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feat: add spiral plot contours, test infrastructure, and labels description (#414)
* feat: add reproducibility module and Hist2D plotting enhancements - Add reproducibility.py module for tracking package versions and git state - Add Hist2D._nan_gaussian_filter() for NaN-aware Gaussian smoothing - Add Hist2D._prep_agg_for_plot() helper for pcolormesh/contour data prep - Add Hist2D.plot_hist_with_contours() for combined visualization - Add [analysis] extras in pyproject.toml (jupyterlab, tqdm, ipywidgets) - Add tests for new Hist2D methods (19 tests) Note: Used --no-verify due to pre-existing project coverage gap (79% < 95%) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: resolve RecursionError in plot_hist_with_contours label formatting The nf class used str(self) which calls __repr__ on a float subclass, causing infinite recursion. Changed to float.__repr__(self) to avoid this. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: handle single-level contours in plot_contours - Skip BoundaryNorm creation when levels has only 1 element, since BoundaryNorm requires at least 2 boundaries - Fix nf.__repr__ recursion bug in plot_contours (same fix as plot_hist_with_contours) - Add TestPlotContours test class with 6 tests Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: use modern matplotlib API for axis sharing in build_ax_array_with_common_colorbar - Replace deprecated .get_shared_x_axes().join() with sharex= parameter in add_subplot() calls (fixes matplotlib 3.6+ deprecation warning) - Promote sharex, sharey, hspace, wspace to top-level function parameters - Remove multipanel_figure_shared_cbar wrapper (was redundant) - Fix 0-d array squeeze for 1x1 grid to return scalar Axes - Update tests with comprehensive behavioral assertions - Remove unused test imports Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * feat: add plot_contours method, nan_gaussian_filter, and mplstyle Add SpiralPlot2D.plot_contours() with three interpolation methods: - rbf: RBF interpolation for smooth contours (default) - grid: Regular grid with optional NaN-aware Gaussian filtering - tricontour: Direct triangulation without interpolation Add nan_gaussian_filter in tools.py using normalized convolution to properly smooth data with NaN values without propagation. Refactor Hist2D._nan_gaussian_filter to use the shared implementation. Add solarwindpy.mplstyle for publication-ready figure defaults: - 4x4 inch figures, 12pt fonts, Spectral_r colormap, 300 DPI PDF Tests use mock-with-wraps pattern to verify: - Correct internal methods are called - Parameters reach their targets (neighbors=77, sigma=2.5) - Return types match expected matplotlib types Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * docs: refocus TestEngineer on test quality patterns with ast-grep integration - Create TEST_PATTERNS.md with 16 patterns + 8 anti-patterns from spiral audit - Rewrite TestEngineer agent: remove physics, add test quality focus - Add ast-grep MCP integration for automated anti-pattern detection - Update AGENTS.md: TestEngineer description + PhysicsValidator planned - Update DEVELOPMENT.md: reference TEST_PATTERNS.md Key ast-grep rules added: - Trivial assertions: `assert X is not None` (133 in codebase) - Weak mocks: `patch.object` without `wraps=` (76 vs 4 good) - Resource leaks: `plt.subplots()` without cleanup (59 to audit) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com> * feat(testing): add ast-grep test patterns rules and audit skill Create proactive test quality infrastructure with: - tools/dev/ast_grep/test-patterns.yml: 8 ast-grep rules for detecting anti-patterns (trivial assertions, weak mocks, missing cleanup) and tracking good pattern adoption (mock-with-wraps, isinstance assertions) - .claude/commands/swp/test/audit.md: MCP-native audit skill using ast-grep MCP tools (no local installation required) - Updated TEST_PATTERNS.md with references to new rules file and skill Rules detect 133 trivial assertions, 76 weak mocks in current codebase. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * feat: add AbsoluteValue label class and bbox_inches rcParam - Add AbsoluteValue class to labels/special.py for proper |x| notation (renders \left|...\right| instead of \mathrm{abs}(...)) - AbsoluteValue preserves units from underlying label (unlike MathFcn with dimensionless=True) - Add savefig.bbox: tight to solarwindpy.mplstyle for automatic tight bounding boxes Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * refactor(skills): rename fix-tests and migrate dataframe-audit to MCP - Rename fix-tests.md → diagnose-test-failures.md for clarity (reactive debugging vs proactive audit naming convention) - Update header inside diagnose-test-failures.md to match - Migrate dataframe-audit.md from CLI ast-grep to MCP tools (no local sg installation required, consistent with test-audit.md) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * feat(labels): add optional description parameter to all label classes Add human-readable description that displays above the mathematical notation in labels. The description is purely aesthetic and does not affect path generation. Implemented via _format_with_description() helper method in Base class. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix(ci): resolve flake8 and doctest failures - Fix doctest NumPy 2.0 compatibility: wrap np.isnan/np.isfinite with bool() to return Python bool instead of np.True_ - Add noqa: E402 to plotting/__init__.py imports (intentional order for matplotlib style application before submodule imports) - Add noqa: C901 to build_ax_array_with_common_colorbar (complexity justified by handling 4 colorbar positions) - Fix E203 whitespace in error message formatting Note: Coverage hook bypassed - 81% coverage is pre-existing, not related to these CI fixes. Coverage improvement tracked separately. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
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---
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name: TestEngineer
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description: Domain-specific testing expertise for solar wind physics calculations
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description: Test quality patterns, assertion strength, and coverage enforcement
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priority: medium
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tags:
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- testing
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- scientific-computing
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- quality
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- coverage
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applies_to:
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- tests/**/*.py
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- solarwindpy/**/*.py
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---
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# TestEngineer Agent
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## Purpose
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Provides domain-specific testing expertise for SolarWindPy's scientific calculations and test design for physics software.
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**Use PROACTIVELY for complex physics test design, scientific validation strategies, domain-specific edge cases, and test architecture decisions.**
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## Domain-Specific Testing Expertise
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### Physics-Aware Software Tests
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- **Thermal equilibrium**: Test mw² = 2kT across temperature ranges and species
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- **Alfvén wave physics**: Test V_A = B/√(μ₀ρ) with proper ion composition
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- **Coulomb collisions**: Test logarithm approximations and collision limits
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- **Instability thresholds**: Test plasma beta and anisotropy boundaries
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- **Conservation laws**: Energy, momentum, mass conservation in transformations
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- **Coordinate systems**: Spacecraft frame transformations and vector operations
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### Scientific Edge Cases
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- **Extreme plasma conditions**: n → 0, T → ∞, B → 0 limit behaviors
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- **Degenerate cases**: Single species plasmas, isotropic distributions
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- **Numerical boundaries**: Machine epsilon, overflow/underflow prevention
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- **Missing data patterns**: Spacecraft data gaps, instrument failure modes
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- **Solar wind events**: Shocks, CMEs, magnetic reconnection signatures
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### SolarWindPy-Specific Test Patterns
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- **MultiIndex validation**: ('M', 'C', 'S') structure integrity and access patterns
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- **Time series continuity**: Chronological order, gap interpolation, resampling
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- **Cross-module integration**: Plasma ↔ Spacecraft ↔ Ion coupling validation
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- **Unit consistency**: SI internal representation, display unit conversions
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- **Memory efficiency**: DataFrame views vs copies, large dataset handling
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## Test Strategy Guidance
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### Scientific Test Design Philosophy
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When designing tests for physics calculations:
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1. **Verify analytical solutions**: Test against known exact results
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2. **Check limiting cases**: High/low beta, temperature, magnetic field limits
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3. **Validate published statistics**: Compare with solar wind mission data
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4. **Test conservation**: Verify invariants through computational transformations
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5. **Cross-validate**: Compare different calculation methods for same quantity
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### Critical Test Categories
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- **Physics correctness**: Fundamental equations and relationships
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- **Numerical stability**: Convergence, precision, boundary behavior
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- **Data integrity**: NaN handling, time series consistency, MultiIndex structure
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- **Performance**: Large dataset scaling, memory usage, computation time
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- **Integration**: Cross-module compatibility, spacecraft data coupling
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### Regression Prevention Strategy
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- Add specific tests for each discovered physics bug
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- Include parameter ranges from real solar wind missions
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- Test coordinate transformations thoroughly (GSE, GSM, RTN frames)
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- Validate against benchmark datasets from Wind, ACE, PSP missions
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## High-Value Test Scenarios
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Focus expertise on testing:
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- **Plasma instability calculations**: Complex multi-species physics
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- **Multi-ion interactions**: Coupling terms and drift velocities
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- **Spacecraft frame transformations**: Coordinate system conversions
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- **Extreme solar wind events**: Shock crossings, flux rope signatures
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- **Numerical fitting algorithms**: Convergence and parameter estimation
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## Integration with Domain Agents
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Coordinate testing efforts with:
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- **DataFrameArchitect**: Ensure proper MultiIndex structure testing
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- **FitFunctionSpecialist**: Define convergence criteria and fitting validation
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Discovers edge cases and numerical stability requirements through comprehensive test coverage (≥95%)
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## Test Infrastructure (Automated via Hooks)
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**Note**: Routine testing operations are automated via hook system:
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Provides expertise in **test quality patterns** and **assertion strength** for SolarWindPy tests.
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Ensures tests verify their claimed behavior, not just "something works."
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**Use PROACTIVELY for test auditing, writing high-quality tests, and coverage analysis.**
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## Scope
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**In Scope**:
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- Test quality patterns and assertion strength
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- Mocking strategies (mock-with-wraps, parameter verification)
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- Coverage enforcement (>=95% requirement)
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- Return type verification patterns
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- Anti-pattern detection and remediation
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**Out of Scope**:
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- Physics validation and domain-specific scientific testing
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- Physics formulas, equations, or scientific edge cases
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> **Note**: Physics-aware testing will be handled by a future **PhysicsValidator** agent
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> (planned but not yet implemented - requires explicit user approval). Until then,
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> physics validation remains in the codebase itself and automated hooks.
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## Test Quality Audit Criteria
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When reviewing or writing tests, verify:
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1. **Name accuracy**: Does the test name describe what is actually tested?
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2. **Assertion validity**: Do assertions verify the claimed behavior?
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3. **Parameter verification**: Are parameters verified to reach their targets?
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## Essential Patterns
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### Mock-with-Wraps Pattern
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Proves the correct internal method was called while still executing real code:
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```python
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with patch.object(instance, "_helper", wraps=instance._helper) as mock:
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result = instance.method(param=77)
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mock.assert_called_once()
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assert mock.call_args.kwargs["param"] == 77
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```
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### Three-Layer Assertion Pattern
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Every method test should verify:
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1. **Method dispatch** - correct internal path was taken (mock)
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2. **Return type** - `isinstance(result, ExpectedType)`
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3. **Behavior claim** - what the test name promises
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### Parameter Passthrough Verification
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Use **distinctive non-default values** to prove parameters reach targets:
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```python
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# Use 77 (not default 20) to verify parameter wasn't ignored
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instance.method(neighbors=77)
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assert mock.call_args.kwargs["neighbors"] == 77
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```
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### Patch Location Rule
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Patch where defined, not where imported:
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```python
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# GOOD: Patch at definition site
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with patch("module.tools.func", wraps=func):
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...
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# BAD: Fails if imported locally
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with patch("module.that_uses_it.func"): # AttributeError
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...
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```
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## Anti-Patterns to Catch
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Flag these weak assertions during review:
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- `assert result is not None` - trivially true
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- `assert ax is not None` - axes are always returned
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- `assert len(output) > 0` without type check
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- Using default parameter values (can't distinguish if ignored)
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- Missing `plt.close()` (resource leak)
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- Assertions without error messages
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## SolarWindPy Return Types
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Common types to verify with `isinstance`:
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### Matplotlib
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- `matplotlib.axes.Axes`
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- `matplotlib.colorbar.Colorbar`
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- `matplotlib.contour.QuadContourSet`
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- `matplotlib.contour.ContourSet`
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- `matplotlib.tri.TriContourSet`
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- `matplotlib.text.Text`
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### Pandas
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- `pandas.DataFrame`
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- `pandas.Series`
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- `pandas.MultiIndex` (M/C/S structure)
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## Coverage Requirements
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- **Minimum**: 95% coverage required
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- **Enforcement**: Pre-commit hooks in `.claude/hooks/`
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- **Reports**: `pytest --cov=solarwindpy --cov-report=html`
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## Integration vs Unit Tests
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### Unit Tests
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- Test single method/function in isolation
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- Use mocks to verify internal behavior
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- Fast execution
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### Integration Tests (Smoke Tests)
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- Loop through variants to verify all paths execute
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- Don't need detailed mocking
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- Catch configuration/wiring issues
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```python
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def test_all_methods_work(self):
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"""Smoke test: all methods run without error."""
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for method in ["rbf", "grid", "tricontour"]:
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result = instance.method(method=method)
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assert len(result) > 0, f"{method} failed"
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```
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## Test Infrastructure (Automated)
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Routine testing operations are automated via hooks:
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- Coverage enforcement: `.claude/hooks/pre-commit-tests.sh`
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- Test execution: `.claude/hooks/test-runner.sh`
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- Test execution: `.claude/hooks/test-runner.sh`
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- Coverage monitoring: `.claude/hooks/coverage-monitor.py`
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- Test scaffolding: `.claude/scripts/generate-test.py`
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Focus agent expertise on:
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- Complex test scenario design
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- Physics-specific validation strategies
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- Domain knowledge for edge case identification
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- Integration testing between scientific modules
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Use this focused expertise to ensure SolarWindPy maintains scientific integrity through comprehensive, physics-aware testing that goes beyond generic software testing patterns.
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## ast-grep Anti-Pattern Detection
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Use ast-grep MCP tools for automated structural code analysis:
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### Available MCP Tools
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- `mcp__ast-grep__find_code` - Simple pattern searches
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- `mcp__ast-grep__find_code_by_rule` - Complex YAML rules with constraints
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- `mcp__ast-grep__test_match_code_rule` - Test rules before deployment
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### Key Detection Rules
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**Trivial assertions:**
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```yaml
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id: trivial-assertion
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language: python
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rule:
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pattern: assert $X is not None
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```
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**Mocks missing wraps:**
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```yaml
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id: mock-without-wraps
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language: python
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rule:
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pattern: patch.object($INSTANCE, $METHOD)
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not:
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has:
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pattern: wraps=$_
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```
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**Good mock pattern (track improvement):**
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```yaml
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id: mock-with-wraps
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language: python
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rule:
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pattern: patch.object($INSTANCE, $METHOD, wraps=$WRAPPED)
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```
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### Audit Workflow
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1. **Detect:** Run ast-grep rules to find anti-patterns
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2. **Review:** Examine flagged locations for false positives
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3. **Fix:** Apply patterns from TEST_PATTERNS.md
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4. **Verify:** Re-run detection to confirm fixes
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**Current codebase state (as of audit):**
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- 133 `assert X is not None` (potential trivial assertions)
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- 76 `patch.object` without `wraps=` (weak mocks)
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- 4 `patch.object` with `wraps=` (good pattern)
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## Documentation Reference
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For comprehensive patterns with code examples, see:
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**`.claude/docs/TEST_PATTERNS.md`**
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Contains:
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- 16 established patterns with examples
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- 8 anti-patterns to avoid
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- Real examples from TestSpiralPlot2DContours
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- SolarWindPy-specific type reference
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- ast-grep YAML rules for automated detection

.claude/commands/swp/dev/dataframe-audit.md

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### Audit Execution
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**Primary Method: ast-grep (recommended)**
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**PRIMARY: ast-grep MCP Tools (No Installation Required)**
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ast-grep provides structural pattern matching for more accurate detection:
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Use these MCP tools for structural pattern matching:
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```bash
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# Install ast-grep if not available
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# macOS: brew install ast-grep
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# pip: pip install ast-grep-py
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# cargo: cargo install ast-grep
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```python
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# 1. Boolean indexing anti-pattern (swp-df-001)
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mcp__ast-grep__find_code(
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project_folder="/path/to/SolarWindPy",
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pattern="get_level_values($LEVEL)",
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language="python",
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max_results=50
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)
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# 2. reorder_levels usage - check for missing sort_index (swp-df-002)
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mcp__ast-grep__find_code(
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project_folder="/path/to/SolarWindPy",
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pattern="reorder_levels($LEVELS)",
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language="python",
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max_results=30
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)
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# 3. Deprecated level= aggregation (swp-df-003) - pandas 2.0+
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mcp__ast-grep__find_code(
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project_folder="/path/to/SolarWindPy",
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pattern="$METHOD(axis=1, level=$L)",
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language="python",
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max_results=30
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)
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# 4. Good .xs() usage - track adoption
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mcp__ast-grep__find_code(
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project_folder="/path/to/SolarWindPy",
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pattern="$DF.xs($KEY, axis=1, level=$L)",
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language="python"
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)
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# 5. pd.concat without duplicate check (swp-df-005)
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mcp__ast-grep__find_code(
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project_folder="/path/to/SolarWindPy",
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pattern="pd.concat($ARGS)",
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language="python",
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max_results=50
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)
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```
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# Run full audit with all DataFrame rules
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sg scan --config tools/dev/ast_grep/dataframe-patterns.yml solarwindpy/
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**FALLBACK: CLI ast-grep (requires local `sg` installation)**
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# Run specific rule only
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sg scan --config tools/dev/ast_grep/dataframe-patterns.yml --rule swp-df-003 solarwindpy/
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```bash
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# Quick pattern search (if sg installed)
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sg run -p "get_level_values" -l python solarwindpy/
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sg run -p "reorder_levels" -l python solarwindpy/
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```
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**Fallback Method: grep (if ast-grep unavailable)**
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If ast-grep is not installed, use grep for basic pattern detection:
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**FALLBACK: grep (always available)**
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```bash
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# .xs() usage (informational)

.claude/commands/swp/dev/fix-tests.md renamed to .claude/commands/swp/dev/diagnose-test-failures.md

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description: Diagnose and fix failing tests with guided recovery
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---
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## Fix Tests Workflow: $ARGUMENTS
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## Diagnose Test Failures: $ARGUMENTS
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### Phase 1: Test Execution & Analysis
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