MATLAB reads my old tsv file instead of the new one after I have updated it.

Question

Alireza Majdi 2023-12-25

0
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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2063902-matlab-reads-my-old-tsv-file-instead-of-the-new-one-after-i-have-updated-it

编辑： Alireza Majdi 2023-12-25

The following code is my script. It seems that it does not read the bold part and gives me a A file that is empty in a step-by-step analysis of the script and when I run the text through it gives me a figure that has used the right path but wrong data from the old and not-updated tsv file.

function [A,ampDCList,alme,trigem] = Fig_RN_Xylo
oldDir = pwd;
cd('L:\GBW-0046_Myles_Mc_Laughlin\OEData\Trigem\Analysis\Figs Paper 2 - RN-LC\');
S = RNXYLODataStruct
cd(oldDir);
ampDCList = [3];
nAmps = length(ampDCList);
for n = 1:nAmps
    A.(['A' num2str(n)]) = [];
end
getSpikeWaveforms = 0;
i=1;
for n = 1:length(S)
    disp(['S(' num2str(n) ').isSpikedSorted: ', num2str(S(n).isSpikedSorted)]);
    if S(n).isSpikedSorted == 1
        disp(['Length of fileListXylo for S(' num2str(n) '): ', num2str(length(S(n).fileListXylo))]);
        for m = 1:length(S(n).fileListXylo)
            dataFolder = [S(n).dataPath S(n).fileListXylo{m}];
            nChans = S(n).nChans;
            dataFolder = unixCheckPath(dataFolder);
            phyFolder = [dataFolder filesep 'continuous_filt' filesep 'continuous_filt.GUI'];
            % get the cluster Info FileName
            clusterInfoFileName = [phyFolder filesep 'cluster_info.tsv'];
            if exist(clusterInfoFileName)
                phySpikeFile = [dataFolder '\' S(n).fileListDC{m} '.physpks'];
                if exist(phySpikeFile)
                    disp(['Loading ' phySpikeFile])
                    load(phySpikeFile,'-mat')
                else
                    I = getPhySpikeTimes(dataFolder,getSpikeWaveforms,nChans);
                    disp(['Saving ' phySpikeFile])
                    save(phySpikeFile,"I")
                end
                if ~isempty(I)
                    I = analysePhySpikeTimes(I,0);
                    I = analysePhySpikeEntropy(I);
                    for h = 1:length(I)
                        I(h).ratName = S(n).ratName;
                        I(h).ratNumber = str2num(I(h).ratName(6:7));
                    end
                    if ~isnan(I(1).ampDC)
                        indAmp = find(I(1).ampDC(1) == ampDCList);
                        if ~isempty(indAmp)
                            eval(['A.A' num2str(indAmp) '= [A.A' num2str(indAmp) ' I];'])
                        end
                    end
                end
            else
                FilesNotFound{i} = clusterInfoFileName;
                i=i+1;
            end
        end
    end
end
% stats
a=[];
j=0;
for n = 1 %1:nAmps
    eval(['B = A.A' num2str(n) ';'])
    pre = [B.spikeRatePreMean];
    dur = [B.spikeRateDurMean];
    post = [B.spikeRatePostMean];
    
    
    dur = dur*0.8;
    for m = 1:length(B)
        B(m).xylo = 1;
        B(m).spikeRateDurMean = dur(m);
    end
    eval(['A.A' num2str(n) '=B;'])
    [a,j] = makeLongData(a,j,pre,dur,post,B);
    
    %[h,p] = ttest(pre,dur);
    x = [pre' dur' post'];
    %     [p,tbl,stats] = friedman(x,1);
    %     STATS(n).p = p;
    %     STATS(n).tbl = tbl;
    %     STATS(n).stats = stats;
    %     if STATS(n).p<0.05 % do post-hoc
    [STATS(n).pPreDur,STATS(n).hPreDur,STATS(n).statsPreDur] = signrank(pre,dur);
    [STATS(n).pPrePost,STATS(n).hPrePost,STATS(n).statsPrePost] = signrank(pre,post);
    [STATS(n).pPostDur,STATS(n).hPostDur,STATS(n).statsPostDur] = signrank(post,dur);
    STATS(n).pPreDur_Corrected = STATS(n).pPreDur*3;
    STATS(n).pPrePost_Corrected = STATS(n).pPrePost*3;
    STATS(n).pPostDur_Corrected = STATS(n).pPostDur*3;
    %   end
end
SPK = [a{:,3}]';
Xylo = [a{:,4}]';
Condition = a(:,2);
Rat = [a{:,1}]';
Neuron = [a{:,5}]';
trigem = table(SPK,Xylo,Condition,Rat,Neuron);
alme = fitlme(trigem,'SPK~Condition+(1|Neuron) +(1|Rat)')
diary('almeLCXyloDetails.txt')
alme 
diary off
anova(alme)
R = residuals(alme);
figure
hist(R,100);
% figure
f1 = figure('position',[1677         585         786         291]);
set(gcf,'color','w');
fSize = 12;
fSizeSmall = 10;
lWidth = 1.2;
mSize = 8;
%set(f1,'position',[681   559   966   420]/2)
nCols = 2;
[map,num,typ,scheme] = brewermap(3,'Accent');
minSR = -5;
maxSR = 20;
f2 = figure;
for n =1
    eval(['B = A.A' num2str(n) ';'])
    %spikeRateOverTime = [];
    spikeRateOverTime = zeros(length(B),length(B(1).spikeRateOverTime));
    smoothline = ones(1,length(spikeRateOverTime));
    thrd = round(length(smoothline)/3);
    smoothline(thrd+1:2*thrd+2) = linspace(1,0.85,thrd+2);
    
    for m = 1:length(B)
        spikeRateOverTime(m,1:length(B(m).spikeRateOverTime)) = B(m).spikeRateOverTime.*smoothline;
    end
    srOverTimeBins = B(m).spikeRateOverTimeBins;
    
    srOverTime = mean(spikeRateOverTime);
    
    srOverTimeSTD = std(spikeRateOverTime);
    
    srOverTimeCI = 0.95*(srOverTimeSTD/sqrt(length(length(B))));
    
    figure(f1)
    subplot(1,2,1)
    p = patch([srOverTimeBins fliplr(srOverTimeBins)],[srOverTime+srOverTimeCI fliplr(srOverTime-srOverTimeCI)],[0.7 0.7 0.7],'facealpha',0.5,'linestyle','none');
    hold on
    p1 = plot(srOverTimeBins,srOverTime,'color',map(1,:))
    text(5,10,'pre','FontName','Arial','fontsize',fSizeSmall)
    text(65,10,'dur','FontName','Arial','fontsize',fSizeSmall)
    text(125,10,'post','FontName','Arial','fontsize',fSizeSmall)
    
    title({'Xylocaine Blocker',['DC  3 mA']})
    xlabel('Time (s)')
    ylabel('Spike Rate (spk/s)')
    xlim([0 180])
    ylim([minSR maxSR])
    timeOn = round(B(1).dcOn/B(1).fs)
    timeOff = round(B(1).dcOff/B(1).fs)
    
    plot([timeOn timeOn],[minSR maxSR-6],'k--','color',[0.5 0.5 0.5])
    plot([timeOff timeOff],[minSR maxSR-6],'k--','color',[0.5 0.5 0.5])
    
    l = legend([p p1],'+/- 95% CI','Mean Spike Rate')
    set(l,'FontName','Arial','fontsize',fSizeSmall)
    legend boxoff
    
    set(gca, 'Ticklength', [0 0])
    set(gca,'linewidth',lWidth)
end
minSR = 0;
maxSR = 20;
for n = 1%1:nAmps
    eval(['B = A.A' num2str(n) ';'])
    pre = [B.spikeRatePreMean];
    dur = [B.spikeRateDurMean];
    post = [B.spikeRatePostMean];
    
    
    srPreMean = mean(pre);
    srDurMean = mean(dur);
    srPostMean = mean(post);
    srPreSTD = std(pre);
    srDurSTD = std(dur);
    srPostSTD = std(post);
    srPreCI = 0.95*(srPreSTD/sqrt(length(pre)));
    srDurCI = 0.95*(srDurSTD/sqrt(length(dur)));
    srPostCI = 0.95*(srPostSTD/sqrt(length(post)));
    
    figure(f1)
    subplot(1,2,2)
    
    dist = unifrnd(-.3,0.3,size(pre));
    plot(1+dist,pre,'.','MarkerSize',mSize,'color',[0.5 0.5 0.5])
    hold on
    plot(2+dist,dur,'.','MarkerSize',mSize,'color',[0.5 0.5 0.5])
    plot(3+dist,post,'.','MarkerSize',mSize,'color',[0.5 0.5 0.5])
    
    if n==1
        fa = 0.5;
    elseif n==2
        fa = 0.8;
    end
    bar([1],[srPreMean],'FaceColor',map(1,:),'FaceAlpha',fa,'linewidth',lWidth)
    bar([2],[srDurMean],'FaceColor',map(1,:),'FaceAlpha',fa,'linewidth',lWidth)
    bar([3],[srPostMean],'FaceColor',map(1,:),'FaceAlpha',fa,'linewidth',lWidth)
    hold on
    er = errorbar([1 2 3],[srPreMean srDurMean srPostMean],[srPreCI srDurCI srPostCI],'color',[0 0 0],'LineWidth',lWidth,'LineStyle','none');
    
    title(['DC  3 mA'])
    set(gca,'xtick',[1 2 3])
    set(gca,'xticklabel',{'pre','dur','post'},'FontName','Arial','fontsize',fSizeSmall)
    ylabel('Spike Rate (spk/s)','FontName','Arial','fontsize',fSizeSmall)
    ylim([minSR maxSR])
    
    if STATS(n).pPreDur_Corrected<=0.05
        sigline([1,2],[],12)
    end
    if STATS(n).pPrePost_Corrected<=0.05
        sigline([1,3],[],14)
    end
    if STATS(n).pPostDur_Corrected<=0.05
        sigline([2,3],[],16)
    end
    box off
    set(gca, 'Ticklength', [0 0])
    set(gca,'linewidth',lWidth)
    % text(2,srDurMean+2*srDurCI,'*','FontWeight','bold','FontSize',14,'Color',[1 0 0])
    
end
%------------------------ Locals ------------------------------------------
function [a,j] = makeLongData(a,j,pre,dur,post,B)
for o = 1:length(dur)
    rn = B(o).ratNumber;
    j = j+1;
    a{j,1} = rn;
    a{j,2} = 'dur';
    a{j,3} = dur(o)+1;
    a{j,4} = B(o).xylo;
    a{j,5} = B(o).cluster_id+rn*1000;
end
for o = 1:length(pre)
    rn = B(o).ratNumber;
    j = j+1;
    a{j,1} = rn;
    a{j,2} = 'pre';
    a{j,3} = pre(o)+1;
    a{j,4} = B(o).xylo;
    a{j,5} = B(o).cluster_id+rn*1000;
    
end
for o = 1:length(post)
    rn = B(o).ratNumber;
    j = j+1;
    a{j,1} = rn;
    a{j,2} = 'post';
    a{j,3} = post(o)+1;
    a{j,4} = B(o).xylo;
    a{j,5} = B(o).cluster_id+rn*1000;
end

5 个评论
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Stephen23 2023-12-25

编辑：Stephen23 2023-12-25

Some notes on the code:

Rather than calling PWD & CD to import/export file data, it is much more efficient (and easier to debug) to simply provide the file import/export function with an absolute or relative filename. So the function RNXYLODataStruct should be improved by accepting an filename/filepath. Avoid CD in code.
EVAL should be avoided. And it is easy to avoid: all of those EVAL calls can (and should) be easily replaced by dynamic fieldnames: https://www.mathworks.com/help/matlab/matlab_prog/generate-field-names-from-variables.html Curiously the code uses dynamic fieldnames near the start: A.(['A' num2str(n)]) = []; but then the author seems to sadly have forgotten them.
Even better would be to replace pseudo-indices in fieldnames with real indices. In other words, replace that structure with lots of dynamic fieldnames with a cell array, a structure array, or similar. Converting numeric to text like this ['A' num2str(n)] every time you want to access your data is complex and slow, due to hiding pseudo-indices, and should be avoided. Using actual indexing is simpler and more efficient.
It is strongly recommended to LOAD into an output variable. This makes code more efficient, easier to debug, avoids the risk of overwriting data without any warning, and also avoids the risk that your code inadvertently uses data from prior loop iteration without any warning.
Superfluous square brackets do nothing, get rid of them: bar([1],[srPreMean],...