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)
print("Fitting GammaPoisson:", dictResults)
# 8 Geometric
dist = RVGs.Geometric(0.3, 1)
dat_geom = np.array(get_samples(dist, np.random)) # generate data
dictResults = Fit.fit_geometric(dat_geom, 'Data', fixed_location=1) # fit
print("Fitting Geometric:", dictResults)
# 9 fitting a JohnsonSb distribution
dist = RVGs.JohnsonSb(a=10, b=3, loc=1, scale=2)
dat_JohnsonSb = np.array(get_samples(dist, np.random)) # generate data
dictResults = Fit.fit_johnsonSb(dat_JohnsonSb, 'Data', fixed_location=1) # fit
print("Fitting johnsonSb:", dictResults)
# 10 fitting a JohnsonSu distribution
dist = RVGs.JohnsonSu(a=10, b=3, loc=1, scale=2)
dat_JohnsonSu = np.array(get_samples(dist, np.random)) # generate data
dictResults = Fit.fit_johnsonSu(dat_JohnsonSu, 'Data', fixed_location=1) # fit
print("Fitting johnsonSu:", dictResults)
# 11 LogNormal
dist = RVGs.LogNormal(s=1, loc=1, scale=2)
dat_lognorm = np.array(get_samples(dist, np.random)) # mean, sigma
dictResults = Fit.fit_lognorm(dat_lognorm, 'Data',
fixed_location=1) # fit (scale=exp(mean))
print("Fitting LogNormal:", dictResults)
# 12 NegativeBinomial
dist = RVGs.NegativeBinomial(3, 0.3, 1)
dat_neg_bin = np.array(get_samples(dist, np.random)) # mean, sigma
dictResults = Fit.fit_negative_binomial(dat_neg_bin, 'Data', fixed_location=1)
