#!/usr/bin/env python
 
# ** What is this code? **
#
# This is an example script for using new biopython modules for
# generating NOE crosspeak peaklists from diagonal peaklist within
# the framework of nmrview.
# nmrview is not required to run this script, only an installed
# version of python.
 
 
# ** What's important? **
#
# The xpktools.py and NOEtools.py modules are what I'm trying to
# demonstrate.  They provide methods and a data class for performing
# some general analysis on NMR data taken directly from peaklist.
# The code in this script will demonstrate how they are used.
 
 
# ** Who wrote this code? **
# Robert Bussell, Jr.
# rgb2003@med.cornell.edu
 
 
# ** Running this script **
#
# To run this script on a UNIX/Linux system, make it executable and
# modify the first line of this script to point to python if necessary.
# First try running the code with the peaklist that I provide to get
# the feel of how things work, then you can use your own peaklist if you
# modify the variables under the "INITS" code block to make it work
# with your data.  The modules xpktools and NOEtools can be called from
# your own scripts when you have them in place on your computer.
# NOTE: It is very important to have an intact peaklist.  If you copy and
# paste mine into a file be prepared to remove inappropriate line breaks.
 
 
# ** Output of this script **
#
# This script generates a human readable standard output version of the
# NOE coordinates as well as an nmrview peaklist out_example.xpk.
 
# ***********************************************************************
 
# ***** LOAD MODULES *****
 
from __future__ import print_function
import getopt
import string
import sys
 
# -- don't need to modify sys.path with the *tools in Biopython
# -- just need Biopython installed somewhere in the PYTHONPATH
#sys.path=[sys.path,"./"]
#sys.path=[sys.path,"/usr/people/robert/development/xpktools"]
from Bio.NMR import xpktools  # Contains data classes and functions for .xpk files
from Bio.NMR import NOEtools  # A module specific for generate NOE predictions
 
# * * * * * * * * * * MAIN * * * * * * * * * *
 
# ***** INITS *****
 
inc = 1                      # The NOE increment (n where i->i+n and i->i-n are noes)
infn = "./noed.xpk"          # Input peaklist
outfn = "./out_example.xpk"  # Output peaklist
detectatom = "H1"           # Directly detected atom
relayatom = "N15"           # J-coupling from here to detected atom
fromatom = "15N2"           # The other labelled nucleus
 
# *-*-*  First the peaklist is read into a data class from xpktools
# *-*-*  that contains methods for easily extracting information from
# *-*-*  the peaklist file
 
peaklist = xpktools.Peaklist(infn)  # infn is the name of the xpk file
 
# *-*-* The class attribute residue_dict allows the data lines
# *-*-* to be separated from the header and returned here to
# *-*-* the dictionary <dict> as a list indexed by the assignment
# *-*-* of any of the nuclei in the file -- here, the detected atom
# *-*-* is used
 
dict = peaklist.residue_dict(detectatom)
 
# *-*-* As well as the data, the dictionary contains two other entries,
# *-*-* corresponding to the maximum and minimum residues indexed
 
MAXRES = dict["maxres"]
MINRES = dict["minres"]
 
#****** CALCULATE AND WRITE CROSSPEAK PEAKLIST *****
 
# *-*-* The peaklist class has a method for writing out the header
# *-*-* information in a format recognizable by nmrview
 
peaklist.write_header(outfn)  # Write the header to the output file
 
# *-*-* Predict the i->i+inc and i->i-inc noe positions if possible
# *-*-* Write each one to the output file as they are calculated
 
count = 0     # A counter that number the output data lines in order
res = MINRES  # minimum residue number in the set
outlist = []  # Holds the output data
 
while (res <= MAXRES):
 
# *-*-* Predicting the NOE positions based on peak assignment data
# *-*-* is done by supplying the peaklist to and specifying the label
# *-*-* of the origin and detected atom in the NOE transfer as well as
# *-*-* the residues between which the NOE transfer takes places.
 
    noe1 = NOEtools.predictNOE(peaklist, "15N2", "H1", res, res + inc)
    noe2 = NOEtools.predictNOE(peaklist, "15N2", "H1", res, res - inc)
 
# *-*-* The output of predictNOE is in the form of an xpk entry line
# *-*-* suitable for printing to an output file
# *-*-* Additionally, it is possible to extract information easily from
# *-*-* these output lines by using the xpktools.XpkEntry class
 
    entry1 = xpktools.XpkEntry(noe1, peaklist.datalabels)
 
    if noe1 != "":
 
  # *-*-* Here I'm using the XpkEntry class to gain access to
  # *-*-* specific fields in the that make the information
  # *-*-* more readable and suitable for creating data tables
  # *-*-* This output will be printed to the screen.
  # *-*-* The data table contains the assignment, coordinates and
  # *-*-* intensity of the resonance.
 
        print(string.split(entry1.fields["15N2.L"], ".")[0], "-->", \
            string.split(entry1.fields["N15.L"], ".")[0], "\t", \
            entry1.fields["H1.P"], entry1.fields["N15.P"], \
            entry1.fields["15N2.P"], entry1.fields["int"])
 
        noe1 = noe1 + "\012"
        noe1 = xpktools.replace_entry(noe1, 1, count)
        outlist.append(noe1)
        count += 1
 
        if noe2 != "":
            noe2 = noe2 + "\012"
            noe2 = xpktools.replace_entry(noe2, 1, count)
            outlist.append(noe2)
            count += 1
    res += 1
 
# Open the output file and write the data
outfile = open(outfn, 'a')
outfile.writelines(outlist)  # Write the output lines to the file
outfile.close()