feat: modular sync architecture with accorr numba/torch optimizations (#250)

feat: modular sync architecture with accorr numba/torch optimizations

Author: Ramdam17

CHANGELOG.md

@@ -3,10 +3,22 @@
 ## [Unreleased]
 
 ### Added
-- New connectivity metric: Adjusted Circular Correlation (`accorr`) in `analyses.py`
-  - Hybrid implementation with vectorized numerator and exact denominator computation
-  - Progress bar support via `tqdm` for monitoring computation progress
-  - Available through `pair_connectivity()` and `compute_sync()` functions with `mode='accorr'`
+- **New `hypyp.sync` module**: Modular architecture for connectivity metrics
+  - Extracted 9 connectivity metrics into separate classes: `PLV`, `CCorr`, `ACorr`, `Coh`, `ImCoh`, `PLI`, `WPLI`, `EnvCorr`, `PowCorr`
+  - `BaseMetric` abstract class for uniform interface across all metrics
+  - `get_metric(mode, backend)` function for easy metric instantiation
+  - Backend support infrastructure (numpy default, with future support for numba/torch)
+  - Helper functions: `multiply_conjugate`, `multiply_conjugate_time`, `multiply_product`
+
+### Changed
+- **BREAKING**: `accorr` metric now returns raw connectivity values with shape `(n_epoch, n_freq, 2*n_ch, 2*n_ch)` like all other metrics. The `swapaxes` and `epochs_average` operations are now handled by `compute_sync()` instead of being applied inside the metric.
+- Refactored `compute_sync()` to use the new `hypyp.sync` module internally
+
+### Deprecated
+- `_multiply_conjugate()` in analyses.py - use `hypyp.sync.multiply_conjugate` instead (will be removed in 1.0.0)
+- `_multiply_conjugate_time()` in analyses.py - use `hypyp.sync.multiply_conjugate_time` instead (will be removed in 1.0.0)
+- `_multiply_product()` in analyses.py - use `hypyp.sync.multiply_product` instead (will be removed in 1.0.0)
+- `_accorr_hybrid()` in analyses.py - use `hypyp.sync.ACorr` instead (will be removed in 1.0.0)
 
 ## [0.5.0b13] - 2025-09-18
 

CONTRIBUTORS.md

@@ -0,0 +1,37 @@
+# Contributors
+
+HyPyP is built by a community of researchers and engineers. Thank you to everyone
+who has contributed code, ideas, feedback, and bug reports.
+
+## Original Authors
+
+- Florence BRUN
+- Anaël AYROLLES
+- Phoebe CHEN
+- Amir DJALOVSKI
+- Yann BEAUXIS
+- Suzanne DIKKER
+- Guillaume DUMAS
+
+## Contributors
+
+- Ryssa MOFFAT
+- Marine Gautier MARTINS
+- Rémy RAMADOUR
+- Patrice FORTIN
+- Ghazaleh RANJBARAN
+- Quentin MOREAU
+- Caitriona DOUGLAS
+- Franck PORTEOUS
+- Jonas MAGO
+- Juan C. AVENDANO
+- Julie BONNAIRE
+- Martín A. MIGUEL ([@m2march](https://github.com/m2march)) —
+  Implemented ACCorr hardware acceleration (numba JIT and PyTorch GPU/MPS backends)
+  and benchmarking infrastructure as part of BrainHack Montréal 2026 (PR #246, #250).
+
+## How to Contribute
+
+We welcome contributions of all kinds — bug fixes, new features, documentation
+improvements, and tutorials. See our
+[GitHub Issues](https://github.com/ppsp-team/HyPyP/issues) to get started.

README.md

@@ -14,8 +14,8 @@ The **Hy**perscanning **Py**thon **P**ipeline
 
 ## Contributors
 
-Original authors: Florence BRUN, Anaël AYROLLES, Phoebe CHEN, Amir DJALOVSKI, Yann BEAUXIS, Suzanne DIKKER, Guillaume DUMAS  
-New contributors: Ryssa MOFFAT, Marine Gautier MARTINS, Rémy RAMADOUR, Patrice FORTIN, Ghazaleh RANJBARAN, Quentin MOREAU, Caitriona DOUGLAS, Franck PORTEOUS, Jonas MAGO, Juan C. AVENDANO, Julie BONNAIRE
+Original authors: Florence BRUN, Anaël AYROLLES, Phoebe CHEN, Amir DJALOVSKI, Yann BEAUXIS, Suzanne DIKKER, Guillaume DUMAS
+New contributors: Ryssa MOFFAT, Marine Gautier MARTINS, Rémy RAMADOUR, Patrice FORTIN, Ghazaleh RANJBARAN, Quentin MOREAU, Caitriona DOUGLAS, Franck PORTEOUS, Jonas MAGO, Juan C. AVENDANO, Julie BONNAIRE, Martín A. MIGUEL, [@m2march](https://github.com/m2march) (ACCorr GPU/numba optimizations, BrainHack Montréal 2026)
 
 ## Installation