feat: Add hypothesis testing and update dependencies

- Added property-based tests for camera affinity calculations using Hypothesis, enhancing test coverage and reliability.
- Updated `.gitignore` to include `.hypothesis` directory.
- Added `hypothesis` and `pytest` as dependencies in `pyproject.toml` and `uv.lock` for improved testing capabilities.
- Refactored imports in `playground.py` to include `Sequence` and `Mapping` from `beartype.typing`.
- Introduced a new test file `tests/test_affinity.py` for structured testing of affinity calculations.

tests/test_affinity.py

@@ -0,0 +1,119 @@
+from datetime import datetime, timedelta
+
+import jax.numpy as jnp
+import numpy as np
+import pytest
+from hypothesis import given, settings, HealthCheck
+from hypothesis import strategies as st
+
+from app.camera import Camera, CameraParams
+from playground import (
+    Detection,
+    Tracking,
+    calculate_affinity_matrix,
+    calculate_camera_affinity_matrix,
+)
+
+# ----------------------------------------------------------------------------
+# Helper functions to generate synthetic cameras / trackings / detections
+# ----------------------------------------------------------------------------
+
+
+def _make_dummy_camera(cam_id: str, rng: np.random.Generator) -> Camera:
+    K = jnp.eye(3)
+    Rt = jnp.eye(4)
+    dist = jnp.zeros(5)
+    image_size = jnp.array([1000, 1000])
+    params = CameraParams(K=K, Rt=Rt, dist_coeffs=dist, image_size=image_size)
+    return Camera(id=cam_id, params=params)
+
+
+def _random_keypoints_3d(rng: np.random.Generator, J: int):
+    return jnp.asarray(rng.uniform(-1.0, 1.0, size=(J, 3)).astype(np.float32))
+
+
+def _random_keypoints_2d(rng: np.random.Generator, J: int):
+    return jnp.asarray(rng.uniform(0.0, 1000.0, size=(J, 2)).astype(np.float32))
+
+
+def _make_trackings(rng: np.random.Generator, camera: Camera, T: int, J: int):
+    now = datetime.now()
+    trackings = []
+    for i in range(T):
+        kps3d = _random_keypoints_3d(rng, J)
+        trk = Tracking(
+            id=i + 1,
+            keypoints=kps3d,
+            last_active_timestamp=now
+            - timedelta(milliseconds=int(rng.integers(20, 50))),
+        )
+        trackings.append(trk)
+    return trackings
+
+
+def _make_detections(rng: np.random.Generator, camera: Camera, D: int, J: int):
+    now = datetime.now()
+    detections = []
+    for _ in range(D):
+        kps2d = _random_keypoints_2d(rng, J)
+        det = Detection(
+            keypoints=kps2d,
+            confidences=jnp.ones(J, dtype=jnp.float32),
+            camera=camera,
+            timestamp=now,
+        )
+        detections.append(det)
+    return detections
+
+
+# ----------------------------------------------------------------------------
+# Property-based test: per-camera vs naive slice should match
+# ----------------------------------------------------------------------------
+
+
+@settings(max_examples=3, deadline=None, suppress_health_check=[HealthCheck.too_slow])
+@given(
+    T=st.integers(min_value=1, max_value=4),
+    D=st.integers(min_value=1, max_value=4),
+    J=st.integers(min_value=5, max_value=15),
+    seed=st.integers(min_value=0, max_value=10000),
+)
+def test_per_camera_matches_naive(T, D, J, seed):
+    rng = np.random.default_rng(seed)
+
+    cam = _make_dummy_camera("C0", rng)
+
+    trackings = _make_trackings(rng, cam, T, J)
+    detections = _make_detections(rng, cam, D, J)
+
+    # Compute per-camera affinity (fast)
+    A_fast = calculate_camera_affinity_matrix(
+        trackings,
+        detections,
+        w_2d=1.0,
+        alpha_2d=1.0,
+        w_3d=1.0,
+        alpha_3d=1.0,
+        lambda_a=0.1,
+    )
+
+    # Compute naive multi-camera affinity and slice out this camera
+    from collections import OrderedDict
+
+    det_dict = OrderedDict({"C0": detections})
+    A_naive, _ = calculate_affinity_matrix(
+        trackings,
+        det_dict,
+        w_2d=1.0,
+        alpha_2d=1.0,
+        w_3d=1.0,
+        alpha_3d=1.0,
+        lambda_a=0.1,
+    )
+
+    # They should be close
+    np.testing.assert_allclose(A_fast, np.asarray(A_naive), rtol=1e-5, atol=1e-5)
+
+
+if __name__ == "__main__" and pytest is not None:
+    pytest.main([__file__])