from scipy import stats
import numpy as np
# siemens_froc = [1.68,1.81,1.44,1.55]
# b400_froc = [3.4,3.93,2.82,]
# b800_froc = [1.58,1.99,1.36,1.6]

# siemens_roc = [0.782, 0.732, 0.775, 0.854]
b400_roc = [0.746, 0.814, 0.789, 0.763]
b800_roc = [0.786, 0.731, 0.67, 0.782]

# stat_test = stats.wilcoxon(siemens_froc,b800_froc,alternative='less')
# print('froc stats siemens > b400',stat_test)
# print(' Mean and std siemens froc:', np.mean(siemens_froc),'+-',np.std(siemens_froc))
# print(' Mean and std b400 froc:', np.mean(b400_froc),'+-',np.std(b400_froc))
# print(' Mean and std b800 froc:', np.mean(b800_froc),'+-',np.std(b800_froc))

# print(' Mean and std siemens roc:', np.mean(siemens_roc),'+-',np.std(siemens_roc))
print(' Mean and std b400 roc:', np.mean(b400_roc),'+-',np.std(b400_roc))
print(' Mean and std b800 roc:', np.mean(b800_roc),'+-',np.std(b800_roc))


# The test has been introduced in [4]. Given n independent samples (xi, yi) from a bivariate distribution 
# (i.e. paired samples), it computes the differences di = xi - yi. One assumption of the test is that the 
# differences are symmetric, see [2]. The two-sided test has the null hypothesis that the median of the 
# differences is zero against the alternative that it is different from zero. The one-sided test has the 
# null hypothesis that the median is positive against the alternative that it is negative 
# (alternative == 'less'), or vice versa (alternative == 'greater.').