weinberz

root = pwd;
directories = dir(root);
directories(ismember( {directories.name}, {'.', '..'})) = [];
directories = directories([directories.isdir]);

for k=1 : length(directories)
    outputdir = [root filesep directories(k).name];
    files = dir(outputdir);
    filename_idx = find(not(cellfun('isempty',strfind({files.name}, 'tif'))));
    filename = files(filename_idx).name;

weinberz

% Use databinner function on all variables in workspace.
for i=1:numel(vars)
    eval(['[' vars{i} '_avg_2, ' vars{i} '_max_2] = dataBinner(' vars{i} ',2);'])
    eval(['[' vars{i} '_avg_5, ' vars{i} '_max_5] = dataBinner(' vars{i} ',5);'])
    eval(['[' vars{i} '_avg_10, ' vars{i} '_max_10] = dataBinner(' vars{i} ',10);'])
end

% Generate tables for allowing export of data back to prism. This pads tables
% for use with 30-wide columns in Prism.
for i=[2 5 10]

weinberz

% Load condition data and then run analysis for files in directory.
% Need to change "directory", Channels in loadConditionData and ChannelNames
directory = '/Users/exark/Desktop/Crops';
dirs = dir(directory);
dirs = dirs(4:end);
dirs = dirs([dirs.isdir]);
data = repmat(struct('name',1,'data',1), 8, 1 );
for i=1:length(dirs)
  data(i).name = dirs(i).name;
  data(i).data = loadConditionData([directory filesep dirs(i).name '/Treatment'], {'Ch1'}, {'alexa568'}, 'Parameters', [1.49 250 16e-6]);

weinberz

names = {}
x = hist(Lifetimes,[50 100 150 200 250 300 350 400])
for i = 1:size(x)
  x(:,i) = x(:,i)./sum(x(:,i))
end
hist(Lifetimes,[50 100 150 200 250 300 350 400])
legend(names)

bins = [50 100 150 200 250 300 350 400];
figure()