def test_HDC(reference_coordinates_HDC):
def _power3(x, a=0.1000, b=1.489, c=0.1901):
return a + b * x**c
# A 3-parameter exponential function (a dependence function).
def _exp3(x, a=0.0400, b=0.1748, c=-0.2243):
return a + b * np.exp(c * x)
bounds = [(0, None), (0, None), (None, None)]
power3 = DependenceFunction(_power3, bounds)
exp3 = DependenceFunction(_exp3, bounds)
dist_description_0 = {
"distribution": WeibullDistribution(alpha=2.776,
beta=1.471,
gamma=0.8888),
}
dist_description_1 = {
"distribution": LogNormalDistribution(),
"conditional_on": 0,
"parameters": {
"mu": power3,
"sigma": exp3
},
}
ghm = GlobalHierarchicalModel([dist_description_0, dist_description_1])
alpha = calculate_alpha(3, 50)
limits = [(0, 20), (0, 18)]
deltas = [0.1, 0.1]
my_contour = HighestDensityContour(ghm, alpha, limits, deltas)
my_coordinates = my_contour.coordinates
np.testing.assert_allclose(my_coordinates, reference_coordinates_HDC)
def test_IFORMContour(seastate_model):
"""
Compare the coordinates of an IFORM contour with the results from Haselsteiner
et al. (2017; DOI: 10.1016/j.coastaleng.2017.03.002)
"""
alpha = calculate_alpha(3, 25)
my_contour = IFORMContour(seastate_model, alpha)
my_coordinates = my_contour.coordinates
np.testing.assert_allclose(max(my_coordinates[:, 0]), 15.23, atol=0.05)
np.testing.assert_allclose(max(my_coordinates[:, 1]), 13.96, atol=0.05)
def test_ISORMContour(seastate_model):
"""
Compare the coordinates of an ISORM contour with the results from Haselsteiner
et al. (2017; DOI: 10.1016/j.coastaleng.2017.03.002, Fig. 8). The shown
308.8-year IFORM contour is the same as an 25-year IFORM contour.
"""
alpha = calculate_alpha(3, 25)
my_contour = ISORMContour(seastate_model, alpha)
my_coordinates = my_contour.coordinates
np.testing.assert_allclose(max(my_coordinates[:, 0]), 17.4, atol=0.1)
np.testing.assert_allclose(max(my_coordinates[:, 1]), 14.9, atol=0.1)
def test_HighestDensityContour(seastate_model):
"""
Compare the coordinates of a HD contour with the results from Haselsteiner
et al. (2017; DOI: 10.1016/j.coastaleng.2017.03.002, Fig. 5)
"""
alpha = calculate_alpha(3, 25)
limits = [(0, 20), (0, 18)]
deltas = [0.05, 0.05]
my_contour = HighestDensityContour(seastate_model, alpha, limits, deltas)
my_coordinates = my_contour.coordinates
np.testing.assert_allclose(max(my_coordinates[:, 0]), 16.79, atol=0.05)
np.testing.assert_allclose(max(my_coordinates[:, 1]), 14.64, atol=0.05)
def test_ISORM(reference_coordinates_ISORM):
# Logarithmic square function.
def _lnsquare2(x, a=3.62, b=5.77):
return np.log(a + b * np.sqrt(x / 9.81))
# 3-parameter function that asymptotically decreases (a dependence function).
def _asymdecrease3(x, a=0, b=0.324, c=0.404):
return a + b / (1 + c * x)
lnsquare2 = DependenceFunction(_lnsquare2)
asymdecrease3 = DependenceFunction(_asymdecrease3)
dist_description_0 = {
"distribution":
ExponentiatedWeibullDistribution(alpha=0.207, beta=0.684, delta=7.79),
}
dist_description_1 = {
"distribution": LogNormalDistribution(),
"conditional_on": 0,
"parameters": {
"mu": lnsquare2,
"sigma": asymdecrease3
},
}
ghm = GlobalHierarchicalModel([dist_description_0, dist_description_1])
state_duration = 3
return_period = 20
alpha = calculate_alpha(state_duration, return_period)
my_isorm = ISORMContour(ghm, alpha)
my_coordinates = my_isorm.coordinates
np.testing.assert_allclose(my_coordinates, reference_coordinates_ISORM)
def test_DirectSamplingContour(reference_data_DSContour):
sample = reference_data_DSContour["sample"]
ref_coordinates = reference_data_DSContour["ref_coordinates"]
def _power3(x, a=0.1000, b=1.489, c=0.1901):
return a + b * x**c
# A 3-parameter exponential function (a dependence function).
def _exp3(x, a=0.0400, b=0.1748, c=-0.2243):
return a + b * np.exp(c * x)
bounds = [(0, None), (0, None), (None, None)]
power3 = DependenceFunction(_power3, bounds)
exp3 = DependenceFunction(_exp3, bounds)
dist_description_0 = {
"distribution": WeibullDistribution(alpha=2.776,
beta=1.471,
gamma=0.8888),
}
dist_description_1 = {
"distribution": LogNormalDistribution(),
"conditional_on": 0,
"parameters": {
"mu": power3,
"sigma": exp3
},
}
ghm = GlobalHierarchicalModel([dist_description_0, dist_description_1])
alpha = calculate_alpha(3, 50)
my_ds_contour = DirectSamplingContour(ghm, alpha, sample=sample)
my_coordinates = my_ds_contour.coordinates
np.testing.assert_allclose(my_coordinates, ref_coordinates)
plot_2D_isodensity,
plot_dependence_functions,
plot_marginal_quantiles,
)
# %% load data, prepare common variables
data = pd.read_csv("datasets/NDBC_buoy_46025.csv", sep=",")[["Hs", "T"]]
dist_descriptions, fit_descriptions, semantics = get_DNVGL_Hs_Tz()
ghm = GlobalHierarchicalModel(dist_descriptions)
ghm.fit(data, fit_descriptions=fit_descriptions)
state_duration = 3
return_period = 50
alpha = calculate_alpha(state_duration, return_period)
iform_contour = IFORMContour(ghm, alpha)
plt.close("all")
# %% plot_qq
axes = plot_marginal_quantiles(ghm, data, semantics=semantics)
# plt.show()
# %% plot_dependence_functions
par_rename = {"mu": r"$\mu$", "sigma": r"$\sigma$"}
axes = plot_dependence_functions(ghm,
semantics=semantics,
par_rename=par_rename)
def test_v_hs_hd_contour():
"""
Use a wind speed - wave height dataset, fit the joint
distribution that was proposed by Haselsteiner et al. (2020)
and compute a highest density contour. This test reproduces
the results presented in Haselestiner et al. (2020). The
coorindates are availble at https://github.com/ec-benchmark-organizers/
ec-benchmark/blob/master/results/exercise-1/contribution-4/haselsteiner_
andreas_dataset_d_50.txt
Such a work flow is for example typical when generationg
a 50-year contour for DLC 1.6 in the offshore wind standard
IEC 61400-3-1.
Haselsteiner, A. F., Sander, A., Ohlendorf, J.-H., & Thoben, K.-D. (2020).
Global hierarchical models for wind and wave contours: Physical
interpretations of the dependence functions. Proc. 39th International
Conference on Ocean, Offshore and Arctic Engineering (OMAE 2020).
https://doi.org/10.1115/OMAE2020-18668
International Electrotechnical Commission. (2019). Wind energy
generation systems - Part 3-1: Design requirements for fixed
offshore wind turbines (IEC 61400-3-1).
"""
data = read_ec_benchmark_dataset("datasets/ec-benchmark_dataset_D.txt")
def _logistics4(x, a=1, b=1, c=-1, d=1):
return a + b / (1 + np.exp(c * (x - d)))
def _alpha3(x, a, b, c, d_of_x):
return (a + b * x**c) / 2.0445**(1 / d_of_x(x))
logistics_bounds = [(0, None), (0, None), (None, 0), (0, None)]
alpha_bounds = [(0, None), (0, None), (None, None)]
beta_dep = DependenceFunction(_logistics4,
logistics_bounds,
weights=lambda x, y: y)
alpha_dep = DependenceFunction(_alpha3,
alpha_bounds,
d_of_x=beta_dep,
weights=lambda x, y: y)
dist_description_v = {
"distribution": ExponentiatedWeibullDistribution(),
"intervals": WidthOfIntervalSlicer(width=2),
}
dist_description_hs = {
"distribution": ExponentiatedWeibullDistribution(f_delta=5),
"conditional_on": 0,
"parameters": {
"alpha": alpha_dep,
"beta": beta_dep,
},
}
model = GlobalHierarchicalModel([dist_description_v, dist_description_hs])
fit_description_vs = {"method": "wlsq", "weights": "quadratic"}
fit_description_hs = {"method": "wlsq", "weights": "quadratic"}
model.fit(data, [fit_description_vs, fit_description_hs])
axs = plot_marginal_quantiles(model, data)
axs = plot_dependence_functions(model)
ax = plot_2D_isodensity(model, data)
alpha = calculate_alpha(1, 50)
limits = [(0, 35), (0, 20)]
contour = HighestDensityContour(model,
alpha,
limits=limits,
deltas=[0.2, 0.2])
coordinates = contour.coordinates
np.testing.assert_allclose(max(coordinates[:, 0]), 29.9, atol=0.2)
np.testing.assert_allclose(max(coordinates[:, 1]), 15.5, atol=0.2)
np.testing.assert_allclose(min(coordinates[:, 0]), 0, atol=0.1)
np.testing.assert_allclose(min(coordinates[:, 1]), 0, atol=0.1)
ax = plot_2D_contour(contour, sample=data)
def test_hs_tz_iform_contour():
"""
Use a sea state dataset with the variables Hs and Tz,
fit the join distribution recommended in DNVGL-RP-C203 to
it and compute an IFORM contour. This tests reproduces
the results published in Haseltseiner et al. (2019).
Such a work flow is for example typical in ship design.
Haselsteiner, A. F., Coe, R. G., Manuel, L., Nguyen, P. T. T.,
Martin, N., & Eckert-Gallup, A. (2019). A benchmarking exercise
on estimating extreme environmental conditions: Methodology &
baseline results. Proc. 38th International Conference on Ocean,
Offshore and Arctic Engineering (OMAE 2019).
https://doi.org/10.1115/OMAE2019-96523
DNV GL. (2017). Recommended practice DNVGL-RP-C205:
Environmental conditions and environmental loads.
"""
data = read_ec_benchmark_dataset("datasets/ec-benchmark_dataset_A.txt")
# A 3-parameter power function (a dependence function).
def _power3(x, a, b, c):
return a + b * x**c
# A 3-parameter exponential function (a dependence function).
def _exp3(x, a, b, c):
return a + b * np.exp(c * x)
bounds = [(0, None), (0, None), (None, None)]
power3 = DependenceFunction(_power3, bounds)
exp3 = DependenceFunction(_exp3, bounds)
dist_description_0 = {
"distribution": WeibullDistribution(),
"intervals": WidthOfIntervalSlicer(width=0.5),
}
dist_description_1 = {
"distribution": LogNormalDistribution(),
"conditional_on": 0,
"parameters": {
"mu": power3,
"sigma": exp3
},
}
model = GlobalHierarchicalModel([dist_description_0, dist_description_1])
model.fit(data)
axs = plot_marginal_quantiles(model, data)
axs = plot_dependence_functions(model)
ax = plot_2D_isodensity(model, data)
alpha = calculate_alpha(1, 20)
contour = IFORMContour(model, alpha)
coordinates = contour.coordinates
np.testing.assert_allclose(max(coordinates[:, 0]), 5.0, atol=0.5)
np.testing.assert_allclose(max(coordinates[:, 1]), 16.1, atol=0.5)
ax = plot_2D_contour(contour, sample=data)
def test_DirectSamplingContour(seastate_model):
"""
Computes a direct sampling contour and compares it with results
from Huseby et al. (2013; DOI: 10.1016/j.oceaneng.2012.12.034, Tab. 5).
"""
ref_contour_hs_1 = [
9.99,
10.65,
10.99,
11.25,
11.25,
11.41,
11.42,
11.46,
11.48,
11.54,
11.57,
11.56,
11.58,
11.59,
11.59,
11.60,
11.60,
11.59,
11.59,
11.56,
11.53,
11.46,
11.26,
10.88,
7.44,
2.05,
]
ref_contour_tz_1 = [
12.34,
12.34,
12.31,
12.25,
12.25,
12.18,
12.17,
12.15,
12.13,
12.06,
12.02,
12.03,
12.00,
11.96,
11.95,
11.86,
11.84,
11.77,
11.76,
11.67,
11.60,
11.47,
11.20,
10.77,
7.68,
3.76,
]
alpha = calculate_alpha(6, 1)
prng = np.random.RandomState(42) # Fix the random seed for consistency.
# Because a random sample is drawn (and fixing the random seed with
# .np.random.RandomState) does not work, results will be different each
# time the test is run. Sometimes the test might fail.
my_ds_contour = DirectSamplingContour(seastate_model, alpha, deg_step=6)
my_coordinates = my_ds_contour.coordinates
np.testing.assert_allclose(my_coordinates[0:26, 0],
ref_contour_hs_1,
atol=0.75)
np.testing.assert_allclose(my_coordinates[0:26, 1],
ref_contour_tz_1,
atol=0.75)
exp3 = DependenceFunction(_exp3, bounds)
dist_description_0 = {
"distribution": WeibullDistribution(alpha=2.776, beta=1.471, gamma=0.8888),
}
dist_description_1 = {
"distribution": LogNormalDistribution(),
"conditional_on": 0,
"parameters": {
"mu": power3,
"sigma": exp3
},
}
ghm = GlobalHierarchicalModel([dist_description_0, dist_description_1])
alpha = calculate_alpha(3, 50)
n = 10000
sample = ghm.draw_sample(n)
my_ds_contour = DirectSamplingContour(ghm, alpha, sample=sample)
my_coordinates = my_ds_contour.coordinates
# %% viroconcom v1
import sys
sys.path.append("../viroconcom")
from viroconcom.distributions import (
WeibullDistribution,
LognormalDistribution,
MultivariateDistribution,
)