function stat=newplt_abs_stats(name_base)
% script to plot abs data, data format has changed since Myrtle Beach
% report, so one file per frequency must be loaded, but the plotting is based
% on Charlene's plot_ABS_stats.m from Myrtle Beach
% emontgomery@usgs.gov; March 2011

% make sure the argument is the right form
if isnumeric(name_base)
    name_base=num2str(name_base);
end
stat=1;
% load in the data for the 3 heads
% first 5KhZ in the 3s file
fname=[name_base 'abss3s.cdf'];
nc=netcdf(fname);
tt5=nc{'time'}(:)+(nc{'time2'}(:)./86400000);
mean5=nc{'abs_trans_mean'}(:,:)';
vr5=nc{'vrange'}(:)';
pctl5=nc{'abs_trans_pctl'}(:,:);
ttl5=nc{'abs_trans_mean'}.long_name(:);
close(nc)
%now for 2.5KhZ in the 2s file
fname=[name_base 'abss2s.cdf'];
nc=netcdf(fname);
tt2_5=nc{'time'}(:)+(nc{'time2'}(:)./86400000);
mean2_5=nc{'abs_trans_mean'}(:,:)';
vr2_5=nc{'vrange'}(:)';
pctl2_5=nc{'abs_trans_pctl'}(:,:);
ttl2_5=nc{'abs_trans_mean'}.long_name(:);
close(nc)
%now for 1KhZ in the 1s file
fname=[name_base 'abss1s.cdf'];
nc=netcdf(fname);
tt1=nc{'time'}(:)+(nc{'time2'}(:)./86400000);
mean1=nc{'abs_trans_mean'}(:,:)';
vr1=nc{'vrange'}(:)';
pctl1=nc{'abs_trans_pctl'}(:,:);
ttl1=nc{'abs_trans_mean'}.long_name(:);
clrttl=nc{'abs_trans_mean'}.units(:);
close(nc)
% assume the times cover the same span since logged together

freq_strs=['1  '; '2_5'; '5  '];
pctiles = {'1st','16th','50th','84th','99th'};
%now do the plot of means
for ik=1:3
    switch ik
        case 1
            figure('pos',[100 100 1000 800])
            orient landscape
            subplot(311)
        case 2
            subplot(312)
        case 3
            subplot(313)
    end
    eval(['xdata=datenum(gregorian(tt' deblank(freq_strs(ik,:)) '));'])
    eval(['depth=vr' deblank(freq_strs(ik,:)) ';'])
    eval(['data=mean' deblank(freq_strs(ik,:)) ';'])
    eval(['ttl=ttl' deblank(freq_strs(ik,:)) ';'])
    pcolor(xdata, flipud(depth), log(data))
    shading flat
    datetick('x',2,'keepticks','keeplimits')
    ylabel('Distance from ABS (m)')
    set(gca,'YDir','reverse')
    set(gca,'Ytick',[0:.25:depth(end)])
    set(gca,'YtickLabel',num2str([0:.25:depth(end)]',2))
    set(gca,'Layer','top')
    axpos = get(gca,'position');
    if ik ==1;
        ca=caxis;
    else
        caxis(ca);
    end
    L = axpos(1)-.055; B = axpos(2); W = axpos(3)+.075; H = .2;
    set(gca,'position',[L B W H])
    clrbr = colorbar;
    cpos = get(clrbr,'position');
    set(clrbr,'position',[L+W+.01 B 0.02 H])
    set(get(clrbr,'Ylabel'),'String',clrttl)
    title(ttl)
    if isequal(ik,3)
        xlabel('Time')
    end
end
%removing the large matrices doesn't help memory much, but removing the
%plot when your done with it helps a lot!
clear xdata depth data ttl mean* nc
eval(['print -djpeg ' name_base 'abs_means.jpg;'])
close

% now do the pctl plots for each transducer
for ik=1:3
    eval(['data=pctl' deblank(freq_strs(ik,:)) ';'])
    eval(['xdata=datenum(gregorian(tt' deblank(freq_strs(ik,:)) '));'])
    eval(['depth=vr' deblank(freq_strs(ik,:)) ';'])
    eval(['ttl=ttl' deblank(freq_strs(ik,:)) ';'])
    [a,b,lvl]=size(data);
    figure('pos',[100 100 1000 800])
    orient landscape
    for k = 1:lvl
        subplot(lvl,1,k)
        pcolor(xdata, flipud(depth), log(data(:,:,k)'))
        shading flat
        datetick('x',2,'keepticks','keeplimits')
        ylabel({'Distance from','ABS (m)'})
        set(gca,'YDir','reverse')
        set(gca,'Layer','top')
        title([pctiles{k},' Percentile'])
        axpos = get(gca,'position');
        if k ==1;
            ca=caxis;
        else
            caxis(ca);
        end
        L = axpos(1)-.05; B = axpos(2); W = axpos(3)+.07; H = .1;
        set(gca,'position',[L B W H])
        clrbr = colorbar;
        cpos = get(clrbr,'position');
        set(clrbr,'position',[L+W+.01 B 0.02 H])
        set(get(clrbr,'Ylabel'),'String',clrttl)
        if isequal(k,1)
            text(((xdata(1)+xdata(end))/2),-0.45,ttl,'horizontalalignment',...
                'center','fontsize',14)
        elseif isequal(k,5)
            xlabel('Time')
        end
    end
    %removing the large matrices doesn't help memory much, but removing the
    %plot when your done with it helps a lot!
    eval (['clear data pctl' deblank(freq_strs(ik,:)) ';'])
    eval(['print -djpeg ' name_base 'abs' deblank(freq_strs(ik,:)) '_pctl.jpg;'])
    close
end