def testInverselyProportionalToPopulationCalculatorWithDensity(self):
# test 3
clusters = clustering.Clusters()
sizes = [6, 2, 3, 1]
contacts = [4, 3, 2, 1]
for size, ncontacts in zip(sizes, contacts):
cluster = clustering.Cluster(None, None, None, None)
cluster.elements = size
cluster.contacts = ncontacts
clusters.addCluster(cluster)
clusteringBlock = {
"density": {
"type": "heaviside",
"params": {
"values": [6, 2, 3, 1],
"conditions": [3, 2, 1]
}
}
}
densityCalculatorBuilder = densitycalculator.DensityCalculatorBuilder()
densityCalculator = densityCalculatorBuilder.build(clusteringBlock)
params = spawning.SpawningParams()
inverselyProp = spawning.InverselyProportionalToPopulationCalculator(
params, densityCalculator)
trajs = 8
degeneracy = inverselyProp.calculate(clusters.clusters, trajs)
golden = [2, 2, 2, 2]
self.assertEqual(degeneracy, golden)
def testInverselyProportionalToPopulationCalculator(self):
clusters = clustering.Clusters()
sizes = [6, 2, 3, 1]
for size in sizes:
cluster = clustering.Cluster(None, None, None, None)
cluster.elements = size
clusters.addCluster(cluster)
params = spawning.SpawningParams()
inverselyProp = spawning.InverselyProportionalToPopulationCalculator(
params)
trajs = 10
degeneracy = inverselyProp.calculate(clusters.clusters, trajs)
golden = [1, 2, 2, 5]
self.assertEqual(degeneracy, golden)
def main(args):
# Parameters
clusteringObj = utilities.readClusteringObject(args.clusteringObj)
native = args.native
pathwayFilename = args.pathwayFilename
ntrajs = args.ntrajs
threshold = args.threshold
RMSDCalc = RMSDCalculator.RMSDCalculator(clusteringObj.symmetries)
# use graph algorithm to establish a path
initial_cluster = 0
final_cluster = getOptimalCluster(clusteringObj, native, RMSDCalc)
distanceMatrix = createNetworkMatrix(clusteringObj, threshold, RMSDCalc)
predecessors = obtainShortestPath(distanceMatrix)
pathway = createPathway(initial_cluster, final_cluster, predecessors)
print "Pathway clusters:"
print pathway
# write pathway into a single trajectory
writePathwayTrajectory(clusteringObj, pathway, pathwayFilename, native)
# create clustering object with only the pathway clusters
ClPath = clustering.Clustering()
ClPath.clusters.clusters = map(
lambda x: clusteringObj.clusters.clusters[x], pathway)
# spawning along the trajectory
spawningParams = spawning.SpawningParams()
densityCalculatorBuilder = densitycalculator.DensityCalculatorBuilder()
densityCalculator = densityCalculatorBuilder.build({})
spawningPathway = spawning.InverselyProportionalToPopulationCalculator(
densityCalculator)
# Set a least 1 processors from the extrems of the path
degeneracies = spawningPathway.calculate(ClPath.clusters.clusters,
ntrajs - 2, spawningParams)
degeneracies[0] += 1
degeneracies[-1] += 1
print "degeneracies over pathway:"
print degeneracies
print ""
def main(jsonBlock):
# Parameters
firstControlFile = jsonBlock["firstControlFile"]
ntrajs = jsonBlock["ntrajs"]
resname = jsonBlock["resname"]
symmetries = jsonBlock["symmetries"]
trajFolder = jsonBlock["trajFolder"]
clusteringObject = jsonBlock["clusteringObject"]
clusteringThreshold = jsonBlock["clusteringThreshold"]
pathwayFilename = jsonBlock["pathwayFilename"]
templetizedInitialName = jsonBlock["templetizedInitialName"].encode()
secondControlFileTemp = jsonBlock["secondControlFileTemp"]
secondControlFile = jsonBlock["secondControlFile"]
distanceThreshold = jsonBlock["distanceThreshold"]
if firstControlFile:
# Run first adaptive
adaptiveSampling.main(firstControlFile)
# Cluster trajectories
ClOrd, thresholdCalculator = clusterTrajectories(resname,
trajFolder,
clusteringObject,
clusteringThreshold,
symmetries=symmetries)
# use graph algorithm to establish a path
initial_cluster = 0
final_cluster = ClOrd.getOptimalMetric()
pathway = createPathway(initial_cluster, final_cluster, ClOrd,
distanceThreshold)
# write pathway into a single trajectory
writePathwayTrajectory(ClOrd, pathway, pathwayFilename)
# create clustering object with only the pathway clusters
ClPath = OrderedContactsClustering(thresholdCalculator,
resname=resname,
reportBaseFilename="report",
columnOfReportFile=4,
symmetries=symmetries)
ClPath.clusters.clusters = map(lambda x: ClOrd.clusters.clusters[x],
pathway)
# spawning along the trajectory
spawningParams = spawning.SpawningParams()
densityCalculatorBuilder = densitycalculator.DensityCalculatorBuilder()
densityCalculator = densityCalculatorBuilder.build({})
spawningPathway = spawning.InverselyProportionalToPopulationCalculator(
densityCalculator)
# Set a least 1 processors from the extrems of the path
degeneracies = spawningPathway.calculate(ClPath.clusters.clusters,
ntrajs - 2, spawningParams)
degeneracies[0] += 1
degeneracies[-1] += 1
print "degeneracies over pathway:"
print degeneracies
print ""
plots = False
if plots:
plotMetricsDegeneracy(ClPath, resname, degeneracies)
# Prepare second adaptive
makeNewControlFile(degeneracies, ClPath, templetizedInitialName,
secondControlFileTemp, secondControlFile)
adaptiveSampling.main(secondControlFile)
# })
ClCont = clustering.ContactsClustering(thresholdCalculator, resname=resname,
reportBaseFilename="report",
columnOfReportFile=4,
contactThresholdDistance=contactThresholdDistance,
symmetries=[], altSelection=altSel)
ClAcc = clustering.ContactMapAccumulativeClustering(thresholdCalculatorAcc,
similarityEvaluator,
resname=resname,
reportBaseFilename="report",
columnOfReportFile=4,
contactThresholdDistance=contactThresholdDistance,
altSelection=altSel)
spawningObject = spawning.InverselyProportionalToPopulationCalculator(densityCalculator)
# spawningObject = spawning.UCBCalculator(densityCalculator)
# spawningObject = spawning.EpsilonDegeneracyCalculator(densityCalculator)
# spawningObject = spawning.REAPCalculator()
# ClAcc.clusterInitialStructures(["/home/jgilaber/PR/PR_prog_initial_adaptive.pdb"])
# ClCont.clusterInitialStructures(["/home/jgilaber/4DAJ/4DAJ_initial_adaptive.pdb"])
# processorMapping = [0 for i in range(ntrajs-1)]
if not os.path.exists("results"):
os.makedirs("results")
for i in range(nEpochs):
path = ["%s/%d/traj*" % (simulation_path, i)]
paths_report = ["%s/%d/report*" % (simulation_path, i)]
trajs = clustering.getAllTrajectories(paths_report)
total_snapshots = 0
for traj in trajs: