[MegaLinter] Apply linters automatic fixes

src/pyrecest/_backend/pytorch/random.py

@@ -190,7 +190,9 @@ def _integer_population_size(a):
     return None
 
 
-def _choice_indices(population_size, size, num_samples, replace, p, device, *, shuffle=True):
+def _choice_indices(
+    population_size, size, num_samples, replace, p, device, *, shuffle=True
+):
     if population_size <= 0:
         if num_samples == 0:
             return _torch.empty(size or (0,), dtype=_torch.long, device=device)

src/pyrecest/evaluation/generate_measurements.py

@@ -250,13 +250,16 @@ def generate_measurements(groundtruth, simulation_config):
                 measurements[t] = curr_dist.sample(n_meas)
             elif isinstance(meas_noise, GaussianDistribution):
                 noise_samples = meas_noise.sample(n_meas)
-                measurements[t] = tile(
-                    groundtruth[t],
-                    (
-                        n_meas,
-                        1,
-                    ),
-                ) + noise_samples
+                measurements[t] = (
+                    tile(
+                        groundtruth[t],
+                        (
+                            n_meas,
+                            1,
+                        ),
+                    )
+                    + noise_samples
+                )
 
     return measurements
 

src/pyrecest/evaluation/pareto.py

@@ -369,5 +369,7 @@ def _coerce_finite_threshold(value: Any, column: str) -> float:
             f"Constraint threshold for {column!r} must be a finite scalar."
         ) from exc
     if not np.isfinite(threshold):
-        raise ValueError(f"Constraint threshold for {column!r} must be a finite scalar.")
+        raise ValueError(
+            f"Constraint threshold for {column!r} must be a finite scalar."
+        )
     return threshold

src/pyrecest/filters/association_hypotheses.py

@@ -337,7 +337,9 @@ def filter(
                 ),
             )
             for hypothesis in sorted_group[: self.k]:
-                accepted_keys.add((_track_index(hypothesis), _measurement_index(hypothesis)))
+                accepted_keys.add(
+                    (_track_index(hypothesis), _measurement_index(hypothesis))
+                )
 
         result = []
         for hypothesis in hypotheses:

src/pyrecest/models/__init__.py

@@ -4,6 +4,7 @@
 and capability-oriented so filters can opt into the pieces they need.
 """
 
+from ._validated_motion_models import nearly_coordinated_turn_model
 from .adapters import (
     LinearMeasurementArguments,
     LinearTransitionArguments,
@@ -66,7 +67,6 @@
     white_noise_jerk_covariance,
     white_noise_snap_covariance,
 )
-from ._validated_motion_models import nearly_coordinated_turn_model
 from .sensor_models import (
     bearing_only_measurement,
     bearing_only_model,

src/pyrecest/models/_validated_motion_models.py

@@ -19,9 +19,11 @@ def nearly_coordinated_turn_model(
         dt,
         "dt",
     )
-    turn_rate_variance = _motion_models._as_nonnegative_float(  # pylint: disable=protected-access
-        turn_rate_variance,
-        "turn_rate_variance",
+    turn_rate_variance = (
+        _motion_models._as_nonnegative_float(  # pylint: disable=protected-access
+            turn_rate_variance,
+            "turn_rate_variance",
+        )
     )
     return _nearly_coordinated_turn_model_impl(
         dt=dt,

tests/backend/test_numpy_random_backend.py

@@ -29,9 +29,7 @@ def test_choice_without_replacement_shuffle_false_preserves_order():
     matrix = np.array([[10, 20, 30], [40, 50, 60]])
 
     random.seed(0)
-    samples = random.choice(
-        values, size=values.shape[0], replace=False, shuffle=False
-    )
+    samples = random.choice(values, size=values.shape[0], replace=False, shuffle=False)
     column_samples = random.choice(
         matrix,
         size=matrix.shape[1],

tests/backend/test_pytorch_random_backend.py

@@ -124,9 +124,7 @@ def test_choice_without_replacement_shuffle_false_preserves_order():
     matrix = torch.tensor([[10, 20, 30], [40, 50, 60]])
 
     random.seed(0)
-    samples = random.choice(
-        values, size=values.shape[0], replace=False, shuffle=False
-    )
+    samples = random.choice(values, size=values.shape[0], replace=False, shuffle=False)
     column_samples = random.choice(
         matrix,
         size=matrix.shape[1],

tests/models/test_validation.py

@@ -119,7 +119,9 @@ class DistributionWithCallableDimAndMean:
             mu = array([0.0, 1.0])
 
             def dim(self):
-                raise AssertionError("dim() must not be called when methods are disabled")
+                raise AssertionError(
+                    "dim() must not be called when methods are disabled"
+                )
 
         self.assertEqual(
             infer_state_dim_from_distribution(

tests/test_multisession_assignment_observation_costs_gap.py

@@ -2,7 +2,10 @@
 
 import unittest
 
-from pyrecest.backend import __backend_name__, array  # pylint: disable=no-name-in-module
+from pyrecest.backend import (  # pylint: disable=no-name-in-module
+    __backend_name__,
+    array,
+)
 from pyrecest.utils import solve_multisession_assignment_with_observation_costs
 
 

tests/test_track_completion.py

@@ -5,7 +5,6 @@
 import unittest
 
 import numpy as np
-
 from pyrecest.utils.track_completion import (
     CompletionCandidate,
     enumerate_fragment_completion_paths,
@@ -69,6 +68,7 @@ def test_rejects_negative_candidate_observations(self) -> None:
 
         for invalid_candidate in invalid_candidates:
             with self.subTest(invalid_candidate=invalid_candidate):
+
                 def provider(session: int, observation: int, target_session: int):
                     del session, observation, target_session
                     return [invalid_candidate]