AstarVienna/METIS_Pipeline
Jan 2025 – Feb 2026
8 Months active
Languages Used
PythonTOMLShellYAMLyaml
Technical Skills
Astronomy SoftwareConfiguration ManagementData ReductionImage ProcessingPipeline DevelopmentSoftware Development
JenniferKarr
PROFILE
Jenniferkarr
Over eight months, J. Karr developed and enhanced the METIS_Pipeline repository, delivering robust astronomical data reduction and imaging workflows. Karr implemented end-to-end pipelines for pupil imaging, LM/N-band reductions, and high-contrast imaging, focusing on reproducibility, calibration accuracy, and automated quality control. Using Python, YAML, and CI/CD practices, Karr engineered modular recipes for bias subtraction, flat-fielding, dark current processing, and noise modeling, embedding QC metadata directly into FITS outputs. The work addressed schema inconsistencies, improved workflow automation, and stabilized test environments, resulting in reliable, maintainable scientific software that supports high-precision astronomical observations and scalable data management for research teams.
Overall Statistics
Feature vs Bugs
81%Features
Repository Contributions
44Total
Bugs
3
Commits
44
Features
13
Lines of code
3,333
Activity Months8
Your Network
18 peopleSame Organization
@asiaa.sinica.edu.tw5
Shared Repositories
13
Benjamin EiseleMember
eiseleb47Member
Chi-Hung YanMember
Gilles OttenMember
Hugo BuddelmeijerMember
janusbrinkMember
eiseleb47Member
WKMember
Kieran LeschinskiMember
Work History
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for AstarVienna/METIS_Pipeline: Reinstated High-Contrast Imaging (HCI) recipes into the METIS Pipeline, adding new calibration and processing classes/methods to enable high-precision astronomical imaging. This work restores end-to-end HCI capabilities and improves reproducibility for observational workflows.
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for AstarVienna/METIS_Pipeline: Reinstated High-Contrast Imaging (HCI) recipes into the METIS Pipeline, adding new calibration and processing classes/methods to enable high-precision astronomical imaging. This work restores end-to-end HCI capabilities and improves reproducibility for observational workflows.
February 2026
November 2025
5 Commits • 3 Features
Nov 1, 2025November 2025 (2025-11) monthly summary for AstarVienna/METIS_Pipeline. Focused on delivering robust imaging workflows and HCI data support to strengthen METIS pipeline automation, reliability, and data quality. Key work centered on LM/N-band imaging workflow enhancements, pupil imaging with CHOPHOME data processing, and HCI workflow support for calibration data products. All work contributes to faster recipe iteration, improved data provenance, and scalable data management across METIS experiments.
November 2025
5 Commits • 3 Features
Nov 1, 2025November 2025 (2025-11) monthly summary for AstarVienna/METIS_Pipeline. Focused on delivering robust imaging workflows and HCI data support to strengthen METIS pipeline automation, reliability, and data quality. Key work centered on LM/N-band imaging workflow enhancements, pupil imaging with CHOPHOME data processing, and HCI workflow support for calibration data products. All work contributes to faster recipe iteration, improved data provenance, and scalable data management across METIS experiments.
October 2025
1 Commits • 1 Features
Oct 1, 2025In October 2025, delivered key enhancements to the METIS_Pipeline calibration data generation workflow, increasing robustness and accuracy of calibration datasets. Addressed minor schema inconsistencies in master dark and dark recipes and introduced association rules to the n-band workflow for flats and darks, improving data processing reliability and enabling more trustworthy downstream analyses. These changes reduce downstream processing errors and lay groundwork for more automated calibration quality checks.
1 Commits • 1 Features
Oct 1, 2025In October 2025, delivered key enhancements to the METIS_Pipeline calibration data generation workflow, increasing robustness and accuracy of calibration datasets. Addressed minor schema inconsistencies in master dark and dark recipes and introduced association rules to the n-band workflow for flats and darks, improving data processing reliability and enabling more trustworthy downstream analyses. These changes reduce downstream processing errors and lay groundwork for more automated calibration quality checks.
October 2025
August 2025
14 Commits • 2 Features
Aug 1, 2025In August 2025, METIS_Pipeline achievements focused on enhancing data quality, stability, and DevOps maturity. Key work delivered robust dark current processing, improved noise modeling, crash protection for zero-valued images, and stabilized CI/CD/test data workflows. These efforts reduce failure risk, improve automated data products, and demonstrate solid software engineering across the pipeline.
August 2025
14 Commits • 2 Features
Aug 1, 2025In August 2025, METIS_Pipeline achievements focused on enhancing data quality, stability, and DevOps maturity. Key work delivered robust dark current processing, improved noise modeling, crash protection for zero-valued images, and stabilized CI/CD/test data workflows. These efforts reduce failure risk, improve automated data products, and demonstrate solid software engineering across the pipeline.
May 2025
7 Commits • 3 Features
May 1, 2025Month: 2025-05 — METIS_Pipeline development focusing on feature delivery, workflow standardization, and QA improvements to increase data quality and downstream usability.
7 Commits • 3 Features
May 1, 2025Month: 2025-05 — METIS_Pipeline development focusing on feature delivery, workflow standardization, and QA improvements to increase data quality and downstream usability.
May 2025
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 – METIS_Pipeline (AstarVienna) delivered an end-to-end LM Data Image Reduction Pipeline with QC Metadata. The primary work focused on implementing a basic reduction recipe that reads calibration and raw LM files, performs bias subtraction and flat-fielding, computes QC parameters, and updates the FITS header with QC values before writing the reduced output. No major bugs were reported this month; development centered on feature delivery and pipeline reliability.
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 – METIS_Pipeline (AstarVienna) delivered an end-to-end LM Data Image Reduction Pipeline with QC Metadata. The primary work focused on implementing a basic reduction recipe that reads calibration and raw LM files, performs bias subtraction and flat-fielding, computes QC parameters, and updates the FITS header with QC values before writing the reduced output. No major bugs were reported this month; development centered on feature delivery and pipeline reliability.
February 2025
9 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for AstarVienna/METIS_Pipeline: Delivered a new pupil imaging workflow integrated into the main pipeline, stabilized CI with the new processing step, and refined data paths. Implemented METIS pipeline configuration fixes to ensure correct file paths and data handling. Improved documentation and code quality. Business value realized through streamlined pupil-imaging processing, reduced manual intervention, and more reliable instrument data processing.
9 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for AstarVienna/METIS_Pipeline: Delivered a new pupil imaging workflow integrated into the main pipeline, stabilized CI with the new processing step, and refined data paths. Implemented METIS pipeline configuration fixes to ensure correct file paths and data handling. Improved documentation and code quality. Business value realized through streamlined pupil-imaging processing, reduced manual intervention, and more reliable instrument data processing.
February 2025
January 2025
6 Commits • 1 Features
Jan 1, 2025Month: 2025-01 — concise monthly summary for METIS_Pipeline development focused on pupil imaging workflow; no major bugs reported this period; feature delivery and process improvements implemented to enhance data quality, reproducibility, and pipeline readiness.
January 2025
6 Commits • 1 Features
Jan 1, 2025Month: 2025-01 — concise monthly summary for METIS_Pipeline development focused on pupil imaging workflow; no major bugs reported this period; feature delivery and process improvements implemented to enhance data quality, reproducibility, and pipeline readiness.
Activity
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Quality Metrics
Correctness81.8%
Maintainability85.2%
Architecture79.4%
Performance73.8%
AI Usage21.4%
Skills & Technologies
Programming Languages
PythonShellTOMLYAMLyaml
Technical Skills
Astronomy SoftwareBug FixingCI/CDCode RefactoringConfiguration ManagementData AnalysisData EngineeringData HandlingData ProcessingData Processing PipelineData ReductionDevOpsDocumentationFITS File HandlingGitHub Actions
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline