view toolboxes/bioakustik_tools/conversion/extract_buchfink_xls.m @ 0:e9a9cd732c1e tip

first hg version after svn

%seglengths
%vols
%periods
%name

% 
% datlen=size(data,1);
% maxseglen=10;
% clear segvol;
% clear seglengths;
% clear repetitions;
% clear name;
% 
% filectr=0;
% lineptr=15;
% 
% while lineptr <= size(textdata,1)
%     if ~strcmp('',textdata{lineptr,1}) 
%         path = textdata{lineptr,1};
%     end
%     if ~strcmp('',textdata{lineptr,2}) 
%         filectr=filectr+1;
%         name{filectr} = strcat(path,'\',textdata{lineptr,2});
%         datapos=lineptr-14;
%         
%         segctr=0;
%         while ~isnan(data(datapos,2))&& datapos < datlen
%             segctr=segctr+1;
%             seglengths(filectr,segctr)= data(datapos,2);
%             if ~isnan(data(datapos,3))
%                 repetitions(filectr,segctr)=data(datapos,3);
%             end
%             segvol(filectr,segctr)=data(datapos,4);
%             datapos=datapos+1;
%             lineptr = lineptr+1;
%         end
%     end
%     lineptr = lineptr+1;
% end


compl_len=sum(seglengths,2);%ins secs
compl_std=std(compl_len);
compl_mean=mean(compl_len);

numsegs=seglengths>0;
numsegs=sum(numsegs,2);
meannumsegs=mean(numsegs)
minnumsegs=min(numsegs)
maxnumsegs=max(numsegs)

for i=1:maxnumsegs %segmentlänge, betrachtet werden jeweils alle segs 
                    % mit ausreichender länge
    valid_cols=find(numsegs>=i);
    mean_seglen(i)=mean(seglengths(valid_cols,i));
    min_seglen(i)=min(seglengths(valid_cols,i));
    max_seglen(i)=max(seglengths(valid_cols,i));
    std_seglen(i)=std(seglengths(valid_cols,i));%in s
end

% valid_cols=find(numsegs==floor(meannumsegs));
% for i=1:floor(mean(numsegs)) %segmentlänge, betrachtet werden jeweils alle segs 
%                     % mit genau floor(mittlerer) länge
%     mean_seglen2(i)=mean(seglengths(valid_cols,i));
%     min_seglen2(i)=min(seglengths(valid_cols,i));
%     max_seglen2(i)=max(seglengths(valid_cols,i));
%     std_seglen2(i)=std(seglengths(valid_cols,i));%in s
% end

%längen
seglen_all=reshape(seglengths,115*6,1);
goodlens=find(seglen_all>0);
mean_seglen_all=mean(seglen_all(goodlens));
std_seglen_all=std(seglen_all(goodlens));
for i=1:maxnumsegs
    segs(i)=sum(numsegs==i);
end

period_segs=sum(repetitions>0,2);
for i=1:maxnumsegs
    havenumperiodsegs(i)=sum(period_segs==i);
end
segs_period=(repetitions>0);
sum((period_segs==3)&(numsegs==4))
sum((period_segs==2)&(numsegs==4))
sum(((period_segs==4)&(numsegs==5)))
sum(((period_segs==3)&(numsegs==5)))

better_half_ind=find((period_segs==3)&(numsegs==4));
better_half_len=seglengths(better_half_ind,:);
better_half_mean=mean(better_half_len,1);
better_half_std=std(better_half_len);

%wiederholungen
period_all=reshape(repetitions,115*6,1);
goodreps=find(period_all>0);
period_all=period_all(goodreps);
peri_all_mean=mean(period_all);
peri_all_std=std(period_all);

%frequenzen
freqs = repetitions./(seglengths./1000);
freqs_all=reshape(freqs,115*6,1);
freqs_all=freqs_all(find(~isnan(freqs_all) & (freqs_all>0)));
freqs_all_mean=mean(freqs_all);
freqs_all_std=std(freqs_all);


better_h_f=repetitions(better_half_ind,2:4)./(seglengths(better_half_ind,2:4)./1000);
better_h_f_mean=mean(better_h_f);
better_h_f_std=std(better_h_f);
better_h_f_min=min(better_h_f);
better_h_f_max=max(better_h_f);

better_half2_ind=find((period_segs==4)&(numsegs==5));

better_h2_f=repetitions(better_half2_ind,2:5)./(seglengths(better_half2_ind,2:5)./1000);
better_h2_f_mean=mean(better_h2_f);
better_h2_f_std=std(better_h2_f);
better_h2_f_min=min(better_h2_f);
better_h2_f_max=max(better_h2_f);

abweichung_f=better_h_f-repmat(better_h_f_mean,size(better_h_f,1),1);
corrcoef(abweichung_f)
abweichung2_f=better_h2_f-repmat(better_h2_f_mean,size(better_h2_f,1),1);
corrcoef(abweichung2_f)