from BayesianOptimization.BOwithGym import BayesianOptimization

import numpy as np

import matplotlib.pyplot as plt

# from ToyTask.MountainCarGym import Continuous_MountainCarEnv
from ToyTask.Pendulum import PendulumEnv

import warnings
from sklearn.exceptions import ConvergenceWarning

warnings.filterwarnings("ignore", category=ConvergenceWarning)

# BO parameters
env = PendulumEnv()
nr_steps = 100
acquisition_fun = 'ei'
iteration_steps = 100

nr_runs = 100

# storage arrays
finished_store = np.zeros((1, nr_runs))
best_policy = np.zeros((nr_steps, nr_runs))
reward_store = np.zeros((iteration_steps, nr_runs))


# post-processing
def post_processing(finished, policy, reward):

    finish_mean = np.nanmean(finished)
    finish_std = np.nanstd(finished)

    policy_mean = np.mean(policy, axis=1)
    policy_std = np.std(policy, axis=1)

    reward_mean = np.mean(reward, axis=1)
    reward_std = np.std(reward, axis=1)

    return finish_mean, finish_std, policy_mean, policy_std, reward_mean, reward_std


# plot functions
def plot_policy(mean, std, fin_mean, fin_std):
    x = np.linspace(0, mean.shape[0], mean.shape[0])
    plt.plot(x, mean)
    plt.fill_between(
        x,
        mean - 1.96 * std,
        mean + 1.96 * std,
        alpha=0.5
    )

    y = np.linspace(-2, 2, 50)
    plt.vlines(fin_mean, -2, 2, colors='red')
    plt.fill_betweenx(
        y,
        fin_mean - 1.96 * fin_std,
        fin_mean + 1.96 * fin_std,
        alpha=0.5,
    )

    plt.show()


def plot_reward(mean, std):
    eps = np.linspace(0, mean.shape[0], mean.shape[0])
    plt.plot(eps, mean)

    plt.fill_between(
        eps,
        mean - 1.96 * std,
        mean + 1.96 * std,
        alpha=0.5
    )
    plt.show()


# main
def main():
    global finished_store, best_policy, reward_store
    bo = BayesianOptimization(env, nr_steps, acq=acquisition_fun)
    for i in range(nr_runs):
        print('Iteration:', str(i))
        bo.env_seed = int(np.random.randint(1, 2147483647, 1)[0])
        bo.initialize()
        for j in range(iteration_steps):
            x_next = bo.next_observation()
            bo.eval_new_observation(x_next)

        finished = bo.get_best_result(plotter=False)

        finished_store[:, i] = finished
        best_policy[:, i] = bo.policy_model.trajectory.T
        reward_store[:, i] = bo.best_reward.T

        print(reward_store[-1, i])

    finish_mean, finish_std, policy_mean, policy_std, reward_mean, reward_std = post_processing(finished_store,
                                                                                                best_policy,
                                                                                                reward_store)
    plot_policy(policy_mean, policy_std, finish_mean, finish_std)
    plot_reward(reward_mean, reward_std)


if __name__ == '__main__':
    main()