Showing posts with label octal dump. Show all posts
Showing posts with label octal dump. Show all posts

20 July 2011

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;"