Upgrade to version 22.3.0 of the Black formatter

fzi-forschungszentrum-informatik · Mar 15, 2023 · 31800c3 · 31800c3
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diff --git a/p3iv_modules/src/p3iv_modules/perception/deprecated/geometry_utils.py b/p3iv_modules/src/p3iv_modules/perception/deprecated/geometry_utils.py
@@ -130,7 +130,7 @@ def seg_arc_intersect(seg, origin, radius, arc):
 
     D = D / t_max
 
-    coef = np.array([1, 2 * np.dot(D, (A - O)), norm(A - O) ** 2 - r ** 2])
+    coef = np.array([1, 2 * np.dot(D, (A - O)), norm(A - O) ** 2 - r**2])
     coef = np.round(coef, decimals=8)
 
     sol = np.roots(coef)

diff --git a/p3iv_modules/src/p3iv_modules/perception/limited.py b/p3iv_modules/src/p3iv_modules/perception/limited.py
@@ -117,29 +117,29 @@ def __init__(
         # position covariance matrix for percepted objects
         self.per_pos_cov = np.asarray(
             [
-                [per_pos_sigma_x ** 2, per_pos_cross_corr * per_pos_sigma_x * per_pos_sigma_y],
-                [per_pos_cross_corr * per_pos_sigma_x * per_pos_sigma_y, per_pos_sigma_y ** 2],
+                [per_pos_sigma_x**2, per_pos_cross_corr * per_pos_sigma_x * per_pos_sigma_y],
+                [per_pos_cross_corr * per_pos_sigma_x * per_pos_sigma_y, per_pos_sigma_y**2],
             ]
         )
         # velocity covariance matrix for percepted objects
         self.per_vel_cov = np.asarray(
             [
-                [per_vel_sigma_x ** 2, per_vel_cross_corr * per_vel_sigma_x * per_vel_sigma_y],
-                [per_vel_cross_corr * per_vel_sigma_x * per_vel_sigma_y, per_vel_sigma_y ** 2],
+                [per_vel_sigma_x**2, per_vel_cross_corr * per_vel_sigma_x * per_vel_sigma_y],
+                [per_vel_cross_corr * per_vel_sigma_x * per_vel_sigma_y, per_vel_sigma_y**2],
             ]
         )
         # position covariance matrix for localization - ego vehicle
         self.loc_pos_cov = np.asarray(
             [
-                [loc_pos_sigma_x ** 2, loc_pos_cross_corr * loc_pos_sigma_x * loc_pos_sigma_y],
-                [loc_pos_cross_corr * loc_pos_sigma_x * loc_pos_sigma_y, loc_pos_sigma_y ** 2],
+                [loc_pos_sigma_x**2, loc_pos_cross_corr * loc_pos_sigma_x * loc_pos_sigma_y],
+                [loc_pos_cross_corr * loc_pos_sigma_x * loc_pos_sigma_y, loc_pos_sigma_y**2],
             ]
         )
         # velocity covariance matrix for localization - ego vehicle
         self.loc_vel_cov = np.asarray(
             [
-                [loc_vel_sigma_x ** 2, loc_vel_cross_corr * loc_vel_sigma_x * loc_vel_sigma_y],
-                [loc_vel_cross_corr * loc_vel_sigma_x * loc_vel_sigma_y, loc_vel_sigma_y ** 2],
+                [loc_vel_sigma_x**2, loc_vel_cross_corr * loc_vel_sigma_x * loc_vel_sigma_y],
+                [loc_vel_cross_corr * loc_vel_sigma_x * loc_vel_sigma_y, loc_vel_sigma_y**2],
             ]
         )
 

diff --git a/p3iv_modules/src/p3iv_modules/perception/perfect.py b/p3iv_modules/src/p3iv_modules/perception/perfect.py
@@ -31,29 +31,29 @@ def __init__(
         # position covariance matrix for percepted objects
         self.per_pos_cov = np.asarray(
             [
-                [per_pos_sigma_x ** 2, per_pos_cross_corr * per_pos_sigma_x * per_pos_sigma_y],
-                [per_pos_cross_corr * per_pos_sigma_x * per_pos_sigma_y, per_pos_sigma_y ** 2],
+                [per_pos_sigma_x**2, per_pos_cross_corr * per_pos_sigma_x * per_pos_sigma_y],
+                [per_pos_cross_corr * per_pos_sigma_x * per_pos_sigma_y, per_pos_sigma_y**2],
             ]
         )
         # velocity covariance matrix for percepted objects
         self.per_vel_cov = np.asarray(
             [
-                [per_vel_sigma_x ** 2, per_vel_cross_corr * per_vel_sigma_x * per_vel_sigma_y],
-                [per_vel_cross_corr * per_vel_sigma_x * per_vel_sigma_y, per_vel_sigma_y ** 2],
+                [per_vel_sigma_x**2, per_vel_cross_corr * per_vel_sigma_x * per_vel_sigma_y],
+                [per_vel_cross_corr * per_vel_sigma_x * per_vel_sigma_y, per_vel_sigma_y**2],
             ]
         )
         # position covariance matrix for localization - ego vehicle
         self.loc_pos_cov = np.asarray(
             [
-                [loc_pos_sigma_x ** 2, loc_pos_cross_corr * loc_pos_sigma_x * loc_pos_sigma_y],
-                [loc_pos_cross_corr * loc_pos_sigma_x * loc_pos_sigma_y, loc_pos_sigma_y ** 2],
+                [loc_pos_sigma_x**2, loc_pos_cross_corr * loc_pos_sigma_x * loc_pos_sigma_y],
+                [loc_pos_cross_corr * loc_pos_sigma_x * loc_pos_sigma_y, loc_pos_sigma_y**2],
             ]
         )
         # velocity covariance matrix for localization - ego vehicle
         self.loc_vel_cov = np.asarray(
             [
-                [loc_vel_sigma_x ** 2, loc_vel_cross_corr * loc_vel_sigma_x * loc_vel_sigma_y],
-                [loc_vel_cross_corr * loc_vel_sigma_x * loc_vel_sigma_y, loc_vel_sigma_y ** 2],
+                [loc_vel_sigma_x**2, loc_vel_cross_corr * loc_vel_sigma_x * loc_vel_sigma_y],
+                [loc_vel_cross_corr * loc_vel_sigma_x * loc_vel_sigma_y, loc_vel_sigma_y**2],
             ]
         )
 

diff --git a/p3iv_utils/src/p3iv_utils/driver_models.py b/p3iv_utils/src/p3iv_utils/driver_models.py
@@ -78,7 +78,7 @@ def accs(self, l_ego, v_ego, l_front_array, v_front_array, N, dt):
                 if idm_acc < v_ego / dt:
                     idm_acc = v_ego / dt
 
-            displacement = v_ego * dt + 0.5 * idm_acc * (dt ** 2)
+            displacement = v_ego * dt + 0.5 * idm_acc * (dt**2)
             l_ego = l_ego + displacement
 
             idm_pos_array[i] = l_ego

diff --git a/p3iv_utils/src/p3iv_utils/extrema_functions.py b/p3iv_utils/src/p3iv_utils/extrema_functions.py
@@ -16,12 +16,12 @@ def get_braking_dist(v_curr, acceleration, **kwargs):
     dist = v_curr * dt - 0.5 * acceleration * dt**2 - 1/6 * jerk * dt**3
     """
 
-    return np.abs(v_curr ** 2 / (2 * acceleration))
+    return np.abs(v_curr**2 / (2 * acceleration))
 
 
 def accelerating_motion(v_curr, acceleration, **kwargs):
     delta_time = kwargs["delta_time"]
-    return v_curr * delta_time + 0.5 * acceleration * delta_time ** 2
+    return v_curr * delta_time + 0.5 * acceleration * delta_time**2
 
 
 def get_max_acc_displacements(vel_array, motion_func, acc, dt):
@@ -55,12 +55,12 @@ def saturation(param, threshold):
 
 
 def get_delta_l(v, a, dt):
-    delta_l = v * dt + 0.5 * a * dt ** 2
+    delta_l = v * dt + 0.5 * a * dt**2
     if delta_l > 0:
         return delta_l
     else:
         t_stop = np.abs(v / a)
-        return v * t_stop + 0.5 * a * t_stop ** 2
+        return v * t_stop + 0.5 * a * t_stop**2
 
 
 def get_delta_v(v, a, dt):

diff --git a/p3iv_utils/src/p3iv_utils/integrate_acceleration.py b/p3iv_utils/src/p3iv_utils/integrate_acceleration.py
@@ -35,7 +35,7 @@ def generate_position_velocity_acc_array(
     # set initial values
     x[:, 0] = np.array([s_0, v_0, 0])
 
-    half_dt_sim_square = 0.5 * dt_sim ** 2
+    half_dt_sim_square = 0.5 * dt_sim**2
 
     for i in range(n_sim_timesteps):
         x[0, i + 1] = x[0, i] + dt_sim * x[1, i] + half_dt_sim_square * acc[i]

diff --git a/p3iv_utils_polyline/src/p3iv_utils_polyline/interpolated_polyline_segment.py b/p3iv_utils_polyline/src/p3iv_utils_polyline/interpolated_polyline_segment.py
@@ -7,7 +7,7 @@
 
 def hypot(a, b):
     # do not use np.hypot(a, b); may not work well with autodiff & jit
-    return (a ** 2 + b ** 2) ** 0.5
+    return (a**2 + b**2) ** 0.5
 
 
 class InterpolatedPolylineSegment(object):

diff --git a/p3iv_utils_polyline/test/test_line.py b/p3iv_utils_polyline/test/test_line.py
@@ -23,10 +23,10 @@ def test_straight_line_w_python_impl(self):
         self.assertAlmostEqual(obs_ip.signed_distance(0, 2), 1)
 
         # test the distance at the LAST-segment
-        self.assertAlmostEqual(obs_ip.signed_distance(4, 3), 2 * 2 ** 0.5)
+        self.assertAlmostEqual(obs_ip.signed_distance(4, 3), 2 * 2**0.5)
 
         # test the distance at the FIRST-segment
-        self.assertAlmostEqual(obs_ip.signed_distance(-4, -1), -2 * 2 ** 0.5)
+        self.assertAlmostEqual(obs_ip.signed_distance(-4, -1), -2 * 2**0.5)
 
     def test_straight_line_w_pybind_impl(self):
         from p3iv_utils_polyline.py_interpolated_polyline import PyInterpolatedPolyline as InterpolatedPolyline
@@ -44,10 +44,10 @@ def test_straight_line_w_pybind_impl(self):
         self.assertAlmostEqual(obs_ip.signed_distance(0, 2), 1)
 
         # test the distance at the LAST-segment
-        self.assertAlmostEqual(obs_ip.signed_distance(4, 3), 2 * 2 ** 0.5)
+        self.assertAlmostEqual(obs_ip.signed_distance(4, 3), 2 * 2**0.5)
 
         # test the distance at the FIRST-segment
-        self.assertAlmostEqual(obs_ip.signed_distance(-4, -1), -2 * 2 ** 0.5)
+        self.assertAlmostEqual(obs_ip.signed_distance(-4, -1), -2 * 2**0.5)
 
 
 class InterpolatedPolylineRoundaboutOFCenterlineTest(unittest.TestCase):

diff --git a/p3iv_utils_polyvision/src/p3iv_utils_polyvision/visualisation.py b/p3iv_utils_polyvision/src/p3iv_utils_polyvision/visualisation.py
@@ -140,7 +140,7 @@ def generateFoVWedge(
     radius = halfwidth + radiusFactor * (baserange - halfwidth)
     # calculate center point of circle sector
     # c_y = leftP_y - sqrt(radius²-leftP_x²)
-    center_y = leftP[1] - math.sqrt(radius ** 2 - leftP[0] ** 2)
+    center_y = leftP[1] - math.sqrt(radius**2 - leftP[0] ** 2)
     center = np.array([0, center_y])
     # calculate angle of circle sector
     circleSectorAngle = 2 * math.asin(halfwidth / radius)  # in radians

diff --git a/p3iv_utils_probability/res/approximation_test/univariate_normal_approximation.py b/p3iv_utils_probability/res/approximation_test/univariate_normal_approximation.py
@@ -27,18 +27,18 @@ def uvn_product(uvn1, uvn2):
 
 
 def _cal_par(mu1, sigma1, mu2, sigma2):
-    mu_12 = (mu1 * sigma2 ** 2 + mu2 * sigma1 ** 2) / (sigma1 ** 2 + sigma2 ** 2)
-    sigma_12 = np.sqrt((sigma1 ** 2 * sigma2 ** 2) / (sigma1 ** 2 + sigma2 ** 2))
+    mu_12 = (mu1 * sigma2**2 + mu2 * sigma1**2) / (sigma1**2 + sigma2**2)
+    sigma_12 = np.sqrt((sigma1**2 * sigma2**2) / (sigma1**2 + sigma2**2))
     s = (
         1
-        / (np.sqrt(2 * np.pi * (sigma1 ** 2 + sigma2 ** 2)))
-        * np.exp(-((mu1 - mu2) ** 2 / (2 * (sigma1 ** 2 + sigma2 ** 2))))
+        / (np.sqrt(2 * np.pi * (sigma1**2 + sigma2**2)))
+        * np.exp(-((mu1 - mu2) ** 2 / (2 * (sigma1**2 + sigma2**2))))
     )
     return s, mu_12, sigma_12
 
 
 def cal_uncertainty_of_mul(mu1, cov1, mu2, cov2):
-    return mu2 ** 2 * cov1 + mu1 ** 2 * cov2
+    return mu2**2 * cov1 + mu1**2 * cov2
 
 
 def get_diff_integral_list(uvn1, uvn2, n_std=4):

diff --git a/p3iv_utils_probability/src/p3iv_utils_probability/distributions/bivariate_distribution.py b/p3iv_utils_probability/src/p3iv_utils_probability/distributions/bivariate_distribution.py
@@ -83,11 +83,11 @@ def _pdf_jointly_normal(self, x, y):
         rho = self.covariance[0, 1] / (sigma_1 * sigma_2)
         f = (
             1
-            / (2 * np.pi * sigma_1 * sigma_2 * np.sqrt(1 - rho ** 2))
+            / (2 * np.pi * sigma_1 * sigma_2 * np.sqrt(1 - rho**2))
             * np.exp(
-                -((x - mu[0]) ** 2) / (2 * (1 - rho ** 2) * sigma_1 ** 2)
-                - (y - mu[1]) ** 2 / (2 * (1 - rho ** 2) * sigma_2 ** 2)
-                + (rho * (x - mu[0]) * (y - mu[1])) / ((1 - rho ** 2) * sigma_1 * sigma_2)
+                -((x - mu[0]) ** 2) / (2 * (1 - rho**2) * sigma_1**2)
+                - (y - mu[1]) ** 2 / (2 * (1 - rho**2) * sigma_2**2)
+                + (rho * (x - mu[0]) * (y - mu[1])) / ((1 - rho**2) * sigma_1 * sigma_2)
             )
         )
         return f
diff --git a/p3iv_utils_probability/src/p3iv_utils_probability/distributions/truncated_distribution.py b/p3iv_utils_probability/src/p3iv_utils_probability/distributions/truncated_distribution.py
@@ -82,7 +82,7 @@ def _intermediate_parameters(self):
     @staticmethod
     def _pdf_standard(x):
         """Probability distribution of standard normal distribution"""
-        return 1 / np.sqrt(2 * np.pi) * np.exp(-(x ** 2) / 2)
+        return 1 / np.sqrt(2 * np.pi) * np.exp(-(x**2) / 2)
 
     @staticmethod
     def _cdf_standard(x):
@@ -104,7 +104,7 @@ def within_n_std(self, x, y, n_std):
         points = self.reform_points(x, y)
 
         mean_x, mean_y, theta, a, b = super(TruncatedBivariateNormalDistribution, self).range(n_std)
-        c = np.sqrt(a ** 2 - b ** 2)  # calculate linear eccentricity
+        c = np.sqrt(a**2 - b**2)  # calculate linear eccentricity
         F1 = np.array([mean_x, mean_y]) + c * np.array([np.cos(theta), np.sin(theta)])  # focal-point 1 coords
         F2 = np.array([mean_x, mean_y]) - c * np.array([np.cos(theta), np.sin(theta)])  # focal-point 2 coords
 

diff --git a/p3iv_utils_probability/src/p3iv_utils_probability/distributions/univariate_distribution.py b/p3iv_utils_probability/src/p3iv_utils_probability/distributions/univariate_distribution.py
@@ -72,7 +72,7 @@ def __div__(self, other):
     def pdf(self, x, *args):
         mu = self.mean
         sigma = np.sqrt(self.covariance)
-        f_val = 1 / (np.sqrt(2 * np.pi) * sigma) * np.exp(-((x - mu) ** 2 / (2 * sigma ** 2)))
+        f_val = 1 / (np.sqrt(2 * np.pi) * sigma) * np.exp(-((x - mu) ** 2 / (2 * sigma**2)))
         return f_val
 
     def cdf(self, x, *args):