def freqs_wrapper(windowQueue, resultQueue, genoFormat, sampleData, minData, target, asCounts, keepNanLines = False):
while True:
windowNumber,window = windowQueue.get() # retrieve window
#make alignment objects
aln = genomics.genoToAlignment(window.seqDict(), sampleData, genoFormat = genoFormat)
popAlns = dict(zip(sampleData.popNames, [aln.subset(groups=[pop]) for pop in sampleData.popNames]))
#target base at each site
if target == "derived":
#use last pop as outgroup
outgroup = sampleData.popNames[-1]
inAln = aln.subset(groups = sampleData.popNames[:-1])
baseColumns = np.array([genomics.derivedAllele(inAln.numArray[:,i][inAln.nanMask[:,i]],
popAlns[outgroup].numArray[:,i][popAlns[outgroup].nanMask[:,i]],
numeric=True)
for i in xrange(aln.l)]).reshape([aln.l,1])
else:
#otherwise get minor allele.
baseColumns = np.array([genomics.minorAllele(aln.numArray[:,i][aln.nanMask[:,i]]) for i in xrange(aln.l)]).reshape([aln.l,1])
goodSites = np.apply_along_axis(lambda(x): ~np.any(np.isnan(x)),1,baseColumns)
#get freqs per pop
popFreqs = []
for pop in sampleData.popNames:
#first find sites with sufficient data
goodData = popAlns[pop].siteNonNan() >= minData
sites = np.where(goodSites & goodData)[0]
baseFreqs = popAlns[pop].siteFreqs(sites, asCounts=asCounts)
popColumns = baseColumns[sites,:].astype(int)
popRows = np.repeat(np.arange(len(sites))[:,np.newaxis],popColumns.shape[1], axis = 1)
targetFreqs = np.empty([aln.l, popColumns.shape[1]], dtype=int if asCounts else float)
targetFreqs.fill(np.nan)
if len(sites) >= 1: targetFreqs[sites,:] = baseFreqs[popRows,popColumns]
popFreqs.append(np.around(targetFreqs, 4))
allFreqs = np.hstack(popFreqs)
if not keepNanLines:
outSites = np.where(~np.apply_along_axis(np.all, 1, np.isnan(allFreqs)))[0]
else: outSites = range(aln.l)
outArray = np.column_stack(([window.scaffold]*len(outSites),
np.array(window.positions)[outSites].astype(str),
allFreqs[outSites,:].astype(str),))
resultStrings = ["\t".join(row) for row in outArray]
resultQueue.put((windowNumber, resultStrings,))
def freqs_wrapper(inQueue, resultQueue, headerLine, genoFormat, sampleData, target, minData, asCounts, threshold, keepNanLines = False):
while True:
sliceNumber,fileSlice = inQueue.get() # retrieve slice
if sliceNumber == -1:
resultQueue.put((-1,None,)) # this is the way of telling everything we're done
break
window = genomics.parseGenoFile(fileSlice, headerLine, names=sampleData.indNames)
#make alignment objects
aln = genomics.genoToAlignment(window.seqDict(), sampleData, genoFormat = genoFormat)
popAlns = dict([(popName, aln.subset(groups=[popName])) for popName in sampleData.popNames])
#this above replaced this below, as it should be faster
#popAlns = dict(zip(sampleData.popNames, [aln.subset(groups=[pop]) for pop in sampleData.popNames]))
#if there is no target, fetch all base counts
if not target:
popFreqs = []
for pop in sampleData.popNames:
goodData = popAlns[pop].siteNonNan() >= minData
sites = np.where(goodData)[0]
baseFreqs = popAlns[pop].siteFreqs(asCounts=asCounts)
popFreqs.append([",".join(row) for row in baseFreqs.astype(str)])
allFreqs = np.column_stack(popFreqs)
else:
#otherwise define the target base at each site
if target == "derived":
#use last pop as outgroup
outgroup = sampleData.popNames[-1]
inAln = aln.subset(groups = sampleData.popNames[:-1])
baseColumns = np.array([genomics.derivedAllele(inAln.numArray[:,i][inAln.nanMask[:,i]],
popAlns[outgroup].numArray[:,i][popAlns[outgroup].nanMask[:,i]],
numeric=True)
for i in range(aln.l)]).reshape([aln.l,1])
else:
#otherwise get minor allele.
baseColumns = np.array([genomics.minorAllele(aln.numArray[:,i][aln.nanMask[:,i]]) for i in xrange(aln.l)]).reshape([aln.l,1])
goodSites = np.apply_along_axis(lambda x: ~np.any(np.isnan(x)),1,baseColumns)
#get freqs per pop
popFreqs = []
for pop in sampleData.popNames:
#first find sites with sufficient data
goodData = popAlns[pop].siteNonNan() >= minData
sites = np.where(goodSites & goodData)[0]
baseFreqs = popAlns[pop].siteFreqs(sites, asCounts=asCounts)
popColumns = baseColumns[sites,:].astype(int)
popRows = np.repeat(np.arange(len(sites))[:,np.newaxis],popColumns.shape[1], axis = 1)
targetFreqs = np.zeros([aln.l, popColumns.shape[1]], dtype=int if asCounts else float)
if not asCounts: targetFreqs.fill(np.nan)
if len(sites) >= 1: targetFreqs[sites,:] = baseFreqs[popRows,popColumns]
popFreqs.append(np.around(targetFreqs, 4))
allFreqs = np.hstack(popFreqs)
if threshold and not asCounts:
allFreqs[allFreqs >= threshold] = 1
allFreqs[allFreqs < threshold] = 0
#fetch scaffold and position
scafPos = np.array([line.split(None, 2)[:2] for line in fileSlice], dtype="str")
if not keepNanLines:
if not asCounts:
outSites = np.where(~np.apply_along_axis(np.all, 1, np.isnan(allFreqs)))[0]
else: outSites = np.where(~np.apply_along_axis(np.all, 1, allFreqs==0))[0]
else: outSites = range(aln.l)
outArray = np.column_stack((scafPos[outSites,:],
allFreqs[outSites,:].astype(str),))
resultStrings = ["\t".join(row) for row in outArray]
resultQueue.put((sliceNumber, resultStrings,))