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alexbooth92

PROFILE

Alexbooth92

Alex Booth developed and maintained core simulation and data processing workflows for the DUNE/2x2_sim and DUNE/ndlar_flow repositories, focusing on automation, configuration management, and throughput optimization. He introduced environment-driven configuration patterns using YAML and shell scripting, enabling reproducible and flexible simulation campaigns. Alex enhanced data pipeline scalability by increasing buffer sizes and aligning workflow scripts with evolving file conventions, reducing downtime and improving maintainability. His work included bug fixes in detector geometry and workflow automation, as well as features supporting multi-neutrino analysis and robust input validation. He primarily used Python, Bash, and YAML, demonstrating depth in DevOps and simulation configuration.

Overall Statistics

Feature vs Bugs

67%Features

Repository Contributions

18Total
Bugs
4
Commits
18
Features
8
Lines of code
222
Activity Months5

Work History

May 2025

1 Commits • 1 Features

May 1, 2025

May 2025 monthly summary focused on delivering a high-impact throughput optimization in DUNE/ndlar_flow. Implemented a tenfold increase of RawEventGeneratorMC.buffer_size to support larger data throughput, validated via interactive testing, and prepared for deployment with traceable commits. No major bugs fixed this month; the work lays groundwork for higher data-rate processing and improved scalability across the data pipeline.

April 2025

1 Commits

Apr 1, 2025

April 2025 (2025-04) Monthly Summary for DUNE/2x2_sim: Maintained and stabilized automation by aligning the ndlar run script with the current workflow YAML filenames. This maintenance ensures charge event building, reconstruction, combined reconstruction, and calibration steps reference the latest workflow file naming, preventing breakages due to workflow structure changes and reducing downtime across the automation pipeline.

February 2025

3 Commits • 2 Features

Feb 1, 2025

February 2025 monthly summary for the DUNE/2x2_sim development thread, focusing on business value, reliability, and maintainability. The month delivered data/MC processing enhancements, improved output organization, and clearer execution semantics that reduce risk and streamline downstream analysis.

January 2025

11 Commits • 4 Features

Jan 1, 2025

January 2025 performance highlights across DUNE/2x2_sim and DUNE/ndlar_flow. Delivered feature enhancements and stability improvements enabling broader multi-neutrino analyses, improved CAF workflow reliability, hardened environment/setup for reproducible builds, and consistent MC configuration naming. Business value: increased analysis capability, reduced runtime errors, clearer user guidance, and streamlined deployment across the 2x2 framework. Notable technical achievements include Neutrino Reconstruction Enhancement (commit 88710e501db4b0847fe4dd2ab492c6549b0ff070), LArRecoND segfault fix (commit 0b034119c7393a0d8847635e3274c0fac24f1106), CAF workflow input validation and mandatory edepsim input (commits 29d158c8836285b2d4289a8d19ebc358c8413266 and cfac6ed41e8139da2e5898d0a9a198c7b65a2922), Pandora/LArNDReco environment hardening (commits 3e88185ee35070f3755e5149fec966a4a087c736, f67af2ed22d9ad7ed68943e45d11ef03ac7362d0, cd7260ba7904577f60f7f5d200bd7ba5b1177577, 8093e67017287255e266de81101ea7c026e64658, 87f35f690e1d25a72ba5775d991a730a1faa0a14), and MC naming alignment for ndlar_flow (commits 2ad31279fe9ae1d20a1740866af7e69aeded26ff and 8ea0206ea3c17e0b7d68cebe3906b4c318db127f).

December 2024

2 Commits • 1 Features

Dec 1, 2024

December 2024 monthly performance summary: Focused on improving simulation accuracy and configurability across DUNE simulation repos. Key outcomes include delivering an environment-driven event generator selection for run_genie (DUNE/2x2_sim) and fixing a geometry alignment bug in detector geometry (DUNE/larnd-sim). These changes enhance business value by enabling reproducible, flexible simulation campaigns with less manual configuration, and strengthen the overall stability of end-to-end workflows. Technologies demonstrated include environment-variable driven configuration, YAML-based parameter tuning, and Git-based change management across multiple repositories.

Activity

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Quality Metrics

Correctness87.8%
Maintainability92.2%
Architecture84.4%
Performance80.0%
AI Usage20.0%

Skills & Technologies

Programming Languages

BashPythonShellXMLYAMLbash

Technical Skills

Build ManagementBuild System ConfigurationConfiguration ManagementData ReconstructionDependency ManagementDetector Simulation ConfigurationDevOpsDocumentationFile ManagementScriptingShell ScriptingSimulationVersion Control

Repositories Contributed To

3 repos

Overview of all repositories you've contributed to across your timeline

DUNE/2x2_sim

Dec 2024 Apr 2025
4 Months active

Languages Used

ShellBashPythonXMLbash

Technical Skills

Configuration ManagementShell ScriptingBuild ManagementBuild System ConfigurationData ReconstructionDependency Management

DUNE/ndlar_flow

Jan 2025 May 2025
2 Months active

Languages Used

YAML

Technical Skills

Configuration Management

DUNE/larnd-sim

Dec 2024 Dec 2024
1 Month active

Languages Used

YAML

Technical Skills

Detector Simulation Configuration

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