Release 0.6.0

Added

• New hypyp.sync module: Modular architecture for connectivity metrics
  • Extracted 9 connectivity metrics into separate classes: PLV, CCorr, ACCorr, Coh, ImCoh, PLI, WPLI, EnvCorr, PowCorr
  • BaseMetric abstract class for uniform interface across all metrics
  • get_metric(mode, optimization) function for easy metric instantiation
  • Helper functions: multiply_conjugate, multiply_conjugate_time, multiply_product
• GPU and numba backends for all 9 sync metrics:
  • numba JIT with prange: PLV, CCorr, Coh, ImCoh, PLI, wPLI, EnvCorr, PowCorr
  • PyTorch (MPS/CUDA/CPU) via batched einsum: all 9 metrics
  • Metal compute shaders (Apple Silicon): PLI, wPLI, ACCorr
  • CUDA raw kernels via CuPy (NVIDIA GPUs): all 9 metrics
• Benchmark-driven AUTO_PRIORITY table for optimization='auto', compiled from
  Mac M4 Max (131 runs) and Narval A100 (111 runs) benchmarks
• priority parameter on get_metric() and compute_sync() for custom backend ordering
• hypyp/sync/kernels/ submodule with Metal and CUDA dispatch infrastructure
• New optional dependencies: pyobjc-framework-Metal (Apple), cupy-cuda12x (NVIDIA)
• multiply_conjugate_torch and multiply_conjugate_time_torch GPU helpers

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.ACCorr instead (will be removed in 1.0.0)