From 492879592caab840753e9598300f1da5d69c5c7d Mon Sep 17 00:00:00 2001
From: Beale John Henry <beale_j@saresc-cons-05.psi.ch>
Date: Wed, 22 Mar 2023 14:10:38 +0100
Subject: [PATCH] rm 16M as now works for Cristallina and Alvra

---
 pyfai-tools/convert-scan-for-pyfai.py | 44 ++++++++++++++++++++-------
 1 file changed, 33 insertions(+), 11 deletions(-)

diff --git a/pyfai-tools/convert-scan-for-pyfai.py b/pyfai-tools/convert-scan-for-pyfai.py
index 0418aac..436fddf 100644
--- a/pyfai-tools/convert-scan-for-pyfai.py
+++ b/pyfai-tools/convert-scan-for-pyfai.py
@@ -4,6 +4,7 @@
 
 """
 # 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#
@@ -36,25 +37,46 @@ import argparse
 def convert_image( path_to_json, jungfrau, name ):
 
     # opens scan
+    print( "opening scane" )
     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]
-        mean = np.mean( subset[ jungfrau ].data, axis=0 )
-        mean_image.append(mean)
+    # 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
-    sum_image = np.sum( mean_image, axis=0 )
+    print( "final average" )
+    mean_image = imgs.mean(axis=0)
+    print("done")
 
     # output to file
-    np.save( "{0}.npy".format( name ), sum_image )
+    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(sum_image, vmin=0, vmax=1000)
+    ax.imshow(mean_image, vmin=0, vmax=1000)
     plt.show()
 
 if __name__ == "__main__":
@@ -78,7 +100,7 @@ if __name__ == "__main__":
         "--name",
         help="name of output file",
         type=str,
-        default="sum_mean_scan"
+        default="mean_scan"
     )
     args = parser.parse_args()