import sys
sys.path.append('./../code')
from utils_quintin import *
import matplotlib.pyplot as plt
import argparse

parser = argparse.ArgumentParser(
    description='Visualise froc results')
parser.add_argument('-saveas', 
    help='')
parser.add_argument('-comparison', 
    help='')
parser.add_argument('--experiment', '-s', 
    metavar='[series_name]', required=True, nargs='+',
    help='List of series to include, must correspond with' +
        "path files in ./data/")
args = parser.parse_args()

if args.comparison:
    colors = ['r','r','b','b','g','g']
    plot_type = ['-','--','-','--','-','--']
else: 
    colors = ['r','b','g','k']
    plot_type = ['-','-','-','-']

experiments = args.experiment
print(experiments)
experiment_path = []
experiment_metrics = {}
auroc = []
for idx in range(len(args.experiment)):
    experiment_path = f'./../train_output/{experiments[idx]}/froc_metrics.yml'
    experiment_metrics = read_yaml_to_dict(experiment_path)
    auroc.append(round(experiment_metrics['auroc'],3))

    plt.figure(1)
    plt.plot(experiment_metrics["FP_per_case"], experiment_metrics["sensitivity"],color=colors[idx],linestyle=plot_type[idx])

    plt.figure(2)
    plt.plot(experiment_metrics["fpr"], experiment_metrics["tpr"],color=colors[idx],linestyle=plot_type[idx])

print(auroc)
experiments = [exp.replace('train_10h_', '') for exp in experiments] 
experiments = [exp.replace('train_n0.001_', '') for exp in experiments] 
experiments = [exp.replace('_', ' ') for exp in experiments] 
# experiments = ['10% noise','1% noise','0.1% noise','0.05% noise']

plt.figure(1)
plt.title('fROC curve')
plt.xlabel('False positive per case')
plt.ylabel('Sensitivity')
plt.legend(experiments,loc='lower right')
plt.xlim([0,3])
plt.ylim([0,1])
plt.yticks([0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1])
plt.grid()
plt.savefig(f"./../train_output/fROC_{args.saveas}.png", dpi=300)

concat_func = lambda x,y: x + " (" + str(y) + ")"
experiments_auroc = list(map(concat_func,experiments,auroc)) # list the map function

plt.figure(2)
plt.title('ROC curve')
plt.legend(experiments_auroc,loc='lower right')
plt.xlabel('False positive rate')
plt.ylabel('True positive rate')
plt.grid()
plt.savefig(f"./../train_output/ROC_{args.saveas}.png", dpi=300)