diff --git a/pyfai-tools/convert-scan-for-pyfai-16M.py b/pyfai-tools/convert-scan-for-pyfai-16M.py
deleted file mode 100644
index 436fddf..0000000
--- a/pyfai-tools/convert-scan-for-pyfai-16M.py
+++ /dev/null
@@ -1,107 +0,0 @@
-#!/usr/bin/env python3
-
-# author J.Beale
-
-"""
-# aim
- - -16M=varient for large detectors
-make image file to input into pyFAI for initial detector beam-centre and detector distance calibration
-refer to Cristallina8M-calibration for complete protocol
-https://docs.google.com/document/d/1RoeUUogvRxX4M6uqGwkjf3dVJBabiMUx4ZxwcA5e9Dc/edit#
-
-# protocol
-take scan of LaB6
-## IMPORTANT ##
-- save image as photon-counts - in slic/run_control scale=beam energy
-- detector_geometry=TRUE - saves detector panels in their correct orientation
-## scan inputs ##
-- <0.01 trans
-- motor scan > 10 um per step
-- 10 images per step, 100 steps
-- use scan.json as input for this script
-
-# usage
-python make-tiff.py -j <jugfrau-name> -s <path to scan file> -n <name of output file>
-
-# output
-creates a .npy file that can be loaded directly into pyFAI
-"""
-
-# modules
-from matplotlib import pyplot as plt
-import numpy as np
-from sfdata import SFScanInfo
-from tqdm import tqdm
-import argparse
-
-def convert_image( path_to_json, jungfrau, name ):
-
-    # opens scan
-    print( "opening scane" )
-    scan = SFScanInfo( path_to_json )
-
-    # steps in scane
-    nsteps = len(scan)
-
-    # define step ch and im_shape
-    step = scan[0]
-    ch = step[jungfrau]
-    img_shape = ch[0].shape
-
-    print("stepping through scan and averaging images at each step")
-    # step through scan and average files from each positions
-    imgs_shape = (nsteps, *img_shape)
-    imgs = np.empty(imgs_shape)
-    for i, subset in tqdm(enumerate(scan)): 
-
-        # go through data in_batches so you don't run out of memory
-        ch = subset[jungfrau]
-        mean = np.zeros(img_shape)
-        for _indices, batch in ch.in_batches(size=2):
-            mean += np.mean(batch, axis=0)
-        
-        # take mean of means for batch opened data
-        imgs[i] = mean
-
-    print( "done" )
-    # sum averaged imaged
-    print( "final average" )
-    mean_image = imgs.mean(axis=0)
-    print("done")
-
-    # output to file
-    print( "saving to .npy = {0}".format( name ) )
-    np.save( "{0}.npy".format( name ), mean_image )
-    print( "done" )
-
-    # create plot of summed, averaged scan
-    fig, ax = plt.subplots()
-    ax.imshow(mean_image, vmin=0, vmax=1000)
-    plt.show()
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "-j",
-        "--jungfrau",
-        help="name of the jungfrau used, i.e., JF17T16V01 for Cristallina MX",
-        type=str,
-        default="JF17T16V01"
-    )
-    parser.add_argument(
-        "-s",
-        "--scan",
-        help="path to json scan file",
-        type=str,
-        default="/sf/cristallina/data/p20590/raw/run0003/meta/scan.json"
-    )
-    parser.add_argument(
-        "-n",
-        "--name",
-        help="name of output file",
-        type=str,
-        default="mean_scan"
-    )
-    args = parser.parse_args()
-
-    convert_image( args.scan, args.jungfrau, args.name )