[MegaLinter] Apply linters automatic fixes

Author: FlorianPfaff

pyrecest/distributions/cart_prod/state_space_subdivision_distribution.py

@@ -26,9 +26,9 @@ def __init__(self, gd, linear_distributions):
             One distribution per grid point representing the conditional
             distribution of the linear state given that grid point.
         """
-        assert gd.n_grid_points == len(linear_distributions), (
-            "Number of grid points in gd must match length of linear_distributions."
-        )
+        assert gd.n_grid_points == len(
+            linear_distributions
+        ), "Number of grid points in gd must match length of linear_distributions."
         self.gd = copy.deepcopy(gd)
         self.linear_distributions = list(copy.deepcopy(linear_distributions))
 

pyrecest/distributions/cart_prod/state_space_subdivision_gaussian_distribution.py

@@ -4,13 +4,17 @@
 # pylint: disable=no-name-in-module,no-member
 from pyrecest.backend import (
     allclose,
-    any as backend_any,
+)
+from pyrecest.backend import any as backend_any
+from pyrecest.backend import (
     argmax,
     array,
     asarray,
     concatenate,
     stack,
-    sum as backend_sum,
+)
+from pyrecest.backend import sum as backend_sum
+from pyrecest.backend import (
     zeros,
 )
 
@@ -39,9 +43,9 @@ def __init__(self, gd, gaussians):
         gaussians : list of GaussianDistribution
             One Gaussian per grid point of *gd*.
         """
-        assert all(isinstance(g, GaussianDistribution) for g in gaussians), (
-            "All elements of gaussians must be GaussianDistribution instances."
-        )
+        assert all(
+            isinstance(g, GaussianDistribution) for g in gaussians
+        ), "All elements of gaussians must be GaussianDistribution instances."
         super().__init__(gd, gaussians)
 
     # ------------------------------------------------------------------
@@ -132,9 +136,7 @@ def multiply(self, other):
         )
         self_grid = asarray(self.gd.get_grid())
         other_grid = asarray(other.gd.get_grid())
-        assert allclose(self_grid, other_grid), (
-            "Can only multiply for equal grids."
-        )
+        assert allclose(self_grid, other_grid), "Can only multiply for equal grids."
 
         n = len(self.linear_distributions)
         new_linear_distributions = []
@@ -148,7 +150,9 @@ def multiply(self, other):
             # N(mu_self_i, C_self_i + C_other_i) evaluated at mu_other_i.
             # This is equivalent to N(0, C_self_i + C_other_i) at 0.
             combined_cov = ld_self.C + ld_other.C
-            temp_g = GaussianDistribution(ld_other.mu, combined_cov, check_validity=False)
+            temp_g = GaussianDistribution(
+                ld_other.mu, combined_cov, check_validity=False
+            )
             pdf_values.append(temp_g.pdf(ld_self.mu))
 
             new_linear_distributions.append(ld_self.multiply(ld_other))
@@ -215,7 +219,7 @@ def mode(self):
 
         # Remove the maximum entry to check for multimodality
         remaining = concatenate(
-            [fun_vals_joint[:index], fun_vals_joint[index + 1:]]  # noqa: E203
+            [fun_vals_joint[:index], fun_vals_joint[index + 1 :]]  # noqa: E203
         )
         if len(remaining) > 0 and (
             backend_any((max_val - remaining) < 1e-15)

pyrecest/tests/distributions/test_state_space_subdivision_gaussian_distribution.py

@@ -33,7 +33,9 @@ def test_multiply_s1_x_r1_identical_precise(self):
         gd = HypertoroidalGridDistribution.from_distribution(
             CircularUniformDistribution(), (n,)
         )
-        gaussians = [GaussianDistribution(array([0.0]), array([[1.0]])) for _ in range(n)]
+        gaussians = [
+            GaussianDistribution(array([0.0]), array([[1.0]])) for _ in range(n)
+        ]
         rbd1 = StateSpaceSubdivisionGaussianDistribution(gd, gaussians)
 
         gaussians2 = [
@@ -95,9 +97,7 @@ def test_hybrid_mean(self):
         gd = HypertoroidalGridDistribution.from_distribution(
             VonMisesDistribution(mu_periodic, 1.0), (n,)
         )
-        gaussians = [
-            GaussianDistribution(mu_linear, 1000.0 * eye(3)) for _ in range(n)
-        ]
+        gaussians = [GaussianDistribution(mu_linear, 1000.0 * eye(3)) for _ in range(n)]
         rbd = StateSpaceSubdivisionGaussianDistribution(gd, gaussians)
         npt.assert_allclose(
             rbd.hybrid_mean(),
@@ -112,9 +112,7 @@ def test_linear_mean(self):
         gd = HypertoroidalGridDistribution.from_distribution(
             VonMisesDistribution(4.0, 1.0), (n,)
         )
-        gaussians = [
-            GaussianDistribution(mu_linear, 1000.0 * eye(3)) for _ in range(n)
-        ]
+        gaussians = [GaussianDistribution(mu_linear, 1000.0 * eye(3)) for _ in range(n)]
         rbd = StateSpaceSubdivisionGaussianDistribution(gd, gaussians)
         npt.assert_allclose(rbd.linear_mean(), mu_linear, rtol=5e-7)
 
@@ -125,9 +123,7 @@ def test_mode_warning_uniform(self):
         gd = HypertoroidalGridDistribution.from_distribution(
             CircularUniformDistribution(), (n,)
         )
-        gaussians = [
-            GaussianDistribution(mu_linear, 1000.0 * eye(3)) for _ in range(n)
-        ]
+        gaussians = [GaussianDistribution(mu_linear, 1000.0 * eye(3)) for _ in range(n)]
         rbd = StateSpaceSubdivisionGaussianDistribution(gd, gaussians)
         with self.assertWarns(UserWarning):
             rbd.mode()
@@ -140,14 +136,13 @@ def test_mode(self):
         gd = HypertoroidalGridDistribution.from_distribution(
             VonMisesDistribution(mu_periodic, 10.0), (n,)
         )
-        gaussians = [
-            GaussianDistribution(mu_linear, eye(3)) for _ in range(n)
-        ]
+        gaussians = [GaussianDistribution(mu_linear, eye(3)) for _ in range(n)]
         rbd = StateSpaceSubdivisionGaussianDistribution(gd, gaussians)
 
         # Should not warn
         with self.assertNoLogs(level="WARNING"):
             import warnings as _warnings
+
             with _warnings.catch_warnings():
                 _warnings.simplefilter("error", UserWarning)
                 m = rbd.mode()