[MegaLinter] Apply linters automatic fixes

Author: FlorianPfaff

pyrecest/distributions/__init__.py

@@ -74,20 +74,21 @@
     PartiallyWrappedNormalDistribution,
 )
 from .cart_prod.se2_bingham_distribution import SE2BinghamDistribution
+from .cart_prod.se2_pwn_distribution import SE2PWNDistribution
 from .cart_prod.state_space_subdivision_distribution import (
     StateSpaceSubdivisionDistribution,
 )
 from .cart_prod.state_space_subdivision_gaussian_distribution import (
     StateSpaceSubdivisionGaussianDistribution,
 )
-from .cart_prod.se2_pwn_distribution import SE2PWNDistribution
 from .circle.abstract_circular_distribution import AbstractCircularDistribution
 from .circle.circular_dirac_distribution import CircularDiracDistribution
 from .circle.circular_fourier_distribution import CircularFourierDistribution
 from .circle.circular_mixture import CircularMixture
 from .circle.circular_uniform_distribution import CircularUniformDistribution
 from .circle.custom_circular_distribution import CustomCircularDistribution
 from .circle.generalized_von_mises_distribution import GvMDistribution
+from .circle.piecewise_constant_distribution import PiecewiseConstantDistribution
 from .circle.sine_skewed_distributions import (
     AbstractSineSkewedDistribution,
     GeneralizedKSineSkewedVonMisesDistribution,
@@ -97,7 +98,6 @@
     SineSkewedWrappedCauchyDistribution,
     SineSkewedWrappedNormalDistribution,
 )
-from .circle.piecewise_constant_distribution import PiecewiseConstantDistribution
 from .circle.von_mises_distribution import VonMisesDistribution
 from .circle.wrapped_cauchy_distribution import WrappedCauchyDistribution
 from .circle.wrapped_laplace_distribution import WrappedLaplaceDistribution
@@ -111,18 +111,6 @@
 from .custom_hyperrectangular_distribution import CustomHyperrectangularDistribution
 from .disk_uniform_distribution import DiskUniformDistribution
 from .ellipsoidal_ball_uniform_distribution import EllipsoidalBallUniformDistribution
-from .nonperiodic.abstract_hyperrectangular_distribution import (
-    AbstractHyperrectangularDistribution,
-)
-from .nonperiodic.abstract_linear_distribution import AbstractLinearDistribution
-from .nonperiodic.custom_linear_distribution import CustomLinearDistribution
-from .nonperiodic.gaussian_distribution import GaussianDistribution
-from .nonperiodic.gaussian_mixture import GaussianMixture
-from .nonperiodic.hyperrectangular_uniform_distribution import (
-    HyperrectangularUniformDistribution,
-)
-from .nonperiodic.linear_dirac_distribution import LinearDiracDistribution
-from .nonperiodic.linear_mixture import LinearMixture
 from .hypersphere_subset.abstract_hemispherical_distribution import (
     AbstractHemisphericalDistribution,
 )
@@ -172,15 +160,12 @@
 from .hypersphere_subset.hyperhemispherical_dirac_distribution import (
     HyperhemisphericalDiracDistribution,
 )
-from .hypersphere_subset.hyperhemispherical_uniform_distribution import (
-    HyperhemisphericalUniformDistribution,
-)
-from .hypersphere_subset.hyperspherical_uniform_distribution import (
-    HypersphericalUniformDistribution,
-)
 from .hypersphere_subset.hyperhemispherical_grid_distribution import (
     HyperhemisphericalGridDistribution,
 )
+from .hypersphere_subset.hyperhemispherical_uniform_distribution import (
+    HyperhemisphericalUniformDistribution,
+)
 from .hypersphere_subset.hyperhemispherical_watson_distribution import (
     HyperhemisphericalWatsonDistribution,
 )
@@ -191,6 +176,9 @@
     HypersphericalGridDistribution,
 )
 from .hypersphere_subset.hyperspherical_mixture import HypersphericalMixture
+from .hypersphere_subset.hyperspherical_uniform_distribution import (
+    HypersphericalUniformDistribution,
+)
 from .hypersphere_subset.spherical_grid_distribution import SphericalGridDistribution
 from .hypersphere_subset.spherical_harmonics_distribution_complex import (
     SphericalHarmonicsDistributionComplex,
@@ -237,6 +225,18 @@
 from .hypertorus.toroidal_wrapped_normal_distribution import (
     ToroidalWrappedNormalDistribution,
 )
+from .nonperiodic.abstract_hyperrectangular_distribution import (
+    AbstractHyperrectangularDistribution,
+)
+from .nonperiodic.abstract_linear_distribution import AbstractLinearDistribution
+from .nonperiodic.custom_linear_distribution import CustomLinearDistribution
+from .nonperiodic.gaussian_distribution import GaussianDistribution
+from .nonperiodic.gaussian_mixture import GaussianMixture
+from .nonperiodic.hyperrectangular_uniform_distribution import (
+    HyperrectangularUniformDistribution,
+)
+from .nonperiodic.linear_dirac_distribution import LinearDiracDistribution
+from .nonperiodic.linear_mixture import LinearMixture
 from .se2_dirac_distribution import SE2DiracDistribution
 from .se3_cart_prod_stacked_distribution import SE3CartProdStackedDistribution
 from .se3_dirac_distribution import SE3DiracDistribution

pyrecest/distributions/circle/piecewise_constant_distribution.py

@@ -1,6 +1,18 @@
 # pylint: disable=no-name-in-module,no-member,redefined-builtin
 import pyrecest.backend
-from pyrecest.backend import mod, pi, array, arange, mean, floor, zeros, exp, sum, log, random
+from pyrecest.backend import (
+    arange,
+    array,
+    exp,
+    floor,
+    log,
+    mean,
+    mod,
+    pi,
+    random,
+    sum,
+    zeros,
+)
 
 from .abstract_circular_distribution import AbstractCircularDistribution
 
@@ -48,7 +60,10 @@ def pdf(self, xs):
         n_intervals = len(self.w)
         xs_mod = array(mod(xs, 2.0 * pi), dtype=float)
         idx = array(
-            [min(int(floor(x / (2.0 * pi) * n_intervals)), n_intervals - 1) for x in xs_mod]
+            [
+                min(int(floor(x / (2.0 * pi) * n_intervals)), n_intervals - 1)
+                for x in xs_mod
+            ]
         )
         return self.w[idx]
 
@@ -66,7 +81,9 @@ def trigonometric_moment(self, n):
             n-th trigonometric moment.
         """
         if pyrecest.backend.__backend_name__ == "jax":  # pylint: disable=no-member
-            raise NotImplementedError("trigonometric_moment is not supported on the JAX backend.")
+            raise NotImplementedError(
+                "trigonometric_moment is not supported on the JAX backend."
+            )
         if n == 0:
             return 1.0 + 0j
         num = len(self.w)
@@ -111,8 +128,13 @@ def sample(self, n):
         # construction. Divide by sum anyway to guard against floating-point drift.
         interval_probs = self.w * interval_width
         interval_probs /= interval_probs.sum()
-        interval_indices = random.choice(arange(num_intervals), size=(n,), p=interval_probs)
-        return interval_indices * interval_width + random.uniform(size=(n,)) * interval_width
+        interval_indices = random.choice(
+            arange(num_intervals), size=(n,), p=interval_probs
+        )
+        return (
+            interval_indices * interval_width
+            + random.uniform(size=(n,)) * interval_width
+        )
 
     @staticmethod
     def left_border(m, n):
@@ -190,14 +212,14 @@ def calculate_parameters_numerically(pdf_func, n):
         from scipy.integrate import quad  # pylint: disable=import-outside-toplevel
 
         if pyrecest.backend.__backend_name__ == "jax":  # pylint: disable=no-member
-            raise NotImplementedError("calculate_parameters_numerically is not supported on the JAX backend.")
+            raise NotImplementedError(
+                "calculate_parameters_numerically is not supported on the JAX backend."
+            )
 
         assert n >= 1
         w = zeros(n)
         for j in range(1, n + 1):
             left = PiecewiseConstantDistribution.left_border(j, n)
             r = PiecewiseConstantDistribution.right_border(j, n)
-            w[j - 1] = quad(
-                lambda x: float(pdf_func(array([x]))), left, r
-            )[0]
+            w[j - 1] = quad(lambda x: float(pdf_func(array([x]))), left, r)[0]
         return w

pyrecest/distributions/se2_dirac_distribution.py

@@ -7,9 +7,7 @@
 )
 
 
-class SE2DiracDistribution(
-    HypercylindricalDiracDistribution, AbstractSE2Distribution
-):
+class SE2DiracDistribution(HypercylindricalDiracDistribution, AbstractSE2Distribution):
     """Partially wrapped Dirac distribution on SE(2).
 
     Represents a distribution on SE(2) = S^1 x R^2 using weighted Dirac
@@ -56,7 +54,11 @@ def covariance_4d(self):
         -------
         array of shape (4, 4)
         """
-        from pyrecest.backend import column_stack, cos, sin  # pylint: disable=import-outside-toplevel
+        from pyrecest.backend import (  # pylint: disable=import-outside-toplevel
+            column_stack,
+            cos,
+            sin,
+        )
 
         S = column_stack(
             (cos(self.d[:, 0:1]), sin(self.d[:, 0:1]), self.d[:, 1:])

pyrecest/filters/__init__.py

@@ -6,13 +6,13 @@
 from .abstract_multitarget_tracker import AbstractMultitargetTracker
 from .abstract_nearest_neighbor_tracker import AbstractNearestNeighborTracker
 from .abstract_particle_filter import AbstractParticleFilter
-from .circular_ukf import CircularUKF
 from .abstract_tracker_with_logging import AbstractTrackerWithLogging
 from .circular_particle_filter import CircularParticleFilter
+from .circular_ukf import CircularUKF
 from .euclidean_particle_filter import EuclideanParticleFilter
-from .hypercylindrical_particle_filter import HypercylindricalParticleFilter
 from .global_nearest_neighbor import GlobalNearestNeighbor
 from .gprhm_tracker import GPRHMTracker
+from .hypercylindrical_particle_filter import HypercylindricalParticleFilter
 from .hyperhemisphere_cart_prod_particle_filter import (
     HyperhemisphereCartProdParticleFilter,
 )

pyrecest/filters/circular_ukf.py

@@ -71,9 +71,7 @@ def __init__(self, alpha: float = 1e-3, beta: float = 2.0, kappa: float = 0.0):
         self._alpha = alpha
         self._beta = beta
         self._kappa = kappa
-        initial_state = GaussianDistribution(
-            array([0.0]), array([[1.0]])
-        )
+        initial_state = GaussianDistribution(array([0.0]), array([[1.0]]))
         CircularFilterMixin.__init__(self)
         AbstractFilter.__init__(self, initial_state)
 
@@ -147,8 +145,16 @@ def hx(x):
             return x
 
         ukf = _make_ukf(
-            fx, hx, dim_z=1, x0=mu0, P0=C0, Q=Q_val, R=array([[C0]]),
-            alpha=self._alpha, beta=self._beta, kappa=self._kappa,
+            fx,
+            hx,
+            dim_z=1,
+            x0=mu0,
+            P0=C0,
+            Q=Q_val,
+            R=array([[C0]]),
+            alpha=self._alpha,
+            beta=self._beta,
+            kappa=self._kappa,
         )
         ukf.predict()
 
@@ -194,9 +200,7 @@ def update_identity(self, gauss_meas: GaussianDistribution, z):
         new_C = (1.0 - K) * C
 
         new_mu = float(mod(array([new_mu]), 2.0 * pi)[0])
-        self._filter_state = GaussianDistribution(
-            array([new_mu]), array([[new_C]])
-        )
+        self._filter_state = GaussianDistribution(array([new_mu]), array([[new_C]]))
 
     def update_nonlinear(  # pylint: disable=too-many-locals
         self, f, gauss_meas: GaussianDistribution, z, measurement_periodic: bool = False
@@ -251,8 +255,16 @@ def hx(x):
             return atleast_1d(array([f(x.flatten()[0])], dtype=float))
 
         ukf = _make_ukf(
-            fx, hx, dim_z=dim_z, x0=mu0, P0=C0, Q=0.0, R=R_mat,
-            alpha=self._alpha, beta=self._beta, kappa=self._kappa,
+            fx,
+            hx,
+            dim_z=dim_z,
+            x0=mu0,
+            P0=C0,
+            Q=0.0,
+            R=R_mat,
+            alpha=self._alpha,
+            beta=self._beta,
+            kappa=self._kappa,
         )
         # predict() with identity fx and Q=0 populates sigmas_f without
         # altering the mean or covariance, which is required before update().

pyrecest/filters/hypercylindrical_particle_filter.py

@@ -11,7 +11,9 @@
 from .manifold_mixins import HypercylindricalFilterMixin
 
 
-class HypercylindricalParticleFilter(AbstractParticleFilter, HypercylindricalFilterMixin):
+class HypercylindricalParticleFilter(
+    AbstractParticleFilter, HypercylindricalFilterMixin
+):
     def __init__(
         self,
         n_particles: Union[int, int32, int64],

pyrecest/tests/distributions/test_piecewise_constant_distribution.py

@@ -4,7 +4,7 @@
 import pyrecest.backend
 
 # pylint: disable=no-name-in-module,no-member,redefined-builtin
-from pyrecest.backend import linspace, sum, exp, log, mean, pi, array
+from pyrecest.backend import array, exp, linspace, log, mean, pi, sum
 from pyrecest.distributions.circle.piecewise_constant_distribution import (
     PiecewiseConstantDistribution,
 )
@@ -33,9 +33,7 @@ def test_pdf(self):
     def test_integral_normalized(self):
         """Verify the distribution integrates to 1 via the exact sum."""
         n = len(self.dist.w)
-        npt.assert_allclose(
-            sum(self.dist.w) * (2.0 * pi / n), 1.0, rtol=5e-7
-        )
+        npt.assert_allclose(sum(self.dist.w) * (2.0 * pi / n), 1.0, rtol=5e-7)
 
     def test_integral_partial(self):
         """Verify partial integrals sum to 1 using a fine grid."""

pyrecest/tests/distributions/test_se2_dirac_distribution.py

@@ -112,7 +112,9 @@ def test_sampling(self):
         s = self.dist.sample(n)
         self.assertEqual(s.shape, (n, 3))
         # Angles should be in [0, 2*pi)
-        from pyrecest.backend import all as backend_all  # pylint: disable=import-outside-toplevel
+        from pyrecest.backend import (
+            all as backend_all,  # pylint: disable=import-outside-toplevel
+        )
 
         self.assertTrue(backend_all(s[:, 0] >= 0))
         self.assertTrue(backend_all(s[:, 0] < 2 * pi))

pyrecest/tests/filters/test_hypercylindrical_particle_filter.py

@@ -29,9 +29,7 @@ def setUp(self):
     def test_initialization(self):
         hpf = HypercylindricalParticleFilter(10, self.bound_dim, self.lin_dim)
         self.assertIsNotNone(hpf.filter_state)
-        self.assertEqual(
-            hpf.filter_state.d.shape, (10, self.bound_dim + self.lin_dim)
-        )
+        self.assertEqual(hpf.filter_state.d.shape, (10, self.bound_dim + self.lin_dim))
 
     @unittest.skipIf(
         pyrecest.backend.__backend_name__ == "jax", reason="Backend not supported"