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

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first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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wolffd@0 1
wolffd@0 2
wolffd@0 3 %seglengths
wolffd@0 4 %vols
wolffd@0 5 %periods
wolffd@0 6 %name
wolffd@0 7
wolffd@0 8 %
wolffd@0 9 % datlen=size(data,1);
wolffd@0 10 % maxseglen=10;
wolffd@0 11 % clear segvol;
wolffd@0 12 % clear seglengths;
wolffd@0 13 % clear repetitions;
wolffd@0 14 % clear name;
wolffd@0 15 %
wolffd@0 16 % filectr=0;
wolffd@0 17 % lineptr=15;
wolffd@0 18 %
wolffd@0 19 % while lineptr <= size(textdata,1)
wolffd@0 20 % if ~strcmp('',textdata{lineptr,1})
wolffd@0 21 % path = textdata{lineptr,1};
wolffd@0 22 % end
wolffd@0 23 % if ~strcmp('',textdata{lineptr,2})
wolffd@0 24 % filectr=filectr+1;
wolffd@0 25 % name{filectr} = strcat(path,'\',textdata{lineptr,2});
wolffd@0 26 % datapos=lineptr-14;
wolffd@0 27 %
wolffd@0 28 % segctr=0;
wolffd@0 29 % while ~isnan(data(datapos,2))&& datapos < datlen
wolffd@0 30 % segctr=segctr+1;
wolffd@0 31 % seglengths(filectr,segctr)= data(datapos,2);
wolffd@0 32 % if ~isnan(data(datapos,3))
wolffd@0 33 % repetitions(filectr,segctr)=data(datapos,3);
wolffd@0 34 % end
wolffd@0 35 % segvol(filectr,segctr)=data(datapos,4);
wolffd@0 36 % datapos=datapos+1;
wolffd@0 37 % lineptr = lineptr+1;
wolffd@0 38 % end
wolffd@0 39 % end
wolffd@0 40 % lineptr = lineptr+1;
wolffd@0 41 % end
wolffd@0 42
wolffd@0 43
wolffd@0 44 compl_len=sum(seglengths,2);%ins secs
wolffd@0 45 compl_std=std(compl_len);
wolffd@0 46 compl_mean=mean(compl_len);
wolffd@0 47
wolffd@0 48 numsegs=seglengths>0;
wolffd@0 49 numsegs=sum(numsegs,2);
wolffd@0 50 meannumsegs=mean(numsegs)
wolffd@0 51 minnumsegs=min(numsegs)
wolffd@0 52 maxnumsegs=max(numsegs)
wolffd@0 53
wolffd@0 54 for i=1:maxnumsegs %segmentlänge, betrachtet werden jeweils alle segs
wolffd@0 55 % mit ausreichender länge
wolffd@0 56 valid_cols=find(numsegs>=i);
wolffd@0 57 mean_seglen(i)=mean(seglengths(valid_cols,i));
wolffd@0 58 min_seglen(i)=min(seglengths(valid_cols,i));
wolffd@0 59 max_seglen(i)=max(seglengths(valid_cols,i));
wolffd@0 60 std_seglen(i)=std(seglengths(valid_cols,i));%in s
wolffd@0 61 end
wolffd@0 62
wolffd@0 63 % valid_cols=find(numsegs==floor(meannumsegs));
wolffd@0 64 % for i=1:floor(mean(numsegs)) %segmentlänge, betrachtet werden jeweils alle segs
wolffd@0 65 % % mit genau floor(mittlerer) länge
wolffd@0 66 % mean_seglen2(i)=mean(seglengths(valid_cols,i));
wolffd@0 67 % min_seglen2(i)=min(seglengths(valid_cols,i));
wolffd@0 68 % max_seglen2(i)=max(seglengths(valid_cols,i));
wolffd@0 69 % std_seglen2(i)=std(seglengths(valid_cols,i));%in s
wolffd@0 70 % end
wolffd@0 71
wolffd@0 72 %längen
wolffd@0 73 seglen_all=reshape(seglengths,115*6,1);
wolffd@0 74 goodlens=find(seglen_all>0);
wolffd@0 75 mean_seglen_all=mean(seglen_all(goodlens));
wolffd@0 76 std_seglen_all=std(seglen_all(goodlens));
wolffd@0 77 for i=1:maxnumsegs
wolffd@0 78 segs(i)=sum(numsegs==i);
wolffd@0 79 end
wolffd@0 80
wolffd@0 81 period_segs=sum(repetitions>0,2);
wolffd@0 82 for i=1:maxnumsegs
wolffd@0 83 havenumperiodsegs(i)=sum(period_segs==i);
wolffd@0 84 end
wolffd@0 85 segs_period=(repetitions>0);
wolffd@0 86 sum((period_segs==3)&(numsegs==4))
wolffd@0 87 sum((period_segs==2)&(numsegs==4))
wolffd@0 88 sum(((period_segs==4)&(numsegs==5)))
wolffd@0 89 sum(((period_segs==3)&(numsegs==5)))
wolffd@0 90
wolffd@0 91 better_half_ind=find((period_segs==3)&(numsegs==4));
wolffd@0 92 better_half_len=seglengths(better_half_ind,:);
wolffd@0 93 better_half_mean=mean(better_half_len,1);
wolffd@0 94 better_half_std=std(better_half_len);
wolffd@0 95
wolffd@0 96 %wiederholungen
wolffd@0 97 period_all=reshape(repetitions,115*6,1);
wolffd@0 98 goodreps=find(period_all>0);
wolffd@0 99 period_all=period_all(goodreps);
wolffd@0 100 peri_all_mean=mean(period_all);
wolffd@0 101 peri_all_std=std(period_all);
wolffd@0 102
wolffd@0 103 %frequenzen
wolffd@0 104 freqs = repetitions./(seglengths./1000);
wolffd@0 105 freqs_all=reshape(freqs,115*6,1);
wolffd@0 106 freqs_all=freqs_all(find(~isnan(freqs_all) & (freqs_all>0)));
wolffd@0 107 freqs_all_mean=mean(freqs_all);
wolffd@0 108 freqs_all_std=std(freqs_all);
wolffd@0 109
wolffd@0 110
wolffd@0 111 better_h_f=repetitions(better_half_ind,2:4)./(seglengths(better_half_ind,2:4)./1000);
wolffd@0 112 better_h_f_mean=mean(better_h_f);
wolffd@0 113 better_h_f_std=std(better_h_f);
wolffd@0 114 better_h_f_min=min(better_h_f);
wolffd@0 115 better_h_f_max=max(better_h_f);
wolffd@0 116
wolffd@0 117 better_half2_ind=find((period_segs==4)&(numsegs==5));
wolffd@0 118
wolffd@0 119 better_h2_f=repetitions(better_half2_ind,2:5)./(seglengths(better_half2_ind,2:5)./1000);
wolffd@0 120 better_h2_f_mean=mean(better_h2_f);
wolffd@0 121 better_h2_f_std=std(better_h2_f);
wolffd@0 122 better_h2_f_min=min(better_h2_f);
wolffd@0 123 better_h2_f_max=max(better_h2_f);
wolffd@0 124
wolffd@0 125 abweichung_f=better_h_f-repmat(better_h_f_mean,size(better_h_f,1),1);
wolffd@0 126 corrcoef(abweichung_f)
wolffd@0 127 abweichung2_f=better_h2_f-repmat(better_h2_f_mean,size(better_h2_f,1),1);
wolffd@0 128 corrcoef(abweichung2_f)