wholeBrain_batch.m

function wholeBrain_batch(filename,handles)
%wholeBrain_batch - A batch processing script for wholeBrain paper
%Examples:
% >> wholeBrain_batch('files.txt')
% >> wholeBrain_batch('files.txt',handles)
%
%**USE**
%Must provide one input:
%
%(1) table name with desired filenames (space delimited txt file, with full filenames in first column)
%files.txt should have TIFF movie filenames in first column, dummy region matlab filenames in second column
%can have an extra columns with descriptor/factor information for the file. This will be the rowinfo that is attached to each measure observation in the following script.
%depends on  readtext.m file script from matlab central -  readtext('files.txt',' ');
%
%Optional:
% handles - handle structure of input variables
% 	handles.makeMovies - string of 'all', 'some', or 'none' to indicate if you want to make avis. Do 'none' or 'some' to speed things up.
% 	handles.hemisphereIndices - vector of integers, region.coord index locations in region.name for gross anatomical brain regions like 'cortex.L' and 'cortex.R' and others (e.g. 'OB.L', 'OB.R', 'SC.L', 'SC.R', etc).
% 	handles.backgroundRemovRadius - single numeric in pixels. Default corresponds to 681 µm radius (round(681/region.spaceres)) for the circular structured element used for background subtraction.
% 	handles.pthr - single numeric. Default is 0.99. Percentile threshold for the sobel edge based detection algorithm in wholeBrain_segmentation.m
% 	handles.makeThresh - single numeric logical. Default is 1, for estimating the graythreshold using Otsu's method for each movie separately. Alternative is to use previous movie graythresh (like for subsequent recordings).
%	handles.sigma - %sigma is the standard deviation in pixels of the gaussian for smoothing. It is 56.75µm at 11.35µm/px dimensions to give a **5px sigma**. gaussSmooth.m multiplies the sigma by 2.25 standard deviations for the filter size by default.
%	handles.useSobel - single numeric logical. Default is 1, to use the sobel gradient transformation of the movie array to select edge pixels within the brain that will be used to determine otsu's threshold for signal detection in the movie and resulting segmentation.
%
% Check the workflow sequence below at wholeBrain_workflow(). Briefly it is:
%	1. Segmentation
%	2. Detection
%	3. Format domain data structures
%	4. Get active fraction signals
%	5. Get correlation matrix and plots
%	6. Get cortical - motor corr results and plots
%	7. Get spatial correlation results and plots
%	8. Make contour activity maps
%	9. Batch fetch datasets
%
%Output:
%Everything is automatically saved. 
%	* A list of processed .mat files with data structures will be saved at filesOutput_*YYYYMMDD-HHMMSS*.txt.
%	* A bunch of .png and .eps figures will be automatically saved with [fnm *YYYYMMDD-HHMMSS*] format.
%	* Output data from batchFetch* functions will be appended to:
%		* 'dDomainProps.txt'
%		* 'dLocationProps.txt'
%		* 'dLocationPropsFreq.txt'
%		* 'dCorr.txt'
%		* 'dMotorCorr.txt'
%		* 'dSpatialCorr.txt'
%
% See also wholeBrain_segmentation.m, wholeBrain_detect.m, wholeBrain_workflow.md
%
%James B. Ackman, 2013-11-19 12:06:20
% Except where otherwise noted, all code in this program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation; either version 2 of the License, or
% (at your option) any later version.

% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
% GNU General Public License for more details.

% You should have received a copy of the GNU General Public License along
% with this program; if not, write to the Free Software Foundation, Inc.,
% 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.


if nargin < 2 || isempty(handles), handles = []; end
filelist = readtext(filename,' ');
datafilename = ['filesOutput_' datestr(now,'yyyymmdd-HHMMSS') '.txt'];
mainfcnLoop(filelist,datafilename,handles)


function mainfcnLoop(filelist,datafilename,handles)
%start loop through files-----------------------------------------------------------------
if size(filelist,2) > 1
	fnms = filelist(:,2);  %Second column is dummy region matfiles
	fnms2 = filelist(:,1); %First column is the original .tif filename
else
	error('TIFF movie filename required in 1st column, region dummy filename required in 2rd column of space-delimited filelist')
end

currdir = pwd;  %This assumes that the script calling wholeBrain_batch has already cd'd into the flat directory containing both .tif movie files and dummy .mat files
%Otherwise this currdir can be changed within this mainfcnloop and the paths to the files for loading .mat and .tif files ('f' and 'fn' below inside 'mainfcnloop') can be changed

if isfield(handles,'dirname')
	dirname = handles.dirname;
else
	dirname = datestr(now,'yyyy-mm-dd-HHMMSS');
end
if exist(fullfile(currdir,dirname),'dir') ~= 7
	%If the directory does not exist locally, create it.
	mkdir(dirname);
end
cd(dirname);

levels = zeros(1,numel(fnms));
for j=1:numel(fnms)
	[pathstr, name, ext] = fileparts(fnms{j});
	matfilename = [name ext]; 
	f = fullfile(currdir,matfilename);
	load(f,'region');  %load the dummy file at fnms{j} containing parcellations, motor signal, etc

	[pathstr, name, ext] = fileparts(fnms2{j});  
	region.filename = [name ext]; %set the .tif file name
	region.matfilename = matfilename;
	fn = fullfile(currdir,region.filename);

	
    sprintf(fnms{j})    
    disp('--------------------------------------------------------------------')
    disp(['Processing ' num2str(j) '/' num2str(numel(fnms)) ' files...'])

	makeThresh = 1;
	ind = detectDrugStimuli(region)
	if ~isempty(ind)
		makeThresh = 0;
		region.graythresh = mean(levels);
	end	

	handles.makeThresh = makeThresh;
	[fnm,thresh] = wholeBrain_workflow(fn,region,handles);
	levels(1,j) = thresh;
	appendCellArray2file(datafilename,{fnm})
    close all
end

function ind = detectDrugStimuli(region,stimuliIndices)
if nargin < 2 || isempty(stimuliIndices), stimuliIndices = {'drug.state.control' 'drug.state.isoflurane'}; end
if isfield(region,'stimuli');
	ind = [];
	for i = 1:length(region.stimuli)
		for k = 1:length(stimuliIndices)
			if strcmp(region.stimuli{i}.description,stimuliIndices{k})
				ind = [ind i];
			end
		end
	end
else
	ind = [];
end

function appendCellArray2file(filename,output)
%---Generic output function-------------
tmp=output;
fid = fopen(filename,'a');
for i=1:numel(tmp); tmp{i} = num2str(tmp{i}); end  %this will be to 4 decimal points (defaut for 'format short'). Can switch to 'format long' before running this loop if need more precision.
tmp2=tmp';
fprintf(fid,[repmat('%s\t',1,size(tmp2,1)-1),'%s\n'],tmp2{:});  %tab delimited
%fprintf(fid,[repmat('%s ',1,size(tmp2,1)-1),'%s\n'],tmp2{:});  %space delimited
fclose(fid);


function [fnm,thresh] = wholeBrain_workflow(fnm,region,handles)
%==1==Segmentation============================
%fnm = '120518_07.tif';
%load('120518_07_dummyHemis2.mat');

fn = fnm;  %fullfile path to the .tif file, for passing to wholeBrain_segmentation for opening. All other output saved to local subdirectory
fnm = region.filename; %set the base .tif file name for making fnm2 filenames below for saving outputs.

if ~isfield(handles,'hemisphereIndices')
	hemisphereIndices = find(strcmp(region.name,'cortex.L') | strcmp(region.name,'cortex.R') | strcmp(region.name,'OB.L') | strcmp(region.name,'OB.R') | strcmp(region.name,'SC.L') | strcmp(region.name,'SC.R'));  %region.coord locations of 'cortex.L' and 'cortex.R' and others.
else
	hemisphereIndices = handles.hemisphereIndices;
end

if ~isfield(handles,'backgroundRemovRadius')
	backgroundRemovRadius = round(681/region.spaceres);  % default is 681 µm radius for the circular structured element used for background subtraction.
else
	backgroundRemovRadius = handles.backgroundRemovRadius;
end

if ~isfield(handles,'makeMovies'), 
	makeMovies = 'all';
else
	makeMovies = handles.makeMovies;
end

if ~isfield(handles,'pthr'), 
	pthr = 0.99; 
else
	pthr = handles.pthr;
end

if ~isfield(handles,'sigma')
	sigma = 56.75/region.spaceres;  %sigma is the standard deviation in pixels of the gaussian for smoothing. It is 56.75µm at 11.35µm/px dimensions to give a **5px sigma**. gaussSmooth.m multiplies the sigma by 2.25 standard deviations for the filter size by default.
else
	sigma = handles.sigma;
end

if ~isfield(handles,'useSobel')
	useSobel = 1;  %sigma is the standard deviation in pixels of the gaussian for smoothing. It is 56.75µm at 11.35µm/px dimensions to give a **5px sigma**. gaussSmooth.m multiplies the sigma by 2.25 standard deviations for the filter size by default.
else
	useSobel = handles.useSobel;
end

if ~isfield(handles,'makeThresh'), 
	makeThresh = 1; 
else
	makeThresh = handles.makeThresh;
end

switch makeThresh
case 1
	grayThresh = [];
case 0
	%grayThresh = region.graythresh;
	grayThresh = [];
end

switch makeMovies
case 'all'
	makeInitMovies = 1; 
case 'some'
	makeInitMovies = 0;
case 'none'
	makeInitMovies = 0;
end

if ~isfield(handles,'thresh'), 
	thresh = 2; 
else
	thresh = handles.thresh;
end

if isfield(handles, 'mov'), 
	preload = 1;
else
	preload = 0;
end

if preload
	if ~isempty(handles.mov)
		[A2, A, thresh, Amin, Amax] = wholeBrain_segmentation(fn,handles.mov,region,thresh,sigma,hemisphereIndices,makeInitMovies);
	else
		error('handles.mov is empty')
	end
else
	[A2, A, thresh, Amin, Amax] = wholeBrain_segmentationOtsu(fn,backgroundRemovRadius,region,hemisphereIndices,0,makeInitMovies,grayThresh,pthr,sigma,useSobel);
end
region.graythresh = thresh;
region.Amin = Amin;
region.Amax = Amax;

disp(['Segmentation finished: ' datestr(now,'yyyymmdd-HHMMSS')])

%==2==Detection============================
[A3, CC, STATS] = wholeBrain_detect(A2,A,[4 3],makeInitMovies,fnm,region,hemisphereIndices);
fnm2 = [fnm(1:length(fnm)-4) '_' datestr(now,'yyyymmdd-HHMMSS') '.mat'];
save([fnm2(1:length(fnm2)-4) '_connComponents' '.mat'],'A2','A3','CC','STATS','-v7.3');  
clear A2 A3;

disp(['Detection finished: ' datestr(now,'yyyymmdd-HHMMSS')])

%rsync -av -e ssh jba38@louise.hpc.yale.edu:~/data/120518i/120518_09....mat ~/Desktop  

%==3==Format domain data structures============================
domains = DomainSegmentationAssignment(CC,STATS, 'false');  

region.domainData.domains = domains;      
region.domainData.CC = CC;      
region.domainData.STATS = STATS;  
clear CC STATS domains;

if ~isfield(region.domainData.STATS, 'descriptor')      
	for i = 1:length(region.domainData.STATS)    
		region.domainData.STATS(i).descriptor = '';      
	end      
end      

if isfield(region, 'taggedCentrBorders')
	region = tagDomains(region, region.taggedCentrBorders, 'artifact');
end

locationIndices = find(~strcmp(region.name,'field') & ~strcmp(region.name,'craniotomy'));  %because region.location may be empty to this point (usually gets tagged only as a lut for cells are grid rois)
region = Domains2region(region.domainData.domains, region.domainData.CC,region.domainData.STATS,region,locationIndices);

fnm = fnm2;  
fnm = [fnm(1:end-4) '_d2r' '.mat'];       
save(fnm,'region','-v7.3');   

disp(['Domain assignment finished: ' datestr(now,'yyyymmdd-HHMMSS')])


%==4==Get active fraction signals=============================
sz=region.domainData.CC.ImageSize;
A3 = false(sz); 

for i = 1:region.domainData.CC.NumObjects      
	if ~strcmp(region.domainData.STATS(i).descriptor, 'artifact')    
		A3(region.domainData.CC.PixelIdxList{i}) = true;      
	end          
end      

if isfield(region, 'taggedCentrBorders') && (strcmp(makeMovies,'all') | strcmp(makeMovies,'some'))
	myMovie2avi(A3,fnm)
end

data = wholeBrain_activeFraction(A3,region);   

region.locationData.data = data; clear data
save(fnm,'region','-v7.3');     

disp('-----')

wholeBrain_activeFraction(A3,region,hemisphereIndices,'drug.state.isoflurane'); %Print just the gross anatomical hemisphereIndices traces instead of all locations
fnm2 = [fnm(1:end-4) 'actvFraction' datestr(now,'yyyymmdd-HHMMSS') '.mat'];
print(gcf, '-dpng', [fnm2(1:end-4) '-' datestr(now,'yyyymmdd-HHMMSS') '.png']);      
print(gcf, '-depsc', [fnm2(1:end-4) '-' datestr(now,'yyyymmdd-HHMMSS') '.eps']);   

disp(['Active fraction finished: ' datestr(now,'yyyymmdd-HHMMSS')])

%==5==More Plots=========================
%--Single contour activity map-----
wholeBrainActivityMapFig(region,[],2,1,20,[],[],'pixelFreq');  
fnm2 = [fnm(1:end-4) 'ActivityMapFigContour' datestr(now,'yyyymmdd-HHMMSS') '.mat'];              
print(gcf, '-dpng', [fnm2(1:end-4) '.png']);                  
print(gcf, '-depsc', [fnm2(1:end-4) '.eps']); 
disp('complete: wholeBrainActivityMapFig, contour pixelFreq')

mapTypes = {'pixelFreq','domainFreq','domainDur','domainDiam','domainAmpl'}
for j =1:length(mapTypes)
	wholeBrainActivityMapFig(region,[],2,1,0,[],[],mapTypes{j});
	fnm2 = [fnm(1:end-4) 'ActivityMapFigRawProj' datestr(now,'yyyymmdd-HHMMSS') '_' mapTypes{j} '.mat'];              
	print(gcf, '-dpng', [fnm2(1:end-4) '.png']);                  
	print(gcf, '-depsc', [fnm2(1:end-4) '.eps']);
	disp(['complete: wholeBrainActivityMapFig, mapType=' mapTypes{j}])
end


%--Drug state contour activity maps if applicable
if isfield(region,'stimuli');
	stimuliIndices = {'drug.state.control' 'drug.state.isoflurane'};
	ind = detectDrugStimuli(region,stimuliIndices);
	if ~isempty(ind)
		wholeBrainActivityMapFig(region,[],2,5,20,ind); 
		fnm2 = [fnm(1:end-4) 'ActivityMapFigDrug' datestr(now,'yyyymmdd-HHMMSS') '.mat'];        
		print(gcf, '-dpng', [fnm2(1:end-4) '.png']);            
		print(gcf, '-depsc', [fnm2(1:end-4) '.eps']);
		
		mapTypes = {'pixelFreq','domainFreq','domainDur','domainDiam','domainAmpl'}
		for j =1:length(mapTypes)
			wholeBrainActivityMapFig(region,[],2,7,0,ind,[],mapTypes{j});
			fnm2 = [fnm(1:end-4) 'ActivityMapFigRawProjDrug' datestr(now,'yyyymmdd-HHMMSS') '_' mapTypes{j} '.mat'];              
			print(gcf, '-dpng', [fnm2(1:end-4) '.png']);                  
			print(gcf, '-depsc', [fnm2(1:end-4) '.eps']);
			disp(['complete: wholeBrainActivityMapFig, mapType=' mapTypes{j}])
		end
	end	
	
	%--Motor state contour activity maps if applicable		
	stimuliIndices = {'motor.state.active' 'motor.state.quiet'};
	ind = detectDrugStimuli(region,stimuliIndices);
	if ~isempty(ind)
		wholeBrainActivityMapFig(region,[],2,5,20,ind); 
		fnm2 = [fnm(1:end-4) 'ActivityMapFigMotor' datestr(now,'yyyymmdd-HHMMSS') '.mat'];        
		print(gcf, '-dpng', [fnm2(1:end-4) '.png']);            
		print(gcf, '-depsc', [fnm2(1:end-4) '.eps']);
		
		mapTypes = {'pixelFreq','domainFreq','domainDur','domainDiam','domainAmpl'}
		for j =1:length(mapTypes)
			wholeBrainActivityMapFig(region,[],2,7,0,ind,[],mapTypes{j});
			fnm2 = [fnm(1:end-4) 'ActivityMapFigRawProjMotorState' datestr(now,'yyyymmdd-HHMMSS') '_' mapTypes{j} '.mat'];              
			print(gcf, '-dpng', [fnm2(1:end-4) '.png']);                  
			print(gcf, '-depsc', [fnm2(1:end-4) '.eps']);
			disp(['complete: wholeBrainActivityMapFig, mapType=' mapTypes{j}])
		end		  		
	end

	%--Motor onset activity maps if applicable			
	stimuliIndices = {'motor.onsets'};
	ind = detectDrugStimuli(region,stimuliIndices); 
	if ~isempty(ind)
		mapTypes = {'domainFreq','domainDur','domainDiam'}  
		for j =1:length(mapTypes)  
			wholeBrainActivityMapFig(region,[],2,7,0,ind,[],mapTypes{j});  
			fnm2 = [fnm(1:end-4) 'ActivityMapFigRawProjMotorOnsets' datestr(now,'yyyymmdd-HHMMSS') '_' mapTypes{j} '.mat'];                
			print(gcf, '-dpng', [fnm2(1:end-4) '.png']);                    
			print(gcf, '-depsc', [fnm2(1:end-4) '.eps']);  
			disp(['complete: wholeBrainActivityMapFig, mapType=' mapTypes{j}])  
		end 
	end	
end 
%}

%DomainPatchesPlot(region.domainData.domains, region.domainData.CC, region.domainData.STATS,1,[],1)   %DomainPatchesPlot won't work on a linux server because of a segmentation fault with drawing alpha transparency in matlab
%fnm2 = [fnm(1:end-4) 'DomainPatchesPlot' datestr(now,'yyyymmdd-HHMMSS') '.mat'];              
%print(gcf, '-dpng', [fnm2(1:end-4) '.png']);                  
%print(gcf, '-depsc', [fnm2(1:end-4) '.eps']);

%--Maximum intensity projections of movie--
	
%-------Make movie array with with raw intensity values within the functional ROIs--------    
A4 = A;    
A4(~A3) = 0;  %A3 is binary movie from above with any 'artifact' tagged domains removed (usually none on first batch analysis pass)
%A4=mat2gray(A4);    
bw = max(A3,[],3);
	
%----Maxproj of detected domains -----
A5 = max(A4,[],3);
tmp = A5(bw);
LOW_HIGH = stretchlim(tmp);
%A6 = imadjust(A5,LOW_HIGH,[]);
A6 = mat2gray(A5);
figure;  
imagesc(A6); title('maxproj of raw detected domains array'); colorbar('location','eastoutside'); axis image   
fnm2 = [fnm(1:length(fnm)-4) '_maxProj1_' datestr(now,'yyyymmdd-HHMMSS')];    
print('-dpng', [fnm2 '.png'])
print('-depsc', [fnm2 '.eps']) 

%----Maxproj of raw dFoF array -----
A5 = max(A,[],3);
%A6 = imadjust(A5,LOW_HIGH,[]);   
A6 = mat2gray(A5);
figure;  
imagesc(A6); title('maxproj of dFoF movie array detected adjust'); colorbar('location','eastoutside'); axis image   
fnm2 = [fnm(1:length(fnm)-4) '_maxProj2_' datestr(now,'yyyymmdd-HHMMSS')];    
print('-dpng', [fnm2 '.png'])
print('-depsc', [fnm2 '.eps']) 

%----Maxproj of raw dFoF array -----
figure;  
%A6 = imadjust(A5);
A6 = mat2gray(A5);
imagesc(A6); title('maxproj of dFoF movie array raw adjust'); colorbar('location','eastoutside'); axis image   
fnm2 = [fnm(1:length(fnm)-4) '_maxProj3_' datestr(now,'yyyymmdd-HHMMSS')];    
print('-dpng', [fnm2 '.png'])
print('-depsc', [fnm2 '.eps']) 
clear A A4 A5 A6;


disp(['Maps finished: ' datestr(now,'yyyymmdd-HHMMSS')])

%==6==Get opticflow==============================
%TODO: change optFlowLk usage to newer opticalFlow.m in piotrImageVideoProcessingToolbox
chkFile = exist('optFlowLk.m');
if chkFile == 2
[Vsum, ~, ~] = wholeBrain_opticFlowByDomain(A3,region,fnm,makeInitMovies);
region.domainData.Vsum = Vsum;
clear Vsum
else
	disp('Skipping opticflow...optFlowLk from piotrImageVideoProcessingToolbox v.2.61 not installed')
end

%==7==Batch fetch datasets=======================
batchFetchDomainProps({fnm},region,fullfile(pwd,'dDomainProps.txt'));
batchFetchLocationProps({fnm},region,fullfile(pwd,'dLocationProps.txt'), 'true', {'motor.state.active' 'motor.state.quiet' 'drug.state.control' 'drug.state.isoflurane'});
batchFetchLocationPropsFreq({fnm},region,fullfile(pwd,'dLocationPropsFreq.txt'), 'true', {'motor.state.active' 'motor.state.quiet' 'drug.state.control' 'drug.state.isoflurane'});	
%}

disp(['All batch fetch domains, location finished: ' datestr(now,'yyyymmdd-HHMMSS')])

%==8==Get spatial correlation results and plots==============
region = wholeBrain_SpatialCOMCorr(fnm,region,{'cortex.L' 'cortex.R'},1);
batchFetchSpatialCOMCorrData({fnm},region,fullfile(pwd,'dCorticalCorr.txt'),1);

%==9==Get temporal correlation results and plots for cortical hemispheres=================
region = wholeBrain_CorticalActiveFractionCorr(fnm,region,{'cortex.L' 'cortex.R'});
batchFetchCorticalCorrData({fnm},region,fullfile(pwd,'dCorticalCorr.txt'),1);
save(fnm,'region','-v7.3') ;

%===If region contains coords for more than just 'field', 'cortex.L', and 'cortex.R' proceed to fetch data and plots making use of these parcellations (functional correlation matrices, motor corr, etc)
if length(region.name) > length(hemisphereIndices)+1

	%==10==Get correlation matrix and plots======================== 
	exclude = {'cortex.L' 'cortex.R'};
	region = wholeBrain_corrData(fnm, region, exclude);  %will also print and save corr matrix and raster plot of the traces (activeFraction) that went into the corr matrix
	save(fnm,'region','-v7.3') ;
	batchFetchCorrData({fnm},region,fullfile(pwd,'dCorr.txt'),1);

	%==11==Get cortical - motor corr results and plots=============
	if isfield(region,'motorSignal')
		if ~isempty(region.motorSignal)
			clear st  
			st(1).str = {'HL.L' 'HL.R' 'T.L' 'T.R' 'FL.L' 'FL.R'};    
			st(2).str = {'M1.L' 'M1.R' 'M2.L' 'M2.R'};    
			st(3).str = {'barrel.L' 'barrel.R' 'AS.L' 'AS.R'};    
			st(4).str = {'barrel.L' 'barrel.R'};    
			st(5).str = {'RSA.L' 'RSA.R'};    
			st(6).str = {'PPC.L' 'PPC.R'};    
			st(7).str = {'V1.L' 'V1.R'};    
			st(8).str = {'V2L.L' 'V2L.R' 'V2M.L' 'V2M.R'};    
			st(9).str = {'V2L.L' 'V2L.R' 'V2M.L' 'V2M.R' 'V1.L' 'V1.R'};    
			st(10).str = {'cortex.L' 'cortex.R'};
			st(11).str = {'OB.L' 'OB.R'};
			st(12).str = {'SC.L' 'SC.R'};	
		
			region = wholeBrain_MotorSignalCorr(fnm,region,st,[],1);
			save(fnm,'region','-v7.3') ;
			batchFetchMotorCorrData({fnm},region,fullfile(pwd,'dMotorCorr.txt'),1);
		end
	end

else
	if isfield(region,'motorSignal')
		if ~isempty(region.motorSignal)
			st(1).str = {'cortex.L' 'cortex.R'};	
			region = wholeBrain_MotorSignalCorr(fnm,region,st,[],1);
			save(fnm,'region','-v7.3') ;
			batchFetchMotorCorrData({fnm},region,fullfile(pwd,'dMotorCorr.txt'),1);
		end
	end
end

disp(['Batch finished: ' datestr(now,'yyyymmdd-HHMMSS')])
close all
clear region