hael/SIMPLE
Nov 2024 – Apr 2026
12 Months active
Languages Used
Fortran
Technical Skills
Data AnalysisImage ProcessingScientific ComputingFortran ProgrammingSignal ProcessingPerformance Optimization
Afan Atif
PROFILE
Afan Atif
Over twelve months, Afanatif1 developed advanced data analysis and image processing capabilities for the hael/SIMPLE repository, focusing on 2D molecular and AFM data workflows. Leveraging Fortran, they engineered robust algorithms for clustering, hierarchical tree modeling, and correlation analysis, integrating features like Fourier-Mellin transforms, K-means clustering, and block-tree structures. Their work included performance optimization, memory management, and extensive test coverage, resulting in scalable, reproducible analytics pipelines. By refactoring core modules and introducing new CLI tools, Afanatif1 improved workflow efficiency, data integrity, and maintainability, enabling researchers to process large datasets with greater reliability and supporting rigorous scientific computing requirements.
Overall Statistics
Feature vs Bugs
70%Features
Repository Contributions
75Total
Bugs
12
Commits
75
Features
28
Lines of code
14,232
Activity Months12
Your Network
18 peopleShared Repositories
18
AtifMember
CaesarMember
CaesarMember
CaesarMember
CaesarMember
CaesarMember
CaesarMember
cyrilrMember
Hans ElmlundMember
Work History
April 2026
3 Commits • 1 Features
Apr 1, 2026April 2026: Focused on performance optimization and robustness for 2D pool analysis in hael/SIMPLE. Delivered Block Tree integration with pre-computed trees, enabling faster tree_rank calculations via binary file reads; improved memory and parameter handling to boost robustness and scalability. Implemented targeted robustness fixes for pool tree initialization, validation, and file checks to ensure reliability in production.
3 Commits • 1 Features
Apr 1, 2026April 2026: Focused on performance optimization and robustness for 2D pool analysis in hael/SIMPLE. Delivered Block Tree integration with pre-computed trees, enabling faster tree_rank calculations via binary file reads; improved memory and parameter handling to boost robustness and scalability. Implemented targeted robustness fixes for pool tree initialization, validation, and file checks to ensure reliability in production.
April 2026
March 2026
8 Commits • 3 Features
Mar 1, 2026March 2026 — hael/SIMPLE: Delivered foundational block-tree and data-management improvements, plus a new analysis CLI, enhancing performance, reliability, and usability of tree-based workflows. These changes drive faster, more accurate analyses and reproducible results across block-tree datasets.
March 2026
8 Commits • 3 Features
Mar 1, 2026March 2026 — hael/SIMPLE: Delivered foundational block-tree and data-management improvements, plus a new analysis CLI, enhancing performance, reliability, and usability of tree-based workflows. These changes drive faster, more accurate analyses and reproducible results across block-tree datasets.
February 2026
18 Commits • 2 Features
Feb 1, 2026February 2026 monthly summary for hael/SIMPLE: Delivered significant improvements to the 2D cleanup and data processing workflow and rearchitected the tree search stack to boost performance, scalability, and user productivity. Key work includes memory allocation optimizations for joint scores and indices, advanced image processing techniques (e.g., Otsu binarization), expanded cleanup capabilities for 2D projects, projections, and data structures, plus a new 2D cleanup commander and enhanced testing capabilities. In parallel, the search algorithms were modularized into distinct 2D tree paths (multi-dendrogram vs. binary tree) with best-first, stochastic, probabilistic traversal, and exhaustive options, accompanied by 2D block-tree utilities and tests. These efforts improve workflow efficiency, data integrity, and analytical throughput, delivering measurable business value and preparing the codebase for larger-scale 2D molecular data analyses.
18 Commits • 2 Features
Feb 1, 2026February 2026 monthly summary for hael/SIMPLE: Delivered significant improvements to the 2D cleanup and data processing workflow and rearchitected the tree search stack to boost performance, scalability, and user productivity. Key work includes memory allocation optimizations for joint scores and indices, advanced image processing techniques (e.g., Otsu binarization), expanded cleanup capabilities for 2D projects, projections, and data structures, plus a new 2D cleanup commander and enhanced testing capabilities. In parallel, the search algorithms were modularized into distinct 2D tree paths (multi-dendrogram vs. binary tree) with best-first, stochastic, probabilistic traversal, and exhaustive options, accompanied by 2D block-tree utilities and tests. These efforts improve workflow efficiency, data integrity, and analytical throughput, delivering measurable business value and preparing the codebase for larger-scale 2D molecular data analyses.
February 2026
January 2026
28 Commits • 11 Features
Jan 1, 2026January 2026 — hael/SIMPLE monthly summary focused on stabilizing the core clustering pipeline, expanding test coverage, and enabling scalable tree-based analysis. Core improvements include a shift to agglomerative clustering, a dendrogram overhaul with sub_trees, and robust state management across tree structures. The month also delivered targeted benchmarks and extensive tests to validate performance and correctness under load.
January 2026
28 Commits • 11 Features
Jan 1, 2026January 2026 — hael/SIMPLE monthly summary focused on stabilizing the core clustering pipeline, expanding test coverage, and enabling scalable tree-based analysis. Core improvements include a shift to agglomerative clustering, a dendrogram overhaul with sub_trees, and robust state management across tree structures. The month also delivered targeted benchmarks and extensive tests to validate performance and correctness under load.
December 2025
4 Commits • 1 Features
Dec 1, 2025December 2025 — hael/SIMPLE delivered a robust hierarchical tree data model with traversal and in-plane angle node types, including a walking subroutine and enhanced dendrogram visualization. The work included tree refactoring to support the new node types and added tests for the structure. A small Fortran test improvement cleaned up a redundant variable declaration to improve clarity and maintainability. These changes deliver better data relationship management, stronger test coverage, and a solid foundation for scalable analytics.
4 Commits • 1 Features
Dec 1, 2025December 2025 — hael/SIMPLE delivered a robust hierarchical tree data model with traversal and in-plane angle node types, including a walking subroutine and enhanced dendrogram visualization. The work included tree refactoring to support the new node types and added tests for the structure. A small Fortran test improvement cleaned up a redundant variable declaration to improve clarity and maintainability. These changes deliver better data relationship management, stronger test coverage, and a solid foundation for scalable analytics.
December 2025
September 2025
1 Commits • 1 Features
Sep 1, 2025Month 2025-09 – hael/SIMPLE: Delivered end-to-end Cavgs and AFM Image Processing, Ranking, and Output Pipeline. Refactored image handling for reading/processing/normalization; added matching distance matrix; produced ranked outputs including reference cavgs and AFM images. No major bugs fixed this month. Business impact: improved analytics throughput, data quality, and traceability.
September 2025
1 Commits • 1 Features
Sep 1, 2025Month 2025-09 – hael/SIMPLE: Delivered end-to-end Cavgs and AFM Image Processing, Ranking, and Output Pipeline. Refactored image handling for reading/processing/normalization; added matching distance matrix; produced ranked outputs including reference cavgs and AFM images. No major bugs fixed this month. Business impact: improved analytics throughput, data quality, and traceability.
August 2025
3 Commits • 2 Features
Aug 1, 2025Month: 2025-08 — Summary focused on hael/SIMPLE contributions, highlighting delivered features, notable improvements, and overall impact for business value and maintainability.
3 Commits • 2 Features
Aug 1, 2025Month: 2025-08 — Summary focused on hael/SIMPLE contributions, highlighting delivered features, notable improvements, and overall impact for business value and maintainability.
August 2025
April 2025
3 Commits • 1 Features
Apr 1, 2025April 2025 (2025-04) monthly summary for hael/SIMPLE feature work focused on enhancing AFM particle analysis accuracy and robustness. Delivered a substantial feature upgrade including 2D K-means clustering and Gaussian fitting for particle analysis, plus refactoring of image processing and correlation calculations. Implemented validation/testing for in-plane correlation function to ensure reliability across datasets. The work improves measurement accuracy, reliability, and maintainability, enabling more precise material characterization and better downstream analytics.
April 2025
3 Commits • 1 Features
Apr 1, 2025April 2025 (2025-04) monthly summary for hael/SIMPLE feature work focused on enhancing AFM particle analysis accuracy and robustness. Delivered a substantial feature upgrade including 2D K-means clustering and Gaussian fitting for particle analysis, plus refactoring of image processing and correlation calculations. Implemented validation/testing for in-plane correlation function to ensure reliability across datasets. The work improves measurement accuracy, reliability, and maintainability, enabling more precise material characterization and better downstream analytics.
February 2025
3 Commits • 2 Features
Feb 1, 2025February 2025 monthly performance summary for hael/SIMPLE. Focused on delivering performance improvements for core analytics, restoring correctness where needed, and expanding analytics capabilities for AFM data analysis. Key efforts include refactoring the correlation matrix computation to a Fourier-Mellin-based approach, reverting changes that affected in-plane invariant corrmat calculations to ensure accuracy, and adding clustering/visualization features to the AFM commander to enhance data insight workflows.
3 Commits • 2 Features
Feb 1, 2025February 2025 monthly performance summary for hael/SIMPLE. Focused on delivering performance improvements for core analytics, restoring correctness where needed, and expanding analytics capabilities for AFM data analysis. Key efforts include refactoring the correlation matrix computation to a Fourier-Mellin-based approach, reverting changes that affected in-plane invariant corrmat calculations to ensure accuracy, and adding clustering/visualization features to the AFM commander to enhance data insight workflows.
February 2025
January 2025
2 Commits • 2 Features
Jan 1, 2025Monthly work summary for 2025-01 focused on the hael/SIMPLE repository. Delivered end-to-end AFM data processing capabilities and targeted performance improvements for correlation analysis. Summary highlights below with top achievements and business impact.
January 2025
2 Commits • 2 Features
Jan 1, 2025Monthly work summary for 2025-01 focused on the hael/SIMPLE repository. Delivered end-to-end AFM data processing capabilities and targeted performance improvements for correlation analysis. Summary highlights below with top achievements and business impact.
December 2024
1 Commits • 1 Features
Dec 1, 2024Summary for 2024-12: Focused on delivering AFM data analysis capabilities in hael/SIMPLE. Key feature delivered: AFM Image Processing and In-Plane Invariant Correlation Analysis with an optional mirror-symmetry consideration (under development). No documented major bug fixes this month. Overall impact: lays groundwork for automated AFM data analysis pipelines, enabling more rigorous materials characterization and potential time savings for researchers. Technologies/skills demonstrated: AFM image processing, feature extraction, invariant correlation analysis, symmetry-aware algorithm design, Git-based development and traceability, and performance-conscious coding practices.
1 Commits • 1 Features
Dec 1, 2024Summary for 2024-12: Focused on delivering AFM data analysis capabilities in hael/SIMPLE. Key feature delivered: AFM Image Processing and In-Plane Invariant Correlation Analysis with an optional mirror-symmetry consideration (under development). No documented major bug fixes this month. Overall impact: lays groundwork for automated AFM data analysis pipelines, enabling more rigorous materials characterization and potential time savings for researchers. Technologies/skills demonstrated: AFM image processing, feature extraction, invariant correlation analysis, symmetry-aware algorithm design, Git-based development and traceability, and performance-conscious coding practices.
December 2024
November 2024
1 Commits • 1 Features
Nov 1, 2024Monthly work summary for 2024-11 focusing on hael/SIMPLE developments, delivering AFM data support and related improvements.
November 2024
1 Commits • 1 Features
Nov 1, 2024Monthly work summary for 2024-11 focusing on hael/SIMPLE developments, delivering AFM data support and related improvements.
Activity
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Quality Metrics
Correctness83.4%
Maintainability81.8%
Architecture82.0%
Performance80.8%
AI Usage26.0%
Skills & Technologies
Programming Languages
Fortran
Technical Skills
2D data visualizationAlgorithm OptimizationCLI DevelopmentClustering AlgorithmsCode FormattingCode RefactoringData AnalysisFortranFortran DevelopmentFortran ProgrammingFortran programmingFull Stack DevelopmentImage ProcessingPerformance OptimizationReadability Improvement
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