[MegaLinter] Apply linters automatic fixes

src/pyrecest/_backend/_common.py

@@ -56,8 +56,7 @@ def _normalize_reduction_axes(axis, ndim_value):
         axes = tuple(axis)
 
     normalized_axes = tuple(
-        axis_index + ndim_value if axis_index < 0 else axis_index
-        for axis_index in axes
+        axis_index + ndim_value if axis_index < 0 else axis_index for axis_index in axes
     )
     if len(set(normalized_axes)) != len(normalized_axes):
         raise ValueError("duplicate value in 'axis'")

src/pyrecest/_backend/jax/random.py

@@ -271,11 +271,7 @@ def _validate_choice_probabilities(p, population_size):
         raise ValueError("p must be 1-dimensional with one entry per population item")
 
     p_sum = p.sum()
-    if (
-        bool(_jnp.any(p < 0))
-        or not bool(_jnp.isfinite(p_sum))
-        or bool(p_sum <= 0)
-    ):
+    if bool(_jnp.any(p < 0)) or not bool(_jnp.isfinite(p_sum)) or bool(p_sum <= 0):
         raise ValueError("probabilities do not sum to a positive value")
     return p / p_sum
 

src/pyrecest/_backend/pytorch/linalg.py

@@ -182,7 +182,9 @@ def solve_sylvester(a, b, q):
     a = a.to(dtype=common_dtype)
     b = b.to(dtype=common_dtype)
     q = q.to(dtype=common_dtype)
-    is_shared_factor = a.shape == b.shape and _torch.allclose(a, b, atol=1e-6, rtol=1e-6)
+    is_shared_factor = a.shape == b.shape and _torch.allclose(
+        a, b, atol=1e-6, rtol=1e-6
+    )
     is_shared_hermitian_factor = is_shared_factor and _torch.all(
         _torch.abs(a - a.transpose(-2, -1).conj()) < 1e-6
     )

src/pyrecest/evaluation/check_and_fix_config.py

@@ -20,9 +20,9 @@ def _expand_meas_per_step(simulation_param):
 
 def _validate_measurement_counts(simulation_param):
     counts = simulation_param["n_meas_at_individual_time_step"]
-    assert len(counts) == simulation_param["n_timesteps"], (
-        "n_meas_at_individual_time_step must have one entry per time step"
-    )
+    assert (
+        len(counts) == simulation_param["n_timesteps"]
+    ), "n_meas_at_individual_time_step must have one entry per time step"
     assert all(
         x > 0 for x in counts
     ), "n_meas_at_individual_time_step must contain positive values"

src/pyrecest/filters/tracklet_viterbi.py

@@ -527,11 +527,7 @@ def _fixed_lag_committed_step_cost(
             return float(
                 unary_cost
                 + config.missed_detection_cost
-                + (
-                    config.consecutive_miss_cost
-                    if committed_miss_streak > 0
-                    else 0.0
-                )
+                + (config.consecutive_miss_cost if committed_miss_streak > 0 else 0.0)
             )
         return float(unary_cost)
 
@@ -543,7 +539,9 @@ def _fixed_lag_committed_step_cost(
             config,
         )
     )
-    return float(unary_cost + transition(previous_committed, selected, committed_miss_streak))
+    return float(
+        unary_cost + transition(previous_committed, selected, committed_miss_streak)
+    )
 
 
 def _reconstruct_path(
@@ -591,12 +589,15 @@ def _motion_cost(
     else:
         predicted = (
             previous_position
-            + np.asarray(previous.velocity, dtype=float).reshape(previous_position.shape)
+            + np.asarray(previous.velocity, dtype=float).reshape(
+                previous_position.shape
+            )
             * dt_s
         )
     position_cost = float(
         np.sum(
-            ((current_position - predicted) / float(config.transition_position_std)) ** 2
+            ((current_position - predicted) / float(config.transition_position_std))
+            ** 2
         )
     )
     speed_cost = 0.0
@@ -614,7 +615,10 @@ def _motion_cost(
         )
         velocity_cost = float(
             np.sum(
-                ((velocity - displacement_velocity) / float(config.transition_velocity_std))
+                (
+                    (velocity - displacement_velocity)
+                    / float(config.transition_velocity_std)
+                )
                 ** 2
             )
         )

tests/backend_support/test_pytorch_fractional_matrix_power_contract.py

@@ -1,7 +1,6 @@
 import importlib.util
 
 import pytest
-
 from tests.support.backend_runner import run_backend_code
 
 

tests/backend_support/test_pytorch_random_contract.py

@@ -1,5 +1,4 @@
 import pytest
-
 from tests.support.backend_runner import run_backend_code
 
 

tests/distributions/test_toroidal_dirac_distribution.py

@@ -2,7 +2,6 @@
 
 import numpy as np
 import numpy.testing as npt
-
 import pyrecest.backend
 from pyrecest.backend import array
 from pyrecest.distributions import ToroidalDiracDistribution

tests/evaluation/test_check_and_fix_config.py

@@ -1,5 +1,4 @@
 import pytest
-
 from pyrecest.evaluation.check_and_fix_config import (
     _expand_meas_per_step,
     _validate_measurement_counts,

tests/test_backend_dot_contract.py

@@ -1,8 +1,7 @@
 import numpy as np
 import numpy.testing as npt
-import pytest
-
 import pyrecest.backend as backend
+import pytest
 from pyrecest.backend import array
 
 

tests/test_jax_choice_probability_validation.py

@@ -8,8 +8,7 @@
 @unittest.skipIf(importlib.util.find_spec("jax") is None, "JAX is not installed")
 class JaxChoiceProbabilityValidationTest(unittest.TestCase):
     def assert_choice_raises_value_error(self, call_source):
-        code = textwrap.dedent(
-            f"""
+        code = textwrap.dedent(f"""
             from pyrecest.backend import array, random
 
             try:
@@ -22,8 +21,7 @@ def assert_choice_raises_value_error(self, call_source):
                 ) from exc
             else:
                 raise AssertionError("expected ValueError")
-            """
-        )
+            """)
         result = run_backend_code("jax", code)
         self.assertEqual(result.returncode, 0, result.stdout + result.stderr)