MAINT: Improve NumPy 2 compat

mne/annotations.py

@@ -1728,4 +1728,4 @@ def count_annotations(annotations):
         {'T0': 2, 'T1': 1}
     """
     types, counts = np.unique(annotations.description, return_counts=True)
-    return {t: count for t, count in zip(types, counts)}
+    return {str(t): int(count) for t, count in zip(types, counts)}

mne/conftest.py

@@ -163,10 +163,6 @@ def pytest_configure(config):
         warning_line = warning_line.strip()
         if warning_line and not warning_line.startswith("#"):
             config.addinivalue_line("filterwarnings", warning_line)
-    # TODO: Fix this with casts?
-    # https://github.com/numpy/numpy/pull/22449
-    if check_version("numpy", "1.26"):
-        np.set_printoptions(legacy="1.25")
 
 
 # Have to be careful with autouse=True, but this is just an int comparison

mne/event.py

@@ -1681,7 +1681,7 @@ def count_events(events, ids=None):
         {1: 2, 11: 0}
     """
     counts = np.bincount(events[:, 2])
-    counts = {i: count for i, count in enumerate(counts) if count > 0}
+    counts = {i: int(count) for i, count in enumerate(counts) if count > 0}
     if ids is not None:
-        return {id: counts.get(id, 0) for id in ids}
+        counts = {id_: int(counts.get(id_, 0)) for id_ in ids}
     return counts

mne/filter.py

@@ -2120,7 +2120,7 @@ def detrend(x, order=1, axis=-1):
         >>> npoints = int(1e3)
         >>> noise = randgen.randn(npoints)
         >>> x = 3 + 2*np.linspace(0, 1, npoints) + noise
-        >>> (detrend(x) - noise).max() < 0.01
+        >>> bool((detrend(x) - noise).max() < 0.01)
         True
     """
     from scipy.signal import detrend

mne/preprocessing/ica.py

@@ -11,7 +11,7 @@
 from copy import deepcopy
 from numbers import Integral
 from time import time
-from dataclasses import dataclass
+from dataclasses import dataclass, is_dataclass
 from typing import Optional, List, Literal
 import warnings
 
@@ -112,15 +112,14 @@
 
 def _make_xy_sfunc(func, ndim_output=False):
     """Aux function."""
-    if ndim_output:
 
-        def sfunc(x, y):
-            return np.array([func(a, y.ravel()) for a in x])[:, 0]
-
-    else:
-
-        def sfunc(x, y):
-            return np.array([func(a, y.ravel()) for a in x])
+    def sfunc(x, y, ndim_output=ndim_output):
+        out = [func(a, y.ravel()) for a in x]
+        if len(out) and is_dataclass(out[0]):  # PermutationTestResult
+            out = [(o.statistic, o.pvalue) for o in out]
+        if ndim_output:
+            out = np.array(out)[:, 0]
+        return out
 
     sfunc.__name__ = ".".join(["score_func", func.__module__, func.__name__])
     sfunc.__doc__ = func.__doc__