From 6201a4e19cab212574f952bc791ac50c5b435ac1 Mon Sep 17 00:00:00 2001
From: sfmig <33267254+sfmig@users.noreply.github.com>
Date: Tue, 17 Sep 2024 16:29:58 +0100
Subject: [PATCH] Add reindex and interpolate example

---
 examples/reindex_and_interpolate.py | 77 +++++++++++++++++++++++++++++
 1 file changed, 77 insertions(+)
 create mode 100644 examples/reindex_and_interpolate.py

diff --git a/examples/reindex_and_interpolate.py b/examples/reindex_and_interpolate.py
new file mode 100644
index 00000000..7c211865
--- /dev/null
+++ b/examples/reindex_and_interpolate.py
@@ -0,0 +1,77 @@
+"""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