def test_multivariate_draw_sample(self):
"""
Create an example MultivariateDistribution (Vanem2012 model).
"""
# Define dependency tuple.
dep1 = (None, None, None)
dep2 = (0, None, 0)
# Define parameters.
shape = ConstantParam(1.471)
loc = ConstantParam(0.8888)
scale = ConstantParam(2.776)
par1 = (shape, loc, scale)
shape = FunctionParam('exp3', 0.0400, 0.1748, -0.2243)
loc = None
scale = FunctionParam('power3', 0.1, 1.489, 0.1901)
par2 = (shape, loc, scale)
del shape, loc, scale
# Create distributions.
dist1 = WeibullDistribution(*par1)
dist2 = LognormalDistribution(*par2)
distributions = [dist1, dist2]
dependencies = [dep1, dep2]
ref_points = 99119
mul_var_dist = MultivariateDistribution(distributions, dependencies)
my_points = mul_var_dist.draw_sample(ref_points)
my_points0 = my_points[0].size
my_points1 = my_points[1].size
assert ref_points == my_points0
assert ref_points == my_points1
def test_draw_sample_distribution(self):
"""
Create an example MultivariateDistribution (Vanem2012 model).
"""
# Define dependency tuple.
dep1 = (None, None, None)
dep2 = (0, None, 0)
# Define parameters.
shape = ConstantParam(1.471)
loc = ConstantParam(0.8888)
scale = ConstantParam(2.776)
par1 = (shape, loc, scale)
shape = FunctionParam('exp3', 0.0400, 0.1748, -0.2243)
loc = None
scale = FunctionParam('power3', 0.1, 1.489, 0.1901)
par2 = (shape, loc, scale)
del shape, loc, scale
# Create distributions.
dist1 = WeibullDistribution(*par1)
dist2 = LognormalDistribution(*par2)
distributions = [dist1, dist2]
dependencies = [dep1, dep2]
points = 1000000
mul_var_dist = MultivariateDistribution(distributions, dependencies)
my_points = mul_var_dist.draw_sample(points)
#Fit the sample
# Describe the distribution that should be fitted to the sample.
dist_description_0 = {
'name': 'Weibull',
'dependency': (None, None, None),
'width_of_intervals': 2
}
dist_description_1 = {
'name': 'Lognormal',
'dependency': (0, None, 0),
'functions': ('exp3', None, 'power3')
}
my_fit = Fit([my_points[0], my_points[1]],
[dist_description_0, dist_description_1])
print(my_fit.mul_var_dist.distributions[0].shape(0))
print(mul_var_dist.distributions[0].shape(0))
assert np.round(my_fit.mul_var_dist.distributions[0].shape(0),
2) == np.round(mul_var_dist.distributions[0].shape(0),
2)
def ecdf(data):
""" Compute ECDF """
x = np.sort(data)
n = x.size
F = np.arange(1, n + 1) / n
return (x, F)
variable_list = [u, hs]
var_symbols = ['$U_{10}$ (m/s)', '$H_s$ (m)']
var_labels = [lu, lhs]
# Use a Monte Carlo sample from the joint model for subsequent calculations
mc_sample = joint_model_4.draw_sample(len(u) * MC_FACTOR)
mc_u = mc_sample[0]
mc_hs = mc_sample[1]
# Probability - Quantile plot
for i, x in enumerate(variable_list):
x_ordered, F = ecdf(x)
alpha = 1 - F
tr = 1 / (alpha * 365.25 * 24)
tr_threshold = 0.0001
axs1[i].plot(tr[tr >= tr_threshold],
x_ordered[tr >= tr_threshold],
'ok',
markerfacecolor='none',
ms=3,
rasterized=True)