////////////////////////////////////////////////////////////////
/*									
	PIPSQUEAK!
	Perineuronal net Intensity Program for the
	Standardization and Quantification of ECM Analysis

	Written by John H. Harkness, PhD.
	>>> Contact: harknessjh@gmail.com
	>>> Copyright (C) 2020, Rewire Neuro, Inc

	
	
	Supported by:
	>>> Rewire Neuroscience (rewireneuroscience.com)
	>>> WSU Alcohol and Drug Abuse Research Program (ADARP)
	>>> The Translational Addiction Research Center (TARC)
	>>> NIH DA 033404 
	>>> Washington State Initiative Measure No. 171
	>>> R43GM134789-01 (NIGMS SBIR Phase I)	
	>>> State of Oregon SBIR matching grant	

	Protocol based on Slaker, Harkness, and Sorg, 2016
	>>> Special thanks to Megan Slaker, PhD

	>>> PLEASE CITE: Slaker, et al., 2016 <<<
*/
////////////////////////////////////////////////////////////////

	//Global variables
var software_key=0;
var software_key_string="blank"; //not in saved pipsqueak parameters
var email_address="blank"; //not in saved pipsqueak parameters
var email_address_raw="blank";
var encry_key="blank";

var TimeString = "BlankDate";

var scale_size = 1.194;
var scale_known = 1;
var scale_pixel = 1;
var scale_unit = "um";
var zsteps = 20;

var background_setting = "Low";

var set_intensity_low_threshold = 1.165;
var set_intensity_high_threshold = 5000;
var set_low_particlesize = 70;
var set_high_particlesize = 500;
var set_low_circularity = 0.02;
var set_high_circularity = 1.00;
var set_roi_enlargement_single = 6.00;
var set_intensity_low_threshold_old = 1.165;
var set_low_particlesize_old = 70;
var set_high_particlesize_old = 99999;
var ROI_type_SL = "Square";

var countSingle = 0;
var first_image = 0;
var second_image = 0;
var series_name = 0;
var subDir = 0;

var first_suffix = 2;
var second_suffix = 3;
var merged_suffix = 1;
var total_images = 3;

var first_stain_type = "PV";
var DL1_set_intensity_low_threshold = 1.5;
var DL1_set_intensity_high_threshold = 5000;
var DL1_set_low_particlesize = 40;
var DL1_set_high_particlesize = 500;
var DL1_set_low_circularity = 0.02;
var DL1_set_high_circularity = 1.00;
var DL1_set_roi_enlargement = -1.00;
var SL1_set_intensity_low_threshold_old = 1.0;
var SL1_set_low_particlesize_old = 40;
var SL1_set_high_particlesize_old = 1000;
//var DL1_set_intensity_low_threshold_old = 1.0;
//var DL1_set_low_particlesize_old = 40;
//var DL1_set_high_particlesize_old = 1000;
var DL1_lower = 0;
var DL1_overlap = 0.8;
var ROI_type_DL1 = "Circle";

var second_stain_type = "WFA";
var DL2_set_intensity_low_threshold = 1.65;
var DL2_set_intensity_high_threshold = 5000;
var DL2_set_low_particlesize = 100;
var DL2_set_high_particlesize = 500;
var DL2_set_low_circularity = 0.02;
var DL2_set_high_circularity = 1.00;
var DL2_set_roi_enlargement = 1.00;
var SL2_set_intensity_low_threshold_old = 1.165;
var SL2_set_low_particlesize_old = 70;
var SL2_set_high_particlesize_old = 99999;
//var DL2_set_intensity_low_threshold_old = 1.165;
//var DL2_set_low_particlesize_old = 70;
//var DL2_set_high_particlesize_old = 5000;
var DL2_lower = 0;
var DL2_overlap = 0.8;
var ROI_type_DL2 = "Square";

var first_image_TL = 0;
var second_image_TL
= 0;
var filler1=0;
var third_image_TL = 0;
var merged_image_TL = 0;
var series_name_TL = 0;
var subDir_TL = 0;

var first_suffix_TL= 2;
var second_suffix_TL = 3;
var third_suffix_TL = 4;
var merged_suffix_TL = 1;
var total_images_TL = 4;

var first_stain_type_TL = "PV";
var TL1_set_intensity_low_threshold = 1.5;
var TL1_set_intensity_high_threshold = 5000;
var TL1_set_low_particlesize = 40;
var TL1_set_high_particlesize = 500;
var TL1_set_low_circularity = 0.02;
var TL1_set_high_circularity = 1.00;
var TL1_set_roi_enlargement = -1.00;
var TL1_lower = 0;
var TL1_overlap = 0.8;
var ROI_type_TL1 = "Circle";

var second_stain_type_TL = "WFA";
var TL2_set_intensity_low_threshold = 1.65;
var TL2_set_intensity_high_threshold = 5000;
var TL2_set_low_particlesize = 100;
var TL2_set_high_particlesize = 500;
var TL2_set_low_circularity = 0.02;
var TL2_set_high_circularity = 1.00;
var TL2_set_roi_enlargement = 1.00;
var TL2_lower = 0;
var TL2_overlap = 0.8;
var ROI_type_TL2 = "Square";

var third_stain_type_TL = "8-oxo";
var TL3_set_intensity_low_threshold = 1.5;
var TL3_set_intensity_high_threshold = 5000;
var TL3_set_low_particlesize = 50;
var TL3_set_high_particlesize = 300;
var TL3_set_low_circularity = 0.02;
var TL3_set_high_circularity = 1.00;
var TL3_set_roi_enlargement = 1.00;
var TL3_lower = 0;
var TL3_overlap = 0.8;
var ROI_type_TL3 = "Circle";

var lower = 0;
var import_type = 0;
var first_open = 0;
var last_open = 0;
var name = 0;
var full_name = 0;
var dir = 0;
var first_time = 0;

var save_location = 0;
var pipsqueakCount = 1;
var countCount = 1;
var countDouble = 0;
var first_open_double = 0;
var last_open_double = 0;

var subject_id = 0;
var first_SL_run=1;

///////////////////////////

macro "PIPSQUEAK" {



macroDir=getDirectory("macros");
	if(File.exists(macroDir+"Pipsqueak_config.txt")){

		parameters = File.openAsString(macroDir+"Pipsqueak_config.txt");
		parLines=split(parameters,"\n");
		
		email_address_raw=parLines[0];
		software_key=parseFloat(parLines[1]);
		encry_key=parLines[2];
		
		TimeString=parLines[3];

 scale_size=parseFloat(parLines[4]);
 scale_known=parseFloat(parLines[5]);
 scale_pixel=parseFloat(parLines[6]);
 scale_unit=parLines[7];
 zsteps=parseFloat(parLines[8]);

 background_setting=parLines[9];

 set_intensity_low_threshold=parseFloat(parLines[10]);
 set_intensity_high_threshold=parseFloat(parLines[11]);
 set_low_particlesize=parseFloat(parLines[12]);
 set_high_particlesize=parseFloat(parLines[13]);
 set_low_circularity=parseFloat(parLines[14]);
 set_high_circularity=parseFloat(parLines[15]);
 set_roi_enlargement_single=parseFloat(parLines[16]);
 set_intensity_low_threshold_old=parseFloat(parLines[17]);
 set_low_particlesize_old=parseFloat(parLines[18]);
 set_high_particlesize_old=parseFloat(parLines[19]);
 ROI_type_SL=parLines[20];

 countSingle=parseFloat(parLines[21]);
 first_image=parLines[22];
 second_image=parLines[23];
 series_name=parLines[24];
 subDir=parLines[25];

 first_suffix=parseFloat(parLines[26]);
 second_suffix=parseFloat(parLines[27]);
 merged_suffix=parseFloat(parLines[28]);
 total_images=parseFloat(parLines[29]);

 first_stain_type=parLines[30];
 DL1_set_intensity_low_threshold=parseFloat(parLines[31]);
 DL1_set_intensity_high_threshold=parseFloat(parLines[32]);
 DL1_set_low_particlesize=parseFloat(parLines[33]);
 DL1_set_high_particlesize=parseFloat(parLines[34]);
 DL1_set_low_circularity=parseFloat(parLines[35]);
 DL1_set_high_circularity=parseFloat(parLines[36]);
 DL1_set_roi_enlargement=parseFloat(parLines[37]);
 SL1_set_intensity_low_threshold_old=parseFloat(parLines[38]);
 SL1_set_low_particlesize_old=parseFloat(parLines[39]);
 SL1_set_high_particlesize_old=parseFloat(parLines[40]);
 DL1_lower=parseFloat(parLines[41]);
 DL1_overlap=parseFloat(parLines[42]);
 ROI_type_DL1=parLines[43];

 second_stain_type=(parLines[44]);
 DL2_set_intensity_low_threshold=parseFloat(parLines[45]);
 DL2_set_intensity_high_threshold=parseFloat(parLines[46]);
 DL2_set_low_particlesize=parseFloat(parLines[47]);
 DL2_set_high_particlesize=parseFloat(parLines[48]);
 DL2_set_low_circularity=parseFloat(parLines[49]);
 DL2_set_high_circularity=parseFloat(parLines[50]);
 DL2_set_roi_enlargement=parseFloat(parLines[51]);
 SL2_set_intensity_low_threshold_old=parseFloat(parLines[52]);
 SL2_set_low_particlesize_old=parseFloat(parLines[53]);
 SL2_set_high_particlesize_old=parseFloat(parLines[54]);

 DL2_lower=parseFloat(parLines[55]);
 DL2_overlap=parseFloat(parLines[56]);
 ROI_type_DL2=parLines[57];

 first_image_TL=parseFloat(parLines[58]);
 second_image_TL=parseFloat(parLines[59]);
 filler1=parseFloat(parLines[60]);
 third_image_TL=parseFloat(parLines[61]);
 merged_image_TL=parseFloat(parLines[62]);
 series_name_TL=parseFloat(parLines[63]);
 subDir_TL=parseFloat(parLines[64]);

 first_suffix_TL=parseFloat(parLines[65]);
 second_suffix_TL=parseFloat(parLines[66]);
 third_suffix_TL=parseFloat(parLines[67]);
 merged_suffix_TL=parseFloat(parLines[68]);
 total_images_TL=parseFloat(parLines[69]);

 first_stain_type_TL=parLines[70];
 TL1_set_intensity_low_threshold=parseFloat(parLines[71]);
 TL1_set_intensity_high_threshold=parseFloat(parLines[72]);
 TL1_set_low_particlesize=parseFloat(parLines[73]);
 TL1_set_high_particlesize=parseFloat(parLines[74]);
 TL1_set_low_circularity=parseFloat(parLines[75]);
 TL1_set_high_circularity=parseFloat(parLines[76]);
 TL1_set_roi_enlargement=parseFloat(parLines[77]);
 TL1_lower=parseFloat(parLines[78]);
 TL1_overlap=parseFloat(parLines[79]);
 ROI_type_TL1=parLines[80];

 second_stain_type_TL=parLines[81];
 TL2_set_intensity_low_threshold=parseFloat(parLines[82]);
 TL2_set_intensity_high_threshold=parseFloat(parLines[83]);
 TL2_set_low_particlesize=parseFloat(parLines[84]);
 TL2_set_high_particlesize=parseFloat(parLines[85]);
 TL2_set_low_circularity=parseFloat(parLines[86]);
 TL2_set_high_circularity=parseFloat(parLines[87]);
 TL2_set_roi_enlargement=parseFloat(parLines[88]);
 TL2_lower=parseFloat(parLines[89]);
 TL2_overlap=parseFloat(parLines[90]);
 ROI_type_TL2=parLines[91];

 third_stain_type_TL=parLines[92];
 TL3_set_intensity_low_threshold=parseFloat(parLines[93]);
 TL3_set_intensity_high_threshold=parseFloat(parLines[94]);
 TL3_set_low_particlesize=parseFloat(parLines[95]);
 TL3_set_high_particlesize=parseFloat(parLines[96]);
 TL3_set_low_circularity=parseFloat(parLines[97]);
 TL3_set_high_circularity=parseFloat(parLines[98]);
 TL3_set_roi_enlargement=parseFloat(parLines[99]);
 TL3_lower=parseFloat(parLines[100]);
 TL3_overlap=parseFloat(parLines[101]);
 ROI_type_TL3=parLines[102];

 lower=parseFloat(parLines[103]);
 import_type=parseFloat(parLines[104]);
 first_open=parseFloat(parLines[105]);
 last_open=parseFloat(parLines[106]);
 name=parseFloat(parLines[107]);
 full_name=parseFloat(parLines[108]);
 dir=parseFloat(parLines[109]);
 first_time=parseFloat(parLines[110]);

 save_location=parLines[111];
 pipsqueakCount=parseFloat(parLines[112]);
 countCount=parseFloat(parLines[113]);
 countDouble=parseFloat(parLines[114]);
 first_open_double=parseFloat(parLines[115]);
 last_open_double=parseFloat(parLines[116]);

 subject_id=parLines[117];
 first_SL_run=parseFloat(parLines[118]);

	}
	


//print("software key before if: "+software_key);
if (software_key!=421387){ /// this will need to change when I get the key to be hashed with the email address
	exec("open", "https://pipsqueak.ai/downloads");

	Dialog.create("Software License key");
	
		Dialog.addMessage("Please enter your Pipsqueak Software License key. \nIf you do not yet have a key, please download Pipsqueak Version 5 from https://pipsqueak.ai/downloads");
		Dialog.addMessage("\n");
		Dialog.addString("Registered Email", "");
		Dialog.addString("Software Key      ", "eg:123456");
		Dialog.addMessage("\n \n Pipsqueak now requires users to register an email and license to ensure fair use \n of Pipsqueak in accord with our terms of use. ");
	
		Dialog.show
	
	email_address_raw = Dialog.getString();
	software_key_string =  Dialog.getString();;
	software_key = parseFloat(software_key_string);
	email_address_raw=toLowerCase(email_address_raw);
	email_raw_length=lengthOf(email_address_raw);

	end="@";
	//end_substring=0;
	end_substring=indexOf(email_address_raw, end);
	
	if(email_raw_length>0 && end_substring>0) {
		email_address=substring(email_address_raw,0,end_substring);
		encry_key=email_address+software_key;

		email_address=toLowerCase(email_address);
		email_length=lengthOf(email_address);
		encry_key=0;
		
		if(email_length>=0) {
			for(i=0; i<email_length;i++) { 
				value=charCodeAt(email_address, i);
				encry_key=encry_key+value;
			}
		}else{
			waitForUser("Please enter your email address again in the format user@example.com (error: 1)");
			software_key = 0;
			run("PIPSQUEAK");
		}
	}else{
		waitForUser("Please enter your email address again in the format user@example.com (error: 2)");
		software_key = 0;
		run("PIPSQUEAK");
	}

	
	if (software_key==421387 && email_raw_length>0 && end_substring>0){ // this will need to change when I get the key to be hashed with the email address
		saveParameters();
		call_home_url = "http://www.google-analytics.com/__utm.gif?utmwv=5.1.7&utms=1&utmn=1894752493&utmhn=www.jabstracts.org/&utmcs=UTF-8&utmsr=1280×1024&utmsc=24-bit&utmul=en-us&utmje=1&utmfl=10.3%20r183&utmdt=Tracking%20QR%20Codes%20with%20Google%20Analytics&utmhid=1681965357&utmr=http%3A%2F%2Fwww.imagejnih.gov%2F&utmp=%2Fjabstracts.org%2Fpipsqueak%2Fcall_home%2Fv4-0-2x-Key-"+email_address_raw+"%2F&utmac=UA-83183702-1&utmcc=__utma%3D230887938.1463229748.1317737798.1317737798.1317737798.1%3B%2B__utmz%3D230887938.1317737798.1.1.utmcsr%3Dgoogle%7Cutmccn%3D(organic)%7Cutmcmd%3Dorganic%7Cutmctr%3DPIPSQUEAK%2Fv4.0.2x.Key."+email_address_raw+"%3B&utmu=DC~";
		File.openUrlAsString(call_home_url);
		wait(50);
		print("\\Clear");
		wait(50);
		run("_ps_initi");
	}else {
		call_home_url = "http://www.google-analytics.com/__utm.gif?utmwv=5.1.7&utms=1&utmn=1894752493&utmhn=www.jabstracts.org/&utmcs=UTF-8&utmsr=1280×1024&utmsc=24-bit&utmul=en-us&utmje=1&utmfl=10.3%20r183&utmdt=Tracking%20QR%20Codes%20with%20Google%20Analytics&utmhid=1681965357&utmr=http%3A%2F%2Fwww.imagejnih.gov%2F&utmp=%2Fjabstracts.org%2Fpipsqueak%2Fcall_home%2Fv4-0-2x-KeyFail%2F&utmac=UA-83183702-1&utmcc=__utma%3D230887938.1463229748.1317737798.1317737798.1317737798.1%3B%2B__utmz%3D230887938.1317737798.1.1.utmcsr%3Dgoogle%7Cutmccn%3D(organic)%7Cutmcmd%3Dorganic%7Cutmctr%3DPIPSQUEAK%2Fv4.1.2x.KeyFail%3B&utmu=DC~";
		File.openUrlAsString(call_home_url);
		wait(50);
		print("\\Clear");
		wait(50);
		waitForUser("Sorry, your Pipsqueak registration is not correct. Please checkyour registration email and software key (error: 3)");
		software_key = 0;
		saveParameters();
		run("PIPSQUEAK");
	}
	
}
if (software_key==421387){
	email_raw_length=lengthOf(email_address_raw);
	if (email_raw_length>0){
		call_home_url = "http://www.google-analytics.com/__utm.gif?utmwv=5.1.7&utms=1&utmn=1894752493&utmhn=www.jabstracts.org/&utmcs=UTF-8&utmsr=1280×1024&utmsc=24-bit&utmul=en-us&utmje=1&utmfl=10.3%20r183&utmdt=Tracking%20QR%20Codes%20with%20Google%20Analytics&utmhid=1681965357&utmr=http%3A%2F%2Fwww.imagejnih.gov%2F&utmp=%2Fjabstracts.org%2Fpipsqueak%2Fcall_home%2Fv4-0-2x-Key-"+email_address_raw+"%2F&utmac=UA-83183702-1&utmcc=__utma%3D230887938.1463229748.1317737798.1317737798.1317737798.1%3B%2B__utmz%3D230887938.1317737798.1.1.utmcsr%3Dgoogle%7Cutmccn%3D(organic)%7Cutmcmd%3Dorganic%7Cutmctr%3DPIPSQUEAK%2Fv4.0.2x.Key."+email_address_raw+"%3B&utmu=DC~";
		File.openUrlAsString(call_home_url);
		wait(50);
		print("\\Clear");
		wait(50);
		run("_ps_initi");
	}else {
		call_home_url = "http://www.google-analytics.com/__utm.gif?utmwv=5.1.7&utms=1&utmn=1894752493&utmhn=www.jabstracts.org/&utmcs=UTF-8&utmsr=1280×1024&utmsc=24-bit&utmul=en-us&utmje=1&utmfl=10.3%20r183&utmdt=Tracking%20QR%20Codes%20with%20Google%20Analytics&utmhid=1681965357&utmr=http%3A%2F%2Fwww.imagejnih.gov%2F&utmp=%2Fjabstracts.org%2Fpipsqueak%2Fcall_home%2Fv4-0-2x-KeyFail%2F&utmac=UA-83183702-1&utmcc=__utma%3D230887938.1463229748.1317737798.1317737798.1317737798.1%3B%2B__utmz%3D230887938.1317737798.1.1.utmcsr%3Dgoogle%7Cutmccn%3D(organic)%7Cutmcmd%3Dorganic%7Cutmctr%3DPIPSQUEAK%2Fv4.1.2x.KeyFail%3B&utmu=DC~";
		File.openUrlAsString(call_home_url);
		wait(50);
		print("\\Clear");
		wait(50);
		waitForUser("Sorry, your Pipsqueak registration is not correct. Please checkyour registration email and software key (error: 4)");
		software_key = 0;
		saveParameters();
		run("PIPSQUEAK");
	}
}else {
		call_home_url = "http://www.google-analytics.com/__utm.gif?utmwv=5.1.7&utms=1&utmn=1894752493&utmhn=www.jabstracts.org/&utmcs=UTF-8&utmsr=1280×1024&utmsc=24-bit&utmul=en-us&utmje=1&utmfl=10.3%20r183&utmdt=Tracking%20QR%20Codes%20with%20Google%20Analytics&utmhid=1681965357&utmr=http%3A%2F%2Fwww.imagejnih.gov%2F&utmp=%2Fjabstracts.org%2Fpipsqueak%2Fcall_home%2Fv4-0-2x-KeyFail%2F&utmac=UA-83183702-1&utmcc=__utma%3D230887938.1463229748.1317737798.1317737798.1317737798.1%3B%2B__utmz%3D230887938.1317737798.1.1.utmcsr%3Dgoogle%7Cutmccn%3D(organic)%7Cutmcmd%3Dorganic%7Cutmctr%3DPIPSQUEAK%2Fv4.1.2x.KeyFail%3B&utmu=DC~";
		File.openUrlAsString(call_home_url);
		wait(50);
		print("\\Clear");
		wait(50);
		waitForUser("Sorry, your Pipsqueak registration is not correct. Please checkyour registration email and software key (error: 3)");
		software_key = 0;
		saveParameters();
		run("PIPSQUEAK");

}

function saveParameters(){
	macroDir=getDirectory("macros");
    parFile = File.open(macroDir+"Pipsqueak_config.txt"); // display file open dialog
	print(parFile,email_address_raw); //0
    print(parFile, software_key); //1
   	print(parFile,encry_key); //2
   		
   	print(parFile,TimeString); //3

	print(parFile, scale_size); //4
	print(parFile, scale_known); //5
	print(parFile, scale_pixel); //6
	print(parFile, scale_unit); //7
	print(parFile, zsteps); //8

	print(parFile, background_setting); //9

	print(parFile, set_intensity_low_threshold); //10
	print(parFile, set_intensity_high_threshold); //11
	print(parFile, set_low_particlesize); //12
	print(parFile, set_high_particlesize); //13
	print(parFile, set_low_circularity); //14
	print(parFile, set_high_circularity); //15
    print(parFile, set_roi_enlargement_single); //16
	print(parFile, set_intensity_low_threshold_old); //17
	print(parFile, set_low_particlesize_old); //18
	print(parFile, set_high_particlesize_old); //19
	print(parFile, ROI_type_SL); //20

	print(parFile, countSingle); //21
	print(parFile, first_image); //22
	print(parFile, second_image); //23
	print(parFile, series_name); //24
	print(parFile, subDir); //25

	print(parFile, first_suffix); //26
	print(parFile, second_suffix); //27
	print(parFile, merged_suffix); //28
	print(parFile, total_images); //29

	print(parFile, first_stain_type); //30
	print(parFile, DL1_set_intensity_low_threshold); //31
	print(parFile, DL1_set_intensity_high_threshold); //32
	print(parFile, DL1_set_low_particlesize); //33
	print(parFile, DL1_set_high_particlesize); //34
	print(parFile, DL1_set_low_circularity); //35
	print(parFile, DL1_set_high_circularity); //36
	print(parFile, DL1_set_roi_enlargement); //37
	print(parFile, SL1_set_intensity_low_threshold_old); //38
	print(parFile, SL1_set_low_particlesize_old); //39
	print(parFile, SL1_set_high_particlesize_old); //40
	print(parFile, DL1_lower); //41
	print(parFile, DL1_overlap); //42
	print(parFile, ROI_type_DL1); //43

	print(parFile, second_stain_type); //44
	print(parFile, DL2_set_intensity_low_threshold); //45
	print(parFile, DL2_set_intensity_high_threshold); //46
	print(parFile, DL2_set_low_particlesize); //47
	print(parFile, DL2_set_high_particlesize); //48
	print(parFile, DL2_set_low_circularity); //49
	print(parFile, DL2_set_high_circularity); //50
	print(parFile, DL2_set_roi_enlargement); //51
	print(parFile, SL2_set_intensity_low_threshold_old); //52
	print(parFile, SL2_set_low_particlesize_old); //53
	print(parFile, SL2_set_high_particlesize_old); //54

	print(parFile, DL2_lower); //55
	print(parFile, DL2_overlap); //56
	print(parFile, ROI_type_DL2); //57

	print(parFile, first_image_TL); //58
	print(parFile, second_image_TL); //59
	print(parFile, filler1); //60
	print(parFile, third_image_TL); //61
	print(parFile, merged_image_TL); //62
	print(parFile, series_name_TL); //63
	print(parFile, subDir_TL); //64

	print(parFile, first_suffix_TL); //65
	print(parFile, second_suffix_TL); //66
	print(parFile, third_suffix_TL); //67
	print(parFile, merged_suffix_TL); //68
	print(parFile, total_images_TL); //69

	print(parFile, first_stain_type_TL); //70
	print(parFile, TL1_set_intensity_low_threshold); //71
	print(parFile, TL1_set_intensity_high_threshold); //72
	print(parFile, TL1_set_low_particlesize); //73
	print(parFile, TL1_set_high_particlesize); //74
	print(parFile, TL1_set_low_circularity); //75
	print(parFile, TL1_set_high_circularity); //76
	print(parFile, TL1_set_roi_enlargement); //77
	print(parFile, TL1_lower); //78
	print(parFile, TL1_overlap); //79
	print(parFile, ROI_type_TL1); //80

	print(parFile, second_stain_type_TL); //81
	print(parFile, TL2_set_intensity_low_threshold); //82
	print(parFile, TL2_set_intensity_high_threshold); //83
	print(parFile, TL2_set_low_particlesize); //84
	print(parFile, TL2_set_high_particlesize); //85
	print(parFile, TL2_set_low_circularity); //86
	print(parFile, TL2_set_high_circularity); //87
	print(parFile, TL2_set_roi_enlargement); //88
	print(parFile, TL2_lower); //89
	print(parFile, TL2_overlap); //90
	print(parFile, ROI_type_TL2); //91

	print(parFile, third_stain_type_TL); //92
	print(parFile, TL3_set_intensity_low_threshold); //93
	print(parFile, TL3_set_intensity_high_threshold); //94
	print(parFile, TL3_set_low_particlesize); //95
	print(parFile, TL3_set_high_particlesize); //96
	print(parFile, TL3_set_low_circularity); //97
	print(parFile, TL3_set_high_circularity); //98
	print(parFile, TL3_set_roi_enlargement); //99
	print(parFile, TL3_lower); //100
	print(parFile, TL3_overlap); //101
	print(parFile, ROI_type_TL3); //102

	print(parFile, lower); //103
	print(parFile, import_type); //104
	print(parFile, first_open); //105
	print(parFile, last_open); //106
	print(parFile, name); //107
	print(parFile, full_name); //108
	print(parFile, dir); //109
	print(parFile, first_time); //110

	print(parFile, save_location); //111
	print(parFile, pipsqueakCount); //112
	print(parFile, countCount); //113
	print(parFile, countDouble); //114
	print(parFile, first_open_double); //115
	print(parFile, last_open_double); //116

	print(parFile, subject_id); //117
	print(parFile, first_SL_run); //118

    File.close(parFile);

}
}

////////////////////////////////////////////////
macro "_ps_initi" {

getDateAndTime(year, month, dayOfWeek, dayOfMonth, hour, minute, second, msec);
TimeString ="_"+month+1+" "+dayOfMonth+" "+year+" at "+hour+" "+minute;

//if (pipsqueakCount==1){
	
///call home tracking URL
//UPDATE HERE (1/4 and 2/4)->

	call_home_url = "http://www.google-analytics.com/__utm.gif?utmwv=5.1.7&utms=1&utmn=1894752493&utmhn=www.jabstracts.org/&utmcs=UTF-8&utmsr=1280×1024&utmsc=24-bit&utmul=en-us&utmje=1&utmfl=10.3%20r183&utmdt=Tracking%20QR%20Codes%20with%20Google%20Analytics&utmhid=1681965357&utmr=http%3A%2F%2Fwww.imagejnih.gov%2F&utmp=%2Fjabstracts.org%2Fpipsqueak%2Fcall_home%2Fv4-0-2x%2F&utmac=UA-83183702-1&utmcc=__utma%3D230887938.1463229748.1317737798.1317737798.1317737798.1%3B%2B__utmz%3D230887938.1317737798.1.1.utmcsr%3Dgoogle%7Cutmccn%3D(organic)%7Cutmcmd%3Dorganic%7Cutmctr%3DPIPSQUEAK%2Fv4.0.2x%3B&utmu=DC~";
	File.openUrlAsString(call_home_url);
	wait(50);
	print("\\Clear");
	wait(50);

///
//UPDATE HERE (3/4)->
	print("Pipsqueak! v4.0.2x Perineuronal net Intensity Program for the Standardization and Quantification of ECM Analysis.");
	print("\n"+"Copyright (C) 2019, John H. Harkness, PhD.");
	print("Lab of Barb Sorg, PhD., Washington State University, Vancouver");
	print("\n"+"FOR ACCURATE RESULTS, IT IS CRITICAL THAT YOUR IMAGE DIMENSIONS ARE CORRECTLY ENTERED (i.e. pixels per known distance).");
	
//UPDATE HERE (4/4)-> 

	//waitForUser("Pipsqueak version 4.0.2x, Nov 2th, 2020 \n \n 4.0.2 CHANGE LOG: This is another big one! (Sorry it's been a minute). Thanks for registering your license and agreeing to our terms of service. \n Squashed a lot of little bugs and made some significant performance improvements under the hood. \n \n Pipsqueak NOW SAVES YOUR SETTINGS!!!");

	exec("open", "https://pipsqueak.ai");
	
	Dialog.createNonBlocking("It's time to upgrade to Pipsqueak Version 5");
	Dialog.addMessage("Hey there Pipsqueak User,");
	Dialog.addMessage("I can't begin to tell you how much your support (and use of PIPSQUEAK) has meant over the last 4 years. Users like you made this a communtiy project, made it better, and gave it wings. \nThank you.");
	Dialog.addMessage("\nPIPSQUEAK has evolved a lot since then. For example, you'll notice big things like the shift from calling it 'PIPSQUEAK' to the more refined 'Pipsqueak.'");
	Dialog.addMessage("\n>>> Rewire Neuro is now working full time to improve Pipsqueak, but the package you are using here (version 4.0.2x) is one year out of date and is being depreciated. <<<");
	Dialog.addMessage(" ");
	Dialog.addMessage("But dont worry!! PIPSQUEAK IS STILL FREE AND WILL CONTINUE TO BE FREE! The free tools you use here are now called Pipsqueak Basic. \nTo download and install Pipsqueak Basic Version 5+, please navigate to https://pipsqueak.ai");
	Dialog.addMessage("\n>>> When you download Version 5, YOU'LL RECEIVE A FREE WEEK TRIAL of Pipsqueak AI! <<< \nWe've made a TON of improvements have been made under the hood. Not the least of which is the shift to using computer vision (machine learning) to detect cells. \nPipsqueak Version 5 has received a significant facelift and many improvements to speed and accuracy. \nCell detection is significantly more accrate with AI. It's amazing, and it can detect hundreds of cells in a fraction of a second. \nYou'll still have an opportunity to manually edit your cell selection, but you probably won't need to very often. ");
	Dialog.addMessage(" ");
	Dialog.addMessage("So, come on over to Pipsqueak AI Version 5 and enjoy a future of image analysis with artificial intelligence. As always, feel free to reach out to me with questions.");
	Dialog.addMessage("Thank you for using Pipsqueak (aka PIPSQUEAK)! \n-John Harkness, Ph.D. \n \njohn.harkness@wsu.edu");
	Dialog.addHelp("https://pipsqueak.ai/downloads/");

	Dialog.show;

	
	
	wait(1000);
	pipsqueakCount = pipsqueakCount+1;
	
//} else {
//	wait(50);
//}
print("Background Setting: "+background_setting);
print("Distance in pixels: "+scale_size+"\n"+"Known distance: "+scale_known+"\n"+"Pixel aspect ratio: "+scale_pixel+"\n"+"Unit of length: "+scale_unit);
print("Date on saved files will be '"+TimeString+"'");
print("\n");
/*
Dialog.create("Please Consider Supporting PIPSQUEAK's Development!");
Dialog.addMessage("We hope that you find that this free tool has benefited your research!");
Dialog.addMessage("PIPSQUEAK's development has been entirely supported by grants funds from Washington State University and NIDA.");
Dialog.addMessage(" ");
Dialog.addMessage("Please consider helping support the continued development of PIPSQUEAK by donating on our GoFundMe page.");
Dialog.addMessage("Your donation will help support, feed, and motivate the postdoc behind this tool.");
Dialog.addMessage(" ");
Dialog.addMessage("Make a contribution here: https://gf.me/u/mxuhkv or by pressing the 'HELP' button below.");
Dialog.addMessage("Thank you for your support!");
Dialog.addHelp("https://gf.me/u/mxuhkv");

Dialog.show;
*/
	
Dialog.create("Welcome");
items0 = newArray("Single label analysis", "->Settings", "Double label analysis", "->Contact Us and/or Report a bug", "Triple label analysis");
Dialog.addRadioButtonGroup("What would you like to do?", items0, 4, 2, " ");

Dialog.show;

choice0 = Dialog.getRadioButton;
	
	
if (choice0 =="Single label analysis") {

	///call home tracking URL
	//UPDATE HERE (1/4 and 2/4)->

	call_home_url = "http://www.google-analytics.com/__utm.gif?utmwv=5.1.7&utms=1&utmn=1894752493&utmhn=www.jabstracts.org/&utmcs=UTF-8&utmsr=1280×1024&utmsc=24-bit&utmul=en-us&utmje=1&utmfl=10.3%20r183&utmdt=Tracking%20QR%20Codes%20with%20Google%20Analytics&utmhid=1681965357&utmr=http%3A%2F%2Fwww.imagejnih.gov%2F&utmp=%2Fjabstracts.org%2Fpipsqueak%2Fcall_home%2Fv4-0-2xsingle%2F&utmac=UA-83183702-1&utmcc=__utma%3D230887938.1463229748.1317737798.1317737798.1317737798.1%3B%2B__utmz%3D230887938.1317737798.1.1.utmcsr%3Dgoogle%7Cutmccn%3D(organic)%7Cutmcmd%3Dorganic%7Cutmctr%3DPIPSQUEAK%2Fv4.0.2xsingle%3B&utmu=DC~";
	File.openUrlAsString(call_home_url);
	wait(50);
	//print("\\Clear");
	
saveParameters();
	run("_ps_single");
	}
	
if (choice0 =="Double label analysis") {
	
	
	///call home tracking URL
	//UPDATE HERE (1/4 and 2/4)->

	call_home_url = "http://www.google-analytics.com/__utm.gif?utmwv=5.1.7&utms=1&utmn=1894752493&utmhn=www.jabstracts.org/&utmcs=UTF-8&utmsr=1280×1024&utmsc=24-bit&utmul=en-us&utmje=1&utmfl=10.3%20r183&utmdt=Tracking%20QR%20Codes%20with%20Google%20Analytics&utmhid=1681965357&utmr=http%3A%2F%2Fwww.imagejnih.gov%2F&utmp=%2Fjabstracts.org%2Fpipsqueak%2Fcall_home%2Fv4-0-2xdouble%2F&utmac=UA-83183702-1&utmcc=__utma%3D230887938.1463229748.1317737798.1317737798.1317737798.1%3B%2B__utmz%3D230887938.1317737798.1.1.utmcsr%3Dgoogle%7Cutmccn%3D(organic)%7Cutmcmd%3Dorganic%7Cutmctr%3DPIPSQUEAK%2Fv4.0.2xdouble%3B&utmu=DC~";
	File.openUrlAsString(call_home_url);
	wait(50);
	print("\\Clear");

	saveParameters();
	run("_ps_double");
	}
	
if (choice0=="Triple label analysis") {
		
	///call home tracking URL
	//UPDATE HERE (1/4 and 2/4)->

	call_home_url = "http://www.google-analytics.com/__utm.gif?utmwv=5.1.7&utms=1&utmn=1894752493&utmhn=www.jabstracts.org/&utmcs=UTF-8&utmsr=1280×1024&utmsc=24-bit&utmul=en-us&utmje=1&utmfl=10.3%20r183&utmdt=Tracking%20QR%20Codes%20with%20Google%20Analytics&utmhid=1681965357&utmr=http%3A%2F%2Fwww.imagejnih.gov%2F&utmp=%2Fjabstracts.org%2Fpipsqueak%2Fcall_home%2Fv4-0-2xtriple%2F&utmac=UA-83183702-1&utmcc=__utma%3D230887938.1463229748.1317737798.1317737798.1317737798.1%3B%2B__utmz%3D230887938.1317737798.1.1.utmcsr%3Dgoogle%7Cutmccn%3D(organic)%7Cutmcmd%3Dorganic%7Cutmctr%3DPIPSQUEAK%2Fv4.0.2xtriple%3B&utmu=DC~";
	File.openUrlAsString(call_home_url);
	wait(50);
	//print("\\Clear");
	saveParameters();	
	run("_ps_triple");
	}
	
if (choice0 =="->Contact Us and/or Report a bug") {
	//Dialog.addHelp("http://www.labs.wsu.edu/sorg/research-resources/");		
	waitForUser("Thanks! Please log bugs as best you can (screen shot, description, etc) and send to john.harkness@wsu.edu . \n For questions or help, please use the same email address. Thanks for using PIPSQUEAK!");
	saveParameters();
	run("_ps_initi");
	}
	
//if (choice0 =="->Help") {
//	waitForUser("Please visit https://labs.wsu.edu/sorg/research-resources");
//	run("_ps_initi");
//}
	
if (choice0 =="->Settings") {
	Dialog.create("Settings Menu");
	items1 = newArray("Image dimensions, scale, and z-steps settings", "Background calculation settings", "Single label analysis settings", "Double label analysis settings", "Triple label analysis settings");
	Dialog.addRadioButtonGroup("What would you like to do?", items1, 5, 1, " ");
	
	Dialog.show;
	
	choice1 = Dialog.getRadioButton;
	
	
	if (choice1 =="Image dimensions, scale, and z-steps settings") {
		Dialog.create("Image dimensions, scale, and z-steps settings");
		Dialog.addMessage("**Image scale measurements (for both SINGLE, DOUBLE, and TRIPLE label analysis)**");
		Dialog.addNumber("Number of z-steps to be summed:", zsteps);
		Dialog.addMessage(" ");
		Dialog.addNumber("Distance in pixels:", scale_size);
		Dialog.addNumber("Known distance:", scale_known);
		Dialog.addNumber("Pixel aspect ratio:", scale_pixel);
		Dialog.addString("Unit of length:", scale_unit);
		
			
		Dialog.show;

		zsteps = Dialog.getNumber();
		scale_size = Dialog.getNumber();;
		scale_known = Dialog.getNumber();;;
		scale_pixel = Dialog.getNumber();;;;
		scale_unit = Dialog.getString();
		
		run("_ps_initi");
		}
		
	if (choice1 =="Background calculation settings") {
		Dialog.create("Background calculation settings");
		Dialog.addMessage("Adjustments will alter cell determination and MAY reduce identification specificity.");
		Dialog.addMessage("Proceed with caution: here be dragons.");
		Dialog.addMessage(" ");
		Dialog.addMessage("High background subtraction: This is the original setting for v2.02 and below. It removes more background, but may miss lightly stained cells.");
		Dialog.addMessage("Low background subtraction: This is the improved background setting. It is more sensitive for lightly stained cells.");
		items2 = newArray("High", "Low");
		Dialog.addRadioButtonGroup("Which background subtraction setting would you like to use?", items2, 2, 1, background_setting);
	
		Dialog.show;
		
		choice2 = Dialog.getRadioButton;
		if (choice2 =="High") {
			background_setting = "High";
			
			run("_ps_initi");
			}
		
		if (choice2 =="Low") {
			background_setting = "Low";
			
			run("_ps_initi");
			}
		}
	if (choice1 =="Single label analysis settings") {
		Dialog.create("Single-label Intensity Analysis Settings");
		Dialog.addMessage("Adjustments will alter cell determination and MAY reduce identification specificity.");
		Dialog.addMessage("Proceed with caution: here be dragons.");
		Dialog.addMessage(" ");
		Dialog.addNumber("Lower threshold multiplier:",set_intensity_low_threshold);
		Dialog.addNumber("Upper threshold limit:", set_intensity_high_threshold);
		Dialog.addNumber("Lower limit particle size:", set_low_particlesize);
		Dialog.addNumber("Upper limit particle size:", set_high_particlesize);
		Dialog.addNumber("Lower limit circularity:", set_low_circularity);
		Dialog.addNumber("Upper limit circularity:", set_high_circularity);
		Dialog.addNumber("ROI Enlargement (pixels):", set_roi_enlargement_single);
		items3 = newArray("As-detected", "Square", "Circle");
		Dialog.addRadioButtonGroup("Shape of ROIs:", items3, 3, 1, ROI_type_SL);
	
		Dialog.show;
		
		set_intensity_low_threshold = Dialog.getNumber();
		set_intensity_high_threshold = Dialog.getNumber();;
		set_low_particlesize = Dialog.getNumber();;;
		set_high_particlesize = Dialog.getNumber();;;;
		set_low_circularity = Dialog.getNumber();;;;;
		set_high_circularity = Dialog.getNumber();;;;;;
		set_roi_enlargement_single = Dialog.getNumber();;;;;;;
		
		choice3 = Dialog.getRadioButton;
		if (choice3 =="As-detected") {
			ROI_type_SL = "As-detected";	
			}
		if (choice3 =="Square") {
			ROI_type_SL = "Square";	
			}
		if (choice3 =="Circle") {
			ROI_type_SL = "Circle";	
			}
		
		run("_ps_initi");
		}
	
	if (choice1 =="Double label analysis settings") {
		Dialog.create("Double-label Intensity Analysis Settings");		
		Dialog.addMessage("**Window 1 of 2**");
		Dialog.addMessage(" ");		
		Dialog.addMessage("Please enter the numbering suffixes of the image series you want to analyze in the space below.");
		Dialog.addMessage("eg: Exp42_SubA_example_1.tif and Exp42_SubA_example_3.tif = #1 and #3");
		Dialog.addMessage(" ");
		Dialog.addNumber("First image (if possible, smaller volume stain):", first_suffix);
		Dialog.addNumber("Second image (if possible, larger volume stain):", second_suffix);
		Dialog.addNumber("Merged image:", merged_suffix);
		Dialog.addMessage(" ");
		Dialog.addMessage("*First stain parameters*");
		Dialog.addString("Stain type:", first_stain_type);
		Dialog.addNumber("Lower threshold multiplier:", DL1_set_intensity_low_threshold);
		Dialog.addNumber("Upper threshold limit:", DL1_set_intensity_high_threshold);
		Dialog.addNumber("Lower limit particle size:", DL1_set_low_particlesize);
		Dialog.addNumber("Upper limit particle size:", DL1_set_high_particlesize);
		Dialog.addNumber("Lower limit circularity:", DL1_set_low_circularity);
		Dialog.addNumber("Upper limit circularity:", DL1_set_high_circularity);
		Dialog.addNumber("ROI Enlargement (pixels):", DL1_set_roi_enlargement);
		Dialog.addNumber("Min overlap % to be considered double-labeled (% as decimal):", DL1_overlap);
		items4 = newArray("As-detected", "Square", "Circle");
		Dialog.addRadioButtonGroup("Shape of ROIs:", items4, 3, 1, ROI_type_DL1);

		Dialog.show;

		first_suffix = Dialog.getNumber();
		second_suffix = Dialog.getNumber();;
		merged_suffix = Dialog.getNumber();;;
		//total_images = Dialog.getNumber();;;;
		first_stain_type =  Dialog.getString();
		DL1_set_intensity_low_threshold = Dialog.getNumber();;
		DL1_set_intensity_high_threshold = Dialog.getNumber();;;
		DL1_set_low_particlesize = Dialog.getNumber();;;;
		DL1_set_high_particlesize = Dialog.getNumber();;;;;
		DL1_set_low_circularity = Dialog.getNumber();;;;;;
		DL1_set_high_circularity = Dialog.getNumber();;;;;;;
		DL1_set_roi_enlargement = Dialog.getNumber();;;;;;;;
		DL1_overlap = Dialog.getNumber();;;;;;;;;
		choice4 = Dialog.getRadioButton;
		if (choice4 =="As-detected") {
			ROI_type_DL1 = "As-detected";	
			}
		if (choice4 == "Square") {
			ROI_type_DL1 = "Square";	
			}
		if (choice4 == "Circle") {
			ROI_type_DL1 = "Circle";	
			}
		
	//next window
		Dialog.create("Double-label Intensity Analysis Settings");		
		Dialog.addMessage("**Window 2 of 2**");
		Dialog.addMessage(" ");		
		Dialog.addMessage("*Second stain parameters*");
		Dialog.addString("Stain type:", second_stain_type);
		Dialog.addNumber("Lower threshold multiplier:", DL2_set_intensity_low_threshold);
		Dialog.addNumber("Upper threshold limit:", DL2_set_intensity_high_threshold);
		Dialog.addNumber("Lower limit particle size:", DL2_set_low_particlesize);
		Dialog.addNumber("Upper limit particle size:", DL2_set_high_particlesize);
		Dialog.addNumber("Lower limit circularity:",  DL2_set_low_circularity);
		Dialog.addNumber("Upper limit circularity:", DL2_set_high_circularity);
		Dialog.addNumber("ROI Enlargement (pixels):", DL2_set_roi_enlargement);
		Dialog.addNumber("Min overlap % to be considered double-labeled (% as decimal):", DL2_overlap);
		items5 = newArray("As-detected", "Square", "Circle");
		Dialog.addRadioButtonGroup("Shape of ROIs:", items5, 3, 1, ROI_type_DL2);

		Dialog.show;

		second_stain_type =  Dialog.getString();
		DL2_set_intensity_low_threshold = Dialog.getNumber();;
		DL2_set_intensity_high_threshold = Dialog.getNumber();;;
		DL2_set_low_particlesize = Dialog.getNumber();;;;
		DL2_set_high_particlesize = Dialog.getNumber();;;;;
		DL2_set_low_circularity = Dialog.getNumber();;;;;;
		DL2_set_high_circularity = Dialog.getNumber();;;;;;;
		DL2_set_roi_enlargement = Dialog.getNumber();;;;;;;;
		DL2_overlap = Dialog.getNumber();;;;;;;;;
		choice5 = Dialog.getRadioButton;
		if (choice5 == "As-detected") {
			ROI_type_DL2 = "As-detected";	
			}
		if (choice5 == "Square") {
			ROI_type_DL2 = "Square";	
			}
		if (choice5 == "Circle") {
			ROI_type_DL2 = "Circle";	
			}
		run("_ps_initi");
		}
		
	if (choice1 == "Triple label analysis settings") {
		Dialog.create(" Triple-label Intensity Analysis Settings");
		Dialog.addMessage("**Window 1 of 3**");		
		Dialog.addMessage(" ");		
		Dialog.addMessage("Please enter the numbering suffixes of the image series you want to analyze in the space below.");
		Dialog.addMessage("eg: Exp42_SubA_example_1.tif and Exp42_SubA_example_3.tif = #1 and #3");
		Dialog.addMessage(" ");
		Dialog.addNumber("First image (if possible, smaller volume stain):", first_suffix_TL);
		Dialog.addNumber("Second image (if possible, larger volume stain):", second_suffix_TL);
		Dialog.addNumber("Third image (if possible, smaller volume stain):", third_suffix_TL);
		Dialog.addNumber("Merged image:", merged_suffix_TL);
		//Dialog.addNumber("Total number of stains plus composite in each subject series:", total_images);
		//REMOVED FOR NOW—SEE NOTE IN "_ps_OPENNEXT_DOUBLE".
		Dialog.addMessage(" ");
		Dialog.addMessage("*First stain parameters*");
		Dialog.addString("Stain type:", first_stain_type_TL);
		Dialog.addNumber("Lower threshold multiplier:", TL1_set_intensity_low_threshold);
		Dialog.addNumber("Upper threshold limit:", TL1_set_intensity_high_threshold);
		Dialog.addNumber("Lower limit particle size:", TL1_set_low_particlesize);
		Dialog.addNumber("Upper limit particle size:", TL1_set_high_particlesize);
		Dialog.addNumber("Lower limit circularity:", TL1_set_low_circularity);
		Dialog.addNumber("Upper limit circularity:", TL1_set_high_circularity);
		Dialog.addNumber("ROI Enlargement (pixels):", TL1_set_roi_enlargement);
		Dialog.addNumber("Min overlap % to be considered double-labeled (% as decimal):", TL1_overlap);
		items6 = newArray("As-detected", "Square", "Circle");
		Dialog.addRadioButtonGroup("Shape of ROIs:", items6, 3, 1, ROI_type_TL1);
		
		Dialog.show
		
		first_suffix_TL = Dialog.getNumber();
		second_suffix_TL = Dialog.getNumber();;
		third_suffix_TL = Dialog.getNumber();;;
		merged_suffix_TL = Dialog.getNumber();;;;
		//total_images = Dialog.getNumber();;;;;
		first_stain_type_TL =  Dialog.getString();
		TL1_set_intensity_low_threshold = Dialog.getNumber();;
		TL1_set_intensity_high_threshold = Dialog.getNumber();;;
		TL1_set_low_particlesize = Dialog.getNumber();;;;
		TL1_set_high_particlesize = Dialog.getNumber();;;;;
		TL1_set_low_circularity = Dialog.getNumber();;;;;;
		TL1_set_high_circularity = Dialog.getNumber();;;;;;;
		TL1_set_roi_enlargement = Dialog.getNumber();;;;;;;;
		TL1_overlap = Dialog.getNumber();;;;;;;;;
		choice6 = Dialog.getRadioButton;
		if (choice6 =="As-detected") {
			ROI_type_TL1 = "As-detected";	
			}
		if (choice6 == "Square") {
			ROI_type_TL1 = "Square";	
			}
		if (choice6 == "Circle") {
			ROI_type_TL1 = "Circle";
			}
		Dialog.create(" Triple-label Intensity Analysis Settings");
		Dialog.addMessage("**Window 2 of 3**");		
		Dialog.addMessage(" ");
		Dialog.addMessage("*Second stain parameters*");
		Dialog.addString("Stain type:", second_stain_type_TL);
		Dialog.addNumber("Lower threshold multiplier:", TL2_set_intensity_low_threshold);
		Dialog.addNumber("Upper threshold limit:", TL2_set_intensity_high_threshold);
		Dialog.addNumber("Lower limit particle size:", TL2_set_low_particlesize);
		Dialog.addNumber("Upper limit particle size:", TL2_set_high_particlesize);
		Dialog.addNumber("Lower limit circularity:",  TL2_set_low_circularity);
		Dialog.addNumber("Upper limit circularity:", TL2_set_high_circularity);
		Dialog.addNumber("ROI Enlargement (pixels):", TL2_set_roi_enlargement);
		Dialog.addNumber("Min overlap % to be considered double-labeled (% as decimal):", TL2_overlap);
		items7 = newArray("As-detected", "Square", "Circle");
		Dialog.addRadioButtonGroup("Shape of ROIs:", items7, 3, 1, ROI_type_TL2);
		
		Dialog.show;
				
		second_stain_type_TL =  Dialog.getString();
		TL2_set_intensity_low_threshold = Dialog.getNumber();;
		TL2_set_intensity_high_threshold = Dialog.getNumber();;;
		TL2_set_low_particlesize = Dialog.getNumber();;;;
		TL2_set_high_particlesize = Dialog.getNumber();;;;;
		TL2_set_low_circularity = Dialog.getNumber();;;;;;
		TL2_set_high_circularity = Dialog.getNumber();;;;;;;
		TL2_set_roi_enlargement = Dialog.getNumber();;;;;;;;
		TL2_overlap = Dialog.getNumber();;;;;;;;;
		choice7 = Dialog.getRadioButton;
		if (choice7 =="As-detected") {
			ROI_type_TL2 = "As-detected";	
			}
		if (choice7 == "Square") {
			ROI_type_TL2 = "Square";	
			}
		if (choice7 == "Circle") {
			ROI_type_TL2 = "Circle";
			}
		Dialog.create(" Triple-label Intensity Analysis Settings");
		Dialog.addMessage("**Window 3 of 3**");;	
		Dialog.addMessage(" ");
		Dialog.addMessage("*Third stain parameters*");
		Dialog.addString("Stain type:", third_stain_type_TL);
		Dialog.addNumber("Lower threshold multiplier:", TL3_set_intensity_low_threshold);
		Dialog.addNumber("Upper threshold limit:", TL3_set_intensity_high_threshold);
		Dialog.addNumber("Lower limit particle size:", TL3_set_low_particlesize);
		Dialog.addNumber("Upper limit particle size:", TL3_set_high_particlesize);
		Dialog.addNumber("Lower limit circularity:",  TL3_set_low_circularity);
		Dialog.addNumber("Upper limit circularity:", TL3_set_high_circularity);
		Dialog.addNumber("ROI Enlargement (pixels):", TL3_set_roi_enlargement);
		Dialog.addNumber("Min overlap % to be considered double-labeled (% as decimal):", TL3_overlap);
		items8 = newArray("As-detected", "Square", "Circle");
		Dialog.addRadioButtonGroup("Shape of ROIs:", items8, 3, 1, ROI_type_TL3);
		
		Dialog.show;
				
		third_stain_type_TL =  Dialog.getString();
		TL3_set_intensity_low_threshold = Dialog.getNumber();;
		TL3_set_intensity_high_threshold = Dialog.getNumber();;;
		TL3_set_low_particlesize = Dialog.getNumber();;;;
		TL3_set_high_particlesize = Dialog.getNumber();;;;;
		TL3_set_low_circularity = Dialog.getNumber();;;;;;
		TL3_set_high_circularity = Dialog.getNumber();;;;;;;
		TL3_set_roi_enlargement = Dialog.getNumber();;;;;;;;
		TL3_overlap = Dialog.getNumber();;;;;;;;;
		choice8 = Dialog.getRadioButton;
		if (choice8 =="As-detected") {
			ROI_type_TL3 = "As-detected";	
			}
		if (choice8 == "Square") {
			ROI_type_TL3 = "Square";	
			}
		if (choice8 == "Circle") {
			ROI_type_TL3 = "Circle";

		
		run("_ps_initi");
			}
		}	
	}

function saveParameters(){
        macroDir=getDirectory("macros");
        parFile = File.open(macroDir+"Pipsqueak_config.txt"); // display file open dialog
    	    	print(parFile,email_address_raw); //0
    	print(parFile, software_key); //1
   		print(parFile,encry_key); //2
   		
   		print(parFile,TimeString); //3

print(parFile, scale_size); //4
print(parFile, scale_known); //5
print(parFile, scale_pixel); //6
print(parFile, scale_unit); //7
print(parFile, zsteps); //8

print(parFile, background_setting); //9

print(parFile, set_intensity_low_threshold); //10
print(parFile, set_intensity_high_threshold); //11
print(parFile, set_low_particlesize); //12
print(parFile, set_high_particlesize); //13
print(parFile, set_low_circularity); //14
print(parFile, set_high_circularity); //15
print(parFile, set_roi_enlargement_single); //16
print(parFile, set_intensity_low_threshold_old); //17
print(parFile, set_low_particlesize_old); //18
print(parFile, set_high_particlesize_old); //19
print(parFile, ROI_type_SL); //20

print(parFile, countSingle); //21
print(parFile, first_image); //22
print(parFile, second_image); //23
print(parFile, series_name); //24
print(parFile, subDir); //25

print(parFile, first_suffix); //26
print(parFile, second_suffix); //27
print(parFile, merged_suffix); //28
print(parFile, total_images); //29

print(parFile, first_stain_type); //30
print(parFile, DL1_set_intensity_low_threshold); //31
print(parFile, DL1_set_intensity_high_threshold); //32
print(parFile, DL1_set_low_particlesize); //33
print(parFile, DL1_set_high_particlesize); //34
print(parFile, DL1_set_low_circularity); //35
print(parFile, DL1_set_high_circularity); //36
print(parFile, DL1_set_roi_enlargement); //37
print(parFile, SL1_set_intensity_low_threshold_old); //38
print(parFile, SL1_set_low_particlesize_old); //39
print(parFile, SL1_set_high_particlesize_old); //40
print(parFile, DL1_lower); //41
print(parFile, DL1_overlap); //42
print(parFile, ROI_type_DL1); //43

print(parFile, second_stain_type); //44
print(parFile, DL2_set_intensity_low_threshold); //45
print(parFile, DL2_set_intensity_high_threshold); //46
print(parFile, DL2_set_low_particlesize); //47
print(parFile, DL2_set_high_particlesize); //48
print(parFile, DL2_set_low_circularity); //49
print(parFile, DL2_set_high_circularity); //50
print(parFile, DL2_set_roi_enlargement); //51
print(parFile, SL2_set_intensity_low_threshold_old); //52
print(parFile, SL2_set_low_particlesize_old); //53
print(parFile, SL2_set_high_particlesize_old); //54

print(parFile, DL2_lower); //55
print(parFile, DL2_overlap); //56
print(parFile, ROI_type_DL2); //57

print(parFile, first_image_TL); //58
print(parFile, second_image_TL); //59
print(parFile, filler1); //60
print(parFile, third_image_TL); //61
print(parFile, merged_image_TL); //62
print(parFile, series_name_TL); //63
print(parFile, subDir_TL); //64

print(parFile, first_suffix_TL); //65
print(parFile, second_suffix_TL); //66
print(parFile, third_suffix_TL); //67
print(parFile, merged_suffix_TL); //68
print(parFile, total_images_TL); //69

print(parFile, first_stain_type_TL); //70
print(parFile, TL1_set_intensity_low_threshold); //71
print(parFile, TL1_set_intensity_high_threshold); //72
print(parFile, TL1_set_low_particlesize); //73
print(parFile, TL1_set_high_particlesize); //74
print(parFile, TL1_set_low_circularity); //75
print(parFile, TL1_set_high_circularity); //76
print(parFile, TL1_set_roi_enlargement); //77
print(parFile, TL1_lower); //78
print(parFile, TL1_overlap); //79
print(parFile, ROI_type_TL1); //80

print(parFile, second_stain_type_TL); //81
print(parFile, TL2_set_intensity_low_threshold); //82
print(parFile, TL2_set_intensity_high_threshold); //83
print(parFile, TL2_set_low_particlesize); //84
print(parFile, TL2_set_high_particlesize); //85
print(parFile, TL2_set_low_circularity); //86
print(parFile, TL2_set_high_circularity); //87
print(parFile, TL2_set_roi_enlargement); //88
print(parFile, TL2_lower); //89
print(parFile, TL2_overlap); //90
print(parFile, ROI_type_TL2); //91

print(parFile, third_stain_type_TL); //92
print(parFile, TL3_set_intensity_low_threshold); //93
print(parFile, TL3_set_intensity_high_threshold); //94
print(parFile, TL3_set_low_particlesize); //95
print(parFile, TL3_set_high_particlesize); //96
print(parFile, TL3_set_low_circularity); //97
print(parFile, TL3_set_high_circularity); //98
print(parFile, TL3_set_roi_enlargement); //99
print(parFile, TL3_lower); //100
print(parFile, TL3_overlap); //101
print(parFile, ROI_type_TL3); //102

print(parFile, lower); //103
print(parFile, import_type); //104
print(parFile, first_open); //105
print(parFile, last_open); //106
print(parFile, name); //107
print(parFile, full_name); //108
print(parFile, dir); //109
print(parFile, first_time); //110

print(parFile, save_location); //111
print(parFile, pipsqueakCount); //112
print(parFile, countCount); //113
print(parFile, countDouble); //114
print(parFile, first_open_double); //115
print(parFile, last_open_double); //116

print(parFile, subject_id); //117
print(parFile, first_SL_run); //118

    
        File.close(parFile);

}
	
}

//////////////////////////

macro "_ps_single" {

Dialog.create("Import Type");

//items = newArray("Semiautomated analysis of raw, non-summed, individual Z-stacks", "Semiautomated analysis of summed single-labeled images", "Automatic analysis of summed single-labeled images", "->Make summed single-labeled images from Z-stacks.");

items = newArray("Semiautomated analysis of summed single-labeled images", "Automatic analysis of summed single-labeled images", "->Make summed single-labeled images from Z-stacks.");
Dialog.addRadioButtonGroup("What type of file do you want to import?", items, 3, 1, "Semiautomated analysis of summed single-labeled images");

Dialog.show;
choice = Dialog.getRadioButton;
/*
if (choice=="Semiautomated analysis of raw, non-summed, individual Tiffs") {
	import_type=1;
	run("_ps_Import");
	}
*/
if (choice=="Semiautomated analysis of summed single-labeled images") {
	import_type=2;
	run("_ps_Import_Summed");
	}
if (choice=="Automatic analysis of summed single-labeled images") {
	import_type=3;
	run("_ps_Auto_Import_Summed");
	}
if (choice=="->Make summed single-labeled images from Z-stacks.") {
	Dialog.create("Stacks folder");
	Dialog.addMessage("Before using this function, please place folders containing Z-stack images within an experiment folder. Then choose the experiment folder.");
	Dialog.addMessage("ie, /Desktop/EXPERIMENT FOLDER/Subject folder/image1... image20");
	Dialog.show(); 
	run("_ps_z-project");
	}

	
	
////////////////////////////

macro "_ps_Import" {
       // Importing stacks
//import_type=1;
run("Image Sequence...", "disable_global convert sort");
name = getTitle; 
dir = getDirectory("image"); 
selectWindow(name);
run("Set Measurements...", "area mean standard min center limit redirect=None decimal=3");  //setting measurements.

run("Z Project...", "projection=[Sum Slices]");
run("Set Scale...", "distance=scale_size known=scale_known pixel=scale_pixel unit=&scale_unit global");
full_name = "SUM_"+name; 
print("\n"+name+" loaded.");
run("_ps_Background");
}

/////////////////////////////

macro "_ps_Import_Summed" {
       // Importing image

//import_type=2;
open();
run("Set Measurements...", "area mean standard min center limit redirect=None decimal=3");  //setting measurements.
run("Set Scale...", "distance=scale_size known=scale_known pixel=scale_pixel unit=&scale_unit global");
dir = getDirectory("image"); 
//print(dir);
name = getTitle; 
full_name = getTitle; 
index = lastIndexOf(name, "."); 
if (index!=-1) name = substring(name, 0, index);
print("\n"+name+" loaded.");

first_open = name;


run("_ps_Background");
}

////////////////////////////////

macro "_ps_Auto_Import_Summed" {
       // Importing image

//import_type=3;
open();
run("Set Measurements...", "area mean standard min center limit redirect=None decimal=3");  //setting measurements.
run("Set Scale...", "distance=scale_size known=scale_known pixel=scale_pixel unit=&scale_unit global");
dir = getDirectory("image"); 
name = getTitle;
full_name = getTitle; 
index = lastIndexOf(name, "."); 
if (index!=-1) name = substring(name, 0, index);
print("\n"+name+" loaded.");

first_open = name;

waitForUser("Please don't touch", "Please let the macro run without clicking windows. Disruption may cause the macro to stall.");

run("_ps_Background");
}

////////////////////////////////

macro "_ps_z-project" {
	
	//makes summed image from z-stack

Ldest = getDirectory("Choose a Directory ");         //load from
Sdest = Ldest+"/summed/";           //save at
sfold = Sdest
stackfold = getFileList(Ldest);

if(!File.exists(sfold)) {
	File.makeDirectory(sfold);	
}

Dialog.create("Enter z-steps");
Dialog.addMessage("Please entere the number of z-steps (slices) in each image series");		
Dialog.addNumber("Number of z-steps:", zsteps);

Dialog.show;

zsteps = Dialog.getNumber();

for(i=0; i<stackfold.length; i++){
	stacks = stackfold[i];
	identify = substring(stacks, 0, lengthOf(stacks)-1);
	names = substring(stacks, lengthOf(stacks)-1);
	if(names == "/" && stacks!="summed/"){
		ISp = "open=[" + Ldest + stacks + "] number=zsteps starting=q increment=1 scale=100 file=[] or=[] sort";

		for(q=1; q<2; q++) { 
			run("Image Sequence...", ISp);
			if(q!=1){
				run("8-bit");
			}

			run("Z Project...", "start=1 stop=zsteps projection=[Sum Slices]");


			saveAs("Tiff", sfold + identify + "_" + q + ".tif");

			close();
			
			{
			while(nImages>0){
				selectImage(nImages);
				close();
				}
			}
		}
	}
}

run("_ps_single");
}

////////////////////////////////

macro "_ps_OpenNext_Single" {

//import_type=3;
makeRectangle(0, 0, 0, 0);
run("Revert");
run("Open Next");
run("Set Measurements...", "area mean standard min center limit redirect=None decimal=3");  //setting measurements.
run("Set Scale...", "distance=scale_size known=scale_known pixel=scale_pixel unit=&scale_unit global");
dir = getDirectory("image"); 
name = getTitle; 
full_name = getTitle; 
index = lastIndexOf(name, "."); 
if (index!=-1) name = substring(name, 0, index);
last_open = name;

if (last_open==first_open) {
	print("\n"+"All images processed! Done.");
	makeRectangle(0, 0, 0, 0);
	run("Revert");
	run("Clear Results");
	//run("Close");
	selectWindow(name+".tif");
	run("Close");
} else {
	print ("\n"+last_open+" loaded.");
	run ("_ps_Background");
	}
}
///////////////////////////////

macro "_ps_Background" {
	// Selecting Background

run("Clear Results");

run("ROI Manager...");
roiManager("reset");
roiManager("Show All");
roiManager("Show All with labels");

Dialog.create("Subject ID");
	Dialog.addMessage("Please enter the Subject ID name or number");
	Dialog.addString("Subject ID:", "eg:42B");
	Dialog.show
subject_id =  Dialog.getString();

height = getHeight;
width = getWidth;

selectWindow(full_name);
run("Duplicate...", " ");
selectWindow(full_name);

makeRectangle((6*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((6*(width-40)/6), (1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((6*(width-40)/6), (2*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((6*(width-40)/6), (3*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((6*(width-40)/6), (4*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((6*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((5*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((4*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((3*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((2*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((1*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (4*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (3*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (2*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((1*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((2*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((3*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((4*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((5*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");

run("Subtract Background...", "rolling=10");
roiManager("Select", newArray(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,21));
roiManager("Measure");


	// Calculating lower threshold background value in "Mean" column

m=0;
bkgd_mean=0;
meanbkgd=0;
std=0;
bkgd_std=0;
stdbkgd=0;

m = nResults;
bkgd_mean = newArray(m);
run("Select None");
for (i=0; i<m; i++) {
	bkgd_mean[i] = getResult('Mean', i);
}

if(background_setting == "High") {
	Array.getStatistics(bkgd_mean, min, max, mean, stdDev);
	Array.sort(bkgd_mean);
	bkgd_high_trim = Array.trim(bkgd_mean, 15);
	Array.getStatistics(bkgd_high_trim, min, max, mean, stdDev);
	Array.reverse(bkgd_high_trim);
	bkgd_low_trim = Array.trim(bkgd_high_trim, 7);
	Array.getStatistics(bkgd_low_trim, min, max, mean, stdDev);
	meanbkgd = mean;

	std = nResults;
	bkgd_std = newArray(std);
	run("Select None");
	for (i=0; i<std; i++) {
		bkgd_std[i] = getResult('StdDev', i);
	}
	Array.getStatistics(bkgd_std, min, max, mean, stdDev);
	Array.sort(bkgd_std);
	bksd_high_trim = Array.trim(bkgd_std, 15);
	Array.getStatistics(bksd_high_trim, min, max, mean, stdDev);
	Array.reverse(bksd_high_trim);
	bksd_low_trim = Array.trim(bksd_high_trim, 7);
	Array.getStatistics(bksd_low_trim, min, max, mean, stdDev);
	stdbkgd = mean;
	
	lower = ((meanbkgd)+(stdbkgd*2));
}
if(background_setting == "Low") {
	Array.getStatistics(bkgd_mean, min, max, mean, stdDev);
	Array.sort(bkgd_mean);
	bkgd_high_trim = Array.trim(bkgd_mean, 5);
	Array.getStatistics(bkgd_high_trim, min, max, mean, stdDev);
	meanbkgd = mean;

	std = nResults;
	bkgd_std = newArray(std);
	run("Select None");
	for (i=0; i<std; i++) {
		bkgd_std[i] = getResult('StdDev', i);
	}
	Array.getStatistics(bkgd_std, min, max, mean, stdDev);
	Array.sort(bkgd_std);
	bksd_high_trim = Array.trim(bkgd_std, 5);
	Array.getStatistics(bksd_high_trim, min, max, mean, stdDev);
	stdbkgd = mean;

	lower = ((meanbkgd)+(stdbkgd*2));
}


// Setting Threshold
print("Mean="+meanbkgd+",_StdDev="+stdbkgd);
print("Lower_Threshold="+lower);
run("Set Measurements...", "area mean standard min center limit redirect=None decimal=3");  //setting measurements.
setAutoThreshold("Default dark");
run("Threshold...");
wait(200);
setThreshold(lower, 500000);
//run("Apply");
run("NaN Background");
run("Close");

run("_ps_Measuring_ROIs");
	
}

/////////////////////////////

macro "_ps_Measuring_ROIs" {
       // Measuring ROIs
           //Finding PNNs with Analyze Particles

roi_count = roiManager("count");
if (roi_count>0) {
	roiManager("Delete");
	}
if (import_type==2) {		
	selectWindow(name+".tif");
	}	
if (import_type==1) {
	selectWindow("SUM_"+name);
	}

roiManager("Show All");
roiManager("Show All with labels");

run("Clear Results");
wait(200);
setAutoThreshold("Default dark");
run("Threshold...");
wait(200);
setThreshold(lower*set_intensity_low_threshold, set_intensity_high_threshold);
wait(200);
makeRectangle(0, 0, 0, 0);
run("Analyze Particles...", "size=set_low_particlesize-set_high_particlesize circularity=set_low_circularity-set_high_circularity clear include add");
wait(200);
selectWindow("Threshold");
resetThreshold();
run("Close");
roiManager("Measure");

           //Making  ROIs from Particles

wait(500);
add_squares=roiManager("count"); 
if (add_squares==0) {

	roiManager("Show All");
	roiManager("Show All with labels");
	waitForUser("Oops", "We didn't see any staining here. Try adjusting the Cell Detection Threshold settings in the next step... ");
	setTool("point");
	makePoint(0, 0);
	selectWindow(full_name);
	} else {
		array=newArray(add_squares); 
		for(i=0; i<add_squares;i++) { 
			roiManager("Select", 0);
				if(ROI_type_SL == "As-detected") {
					wait(10);
				}
				if(ROI_type_SL == "Square") {
       				run("Specify...", "centered scaled");
       			}
 	      		if(ROI_type_SL == "Circle") {
    	   			run("Specify...", "oval constrain centered scaled");
				}	
       			
       			run("Enlarge...", "enlarge=set_roi_enlargement_single");
       			roiManager("Add");
       			roiManager("Select", 0);
       			roiManager("Delete");
			}
		} 

if (import_type==3) {
	wait(300);
} else {
	setTool("rectangle");
	if (import_type==2) {		
		selectWindow(name+"-1.tif");
		roiManager("Show All");
		roiManager("Show All with labels");
		}	
	if (import_type==1) {
		selectWindow(name);
		run("Z Project...", "projection=[Sum Slices]");
		selectWindow("SUM_"+name+"-1");
		roiManager("Show All");
		roiManager("Show All with labels");
		}

	set_intensity_low_threshold_old = set_intensity_low_threshold;
	set_low_particlesize_old = set_low_particlesize; 
	set_high_particlesize_old = set_high_particlesize;

	Dialog.create("Adjust Cell Detection Threshold");
	items = newArray("Yes, rerun cell detection with these new settings.", "No, let me add / delete ROIs by hand.", "I like the ROIs as they are. Lets move on.");
	Dialog.addSlider("Threshold of Detection:", (set_intensity_low_threshold*0.1), (set_intensity_low_threshold*2), set_intensity_low_threshold);
	Dialog.addSlider("Smallest Cell Detected:", 0.0, 10000, set_low_particlesize);
	Dialog.addSlider("Largest Cell Detected:", 0.0, 100000, set_high_particlesize);

	Dialog.addRadioButtonGroup("Would you like to change the cell detection threshold settings?", items, 3, 1, "No, let me add / delete ROIs by hand.");
	Dialog.show;
	set_intensity_low_threshold = Dialog.getNumber();
	set_low_particlesize = Dialog.getNumber();
	set_high_particlesize = Dialog.getNumber();
	choice = Dialog.getRadioButton;

	if (choice=="No, let me add / delete ROIs by hand." && set_intensity_low_threshold_old==set_intensity_low_threshold && set_low_particlesize_old==set_low_particlesize && set_high_particlesize_old==set_high_particlesize)  {
		
		if (import_type==2) {		
			waitForUser("How Does That Look?", "Here is your chance to adjust the ROIs to better identify PNNs. Try adding or deleting ROIs in the ROI manager. **THEN** press OK.");
			selectWindow(name+"-1.tif");
			close(name+"-1.tif");
			}

		if (import_type==1) {
			waitForUser("How Does That Look?", "Here is your chance to adjust the ROIs to better identify PNNs. Try adding or deleting ROIs in the ROI manager. **THEN** press OK.");
			selectWindow("SUM_"+name+"-1");
			close("SUM_"+name+"-1");
			}

		}
	
	if (choice=="Yes, rerun cell detection with these new settings." || set_intensity_low_threshold_old != set_intensity_low_threshold || set_low_particlesize_old != set_low_particlesize || set_high_particlesize_old != set_high_particlesize) {
		if (import_type==2) {		
			selectWindow(full_name);
			}

		if (import_type==1) {
			selectWindow("SUM_"+name+"-1");
			close("SUM_"+name+"-1");
			}

		run("_ps_Measuring_ROIs");
		}

	if (choice=="I like the ROIs as they are. Lets move on." && set_intensity_low_threshold_old==set_intensity_low_threshold && set_low_particlesize_old==set_low_particlesize && set_high_particlesize_old==set_high_particlesize)  {
		if (import_type==2) {		
			selectWindow(name+"-1.tif");
			close(name+"-1.tif");
			}
		if (import_type==1) {
			selectWindow("SUM_"+name+"-1");
			close("SUM_"+name+"-1");
			}
		}
	}

//saving ROI overlay

//name = getTitle; 
save_ROIs=roiManager("count");
array=newArray(save_ROIs);
for(i=0; i<save_ROIs; i++) {
	array[i] = i;
}
roiManager("Select", array);
roiManager("Save", dir+name+"_ROIoverlay.zip");

//Measuring ROIs

run("Clear Results");
roiManager("Show All");
roiManager("Show All with labels");
measure_squares=0;
measure_squares=roiManager("count"); 
array=newArray(measure_squares); 
for(i=0; i<measure_squares;i++) { 
	array[i] = i; 
}
roiManager("Select", array);
roiManager("Measure");

m=0;
means=0;
m = nResults;
means = newArray(m);
run("Select None");
for (i=0; i<m; i++) {
	means[i] = getResult('Mean', i);
}
run("Select None");
for (i=0; i<m; i++) {
	setResult("Background", i, lower);
	setResult("Mean-Background", i, means[i]-lower);
}
setResult("File Name", 0, name);
updateResults();

m2 = 0;
avgmeans = 0;
m2 = nResults;
avgmeans = newArray(m2);
run("Select None");
for (i=0; i<m2; i++) {
	avgmeans[i] = getResult('Mean-Background', i);
}
Array.getStatistics(avgmeans, min, max, mean, stdDev);
print("Mean_Intensity="+mean);

numberPNNs=0;
numberPNNs=getValue("results.count");
print("Number_of_PNNs(cells)="+numberPNNs);

n = nResults;
area = newArray(n);
XM = newArray(n);
YM = newArray(n);
mean = newArray(n);
stddev = newArray(n);
min = newArray(n);
max = newArray(n);
bkgd = newArray(n);
meanbkgd = newArray(n);
print("\n");
print("Individual cell data:");
print("Subject# Image_name Cell# Area XM YM Mean StdDev Min Max Background Mean-Background");
for (i=0; i<n; i++) {
	area[i] = getResult('Area', i);
	XM[i] = getResult('XM', i);
	YM[i] = getResult('YM', i);
	mean[i] = getResult('Mean', i);
	stddev[i] = getResult('StdDev', i);
	min[i] = getResult('Min', i);
	max[i] = getResult('Max', i);
	bkgd[i] = getResult('Background', i);
	meanbkgd[i] = getResult('Mean-Background', i);
	print(subject_id+" "+name+" "+(i+1)+" "+area[i]+" "+XM[i]+" "+YM[i]+" "+mean[i]+" "+stddev[i]+" "+min[i]+" "+max[i]+" "+bkgd[i]+" "+meanbkgd[i]);
}


run("_ps_Save");
}

/////////////////////////////

//macro "_ps_count_ROIs" {
       // count labeled cells by making ROIs
           //Finding cells with Analyze Particles

roiManager("reset");
run("Clear Results");

if(countCount==2 && countWinROI==1) {
	waitForUser("Please select your ROI file in the next screen.");
	ROI_Dir=File.openDialog("Please open ROI file"); 
}

if(countWinROI==1){
	roiManager("Open", ROI_Dir);
	ROI_Dir_path = File.directory;
	wait(50);

	roiManager("Show All");
	roiManager("Show All with labels");

	waitForUser("Adjust your ROI, ****THEN**** press OK.");
}

wait(100);
setAutoThreshold("Default dark");
run("Threshold...");
setThreshold(lower*set_count_low_threshold, set_count_high_threshold);
wait(200);
roiManager("Select", 0);
run("Analyze Particles...", "size=set_count_low_particlesize-set_count_high_particlesize circularity=set_count_low_circularity-set_count_high_circularity clear include add");
wait(200);
selectWindow("Threshold");
resetThreshold();

wait(500);

setTool("oval"); //selects the right tool. The next lines creates a dialogue to give the user ability to correct ROI selection.

set_count_low_threshold_old = set_count_low_threshold;
set_count_low_particlesize_old = set_count_low_particlesize; 
set_count_high_particlesize_old = set_count_high_particlesize;

Dialog.create("Adjust Cell Detection Threshold");
items = newArray("Yes, rerun cell detection with these new settings.", "No, let me add / delete ROIs by hand.", "I like the ROIs as they are. Lets move on to counting.");
	
Dialog.addSlider("Threshold of Detection:", (set_count_low_threshold*0.1), (set_count_low_threshold*2), set_count_low_threshold);
Dialog.addSlider("Smallest Cell Detected:", 0.0, 10000, set_count_low_particlesize);
Dialog.addSlider("Largest Cell Detected:", 0.0, 100000, set_count_high_particlesize);

Dialog.addRadioButtonGroup("Would you like to change the cell detection threshold settings?", items, 3, 1, "I like the ROIs as they are. Lets move on to counting.");
Dialog.show;
set_count_low_threshold = Dialog.getNumber();
set_count_low_particlesize = Dialog.getNumber();
set_count_high_particlesize = Dialog.getNumber();
choice = Dialog.getRadioButton;

if (choice=="No, let me add / delete ROIs by hand." && set_count_low_threshold_old==set_count_low_threshold && set_count_low_particlesize_old==set_count_low_particlesize && set_count_high_particlesize_old==set_count_high_particlesize)  {
	waitForUser("How Does That Look?", "Here is your chance to adjust the ROIs to better identify the cells. Try adding or deleting ROIs in the ROI manager. **THEN** press OK.");
}
	
if (choice=="Yes, rerun cell detection with these new settings." || set_count_low_threshold_old != set_count_low_threshold || set_count_low_particlesize_old != set_count_low_particlesize || set_count_high_particlesize_old != set_count_high_particlesize) {
				
	run("_ps_count_ROIs");
}

if (choice=="I like the ROIs as they are. Lets move on to double-labeled staining." && set_count_low_threshold_old==set_count_low_threshold && set_count_low_particlesize_old==set_count_low_particlesize && set_count_high_particlesize_old==set_count_high_particlesize) {
	wait(10);
}
	
roiManager("Measure");
numbercells=0;
numbercells=getValue("results.count");

if(countCount==2){
	print("\n");
	print("Results will be saved as: "+ROI_Dir_path+"All_Results.txt");
	print("\n");
	print("Subject# Image_name . Cells_counted");
}	
print(subject_id+" "+name+" Cells= "+ numbercells);

run("Clear Results");
roiManager("reset");
run("Close");
selectWindow(name+".tif");
run("Close");

Dialog.create("Analyze another image?");
items = newArray("Open another", "Return to main menu", "Exit");
Dialog.addRadioButtonGroup("Analysis done! \n Next Move:", items, 3, 1, "Open another");
Dialog.show;
choice = Dialog.getRadioButton;
if (choice=="Open another") {
	run("_ps_count");

} else {
	selectWindow("Log");
	saveAs("Text", dir+"/All_Results"+TimeString+".txt");

	if (choice=="Return to main menu") {	
		run("_ps_initi");
	} 
	if (choice=="Exit") {
		print("\n"+"Thanks for using PIPSQUEAK. Bye!");
		}
	}
}


/////////////////////////////

macro "_ps_Save" {

	// Saving data

	name_text = 0;
	name_text = name + ".txt"; 
	selectWindow("Log");
	saveAs("Measurements", dir+name_text); 
	saveAs("Text", dir+"/All_Results"+TimeString+".txt");
	wait(100);
	print("Results_saved_as:_"+dir+name);
	print("\n");
	wait(200);

	// Closing project


clear_ROIs=roiManager("count"); 
array=newArray(clear_ROIs); 
for(i=0; i<clear_ROIs;i++) { 
    array[i] = i;
}
roiManager("Select", array); 
roiManager("Delete");

selectWindow("ROI Manager");
wait(15);
run("Close");


if (import_type==3) {
	run("Clear Results");
	run("_ps_OpenNext_Single");
} else 
	if (import_type==1) {
		selectWindow(full_name);
		wait(15);
		run("Close");
		selectWindow(name);
		wait(15);
		run("Close");

		Dialog.create("Analyze another image?");
		items = newArray("Open another", "Exit to main menu");
		Dialog.addRadioButtonGroup("Analysis done! Results saves as .txt"+"\n"+"Next Move:", items, 2, 1, "Open another");
		Dialog.show;
		choice = Dialog.getRadioButton;
		if (choice=="Open another") {
			run("_ps_Import");

		} else {
			run("_ps_initi");
			print("\n"+"Thanks for using PIPSQUEAK. Bye!");
			}
		}

	if (import_type==2) {
		
		selectWindow(name+".tif");
		wait(15);
		run("Close");
		Dialog.create("Analyze another image?");
		items = newArray("Open Next Image", "Open another", "Exit to main menu");
		Dialog.addRadioButtonGroup("Analysis done! Results saves as .txt"+"\n"+"Next Move:", items, 3, 1, "Open Next Image");
		Dialog.show;
		choice = Dialog.getRadioButton;
		if (choice=="Open another") {
			run("_ps_Import_Summed");
			
		} else {
			if (choice=="Open Next Image") {
				run("_ps_Import_Summed");
				} else {
					run("_ps_initi");
					print("\n"+"Thanks for using PIPSQUEAK. Bye!");
				}
			}
		}
	}
}

/////////////////////////////////////////////////////
//#################################################//
//#################################################//
/////////////////////////////////////////////////////

	//Here be double label intensity analysis

macro "_ps_double" {


Dialog.create("Import Type"); // initial dialogue.
items = newArray("Semiautomated analysis of summed double-labeled images", "Automatic analysis of summed double-labeled images", "->Make summed double-labeled images from Z-stacks.");
Dialog.addRadioButtonGroup("What type of file do you want to import?", items, 3, 1, "Semiautomated analysis of summed double-labeled images");
Dialog.show;
choice = Dialog.getRadioButton;
if (choice=="Semiautomated analysis of summed double-labeled images") {
	import_type = 5;
	run("_ps_Open_Double");
	} else {		
	if (choice=="Automatic analysis of summed double-labeled images") {
		import_type = 6;		
		run("_ps_Open_Double");
		}
	if (choice=="->Make summed double-labeled images from Z-stacks.") {
		Dialog.create("Stacks folder");
		Dialog.addMessage("Before using this function, please place folders containing Z-stack images within an experiment folder. Then choose the experiment folder.");
		Dialog.addMessage("ie, /Desktop/EXPERIMENT FOLDER/Subject folder/image1... image20");
		Dialog.show(); 
		run("_ps_double_sum");
		}
	}
}

/////////////////////////////

macro "_ps_double_sum" { // summing images.


Ldest = getDirectory("Choose a Directory ");         //load from
Sdest = Ldest+"/summed/";           //save at
sfold = Sdest
stackfold = getFileList(Ldest);

if(!File.exists(sfold)) {
	File.makeDirectory(sfold);	
}

Dialog.create("Enter z-steps");
Dialog.addMessage("Please entere the number of z-steps (slices) in each image series");		
Dialog.addNumber("Number of z-steps:", zsteps);

Dialog.show;

zsteps = Dialog.getNumber();

for(i=0; i<stackfold.length; i++){
	stacks = stackfold[i];
	identify = substring(stacks, 0, lengthOf(stacks)-1);
	names = substring(stacks, lengthOf(stacks)-1);
	if(names == "/" && stacks!="summed/"){
		ISp = "open=[" + Ldest + stacks + "] number=zsteps starting=q increment=3 scale=100 file=[] or=[] sort";

		for(q=1; q<4; q++) { 
			run("Image Sequence...", ISp);
			if(q!=1){
				run("8-bit");
			}

			run("Z Project...", "start=1 stop=zsteps projection=[Sum Slices]");


			saveAs("Tiff", sfold + identify + "_" + q + ".tif");

			close();
			
			{
			while(nImages>0){
				selectImage(nImages);
				close();
				}
			}
		}
	}
}

run("_ps_double");
}

/////////////////////////////

macro "_ps_Open_Double" { // opening previously summed images

waitForUser("Select First Image", "Please open the first image of the subject series.");
open();

Dialog.create("Subject ID");
	Dialog.addMessage("Please enter the Subject ID name or number");
	Dialog.addString("Subject ID:", "eg:42B");
	Dialog.show
subject_id =  Dialog.getString();

run("Set Measurements...", "area mean standard min center limit redirect=None decimal=3");  //setting measurements.
run("Set Scale...", "distance=scale_size known=scale_known pixel=scale_pixel unit=&scale_unit global");
first_image = getTitle;
dir = getDirectory("image"); 
index = lastIndexOf(first_image, "."); 
if (index!=-1) first_image = substring(first_image, 0, index);
	print("\n"+first_image+" loaded.");

series_name = substring(first_image, 0, index-2);
first_open_double = series_name;

subDir = dir+"/"+series_name+"/";
File.makeDirectory(subDir);

if(endsWith(first_image,first_suffix)) {
	open(series_name+"_"+second_suffix+".tif");
	second_image = getTitle;
	if (index!=-1) second_image = substring(second_image, 0, index);
		print(second_image+" loaded.");
		run("_ps_Double_Background");
	} else {		
		waitForUser("Please update Settings to match numbering suffixes of double-label image series");
		run("_ps_initi");
	}

}

//////////////////////////////

macro "_ps_Double_Background" { //the meat of the pipsqueak. DL= double-label. SL= single-label.

//First Image
	// Selecting Background

first_image_tif = first_image+".tif";  //image names are consistently kept as "first_image_tif" and "second_image_tif" throughout the double-labeled macro(s) second_image_tif = second_image+".tif"; //even though the actual images are duplicated and named things like "first_image-2.tif"
name = series_name+" "+first_stain_type+".txt"; //saving the results table in a sub folder.

run("Clear Results"); //clearing the template

run("ROI Manager..."); //opening ROI manager
roiManager("reset"); // clearing ROI manager

roiManager("Show All"); //turning on show all labels
roiManager("Show All with labels");

selectWindow(first_image_tif); //grabbing first image

height = getHeight; //getting dimensions in order to place background ROIs around images of any size
width = getWidth;

makeRectangle((6*(width-40)/6), (0.1*(height-40)/5), 30, 30); //placing 20 ROIs around the perimeter of the image
roiManager("Add");
makeRectangle((6*(width-40)/6), (1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((6*(width-40)/6), (2*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((6*(width-40)/6), (3*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((6*(width-40)/6), (4*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((6*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((5*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((4*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((3*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((2*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((1*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (4*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (3*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (2*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((1*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((2*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((3*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((4*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((5*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");

run("Subtract Background...", "rolling=10"); //before measuring the image background for threshold, running rolling ball subtraction
roiManager("Select", newArray(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,21)); //measuring all the ROIs in order to run stats and select the right ones
roiManager("Measure");


	// Calculating lower threshold background value in "Mean" column

DL1_m=0;
DL1_bkgd_mean=0;
DL1_meanbkgd=0;
DL1_std=0;
DL1_bkgd_std=0;
DL1_stdbkgd=0;

DL1_m = nResults; //building an array for the ROI means.
DL1_bkgd_mean = newArray(DL1_m);
run("Select None");
for (i=0; i<DL1_m; i++) { //getting ROI mean one at a time from ROI manager and putting mean into array.
	DL1_bkgd_mean[i] = getResult('Mean', i);
}

if (background_setting == "High") {
	Array.getStatistics(DL1_bkgd_mean, min, max, mean, stdDev); //getting other stats from mean array
	Array.sort(DL1_bkgd_mean); //sorting array by mean values.
	DL1_bkgd_high_trim = Array.trim(DL1_bkgd_mean, 15); //removing highest 5 mean values and putting remaining into new array
	Array.getStatistics(DL1_bkgd_high_trim, min, max, mean, stdDev); // getting stats for new array
	Array.reverse(DL1_bkgd_high_trim); //reversing the order of the array values.
	DL1_bkgd_low_trim = Array.trim(DL1_bkgd_high_trim, 7); // removing the lowest 8 values (since the array order had been reversed).
	Array.getStatistics(DL1_bkgd_low_trim, min, max, mean, stdDev); // getting stats from this new new array
	DL1_meanbkgd = mean; //assigning mean value.

	DL1_std = nResults;  //counting the number of ROIs.
	DL1_bkgd_std = newArray(DL1_std); 
	run("Select None");
	for (i=0; i<DL1_std; i++) {  //for each ROI, get the standard deviation value (this will lead to a similar function as was used for means above.
		DL1_bkgd_std[i] = getResult('StdDev', i);
	}
	Array.getStatistics(DL1_bkgd_std, min, max, mean, stdDev);
	Array.sort(DL1_bkgd_std);
	DL1_bksd_high_trim = Array.trim(DL1_bkgd_std, 15);
	Array.getStatistics(DL1_bksd_high_trim, min, max, mean, stdDev);
	Array.reverse(DL1_bksd_high_trim);
	DL1_bksd_low_trim = Array.trim(DL1_bksd_high_trim, 7);
	Array.getStatistics(DL1_bksd_low_trim, min, max, mean, stdDev);
	DL1_stdbkgd = mean;
}

if (background_setting == "Low") {
	Array.getStatistics(DL1_bkgd_mean, min, max, mean, stdDev); //getting other stats from mean array
	Array.sort(DL1_bkgd_mean); //sorting array by mean values.
	DL1_bkgd_high_trim = Array.trim(DL1_bkgd_mean, 5); //removing highest 17 mean values and putting remaining into new array
	Array.getStatistics(DL1_bkgd_high_trim, min, max, mean, stdDev); // getting stats for new array
	DL1_meanbkgd = mean; //assigning mean value.

	DL1_std = nResults;  //counting the number of ROIs.
	DL1_bkgd_std = newArray(DL1_std); 
	run("Select None");
	for (i=0; i<DL1_std; i++) {  //for each ROI, get the standard deviation value (this will lead to a similar function as was used for means above.
		DL1_bkgd_std[i] = getResult('StdDev', i);
	}
	Array.getStatistics(DL1_bkgd_std, min, max, mean, stdDev);
	Array.sort(DL1_bkgd_std);
	DL1_bksd_high_trim = Array.trim(DL1_bkgd_std, 5);
	Array.getStatistics(DL1_bksd_high_trim, min, max, mean, stdDev);
	DL1_stdbkgd = mean;
}

DL1_lower = ((DL1_meanbkgd)+(DL1_stdbkgd*2));  //THIS IS THE VALUE THAT IS USED AS THE NaN VALUE FOR OUR INTENSITY MEASUREMENTS. 
print("\n");
print("Single "+first_stain_type+" label:");
print(first_stain_type+" Mean Background = "+DL1_meanbkgd+", Background StdDev = "+DL1_stdbkgd);
print(first_stain_type+" Lower Threshold= "+DL1_lower);

	// Setting Threshold

run("Set Measurements...", "area mean standard min center limit redirect=None decimal=3");  //setting measurements.
setAutoThreshold("Default dark");
run("Threshold..."); //Opening the threshold function
wait(200);
setThreshold(DL1_lower, 500000); //setting the NaN value.
//run("Apply");
run("NaN Background");
//run("Close");


selectWindow(first_image_tif); //duplicating the original image. Here it is important that the original is kept 
run("Duplicate...", " "); //alive because it is the only version that can be reverted for the user to view. (alternatively, just reopen fresh image).
//run("8-bit");
run("Duplicate...", " ");
//run("8-bit");
selectWindow(first_image+"-1.tif");

run("_ps_Double_Background_SL1_ROIs");
}	
////////////////////////////////

macro "_ps_Double_Background_SL1_ROIs" { // this section is macro-ed because we want to be able to return to this point. Good place to use function.

       // Measuring ROIs
           //Finding PNNs with Analyze Particles

roiManager("reset");

first_image_tif = first_image+".tif";
second_image_tif = second_image+".tif";

selectWindow(first_image+"-1.tif");
roiManager("Show All");
roiManager("Show All with labels");

run("Clear Results"); 
wait(200);
setAutoThreshold("Default dark"); //setting the threshold in the next couple lines for the analyze particle function (identifying cells).
run("Threshold...");
wait(200);
setThreshold(DL1_lower*DL1_set_intensity_low_threshold, DL1_set_intensity_high_threshold);
wait(200);
makeRectangle(0, 0, 0, 0); //clear temp ROIs
run("Analyze Particles...", "size=DL1_set_low_particlesize-DL1_set_high_particlesize circularity=DL1_set_low_circularity-DL1_set_high_circularity clear include add");
wait(200); //THE PREVIOUS LINE IS IMPORTANT. set limits for ROI construction based on pixels above threshold and added them to the ROI manager.
selectWindow("Threshold");
resetThreshold();
run("Close");
roiManager("Measure");

           //Making ROIs from Particles

wait(500);
DL1_add_circles=roiManager("count"); // here to stop the user if no ROIs were detected

if (DL1_add_circles>0) { //if yes ROIs, change the size of the ROIs based on global enlargement variable.

	array=newArray(DL1_add_circles); 
	for(i=0; i<DL1_add_circles;i++) { 
		roiManager("Select", 0);
			if(ROI_type_DL1 == "As-detected") {
				wait(10);
			}
			if(ROI_type_DL1 == "Square") {
   				run("Specify...", "centered scaled");
    		}
       		if(ROI_type_DL1 == "Circle") {
       			run("Specify...", "oval constrain centered scaled");
       		}
       		run("Enlarge...", "enlarge=DL1_set_roi_enlargement");
       		roiManager("Add");
       		roiManager("Select", 0);
       		roiManager("Delete");
	} 
	
} else {

	waitForUser("Oops", "We didn't see any staining here. Try adding some ROIs in the next step, or changing the image detection settings.");
	if (import_type==6) {
		run("_ps_Open_Double");
	} else {
		//nothing
	}
}

if (import_type==6) { //when in automatic analysis, will skip the user check step below.
	wait(300);
} else {
	setTool("oval"); //selects the right tool. The next lines creates a dialogue to give the user ability to correct ROI selection.
	selectWindow(first_image_tif);
	run("Revert"); //reverts the background correction
	roiManager("Show All");
	roiManager("Show All with labels");
	
	SL1_set_intensity_low_threshold_old = DL1_set_intensity_low_threshold;
	SL1_set_low_particlesize_old = DL1_set_low_particlesize; 
	SL1_set_high_particlesize_old = DL1_set_high_particlesize;

	Dialog.create("Adjust Cell Detection Threshold");
	items = newArray("Yes, rerun cell detection with these new settings.", "No, let me add / delete ROIs by hand.", "I like the ROIs as they are. Lets move on to the next stain.");
	Dialog.addSlider("Threshold of Detection:", (DL1_set_intensity_low_threshold*0.1), (DL1_set_intensity_low_threshold*2), DL1_set_intensity_low_threshold);
	Dialog.addSlider("Smallest Cell Detected:", 0.0, 10000, DL1_set_low_particlesize);
	Dialog.addSlider("Largest Cell Detected:", 0.0, 100000, DL1_set_high_particlesize);

	Dialog.addRadioButtonGroup("Would you like to change the "+first_stain_type+" cell detection threshold settings?", items, 3, 1, "No, let me add / delete ROIs by hand.");
	Dialog.show;
	DL1_set_intensity_low_threshold = Dialog.getNumber();
	DL1_set_low_particlesize = Dialog.getNumber();
	DL1_set_high_particlesize = Dialog.getNumber();
	choice = Dialog.getRadioButton;

	if (choice=="No, let me add / delete ROIs by hand." && SL1_set_intensity_low_threshold_old==DL1_set_intensity_low_threshold && SL1_set_low_particlesize_old==DL1_set_low_particlesize && SL1_set_high_particlesize_old==DL1_set_high_particlesize)  {
		waitForUser("How Does That Look?", "Here is your chance to adjust the ROIs to better identify PNNs. Try adding or deleting ROIs in the ROI manager. **THEN** press OK.");
		}

	if (choice=="Yes, rerun cell detection with these new settings." || SL1_set_intensity_low_threshold_old != DL1_set_intensity_low_threshold || SL1_set_low_particlesize_old != DL1_set_low_particlesize || SL1_set_high_particlesize_old != DL1_set_high_particlesize) {

		run("_ps_Double_Background_SL1_ROIs");
		}

	if (choice=="I like the ROIs as they are. Lets move on to the next stain." && SL1_set_intensity_low_threshold_old==DL1_set_intensity_low_threshold && SL1_set_low_particlesize_old==DL1_set_low_particlesize && SL1_set_high_particlesize_old==DL1_set_high_particlesize)  {
		wait(10);
		}
	}

selectWindow(first_image_tif); //closing the reverted image
run("Close");

//saving SL1 ROI overlay


SL1_save_ROIs=roiManager("count"); //select all the ROIs in order to save the SL set.
array=newArray(SL1_save_ROIs);
for(i=0; i<SL1_save_ROIs; i++) {
	array[i] = i;
	}
roiManager("Select", array);
roiManager("Save", subDir+first_image+first_stain_type+"_single-label_ROIoverlay.zip");
	

//Measuring ROIs

selectWindow(first_image+"-1.tif"); 
run("Clear Results");
DL1_measure_circles=0;  //Selects all the ROIs and uses measure function to populate the results window.
DL1_measure_circles=roiManager("count"); 
array=newArray(DL1_measure_circles); 
for(i=0; i<DL1_measure_circles;i++) { 
        array[i] = i; 
}
roiManager("Select", array);
roiManager("Measure");
roiManager("Show All");
roiManager("Show All with labels");

DL1_m = nResults; //defining array variable.
DL1_means = newArray(DL1_m); //building array
roiManager("deselect");
for (i=0; i<DL1_m; i++) { //populating array with mean from each result
      DL1_means[i] = getResult('Mean', i);
    }
roiManager("deselect");
for (i=0; i<DL1_m; i++) { //adding columns for "background" and "mean-background" to the results table.
	setResult("Background", i, DL1_lower);
	setResult("Mean-Background", i, DL1_means[i]-DL1_lower);
}
setResult("File Name", 0, name); //column for file name.
updateResults(); //updating the results window to reflect these changes.

DL1_m2 = nResults; //making a average of the ROI means in order to print a "mean" value for this image.
DL1_avgmeans = newArray(DL1_m2);
run("Select None");
for (i=0; i<DL1_m2; i++) {
	DL1_avgmeans[i] = getResult('Mean-Background', i);
}
Array.getStatistics(DL1_avgmeans, min, max, mean, stdDev);
print(first_stain_type+" Mean Intensity = "+mean);

DL1_numberStains = 0; //printing the number of cells counted.
DL1_numberStains=getValue("results.count");
print("Number of "+first_stain_type+" Stained Cells = "+DL1_numberStains);

n_sl1 = nResults; //THIS IS AN IMPORTANT FUNCTION FOR THE RESULTS. SAME RESULTS AS IN TABLE, BUT PRINTED IN LOG, WHICH IS BETTER FOR IMPORTING INTO EXCEL.
area_sl1 = newArray(n_sl1);
XM_sl1 = newArray(n_sl1);
YM_sl1 = newArray(n_sl1);
mean_sl1 = newArray(n_sl1);
stddev_sl1 = newArray(n_sl1);
min_sl1 = newArray(n_sl1);
max_sl1 = newArray(n_sl1);
bkgd_sl1 = newArray(n_sl1);
meanbkgd_sl1 = newArray(n_sl1);
print("\n");
print("Individual "+first_stain_type+" cell data:");
print("Subject# Image_name Stain_type Cell# Area XM YM Mean StdDev Min Max Background Mean-Background");
for (i=0; i<n_sl1; i++) { // for each line in the results table, populate an array with these parameters and print them in the log. 
	area_sl1[i] = getResult('Area', i);
	XM_sl1[i] = getResult('XM', i);
	YM_sl1[i] = getResult('YM', i);
	mean_sl1[i] = getResult('Mean', i);
	stddev_sl1[i] = getResult('StdDev', i);
	min_sl1[i] = getResult('Min', i);
	max_sl1[i] = getResult('Max', i);
	bkgd_sl1[i] = getResult('Background', i);
	meanbkgd_sl1[i] = getResult('Mean-Background', i);
	print(subject_id+" "+first_image+" "+first_stain_type+" "+(i+1)+" "+area_sl1[i]+" "+XM_sl1[i]+" "+YM_sl1[i]+" "+mean_sl1[i]+" "+stddev_sl1[i]+" "+min_sl1[i]+" "+max_sl1[i]+" "+bkgd_sl1[i]+" "+meanbkgd_sl1[i]);
}

roiManager("reset");
selectWindow("ROI Manager");
run("Close");

wait(100);

selectWindow(first_image+"-1.tif");
run("Close");

	// Saving data


saveAs("Measurements", subDir+name);
print("\n"); 
print("Results saved as: "+subDir+name);


//\\//\\//\\//\\//\\//\\

//Second Image //THIS SECTION DOES THE SAME SINGLE-LABEL ROI WORK AS ABOVE, BUT FOR THE SECOND STAIN.	
	// Selecting Background

run("Clear Results");

roiManager("reset");

roiManager("Show All");
roiManager("Show All with labels");

selectWindow(second_image_tif);

height = getHeight;
width = getWidth;

makeRectangle((6*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((6*(width-40)/6), (1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((6*(width-40)/6), (2*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((6*(width-40)/6), (3*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((6*(width-40)/6), (4*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((6*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((5*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((4*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((3*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((2*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((1*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (4*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (3*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (2*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((1*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((2*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((3*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((4*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((5*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");

run("Subtract Background...", "rolling=10");
roiManager("Select", newArray(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,21));
roiManager("Measure");


	// Calculating lower threshold background value in "Mean" column

DL2_m=0;
DL2_bkgd_mean=0;
DL2_meanbkgd=0;
DL2_std=0;
DL2_bkgd_std=0;
DL2_stdbkgd=0;

DL2_m = nResults;
DL2_bkgd_mean = newArray(DL2_m);
run("Select None");
for (i=0; i<DL2_m; i++) {
	DL2_bkgd_mean[i] = getResult('Mean', i);
}
if (background_setting == "High") {
	Array.getStatistics(DL2_bkgd_mean, min, max, mean, stdDev);
	Array.sort(DL2_bkgd_mean);
	DL2_bkgd_high_trim = Array.trim(DL2_bkgd_mean, 15);
	Array.getStatistics(DL2_bkgd_high_trim, min, max, mean, stdDev);
	Array.reverse(DL2_bkgd_high_trim);
	DL2_bkgd_low_trim = Array.trim(DL2_bkgd_high_trim, 7);
	Array.getStatistics(DL2_bkgd_low_trim, min, max, mean, stdDev);
	DL2_meanbkgd = mean;

	DL2_std = nResults;
	DL2_bkgd_std = newArray(DL2_std);
	run("Select None");
	for (i=0; i<DL2_std; i++) {
		DL2_bkgd_std[i] = getResult('StdDev', i);
	}
	Array.getStatistics(DL2_bkgd_std, min, max, mean, stdDev);
	Array.sort(DL2_bkgd_std);
	DL2_bksd_high_trim = Array.trim(DL2_bkgd_std, 15);
	Array.getStatistics(DL2_bksd_high_trim, min, max, mean, stdDev);
	Array.reverse(DL2_bksd_high_trim);
	DL2_bksd_low_trim = Array.trim(DL2_bksd_high_trim, 7);
	Array.getStatistics(DL2_bksd_low_trim, min, max, mean, stdDev);
	DL2_stdbkgd = mean;
}

if (background_setting == "Low") {
	Array.getStatistics(DL2_bkgd_mean, min, max, mean, stdDev);
	Array.sort(DL2_bkgd_mean);
	DL2_bkgd_high_trim = Array.trim(DL2_bkgd_mean, 5);
	Array.getStatistics(DL2_bkgd_high_trim, min, max, mean, stdDev);
	DL2_meanbkgd = mean;

	DL2_std = nResults;
	DL2_bkgd_std = newArray(DL2_std);
	run("Select None");
	for (i=0; i<DL2_std; i++) {
		DL2_bkgd_std[i] = getResult('StdDev', i);
	}
	Array.getStatistics(DL2_bkgd_std, min, max, mean, stdDev);
	Array.sort(DL2_bkgd_std);
	DL2_bksd_high_trim = Array.trim(DL2_bkgd_std, 5);
	Array.getStatistics(DL2_bksd_high_trim, min, max, mean, stdDev);
	DL2_stdbkgd = mean;
}

DL2_lower = ((DL2_meanbkgd)+(DL2_stdbkgd*2));
print("\n");
print("Single "+second_stain_type+" label:");
print(second_stain_type+" Mean Background = "+DL2_meanbkgd+", Background StdDev = "+DL2_stdbkgd);
print(second_stain_type+" Lower Threshold= "+DL2_lower);

	// Setting Threshold

run("Set Measurements...", "area mean standard min center limit redirect=None decimal=3");  //setting measurements.
setAutoThreshold("Default dark");
run("Threshold...");
wait(200);
setThreshold(DL2_lower, 500000);
//run("Apply");
run("NaN Background");

selectWindow(second_image_tif);
run("Duplicate...", " ");
//run("8-bit");
run("Duplicate...", " ");
//run("8-bit");
selectWindow(second_image+"-1.tif");

run("_ps_Double_Background_SL2_ROIs");

}

/////////////////////////////

macro "_ps_Double_Background_SL2_ROIs" {


       // Measuring ROIs
           //Finding PNNs with Analyze Particles

second_image_tif = second_image+".tif";
name = series_name+" "+second_stain_type+".txt"; 

roiManager("reset");

selectWindow(second_image+"-1.tif");
roiManager("Show All");
roiManager("Show All with labels");

run("Clear Results");
wait(200);
setAutoThreshold("Default dark");
run("Threshold...");
wait(200);
setThreshold(DL2_lower*DL2_set_intensity_low_threshold, DL2_set_intensity_high_threshold);
wait(200);
makeRectangle(0, 0, 0, 0);
run("Analyze Particles...", "size=DL2_set_low_particlesize-DL2_set_high_particlesize circularity=DL2_set_low_circularity-DL2_set_high_circularity clear include add");
wait(200);
selectWindow("Threshold");
resetThreshold();
run("Close");
roiManager("Measure");

           //Making ROIs from Particles

wait(500);
DL2_add_squares=roiManager("count"); 
if (DL2_add_squares>0) {

	array=newArray(DL2_add_squares); 
	for(i=0; i<DL2_add_squares;i++) { 
		roiManager("Select", 0);
		if(ROI_type_DL2 == "As-detected") {
			wait(10);
		}
		if(ROI_type_DL2 == "Square") {
			run("Specify...", "centered scaled");
		}
		if(ROI_type_DL2 == "Circle") {
			run("Specify...", "oval constrain centered scaled");
		}
		
		run("Enlarge...", "enlarge=DL2_set_roi_enlargement");
		roiManager("Add");
		roiManager("Select", 0);
		roiManager("Delete");
	} 
	
} else {

	waitForUser("Oops", "We didn't see any staining here. Try adding some ROIs in the next step, or changing the image detection settings.");
	if (import_type==6) {
		run("_ps_Open_Double");
	} else {
		//nothing
	}
}

if (import_type==6) {
	wait(300);
} else {

	setTool("rectangle");
	selectWindow(second_image_tif);
	run("Revert");
	roiManager("Show All");
	roiManager("Show All with labels");

	SL2_set_intensity_low_threshold_old = DL2_set_intensity_low_threshold;
	SL2_set_low_particlesize_old = DL2_set_low_particlesize; 
	SL2_set_high_particlesize_old = DL2_set_high_particlesize;

	Dialog.create("Adjust Cell Detection Threshold");
	items = newArray("Yes, rerun cell detection with these new settings.", "No, let me add / delete ROIs by hand.", "I like the ROIs as they are. Lets move on to double-labeled staining.");
	
	Dialog.addSlider("Threshold of Detection:", (DL2_set_intensity_low_threshold*0.1), (DL2_set_intensity_low_threshold*2), DL2_set_intensity_low_threshold);
	Dialog.addSlider("Smallest Cell Detected:", 0.0, 10000, DL2_set_low_particlesize);
	Dialog.addSlider("Largest Cell Detected:", 0.0, 100000, DL2_set_high_particlesize);

	Dialog.addRadioButtonGroup("Would you like to change the "+second_stain_type+" cell detection threshold settings?", items, 3, 1, "No, let me add / delete ROIs by hand.");
	Dialog.show;
	DL2_set_intensity_low_threshold = Dialog.getNumber();
	DL2_set_low_particlesize = Dialog.getNumber();
	DL2_set_high_particlesize = Dialog.getNumber();
	choice = Dialog.getRadioButton;

	if (choice=="No, let me add / delete ROIs by hand." && SL2_set_intensity_low_threshold_old==DL2_set_intensity_low_threshold && SL2_set_low_particlesize_old==DL2_set_low_particlesize && SL2_set_high_particlesize_old==DL2_set_high_particlesize)  {
		waitForUser("How Does That Look?", "Here is your chance to adjust the ROIs to better identify PNNs. Try adding or deleting ROIs in the ROI manager. **THEN** press OK.");
		}
	
	if (choice=="Yes, rerun cell detection with these new settings." || SL2_set_intensity_low_threshold_old != DL2_set_intensity_low_threshold || SL2_set_low_particlesize_old != DL2_set_low_particlesize || SL2_set_high_particlesize_old != DL2_set_high_particlesize) {
				
		run("_ps_Double_Background_SL2_ROIs");
		}

	if (choice=="I like the ROIs as they are. Lets move on to double-labeled staining." && SL2_set_intensity_low_threshold_old==DL2_set_intensity_low_threshold && SL2_set_low_particlesize_old==DL2_set_low_particlesize && SL2_set_high_particlesize_old==DL2_set_high_particlesize) {
		wait(10);
		}
	}


selectWindow(second_image_tif);
run("Close");
	

//saving SL2 ROI overlay

SL2_save_ROIs=roiManager("count");
array=newArray(SL2_save_ROIs);
for(i=0; i<SL2_save_ROIs; i++) {
	array[i] = i;
	}
roiManager("Select", array);
roiManager("Save", subDir+second_image+second_stain_type+"_single-label_ROIoverlay.zip");
	

//Measuring ROIs	

selectWindow(second_image+"-1.tif");
run("Clear Results");
DL2_measure_squares=0;
DL2_measure_squares =roiManager("count"); 
array=newArray(DL2_measure_squares); 
for(i=0; i<DL2_measure_squares;i++) { 
        array[i] = i; 
	}
roiManager("Select", array);
roiManager("Measure");
roiManager("Show All");
roiManager("Show All with labels");

DL2_m=0;
DL2_means=0;
DL2_m = nResults;
DL2_means = newArray(DL2_m);
run("Select None");
    for (i=0; i<DL2_m; i++) {
      DL2_means[i] = getResult('Mean', i);
    }
run("Select None");
    for (i=0; i<DL2_m; i++) {
      setResult("Background", i, DL2_lower);
      setResult("Mean-Background", i, DL2_means[i]-DL2_lower);
    }
setResult("File Name", 0, name);
updateResults();

DL2_m2 = 0;
DL2_avgmeans = 0;
DL2_m2 = nResults;
DL2_avgmeans = newArray(DL2_m2);
run("Select None");
    for (i=0; i<DL2_m2; i++) {
     DL2_avgmeans[i] = getResult('Mean-Background', i);
    }
Array.getStatistics(DL2_avgmeans, min, max, mean, stdDev);
print(second_stain_type+" Mean Intensity = "+mean);


DL2_numberStains=0;
DL2_numberStains=getValue("results.count");
print("Number of "+second_stain_type+" Stained Cells = "+DL2_numberStains);

n_sl2 = nResults;
area_sl2 = newArray(n_sl2);
XM_sl2 = newArray(n_sl2);
YM_sl2 = newArray(n_sl2);
mean_sl2 = newArray(n_sl2);
stddev_sl2 = newArray(n_sl2);
min_sl2 = newArray(n_sl2);
max_sl2 = newArray(n_sl2);
bkgd_sl2 = newArray(n_sl2);
meanbkgd_sl2 = newArray(n_sl2);
print("\n");
print("Individual "+second_stain_type+" cell data:");
print("Subject# Image_name Stain_type Cell# Area XM YM Mean StdDev Min Max Background Mean-Background");
for (i=0; i<n_sl2; i++) {
	area_sl2[i] = getResult('Area', i);
	XM_sl2[i] = getResult('XM', i);
	YM_sl2[i] = getResult('YM', i);
	mean_sl2[i] = getResult('Mean', i);
	stddev_sl2[i] = getResult('StdDev', i);
	min_sl2[i] = getResult('Min', i);
	max_sl2[i] = getResult('Max', i);
	bkgd_sl2[i] = getResult('Background', i);
	meanbkgd_sl2[i] = getResult('Mean-Background', i);
	print(subject_id+" "+second_image+" "+second_stain_type+" "+(i+1)+" "+area_sl2[i]+" "+XM_sl2[i]+" "+YM_sl2[i]+" "+mean_sl2[i]+" "+stddev_sl2[i]+" "+min_sl2[i]+" "+max_sl2[i]+" "+bkgd_sl2[i]+" "+meanbkgd_sl2[i]);
	}

selectWindow(second_image+"-1.tif");
run("Close");

	// Saving data


saveAs("Measurements", subDir+name);
print("\n"); 
print("Results saved as: "+subDir+name);

run("_ps_Double_Background_DL1and2_ROIs");

}

//\\//\\//\\//\\//\\//\\//\\

macro "_ps_Double_Background_DL1and2_ROIs" {  //this is the macro that identifies double-labeled cells.

	//Double labeled cell identification

first_image_tif = first_image+"-2.tif";
second_image_tif = second_image+"-2.tif";

//////////////////////////////////////////NEW DOUBLE ROI STUFF HERE DOWN///////////////////////////////////////////////////

selectWindow(second_image_tif); //select the WFA image

roiManager("deselect");
roiManager("reset");
wait(50);

roiManager("Open", subDir+first_image+first_stain_type+"_single-label_ROIoverlay.zip");
double_label_first_type = roiManager("count"); // determines the number of circle ROIs
double_label_first_type_array = Array.getSequence(double_label_first_type); 

roiManager("Open", subDir+second_image+second_stain_type+"_single-label_ROIoverlay.zip");
double_label_all_types = roiManager("count");
double_label_all_types_array = Array.getSequence(double_label_all_types); 
double_label_second_type = double_label_all_types-double_label_first_type; // determines the number of square ROIs
double_label_second_type_start = double_label_first_type; // the ROI manager position of the first Square

roiManager("Show All");
roiManager("Show All with labels");

/* 
 In this section we are identifying the squares that have overlapping cicrles. 
 */
n=0; // number of time the square loop was run.
no_overlap=0; //number of squares without circle overlap.
multi_b_removed=0; //number of circles removed because resolved multiple overlap;
removed_circles_array = newArray(n);
saved_squares_array = newArray(n);
saved_circles_array1 = newArray(n);

for(a=double_label_second_type_start; a<(double_label_all_types-no_overlap-multi_b_removed); a++){  // squares.
	roiManager("Select", a);
	a2=a+1; //a2 is a counting variable to correct for the ROI manager starting at ROI #0.
	bTotal=0; // number of circles checked against square "a2".
	no_co=0; // yes/no variable determining whether overlap was found. 
	co_roi_array = newArray(no_co); // this array will be concatinated with the "b" value for each overlapping circle so the ROIs can be selected.
	co_roi_array_UI = newArray(no_co); // this array will be concatinated with the "b2" value for each overlapping circle so the user can see multiple overlaps.
	CoM_distance_array = newArray(no_co); // making an array of the distances between the square CoM and the circle's CoM.
	for(b=0; b<(double_label_first_type-multi_b_removed); b++){ //circles.
		b2=b+1; //b2 is a counting variable to correct for the ROI manager starting at ROI #0.
		roiManager("Select", newArray(a, b)); //select the square then the circle
		roiManager("AND"); //build an overlapping ROI
		roiType = selectionType();
		bTotal=bTotal+1;
		if(roiType==-1){ // and there is not overlapping area under the square and circle ROIs
			//print("No overlap: circle "+b2); //then, move on to the next (ie, restart the loop).
			//waitForUser("circle rejected "+b2);
			//wait(10);
			}
		if(roiType!=-1){ // if there is overlap from the square and the circle,
			c = roiManager("Add"); //add the overlap to the ROI manager so that we can measure its size.
			no_co = no_co+1; // to remember that, yes, there is overlap.
				
			roiManager("deselect");
			run("Clear Results");
			roiManager("Select", b); //measure the size of the circle
			roiManager("Measure");
			co_roi = (roiManager("count")-1); //find the overlap ROI (it's going to be the last one on the ROI manager list).
			roiManager("Select", co_roi); // select it.
			roiManager("Measure"); //measure the area.
			roiManager("Select", co_roi);
			roiManager("Delete"); //now removing it beause we only wanted that new ROI to measure the area of overlap.
			b2_area=getResult("Area", 0); // grab the circle-ROI's area from the results 
			c_area=getResult("Area", 1); // grab the overlap-ROI's area from the results
			b2_area_80 = b2_area*DL1_overlap;//0.8; // calculate 80% of the circle area (we're using 80% as an arbitrary threshold of minimum circle that should be within the square). 
			//print("overlap = "+c_area);
			//print("square area = "+b2_area);
			//print("80% threshold = "+b2_area_80);
			//print("comparison is "+b2_area_80+":"+c_area);
			if(b2_area_80>c_area){ // if the overlapping area is less 80% of the circle,
				no_co = no_co-1; //then, forget that an overlap was identified and move on (restart the loop).
				//waitForUser("circle "+b2+" identified but <80% overlap");
			} else {
				co_roi_array = Array.concat(co_roi_array, b); //building an array to remember which circle ROIs overlap (for ROI selection)
				co_roi_array_UI = Array.concat(co_roi_array_UI, b2); //building an array to remember which circle ROIs overlap (for the human to understand)
				saved_squares_array = Array.concat(saved_squares_array, a);

				//print("co roi's:");
				//Array.print(co_roi_array_UI);
				//waitForUser("circle "+b2+" identified and >80% overlap");
				}
			}
		if(b2==(double_label_first_type-multi_b_removed) && (no_co==0)){ //if we have checked every circle and still not found a circle that overlap (>80%) with the square,
     		roiManager("deselect");
     		roiManager("Select", a2-1); //move back one spot and select that square,
			roiManager("Delete"); // then delete it because we know that it was a non-overlapping square and should no longer be considered for colabeling.
			no_overlap=no_overlap+1; // add one to the number of square not overlapping so that we can lower the total squares.
			a=a-1; //remove one square from the counted number of squares. Semi repetitive with last variable, but each serve a function in the top IF loop.
			//waitForUser("Square "+a2+" is a loner");
     		}
     	if(b2==(double_label_first_type-multi_b_removed) && (no_co==1)){ // if we have checked all circles and there was an overlap detected
     		//print("Total circles checked: "+bTotal); //then, print # circles checked. (next step is to exit loop because a!<(double_label_all_types-no_overlap)).
			//print("loop = "+n+1);
			//print("\n");
			//waitForUser("Square "+a2+" has a friend");
			//wait(10);
     		}
     	
     	if(b2==(double_label_first_type-multi_b_removed) && (no_co>1)){ // if checked all circles and there were multiple overlaps, 
     		//i think there needs to be a check here to see if any of the circles have been previously identified. Something like:
     		//
      		//for(i=0; i<saved_circles_array1.length; i++){ //
     			//for(j=0; j<co_roi_array.length; j++){ 
     				//if(saved_circles_array1[i]==co_roi_array[j]{
     					//then check the circles -> if the previous square is the better overlap
							//then no_co-1, and the no_co>1 is resolved
     					//else it resolves below with better CoM analysis. 
     						//if the other circle wins, 
     							//then saved_circles_array[i] should be retained in co-labeling with the previous square.
     						//else it is deleted
     				//else no problem, check next			
     		//}
     		
     		
     		
     		selectWindow(second_image_tif); //select the WFA image
     		run("Clear Results");
     		roiManager("deselect");
     		roiManager("Select", a); //select the square ROI
     		roiManager("Measure"); //measure the square ROI so we can...
     		CoM_x_square=getResult("XM", 0); // grab the squares's center of mass x-coordinate from the results. 
     		CoM_y_square=getResult("YM", 0); // grab the squares's center of mass y-coordinate from the results.
     		
    		CoM_tot_square = CoM_x_square+CoM_y_square; //making the total value (number only used for comparison to circles).
     		
     		run("Clear Results"); 
     		selectWindow(first_image_tif); //select the PV image
     		roiManager("deselect");
     		roiManager("Select", co_roi_array); //select the overlapping ROIs
     		roiManager("Measure"); 
      		for(i=0; i<no_co; i++){ //doing this as a FOR loop to allow for any range of overlapping ROIs
     			CoM_x=getResult("XM", i); // grab the circle's center of mass x-coordinate from the results.
   				CoM_y=getResult("YM", i); // grab the first circle's center of mass y-coordinate from the results.
   				CoM_tot=CoM_x+CoM_y; // the circle's total value
	   			distance=abs(CoM_tot_square-CoM_tot); //measuring the distande between the center of the square ROI's brightest area and the circle ROI
				CoM_distance_array = Array.concat(CoM_distance_array, distance); // building an array of the distances between the square CoM and the circle's CoM.
				//waitForUser("Square "+a2+" Center of mass:\n X="+CoM_x_square+"\n Y="+CoM_y_square+"\n Total CoM="+CoM_tot_square+"\n Circle CoM:\n X="+CoM_x+"\n Y="+CoM_y+"\n Total CoM="+CoM_tot);
     			}
     		rankPosition_CoM = Array.rankPositions(CoM_distance_array); //determining the shortest distance
     		rank_CoM = Array.rankPositions(rankPosition_CoM);  		
     		rankPositionWinner=Array.slice(rankPosition_CoM, 0, 1);
     		
     		/*
     		print("Rank Array:");
     		Array.print(rankPosition_CoM);
     		print("Winner:");
     		Array.print(rankPositionWinner);
     		waitForUser("rank array printed");
			*/
			
     		co_roi_array_winner_position = rankPositionWinner[0];
     		co_roi_array_low = Array.slice(co_roi_array, 0, co_roi_array_winner_position); //saving multiple ROIs before the shortest distance ROI
     		co_roi_array_high = Array.slice(co_roi_array, co_roi_array_winner_position+1, no_co); // saving multiple ROIs after the shortest distance ROI
     		co_roi_array_losers = Array.concat(co_roi_array_low, co_roi_array_high); 

     		co_roi_array_winner_low = Array.slice(co_roi_array, 0, co_roi_array_winner_position+1); //saving multiple ROIs before the shortest distance ROI
     		co_roi_array_winner = Array.slice(co_roi_array, co_roi_array_winner_position, no_co-1); // saving multiple ROIs after the shortest distance ROI
			removed_circles_array = Array.concat(removed_circles_array, co_roi_array_losers);
			roiManager("deselect");
			roiManager("Select", co_roi_array_losers); //deleting the remaining multiple ROI (except for the closest to the square ROI)
			roiManager("Delete");  

			a=a-(no_co-1); //since square ROIs are numbered after circles, we have to revise this number to keep the counting correct in the top IF loop.	
   			multi_b_removed=multi_b_removed+(no_co-1); //since square ROIs are numbered after circles, we have to revise this number to keep the counting correct in the top IF loop.
			for(i=0; i<saved_squares_array.length; i++){
 				saved_squares_array[i]=saved_squares_array[i]-(no_co-1);
				}
			//waitForUser("Square "+a2+" is in a group situation, but it's cool");
     		}
     	if (no_overlap == double_label_second_type) {
			selectWindow(first_image_tif);
			run("Close");

			selectWindow(second_image_tif);
			run("Close");
		
			Dialog.create("No Co-labeling");
			items = newArray("Open new image for analysis.", "Return to main menu.");
			Dialog.addMessage("There appear to be no co-labeled cells in this image.");
			Dialog.addMessage(" ");	
			Dialog.addMessage("If you think this is an error, consider selecting this image again and");
			Dialog.addMessage("re-running this analysis with altered settings and ROIs.");
			Dialog.addMessage("Otherwise, analysis of this image is done and you should select the next image.");
			Dialog.addMessage(" ");
			Dialog.addRadioButtonGroup("What would you like to do next?", items, 2, 1, "Open new image for analysis.");
			Dialog.show;
			choice = Dialog.getRadioButton;
			if (choice=="Return to main menu.") {
				run("_ps_initi");
				}
			if (choice=="Open new image for analysis.") {
				run("_ps_Open_Double");
				}
			}
		saved_circles_array1 = Array.concat(saved_circles_array1, co_roi_array); 
		}
	
	n=n+1;

	}
//saving ROI overlay

roiManager("deselect"); //by not making an ROI manager selection, and actually deselecting, the ROI manager selects all by default. 
roiManager("Save", subDir+second_image+second_stain_type+"_first_step_double-label_ROIoverlay.zip");
wait(100);

roiManager("deselect");
roiManager("Select", saved_squares_array);
roiManager("Delete");

save_circle_ROIs=roiManager("count");
circle_array=newArray(save_circle_ROIs);
for(i=0; i<save_circle_ROIs; i++) {
	circle_array[i] = i;
}

roiManager("deselect");
roiManager("Save", subDir+first_image+first_stain_type+"_refined-single-label_ROIoverlay.zip");
wait(100);
roiManager("reset");

roiManager("Open", subDir+second_image+second_stain_type+"_first_step_double-label_ROIoverlay.zip");
roiManager("Select", circle_array);
roiManager("Delete");

save_squareDL_ROIs=roiManager("count");
square_DL_array=newArray(save_squareDL_ROIs);
for(i=0; i<save_squareDL_ROIs; i++) {
	square_DL_array[i] = i;
	}
	
roiManager("deselect");
roiManager("Save", subDir+second_image+second_stain_type+"_double-label_ROIoverlay.zip");
wait(100);

roiManager("reset");

//////////////////////////////////////////////
/* 
 In this section we are identifying the CIRCLES that have overlapping SQUARES. 
 */
 
selectWindow(first_image_tif); //select the PV image
 
roiManager("Open", subDir+second_image+second_stain_type+"_double-label_ROIoverlay.zip");
double_label_second_type_b = roiManager("count");
double_label_second_type_b_array = Array.getSequence(double_label_second_type_b); 
//waitForUser("");
roiManager("Open", subDir+first_image+first_stain_type+"_refined-single-label_ROIoverlay.zip");
double_label_all_types = roiManager("count");
double_label_all_types_array = Array.getSequence(double_label_all_types); 
double_label_first_type = double_label_all_types-double_label_second_type_b;
double_label_first_type_start = double_label_second_type_b; 

roiManager("Show All");
roiManager("Show All with labels");

n=0; //count loops
no_overlap=0; //number of circles without square overlap. 
co_roi_array2 = newArray(n); // this array will be concatenated with the "d" value for each overlapping square so the ROIs can be selected.
co_roi_array2_UI = newArray(n); // this array will be concatenated with the "d2" value for each overlapping square so the user can see multiple overlaps.
saved_circles_array = newArray(n); //building an array to remember which circle ROIs overlap (for ROI selection)

for(c=double_label_first_type_start; c<(double_label_all_types-no_overlap); c++){ //circles
	roiManager("Select", c);
	c2=c+1; //c2 is a counting variable to correct for the ROI manager starting at ROI #0.
	dTotal=0; // number of squares checked against circle "c2".
	no_co=0; // yes/no variable determining whether overlap was found. 
	for(d=0; d<double_label_second_type_b; d++){ //squares
		roiManager("Select", newArray(c, d)); //select the square the the circle
		roiManager("AND"); //build an overlapping ROI
		roiType = selectionType(); //determine the type of overlap represented by the overlapping ROI
		d2=d+1;
		dTotal=dTotal+1;
		if(roiType==-1){ //"-1" represents the type of ROI that does not overlap
			//print("No overlap: square "+c2); //then, move on to the next (ie, restart the loop).
			//waitForUser("circle rejected "+c2);
     		}
		if(roiType!=-1){ //if not "-1 type", then there is overlap, so mark it.
			roiManager("Add"); //add the overlap to the ROI manager so that we can measure its size.
			no_co = no_co+1; // to remember that, yes, there is overlap.
				
			roiManager("deselect");
			run("Clear Results");
			roiManager("Select", c); //measure the size of the circle
			roiManager("Measure");
			co_roi = (roiManager("count")-1); //find the overlap ROI (it's going to be the last one on the ROI manager list).
			roiManager("Select", co_roi); // select it.
			roiManager("Measure"); //measure the area.
			roiManager("Select", co_roi);
			roiManager("Delete"); //now removing it beause we only wanted that new ROI to measure the area of overlap.
			c2_area=getResult("Area", 0); // grab the circle-ROI's area from the results 
			co_area=getResult("Area", 1); // grab the overlap-ROI's area from the results
			c2_area_80 = c2_area*DL2_overlap;//0.8; // calculate 80% of the circle area (we're using 80% as an arbitrary threshold of minimum circle that should be within the square). 
			//print("overlap = "+co_area);
			//print("circle area = "+c2_area);
			//print("80% threshold = "+c2_area_80);
			//print("comparison is "+c2_area_80+":"+co_area);
			if(c2_area_80>co_area){ // if the overlapping area is less 80% of the square,
				no_co = no_co-1; //then, forget that an overlap was identified and move on (restart the loop).
				//waitForUser("circle "+c2+" identified but <80% overlap");
			} else {
				co_roi_array2 = Array.concat(co_roi_array2, d); //building an array to remember which circle ROIs overlap (for ROI selection)
				co_roi_array2_UI = Array.concat(co_roi_array2_UI, d2); //building an array to remember which circle ROIs overlap (for the human to understand)
				saved_circles_array = Array.concat(saved_circles_array, c); //building an array to remember which circle ROIs overlap (for ROI selection)
				//waitForUser("circle "+c2+" identified and >80% overlap");
				}
			}
		if((d2==double_label_second_type_b) && (no_co==0)){ //if we have checked every square and still not found a square that overlap (>80%) with the circle,
			roiManager("deselect");
     		roiManager("Select", c2-1);
			roiManager("Delete");
			no_overlap=no_overlap+1; // add one to the number of circles not overlapping so that we can lower the total circles.
			c=c-1;//remove one circle from the counted number of circle. Semi repetitive with last variable, but each serve a function in the top IF loop.
			//waitForUser("circle "+c2+" is a loner");
     	}
	}
	
	n=n+1;
}
roiManager("Select", double_label_second_type_b_array);
roiManager("Delete");

//saving ROI overlay

save_circleDL_ROIs=roiManager("count");

circle_DL_array=newArray(save_circleDL_ROIs); // saving the list of DL circle ROIs
for(i=0; i<save_circleDL_ROIs; i++) {
	circle_DL_array[i] = i;
	}

roiManager("deselect");
roiManager("Save", subDir+first_image+first_stain_type+"_double-label_ROIoverlay.zip");
wait(100);


print("\n"+first_image);  //THE NEXT COUPLE OF LINES PRINT FEEDBACK ABOUT THE NUMBER OF DL CELLS FOUND, AND THE NUMBER OF CIRCLE ROIs DELETED IN THE FIRST STEP.
if(removed_circles_array.length>0){
	print("Removed circle ROIs (these were multiple overlaps with square ROIs):");
	Array.print(removed_circles_array);
}
print("Circle DL ROIs = "+save_circleDL_ROIs);
print("\n"+second_image);
print("Square DL ROIs = "+save_squareDL_ROIs);

roiManager("reset");

run("_ps_measure_Double_Background_DL1and2_ROIs");

}

//\\//\\//\\//\\//\\//\\//\\

macro "_ps_measure_Double_Background_DL1and2_ROIs" { //this macro simply recalls the ROIs defined in the last macro in order to measure the values again.

first_image_tif = first_image+"-2.tif";
second_image_tif = second_image+"-2.tif";

roiManager("Open", subDir+first_image+first_stain_type+"_double-label_ROIoverlay.zip");
roiManager("Open", subDir+second_image+second_stain_type+"_double-label_ROIoverlay.zip");

if (import_type==6) {  //if run in automatic mode, no ROI check step offered.
	wait(300);
} else {  //otherwise, it opens the merge image and overlays the ROIs.
open(series_name+"_"+merged_suffix+".tif");
merged_image = getTitle;
roiManager("Show All");
roiManager("Show All with labels");
selectWindow(merged_image);

Dialog.create("How Does That Look?"); //the first step is asking the user if they want to edit either ROI set.
	items = newArray(first_stain_type, second_stain_type, "Accept double labeling as is.");
	Dialog.addRadioButtonGroup("Here is your chance to adjust the ROIs to better identify PNNs. Which double-labeled cells would you like to adjust?", items, 3, 1, "Accept double labeling as is.");
	Dialog.show;
	choice = Dialog.getRadioButton;

	if (choice==first_stain_type) {
		setTool("oval");
		roiManager("reset");
		roiManager("Open", subDir+first_image+first_stain_type+"_double-label_ROIoverlay.zip");
		waitForUser("How Does That Look?", "Here is your chance to adjust the double-labeling. Try moving, adding, or deleting ROIs. **THEN** press OK.");
		roiManager("deselect");
		roiManager("Save", subDir+first_image+first_stain_type+"_double-label_ROIoverlay.zip");
		wait(100);

		Dialog.create("Adjust the other stain?"); //then the second step is to ask the user if they want to edit the other stain's ROIs.
			items = newArray("Yes", "No, accept double labeling as is.");
			Dialog.addRadioButtonGroup("Do you want to also adjust "+second_stain_type+" double-labeling?", items, 2, 1, "Yes");
			Dialog.show;
			choice = Dialog.getRadioButton;

		if (choice=="Yes") {
			setTool("rectangle");
			roiManager("reset");
			roiManager("Open", subDir+second_image+second_stain_type+"_double-label_ROIoverlay.zip");
			waitForUser("How Does That Look?", "Here is your chance to adjust the double-labeling. Try moving, adding, or deleting ROIs. **THEN** press OK.");
			roiManager("deselect");
			roiManager("Save", subDir+second_image+second_stain_type+"_double-label_ROIoverlay.zip");
			wait(100);
			roiManager("reset");
			selectWindow(merged_image);
			run("Close");
			run("_ps_measure_Double_Background_DL1and2_ROIs");
		}
		
		if (choice=="No, accept double labeling as is.") {
			//well, aren't you confident.
		}
	}
	if (choice==second_stain_type) {
		setTool("rectangle");
		roiManager("reset");
		roiManager("Open", subDir+second_image+second_stain_type+"_double-label_ROIoverlay.zip");
		waitForUser("How Does That Look?", "Here is your chance to adjust the double-labeling. Try moving, adding, or deleting ROIs. **THEN** press OK.");
		roiManager("deselect");
		roiManager("Save", subDir+second_image+second_stain_type+"_double-label_ROIoverlay.zip");
		wait(100);

		Dialog.create("Adjust the other stain?"); //again, second step, but after starting with the alternative stain.
			items = newArray("Yes", "No, accept double labeling as is.");
			Dialog.addRadioButtonGroup("Do you want to also adjust "+first_stain_type+" double-labeling?", items, 2, 1, "Yes");
			Dialog.show;
			choice = Dialog.getRadioButton;

		if (choice=="Yes") {
			setTool("oval");
			roiManager("reset");
			roiManager("Open", subDir+first_image+first_stain_type+"_double-label_ROIoverlay.zip");
			waitForUser("How Does That Look?", "Here is your chance to adjust the double-labeling. Try moving, adding, or deleting ROIs. **THEN** press OK.");
			roiManager("deselect");
			roiManager("Save", subDir+first_image+first_stain_type+"_double-label_ROIoverlay.zip");
			wait(100);
			roiManager("reset");
			selectWindow(merged_image);
			run("Close");
			run("_ps_measure_Double_Background_DL1and2_ROIs");
		}	
		if (choice=="No, accept double labeling as is.") {
			//well, aren't you confident.
		}	
	}
	if (choice=="Accept double labeling as is.") {
		//well, aren't you confident.
	}
}
if (import_type==5){
	selectWindow(merged_image);
	run("Close");
	}
roiManager("reset");

//\\\\\\\\\\\\\
//actually measuring the double labeled ROIs (image 1)

run("Clear Results");
selectWindow(first_image_tif);

roiManager("Open", subDir+first_image+first_stain_type+"_double-label_ROIoverlay.zip");
name = series_name+" "+first_stain_type+" double-label.txt";

DL_ROIs=0;
DL_ROIs =roiManager("count"); 
array=newArray(DL_ROIs); 
for(i=0; i<DL_ROIs;i++) { 
        array[i] = i; 
	}
//roiManager("Select", array); //by not making an ROI manager selection, and actually deselecting, the ROI manager selects all by default. 
roiManager("deselect");
roiManager("Measure");
roiManager("Show All");
roiManager("Show All with labels");

DL_ROIs1_m=0;
DL_ROIs1_means=0;
DL_ROIs1_m = nResults;
DL_ROIs1_means = newArray(DL_ROIs1_m);
roiManager("deselect");
for (i=0; i<DL_ROIs1_m; i++) {
	DL_ROIs1_means[i] = getResult('Mean', i);
}
roiManager("deselect");
for (i=0; i<DL_ROIs1_m; i++) {
	setResult("Background", i, DL1_lower);
	setResult("Mean-Background", i, DL_ROIs1_means[i]-DL1_lower);
}
setResult("File Name", 0, name);
updateResults();

DL_ROIs1_m2 = 0;
DL_ROIs1_avgmeans = 0;
DL_ROIs1_m2 = nResults;
DL_ROIs1_avgmeans = newArray(DL_ROIs1_m2);
roiManager("deselect");
for (i=0; i<DL_ROIs1_m2; i++) {
	DL_ROIs1_avgmeans[i] = getResult('Mean-Background', i);
	}
Array.getStatistics(DL_ROIs1_avgmeans, min, max, mean, stdDev);
print("\n");
print("Double-labeled "+first_stain_type+" Mean Intensity = "+mean);


DL_ROIs1_numberStains=0;
DL_ROIs1_numberStains=getValue("results.count");
print("Final number of Double-labeled "+first_stain_type+" Cells = "+DL_ROIs1_numberStains);

n_dl1 = nResults;
area_dl1 = newArray(n_dl1);
XM_dl1 = newArray(n_dl1);
YM_dl1 = newArray(n_dl1);
mean_dl1 = newArray(n_dl1);
stddev_dl1 = newArray(n_dl1);
min_dl1 = newArray(n_dl1);
max_dl1 = newArray(n_dl1);
bkgd_dl1 = newArray(n_dl1);
meanbkgd_dl1 = newArray(n_dl1);
print("\n");
print("Double-labeled "+first_stain_type+" cell data:");
print("Subject# Image_name Stain_type Cell# Area XM YM Mean StdDev Min Max Background Mean-Background");
for (i=0; i<n_dl1; i++) {
	area_dl1[i] = getResult('Area', i);
	XM_dl1[i] = getResult('XM', i);
	YM_dl1[i] = getResult('YM', i);
	mean_dl1[i] = getResult('Mean', i);
	stddev_dl1[i] = getResult('StdDev', i);
	min_dl1[i] = getResult('Min', i);
	max_dl1[i] = getResult('Max', i);
	bkgd_dl1[i] = getResult('Background', i);
	meanbkgd_dl1[i] = getResult('Mean-Background', i);
	print(subject_id+" "+first_image+" "+"Double-labeled_"+first_stain_type+" "+(i+1)+" "+area_dl1[i]+" "+XM_dl1[i]+" "+YM_dl1[i]+" "+mean_dl1[i]+" "+stddev_dl1[i]+" "+min_dl1[i]+" "+max_dl1[i]+" "+bkgd_dl1[i]+" "+meanbkgd_dl1[i]);
	}

	// Saving data

 
saveAs("Measurements", subDir+name);
print("\n"); 
print("Results saved as: "+subDir+name);

roiManager("reset");


//\\\\\\\\\\\\\\\
//measure double labeled ROIs (image 2)


roiManager("Open", subDir+second_image+second_stain_type+"_double-label_ROIoverlay.zip");
name = series_name+" "+second_stain_type+" double-label.txt"; 

run("Clear Results");
selectWindow(second_image_tif);


roiManager("Show All");
roiManager("Show All with labels");
roiManager("deselect");

DL_ROIs=0;
DL_ROIs =roiManager("count"); 
array=newArray(DL_ROIs); 
for(i=0; i<DL_ROIs;i++) { 
	array[i] = i; 
	}

roiManager("deselect");
roiManager("Measure");
roiManager("Show All");
roiManager("Show All with labels");

DL_ROIs2_m=0;
DL_ROIs2_means=0;
DL_ROIs2_m = nResults;
DL_ROIs2_means = newArray(DL_ROIs2_m);
run("Select None");
for (i=0; i<DL_ROIs2_m; i++) {
	DL_ROIs2_means[i] = getResult('Mean', i);
	}
roiManager("deselect");
for (i=0; i<DL_ROIs2_m; i++) {
	setResult("Background", i, DL2_lower);
	setResult("Mean-Background", i, DL_ROIs2_means[i]-DL2_lower);
	}

setResult("File Name", 0, name);
updateResults();

DL_ROIs2_m2 = 0;
DL_ROIs2_avgmeans = 0;
DL_ROIs2_m2 = nResults;
DL_ROIs2_avgmeans = newArray(DL_ROIs2_m2);
roiManager("deselect");
for (i=0; i<DL_ROIs2_m2; i++) {
	DL_ROIs2_avgmeans[i] = getResult('Mean-Background', i);
	}

Array.getStatistics(DL_ROIs2_avgmeans, min, max, mean, stdDev);
print("\n");
print("Double-labeled "+second_stain_type+" Mean Intensity = "+mean);


DL_ROIs2_numberStains=0;
DL_ROIs2_numberStains=getValue("results.count");
print("Final number of Double-labeled "+second_stain_type+" Cells = "+DL_ROIs2_numberStains);

n_dl2 = nResults;
area_dl2 = newArray(n_dl2);
XM_dl2 = newArray(n_dl2);
YM_dl2 = newArray(n_dl2);
mean_dl2 = newArray(n_dl2);
stddev_dl2 = newArray(n_dl2);
min_dl2 = newArray(n_dl2);
max_dl2 = newArray(n_dl2);
bkgd_dl2 = newArray(n_dl2);
meanbkgd_dl2 = newArray(n_dl2);
print("\n");
print("Double-labeled "+second_stain_type+" cell data:");
print("Subject# Image_name Stain_type Cell# Area XM YM Mean StdDev Min Max Background Mean-Background");
for (i=0; i<n_dl2; i++) {
	area_dl2[i] = getResult('Area', i);
	XM_dl2[i] = getResult('XM', i);
	YM_dl2[i] = getResult('YM', i);
	mean_dl2[i] = getResult('Mean', i);
	stddev_dl2[i] = getResult('StdDev', i);
	min_dl2[i] = getResult('Min', i);
	max_dl2[i] = getResult('Max', i);
	bkgd_dl2[i] = getResult('Background', i);
	meanbkgd_dl2[i] = getResult('Mean-Background', i);
	print(subject_id+" "+second_image+" "+"Double-labeled_"+second_stain_type+" "+(i+1)+" "+area_dl2[i]+" "+XM_dl2[i]+" "+YM_dl2[i]+" "+mean_dl2[i]+" "+stddev_dl2[i]+" "+min_dl2[i]+" "+max_dl2[i]+" "+bkgd_dl2[i]+" "+meanbkgd_dl2[i]);
	}

	// Saving data


saveAs("Measurements", subDir+name);
selectWindow("Log");
saveAs("Text", dir+"/All_Results"+TimeString+".txt");
wait(100);
print("\n"); 
print("Results saved as: "+subDir+name);
wait(200);


selectWindow(first_image_tif);
wait(50);
run("Close");
wait(50);
selectWindow(second_image_tif);
wait(50);
run("Close");
wait(50);	

///////////////////////////////////

	// Closing project

roiManager("reset");
selectWindow("ROI Manager");
run("Close");
run("Clear Results");
print("Done!");

	Dialog.create("Next image?");
	items = newArray("Open next image for analysis.", "Return to main menu.");
	Dialog.addRadioButtonGroup("What would you like to do next?", items, 2, 1, "Open next image for analysis.");
	Dialog.show;
	choice = Dialog.getRadioButton;
	if (choice=="Return to main menu.") {
		run("_ps_initi");
	} else {
//		run("_ps_OpenNext_Double");
//SEE NOT IN "_ps_OPENNEXT_DOUBLE" ABOUT REMOVAL OF AUTO OPEN FUNCTION.

		run("_ps_Open_Double");
		}
	}
		
}

/////////////////////////////

	

/////////////////////////////////////////////////////
//#################################################//
//#################################################//
/////////////////////////////////////////////////////


	//Here be triple label intensity analysis

macro "_ps_triple" {



Dialog.create("Import Type"); // initial dialogue.
items = newArray("Semiautomated analysis of summed triple-labeled images", "Automatic analysis of summed triple-labeled images", "Automatic REanalysis using previous ROIs", "->Make summed triple-labeled images from Z-stacks.");
Dialog.addRadioButtonGroup("What type of file do you want to import?", items, 4, 1, "Semiautomated analysis of summed triple-labeled Z-stack");
Dialog.show;
choice = Dialog.getRadioButton;
if (choice=="Semiautomated analysis of summed triple-labeled images") {
	import_type = 5;
	run("_ps_Open_Triple");
	} else {		
	if (choice=="Automatic analysis of summed triple-labeled images") {
		import_type = 6;		
		run("_ps_Open_Triple");
		}
	if (choice=="Automatic REanalysis using previous ROIs") {
		import_type = 7;
		run("_ps_Open_Triple");
		}
	if (choice=="->Make summed triple-labeled images from Z-stacks.") {
		Dialog.create("Stacks folder");
		Dialog.addMessage("Before using this function, please place folders containing Z-stack images within an experiment folder. Then choose the experiment folder.");
		Dialog.addMessage("ie, /Desktop/EXPERIMENT FOLDER/Subject folder/image1... image20");
		Dialog.show(); 
		run("_ps_triple_sum");
		}
	}
}

/////////////////////////////

macro "_ps_triple_sum" { // summing images.

Ldest = getDirectory("Choose a Directory ");         //load from
Sdest = Ldest+"/Summed/";           //save at
sfold = Sdest
stackfold = getFileList(Ldest);

if(!File.exists(sfold)) {
	File.makeDirectory(sfold);	
}

Dialog.create("Enter z-steps");
Dialog.addMessage("Please entere the number of z-steps (slices) in each image series");		
Dialog.addNumber("Number of z-steps:", zsteps);

Dialog.show;

zsteps = Dialog.getNumber();

for(i=0; i<stackfold.length; i++){
	stacks = stackfold[i];
	identify = substring(stacks, 0, lengthOf(stacks)-1);
	names = substring(stacks, lengthOf(stacks)-1);
	if(names == "/" && stacks!="Summed/"){
		ISp = "open=[" + Ldest + stacks + "] number=zsteps starting=q increment=4 scale=100 file=[] or=[] sort";

		for(q=1; q<5; q++) { 
			run("Image Sequence...", ISp);
			if(q!=1){
				run("8-bit");
			}

			run("Z Project...", "start=1 stop=zsteps projection=[Sum Slices]");


			saveAs("Tiff", sfold + identify + "_" + q + ".tif");

			close();
			
			{
			while(nImages>0){
				selectImage(nImages);
				close();
				}
			}
		}
	}
}

run("_ps_triple");
}

/////////////////////////////

macro "_ps_Open_Triple" { // opening previously summed images

waitForUser("Select first Image", "Please open the "+first_stain_type_TL+" image of the subject series.");
open();

Dialog.create("Subject ID");
Dialog.addMessage("Please enter the Subject ID name or number");
Dialog.addString("Subject ID:", "eg:42B");
Dialog.show
subject_id =  Dialog.getString();

run("Set Measurements...", "area mean standard min center limit redirect=None decimal=3");  //setting measurements.
run("Set Scale...", "distance=scale_size known=scale_known pixel=scale_pixel unit=&scale_unit global");
first_image_TL = getTitle;
dir = getDirectory("image"); 
index = lastIndexOf(first_image_TL, "."); 
if (index!=-1) first_image_TL = substring(first_image_TL, 0, index);
	print("\n"+first_image_TL+" loaded.");

series_name_TL = substring(first_image_TL, 0, index-2);
first_open_triple = series_name_TL;

subDir_TL = dir+"/"+series_name_TL+"/";
File.makeDirectory(subDir_TL);


//print("subDir_TL = "+subDir_TL_TL);

if(endsWith(first_image_TL,first_suffix_TL)) {
	open(series_name_TL+"_"+second_suffix_TL+".tif");
	second_image_TL = getTitle;
	if (index!=-1) second_image_TL = substring(second_image_TL, 0, index);
		print(second_image_TL+" loaded.");
	
	} else {
	waitForUser("Please update Settings to match numbering suffixes of triple-label image series");
	run("_ps_initi");
	}

	if(endsWith(second_image_TL,second_suffix_TL)) {
		open(series_name_TL+"_"+third_suffix_TL+".tif");
		third_image_TL = getTitle;
		if (index!=-1) third_image_TL = substring(third_image_TL, 0, index);
			print(third_image_TL+" loaded.");
			
		} else {		
		waitForUser("Please update Settings to match numbering suffixes of triple-label image series");
		run("_ps_initi");
		}

		if(endsWith(third_image_TL,third_suffix_TL)) {
			open(series_name_TL+"_"+merged_suffix_TL+".tif");
			merged_image_TL = getTitle;
			if (index!=-1) merged_image_TL = substring(merged_image_TL, 0, index);
				print(merged_image_TL+" loaded.");
				run("_ps_Triple_Background");
			
			} else {		
			waitForUser("Please update Settings to match numbering suffixes of triple-label image series");
			run("_ps_initi");
			}
		}
	}

}
}

///////////////////////////////

macro "_ps_Triple_Background" { //the meat of the pipsqueak. TL= triple-label. DL= double-label. SL= single-label.

//First Image
	// Selecting Background

first_image_tif = first_image_TL+".tif";  //image names are consistently kept as "first_image_tif" and "second_image_tif" throughout the triple-labeled macro(s) 
second_image_tif = second_image_TL+".tif"; //even though the actual images are duplicated and named things like "first_image_TL-2.tif"
third_image_tif = third_image_TL+".tif"; 

run("Clear Results"); //clearing the template

run("ROI Manager..."); //opening ROI manager
roiManager("reset"); // clearing ROI manager

roiManager("Show All"); //turning on show all labels
roiManager("Show All with labels");

selectWindow(first_image_tif); //grabbing first image

height = getHeight; //getting dimensions in order to place background ROIs around images of any size
width = getWidth;

makeRectangle((6*(width-40)/6), (0.1*(height-40)/5), 30, 30); //placing 20 ROIs around the perimeter of the image
roiManager("Add");
makeRectangle((6*(width-40)/6), (1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((6*(width-40)/6), (2*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((6*(width-40)/6), (3*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((6*(width-40)/6), (4*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((6*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((5*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((4*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((3*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((2*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((1*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (4*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (3*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (2*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((1*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((2*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((3*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((4*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((5*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");

run("Subtract Background...", "rolling=10"); //before measuring the image background for threshold, running rolling ball subtraction
roiManager("Select", newArray(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,21)); //measuring all the ROIs in order to run stats and select the right ones
roiManager("Measure");


	// Calculating lower threshold background value in "Mean" column

DL1_m=0;
DL1_bkgd_mean=0;
DL1_meanbkgd=0;
DL1_std=0;
DL1_bkgd_std=0;
DL1_stdbkgd=0;

DL1_m = nResults; //building an array for the ROI means.
DL1_bkgd_mean = newArray(DL1_m);
run("Select None");
for (i=0; i<DL1_m; i++) { //getting ROI mean one at a time from ROI manager and putting mean into array.
	DL1_bkgd_mean[i] = getResult('Mean', i);
	}

if (background_setting == "High") {
	Array.getStatistics(DL1_bkgd_mean, min, max, mean, stdDev); //getting other stats from mean array
	Array.sort(DL1_bkgd_mean); //sorting array by mean values.
	DL1_bkgd_high_trim = Array.trim(DL1_bkgd_mean, 15); //removing highest 5 mean values and putting remaining into new array
	Array.getStatistics(DL1_bkgd_high_trim, min, max, mean, stdDev); // getting stats for new array
	Array.reverse(DL1_bkgd_high_trim); //reversing the order of the array values.
	DL1_bkgd_low_trim = Array.trim(DL1_bkgd_high_trim, 7); // removing the lowest 8 values (since the array order had been reversed).
	Array.getStatistics(DL1_bkgd_low_trim, min, max, mean, stdDev); // getting stats from this new new array
	DL1_meanbkgd = mean; //assigning mean value.

	DL1_std = nResults;  //counting the number of ROIs.
	DL1_bkgd_std = newArray(DL1_std); 
	run("Select None");
	for (i=0; i<DL1_std; i++) {  //for each ROI, get the standard deviation value (this will lead to a similar function as was used for means above).
		DL1_bkgd_std[i] = getResult('StdDev', i);
		}
	Array.getStatistics(DL1_bkgd_std, min, max, mean, stdDev);
	Array.sort(DL1_bkgd_std);
	DL1_bksd_high_trim = Array.trim(DL1_bkgd_std, 15);
	Array.getStatistics(DL1_bksd_high_trim, min, max, mean, stdDev);
	Array.reverse(DL1_bksd_high_trim);
	DL1_bksd_low_trim = Array.trim(DL1_bksd_high_trim, 7);
	Array.getStatistics(DL1_bksd_low_trim, min, max, mean, stdDev);
	DL1_stdbkgd = mean;
	}

if (background_setting == "Low") {
	Array.getStatistics(DL1_bkgd_mean, min, max, mean, stdDev); //getting other stats from mean array
	Array.sort(DL1_bkgd_mean); //sorting array by mean values.
	DL1_bkgd_high_trim = Array.trim(DL1_bkgd_mean, 5); //removing highest 17 mean values and putting remaining into new array
	Array.getStatistics(DL1_bkgd_high_trim, min, max, mean, stdDev); // getting stats for new array
	DL1_meanbkgd = mean; //assigning mean value.

	DL1_std = nResults;  //counting the number of ROIs.
	DL1_bkgd_std = newArray(DL1_std); 
	run("Select None");
	for (i=0; i<DL1_std; i++) {  //for each ROI, get the standard deviation value (this will lead to a similar function as was used for means above).
		DL1_bkgd_std[i] = getResult('StdDev', i);
		}
	Array.getStatistics(DL1_bkgd_std, min, max, mean, stdDev);
	Array.sort(DL1_bkgd_std);
	DL1_bksd_high_trim = Array.trim(DL1_bkgd_std, 5);
	Array.getStatistics(DL1_bksd_high_trim, min, max, mean, stdDev);
	DL1_stdbkgd = mean;
	}

TL1_lower = ((DL1_meanbkgd)+(DL1_stdbkgd*2));  //THIS IS THE VALUE THAT IS USED AS THE NaN VALUE FOR OUR INTENSITY MEASUREMENTS. 
print("\n");
print("Single "+first_stain_type_TL+" label:");
print(first_stain_type_TL+" Mean Background = "+DL1_meanbkgd+", Background StdDev = "+DL1_stdbkgd);
print(first_stain_type_TL+" Lower Threshold= "+TL1_lower);

	// Setting Threshold

run("Set Measurements...", "area mean standard min center limit redirect=None decimal=3");  //setting measurements.
setAutoThreshold("Default dark");
run("Threshold..."); //Opening the threshold function
wait(200);
setThreshold(TL1_lower, 500000); //setting the NaN value.
//run("Apply");
run("NaN Background");
//run("Close");


selectWindow(first_image_tif); //duplicating the original image. Here it is important that the original is kept 
run("Duplicate...", " "); //alive because it is the only version that can be reverted for the user to view. (alternatively, just reopen fresh image).
//run("8-bit");
run("Duplicate...", " ");
//run("8-bit");
selectWindow(first_image_TL+"-1.tif");

run("_ps_Triple_Background_SL1_ROIs");
}	
////////////////////////////////

macro "_ps_Triple_Background_SL1_ROIs" { // this section is macro-ed because we want to be able to return to this point. Good place to use function.

       // Measuring ROIs
           //Finding PNNs with Analyze Particles

	name = series_name_TL+" "+first_stain_type_TL+".txt"; //saving the results table in a sub folder.

	first_image_tif = first_image_TL+".tif";
	second_image_tif = second_image_TL+".tif";
	third_image_tif = third_image_TL+".tif";

	selectWindow(first_image_TL+"-1.tif");
	roiManager("Show All");
	roiManager("Show All with labels");
	
	run("Clear Results"); 
	
if(first_SL_run==1 && import_type!=7) {
	roiManager("reset");
	wait(200);
	setAutoThreshold("Default dark"); //setting the threshold in the next couple lines for the analyze particle function (identifying cells).
	run("Threshold...");
	wait(200);
	setThreshold(TL1_lower*TL1_set_intensity_low_threshold, TL1_set_intensity_high_threshold);
	wait(200);
	makeRectangle(0, 0, 0, 0); //clear temp ROIs
	run("Analyze Particles...", "size=TL1_set_low_particlesize-TL1_set_high_particlesize circularity=TL1_set_low_circularity-TL1_set_high_circularity clear include add");
	wait(200); //THE PREVIOUS LINE IS IMPORTANT. set limits for ROI construction based on pixels above threshold and added them to the ROI manager.
	selectWindow("Threshold");
	resetThreshold();
	run("Close");
	roiManager("Measure");

			   //Making circular ROIs from Particles

	wait(500);
	DL1_add_circles=roiManager("count"); // here to stop the user if no ROIs were detected

	if (DL1_add_circles>0) { //if yes ROIs, change the size of the ROIs based on global enlargement variable.

		array=newArray(DL1_add_circles); 
		for(i=0; i<DL1_add_circles;i++) { 
			roiManager("Select", 0);
			if(ROI_type_TL1 == "As-detected") {
				wait(10);
				}
			if(ROI_type_TL1 == "Square") {
				run("Specify...", "centered scaled");
				}
			if(ROI_type_TL1 == "Circle") {
				run("Specify...", "oval constrain centered scaled");
				}
			run("Enlarge...", "enlarge=TL1_set_roi_enlargement");
			roiManager("Add");
			roiManager("Select", 0);
			roiManager("Delete");
			} 
	
	} else {
		waitForUser("Oops", "We didn't see any staining here. Try adding some ROIs in the next step, or changing the image detection settings.");
		if (import_type==6) {
			run("_ps_Open_Triple");
			}	 
		}
	} 
	
roiManager("Set Color", "red");

if (import_type==6) { //when in automatic analysis, will skip the user check step below.
	wait(300);
	} 
if (import_type==7) {
	roiManager("reset");
	roiManager("Open", subDir_TL+first_image_TL+first_stain_type_TL+"_single-label_ROIoverlay.zip");
	roiManager("Show All");
	roiManager("Show All with labels");
	first_SL_run=(first_SL_run+1);
	//run("_ps_Triple_Background_SL1_ROIs");
	
	} else {
	setTool("oval"); //selects the right tool. The next lines creates a dialogue to give the user ability to correct ROI selection.
	selectWindow(first_image_tif);
	run("Revert"); //reverts the background correction
	roiManager("Show All");
	roiManager("Show All with labels");

	TL1_set_intensity_low_threshold_old = TL1_set_intensity_low_threshold;
	TL1_set_low_particlesize_old = TL1_set_low_particlesize; 
	TL1_set_high_particlesize_old = TL1_set_high_particlesize;

	Dialog.create("Adjust Cell Detection Threshold");
	items = newArray("Rerun cell detection with these new settings.", "Let me add / delete ROIs by hand.", "Load previous ROI set.", "Save these ROIs and continue to "+second_stain_type_TL+" staining.");
	Dialog.addSlider("Threshold of Detection:", (TL1_set_intensity_low_threshold*0.1), (TL1_set_intensity_low_threshold*2), TL1_set_intensity_low_threshold);
	Dialog.addSlider("Smallest Cell Detected:", 0.0, 10000, TL1_set_low_particlesize);
	Dialog.addSlider("Largest Cell Detected:", 0.0, 100000, TL1_set_high_particlesize);

	Dialog.addRadioButtonGroup("Would you like to change the "+first_stain_type_TL+" cell detection threshold settings?", items, 4, 1, "Let me add / delete ROIs by hand.");
	Dialog.show;
	TL1_set_intensity_low_threshold = Dialog.getNumber();
	TL1_set_low_particlesize = Dialog.getNumber();
	TL1_set_high_particlesize = Dialog.getNumber();
	choice = Dialog.getRadioButton;

	if (choice=="Let me add / delete ROIs by hand." && TL1_set_intensity_low_threshold_old==TL1_set_intensity_low_threshold && TL1_set_low_particlesize_old==TL1_set_low_particlesize && TL1_set_high_particlesize_old==TL1_set_high_particlesize)  {
		waitForUser("How Does That Look?", "Here is your chance to adjust the ROIs to better identify PNNs. Try adding or deleting ROIs in the ROI manager. **THEN** press OK.");
		first_SL_run=(first_SL_run+1);
		run("_ps_Triple_Background_SL1_ROIs");
		}

	if (choice=="Rerun cell detection with these new settings." || TL1_set_intensity_low_threshold_old != TL1_set_intensity_low_threshold || TL1_set_low_particlesize_old != TL1_set_low_particlesize || TL1_set_high_particlesize_old != TL1_set_high_particlesize) {
		first_SL_run=1;
		run("_ps_Triple_Background_SL1_ROIs");
		}

	if (choice=="Save these ROIs and continue to "+second_stain_type_TL+" staining." && TL1_set_intensity_low_threshold_old==TL1_set_intensity_low_threshold && TL1_set_low_particlesize_old==TL1_set_low_particlesize && TL1_set_high_particlesize_old==TL1_set_high_particlesize)  {
		first_SL_run=1;
		wait(10);
		}
	if (choice=="Load previous ROI set.") {
		roiManager("reset");
		roiManager("Open", "");
		roiManager("Show All");
		roiManager("Show All with labels");
		first_SL_run=(first_SL_run+1);
		run("_ps_Triple_Background_SL1_ROIs");
		}
	}

selectWindow(first_image_tif); //closing the reverted image
run("Close");

//saving SL1 ROI overlay

SL1_save_ROIs=roiManager("count"); //select all the ROIs in order to save the SL set.
array=newArray(SL1_save_ROIs);
for(i=0; i<SL1_save_ROIs; i++) {
	array[i] = i;
	}
roiManager("Select", array);
roiManager("Save", subDir_TL+first_image_TL+first_stain_type_TL+"_single-label_ROIoverlay.zip");
	

//Measuring ROIs

selectWindow(first_image_TL+"-1.tif");

run("Clear Results");
DL1_measure_circles=0;  //Selects all the ROIs and uses measure function to populate the results window.
DL1_measure_circles=roiManager("count"); 
array=newArray(DL1_measure_circles); 
for(i=0; i<DL1_measure_circles;i++) { 
        array[i] = i; 
}
roiManager("Select", array);
roiManager("Measure");
roiManager("Show All");
roiManager("Show All with labels");

DL1_m = nResults; //defining array variable.
DL1_means = newArray(DL1_m); //building array
roiManager("deselect");
for (i=0; i<DL1_m; i++) { //populating array with mean from each result
      DL1_means[i] = getResult('Mean', i);
    }
roiManager("deselect");
for (i=0; i<DL1_m; i++) { //adding columns for "background" and "mean-background" to the results table.
	setResult("Background", i, TL1_lower);
	setResult("Mean-Background", i, DL1_means[i]-TL1_lower);
}
setResult("File Name", 0, name); //column for file name.
updateResults(); //updating the results window to reflect these changes.

DL1_m2 = nResults; //making a average of the ROI means in order to print a "mean" value for this image.
DL1_avgmeans = newArray(DL1_m2);
run("Select None");
for (i=0; i<DL1_m2; i++) {
	DL1_avgmeans[i] = getResult('Mean-Background', i);
}
Array.getStatistics(DL1_avgmeans, min, max, mean, stdDev);
print(first_stain_type_TL+" Mean Intensity = "+mean);

DL1_numberStains = 0; //printing the number of cells counted.
DL1_numberStains=getValue("results.count");
print("Number of "+first_stain_type_TL+" Stained Cells = "+DL1_numberStains);

n_sl1 = nResults; //THIS IS AN IMPORTANT FUNCTION FOR THE RESULTS. SAME RESULTS AS IN TABLE, BUT PRINTED IN LOG, WHICH IS BETTER FOR IMPORTING INTO EXCEL.
area_sl1 = newArray(n_sl1);
XM_sl1 = newArray(n_sl1);
YM_sl1 = newArray(n_sl1);
mean_sl1 = newArray(n_sl1);
stddev_sl1 = newArray(n_sl1);
min_sl1 = newArray(n_sl1);
max_sl1 = newArray(n_sl1);
bkgd_sl1 = newArray(n_sl1);
meanbkgd_sl1 = newArray(n_sl1);
print("\n");
print("Individual "+first_stain_type_TL+" cell data:");
print("Subject# Image_name Stain_type Cell# Area XM YM Mean StdDev Min Max Background Mean-Background");
n=0;
for (i=0; i<n_sl1; i++) { // for each line in the results table, populate an array with these parameters and print them in the log. 
	area_sl1[i] = getResult('Area', i);
	XM_sl1[i] = getResult('XM', i);
	YM_sl1[i] = getResult('YM', i);
	mean_sl1[i] = getResult('Mean', i);
	stddev_sl1[i] = getResult('StdDev', i);
	min_sl1[i] = getResult('Min', i);
	max_sl1[i] = getResult('Max', i);
	bkgd_sl1[i] = getResult('Background', i);
	meanbkgd_sl1[i] = getResult('Mean-Background', i);
	print(subject_id+" "+first_image_TL+" "+first_stain_type_TL+" "+(i+1)+" "+area_sl1[i]+" "+XM_sl1[i]+" "+YM_sl1[i]+" "+mean_sl1[i]+" "+stddev_sl1[i]+" "+min_sl1[i]+" "+max_sl1[i]+" "+bkgd_sl1[i]+" "+meanbkgd_sl1[i]);
	
	//roiManager("Select", i);
	//run("Duplicate...", " ");
	//selectWindow(first_image_TL+"-3.tif");
	n=n+1;
	
	}
	
roiManager("reset");
selectWindow("ROI Manager");
run("Close");

wait(100);

selectWindow(first_image_TL+"-1.tif");
run("Close");

	// Saving data

saveAs("Measurements", subDir_TL+name);
print("\n"); 
print("Results saved as: "+subDir_TL+name);
selectWindow("Log");
saveAs("Text", dir+"/All_Results"+TimeString+".txt");


//\\//\\//\\//\\//\\//\\

//Second Image //THIS SECTION DOES THE SAME SINGLE-LABEL ROI WORK AS ABOVE, BUT FOR THE SECOND STAIN.	
	// Selecting Background

run("Clear Results");

roiManager("reset");

roiManager("Show All");
roiManager("Show All with labels");

selectWindow(second_image_tif);


height = getHeight;
width = getWidth;

makeRectangle((6*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((6*(width-40)/6), (1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((6*(width-40)/6), (2*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((6*(width-40)/6), (3*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((6*(width-40)/6), (4*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((6*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((5*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((4*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((3*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((2*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((1*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (4*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (3*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (2*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((1*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((2*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((3*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((4*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((5*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");

run("Subtract Background...", "rolling=10");
roiManager("Select", newArray(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,21));
roiManager("Measure");


	// Calculating lower threshold background value in "Mean" column

DL2_m=0;
DL2_bkgd_mean=0;
DL2_meanbkgd=0;
DL2_std=0;
DL2_bkgd_std=0;
DL2_stdbkgd=0;

DL2_m = nResults;
DL2_bkgd_mean = newArray(DL2_m);
run("Select None");
for (i=0; i<DL2_m; i++) {
	DL2_bkgd_mean[i] = getResult('Mean', i);
	}
if (background_setting == "High") {
	Array.getStatistics(DL2_bkgd_mean, min, max, mean, stdDev); //getting other stats from mean array
	Array.sort(DL2_bkgd_mean); //sorting array by mean values.
	DL2_bkgd_high_trim = Array.trim(DL2_bkgd_mean, 15); //removing highest 5 mean values and putting remaining into new array
	Array.getStatistics(DL2_bkgd_high_trim, min, max, mean, stdDev); // getting stats for new array
	Array.reverse(DL2_bkgd_high_trim); //reversing the order of the array values.
	DL2_bkgd_low_trim = Array.trim(DL2_bkgd_high_trim, 7); // removing the lowest 8 values (since the array order had been reversed).
	Array.getStatistics(DL2_bkgd_low_trim, min, max, mean, stdDev); // getting stats from this new new array
	DL2_meanbkgd = mean; //assigning mean value.

	DL2_std = nResults;  //counting the number of ROIs.
	DL2_bkgd_std = newArray(DL2_std); 
	run("Select None");
	for (i=0; i<DL2_std; i++) {  //for each ROI, get the standard deviation value (this will lead to a similar function as was used for means above).
		DL2_bkgd_std[i] = getResult('StdDev', i);
		}
	Array.getStatistics(DL2_bkgd_std, min, max, mean, stdDev);
	Array.sort(DL2_bkgd_std);
	DL2_bksd_high_trim = Array.trim(DL2_bkgd_std, 15);
	Array.getStatistics(DL2_bksd_high_trim, min, max, mean, stdDev);
	Array.reverse(DL2_bksd_high_trim);
	DL2_bksd_low_trim = Array.trim(DL2_bksd_high_trim, 7);
	Array.getStatistics(DL2_bksd_low_trim, min, max, mean, stdDev);
	DL2_stdbkgd = mean;
	}

if (background_setting == "Low") {
	Array.getStatistics(DL2_bkgd_mean, min, max, mean, stdDev); //getting other stats from mean array
	Array.sort(DL2_bkgd_mean); //sorting array by mean values.
	DL2_bkgd_high_trim = Array.trim(DL2_bkgd_mean, 5); //removing highest 17 mean values and putting remaining into new array
	Array.getStatistics(DL2_bkgd_high_trim, min, max, mean, stdDev); // getting stats for new array
	DL2_meanbkgd = mean; //assigning mean value.

	DL2_std = nResults;  //counting the number of ROIs.
	DL2_bkgd_std = newArray(DL2_std); 
	run("Select None");
	for (i=0; i<DL2_std; i++) {  //for each ROI, get the standard deviation value (this will lead to a similar function as was used for means above).
		DL2_bkgd_std[i] = getResult('StdDev', i);
		}
	Array.getStatistics(DL2_bkgd_std, min, max, mean, stdDev);
	Array.sort(DL2_bkgd_std);
	DL2_bksd_high_trim = Array.trim(DL2_bkgd_std, 5);
	Array.getStatistics(DL2_bksd_high_trim, min, max, mean, stdDev);
	DL2_stdbkgd = mean;
	}
	
TL2_lower = ((DL2_meanbkgd)+(DL2_stdbkgd*2));
print("\n");
print("Single "+second_stain_type_TL+" label:");
print(second_stain_type_TL+" Mean Background = "+DL2_meanbkgd+", Background StdDev = "+DL2_stdbkgd);
print(second_stain_type_TL+" Lower Threshold= "+TL2_lower);

	// Setting Threshold

run("Set Measurements...", "area mean standard min center limit redirect=None decimal=3");  //setting measurements.
setAutoThreshold("Default dark");
run("Threshold...");
wait(200);
setThreshold(TL2_lower, 500000);
//run("Apply");
run("NaN Background");

selectWindow(second_image_tif);
run("Duplicate...", " ");
//run("8-bit");
run("Duplicate...", " ");
//run("8-bit");
selectWindow(second_image_TL+"-1.tif");

run("_ps_Triple_Background_SL2_ROIs");

}

/////////////////////////////

macro "_ps_Triple_Background_SL2_ROIs" {


       // Measuring ROIs
           //Finding PNNs with Analyze Particles

second_image_tif = second_image_TL+".tif";
third_image_tif = third_image_TL+".tif";

	name = series_name_TL+" "+second_stain_type_TL+".txt"; 
	selectWindow(second_image_TL+"-1.tif");
	roiManager("Show All");
	roiManager("Show All with labels");

	run("Clear Results");

if(first_SL_run==1) {
	roiManager("reset");
	wait(200);
	setAutoThreshold("Default dark");
	run("Threshold...");
	wait(200);
	setThreshold(TL2_lower*TL2_set_intensity_low_threshold, TL2_set_intensity_high_threshold);
	wait(200);
	makeRectangle(0, 0, 0, 0);
	run("Analyze Particles...", "size=TL2_set_low_particlesize-TL2_set_high_particlesize circularity=TL2_set_low_circularity-TL2_set_high_circularity clear include add");
	wait(200);
	selectWindow("Threshold");
	resetThreshold();
	run("Close");
	roiManager("Measure");

			   //Making square ROIs from Particles

	wait(500);
	DL2_add_squares=roiManager("count"); 

	if (DL2_add_squares>0) {

		array=newArray(DL2_add_squares); 
		for(i=0; i<DL2_add_squares;i++) { 
			roiManager("Select", 0);
			if(ROI_type_TL2 == "As-detected") {
				wait(10);
				}
			if(ROI_type_TL2 == "Square") {
				run("Specify...", "centered scaled");
				}
			if(ROI_type_TL2 == "Circle") {
				run("Specify...", "oval constrain centered scaled");
				}
		
			run("Enlarge...", "enlarge=TL2_set_roi_enlargement");
			roiManager("Add");
			roiManager("Select", 0);
			roiManager("Delete");
			} 
	
	} else {

		waitForUser("Oops", "We didn't see any staining here. Try adding some ROIs in the next step, or changing the image detection settings.");
		if (import_type==6) {
			run("_ps_Open_Triple");
			}	
		}
	}
if (import_type==6) {
	wait(300);
	} 
if (import_type==7) {
	roiManager("reset");
	roiManager("Open", subDir_TL+second_image_TL+second_stain_type_TL+"_single-label_ROIoverlay.zip");
	roiManager("Show All");
	roiManager("Show All with labels");
	first_SL_run=(first_SL_run+1);

} else {

	setTool("rectangle");
	selectWindow(second_image_tif);
	run("Revert");
	roiManager("Show All");
	roiManager("Show All with labels");

	TL2_set_intensity_low_threshold_old = TL2_set_intensity_low_threshold;
	TL2_set_low_particlesize_old = TL2_set_low_particlesize; 
	TL2_set_high_particlesize_old = TL2_set_high_particlesize;

	Dialog.create("Adjust Cell Detection Threshold");
	items = newArray("Rerun cell detection with these new settings.", "Let me add / delete ROIs by hand.", "Load previous ROI set.", "Save these ROIs and continue to "+third_stain_type_TL+" staining.");

	Dialog.addSlider("Threshold of Detection:", (TL2_set_intensity_low_threshold*0.1), (TL2_set_intensity_low_threshold*2), TL2_set_intensity_low_threshold);
	Dialog.addSlider("Smallest Cell Detected:", 0.0, 10000, TL2_set_low_particlesize);
	Dialog.addSlider("Largest Cell Detected:", 0.0, 100000, TL2_set_high_particlesize);

	Dialog.addRadioButtonGroup("Would you like to change the "+second_stain_type_TL+" cell detection threshold settings?", items, 4, 1, "Let me add / delete ROIs by hand.");
	Dialog.show;
	TL2_set_intensity_low_threshold = Dialog.getNumber();
	TL2_set_low_particlesize = Dialog.getNumber();
	TL2_set_high_particlesize = Dialog.getNumber();
	choice = Dialog.getRadioButton;

	if (choice=="Let me add / delete ROIs by hand." && TL2_set_intensity_low_threshold_old==TL2_set_intensity_low_threshold && TL2_set_low_particlesize_old==TL2_set_low_particlesize && TL2_set_high_particlesize_old==TL2_set_high_particlesize)  {
		waitForUser("How Does That Look?", "Here is your chance to adjust the ROIs to better identify PNNs. Try adding or deleting ROIs in the ROI manager. **THEN** press OK.");
		first_SL_run=(first_SL_run+1);
		run("_ps_Triple_Background_SL2_ROIs");
		}

	if (choice=="Rerun cell detection with these new settings." || TL2_set_intensity_low_threshold_old != TL2_set_intensity_low_threshold || TL2_set_low_particlesize_old != TL2_set_low_particlesize || TL2_set_high_particlesize_old != TL2_set_high_particlesize) {
		first_SL_run=1;		
		run("_ps_Triple_Background_SL2_ROIs");
		}

	if (choice=="Save these ROIs and continue to "+third_stain_type_TL+" staining." && TL2_set_intensity_low_threshold_old==TL2_set_intensity_low_threshold && TL2_set_low_particlesize_old==TL2_set_low_particlesize && TL2_set_high_particlesize_old==TL2_set_high_particlesize) {
		first_SL_run=1;
		wait(10);
		}
	if (choice=="Load previous ROI set.") {
		roiManager("reset");
		roiManager("Open", "");
		roiManager("Show All");
		roiManager("Show All with labels");
		first_SL_run=(first_SL_run+1);
		run("_ps_Triple_Background_SL2_ROIs");
		}
	}
	

selectWindow(second_image_tif);
run("Close");
	
//saving SL2 ROI overlay

SL2_save_ROIs=roiManager("count");
array=newArray(SL2_save_ROIs);
for(i=0; i<SL2_save_ROIs; i++) {
	array[i] = i;
	}
roiManager("Select", array);
roiManager("Save", subDir_TL+second_image_TL+second_stain_type_TL+"_single-label_ROIoverlay.zip");
	

//Measuring ROIs	

selectWindow(second_image_TL+"-1.tif");
run("Clear Results");
DL2_measure_squares=0;
DL2_measure_squares =roiManager("count"); 
array=newArray(DL2_measure_squares); 
for(i=0; i<DL2_measure_squares;i++) { 
        array[i] = i; 
	}
roiManager("Select", array);
roiManager("Measure");
roiManager("Show All");
roiManager("Show All with labels");

DL2_m=0;
DL2_means=0;
DL2_m = nResults;
DL2_means = newArray(DL2_m);
run("Select None");
    for (i=0; i<DL2_m; i++) {
      DL2_means[i] = getResult('Mean', i);
    }
run("Select None");
    for (i=0; i<DL2_m; i++) {
      setResult("Background", i, TL2_lower);
      setResult("Mean-Background", i, DL2_means[i]-TL2_lower);
    }
setResult("File Name", 0, name);
updateResults();

DL2_m2 = 0;
DL2_avgmeans = 0;
DL2_m2 = nResults;
DL2_avgmeans = newArray(DL2_m2);
run("Select None");
    for (i=0; i<DL2_m2; i++) {
     DL2_avgmeans[i] = getResult('Mean-Background', i);
    }
Array.getStatistics(DL2_avgmeans, min, max, mean, stdDev);
print(second_stain_type_TL+" Mean Intensity = "+mean);


DL2_numberStains=0;
DL2_numberStains=getValue("results.count");
print("Number of "+second_stain_type_TL+" Stained Cells = "+DL2_numberStains);

n_sl2 = nResults; //THIS IS AN IMPORTANT FUNCTION FOR THE RESULTS. SAME RESULTS AS IN TABLE, BUT PRINTED IN LOG, WHICH IS BETTER FOR IMPORTING INTO EXCEL.
area_sl2 = newArray(n_sl2);
XM_sl2 = newArray(n_sl2);
YM_sl2 = newArray(n_sl2);
mean_sl2 = newArray(n_sl2);
stddev_sl2 = newArray(n_sl2);
min_sl2 = newArray(n_sl2);
max_sl2 = newArray(n_sl2);
bkgd_sl2 = newArray(n_sl2);
meanbkgd_sl2 = newArray(n_sl2);
print("\n");
print("Individual "+second_stain_type_TL+" cell data:");
print("Subject# Image_name Stain_type Cell# Area XM YM Mean StdDev Min Max Background Mean-Background");
n=0;
for (i=0; i<n_sl2; i++) { // for each line in the results table, populate an array with these parameters and print them in the log. 
	area_sl2[i] = getResult('Area', i);
	XM_sl2[i] = getResult('XM', i);
	YM_sl2[i] = getResult('YM', i);
	mean_sl2[i] = getResult('Mean', i);
	stddev_sl2[i] = getResult('StdDev', i);
	min_sl2[i] = getResult('Min', i);
	max_sl2[i] = getResult('Max', i);
	bkgd_sl2[i] = getResult('Background', i);
	meanbkgd_sl2[i] = getResult('Mean-Background', i);
	print(subject_id+" "+second_image_TL+" "+second_stain_type_TL+" "+(i+1)+" "+area_sl2[i]+" "+XM_sl2[i]+" "+YM_sl2[i]+" "+mean_sl2[i]+" "+stddev_sl2[i]+" "+min_sl2[i]+" "+max_sl2[i]+" "+bkgd_sl2[i]+" "+meanbkgd_sl2[i]);
		
	//roiManager("Select", i);
	//run("Duplicate...", " ");
	//selectWindow(second_image_TL+"-3.tif");
	n=n+1;
	
	}
	
roiManager("reset");
selectWindow("ROI Manager");
run("Close");

wait(100);

selectWindow(second_image_TL+"-1.tif");
run("Close");

	// Saving data

saveAs("Measurements", subDir_TL+name);
print("\n"); 
print("Results saved as: "+subDir_TL+name);
selectWindow("Log");
saveAs("Text", dir+"/All_Results"+TimeString+".txt");


//\\//\\//\\//\\//\\//\\

//Third Image //THIS SECTION DOES THE SAME SINGLE-LABEL ROI WORK AS ABOVE, BUT FOR THE third STAIN.	
	// Selecting Background

run("Clear Results");

roiManager("reset");

roiManager("Show All");
roiManager("Show All with labels");


selectWindow(third_image_tif);

height = getHeight;
width = getWidth;

makeRectangle((6*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((6*(width-40)/6), (1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((6*(width-40)/6), (2*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((6*(width-40)/6), (3*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((6*(width-40)/6), (4*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((6*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((5*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((4*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((3*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((2*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((1*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (5*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (4*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (3*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (2*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((0.1*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((1*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((2*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((3*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((4*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");
makeRectangle((5*(width-40)/6), (0.1*(height-40)/5), 30, 30);
roiManager("Add");

run("Subtract Background...", "rolling=10");
roiManager("Select", newArray(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,21));
roiManager("Measure");


	// Calculating lower threshold background value in "Mean" column

DL3_m=0;
DL3_bkgd_mean=0;
DL3_meanbkgd=0;
DL3_std=0;
DL3_bkgd_std=0;
DL3_stdbkgd=0;

DL3_m = nResults;
DL3_bkgd_mean = newArray(DL3_m);
run("Select None");
for (i=0; i<DL3_m; i++) {
	DL3_bkgd_mean[i] = getResult('Mean', i);
	}
if (background_setting == "High") {
	Array.getStatistics(DL3_bkgd_mean, min, max, mean, stdDev);
	Array.sort(DL3_bkgd_mean);
	DL3_bkgd_high_trim = Array.trim(DL3_bkgd_mean, 15);
	Array.getStatistics(DL3_bkgd_high_trim, min, max, mean, stdDev);
	Array.reverse(DL3_bkgd_high_trim);
	DL3_bkgd_low_trim = Array.trim(DL3_bkgd_high_trim, 7);
	Array.getStatistics(DL3_bkgd_low_trim, min, max, mean, stdDev);
	DL3_meanbkgd = mean;

	DL3_std = nResults;
	DL3_bkgd_std = newArray(DL3_std);
	run("Select None");
	for (i=0; i<DL3_std; i++) {
		DL3_bkgd_std[i] = getResult('StdDev', i);
		}
	Array.getStatistics(DL3_bkgd_std, min, max, mean, stdDev);
	Array.sort(DL3_bkgd_std);
	DL3_bksd_high_trim = Array.trim(DL3_bkgd_std, 15);
	Array.getStatistics(DL3_bksd_high_trim, min, max, mean, stdDev);
	Array.reverse(DL3_bksd_high_trim);
	DL3_bksd_low_trim = Array.trim(DL3_bksd_high_trim, 7);
	Array.getStatistics(DL3_bksd_low_trim, min, max, mean, stdDev);
	DL3_stdbkgd = mean;
	}

if (background_setting == "Low") {
	Array.getStatistics(DL3_bkgd_mean, min, max, mean, stdDev);
	Array.sort(DL3_bkgd_mean);
	DL3_bkgd_high_trim = Array.trim(DL3_bkgd_mean, 5);
	Array.getStatistics(DL3_bkgd_high_trim, min, max, mean, stdDev);
	DL3_meanbkgd = mean;

	DL3_std = nResults;
	DL3_bkgd_std = newArray(DL3_std);
	run("Select None");
	for (i=0; i<DL3_std; i++) {
		DL3_bkgd_std[i] = getResult('StdDev', i);
		}
	Array.getStatistics(DL3_bkgd_std, min, max, mean, stdDev);
	Array.sort(DL3_bkgd_std);
	DL3_bksd_high_trim = Array.trim(DL3_bkgd_std, 5);
	Array.getStatistics(DL3_bksd_high_trim, min, max, mean, stdDev);
	DL3_stdbkgd = mean;
	}

TL3_lower = ((DL3_meanbkgd)+(DL3_stdbkgd*2));
print("\n");
print("Single "+third_stain_type_TL+" label:");
print(third_stain_type_TL+" Mean Background = "+DL3_meanbkgd+", Background StdDev = "+DL3_stdbkgd);
print(third_stain_type_TL+" Lower Threshold= "+TL3_lower);

	// Setting Threshold

run("Set Measurements...", "area mean standard min center limit redirect=None decimal=3");  //setting measurements.
setAutoThreshold("Default dark");
run("Threshold...");
wait(200);
setThreshold(TL3_lower, 500000);
//run("Apply");
run("NaN Background");


selectWindow(third_image_tif);
run("Duplicate...", " ");
//run("8-bit");
run("Duplicate...", " ");
//run("8-bit");
selectWindow(third_image_TL+"-1.tif");

run("_ps_Triple_Background_SL3_ROIs");

}

/////////////////////////////

macro "_ps_Triple_Background_SL3_ROIs" {


       // Measuring ROIs
           //Finding PNNs with Analyze Particles

third_image_tif = third_image_TL+".tif";
name = series_name_TL+" "+third_stain_type_TL+".txt"; 

selectWindow(third_image_TL+"-1.tif");
roiManager("Show All");
roiManager("Show All with labels");

run("Clear Results");
	
if(first_SL_run==1) {
	roiManager("reset");
	wait(200);
	setAutoThreshold("Default dark");
	run("Threshold...");
	wait(200);
	setThreshold(TL3_lower*TL3_set_intensity_low_threshold, TL3_set_intensity_high_threshold);
	wait(200);
	makeRectangle(0, 0, 0, 0);
	run("Analyze Particles...", "size=TL3_set_low_particlesize-TL3_set_high_particlesize circularity=TL3_set_low_circularity-TL3_set_high_circularity clear include add");
	wait(200);
	selectWindow("Threshold");
	resetThreshold();
	run("Close");
	roiManager("Measure");

			   //Making square ROIs from Particles

	wait(500);
	DL3_add_circles=roiManager("count"); 

	if (DL3_add_circles>0) {

		array=newArray(DL3_add_circles); 
		for(i=0; i<DL3_add_circles;i++) { 
			roiManager("Select", 0);
			if(ROI_type_TL3 == "As-detected") {
				wait(5);
			}
			if(ROI_type_TL3 == "Square") {
				run("Specify...", "centered scaled");
			}
			if(ROI_type_TL3 == "Circle") {
				run("Specify...", "oval constrain centered scaled");
			}
		
			run("Enlarge...", "enlarge=TL3_set_roi_enlargement");
			roiManager("Add");
			roiManager("Select", 0);
			roiManager("Delete");
		} 
	
	} else {

		waitForUser("Oops", "We didn't see any staining here. Try adding some ROIs in the next step, or changing the image detection settings.");
		if (import_type==6) {
			run("_ps_Open_Triple");
			}
		}
	}
	
if (import_type==6) {
	wait(300);
	} 
if (import_type==7) {
	roiManager("reset");
	roiManager("Open", subDir_TL+third_image_TL+third_stain_type_TL+"_single-label_ROIoverlay.zip");
	roiManager("Show All");
	roiManager("Show All with labels");
	first_SL_run=(first_SL_run+1);
} else {

	setTool("oval");
	selectWindow(third_image_tif);
	run("Revert");
	roiManager("Show All");
	roiManager("Show All with labels");

	TL3_set_intensity_low_threshold_old = TL3_set_intensity_low_threshold;
	TL3_set_low_particlesize_old = TL3_set_low_particlesize; 
	TL3_set_high_particlesize_old = TL3_set_high_particlesize;

	Dialog.create("Adjust Cell Detection Threshold");
	items = newArray("Rerun cell detection with these new settings.", "Let me add / delete ROIs by hand.", "Load previous ROI set.", "Save these ROIs and continue to triple-labeled staining.");

	Dialog.addSlider("Threshold of Detection:", (TL3_set_intensity_low_threshold*0.1), (TL3_set_intensity_low_threshold*2), TL3_set_intensity_low_threshold);
	Dialog.addSlider("Smallest Cell Detected:", 0.0, 10000, TL3_set_low_particlesize);
	Dialog.addSlider("Largest Cell Detected:", 0.0, 100000, TL3_set_high_particlesize);

	Dialog.addRadioButtonGroup("Would you like to change the "+third_stain_type_TL+" cell detection threshold settings?", items, 4, 1, "Let me add / delete ROIs by hand.");
	Dialog.show;
	TL3_set_intensity_low_threshold = Dialog.getNumber();
	TL3_set_low_particlesize = Dialog.getNumber();
	TL3_set_high_particlesize = Dialog.getNumber();
	choice = Dialog.getRadioButton;

	if (choice=="Let me add / delete ROIs by hand." && TL3_set_intensity_low_threshold_old==TL3_set_intensity_low_threshold && TL3_set_low_particlesize_old==TL3_set_low_particlesize && TL3_set_high_particlesize_old==TL3_set_high_particlesize)  {
		waitForUser("How Does That Look?", "Here is your chance to adjust the ROIs to better identify PNNs. Try adding or deleting ROIs in the ROI manager. **THEN** press OK.");
		first_SL_run=(first_SL_run+1);
		run("_ps_Triple_Background_SL3_ROIs");
		}

	if (choice=="Rerun cell detection with these new settings." || TL3_set_intensity_low_threshold_old != TL3_set_intensity_low_threshold || TL3_set_low_particlesize_old != TL3_set_low_particlesize || TL3_set_high_particlesize_old != TL3_set_high_particlesize) {
		first_SL_run=1;	
		run("_ps_Triple_Background_SL3_ROIs");
		}

	if (choice=="Save these ROIs and continue to triple-labeled staining." && TL3_set_intensity_low_threshold_old==TL3_set_intensity_low_threshold && TL3_set_low_particlesize_old==TL3_set_low_particlesize && TL3_set_high_particlesize_old==TL3_set_high_particlesize) {
		first_SL_run=1;
		wait(10);
		}
	if (choice=="Load previous ROI set.") {
		roiManager("reset");
		roiManager("Open", "");
		roiManager("Show All");
		roiManager("Show All with labels");
		first_SL_run=(first_SL_run+1);
		run("_ps_Triple_Background_SL3_ROIs");
		}
	}

selectWindow(third_image_tif);
run("Close");
	

//saving TL3 ROI overlay

TL3_save_ROIs=roiManager("count");
array=newArray(TL3_save_ROIs);
for(i=0; i<TL3_save_ROIs; i++) {
	array[i] = i;
	}
roiManager("Select", array);
roiManager("Save", subDir_TL+third_image_TL+third_stain_type_TL+"_single-label_ROIoverlay.zip");
	

//Measuring ROIs	

selectWindow(third_image_TL+"-1.tif");
run("Clear Results");
DL3_measure_circles=0;
DL3_measure_circles =roiManager("count"); 
array=newArray(DL3_measure_circles); 
for(i=0; i<DL3_measure_circles;i++) { 
        array[i] = i; 
	}
roiManager("Select", array);
roiManager("Measure");
roiManager("Show All");
roiManager("Show All with labels");

DL3_m=0;
DL3_means=0;
DL3_m = nResults;
DL3_means = newArray(DL3_m);
run("Select None");
    for (i=0; i<DL3_m; i++) {
      DL3_means[i] = getResult('Mean', i);
    }
run("Select None");
    for (i=0; i<DL3_m; i++) {
      setResult("Background", i, TL3_lower);
      setResult("Mean-Background", i, DL3_means[i]-TL3_lower);
    }
setResult("File Name", 0, name);
updateResults();

DL3_m3 = 0;
DL3_avgmeans = 0;
DL3_m3 = nResults;
DL3_avgmeans = newArray(DL3_m3);
run("Select None");
    for (i=0; i<DL3_m3; i++) {
     DL3_avgmeans[i] = getResult('Mean-Background', i);
    }
Array.getStatistics(DL3_avgmeans, min, max, mean, stdDev);
print(third_stain_type_TL+" Mean Intensity = "+mean);


DL3_numberStains=0;
DL3_numberStains=getValue("results.count");
print("Number of "+third_stain_type_TL+" Stained Cells = "+DL3_numberStains);

n_sl3 = nResults; //THIS IS AN IMPORTANT FUNCTION FOR THE RESULTS. SAME RESULTS AS IN TABLE, BUT PRINTED IN LOG, WHICH IS BETTER FOR IMPORTING INTO EXCEL.
area_sl3 = newArray(n_sl3);
XM_sl3 = newArray(n_sl3);
YM_sl3 = newArray(n_sl3);
mean_sl3 = newArray(n_sl3);
stddev_sl3 = newArray(n_sl3);
min_sl3 = newArray(n_sl3);
max_sl3 = newArray(n_sl3);
bkgd_sl3 = newArray(n_sl3);
meanbkgd_sl3 = newArray(n_sl3);
print("\n");
print("Individual "+third_stain_type_TL+" cell data:");
print("Subject# Image_name Stain_type Cell# Area XM YM Mean StdDev Min Max Background Mean-Background");
n=0;
for (i=0; i<n_sl3; i++) { // for each line in the results table, populate an array with these parameters and print them in the log. 
	area_sl3[i] = getResult('Area', i);
	XM_sl3[i] = getResult('XM', i);
	YM_sl3[i] = getResult('YM', i);
	mean_sl3[i] = getResult('Mean', i);
	stddev_sl3[i] = getResult('StdDev', i);
	min_sl3[i] = getResult('Min', i);
	max_sl3[i] = getResult('Max', i);
	bkgd_sl3[i] = getResult('Background', i);
	meanbkgd_sl3[i] = getResult('Mean-Background', i);
	print(subject_id+" "+third_image_TL+" "+third_stain_type_TL+" "+(i+1)+" "+area_sl3[i]+" "+XM_sl3[i]+" "+YM_sl3[i]+" "+mean_sl3[i]+" "+stddev_sl3[i]+" "+min_sl3[i]+" "+max_sl3[i]+" "+bkgd_sl3[i]+" "+meanbkgd_sl3[i]);
		
	//roiManager("Select", i);
	//run("Duplicate...", " ");
	//selectWindow(third_image_TL+"-3.tif");
	n=n+1;
	
	}

roiManager("reset");
selectWindow("ROI Manager");
run("Close");

wait(100);

selectWindow(third_image_TL+"-1.tif");
run("Close");

	// Saving data

saveAs("Measurements", subDir_TL+name);
print("\n"); 
print("Results saved as: "+subDir_TL+name);
selectWindow("Log");
saveAs("Text", dir+"/All_Results"+TimeString+".txt");

run("_ps_Triple_Background_DL1and2_ROIs");

}


//\\//\\//\\//\\//\\//\\//\\

macro "_ps_Triple_Background_DL1and2_ROIs" {  //this is the macro that identifies double-labeled cells between stains 1 and 2.

	//Double labeled cell identification

first_image_tif = first_image_TL+"-2.tif";
second_image_tif = second_image_TL+"-2.tif";

//////////////////////////////////////////NEW DOUBLE ROI STUFF HERE DOWN///////////////////////////////////////////////////

selectWindow(second_image_tif); //select the WFA image

roiManager("deselect");
roiManager("reset");
wait(50);

roiManager("Open", subDir_TL+first_image_TL+first_stain_type_TL+"_single-label_ROIoverlay.zip");

double_label_first_type = roiManager("count"); // determines the number of circle ROIs
double_label_first_type_array = Array.getSequence(double_label_first_type); 

roiManager("Open", subDir_TL+second_image_TL+second_stain_type_TL+"_single-label_ROIoverlay.zip");

roiManager("Show All");
roiManager("Show All with labels");

double_label_all_types = roiManager("count");
double_label_all_types_array = Array.getSequence(double_label_all_types); 
double_label_second_type = double_label_all_types-double_label_first_type; // determines the number of square ROIs
double_label_second_type_start = double_label_first_type; // the ROI manager position of the first Square

/* 
 In this section we are identifying the squares that have overlapping cicrles. 
 */


n=0; // number of time the square loop was run.
no_overlap=0; //number of squares without circle overlap.
multi_b_removed=0; //number of circles removed because resolved multiple overlap;
removed_circles_array_a = newArray(n);
saved_squares_array_a = newArray(n);

for(a=double_label_second_type_start; a<(double_label_all_types-no_overlap-multi_b_removed); a++){  // squares.
	roiManager("Select", a);
	a2=a+1; //a2 is a counting variable to correct for the ROI manager starting at ROI #0.
	bTotal=0; // number of circles checked against square "a2".
	no_co=0; // yes/no variable determining whether overlap was found. 
	co_roi_array = newArray(no_co); // this array will be concatinated with the "b" value for each overlapping circle so the ROIs can be selected.
	co_roi_array_UI = newArray(no_co); // this array will be concatinated with the "b2" value for each overlapping circle so the user can see multiple overlaps.
	CoM_distance_array = newArray(no_co); // making an array of the distances between the square CoM and the circle's CoM.
	for(b=0; b<double_label_first_type-multi_b_removed; b++){ //circles.
		b2=b+1; //b2 is a counting variable to correct for the ROI manager starting at ROI #0.
		roiManager("Select", newArray(a, b)); //select the square the the circle
		roiManager("AND"); //build an overlapping ROI
		roiType = selectionType();
		bTotal=bTotal+1;
		if(roiType==-1){ // and there is not overlapping area under the square and circle ROIs
			//print("No overlap: circle "+b2); //then, move on to the next (ie, restart the loop).
			//wait(10);
			}
		if(roiType!=-1){ // if there is overlap from the square and the circle,
			c = roiManager("Add"); //add the overlap to the ROI manager so that we can measure its size.
			//print("HIT!!! = circle "+b2);
			no_co = no_co+1; // to remember that, yes, there is overlap.
				
			roiManager("deselect");
			run("Clear Results");
			//selectWindow(first_image_tif); //select the PV image
			roiManager("Select", b); //measure the size of the circle
			roiManager("Measure");
			co_roi = (roiManager("count")-1); //find the overlap ROI (it's going to be the last one on the ROI manager list).
			//selectWindow(second_image_tif); //select the WFA image
			roiManager("Select", co_roi); // select it.
			roiManager("Measure"); //measure the area.
			
			b2_area=getResult("Area", 0); // grab the circle-ROI's area from the results 
			c_area=getResult("Area", 1); // grab the overlap-ROI's area from the results
			b2_area_80 = b2_area*TL1_overlap; // calculate 80% of the circle area (we're using 80% as an arbitrary threshold of minimum circle that should be within the square). 
			wait(5);
			
			//print("b = "+b2_area);
			//print("c = "+c_area);
			//print("b2_80 = "+b2_area_80);
			
			roiManager("Select", co_roi);
			roiManager("Delete"); //now removing it beause we only wanted that new ROI to measure the area of overlap.
			
			if(b2_area_80>c_area){ // if the overlapping area is less 80% of the circle,
				no_co = no_co-1; //then, forget that an overlap was identified and move on (restart the loop).
				wait(5);
				//print("Nevermind, It failed 80% overlap. YYYYEEEEEAAAAH");
			} else {
				co_roi_array = Array.concat(co_roi_array, b); //building an array to remember which circle ROIs overlap (for ROI selection)
				co_roi_array_UI = Array.concat(co_roi_array_UI, b2); //building an array to remember which circle ROIs overlap (for the human to understand)
				saved_squares_array_a = Array.concat(saved_squares_array_a, a);
				//print("Squares saved so far:");
				//Array.print(saved_squares_array_a);
				}
			}
		if(b2==(double_label_first_type-multi_b_removed) && (no_co==0)){ //if we have checked every circle and still not found a circle that overlap (>80%) with the square,
     		roiManager("deselect");
     		roiManager("Select", a2-1); //move back one spot and select that square,
			roiManager("Delete"); // then delete it because we know that it was a non-overlapping square and should no longer be considered for colabeling.
			//print("Square "+a2+" deleted. AAAAAAAAAAAAAAAAAAAAAAAHHHHHHHHHHHHHHH"); //square ROI death scene.
			no_overlap=no_overlap+1; // add one to the number of square not overlapping so that we can lower the total squares.
			a=a-1; //remove one square from the counted number of squares. Semi repetitive with last variable, but each serve a function in the top IF loop.
     		}
     	if(b2==(double_label_first_type-multi_b_removed) && (no_co==1)){ // if we have checked all circles and there was an overlap detected
     		//print("Total circles checked: "+bTotal); //then, print # circles checked. (next step is to exit loop because a!<(double_label_all_types-no_overlap)).
			//print("loop = "+n+1);
			//print("\n");
			//wait(10);
     		}
     	
     	if(b2==(double_label_first_type-multi_b_removed) && (no_co>1)){ // if checked all circles and there were multiple overlaps, 
     		selectWindow(second_image_tif); //select the WFA image
     		run("Clear Results");
     		roiManager("deselect");
     		roiManager("Select", a); //select the square ROI
     		roiManager("Measure"); //measure the square ROI so we can...
     		CoM_x_square=getResult("XM", 0); // grab the squares's center of mass x-coordinate from the results. 
     		CoM_y_square=getResult("YM", 0); // grab the squares's center of mass y-coordinate from the results.
     		CoM_tot_square = CoM_x_square+CoM_y_square; //making the total value (number only used for comparison to circles).
     		wait(10);
     		
     		run("Clear Results"); 
     		selectWindow(first_image_tif); //select the PV image
     		roiManager("deselect");
     		roiManager("Select", co_roi_array); //select the overlapping ROIs
     		roiManager("Measure"); 
      		for(i=0; i<no_co; i++){ //doing this as a FOR loop to allow for any range of overlapping ROIs
     			CoM_x=getResult("XM", i); // grab the circle's center of mass x-coordinate from the results.
   				CoM_y=getResult("YM", i); // grab the first circle's center of mass y-coordinate from the results.
   				CoM_tot=CoM_x+CoM_y; // the circle's total value
   				//print("CoM_x for ROI "+(i)+": "+CoM_x);
   				//print("CoM_y for ROI "+(i)+": "+CoM_y);
   				//print("CoM_tot for ROI "+(i)+": "+CoM_tot);
	   			distance=abs(CoM_tot_square-CoM_tot); //measuring the distance between the center of the square ROI's brightest area and the circle ROI
				CoM_distance_array = Array.concat(CoM_distance_array, distance); // building an array of the distances between the square CoM and the circle's CoM.
   				//print("CoM_distance_array:");
   				//Array.print(CoM_distance_array);
   				wait(5);
     			}
     		
     		selectWindow(second_image_tif); //select the WFA image
     		rankPosition_CoM = Array.rankPositions(CoM_distance_array); //determining the shortest distance
     		rank_CoM = Array.rankPositions(rankPosition_CoM);
     		rankPositionWinner=Array.slice(rankPosition_CoM, 0, 1);

     		co_roi_array_winner_position = rankPositionWinner[0];
     		co_roi_array_low = Array.slice(co_roi_array, 0, co_roi_array_winner_position); //saving multiple ROIs before the shortest distance ROI
     		co_roi_array_high = Array.slice(co_roi_array, co_roi_array_winner_position+1, no_co); // saving multiple ROIs after the shortest distance ROI
     		co_roi_array_losers = Array.concat(co_roi_array_low, co_roi_array_high); 

     		co_roi_array_winner_low = Array.slice(co_roi_array, 0, co_roi_array_winner_position+1); //saving multiple ROIs before the shortest distance ROI
     		co_roi_array_winner = Array.slice(co_roi_array, co_roi_array_winner_position, no_co-1); // saving multiple ROIs after the shortest distance ROI
			
			removed_circles_array_a = Array.concat(removed_circles_array_a, co_roi_array_losers);
			roiManager("deselect");
			roiManager("Select", co_roi_array_losers); //deleting the remaining multiple ROI (except for the closest to the square ROI)
			roiManager("Delete");  

			a=a-(no_co-1); //since square ROIs are numbered after circles, we have to revise this number to keep the counting correct in the top IF loop.	
   			multi_b_removed=multi_b_removed+(no_co-1); //since square ROIs are numbered after circles, we have to revise this number to keep the counting correct in the top IF loop.					
			for(i=0; i<saved_squares_array_a.length; i++){
 				saved_squares_array_a[i]=saved_squares_array_a[i]-(no_co-1);
				
				}
     		}
		}
	
		n=n+1;

	}
//saving ROI overlay

roiManager("deselect"); //by not making an ROI manager selection, and actually deselecting, the ROI manager selects all by default. 
roiManager("Save", subDir_TL+second_image_TL+second_stain_type_TL+"_vs_"+first_stain_type_TL+"_first_step_double-label_ROIoverlay.zip");
wait(100);

roiManager("deselect");
roiManager("Select", saved_squares_array_a);
roiManager("Delete");

save_circle_ROIs=roiManager("count");
circle_array=newArray(save_circle_ROIs);
for(i=0; i<save_circle_ROIs; i++) {
	circle_array[i] = i;
	}

roiManager("deselect");
roiManager("Save", subDir_TL+first_image_TL+first_stain_type_TL+"_vs_"+second_stain_type_TL+"_refined-single-label_ROIoverlay.zip");
wait(100);
roiManager("reset");

roiManager("Open", subDir_TL+second_image_TL+second_stain_type_TL+"_vs_"+first_stain_type_TL+"_first_step_double-label_ROIoverlay.zip");
roiManager("Select", circle_array);
roiManager("Delete");

save_squareDL_ROIs=roiManager("count");
square_DL_array=newArray(save_squareDL_ROIs);
for(i=0; i<save_squareDL_ROIs; i++) {
	square_DL_array[i] = i;
	}
	
roiManager("deselect");
roiManager("Save", subDir_TL+second_image_TL+second_stain_type_TL+"_vs_"+first_stain_type_TL+"_double-label_ROIoverlay.zip");
wait(100);

roiManager("reset");

//////////////////////////////////////////////
/* 
 In this section we are identifying the CIRCLES that have overlapping SQUARES. 
 */

selectWindow(first_image_tif); //select the PV image

roiManager("Open", subDir_TL+second_image_TL+second_stain_type_TL+"_vs_"+first_stain_type_TL+"_double-label_ROIoverlay.zip");
double_label_second_type_b = roiManager("count");
double_label_second_type_b_array = Array.getSequence(double_label_second_type_b); 

roiManager("Open", subDir_TL+first_image_TL+first_stain_type_TL+"_vs_"+second_stain_type_TL+"_refined-single-label_ROIoverlay.zip");
double_label_all_types = roiManager("count");
double_label_all_types_array = Array.getSequence(double_label_all_types); 
double_label_first_type = double_label_all_types-double_label_second_type_b;
double_label_first_type_start = double_label_second_type_b; 

n=0; //count loops
no_overlap=0; //number of circles without square overlap. 
co_roi_array2 = newArray(n); // this array will be concatenated with the "d" value for each overlapping square so the ROIs can be selected.
co_roi_array2_UI = newArray(n); // this array will be concatenated with the "d2" value for each overlapping square so the user can see multiple overlaps.
saved_circles_array = newArray(n); //building an array to remember which circle ROIs overlap (for ROI selection)

for(c=double_label_first_type_start; c<(double_label_all_types-no_overlap); c++){ //circles
	roiManager("Select", c);
	c2=c+1; //c2 is a counting variable to correct for the ROI manager starting at ROI #0.

	dTotal=0; // number of squares checked against circle "c2".
	no_co=0; // yes/no variable determining whether overlap was found. 
	for(d=0; d<double_label_second_type_b; d++){ //squares
		roiManager("Select", newArray(c, d)); //select the square the the circle
		roiManager("AND"); //build an overlapping ROI
		roiType = selectionType(); //determine the type of overlap represented by the overlapping ROI
		d2=d+1;
		dTotal=dTotal+1;
		if(roiType==-1){ //"-1" represents the type of ROI that does not overlap
     	}

		if(roiType!=-1){ //if not "-1 type", then there is overlap, so mark it.
			roiManager("Add"); //add the overlap to the ROI manager so that we can measure its size.
			no_co = no_co+1; // to remember that, yes, there is overlap.
				
			roiManager("deselect");
			run("Clear Results");
			//selectWindow(first_image_tif); //select the PV image
			roiManager("Select", c); //measure the size of the circle
			roiManager("Measure");
			co_roi = (roiManager("count")-1); //find the overlap ROI (it's going to be the last one on the ROI manager list).
			//selectWindow(second_image_tif); //select the WFA image
			roiManager("Select", co_roi); // select it.
			roiManager("Measure"); //measure the area.
			
			c2_area=getResult("Area", 0); // grab the circle-ROI's area from the results 
			co_area=getResult("Area", 1); // grab the overlap-ROI's area from the results
			c2_area_80 = c2_area*TL2_overlap; // calculate 80% of the circle area (we're using 80% as an arbitrary threshold of minimum circle that should be within the square). 
			
			//selectWindow(first_image_tif); //select the PV image
			wait(5);
			
			roiManager("Select", co_roi);
			roiManager("Delete"); //now removing it beause we only wanted that new ROI to measure the area of overlap.
			
			if(c2_area_80>co_area){ // if the overlapping area is less 80% of the square,
				no_co = no_co-1; //then, forget that an overlap was identified and move on (restart the loop).
				//print("Nevermind, It failed 80% overlap. YYYYEEEEEAAAAH");
			} else {
				co_roi_array2 = Array.concat(co_roi_array2, d); //building an array to remember which circle ROIs overlap (for ROI selection)
				co_roi_array2_UI = Array.concat(co_roi_array2_UI, d2); //building an array to remember which circle ROIs overlap (for the human to understand)
				saved_circles_array = Array.concat(saved_circles_array, c); //building an array to remember which circle ROIs overlap (for ROI selection)
				}
			}
		if((d2==double_label_second_type_b) && (no_co==0)){ //if we have checked every square and still not found a square that overlap (>80%) with the circle,
			roiManager("deselect");
     		roiManager("Select", c2-1);
			roiManager("Delete");
			no_overlap=no_overlap+1; // add one to the number of circles not overlapping so that we can lower the total circles.
			c=c-1;//remove one circle from the counted number of circle. Semi repetitive with last variable, but each serve a function in the top IF loop.
     		}
		}
	
	n=n+1;
	
	}

roiManager("Select", double_label_second_type_b_array);
roiManager("Delete");

//saving ROI overlay

save_circleDL_ROIs=roiManager("count");

circle_DL_array=newArray(save_circleDL_ROIs); // saving the list of DL circle ROIs
for(i=0; i<save_circleDL_ROIs; i++) {
	circle_DL_array[i] = i;
	}

roiManager("deselect");
roiManager("Save", subDir_TL+first_image_TL+first_stain_type_TL+"_vs_"+second_stain_type_TL+"_double-label_ROIoverlay.zip");

wait(100);


print("\n"+first_image_TL);  //THE NEXT COUPLE OF LINES PRINT FEEDBACK ABOUT THE NUMBER OF DL CELLS FOUND, AND THE NUMBER OF CIRCLE ROIs DELETED IN THE FIRST STEP.
if(removed_circles_array_a.length>0){
	print("Removed circle ROIs (these were multiple overlaps with square ROIs):");
	Array.print(removed_circles_array_a);
}
print("Circle DL ROIs = "+save_circleDL_ROIs);
print("\n"+second_image_TL);
print("Square DL ROIs = "+save_squareDL_ROIs);

roiManager("reset");

run("_ps_Triple_Background_DL3and2_ROIs");
}


//\\//\\//\\//\\//\\//\\//\\

macro "_ps_Triple_Background_DL3and2_ROIs" {  //this is the macro that identifies double-labeled cells between stains 3 and 2.

	//Double labeled cell identification

third_image_tif = third_image_TL+"-2.tif";
second_image_tif = second_image_TL+"-2.tif";

//////////////////////////////////////////NEW DOUBLE ROI STUFF HERE DOWN///////////////////////////////////////////////////

roiManager("deselect");
roiManager("reset");
wait(50);

selectWindow(second_image_tif); //select the WFA image

roiManager("Open", subDir_TL+third_image_TL+third_stain_type_TL+"_single-label_ROIoverlay.zip");

double_label_third_type = roiManager("count"); // determines the number of circle ROIs
double_label_third_type_array = Array.getSequence(double_label_third_type); 

roiManager("Open", subDir_TL+second_image_TL+second_stain_type_TL+"_single-label_ROIoverlay.zip");

roiManager("Show All");
roiManager("Show All with labels");

double_label_all_types = roiManager("count");
double_label_all_types_array = Array.getSequence(double_label_all_types); 
double_label_second_type = double_label_all_types-double_label_third_type; // determines the number of square ROIs
double_label_second_type_start = double_label_third_type; // the ROI manager position of the first Square

/* 
 In this section we are identifying the squares that have overlapping cicrles. 
 */
//waitForUser("Everything is loaded, let's start.");

n=0; // number of time the square loop was run.
no_overlap=0; //number of squares without circle overlap.
multi_b_removed=0; //number of circles removed because resolved multiple overlap;
removed_circles_array_c = newArray(n);
saved_squares_array_c = newArray(n);

for(a=double_label_second_type_start; a<(double_label_all_types-no_overlap-multi_b_removed); a++){  // squares.
	roiManager("Select", a);
	a2=a+1; //a2 is a counting variable to correct for the ROI manager starting at ROI #0.
	//print("square = "+a2);
	bTotal=0; // number of circles checked against square "a2".
	no_co=0; // yes/no variable determining whether overlap was found. 
	co_roi_array = newArray(no_co); // this array will be concatenated with the "b" value for each overlapping circle so the ROIs can be selected.
	co_roi_array_UI = newArray(no_co); // this array will be concatenated with the "b2" value for each overlapping circle so the user can see multiple overlaps.
	CoM_distance_array = newArray(no_co); // making an array of the distances between the square CoM and the circle's CoM.
	
	for(b=0; b<(double_label_third_type-multi_b_removed); b++){ //circles.
		b2=b+1; //b2 is a counting variable to correct for the ROI manager starting at ROI #0.
		roiManager("Select", newArray(a, b)); //select the square the the circle
		roiManager("AND"); //build an overlapping ROI
		roiType = selectionType();
		bTotal=bTotal+1;
		if(roiType==-1){ // and there is not overlapping area under the square and circle ROIs
			//print("No overlap: circle "+b2); //then, move on to the next (ie, restart the loop).	
			}
		if(roiType!=-1){ // if there is overlap from the square and the circle,
			c = roiManager("Add"); //add the overlap to the ROI manager so that we can measure its size.
			no_co = no_co+1; // to remember that, yes, there is overlap.
				
			roiManager("deselect");
			run("Clear Results");
			//selectWindow(third_image_tif); //select the 8-oxo image
			roiManager("Select", b); //measure the size of the circle
			roiManager("Measure");
			//selectWindow(second_image_tif); //select the pv image
			co_roi = (roiManager("count")-1); //find the overlap ROI (it's going to be the last one on the ROI manager list).
			roiManager("Select", co_roi); // select it.
			roiManager("Measure"); //measure the area.
						
			b2_area=getResult("Area", 0); // grab the circle-ROI's area from the results 
			c_area=getResult("Area", 1); // grab the overlap-ROI's area from the results
			b2_area_80 = b2_area*TL2_overlap; // calculate 80% of the circle area (we're using 80% as an arbitrary threshold of minimum circle that should be within the square). 
			wait(5);
			
			//print("b2 = "+b2_area);
			//print("c_area= "+c_area);
			//print("b2_80 = "+b2_area_80);
			
			//waitForUser("check area comparisons in log");
			
			roiManager("Select", co_roi);
			roiManager("Delete"); //now removing it because we only wanted that new ROI to measure the area of overlap.
				
			if(b2_area_80>c_area){ // if the overlapping area is less 80% of the circle,
				no_co = no_co-1; //then, forget that an overlap was identified and move on (restart the loop).
				wait(5);
				
			} else {
				co_roi_array = Array.concat(co_roi_array, b); //building an array to remember which circle ROIs overlap (for ROI selection)
				co_roi_array_UI = Array.concat(co_roi_array_UI, b2); //building an array to remember which circle ROIs overlap (for the human to understand)
				saved_squares_array_c = Array.concat(saved_squares_array_c, a);
				}
			}
		if(b2==(double_label_third_type-multi_b_removed) && (no_co==0)) { //if we have checked every circle and still not found a circle that overlap (>80%) with the square,
     		roiManager("deselect");
     		roiManager("Select", a2-1); //move back one spot and select that square,
			roiManager("Delete"); // then delete it because we know that it was a non-overlapping square and should no longer be considered for colabeling.
			no_overlap=no_overlap+1; // add one to the number of square not overlapping so that we can lower the total squares.
			a=a-1; //remove one square from the counted number of squares. Semi repetitive with last variable, but each serve a function in the top IF loop.
     		wait(5);
			}
     	if(b2==(double_label_third_type-multi_b_removed) && (no_co==1)) { // if we have checked all circles and there was an overlap detected
			wait(5);
			}
     	
     	if(b2==(double_label_third_type-multi_b_removed) && (no_co>1)) { // if checked all circles and there were multiple overlaps, 
     		selectWindow(second_image_tif); //select the WFA image
     		run("Clear Results");
     		roiManager("deselect");
     		roiManager("Select", a); //select the square ROI
     		roiManager("Measure"); //measure the square ROI so we can...
     		CoM_x_square=getResult("XM", 0); // grab the squares's center of mass x-coordinate from the results. 
     		CoM_y_square=getResult("YM", 0); // grab the squares's center of mass y-coordinate from the results.
     		CoM_tot_square = CoM_x_square+CoM_y_square; //making the total value (number only used for comparison to circles).
			wait(10);     		

     		run("Clear Results"); 
     		selectWindow(third_image_tif); //select the 8-oxo image
     		roiManager("deselect");
     		roiManager("Select", co_roi_array); //select the overlapping ROIs
     		roiManager("Measure"); 
			wait(10); 

      		for(i=0; i<no_co; i++){ //doing this as a FOR loop to allow for any range of overlapping ROIs
     			CoM_x=getResult("XM", i); // grab the circle's center of mass x-coordinate from the results.
   				CoM_y=getResult("YM", i); // grab the first circle's center of mass y-coordinate from the results.
   				CoM_tot=CoM_x+CoM_y; // the circle's total value
	   			distance=abs(CoM_tot_square-CoM_tot); //measuring the distance between the center of the square ROI's brightest area and the circle ROI
				CoM_distance_array = Array.concat(CoM_distance_array, distance); // building an array of the distances between the square CoM and the circle's CoM.
				wait(5);
     			}
     		
     		selectWindow(second_image_tif); //select the WFA image
			rankPosition_CoM = Array.rankPositions(CoM_distance_array); //determining the shortest distance
			rank_CoM = Array.rankPositions(rankPosition_CoM);
			rankPositionWinner=Array.slice(rankPosition_CoM, 0, 1);

			co_roi_array_winner_position = rankPositionWinner[0];
			co_roi_array_low = Array.slice(co_roi_array, 0, co_roi_array_winner_position); //saving multiple ROIs before the shortest distance ROI
			co_roi_array_high = Array.slice(co_roi_array, co_roi_array_winner_position+1, no_co); // saving multiple ROIs after the shortest distance ROI
			co_roi_array_losers = Array.concat(co_roi_array_low, co_roi_array_high); 

			co_roi_array_winner_low = Array.slice(co_roi_array, 0, co_roi_array_winner_position+1); //saving multiple ROIs before the shortest distance ROI
			co_roi_array_winner = Array.slice(co_roi_array, co_roi_array_winner_position, no_co-1); // saving multiple ROIs after the shortest distance ROI
		
			removed_circles_array_c = Array.concat(removed_circles_array_c, co_roi_array_losers);
			roiManager("deselect");
			roiManager("Select", co_roi_array_losers); //deleting the remaining multiple ROI (except for the closest to the square ROI)
			roiManager("Delete");  

			a=a-(no_co-1); //since square ROIs are numbered after circles, we have to revise this number to keep the counting correct in the top IF loop.	
			multi_b_removed=multi_b_removed+(no_co-1); //since square ROIs are numbered after circles, we have to revise this number to keep the counting correct in the top IF loop.					
			for(i=0; i<saved_squares_array_c.length; i++){
				saved_squares_array_c[i]=saved_squares_array_c[i]-(no_co-1);
				}
			}
		}
	n=n+1;
	}

//saving ROI overlay

roiManager("deselect"); //by not making an ROI manager selection, and actually deselecting, the ROI manager selects all by default. 
roiManager("Save", subDir_TL+second_image_TL+second_stain_type_TL+"_vs_"+third_stain_type_TL+"_first_step_double-label_ROIoverlay.zip");
wait(100);

roiManager("deselect");
roiManager("Select", saved_squares_array_c);
roiManager("Delete");

save_circle_ROIs=roiManager("count");
circle_array=newArray(save_circle_ROIs);
for(i=0; i<save_circle_ROIs; i++) {
	circle_array[i] = i;
	}

roiManager("deselect");
roiManager("Save", subDir_TL+third_image_TL+third_stain_type_TL+"_vs_"+second_stain_type_TL+"_refined-single-label_ROIoverlay.zip");
wait(100);
roiManager("reset");

roiManager("Open", subDir_TL+second_image_TL+second_stain_type_TL+"_vs_"+third_stain_type_TL+"_first_step_double-label_ROIoverlay.zip");
roiManager("Select", circle_array);
roiManager("Delete");

save_squareDL_ROIs=roiManager("count");
square_DL_array=newArray(save_squareDL_ROIs);
for(i=0; i<save_squareDL_ROIs; i++) {
	square_DL_array[i] = i;
	}
	
roiManager("deselect");
roiManager("Save", subDir_TL+second_image_TL+second_stain_type_TL+"_vs_"+third_stain_type_TL+"_double-label_ROIoverlay.zip");
wait(100);

roiManager("reset");

//////////////////////////////////////////////
/* 
 In this section we are identifying the CIRCLES that have overlapping SQUARES. 
 */
 
selectWindow(third_image_tif); //select the 8oxo image
 
roiManager("Open", subDir_TL+second_image_TL+second_stain_type_TL+"_vs_"+third_stain_type_TL+"_double-label_ROIoverlay.zip");
double_label_second_type_b = roiManager("count");
double_label_second_type_b_array = Array.getSequence(double_label_second_type_b); 

roiManager("Open", subDir_TL+third_image_TL+third_stain_type_TL+"_vs_"+second_stain_type_TL+"_refined-single-label_ROIoverlay.zip");
double_label_all_types = roiManager("count");
double_label_all_types_array = Array.getSequence(double_label_all_types); 
double_label_third_type = double_label_all_types-double_label_second_type_b;
double_label_third_type_start = double_label_second_type_b; 

n=0; //count loops
no_overlap=0; //number of circles without square overlap. 
co_roi_array2 = newArray(n); // this array will be concatenated with the "d" value for each overlapping square so the ROIs can be selected.
co_roi_array2_UI = newArray(n); // this array will be concatenated with the "d2" value for each overlapping square so the user can see multiple overlaps.
saved_circles_array = newArray(n); //building an array to remember which circle ROIs overlap (for ROI selection)

for(c=double_label_third_type_start; c<(double_label_all_types-no_overlap); c++){ //circles
	roiManager("Select", c);
	c2=c+1; //c2 is a counting variable to correct for the ROI manager starting at ROI #0.
	//print("circle = "+c2);
	dTotal=0; // number of squares checked against circle "c2".
	no_co=0; // yes/no variable determining whether overlap was found. 
	for(d=0; d<double_label_second_type_b; d++){ //squares
		roiManager("Select", newArray(c, d)); //select the square the the circle
		roiManager("AND"); //build an overlapping ROI
		roiType = selectionType(); //determine the type of overlap represented by the overlapping ROI
		d2=d+1;
		dTotal=dTotal+1;
		if(roiType==-1){ //"-1" represents the type of ROI that does not overlap
			//print("No overlap: square "+d2);
			
     		}
		if(roiType!=-1){ //if not "-1 type", then there is overlap, so mark it.
			roiManager("Add"); //add the overlap to the ROI manager so that we can measure its size.
			//print("HIT!!! = square "+d2);
			no_co = no_co+1; // to remember that, yes, there is overlap.
				
			roiManager("deselect");
			run("Clear Results");
			//selectWindow(third_image_tif); //select the 8-oxo image
			roiManager("Select", c); //measure the size of the circle
			roiManager("Measure");
			co_roi = (roiManager("count")-1); //find the overlap ROI (it's going to be the last one on the ROI manager list).
			roiManager("Select", co_roi); // select it.
			//selectWindow(second_image_tif); //select the pv image
			roiManager("Measure"); //measure the area.
			
			c2_area=getResult("Area", 0); // grab the circle-ROI's area from the results 
			co_area=getResult("Area", 1); // grab the overlap-ROI's area from the results
			c2_area_80 = c2_area*TL3_overlap; // calculate 80% of the circle area (we're using 80% as an arbitrary threshold of minimum circle that should be within the square). 
			//print("c = "+c2_area);
			//print("co = "+co_area);
			//print("c2_80 = "+c2_area_80);
			wait(5);
			//selectWindow(third_image_tif); //select the 8-oxo image
			
			//waitForUser("check area comparisons in log");
			
			roiManager("Select", co_roi);
			roiManager("Delete"); //now removing it beause we only wanted that new ROI to measure the area of overlap.
			
			if(c2_area_80>co_area){ // if the overlapping area is less 80% of the square,
				no_co = (no_co-1); //then, forget that an overlap was identified and move on (restart the loop).
				//print("Nevermind, It failed 80% overlap. YYYYEEEEEAAAAH");
			} else {
				co_roi_array2 = Array.concat(co_roi_array2, d); //building an array to remember which square ROIs overlap (for ROI selection)
				wait(5);
				co_roi_array2_UI = Array.concat(co_roi_array2_UI, d2); //building an array to remember which square ROIs overlap (for the human to understand)
				wait(5);
				saved_circles_array = Array.concat(saved_circles_array, c); //building an array to remember which circle ROIs overlap (for ROI selection)
				//print("Circles saved so far:");
				//Array.print(saved_circles_array);
				wait(5);
				}
			}
		if((d2==double_label_second_type_b) && (no_co==0)){ //if we have checked every square and still not found a square that overlap (>80%) with the circle,
			roiManager("deselect");
			wait(10);
     		roiManager("Select", c2-1);
			roiManager("Delete");
			//print("circle "+c2+" deleted. AAAAAAAAAAAAAAAAAAAAAAAHHHHHHHHHHHHHHH");
			no_overlap=no_overlap+1; // add one to the number of circles not overlapping so that we can lower the total circles.
			c=c-1;//remove one circle from the counted number of circle. Semi repetitive with last variable, but each serve a function in the top IF loop.
			//wait(5);
     		}
	}
	
	n=n+1;
}
//waitForUser("");
roiManager("deselect");
roiManager("Select", double_label_second_type_b_array);
roiManager("Delete");

//saving ROI overlay

save_circleDL_ROIs=roiManager("count");

circle_DL_array=newArray(save_circleDL_ROIs); // saving the list of DL circle ROIs
for(i=0; i<save_circleDL_ROIs; i++) {
	circle_DL_array[i] = i;
	}

roiManager("deselect");
roiManager("Save", subDir_TL+third_image_TL+third_stain_type_TL+"_vs_"+second_stain_type_TL+"_double-label_ROIoverlay.zip");
//waitForUser(" ");
wait(100);

print("Circle DL ROIs = "+save_circleDL_ROIs);
print("\n"+second_image_TL);
print("Square DL ROIs = "+save_squareDL_ROIs);
//print("\n"+"Done");

roiManager("reset");

run("_ps_Triple_Background_DL1and3_ROIs");
}

//\\//\\//\\//\\//\\//\\//\\

macro "_ps_Triple_Background_DL1and3_ROIs" {  //this is the macro that identifies double-labeled cells between stains 1 and 3.

	//Double labeled cell identification

first_image_tif = first_image_TL+"-2.tif";
third_image_tif = third_image_TL+"-2.tif";

//////////////////////////////////////////NEW DOUBLE ROI STUFF HERE DOWN///////////////////////////////////////////////////

roiManager("deselect");
roiManager("reset");
wait(50);

selectWindow(third_image_tif); //select the 8oxo image

roiManager("Open", subDir_TL+first_image_TL+first_stain_type_TL+"_single-label_ROIoverlay.zip");

double_label_first_type = roiManager("count"); // determines the number of 1circle ROIs
double_label_first_type_array = Array.getSequence(double_label_first_type); 

roiManager("Open", subDir_TL+third_image_TL+third_stain_type_TL+"_single-label_ROIoverlay.zip");

roiManager("Show All");
roiManager("Show All with labels");

double_label_all_types = roiManager("count");
double_label_all_types_array = Array.getSequence(double_label_all_types); 
double_label_third_type = double_label_all_types-double_label_first_type; // determines the number of 3circles ROIs
double_label_third_type_start = double_label_first_type; // the ROI manager position of the first 3circle

/* 
 In this section we are identifying the 3circles that have overlapping 1circles. 
 */
//waitForUser("Everything is loaded, let's start.");
n=0; // number of time the 3circle loop was run.
no_overlap=0; //number of 3circles without 1circle overlap.
multi_b_removed=0; //number of 1circles removed because resolved multiple overlap;
removed_1circles_array = newArray(n);
saved_3circles_array = newArray(n);

for(a=double_label_third_type_start; a<(double_label_all_types-no_overlap-multi_b_removed); a++){  // 3circles.
	roiManager("Select", a);
	a2=a+1; //a2 is a counting variable to correct for the ROI manager starting at ROI #0.
	//print("3circle = "+a2);
	bTotal=0; // number of 1circles checked against 3circle "a2".
	no_co=0; // yes/no variable determining whether overlap was found. 
	co_roi_array = newArray(no_co); // this array will be concatinated with the "b" value for each overlapping 1circle so the ROIs can be selected.
	co_roi_array_UI = newArray(no_co); // this array will be concatinated with the "b2" value for each overlapping 1circle so the user can see multiple overlaps.
	CoM_distance_array = newArray(no_co); // making an array of the distances between the 3circle CoM and the 1circle's CoM.
	for(b=0; b<double_label_first_type-multi_b_removed; b++){ //1circles.
		b2=b+1; //b2 is a counting variable to correct for the ROI manager starting at ROI #0.
		roiManager("Select", newArray(a, b)); //select the 3circle the the circle
		roiManager("AND"); //build an overlapping ROI
		roiType = selectionType();
		bTotal=bTotal+1;
		if(roiType==-1){ // and there is not overlapping area under the 3circle and 1circle ROIs
			//print("No overlap: 1circle "+b2); //then, move on to the next (ie, restart the loop).
			
			}
		if(roiType!=-1){ // if there is overlap from the 3circle and the 1circle,
			c = roiManager("Add"); //add the overlap to the ROI manager so that we can measure its size.
			//print("HIT!!! = 1circle "+b2);
			no_co = no_co+1; // to remember that, yes, there is overlap.
				
			roiManager("deselect");
			run("Clear Results");
			
			//selectWindow(first_image_tif); //select the PV image
			roiManager("Select", b); //measure the size of the 1circle
			roiManager("Measure");
			co_roi = (roiManager("count")-1); //find the overlap ROI (it's going to be the last one on the ROI manager list).
			
			//selectWindow(third_image_tif); //select the 8oxo image
			roiManager("Select", co_roi); // select it.
			roiManager("Measure"); //measure the area.
			
			b2_area=getResult("Area", 0); // grab the 1circle-ROI's area from the results 
			c_area=getResult("Area", 1); // grab the overlap-ROI's area from the results
			b2_area_80 = b2_area*TL1_overlap; // calculate 80% of the 1circle area (we're using 80% as an arbitrary threshold of minimum 1circle that should be within the 3circle). 
			//print("b = "+b2_area);
			//print("c = "+c_area);
			//print("b2_80 = "+b2_area_80);
			wait(5);
			
			roiManager("Select", co_roi);
			roiManager("Delete"); //now removing it beause we only wanted that new ROI to measure the area of overlap.
			
			if(b2_area_80>c_area){ // if the overlapping area is less 80% of the 1circle,
				no_co = no_co-1; //then, forget that an overlap was identified and move on (restart the loop).
				//print("Nevermind, It failed 80% overlap. YYYYEEEEEAAAAH");
			} else {
				co_roi_array = Array.concat(co_roi_array, b); //building an array to remember which 1circle ROIs overlap (for ROI selection)
				co_roi_array_UI = Array.concat(co_roi_array_UI, b2); //building an array to remember which 1circle ROIs overlap (for the human to understand)
				saved_3circles_array = Array.concat(saved_3circles_array, a);
				//print("3circles saved so far:");
				//Array.print(saved_3circles_array);
			}
		}
		if(b2==(double_label_first_type-multi_b_removed) && (no_co==0)){ //if we have checked every 1circle and still not found a 1circle that overlap (>80%) with the 3circle,
     		roiManager("deselect");
     		roiManager("Select", a2-1); //move back one spot and select that 3circle,
			roiManager("Delete"); // then delete it because we know that it was a non-overlapping 3circle and should no longer be considered for colabeling.
			//print("3circle "+a2+" deleted. AAAAAAAAAAAAAAAAAAAAAAAHHHHHHHHHHHHHHH"); //3circle ROI death scene.
			no_overlap=no_overlap+1; // add one to the number of 3circles not overlapping so that we can lower the total 3circles.
			a=a-1; //remove one 3circles from the counted number of 3circles. Semi repetitive with last variable, but each serve a function in the top IF loop.
			//wait(5);
     		}
		if(b2==(double_label_first_type-multi_b_removed) && (no_co==1)){ // if we have checked all 1circles and there was an overlap detected
			//print("Total "+first_stain_type_TL+" checked: "+bTotal); //then, print # 1circles checked. (next step is to exit loop because a!<(double_label_all_types-no_overlap)).
			//print("loop = "+n+1);
			//print("\n");
			//wait(5);
			}
     	
		if(b2==(double_label_first_type-multi_b_removed) && (no_co>1)){ // if checked all 1circles and there were multiple overlaps, 
			selectWindow(third_image_tif); //select the 8-oxo image
			run("Clear Results");
			roiManager("deselect");
			roiManager("Select", a); //select the 3circle ROI
			roiManager("Measure"); //measure the 3circle ROI so we can...
			CoM_x_3circle=getResult("XM", 0); // grab the 3circle's center of mass x-coordinate from the results. 
			CoM_y_3circle=getResult("YM", 0); // grab the 3circle's center of mass y-coordinate from the results.
			CoM_tot_3circle= CoM_x_3circle+CoM_y_3circle; //making the total value (number only used for comparison to 1circles).
			wait(10);
			
			run("Clear Results"); 
			selectWindow(first_image_tif); //select the PV image
			roiManager("deselect");
			roiManager("Select", co_roi_array); //select the overlapping ROIs
			roiManager("Measure"); 
			
			wait(5);
			for(i=0; i<no_co; i++){ //doing this as a FOR loop to allow for any range of overlapping ROIs
				CoM_x=getResult("XM", i); // grab the 1circle's center of mass x-coordinate from the results.
				CoM_y=getResult("YM", i); // grab the first 1circle's center of mass y-coordinate from the results.
				CoM_tot=CoM_x+CoM_y; // the 1circle's total value
				//print("CoM_x for ROI "+(i)+": "+CoM_x);
				//print("CoM_y for ROI "+(i)+": "+CoM_y);
				//print("CoM_tot for ROI "+(i)+": "+CoM_tot);
				distance=abs(CoM_tot_3circle-CoM_tot); //measuring the distance between the center of the 3circle ROI's brightest area and the 1circle ROI
				CoM_distance_array = Array.concat(CoM_distance_array, distance); // building an array of the distances between the 3circle CoM and the 1circle's CoM.
				//print("CoM_distance_array:");
				//Array.print(CoM_distance_array);
			
				}
			selectWindow(third_image_tif); //select the 8-oxo image
			rankPosition_CoM = Array.rankPositions(CoM_distance_array); //determining the shortest distance
			rank_CoM = Array.rankPositions(rankPosition_CoM);
			rankPositionWinner=Array.slice(rankPosition_CoM, 0, 1);

			co_roi_array_winner_position = rankPositionWinner[0];
			co_roi_array_low = Array.slice(co_roi_array, 0, co_roi_array_winner_position); //saving multiple ROIs before the shortest distance ROI  		
			co_roi_array_high = Array.slice(co_roi_array, co_roi_array_winner_position+1, no_co); // saving multiple ROIs after the shortest distance ROI
			co_roi_array_losers = Array.concat(co_roi_array_low, co_roi_array_high); 
			co_roi_array_winner_low = Array.slice(co_roi_array, 0, co_roi_array_winner_position+1); //saving multiple ROIs before the shortest distance ROI
			co_roi_array_winner = Array.slice(co_roi_array, co_roi_array_winner_position, no_co-1); // saving multiple ROIs after the shortest distance ROI
		
			removed_1circles_array = Array.concat(removed_1circles_array, co_roi_array_losers);
			roiManager("deselect");
			roiManager("Select", co_roi_array_losers); //deleting the remaining multiple ROI (except for the closest to the 3circle ROI)
			roiManager("Delete");  
		
			a=a-(no_co-1); //since 3circle ROIs are numbered after 1circles, we have to revise this number to keep the counting correct in the top IF loop.	
	   		multi_b_removed=multi_b_removed+(no_co-1); //since 3circle ROIs are numbered after 1circles, we have to revise this number to keep the counting correct in the top IF loop.					
			//wait(20);
			for(i=0; i<saved_3circles_array.length; i++){
				saved_3circles_array[i]=saved_3circles_array[i]-(no_co-1);
				
			}
			
		}
	}
	
	n=n+1;

}
//saving ROI overlay

roiManager("deselect"); //by not making an ROI manager selection, and actually deselecting, the ROI manager selects all by default. 
roiManager("Save", subDir_TL+third_image_TL+third_stain_type_TL+"_vs_"+first_stain_type_TL+"_first_step_double-label_ROIoverlay.zip");
wait(100);

roiManager("deselect");
roiManager("Select", saved_3circles_array);
roiManager("Delete");

//waitForUser("pause");
save_1circle_ROIs=roiManager("count");
circle1_array=newArray(save_1circle_ROIs);
for(i=0; i<save_1circle_ROIs; i++) {
	circle1_array[i] = i;
	}

roiManager("deselect");
roiManager("Save", subDir_TL+first_image_TL+first_stain_type_TL+"_vs_"+third_stain_type_TL+"_refined-single-label_ROIoverlay.zip");
wait(100);
roiManager("reset");

roiManager("Open", subDir_TL+third_image_TL+third_stain_type_TL+"_vs_"+first_stain_type_TL+"_first_step_double-label_ROIoverlay.zip");
roiManager("Select", circle1_array);
roiManager("Delete");
//waitForUser("pause");

save_3circleDL_ROIs=roiManager("count");
circle3_DL_array=newArray(save_3circleDL_ROIs);
for(i=0; i<save_3circleDL_ROIs; i++) {
	circle3_DL_array[i] = i;
	}
	
roiManager("deselect");
roiManager("Save", subDir_TL+third_image_TL+third_stain_type_TL+"_vs_"+first_stain_type_TL+"_double-label_ROIoverlay.zip");
wait(100);
/*
print("\n"+third_image_TL);
if(removed_1circles_array.length>0){
	print("Removed "+first_stain_type_TL+" ROIs (these were multiple overlaps with "+third_stain_type_TL+" ROIs):");
	Array.print(removed_1circles_array);
}
print(third_stain_type_TL+" saved:");
Array.print(saved_3circles_array);
*/
roiManager("reset");

//////////////////////////////////////////////
/* 
 In this section we are identifying the 1CIRCLES that have overlapping 3CIRCLES. 
 */
 
selectWindow(first_image_tif); //select the PV image
 
roiManager("Open", subDir_TL+third_image_TL+third_stain_type_TL+"_vs_"+first_stain_type_TL+"_double-label_ROIoverlay.zip");
double_label_third_type_b = roiManager("count");
double_label_third_type_b_array = Array.getSequence(double_label_third_type_b); 
//waitForUser("");
roiManager("Open", subDir_TL+first_image_TL+first_stain_type_TL+"_vs_"+third_stain_type_TL+"_refined-single-label_ROIoverlay.zip");
double_label_all_types = roiManager("count");
double_label_all_types_array = Array.getSequence(double_label_all_types); 
double_label_first_type = double_label_all_types-double_label_third_type_b;
double_label_first_type_start = double_label_third_type_b; 

n=0; //count loops
no_overlap=0; //number of 1circles without 3circles overlap. 
co_roi_array2 = newArray(n); // this array will be concatenated with the "d" value for each overlapping 3circle so the ROIs can be selected.
co_roi_array2_UI = newArray(n); // this array will be concatenated with the "d2" value for each overlapping 3circle so the user can see multiple overlaps.
saved_1circles_array = newArray(n); //building an array to remember which 1circle ROIs overlap (for ROI selection)

for(c=double_label_first_type_start; c<(double_label_all_types-no_overlap); c++){ //1circles
	roiManager("Select", c);
	c2=c+1; //c2 is a counting variable to correct for the ROI manager starting at ROI #0.
	//print("1circle = "+c2);
	dTotal=0; // number of 3circles checked against 1circle "c2".
	no_co=0; // yes/no variable determining whether overlap was found. 
	for(d=0; d<double_label_third_type_b; d++){ //3circles
		roiManager("Select", newArray(c, d)); //select the 3circle the 1circle
		roiManager("AND"); //build an overlapping ROI
		roiType = selectionType(); //determine the type of overlap represented by the overlapping ROI
		d2=d+1;
		dTotal=dTotal+1;
		if(roiType==-1){ //"-1" represents the type of ROI that does not overlap
			//print("No overlap: 3circle "+d2);
			//wait(5);
     		}
		if(roiType!=-1){ //if not "-1 type", then there is overlap, so mark it.
			roiManager("Add"); //add the overlap to the ROI manager so that we can measure its size.
			//print("HIT!!! = 3circle "+d2);
			no_co = no_co+1; // to remember that, yes, there is overlap.
				
			roiManager("deselect");
			run("Clear Results");
			//selectWindow(first_image_tif); //select the PV image
			roiManager("Select", c); //measure the size of the 1circle
			roiManager("Measure");
			
			//selectWindow(third_image_tif); //select the 8oxo image
			co_roi = (roiManager("count")-1); //find the overlap ROI (it's going to be the last one on the ROI manager list).
			roiManager("Select", co_roi); // select it.
			roiManager("Measure"); //measure the area.
			
			c2_area=getResult("Area", 0); // grab the 1circle-ROI's area from the results 
			co_area=getResult("Area", 1); // grab the overlap-ROI's area from the results
			c2_area_80 = c2_area*TL3_overlap; // calculate 80% of the 1circle area (we're using 80% as an arbitrary threshold of minimum circle that should be within the 3circle). 
			//print("c = "+c2_area);
			//print("co = "+co_area);
			//print("c2_80 = "+c2_area_80);
			wait(5);
			
			roiManager("Select", co_roi);
			roiManager("Delete"); //now removing it beause we only wanted that new ROI to measure the area of overlap.
			
			//selectWindow(first_image_tif); //select the PV image
			
			if(c2_area_80>co_area){ // if the overlapping area is less 80% of the 3circle,
				no_co = no_co-1; //then, forget that an overlap was identified and move on (restart the loop).
				//print("Nevermind, It failed 80% overlap. YYYYEEEEEAAAAH");
			} else {
				co_roi_array2 = Array.concat(co_roi_array2, d); //building an array to remember which 1circle ROIs overlap (for ROI selection)
				co_roi_array2_UI = Array.concat(co_roi_array2_UI, d2); //building an array to remember which 1circle ROIs overlap (for the human to understand)
				saved_1circles_array = Array.concat(saved_1circles_array, c); //building an array to remember which 1circle ROIs overlap (for ROI selection)
				//print("1Circles saved so far:");
				//Array.print(saved_1circles_array);
			}
		}
		if((d2==double_label_third_type_b) && (no_co==0)){ //if checked every 3circle and still not found a 3circle that overlap (>80%) with the 1circle,
			roiManager("deselect");
			wait(10);
     		roiManager("Select", c2-1);
			roiManager("Delete");
			//print("1circle "+c2+" deleted. AAAAAAAAAAAAAAAAAAAAAAAHHHHHHHHHHHHHHH");
			no_overlap=no_overlap+1; // add one to the number of 1circles not overlapping so that we can lower the total 1circles.
			c=c-1;//remove one 1circle from the counted number of 1circle. Semi repetitive with last variable, but each serve a function in the top IF loop.
     		}
	}
	
	n=n+1;
}
//waitForUser("");
roiManager("deselect");
roiManager("Select", double_label_third_type_b_array);
roiManager("Delete");

//saving ROI overlay

save_1circleDL_ROIs=roiManager("count");

circle1_DL_array=newArray(save_1circleDL_ROIs); // saving the list of DL 1circle ROIs
for(i=0; i<save_1circleDL_ROIs; i++) {
	circle1_DL_array[i] = i;
	}

roiManager("deselect");
roiManager("Save", subDir_TL+first_image_TL+first_stain_type_TL+"_vs_"+third_stain_type_TL+"_double-label_ROIoverlay.zip");
//waitForUser(" ");
wait(100);

/*
print("\n"+first_image_TL);  //THE NEXT COUPLE OF LINES PRINT FEEDBACK ABOUT THE NUMBER OF DL CELLS FOUND, AND THE NUMBER OF 1CIRCLE ROIs DELETED IN THE FIRST STEP.
if(removed_1circles_array.length>0){
	print("Removed "+first_stain_type_TL+" ROIs (these were multiple overlaps with "+third_stain_type_TL+" ROIs):");
	Array.print(removed_1circles_array);
}
*/

print(first_stain_type_TL+" DL ROIs = "+save_1circleDL_ROIs);
print("\n"+third_image_TL);
print(third_stain_type_TL+" DL ROIs = "+save_3circleDL_ROIs);
//print("\n"+"Done");

roiManager("reset");
run("Clear Results"); 

run("_ps_measure_tpl_Background_DL1and2_ROIs");

}

//\\//\\//\\//\\//\\//\\//\\

macro "_ps_measure_tpl_Background_DL1and2_ROIs" { //this macro simply recalls the ROIs defined in the last macro in order to measure the values again.

wait(50);

first_image_tif = first_image_TL+"-2.tif";
second_image_tif = second_image_TL+"-2.tif";
merged_image_tif = merged_image_TL+".tif";

roiManager("Open", subDir_TL+first_image_TL+first_stain_type_TL+"_vs_"+second_stain_type_TL+"_double-label_ROIoverlay.zip");
roiManager("Open", subDir_TL+second_image_TL+second_stain_type_TL+"_vs_"+first_stain_type_TL+"_double-label_ROIoverlay.zip");

if (import_type==6 || import_type==7) {  //if run in automatic mode, no ROI check step offered.
	wait(300);
} else {  //otherwise, it opens the merge image and overlays the ROIs.
	
	selectWindow(merged_image_tif);
	roiManager("Show All");
	roiManager("Show All with labels");


	Dialog.create("How Does That Look?"); //the first step is asking the user if they want to edit either ROI set.
	items = newArray(first_stain_type_TL, second_stain_type_TL, "Accept double labeling as is.");
	Dialog.addRadioButtonGroup("Here is your chance to adjust the ROIs to better identify PNNs. Which double-labeled cells would you like to adjust?", items, 3, 1, "Accept double labeling as is.");
	Dialog.show;
	choice = Dialog.getRadioButton;

	if (choice==first_stain_type_TL) {
		setTool("oval");
		roiManager("reset");
		roiManager("Open", subDir_TL+first_image_TL+first_stain_type_TL+"_vs_"+second_stain_type_TL+"_double-label_ROIoverlay.zip");
		waitForUser("How Does That Look?", "Here is your chance to adjust the double-labeling. Try moving, adding, or deleting ROIs. **THEN** press OK.");
		roiManager("deselect");
		roiManager("Save", subDir_TL+first_image_TL+first_stain_type_TL+"_vs_"+second_stain_type_TL+"_double-label_ROIoverlay.zip");
		wait(100);

		Dialog.create("Adjust the other stain?"); //then the second step is to ask the user if they want to edit the other stain's ROIs.
			items = newArray("Yes", "No, accept double labeling as is.");
			Dialog.addRadioButtonGroup("Do you want to also adjust "+second_stain_type_TL+" double-labeling?", items, 2, 1, "Yes");
			Dialog.show;
			choice = Dialog.getRadioButton;

		if (choice=="Yes") {
			setTool("rectangle");
			roiManager("reset");
			roiManager("Open", subDir_TL+second_image_TL+second_stain_type_TL+"_vs_"+first_stain_type_TL+"_double-label_ROIoverlay.zip");
			waitForUser("How Does That Look?", "Here is your chance to adjust the double-labeling. Try moving, adding, or deleting ROIs. **THEN** press OK.");
			roiManager("deselect");
			roiManager("Save", subDir_TL+second_image_TL+second_stain_type_TL+"_vs_"+first_stain_type_TL+"_double-label_ROIoverlay.zip");
			wait(100);
			roiManager("reset");
			run("_ps_measure_tpl_Background_DL1and2_ROIs");
		}
		
		if (choice=="No, accept double labeling as is.") {
			//well, aren't you confident.
		}
	}
	if (choice==second_stain_type_TL) {
		setTool("rectangle");
		roiManager("reset");
		roiManager("Open", subDir_TL+second_image_TL+second_stain_type_TL+"_vs_"+first_stain_type_TL+"_double-label_ROIoverlay.zip");
		waitForUser("How Does That Look?", "Here is your chance to adjust the double-labeling. Try moving, adding, or deleting ROIs. **THEN** press OK.");
		roiManager("deselect");
		roiManager("Save", subDir_TL+second_image_TL+second_stain_type_TL+"_vs_"+first_stain_type_TL+"_double-label_ROIoverlay.zip");
		wait(100);

		Dialog.create("Adjust the other stain?"); //again, second step, but after starting with the alternative stain.
			items = newArray("Yes", "No, accept double labeling as is.");
			Dialog.addRadioButtonGroup("Do you want to also adjust "+first_stain_type_TL+" double-labeling?", items, 2, 1, "Yes");
			Dialog.show;
			choice = Dialog.getRadioButton;

		if (choice=="Yes") {
			setTool("oval");
			roiManager("reset");
			roiManager("Open", subDir_TL+first_image_TL+first_stain_type_TL+"_vs_"+second_stain_type_TL+"_double-label_ROIoverlay.zip");
			waitForUser("How Does That Look?", "Here is your chance to adjust the double-labeling. Try moving, adding, or deleting ROIs. **THEN** press OK.");
			roiManager("deselect");
			roiManager("Save", subDir_TL+first_image_TL+first_stain_type_TL+"_vs_"+second_stain_type_TL+"_double-label_ROIoverlay.zip");
			wait(100);
			roiManager("reset");
			run("_ps_measure_tpl_Background_DL1and2_ROIs");
		}	
		if (choice=="No, accept double labeling as is.") {
			//well, aren't you confident.
		}	
	}
	if (choice=="Accept double labeling as is.") {
		//well, aren't you confident.
	}


}

roiManager("reset");

//\\\\\\\\\\\\\
//actually measuring the double labeled ROIs (image 1)

run("Clear Results");

selectWindow(first_image_tif);
roiManager("Open", subDir_TL+first_image_TL+first_stain_type_TL+"_vs_"+second_stain_type_TL+"_double-label_ROIoverlay.zip");
name = series_name_TL+" "+first_stain_type_TL+" vs "+second_stain_type_TL+" double-label.txt"; 

DL_ROIs=0;
DL_ROIs =roiManager("count"); 
array=newArray(DL_ROIs); 
for(i=0; i<DL_ROIs;i++) { 
        array[i] = i; 
	}
//roiManager("Select", array); //by not making an ROI manager selection, and actually deselecting, the ROI manager selects all by default. 
roiManager("deselect");
roiManager("Measure");
roiManager("Show All");
roiManager("Show All with labels");

DL_ROIs1_m=0;
DL_ROIs1_means=0;
DL_ROIs1_m = nResults;
DL_ROIs1_means = newArray(DL_ROIs1_m);
roiManager("deselect");
for (i=0; i<DL_ROIs1_m; i++) {
	DL_ROIs1_means[i] = getResult('Mean', i);
}
roiManager("deselect");
for (i=0; i<DL_ROIs1_m; i++) {
	setResult("Background", i, TL1_lower);
	setResult("Mean-Background", i, DL_ROIs1_means[i]-TL1_lower);
}
setResult("File Name", 0, name);
updateResults();

DL_ROIs1_m2 = 0;
DL_ROIs1_avgmeans = 0;
DL_ROIs1_m2 = nResults;
DL_ROIs1_avgmeans = newArray(DL_ROIs1_m2);
roiManager("deselect");
for (i=0; i<DL_ROIs1_m2; i++) {
	DL_ROIs1_avgmeans[i] = getResult('Mean-Background', i);
	}
Array.getStatistics(DL_ROIs1_avgmeans, min, max, mean, stdDev);
print("\n");
print("Double-labeled "+first_stain_type_TL+" co-occurring with "+second_stain_type_TL+" Mean Intensity = "+mean);


DL_ROIs1_numberStains=0;
DL_ROIs1_numberStains=getValue("results.count");
print("Final number of Double-labeled "+first_stain_type_TL+" co-occurring with "+second_stain_type_TL+" Cells = "+DL_ROIs1_numberStains);

n_dl1 = nResults;
area_dl1 = newArray(n_dl1);
XM_dl1 = newArray(n_dl1);
YM_dl1 = newArray(n_dl1);
mean_dl1 = newArray(n_dl1);
stddev_dl1 = newArray(n_dl1);
min_dl1 = newArray(n_dl1);
max_dl1 = newArray(n_dl1);
bkgd_dl1 = newArray(n_dl1);
meanbkgd_dl1 = newArray(n_dl1);
print("\n");
print("Double-labeled "+first_stain_type_TL+" vs "+second_stain_type_TL+" cell data:");
print("Subject# Image_name Stain_type Cell# Area XM YM Mean StdDev Min Max Background Mean-Background");
for (i=0; i<n_dl1; i++) {
	area_dl1[i] = getResult('Area', i);
	XM_dl1[i] = getResult('XM', i);
	YM_dl1[i] = getResult('YM', i);
	mean_dl1[i] = getResult('Mean', i);
	stddev_dl1[i] = getResult('StdDev', i);
	min_dl1[i] = getResult('Min', i);
	max_dl1[i] = getResult('Max', i);
	bkgd_dl1[i] = getResult('Background', i);
	meanbkgd_dl1[i] = getResult('Mean-Background', i);
	print(subject_id+" "+first_image_TL+" "+"Double-labeled_"+first_stain_type_TL+"_co-occurring_with_"+second_stain_type_TL+" "+(i+1)+" "+area_dl1[i]+" "+XM_dl1[i]+" "+YM_dl1[i]+" "+mean_dl1[i]+" "+stddev_dl1[i]+" "+min_dl1[i]+" "+max_dl1[i]+" "+bkgd_dl1[i]+" "+meanbkgd_dl1[i]);
	}

	// Saving data

saveAs("Measurements", subDir_TL+name);
print("\n"); 
print("Results saved as: "+subDir_TL+name);
selectWindow("Log");
saveAs("Text", dir+"/All_Results"+TimeString+".txt");

roiManager("reset");

//\\\\\\\\\\\\\\\
//measure double labeled ROIs (image 2)

roiManager("Open", subDir_TL+second_image_TL+second_stain_type_TL+"_vs_"+first_stain_type_TL+"_double-label_ROIoverlay.zip");
name = series_name_TL+" "+second_stain_type_TL+" vs "+first_stain_type_TL+" double-label.txt"; 

run("Clear Results");
selectWindow(second_image_tif);

//waitForUser("Pause5");

roiManager("Show All");
roiManager("Show All with labels");
roiManager("deselect");

DL_ROIs=0;
DL_ROIs =roiManager("count"); 
array=newArray(DL_ROIs); 
for(i=0; i<DL_ROIs;i++) { 
	array[i] = i; 
	}

//roiManager("Select", array);
roiManager("deselect");
roiManager("Measure");
roiManager("Show All");
roiManager("Show All with labels");

DL_ROIs2_m=0;
DL_ROIs2_means=0;
DL_ROIs2_m = nResults;
DL_ROIs2_means = newArray(DL_ROIs2_m);
run("Select None");
for (i=0; i<DL_ROIs2_m; i++) {
	DL_ROIs2_means[i] = getResult('Mean', i);
	}
roiManager("deselect");
for (i=0; i<DL_ROIs2_m; i++) {
	setResult("Background", i, TL2_lower);
	setResult("Mean-Background", i, DL_ROIs2_means[i]-TL2_lower);
	}

setResult("File Name", 0, name);
updateResults();

DL_ROIs2_m2 = 0;
DL_ROIs2_avgmeans = 0;
DL_ROIs2_m2 = nResults;
DL_ROIs2_avgmeans = newArray(DL_ROIs2_m2);
roiManager("deselect");
for (i=0; i<DL_ROIs2_m2; i++) {
	DL_ROIs2_avgmeans[i] = getResult('Mean-Background', i);
	}

Array.getStatistics(DL_ROIs2_avgmeans, min, max, mean, stdDev);
print("\n");
print("Double-labeled "+second_stain_type_TL+" co-occurring with "+first_stain_type_TL+" Mean Intensity = "+mean);


DL_ROIs2_numberStains=0;
DL_ROIs2_numberStains=getValue("results.count");
print("Final number of Double-labeled "+second_stain_type_TL+" co-occurring with "+first_stain_type_TL+" Cells = "+DL_ROIs2_numberStains);

n_dl2 = nResults;
area_dl2 = newArray(n_dl2);
XM_dl2 = newArray(n_dl2);
YM_dl2 = newArray(n_dl2);
mean_dl2 = newArray(n_dl2);
stddev_dl2 = newArray(n_dl2);
min_dl2 = newArray(n_dl2);
max_dl2 = newArray(n_dl2);
bkgd_dl2 = newArray(n_dl2);
meanbkgd_dl2 = newArray(n_dl2);
print("\n");
print("Double-labeled "+second_stain_type_TL+" vs "+first_stain_type_TL+" cell data:");
print("Subject# Image_name Stain_type Cell# Area XM YM Mean StdDev Min Max Background Mean-Background");
for (i=0; i<n_dl2; i++) {
	area_dl2[i] = getResult('Area', i);
	XM_dl2[i] = getResult('XM', i);
	YM_dl2[i] = getResult('YM', i);
	mean_dl2[i] = getResult('Mean', i);
	stddev_dl2[i] = getResult('StdDev', i);
	min_dl2[i] = getResult('Min', i);
	max_dl2[i] = getResult('Max', i);
	bkgd_dl2[i] = getResult('Background', i);
	meanbkgd_dl2[i] = getResult('Mean-Background', i);
	print(subject_id+" "+second_image_TL+" "+"Double-labeled_"+second_stain_type_TL+"_co-occurring_with_"+first_stain_type_TL+" "+(i+1)+" "+area_dl2[i]+" "+XM_dl2[i]+" "+YM_dl2[i]+" "+mean_dl2[i]+" "+stddev_dl2[i]+" "+min_dl2[i]+" "+max_dl2[i]+" "+bkgd_dl2[i]+" "+meanbkgd_dl2[i]);
	}

	// Saving data

saveAs("Measurements", subDir_TL+name);
selectWindow("Log");
saveAs("Text", dir+"/All_Results"+TimeString+".txt");
print("\n"); 
print("Results saved as: "+subDir_TL+name);

roiManager("reset");
run("Clear Results");

run("_ps_measure_tpl_Background_DL3and2_ROIs");

}
//\\//\\//\\//\\//\\//\\//\\

macro "_ps_measure_tpl_Background_DL3and2_ROIs" { //this macro simply recalls the ROIs defined in the last macro in order to measure the values again.

third_image_tif = third_image_TL+"-2.tif";
second_image_tif = second_image_TL+"-2.tif";
merged_image_tif = merged_image_TL+".tif";

roiManager("Open", subDir_TL+third_image_TL+third_stain_type_TL+"_vs_"+second_stain_type_TL+"_double-label_ROIoverlay.zip");
roiManager("Open", subDir_TL+second_image_TL+second_stain_type_TL+"_vs_"+third_stain_type_TL+"_double-label_ROIoverlay.zip");

if (import_type==6 || import_type==7) {  //if run in automatic mode, no ROI check step offered.
	wait(300);
} else {  //otherwise, it opens the merge image and overlays the ROIs.
	selectWindow(merged_image_tif);
	roiManager("Show All");
	roiManager("Show All with labels");


	Dialog.create("How Does That Look?"); //the first step is asking the user if they want to edit either ROI set.
	items = newArray(third_stain_type_TL, second_stain_type_TL, "Accept double labeling as is.");
	Dialog.addRadioButtonGroup("Here is your chance to adjust the ROIs to better identify PNNs. Which double-labeled cells would you like to adjust?", items, 3, 1, "Accept double labeling as is.");
	Dialog.show;
	choice = Dialog.getRadioButton;

	if (choice==third_stain_type_TL) {
		setTool("oval");
		roiManager("reset");
		roiManager("Open", subDir_TL+third_image_TL+third_stain_type_TL+"_vs_"+second_stain_type_TL+"_double-label_ROIoverlay.zip");
		waitForUser("How Does That Look?", "Here is your chance to adjust the double-labeling. Try moving, adding, or deleting ROIs. **THEN** press OK.");
		roiManager("deselect");
		roiManager("Save", subDir_TL+third_image_TL+third_stain_type_TL+"_vs_"+second_stain_type_TL+"_double-label_ROIoverlay.zip");
		wait(100);

		Dialog.create("Adjust the other stain?"); //then the second step is to ask the user if they want to edit the other stain's ROIs.
			items = newArray("Yes", "No, accept double labeling as is.");
			Dialog.addRadioButtonGroup("Do you want to also adjust "+second_stain_type_TL+" double-labeling?", items, 2, 1, "Yes");
			Dialog.show;
			choice = Dialog.getRadioButton;

		if (choice=="Yes") {
			setTool("rectangle");
			roiManager("reset");
			roiManager("Open", subDir_TL+second_image_TL+second_stain_type_TL+"_vs_"+third_stain_type_TL+"_double-label_ROIoverlay.zip");
			waitForUser("How Does That Look?", "Here is your chance to adjust the double-labeling. Try moving, adding, or deleting ROIs. **THEN** press OK.");
			roiManager("deselect");
			roiManager("Save", subDir_TL+second_image_TL+second_stain_type_TL+"_vs_"+third_stain_type_TL+"_double-label_ROIoverlay.zip");
			wait(100);
			roiManager("reset");

			run("_ps_measure_tpl_Background_DL3and2_ROIs");
		}
		
		if (choice=="No, accept double labeling as is.") {
			//well, aren't you confident.
		}
	}
	if (choice==second_stain_type_TL) {
		setTool("rectangle");
		roiManager("reset");
		roiManager("Open", subDir_TL+second_image_TL+second_stain_type_TL+"_vs_"+third_stain_type_TL+"_double-label_ROIoverlay.zip");
		waitForUser("How Does That Look?", "Here is your chance to adjust the double-labeling. Try moving, adding, or deleting ROIs. **THEN** press OK.");
		roiManager("deselect");
		roiManager("Save", subDir_TL+second_image_TL+second_stain_type_TL+"_vs_"+third_stain_type_TL+"_double-label_ROIoverlay.zip");
		wait(100);

		Dialog.create("Adjust the other stain?"); //again, second step, but after starting with the alternative stain.
			items = newArray("Yes", "No, accept double labeling as is.");
			Dialog.addRadioButtonGroup("Do you want to also adjust "+third_stain_type_TL+" double-labeling?", items, 2, 1, "Yes");
			Dialog.show;
			choice = Dialog.getRadioButton;

		if (choice=="Yes") {
			setTool("oval");
			roiManager("reset");
			roiManager("Open", subDir_TL+third_image_TL+third_stain_type_TL+"_vs_"+second_stain_type_TL+"_double-label_ROIoverlay.zip");
			waitForUser("How Does That Look?", "Here is your chance to adjust the double-labeling. Try moving, adding, or deleting ROIs. **THEN** press OK.");
			roiManager("deselect");
			roiManager("Save", subDir_TL+third_image_TL+third_stain_type_TL+"_vs_"+second_stain_type_TL+"_double-label_ROIoverlay.zip");
			wait(100);
			roiManager("reset");
			run("_ps_measure_tpl_Background_DL3and2_ROIs");
		}	
		if (choice=="No, accept double labeling as is.") {
			//well, aren't you confident.
		}	
	}
	if (choice=="Accept double labeling as is.") {
		//well, aren't you confident.
	}

}
roiManager("reset");

//\\\\\\\\\\\\\
//actually measuring the double labeled ROIs (image 3)

run("Clear Results");

selectWindow(third_image_tif);
roiManager("Open", subDir_TL+third_image_TL+third_stain_type_TL+"_vs_"+second_stain_type_TL+"_double-label_ROIoverlay.zip");
name = series_name_TL+" "+third_stain_type_TL+"_vs_"+second_stain_type_TL+" double-label.txt"; 

DL_ROIs=0;
DL_ROIs =roiManager("count"); 
array=newArray(DL_ROIs); 
for(i=0; i<DL_ROIs;i++) { 
    array[i] = i; 
	}
//roiManager("Select", array); //by not making an ROI manager selection, and actually deselecting, the ROI manager selects all by default. 
roiManager("deselect");
roiManager("Measure");
roiManager("Show All");
roiManager("Show All with labels");

DL_ROIs1_m=0;
DL_ROIs1_means=0;
DL_ROIs1_m = nResults;
DL_ROIs1_means = newArray(DL_ROIs1_m);
roiManager("deselect");
for (i=0; i<DL_ROIs1_m; i++) {
	DL_ROIs1_means[i] = getResult('Mean', i);
}
roiManager("deselect");
for (i=0; i<DL_ROIs1_m; i++) {
	setResult("Background", i, TL3_lower);
	setResult("Mean-Background", i, DL_ROIs1_means[i]-TL3_lower);
}
setResult("File Name", 0, name);
updateResults();

DL_ROIs1_m2 = 0;
DL_ROIs1_avgmeans = 0;
DL_ROIs1_m2 = nResults;
DL_ROIs1_avgmeans = newArray(DL_ROIs1_m2);
roiManager("deselect");
for (i=0; i<DL_ROIs1_m2; i++) {
	DL_ROIs1_avgmeans[i] = getResult('Mean-Background', i);
	}
Array.getStatistics(DL_ROIs1_avgmeans, min, max, mean, stdDev);
print("\n");
print("Double-labeled "+third_stain_type_TL+" co-occurring with "+second_stain_type_TL+" Mean Intensity = "+mean);


DL_ROIs1_numberStains=0;
DL_ROIs1_numberStains=getValue("results.count");
print("Final number of Double-labeled "+third_stain_type_TL+" co-occurring with "+second_stain_type_TL+" Cells = "+DL_ROIs1_numberStains);

n_dl1 = nResults;
area_dl1 = newArray(n_dl1);
XM_dl1 = newArray(n_dl1);
YM_dl1 = newArray(n_dl1);
mean_dl1 = newArray(n_dl1);
stddev_dl1 = newArray(n_dl1);
min_dl1 = newArray(n_dl1);
max_dl1 = newArray(n_dl1);
bkgd_dl1 = newArray(n_dl1);
meanbkgd_dl1 = newArray(n_dl1);
print("\n");
print("Double-labeled "+third_stain_type_TL+" vs "+second_stain_type_TL+" cell data:");
print("Subject# Image_name Stain_type Cell# Area XM YM Mean StdDev Min Max Background Mean-Background");
for (i=0; i<n_dl1; i++) {
	area_dl1[i] = getResult('Area', i);
	XM_dl1[i] = getResult('XM', i);
	YM_dl1[i] = getResult('YM', i);
	mean_dl1[i] = getResult('Mean', i);
	stddev_dl1[i] = getResult('StdDev', i);
	min_dl1[i] = getResult('Min', i);
	max_dl1[i] = getResult('Max', i);
	bkgd_dl1[i] = getResult('Background', i);
	meanbkgd_dl1[i] = getResult('Mean-Background', i);
	print(subject_id+" "+third_image_TL+" "+"Double-labeled_"+third_stain_type_TL+"_co-occurring_with_"+second_stain_type_TL+" "+(i+1)+" "+area_dl1[i]+" "+XM_dl1[i]+" "+YM_dl1[i]+" "+mean_dl1[i]+" "+stddev_dl1[i]+" "+min_dl1[i]+" "+max_dl1[i]+" "+bkgd_dl1[i]+" "+meanbkgd_dl1[i]);
	}


	// Saving data

saveAs("Measurements", subDir_TL+name);
print("\n"); 
print("Results saved as: "+subDir_TL+name);
selectWindow("Log");
saveAs("Text", dir+"/All_Results"+TimeString+".txt");

roiManager("reset");

//\\\\\\\\\\\\\\\
//measure double labeled ROIs (image 2)

roiManager("Open", subDir_TL+second_image_TL+second_stain_type_TL+"_vs_"+third_stain_type_TL+"_double-label_ROIoverlay.zip");
name = series_name_TL+" "+second_stain_type_TL+"_vs_"+third_stain_type_TL+" double-label.txt"; 

run("Clear Results");
selectWindow(second_image_tif);

//waitForUser("Pause5");

roiManager("Show All");
roiManager("Show All with labels");
roiManager("deselect");

DL_ROIs=0;
DL_ROIs =roiManager("count"); 
array=newArray(DL_ROIs); 
for(i=0; i<DL_ROIs;i++) { 
	array[i] = i; 
	}

//roiManager("Select", array);
roiManager("deselect");
roiManager("Measure");
roiManager("Show All");
roiManager("Show All with labels");

DL_ROIs2_m=0;
DL_ROIs2_means=0;
DL_ROIs2_m = nResults;
DL_ROIs2_means = newArray(DL_ROIs2_m);
run("Select None");
for (i=0; i<DL_ROIs2_m; i++) {
	DL_ROIs2_means[i] = getResult('Mean', i);
	}
roiManager("deselect");
for (i=0; i<DL_ROIs2_m; i++) {
	setResult("Background", i, TL2_lower);
	setResult("Mean-Background", i, DL_ROIs2_means[i]-TL2_lower);
	}

setResult("File Name", 0, name);
updateResults();

DL_ROIs2_m2 = 0;
DL_ROIs2_avgmeans = 0;
DL_ROIs2_m2 = nResults;
DL_ROIs2_avgmeans = newArray(DL_ROIs2_m2);
roiManager("deselect");
for (i=0; i<DL_ROIs2_m2; i++) {
	DL_ROIs2_avgmeans[i] = getResult('Mean-Background', i);
	}

Array.getStatistics(DL_ROIs2_avgmeans, min, max, mean, stdDev);
print("\n");
print("Double-labeled "+second_stain_type_TL+" co-occurring with "+third_stain_type_TL+" Mean Intensity = "+mean);


DL_ROIs2_numberStains=0;
DL_ROIs2_numberStains=getValue("results.count");
print("Final number of Double-labeled "+second_stain_type_TL+" co-occurring with "+third_stain_type_TL+" Cells = "+DL_ROIs2_numberStains);

n_dl2 = nResults;
area_dl2 = newArray(n_dl2);
XM_dl2 = newArray(n_dl2);
YM_dl2 = newArray(n_dl2);
mean_dl2 = newArray(n_dl2);
stddev_dl2 = newArray(n_dl2);
min_dl2 = newArray(n_dl2);
max_dl2 = newArray(n_dl2);
bkgd_dl2 = newArray(n_dl2);
meanbkgd_dl2 = newArray(n_dl2);
print("\n");
print("Double-labeled "+second_stain_type_TL+" vs "+third_stain_type_TL+" cell data:");
print("Subject# Image_name Stain_type Cell# Area XM YM Mean StdDev Min Max Background Mean-Background");
for (i=0; i<n_dl2; i++) {
	area_dl2[i] = getResult('Area', i);
	XM_dl2[i] = getResult('XM', i);
	YM_dl2[i] = getResult('YM', i);
	mean_dl2[i] = getResult('Mean', i);
	stddev_dl2[i] = getResult('StdDev', i);
	min_dl2[i] = getResult('Min', i);
	max_dl2[i] = getResult('Max', i);
	bkgd_dl2[i] = getResult('Background', i);
	meanbkgd_dl2[i] = getResult('Mean-Background', i);
	print(subject_id+" "+second_image_TL+" "+"Double-labeled_"+second_stain_type_TL+"_co-occurring_with_"+third_stain_type_TL+" "+(i+1)+" "+area_dl2[i]+" "+XM_dl2[i]+" "+YM_dl2[i]+" "+mean_dl2[i]+" "+stddev_dl2[i]+" "+min_dl2[i]+" "+max_dl2[i]+" "+bkgd_dl2[i]+" "+meanbkgd_dl2[i]);
	}

	// Saving data

saveAs("Measurements", subDir_TL+name);
selectWindow("Log");
saveAs("Text", dir+"/All_Results"+TimeString+".txt");
print("\n"); 
print("Results saved as: "+subDir_TL+name);	

roiManager("reset");
run("Clear Results");

run("_ps_measure_tpl_Background_DL1and3_ROIs");

}
//\\//\\//\\//\\//\\//\\//\\

macro "_ps_measure_tpl_Background_DL1and3_ROIs" { //this macro simply recalls the ROIs defined in the last macro in order to measure the values again.

first_image_tif = first_image_TL+"-2.tif";
second_image_tif = second_image_TL+"-2.tif";
third_image_tif = third_image_TL+"-2.tif";
merged_image_tif = merged_image_TL+".tif";

roiManager("Open", subDir_TL+first_image_TL+first_stain_type_TL+"_vs_"+third_stain_type_TL+"_double-label_ROIoverlay.zip");
roiManager("Open", subDir_TL+third_image_TL+third_stain_type_TL+"_vs_"+first_stain_type_TL+"_double-label_ROIoverlay.zip");

if (import_type==6 || import_type==7) {  //if run in automatic mode, no ROI check step offered.
	wait(300);
} else {  //otherwise, it opens the merge image and overlays the ROIs.
	selectWindow(merged_image_tif);
	roiManager("Show All");
	roiManager("Show All with labels");


	Dialog.create("How Does That Look?"); //the first step is asking the user if they want to edit either ROI set.
	items = newArray(first_stain_type_TL, third_stain_type_TL, "Accept double labeling as is.");
	Dialog.addRadioButtonGroup("Here is your chance to adjust the ROIs to better identify PNNs. Which double-labeled cells would you like to adjust?", items, 3, 1, "Accept double labeling as is.");
	Dialog.show;
	choice = Dialog.getRadioButton;

	if (choice==first_stain_type_TL) {
		setTool("oval");
		roiManager("reset");
		roiManager("Open", subDir_TL+first_image_TL+first_stain_type_TL+"_vs_"+third_stain_type_TL+"_double-label_ROIoverlay.zip");
		waitForUser("How Does That Look?", "Here is your chance to adjust the double-labeling. Try moving, adding, or deleting ROIs. **THEN** press OK.");
		roiManager("deselect");
		roiManager("Save", subDir_TL+first_image_TL+first_stain_type_TL+"_vs_"+third_stain_type_TL+"_double-label_ROIoverlay.zip");
		wait(100);

		Dialog.create("Adjust the other stain?"); //then the second step is to ask the user if they want to edit the other stain's ROIs.
			items = newArray("Yes", "No, accept double labeling as is.");
			Dialog.addRadioButtonGroup("Do you want to also adjust "+third_stain_type_TL+" double-labeling?", items, 2, 1, "Yes");
			Dialog.show;
			choice = Dialog.getRadioButton;

		if (choice=="Yes") {
			setTool("oval");
			roiManager("reset");
			roiManager("Open", subDir_TL+third_image_TL+third_stain_type_TL+"_vs_"+first_stain_type_TL+"_double-label_ROIoverlay.zip");
			waitForUser("How Does That Look?", "Here is your chance to adjust the double-labeling. Try moving, adding, or deleting ROIs. **THEN** press OK.");
			roiManager("deselect");
			roiManager("Save", subDir_TL+third_image_TL+third_stain_type_TL+"_vs_"+first_stain_type_TL+"_double-label_ROIoverlay.zip");
			wait(100);
			roiManager("reset");

			run("_ps_measure_tpl_Background_DL1and3_ROIs");
		}
		
		if (choice=="No, accept double labeling as is.") {
			//well, aren't you confident.
		}
	}
	if (choice==third_stain_type_TL) {
		setTool("oval");
		roiManager("reset");
		roiManager("Open", subDir_TL+third_image_TL+third_stain_type_TL+"_vs_"+first_stain_type_TL+"_double-label_ROIoverlay.zip");
		waitForUser("How Does That Look?", "Here is your chance to adjust the double-labeling. Try moving, adding, or deleting ROIs. **THEN** press OK.");
		roiManager("deselect");
		roiManager("Save", subDir_TL+third_image_TL+third_stain_type_TL+"_vs_"+first_stain_type_TL+"_double-label_ROIoverlay.zip");
		wait(100);

		Dialog.create("Adjust the other stain?"); //again, second step, but after starting with the alternative stain.
			items = newArray("Yes", "No, accept double labeling as is.");
			Dialog.addRadioButtonGroup("Do you want to also adjust "+first_stain_type_TL+" double-labeling?", items, 2, 1, "Yes");
			Dialog.show;
			choice = Dialog.getRadioButton;

		if (choice=="Yes") {
			setTool("oval");
			roiManager("reset");
			roiManager("Open", subDir_TL+first_image_TL+first_stain_type_TL+"_vs_"+third_stain_type_TL+"_double-label_ROIoverlay.zip");
			waitForUser("How Does That Look?", "Here is your chance to adjust the double-labeling. Try moving, adding, or deleting ROIs. **THEN** press OK.");
			roiManager("deselect");
			roiManager("Save", subDir_TL+first_image_TL+first_stain_type_TL+"_vs_"+third_stain_type_TL+"_double-label_ROIoverlay.zip");
			wait(100);
			roiManager("reset");

			run("_ps_measure_tpl_Background_DL1and3_ROIs");
		}	
		if (choice=="No, accept double labeling as is.") {
			//well, aren't you confident.
		}	
	}
	if (choice=="Accept double labeling as is.") {
		//well, aren't you confident.
	}

}
roiManager("reset");

//\\\\\\\\\\\\\
//actually measuring the double labeled ROIs (image 1)

run("Clear Results");

selectWindow(first_image_tif);
roiManager("Open", subDir_TL+first_image_TL+first_stain_type_TL+"_vs_"+third_stain_type_TL+"_double-label_ROIoverlay.zip");
name = series_name_TL+" "+first_stain_type_TL+"_vs_"+third_stain_type_TL+" double-label.txt"; 

DL_ROIs=0;
DL_ROIs =roiManager("count"); 
array=newArray(DL_ROIs); 
for(i=0; i<DL_ROIs;i++) { 
        array[i] = i; 
	}
//roiManager("Select", array); //by not making an ROI manager selection, and actually deselecting, the ROI manager selects all by default. 
roiManager("deselect");
roiManager("Measure");
roiManager("Show All");
roiManager("Show All with labels");

DL_ROIs1_m=0;
DL_ROIs1_means=0;
DL_ROIs1_m = nResults;
DL_ROIs1_means = newArray(DL_ROIs1_m);
roiManager("deselect");
for (i=0; i<DL_ROIs1_m; i++) {
	DL_ROIs1_means[i] = getResult('Mean', i);
}
roiManager("deselect");
for (i=0; i<DL_ROIs1_m; i++) {
	setResult("Background", i, TL1_lower);
	setResult("Mean-Background", i, DL_ROIs1_means[i]-TL1_lower);
}
setResult("File Name", 0, name);
updateResults();

DL_ROIs1_m2 = 0;
DL_ROIs1_avgmeans = 0;
DL_ROIs1_m2 = nResults;
DL_ROIs1_avgmeans = newArray(DL_ROIs1_m2);
roiManager("deselect");
for (i=0; i<DL_ROIs1_m2; i++) {
	DL_ROIs1_avgmeans[i] = getResult('Mean-Background', i);
	}
Array.getStatistics(DL_ROIs1_avgmeans, min, max, mean, stdDev);
print("\n");
print("Double-labeled "+first_stain_type_TL+" co-occurring with "+third_stain_type_TL+" Mean Intensity = "+mean);


DL_ROIs1_numberStains=0;
DL_ROIs1_numberStains=getValue("results.count");
print("Final number of Double-labeled "+first_stain_type_TL+" co-occurring with "+third_stain_type_TL+" Cells = "+DL_ROIs1_numberStains);

n_dl1 = nResults;
area_dl1 = newArray(n_dl1);
XM_dl1 = newArray(n_dl1);
YM_dl1 = newArray(n_dl1);
mean_dl1 = newArray(n_dl1);
stddev_dl1 = newArray(n_dl1);
min_dl1 = newArray(n_dl1);
max_dl1 = newArray(n_dl1);
bkgd_dl1 = newArray(n_dl1);
meanbkgd_dl1 = newArray(n_dl1);
print("\n");
print("Double-labeled "+first_stain_type_TL+" vs "+third_stain_type_TL+" cell data:");
print("Subject# Image_name Stain_type Cell# Area XM YM Mean StdDev Min Max Background Mean-Background");
for (i=0; i<n_dl1; i++) {
	area_dl1[i] = getResult('Area', i);
	XM_dl1[i] = getResult('XM', i);
	YM_dl1[i] = getResult('YM', i);
	mean_dl1[i] = getResult('Mean', i);
	stddev_dl1[i] = getResult('StdDev', i);
	min_dl1[i] = getResult('Min', i);
	max_dl1[i] = getResult('Max', i);
	bkgd_dl1[i] = getResult('Background', i);
	meanbkgd_dl1[i] = getResult('Mean-Background', i);
	print(subject_id+" "+first_image_TL+" "+"Double-labeled_"+first_stain_type_TL+"_co-occurring_with_"+third_stain_type_TL+" "+(i+1)+" "+area_dl1[i]+" "+XM_dl1[i]+" "+YM_dl1[i]+" "+mean_dl1[i]+" "+stddev_dl1[i]+" "+min_dl1[i]+" "+max_dl1[i]+" "+bkgd_dl1[i]+" "+meanbkgd_dl1[i]);
	}

	// Saving data

saveAs("Measurements", subDir_TL+name);
print("\n"); 
print("Results saved as: "+subDir_TL+name);
selectWindow("Log");
saveAs("Text", dir+"/All_Results"+TimeString+".txt");

roiManager("reset");

//\\\\\\\\\\\\\\\
//measure double labeled ROIs (image 3)

roiManager("Open", subDir_TL+third_image_TL+third_stain_type_TL+"_vs_"+first_stain_type_TL+"_double-label_ROIoverlay.zip");
name = series_name_TL+" "+third_stain_type_TL+"_vs_"+first_stain_type_TL+" double-label.txt"; 

run("Clear Results");
selectWindow(third_image_tif);

//waitForUser("Pause5");

roiManager("Show All");
roiManager("Show All with labels");
roiManager("deselect");

DL_ROIs=0;
DL_ROIs =roiManager("count"); 
array=newArray(DL_ROIs); 
for(i=0; i<DL_ROIs;i++) { 
	array[i] = i; 
	}

//roiManager("Select", array);
roiManager("deselect");
roiManager("Measure");
roiManager("Show All");
roiManager("Show All with labels");

DL_ROIs2_m=0;
DL_ROIs2_means=0;
DL_ROIs2_m = nResults;
DL_ROIs2_means = newArray(DL_ROIs2_m);
run("Select None");
for (i=0; i<DL_ROIs2_m; i++) {
	DL_ROIs2_means[i] = getResult('Mean', i);
	}
roiManager("deselect");
for (i=0; i<DL_ROIs2_m; i++) {
	setResult("Background", i, TL3_lower);
	setResult("Mean-Background", i, DL_ROIs2_means[i]-TL3_lower);
	}

setResult("File Name", 0, name);
updateResults();

DL_ROIs2_m2 = 0;
DL_ROIs2_avgmeans = 0;
DL_ROIs2_m2 = nResults;
DL_ROIs2_avgmeans = newArray(DL_ROIs2_m2);
roiManager("deselect");
for (i=0; i<DL_ROIs2_m2; i++) {
	DL_ROIs2_avgmeans[i] = getResult('Mean-Background', i);
	}

Array.getStatistics(DL_ROIs2_avgmeans, min, max, mean, stdDev);
print("\n");
print("Double-labeled "+third_stain_type_TL+" co-occurring with "+first_stain_type_TL+" Mean Intensity = "+mean);


DL_ROIs2_numberStains=0;
DL_ROIs2_numberStains=getValue("results.count");
print("Final number of Double-labeled "+third_stain_type_TL+" co-occurring with "+first_stain_type_TL+" Cells = "+DL_ROIs2_numberStains);

n_dl2 = nResults;
area_dl2 = newArray(n_dl2);
XM_dl2 = newArray(n_dl2);
YM_dl2 = newArray(n_dl2);
mean_dl2 = newArray(n_dl2);
stddev_dl2 = newArray(n_dl2);
min_dl2 = newArray(n_dl2);
max_dl2 = newArray(n_dl2);
bkgd_dl2 = newArray(n_dl2);
meanbkgd_dl2 = newArray(n_dl2);
print("\n");
print("Double-labeled "+third_stain_type_TL+" vs "+first_stain_type_TL+" cell data:");
print("Subject# Image_name Stain_type Cell# Area XM YM Mean StdDev Min Max Background Mean-Background");
for (i=0; i<n_dl2; i++) {
	area_dl2[i] = getResult('Area', i);
	XM_dl2[i] = getResult('XM', i);
	YM_dl2[i] = getResult('YM', i);
	mean_dl2[i] = getResult('Mean', i);
	stddev_dl2[i] = getResult('StdDev', i);
	min_dl2[i] = getResult('Min', i);
	max_dl2[i] = getResult('Max', i);
	bkgd_dl2[i] = getResult('Background', i);
	meanbkgd_dl2[i] = getResult('Mean-Background', i);
	print(subject_id+" "+third_image_TL+" "+"Double-labeled_"+third_stain_type_TL+"_co-occurring_with_"+first_stain_type_TL+" "+(i+1)+" "+area_dl2[i]+" "+XM_dl2[i]+" "+YM_dl2[i]+" "+mean_dl2[i]+" "+stddev_dl2[i]+" "+min_dl2[i]+" "+max_dl2[i]+" "+bkgd_dl2[i]+" "+meanbkgd_dl2[i]);
	}

	// Saving data

saveAs("Measurements", subDir_TL+name);
selectWindow("Log");
saveAs("Text", dir+"/All_Results"+TimeString+".txt");
print("\n"); 
print("Results saved as: "+subDir_TL+name);

//\\//\\//\\//\\//\\//\\//\\

macro "_ps_TL_Overlapping_ROIs" {  //this is the macro that identifies triple-labeled cells between all stains. Adapted from "New" double-label overlay detection

	//triple labeled cell identification

selectWindow(merged_image_tif); //select the merged image

roiManager("deselect");
roiManager("reset");
wait(50);

roiManager("Open", subDir_TL+first_image_TL+first_stain_type_TL+"_vs_"+second_stain_type_TL+"_double-label_ROIoverlay.zip");
TL_1v2_type = roiManager("count"); // determines the number of DL ROIs
TL_1v2_type_array = Array.getSequence(TL_1v2_type); 

roiManager("Open", subDir_TL+second_image_TL+second_stain_type_TL+"_vs_"+third_stain_type_TL+"_double-label_ROIoverlay.zip");
TL_1v2and2v3_types = roiManager("count");
TL_1v2and2v3_types_array = Array.getSequence(TL_1v2and2v3_types); 
TL_2v3_type = TL_1v2and2v3_types-TL_1v2_type; // determines the number of 1 plus 2 overlapping ROIs
TL_2v3_type_start = TL_1v2_type; // the ROI manager position of the first overlapping ROI

roiManager("Show All");
roiManager("Show All with labels");

//waitForUser("Everything is loaded, let's start.");

// In this section we are identifying the 2v3 ROIs that have overlapping 1v2 ROIs. 

n=0; // number of time the 2v3 ROIs loop was run.
no_overlap=0; //number of 2v3 ROIs without 1v2 ROIs overlap.
multi_b_removed=0; //number of 1v2 ROIs removed because resolved multiple overlap;
removed_1v2_array = newArray(n);
saved_2v3_array = newArray(n);

for(a=TL_2v3_type_start; a<(TL_1v2and2v3_types-no_overlap-multi_b_removed); a++){  // 2v3 ROIs
	roiManager("Select", a);
	a2=a+1; //a2 is a counting variable to correct for the ROI manager starting at ROI #0.
	bTotal=0; // number of 1v2 ROIs checked against 2v3 ROIs "a2".
	no_co=0; // yes/no variable determining whether overlap was found. 
	co_roi_array = newArray(no_co); // this array will be concatinated with the "b" value for each overlapping 1v2 ROIs so the ROIs can be selected.
	co_roi_array_UI = newArray(no_co); // this array will be concatinated with the "b2" value for each overlapping 1v2 ROIs so the user can see multiple overlaps.
	CoM_distance_array = newArray(no_co); // making an array of the distances between the 2v3 ROIs CoM and the 1v2 ROIs CoM.
	for(b=0; b<TL_1v2_type; b++){ //1v2 ROI.
		b2=b+1; //b2 is a counting variable to correct for the ROI manager starting at ROI #0.
		roiManager("Select", newArray(a, b)); //select the 2v3 ROIs
		roiManager("AND"); //build an overlapping ROI
		roiType = selectionType();
		bTotal=bTotal+1;
		if(roiType==-1){ // and there is not overlapping area under the 2v3 and 1v2 ROIs
			//print("No overlap: 1circle "+b2); //then, move on to the next (ie, restart the loop).
			}
		if(roiType!=-1){ // if there is overlap from the 2v3 and 1v2 ROIs,
			c = roiManager("Add"); //add the overlap to the ROI manager so that we can measure its size.
			no_co = no_co+1; // to remember that, yes, there is overlap.
				
			roiManager("deselect");
			run("Clear Results");
			roiManager("Select", b); //measure the size of the 1v2 ROI
			roiManager("Measure");
			co_roi = (roiManager("count")-1); //find the overlap ROI (it's going to be the last one on the ROI manager list).
			roiManager("Select", co_roi); // select it.
			roiManager("Measure"); //measure the area.
			roiManager("Select", co_roi);
			roiManager("Delete"); //now removing it beause we only wanted that new ROI to measure the area of overlap.
			b2_area=getResult("Area", 0); // grab the 1v2 ROI's area from the results 
			c_area=getResult("Area", 1); // grab the overlap-ROI's area from the results
			b2_area_80 = b2_area*TL2_overlap; // calculate 80% of the 1v2 ROI area (we're using 80% as an arbitrary threshold of minimum 1v2 ROI that should be within the 2v3 ROI). 
			if(b2_area_80>c_area){ // if the overlapping area is less 80% of the 1v2 ROI,
				no_co = no_co-1; //then, forget that an overlap was identified and move on (restart the loop).
				//print("Nevermind, It failed 80% overlap. YYYYEEEEEAAAAH");
			} else {
				co_roi_array = Array.concat(co_roi_array, b); //building an array to remember which 1v2 ROIs overlap (for ROI selection)
				co_roi_array_UI = Array.concat(co_roi_array_UI, b2); //building an array to remember which 1v2 ROI ROIs overlap (for the human to understand)
				saved_2v3_array = Array.concat(saved_2v3_array, a);
				}
			}
		if((b2==TL_1v2_type) && (no_co==0)){ //if we have checked every 1v2 ROI and still not found a 1v2 ROI that overlap (>80%) with a 2v3 ROI,
     		roiManager("deselect");
     		roiManager("Select", a2-1); //move back one spot and select that 2v3 ROI,
			roiManager("Delete"); // then delete it because we know that it was a non-overlapping 2v3 ROI and should no longer be considered for colabeling.
			no_overlap=no_overlap+1; // add one to the number of 2v3 ROIs not overlapping so that we can lower the total 2v3 ROIs.
			a=a-1; //remove one 2v3 ROIs from the counted number of 2v3 ROIs. Semi repetitive with last variable, but each serve a function in the top IF loop.
			}
     	if(b2==(TL_1v2_type) && (no_co==1)){ // if we have checked all 1v2 ROIs and there was an overlap detected
     		//wait(5);
     		}
     	
     	if(b2==(TL_1v2_type) && (no_co>1)){ // if checked all 1v2 ROIs and there were multiple overlaps, 
     		selectWindow(second_image_tif); //select the WFA image
     		run("Clear Results");
			roiManager("deselect");
			roiManager("Select", a); //select the 2v3 ROI ROI
			roiManager("Measure"); //measure the 2v3 ROI ROI so we can...
			CoM_x_2v3_ROI=getResult("XM", 0); // grab the 2v3 ROI's center of mass x-coordinate from the results. 
			CoM_y_2v3_ROI=getResult("YM", 0); // grab the 2v3 ROI's center of mass y-coordinate from the results.
			
			CoM_tot_2v3_ROI= CoM_x_2v3_ROI+CoM_y_2v3_ROI; //making the total value (number only used for comparison to 1v2 ROIs).
			
			run("Clear Results"); 
			selectWindow(first_image_tif); //select the PV image
			roiManager("deselect");
			roiManager("Select", co_roi_array); //select the overlapping ROIs
			roiManager("Measure"); 
			for(i=0; i<no_co; i++){ //doing this as a FOR loop to allow for any range of overlapping ROIs
				CoM_x=getResult("XM", i); // grab the 1v2 ROI's center of mass x-coordinate from the results.
				CoM_y=getResult("YM", i); // grab the first 1v2 ROI's center of mass y-coordinate from the results.
				CoM_tot=CoM_x+CoM_y; // the 1v2 ROI's total value
				distance=abs(CoM_tot_2v3_ROI-CoM_tot); //measuring the distance between the center of the 2v3_ROI ROI's brightest area and the 1v2 ROI
				CoM_distance_array = Array.concat(CoM_distance_array, distance); // building an array of the distances between the 2v3 ROI's CoM and the 1v2 ROI's CoM.
				}
			rankPosition_CoM = Array.rankPositions(CoM_distance_array); //determining the shortest distance
			rank_CoM = Array.rankPositions(rankPosition_CoM);
			rankPositionWinner=Array.slice(rankPosition_CoM, 0, 1);

			co_roi_array_winner_position = rankPositionWinner[0];
			co_roi_array_low = Array.slice(co_roi_array, 0, co_roi_array_winner_position); //saving multiple ROIs before the shortest distance ROI  		
			co_roi_array_high = Array.slice(co_roi_array, co_roi_array_winner_position+1, no_co); // saving multiple ROIs after the shortest distance ROI
			co_roi_array_losers = Array.concat(co_roi_array_low, co_roi_array_high); 
			
			co_roi_array_winner_low = Array.slice(co_roi_array, 0, co_roi_array_winner_position+1); //saving multiple ROIs before the shortest distance ROI
			co_roi_array_winner = Array.slice(co_roi_array, co_roi_array_winner_position, no_co-1); // saving multiple ROIs after the shortest distance ROI
			removed_1v2_array = Array.concat(removed_1v2_array, co_roi_array_losers);
			roiManager("deselect");
			roiManager("Select", co_roi_array_losers); //deleting the remaining multiple ROI (except for the closest to the 1v2_ROI ROI)
			roiManager("Delete");  
	
			a=a-(no_co-1); //since 2v3 ROIs are numbered after 1v2 ROIs, we have to revise this number to keep the counting correct in the top IF loop.	
			multi_b_removed=multi_b_removed+(no_co-1); //since 2v3 ROIs are numbered after 1v2 ROIs, we have to revise this number to keep the counting correct in the top IF loop.					
			for(i=0; i<saved_2v3_array.length; i++){
				saved_2v3_array[i]=saved_2v3_array[i]-(no_co-1);
				}
			}
		/*
		if (no_overlap == TL_2v3_type {
			Dialog.create("No Co-labeling");
			items = newArray("Open new image for analysis.", "Return to main menu.");
			Dialog.addMessage("There appear to be no co-labeled cells in this image.");
			Dialog.addMessage(" ");	
			Dialog.addMessage("If you think this is an error, consider selecting this image again and");
			Dialog.addMessage("re-running this analysis with altered settings and ROIs.");
			Dialog.addMessage("Otherwise, analysis of this image is done and you should select the next image.");
			Dialog.addMessage(" ");
			Dialog.addRadioButtonGroup("What would you like to do next?", items, 2, 1, "Open new image for analysis.");
			Dialog.show;
			choice = Dialog.getRadioButton;
			if (choice=="Return to main menu.") {
				run("_ps_initi");
				}
			if (choice=="Open new image for analysis.") {
				run("_ps_Open_Double");
				}
			}
		*/
		}
	n=n+1;
	}
	
//saving ROI overlay

roiManager("deselect"); //by not making an ROI manager selection, and actually deselecting, the ROI manager selects all by default. 
roiManager("Save", subDir_TL+second_stain_type_TL+"_vs_"+third_stain_type_TL+"_FirstStep_TripleLabel_ROIoverlay.zip");
wait(100);

roiManager("deselect");
roiManager("Select", saved_2v3_array);
roiManager("Delete");

save_1v2_ROIs=roiManager("count");
TL_1v2_ROI_array=newArray(save_1v2_ROIs);
for(i=0; i<save_1v2_ROIs; i++) {
	TL_1v2_ROI_array[i] = i;
	}

roiManager("deselect");
roiManager("Save", subDir_TL+first_stain_type_TL+"_vs_"+second_stain_type_TL+"_refined-DoubleLabel_ROIoverlay.zip");
wait(100);
roiManager("reset");
	
roiManager("Open", subDir_TL+second_stain_type_TL+"_vs_"+third_stain_type_TL+"_FirstStep_TripleLabel_ROIoverlay.zip");
roiManager("Select", TL_1v2_ROI_array);
roiManager("Delete");

//waitForUser("are there 2's?");

save_2v3_TL_ROIs=roiManager("count");

TL_2v3_ROI_1_array=newArray(save_2v3_TL_ROIs);
for(i=0; i<save_2v3_TL_ROIs; i++) {
	TL_2v3_ROI_1_array[i] = i;
	}
	
//waitForUser("are there 2's?");

roiManager("deselect");
roiManager("Save", subDir_TL+second_stain_type_TL+"_vs_"+third_stain_type_TL+"_TripleLabel_ROIoverlay.zip");
wait(100);

roiManager("reset");
run("Clear Results");

//////////////////////////////////////////////
/* 
 In this section we are identifying the 1v2 ROIS that have overlapping 2v3_ROIS. 
 */
 
selectWindow(merged_image_tif); //select the merged image

roiManager("Open", subDir_TL+second_stain_type_TL+"_vs_"+third_stain_type_TL+"_TripleLabel_ROIoverlay.zip");
TL_2v3_type_b = roiManager("count");
TL_2v3_type_b_array = Array.getSequence(TL_2v3_type_b); 

roiManager("Open", subDir_TL+first_stain_type_TL+"_vs_"+second_stain_type_TL+"_refined-DoubleLabel_ROIoverlay.zip");
TL_1v2and2v3_types = roiManager("count");
TL_1v2and2v3_types_array = Array.getSequence(TL_1v2and2v3_types); 
TL_1v2_type = TL_1v2and2v3_types-TL_2v3_type_b;
TL_1v2_type_start = TL_2v3_type_b; 
//waitForUser("Starting TL #2");

n=0; //count loops
no_overlap=0; //number of 1v2 ROIs without 2v3 ROI overlap. 
co_roi_array2 = newArray(n); // this array will be concatenated with the "d" value for each overlapping 2v3 ROI so the ROIs can be selected.
co_roi_array2_UI = newArray(n); // this array will be concatenated with the "d2" value for each overlapping 2v3 ROI so the user can see multiple overlaps.
saved_1v2_ROIs_array = newArray(n); //building an array to remember which 1v2 ROIs overlap (for ROI selection)

for(c=TL_1v2_type_start; c<(TL_1v2and2v3_types-no_overlap); c++){ //1v2 ROIs
	roiManager("Select", c);
	c2=c+1; //c2 is a counting variable to correct for the ROI manager starting at ROI #0.
	dTotal=0; // number of 2v3 ROIs checked against 1v2 ROI "c2".
	no_co=0; // yes/no variable determining whether overlap was found. 
	for(d=0; d<TL_2v3_type_b; d++){ //2v3 ROIs
		roiManager("Select", newArray(c, d)); //select the 2v3 and 1v2 ROI
		roiManager("AND"); //build an overlapping ROI
		roiType = selectionType(); //determine the type of overlap represented by the overlapping ROI
		d2=d+1;
		dTotal=dTotal+1;
		if(roiType==-1){ //"-1" represents the type of ROI that does not overlap
			//wait(5);
     		}
		if(roiType!=-1){ //if not "-1 type", then there is overlap, so mark it.
			roiManager("Add"); //add the overlap to the ROI manager so that we can measure its size.
			no_co = no_co+1; // to remember that, yes, there is overlap.
				
			roiManager("deselect");
			run("Clear Results");
			roiManager("Select", c); //measure the size of the 1v2 ROI
			roiManager("Measure");
			co_roi = (roiManager("count")-1); //find the overlap ROI (it's going to be the last one on the ROI manager list).
			roiManager("Select", co_roi); // select it.
			roiManager("Measure"); //measure the area.
			roiManager("Select", co_roi);
			roiManager("Delete"); //now removing it beause we only wanted that new ROI to measure the area of overlap.
			c2_area=getResult("Area", 0); // grab the 1v2 ROI's area from the results 
			co_area=getResult("Area", 1); // grab the overlap-ROI's area from the results
			c2_area_80 = c2_area*TL1_overlap; // calculate 80% of the 1v2 ROI area (we're using 80% as an arbitrary threshold of minimum 1v2 ROI that should be within the  2v3 ROI). 
			if(c2_area_80>co_area){ // if the overlapping area is less 80% of the 2v3 ROI,
				no_co = no_co-1; //then, forget that an overlap was identified and move on (restart the loop).
			} else {
				co_roi_array2 = Array.concat(co_roi_array2, d); //building an array to remember which 1v2 ROIs overlap (for ROI selection)
				co_roi_array2_UI = Array.concat(co_roi_array2_UI, d2); //building an array to remember which 1v2 ROIs overlap (for the human to understand)
				saved_1v2_ROIs_array = Array.concat(saved_1v2_ROIs_array, c); //building an array to remember which 1v2 ROIs overlap (for ROI selection)
				}
			}
		if((d2==TL_2v3_type_b) && (no_co==0)){ //if checked every 2v3 and still not found a 2v3 ROI that overlap (>80%) with the 1v2 ROI,
			roiManager("deselect");
     		roiManager("Select", c2-1);
			roiManager("Delete");
			no_overlap=no_overlap+1; // add one to the number of 1v2 ROIs not overlapping so that we can lower the total 1v2 ROIs.
			c=c-1;//remove one 1v2 ROI from the counted number of 1v2 ROI. Semi repetitive with last variable, but each serve a function in the top IF loop.
     		}
		}
	n=n+1;
	
	}

roiManager("Select", TL_2v3_type_b_array);
roiManager("Delete");

//saving ROI overlay

save_1v2_TL_ROIs=roiManager("count");

TL_1v2_ROI_2_array=newArray(save_1v2_TL_ROIs); // saving the list of DL 1v2 ROIs
for(i=0; i<save_1v2_TL_ROIs; i++) {
	TL_1v2_ROI_2_array[i] = i;
	}

roiManager("deselect");
roiManager("Save", subDir_TL+first_stain_type_TL+"_vs_"+second_stain_type_TL+"_TripleLabel_ROIoverlay.zip");
wait(100);

print("\n"+first_image_TL);  //THE NEXT COUPLE OF LINES PRINT FEEDBACK ABOUT THE NUMBER OF DL CELLS FOUND, AND THE NUMBER OF 1v2 ROI ROIs DELETED IN THE FIRST STEP.
if(removed_1v2_array.length>0){
	print("Removed "+first_stain_type_TL+" ROIs (these were multiple overlaps with "+second_stain_type_TL+" ROIs):");
	Array.print(removed_1v2_array);
	}
print("1v2 DL ROIs = "+save_1v2_TL_ROIs);
print("\n"+second_image_TL);
print("2v3 DL ROIs = "+save_2v3_TL_ROIs);

roiManager("reset");
run("Clear Results");

///////////////////////////////////
/* 
 In this section we are identifying the 3v2 ROIS that have overlapping 2v3 TL_ROIS. 
 */
 
selectWindow(merged_image_tif); //select the merged image

roiManager("Open", subDir_TL+second_stain_type_TL+"_vs_"+third_stain_type_TL+"_TripleLabel_ROIoverlay.zip");
TL_2v3_type_c = roiManager("count");
TL_2v3_type_c_array = Array.getSequence(TL_2v3_type_c); 

roiManager("Open", subDir_TL+third_image_TL+third_stain_type_TL+"_vs_"+second_stain_type_TL+"_double-label_ROIoverlay.zip");
TL_2v3and3v2_types = roiManager("count");
TL_2v3and3v2_types_array = Array.getSequence(TL_2v3and3v2_types); 
TL_3v2_type = TL_2v3and3v2_types-TL_2v3_type_c;
TL_3v2_type_start = TL_2v3_type_c; 

//waitForUser("Starting TL #3");

n=0; //count loops
no_overlap=0; //number of 3v2 ROIs without 2v3 ROIs overlap. 
co_roi_array2 = newArray(n); // this array will be concatenated with the "d" value for each overlapping 2v3 ROI so the ROIs can be selected.
co_roi_array2_UI = newArray(n); // this array will be concatenated with the "d2" value for each overlapping 2v3 ROI so the user can see multiple overlaps.
saved_3v2_ROIs_array = newArray(n); //building an array to remember which 3v2 ROIs overlap (for ROI selection)

for(c=TL_3v2_type_start; c<(TL_2v3and3v2_types-no_overlap); c++){ //3v2 ROIs
	roiManager("Select", c);
	c2=c+1; //c2 is a counting variable to correct for the ROI manager starting at ROI #0.
	dTotal=0; // number of 2v3 ROIs checked against 3v2 ROI "c2".
	no_co=0; // yes/no variable determining whether overlap was found. 
	for(d=0; d<TL_2v3_type_c; d++){ //2v3 ROIs
		roiManager("Select", newArray(c, d)); //select the 3v2 ROI the 2v3 ROI
		roiManager("AND"); //build an overlapping ROI
		roiType = selectionType(); //determine the type of overlap represented by the overlapping ROI
		d2=d+1;
		dTotal=dTotal+1;
		if(roiType==-1){ //"-1" represents the type of ROI that does not overlap
			//wait(5);
			//print("no overlap");
     		}
		if(roiType!=-1){ //if not "-1 type", then there is overlap, so mark it.
			roiManager("Add"); //add the overlap to the ROI manager so that we can measure its size.
			no_co = no_co+1; // to remember that, yes, there is overlap.
				
			roiManager("deselect");
			run("Clear Results");
			roiManager("Select", c); //measure the size of the 3v2 ROI
			roiManager("Measure");
			co_roi = (roiManager("count")-1); //find the overlap ROI (it's going to be the last one on the ROI manager list).
			roiManager("Select", co_roi); // select it.
			roiManager("Measure"); //measure the area.
			roiManager("Select", co_roi);
			roiManager("Delete"); //now removing it beause we only wanted that new ROI to measure the area of overlap.
			c2_area=getResult("Area", 0); // grab the 3v2 ROI's area from the results 
			co_area=getResult("Area", 1); // grab the overlap-ROI's area from the results
			c2_area_80 = c2_area*TL3_overlap; // calculate 80% of the 3v2 ROI area (we're using 80% as an arbitrary threshold of minimum 3v2 ROI that should be within the 2v3 ROI). 
			if(c2_area_80>co_area){ // if the overlapping area is less 80% of the 2v3 ROI,
				no_co = no_co-1; //then, forget that an overlap was identified and move on (restart the loop).
			} else {
				co_roi_array2 = Array.concat(co_roi_array2, d); //building an array to remember which 3v2 ROIs overlap (for ROI selection)
				co_roi_array2_UI = Array.concat(co_roi_array2_UI, d2); //building an array to remember which 3v2 ROIs overlap (for the human to understand)
				saved_3v2_ROIs_array = Array.concat(saved_3v2_ROIs_array, c); //building an array to remember which 3v2 ROIs overlap (for ROI selection)
				}
			}
			//print("overlap");
		if((d2==TL_2v3_type_c) && (no_co==0)){ //if checked every 2v3 ROI and still not found a 2v3 ROI that overlap (>80%) with the 3v2 ROI,
			roiManager("deselect");
     		roiManager("Select", c2-1);
			roiManager("Delete");
			no_overlap=no_overlap+1; // add one to the number of 3v2 ROIs not overlapping so that we can lower the total 3v2 ROIs.
			c=c-1;//remove one 3v2 ROI from the counted number of 3v2 ROIs. Semi repetitive with last variable, but each serve a function in the top IF loop.
     		}
		}
		n=n+1;
	}
//waitForUser("finished TL #3");

roiManager("Select", TL_2v3_type_c_array);
roiManager("Delete");

//waitForUser("");

//saving ROI overlay

save_3v2_TL_ROIs=roiManager("count");

TL_3v2_ROI_2_array=newArray(save_3v2_TL_ROIs); // saving the list of TL 3v2  ROIs
for(i=0; i<save_3v2_TL_ROIs; i++) {
	TL_3v2_ROI_2_array[i] = i;
	}

roiManager("deselect");
roiManager("Save", subDir_TL+third_stain_type_TL+"_vs_"+first_stain_type_TL+"_TripleLabel_ROIoverlay.zip");
//waitForUser(" ");
wait(100);

print("\n"+third_image_TL);
print("3v2 TL ROIs = "+save_3v2_TL_ROIs);

roiManager("reset");
run("Clear Results"); 

run("_ps_measure_TL_ROIs");
}

//\\//\\//\\//\\//\\//\\//\\

macro "_ps_measure_TL_ROIs" { //this macro simply recalls the TL ROIs defined in the last macro in order to measure the values again.

//waitForUser("TL_measure macro");

first_image_tif = first_image_TL+"-2.tif";
second_image_tif = second_image_TL+"-2.tif";
third_image_tif = third_image_TL+"-2.tif";
merged_image_tif = merged_image_TL+".tif";

roiManager("Open", subDir_TL+first_stain_type_TL+"_vs_"+second_stain_type_TL+"_TripleLabel_ROIoverlay.zip");
roiManager("Open", subDir_TL+second_stain_type_TL+"_vs_"+third_stain_type_TL+"_TripleLabel_ROIoverlay.zip");
roiManager("Open", subDir_TL+third_stain_type_TL+"_vs_"+first_stain_type_TL+"_TripleLabel_ROIoverlay.zip");

if (import_type==6 || import_type==7) {  //if run in automatic mode, no ROI check step offered.
	wait(300);
} else {  //otherwise, it opens the merge image and overlays the ROIs.
	selectWindow(merged_image_tif);
	roiManager("Show All");
	roiManager("Show All with labels");


	Dialog.create("How Does That Look?"); //the first step is asking the user if they want to edit either ROI set.
	items = newArray(first_stain_type_TL, second_stain_type_TL, third_stain_type_TL, "Accept labeling as is.");
	Dialog.addRadioButtonGroup("Here is your chance to adjust the ROIs to better identify PNNs. Which triple-labeled cells would you like to adjust?", items, 4, 1, "Accept labeling as is.");
	Dialog.show;
	choice = Dialog.getRadioButton;

	if (choice==first_stain_type_TL) {
		setTool("oval");
		roiManager("reset");
		roiManager("Open", subDir_TL+first_stain_type_TL+"_vs_"+second_stain_type_TL+"_TripleLabel_ROIoverlay.zip");
		waitForUser("How Does That Look?", "Here is your chance to adjust the triple-labeling. Try moving or deleting ROIs. **THEN** press OK.");
		roiManager("deselect");
		roiManager("Save", subDir_TL+first_stain_type_TL+"_vs_"+second_stain_type_TL+"_TripleLabel_ROIoverlay.zip");
		wait(100);

		Dialog.create("Adjust the other stains?"); //then the second step is to ask the user if they want to edit the other stain's ROIs.
			items = newArray("Yes", "No, accept labeling as is.");
			Dialog.addRadioButtonGroup("Do you want to also adjust the other stains' ROIs?", items, 2, 1, "Yes");
			Dialog.show;
			choice = Dialog.getRadioButton;

		if (choice=="Yes") {
			setTool("rectangle");
			roiManager("reset");
			roiManager("Open", subDir_TL+second_stain_type_TL+"_vs_"+third_stain_type_TL+"_TripleLabel_ROIoverlay.zip");
			waitForUser("How Does That Look?", "Here is your chance to adjust the labeling. Try moving or deleting ROIs. **THEN** press OK.");
			roiManager("deselect");
			roiManager("Save", subDir_TL+second_stain_type_TL+"_vs_"+third_stain_type_TL+"_TripleLabel_ROIoverlay.zip");
			wait(100);
			roiManager("reset");
			
			setTool("oval");
			roiManager("Open", subDir_TL+third_stain_type_TL+"_vs_"+first_stain_type_TL+"_TripleLabel_ROIoverlay.zip");
			waitForUser("How Does That Look?", "Here is your chance to adjust the labeling. Try moving or deleting ROIs. **THEN** press OK.");
			roiManager("deselect");
			roiManager("Save", subDir_TL+third_stain_type_TL+"_vs_"+first_stain_type_TL+"_TripleLabel_ROIoverlay.zip");
			wait(100);
			roiManager("reset");

			run("_ps_measure_TL_ROIs");
			}
		
		if (choice=="No, accept labeling as is.") {
			//well, aren't you confident.
			}
		}
	
	if (choice==second_stain_type_TL) {
		setTool("rectangle");
		roiManager("reset");
		roiManager("Open", subDir_TL+second_stain_type_TL+"_vs_"+third_stain_type_TL+"_TripleLabel_ROIoverlay.zip");
		waitForUser("How Does That Look?", "Here is your chance to adjust the labeling. Try moving or deleting ROIs. **THEN** press OK.");
		roiManager("deselect");
		roiManager("Save", subDir_TL+second_stain_type_TL+"_vs_"+third_stain_type_TL+"_TripleLabel_ROIoverlay.zip");
		wait(100);

		Dialog.create("Adjust the other stains?"); //then the second step is to ask the user if they want to edit the other stain's ROIs.
			items = newArray("Yes", "No, accept labeling as is.");
			Dialog.addRadioButtonGroup("Do you want to also adjust the other stains' ROIs?", items, 2, 1, "Yes");
			Dialog.show;
			choice = Dialog.getRadioButton;

		if (choice=="Yes") {
			setTool("oval");
			roiManager("reset");
			roiManager("Open", subDir_TL+first_stain_type_TL+"_vs_"+second_stain_type_TL+"_TripleLabel_ROIoverlay.zip");
			waitForUser("How Does That Look?", "Here is your chance to adjust the labeling. Try moving or deleting ROIs. **THEN** press OK.");
			roiManager("deselect");
			roiManager("Save", subDir_TL+first_stain_type_TL+"_vs_"+second_stain_type_TL+"_TripleLabel_ROIoverlay.zip");
			wait(100);
			roiManager("reset");
			
			roiManager("Open", subDir_TL+third_stain_type_TL+"_vs_"+first_stain_type_TL+"_TripleLabel_ROIoverlay.zip");
			waitForUser("How Does That Look?", "Here is your chance to adjust the labeling. Try moving or deleting ROIs. **THEN** press OK.");
			roiManager("deselect");
			roiManager("Save", subDir_TL+third_stain_type_TL+"_vs_"+_first_type_TL+"_TripleLabel_ROIoverlay.zip");
			wait(100);
			roiManager("reset");

			run("_ps_measure_TL_ROIs");
			}
		
		if (choice=="No, accept labeling as is.") {
			//well, aren't you confident.
			}
		}
	
	if (choice==third_stain_type_TL) {
		setTool("oval");
		roiManager("reset");
		roiManager("Open", subDir_TL+third_stain_type_TL+"_vs_"+first_stain_type_TL+"_TripleLabel_ROIoverlay.zip");
		waitForUser("How Does That Look?", "Here is your chance to adjust the labeling. Try moving or deleting ROIs. **THEN** press OK.");
		roiManager("deselect");
		roiManager("Save", subDir_TL+third_stain_type_TL+"_vs_"+first_stain_type_TL+"_TripleLabel_ROIoverlay.zip");
		wait(100);

		Dialog.create("Adjust the other stains?"); //again, second step, but after starting with the alternative stain.
			items = newArray("Yes", "No, accept labeling as is.");
			Dialog.addRadioButtonGroup("Do you want to also adjust other stains' ROIs?", items, 2, 1, "Yes");
			Dialog.show;
			choice = Dialog.getRadioButton;

		if (choice=="Yes") {
			setTool("oval");
			roiManager("reset");
			roiManager("Open", subDir_TL+first_stain_type_TL+"_vs_"+second_stain_type_TL+"_TripleLabel_ROIoverlay.zip");
			waitForUser("How Does That Look?", "Here is your chance to adjust the lableing. Try moving or deleting ROIs. **THEN** press OK.");
			roiManager("deselect");
			roiManager("Save", subDir_TL+first_stain_type_TL+"_vs_"+second_stain_type_TL+"TripleLabel_ROIoverlay.zip");
			wait(100);
			roiManager("reset");
			
			setTool("oval");
			roiManager("Open", subDir_TL+second_stain_type_TL+"_vs_"+third_stain_type_TL+"_TripleLabel_ROIoverlay.zip");
			waitForUser("How Does That Look?", "Here is your chance to adjust the lableing. Try moving or deleting ROIs. **THEN** press OK.");
			roiManager("deselect");
			roiManager("Save", subDir_TL+second_stain_type_TL+"_vs_"+third_stain_type_TL+"TripleLabel_ROIoverlay.zip");
			wait(100);
			roiManager("reset");

			run("_ps_measure_TL_ROIs");
			}	
		if (choice=="No, accept labeling as is.") {
			//well, aren't you confident.
			}	
		}
	if (choice=="Accept labeling as is.") {
		//well, aren't you confident.
		}
	}
roiManager("reset");

//\\\\\\\\\\\\\
//actually measuring the triple labeled ROIs (image 1)

run("Clear Results");
selectWindow(first_image_tif);

roiManager("Open", subDir_TL+first_stain_type_TL+"_vs_"+second_stain_type_TL+"_TripleLabel_ROIoverlay.zip");
name = series_name_TL+" "+first_stain_type_TL+" triple-label.txt"; 

TL_ROIs=0;
TL_ROIs =roiManager("count"); 
TL_array=newArray(TL_ROIs); 
for(i=0; i<TL_ROIs;i++) { 
    TL_array[i] = i; 
	}

roiManager("deselect");
roiManager("Measure");
roiManager("Show All");
roiManager("Show All with labels");

TL_ROIs1_m=0;
TL_ROIs1_means=0;
TL_ROIs1_m = nResults;
TL_ROIs1_means = newArray(TL_ROIs1_m);
roiManager("deselect");
for (i=0; i<TL_ROIs1_m; i++) {
	TL_ROIs1_means[i] = getResult('Mean', i);
	}
roiManager("deselect");
for (i=0; i<TL_ROIs1_m; i++) {
	setResult("Background", i, TL1_lower);
	setResult("Mean-Background", i, TL_ROIs1_means[i]-TL1_lower);
	}
setResult("File Name", 0, name);
updateResults();

TL_ROIs1_m2 = 0;
TL_ROIs1_avgmeans = 0;
TL_ROIs1_m2 = nResults;
TL_ROIs1_avgmeans = newArray(TL_ROIs1_m2);
roiManager("deselect");
for (i=0; i<TL_ROIs1_m2; i++) {
	TL_ROIs1_avgmeans[i] = getResult('Mean-Background', i);
	}
Array.getStatistics(TL_ROIs1_avgmeans, min, max, mean, stdDev);
print("\n");
print("Triple-labeled "+first_stain_type_TL+" Mean Intensity = "+mean);


TL_ROIs1_numberStains=0;
TL_ROIs1_numberStains=getValue("results.count");
print("Final number of Triple-labeled "+first_stain_type_TL+" Cells = "+TL_ROIs1_numberStains);

n_tl1 = nResults;
area_tl1 = newArray(n_tl1);
XM_tl1 = newArray(n_tl1);
YM_tl1 = newArray(n_tl1);
mean_tl1 = newArray(n_tl1);
stddev_tl1 = newArray(n_tl1);
min_tl1 = newArray(n_tl1);
max_tl1 = newArray(n_tl1);
bkgd_tl1 = newArray(n_tl1);
meanbkgd_tl1 = newArray(n_tl1);
print("\n");
print("Triple-labeled "+first_stain_type_TL+" cell data:");
print("Subject# Image_name Stain_type Cell# Area XM YM Mean StdDev Min Max Background Mean-Background");
for (i=0; i<n_tl1; i++) {
	area_tl1[i] = getResult('Area', i);
	XM_tl1[i] = getResult('XM', i);
	YM_tl1[i] = getResult('YM', i);
	mean_tl1[i] = getResult('Mean', i);
	stddev_tl1[i] = getResult('StdDev', i);
	min_tl1[i] = getResult('Min', i);
	max_tl1[i] = getResult('Max', i);
	bkgd_tl1[i] = getResult('Background', i);
	meanbkgd_tl1[i] = getResult('Mean-Background', i);
	print(subject_id+" "+first_image_TL+" "+"Triple-labeled_"+first_stain_type_TL+" "+(i+1)+" "+area_tl1[i]+" "+XM_tl1[i]+" "+YM_tl1[i]+" "+mean_tl1[i]+" "+stddev_tl1[i]+" "+min_tl1[i]+" "+max_tl1[i]+" "+bkgd_tl1[i]+" "+meanbkgd_tl1[i]);
	}

// Saving data

saveAs("Measurements", subDir_TL+name);
print("\n"); 
print("Results saved as: "+subDir_TL+name);
selectWindow("Log");
saveAs("Text", dir+"/All_Results"+TimeString+".txt");

roiManager("reset");

//\\\\\\\\\\\\\\\
//actually measuring the triple labeled ROIs (image 2)

run("Clear Results");
selectWindow(second_image_tif);

roiManager("Open", subDir_TL+second_stain_type_TL+"_vs_"+third_stain_type_TL+"_TripleLabel_ROIoverlay.zip");
name = series_name_TL+" "+second_stain_type_TL+" triple-label.txt"; 

TL_ROIs=0;
TL_ROIs =roiManager("count"); 
TL_array=newArray(TL_ROIs); 
for(i=0; i<TL_ROIs;i++) { 
    TL_array[i] = i; 
	}

roiManager("deselect");
roiManager("Measure");
roiManager("Show All");
roiManager("Show All with labels");

TL_ROIs2_m=0;
TL_ROIs2_means=0;
TL_ROIs2_m = nResults;
TL_ROIs2_means = newArray(TL_ROIs2_m);
roiManager("deselect");
for (i=0; i<TL_ROIs2_m; i++) {
	TL_ROIs2_means[i] = getResult('Mean', i);
	}
roiManager("deselect");
for (i=0; i<TL_ROIs2_m; i++) {
	setResult("Background", i, TL2_lower);
	setResult("Mean-Background", i, TL_ROIs2_means[i]-TL2_lower);
	}
setResult("File Name", 0, name);
updateResults();

TL_ROIs2_m2 = 0;
TL_ROIs2_avgmeans = 0;
TL_ROIs2_m2 = nResults;
TL_ROIs2_avgmeans = newArray(TL_ROIs2_m2);
roiManager("deselect");
for (i=0; i<TL_ROIs2_m2; i++) {
	TL_ROIs2_avgmeans[i] = getResult('Mean-Background', i);
	}
Array.getStatistics(TL_ROIs2_avgmeans, min, max, mean, stdDev);
print("\n");
print("Triple-labeled "+second_stain_type_TL+" Mean Intensity = "+mean);


TL_ROIs2_numberStains=0;
TL_ROIs2_numberStains=getValue("results.count");
print("Final number of Triple-labeled "+second_stain_type_TL+" Cells = "+TL_ROIs2_numberStains);

n_tl2 = nResults;
area_tl2 = newArray(n_tl2);
XM_tl2 = newArray(n_tl2);
YM_tl2 = newArray(n_tl2);
mean_tl2 = newArray(n_tl2);
stddev_tl2 = newArray(n_tl2);
min_tl2 = newArray(n_tl2);
max_tl2 = newArray(n_tl2);
bkgd_tl2 = newArray(n_tl2);
meanbkgd_tl2 = newArray(n_tl2);
print("\n");
print("Triple-labeled "+second_stain_type_TL+" cell data:");
print("Subject# Image_name Stain_type Cell# Area XM YM Mean StdDev Min Max Background Mean-Background");
for (i=0; i<n_tl2; i++) {
	area_tl2[i] = getResult('Area', i);
	XM_tl2[i] = getResult('XM', i);
	YM_tl2[i] = getResult('YM', i);
	mean_tl2[i] = getResult('Mean', i);
	stddev_tl2[i] = getResult('StdDev', i);
	min_tl2[i] = getResult('Min', i);
	max_tl2[i] = getResult('Max', i);
	bkgd_tl2[i] = getResult('Background', i);
	meanbkgd_tl2[i] = getResult('Mean-Background', i);
	print(subject_id+" "+second_image_TL+" "+"Triple-labeled_"+second_stain_type_TL+" "+(i+1)+" "+area_tl2[i]+" "+XM_tl2[i]+" "+YM_tl2[i]+" "+mean_tl2[i]+" "+stddev_tl2[i]+" "+min_tl2[i]+" "+max_tl2[i]+" "+bkgd_tl2[i]+" "+meanbkgd_tl2[i]);
	}

	// Saving data

saveAs("Measurements", subDir_TL+name);
print("\n"); 
print("Results saved as: "+subDir_TL+name);
selectWindow("Log");
saveAs("Text", dir+"/All_Results"+TimeString+".txt");

roiManager("reset");

//\\\\\\\\\\\\\\\
//actually measuring the triple labeled ROIs (image 3)

run("Clear Results");
selectWindow(third_image_tif);

roiManager("Open", subDir_TL+third_stain_type_TL+"_vs_"+first_stain_type_TL+"_TripleLabel_ROIoverlay.zip");
name = series_name_TL+" "+third_stain_type_TL+" triple-label.txt"; 

TL_ROIs=0;
TL_ROIs =roiManager("count"); 
TL_array=newArray(TL_ROIs); 
for(i=0; i<TL_ROIs;i++) { 
    TL_array[i] = i; 
	}
//roiManager("Select", TL_array); //by not making an ROI manager selection, and actually deselecting, the ROI manager selects all by default. 
roiManager("deselect");
roiManager("Measure");
roiManager("Show All");
roiManager("Show All with labels");

TL_ROIs3_m=0;
TL_ROIs3_means=0;
TL_ROIs3_m = nResults;
TL_ROIs3_means = newArray(TL_ROIs3_m);
roiManager("deselect");
for (i=0; i<TL_ROIs3_m; i++) {
	TL_ROIs3_means[i] = getResult('Mean', i);
	}
roiManager("deselect");
for (i=0; i<TL_ROIs3_m; i++) {
	setResult("Background", i, TL3_lower);
	setResult("Mean-Background", i, TL_ROIs3_means[i]-TL3_lower);
	}
setResult("File Name", 0, name);
updateResults();

TL_ROIs3_m2 = 0;
TL_ROIs3_avgmeans = 0;
TL_ROIs3_m2 = nResults;
TL_ROIs3_avgmeans = newArray(TL_ROIs3_m2);
roiManager("deselect");
for (i=0; i<TL_ROIs3_m2; i++) {
	TL_ROIs3_avgmeans[i] = getResult('Mean-Background', i);
	}
Array.getStatistics(TL_ROIs3_avgmeans, min, max, mean, stdDev);
print("\n");
print("Triple-labeled "+third_stain_type_TL+" Mean Intensity = "+mean);


TL_ROIs3_numberStains=0;
TL_ROIs3_numberStains=getValue("results.count");
print("Final number of Triple-labeled "+third_stain_type_TL+" Cells = "+TL_ROIs3_numberStains);

n_tl3 = nResults;
area_tl3 = newArray(n_tl3);
XM_tl3 = newArray(n_tl3);
YM_tl3 = newArray(n_tl3);
mean_tl3 = newArray(n_tl3);
stddev_tl3 = newArray(n_tl3);
min_tl3 = newArray(n_tl3);
max_tl3 = newArray(n_tl3);
bkgd_tl3 = newArray(n_tl3);
meanbkgd_tl3 = newArray(n_tl3);
print("\n");
print("Triple-labeled "+third_stain_type_TL+" cell data:");
print("Subject# Image_name Stain_type Cell# Area XM YM Mean StdDev Min Max Background Mean-Background");
for (i=0; i<n_tl3; i++) {
	area_tl3[i] = getResult('Area', i);
	XM_tl3[i] = getResult('XM', i);
	YM_tl3[i] = getResult('YM', i);
	mean_tl3[i] = getResult('Mean', i);
	stddev_tl3[i] = getResult('StdDev', i);
	min_tl3[i] = getResult('Min', i);
	max_tl3[i] = getResult('Max', i);
	bkgd_tl3[i] = getResult('Background', i);
	meanbkgd_tl3[i] = getResult('Mean-Background', i);
	print(subject_id+" "+second_image_TL+" "+"Triple-labeled_"+third_stain_type_TL+" "+(i+1)+" "+area_tl3[i]+" "+XM_tl3[i]+" "+YM_tl3[i]+" "+mean_tl3[i]+" "+stddev_tl3[i]+" "+min_tl3[i]+" "+max_tl3[i]+" "+bkgd_tl3[i]+" "+meanbkgd_tl3[i]);
	}

	// Saving data

saveAs("Measurements", subDir_TL+name);
print("\n"); 
print("Results saved as: "+subDir_TL+name);
selectWindow("Log");
saveAs("Text", dir+"/All_Results"+TimeString+".txt");
waitForUser("Pause before close");
wait(10);
roiManager("reset");
wait(10);
//\\\\\\\\\\\\\\\


//waitForUser("Pause before close");
wait(10);
selectWindow(first_image_tif);
wait(10);
run("Close");
selectWindow(second_image_tif);
wait(10);
run("Close");
selectWindow(third_image_tif);
wait(10);
run("Close");	
selectWindow(merged_image_tif);
wait(10);
run("Close");



///////////////////////////////////




	// Closing project

roiManager("reset");
//selectWindow("ROI Manager");
//wait(15);
//run("Close");
run("Clear Results");
print("Done!");

Dialog.create("Next image?");
items = newArray("Open next image for analysis.", "Return to main menu.");
Dialog.addRadioButtonGroup("What would you like to do next?", items, 2, 1, "Open next image for analysis.");
Dialog.show;
choice = Dialog.getRadioButton;
if (choice=="Return to main menu.") {
	run("_ps_initi");
} else {
	run("_ps_Open_Triple");
}
}
		
}