Applied recommended code changes & bug fix in _scalp_coupling_index iteration

Author: zEdS15B3GCwq

mne/preprocessing/nirs/_beer_lambert_law.py

@@ -119,7 +119,7 @@ def beer_lambert_law(raw, ppf=6.0):
 
 
 def _load_absorption(freqs):
-    """Load molar extinction coefficients"""
+    """Load molar extinction coefficients."""
     # Data from https://omlc.org/spectra/hemoglobin/summary.html
     # The text was copied to a text file. The text before and
     # after the table was deleted. The the following was run in

mne/preprocessing/nirs/_scalp_coupling_index.py

@@ -65,42 +65,25 @@ def scalp_coupling_index(
 
     sci = np.zeros(picks.shape)
 
-    if n_wavelengths == 2:
-        # Use pairwise correlation for 2 wavelengths (backward compatibility)
-        for ii in range(0, len(picks), 2):
+    # Calculate all pairwise correlations within each group and use the minimum as SCI
+    pair_indices = np.triu_indices(n_wavelengths, k=1)
+    for gg in range(0, len(picks), n_wavelengths):
+        group_data = filtered_data[gg : gg + n_wavelengths]
+
+        # Calculate pairwise correlations within the group
+        correlations = np.zeros(pair_indices[0].shape[0])
+
+        for n, (ii, jj) in enumerate(zip(*pair_indices)):
             with np.errstate(invalid="ignore"):
-                c = np.corrcoef(filtered_data[ii], filtered_data[ii + 1])[0][1]
-            if not np.isfinite(c):  # someone had std=0
-                c = 0
-            sci[ii] = c
-            sci[ii + 1] = c
-    else:
-        # For multiple wavelengths: calculate all pairwise correlations within each group
-        # and use the minimum as the quality metric
-
-        # Group picks by number of wavelengths
-        # Drops last incomplete group, but we're assuming valid data
-        pick_iter = iter(picks)
-        pick_groups = zip(*[pick_iter] * n_wavelengths)
-
-        for group_picks in pick_groups:
-            group_data = filtered_data[group_picks]
-
-            # Calculate pairwise correlations within the group
-            pair_indices = np.triu_indices(len(group_picks), k=1)
-            correlations = np.zeros(pair_indices[0].shape[0])
-
-            for n, (ii, jj) in enumerate(zip(*pair_indices)):
-                with np.errstate(invalid="ignore"):
-                    c = np.corrcoef(group_data[ii], group_data[jj])[0][1]
-                if np.isfinite(c):
-                    correlations[n] = c
-
-            # Use minimum correlation as the quality metric
-            group_sci = correlations.min()
-
-            # Assign the same SCI value to all channels in the group
-            sci[group_picks] = group_sci
+                c = np.corrcoef(group_data[ii], group_data[jj])[0][1]
+            if np.isfinite(c):
+                correlations[n] = c
+
+        # Use minimum correlation as SCI
+        group_sci = correlations.min()
+
+        # Assign the same SCI value to all channels in the group
+        sci[gg : gg + n_wavelengths] = group_sci
 
     sci[zero_mask] = 0
     sci = sci[np.argsort(picks)]  # restore original order

mne/preprocessing/nirs/nirs.py

@@ -149,11 +149,12 @@ def _check_channels_ordered(info, pair_vals, *, throw_errors=True, check_bads=Tr
     # (e.g., for 2 wavelengths, we need even number of channels)
     if len(picks) % len(pair_vals) != 0:
         picks = _throw_or_return_empty(
-            f"NIRS channels not ordered correctly. The number of channels "
+            "NIRS channels not ordered correctly. The number of channels "
             f"must be a multiple of {len(pair_vals)} values, but "
             f"{len(picks)} channels were provided.",
             throw_errors,
         )
+
     # Ensure wavelength info exists for waveform data
     all_freqs = [info["chs"][ii]["loc"][9] for ii in picks_wave]
     if len(pair_vals) != len(set(all_freqs)):
@@ -197,9 +198,9 @@ def _check_channels_ordered(info, pair_vals, *, throw_errors=True, check_bads=Tr
     picks = picks[np.argsort([info["ch_names"][pick] for pick in picks])]
 
     # Validate channel grouping (same source-detector pairs, all pair_vals match)
-    for i in range(0, len(picks), len(pair_vals)):
+    for ii in range(0, len(picks), len(pair_vals)):
         # Extract a group of channels (e.g., all wavelengths for one S-D pair)
-        group_picks = picks[i : i + len(pair_vals)]
+        group_picks = picks[ii : ii + len(pair_vals)]
 
         # Parse channel names using regex to extract source, detector, and value info
         group_info = [
@@ -228,8 +229,8 @@ def _check_channels_ordered(info, pair_vals, *, throw_errors=True, check_bads=Tr
             break
 
     if check_bads:
-        for i in range(0, len(picks), len(pair_vals)):
-            group_picks = picks[i : i + len(pair_vals)]
+        for ii in range(0, len(picks), len(pair_vals)):
+            group_picks = picks[ii : ii + len(pair_vals)]
 
             want = [info.ch_names[pick] for pick in group_picks]
             got = list(set(info["bads"]).intersection(want))