add # pragma: no cover

AerialNavigation/rocket_powered_landing/rocket_powered_landing.py

@@ -656,7 +656,7 @@ def main():
             print(f'Converged after {it + 1} iterations.')
             break
 
-    if show_animation:
+    if show_animation:  # pragma: no cover
         plot_animation(X, U)
 
     print("done!!")

Mapping/circle_fitting/circle_fitting.py

@@ -122,7 +122,7 @@ def main():
         ex, ey, er, error = circle_fitting(x, y)
         print("Error:", error)
 
-        if show_animation:
+        if show_animation:  # pragma: no cover
             plt.cla()
             plt.axis("equal")
             plt.plot(0.0, 0.0, "*r")

Mapping/gaussian_grid_map/gaussian_grid_map.py

@@ -71,7 +71,7 @@ def main():
         gmap, minx, maxx, miny, maxy = generate_gaussian_grid_map(
             ox, oy, xyreso, STD)
 
-        if show_animation:
+        if show_animation:  # pragma: no cover
             plt.cla()
             draw_heatmap(gmap, minx, maxx, miny, maxy, xyreso)
             plt.plot(ox, oy, "xr")

Mapping/kmeans_clustering/kmeans_clustering.py

@@ -161,7 +161,7 @@ def main():
         clusters = kmeans_clustering(rx, ry, ncluster)
 
         # for animation
-        if show_animation:
+        if show_animation:  # pragma: no cover
             plt.cla()
             inds = calc_association(cx, cy, clusters)
             for ic in inds:

Mapping/raycasting_grid_map/raycasting_grid_map.py

@@ -121,7 +121,8 @@ def main():
         oy = (np.random.rand(4) - 0.5) * 10.0
         pmap, minx, maxx, miny, maxy, xyreso = generate_ray_casting_grid_map(
             ox, oy, xyreso, yawreso)
-        if show_animation:
+
+        if show_animation:  # pragma: no cover
             plt.cla()
             draw_heatmap(pmap, minx, maxx, miny, maxy, xyreso)
             plt.plot(ox, oy, "xr")

PathPlanning/AStar/a_star.py

@@ -68,7 +68,7 @@ def a_star_planning(sx, sy, gx, gy, ox, oy, reso, rr):
         current = openset[c_id]
 
         # show graph
-        if show_animation:
+        if show_animation:  # pragma: no cover
             plt.plot(current.x * reso, current.y * reso, "xc")
             if len(closedset.keys()) % 10 == 0:
                 plt.pause(0.001)
@@ -214,7 +214,7 @@ def main():
         ox.append(40.0)
         oy.append(60.0 - i)
 
-    if show_animation:
+    if show_animation:  # pragma: no cover
         plt.plot(ox, oy, ".k")
         plt.plot(sx, sy, "xr")
         plt.plot(gx, gy, "xb")
@@ -223,7 +223,7 @@ def main():
 
     rx, ry = a_star_planning(sx, sy, gx, gy, ox, oy, grid_size, robot_size)
 
-    if show_animation:
+    if show_animation:  # pragma: no cover
         plt.plot(rx, ry, "-r")
         plt.show()
 

PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py

@@ -313,7 +313,7 @@ def test1(max_vel=0.5):
         for j, t in enumerate(times):
             state[:, j] = traj.calc_traj_point(t)
 
-        if show_animation:
+        if show_animation:  # pragma: no cover
             # plot the path
             plt.plot(state[0, :], state[1, :])
             plt.pause(1.0)

PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py

@@ -343,7 +343,7 @@ def main():
             print("Goal")
             break
 
-        if show_animation:
+        if show_animation:  # pragma: no cover
             plt.cla()
             plt.plot(tx, ty)
             plt.plot(ob[:, 0], ob[:, 1], "xk")
@@ -356,7 +356,7 @@ def main():
             plt.pause(0.0001)
 
     print("Finish")
-    if show_animation:
+    if show_animation:  # pragma: no cover
         plt.grid(True)
         plt.pause(0.0001)
         plt.show()

PathPlanning/LQRRRTStar/lqr_rrt_star.py

@@ -304,7 +304,7 @@ def main():
     path = rrt.planning(animation=show_animation)
 
     # Draw final path
-    if show_animation:
+    if show_animation:  # pragma: no cover
         rrt.draw_graph()
         plt.plot([x for (x, y) in path], [y for (x, y) in path], '-r')
         plt.grid(True)

PathPlanning/ModelPredictiveTrajectoryGenerator/model_predictive_trajectory_generator.py

@@ -123,7 +123,7 @@ def optimize_trajectory(target, k0, p):
         p += alpha * np.array(dp)
         #  print(p.T)
 
-        if show_animation:
+        if show_animation:  # pragma: no cover
             show_trajectory(target, xc, yc)
     else:
         xc, yc, yawc, p = None, None, None, None

PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py

@@ -393,7 +393,7 @@ def test():
         px, py, pyaw, mode, clen = reeds_shepp_path_planning(
             start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature, step_size)
 
-        if show_animation:
+        if show_animation:  # pragma: no cover
             plt.cla()
             plt.plot(px, py, label="final course " + str(mode))
 
@@ -430,7 +430,7 @@ def main():
     px, py, pyaw, mode, clen = reeds_shepp_path_planning(
         start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature, step_size)
 
-    if show_animation:
+    if show_animation:  # pragma: no cover
         plt.cla()
         plt.plot(px, py, label="final course " + str(mode))
 

PathPlanning/VoronoiRoadMap/voronoi_road_map.py

@@ -78,7 +78,7 @@ def VRM_planning(sx, sy, gx, gy, ox, oy, rr):
     obkdtree = KDTree(np.vstack((ox, oy)).T)
 
     sample_x, sample_y = sample_points(sx, sy, gx, gy, rr, ox, oy, obkdtree)
-    if show_animation:
+    if show_animation:  # pragma: no cover
         plt.plot(sample_x, sample_y, ".b")
 
     road_map = generate_roadmap(sample_x, sample_y, rr, obkdtree)
@@ -183,7 +183,7 @@ def dijkstra_planning(sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y):
         current = openset[c_id]
 
         # show graph
-        if show_animation and len(closedset.keys()) % 2 == 0:
+        if show_animation and len(closedset.keys()) % 2 == 0:  # pragma: no cover
             plt.plot(current.x, current.y, "xg")
             plt.pause(0.001)
 
@@ -287,7 +287,7 @@ def main():
         ox.append(40.0)
         oy.append(60.0 - i)
 
-    if show_animation:
+    if show_animation:  # pragma: no cover
         plt.plot(ox, oy, ".k")
         plt.plot(sx, sy, "^r")
         plt.plot(gx, gy, "^c")
@@ -298,7 +298,7 @@ def main():
 
     assert rx, 'Cannot found path'
 
-    if show_animation:
+    if show_animation:  # pragma: no cover
         plt.plot(rx, ry, "-r")
         plt.show()
 

PathTracking/cgmres_nmpc/cgmres_nmpc.py

@@ -584,7 +584,7 @@ def main():
         # update state
         plant_system.update_state(u_1s[0], u_2s[0])
 
-    if show_animation:
+    if show_animation:  # pragma: no cover
         animation(plant_system, controller, dt)
         plot_figures(plant_system, controller, iteration_num, dt)
 

PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py

@@ -1,3 +1,8 @@
+import cubic_spline_planner
+import scipy.linalg as la
+import matplotlib.pyplot as plt
+import math
+import numpy as np
 """
 
 Path tracking simulation with LQR speed and steering control
@@ -8,11 +13,6 @@ author Atsushi Sakai (@Atsushi_twi)
 import sys
 sys.path.append("../../PathPlanning/CubicSpline/")
 
-import numpy as np
-import math
-import matplotlib.pyplot as plt
-import scipy.linalg as la
-import cubic_spline_planner
 
 # LQR parameter
 Q = np.eye(5)
@@ -206,8 +206,8 @@ def closed_loop_prediction(cx, cy, cyaw, ck, speed_profile, goal):
             plt.plot(cx[target_ind], cy[target_ind], "xg", label="target")
             plt.axis("equal")
             plt.grid(True)
-            plt.title("speed[km/h]:" + str(round(state.v * 3.6, 2)) +
+            plt.title("speed[km/h]:" + str(round(state.v * 3.6, 2))
-                      ",target index:" + str(target_ind))
+                      + ",target index:" + str(target_ind))
             plt.pause(0.0001)
 
     return t, x, y, yaw, v
@@ -257,7 +257,7 @@ def main():
 
     t, x, y, yaw, v = closed_loop_prediction(cx, cy, cyaw, ck, sp, goal)
 
-    if show_animation:
+    if show_animation:  # pragma: no cover
         plt.close()
         plt.subplots(1)
         plt.plot(ax, ay, "xb", label="waypoints")

PathTracking/lqr_steer_control/lqr_steer_control.py

@@ -255,7 +255,7 @@ def main():
 
     t, x, y, yaw, v = closed_loop_prediction(cx, cy, cyaw, ck, sp, goal)
 
-    if show_animation:
+    if show_animation:  # pragma: no cover
         plt.close()
         plt.subplots(1)
         plt.plot(ax, ay, "xb", label="input")

PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py

@@ -427,7 +427,7 @@ def do_simulation(cx, cy, cyaw, ck, sp, dl, initial_state):
             print("Goal")
             break
 
-        if show_animation:
+        if show_animation:  # pragma: no cover
             plt.cla()
             if ox is not None:
                 plt.plot(ox, oy, "xr", label="MPC")
@@ -563,7 +563,7 @@ def main():
     t, x, y, yaw, v, d, a = do_simulation(
         cx, cy, cyaw, ck, sp, dl, initial_state)
 
-    if show_animation:
+    if show_animation:  # pragma: no cover
         plt.close("all")
         plt.subplots()
         plt.plot(cx, cy, "-r", label="spline")
@@ -596,7 +596,7 @@ def main2():
     t, x, y, yaw, v, d, a = do_simulation(
         cx, cy, cyaw, ck, sp, dl, initial_state)
 
-    if show_animation:
+    if show_animation:  # pragma: no cover
         plt.close("all")
         plt.subplots()
         plt.plot(cx, cy, "-r", label="spline")

PathTracking/move_to_pose/move_to_pose.py

@@ -67,7 +67,7 @@ def move_to_pose(x_start, y_start, theta_start, x_goal, y_goal, theta_goal):
         x = x + v * np.cos(theta) * dt
         y = y + v * np.sin(theta) * dt
 
-        if show_animation:
+        if show_animation:  # pragma: no cover
             plt.cla()
             plt.arrow(x_start, y_start, np.cos(theta_start),
                       np.sin(theta_start), color='r', width=0.1)

PathTracking/pure_pursuit/pure_pursuit.py

@@ -126,7 +126,7 @@ def main():
         v.append(state.v)
         t.append(time)
 
-        if show_animation:
+        if show_animation:  # pragma: no cover
             plt.cla()
             plt.plot(cx, cy, ".r", label="course")
             plt.plot(x, y, "-b", label="trajectory")
@@ -139,7 +139,7 @@ def main():
     # Test
     assert lastIndex >= target_ind, "Cannot goal"
 
-    if show_animation:
+    if show_animation:  # pragma: no cover
         plt.cla()
         plt.plot(cx, cy, ".r", label="course")
         plt.plot(x, y, "-b", label="trajectory")

PathTracking/rear_wheel_feedback/rear_wheel_feedback.py

@@ -1,3 +1,6 @@
+import cubic_spline_planner
+import matplotlib.pyplot as plt
+import math
 """
 
 Path tracking simulation with rear wheel feedback steering control and PID speed control.
@@ -8,10 +11,6 @@ author: Atsushi Sakai(@Atsushi_twi)
 import sys
 sys.path.append("../../PathPlanning/CubicSpline/")
 
-import math
-import matplotlib.pyplot as plt
-import cubic_spline_planner
-
 
 Kp = 1.0  # speed propotional gain
 # steering control parameter
@@ -202,7 +201,7 @@ def main():
     # Test
     assert goal_flag, "Cannot goal"
 
-    if show_animation:
+    if show_animation:  # pragma: no cover
         plt.close()
         plt.subplots(1)
         plt.plot(ax, ay, "xb", label="input")

PathTracking/stanley_controller/stanley_controller.py

@@ -184,7 +184,7 @@ def main():
         v.append(state.v)
         t.append(time)
 
-        if show_animation:
+        if show_animation:  # pragma: no cover
             plt.cla()
             plt.plot(cx, cy, ".r", label="course")
             plt.plot(x, y, "-b", label="trajectory")
@@ -197,7 +197,7 @@ def main():
     # Test
     assert last_idx >= target_idx, "Cannot reach goal"
 
-    if show_animation:
+    if show_animation:  # pragma: no cover
         plt.plot(cx, cy, ".r", label="course")
         plt.plot(x, y, "-b", label="trajectory")
         plt.legend()

SLAM/EKFSLAM/ekf_slam.py

@@ -97,12 +97,12 @@ def observation(xTrue, xd, u, RFID):
 def motion_model(x, u):
 
     F = np.array([[1.0, 0, 0],
-                   [0, 1.0, 0],
+                  [0, 1.0, 0],
-                   [0, 0, 1.0]])
+                  [0, 0, 1.0]])
 
     B = np.array([[DT * math.cos(x[2, 0]), 0],
-                   [DT * math.sin(x[2, 0]), 0],
+                  [DT * math.sin(x[2, 0]), 0],
-                   [0.0, DT]])
+                  [0.0, DT]])
 
     x = (F @ x) + (B @ u)
     return x
@@ -119,8 +119,8 @@ def jacob_motion(x, u):
         (STATE_SIZE, LM_SIZE * calc_n_LM(x)))))
 
     jF = np.array([[0.0, 0.0, -DT * u[0] * math.sin(x[2, 0])],
-                    [0.0, 0.0, DT * u[0] * math.cos(x[2, 0])],
+                   [0.0, 0.0, DT * u[0] * math.cos(x[2, 0])],
-                    [0.0, 0.0, 0.0]])
+                   [0.0, 0.0, 0.0]])
 
     G = np.eye(STATE_SIZE) + Fx.T * jF * Fx
 
@@ -170,7 +170,7 @@ def calc_innovation(lm, xEst, PEst, z, LMid):
     delta = lm - xEst[0:2]
     q = (delta.T @ delta)[0, 0]
     #zangle = math.atan2(delta[1], delta[0]) - xEst[2]
-    zangle = math.atan2(delta[1,0], delta[0,0]) - xEst[2]
+    zangle = math.atan2(delta[1, 0], delta[0, 0]) - xEst[2]
     zp = np.array([[math.sqrt(q), pi_2_pi(zangle)]])
     y = (z - zp).T
     y[1] = pi_2_pi(y[1])
@@ -183,7 +183,7 @@ def calc_innovation(lm, xEst, PEst, z, LMid):
 def jacobH(q, delta, x, i):
     sq = math.sqrt(q)
     G = np.array([[-sq * delta[0, 0], - sq * delta[1, 0], 0, sq * delta[0, 0], sq * delta[1, 0]],
-                   [delta[1, 0], - delta[0, 0], - 1.0, - delta[1, 0], delta[0, 0]]])
+                  [delta[1, 0], - delta[0, 0], - 1.0, - delta[1, 0], delta[0, 0]]])
 
     G = G / q
     nLM = calc_n_LM(x)
@@ -235,13 +235,12 @@ def main():
 
         x_state = xEst[0:STATE_SIZE]
 
-
         # store data history
         hxEst = np.hstack((hxEst, x_state))
         hxDR = np.hstack((hxDR, xDR))
         hxTrue = np.hstack((hxTrue, xTrue))
 
-        if show_animation:
+        if show_animation:  # pragma: no cover
             plt.cla()
 
             plt.plot(RFID[:, 0], RFID[:, 1], "*k")
@@ -263,6 +262,5 @@ def main():
             plt.pause(0.001)
 
 
-
 if __name__ == '__main__':
     main()

SLAM/FastSLAM1/fast_slam1.py

@@ -373,7 +373,7 @@ def main():
         hxDR = np.hstack((hxDR, xDR))
         hxTrue = np.hstack((hxTrue, xTrue))
 
-        if show_animation:
+        if show_animation:  # pragma: no cover
             plt.cla()
             plt.plot(RFID[:, 0], RFID[:, 1], "*k")
 

SLAM/FastSLAM2/fast_slam2.py

@@ -402,7 +402,7 @@ def main():
         hxDR = np.hstack((hxDR, xDR))
         hxTrue = np.hstack((hxTrue, xTrue))
 
-        if show_animation:
+        if show_animation:  # pragma: no cover
             plt.cla()
             plt.plot(RFID[:, 0], RFID[:, 1], "*k")
 

SLAM/GraphBasedSLAM/graph_based_slam.py

@@ -295,14 +295,13 @@ def main():
 
         xTrue, z, xDR, ud = observation(xTrue, xDR, u, RFID)
 
-
         hz.append(z)
 
         if dtime >= show_graph_dtime:
             x_opt = graph_based_slam(hxDR, hz)
             dtime = 0.0
 
-            if show_animation:
+            if show_animation:  # pragma: no cover
                 plt.cla()
 
                 plt.plot(RFID[:, 0], RFID[:, 1], "*k")

SLAM/iterative_closest_point/iterative_closest_point.py

@@ -33,7 +33,7 @@ def ICP_matching(ppoints, cpoints):
     while dError >= EPS:
         count += 1
 
-        if show_animation:
+        if show_animation:  # pragma: no cover
             plt.cla()
             plt.plot(ppoints[0, :], ppoints[1, :], ".r")
             plt.plot(cpoints[0, :], cpoints[1, :], ".b")