A good start

This page represents a good script to get started in MVAPACK/Octave. Copy the script below, or download this one, and save as a .m file in your favorite simple text editor.

The Script

Get Files

display('Using typical unix filename syntax (i.e. ./* or ../your/file/is/here/* or ???). This will only use directories.') filepath = input('What is the path to your data? ',"s"); F.fileglob = glob(filepath); %% Number of ? is the number of digits of the file name

%% We only use directories here count = 1; sizeoffileglob = numel(F.fileglob); for i = [1:sizeoffileglob]

 if isfolder(F.fileglob{i})
   if (fexist([F.fileglob{1}, '/ser']) || fexist([F.fileglob{1}, '/fid']))
     continue;
   end
   F.dirs{count++} = F.fileglob{i};
 end

end checkforexistence = exist('F.dirs') if ~checkforexistence

 error('There is no NMR data here, seems you have the wrong filepath.')

end

Extract Data

%% This could be loadnmr or loaddmx - default should be loadnmr display('With "@loadnmr" and "@loaddmx" as your choices. Refer to the manual for these functions.'); loadingfunc = input('Which would you like to use? ', "s"); if loadingfunc ~= '@loadnmr' || loadingfunc ~= '@loaddmx'

 error('A loading function was not chosen correctly');

end [F.data, F.parms, F.t] = feval(loadingfunc, F.dirs); %% Assumes F.dirs were all run with similar parameters

Apodization, Zerofill and Fourier Transform

apodizing = input('Would you like to apodize?(y,n) ', "s") if apodizing == 'y' || apodizing == 'Y'

 display('With "@expwindow", "@gausswindow", and "@sinewindow" being the options.(Refer to the manual)')
 functionhandleofapodization = input('Which function would you like to use? ',"s"); %% User choice on function handle
 F.data = apodize(F.data,F.parms,functionhandleofapodization);

end

zerofilling = input('Would you like to zerofill?(y,n) ', "s"); if zerofilling == 'y' || zerofilling == 'Y'

 numberofzerofills = input('How many zerofills would you like? '); %% User choice on number
 F.data = zerofill(F.data,F.parms,numberofzerofills);

end [S.data, S.ppm] = nmrft(F.data, F.parms);%% finally do something

Phase and extract real

%% phase correction method objective can be decided display('Automatic phasing will be performed, you can choose an objective function. Your options are "@simplex_minimum", "@simplex_entropy", "@simplex_whiten", or "@simplex_integral". (Refer to manual)') phaseobjective = ('Which objective do you choose? ', "s"); [S.data, S.phc0, S.phc1] = autophase(S.data, F.parms, phaseobjective); [S.data, S.phc0, S.phc1] = autophase(S.data, F.parms, phaseobjective); X.data = realnmr(S.data, F.parms); %% extract numerical data X.ppm = S.ppm;

Alignment and Referencing

%% an if could be used to decide if icosshift or cosshift here** display('With "@coshift" and "@icoshift" as your options.') alignmentoption = input('Which alignment method would you like to use? ',"s"); X.data = feval(alignmentoption, X.data, X.ppm); X.data = feval(alignmentoption, X.data, X.ppm); X.data = feval(alignmentoption, X.data, X.ppm); plot(X.ppm, X.data);

%% an automatic referencing method could be used instead of this (no automatic method currently available) adjust_zero = 1 zero_adjustment = 0

 while adjust_zero

 	X.ppm = refadj(X.ppm, zero_adjustment, 0.0);

 	plot(X.ppm, X.data);

 	adjust_input = input('Do you need to zero?(y/n)  ',"s");
 	if adjust_input == "Y" || adjust_input == "y"
         	adjust_zero = 1;
 		      zero_adjustment = input('How much Adjustment?  ');
   else
         	adjust_zero = 0;
   end
 end

%% this can be selected as the manual removal method - otherwise ROI methods could be used?**

 remove_var = 1;
 while remove_var

 	remove_input = input('Do you need to remove data?(y/n)  ',"s");
 	if remove_input == "Y" || remove_input == "y"
         	remove_var = 1;
 		      kmin_input = input('What is the minumum of the range?  ');
           kmax_input = input('What is the maximum of the range?  ');
           idxmin = findnearest(X.ppm, kmax_input);
           idxmax = findnearest(X.ppm, kmax_input);
           X.rmvar = [idxmax:idxmin];
           [X.data, X.ppm] = rmvar(X.data, X.ppm, X.rmvar);
           plot(X.ppm, X.data);
   else
         	remove_var = 0;
   end
 end

%% binning can be done a number of ways we default to adaptive binning*** display('There are multiple ways of binning, this walkthrough uses binadapt.') [X.bindata, X.binppm] = binadapt(X.data, X.ppm, F.parms); %% normalization has multiple options these need to be considered *** X.bindata = histmatch(X.bindata);

display('To this point, the important data is extracted and binned as "X.bindata" and can be analyzed');