////////////////////////////////////////////////////////////////
/*									
	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) 2016, John H. Harkness

	Washington State University, Vancouver
	
	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

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

	>>> PLEASE CITE: Slaker, et al., 2016 (IN SUBMISSION) <<<
*/
////////////////////////////////////////////////////////////////



	//Global variables

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

var set_intensity_low_threshold = 1.165;
var set_intensity_high_threshold = 5000;
var set_low_particlesize = 70;
var set_high_particlesize = 99999;
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 set_count_low_threshold = 0.5;
var set_count_high_threshold = 5000;
var set_count_low_particlesize = 20;
var set_count_high_particlesize = 100000;
var set_count_low_circularity = 0.02;
var set_count_high_circularity = 1.00;

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

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

var first_stain_type = "PV";
var DL1_set_intensity_low_threshold = 1.0;
var DL1_set_intensity_high_threshold = 5000;
var DL1_set_low_particlesize = 40;
var DL1_set_high_particlesize = 1000;
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 second_stain_type = "WFA";
var DL2_set_intensity_low_threshold = 1.165;
var DL2_set_intensity_high_threshold = 5000;
var DL2_set_low_particlesize = 70;
var DL2_set_high_particlesize = 99999;
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 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;

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

macro "PIPSQUEAK" {

if (pipsqueakCount==1){
	
///call home tracking URL
//UPDATE HERE (1/3)->

	call_home_url = "http://jabstracts.org/PIPSQUEAK/call_home/v2-00/";
	File.openUrlAsString(call_home_url);
	wait(200);
	print("\\Clear");

///
//UPDATE HERE (2/3)->

	print("PIPSQUEAK! v2.01 Perineuronal net Intensity Program for the Standardization and Quantification of ECM Analysis.");
	print("\n"+"Copyright (C) 2016, John H. Harkness, PhD.");
	print("Protocol by Megan Slaker, PhD.");
	print("Lab of Barb Sorg, PhD., Washington State University, Vancouver");
	print("\n"+"\n"+"Distance in pixels: "+scale_size+"\n"+"Known distance: "+scale_known+"\n"+"Pixel aspect ratio: "+scale_pixel+"\n"+"Unit of length: "+scale_unit);
	
//UPDATE HERE (3/3)-> 

	waitForUser("PIPSQUEAK version 2.01, Oct 5, 2016 \n \n 2.00 CHANGE LOG: Complete redesign for identification of double-labeled cells. \n Now, ROIs that are built during single-label identification will be maintained carried forward to the double-label step. \n Cells labeled by either single label are compared between labels and those with overlapping labels are identified as double labeled. \n This method accounts and corrects for partial overlap (removed if less that 80% overlap) and multiple overlap \n (two PV cells for one WFA cell). Pretty neat! One more thing, results output is improved in the Log Window. \n Now, each cell row contains subject, image name, and stain type information. Therefore, importing Log results into Excel \n is easier because you can use the 'sort data' tool to help remove unwanted rows.\n NOTE: Results from 2.00 and above should not be combined with earlier versions of PIPSQUEAK for statistical analysis. \n \n 2.01 CHANGE LOG: UI improvements in cell counting function.");

	wait(1000);
	pipsqueakCount = pipsqueakCount+1;
	
	} else {
		wait(50);
		}

Dialog.create("Welcome.");
items = newArray("Count labeled cells.", "Single label intensity analysis.", "Double label intensity analysis.", "->Change image scale settings.");
Dialog.addRadioButtonGroup("What would you like to do?", items, 4, 1, " ");
Dialog.show;
choice = Dialog.getRadioButton;
if (choice=="Count labeled cells.") {
       run("__sub_PIPSQUEAK_count");
	
	} else {
	if (choice=="Single label intensity analysis.") {
		run("__sub_PIPSQUEAK_single");
		}
	if (choice=="Double label intensity analysis.") {
		run("__sub_PIPSQUEAK_double");
		}
	if (choice=="->Change image scale settings.") {
		Dialog.create("Settings 1 of 2");
		Dialog.addMessage("**Image scale measurements (for both single and double label analysis)**");
		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.addMessage(" ");
		Dialog.addMessage("---------------------------------------------------------------------");
		Dialog.addMessage("                                    **ROI identification settings.**");
		Dialog.addMessage("Adjustments will alter cell determination and MAY reduce identification specificity.");
		Dialog.addMessage("Proceed with caution: here be dragons.");

//this can be improved in the future by adding a drop down menu that calls up set stain parameters.
//!!need additional settings for double labeled analysis?!!

		Dialog.addMessage(" ");
		Dialog.addMessage("**Intensity measurements**");
		//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);
		
		Dialog.addMessage(" ");		
		Dialog.addMessage(" ");
		Dialog.addMessage("**Cell count**");
		//Dialog.addMessage(" ");
		Dialog.addNumber("Lower threshold multiplier:", set_count_low_threshold );
		Dialog.addNumber("Upper threshold limit:", set_count_high_threshold);
		Dialog.addNumber("Lower limit particle size:", set_count_low_particlesize);
		Dialog.addNumber("Upper limit particle size:", set_count_high_particlesize);
		Dialog.addNumber("Lower limit circularity:", set_count_low_circularity);
		Dialog.addNumber("Upper limit circularity:", set_count_high_circularity);


		
		Dialog.show;

		scale_size = Dialog.getNumber();
		scale_known = Dialog.getNumber();;
		scale_pixel = Dialog.getNumber();;;
		scale_unit =  Dialog.getString();
		
		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();;;;;;;

		set_count_low_threshold = Dialog.getNumber();;;;;;;
		set_count_high_threshold = Dialog.getNumber();;;;;;;;
		set_count_low_particlesize = Dialog.getNumber();;;;;;;;;
		set_count_high_particlesize = Dialog.getNumber();;;;;;;;;;
		set_count_low_circularity = Dialog.getNumber();;;;;;;;;;;
		set_count_high_circularity = Dialog.getNumber();;;;;;;;;;;;
		
		Dialog.create("Settings 1 of 2");		
		Dialog.addMessage("**Double-label Analysis**");
		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.addNumber("Total number of stains plus composite in each subject series:", total_images);
		//REMOVED FOR NOW—SEE NOTE IN "__SUB_PIPSQUEAK_OPENNEXT_DOUBLE".
		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.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.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();;;;;;;;
		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();;;;;;;;

		run("PIPSQUEAK");
		}
	}
}

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

macro "__sub_PIPSQUEAK_count" {
	//count labeled cells

import_type=4;

if (countCount == 1) {
	Dialog.create("Count Labeled Cells.");
	items = newArray("Yes, continue.", "No. Go back.");
	Dialog.addRadioButtonGroup("For this analysis, you NEED TO SET THE CELL DETECTION PARAMETERS in Settings. Also, you need previously summed z-stacks. \n Do you have the correct settings and summed files ready?", items, 2, 1, "Yes, continue.");
	Dialog.show;
	choice = Dialog.getRadioButton;
	if (choice=="No. Go back.") {
			run("PIPSQUEAK");
		} else {
			wait(50);
		}
	} else {
		wait(50);
		}
countCount = countCount+1;

Dialog.create("Cell Count");
items = newArray("Open image to count", "Adjust cell detection parameters", "Return to main menu");
Dialog.addRadioButtonGroup("What would you like to do?", items, 3, 1, "Open image to count");
Dialog.show;
choice = Dialog.getRadioButton;
if (choice=="Open image to count") {
	wait(100);
	
	} else {
	if (choice=="Return to main menu") {
		run("PIPSQUEAK");
		}
	if (choice=="Adjust cell detection parameters") {
		Dialog.create("Settings");
		Dialog.addMessage("**Cell count**");
		//Dialog.addMessage(" ");
		Dialog.addNumber("Lower threshold multiplier:", set_count_low_threshold );
		Dialog.addNumber("Upper threshold limit:", set_count_high_threshold);
		Dialog.addNumber("Lower limit particle size:", set_count_low_particlesize);
		Dialog.addNumber("Upper limit particle size:", set_count_high_particlesize);
		Dialog.addNumber("Lower limit circularity:", set_count_low_circularity);
		Dialog.addNumber("Upper limit circularity:", set_count_high_circularity);
		
		Dialog.show;

		set_count_low_threshold = Dialog.getNumber();;;;;;;
		set_count_high_threshold = Dialog.getNumber();;;;;;;;
		set_count_low_particlesize = Dialog.getNumber();;;;;;;;;
		set_count_high_particlesize = Dialog.getNumber();;;;;;;;;;
		set_count_low_circularity = Dialog.getNumber();;;;;;;;;;;
		set_count_high_circularity = Dialog.getNumber();;;;;;;;;;;;

		run("__sub_PIPSQUEAK_count");
		}
	}


open();
run("Set Measurements...", "area mean standard min redirect=None decimal=3");
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("__sub_PIPSQUEAK_Background");
}

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

macro "__sub_PIPSQUEAK_single" {

//
/*  
print("PIPSQUEAK! Perineuronal net Intensity Program for the Standardization and Quantification of ECM Analysis.");
print("\n"+"Copyright (C) 2016, John H. Harkness, PhD.");
print("Protocol by Megan Slaker, PhD.");
print("Lab of Barb Sorg, PhD., Washington State University, Vancouver");
print("\n"+"Distance in pixels: "+scale_size+"\n"+"Known distance: "+scale_known+"\n"+"Pixel aspect ratio: "+scale_pixel+"\n"+"Unit of length: "+scale_unit);
wait(1000);
*/

Dialog.create("Import Type");

//
/*items = newArray("Semiautomated analysis of raw, non-summed, individual Tiffs", "Semiautomated analysis of summed Z-stacks", "Automatic analysis of summed Z-stacks","->Make summed image from z-stacks.");
Dialog.addRadioButtonGroup("What type of file do you want to import?", items, 4, 1, "Semiautomated analysis of summed Z-stacks");

*/
//
items = newArray("Semiautomated analysis of raw, non-summed, individual Tiffs", "Semiautomated analysis of summed Z-stacks", "Automatic analysis of summed Z-stacks");
Dialog.addRadioButtonGroup("What type of file do you want to import?", items, 3, 1, "Semiautomated analysis of summed Z-stacks");

Dialog.show;
choice = Dialog.getRadioButton;
if (choice=="Semiautomated analysis of raw, non-summed, individual Tiffs") {
	import_type=1;
	run("__sub_PIPSQUEAK_Import");
	
} else {
	if (choice=="Semiautomated analysis of summed Z-stacks") {
		import_type=2;
		run("__sub_PIPSQUEAK_Import_Summed");
		}
	if (choice=="Automatic analysis of summed Z-stacks") {
		import_type=3;
		run("__sub_PIPSQUEAK_Auto_Import_Summed");
		}
//
/*	if (choice=="->Make summed image from z-stacks.") {
		//Dialog.create("Stacks folder");
		//Dialog.addMessage("Before using this function, please place z-stack images into a folder titled 'Stacks' within your experiment folder.");
		//Dialog.addMessage("ie, /Desktop/experiment folder/Stacks/image1... image20");
		//Dialog.show(); 
		run("__sub_PIPSQUEAK_z-project");
		}
*/
	}
}
/////////////////////////////

macro "__sub_PIPSQUEAK_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 redirect=None decimal=3");

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("__sub_PIPSQUEAK_Background");
}

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

macro "__sub_PIPSQUEAK_Import_Summed" {
       // Importing image

//import_type=2;
open();
run("Set Measurements...", "area mean standard min redirect=None decimal=3");
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("__sub_PIPSQUEAK_Background");
}

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

macro "__sub_PIPSQUEAK_Auto_Import_Summed" {
       // Importing image

//import_type=3;
open();
run("Set Measurements...", "area mean standard min redirect=None decimal=3");
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("__sub_PIPSQUEAK_Background");
}

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

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


top_dir = getDirectory("Choose a Directory ");
count = 1;
listFiles(top_dir); 


function listFiles(top_dir) {
	list = getFileList(top_dir+"/..");
	//child_list = getFileList(top_dir);
	parentlist = File.getParent(top_dir);
	for (i=0; i<list.length; i++) {
		    	if (endsWith(list[i], ".tif")){
    	
        	listFiles(""+parentlist+File.separator+list[i]);

	} else {
			print((count++) + ": " + parentlist+File.separator+ list[i]);
			//child_list = getFileList();
			//print(child_list[i]);
			child_list = getFileList(parentlist+File.separator+ list[i]);
			for (a=0; a<child_list.length; a++) {
        		if (endsWith(child_list[a], ".tif")){
           			listFiles(""+child_list[a]);
			}else{
           			print(parentlist+File.separator+ list[i]+child_list[a]);
           			open_list = parentlist+File.separator+list[i]+child_list[a];
           			print("\n"+"Image total: "+(count++)+"= "+open_list);
           			run("Image Sequence...", "open=&open_list disable_global convert sort");
           			run("Set Measurements...", "area mean standard min redirect=None decimal=3");
					run("Z Project...", "projection=[Sum Slices]");
					run("Set Scale...", "distance=scale_size known=scale_known pixel=scale_pixel unit=&scale_unit global");
					name = getTitle;
					file_name = File.getName(list[i]);
					folder_name = File.getName(File.getParent(File.directory));
					
						if (count == 2) {
							save_location = "/Users/John 1/Desktop/";
							Dialog.create("Save location");
							Dialog.addMessage("Select a place to save the summed image. ie, /Users/John/Desktop");
							Dialog.addString("Save to: ", save_location);
							Dialog.show();
							save_location = Dialog.getString();
							print("Save location: "+save_location);
							} else {
								wait(1);
								}

						if (File.exists(save_location+"PIPSQUEAK_Summed Stacks/")) {
							wait(1);
							} else { 
								File.makeDirectory(save_location+"PIPSQUEAK_Summed Stacks/");
								}
						saveAs("Tiff", save_location+"PIPSQUEAK_Summed Stacks/"+file_name+"-"+name+".tif");
						close(file_name+"-"+name+".tif");
						print("Image: "+file_name+"-"+name+"\n"+"Saved in: "+save_location+"PIPSQUEAK_Summed Stacks/");
						run("Close All");
					
	}
	}
}
}
}
Dialog.create("Again?");
items = newArray("Yes, save another.", "No, let's analyze.", "Exit");
Dialog.addRadioButtonGroup("Would you like to save another summed image?", items, 3, 1, "Yes, save another.");
Dialog.show;
choice = Dialog.getRadioButton;
if (choice=="Yes, save another.") {
       run("__sub_PIPSQUEAK_z-project");
	
	} else {
	if (choice=="No, let's analyze.") {
		run("PIPSQUEAK");
		}
	if (choice=="Exit") {
		print("\n"+"Thanks for using PIPSQUEAK. Bye!");
		}
	}
}

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

macro "__sub_PIPSQUEAK_OpenNext_Single" {

//import_type=3;
makeRectangle(0, 0, 0, 0);
run("Revert");
run("Open Next");
run("Set Measurements...", "area mean standard min redirect=None decimal=3");
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 ("__sub_PIPSQUEAK_Background");
	}
}
///////////////////////////////

macro "__sub_PIPSQUEAK_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: 2B");
	Dialog.show
subject_id =  Dialog.getString();

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

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);
}
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 (import_type==4) {
	run("__sub_PIPSQUEAK_count_ROIs");
} else {

	// Setting Threshold

	print("Mean="+meanbkgd+",_StdDev="+stdbkgd);
	print("Lower_Threshold="+lower);
	run("Set Measurements...", "area mean standard min limit redirect=None decimal=3");
	setAutoThreshold("Default dark");
	run("Threshold...");
	wait(200);
	setThreshold(lower, 5000);
	//run("Apply");
	run("NaN Background");
	run("Close");

	run("__sub_PIPSQUEAK_Measuring_ROIs");
	}
}

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

macro "__sub_PIPSQUEAK_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 square 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);
       			run("Specify...", "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) {		
		run("Duplicate...", " ");
		selectWindow(name+"-1.tif");
		run("Revert", " ");
		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, "Yes, rerun cell detection with these new settings.");
	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(name+"-1.tif");
			close(name+"-1.tif");
			}

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

		run("__sub_PIPSQUEAK_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);
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 Mean StdDev Min Max Background Mean-Background");
for (i=0; i<n; i++) {
	area[i] = getResult('Area', 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]+" "+mean[i]+" "+stddev[i]+" "+min[i]+" "+max[i]+" "+bkgd[i]+" "+meanbkgd[i]);
}

run("__sub_PIPSQUEAK_Save");


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

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

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

wait(100);
setAutoThreshold("Default dark");
run("Threshold...");
setThreshold(lower*set_count_low_threshold, set_count_high_threshold);
wait(200);
makeRectangle(0, 0, 0, 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);
//waitForUser("How Does That Look?", "Here is your chance to adjust ROIs to better count labeled cells. Try adding or deleting circular ROIs in the ROI manager. **THEN** press OK.");

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, 500, 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("__sub_PIPSQUEAK_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");



//print("\n");
if(countCount==2){
	print("\n");
	print("Subject# Image_name . Cells_counted");
}	
print(subject_id+" "+name+" Cells= "+ numbercells);
//print("Number of cells counted = "+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("__sub_PIPSQUEAK_count");

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


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

macro "__sub_PIPSQUEAK_Save" {

	// Saving data
//
/*
if (import_type==1) {
	name = getTitle; 
	name = name + ".txt"; 
	saveAs("Measurements"); 
	saveAs("Text", dir+"/All_Results.txt");
	print("Results saved as: "+name);
	print("\n");	
	makeRectangle(0, 0, 0, 0);
	run("Revert");
	run("Close");
} else {
*/
//
	name_text = 0;
	name_text = name + ".txt"; 
	selectWindow("Log");
	saveAs("Measurements", dir+name_text); 
	saveAs("Text", dir+"/All_Results.txt");
	print("Results_saved_as:_"+dir+name);
	print("\n");
// 	}

	// 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");
run("Close");


if (import_type==3) {
	run("Clear Results");
	run("__sub_PIPSQUEAK_OpenNext_Single");
} else 
	if (import_type==1) {
		selectWindow(full_name);
		run("Close");
		selectWindow(name);
		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("__sub_PIPSQUEAK_Import");

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

	if (import_type==2) {
		
		selectWindow(name+".tif");
		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("__sub_PIPSQUEAK_Import_Summed");
			
		} else {
			if (choice=="Open Next Image") {
				run("__sub_PIPSQUEAK_OpenNext_Single");
				} else {
					run("PIPSQUEAK");
					print("\n"+"Thanks for using PIPSQUEAK. Bye!");
				}
			}
		}
	}
}



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


	//Here be double label intensity analysis

macro "__sub_PIPSQUEAK_double" {


Dialog.create("Import Type"); // initial dialogue.
items = newArray("Semiautomated analysis of summed double-labeled Z-stack", "Automatic analysis of summed double-labeled Z-stacks", "->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 Z-stack");
Dialog.show;
choice = Dialog.getRadioButton;
if (choice=="Semiautomated analysis of summed double-labeled Z-stack") {
	import_type = 5;
	run("__sub_PIPSQUEAK_Open_Double");
	} else {		
	if (choice=="Automatic analysis of summed double-labeled Z-stacks") {
		import_type = 6;		
		run("__sub_PIPSQUEAK_Open_Double");
		}
	if (choice=="->Make summed double-labeled images from z-stacks.") {
		//Dialog.create("Stacks folder");
		//Dialog.addMessage("Before using this function, please place z-stack images into a folder titled 'Stacks' within your experiment folder.");
		//Dialog.addMessage("ie, /Desktop/experiment folder/Stacks/image1... image20");
		//Dialog.show(); 
		run("__sub_PIPSQUEAK_double_sum"");
		}
	}
}


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

macro "__sub_PIPSQUEAK_double_sum" { // summing images.

countDouble = 1
Ldest = getDirectory("Choose a Directory ");		//load from
name = getTitle;
countDouble++

if (countDouble == 2) {
		Sdest = Ldest+"/summed/";	//save to
		Dialog.create("Save location");
		Dialog.addMessage("Select a place to save the summed image. ie, /Users/John/Desktop");
		Dialog.addString("Save to: ", Sdest);
		Dialog.show();
		Sdest = Dialog.getString(Ldest="/summed/");
		print("Save location: "+Sdest);
	} else {
		wait(100);
		}
	
	if (File.exists(Sdest+name+"/")) {
			wait(100);
		} else { 
			File.makeDirectory(Sdest+name+"/");
			}
		

sfold = Sdest + name;
stackfold = getFileList(Ldest);

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

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!="Sum Slices/"){
		ISp = "open=[" + Ldest + stacks + "] number=20 starting=q increment=3 scale=100 file=[] or=[] sort";

		for(q=1; q<4; q++) { 
			run("Image Sequence...", ISp);
			if(q!=2){
				run("8-bit");
			}

			run("Z Project...", "start=1 stop=20 projection=[Sum Slices]");


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

			close();
		}
	}
}

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

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

macro "__sub_PIPSQUEAK_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: 2B");
	Dialog.show
subject_id =  Dialog.getString();

run("Set Measurements...", "area mean standard min center redirect=None decimal=3");
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);
//print("subDir = "+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("__sub_PIPSQUEAK_Double_Background");
	} else {		
		waitForUser("Please update Settings to match numbering suffixes of double-label image series");
		run("PIPSQUEAK");
	}

}

//////////////////////////////
/*
//MACRO REMOVED BECAUSE SUBJECT SEQUENCE LOADING COMPLICATED BY IMAGE ORDER. MUST REVISIT STRATEGY TO MAKE THIS FUNCTION WORK.
macro "__sub_PIPSQUEAK_OpenNext_Double" {

if (total_images == second_suffix) { // this macro automatically opened the next image
	run("Open Next");
	} else {
	series_diff = total_images-second_suffix;
	for (i = 0; i < series_diff; i++) {
		run("Open Next");
		}}
	
first_image = getTitle;
index = lastIndexOf(first_image, ".");

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

last_open_double = series_name;
//print(series_name);


//if (last_open_double==first_open_double) {
//	print("\n"+"All images processed! Done.");
//	makeRectangle(0, 0, 0, 0);
//	run("Revert");
//	run("Clear Results");
//	run("Close");
//	selectWindow(first_image+".tif");
//	run("Close");
//	} else {
//	wait(100);
//	}

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

*/
///////////////////////////////

macro "__sub_PIPSQUEAK_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"

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);
}
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;

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, 5000); //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("__sub_PIPSQUEAK_Double_Background_SL1_ROIs");
}	
////////////////////////////////

macro "__sub_PIPSQUEAK_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 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) {
	waitForUser("Oops", "We didn't see any staining here. Try adjusting the Cell Detection Threshold settings in the next step... ");
		
} else { //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);
       		run("Specify...", "oval constrain centered scaled");
       		run("Enlarge...", "enlarge=DL1_set_roi_enlargement");
       		roiManager("Add");
       		roiManager("Select", 0);
       		roiManager("Delete");
		} 
	}

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, 1000, DL1_set_low_particlesize);
	Dialog.addSlider("Largest Cell Detected:", 0.0, 10000, 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("__sub_PIPSQUEAK_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

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);
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 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);
	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]+" "+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

name = series_name+" "+first_stain_type+".txt"; //saving the results table in a sub folder.
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);
}
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;

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");
setAutoThreshold("Default dark");
run("Threshold...");
wait(200);
setThreshold(DL2_lower, 5000);
//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("__sub_PIPSQUEAK_Double_Background_SL2_ROIs");

}

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

macro "__sub_PIPSQUEAK_Double_Background_SL2_ROIs" {


       // Measuring ROIs
           //Finding PNNs with Analyze Particles

second_image_tif = second_image+".tif";

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 square ROIs from Particles

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

	waitForUser("Oops", "We didn't see any staining here. Try adjusting the Cell Detection Threshold settings in the next step... ");

} else {
	array=newArray(DL2_add_squares); 
	for(i=0; i<DL2_add_squares;i++) { 
		roiManager("Select", 0);
        	run("Specify...", "centered scaled");
        	run("Enlarge...", "enlarge=DL2_set_roi_enlargement");
        	roiManager("Add");
        	roiManager("Select", 0);
        	roiManager("Delete");
		} 
	}

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, 1000, 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("__sub_PIPSQUEAK_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	

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);
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 Mean StdDev Min Max Background Mean-Background");
for (i=0; i<n_sl2; i++) {
	area_sl2[i] = getResult('Area', 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]+" "+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

name = series_name+" "+second_stain_type+".txt"; 
saveAs("Measurements", subDir+name);
print("\n"); 
print("Results saved as: "+subDir+name);

run("__sub_PIPSQUEAK_Double_Background_DL1and2_ROIs");

}

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

macro "__sub_PIPSQUEAK_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///////////////////////////////////////////////////


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

roiManager("Open", subDir+first_image+first_stain_type+"_single-label_ROIoverlay.zip");
//waitForUser("");
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
////////
/*
print("double_label_first_type = "+double_label_first_type);
print("double_label_second_type = "+double_label_second_type);
print("double_label_all_types = "+double_label_all_types);
print("double_label_second_type_start = "+double_label_second_type_start);
*/
////////
/* 
 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 = newArray(n);
saved_squares_array = 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 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; 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");
			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*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("b = "+b2_area);
			//print("c = "+c_area);
			//print("b2_80 = "+b2_area_80);
			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).
				//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 = Array.concat(saved_squares_array, a);
				//print("Squares saved so far:");
				//Array.print(saved_squares_array);
			}
		}
		if((b2==double_label_first_type) && (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) && (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) && (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).
     		
     		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 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.
   				//print("CoM_distance_array:");
   				//Array.print(CoM_distance_array);
     		}
     		rankPosition_CoM = Array.rankPositions(CoM_distance_array); //determining the shortest distance
     		rank_CoM = Array.rankPositions(rankPosition_CoM);
     		rankPositionWinner=Array.slice(rankPosition_CoM, 0, 1);
//////						this section is just for development //////////////////////
/*     		
 	     	print("no_co = "+no_co);
     		print("Square's COM_x: "+CoM_x_square);
     		print("Square's COM_y: "+CoM_y_square);
     		print("Square's COM_tot: "+CoM_tot_square);
     		print("Here are the overlaps:");
     		Array.print(co_roi_array_UI);
     		print("Here were their distances:");
     		Array.print(CoM_distance_array);
     		print("Here were their ranks (starting with index of smallest value):");
     		for (jj = 0; jj < CoM_distance_array.length; jj++){
				print(rankPosition_CoM[jj]);
			}
*/
///////////////
     		co_roi_array_winner_position = rankPositionWinner[0];
     		//print("Winner = "+co_roi_array_winner_position);
     		co_roi_array_low = Array.slice(co_roi_array, 0, co_roi_array_winner_position); //saving multiple ROIs before the shortest distance ROI
     		//print("low loser array:");
			//Array.print(co_roi_array_low);
     		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
     		//print("high loser array:");
			//Array.print(co_roi_array_high);
     		co_roi_array_losers = Array.concat(co_roi_array_low, co_roi_array_high); 
     		//print("Here's what will be deleted:");
     		//Array.print(co_roi_array_losers);

     		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
     		//print("low winner array:");
			//Array.print(co_roi_array_winner_low);
     		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
     		//print("winner:");
			//Array.print(co_roi_array_winner);
			
     		//print("Total circles checked: "+bTotal);
			//print("loop = "+n);
			//print("\n");
			removed_circles_array = Array.concat(removed_circles_array, co_roi_array_losers);
			//print("Removed circle ROIs thus far:");
			//Array.print(removed_circles_array);
			roiManager("deselect");
			roiManager("Select", co_roi_array_losers); //deleting the remaining multiple ROI (except for the closest to the square ROI)
			//waitForUser("pause");
			roiManager("Delete");  
			//waitForUser("pause");

			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.					
			//print("Squares saved:");
			//Array.print(saved_squares_array);
			for(i=0; i<saved_squares_array.length; i++){
 				saved_squares_array[i]=saved_squares_array[i]-(no_co-1);
			}
			//print("Squares saved:");
			//Array.print(saved_squares_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");



//waitForUser("pause");
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");
//waitForUser("pause");

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);
/*
print("\n"+second_image);
if(removed_circles_array.length>0){
	print("Removed circle ROIs (these were multiple overlaps with square ROIs):");
	Array.print(removed_circles_array);
}
print("Squares saved:");
Array.print(saved_squares_array);
*/
roiManager("reset");

//////////////////////////////////////////////
/* 
 In this section we are identifying the CIRCLES that have overlapping SQUARES. 
 */
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; 
////////
/*
print("double_label_first_type = "+double_label_first_type);
print("double_label_second_type_b = "+double_label_second_type_b);
print("double_label_all_types = "+double_label_all_types);
print("double_label_first_type_start = "+double_label_first_type_start);
*/
////////
//waitForUser("");
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.
	//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);
			//wait(10);
     	}
		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");
			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*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("c = "+c2_area);
			//print("co = "+co_area);
			//print("c2_80 = "+c2_area_80);
			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)
				//print("Circles saved so far:");
				//Array.print(saved_circles_array);
			}
		}
		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");
			//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.
     	}
	}
	
	n=n+1;
	//print("Total squares checked: "+dTotal);
	//print("loop = "+n);
	//print("\n");
}
//waitForUser("");
roiManager("Select", double_label_second_type_b_array);
roiManager("Delete");

//saving ROI overlay

save_circleDL_ROIs=roiManager("count");
//print("number of saved circle ROIs = "+save_circleDL_ROIs);

//print("\n"+first_image);
//print("Circles ROIs saved:");
//Array.print(saved_circles_array);

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");
//waitForUser(" ");
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);
//print("\n"+"Done");

roiManager("reset");

run("__sub_PIPSQUEAK_measure_Double_Background_DL1and2_ROIs");

}

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

macro "__sub_PIPSQUEAK_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");


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("__sub_PIPSQUEAK_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("__sub_PIPSQUEAK_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.
	}
}
selectWindow(merged_image);
run("Close");
roiManager("reset");

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

run("Clear Results");

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

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);
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 Mean StdDev Min Max Background Mean-Background");
for (i=0; i<n_dl1; i++) {
	area_dl1[i] = getResult('Area', 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]+" "+mean_dl1[i]+" "+stddev_dl1[i]+" "+min_dl1[i]+" "+max_dl1[i]+" "+bkgd_dl1[i]+" "+meanbkgd_dl1[i]);
	}

	// Saving data

name = series_name+" "+first_stain_type+" double-label.txt"; 
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");

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, 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);
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 Mean StdDev Min Max Background Mean-Background");
for (i=0; i<n_dl2; i++) {
	area_dl2[i] = getResult('Area', 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]+" "+mean_dl2[i]+" "+stddev_dl2[i]+" "+min_dl2[i]+" "+max_dl2[i]+" "+bkgd_dl2[i]+" "+meanbkgd_dl2[i]);
	}

	// Saving data

name = series_name+" "+second_stain_type+" double-label.txt"; 
saveAs("Measurements", subDir+name);
selectWindow("Log");
saveAs("Text", dir+"/All_Results.txt");
print("\n"); 
print("Results saved as: "+subDir+name);

//waitForUser("Pause");

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

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

	// Closing project

roiManager("reset");
selectWindow("ROI Manager");
run("Close");
run("Clear Results");
print("Done!");
//if (import_type==6) {
//	run("__sub_PIPSQUEAK_OpenNext_Double");
//	} else {

	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("PIPSQUEAK");
	} else {
//		run("__sub_PIPSQUEAK_OpenNext_Double");
//SEE NOT IN "__SUB_PIPSQUEAK_OPENNEXT_DOUBLE" ABOUT REMOVAL OF AUTO OPEN FUNCTION.

		run("__sub_PIPSQUEAK_Open_Double");
		}
	}
		
}

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