Patrick Peglar is a seasoned software engineer based in Exeter with 13 years of experience building robust scientific and geospatial tooling at the Met Office. He combines backend, QA and full‑stack skills to improve complex data pipelines and visualisation libraries, contributing notable fixes and features to high-profile open-source projects like Cartopy and Iris. His work on Cartopy refined map gridline labeling and projection handling, while his contributions to Iris strengthened GRIB2 support and introduced resilient testing for scientific file formats. Comfortable navigating both production systems and domain-specific scientific code, he brings a pragmatic focus on reliability and reproducible data handling to geoscience software.
A powerful, format-agnostic, and community-driven Python package for analysing and visualising Earth science data
Role in this project:
Backend & QA Engineer
Contributions:9 releases, 946 reviews, 545 commits in 10 years 2 months
Contributions summary:Patrick primarily contributed to the improvement of GRIB format support, demonstrating expertise in handling and testing scientific data formats. They added tests for GRIB ProductDefinition Template 4.9, developed a testing framework involving a "fake GribApi," and fixed issues related to file loading. The contributions focused on enhancing the reliability and functionality of the Iris library's ability to handle GRIB2 files.
Cartopy - a cartographic python library with matplotlib support
Role in this project:
Full-stack Developer
Contributions:13 commits, 30 PRs, 7 pushes in 5 years 1 month
Contributions summary:Patrick contributed to the cartopy library by implementing new features and fixing bugs related to map gridlines and reprojection. They added optional labeling for GeoAxes map gridlines and resolved an 'endpoint' problem in gridline rendering, improving the visual accuracy of maps. Further, they refactored and improved the handling of projections, specifically the Robinson and NearsidePerspective projections, to avoid errors and enhance the robustness of the library. These changes enhance the library's usability and reliability for geospatial data visualization.
python-librarycartopypythongeographyspatial
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