
David Riley developed and enhanced reweighting workflows for the mach3-software/MaCh3 repository, focusing on robust MCMC analysis and improved maintainability. He implemented end-to-end reweighting tools, including a new ReweightMCMC executable, and overhauled memory management for graph-based data structures using C++ and the ROOT framework. His work addressed error handling, configuration parsing, and YAML validation, enabling flexible 1D and 2D reweighting scenarios. David also refactored code for clarity, stabilized build systems, and expanded CI/CD validation in MaCh3Tutorial. These contributions resulted in more reliable, scalable analysis pipelines and streamlined experimentation for scientific computing and statistical modeling workflows.

October 2025 performance summary for MaCh3 and MaCh3Tutorial. Delivered consolidated reweighting across 1D/2D aligned with ProcessMCMC, introduced YAML conflict checks for OscProcessor, stabilized builds via memory-management fixes and TTree lifecycle corrections, and expanded the reweighting workflow to support multiple 1D reweights and single-tuple Gaussian reweights. Architecture clarified by removing legacy reweighting path from ProcessMCMC.cpp and enhancing progress indicators during MCMC reweighting. Documentation and CI pipelines were restructured to improve maintainability and validation coverage, including moving configs to tutorial repos and validating ReweightMCMC in CI. Business value: improved correctness and scalability of the reweighting workflow, reduced configuration errors, more reliable builds, and faster integration/validation cycles in CI, enabling more rapid experimentation and deployment of reweighting scenarios.
October 2025 performance summary for MaCh3 and MaCh3Tutorial. Delivered consolidated reweighting across 1D/2D aligned with ProcessMCMC, introduced YAML conflict checks for OscProcessor, stabilized builds via memory-management fixes and TTree lifecycle corrections, and expanded the reweighting workflow to support multiple 1D reweights and single-tuple Gaussian reweights. Architecture clarified by removing legacy reweighting path from ProcessMCMC.cpp and enhancing progress indicators during MCMC reweighting. Documentation and CI pipelines were restructured to improve maintainability and validation coverage, including moving configs to tutorial repos and validating ReweightMCMC in CI. Business value: improved correctness and scalability of the reweighting workflow, reduced configuration errors, more reliable builds, and faster integration/validation cycles in CI, enabling more rapid experimentation and deployment of reweighting scenarios.
September 2025 for MaCh3 focused on hardening reweighting workflows and improving maintainability. Key fixes include a memory-safety fix for 2D reweighting, overhaul of ReweightMCMC for 1D/2D reweighting with TGraph support, and a robust fix to copying ROOT files, complemented by targeted maintenance and refactoring for reliability and future extensibility. These changes deliver clearer weight calculations, more flexible reweighting scenarios, and a cleaner, well-documented codebase, enabling faster iterations and more trustworthy results.
September 2025 for MaCh3 focused on hardening reweighting workflows and improving maintainability. Key fixes include a memory-safety fix for 2D reweighting, overhaul of ReweightMCMC for 1D/2D reweighting with TGraph support, and a robust fix to copying ROOT files, complemented by targeted maintenance and refactoring for reliability and future extensibility. These changes deliver clearer weight calculations, more flexible reweighting scenarios, and a cleaner, well-documented codebase, enabling faster iterations and more trustworthy results.
Month 2025-08: Delivered end-to-end reweighting capabilities for MCMC workflows in mach3-software/MaCh3, including plot reweighting and a new ReweightMCMC executable to apply user-defined weight constraints to MCMC samples. Strengthened robustness with improved error handling and memory management for 2D graphs and graph loading, addressing null-graph interpolation issues and proper cloning/memory handling. Result: more flexible, reliable MCMC analysis, enabling quick exploration of weighted scenarios with reduced risk of runtime failures. Technologies demonstrated: C++ development, graph data structures (e.g., TGraph2D), memory management, error handling, and modular tool integration.
Month 2025-08: Delivered end-to-end reweighting capabilities for MCMC workflows in mach3-software/MaCh3, including plot reweighting and a new ReweightMCMC executable to apply user-defined weight constraints to MCMC samples. Strengthened robustness with improved error handling and memory management for 2D graphs and graph loading, addressing null-graph interpolation issues and proper cloning/memory handling. Result: more flexible, reliable MCMC analysis, enabling quick exploration of weighted scenarios with reduced risk of runtime failures. Technologies demonstrated: C++ development, graph data structures (e.g., TGraph2D), memory management, error handling, and modular tool integration.
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