def test_johnsonsb(rnd, a, b, loc, scale):
# johnsonSb random variate generator
johnsonsb_dist = RVGs.JohnsonSb(a, b, loc, scale)
# obtain samples
samples = get_samples(johnsonsb_dist, rnd)
# report mean and variance
mean = scipy.johnsonsb.mean(a, b, loc, scale)
var = scipy.johnsonsb.var(a, b, loc, scale)
print_test_results('JohnsonSb', samples, expectation=mean, variance=var)
def test_fitting_johnson_sb():
print("\nTesting Johnson Sb with a=10, b=5, loc=10, scale=100")
dist = RVGs.JohnsonSb(a=10, b=5, loc=10, scale=100)
print(' percentile interval: ', dist.get_percentile_interval(alpha=0.05))
data = np.array(get_samples(dist, np.random))
dict_ml_results = RVGs.JohnsonSb.fit_ml(data=data, fixed_location=10)
print(" Fit:")
print(" ML:", dict_ml_results)
# plot the fitted distributions
Plot.plot_johnson_sb_fit(data=data,
fit_results=dict_ml_results,
title='Maximum Likelihood')
