def test_plot_confidence_interval(self):
"""
Plots a contour's confidence interval.
"""
dataset_d_v, dataset_d_hs, label_v, label_hs = \
read_ecbenchmark_dataset('datasets/1year_dataset_D.txt')
# Read the contours that have beem computed previously from csv files.
folder_name = 'contour-coordinates/'
file_name_median = 'doe_john_years_25_median.txt'
file_name_bottom = 'doe_john_years_25_bottom.txt'
file_name_upper = 'doe_john_years_25_upper.txt'
(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 = SamplePlotData(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,
x_upper=contour_v_upper,
y_upper=contour_hs_upper,
ax=ax,
x_label=label_v,
y_label=label_hs,
contour_labels=contour_labels,
plotted_sample=plotted_sample)
def test_read_write_contour(self):
"""
Read a contour, then writes this contour to a new file.
"""
folder_name = 'contour-coordinates/'
file_name_median = 'doe_john_years_25_median.txt'
(contour_v_median,
contour_hs_median) = read_contour(folder_name + file_name_median)
new_file_path = folder_name + 'test_contour.txt'
write_contour(contour_v_median, contour_hs_median, new_file_path,
'Wind speed (m/s)', 'Significant wave height (m)')
os.remove(new_file_path)
contours_hs = []
contours_tz = []
max_hs_on_contour = np.empty(n_contours_to_analyze)
for i in range(n_contours_to_analyze):
contribution_nr = i + 1
if 11 >= contribution_nr >= 9:
contribution_nr = 9
elif contribution_nr > 11:
# Because contribution 9 holds 3 sets of contours.
contribution_nr = contribution_nr - 2
folder_name = 'results/exercise-1/contribution-' + str(
contribution_nr)
file_name = folder_name + '/' + lastname_firstname[i] + '_dataset_' + \
dataset_char + '_' + str(return_period) + '.txt'
(hs, tz) = read_contour(file_name)
if i in (7, 8, 9, 10):
(tz, hs) = read_contour(file_name)
contours_hs.append(hs)
contours_tz.append(tz)
max_hs_on_contour[i] = max(hs[~np.isnan(tz)])
# Plot the data and the contour.
ax1.scatter(tz_p, hs_p, c='black', alpha=0.5, zorder=-2)
ax1.scatter(tz_r,
hs_r,
marker='v',
facecolor='None',
edgecolor='black',
alpha=0.5,
zorder=-2)
contours = []
contours_v = []
contours_hs = []
max_hs_on_contours = np.empty(n_contours_to_analyze)
for i in range(n_contours_to_analyze):
contribution_nr = i + 1
if 11 >= contribution_nr >= 9:
contribution_nr = 9
elif contribution_nr > 11:
# Because contribution 9 holds 3 sets of contours.
contribution_nr = contribution_nr - 2
folder_name = 'results/exercise-1/contribution-' + str(contribution_nr)
file_name = folder_name + '/' + lastname_firstname[i] + '_dataset_' + \
dataset_char + '_' + str(return_period) + '.txt'
if contribution_nr in (1, 2, 3, 5, 6, 8, 10):
(hs, v) = read_contour(file_name)
else:
(v, hs) = read_contour(file_name)
contour = Object()
contour.c = (np.append(v, v[0]), np.append(hs, hs[0]))
contours.append(contour)
contours_v.append(v)
contours_hs.append(hs)
max_hs_on_contours[i] = max(hs[~np.isnan(hs)])
# Compute the min, max and median contour.
# First, define the origin and angles based on normalization.
v0 = np.mean(v_p)
hs0 = np.mean(hs_p)
angle_step_for_ci = 2
theta_stars = np.arange(0, 360, angle_step_for_ci) / 180 * np.pi
]
slengths = [1, 5, 25]
ylims = [18, 18, 18]
fig, axs = plt.subplots(4, 3, sharex=True, sharey=True, figsize=(8.5, 11))
for idx, (axi, name, style, leg_str,
num) in enumerate(zip(axs, names, styles, leg_strs, nums)):
for (ax, slength, ylim) in zip(axi, slengths, ylims):
folder_name = 'results/exercise-2/contribution-' + str(num)
temp = folder_name + '/' + name + '_years_' + str(slength)
file_name_median = temp + '_median.txt'
file_name_lower = temp + '_bottom.txt'
file_name_upper = temp + '_upper.txt'
v_median, hs_median = read_contour(file_name_median)
v_lower, hs_lower = read_contour(file_name_lower)
v_upper, hs_upper = read_contour(file_name_upper)
sample_plot_data = SamplePlotData(x=np.asarray(sample_v),
y=np.asarray(sample_hs),
ax=ax,
label='Dataset D (provided)')
plot_confidence_interval(x_median=v_median,
y_median=hs_median,
x_bottom=v_lower,
y_bottom=hs_lower,
x_upper=v_upper,
y_upper=hs_upper,
ax=ax,