fig, axs = plt.subplots(2, 1, figsize=(5, 8), sharex=True)
axs[0].plot(v, r, c='black')
axs[0].plot([25, 25], [0, 1.1], '--k', linewidth=0.8)
axs[0].text(23.5,
0.25,
'Cut-out wind speed',
fontsize=fs,
rotation=90,
verticalalignment='center')
axs[0].spines['right'].set_visible(False)
axs[0].spines['top'].set_visible(False)
axs[0].set_ylabel(r_label)
axs[0].set_ylim([0, 1.1])
# Load data, fit joint model, compute contour.
data = read_ec_benchmark_dataset('ec-benchmark_dataset_D.txt')
dist_descriptions, fit_descriptions, semantics = get_OMAE2020_V_Hs()
model = GlobalHierarchicalModel(dist_descriptions)
model.fit(data, fit_descriptions)
c = IFORMContour(model, 1 / (50 * 365.25 * 24))
contour_v = c.coordinates[:, 0] / 0.95
contour_hs = c.coordinates[:, 1]
contour_v = contour_v * (90 / 10)**0.14 # Convert wind speed to hub height.
axs[1].plot(np.append(contour_v, contour_v[0]),
np.append(contour_hs, contour_hs[0]),
'--b',
label='Environmental contour')
axs[1].annotate('environmental contour',
"svg.fonttype"] = "none" # Do avoid outputting font as path.
matplotlib.rcParams.update({"font.size": 6})
matplotlib.rcParams["font.sans-serif"] = "Arial"
matplotlib.rcParams["font.family"] = "sans-serif"
matplotlib.rcParams["mathtext.fontset"] = "custom"
matplotlib.rcParams["mathtext.rm"] = "Arial"
matplotlib.rcParams["mathtext.it"] = "Arial:italic"
matplotlib.rcParams["mathtext.bf"] = "Arial:bold"
# Read dataset A, B or C.
DATASET_CHARS = ["A", "B", "C"]
fig, axes = plt.subplots(2, 3, figsize=(7, 5), sharey="row")
for i, dataset_char in enumerate(DATASET_CHARS):
file_path = "datasets/" + dataset_char + ".txt"
sample = read_ec_benchmark_dataset(file_path)
# Define the structure of the joint distribution model and fit it to the data.
dist_descriptions, fit_descriptions, semantics = get_OMAE2020_Hs_Tz()
model = GlobalHierarchicalModel(dist_descriptions)
model.fit(sample)
two_axes = [axes[0, i], axes[1, i]]
plot_marginal_quantiles(model, sample, semantics, two_axes)
for j, ax in enumerate(two_axes):
if j == 0:
ax.set_title("Dataset $" + dataset_char + "$")
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
if i > 0:
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)
import matplotlib.pyplot as plt
from virocon import (
read_ec_benchmark_dataset,
GlobalHierarchicalModel,
get_OMAE2020_V_Hs,
IFORMContour,
ISORMContour,
DirectSamplingContour,
HighestDensityContour,
plot_2D_contour,
)
# Load dataset (this is dataset D from a benchmarking exercise on environmental
# contours, see https://github.com/ec-benchmark-organizers/ec-benchmark
data = read_ec_benchmark_dataset("datasets/ec-benchmark_dataset_D.txt")
# Define the structure of the joint distribution model.
dist_descriptions, fit_descriptions, semantics = get_OMAE2020_V_Hs()
model = GlobalHierarchicalModel(dist_descriptions)
# Fit the model to the data (estimate the model's parameter values).
model.fit(data, fit_descriptions)
# Compute four type of contours with a return period of 50 years.
state_duration = 1 # hours
return_period = 50 # years
alpha = state_duration / (return_period * 365.25 * 24)
iform = IFORMContour(model, alpha)
isorm = ISORMContour(model, alpha)
direct_samplig = DirectSamplingContour(model, alpha)
def dataset_dnvgl_hstz():
data = read_ec_benchmark_dataset("datasets/ec-benchmark_dataset_A.txt")
return data
def dataset_omae2020_vhs():
data = read_ec_benchmark_dataset("datasets/ec-benchmark_dataset_D.txt")
data.columns = ["Datetime", "V", "Hs"]
data = data[["V", "Hs"]]
return data