def test_catch_combos_too_large():
N = 1e6
k_choices = 4
num_params = 2
input_sample = np.random.random_sample((N, num_params))
try:
find_most_distant(input_sample, N, num_params, k_choices)
except:
pass
else:
raise AssertionError("Test did not fail when number of \
combinations exceeded system size")
def test_catch_combos_too_large():
N = 1e6
k_choices = 4
num_params = 2
input_sample = np.random.random_sample((N, num_params))
try:
find_most_distant(input_sample, N, num_params, k_choices)
except:
pass
else:
raise AssertionError("Test did not fail when number of \
combinations exceeded system size")
def test_combo_from_find_most_distant():
'''
Tests whether the correct combination is picked from the fixture drawn
from Saltelli et al. 2008, in the solution to exercise 3a,
Chapter 3, page 134.
'''
sample_inputs = setup()
N = 6
num_params = 2
k_choices = 4
scores = find_most_distant(sample_inputs, N, num_params, k_choices)
output = find_maximum(scores, N, k_choices)
expected = [0, 2, 3, 5] # trajectories 1, 3, 4, 6
assert_equal(output, expected)
def test_combo_from_find_most_distant():
'''
Tests whether the correct combination is picked from the fixture drawn
from Saltelli et al. 2008, in the solution to exercise 3a,
Chapter 3, page 134.
'''
sample_inputs = setup()
N = 6
num_params = 2
k_choices = 4
scores = find_most_distant(sample_inputs, N, num_params, k_choices)
output = find_maximum(scores, N, k_choices)
expected = [0, 2, 3, 5] # trajectories 1, 3, 4, 6
assert_equal(output, expected)
def test_find_local_maximum_distance():
'''
Test whether finding the local maximum distance equals the global maximum distance
in a simple case. From Saltelli et al. 2008, in the solution to exercise 3a,
Chapter 3, page 134.
'''
sample_inputs = setup()
N=6
num_params = 2
k_choices = 4
scores_global = find_most_distant(sample_inputs, N, num_params, k_choices)
output_global = find_maximum(scores_global, N, k_choices)
output_local = find_local_maximum(sample_inputs, N, num_params, k_choices)
assert_equal(output_global, output_local)
def test_find_local_maximum_distance():
'''
Test whether finding the local maximum distance equals the global maximum distance
in a simple case. From Saltelli et al. 2008, in the solution to exercise 3a,
Chapter 3, page 134.
'''
sample_inputs = setup()
N = 6
num_params = 2
k_choices = 4
scores_global = find_most_distant(sample_inputs, N, num_params, k_choices)
output_global = find_maximum(scores_global, N, k_choices)
output_local = find_local_maximum(sample_inputs, N, num_params, k_choices)
assert_equal(output_global, output_local)
def test_scores_from_find_most_distant():
'''
Checks whether array of scores from (6 4) is correct.
Data is derived from Saltelli et al. 2008,
in the solution to exercise 3a, Chapter 3, page 134.
'''
sample_inputs = setup()
N = 6
num_params = 2
k_choices = 4
output = find_most_distant(sample_inputs, N, num_params, k_choices)
expected = np.array([15.022, 13.871, 14.815, 14.582, 16.178, 14.912, 15.055, 16.410,
15.685, 16.098, 14.049, 15.146, 14.333, 14.807, 14.825],
dtype=np.float32)
assert_allclose(output, expected, rtol=1e-1, atol=1e-2)
def test_scores_from_find_most_distant():
'''
Checks whether array of scores from (6 4) is correct.
Data is derived from Saltelli et al. 2008,
in the solution to exercise 3a, Chapter 3, page 134.
'''
sample_inputs = setup()
N = 6
num_params = 2
k_choices = 4
output = find_most_distant(sample_inputs, N, num_params, k_choices)
expected = np.array([
15.022, 13.871, 14.815, 14.582, 16.178, 14.912, 15.055, 16.410, 15.685,
16.098, 14.049, 15.146, 14.333, 14.807, 14.825
],
dtype=np.float32)
assert_allclose(output, expected, rtol=1e-1, atol=1e-2)
