def test_johnsonsu(rnd, a, b, loc, scale):
# johnsonSu random variate generator
johnsonsu_dist = RVGs.JohnsonSu(a, b, loc, scale)
# obtain samples
samples = get_samples(johnsonsu_dist, rnd)
# report mean and variance
mean = scipy.johnsonsu.mean(a, b, loc, scale)
var = scipy.johnsonsu.var(a, b, loc, scale)
print_test_results('JohnsonSu', samples, expectation=mean, variance=var)
def test_fitting_johnson_su():
print("\nTesting Johnson Su with a=2, b=3, loc=1, scale=4")
dist = RVGs.JohnsonSu(a=2, b=3, loc=1, scale=4)
print(' percentile interval: ', dist.get_percentile_interval(alpha=0.05))
data = np.array(get_samples(dist, np.random))
dict_ml_results = RVGs.JohnsonSu.fit_ml(data=data, fixed_location=1)
print(" Fit:")
print(" ML:", dict_ml_results)
# plot the fitted distributions
Plot.plot_johnson_su_fit(data=data,
fit_results=dict_ml_results,
title='Maximum Likelihood')
