fix readonly issues in pandas

Author: stes

cebra/integrations/deeplabcut.py

@@ -169,7 +169,8 @@ def load_data(self, pcutoff: float = 0.6) -> npt.NDArray:
         pred_xy = []
         for i, _ in enumerate(self.dlc_df.index):
             data = (self.dlc_df.iloc[i].loc[self.scorer].loc[
-                self.keypoints_list].to_numpy().reshape(-1, len(dlc_df_coords)))
+                self.keypoints_list].to_numpy().copy().reshape(
+                    -1, len(dlc_df_coords)))
 
             # Handles nan values with interpolation
             if i > 0 and i < len(self.dlc_df) - 1:

cebra/integrations/sklearn/dataset.py

@@ -110,6 +110,9 @@ def _parse_data(self, X: npt.NDArray):
         # one sample is a conservative default here to ensure that sklearn tests
         # passes with the correct error messages.
         X = cebra_sklearn_utils.check_input_array(X, min_samples=2)
+        # Ensure array is writable (pandas 3.0+ may return read-only arrays)
+        if not X.flags.writeable:
+            X = X.copy()
         self.neural = torch.from_numpy(X).float().to(self.device)
 
     def _parse_labels(self, labels: Optional[tuple]):
@@ -145,6 +148,9 @@ def _parse_labels(self, labels: Optional[tuple]):
 
             # Define the index as either continuous or discrete indices, depending
             # on the dtype in the index array.
+            # Ensure array is writable (pandas 3.0+ may return read-only arrays)
+            if not y.flags.writeable:
+                y = y.copy()
             if cebra.helper._is_floating(y):
                 y = torch.from_numpy(y).float()
                 if y.dim() == 1: