# analysis
# board, trial
# 200, 200

algos = ["rand ", "elem ", "sumsq", "area "]

stats = []

for i in range(len(algos)):
    filename = f"{algos[i]}_raw2.csv"
    with open(filename, "r") as f:
        # f.readline()  # avg_best,avg_worst,avg_mean
        board=[]
        for l in f:
            val = list(map(float, l.strip().split(',')))
            board += [(val)]
        stats += [board]

n_algs = len(stats)
n_boards = len(stats[0])
n_trials = len(stats[0][0])

stat_li = []

for i in range(len(algos)):
    tmp1 = []
    for j in range(len(stats[i])):
        best=0
        worst=999
        sums=0
        sumsq=0
        tmp2 = []
        for k in range(len(stats[i][j])):
            v = stats[i][j][k]
            best, worst, sums, sumsq = max(best, v), min(worst, v), sums+v, sumsq+v*v
            mean=sums/(k+1)
            variance=(sumsq/(k+1)-mean*mean)**.5
            tmp2 += [(best,worst,mean,variance)]
        tmp1 += [tmp2]
    stat_li += [tmp1]

import numpy as np
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (10,8)

stat_np = np.array(stat_li)

best_np = stat_np[:,:,:,0]
mean_best_np = np.mean(best_np, axis=1)
#worst_np = stat_np[:,:,:,1]
#avg_np = stat_np[:,:,:,2]
#stdev_np = stat_np[:,:,:,3]


for i in range(n_algs):
    plt.plot(range(1,n_trials+1), mean_best_np[i], label=algos[i])

plt.title("Best")
plt.legend()
plt.xlabel("Trials")
plt.ylabel("Score")
plt.savefig('fig2', bbox_inches='tight')
plt.show()