% [spikeTimes] = findSpikes(data, time)
% returns cellarray with spiketimes for different runs
% data = matrix, size is runLen rows and runNum cols
function [spikeTimes] = findSpikes(data, time)
[runLen runs] = size(data);
thresh = 0.035; % Spike filter threshold
events = (data > thresh).*data + (data <= thresh)*thresh;
% A spike is a datapoint D above threshhold where the datapoints to
% the left and right are lower than D.
% events(i1) < events(i)
chkLeft = events(2:end,:) > events(1:end1,:);
left = [zeros(1, runs); chkLeft];
clear chkLeft
% events(i) > events(i+1)
chkRight = events(1:end1,:) > events(2:end,:);
right = [chkRight; zeros(1, runs)];
clear chkRight
spikes = left & right;
clear left right
% The above definition misses spikes where two consequtive points
% above threshold are identical, lets find them too...
newspikes = [(events(1:end1,:) == events(2:end,:)) ...
& (events(1:end1,:) > thresh); zeros(1, runs)];
spikes = spikes + newspikes;
clear newspikes
if(max(spikes) > 1)
error('Matlab:OUCH!', 'Warning binary spikes matrix corrupt!')
end
for runIdx=1:runs
spikeIdx = find(spikes(:, runIdx));
spikeTimes{runIdx} = time(spikeIdx);
end
