AstarVienna/METIS_Pipeline
Oct 2024 – Dec 2025
14 Months active
Languages Used
PythonShellYAMLINITOMLMarkdown
Technical Skills
Backend DevelopmentData ProcessingAstronomy SoftwareClean CodeCode QualityCode Refactoring
Martin Baláž
PROFILE
Martin Baláž
Martin Balaz engineered core data processing and quality control infrastructure for the AstarVienna/METIS_Pipeline, focusing on scalable, maintainable astronomy data workflows. Over 14 months, he refactored the pipeline’s architecture to introduce robust DataItem models, unified data loading, and a centralized quality control parameter framework, enabling reliable image calibration and reduction. Using Python and leveraging object-oriented design, Martin modernized parameter handling, improved error propagation, and standardized FITS file processing. His work emphasized test-driven development, code clarity, and extensibility, resulting in improved data integrity, reproducibility, and maintainability across the pipeline. These enhancements streamlined downstream analysis and reduced manual validation overhead.
Overall Statistics
Feature vs Bugs
71%Features
Repository Contributions
298Total
Bugs
36
Commits
298
Features
90
Lines of code
43,601
Activity Months14
Your Network
13 peopleShared Repositories
13
Benjamin EiseleMember
eiseleb47Member
Chi-Hung YanMember
Gilles OttenMember
Hugo BuddelmeijerMember
janusbrinkMember
JenniferKarrMember
eiseleb47Member
WKMember
Work History
December 2025
3 Commits • 1 Features
Dec 1, 2025December 2025 Monthly Summary for METIS_Pipeline: Focused on strengthening data quality controls by delivering a unified Quality Control parameter framework. Delivered QcParameter skeletons, centralized QC introspection/refactoring, and expanded QC parameter coverage for image processing. No major bugs fixed this month; primary activity was feature development, refactoring, and establishing a maintainable QC infrastructure. Impact: enables configurable, auditable QC checks, reduces manual validation time, and provides scalable QC definitions for governance and reliability. Technologies/skills demonstrated include Python object-oriented design, refactoring for maintainability, QC metrics integration, and pipeline instrumentation.
3 Commits • 1 Features
Dec 1, 2025December 2025 Monthly Summary for METIS_Pipeline: Focused on strengthening data quality controls by delivering a unified Quality Control parameter framework. Delivered QcParameter skeletons, centralized QC introspection/refactoring, and expanded QC parameter coverage for image processing. No major bugs fixed this month; primary activity was feature development, refactoring, and establishing a maintainable QC infrastructure. Impact: enables configurable, auditable QC checks, reduces manual validation time, and provides scalable QC definitions for governance and reliability. Technologies/skills demonstrated include Python object-oriented design, refactoring for maintainability, QC metrics integration, and pipeline instrumentation.
December 2025
November 2025
7 Commits • 2 Features
Nov 1, 2025November 2025 performance summary for METIS_Pipeline (AstarVienna). Focused on strengthening data robustness, pipeline reliability, and maintainability in METIS/IFU workflows. Delivered two primary features and associated optimizations; addressed data loading, header handling, and schema enhancements; and improved developer guidance via documentation and introspection warnings.
November 2025
7 Commits • 2 Features
Nov 1, 2025November 2025 performance summary for METIS_Pipeline (AstarVienna). Focused on strengthening data robustness, pipeline reliability, and maintainability in METIS/IFU workflows. Delivered two primary features and associated optimizations; addressed data loading, header handling, and schema enhancements; and improved developer guidance via documentation and introspection warnings.
October 2025
27 Commits • 6 Features
Oct 1, 2025October 2025 METIS_Pipeline monthly summary: Key features delivered, critical bugs fixed, and improvements in pipeline reliability and maintainability. Highlights include IFU data processing enhancements, robust data I/O, architectural data handling changes, and Metis imaging/LM/N IMG recipe migrations, complemented by stability fixes and expanded tests.
27 Commits • 6 Features
Oct 1, 2025October 2025 METIS_Pipeline monthly summary: Key features delivered, critical bugs fixed, and improvements in pipeline reliability and maintainability. Highlights include IFU data processing enhancements, robust data I/O, architectural data handling changes, and Metis imaging/LM/N IMG recipe migrations, complemented by stability fixes and expanded tests.
October 2025
September 2025
10 Commits • 3 Features
Sep 1, 2025Month: 2025-09 | Repository: AstarVienna/METIS_Pipeline Concise monthly summary focusing on business value and technical achievements: - Key features delivered: - Unified METIS data loading and processing enhancements: Consolidated updates to data loading, access, and preparation across METIS image processing pipelines; adopted a standardized loading format; enabled reuse of loaded data for background products. - Data model and pipeline infrastructure modernization: Refactors to data item models, HDU scaffolding, and core imports to improve clarity, logging, and maintainability. - Improved dark current correction in det_dark: Deduplicated code, improved error handling, and introduced a robust method for combining images with proper error propagation. - Major bugs fixed: - Fixed wrong loading in metis_ifu_postprocess, addressing a critical data integrity issue in the post-processing path. - Overall impact and accomplishments: - Increased reliability and maintainability of METIS data processing, enabling faster iteration and safer background product generation. Clearer data models and logging support scale, reduce maintenance overhead, and lower risk of data loading errors. End-to-end data integrity improved with robust dark current correction. - Technologies/skills demonstrated: - Data loading standardization, pipeline modernization, Python refactoring, error handling, testing practices, and commit-level traceability (mapping to multiple commits across features).
September 2025
10 Commits • 3 Features
Sep 1, 2025Month: 2025-09 | Repository: AstarVienna/METIS_Pipeline Concise monthly summary focusing on business value and technical achievements: - Key features delivered: - Unified METIS data loading and processing enhancements: Consolidated updates to data loading, access, and preparation across METIS image processing pipelines; adopted a standardized loading format; enabled reuse of loaded data for background products. - Data model and pipeline infrastructure modernization: Refactors to data item models, HDU scaffolding, and core imports to improve clarity, logging, and maintainability. - Improved dark current correction in det_dark: Deduplicated code, improved error handling, and introduced a robust method for combining images with proper error propagation. - Major bugs fixed: - Fixed wrong loading in metis_ifu_postprocess, addressing a critical data integrity issue in the post-processing path. - Overall impact and accomplishments: - Increased reliability and maintainability of METIS data processing, enabling faster iteration and safer background product generation. Clearer data models and logging support scale, reduce maintenance overhead, and lower risk of data loading errors. End-to-end data integrity improved with robust dark current correction. - Technologies/skills demonstrated: - Data loading standardization, pipeline modernization, Python refactoring, error handling, testing practices, and commit-level traceability (mapping to multiple commits across features).
August 2025
13 Commits • 2 Features
Aug 1, 2025Monthly performance recap for 2025-08 focused on METIS_Pipeline improvements. Delivered a substantial overhaul of data handling and integrity, plus targeted enhancements to distortion-reduced product processing. Core work included data loading standardization, explicit data item semantics, unified image handling, and data schemas with usage tracking, accompanied by a major refactor of DataItems and related utilities. Implemented distortion-reduced product improvements with inheritance from ImageDataItem, ensured dummy image generation, and refined loading pathways. Achieved foundational maintainability gains with the first multisave, removal of the MultipleDataItem, and reworked recipes for the new standard. These changes enhance data reliability, reproducibility, and readiness for downstream analysis and distortion studies.
13 Commits • 2 Features
Aug 1, 2025Monthly performance recap for 2025-08 focused on METIS_Pipeline improvements. Delivered a substantial overhaul of data handling and integrity, plus targeted enhancements to distortion-reduced product processing. Core work included data loading standardization, explicit data item semantics, unified image handling, and data schemas with usage tracking, accompanied by a major refactor of DataItems and related utilities. Implemented distortion-reduced product improvements with inheritance from ImageDataItem, ensured dummy image generation, and refined loading pathways. Achieved foundational maintainability gains with the first multisave, removal of the MultipleDataItem, and reworked recipes for the new standard. These changes enhance data reliability, reproducibility, and readiness for downstream analysis and distortion studies.
August 2025
July 2025
39 Commits • 9 Features
Jul 1, 2025July 2025 METIS_Pipeline (AstarVienna) — Focused on completing the core migration to DataItems, stabilizing the test and import pipeline, and laying groundwork for future DataItems-driven analytics. Delivered a data-model upgrade, significant refactors, and robustness improvements that reduce technical debt and improve data integrity and maintainability.
July 2025
39 Commits • 9 Features
Jul 1, 2025July 2025 METIS_Pipeline (AstarVienna) — Focused on completing the core migration to DataItems, stabilizing the test and import pipeline, and laying groundwork for future DataItems-driven analytics. Delivered a data-model upgrade, significant refactors, and robustness improvements that reduce technical debt and improve data integrity and maintainability.
June 2025
30 Commits • 11 Features
Jun 1, 2025Month: 2025-06. Summary: The METIS_Pipeline project delivered a major data-model overhaul and robustness improvements, enabling more scalable data processing and stronger data integrity across DRLD workflows. Key deliveries included core DataItems expansion and hierarchy refactor, DRLD-integrated data item work, modernization of parameter handling to the pyesorex Parameter class hierarchy, extended conversions and final recipe pass, and comprehensive data model refinements including frame attributes and WCU_OFF raws. In addition, targeted image processing fixes and a reaffirmed tests baseline improved stability and reliability. Commits referenced include: core refactors such as Splitting the dataitems and Moving to DataItems; Refactored the data item hierarchy; Digging through DRLD and adding data items; Switched to pyesorex Parameter class hierarchy; More conversions and Last recipe converted; Slightly tidied up + new test; Toying with descriptors, mixins and subclassing; Added WCU_OFF raws; More data items added; Moved cpl frame attributes to DataItem; Converted all IFU data items; Added _frame attributes to LM recipes; More missing attributes added; Simplifying classes; Added subclass scanning mechanism; Ironed out class hierarchy; Reworked the saving mechanism for image products; Fixed image-related attributes (distortion, restore, lm_img_basic_reduce); Misc stability fixes.
30 Commits • 11 Features
Jun 1, 2025Month: 2025-06. Summary: The METIS_Pipeline project delivered a major data-model overhaul and robustness improvements, enabling more scalable data processing and stronger data integrity across DRLD workflows. Key deliveries included core DataItems expansion and hierarchy refactor, DRLD-integrated data item work, modernization of parameter handling to the pyesorex Parameter class hierarchy, extended conversions and final recipe pass, and comprehensive data model refinements including frame attributes and WCU_OFF raws. In addition, targeted image processing fixes and a reaffirmed tests baseline improved stability and reliability. Commits referenced include: core refactors such as Splitting the dataitems and Moving to DataItems; Refactored the data item hierarchy; Digging through DRLD and adding data items; Switched to pyesorex Parameter class hierarchy; More conversions and Last recipe converted; Slightly tidied up + new test; Toying with descriptors, mixins and subclassing; Added WCU_OFF raws; More data items added; Moved cpl frame attributes to DataItem; Converted all IFU data items; Added _frame attributes to LM recipes; More missing attributes added; Simplifying classes; Added subclass scanning mechanism; Ironed out class hierarchy; Reworked the saving mechanism for image products; Fixed image-related attributes (distortion, restore, lm_img_basic_reduce); Misc stability fixes.
June 2025
May 2025
23 Commits • 10 Features
May 1, 2025May 2025 Monthly Summary for AstarVienna/METIS_Pipeline: Key engineering work focused on API/architecture refactorings, code quality, test coverage, and targeted bug fixes. The work delivered improved maintainability, transparency of pipeline inputs, better diagnostics, and extended functionality across the METIS_Pipeline. Highlights include API consolidation and refactoring (PipelineInput to Table and Image), added used_frames API, and data-structure updates (products as sets) with docstrings; documentation enhancements; Metis-related bug fixes; test metadata update; linting and static analysis improvements; external mark for batch metadata; enhanced diagnostic messages; FrameType.TABLE support in MultipleProduct; DRLD reconstruction classmethods; abstracted saving mechanism for lm_img_basic_reduce.
May 2025
23 Commits • 10 Features
May 1, 2025May 2025 Monthly Summary for AstarVienna/METIS_Pipeline: Key engineering work focused on API/architecture refactorings, code quality, test coverage, and targeted bug fixes. The work delivered improved maintainability, transparency of pipeline inputs, better diagnostics, and extended functionality across the METIS_Pipeline. Highlights include API consolidation and refactoring (PipelineInput to Table and Image), added used_frames API, and data-structure updates (products as sets) with docstrings; documentation enhancements; Metis-related bug fixes; test metadata update; linting and static analysis improvements; external mark for batch metadata; enhanced diagnostic messages; FrameType.TABLE support in MultipleProduct; DRLD reconstruction classmethods; abstracted saving mechanism for lm_img_basic_reduce.
March 2025
9 Commits • 6 Features
Mar 1, 2025March 2025 monthly summary for AstarVienna/METIS_Pipeline focusing on packaging, API exposure, import structure, documentation, and testing readiness to accelerate integration and long-term maintainability.
9 Commits • 6 Features
Mar 1, 2025March 2025 monthly summary for AstarVienna/METIS_Pipeline focusing on packaging, API exposure, import structure, documentation, and testing readiness to accelerate integration and long-term maintainability.
March 2025
February 2025
76 Commits • 20 Features
Feb 1, 2025February 2025 – METIS_Pipeline (AstarVienna): Delivered substantial testing, data infrastructure, and architectural improvements that strengthen reliability, data quality, and delivery velocity across the pipeline. Focused on expanding test coverage, stabilizing CI/CD workflows, and refactoring core data models to enable easier feature integration and future enhancements. Key features delivered and associated outcomes: - Expanded LM imaging test coverage and distortion testing, with targeted fixes to metis_lm_img_distortion and associated test suites, increasing test coverage and confidence in image quality processing. - Data inputs modernization: moved fluxstd to common inputs and added inputs used by ifu_telluric, improving test data organization and reuse across modules. - Pupil imaging enhancements: migrated to a Product-list approach for flow, unified tests for SOFs, and expanded test coverage and inputs. - Distortion input re-enabled and maintained: ensured availability for downstream testing and processing. - Major code quality and architectural improvements: added type annotations, introduced private attributes where appropriate, stabilized simulations alignment, and reworked IFU/product handling; aligned with new class attribute patterns. - Metadata and recipe improvements: upgraded metadata schema, reorganized test coverage, and refined recipe hierarchy to better reflect data relationships and testing needs. - Core stability and test framework improvements: refactored core promotion mechanisms, improved test messaging, and stabilized test suites, including gating tests dependent on SOFs/SKY for consistent batch runs. Overall impact: - Reduced defect leakage and accelerated release cadence through comprehensive testing, robust data handling, and cleaner, more maintainable code. The work lays a stronger foundation for end-to-end pipeline reliability, data quality, and scalability of new features. Technologies/skills demonstrated: - Python development, type hints, private attributes, class attributes, and large-scale refactoring. - Test frameworks (pytest), test scaffolding, EDPS markers, and advanced assertion messaging. - CI/CD workflow maintenance and optimization, including target/workflow fixes and gating strategies. - Data modeling improvements (metadata and product flow) and test-driven validation across LM, IFU, and pupil imaging modules.
February 2025
76 Commits • 20 Features
Feb 1, 2025February 2025 – METIS_Pipeline (AstarVienna): Delivered substantial testing, data infrastructure, and architectural improvements that strengthen reliability, data quality, and delivery velocity across the pipeline. Focused on expanding test coverage, stabilizing CI/CD workflows, and refactoring core data models to enable easier feature integration and future enhancements. Key features delivered and associated outcomes: - Expanded LM imaging test coverage and distortion testing, with targeted fixes to metis_lm_img_distortion and associated test suites, increasing test coverage and confidence in image quality processing. - Data inputs modernization: moved fluxstd to common inputs and added inputs used by ifu_telluric, improving test data organization and reuse across modules. - Pupil imaging enhancements: migrated to a Product-list approach for flow, unified tests for SOFs, and expanded test coverage and inputs. - Distortion input re-enabled and maintained: ensured availability for downstream testing and processing. - Major code quality and architectural improvements: added type annotations, introduced private attributes where appropriate, stabilized simulations alignment, and reworked IFU/product handling; aligned with new class attribute patterns. - Metadata and recipe improvements: upgraded metadata schema, reorganized test coverage, and refined recipe hierarchy to better reflect data relationships and testing needs. - Core stability and test framework improvements: refactored core promotion mechanisms, improved test messaging, and stabilized test suites, including gating tests dependent on SOFs/SKY for consistent batch runs. Overall impact: - Reduced defect leakage and accelerated release cadence through comprehensive testing, robust data handling, and cleaner, more maintainable code. The work lays a stronger foundation for end-to-end pipeline reliability, data quality, and scalability of new features. Technologies/skills demonstrated: - Python development, type hints, private attributes, class attributes, and large-scale refactoring. - Test frameworks (pytest), test scaffolding, EDPS markers, and advanced assertion messaging. - CI/CD workflow maintenance and optimization, including target/workflow fixes and gating strategies. - Data modeling improvements (metadata and product flow) and test-driven validation across LM, IFU, and pupil imaging modules.
January 2025
38 Commits • 13 Features
Jan 1, 2025January 2025: Delivered core METIS_Pipeline enhancements focused on reliability, extensibility, and maintainability. Implemented dynamic and explicit class dispatch paths for configurable workflows; simplified inputs; hardened tag handling; expanded test coverage; cleaned up code and documentation; added calibration placeholders for future METIS I/F U work. These changes reduce onboarding friction, improve API clarity, and increase robustness across detectors.
38 Commits • 13 Features
Jan 1, 2025January 2025: Delivered core METIS_Pipeline enhancements focused on reliability, extensibility, and maintainability. Implemented dynamic and explicit class dispatch paths for configurable workflows; simplified inputs; hardened tag handling; expanded test coverage; cleaned up code and documentation; added calibration placeholders for future METIS I/F U work. These changes reduce onboarding friction, improve API clarity, and increase robustness across detectors.
January 2025
December 2024
11 Commits • 3 Features
Dec 1, 2024December 2024 METIS_Pipeline monthly summary: Focused on making the pipeline detector-aware, robust, and maintainable. Delivered architectural refactors for detector-specific product/input handling, advanced calibration/RSRF processing improvements, and expanded testing for broader validation. These changes improved data quality, processing reliability, and maintainability across detectors.
December 2024
11 Commits • 3 Features
Dec 1, 2024December 2024 METIS_Pipeline monthly summary: Focused on making the pipeline detector-aware, robust, and maintainable. Delivered architectural refactors for detector-specific product/input handling, advanced calibration/RSRF processing improvements, and expanded testing for broader validation. These changes improved data quality, processing reliability, and maintainability across detectors.
November 2024
11 Commits • 3 Features
Nov 1, 2024November 2024 performance-focused monthly summary for METIS_Pipeline. Delivered a comprehensive pipeline overhaul addressing dark imaging paths and IFU data processing, with an emphasis on maintainability, extensibility, and data integrity. Architecture refactor introduces detector mixin-based implementations (MetisDetDark, Metis2rgDarkImpl, MetisGeoDarkImpl, MetisIfuDarkImpl) to support multi-detector scenarios and simplify future enhancements, enabling more robust dark imaging workflows. IFU data reduction pipeline structural improvements redefine input/product structures for metis_ifu_distortion and metis_ifu_postprocess, while reducing metis_ifu_reduce inputs to streamline data flow and support new product types for IFU processing. Data source/configuration alignment for SOF data updates environment variables and removes obsolete paths, ensuring the pipeline consumes current data sources. Test infrastructure and code quality improvements include fixture consolidation to conftest.py, cleanup of imports and docstrings, added type hints, and expanded test coverage, resulting in more reliable CI and faster feedback.
11 Commits • 3 Features
Nov 1, 2024November 2024 performance-focused monthly summary for METIS_Pipeline. Delivered a comprehensive pipeline overhaul addressing dark imaging paths and IFU data processing, with an emphasis on maintainability, extensibility, and data integrity. Architecture refactor introduces detector mixin-based implementations (MetisDetDark, Metis2rgDarkImpl, MetisGeoDarkImpl, MetisIfuDarkImpl) to support multi-detector scenarios and simplify future enhancements, enabling more robust dark imaging workflows. IFU data reduction pipeline structural improvements redefine input/product structures for metis_ifu_distortion and metis_ifu_postprocess, while reducing metis_ifu_reduce inputs to streamline data flow and support new product types for IFU processing. Data source/configuration alignment for SOF data updates environment variables and removes obsolete paths, ensuring the pipeline consumes current data sources. Test infrastructure and code quality improvements include fixture consolidation to conftest.py, cleanup of imports and docstrings, added type hints, and expanded test coverage, resulting in more reliable CI and faster feedback.
November 2024
October 2024
1 Commits • 1 Features
Oct 1, 2024October 2024 (AstarVienna/METIS_Pipeline): Delivered IFU RSRF data input support for the METIS pipeline, introducing DistortionTableInput and WavecalInput classes to enable robust ingestion and handling of IFU RSRF data. This enhancement directly strengthens calibration workflows and end-to-end data processing, improving data quality and pipeline reliability for IFU observations.
October 2024
1 Commits • 1 Features
Oct 1, 2024October 2024 (AstarVienna/METIS_Pipeline): Delivered IFU RSRF data input support for the METIS pipeline, introducing DistortionTableInput and WavecalInput classes to enable robust ingestion and handling of IFU RSRF data. This enhancement directly strengthens calibration workflows and end-to-end data processing, improving data quality and pipeline reliability for IFU observations.
Activity
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Quality Metrics
Correctness82.6%
Maintainability86.4%
Architecture80.8%
Performance68.4%
AI Usage21.0%
Skills & Technologies
Programming Languages
INIMarkdownPythonShellTOMLYAML
Technical Skills
API DesignAPI DevelopmentAbstract ClassesAstronomy Data ReductionAstronomy SoftwareAstronomy Software DevelopmentBackend DevelopmentBug FixingCI/CDCalibrationClass DesignClass Hierarchy DesignClass InheritanceClass MethodsClass Refactoring
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