[FIX] Make outlier detection robust to nans (nbara#73)

Author: nbara

citation.cff

@@ -5,7 +5,7 @@ authors:
   given-names: "Nicolas"
   orcid: "https://orcid.org/0000-0003-1495-561X"
 title: "MEEGkit"
-version: 0.1.6
+version: 0.1.7
 doi: 10.5281/zenodo.10210992
-date-released: 2023-11-28
+date-released: 2023-12-08
 url: "https://github.com/nbara/python-meegkit"

meegkit/__init__.py

@@ -1,5 +1,5 @@
 """M/EEG denoising utilities in python."""
-__version__ = "0.1.6"
+__version__ = "0.1.7"
 
 from . import asr, cca, detrend, dss, lof, ress, sns, star, trca, tspca, utils
 

meegkit/utils/denoise.py

@@ -187,11 +187,11 @@ def find_outlier_trials(X, thresh=None, show=True):
     else:
         n_chans, n_trials = X.shape
 
-    avg = np.mean(X, axis=-1, keepdims=True)  # mean over trials
+    avg = np.nanmean(X, axis=-1, keepdims=True)  # mean over trials
     d = X - avg  # difference from mean
-    d = np.sum(d ** 2, axis=0)
+    d = np.nansum(d ** 2, axis=0)
 
-    d = d / (np.sum(X ** 2) / n_trials)
+    d = d / (np.nansum(X ** 2) / n_trials)
     idx = np.where(d < thresh[0])[0]
 
     if show: