FIX: Dim

mne_nirs/tests/test_examples.py

@@ -44,6 +44,8 @@ def examples_path():
 )
 
 
+# TODO: Change ax= to axes= once nilearn 0.11 is out
+@pytest.mark.filterwarnings('ignore:The parameter "ax" will be removed.*:')
 @pytest.mark.filterwarnings("ignore:No bad channels to interpolate.*:")
 @pytest.mark.filterwarnings("ignore:divide by zero encountered.*:")
 @pytest.mark.filterwarnings("ignore:invalid value encountered.*:")

mne_nirs/utils/_io.py

@@ -70,6 +70,10 @@ def _tidy_Contrast(data, glm_est, design_matrix):
         contrast_type = glm_est.stat_type
     else:  # nilearn < 0.10
         contrast_type = glm_est.contrast_type
+    effect = glm_est.effect
+    if effect.ndim == 2:  # old nilearn
+        assert effect.shape[0] == 1
+        effect = effect[0]
     for idx, ch in enumerate(data.ch_names):
         df = pd.concat(
             [
@@ -79,7 +83,7 @@ def _tidy_Contrast(data, glm_est, design_matrix):
                         "ch_name": ch,
                         "ContrastType": contrast_type,
                         "variable": "effect",
-                        "value": glm_est.effect[0][idx],
+                        "value": effect[idx],
                     },
                     index=[0],
                 ),

mne_nirs/visualisation/_plot_GLM_topo.py

@@ -118,7 +118,10 @@ def _plot_glm_contrast_topo(inst, contrast, figsize=(12, 7), sphere=None):
     types = np.unique(info.get_channel_types())
 
     # Extract values to plot and rescale to uM
-    estimates = contrast.effect[0]
+    estimates = contrast.effect
+    if estimates.ndim == 2:  # old nilearn
+        assert estimates.shape[0] == 1
+        estimates = estimates[0]
     estimates = estimates * 1e6
 
     # Create subplots for figures