diff --git a/.gitattributes b/.gitattributes
index 1ef325f1b111266a6b26e0196871bd78baa8c2f3..de6409ef47cab5ef4475c169534c5d58f28afc9a 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -57,3 +57,29 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/ImageJ_plugins/3D_suite/imageware.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/ImageJ_plugins/3D_suite/mcib3d-core3.93.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/ImageJ_plugins/3D_suite/mcib3d_plugins3.93.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/ImageJ_plugins/3D_viewer/3D_Viewer-4.0.1.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/ImageJ_plugins/3D_viewer/VIB-lib-2.1.1.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/ImageJ_plugins/3D_viewer/gluegen-rt-2.3.2.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/ImageJ_plugins/3D_viewer/j3dcore-1.6.0-scijava-2.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/ImageJ_plugins/3D_viewer/j3dutils-1.6.0-scijava-2.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/ImageJ_plugins/3D_viewer/jogl-all-2.3.2-natives-linux-amd64.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/ImageJ_plugins/3D_viewer/jogl-all-2.3.2-natives-macosx-universal.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/ImageJ_plugins/3D_viewer/jogl-all-2.3.2-natives-windows-amd64.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/ImageJ_plugins/3D_viewer/jogl-all-2.3.2.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/ImageJ_plugins/3D_viewer/vecmath-1.6.0-scijava-2.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/ImageJ_plugins/spatial3dtissuej_plugin/TissueJ4Merfish_v14.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/ImageJ_plugins/utils/3D_Convex_Hull.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/ImageJ_plugins/utils/imagescience.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/ImageJ_plugins/utils/mpicbg_-1.4.1.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/cell_zone_detection/TissueJ4Merfish_v14.jar filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/cell_zone_detection/merFISH_01_025_08-SEG.tif filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/cell_zone_detection/merFISH_01_025_08-mask.tif filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/nuclei_segmentation/cell_zone_results/aligned_images_fov0_z18_01_cell_zone_rad_5.0.tif filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/nuclei_segmentation/demo_results/aligned_images_fov0_z18_01_SEG_demo_only.tif filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/nuclei_segmentation/seg_results/aligned_images_fov0_z18_01_SEG.tif filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/nuclei_segmentation/seg_results/aligned_images_fov0_z18_02_SEG.tif filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/nuclei_segmentation/testing_images/aligned_images_fov0_z18_01.tif filter=lfs diff=lfs merge=lfs -text
+data/imagej_macro/nuclei_segmentation/testing_images/aligned_images_fov0_z18_02.tif filter=lfs diff=lfs merge=lfs -text
diff --git a/data/Correlation/correlation.py b/data/Correlation/correlation.py
new file mode 100644
index 0000000000000000000000000000000000000000..afdb1c3ff10a331dcd232b192f6b1c8798610578
--- /dev/null
+++ b/data/Correlation/correlation.py
@@ -0,0 +1,204 @@
+
+
+
+
+# Import
+import cProfile
+from distutils import core
+from pathlib import Path
+import numpy as np
+import scipy as sp
+import pandas as pd
+import matplotlib.pyplot as plt
+from matplotlib.backends.backend_pdf import PdfPages
+import seaborn as sns
+import os
+import pickle
+import scipy.stats as stats
+import argparse
+from adjustText import adjust_text
+import matplotlib.backends.backend_pdf
+
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-b', type = str, help = 'Path to barcodes')
+    parser.add_argument('-xp', type = str, help = 'Experiment ID')
+    parser.add_argument('-c', type = str, default = "D:\BCCAncer\FILES\codebook_0_C1E1_van.csv", help = 'Path to codebook')
+    parser.add_argument('-a', type = str, default = "D:\BCCAncer\FILES\XP2474_4T1_C1E1_new_bulk.csv", help = 'Path to bulk file (abundance file)')
+    parser.add_argument('-o', type = str, default = "D:\BCCAncer\EXP\XP7174\Correlations\BulkCorr", help = 'Path to save processed files')
+    parser.add_argument('-is_cambridge', type = bool, default = False, help = 'Are the results for Cambridge datasets?')
+    parser.add_argument('-drop_blanks', type = bool, default = False, help = 'Remove blanks before correlation')
+    parser.add_argument('-log', type =bool, default = True, help = 'Do we want to take log while correlation of bulk?')
+    parser.add_argument("-d", nargs="+", type = float, default=0.65, help = 'Max mean distance threshold for an area of detected barcode')
+    parser.add_argument("-removeZ", nargs="+", type=int, default=None, help="Z slices to remove from evaluation")
+    #parser.add_argument('-gene_list', type = str, action='append', required=True, help = 'List of genes which we want to exclude from analysis')
+
+
+    args = parser.parse_args()
+
+    if not os.path.exists(args.o):
+        os.makedirs(args.o)
+
+    info_np = np.zeros(shape = (len(args.d), 7))
+
+    # read sheet and remove undesired z slices
+    df = read_sheet(args.b)
+    if args.removeZ is not None:
+        removeZ_set = set(args.removeZ)
+        df = df[~df['z'].isin(removeZ_set)]
+    
+    
+    
+    pdf = matplotlib.backends.backend_pdf.PdfPages(f'{args.o}/{args.xp}_correlation.pdf')
+    correlation = Correlation(args.c, df, args.a, args.o, args.xp, args.is_cambridge, pdf)
+    args.d.sort(reverse = True)
+
+    
+    for idx, dist_threshold in enumerate(args.d):
+        filter_df = correlation.filter_distance(dist_threshold)
+        gp_df = correlation.groupby(filter_df)
+        gp_df = correlation.merge_df_cb(gp_df)
+
+        tot_counts = gp_df.counts.sum()
+        blank_counts = gp_df.loc[gp_df['gene_symbol'].isin(['Blank_01', 'Blank_02', 'Blank_03', 'Blank_04', 'Blank_05', 'Blank_06'])].counts.sum()
+
+        info_np[idx][0] = dist_threshold
+        info_np[idx][3] = int(tot_counts)
+        info_np[idx][4] = int(blank_counts)
+        info_np[idx][5] = int(len(gp_df))
+
+        gp_df = correlation.df_bulk(gp_df)
+
+        if args.drop_blanks:
+            gp_df = correlation.remove_blanks(gp_df) 
+
+        """
+            if args.gene_list:
+            print('removing')
+            gp_df = correlation.remove_specific_genes(gp_df)
+        """
+
+        info_np[idx][-1] = len(gp_df)
+
+        gp_df['log_counts'] = np.log2(gp_df['counts']+1)
+        gp_df['log_tpm'] = np.log2(gp_df['bulk_exp']+0.0001)
+
+        gp_df.to_csv(f'{args.o}/{args.xp}_{dist_threshold}.csv')
+
+
+        info_np[idx][1], info_np[idx][2] = correlation.log_correlation(gp_df, dist_threshold)
+
+    pdf.close()
+
+    info_df = pd.DataFrame(info_np, columns = ['Distance Threshold',\
+                                        'Pearson correlation',\
+                                        'Spearman correlation',\
+                                        '# detected barcodes (including control barcodes)',\
+                                        '# detected control (blanks) barcodes',\
+                                        '# genes at dist threshold in correlation',\
+                                        '# barcodes counted towards correlaton estimation'])
+    info_df.to_csv(f'{args.o}/info_{args.xp}.csv')
+ 
+def read_sheet(file):
+    
+    ext = file.split(".")[-1]
+    if ext == "csv":
+        df = pd.read_csv(file)
+    elif ext == "tsv":
+        df = pd.read_csv(file, '\t')
+    elif ext in {"xls", "xlsx", "xlsm", "xlsb"}:
+        df = pd.read_excel(file)
+    else:
+        raise ValueError("Unexpected file extension")
+    return df    
+
+class Correlation:
+    def __init__(self, codebook, barcodes, bulk, output_dir, name, is_cambridge, pdf_object):
+        self.codebook = read_sheet(codebook)
+        self.barcodes = barcodes
+        self.bulk = read_sheet(bulk)
+        self.output_dir = output_dir
+        self.name = name
+        self.is_cambridge = is_cambridge
+        self.pdf_object = pdf_object
+        #self.remove_genes_list = remove_genes_list
+
+    def filter_distance(self, distance_threshold):
+        return self.barcodes.loc[self.barcodes['mean_distance']<distance_threshold]
+
+    def groupby(self, df):
+        return (df.groupby('barcode_id').size().reset_index(name='counts'))
+
+    def modify_codebook(self):
+        self.codebook['barcode_id'] = self.codebook.index
+        self.codebook.rename(columns={"name": "gene_symbol"}, inplace = True)
+
+    def merge_df_cb(self,df):
+        self.modify_codebook()
+        return pd.merge(self.codebook, df, how='inner', on='barcode_id')
+
+    def df_bulk(self, df):
+        return pd.merge(self.bulk, df, how='inner', on='gene_symbol')
+
+    def remove_blanks(self,df):
+        return df[df['bulk_exp'] != 0]
+
+    """
+    def remove_specific_genes(self,df):
+        c = 0
+        print(self.remove_genes_list)
+        for gene in self.remove_genes_list:
+            print(c, gene)
+            df = df.loc[df['gene_symbol']!=gene]
+            #cccprint(df.loc[df['gene_symbol']==gene])
+            c+=1
+        return df
+    """
+    
+
+    def log_correlation(self, df, dist_threshold):
+            f1, ax = plt.subplots(figsize=(9, 9))
+            sns.set_palette("deep")
+            sns.scatterplot(x="log_tpm",y="log_counts",data=df,ax=ax)
+                           
+            if self.is_cambridge == True:
+                count_type = "C_counts"
+            else: count_type = "V_counts"
+                    
+                
+            ax.set_title("TPM Correlation for " + self.name, fontsize = 15)
+            ax.set_xlabel("log2(TPM+1e-4), V_bulk", fontsize = 20)
+            ax.set_ylabel("log2(# detected counts+1), "+ count_type, fontsize = 20)
+            
+            
+            def plotlabel(xvar, yvar, label):
+                ax.text(xvar+0.02, yvar, label)
+      
+                
+            pearson, _ = stats.pearsonr(df["log_tpm"],df["log_counts"])
+            spearman, _ = stats.spearmanr(df["log_tpm"],df["log_counts"])
+            #kendalltau, _ = stats.kendalltau(df["log_tpm"],df["log_counts"])
+               
+            ax.text(.01, .95, 'Pearson = {:.2f}\nSpearman = {:.2f}'.format(pearson,spearman),transform=ax.transAxes)
+            #df.apply(lambda x: plotlabel(x['log_tpm'],  x['log_counts'], x['gene_symbol']), axis=1)
+            #plt.axvline(x = 0, color = 'r', label = 'axvline - full height')
+            
+            self.pdf_object.savefig(f1)
+
+            texts = []
+            for xs,ys,label in zip(df['log_tpm'],df['log_counts'],df['gene_symbol']):
+                    texts.append(ax.text(xs,ys,label))
+            adjust_text(texts, force_points=0.2, force_text=0.2,expand_points=(1, 1), expand_text=(1, 1), arrowprops=dict(arrowstyle="-", color='black', lw=0.5))
+            plt.tight_layout()
+            
+            #plt.savefig(f'{self.output_dir}/{self.name}_{dist_threshold}.pdf')
+            self.pdf_object.savefig(f1)
+
+            return pearson, spearman
+
+
+if __name__ == '__main__':
+    #cProfile.run('main()')
+    main()
diff --git a/data/Snakefile b/data/Snakefile
new file mode 100644
index 0000000000000000000000000000000000000000..54e38673e1205e407b7bcf5d9f859ae8c46c0bf8
--- /dev/null
+++ b/data/Snakefile
@@ -0,0 +1,261 @@
+from pathlib import Path
+import re
+import os
+import makereports as reports
+from utils import fileIO,imgproc
+
+configfile: "config.json"
+
+
+def download_azure():
+    
+    if config["isRemote"]=="N" or os.path.exists(os.path.join(config['results_path'],'data')):
+        return None
+    
+    shell(config["azure_command"].format(config["raw_data_path"],os.path.join(config['results_path'],'data')))
+    
+    return None
+
+def check_params():
+
+    # Check to see if there are any params that need to be found
+    b_findz = len(config['z'])==0
+    b_findfov = len(config['fov'])==0
+    b_findir = len(config['ir'])==0
+    b_findwv = len(config['channel'])==0
+
+
+    # get all the images
+    if config["isRemote"] == "Y":
+        files = Path(os.path.join(config['results_path'],'data')).rglob('*.TIFF')
+    else:
+        files = Path(config['raw_data_path']).rglob('*.TIFF')
+    files = list(map(str,files))
+    # Find all channel, ir, fov, and z from the file names
+    re_filter = r"(.*)(\d{3})(?=nm).*(\B\d{2})\D(\B\d{3})\D(\B\d{2}\b)"
+    param_filter = re.compile(re_filter)
+    results = list(map(param_filter.search,files))
+    
+    results =list(filter(lambda x: isinstance(x,re.Match), results))
+    # Get the list of parameters if they are needed
+    #   Group 0 is the full match, so each capture group is 1 indexed
+    if b_findz:
+        zs = list(map(lambda x: x.group(5),results))
+    else:
+        zs = config['z']
+
+    if b_findir:
+        irs = list(map(lambda x: x.group(3),results))
+    else:
+        irs = config['ir'] 
+
+    if b_findfov:
+        fovs = list(map(lambda x: x.group(4),results))
+    else:
+        fovs = config['fov']
+
+    if b_findwv:
+        wvs = list(map(lambda x: x.group(2),results))
+    else:
+        wvs = config['channel']
+
+    if isinstance(results,list) and len(results)>0:
+        full_raw_path = results[0].group(1)
+    else:
+        full_raw_path=''
+
+
+    return sorted(list(set(zs)),key=int),sorted(list(set(fovs)),key=int),sorted(list(set(irs)),key=int),sorted(list(set(wvs)),key=int),full_raw_path
+
+
+download_azure()
+zs,fovs,irs,wvs,full_raw_path = check_params()
+
+
+default_message= "rule {rule}, {wildcards}, threads: {threads}"
+
+rule all_done:
+    input:
+        os.path.join(config['results_path'],'brightness_report.t'),
+        os.path.join(config['results_path'],'focus_report.t'),
+        os.path.join(config['results_path'],'deconvolved_brightness_report.t'),
+        os.path.join(config['results_path'],'masked_brightness_report.t'),
+        os.path.join(config['results_path'],'decodability_report.t')
+    output:
+        os.path.join(config['results_path'],'all_done.t')
+    run:
+        if config["isRemote"]=="Y":
+            shell("rm -rf \"{}\"".format(os.path.join(config['results_path'],'data')))
+        if config["delete_stack"]=="Y":
+            shell("rm -f \"{}\"".format(os.path.join(config['results_path'],'img*')))
+        shell("touch \"{output[0]}\"")
+
+rule all:
+    threads:1
+    input:
+        os.path.join(config['results_path'],'all_done.t')
+
+
+def isRemote(wildcards):
+    
+    if config["isRemote"]=="N":
+        return config["raw_data_path"]
+    else:
+        return directory(os.path.join(config['results_path'],'data'))
+
+rule create_image_stack:
+    threads:1
+    message: default_message
+    input:
+        isRemote
+    output:
+        out_file = os.path.join(config['results_path'],'imgstack_{fov}_{z}.npy'),
+        coord_file = os.path.join(config['results_path'],'coord_{fov}_{z}.json')
+    run:
+        fileIO.create_image_stack(os.path.join(full_raw_path,config['raw_image_format']),
+                                    wildcards.fov,wildcards.z,irs,wvs,output.out_file,output.coord_file)
+
+
+rule deconvolve_images:
+    threads:1
+    message: default_message
+    input:
+        in_file = os.path.join(config['results_path'],'imgstack_{fov}_{z}.npy'),
+    output:
+        out_file = os.path.join(config['results_path'],'deconvolved','deconvolved_{fov}_{z}.npy')
+    run:
+        imgproc._deconvolute(input.in_file,output.out_file)
+
+
+
+
+rule deconvolved_brightness_report:
+    threads:1
+    message: default_message
+    input:
+        img_stack = os.path.join(config['results_path'],'deconvolved','deconvolved_{fov}_{z}.npy'),
+        coord_file = os.path.join(config['results_path'],'coord_{fov}_{z}.json')
+    output:
+        out=os.path.join(config['results_path'],'deconvolved_brightness_report_{fov}_{z}.pdf')
+    run:
+        reports.generate_brightness_reports(input.img_stack,input.coord_file,output.out,wildcards.fov,wildcards.z)
+
+
+rule compile_deconvolved_brightness_report:
+    threads:1
+    message: default_message
+    input:
+        expand(os.path.join(config['results_path'],'deconvolved_brightness_report_{fov}_{z}.pdf'),fov=fovs,z=zs)
+    output:
+        os.path.join(config['results_path'],'deconvolved_brightness_report.t')
+    shell:
+        "touch \"{output}\""
+
+
+rule create_mask_images:
+    threads:1
+    message: default_message
+    input:
+        img_stack = os.path.join(config['results_path'],'deconvolved','deconvolved_{fov}_{z}.npy'),
+    output:
+        out_mask=os.path.join(config['results_path'],'masked','mask_{fov}_{z}.npy')
+    run:
+        imgproc.maskImages(input.img_stack,output.out_mask)
+
+
+rule masked_brightness_report:
+    threads:1
+    message: default_message
+    input:
+        img_stack = os.path.join(config['results_path'],'imgstack_{fov}_{z}.npy'),
+        masks = os.path.join(config['results_path'],'masked','mask_{fov}_{z}.npy'),
+        coord_file = os.path.join(config['results_path'],'coord_{fov}_{z}.json')
+    output:
+        out=os.path.join(config['results_path'],'masked_brightness_report_{fov}_{z}.pdf')
+    run:
+        reports.generate_masked_brightness_reports(input.img_stack,input.coord_file,output.out,wildcards.fov,wildcards.z,input.masks)
+
+rule compile_masked_brightness_report:
+    threads:1
+    message: default_message
+    input:
+        expand(os.path.join(config['results_path'],'masked_brightness_report_{fov}_{z}.pdf'),fov=fovs,z=zs)
+    output:
+        os.path.join(config['results_path'],'masked_brightness_report.t')
+    shell:
+        "touch \"{output}\""
+
+rule brightness_report:
+    threads:1
+    message: default_message
+    input:
+        img_stack = os.path.join(config['results_path'],'imgstack_{fov}_{z}.npy'),
+        coord_file = os.path.join(config['results_path'],'coord_{fov}_{z}.json')
+    output:
+        out=os.path.join(config['results_path'],'brightness_report_{fov}_{z}.pdf')
+    run:
+        reports.generate_brightness_reports(input.img_stack,input.coord_file,output.out,wildcards.fov,wildcards.z)
+
+rule compile_brightness_report:
+    threads:1
+    message: default_message
+    input:
+        expand(os.path.join(config['results_path'],'brightness_report_{fov}_{z}.pdf'),fov=fovs,z=zs)
+    output:
+        os.path.join(config['results_path'],'brightness_report.t')
+    shell:
+        "touch \"{output}\""
+
+
+
+
+rule focus_report:
+    threads:1
+    message: default_message
+    input:
+        img_stack = expand(os.path.join(config['results_path'],'imgstack_{{fov}}_{z}.npy'),z=zs),
+        coord_file = expand(os.path.join(config['results_path'],'coord_{{fov}}_{z}.json'),z=zs)
+    output:
+        out=os.path.join(config['results_path'],'focus_report_{fov}.pdf'),
+        out_csvs = os.path.join(config['results_path'],'focus_report_{fov}.csv')
+    run:
+        reports.generate_focus_reports(input.img_stack,input.coord_file,output.out,output.out_csvs,wildcards.fov)
+
+rule compile_focus_report:
+    threads:1
+    message: default_message
+    input:
+        files = expand(os.path.join(config['results_path'],'focus_report_{fov}.pdf'),fov=fovs),
+        csvs = expand(os.path.join(config['results_path'],'focus_report_{fov}.csv'),fov=fovs)
+    output:
+        combined = os.path.join(config['results_path'],'focus_report_all_fov.pdf'),
+        out = os.path.join(config['results_path'],'focus_report.t')
+    run:
+        reports.compile_focus_report(input.csvs,output.combined,irs,wvs)
+        shell("touch \"{output.out}\"")
+
+
+rule decodability_report:
+    threads:1
+    message: default_message
+    input:
+        img_stack = os.path.join(config['results_path'],'deconvolved','deconvolved_{fov}_{z}.npy'),
+        coord_file = os.path.join(config['results_path'],'coord_{fov}_{z}.json'),
+        codebook_file = config["codebook_file"],
+        data_organization_file = config["data_org_file"]
+    output:
+        out=os.path.join(config['results_path'],'decodability_report_{fov}_{z}.pdf'),
+        out_stats = os.path.join(config['results_path'],'decodability_stats_{fov}_{z}.txt')
+    run:
+        reports.generate_decodability_reports(input.img_stack,input.coord_file,output.out,input.codebook_file,input.data_organization_file,wildcards.fov,wildcards.z,output.out_stats)
+
+
+rule compile_decodability_report:
+    threads:1
+    message: default_message
+    input:
+        expand(os.path.join(config['results_path'],'decodability_report_{fov}_{z}.pdf'),fov=fovs,z=zs)
+    output:
+        os.path.join(config['results_path'],'decodability_report.t')
+    shell:
+        "touch \"{output}\""
\ No newline at end of file
diff --git a/data/config.json b/data/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..25605241ae1364830f7f5728b36344e549160546
--- /dev/null
+++ b/data/config.json
@@ -0,0 +1,14 @@
+{
+    "raw_data_path":"/Volumes/MERFISH_COLD/XP2059",
+    "results_path":"/Volumes/MERFISH_COLD/XP2059/1/1/2059_merFISH_report_results",
+    "raw_image_format": "{wv}nm, Raw/merFISH_{ir}_{fov}_{z}.TIFF",
+    "channel": [],
+    "fov": ["001"],
+    "ir": [],
+    "z": ["01","02","03"],
+    "isRemote":"N",
+    "azure_command":"azcopy cp \"{}\" \"{}\" --recursive",
+    "delete_stack":"N",
+    "codebook_file": "/Volumes/MERFISH_COLD/Codebooks/C1E1_codebook_shahid.csv",
+    "data_org_file":"/Volumes/MERFISH_COLD/data_organization/data_organization.csv"
+}
diff --git a/data/debug_reports.py b/data/debug_reports.py
new file mode 100644
index 0000000000000000000000000000000000000000..ffcaa731b6d3c566e1a85c0d4bc8fc7eb9fbb696
--- /dev/null
+++ b/data/debug_reports.py
@@ -0,0 +1,176 @@
+import json
+import os
+import numpy as np
+from utils import fileIO,imgproc
+import reports
+import makereports
+from pathlib import Path
+import time
+import re
+
+coord_file = "config.json"
+a_file = open(coord_file, "r")
+config = json.load(a_file)
+
+
+fov = '001'
+z='02'
+
+def download_azure():
+    
+    if config["isRemote"]=="N" or os.path.exists(os.path.join(config['results_path'],'data')):
+        return None
+    
+    os.system(config["azure_command"].format(config["raw_data_path"],os.path.join(config['results_path'],'data')))
+    
+    return None
+
+def check_params():
+
+    # Check to see if there are any params that need to be found
+    b_findz = len(config['z'])==0
+    b_findfov = len(config['fov'])==0
+    b_findir = len(config['ir'])==0
+    b_findwv = len(config['channel'])==0
+
+
+    # get all the images
+    if config["isRemote"] == "Y":
+        files = Path(os.path.join(config['results_path'],'data')).rglob('*.TIFF')
+    else:
+        files = Path(config['raw_data_path']).rglob('*.TIFF')
+    files = list(map(str,files))
+    # Find all channel, ir, fov, and z from the file names
+    re_filter = r"(.*)(\d{3})(?=nm).*(\B\d{2})\D(\B\d{3})\D(\B\d{2}\b)"
+    param_filter = re.compile(re_filter)
+    results = list(map(param_filter.search,files))
+    
+    results =list(filter(lambda x: isinstance(x,re.Match), results))
+    # Get the list of parameters if they are needed
+    #   Group 0 is the full match, so each capture group is 1 indexed
+    if b_findz:
+        zs = list(map(lambda x: x.group(5),results))
+    else:
+        zs = config['z']
+
+    if b_findir:
+        irs = list(map(lambda x: x.group(3),results))
+    else:
+        irs = config['ir'] 
+
+    if b_findfov:
+        fovs = list(map(lambda x: x.group(4),results))
+    else:
+        fovs = config['fov']
+
+    if b_findwv:
+        wvs = list(map(lambda x: x.group(2),results))
+    else:
+        wvs = config['channel']
+
+    if isinstance(results,list) and len(results)>0:
+        full_raw_path = results[0].group(1)
+    else:
+        full_raw_path=''
+
+
+    return sorted(list(set(zs)),key=int),sorted(list(set(fovs)),key=int),sorted(list(set(irs)),key=int),sorted(list(set(wvs)),key=int),full_raw_path
+
+
+def check_dirs(files):
+    """Checks to see if the directories for the files in the list exist. If they dont, then make those directories
+
+    Args:
+        files (list[str]): the list of files whose directory paths to create. Needs to be the full, not relative paths
+        
+    """
+    if not type(files) == list:
+        d = os.path.dirname(files)
+        if not os.path.isdir(d):
+            # print(files+'files' + d +'Does not exist')
+            os.makedirs(d)
+    else:
+        for f in files:
+
+            d = os.path.dirname(f)
+            if d != "" and not os.path.isdir(d):
+                # print(f+'files' + d +'Does not exist')
+                os.makedirs(d)
+
+download_azure()
+zs,fovs,irs,wvs,full_raw_path = check_params()
+
+def create_image_stack():
+
+    out_file = os.path.join(config['results_path'],f'imgstack_{fov}_{z}.npy')
+    coord_file = os.path.join(config['results_path'],f'coord_{fov}_{z}.json')
+    check_dirs(out_file)
+
+    fileIO.create_image_stack(os.path.join(full_raw_path,config['raw_image_format']),fov,z,irs,wvs,out_file,coord_file)
+
+def create_deconvolved_images():
+    in_file = os.path.join(config['results_path'],f'imgstack_{fov}_{z}.npy')
+
+    out_file = os.path.join(config['results_path'],'deconvolved',f'deconvolved_{fov}_{z}.npy')
+    check_dirs(out_file)
+    imgproc._deconvolute(in_file,out_file)
+
+def create_brightness_report():
+    
+    img_stack = os.path.join(config['results_path'],f'imgstack_{fov}_{z}.npy')
+    coord_file = os.path.join(config['results_path'],f'coord_{fov}_{z}.json')
+    
+    out=os.path.join(config['results_path'],f'brightness_report_{fov}_{z}.pdf')
+    check_dirs(out)
+    
+    makereports.brightness_worker(img_stack,coord_file,out,fov,z)
+
+def create_focus_report():
+    
+    img_stack = [os.path.join(config['results_path'],f'imgstack_{fov}_{z}.npy')]
+    coord_file = [os.path.join(config['results_path'],f'coord_{fov}_{z}.json')]
+    
+    out=os.path.join(config['results_path'],f'focus_report_{fov}.pdf')
+    out_csv = os.path.join(config['results_path'],f'focus_report_{fov}.csv')
+    check_dirs(out)
+    makereports.focus_worker(img_stack,coord_file,out,out_csv,fov)
+
+
+def compile_focus_reports():
+
+    in_files = [os.path.join(config['results_path'],f'focus_report_{fov}.csv')]
+    output = 'full_report_debug.csv'
+    
+    makereports.compile_focus_report(in_files,output=output,irs=irs,wvs=wvs)
+
+
+if __name__=='__main__':
+    start_time = time.time()
+
+    sub_start_time = time.time()
+    create_image_stack()
+    sub_end_time = time.time()
+    print(f'Image Stack: {sub_end_time-sub_start_time}')
+    
+    sub_start_time = time.time()
+    create_deconvolved_images()
+    sub_end_time = time.time()
+    print(f'Deconvolved Stack: {sub_end_time-sub_start_time}')
+
+    sub_start_time = time.time()
+    create_brightness_report()
+    sub_end_time = time.time()
+    print(f'Brightness Report: {sub_end_time-sub_start_time}')
+
+    sub_start_time = time.time()
+    create_focus_report()
+    sub_end_time = time.time()
+    print(f'Focus Report: {sub_end_time-sub_start_time}')
+
+    sub_start_time = time.time()
+    compile_focus_reports()
+    sub_end_time = time.time()
+    print(f'Compiled Focus Report: {sub_end_time-sub_start_time}')    
+
+    end_time = time.time()
+    print(f'Total Time: {end_time-start_time}')
\ No newline at end of file
diff --git a/data/environment.yml b/data/environment.yml
new file mode 100644
index 0000000000000000000000000000000000000000..34464c21f1a70d1efe631081e09094bbc9d76d69
--- /dev/null
+++ b/data/environment.yml
@@ -0,0 +1,11 @@
+name: experiment_report
+channels:
+  - conda-forge
+  - bioconda
+dependencies:
+  - python=3.7
+  - snakemake
+  - matplotlib
+  - scikit-image
+  - numpy
+  - scikit-learn
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diff --git a/data/imagej_macro/ImageJ_plugins/3D_suite/imageware.jar b/data/imagej_macro/ImageJ_plugins/3D_suite/imageware.jar
new file mode 100644
index 0000000000000000000000000000000000000000..6c51d38d72293e5589f8612a409dd313c845ca2b
--- /dev/null
+++ b/data/imagej_macro/ImageJ_plugins/3D_suite/imageware.jar
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d0f521c689ff44fbbea7f87b8125c9f86e12923388a88892ae6120c4062b511d
+size 103743
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new file mode 100644
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--- /dev/null
+++ b/data/imagej_macro/ImageJ_plugins/3D_suite/mcib3d-core3.93.jar
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 814496
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new file mode 100644
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--- /dev/null
+++ b/data/imagej_macro/ImageJ_plugins/3D_suite/mcib3d_plugins3.93.jar
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 252590
diff --git a/data/imagej_macro/ImageJ_plugins/3D_viewer/.directory b/data/imagej_macro/ImageJ_plugins/3D_viewer/.directory
new file mode 100644
index 0000000000000000000000000000000000000000..0012716db12436039259fb6266c6cc025e4e5fa0
--- /dev/null
+++ b/data/imagej_macro/ImageJ_plugins/3D_viewer/.directory
@@ -0,0 +1,4 @@
+[Dolphin]
+PreviewsShown=true
+Timestamp=2016,7,1,12,4,53
+Version=3
diff --git a/data/imagej_macro/ImageJ_plugins/3D_viewer/3D_Viewer-4.0.1.jar b/data/imagej_macro/ImageJ_plugins/3D_viewer/3D_Viewer-4.0.1.jar
new file mode 100644
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--- /dev/null
+++ b/data/imagej_macro/ImageJ_plugins/3D_viewer/3D_Viewer-4.0.1.jar
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 548054
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new file mode 100644
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+++ b/data/imagej_macro/ImageJ_plugins/3D_viewer/VIB-lib-2.1.1.jar
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 635940
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+size 345605
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diff --git a/data/imagej_macro/ImageJ_plugins/3D_viewer/vecmath-1.6.0-scijava-2.jar b/data/imagej_macro/ImageJ_plugins/3D_viewer/vecmath-1.6.0-scijava-2.jar
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+++ b/data/imagej_macro/ImageJ_plugins/3D_viewer/vecmath-1.6.0-scijava-2.jar
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/data/imagej_macro/ImageJ_plugins/readme.txt b/data/imagej_macro/ImageJ_plugins/readme.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3ae42d3cd71c05062ff93df8b6dec7da0e1cfc71
--- /dev/null
+++ b/data/imagej_macro/ImageJ_plugins/readme.txt
@@ -0,0 +1,35 @@
+Please copy all folders here to ImageJ/plugins/ folder and relaunch ImageJ platform
+
+For update, you just need to replace TissueJ4Merfish.jar in the folder spatial3dtissuej_plugin/ by the most updated plugin and relaunch ImageJ. 
+
+
+
+Other notes: 
+For 3D viewer, you can copy 3dviewer plugins folder into your plugins directory
+or install latest 3d viewer plugin from Fiij
+Note: if the program do not work, the main issue may be due lack of supporting packages from 3D viewers. In this case, you can delete your current 3d viewer version, and replaced by the folder that I provided in this github
+
+For Fiji_plugins.jar you can use the latest version in your local ImageJ
+
+When launching ImageJ, platform will notice you a plugin with multiple versions in plugins folder, and overwrite it using one version. In this case, you can go into your local ImageJ/plugins folder, and remove one old version of plugin
+
+Acknowledgement: 
+See more at: 
+https://mcib3d.frama.io/3d-suite-imagej/
+J. Ollion, J. Cochennec, F. Loll, C. Escudé, T. Boudier. (2013) TANGO: A Generic Tool for High-throughput 3D Image Analysis for Studying Nuclear Organization. Bioinformatics 2013 Jul 15;29(14):1840-1.
+
+The 3D suite would like to thank P. Andrey, J.-F. Gilles and the developers of the following plugins :
+
+    Imagescience
+    LocalThickness
+    ConvexHull3D
+    3D Object Counter
+    Droplet Counter
+
+Links
+    BoneJ
+    3D Shapes
+    LabKit
+    3D Viewer
+    MorphoLibJ
+    CLIJ
diff --git a/data/imagej_macro/ImageJ_plugins/spatial3dtissuej_plugin/TissueJ4Merfish_v14.jar b/data/imagej_macro/ImageJ_plugins/spatial3dtissuej_plugin/TissueJ4Merfish_v14.jar
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--- /dev/null
+++ b/data/imagej_macro/ImageJ_plugins/spatial3dtissuej_plugin/TissueJ4Merfish_v14.jar
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7b4cbabea978a48df86ca4f3ace607854ca44c273a8a30cad872d4234f3807b7
+size 841067
diff --git a/data/imagej_macro/ImageJ_plugins/utils/3D_Convex_Hull.jar b/data/imagej_macro/ImageJ_plugins/utils/3D_Convex_Hull.jar
new file mode 100644
index 0000000000000000000000000000000000000000..b48c742b016a10cbc53040f07fab2224f7731ac6
--- /dev/null
+++ b/data/imagej_macro/ImageJ_plugins/utils/3D_Convex_Hull.jar
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:52c261d7c666b964ec6391a92739e7a65bc827e28a79983005e0883bf1741bb4
+size 130741
diff --git a/data/imagej_macro/ImageJ_plugins/utils/Fiji_Plugins-3.1.1.jar b/data/imagej_macro/ImageJ_plugins/utils/Fiji_Plugins-3.1.1.jar
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diff --git a/data/imagej_macro/ImageJ_plugins/utils/fiji-lib-2.1.2.jar b/data/imagej_macro/ImageJ_plugins/utils/fiji-lib-2.1.2.jar
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diff --git a/data/imagej_macro/ImageJ_plugins/utils/imagescience.jar b/data/imagej_macro/ImageJ_plugins/utils/imagescience.jar
new file mode 100644
index 0000000000000000000000000000000000000000..7a3a4fddaae8149d09754d7be73b2777a3dc93d7
--- /dev/null
+++ b/data/imagej_macro/ImageJ_plugins/utils/imagescience.jar
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6975dcc17d4d9e0dc618060e93266c816dc62764379fe46bae2f7c6eaecfe134
+size 283410
diff --git a/data/imagej_macro/ImageJ_plugins/utils/mpicbg_-1.4.1.jar b/data/imagej_macro/ImageJ_plugins/utils/mpicbg_-1.4.1.jar
new file mode 100644
index 0000000000000000000000000000000000000000..8d0f88ef288d5732463d10b3c1e31d483f54cd89
--- /dev/null
+++ b/data/imagej_macro/ImageJ_plugins/utils/mpicbg_-1.4.1.jar
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a1c962431c6f7998d3e263a32308471128dd050373603d7419954fba456ce859
+size 138726
diff --git a/data/imagej_macro/ImageJ_plugins/utils/quickhull3d-1.0.0.jar b/data/imagej_macro/ImageJ_plugins/utils/quickhull3d-1.0.0.jar
new file mode 100644
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diff --git a/data/imagej_macro/bleed_throught_validate/bleed_throught_macro.ijm b/data/imagej_macro/bleed_throught_validate/bleed_throught_macro.ijm
new file mode 100644
index 0000000000000000000000000000000000000000..586532936da35e05e7b067cfb5ef037611156ec4
--- /dev/null
+++ b/data/imagej_macro/bleed_throught_validate/bleed_throught_macro.ijm
@@ -0,0 +1,155 @@
+//// How to use
+//// Open macro from ImageJ menu
+//// Change parameter setting here, and run entire macro
+//// For input folder browser, choose the input image folder 
+//// It takes ~20 seconds to run this macro
+
+print("\\Clear");
+
+
+// Parameters setting
+source_image="merFISH_02_007_01_wavelength_561.TIFF";  // change parameter here
+target_image="merFISH_02_007_01_wavelength_647.TIFF";  // change parameter here
+suffixe=".TIFF";
+
+
+
+
+
+
+print("----------------------------------------------------------------");
+//dir = getArgument;
+dir=getDirectory("mouse"); // open a browser, allow you to choose input directory
+//dir = "yourlocaldir/bleed_throught_validate/raw/";  
+// ex: dir="/Users/htran/Documents/storage_tmp/merfish_XP2059/bleed_throught_validate/raw/";
+if (dir=="") 
+	exit ("No argument!");
+
+print("Working dir: "+dir+"\n");
+
+results_dir=File.getParent(dir)+"/results/";
+if(!File.exists(results_dir)) 
+      File.mkdir(results_dir);
+
+            
+
+
+					
+					
+short_name_source = substring(source_image,0,lastIndexOf(source_image,suffixe));
+short_name_target = substring(target_image,0,lastIndexOf(target_image,suffixe));
+IJ.log(short_name_source);
+IJ.log(short_name_target);
+
+// Source image first
+print("Loading image: "+source_image);
+open(dir+source_image);																				
+selectWindow(source_image);
+run("Enhance Contrast", "saturated=0.35");
+if (bitDepth > 8) {run("8-bit");}
+//run("Median...", "radius=2");  // in case you see lots of noises detected as signals
+run("Convolve...", "text1=[-1 -1 -1 -1 -1\n-1 -1 -1 -1 -1\n-1 -1 24 -1 -1\n-1 -1 -1 -1 -1\n-1 -1 -1 -1 -1\n] normalize");
+
+
+////only signals with intensity values from 250 to 255 are considered as signals, from my observation of spots and noise in images. 
+////You can use other thresholds, this macro just give an estimation, not provide accurate results for publication. 
+setThreshold(250, 255, "raw"); 
+//setThreshold(250, 255);
+setOption("BlackBackground", true);
+run("Convert to Mask");
+run("Grays");
+
+selectWindow(source_image);
+bin_source=short_name_source+"_BINARY";  //can open zip file from ImageJ to have tif format
+saveAs("ZIP", results_dir+bin_source+".zip");
+print("Deconvolution done!");
+
+
+
+					
+print("Loading image: "+target_image);
+open(dir+target_image);
+selectWindow(target_image);
+run("Enhance Contrast", "saturated=0.35");
+if (bitDepth > 8) {run("8-bit");}
+
+// ATTENTION: in case you see lots of noises detected as signals, because this image contains large amount of noises --> need to use median filter here
+// If you don't see lots of noise, please comment median filter.  
+run("Median...", "radius=2"); 
+
+run("Convolve...", "text1=[-1 -1 -1 -1 -1\n-1 -1 -1 -1 -1\n-1 -1 24 -1 -1\n-1 -1 -1 -1 -1\n-1 -1 -1 -1 -1\n] normalize");
+setThreshold(250, 255, "raw");
+//setThreshold(250, 255);
+setOption("BlackBackground", true);
+run("Convert to Mask");
+run("Grays");
+
+selectWindow(target_image);
+bin_target=short_name_target+"_BINARY"; //can open zip file from ImageJ to have tif format
+saveAs("ZIP",results_dir+bin_target+".zip");
+print("Deconvolution done!");
+
+
+
+
+
+
+// histogram here
+
+//// First, extracting signals that are overlapped in source and target images
+imageCalculator("AND create", bin_source+".tif", bin_target+".tif");
+selectWindow("Result of "+bin_source+".tif");
+output_image="bleedthrought_signals"; //can open zip file from ImageJ to have tif format
+saveAs("ZIP",results_dir+output_image+".zip");
+
+
+//Counting number of spots in source image
+selectWindow(bin_source+".tif");
+nBins = 256; 
+getHistogram(values, counts, nBins);
+source_spots=counts[255]; // number of spots
+IJ.log("Source image is: "+source_image);
+IJ.log("Number of spots in source image is: "+source_spots);
+
+
+//Counting number of spots in bleedthrought image
+selectWindow(output_image+".tif");
+getHistogram(values, counts, nBins);
+bleedthrought_spots=counts[255]; // number of spots
+IJ.log("Number of spots that bleed throught other wavelength channel is: "+bleedthrought_spots);
+
+
+//Counting number of spots in target image
+selectWindow(bin_target+".tif");
+getHistogram(values, counts, nBins);
+target_spots=counts[255]; // number of spots
+IJ.log("Target image is: "+target_image);
+IJ.log("Number of spots in target image is: "+target_spots);
+
+
+pct_bleed=100*bleedthrought_spots/source_spots;
+IJ.log("Percentage of bleed throught is: "+pct_bleed);
+
+pct_bleed_target=100*bleedthrought_spots/target_spots;
+IJ.log("Percentage of bleed throught is: "+pct_bleed_target);
+
+
+// Save results into a csv file
+setResult("source_img", 0, source_image);
+setResult("target_img", 0, target_image);
+setResult("pct_bleedthrought_source", 0, pct_bleed);
+setResult("pct_bleedthrought_target", 0, pct_bleed_target);
+updateResults();
+
+selectWindow("Results");
+saveAs("Results",results_dir+"bleed_throught_report.csv");
+selectWindow("Results");
+run("Close");
+print("Save output into the folder: "+results_dir);
+
+run("Close All");
+
+print("Completed");
+print("----------------------------------------------------------------");
+
+
diff --git a/data/imagej_macro/bleed_throught_validate/raw/merFISH_02_007_01_wavelength_561.TIFF b/data/imagej_macro/bleed_throught_validate/raw/merFISH_02_007_01_wavelength_561.TIFF
new file mode 100644
index 0000000000000000000000000000000000000000..dea22dcd671a5a00991314eab72471d0b1c12a3a
--- /dev/null
+++ b/data/imagej_macro/bleed_throught_validate/raw/merFISH_02_007_01_wavelength_561.TIFF
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:224c7e7ed6eb8e4a3dc655412cab88971a255dcc72d43433a0fe746656f20260
+size 5171598
diff --git a/data/imagej_macro/bleed_throught_validate/raw/merFISH_02_007_01_wavelength_647.TIFF b/data/imagej_macro/bleed_throught_validate/raw/merFISH_02_007_01_wavelength_647.TIFF
new file mode 100644
index 0000000000000000000000000000000000000000..b37c29e082880af127f9cfbf2af081b5812a1d09
--- /dev/null
+++ b/data/imagej_macro/bleed_throught_validate/raw/merFISH_02_007_01_wavelength_647.TIFF
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8d1ab714c0baf8900a2b8e2f6e5d76b2a3308f16ed301567b7636ee55d40225f
+size 5171598
diff --git a/data/imagej_macro/bleed_throught_validate/results/bleed_throught_report.csv b/data/imagej_macro/bleed_throught_validate/results/bleed_throught_report.csv
new file mode 100644
index 0000000000000000000000000000000000000000..740e3040be5004ee42267b98b05b197ee57ede48
--- /dev/null
+++ b/data/imagej_macro/bleed_throught_validate/results/bleed_throught_report.csv
@@ -0,0 +1,2 @@
+ ,source_img,target_img,pct_bleedthrought_source,pct_bleedthrought_target
+1,merFISH_02_007_01_wavelength_561.TIFF,merFISH_02_007_01_wavelength_647.TIFF,0.258,17.386
diff --git a/data/imagej_macro/bleed_throught_validate/results/bleedthrought_signals.zip b/data/imagej_macro/bleed_throught_validate/results/bleedthrought_signals.zip
new file mode 100644
index 0000000000000000000000000000000000000000..88724038d2b713f0dd10aac4f59d28da76a34243
--- /dev/null
+++ b/data/imagej_macro/bleed_throught_validate/results/bleedthrought_signals.zip
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:169ff3492e5cff7155db973c062af8d630354b5898c2609de67e47438628b843
+size 3099
diff --git a/data/imagej_macro/bleed_throught_validate/results/merFISH_02_007_01_wavelength_561_BINARY.zip b/data/imagej_macro/bleed_throught_validate/results/merFISH_02_007_01_wavelength_561_BINARY.zip
new file mode 100644
index 0000000000000000000000000000000000000000..401c173f11c75870863398180bff78ce074cf8f9
--- /dev/null
+++ b/data/imagej_macro/bleed_throught_validate/results/merFISH_02_007_01_wavelength_561_BINARY.zip
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7cf64e0aa94e2cd897db6da10ae9e84cd84c700b8539e7f97fc947b84ef10712
+size 72243
diff --git a/data/imagej_macro/bleed_throught_validate/results/merFISH_02_007_01_wavelength_647_BINARY.zip b/data/imagej_macro/bleed_throught_validate/results/merFISH_02_007_01_wavelength_647_BINARY.zip
new file mode 100644
index 0000000000000000000000000000000000000000..04d611eefbca64e39455e6a90f946d234738cc6a
--- /dev/null
+++ b/data/imagej_macro/bleed_throught_validate/results/merFISH_02_007_01_wavelength_647_BINARY.zip
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:81d81d26f2259ef6cb28be1943b649b131f70f8683b25cb57c3b5bc9eeaf202d
+size 3953
diff --git a/data/imagej_macro/blur_detector/dataset1/merFISH_01_025_05.TIFF b/data/imagej_macro/blur_detector/dataset1/merFISH_01_025_05.TIFF
new file mode 100644
index 0000000000000000000000000000000000000000..26be9bd0c5ab9051d582161a17d5359a6bcd4337
--- /dev/null
+++ b/data/imagej_macro/blur_detector/dataset1/merFISH_01_025_05.TIFF
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:87d350e4df369288588e2e235342641861845a827a7893a9e3bb21d6a4715b05
+size 500247
diff --git a/data/imagej_macro/blur_detector/dataset1/merFISH_08_025_05.TIFF b/data/imagej_macro/blur_detector/dataset1/merFISH_08_025_05.TIFF
new file mode 100644
index 0000000000000000000000000000000000000000..e873cda98d8c9f830dffa674ee8bea2c21b3d948
--- /dev/null
+++ b/data/imagej_macro/blur_detector/dataset1/merFISH_08_025_05.TIFF
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b165e1ea8c361cad39638197ffccb95ab68de06223b6bf86e8ed9b186e811ead
+size 500247
diff --git a/data/imagej_macro/blur_detector/dataset2/merFISH_05_025_05.TIFF b/data/imagej_macro/blur_detector/dataset2/merFISH_05_025_05.TIFF
new file mode 100644
index 0000000000000000000000000000000000000000..d73d7980e9e254dff179838b3a904b554fe11976
--- /dev/null
+++ b/data/imagej_macro/blur_detector/dataset2/merFISH_05_025_05.TIFF
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a7ab993b728c287b4506bfe2792f724fa8bca5632c47e88aba807c3b317d27cb
+size 500247
diff --git a/data/imagej_macro/blur_detector/dataset2/merFISH_06_025_05.TIFF b/data/imagej_macro/blur_detector/dataset2/merFISH_06_025_05.TIFF
new file mode 100644
index 0000000000000000000000000000000000000000..58bf6f8a6c24126244ddf251b7b9b9fcb77e9bfc
--- /dev/null
+++ b/data/imagej_macro/blur_detector/dataset2/merFISH_06_025_05.TIFF
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d753615bc135dd70608e3bd09fbca6caa56e18eb97797f9bb26ff527b677378d
+size 500247
diff --git a/data/imagej_macro/blur_detector/detecting_blur_image.R b/data/imagej_macro/blur_detector/detecting_blur_image.R
new file mode 100644
index 0000000000000000000000000000000000000000..355c35e4a62f049e73fa6860635d3cfd0fc86403
--- /dev/null
+++ b/data/imagej_macro/blur_detector/detecting_blur_image.R
@@ -0,0 +1,85 @@
+suppressPackageStartupMessages({
+  # library(tidyverse)
+  library(dplyr)
+  library(ggplot2)
+  # library(igraph)
+  # library(ggraph)
+  library(data.table)
+  library(RColorBrewer)
+})
+
+
+read_input_data <- function(input_dir, total_pixels){
+  fns <- list.files(input_dir)
+  fns <- fns[grepl('.csv',fns)]
+  print("Number of csv histogram files: ")
+  print(length(fns))
+  good_signals_intensity_thrs <- 5
+  summary_stat <- tibble::tibble()
+  for(fn in fns){
+    df <- data.table::fread(paste0(input_dir, fn))
+    nbOverExp <- df %>%
+      dplyr::filter(intensity_val=='greater_30') %>%
+      dplyr::pull(counts)
+    total_counts <- df %>%
+      dplyr::filter(intensity_val!='greater_30')%>%
+      dplyr::mutate(intensity_val=as.numeric(intensity_val)) %>%
+      dplyr::filter(intensity_val>good_signals_intensity_thrs)%>%
+      dplyr::summarise(total_counts=sum(counts)) %>%
+      dplyr::pull(total_counts)
+    
+    stat <- tibble::tibble(image_fn=fn,
+                           pct_signals=round(100*total_counts/total_pixels,2),
+                           nb_overExp=nbOverExp)
+    summary_stat <- dplyr::bind_rows(summary_stat, stat)
+    
+    
+  }
+  return(summary_stat)
+}
+
+get_stat <- function(summary_stat, datatag, save_dir){
+  # fn <- 'merFISH_02_006_05_histogram.csv'
+  # View(df)
+  outliers <- tibble::tibble()
+  dim(summary_stat)
+  summary_stat$pct_overExp <- round(100*summary_stat$nb_overExp/total_pixels,3)
+  # summary(summary_stat$pct_signals)
+  # summary(summary_stat$pct_overExp)
+  # head(summary_stat)
+  
+  ## Detecting all outliers
+  outliers_top_thrs <- 0.95
+  outliers_top_FOVs <- summary_stat %>%
+    dplyr::filter(pct_signals > quantile(pct_signals, outliers_top_thrs))
+  print('Amount of outliers with large number of signals: ')
+  print(dim(outliers_top_FOVs))
+  outliers_top_FOVs$desc <- 'high_pct_signals'
+  outliers <- dplyr::bind_rows(outliers, outliers_top_FOVs)
+  
+  outliers_bottom_thrs <- 0.05
+  outliers_bottom_FOVs <- summary_stat %>%
+    dplyr::filter(pct_signals < quantile(pct_signals, outliers_bottom_thrs))
+  print('Amount of outliers with low signals: ')
+  print(dim(outliers_bottom_FOVs))
+  outliers_bottom_FOVs$desc <- 'low_pct_signals'
+  outliers <- dplyr::bind_rows(outliers, outliers_bottom_FOVs)
+  
+  artifact_thres <- 0.3
+  outliers_artifacts <- summary_stat %>%
+    dplyr::filter(pct_overExp>=artifact_thres)
+  outliers_artifacts$desc <- 'contain_artifacts'
+  outliers <- dplyr::bind_rows(outliers, outliers_artifacts)
+  print('Amount of artifacts: ')
+  print(dim(outliers_artifacts))
+  data.table::fwrite(outliers, paste0(save_dir, datatag, '_outliers.csv'))
+  
+}
+
+
+total_pixels = 1608 * 1608
+datatag <- 'XP2509'
+input_dir <- '/Users/htran/Documents/merfish_temp_storage/testing/results/'
+summary_stat <- read_input_data(input_dir, total_pixels)
+save_dir <- input_dir
+get_stat(summary_stat, datatag, save_dir)
diff --git a/data/imagej_macro/blur_detector/execute_blur_detector_dataset1.sh b/data/imagej_macro/blur_detector/execute_blur_detector_dataset1.sh
new file mode 100644
index 0000000000000000000000000000000000000000..38fcd4b0c38466a71f8128e55ab03f230e6733fd
--- /dev/null
+++ b/data/imagej_macro/blur_detector/execute_blur_detector_dataset1.sh
@@ -0,0 +1,59 @@
+#!/bin/sh
+
+# batch macro input_filename
+# Ref: page 20: https://imagej.nih.gov/ij/docs/macro_reference_guide.pdf
+
+## Configuration MacOS
+imagej_dir="/Applications/ImageJ.app" ## MacOS ImageJ java run folder
+macro_dir="/Users/hoatran/Documents/BCCRC_projects/merfish/datasets_reduced_size/blur_detector/"
+project_dir="/Users/hoatran/Documents/BCCRC_projects/merfish/datasets_reduced_size/blur_detector/"
+
+series="dataset1/" 
+task="blur_detector1" 
+
+## ImageJ/ Fiji can be installed to Applications, or put into any folder in your drive. 
+# imagej_dir="/Applications/ImageJ.app" ## MacOS ImageJ java run folder
+# imagej_dir="/Applications/Fiji.app" ## MacOS Fiji java run folder
+# imagej_dir="/Users/htran/Downloads/Fiji.app/" ## MacOS Fiji java run folder - from my computer
+
+
+# imagej_exe_file=${imagej_dir}jars/ij-1.53q.jar  ## Fiji file location, in general file name is ij.jar, sometimes include version here
+imagej_exe_file=${imagej_dir}/Contents/Java/ij.jar ## ImageJ exe file location, in general file name is ij.jar, sometimes include version here
+
+
+macro_fn="${macro_dir}macro_blur_detector.ijm"
+
+
+
+log_file="${macro_dir}${task}.log"
+echo $log_file
+exec >> $log_file 2>&1 && tail $log_file
+
+input_dir="${project_dir}${series}"
+echo "__________________________________\n"
+echo "Input directory is: \n"
+echo $input_dir
+echo "Blur detector \n"
+## You can change memory amount, ex: 20000m to 30000m so program will run faster.
+## MacOS background mode here
+
+## If you have java environment jre installed in your computer
+java -Xmx10000m -jar ${imagej_exe_file} -ijpath $imagej_dir/ -batch $macro_fn $input_dir
+
+## Otherwise using existing jre env from Fiji here
+# /Users/htran/Downloads/Fiji.app/java/macosx/adoptopenjdk-8.jdk/jre/Contents/Home/bin/java -Xmx20000m -jar $imagej_dir/Contents/Java/ij.jar -ijpath $imagej_dir/ -batch $macro_fn $input_dir
+# ${imagej_dir}java/macosx/adoptopenjdk-8.jdk/jre/Contents/Home/bin/java -Xmx20000m -jar ${imagej_exe_file} -ijpath $imagej_dir/ -batch $macro_fn $input_dir
+
+## Linux background mode here, using xvfb-run in case you run in server (graphical env), in local computer, java command is enough
+# xvfb-run -a java -Xmx15000m -jar $imagej_dir/ij.jar -ijpath $imagej_dir/ -batch $macro_fn $input_dir 
+
+## In Linux local computer, java command is sufficient
+# java -Xmx15000m -jar $imagej_dir/ij.jar -ijpath $imagej_dir/ -batch $macro_fn $input_dir 
+
+echo "Nucleus Segmentation Completed!"
+echo "__________________________________\n"
+
+
+
+
+
diff --git a/data/imagej_macro/blur_detector/execute_blur_detector_dataset2.sh b/data/imagej_macro/blur_detector/execute_blur_detector_dataset2.sh
new file mode 100644
index 0000000000000000000000000000000000000000..3467d156c7c1287e376e3ca41dc2a75ed3949398
--- /dev/null
+++ b/data/imagej_macro/blur_detector/execute_blur_detector_dataset2.sh
@@ -0,0 +1,59 @@
+#!/bin/sh
+
+# batch macro input_filename
+# Ref: page 20: https://imagej.nih.gov/ij/docs/macro_reference_guide.pdf
+
+## Configuration MacOS
+imagej_dir="/Applications/ImageJ.app" ## MacOS ImageJ java run folder
+macro_dir="/Users/hoatran/Documents/BCCRC_projects/merfish/datasets_reduced_size/blur_detector/"
+project_dir="/Users/hoatran/Documents/BCCRC_projects/merfish/datasets_reduced_size/blur_detector/"
+
+series="dataset2/" 
+task="blur_detector2" 
+
+## ImageJ/ Fiji can be installed to Applications, or put into any folder in your drive. 
+# imagej_dir="/Applications/ImageJ.app" ## MacOS ImageJ java run folder
+# imagej_dir="/Applications/Fiji.app" ## MacOS Fiji java run folder
+# imagej_dir="/Users/htran/Downloads/Fiji.app/" ## MacOS Fiji java run folder - from my computer
+
+
+# imagej_exe_file=${imagej_dir}jars/ij-1.53q.jar  ## Fiji file location, in general file name is ij.jar, sometimes include version here
+imagej_exe_file=${imagej_dir}/Contents/Java/ij.jar ## ImageJ exe file location, in general file name is ij.jar, sometimes include version here
+
+
+macro_fn="${macro_dir}macro_blur_detector.ijm"
+
+
+
+log_file="${macro_dir}${task}.log"
+echo $log_file
+exec >> $log_file 2>&1 && tail $log_file
+
+input_dir="${project_dir}${series}"
+echo "__________________________________\n"
+echo "Input directory is: \n"
+echo $input_dir
+echo "Blur detector \n"
+## You can change memory amount, ex: 20000m to 30000m so program will run faster.
+## MacOS background mode here
+
+## If you have java environment jre installed in your computer
+java -Xmx10000m -jar ${imagej_exe_file} -ijpath $imagej_dir/ -batch $macro_fn $input_dir
+
+## Otherwise using existing jre env from Fiji here
+# /Users/htran/Downloads/Fiji.app/java/macosx/adoptopenjdk-8.jdk/jre/Contents/Home/bin/java -Xmx20000m -jar $imagej_dir/Contents/Java/ij.jar -ijpath $imagej_dir/ -batch $macro_fn $input_dir
+# ${imagej_dir}java/macosx/adoptopenjdk-8.jdk/jre/Contents/Home/bin/java -Xmx20000m -jar ${imagej_exe_file} -ijpath $imagej_dir/ -batch $macro_fn $input_dir
+
+## Linux background mode here, using xvfb-run in case you run in server (graphical env), in local computer, java command is enough
+# xvfb-run -a java -Xmx15000m -jar $imagej_dir/ij.jar -ijpath $imagej_dir/ -batch $macro_fn $input_dir 
+
+## In Linux local computer, java command is sufficient
+# java -Xmx15000m -jar $imagej_dir/ij.jar -ijpath $imagej_dir/ -batch $macro_fn $input_dir 
+
+echo "Nucleus Segmentation Completed!"
+echo "__________________________________\n"
+
+
+
+
+
diff --git a/data/imagej_macro/blur_detector/macro_blur_detector.ijm b/data/imagej_macro/blur_detector/macro_blur_detector.ijm
new file mode 100644
index 0000000000000000000000000000000000000000..665f4c431ba9938ed94d0adcc01de5a6b9e91c44
--- /dev/null
+++ b/data/imagej_macro/blur_detector/macro_blur_detector.ijm
@@ -0,0 +1,24 @@
+setBatchMode(true);
+print("\\Clear");
+
+input_dir = getArgument;
+//input_dir = "/Users/yourdir/dataset1/";  // for testing purpose, you can disable line above, and use this
+if (input_dir=="") 
+	exit ("No argument!");
+
+
+
+save_dir=File.getParent(input_dir)+"/"+File.getName(input_dir)+"_results/";
+if(!File.exists(save_dir)) 
+      File.mkdir(save_dir);
+      
+print("Working dir: "+input_dir+"\n");      
+print("Output dir: "+save_dir+"\n");
+
+number_neighbors=48;
+
+// Running program
+run("BLUR DETECTOR ENTIRE FOLDER", "input="+input_dir+" save="+save_dir+" number="+number_neighbors);
+
+
+setBatchMode(false);
diff --git a/data/imagej_macro/cell_zone_detection/Macro_cell_zone_extension.ijm b/data/imagej_macro/cell_zone_detection/Macro_cell_zone_extension.ijm
new file mode 100644
index 0000000000000000000000000000000000000000..7edce6cadad86611cf3e27688b83659119e3288b
--- /dev/null
+++ b/data/imagej_macro/cell_zone_detection/Macro_cell_zone_extension.ijm
@@ -0,0 +1,81 @@
+//======================================================================================
+//// Installation
+//// See at https://github.com/nhuhoa/Spatial3DTissueJ/tree/master/ImageJ_plugins_jar/list_plugin_readme.txt
+//// Specific plugin to run functions here: TissueJ4Merfish_v14.jar, need version >=14
+//======================================================================================
+//// Preparation
+//// nuclei segmented input images
+
+
+
+//======================================================================================
+setBatchMode(true);
+print("________________cell zone detection with pre-defined radius___________________________");
+
+// Please modify the dir input parameter here
+//dir="YourDirectory/demo_watershed_seg/";
+dir="/Users/hoatran/Documents/BCCRC_projects/merfish/image_processing/demo_watershed_seg/";
+seg_image_fn="merFISH_01_025_08-SEG.tif";
+radius_extension=15;
+save_dir=File.getParent(dir)+"/cell_zone_detection/";
+
+open(dir + seg_image_fn);  
+run("DETECTING CELL ZONE", "save_dir="+save_dir+" nuclei="+seg_image_fn+" radius_max="+radius_extension+" save");
+
+run("Close All"); 
+print("Completed!");
+setBatchMode(false);
+
+
+
+
+//======================================================================================
+setBatchMode(true);
+print("______________ cell zone detection with infinite extension _____________________________");
+print(" Radius = 0 means no max radius");
+
+// Please modify the dir input parameter here
+dir="YourDirectory/demo_watershed_seg/";
+dir="/Users/hoatran/Documents/BCCRC_projects/merfish/image_processing/demo_watershed_seg/";
+seg_image_fn="merFISH_01_025_08-SEG.tif";
+radius_extension=0;
+save_dir=File.getParent(dir)+"/cell_zone_detection/";
+
+open(dir + seg_image_fn);  
+run("DETECTING CELL ZONE", "save_dir="+save_dir+" nuclei="+seg_image_fn+" radius_max="+radius_extension+" save");
+
+run("Close All"); 
+print("Completed!");
+setBatchMode(false);
+
+
+//======================================================================================
+setBatchMode(true);
+print("_______________cell zone detection with nuclei as seed, and binary cell mask and max radius of extension____________________________");
+
+// Please modify the dir input parameter here
+//dir="YourDirectory/demo_watershed_seg/";
+dir="/Users/hoatran/Documents/BCCRC_projects/merfish/image_processing/demo_watershed_seg/";
+seg_image_fn="merFISH_01_025_08-SEG.tif";
+cell_mask_fn="merFISH_01_025_08-mask.tif";
+radius_extension=15;
+save_dir=File.getParent(dir)+"/cell_zone_detection/";
+
+open(dir + seg_image_fn);
+open(dir + cell_mask_fn);
+suffixe_filename=".tif";
+cell_mask_fn = substring(cell_mask_fn,0,lastIndexOf(cell_mask_fn,suffixe_filename));
+seg_image_fn = substring(seg_image_fn,0,lastIndexOf(seg_image_fn,suffixe_filename));
+
+run("3D Watershed Split", "binary="+cell_mask_fn+" seeds="+seg_image_fn+" radius="+radius_extension);
+
+
+selectWindow("Split");
+saveAs("Tiff", save_dir+seg_image_fn+"_cell_zone_detection_with_cell_mask.tif");
+
+// For compressed format, that can open from ImageJ, compressed ~100 times but same quality
+//saveAs("ZIP", save_dir+seg_image_fn+"_cell_zone_detection_with_cell_mask.zip");
+
+run("Close All"); 
+print("Completed!");
+setBatchMode(false);
diff --git a/data/imagej_macro/cell_zone_detection/TissueJ4Merfish_v14.jar b/data/imagej_macro/cell_zone_detection/TissueJ4Merfish_v14.jar
new file mode 100644
index 0000000000000000000000000000000000000000..ede9263bfdad80ffbde246927aa10b53dcd8b8f7
--- /dev/null
+++ b/data/imagej_macro/cell_zone_detection/TissueJ4Merfish_v14.jar
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7b4cbabea978a48df86ca4f3ace607854ca44c273a8a30cad872d4234f3807b7
+size 841067
diff --git a/data/imagej_macro/cell_zone_detection/merFISH_01_025_08-SEG.tif b/data/imagej_macro/cell_zone_detection/merFISH_01_025_08-SEG.tif
new file mode 100644
index 0000000000000000000000000000000000000000..e1c2b07148e8e074ffa843ab955e58ab4459f386
--- /dev/null
+++ b/data/imagej_macro/cell_zone_detection/merFISH_01_025_08-SEG.tif
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:388fd61b0a5cff353623bc4c4f6b06867906910fa68d77b674f75155bc448286
+size 1281812
diff --git a/data/imagej_macro/cell_zone_detection/merFISH_01_025_08-mask.tif b/data/imagej_macro/cell_zone_detection/merFISH_01_025_08-mask.tif
new file mode 100644
index 0000000000000000000000000000000000000000..126827f65630ba2ff30b3bf2482a1b321b581342
--- /dev/null
+++ b/data/imagej_macro/cell_zone_detection/merFISH_01_025_08-mask.tif
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b8a6a731ec94b4986e7c673a1e2f3bd5fc4df4b6d508a222176c8fb83352a7d0
+size 640236
diff --git a/data/imagej_macro/counting_spots/counting_spots_FOVs_display.py b/data/imagej_macro/counting_spots/counting_spots_FOVs_display.py
new file mode 100644
index 0000000000000000000000000000000000000000..f6adae0999d4421dbf8658bb5989666778c4b07f
--- /dev/null
+++ b/data/imagej_macro/counting_spots/counting_spots_FOVs_display.py
@@ -0,0 +1,98 @@
+from re import L
+from turtle import shape
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+from pathlib import Path
+import os
+import numpy as np
+#load csv files
+#returns files names as well to preserve laser channel information
+def load_data(path:Path) -> tuple:
+    files=[os.path.join(path,file) for file in os.listdir(path)]
+    dfs=[]
+    for f in files:
+        if ".csv" in f.lower(): 
+            df=pd.read_csv(f)
+            dfs.append(df)
+    return tuple(dfs)
+
+
+def find_rect(fovs:int):
+    area=fovs
+    w=area
+    l=2
+    while l <=w :    
+        if (area/l)%1==0: w=area/l     
+        l+=1
+    return int(w)
+
+def min_max_scale_percent(df,val):
+    max_spot=np.max(df["total_spots"])
+    min_spot=np.min(df["total_spots"])
+    return round((val-min_spot)/(max_spot-min_spot)*100,2) 
+
+#shows simple heatmap with median spot count per tile
+#counts are scaled between 1-100 just to get a sense of a good tile
+def plot_heatmap(df):
+    total_fov=np.max(df["FOV"])
+    
+    w=find_rect(total_fov)
+    l=int(total_fov/w)
+    heatmap=np.empty(shape=(w,l),dtype=int)
+    index=1
+    for y in range(heatmap.shape[0]):
+        for x in range(heatmap.shape[1]):
+            print(index)
+            val=min_max_scale_percent( 
+                df,
+                np.median(df["total_spots"][df["FOV"] == index])
+            )
+            heatmap[y][x]=val
+            index+=1
+    sns.heatmap(data=heatmap,annot=True)
+    plt.show()
+
+
+
+#plots 2*num_tiles bar graphs each showing spots captured in a round for that tile  
+def plot_grid(ch1,ch2,savePath:Path):
+    os.chdir(savePath)
+    total_fov=np.max(ch1["FOV"])
+    ymax=np.max(ch1["total_spots"]) if np.max(ch1["total_spots"])>np.max(ch2["total_spots"])  else np.max(ch2["total_spots"])
+    fig, axs= plt.subplots(figsize=(16,120),nrows=total_fov,ncols=2,tight_layout=True)
+    
+    for i in range(axs.shape[0]):
+        axs[i][0].set_title(f"FOV: {i+1}")
+        axs[i][0].set_ylabel(f"# of spots", fontsize=15)
+        axs[i][0].set_xlabel("imaging round", fontsize=10)
+        axs[i][0].set_ylim(ymax)
+        axs[i][0].bar(
+            ch1["imaging_round"][ch1["FOV"]==i+1],
+            ch1["total_spots"][ch1["FOV"]==i+1]
+        )
+        axs[i][0].invert_yaxis()
+    axs[0][0].set_title("647\nFOV: 1")
+    for i in range(axs.shape[0]):
+        axs[i][1].set_title(f"FOV: {i+1}")
+        axs[i][1].set_ylabel(f"# of spots", fontsize=15)
+        axs[i][1].set_xlabel("imaging round", fontsize=10)
+        axs[i][1].set_ylim(ymax)
+        axs[i][1].bar(
+            ch2["imaging_round"][ch2["FOV"]==i+1],
+            ch2["total_spots"][ch2["FOV"]==i+1]
+        )
+        axs[i][1].invert_yaxis()
+    axs[0][1].set_title("750\nFOV: 1")
+    
+    plt.tight_layout(pad=0.4,w_pad=0.5,h_pad=1)
+    plt.savefig(os.path.join(savePath, "spots_accross_FOVs.pdf"),format="pdf")
+   
+    
+
+
+if __name__ == "__main__":
+    path= r"C:\Users\Isaac von Riedemann\Downloads\counting_spots_across_FOVs_dataset_XP2059_647"
+    dfs=load_data(path)
+    
+    plot_grid(dfs[0],dfs[1],path)
\ No newline at end of file
diff --git a/data/imagej_macro/counting_spots/counting_spots_z_display.py b/data/imagej_macro/counting_spots/counting_spots_z_display.py
new file mode 100644
index 0000000000000000000000000000000000000000..34ea875398b213aa61cf80d58380e68c2d727426
--- /dev/null
+++ b/data/imagej_macro/counting_spots/counting_spots_z_display.py
@@ -0,0 +1,70 @@
+import os
+from pathlib import Path
+import numpy as np
+import pandas as pd
+from matplotlib import pyplot as plt
+import skimage
+import argparse
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--input_dir', type = str, help = 'Input directory to csv files')
+    parser.add_argument('--output_dir', type = str, help = 'Output directory to store the results')
+    parser.add_argument('--exp_name', type = str, help = 'Experiment name, e.g.: XP2059')
+    parser.add_argument('--ch', type = int, help='Channel of concern')
+    parser.add_argument('--bar_width', type = float, default = 0.4, help = 'Width of bar')
+    
+    args = parser.parse_args()
+
+    if not args.output_dir:
+        args.output_dir = args.input_dir
+
+    f_list = irlist(args.input_dir, '*.csv')
+
+    num_zslices = len(pd.read_csv(f_list[0]))
+
+    num_irs = len(f_list)
+
+    fov = f_list[0].split('_')[-3][3:]
+
+    spots, xlist, X = get_xy(f_list, num_zslices)
+
+    ymax = np.amax(spots)
+    
+    fig, axes = plt.subplots(nrows=num_irs, ncols=1, figsize=(30, 60))
+    fig.suptitle(f'Counting spots for {args.exp_name}, FOV = {fov}, channel = {args.ch} nm', fontsize = 40)
+
+    
+    for i, ax in enumerate(axes.flat): 
+        ax.set_xticks(X, xlist[i], fontsize = 25)
+        ax.set_xticklabels(xlist[i], rotation = 45)
+        ax.set_ylabel(f'# spots in IR {i+1}', fontsize = 28, fontweight="bold")
+        ax.set_ylim([0,ymax])
+        ax.tick_params(axis='y', labelsize=25)
+        ax.bar(X, spots[:,i], width = args.bar_width)
+        
+   
+    plt.tight_layout()
+    
+    fig.savefig(args.output_dir+f'/{args.exp_name}_{fov}_{args.ch}.pdf', transparent = 'False', format = 'pdf')
+
+def irlist(idir, match_str):
+    idir = Path(idir)
+    return [str(i) for i in sorted(list(idir.rglob(match_str)))]
+
+def get_xy(csv_list, zslices):
+    num_ir = len(csv_list)
+    spots_arr = np.zeros(shape=(zslices,num_ir))
+    x_ticks = []
+    for i in range(num_ir):
+        df = pd.read_csv(csv_list[i])
+        spots_arr[:,i] = df['Spots'].to_numpy()
+        x = [s.replace('merFISH_', "") for s in sorted(list(df['Image']))]
+        x_ticks.append(x) 
+    X = np.arange(zslices)
+    return spots_arr, x_ticks, X
+
+if __name__ == '__main__':
+    main()
+    #python counting_spots_z_display.py --input_dir /Users/ythapliyal/Counting_spots_old/XP2059/647 --exp_name XP2059 --ch 647
+
diff --git a/data/imagej_macro/counting_spots/macro_counting_metric.txt b/data/imagej_macro/counting_spots/macro_counting_metric.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1c4abb77c47f57f368839272cb3e83b1840c5412
--- /dev/null
+++ b/data/imagej_macro/counting_spots/macro_counting_metric.txt
@@ -0,0 +1,85 @@
+// Open this macro from ImageJ
+// Change the input_dir: a directory which contain 8 images of same wavelength. 
+// or a big folder image, and define the pattern of image as image_fn below. 
+// then run entire macro: run macro from menu bar or Cmd + R in mac, ctrl + R in other os sys I think. 
+
+input_dir="/Users/hoatran/Documents/BCCRC_projects/merfish/XP2059_FOV25/";
+
+
+output_dir=input_dir+"counting_spots/";
+
+if(!File.exists(output_dir)) 
+      File.mkdir(output_dir);
+
+for (r=1; r<=8; r++) {
+
+//r=8;
+
+
+
+image_fn="merFISH_0"+r+"_025_05.TIFF";
+
+open(input_dir+image_fn);
+short_name = substring(image_fn,0,indexOf(image_fn,'.TIFF'));
+
+run("8-bit");
+
+run("Enhance Contrast...", "saturated=0.2 normalize");
+
+run("Convolve...", "text1=[-1 -1 -1 -1 -1\n-1 -1 -1 -1 -1\n-1 -1 24 -1 -1\n-1 -1 -1 -1 -1\n-1 -1 -1 -1 -1\n] normalize");
+
+//open(input_dir+"demo/mask.tif");
+
+selectWindow(image_fn);
+
+//imageCalculator("AND create", image_fn,"mask.tif");
+
+//selectWindow("mask.tif");
+
+//close();
+
+//selectWindow("Result of "+image_fn);
+
+//selectWindow(image_fn);
+
+nBins = 256;  
+
+row = 0;
+
+getHistogram(values, counts, nBins);
+
+for (i=250; i<nBins; i++) {
+
+      setResult("Value", row, values[i]);
+
+      setResult("Count", row, counts[i]);
+
+      row++;
+
+}
+
+updateResults();
+
+
+saveAs("Results",output_dir+short_name+"_hist.csv");
+
+//selectWindow("Result of "+image_fn);
+selectWindow(image_fn);
+saveAs("Tiff",output_dir+image_fn);
+
+run("Close All"); 
+
+//selectWindow(image_fn);
+
+//close();
+run("Close All");
+list = getList("window.titles");
+if(list.length>0){
+  	for (i=0; i<list.length; i++){
+  		winame = list[i];
+	  	print(winame);
+  		selectWindow(winame);
+	  	run("Close");
+	}
+	}
+}
diff --git a/data/imagej_macro/counting_spots/macro_counting_metric_across_FOVs.txt b/data/imagej_macro/counting_spots/macro_counting_metric_across_FOVs.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5314bf8f1d94122aa31559d269028cc30ad9857b
--- /dev/null
+++ b/data/imagej_macro/counting_spots/macro_counting_metric_across_FOVs.txt
@@ -0,0 +1,125 @@
+// Open this macro from ImageJ
+// Change the input_dir: a directory which contain merfish images of a dataset
+// or a big folder image, and define the pattern of image as image_fn below. 
+// then run entire macro: run macro from menu bar or Cmd + R in mac, ctrl + R, ctrl shift R in other os sys. 
+
+// With a given FOV, and given round
+// Counting pixels corresponding to spots across different z-slices. 
+
+setBatchMode(true);
+print("\\Clear");
+print("Counting pixels corresponding to spots across different z-slices");
+print("----------------------------------------------------------------");
+
+
+print("-----------------Parameter Setting---------------------------");
+input_dir="D:/Co-op/XP2059/1/1/";
+ch1="647nm, Raw/"
+ch2="750nm, Raw/"
+dataset="XP2059";
+nbFOVs=42;
+nbRounds=8;
+nbZslices=16;
+
+
+suffix=".TIFF";
+test_case = "counting_spots_across_FOVs_dataset_"+dataset;
+output_dir=File.getParent(input_dir)+"/"+test_case+"/";
+nBins = 256; 
+if(!File.exists(output_dir)) 
+      File.mkdir(output_dir);
+
+print(input_dir);
+print(output_dir);
+
+
+
+function deconvolute_binarization_image (image_fn) {
+  selectWindow(image_fn);
+  run("Enhance Contrast", "saturated=0.35");
+  if (bitDepth > 8) {run("8-bit");}
+  //run("Median...", "radius=2");  // in case you see lots of noises detected as signals
+  run("Convolve...", "text1=[-1 -1 -1 -1 -1\n-1 -1 -1 -1 -1\n-1 -1 24 -1 -1\n-1 -1 -1 -1 -1\n-1 -1 -1 -1 -1\n] normalize");
+
+
+  ////only signals with intensity values from 250 to 255 are considered as signals, from my observation of spots and noise in images. 
+  ////You can use other thresholds, this macro just give an estimation, not provide accurate results for publication. 
+  setThreshold(250, 255, "raw"); 
+  //setThreshold(250, 255);
+  setOption("BlackBackground", true);
+  run("Convert to Mask");
+  run("Grays");
+
+}
+
+//FOV=25;
+//r=1;
+c=0;
+for (FOV=1; FOV<=nbFOVs; FOV++) {
+  if(FOV<10){  // Isaac: is this the correct format for FOV?
+        FOV_label="0"+FOV;
+  }else{
+        FOV_label=""+FOV;
+  }
+  for (r=1; r<=nbRounds; r++) {
+  print("--------------------------------------------");
+  print("FOV:  "+FOV_label+"      round:  "+r);
+    output_img = "total_signals_round"+r;
+    for (z=1; z<=nbZslices; z++) {
+      if(z<10){
+        zslice="0"+z;
+      }else{
+        zslice=""+z;
+      }
+      short_name = "merFISH_0"+r+"_0"+FOV_label+"_"+zslice;
+      image_fn=short_name+suffix;
+      //print(image_fn);
+      open(input_dir+image_fn);
+      //short_name = substring(image_fn,0,indexOf(image_fn, suffix));
+      deconvolute_binarization_image (image_fn);
+      if(z==1){
+        selectWindow(image_fn);
+        rename(output_img);
+        //IJ.log('First assignment:  '+output_img);
+      }else{
+        //IJ.log('Sum: '+image_fn);
+        imageCalculator("OR create", output_img, image_fn);
+        selectWindow(image_fn);
+        close();
+        selectWindow(output_img);
+        close();	
+        selectWindow("Result of "+output_img); 
+        rename(output_img);	
+    
+      }
+    }
+    nBins = 256; 
+    getHistogram(values, counts, nBins);
+    IJ.log("Number of spots: "+counts[255]);
+    // //Save results into a csv file
+    setResult("FOV", c, FOV);
+    setResult("imaging_round", c, r);
+    setResult("total_spots", c, counts[255]);
+    updateResults();
+    c++;
+    selectWindow(output_img);
+    close();	
+  }  
+}
+selectWindow("Results");
+saveAs("Results",output_dir+"spots_across_FOV_report.csv");
+selectWindow("Results");
+run("Close");
+print("Save output into the folder: "+output_dir);
+
+run("Close All");
+
+print("Completed");
+print("----------------------------------------------------------------");
+setBatchMode(false);
+
+
+
+
+
+
diff --git a/data/imagej_macro/counting_spots/macro_counting_metric_v3_across_rounds.txt b/data/imagej_macro/counting_spots/macro_counting_metric_v3_across_rounds.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6bb78c3f61d008a98fa5e4ae3f97e2c6837fd4b4
--- /dev/null
+++ b/data/imagej_macro/counting_spots/macro_counting_metric_v3_across_rounds.txt
@@ -0,0 +1,104 @@
+// Open this macro from ImageJ
+// Change the input_dir: a directory which contain 8 images of same wavelength. 
+// or a big folder image, and define the pattern of image as image_fn below. 
+// then run entire macro: run macro from menu bar or Cmd + R in mac, ctrl + R in other os sys I think. 
+
+// Counting pixels corresponding to spots across different rounds. 
+
+setBatchMode(true);
+print("\\Clear");
+print("Counting pixels corresponding to spots across different rounds");
+print("----------------------------------------------------------------");
+input_dir="/Users/hoatran/Documents/BCCRC_projects/merfish/XP2059_FOV25/";
+FOV=25;
+z=5;
+suffix=".TIFF";
+test_case = "counting_pixels_spots_FOV0"+FOV+"_z0"+z;
+output_dir=input_dir+test_case+"/";
+
+if(!File.exists(output_dir)) 
+      File.mkdir(output_dir);
+
+print(input_dir);
+print(output_dir);
+
+
+for (r=1; r<=8; r++) {
+
+//r=8;
+
+
+short_name="merFISH_0"+r+"_0"+FOV+"_0"+z;
+image_fn=short_name+suffix;
+
+open(input_dir+image_fn);
+//short_name = substring(image_fn,0,indexOf(image_fn, suffix));
+
+run("8-bit");
+
+run("Enhance Contrast...", "saturated=0.2 normalize");
+
+run("Convolve...", "text1=[-1 -1 -1 -1 -1\n-1 -1 -1 -1 -1\n-1 -1 24 -1 -1\n-1 -1 -1 -1 -1\n-1 -1 -1 -1 -1\n] normalize");
+
+//open(input_dir+"demo/mask.tif");
+
+selectWindow(image_fn);
+
+//imageCalculator("AND create", image_fn,"mask.tif");
+
+//selectWindow("mask.tif");
+
+//close();
+
+//selectWindow("Result of "+image_fn);
+
+//selectWindow(image_fn);
+
+nBins = 256; 
+
+
+
+getHistogram(values, counts, nBins);
+
+nb_spots=0;
+
+for (i=250; i<nBins; i++) {
+	nb_spots = nb_spots + counts[i];
+
+}
+//setResult("Value", row, values[i]);
+setResult("Image", r-1, short_name);
+setResult("Spots", r-1, nb_spots);
+
+
+
+updateResults();
+
+
+
+//selectWindow("Result of "+image_fn);
+//selectWindow(image_fn);
+//saveAs("Tiff",output_dir+image_fn);
+
+run("Close All"); 
+
+//selectWindow(image_fn);
+//close();
+//run("Close All");
+}
+
+selectWindow("Results");
+saveAs("Results",output_dir+test_case+"_across_rounds.csv");
+selectWindow("Results");
+run("Close");
+print("Save output into the folder: "+output_dir);
+
+print("Completed");
+print("----------------------------------------------------------------");
+setBatchMode(false);
+
+
+
+
+
+
diff --git a/data/imagej_macro/counting_spots/macro_counting_metric_v3_across_zslices.txt b/data/imagej_macro/counting_spots/macro_counting_metric_v3_across_zslices.txt
new file mode 100644
index 0000000000000000000000000000000000000000..646f100190a95ac29ab5eb29d24ec54e9b7c1d36
--- /dev/null
+++ b/data/imagej_macro/counting_spots/macro_counting_metric_v3_across_zslices.txt
@@ -0,0 +1,99 @@
+// Open this macro from ImageJ
+// Change the input_dir: a directory which contain 8 images of same wavelength. 
+// or a big folder image, and define the pattern of image as image_fn below. 
+// then run entire macro: run macro from menu bar or Cmd + R in mac, ctrl + R in other os sys I think. 
+
+// With a given FOV, and given round
+// Counting pixels corresponding to spots across different z-slices. 
+
+setBatchMode(true);
+print("\\Clear");
+print("Counting pixels corresponding to spots across different z-slices");
+print("----------------------------------------------------------------");
+input_dir="/Users/hoatran/Documents/BCCRC_projects/merfish/XP2059_FOV25/demo_zslice_eval/";
+FOV=25;
+r=1;
+suffix=".TIFF";
+test_case = "counting_pixels_spots_r0"+r+"_FOV0"+FOV;
+output_dir=File.getParent(input_dir)+"/"+test_case+"/";
+
+if(!File.exists(output_dir)) 
+      File.mkdir(output_dir);
+
+print(input_dir);
+print(output_dir);
+
+
+for (z=1; z<=16; z++) {
+  if(z<10){
+    zslice="0"+z;
+  }else{
+    zslice=""+z;
+  }
+  short_name = "merFISH_0"+r+"_0"+FOV+"_"+zslice;
+  image_fn=short_name+suffix;
+  print(image_fn);
+  open(input_dir+image_fn);
+  //short_name = substring(image_fn,0,indexOf(image_fn, suffix));
+  
+  run("8-bit");
+  
+  run("Enhance Contrast...", "saturated=0.2 normalize");
+  
+  run("Convolve...", "text1=[-1 -1 -1 -1 -1\n-1 -1 -1 -1 -1\n-1 -1 24 -1 -1\n-1 -1 -1 -1 -1\n-1 -1 -1 -1 -1\n] normalize");
+  
+  //open(input_dir+"demo/mask.tif");
+  
+  selectWindow(image_fn);
+  
+  //imageCalculator("AND create", image_fn,"mask.tif");
+  
+  //selectWindow("mask.tif");
+  
+  //close();
+  
+  //selectWindow("Result of "+image_fn);
+  
+  //selectWindow(image_fn);
+  
+  nBins = 256; 
+
+  getHistogram(values, counts, nBins);
+
+  nb_spots=0;
+  
+  for (i=250; i<nBins; i++) {
+  	nb_spots = nb_spots + counts[i];
+  
+  }
+  //setResult("Value", row, values[i]);
+  setResult("Image", z-1, short_name);
+  setResult("Spots", z-1, nb_spots);
+  updateResults();
+
+  //selectWindow("Result of "+image_fn);
+  //selectWindow(image_fn);
+  //saveAs("Tiff",output_dir+image_fn);
+
+  run("Close All"); 
+
+  //selectWindow(image_fn);
+
+//close();
+//run("Close All");
+}
+
+selectWindow("Results");
+saveAs("Results",output_dir+test_case+"_across_zslices.csv");
+print("Save output into the folder: "+output_dir);
+selectWindow("Results");
+run("Close");
+print("Completed");
+print("----------------------------------------------------------------");
+setBatchMode(false);
+
+
+
+
+
+
diff --git a/data/imagej_macro/counting_spots/plotting_counting_objects.R b/data/imagej_macro/counting_spots/plotting_counting_objects.R
new file mode 100644
index 0000000000000000000000000000000000000000..4dc688fc2c4c8b123452e70577cbb9037365e147
--- /dev/null
+++ b/data/imagej_macro/counting_spots/plotting_counting_objects.R
@@ -0,0 +1,45 @@
+library(dplyr)
+library(ggplot2)
+input_dir <-'/Users/hoatran/Documents/BCCRC_projects/merfish/XP2059_FOV25/counting_spots/'
+FOV <- '025'
+z <- '05'
+prefix <- 'merFISH_0'
+
+# Read all csv files histogram and combine them into 1 file for plotting
+fns <- list.files(input_dir, pattern = '*_hist.csv')
+fns
+
+counts <- tibble::tibble()
+for(f in fns){
+  df <- data.table::fread(paste0(input_dir,f)) %>% as.data.frame()
+  f <- gsub(paste0('_',FOV,'_',z,'_hist.csv'),'',f)
+  f <- gsub(prefix,'R',f)
+  df$round <- f
+  counts <- dplyr::bind_rows(counts, df)
+}
+dim(counts)
+head(counts)
+
+# Counting signals from 250 to 255 intensity values
+stat <- counts %>%
+  dplyr::group_by(round) %>%
+  dplyr::summarise(Count=sum(Count))
+stat
+
+# Counting signals=255 intensity values only
+stat_255 <- counts %>%
+  dplyr::filter(Value==255) %>%
+  dplyr::group_by(round) %>%
+  dplyr::summarise(Count=sum(Count))
+stat_255
+
+p <- ggplot(stat, aes(x=round, y=Count)) +
+  geom_bar(stat="identity", fill="steelblue", width=0.4)+
+  theme_minimal() +
+  labs(title='Counting pixels spots XP2059', x='Round',y='#pixels-spots')
+p  
+
+png(paste0(input_dir,"XP2059_spots_count.png"),height = 2*350, width=2*500,res = 2*72)
+print(p)
+dev.off()
+
diff --git a/data/imagej_macro/counting_spots/plotting_spot_counts_FOVs.R b/data/imagej_macro/counting_spots/plotting_spot_counts_FOVs.R
new file mode 100644
index 0000000000000000000000000000000000000000..15d4b0628ca689def8a4b1e167971c0e914b27cd
--- /dev/null
+++ b/data/imagej_macro/counting_spots/plotting_spot_counts_FOVs.R
@@ -0,0 +1,136 @@
+
+library(ggplot2)
+library(dplyr)
+
+
+library(dplyr)
+library(ggplot2)
+load_spots_counts <- function(input_dir, save_dir){
+  
+  input_dir <- '/Users/htran/Documents/storage_tmp/merfish/merfish_materials-main/quality_control_results/'
+  save_dir <- '/Users/htran/Documents/storage_tmp/merfish/spots_across_FOVs/'
+  save_dir <- paste0(save_dir, 'figs_same_scales/')
+  dir.create(save_dir)
+  fns <- list.files(input_dir)
+  fns <- fns[grepl('XP',fns)]
+  suffix <- 'spots_across_FOV_report.csv'
+  plot_ls <- list()
+  for(datatag in fns){
+    xp_dir <- paste0(input_dir, datatag,'/')
+    supfns <- list.files(xp_dir)
+    supfns <- supfns[grepl(suffix,supfns)]
+    if(length(supfns)==2){ #2 wavelength outputs
+      print(datatag)
+      # print(supfns)
+      df_w1 <- data.table::fread(paste0(xp_dir,supfns[1]))
+      df_w1$wavelength <- substr(supfns[1], 1, 3)
+      df_w2 <- data.table::fread(paste0(xp_dir,supfns[2]))
+      df_w2$wavelength <- substr(supfns[2], 1, 3)
+      df <- dplyr::bind_rows(df_w1, df_w2)
+      print(dim(df))
+      # data.table::fwrite(df, paste0(save_dir, datatag,'_pixels_spots_counts.csv'))
+      p <- viz_spots_across_FOVs(df, datatag, save_dir)
+      plot_ls[[datatag]] <- p
+    }
+  }
+  
+  
+  
+  
+}
+viz_spots_across_FOVs <- function(df, datatag, save_dir){
+  df$imaging_round <- as.factor(df$imaging_round)
+  df$V1 <- NULL
+  df$FOV <- as.factor(df$FOV)
+  p1 <- ggplot(df, aes(FOV, total_spots)) +
+    geom_jitter(aes(colour = imaging_round), width = 0.25) + 
+    geom_violin()+
+    # geom_hline(yintercept=250000, linetype="dashed", color = "red") + 
+    facet_grid(rows = vars(wavelength)) + #, scales="free"
+    guides(colour = guide_legend(override.aes = list(shape = 15, size=6))) + 
+    theme_bw()+ 
+    theme(axis.text.x = element_text(size=15, color="black"),
+          strip.text = element_text(size=15, color="black"), 
+          legend.position ='bottom')
+  # p1
+  png(paste0(save_dir,datatag, "_eval_spots_across_FOVs.png"), height = 2*500, width=2*1200, res = 2*72)
+  print(p1)
+  dev.off()
+  return(p1)
+}
+
+
+
+
+
+
+
+# input_dir <- '~/Documents/storage_tmp/merfish/counting_spots_across_FOVs_dataset_XP2059/'
+# 
+# df1 <- data.table::fread(paste0(input_dir,'counting_spots_across_FOVs_dataset_XP2059_647/spots_across_FOV_report.csv')) %>% as.data.frame()
+# df2 <- data.table::fread(paste0(input_dir,'counting_spots_across_FOVs_dataset_XP2059_750/spots_across_FOV_report.csv')) %>% as.data.frame()  
+# 
+# dim(df1)
+# dim(df2)
+# 
+# df1$wavelength <- '647'
+# df2$wavelength <- '750'
+# df <- dplyr::bind_rows(df1, df2)
+# dim(df)
+# colnames(df)
+# df$V1[1]
+# 
+# data.table::fwrite(df, paste0('~/Documents/storage_tmp/merfish_XP2059/spots_counting_Hoa/pixels_spots_counts.csv'))
+# df$imaging_round <- as.factor(df$imaging_round)
+# df$V1 <- NULL
+# df$FOV <- as.factor(df$FOV)
+# p1 <- ggplot(df, aes(FOV, total_spots)) +
+#   geom_jitter(aes(colour = imaging_round), width = 0.25) + 
+#   geom_violin()+
+#   # geom_hline(yintercept=250000, linetype="dashed", color = "red") + 
+#   facet_grid(rows = vars(wavelength)) + 
+#   guides(colour = guide_legend(override.aes = list(shape = 15, size=6))) + 
+#   theme_bw()+ 
+#   theme(axis.text.x = element_text(size=15, color="black"),
+#         strip.text = element_text(size=15, color="black"), 
+#         legend.position ='bottom')
+# p1
+# 
+# 
+# stat1$FOV <- as.numeric(stat1$FOV)
+# stat1$FOV <- as.factor(stat1$FOV)
+# 
+# p2 <- ggplot(stat1, aes(FOV, nb_objects)) +
+#   geom_col(width = 0.3) + 
+#   ylim(0, max(stat1$nb_objects)+5) + 
+#   theme_bw() +
+#   theme(axis.text.x = element_text(size=15, color="black"),
+#         axis.title.y = element_text(size=16, color="black"),
+#         axis.text.y = element_text(size=16, color="black"))
+# 
+# p2  
+# # BiocManager::install('cowplot')
+# library(cowplot)
+# p2 <- cowplot::plot_grid(p2, NULL, align = 'hozirontal',nrow=1, rel_widths = c(2,0.025))
+# p <- cowplot::plot_grid(p2, p1, align = 'vertical',ncol=1, rel_heights = c(1,3), 
+#                         labels = c('Number of objects across FOV','Pixel-Spots Counting'))
+# p
+# png(paste0(input_dir,"Evaluate_spots_across_FOV_XP2059.png"), height = 2*900, width=2*1400, res = 2*72)
+# print(p)
+# dev.off()
+# 
+# 
+# df <- data.table::fread('~/Documents/storage_tmp/merfish_XP2059/spots_counting_Hoa/total_cell_areas_across_FOV.csv')
+# 
+# 
+# 
+# df$FOV <- substr(df$cell_image_name, 12,14)
+# df$FOV <- as.numeric(df$FOV)
+# df$cell_image_name <- NULL
+# dim(df)
+# df$total_foreground_area
+# sum(df$V1==df$FOV)
+# df$V1 <- NULL
+# data.table::fwrite(df, '~/Documents/storage_tmp/merfish_XP2059/spots_counting_Hoa/total_cell_areas_across_FOV.csv')
+# 
+# 
diff --git a/data/imagej_macro/nuclei_segmentation/cell_zone_results/aligned_images_fov0_z18_01_cell_zone_rad_5.0.tif b/data/imagej_macro/nuclei_segmentation/cell_zone_results/aligned_images_fov0_z18_01_cell_zone_rad_5.0.tif
new file mode 100644
index 0000000000000000000000000000000000000000..3bfd561314edf40708773ac58565a1bbebc441c8
--- /dev/null
+++ b/data/imagej_macro/nuclei_segmentation/cell_zone_results/aligned_images_fov0_z18_01_cell_zone_rad_5.0.tif
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eb1f9c41a44eade88e13d693a3edcf31c851eb03a85d28dd659b1ac5a4b68f07
+size 5171494
diff --git a/data/imagej_macro/nuclei_segmentation/cell_zone_results/aligned_images_fov0_z18_01_cell_zone_rad_5.0.zip b/data/imagej_macro/nuclei_segmentation/cell_zone_results/aligned_images_fov0_z18_01_cell_zone_rad_5.0.zip
new file mode 100644
index 0000000000000000000000000000000000000000..549214474e4863b8f04cdadfb8c730aae8410b02
--- /dev/null
+++ b/data/imagej_macro/nuclei_segmentation/cell_zone_results/aligned_images_fov0_z18_01_cell_zone_rad_5.0.zip
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e414e57ddb1ecc0b5b5e0f7139d8d7bab69b1555c1029dc01c6e88bca0037453
+size 100356
diff --git a/data/imagej_macro/nuclei_segmentation/contour_ROI_extraction.txt b/data/imagej_macro/nuclei_segmentation/contour_ROI_extraction.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e84a36764426fa8f93c7459a5700ab7a43fb213a
--- /dev/null
+++ b/data/imagej_macro/nuclei_segmentation/contour_ROI_extraction.txt
@@ -0,0 +1,76 @@
+//dir=getDirectory("mouse");
+//print(dir)
+setBatchMode(true);
+print("___________________________________________");
+
+
+suffixe=".tif"; // change this setting if your image ends with .tiff
+
+
+// get input direction from shell script command
+dir = getArgument;  // dir contains segmented images
+
+// for testing purpose, you can disable line above, and set input dir from this param
+//dir = "/Users/hoatran/Documents/BCCRC_projects/merfish/merFISH_report_pipeline/imagej_macro/nuclei_segmentation/testing_images/";  
+
+if (dir=="") 
+	exit ("No argument! Exit program");
+
+print("Working dir: "+dir+"\n");
+
+
+demo_dir=File.getParent(dir)+"/demo_results/";
+if(!File.exists(demo_dir)) 
+      File.mkdir(demo_dir);
+
+
+print("Creating demo ROI folder: "+demo_dir+"\n");
+
+files=getFileList(dir);
+
+print("Nb of processing images: "+files.length+"\n");
+
+for(f=0;f<files.length;f++)
+{
+     if(!File.isDirectory(dir+files[f])) {
+				name=files[f];
+				
+				if(endsWith(name, suffixe)){ // image ends with .tif, only process image
+					
+					short_name = substring(name,0,lastIndexOf(name,suffixe));  
+					print("_______________________________________\n");
+					print(name);
+					//print(short_name);
+          start = getTime();
+					print("Loading image: "+name);
+					open(dir+name);
+					  selectWindow(name);
+					  run("Duplicate...", "title=["+ short_name +"_raw]");
+					  
+					  // Get demo output of segmentation, overlapped with raw image, can comment this function if it takes time to run with large image
+						run("3D Draw Rois", "raw="+short_name+"_raw seg="+short_name);
+						saveAs("Tiff", demo_dir+short_name+"_ROI.tif");
+						
+						print("Time of execution is: "+(getTime()-start)/1000+" seconds"); 
+						
+						run("Close All"); // comment this command if you want to see the results
+
+					}
+					
+           }
+           
+    }
+
+}
+print("Hura, completed!!!");
+
+//if(!File.exists(dir+"log")) File.mkdir(dir+"log");
+//selectWindow("Log");
+//saveAs("Text", dir+ "segment_log.txt");
+setBatchMode(false);
+
+
+
+
+
+
diff --git a/data/imagej_macro/nuclei_segmentation/demo_results/aligned_images_fov0_z18_01_SEG_demo_only.tif b/data/imagej_macro/nuclei_segmentation/demo_results/aligned_images_fov0_z18_01_SEG_demo_only.tif
new file mode 100644
index 0000000000000000000000000000000000000000..12001e4858665833fb4a3aaa13e1a60edfb3e959
--- /dev/null
+++ b/data/imagej_macro/nuclei_segmentation/demo_results/aligned_images_fov0_z18_01_SEG_demo_only.tif
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a168849346fe45f1d1e6cebb206d65b4d538e0105e1fd0e26b84fc708acfb001
+size 7757222
diff --git a/data/imagej_macro/nuclei_segmentation/execute_segmentation.sh b/data/imagej_macro/nuclei_segmentation/execute_segmentation.sh
new file mode 100644
index 0000000000000000000000000000000000000000..e6f1f27962cd76246fcd3b1ed86094f27dea96ce
--- /dev/null
+++ b/data/imagej_macro/nuclei_segmentation/execute_segmentation.sh
@@ -0,0 +1,65 @@
+#!/bin/sh
+
+# batch macro input_filename
+# Ref: page 20: https://imagej.nih.gov/ij/docs/macro_reference_guide.pdf
+
+## Configuration MacOS
+imagej_dir="/Applications/ImageJ.app/" ## MacOS ImageJ java run folder
+macro_dir="/Users/hoatran/Documents/BCCRC_projects/merfish/merFISH_report_pipeline/imagej_macro/nuclei_segmentation/"
+project_dir="/Users/hoatran/Documents/BCCRC_projects/merfish/merFISH_report_pipeline/imagej_macro/nuclei_segmentation/testing_images/"
+
+series="merfish_4T1" 
+task="NUC_SEG" # Nuc segmentation
+
+## ImageJ/ Fiji can be installed to Applications, or put into any folder in your drive. 
+# imagej_dir="/Applications/ImageJ.app" ## MacOS ImageJ java run folder
+# imagej_dir="/Applications/Fiji.app" ## MacOS Fiji java run folder
+# imagej_dir="/Users/htran/Downloads/Fiji.app/" ## MacOS Fiji java run folder - from my computer
+
+
+# imagej_exe_file=${imagej_dir}jars/ij-1.53q.jar  ## Fiji file location, in general file name is ij.jar, sometimes include version here, ex: ij-1.53q.jar, version 1.53
+imagej_exe_file=${imagej_dir}Contents/Java/ij.jar ## ImageJ exe file location, in general file name is ij.jar, sometimes include version here
+
+
+macro_fn="${macro_dir}nuc_seg.txt"
+
+
+
+log_file="${macro_dir}${series}_${task}.log"
+echo $log_file
+exec >> $log_file 2>&1 && tail $log_file
+
+input_dir="${project_dir}"
+echo "__________________________________\n"
+echo "Input directory is: \n"
+echo $input_dir
+echo "Segment images \n"
+
+
+## You can change memory amount, ex: 20000m to 30000m so program will run faster.
+## MacOS background mode here
+
+## If you have java environment jre installed in your computer
+# java -Xmx20000m -jar ${imagej_exe_file} -ijpath $imagej_dir/ -batch $macro_fn $input_dir
+
+## Otherwise using existing jre env from Fiji here
+# /Users/htran/Downloads/Fiji.app/java/macosx/adoptopenjdk-8.jdk/jre/Contents/Home/bin/java -Xmx20000m -jar ${imagej_dir}Contents/Java/ij.jar -ijpath ${imagej_dir} -batch $macro_fn $input_dir
+# ${imagej_dir}java/macosx/adoptopenjdk-8.jdk/jre/Contents/Home/bin/java -Xmx20000m -jar ${imagej_exe_file} -ijpath ${imagej_dir} -batch $macro_fn ${input_dir}
+
+## MacOS mode 
+${imagej_dir}jre/bin/java -Xmx20000m -jar $imagej_exe_file -ijpath $imagej_dir -batch $macro_fn $input_dir
+
+
+## Linux background mode here, using xvfb-run in case you run in server (graphical env), in local computer, java command is enough
+# xvfb-run -a java -Xmx15000m -jar $imagej_dir/ij.jar -ijpath $imagej_dir/ -batch $macro_fn $input_dir 
+
+## In Linux local computer, java command is sufficient
+# java -Xmx15000m -jar $imagej_dir/ij.jar -ijpath $imagej_dir/ -batch $macro_fn $input_dir 
+
+echo "Nucleus Segmentation Completed!"
+echo "__________________________________\n"
+
+
+
+
+
diff --git a/data/imagej_macro/nuclei_segmentation/nuc_seg.txt b/data/imagej_macro/nuclei_segmentation/nuc_seg.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e3310680665934a45789e34ffdff4d5c6946b1c2
--- /dev/null
+++ b/data/imagej_macro/nuclei_segmentation/nuc_seg.txt
@@ -0,0 +1,144 @@
+//dir=getDirectory("mouse");
+//print(dir)
+setBatchMode(true);
+print("___________________________________________");
+
+// SEGMENTATION PARAMETERS
+minSize=23; // minimum diameter
+maxSize=41; // maximum diameter
+// CELL ZONE DETECTION PARAMETERS
+radius_max_cell_zone_extension=5; // from border of nucleus, extend into space with this radius
+
+suffixe=".tif"; // change this setting if your image ends with .tiff
+
+
+background_threshold=50;	// [0,255] intensity value range, if no pixel value higher than this threshold means background objects, skip all functions.
+
+// get input direction from shell script command
+dir = getArgument;  
+
+// for testing purpose, you can disable line above, and set input dir from this param
+//dir = "/Users/hoatran/Documents/BCCRC_projects/merfish/merFISH_report_pipeline/imagej_macro/nuclei_segmentation/testing_images/";  
+
+if (dir=="") 
+	exit ("No argument! Exit program");
+
+print("Working dir: "+dir+"\n");
+
+seg_dir=File.getParent(dir)+"/seg_results/";
+if(!File.exists(seg_dir)) 
+      File.mkdir(seg_dir);
+
+demo_dir=File.getParent(dir)+"/demo_results/";
+if(!File.exists(demo_dir)) 
+      File.mkdir(demo_dir);
+
+cell_zone_dir=File.getParent(dir)+"/cell_zone_results/";
+if(!File.exists(cell_zone_dir)) 
+      File.mkdir(cell_zone_dir);
+
+
+print("Creating segmentation folder: "+seg_dir+"\n");
+
+files=getFileList(dir);
+
+//print("Nb of processing images: "+files.length+"\n");
+
+for(f=0;f<files.length;f++)
+{
+     if(!File.isDirectory(dir+files[f])) {
+				name=files[f];
+				
+				if(endsWith(name, suffixe)){ // image ends with .tif, only process image
+					
+					short_name = substring(name,0,lastIndexOf(name,suffixe));  
+					print("_______________________________________\n");
+					print(name);
+					//print(short_name);
+          start = getTime();
+					print("Loading image: "+name);
+					open(dir+name);
+					h=getHeight();
+					w=getWidth();
+					//print(' t: '+title+"   h:   "+h+"w:   "+w);
+					setOption("ScaleConversions", true);
+					selectWindow(name);
+					run("Enhance Contrast", "saturated=0.35");
+					if (bitDepth > 8) {run("8-bit");}
+					getStatistics(area, mean, min, max, std);
+					print(getTitle+":   max:   "+max+"  min: "+min+"  mean: "+mean+"  area: "+area+"  std:"+std);
+					if(max<background_threshold){
+						print("******* Background image, low intensity, please verify your input image");
+						//newImage(short_name+"_SEG", "16-bit black", w, h, 1);
+						//saveAs("Tiff",dir+"SEG/"+short_name+"_SEG.tif");	
+						run("Close All"); 
+					} else{
+					  
+			      selectWindow(name);
+					  run("Grays");
+						//run("Median...", "radius=2");
+						//run("Mean...", "radius=2");
+					  //print("Band pass filtering...");
+					  selectWindow(name);
+					  //run("Duplicate...", " ");
+					  run("Duplicate...", "title=["+ short_name +"_raw]");
+					  //selectWindow(short_name+"-1.tif");
+						//rename(short_name+"_raw");
+					  selectWindow(name);
+						run("32-bit");
+			      run("Bandpass Filter...", "filter_large="+maxSize+" filter_small="+minSize+" suppress=None tolerance=5");
+						//run("Enhance Contrast...", "saturated=0");
+						run("16-bit");
+						
+						//saveAs("Tiff",dir+"FILTERED/"+"BP_"+name);
+						//open(dir+"FILTERED/"+"BP_"+name);
+						//selectWindow("BP_"+name);
+						//rename("BP_dapi");
+						selectWindow(name);
+						print("Segmenting...");
+						getStatistics(area, mean, min, max, std);
+						print(getTitle+"  mean value: "+mean);
+						sd=700;
+						spot_seeds_threshold=floor(mean)+sd;  // if you obtain too many objects, you can increase the sd value here. And in contrast, if you obtain small number of objects than you expected, you can decrease the sd value here
+						print("_________________________________________");
+						print("ATTENTION: seeds threshold value is:  "+spot_seeds_threshold);
+						// usually seeds_threshold=30000 for 16 bits image
+						run("3D Spot Segmentation", "seeds_threshold="+spot_seeds_threshold+" local_background=0 radius_0=2 radius_1=4 radius_2=6 weigth=0.50 radius_max=20 sd_value=1.90 local_threshold=[Gaussian fit] seg_spot=Block watershed volume_min=15 volume_max=1000000 seeds=Automatic spots="+short_name+" radius_for_seeds=2 output=[Label Image]"); 
+						//run("SEGMENTING NUCLEUS", "save_dir="+seg_dir+" dapi="+name+" maximal="+maxSize+" minimal="+minSize+" automatic seed="+spot_seeds_threshold);
+
+//run("SEGMENTING NUCLEUS", "save_dir="+seg_dir+" dapi="+name+" maximal="+maxSize+" minimal="+minSize+" seed="+spot_seeds_threshold);
+
+						print("Time of execution is: "+(getTime()-start)/1000+" seconds"); 
+						selectWindow("seg");
+						run("3-3-2 RGB");
+					 	saveAs("Tiff",seg_dir+short_name+"_SEG.tif");	
+					 	
+					 	// Get demo output of segmentation, overlapped with raw image, can comment this function if it takes time to run with large image
+						run("3D Draw Rois", "raw="+short_name+"_raw seg="+short_name+"_SEG");
+						saveAs("Tiff", demo_dir+short_name+"_SEG_demo_only.tif");
+						
+						//run("DETECTING CELL ZONE", "save_dir="+cell_zone_dir+" nuclei="+short_name+"_SEG.tif radius_max="+radius_max_cell_zone_extension+" save save="+cell_zone_dir+short_name+"_cell_zone_rad_5.0.zip");
+						run("DETECTING CELL ZONE", "save_dir="+cell_zone_dir+" nuclei="+short_name+"_SEG.tif radius_max="+radius_max_cell_zone_extension+" save");
+
+						run("Close All"); 
+
+					}
+					
+           }
+           
+    }
+
+}
+print("Segmented nuc images results are saved into folder: "+seg_dir+"\n");
+print("Hura, completed!!!");
+
+//if(!File.exists(dir+"log")) File.mkdir(dir+"log");
+//selectWindow("Log");
+//saveAs("Text", dir+ "segment_log.txt");
+setBatchMode(false);
+
+
+
+
+
+
diff --git a/data/imagej_macro/nuclei_segmentation/readme.md b/data/imagej_macro/nuclei_segmentation/readme.md
new file mode 100644
index 0000000000000000000000000000000000000000..d52e9a7c8daacec4dbd8ae97cb60b651543d457a
--- /dev/null
+++ b/data/imagej_macro/nuclei_segmentation/readme.md
@@ -0,0 +1,21 @@
+## Nuclei Segmentation
+
+### Installation
+First, you can copy supporting plugins from this [directory](https://github.com/molonc/merFISH_report_pipeline/tree/main/imagej_macro/ImageJ_plugins/) into your `ImageJ/plugins/` folder.  After that, your plugins folder will contains: utilities plugins at: `ImageJ/plugins/3D_suite`,`ImageJ/plugins/3D_viewer`,`ImageJ/plugins/utils`, and main plugin for this computation at: `ImageJ/plugins/spatial3dtissuej_plugin/TissueJ4Merfish_v14.jar`. Note: please use the latest package in this folder. 
+
+### Run scripts
+- Option 1: manual option: you can run macro by opening nuclei segmentation [macro](https://github.com/molonc/merFISH_report_pipeline/blob/main/imagej_macro/nuclei_segmentation/nuc_seg.txt) from ImageJ, and change input directory, input parameters and run macro.
+- Option 2: background mode, using [shell script](https://github.com/molonc/merFISH_report_pipeline/blob/main/imagej_macro/nuclei_segmentation/execute_segmentation.sh) to run program under background mode. See setting instruction in this file for different OS system. Note: java program will pop up a Java icon during the run, so program require X11 display syste, if you run this program from server, you need to install virtual environment xvfb-run in order to run it. See details in shell script.
+-  Option 2: background mode. Similar to the second option above. But in case you have large number of input images, you can divide images into multiple folders, ex: 3 folders, and run shell script from each folder. Program will use multiple threads from your computer, so execution time will be much faster. 
+
+### Parameters
+- Nuclei segmentation: `minSize` minimum diameter of a nucleus, `maxSize` maximum diameter of a nucleus. Tell program that objects we expect are in this range. So program can work better.
+- `suffixe` usually .tif or .tiff. Tell program to scan entire folder to get the list of input files, and only file that ends with this suffixe will be taken into account.
+- `radius_max_cell_zone_extension`: for cell zone estimation step, from the periphery of a nucleus, extending into space with max radius to use as a cell membrane area.
+
+### Testing dataset
+- You can run a testing example from this git repo first, before moving to your input images. See [Testing dataset](https://github.com/molonc/merFISH_report_pipeline/tree/main/imagej_macro/nuclei_segmentation/testing_images)
+- Output of nuclei segmentation for this testing dataset is at [directory](https://github.com/molonc/merFISH_report_pipeline/tree/main/imagej_macro/nuclei_segmentation/seg_results)
+- Output of ROI: region of interest, nuclei contours from segmented results will be at [directory](https://github.com/molonc/merFISH_report_pipeline/tree/main/imagej_macro/nuclei_segmentation/demo_results/). It helps to debug program, and see the performance of algo. Note: you can comment this function from bottom part of script if you don't need it [macro](https://github.com/molonc/merFISH_report_pipeline/blob/main/imagej_macro/nuclei_segmentation/nuc_seg.txt)
+- Output of cell zone detection will be stored at [directory](https://github.com/molonc/merFISH_report_pipeline/tree/main/imagej_macro/nuclei_segmentation/cell_zone_results) with two different formats: .tif and .zip. ZIP format is much smaller file compared to .tif. You can keep one only .zip format if you don't have space, and open .zip image from ImageJ. 
+
diff --git a/data/imagej_macro/nuclei_segmentation/readme.txt b/data/imagej_macro/nuclei_segmentation/readme.txt
new file mode 100644
index 0000000000000000000000000000000000000000..aaebfe2e37ba16b565f433dbf70c28b921729018
--- /dev/null
+++ b/data/imagej_macro/nuclei_segmentation/readme.txt
@@ -0,0 +1,6 @@
+First, granting permission for this file:
+  chmod 700 execute_segmentation.sh
+Then, launch program in batch mode: 
+  ./execute_segmentation.sh
+
+
diff --git a/data/imagej_macro/nuclei_segmentation/seg_results/aligned_images_fov0_z18_01_SEG.tif b/data/imagej_macro/nuclei_segmentation/seg_results/aligned_images_fov0_z18_01_SEG.tif
new file mode 100644
index 0000000000000000000000000000000000000000..2a0f6c92da004bfebcbb2dcf7e2e6c5bb1c7b358
--- /dev/null
+++ b/data/imagej_macro/nuclei_segmentation/seg_results/aligned_images_fov0_z18_01_SEG.tif
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:edf98aa071998043a532cc16dd5e74701018a1d551d733866fd030f233f52110
+size 5173130
diff --git a/data/imagej_macro/nuclei_segmentation/seg_results/aligned_images_fov0_z18_02_SEG.tif b/data/imagej_macro/nuclei_segmentation/seg_results/aligned_images_fov0_z18_02_SEG.tif
new file mode 100644
index 0000000000000000000000000000000000000000..2a0f6c92da004bfebcbb2dcf7e2e6c5bb1c7b358
--- /dev/null
+++ b/data/imagej_macro/nuclei_segmentation/seg_results/aligned_images_fov0_z18_02_SEG.tif
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:edf98aa071998043a532cc16dd5e74701018a1d551d733866fd030f233f52110
+size 5173130
diff --git a/data/imagej_macro/nuclei_segmentation/testing_images/aligned_images_fov0_z18_01.tif b/data/imagej_macro/nuclei_segmentation/testing_images/aligned_images_fov0_z18_01.tif
new file mode 100644
index 0000000000000000000000000000000000000000..13a9ca4e2fcb5e92cc248b564fb3a6cdfc8780dc
--- /dev/null
+++ b/data/imagej_macro/nuclei_segmentation/testing_images/aligned_images_fov0_z18_01.tif
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ed817b3fd4433f94c9569292e6338a6b6b62ad6ac1f72dbd7823de934e42c345
+size 5171594
diff --git a/data/imagej_macro/nuclei_segmentation/testing_images/aligned_images_fov0_z18_02.tif b/data/imagej_macro/nuclei_segmentation/testing_images/aligned_images_fov0_z18_02.tif
new file mode 100644
index 0000000000000000000000000000000000000000..13a9ca4e2fcb5e92cc248b564fb3a6cdfc8780dc
--- /dev/null
+++ b/data/imagej_macro/nuclei_segmentation/testing_images/aligned_images_fov0_z18_02.tif
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ed817b3fd4433f94c9569292e6338a6b6b62ad6ac1f72dbd7823de934e42c345
+size 5171594
diff --git a/data/imagej_macro/registration_evaluation/Macro_extract_centroids_image.ijm b/data/imagej_macro/registration_evaluation/Macro_extract_centroids_image.ijm
new file mode 100644
index 0000000000000000000000000000000000000000..08c1838c52d461115229013d3ecd050260b72454
--- /dev/null
+++ b/data/imagej_macro/registration_evaluation/Macro_extract_centroids_image.ijm
@@ -0,0 +1,67 @@
+
+
+
+setBatchMode(true);
+print("\\Clear");
+print("Counting pixels corresponding to spots across different z-slices");
+print("----------------------------------------------------------------");
+
+
+print("-----------------Parameter Setting---------------------------");
+input_dir="/Users/htran/Documents/storage_tmp/merfish/registration_evaluation/Aligned_fiducial_images/XP2457/aligned_images/";
+
+suffix=".tif";
+output_dir=input_dir+"results/";
+
+if(!File.exists(output_dir)) 
+      File.mkdir(output_dir);
+
+print(input_dir);
+print(output_dir);
+
+
+
+function get_centroids_fiducials (image_fn, output_dir) {
+	suffixe=".tif";
+	short_name_img = substring(image_fn,0,lastIndexOf(image_fn,suffixe));
+	selectWindow(image_fn);
+	
+	run("Gaussian Blur...", "sigma=2");
+	run("Convolve...", "text1=[-1 -1 -1 -1 -1\n-1 -1 -1 -1 -1\n-1 -1 24 -1 -1\n-1 -1 -1 -1 -1\n-1 -1 -1 -1 -1\n] normalize");
+	if (bitDepth > 8) {run("8-bit");}
+	//run("Find Maxima...");
+	setOption("ScaleConversions", true);
+	run("Find Maxima...", "prominence=10 output=List");
+	selectWindow("Results");
+	saveAs("Results",output_dir+short_name_img+"_centroids.csv");
+	selectWindow("Results");
+	run("Close");
+	//print("Save output into the folder: "+results_dir);
+	run("Close All");
+	
+}
+
+
+files=getFileList(input_dir);
+
+//print("Nb of processing images: "+files.length+"\n");
+
+for(f=0;f<files.length;f++)
+{
+     if(!File.isDirectory(input_dir+files[f])) {
+				image_fn=files[f];
+				// NUC image ends with NUC.tif or NUC.tiff
+				if(endsWith(image_fn,suffix)){
+				  print(image_fn);
+					open(input_dir+image_fn);
+					get_centroids_fiducials(image_fn, output_dir);
+				}
+     }
+     
+}     								
+					
+					
+print("Completed");
+
+print("----------------------------------------------------------------");
+setBatchMode(false);
\ No newline at end of file
diff --git a/data/imagej_macro/registration_evaluation/evaluate_registration.R b/data/imagej_macro/registration_evaluation/evaluate_registration.R
new file mode 100644
index 0000000000000000000000000000000000000000..6af1798ab4565e68721b1d66e6ac99ebcdb797ce
--- /dev/null
+++ b/data/imagej_macro/registration_evaluation/evaluate_registration.R
@@ -0,0 +1,146 @@
+library(dplyr)
+library(ggplot2)
+get_neighbour_stat <- function(df1, df2, tag='R21', datatag=''){ # df1: reference, df2: query
+  nearest <- RANN::nn2(df1,df2,k=1, searchtype='radius',radius=1.5) # 1.5, one pixel different at X or Y
+  
+  nidx <- as.data.frame(nearest$nn.idx)
+  colnames(nidx) <- 'nearest_neighbour'
+  nidx$fb_idx <- seq_len(dim(nidx)[1]) # fiducial bead - spot index
+  
+  ndist <- as.data.frame(nearest$nn.dists)
+  colnames(ndist) <- 'eu_dist'
+  ndist$fb_idx <- seq_len(dim(ndist)[1])
+  
+  nidx_stat <- nidx %>% left_join(ndist, by='fb_idx')
+  
+  nidx_stat1 <- nidx_stat %>% 
+    dplyr::filter(nearest_neighbour!=0) # can not find the neighbour with this radius
+  
+  nidx_stat2 <- nidx_stat %>% 
+    dplyr::filter(eu_dist==0) # can not find the neighbour with this radius
+  
+  total_stat <- tibble::tibble(datatag=datatag,
+                           roundSt=tag, pct_exact_match=round(100 * dim(nidx_stat2)[1]/dim(df2)[1],1),
+                           pct_approx_match=round(100 * dim(nidx_stat1)[1]/dim(df2)[1],1),
+                           avg_error_rate=round(mean(nidx_stat1$eu_dist),3))
+  return(total_stat)
+  
+}
+viz_error_registration <- function(df){
+  df$roundSt <- as.numeric(df$roundSt)
+  # df <- df[gtools::mixedorder(df$roundSt),]
+  # gtools::mixedorder(c('1','10','2'))
+  df_plt <- df %>%
+    dplyr::select(-avg_error_rate)
+  stat <- df_plt %>%
+    dplyr::group_by(datatag) %>%
+    dplyr::summarise(pct_approx_match_stat=round(mean(pct_approx_match),1))
+  df_plt <- df_plt %>% left_join(stat, by='datatag')
+  df_plt$datatag <- paste0(df_plt$datatag,'  ',df_plt$pct_approx_match_stat,'%')
+  df_plt <- df_plt %>%  
+    dplyr::select(-pct_approx_match_stat) %>% 
+    tidyr::pivot_longer(cols = starts_with('pct_'),
+                        names_to = 'match_type', values_to='pct_match')
+  
+  # Grouped
+  p_pct <- ggplot(df_plt, aes(fill=match_type, y=pct_match, x=roundSt)) + 
+    geom_bar(position="dodge", stat="identity", width=0.4) + 
+    facet_wrap(~datatag, ncol = 3) + 
+    theme_bw() + 
+    scale_x_continuous(name="Round", breaks=rep(2:16,1))+
+    theme(axis.text = element_text(size=12),
+          strip.text = element_text(size=13, face = 'bold'),
+          strip.background = element_rect(fill = 'white', colour = 'white'),
+          panel.grid.major = element_blank(), 
+          panel.grid.minor = element_blank(),
+          legend.position = 'bottom')
+  # df$roundSt <- as.factor(df$roundSt)
+  
+  df_plt1 <- df 
+  stat1 <- df_plt1 %>%
+    dplyr::group_by(datatag) %>%
+    dplyr::summarise(avg_error_rate_stat=round(mean(avg_error_rate),3))
+  df_plt1 <- df_plt1 %>% left_join(stat1, by='datatag')
+  df_plt1$datatag <- paste0(df_plt1$datatag,': ',df_plt1$avg_error_rate_stat,' ')
+  
+  
+  p_dist <- ggplot(df_plt1, aes(x=roundSt, y= avg_error_rate, fill=as.factor(roundSt))) + 
+    geom_bar(stat="identity", width=0.4) + 
+    facet_wrap(~datatag, ncol = 3) + 
+    theme_bw() + 
+    scale_x_continuous(name="Round", breaks=rep(2:16,1))+
+    theme(axis.text = element_text(size=12),
+          strip.text = element_text(size=13, face = 'bold'),
+          strip.background = element_rect(fill = 'white', colour = 'white'),
+          panel.grid.major = element_blank(), 
+          panel.grid.minor = element_blank(),
+          legend.position = 'bottom')
+  return(list(p_pct=p_pct, p_dist=p_dist))
+  
+}
+
+base_dir <- '/Users/hoatran/Documents/BCCRC_projects/merfish/evaluate_registration/Aligned_fiducial_images/'
+
+series <- c("XP2059","XP4516","XP2504","XP2457","XP2453")
+
+input_dir <- paste0(base_dir,'results/')
+fns <- list.files(input_dir)
+fns <- fns[grepl('.csv', fns)]
+length(fns)
+
+tags <- sapply(strsplit(fns,'_'), function(x){
+  return(x[1])
+})
+fovs <- sapply(strsplit(fns,'_'), function(x){
+  return(x[2])
+})
+rounds <- sapply(strsplit(fns,'_'), function(x){
+  return(x[3])
+})
+
+metadata <- data.frame(datatag=as.character(tags),
+                       fov=as.character(fovs),
+                       roundSt=gsub('.csv','',as.character(rounds)),
+                       fn=fns)
+head(metadata)
+data.table::fwrite(metadata, paste0(base_dir, 'metadata_all_files.csv.gz'))
+rownames(metadata) <- metadata$fn
+ref_round <- '1'
+summary_total <- tibble::tibble()
+for(fn in fns){
+  
+  if(metadata[fn,'roundSt']!='1'){
+    df_ref <- data.table::fread(paste0(input_dir, metadata[fn,'datatag'],'_'
+                                       ,metadata[fn,'fov'],'_',ref_round,'.csv')) %>% as.data.frame()
+    df_aligned <- data.table::fread(paste0(input_dir, fn)) %>% as.data.frame()
+    df_ref$V1 <- NULL
+    df_aligned$V1 <- NULL
+    stat <- get_neighbour_stat(df_ref, df_aligned, 
+                               tag=metadata[fn,'roundSt'], 
+                               datatag=paste0(metadata[fn,'datatag'],'_',metadata[fn,'fov']))
+    summary_total <- dplyr::bind_rows(summary_total, stat)
+    
+  }
+    
+}
+dim(df_ref)
+dim(df_aligned)
+metadata1 <- metadata %>%
+  dplyr::filter(datatag=='XP2504' & fov=='FOV26')
+fn <- 'XP2504_FOV26_4.csv'
+dim(summary_total)
+summary_total$roundSt[1:3]
+data.table::fwrite(summary_total, paste0(base_dir, 'summary_mapping_from_rounds_to_R1.csv.gz'))
+colnames(summary_total)
+plt_ls <- viz_error_registration(summary_total)
+
+
+png(paste0(base_dir,"Evaluate_registration_matching.png"), 
+    height = 2*450, width=2*900, res = 2*72)
+print(plt_ls$p_pct)
+dev.off()
+
+png(paste0(base_dir,"Evaluate_registration_error_rates.png"), 
+    height = 2*450, width=2*900, res = 2*72)
+print(plt_ls$p_dist)
+dev.off()
\ No newline at end of file
diff --git a/data/imagej_macro/registration_evaluation/registration_evaluation_v2.txt b/data/imagej_macro/registration_evaluation/registration_evaluation_v2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..832121812de07bab1bea811830fa49e6a60aaff6
--- /dev/null
+++ b/data/imagej_macro/registration_evaluation/registration_evaluation_v2.txt
@@ -0,0 +1,100 @@
+
+
+
+setBatchMode(true);
+print("\\Clear");
+
+print("-----------------Parameter Setting---------------------------");
+base_dir="/Users/hoatran/Documents/BCCRC_projects/merfish/evaluate_registration/Aligned_fiducial_images/";
+
+//datatag="XP2059";
+//datatag="XP4516";
+datatag="XP2504";
+//datatag="XP2457";
+//datatag="XP2453";
+
+input_dir=base_dir+datatag+"/";
+
+suffix=".tif";
+output_dir=base_dir+"results/";
+
+if(!File.exists(output_dir)) 
+      File.mkdir(output_dir);
+
+print(input_dir);
+print(output_dir);
+
+files=getFileList(input_dir);
+
+//print("Nb of processing images: "+files.length+"\n");
+
+for(f=0;f<files.length;f++)
+{
+     if(!File.isDirectory(input_dir+files[f])) {
+				image_fn=files[f];
+				// NUC image ends with NUC.tif or NUC.tiff
+				if(endsWith(image_fn,suffix)){
+				  print(image_fn);
+					open(input_dir+image_fn);
+				short_name_img = substring(image_fn,0,lastIndexOf(image_fn,suffixe));
+					for (n=1; n<=nSlices; n++) {
+						setSlice(n);
+						run("Gaussian Blur...", "sigma=2");
+						run("Convolve...", "text1=[-1 -1 -1 -1 -1\n-1 -1 -1 -1 -1\n-1 -1 24 -1 -1\n-1 -1 -1 -1 -1\n-1 -1 -1 -1 -1\n] normalize");
+						if (bitDepth > 8) {run("8-bit");}
+						//run("Find Maxima...");
+						//setOption("ScaleConversions", true);
+						run("Find Maxima...", "prominence=10 output=List");
+						selectWindow("Results");
+						saveAs("Results",output_dir+short_name_img+"_"+n+".csv");
+						selectWindow("Results");
+						run("Close");	
+	
+					}
+				selectWindow(image_fn);
+				run("Close");	
+
+				}
+     }
+     
+}     								
+					
+					
+print("Completed");
+
+print("----------------------------------------------------------------");
+setBatchMode(false);
+
+
+
+image_fn="XP2504_FOV52.tif";
+
+
+suffixe=".tif";
+				base_dir="/Users/hoatran/Documents/BCCRC_projects/merfish/evaluate_registration/Aligned_fiducial_images/";
+				output_dir=base_dir+"results/";
+				short_name_img = substring(image_fn,0,lastIndexOf(image_fn,suffixe));
+for (n=1; n<=1; n++) {
+
+
+if(n<10){
+prefix_fn="0"+n;
+}else{
+prefix_fn=""+n;
+}
+selectWindow("XP2504_FOV52-00"+prefix_fn);
+						run("Gaussian Blur...", "sigma=2");
+						run("Convolve...", "text1=[-1 -1 -1 -1 -1\n-1 -1 -1 -1 -1\n-1 -1 24 -1 -1\n-1 -1 -1 -1 -1\n-1 -1 -1 -1 -1\n] normalize");
+						if (bitDepth > 8) {run("8-bit");}
+						//run("Find Maxima...");
+						setOption("ScaleConversions", true);
+						run("Find Maxima...", "prominence=10 output=List");
+						selectWindow("Results");
+						saveAs("Results",output_dir+short_name_img+"_"+n+".csv");
+						selectWindow("Results");
+						run("Close");	
+selectWindow("XP2504_FOV52-00"+prefix_fn);
+				run("Close");	
+
+}
+
diff --git a/data/imagej_macro/resize_images/resize_images_macro.ijm b/data/imagej_macro/resize_images/resize_images_macro.ijm
new file mode 100644
index 0000000000000000000000000000000000000000..62fe1c56d54d3f7b91d292b126eff20ceded52da
--- /dev/null
+++ b/data/imagej_macro/resize_images/resize_images_macro.ijm
@@ -0,0 +1,65 @@
+
+
+
+
+setBatchMode(true);
+print("\\Clear");
+print("Zipping func");
+print("Noted: just for observation, can not use for other purposes, data will slightly change if you convert image into 8 bits");
+print("----------------------------------------------------------------");
+
+
+print("-----------------Parameter Setting---------------------------");
+input_dir="/Users/htran/Documents/storage_tmp/merfish_XP2059/wavelength647/";
+output_dir=File.getParent(input_dir)+"/"+File.getName(input_dir)+"_zip_format/";
+rescale_ratio=0.25;
+suffix=".TIFF";
+
+
+
+if(!File.exists(output_dir)) 
+      File.mkdir(output_dir);
+
+print(input_dir);
+print(output_dir);
+
+
+start = getTime();
+
+files=getFileList(input_dir);
+
+//print("Nb of processing images: "+files.length+"\n");
+
+for(f=0;f<files.length;f++)
+{
+     if(!File.isDirectory(input_dir+files[f])) {
+				name=files[f];
+				// NUC image ends with NUC.tif or NUC.tiff
+				if(endsWith(name,suffix)){
+					short_name = substring(name,0,lastIndexOf(name,suffix));  // TO DO: cut suffixe instead of using indexOf function here, issue when there are 2 .tif words in file name					
+					print("_______________________________________\n");
+					//print(name);
+					//print(short_name);
+          			
+					print("Resizing image: "+name);
+					open(input_dir+name);
+					run("Enhance Contrast", "saturated=0.35");
+				    setOption("ScaleConversions", true);
+      				if (bitDepth > 8) {run("8-bit");}
+//	  				run("Scale...", "x=0.25 y=0.25 width=161 height=161 interpolation=Bilinear average create");
+	  				wd=round(getWidth()*rescale_ratio);
+					ht=round(getHeight()*rescale_ratio);
+					run("Scale...", "x="+rescale_ratio+" y="+rescale_ratio+" width="+wd+" "+"height="+ht+" interpolation=Bilinear average create");
+      				saveAs("ZIP", output_dir+short_name+".zip");
+      				close();
+      				
+				}
+     }
+}     		
+run("Close All"); 		
+print("Time of execution is: "+(getTime()-start)/1000+" seconds"); 
+print("Save output into folder: "+output_dir);
+print("Completed");
+print("----------------------------------------------------------------");
+setBatchMode(false);
+						
diff --git a/data/imagej_macro/viz_deconvolution_img.zip b/data/imagej_macro/viz_deconvolution_img.zip
new file mode 100644
index 0000000000000000000000000000000000000000..26a85d4af26899f5c1cbf8004c837f7e4ccd5bc0
--- /dev/null
+++ b/data/imagej_macro/viz_deconvolution_img.zip
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d3436cf5dfbd37b76b73c280a11386c2a109f4ad072a80a789a91b93e14792a4
+size 770594
diff --git a/data/makereports.py b/data/makereports.py
new file mode 100644
index 0000000000000000000000000000000000000000..e77317671536599778213a639c84b78ac7947c8d
--- /dev/null
+++ b/data/makereports.py
@@ -0,0 +1,132 @@
+from reports.BrightnessReport import BrightnessReport
+from reports.DecodabilityReport import DecodabilityReport
+from reports.MaskedBrightnessReport import MaskedBrightnessReport
+from reports.FocusReport import FocusReport
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.backends.backend_pdf import PdfPages
+
+import pandas as pd
+
+
+# Brightness report worker-------
+def generate_brightness_reports(image_stack_file,coord_info,out_file,fov,z):
+    #This was necessary to prototype on OSX...consider removing in the future after testing
+    import multiprocessing
+    job = multiprocessing.Process(target=brightness_worker,args=(image_stack_file,coord_info,out_file,fov,z))
+    job.start()
+
+    #brightness_worker(image_stack_file,coord_info,out_file,fov,fovs)
+
+def brightness_worker(image_stack_file,coord_info,out_file,fov,z):
+    br = BrightnessReport(image_stack_file,coord_info,fov,z)
+    br.set_pdf(PdfPages(filename=out_file))
+    
+    br.preview_images()
+    br.brightness_infov_z()
+    br.brightness_on_images()
+    br.contrast_heatmap()
+    br.closePdf()
+
+# Masked Brightness report worker-------
+def generate_masked_brightness_reports(image_stack_file,coord_info,out_file,fov,z,mask_stack):
+    #This was necessary to prototype on OSX...consider removing in the future after testing
+    import multiprocessing
+    job = multiprocessing.Process(target=masked_brightness_worker,args=(image_stack_file,mask_stack,coord_info,out_file,fov,z))
+    job.start()
+
+def masked_brightness_worker(image_stack_file,mask_stack,coord_info,out_file,fov,z):
+    mbr = MaskedBrightnessReport(image_stack_file,mask_stack,coord_info,fov,z)
+    mbr.set_pdf(PdfPages(filename=out_file))
+    
+    mbr.preview_images()
+    mbr.brightness_infov_z()
+    mbr.brightness_on_images()
+    mbr.contrast_heatmap()
+    mbr.closePdf()
+
+
+# Decodability Report worker --------
+def generate_decodability_reports(image_stack_file,coord_info,out_file,codebook_file,data_org_file,fov,z,out_detection_stats,):
+    #This was necessary to prototype on OSX...consider removing in the future after testing
+    import multiprocessing
+    job = multiprocessing.Process(target=decodability_worker,args=(image_stack_file,coord_info,out_file,codebook_file,data_org_file,fov,z,out_detection_stats))
+    job.start()
+
+def decodability_worker(image_stack_file,coord_info,out_file,codebook_file,data_org_file,fov,z,out_detection_stats):
+    dr = DecodabilityReport(image_stack_file,coord_info,codebook_file,data_org_file,fov,z,out_detection_stats)
+    dr.set_pdf(PdfPages(filename=out_file))
+    
+    dr.make_bit_imgs()
+    dr.make_decodability_hists()
+    dr.closePdf()
+
+
+# Focus report worker-------
+def generate_focus_reports(image_stack_file,coord_info,out_file,out_csv,fov):
+    #This was necessary to prototype on OSX...consider removing in the future after testing
+    import multiprocessing
+    job = multiprocessing.Process(target=focus_worker,args=(image_stack_file,coord_info,out_file,out_csv,fov))
+    job.start()
+    # focus_worker(image_stack_file,coord_info,out_file,out_csv,fov,fovs)
+
+
+def focus_worker(image_stack_file,coord_info,out_file,out_csv,fov):
+    fr = FocusReport(image_stack_file,coord_info,fov,out_csv)
+    fr.set_pdf(PdfPages(filename=out_file))
+    fr.f_measure_report()
+    fr.closePdf()
+
+# Compile reports----------------
+
+def compile_focus_report(file_list:list,output,irs,wvs):
+    
+    full_df = pd.DataFrame()
+    for fl in file_list:
+        test = pd.read_csv(fl)
+        full_df = full_df.append(test,ignore_index=True)
+    
+    full_df.to_csv(output)
+    if not isinstance(irs,list):
+        irs = list(irs)
+    report_pdf = PdfPages(filename = output)
+    
+
+    compiled_matrix = np.zeros((
+        len(file_list), # length of fovs
+        len(wvs),
+        len(irs)
+    ))
+
+    for iir, ir in enumerate(irs):#for each ir
+        
+        for iwv, wv in enumerate(wvs):#for each wv
+            data = full_df[["FOV",str(wv)]][(full_df["IR"]==int(ir))] #extract the FOVs...assume stuff is ordered
+            
+            data = data.to_numpy() # FOV x 2
+
+            compiled_matrix[:,iwv,iir] = data[:,1] #this is the z
+
+            #ax[iir].plot("FOV",str(wv),data=data)
+    #x:FOV y:z spy:IR
+    f,ax = plt.subplots(nrows = len(wvs),ncols = 1,sharex=True,sharey=True,figsize=(8,len(irs)*4))    
+    
+    if not isinstance(ax,np.ndarray):
+        ax = np.array(ax)
+    
+    for iwv, wv in enumerate(wvs):#for each wv
+        ax[iwv].imshow(compiled_matrix[:,iwv,:])
+        ax[iwv].set_xticks(np.arange(len(irs)), irs)
+        ax[iwv].set_yticks(np.arange(len(file_list)), np.arange(len(file_list)))
+        ax[iwv].set_ylabel("FOVS ")
+        ax[iwv].set_xlabel("Imaging Round")
+        plt.setp(ax[iwv].get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor")
+
+        for iir in range(len(irs)):
+            for ifl in range(len(file_list)):
+                text = ax[iwv].text(iir, ifl, int(compiled_matrix[ifl,iwv, iir]), ha="center", va="center", color="w")
+        ax[iwv].set_title(f"Wavelength: {wv} nm")
+
+
+    report_pdf.savefig()
+    report_pdf.close()
diff --git a/data/merfish_barcode/LICENSE b/data/merfish_barcode/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..f288702d2fa16d3cdf0035b15a9fcbc552cd88e7
--- /dev/null
+++ b/data/merfish_barcode/LICENSE
@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU General Public License is a free, copyleft license for
+software and other kinds of works.
+
+  The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works.  By contrast,
+the GNU General Public License is intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
+software for all its users.  We, the Free Software Foundation, use the
+GNU General Public License for most of our software; it applies also to
+any other work released this way by its authors.  You can apply it to
+your programs, too.
+
+  When we speak of free software, we are referring to freedom, not
+price.  Our General Public Licenses are designed to make sure that you
+have the freedom to distribute copies of free software (and charge for
+them if you wish), that you receive source code or can get it if you
+want it, that you can change the software or use pieces of it in new
+free programs, and that you know you can do these things.
+
+  To protect your rights, we need to prevent others from denying you
+these rights or asking you to surrender the rights.  Therefore, you have
+certain responsibilities if you distribute copies of the software, or if
+you modify it: responsibilities to respect the freedom of others.
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+  For example, if you distribute copies of such a program, whether
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+
+  Developers that use the GNU GPL protect your rights with two steps:
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+giving you legal permission to copy, distribute and/or modify it.
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+  For the developers' and authors' protection, the GPL clearly explains
+that there is no warranty for this free software.  For both users' and
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+changed, so that their problems will not be attributed erroneously to
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+  The precise terms and conditions for copying, distribution and
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diff --git a/data/merfish_barcode/environment.yml b/data/merfish_barcode/environment.yml
new file mode 100644
index 0000000000000000000000000000000000000000..a273bda34ba2a24f077722b559a81ca8c6ea52b6
--- /dev/null
+++ b/data/merfish_barcode/environment.yml
@@ -0,0 +1,13 @@
+name: barcode_env
+channels:
+  - conda-forge
+  - defaults
+dependencies:
+  - opencv
+  - scikit-image
+  - python=3.7
+  - matplotlib
+  - numpy
+  - click
+  - pip:
+    - reliability
diff --git a/data/merfish_barcode/main.py b/data/merfish_barcode/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..43e109b50444eb3d62c0296b0688b5f2330303bf
--- /dev/null
+++ b/data/merfish_barcode/main.py
@@ -0,0 +1,173 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import skimage.io as skio
+from glob import glob
+import click
+import os
+from skimage import registration
+import scipy.ndimage as ndi
+from skimage.exposure import equalize_adapthist
+import re
+import cv2
+from reliability.Other_functions import crosshairs
+
+@click.group()
+def cli():
+    pass
+
+refPt = []
+@cli.command('makeBarcodeWithBead')
+@click.argument('img647_dir',type=click.Path(exists=True))
+@click.argument('img750_dir',type=click.Path(exists=True))
+@click.argument('bead_dir',type=click.Path(exists=True))
+@click.option('--n_ref',default=0,type=int,help='The index of the file that will be used as datum for image registration (Default: 0)')
+@click.option('--pattern',default=None,type=str,help='The glob pattern to be used for file extraction (Default: "*.TIFF")')
+@click.option('--circle_size',default=15,type=int,help='The approximate diameter of a probe (Default: 15)')
+@click.option('--thresh',default=0.995,type=float,help='Local percentile for intensity extraction (Default: 0.995)')
+@click.option('--window_size',default=100,type=int,help='Size of the window used for local percentile extraction (Default: 100)')
+@click.option('--img_scale',default=1.0,type=float,help='Multiplier for image values to make it viewable (Default: 1)')
+def makeBarcodeWithBead(img647_dir:str,img750_dir:str,bead_dir:str,n_ref:int,pattern:str,circle_size:int,thresh:float,window_size:int,img_scale:float):
+
+    
+
+
+    global refPt
+    if pattern is None:
+        pattern = "*.TIFF"
+    pattern647 = os.path.join(img647_dir,pattern)
+    pattern750 = os.path.join(img750_dir,pattern)
+    file_list_647 = glob(pattern647)
+    file_list_750 = glob(pattern750)
+    
+    file_list_647.sort(key=lambda f: int(re.sub('\D', '', f)))
+    file_list_750.sort(key=lambda f: int(re.sub('\D', '', f)))
+
+    bead_dir_pattern = os.path.join(bead_dir,pattern)
+    bead_file_list = glob(bead_dir_pattern)
+    bead_file_list.sort(key=lambda f: int(re.sub('\D', '', f)))
+
+    #Register the beads
+    bead_imgs = dict()
+    for idx,fn in enumerate(bead_file_list):
+        img = skio.imread(fn)
+        bead_imgs[idx]=img
+    #load images
+    imgs = dict()
+    file_list = file_list_647+file_list_750
+    for idx,fn in enumerate(file_list):
+        
+        _img = skio.imread(fn)
+        
+        imgs[idx]=equalize_adapthist(_img, clip_limit=0.03)
+
+    
+    for idx in range(1,len(bead_imgs)):
+        shift,_,_ = registration.phase_cross_correlation(bead_imgs[n_ref],bead_imgs[idx])
+        
+        imgs[idx] = ndi.shift(imgs[idx],shift)
+
+
+    #Window size
+    window_Size =window_size
+
+    ref_img = imgs[n_ref]
+    ref_img = ref_img*img_scale
+
+    fig,ax = plt.subplots(num=1)
+    cid = fig.canvas.mpl_connect('button_release_event', recordClickLoc_mpl)
+    ax.imshow(ref_img,cmap='gray')
+    crosshairs()
+    plt.show()
+    while True:
+  
+        if len(refPt)<2:
+            if not plt.fignum_exists(num=1):
+                fig,ax = plt.subplots(num=1)
+                cid = fig.canvas.mpl_connect('button_release_event', recordClickLoc_mpl)
+                ax.imshow(ref_img,cmap='gray')
+                crosshairs()
+                plt.show()
+            continue
+        else:
+            print('Accepted')
+            print(refPt)
+            break
+    
+    #load and register all the images based on the bead registration
+
+
+    #Draw a circle of a certain radius in the image
+    img_circle = dict()
+
+    n_cols = 3
+    n_rows = int(np.ceil(len(imgs)/n_cols))
+    plt.rcParams.update({'font.size': 22})
+    f,ax = plt.subplots(n_rows,n_cols,figsize=(20,20),dpi=200)
+    ax = ax.flatten()
+
+    for idx in range(len(imgs)):
+        img = imgs[idx]
+        spot_thresh = calcSpotThresh(img,thresh_percent=thresh)
+        canvas = np.zeros((img.shape[0],img.shape[1],3))
+        circle_mask = cv2.circle(canvas,tuple(refPt),circle_size//2,(1,1,1),-1)
+        circle_test = circleTest(img,circle_mask,spot_thresh)
+        img_circle[idx] = centreCrop(cv2.circle(img,tuple(refPt),circle_size//2,(0,255,255),1),
+                                    refPt,
+                                    window_Size)
+        ax[idx].imshow(img_circle[idx]*img_scale,vmin=0,cmap='gray')#,vmax=np.max(img[idx]),)
+        ax[idx].axis('off')
+        ax[idx].set_title(circle_test)
+    plt.tight_layout()
+
+    plt.savefig('./barcode_bead_example.png')
+
+
+
+
+    
+
+def recordClickLoc_mpl(event):
+    global refPt
+    refPt=[int(event.xdata),int(event.ydata)]
+
+def centreCrop(img,centrePt,window_size):
+    h=window_size//2
+
+    
+    y = np.maximum(centrePt[1] - h,0)
+    x = np.maximum(centrePt[0] - h,0)
+
+    crop_img = img[int(y):int(y+window_size), int(x):int(x+window_size)]
+    return crop_img
+
+def circleTest(img,circle_mask, thresh):
+    res = np.multiply(img[:,:,np.newaxis].copy(),circle_mask)[:,:]
+    test = res[res>thresh].sum()
+    if test>0:
+        return 1
+    else:
+        return 0
+
+def calcSpotThresh(img:np.ndarray,thresh_percent:float = 0.9)->float:
+    _img = img[:,:].flatten()
+
+    sorted_img = np.sort(_img)
+
+    idx = np.floor(thresh_percent*sorted_img.size).astype(int)
+
+    _thresh = sorted_img[idx]
+    
+    return _thresh
+    #create histogram of the image
+    #find the threshpercentile. i.e. sort the list, and then find the thresh_percent*len(list)
+
+if __name__=='__main__':
+    cli()
+
+    # img647_dir = '/Volumes/shahidsWORK/647nm, Raw'
+
+    # img750_dir = '/Volumes/shahidsWORK/750nm, Raw'
+
+    # beads_dir = '/Volumes/shahidsWORK/561nm, Raw'
+
+    # makeBarcodeWithBead(img647_dir,img750_dir,beads_dir,n_ref=0,pattern="merFISH_*_002_01.TIFF",circle_size=9,thresh=0.995,window_size=50,img_scale=1)
diff --git a/data/napari_merfish/LICENSE b/data/napari_merfish/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..f288702d2fa16d3cdf0035b15a9fcbc552cd88e7
--- /dev/null
+++ b/data/napari_merfish/LICENSE
@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
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+  Nothing in this License shall be construed as excluding or limiting
+any implied license or other defenses to infringement that may
+otherwise be available to you under applicable patent law.
+
+  12. No Surrender of Others' Freedom.
+
+  If conditions are imposed on you (whether by court order, agreement or
+otherwise) that contradict the conditions of this License, they do not
+excuse you from the conditions of this License.  If you cannot convey a
+covered work so as to satisfy simultaneously your obligations under this
+License and any other pertinent obligations, then as a consequence you may
+not convey it at all.  For example, if you agree to terms that obligate you
+to collect a royalty for further conveying from those to whom you convey
+the Program, the only way you could satisfy both those terms and this
+License would be to refrain entirely from conveying the Program.
+
+  13. Use with the GNU Affero General Public License.
+
+  Notwithstanding any other provision of this License, you have
+permission to link or combine any covered work with a work licensed
+under version 3 of the GNU Affero General Public License into a single
+combined work, and to convey the resulting work.  The terms of this
+License will continue to apply to the part which is the covered work,
+but the special requirements of the GNU Affero General Public License,
+section 13, concerning interaction through a network will apply to the
+combination as such.
+
+  14. Revised Versions of this License.
+
+  The Free Software Foundation may publish revised and/or new versions of
+the GNU General Public License from time to time.  Such new versions will
+be similar in spirit to the present version, but may differ in detail to
+address new problems or concerns.
+
+  Each version is given a distinguishing version number.  If the
+Program specifies that a certain numbered version of the GNU General
+Public License "or any later version" applies to it, you have the
+option of following the terms and conditions either of that numbered
+version or of any later version published by the Free Software
+Foundation.  If the Program does not specify a version number of the
+GNU General Public License, you may choose any version ever published
+by the Free Software Foundation.
+
+  If the Program specifies that a proxy can decide which future
+versions of the GNU General Public License can be used, that proxy's
+public statement of acceptance of a version permanently authorizes you
+to choose that version for the Program.
+
+  Later license versions may give you additional or different
+permissions.  However, no additional obligations are imposed on any
+author or copyright holder as a result of your choosing to follow a
+later version.
+
+  15. Disclaimer of Warranty.
+
+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
+APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
+HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
+OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
+THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.
diff --git a/data/napari_merfish/barcode_viewer.ui b/data/napari_merfish/barcode_viewer.ui
new file mode 100644
index 0000000000000000000000000000000000000000..c4a59b516c67339acecbe9eee96d7fa3669e4121
--- /dev/null
+++ b/data/napari_merfish/barcode_viewer.ui
@@ -0,0 +1,93 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<ui version="4.0">
+ <class>MainWindow</class>
+ <widget class="QMainWindow" name="MainWindow">
+  <property name="geometry">
+   <rect>
+    <x>0</x>
+    <y>0</y>
+    <width>381</width>
+    <height>312</height>
+   </rect>
+  </property>
+  <property name="windowTitle">
+   <string>MainWindow</string>
+  </property>
+  <widget class="QWidget" name="centralwidget">
+   <widget class="QWidget" name="verticalLayoutWidget">
+    <property name="geometry">
+     <rect>
+      <x>20</x>
+      <y>20</y>
+      <width>321</width>
+      <height>121</height>
+     </rect>
+    </property>
+    <layout class="QVBoxLayout" name="verticalLayout">
+     <item>
+      <layout class="QHBoxLayout" name="horizontalLayout">
+       <item>
+        <widget class="QLabel" name="lblGeneName">
+         <property name="text">
+          <string>Gene Name</string>
+         </property>
+        </widget>
+       </item>
+       <item>
+        <widget class="QLabel" name="lbDecodedGeneName">
+         <property name="text">
+          <string/>
+         </property>
+        </widget>
+       </item>
+      </layout>
+     </item>
+     <item>
+      <layout class="QHBoxLayout" name="horizontalLayout_2">
+       <item>
+        <widget class="QLabel" name="lblBarcode">
+         <property name="text">
+          <string>Barcode</string>
+         </property>
+        </widget>
+       </item>
+       <item>
+        <widget class="QLabel" name="lblDecodedBarcode">
+         <property name="text">
+          <string/>
+         </property>
+        </widget>
+       </item>
+      </layout>
+     </item>
+    </layout>
+   </widget>
+   <widget class="QPushButton" name="btnMakeLayer">
+    <property name="geometry">
+     <rect>
+      <x>20</x>
+      <y>160</y>
+      <width>100</width>
+      <height>32</height>
+     </rect>
+    </property>
+    <property name="text">
+     <string>Make Layer</string>
+    </property>
+   </widget>
+  </widget>
+  <widget class="QMenuBar" name="menubar">
+   <property name="geometry">
+    <rect>
+     <x>0</x>
+     <y>0</y>
+     <width>381</width>
+     <height>22</height>
+    </rect>
+   </property>
+  </widget>
+  <widget class="QStatusBar" name="statusbar"/>
+ </widget>
+ <resources/>
+ <connections/>
+</ui>
diff --git a/data/napari_merfish/config.json b/data/napari_merfish/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..bde3729a3fdc0fba84580e6990b674636518f4f8
--- /dev/null
+++ b/data/napari_merfish/config.json
@@ -0,0 +1,12 @@
+{
+    "raw_data_dir": "/Volumes/MERFISH_COLD/XP1678 20220309 ONI merfish/",
+    "analysis_dir": "/Volumes/MERFISH_COLD/XP1678 20220309 ONI merfish/Analysis",
+    "stage2pix_scaling":0.116999998688698,
+    "stage2z_scaling":1,
+    "decoded_img":true,
+    "ir_upper":9,
+    "z_lower":1,
+    "z_upper":10,
+    "file_pattern": "merFISH_{{ir:02d}}_{fov}_*.tiff",
+    "codebook_name":"C1E1_codebook_shahid.csv"
+}
diff --git a/data/napari_merfish/environment.yaml b/data/napari_merfish/environment.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..9670eafbf3189adb3f92915abc3aef3072a827a8
--- /dev/null
+++ b/data/napari_merfish/environment.yaml
@@ -0,0 +1,12 @@
+name: conda 
+channels:
+  - defaults
+dependencies:
+  - python=3.8
+  - click
+  - pandas
+  - openpyxl
+  - dask-image
+  - pip
+  - pip:
+    - napari[all]
diff --git a/data/napari_merfish/main.py b/data/napari_merfish/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..041b7616953aa02f58003a031894dfbe39a4027a
--- /dev/null
+++ b/data/napari_merfish/main.py
@@ -0,0 +1,221 @@
+from magicgui import magicgui
+import napari
+import skimage.io as skio
+import scipy.ndimage as ndi
+import numpy as np
+import pandas as pd
+from dask_image.imread import imread
+from dask_image.ndfourier import fourier_shift
+import dask.array as da
+from dask.array.image import imread as daimread
+from dask import delayed
+import json
+import os
+import functools
+import pandas as pd
+import matplotlib.pyplot as plt
+from napari.types import ImageData, PointsData
+import widgets
+from magicgui import magicgui
+import functools
+
+def gene_mask(arr,g):
+    v = da.equal(arr,g)
+    return v
+
+def dask_reshape(arr,gmax):
+    
+    gene_map = [gene_mask(arr-1,g) for g in range(gmax)]
+    x=da.stack(1*gene_map,axis=0)
+    return x
+    
+def getTLBF(df:pd.DataFrame,im_shape):
+    # TODO: THIS NEEDS TO BE CHANGED WITH THE NEW CSV FORMAT
+    shift_r = [0]
+    shift_r.extend(df['shift_y'].to_list())
+    shift_c = [0]
+    shift_c.extend(df['shift_x'].to_list())
+
+    dx_tl = np.max(np.maximum(shift_c,0))
+    dy_tl = np.max(np.maximum(shift_r,0))
+
+    dx_br = np.min(np.minimum(shift_c,0))
+    dy_br = np.min(np.minimum(shift_r,0))
+
+    tl = np.ceil([dy_tl,dx_tl]).astype(int)
+    br = np.floor([im_shape[0]+dy_br,im_shape[1]+dx_br]).astype(int)
+    return shift_r,shift_c,tl, br,dx_tl,dy_tl
+
+def load_img_and_shift(fn,shift,tl,br):
+    _img = skio.imread(fn)
+    #just perform the shift and then trim
+
+    shifted_img = ndi.shift(_img,shift)
+
+    cropped_img = shifted_img#[tl[0]:br[0],tl[1]:br[1]]
+
+    return cropped_img
+
+if __name__=="__main__":
+    config = json.load(open('config.json'))
+    ir_upper=config["ir_upper"] #Has to 9 or less
+    raw_data_dir = config["raw_data_dir"]
+    analysis_dir = config["analysis_dir"]
+    stagepos_file = os.path.join(raw_data_dir,'stagePos_Round#1.xlsx')
+
+    if os.path.isfile(stagepos_file):
+        stage = pd.read_excel(stagepos_file)
+    else:
+        stagepos_file = os.path.join(raw_data_dir,'stage_position_1.csv')
+        stage = pd.read_csv(stagepos_file)
+        
+    
+    z_lower = config["z_lower"]
+    z_num = config["z_upper"]#len(stage.columns)-5
+    _fovs = [i for i in range(1,len(stage))]
+    fovs = list(map(lambda x: f'{x:03d}',_fovs))
+    if 'Var1_ 1' in stage.columns: #Backwards compatability with old stagepos files
+        x_loc = stage['Var1_ 5']
+        y_loc = stage['Var1_ 4']
+        z_spacing = np.abs(stage['Var1_ 6'][0]-stage['Var1_ 7'][0])
+    elif 'x_pos' in stage.columns:
+        x_loc = stage['y_pos']
+        y_loc = stage['x_pos']
+        z_spacing = np.abs(stage['z_slice_0'][0]-stage['z_slice_1'][0])
+    else:
+        x_loc = stage['stage_pos_y']
+        y_loc = stage['stage_pos_x']
+        z_spacing = np.abs(stage['z_position_1'][0]-stage['z_position_2'][0])
+    
+    x_loc = x_loc-x_loc.iloc[0]
+    y_loc = y_loc-y_loc.iloc[0]
+
+    stage2pix_scaling=config["stage2pix_scaling"] # nikon/pix
+    stage2z_scaling = config["stage2pix_scaling"] #nikon/z
+    
+    viewer = napari.Viewer()
+    for idx,fov in enumerate(fovs):
+        shift_r=np.full((ir_upper,1),0)
+        shift_c=np.full((ir_upper,1),0)
+        tl,br = 0,0 
+        pattern_img =  config["file_pattern"].format(fov=fov)
+        image_root = f'/decoding/decoded_images/decoded_{fov}'+'_{:02d}.npy'
+        alignment_root = f'/aligned/shift_{fov}.csv'
+        
+        if config["decoded_img"]:
+            shift_name =analysis_dir+alignment_root
+            
+            codebook_name = os.path.join(config["raw_data_dir"],config["codebook_name"])
+            codebook_df = pd.read_csv(codebook_name,skiprows=3)
+            codebook_df=codebook_df.rename(columns={c:c.strip() for c in codebook_df.columns},errors='raise')
+            name =analysis_dir+image_root.format(z_lower)
+            if not os.path.isfile(name):
+                print(f"{name} does not exist")
+                continue
+            sample = np.load(name)
+            if os.path.isfile(shift_name):
+                df= pd.read_csv(shift_name)
+                shift_r,shift_c,tl, br, dx_tl,dy_tl= getTLBF(df,sample.shape)
+            num_genes = sample.max()
+            
+            lazy_npload = delayed(np.load)
+            lazy_reshapefn = delayed(dask_reshape)
+            _zs = np.arange(z_lower,z_num+1)
+            lazy_decodes = [lazy_npload(analysis_dir+image_root.format(z)) for z in _zs]
+            
+            dask_arrays = [
+            da.from_delayed(delayed_reader, shape=sample.shape, dtype=sample.dtype)
+            for delayed_reader in lazy_decodes
+            ]
+            lazy_reshapes = [lazy_reshapefn(da,num_genes) for da in dask_arrays]
+            dask_arrays = [
+            da.from_delayed(lr, shape=(num_genes,*sample.shape), dtype=sample.dtype)
+            for lr in lazy_reshapes
+            ]
+
+            stack = da.stack(dask_arrays, axis=0)
+            
+            stack = da.broadcast_to(stack,(1,stack.shape[0],stack.shape[1],stack.shape[2],stack.shape[3]))
+            stack = da.transpose(stack,[2,0,1,3,4])
+            #add decoded image
+
+            viewer.add_image(stack,translate=(x_loc.iloc[idx]/stage2pix_scaling,-y_loc[idx]/stage2pix_scaling),name=f'decoded',scale=[1,1,z_spacing/stage2z_scaling,-1,1],blending='additive')
+
+        #add 473 volume
+        channel = 473
+        channel_format = f'{channel}nm, Raw/'
+        irs = [imread(raw_data_dir + channel_format + pattern_img.format(ir=ir)) for ir in range(1,ir_upper)]
+        stack = da.stack(irs)    
+        viewer.add_image(stack,
+                                translate=((x_loc[idx])/stage2pix_scaling,
+                                            (-y_loc[idx])/stage2pix_scaling),
+                                name=f'fov:{fov}, {channel}nm volume',
+                                opacity=0.5,
+                                scale=[1,z_spacing/stage2z_scaling,-1,1],
+                                contrast_limits=[0,2**16]) 
+        #add 561 volume
+        channel = 561
+        channel_format = f'{channel}nm, Raw/'
+        irs = [
+            daimread(raw_data_dir + channel_format + pattern_img.format(ir=ir),functools.partial(load_img_and_shift,
+                                                                                                                shift = (shift_r[ir-1],shift_c[ir-1]),tl=tl,br=br
+                                                                                                )
+                    ) 
+            for ir in range(1,ir_upper)
+            ]
+        # irs = [imread(raw_data_dir + channel_format + pattern_img.format(ir)) for ir in range(1,ir_upper)]
+        stack = da.stack(irs)    
+        viewer.add_image(stack,
+                                translate=(
+                                            x_loc.iloc[idx]/stage2pix_scaling,
+                                            -y_loc[idx]/stage2pix_scaling),
+                                name=f'fov:{fov}, {channel}nm volume',
+                                opacity=0.5,
+                                scale=[1,z_spacing/stage2z_scaling,-1,1],
+                                contrast_limits=[0,2**16])         
+        #add 647 volume 
+        channel = 647
+        channel_format = f'{channel}nm, Raw/'
+        irs = [
+            daimread(raw_data_dir + channel_format + pattern_img.format(ir=ir),functools.partial(load_img_and_shift,
+                                                                                                                shift = (shift_r[ir-1],shift_c[ir-1]),tl=tl,br=br
+                                                                                                )
+                    ) 
+            for ir in range(1,ir_upper)
+            ]
+        stack = da.stack(irs)    
+        viewer.add_image(stack,
+                                translate=(x_loc.iloc[idx]/stage2pix_scaling,-y_loc[idx]/stage2pix_scaling),
+                                name=f'fov:{fov}, {channel}nm volume',
+                                opacity=0.5,
+                                scale=[1,z_spacing/stage2z_scaling,-1,1],
+                                contrast_limits=[0,2**16],
+                                colormap='yellow')
+        #add 750 volume 
+        channel = 750
+        channel_format = f'{channel}nm, Raw/'
+        irs = [
+            daimread(raw_data_dir + channel_format + pattern_img.format(ir=ir),functools.partial(load_img_and_shift,
+                                                                                                                shift = (shift_r[ir-1],shift_c[ir-1]),tl=tl,br=br
+                                                                                                )
+                    ) 
+            for ir in range(1,ir_upper)
+            ]
+        stack = da.stack(irs)    
+        viewer.add_image(stack,
+                                translate=(x_loc.iloc[idx]/stage2pix_scaling,-y_loc[idx]/stage2pix_scaling),
+                                name=f'fov:{fov}, {channel}nm volume',
+                                opacity=0.5,
+                                scale=[1,z_spacing/stage2z_scaling,-1,1],
+                                contrast_limits=[0,2**16],
+                                colormap='green')
+        viewer.dims.axis_labels = ['GN','IR', 'Z', 'Y', 'X']
+
+
+
+    if config["decoded_img"]:
+        barcode_viewer=widgets.FancyGUI(viewer,codebook_df,fovs=fovs,x_loc=x_loc.to_numpy()/stage2pix_scaling,y_loc=y_loc.to_numpy()/stage2pix_scaling,img_scale=[1,z_spacing/stage2z_scaling,-1,1])
+        viewer.window.add_dock_widget(barcode_viewer,area='right')
+
+    viewer.reset_view()# start the event loop and show the viewer
+    napari.run()
diff --git a/data/napari_merfish/widgets.py b/data/napari_merfish/widgets.py
new file mode 100644
index 0000000000000000000000000000000000000000..3a38e2db83e3841c6935b1073c6a1954d85c2102
--- /dev/null
+++ b/data/napari_merfish/widgets.py
@@ -0,0 +1,65 @@
+from qtpy.QtWidgets import QMainWindow, QLabel,QGridLayout
+import napari
+from qtpy import uic
+from pathlib import Path
+import pandas as pd
+import dask.array as da
+
+# Define the main window class
+class FancyGUI(QMainWindow):
+    def __init__(self, napari_viewer:napari.Viewer,codebook:pd.DataFrame,fovs,x_loc,y_loc,img_scale):          # include napari_viewer as argument (it has to have this name)
+        super().__init__()
+        self.viewer = napari_viewer
+        self.fovs = fovs
+        self.x_loc = x_loc
+        self.y_loc = y_loc
+        self.img_scale=img_scale
+
+
+
+        self.codebook = codebook
+        self.UI_FILE = str(Path(__file__).parent / "barcode_viewer.ui")  # path to .ui file
+        uic.loadUi(self.UI_FILE, self)           # load QtDesigner .ui file
+        # self.widgetArrangement = QGridLayout()
+        # self.widgetArrangement.addWidget(self.geneName)
+        # self.widgetArrangement.addWidget(self.barcode)
+
+        # self.setLayout(self.widgetArrangement)
+        self.btnMakeLayer.clicked.connect(self.makeGeneLayer)
+        self.viewer.dims.events.connect(self.updateBarcode)
+        
+
+    def updateBarcode(self):
+
+        current_slice = self.viewer.dims.current_step
+
+        self.lblDecodedBarcode.setText(self.codebook.iloc[current_slice[0]]['barcode'])
+        self.lbDecodedGeneName.setText(self.codebook.iloc[current_slice[0]]['name'])
+    
+
+    def makeGeneLayer(self):
+        
+        current_slice = self.viewer.dims.current_step
+        for idx,fov in enumerate(self.fovs):
+            
+
+            if idx==0:
+                l = self.viewer.layers[f"decoded"]    
+            else:
+                try:
+                    l = self.viewer.layers[f"decoded [{idx}]"]
+                except KeyError:
+                    continue
+            
+            self.viewer.add_image(
+                            da.repeat(
+                                da.stack([l.data[current_slice[0],:,:,:,:]],axis=0),
+                            repeats = len(self.codebook),
+                            axis=0),
+                        name=f"Gene: {current_slice[0]} decoded [{idx}]",
+                        translate=(self.x_loc[idx],-self.y_loc[idx]),
+                        scale=self.img_scale,
+                        )
+    
+        print("Layers added")
+        
\ No newline at end of file
diff --git a/data/reports/BaseReport.py b/data/reports/BaseReport.py
new file mode 100644
index 0000000000000000000000000000000000000000..393c3df3d69b883100d088164a78f0946df113ce
--- /dev/null
+++ b/data/reports/BaseReport.py
@@ -0,0 +1,110 @@
+import json
+from typing import List, Union
+import numpy as np
+
+
+class BaseReport:
+    """The Abstract base class for all reports
+    """
+
+    def __init__(self, imgstack_files: str, coord_infos: str):
+
+        self.imgstack = self._read_imgstack(imgstack_files)
+
+        if isinstance(coord_infos, list):
+            merged_json = dict()
+            for coord_info in coord_infos:
+                a_file = open(coord_info, "r")
+                _contents = json.load(a_file)
+
+                for k, v in _contents.items():
+                    # print(v)
+                    if not (k in merged_json):
+                        # if the key isnt in the merged dictionary, then add and initialise it. This is python 3.6+ behaviour
+                        merged_json[k] = v
+                        continue
+
+                    if not isinstance(merged_json[k], list) or isinstance(
+                        merged_json[k], str
+                    ):
+                        # If there is unique data and the value at k in merged dict is not a list already, make it one
+                        merged_json[k] = [merged_json[k]]
+                    _test_dict = merged_json[k].copy()
+                    _base_set = set(_test_dict)
+
+                    if isinstance(v, list):
+                        _test_dict.extend(v)
+                    else:
+                        _test_dict.append(v)
+
+                    _test_set = set(_test_dict)
+                    # print(_test_set)
+                    # print(_base_set)
+                    if len(_test_set) == len(_base_set):
+                        # If there is nothing unique when making the set of the new content at that key and the old content, then skip
+                        continue
+                    if isinstance(v, list):
+                        merged_json[k].extend(v)
+                    else:
+                        merged_json[k].append(v)
+
+            self.coords = merged_json
+            # print(self.coords)
+        else:
+            a_file = open(coord_infos, "r")
+            self.coords = json.load(a_file)
+
+        self.pdf = None
+
+    # PDF controls--------------------------------------------
+    def set_pdf(self, pdf) -> None:
+        """Takes a PdfPages object and sets that as the internal variable 
+
+        Args:
+            pdf : the pdf pages object
+        """
+        self.pdf = pdf
+
+    def get_pdf(self):
+        """Return the pdfpages object
+
+        Returns:
+            _type_: _description_
+        """
+        return self.pdf
+
+    def isPdf(self) -> bool:
+        """Checks if the pdf has been set
+
+        Returns:
+            bool: _description_
+        """
+        if self.pdf is None:
+            return False
+        return True
+
+    def closePdf(self) -> None:
+        """Closes the pdf
+        """
+        if self.isPdf():
+            self.pdf.close()
+            self.pdf = None
+
+    def _read_imgstack(self, imgstack_files: Union[List[str], str]):
+        """Read in an image stack created by utils.fileIO function
+
+        Args:
+            imgstack_files (str): The 
+
+        Returns:
+            _type_: Loaded Image stack
+        """
+        if isinstance(imgstack_files, list):
+            # If there is a list of images, then extend the image stack along the last dimension
+            _imgstack = np.array(
+                [np.load(image_stack) for image_stack in imgstack_files]
+            )
+            imgstack = np.stack(_imgstack, axis=-1)
+        else:
+            imgstack = np.load(imgstack_files)
+        return imgstack
diff --git a/data/reports/BrightnessReport.py b/data/reports/BrightnessReport.py
new file mode 100644
index 0000000000000000000000000000000000000000..297203055b888ee71f9ff2ebb8bbefb9774c40d2
--- /dev/null
+++ b/data/reports/BrightnessReport.py
@@ -0,0 +1,278 @@
+from .BaseReport import BaseReport
+
+from utils import imgproc
+
+import skimage.exposure as ske
+import numpy as np
+
+import matplotlib.pyplot as plt
+from mpl_toolkits.axes_grid1.inset_locator import inset_axes
+
+
+
+
+class BrightnessReport(BaseReport):
+    def __init__(self,imgstack_file,coord_info,fov,z):
+        super().__init__(imgstack_file,coord_info)
+        
+        self.fov_name = fov
+        self.z_name = z
+        self.imgstack = self.imgstack # (y,x,wvs,irs)
+        self.contrast_tape = np.zeros((self.imgstack.shape[2],
+                                    self.imgstack.shape[3]))
+
+
+
+
+    #Helper----------------------------------------------
+    def calc_HS_metric(self,img):
+        '''
+        https://ieeexplore.ieee.org/document/6108900
+        '''
+        vals = np.percentile(img.ravel(),[75,25])
+        max_val = np.max(img)
+        min_val = np.min(img)
+
+        return (vals[0]-vals[1])/(max_val-min_val)
+    def calc_HF_metric(self,img):
+        '''
+        https://ieeexplore.ieee.org/document/6108900
+        '''
+
+        hist,_ = np.histogram(img.flatten(),bins = int(img.max()//2),range=(0,img.max()))
+
+        return np.power(np.prod(hist),1/len(hist)) / hist.sum() * len(hist)
+
+        
+    def ski_is_low_contrast(self,img,fraction_threshold = 0.25):
+        return ske.is_low_contrast(img,fraction_threshold=fraction_threshold)
+
+    def contrast_test(self,img,threshold=0.25,method='ski'):
+        if method=='ski':
+            return self.ski_is_low_contrast(img,threshold)
+        elif method =='HS':
+            res = self.calc_HS_metric(img)
+            return res>threshold
+        elif method =='HF':
+            res = self.calc_HF_metric(img)
+            return res>threshold
+
+        
+
+
+
+    #Reports----------------------------------------------
+    
+    def preview_images(self):
+        max_wv_val = self.imgstack.max(axis=0).max(axis=0).max(axis=1)
+        val_range = max_wv_val*0.9#self.imgstack.max()*0.90
+        f,ax = plt.subplots(nrows=len(self.coords['irs']),ncols=len(self.coords['wvs']),sharex=True,sharey=True,figsize=(len(self.coords['wvs'])*4,len(self.coords['irs'])*4))
+        plt.suptitle(f'FOV: {self.fov_name}; Z: {self.z_name}')
+        if not isinstance(ax,np.ndarray):
+            ax = np.array([ax])
+        if len(ax.shape)==1:
+            if len(self.coords['irs'])==1:
+                ax=ax[np.newaxis,:]
+            elif len(self.coords['wvs'])==1:
+                ax=ax[:,np.newaxis]
+        for iwv,wv in enumerate(self.coords['wvs']):
+            for iir,ir in enumerate(self.coords['irs']):
+                img = self.imgstack[:,:,iwv,iir]
+                ax[iir,iwv].imshow(img,vmax=val_range[iwv],cmap='gray')
+                if iwv==0:
+                    ax[iir,iwv].set_ylabel(f'ir:{ir}')
+                if iir==0:
+                    ax[iir,iwv].set_title(f'channel: {wv}nm')
+
+        plt.tight_layout()
+        self.pdf.savefig()
+        plt.close(f)
+
+
+    def brightness_infov_z(self):
+        largest = self.imgstack.max()
+        f,ax = plt.subplots(nrows=len(self.coords['irs']),ncols=len(self.coords['wvs']),sharex=True,sharey=True,figsize=(len(self.coords['wvs'])*4,len(self.coords['irs'])*4))
+        if not isinstance(ax,np.ndarray):
+            ax = np.array([ax])
+        if len(ax.shape)==1:
+            if len(self.coords['irs'])==1:
+                ax=ax[np.newaxis,:]
+            elif len(self.coords['wvs'])==1:
+                ax=ax[:,np.newaxis]
+        plt.suptitle(f'FOV: {self.fov_name}; Z: {self.z_name}')
+        
+        for iwv,wv in enumerate(self.coords['wvs']):
+            for iir,ir in enumerate(self.coords['irs']):
+                bin_num = int(largest//2)
+
+                data = self.imgstack[:,:,iwv,iir]
+                self.contrast_tape[iwv,iir] = self.calc_HS_metric(data)
+                flat_data = data.ravel()
+
+                ax[iir,iwv].hist(flat_data,bins=bin_num,range=(0,largest),log=True,histtype='step')
+                ax[iir,iwv].text(0.5, 0.5, f'HS:{self.contrast_tape[iwv,iir]}',
+                                ha="center", va="center",
+                                transform=ax[iir,iwv].transAxes)
+                if iwv==0:
+                    ax[iir,iwv].set_ylabel(f'ir:{ir}')
+                if iir==0:
+                    ax[iir,iwv].set_title(f'channel: {wv}nm')
+
+        plt.tight_layout()
+        self.pdf.savefig()
+        plt.close(f)
+    
+    def brightness_on_images(self):
+
+
+        max_wv_val = self.imgstack.max(axis=0).max(axis=0).max(axis=1)
+        val_range = max_wv_val*0.9
+        largest = self.imgstack.max()
+        f,ax = plt.subplots(nrows=len(self.coords['irs']),ncols=len(self.coords['wvs']),sharex=True,sharey=True,figsize=(len(self.coords['wvs'])*4,len(self.coords['irs'])*4))
+        plt.suptitle(f'FOV: {self.fov_name}; Z: {self.z_name}')
+        if not isinstance(ax,np.ndarray):
+            ax = np.array([ax])
+        if len(ax.shape)==1:
+            if len(self.coords['irs'])==1:
+                ax=ax[np.newaxis,:]
+            elif len(self.coords['wvs'])==1:
+                ax=ax[:,np.newaxis]
+        for iwv,wv in enumerate(self.coords['wvs']):
+            for iir,ir in enumerate(self.coords['irs']):
+                img = self.imgstack[:,:,iwv,iir]
+                bin_num = int(largest//2)
+                ax[iir,iwv].imshow(img,vmax=val_range[iwv],cmap='gray')
+                # Add histogram to the corner of the image
+                axins = inset_axes(ax[iir,iwv], width="25%", height="25%", loc=4, borderpad=1)
+                data = img.ravel()
+                axins.hist(data,bins=bin_num,range=(0,largest),log=True,histtype='step')
+                axins.tick_params(labelleft=False, labelbottom=False)
+                if iwv==0:
+                    ax[iir,iwv].set_ylabel(f'ir:{ir}')
+                if iir==0:
+                    ax[iir,iwv].set_title(f'channel: {wv}nm')
+
+        plt.tight_layout()
+        self.pdf.savefig()
+        plt.close(f)
+    
+    def contrast_heatmap(self):
+
+        f,ax = plt.subplots()
+
+        ims = ax.imshow(self.contrast_tape)
+        ax.set_yticks(np.arange(len(self.coords['wvs'])), self.coords['wvs'])
+        ax.set_xticks(np.arange(len(self.coords['irs'])), self.coords['irs'])
+        ax.set_ylabel("Wavelength (nm)")
+        ax.set_xlabel("Imaging Round")
+
+        ax.set_title(f'FOV: {self.fov_name}; Z: {self.z_name}')
+        plt.colorbar(ims)
+        plt.tight_layout()
+        self.pdf.savefig()
+        plt.close(f)
+        
+    def _brightness_through_z(self):
+        """This is deprecated.
+        """
+        #Take the image stack and do a max projection from all the pixels through z
+        mip_z_stack = self.imgstack.max(axis=4)
+
+        largest = mip_z_stack.max()
+        f,ax = plt.subplots(nrows=len(self.coords['irs']),ncols=len(self.coords['wvs']),sharex=True,sharey=True,figsize=(len(self.coords['wvs'])*4,len(self.coords['irs'])*4))
+        if not isinstance(ax,np.ndarray):
+            ax = np.array([ax])
+        if len(ax.shape)==1:
+            if len(self.coords['irs'])==1:
+                ax=ax[np.newaxis,:]
+            elif len(self.coords['wvs'])==1:
+                ax=ax[:,np.newaxis]
+        plt.suptitle(f'FOV: {self.fov_name}')
+        for iwv,wv in enumerate(self.coords['wvs']):
+            for iir,ir in enumerate(self.coords['irs']):
+                bin_num = int(largest//2)
+
+                data = mip_z_stack[:,:,iwv,iir]
+                self.contrast_tape[iwv,iir] = self.calc_HS_metric(data)
+                flat_data = data.ravel()
+
+                ax[iir,iwv].hist(flat_data,bins=bin_num,range=(0,largest),log=True,histtype='step')
+                ax[iir,iwv].text(0.5, 0.5, f'HS:{self.contrast_tape[iwv,iir]}',
+                                ha="center", va="center",
+                                transform=ax[iir,iwv].transAxes)
+                if iwv==0:
+                    ax[iir,iwv].set_ylabel(f'ir:{ir}')
+                if iir==0:
+                    ax[iir,iwv].set_title(f'channel: {wv}nm')
+
+        plt.tight_layout()
+        self.pdf.savefig()
+        plt.close(f)
+
+
+    def _brightness_through_z_on_images(self):
+        """This is Deprecated.
+        """
+        mip_z_stack = self.imgstack.max(axis=4)
+
+        max_wv_val = self.imgstack.max(axis=0).max(axis=0).max(axis=1).max(axis=1)
+        val_range = max_wv_val*0.9
+        largest = mip_z_stack.max()
+        f,ax = plt.subplots(nrows=len(self.coords['irs']),ncols=len(self.coords['wvs']),sharex=True,sharey=True,figsize=(len(self.coords['wvs'])*4,len(self.coords['irs'])*4))
+        plt.suptitle(f'FOV: {self.fov_name}')
+        if not isinstance(ax,np.ndarray):
+            ax = np.array([ax])
+        if len(ax.shape)==1:
+            if len(self.coords['irs'])==1:
+                ax=ax[np.newaxis,:]
+            elif len(self.coords['wvs'])==1:
+                ax=ax[:,np.newaxis]
+        for iwv,wv in enumerate(self.coords['wvs']):
+            for iir,ir in enumerate(self.coords['irs']):
+                img = mip_z_stack[:,:,iwv,iir]
+                bin_num = int(largest//2)
+                ax[iir,iwv].imshow(img,vmax=val_range[iwv],cmap='gray')
+                # Add histogram to the corner of the image
+                axins = inset_axes(ax[iir,iwv], width="25%", height="25%", loc=4, borderpad=1)
+                data = img.flatten()
+                axins.hist(data,bins=bin_num,range=(0,largest),log=True,histtype='step')
+                axins.tick_params(labelleft=False, labelbottom=False)
+                if iwv==0:
+                    ax[iir,iwv].set_ylabel(f'ir:{ir}')
+                if iir==0:
+                    ax[iir,iwv].set_title(f'channel: {wv}nm')
+
+        plt.tight_layout()
+        self.pdf.savefig()
+        plt.close(f)
+
+
+        
+    def _preview_images(self):
+        """This is deprecated.
+        """
+        mip_z_stack = self.imgstack.max(axis=4)
+        max_wv_val = self.imgstack.max(axis=0).max(axis=0).max(axis=1).max(axis=1)
+        val_range = max_wv_val*0.9#self.imgstack.max()*0.90
+
+        f,ax = plt.subplots(nrows=len(self.coords['irs']),ncols=len(self.coords['wvs']),sharex=True,sharey=True,figsize=(len(self.coords['wvs'])*4,len(self.coords['irs'])*4))
+        plt.suptitle(f'FOV: {self.fov_name}')
+        if not isinstance(ax,np.ndarray):
+            ax = np.array([ax])
+        if len(ax.shape)==1:
+            if len(self.coords['irs'])==1:
+                ax=ax[np.newaxis,:]
+            elif len(self.coords['wvs'])==1:
+                ax=ax[:,np.newaxis]
+        for iwv,wv in enumerate(self.coords['wvs']):
+            for iir,ir in enumerate(self.coords['irs']):
+                img = mip_z_stack[:,:,iwv,iir]
+                ax[iir,iwv].imshow(img,vmax=val_range[iwv],cmap='gray')
+                if iwv==0:
+                    ax[iir,iwv].set_ylabel(f'ir:{ir}')
+                if iir==0:
+                    ax[iir,iwv].set_title(f'channel: {wv}nm')
+
+        plt.tight_layout()
+        self.pdf.savefig()
+        plt.close(f)
diff --git a/data/reports/DecodabilityReport.py b/data/reports/DecodabilityReport.py
new file mode 100644
index 0000000000000000000000000000000000000000..2bd9908f19dfd5009c0d0f3470c281009ef35952
--- /dev/null
+++ b/data/reports/DecodabilityReport.py
@@ -0,0 +1,522 @@
+import sys
+
+sys.path.append("../")
+from utils.fileIO import Codebook, read_table
+from utils.imgproc import warp_image,register_stack
+from utils.helper_functions import performDumbExtraction, findErrorBits
+from .BaseReport import BaseReport
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib as mpl
+import scipy.ndimage as ndi
+import skimage
+from sklearn.neighbors import NearestNeighbors
+
+mpl.rcParams["image.interpolation"] = "none"
+
+
+class DecodabilityReport(BaseReport):
+    """This report produces graphs and figures that provide insight into the decodeability of a dataset.
+
+        Args:
+            deconvolved_img_stack (str): The path to the img stack of deconvolved images
+            coord_info (str): The path to the coordinate information file for the fov and z
+            codebook_file (str): The path to the codebook csv file 
+            data_org_file (str): The path to the data organization file
+            fov (str): The fov name that belongs to the image stack
+            z (str): The z name that belongs to the image stack
+            out_detection_stats (str): The path that the detection stats will be printed into
+
+        Raises:
+            AssertionError: If the number of wavelenghts is not 4 in the coordinate file, then the image warping in the data organization file wont work. 
+        """
+    def __init__(
+        self,
+        deconvolved_img_stack: str,
+        coord_info: str,
+        codebook_file: str,
+        data_org_file: str,
+        fov: int,
+        z: int,
+        out_detection_stats: str,
+    ):
+        
+        super().__init__(deconvolved_img_stack, coord_info)
+        if not (len(self.coords["wvs"]) == 4):
+            raise AssertionError("Image Stack must contain 4 channels")
+
+        self.fov_name = fov
+        self.z_name = z
+        self.out_detection_stats = out_detection_stats
+        # Load the codebook file
+        self.codebook = Codebook(codebook_file)
+
+        # Load the dataorg file
+        data_org = read_table(data_org_file).to_dict("records")
+        self.registered_images = register_stack(self.imgstack)
+        # Warp the deconvolved image stack into (16,x,y)
+        self.warped_imgs = warp_image(data_org, self.registered_images, 16)
+
+        # Pass into the perform the dumb extraction
+        (
+            self.filtered_warped_images,
+            self.bright_pixel_count,
+            self.bit_map,
+            self.bit_vector_map,
+            self.dist_map,
+            self.barcode_map,
+            self.error_bit_map,
+            self.detection_number_map,
+            self.codebook_bits,
+        ) = performDumbExtraction(
+            self.warped_imgs, self.codebook, clip_thresh=98.5
+        )
+
+    """
+    ====MAKE REPORTS=====
+    """
+
+    def make_bit_imgs(self):
+        """Generates the 3,4, and 5 bit detection images. 
+        These are bits that are within 1 manhattan distance 
+        to an entry in the codebook
+        """
+        # make 3,4,5 bit images
+        image_blank = np.zeros(
+            (self.warped_imgs.shape[1], self.warped_imgs.shape[2])
+        )
+
+        bc3_img = np.multiply(
+            np.reshape(self.barcode_map[3], image_blank.shape) + 1,
+            np.reshape(self.dist_map[3] == 1, image_blank.shape),
+        )
+        bc4_img = np.multiply(
+            np.reshape(self.barcode_map[4], image_blank.shape) + 1,
+            np.reshape(self.dist_map[4] == 0, image_blank.shape),
+        )
+        bc5_img = np.multiply(
+            np.reshape(self.barcode_map[5], image_blank.shape) + 1,
+            np.reshape(self.dist_map[5] == 1, image_blank.shape),
+        )
+
+        bc3_img = np.where(bc3_img > 0, 1, 0)
+
+        bc4_img = np.where(bc4_img > 0, 1, 0)
+
+        bc5_img = np.where(bc5_img > 0, 1, 0)
+
+        f, ax = plt.subplots(
+            nrows=1, ncols=3, sharex=True, sharey=True, figsize=(3 * 10, 10)
+        )
+
+        ax[0].imshow(
+            bc3_img, cmap="gray", vmin=0, vmax=1, interpolation="none"
+        )
+        ax[1].imshow(
+            bc4_img, cmap="gray", vmin=0, vmax=1, interpolation="none"
+        )
+        ax[2].imshow(
+            bc5_img, cmap="gray", vmin=0, vmax=1, interpolation="none"
+        )
+
+        ax[0].set_title("Image of 3-bit Decodable barcodes")
+        ax[1].set_title("Image of 4-bit Decodable barcodes")
+        ax[2].set_title("Image of 5-bit Decodable barcodes")
+
+        plt.tight_layout()
+        self.pdf.savefig()
+        plt.close(f)
+
+        # Set the colour images
+
+        colour_img = np.zeros((bc5_img.shape[0], bc5_img.shape[1], 3))
+
+        colour_img[:, :, 0] = bc3_img
+        colour_img[:, :, 1] = bc4_img
+        colour_img[:, :, 2] = bc5_img
+
+        f, ax = plt.subplots(figsize=(40, 40))
+        ax.imshow(colour_img, vmin=0, vmax=1, interpolation="none")
+        plt.title("Red: 3-bit words; Green: 4-bit words; Blue: 5-bit words")
+        plt.tight_layout()
+        self.pdf.savefig()
+        plt.close(f)
+
+        # For each bit in the filtered warped image, spit out an RGB image
+
+        f, axs = plt.subplots(4, 4, figsize=(43, 43))
+        axs = axs.flatten()
+        window_half_width = 50
+
+        while True:
+            r, c = (
+                np.random.randint(0, bc3_img.shape[0]),
+                np.random.randint(0, bc3_img.shape[1]),
+            )
+
+            tl = [int(r - window_half_width), int(c - window_half_width)]
+
+            br = [int(r + window_half_width), int(c + window_half_width)]
+
+            fwi = np.moveaxis(self.filtered_warped_images, 0, -1)
+            wi = np.moveaxis(self.warped_imgs, 0, -1)
+            masked3 = np.multiply(
+                fwi,
+                np.reshape(self.dist_map[3] == 1, image_blank.shape)[
+                    ..., None
+                ],
+            )
+
+            masked4 = np.multiply(
+                fwi,
+                np.reshape(self.dist_map[4] == 0, image_blank.shape)[
+                    ..., None
+                ],
+            )
+
+            masked5 = np.multiply(
+                fwi,
+                np.reshape(self.dist_map[5] == 1, image_blank.shape)[
+                    ..., None
+                ],
+            )
+
+            cropped_mask3 = masked3[tl[0]: br[0] + 1, tl[1]: br[1] + 1, :]
+            cropped_mask4 = masked4[tl[0]: br[0] + 1, tl[1]: br[1] + 1, :]
+            cropped_mask5 = masked5[tl[0]: br[0] + 1, tl[1]: br[1] + 1, :]
+
+            if np.all(cropped_mask3 != 1) and np.all(cropped_mask5 != 1):
+                pass  # If the cropped area is empty, then try it all again
+            else:
+                break
+
+        mpl.rcParams["image.interpolation"] = "none"
+        for iax, ax in enumerate(axs):
+            colour_image = np.stack(
+                (
+                    cropped_mask3[:, :, iax],
+                    cropped_mask4[:, :, iax],
+                    cropped_mask5[:, :, iax],
+                ),
+                axis=2,
+            )
+
+            ax.imshow(colour_image * 255, vmin=0, vmax=1)
+            ax.set_title(f"Bit: {iax}")
+        plt.suptitle(
+            f"Red: 3-bit words; Green: 4-bit words; Blue: 5-bit words \n \
+            Row: {r}, Column {c}, CropSize {2*window_half_width+1}"
+        )
+        # plt.tight_layout()
+        self.pdf.savefig()
+        plt.close(f)
+
+        f, axs = plt.subplots(4, 4, figsize=(43, 43))
+        axs = axs.flatten()
+
+        masked3 = np.multiply(
+            fwi,
+            np.reshape(self.dist_map[3] == 1, image_blank.shape)[..., None],
+        )
+
+        masked4 = np.multiply(
+            fwi,
+            np.reshape(self.dist_map[4] == 0, image_blank.shape)[..., None],
+        )
+
+        masked5 = np.multiply(
+            fwi,
+            np.reshape(self.dist_map[5] == 1, image_blank.shape)[..., None],
+        )
+
+        cropped_mask3 = masked3[tl[0]: br[0] + 1, tl[1]: br[1] + 1, :]
+        cropped_mask4 = masked4[tl[0]: br[0] + 1, tl[1]: br[1] + 1, :]
+        cropped_mask5 = masked5[tl[0]: br[0] + 1, tl[1]: br[1] + 1, :]
+
+        row3, col3, z3 = np.where(cropped_mask3 == 1)
+
+        row4, col4, z4 = np.where(cropped_mask4 == 1)
+
+        row5, col5, z5 = np.where(cropped_mask5 == 1)
+
+        for iax, ax in enumerate(axs):
+            crop = wi[tl[0]: br[0] + 1, tl[1]: br[1] + 1, iax]
+            crap_max = ndi.maximum_filter(crop, size=2, mode="constant")
+            peak = skimage.feature.peak_local_max(crap_max, min_distance=2)
+            # print(peak)
+            ax.imshow(crop, cmap="gray")
+            ax.scatter(col3, row3, s=15, color="red", marker="*")
+            ax.scatter(col4, row4, s=15, color="green", marker="*")
+            ax.scatter(col5, row5, s=15, color="blue", marker="*")
+            ax.scatter(
+                peak[:, 1], peak[:, 0], s=10, color="yellow", marker="*"
+            )
+            ax.set_title(f"Bit: {iax}")
+        plt.suptitle(
+            "Red: 3-bit words; Green: 4-bit words; Blue: 5-bit words\n \
+            Overlayed on smoothed deconvolved image"
+        )
+        # plt.tight_layout()
+        self.pdf.savefig()
+        plt.close(f)
+
+        f, axs = plt.subplots(4, 4, figsize=(40, 40))
+        axs = axs.flatten()
+        for iax, ax in enumerate(axs):
+            crop = fwi[tl[0]: br[0] + 1, tl[1]: br[1] + 1, iax]
+            crap_max = ndi.maximum_filter(crop, size=2, mode="constant")
+            peak = skimage.feature.peak_local_max(crap_max, min_distance=2)
+            # print(peak)
+            ax.imshow(crop, cmap="gray")
+            ax.scatter(col3, row3, s=15, color="red", marker="*")
+            ax.scatter(col4, row4, s=15, color="green", marker="*")
+            ax.scatter(col5, row5, s=15, color="blue", marker="*")
+            ax.scatter(
+                peak[:, 1], peak[:, 0], s=10, color="yellow", marker="*"
+            )
+            ax.set_title(f"Bit: {iax}")
+        plt.suptitle(
+            "Red: 3-bit words; Green: 4-bit words; Blue: 5-bit words\n \
+            Overlayed thresholded deconvolved image"
+        )
+        # plt.tight_layout()
+        self.pdf.savefig()
+        plt.close(f)
+
+    def make_decodability_hists(self):
+        """Make the histograms that for determining decoability based on 
+            thresholded pixels
+        """
+        f, ax = plt.subplots(figsize=(10, 10))
+        plt.hist(
+            self.bright_pixel_count.ravel(), bins=[i for i in range(17)],
+        )
+        ax.set_xlim([1, 17])
+        ax.set_ylim([0, 0.6e6])
+        ax.vlines(x=[3, 6], ymin=0, ymax=ax.get_ylim()[1], colors=["r", "r"])
+        ax.set_xticks(
+            ticks=[i + 0.5 for i in range(17)], labels=[i for i in range(17)]
+        )
+        ax.set_title("Number of bright pixels stacked")
+        plt.tight_layout()
+        self.pdf.savefig()
+        plt.close(f)
+        codebook_bits = np.array(self.codebook.barcode_arrays)
+        nbrs = NearestNeighbors(
+            n_neighbors=1, algorithm="ball_tree", p=1
+        ).fit(codebook_bits)
+
+        bright_pix_3bcs = np.reshape(self.bit_map[3], (16, -1)).T
+        bright_pix_4bcs = np.reshape(self.bit_map[4], (16, -1)).T
+        bright_pix_5bcs = np.reshape(self.bit_map[5], (16, -1)).T
+
+        bright_pix_3bcs = bright_pix_3bcs[bright_pix_3bcs.sum(axis=1) > 0, :]
+        bright_pix_4bcs = bright_pix_4bcs[bright_pix_4bcs.sum(axis=1) > 0, :]
+        bright_pix_5bcs = bright_pix_5bcs[bright_pix_5bcs.sum(axis=1) > 0, :]
+
+        # Look at distances for 3 pixel barcodes
+        dist3, barcode3_ids = nbrs.kneighbors(bright_pix_3bcs)
+        dist3_fraction = (dist3 == 1).sum() / (dist3 > 0).sum()
+        f, ax = plt.subplots(figsize=(10, 10))
+        plt.hist(dist3.ravel(), bins=[i for i in range(17)])
+        plt.vlines(x=[1, 2], ymin=0, ymax=ax.get_ylim()[1], colors=["r", "r"])
+        ax.set_xticks(
+            ticks=[i + 0.5 for i in range(17)], labels=[i for i in range(17)]
+        )
+        ax.set_xlabel("Manhattan Distance away from entry in codebook")
+        ax.set_ylabel("Number of Pixels")
+        plt.title(
+            "3 Bright Pixel Barcodes: Manhattan Distance \n Detection fraction: \
+            {:.02f}".format(dist3_fraction)
+        )
+        plt.tight_layout()
+        self.pdf.savefig()
+        plt.close(f)
+        # Look at distances for 4 pixel barcodes
+        dist4, barcode4_ids = nbrs.kneighbors(bright_pix_4bcs)
+        dist4_fraction = (dist4 == 0).sum() / (dist4 > -1).sum()
+        f, ax = plt.subplots(figsize=(10, 10))
+        plt.hist(dist4.ravel(), bins=[i for i in range(17)])
+        plt.vlines(x=[0, 1], ymin=0, ymax=ax.get_ylim()[1], colors=["r", "r"])
+        ax.set_xticks(
+            ticks=[i + 0.5 for i in range(17)], labels=[i for i in range(17)]
+        )
+        ax.set_xlabel("Manhattan Distance away from entry in codebook")
+        ax.set_ylabel("Number of Pixels")
+        plt.title(
+            "4 Bright Pixel Barcodes: Manhattan Distance \n Detection fraction: \
+            {:.02f}".format(dist4_fraction)
+        )
+        plt.tight_layout()
+        self.pdf.savefig()
+        plt.close(f)
+
+        # Look at distances for 5 pixel barcodes
+        dist5, barcode5_ids = nbrs.kneighbors(bright_pix_5bcs)
+        dist5_fraction = (dist5 == 1).sum() / (dist5 > -1).sum()
+        f, ax = plt.subplots(figsize=(10, 10))
+        plt.hist(dist5.ravel(), bins=[i for i in range(17)])
+        plt.vlines(x=[1, 2], ymin=0, ymax=ax.get_ylim()[1], colors=["r", "r"])
+        ax.set_xticks(
+            ticks=[i + 0.5 for i in range(17)], labels=[i for i in range(17)]
+        )
+        ax.set_xlabel("Manhattan Distance away from entry in codebook")
+        ax.set_ylabel("Number of Pixels")
+        plt.title(
+            "5 Bright Pixel Barcodes: Manhattan Distance \
+            \n Detection fraction: {:.02f}".format(
+                dist5_fraction
+            )
+        )
+        plt.tight_layout()
+        self.pdf.savefig()
+        plt.close(f)
+
+        """
+        Find out what is the bit distribution of the spots that had 3-5 bits, 
+        but didnt match the codebook
+        """
+
+        unique_dist3 = np.unique(dist3)
+        dist3_barcode_hist = np.zeros((16, 16))
+        for ud3 in unique_dist3:
+            dist3_barcode_hist[int(ud3), :] = np.sum(
+                bright_pix_3bcs[np.squeeze(dist3 == ud3), :], axis=0
+            )
+            dist3_barcode_hist[int(ud3), :] = np.nan_to_num(
+                dist3_barcode_hist[int(ud3), :]
+                / dist3_barcode_hist[int(ud3), :].sum()
+            )
+
+        unique_dist4 = np.unique(dist4)
+        dist4_barcode_hist = np.zeros((16, 16))
+        for ud4 in unique_dist4:
+            dist4_barcode_hist[int(ud4), :] = np.sum(
+                bright_pix_4bcs[np.squeeze(dist4 == ud4), :], axis=0
+            )
+            dist4_barcode_hist[int(ud4), :] = np.nan_to_num(
+                dist4_barcode_hist[int(ud4), :]
+                / dist4_barcode_hist[int(ud4), :].sum()
+            )
+
+        unique_dist5 = np.unique(dist5)
+        dist5_barcode_hist = np.zeros((16, 16))
+        for ud5 in unique_dist5:
+            dist5_barcode_hist[int(ud5), :] = np.sum(
+                bright_pix_5bcs[np.squeeze(dist5 == ud5), :], axis=0
+            )
+            dist5_barcode_hist[int(ud5), :] = np.nan_to_num(
+                dist5_barcode_hist[int(ud5), :]
+                / dist5_barcode_hist[int(ud5), :].sum()
+            )
+
+        f, ax = plt.subplots(figsize=(10, 10))
+        plt.imshow(dist3_barcode_hist)
+        for ud in range(dist3_barcode_hist.shape[0]):
+            for bp in range(dist3_barcode_hist.shape[0]):
+                _ = ax.text(
+                    bp,
+                    ud,
+                    f"{dist3_barcode_hist[ud, bp]*100:.2f}%",
+                    ha="center",
+                    va="center",
+                    color="w",
+                )
+        ax.set_ylabel("Manhattan Distance away from entry in codebook")
+        ax.set_xlabel("Bit Number")
+        plt.title("3 Bright Pixel bit distribution")
+        plt.tight_layout()
+        self.pdf.savefig()
+        plt.close(f)
+
+        f, ax = plt.subplots(figsize=(10, 10))
+        plt.imshow(dist4_barcode_hist)
+        for ud in range(dist4_barcode_hist.shape[0]):
+            for bp in range(dist4_barcode_hist.shape[0]):
+                _ = ax.text(
+                    bp,
+                    ud,
+                    f"{dist4_barcode_hist[ud, bp]*100:.2f}%",
+                    ha="center",
+                    va="center",
+                    color="w",
+                )
+        ax.set_ylabel("Manhattan Distance away from entry in codebook")
+        ax.set_xlabel("Bit Number")
+        plt.title("4 Bright Pixel bit distribution")
+        plt.tight_layout()
+        self.pdf.savefig()
+        plt.close(f)
+
+        f, ax = plt.subplots(figsize=(10, 10))
+        plt.imshow(dist5_barcode_hist)
+        for ud in range(dist5_barcode_hist.shape[0]):
+            for bp in range(dist5_barcode_hist.shape[0]):
+                _ = ax.text(
+                    bp,
+                    ud,
+                    f"{dist5_barcode_hist[ud, bp]*100:.2f}%",
+                    ha="center",
+                    va="center",
+                    color="w",
+                )
+        ax.set_ylabel("Manhattan Distance away from entry in codebook")
+        ax.set_xlabel("Bit Number")
+        plt.title("5 Bright Pixel bit distribution")
+        plt.tight_layout()
+        self.pdf.savefig()
+        plt.close(f)
+
+        errorBit3 = findErrorBits(
+            bright_pix_3bcs,
+            self.codebook_bits,
+            dist3,
+            barcode3_ids,
+            filter_val=1,
+        )
+
+        errorBit4 = findErrorBits(
+            bright_pix_4bcs,
+            self.codebook_bits,
+            dist4,
+            barcode4_ids,
+            filter_val=0,
+        )
+
+        errorBit5 = findErrorBits(
+            bright_pix_5bcs,
+            self.codebook_bits,
+            dist5,
+            barcode5_ids,
+            filter_val=1,
+        )
+
+        f, ax = plt.subplots(1, 3, figsize=(3 * 10, 10))
+        ax[0].hist(
+            errorBit3.ravel(), bins=[i for i in range(17)], align="left"
+        )
+        ax[1].hist(
+            errorBit4.ravel(), bins=[i for i in range(17)], align="left"
+        )
+        ax[2].hist(
+            errorBit5.ravel(), bins=[i for i in range(17)], align="left"
+        )
+        ax[0].set_title("Bits to correct for 3-bit detections")
+        ax[1].set_title("Bits to correct for 4-bit detections")
+        ax[2].set_title("Bits to correct for 5-bit detections")
+        self.pdf.savefig()
+        plt.close(f)
+
+        # # Save the output of detected barcodes
+        number_report = [
+            f"Number of 3bit Detections: {dist3[dist3==1].sum()}\n",
+            f"Number of 4bit Detections: {(1+dist4[dist4==0]).sum()}\n",
+            f"Number of 5bit Detections: {dist5[dist5==1].sum()}",
+        ]
+
+        with open(f"{self.out_detection_stats}", "w") as f:
+            f.writelines(number_report)
+
+
+if __name__ == "__main__":
+    print("test")
diff --git a/data/reports/FocusReport.py b/data/reports/FocusReport.py
new file mode 100644
index 0000000000000000000000000000000000000000..eeffdd051bbf8d3d7aa7ec89b4cb4eba2eca3564
--- /dev/null
+++ b/data/reports/FocusReport.py
@@ -0,0 +1,76 @@
+from .BaseReport import BaseReport
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+
+import scipy.ndimage as ndi
+
+class FocusReport(BaseReport):
+    def __init__(self,imgstack_files,coord_infos,fov,out_csv):
+        super().__init__(imgstack_files,coord_infos)
+        
+        self.fov_name = fov
+        self.imgstack = self.imgstack #(y,x,wvs,irs,zs)
+
+        self.peak_idx = np.zeros((self.imgstack.shape[2],self.imgstack.shape[3]))
+        self.out_csv = out_csv
+    def f_measure(self,img):
+        filter = np.array([ [1],
+                            [0],
+                            [-1],
+                            [0],
+                            [0]])
+
+        res = ndi.convolve(img,filter,mode='reflect')
+        res = np.power(res,2)
+        return res.sum()
+
+    #Reports-----
+    def f_measure_report(self):
+
+
+        #create an empty matrix with dimensions of ir x wv x z (rows v cols v depth) 
+        #Populate the matrix in the loop
+        #Then imshow and add text
+
+        focus_matrix = np.zeros((len(self.coords['irs']),
+                                len(self.coords['wvs']),
+                                len(self.coords['zs']))
+                                )
+
+        for iir,ir in enumerate(self.coords['irs']):
+            for iwv,wv in enumerate(self.coords['wvs']):
+                for iz,z in enumerate(self.coords['zs']):
+                    focus_matrix[iir,iwv,iz] = self.f_measure(self.imgstack[:,:,iwv,iir,iz])
+
+        viz_focus_matrix = np.argmax(focus_matrix,axis=2)
+
+        f, ax = plt.subplots()
+        im = ax.imshow(viz_focus_matrix)     
+
+        ax.set_xticks(np.arange(len(self.coords['wvs'])), self.coords['wvs'])
+        ax.set_yticks(np.arange(len(self.coords['irs'])), self.coords['irs'])
+        ax.set_xlabel("Wavelength (nm)")
+        ax.set_ylabel("Imaging Round")
+        plt.setp(ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor")
+
+        for iir in range(len(self.coords['irs'])):
+            for iwv in range(len(self.coords['wvs'])):
+                text = ax.text(iwv, iir, viz_focus_matrix[iir, iwv], ha="center", va="center", color="w")
+
+
+
+        plt.tight_layout()
+        self.pdf.savefig()
+        plt.close(f)
+
+        #Save the pk idx to a csv information for each imaging round
+        df = pd.DataFrame()
+        for iwv,wv in enumerate(self.coords['wvs']):
+            df[str(wv)] = viz_focus_matrix[:,iwv] # each imaging round is a row
+            df["FOV"] = len(viz_focus_matrix[:,0])*[self.fov_name]
+            df["IR"] = self.coords['irs']
+        df.to_csv(self.out_csv)    
+
+
diff --git a/data/reports/MaskedBrightnessReport.py b/data/reports/MaskedBrightnessReport.py
new file mode 100644
index 0000000000000000000000000000000000000000..32e15ca51599cc7054f89ce5a45756db9b5e9778
--- /dev/null
+++ b/data/reports/MaskedBrightnessReport.py
@@ -0,0 +1,11 @@
+from reports.BrightnessReport import BrightnessReport
+import numpy as np
+
+
+class MaskedBrightnessReport(BrightnessReport):
+    def __init__(self,imgstack_file,masked_images,coord_info,fov,z):
+        super().__init__(imgstack_file,coord_info,fov,z)
+        self.mask_imgstack = self._read_imgstack(masked_images)# (y,x,wvs,irs)
+
+        self.imgstack = np.multiply(self.imgstack,self.mask_imgstack)
+    
\ No newline at end of file
diff --git a/data/reports/__init__.py b/data/reports/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/data/test_notebooks/clutering_metrics.ipynb b/data/test_notebooks/clutering_metrics.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/data/test_notebooks/get_registration_offsets.py b/data/test_notebooks/get_registration_offsets.py
new file mode 100644
index 0000000000000000000000000000000000000000..7667a354b814f42422a965b0b0b68a577f6443b5
--- /dev/null
+++ b/data/test_notebooks/get_registration_offsets.py
@@ -0,0 +1,72 @@
+import argparse
+import os
+from pathlib import Path
+import numpy as np
+import pandas as pd
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-i', type = str, help ="Input directory")
+    parser.add_argument('-o', type = str, help="Output directory")
+    parser.add_argument('-file_format', default = "*.npy", type = str, help = "File to open")
+
+    args = parser.parse_args()
+
+    if not args.o:
+        args.o = args.i
+
+    if not os.path.exists(args.o):
+        os.makedirs(args.o)
+
+    input_list = _get_file_paths(args.i, args.file_format)
+
+    offset_x, offset_y = _get_x_y_offsets(input_list)
+
+    _save_csv(args.o, offset_x, "x_offsets")
+
+    _save_csv(args.o, offset_y, "y_offsets")
+
+def _get_file_paths(input_dir, img_format):
+    input_dir = Path(input_dir)
+    path_list = [str(path) for path in list(sorted(input_dir.rglob(img_format)))]
+    return path_list
+
+def _save_csv(output_dir, df, name):
+    df.to_csv(output_dir+"/"+name+".csv")
+
+
+def _get_x_y_offsets(input_list):
+    nFOV = len(input_list)
+    num_datachannels = 16
+
+    offset_array_x = np.zeros(shape = (nFOV, num_datachannels+1))
+    offset_array_y = np.zeros(shape = (nFOV, num_datachannels+1))
+
+    
+    for fov in range(nFOV):
+        tnx = np.load(input_list[fov], allow_pickle = True)
+        offset_array_x[fov,0] = int(fov+1)
+        offset_array_y[fov,0] = int(fov+1)
+        for j in range(num_datachannels):
+            offset_array_x[fov,j+1] = tnx[j].params[0][-1]
+            offset_array_y[fov,j+1] = tnx[j].params[1][-1]
+
+    pd_columns = ['FOV', 
+    'Offset b/w IR1 & IR 1', 
+    'Offset b/w IR1 & IR 2', 
+    'Offset b/w IR1 & IR 3', 
+    'Offset b/w IR1 & IR 4', 
+    'Offset b/w IR1 & IR 5', 
+    'Offset b/w IR1 & IR 6', 
+    'Offset b/w IR1 & IR 7', 
+    'Offset b/w IR1 & IR 8']
+
+    offset_pd_x = pd.DataFrame(offset_array_x[:, ::2], columns=pd_columns)
+    offset_pd_y = pd.DataFrame(offset_array_y[:, ::2], columns=pd_columns)
+
+    return offset_pd_x, offset_pd_y
+
+
+if __name__ == '__main__':
+    main()
+    # python get_registration_offsets.py -i "/Users/ythapliyal/Documents/Merlin_results/FiducialCorrelationWarp/XP4516"
diff --git a/data/utils/__init__.py b/data/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/data/utils/fileIO.py b/data/utils/fileIO.py
new file mode 100644
index 0000000000000000000000000000000000000000..966a20840281a9ece2dcb1a72ef31f25206ac866
--- /dev/null
+++ b/data/utils/fileIO.py
@@ -0,0 +1,144 @@
+import csv
+import re
+from typing import List
+import skimage.io as skio
+import numpy as np
+import json
+import pandas as pds
+
+import matplotlib.colors as mcolors
+
+
+base_colors = list(mcolors.BASE_COLORS)
+
+
+def create_image_stack(
+    image_format: str,
+    fov: str,
+    z: str,
+    irs: List[str],
+    wvs: List[str],
+    out_file: str,
+    coord_file: str,
+):
+    """Creates the images in an memory format for convenient data access in later reports
+
+    Args:
+        image_format (str): The file image pattern that will be used to construct the image stacks
+        fov (str): The Fov of images to use for the stack
+        z (str): The z of the images to use for the stack
+        irs (List[str]): The list of imaging rounds for the stack
+        wvs (List[str]): The list of channels for the stack
+        out_file (str): The filename of the image stack produced
+        coord_file (str): The filename of the coordinate file produced
+    """
+    # if any parameter is a single string, then make it into an iterable
+    # list object
+
+    if not isinstance(irs, list):
+        irs = [irs]
+    if not isinstance(wvs, list):
+        wvs = [wvs]
+
+    # Extract a test image to get size parameter out of
+    test_img = skio.imread(image_format.format(wv=wvs[0], fov=fov, ir=irs[0], z=z))
+
+    xyshape = test_img.shape
+    x = xyshape[1]
+    y = xyshape[0]
+
+    # Load in all the data
+
+    # Pre-allocate the image stack
+    img_stack = np.zeros((y, x, len(wvs), len(irs),))
+    for iir, ir in enumerate(irs):
+        for iwv, wv in enumerate(wvs):
+
+            img_stack[:, :, iwv, iir] = skio.imread(
+                image_format.format(wv=wv, fov=fov, ir=ir, z=z)
+            )
+    # Drop it all into an npy file
+    np.save(out_file, img_stack)
+
+    # Save all the coordinates for the dimensions into a seperate file
+    data = {"y": y, "x": x, "wvs": wvs, "irs": irs, "fovs": [fov], "zs": [z]}
+    a_file = open(coord_file, "w")
+    a_file = json.dump(data, a_file)
+
+
+class Codebook:
+    """The Codebook helper class
+
+    Args:
+        codebook_path (str): The path to the codebook file
+    """
+    def __init__(self, codebook_path: str):
+        # TODO this could probably work well as a pandas df
+        self.names = []
+        self.ids = []
+        self.barcode_strings = []
+        with open(codebook_path, encoding="utf8") as csv_file:
+            codebook_reader = csv.reader(csv_file)
+            for i, row in enumerate(codebook_reader):
+                if i == 0:
+                    self.version = row[1]
+                elif i == 1:
+                    self.codebook_name = row[1]
+                elif i == 2:
+                    self.bit_names = row[1:]
+                elif i >= 4:
+                    self.names.append(row[0])
+                    self.ids.append(row[1])
+                    self.barcode_strings.append(row[2])
+        self.barcode_arrays = [
+            np.array([int(char) for char in re.sub(r"\s", "", barcode)], dtype="uint8")
+            for barcode in self.barcode_strings
+        ]
+
+    def __len__(self):
+        return len(self.names)
+
+    def normalize_barcode(self, barcode_array):
+        if np.sum(barcode_array) == 0:
+            return barcode_array
+        return barcode_array / np.sqrt(np.sum(barcode_array ** 2))
+
+    def get_weighted_barcodes(self):
+        magnitudes = [np.sqrt(sum(barcodes ** 2)) for barcodes in self.barcode_arrays]
+        return [
+            (self.barcode_arrays[i] / magnitudes[i]).astype("float16")
+            for i in range(len(self.barcode_arrays))
+        ]
+
+    def get_single_bit_error_matrix(self, barcode_id):
+        barcode_array = self.barcode_arrays[barcode_id]
+        bit_error_matrix = [self.normalize_barcode(barcode_array)]
+        for i in range(len(barcode_array)):
+            corrected_barcode = barcode_array.copy()
+            corrected_barcode[i] = np.logical_not(corrected_barcode[i])
+            bit_error_matrix.append(self.normalize_barcode(corrected_barcode))
+        return np.array(bit_error_matrix)
+
+
+def read_table(file: str) -> pds.DataFrame:
+    """Reads a file differently depending on its extension
+
+    Args:
+        file (str): The filename 
+
+    Raises:
+        ValueError: If the file extension is unrecognized
+
+    Returns:
+        pandas.DataFrame: _description_
+    """
+    ext = file.split(".")[-1]
+    if ext == "csv":
+        df = pds.read_csv(file)
+    elif ext == "tsv":
+        df = pds.read_csv(file, "\t")
+    elif ext in {"xls", "xlsx", "xlsm", "xlsb"}:
+        df = pds.read_excel(file)
+    else:
+        raise ValueError("Unexpected file extension")
+    return df
diff --git a/data/utils/helper_functions.py b/data/utils/helper_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..a4cb5b801f2c7d49489c0b792a0c6bc6d0e297f7
--- /dev/null
+++ b/data/utils/helper_functions.py
@@ -0,0 +1,147 @@
+import os
+import numpy as np
+from typing import List
+from sklearn.neighbors import NearestNeighbors
+
+
+def check_dirs(files: List[str]) -> None:
+    """Checks to see if the directories for the files in the list exist. If they dont, then make those directories
+
+    Args:
+        files (list[str]): the list of files whose directory paths to create. Needs to be the full, not relative paths
+        
+    """
+    if not type(files) == list:
+        d = os.path.dirname(files)
+        if not os.path.isdir(d):
+            # print(files+'files' + d +'Does not exist')
+            os.makedirs(d)
+    else:
+        for f in files:
+
+            d = os.path.dirname(f)
+            if d != "" and not os.path.isdir(d):
+                # print(f+'files' + d +'Does not exist')
+                os.makedirs(d)
+
+
+def identity(img):
+    return img
+
+
+def transformWarpedImages(warped_images, transform, clip_thresh):
+
+    pp_wi = list()
+    filtered_wi = list()
+    for wi in range(warped_images.shape[0]):
+        _img = transform(warped_images[wi, :, :])
+
+        pp_wi.append(_img)
+
+    for wi in pp_wi:
+        _img = np.where(wi > np.percentile(wi, clip_thresh), 1, 0)
+
+        filtered_wi.append(_img)
+
+    filtered_wi = np.stack(np.array(filtered_wi), axis=0)
+
+    return filtered_wi
+
+
+def getBrightPixelCount(filtered_warped_images):
+
+    bright_pix_count = filtered_warped_images.sum(axis=0)
+
+    return bright_pix_count
+
+
+def getBarcodesFromPixelStack(codebook, pixel_stack, filtered_wi):
+
+    codebook_bits = np.array(codebook.barcode_arrays)
+    nbrs = NearestNeighbors(n_neighbors=1, algorithm="ball_tree", p=1).fit(
+        codebook_bits
+    )
+    pixel_stack = pixel_stack[np.newaxis, :, :]
+    bit_map = dict()
+    barcode_map = dict()
+    dist_map = dict()
+    bit_vector_map = dict()
+    print(f"Pixel Stack Shape={pixel_stack.shape}")
+    print(f"Filtered WI Shape={filtered_wi.shape}")
+
+    bit_map[3] = np.multiply(pixel_stack == 3, filtered_wi)
+    bit_map[4] = np.multiply(pixel_stack == 4, filtered_wi)
+    bit_map[5] = np.multiply(pixel_stack == 5, filtered_wi)
+    print(f"Bitmap 5 Shape={bit_map[5].shape}")
+    bit_vector_map[3] = np.reshape(bit_map[3], (16, -1)).T
+    bit_vector_map[4] = np.reshape(bit_map[4], (16, -1)).T
+    bit_vector_map[5] = np.reshape(bit_map[5], (16, -1)).T
+
+    # Look at distances for 3 pixel barcodes
+    dist_map[3], barcode_map[3] = nbrs.kneighbors(bit_vector_map[3])
+    dist_map[4], barcode_map[4] = nbrs.kneighbors(bit_vector_map[4])
+    dist_map[5], barcode_map[5] = nbrs.kneighbors(bit_vector_map[5])
+
+    return bit_map, bit_vector_map, dist_map, barcode_map, codebook_bits
+
+
+def filterDetections(barcode_ids, distances, filter_val):
+    return barcode_ids[distances == filter_val]
+
+
+def findErrorBits(bright_pix, codebook_bits, dist, barcode_ids, filter_val=0):
+
+    barcode_subset = barcode_ids[dist == filter_val]
+    bright_pix_subset = bright_pix[np.where(dist == filter_val)[0], :]
+    codebook_subset = codebook_bits[barcode_subset]
+    error_bit_locs = np.argmax(np.abs(bright_pix_subset - codebook_subset), axis=1)
+
+    return error_bit_locs
+
+
+def performDumbExtraction(warped_images, codebook, clip_thresh=98.5):
+    transform = identity
+
+    filtered_warped_images = transformWarpedImages(
+        warped_images, transform, clip_thresh
+    )
+
+    bright_pixel_count = getBrightPixelCount(filtered_warped_images)
+
+    (
+        bit_map,
+        bit_vector_map,
+        dist_map,
+        barcode_map,
+        codebook_bits,
+    ) = getBarcodesFromPixelStack(codebook, bright_pixel_count, filtered_warped_images)
+
+    error_bit_map = dict()
+    error_bit_map[3] = findErrorBits(
+        bit_vector_map[3], codebook_bits, dist_map[3], barcode_map[3], filter_val=1
+    )
+
+    error_bit_map[4] = findErrorBits(
+        bit_vector_map[4], codebook_bits, dist_map[4], barcode_map[4], filter_val=0
+    )
+
+    error_bit_map[5] = findErrorBits(
+        bit_vector_map[5], codebook_bits, dist_map[5], barcode_map[5], filter_val=1
+    )
+
+    detection_number_map = dict()
+    detection_number_map[3] = dist_map[3][dist_map[3] == 1].sum()
+    detection_number_map[4] = (1 + dist_map[4][dist_map[4] == 0]).sum()
+    detection_number_map[5] = dist_map[5][dist_map[5] == 1].sum()
+
+    return (
+        filtered_warped_images,
+        bright_pixel_count,
+        bit_map,
+        bit_vector_map,
+        dist_map,
+        barcode_map,
+        error_bit_map,
+        detection_number_map,
+        codebook_bits,
+    )
diff --git a/data/utils/imgproc.py b/data/utils/imgproc.py
new file mode 100644
index 0000000000000000000000000000000000000000..34371b7e1cc2017566d4bd5d328272596d3ff0d4
--- /dev/null
+++ b/data/utils/imgproc.py
@@ -0,0 +1,218 @@
+import numpy as np
+from functools import partial
+from skimage import restoration,filters
+import skimage.io as skio
+from skimage.exposure import equalize_adapthist
+import skimage
+from skimage import registration
+import scipy.ndimage as ndi
+import pandas as pd
+def read_table(file):
+    """
+    Reads a file into a data frame based on it's extension
+    :param file: the file to read
+    :return: the pandas DataFrame
+    """
+    ext = file.split(".")[-1]
+    if ext == "csv":
+        df = pd.read_csv(file)
+    elif ext == "tsv":
+        df = pd.read_csv(file, '\t')
+    elif ext in {"xls", "xlsx", "xlsm", "xlsb"}:
+        df = pd.read_excel(file)
+    else:
+        raise ValueError("Unexpected file extension")
+    return df
+
+def load_data_organization(data_org_file):
+    """Loads the data organization file as a list of dictionaries
+     based on the extension
+
+    Args:
+        data_org_file: a path to a data organaization in the standard format
+    
+    Returns:
+        data_org: a list of ditionaries containing each imaging rounds information
+    """
+    return read_table(data_org_file).to_dict("records")
+
+def warp_image(data_org, image_stack,bit_num):
+    
+    warped_image_stack = []
+    for bit_num in range(bit_num):
+        data_org_row = data_org[bit_num]
+        wv_idx =int(data_org_row["frame"]) - 1
+        ir_idx = int(data_org_row["imagingRound"])
+        im = image_stack[:,:,wv_idx,ir_idx]
+        
+        warped_image_stack.append(im)
+
+    warped_imgs = np.stack(warped_image_stack,axis=0)
+
+    return warped_imgs    
+    
+
+def register_stack(image_stack):
+    # image stack is a 4d numpy array
+    # (x,y,wv,ir)
+    # take out the 2nd wavelength [should be 561] and then register all the images with respect to the first imaging round
+    fiducial_wv = 1 # wavelength index for beads channel
+    ref_img = image_stack[:,:,fiducial_wv,0]
+    registered_images = image_stack.copy()
+    for ir_idx in range(1,image_stack.shape[3]):
+        img = image_stack[:,:,fiducial_wv,ir_idx]
+        shift, _, _ = registration.phase_cross_correlation(
+            ref_img, img, upsample_factor=100
+        )
+        for wv_idx in range(image_stack.shape[2]):
+            _img = image_stack[:,:,wv_idx,ir_idx]
+            registered_images[:,:,wv_idx,ir_idx] = ndi.shift(_img,shift)
+    return registered_images
+
+# Deconvolute Images
+def _deconvolute(image_stack,out_file):
+    """ Deconvolutes image to sharpen features with filtering and richardson lucy restoration
+    Args:
+        raw_image: 2D image
+        dtype: the dtype of the raw_image being passed in
+    
+    Returns:
+        img: filtered and restored image. This image maintains the original dtype
+    """
+
+    _imgstack = np.load(image_stack) #(y,x,wv,ir)
+    d_imgstack = _imgstack.copy()
+    for iir in range(_imgstack.shape[3]):
+        for iwv in range(_imgstack.shape[2]):
+            if iwv==1:
+                continue
+            raw_image = _imgstack[:,:,iwv,iir]
+
+            img = raw_image.astype("uint16")
+            # High pass filtering
+            filt = (
+                filters.gaussian(
+                    img, sigma=3, mode="nearest", truncate=2, preserve_range=True
+                )
+                .round()
+                .astype("uint16")
+            )
+            img = np.subtract(img, filt, where=img > filt, out=np.zeros_like(img))
+
+            # Point spread function deconvolution
+            psf = _gaussian_kernel((10, 10), 2)
+
+            np.where(img == np.nan, 0, img)
+            img = restoration.richardson_lucy(img, psf, iterations=20, clip=False)
+
+            # Low pass filtering
+            img = filters.gaussian(
+                img,
+                sigma=1,
+                output=None,
+                cval=0,
+                multichannel=False,
+                preserve_range=True,
+                truncate=4.0,
+            )
+
+            d_imgstack[:,:,iwv,iir] = img
+    
+    np.save(out_file,d_imgstack)
+
+
+def _get_psf(sigma):
+    """ Gets a point spread function for a gaussian kernel
+    Args:
+        sigma: variance
+    
+    Returns:
+        gaussian_kernel
+    """
+    kernel_size = int(2 * np.ceil(2 * sigma) + 1)
+    return _gaussian_kernel(shape=(kernel_size, kernel_size), sigma=sigma)
+
+
+def _gaussian_kernel(shape=(3, 3), sigma=0.5):
+    """ Generates a gaussian kernel
+    Args:
+        shape: kernel dimensions
+        sigma: variance
+    
+    Returns:
+        kernel: a gaussian kernel
+    """
+    m, n = [int((ss - 1.0) / 2.0) for ss in shape]
+    y, x = np.ogrid[-m : m + 1, -n : n + 1]
+    kernel = np.exp(-(x * x + y * y) / (2.0 * sigma * sigma))
+    kernel[kernel < np.finfo(kernel.dtype).eps * kernel.max()] = 0
+    sumh = kernel.sum()
+    if sumh != 0:
+        kernel /= sumh
+    return kernel
+
+
+
+
+def deconvolve_img(img,psf=None):
+
+    if psf is None:
+        psf = getPSF()
+    
+    #perform high pass filtering first
+    
+    
+    
+    
+    deconvolved_RL = restoration.richardson_lucy(img, psf, num_iter=10)
+
+    return deconvolved_RL
+
+def equalize_hist(img):
+    
+
+    clip_limit=0.05
+    kernel_size = 10
+
+    _img = equalize_adapthist(img,kernel_size=kernel_size,clip_limit=clip_limit)
+
+    return _img
+
+def getPSF(sig=None,size=5):
+    if sig is None:
+        sig = size/6
+
+    x,y = np.mgrid[-int(size//2):int(size//2),
+            -int(size//2):int(size//2)]
+
+
+    return gaussianPSF(x,y,sig)
+
+
+def gaussianPSF(x,y,sig):
+    t = np.array([[x],[y]])
+
+    if not isinstance(sig,np.array):
+        sigma = np.array([[sig]])
+    else:
+        sigma = sig
+
+    if sigma.shape[0] != sigma.shape[1] or len(sigma.shape)!=2:
+        print('sigma is baddddd')
+    
+    t = -0.5*t.T@np.linalg.inv(sigma)@t
+
+    return np.exp(t)
+
+
+
+def maskImages(d_img_file,out_mask,percentile=98.5):
+    d_imgstack = np.load(d_img_file)
+    mask_imgstack = np.zeros_like(d_imgstack)
+    for iir in range(d_imgstack.shape[2]):
+        for iwv in range(d_imgstack.shape[3]):
+            _img = d_imgstack[:,:,iir,iwv]
+            clip = np.percentile(_img,percentile)
+            mask_imgstack[:,:,iir,iwv] = np.where(_img>clip,1,0)
+
+    np.save(out_mask,mask_imgstack)
\ No newline at end of file