' no bin: %s' %(logArray[2])
)
# Find and process bins below minimum bin count
colSums = countMatrix.sum(axis=0)
binsBelowMin = colSums < args['<mincount>']
failedBins = np.logical_or(failedBins, binsBelowMin)
# Print bin data
print ' Bin Data:\n%s\n%s\n%s\n%s' %(
' bin number: %s' %(genomeBins.binCount),
' bins below minimum: %s' %(sum(binsBelowMin)),
' mean bincount: %.0f' %(np.mean(colSums)),
' median bin count: %.0f' %(np.median(colSums))
)
# Print combined bin data
print '\nCombined Samples:\n Bin Data:\n%s\n%s' %(
' bin number: %s' %(genomeBins.binCount),
' bins below minimum: %s' %(sum(failedBins))
)
# Normalise matrices
if sum(failedBins) < genomeBins.binCount:
# Loop through count matrices
for files in matrixFileList:
# Normalise matrix
normMatrix, biasData = interactionMatrix.normaliseMatrix(
files[0], failedBins)
# Save interactions
np.savetxt(files[1], np.array([genomeBins.binNames,biasData]).T,
'%s', '\t')
np.savetxt(files[2], normMatrix, '%.6f', '\t',
header = '\t'.join(genomeBins.binNames), comments = '')
# Save interactions
np.savetxt(args.countMatrix, countMatrix, '%s', '\t',
header = '\t'.join(genomeBins.binNames), comments = '')
# Print Interaction Data
print '\nInteraction Data\n\t%s\n\t%s\n\t%s\n\t%s' %(
'total: %s' %(logArray[0]),
'accepted: %s' %(logArray[3]),
'no chromosome: %s' %(logArray[1]),
'no bin: %s' %(logArray[2])
)
# Count number of bins above minimum
colSums = countMatrix.sum(axis=0)
binsAboveMin = sum(colSums >= args.minCount)
# Print bin data
print '\nBin Data\n\t%s\n\t%s\n\t%s\n\t%s' %(
'total: %s' %(len(colSums)),
'bins above minimum: %s' %(binsAboveMin),
'mean count: %.0f' %(np.mean(colSums)),
'median count: %.0f' %(np.median(colSums))
)
if binsAboveMin > 0:
# Normalise matrix
normMatrix, biasData = interactionMatrix.normaliseMatrix(
countMatrix, args.minCount)
# Save interactions
np.savetxt(args.normMatrix, normMatrix, '%.6f', '\t',
header = '\t'.join(genomeBins.binNames), comments = '')
np.savetxt(args.biasFile, np.array([genomeBins.binNames,biasData]).T,
'%s', '\t')
