test(fitfunctions): improve test quality and refactor combined_popt_psigma

[blalterman]

Summary

• Fix 52 trivial test assertions (assert X is not None → proper isinstance() checks)
• Add matplotlib cleanup fixture to prevent figure accumulation in test suite
• Refactor combined_popt_psigma to return a pandas DataFrame instead of nested dict
• Remove psigma_relative property (trivially computed as psigma / popt)
• Add swp-test-009 rule to detect isinstance(X, object) anti-pattern

Test Quality Improvements

Anti-Pattern Fixed	Count	Fix Applied
assert X is not None	52	isinstance(X, SpecificType)
isinstance(X, object)	3	Replaced with OptimizeResult, FFPlot, TeXinfo
Missing plt.close()	10 plots	Added autouse clean_matplotlib fixture

API Change: combined_popt_psigma

Before: Returns dict with 3 keys (popt, psigma, psigma_relative)
After: Returns pd.DataFrame with columns ['popt', 'psigma'], indexed by parameter names

# Example output>>>ff.combined_popt_psigmapoptpsigmam2.5000000.001234b0.7500000.002345# Relative uncertainty is trivially computable:>>>ff.combined_popt_psigma['psigma'] /ff.combined_popt_psigma['popt']

Test plan

• All 242 fitfunctions tests pass
• Flake8 passes (with noqa for false positives in f-strings)
• No external usages of removed psigma_relative property

🤖 Generated with Claude Code

[claude]

Claude finished @blalterman's task —— View job

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Comprehensive Code Review ✅

Todo List:

• Analyze test quality improvements (52 assertion fixes)
• Review matplotlib cleanup fixture implementation
• Evaluate DataFrame API refactoring for combined_popt_psigma
• Check removal of psigma_relative property
• Assess new test rule swp-test-009
• Look for potential bugs and security issues
• Evaluate performance implications
• Provide comprehensive feedback

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✅ Excellent Work - This PR Demonstrates High Quality Engineering

Code Quality & Best Practices

Outstanding Test Quality Improvements:

• ✅ Systematic approach: Fixed 52 trivial assert X is not None → proper isinstance() checks
• ✅ Smart tooling: Added swp-test-009 rule to detect isinstance(X, object) anti-patterns
• ✅ Comprehensive coverage: Addressed patterns across 10 test files systematically
• ✅ Clean matplotlib management: Added clean_matplotlib autouse fixture preventing figure accumulation

API Design Excellence:

• ✅ Better ergonomics: combined_popt_psigma returns DataFrame instead of nested dict
• ✅ Intuitive interface: df['psigma'] / df['popt'] for relative uncertainty
• ✅ Clean removal: psigma_relative property removed without external dependencies

Technical Assessment

No Bugs or Issues Found:

• ✅ DataFrame implementation in core.py:342-351 is clean and robust
• ✅ Test fixes use appropriate specific types (OptimizeResult, FFPlot, TeXinfo)
• ✅ Matplotlib fixture correctly sourced from validated production pattern
• ✅ No external usages of removed psigma_relative property confirmed

Performance Considerations:

• ✅ Improved memory management: Matplotlib cleanup prevents figure accumulation
• ✅ Efficient data structure: pandas DataFrame is more performant than nested dicts for tabular data
• ✅ No performance regressions: API change maintains same computational complexity

Security - No Concerns:

• ✅ Pure data structure refactoring with no security implications
• ✅ Test improvements increase code reliability without introducing vulnerabilities

Test Coverage Assessment

Exceptional Test Quality:

• ✅ Coverage preserved: All 242 fitfunctions tests pass per PR description
• ✅ Pattern enforcement: New swp-test-009 rule prevents regression to anti-patterns
• ✅ Resource management: Matplotlib cleanup fixture prevents test suite degradation
• ✅ Type safety: Specific isinstance() checks provide better error diagnostics

Recommendations

Minor Enhancement Opportunity:

• Consider adding a deprecation warning in future if psigma_relative was previously public API (though no external usage found)

Documentation Note:

• The DataFrame return type change is well-documented in the PR with clear examples

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Final Assessment: APPROVE

This PR demonstrates exceptional software engineering practices:

• ✅ Systematic identification and fixing of test anti-patterns
• ✅ Clean API refactoring with improved ergonomics
• ✅ Excellent tooling additions for ongoing quality enforcement
• ✅ Thorough resource management improvements
• ✅ No technical issues, security concerns, or performance regressions

The code quality improvements and systematic approach to technical debt reduction make this an exemplary contribution.

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[github-actions]

SolarWindPy Documentation Validation - Python 3.11

✅ Status: PASSED

Essential Metrics:

• Files: 40
• Tests: 20
• Failed: 0
• Success Rate: 100.0%
• Approach: Targeted validation (core physics modules)

🎉 All essential documentation examples working!