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))