PySATL/pysatl-criterion
Mar 2025 – Aug 2025
4 Months active
Languages Used
MarkdownPythonYAML
Technical Skills
Bug FixingCI/CDDependency ManagementDevOpsDeveloper SetupDocumentation
Ivan Pokhabov
PROFILE
Ivan Pokhabov
Worked on the PySATL/pysatl-criterion repository, delivering new graph-based statistical features and improving developer experience over four months. Developed and integrated goodness-of-fit statistics for exponential distributions, including metrics like average degree, connected components, clique number, and independence number, using Python and graph theory techniques. Enhanced code reliability by expanding unit test coverage for these features and refactoring existing statistics for maintainability. Addressed critical bugs in statistical normality tests and streamlined developer onboarding with updated documentation and Poetry-based setup. Maintained CI/CD pipelines and dependency management with GitHub Actions and YAML, ensuring robust validation and efficient development workflows throughout the project.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
8Total
Bugs
1
Commits
8
Features
4
Lines of code
614
Activity Months4
Your Network
7 peopleWork History
August 2025
1 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for PySATL/pysatl-criterion: Implemented new graph statistics (clique number and independence number), refactored existing statistics to support these features, and updated dependencies and CI configurations to improve reliability and validation. No major bugs fixed this month; the focus was on feature delivery and maintainability. This work enhances graph analysis capabilities for users and reduces time to derive key metrics, contributing to more robust benchmarking and analytics for graph datasets.
1 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for PySATL/pysatl-criterion: Implemented new graph statistics (clique number and independence number), refactored existing statistics to support these features, and updated dependencies and CI configurations to improve reliability and validation. No major bugs fixed this month; the focus was on feature delivery and maintainability. This work enhances graph analysis capabilities for users and reduces time to derive key metrics, contributing to more robust benchmarking and analytics for graph datasets.
August 2025
May 2025
1 Commits • 1 Features
May 1, 2025Month: 2025-05. Focused on expanding test coverage for GOF criteria in PySATL/pysatl-criterion. Delivered graph-based test coverage for exponentiality and normality GOF tests, emphasizing average degree and connected components. This work strengthens statistical validation and code reliability, enabling higher confidence in downstream usage and releases.
May 2025
1 Commits • 1 Features
May 1, 2025Month: 2025-05. Focused on expanding test coverage for GOF criteria in PySATL/pysatl-criterion. Delivered graph-based test coverage for exponentiality and normality GOF tests, emphasizing average degree and connected components. This work strengthens statistical validation and code reliability, enabling higher confidence in downstream usage and releases.
April 2025
1 Commits • 1 Features
Apr 1, 2025Concise monthly summary for 2025-04: Implemented graph-based goodness-of-fit statistics for exponential distributions in PySATL/pysatl-criterion, including average degree and connected components, along with new distribution computations to enable end-to-end graph data analysis. No major bugs fixed this month. Overall impact includes strengthened graph-model validation workflows, expanded analytics capabilities for exponential models, and clearer traceability of work through a single feature-focused commit. Technologies/skills demonstrated include Python, graph algorithms, statistical modeling, and Git-based development in a research-oriented codebase.
1 Commits • 1 Features
Apr 1, 2025Concise monthly summary for 2025-04: Implemented graph-based goodness-of-fit statistics for exponential distributions in PySATL/pysatl-criterion, including average degree and connected components, along with new distribution computations to enable end-to-end graph data analysis. No major bugs fixed this month. Overall impact includes strengthened graph-model validation workflows, expanded analytics capabilities for exponential models, and clearer traceability of work through a single feature-focused commit. Technologies/skills demonstrated include Python, graph algorithms, statistical modeling, and Git-based development in a research-oriented codebase.
April 2025
March 2025
5 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for PySATL/pysatl-criterion: Delivered developer-experience improvements and fixed critical statistical normality test bugs, enhancing reliability for contributors and users.
March 2025
5 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for PySATL/pysatl-criterion: Delivered developer-experience improvements and fixed critical statistical normality test bugs, enhancing reliability for contributors and users.
Activity
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Quality Metrics
Correctness93.8%
Maintainability92.6%
Architecture91.2%
Performance87.6%
AI Usage25.0%
Skills & Technologies
Programming Languages
MarkdownPythonYAML
Technical Skills
Bug FixingCI/CDDependency ManagementDevOpsDeveloper SetupDocumentationGitHub ActionsGraph TheoryPythonSoftware DevelopmentStatistical AnalysisStatisticsTestingUnit Testing
Repositories Contributed To
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