Repository: tobac-project/tobac

Issue:
Update citation in documentation
The citation on the documentation homepage is to the in-review version of the GMD paper for v1.5. This has now moved to final publication and needs to be updated.

Discussion:
@freemansw1: Resolved with #450

Solution patch:
diff --git a/.github/workflows/matrix_ci.yml b/.github/workflows/matrix_ci.yml
index 2da6b38f..953acc27 100644
--- a/.github/workflows/matrix_ci.yml
+++ b/.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:
diff --git a/doc/index.rst b/doc/index.rst
index 1b215f20..d4f4b4e7 100644
--- a/doc/index.rst
+++ b/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.
 
diff --git a/doc/publications.rst b/doc/publications.rst
index 0a0d4776..b22d7856 100644
--- a/doc/publications.rst
+++ b/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).
diff --git a/tobac/feature_detection.py b/tobac/feature_detection.py
index fc491ff0..c0b2c2f4 100644
--- a/tobac/feature_detection.py
+++ b/tobac/feature_detection.py
@@ -1468,7 +1468,7 @@ def filter_min_distance(
     target: {'maximum', 'minimum'}, optional
         Flag to determine if tracking is targeting minima or maxima in
         the data. Default is 'maximum'.
-    PBC_flag : str('none', 'hdim_1', 'hdim_2', 'both')
+    PBC_flag : str('none', 'hdim_1', 'hdim_2', 'both'), optional
         Sets whether to use periodic boundaries, and if so in which directions.
         'none' means that we do not have periodic boundaries
         'hdim_1' means that we are periodic along hdim1
@@ -1540,8 +1540,8 @@ def filter_min_distance(
         feature_locations = features[
             [z_coordinate_name, y_coordinate_name, x_coordinate_name]
         ].to_numpy()
-        feature_locations[0] *= dz
-        feature_locations[1:] *= dxy
+        feature_locations[:, 0] *= dz
+        feature_locations[:, 1:] *= dxy
     else:
         feature_locations = (
             features[[y_coordinate_name, x_coordinate_name]].to_numpy() * dxy
diff --git a/tobac/merge_split.py b/tobac/merge_split.py
index 6e53eb97..012856b9 100644
--- a/tobac/merge_split.py
+++ b/tobac/merge_split.py
@@ -7,30 +7,91 @@
     
 """
 
-
-def merge_split_MEST(TRACK, dxy, distance=None, frame_len=5):
+import logging
+from typing import Optional
+from typing_extensions import Literal
+
+import numpy as np
+import pandas as pd
+import xarray as xr
+import scipy.sparse
+from sklearn.neighbors import BallTree
+
+from tobac.utils.periodic_boundaries import build_distance_function
+from tobac.utils import internal as internal_utils
+
+
+def merge_split_MEST(
+    tracks: pd.DataFrame,
+    dxy: float,
+    dz: Optional[float] = None,
+    distance: Optional[float] = None,
+    frame_len: int = 5,
+    cell_number_unassigned: int = -1,
+    vertical_coord: Optional[str] | None = None,
+    PBC_flag: Literal["none", "hdim_1", "hdim_2", "both"] = None,
+    min_h1: Optional[int] = None,
+    max_h1: Optional[int] = None,
+    min_h2: Optional[int] = None,
+    max_h2: Optional[int] = None,
+) -> xr.Dataset:
     """
-    function to  postprocess tobac track data for merge/split cells using a minimum euclidian spanning tree
-
+    Search for merging splitting cells  in tobac tracking data using a minimum
+    euclidian spanning tree, and combine the merged cells into unique tracks.
 
     Parameters
     ----------
-    TRACK : pandas.core.frame.DataFrame
+    tracks : pandas.core.frame.DataFrame
         Pandas dataframe of tobac Track information
 
-    dxy : float, mandatory
+    dxy : float
         The x/y grid spacing of the data.
         Should be in meters.
 
+    dz : float, optional
+        Constant vertical grid spacing (m), default None. If None, the vertical
+        coord will be inferred automatically or from a specified coord given by
+        the vertical_coord parameter. An exception is raised if both dz and
+        vertical_coord are provided.
 
     distance : float, optional
-        Distance threshold determining how close two features must be in order to consider merge/splitting.
-        Default is 25x the x/y grid spacing of the data, given in dxy.
-        The distance should be in units of meters.
+        Distance threshold determining how close two features must be in order
+        to consider merge/splitting. Default is 25x the x/y grid spacing of the
+        data, given in dxy. The distance should be in units of meters.
 
     frame_len : float, optional
-        Threshold for the maximum number of frames that can separate the end of cell and the start of a related cell.
-        Default is five (5) frames.
+        Threshold for the maximum number of frames that can separate the end of
+        cell and the start of a related cell, by default 5 frames.
+
+    cell_number_unassigned: int, optional
+        Value given tp unassigned/non-tracked cells by tracking, by default -1.
+
+    vertical_coord: str, optional
+        Name of the vertical coordinate, default None. The vertical coordinate
+        used must have values in meters. If None, tries to auto-detect, or uses
+        constant vertical grid spacing if dz is specified. An exception is
+        raised if both dz and vertical_coord are provided.
+
+    PBC_flag : str('none', 'hdim_1', 'hdim_2', 'both'), optional
+        Sets whether to use periodic boundaries, and if so in which directions.
+        'none' means that we do not have periodic boundaries
+        'hdim_1' means that we are periodic along hdim1
+        'hdim_2' means that we are periodic along hdim2
+        'both' means that we are periodic along both horizontal dimensions
+
+    min_h1: int, optional
+        Minimum real point in hdim_1, for use with periodic boundaries.
+
+    max_h1: int, optional
+        Maximum point in hdim_1, exclusive. max_h1-min_h1 should be the size of
+        hdim_1.
+
+    min_h2: int, optional
+        Minimum real point in hdim_2, for use with periodic boundaries.
+
+    max_h2: int, optional
+        Maximum point in hdim_2, exclusive. max_h2-min_h2 should be the size of
+        hdim_2.
 
     Returns
     -------
@@ -45,7 +106,6 @@ def merge_split_MEST(TRACK, dxy, distance=None, frame_len=5):
         - track_child_cell_count: The total number of features belonging to all child cells of a given track id.
         - cell_child_feature_count: The total number of features for each cell.
 
-
     Example usage:
         d = merge_split_MEST(Track)
         ds = tobac.utils.standardize_track_dataset(Track, refl_mask)
@@ -54,172 +114,205 @@ def merge_split_MEST(TRACK, dxy, distance=None, frame_len=5):
         both_ds.to_netcdf(os.path.join(savedir,'Track_features_merges.nc'))
 
     """
-    try:
-        import networkx as nx
-    except ImportError:
-        networkx = None
-
-    import logging
-    import numpy as np
-    from pandas.core.common import flatten
-    import xarray as xr
-    from scipy.spatial.distance import cdist
-
-    # Immediately convert pandas dataframe of track information to xarray:
-    TRACK = TRACK.to_xarray()
-    track_groups = TRACK.groupby("cell")
+
+    track_groups = tracks[tracks["cell"] != cell_number_unassigned].groupby("cell")
     first = track_groups.first()
     last = track_groups.last()
 
     if distance is None:
         distance = dxy * 25.0
 
-    a_names = list()
-    b_names = list()
-    dist = list()
-
-    # write all sets of points (a and b) as Nx2 arrays
-    l = len(last["hdim_2"].values)
-    cells = first["cell"].values
-    a_xy = np.zeros((l, 2))
-    a_xy[:, 0] = last["hdim_2"].values * dxy
-    a_xy[:, 1] = last["hdim_1"].values * dxy
-    b_xy = np.zeros((l, 2))
-    b_xy[:, 0] = first["hdim_2"].values * dxy
-    b_xy[:, 1] = first["hdim_1"].values * dxy
-    # Use cdist to find distance matrix
-    out = cdist(a_xy, b_xy)
-    # Find all cells under the distance threshold
-    j = np.where(out <= distance)
-
-    # Compile cells meeting the criteria to an array of both the distance and cell ids
-    a_names = cells[j[0]]
-    b_names = cells[j[1]]
-    dist = out[j]
-
-    # This is inputing data to the object which will perform the spanning tree.
-    g = nx.Graph()
-    for i in np.arange(len(dist)):
-        g.add_edge(a_names[i], b_names[i], weight=dist[i])
-
-    tree = nx.minimum_spanning_edges(g)
-    tree_list = list(tree)
-
-    new_tree = []
-
-    # Pruning the tree for time limits.
-    for i, j in enumerate(tree_list):
-        frame_a = np.nanmax(track_groups[j[0]].frame.values)
-        frame_b = np.nanmin(track_groups[j[1]].frame.values)
-        if np.abs(frame_a - frame_b) <= frame_len:
-            new_tree.append(tree_list[i][0:2])
-    new_tree_arr = np.array(new_tree)
-
-    TRACK["cell_parent_track_id"] = np.zeros(len(TRACK["cell"].values))
-    cell_id = np.unique(
-        TRACK.cell.values.astype(int)[~np.isnan(TRACK.cell.values.astype(int))]
-    )
-    track_id = dict()  # same size as number of total merged tracks
-
-    # Cleaning up tracks, combining tracks which contain the same cells.
-    arr = np.array([0])
-    for p in cell_id:
-        j = np.where(arr == int(p))
-        if len(j[0]) > 0:
-            continue
+    # As optional coordinate names are not yet implemented, set to defaults here:
+    y_coordinate_name = "hdim_1"
+    x_coordinate_name = "hdim_2"
+
+    # Check if we are 3D.
+    is_3D = "vdim" in tracks
+    if is_3D:
+        if dz is None:
+            # Find vertical coord name
+            z_coordinate_name = internal_utils.find_dataframe_vertical_coord(
+                variable_dataframe=tracks, vertical_coord=vertical_coord
+            )
+            dz = 1
         else:
-            k = np.where(new_tree_arr == p)
-            if len(k[0]) == 0:
-                track_id[p] = [p]
-                arr = np.append(arr, p)
+            # Use dz, raise error if both are set
+            if vertical_coord is None:
+                z_coordinate_name = "vdim"
             else:
-                temp1 = list(np.unique(new_tree_arr[k[0]]))
-                temp = list(np.unique(new_tree_arr[k[0]]))
-
-                for l in range(len(cell_id)):
-                    for i in temp1:
-                        k2 = np.where(new_tree_arr == i)
-                        temp.append(list(np.unique(new_tree_arr[k2[0]]).squeeze()))
-                        temp = list(flatten(temp))
-                        temp = list(np.unique(temp))
-
-                    if len(temp1) == len(temp):
-                        break
-                    temp1 = np.array(temp)
-
-                for i in temp1:
-                    k2 = np.where(new_tree_arr == i)
-                    temp.append(list(np.unique(new_tree_arr[k2[0]]).squeeze()))
-
-                temp = list(flatten(temp))
-                temp = list(np.unique(temp))
-                arr = np.append(arr, np.unique(temp))
-
-                track_id[np.nanmax(np.unique(temp))] = list(np.unique(temp))
+                raise ValueError(
+                    "dz and vertical_coord both set, vertical"
+                    " spacing is ambiguous. Set one to None."
+                )
+
+    # Calculate feature locations in cartesian coordinates
+    if is_3D:
+        cell_start_locations = np.stack(
+            [
+                first[var].values
+                for var in [z_coordinate_name, y_coordinate_name, x_coordinate_name]
+            ],
+            axis=-1,
+        )
+        cell_start_locations[:, 0] *= dz
+        cell_start_locations[:, 1:] *= dxy
+        cell_end_locations = np.stack(
+            [
+                last[var].values
+                for var in [z_coordinate_name, y_coordinate_name, x_coordinate_name]
+            ],
+            axis=-1,
+        )
+        cell_end_locations[0] *= dz
+        cell_end_locations[1:] *= dxy
+    else:
+        cell_start_locations = (
+            np.stack(
+                [first[var].values for var in [y_coordinate_name, x_coordinate_name]],
+                axis=-1,
+            )
+            * dxy
+        )
+        cell_end_locations = (
+            np.stack(
+                [last[var].values for var in [y_coordinate_name, x_coordinate_name]],
+                axis=-1,
+            )
+            * dxy
+        )
+
+    if PBC_flag in ["hdim_1", "hdim_2", "both"]:
+        # Note that we multiply by dxy to get the distances in spatial coordinates
+        dist_func = build_distance_function(
+            min_h1 * dxy, max_h1 * dxy, min_h2 * dxy, max_h2 * dxy, PBC_flag, is_3D
+        )
+        cell_start_tree = BallTree(
+            cell_start_locations, metric="pyfunc", func=dist_func
+        )
+
+    else:
+        cell_start_tree = BallTree(cell_start_locations, metric="euclidean")
+
+    neighbours, distances = cell_start_tree.query_radius(
+        cell_end_locations, r=distance, return_distance=True
+    )
 
-    cell_id = list(np.unique(TRACK.cell.values.astype(int)))
-    logging.debug("found cell ids")
+    # Input data to the graph which will perform the spanning tree.
+    nodes = np.repeat(
+        np.arange(len(neighbours), dtype=int), [len(n) for n in neighbours]
+    )
+    neighbours = np.concatenate(neighbours)
+    weights = np.concatenate(distances)
 
-    cell_parent_track_id = np.zeros(len(cell_id))
-    cell_parent_track_id[:] = -1
+    # Remove edges where the frame gap is greater than frame_len, and also remove connections to the same cell
+    wh_frame_len = (
+        np.abs(first["frame"].values[nodes] - last["frame"].values[neighbours])
+        <= frame_len
+    )
+    wh_valid_edge = np.logical_and(wh_frame_len, nodes != neighbours)
+    start_node_cells = first.index.values[nodes[wh_valid_edge]].astype(np.int32)
+    end_node_cells = last.index.values[neighbours[wh_valid_edge]].astype(np.int32)
+
+    cell_id = np.unique(tracks.cell.values)
+    cell_id = cell_id[cell_id != cell_number_unassigned].astype(int)
+    max_cell = np.max(cell_id)
+
+    if len(start_node_cells):
+        # We need to add a small value to the dists to prevent 0-length edges
+        cell_graph = scipy.sparse.coo_array(
+            (weights[wh_valid_edge] + 0.01, (start_node_cells, end_node_cells)),
+            shape=(max_cell + 1, max_cell + 1),
+        )
+        cell_graph = scipy.sparse.csgraph.minimum_spanning_tree(
+            cell_graph, overwrite=True
+        )
+        # Find remaining start/end nodes after calculating minimum spanning tree
+        start_node_cells, end_node_cells = cell_graph.nonzero()
+
+        cell_parent_track_id = scipy.sparse.csgraph.connected_components(cell_graph)[1][
+            cell_id
+        ]
+        cell_parent_track_id = (
+            np.unique(cell_parent_track_id, return_inverse=True)[1] + 1
+        )
+    else:
+        cell_parent_track_id = np.arange(cell_id.size, dtype=int) + 1
 
-    for i, id in enumerate(track_id, start=0):
-        for j in track_id[int(id)]:
-            cell_parent_track_id[cell_id.index(j)] = int(i)
+    track_dim = "track"
+    cell_dim = "cell"
+    feature_dim = "feature"
 
+    cell_parent_track_id = xr.DataArray(
+        cell_parent_track_id, dims=(cell_dim,), coords={cell_dim: cell_id}
+    )
     logging.debug("found cell parent track ids")
 
-    track_ids = np.array(np.unique(cell_parent_track_id))
+    track_id = np.unique(cell_parent_track_id)
     logging.debug("found track ids")
 
-    feature_parent_cell_id = list(TRACK.cell.values.astype(int))
-    logging.debug("found feature parent cell ids")
-
-    #     # This version includes all the feature regardless of if they are used in cells or not.
-    feature_id = list(TRACK.feature.values.astype(int))
+    # This version includes all the feature regardless of if they are used in cells or not.
+    feature_id = tracks.feature.values.astype(int)
     logging.debug("found feature ids")
 
-    feature_parent_track_id = []
-    feature_parent_track_id = np.zeros(len(feature_id))
-    for i, id in enumerate(feature_id):
-        cellid = feature_parent_cell_id[i]
-        if cellid < 0:
-            feature_parent_track_id[i] = -1
-        else:
-            feature_parent_track_id[i] = cell_parent_track_id[cell_id.index(cellid)]
+    feature_parent_cell_id = tracks.cell.values.astype(int)
+    feature_parent_cell_id = xr.DataArray(
+        feature_parent_cell_id,
+        dims=(feature_dim,),
+        coords={feature_dim: feature_id},
+    )
+    logging.debug("found feature parent cell ids")
 
-    track_child_cell_count = np.zeros(len(track_id))
-    for i, id in enumerate(track_id):
-        track_child_cell_count[i] = len(np.where(cell_parent_track_id == i)[0])
-    logging.debug("found track child cell count")
+    wh_feature_in_cell = (feature_parent_cell_id != cell_number_unassigned).values
+    feature_parent_track_id = np.full(wh_feature_in_cell.shape, cell_number_unassigned)
+    feature_parent_track_id[wh_feature_in_cell] = cell_parent_track_id.loc[
+        feature_parent_cell_id[wh_feature_in_cell]
+    ].values
+    feature_parent_track_id = xr.DataArray(
+        feature_parent_track_id,
+        dims=(feature_dim,),
+        coords={feature_dim: feature_id},
+    )
 
-    cell_child_feature_count = np.zeros(len(cell_id))
-    for i, id in enumerate(cell_id):
-        cell_child_feature_count[i] = len(track_groups[id].feature.values)
-    logging.debug("found cell child feature count")
+    track_child_cell_count = (
+        cell_parent_track_id.groupby(cell_parent_track_id).reduce(np.size).values
+    )
+    track_child_cell_count = xr.DataArray(
+        track_child_cell_count,
+        dims=(track_dim,),
+        coords={track_dim: track_id},
+    )
 
-    track_dim = "track"
-    cell_dim = "cell"
-    feature_dim = "feature"
+    cell_child_feature_count = (
+        feature_parent_cell_id[wh_feature_in_cell]
+        .groupby(feature_parent_cell_id[wh_feature_in_cell])
+        .reduce(np.size)
+        .values
+    )
+    cell_child_feature_count = xr.DataArray(
+        cell_child_feature_count, dims=(cell_dim), coords={cell_dim: cell_id}
+    )
 
-    d = xr.Dataset(
-        {
-            "track": (track_dim, track_ids),
-            "cell": (cell_dim, cell_id),
-            "cell_parent_track_id": (cell_dim, cell_parent_track_id),
-            "feature": (feature_dim, feature_id),
-            "feature_parent_cell_id": (feature_dim, feature_parent_cell_id),
-            "feature_parent_track_id": (feature_dim, feature_parent_track_id),
-            "track_child_cell_count": (track_dim, track_child_cell_count),
-            "cell_child_feature_count": (cell_dim, cell_child_feature_count),
-        }
+    cell_starts_with_split = np.isin(cell_id, start_node_cells)
+    cell_starts_with_split = xr.DataArray(
+        cell_starts_with_split, dims=(cell_dim), coords={cell_dim: cell_id}
     )
 
-    d = d.set_coords(["feature", "cell", "track"])
+    cell_ends_with_merge = np.isin(cell_id, end_node_cells)
+    cell_ends_with_merge = xr.DataArray(
+        cell_ends_with_merge, dims=(cell_dim), coords={cell_dim: cell_id}
+    )
 
-    #     assert len(cell_id) == len(cell_parent_track_id)
-    #     assert len(feature_id) == len(feature_parent_cell_id)
-    #     assert sum(track_child_cell_count) == len(cell_id)
-    #     assert sum(cell_child_feature_count) == len(feature_id)
+    merge_split_ds = xr.Dataset(
+        data_vars={
+            "cell_parent_track_id": cell_parent_track_id,
+            "feature_parent_cell_id": feature_parent_cell_id,
+            "feature_parent_track_id": feature_parent_track_id,
+            "track_child_cell_count": track_child_cell_count,
+            "cell_child_feature_count": cell_child_feature_count,
+            "cell_starts_with_split": cell_starts_with_split,
+            "cell_ends_with_merge": cell_ends_with_merge,
+        },
+        coords={feature_dim: feature_id, cell_dim: cell_id, track_dim: track_id},
+    )
 
-    return d
+    return merge_split_ds
diff --git a/tobac/utils/general.py b/tobac/utils/general.py
index 394034c4..89ab6aa1 100644
--- a/tobac/utils/general.py
+++ b/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)


Test patch:
diff --git a/tobac/tests/test_merge_split.py b/tobac/tests/test_merge_split.py
index 83200192..8b3ecdb4 100644
--- a/tobac/tests/test_merge_split.py
+++ b/tobac/tests/test_merge_split.py
@@ -3,6 +3,7 @@
 
 import pandas as pd
 import numpy as np
+import pytest
 
 import datetime
 
@@ -78,7 +79,7 @@ def test_merge_split_MEST():
 
     # Test a successful merger
     mergesplit_output_merged = mergesplit.merge_split_MEST(
-        output, dxy=1, distance=dist_between + 50
+        output, dxy=10, distance=(dist_between + 50) * 10
     )
 
     # These cells should have merged together.
@@ -86,9 +87,256 @@ def test_merge_split_MEST():
 
     # Test an unsuccessful merger.
     mergesplit_output_unmerged = mergesplit.merge_split_MEST(
-        output, dxy=1, distance=dist_between - 50
+        output, dxy=10, distance=(dist_between - 50) * 10
     )
 
     # These cells should NOT have merged together.
     print(mergesplit_output_unmerged["track"])
     assert len(mergesplit_output_unmerged["track"]) == 2
+
+
+def test_merge_split_MEST_PBC():
+    """
+    Test PBC handling for merge_split_MEST
+    """
+    test_features = pd.DataFrame(
+        {
+            "feature": [1, 2, 3, 4],
+            "hdim_1": [1, 89, 1, 99],
+            "hdim_2": [50, 50, 50, 50],
+            "cell": [1, 2, 1, 2],
+            "frame": [0, 0, 1, 1],
+            "time": [
+                datetime.datetime(2000, 1, 1),
+                datetime.datetime(2000, 1, 1),
+                datetime.datetime(2000, 1, 1, 0, 5),
+                datetime.datetime(2000, 1, 1, 0, 5),
+            ],
+        }
+    )
+    # Test without PBCs
+    mergesplit_output_no_pbc = mergesplit.merge_split_MEST(
+        test_features,
+        dxy=1,
+        distance=25,
+    )
+
+    assert len(mergesplit_output_no_pbc["track"]) == 2
+
+    # Test with PBC in hdim_1, cells should merge
+    mergesplit_output_hdim_1_pbc = mergesplit.merge_split_MEST(
+        test_features,
+        dxy=1,
+        distance=25,
+        PBC_flag="hdim_1",
+        min_h1=0,
+        max_h1=100,
+        min_h2=0,
+        max_h2=100,
+    )
+
+    assert len(mergesplit_output_hdim_1_pbc["track"]) == 1
+
+    # Test with PBC in hdim_2, cells should not merge
+    mergesplit_output_hdim_2_pbc = mergesplit.merge_split_MEST(
+        test_features,
+        dxy=1,
+        distance=25,
+        PBC_flag="hdim_2",
+        min_h1=0,
+        max_h1=100,
+        min_h2=0,
+        max_h2=100,
+    )
+
+    assert len(mergesplit_output_hdim_2_pbc["track"]) == 2
+
+
+def test_merge_split_MEST_frame_len():
+    """
+    Test the frame_len parameter of merge_split_MEST
+    """
+    test_features = pd.DataFrame(
+        {
+            "feature": [1, 2, 3, 4],
+            "hdim_1": [50, 50, 50, 50],
+            "hdim_2": [50, 50, 50, 50],
+            "cell": [1, 1, 2, 2],
+            "frame": [0, 1, 3, 4],
+            "time": [
+                datetime.datetime(2000, 1, 1),
+                datetime.datetime(2000, 1, 1),
+                datetime.datetime(2000, 1, 1, 0, 5),
+                datetime.datetime(2000, 1, 1, 0, 5),
+            ],
+        }
+    )
+
+    # Test with short frame_len, expect no link
+    mergesplit_output = mergesplit.merge_split_MEST(
+        test_features,
+        dxy=1,
+        distance=25,
+        frame_len=1,
+    )
+    assert len(mergesplit_output["track"]) == 2
+
+    # Test with longer frame_len, expect link
+    mergesplit_output = mergesplit.merge_split_MEST(
+        test_features,
+        dxy=1,
+        distance=25,
+        frame_len=2,
+    )
+    assert len(mergesplit_output["track"]) == 1
+
+
+def test_merge_split_MEST_no_cell():
+    """
+    Test merge/split in cases with features with no cell
+    """
+    test_features = pd.DataFrame(
+        {
+            "feature": [1, 2, 3],
+            "hdim_1": [25, 30, 50],
+            "hdim_2": [25, 30, 50],
+            "cell": [1, -1, 1],
+            "frame": [0, 0, 1],
+            "time": [
+                datetime.datetime(2000, 1, 1),
+                datetime.datetime(2000, 1, 1),
+                datetime.datetime(2000, 1, 1, 0, 5),
+            ],
+        }
+    )
+
+    mergesplit_output = mergesplit.merge_split_MEST(
+        test_features,
+        dxy=1,
+        distance=25,
+    )
+
+    assert len(mergesplit_output["track"]) == 1
+
+    assert mergesplit_output["feature_parent_cell_id"].values[1] == -1
+
+
+def test_merge_split_MEST_3D():
+    """
+    Test merge/split support for 3D tracks and dz input
+    """
+
+    test_features = pd.DataFrame(
+        {
+            "feature": [1, 2, 3, 4],
+            "vdim": [1, 2, 1, 2],
+            "hdim_1": [50, 40, 50, 40],
+            "hdim_2": [50, 50, 50, 50],
+            "cell": [1, 2, 1, 2],
+            "frame": [0, 0, 1, 1],
+            "altitude": [500, 750, 1500, 2000],
+            "time": [
+                datetime.datetime(2000, 1, 1),
+                datetime.datetime(2000, 1, 1),
+                datetime.datetime(2000, 1, 1, 0, 5),
+                datetime.datetime(2000, 1, 1, 0, 5),
+            ],
+        }
+    )
+
+    # Test with dz=10, expect merge
+    mergesplit_output_3d_merge = mergesplit.merge_split_MEST(
+        test_features,
+        dxy=1,
+        dz=10,
+        distance=20,
+    )
+
+    assert len(mergesplit_output_3d_merge["track"]) == 1
+
+    # Test with dz=25, expect no merge
+    mergesplit_output_3d_nomerge = mergesplit.merge_split_MEST(
+        test_features,
+        dxy=1,
+        dz=25,
+        distance=20,
+    )
+
+    assert len(mergesplit_output_3d_nomerge["track"]) == 2
+
+    # Test providing vertical_coord
+    mergesplit_output_3d_coord_merge = mergesplit.merge_split_MEST(
+        test_features,
+        dxy=1,
+        vertical_coord="altitude",
+        distance=1100,
+    )
+    mergesplit_output_3d_coord_nomerge = mergesplit.merge_split_MEST(
+        test_features,
+        dxy=1,
+        vertical_coord="altitude",
+        distance=20,
+    )
+
+    assert len(mergesplit_output_3d_coord_merge["track"]) == 1
+    assert len(mergesplit_output_3d_coord_nomerge["track"]) == 2
+
+    # Test auto find vertical_coord
+    mergesplit_output_3d_coord_merge = mergesplit.merge_split_MEST(
+        test_features,
+        dxy=1,
+        distance=1100,
+    )
+    mergesplit_output_3d_coord_nomerge = mergesplit.merge_split_MEST(
+        test_features,
+        dxy=1,
+        distance=20,
+    )
+
+    assert len(mergesplit_output_3d_coord_merge["track"]) == 1
+    assert len(mergesplit_output_3d_coord_nomerge["track"]) == 2
+
+    # Test error if both dz and coord are provided
+    with pytest.raises(ValueError):
+        mergesplit.merge_split_MEST(
+            test_features,
+            dxy=1,
+            dz=1,
+            vertical_coord="auto",
+            distance=20,
+        )
+
+    # Test that wrong vertical coord name causes an error
+    with pytest.raises(ValueError):
+        mergesplit.merge_split_MEST(
+            test_features,
+            dxy=1,
+            vertical_coord="invalid_coord_name",
+            distance=20,
+        )
+
+    # Test that auto search fails if coordinate name is not in default list
+    test_features = pd.DataFrame(
+        {
+            "feature": [1, 2, 3, 4],
+            "vdim": [1, 2, 1, 2],
+            "hdim_1": [50, 40, 50, 40],
+            "hdim_2": [50, 50, 50, 50],
+            "cell": [1, 2, 1, 2],
+            "frame": [0, 0, 1, 1],
+            "invalid_coord_name": [500, 1500, 1000, 2000],
+            "time": [
+                datetime.datetime(2000, 1, 1),
+                datetime.datetime(2000, 1, 1),
+                datetime.datetime(2000, 1, 1, 0, 5),
+                datetime.datetime(2000, 1, 1, 0, 5),
+            ],
+        }
+    )
+
+    with pytest.raises(ValueError):
+        mergesplit.merge_split_MEST(
+            test_features,
+            dxy=1,
+            distance=20,
+        )
diff --git a/tobac/tests/test_segmentation.py b/tobac/tests/test_segmentation.py
index 887d3e4b..862e0a3b 100644
--- a/tobac/tests/test_segmentation.py
+++ b/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
diff --git a/tobac/tests/test_utils.py b/tobac/tests/test_utils.py
index e9953d7f..157bfdf2 100644
--- a/tobac/tests/test_utils.py
+++ b/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"]