from plot import plot_contour, PlottedSample, plot_marginal_fit, plot_dependence_functions
from contour_statistics import points_outside, sort_points_to_form_continous_line
from read_write import read_dataset
from datetime import datetime
import matplotlib
from palettable.colorbrewer.qualitative import Set2_3 as my_colors
from palettable.colorbrewer.sequential import BuPu_5 as my_seq_colors
from scipy.stats import gaussian_kde
from virocon import get_OMAE2020_Hs_Tz, GlobalHierarchicalModel
# Read dataset A, B or C.
DATASET_CHARS = ["A", "B", "C"]
for dataset_char in DATASET_CHARS:
file_path = "datasets/" + dataset_char + ".txt"
hs, tz, label_hs, label_tz = read_dataset(file_path)
label_hs = "Significant wave height (m)"
label_tz = "Zero-up-crossing period (s)"
every_x_point = 1
hs = hs[0::every_x_point]
tz = tz[0::every_x_point]
# Create figure, start with scatter plot.
matplotlib.rcParams[
"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"
import numpy as np
import matplotlib.pyplot as plt
from viroconcom.fitting import Fit
from viroconcom.contours import IFormContour
from plot import plot_contour, PlottedSample, plot_marginal_fit, plot_dependence_functions
from contour_statistics import points_outside
from read_write import read_dataset, determine_file_name_e1, write_contour, read_contour
# Read dataset A, B or C.
DATASET_CHAR = 'A'
file_path = '../datasets/' + DATASET_CHAR + '.txt'
sample_hs, sample_tz, label_hs, label_tz = read_dataset(file_path)
# Define the structure of the probabilistic model that will be fitted to the
# dataset. We will use the model that is recommended in DNV-RP-C205 (2010) on
# page 38 and that is called 'conditonal modeling approach' (CMA).
dist_description_hs = {
'name': 'Weibull_3p',
'dependency': (None, None, None),
'width_of_intervals': 0.5
}
dist_description_tz = {
'name': 'Lognormal_SigmaMu',
'dependency': (0, None, 0), #Shape, Location, Scale
'functions': ('exp3', None, 'power3') #Shape, Location, Scale
}
# Fit the model to the data.
fit = Fit((sample_hs, sample_tz), (dist_description_hs, dist_description_tz))
dist0 = fit.mul_var_dist.distributions[0]
import numpy as np
import matplotlib.pyplot as plt
from viroconcom.fitting import Fit
from viroconcom.contours import IFormContour
from plot import PlottedSample, plot_contour, plot_marginal_fit, plot_dependence_functions
from contour_statistics import points_outside
from read_write import read_dataset, determine_file_name_e1, write_contour, read_contour
# Read dataset D, E or F.
DATASET_CHAR = 'D'
file_path = '../datasets/' + DATASET_CHAR + '.txt'
sample_v, sample_hs, label_v, label_hs= read_dataset(file_path)
# Define the structure of the probabilistic model that will be fitted to the
# dataset. We will use the model that is recommended in DNV-RP-C205 (2010) on
# page 38 and that is called 'conditonal modeling approach' (CMA).
dist_description_hs = {'name': 'Weibull_3p',
'dependency': (None, None, None),
'width_of_intervals': 0.5}
dist_description_v = {'name': 'Weibull_2p',
'dependency': (0, None, 0), #Shape, Location, Scale
'functions': ('power3', None, 'power3') #Shape, Location, Scale
}
# Fit the model to the data.
fit = Fit((sample_hs, sample_v), (dist_description_hs, dist_description_v))
dist0 = fit.mul_var_dist.distributions[0]
print('First variable: ' + dist0.name + ' with '
+ ' scale: ' + str(dist0.scale) + ', '
+ ' shape: ' + str(dist0.shape) + ', '
import numpy as np
import matplotlib.pyplot as plt
from viroconcom.fitting import Fit
from viroconcom.contours import IFormContour
from statistics import median
from plot import plot_contour, PlottedSample
from read_write import read_dataset
# Read dataset A
DATASET_CHAR = 'A'
file_path = 'datasets/' + DATASET_CHAR + '.txt'
a_hs, a_tz, label_hs, label_tz = read_dataset(file_path)
# Define the structure of the probabilistic model that will be fitted to the
# dataset. We will use the model that is recommended in DNV-RP-C205 (2010) on
# page 38 and that is called 'conditonal modeling approach' (CMA).
dist_description_hs = {
'name': 'Weibull_3p',
'dependency': (None, None, None),
'width_of_intervals': 0.5
}
dist_description_tz = {
'name': 'Lognormal_SigmaMu',
'dependency': (0, None, 0), #Shape, Location, Scale
'functions': ('exp3', None, 'power3') #Shape, Location, Scale
}
# Fit the hs-tz model to the data.
fit = Fit((a_hs, a_tz), (dist_description_hs, dist_description_tz))
import numpy as np
import matplotlib.pyplot as plt
from plot import PlottedSample, plot_confidence_interval
from read_write import read_dataset, determine_file_name_e2, read_contour
NR_OF_YEARS_TO_DRAW = 1 # Must be 1, 5 or 25.
# Read dataset D.
file_path = '../datasets/D.txt'
dataset_d_v, dataset_d_hs, label_v, label_hs = read_dataset(file_path)
# Read the contours that have beem computed previously from csv files.
folder_name = 'contour_coordinates/'
file_name_median = determine_file_name_e2('John', 'Doe', NR_OF_YEARS_TO_DRAW,
'median')
file_name_bottom = determine_file_name_e2('John', 'Doe', NR_OF_YEARS_TO_DRAW,
'bottom')
file_name_upper = determine_file_name_e2('John', 'Doe', NR_OF_YEARS_TO_DRAW,
'upper')
(contour_v_median,
contour_hs_median) = read_contour(folder_name + file_name_median)
(contour_v_bottom,
contour_hs_bottom) = read_contour(folder_name + file_name_bottom)
(contour_v_upper,
contour_hs_upper) = read_contour(folder_name + file_name_upper)
# Plot the sample, the median contour and the confidence interval.
fig = plt.figure(figsize=(5, 5), dpi=150)
ax = fig.add_subplot(111)
plotted_sample = PlottedSample(x=np.asarray(dataset_d_v),
import numpy as np
import matplotlib.pyplot as plt
from plot import plot_contour, PlottedSample
from contour_statistics import points_outside
from read_write import read_dataset, read_contour, determine_file_name_e1, \
determine_file_name_e2
DATASET_CHARS = ['A', 'B', 'C', 'D', 'E', 'F']
for dataset_char in DATASET_CHARS:
# Read dataset A, B or C.
file_path = 'datasets/' + dataset_char + '.txt'
sample_x, sample_y, label_x, label_y = read_dataset(file_path)
# Differentiate between sea state and wind wave contours.
if dataset_char in ('A', 'B', 'C'):
return_period_long_tr = 20
else:
return_period_long_tr = 50
# Read the contours from the csv files.
folder_name = 'contour-coordinates/'
file_name_1 = determine_file_name_e1('Andreas', 'Haselsteiner',
dataset_char, 1)
file_name_long_tr = determine_file_name_e1('Andreas', 'Haselsteiner',
dataset_char,
return_period_long_tr)
(contour_x_1, contour_y_1) = read_contour(folder_name + file_name_1)
(contour_x_long,
