diff --git a/PyNutil/folder_of_segmentations_to_meshview.py b/PyNutil/folder_of_segmentations_to_meshview.py
deleted file mode 100644
index 2cfe10cc1585891e3f2ed5afc65235b22fe569b2..0000000000000000000000000000000000000000
--- a/PyNutil/folder_of_segmentations_to_meshview.py
+++ /dev/null
@@ -1,94 +0,0 @@
-# pandas is used for working with csv files
-import pandas as pd
-
-# nrrd just lets us open nrrd files
-import nrrd
-import numpy as np
-import csv
-import json
-
-from datetime import datetime
-
-# import our function for converting a folder of segmentations to points
-from PyNutil import FolderToAtlasSpace, labelPoints, WritePointsToMeshview
-
-volume_path = "../annotation_volumes//annotation_10_reoriented.nrrd"
-data, header = nrrd.read(volume_path)
-
-startTime = datetime.now()
-
-segmentation_folder = "../test_data/ext-d000033_PVMouseExtraction_pub-Nutil_Quantifier_analysis-81264-Input_dir/"
-alignment_json = "../test_data/PVMouse_81264_nonlin.json"
-# now we can use our function to convert the folder of segmentations to points
-points = FolderToAtlasSpace(
- segmentation_folder, alignment_json, pixelID=[255, 0, 0], 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
-label_df = pd.read_csv(label_path)
-# read the annotation volume, it also has a header but we don't need it
-# now we can get the labels for each point
-labels = labelPoints(points, data, scale_factor=1)
-# save points to a meshview json
-WritePointsToMeshview(points, labels, "../outputs/points.json", label_df)
-
-# Task:
-# Make a pandas dataframe
-# Column 1: counted_labels
-# Column 2: label_counts
-# Column 3: region_name (look up by reading Allen2022_colours.csv, look up name and RGB)
-# Save dataframe in output as CSV
-# next task is to create functions from this.
-counted_labels, label_counts = np.unique(labels, return_counts=True)
-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
-
-# look up name, r, g, b in df_allen_colours in df_counts_per_label based on "allenID"
-new_rows = []
-for index, row in df_counts_per_label.iterrows():
- mask = df_allen_colours["allenID"] == row["allenID"]
- current_region_row = df_allen_colours[mask]
- current_region_name = current_region_row["name"].values
- current_region_red = current_region_row["r"].values
- current_region_green = current_region_row["g"].values
- current_region_blue = current_region_row["b"].values
-
- row["name"] = current_region_name[0]
- row["r"] = current_region_red[0]
- row["g"] = current_region_green[0]
- row["b"] = current_region_blue[0]
-
- new_rows.append(row)
-
-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
-)
-
-# r = df_allen_colours["r"]
-# g = df_allen_colours["g"]
-# b = df_allen_colours["b"]
-# region_name = df_allen_colours["name"]
-
-# while we havent added it here it would be good to next quantify the number of cells for each label.
-
-time_taken = datetime.now() - startTime
-
-print(f"time taken was: {time_taken}")
diff --git a/PyNutil/folder_of_segmentations_to_meshview_multithreaded.py b/PyNutil/folder_of_segmentations_to_meshview_multithreaded.py
deleted file mode 100644
index 4face1fc061d66275cac8f8a1e06549a1efb4229..0000000000000000000000000000000000000000
--- a/PyNutil/folder_of_segmentations_to_meshview_multithreaded.py
+++ /dev/null
@@ -1,64 +0,0 @@
-# pandas is used for working with csv files
-import pandas as pd
-
-# nrrd just lets us open nrrd files
-import nrrd
-import numpy as np
-import csv
-import json
-
-from datetime import datetime
-
-# import json, use to define input parameters
-with open("../test/test5_NOP_s037.json", "r") as f:
- input = json.load(f)
-# print(input)
-
-# import our function for converting a folder of segmentations to points
-from coordinate_extraction import FolderToAtlasSpace, FolderToAtlasSpace
-from read_and_write import SaveDataframeasCSV, WritePointsToMeshview, FilesinDirectory
-from counting_and_load import PixelCountPerRegion, labelPoints
-
-startTime = datetime.now()
-
-# now we can use our function to convert the folder of segmentations to points
-points = FolderToAtlasSpace(
- input["segmentation_folder"],
- input["alignment_json"],
- pixelID=input["colour"],
- nonLinear=input["nonlinear"],
-)
-
-time_taken = datetime.now() - startTime
-print(f"Folder to atlas took: {time_taken}")
-# first we need to find the label file for the volume
-# then the path to the volume
-
-# read the label files
-label_df = pd.read_csv(input["label_path"])
-# read the annotation volume, it also has a header but we don't need it
-data, header = nrrd.read(input["volume_path"])
-# now we can get the labels for each point
-labels = labelPoints(points, data, scale_factor=2.5)
-
-# save points to a meshview jsonv
-WritePointsToMeshview(points, labels, input["points_json_path"], label_df)
-
-df_counts_per_label_name = PixelCountPerRegion(labels, input["label_path"])
-SaveDataframeasCSV(df_counts_per_label_name, input["counts_per_label_name"])
-
-time_taken = datetime.now() - startTime
-
-print(f"overall time taken was: {time_taken}")
-
-# while we havent added it here it would be good to next quantify the number of cells for each label.
-# get centroids and areas returns a list of objects and the center coordinate.
-# we need to deform the center coordinate according to visualign deformations¨
-# we need to then transform the coordinate into atlas space
-# and then save labels like before.
-
-
-# Create a list of flat file names from a directory:
-
-# flatfiles = FilesinDirectory('../test_data/ttA_2877_NOP_atlasmaps')
-# print(flatfiles)