import numpy as np
import matplotlib.pyplot as plt


def plot(table, title='', log=False):
    plt.figure(figsize=(5, 2))
    plt.rcParams['font.size'] = 8
    plt.suptitle(title)

    plt.subplot(111)
    if log:
        plt.xscale('log')
    plt.title('EMD' + ' '*10, loc='right')

    plt.plot(table[0], table[1], color='#92182b')
    plt.tick_params(axis='y', labelcolor='#92182b')
    plt.ylabel('average', color='#92182b')
    plt.ticklabel_format(style='sci', axis='y',
                         scilimits=(0, 0), useMathText=True)

    twin = plt.twinx()
    twin.plot(table[0], table[2], color='gray')
    twin.tick_params(axis='y', labelcolor='gray')
    twin.set_ylabel('standard deviation', color='gray')
    twin.ticklabel_format(style='sci', axis='y',
                          scilimits=(0, 0), useMathText=True)

    # plt.subplot(212)
    # if log:
    #     plt.xscale('log')
    # plt.title('skewness', loc='right')
    # plt.xlabel('RL rounds')
    # plt.plot(table[0], table[3], color='xkcd:gray')

    plt.tight_layout()


table = np.loadtxt('ex-6/plots/emd-round-noise.txt')

# plot(table[:27].T, title='noiseless', log=True)
plot(table[27:47].T, title=r'noise at $\sigma_N = 0.005$')
plt.savefig('notes/images/6-rounds-noise-0.005.pdf')
plot(table[47:67].T, title=r'noise at $\sigma_N = 0.01$')
plt.savefig('notes/images/6-rounds-noise-0.01.pdf')
plot(table[67:].T,   title=r'noise at $\sigma_N = 0.05$')
plt.savefig('notes/images/6-rounds-noise-0.05.pdf')