108 lines
2.9 KiB
Python
108 lines
2.9 KiB
Python
#!/usr/bin/env python3
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# authors J.Beale + lots of help from Alexander Steppke and Sven Augustin - love you both!
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"""
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# aim
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- -16M=varient for large detectors
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make image file to input into pyFAI for initial detector beam-centre and detector distance calibration
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refer to Cristallina8M-calibration for complete protocol
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https://docs.google.com/document/d/1RoeUUogvRxX4M6uqGwkjf3dVJBabiMUx4ZxwcA5e9Dc/edit#
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# protocol
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take scan of LaB6
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## IMPORTANT ##
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- save image as photon-counts - in slic/run_control scale=beam energy
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- detector_geometry=TRUE - saves detector panels in their correct orientation
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## scan inputs ##
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- <0.01 trans
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- motor scan > 10 um per step
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- 10 images per step, 100 steps
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- use scan.json as input for this script
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# usage
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python convert-scan-for-pyfai.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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print( "opening scane" )
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scan = SFScanInfo( path_to_json )
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# steps in scane
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nsteps = len(scan)
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# define step ch and im_shape
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step = scan[0]
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ch = step[jungfrau]
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img_shape = ch[0].shape
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print("stepping through scan and averaging images at each step")
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# step through scan and average files from each positions
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imgs_shape = (nsteps, *img_shape)
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imgs = np.empty(imgs_shape)
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for i, subset in tqdm(enumerate(scan)):
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# go through data in_batches so you don't run out of memory
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ch = subset[jungfrau]
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mean = np.zeros(img_shape)
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for _indices, batch in ch.in_batches(size=2):
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mean += np.mean(batch, axis=0)
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# take mean of means for batch opened data
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imgs[i] = mean
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print( "done" )
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# sum averaged imaged
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print( "final average" )
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mean_image = imgs.mean(axis=0)
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print("done")
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# output to file
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print( "saving to .npy = {0}".format( name ) )
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np.save( "{0}.npy".format( name ), mean_image )
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print( "done" )
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# create plot of summed, averaged scan
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fig, ax = plt.subplots()
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ax.imshow(mean_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, i.e., JF17T16V01 for Cristallina MX",
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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="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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