refactor: Improve timestamp handling in camera affinity matrix calculation

- Replaced direct access to tracking and detection timestamps with a more efficient use of `itertools.chain` to gather timestamps, enhancing readability and performance in `calculate_camera_affinity_matrix_jax`.
- Maintained precision in timestamp calculations to ensure accurate results in affinity computations.

playground.py

@@ -20,6 +20,7 @@ from copy import deepcopy as deep_copy
 from dataclasses import dataclass
 from datetime import datetime, timedelta
 from functools import partial, reduce
+from itertools import chain
 from pathlib import Path
 from typing import (
     Any,
@@ -1055,10 +1056,11 @@ def calculate_camera_affinity_matrix_jax(
     # sub‑second detail (resolution ≈ 200 ms).  Keep them in float64 until
     # after subtraction so we preserve Δt‑on‑the‑order‑of‑milliseconds.
     # --- timestamps ----------
-    tracking0 = next(iter(trackings))
-    detection0 = next(iter(camera_detections))
     t0 = min(
-        tracking0.last_active_timestamp, detection0.timestamp
+        chain(
+            (trk.last_active_timestamp for trk in trackings),
+            (det.timestamp for det in camera_detections),
+        )
     ).timestamp()  # common origin (float)
     ts_trk = jnp.array(
         [trk.last_active_timestamp.timestamp() - t0 for trk in trackings],