def test_pdf_nD(self, mock_show, tmp):
fig_pdf = pdf(data,
xdata=np.linspace(1, 12, 12),
range_cbar=[0, 0.5],
ticks_nbr=6,
fname=os.path.join(tmp, 'pdf_nd.pdf'))
reshow(fig_pdf)
plt.plot([0, 10], [25, 25])
plt.show()
plt.close()
def test_sobols_aggregated(self, mock_show, tmp):
fun = Ishigami()
indices = [fun.s_first, fun.s_total]
fig = sensitivity_indices(indices, conf=0.05)
fig = reshow(fig[0])
plt.plot([0, 10], [0.5, 0.5])
fig.show()
sensitivity_indices(indices,
plabels=['x1', 't', 'y'],
fname=os.path.join(tmp, 'sobol.pdf'))
sensitivity_indices(indices,
polar=True,
conf=[[0.2, 0.1, 0.1], [0.1, 0.1, 0.1]])
sensitivity_indices([indices[0]])
sample_scaled = np.concatenate([sample_scaled, sample_])
data = np.concatenate([data, fun(scaler_inverse_transform(sample_))])
sample.empty()
sample += scaler_inverse_transform(sample_scaled)
doe(sample, fname='final_doe.pdf')
# Analysis
print(f'\n########### N-samples ###########\n {sample.shape}')
# Filtering in/out
mask_threashold = data >= threashold
mask_threashold = mask_threashold.flatten()
inv_mask_threashold = np.logical_not(mask_threashold)
sample_in = sample[mask_threashold]
sample_out = sample[inv_mask_threashold]
ratio = len(sample_in) / len(sample) * 100
print(f'{ratio:.2f}% of sampling is inside')
####### Visualization #######
fig = response_surface(corners, fun=fun, ticks_nbr=20, contours=[-20])
fig = reshow(fig)
plt.scatter(sample_in[:, 0], sample_in[:, 1], c='r')
plt.scatter(sample_out[:, 0], sample_out[:, 1], c='b')
plt.show()
def test_doe_3D(self, ishigami_data, tmp):
fig, ax = doe(ishigami_data.space, fname=os.path.join(tmp, 'DOE.pdf'))
fig = reshow(fig)
ax[0].plot([0, 6], [4, -3])
fig.savefig(os.path.join(tmp, 'DOE_change.pdf'))
def test_hdr_basic(self, hdr, tmp, seed):
print('Data shape: ', data.shape)
assert len(hdr.extra_quantiles) == 0
median_t = [
24.27, 25.67, 25.98, 25.05, 23.76, 22.40, 21.31, 20.43, 20.20,
20.47, 21.17, 22.37
]
npt.assert_almost_equal(hdr.median, median_t, decimal=2)
quant = np.vstack([hdr.outliers, hdr.hdr_90, hdr.hdr_50])
quant_t = np.vstack([[
27.20, 28.16, 29.00, 28.94, 28.27, 27.24, 25.84, 24.01, 22.37,
22.24, 22.38, 23.26
],
[
23.94, 26.16, 27.07, 26.50, 26.40, 25.92,
25.36, 24.70, 24.52, 24.67, 25.76, 27.02
],
[
28.01, 28.83, 29.12, 28.23, 27.18, 25.33,
23.41, 22.11, 21.25, 21.56, 21.64, 23.01
],
[
25.63, 26.99, 27.63, 27.11, 26.10, 24.65,
23.55, 22.50, 22.13, 22.51, 23.37, 24.54
],
[
23.04, 24.58, 24.71, 23.41, 21.98, 20.74,
19.85, 19.09, 18.85, 19.04, 19.58, 20.80
],
[
24.85, 26.15, 26.56, 25.78, 24.58, 23.20,
22.11, 21.17, 20.93, 21.25, 22.00, 23.23
],
[
23.67, 25.14, 25.46, 24.28, 22.94, 21.62,
20.59, 19.75, 19.51, 19.73, 20.37, 21.54
]])
npt.assert_almost_equal(quant, quant_t, decimal=0)
figs, axs = hdr.plot(fname=os.path.join(tmp, 'hdr_boxplot.pdf'),
labels=labels,
x_common=np.linspace(1, 12, 12),
xlabel='Month of the year (-)',
flabel='Water surface temperature (C)')
assert len(figs) == 3
assert len(axs) == 3
fig = reshow(figs[2])
plt.plot([0, 10], [25, 25])
axs[2].plot([0, 6], [4, -3])
fig.savefig(os.path.join(tmp, 'hdr_boxplot_change_sample.pdf'))
fig = reshow(figs[1])
axs[1][0].plot([0, 6], [4, -3])
fig.savefig(os.path.join(tmp, 'hdr_boxplot_change_scatter.pdf'))