foggie-sims/foggie
Nov 2024 – Oct 2025
4 Months active
Languages Used
PythonJupyter NotebookMarkdownText
Technical Skills
AstrophysicsAstrophysics Data AnalysisBug FixingCode RefactoringData AnalysisData Visualization
ayanacharyya
PROFILE
Ayanacharyya
Ayan Acharyya developed and enhanced scientific data analysis and visualization tools in the foggie-sims/foggie repository, focusing on astrophysical simulation workflows. Over four months, Ayan delivered features for robust file I/O, parallel data processing, and reproducible plotting, integrating external datasets and literature-based overlays to support cross-redshift and multi-halo analyses. Using Python, Pandas, and Matplotlib, Ayan implemented utilities for generating and visualizing baryonic mass and metallicity gradients, improved error handling, and reorganized project structure for maintainability. The work addressed both feature development and bug fixes, demonstrating depth in scientific computing, code refactoring, and configuration management for collaborative research environments.
Overall Statistics
Feature vs Bugs
78%Features
Repository Contributions
36Total
Bugs
2
Commits
36
Features
7
Lines of code
105,798
Activity Months4
Work History
October 2025
1 Commits
Oct 1, 2025October 2025 monthly summary for foggie-sims/foggie focusing on stabilizing time-related operations and configuration loading. Addressed critical datetime handling and foggie_load usage; adjusted system configuration path to align with system-specific config resolution; these fixes improve reliability of time-based workflows and prevent runtime errors across environments.
1 Commits
Oct 1, 2025October 2025 monthly summary for foggie-sims/foggie focusing on stabilizing time-related operations and configuration loading. Addressed critical datetime handling and foggie_load usage; adjusted system configuration path to align with system-specific config resolution; these fixes improve reliability of time-based workflows and prevent runtime errors across environments.
October 2025
July 2025
1 Commits • 1 Features
Jul 1, 2025Month: 2025-07 — This month focused on feature enhancements in foggie-sims/foggie to improve spectral line data integration, visualization, and preparation for observational-data comparisons. Key work centered on adding spectral line data handling and plotting enhancements, configuring model paths, and enabling overplotting of observational data. No major bugs fixed this period; the emphasis was on delivering end-to-end plotting and data-prep capabilities to accelerate science output and publication readiness.
July 2025
1 Commits • 1 Features
Jul 1, 2025Month: 2025-07 — This month focused on feature enhancements in foggie-sims/foggie to improve spectral line data integration, visualization, and preparation for observational-data comparisons. Key work centered on adding spectral line data handling and plotting enhancements, configuring model paths, and enabling overplotting of observational data. No major bugs fixed this period; the emphasis was on delivering end-to-end plotting and data-prep capabilities to accelerate science output and publication readiness.
December 2024
16 Commits • 3 Features
Dec 1, 2024December 2024 performance summary for foggie project. Delivered core data tooling and analysis capabilities, improved data availability for halo analysis, and strengthened project organization to support long-term maintainability and collaboration. Emphasis on business value: enabling faster, more reliable scientific analysis and reproducible workflows with scalable data generation and visualization utilities.
16 Commits • 3 Features
Dec 1, 2024December 2024 performance summary for foggie project. Delivered core data tooling and analysis capabilities, improved data availability for halo analysis, and strengthened project organization to support long-term maintainability and collaboration. Emphasis on business value: enabling faster, more reliable scientific analysis and reproducible workflows with scalable data generation and visualization utilities.
December 2024
November 2024
18 Commits • 3 Features
Nov 1, 2024November 2024 (foggie-sims/foggie) delivered robustness, visualization, and data-persistence improvements that directly enhance scientific reliability, reproducibility, and workflow efficiency. Key features delivered include plotting enhancements with Z-z overlays and a solar reference line, updated colormap, multi-redshift and multi-halo support, and broader dataset integration (FIRE-2) along with Graf+24 related visuals; addition of literature MZR overlays (Sanders+2021) on existing plots; and an option to export metallicity gradient data with safeguards to prevent overwriting when plotting is enabled. Major bug fixes focused on Foggie Analysis IO robustness, refined argument parsing, improved output directory handling, and the ability to skip existing files or gracefully handle fitting errors. Together these changes improve cross-redshift analyses, data persistence, and overall workflow efficiency. Technologies demonstrated include Python plotting, datashader colormap customization, robust IO and error handling, and integration of external datasets and literature relations.
November 2024
18 Commits • 3 Features
Nov 1, 2024November 2024 (foggie-sims/foggie) delivered robustness, visualization, and data-persistence improvements that directly enhance scientific reliability, reproducibility, and workflow efficiency. Key features delivered include plotting enhancements with Z-z overlays and a solar reference line, updated colormap, multi-redshift and multi-halo support, and broader dataset integration (FIRE-2) along with Graf+24 related visuals; addition of literature MZR overlays (Sanders+2021) on existing plots; and an option to export metallicity gradient data with safeguards to prevent overwriting when plotting is enabled. Major bug fixes focused on Foggie Analysis IO robustness, refined argument parsing, improved output directory handling, and the ability to skip existing files or gracefully handle fitting errors. Together these changes improve cross-redshift analyses, data persistence, and overall workflow efficiency. Technologies demonstrated include Python plotting, datashader colormap customization, robust IO and error handling, and integration of external datasets and literature relations.
Activity
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Quality Metrics
Correctness86.6%
Maintainability86.4%
Architecture80.8%
Performance77.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookMarkdownPythonText
Technical Skills
AstrophysicsAstrophysics Data AnalysisBug FixingCode OrganizationCode RefactoringConfiguration ManagementData AnalysisData VisualizationDocumentationError HandlingFile I/OFile ManagementHigh-Performance Computing (HPC)MPIMatplotlib
Repositories Contributed To
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