function [retval, s, errorb, tau] = allan_modified(data,tau,name,verbose)
  % ALLAN_MODIFIED  Compute the modified Allan deviation for a set of
  %   time-domain frequency data
  % [RETVAL, S, ERRORB, TAU] = ALLAN_MODIFIED(DATA,TAU,NAME,VERBOSE)
  %
  % Inputs:
  % DATA should be a struct and have the following fields:
  %  DATA.freq or DATA.phase

  %			   A vector of fractional frequency measurements (df/f) in
  %			   DATA.freq *or* phase offset data (seconds) in DATA.phase
  %			   If phase data is not present, it will be generated by
  %			   integrating the fractional frequency data.
  %			   If both fields are present, then DATA.phase will be used.

  %
  %  DATA.rate or DATA.time

  %			   The sampling rate in Hertz (DATA.rate) or a vector of
  %			   timestamps for each measurement in seconds (DATA.time).
  %			   DATA.rate is used if both fields are present.
  %			   If DATA.rate == 0, then the timestamps are used.

  %
  % TAU is an array of tau values for computing Allan deviation.

  %	 TAU values must be divisible by 1/DATA.rate (data points cannot be
  %	 grouped in fractional quantities!). Invalid values are ignored.

  % NAME is an optional label that is added to the plot titles.
  % VERBOSE sets the level of status messages:

  %	 0 = silent & no data plots; 1 = status messages; 2 = all messages

  %
  % Outputs:
  % RETVAL is the array of modified Allan deviation values at each TAU.
  % S is an optional output of other statistical measures of the data (mean, std, etc).
  % ERRORB is an optional output containing the error estimates for a
  %   1-sigma confidence interval. These values are shown on the figure for each point.
  % TAU is an optional output containing the array of tau values used in the
  %   calculation (which may be a truncated subset of the input or default values).
  %
  % Example:
  %
  % To compute the modified Allan deviation for the data in the variable "lt":
  % >> lt
  % lt = 

  %	 freq: [1x86400 double]
  %	 rate: 0.5

  %
  % Use:
  %
  % >> adm = allan_modified(lt,[2 10 100],'lt data',1);
  %
  % The modified Allan deviation will be computed and plotted at tau = 2,10,100 seconds.
  %  1-sigma confidence intervals will be indicated by vertical lines at each point.
  % You can also use the default settings, which are usually a good starting point:
  %
  % >> adm = allan_modified(lt);
  %
  %
  % Notes:
  %  This function calculates the modifed Allan deviation (MDEV).
  %  The calculation is performed using phase data. If only frequency data is
  %   provided, phase data is generated by integrating the frequency data.
  %  No pre-processing of the data is performed.
  %  For rate-based data, MDEV is computed only for tau values greater than the
  %   minimum time between samples and less than the half the total time. For
  %   time-stamped data, only tau values greater than the maximum gap between
  %   samples and less than half the total time are used.
  %  The calculation for fixed sample rate data is *much* faster than for
  %   time-stamp data. You may wish to run the rate-based calculation first,
  %   then compare with time-stamp-based. Often the differences are insignificant.
  %  When phase data is generated by integrating time-stamped frequency data,
  %   the final data point is dropped, because there is no timestamp for it.
  %   This will create a [usually small] difference between the results from
  %   analyzing the same data set with timestamp data and analyzing with a
  %   fixed sample rate. See note in the code near line 350.
  %  You can choose between loglog and semilog plotting of results by
  %   commenting in/out the appropriate line. Search for "#PLOTLOG".
  %  This function has been validated using the test data from NBS Monograph
  %   140, the 1000-point test data set given by Riley [1], and the example data
  %   given in IEEE standard 1139-1999, Annex C.
  %   The author welcomes other validation results, see contact info below.
  %
  % For more information, see:
  % [1] W. J. Riley, "The Calculation of Time Domain Frequency Stability,"
  % Available on the web:
  %  http://www.ieee-uffc.org/frequency_control/teaching.asp?name=paper1ht
  %
  %
  % M.A. Hopcroft

  %	  mhopeng at gmail dot com

  %
  % I welcome your comments and feedback!
  %
  % MH Mar2014
  % v1.24 fix bug related to generating freq data from phase with timestamps

  %	   (thanks to S. David-Grignot for finding the bug)

  % MH Oct2010
  % v1.22 tau truncation to integer groups; tau sort

  %	   plotting bugfix

  % v1.20 update to match allan.m (dsplot.m, columns)

  %	   discard tau values with timestamp irregularities

  %
  
  versionstr = 'allan_modified v1.24';
  
  % MH MAR2010
  % v1.1  bugfixes for irregular sample rates

  %	   update consistency check

  %
  % MH FEB2010
  % v1.0  based on allan_overlap v2.0
  %
  
  %#ok<*AGROW>
  
  
  % defaults
  if nargin < 4, verbose = 2; end
  if nargin < 3, name=''; end
  if nargin < 2 || isempty(tau), tau=2.^(-10:10); end
  if isfield(data,'rate') && isempty(data.rate), data.rate=0; end % v1.1
  
  % Formatting for plots
  FontName = 'Arial';
  FontSize = 14;
  plotlinewidth=2;
  
  if verbose >= 1, fprintf(1,'allan_modified: %s
  
  ',versionstr); end
  
  %% Data consistency checks
  if ~(isfield(data,'phase') || isfield(data,'freq'))

  	error('Either ''phase'' or ''freq'' must be present in DATA. See help file for details. [con0]');

  end
  if isfield(data,'time')

  	if isfield(data,'phase') && (length(data.phase) ~= length(data.time))
  		if isfield(data,'freq') && (length(data.freq) ~= length(data.time))
  			error('The time and freq vectors are not the same length. See help for details. [con2]');
  		else
  			error('The time and phase vectors are not the same length. See help for details. [con1]');
  		end
  	end
  	if isfield(data,'phase') && (any(isnan(data.phase)) || any(isinf(data.phase)))
  		error('The phase vector contains invalid elements (NaN/Inf). [con3]');
  	end
  	if isfield(data,'freq') && (any(isnan(data.freq)) || any(isinf(data.freq)))
  		error('The freq vector contains invalid elements (NaN/Inf). [con4]');
  	end
  	if isfield(data,'time') && (any(isnan(data.time)) || any(isinf(data.time)))
  		error('The time vector contains invalid elements (NaN/Inf). [con5]');
  	end

  end
  
  % sort tau vector
  tau=sort(tau);
  
  
  %% Basic statistical tests on the data set
  if ~isfield(data,'freq')

  	if isfield(data,'rate') && data.rate ~= 0
  		data.freq=diff(data.phase).*data.rate;
  	elseif isfield(data,'time')
  		data.freq=diff(data.phase)./diff(data.time);
  	end
  	if verbose >= 1, fprintf(1,'allan_modified: Fractional frequency data generated from phase data (M=%g).
  ',length(data.freq)); end

  end
  if size(data.freq,2) > size(data.freq,1), data.freq=data.freq'; end % ensure columns
  
  s.numpoints=length(data.freq);
  s.max=max(data.freq);
  s.min=min(data.freq);
  s.mean=mean(data.freq);
  s.median=median(data.freq);
  if isfield(data,'time')

  	if size(data.time,2) > size(data.time,1), data.time=data.time'; end % ensure columns
  	s.linear=polyfit(data.time(1:length(data.freq)),data.freq,1);

  elseif isfield(data,'rate') && data.rate ~= 0;

  	s.linear=polyfit((1/data.rate:1/data.rate:length(data.freq)/data.rate)',data.freq,1);

  else

  	error('Either "time" or "rate" must be present in DATA. Type "help allan_modified" for details. [err1]');

  end
  s.std=std(data.freq);
  
  if verbose >= 2

  	fprintf(1,'allan_modified: fractional frequency data statistics:
  ');
  	disp(s);

  end
  
  % scale to median for plotting
  medianfreq=data.freq-s.median;
  sm=[]; sme=[];
  
  % Screen for outliers using 5x Median Absolute Deviation (MAD) criteria
  MAD = median(abs(medianfreq)/0.6745);
  if verbose >= 1 && any(abs(medianfreq) > 5*MAD)

  	fprintf(1, 'allan_modified: NOTE: There appear to be outliers in the frequency data. See plot.
  ');

  end
  
  %%%%
  % There are two cases, either using timestamps or rate:
  
  %% Fixed Sample Rate Data
  %   If there is a regular interval between measurements, calculation is much
  %   easier/faster
  if isfield(data,'rate') && data.rate > 0 % if data rate was given

  	if verbose >= 1
  		fprintf(1, 'allan_modified: regular data ');
  		if isfield(data,'freq')
  			fprintf(1, '(%g freq data points @ %g Hz)
  ',length(data.freq),data.rate);
  		elseif isfield(data,'phase')
  			fprintf(1, '(%g phase data points @ %g Hz)
  ',length(data.phase),data.rate);
  		else
  			error('
   phase or freq data missing [err10]');
  		end
  	end
  	
  	% string for plot title
  	name=[name ' (' num2str(data.rate) ' Hz)'];
  
  	% what is the time interval between data points?
  	tmstep = 1/data.rate;
  	
  	% Is there time data? Just for curiosity/plotting, does not impact calculation
  	if isfield(data,'time')
  		% adjust time data to remove any starting gap; first time step
  		%  should not be zero for comparison with freq data
  		dtime=data.time-data.time(1)+mean(diff(data.time));
  		dtime=dtime(1:length(medianfreq)); % equalize the data vector lengths for plotting (v1.1)
  		if verbose >= 2
  			fprintf(1,'allan_modified: End of timestamp data: %g sec.
  ',dtime(end));
  			if (data.rate - 1/mean(diff(dtime))) > 1e-6
  				fprintf(1,'allan_modified: NOTE: data.rate (%f Hz) does not match average timestamped sample rate (%f Hz)
  ',data.rate,1/mean(diff(dtime)));
  			end
  		end
  	else
  		% create time axis data using rate (for plotting only)
  		dtime=(tmstep:tmstep:length(data.freq)*tmstep);
  	end
  	
  	
  	% is phase data present? If not, generate it
  	if ~isfield(data,'phase')
  		nfreq=data.freq-s.mean;
  		dphase=zeros(1,length(nfreq)+1);
  		dphase(2:end) = cumsum(nfreq).*tmstep;
  		if verbose >= 1, fprintf(1,'allan_modified: phase data generated from fractional frequency data (N=%g).
  ',length(dphase)); end
  	else
  		dphase=data.phase;
  	end
  
  	
  	% check the range of tau values and truncate if necessary
  	% find halfway point of time record
  	halftime = round(tmstep*length(data.freq)/2);
  	% truncate tau to appropriate values
  	tau = tau(tau >= tmstep & tau <= halftime);
  	if verbose >= 2, fprintf(1, 'allan_modified: allowable tau range: %g to %g sec. (1/rate to total_time/2)
  ',tmstep,halftime); end

  	% find the number of data points in each tau group
  	% number of samples
  	N=length(dphase);
  	m = data.rate.*tau;
  	% only integer values allowed (no fractional groups of points)
  	%tau = tau(m-round(m)<1e-8); % numerical precision issues (v1.1)
  	tau = tau(m==round(m));  % The round() test is only correct for values < 2^53
  	%m = m(m-round(m)<1e-8); % change to round(m) for integer test v1.22
  	m = m(m==round(m));
  	%m=round(m);
  	
  	if verbose >= 1, fprintf(1,'allan_modified: calculating modified Allan deviation...
  	   '); end
  	
  	
  	% calculate the modified Allan deviation for each value of tau
  	k=0; tic;
  	for i = tau
  		k=k+1;
  		pa=[];
  		if verbose >= 2, fprintf(1,'%d ',i); end
  		
  		mphase = dphase;
  			
  		% calculate overlapping "phase averages" (x_k)
  		for p=1:m(k)
  			
  			% truncate frequency set length to an even multiple of this tau value
  			mphase=mphase(1:end-rem(length(mphase),m(k)));
  			% group phase values
  			mp=reshape(mphase,m(k),[]);
  			% find average in each "tau group" (each column of mp)
  			pa(p,:)=mean(mp,1);
  			% shift data vector by -1 and repeat
  			mphase=circshift(dphase,(size(dphase)>1)*-p);
  			
  		end
  			
  		% create "modified" y_k freq values
  		mfreq=diff(pa,1,2)./i;
  		mfreq=reshape(mfreq,1,[]);
  		
  		% calculate modified frequency differences
  		mfreqd=reshape(mfreq,m(k),[]); % Vectorize!
  		mfreqd=diff(mfreqd,1,2);
  		mfreqd=reshape(mfreqd,1,[]);
  		
  	   
  		% calculate two-sample variance for this tau
  		sm(k)=sqrt((1/(2*(N-3*m(k)+1)))*(sum(mfreqd(1:N-3*m(k)+1).^2)));
  
  		% estimate error bars
  		sme(k)=sm(k)/sqrt(N-3*m(k)+1);
  
  		
  	end % repeat for each value of tau
  	
  	if verbose >= 2, fprintf(1,'
  '); end
  	calctime=toc; if verbose >= 2, fprintf(1,'allan_modified: Elapsed time for calculation: %g seconds
  ',calctime); end
  
  	   
  	

  %% Irregular data (timestamp)   
  elseif isfield(data,'time')

  	% the interval between measurements is irregular
  	%  so we must group the data by time
  	if verbose >= 1, fprintf(1, 'allan_modified: irregular rate data (no fixed sample rate)
  '); end
  	
  	% string for plot title
  	name=[name ' (timestamp)'];
  	
  	% adjust time to remove any initial offset
  	dtime=data.time-data.time(1)+mean(diff(data.time));
  	%dtime=data.time-data.time(1);
  	% where is the maximum gap in time record?
  	gap_pos=find(diff(dtime)==max(diff(dtime)));
  	% what is average data spacing?
  	avg_gap = mean(diff(dtime));
  	
  	if verbose >= 2
  		fprintf(1, 'allan_modified: WARNING: irregular timestamp data (no fixed sample rate).
  ');
  		fprintf(1, '	   Calculation time may be long and the results subject to interpretation.
  ');
  		fprintf(1, '	   You are advised to estimate using an average sample rate (%g Hz) instead of timestamps.
  ',1/avg_gap);
  		fprintf(1, '	   Continue at your own risk! (press any key to continue)
  ');
  		pause;
  	end
  	
  	if verbose >= 1
  		fprintf(1, 'allan_modified: End of timestamp data: %g sec
  ',dtime(end));
  		fprintf(1, '	   Average sample rate: %g Hz (%g sec/measurement)
  ',1/avg_gap,avg_gap);
  		if max(diff(dtime)) ~= 1/mean(diff(dtime))
  			fprintf(1, '	   Max. gap in time record: %g sec at position %d
  ',max(diff(dtime)),gap_pos(1));
  		end
  		if max(diff(dtime)) > 5*avg_gap
  			fprintf(1, '	   WARNING: Max. gap in time record is suspiciously large (>5x the average interval).
  ');
  		end
  	end
  
  	% is phase data present? If not, generate it
  	if ~isfield(data,'phase')
  		nfreq=data.freq-s.mean;
  		% NOTE: uncommenting the following two lines will artificially
  		% allow the code to give equivalent results for validation data
  		% sets using fixed rate data and timestamped data by adding a
  		% "phantom" data point for frequency integration. Use of this
  		% phantom point can skew the results of calculations on real data.
  		%nfreq(end+1)=0; % phantom freq point, with average value
  		%dtime(end+1)=dtime(end)+avg_gap; % phantom average time step
  		dphase=zeros(1,length(nfreq));
  		dphase(2:end) = cumsum(nfreq(1:end-1)).*diff(dtime);
  		if verbose >= 1, fprintf(1,'allan_modified: Phase data generated from fractional frequency data (N=%g).
  ',length(dphase)); end
  	else
  		dphase=data.phase;
  	end
  
  	% find halfway point
  	halftime = fix(dtime(end)/2);
  	% truncate tau to appropriate values
  	tau = tau(tau >= max(diff(dtime)) & tau <= halftime);
  	if isempty(tau)
  		error('allan_modified: ERROR: no appropriate tau values (> %g s, < %g s)
  ',max(diff(dtime)),halftime);
  	end
  
  %	 % save the freq data for the loop
  %	 dfreq=data.freq;
  	
  	% number of samples
  	N=length(dphase);
  	m=round(tau./mean(diff(dtime)));
  	
  	if verbose >= 1, fprintf(1,'allan_modified: calculating modified Allan deviation...
  '); end
  
  	k=0; tic;
  	for i = tau
  		
  		k=k+1; pa=[];
  		
  		mphase = dphase; time = dtime;
  
  		if verbose >= 2, fprintf(1,'%d ',i); end
  		
  		% calculate overlapping "phase averages" (x_k)
  		%for j = 1:i
  		for j = 1:m(k) % (v1.1)
  			km=0;
  			%fprintf(1,'j: %d ',j);
  			
  			% (v1.1) truncating not correct for overlapping samples
  			% truncate data set to an even multiple of this tau value
  			%mphase = mphase(time <= time(end)-rem(time(end),i));
  			%time = time(time <= time(end)-rem(time(end),i));
  			
  		
  			% break up the data into overlapping groups of tau length
  			while i*km < time(end)
  				km=km+1;
  
  				% progress bar
  				if verbose >= 2
  					if rem(km,100)==0, fprintf(1,'.'); end
  					if rem(km,1000)==0, fprintf(1,'%g/%g
  ',km,round(time(end)/i)); end
  				end
  
  				mp = mphase(i*(km-1) < (time) & (time) <= i*km);
  
  				if ~isempty(mp)
  					pa(j,km)=mean(mp);
  				else
  					pa(j,km)=0;
  				end
  
  			end
  						
  			% shift data vector by -1 and repeat
  			mphase=circshift(dphase,(size(mphase)>1)*-j);
  			mphase(end-j+1:end)=[];
  			time=circshift(dtime,(size(time)>1)*-j);
  			time(end-j+1:end)=[];
  			time=time-time(1)+avg_gap; % remove time offset
  			
  		end		
  
  		% create "modified" y_k freq values
  		mfreq=diff(pa,1,2)./i;
  		mfreq=reshape(mfreq,1,[]);
  		
  		% calculate modified frequency differences
  		mfreqd=reshape(mfreq,m(k),[]); % Vectorize!
  		mfreqd=diff(mfreqd,1,2);
  		mfreqd=reshape(mfreqd,1,[]);
  
  		% calculate two-sample variance for this tau
  		%  only the first N-3*m(k)+1 samples are valid
  		if length(mfreqd) >= N-3*m(k)+1
  			sm(k)=sqrt((1/(2*(N-3*m(k)+1)))*(sum(mfreqd(1:N-3*m(k)+1).^2)));
  
  			% estimate error bars
  			sme(k)=sm(k)/sqrt(N);
  			
  			if verbose >= 2, fprintf(1,'
  '); end
  		else
  			if verbose >=2, fprintf(1,' tau=%g dropped due to timestamp irregularities
  ',tau(k)); end
  			sm(k)=0; sme(k)=0;
  		end			
  		
  
  	end
  
  	if verbose >= 2, fprintf(1,'
  '); end
  	calctime=toc; if verbose >= 2, fprintf(1,'allan_modified: Elapsed time for calculation: %g seconds
  ',calctime); end
  	
  	% remove any points that were dropped
  	tau(sm==0)=[];
  	sm(sm==0)=[];
  	sme(sme==0)=[];
  	
  	% modify time vector for plotting
  	dtime=dtime(1:length(medianfreq));

  
  else

  	error('allan_modified: WARNING: no DATA.rate or DATA.time! Type "help allan_modified" for more information. [err2]');

  end
  
  
  %%%%%%%%
  %% Plotting
  
  if verbose >= 2 % show all data

  	
  	% plot the frequency data, centered on median
  	if size(dtime,2) > size(dtime,1), dtime=dtime'; end % this should not be necessary, but dsplot 1.1 is a little bit brittle
  	try
  		% dsplot makes a new figure
  		hd=dsplot(dtime,medianfreq);
  	catch ME
  		figure;
  		hd=plot(dtime,medianfreq);
  		if verbose >= 1, fprintf(1,'allan_modified: Note: Install dsplot.m for improved plotting of large data sets (File Exchange File ID: #15850).
  '); end
  		if verbose >= 2, fprintf(1,'			 (Message: %s)
  ',ME.message); end
  	end
  	set(hd,'Marker','.','LineStyle','none','Color','b'); % equivalent to '.-'
  	hold on;
  
  	fx = xlim;
  	% plot([fx(1) fx(2)],[s.median s.median],'-k');
  	plot([fx(1) fx(2)],[0 0],':k');
  	% show 5x Median Absolute deviation (MAD) values
  	hm=plot([fx(1) fx(2)],[5*MAD 5*MAD],'-r');
  	plot([fx(1) fx(2)],[-5*MAD -5*MAD],'-r');
  	% show linear fit line
  	hf=plot(xlim,polyval(s.linear,xlim)-s.median,'-g');	
  	title(['Data: ' name],'FontSize',FontSize+2,'FontName',FontName);
  	%set(get(gca,'Title'),'Interpreter','none');
  	xlabel('Time [sec]','FontSize',FontSize,'FontName',FontName);
  	if isfield(data,'units')
  		ylabel(['data - median(data) [' data.units ']'],'FontSize',FontSize,'FontName',FontName);
  	else
  		ylabel('freq - median(freq)','FontSize',FontSize,'FontName',FontName);
  	end
  	set(gca,'FontSize',FontSize,'FontName',FontName);
  	legend([hd hm hf],{'data (centered on median)','5x MAD outliers',['Linear Fit (' num2str(s.linear(1),'%g') ')']},'FontSize',max(10,FontSize-2));
  	% tighten up
  	xlim([dtime(1) dtime(end)]);

  
  
  end % end plot raw data
  
  
  if verbose >= 1 % show analysis results

  	% plot Allan deviation results
  	if ~isempty(sm)
  		figure
  
  		% Choose loglog or semilogx plot here	#PLOTLOG
  		%semilogx(tau,sm,'.-b','LineWidth',plotlinewidth,'MarkerSize',24);
  		loglog(tau,sm,'.-b','LineWidth',plotlinewidth,'MarkerSize',24);
  
  		% in R14SP3, there is a bug that screws up the error bars on a semilog plot.
  		%  When this is fixed, uncomment below to use normal errorbars
  		%errorbar(tau,sm,sme,'.-b'); set(gca,'XScale','log');
  		% this is a hack to approximate the error bars
  		hold on; plot([tau; tau],[sm+sme; sm-sme],'-k','LineWidth',max(plotlinewidth-1,2));
  
  		grid on;
  		title(['Modified Allan Deviation: ' name],'FontSize',FontSize+2,'FontName',FontName);
  		%set(get(gca,'Title'),'Interpreter','none');
  		xlabel('\tau [sec]','FontSize',FontSize,'FontName',FontName);
  		ylabel('Modified \sigma_y(\tau)','FontSize',FontSize,'FontName',FontName);
  		set(gca,'FontSize',FontSize,'FontName',FontName);
  		% expand the x axis a little bit so that the errors bars look nice
  		adax = axis;
  		axis([adax(1)*0.9 adax(2)*1.1 adax(3) adax(4)]);
  		
  		% display the minimum value
  		fprintf(1,'allan: Minimum modified ADEV value: %g at tau = %g seconds
  ',min(sm),tau(sm==min(sm)));		
  		
  	elseif verbose >= 1
  		fprintf(1,'allan_modified: WARNING: no values calculated.
  ');
  		fprintf(1,'	   Check that TAU > 1/DATA.rate and TAU values are divisible by 1/DATA.rate
  ');
  		fprintf(1,'Type "help allan_modified" for more information.
  
  ');
  	end

  
  end % end plot analysis

  retval = sm;
  errorb = sme;
  
  return