import matplotlib.pyplot as plt
import cvxpy
import math
import numpy as np
import sys
import cubic_spline_planner

NX = 4  # x = x, y, v, yaw
NU = 2  # a = [accel, steer]
T = 5  # horizon length

# mpc parameters
R = np.diag([0.01, 0.01])  # input cost matrix
Rd = np.diag([0.01, 1.0])  # input difference cost matrix
Q = np.diag([1.0, 1.0, 0.5, 0.5])  # state cost matrix
Qf = Q  # state final matrix
GOAL_DIS = 2  # goal distance
STOP_SPEED = 0.2  # stop speed
MAX_TIME = 500.0  # max simulation time

# iterative paramter
MAX_ITER = 3  # Max iteration
DU_TH = 0.1  # iteration finish param

TARGET_SPEED = 4.0  # [cm/s] target speed
N_IND_SEARCH = 10  # Search index number

DT = 0.2  # [s] time tick

# Whiteboard robot dimensions
LENGTH = 12  # cm
WIDTH = 12  # cm
BACKTOWHEEL = 3  # cm
WHEEL_LENGTH = 1.8  # cm
WHEEL_WIDTH = 1  # cm
WHEEL_SPACING_HORIZONTAL = 3  # cm
WHEELTOBACK = 6  # cm

MAX_STEER = np.deg2rad(70.0)  # maximum steering angle [rad] 45
MAX_DSTEER = np.deg2rad(30.0)  # maximum steering speed [rad/s] 30
MAX_SPEED = 6.0  # maximum speed [cm/s]
MIN_SPEED = -2.0  # minimum speed [cm/s]
MAX_ACCEL = 0.8  # maximum accel [cm/ss]

execute = True

class State:
    """
    vehicle state class
    """

    def __init__(self, x=0.0, y=0.0, yaw=0.0, v=0.0):
        self.x = x
        self.y = y
        self.yaw = yaw
        self.v = v
        self.predelta = None


def pi_2_pi(angle):
    while(angle > math.pi):
        angle = angle - 2.0 * math.pi

    while(angle < -math.pi):
        angle = angle + 2.0 * math.pi

    return angle


def get_linear_model_matrix(v, phi, delta):

    A = np.zeros((NX, NX))
    A[0, 0] = 1.0
    A[1, 1] = 1.0
    A[2, 2] = 1.0
    A[3, 3] = 1.0
    A[0, 2] = DT * math.cos(phi)
    A[0, 3] = - DT * v * math.sin(phi)
    A[1, 2] = DT * math.sin(phi)
    A[1, 3] = DT * v * math.cos(phi)
    A[3, 2] = DT * math.tan(delta) / WHEELTOBACK

    B = np.zeros((NX, NU))
    B[2, 0] = DT
    B[3, 1] = DT * v / (WHEELTOBACK * math.cos(delta) ** 2)

    C = np.zeros(NX)
    C[0] = DT * v * math.sin(phi) * phi
    C[1] = - DT * v * math.cos(phi) * phi
    C[3] = - DT * v * delta / (WHEELTOBACK * math.cos(delta) ** 2)

    return A, B, C


def plot_car(x, y, yaw, steer=0.0, robot_color="-g"):  # pragma: no cover

    outline = np.array([[-BACKTOWHEEL, (LENGTH - BACKTOWHEEL), (LENGTH - BACKTOWHEEL), -BACKTOWHEEL, -BACKTOWHEEL],
                        [WIDTH / 2, WIDTH / 2, - WIDTH / 2, -WIDTH / 2, WIDTH / 2]])

    fr_wheel = np.array([[WHEEL_LENGTH, -WHEEL_LENGTH, -WHEEL_LENGTH, WHEEL_LENGTH, WHEEL_LENGTH],
                         [-WHEEL_WIDTH - WHEEL_SPACING_HORIZONTAL, -WHEEL_WIDTH - WHEEL_SPACING_HORIZONTAL, WHEEL_WIDTH - WHEEL_SPACING_HORIZONTAL, WHEEL_WIDTH - WHEEL_SPACING_HORIZONTAL, -WHEEL_WIDTH - WHEEL_SPACING_HORIZONTAL]])

    rr_wheel = np.copy(fr_wheel)

    fl_wheel = np.copy(fr_wheel)
    fl_wheel[1, :] *= -1
    rl_wheel = np.copy(rr_wheel)
    rl_wheel[1, :] *= -1

    Rot1 = np.array([[math.cos(yaw), math.sin(yaw)],
                     [-math.sin(yaw), math.cos(yaw)]])
    Rot2 = np.array([[math.cos(steer), math.sin(steer)],
                     [-math.sin(steer), math.cos(steer)]])

    fr_wheel = (fr_wheel.T.dot(Rot2)).T
    fl_wheel = (fl_wheel.T.dot(Rot2)).T
    fr_wheel[0, :] += WHEELTOBACK
    fl_wheel[0, :] += WHEELTOBACK

    fr_wheel = (fr_wheel.T.dot(Rot1)).T
    fl_wheel = (fl_wheel.T.dot(Rot1)).T

    outline = (outline.T.dot(Rot1)).T
    rr_wheel = (rr_wheel.T.dot(Rot1)).T
    rl_wheel = (rl_wheel.T.dot(Rot1)).T

    outline[0, :] += x
    outline[1, :] += y
    fr_wheel[0, :] += x
    fr_wheel[1, :] += y
    rr_wheel[0, :] += x
    rr_wheel[1, :] += y
    fl_wheel[0, :] += x
    fl_wheel[1, :] += y
    rl_wheel[0, :] += x
    rl_wheel[1, :] += y

    # Change whiteboard image source here:
    img = plt.imread(r"C:\Users\Gabriel\Desktop\hello_s.jpg")
    plt.imshow(img, extent=[0, 250, 0, 125])

    plt.plot(np.array(outline[0, :]).flatten(),
             np.array(outline[1, :]).flatten(), robot_color)
    plt.plot(np.array(fr_wheel[0, :]).flatten(),
             np.array(fr_wheel[1, :]).flatten(), robot_color)
    plt.plot(np.array(rr_wheel[0, :]).flatten(),
             np.array(rr_wheel[1, :]).flatten(), robot_color)
    plt.plot(np.array(fl_wheel[0, :]).flatten(),
             np.array(fl_wheel[1, :]).flatten(), robot_color)
    plt.plot(np.array(rl_wheel[0, :]).flatten(),
             np.array(rl_wheel[1, :]).flatten(), robot_color)
    plt.plot(x, y, "")

def update_state(state, a, delta):

    # input check
    if delta >= MAX_STEER:
        delta = MAX_STEER
    elif delta <= -MAX_STEER:
        delta = -MAX_STEER

    state.x = state.x + state.v * math.cos(state.yaw) * DT
    state.y = state.y + state.v * math.sin(state.yaw) * DT
    state.yaw = state.yaw + state.v / WHEELTOBACK * math.tan(delta) * DT
    state.v = state.v + a * DT

    if state. v > MAX_SPEED:
        state.v = MAX_SPEED
    elif state. v < MIN_SPEED:
        state.v = MIN_SPEED

    return state

def get_nparray_from_matrix(x):
    return np.array(x).flatten()

def calc_nearest_index(state, cx, cy, cyaw, pind):

    dx = [state.x - icx for icx in cx[pind:(pind + N_IND_SEARCH)]]
    dy = [state.y - icy for icy in cy[pind:(pind + N_IND_SEARCH)]]

    d = [idx ** 2 + idy ** 2 for (idx, idy) in zip(dx, dy)]

    mind = min(d)

    ind = d.index(mind) + pind

    mind = math.sqrt(mind)

    dxl = cx[ind] - state.x
    dyl = cy[ind] - state.y

    angle = pi_2_pi(cyaw[ind] - math.atan2(dyl, dxl))
    if angle < 0:
        mind *= -1

    return ind, mind

def predict_motion(x0, oa, od, xref):
    xbar = xref * 0.0
    for i, _ in enumerate(x0):
        xbar[i, 0] = x0[i]

    state = State(x=x0[0], y=x0[1], yaw=x0[3], v=x0[2])
    for (ai, di, i) in zip(oa, od, range(1, T + 1)):
        state = update_state(state, ai, di)
        xbar[0, i] = state.x
        xbar[1, i] = state.y
        xbar[2, i] = state.v
        xbar[3, i] = state.yaw

    return xbar

def iterative_linear_mpc_control(xref, x0, dref, oa, od):
    """
    MPC control with updating operational point iteraitvely
    """

    if oa is None or od is None:
        oa = [0.0] * T
        od = [0.0] * T

    for i in range(MAX_ITER):
        xbar = predict_motion(x0, oa, od, xref)
        poa, pod = oa[:], od[:]
        oa, od, ox, oy, oyaw, ov = linear_mpc_control(xref, xbar, x0, dref)
        du = sum(abs(oa - poa)) + sum(abs(od - pod))  # calc u change value
        if du <= DU_TH:
            break
    else:
        print("Iterative is max iter")

    return oa, od, ox, oy, oyaw, ov

def linear_mpc_control(xref, xbar, x0, dref):
    """
    linear mpc control
    xref: reference point
    xbar: operational point
    x0: initial state
    dref: reference steer angle
    """

    x = cvxpy.Variable((NX, T + 1))
    u = cvxpy.Variable((NU, T))

    cost = 0.0
    constraints = []

    for t in range(T):
        cost += cvxpy.quad_form(u[:, t], R)

        if t != 0:
            cost += cvxpy.quad_form(xref[:, t] - x[:, t], Q)

        A, B, C = get_linear_model_matrix(
            xbar[2, t], xbar[3, t], dref[0, t])
        constraints += [x[:, t + 1] == A * x[:, t] + B * u[:, t] + C]

        if t < (T - 1):
            cost += cvxpy.quad_form(u[:, t + 1] - u[:, t], Rd)
            constraints += [cvxpy.abs(u[1, t + 1] - u[1, t]) <=
                            MAX_DSTEER * DT]

    cost += cvxpy.quad_form(xref[:, T] - x[:, T], Qf)

    constraints += [x[:, 0] == x0]
    constraints += [x[2, :] <= MAX_SPEED]
    constraints += [x[2, :] >= MIN_SPEED]
    constraints += [cvxpy.abs(u[0, :]) <= MAX_ACCEL]
    constraints += [cvxpy.abs(u[1, :]) <= MAX_STEER]

    prob = cvxpy.Problem(cvxpy.Minimize(cost), constraints)
    prob.solve(solver=cvxpy.ECOS, verbose=False)

    if prob.status == cvxpy.OPTIMAL or prob.status == cvxpy.OPTIMAL_INACCURATE:
        ox = get_nparray_from_matrix(x.value[0, :])
        oy = get_nparray_from_matrix(x.value[1, :])
        ov = get_nparray_from_matrix(x.value[2, :])
        oyaw = get_nparray_from_matrix(x.value[3, :])
        oa = get_nparray_from_matrix(u.value[0, :])
        odelta = get_nparray_from_matrix(u.value[1, :])

    else:
        print("Error: Cannot solve mpc..")
        oa, odelta, ox, oy, oyaw, ov = None, None, None, None, None, None

    return oa, odelta, ox, oy, oyaw, ov


def calc_ref_trajectory(state, cx, cy, cyaw, ck, sp, dl, pind):
    xref = np.zeros((NX, T + 1))
    dref = np.zeros((1, T + 1))
    ncourse = len(cx)

    ind, _ = calc_nearest_index(state, cx, cy, cyaw, pind)

    if pind >= ind:
        ind = pind

    xref[0, 0] = cx[ind]
    xref[1, 0] = cy[ind]
    xref[2, 0] = sp[ind]
    xref[3, 0] = cyaw[ind]
    dref[0, 0] = 0.0  # steer operational point should be 0

    travel = 0.0

    for i in range(T + 1):
        travel += abs(state.v) * DT
        dind = int(round(travel / dl))

        if (ind + dind) < ncourse:
            xref[0, i] = cx[ind + dind]
            xref[1, i] = cy[ind + dind]
            xref[2, i] = sp[ind + dind]
            xref[3, i] = cyaw[ind + dind]
            dref[0, i] = 0.0
        else:
            xref[0, i] = cx[ncourse - 1]
            xref[1, i] = cy[ncourse - 1]
            xref[2, i] = sp[ncourse - 1]
            xref[3, i] = cyaw[ncourse - 1]
            dref[0, i] = 0.0

    return xref, ind, dref

def check_goal(state, goal, tind, nind):

    # check goal
    dx = state.x - goal[0]
    dy = state.y - goal[1]
    d = math.hypot(dx, dy)

    isgoal = (d <= GOAL_DIS)

    if abs(tind - nind) >= 5:
        isgoal = False

    isstop = (abs(state.v) <= STOP_SPEED)

    if isgoal and isstop:
        return True

    return False

def do_simulation(cx, cy, cyaw, ck, sp, dl, initial_state):
    """
    Simulation
    cx: course x position list
    cy: course y position list
    cy: course yaw position list
    ck: course curvature list
    sp: speed profile
    dl: course tick [cm]
    """

    goal = [cx[-1], cy[-1]]

    state = initial_state

    # initial yaw compensation
    if state.yaw - cyaw[0] >= math.pi:
        state.yaw -= math.pi * 2.0
    elif state.yaw - cyaw[0] <= -math.pi:
        state.yaw += math.pi * 2.0

    time = 0.0
    x = [state.x]
    y = [state.y]
    yaw = [state.yaw]
    v = [state.v]
    t = [0.0]
    d = [0.0]
    a = [0.0]
    target_ind, _ = calc_nearest_index(state, cx, cy, cyaw, 0)

    odelta, oa = None, None

    cyaw = smooth_yaw(cyaw)

    while MAX_TIME >= time:
        xref, target_ind, dref = calc_ref_trajectory(
            state, cx, cy, cyaw, ck, sp, dl, target_ind)

        x0 = [state.x, state.y, state.v, state.yaw]  # current state

        oa, odelta, ox, oy, oyaw, ov = iterative_linear_mpc_control(
            xref, x0, dref, oa, odelta)

        if odelta is not None:
            di, ai = odelta[0], oa[0]

        state = update_state(state, ai, di)
        time = time + DT

        x.append(state.x)
        y.append(state.y)
        yaw.append(state.yaw)
        v.append(state.v)
        t.append(time)
        d.append(di)
        a.append(ai)

        if check_goal(state, goal, target_ind, len(cx)):
            #print("Goal")
            break

        if execute:  # pragma: no cover
            plt.cla()
            # for stopping simulation with the esc key.
            plt.gcf().canvas.mpl_connect('key_release_event',
                    lambda event: [exit(0) if event.key == 'escape' else None])
            if ox is not None:
                plt.plot(ox, oy, "xg", label="MPC")
            plt.plot(cx, cy, "-y", label="course")
            plt.plot(x, y, "*c", linewidth=2, markersize=14, label="trajectory")
            plt.plot(xref[0, :], xref[1, :], "xg", label="xref")
            plt.plot(cx[target_ind], cy[target_ind], "xg", label="target")

            plot_car(state.x, state.y, state.yaw, steer=di)

            plt.grid(False)
            plt.title("Time[s]:" + str(round(time, 2))
                      + ", Speed[cm/s]:" + str(round(state.v, 2)))
            plt.pause(0.0001)

    return t, x, y, yaw, v, d, a

def calc_speed_profile(cx, cy, cyaw, target_speed):

    speed_profile = [target_speed] * len(cx)
    direction = 1.0  # forward

    # Set stop point
    for i in range(len(cx) - 1):
        dx = cx[i + 1] - cx[i]
        dy = cy[i + 1] - cy[i]

        move_direction = math.atan2(dy, dx)

        if dx != 0.0 and dy != 0.0:
            dangle = abs(pi_2_pi(move_direction - cyaw[i]))
            if dangle >= math.pi / 4.0:
                direction = -1.0
            else:
                direction = 1.0

        if direction != 1.0:
            speed_profile[i] = - target_speed
        else:
            speed_profile[i] = target_speed

    speed_profile[-1] = 0.0

    return speed_profile

def smooth_yaw(yaw):

    for i in range(len(yaw) - 1):
        dyaw = yaw[i + 1] - yaw[i]

        while dyaw >= math.pi / 2.0:
            yaw[i + 1] -= math.pi * 2.0
            dyaw = yaw[i + 1] - yaw[i]

        while dyaw <= -math.pi / 2.0:
            yaw[i + 1] += math.pi * 2.0
            dyaw = yaw[i + 1] - yaw[i]

    return yaw

def path_plan(dl):
    # Insert path plan coordinates here:
    ax = [10.0, 20.0]
    ay = [10.0, 20.0]

    cx, cy, cyaw, ck, s = cubic_spline_planner.calc_spline_course(ax, ay, ds=dl)

    return cx, cy, cyaw, ck

def main():

    dl = 1.0  # course tick
    cx, cy, cyaw, ck = path_plan(dl)

    sp = calc_speed_profile(cx, cy, cyaw, TARGET_SPEED)

    initial_state = State(x=cx[0], y=cy[0], yaw=cyaw[0], v=0.0)

    t, x, y, yaw, v, d, a = do_simulation(
        cx, cy, cyaw, ck, sp, dl, initial_state)

    if execute:  # pragma: no cover
        plt.subplots()
        # Change whiteboard image source here:
        img = plt.imread(r"C:\Users\Gabriel\Desktop\hello_s.jpg")
        plt.imshow(img, extent=[0, 250, 0, 125])
        plt.plot(x, y, "*w", linewidth=2, markersize=14)
        plt.grid(False)
        plt.axis(xlim=(0, 250), ylim=(0, 125))
        plt.xlabel("x[cm]")
        plt.ylabel("y[cm]")
        plt.show()

if __name__ == '__main__':
    main()
    # main2()
