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mne/preprocessing/bads.py

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+# Authors: The MNE-Python contributors.
+# License: BSD-3-Clause
+# Copyright the MNE-Python contributors.
+
+import numpy as np
+from scipy.stats import zscore
+
+
+def _find_outliers(X, threshold=3.0, max_iter=2, tail=0):
+    """Find outliers based on iterated Z-scoring.
+
+    This procedure compares the absolute z-score against the threshold.
+    After excluding local outliers, the comparison is repeated until no
+    local outlier is present any more.
+
+    Parameters
+    ----------
+    X : np.ndarray of float, shape (n_elemenets,)
+        The scores for which to find outliers.
+    threshold : float
+        The value above which a feature is classified as outlier.
+    max_iter : int
+        The maximum number of iterations.
+    tail : {0, 1, -1}
+        Whether to search for outliers on both extremes of the z-scores (0),
+        or on just the positive (1) or negative (-1) side.
+
+    Returns
+    -------
+    bad_idx : np.ndarray of int, shape (n_features)
+        The outlier indices.
+    """
+    my_mask = np.zeros(len(X), dtype=bool)
+    for _ in range(max_iter):
+        X = np.ma.masked_array(X, my_mask)
+        if tail == 0:
+            this_z = np.abs(zscore(X))
+        elif tail == 1:
+            this_z = zscore(X)
+        elif tail == -1:
+            this_z = -zscore(X)
+        else:
+            raise ValueError(f"Tail parameter {tail} not recognised.")
+        local_bad = this_z > threshold
+        my_mask = np.max([my_mask, local_bad], 0)
+        if not np.any(local_bad):
+            break
+
+    bad_idx = np.where(my_mask)[0]
+    return bad_idx