blalterman
diff --git a/‎.claude/compacted_state.md‎
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Lines changed: 49 additions & 121 deletions
diff --git a/‎benchmarks/fitfunctions_performance.py‎
Lines changed: 179 additions & 0 deletions b/‎benchmarks/fitfunctions_performance.py‎
Lines changed: 179 additions & 0 deletions
@@ -1,134 +1,62 @@
-# Compacted Context State - 2025-12-23T19:30:21Z
+# Compacted State: FitFunctions Phase 6 Execution
 
-## Compaction Metadata
-- **Timestamp**: 2025-12-23T19:30:21Z
-- **Branch**: feature/dependency-consolidation
-- **Plan**: tests-audit
-- **Pre-Compaction Context**: ~6,856 tokens (1,404 lines)
-- **Target Compression**: light (20% reduction)
-- **Target Tokens**: ~5,484 tokens
-- **Strategy**: light compression with prose focus
+## Branch: plan/fitfunctions-audit-execution @ e0ca3659
 
-## Content Analysis
-- **Files Analyzed**: 6
-- **Content Breakdown**:
-  - Code: 311 lines
-  - Prose: 347 lines
-  - Tables: 15 lines
-  - Lists: 314 lines
-  - Headers: 168 lines
-- **Token Estimates**:
-  - Line-based: 4,212
-  - Character-based: 12,289
-  - Word-based: 7,694
-  - Content-weighted: 3,229
-  - **Final estimate**: 6,856 tokens
+## Current Status
+| Stage | Status | Notes |
+|-------|--------|-------|
+| 1. Merge | ✅ DONE | Bug fix committed e0ca3659 |
+| 2. Environment | 🔧 BLOCKED | Editable install wrong dir |
+| 3-7 | ⏳ Pending | After env fix |
 
-## Git State
-### Current Branch: feature/dependency-consolidation
-### Last Commit: ab14e428 - feat(core): enhance Core.__repr__() to include species information (blalterman, 12 days ago)
-
-### Recent Commits:
-```
-ab14e428 (HEAD -> feature/dependency-consolidation, master) feat(core): enhance Core.__repr__() to include species information
-db3d43e1 docs(feature_integration): complete agent removal documentation updates
-dbf3824d refactor(agents): remove PhysicsValidator and NumericalStabilityGuard agents
-043b8932 refactor(agents): remove PhysicsValidator from active infrastructure (Phase 2.1)
-d27f2912 feat(phase0-memory): add agent-coordination.md and testing-templates.md
-```
-
-### Working Directory Status:
-```
-M docs/requirements.txt
- M pyproject.toml
- M requirements.txt
-?? .claude/logs/
-?? baseline-coverage.json
-?? requirements-dev.lock
-?? tests/fitfunctions/test_metaclass_compatibility.py
+## Critical Blocker
+**Problem**: Tests run against wrong installation
 ```
-
-### Uncommitted Changes Summary:
-```
-docs/requirements.txt | 175 +++++++++++++++++++++++++++++++++++++++++++++++---
- pyproject.toml        |  54 +++++++++-------
- requirements.txt      |  85 ++++++++++++++++++------
- 3 files changed, 261 insertions(+), 53 deletions(-)
+pip show solarwindpy | grep Editable
+# Returns: SolarWindPy-2 (WRONG)
+# Should be: SolarWindPy (current directory)
 ```
 
-## Critical Context Summary
-
-### Active Tasks (Priority Focus)
-- No active tasks identified
-
-### Recent Key Decisions
-- No recent decisions captured
-
-### Blockers & Issues
-⚠️ - **Process Issues**: None - agent coordination worked smoothly throughout
-⚠️ - [x] **Document risk assessment matrix** (Est: 25 min) - Create risk ratings for identified issues (Critical, High, Medium, Low)
-⚠️ ### Blockers & Issues
-
-### Immediate Next Steps
-➡️ - Notes: Show per-module coverage changes and remaining gaps
-➡️ - [x] **Generate recommendations summary** (Est: 20 min) - Provide actionable next steps for ongoing test suite maintenance
-➡️ - [x] Recommendations summary providing actionable next steps
-
-## Session Context Summary
-
-### Active Plan: tests-audit
-## Plan Metadata
-- **Plan Name**: Physics-Focused Test Suite Audit
-- **Created**: 2025-08-21
-- **Branch**: plan/tests-audit
-- **Implementation Branch**: feature/tests-hardening
-- **PlanManager**: UnifiedPlanCoordinator
-- **PlanImplementer**: UnifiedPlanCoordinator with specialized agents
-- **Structure**: Multi-Phase
-- **Total Phases**: 6
-- **Dependencies**: None
-- **Affects**: tests/*, plans/tests-audit/artifacts/, documentation files
-- **Estimated Duration**: 12-18 hours
-- **Status**: Completed
-
-
-### Plan Progress Summary
-- Plan directory: plans/tests-audit
-- Last modified: 2025-09-03 16:47
-
-## Session Resumption Instructions
-
-### 🚀 Quick Start Commands
+**Solution**:
 ```bash
-# Restore session environment
-git checkout feature/dependency-consolidation
-cd plans/tests-audit && ls -la
-git status
-pwd  # Verify working directory
-conda info --envs  # Check active environment
+pip uninstall -y solarwindpy
+pip install -e ".[dev,performance]"
+pytest tests/fitfunctions/test_phase4_performance.py -v
 ```
 
-### 🎯 Priority Actions for Next Session
-1. Review plan status: cat plans/tests-audit/0-Overview.md
-2. Resolve: - **Process Issues**: None - agent coordination worked smoothly throughout
-3. Resolve: - [x] **Document risk assessment matrix** (Est: 25 min) - Create risk ratings for identified issues (Critical, High, Medium, Low)
-4. Review uncommitted changes and decide on commit strategy
+## Bug Fix (COMMITTED e0ca3659)
+File: `solarwindpy/fitfunctions/trend_fits.py`
+- Line 221-223: Filter n_jobs/verbose/backend from kwargs
+- Line 241, 285: Use `**fit_kwargs` instead of `**kwargs`
+
+## Phase 6 Coverage Targets
+| Module | Current | Target | Priority |
+|--------|---------|--------|----------|
+| gaussians.py | 73% | 96% | CRITICAL |
+| exponentials.py | 82% | 96% | CRITICAL |
+| core.py | 90% | 95% | HIGH |
+| trend_fits.py | 80% | 91% | MEDIUM |
+| plots.py | 90% | 95% | MEDIUM |
+| moyal.py | 86% | 95% | LOW |
+
+## Parallel Agent Strategy
+After Stage 2, launch 6 TestEngineer agents in parallel:
+```python
+Task(TestEngineer, "gaussians tests", run_in_background=True)
+Task(TestEngineer, "exponentials tests", run_in_background=True)
+# ... (all 6 modules simultaneously)
+```
+Time: 4-5 hrs sequential → 1.5 hrs parallel
 
-### 🔄 Session Continuity Checklist
-- [ ] **Environment**: Verify correct conda environment and working directory
-- [ ] **Branch**: Confirm on correct git branch (feature/dependency-consolidation)
-- [ ] **Context**: Review critical context summary above
-- [ ] **Plan**: Check plan status in plans/tests-audit
-- [ ] **Changes**: Review uncommitted changes
+## Key Files
+- Plan: `/Users/balterma/.claude/plans/gentle-hugging-sundae.md`
+- Handoff: `plans/fitfunctions-audit/phase6-session-handoff.md`
 
-### 📊 Efficiency Metrics
-- **Context Reduction**: 20.0% (6,856 → 5,484 tokens)
-- **Estimated Session Extension**: 12 additional minutes of productive work
-- **Compaction Strategy**: light compression focused on prose optimization
+## Next Actions
+1. Fix environment (Stage 2)
+2. Verify tests pass
+3. Run coverage analysis (Stage 3)
+4. Launch parallel agents (Stage 4)
 
 ---
-*Automated intelligent compaction - 2025-12-23T19:30:21Z*
-
-## Compaction File
-Filename: `compaction-2025-12-23-193021-20pct.md` - Unique timestamp-based compaction file
-No git tags created - using file-based state preservation
+*Updated: 2025-12-31 - FitFunctions Phase 6 Execution*
@@ -0,0 +1,179 @@
+#!/usr/bin/env python
+"""Benchmark Phase 4 performance optimizations."""
+
+import time
+import numpy as np
+import pandas as pd
+import sys
+import os
+
+# Add the parent directory to sys.path to import solarwindpy
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
+
+from solarwindpy.fitfunctions import Gaussian
+from solarwindpy.fitfunctions.trend_fits import TrendFit
+
+
+def benchmark_trendfit(n_fits=50):
+    """Compare sequential vs parallel TrendFit performance."""
+    print(f"\nBenchmarking with {n_fits} fits...")
+    
+    # Create synthetic data that's realistic for fitting
+    np.random.seed(42)
+    x = np.linspace(0, 10, 100)
+    data = pd.DataFrame({
+        f'col_{i}': 5 * np.exp(-(x-5)**2/2) + np.random.normal(0, 0.1, 100)
+        for i in range(n_fits)
+    }, index=x)
+    
+    # Sequential execution
+    print("  Running sequential...")
+    tf_seq = TrendFit(data, Gaussian, ffunc1d=Gaussian)
+    tf_seq.make_ffunc1ds()
+    
+    start = time.perf_counter()
+    tf_seq.make_1dfits(n_jobs=1)
+    seq_time = time.perf_counter() - start
+    
+    # Parallel execution
+    print("  Running parallel...")
+    tf_par = TrendFit(data, Gaussian, ffunc1d=Gaussian)
+    tf_par.make_ffunc1ds()
+    
+    start = time.perf_counter()
+    tf_par.make_1dfits(n_jobs=-1)
+    par_time = time.perf_counter() - start
+    
+    speedup = seq_time / par_time
+    print(f"  Sequential: {seq_time:.2f}s")
+    print(f"  Parallel:   {par_time:.2f}s")
+    print(f"  Speedup:    {speedup:.1f}x")
+    
+    # Verify results match
+    print("  Verifying results match...")
+    successful_fits = 0
+    for key in tf_seq.ffuncs.index:
+        if key in tf_par.ffuncs.index:  # Both succeeded
+            seq_popt = tf_seq.ffuncs[key].popt
+            par_popt = tf_par.ffuncs[key].popt
+            for param in seq_popt:
+                np.testing.assert_allclose(
+                    seq_popt[param], par_popt[param], 
+                    rtol=1e-10, atol=1e-10
+                )
+            successful_fits += 1
+    
+    print(f"  ✓ {successful_fits} fits verified identical")
+    
+    return speedup, successful_fits
+
+
+def benchmark_single_fitfunction():
+    """Benchmark single FitFunction to understand baseline performance."""
+    print("\nBenchmarking single FitFunction...")
+    
+    np.random.seed(42)
+    x = np.linspace(0, 10, 100)
+    y = 5 * np.exp(-(x-5)**2/2) + np.random.normal(0, 0.1, 100)
+    
+    # Time creation and fitting
+    start = time.perf_counter()
+    ff = Gaussian(x, y)
+    creation_time = time.perf_counter() - start
+    
+    start = time.perf_counter()
+    ff.make_fit()
+    fit_time = time.perf_counter() - start
+    
+    total_time = creation_time + fit_time
+    
+    print(f"  Creation time: {creation_time*1000:.1f}ms")
+    print(f"  Fitting time:  {fit_time*1000:.1f}ms")
+    print(f"  Total time:    {total_time*1000:.1f}ms")
+    
+    return total_time
+
+
+def check_joblib_availability():
+    """Check if joblib is available for parallel processing."""
+    try:
+        import joblib
+        print(f"✓ joblib {joblib.__version__} available")
+        
+        # Check number of cores
+        import os
+        n_cores = os.cpu_count()
+        print(f"✓ {n_cores} CPU cores detected")
+        return True
+    except ImportError:
+        print("✗ joblib not available - only sequential benchmarks will run")
+        return False
+
+
+if __name__ == "__main__":
+    print("FitFunctions Phase 4 Performance Benchmark")
+    print("=" * 50)
+    
+    # Check system capabilities
+    has_joblib = check_joblib_availability()
+    
+    # Single fit baseline
+    single_time = benchmark_single_fitfunction()
+    
+    # TrendFit scaling benchmarks
+    speedups = []
+    fit_counts = []
+    
+    test_sizes = [10, 25, 50, 100]
+    if has_joblib:
+        # Only run larger tests if joblib is available
+        test_sizes.extend([200])
+    
+    for n in test_sizes:
+        expected_seq_time = single_time * n
+        print(f"\nExpected sequential time for {n} fits: {expected_seq_time:.1f}s")
+        
+        try:
+            speedup, n_successful = benchmark_trendfit(n)
+            speedups.append(speedup)
+            fit_counts.append(n_successful)
+        except Exception as e:
+            print(f"  ✗ Benchmark failed: {e}")
+            speedups.append(1.0)
+            fit_counts.append(0)
+    
+    # Summary report
+    print("\n" + "=" * 50)
+    print("BENCHMARK SUMMARY")
+    print("=" * 50)
+    
+    print(f"Single fit baseline: {single_time*1000:.1f}ms")
+    
+    if speedups:
+        print("\nTrendFit Scaling Results:")
+        print("Fits | Successful | Speedup")
+        print("-" * 30)
+        for i, n in enumerate(test_sizes):
+            if i < len(speedups):
+                print(f"{n:4d} | {fit_counts[i]:10d} | {speedups[i]:7.1f}x")
+        
+        if has_joblib:
+            avg_speedup = np.mean(speedups)
+            best_speedup = max(speedups)
+            print(f"\nAverage speedup: {avg_speedup:.1f}x")
+            print(f"Best speedup:    {best_speedup:.1f}x")
+            
+            # Efficiency analysis
+            if avg_speedup > 1.5:
+                print("✓ Parallelization provides significant benefit")
+            else:
+                print("⚠ Parallelization benefit limited (overhead or few cores)")
+        else:
+            print("\nInstall joblib for parallel processing:")
+            print("  pip install joblib")
+            print("  or")
+            print("  pip install solarwindpy[performance]")
+    
+    print("\nTo use parallel fitting in your code:")
+    print("  tf.make_1dfits(n_jobs=-1)  # Use all cores")
+    print("  tf.make_1dfits(n_jobs=4)   # Use 4 cores")