use neogen for docstrings

mpc_pybullet_demo/cvxpy_mpc/cvxpy_mpc.py

@@ -10,15 +10,16 @@ class MPC:
         self, vehicle, T, DT, state_cost, final_state_cost, input_cost, input_rate_cost
     ):
         """
-        :param vehicle:
-        :param T:
-        :param DT:
-        :param state_cost:
-        :param final_state_cost:
-        :param input_cost:
-        :param input_rate_cost:
-        """
 
+        Args:
+            vehicle ():
+            T ():
+            DT ():
+            state_cost ():
+            final_state_cost ():
+            input_cost ():
+            input_rate_cost ():
+        """
         self.nx = 4  # number of state vars
         self.nu = 2  # umber of input/control vars
 
@@ -44,9 +45,13 @@ class MPC:
     def get_linear_model_matrices(self, x_bar, u_bar):
         """
         Computes the LTI approximated state space model x' = Ax + Bu + C
-        :param x_bar:
-        :param u_bar:
-        :return:
+
+        Args:
+            x_bar ():
+            u_bar ():
+
+        Returns:
+
         """
 
         x = x_bar[0]
@@ -96,13 +101,16 @@ class MPC:
         verbose=False,
     ):
         """
-        Optimisation problem defined for the linearised model,
-        :param initial_state:
-        :param target:
-        :param verbose:
-        :return:
-        """
 
+        Args:
+            initial_state ():
+            target ():
+            prev_cmd ():
+            verbose ():
+
+        Returns:
+
+        """
         assert len(initial_state) == self.nx
 
         # Create variables needed for setting up cvxpy problem

mpc_pybullet_demo/cvxpy_mpc/utils.py

@@ -4,11 +4,14 @@ from scipy.interpolate import interp1d
 
 def compute_path_from_wp(start_xp, start_yp, step=0.1):
     """
-    Computes a reference path given a set of waypoints
-    :param start_xp:
-    :param start_yp:
-    :param step:
-    :return:
+
+    Args:
+        start_xp ():
+        start_yp ():
+        step ():
+
+    Returns:
+
     """
     final_xp = []
     final_yp = []
@@ -34,10 +37,13 @@ def compute_path_from_wp(start_xp, start_yp, step=0.1):
 
 def get_nn_idx(state, path):
     """
-    Computes the index of the waypoint closest to vehicle
-    :param state:
-    :param path:
-    :return:
+
+    Args:
+        state ():
+        path ():
+
+    Returns:
+
     """
     dx = state[0] - path[0, :]
     dy = state[1] - path[1, :]
@@ -61,11 +67,16 @@ def get_nn_idx(state, path):
 
 def get_ref_trajectory(state, path, target_v, T, DT):
     """
-    Reinterpolate the trajectory to get a set N desired target states
-    :param state:
-    :param path:
-    :param target_v:
-    :return:
+
+    Args:
+        state ():
+        path ():
+        target_v ():
+        T ():
+        DT ():
+
+    Returns:
+
     """
     K = int(T / DT)
 
@@ -98,7 +109,13 @@ def get_ref_trajectory(state, path, target_v, T, DT):
 
     def fix_angle_reference(angle_ref, angle_init):
         """
-        This function returns a "smoothened" angle_ref wrt angle_init so there are no jumps.
+
+        Args:
+            angle_ref ():
+            angle_init ():
+
+        Returns:
+
         """
         diff_angle = angle_ref - angle_init
         diff_angle = np.unwrap(diff_angle)

mpc_pybullet_demo/cvxpy_mpc/vehicle_model.py

@@ -3,7 +3,14 @@ import numpy as np
 
 class VehicleModel:
     """
-    Helper class to hold vehicle info
+
+    Attributes:
+        wheelbase:
+        max_speed:
+        max_acc:
+        max_d_acc:
+        max_steer:
+        max_d_steer:
     """
 
     def __init__(self):

mpc_pybullet_demo/mpc_demo_nosim.py

@@ -70,9 +70,9 @@ class MPCSim:
 
     def preview(self, mpc_out):
         """
-        [TODO:summary]
 
-        [TODO:description]
+        Args:
+            mpc_out ():
         """
         predicted = np.zeros((2, self.K))
         predicted[:, :] = mpc_out[0:2, 1:]
@@ -85,7 +85,7 @@ class MPCSim:
         predicted = (predicted.T.dot(Rotm)).T
         predicted[0, :] += self.state[0]
         predicted[1, :] += self.state[1]
-        self.predicted = predicted
+        return predicted
 
     def run(self):
         """
@@ -124,10 +124,10 @@ class MPCSim:
 
             # only the first one is used to advance the simulation
             self.action[:] = [u_mpc.value[0, 0], u_mpc.value[1, 0]]
-            self.predict([self.action[0], self.action[1]], DT)
+            self.state = self.predict([self.action[0], self.action[1]], DT)
 
             # use the state trajectory to preview the optimizer output
-            self.preview(x_mpc.value)
+            self.predicted = self.preview(x_mpc.value)
             self.plot_sim()
 
     def predict(self, u, dt):
@@ -141,14 +141,9 @@ class MPCSim:
 
         # solve ODE
         tspan = [0, dt]
-        self.state = odeint(kinematics_model, self.state, tspan, args=(u[:],))[1]
+        return odeint(kinematics_model, self.state, tspan, args=(u[:],))[1]
 
     def plot_sim(self):
-        """
-        [TODO:summary]
-
-        [TODO:description]
-        """
         self.sim_time = self.sim_time + DT
         self.x_history.append(self.state[0])
         self.y_history.append(self.state[1])
@@ -243,18 +238,11 @@ class MPCSim:
 
 def plot_car(x, y, yaw):
     """
-    [TODO:summary]
 
-    [TODO:description]
-
-    Parameters
-    ----------
-    x : [TODO:type]
-        [TODO:description]
-    y : [TODO:type]
-        [TODO:description]
-    yaw : [TODO:type]
-        [TODO:description]
+    Args:
+        x ():
+        y ():
+        yaw ():
     """
     LENGTH = 0.5  # [m]
     WIDTH = 0.25  # [m]

mpc_pybullet_demo/mpc_demo_pybullet.py

@@ -18,7 +18,14 @@ DT = 0.2  # discretization step [s]
 
 
 def get_state(robotId):
-    """ """
+    """
+
+    Args:
+        robotId ():
+
+    Returns:
+
+    """
     robPos, robOrn = p.getBasePositionAndOrientation(robotId)
     linVel, angVel = p.getBaseVelocity(robotId)
 
@@ -33,6 +40,14 @@ def get_state(robotId):
 
 
 def set_ctrl(robotId, currVel, acceleration, steeringAngle):
+    """
+
+    Args:
+        robotId ():
+        currVel ():
+        acceleration ():
+        steeringAngle ():
+    """
     gearRatio = 1.0 / 21
     steering = [0, 2]
     wheels = [8, 15]
@@ -56,7 +71,6 @@ def set_ctrl(robotId, currVel, acceleration, steeringAngle):
 
 
 def plot_results(path, x_history, y_history):
-    """ """
     plt.style.use("ggplot")
     plt.figure()
     plt.title("MPC Tracking Results")
@@ -78,7 +92,6 @@ def plot_results(path, x_history, y_history):
 
 
 def run_sim():
-    """ """
     p.connect(p.GUI)
     p.resetDebugVisualizerCamera(
         cameraDistance=1.0,