
Jan Legerský developed and maintained the PyRigi/PyRigi repository, delivering a modular Python framework for graph rigidity analysis and visualization. Over nine months, Jan engineered core features such as motion simulation, plotting, and API consistency, while refactoring the codebase for maintainability and onboarding ease. He applied Python, NetworkX, and Sphinx to implement robust testing, CI/CD pipelines, and comprehensive documentation, ensuring reliable releases and clear developer guidance. Jan’s work included modularizing graph and framework components, enhancing error handling, and stabilizing documentation deployment. The result was a maintainable, well-documented platform that improved developer productivity and reduced risk of breaking changes.

June 2025 monthly performance summary for PyRigi/PyRigi focused on documentation enhancements for motion APIs and docs deployment stabilization, delivering improved API discoverability, onboarding, and release-readiness with a stable docs pipeline.
June 2025 monthly performance summary for PyRigi/PyRigi focused on documentation enhancements for motion APIs and docs deployment stabilization, delivering improved API discoverability, onboarding, and release-readiness with a stable docs pipeline.
May 2025 performance summary for PyRigi/PyRigi: Delivered foundational framework scaffolding and core module relocation to enable a cleaner, more maintainable architecture. Implemented Graph and tests restructuring to modularize graph components and mirror test structure, improving test reliability and developer onboarding. Resolved automodule import issues by using full paths, reducing import-time failures across packages. Strengthened testing configuration and import reliability with explicit pytest folders, initialization files, and import tidiness. Hardened the API surface by restricting internal modules visibility and consolidating rigidity/general components into dedicated framework submodules, reducing exposure risk and facilitating a cleaner public API. These changes collectively improve maintainability, enable faster release cycles, and deliver measurable business value by stabilizing builds, reducing onboarding time, and reducing risk of breaking changes downstream.
May 2025 performance summary for PyRigi/PyRigi: Delivered foundational framework scaffolding and core module relocation to enable a cleaner, more maintainable architecture. Implemented Graph and tests restructuring to modularize graph components and mirror test structure, improving test reliability and developer onboarding. Resolved automodule import issues by using full paths, reducing import-time failures across packages. Strengthened testing configuration and import reliability with explicit pytest folders, initialization files, and import tidiness. Hardened the API surface by restricting internal modules visibility and consolidating rigidity/general components into dedicated framework submodules, reducing exposure risk and facilitating a cleaner public API. These changes collectively improve maintainability, enable faster release cycles, and deliver measurable business value by stabilizing builds, reducing onboarding time, and reducing risk of breaking changes downstream.
April 2025 (2025-04) monthly summary for PyRigi/PyRigi focusing on business value and technical achievements. Highlights include feature deliveries, quality improvements, API/documentation modernization, and strengthened test coverage. Key outcomes enabled easier integration, higher reliability, and maintainable code growth.
April 2025 (2025-04) monthly summary for PyRigi/PyRigi focusing on business value and technical achievements. Highlights include feature deliveries, quality improvements, API/documentation modernization, and strengthened test coverage. Key outcomes enabled easier integration, higher reliability, and maintainable code growth.
Summary for PyRigi/PyRigi — March 2025: Delivered core feature and reliability improvements with a focus on API consistency, test coverage, and documentation. Implemented parametrized tests for rigid_components and relocated pebble game computation to Rd_closure, improving test reliability and modularity. Fixed a bug in rigid_components when alg=subgraphs-pebble. Standardized API conventions (M->motion, interval as a list) and improved repr() usage, yielding more predictable representations. Expanded documentation and cleaned docstrings across framework, graph, and related modules; enforced PEP8 standards. Strengthened testing and release tooling, including Sphinx warning fixes, added tests for __str__/__repr__, and introduced Poetry-based tooling and release notes workflow. This work enhances stability, developer onboarding, and business value through more reliable simulations and clearer APIs.
Summary for PyRigi/PyRigi — March 2025: Delivered core feature and reliability improvements with a focus on API consistency, test coverage, and documentation. Implemented parametrized tests for rigid_components and relocated pebble game computation to Rd_closure, improving test reliability and modularity. Fixed a bug in rigid_components when alg=subgraphs-pebble. Standardized API conventions (M->motion, interval as a list) and improved repr() usage, yielding more predictable representations. Expanded documentation and cleaned docstrings across framework, graph, and related modules; enforced PEP8 standards. Strengthened testing and release tooling, including Sphinx warning fixes, added tests for __str__/__repr__, and introduced Poetry-based tooling and release notes workflow. This work enhances stability, developer onboarding, and business value through more reliable simulations and clearer APIs.
February 2025 (PyRigi/PyRigi): Delivered reliability, usability, and documentation enhancements that strengthen developer productivity and product stability. Key features include the Long Cell Magic (long_cell) with tagging semantics and robust error handling; stabilized IPython integration with a proper __main__ entry and extended main timeout; and API clarity improvements via the animation_format refactor. In addition, this batch advanced API robustness, added explicit error signaling, improved input validation, and elevated code quality and test/documentation coverage, reducing maintenance risk and accelerating future feature delivery.
February 2025 (PyRigi/PyRigi): Delivered reliability, usability, and documentation enhancements that strengthen developer productivity and product stability. Key features include the Long Cell Magic (long_cell) with tagging semantics and robust error handling; stabilized IPython integration with a proper __main__ entry and extended main timeout; and API clarity improvements via the animation_format refactor. In addition, this batch advanced API robustness, added explicit error signaling, improved input validation, and elevated code quality and test/documentation coverage, reducing maintenance risk and accelerating future feature delivery.
January 2025 monthly performance summary for PyRigi/PyRigi: Focused on maintainability, API consistency, and plotting capabilities while delivering solid testing improvements. Key features delivered include: Documentation and Docstring Refinements across the project (including K33plusEdge coverage); API naming consistency and internal refactors (Inf_Flex -> InfFlex, Coordinate -> Number, Plot2D -> plot2D, and relocation of non-public methods); Plot styling and plotting enhancements with PlotStyle, plot_style parameter support, and 2D/3D variants with related documentation; Geometry features merge for Octahedron and BricardsOctahedron; and Testing enhancements with new tests and reduced test output, alongside PEP8/type hints cleanup.
January 2025 monthly performance summary for PyRigi/PyRigi: Focused on maintainability, API consistency, and plotting capabilities while delivering solid testing improvements. Key features delivered include: Documentation and Docstring Refinements across the project (including K33plusEdge coverage); API naming consistency and internal refactors (Inf_Flex -> InfFlex, Coordinate -> Number, Plot2D -> plot2D, and relocation of non-public methods); Plot styling and plotting enhancements with PlotStyle, plot_style parameter support, and 2D/3D variants with related documentation; Geometry features merge for Octahedron and BricardsOctahedron; and Testing enhancements with new tests and reduced test output, alongside PEP8/type hints cleanup.
December 2024 monthly summary for PyRigi/PyRigi focusing on delivering documentation-driven features, robust test coverage, and packaging/CI reliability to reduce risk and accelerate onboarding.
December 2024 monthly summary for PyRigi/PyRigi focusing on delivering documentation-driven features, robust test coverage, and packaging/CI reliability to reduce risk and accelerate onboarding.
November 2024 — PyRigi/PyRigi delivered notable advancements focused on notebook workflow, kernel support, graph modeling, and documentation. Key features delivered include enhanced notebook linking across tutorials, consistency in Getting Started notebook by converting md to ipynb, and a refactor of the pebble digraph API (renaming and related helpers) to align with domain semantics. Major bugs fixed include the notebook ipynb handling policy to correctly include/exclude notebooks, and critical fixes in is_Rd_circuit along with K1/K2 parameter corrections. Additional improvements strengthened CI and documentation (gh-pages workflow, Sphinx usage, docstrings), expanded testing coverage (realizations and time-based markers), and introduced Python kernel integration. The overall impact is improved end-user experience, onboarding, reproducibility, and a more robust development and release process, enabling faster feature delivery with lower maintenance costs. Technologies demonstrated include Python kernel, Jupyter notebooks, Sphinx and gh-pages, CI workflows, refactoring and testing practices, dependency management (lnumber), and plotting utilities.
November 2024 — PyRigi/PyRigi delivered notable advancements focused on notebook workflow, kernel support, graph modeling, and documentation. Key features delivered include enhanced notebook linking across tutorials, consistency in Getting Started notebook by converting md to ipynb, and a refactor of the pebble digraph API (renaming and related helpers) to align with domain semantics. Major bugs fixed include the notebook ipynb handling policy to correctly include/exclude notebooks, and critical fixes in is_Rd_circuit along with K1/K2 parameter corrections. Additional improvements strengthened CI and documentation (gh-pages workflow, Sphinx usage, docstrings), expanded testing coverage (realizations and time-based markers), and introduced Python kernel integration. The overall impact is improved end-user experience, onboarding, reproducibility, and a more robust development and release process, enabling faster feature delivery with lower maintenance costs. Technologies demonstrated include Python kernel, Jupyter notebooks, Sphinx and gh-pages, CI workflows, refactoring and testing practices, dependency management (lnumber), and plotting utilities.
October 2024 (2024-10) PyRigi/PyRigi — Focused on stability, directed-graph capabilities, and developer experience. Key outcomes include dependency management with ipyevents integration, a core GraphDrawer refactor introducing DirectedEdge support, and comprehensive documentation improvements for math notation, topic-based organization, and Python-based workflows. No explicit major bug fixes were required this month; stability improvements came from dependency updates and formatting enhancements. Impact: faster interactive graph work, easier adoption for Python-based workflows, and higher maintainability.
October 2024 (2024-10) PyRigi/PyRigi — Focused on stability, directed-graph capabilities, and developer experience. Key outcomes include dependency management with ipyevents integration, a core GraphDrawer refactor introducing DirectedEdge support, and comprehensive documentation improvements for math notation, topic-based organization, and Python-based workflows. No explicit major bug fixes were required this month; stability improvements came from dependency updates and formatting enhancements. Impact: faster interactive graph work, easier adoption for Python-based workflows, and higher maintainability.
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