import matplotlib.pyplot as plt
from sklearn.metrics import auc

tpr_harvard = [0.72, 0.72, 0.74, 0.74, 0.78, 0.78, 0.84, 0.84, 0.86, 0.86, 0.91, 0.91, 0.925, 0.925, 0.935, 0.935,
               0.955, 1.0]
fpr_harvard = [0.0, 0.03, 0.03, 0.05, 0.05, 0.09, 0.09, 0.12, 0.12, 0.135, 0.135, 0.16, 0.16, 0.24, 0.24, 0.78, 0.78,
               1.0]

tpr_exb = [0.48, 0.48, 0.55, 0.55, 0.62, 0.62, 0.78, 0.78, 0.8, 0.8, 0.82, 0.82, 0.84, 0.84, 0.87, 0.87, 0.9, 0.9, 0.92,
           0.92, 0.95, 0.95, 0.98, 1.0]
fpr_exb = [0.0, 0.02, 0.02, 0.042, 0.042, 0.05, 0.05, 0.085, 0.085, 0.09, 0.09, 0.115, 0.115, 0.17, 0.17, 0.22, 0.22,
           0.25, 0.25, 0.48, 0.48, 0.58, 1.0, 1.0]

tpr_quinch_wong = [0.03, 0.03, 0.63, 0.63, 0.655, 0.655, 0.72, 0.72, 0.77, 0.77, 0.78, 0.78, 0.8, 0.8, 0.825, 0.825,
                   0.84, 0.84, 0.85, 0.85, 0.92, 0.92, 0.95, 0.95, 0.965, 1.0]
fpr_quinch_wong = [0.0, 0.03, 0.03, 0.04, 0.04, 0.07, 0.07, 0.165, 0.165, 0.22, 0.22, 0.24, 0.24, 0.25, 0.25, 0.26,
                   0.26, 0.44, 0.44, 0.48, 0.48, 0.55, 0.55, 0.6, 1.0, 1.0]

tpr_mtu = [0.48, 0.48, 0.56, 0.56, 0.6, 0.6, 0.64, 0.64, 0.67, 0.67, 0.72, 0.72, 0.75, 0.75, 0.78, 0.78, 0.8, 0.8,
           0.825, 0.825, 0.84, 0.84, 0.86, 0.86, 0.9, 0.9, 0.92, 0.92, 0.95, 0.95, 0.965, 0.965, 0.98, 1.0]
fpr_mtu = [0.0, 0.02, 0.02, 0.045, 0.045, 0.06, 0.06, 0.08, 0.08, 0.115, 0.115, 0.13, 0.13, 0.135, 0.135, 0.23, 0.23,
           0.25, 0.25, 0.26, 0.26, 0.4, 0.4, 0.475, 0.475, 0.495, 0.495, 0.66, 0.66, 0.765, 0.765, 0.8, 1.0, 1.0]

tpr_nlp = [0.525, 0.525, 0.62, 0.62, 0.64, 0.64, 0.66, 0.66, 0.72, 0.72, 0.74, 0.74, 0.76, 0.76, 0.78, 0.78, 0.8, 0.8,
           0.82, 0.82, 0.84, 0.84, 0.88, 0.88, 0.915, 0.915, 0.94, 0.94, 0.965, 0.965, 0.98, 0.98, 1.0, 1.0]
fpr_nlp = [0.0, 0.02, 0.02, 0.06, 0.06, 0.16, 0.16, 0.22, 0.22, 0.29, 0.29, 0.31, 0.31, 0.36, 0.36, 0.372, 0.372, 0.42,
           0.42, 0.44, 0.44, 0.48, 0.48, 0.58, 0.58, 0.72, 0.72, 0.735, 0.735, 0.8, 0.8, 0.9, 1.0, 1.0]

# tpr = []
# fpr = []
#
# tpr_aug = []
# fpr_aug = []
#
# for i in range(len(tpr) - 2):
#     tpr_aug.extend([tpr[i]] * 2)
# tpr_aug.append(tpr[-2])
# tpr_aug.append(tpr[-1])
#
# fpr_aug.append(fpr[0])
# for i in range(1, len(fpr) - 2):
#     fpr_aug.extend([fpr[i]] * 2)
# fpr_aug.append(fpr[-2])
# fpr_aug.append(fpr[-1])
# fpr_aug.append(1.0)
#
# print(tpr_aug)
# print(fpr_aug)


#
# plt.title('ROC Harvard-MIT')
# plt.plot(fpr_aug, tpr_aug, 'b', label='AUC = %0.4f' % 0.9250)
# plt.legend(loc='lower right')
# plt.plot([0, 1], [0, 1], 'r--')
# plt.xlim([0, 1])
# plt.ylim([0, 1])
# plt.ylabel('True Positive Rate')
# plt.xlabel('False Positive Rate')
plt.legend(loc=0)
plt.show()
