combine.m

function t = combine(fileList, n)
% this combines all the files in fileList and saves some output files
    addpath('jsonlab/');

    % read in the file
    x = [];
    for ii = 1:numel(fileList)
        fid = fopen(fileList{ii}, 'r');
        raw = fscanf(fid, '%g,%g', [2 inf])';
        x = [x; raw];
        fclose(fid);
    end

    % get the dates in matlab's format (subtract 5 h to get local EST)
    for i = 1:length(x)
        d{i} = datestr(datenum([1970 1 1 -5 0 x(i,1)]));
    end

    % remove everything bigger than 7000 watts (don't like this, but I have outliers)
    data = x(:,2);
    data(data>7e3) = 6e3;

    % transpose
    time_data = d';

    % JSON requires milliseconds
    millisecond_time = x(:,1).*1000;
    savejson('',[millisecond_time, data], fullfile('output',[n '.json']));

    % now save CSV
    print_csv(n, data, time_data)

    %     create a matlab timeseries
    t = timeseries(data, time_data, 'Name', n);
    t.TimeInfo.Units = 'days'; % this is the default from the datestr input
    t.TimeInfo.Format = 'dd, HH:MM';
    save(n, 't');
    
end

combine_files.m

function combine_files

    P1files = {'10_1.txt', '10_2.txt'};
    P2files = {'11_1.txt', '11_2.txt'};

    p1 = combine(P1files, 'Meter_1');
    p2 = combine(P2files, 'Meter_2');

    save('power_usage', 'p1', 'p2');
    system('rm *.txt');

end

combined_plot.m

function [sync_data] = combined_plot

	load power_usage;
	close all;

	% now combine the time series
	[ts1,ts2] = synchronize(p1,p2, 'union');
	combined_usage = ts1+ts2;
	% combined_usage.TimeInfo.Units = 'hours'; 
	% last_time = combined_usage.Time(end);
	% t = combined_usage.Time;
	% time_step = 10/(24*60); % days
	% c = resample(combined_usage, t(1):time_step:t(end));
	c = combined_usage;
	c.Name = 'combined power usage';
	c.DataInfo.Interpolation = tsdata.interpolation('zoh');
    figure;
	plot(c);
	print_csv('combined', c.Data, getabstime(c));

	% now we want a chart for each day
	dv = datevec(getabstime(c));
	days = unique(dv(:,1:3),'rows');
	[~, subs] = ismember(dv(:,1:3), days, 'rows');
	% now we want a time series for each day
	daily_data = {}; % cell array of each days' data
    subplots = figure;
    together = figure;
    first_day = getsamples(c, subs==2);
    first_day = setabstime(first_day, datestr(getabstime(first_day)));
    start_date = datevec(first_day.TimeInfo.StartDate);
    start_day_date = start_date(1:3);
    first_day.Name = 'reference';
    colors = lines(length(days));
%     tsc = tscollection(first_day);
	for iDay = 2:(length(days)-1)
		% create subarray for these days
		k = getsamples(c, subs==iDay);
        k_sync_time = datevec(getabstime(k));
        k_sync_time(:,1:3) = ones(length(k_sync_time),1)*start_day_date;
        k_to_sync = setabstime(k, datestr(k_sync_time));
        [k_sync, ~] = synchronize(k_to_sync, first_day, 'union');
%         k2 = k;
%         k2.Time = kp.Time;
		daily_data{iDay-1} = k;
        k_sync.Name = ['Day ' num2str(iDay-1)];
        sync_data{iDay-1} = k_sync;
%         tsc = addts(tsc,k_sync);
%         daily_data_common{iDay} = k;
        figure(subplots);        
        subplot(5,2,iDay-1);
        plot(k);
        k.data = smooth(k.Time, k.Data, 0.1,'rloess');
        plot(k, 'ro-');
        st_time = getabstime(k);
        title(datestr(st_time(1),'dddd, dd-mm'));
        % combined plot
        figure(together);
        hold on;
        k_sync.data = smooth(k_sync.Time, k_sync.Data, 0.1,'rloess');
        plot(k_sync,'Color', colors(iDay -1, :));
	end
	daily_sum = [datenum(days) accumarray(subs, c.Data)];
	figure;
	bar(daily_sum(2:(end-1),1), daily_sum(2:(end-1),2));
	datetick('x');
end

compute_watt_hr.m

function watt_hr = compute_watt_hr(p)
    st = p.Time(1);
    ed = p.Time(end);
    total_power = sum([0;diff(p.Time)]*24.*p.Data);
    total_time = (ed - st)*24; % hours
    watt_hr = total_power/total_time;
    disp(' *** ');
	disp(['Average watt/hr for this period: ' num2str(watt_hr)]);
	disp(' *** ');
end

plot_power.m

function [P1, P2] = plot_power

t1 = load('Meter_1');
t2 = load('Meter_2');
p1 = t1.t;
p2 = t2.t;

close all;
figure;

subplot(2,1,1);
w1 = get_ticks_and_plot(p1);
subplot(2,1,2);
w2 = get_ticks_and_plot(p2);

disp(' *** ');
disp(['Average watt/hr together: ' num2str(w1 + w2)]);
disp(' *** ');

end

function watt_hr = get_ticks_and_plot(p)

    p.DataInfo.Interpolation = tsdata.interpolation('zoh');
    plot(p);
    watt_hr = compute_watt_hr(p);
    text(0.2,4500,num2str(watt_hr));
%     xt = (st : (2/24) : ed);
%     set(gca, 'XTick', xt );
%     fun = @(n) sprintf('%02d:00',n);
%     xtl = arrayfun(fun,xt,'uniformoutput',false);
%     set(gca,'xticklabel',xtl);
    grid on;
end

print_csv.m

function print_csv(n, data, t)

    [nrows,~]= size(t);
    filename = [n '.csv'];
    fid = fopen(fullfile('output',filename), 'w');
    fprintf(fid, '%s,%s\n', 'date,price');
    for row=1:nrows
        fprintf(fid, '%s,%d\n', t{row}, data(row));
    end
    fclose(fid);
    
end