Correcting type

src/cloudcasting/utils.py

@@ -158,7 +158,7 @@ def numpy_validation_collate_fn(
 def create_cutout_mask(
     mask_size: tuple[int, int, int, int],
     image_size: tuple[int, int],
-) -> NDArray[np.int8]:
+) -> NDArray[np.float64]:
     """Create a mask with a cutout in the center.
     Args:
         x: x-coordinate of the center of the cutout
@@ -173,7 +173,7 @@ def create_cutout_mask(
     height, width = image_size
     min_x, max_x, min_y, max_y = mask_size
 
-    mask = np.empty((height, width), dtype=np.int8)
+    mask = np.empty((height, width), dtype=np.float64)
     mask[:] = np.nan
     mask[min_y:max_y, min_x:max_x] = 1
     return mask

src/cloudcasting/validation.py

@@ -192,7 +192,7 @@ def score_model_on_all_metrics(
     batch_limit: int | None = None,
     metric_names: tuple[str, ...] | list[str] = ("mae", "mse", "ssim"),
     metric_kwargs: dict[str, dict[str, Any]] | None = None,
-    mask: NDArray[np.int8] = CUTOUT_MASK,
+    mask: NDArray[np.float64] = CUTOUT_MASK,
 ) -> tuple[dict[str, MetricArray], list[str]]:
     """Calculate the scoreboard metrics for the given model on the validation dataset.
 
@@ -368,7 +368,7 @@ def validate(
     batch_size: int = 1,
     num_workers: int = 0,
     batch_limit: int | None = None,
-    mask: NDArray[np.int8] = CUTOUT_MASK,
+    mask: NDArray[np.float64] = CUTOUT_MASK,
 ) -> None:
     """Run the full validation procedure on the model and log the results to wandb.