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diff --git a/utils/raspberrypi/ctt/cac_only.py b/utils/raspberrypi/ctt/cac_only.py
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+#!/usr/bin/env python3
+#
+# SPDX-License-Identifier: BSD-2-Clause
+#
+# Copyright (C) 2023, Raspberry Pi (Trading) Limited
+#
+# cac_only.py - cac tuning tool
+
+
+# This file allows you to tune only the chromatic aberration correction
+# Specify any number of files in the command line args, and it shall iterate through
+# and generate an averaged cac table from all the input images, which you can then
+# input into your tuning file.
+
+# Takes .dng files produced by the camera modules of the dots grid and calculates the chromatic abberation of each dot.
+# Then takes each dot, and works out where it was in the image, and uses that to output a tables of the shifts
+# across the whole image.
+
+from PIL import Image
+import numpy as np
+import rawpy
+import sys
+import getopt
+
+from ctt_cac import *
+
+
+def cac(filelist, output_filepath, plot_results=False):
+    np.set_printoptions(precision=3)
+    np.set_printoptions(suppress=True)
+
+    # Create arrays to hold all the dots data and their colour offsets
+    red_shift = []  # Format is: [[Dot Center X, Dot Center Y, x shift, y shift]]
+    blue_shift = []
+    # Iterate through the files
+    # Multiple files is reccomended to average out the lens aberration through rotations
+    for file in filelist:
+        print("\n Processing file " + str(file))
+        # Read the raw RGB values from the .dng file
+        with rawpy.imread(file) as raw:
+            rgb = raw.postprocess()
+            sizes = (raw.sizes)
+
+        image_size = [sizes[2], sizes[3]]  # Image size, X, Y
+        # Create a colour copy of the RGB values to use later in the calibration
+        imout = Image.new(mode="RGB", size=image_size)
+        rgb_image = np.array(imout)
+        # The rgb values need reshaping from a 1d array to a 3d array to be worked with easily
+        rgb.reshape((image_size[0], image_size[1], 3))
+        rgb_image = rgb
+
+        # Pass the RGB image through to the dots locating program
+        # Returns an array of the dots (colour rectangles around the dots), and an array of their locations
+        print("Finding dots")
+        dots, dots_locations = find_dots_locations(rgb_image)
+
+        # Now, analyse each dot. Work out the centroid of each colour channel, and use that to work out
+        # by how far the chromatic aberration has shifted each channel
+        print('Dots found: ' + str(len(dots)))
+
+        for dot, dot_location in zip(dots, dots_locations):
+            if len(dot) > 0:
+                if (dot_location[0] > 0) and (dot_location[1] > 0):
+                    ret = analyse_dot(dot, dot_location)
+                    red_shift.append(ret[0])
+                    blue_shift.append(ret[1])
+
+    # Take our arrays of red shifts and locations, push them through to be interpolated into a 9x9 matrix
+    # for the CAC block to handle and then store these as a .json file to be added to the camera
+    # tuning file
+    print("\nCreating output grid")
+    rx, ry, bx, by = shifts_to_yaml(red_shift, blue_shift, image_size)
+
+    print("CAC correction complete!")
+
+    # The json format that we then paste into the tuning file (manually)
+    sample = '''
+    {
+        "rpi.cac" :
+        {
+            "strength": 1.0,
+            "lut_rx" : [
+                   rx_vals
+            ],
+            "lut_ry" : [
+                   ry_vals
+            ],
+            "lut_bx" : [
+                   bx_vals
+            ],
+            "lut_by" : [
+                   by_vals
+            ]
+        }
+    }
+    '''
+
+    # Below, may look incorrect, however, the PiSP (standard) dimensions are flipped in comparison to
+    # PIL image coordinate directions, hence why xr -> yr. Also, the shifts calculated are colour shifts,
+    # and the PiSP block asks for the values it should shift (hence the * -1, to convert from colour shift to a pixel shift)
+    sample = sample.replace("rx_vals", pprint_array(ry * -1))
+    sample = sample.replace("ry_vals", pprint_array(rx * -1))
+    sample = sample.replace("bx_vals", pprint_array(by * -1))
+    sample = sample.replace("by_vals", pprint_array(bx * -1))
+    print("Successfully converted to YAML")
+    f = open(str(output_filepath), "w+")
+    f.write(sample)
+    f.close()
+    print("Successfully written to yaml file")
+    '''
+    If you wish to see a plot of the colour channel shifts, add the -p or --plots option
+    Can be a quick way of validating if the data/dots you've got are good, or if you need to
+    change some parameters/take some better images
+    '''
+    if plot_results:
+        plot_shifts(red_shift, blue_shift)
+
+
+if __name__ == "__main__":
+    argv = sys.argv
+    # Detect the input and output file paths
+    arg_output = "output.json"
+    arg_help = "{0} -i <input> -o <output> -p <plot results>".format(argv[0])
+    opts, args = getopt.getopt(argv[1:], "hi:o:p", ["help", "input=", "output=", "plot"])
+
+    output_location = 0
+    input_location = 0
+    filelist = []
+    plot_results = False
+    for i in range(len(argv)):
+        if ("-h") in argv[i]:
+            print(arg_help)  # print the help message
+            sys.exit(2)
+        if "-o" in argv[i]:
+            output_location = i
+        if ".dng" in argv[i]:
+            filelist.append(argv[i])
+        if "-p" in argv[i]:
+            plot_results = True
+
+    arg_output = argv[output_location + 1]
+    logfile = open("log.txt", "a+")
+    cac(filelist, arg_output, plot_results, logfile)