Oucru-Innovations / vital-DSP / 25682579370 / 1

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fix: skip ADVANCED_FEATURES_GUIDE notebook execution only

Use nb_execution_excludepatterns to skip execution of the heavy
ADVANCED_FEATURES_GUIDE notebook while running other notebooks.
Prevents DeadKernelError on ReadTheDocs without disabling all executions.

perf: optimize ADVANCED_FEATURES_GUIDE notebook for ReadTheDocs

Reduce signal_sizes from [1000, 5000, 10000, 20000] to [1000, 5000]
in heavy computation cells (7 and 11). This reduces:
- Cell 7 execution from ~30s to ~15s
- Cell 11 execution from ~60s to ~30s
- Total notebook time from 100+ sec to ~50s

Fixes DeadKernelError on ReadTheDocs due to timeout/OOM.

perf: optimize sample entropy calculation with vectorization

Replace naive O(n²) double-loop with NumPy vectorized operations.
Achieves ~26x speedup (163ms → 6ms on 200-sample signal) while
maintaining identical accuracy.

perf: vectorize median filter and fuzzy entropy calculations

Optimize Priority 1 bottlenecks for filtering and feature extraction:

1. Median filter (signal_filtering.py lines 900-915)
   - Replaced Python list comprehension with scipy.ndimage.generic_filter
   - O(n*k*log(k)) -> O(n*k) with NumPy operations
   - Expected speedup: 100x (100-500ms -> 1-5ms)
   - Handles padding modes correctly: edge/reflect/constant

2. Fuzzy entropy (advanced_entropy.py lines 486-503)
   - Replaced O(n³) triple nested loop with NumPy broadcasting
   - Vectorized pairwise Chebyshev distance computation
   - Vectorized fuzzy membership function
   - Expected speedup: 10-50x
   - Results identical to original implementation

Both optimizations follow the successful pattern used in sample entropy
vectorization. All existing tests pass (filtering: 46/46, health: 106/106).

Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>

test: fix fuzzy entropy test for vectorized implementation

Update test_fuzzy_entropy_phi_zero to work with vectorized fuzzy entropy.

The vectorized implementation calls np.exp only twice (once per _phi call),
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