diff --git a/examples/reindex_and_interpolate.py b/examples/reindex_and_interpolate.py
deleted file mode 100644
index 7c211865f..000000000
--- a/examples/reindex_and_interpolate.py
+++ /dev/null
@@ -1,77 +0,0 @@
-"""Reindex and interpolate bboxes tracks
-===============================
-
-Load and explore an example dataset of bounding boxes tracks.
-"""
-
-# %%
-from movement import sample_data
-from movement.filtering import interpolate_over_time
-from movement.io import load_bboxes
-
-# %%
-# Select sample data file
-# --------------------
-# For the sake of this example, we will use the path to one of
-# the sample datasets provided with ``movement``.
-
-file_path = sample_data.fetch_dataset_paths("VIA_single-crab_MOCA-crab-1.csv")[
- "bboxes"
-]
-print(file_path)
-
-ds = load_bboxes.from_via_tracks_file(
- file_path, use_frame_numbers_from_file=True
-)
-
-# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-# Only 1 in 5 frames are labelled!
-print(ds)
-print(ds.time)
-print(ds.position.data[:, 0, :])
-
-
-# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-# Extend the dataset to every frame by forward filling
-ds_ff = ds.reindex(
- {"time": list(range(ds.time[-1].item()))},
- method="ffill", # propagate last valid index value forward
-)
-
-print(ds_ff.position.data[:, 0, :])
-print(ds_ff.shape.data[:, 0, :])
-
-# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-# Extend the dataset to every frame and fill empty values with nan
-ds_nan = ds.reindex(
- {"time": list(range(ds.time[-1].item()))},
- method=None, # default
-)
-
-print("Position data array:")
-print(ds_nan.position.data[:11, 0, :])
-
-print("Shape data array:")
-print(ds_nan.shape.data[:11, 0, :])
-
-# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-# Linearly interpolate position and shape with nan
-
-ds_interp = ds_nan.copy()
-
-for data_array_str in ["position", "shape"]:
- ds_interp[data_array_str] = interpolate_over_time(
- data=ds_interp[data_array_str],
- method="linear",
- max_gap=None,
- print_report=False,
- )
-
-print("Position data array:")
-print(ds_interp.position.data[:11, 0, :])
-
-print("Shape data array:")
-print(ds_interp.shape.data[:11, 0, :])
-
-# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-# Export as csv file
diff --git a/examples/reindex_and_interpolate_bboxes.py b/examples/reindex_and_interpolate_bboxes.py
new file mode 100644
index 000000000..cdddba8f7
--- /dev/null
+++ b/examples/reindex_and_interpolate_bboxes.py
@@ -0,0 +1,220 @@
+"""Reindex and interpolate bounding boxes tracks
+===============================
+
+Load an example dataset of bounding boxes' tracks and reindex
+it to every frame.
+"""
+
+# %%
+import math
+
+import sleap_io as sio
+from cycler import cycler
+from matplotlib import pyplot as plt
+
+from movement import sample_data
+from movement.filtering import interpolate_over_time
+from movement.io import load_bboxes
+
+# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+# Select sample data file
+# --------------------
+# For this example, we will use the path to one of
+# the sample datasets provided with ``movement``.
+
+dataset_dict = sample_data.fetch_dataset_paths(
+ "VIA_single-crab_MOCA-crab-1.csv",
+ with_video=True, # for visualisation
+)
+
+file_path = dataset_dict["bboxes"]
+print(file_path)
+
+ds = load_bboxes.from_via_tracks_file(
+ file_path, use_frame_numbers_from_file=True
+)
+
+# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+# Only 1 in 5 frames are annotated, plus the last frame (167)
+print(ds)
+print("-----")
+print(ds.time)
+
+
+# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+# Extend the dataset to every frame by forward filling
+# The position and shape data arrays are filled with the last valid value
+# So position and shape are kept constant when no annotation is available
+ds_ff = ds.reindex(
+ {"time": list(range(ds.time[-1].item()))},
+ method="ffill", # propagate last valid index value forward
+)
+
+print("Position data array (first 14 frames):")
+print(ds_ff.position.data[:14, 0, :]) # time, individual, space
+
+print("----")
+print("Shape data array (first 14 frames):")
+print(ds_ff.shape.data[:14, 0, :]) # time, individual, space
+
+# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+# Extend the dataset to every frame and fill empty values with nan
+ds_nan = ds.reindex(
+ {"time": list(range(ds.time[-1].item()))},
+ method=None, # default
+)
+
+print("Position data array (first 14 frames):")
+print(ds_nan.position.data[:14, 0, :])
+
+print("----")
+print("Shape data array (first 14 frames):")
+print(ds_nan.shape.data[:14, 0, :])
+
+# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+# Linearly interpolate position and shape with nan
+
+ds_interp = ds_nan.copy()
+
+for data_array_str in ["position", "shape"]:
+ ds_interp[data_array_str] = interpolate_over_time(
+ data=ds_interp[data_array_str],
+ method="linear",
+ max_gap=None,
+ print_report=False,
+ )
+
+print("Position data array (first 14 frames):")
+print(ds_interp.position.data[:14, 0, :])
+
+print("----")
+print("Shape data array (first 14 frames):")
+print(ds_interp.shape.data[:14, 0, :])
+
+
+# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+# Inspect associated video
+
+video_path = dataset_dict["video"]
+
+
+video = sio.load_video(video_path)
+
+n_frames, height, width, channels = video.shape
+
+print(f"Number of frames: {n_frames}") # The video contains all frames
+print(f"Frame size: {width}x{height}")
+print(f"Number of channels: {channels}")
+
+
+# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+# Plot data
+# OJO camera movement
+
+# select indices of data to plot
+data_start_idx = 0
+data_end_idx = 11
+
+# initialise figure
+fig = plt.figure(figsize=(15, 12))
+
+# add color cycler to axes
+plt.rcParams["axes.prop_cycle"] = cycler(color=plt.get_cmap("tab10").colors)
+color_cycle = plt.rcParams["axes.prop_cycle"].by_key()["color"]
+
+# loop over data and plot over corresponding frame
+for p_i, data_idx in enumerate(range(data_start_idx, data_end_idx)):
+ # add subplot axes
+ ax = plt.subplot(math.ceil(data_end_idx / 5), 5, p_i + 1)
+
+ # plot frame
+ ax.imshow(
+ video[ds.time[data_idx].item()]
+ ) # the video is indexed at every frame! use frame number as index
+
+ # plot annotated boxes
+ top_left_corner = (
+ ds.position[data_idx, 0, :].data - ds.shape[data_idx, 0, :].data / 2
+ )
+ bbox = plt.Rectangle(
+ xy=tuple(top_left_corner),
+ width=ds.shape[data_idx, 0, 0].data, # x coord
+ height=ds.shape[data_idx, 0, 1].data, # y coord of shape array
+ edgecolor=color_cycle[0], # [data_idx % len(color_cycle)],
+ facecolor="none", # transparent fill
+ linewidth=1.5,
+ )
+ ax.add_patch(bbox)
+
+ ax.set_title(f"Frame {ds.time[data_idx].item()}")
+
+fig.tight_layout()
+
+# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+# Compare interpolation methods
+
+# select frames to inspect
+frame_number_start = 0
+frame_number_end = 6
+
+# add color cycler to axes
+plt.rcParams["axes.prop_cycle"] = cycler(color=plt.get_cmap("tab10").colors)
+color_cycle = plt.rcParams["axes.prop_cycle"].by_key()["color"]
+
+
+# initialise figure
+fig = plt.figure(figsize=(15, 12))
+
+
+# loop over data and plot over corresponding frame
+for frame_n in range(frame_number_start, frame_number_end):
+ # add subplot axes
+ ax = plt.subplot(1, 6, frame_n + 1)
+
+ # plot frame
+ ax.imshow(video[frame_n])
+ # the video is indexed at every frame! use frame number as index
+
+ # plot bounding box: box and centroid
+ for ds_i, ds in enumerate([ds_nan, ds_ff, ds_interp]):
+ # plot box
+ top_left_corner = (
+ ds.position.sel(time=frame_n, individuals="id_1").data
+ - ds.shape.sel(time=frame_n, individuals="id_1").data / 2
+ )
+ bbox = plt.Rectangle(
+ xy=tuple(top_left_corner),
+ width=ds.shape.sel(
+ time=frame_n, individuals="id_1", space="x"
+ ).data, # x coord
+ height=ds.shape.sel(
+ time=frame_n, individuals="id_1", space="y"
+ ).data, # y coord of shape array
+ edgecolor=color_cycle[ds_i],
+ facecolor="none", # transparent fill
+ linewidth=[4.5, 1.5, 1.5][ds_i],
+ linestyle=["dotted", "solid", "solid"][ds_i],
+ label=["nan", "ffill", "linear"][ds_i],
+ )
+ ax.add_patch(bbox)
+
+ # plot centroid
+ ax.scatter(
+ x=ds.position.sel(
+ time=frame_n, individuals="id_1", space="x"
+ ).data,
+ y=ds.position.sel(
+ time=frame_n, individuals="id_1", space="y"
+ ).data,
+ s=5,
+ color=color_cycle[ds_i],
+ )
+
+ if frame_n == 0:
+ ax.legend()
+ ax.set_title(f"Frame {frame_n}")
+
+fig.tight_layout()
+
+# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+# Export as csv file