76 lines
1.8 KiB
Python
76 lines
1.8 KiB
Python
#!/usr/bin/env python
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"""
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# aim
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make image file for input into pyFAI for initial detector
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# protocol
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take scan of LaB6
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vanile settings for crystallina 8M are 100 scan positions, 10 shots per position
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run this on output
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# usage
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python make-tiff.py -j <jugfrau-name> -s <path to scan file> -n <name of output file>
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# output
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creates a .npy file that can be loaded directly into pyFAI
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"""
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# modules
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from matplotlib import pyplot as plt
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import numpy as np
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from sfdata import SFScanInfo
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from tqdm import tqdm
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import argparse
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def convert_image( path_to_json, jungfrau, name ):
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# opens scan
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scan = SFScanInfo( path_to_json )
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# step through scan and average files from each positions
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mean_image = []
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for step in tqdm( enumerate(scan) ):
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# step is a SFDataFiles object
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subset = step[1]
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mean = np.mean( subset[ jungfrau ].data, axis=0 )
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mean_image.append(mean)
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# sum averaged imaged
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sum_image = np.sum( mean_image, axis=0 )
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# output to file
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np.save( "{0}.npy".format( name ), sum_image )
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# create plot of summed, averaged scan
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fig, ax = plt.subplots()
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ax.imshow(sum_image, vmin=0, vmax=1000)
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plt.show()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"-j",
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"--jungfrau",
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help="name of the jungfrau used",
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type=str,
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default="JF17T16V01"
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)
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parser.add_argument(
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"-s",
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"--scan",
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help="path to json scan file",
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type=str,
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default="/sf/cristallina/data/p20590/raw/run0003/meta/scan.json"
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)
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parser.add_argument(
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"-n",
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"--name",
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help="name of output file",
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type=str,
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default="sum_mean_scan"
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)
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args = parser.parse_args()
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convert_image( args.scan, args.jungfrau, args.name )
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