Blog entries

  • 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.

  • Quizz WolframAlpha

    2009/07/10 by Nicolas Chauvat

    Wolfram Alpha is a web front-end to huge database of information covering very different topics ranging from mathematical functions to genetics, geography, astronomy, etc.

    When you search for a word, it will try to match it with one of the objects it as in its database and display all the information it has concerning that object. For example it can tell you a lot about the Halley Comet, including where it is at the moment you ask the query. This is the main difference with, say Wikipedia, that will know a lot about that comet in general, but is not meant to compute its location in the sky at the moment you enter your query.

    Searches are not limited to words. One can key in commands like weather in Paris in june 2009 or x^2+sin(x) and get results for those precise queries. The processing of the input query is far from bad, since it returns results to questions like what are the cities of France, but I would not call it state of the art natural language processing since that query returns the largest cities instead of just the cities it knows about and the question what are the smallest cities of France will not return any result. Natural language processing is a very difficult problem, though, especially when done in the open world as it is the case there with a engine available to the wide public on the internet.

    For more examples, visit the WolframAlpha website, where you will also be able to post feature requests or, if you are a developer, get documentation about the WolframAlpha API and maybe use it as a web service in your application when you need to answer certain types of questions.

  • Salomé accepted into Debian unstable

    2010/06/03 by Andre Espaze

    Salomé is a platform for pre and post-processing of numerical simulation available at It is now available as a Debian package and should soon appear in Ubuntu as well.

    A difficult packaging work

    A first package of Salomé 3 was made by the courageous Debian developper Adam C. Powell, IV on January 2008. Such packaging is very resources intensive because of the building of many modules. But the most difficult part was to bring Salomé to an unported environment. Even today, Salomé 5 binaries are only provided by upstream as a stand-alone piece of software ready to unpack on a Debian Sarge/Etch or a Mandriva 2006/2008. This is the first reason why several patches were required for adapting the code to new versions of the dependencies. The version 3 of Salomé was so difficult and time consuming to package that Adam decided to stop during two years.

    The packaging of Salomé started back with the version 5.1.3 in January 2010. Thanks to Logilab and the OpenHPC project, I could join him during 14 weeks of work for adapting every module to Debian unstable. Porting to the new versions of the dependencies was a first step, but we had also to adapt the code to the Debian packaging philosophy with binaries, librairies and data shipped to dedicated directories.

    A promising future

    Salomé being accepted to Debian unstable means that porting it to Ubuntu should follow in a near future. Moreover the work done for adapting Salomé to a GNU/Linux distribution may help developpers on others platforms as well.

    That is excellent news for all people involved in numerical simulation because they are going to have access to Salomé services by using their packages management tools. It will help the spreading of Salomé code on any fresh install and moreover keep it up to date.

    Join the fun

    For mechanical engineers, a derived product called Salomé-Méca has recently been published. The goal is to bring the functionalities from the Code Aster finite element solver to Salomé in order to ease simulation workflows. If you are as well interested in Debian packages for those tools, you are invited to come with us and join the fun.

    I have submitted a proposal to talk about Salomé at EuroSciPy 2010. I look forward to meet other interested parties during this conference that will take place in Paris on July 8th-11th.

  • EuroSciPy 2010 schedule is out !

    2010/06/06 by Nicolas Chauvat

    The EuroSciPy 2010 conference will be held in Paris from july 8th to 11th at Ecole Normale Supérieure. Two days of tutorials, two days of conference, two interesting keynotes, a lightning talk session, an open space for collaboration and sprinting, thirty quality talks in the schedule and already 100 delegates registered.

    If you are doing science and using Python, you want to be there!

  • EuroSciPy'10

    2010/07/13 by Adrien Chauve

    The EuroSciPy2010 conference was held in Paris at the Ecole Normale Supérieure from July 8th to 11th and was organized and sponsored by Logilab and other companies.

    July, 8-9: Tutorials

    The first two days were dedicated to tutorials and I had the chance to talk about SciPy with André Espaze, Gaël Varoquaux and Emanuelle Gouillart in the introductory track. This was nice but it was a bit tricky to present SciPy in such a short time while trying to illustrate the material with real and interesting examples. One very nice thing for the introductory track is that all the material was contributed by different speakers and is freely available in a github repository (licensed under CC BY).

    July, 10-11: Scientific track

    The next two days were dedicated to scientific presentations and why python is such a great tool to develop scientific software and carry out research.


    I had a very great time listening to the presentations, starting with the two very nice keynotes given by Hans Petter Langtangen and Konrad Hinsen. The latter gave us a very nice summary of what happened in the scientific python world during the past 15 years, what is happening now and of course what could happen during the next 15 years. Using a crystal ball and a very humorous tone, he made it very clear that the challenge in the next years will be about how using our hundreds, thousands or even more cores in a bug-free and efficient way. Functional programming may be a very good solution to this challenge as it provides a deterministic way of parallelizing our programs. Konrad also provided some hints about future versions of python that could provide a deeper and more efficient support of functional programming and maybe the addition of a keyword 'async' to handle the computation of a function in another core.

    In fact, the PEP 3148 entitled "Futures - execute computations asynchronously" was just accepted two days ago. This PEP describes the new package called "futures" designed to facilitate the evaluation of callables using threads and processes in future versions of python. A full implementation is already available.


    Parallelization was indeed a very popular issue across presentations, and as for resolving embarrassingly parallel problems, several solutions were presented.

    • Playdoh: Distributes computations over computers connected to a secure network (see playdoh presentation).

      Distributing the computation of a function over two machines is as simple as:

      import playdoh
      result1, result2 =, [arg1, arg2], _machines = ['', ''])
    • Theano: Allows to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. In particular it can use GPU transparently and generate optimized C code (see theano presentation).

    • joblib: Provides among other things helpers for embarrassingly parallel problems. It's built over the multiprocessing package introduced in python 2.6 and brings more readable code and easier debugging.


    Concerning speed, Fransesc Alted has showed us interesting tools for memory optimization currently successfully used in PyTables 2.2. You can read more details on these kind of optimizations in EuroSciPy'09 (part 1/2): The Need For Speed.


    Last but not least, I talked with Cristophe Pradal who is one of the core developer of OpenAlea. He convinced me that SCons is worth using once you have built a nice extension for it: SConsX. I'm looking forward to testing it.

  • EuroSciPy'11 - Annual European Conference for Scientists using Python.

    2011/08/24 by Alain Leufroy

    The EuroScipy2011 conference will be held in Paris at the Ecole Normale Supérieure from August 25th to 28th and is co-organized and sponsored by INRIA, Logilab and other companies.

    The conference is dedicated to cross-disciplinary gathering focused on the use and development of the Python language in scientific research.

    August 25th and 26th are dedicated to tutorial tracks -- basic and advanced tutorials. August 27th and 28th are dedicated to talks, posters and demos sessions.

    Damien Garaud, Vincent Michel and Alain Leufroy (and others) from Logilab will be there. We will talk about a RSS feeds aggregator based on Scikits.learn and CubicWeb and we have a poster about LibAster (a python library for thermomechanical simulation based on Code_Aster).

  • Deuxième hackathon codes libres de mécanique

    2014/04/07 by Nicolas Chauvat


    Le 27 mars 2014, Logilab a accueilli un hackathon consacré aux codes libres de simulation des phénomènes mécaniques. Etaient présents:

    • Patrick Pizette, Sébastien Rémond (Ecole des Mines de Douai / DemGCE)
    • Frédéric Dubois, Rémy Mozul (LMGC Montpellier / LMGC90)
    • Mickaël Abbas, Mathieu Courtois (EDF R&D / Code_Aster)
    • Alexandre Martin (LAMSID / Code_Aster)
    • Luca Dall'Olio, Maximilien Siavelis (Alneos)
    • Florent Cayré, Nicolas Chauvat, Denis Laxalde, Alain Leufroy (Logilab)

    DemGCE et LMGC90

    Patrick Pizette et Sébastien Rémond des Mines de Douai sont venus parler de leur code de modélisation DemGCE de "sphères molles" (aussi appelé smooth DEM), des potentialités d'intégration de leurs algorithmes dans LMGC90 avec Frédéric Dubois du LMGC et de l'interface Simulagora développée par Logilab. DemGCE est un code DEM en 3D développé en C par le laboratoire des Mines de Douai. Il effectuera bientôt des calculs parallèles en mémoire partagée grâce à OpenMP. Après une présentation générale de LMGC90, de son écosystème et de ses applications, ils ont pu lancer leurs premiers calculs en mode dynamique des contacts en appelant via l'interface Python leurs propres configurations d'empilements granulaires.

    Ils ont grandement apprécié l'architecture logicielle de LMGC90, et en particulier son utilisation comme une bibliothèque de calcul via Python, la prise en compte de particules de forme polyhédrique et les aspects visualisations avec Paraview. Il a été discuté de la réutilisation de la partie post/traitement visualisation via un fichier standard ou une bibliothèque dédiée visu DEM.

    Frédéric Dubois semblait intéressé par l'élargissement de la communauté et du spectre des cas d'utilisation, ainsi que par certains algorithmes mis au point par les Mines de Douai sur la génération géométrique d'empilements. Il serait envisageable d'ajouter à LMGC90 les lois d'interaction de la "smooth DEM" en 3D, car elles ne sont aujourd'hui implémentées dans LMGC90 que pour les cas 2D. Cela permettrait de tester en mode "utilisateur" le code LMGC90 et de faire une comparaison avec le code des Mines de Douai (efficacité parallélisation, etc.).

    Florent Cayré a fait une démonstration du potentiel de Simulagora.

    LMGC90 et Code_Aster dans Debian

    Denis Laxalde de Logilab a travaillé d'une part avec Rémy Mozul du LMGC sur l'empaquetage Debian de LMGC90 (pour intégrer en amont les modifications nécessaires), et d'autre part avec Mathieu Courtois d'EDF R&D, pour finaliser l'empaquetage de Code_Aster et notamment discuter de la problématique du lien avec la bibliothèque Metis: la version actuellement utilisée dans Code_Aster (Metis 4), n'est pas publiée dans une licence compatible avec la section principale de Debian. Pour cette raison, Code_Aster n'est pas compilé avec le support MED dans Debian actuellement. En revanche la version 5 de Metis a une licence compatible et se trouve déjà dans Debian. Utiliser cette version permettrait d'avoir Code_Aster avec le support Metis dans Debian. Cependant, le passage de la version 4 à la version 5 de Metis ne semble pas trivial.

    Voir les tickets:

    Replier LibAster dans Code_Aster

    Alain Leufroy et Nicolas Chauvat de Logilab ont travaillé à transformer LibAster en une liste de pull request sur la forge bitbucket de Code_Aster. Ils ont présenté leurs modifications à Mathieu Courtois d'EDF R&D ce qui facilitera leur intégration.

    Voir les tickets:

    Suppression du superviseur dans Code_Aster

    En fin de journée, Alain Leufroy, Nicolas Chauvat et Mathieu Courtois ont échangé leurs idées sur la simplification/suppression du superviseur de commandes actuel de Code_Aster. Il est souhaitable que la vérification de la syntaxe (choix des mots-clés) soit dissociée de l'étape d'exécution.

    La vérification pourrait s'appuyer sur un outil comme pylint, la description de la syntaxe des commandes de Code_Aster pour pylint pourrait également permettre de produire un catalogue compréhensible par Eficas.

    L'avantage d'utiliser pylint serait de vérifier le fichier de commandes avant l'exécution même si celui-ci contient d'autres instructions Python.

    Allocation mémoire dans Code_Aster

    Mickaël Abbas d'EDF R&D s'est intéressé à la modernisation de l'allocation mémoire dans Code_Aster et a listé les difficultés techniques à surmonter ; l'objectif visé est un accès facilité aux données numériques du Fortran depuis l'interface Python. Une des difficultés est le partage des types dérivés Fortran en Python. Rémy Mozul du LMGC et Denis Laxalde de Logilab ont exploré une solution technique basée sur Cython et ISO-C-Bindings. De son côté Mickaël Abbas a contribué à l'avancement de cette tâche directement dans Code_Aster.

    Doxygen pour documentation des sources de Code_Aster

    Luca Dall'Olio d'Alneos et Mathieu Courtois ont testé la mise en place de Doxygen pour documenter Code_Aster. Le fichier de configuration pour doxygen a été modifié pour extraire les commentaires à partir de code Fortran (les commentaires doivent se trouver au dessus de la déclaration de la fonction, par exemple). La configuration doxygen a été restituée dans le depôt Bitbucket. Reste à évaluer s'il y aura besoin de plusieurs configurations (pour la partie C, Python et Fortran) ou si une seule suffira. Une configuration particulière permet d'extraire, pour chaque fonction, les points où elle est appelée et les autres fonctions utilisées. Un exemple a été produit pour montrer comment écrire des équations en syntaxe Latex, la génération de la documentation nécessite plus d'une heure (seule la partie graphique peut être parallélisée). La documentation produite devrait être publiée sur le site de Code_Aster.

    La suite envisagée est de coupler Doxygen avec Breathe et Sphinx pour compléter la documentation extraite du code source de textes plus détaillés.

    La génération de cette documentation devrait être une cible de waf, par exemple waf doc. Un aperçu rapide du rendu de la documentation d'un module serait possible par waf doc file1.F90 [file2.c [...]].

    Voir Code Aster #18 configure doxygen to comment the source files

    Catalogue d'éléments finis

    Maximilien Siavelis d'Alneos et Alexandre Martin du LAMSID, rejoints en fin de journée par Frédéric Dubois du LMGC ainsi que Nicolas Chauvat et Florent Cayré de Logilab, ont travaillé à faciliter la description des catalogues d'éléments finis dans Code_Aster. La définition de ce qui caractérise un élément fini a fait l'objet de débats passionnés. Les points discutés nourriront le travail d'Alexandre Martin sur ce sujet dans Code_Aster. Alexandre Martin a déjà renvoyé aux participants un article qu'il a écrit pour résumer les débats.

    Remontée d'erreurs de fortran vers Python

    Mathieu Courtois d'EDF R&D a montré à Rémy Mozul du LMGC un mécanisme de remontée d'exception du Fortran vers le Python, qui permettra d'améliorer la gestion des erreurs dans LMGC90, qui a posé problème dans un projet réalisé par Denis Laxalde de Logilab pour la SNCF.

    Voir aster_exceptions.c


    Tous les participants semblaient contents de ce deuxième hackathon, qui faisait suite à la première édition de mars 2013 . La prochaine édition aura lieu à l'automne 2014 ou au printemps 2015, ne la manquez pas !

  • SciviJS

    2016/10/10 by Martin Renou


    The goal of my work at Logilab is to create tools to visualize scientific 3D volumic-mesh-based data (mechanical data, electromagnetic...) in a standard web browser. It's a part of the european OpenDreamKit project. Franck Wang has been working on this subject last year. I based my work on his results and tried to improve them.

    Our goal is to create widgets to be used in Jupyter Notebook (formerly IPython) for easy 3D visualization and analysis. We also want to create a graphical user interface in order to enable users to intuitively compute multiple effects on their meshes.

    As Franck Wang worked with X3DOM, which is an open source JavaScript framework that makes it possible to display 3D scenes using HTML nodes, we first thought it was a good idea to keep on working with this framework. But X3DOM is not very well maintained these days, as can be seen on their GitHub repository.

    As a consequence, we decided to take a look at another 3D framework. Our best candidates were:

    • ThreeJS
    • BabylonJS

    ThreeJS and BabylonJS are two well-known Open Source frameworks for 3D web visualization. They are well maintained by hundreds of contributors since several years. Even if BabylonJS was first thought for video games, these two engines are interesting for our project. Some advantages of ThreeJS are:

    Finally, the choice of using ThreeJS was quite obvious because of its Nodes feature, contributed by Sunag Entertainment. It allows users to compose multiple effects like isocolor, threshold, clip plane, etc. As ThreeJS is an Open Source framework, it is quite easy to propose new features and contributors are very helpful.


    As we want to compose multiple effects like isocolor and threshold (the pixel color correspond to a pressure but if this pressure is under a certain threshold we don't want to display it), it seems a good idea to compose shaders instead of creating a big shader with all the features we want to implement. The problem is that WebGL is still limited (as of the 1.x version) and it's not possible for shaders to exchange data with other shaders. Only the vertex shader can send data to the fragment shader through varyings.

    So it's not really possible to compose shaders, but the good news is we can use the new node system of ThreeJS to easily compute and compose a complex material for a mesh.

    alternate text

    It's the graphical view of what you can do in your code, but you can see that it's really simple to implement effects in order to visualize your data.


    With this great tools as a solid basis, I designed a first version of a javascript library, SciviJS, that aims at loading, displaying and analyzing mesh data in a standard web browser (i.e. without any plugin).

    You can define your visualization in a .yml file containing urls to your mesh and data and a hierarchy of effects (called block structures).

    See for an online demo.

    You can see the block structure like following:

    Data blocks are instantiated to load the mesh and define basic parameters like color, position etc. Blocks are connected together to form a tree that helps building a visual analysis of your mesh data. Each block receives data (like mesh variables, color and position) from its parent and can modify them independently.

    Following parameters must be set on dataBlocks:

    • coordURL: URL to the binary file containing coordinate values of vertices.
    • facesURL: URL to the binary file containing indices of faces defining the skin of the mesh.
    • tetrasURL: URL to the binary file containing indices of tetrahedrons. Default is ''.
    • dataURL: URL to the binary file containing data that you want to visualize for each vertices.

    Following parameters can be set on dataBlocks or plugInBlocks:

    • type: type of the block, which is dataBlock or the name of the plugInBlock that you want.
    • colored: define whether or not the 3D object is colored. Default is false, object is rendered gray.
    • colorMap: color map used for coloration, available values are rainbow and gray. Default is rainbow.
    • colorMapMin and colorMapMax: bounds for coloration scaled in [0, 1]. Default is (0, 1).
    • visualizedData: data used as input for coloration. If data are 3D vectors available values are magnitude, X, Y, Z, and default is magnitude. If data are scalar values you don't need to set this parameter.
    • position, rotation, scale: 3D vectors representing position, rotation and scale of the object. Default are [0., 0., 0.], [0., 0., 0.] and [1., 1., 1.].
    • visible: define whether or not the object is visible. Default is true if there's no childrenBlock, false otherwise.
    • childrenBlocks: array of children blocks. Default is empty.

    As of today, there are 6 types of plug-in blocks:

    • Threshold: hide areas of your mesh based on a variable's value and bound parameters

      • lowerBound: lower bound used for threshold. Default is 0 (representing dataMin). If inputData is under lowerBound, then it's not displayed.
      • upperBound: upper bound used for threshold. Default is 1 (representing dataMax). If inputData is above upperBound, then it's not displayed.
      • inputData: data used for threshold effect. Default is visualizedData, but you can set it to magnitude, X, Y or Z.
    • ClipPlane: hide a part of the mesh by cutting it with a plane

      • planeNormal: 3D array representing the normal of the plane used for section. Default is [1., 0., 0.].
      • planePosition: position of the plane for the section. It's a scalar scaled bewteen -1 and 1. Default is 0.
    • Slice: make a slice of your mesh

      • sliceNormal
      • slicePosition
    • Warp: deform the mesh along the direction of an input vector data

      • warpFactor: deformation factor. Default is 1, can be negative.
      • inputData: vector data used for warp effect. Default is data, but you can set it to X, Y or Z to use only one vector component.
    • VectorField: represent the input vector data with arrow glyphs

      • lengthFactor: factor of length of vectors. Default is 1, can be negative.
      • inputData
      • nbVectors: max number of vectors. Default is the number of vertices of the mesh (which is the maximum value).
      • mode: mode of distribution. Default is volume, you can set it to surface.
      • distribution: type of distribution. Default is regular, you can set it to random.
    • Points: represent the data with points

      • pointsSize: size of points in pixels. Default is 3.
      • nbPoints
      • mode
      • distribution

    Using those blocks you can easily render interesting 3D scenes like this:

    Future works

    • Integration to Jupyter Notebook
    • As of today you only can define a .yml file defining the tree of blocks, we plan to develop a Graphical User Interface to enable users to define this tree interactively with drag and drop
    • Support of most file types (for now it only supports binary files)