def writeMatrix(self, matrixParams, outFilename):
print datetime.now(), 'writing out data matrix to %s' % outFilename
newFeatureName = "C:SAMP:" + self.configuration['fm_feature_type'].lower() + "Platform:::::" + self.configuration['fm_platform_type']
newFeatureValue = self.configuration['techtype']
tsvIO.addConstFeature (matrixParams, newFeatureName, newFeatureValue)
tsvIO.writeTSV_dataMatrix (matrixParams, matrixParams['sortRowFlag'], matrixParams['sortColFlag'], outFilename)
print datetime.now(), 'finished writing out data matrix\n'
def writeMatrix(self, matrixParams, outFilename):
print datetime.now(), 'writing out data matrix to %s' % outFilename
newFeatureName = "C:SAMP:" + self.configuration[
'fm_feature_type'].lower(
) + "Platform:::::" + self.configuration['fm_platform_type']
newFeatureValue = self.configuration['techtype']
tsvIO.addConstFeature(matrixParams, newFeatureName, newFeatureValue)
tsvIO.writeTSV_dataMatrix(matrixParams, matrixParams['sortRowFlag'],
matrixParams['sortColFlag'], outFilename)
print datetime.now(), 'finished writing out data matrix\n'
def main():
args = parseArgs()
chr2data = {}
chr2maxcoord = {}
for index in range(1, 25):
chrom = new_Level3_matrix_MM28may13.unifychr(str(index))
chr2data[chrom] = new_Level3_matrix_MM28may13.AutoVivification()
chr2maxcoord[chrom] = 0
steplength = 1000
sampleList = _readAllSnpDataFile(
args.infile, args.include, chr2data, chr2maxcoord, steplength)
cutFrac = 0.02
resegment.NA_VALUE = -999999
resegment.NEAR_ZERO = 0.0001
segList, _, dataMatrix = _resegmentCNdata(
sampleList, chr2data, chr2maxcoord, steplength, cutFrac)
try:
dataD = {}
dataD['rowLabels'] = segList
dataD['colLabels'] = sampleList
dataD['dataMatrix'] = dataMatrix
dataD['dataType'] = "%s:%s" % ("N", "CNVR")
newFeatureName = "C:SAMP:" + "cnvrPlatform"
newFeatureValue = "Genome_Wide_SNP_6"
dataD = tsvIO.addConstFeature(dataD, newFeatureName, newFeatureValue)
sortRowFlag = 0
sortColFlag = 1
tsvIO.writeTSV_dataMatrix(
dataD, sortRowFlag, sortColFlag, args.outfile)
except:
print " FATAL ERROR: failed to write out any resegmented copy-number data "
# -#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#
if __name__ == "__main__":
if (1):
if (len(sys.argv) == 3):
inFile = sys.argv[1]
outFile = sys.argv[2]
else:
print " "
print " Usage: %s <input TSV file> <output TSV file> "
print " "
print " ERROR -- bad command line arguments "
sys.exit(-1)
print " "
print " Running : %s %s %s " % (sys.argv[0], sys.argv[1], sys.argv[2])
print " "
print " "
# now read in the input feature matrix ...
dataD = tsvIO.readTSV(inFile)
# add new custom features ...
dataD = addCustomFeatures(dataD)
# and write the matrix back out
tsvIO.writeTSV_dataMatrix(dataD, 0, 0, outFile)
# -#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#
numCol = len(dataMatrix[0])
skipRowList = []
for iRow in range(numRow):
allNA = 1
iCol = 0
while (allNA == 1 and iCol < numCol):
if (dataMatrix[iRow][iCol] != NA_VALUE):
allNA = 0
iCol += 1
if (allNA):
skipRowList += [iRow]
if (len(skipRowList) > 0):
print " after checking for all-NA features ... "
print " number of rows to be skipped : ", len(skipRowList)
print " --> number of rows remaining : ", (numRow - len(skipRowList))
outD2 = tsvIO.filter_dataMatrix(outD, skipRowList, [])
outD = outD2
# print " "
# print " calling writeTSV_dataMatrix ... ", outFile
sortRowFlag = 0
sortColFlag = 0
tsvIO.writeTSV_dataMatrix(outD, sortRowFlag, sortColFlag, outFile)
print " "
print " DONE "
print " "
# -#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#
outD['colLabels'] = unionColLabels
outD['dataMatrix'] = outMatrix
sortRowFlag = 0 # seems best not to sort the rows
sortColFlag = 0
if (sortRowFlag):
fTypeList.sort()
outD['dataType'] = makeDataTypeString(dTypeList, fTypeList)
if (pruneOrder != "NA"):
print " "
print " now calling pruneTSV_dataMatrix on the merged dataMatrix ... ", pruneOrder
outD = tsvIO.pruneTSV_dataMatrix(outD, rowMaxNAfrac, colMaxNAfrac,
pruneOrder)
else:
print " "
print " NOT doing any pruning of the merged dataMatrix "
print " "
print ' (d) TIME ', time.asctime(time.localtime(time.time()))
print " calling writeTSV_dataMatrix ... ", outFile
tsvIO.writeTSV_dataMatrix(outD, sortRowFlag, sortColFlag, outFile)
print " "
print " DONE ", dTypeList, fTypeList
print ' (e) TIME ', time.asctime(time.localtime(time.time()))
print " "
# -#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#
if __name__ == "__main__":
if (1):
if (len(sys.argv) != 4):
print " Usage : %s <input feature matrix> <output feature matrix> <featType> " % sys.argv[0]
print " to avoid filtering on featType, use ANY "
sys.exit(-1)
inFile = sys.argv[1]
outFile = sys.argv[2]
featType = sys.argv[3]
print " "
print " "
print " ******************** "
print " in filterIdentFeat "
print " ******************** "
inD = tsvIO.readTSV(inFile)
rowLabels = inD['rowLabels']
numRow = len(rowLabels)
outD = removeIdenticalFeatures(inD, featType)
tsvIO.writeTSV_dataMatrix(outD, 0, 0, outFile)
print " FINISHED "
print " "
# -#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#
else:
print " "
print " Usage: %s <input TSV file> <output TSV file> "
print " "
print " ERROR -- bad command line arguments "
sys.exit(-1)
print " "
print " Running : %s %s %s " % (sys.argv[0], sys.argv[1], sys.argv[2])
print " "
# read in the input feature matrix first, just in case there
# actually isn't one yet available ...
testD = tsvIO.readTSV(inFile)
try:
print len(testD['rowLabels']), len(testD['colLabels'])
except:
print " --> invalid / missing input feature matrix "
sys.exit(-1)
# we want to "check" for "deleted" METH probes
if (do_summaryMeth):
newD = summaryMeth(testD)
## tsvIO.writeTSV_dataMatrix ( newD, 0, 0, outFile )
testD = newD
# and finally write it out ...
tsvIO.writeTSV_dataMatrix(testD, 0, 0, outFile)
# -#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#
fVal = float(tokenList[iC + 1])
dataMatrix[iR][iC] = fVal
except:
dataMatrix[iR][iC] = NA_VALUE
numNA += 1
iR += 1
print " iR=%d numNA=%d " % (iR, numNA)
dataD = {}
dataD['rowLabels'] = rowLabels
dataD['colLabels'] = hdrTokens[1:]
dataD['dataMatrix'] = dataMatrix
dataD['dataType'] = "N:MIRN"
print ' writing out data matrix to ', outFilename
newFeatureName = "C:SAMP:mirnPlatform:::::seq"
newFeatureValue = zPlat
dataD = tsvIO.addConstFeature(dataD, newFeatureName, newFeatureValue)
sortRowFlag = 0
sortColFlag = 0
tsvIO.writeTSV_dataMatrix(
dataD, sortRowFlag, sortColFlag, outFilename)
print ' '
print ' DONE !!! '
print ' '
# -#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#
iCol = 0
while (allNA == 1 and iCol < numCol):
if (dataMatrix[iRow][iCol] != NA_VALUE):
allNA = 0
iCol += 1
if (allNA):
skipRowList += [iRow]
if (len(skipRowList) > 0):
print " after checking for all-NA features ... "
print " number of rows to be skipped : ", len(skipRowList)
print " --> number of rows remaining : ", (numRow - len(skipRowList))
outD2 = tsvIO.filter_dataMatrix(outD, skipRowList, [])
outD = outD2
# set up sorting options ...
sortRowFlag = 0
sortColFlag = 0
rowOrder = []
colOrder = []
# print " "
# print " calling writeTSV_dataMatrix ... ", outFile
tsvIO.writeTSV_dataMatrix(outD, sortRowFlag, sortColFlag, outFile, rowOrder, colOrder)
print " "
print " DONE "
print " "
# -#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#
if __name__ == "__main__":
if (len(sys.argv) != 3):
print " Usage : %s <input TSV> <output TSV> " % sys.argv[0]
sys.exit(-1)
tsvNameIn = sys.argv[1]
tsvNameOut = sys.argv[2]
print " "
print " ****************************************************************** "
print " reading input file <%s> " % tsvNameIn
tmpD = tsvIO.readTSV(tsvNameIn)
if (len(tmpD) == 0):
print " in addIndicators ... no input data ... nothing to do here ... "
sys.exit(-1)
# automatically generate indicator features for categorical features
tmpD = addIndicatorFeatures(tmpD)
tsvIO.writeTSV_dataMatrix(tmpD, 0, 0, tsvNameOut)
print " "
print " "
print " FINISHED "
print " "
print " "
# -#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#
outFile = sys.argv[2]
else:
print " "
print " Usage: %s <input TSV file> <output TSV file> "
print " "
print " ERROR -- bad command line arguments "
sys.exit(-1)
print " "
print " Running : %s %s %s " % (sys.argv[0], sys.argv[1], sys.argv[2])
print " "
# read in the input feature matrix first, just in case there
# actually isn't one yet available ...
testD = tsvIO.readTSV(inFile)
try:
print len(testD['rowLabels']), len(testD['colLabels'])
except:
print " --> invalid / missing input feature matrix "
sys.exit(-1)
# and write it back out ...
sortColFlag = 1 # sort the sample barcodes
sortRowFlag = 0 # do NOT sort the feature names
simpleNamesFlag = 1 # and write out 'simple' names
tsvIO.writeTSV_dataMatrix ( testD, sortRowFlag, sortColFlag, \
outFile, [], [], simpleNamesFlag )
# -#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#
outFile = sys.argv[2]
else:
print " "
print " Usage: %s <input TSV file> <output TSV file> "
print " "
print " ERROR -- bad command line arguments "
sys.exit(-1)
print " "
print " Running : %s %s %s " % (sys.argv[0], sys.argv[1], sys.argv[2])
print " "
# read in the input feature matrix first, just in case there
# actually isn't one yet available ...
testD = tsvIO.readTSV(inFile)
try:
print len(testD['rowLabels']), len(testD['colLabels'])
except:
print " --> invalid / missing input feature matrix "
sys.exit(-1)
# and write it back out ...
sortColFlag = 1 # sort the sample barcodes
sortRowFlag = 0 # do NOT sort the feature names
simpleNamesFlag = 1 # and write out 'simple' names
tsvIO.writeTSV_dataMatrix ( testD, sortRowFlag, sortColFlag, \
outFile, [], [], simpleNamesFlag )
# -#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#
if (len(sys.argv) != 3):
print " Usage : %s <input TSV> <output TSV> " % sys.argv[0]
print " ERROR -- bad command line arguments "
sys.exit(-1)
tsvNameIn = sys.argv[1]
tsvNameOut = sys.argv[2]
print " "
print " ****************************************************************** "
print " reading input file <%s> " % tsvNameIn
tmpD = tsvIO.readTSV(tsvNameIn)
if (len(tmpD) == 0):
print " in addIndicators ... no input data ... nothing to do here ... "
sys.exit(-1)
# automatically generate indicator features for categorical features
tmpD = addIndicatorFeatures(tmpD)
tsvIO.writeTSV_dataMatrix(tmpD, 0, 0, tsvNameOut)
print " "
print " "
print " FINISHED "
print " "
print " "
# -#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#
while (allNA == 1 and iCol < numCol):
if (dataMatrix[iRow][iCol] != NA_VALUE):
allNA = 0
iCol += 1
if (allNA):
skipRowList += [iRow]
if (len(skipRowList) > 0):
print " after checking for all-NA features ... "
print " number of rows to be skipped : ", len(skipRowList)
print " --> number of rows remaining : ", (numRow - len(skipRowList))
outD2 = tsvIO.filter_dataMatrix(outD, skipRowList, [])
outD = outD2
# set up sorting options ...
sortRowFlag = 0
sortColFlag = 0
rowOrder = []
colOrder = []
# print " "
# print " calling writeTSV_dataMatrix ... ", outFile
tsvIO.writeTSV_dataMatrix(outD, sortRowFlag, sortColFlag, outFile,
rowOrder, colOrder)
print " "
print " DONE "
print " "
# -#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#
# check that the feature names are still unique ...
print " --> verify that the feature names are unique ... "
(newLabels, rmList) = tsvIO.uniqueFeatureLabels(annotD['rowLabels'],
annotD['dataMatrix'])
print " back from tsvIO.uniqueFeatureLabels "
# quick sanity check that labels are still what I think they are ...
for ii in range(len(newLabels)):
if (not (newLabels[ii] == annotD['rowLabels'][ii])):
print " "
print " BAILING !!! ", newLabels[ii], annotD['rowLabels'][ii]
print " "
sys.exit(-1)
# remove any 'extra' features that need removing ...
if (len(rmList) > 0):
print " --> need to remove these rows ", rmList
tmpD = tsvIO.filter_dataMatrix(annotD, rmList, [])
annotD = tmpD
# and write the matrix back out
print " --> calling tsvIO.writeTSV_dataMatrix ... "
tsvIO.writeTSV_dataMatrix(annotD, 0, 0, outFile)
print " "
print " DONE "
print " "
# -#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#
