import numpy as np
from matplotlib import pyplot as plt
import scipy as sp
from scipy.stats import pearsonr
from matplotlib import markers
from sys import argv
import json
import random
blue = '#0984e3'
red = '#d63031'
green = '#227D51'

sim = [0.79844266, 0.8377947, 0.87015217, 0.91429971, 0.94337877, 0.89212607, 0.99805755, 0.98048562, 0.98530593, 0.99598291, 0.87856398, 0.99217795, 0.62392839, 0.99253731, 0.99703892]
ana = [0.8, 0.8290884722222223, 0.8581769444444445, 0.8872654166666667, 0.9163538888888889, 0.9454423611111111, 0.9745308333333333, 1.0, 1.0, 1.0, 1.0, 1.0, 0.5, 1.0, 1.0]

def p1(x, coopr, crawx, crawy, postfix, show=True):
    fig = plt.figure(figsize=(4.5, 1.8))
    ax = fig.gca()
    if postfix == 'SURVIVE':
        ax.plot(x, sim, marker='s', linestyle='dotted', markerfacecolor='none', color=red, linewidth=2, label="Simulation")
        ax.plot(x, ana, marker='^', linestyle='dotted', markerfacecolor='none', color=green, linewidth=2, label="Analytical")
    ax.plot(x, coopr, marker='o', linestyle='dotted', markerfacecolor='none', color=blue, linewidth=2, label="Human")
    ax.scatter(crawx, crawy, s=15, c='dimgray', marker='x', linewidth=0.5, alpha=0.4)
    ax.set_ylim(-0.05, 1.05)
    ax.set_yticks(sp.linspace(0, 1, 5))
    ax.tick_params(labelsize=10)
    ax.tick_params(direction='in')
    ax.set_xlim(0, 16)
    ax.set_xticks([1, 5, 10, 15])
    # ax.set_xticklabels([''])
    ax.set_xlabel("Rounds", size=11)
    ax.set_ylabel(r"f$_{\rm c}$", size=11, fontstyle='normal')
    ax.yaxis.grid(True, linestyle='--')
    # if postfix == 'SURVIVE':
    #     ax.legend(loc='best', fontsize=10, ncol=3)

    plt.tight_layout()
    if show:
        plt.show()
    else:
        plt.savefig("graph/fc_plot_%s.eps" % postfix)


def plot(mode, show):
    coopr = np.loadtxt("outputs/CR_%s.csv" % mode, delimiter=',')
    crawx, crawy = [],[]
    with open("outputs/CRAW_%s.json" % mode) as f:
        data = json.load(f)
        for l in data:
            for i in range(len(l)):
                crawx.append((i + 1) + random.uniform(-0.25, 0.25))
                crawy.append(l[i])
    x = np.arange(1, 16)
    p1(x, coopr, crawx, crawy, mode, show)

if __name__ == '__main__':
    # fc_plot c/s t/f
    mode = 'CLASSIC' if argv[1] == 'c' else 'SURVIVE'
    show = argv[2] == 't'
    plot(mode, show)