return_period_1)
write_contour(iform_contour_1.coordinates[0][0],
iform_contour_1.coordinates[0][1],
folder_name + file_name_1,
label_x=label_hs,
label_y=label_tz)
file_name_20 = determine_file_name_e1('John', 'Doe', DATASET_CHAR,
return_period_20)
write_contour(iform_contour_20.coordinates[0][0],
iform_contour_20.coordinates[0][1],
folder_name + file_name_20,
label_x=label_hs,
label_y=label_tz)
# Read the contours from the csv files.
(contour_hs_1, contour_tz_1) = read_contour(folder_name + file_name_1)
(contour_hs_20, contour_tz_20) = read_contour(folder_name + file_name_20)
# Find datapoints that exceed the 20-yr contour.
hs_outside, tz_outside, hs_inside, tz_inside = \
points_outside(contour_hs_20,
contour_tz_20,
np.asarray(sample_hs),
np.asarray(sample_tz))
print('Number of points outside the contour: ' + str(len(hs_outside)))
fig = plt.figure(figsize=(5, 5), dpi=150)
ax = fig.add_subplot(111)
# Plot the 1-year contour.
plot_contour(x=contour_tz_1,
folder_name = 'contour_coordinates/'
file_name_1 = determine_file_name_e1('John', 'Doe', DATASET_CHAR, return_period_1)
write_contour(iform_contour_1.coordinates[0][0],
iform_contour_1.coordinates[0][1],
folder_name + file_name_1,
label_x=label_hs,
label_y=label_v)
file_name_50 = determine_file_name_e1('John', 'Doe', DATASET_CHAR, return_period_50)
write_contour(iform_contour_50.coordinates[0][0],
iform_contour_50.coordinates[0][1],
folder_name + file_name_50,
label_x=label_hs,
label_y=label_v)
# Read the contour coordinates from the created csv files.
(contour_hs_1, contour_v_1) = read_contour(folder_name + file_name_1)
(contour_hs_50, contour_v_50) = read_contour(folder_name + file_name_50)
# Find datapoints that exceed the contour.
hs_outside, v_outside, hs_inside, v_inside = \
points_outside(contour_hs_50,
contour_v_50,
np.asarray(sample_hs),
np.asarray(sample_v))
print('Number of points outside the contour: ' + str(len(hs_outside)))
fig = plt.figure(figsize=(5, 5), dpi=150)
ax = fig.add_subplot(111)
# Plot the 1-year contour.
plot_contour(x=contour_v_1,
'Asta', 'Hannesdottir', NR_OF_YEARS_TO_DRAW, 'bottom')
write_contour(sorted_v[bottom_percentile_index, :],
sorted_hs[bottom_percentile_index, :],
folder_name + file_name_bottom,
label_x=label_v,
label_y=label_hs)
file_name_upper = determine_file_name_e2(
'Asta', 'Hannesdottir', NR_OF_YEARS_TO_DRAW, 'upper')
write_contour(sorted_v[upper_percentile_index, :],
sorted_hs[upper_percentile_index, :],
folder_name + file_name_upper,
label_x=label_v,
label_y=label_hs)
# Read the contours from the csv files.
(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),
y=np.asarray(dataset_d_hs),
ax=ax,
label='dataset D')
contour_labels = ['50th percentile contour', '2.5th percentile contour',
'97.5th percentile contour']
plot_confidence_interval(
x_median=contour_v_median, y_median=contour_hs_median,
x_bottom=contour_v_bottom, y_bottom=contour_hs_bottom,
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),
y=np.asarray(dataset_d_hs),
ax=ax,
label='dataset D')
contour_labels = [
'50th percentile contour', '2.5th percentile contour',
'97.5th percentile contour'
# 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,
contour_y_long) = read_contour(folder_name + file_name_long_tr)
# Switch the order of variables for plotting Hs over Tz.
if dataset_char in ('A', 'B', 'C'):
sample_x, sample_y = sample_y, sample_x
label_x, label_y = label_y, label_x
contour_x_1, contour_y_1 = contour_y_1, contour_x_1
contour_x_long, contour_y_long = contour_y_long, contour_x_long
# Find datapoints that exceed the 20/50-yr contour.
x_outside, y_outside, x_inside, y_inside = \
points_outside(contour_x_long,
contour_y_long,
np.asarray(sample_x),
