def test_analyze_clustering(self):
separated_decomposed_clusters = {
'mixed': {
'1': {
'traj_A': [3],
'traj_B': [8, 10]
},
'2': {
'traj_A': [4],
'traj_B': [14, 15]
}
},
'pure': {
'0': {
'traj_A': [0, 1, 2]
},
'3': {
'traj_A': [5, 6]
},
'4': {
'traj_B': [9, 11, 12, 13, 7]
}
}
}
expected_analysis = {
'num_pure': 3,
'num_mixed': 2,
'num_mixed_elements': 6,
'total_num_clusters': 5,
'num_pure_elements': 10,
'total_num_elements': 16,
'overlap': 0.054930609464645341,
}
analysis = {}
Analyzer.analyze_clustering(separated_decomposed_clusters, self.matrix,
analysis)
self.assertDictEqual(expected_analysis, analysis)
def test_analyze_clustering(self):
separated_decomposed_clusters = {
'mixed': {
'1': {
'traj_A': [3],
'traj_B': [8, 10]
},
'2': {
'traj_A': [4],
'traj_B': [14, 15]
}
},
'pure': {
'0': {
'traj_A': [0, 1, 2]
},
'3': {
'traj_A': [5, 6]
},
'4': {
'traj_B': [9, 11, 12, 13, 7]
}
}
}
expected_analysis = {
'num_pure': 3,
'num_mixed': 2,
'num_mixed_elements': 6,
'total_num_clusters': 5,
'num_pure_elements': 10,
'total_num_elements': 16,
'overlap': 0.054930609464645341,
}
analysis = {}
Analyzer.analyze_clustering(separated_decomposed_clusters, self.matrix, analysis)
self.assertDictEqual(expected_analysis, analysis)
def conformational_space_comparison(clustering, trajectoryHandler, matrixHandler,
clustering_parameters, refinement_parameters):
# clustering = Refiner(matrixHandler,
# trajectoryHandler,
# clustering_parameters,
# refinement_parameters,
# observer).run(clustering)
traj_ranges = {}
current = 0
for i, pdb_source in enumerate(trajectoryHandler.sources):
num_confs = pdb_source.get_info("number_of_conformations")
traj_ranges["traj_%d"%i] = (current, current + num_confs -1)
current = current + num_confs
decomposed_clusters = Separator.separate(clustering.clusters, traj_ranges)
analysis = Analyzer.run(decomposed_clusters, matrixHandler.distance_matrix)
return analysis
def conformational_space_comparison(clustering, trajectoryHandler,
matrixHandler, clustering_parameters,
refinement_parameters):
# clustering = Refiner(matrixHandler,
# trajectoryHandler,
# clustering_parameters,
# refinement_parameters,
# observer).run(clustering)
traj_ranges = {}
current = 0
for i, pdb_source in enumerate(trajectoryHandler.sources):
num_confs = pdb_source.get_info("number_of_conformations")
traj_ranges["traj_%d" % i] = (current, current + num_confs - 1)
current = current + num_confs
decomposed_clusters = Separator.separate(clustering.clusters, traj_ranges)
analysis = Analyzer.run(decomposed_clusters, matrixHandler.distance_matrix)
return analysis
def conformational_space_comparison(clustering, matrixHandler,
trajectoryHandler, clustering_parameters,
refinement_parameters, observer):
# clustering = Refiner(matrixHandler,
# trajectoryHandler,
# clustering_parameters,
# refinement_parameters,
# observer).run(clustering)
# TODO: testing
traj_ranges = {}
current = 0
for i, pdb in enumerate(trajectoryHandler.pdbs):
traj_ranges["traj_%d" % i] = (current,
current + pdb["conformations"] - 1)
current = current + pdb["conformations"]
decomposed_clusters = Separator.separate(clustering.clusters, traj_ranges)
analysis = Analyzer.run(decomposed_clusters, matrixHandler.distance_matrix)
return analysis
def test_analyze_clusters(self):
expected_analysis = {
'cluster_2': {
'components': ['traj_A'],
'global': {
'std': 0.5656854152679444,
'max': 1.4142135381698608,
'num_elements': 5,
'mean': 1.1313708305358887,
"traj_A": {
"max": 1.4142,
"mean": 1.1314,
"num_elements": 5,
"std": 0.5657
}
}
},
'cluster_3': {
'components': ['traj_B'],
'global': {
'std': 0.56568541526794436,
'max': 1.4142135381698608,
'num_elements': 5,
'mean': 1.1313708305358887,
"traj_B": {
"max": 1.4142,
"mean": 1.1314,
"num_elements": 5,
"std": 0.5657
}
}
},
'cluster_1': {
'components': ['traj_A', 'traj_B', 'traj_C'],
'centers_mean_diff': 5.6903559366861982,
'global': {
'std': 1.5095995219901064,
'num_elements': 15,
'max': 5.385164737701416,
'overlap': 0.49575698403605678,
'traj_C': {
'std': 0.56568541526794436,
'max': 1.4142135381698608,
'num_elements': 5,
'mean': 1.1313708305358887
},
'traj_A': {
'std': 0.56568541526794436,
'max': 1.4142135381698608,
'num_elements': 5,
'mean': 1.1313708305358887
},
'traj_B': {
'std': 0.56568541526794436,
'max': 1.4142135381698608,
'num_elements': 5,
'mean': 1.1313708305358887
},
'mean': 3.3646855751673379
}
}
}
analysis = {}
Analyzer.analyze_clusters(self.separated_decomposed_clusters,
self.matrix, analysis)
self.maxDiff = None
self.assertEqual(
json.dumps(expected_analysis,
sort_keys=True,
indent=0,
separators=(',', ':')),
json.dumps(analysis,
sort_keys=True,
indent=0,
separators=(',', ':')))
def test_analyze_clusters(self):
expected_analysis = {
'cluster_2': {
'components': ['traj_A'],
'global': {
'std': 0.5656854152679444,
'max': 1.4142135381698608,
'num_elements': 5,
'mean': 1.1313708305358887,
"traj_A":{
"max":1.4142,
"mean":1.1314,
"num_elements":5,
"std":0.5657
}
}
},
'cluster_3': {
'components': ['traj_B'],
'global': {
'std': 0.56568541526794436,
'max': 1.4142135381698608,
'num_elements': 5,
'mean': 1.1313708305358887,
"traj_B":{
"max":1.4142,
"mean":1.1314,
"num_elements":5,
"std":0.5657
}
}
},
'cluster_1': {
'components': ['traj_A', 'traj_B', 'traj_C'],
'centers_mean_diff': 5.6903559366861982,
'global': {
'std': 1.5095995219901064,
'num_elements': 15,
'max': 5.385164737701416,
'overlap': 0.49575698403605678,
'traj_C': {
'std': 0.56568541526794436,
'max': 1.4142135381698608,
'num_elements': 5,
'mean': 1.1313708305358887
},
'traj_A': {
'std': 0.56568541526794436,
'max': 1.4142135381698608,
'num_elements': 5,
'mean': 1.1313708305358887
},
'traj_B': {
'std': 0.56568541526794436,
'max': 1.4142135381698608,
'num_elements': 5,
'mean': 1.1313708305358887
},
'mean': 3.3646855751673379
}
}
}
analysis = {}
Analyzer.analyze_clusters(self.separated_decomposed_clusters, self.matrix, analysis)
self.maxDiff = None
self.assertEqual(json.dumps(expected_analysis,
sort_keys = True,
indent = 0,
separators = (',', ':')),
json.dumps(analysis,
sort_keys = True,
indent = 0,
separators = (',', ':')))
