chore(format): formatting code

docs/source/video_encoding_parameters.mdx

@@ -100,7 +100,7 @@ lerobot-record \
 | `use_log`   | `bool`  | `True`       | If `true`, quantize in log-space (recommended for typical depth sensors). Set to `false` for uniform/linear quantization.         |
 
 > [!TIP]
-> `depth_min`, `depth_max`, and `shift` are always interpreted in **metres**, regardless of the input depth's unit. Inputs are auto-detected: integer arrays (e.g. `uint16` millimetres straight from a RealSense) are treated as millimetres, floating arrays as metres. 
+> `depth_min`, `depth_max`, and `shift` are always interpreted in **metres**, regardless of the input depth's unit. Inputs are auto-detected: integer arrays (e.g. `uint16` millimetres straight from a RealSense) are treated as millimetres, floating arrays as metres.
 > Pick `depth_min` / `depth_max` to bracket the actual working range of your sensor — quanta outside that range saturate, which can crush detail at the boundaries.
 
 Depth features are flagged with `"is_depth_map": true` in `meta/info.json`, and their quantizer settings (`video.depth_min`, `video.depth_max`, `video.shift`, `video.use_log`) are persisted — which is what lets depth be **dequantized back to physical units** on load.

tests/datasets/test_depth.py

@@ -90,16 +90,16 @@ class TestQuantizeDequantize:
     @pytest.mark.parametrize("output_unit", ["m", "mm"])
     def test_numpy_torch_agree(self, use_log, output_unit):
         """Batched torch path produces the same values as the numpy path."""
-        T = 3
+        batch_size = 3
         per_frame = np.linspace(0, DEPTH_QMAX, H * W, dtype=np.uint16).reshape(H, W)
-        batch_np = np.broadcast_to(per_frame[None, None, ...], (T, 1, H, W)).copy()
+        batch_np = np.broadcast_to(per_frame[None, None, ...], (batch_size, 1, H, W)).copy()
         batch_t = torch.from_numpy(batch_np.astype(np.int32))  # torch.uint16 support is patchy.
 
         ref = dequantize_depth(batch_np, use_log=use_log, output_unit=output_unit, output_tensor=False)
         out = dequantize_depth(batch_t, use_log=use_log, output_unit=output_unit, output_tensor=True)
 
         assert isinstance(out, torch.Tensor)
-        assert out.shape == (T, 1, H, W)
+        assert out.shape == (batch_size, 1, H, W)
         # ``m``: float32 noise (~10 µm in log mode, after ``exp``) — still 200× below the ~2 mm quant step.
         # ``mm`` + tensor stays in float32 (no uint16 round-trip), so allow 1 mm slop.
         atol = 1e-5 if output_unit == "m" else 1.0

tests/datasets/test_video_encoding.py

@@ -66,7 +66,7 @@ require_qsv = _require_encoder("h264_qsv")
 TEST_ARTIFACTS_DIR = Path(__file__).parent.parent / "artifacts" / "encoded_videos"
 
 
-def _write_RGB_frames(imgs_dir: Path, num_frames: int = 4, height: int = 64, width: int = 96) -> None:
+def _write_color_frames(imgs_dir: Path, num_frames: int = 4, height: int = 64, width: int = 96) -> None:
     imgs_dir.mkdir(parents=True, exist_ok=True)
     for i in range(num_frames):
         arr = np.random.randint(0, 256, (height, width, 3), dtype=np.uint8)
@@ -107,7 +107,7 @@ def _encode_video(
         _write_depth_frames(imgs_dir, num_frames=num_frames)
         cfg = cfg or DepthEncoderConfig()
     else:
-        _write_RGB_frames(imgs_dir, num_frames=num_frames)
+        _write_color_frames(imgs_dir, num_frames=num_frames)
     encode_video_frames(imgs_dir, path, fps=fps, video_encoder=cfg, overwrite=True)
     return path
 
@@ -449,7 +449,7 @@ class TestEncodeVideoFrames:
 
     def test_overwrite_false_skips_existing_file(self, tmp_path):
         imgs_dir = tmp_path / "imgs"
-        _write_RGB_frames(imgs_dir)
+        _write_color_frames(imgs_dir)
         video_path = tmp_path / "out.mp4"
         sentinel = b"pre-existing content"
         video_path.write_bytes(sentinel)
@@ -461,7 +461,7 @@ class TestEncodeVideoFrames:
     @require_libsvtav1
     def test_overwrite_true_replaces_existing_file(self, tmp_path):
         imgs_dir = tmp_path / "imgs"
-        _write_RGB_frames(imgs_dir)
+        _write_color_frames(imgs_dir)
         video_path = tmp_path / "out.mp4"
         video_path.write_bytes(b"stale content")