66 lines
2.2 KiB
Mathematica
66 lines
2.2 KiB
Mathematica
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function[trials] = calculate_statistics(trials)
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item_struct = struct('time', [], 'value', []);
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stats_struct = struct('max', item_struct, ...
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'mean', [], ...
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'median', [], ...
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'mode', [], ...
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'std', [], ...
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'q1', [],...
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'q2', [],...
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'q3', [],...
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'auc', []);
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for t = 1:length(trials)
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trial = trials(t);
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eyes = fieldnames(trial.data);
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for e = 1:length(eyes)
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try
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eye = eyes{e};
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trial.stats.(eye) = stats_struct;
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signal = trial.data.(eye).baseline_corrected.filtered;
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if isempty(signal)
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continue;
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end
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% only relevant after stimulus onset
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trial_after_so = trial.data.(eye).baseline_corrected.filtered(find(trial.time > trial.stimulus_onset));
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trial_before_so = trial.data.(eye).baseline_corrected.filtered(find(trial.time <= trial.stimulus_onset));
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% calculate stats from trial trace
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max_after_so = max(trial_after_so);
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max_ind = length(trial_before_so) + find(trial_after_so==max_after_so);
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delta_t = trial.time(max_ind(1)) - trial.stimulus_onset;
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mean_after_so = mean(trial_after_so);
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std_after_so = std(trial_after_so);
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mode_after_so = mode(trial_after_so);
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median_after_so = median(trial_after_so);
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q1 = prctile(trial_after_so, 25);
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q2 = median_after_so;
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q3 = prctile(trial_after_so, 75);
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% store values in trial
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trial.stats.(eye).max.value = max_after_so;
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trial.stats.(eye).max.time = delta_t;
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trial.stats.(eye).median = median_after_so;
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trial.stats.(eye).mean = mean_after_so;
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trial.stats.(eye).std = std_after_so;
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trial.stats.(eye).mode = mode_after_so;
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trial.stats.(eye).q1 = q1;
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trial.stats.(eye).q2 = q2;
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trial.stats.(eye).q3 = q3;
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trial.stats.(eye).auc = trapz(trial_after_so);
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catch
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warning('No statistics generated for trial');
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end
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end
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% store trial back in array
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trials(t) = trial;
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end
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end
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