def printClusters(clusters):
cid = 0
for c in clusters:
cid += 1
progress.Print("ClusterID: %s" % cid)
for s in c:
progress.Print(s)
def GetBasePairsFromStructFile(
faPath): # return dic={structure:[liste de pairs de base ],....}
# print faPath
DicStruct = {}
lines = FF.Parsefile(faPath)
# print lines
SeqLen = len(lines[1]) - 1
# print SeqLen,"seq length"
rawStructs = []
for j in range(len(lines)):
sec_str = lines[j].strip().split(' ')[0]
rawStructs.append(sec_str)
DicStruct[j] = BasePairsFromStruct(sec_str)
progress.Print("Loaded %s structures (%s distinct)" %
(len(rawStructs), len(set(rawStructs))))
return len(lines), DicStruct
def StructSampling(Pathconstraints, Conditions, numberStructures, T, m, b, defaultFasta):
conf = loadConfig()
dir = os.path.join(conf.OutputFolder, "tmp", 'OutputSamples' + str(numberStructures))
FF.CreateFold(dir)
thermoMsgShown = False
for filename in Conditions:
lines = []
header = []
progress.StartTask("Processing %s"%(filename))
while len(lines) - NUM_HEADER_LINES < numberStructures:
# If alternative sequence file found in constraints folder, use it rather than default
Input = defaultFasta
for p in Pathconstraints:
tmpInput = os.path.join(p, filename + '.' + IPANEMAP.FASTA_EXTENSION)
if os.path.isfile(tmpInput):
Input = tmpInput
output = os.path.join(dir, filename)
Command = 'RNAsubopt -p ' + str(numberStructures) + ' -s -T ' + str(T)
(hasHardConstraints, hasSoftConstraints) = (False, False)
hardConstraintFile = os.path.join(conf.PathConstraintsFile, filename + '.txt')
if os.path.isfile(hardConstraintFile):
Command += ' -C --enforceConstraint '
hasHardConstraints = True
Input = hardConstraintFile
ShapeFile = os.path.join(conf.PathConstraintsFileShape, filename + '.txt')
if os.path.isfile(ShapeFile):
Command += ' --shape ' + ShapeFile + ' --shapeMethod="Dm' + str(m) + 'b' + str(b) + '"'
hasSoftConstraints = True
if not (hasHardConstraints or hasSoftConstraints or thermoMsgShown):
progress.Print("Warning: Did not find suitable constraint file for this condition, using purely thermodynamic sampling")
thermoMsgShown = True
subprocess.call(Command, stdin=open(Input, 'r'), stdout=open(output, 'wb'),
stderr=open(os.devnull, 'w'), shell=True)
with open(output, 'r') as f:
nlines = f.readlines()
header = nlines[:NUM_HEADER_LINES]
lines += nlines[NUM_HEADER_LINES:]
with open(output, 'w') as f:
f.writelines(header+lines[:numberStructures])
progress.EndTask()
return dir
ProbingConditions = [RNAName + state for state in conf.Conditions]
# Specify whether to generate new sample or use a previously generated one
OutputSamples = os.path.join(conf.OutputFolder, "tmp",
'OutputSamples') + conf.SampleSize
if str.lower(
conf.Sampling) == "true" or not os.path.isdir(OutputSamples):
progress.StartTask("Sampling %s structures for each condition" %
(conf.SampleSize))
OutputSamples = SP.StructSampling(
[conf.PathConstraintsFile, conf.PathConstraintsFileShape],
ProbingConditions, int(conf.SampleSize), conf.Temperature,
conf.m, conf.b, conf.RNA)
progress.EndTask()
else:
progress.Print("Using existing sample")
progress.Print("Probing conditions: %s" % (ProbingConditions))
# Create a global file that contains structures sampled from the list of Probing conditions
FF.MergeFiles(OutputSamples, os.path.join(OutputSamples,
'Samples.txt'),
ProbingConditions, SP.NUM_HEADER_LINES)
# Create a distance matrix file
progress.StartTask("Computing dissimilarity matrix")
SVMlFile = "DissimilarityMatrix" + conf.SampleSize
# Calculate distance and identify redundant structures within the same condition
SF.DistanceStruct(os.path.join(OutputSamples, 'Samples.txt'), SVMlFile,
int(conf.SampleSize), ProbingConditions)
progress.EndTask()
def DefineNumberCluster(SVMLMatrix, Redundant, method, DM, BoltzmanFactor,
Probingconditions, rna):
conf = loadConfig()
epsilon = 1 # Cetroid base pair distance threshold
Cluster = defaultdict(lambda: defaultdict(CL.a))
Clust = defaultdict(lambda: defaultdict(CL.a))
CumulBE = defaultdict(lambda: defaultdict(CL.a))
Centroids = defaultdict(lambda: defaultdict(CL.a))
progress.StartTask("Initialization step")
# Initialization step
Cluster[1] = CL.MiniBatchKMeans(SVMLMatrix, 1)
Centroids[1], BZ, X, Y, Z, IntradistanceStop = CT.CentroidBycluster(
Cluster[1],
os.path.join(conf.OutputFolder, "tmp",
'OutputSamples' + str(conf.SampleSize), 'Samples.txt'),
BoltzmanFactor, int(conf.SampleSize), Probingconditions, rna)
CumulBE[1] = CumulatedBoltzmannsbyCluster(Cluster[1], BZ,
int(conf.SampleSize),
Probingconditions)
#print "***************************************verification bz", "Cluster \t Centroids \t CumulBE \t ", Centroids[1], CumulBE[1]
progress.EndTask()
for nb in range(2, 21):
progress.StartTask("Clustering with %s clusters" % nb)
progress.StartTask("Run MBKM")
Clust[nb] = CL.MiniBatchKMeans(SVMLMatrix, nb)
progress.EndTask()
Centroids[nb], BZ, X, Y, Z, Intradistance = CT.CentroidBycluster(
Clust[nb],
os.path.join(conf.OutputFolder, "tmp",
'OutputSamples' + str(conf.SampleSize),
'Samples.txt'), BoltzmanFactor, int(conf.SampleSize),
Probingconditions, rna)
CumulBE[nb] = CumulatedBoltzmannsbyCluster(Clust[nb], BZ,
int(conf.SampleSize),
Probingconditions)
lista = []
'''
####***************************************************First crierion:
if len([ elem for elem in IntradistanceStop if elem <= epsilon_intradist ] )!=0:
print "************************************* Clustering done with ", nb ," as the optimal number of clusters using the first criterion intradistance*********************************************************************"
break
# ************************************* second criterion
'''
for elem1 in Centroids[nb - 1].keys():
rep = []
'''
print "distance to all elements"
print "Ref \t i \t i+1 \t BPdist \t CumulatedBz i \t CumulatedBz i+1 \t CumulatedBzdist"
'''
for elem2 in Centroids[nb].keys():
rep.append(
(elem2,
SF.DistanceTwoStructs(
SF.BasePairsFromStruct(Centroids[nb - 1][elem1]),
SF.BasePairsFromStruct(Centroids[nb][elem2]))))
minima = np.min([item[1] for item in rep])
pos = [elem[0] for elem in rep if elem[1] == minima][0]
l1 = CumulBE[nb - 1][elem1]
l2 = CumulBE[nb][pos]
# print "what s wrong!", l1,l2
Dist = l1 - l2
lista.append((minima, (l1, l2, Dist)))
########## The new criterion i about the existence of probable cluster
Bzmepsilon = 0.3 * CumulBE[1][0]
BP_All_probable_centroids = [
BPdist for BPdist, Bzmandist in lista if Bzmandist[0] >= Bzmepsilon
]
progress.EndTask()
if (len([elem for elem in Intradistance if elem <= epsilon]) != 0
or len([
distance for distance in BP_All_probable_centroids
if distance <= epsilon
]) == len(BP_All_probable_centroids)):
FF.PickleVariable(Cluster[nb], "Clusters" + method + ".pkl")
progress.Print("Choosing %s as the optimal number of clusters" %
nb)
break
# for the entire clusters while keeping redundancy
return Clust[nb], Centroids[nb]