def runSample(params):
#retrieving the given parameter
ccs=params[0]
tree=params[1]
extantAdjacencies=params[2]
adjacencyProbs=params[3]
alpha=params[4]
i=params[5]
extantAdjacencies_species_adj=params[6]
outputDirectory=params[7]
reconstructedMarkerCount=params[8]
allSampleReconstructionStatistic={}
dict_SCJ={}
#lock = multiprocessing.Lock()
#output text log
outLog="Sample: "+str(i)+"\n"
#start sampling method like in the Main.py
outLog+="Enumerate joint labelings...\n"
jointLabels, first = SR.enumJointLabelings(ccs)
outLog+="Check valid labels...\n"
validLabels, validAtNode = SR.validLabels(jointLabels, first)
#lock.acquire()
outLog+= "Compute ancestral labels with SR...\n"
topDown = SR.sampleLabelings(tree, ccs, validAtNode, extantAdjacencies, adjacencyProbs, alpha)
#lock.release()
reconstructedAdj = SR.reconstructedAdjacencies(topDown)
SR.outputReconstructedAdjacencies(reconstructedAdj, outputDirectory+"/reconstructed_adjacencies_" + str(i))
for node in reconstructedAdj:
# count for each adjaency on each internal node, how often this adjacencies over all samples occurs there
for adjacency in reconstructedAdj[node]:
#lock.acquire()
if (node,adjacency) in allSampleReconstructionStatistic:
allSampleReconstructionStatistic[(node,adjacency)] += 1
else:
allSampleReconstructionStatistic.update({(node,adjacency):1})
#lock.release()
outLog+="Scaffolding...\n"
scaffolds = scaffolding.scaffoldAdjacencies(reconstructedAdj)
undoubled = scaffolding.undoubleScaffolds(scaffolds)
scaffolding.outputUndoubledScaffolds(undoubled, outputDirectory+"/undoubled_scaffolds_" + str(i))
scaffolding.outputScaffolds(scaffolds, outputDirectory+"/doubled_scaffolds_" + str(i))
log=scaffolding.sanityCheckScaffolding(undoubled)
outLog+=log
for node in undoubled:
outLog+= str(node)+'\n'
markerCounter = 0
for scaffold in undoubled[node]:
first = scaffold[0]
last = scaffold[-1]
if not first == last:
markerCounter = markerCounter + len(scaffold)
else:
markerCounter = markerCounter + len(scaffold) - 1
outLog+= str(node) + " number of reconstructed undoubled marker in scaffolds: " + str(markerCounter)+'\n'
# number of reconstructed markerIds given by reconstructedMarkerCount
# singleton scaffolds number / number of not reconstructed marker
notReconstructedMarkerCount = reconstructedMarkerCount - markerCounter
# number of all scaffolds
allScaffoldCount = len(undoubled[node]) + notReconstructedMarkerCount
outLog+= str(node) + " number of singleton scaffolds (not reconstructed marker): " + str(
notReconstructedMarkerCount)+'\n'
outLog+= str(node) + " number of scaffolds: " + str(allScaffoldCount)+'\n'
#lock.acquire()
scj = calculate_SCJ(tree, reconstructedAdj, extantAdjacencies_species_adj)
outLog+="Single-Cut-or-Join-Distance: " + str(scj)+'\n'
dict_SCJ.update({'Sample_' + str(i): scj})
#lock.release()
return (allSampleReconstructionStatistic,dict_SCJ,outLog)
if not first == last:
markerCounter = markerCounter + len(scaffold)
else:
markerCounter = markerCounter + len(scaffold)-1
print node+" number of reconstructed undoubled marker in scaffolds: "+str(markerCounter)
# number of reconstructed markerIds
reconstructedMarkerCount = len(reconstructedMarker)
# singleton scaffolds number / number of not reconstructed marker
notReconstructedMarkerCount = reconstructedMarkerCount - markerCounter
# number of all scaffolds
allScaffoldCount = len(undoubled[node]) + notReconstructedMarkerCount
print node + " number of singleton scaffolds (not reconstructed marker): " + str(notReconstructedMarkerCount)
print node + " number of scaffolds: " + str(allScaffoldCount)
#calculate SCJ-distance for unsampled solution
scj_unsampled=calculate_SCJ(tree, reconstructedAdj, extantAdjacencies_species_adj)
dict_SCJ.update({'Unsampled':scj_unsampled})
print time.time() - t0, "seconds process time"
t_sampling=time.time()
#dictionary for statistics of reconstructed Adjacencies
#structure: >internal node adjacency number of how often this adj was reconstructed at this node among all samples
allSampleReconstructionStatistic={}
#Sampling
#only if a number of samples is given and if the sript is called as standalone (not imported) script
if args.sampling and __name__ == '__main__':
print "SAMPLING"
# reconstruct marker pairs out of extantAdjacencies
reconstructedMarker = set()
