[MegaLinter] Apply linters automatic fixes

src/pyrecest/backend_support/_pytorch_take_index_contract.py

@@ -3,7 +3,6 @@
 from __future__ import annotations
 
 import numpy as np
-
 from pyrecest.backend_support._pytorch_assignment_index_contract import (
     patch_pytorch_assignment_index_contract as _patch_pytorch_assignment_index_contract,
 )

src/pyrecest/filters/circular_fourier_filter.py

@@ -113,13 +113,10 @@ def predict_identity(self, d_sys):
             # convolution.  The inverse FFT returns the discrete circular sum;
             # multiplying by the grid spacing approximates the convolution
             # integral over [0, 2*pi).
-            predicted_values = (
-                fft.irfft(
-                    fft.rfft(density_values) * fft.rfft(d_sys),
-                    n=no_coefficients,
-                )
-                * (2.0 * pi / no_coefficients)
-            )
+            predicted_values = fft.irfft(
+                fft.rfft(density_values) * fft.rfft(d_sys),
+                n=no_coefficients,
+            ) * (2.0 * pi / no_coefficients)
             # Round-off in the FFT can produce tiny negative values although a
             # convolution of nonnegative densities is nonnegative.
             predicted_values = maximum(predicted_values, 0.0)

src/pyrecest/filters/global_nearest_neighbor.py

@@ -433,4 +433,4 @@ def update_linear(
                 )
 
         if self.log_posterior_estimates:
-            self.store_posterior_estimates()
+            self.store_posterior_estimates()

src/pyrecest/filters/kalman_filter.py

@@ -56,9 +56,7 @@ def _validate_kalman_state_covariance(covariance, dim):
         check_symmetric=True,
     )
     eigenvalues = linalg.eigvalsh(covariance)
-    if not bool(
-        backend_all(eigenvalues >= -_STATE_COVARIANCE_EIGENVALUE_ATOL)
-    ):
+    if not bool(backend_all(eigenvalues >= -_STATE_COVARIANCE_EIGENVALUE_ATOL)):
         raise ValueError("state.covariance must be positive semidefinite.")
     return covariance
 

src/pyrecest/filters/state_space_subdivision_filter.py

@@ -152,9 +152,7 @@ def _validated_input_argument(values):
                 )
             return values
 
-        system_matrices = _validated_matrix_argument(
-            system_matrices, "system_matrices"
-        )
+        system_matrices = _validated_matrix_argument(system_matrices, "system_matrices")
         covariance_matrices = _validated_matrix_argument(
             covariance_matrices, "covariance_matrices"
         )

src/pyrecest/filters/unscented_kalman_filter.py

@@ -3,8 +3,9 @@
 from typing import Callable
 
 import pyrecest.backend
-from pyrecest.backend import atleast_1d, zeros
+from pyrecest.backend import atleast_1d
 from pyrecest.backend import copy as backend_copy
+from pyrecest.backend import zeros
 from pyrecest.distributions import GaussianDistribution
 from pyrecest.models import AdditiveNoiseMeasurementModel, AdditiveNoiseTransitionModel
 from pyrecest.sampling.sigma_points import MerweScaledSigmaPoints

src/pyrecest/models/_validated_motion_models.py

@@ -111,9 +111,7 @@ def coordinated_turn_transition(
     )
 
 
-def coordinated_turn_model(
-    dt: float = 1.0, noise_covariance: Any | None = None
-) -> Any:
+def coordinated_turn_model(dt: float = 1.0, noise_covariance: Any | None = None) -> Any:
     """Return a coordinated-turn model with validated process-noise covariance."""
     dt = _motion_models._as_scalar_float(  # pylint: disable=protected-access
         dt,
@@ -180,9 +178,7 @@ def se2_unicycle_transition(
     )
 
 
-def se2_unicycle_model(
-    dt: float = 1.0, noise_covariance: Any | None = None
-) -> Any:
+def se2_unicycle_model(dt: float = 1.0, noise_covariance: Any | None = None) -> Any:
     """Return an SE(2) unicycle model with validated process noise."""
     dt = _motion_models._as_scalar_float(  # pylint: disable=protected-access
         dt,
@@ -193,9 +189,7 @@ def se2_unicycle_model(
     return _se2_unicycle_model_impl(dt=dt, noise_covariance=noise_covariance)
 
 
-def se3_pose_twist_model(
-    dt: float = 1.0, noise_covariance: Any | None = None
-) -> Any:
+def se3_pose_twist_model(dt: float = 1.0, noise_covariance: Any | None = None) -> Any:
     """Return an SE(3) pose/twist model with validated process noise."""
     dt = _motion_models._as_scalar_float(  # pylint: disable=protected-access
         dt,

tests/backend_support/test_pytorch_assignment_index_contract.py

@@ -5,7 +5,6 @@
 import importlib.util
 
 import pytest
-
 from tests.support.backend_runner import run_backend_code
 
 pytestmark = pytest.mark.backend_portable

tests/filters/test_abstract_grid_filter_state_ownership.py

@@ -1,5 +1,4 @@
 import numpy.testing as npt
-
 from pyrecest.backend import array
 from pyrecest.distributions.circle.circular_grid_distribution import (
     CircularGridDistribution,

tests/filters/test_fejer_filter_control_validation.py

@@ -1,6 +1,6 @@
 import numpy as np
-import pytest
 import pyrecest.backend
+import pytest
 from pyrecest.filters import FejerIdentityFilter
 
 

tests/filters/test_global_nearest_neighbor_gating.py

@@ -1,15 +1,12 @@
 import unittest
 
-from scipy.stats import chi2
-
 from pyrecest.filters import GlobalNearestNeighbor
+from scipy.stats import chi2
 
 
 class GlobalNearestNeighborGatingTest(unittest.TestCase):
     def test_default_gate_matches_distance_representation(self):
-        squared_tracker = GlobalNearestNeighbor(
-            association_param={"square_dist": True}
-        )
+        squared_tracker = GlobalNearestNeighbor(association_param={"square_dist": True})
         unsquared_tracker = GlobalNearestNeighbor(
             association_param={"square_dist": False}
         )

tests/filters/test_hyperhemispherical_grid_filter_state_ownership.py

@@ -3,7 +3,6 @@
 
 import numpy.testing as npt
 import pyrecest.backend
-
 from pyrecest.filters.hyperhemispherical_grid_filter import (
     HyperhemisphericalGridFilter,
 )

tests/filters/test_hyperspherical_ukf_dimension_validation.py

@@ -1,10 +1,8 @@
 import numpy as np
 import pyrecest.backend
 import pytest
-
 from pyrecest.filters.hyperspherical_ukf import HypersphericalUKF
 
-
 pytestmark = pytest.mark.skipif(
     pyrecest.backend.__backend_name__ == "jax",
     reason="HypersphericalUKF is unsupported on JAX.",

tests/filters/test_hyperspherical_ukf_state_ownership.py

@@ -1,12 +1,10 @@
 import numpy.testing as npt
 import pyrecest.backend
 import pytest
-
 from pyrecest.backend import array, eye, to_numpy
 from pyrecest.distributions import GaussianDistribution
 from pyrecest.filters.hyperspherical_ukf import HypersphericalUKF
 
-
 pytestmark = pytest.mark.skipif(
     pyrecest.backend.__backend_name__ == "jax",
     reason="HypersphericalUKF is unsupported on JAX.",

tests/filters/test_imm_shared_python_matrices.py

@@ -25,9 +25,7 @@ def predict_linear(self, system_matrix, sys_noise_cov, sys_input=None):
         covariance = (
             system_matrix @ self.filter_state.C @ system_matrix.T + sys_noise_cov
         )
-        self.filter_state = GaussianDistribution(
-            mean, covariance, check_validity=False
-        )
+        self.filter_state = GaussianDistribution(mean, covariance, check_validity=False)
 
 
 @unittest.skipIf(

tests/filters/test_kalman_filter_covariance_validation.py

@@ -1,5 +1,4 @@
 import pytest
-
 from pyrecest import backend
 from pyrecest.distributions import GaussianDistribution
 from pyrecest.filters import KalmanFilter

tests/filters/test_low_rank_hypertoroidal_fourier_filter.py

@@ -80,7 +80,9 @@ def test_filter_state_assignment_does_not_alias_low_rank_input(self):
         state.coefficients = LowRankHypertoroidalFourierDistribution.uniform(
             (5,), "identity"
         ).coefficients
-        npt.assert_allclose(low_rank_filter.filter_state.to_dense(), expected, atol=1e-12)
+        npt.assert_allclose(
+            low_rank_filter.filter_state.to_dense(), expected, atol=1e-12
+        )
 
     def test_predict_identity_matches_dense_1d(self):
         dense_filter = HypertoroidalFourierFilter((5,), "identity")

tests/filters/test_nearest_neighbor_prediction_validation.py

@@ -1,7 +1,6 @@
 import unittest
 
 import numpy as np
-
 import pyrecest.backend
 from pyrecest.distributions import GaussianDistribution
 from pyrecest.filters.global_nearest_neighbor import GlobalNearestNeighbor

tests/filters/test_piecewise_constant_filter_state_ownership.py

@@ -1,7 +1,6 @@
 import unittest
 
 import numpy.testing as npt
-
 from pyrecest.backend import array, copy
 from pyrecest.distributions.circle.piecewise_constant_distribution import (
     PiecewiseConstantDistribution,

tests/models/test_motion_model_noise_covariance_validation.py

@@ -7,11 +7,11 @@
 import numpy as np
 from pyrecest.models import (
     coordinated_turn_model,
+    motion_models,
     nearly_constant_speed_model,
     se2_unicycle_model,
     se3_pose_twist_model,
 )
-from pyrecest.models import motion_models
 
 
 class TestMotionModelNoiseCovarianceValidation(unittest.TestCase):