From a46b44f6d44c8018eef6b02a00e60d549ab539af Mon Sep 17 00:00:00 2001
From: beale_j <john.beale@psi.ch>
Date: Wed, 22 Mar 2023 13:04:21 +0000
Subject: [PATCH] Delete convert-scan-for-pyfai-16M.py

---
 convert-scan-for-pyfai-16M.py | 92 -----------------------------------
 1 file changed, 92 deletions(-)
 delete mode 100644 convert-scan-for-pyfai-16M.py

diff --git a/convert-scan-for-pyfai-16M.py b/convert-scan-for-pyfai-16M.py
deleted file mode 100644
index 921ecf1..0000000
--- a/convert-scan-for-pyfai-16M.py
+++ /dev/null
@@ -1,92 +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
-    scan = SFScanInfo( path_to_json )
-
-    # step through scan and average files from each positions
-    mean_image = []
-    for step in tqdm( enumerate(scan) ):
-        # step is a SFDataFiles object 
-        subset = step[1]
-
-        # go through data in_batches so you don't run out of memory
-        means = np.empty(subset[jungfrau].data.shape)
-        for indices, batch in subset[jungfrau].in_batches(size=2):
-            means[indices] = np.mean(batch.data, axis=(0))
-        
-        # take mean of means for batch opened data
-        mean_image.append(np.mean(means, axis=0))
-
-    # sum averaged imaged
-    sum_image = np.sum( mean_image, axis=0 )
-
-    # output to file
-    np.save( "{0}.npy".format( name ), sum_image )
-
-    # create plot of summed, averaged scan
-    fig, ax = plt.subplots()
-    ax.imshow(sum_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="sum_mean_scan"
-    )
-    args = parser.parse_args()
-
-    convert_image( args.scan, args.jungfrau, args.name )