AutoWIG : automatisation de l'encapsulation de librairies C++ en Python et en R

National audiencePython and R programming languages are two of the most popular languages in scientific computing. However, most scientific packages incorporates C and C++ libraries. While several semi-automatic solutions and tools exist to wrap C++ libraries (RCPP, Boost.Python), the process of wrapping a large C++ library is cumbersome and time consuming. Some solutions have been developed in the past (e.g. Py++ or XDress) but require to write complex code to automate the process, and rely on technologies that are not maintained. AutoWIG relies on the LLVM/Clang technology for parsing C/C++ code and the Mako templating engine for generating Boost.Python wrappers. We will illustrate the usage of AutoWIG on a complex collection of C++ libraries for statistical analysis.Les langages de programmation Python et R sont deux des langages les plus populaires pour le calcul scientifique. Cependant, la plupart des logiciels scientifiques incorporent des biblioth eques C ou C++. Bien qu'il existe plusieurs solutions et des outils semi-automatiques pour encapsuler des biblioth eques C++ (RCPP, Boost.Python), le processus d'encapsulation d'une grande biblioth eque C++ est long et fastidieux. Certaines solutions pour Python ont eté développées dans le passé (par exemple Py++ ou XDress) mais nécessitent d'´ ecrire du code complexe pour automatiser le processus, et de compter sur des technologies qui ne sont pas entretenues. Le logiciel AutoWIG fait appeì a la technologie LLVM/Clang pour l'analyse syntaxique de code C/C++ et a l'outil Mako pour générer l'encapsulation des biblioth eques C++ avec Boost.Python et RCPP. Nous illustrerons l'utilisation d'AutoWIG sur un ensemble complexe de biblioth eques C++ pour l'analyse statistique. Mots-clés. C++, Python, R, calcul scientifique Abstract. Python and R programming languages are two of the most popular languages in scientific computing. However, most scientific packages incorporates C and C++ libraries. While several semi-automatic solutions and tools exist to wrap C++ libraries (RCPP, Boost.Python), the process of wrapping a large C++ library is cumbersome and time consuming. Some solutions have been developed in the past (e.g. Py++ or XDress) but require to write complex code to automate the process, and rely on technologies that are not maintained. AutoWIG relies on the LLVM/Clang technology for parsing C/C++ code and the Mako templating engine for generating Boost.Python wrappers. We will illustrate the usage of AutoWIG on a complex collection of C++ libraries for statistical analysis

Parametric Modelling of Multivariate Count Data Using Probabilistic Graphical Models

Multivariate count data are defined as the number of items of different categories issued from sampling within a population, which individuals are grouped into categories. The analysis of multivariate count data is a recurrent and crucial issue in numerous modelling problems, particularly in the fields of biology and ecology (where the data can represent, for example, children counts associated with multitype branching processes), sociology and econometrics. We focus on I) Identifying categories that appear simultaneously, or on the contrary that are mutually exclusive. This is achieved by identifying conditional independence relationships between the variables; II)Building parsimonious parametric models consistent with these relationships; III) Characterising and testing the effects of covariates on the joint distribution of the counts. To achieve these goals, we propose an approach based on graphical probabilistic models, and more specifically partially directed acyclic graphs

Approche graphique pour la modélisation statistique de la dépendance entre activités journalières

http://mistis.inrialpes.fr/workshop-statistique-transport.html Transparents disponibles sur http://mistis.inrialpes.fr/docs/workshop-statistique-transport/slidesDurand.pdfInternational audienceIn this presentation, we introduce a new family of statistical models for the analysis of multivariate count data. We propose an application in modelling daily activity programs at the scale of individuals or families

Détection de motifs disruptifs au sein de plantes : une approche de quotientement/classification d'arborescences

National audienceMultiple change-point models for path-indexed data are transposed to tree-indexed data. The objective of multiple change-point models is to partition a heterogeneous tree into homogeneous subtrees. Since optimal algorithms for segmenting sequences cannot be transposed to trees, we propose here an efficient heuristic for tree segmentation. Segmented subtrees are grouped together in a post-processing phase since similar disjoint patches are often observed in tree canopy. Application of such models is illustrated on mango tree where subtrees are assimilated to plant patches and clusters of patches to patch types (e.g. vegetative, flowering or resting patch).Les modèles de détection de ruptures multiples pour séquences sont transposés aux arborescences. L'objectif est de quotienter une arborescence en sous-arborescences homogènes. Comme les algorithmes optimaux de segmentation de séquences ne peuvent être transposés aux arborescences, nous proposons ici une méthode heuristique permettant de segmenter efficacement une arborescence. Les sous-arborescences obtenues sont ensuite groupées dans une phase de post-traitement car des sous-arborescences disjointes relativement similaires sont observées dans les canopées d'arbre. Ces modèles sont illustrés par le cas du manguier où les collections de sous-arborescences permettent d'identifier les motifs disruptifs (juxtaposition de sous-arborescences végétatives, florifères ou en pause) observés dans les canopées

A statistical modeling framework for analyzing tree-indexed data: Application to plant development on microscopic and macroscopic scales

We address statistical models for tree-indexed data.In Virtual Plants team, the host team for this thesis, applications of interest focus on plant development and its modulation by environmental and genetic factors.We thus focus on plant developmental applications both at a microscopic level with the study of the cell lineage in the biological tissue responsible for the plant growth, and at a macroscopic level with the mechanism of branch production.Far fewer models are available for tree-indexed data than for path-indexed data.This thesis therefore aims to propose a statistical modeling framework for studying patterns in tree-indexed data.To this end, two different classes of statistical models, Markov and change-point models, are investigatedNous nous intéressons à des modèles statistiques pour données indexées par des arborescences. Dans le contexte de l'équipe Virtual Plants, les applications portent sur le développement de la plante et sa modulation par des facteurs génétiques et environnementaux. Les modèles statistiques pour données indexées par des arborescences sont beaucoup moins développés que ceux pour séquences ou séries temporelles. Cette thèse vise à proposer un cadre de modélisation statistique pour l'identification de patterns dans des données indexées par des arborescences. Deux classes de modèles statistiques, les modèles de Markov et leur extension aux modèles de Markov cachés et les modèles de détection de ruptures multiples, sont étudiés. Nous proposons notamment de nouvelles méthodes dinférence de la structure dindépendance conditionnelle entre nuds parent et enfants dans les modèles de Markov reposant sur des algorithmes de sélection de graphes dans des modèles graphiques probabilistes. Les modèles étudiés sont appliqués dune part à des arborescences de lignage cellulaire à léchelle microscopique et dautre part à des systèmes ramifiés à léchelle macroscopique

Estimation of Discrete Partially Directed Acyclic Graphical Models in Multitype Branching Processes

International audienceWe address the inference of discrete-state models for tree-structured data. Our aim is to introduce parametric multitype branching processes that can be efficiently estimated on the basis of data of limited size. Each generation distribution within this macroscopic model is modeled by a partially directed acyclic graphical model. The estimation of each graphical model relies on a greedy algorithm for graph selection. We present an algorithm for discrete graphical model which is applied on multivariate count data. The proposed modeling approach is illustrated on plant architecture datasets

Functional Stepped Surfaces, Flips and Generalized Substitutions

Remplace N° 06014 (2006) 23 [lirmm-00102710 ? version 1]International audienceA substitution is a non-erasing morphism of the free monoid. The notion of multidimensional substitution of non-constant length acting on multidimensional words is proved to be well-defined on the set of two-dimensional words related to discrete approximations of irrational planes. Such a multidimensional substitution can be associated with any usual unimodular substitution. The aim of this paper is to extend the domain of definition of such multidimensional substitutions to functional stepped surfaces. One central tool for this extension is the notion of flips acting on tilings by lozenges of the plane

The ALADIN Interactive Sky Atlas

The Aladin interactive sky atlas, developed at CDS, is a service providing simultaneous access to digitized images of the sky, astronomical catalogues, and databases. The driving motivation is to facilitate direct, visual comparison of observational data at any wavelength with images of the optical sky, and with reference catalogues. The set of available sky images consists of the STScI Digitized Sky Surveys, completed with high resolution images of crowded regions scanned at the MAMA facility in Paris. A Java WWW interface to the system is available at: http://aladin.u-strasbg.fr/Comment: 8 pages, 3 Postscript figures; to be published in A&