From 654c813c444d18f66b5748342e0d77205133aaff Mon Sep 17 00:00:00 2001
From: Sean Freeman <sean.freeman@uah.edu>
Date: Tue, 16 Jan 2024 13:20:07 -0600
Subject: [PATCH 01/19] add initial family code to tobac

---
 tobac/tests/test_utils.py | 12 ++++++++++++
 tobac/utils/general.py    | 22 ++++++++++++++++++++++
 2 files changed, 34 insertions(+)

diff --git a/tobac/tests/test_utils.py b/tobac/tests/test_utils.py
index eb3c745d..f6bed556 100644
--- a/tobac/tests/test_utils.py
+++ b/tobac/tests/test_utils.py
@@ -586,3 +586,15 @@ def test_transform_feature_points_3D():
     assert np.all(new_feat_df["hdim_1"] == [25, 30])
     assert np.all(new_feat_df["hdim_2"] == [5, 15])
     assert np.all(new_feat_df["vdim"] == [5, 10])
+
+
+def test_identify_feature_families():
+    """tests tobac.utils.general.identify_feature_families"""
+    orig_feat_df_1 = tb_test.generate_single_feature(
+        0, 95, 10, max_h1=1000, max_h2=1000
+    )
+    orig_feat_df_2 = tb_test.generate_single_feature(
+        5, 105, 20, max_h1=1000, max_h2=1000
+    )
+
+    orig_feat_df = tb_utils.combine_feature_dataframes([orig_feat_df_1, orig_feat_df_2])
diff --git a/tobac/utils/general.py b/tobac/utils/general.py
index 7836a9c6..4ef58f4c 100644
--- a/tobac/utils/general.py
+++ b/tobac/utils/general.py
@@ -913,3 +913,25 @@ def standardize_track_dataset(TrackedFeatures, Mask, Projection=None):
         ds["ProjectionCoordinateSystem"] = Projection
 
     return ds
+
+
+@internal_utils.iris_to_xarray
+def identify_feature_families(
+    feature_df: pd.DataFrame,
+    in_segmentation: xr.DataArray,
+    start_family_number: int = 0,
+):
+    """
+
+    Parameters
+    ----------
+    feature_df: pd.DataFrame
+        Input feature dataframe
+    in_segmentation: xr.Data
+    start_family_number
+
+    Returns
+    -------
+
+    """
+    pass

From 429ee42780eb8b22487d0be82b91a04f525487b5 Mon Sep 17 00:00:00 2001
From: Sean Freeman <freemansw1@gmail.com>
Date: Wed, 17 Jan 2024 13:12:25 -0600
Subject: [PATCH 02/19] add family finding

---
 tobac/tests/test_utils.py | 22 +++++++++++++---
 tobac/utils/general.py    | 53 ++++++++++++++++++++++++++++++++++++---
 tobac/utils/internal.py   |  2 +-
 3 files changed, 70 insertions(+), 7 deletions(-)

diff --git a/tobac/tests/test_utils.py b/tobac/tests/test_utils.py
index f6bed556..8abe0397 100644
--- a/tobac/tests/test_utils.py
+++ b/tobac/tests/test_utils.py
@@ -591,10 +591,26 @@ def test_transform_feature_points_3D():
 def test_identify_feature_families():
     """tests tobac.utils.general.identify_feature_families"""
     orig_feat_df_1 = tb_test.generate_single_feature(
-        0, 95, 10, max_h1=1000, max_h2=1000
+        10, 30, 10, max_h1=50, max_h2=50, feature_num=1
     )
     orig_feat_df_2 = tb_test.generate_single_feature(
-        5, 105, 20, max_h1=1000, max_h2=1000
+        30, 30, 20, max_h1=50, max_h2=50, feature_num=2
+    )
+
+    orig_feat_df = tb_utils.combine_feature_dataframes(
+        [orig_feat_df_1, orig_feat_df_2], renumber_features=False
     )
 
-    orig_feat_df = tb_utils.combine_feature_dataframes([orig_feat_df_1, orig_feat_df_2])
+    # make fake segmentation
+    test_arr = np.zeros((2, 50, 50), dtype=int)
+    test_arr[0, 5:15, 20:40] = 1
+    test_arr[0, 15:40, 20:40] = 2
+
+    test_xr = xr.DataArray(data=test_arr, dims=["time", "hdim_1", "hdim_2"])
+
+    out_df, out_grid = tb_utils.general.identify_feature_families(
+        orig_feat_df, test_xr, return_grid=True, family_column_name="family"
+    )
+    assert np.unique(out_df["family"] == 1)
+    assert np.all(out_grid[0, 5:15, 20:40] == 1)
+    assert np.all(out_grid[0, 15:40, 20:40] == 1)
diff --git a/tobac/utils/general.py b/tobac/utils/general.py
index 4ef58f4c..bf77723b 100644
--- a/tobac/utils/general.py
+++ b/tobac/utils/general.py
@@ -3,8 +3,10 @@
 """
 import copy
 import logging
+from typing import Union
 
 import pandas as pd
+import skimage
 
 from . import internal as internal_utils
 import numpy as np
@@ -919,8 +921,11 @@ def standardize_track_dataset(TrackedFeatures, Mask, Projection=None):
 def identify_feature_families(
     feature_df: pd.DataFrame,
     in_segmentation: xr.DataArray,
-    start_family_number: int = 0,
-):
+    return_grid: bool = False,
+    family_column_name: str = "feature_family_id",
+    unsegmented_point_values: int = 0,
+    below_threshold_values: int = -1,
+) -> Union[tuple[pd.DataFrame, xr.DataArray], pd.DataFrame]:
     """
 
     Parameters
@@ -934,4 +939,46 @@ def identify_feature_families(
     -------
 
     """
-    pass
+
+    # we need to label the data, but we currently label using skimage label, not dask label.
+
+
+    # 3D should be 4-D (time, then 3 spatial).
+    # 2D should be 3-D (time, then 2 spatial)
+    is_3D = len(in_segmentation.shape) ==4
+    seg_family_dict = dict()
+    out_families = copy.deepcopy(in_segmentation)
+
+    for time_index in range(in_segmentation.shape[0]):
+        in_arr = np.array(in_segmentation.values[time_index])
+
+        segmented_arr = np.logical_and(
+            in_arr != unsegmented_point_values, in_arr != below_threshold_values
+        )
+        # These are our families
+        family_labeled_data = skimage.measure.label(
+            segmented_arr,
+        )
+
+        # now we need to note feature->family relationship in the dataframe.
+        segmentation_props = skimage.measure.regionprops(in_arr)
+
+        # associate feature ID -> family ID
+        for seg_area in segmentation_props:
+            if is_3D:
+                seg_family = family_labeled_data[seg_area.coords[0, 0], seg_area.coords[0, 1], seg_area.cords[0,2]]
+            else:
+                seg_family = family_labeled_data[seg_area.coords[0, 0], seg_area.coords[0, 1]]
+            seg_family_dict[seg_area.label] = seg_family
+
+        out_families[time_index] = segmented_arr
+
+    family_series = pd.Series(seg_family_dict, name=family_column_name)
+    feature_series = pd.Series({x: x for x in seg_family_dict.keys()}, name="feature")
+    family_df = pd.concat([family_series, feature_series], axis=1)
+    out_df = feature_df.merge(family_df, on="feature", how="inner")
+
+    if return_grid:
+        return out_df, out_families
+    else:
+        return out_df
diff --git a/tobac/utils/internal.py b/tobac/utils/internal.py
index 55888f7d..08424c89 100644
--- a/tobac/utils/internal.py
+++ b/tobac/utils/internal.py
@@ -4,6 +4,7 @@
 import skimage.measure
 import xarray as xr
 import iris
+import iris.cube
 import warnings
 
 
@@ -110,7 +111,6 @@ def iris_to_xarray(func):
         Function including decorator
     """
 
-    import iris
     import xarray
 
     def wrapper(*args, **kwargs):

From 043e5d1347ec080298c024443d9f832f8eba03bd Mon Sep 17 00:00:00 2001
From: Sean Freeman <freemansw1@gmail.com>
Date: Wed, 17 Jan 2024 13:31:43 -0600
Subject: [PATCH 03/19] formatting and documentation

---
 tobac/utils/general.py | 30 ++++++++++++++++++++++++------
 1 file changed, 24 insertions(+), 6 deletions(-)

diff --git a/tobac/utils/general.py b/tobac/utils/general.py
index bf77723b..047cc68b 100644
--- a/tobac/utils/general.py
+++ b/tobac/utils/general.py
@@ -927,25 +927,39 @@ def identify_feature_families(
     below_threshold_values: int = -1,
 ) -> Union[tuple[pd.DataFrame, xr.DataArray], pd.DataFrame]:
     """
+    Function to identify families/storm systems by identifying where segmentation touches.
+    At a given time, segmentation areas are considered part of the same family if they
+    touch at any point.
 
     Parameters
     ----------
     feature_df: pd.DataFrame
         Input feature dataframe
-    in_segmentation: xr.Data
-    start_family_number
+    in_segmentation: xr.DataArray
+        Input segmentation
+    return_grid: bool
+        Whether to return the segmentation grid showing families
+    family_column_name: str
+        The name in the output dataframe of the family ID
+    unsegmented_point_values: int
+        The value in the input segmentation for unsegmented but above threshold points
+    below_threshold_values: int
+        The value in the input segmentation for below threshold points
 
     Returns
     -------
+    pd.DataFrame and xr.DataArray or pd.DataFrame
+        Input dataframe with family IDs associated with each feature
+        if return_grid is True, the segmentation grid showing families is
+        also returned.
 
     """
 
     # we need to label the data, but we currently label using skimage label, not dask label.
 
-
     # 3D should be 4-D (time, then 3 spatial).
     # 2D should be 3-D (time, then 2 spatial)
-    is_3D = len(in_segmentation.shape) ==4
+    is_3D = len(in_segmentation.shape) == 4
     seg_family_dict = dict()
     out_families = copy.deepcopy(in_segmentation)
 
@@ -966,9 +980,13 @@ def identify_feature_families(
         # associate feature ID -> family ID
         for seg_area in segmentation_props:
             if is_3D:
-                seg_family = family_labeled_data[seg_area.coords[0, 0], seg_area.coords[0, 1], seg_area.cords[0,2]]
+                seg_family = family_labeled_data[
+                    seg_area.coords[0, 0], seg_area.coords[0, 1], seg_area.cords[0, 2]
+                ]
             else:
-                seg_family = family_labeled_data[seg_area.coords[0, 0], seg_area.coords[0, 1]]
+                seg_family = family_labeled_data[
+                    seg_area.coords[0, 0], seg_area.coords[0, 1]
+                ]
             seg_family_dict[seg_area.label] = seg_family
 
         out_families[time_index] = segmented_arr

From 0837aa4d176b8be35482beba4a17b3619d3df1de Mon Sep 17 00:00:00 2001
From: Sean Freeman <sean.freeman@uah.edu>
Date: Thu, 14 Aug 2025 11:27:52 -0500
Subject: [PATCH 04/19] fixes post merging

---
 tobac/tests/test_utils.py | 1 -
 tobac/utils/general.py    | 1 -
 2 files changed, 2 deletions(-)

diff --git a/tobac/tests/test_utils.py b/tobac/tests/test_utils.py
index d0f2794c..6d719df7 100644
--- a/tobac/tests/test_utils.py
+++ b/tobac/tests/test_utils.py
@@ -1,4 +1,3 @@
-from cycler import V
 import pytest
 import tobac.testing
 import tobac.testing as tbtest
diff --git a/tobac/utils/general.py b/tobac/utils/general.py
index 983d1731..b4686a92 100644
--- a/tobac/utils/general.py
+++ b/tobac/utils/general.py
@@ -744,7 +744,6 @@ def standardize_track_dataset(TrackedFeatures, Mask, Projection=None):
     return ds
 
 
-@internal_utils.iris_to_xarray
 def identify_feature_families(
     feature_df: pd.DataFrame,
     in_segmentation: xr.DataArray,

From 30f3fe7d233ab0e435ddb19cab44ea4fcb505f62 Mon Sep 17 00:00:00 2001
From: Sean Freeman <sean.freeman@uah.edu>
Date: Thu, 14 Aug 2025 12:40:09 -0500
Subject: [PATCH 05/19] refactor: move merge_split into a new directory

---
 tobac/merge_split/__init__.py                                   | 1 +
 .../merge_split_feature_points.py}                              | 0
 tobac/tests/test_merge_split.py                                 | 2 +-
 3 files changed, 2 insertions(+), 1 deletion(-)
 create mode 100644 tobac/merge_split/__init__.py
 rename tobac/{merge_split.py => merge_split/merge_split_feature_points.py} (100%)

diff --git a/tobac/merge_split/__init__.py b/tobac/merge_split/__init__.py
new file mode 100644
index 00000000..15707047
--- /dev/null
+++ b/tobac/merge_split/__init__.py
@@ -0,0 +1 @@
+from .merge_split_feature_points import merge_split_MEST
diff --git a/tobac/merge_split.py b/tobac/merge_split/merge_split_feature_points.py
similarity index 100%
rename from tobac/merge_split.py
rename to tobac/merge_split/merge_split_feature_points.py
diff --git a/tobac/tests/test_merge_split.py b/tobac/tests/test_merge_split.py
index 623de3a2..a80520b9 100644
--- a/tobac/tests/test_merge_split.py
+++ b/tobac/tests/test_merge_split.py
@@ -8,7 +8,7 @@
 
 import tobac.testing as tbtest
 import tobac.tracking as tbtrack
-import tobac.merge_split as mergesplit
+import tobac.merge_split.merge_split_feature_points as mergesplit
 
 
 def test_merge_split_MEST():

From f62d69d22b65b2fe7c551dbde2aaafcd8c99ba06 Mon Sep 17 00:00:00 2001
From: Sean Freeman <sean.freeman@uah.edu>
Date: Mon, 18 Aug 2025 14:30:24 -0500
Subject: [PATCH 06/19] refactor: move PBC labeling to a new file

---
 tobac/feature_detection.py              | 234 ++--------------------
 tobac/utils/internal/label_functions.py | 247 ++++++++++++++++++++++++
 2 files changed, 258 insertions(+), 223 deletions(-)
 create mode 100644 tobac/utils/internal/label_functions.py

diff --git a/tobac/feature_detection.py b/tobac/feature_detection.py
index 2027074d..c1e98ad9 100644
--- a/tobac/feature_detection.py
+++ b/tobac/feature_detection.py
@@ -37,6 +37,7 @@
 from tobac.utils import periodic_boundaries as pbc_utils
 from tobac.utils.general import spectral_filtering
 from tobac.utils import get_statistics
+from tobac.utils.internal import label_functions
 import warnings
 
 # from typing_extensions import Literal
@@ -505,230 +506,8 @@ def feature_detection_threshold(
             selem = np.ones((n_erosion_threshold, n_erosion_threshold))
         mask = binary_erosion(mask, selem)
         # detect individual regions, label  and count the number of pixels included:
-    labels, num_labels = label(mask, background=0, return_num=True)
-    if not is_3D:
-        # let's transpose labels to a 1,y,x array to make calculations etc easier.
-        labels = labels[np.newaxis, :, :]
-    # these are [min, max], meaning that the max value is inclusive and a valid
-    # value.
-    z_min = 0
-    z_max = labels.shape[0] - 1
-    y_min = 0
-    y_max = labels.shape[1] - 1
-    x_min = 0
-    x_max = labels.shape[2] - 1
-
-    # deal with PBCs
-    # all options that involve dealing with periodic boundaries
-    pbc_options = ["hdim_1", "hdim_2", "both"]
-    if PBC_flag not in pbc_options and PBC_flag != "none":
-        raise ValueError(
-            "Options for periodic are currently: none, " + ", ".join(pbc_options)
-        )
-
-    # we need to deal with PBCs in some way.
-    if PBC_flag in pbc_options and num_labels > 0:
-        #
-        # create our copy of `labels` to edit
-        labels_2 = deepcopy(labels)
-        # points we've already edited
-        skip_list = np.array([])
-        # labels that touch the PBC walls
-        wall_labels = np.array([], dtype=np.int32)
-
-        all_label_props = internal_utils.get_label_props_in_dict(labels)
-        [
-            all_labels_max_size,
-            all_label_locs_v,
-            all_label_locs_h1,
-            all_label_locs_h2,
-        ] = internal_utils.get_indices_of_labels_from_reg_prop_dict(all_label_props)
-
-        # find the points along the boundaries
-
-        # along hdim_1 or both horizontal boundaries
-        if PBC_flag == "hdim_1" or PBC_flag == "both":
-            # north and south wall
-            ns_wall = np.unique(labels[:, (y_min, y_max), :])
-            wall_labels = np.append(wall_labels, ns_wall)
-
-        # along hdim_2 or both horizontal boundaries
-        if PBC_flag == "hdim_2" or PBC_flag == "both":
-            # east/west wall
-            ew_wall = np.unique(labels[:, :, (x_min, x_max)])
-            wall_labels = np.append(wall_labels, ew_wall)
-
-        wall_labels = np.unique(wall_labels)
-
-        for label_ind in wall_labels:
-            new_label_ind = label_ind
-            # 0 isn't a real index
-            if label_ind == 0:
-                continue
-            # skip this label if we have already dealt with it.
-            if np.any(label_ind == skip_list):
-                continue
-
-            # create list for skip labels for this wall label only
-            skip_list_thisind = list()
 
-            # get all locations of this label.
-            # TODO: harmonize x/y/z vs hdim1/hdim2/vdim.
-            label_locs_v = all_label_locs_v[label_ind]
-            label_locs_h1 = all_label_locs_h1[label_ind]
-            label_locs_h2 = all_label_locs_h2[label_ind]
-
-            # loop through every point in the label
-            for label_z, label_y, label_x in zip(
-                label_locs_v, label_locs_h1, label_locs_h2
-            ):
-                # check if this is the special case of being a corner point.
-                # if it's doubly periodic AND on both x and y boundaries, it's a corner point
-                # and we have to look at the other corner.
-                # here, we will only look at the corner point and let the below deal with x/y only.
-                if PBC_flag == "both" and (
-                    np.any(label_y == [y_min, y_max])
-                    and np.any(label_x == [x_min, x_max])
-                ):
-                    # adjust x and y points to the other side
-                    y_val_alt = pbc_utils.adjust_pbc_point(label_y, y_min, y_max)
-                    x_val_alt = pbc_utils.adjust_pbc_point(label_x, x_min, x_max)
-
-                    label_on_corner = labels[label_z, y_val_alt, x_val_alt]
-
-                    if (label_on_corner != 0) and (
-                        ~np.any(label_on_corner == skip_list)
-                    ):
-                        # alt_inds = np.where(labels==alt_label_3)
-                        # get a list of indices where the label on the corner is so we can switch
-                        # them in the new list.
-
-                        labels_2[
-                            all_label_locs_v[label_on_corner],
-                            all_label_locs_h1[label_on_corner],
-                            all_label_locs_h2[label_on_corner],
-                        ] = label_ind
-                        skip_list = np.append(skip_list, label_on_corner)
-                        skip_list_thisind = np.append(
-                            skip_list_thisind, label_on_corner
-                        )
-
-                    # if it's labeled and has already been dealt with for this label
-                    elif (
-                        (label_on_corner != 0)
-                        and (np.any(label_on_corner == skip_list))
-                        and (np.any(label_on_corner == skip_list_thisind))
-                    ):
-                        # print("skip_list_thisind label - has already been treated this index")
-                        continue
-
-                    # if it's labeled and has already been dealt with via a previous label
-                    elif (
-                        (label_on_corner != 0)
-                        and (np.any(label_on_corner == skip_list))
-                        and (~np.any(label_on_corner == skip_list_thisind))
-                    ):
-                        # find the updated label, and overwrite all of label_ind indices with
-                        # updated label
-                        labels_2_alt = labels_2[label_z, y_val_alt, x_val_alt]
-                        labels_2[label_locs_v, label_locs_h1, label_locs_h2] = (
-                            labels_2_alt
-                        )
-                        skip_list = np.append(skip_list, label_ind)
-                        break
-
-                # on the hdim1 boundary and periodic on hdim1
-                if (PBC_flag == "hdim_1" or PBC_flag == "both") and np.any(
-                    label_y == [y_min, y_max]
-                ):
-                    y_val_alt = pbc_utils.adjust_pbc_point(label_y, y_min, y_max)
-
-                    # get the label value on the opposite side
-                    label_alt = labels[label_z, y_val_alt, label_x]
-
-                    # if it's labeled and not already been dealt with
-                    if (label_alt != 0) and (~np.any(label_alt == skip_list)):
-                        # find the indices where it has the label value on opposite side and change
-                        # their value to original side
-                        # print(all_label_locs_v[label_alt], alt_inds[0])
-                        labels_2[
-                            all_label_locs_v[label_alt],
-                            all_label_locs_h1[label_alt],
-                            all_label_locs_h2[label_alt],
-                        ] = new_label_ind
-                        # we have already dealt with this label.
-                        skip_list = np.append(skip_list, label_alt)
-                        skip_list_thisind = np.append(skip_list_thisind, label_alt)
-
-                    # if it's labeled and has already been dealt with for this label
-                    elif (
-                        (label_alt != 0)
-                        and (np.any(label_alt == skip_list))
-                        and (np.any(label_alt == skip_list_thisind))
-                    ):
-                        continue
-
-                    # if it's labeled and has already been dealt with
-                    elif (
-                        (label_alt != 0)
-                        and (np.any(label_alt == skip_list))
-                        and (~np.any(label_alt == skip_list_thisind))
-                    ):
-                        # find the updated label, and overwrite all of label_ind indices with
-                        # updated label
-                        labels_2_alt = labels_2[label_z, y_val_alt, label_x]
-                        labels_2[label_locs_v, label_locs_h1, label_locs_h2] = (
-                            labels_2_alt
-                        )
-                        new_label_ind = labels_2_alt
-                        skip_list = np.append(skip_list, label_ind)
-
-                if (PBC_flag == "hdim_2" or PBC_flag == "both") and np.any(
-                    label_x == [x_min, x_max]
-                ):
-                    x_val_alt = pbc_utils.adjust_pbc_point(label_x, x_min, x_max)
-
-                    # get the label value on the opposite side
-                    label_alt = labels[label_z, label_y, x_val_alt]
-
-                    # if it's labeled and not already been dealt with
-                    if (label_alt != 0) and (~np.any(label_alt == skip_list)):
-                        # find the indices where it has the label value on opposite side and change
-                        # their value to original side
-                        labels_2[
-                            all_label_locs_v[label_alt],
-                            all_label_locs_h1[label_alt],
-                            all_label_locs_h2[label_alt],
-                        ] = new_label_ind
-                        # we have already dealt with this label.
-                        skip_list = np.append(skip_list, label_alt)
-                        skip_list_thisind = np.append(skip_list_thisind, label_alt)
-
-                    # if it's labeled and has already been dealt with for this label
-                    elif (
-                        (label_alt != 0)
-                        and (np.any(label_alt == skip_list))
-                        and (np.any(label_alt == skip_list_thisind))
-                    ):
-                        continue
-
-                    # if it's labeled and has already been dealt with
-                    elif (
-                        (label_alt != 0)
-                        and (np.any(label_alt == skip_list))
-                        and (~np.any(label_alt == skip_list_thisind))
-                    ):
-                        # find the updated label, and overwrite all of label_ind indices with
-                        # updated label
-                        labels_2_alt = labels_2[label_z, label_y, x_val_alt]
-                        labels_2[label_locs_v, label_locs_h1, label_locs_h2] = (
-                            labels_2_alt
-                        )
-                        new_label_ind = labels_2_alt
-                        skip_list = np.append(skip_list, label_ind)
-
-        # copy over new labels after we have adjusted everything
-        labels = labels_2
+    labels, num_labels = label_functions.label_with_pbcs(mask, PBC_flag=PBC_flag)
 
     # END PBC treatment
     # we need to get label properties again after we handle PBCs.
@@ -814,6 +593,15 @@ def feature_detection_threshold(
             separate arrays. This also makes comparisons in remove_parents
             substantially faster. 
             """
+            # these are [min, max], meaning that the max value is inclusive and a valid
+            # value.
+            z_min = 0
+            z_max = labels.shape[0] - 1
+            y_min = 0
+            y_max = labels.shape[1] - 1
+            x_min = 0
+            x_max = labels.shape[2] - 1
+
             if is_3D:
                 region_i = np.ravel_multi_index(
                     (hdim1_indices, hdim2_indices, vdim_indices),
diff --git a/tobac/utils/internal/label_functions.py b/tobac/utils/internal/label_functions.py
new file mode 100644
index 00000000..ef81c2b5
--- /dev/null
+++ b/tobac/utils/internal/label_functions.py
@@ -0,0 +1,247 @@
+"""
+Module to allow for labeling of features internally to tobac.
+"""
+
+import skimage
+import numpy as np
+from typing import Literal
+import copy
+from tobac.utils.internal import label_props
+from tobac.utils import periodic_boundaries as pbc_utils
+
+
+def label_with_pbcs(
+    in_label_arr: np.typing.ArrayLike,
+    PBC_flag: Literal["none", "hdim_1", "hdim_2", "both"] = "none",
+):
+
+    is_3d = len(np.shape(in_label_arr)) == 3
+
+    labels, num_labels = skimage.measure.label(
+        in_label_arr, background=0, return_num=True
+    )
+    if not is_3d:
+        # let's transpose labels to a 1,y,x array to make calculations etc easier.
+        labels = labels[np.newaxis, :, :]
+    # these are [min, max], meaning that the max value is inclusive and a valid
+    # value.
+    z_min = 0
+    z_max = labels.shape[0] - 1
+    y_min = 0
+    y_max = labels.shape[1] - 1
+    x_min = 0
+    x_max = labels.shape[2] - 1
+
+    # deal with PBCs
+    # all options that involve dealing with periodic boundaries
+    pbc_options = ["hdim_1", "hdim_2", "both"]
+    if PBC_flag not in pbc_options and PBC_flag != "none":
+        raise ValueError(
+            "Options for periodic are currently: none, " + ", ".join(pbc_options)
+        )
+
+    # we need to deal with PBCs in some way.
+    if PBC_flag in pbc_options and num_labels > 0:
+        #
+        # create our copy of `labels` to edit
+        labels_2 = copy.deepcopy(labels)
+        # points we've already edited
+        skip_list = np.array([])
+        # labels that touch the PBC walls
+        wall_labels = np.array([], dtype=np.int32)
+
+        all_label_props = label_props.get_label_props_in_dict(labels)
+        [
+            all_labels_max_size,
+            all_label_locs_v,
+            all_label_locs_h1,
+            all_label_locs_h2,
+        ] = label_props.get_indices_of_labels_from_reg_prop_dict(all_label_props)
+
+        # find the points along the boundaries
+
+        # along hdim_1 or both horizontal boundaries
+        if PBC_flag == "hdim_1" or PBC_flag == "both":
+            # north and south wall
+            ns_wall = np.unique(labels[:, (y_min, y_max), :])
+            wall_labels = np.append(wall_labels, ns_wall)
+
+        # along hdim_2 or both horizontal boundaries
+        if PBC_flag == "hdim_2" or PBC_flag == "both":
+            # east/west wall
+            ew_wall = np.unique(labels[:, :, (x_min, x_max)])
+            wall_labels = np.append(wall_labels, ew_wall)
+
+        wall_labels = np.unique(wall_labels)
+
+        for label_ind in wall_labels:
+            new_label_ind = label_ind
+            # 0 isn't a real index
+            if label_ind == 0:
+                continue
+            # skip this label if we have already dealt with it.
+            if np.any(label_ind == skip_list):
+                continue
+
+            # create list for skip labels for this wall label only
+            skip_list_thisind = list()
+
+            # get all locations of this label.
+            label_locs_v = all_label_locs_v[label_ind]
+            label_locs_h1 = all_label_locs_h1[label_ind]
+            label_locs_h2 = all_label_locs_h2[label_ind]
+
+            # loop through every point in the label
+            for label_z, label_y, label_x in zip(
+                label_locs_v, label_locs_h1, label_locs_h2
+            ):
+                # check if this is the special case of being a corner point.
+                # if it's doubly periodic AND on both x and y boundaries, it's a corner point
+                # and we have to look at the other corner.
+                # here, we will only look at the corner point and let the below deal with x/y only.
+                if PBC_flag == "both" and (
+                    np.any(label_y == [y_min, y_max])
+                    and np.any(label_x == [x_min, x_max])
+                ):
+                    # adjust x and y points to the other side
+                    y_val_alt = pbc_utils.adjust_pbc_point(label_y, y_min, y_max)
+                    x_val_alt = pbc_utils.adjust_pbc_point(label_x, x_min, x_max)
+
+                    label_on_corner = labels[label_z, y_val_alt, x_val_alt]
+
+                    if (label_on_corner != 0) and (
+                        ~np.any(label_on_corner == skip_list)
+                    ):
+                        # alt_inds = np.where(labels==alt_label_3)
+                        # get a list of indices where the label on the corner is so we can switch
+                        # them in the new list.
+
+                        labels_2[
+                            all_label_locs_v[label_on_corner],
+                            all_label_locs_h1[label_on_corner],
+                            all_label_locs_h2[label_on_corner],
+                        ] = label_ind
+                        skip_list = np.append(skip_list, label_on_corner)
+                        skip_list_thisind = np.append(
+                            skip_list_thisind, label_on_corner
+                        )
+
+                    # if it's labeled and has already been dealt with for this label
+                    elif (
+                        (label_on_corner != 0)
+                        and (np.any(label_on_corner == skip_list))
+                        and (np.any(label_on_corner == skip_list_thisind))
+                    ):
+                        # print("skip_list_thisind label - has already been treated this index")
+                        continue
+
+                    # if it's labeled and has already been dealt with via a previous label
+                    elif (
+                        (label_on_corner != 0)
+                        and (np.any(label_on_corner == skip_list))
+                        and (~np.any(label_on_corner == skip_list_thisind))
+                    ):
+                        # find the updated label, and overwrite all of label_ind indices with
+                        # updated label
+                        labels_2_alt = labels_2[label_z, y_val_alt, x_val_alt]
+                        labels_2[label_locs_v, label_locs_h1, label_locs_h2] = (
+                            labels_2_alt
+                        )
+                        skip_list = np.append(skip_list, label_ind)
+                        break
+
+                # on the hdim1 boundary and periodic on hdim1
+                if (PBC_flag == "hdim_1" or PBC_flag == "both") and np.any(
+                    label_y == [y_min, y_max]
+                ):
+                    y_val_alt = pbc_utils.adjust_pbc_point(label_y, y_min, y_max)
+
+                    # get the label value on the opposite side
+                    label_alt = labels[label_z, y_val_alt, label_x]
+
+                    # if it's labeled and not already been dealt with
+                    if (label_alt != 0) and (~np.any(label_alt == skip_list)):
+                        # find the indices where it has the label value on opposite side and change
+                        # their value to original side
+                        # print(all_label_locs_v[label_alt], alt_inds[0])
+                        labels_2[
+                            all_label_locs_v[label_alt],
+                            all_label_locs_h1[label_alt],
+                            all_label_locs_h2[label_alt],
+                        ] = new_label_ind
+                        # we have already dealt with this label.
+                        skip_list = np.append(skip_list, label_alt)
+                        skip_list_thisind = np.append(skip_list_thisind, label_alt)
+
+                    # if it's labeled and has already been dealt with for this label
+                    elif (
+                        (label_alt != 0)
+                        and (np.any(label_alt == skip_list))
+                        and (np.any(label_alt == skip_list_thisind))
+                    ):
+                        continue
+
+                    # if it's labeled and has already been dealt with
+                    elif (
+                        (label_alt != 0)
+                        and (np.any(label_alt == skip_list))
+                        and (~np.any(label_alt == skip_list_thisind))
+                    ):
+                        # find the updated label, and overwrite all of label_ind indices with
+                        # updated label
+                        labels_2_alt = labels_2[label_z, y_val_alt, label_x]
+                        labels_2[label_locs_v, label_locs_h1, label_locs_h2] = (
+                            labels_2_alt
+                        )
+                        new_label_ind = labels_2_alt
+                        skip_list = np.append(skip_list, label_ind)
+
+                if (PBC_flag == "hdim_2" or PBC_flag == "both") and np.any(
+                    label_x == [x_min, x_max]
+                ):
+                    x_val_alt = pbc_utils.adjust_pbc_point(label_x, x_min, x_max)
+
+                    # get the label value on the opposite side
+                    label_alt = labels[label_z, label_y, x_val_alt]
+
+                    # if it's labeled and not already been dealt with
+                    if (label_alt != 0) and (~np.any(label_alt == skip_list)):
+                        # find the indices where it has the label value on opposite side and change
+                        # their value to original side
+                        labels_2[
+                            all_label_locs_v[label_alt],
+                            all_label_locs_h1[label_alt],
+                            all_label_locs_h2[label_alt],
+                        ] = new_label_ind
+                        # we have already dealt with this label.
+                        skip_list = np.append(skip_list, label_alt)
+                        skip_list_thisind = np.append(skip_list_thisind, label_alt)
+
+                    # if it's labeled and has already been dealt with for this label
+                    elif (
+                        (label_alt != 0)
+                        and (np.any(label_alt == skip_list))
+                        and (np.any(label_alt == skip_list_thisind))
+                    ):
+                        continue
+
+                    # if it's labeled and has already been dealt with
+                    elif (
+                        (label_alt != 0)
+                        and (np.any(label_alt == skip_list))
+                        and (~np.any(label_alt == skip_list_thisind))
+                    ):
+                        # find the updated label, and overwrite all of label_ind indices with
+                        # updated label
+                        labels_2_alt = labels_2[label_z, label_y, x_val_alt]
+                        labels_2[label_locs_v, label_locs_h1, label_locs_h2] = (
+                            labels_2_alt
+                        )
+                        new_label_ind = labels_2_alt
+                        skip_list = np.append(skip_list, label_ind)
+
+        # remove skipped labels from the number, remove 0 from the unique list.
+        num_labels = len(np.unique(labels_2)) - 1
+        return labels_2, num_labels
+    else:
+        return labels, num_labels

From 6d604139de66281cef2fadd4df6a8638989c59b7 Mon Sep 17 00:00:00 2001
From: Sean Freeman <sean.freeman@uah.edu>
Date: Thu, 4 Sep 2025 16:14:12 -0500
Subject: [PATCH 07/19] update to PBC labeling to include connectivity options.

---
 tobac/utils/internal/label_functions.py | 24 +++++++++++++++++++++---
 1 file changed, 21 insertions(+), 3 deletions(-)

diff --git a/tobac/utils/internal/label_functions.py b/tobac/utils/internal/label_functions.py
index ef81c2b5..73db54ec 100644
--- a/tobac/utils/internal/label_functions.py
+++ b/tobac/utils/internal/label_functions.py
@@ -13,12 +13,30 @@
 def label_with_pbcs(
     in_label_arr: np.typing.ArrayLike,
     PBC_flag: Literal["none", "hdim_1", "hdim_2", "both"] = "none",
+    connectivity: int = 2,
 ):
+    """Function to run labeling, with checks for periodic boundaries.
+
+    Parameters
+    ----------
+    in_label_arr: ArrayLike (bool)
+        Input array to label. Can be 2D or 3D, but needs to be a binary
+    PBC_flag: {"none", "hdim_1", "hdim_2", "both"}
+        Flag to indicate which boundaries are periodic
+    connectivity: int
+        What kind of connectivity to use - the sklearn default is 2 (meaning diagonals are included).
+
+
+    Returns
+    -------
+    ArrayLike (int)
+        A labeled field
+    """
 
     is_3d = len(np.shape(in_label_arr)) == 3
 
     labels, num_labels = skimage.measure.label(
-        in_label_arr, background=0, return_num=True
+        in_label_arr, background=0, return_num=True, connectivity=connectivity
     )
     if not is_3d:
         # let's transpose labels to a 1,y,x array to make calculations etc easier.
@@ -196,8 +214,8 @@ def label_with_pbcs(
                         new_label_ind = labels_2_alt
                         skip_list = np.append(skip_list, label_ind)
 
-                if (PBC_flag == "hdim_2" or PBC_flag == "both") and np.any(
-                    label_x == [x_min, x_max]
+                if (PBC_flag == "hdim_2" or PBC_flag == "both") and (
+                    np.any(label_x == x_min) or np.any(label_x == x_max)
                 ):
                     x_val_alt = pbc_utils.adjust_pbc_point(label_x, x_min, x_max)
 

From d8fb2f2c57f2733cea59272f7ad2cc54f7b11bbf Mon Sep 17 00:00:00 2001
From: Sean Freeman <sean.freeman@uah.edu>
Date: Wed, 8 Oct 2025 11:56:02 -0600
Subject: [PATCH 08/19] first cut at adding feature family ID. Need to add
 tests

---
 tobac/merge_split/families/__init__.py        |   0
 .../merge_split/families/feature_family_id.py | 352 ++++++++++++++++++
 tobac/utils/datetime.py                       |  26 ++
 3 files changed, 378 insertions(+)
 create mode 100644 tobac/merge_split/families/__init__.py
 create mode 100644 tobac/merge_split/families/feature_family_id.py

diff --git a/tobac/merge_split/families/__init__.py b/tobac/merge_split/families/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tobac/merge_split/families/feature_family_id.py b/tobac/merge_split/families/feature_family_id.py
new file mode 100644
index 00000000..8ff479a4
--- /dev/null
+++ b/tobac/merge_split/families/feature_family_id.py
@@ -0,0 +1,352 @@
+"""Code to identify "families" using features from tobac output.
+
+A family is defined as a set of contiguous points above a single threshold that contain at least
+one but can contain many detected features.
+"""
+
+import pandas as pd
+import numpy as np
+import xarray as xr
+import skimage.measure
+import copy
+from typing import Optional, Literal
+import datetime
+import tobac.utils.internal.label_functions as tb_label
+import tobac.utils.datetime as tb_datetime
+
+
+def identify_feature_families_from_segmentation(
+    feature_df: pd.DataFrame,
+    in_segmentation: xr.DataArray,
+    return_grid: bool = False,
+    family_column_name: str = "feature_family_id",
+    unsegmented_point_values: int = 0,
+    below_threshold_values: int = -1,
+):
+    """
+    Function to identify families/storm systems by identifying where segmentation touches.
+    At a given time, segmentation areas are considered part of the same family if they
+    touch at any point.
+
+    Parameters
+    ----------
+    feature_df: pd.DataFrame
+        Input feature dataframe
+    in_segmentation: xr.DataArray
+        Input segmentation data. Must be derived from the features submitted, but can be
+        a subset of those times.
+    return_grid: bool
+        Whether to return the segmentation grid showing families
+    family_column_name: str
+        The name in the output dataframe of the family ID
+    unsegmented_point_values: int
+        The value in the input segmentation for unsegmented but above threshold points
+    below_threshold_values: int
+        The value in the input segmentation for below threshold points
+
+    Returns
+    -------
+    pd.DataFrame and xr.DataArray or pd.DataFrame
+        Input dataframe with family IDs associated with each feature
+        if return_grid is True, the segmentation grid showing families is
+        also returned.
+
+    """
+
+    # TODO: This does not currently work if you have segmentation data that
+    # uses feature numbers pre-merging
+
+    # we need to label the data, but we currently label using skimage label, not dask label.
+
+    # 3D should be 4-D (time, then 3 spatial).
+    # 2D should be 3-D (time, then 2 spatial)
+    is_3D = len(in_segmentation.shape) == 4
+    seg_family_dict = dict()
+    out_families = copy.deepcopy(in_segmentation)
+    max_family_number = 0
+    enable_family_statistics = True
+
+    if enable_family_statistics:
+        region_props_vals = ["bbox", "centroid", "num_pixels"]
+        family_stats = dict()
+
+    for time_index in range(in_segmentation.shape[0]):
+        in_arr = np.array(in_segmentation.values[time_index])
+
+        segmented_arr = np.logical_and(
+            in_arr != unsegmented_point_values, in_arr != below_threshold_values
+        )
+        # These are our families
+        family_labeled_data, number_families = skimage.measure.label(
+            segmented_arr, return_num=True
+        )
+        if enable_family_statistics:
+            all_family_nums = list()
+
+            family_props = skimage.measure.regionprops(family_labeled_data)
+            for family in family_props:
+                all_family_nums.append(family.label + max_family_number)
+                family_stats[family.label + max_family_number] = dict()
+                # family_stats[family.label+max_family_number]['frame'] =
+
+                family_stats[family.label + max_family_number]["num_pixels"] = family[
+                    "num_pixels"
+                ]
+                family_stats[family.label + max_family_number][family_column_name] = (
+                    family.label + max_family_number
+                )
+                if not is_3D:
+                    family_stats[family.label + max_family_number]["hdim_1_center"] = (
+                        family["centroid"][0]
+                    )
+                    family_stats[family.label + max_family_number]["hdim_2_center"] = (
+                        family["centroid"][1]
+                    )
+
+                else:
+                    # TODO: integrate 3D stats - mostly around center coordinates - need the functions in tobac proper
+                    raise NotImplementedError("3D stats not implemented yet")
+
+        # now we need to note feature->family relationship in the dataframe.
+        segmentation_props = skimage.measure.regionprops(in_arr)
+
+        # associate feature ID -> family ID
+        for seg_area in segmentation_props:
+            if is_3D:
+                seg_family = family_labeled_data[
+                    seg_area.coords[0, 0], seg_area.coords[0, 1], seg_area.cords[0, 2]
+                ]
+            else:
+                seg_family = family_labeled_data[
+                    seg_area.coords[0, 0], seg_area.coords[0, 1]
+                ]
+            seg_family_dict[seg_area.label] = seg_family + max_family_number
+        """
+        if seg_area is not None and enable_family_statistics:
+            curr_feat = seg_area.label
+            curr_frame = feature_df[feature_df['feature'] == curr_feat]['frame']
+            for family_id in all_family_nums:
+                family_stats[family_id]['frame'] = curr_frame.values[0]
+
+        """
+        out_families[time_index] = family_labeled_data + max_family_number * (
+            (family_labeled_data > unsegmented_point_values).astype(int)
+        )
+        max_family_number = max_family_number + number_families
+
+    family_series = pd.Series(seg_family_dict, name=family_column_name)
+    feature_series = pd.Series({x: x for x in seg_family_dict.keys()}, name="feature")
+    family_df = pd.concat([family_series, feature_series], axis=1)
+    out_df = feature_df.merge(family_df, on="feature", how="inner")
+
+    if enable_family_statistics:
+        family_stats_df = pd.DataFrame.from_dict(family_stats, orient="index")
+
+    if return_grid:
+        if enable_family_statistics:
+            return out_df, family_stats_df, out_families
+
+    else:
+        return out_df, family_stats_df
+
+
+def identify_feature_families_from_data(
+    feature_df: pd.DataFrame,
+    in_data: xr.DataArray,
+    threshold: float,
+    return_grid: bool = False,
+    family_column_name: str = "feature_family_id",
+    time_padding: Optional[datetime.timedelta] = datetime.timedelta(seconds=0.5),
+    PBC_flag: Literal["none", "hdim_1", "hdim_2", "both"] = "none",
+    target: Literal["minimum", "maximum"] = "maximum",
+):
+    """
+    Function to identify families/storm systems by identifying where segmentation touches.
+    At a given time, segmentation areas are considered part of the same family if they
+    touch at any point.
+
+    Parameters
+    ----------
+    feature_df: pd.DataFrame
+        Input feature dataframe
+    in_data: xr.DataArray
+        Input data. Should match the data that feature_df was generated from.
+    threshold: float
+        Threshold to define your feature family at
+    return_grid: bool
+        Whether to return the segmentation grid showing families
+    family_column_name: str
+        The name in the output dataframe of the family ID
+    time_padding: datetime.timedelta
+        Time padding to find the matching time between the feature_df and the in_data.
+        By default, this is a half second to deal with random errors around time data type
+        conversions.
+    PBC_flag: {"none", "hdim_1", "hdim_2", "both"}
+        What axes to do periodic boundaries on
+    target: {"minimum", "maximum"}
+        Whether we are looking for things ascending ("maximum") or descending ("minimum")
+
+    Returns
+    -------
+    pd.DataFrame and xr.DataArray or pd.DataFrame
+        Input dataframe with family IDs associated with each feature
+        if return_grid is True, the segmentation grid showing families is
+        also returned.
+
+    """
+
+    # we need to label the data, but we currently label using skimage label, not dask label.
+
+    time_var_name = "time"
+
+    # 3D should be 4-D (time, then 3 spatial).
+    # 2D should be 3-D (time, then 2 spatial)
+    is_3D = len(in_data.shape) == 4
+
+    seg_family_dict = dict()
+    out_families = copy.deepcopy(in_data)
+    out_families = out_families.astype(np.int64)
+    out_families.name = "family_grid"
+    max_family_number = 0
+    enable_family_statistics = True
+
+    if enable_family_statistics:
+        region_props_vals = ["bbox", "centroid", "num_pixels"]
+        family_stats = dict()
+    # TODO: find index of time variable
+    for time_index in range(in_data.shape[0]):
+        # TODO: fix time_var_name for isel?
+        in_data_at_time = in_data.isel(time=time_index)
+        in_arr = np.array(in_data_at_time)
+
+        # These are our families
+        if target == "minimum":
+            mask = in_arr < threshold
+        elif target == "maximum":
+            mask = in_arr > threshold
+        else:
+            raise ValueError("target must be minimum or maximum")
+        family_labeled_data, number_families = tb_label.label_with_pbcs(
+            mask, PBC_flag=PBC_flag, connectivity=1
+        )
+        if not is_3D:
+            family_labeled_data = family_labeled_data[0]
+        if enable_family_statistics:
+            all_family_nums = list()
+
+            family_props = skimage.measure.regionprops(family_labeled_data)
+            for family in family_props:
+                all_family_nums.append(family.label + max_family_number)
+                family_stats[family.label + max_family_number] = dict()
+                # family_stats[family.label+max_family_number]['frame'] =
+
+                family_stats[family.label + max_family_number]["num_pixels"] = family[
+                    "num_pixels"
+                ]
+                family_stats[family.label + max_family_number][family_column_name] = (
+                    family.label + max_family_number
+                )
+                if not is_3D:
+                    family_stats[family.label + max_family_number]["hdim_1_center"] = (
+                        family["centroid"][0]
+                    )
+                    family_stats[family.label + max_family_number]["hdim_2_center"] = (
+                        family["centroid"][1]
+                    )
+
+                else:
+                    # TODO: integrate 3D stats - mostly around center coordinates - need the functions in tobac proper
+                    raise NotImplementedError("3D stats not implemented yet")
+
+        # associate feature ID -> family ID
+        #
+
+        # need to associate family ID with each feature ID
+
+        # get rows at current time
+
+        rows_at_time = tb_datetime.find_df_rows_at_time(
+            feature_df,
+            in_data_at_time["time"].values,
+            time_var_name=time_var_name,
+            time_padding=time_padding,
+        )
+        rows_at_time = rows_at_time.copy()
+
+        if is_3D:
+            v_max, h1_max, h2_max = family_labeled_data.shape
+        else:
+            # print(family_labeled_data.shape)
+            h1_max, h2_max = family_labeled_data.shape
+
+        rows_at_time["hdim_1_adj"] = np.clip(
+            (rows_at_time["hdim_1"] + 0.5).astype(int), a_min=0, a_max=h1_max - 1
+        )
+        rows_at_time["hdim_2_adj"] = np.clip(
+            (rows_at_time["hdim_2"] + 0.5).astype(int), a_min=0, a_max=h2_max - 1
+        )
+
+        data_in_shape = family_labeled_data.shape
+        # TODO: deal with dim order for 3D
+        if is_3D:
+            if "vdim" not in rows_at_time:
+                raise NotImplementedError(
+                    "Family ID from raw field not supported going from 2D features to 3D family"
+                )
+
+            rows_at_time["vdim_adj"] = np.clip(
+                int(rows_at_time["vdim"] + 0.5).astype(int), a_min=0, a_max=v_max - 1
+            )
+            points_list = (
+                rows_at_time["hdim_1_adj"].values,
+                rows_at_time["hdim_2_adj"].values,
+                rows_at_time["vdim_adj"].values,
+            )
+
+        else:
+            points_list = (
+                rows_at_time["hdim_1_adj"].values,
+                rows_at_time["hdim_2_adj"].values,
+            )
+
+        # print(family_labeled_data.shape)
+        family_ids = family_labeled_data[points_list]
+        # we want to get rid of points that aren't features in the grid output
+        suppressing_families = np.isin(family_labeled_data, family_ids)
+        feature_id_family_id_match_ct = {
+            feat: fam_id + max_family_number
+            for feat, fam_id in zip(
+                rows_at_time["feature"].values, family_ids + max_family_number
+            )
+        }
+        seg_family_dict.update(feature_id_family_id_match_ct)
+        out_families[time_index] = (
+            family_labeled_data + max_family_number
+        ) * suppressing_families.astype(int)
+
+        max_family_number = max_family_number + number_families
+
+    family_series = pd.Series(seg_family_dict, name=family_column_name)
+    feature_series = pd.Series({x: x for x in seg_family_dict.keys()}, name="feature")
+    family_df = pd.concat([family_series, feature_series], axis=1)
+    out_df = feature_df.merge(family_df, on="feature", how="inner")
+
+    if enable_family_statistics:
+        family_stats_df = pd.DataFrame.from_dict(family_stats, orient="index")
+        fam_to_time_df = out_df[["time", family_column_name]].set_index(
+            family_column_name
+        )
+
+        fam_to_time_df = fam_to_time_df.loc[
+            ~fam_to_time_df.index.duplicated(keep="first"), :
+        ].sort_index()
+        family_stats_df = family_stats_df.join(
+            fam_to_time_df, on=family_column_name
+        ).dropna(subset="time")
+
+    if return_grid:
+        if enable_family_statistics:
+            return out_df, family_stats_df, out_families
+
+    else:
+        return out_df, family_stats_df
diff --git a/tobac/utils/datetime.py b/tobac/utils/datetime.py
index 611603cb..377dae2b 100644
--- a/tobac/utils/datetime.py
+++ b/tobac/utils/datetime.py
@@ -6,6 +6,7 @@
 import pandas as pd
 import xarray as xr
 import cftime
+from typing import Optional
 
 
 def to_cftime(
@@ -182,3 +183,28 @@ def match_datetime_format(
     if isinstance(target, datetime.datetime):
         return to_datetime(dates)
     raise ValueError("Target is not a valid datetime format")
+
+
+def find_df_rows_at_time(
+    in_df: pd.DataFrame,
+    in_time,
+    time_var_name="time",
+    time_padding: Optional[datetime.timedelta] = None,
+):
+    all_times = pd.Series(
+        match_datetime_format(in_df[time_var_name], in_time),
+        index=in_df.index,
+    )
+
+    if time_padding is not None:
+        # padded_conv = pd.Timedelta(time_padding).to_timedelta64()
+        if isinstance(in_time, (int, np.datetime64)):
+            min_time = in_time - pd.Timedelta(time_padding).to_timedelta64()
+            max_time = in_time + pd.Timedelta(time_padding).to_timedelta64()
+        else:
+            min_time = in_time - time_padding
+            max_time = in_time + time_padding
+        features_i = in_df.loc[all_times.between(min_time, max_time)]
+    else:
+        features_i = in_df.loc[all_times == in_time]
+    return features_i

From f34dd6b9599099acc872020d395a38ae762a39d2 Mon Sep 17 00:00:00 2001
From: Sean Freeman <sean.freeman@uah.edu>
Date: Mon, 19 Jan 2026 14:38:41 -0600
Subject: [PATCH 09/19] add test for ID from data

---
 tobac/merge_split/families/__init__.py        |  4 ++
 .../merge_split/families/feature_family_id.py |  8 +--
 tobac/tests/family_tests/test_family_id.py    | 70 +++++++++++++++++++
 3 files changed, 78 insertions(+), 4 deletions(-)
 create mode 100644 tobac/tests/family_tests/test_family_id.py

diff --git a/tobac/merge_split/families/__init__.py b/tobac/merge_split/families/__init__.py
index e69de29b..b84fcaec 100644
--- a/tobac/merge_split/families/__init__.py
+++ b/tobac/merge_split/families/__init__.py
@@ -0,0 +1,4 @@
+from .feature_family_id import (
+    identify_feature_families_from_segmentation,
+    identify_feature_families_from_data,
+)
diff --git a/tobac/merge_split/families/feature_family_id.py b/tobac/merge_split/families/feature_family_id.py
index 8ff479a4..f6853fb7 100644
--- a/tobac/merge_split/families/feature_family_id.py
+++ b/tobac/merge_split/families/feature_family_id.py
@@ -240,18 +240,18 @@ def identify_feature_families_from_data(
                 family_stats[family.label + max_family_number] = dict()
                 # family_stats[family.label+max_family_number]['frame'] =
 
-                family_stats[family.label + max_family_number]["num_pixels"] = family[
+                family_stats[family.label + max_family_number][
                     "num_pixels"
-                ]
+                ] = family.area
                 family_stats[family.label + max_family_number][family_column_name] = (
                     family.label + max_family_number
                 )
                 if not is_3D:
                     family_stats[family.label + max_family_number]["hdim_1_center"] = (
-                        family["centroid"][0]
+                        family.centroid[0]
                     )
                     family_stats[family.label + max_family_number]["hdim_2_center"] = (
-                        family["centroid"][1]
+                        family.centroid[1]
                     )
 
                 else:
diff --git a/tobac/tests/family_tests/test_family_id.py b/tobac/tests/family_tests/test_family_id.py
new file mode 100644
index 00000000..5faba0ed
--- /dev/null
+++ b/tobac/tests/family_tests/test_family_id.py
@@ -0,0 +1,70 @@
+import tobac
+import tobac.testing as tbtest
+import tobac.feature_detection as feat_detect
+import pytest
+import numpy as np
+
+import tobac.merge_split.families as tb_fam
+
+
+def test_family_id_from_data_basic():
+    """
+    Tests that family id
+    (tobac.merge_split_families.feature_family_id.identify_feature_families_from_data)
+    functions at all. Not comprehensive, but a decent look at it.
+    Returns
+    -------
+    """
+
+    test_dset_size = (100, 100)
+    # make a few points that overlap one another so we end up with multiple
+    # features that should agglomerate to one family
+    test_hdim_1_pts = [20.0, 23, 15, 17]
+    test_hdim_2_pts = [20.0, 23, 15, 17]
+    test_hdim_1_szs = [5, 5, 5, 5]
+    test_hdim_2_szs = [5, 5, 5, 5]
+    test_amps = [3, 5, 2, 5]
+
+    test_data = np.zeros(test_dset_size)
+
+    for test_h1, test_h2, test_sz1, test_sz2, test_amp in zip(
+        test_hdim_1_pts, test_hdim_2_pts, test_hdim_1_szs, test_hdim_2_szs, test_amps
+    ):
+        test_data = tbtest.make_feature_blob(
+            test_data,
+            test_h1,
+            test_h2,
+            h1_size=test_sz1,
+            h2_size=test_sz2,
+            amplitude=test_amp,
+        )
+    test_data = np.expand_dims(test_data, 0)
+    test_data_xr = tbtest.make_dataset_from_arr(
+        test_data, data_type="xarray", time_dim_num=0
+    )
+
+    test_threshs = [1.5, 2.5, 4.5]
+    n_min_threshold = 1
+    dxy = 100
+
+    fd_output = feat_detect.feature_detection_multithreshold(
+        test_data_xr,
+        dxy=dxy,
+        threshold=test_threshs,
+        n_min_threshold=n_min_threshold,
+    )
+
+    assert len(fd_output) == 2, f"Expected 2 features, but got {len(fd_output)}"
+
+    # detect families from data
+
+    families_fd, stats_fd = tb_fam.identify_feature_families_from_data(
+        fd_output, test_data_xr, threshold=1.5
+    )
+    assert (
+        len(families_fd) == 2
+    ), f"families: Expected 2 features, but got {len(fd_output)}"
+    assert (
+        len(stats_fd) == 1
+    ), f"family stats: Expected 1 family, but got {len(fd_output)}"
+    assert np.all(families_fd["feature_family_id"].values == [1, 1])

From e6edefcad699657e95face719cee7fa8fb0847b6 Mon Sep 17 00:00:00 2001
From: Sean Freeman <freemansw1@gmail.com>
Date: Mon, 19 Jan 2026 15:03:53 -0600
Subject: [PATCH 10/19] added basic test for segmentation pathway for feature
 family ID

---
 tobac/tests/family_tests/test_family_id.py | 70 ++++++++++++++++++++++
 1 file changed, 70 insertions(+)

diff --git a/tobac/tests/family_tests/test_family_id.py b/tobac/tests/family_tests/test_family_id.py
index 5faba0ed..8a4eb49c 100644
--- a/tobac/tests/family_tests/test_family_id.py
+++ b/tobac/tests/family_tests/test_family_id.py
@@ -1,6 +1,7 @@
 import tobac
 import tobac.testing as tbtest
 import tobac.feature_detection as feat_detect
+import tobac.segmentation as tb_seg
 import pytest
 import numpy as np
 
@@ -68,3 +69,72 @@ def test_family_id_from_data_basic():
         len(stats_fd) == 1
     ), f"family stats: Expected 1 family, but got {len(fd_output)}"
     assert np.all(families_fd["feature_family_id"].values == [1, 1])
+
+
+def test_family_id_from_seg_basic():
+    """
+    Tests that family id
+    (tobac.merge_split_families.feature_family_id.identify_feature_families_from_segmentation)
+    functions work at all. Not comprehensive, but a decent look at it.
+    Returns
+    -------
+    """
+
+    test_dset_size = (100, 100)
+    # make a few points that overlap one another so we end up with multiple
+    # features that should agglomerate to one family
+    test_hdim_1_pts = [20.0, 23, 15, 17]
+    test_hdim_2_pts = [20.0, 23, 15, 17]
+    test_hdim_1_szs = [5, 5, 5, 5]
+    test_hdim_2_szs = [5, 5, 5, 5]
+    test_amps = [3, 5, 2, 5]
+
+    test_data = np.zeros(test_dset_size)
+
+    for test_h1, test_h2, test_sz1, test_sz2, test_amp in zip(
+        test_hdim_1_pts, test_hdim_2_pts, test_hdim_1_szs, test_hdim_2_szs, test_amps
+    ):
+        test_data = tbtest.make_feature_blob(
+            test_data,
+            test_h1,
+            test_h2,
+            h1_size=test_sz1,
+            h2_size=test_sz2,
+            amplitude=test_amp,
+        )
+    test_data = np.expand_dims(test_data, 0)
+    test_data_xr = tbtest.make_dataset_from_arr(
+        test_data, data_type="xarray", time_dim_num=0
+    )
+
+    test_threshs = [1.5, 2.5, 4.5]
+    n_min_threshold = 1
+    dxy = 100
+
+    fd_output = feat_detect.feature_detection_multithreshold(
+        test_data_xr,
+        dxy=dxy,
+        threshold=test_threshs,
+        n_min_threshold=n_min_threshold,
+    )
+
+    assert len(fd_output) == 2, f"Expected 2 features, but got {len(fd_output)}"
+
+    # run segmentation
+
+    seg_grid, seg_feats = tb_seg.segmentation_3D(
+        fd_output, test_data_xr, dxy=dxy, threshold=1.5
+    )
+
+    # detect families from data
+
+    families_fd, stats_fd = tb_fam.identify_feature_families_from_segmentation(
+        seg_feats, seg_grid
+    )
+    assert (
+        len(families_fd) == 2
+    ), f"families: Expected 2 features, but got {len(fd_output)}"
+    assert (
+        len(stats_fd) == 1
+    ), f"family stats: Expected 1 family, but got {len(fd_output)}"
+    assert np.all(families_fd["feature_family_id"].values == [1, 1])

From d0c337946b0480b25346c17f62fa5e0bff2901a9 Mon Sep 17 00:00:00 2001
From: Sean Freeman <freemansw1@gmail.com>
Date: Mon, 19 Jan 2026 15:42:35 -0600
Subject: [PATCH 11/19] add tests (and code for) what to do if the feature
 can't be linked to a family

---
 .../merge_split/families/feature_family_id.py | 61 ++++++++----
 tobac/tests/family_tests/test_family_id.py    | 95 +++++++++++++++++++
 2 files changed, 136 insertions(+), 20 deletions(-)

diff --git a/tobac/merge_split/families/feature_family_id.py b/tobac/merge_split/families/feature_family_id.py
index f6853fb7..812a2ec9 100644
--- a/tobac/merge_split/families/feature_family_id.py
+++ b/tobac/merge_split/families/feature_family_id.py
@@ -9,7 +9,7 @@
 import xarray as xr
 import skimage.measure
 import copy
-from typing import Optional, Literal
+from typing import Optional, Literal, Union
 import datetime
 import tobac.utils.internal.label_functions as tb_label
 import tobac.utils.datetime as tb_datetime
@@ -96,12 +96,12 @@ def identify_feature_families_from_segmentation(
                     family.label + max_family_number
                 )
                 if not is_3D:
-                    family_stats[family.label + max_family_number]["hdim_1_center"] = (
-                        family["centroid"][0]
-                    )
-                    family_stats[family.label + max_family_number]["hdim_2_center"] = (
-                        family["centroid"][1]
-                    )
+                    family_stats[family.label + max_family_number][
+                        "hdim_1_center"
+                    ] = family["centroid"][0]
+                    family_stats[family.label + max_family_number][
+                        "hdim_2_center"
+                    ] = family["centroid"][1]
 
                 else:
                     # TODO: integrate 3D stats - mostly around center coordinates - need the functions in tobac proper
@@ -159,6 +159,7 @@ def identify_feature_families_from_data(
     time_padding: Optional[datetime.timedelta] = datetime.timedelta(seconds=0.5),
     PBC_flag: Literal["none", "hdim_1", "hdim_2", "both"] = "none",
     target: Literal["minimum", "maximum"] = "maximum",
+    unlinked_family_id: Union[int, None] = -1,
 ):
     """
     Function to identify families/storm systems by identifying where segmentation touches.
@@ -185,6 +186,11 @@ def identify_feature_families_from_data(
         What axes to do periodic boundaries on
     target: {"minimum", "maximum"}
         Whether we are looking for things ascending ("maximum") or descending ("minimum")
+    unlinked_family_id: int or None
+        The value to have in the dataframe for any feature that cannot be linked to a family.
+        This is unusual (as every feature should link to a family), but this can happen
+        if e.g., the feature position is located outside of the feature area above the threshold.
+        If "None", these features are dropped from the output.
 
     Returns
     -------
@@ -213,9 +219,9 @@ def identify_feature_families_from_data(
     if enable_family_statistics:
         region_props_vals = ["bbox", "centroid", "num_pixels"]
         family_stats = dict()
-    # TODO: find index of time variable
     for time_index in range(in_data.shape[0]):
         # TODO: fix time_var_name for isel?
+        # print("time_index: ", time_index)
         in_data_at_time = in_data.isel(time=time_index)
         in_arr = np.array(in_data_at_time)
 
@@ -235,6 +241,7 @@ def identify_feature_families_from_data(
             all_family_nums = list()
 
             family_props = skimage.measure.regionprops(family_labeled_data)
+            # print(max_family_number)
             for family in family_props:
                 all_family_nums.append(family.label + max_family_number)
                 family_stats[family.label + max_family_number] = dict()
@@ -247,20 +254,17 @@ def identify_feature_families_from_data(
                     family.label + max_family_number
                 )
                 if not is_3D:
-                    family_stats[family.label + max_family_number]["hdim_1_center"] = (
-                        family.centroid[0]
-                    )
-                    family_stats[family.label + max_family_number]["hdim_2_center"] = (
-                        family.centroid[1]
-                    )
+                    family_stats[family.label + max_family_number][
+                        "hdim_1_center"
+                    ] = family.centroid[0]
+                    family_stats[family.label + max_family_number][
+                        "hdim_2_center"
+                    ] = family.centroid[1]
 
                 else:
                     # TODO: integrate 3D stats - mostly around center coordinates - need the functions in tobac proper
                     raise NotImplementedError("3D stats not implemented yet")
 
-        # associate feature ID -> family ID
-        #
-
         # need to associate family ID with each feature ID
 
         # get rows at current time
@@ -310,11 +314,16 @@ def identify_feature_families_from_data(
             )
 
         # print(family_labeled_data.shape)
+
         family_ids = family_labeled_data[points_list]
+        # remove 0 (background) if needed
+
+        family_ids_sorted = np.unique(np.sort(family_ids))
+
         # we want to get rid of points that aren't features in the grid output
-        suppressing_families = np.isin(family_labeled_data, family_ids)
+        suppressing_families = np.isin(family_labeled_data, family_ids_sorted)
         feature_id_family_id_match_ct = {
-            feat: fam_id + max_family_number
+            feat: fam_id
             for feat, fam_id in zip(
                 rows_at_time["feature"].values, family_ids + max_family_number
             )
@@ -324,12 +333,16 @@ def identify_feature_families_from_data(
             family_labeled_data + max_family_number
         ) * suppressing_families.astype(int)
 
-        max_family_number = max_family_number + number_families
+        max_family_number = out_families.max().values
 
     family_series = pd.Series(seg_family_dict, name=family_column_name)
     feature_series = pd.Series({x: x for x in seg_family_dict.keys()}, name="feature")
     family_df = pd.concat([family_series, feature_series], axis=1)
     out_df = feature_df.merge(family_df, on="feature", how="inner")
+    if unlinked_family_id is not None:
+        out_df.loc[out_df["feature_family_id"] == 0, "feature_family_id"] = -1
+    else:
+        out_df = out_df[out_df["feature_family_id"] != 0]
 
     if enable_family_statistics:
         family_stats_df = pd.DataFrame.from_dict(family_stats, orient="index")
@@ -343,6 +356,14 @@ def identify_feature_families_from_data(
         family_stats_df = family_stats_df.join(
             fam_to_time_df, on=family_column_name
         ).dropna(subset="time")
+        family_stats_df = family_stats_df.drop(
+            [
+                0,
+            ],
+            axis=0,
+            errors="ignore",
+        )
+        family_stats_df = family_stats_df.set_index(family_column_name)
 
     if return_grid:
         if enable_family_statistics:
diff --git a/tobac/tests/family_tests/test_family_id.py b/tobac/tests/family_tests/test_family_id.py
index 8a4eb49c..8d7c70e0 100644
--- a/tobac/tests/family_tests/test_family_id.py
+++ b/tobac/tests/family_tests/test_family_id.py
@@ -138,3 +138,98 @@ def test_family_id_from_seg_basic():
         len(stats_fd) == 1
     ), f"family stats: Expected 1 family, but got {len(fd_output)}"
     assert np.all(families_fd["feature_family_id"].values == [1, 1])
+
+
+def test_family_id_data_features_unlinked():
+    """
+    tests identify_feature_families_from_data
+    Ensures that features whose positions fall outside of the feature area
+    are still in the output but are unlinked (-1)
+    """
+
+    test_dset_size = (100, 100)
+    # pretty darn circular feature
+    test_hdim_1_pts = [
+        60.0,
+        58.7,
+        55.0,
+        50.0,
+        45.0,
+        41.3,
+        40.0,
+        41.3,
+        45.0,
+        50.0,
+        55.0,
+        58.7,
+    ]
+    test_hdim_2_pts = [
+        50.0,
+        55.0,
+        58.7,
+        60.0,
+        58.7,
+        55.0,
+        50.0,
+        45.0,
+        41.3,
+        40.0,
+        41.3,
+        45.0,
+    ]
+    test_hdim_1_szs = [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]
+    test_hdim_2_szs = [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]
+    test_amps = [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]
+
+    test_data = np.zeros(test_dset_size)
+
+    for test_h1, test_h2, test_sz1, test_sz2, test_amp in zip(
+        test_hdim_1_pts, test_hdim_2_pts, test_hdim_1_szs, test_hdim_2_szs, test_amps
+    ):
+        test_data = tbtest.make_feature_blob(
+            test_data,
+            test_h1,
+            test_h2,
+            h1_size=test_sz1,
+            h2_size=test_sz2,
+            amplitude=test_amp,
+        )
+    test_data = np.expand_dims(test_data, 0)
+    test_data_xr = tbtest.make_dataset_from_arr(
+        test_data, data_type="xarray", time_dim_num=0
+    )
+
+    test_threshs = [1.5, 2.5, 4.5]
+    n_min_threshold = 1
+    dxy = 100
+
+    fd_output = feat_detect.feature_detection_multithreshold(
+        test_data_xr,
+        dxy=dxy,
+        threshold=test_threshs,
+        n_min_threshold=n_min_threshold,
+    )
+    assert len(fd_output) == 1, f"Expected 1 feature, but got {len(fd_output)}"
+
+    # detect families from data
+
+    families_fd, stats_fd = tb_fam.identify_feature_families_from_data(
+        fd_output, test_data_xr, threshold=1.5, unlinked_family_id=-1
+    )
+    assert (
+        len(families_fd) == 1
+    ), f"families: Expected 1 feature, but got {len(fd_output)}"
+    assert (
+        len(stats_fd) == 0
+    ), f"family stats: Expected 0 families, but got {len(fd_output)}"
+    assert np.all(families_fd["feature_family_id"].values == [])
+
+    families_fd, stats_fd = tb_fam.identify_feature_families_from_data(
+        fd_output, test_data_xr, threshold=1.5, unlinked_family_id=None
+    )
+    assert (
+        len(families_fd) == 0
+    ), f"families: Expected 0 features, but got {len(fd_output)}"
+    assert (
+        len(stats_fd) == 0
+    ), f"family stats: Expected 0 families, but got {len(fd_output)}"

From f448a7b81a1dead5cfc7ebfa44d68f289d767408 Mon Sep 17 00:00:00 2001
From: Sean Freeman <freemansw1@gmail.com>
Date: Mon, 19 Jan 2026 15:50:24 -0600
Subject: [PATCH 12/19] refactor of segmentation feature families code

---
 .../merge_split/families/feature_family_id.py | 122 ++++--------------
 1 file changed, 23 insertions(+), 99 deletions(-)

diff --git a/tobac/merge_split/families/feature_family_id.py b/tobac/merge_split/families/feature_family_id.py
index 812a2ec9..04503734 100644
--- a/tobac/merge_split/families/feature_family_id.py
+++ b/tobac/merge_split/families/feature_family_id.py
@@ -20,6 +20,7 @@ def identify_feature_families_from_segmentation(
     in_segmentation: xr.DataArray,
     return_grid: bool = False,
     family_column_name: str = "feature_family_id",
+    PBC_flag: Literal["none", "hdim_1", "hdim_2", "both"] = "none",
     unsegmented_point_values: int = 0,
     below_threshold_values: int = -1,
 ):
@@ -43,6 +44,8 @@ def identify_feature_families_from_segmentation(
         The value in the input segmentation for unsegmented but above threshold points
     below_threshold_values: int
         The value in the input segmentation for below threshold points
+    PBC_flag: {"none", "hdim_1", "hdim_2", "both"}
+        What axes to do periodic boundaries on
 
     Returns
     -------
@@ -53,101 +56,18 @@ def identify_feature_families_from_segmentation(
 
     """
 
-    # TODO: This does not currently work if you have segmentation data that
-    # uses feature numbers pre-merging
-
-    # we need to label the data, but we currently label using skimage label, not dask label.
-
-    # 3D should be 4-D (time, then 3 spatial).
-    # 2D should be 3-D (time, then 2 spatial)
-    is_3D = len(in_segmentation.shape) == 4
-    seg_family_dict = dict()
-    out_families = copy.deepcopy(in_segmentation)
-    max_family_number = 0
-    enable_family_statistics = True
-
-    if enable_family_statistics:
-        region_props_vals = ["bbox", "centroid", "num_pixels"]
-        family_stats = dict()
-
-    for time_index in range(in_segmentation.shape[0]):
-        in_arr = np.array(in_segmentation.values[time_index])
-
-        segmented_arr = np.logical_and(
-            in_arr != unsegmented_point_values, in_arr != below_threshold_values
-        )
-        # These are our families
-        family_labeled_data, number_families = skimage.measure.label(
-            segmented_arr, return_num=True
-        )
-        if enable_family_statistics:
-            all_family_nums = list()
-
-            family_props = skimage.measure.regionprops(family_labeled_data)
-            for family in family_props:
-                all_family_nums.append(family.label + max_family_number)
-                family_stats[family.label + max_family_number] = dict()
-                # family_stats[family.label+max_family_number]['frame'] =
-
-                family_stats[family.label + max_family_number]["num_pixels"] = family[
-                    "num_pixels"
-                ]
-                family_stats[family.label + max_family_number][family_column_name] = (
-                    family.label + max_family_number
-                )
-                if not is_3D:
-                    family_stats[family.label + max_family_number][
-                        "hdim_1_center"
-                    ] = family["centroid"][0]
-                    family_stats[family.label + max_family_number][
-                        "hdim_2_center"
-                    ] = family["centroid"][1]
-
-                else:
-                    # TODO: integrate 3D stats - mostly around center coordinates - need the functions in tobac proper
-                    raise NotImplementedError("3D stats not implemented yet")
-
-        # now we need to note feature->family relationship in the dataframe.
-        segmentation_props = skimage.measure.regionprops(in_arr)
-
-        # associate feature ID -> family ID
-        for seg_area in segmentation_props:
-            if is_3D:
-                seg_family = family_labeled_data[
-                    seg_area.coords[0, 0], seg_area.coords[0, 1], seg_area.cords[0, 2]
-                ]
-            else:
-                seg_family = family_labeled_data[
-                    seg_area.coords[0, 0], seg_area.coords[0, 1]
-                ]
-            seg_family_dict[seg_area.label] = seg_family + max_family_number
-        """
-        if seg_area is not None and enable_family_statistics:
-            curr_feat = seg_area.label
-            curr_frame = feature_df[feature_df['feature'] == curr_feat]['frame']
-            for family_id in all_family_nums:
-                family_stats[family_id]['frame'] = curr_frame.values[0]
-
-        """
-        out_families[time_index] = family_labeled_data + max_family_number * (
-            (family_labeled_data > unsegmented_point_values).astype(int)
-        )
-        max_family_number = max_family_number + number_families
-
-    family_series = pd.Series(seg_family_dict, name=family_column_name)
-    feature_series = pd.Series({x: x for x in seg_family_dict.keys()}, name="feature")
-    family_df = pd.concat([family_series, feature_series], axis=1)
-    out_df = feature_df.merge(family_df, on="feature", how="inner")
-
-    if enable_family_statistics:
-        family_stats_df = pd.DataFrame.from_dict(family_stats, orient="index")
-
-    if return_grid:
-        if enable_family_statistics:
-            return out_df, family_stats_df, out_families
-
-    else:
-        return out_df, family_stats_df
+    booled_values = (in_segmentation != unsegmented_point_values) & (
+        in_segmentation != below_threshold_values
+    )
+    return identify_feature_families_from_data(
+        feature_df,
+        booled_values,
+        threshold=0,
+        target="bool",
+        return_grid=return_grid,
+        PBC_flag=PBC_flag,
+        family_column_name=family_column_name,
+    )
 
 
 def identify_feature_families_from_data(
@@ -158,7 +78,7 @@ def identify_feature_families_from_data(
     family_column_name: str = "feature_family_id",
     time_padding: Optional[datetime.timedelta] = datetime.timedelta(seconds=0.5),
     PBC_flag: Literal["none", "hdim_1", "hdim_2", "both"] = "none",
-    target: Literal["minimum", "maximum"] = "maximum",
+    target: Literal["minimum", "maximum", "bool"] = "maximum",
     unlinked_family_id: Union[int, None] = -1,
 ):
     """
@@ -184,8 +104,10 @@ def identify_feature_families_from_data(
         conversions.
     PBC_flag: {"none", "hdim_1", "hdim_2", "both"}
         What axes to do periodic boundaries on
-    target: {"minimum", "maximum"}
-        Whether we are looking for things ascending ("maximum") or descending ("minimum")
+    target: {"minimum", "maximum", "bool"}
+        Whether we are looking for things ascending ("maximum") or descending ("minimum").
+        There is the special case where you already have a true/false array, then you can put
+        "bool" as the output.
     unlinked_family_id: int or None
         The value to have in the dataframe for any feature that cannot be linked to a family.
         This is unusual (as every feature should link to a family), but this can happen
@@ -230,8 +152,10 @@ def identify_feature_families_from_data(
             mask = in_arr < threshold
         elif target == "maximum":
             mask = in_arr > threshold
+        elif target == "bool":
+            mask = in_arr
         else:
-            raise ValueError("target must be minimum or maximum")
+            raise ValueError("target must be minimum, maximum, or bool")
         family_labeled_data, number_families = tb_label.label_with_pbcs(
             mask, PBC_flag=PBC_flag, connectivity=1
         )

From 29e3661004968616e78bc792e406f9d81ec54839 Mon Sep 17 00:00:00 2001
From: Sean Freeman <freemansw1@gmail.com>
Date: Mon, 19 Jan 2026 17:25:43 -0600
Subject: [PATCH 13/19] add documentation of family detection

---
 doc/api/index.rst                             |   2 +
 doc/conf.py                                   |   1 +
 doc/getting_started/family_detection.md       |  13 +
 doc/getting_started/index.md                  |   1 +
 .../Basics/Idealized-Family-Detection.ipynb   | 401 ++++++++++++++++++
 tobac/merge_split/__init__.py                 |   1 +
 6 files changed, 419 insertions(+)
 create mode 100644 doc/getting_started/family_detection.md
 create mode 100644 examples/Basics/Idealized-Family-Detection.ipynb

diff --git a/doc/api/index.rst b/doc/api/index.rst
index 0c47e1a3..cd15ee5a 100644
--- a/doc/api/index.rst
+++ b/doc/api/index.rst
@@ -17,9 +17,11 @@ Documentation is broken down by directory and module.
    tracking
    segmentation
    merge_split
+   merge_split.families
    utils.bulk_statistics
    utils.general
    plotting
    analysis.cell_analysis
    analysis.feature_analysis
    analysis.spatial
+
diff --git a/doc/conf.py b/doc/conf.py
index 330884cc..20fec2b1 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -182,6 +182,7 @@ def setup(app):
     "examples/Basics/Methods-and-Parameters-for-Feature-Detection_Part_2": "_static/thumbnails/Basics_Methods-and-Parameters-for-Feature-Detection_Part_2_Thumbnail.png",
     "examples/Basics/Methods-and-Parameters-for-Linking": "_static/thumbnails/Basics_Methods-and-Parameters-for-Linking_Thumbnail.png",
     "examples/Basics/Methods-and-Parameters-for-Segmentation": "_static/thumbnails/Basics_Methods-and-Parameters-for-Segmentation_Thumbnail.png",
+    "examples/Basics/Idealized-Family-Detection": "_static/thumbnails/Idealized-Family-Detection_Thumbnail.png",
     "examples/Example_OLR_Tracking_model/Example_OLR_Tracking_model": "_static/thumbnails/Example_OLR_Tracking_model_Thumbnail.png",
     "examples/Example_OLR_Tracking_satellite/Example_OLR_Tracking_satellite": "_static/thumbnails/Example_OLR_Tracking_satellite_Thumbnail.png",
     "examples/Example_Precip_Tracking/Example_Precip_Tracking": "_static/thumbnails/Example_Precip_Tracking_Thumbnail.png",
diff --git a/doc/getting_started/family_detection.md b/doc/getting_started/family_detection.md
new file mode 100644
index 00000000..52676cfe
--- /dev/null
+++ b/doc/getting_started/family_detection.md
@@ -0,0 +1,13 @@
+# Families
+
+Features ({doc}`/userguide/feature_detection/index`) are the *base unit* of *tobac*. However, sometimes you want to identify how features interact together. **Families** are the way to do this. Families aggregate features, allowing one to link features together spatially. 
+
+## Family Examples
+- Identifying individual convective cores (features) within a broader MCS (family)
+- Identifying individual updrafts (features) within a single cloud (family)
+
+## Algorithm Basics
+You can either detect families from data ({py:func}`tobac.merge_split.families.identify_feature_families_from_data`) or from segmentation output ({py:func}`tobac.merge_split.families.identify_feature_families_from_segmentation`). Similar to segmentation, families are identified based on a single threshold when detected from data (defined by {py:code}`threshold`), but it does *not* need to be the same field that you detect the features on[^3dcaveat].
+
+
+[^3dcaveat]: Although, you cannot currently project 2D features to 3D families.
\ No newline at end of file
diff --git a/doc/getting_started/index.md b/doc/getting_started/index.md
index 09a555ae..b97655be 100644
--- a/doc/getting_started/index.md
+++ b/doc/getting_started/index.md
@@ -11,6 +11,7 @@ feature_detection_overview
 tracking_basics
 segmentation
 merge_split
+family_detection
 plotting
 get_help
 ```
diff --git a/examples/Basics/Idealized-Family-Detection.ipynb b/examples/Basics/Idealized-Family-Detection.ipynb
new file mode 100644
index 00000000..f92fa2f2
--- /dev/null
+++ b/examples/Basics/Idealized-Family-Detection.ipynb
@@ -0,0 +1,401 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Basic Family Detection\n",
+    "\n",
+    "This example demonstrates how to use `tobac` for family detection on synthetic data containing a large blob with multiple smaller, more intense blobs nested within it.\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "In this notebook, we will:\n",
+    "1. Create synthetic data, with one large, less intense blob, and several smaller, more intense blobs within it.\n",
+    "3. Use tobac's feature detection to identify the intense blobs\n",
+    "4. Use tobac's family detection to link these features as one family.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import Required Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import xarray as xr\n",
+    "from scipy.ndimage import gaussian_filter\n",
+    "import tobac\n",
+    "\n",
+    "# Set random seed for reproducibility\n",
+    "np.random.seed(42)"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Synthetic Data\n",
+    "\n",
+    "We'll create a 2D field with:\n",
+    "- A large, diffuse background blob with moderate intensity\n",
+    "- 5 smaller, intense blobs positioned within the larger blob\n",
+    "\n",
+    "This simulates a scenario where you might have a broad atmospheric feature (like a mesoscale convective system) with embedded intense convective cells."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "source": [
+    "# Define grid dimensions\n",
+    "nx, ny = 200, 200\n",
+    "x = np.arange(nx)\n",
+    "y = np.arange(ny)\n",
+    "X, Y = np.meshgrid(x, y)\n",
+    "\n",
+    "# Initialize the field\n",
+    "field = np.zeros((ny, nx))\n",
+    "\n",
+    "# Create a large background blob centered in the domain\n",
+    "center_x, center_y = 100, 100\n",
+    "sigma_large = 40  # Large blob spread\n",
+    "amplitude_large = 60  # Moderate intensity\n",
+    "\n",
+    "# Add the large Gaussian blob\n",
+    "large_blob = amplitude_large * np.exp(\n",
+    "    -((X - center_x)**2 + (Y - center_y)**2) / (2 * sigma_large**2)\n",
+    ")\n",
+    "field += large_blob\n",
+    "\n",
+    "# Define positions for 5 smaller, intense blobs within the large blob\n",
+    "# These are positioned in a pattern within the larger blob\n",
+    "small_blob_positions = [\n",
+    "    (80, 80),   # Upper left\n",
+    "    (120, 80),  # Upper right\n",
+    "    (100, 100), # Center\n",
+    "    (80, 120),  # Lower left\n",
+    "    (120, 120)  # Lower right\n",
+    "]\n",
+    "\n",
+    "sigma_small = 8  # Small blob spread\n",
+    "amplitude_small = 50  # High intensity (higher than background)\n",
+    "\n",
+    "# Add the 5 intense blobs\n",
+    "for pos_x, pos_y in small_blob_positions:\n",
+    "    small_blob = amplitude_small * np.exp(\n",
+    "        -((X - pos_x)**2 + (Y - pos_y)**2) / (2 * sigma_small**2)\n",
+    "    )\n",
+    "    field += small_blob\n",
+    "\n",
+    "# Add some noise to make it more realistic\n",
+    "noise = np.random.normal(0, 1, field.shape)\n",
+    "field += noise\n",
+    "\n",
+    "# Smooth the field slightly to reduce noise\n",
+    "field = gaussian_filter(field, sigma=0.5)\n",
+    "\n",
+    "print(f\"Field shape: {field.shape}\")\n",
+    "print(f\"Field range: {field.min():.2f} to {field.max():.2f}\")"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prepare Data for tobac\n",
+    "\n",
+    "tobac works with xarray DataArrays that have specific coordinate and dimension names. We'll create an xarray DataArray with appropriate metadata."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "source": [
+    "# Create coordinate arrays\n",
+    "# For this example, we'll use simple grid coordinates\n",
+    "x = np.arange(nx)\n",
+    "y = np.arange(ny)\n",
+    "time = np.array(['2026-01-01T00:00:00'], dtype='datetime64[ns]')  # Single time step\n",
+    "\n",
+    "# Create xarray DataArray\n",
+    "data = xr.DataArray(\n",
+    "    field[np.newaxis, :, :],  # Add time dimension\n",
+    "    coords={\n",
+    "        'time': time,\n",
+    "        'x': x,\n",
+    "        'y': y\n",
+    "    },\n",
+    "    dims=['time', 'y', 'x'],\n",
+    "    name='intensity'\n",
+    ")\n",
+    "data"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "plt.pcolormesh(data[0])\n",
+    "plt.colorbar()"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configure tobac Feature Detection\n",
+    "\n",
+    "We need to set parameters for feature detection:\n",
+    "- `threshold`: Minimum intensity for feature detection\n",
+    "- `target`: Set to \"maximum\" as we're dealing with a field that increases as it gets more intense\n",
+    "- `position_threshold`: Sets the algorithm for choosing the feature position from the 2D/3D field\n",
+    "- `sigma_threshold`: Sets the smoothing amount\n",
+    "\n",
+    "The threshold should be set high enough to detect only the 5 intense blobs, not the background."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "source": [
+    "# Set parameters for feature detection\n",
+    "parameters = {\n",
+    "    'threshold': [30,80],\n",
+    "    'target': 'maximum',\n",
+    "    'position_threshold': 'weighted_diff',  # Use weighted difference for position\n",
+    "    'sigma_threshold': 1,  # Smoothing parameter\n",
+    "    'n_erosion_threshold': 0,  # No erosion\n",
+    "    'n_min_threshold': 1,  # Minimum number of pixels per feature\n",
+    "}"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Run tobac Feature Detection\n",
+    "\n",
+    "Now we'll run tobac's feature detection algorithm to identify the 5 intense blobs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "source": [
+    "# Run feature detection\n",
+    "features = tobac.feature_detection_multithreshold(\n",
+    "    data,\n",
+    "    dxy=1,  # Grid spacing (1 unit in both directions)\n",
+    "    **parameters\n",
+    ")\n",
+    "features"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualize Results\n",
+    "\n",
+    "Finally, we'll create a visualization showing:\n",
+    "1. The original field with both the large blob and the 5 intense blobs\n",
+    "2. Detected feature locations overlaid as scatter points\n",
+    "\n",
+    "This allows us to verify that tobac correctly identified the 5 intense blobs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "source": [
+    "# Create figure\n",
+    "fig, ax = plt.subplots(figsize=(10, 8))\n",
+    "\n",
+    "# Plot the field as a filled contour\n",
+    "im = ax.contourf(X, Y, field, levels=20, cmap='YlOrRd')\n",
+    "cbar = plt.colorbar(im, ax=ax, label='Intensity (arbitrary units)')\n",
+    "\n",
+    "# Overlay the detected features as scatter points\n",
+    "if len(features) > 0:\n",
+    "    ax.scatter(\n",
+    "        features['hdim_1'],\n",
+    "        features['hdim_2'],\n",
+    "        c='blue',\n",
+    "        s=200,\n",
+    "        marker='x',\n",
+    "        linewidths=3,\n",
+    "        label=f'Detected features (n={len(features)})',\n",
+    "        zorder=5\n",
+    "    )\n",
+    "    \n",
+    "    # Add feature numbers as labels\n",
+    "    for idx, row in features.iterrows():\n",
+    "        ax.text(\n",
+    "            row['hdim_1'] + 3,\n",
+    "            row['hdim_2'] + 3,\n",
+    "            str(row['feature']),\n",
+    "            color='blue',\n",
+    "            fontsize=12,\n",
+    "            fontweight='bold',\n",
+    "            bbox=dict(boxstyle='round,pad=0.3', facecolor='white', alpha=0.7)\n",
+    "        )\n",
+    "\n",
+    "# Labels and title\n",
+    "ax.set_xlabel('X coordinate (grid points)', fontsize=12)\n",
+    "ax.set_ylabel('Y coordinate (grid points)', fontsize=12)\n",
+    "ax.set_title('tobac Feature Detection', fontsize=14, fontweight='bold')\n",
+    "ax.legend(loc='upper right', fontsize=10)\n",
+    "ax.grid(True, alpha=0.3, linestyle=':')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "## Family Detection from Data"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": "family_df, family_stats_df, family_grid = tobac.merge_split.families.identify_feature_families_from_data(features, data, threshold=40, return_grid=True)",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "We can now see that all of the features are assigned the same family in the `feature_family_id` column."
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": "family_df",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "In addition to modifying the feature DataFrame, we also get a new *statistics* dataframe, which is ordered by the list of families, rather than by the features."
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": "family_stats_df",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "And, optionally, we get a grid output."
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": "family_grid",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "# Create figure\n",
+    "fig, ax = plt.subplots(figsize=(10, 8))\n",
+    "\n",
+    "# Plot the field as a filled contour\n",
+    "im = ax.contourf(X, Y, field, levels=20, cmap='YlOrRd')\n",
+    "cbar = plt.colorbar(im, ax=ax, label='Intensity (arbitrary units)')\n",
+    "\n",
+    "# Overlay the detected features as scatter points\n",
+    "if len(features) > 0:\n",
+    "    ax.scatter(\n",
+    "        features['hdim_1'],\n",
+    "        features['hdim_2'],\n",
+    "        c='blue',\n",
+    "        s=200,\n",
+    "        marker='x',\n",
+    "        linewidths=3,\n",
+    "        label=f'Detected features (n={len(features)})',\n",
+    "        zorder=5\n",
+    "    )\n",
+    "\n",
+    "    # Add feature numbers as labels\n",
+    "    for idx, row in features.iterrows():\n",
+    "        ax.text(\n",
+    "            row['hdim_1'] + 3,\n",
+    "            row['hdim_2'] + 3,\n",
+    "            str(row['feature']),\n",
+    "            color='blue',\n",
+    "            fontsize=12,\n",
+    "            fontweight='bold',\n",
+    "            bbox=dict(boxstyle='round,pad=0.3', facecolor='white', alpha=0.7)\n",
+    "        )\n",
+    "\n",
+    "ax.contour(X, Y, family_grid[0].values, levels=[0.9])\n",
+    "\n",
+    "# Labels and title\n",
+    "ax.set_xlabel('X coordinate (grid points)', fontsize=12)\n",
+    "ax.set_ylabel('Y coordinate (grid points)', fontsize=12)\n",
+    "ax.set_title('tobac Family Detection', fontsize=14, fontweight='bold')\n",
+    "ax.legend(loc='upper right', fontsize=10)\n",
+    "ax.grid(True, alpha=0.3, linestyle=':')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "Unlike feature detection, as you can see, family output is *not* smoothed."
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/tobac/merge_split/__init__.py b/tobac/merge_split/__init__.py
index 15707047..86a3f5aa 100644
--- a/tobac/merge_split/__init__.py
+++ b/tobac/merge_split/__init__.py
@@ -1 +1,2 @@
 from .merge_split_feature_points import merge_split_MEST
+from . import families

From 2215b1e18630838bad1802354e3363344e2be84a Mon Sep 17 00:00:00 2001
From: Sean Freeman <freemansw1@gmail.com>
Date: Mon, 19 Jan 2026 21:23:01 -0600
Subject: [PATCH 14/19] update examples

---
 doc/getting_started/family_detection.md | 8 ++++++++
 examples/index.md                       | 1 +
 2 files changed, 9 insertions(+)

diff --git a/doc/getting_started/family_detection.md b/doc/getting_started/family_detection.md
index 52676cfe..d043a078 100644
--- a/doc/getting_started/family_detection.md
+++ b/doc/getting_started/family_detection.md
@@ -9,5 +9,13 @@ Features ({doc}`/userguide/feature_detection/index`) are the *base unit* of *tob
 ## Algorithm Basics
 You can either detect families from data ({py:func}`tobac.merge_split.families.identify_feature_families_from_data`) or from segmentation output ({py:func}`tobac.merge_split.families.identify_feature_families_from_segmentation`). Similar to segmentation, families are identified based on a single threshold when detected from data (defined by {py:code}`threshold`), but it does *not* need to be the same field that you detect the features on[^3dcaveat].
 
+## Family Example Notebooks
+
+```{nblinkgallery}
+:caption: Jupyter Notebook Examples
+
+../examples/Basics/Idealized-Family-Detection.ipynb
+```
+
 
 [^3dcaveat]: Although, you cannot currently project 2D features to 3D families.
\ No newline at end of file
diff --git a/examples/index.md b/examples/index.md
index 4e6d0e57..1d9a6e46 100644
--- a/examples/index.md
+++ b/examples/index.md
@@ -15,6 +15,7 @@ tobac is provided with a set of Jupyter notebooks that show examples of the appl
    On Feature Detection: Part 2 <./Basics/Methods-and-Parameters-for-Feature-Detection_Part_2.ipynb>
    On Segmentation <./Basics/Methods-and-Parameters-for-Segmentation.ipynb>
    On Linking <./Basics/Methods-and-Parameters-for-Linking.ipynb>
+   Basic Family Detection <./Basics/Idealized-Family-Detection.ipynb>
 ```
 
 ## Examples of Using _tobac_ with Observations

From fab3488311a95b08505078d833ca5653f7f7c3c3 Mon Sep 17 00:00:00 2001
From: Sean Freeman <sean.freeman@uah.edu>
Date: Tue, 20 Jan 2026 08:39:31 -0600
Subject: [PATCH 15/19] fix formatting

---
 .../merge_split/families/feature_family_id.py | 19 ++++++++++---------
 1 file changed, 10 insertions(+), 9 deletions(-)

diff --git a/tobac/merge_split/families/feature_family_id.py b/tobac/merge_split/families/feature_family_id.py
index 04503734..f2343876 100644
--- a/tobac/merge_split/families/feature_family_id.py
+++ b/tobac/merge_split/families/feature_family_id.py
@@ -4,13 +4,14 @@
 one but can contain many detected features.
 """
 
+import copy
+from typing import Optional, Literal, Union
+import datetime
+
 import pandas as pd
 import numpy as np
 import xarray as xr
 import skimage.measure
-import copy
-from typing import Optional, Literal, Union
-import datetime
 import tobac.utils.internal.label_functions as tb_label
 import tobac.utils.datetime as tb_datetime
 
@@ -178,12 +179,12 @@ def identify_feature_families_from_data(
                     family.label + max_family_number
                 )
                 if not is_3D:
-                    family_stats[family.label + max_family_number][
-                        "hdim_1_center"
-                    ] = family.centroid[0]
-                    family_stats[family.label + max_family_number][
-                        "hdim_2_center"
-                    ] = family.centroid[1]
+                    family_stats[family.label + max_family_number]["hdim_1_center"] = (
+                        family.centroid[0]
+                    )
+                    family_stats[family.label + max_family_number]["hdim_2_center"] = (
+                        family.centroid[1]
+                    )
 
                 else:
                     # TODO: integrate 3D stats - mostly around center coordinates - need the functions in tobac proper

From 7f1526b0f5070c0c762ffc2f277b75b64cc54eda Mon Sep 17 00:00:00 2001
From: Sean Freeman <sean.freeman@uah.edu>
Date: Sun, 25 Jan 2026 18:09:41 -0600
Subject: [PATCH 16/19] fix blank notebook

---
 .../Basics/Idealized-Family-Detection.ipynb   | 1624 ++++++++++++++++-
 1 file changed, 1591 insertions(+), 33 deletions(-)

diff --git a/examples/Basics/Idealized-Family-Detection.ipynb b/examples/Basics/Idealized-Family-Detection.ipynb
index f92fa2f2..dc9bbf9b 100644
--- a/examples/Basics/Idealized-Family-Detection.ipynb
+++ b/examples/Basics/Idealized-Family-Detection.ipynb
@@ -25,7 +25,12 @@
   },
   {
    "cell_type": "code",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-26T00:05:54.524125Z",
+     "start_time": "2026-01-26T00:05:45.146709Z"
+    }
+   },
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
@@ -37,7 +42,7 @@
     "np.random.seed(42)"
    ],
    "outputs": [],
-   "execution_count": null
+   "execution_count": 1
   },
   {
    "cell_type": "markdown",
@@ -54,7 +59,12 @@
   },
   {
    "cell_type": "code",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-26T00:05:54.549620Z",
+     "start_time": "2026-01-26T00:05:54.537846Z"
+    }
+   },
    "source": [
     "# Define grid dimensions\n",
     "nx, ny = 200, 200\n",
@@ -106,8 +116,17 @@
     "print(f\"Field shape: {field.shape}\")\n",
     "print(f\"Field range: {field.min():.2f} to {field.max():.2f}\")"
    ],
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Field shape: (200, 200)\n",
+      "Field range: -1.83 to 110.11\n"
+     ]
+    }
+   ],
+   "execution_count": 2
   },
   {
    "cell_type": "markdown",
@@ -120,7 +139,12 @@
   },
   {
    "cell_type": "code",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-26T00:05:54.708097Z",
+     "start_time": "2026-01-26T00:05:54.589038Z"
+    }
+   },
    "source": [
     "# Create coordinate arrays\n",
     "# For this example, we'll use simple grid coordinates\n",
@@ -141,18 +165,613 @@
     ")\n",
     "data"
    ],
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
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+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 20px auto;\n",
+       "  width: 100%;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 20px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;intensity&#x27; (time: 1, y: 200, x: 200)&gt; Size: 320kB\n",
+       "array([[[ 0.54064717,  0.19960743,  0.82789391, ...,  0.26231846,\n",
+       "          0.09810755, -0.66419651],\n",
+       "        [ 0.31031947,  0.53221927,  1.00906837, ...,  0.74671614,\n",
+       "          0.23523976,  0.91479192],\n",
+       "        [-0.93599967, -0.36009621,  0.29189713, ..., -0.35778644,\n",
+       "         -0.14666944,  0.48745001],\n",
+       "        ...,\n",
+       "        [ 0.12690153,  0.42790158, -0.35233134, ...,  0.57253101,\n",
+       "          0.45695974, -0.20042559],\n",
+       "        [-0.10049394,  0.50000217, -0.71292123, ...,  1.04282455,\n",
+       "          1.45482585, -0.07086827],\n",
+       "        [-0.72549437,  0.389674  , -0.91807389, ...,  0.78870583,\n",
+       "          0.97036958,  0.465329  ]]], shape=(1, 200, 200))\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 8B 2026-01-01\n",
+       "  * y        (y) int64 2kB 0 1 2 3 4 5 6 7 8 ... 192 193 194 195 196 197 198 199\n",
+       "  * x        (x) int64 2kB 0 1 2 3 4 5 6 7 8 ... 192 193 194 195 196 197 198 199</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-obj-name'>&#x27;intensity&#x27;</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>y</span>: 200</li><li><span class='xr-has-index'>x</span>: 200</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b9924aad-d0b3-4501-a578-23f33e35ddae' class='xr-array-in' type='checkbox' checked><label for='section-b9924aad-d0b3-4501-a578-23f33e35ddae' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.5406 0.1996 0.8279 1.334 -0.006727 ... 0.5633 0.7887 0.9704 0.4653</span></div><div class='xr-array-data'><pre>array([[[ 0.54064717,  0.19960743,  0.82789391, ...,  0.26231846,\n",
+       "          0.09810755, -0.66419651],\n",
+       "        [ 0.31031947,  0.53221927,  1.00906837, ...,  0.74671614,\n",
+       "          0.23523976,  0.91479192],\n",
+       "        [-0.93599967, -0.36009621,  0.29189713, ..., -0.35778644,\n",
+       "         -0.14666944,  0.48745001],\n",
+       "        ...,\n",
+       "        [ 0.12690153,  0.42790158, -0.35233134, ...,  0.57253101,\n",
+       "          0.45695974, -0.20042559],\n",
+       "        [-0.10049394,  0.50000217, -0.71292123, ...,  1.04282455,\n",
+       "          1.45482585, -0.07086827],\n",
+       "        [-0.72549437,  0.389674  , -0.91807389, ...,  0.78870583,\n",
+       "          0.97036958,  0.465329  ]]], shape=(1, 200, 200))</pre></div></div></li><li class='xr-section-item'><input id='section-3acb7435-0adb-4769-95b7-6e83d9ddaa6c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3acb7435-0adb-4769-95b7-6e83d9ddaa6c' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2026-01-01</div><input id='attrs-7e6fbd7b-448a-4a85-b045-7d02ec755e2d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7e6fbd7b-448a-4a85-b045-7d02ec755e2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9709dd18-4ae1-490e-a78b-2a3221274aee' class='xr-var-data-in' type='checkbox'><label for='data-9709dd18-4ae1-490e-a78b-2a3221274aee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2026-01-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 195 196 197 198 199</div><input id='attrs-5660ed70-aca8-49d4-82b8-3edd96c9f6b2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5660ed70-aca8-49d4-82b8-3edd96c9f6b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a339ffe-d3f3-45b4-b2f8-7376face4c29' class='xr-var-data-in' type='checkbox'><label for='data-4a339ffe-d3f3-45b4-b2f8-7376face4c29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
+       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
+       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
+       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
+       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
+       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
+       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
+       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
+       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
+       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
+       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
+       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
+       "       168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
+       "       182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n",
+       "       196, 197, 198, 199])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 195 196 197 198 199</div><input id='attrs-427a811b-d9c8-4d70-88e6-50571b2c7af7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-427a811b-d9c8-4d70-88e6-50571b2c7af7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68fd11af-31d8-4ac0-aa49-1be84f819bf7' class='xr-var-data-in' type='checkbox'><label for='data-68fd11af-31d8-4ac0-aa49-1be84f819bf7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
+       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
+       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
+       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
+       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
+       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
+       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
+       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
+       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
+       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
+       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
+       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
+       "       168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
+       "       182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n",
+       "       196, 197, 198, 199])</pre></div></li></ul></div></li></ul></div></div>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 3
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-26T00:05:54.906926Z",
+     "start_time": "2026-01-26T00:05:54.729925Z"
+    }
+   },
    "cell_type": "code",
    "source": [
     "plt.pcolormesh(data[0])\n",
     "plt.colorbar()"
    ],
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar at 0x3058a3e00>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 4
   },
   {
    "cell_type": "markdown",
@@ -171,7 +790,12 @@
   },
   {
    "cell_type": "code",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-26T00:05:54.921140Z",
+     "start_time": "2026-01-26T00:05:54.918327Z"
+    }
+   },
    "source": [
     "# Set parameters for feature detection\n",
     "parameters = {\n",
@@ -184,7 +808,7 @@
     "}"
    ],
    "outputs": [],
-   "execution_count": null
+   "execution_count": 5
   },
   {
    "cell_type": "markdown",
@@ -197,7 +821,12 @@
   },
   {
    "cell_type": "code",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-26T00:05:55.289501Z",
+     "start_time": "2026-01-26T00:05:54.927783Z"
+    }
+   },
    "source": [
     "# Run feature detection\n",
     "features = tobac.feature_detection_multithreshold(\n",
@@ -207,8 +836,138 @@
     ")\n",
     "features"
    ],
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "   frame  idx      hdim_1      hdim_2  num  threshold_value  feature  \\\n",
+       "0      0    3   81.212075   81.216162  174               80        1   \n",
+       "1      0    4   81.219630  118.810947  175               80        2   \n",
+       "2      0    5  100.021091   99.990637  397               80        3   \n",
+       "3      0    6  118.814906   81.229949  172               80        4   \n",
+       "4      0    7  118.763451  118.781232  169               80        5   \n",
+       "\n",
+       "        time              timestr           x           y  \n",
+       "0 2026-01-01  2026-01-01 00:00:00   81.216162   81.212075  \n",
+       "1 2026-01-01  2026-01-01 00:00:00  118.810947   81.219630  \n",
+       "2 2026-01-01  2026-01-01 00:00:00   99.990637  100.021091  \n",
+       "3 2026-01-01  2026-01-01 00:00:00   81.229949  118.814906  \n",
+       "4 2026-01-01  2026-01-01 00:00:00  118.781232  118.763451  "
+      ],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>frame</th>\n",
+       "      <th>idx</th>\n",
+       "      <th>hdim_1</th>\n",
+       "      <th>hdim_2</th>\n",
+       "      <th>num</th>\n",
+       "      <th>threshold_value</th>\n",
+       "      <th>feature</th>\n",
+       "      <th>time</th>\n",
+       "      <th>timestr</th>\n",
+       "      <th>x</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>81.212075</td>\n",
+       "      <td>81.216162</td>\n",
+       "      <td>174</td>\n",
+       "      <td>80</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2026-01-01</td>\n",
+       "      <td>2026-01-01 00:00:00</td>\n",
+       "      <td>81.216162</td>\n",
+       "      <td>81.212075</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>81.219630</td>\n",
+       "      <td>118.810947</td>\n",
+       "      <td>175</td>\n",
+       "      <td>80</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2026-01-01</td>\n",
+       "      <td>2026-01-01 00:00:00</td>\n",
+       "      <td>118.810947</td>\n",
+       "      <td>81.219630</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>100.021091</td>\n",
+       "      <td>99.990637</td>\n",
+       "      <td>397</td>\n",
+       "      <td>80</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2026-01-01</td>\n",
+       "      <td>2026-01-01 00:00:00</td>\n",
+       "      <td>99.990637</td>\n",
+       "      <td>100.021091</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>118.814906</td>\n",
+       "      <td>81.229949</td>\n",
+       "      <td>172</td>\n",
+       "      <td>80</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2026-01-01</td>\n",
+       "      <td>2026-01-01 00:00:00</td>\n",
+       "      <td>81.229949</td>\n",
+       "      <td>118.814906</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>118.763451</td>\n",
+       "      <td>118.781232</td>\n",
+       "      <td>169</td>\n",
+       "      <td>80</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2026-01-01</td>\n",
+       "      <td>2026-01-01 00:00:00</td>\n",
+       "      <td>118.781232</td>\n",
+       "      <td>118.763451</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 6
   },
   {
    "cell_type": "markdown",
@@ -225,7 +984,12 @@
   },
   {
    "cell_type": "code",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-26T00:05:55.461071Z",
+     "start_time": "2026-01-26T00:05:55.302460Z"
+    }
+   },
    "source": [
     "# Create figure\n",
     "fig, ax = plt.subplots(figsize=(10, 8))\n",
@@ -269,8 +1033,22 @@
     "plt.tight_layout()\n",
     "plt.show()"
    ],
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 7
   },
   {
    "metadata": {},
@@ -278,11 +1056,16 @@
    "source": "## Family Detection from Data"
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-26T00:05:55.497989Z",
+     "start_time": "2026-01-26T00:05:55.488977Z"
+    }
+   },
    "cell_type": "code",
    "source": "family_df, family_stats_df, family_grid = tobac.merge_split.families.identify_feature_families_from_data(features, data, threshold=40, return_grid=True)",
    "outputs": [],
-   "execution_count": null
+   "execution_count": 8
   },
   {
    "metadata": {},
@@ -290,11 +1073,152 @@
    "source": "We can now see that all of the features are assigned the same family in the `feature_family_id` column."
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-26T00:05:55.718733Z",
+     "start_time": "2026-01-26T00:05:55.714195Z"
+    }
+   },
    "cell_type": "code",
    "source": "family_df",
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "   frame  idx      hdim_1      hdim_2  num  threshold_value  feature  \\\n",
+       "0      0    3   81.212075   81.216162  174               80        1   \n",
+       "1      0    4   81.219630  118.810947  175               80        2   \n",
+       "2      0    5  100.021091   99.990637  397               80        3   \n",
+       "3      0    6  118.814906   81.229949  172               80        4   \n",
+       "4      0    7  118.763451  118.781232  169               80        5   \n",
+       "\n",
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+   "execution_count": 9
   },
   {
    "metadata": {},
@@ -302,11 +1226,73 @@
    "source": "In addition to modifying the feature DataFrame, we also get a new *statistics* dataframe, which is ordered by the list of families, rather than by the features."
   },
   {
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+    }
+   },
    "cell_type": "code",
    "source": "family_stats_df",
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "                   num_pixels  hdim_1_center  hdim_2_center       time\n",
+       "feature_family_id                                                     \n",
+       "1                      5147.0     100.009909     100.058092 2026-01-01"
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   {
    "metadata": {},
@@ -314,14 +1300,572 @@
    "source": "And, optionally, we get a grid output."
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-   "metadata": {},
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    "cell_type": "code",
    "source": "family_grid",
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
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+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;family_grid&#x27; (time: 1, y: 200, x: 200)&gt; Size: 320kB\n",
+       "array([[[0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        ...,\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0]]], shape=(1, 200, 200))\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 8B 2026-01-01\n",
+       "  * y        (y) int64 2kB 0 1 2 3 4 5 6 7 8 ... 192 193 194 195 196 197 198 199\n",
+       "  * x        (x) int64 2kB 0 1 2 3 4 5 6 7 8 ... 192 193 194 195 196 197 198 199</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-obj-name'>&#x27;family_grid&#x27;</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>y</span>: 200</li><li><span class='xr-has-index'>x</span>: 200</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-0dd69efb-5549-46a6-9003-672fe5da0db0' class='xr-array-in' type='checkbox' checked><label for='section-0dd69efb-5549-46a6-9003-672fe5da0db0' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0</span></div><div class='xr-array-data'><pre>array([[[0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        ...,\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0],\n",
+       "        [0, 0, 0, ..., 0, 0, 0]]], shape=(1, 200, 200))</pre></div></div></li><li class='xr-section-item'><input id='section-a754324e-3125-450e-85fc-a74f65a1e16d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a754324e-3125-450e-85fc-a74f65a1e16d' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2026-01-01</div><input id='attrs-06bcfbd8-00d8-4ad6-82b2-f9e16e960eea' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-06bcfbd8-00d8-4ad6-82b2-f9e16e960eea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5820f6b8-ad8b-47c2-bfd1-465657182692' class='xr-var-data-in' type='checkbox'><label for='data-5820f6b8-ad8b-47c2-bfd1-465657182692' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2026-01-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 195 196 197 198 199</div><input id='attrs-c2e34c9e-526d-4bd9-83c7-92ee003c1c50' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c2e34c9e-526d-4bd9-83c7-92ee003c1c50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a001f261-4a3d-41b9-b363-46d986c4751d' class='xr-var-data-in' type='checkbox'><label for='data-a001f261-4a3d-41b9-b363-46d986c4751d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
+       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
+       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
+       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
+       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
+       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
+       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
+       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
+       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
+       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
+       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
+       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
+       "       168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
+       "       182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n",
+       "       196, 197, 198, 199])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 195 196 197 198 199</div><input id='attrs-04caa090-85d4-4336-8f5e-5992db16d5ad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-04caa090-85d4-4336-8f5e-5992db16d5ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c798e767-145c-4ba9-a7f9-3859c593ac33' class='xr-var-data-in' type='checkbox'><label for='data-c798e767-145c-4ba9-a7f9-3859c593ac33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
+       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
+       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
+       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
+       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
+       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
+       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
+       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
+       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
+       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
+       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
+       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
+       "       168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
+       "       182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n",
+       "       196, 197, 198, 199])</pre></div></li></ul></div></li></ul></div></div>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 11
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-01-26T00:05:56.107322Z",
+     "start_time": "2026-01-26T00:05:55.992264Z"
+    }
+   },
    "cell_type": "code",
    "source": [
     "# Create figure\n",
@@ -368,8 +1912,22 @@
     "plt.tight_layout()\n",
     "plt.show()"
    ],
-   "outputs": [],
-   "execution_count": null
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "jetTransient": {
+      "display_id": null
+     }
+    }
+   ],
+   "execution_count": 12
   },
   {
    "metadata": {},

From 3af3f28203333efc609a76d893b723d0bde9f121 Mon Sep 17 00:00:00 2001
From: Sean Freeman <sean.freeman@uah.edu>
Date: Tue, 10 Mar 2026 16:18:46 -0500
Subject: [PATCH 17/19] fixes for multi-time, include tests for multi-time

---
 .../merge_split/families/feature_family_id.py | 19 ++++++--
 tobac/tests/family_tests/test_family_id.py    | 48 +++++++++++--------
 2 files changed, 42 insertions(+), 25 deletions(-)

diff --git a/tobac/merge_split/families/feature_family_id.py b/tobac/merge_split/families/feature_family_id.py
index f2343876..e9f195ec 100644
--- a/tobac/merge_split/families/feature_family_id.py
+++ b/tobac/merge_split/families/feature_family_id.py
@@ -248,10 +248,8 @@ def identify_feature_families_from_data(
         # we want to get rid of points that aren't features in the grid output
         suppressing_families = np.isin(family_labeled_data, family_ids_sorted)
         feature_id_family_id_match_ct = {
-            feat: fam_id
-            for feat, fam_id in zip(
-                rows_at_time["feature"].values, family_ids + max_family_number
-            )
+            feat: (fam_id + max_family_number if fam_id != 0 else -1)
+            for feat, fam_id in zip(rows_at_time["feature"].values, family_ids)
         }
         seg_family_dict.update(feature_id_family_id_match_ct)
         out_families[time_index] = (
@@ -267,10 +265,20 @@ def identify_feature_families_from_data(
     if unlinked_family_id is not None:
         out_df.loc[out_df["feature_family_id"] == 0, "feature_family_id"] = -1
     else:
-        out_df = out_df[out_df["feature_family_id"] != 0]
+        out_df = out_df[
+            np.logical_and(
+                out_df["feature_family_id"] != 0, out_df["feature_family_id"] != -1
+            )
+        ]
 
     if enable_family_statistics:
         family_stats_df = pd.DataFrame.from_dict(family_stats, orient="index")
+        # we need to drop any family_stats that aren't in the feature DF
+        family_stats_df = family_stats_df[
+            family_stats_df[family_column_name].isin(
+                np.unique(family_df[family_column_name].dropna().values)
+            )
+        ]
         fam_to_time_df = out_df[["time", family_column_name]].set_index(
             family_column_name
         )
@@ -278,6 +286,7 @@ def identify_feature_families_from_data(
         fam_to_time_df = fam_to_time_df.loc[
             ~fam_to_time_df.index.duplicated(keep="first"), :
         ].sort_index()
+        fam_to_time_df = fam_to_time_df[fam_to_time_df.index != -1]
         family_stats_df = family_stats_df.join(
             fam_to_time_df, on=family_column_name
         ).dropna(subset="time")
diff --git a/tobac/tests/family_tests/test_family_id.py b/tobac/tests/family_tests/test_family_id.py
index 8d7c70e0..1c0bbc8c 100644
--- a/tobac/tests/family_tests/test_family_id.py
+++ b/tobac/tests/family_tests/test_family_id.py
@@ -147,7 +147,7 @@ def test_family_id_data_features_unlinked():
     are still in the output but are unlinked (-1)
     """
 
-    test_dset_size = (100, 100)
+    test_dset_size = (2, 100, 100)
     # pretty darn circular feature
     test_hdim_1_pts = [
         60.0,
@@ -182,22 +182,25 @@ def test_family_id_data_features_unlinked():
     test_amps = [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]
 
     test_data = np.zeros(test_dset_size)
-
-    for test_h1, test_h2, test_sz1, test_sz2, test_amp in zip(
-        test_hdim_1_pts, test_hdim_2_pts, test_hdim_1_szs, test_hdim_2_szs, test_amps
-    ):
-        test_data = tbtest.make_feature_blob(
-            test_data,
-            test_h1,
-            test_h2,
-            h1_size=test_sz1,
-            h2_size=test_sz2,
-            amplitude=test_amp,
+    for i in range(test_data.shape[0]):
+        for test_h1, test_h2, test_sz1, test_sz2, test_amp in zip(
+            test_hdim_1_pts,
+            test_hdim_2_pts,
+            test_hdim_1_szs,
+            test_hdim_2_szs,
+            test_amps,
+        ):
+            test_data[i] = tbtest.make_feature_blob(
+                test_data[i],
+                test_h1,
+                test_h2,
+                h1_size=test_sz1,
+                h2_size=test_sz2,
+                amplitude=test_amp,
+            )
+        test_data_xr = tbtest.make_dataset_from_arr(
+            test_data, data_type="xarray", time_dim_num=0
         )
-    test_data = np.expand_dims(test_data, 0)
-    test_data_xr = tbtest.make_dataset_from_arr(
-        test_data, data_type="xarray", time_dim_num=0
-    )
 
     test_threshs = [1.5, 2.5, 4.5]
     n_min_threshold = 1
@@ -209,7 +212,9 @@ def test_family_id_data_features_unlinked():
         threshold=test_threshs,
         n_min_threshold=n_min_threshold,
     )
-    assert len(fd_output) == 1, f"Expected 1 feature, but got {len(fd_output)}"
+    assert (
+        len(fd_output) == test_data.shape[0]
+    ), f"Expected {test_data.shape[0]} feature(s), but got {len(fd_output)}"
 
     # detect families from data
 
@@ -217,12 +222,15 @@ def test_family_id_data_features_unlinked():
         fd_output, test_data_xr, threshold=1.5, unlinked_family_id=-1
     )
     assert (
-        len(families_fd) == 1
-    ), f"families: Expected 1 feature, but got {len(fd_output)}"
+        len(families_fd) == test_data.shape[0]
+    ), f"families: Expected {test_data.shape[0]} feature(s), but got {len(fd_output)}"
     assert (
         len(stats_fd) == 0
     ), f"family stats: Expected 0 families, but got {len(fd_output)}"
-    assert np.all(families_fd["feature_family_id"].values == [])
+
+    assert np.all(
+        families_fd["feature_family_id"].values == -1
+    ), f'families: Expected all families to be -1, but got {np.unique(families_fd["feature_family_id"].values)}.'
 
     families_fd, stats_fd = tb_fam.identify_feature_families_from_data(
         fd_output, test_data_xr, threshold=1.5, unlinked_family_id=None

From fb8c6730560601d0ca5fe5078ce6fcbda90f1ae1 Mon Sep 17 00:00:00 2001
From: Sean Freeman <freemansw1@gmail.com>
Date: Tue, 10 Mar 2026 22:10:57 -0500
Subject: [PATCH 18/19] added early family detection info to user guide

---
 doc/userguide/families/family_detection.md | 23 ++++++++++++++++++++++
 1 file changed, 23 insertions(+)
 create mode 100644 doc/userguide/families/family_detection.md

diff --git a/doc/userguide/families/family_detection.md b/doc/userguide/families/family_detection.md
new file mode 100644
index 00000000..4f83a8db
--- /dev/null
+++ b/doc/userguide/families/family_detection.md
@@ -0,0 +1,23 @@
+# Families
+
+As described in Getting Started ({doc}`/getting_started/family_detection`), features ({doc}`/userguide/feature_detection/index`) are the *base unit* of *tobac*, whereas **families** stitch multiple *features* together. Families aggregate features, allowing one to link features together spatially. 
+
+One can think about *families* as a kind of spatial clustering, although at the moment not using any preexisting clustering methods. As with every other method in *tobac*, family detection is modular, meaning that you can choose to use it or not.  
+
+## Family Examples
+- Identifying individual convective cores (features) within a broader MCS (family)
+- Identifying individual updrafts (features) within a single cloud (family)
+
+## Algorithm Basics
+You can either detect families from data ({py:func}`tobac.merge_split.families.identify_feature_families_from_data`) or from segmentation output ({py:func}`tobac.merge_split.families.identify_feature_families_from_segmentation`). Similar to segmentation, families are identified based on a single threshold when detected from data (defined by {py:code}`threshold`), but it does *not* need to be the same field that you detect the features on[^3dcaveat].
+
+## Family Example Notebooks
+
+```{nblinkgallery}
+:caption: Jupyter Notebook Examples
+
+../examples/Basics/Idealized-Family-Detection.ipynb
+```
+
+
+[^3dcaveat]: Although, you cannot currently project 2D features to 3D families.
\ No newline at end of file

From 9f12f17a7d40ea7cd62bff2fff5fa7b59debdbf6 Mon Sep 17 00:00:00 2001
From: Sean Freeman <sean.freeman@uah.edu>
Date: Wed, 25 Mar 2026 10:19:01 -0500
Subject: [PATCH 19/19] fix a bug around 0 points not being identified.

---
 tobac/merge_split/families/feature_family_id.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tobac/merge_split/families/feature_family_id.py b/tobac/merge_split/families/feature_family_id.py
index e9f195ec..5545a023 100644
--- a/tobac/merge_split/families/feature_family_id.py
+++ b/tobac/merge_split/families/feature_family_id.py
@@ -243,7 +243,7 @@ def identify_feature_families_from_data(
         family_ids = family_labeled_data[points_list]
         # remove 0 (background) if needed
 
-        family_ids_sorted = np.unique(np.sort(family_ids))
+        family_ids_sorted = np.unique(family_ids[family_ids > 0])
 
         # we want to get rid of points that aren't features in the grid output
         suppressing_families = np.isin(family_labeled_data, family_ids_sorted)