[FIX] ASR: reuse full SHAPE_RANGE grid in clean_windows (include beta=3.5)

clean_windows hardcoded np.arange(1.7, 3.5, 0.15), a 12-point grid that
excludes the beta=3.5 endpoint, overriding the correct 13-point SHAPE_RANGE
that fit_eeg_distribution defaults to. Reuse SHAPE_RANGE so the KL grid
search can select beta=3.5.

Author: sappelhoff

meegkit/asr.py

@@ -8,7 +8,13 @@
 from statsmodels.robust.scale import mad
 
 from .utils import block_covariance, nonlinear_eigenspace
-from .utils.asr import fit_eeg_distribution, geometric_median, yulewalk, yulewalk_filter
+from .utils.asr import (
+    SHAPE_RANGE,
+    fit_eeg_distribution,
+    geometric_median,
+    yulewalk,
+    yulewalk_filter,
+)
 
 
 class ASR:
@@ -324,7 +330,7 @@ def clean_windows(X, sfreq, max_bad_chans=0.2, zthresholds=[-3.5, 5],
     # set internal variables
     truncate_quant = [0.0220, 0.6000]
     step_sizes = [0.01, 0.01]
-    shape_range = np.arange(1.7, 3.5, 0.15)
+    shape_range = SHAPE_RANGE
     max_bad_chans = np.round(X.shape[0] * max_bad_chans)
 
     # set data indices

tests/test_asr.py

@@ -1,14 +1,15 @@
 """ASR test."""
 import inspect
 import os
+from unittest.mock import patch
 
 import matplotlib.pyplot as plt
 import numpy as np
 import pytest
 from scipy import signal
 
 from meegkit.asr import ASR, asr_calibrate, asr_process, clean_windows
-from meegkit.utils.asr import yulewalk, yulewalk_filter
+from meegkit.utils.asr import SHAPE_RANGE, fit_eeg_distribution, yulewalk, yulewalk_filter
 from meegkit.utils.matrix import sliding_window
 
 # Data files
@@ -276,6 +277,44 @@ def test_asr_class(method, reref, show=False):
     ASR(sfreq=125)
     ASR(Sfreq=150)
 
+def test_clean_windows_uses_full_shape_range():
+    """clean_windows passes the full 13-point SHAPE_RANGE (incl beta=3.5) down.
+
+    Spies on the shape_range argument to fit_eeg_distribution (6th positional).
+    """
+    raw = np.load(os.path.join(THIS_FOLDER, "data", "eeg_raw.npy"))
+    sfreq = 250
+
+    with patch(
+        "meegkit.asr.fit_eeg_distribution", side_effect=fit_eeg_distribution
+    ) as mock_fit:
+        clean_windows(raw, sfreq)
+
+    assert mock_fit.called
+    captured = mock_fit.call_args.args[5]
+    assert len(captured) == 13
+    assert np.isclose(captured[-1], 3.5)
+    assert np.allclose(captured, SHAPE_RANGE)
+
+
+def test_fit_eeg_distribution_shape_range_materiality():
+    """Dropping the beta=3.5 endpoint changes the selected shape parameter.
+
+    A bounded, near-uniform sample drives the grid search to the largest beta.
+    """
+    # Bounded, near-uniform sample: favors the largest available beta.
+    Y = np.linspace(-1, 1, 2000)
+
+    shape_range_trunc = np.arange(1.7, 3.5, 0.15)
+
+    beta_full = fit_eeg_distribution(Y, shape_range=SHAPE_RANGE)[3]
+    beta_trunc = fit_eeg_distribution(Y, shape_range=shape_range_trunc)[3]
+
+    assert np.isclose(beta_full, 3.5)
+    assert beta_trunc <= np.max(shape_range_trunc) + 1e-9
+    assert beta_full > beta_trunc
+
+
 if __name__ == "__main__":
     pytest.main([__file__])
     # test_yulewalk(250, True)