Noei_PNAS_2022/Aux_functions/Data_prepare.m

function [R_base,Spiketimes_single_rat] = Data_prepare(Spike_times,animal_id,Trial_times,binsize)
% This function rearranges and perepares the data for the decomposition
% Inputs : 
% Spike_times : a cell array consisiting the spike times of the each unit
% animal_id : a cell array consisiting the id of the animal for each unit
% Trial Times : A vector containing the end time of the trials 
% binsize : the time (in msc) used for the binning 
% Outputs :
% R_whole : The cell of the binned data (size : N*T*S N = Number of units
% T = length of the experiment, S = Number of Trials/Animals )
% R_base : Same as R_whole but before the stimulus
animal_id_number = zeros(length(animal_id),1); 
for i=1:length(animal_id)
    animal_id_number(i) = str2num(animal_id{i});
end
%animal_id_number_sorted = sort(animal_id_number);
animal_id_number_sorted = animal_id_number;
%animal_id_unique = unique(animal_id_number_sorted);
animal_id_unique = unique(animal_id_number,'stable');
R_whole = cell(1,length(animal_id_unique));
R_base = R_whole;
parfor i=1:length(animal_id_unique)
    idx_first = find(animal_id_number_sorted == animal_id_unique(i),1,'first');
    idx_last = find(animal_id_number_sorted == animal_id_unique(i),1,'last');
    Spikes_single_rat = Spike_times(idx_first:idx_last);
    Spiketimes_single_rat{i} = Spike_times(idx_first:idx_last);
    Spikes_single_rat =  cellfun(@(x) x*1000,Spikes_single_rat,'un',0);
    endtime_base = Trial_times(idx_last)*1000;
    %endtime = max(cell2mat(Spikes_single_rat)) + 10*1000;
    R_base{i} = count_spikes(Spikes_single_rat',binsize,endtime_base);
    %R_whole{i} = count_spikes(Spikes_single_rat',binsize,endtime);
end
end