[MegaLinter] Apply linters automatic fixes

src/pyrecest/_backend/numpy/linalg.py

@@ -20,17 +20,19 @@
     expm,
 )
 
+from .._shared_numpy.linalg import fractional_matrix_power as _fractional_matrix_power
 from .._shared_numpy.linalg import (
-    fractional_matrix_power as _fractional_matrix_power,
     is_single_matrix_pd,
-    logm as _logm,
+)
+from .._shared_numpy.linalg import logm as _logm
+from .._shared_numpy.linalg import (
     polar,
     qr,
     quadratic_assignment,
     solve,
     solve_sylvester,
-    sqrtm as _sqrtm,
 )
+from .._shared_numpy.linalg import sqrtm as _sqrtm
 
 
 def _empty_zero_by_zero_matrix_result(value):

src/pyrecest/_backend/pytorch/random.py

@@ -267,9 +267,7 @@ def _validate_randint_array_dtype_bounds(low, high, dtype):
     # representable by the output dtype, as in randint(255, 256, dtype=uint8).
     # For int64, input tensors cannot represent max + 1, so every accepted high
     # value is already within the valid endpoint range.
-    if dtype != _torch.int64 and bool(
-        _torch.any(high_int64 > dtype_info.max + 1)
-    ):
+    if dtype != _torch.int64 and bool(_torch.any(high_int64 > dtype_info.max + 1)):
         raise ValueError(f"high is out of bounds for {dtype_name}")
 
 

src/pyrecest/distributions/abstract_custom_distribution.py

@@ -10,7 +10,6 @@
 
 from .abstract_distribution_type import AbstractDistributionType
 
-
 _INVALID_INTEGRAL_TYPES = (
     bool,
     np.bool_,

src/pyrecest/distributions/cart_prod/hypercylindrical_state_space_subdivision_distribution.py

@@ -38,7 +38,9 @@
 from pyrecest.distributions.nonperiodic.custom_linear_distribution import (
     CustomLinearDistribution,
 )
-from pyrecest.distributions.nonperiodic.gaussian_distribution import GaussianDistribution
+from pyrecest.distributions.nonperiodic.gaussian_distribution import (
+    GaussianDistribution,
+)
 from pyrecest.distributions.nonperiodic.linear_mixture import LinearMixture
 
 

src/pyrecest/distributions/hypersphere_subset/von_mises_fisher_distribution.py

@@ -33,7 +33,6 @@
 
 from .abstract_hyperspherical_distribution import AbstractHypersphericalDistribution
 
-
 _INVALID_REAL_SCALAR_TYPES = (
     bool,
     np.bool_,

src/pyrecest/distributions/hypersphere_subset/watson_distribution.py

@@ -29,7 +29,6 @@
 from .abstract_hyperspherical_distribution import AbstractHypersphericalDistribution
 from .bingham_distribution import BinghamDistribution
 
-
 _INVALID_REAL_SCALAR_TYPES = (
     bool,
     np.bool_,

src/pyrecest/evaluation/check_and_fix_config.py

@@ -1,7 +1,6 @@
 from numbers import Integral, Real
 
 import numpy as np
-
 from pyrecest.distributions import AbstractManifoldSpecificDistribution
 
 
@@ -12,9 +11,9 @@ def _is_integer_count(value):
 
 
 def _validate_probability(value, name):
-    if isinstance(value, (bool, np.bool_, np.datetime64, np.timedelta64)) or not isinstance(
-        value, Real
-    ):
+    if isinstance(
+        value, (bool, np.bool_, np.datetime64, np.timedelta64)
+    ) or not isinstance(value, Real):
         raise TypeError(f"{name} must be a real scalar")
     value = float(value)
     if not np.isfinite(value) or not 0.0 <= value <= 1.0:

src/pyrecest/evaluation/get_distance_function.py

@@ -164,9 +164,7 @@ def distance_function(xest, xtrue):
     return distance_function
 
 
-def _target_matrix_candidates(
-    value, name: str
-) -> list[tuple[numpy.ndarray, int]]:
+def _target_matrix_candidates(value, name: str) -> list[tuple[numpy.ndarray, int]]:
     value = _as_real_numeric_array(value, name)
     if value.ndim not in (1, 2):
         raise ValueError(f"{name} must be a one- or two-dimensional target set")

src/pyrecest/filters/global_nearest_neighbor.py

@@ -22,7 +22,6 @@
 
 from .abstract_nearest_neighbor_tracker import AbstractNearestNeighborTracker
 
-
 _INVALID_PAIRWISE_COST_SCALAR_TYPES = (
     type(None),
     bool,

src/pyrecest/filters/mode_rbpf_manifold_ukf_tracker.py

@@ -876,9 +876,7 @@ def _normalize_probs(probs):
             )
         total = float(np.sum(probs))
         if total <= 0.0:
-            raise ValueError(
-                "initial_mode_probs must have positive total probability"
-            )
+            raise ValueError("initial_mode_probs must have positive total probability")
         return probs / total
 
     @staticmethod

src/pyrecest/filters/sequence_association.py

@@ -360,9 +360,8 @@ def _validate_integer(value: object, name: str) -> int:
         raise ValueError(message) from exc
     if value_array.ndim != 0 or value_array.dtype == np.bool_:
         raise ValueError(message)
-    if (
-        value_array.dtype.kind in {"S", "U", "c"}
-        or _is_temporal_scalar_array(value_array)
+    if value_array.dtype.kind in {"S", "U", "c"} or _is_temporal_scalar_array(
+        value_array
     ):
         raise ValueError(message)
 

src/pyrecest/models/linear_gaussian.py

@@ -2,8 +2,8 @@
 
 from numbers import Complex, Integral, Real
 
+from pyrecest.backend import all as backend_all
 from pyrecest.backend import (
-    all as backend_all,
     asarray,
 )
 from pyrecest.backend import copy as backend_copy

src/pyrecest/smoothers/abstract_smoother.py

@@ -161,9 +161,7 @@ def _normalize_vector_sequence(  # pylint: disable=too-many-return-statements
         expected_shape = (vector_dim,)
         shape_error = f"{name} must contain vectors with shape {expected_shape}."
 
-        if isinstance(values, (list, tuple)) and any(
-            value is None for value in values
-        ):
+        if isinstance(values, (list, tuple)) and any(value is None for value in values):
             values_arr = None
         else:
             try:

src/pyrecest/utils/_point_set_registration_common.py

@@ -25,7 +25,6 @@
 from scipy.optimize import linear_sum_assignment
 from scipy.spatial.distance import cdist
 
-
 _INVALID_REAL_SCALAR_TYPES = (
     type(None),
     bool,

src/pyrecest/utils/metrics.py

@@ -579,11 +579,7 @@ def _as_covariance_stack(
 
 def _as_positive_int(value: Any, name: str) -> int:
     array = np.asarray(value)
-    if (
-        array.ndim != 0
-        or array.dtype == np.bool_
-        or array.dtype.kind in {"M", "m"}
-    ):
+    if array.ndim != 0 or array.dtype == np.bool_ or array.dtype.kind in {"M", "m"}:
         raise ValueError(f"{name} must be a positive integer")
     scalar = array.item()
     if isinstance(scalar, (int, np.integer)) and not isinstance(scalar, bool):

src/pyrecest/utils/point_set_registration.py

@@ -214,8 +214,10 @@ def _validate_positive_integer(value, name: str, *, minimum: int = 1) -> int:
         except TypeError:
             pass
 
-    if isinstance(value, np.ndarray) and value.shape == () and isinstance(
-        value.item(), temporal_types
+    if (
+        isinstance(value, np.ndarray)
+        and value.shape == ()
+        and isinstance(value.item(), temporal_types)
     ):
         raise ValueError(error_message)
 

tests/backend/test_numpy_fractional_matrix_power_exponent_validation.py

@@ -2,7 +2,6 @@
 import pytest
 from pyrecest._backend.numpy import linalg
 
-
 _MATRIX = np.diag([4.0, 9.0])
 
 
@@ -16,9 +15,7 @@
         np.timedelta64(2, "ns"),
         np.datetime64("1970-01-01T00:00:00.000000002"),
         np.array(np.timedelta64(2, "ns"), dtype=object),
-        np.array(
-            np.datetime64("1970-01-01T00:00:00.000000002"), dtype=object
-        ),
+        np.array(np.datetime64("1970-01-01T00:00:00.000000002"), dtype=object),
         "0.5",
         0.5 + 0.0j,
     ],

tests/backend/test_numpy_linalg_zero_by_zero_matrix_functions.py

@@ -1,6 +1,5 @@
 import numpy as np
 import pytest
-
 from pyrecest._backend.numpy import linalg
 
 

tests/backend/test_pytorch_randint_dtype_bounds.py

@@ -1,7 +1,6 @@
 import numpy as np
 import pytest
 
-
 torch = pytest.importorskip("torch")
 
 from pyrecest._backend.pytorch import random  # noqa: E402
@@ -16,9 +15,7 @@
         ([0], [129], np.int8, "high is out of bounds for int8"),
     ],
 )
-def test_array_randint_rejects_bounds_outside_output_dtype(
-    low, high, dtype, message
-):
+def test_array_randint_rejects_bounds_outside_output_dtype(low, high, dtype, message):
     with pytest.raises(ValueError, match=message):
         random.randint(low, high, dtype=dtype)
 

tests/backend/test_pytorch_random_multivariate_normal_keywords.py

@@ -1,7 +1,6 @@
 import numpy as np
 import pytest
 
-
 torch = pytest.importorskip("torch")
 from pyrecest._backend.pytorch import random  # noqa: E402
 

tests/distributions/test_abstract_mixture_temporal_weights.py

@@ -1,7 +1,6 @@
 import unittest
 
 import numpy as np
-
 from pyrecest.distributions.abstract_mixture import _validate_mixture_weight_values
 
 

tests/distributions/test_complex_acg_sample_count_precision.py

@@ -2,7 +2,6 @@
 from fractions import Fraction
 
 import numpy as np
-
 from pyrecest.distributions.hypersphere_subset.complex_angular_central_gaussian_distribution import (
     _validate_positive_sample_count,
 )

tests/distributions/test_complex_watson_temporal_sample_count.py

@@ -1,11 +1,9 @@
 import numpy as np
-import pytest
-
 import pyrecest.backend
+import pytest
 from pyrecest.backend import array, complex128
 from pyrecest.distributions import ComplexWatsonDistribution
 
-
 pytestmark = pytest.mark.skipif(
     pyrecest.backend.__backend_name__ == "jax",
     reason="Complex Watson sampling is not supported on the JAX backend",

tests/distributions/test_ellipsoidal_ball_temporal_sample_count.py

@@ -1,6 +1,5 @@
 import numpy as np
 import pytest
-
 from pyrecest.backend import array, diag
 from pyrecest.distributions import EllipsoidalBallUniformDistribution
 

tests/distributions/test_gaussian_marginalize_out_validation.py

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

tests/distributions/test_gaussian_mixture_complex_mean.py

@@ -11,12 +11,8 @@
 
 class GaussianMixtureComplexMeanTest(unittest.TestCase):
     def test_set_mean_rejects_complex_target_without_mutating_components(self):
-        component_1 = GaussianDistribution(
-            array([0.0, 1.0]), diag(array([1.0, 2.0]))
-        )
-        component_2 = GaussianDistribution(
-            array([2.0, 3.0]), diag(array([3.0, 4.0]))
-        )
+        component_1 = GaussianDistribution(array([0.0, 1.0]), diag(array([1.0, 2.0])))
+        component_2 = GaussianDistribution(array([2.0, 3.0]), diag(array([3.0, 4.0])))
         mixture = GaussianMixture([component_1, component_2], array([0.25, 0.75]))
         original_means = [to_numpy(dist.mu).copy() for dist in mixture.dists]
 

tests/distributions/test_hypercylindrical_zero_marginals.py

@@ -2,7 +2,6 @@
 from math import pi
 
 import numpy as np
-
 from pyrecest.backend import __backend_name__ as backend_name
 from pyrecest.backend import array
 from pyrecest.distributions.cart_prod.hypercylindrical_state_space_subdivision_distribution import (

tests/distributions/test_hypertoroidal_mixed_boolean_input_validation.py

@@ -23,9 +23,7 @@ def test_rejects_mixed_boolean_evaluation_points(self):
 
     def test_numeric_python_sequences_remain_valid(self):
         npt.assert_allclose(as_shift_vector([0.0, 1.0], 2), [0.0, 1.0])
-        npt.assert_allclose(
-            as_hypertoroidal_points([[0.0, 1.0]], 2), [[0.0, 1.0]]
-        )
+        npt.assert_allclose(as_hypertoroidal_points([[0.0, 1.0]], 2), [[0.0, 1.0]])
 
 
 if __name__ == "__main__":

tests/distributions/test_hypertoroidal_python_scalar_integrand.py

@@ -1,7 +1,6 @@
 import numpy as np
 import numpy.testing as npt
 import pytest
-
 from pyrecest import backend
 from pyrecest.distributions import AbstractHypertoroidalDistribution
 

tests/distributions/test_linear_box_particle_temporal_count.py

@@ -1,6 +1,5 @@
 import numpy as np
 import pytest
-
 from pyrecest.backend import array
 from pyrecest.distributions.nonperiodic.linear_box_particle_distribution import (
     LinearBoxParticleDistribution,

tests/distributions/test_partially_wrapped_normal_hybrid_moment_order.py

@@ -2,7 +2,6 @@
 
 import numpy as np
 import numpy.testing as npt
-
 from pyrecest.backend import array
 from pyrecest.distributions.cart_prod.partially_wrapped_normal_distribution import (
     PartiallyWrappedNormalDistribution,

tests/distributions/test_piecewise_constant_interval_index_validation.py

@@ -1,6 +1,5 @@
 import numpy as np
 import pytest
-
 from pyrecest.distributions.circle.piecewise_constant_distribution import (
     PiecewiseConstantDistribution,
 )

tests/distributions/test_se2_dirac_temporal_particle_count.py

@@ -1,6 +1,5 @@
 import numpy as np
 import pytest
-
 from pyrecest.distributions import SE2DiracDistribution
 from pyrecest.distributions.cart_prod.abstract_hypercylindrical_distribution import (
     AbstractHypercylindricalDistribution,
@@ -18,10 +17,14 @@ def sample(self, n):
         raise AssertionError("invalid temporal counts must not reach sampling")
 
     def marginalize_linear(self):
-        raise AssertionError("marginalization must not be evaluated for count validation")
+        raise AssertionError(
+            "marginalization must not be evaluated for count validation"
+        )
 
     def marginalize_periodic(self):
-        raise AssertionError("marginalization must not be evaluated for count validation")
+        raise AssertionError(
+            "marginalization must not be evaluated for count validation"
+        )
 
 
 @pytest.mark.parametrize(

tests/distributions/test_so3_conversion_validation.py

@@ -16,7 +16,6 @@
     SO3TangentGaussianDistribution,
 )
 
-
 _TEMPORAL_VALUES = (
     np.timedelta64(3, "ns"),
     np.timedelta64(3, "us"),

tests/distributions/test_von_mises_temporal_sample_count.py

@@ -1,6 +1,5 @@
 import numpy as np
 import pytest
-
 from pyrecest.distributions import VonMisesDistribution
 
 

tests/distributions/test_wrapped_normal_temporal_sample_counts.py

@@ -1,6 +1,5 @@
 import numpy as np
 import pytest
-
 from pyrecest.distributions import WrappedNormalDistribution