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
András Mihályko developed and enhanced the PyRigi/PyRigi graph analytics library over three months, focusing on robust algorithm design and maintainability. He refactored core APIs for clarity, consolidated modules, and introduced a mechanism to reuse precomputed directed graphs, improving performance in graph analysis workflows. Using Python and leveraging Pytest for testing, he expanded the test infrastructure to cover large-scale, tight, and sparse6 graph formats, and implemented comprehensive unit tests for edge cases such as self-loops. His work emphasized code organization, documentation, and reliability, resulting in a more stable, maintainable codebase that supports scalable and accurate graph computations.
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 people
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
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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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