A direct comparison of high-speed methods for the numerical Abel transform

The Abel transform is a mathematical operation that transforms a cylindrically symmetric three-dimensional (3D) object into its two-dimensional (2D) projection. The inverse Abel transform reconstructs the 3D object from the 2D projection. Abel transforms have wide application across numerous fields of science, especially chemical physics, astronomy, and the study of laser-plasma plumes. Consequently, many numerical methods for the Abel transform have been developed, which makes it challenging to select the ideal method for a specific application. In this work eight transform methods have been incorporated into a single, open-source Python software package (PyAbel) to provide a direct comparison of the capabilities, advantages, and relative computational efficiency of each transform method. Most of the tested methods provide similar, high-quality results. However, the computational efficiency varies across several orders of magnitude. By optimizing the algorithms, we find that some transform methods are sufficiently fast to transform 1-megapixel images at more than 100 frames per second on a desktop personal computer. In addition, we demonstrate the transform of gigapixel images.Comment: 9 pages, 5 figure

Sustainable computational science: the ReScience initiative

Computer science o ers a large set of tools for prototyping, writing, running, testing, validating, sharing and reproducing results, however computational science lags behind. In the best case, authors may provide their source code as a compressed archive and they may feel con dent their research is reproducible. But this is not exactly true. Jonathan Buckheit and David Donoho proposed more than two decades ago that an article about computational results is advertising, not scholarship. e actual scholarship is the full so ware environment, code, and data that produced the result. is implies new work ows, in particular in peer-reviews. Existing journals have been slow to adapt: source codes are rarely requested, hardly ever actually executed to check that they produce the results advertised in the article. ReScience is a peer-reviewed journal that targets computational research and encourages the explicit replication of already published research, promoting new and open-source implementations in order to ensure that the original research can be replicated from its description. To achieve this goal, the whole publishing chain is radically di erent from other traditional scienti c journals. ReScience resides on GitHub where each new implementation of a computational study is made available together with comments, explanations, and so ware tests

Étude numérique et expérimentale des phénomènes d'accrétion-éjection en astrophysique de laboratoire

With the development of high power laser facilities able to focus microscopic amounts of energy in less than 1 mm³ and in a few nanoseconds, it became possible to reach High Energy Density (HED) regimes of matter previously found only in planetary and stellar interiors. Laboratory astrophysics thus aims to study certain classes of astrophysical objects in a controlled environment of HED facilities using appropriate scaling laws.The modelling of hydrodynamic plasma flows in such experiments requires the use of numerical tools that will be presented in this thesis work. In particular, we will start by discussing the multi-physics Adaptive Mesh Refinement (AMR) code FLASH that can be used to model laser driven plasma flows. Then, we will discuss the equation of state and opacities necessary to precisely model the experimental conditions. Finally, in a validation effort, a code to code comparison study for MULTI, DUED and FLASH codes will be illustrated with several test cases. In a second part, we will present experimental results related to the accretion-ejection mechanisms in astrophysical objects, and in particular, magnetic Cataclysmic Variables (mCV) and astrophysical jets. The former is a binary star system, where a white dwarf accretes matter from a companion star. The matter is then confined by magnetic field lines and falls with a supersonic velocity onto the white dwarf surface, generating a stationary radiative shock wave in the accretion column. This work is part of the POLAR project and lead to experiments performed on the LULI2000 (France), GEKKO XII (Japan) and Orion (UK) laser facilities. The second type of objects that were studied in this work are astrophysical jets, where matter is ejected from a central compact object, forming plasma flows collimated over large distances. In particular, we have performed an experimental demonstration of the Shock Focused Inertial Confinement (SFIC) mechanism on the LULI2000 laser facility.Avec le développement des installations laser de puissance, qui focalisent des quantités macroscopique d'énergie dans moins d'un mm3 en quelques nanosecondes, il devenu possible d'atteindre des conditions de matière à Haute Densité d'Énergie (HDE) précédemment accessibles uniquement à l'intérieur des intérieurs planétaires ou stellaires. L’astrophysique de laboratoire vise ainsi à étudier certaines classes d'objets astrophysiques dans un environnement contrôlé sur des installations HDE à l’aide des lois d’échelles appropriées. La modélisation des écoulements hydrodynamiques dans ces expériences nécessite l’utilisation d’un ensemble d’outils numériques, qui seront abordés dans une première partie de ce travail. En particulier, je présenterai un code d’hydrodynamique/MHD radiative à raffinement automatique de maillage (AMR), FLASH, développé à l’université de Chicago, permettant de modéliser les plasmas produits par laser. Puis, je discuterai de l’amélioration des données de base (équations d’état et opacités), indispensables pour simuler avec précision les conditions expérimentales. Enfin, dans un effort de validation, une comparaison entre les codes MULTI, DUED et FLASH sera illustrée sur un cas test. Dans une deuxième partie, seront abordés les résultats expérimentaux relatifs aux mécanismes d’accrétion-éjection de matière dans les objets astrophysiques que sont les variables cataclysmiques magnétiques (mCV) et les jets astrophysiques. Les premiers sont des systèmes binaires dans lesquels une naine blanche accréte de la matière provenant d'une étoile compagnon. La matière est alors confinée par des lignes de champ magnétiques et tombe avec une vitesse supersonique à la surface de la naine blanche, créant un choc radiatif stationnaire dans la colonne d’accrétion. Ce travail s'inscrit dans le cadre d'un projet national POLAR et a donné lieu à des expériences effectuées sur les installations laser LULI2000, GEKKO XII (Japon) et Orion (R.-U.). Le second type d'objets étudiés est les jets astrophysiques où la matière est éjectée d’un objet compact, formant des flots de plasmas collimatés sur de larges distances. On s’intéressera en particulier, aux mécanismes de collimation hydrodynamique par choc (SFIC) avec des expériences effectuées sur l’installation LULI2000

PyAbel: PyAbel 0.7.3

Incorporates new methods: Onion peeling, two point, and Onion_bordas. (Small tweaks to Travis CI script from previous release.

PyAbel/PyAbel: PyAbel 0.7.6

The latest and greatest PyAbel, v0.7.6 should correctly compile the "direct-C" method, even with older C-compilers on Windows

PyAbel (v0.7): A Python Package for Abel Transforms

<p>PyAbel is an open-source Python package for performing Abel transforms (both forward and inverse). The forward Abel transform is used to transform a slice of a 3D object into a 2D projection of the object. The inverse Abel transform accomplished the opposite, taking a 2D projection of the object and providing a slice of the 3D object. To use the Abel transform, the object must have cylindrical symmetry, and that axis of cylindrical symmetry must lie in the plane of the 2D image.</p> <p>Inverse Abel transforms are commonly used in processing images derived from photoelectron/photoion spectroscopy experiments, ultracold atom (Bose-Einstein condensates) experiments, and in the analysis of flames and plasma plumes. Basically, these are all cases where projections of cylindrically symmetric structures are recorded. </p> <p>PyAbel incorporates several of the most popular Abel Transform algorithms, including:</p> <p>    1. The Gaussian basis set expansion of Dribinski and co-workers (BASEX)</p> <p>    2.  The recursive method of Hansen and Law.</p> <p>    3. Direct numerical integration of the analytical Abel transform equations.</p> <p>    4. The "three point" method of Dasch and co-workers.</p> <p>PyAbel achieves highly efficient implementations of these Abel transform methods in an easy-to-use Python package. PyAbel also includes a set of tools for centering, symmetrizing, and integrating images.</p> <p>PyAbel is available under the MIT license.</p

Tslearn, A Machine Learning Toolkit for Time Series Data

International audiencetslearn is a general-purpose Python machine learning library for time series that offers tools for pre-processing and feature extraction as well as dedicated models for clustering, classification and regression. It follows scikit-learn's Application Programming Interface for transformers and estimators, allowing the use of standard pipelines and model selection tools on top of tslearn objects. It is distributed under the BSD-2-Clause license, and its source code is available at https://github.com/tslearn-team/tslearn

ICU Bed Availability Monitoring and analysis in the Grand Est region of France during the COVID-19 epidemic

International audienceBackground: Reliable information is an essential component for responding to the COVID-19 epidemic, especially regarding the availability of critical care beds (CCBs). We propose three contributions: a) ICUBAM (ICU Bed Availability Monitor), a tool which both collects and visualizes information on CCB availability entered directly by intensivists. b) An analysis of CCB availability and ICU admissions and outcomes using collected by ICUBAM during a 6-week period in the hard-hit Grand Est region of France, and c) Explanatory and predictive models adapted to CCB availability prediction, and fitted to availability information collected by ICUBAM.Methods: We interact directly with intensivists twice a day, by sending a SMS with a web link to the ICUBAM form where they enter 8 numbers: number of free and occupied CCBs (ventilator-equipped) for both COVID-19 positive and COVID-19- negative patients, the number of COVID-19 related ICU deaths and discharges, the number of ICU refusals, and the number of patients transferred to another region due to bed shortages. The collected data are described using univariate and multivariate methods such as correspondence analysis and then modeled at different scales: a medium and long term prediction using SEIR models, and a short term statistical model to predict the number of CCBs.Results: ICUBAM was brought online March 25, and is currently being used in the Grand-Est region by 109 intensivists representing 40 ICUs (95% of ICUs). ICUBAM allows for the calculation of CCB availability, admission and discharge statistics. Our analysis of data describes the evolution and extent of the COVID-19 health crisis in the Grand-Est region: on April 6th, at maximum bed capacity, 1056 ventilator-equipped CCBs were present, representing 211% of the nominal regional capacity of 501 beds. From March 19th to March 31st, average daily COVID-19 ICU inflow was 68 patients/day, and 314 critical care patients were transferred out of the Grand-Est region. With French lockdown starting on March 17th, a decrease of the daily inflow was found starting on April 1st: 23 patients/day during the first fortnight of April, and 7 patients/day during the last fortnight. However, treatment time for COVID-19 occupied CCBs is long: 15 days after the peak on March 31st, only 20% of ICU beds have been freed (50% after 1 month). Region-wide COVID-19 related in-ICU mortality is evaluated at 31%. Models trained from ICUBAM data are able to describe and predict the evolution of bed usage for the Grand-Estregion.Conclusion: We observe strong uptake of the ICUBAM tool, amongst both physicians and local healthcare stakeholders (health agencies, first responders etc.). We are able to leverage data collected with ICUBAM to better understand the evolution of the COVID-19 epidemic in the Grand Est region. We also present how data ingested by ICUBAM can be used to anticipate CCB shortages and predict future admissions. Most importantly, we demonstrate the importance of having a cross-functional team involving physicians, statisticians and computer scientists working both with first-line medical responders and local health agencies. This allowed us to quickly implement effective tools to assist in critical decision-making processes

pyodide/pyodide: 0.24.0

Pyodide is a Python distribution for the browser and Node.js based on WebAssembl