Apply suggestions from code review

Co-authored-by: Eric Larson <larson.eric.d@gmail.com>

Author: Kallemakela

mne/preprocessing/nirs/_beer_lambert_law.py

@@ -19,7 +19,7 @@
 )
 
 
-def beer_lambert_law(raw, ppf=6.0, sd_distances=None):
+def beer_lambert_law(raw, ppf=6.0, *, sd_distances=None):
     r"""Convert NIRS optical density data to haemoglobin concentration.
 
     Parameters
@@ -36,6 +36,8 @@ def beer_lambert_law(raw, ppf=6.0, sd_distances=None):
         from ``raw.info['chs']``. If array-like, the values must have a distance
         for each channel, matching the order in ``info['chs']``.
 
+        .. versionadded:: 1.13
+
     Returns
     -------
     raw : instance of Raw
@@ -167,13 +169,13 @@ def _get_sd_distances(raw, sd_distances):
             "sd_distances must be a float or a 1D array-like, got "
             f"shape {sd_distances.shape}"
         )
-    if len(sd_distances) == n_channels:
-        return sd_distances
-    raise ValueError(
-        "sd_distances must be a float or an array-like with length matching "
-        f"the len(raw.info['chs']) ({n_channels}), "
-        f"got length {len(sd_distances)}"
-    )
+    if len(sd_distances) != n_channels:
+        raise ValueError(
+            "sd_distances must be a float or an array-like with length matching "
+            f"the len(raw.info['chs']) ({n_channels}), "
+            f"got length {len(sd_distances)}"
+        )
+    return sd_distances
 
 
 def _load_absorption(freqs):

mne/preprocessing/nirs/nirs.py

@@ -65,7 +65,7 @@ def short_channels(info, threshold=0.01):
 def _has_source_detector_distances(info, picks=None):
     """Return True if source-detector distances can be computed."""
     distances = source_detector_distances(info, picks=picks)
-    return len(distances) > 0 and np.isfinite(distances).all() and np.all(distances > 0)
+    return len(distances) > 0 and np.all(distances > 0)
 
 
 def _channel_frequencies(info):

mne/preprocessing/nirs/tests/test_beer_lambert_law.py

@@ -187,10 +187,7 @@ def test_get_sd_distances():
 
     for idx in range(len(raw.info["chs"])):
         raw.info["chs"][idx]["loc"][3:9] = np.nan
-    with warnings.catch_warnings(record=True) as caught:
-        warnings.simplefilter("always")
-        assert_allclose(_get_sd_distances(raw, expected), expected, rtol=1e-12, atol=0)
-    assert len(caught) == 0
+    assert_allclose(_get_sd_distances(raw, expected), expected, rtol=1e-12, atol=0)
 
     with pytest.raises(ValueError, match=r"1D array-like"):
         _get_sd_distances(raw, np.ones((2, 2)))