PyRigi/PyRigi
Nov 2024 – Mar 2025
3 Months active
Languages Used
NonePythonTexttext
Technical Skills
Algorithm DesignAlgorithm OptimizationCode CleanupCode OrganizationCode RefactoringData Serialization
Andras Mihalyko
PROFILE
Andras Mihalyko
Worked on the PyRigi/PyRigi repository to enhance graph analytics and rigidity analysis workflows, focusing on Python-based algorithm design and implementation. Delivered features such as 2D rigidity analysis using the pebble game, robust handling of self-loops in graph structures, and expanded support for data formats like sparse6. Refactored APIs for clarity, improved error messaging, and consolidated modules to streamline code organization. Strengthened the testing infrastructure with parameterized unit tests and large-scale integration tests, ensuring reliability and maintainability. Emphasized code readability, documentation, and test automation, resulting in a more consistent, performant, and scalable foundation for graph theory applications.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
41Total
Bugs
0
Commits
41
Features
7
Lines of code
9,053
Activity Months3
Your Network
10 peopleShared Repositories
10
Work History
March 2025
2 Commits • 1 Features
Mar 1, 2025Month: 2025-03. Delivered Graph Loop Handling and Validation Enhancements for PyRigi/PyRigi, focusing on self-loop integrity and test coverage. Fixed self-loop handling in edge removal/addition and adjusted degree checks to account for self-referential edges. Added parameterized unit tests validating is_kl_tight and is_kl_sparse across scenarios (single vertex, disjoint loops, mixed graphs). This work improves correctness, robustness, and reliability of the graph library. Commits: f0047074a5fdd6739993e84a342ef5f395207dbc; 1603e77ba26ca0270acf8323394f268241430288.
2 Commits • 1 Features
Mar 1, 2025Month: 2025-03. Delivered Graph Loop Handling and Validation Enhancements for PyRigi/PyRigi, focusing on self-loop integrity and test coverage. Fixed self-loop handling in edge removal/addition and adjusted degree checks to account for self-referential edges. Added parameterized unit tests validating is_kl_tight and is_kl_sparse across scenarios (single vertex, disjoint loops, mixed graphs). This work improves correctness, robustness, and reliability of the graph library. Commits: f0047074a5fdd6739993e84a342ef5f395207dbc; 1603e77ba26ca0270acf8323394f268241430288.
March 2025
February 2025
19 Commits • 3 Features
Feb 1, 2025February 2025 monthly summary for PyRigi/PyRigi: Delivered a robust 2D rigidity analysis workflow using the pebble game, including maximal rigid components detection, expanded unit tests, and targeted refactors for clarity. Strengthened test infrastructure, reorganized test assets, and aligned tests with the latest API naming. Performed comprehensive internal cleanup and documentation updates to improve maintainability and clarity. These efforts reduce risk of regressions in rigidity calculations, improve reliability for downstream users, and enable smoother integration into CI and release workflows.
February 2025
19 Commits • 3 Features
Feb 1, 2025February 2025 monthly summary for PyRigi/PyRigi: Delivered a robust 2D rigidity analysis workflow using the pebble game, including maximal rigid components detection, expanded unit tests, and targeted refactors for clarity. Strengthened test infrastructure, reorganized test assets, and aligned tests with the latest API naming. Performed comprehensive internal cleanup and documentation updates to improve maintainability and clarity. These efforts reduce risk of regressions in rigidity calculations, improve reliability for downstream users, and enable smoother integration into CI and release workflows.
November 2024
20 Commits • 3 Features
Nov 1, 20242024-11 Monthly Summary for PyRigi/PyRigi: Key features delivered: - PebbleDiGraph/Graph API Cleanup and Enhancement: Consolidated and renamed internal modules and APIs for PebbleDiGraph/Graph; clarified edge/circuit computations; renamed Edge to DirectedEdge; removed redundant APIs; improved error messages. - Performance optimization: Added use_precomputed_pebble_digraph parameter across graph analysis methods to reuse a precomputed directed graph and avoid redundant computations. - Testing infrastructure and data format support: Expanded test graphs with tight/non-sparse structures, added sparse6 format support, and introduced large-scale tests to improve validation of graph algorithms (including circuit tests). Major bugs fixed: - Clarified and corrected error messages; completed API renaming (Edge to DirectedEdge). - Removed deprecated APIs edge_is_kl_independent() and edge_kl_circuit(), reducing technical debt and future maintenance risk. - Addressed inconsistencies uncovered during refactorings to stabilize the public API. Overall impact and accomplishments: - Improved API consistency and maintainability, enabling faster onboarding and less confusion during usage. - Achieved measurable performance improvements by reusing precomputed PebbleDiGraph across analyses, reducing redundant computations. - Strengthened validation and reliability through expanded testing (sparse6, dense/large graphs, and circuit scenarios), supporting scalable graph analytics in production. Technologies/skills demonstrated: - Python module refactoring, API design, and naming clarity (PebbleDiGraph -> pebble_digraph). - Graph algorithms and circuit computations with improvements to fundamental circuit logic and edge handling. - Performance optimization patterns (shared precomputed structures). - Testing strategy and data-format support (sparse6, tight graphs, large-scale test suites).
20 Commits • 3 Features
Nov 1, 20242024-11 Monthly Summary for PyRigi/PyRigi: Key features delivered: - PebbleDiGraph/Graph API Cleanup and Enhancement: Consolidated and renamed internal modules and APIs for PebbleDiGraph/Graph; clarified edge/circuit computations; renamed Edge to DirectedEdge; removed redundant APIs; improved error messages. - Performance optimization: Added use_precomputed_pebble_digraph parameter across graph analysis methods to reuse a precomputed directed graph and avoid redundant computations. - Testing infrastructure and data format support: Expanded test graphs with tight/non-sparse structures, added sparse6 format support, and introduced large-scale tests to improve validation of graph algorithms (including circuit tests). Major bugs fixed: - Clarified and corrected error messages; completed API renaming (Edge to DirectedEdge). - Removed deprecated APIs edge_is_kl_independent() and edge_kl_circuit(), reducing technical debt and future maintenance risk. - Addressed inconsistencies uncovered during refactorings to stabilize the public API. Overall impact and accomplishments: - Improved API consistency and maintainability, enabling faster onboarding and less confusion during usage. - Achieved measurable performance improvements by reusing precomputed PebbleDiGraph across analyses, reducing redundant computations. - Strengthened validation and reliability through expanded testing (sparse6, dense/large graphs, and circuit scenarios), supporting scalable graph analytics in production. Technologies/skills demonstrated: - Python module refactoring, API design, and naming clarity (PebbleDiGraph -> pebble_digraph). - Graph algorithms and circuit computations with improvements to fundamental circuit logic and edge handling. - Performance optimization patterns (shared precomputed structures). - Testing strategy and data-format support (sparse6, tight graphs, large-scale test suites).
November 2024
Activity
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Quality Metrics
Correctness92.2%
Maintainability93.2%
Architecture89.2%
Performance84.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
NonePythonTexttext
Technical Skills
Algorithm DesignAlgorithm ImplementationAlgorithm OptimizationAlgorithm RefactoringAlgorithm TestingCode CleanupCode FormattingCode OrganizationCode RefactoringData ConversionData SerializationData StructuresDocumentationFile I/OFile Management
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