7. Processing 1D Bruker nmr data

Bruker 1D binary NMR files can be processed using a combination of cat, grep, sed, gawk and od, together with python and octave (w/ octave-optim) for some fancy line-fitting.

 brukdig2asc:
 #!/bin/bash
#usage: brukdig2asc
SW=`cat acqus | grep 'SW_h' | sed 's/\=/\t/g' | gawk '{print $2}'| tr -d '\n'`
TD=`cat acqus | grep 'TD=' | sed 's/\=/\t/g' | gawk '{print $2}'| tr -d '\n'`
O=`cat acqus | grep '$O1=' | sed 's/\=/\t/g' | gawk '{print $2}'`
SFO=`cat acqus | grep 'SFO1=' | sed 's/\=/\t/g' | gawk '{print $2}'`
#TIME=16384
#SWEEP=23809.5238095238
#AQ=`echo "1/(23809.5238095238/(16384/2))" | bc -lq`
cp fid fid.bin
ls fid.bin | cpio -o | cpio -i --swap -u
od -An -t dI -v -w8 fid.bin| gawk '{print NR,$1,$2}'| sed '1,64d' >fid.asc1
pynmr $SW $TD $O $SFO
makespec

pynmr:
#!/usr/bin/python2.6
import sys
#print str(sys.argv)
sweepwidth=float(sys.argv[1])
nopts=int(sys.argv[2])
centrefreq=float(sys.argv[3])
basefreq=float(sys.argv[4])

aq=1/(sweepwidth/(nopts/2))
#print str(sweepwidth),str(nopts)
f=open('fid.asc1','r')
g=open('fid.asc','w')
for line in f:
    line=line.rstrip('\n')
    line=line.split(' ')
#    print line
    freq=float(line[0])/(nopts/2)*sweepwidth+(centrefreq-sweepwidth/2)
    line[0]=(float(line[0])/(nopts/2))*aq
    g.write(str(line[0])+'\t'+str(line[1])+'\t'+str(line[2])+'\t'+str(freq)+'\n')
f.close
g.close

makespec:

#!/bin/bash
octave --silent --eval "fid=load('fid.asc');
#make xaxis
[nopts b]=size(fid);
aq=max(fid(:,1));
sw=nopts/aq;
freqx=linspace(0,sw,nopts)';

#apodizing
lb=5/10000;
fid(:,2)=fid(:,2).*exp(-lb.*freqx);
fid(:,3)=fid(:,3).*exp(-lb.*freqx);

#phasing
spec=[fid(:,1) real(fftshift(fft(fid(:,2)+i*fid(:,3)))) imag(fftshift(fft(fid(:,2)+i*fid(:,3))))];
[a b]=size(spec); spec(a/2,2:3)=[0 0];
phc=linspace(0,2*pi,180);
maxsig=0;k=1;
for n=1:180;
        localmax=max( real( (spec(:,2)+i*spec(:,3)).*exp(i*phc(n)) ));
        if (localmax>maxsig)
                maxsig=localmax;
                k=n;
        endif
endfor;
#simple baseline
absd=inline('m+t*0','t','m');
guess=0;
[f m kvg iter corp covp covr stdresid z r2]=leasqr(fid(:,4),real((spec(:,2)+i*spec(:,3)).*exp(i*phc(k))),guess,absd);
#disp(m)
#disp(sqrt(diag(covp)))

#make spectrum
spectrum=[fid(:,4) real((spec(:,2)+i*spec(:,3)).*exp(i*phc(k)))-m imag((spec(:,2)+i*spec(:,3)).*exp(i*(phc(k)+pi/2)))-m];

#fitting
pkg load optim
[a b]=max(spectrum(:,2));
centre=fid(b,4);
guess=[10 max(spec(:,2))]; #centre width height
#disp(guess)
lorentzian=inline('p(2)*(1/pi)*(p(1)/2)./((t-centre).^2+(0.5*p(1))^2)','t','p');
[f p r2]=leasqr(fid((b-150):(b+150),4),spectrum((b-150):(b+150),3),guess,lorentzian);

#filter out artefacts from fitting set
filtered=[0 0];
res=floor((max(fid(:,4))-min(fid(:,4)))/nopts);
for l=(b-ceil(5*p(1)/res)):(b+5*ceil(p(1))/res)
delta=lorentzian(fid(l,4),p)-spectrum(l,2);

if (delta>(lorentzian(fid(l,4),p))/1.2)
# do nothing
else
filtered=[filtered; fid(l,4) spectrum(l,2)];
endif
endfor

filtered=[ filtered(2:size(filtered(:,2)),1) filtered(2:(size(filtered(:,2))),2)  ];
[f p r2]=leasqr(filtered(:,1),filtered(:,2),p,lorentzian);

#disp(p')
#disp(r2)
params=[centre centre/67.8 max(lorentzian(fid(:,4),p)) p(1) 1.000 p(2)];
disp(params)
#save
spex=[fid(:,4) real((spec(:,2)+i*spec(:,3)).*exp(i*phc(k)))-m imag((spec(:,2)+i*spec(:,3)).*exp(i*(phc(k)+pi/2)))-m lorentzian(fid(:,4),p)];
save spectrum.dat spex;"