precursorAlleleACount = int(settings.general['precursorAlleleACount'])
precursorAlleleBCount = int(settings.general['precursorAlleleBCount'])
#Check if the ploidy is correct
totalPloidy = precursorAlleleACount + precursorAlleleBCount
precursorTumorFrequency = 100 #Check if the ploidy is different from 2 (or allele balance). If true, then the precursor is not a healthy cell and we have 100% tumor
if precursorAlleleACount != 1 or precursorAlleleBCount != 1 or precursorPloidy != 2:
precursorTumorFrequency = 0 #In this case we have 100% tumor in the precursor. Only if the precursor it is normal it is 0% tumor.
#Initialize the 'healthy' sample, this can now also be a precursor
healthySample = Sample(None, None)
healthySample.C = [C([2, precursorPloidy])] * measurementLength
healthySample.A = [Alleles(precursorAlleleACount, precursorAlleleBCount)
] * measurementLength
healthySample.Mu = [Mu(precursorTumorFrequency)]
#obtain the chromosome, start and end information from the other samples
healthySample.measurements = LAF([0.5] * measurementLength,
tmpSamples[0].measurements.chromosomes,
tmpSamples[0].measurements.starts,
tmpSamples[0].measurements.ends)
healthySample.somaticVariants = [0] * somVarNum
healthySample.somaticVariantsInd = tmpSamples[0].somaticVariantsInd
healthySample.setParent(None)
healthySample.name = 'Precursor' #do not call it healthy, it may also be a 4N precursor.
#Make a dummy bestCMu for the healthy sample
eventDistances = targetClone.eventDistances
bestCMuHealthy = CMuCombination(C([2, precursorPloidy]),
Mu(precursorTumorFrequency), eventDistances)
