v3.2: update nqr_snn/data/dataset.py

nqr_snn/data/dataset.py

@@ -147,16 +147,10 @@ class NQRDatasetV2(Dataset):
         self.snr_dbs = snr_dbs  # v3.0: for curriculum training
         
         # Apply denoiser if provided (LM/SSA/Wavelet pipeline integration)
+        # v3.2: use consolidated helper (was duplicated 4×)
         if denoiser is not None:
-            denoised = np.zeros_like(signals)
-            for i in range(len(signals)):
-                if hasattr(denoiser, 'fit_one'):  # LMDenoiser
-                    denoised[i] = denoiser.fit_one(signals[i])
-                elif hasattr(denoiser, 'denoise'):  # SSA/Wavelet
-                    denoised[i] = denoiser.denoise(signals[i])
-                else:
-                    denoised[i] = signals[i]
-            signals_to_extract = denoised
+            from nqr_snn.denoising import denoise_batch
+            signals_to_extract = denoise_batch(denoiser, signals)
         else:
             signals_to_extract = signals