def testIntegration3(self):
"""
Simulations are actually run
"""
controlFile = "tests/data/3ptb_data/integrationTest3.conf"
goldenPath = "tests/data/3ptb_data/originTest3"
outputPath = "tests/data/3ptb_data/Test3"
elements = [22, 14, 16, 19, 1]
goldenClusters = []
metrics = [[1.00000e+00, 0.00000e+00, 0.00000e+00, -5.28675e+02, 6.41969e-03],
[1.00000e+00, 0.00000e+00, 0.00000e+00, -5.28657e+02, 1.88599e-02],
[1.00000e+00, 0.00000e+00, 0.00000e+00, -5.28675e+02, 6.41969e-03],
[1.00000e+00, 0.00000e+00, 0.00000e+00, -5.28665e+02, 1.24213e-02],
[1.00000e+00, 5.00000e+00, 5.00000e+00, -5.28660e+02, 1.75149e-02]]
for i in range(5):
pdb = atomset.PDB()
pdb.initialise(goldenPath+"/2/clustering/cluster_%d.pdb" % i, resname="AEN")
cluster = clustering.Cluster(pdb, 4, metrics=metrics[i])
cluster.elements = elements[i]
cluster.contacts = 0
goldenClusters.append(cluster)
# name = socket.gethostname()
# if "bsccv" not in name and "login" not in name:
# print("Some integration can't be run due to not having PELE installed")
# return True
# self.integrationTest(controlFile, goldenPath, outputPath, goldenClusters)
tmpFolder = "tmp_" + outputPath.replace("/", "_")
adaptiveSampling.main(controlFile)
self.check_succesful_simulation(outputPath, 3)
# cleanup
shutil.rmtree(outputPath)
shutil.rmtree(tmpFolder)
def test_symmetryContactMapDifference(self):
pdb_1 = atomset.PDB()
pdb_1.initialise("tests/data/symmetries/cluster_1.pdb", resname='AEN')
pdb_1_sym = atomset.PDB()
pdb_1_sym.initialise("tests/data/symmetries/cluster_1_sym.pdb",
resname='AEN')
symmetries3PTB = [{"3230:N1:AEN": "3231:N2:AEN"}]
symmetryEvaluator = sym.SymmetryContactMapEvaluator(symmetries3PTB)
symmetryEvaluatorEmpty = sym.SymmetryContactMapEvaluator()
contactMap1, contacts1 = symmetryEvaluator.buildContactMap(
pdb_1, 'AEN', 16)
cluster = clustering.Cluster(pdb_1, contactMap=contactMap1)
contactMap1Sym, contactsSym = symmetryEvaluator.createContactMap(
pdb_1_sym, 'AEN', 16)
contactMapNoSym, _ = symmetryEvaluator.createContactMap(
pdb_1_sym, 'AEN', 16)
goldenDifference = 0.0
DifferenceSym = symmetryEvaluator.evaluateDifferenceDistance(
contactMap1Sym, cluster.contactMap)
DifferenceNosym = symmetryEvaluatorEmpty.evaluateDifferenceDistance(
contactMapNoSym, cluster.contactMap)
self.assertEqual(contacts1, contactsSym)
self.assertAlmostEqual(goldenDifference, DifferenceSym)
self.assertNotAlmostEqual(DifferenceSym, DifferenceNosym)
def test_symmetryContactMapJaccard_XTC(self):
xtc_obj = mdtraj.load("tests/data/symmetries/cluster_1.xtc",
top="tests/data/symmetries/cluster_1.pdb")
topology = utilities.getTopologyFile(
"tests/data/symmetries/cluster_1.pdb")
pdb_1 = atomset.PDB()
pdb_1.initialise(10 * xtc_obj.xyz[0], resname='AEN', topology=topology)
topology = utilities.getTopologyFile(
"tests/data/symmetries/cluster_1_sym.pdb")
xtc_obj = mdtraj.load("tests/data/symmetries/cluster_1_sym.xtc",
top="tests/data/symmetries/cluster_1_sym.pdb")
pdb_1_sym = atomset.PDB()
pdb_1_sym.initialise(10 * xtc_obj.xyz[0],
resname='AEN',
topology=topology)
symmetries3PTB = [{"3230:N1:AEN": "3231:N2:AEN"}]
symmetryEvaluator = sym.SymmetryContactMapEvaluator(symmetries3PTB)
symmetryEvaluatorEmpty = sym.SymmetryContactMapEvaluator()
contactMap1, contacts1 = symmetryEvaluator.buildContactMap(
pdb_1, 'AEN', 16)
cluster = clustering.Cluster(pdb_1, contactMap=contactMap1)
contactMap1Sym, contactsSym = symmetryEvaluator.createContactMap(
pdb_1_sym, 'AEN', 16)
contactMapNoSym, _ = symmetryEvaluator.createContactMap(
pdb_1_sym, 'AEN', 16)
goldenJaccard = 0.0
Jaccard = symmetryEvaluator.evaluateJaccard(contactMap1Sym,
cluster.contactMap)
JaccardNosym = symmetryEvaluatorEmpty.evaluateJaccard(
contactMapNoSym, cluster.contactMap)
self.assertEqual(contacts1, contactsSym)
self.assertAlmostEqual(goldenJaccard, Jaccard)
self.assertNotAlmostEqual(Jaccard, JaccardNosym)
def testPeriodicVariableEpsilonCalculator(self):
params = spawning.SpawningParams()
params.epsilon = 0.5
params.varEpsilonType = "linearVariation"
params.maxEpsilon = 0.75
params.minEpsilon = 0.5
params.variationWindow = 20
params.maxEpsilonWindow = 1
params.metricWeights = "linear"
params.nclusters = 100
params_test = {"period": 8}
params.period = params_test.get("period", params.variationWindow)
params.period += np.sign(np.abs(params.variationWindow -
params.period))
variable_epsilon = spawning.VariableEpsilonDegeneracyCalculator(params)
# rateVariation = (params.maxEpsilon-params.minEpsilon)/3
clusters = clustering.Clusters()
sizes = [6, 2, 3, 1]
energies = [-4, -2, -2, -1]
for size, energy in zip(sizes, energies):
cluster = clustering.Cluster(None, None, None, None, metricCol=0)
cluster.elements = size
cluster.metrics = [energy]
clusters.addCluster(cluster)
trajs = 20
degeneracy6 = variable_epsilon.calculate(clusters.clusters, trajs, 0)
golden6 = np.array([7, 4, 4, 5])
np.testing.assert_array_equal(degeneracy6, golden6)
self.assertAlmostEqual(params.epsilon, params.minEpsilon)
for i in range(1, params.variationWindow):
degeneracy7 = variable_epsilon.calculate(clusters.clusters, trajs,
i)
def testEpsilonCalculatorWithDensity(self):
params = spawning.SpawningParams()
params.epsilon = 0.5
params.metricWeights = "linear"
params.nclusters = 100
clusters = clustering.Clusters()
sizes = [6, 2, 3, 1]
energies = [-4, -2, -2, 0]
contacts = [4, 3, 2, 1]
for size, energy, ncontacts in zip(sizes, energies, contacts):
cluster = clustering.Cluster(None, None, None, None, metricCol=0)
cluster.elements = size
cluster.metrics = [energy]
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)
epsilon = spawning.EpsilonDegeneracyCalculator(params,
densityCalculator)
trajs = 16
degeneracy = epsilon.calculate(clusters.clusters, trajs)
golden = np.array([6, 4, 4, 2])
np.testing.assert_array_equal(degeneracy, golden)
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 testUCBCalculator(self):
params = spawning.SpawningParams()
params.alpha = 8.0
UCB = spawning.UCBCalculator(params)
clusters = clustering.Clusters()
sizes = [6, 2, 3, 1]
energies = [-4, -2, -2, -1]
for size, energy in zip(sizes, energies):
cluster = clustering.Cluster(None, None, None, None, metricCol=0)
cluster.elements = size
cluster.metrics = [energy]
clusters.addCluster(cluster)
trajs = 20
degeneracy = UCB.calculate(clusters.clusters, trajs)
golden = np.array([3, 5, 3, 9])
np.testing.assert_array_equal(degeneracy, golden)
def testSameWeightDegeneracyCalculator(self):
params = spawning.SpawningParams()
sameWeightDegCalculator = spawning.SameWeightDegeneracyCalculator(
params)
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)
trajs = 10
degeneracy = sameWeightDegCalculator.calculate(clusters.clusters,
trajs)
golden = [1, 1, 1, 1]
self.assertEqual(degeneracy, golden)
def addSnapshotToCluster(self, snapshot, metrics=[], col=None):
pdb = atomset.PDB()
pdb.initialise(snapshot, resname=self.resname)
if not self.initialPDB:
self.initialPDB = pdb
contacts = pdb.countContacts(self.resname,
self.contactThresholdDistance)
numberOfLigandAtoms = pdb.getNumberOfAtoms()
contactsPerAtom = float(contacts) / numberOfLigandAtoms
threshold = self.thresholdCalculator.calculate(contactsPerAtom)
threshold2 = threshold**2
threshold2 = 10
initial_scd = atomset.computeSquaredCentroidDifference(
self.initialPDB, pdb)
snapshotPosition, closerClusterInd, closerClusterRMSD = self.getCloserCluster(
initial_scd, threshold2, pdb)
try:
clusterToAssign = self.clusters.clusters[closerClusterInd]
if closerClusterRMSD < clusterToAssign.threshold:
clusterToAssign.addElement(metrics)
return
except TypeError:
# When the first snapshot is read the index of the closest cluster
# center is set to None
pass
# if made it here, the snapshot was not added into any cluster
cluster = clustering.Cluster(pdb,
thresholdRadius=threshold,
contacts=contactsPerAtom,
metrics=metrics,
metricCol=col,
density=1)
if snapshotPosition is not None:
self.clusters.insertCluster(snapshotPosition, cluster)
self.distancesList.insert(snapshotPosition, initial_scd)
else:
self.clusters.addCluster(cluster)
self.distancesList.append(initial_scd)
def testEpsilonCalculator(self):
params = spawning.SpawningParams()
params.epsilon = 0.5
params.metricWeights = "linear"
params.nclusters = 100
epsilon = spawning.EpsilonDegeneracyCalculator(params)
clusters = clustering.Clusters()
sizes = [6, 2, 3, 1]
energies = [-4, -2, -2, -1]
for size, energy in zip(sizes, energies):
cluster = clustering.Cluster(None, None, None, None, metricCol=0)
cluster.elements = size
cluster.metrics = [energy]
clusters.addCluster(cluster)
trajs = 20
degeneracy = epsilon.calculate(clusters.clusters, trajs)
golden = np.array([7, 4, 4, 5])
np.testing.assert_array_equal(degeneracy, golden)
def test_CMInnerLimit(self):
CMEvaluator = clustering.CMClusteringEvaluator(None, None)
cluster1_8 = clustering.Cluster(None, contacts=1, contactThreshold=8)
cluster2_8 = clustering.Cluster(None, contacts=2.2, contactThreshold=8)
cluster2_6 = clustering.Cluster(None, contacts=2.2, contactThreshold=6)
cluster1_6 = clustering.Cluster(None, contacts=1.0, contactThreshold=6)
cluster1_4 = clustering.Cluster(None, contacts=1.0, contactThreshold=4)
cluster2_4 = clustering.Cluster(None, contacts=2.2, contactThreshold=4)
# Reduced slope to 8
self.assertAlmostEqual(CMEvaluator.getInnerLimit(cluster1_8), 12)
self.assertAlmostEqual(CMEvaluator.getInnerLimit(cluster2_8), 4)
self.assertAlmostEqual(CMEvaluator.getInnerLimit(cluster1_6), 12)
self.assertAlmostEqual(CMEvaluator.getInnerLimit(cluster2_6), 4)
self.assertAlmostEqual(CMEvaluator.getInnerLimit(cluster1_4), 12)
self.assertAlmostEqual(CMEvaluator.getInnerLimit(cluster2_4), 4)
def testIntegration1(self):
"""
Simulations are not run, trajectories and reports are copied
"""
controlFile = "tests/data/3ptb_data/integrationTest1.conf"
goldenPath = "tests/data/3ptb_data/originTest1"
outputPath = "tests/data/3ptb_data/Test1"
elements = [28, 17, 5, 18, 1, 3]
goldenClusters = []
metrics = [[1.00000e+00, 0.00000e+00, 0.00000e+00, -7.49807e+03, 2.01909e+01, 2.16436e-01],
[1.00000e+00, 0.00000e+00, 0.00000e+00, -7.49806e+03, 1.85232e+01, 2.29384e-01],
[1.00000e+00, 0.00000e+00, 0.00000e+00, -7.49801e+03, 1.82444e+01, 2.53929e-01],
[1.00000e+00, 0.00000e+00, 0.00000e+00, -7.49800e+03, 2.24539e+01, 2.66941e-01],
[1.00000e+00, 5.00000e+00, 5.00000e+00, -7.49797e+03, 2.23322e+01, 3.08604e-01],
[1.00000e+00, 3.00000e+00, 3.00000e+00, -7.49829e+03, 1.48606e+01, 7.32479e-03]]
for i in range(6):
pdb = atomset.PDB()
pdb.initialise(goldenPath+"/2/clustering/cluster_%d.pdb" % i, resname="AEN")
cluster = clustering.Cluster(pdb, thresholdRadius=4, metrics=metrics[i])
cluster.elements = elements[i]
cluster.contacts = 0
goldenClusters.append(cluster)
self.integrationTest(controlFile, goldenPath, outputPath, goldenClusters)
def testIntegration2(self):
"""
Simulations are not run, trajectories and reports are copied
"""
controlFile = "tests/data/3ptb_data/integrationTest2.conf"
goldenPath = "tests/data/3ptb_data/srcTest2Epsilon"
outputPath = "tests/data/3ptb_data/Test2"
elements = [35, 14, 8, 14, 1]
metrics = [[1.0, 0.0, 0.0, -7498.07, 20.1909, 0.216436],
[1.0, 0.0, 0.0, -7498.06, 18.5232, 0.229384],
[1.0, 0.0, 0.0, -7498.01, 18.6059, 0.253929],
[1.0, 0.0, 0.0, -7498.05, 22.4539, 0.238184],
[1.0, 4.0, 4.0, -7498.05, 22.7766, 0.242335]]
goldenClusters = []
for i in range(5):
pdb = atomset.PDB()
pdb.initialise(goldenPath+"/2/clustering/cluster_%d.pdb" % i, resname="AEN")
cluster = clustering.Cluster(pdb, 4, metrics=metrics[i])
cluster.elements = elements[i]
cluster.contacts = 0
goldenClusters.append(cluster)
self.integrationTest(controlFile, goldenPath, outputPath, goldenClusters)
