def test_dres(self, ens1, ens2):
results, details = encore.dres([ens1, ens2],
selection="name CA and resnum 1-10")
result_value = results[0, 1]
upper_bound = 0.6
assert result_value < upper_bound, "Unexpected value for Dim. " \
"reduction Ensemble Similarity: {0:f}. Expected {1:f}.".format(result_value, upper_bound)
def test_dres_without_superimposition(self, ens1, ens2):
distance_matrix = encore.get_distance_matrix(
encore.merge_universes([ens1, ens2]),
superimpose=False)
results, details = encore.dres([ens1, ens2],
distance_matrix = distance_matrix)
result_value = results[0,1]
expected_value = 0.68
assert_almost_equal(result_value, expected_value, decimal=1,
err_msg="Unexpected value for Dim. reduction Ensemble Similarity: {0:f}. Expected {1:f}.".format(result_value, expected_value))
def test_dres_to_self(self, ens1):
results, details = encore.dres([ens1, ens1])
result_value = results[0, 1]
expected_value = 0.
assert_almost_equal(
result_value,
expected_value,
decimal=2,
err_msg=
"Dim. Reduction Ensemble Similarity to itself not zero: {0:f}".
format(result_value))
def test_dres(self):
results, details = encore.dres([self.ens1, self.ens2],
selection="name CA and resnum 1-10")
result_value = results[0, 1]
upper_bound = 0.6
self.assertLess(
result_value,
upper_bound,
msg=
"Unexpected value for Dim. reduction Ensemble Similarity: {0:f}. Expected {1:f}."
.format(result_value, upper_bound))
def test_dres_error_estimation(self, ens1):
average_upper_bound = 0.3
stdev_upper_bound = 0.2
averages, stdevs = encore.dres([ens1, ens1], estimate_error = True,
bootstrapping_samples=10,
selection="name CA and resnum 1-10")
average = averages[0,1]
stdev = stdevs[0,1]
assert average < average_upper_bound, "Unexpected average value for " \
"bootstrapped samples in Dim. reduction Ensemble similarity"
assert stdev < stdev_upper_bound, "Unexpected standard deviation for" \
" bootstrapped samples in Dim. reduction Ensemble imilarity"
def test_dres_error_estimation(self, ens1):
average_upper_bound = 0.3
stdev_upper_bound = 0.2
averages, stdevs = encore.dres([ens1, ens1],
estimate_error=True,
bootstrapping_samples=10,
selection="name CA and resnum 1-10")
average = averages[0, 1]
stdev = stdevs[0, 1]
assert average < average_upper_bound, "Unexpected average value for " \
"bootstrapped samples in Dim. reduction Ensemble similarity"
assert stdev < stdev_upper_bound, "Unexpected standard deviation for" \
" bootstrapped samples in Dim. reduction Ensemble imilarity"
def test_dres_without_superimposition(self, ens1, ens2):
distance_matrix = encore.get_distance_matrix(encore.merge_universes(
[ens1, ens2]),
superimpose=False)
results, details = encore.dres([ens1, ens2],
distance_matrix=distance_matrix)
result_value = results[0, 1]
expected_value = 0.68
assert_almost_equal(
result_value,
expected_value,
decimal=1,
err_msg=
"Unexpected value for Dim. reduction Ensemble Similarity: {0:f}. Expected {1:f}."
.format(result_value, expected_value))
def test_dres_error_estimation(self):
average_upper_bound = 0.3
stdev_upper_bound = 0.2
averages, stdevs = encore.dres([self.ens1, self.ens1],
estimate_error=True,
bootstrapping_samples=10,
selection="name CA and resnum 1-10")
average = averages[0, 1]
stdev = stdevs[0, 1]
self.assertLess(
average,
average_upper_bound,
msg=
"Unexpected average value for bootstrapped samples in Dim. reduction Ensemble similarity"
)
self.assertLess(
stdev,
stdev_upper_bound,
msg=
"Unexpected standard deviation for bootstrapped samples in Dim. reduction Ensemble imilarity"
)
def test_dres(self, ens1, ens2):
results, details = encore.dres([ens1, ens2], selection="name CA and resnum 1-10")
result_value = results[0,1]
upper_bound = 0.6
assert result_value < upper_bound, "Unexpected value for Dim. " \
"reduction Ensemble Similarity: {0:f}. Expected {1:f}.".format(result_value, upper_bound)
def test_dres_to_self(self, ens1):
results, details = encore.dres([ens1, ens1])
result_value = results[0,1]
expected_value = 0.
assert_almost_equal(result_value, expected_value, decimal=2,
err_msg="Dim. Reduction Ensemble Similarity to itself not zero: {0:f}".format(result_value))
