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9 import string
10 import xpktools
11
12 -def predictNOE(peaklist,originNuc,detectedNuc,originResNum,toResNum):
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26 returnLine=""
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28 datamap=_data_map(peaklist.datalabels)
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31 originAssCol = datamap[originNuc+".L"]+1
32 originPPMCol = datamap[originNuc+".P"]+1
33 detectedPPMCol = datamap[detectedNuc+".P"]+1
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35
36 if str(toResNum) in peaklist.residue_dict(detectedNuc) \
37 and str(originResNum) in peaklist.residue_dict(detectedNuc):
38 detectedList=peaklist.residue_dict(detectedNuc)[str(toResNum)]
39 originList=peaklist.residue_dict(detectedNuc)[str(originResNum)]
40 returnLine=detectedList[0]
41
42 for line in detectedList:
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44 aveDetectedPPM =_col_ave(detectedList,detectedPPMCol)
45 aveOriginPPM =_col_ave(originList,originPPMCol)
46 originAss =string.splitfields(originList[0])[originAssCol]
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48 returnLine=xpktools.replace_entry(returnLine,originAssCol+1,originAss)
49 returnLine=xpktools.replace_entry(returnLine,originPPMCol+1,aveOriginPPM)
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51 return returnLine
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57 i=0
58 datamap={}
59 labelList=string.splitfields(labelline)
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62 for i in range(len(labelList)):
63 datamap[labelList[i]]=i
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65 return datamap
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69 total=0; n=0
70 for element in list:
71 total+=string.atof(string.split(element)[col])
72 n+=1
73 return total/n
74