copies of swissmx related tools

beamline_tools/edge_scan.py

@@ -0,0 +1,104 @@
+#!/usr/bin/env python3
+
+# authors M.Appleby + some code donated by J.Beale
+
+"""
+# aim
+Calculate FWHM of x-ray beam from an edge scan
+
+# protocol
+complete edge scan
+## IMPORTANT ##
+- save data as .txt file 
+
+# usage
+python convert-scan-for-pyfai.py -j <jugfrau-name> -s <path to scan file> -n <name of output file>
+
+# output
+creates a .png of fit including FWHM title
+"""
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from scipy import stats
+from scipy.optimize import curve_fit
+from scipy import asarray as ar,exp
+from math import sqrt, pi
+import argparse
+
+def gaus(x, C, A, x0, sigma):
+	return C + A*np.exp(-(x-x0)**2/(2*sigma**2))
+
+
+def getFWHM(filename, output_name):
+	df = pd.read_csv(filename, sep='\t', index_col=0)
+	print(df)
+	#print(df.columns)
+
+	x_label=df.index.name
+	y_label=df.columns[0]
+	x_vals = df.index.tolist()
+	y_vals = df[y_label].tolist()
+
+	plt.plot(x_vals, y_vals, label = 'edge_scan')
+	plt.ylabel('Intensity (counts)')
+	plt.xlabel('Motor position (mm)')
+
+	dydx = np.gradient(y_vals, x_vals)
+
+	mean = sum((x_vals)*dydx)/sum(dydx)
+	sigma = sum(dydx*(mean)**2)/sum(dydx)
+	print(mean, sigma)
+
+	A= 1/(sigma*2*sqrt(pi))
+	print(x_vals, '\\', y_vals)
+	print(dydx)
+	dydx[np.isnan(dydx)] = 0
+	print(dydx)	
+
+	param_optimised,param_covariance_matrix = curve_fit(gaus,x_vals,dydx,p0=[min(y_vals), A, mean,sigma],maxfev=5000)
+	offset_op, amp_op, x0_op, sigma_op = param_optimised[0], param_optimised[1], param_optimised[2], param_optimised[3]
+
+	print(param_optimised)
+
+	gauss_y = gaus(x_vals,*param_optimised)
+	FWHM_x = np.abs(2*np.sqrt(2*np.log(2))*sigma_op)
+
+	plt.plot(x_vals, dydx, label = 'derivative')
+	plt.plot(x_vals,gauss_y,label='Gaussian fit',color ='orange')
+	#plt.fill_between(x_vals,gauss_y,color='orange',alpha=0.5)
+	plt.axvspan(x0_op+FWHM_x/2,x0_op-FWHM_x/2, color='green', alpha=0.75, lw=0, label='FWHM = {0}'.format(FWHM_x))
+	print(FWHM_x)
+	#plt.plot(x_label, y_label, data=df)
+	plt.show()
+	plt.savefig(output_name+filename.split('/')[-1]+'FWHM_{0}.png'.format(FWHM_x))
+	return
+
+if __name__ == '__main__':
+	parser = argparse.ArgumentParser()
+	parser.add_argument(
+		"-p",
+		"--path",
+		help="path of input and output file, not currently in use",
+		type=str,
+		default="/sf/cristallina/data/p21224/res/pshell/edge_scans/"
+	)
+	parser.add_argument(
+		"-i",
+		"--input",
+		help="location of input file",
+		type=str,
+		default="/sf/cristallina/data/p21224/res/pshell/edge_scans/0.5_x/0.5_x"
+	)
+	parser.add_argument(
+		"-o",
+		"--output",
+		help="output path to save figure",
+		type=str,
+		default="/sf/cristallina/data/p21224/res/pshell/edge_scans/"
+	)
+	args = parser.parse_args()
+	getFWHM(args.input, args.output)
+
+

beamline_tools/swissmx.sh

@@ -0,0 +1,7 @@
+#!/bin/bash 
+
+source /sf/cristallina/applications/mx/conda/miniconda/bin/activate
+conda activate crmx38
+
+cd /sf/cristallina/applications/mx/zamofing_t/ESB_MX/python/SwissMX/
+python swissmx.py

clen_tools/detector-distance-refinement.py

@@ -87,8 +87,7 @@ def write_crystfel_run( clen, sample_h5_file, clen_geom_file, cell_file, thresho
     run_sh = open( cryst_run_file, "w" )
     run_sh.write( "#!/bin/sh\n\n" )
     run_sh.write( "module purge\n" )
-    run_sh.write( "module use MX unstable\n" )
-    run_sh.write( "module load crystfel/0.10.2-rhel8\n" )
+    run_sh.write( "module load crystfel/0.11.1\n" )
     run_sh.write( "indexamajig -i {0} \\\n".format( sample_h5_file ) )
     run_sh.write( "  --output={0}.stream \\\n".format( clen ) )
     run_sh.write( "  --geometry={0}\\\n".format( clen_geom_file ) )