classdef MaxVar_Timing_Recovery < handle

    properties(Access=public)
        mode
        sps
        fsym
        fadc
        num_tau
        comp_signal
        comp_mode
    end

    methods(Access=public)
        function obj = MaxVar_Timing_Recovery(options)
            arguments(Input)

                options.mode = 0;
                options.sps = 2;
                options.fsym = 32e9;
                options.fadc = 80e9;
                options.num_tau = 64;
                options.comp_signal = 0;
                options.comp_mode = 0;
            end

            fn = fieldnames(options);
            for n = 1:numel(fn)
                obj.(fn{n}) = options.(fn{n});
            end

            %obj-Initialization here%

        end

        function [data_out] = process(obj, data_in)

            data_out = data_in;
            x = data_in.signal;

            if obj.comp_mode
                our_signal = data_in;
                their_signal = obj.comp_signal;
            end

            if obj.mode == 0
                vars = zeros(obj.sps,1);
                for phi = 1:obj.sps

                    % Test variance for different start samples
                    y_phi = x(phi:obj.sps:end);
                    vars(phi) = var(y_phi);

                    % Comparison to reference signal
                    if obj.comp_mode
                        our_signal.signal = x(phi:obj.sps:end);
                        our_signal.fs = 6e9;
                        our_signal.normalize("mode","rms").plot("displayname",['Our signal, ' num2str(vars(phi))],'fignum',1231+phi);
                        their_signal.normalize("mode","rms").plot("displayname",'Their signal','fignum',1231+phi);
                    end

                end
                
                % Choose signal configuration with the maximum variance
                [~,phi_opt] = max(vars);
                
                y = x(phi_opt:obj.sps:end).';   

            elseif obj.mode == 1
                % Create a grid with different (sub)sample starting points
                T = 1/obj.fsym;
                t = (0:length(x)-1)/obj.fadc;
                tauGrid = linspace(0, T, obj.num_tau);
                
                % Interpolate the signal starting from every defined point
                % and calculate the MMSE/variance
                vars = zeros(size(tauGrid));
                for i = 1:numel(tauGrid)
                    tau = tauGrid(i);
                    % tk = linspace(tau, t(end), length(x)/obj.sps);
                    tk = tau : T : t(end)+tau;
                    xk = interp1(t, x, tk, 'linear', 'extrap');
                    if obj.comp_mode % MMSE
                        e = xk.' - obj.comp_signal.signal;
                        vars(i) = mean(abs(e).^2);
                    else % Variance
                        vars(i) = var(xk, 1);
                    end
                end
                
                if obj.comp_mode % MMSE
                    [~,idx] = min(vars);
                else % Variance
                    [~,idx] = max(vars);
                end
                tau_opt = tauGrid(idx);
                
                % Choosing the signal at optimum
                % tk = linspace(tau_opt, t(end), length(x)/obj.sps);
                tk = tau_opt : T : t(end)+tau_opt;
                y = interp1(t, x, tk, 'linear', 'extrap');
            end
            % if length(y) ~= length(x)/obj.sps
            %     y = [y 0];
            % end
            data_out.signal = y.';
            data_out.fs = obj.fsym;
        end


    end
end