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# NOEtools.py: A python module for predicting NOE coordinates from
# assignment data.
#
# The input and output are modelled on nmrview peaklists.
# This modules is suitable for directly generating an nmrview
# peaklist with predicted crosspeaks directly from the
# input assignment peaklist.
import string
import sys
sys.path=[sys.path,"/usr/people/robert/development/xpktools/"]
import xpktools
def predictNOE(peaklist,originNuc,detectedNuc,originResNum,toResNum):
# Predict the i->j NOE position based on self peak (diagonal) assignments
#
# example predictNOE(peaklist,"N15","H1",10,12)
# where peaklist is of the type xpktools.peaklist
# would generate a .xpk file entry for a crosspeak
# that originated on N15 of residue 10 and ended up
# as magnetization detected on the H1 nucleus of
# residue 12.
# CAVEAT: The initial peaklist is assumed to be diagonal (self peaks only)
# and currently there is not checking done to insure that this
# assumption holds true. Check your peaklist for errors and
# off diagonal peaks before attempting to use predictNOE.
returnLine="" # The modified line to be returned to the caller
datamap=_data_map(peaklist.datalabels)
# Construct labels for keying into dictionary
originAssCol = datamap[originNuc+".L"]+1
originPPMCol = datamap[originNuc+".P"]+1
detectedPPMCol = datamap[detectedNuc+".P"]+1
# Make a list of the data lines involving the detected
if (peaklist.residue_dict(detectedNuc).has_key(str(toResNum)) and
peaklist.residue_dict(detectedNuc).has_key(str(originResNum))):
detectedList=peaklist.residue_dict(detectedNuc)[str(toResNum)]
originList=peaklist.residue_dict(detectedNuc)[str(originResNum)]
returnLine=detectedList[0]
for line in detectedList:
aveDetectedPPM =_col_ave(detectedList,detectedPPMCol)
aveOriginPPM =_col_ave(originList,originPPMCol)
originAss =string.splitfields(originList[0])[originAssCol]
returnLine=xpktools.replace_entry(returnLine,originAssCol+1,originAss)
returnLine=xpktools.replace_entry(returnLine,originPPMCol+1,aveOriginPPM)
return returnLine
def _data_map(labelline):
# Generate a map between datalabels and column number
# based on a labelline
i=0 # A counter
datamap={} # The data map dictionary
labelList=string.splitfields(labelline) # Get the label line
# Get the column number for each label
for i in range(len(labelList)):
datamap[labelList[i]]=i
return datamap
def _col_ave(list,col):
# Compute average values from a particular column in a string list
sum=0; n=0
for element in list:
sum=sum+string.atof(string.split(element)[col])
n=n+1
return sum/n
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