def reduce(self, **keywords):
"""
2012.10.15
run after all files have been walked through
"""
meanStdData = statistics.estimateMeanStdFromData(
dataVector=self.dataLs, excludeTopFraction=0.2)
chiSqData = statistics.calculateChiSqStatOfDeltaVector(dataVector=self.dataLs, mean=meanStdData.mean, \
std=meanStdData.std)
xMedianValue = numpy.median(self.x_value_ls)
yMedianValue = numpy.median(self.y_value_ls)
corr = numpy.corrcoef(self.x_value_ls, self.y_value_ls)[0, 1]
thisOutputID = os.path.basename(self.outputFname)
outlierFraction = float(self.noOfOutliers) / self.noOfNonMissing
dataRow = [thisOutputID, self.noOfOutliers, self.noOfNonMissing, outlierFraction, chiSqData.chiSqStat,\
chiSqData.chiSqMinusLogPvalue, xMedianValue, yMedianValue, corr]
self.writer.writerow(dataRow)
sys.stderr.write("%s/%s (%.3f) outliers, chiSqStat=%.3f, chiSqMinusLogPvalue=%.3f.\n"%\
(self.noOfOutliers, self.noOfNonMissing, outlierFraction,\
chiSqData.chiSqStat, chiSqData.chiSqMinusLogPvalue))
#close the self.invariantPData.writer
AbstractMatrixFileWalker.reduce(self, **keywords)
def setup(self, **keywords):
"""
"""
AbstractMatrixFileWalker.setup(self, **keywords)
#construct a individualCode2readGroup from readGroupFname
self.invariantPData.individualCode2readGroup = {}
reader = MatrixFile(inputFname=self.readGroupFname)
reader.constructColName2IndexFromHeader()
if self.readGroupHeader:
readGroupIndex = reader.getColIndexGivenColHeader(self.readGroupHeader)
else:
readGroupIndex = 0
for row in reader:
readGroup = row[readGroupIndex]
individualAlignment = self.db_vervet.parseAlignmentReadGroup(readGroup).individualAlignment
if individualAlignment:
individual_code = individualAlignment.individual_sequence.individual.code
self.invariantPData.individualCode2readGroup[individual_code] = readGroup
del reader
return 1
def __init__(self, inputFnameLs=None, **keywords): """ """ AbstractMatrixFileWalker.__init__(self, inputFnameLs=inputFnameLs, **keywords)
