diff --git a/PyNutil/PyNutil.py b/PyNutil/coordinate_extraction.py
similarity index 91%
rename from PyNutil/PyNutil.py
rename to PyNutil/coordinate_extraction.py
index 89ef7476fed2554baa176a4397be4bfdbf4d3246..453fa27dc85e9bf598b2635589f0004f806ce707 100644
--- a/PyNutil/PyNutil.py
+++ b/PyNutil/coordinate_extraction.py
@@ -29,6 +29,8 @@ def getCentroidsAndArea(Segmentation, pixelCutOff=0):
 
 
 
+
+
 def transformToRegistration(SegHeight, SegWidth, RegHeight, RegWidth):
     """this function returns the scaling factors to transform the segmentation to the registration space"""
     Yscale = RegHeight/SegHeight
@@ -94,14 +96,19 @@ def SegmentationToAtlasSpace(slice, SegmentationPath, pixelID='auto', nonLinear=
     RegWidth  = slice["width"]
     #this calculates reg/seg
     Yscale , Xscale = transformToRegistration(SegHeight,SegWidth,  RegHeight,RegWidth)
-    #this creates a triangulation using the reg width
-    triangulation   = triangulate(RegWidth, RegHeight, slice["markers"])
-    #scale the seg coordinates to reg/seg
     scaledY,scaledX = scalePositions(ID_pixels[0], ID_pixels[1], Yscale, Xscale)
     if nonLinear:
-        newX, newY = transform_vec(triangulation, scaledX, scaledY)
+        if "markers" in slice:
+            #this creates a triangulation using the reg width
+            triangulation   = triangulate(RegWidth, RegHeight, slice["markers"])
+            newX, newY = transform_vec(triangulation, scaledX, scaledY)
+        else:
+            print(f"no markers found for " + slice["filename"])
+            newX, newY = scaledX, scaledY
     else:
         newX, newY = scaledX, scaledY
+
+
     #scale U by Uxyz/RegWidth and V by Vxyz/RegHeight
     points = transformToAtlasSpace(slice['anchoring'], newY, newX, RegHeight, RegWidth)
     # points = points.reshape(-1)
@@ -113,7 +120,7 @@ def FolderToAtlasSpace(folder, QUINT_alignment, pixelID=[0, 0, 0], nonLinear=Tru
     slices = loadVisuAlignJson(QUINT_alignment)
     points = []
     segmentationFileTypes = [".png", ".tif", ".tiff", ".jpg", ".jpeg"] 
-    Segmentations = [file for file in glob(folder + "*") if any([file.endswith(type) for type in segmentationFileTypes])]
+    Segmentations = [file for file in glob(folder + "/*") if any([file.endswith(type) for type in segmentationFileTypes])]
     SectionNumbers = number_sections(Segmentations)
     #order segmentations and sectionNumbers
     # Segmentations = [x for _,x in sorted(zip(SectionNumbers,Segmentations))]
@@ -125,12 +132,13 @@ def FolderToAtlasSpace(folder, QUINT_alignment, pixelID=[0, 0, 0], nonLinear=Tru
         ##this converts the segmentation to a point cloud
         points.extend(SegmentationToAtlasSpace(current_slice, SegmentationPath, pixelID, nonLinear))
     return np.array(points)
+
 def FolderToAtlasSpaceMultiThreaded(folder, QUINT_alignment, pixelID=[0, 0, 0], nonLinear=True):
     "apply Segmentation to atlas space to all segmentations in a folder"
     slices = loadVisuAlignJson(QUINT_alignment)
     
     segmentationFileTypes = [".png", ".tif", ".tiff", ".jpg", ".jpeg"] 
-    Segmentations = [file for file in glob(folder + "*") if any([file.endswith(type) for type in segmentationFileTypes])]
+    Segmentations = [file for file in glob(folder + "/*") if any([file.endswith(type) for type in segmentationFileTypes])]
     SectionNumbers = number_sections(Segmentations)
     #order segmentations and sectionNumbers
     # Segmentations = [x for _,x in sorted(zip(SectionNumbers,Segmentations))]
@@ -172,12 +180,17 @@ def SegmentationToAtlasSpaceMultiThreaded(slice, SegmentationPath, pixelID='auto
     RegWidth  = slice["width"]
     #this calculates reg/seg
     Yscale , Xscale = transformToRegistration(SegHeight,SegWidth,  RegHeight,RegWidth)
-    #this creates a triangulation using the reg width
-    triangulation   = triangulate(RegWidth, RegHeight, slice["markers"])
+
     #scale the seg coordinates to reg/seg
     scaledY,scaledX = scalePositions(ID_pixels[0], ID_pixels[1], Yscale, Xscale)
     if nonLinear:
-        newX, newY = transform_vec(triangulation, scaledX, scaledY)
+        if "markers" in slice:
+            #this creates a triangulation using the reg width
+            triangulation   = triangulate(RegWidth, RegHeight, slice["markers"])
+            newX, newY = transform_vec(triangulation, scaledX, scaledY)
+        else:
+            print(f"no markers found for " + slice["filename"])
+            newX, newY = scaledX, scaledY
     else:
         newX, newY = scaledX, scaledY
     #scale U by Uxyz/RegWidth and V by Vxyz/RegHeight
diff --git a/PyNutil/dream_workflow.py b/PyNutil/dream_workflow.py
new file mode 100644
index 0000000000000000000000000000000000000000..5f53d9eb732967c4cfc0a1619976e92ea5fa6c5d
--- /dev/null
+++ b/PyNutil/dream_workflow.py
@@ -0,0 +1,36 @@
+import PyNutil
+
+#define parameters
+#specify loacation of segmentation folder
+segmentation_folder = r"blabla/blabla"
+#specify location of json file
+json_file = r"blabla/blabla.json"
+#specify colour to quantify
+colour = [255, 255, 255]
+#specify output location
+output_path = r"blabla/blabla/output"
+
+quantifier = PyNutil(segmentation_folder,
+                                    json_file,
+                                    colour,
+                                    output_path)
+
+quantifier.build_quantifier()
+
+#define your mask as either a png, or a qcalign damage map
+#this mask will be applied to all 
+quantifier.load_mask(mask_path=r"blablabla/")
+
+#load a custom region file
+quantifier.load_custom_regions(custom_region_json=r"blablabla/")
+#run coordinate extraction
+#ideally extract coordinates per section and whole brain
+points = quantifier.get_coordinates()
+
+quantifier.save_coordinates()
+
+objects = quantifier.get_objects()
+
+loads = quantifier.get_loads()
+
+quantifier.save_segmentation_atlas_overlays()
\ No newline at end of file
diff --git a/PyNutil/folder_of_segmentations_to_meshview_multithreaded.py b/PyNutil/folder_of_segmentations_to_meshview_multithreaded.py
index fdb50aa563315d20051f0680c8cc3aec4d445fd7..20cf0323370ab27961c7261071c225a44575187f 100644
--- a/PyNutil/folder_of_segmentations_to_meshview_multithreaded.py
+++ b/PyNutil/folder_of_segmentations_to_meshview_multithreaded.py
@@ -8,24 +8,31 @@ import csv
 
 from datetime import datetime
 
+#import json and use it to define volume_path, segmentation_folder, alignment_json, label_path, colour, allen_colours
+
+
 #import our function for converting a folder of segmentations to points
 from PyNutil import FolderToAtlasSpace, labelPoints, WritePointsToMeshview, FolderToAtlasSpaceMultiThreaded
-
+label_path = "../annotation_volumes//allen2022_colours.csv"
+colour = [255, 0, 255]
 volume_path = "../annotation_volumes//annotation_10_reoriented.nrrd"
 data, header = nrrd.read(volume_path)
+points_json_path = "../outputs/points.json"
+segmentation_folder = "../test_data/oneSection15"
+alignment_json = "../test_data/C68_nonlinear_no_markers.json"
+allen_colours = "../annotation_volumes//allen2022_colours.csv"
+counts_per_label_name = "../outputs/counts_per_allenID.csv"
 
 startTime = datetime.now()
 
-segmentation_folder = "../test_data/tTA_2877_NOP/"
-alignment_json = "../test_data/tTA_2877_NOP_horizontal_final_2017.json"
+
 #now we can use our function to convert the folder of segmentations to points
-points = FolderToAtlasSpaceMultiThreaded(segmentation_folder,alignment_json, pixelID=[0, 0, 255], nonLinear=True)
+points = FolderToAtlasSpaceMultiThreaded(segmentation_folder,alignment_json, pixelID=colour, nonLinear=True)
 
 time_taken = datetime.now() - startTime
 
 print(f"Folder to atlas took: {time_taken}")
 #first we need to find the label file for the volume
-label_path = "../annotation_volumes//allen2022_colours.csv"
 #then the path to the volume
 
 #read the label files
@@ -34,7 +41,7 @@ label_df = pd.read_csv(label_path)
 #now we can get the labels for each point
 labels = labelPoints(points, data, scale_factor=2.5)
 #save points to a meshview json
-WritePointsToMeshview(points, labels,"../outputs/points.json", label_df)
+WritePointsToMeshview(points, labels,points_json_path, label_df)
 
 #Task:
 # Make a pandas dataframe
@@ -48,7 +55,6 @@ counts_per_label = list(zip(counted_labels,label_counts))
 
 df_counts_per_label = pd.DataFrame(counts_per_label, columns=["allenID","pixel count"])
 
-allen_colours = "../annotation_volumes//allen2022_colours.csv"
 
 df_allen_colours =pd.read_csv(allen_colours, sep=",")
 df_allen_colours
@@ -74,7 +80,7 @@ df_counts_per_label_name = pd.DataFrame(new_rows)
 df_counts_per_label_name
 
 # write to csv file
-df_counts_per_label_name.to_csv("../outputs/counts_per_allenID.csv", sep=";", na_rep='', index= False)
+df_counts_per_label_name.to_csv(counts_per_label_name, sep=";", na_rep='', index= False)
 
 #r = df_allen_colours["r"]
 #g = df_allen_colours["g"]
diff --git a/PyNutil/main.py b/PyNutil/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf284b4c229aa48f7caab40cb82444fb4e3acf85
--- /dev/null
+++ b/PyNutil/main.py
@@ -0,0 +1,14 @@
+
+
+
+
+class PyNutil:
+    def __init__(self, segmentation_folder) -> None:
+        self.segmentation_folder = segmentation_folder
+        self.json_file = json_file
+        self.colour = colour
+        self.output_path = output_path
+
+    def build_quantifier(self):
+        #do all the expensive computations
+
diff --git a/PyNutil/object_counting.py b/PyNutil/object_counting.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/PyNutil/read_and_write.py b/PyNutil/read_and_write.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/test_data/C68_nonlinear_no_markers.json b/test_data/C68_nonlinear_no_markers.json
new file mode 100644
index 0000000000000000000000000000000000000000..72dffc3182462cbcb07e3f89b258c85f4a75545b
--- /dev/null
+++ b/test_data/C68_nonlinear_no_markers.json
@@ -0,0 +1,51 @@
+{"name":"Test series","target":"ABA_Mouse_CCFv3_2017_25um.cutlas","target-resolution":[456.0, 528.0, 320.0],"slices":[
+{"filename":"1362_5366_7648_D1R_P49_F_C68_s001.png","nr":1,"width":13097,"height":18492,"anchoring":[230.14674200004373, 431.6345492635493, 253.5698870936448, -112.76432204654904, -0.4109929005257129, -7.325477280745247, 12.919121055200655, 43.01607526489645, -201.28316180093938]},
+{"filename":"1362_5366_7648_D1R_P49_F_C68_s002.png","nr":2,"width":27531,"height":21723,"anchoring":[353.6123415897215, 421.48087125870757, 256.41882506743565, -237.68709542861797, -8.083848113249593, 18.509925534916544, -18.215706928048096, 45.2109471833391, -214.1640188310091],"markers":[[11890.247471743012, 1571.520723436323, 11955.758477096968, 1162.2705350414465], [5531.372609278058, 1999.4940737739316, 4782.303390838785, 1358.7106254709875], [5078.976552776763, 10168.902827534404, 5027.96966091612, 11098.865109269027], [2143.4229148869467, 5140.00467490576, 753.3765615704938, 4960.112283345893], [8939.472272322024, 8725.833099785448, 9056.896490184414, 10100.294649585534], [9922.60021900368, 19364.441071812027, 10072.317073170732, 18285.29841748305], [5896.039080782744, 10971.713944657618, 6338.18976799524, 11900.995478522982], [6796.323678634961, 18047.43510392754, 7157.077334919692, 17335.837980406934], [13748.23031296856, 9084.597294556723, 13708.177870315289, 8987.13413715147], [20166.212177360023, 16162.540125918491, 20701.47769185009, 16419.11755840241], [25803.592847639688, 9495.57416585216, 25254.492563950032, 9019.874152223058], [23484.52236649051, 3391.332526858394, 23059.873884592504, 3683.2516955538813], [16401.005282920283, 1940.3822556412397, 16688.92861392029, 2259.0610399397137], [15570.354648859375, 9869.0382255145, 15493.35276621059, 9298.164280331577], [20112.643823426723, 10017.776120699516, 19538.657346817366, 9772.894498869631], [15770.508340184544, 19777.23666856384, 15624.374776918501, 18841.878673700074]]}, 
+{"filename":"1362_5366_7648_D1R_P49_F_C68_s003.png","nr":3,"width":30402,"height":20630,"anchoring":[365.3330535252807, 409.6529969316114, 253.73864201997074, -263.4290151901554, -9.46040112187291, 22.871515959985697, -19.332911399154455, 43.201324903157136, -204.80268803574717],"markers":[[15379.835294117645, 3808.8545591559905, 15566.446035805626, 3933.2253202712886], [1940.0602738379098, 6169.365349387863, 1446.2332480818413, 6171.899020346647], [6072.849921357663, 11571.978252731436, 6593.5795396419435, 12234.973624717406], [8623.362568529252, 18217.8095927076, 8288.627109974424, 18375.779954785226], [12922.424394759033, 17964.719798336017, 13156.057289002558, 18095.94574227581], [16645.335700696585, 19979.475650245593, 16888.272122762144, 19448.47776940467], [14772.597349688142, 17730.933565216637, 14508.985166240407, 17816.11152976639], [23246.99109686503, 13129.732535093819, 22315.53452685422, 13245.4860587792], [19774.64390877671, 18166.525763974125, 19858.493094629153, 18033.760361718163], [28533.395713305385, 9551.908118086012, 28022.713043478263, 8861.416729464956], [12260.88293235072, 170.34473380395048, 12160.8, 419.75131876412956], [20018.621343465973, 663.9989201712581, 20433.87621483376, 1026.0587792012057], [14719.830638132058, 10583.351485232713, 14680.045012787725, 11442.110022607385], [20212.389473119274, 10852.988436963104, 19469.720716112537, 11597.573474001507], [17470.134971688323, 11345.75197713033, 17339.248081841437, 12639.178598342123]]}, 
+{"filename":"1362_5366_7648_D1R_P49_F_C68_s004.png","nr":4,"width":27509,"height":21707,"anchoring":[339.0765031967628, 399.8075106604399, 263.77795561793414, -240.36460376655967, -5.675552029528489, 7.007095236580368, -7.446481686092206, 46.206622453183975, -218.01081311350754],"markers":[[12682.61451516954, 3287.948003014318, 12535.332540154672, 4138.561416729464], [22354.908993019188, 2251.085634768103, 22386.86020226056, 1586.721175584024], [16640.630205936475, 403.0602932890524, 16561.039857227843, 899.6872645064054], [26577.695708450636, 9698.929415441413, 26478.02617489589, 7835.458176337603], [21266.899872598093, 13440.349128568709, 21503.16835217133, 13004.570459683497], [12267.607197288242, 11526.803144884805, 13271.742415229028, 12219.388847023361], [12720.74201420031, 19515.807564988787, 15399.148720999407, 19580.466465712136], [17463.08503993057, 18389.228071586447, 19523.044021415826, 18729.853051996983], [15489.323882369255, 11470.384745866711, 16201.01725163593, 12088.525244913339], [17542.03032347336, 13786.429460846113, 18328.42355740631, 14378.638281838732]]}, 
+{"filename":"1362_5366_7648_D1R_P49_F_C68_s005.png","nr":5,"width":31847,"height":22804,"anchoring":[362.8206652853157, 396.55277899326546, 266.29989017676274, -279.4951443291282, -7.409199149642796, 11.930875762988302, -11.151936831334133, 48.884536521731114, -230.88975046921396],"markers":[[15227.437668645438, 3505.6639035418243, 15416.491635186185, 3574.402411454409], [6585.318167886053, 1750.726545194821, 6324.714516999459, 1477.8779201205728], [2816.730399075127, 11557.215103708158, 3196.730706961683, 11273.115297663902], [5716.06235635927, 13206.666540608248, 6668.449001618997, 13180.608892238131], [14497.78091120998, 16841.83254810219, 14196.234214786831, 16497.241899020348], [11801.495005202774, 20074.85880500027, 11291.677819751754, 19023.38206480784], [23589.770727688458, 1929.6279103454278, 23872.359956826767, 1632.5395629238885], [17162.38945575009, 20248.703442003338, 17066.417161359957, 19435.813112283347], [23743.99352061202, 14017.28253674331, 22566.16891527253, 14744.409947249434], [12894.688051212885, 12648.662726275468, 12735.362655153807, 12218.269781461942], [9435.704235607775, 11482.063398706308, 9796.432811656774, 11341.853805576486], [21040.7932694398, 12098.590291453678, 20091.280626011874, 12768.17784476262]]}, 
+{"filename":"1362_5366_7648_D1R_P49_F_C68_s006.png","nr":6,"width":34725,"height":22801,"anchoring":[381.9556105399471, 390.3618973531922, 265.4632480151974, -306.11102806816587, -9.08168942115185, 17.585592175795057, -14.834673167135909, 49.232020840107126, -232.80120967644288],"markers":[[17259.458456973294, 3384.926149208741, 17087.72255192878, 3556.749811605124], [7631.185067333177, 1849.581547851796, 6732.047477744806, 1374.5892991710625], [2641.814631490097, 6258.203313036547, 1974.9629080118689, 5618.633760361719], [6806.739816439247, 13842.101989145827, 7367.470326409496, 13848.987189148454], [10886.064356054892, 18845.235569012068, 10802.188427299701, 18986.514694800302], [16359.822027440505, 15471.263528223459, 15524.925816023739, 15550.041446872645], [15110.865404084265, 19317.472032794172, 13738.872403560832, 19519.168048229083]]}, 
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