refactor region id selection and mask label construction in r2d.mask

echoregions/regions2d/regions2d.py

@@ -195,6 +195,8 @@ def select_region(
             if isinstance(region_id, (float, int, str)):
                 region_id = [region_id]
             elif isinstance(region_id, list):
+                if len(region_id) == 0:
+                    raise ValueError("region_id list is empty. Cannot be empty.")
                 for value in region_id:
                     if not isinstance(value, (float, int, str)):
                         raise TypeError(
@@ -483,36 +485,9 @@ def mask(
         region_contours : pd.DataFrame
             DataFrame containing region_id, depth, and time.
         """
-        if isinstance(region_id, list):
-            if len(region_id) == 0:
-                raise ValueError("region_id list is empty. Cannot be empty.")
-            mask_label_region_ids = region_id
-        elif region_id is None:
-            # Extract all region_id values
-            mask_label_region_ids = self.data.region_id.astype(int).to_list()
-        else:
-            raise TypeError(
-                f"region_id must be of type list. Currently is of type {type(region_id)}"
-            )
-
-        if mask_labels is None:
-            # Create mask_labels with each region_id as a key and values starting from 0
-            mask_labels = {key: idx for idx, key in enumerate(mask_label_region_ids)}
-
-        # Check that region_id and mask_labels are of the same size
-        if len(set(mask_label_region_ids) - set(mask_labels.keys())) > 0:
-            raise ValueError(
-                "Each region_id' must be a key in 'mask_labels'. "
-                "If you would prefer 0 based indexing as values for mask_labels, leave "
-                "mask_labels as None."
-            )
-
         # Dataframe containing region information.
         region_df = self.select_region(region_id, region_class)
 
-        # Select only important columns
-        region_df = region_df[["region_id", "time", "depth"]]
-
         # Filter for rows with depth values within self min and self max depth and
         # for rows that have positive depth.
         region_df = region_df[
@@ -529,6 +504,23 @@ def mask(
                 "between min_depth and max_depth."
             )
         else:
+            # Grab subset region ids
+            subset_region_ids = region_df.region_id.astype(int).to_list()
+
+            if mask_labels is None:
+                # Create mask_labels with each subset_region_ids as a key and values starting from 0
+                mask_labels = {key: idx for idx, key in enumerate(subset_region_ids)}
+
+            # Check that subset_region_ids and mask_labels are of the same size
+            if len(set(subset_region_ids) - set(mask_labels.keys())) > 0:
+                raise ValueError(
+                    "Each value in subset_region_ids must be a key in 'mask_labels'. "
+                    "If you would prefer 0 based indexing as values for mask_labels, leave "
+                    "mask_labels as None."
+                )
+            # Select only important columns
+            region_df = region_df[["region_id", "time", "depth"]]
+
             # Organize the regions in a format for region mask.
             df = region_df.explode(["time", "depth"])