From a9e3e74ffc20bb9a152319892672cffe71ccb7a7 Mon Sep 17 00:00:00 2001
From: Beale John Henry <beale_j@saresc-cons-05.psi.ch>
Date: Tue, 21 Mar 2023 12:19:45 +0100
Subject: [PATCH] new version of convert-scan-for-pyfai for 16M detector -
 added in_batch line to help with the opening of images

---
 convert-scan-for-pyfai-16M.py | 92 +++++++++++++++++++++++++++++++++++
 1 file changed, 92 insertions(+)
 create mode 100644 convert-scan-for-pyfai-16M.py

diff --git a/convert-scan-for-pyfai-16M.py b/convert-scan-for-pyfai-16M.py
new file mode 100644
index 0000000..921ecf1
--- /dev/null
+++ b/convert-scan-for-pyfai-16M.py
@@ -0,0 +1,92 @@
+#!/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 )