Fix format

PythonLinearNonlinearControl/common/utils.py

@@ -1,5 +1,6 @@
 import numpy as np
 
+
 def rotate_pos(pos, angle):
     """ Transformation the coordinate in the angle
 
@@ -14,6 +15,7 @@ def rotate_pos(pos, angle):
 
     return np.dot(pos, rot_mat.T)
 
+
 def fit_angle_in_range(angles, min_angle=-np.pi, max_angle=np.pi):
     """ Check angle range and correct the range
 
@@ -43,6 +45,7 @@ def fit_angle_in_range(angles, min_angle=-np.pi, max_angle=np.pi):
     output = np.minimum(max_angle, np.maximum(min_angle, output))
     return output.reshape(output_shape)
 
+
 def update_state_with_Runge_Kutta(state, u, functions, dt=0.01):
     """ update state in Runge Kutta methods
     Args:

PythonLinearNonlinearControl/configs/cartpole.py

@@ -1,5 +1,6 @@
 import numpy as np
 
+
 class CartPoleConfigModule():
     # parameters
     ENV_NAME = "CartPole-v0"
@@ -39,21 +40,21 @@ class CartPoleConfigModule():
                 "num_elites": 50,
                 "max_iters": 15,
                 "alpha": 0.3,
-                "init_var":9.,
+                "init_var": 9.,
-                "threshold":0.001
+                "threshold": 0.001
             },
-            "MPPI":{
+            "MPPI": {
-                "beta" : 0.6,
+                "beta": 0.6,
                 "popsize": 5000,
                 "kappa": 0.9,
                 "noise_sigma": 0.5,
             },
-            "MPPIWilliams":{
+            "MPPIWilliams": {
                 "popsize": 5000,
                 "lambda": 1.,
                 "noise_sigma": 0.9,
             },
-           "iLQR":{
+            "iLQR": {
                 "max_iter": 500,
                 "init_mu": 1.,
                 "mu_min": 1e-6,
@@ -61,7 +62,7 @@ class CartPoleConfigModule():
                 "init_delta": 2.,
                 "threshold": 1e-6,
             },
-           "DDP":{
+            "DDP": {
                 "max_iter": 500,
                 "init_mu": 1.,
                 "mu_min": 1e-6,
@@ -69,9 +70,9 @@ class CartPoleConfigModule():
                 "init_delta": 2.,
                 "threshold": 1e-6,
             },
-           "NMPC-CGMRES":{
+            "NMPC-CGMRES": {
             },
-           "NMPC-Newton":{
+            "NMPC-Newton": {
             },
         }
 
@@ -103,15 +104,15 @@ class CartPoleConfigModule():
         """
 
         if len(x.shape) > 2:
-            return (6. * (x[:, :, 0]**2) \
+            return (6. * (x[:, :, 0]**2)
-                   + 12. * ((np.cos(x[:, :, 2]) + 1.)**2) \
+                    + 12. * ((np.cos(x[:, :, 2]) + 1.)**2)
-                   + 0.1 * (x[:, :, 1]**2) \
+                    + 0.1 * (x[:, :, 1]**2)
                     + 0.1 * (x[:, :, 3]**2))[:, :, np.newaxis]
 
         elif len(x.shape) > 1:
-            return (6. * (x[:, 0]**2) \
+            return (6. * (x[:, 0]**2)
-                   + 12. * ((np.cos(x[:, 2]) + 1.)**2) \
+                    + 12. * ((np.cos(x[:, 2]) + 1.)**2)
-                   + 0.1 * (x[:, 1]**2) \
+                    + 0.1 * (x[:, 1]**2)
                     + 0.1 * (x[:, 3]**2))[:,  np.newaxis]
 
         return 6. * (x[0]**2) \
@@ -134,15 +135,15 @@ class CartPoleConfigModule():
         """
 
         if len(terminal_x.shape) > 1:
-            return (6. * (terminal_x[:, 0]**2) \
+            return (6. * (terminal_x[:, 0]**2)
-                   + 12. * ((np.cos(terminal_x[:, 2]) + 1.)**2) \
+                    + 12. * ((np.cos(terminal_x[:, 2]) + 1.)**2)
-                   + 0.1 * (terminal_x[:, 1]**2) \
+                    + 0.1 * (terminal_x[:, 1]**2)
                     + 0.1 * (terminal_x[:, 3]**2))[:, np.newaxis] \
                 * CartPoleConfigModule.TERMINAL_WEIGHT
 
-        return (6. * (terminal_x[0]**2) \
+        return (6. * (terminal_x[0]**2)
-               + 12. * ((np.cos(terminal_x[2]) + 1.)**2) \
+                + 12. * ((np.cos(terminal_x[2]) + 1.)**2)
-               + 0.1 * (terminal_x[1]**2) \
+                + 0.1 * (terminal_x[1]**2)
                 + 0.1 * (terminal_x[3]**2)) \
             * CartPoleConfigModule.TERMINAL_WEIGHT
 
@@ -163,7 +164,7 @@ class CartPoleConfigModule():
             cost_dx1 = 0.2 * x[:, 1]
             cost_dx2 = 24. * (1 + np.cos(x[:, 2])) * -np.sin(x[:, 2])
             cost_dx3 = 0.2 * x[:, 3]
-            cost_dx = np.stack((cost_dx0, cost_dx1,\
+            cost_dx = np.stack((cost_dx0, cost_dx1,
                                 cost_dx2, cost_dx3), axis=1)
             return cost_dx
 

PythonLinearNonlinearControl/configs/first_order_lag.py

@@ -1,5 +1,6 @@
 import numpy as np
 
+
 class FirstOrderLagConfigModule():
     # parameters
     ENV_NAME = "FirstOrderLag-v0"
@@ -34,23 +35,23 @@ class FirstOrderLagConfigModule():
                 "num_elites": 50,
                 "max_iters": 15,
                 "alpha": 0.3,
-                "init_var":1.,
+                "init_var": 1.,
-                "threshold":0.001
+                "threshold": 0.001
             },
-            "MPPI":{
+            "MPPI": {
-                "beta" : 0.6,
+                "beta": 0.6,
                 "popsize": 5000,
                 "kappa": 0.9,
                 "noise_sigma": 0.5,
             },
-            "MPPIWilliams":{
+            "MPPIWilliams": {
                 "popsize": 5000,
                 "lambda": 1.,
                 "noise_sigma": 0.9,
             },
-           "MPC":{
+            "MPC": {
             },
-           "iLQR":{
+            "iLQR": {
                 "max_iter": 500,
                 "init_mu": 1.,
                 "mu_min": 1e-6,
@@ -58,7 +59,7 @@ class FirstOrderLagConfigModule():
                 "init_delta": 2.,
                 "threshold": 1e-6,
             },
-           "DDP":{
+            "DDP": {
                 "max_iter": 500,
                 "init_mu": 1.,
                 "mu_min": 1e-6,
@@ -66,9 +67,9 @@ class FirstOrderLagConfigModule():
                 "init_delta": 2.,
                 "threshold": 1e-6,
             },
-           "NMPC-CGMRES":{
+            "NMPC-CGMRES": {
             },
-           "NMPC-Newton":{
+            "NMPC-Newton": {
             },
         }
 
@@ -128,7 +129,7 @@ class FirstOrderLagConfigModule():
         if not terminal:
             return 2. * (x - g_x) * np.diag(FirstOrderLagConfigModule.Q)
 
-        return (2. * (x - g_x) \
+        return (2. * (x - g_x)
                 * np.diag(FirstOrderLagConfigModule.Sf))[np.newaxis, :]
 
     @staticmethod

PythonLinearNonlinearControl/configs/make_configs.py

@@ -2,6 +2,7 @@ from .first_order_lag import FirstOrderLagConfigModule
 from .two_wheeled import TwoWheeledConfigModule
 from .cartpole import CartPoleConfigModule
 
+
 def make_config(args):
     """
     Returns:

PythonLinearNonlinearControl/configs/two_wheeled.py

@@ -4,6 +4,7 @@ from matplotlib.axes import Axes
 from ..plotters.plot_objs import square_with_angle, square
 from ..common.utils import fit_angle_in_range
 
+
 class TwoWheeledConfigModule():
     # parameters
     ENV_NAME = "TwoWheeled-v0"
@@ -46,21 +47,21 @@ class TwoWheeledConfigModule():
                 "num_elites": 50,
                 "max_iters": 15,
                 "alpha": 0.3,
-                "init_var":1.,
+                "init_var": 1.,
-                "threshold":0.001
+                "threshold": 0.001
             },
-            "MPPI":{
+            "MPPI": {
-                "beta" : 0.6,
+                "beta": 0.6,
                 "popsize": 5000,
                 "kappa": 0.9,
                 "noise_sigma": 0.5,
             },
-            "MPPIWilliams":{
+            "MPPIWilliams": {
                 "popsize": 5000,
                 "lambda": 1,
                 "noise_sigma": 1.,
             },
-           "iLQR":{
+            "iLQR": {
                 "max_iter": 500,
                 "init_mu": 1.,
                 "mu_min": 1e-6,
@@ -68,7 +69,7 @@ class TwoWheeledConfigModule():
                 "init_delta": 2.,
                 "threshold": 1e-6,
             },
-           "DDP":{
+            "DDP": {
                 "max_iter": 500,
                 "init_mu": 1.,
                 "mu_min": 1e-6,
@@ -76,9 +77,9 @@ class TwoWheeledConfigModule():
                 "init_delta": 2.,
                 "threshold": 1e-6,
             },
-           "NMPC-CGMRES":{
+            "NMPC-CGMRES": {
             },
-           "NMPC-Newton":{
+            "NMPC-Newton": {
             },
         }
 
@@ -142,7 +143,7 @@ class TwoWheeledConfigModule():
             cost (numpy.ndarray): cost of state, shape(pred_len, ) or
                 shape(pop_size, pred_len)
         """
-        terminal_diff = TwoWheeledConfigModule.fit_diff_in_range(terminal_x \
+        terminal_diff = TwoWheeledConfigModule.fit_diff_in_range(terminal_x
                                                                  - terminal_g_x)
 
         return ((terminal_diff)**2) * np.diag(TwoWheeledConfigModule.Sf)
@@ -164,7 +165,7 @@ class TwoWheeledConfigModule():
         if not terminal:
             return 2. * (diff) * np.diag(TwoWheeledConfigModule.Q)
 
-        return (2. * (diff) \
+        return (2. * (diff)
                 * np.diag(TwoWheeledConfigModule.Sf))[np.newaxis, :]
 
     @staticmethod

PythonLinearNonlinearControl/controllers/cem.py

@@ -8,6 +8,7 @@ from ..envs.cost import calc_cost
 
 logger = getLogger(__name__)
 
+
 class CEM(Controller):
     """ Cross Entropy Method for linear and nonlinear method
 
@@ -19,6 +20,7 @@ class CEM(Controller):
         using probabilistic dynamics models.
         In Advances in Neural Information Processing Systems (pp. 4754-4765).
     """
+
     def __init__(self, config, model):
         super(CEM, self).__init__(config, model)
 
@@ -50,7 +52,7 @@ class CEM(Controller):
         self.input_cost_fn = config.input_cost_fn
 
         # init mean
-        self.init_mean = np.tile((config.INPUT_UPPER_BOUND \
+        self.init_mean = np.tile((config.INPUT_UPPER_BOUND
                                   + config.INPUT_LOWER_BOUND) / 2.,
                                  self.pred_len)
         self.prev_sol = self.init_mean.copy()
@@ -86,7 +88,7 @@ class CEM(Controller):
 
         # make distribution
         X = stats.truncnorm(-1, 1,
-                            loc=np.zeros_like(mean),\
+                            loc=np.zeros_like(mean),
                             scale=np.ones_like(mean))
 
         while (opt_count < self.max_iters) and np.max(var) > self.epsilon:

PythonLinearNonlinearControl/controllers/controller.py

@@ -2,9 +2,11 @@ import numpy as np
 
 from ..envs.cost import calc_cost
 
+
 class Controller():
     """ Controller class
     """
+
     def __init__(self, config, model):
         """
         """
@@ -49,7 +51,7 @@ class Controller():
 
         # get particle cost
         costs = calc_cost(pred_xs, samples, g_xs,
-                          self.state_cost_fn, self.input_cost_fn, \
+                          self.state_cost_fn, self.input_cost_fn,
                           self.terminal_state_cost_fn)
 
         return costs
@@ -58,10 +60,12 @@ class Controller():
     def gradient_hamiltonian_x(x, u, lam):
         """ gradient of hamitonian with respect to the state, 
         """
-        raise NotImplementedError("Implement gradient of hamitonian with respect to the state")
+        raise NotImplementedError(
+            "Implement gradient of hamitonian with respect to the state")
 
     @staticmethod
     def gradient_hamiltonian_u(x, u, lam):
         """  gradient of hamitonian with respect to the input
         """
-        raise NotImplementedError("Implement gradient of hamitonian with respect to the input")
+        raise NotImplementedError(
+            "Implement gradient of hamitonian with respect to the input")

PythonLinearNonlinearControl/controllers/ddp.py

@@ -8,6 +8,7 @@ from ..envs.cost import calc_cost
 
 logger = getLogger(__name__)
 
+
 class DDP(Controller):
     """ Differential Dynamic Programming
 
@@ -18,6 +19,7 @@ class DDP(Controller):
         https://github.com/studywolf/control, and
         https://github.com/anassinator/ilqr
     """
+
     def __init__(self, config, model):
         """
         """
@@ -98,7 +100,7 @@ class DDP(Controller):
 
             try:
                 # backward
-                k, K = self.backward(f_x, f_u, f_xx, f_ux, f_uu, \
+                k, K = self.backward(f_x, f_u, f_xx, f_ux, f_uu,
                                      l_x, l_xx, l_u, l_uu, l_ux)
 
                 # line search
@@ -139,7 +141,7 @@ class DDP(Controller):
                 # increase regularization term.
                 self.delta = max(1.0, self.delta) * self.init_delta
                 self.mu = max(self.mu_min, self.mu * self.delta)
-                logger.debug("Update regularization term to {}"\
+                logger.debug("Update regularization term to {}"
                              .format(self.mu))
                 if self.mu >= self.mu_max:
                     logger.debug("Reach Max regularization term")

PythonLinearNonlinearControl/controllers/ilqr.py

@@ -8,6 +8,7 @@ from ..envs.cost import calc_cost
 
 logger = getLogger(__name__)
 
+
 class iLQR(Controller):
     """ iterative Liner Quadratique Regulator
 
@@ -16,6 +17,7 @@ class iLQR(Controller):
         Intelligent Robots and Systems (pp. 4906-4913). and Study Wolf,
         https://github.com/studywolf/control
     """
+
     def __init__(self, config, model):
         """
         """
@@ -130,7 +132,7 @@ class iLQR(Controller):
                 # increase regularization term.
                 self.delta = max(1.0, self.delta) * self.init_delta
                 self.mu = max(self.mu_min, self.mu * self.delta)
-                logger.debug("Update regularization term to {}"\
+                logger.debug("Update regularization term to {}"
                              .format(self.mu))
                 if self.mu >= self.mu_max:
                     logger.debug("Reach Max regularization term")

PythonLinearNonlinearControl/controllers/make_controllers.py

@@ -6,6 +6,7 @@ from .mppi_williams import MPPIWilliams
 from .ilqr import iLQR
 from .ddp import DDP
 
+
 def make_controller(args, config, model):
 
     if args.controller_type == "MPC":

PythonLinearNonlinearControl/controllers/mpc.py

@@ -9,6 +9,7 @@ from ..envs.cost import calc_cost
 
 logger = getLogger(__name__)
 
+
 class LinearMPC(Controller):
     """ Model Predictive Controller for linear model
 
@@ -21,6 +22,7 @@ class LinearMPC(Controller):
     Ref:
         Maciejowski, J. M. (2002). Predictive control: with constraints.
     """
+
     def __init__(self, config, model):
         """
         Args:
@@ -91,8 +93,8 @@ class LinearMPC(Controller):
 
         for i in range(self.pred_len - 1):
             temp_mat = np.zeros_like(self.gamma_mat)
-            temp_mat[int((i + 1)*self.state_size): , :] =\
+            temp_mat[int((i + 1)*self.state_size):, :] =\
-                self.gamma_mat[:-int((i + 1)*self.state_size) , :]
+                self.gamma_mat[:-int((i + 1)*self.state_size), :]
 
             self.theta_mat = np.hstack((self.theta_mat, temp_mat))
 
@@ -114,7 +116,7 @@ class LinearMPC(Controller):
 
             for i in range(self.pred_len - 1):
                 for j in range(self.input_size):
-                    temp_F[j * 2: (j + 1) * 2,\
+                    temp_F[j * 2: (j + 1) * 2,
                            ((i+1) * self.input_size) + j] = np.array([1., -1.])
                 self.F = np.vstack((self.F, temp_F))
 
@@ -187,14 +189,14 @@ class LinearMPC(Controller):
 
         # using cvxopt
         def optimized_func(dt_us):
-            return (np.dot(dt_us, np.dot(H, dt_us.reshape(-1, 1))) \
+            return (np.dot(dt_us, np.dot(H, dt_us.reshape(-1, 1)))
                     - np.dot(G.T, dt_us.reshape(-1, 1)))[0]
 
         # constraint
         lb = np.array([-np.inf for _ in range(len(ub))])  # one side cons
         cons = LinearConstraint(A, lb, ub)
         # solve
-        opt_sol = minimize(optimized_func, self.prev_sol.flatten(),\
+        opt_sol = minimize(optimized_func, self.prev_sol.flatten(),
                            constraints=[cons])
         opt_dt_us = opt_sol.x
 
@@ -213,7 +215,7 @@ class LinearMPC(Controller):
         """
 
         # to dt form
-        opt_dt_u_seq = np.cumsum(opt_dt_us.reshape(self.pred_len,\
+        opt_dt_u_seq = np.cumsum(opt_dt_us.reshape(self.pred_len,
                                                    self.input_size),
                                  axis=0)
         self.prev_sol = opt_dt_u_seq.copy()

PythonLinearNonlinearControl/controllers/mppi.py

@@ -8,6 +8,7 @@ from ..envs.cost import calc_cost
 
 logger = getLogger(__name__)
 
+
 class MPPI(Controller):
     """ Model Predictive Path Integral for linear and nonlinear method
 
@@ -18,6 +19,7 @@ class MPPI(Controller):
         Deep Dynamics Models for Learning Dexterous Manipulation.
         arXiv preprint arXiv:1909.11652.
     """
+
     def __init__(self, config, model):
         super(MPPI, self).__init__(config, model)
 
@@ -47,7 +49,7 @@ class MPPI(Controller):
         self.input_cost_fn = config.input_cost_fn
 
         # init mean
-        self.prev_sol = np.tile((config.INPUT_UPPER_BOUND \
+        self.prev_sol = np.tile((config.INPUT_UPPER_BOUND
                                  + config.INPUT_LOWER_BOUND) / 2.,
                                 self.pred_len)
         self.prev_sol = self.prev_sol.reshape(self.pred_len, self.input_size)
@@ -81,13 +83,13 @@ class MPPI(Controller):
         for t in range(self.pred_len):
             if t > 0:
                 noised_inputs[:, t, :] = self.beta \
-                                         * (self.prev_sol[t, :] \
+                    * (self.prev_sol[t, :]
                        + noise[:, t, :]) \
                     + (1 - self.beta) \
                     * noised_inputs[:, t-1, :]
             else:
                 noised_inputs[:, t, :] = self.beta \
-                                         * (self.prev_sol[t, :] \
+                    * (self.prev_sol[t, :]
                        + noise[:, t, :]) \
                     + (1 - self.beta) \
                     * self.history_u[-1]

PythonLinearNonlinearControl/controllers/mppi_williams.py

@@ -8,6 +8,7 @@ from ..envs.cost import calc_cost
 
 logger = getLogger(__name__)
 
+
 class MPPIWilliams(Controller):
     """ Model Predictive Path Integral for linear and nonlinear method
 
@@ -19,6 +20,7 @@ class MPPIWilliams(Controller):
         2017 IEEE International Conference on Robotics and Automation (ICRA),
         Singapore, 2017, pp. 1714-1721.
     """
+
     def __init__(self, config, model):
         super(MPPIWilliams, self).__init__(config, model)
 
@@ -47,7 +49,7 @@ class MPPIWilliams(Controller):
         self.input_cost_fn = config.input_cost_fn
 
         # init mean
-        self.prev_sol = np.tile((config.INPUT_UPPER_BOUND \
+        self.prev_sol = np.tile((config.INPUT_UPPER_BOUND
                                  + config.INPUT_LOWER_BOUND) / 2.,
                                 self.pred_len)
         self.prev_sol = self.prev_sol.reshape(self.pred_len, self.input_size)
@@ -85,7 +87,7 @@ class MPPIWilliams(Controller):
 
         # get particle cost
         costs = calc_cost(pred_xs, samples, g_xs,
-                          self.state_cost_fn, None, \
+                          self.state_cost_fn, None,
                           self.terminal_state_cost_fn)
 
         return costs

PythonLinearNonlinearControl/controllers/random.py

@@ -8,6 +8,7 @@ from ..envs.cost import calc_cost
 
 logger = getLogger(__name__)
 
+
 class RandomShooting(Controller):
     """ Random Shooting Method for linear and nonlinear method
 
@@ -19,6 +20,7 @@ class RandomShooting(Controller):
         using probabilistic dynamics models.
         In Advances in Neural Information Processing Systems (pp. 4754-4765).
     """
+
     def __init__(self, config, model):
         super(RandomShooting, self).__init__(config, model)
 

PythonLinearNonlinearControl/envs/cartpole.py

@@ -4,6 +4,7 @@ from matplotlib.axes import Axes
 from .env import Env
 from ..plotters.plot_objs import square
 
+
 class CartPoleEnv(Env):
     """ Cartpole Environment
 
@@ -13,13 +14,14 @@ class CartPoleEnv(Env):
         6-832-underactuated-robotics-spring-2009/readings/
         MIT6_832s09_read_ch03.pdf
     """
+
     def __init__(self):
         """
         """
-        self.config = {"state_size" : 4,
+        self.config = {"state_size": 4,
-                       "input_size" : 1,
+                       "input_size": 1,
-                       "dt" : 0.02,
+                       "dt": 0.02,
-                       "max_step" : 500,
+                       "max_step": 500,
                        "input_lower_bound": [-3.],
                        "input_upper_bound": [3.],
                        "mp": 0.2,
@@ -76,21 +78,21 @@ class CartPoleEnv(Env):
         # x
         d_x0 = self.curr_x[1]
         # v_x
-        d_x1 = (u[0] + self.config["mp"] * np.sin(self.curr_x[2]) \
+        d_x1 = (u[0] + self.config["mp"] * np.sin(self.curr_x[2])
-               * (self.config["l"] * (self.curr_x[3]**2) \
+                * (self.config["l"] * (self.curr_x[3]**2)
                    + self.config["g"] * np.cos(self.curr_x[2]))) \
-               / (self.config["mc"] + self.config["mp"] \
+            / (self.config["mc"] + self.config["mp"]
                * (np.sin(self.curr_x[2])**2))
         # theta
         d_x2 = self.curr_x[3]
 
         # v_theta
-        d_x3 = (-u[0] * np.cos(self.curr_x[2]) \
+        d_x3 = (-u[0] * np.cos(self.curr_x[2])
-                - self.config["mp"] * self.config["l"] * (self.curr_x[3]**2) \
+                - self.config["mp"] * self.config["l"] * (self.curr_x[3]**2)
-                  * np.cos(self.curr_x[2]) * np.sin(self.curr_x[2]) \
+                * np.cos(self.curr_x[2]) * np.sin(self.curr_x[2])
-                - (self.config["mc"] + self.config["mp"]) * self.config["g"] \
+                - (self.config["mc"] + self.config["mp"]) * self.config["g"]
                 * np.sin(self.curr_x[2])) \
-               / (self.config["l"] * (self.config["mc"] + self.config["mp"] \
+            / (self.config["l"] * (self.config["mc"] + self.config["mp"]
                                    * (np.sin(self.curr_x[2])**2)))
 
         next_x = self.curr_x +\
@@ -115,7 +117,7 @@ class CartPoleEnv(Env):
 
         return next_x.flatten(), costs, \
             self.step_count > self.config["max_step"], \
-               {"goal_state" : self.g_x}
+            {"goal_state": self.g_x}
 
     def plot_func(self, to_plot, i=None, history_x=None, history_g_x=None):
         """ plot cartpole object function
@@ -134,10 +136,10 @@ class CartPoleEnv(Env):
 
             imgs["cart"] = to_plot.plot([], [], c="k")[0]
             imgs["pole"] = to_plot.plot([], [], c="k", linewidth=5)[0]
-            imgs["center"] = to_plot.plot([], [], marker="o", c="k",\
+            imgs["center"] = to_plot.plot([], [], marker="o", c="k",
                                           markersize=10)[0]
             # centerline
-            to_plot.plot(np.linspace(-1., 1., num=50), np.zeros(50),\
+            to_plot.plot(np.linspace(-1., 1., num=50), np.zeros(50),
                          c="k", linestyle="dashed")
 
             # set axis
@@ -166,13 +168,13 @@ class CartPoleEnv(Env):
             pole_y (numpy.ndarray): y data of pole 
         """
         # cart
-        cart_x, cart_y = square(curr_x[0], 0.,\
+        cart_x, cart_y = square(curr_x[0], 0.,
                                 self.config["cart_size"], 0.)
 
         # pole
-        pole_x = np.array([curr_x[0], curr_x[0] + self.config["l"] \
+        pole_x = np.array([curr_x[0], curr_x[0] + self.config["l"]
                            * np.cos(curr_x[2]-np.pi/2)])
-        pole_y = np.array([0., self.config["l"] \
+        pole_y = np.array([0., self.config["l"]
                            * np.sin(curr_x[2]-np.pi/2)])
 
         return cart_x, cart_y, pole_x, pole_y

PythonLinearNonlinearControl/envs/cost.py

@@ -4,6 +4,7 @@ import numpy as np
 
 logger = getLogger(__name__)
 
+
 def calc_cost(pred_xs, input_sample, g_xs,
               state_cost_fn, input_cost_fn, terminal_state_cost_fn):
     """ calculate the cost 
@@ -24,7 +25,8 @@ def calc_cost(pred_xs, input_sample, g_xs,
     # state cost
     state_cost = 0.
     if state_cost_fn is not None:
-        state_pred_par_cost = state_cost_fn(pred_xs[:, 1:-1, :], g_xs[:, 1:-1, :])
+        state_pred_par_cost = state_cost_fn(
+            pred_xs[:, 1:-1, :], g_xs[:, 1:-1, :])
         state_cost = np.sum(np.sum(state_pred_par_cost, axis=-1), axis=-1)
 
     # terminal cost

PythonLinearNonlinearControl/envs/env.py

@@ -1,5 +1,6 @@
 import numpy as np
 
+
 class Env():
     """ Environments class
     Attributes:
@@ -8,6 +9,7 @@ class Env():
         history_x (list[numpy.ndarray]): historty of state, shape(step_count*state_size)
         step_count (int): step count
     """
+
     def __init__(self, config):
         """
         """

PythonLinearNonlinearControl/envs/first_order_lag.py

@@ -3,17 +3,19 @@ import scipy
 from scipy import integrate
 from .env import Env
 
+
 class FirstOrderLagEnv(Env):
     """ First Order Lag System Env
     """
+
     def __init__(self, tau=0.63):
         """
         """
-        self.config = {"state_size" : 4,\
+        self.config = {"state_size": 4,
-                       "input_size" : 2,\
+                       "input_size": 2,
-                       "dt" : 0.05,\
+                       "dt": 0.05,
-                       "max_step" : 500,\
+                       "max_step": 500,
-                       "input_lower_bound": [-0.5, -0.5],\
+                       "input_lower_bound": [-0.5, -0.5],
                        "input_upper_bound": [0.5, 0.5],
                        }
 
@@ -112,7 +114,7 @@ class FirstOrderLagEnv(Env):
 
         return next_x.flatten(), cost, \
             self.step_count > self.config["max_step"], \
-               {"goal_state" : self.g_x}
+            {"goal_state": self.g_x}
 
     def plot_func(self, to_plot, i=None, history_x=None, history_g_x=None):
         """

PythonLinearNonlinearControl/envs/make_envs.py

@@ -3,6 +3,7 @@ from .two_wheeled import TwoWheeledConstEnv
 from .two_wheeled import TwoWheeledTrackEnv
 from .cartpole import CartPoleEnv
 
+
 def make_env(args):
 
     if args.env == "FirstOrderLag":

PythonLinearNonlinearControl/envs/nonlinear_sample_system.py

@@ -4,17 +4,19 @@ from scipy import integrate
 from .env import Env
 from ..common.utils import update_state_with_Runge_Kutta
 
+
 class NonlinearSampleEnv(Env):
     """ Nonlinear Sample Env
     """
+
     def __init__(self):
         """
         """
-        self.config = {"state_size" : 2,\
+        self.config = {"state_size": 2,
-                       "input_size" : 1,\
+                       "input_size": 1,
-                       "dt" : 0.01,\
+                       "dt": 0.01,
-                       "max_step" : 250,\
+                       "max_step": 250,
-                       "input_lower_bound": [-0.5],\
+                       "input_lower_bound": [-0.5],
                        "input_upper_bound": [0.5],
                        }
 
@@ -78,7 +80,7 @@ class NonlinearSampleEnv(Env):
 
         return next_x.flatten(), cost, \
             self.step_count > self.config["max_step"], \
-               {"goal_state" : self.g_x}
+            {"goal_state": self.g_x}
 
     def _func_x_1(self, x_1, x_2, u):
         """

PythonLinearNonlinearControl/envs/two_wheeled.py

@@ -5,6 +5,7 @@ import matplotlib.pyplot as plt
 from .env import Env
 from ..plotters.plot_objs import circle_with_angle, square, circle
 
+
 def step_two_wheeled_env(curr_x, u, dt, method="Oylar"):
     """ step two wheeled enviroment
 
@@ -28,19 +29,21 @@ def step_two_wheeled_env(curr_x, u, dt, method="Oylar"):
 
     return next_x
 
+
 class TwoWheeledConstEnv(Env):
     """ Two wheeled robot with constant goal Env
     """
+
     def __init__(self):
         """
         """
-        self.config = {"state_size" : 3,\
+        self.config = {"state_size": 3,
-                       "input_size" : 2,\
+                       "input_size": 2,
-                       "dt" : 0.01,\
+                       "dt": 0.01,
-                       "max_step" : 500,\
+                       "max_step": 500,
-                       "input_lower_bound": (-1.5, -3.14),\
+                       "input_lower_bound": (-1.5, -3.14),
-                       "input_upper_bound": (1.5, 3.14),\
+                       "input_upper_bound": (1.5, 3.14),
-                       "car_size": 0.2,\
+                       "car_size": 0.2,
                        "wheel_size": (0.075, 0.015)
                        }
 
@@ -104,7 +107,7 @@ class TwoWheeledConstEnv(Env):
 
         return next_x.flatten(), costs, \
             self.step_count > self.config["max_step"], \
-               {"goal_state" : self.g_x}
+            {"goal_state": self.g_x}
 
     def plot_func(self, to_plot, i=None, history_x=None, history_g_x=None):
         """ plot cartpole object function
@@ -160,10 +163,10 @@ class TwoWheeledConstEnv(Env):
                               self.config["car_size"], curr_x[2])
 
         # left tire
-        center_x = (self.config["car_size"] \
+        center_x = (self.config["car_size"]
                     + self.config["wheel_size"][1]) \
             * np.cos(curr_x[2]-np.pi/2.) + curr_x[0]
-        center_y = (self.config["car_size"] \
+        center_y = (self.config["car_size"]
                     + self.config["wheel_size"][1]) \
             * np.sin(curr_x[2]-np.pi/2.) + curr_x[1]
 
@@ -172,10 +175,10 @@ class TwoWheeledConstEnv(Env):
                    self.config["wheel_size"], curr_x[2])
 
         # right tire
-        center_x = (self.config["car_size"] \
+        center_x = (self.config["car_size"]
                     + self.config["wheel_size"][1]) \
             * np.cos(curr_x[2]+np.pi/2.) + curr_x[0]
-        center_y = (self.config["car_size"] \
+        center_y = (self.config["car_size"]
                     + self.config["wheel_size"][1]) \
             * np.sin(curr_x[2]+np.pi/2.) + curr_x[1]
 
@@ -187,19 +190,21 @@ class TwoWheeledConstEnv(Env):
             left_tire_x, left_tire_y,\
             right_tire_x, right_tire_y
 
+
 class TwoWheeledTrackEnv(Env):
     """ Two wheeled robot with constant goal Env
     """
+
     def __init__(self):
         """
         """
-        self.config = {"state_size" : 3,\
+        self.config = {"state_size": 3,
-                       "input_size" : 2,\
+                       "input_size": 2,
-                       "dt" : 0.01,\
+                       "dt": 0.01,
-                       "max_step" : 1000,\
+                       "max_step": 1000,
-                       "input_lower_bound": (-1.5, -3.14),\
+                       "input_lower_bound": (-1.5, -3.14),
-                       "input_upper_bound": (1.5, 3.14),\
+                       "input_upper_bound": (1.5, 3.14),
-                       "car_size": 0.2,\
+                       "car_size": 0.2,
                        "wheel_size": (0.075, 0.015)
                        }
 
@@ -221,11 +226,11 @@ class TwoWheeledTrackEnv(Env):
         # circle
         circle_1_x, circle_1_y = circle(linelength/2., circle_radius,
                                         circle_radius, start=-np.pi/2., end=np.pi/2., n_point=50)
-        circle_1 = np.stack((circle_1_x , circle_1_y), axis=1)
+        circle_1 = np.stack((circle_1_x, circle_1_y), axis=1)
 
         circle_2_x, circle_2_y = circle(-linelength/2., circle_radius,
                                         circle_radius, start=np.pi/2., end=3*np.pi/2., n_point=50)
-        circle_2 = np.stack((circle_2_x , circle_2_y), axis=1)
+        circle_2 = np.stack((circle_2_x, circle_2_y), axis=1)
 
         road_pos = np.concatenate((line_1, circle_1, line_2, circle_2), axis=0)
 
@@ -294,7 +299,7 @@ class TwoWheeledTrackEnv(Env):
 
         return next_x.flatten(), costs, \
             self.step_count > self.config["max_step"], \
-               {"goal_state" : self.g_traj}
+            {"goal_state": self.g_traj}
 
     def plot_func(self, to_plot, i=None, history_x=None, history_g_x=None):
         """ plot cartpole object function
@@ -354,10 +359,10 @@ class TwoWheeledTrackEnv(Env):
                               self.config["car_size"], curr_x[2])
 
         # left tire
-        center_x = (self.config["car_size"] \
+        center_x = (self.config["car_size"]
                     + self.config["wheel_size"][1]) \
             * np.cos(curr_x[2]-np.pi/2.) + curr_x[0]
-        center_y = (self.config["car_size"] \
+        center_y = (self.config["car_size"]
                     + self.config["wheel_size"][1]) \
             * np.sin(curr_x[2]-np.pi/2.) + curr_x[1]
 
@@ -366,10 +371,10 @@ class TwoWheeledTrackEnv(Env):
                    self.config["wheel_size"], curr_x[2])
 
         # right tire
-        center_x = (self.config["car_size"] \
+        center_x = (self.config["car_size"]
                     + self.config["wheel_size"][1]) \
             * np.cos(curr_x[2]+np.pi/2.) + curr_x[0]
-        center_y = (self.config["car_size"] \
+        center_y = (self.config["car_size"]
                     + self.config["wheel_size"][1]) \
             * np.sin(curr_x[2]+np.pi/2.) + curr_x[1]
 

PythonLinearNonlinearControl/helper.py

@@ -7,6 +7,7 @@ import six
 import pickle
 from logging import DEBUG, basicConfig, getLogger, FileHandler, StreamHandler, Formatter, Logger
 
+
 def make_logger(save_dir):
     """
     Args:
@@ -33,6 +34,7 @@ def make_logger(save_dir):
     # sh_handler = StreamHandler()
     # logger.addHandler(sh_handler)
 
+
 def int_tuple(s):
     """ transform str to tuple
     Args:
@@ -42,6 +44,7 @@ def int_tuple(s):
     """
     return tuple(int(i) for i in s.split(','))
 
+
 def bool_flag(s):
     """ transform str to bool flg
     Args:
@@ -54,6 +57,7 @@ def bool_flag(s):
     msg = 'Invalid value "%s" for bool flag (should be 0 or 1)'
     raise ValueError(msg % s)
 
+
 def file_exists(path):
     """ Check file existence on given path
     Args:
@@ -63,6 +67,7 @@ def file_exists(path):
     """
     return os.path.exists(path)
 
+
 def create_dir_if_not_exist(outdir):
     """ Check directory existence and creates new directory if not exist
     Args:  
@@ -77,6 +82,7 @@ def create_dir_if_not_exist(outdir):
             return
     os.makedirs(outdir)
 
+
 def write_text_to_file(file_path, data):
     """ Write given text data to file
     Args:
@@ -86,6 +92,7 @@ def write_text_to_file(file_path, data):
     with open(file_path, 'w') as f:
         f.write(data)
 
+
 def read_text_from_file(file_path):
     """ Read given file as text
     Args:
@@ -96,6 +103,7 @@ def read_text_from_file(file_path):
     with open(file_path, 'r') as f:
         return f.read()
 
+
 def save_pickle(file_path, data):
     """ pickle given data to file
     Args:
@@ -105,6 +113,7 @@ def save_pickle(file_path, data):
     with open(file_path, 'wb') as f:
         pickle.dump(data, f)
 
+
 def load_pickle(file_path):
     """ load pickled data from file
     Args:
@@ -118,6 +127,7 @@ def load_pickle(file_path):
         else:
             return pickle.load(f, encoding='bytes')
 
+
 def prepare_output_dir(base_dir, args, time_format='%Y-%m-%d-%H%M%S'):
     """ prepare a directory with current datetime as name.
     created directory contains the command and args when the script was called as text file.

PythonLinearNonlinearControl/models/cartpole.py

@@ -2,9 +2,11 @@ import numpy as np
 
 from .model import Model
 
+
 class CartPoleModel(Model):
     """ cartpole model
     """
+
     def __init__(self, config):
         """
         """
@@ -31,16 +33,16 @@ class CartPoleModel(Model):
             # x
             d_x0 = curr_x[1]
             # v_x
-            d_x1 = (u[0] + self.mp * np.sin(curr_x[2]) \
+            d_x1 = (u[0] + self.mp * np.sin(curr_x[2])
-                        * (self.l * (curr_x[3]**2) \
+                    * (self.l * (curr_x[3]**2)
                        + self.g * np.cos(curr_x[2]))) \
                 / (self.mc + self.mp * (np.sin(curr_x[2])**2))
             # theta
             d_x2 = curr_x[3]
             # v_theta
-            d_x3 = (-u[0] * np.cos(curr_x[2]) \
+            d_x3 = (-u[0] * np.cos(curr_x[2])
-                    - self.mp * self.l * (curr_x[3]**2) \
+                    - self.mp * self.l * (curr_x[3]**2)
-                      * np.cos(curr_x[2]) * np.sin(curr_x[2]) \
+                    * np.cos(curr_x[2]) * np.sin(curr_x[2])
                     - (self.mc + self.mp) * self.g * np.sin(curr_x[2])) \
                 / (self.l * (self.mc + self.mp * (np.sin(curr_x[2])**2)))
 
@@ -53,16 +55,16 @@ class CartPoleModel(Model):
             # x
             d_x0 = curr_x[:, 1]
             # v_x
-            d_x1 = (u[:, 0] + self.mp * np.sin(curr_x[:, 2]) \
+            d_x1 = (u[:, 0] + self.mp * np.sin(curr_x[:, 2])
-                        * (self.l * (curr_x[:, 3]**2) \
+                    * (self.l * (curr_x[:, 3]**2)
                        + self.g * np.cos(curr_x[:, 2]))) \
                 / (self.mc + self.mp * (np.sin(curr_x[:, 2])**2))
             # theta
             d_x2 = curr_x[:, 3]
             # v_theta
-            d_x3 = (-u[:, 0] * np.cos(curr_x[:, 2]) \
+            d_x3 = (-u[:, 0] * np.cos(curr_x[:, 2])
-                    - self.mp * self.l * (curr_x[:, 3]**2) \
+                    - self.mp * self.l * (curr_x[:, 3]**2)
-                      * np.cos(curr_x[:, 2]) * np.sin(curr_x[:, 2]) \
+                    * np.cos(curr_x[:, 2]) * np.sin(curr_x[:, 2])
                     - (self.mc + self.mp) * self.g * np.sin(curr_x[:, 2])) \
                 / (self.l * (self.mc + self.mp * (np.sin(curr_x[:, 2])**2)))
 
@@ -99,31 +101,31 @@ class CartPoleModel(Model):
         tmp2 = 1. / (self.mc + self.mp * (np.sin(xs[:, 2])**2))
 
         f_x[:, 1, 2] = - us[:, 0] * tmp \
-                       - tmp * (self.mp * np.sin(xs[:, 2]) \
+                       - tmp * (self.mp * np.sin(xs[:, 2])
-                                * (self.l * xs[:, 3]**2 \
+                                * (self.l * xs[:, 3]**2
                                    + self.g * np.cos(xs[:, 2]))) \
-                       + tmp2 * (self.mp * np.cos(xs[:, 2]) * self.l \
+            + tmp2 * (self.mp * np.cos(xs[:, 2]) * self.l
-                                 * xs[:, 3]**2 \
+                      * xs[:, 3]**2
-                                 + self.mp * self.g * (np.cos(xs[:, 2])**2 \
+                      + self.mp * self.g * (np.cos(xs[:, 2])**2
                                             - np.sin(xs[:, 2])**2))
         f_x[:, 3, 2] = - 1. / self.l * tmp \
-                        * (-us[:, 0] * np.cos(xs[:, 2]) \
+            * (-us[:, 0] * np.cos(xs[:, 2])
-                           - self.mp * self.l * (xs[:, 3]**2) \
+               - self.mp * self.l * (xs[:, 3]**2)
-                           * np.cos(xs[:, 2]) * np.sin(xs[:, 2]) \
+               * np.cos(xs[:, 2]) * np.sin(xs[:, 2])
                - (self.mc + self.mp) * self.g * np.sin(xs[:, 2])) \
             + 1. / self.l * tmp2 \
-                           * (us[:, 0] * np.sin(xs[:, 2]) \
+            * (us[:, 0] * np.sin(xs[:, 2])
-                              - self.mp * self.l * xs[:, 3]**2 \
+               - self.mp * self.l * xs[:, 3]**2
-                                * (np.cos(xs[:, 2])**2 - np.sin(xs[:, 2])**2) \
+               * (np.cos(xs[:, 2])**2 - np.sin(xs[:, 2])**2)
-                              - (self.mc + self.mp) \
+               - (self.mc + self.mp)
                * self.g * np.cos(xs[:, 2]))
 
         # f_theta_dot
-        f_x[:, 1, 3] = tmp2 * (self.mp * np.sin(xs[:, 2]) \
+        f_x[:, 1, 3] = tmp2 * (self.mp * np.sin(xs[:, 2])
                                * self.l * 2 * xs[:, 3])
         f_x[:, 2, 3] = np.ones(pred_len)
         f_x[:, 3, 3] = 1. / self.l * tmp2 \
-                        * (-2. * self.mp * self.l * xs[:, 3] \
+            * (-2. * self.mp * self.l * xs[:, 3]
                * np.cos(xs[:, 2]) * np.sin(xs[:, 2]))
 
         return f_x * dt + np.eye(state_size)  # to discrete form
@@ -150,7 +152,7 @@ class CartPoleModel(Model):
         f_u[:, 1, 0] = 1. / (self.mc + self.mp * (np.sin(xs[:, 2])**2))
 
         f_u[:, 3, 0] = -np.cos(xs[:, 2]) \
-                       / (self.l * (self.mc \
+            / (self.l * (self.mc
                          + self.mp * (np.sin(xs[:, 2])**2)))
 
         return f_u * dt  # to discrete form

PythonLinearNonlinearControl/models/first_order_lag.py

@@ -3,6 +3,7 @@ import scipy.linalg
 from scipy import integrate
 from .model import LinearModel
 
+
 class FirstOrderLagModel(LinearModel):
     """ first order lag model
     Attributes:
@@ -10,13 +11,15 @@ class FirstOrderLagModel(LinearModel):
         u (numpy.ndarray):
         history_pred_xs (numpy.ndarray):
     """
+
     def __init__(self, config, tau=0.63):
         """
         Args:
             tau (float): time constant 
         """
         # param
-        self.A, self.B = self._to_state_space(tau, dt=config.DT)  # discrete system
+        self.A, self.B = self._to_state_space(
+            tau, dt=config.DT)  # discrete system
         super(FirstOrderLagModel, self).__init__(self.A, self.B)
 
     @staticmethod
@@ -44,7 +47,8 @@ class FirstOrderLagModel(LinearModel):
         B = np.zeros_like(Bc)
         for m in range(Bc.shape[0]):
             for n in range(Bc.shape[1]):
-                integrate_fn = lambda tau: np.matmul(scipy.linalg.expm(Ac*tau), Bc)[m, n]
+                def integrate_fn(tau): return np.matmul(
+                    scipy.linalg.expm(Ac*tau), Bc)[m, n]
                 sol = integrate.quad(integrate_fn, 0, dt)
                 B[m, n] = sol[0]
 

PythonLinearNonlinearControl/models/make_models.py

@@ -2,6 +2,7 @@ from .first_order_lag import FirstOrderLagModel
 from .two_wheeled import TwoWheeledModel
 from .cartpole import CartPoleModel
 
+
 def make_model(args, config):
 
     if args.env == "FirstOrderLag":

PythonLinearNonlinearControl/models/model.py

@@ -1,8 +1,10 @@
 import numpy as np
 
+
 class Model():
     """ base class of model
     """
+
     def __init__(self):
         """
         """
@@ -22,7 +24,7 @@ class Model():
                 or shape(pop_size, pred_len+1, state_size)
         """
         if len(us.shape) == 3:
-            pred_xs =self._predict_traj_alltogether(curr_x, us)
+            pred_xs = self._predict_traj_alltogether(curr_x, us)
         elif len(us.shape) == 2:
             pred_xs = self._predict_traj(curr_x, us)
         else:
@@ -75,7 +77,7 @@ class Model():
             # next_x.shape = (pop_size, state_size)
             next_x = self.predict_next_state(x, us[t])
             # update
-            pred_xs = np.concatenate((pred_xs, next_x[np.newaxis, :, :]),\
+            pred_xs = np.concatenate((pred_xs, next_x[np.newaxis, :, :]),
                                      axis=0)
             x = next_x
 
@@ -99,13 +101,13 @@ class Model():
         # get size
         (pred_len, input_size) = us.shape
         # pred final adjoint state
-        lam = self.predict_terminal_adjoint_state(xs[-1],\
+        lam = self.predict_terminal_adjoint_state(xs[-1],
                                                   terminal_g_x=g_xs[-1])
         lams = lam[np.newaxis, :]
 
         for t in range(pred_len-1, 0, -1):
             prev_lam = \
-                self.predict_adjoint_state(lam, xs[t], us[t],\
+                self.predict_adjoint_state(lam, xs[t], us[t],
                                            goal=g_xs[t], t=t)
             # update
             lams = np.concatenate((prev_lam[np.newaxis, :], lams), axis=0)
@@ -175,6 +177,7 @@ class Model():
         raise NotImplementedError("Implement hessian of model \
                                    with respect to the input")
 
+
 class LinearModel(Model):
     """ discrete linear model, x[k+1] = Ax[k] + Bu[k]
 
@@ -182,6 +185,7 @@ class LinearModel(Model):
         A (numpy.ndarray): shape(state_size, state_size)
         B (numpy.ndarray): shape(state_size, input_size)
     """
+
     def __init__(self, A, B):
         """
         """

PythonLinearNonlinearControl/models/two_wheeled.py

@@ -2,9 +2,11 @@ import numpy as np
 
 from .model import Model
 
+
 class TwoWheeledModel(Model):
     """ two wheeled model
     """
+
     def __init__(self, config):
         """
         """

PythonLinearNonlinearControl/planners/closest_point_planner.py

@@ -1,9 +1,11 @@
 import numpy as np
 from .planner import Planner
 
+
 class ClosestPointPlanner(Planner):
     """ This planner make goal state according to goal path
     """
+
     def __init__(self, config):
         """
         """

PythonLinearNonlinearControl/planners/const_planner.py

@@ -1,9 +1,11 @@
 import numpy as np
 from .planner import Planner
 
+
 class ConstantPlanner(Planner):
     """ This planner make constant goal state
     """
+
     def __init__(self, config):
         """
         """

PythonLinearNonlinearControl/planners/planner.py

@@ -1,8 +1,10 @@
 import numpy as np
 
+
 class Planner():
     """
     """
+
     def __init__(self):
         """
         """

PythonLinearNonlinearControl/plotters/animator.py

@@ -8,9 +8,11 @@ import matplotlib.animation as animation
 
 logger = getLogger(__name__)
 
+
 class Animator():
     """ animation class
     """
+
     def __init__(self, env, args=None):
         """
         """
@@ -65,7 +67,7 @@ class Animator():
         """
         # set up animation figures
         self._setup()
-        _update_img = lambda i: self._update_img(i, history_x, history_g_x)
+        def _update_img(i): return self._update_img(i, history_x, history_g_x)
 
         # Set up formatting for the movie files
         Writer = animation.writers['ffmpeg']

PythonLinearNonlinearControl/plotters/plot_func.py

@@ -5,6 +5,7 @@ import matplotlib.pyplot as plt
 
 from ..helper import save_pickle, load_pickle
 
+
 def plot_result(history, history_g=None, ylabel="x",
                 save_dir="./result", name="state_history"):
     """
@@ -28,7 +29,7 @@ def plot_result(history, history_g=None, ylabel="x",
         def plot(axis, history, history_g=None):
             axis.plot(range(iters), history, c="r", linewidth=3)
             if history_g is not None:
-                axis.plot(range(iters), history_g,\
+                axis.plot(range(iters), history_g,
                           c="b", linewidth=3, label="goal")
 
         if i < size:
@@ -47,6 +48,7 @@ def plot_result(history, history_g=None, ylabel="x",
         axis1.legend(ncol=1, bbox_to_anchor=(0., 1.02, 1., 0.102), loc=3)
         figure.savefig(path, bbox_inches="tight", pad_inches=0.05)
 
+
 def plot_results(history_x, history_u, history_g=None, args=None):
     """
 
@@ -64,12 +66,13 @@ def plot_results(history_x, history_u, history_g=None, args=None):
         controller_type = args.controller_type
 
     plot_result(history_x, history_g=history_g, ylabel="x",
-                name= env + "-state_history",
+                name=env + "-state_history",
                 save_dir="./result/" + controller_type)
     plot_result(history_u, history_g=np.zeros_like(history_u), ylabel="u",
-                name= env + "-input_history",
+                name=env + "-input_history",
                 save_dir="./result/" + controller_type)
 
+
 def save_plot_data(history_x, history_u, history_g=None, args=None):
     """ save plot data
 
@@ -98,6 +101,7 @@ def save_plot_data(history_x, history_u, history_g=None, args=None):
                         env + "-history_g.pkl")
     save_pickle(path, history_g)
 
+
 def load_plot_data(env, controller_type, result_dir="./result"):
     """
     Args:
@@ -123,6 +127,7 @@ def load_plot_data(env, controller_type, result_dir="./result"):
 
     return history_x, history_u, history_g
 
+
 def plot_multi_result(histories, histories_g=None, labels=None, ylabel="x",
                       save_dir="./result", name="state_history"):
     """
@@ -146,7 +151,7 @@ def plot_multi_result(histories, histories_g=None, labels=None, ylabel="x",
             axis.plot(range(iters), history,
                       linewidth=3, label=label, alpha=0.7, linestyle="dashed")
             if history_g is not None:
-                axis.plot(range(iters), history_g,\
+                axis.plot(range(iters), history_g,
                           c="b", linewidth=3)
 
         if i < size:

PythonLinearNonlinearControl/plotters/plot_objs.py

@@ -5,6 +5,7 @@ import matplotlib.pyplot as plt
 
 from ..common.utils import rotate_pos
 
+
 def circle(center_x, center_y, radius, start=0., end=2*np.pi, n_point=100):
     """ Create circle matrix
 
@@ -29,6 +30,7 @@ def circle(center_x, center_y, radius, start=0., end=2*np.pi, n_point=100):
 
     return np.array(circle_xs), np.array(circle_ys)
 
+
 def circle_with_angle(center_x, center_y, radius, angle):
     """ Create circle matrix with angle line matrix
 
@@ -50,6 +52,7 @@ def circle_with_angle(center_x, center_y, radius, angle):
 
     return circle_x, circle_y, angle_x, angle_y
 
+
 def square(center_x, center_y, shape, angle):
     """ Create square
 
@@ -77,6 +80,7 @@ def square(center_x, center_y, shape, angle):
 
     return trans_points[:, 0], trans_points[:, 1]
 
+
 def square_with_angle(center_x, center_y, shape, angle):
     """ Create square with angle line
 

PythonLinearNonlinearControl/runners/make_runners.py

@@ -1,4 +1,5 @@
 from .runner import ExpRunner
 
+
 def make_runner(args):
     return ExpRunner()

PythonLinearNonlinearControl/runners/runner.py

@@ -4,9 +4,11 @@ import numpy as np
 
 logger = getLogger(__name__)
 
+
 class ExpRunner():
     """ experiment runner
     """
+
     def __init__(self):
         """
         """
@@ -46,6 +48,6 @@ class ExpRunner():
             score += cost
             step_count += 1
 
-        logger.debug("Controller type = {}, Score = {}"\
+        logger.debug("Controller type = {}, Score = {}"
                      .format(controller, score))
         return np.array(history_x), np.array(history_u), np.array(history_g)