first scripts

convert-scan-for-pyfai.py

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+#!/usr/bin/env python
+
+"""
+# aim
+make image file for input into pyFAI for initial detector 
+
+# protocol
+take scan of LaB6
+vanile settings for crystallina 8M are 100 scan positions, 10 shots per position
+run this on output
+
+# 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]
+        mean = np.mean( subset[ jungfrau ].data, axis=0 )
+        mean_image.append(mean)
+
+    # 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",
+        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 )