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 test_separate(self):
traj_ranges = {"traj_A": (0, 6), "traj_B": (7, 15)}
clusters = [
Cluster(None, [0, 1, 2]),
Cluster(None, [3, 8, 10]),
Cluster(None, [14, 4, 15]),
Cluster(None, [5, 6]),
Cluster(None, [7, 9, 11, 12, 13]),
]
for i in range(len(clusters)):
clusters[i].id = str(i)
expected = {
'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]
}
}
}
self.assertDictEqual(expected,
Separator.separate(clusters, traj_ranges))
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_separate(self):
traj_ranges = {"traj_A":(0,6),"traj_B":(7,15)}
clusters = [
Cluster(None,[0,1,2]),
Cluster(None,[3,8,10]),
Cluster(None,[14,4,15]),
Cluster(None,[5,6]),
Cluster(None,[7,9,11,12,13]),
]
for i in range(len(clusters)):
clusters[i].id = str(i)
expected = {
'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]
}
}
}
self.assertDictEqual(expected,Separator.separate(clusters, traj_ranges))
