AstarVienna/METIS_Pipeline
Jan 2025 – May 2026
10 Months active
Languages Used
PythonTOMLShellYAMLyamlMarkdown
Technical Skills
Astronomy SoftwareConfiguration ManagementData ReductionImage ProcessingPipeline DevelopmentSoftware Development
JenniferKarr
PROFILE
Jenniferkarr
Developed and maintained the METIS_Pipeline repository, delivering end-to-end astronomical data reduction and image processing workflows with a focus on reproducibility, automation, and data quality. Leveraged Python, YAML, and Jupyter Notebooks to implement robust pipelines for pupil imaging, LM/N-band reductions, and high-contrast imaging, integrating calibration, noise modeling, and quality control. Enhanced CI/CD processes using GitHub Actions, stabilized test data management, and improved documentation for user onboarding and operational reliability. Addressed workflow standardization, bug fixes, and schema consistency, enabling scalable, automated processing of complex FITS datasets and supporting advanced scientific computing needs in astronomical research and instrument data analysis.
Overall Statistics
Feature vs Bugs
84%Features
Repository Contributions
49Total
Bugs
3
Commits
49
Features
16
Lines of code
4,182
Activity Months10
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
May 2026
4 Commits • 2 Features
May 1, 2026May 2026 performance summary for METIS_Pipeline (AstarVienna/METIS_Pipeline): Delivered robust image processing improvements and onboarding-ready documentation, driving higher data quality and faster deployment of METIS with MTR/GUI. Focused on two features: (1) image processing enhancements for pixel masking and bad-pixel handling, including a two-stage mask calculation and median-based clipping; and (2) documentation updates to streamline MTR/GUI usage and METIS Test Runner installation. These changes reduce manual debugging, improve pipeline reliability, and shorten onboarding time for new users and collaborators.
4 Commits • 2 Features
May 1, 2026May 2026 performance summary for METIS_Pipeline (AstarVienna/METIS_Pipeline): Delivered robust image processing improvements and onboarding-ready documentation, driving higher data quality and faster deployment of METIS with MTR/GUI. Focused on two features: (1) image processing enhancements for pixel masking and bad-pixel handling, including a two-stage mask calculation and median-based clipping; and (2) documentation updates to streamline MTR/GUI usage and METIS Test Runner installation. These changes reduce manual debugging, improve pipeline reliability, and shorten onboarding time for new users and collaborators.
May 2026
April 2026
1 Commits • 1 Features
Apr 1, 2026Concise monthly summary for 2026-04 focusing on METIS_Pipeline work. Delivered demo notebooks that illustrate METIS simulations and pyCPL/pyHDRL workflows, enhancing user onboarding and practical experimentation. No major bugs fixed this month in the METIS_Pipeline scope.
April 2026
1 Commits • 1 Features
Apr 1, 2026Concise monthly summary for 2026-04 focusing on METIS_Pipeline work. Delivered demo notebooks that illustrate METIS simulations and pyCPL/pyHDRL workflows, enhancing user onboarding and practical experimentation. No major bugs fixed this month in the METIS_Pipeline scope.
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
Loading activity data...
Quality Metrics
Correctness83.2%
Maintainability86.0%
Architecture80.6%
Performance75.8%
AI Usage21.2%
Skills & Technologies
Programming Languages
MarkdownPythonShellTOMLYAMLyaml
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