1
2
3
4
5
6
7
8
9 import xpktools
10
11 -def predictNOE(peaklist,originNuc,detectedNuc,originResNum,toResNum):
12
13
14
15
16
17
18
19
20
21
22
23
24
25 returnLine=""
26
27 datamap=_data_map(peaklist.datalabels)
28
29
30 originAssCol = datamap[originNuc+".L"]+1
31 originPPMCol = datamap[originNuc+".P"]+1
32 detectedPPMCol = datamap[detectedNuc+".P"]+1
33
34
35 if str(toResNum) in peaklist.residue_dict(detectedNuc) \
36 and str(originResNum) in peaklist.residue_dict(detectedNuc):
37 detectedList=peaklist.residue_dict(detectedNuc)[str(toResNum)]
38 originList=peaklist.residue_dict(detectedNuc)[str(originResNum)]
39 returnLine=detectedList[0]
40
41 for line in detectedList:
42
43 aveDetectedPPM =_col_ave(detectedList,detectedPPMCol)
44 aveOriginPPM =_col_ave(originList,originPPMCol)
45 originAss =originList[0].split()[originAssCol]
46
47 returnLine=xpktools.replace_entry(returnLine,originAssCol+1,originAss)
48 returnLine=xpktools.replace_entry(returnLine,originPPMCol+1,aveOriginPPM)
49
50 return returnLine
51
52
54
55
56 i=0
57 datamap={}
58 labelList=labelline.split()
59
60
61 for i in range(len(labelList)):
62 datamap[labelList[i]]=i
63
64 return datamap
65
67
68 total=0; n=0
69 for element in list:
70 total+=float(element.split()[col])
71 n+=1
72 return total/n
73