Software

• Python Package (2023): MCRPPy

  

  

  Figure: A sample from a Poisson point process (left) and the corresponding repelled point process (right). The force field driving the repulsion is represented in the middle.

  Python package for sampling Repelled point processes, estimating function integrals using various Monte Carlo methods (including a method with the repelled point process), and illustrating 2D gravitational allocation from the Lebesgue measure to a point process.

  • Open source available on GitHub
  • Tutorial Jupyter Notebooks
  • Corresponding paper
  

  Figure: Illustration of a gravitational allocation from Lebesgue to a realization (black dots) of a Poisson point process (PPP) in a disk. The region underlying the curves sharing the same color illustrates a basin, which collects the points of the space allocated to the point of the PPP that belongs to that particular colored region.

• Python Package (2022): structure_factor

  

  

  Figure: A sample from the Ginibre ensemble (left) and the corresponding estimated structure factor represented radially (middle) and in 2D (right).

  Python package for studying the hyperuniformity of a point process. Approximate the structure factor, the pair correlation function, testing the effective hyperuniformity, testing the hyperuniformity, and identifying the hyperuniformity class of a stationary point process.

  • Open source available on GitHub and PyPi
  • Documentation
  • Tutorial Jupyter Notebook
  • Corresponding paper

• Python project (2021): assess_data_quality

  Python plug-and-play algorithm for assessing data quality and finding bad data within a dataset. Project developed during the challenge Mathematics and Companies organized by AMIES, SFdS, SMF, and the SMAI for Ph.D. students in France.

  • plug-and-play algorithm available on GitHub