updated v1.5 reference to peer-reviewed

.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:

doc/index.rst

@@ -10,14 +10,14 @@ The software is set up in a modular way to include different algorithms for feat
 
 Individual features are identified as either maxima or minima in a 2D or 3D time-varying field (see :doc:`feature_detection_overview`). An associated volume can then be determined using these features with a separate (or identical) time-varying 2D or 3D field and a threshold value (see :doc:`segmentation`). The identified objects are linked into consistent trajectories representing the cloud over its lifecycle in the tracking step. Analysis and visualization methods provide a convenient way to use and display the tracking results.
 
-Version 1.2 of tobac and some example applications are described in a peer-reviewed article in Geoscientific Model Development as:
+**Version 1.2 of tobac** and some example applications are described in a peer-reviewed article in Geoscientific Model Development as:
 
 Heikenfeld, M., Marinescu, P. J., Christensen, M., Watson-Parris, D., Senf, F., van den Heever, S. C., and Stier, P.: tobac 1.2: towards a flexible framework for tracking and analysis of clouds in diverse datasets, Geosci. Model Dev., 12, 4551–4570, https://doi.org/10.5194/gmd-12-4551-2019, 2019.
 
-Version 1.5 of tobac and the major enhancements that came with that version are currently under review in Geoscientific Model Development:
+**Version 1.5 of tobac** and the major enhancements that came with that version are described in the following peer-reviewed article in Geoscientific Model Development:
 
+Sokolowsky, G. A., Freeman, S. W., Jones, W. K., Kukulies, J., Senf, F., Marinescu, P. J., Heikenfeld, M., Brunner, K. N., Bruning, E. C., Collis, S. M., Jackson, R. C., Leung, G. R., Pfeifer, N., Raut, B. A., Saleeby, S. M., Stier, P., and van den Heever, S. C.: tobac v1.5: Introducing Fast 3D Tracking, Splits and Mergers, and Other Enhancements for Identifying and Analysing Meteorological Phenomena. Geoscientific Model Development, 17(13), 5309-5330. https://doi.org/10.5194/gmd-17-5309-2024, 2024.
 
-Sokolowsky, G. A., Freeman, S. W., Jones, W. K., Kukulies, J., Senf, F., Marinescu, P. J., Heikenfeld, M., Brunner, K. N., Bruning, E. C., Collis, S. M., Jackson, R. C., Leung, G. R., Pfeifer, N., Raut, B. A., Saleeby, S. M., Stier, P., and van den Heever, S. C.: tobac v1.5: Introducing Fast 3D Tracking, Splits and Mergers, and Other Enhancements for Identifying and Analysing Meteorological Phenomena, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-1722, 2023.
 
 The project is currently being extended by several contributors to include additional workflows and algorithms using the same structure, syntax, and data formats.
 

doc/publications.rst

@@ -11,6 +11,10 @@ Refereed Publications
       :widths: 30
       :class: wy-table-responsive
 
+      * - Sokolowsky, G. A., Freeman, S. W., Jones, W. K., Kukulies, J., Senf, F., Marinescu, P. J., Heikenfeld, M., Brunner, K. N., Bruning, E. C., Collis, S. M., Jackson, R. C., Leung, G. R., Pfeifer, N., Raut, B. A., Saleeby, S. M., Stier, P., and van den Heever, S. C.: tobac v1.5 (2024). Introducing Fast 3D Tracking, Splits and Mergers, and Other Enhancements for Identifying and Analysing Meteorological Phenomena. Geoscientific Model Development, 17(13), 5309-5330. https://doi.org/10.5194/gmd-17-5309-2024. 
+
+      * - Heikenfeld, M., Marinescu, P. J., Christensen, M., Watson-Parris, D., Senf, F., van den Heever, S. C., and Stier, P. (2019). tobac 1.2: towards a flexible framework for tracking and analysis of clouds in diverse datasets, Geosci. Model Dev., 12, 4551–4570, https://doi.org/10.5194/gmd-12-4551-2019
+
       * - Bukowski, J., & van den Heever, S. C. (2021). Direct radiative effects in haboobs. *Journal of Geophysical Research: Atmospheres*, 126(21), e2021JD034814, doi:10.1029/2021JD034814.
 
       * - Bukowski, J. (2021). Mineral Dust Lofting and Interactions with Cold Pools (Doctoral dissertation, Colorado State University).

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)