Pierre Fernique

AutoWIG: automatic generation of python bindings for C++ libraries

Most of Python and R scientific packages incorporate compiled scientific libraries to speed up the code and reuse legacy libraries. While several semi-automatic solutions exist to wrap these compiled libraries, the process of wrapping a large library is cumbersome and time consuming. In this paper, we introduce AutoWIG, a Python package that wraps automatically compiled libraries into high-level languages using LLVM/Clang technologies and the Mako templating engine. Our approach is automatic, extensible, and applies to complex C++ libraries, composed of thousands of classes or incorporating modern meta-programming constructs.

Characterization of mango tree patchiness using a tree-segmentation/clustering approach

In functional-structural plant models, inferring latent levels of organization from data while accounting for both connections between levels and within-individual heterogeneity is a challenging task. Here, we develop an approach based on multiple change-point models. It aims at partitioning a heterogeneous tree into homogeneous subtrees of consequent sizes. While multiple change-point models for sequences have been studied in depth, their transposition to tree-indexed data remains unaddressed. Since optimal algorithms of multiple change-point models for sequences cannot be transposed to trees, we propose here an efficient heuristic for tree segmentation. The segmented subtrees are grouped in a post-processing phase since similar disjoint patches in the canopy are observed. Application of such models is illustrated in mango tree where subtrees are assimilated to plant patches and clusters of patches to patch types (e.g. vegetative, flowering or resting patch).