ISISNeutronMuon/MDANSE
Nov 2024 – Jun 2025
8 Months active
Languages Used
C++PyQtPythonTOMLHTMLFortranRSTmd
Technical Skills
3D VisualizationAlgorithm OptimizationBackend DevelopmentCode DocumentationCode FormattingConfiguration Management
Chi Cheng
PROFILE
Chi Cheng
Chi Cheng developed and maintained core scientific data analysis workflows for the ISISNeutronMuon/MDANSE repository, focusing on molecular dynamics simulation analytics and output management. Over eight months, he delivered robust grouping logic, enhanced trajectory import and atom mapping, and improved calculation consistency across multiple analysis modules. Using Python, NumPy, and PyQt, he refactored data models, optimized performance, and expanded test coverage to ensure reliable, reproducible results. His work included integrating CP2K and MDTraj support, refining the atom database editor, and standardizing output formats. Through systematic code quality improvements and comprehensive documentation, he enabled scalable, maintainable, and user-friendly scientific computing pipelines.
Overall Statistics
Feature vs Bugs
52%Features
Repository Contributions
266Total
Bugs
69
Commits
266
Features
75
Lines of code
122,500
Activity Months8
Your Network
7 people
Work History
June 2025
60 Commits • 11 Features
Jun 1, 2025June 2025 Monthly Summary for ISISNeutronMuon/MDANSE: Delivered substantial grouping and output-management improvements across multiple fluctuation analysis signals, expanded grouping across additional jobs, and strengthened code quality and tests. The work stabilizes and standardizes grouped results, enabling faster downstream analytics and more reliable comparisons across experiments. Key features and enhancements delivered include: (1) comprehensive grouping across PACF, PPS, self-part van Hove, and VACF with updated output naming and grouping configuration; (2) broad extension of grouping to CCF, DISF, EISF, GDISF, SFFSF, NDTSF, coordination number, and PDF, with bug fixes for grouping when molecules are not selected; (3) updated output keys and documentation for the coordination number, PDFs, and related results; (4) code quality and maintainability improvements (linting with Ruff, formatting, docstrings) and code refactoring for readability and stability; (5) unit tests and documentation updates to reflect new grouping behavior, including a new selection sum test. Major bugs fixed include SSF/CCF calculation and output issues, PDF unit test reference alignment, Windows plotting reliability, main/partial van Hove results, and various workflow/test fixes. Representative fixes include: fixed ssf and ccf output (commit 9751edd6...), fixed PDF unit test references (commit 989e7aeb...), Windows plotting fix (commit fa291aa3...), coordination number output key updates (commit 12682333...), DISF/GDISF grouped totals and units fixes (commit add93754...), and multiple code-quality fixes (ruff, typing, docstrings) across the period. Overall impact and accomplishments: This work delivers reliable, consistent grouped results across a broad set of analyses, reducing post-processing effort and enabling direct, apples-to-apples comparisons across simulation runs. The improvements lay a solid foundation for reproducibility and faster business insights from MD simulations, while also reducing maintenance costs through improved code quality and test coverage. Technologies/skills demonstrated: Python-based data analytics and grouping logic, Ruff linting and typing improvements, docstring and documentation enhancements, unit testing, and code refactoring. The changes include both feature work (grouping logic and output keys) and quality improvements (linting, tests, and docs) across a multi-repo workflow.
60 Commits • 11 Features
Jun 1, 2025June 2025 Monthly Summary for ISISNeutronMuon/MDANSE: Delivered substantial grouping and output-management improvements across multiple fluctuation analysis signals, expanded grouping across additional jobs, and strengthened code quality and tests. The work stabilizes and standardizes grouped results, enabling faster downstream analytics and more reliable comparisons across experiments. Key features and enhancements delivered include: (1) comprehensive grouping across PACF, PPS, self-part van Hove, and VACF with updated output naming and grouping configuration; (2) broad extension of grouping to CCF, DISF, EISF, GDISF, SFFSF, NDTSF, coordination number, and PDF, with bug fixes for grouping when molecules are not selected; (3) updated output keys and documentation for the coordination number, PDFs, and related results; (4) code quality and maintainability improvements (linting with Ruff, formatting, docstrings) and code refactoring for readability and stability; (5) unit tests and documentation updates to reflect new grouping behavior, including a new selection sum test. Major bugs fixed include SSF/CCF calculation and output issues, PDF unit test reference alignment, Windows plotting reliability, main/partial van Hove results, and various workflow/test fixes. Representative fixes include: fixed ssf and ccf output (commit 9751edd6...), fixed PDF unit test references (commit 989e7aeb...), Windows plotting fix (commit fa291aa3...), coordination number output key updates (commit 12682333...), DISF/GDISF grouped totals and units fixes (commit add93754...), and multiple code-quality fixes (ruff, typing, docstrings) across the period. Overall impact and accomplishments: This work delivers reliable, consistent grouped results across a broad set of analyses, reducing post-processing effort and enabling direct, apples-to-apples comparisons across simulation runs. The improvements lay a solid foundation for reproducibility and faster business insights from MD simulations, while also reducing maintenance costs through improved code quality and test coverage. Technologies/skills demonstrated: Python-based data analytics and grouping logic, Ruff linting and typing improvements, docstring and documentation enhancements, unit testing, and code refactoring. The changes include both feature work (grouping logic and output keys) and quality improvements (linting, tests, and docs) across a multi-repo workflow.
June 2025
May 2025
24 Commits • 11 Features
May 1, 2025May 2025 monthly performance summary for ISISNeutronMuon/MDANSE focusing on delivered business value and technical achievements. The team delivered meaningful feature enhancements, fixed critical calculation and output issues, expanded test coverage, and improved code quality and user-facing UI. These efforts improved result accuracy, reduced maintenance risk, and accelerated downstream workflows in data analysis and reporting.
May 2025
24 Commits • 11 Features
May 1, 2025May 2025 monthly performance summary for ISISNeutronMuon/MDANSE focusing on delivered business value and technical achievements. The team delivered meaningful feature enhancements, fixed critical calculation and output issues, expanded test coverage, and improved code quality and user-facing UI. These efforts improved result accuracy, reduced maintenance risk, and accelerated downstream workflows in data analysis and reporting.
April 2025
29 Commits • 7 Features
Apr 1, 2025April 2025 MDANSE monthly summary focusing on delivering core capabilities, improving data integrity, and strengthening code quality. The team delivered major Atom Database Editor enhancements, refined the data model, tightened UI behavior, and implemented comprehensive code quality and testing improvements to support reliable, scalable data management and analysis workflows.
29 Commits • 7 Features
Apr 1, 2025April 2025 MDANSE monthly summary focusing on delivering core capabilities, improving data integrity, and strengthening code quality. The team delivered major Atom Database Editor enhancements, refined the data model, tightened UI behavior, and implemented comprehensive code quality and testing improvements to support reliable, scalable data management and analysis workflows.
April 2025
March 2025
26 Commits • 6 Features
Mar 1, 2025Month: 2025-03 — ISISNeutronMuon/MDANSE Overview - Delivered key features, fixed critical inconsistencies, and improved code quality and documentation. Efforts focused on strengthening the MDANSE weight scheme integration, ensuring cross-module consistency (DISF/ DCSF/ CCF/ NDTSF/ SSF/ X-ray), and enhancing maintainability and user guidance. Key features delivered - Weight scheme integration and prefactor alignment: Updated weight scheme section and aligned DISF/DCSF prefactors with SSF, van Hove function, and PDF; synchronized diagonal parts of partial PDF, SSF, and van Hove with the weight scheme. - X-ray/SSF scaling integration: Introduced a scaling factor for inter+intra SSF and X-ray results; ensured full compatibility with the MDANSE weight scheme. - Documentation updates: Weighting scheme and notation refreshed; dynamics section updated; broader editorial improvements across docs. - General code quality and maintenance: Linting with Ruff, updated docstrings, and refactor fixes to improve readability and reliability. - NDTSF/DCSF/CCF consistency and plotting: Stabilized NDTSF references; aligned DCSF/CCF with updated prefactors; NDTSF now plottable with apply weight option consistent with DCSF/DISF. Major bugs fixed - DCSF/CCF/NDTSF consistency and tests: Updated tests for stable comparisons; fixed prefactors and NDTSF references; improved overall consistency. - Documentation corrections: Fixed issues in docs; fixed broken links and typos (GDISF equation typos addressed); file-naming consistency improvements. - Plot/plotter behavior: Ensured NDTSF can be plotted like DCSF/DISF with the weight-apply option. Overall impact and accomplishments - Improved accuracy and reliability: End-to-end weight scheme consistency across MDANSE components reduces discrepancies between DISF/ DCSF/ CCF/ NDTSF/ SSF and related functions, enhancing predictive reliability for users. - Increased maintainability and onboarding efficiency: Code quality improvements and comprehensive documentation reduce long-term maintenance costs and accelerate contributions. - Better user guidance and reproducibility: Updated docs and plotting behavior provide clearer guidance and more reproducible results for researchers. Technologies/skills demonstrated - Python development and MDANSE domain expertise: weight scheme logic, prefactor handling, and cross-module integration. - Software quality practices: linting with Ruff, docstring standards, and systematic refactors. - Testing and validation: Updated tests for stable comparisons across components; validated plotting behavior and scaling logic.
March 2025
26 Commits • 6 Features
Mar 1, 2025Month: 2025-03 — ISISNeutronMuon/MDANSE Overview - Delivered key features, fixed critical inconsistencies, and improved code quality and documentation. Efforts focused on strengthening the MDANSE weight scheme integration, ensuring cross-module consistency (DISF/ DCSF/ CCF/ NDTSF/ SSF/ X-ray), and enhancing maintainability and user guidance. Key features delivered - Weight scheme integration and prefactor alignment: Updated weight scheme section and aligned DISF/DCSF prefactors with SSF, van Hove function, and PDF; synchronized diagonal parts of partial PDF, SSF, and van Hove with the weight scheme. - X-ray/SSF scaling integration: Introduced a scaling factor for inter+intra SSF and X-ray results; ensured full compatibility with the MDANSE weight scheme. - Documentation updates: Weighting scheme and notation refreshed; dynamics section updated; broader editorial improvements across docs. - General code quality and maintenance: Linting with Ruff, updated docstrings, and refactor fixes to improve readability and reliability. - NDTSF/DCSF/CCF consistency and plotting: Stabilized NDTSF references; aligned DCSF/CCF with updated prefactors; NDTSF now plottable with apply weight option consistent with DCSF/DISF. Major bugs fixed - DCSF/CCF/NDTSF consistency and tests: Updated tests for stable comparisons; fixed prefactors and NDTSF references; improved overall consistency. - Documentation corrections: Fixed issues in docs; fixed broken links and typos (GDISF equation typos addressed); file-naming consistency improvements. - Plot/plotter behavior: Ensured NDTSF can be plotted like DCSF/DISF with the weight-apply option. Overall impact and accomplishments - Improved accuracy and reliability: End-to-end weight scheme consistency across MDANSE components reduces discrepancies between DISF/ DCSF/ CCF/ NDTSF/ SSF and related functions, enhancing predictive reliability for users. - Increased maintainability and onboarding efficiency: Code quality improvements and comprehensive documentation reduce long-term maintenance costs and accelerate contributions. - Better user guidance and reproducibility: Updated docs and plotting behavior provide clearer guidance and more reproducible results for researchers. Technologies/skills demonstrated - Python development and MDANSE domain expertise: weight scheme logic, prefactor handling, and cross-module integration. - Software quality practices: linting with Ruff, docstring standards, and systematic refactors. - Testing and validation: Updated tests for stable comparisons across components; validated plotting behavior and scaling logic.
February 2025
28 Commits • 10 Features
Feb 1, 2025February 2025: Delivered end-to-end enhancements to MDANSE data workflows, improved analytics reliability, and strengthened code quality. Key features include CP2K integration with optional velocity support, initial CP2K forces file handling, and test coverage for force conversion. Added a plotter scaling option with fixed data header alignment. Implemented substantial bug fixes across statistical tests (EISF, VACF, PACF) and updated the total neutron calculation to remove weighted results. Also advanced maintainability with core refactorings (get_weights), weight assignment fixes, and code quality improvements (ruff/black), plus documentation updates and tutorials. Overall impact: more reliable data processing, faster validation, and clearer analytics for better scientific decision-making and collaboration.
28 Commits • 10 Features
Feb 1, 2025February 2025: Delivered end-to-end enhancements to MDANSE data workflows, improved analytics reliability, and strengthened code quality. Key features include CP2K integration with optional velocity support, initial CP2K forces file handling, and test coverage for force conversion. Added a plotter scaling option with fixed data header alignment. Implemented substantial bug fixes across statistical tests (EISF, VACF, PACF) and updated the total neutron calculation to remove weighted results. Also advanced maintainability with core refactorings (get_weights), weight assignment fixes, and code quality improvements (ruff/black), plus documentation updates and tutorials. Overall impact: more reliable data processing, faster validation, and clearer analytics for better scientific decision-making and collaboration.
February 2025
January 2025
39 Commits • 9 Features
Jan 1, 2025January 2025 (2025-01) – ISISNeutronMuon/MDANSE monthly summary covering key features delivered, major bugs fixed, impact, and technologies demonstrated. The past month focused on stabilizing the data processing pipeline, improving user experience through robust file handling, and advancing core scientific capabilities and tests.
January 2025
39 Commits • 9 Features
Jan 1, 2025January 2025 (2025-01) – ISISNeutronMuon/MDANSE monthly summary covering key features delivered, major bugs fixed, impact, and technologies demonstrated. The past month focused on stabilizing the data processing pipeline, improving user experience through robust file handling, and advancing core scientific capabilities and tests.
December 2024
31 Commits • 10 Features
Dec 1, 2024December 2024 monthly summary for ISISNeutronMuon/MDANSE focused on delivering core MDTraj integration, strengthening atom-mapping correctness and performance, expanding UI capabilities, and improving code quality and test reliability. The work enhances reliability, scalability for large systems, and faster feedback loops for end users, driving clear business value in simulation accuracy and developer velocity.
31 Commits • 10 Features
Dec 1, 2024December 2024 monthly summary for ISISNeutronMuon/MDANSE focused on delivering core MDTraj integration, strengthening atom-mapping correctness and performance, expanding UI capabilities, and improving code quality and test reliability. The work enhances reliability, scalability for large systems, and faster feedback loops for end users, driving clear business value in simulation accuracy and developer velocity.
December 2024
November 2024
29 Commits • 11 Features
Nov 1, 2024November 2024 monthly summary for ISISNeutronMuon/MDANSE: Delivered MDAnalysis-based trajectory import, multi-file handling, and UI improvements that enhance data fidelity, scalability, and usability. Key capabilities include configurable MDAnalysis converter (time steps, optional coordinate file) with velocity and force data extraction; multi-file coordinate reading and stitching; UI improvements for atom mapping and labeling (scrollable dialog, element-data labels) including corrected SOD/Na mapping; 3D viewer performance enhancements (parallel KD-tree bond calculation, improved bond calculator and view picker); and dependencies/data improvements (MDAnalysis dependency added, charges read from topology file when available). These changes reduce setup friction, improve reproducibility, and enable more complete trajectory analysis.
November 2024
29 Commits • 11 Features
Nov 1, 2024November 2024 monthly summary for ISISNeutronMuon/MDANSE: Delivered MDAnalysis-based trajectory import, multi-file handling, and UI improvements that enhance data fidelity, scalability, and usability. Key capabilities include configurable MDAnalysis converter (time steps, optional coordinate file) with velocity and force data extraction; multi-file coordinate reading and stitching; UI improvements for atom mapping and labeling (scrollable dialog, element-data labels) including corrected SOD/Na mapping; 3D viewer performance enhancements (parallel KD-tree bond calculation, improved bond calculator and view picker); and dependencies/data improvements (MDAnalysis dependency added, charges read from topology file when available). These changes reduce setup friction, improve reproducibility, and enable more complete trajectory analysis.
Activity
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Quality Metrics
Correctness88.2%
Maintainability88.0%
Architecture83.0%
Performance79.2%
AI Usage20.4%
Skills & Technologies
Programming Languages
C++FortranHTMLJSONMMATLABNumpyPyQtPythonPython (GUI)
Technical Skills
3D VisualizationAlgorithm DevelopmentAlgorithm OptimizationBackend DevelopmentBug FixBug FixingClean CodeCode ClarityCode CleanupCode CommentingCode DocumentationCode FormattingCode OptimizationCode OrganizationCode Refactoring
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