merge in matrix test fix

Author: JuliaKukulies

.github/workflows/matrix_ci.yml

@@ -20,7 +20,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
         os: [macos, ubuntu, windows]
 
     steps:

tobac/tests/test_segmentation.py

@@ -630,8 +630,8 @@ def test_segmentation_multiple_features():
     out_seg_mask_arr = out_seg_mask.core_data()
 
     # assure that the number of grid cells belonging to each feature (ncells) are consistent with segmentation mask
-    assert int(out_df[out_df.feature == 1].ncells.values) == size_feature1
-    assert int(out_df[out_df.feature == 2].ncells.values) == size_feature2
+    assert int(out_df[out_df.feature == 1].ncells.values[0]) == size_feature1
+    assert int(out_df[out_df.feature == 2].ncells.values[0]) == size_feature2
     # assure that bulk statistic columns are created in output (one column added after segmentation)
     assert out_df.columns.size - fd_output.columns.size > 1
     # assure that statistics are calculated everywhere where an area for ncells is found

tobac/tests/test_utils.py

@@ -559,7 +559,7 @@ def test_transform_feature_points_3D():
         5, 105, 20, max_h1=1000, max_h2=1000
     )
 
-    orig_feat_df = tb_utils.combine_tobac_feats([orig_feat_df_1, orig_feat_df_2])
+    orig_feat_df = tb_utils.combine_feature_dataframes([orig_feat_df_1, orig_feat_df_2])
 
     orig_feat_df["latitude"] = orig_feat_df["hdim_1"]
     orig_feat_df["longitude"] = orig_feat_df["hdim_2"]

tobac/utils/general.py

@@ -714,7 +714,6 @@ def transform_feature_points(
         the new grid, suitable for use in segmentation
 
     """
-    from .. import analysis as tb_analysis
 
     RADIUS_EARTH_M = 6371000
     is_3D = "vdim" in features
@@ -812,14 +811,12 @@ def transform_feature_points(
         )
 
     if warn_dropped_features:
-        returned_features = ret_features["feature"]
-        all_features = features["feature"]
-        removed_features = np.delete(
-            all_features, np.where(np.any(all_features == returned_features))
-        )
-        warnings.warn(
-            "Dropping feature numbers: " + str(removed_features.values), UserWarning
-        )
+        removed_features = np.setdiff1d(features["feature"], ret_features["feature"])
+        if len(removed_features):
+            warnings.warn(
+                "Dropping feature numbers: " + str(removed_features.tolist()),
+                UserWarning,
+            )
 
     # make sure that feature points are converted back to int64
     ret_features["feature"] = ret_features.feature.astype(int)