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interpl_plus.m
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interpl_plus.m
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function [itime,ifits,isumm,ifitsext] = interpl_plus(traces, delta)
M = size(traces,1); % number of methods
R = size(traces,2); % number of runs per method
endtimes = cellfun(@(x) x.time(end), traces);
% extract the maximum time for each individual run
maxtime = max(endtimes,[],2);
% generate time ticks that all will share for interpolation to calculate
% bounds and median
itime = 0:delta:ceil(max(maxtime));
% do all the interpolation at once
ff = cellfun(@(x) interp1(x.time, x.fit, itime, 'linear', 'extrap'), traces, 'UniformOutput', false);
% create interpolated traces
ifits = cell(M,R);
ifitsext = cell(M,R);
for m = 1:M
grplen = find(itime > maxtime(m),1,'first');
for r = 1:R
% Get the interpolated data
ifittmp1 = transpose(ff{m,r});
ifittmp2 = ifittmp1;
% Find the location of the time point that is just past the last
% recorded timepoint in the trace data.
len = find(itime > traces{m,r}.time(end),1,'first');
% Set the interpolated point just past that last time value equal
% to the last recorded fit value.
ifittmp1(len) = traces{m,r}.fit(end);
ifittmp2(len:grplen) = traces{m,r}.fit(end);
% Set fit values past the end to NaN
ifittmp1(len+1:end) = NaN;
ifittmp2(grplen+1:end) = NaN;
% Save the data
ifits{m,r} = ifittmp1;
ifitsext{m,r} = ifittmp2;
end
end
% compute summary info
isumm = cell(M,1);
for m = 1:M
tmp = transpose(cell2mat(ifitsext(m,:)));
isumm{m}.median = transpose(prctile(tmp,50));
isumm{m}.max = transpose(max(tmp,[],1));
isumm{m}.min = transpose(min(tmp,[],1));
end