[ENH] return input data if score is negative on first iter

meegkit/dss.py

@@ -291,10 +291,10 @@ def dss_line_iter(data, fline, sfreq, win_sz=10, spot_sz=2.5,
     show: bool
         Produce a visual output of each iteration (default=False).
     dirname: str
-        Path to the directory where visual outputs are saved when show is 'True'. 
+        Path to the directory where visual outputs are saved when show is 'True'.
         If 'None', does not save the outputs. (default=None)
     extension: str
-        Extension of the images filenames. Must be compatible with plt.savefig() 
+        Extension of the images filenames. Must be compatible with plt.savefig()
         function. (default=".png")
     n_iter_max : int
         Maximum number of iterations (default=100).
@@ -313,11 +313,13 @@ def nan_basic_interp(array):
         array[nans] = np.interp(ix(nans), ix(~nans), array[~nans])
         return array
 
+    data_clean = data.copy()
+
     freq_rn = [fline - win_sz, fline + win_sz]
     freq_sp = [fline - spot_sz, fline + spot_sz]
     freq, psd = welch(data, fs=sfreq, nfft=nfft, axis=0)
 
-    freq_rn_ix = np.logical_and(freq >= freq_rn[0], 
+    freq_rn_ix = np.logical_and(freq >= freq_rn[0],
                                 freq <= freq_rn[1])
     freq_used = freq[freq_rn_ix]
     freq_sp_ix = np.logical_and(freq_used >= freq_sp[0],
@@ -338,8 +340,8 @@ def nan_basic_interp(array):
     aggr_resid = []
     iterations = 0
     while iterations < n_iter_max:
-        data, _ = dss_line(data, fline, sfreq, nfft=nfft, nremove=1)
-        freq, psd = welch(data, fs=sfreq, nfft=nfft, axis=0)
+        data_clean, _ = dss_line(data_clean, fline, sfreq, nfft=nfft, nremove=1)
+        freq, psd = welch(data_clean, fs=sfreq, nfft=nfft, axis=0)
         if psd.ndim == 3:
             mean_psd = np.mean(psd, axis=(1, 2))[freq_rn_ix]
         elif psd.ndim == 2:
@@ -366,7 +368,7 @@ def nan_basic_interp(array):
             ax.flat[0].set_xlabel("Frequency (Hz)")
             ax.flat[0].set_ylabel("Power")
 
-            ax.flat[1].plot(freq_used, mean_psd_tf, c="gray", 
+            ax.flat[1].plot(freq_used, mean_psd_tf, c="gray",
                             label="Interpolated mean PSD")
             ax.flat[1].plot(freq_used, mean_psd, c="blue", label="Mean PSD")
             ax.flat[1].plot(freq_used, clean_fit_line, c="red", label="Fitted polynomial")
@@ -396,6 +398,9 @@ def nan_basic_interp(array):
             plt.show()
 
         if mean_score <= 0:
+            # Return original data if score is negative on first iteration
+            if iterations == 0:
+                return data, 0
             break
 
         iterations += 1
@@ -404,4 +409,4 @@ def nan_basic_interp(array):
         raise RuntimeError("Could not converge. Consider increasing the "
                            "maximum number of iterations")
 
-    return data, iterations
+    return data_clean, iterations

tests/test_dss.py

@@ -176,6 +176,30 @@ def _plot(before, after):
     plt.close("all")
 
 
+def test_dss_line_iter_no_noise():
+    """
+    Test that dss_line_iter returns original data unchanged when DSS
+    cannot improve the signal.
+    """
+    sr = 200
+    fline = 50
+    n_samples = 9000
+    n_chans = 10
+    rng = np.random.RandomState(42)
+
+    # create data without line noise at target frequency
+    x = rng.randn(n_samples, n_chans)
+    x_original = x.copy()
+
+    x_out, n_iters = dss.dss_line_iter(x, fline, sr, n_iter_max=10)
+
+    assert n_iters == 0, f"Expected 0 iterations (no improvement), got {n_iters}"
+    assert np.allclose(x_out, x_original), (
+        "When DSS cannot improve signal, should return original data unchanged"
+    )
+    assert np.allclose(x, x_original), "Input data should never be mutated"
+
+
 def profile_dss_line(nkeep):
     """Test line noise removal."""
     import cProfile