linting and formatting

src/neuropose/_model.py

@@ -54,8 +54,7 @@ logger = logging.getLogger(__name__)
 # human. These are supply-chain constants.
 
 _MODEL_URL = (
-    "https://omnomnom.vision.rwth-aachen.de/data/metrabs/"
+    "https://omnomnom.vision.rwth-aachen.de/data/metrabs/metrabs_eff2l_y4_384px_800k_28ds.tar.gz"
-    "metrabs_eff2l_y4_384px_800k_28ds.tar.gz"
 )
 _MODEL_SHA256 = "fa31b5b043f227588c3d224e56db89307d021bfbbb52e36028919f90e1f96c89"
 _MODEL_ARCHIVE_NAME = "metrabs_eff2l_y4_384px_800k_28ds.tar.gz"
@@ -174,13 +173,13 @@ def _download_with_progress(url: str, dest: Path) -> None:
     logger.info("Downloading %s → %s", url, dest)
     dest.parent.mkdir(parents=True, exist_ok=True)
 
-    request = urllib.request.Request(  # noqa: S310
+    request = urllib.request.Request(
         url,
         headers={"User-Agent": "neuropose/0.1"},
     )
 
     try:
-        with urllib.request.urlopen(  # noqa: S310
+        with urllib.request.urlopen(
             request,
             timeout=_DOWNLOAD_SOCKET_TIMEOUT,
         ) as response:
@@ -210,15 +209,12 @@ def _download_with_progress(url: str, dest: Path) -> None:
     except Exception as exc:
         # Clean up partial file so the next call re-downloads cleanly.
         dest.unlink(missing_ok=True)
-        raise RuntimeError(
+        raise RuntimeError(f"Failed to download MeTRAbs model from {url}: {exc}") from exc
-            f"Failed to download MeTRAbs model from {url}: {exc}"
-        ) from exc
 
     if total_bytes > 0 and downloaded != total_bytes:
         dest.unlink(missing_ok=True)
         raise RuntimeError(
-            f"Download from {url} was truncated: "
+            f"Download from {url} was truncated: got {downloaded} bytes, expected {total_bytes}."
-            f"got {downloaded} bytes, expected {total_bytes}."
         )
     logger.info("Download complete: %d bytes", downloaded)
 
@@ -308,9 +304,7 @@ def _find_saved_model(root: Path) -> Path:
     """
     candidates = list(root.rglob("saved_model.pb"))
     if not candidates:
-        raise RuntimeError(
+        raise RuntimeError(f"no saved_model.pb found under {root}; tarball layout unexpected")
-            f"no saved_model.pb found under {root}; tarball layout unexpected"
-        )
     if len(candidates) > 1:
         raise RuntimeError(
             f"multiple saved_model.pb files found under {root}: "
@@ -328,7 +322,7 @@ def _tf_load(saved_model_dir: Path) -> Any:
     code paths that never reach the loader.
     """
     try:
-        import tensorflow as tf  # noqa: PLC0415
+        import tensorflow as tf
     except ImportError as exc:
         raise RuntimeError(
             "TensorFlow is required to load the MeTRAbs model but is not installed. "
@@ -340,9 +334,7 @@ def _tf_load(saved_model_dir: Path) -> Any:
     try:
         model = tf.saved_model.load(str(saved_model_dir))
     except Exception as exc:
-        raise RuntimeError(
+        raise RuntimeError(f"Failed to load SavedModel from {saved_model_dir}: {exc}") from exc
-            f"Failed to load SavedModel from {saved_model_dir}: {exc}"
-        ) from exc
 
     missing = [attr for attr in _REQUIRED_MODEL_ATTRS if not hasattr(model, attr)]
     if missing:

src/neuropose/analyzer/dtw.py

@@ -67,7 +67,7 @@ def _require_fastdtw() -> tuple[Callable, Callable]:
         points the user at the ``analysis`` optional-dependencies extra.
     """
     try:
-        from fastdtw import fastdtw
+        from fastdtw import fastdtw  # type: ignore[attr-defined]
         from scipy.spatial.distance import euclidean
     except ImportError as exc:
         raise ImportError(
@@ -143,9 +143,7 @@ def dtw_per_joint(a: np.ndarray, b: np.ndarray) -> list[DTWResult]:
         a_joint = a[:, joint_idx, :]
         b_joint = b[:, joint_idx, :]
         distance, path = fastdtw(a_joint, b_joint, dist=euclidean)
-        results.append(
+        results.append(DTWResult(distance=float(distance), path=[tuple(p) for p in path]))
-            DTWResult(distance=float(distance), path=[tuple(p) for p in path])
-        )
     return results
 
 
@@ -188,8 +186,7 @@ def dtw_relation(
     num_joints = a.shape[1]
     if not (0 <= joint_i < num_joints) or not (0 <= joint_j < num_joints):
         raise ValueError(
-            f"joint indices must be in [0, {num_joints}); "
+            f"joint indices must be in [0, {num_joints}); got joint_i={joint_i}, joint_j={joint_j}"
-            f"got joint_i={joint_i}, joint_j={joint_j}"
         )
     fastdtw, euclidean = _require_fastdtw()
     disp_a = a[:, joint_j, :] - a[:, joint_i, :]
@@ -202,8 +199,7 @@ def _validate_same_joint_count(a: np.ndarray, b: np.ndarray) -> None:
     """Raise :class:`ValueError` if ``a`` and ``b`` disagree on joint count."""
     if a.ndim < 2 or b.ndim < 2:
         raise ValueError(
-            f"expected 3D arrays of shape (frames, joints, 3); "
+            f"expected 3D arrays of shape (frames, joints, 3); got a.ndim={a.ndim}, b.ndim={b.ndim}"
-            f"got a.ndim={a.ndim}, b.ndim={b.ndim}"
         )
     if a.shape[1] != b.shape[1]:
         raise ValueError(

src/neuropose/analyzer/features.py

@@ -26,12 +26,12 @@ from __future__ import annotations
 
 from collections.abc import Sequence
 from dataclasses import dataclass
+from typing import Any
 
 import numpy as np
 
 from neuropose.io import VideoPredictions
 
-
 # ---------------------------------------------------------------------------
 # VideoPredictions → numpy
 # ---------------------------------------------------------------------------
@@ -122,9 +122,7 @@ def normalize_pose_sequence(
         sequence is degenerate (zero extent on every axis).
     """
     if sequence.ndim != 3 or sequence.shape[-1] != 3:
-        raise ValueError(
+        raise ValueError(f"expected (frames, joints, 3); got shape {sequence.shape}")
-            f"expected (frames, joints, 3); got shape {sequence.shape}"
-        )
     result = sequence.astype(float, copy=True)
     mins = result.reshape(-1, 3).min(axis=0)
     maxs = result.reshape(-1, 3).max(axis=0)
@@ -187,9 +185,7 @@ def pad_sequences(
     """
     if not sequences:
         if target_length is None:
-            raise ValueError(
+            raise ValueError("cannot infer target_length from an empty sequence list")
-                "cannot infer target_length from an empty sequence list"
-            )
         return []
 
     first = sequences[0]
@@ -197,13 +193,10 @@ def pad_sequences(
     for idx, seq in enumerate(sequences):
         if seq.shape[1:] != trailing_shape:
             raise ValueError(
-                f"sequence {idx} has trailing shape {seq.shape[1:]}; "
+                f"sequence {idx} has trailing shape {seq.shape[1:]}; expected {trailing_shape}"
-                f"expected {trailing_shape}"
             )
 
-    length = target_length if target_length is not None else max(
+    length = target_length if target_length is not None else max(s.shape[0] for s in sequences)
-        s.shape[0] for s in sequences
-    )
 
     padded: list[np.ndarray] = []
     for seq in sequences:
@@ -255,17 +248,13 @@ def extract_joint_angles(
         If any joint index in ``triplets`` is out of range.
     """
     if sequence.ndim != 3 or sequence.shape[-1] != 3:
-        raise ValueError(
+        raise ValueError(f"expected (frames, joints, 3); got shape {sequence.shape}")
-            f"expected (frames, joints, 3); got shape {sequence.shape}"
-        )
     num_joints = sequence.shape[1]
     columns: list[np.ndarray] = []
     for a_idx, b_idx, c_idx in triplets:
         for idx in (a_idx, b_idx, c_idx):
             if not (0 <= idx < num_joints):
-                raise ValueError(
+                raise ValueError(f"joint index {idx} out of range [0, {num_joints})")
-                    f"joint index {idx} out of range [0, {num_joints})"
-                )
         v1 = sequence[:, a_idx, :] - sequence[:, b_idx, :]
         v2 = sequence[:, c_idx, :] - sequence[:, b_idx, :]
         n1 = np.linalg.norm(v1, axis=1)
@@ -343,7 +332,7 @@ def extract_feature_statistics(values: np.ndarray) -> FeatureStatistics:
 # ---------------------------------------------------------------------------
 
 
-def find_peaks(values: np.ndarray, **kwargs: object) -> np.ndarray:
+def find_peaks(values: np.ndarray, **kwargs: Any) -> np.ndarray:
     """Return indices of local maxima in a 1D series.
 
     Thin wrapper around :func:`scipy.signal.find_peaks` that returns

src/neuropose/config.py

@@ -84,8 +84,7 @@ class Settings(BaseSettings):
     def _validate_device(cls, value: str) -> str:
         if not _DEVICE_PATTERN.match(value):
             raise ValueError(
-                f"device must match '/(CPU|GPU):<index>' "
+                f"device must match '/(CPU|GPU):<index>' (e.g. '/CPU:0', '/GPU:0'); got {value!r}"
-                f"(e.g. '/CPU:0', '/GPU:0'); got {value!r}"
             )
         return value
 
@@ -140,9 +139,7 @@ class Settings(BaseSettings):
         if data is None:
             data = {}
         if not isinstance(data, dict):
-            raise ValueError(
+            raise ValueError(f"config file must contain a YAML mapping; got {type(data).__name__}")
-                f"config file must contain a YAML mapping; got {type(data).__name__}"
-            )
         return cls(**data)
 
     def ensure_dirs(self) -> None:

src/neuropose/interfacer.py

@@ -26,6 +26,7 @@ Key guarantees
 
 from __future__ import annotations
 
+import contextlib
 import fcntl
 import logging
 import os
@@ -381,15 +382,11 @@ class Interfacer:
     def _restore_signal_handlers(self) -> None:
         """Restore the signal handlers that were in place before :meth:`run`."""
         if self._prev_sigint is not None:
-            try:
+            with contextlib.suppress(ValueError, TypeError):
                 signal.signal(signal.SIGINT, self._prev_sigint)  # type: ignore[arg-type]
-            except (ValueError, TypeError):
-                pass
         if self._prev_sigterm is not None:
-            try:
+            with contextlib.suppress(ValueError, TypeError):
                 signal.signal(signal.SIGTERM, self._prev_sigterm)  # type: ignore[arg-type]
-            except (ValueError, TypeError):
-                pass
         self._prev_sigint = None
         self._prev_sigterm = None
 
@@ -421,14 +418,10 @@ class Interfacer:
         """Release the single-instance lock if held."""
         if self._lock_fd is None:
             return
-        try:
+        with contextlib.suppress(OSError):
             fcntl.flock(self._lock_fd, fcntl.LOCK_UN)
-        except OSError:
+        with contextlib.suppress(OSError):
-            pass
-        try:
             os.close(self._lock_fd)
-        except OSError:
-            pass
         self._lock_fd = None
 
 
@@ -439,11 +432,7 @@ class Interfacer:
 
 def _discover_videos(job_dir: Path) -> Iterable[Path]:
     """Yield paths to all supported video files in ``job_dir`` (non-recursive)."""
-    return (
+    return (p for p in job_dir.iterdir() if p.is_file() and p.suffix.lower() in VIDEO_EXTENSIONS)
-        p
-        for p in job_dir.iterdir()
-        if p.is_file() and p.suffix.lower() in VIDEO_EXTENSIONS
-    )
 
 
 def _is_empty_dir(path: Path) -> bool:

src/neuropose/io.py

@@ -17,14 +17,14 @@ from __future__ import annotations
 import json
 from collections.abc import Iterator
 from datetime import datetime
-from enum import Enum
+from enum import StrEnum
 from pathlib import Path
 from typing import Any
 
 from pydantic import BaseModel, ConfigDict, Field, RootModel
 
 
-class JobStatus(str, Enum):
+class JobStatus(StrEnum):
     """Lifecycle state of a single processing job."""
 
     PROCESSING = "processing"
@@ -94,12 +94,15 @@ class VideoPredictions(BaseModel):
         return list(self.frames.keys())
 
     def __len__(self) -> int:
+        """Return the number of frames."""
         return len(self.frames)
 
     def __iter__(self) -> Iterator[str]:  # type: ignore[override]
+        """Iterate over frame identifiers in insertion order."""
         return iter(self.frames)
 
     def __getitem__(self, key: str) -> FramePrediction:
+        """Return the :class:`FramePrediction` for ``key``."""
         return self.frames[key]
 
 
@@ -115,12 +118,15 @@ class JobResults(RootModel[dict[str, VideoPredictions]]):
         return list(self.root.keys())
 
     def __len__(self) -> int:
+        """Return the number of videos in the job."""
         return len(self.root)
 
     def __iter__(self) -> Iterator[str]:  # type: ignore[override]
+        """Iterate over video names in insertion order."""
         return iter(self.root)
 
     def __getitem__(self, key: str) -> VideoPredictions:
+        """Return the :class:`VideoPredictions` for ``key``."""
         return self.root[key]
 
 
@@ -150,9 +156,11 @@ class StatusFile(RootModel[dict[str, JobStatusEntry]]):
         return len(self.root) == 0
 
     def __len__(self) -> int:
+        """Return the number of job entries."""
         return len(self.root)
 
     def __iter__(self) -> Iterator[str]:  # type: ignore[override]
+        """Iterate over job names in insertion order."""
         return iter(self.root)
 
 

src/neuropose/visualize.py

@@ -145,7 +145,7 @@ def _render_frame(
     view: str,
     joint_edges: Sequence[tuple[int, int]] | None,
     plt: Any,
-    Rectangle: Any,
+    Rectangle: Any,  # noqa: N803  — matches matplotlib.patches.Rectangle class name
 ) -> None:
     """Render one frame's 2D overlay + 3D scatter to ``out_path``."""
     # Explicit copies. The previous prototype mutated the caller's data via

tests/conftest.py

@@ -3,7 +3,6 @@
 from __future__ import annotations
 
 import os
-from collections.abc import Iterator
 from pathlib import Path
 from typing import Any
 
@@ -11,7 +10,6 @@ import cv2
 import numpy as np
 import pytest
 
-
 # ---------------------------------------------------------------------------
 # Slow test opt-in
 # ---------------------------------------------------------------------------
@@ -57,7 +55,7 @@ def pytest_collection_modifyitems(
 def _isolate_environment(
     monkeypatch: pytest.MonkeyPatch,
     tmp_path_factory: pytest.TempPathFactory,
-) -> Iterator[None]:
+) -> None:
     """Isolate every test from the developer's real home directory.
 
     Points ``$HOME`` and ``$XDG_DATA_HOME`` at per-test temp directories so
@@ -73,7 +71,6 @@ def _isolate_environment(
     for key in list(os.environ):
         if key.startswith("NEUROPOSE_"):
             monkeypatch.delenv(key, raising=False)
-    yield
 
 
 @pytest.fixture
@@ -91,13 +88,13 @@ def xdg_home() -> Path:
 def synthetic_video(tmp_path: Path) -> Path:
     """Generate a tiny synthetic video at test time.
 
-    The fixture writes a 5-frame, 32×32 MJPG-encoded ``.avi`` file. MJPG is
+    The fixture writes a 5-frame, 32x32 MJPG-encoded ``.avi`` file. MJPG is
     chosen over ``mp4v`` because it ships with ``opencv-python-headless`` on
     every platform we target, whereas ``mp4v`` occasionally requires an
     ffmpeg binary that may not be present on minimal CI runners.
     """
     path = tmp_path / "synthetic.avi"
-    fourcc = cv2.VideoWriter_fourcc(*"MJPG")
+    fourcc = cv2.VideoWriter_fourcc(*"MJPG")  # type: ignore[attr-defined]
     writer = cv2.VideoWriter(str(path), fourcc, 30.0, (32, 32))
     assert writer.isOpened(), "cv2.VideoWriter failed to open; MJPG codec missing?"
     for i in range(5):
@@ -106,7 +103,8 @@ def synthetic_video(tmp_path: Path) -> Path:
         frame = np.full((32, 32, 3), i * 40, dtype=np.uint8)
         writer.write(frame)
     writer.release()
-    assert path.exists() and path.stat().st_size > 0, "Synthetic video is empty."
+    assert path.exists(), "Synthetic video was not written."
+    assert path.stat().st_size > 0, "Synthetic video is empty."
     return path
 
 

tests/integration/test_estimator_smoke.py

@@ -41,16 +41,16 @@ pytestmark = pytest.mark.slow
 
 @pytest.fixture
 def integration_video(tmp_path: Path) -> Path:
-    """Generate a 384×288 synthetic video sized for MeTRAbs input.
+    """Generate a 384x288 synthetic video sized for MeTRAbs input.
 
     The default ``synthetic_video`` fixture in ``tests/conftest.py``
-    produces 32×32 frames, which is too small for MeTRAbs's 384 px
+    produces 32x32 frames, which is too small for MeTRAbs's 384 px
     input and may cause the detector pipeline to short-circuit
     unpredictably. This fixture produces a modestly-sized video so
     the smoke test's plumbing assertions are meaningful.
     """
     path = tmp_path / "integration.avi"
-    fourcc = cv2.VideoWriter_fourcc(*"MJPG")
+    fourcc = cv2.VideoWriter_fourcc(*"MJPG")  # type: ignore[attr-defined]
     writer = cv2.VideoWriter(str(path), fourcc, 30.0, (384, 288))
     assert writer.isOpened(), "cv2.VideoWriter failed to open; MJPG codec missing?"
     for i in range(5):
@@ -60,7 +60,8 @@ def integration_video(tmp_path: Path) -> Path:
         frame = np.full((288, 384, 3), 100 + i * 10, dtype=np.uint8)
         writer.write(frame)
     writer.release()
-    assert path.exists() and path.stat().st_size > 0
+    assert path.exists()
+    assert path.stat().st_size > 0
     return path
 
 
@@ -95,9 +96,7 @@ class TestMetrabsLoader:
         assert hasattr(model_a, "detect_poses")
         assert hasattr(model_b, "detect_poses")
 
-    def test_berkeley_mhad_skeleton_is_present(
+    def test_berkeley_mhad_skeleton_is_present(self, shared_model_cache_dir: Path) -> None:
-        self, shared_model_cache_dir: Path
-    ) -> None:
         """The estimator pins skeleton='berkeley_mhad_43'; verify it exists."""
         model = load_metrabs_model(cache_dir=shared_model_cache_dir)
         joint_names = model.per_skeleton_joint_names["berkeley_mhad_43"]

tests/unit/test_analyzer_dtw.py

@@ -12,7 +12,6 @@ from neuropose.analyzer.dtw import (
     dtw_relation,
 )
 
-
 # ---------------------------------------------------------------------------
 # Fixtures
 # ---------------------------------------------------------------------------
@@ -31,18 +30,14 @@ def simple_sequence() -> np.ndarray:
 
 
 class TestDtwAll:
-    def test_identical_sequences_distance_zero(
+    def test_identical_sequences_distance_zero(self, simple_sequence: np.ndarray) -> None:
-        self, simple_sequence: np.ndarray
-    ) -> None:
         result = dtw_all(simple_sequence, simple_sequence)
         assert isinstance(result, DTWResult)
         assert result.distance == pytest.approx(0.0, abs=1e-9)
         # Identical sequences produce a diagonal warping path.
         assert all(i == j for i, j in result.path)
 
-    def test_shifted_sequences_distance_zero(
+    def test_shifted_sequences_distance_zero(self, simple_sequence: np.ndarray) -> None:
-        self, simple_sequence: np.ndarray
-    ) -> None:
         """DTW should absorb a pure time shift without penalty."""
         # Duplicate the first frame to create a one-frame shift.
         shifted = np.concatenate([simple_sequence[:1], simple_sequence], axis=0)
@@ -74,9 +69,7 @@ class TestDtwAll:
 
 
 class TestDtwPerJoint:
-    def test_returns_one_result_per_joint(
+    def test_returns_one_result_per_joint(self, simple_sequence: np.ndarray) -> None:
-        self, simple_sequence: np.ndarray
-    ) -> None:
         results = dtw_per_joint(simple_sequence, simple_sequence)
         assert len(results) == simple_sequence.shape[1]
         for result in results:
@@ -112,9 +105,7 @@ class TestDtwPerJoint:
 
 
 class TestDtwRelation:
-    def test_identical_sequences_distance_zero(
+    def test_identical_sequences_distance_zero(self, simple_sequence: np.ndarray) -> None:
-        self, simple_sequence: np.ndarray
-    ) -> None:
         result = dtw_relation(simple_sequence, simple_sequence, joint_i=0, joint_j=1)
         assert result.distance == pytest.approx(0.0, abs=1e-9)
 

tests/unit/test_analyzer_features.py

@@ -18,7 +18,6 @@ from neuropose.analyzer.features import (
 )
 from neuropose.io import VideoPredictions
 
-
 # ---------------------------------------------------------------------------
 # Fixtures
 # ---------------------------------------------------------------------------
@@ -30,10 +29,7 @@ def _make_predictions(num_frames: int, num_persons: int = 1) -> VideoPredictions
     for i in range(num_frames):
         frames[f"frame_{i:06d}"] = {
             "boxes": [[0.0, 0.0, 1.0, 1.0, 0.9]] * num_persons,
-            "poses3d": [
+            "poses3d": [[[float(i), float(i) * 2, float(i) * 3], [0.0, 0.0, 0.0]]] * num_persons,
-                [[float(i), float(i) * 2, float(i) * 3], [0.0, 0.0, 0.0]]
-            ]
-            * num_persons,
             "poses2d": [[[0.0, 0.0], [1.0, 1.0]]] * num_persons,
         }
     return VideoPredictions.model_validate(

tests/unit/test_cli.py

@@ -73,9 +73,7 @@ class TestTopLevelOptions:
             result = runner.invoke(app, [subcommand, "--help"])
             assert result.exit_code == EXIT_OK, f"{subcommand} --help failed"
 
-    def test_verbose_and_quiet_are_mutually_exclusive(
+    def test_verbose_and_quiet_are_mutually_exclusive(self, runner: CliRunner) -> None:
-        self, runner: CliRunner
-    ) -> None:
         result = runner.invoke(app, ["--verbose", "--quiet", "watch"])
         assert result.exit_code != EXIT_OK
 
@@ -94,9 +92,7 @@ class TestConfigOption:
         # We just verify it mentions the missing file's name.
         assert "nope.yaml" in result.output
 
-    def test_invalid_config_yaml_structure(
+    def test_invalid_config_yaml_structure(self, runner: CliRunner, tmp_path: Path) -> None:
-        self, runner: CliRunner, tmp_path: Path
-    ) -> None:
         path = tmp_path / "bad.yaml"
         path.write_text("- not a mapping\n- another item\n")
         result = runner.invoke(app, ["--config", str(path), "watch"])
@@ -111,9 +107,7 @@ class TestConfigOption:
         assert result.exit_code == EXIT_USAGE
         assert "invalid config" in result.output.lower()
 
-    def test_valid_config_reaches_subcommand(
+    def test_valid_config_reaches_subcommand(self, runner: CliRunner, tmp_path: Path) -> None:
-        self, runner: CliRunner, tmp_path: Path
-    ) -> None:
         # A valid config should flow through the callback and into the
         # subcommand. For ``watch``, the subcommand will then fail on the
         # model load (NotImplementedError from the commit-11 stub), which
@@ -158,9 +152,7 @@ class TestWatch:
 
 
 class TestProcess:
-    def test_missing_video_exits_usage(
+    def test_missing_video_exits_usage(self, runner: CliRunner, tmp_path: Path) -> None:
-        self, runner: CliRunner, tmp_path: Path
-    ) -> None:
         result = runner.invoke(app, ["process", str(tmp_path / "nope.mp4")])
         # click's path existence check fires before our callback, so the
         # exit is a usage error.

tests/unit/test_config.py

@@ -77,7 +77,7 @@ class TestValidation:
 
     def test_extra_fields_rejected(self, xdg_home: Path) -> None:
         with pytest.raises(ValidationError):
-            Settings(nonexistent_field=True)
+            Settings(nonexistent_field=True)  # type: ignore[call-arg]
 
 
 class TestYamlLoad:
@@ -85,9 +85,7 @@ class TestYamlLoad:
 
     def test_valid(self, tmp_path: Path, xdg_home: Path) -> None:
         config_path = tmp_path / "config.yaml"
-        config_path.write_text(
+        config_path.write_text(yaml.safe_dump({"device": "/GPU:0", "poll_interval_seconds": 30}))
-            yaml.safe_dump({"device": "/GPU:0", "poll_interval_seconds": 30})
-        )
         settings = Settings.from_yaml(config_path)
         assert settings.device == "/GPU:0"
         assert settings.poll_interval_seconds == 30

tests/unit/test_interfacer.py

@@ -30,7 +30,6 @@ from neuropose.io import (
     save_status,
 )
 
-
 # ---------------------------------------------------------------------------
 # Stubs and helpers
 # ---------------------------------------------------------------------------
@@ -133,9 +132,7 @@ class TestDiscoverNewJobs:
         interfacer = Interfacer(settings, Estimator(model=fake_metrabs_model))
         assert interfacer._discover_new_jobs(StatusFile(root={})) == []
 
-    def test_skips_empty_directories_silently(
+    def test_skips_empty_directories_silently(self, tmp_path: Path, fake_metrabs_model) -> None:
-        self, tmp_path: Path, fake_metrabs_model
-    ) -> None:
         settings = _make_settings(tmp_path)
         settings.ensure_dirs()
         (settings.input_dir / "empty_job").mkdir()
@@ -179,9 +176,7 @@ class TestDiscoverNewJobs:
         interfacer = Interfacer(settings, Estimator(model=fake_metrabs_model))
         assert interfacer._discover_new_jobs(status) == ["job_b"]
 
-    def test_dir_with_non_video_files_is_returned(
+    def test_dir_with_non_video_files_is_returned(self, tmp_path: Path, fake_metrabs_model) -> None:
-        self, tmp_path: Path, fake_metrabs_model
-    ) -> None:
         # Dirs that contain files but no *videos* are NOT silently skipped
         # — they should be returned so process_job marks them failed.
         settings = _make_settings(tmp_path)
@@ -286,7 +281,7 @@ class TestProcessJobFailure:
     ) -> None:
         settings = _make_settings(tmp_path)
         _prepare_job(settings, "job_a", videos=[synthetic_video])
-        interfacer = Interfacer(settings, _RaisingEstimator())
+        interfacer = Interfacer(settings, _RaisingEstimator())  # type: ignore[arg-type]
 
         entry = interfacer.process_job("job_a")
 
@@ -297,9 +292,7 @@ class TestProcessJobFailure:
         assert not (settings.input_dir / "job_a").exists()
         assert (settings.failed_dir / "job_a").exists()
 
-    def test_quarantine_collision_suffixes(
+    def test_quarantine_collision_suffixes(self, tmp_path: Path, fake_metrabs_model) -> None:
-        self, tmp_path: Path, fake_metrabs_model
-    ) -> None:
         settings = _make_settings(tmp_path)
         settings.ensure_dirs()
         # Pre-populate failed_dir with an existing entry for "job_a".
@@ -324,7 +317,7 @@ class TestProcessJobFailure:
         _prepare_job(settings, "job_a", videos=[synthetic_video])
         interfacer = Interfacer(
             settings,
-            _RaisingEstimator(exc=JobProcessingError("custom boom")),
+            _RaisingEstimator(exc=JobProcessingError("custom boom")),  # type: ignore[arg-type]
         )
 
         entry = interfacer.process_job("job_a")
@@ -369,9 +362,7 @@ class TestRecoverStuckJobs:
         assert not (settings.input_dir / "job_a").exists()
         assert (settings.failed_dir / "job_a").exists()
 
-    def test_does_not_touch_completed_entries(
+    def test_does_not_touch_completed_entries(self, tmp_path: Path, fake_metrabs_model) -> None:
-        self, tmp_path: Path, fake_metrabs_model
-    ) -> None:
         settings = _make_settings(tmp_path)
         settings.ensure_dirs()
         completed = datetime(2026, 4, 13, 10, 0, 0, tzinfo=UTC)
@@ -400,9 +391,7 @@ class TestRecoverStuckJobs:
         assert loaded.root["job_b"].status == JobStatus.FAILED
         assert loaded.root["job_b"].error == "old failure"
 
-    def test_no_status_file_is_noop(
+    def test_no_status_file_is_noop(self, tmp_path: Path, fake_metrabs_model) -> None:
-        self, tmp_path: Path, fake_metrabs_model
-    ) -> None:
         settings = _make_settings(tmp_path)
         interfacer = Interfacer(settings, Estimator(model=fake_metrabs_model))
         # Must not raise even though status_file does not exist.
@@ -476,9 +465,7 @@ class TestRunOnce:
 
 
 class TestLock:
-    def test_first_acquire_succeeds(
+    def test_first_acquire_succeeds(self, tmp_path: Path, fake_metrabs_model) -> None:
-        self, tmp_path: Path, fake_metrabs_model
-    ) -> None:
         settings = _make_settings(tmp_path)
         interfacer = Interfacer(settings, Estimator(model=fake_metrabs_model))
         try:
@@ -500,9 +487,7 @@ class TestLock:
         finally:
             first._release_lock()
 
-    def test_release_allows_subsequent_acquire(
+    def test_release_allows_subsequent_acquire(self, tmp_path: Path, fake_metrabs_model) -> None:
-        self, tmp_path: Path, fake_metrabs_model
-    ) -> None:
         settings = _make_settings(tmp_path)
         first = Interfacer(settings, Estimator(model=fake_metrabs_model))
         first._acquire_lock()
@@ -514,9 +499,7 @@ class TestLock:
         finally:
             second._release_lock()
 
-    def test_lock_file_contains_pid(
+    def test_lock_file_contains_pid(self, tmp_path: Path, fake_metrabs_model) -> None:
-        self, tmp_path: Path, fake_metrabs_model
-    ) -> None:
         settings = _make_settings(tmp_path)
         interfacer = Interfacer(settings, Estimator(model=fake_metrabs_model))
         try:
@@ -536,9 +519,7 @@ class TestLock:
 
 
 class TestInterruptibleSleep:
-    def test_zero_returns_immediately(
+    def test_zero_returns_immediately(self, tmp_path: Path, fake_metrabs_model) -> None:
-        self, tmp_path: Path, fake_metrabs_model
-    ) -> None:
         settings = _make_settings(tmp_path)
         interfacer = Interfacer(settings, Estimator(model=fake_metrabs_model))
         import time
@@ -548,9 +529,7 @@ class TestInterruptibleSleep:
         elapsed = time.monotonic() - start
         assert elapsed < 0.1
 
-    def test_stop_flag_wakes_sleep_early(
+    def test_stop_flag_wakes_sleep_early(self, tmp_path: Path, fake_metrabs_model) -> None:
-        self, tmp_path: Path, fake_metrabs_model
-    ) -> None:
         settings = _make_settings(tmp_path)
         interfacer = Interfacer(settings, Estimator(model=fake_metrabs_model))
         interfacer.stop()

tests/unit/test_io.py

@@ -24,7 +24,6 @@ from neuropose.io import (
     save_video_predictions,
 )
 
-
 # ---------------------------------------------------------------------------
 # Fixtures
 # ---------------------------------------------------------------------------
@@ -107,7 +106,7 @@ class TestVideoMetadata:
                 fps=30.0,
                 width=640,
                 height=480,
-                source_path="/leak/me",
+                source_path="/leak/me",  # type: ignore[call-arg]
             )
 
     def test_is_frozen(self) -> None:

tests/unit/test_visualize.py

@@ -36,9 +36,7 @@ class TestVisualizePredictions:
         predictions_for_synthetic: VideoPredictions,
     ) -> None:
         output_dir = tmp_path / "viz"
-        written = visualize_predictions(
+        written = visualize_predictions(synthetic_video, predictions_for_synthetic, output_dir)
-            synthetic_video, predictions_for_synthetic, output_dir
-        )
         assert len(written) == 5
         for path in written:
             assert path.exists()