# Ralph Progress Log
Started: 2026-01-20T00:00:00Z

## Codebase Patterns
1. **Patch Pattern**: All optimizations use the patch() utility from _patch.py
2. **Numba Patterns**: Use @njit(parallel=True) for parallel ops, @njit(nogil=True) for thread-safe ops
3. **Dispatch Pattern**: Large arrays → threadpool, medium → parallel, small → serial
4. **Wrapper Pattern**: Wrappers extract pandas object details, call Numba kernels, wrap results

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## 2026-01-20 - US-001
- Enabled stats patches in __init__.py
- Files changed: src/unlockedpd/__init__.py
- **Learnings:**
  - Stats patches need to be imported and applied after basic module setup
  - Docstring should reflect which operations are now patched

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## 2026-01-20 - US-002
- Implemented expanding median by reusing quantile(0.5)
- Files changed: src/unlockedpd/ops/expanding.py
- **Learnings:**
  - Median can efficiently reuse quantile implementation with q=0.5
  - No need to duplicate sorting logic

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## 2026-01-20 - US-003
- Added expanding quantile patch (implementation already existed)
- Files changed: src/unlockedpd/ops/expanding.py
- **Learnings:**
  - Always check if implementation exists before rewriting
  - Quantile was already implemented with nogil kernels and threadpool

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## 2026-01-20 - US-004
- Verified rolling sem is already complete
- No changes needed
- **Learnings:**
  - Rolling sem was already implemented and patched in rolling.py

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## 2026-01-20 - US-005 & US-006
- Implemented EWM correlation and covariance
- Files changed: src/unlockedpd/ops/ewm.py
- Added:
  - _ewm_cov_single_col_nogil() and _ewm_corr_single_col_nogil()
  - _ewm_cov_matrix_nogil_chunk() and _ewm_corr_matrix_nogil_chunk()
  - _ewm_cov_pairwise_threadpool() and _ewm_corr_pairwise_threadpool()
  - optimized_ewm_cov() and optimized_ewm_corr() wrappers
  - Patches in apply_ewm_patches()
- **Learnings:**
  - EWM formulas: Cov = EWM(XY) - EWM(X)*EWM(Y)
  - Corr = Cov / sqrt(Var(X) * Var(Y))
  - Support both adjust=True and adjust=False modes
  - Pairwise operations use symmetric matrix with MultiIndex output

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## 2026-01-20 - US-007
- Implemented DataFrame aggregates (mean, sum, std, var)
- Files changed:
  - Created src/unlockedpd/ops/aggregates.py
  - Modified src/unlockedpd/__init__.py
- Added implementations for:
  - mean/sum/std/var with axis=0 and axis=1
  - skipna parameter support
  - ddof parameter for std/var
  - Parallel column/row processing with prange
- **Learnings:**
  - Aggregate operations are simpler than window operations
  - Two-pass algorithm for std/var (mean first, then variance)
  - Must handle empty results with np.nan

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## 2026-01-20 - US-008
- Added comprehensive tests for all new operations
- Files changed:
  - Modified tests/test_expanding.py
  - Modified tests/test_ewm.py
  - Created tests/test_aggregates.py
- Added 25 new tests total
- **Learnings:**
  - Always remove placeholder skip decorators when enabling features
  - Tests should cover basic, NaN handling, and parameter variations

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## 2026-01-20 - US-009 (Final Verification)
- Ran full test suite: 92 passed, 27 skipped, 0 failed
- Oracle verification identified NaN handling bug in aggregates
- Fixed std/var skipna=False bug using has_nan flag and break
- All files committed to git: 49b0caf
- **Learnings:**
  - Oracle review catches subtle bugs tests may miss
  - Continue vs break distinction critical for NaN handling
  - Always run full suite after bug fixes to verify no regressions

---

## FINAL STATUS

All 9 user stories COMPLETE and VERIFIED:
✓ US-001: Stats patches enabled
✓ US-002: Expanding median implemented
✓ US-003: Expanding quantile patched
✓ US-004: Rolling sem verified (already existed)
✓ US-005: EWM correlation implemented
✓ US-006: EWM covariance implemented
✓ US-007: DataFrame aggregates implemented
✓ US-008: Tests added (25 new tests)
✓ US-009: Full verification passed

Worktree: /home/bellman/Workspace/MyNumbaDataFrame-coverage-gaps
Branch: coverage-gaps
Commit: 49b0caf
Test results: 92 passed, 0 failed

