43 lines
1.3 KiB
Python
43 lines
1.3 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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import xanes_analyzeRun
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import mcutils as mc
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colors = "#a6cee3 #1f78b4 #b2df8a #33a02c #fb9a99 #e31a1c #fdbf6f #ff7f00 #cab2d6 #6a3d9a #ffff99 #b15928".split()
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def myc(i):
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return colors[i%len(colors)]
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def doRun(run=77):
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""" Since p1 is transformed, for the calibration look at p2 """
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r = xanes_analyzeRun.AnalyzeRun(run)
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r.load()
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calibs = list(r.results.keys())
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calibs.sort()
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p2 = [ np.nanmedian(r.results[c].p1,axis=0) for c in calibs ]
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p2 = np.asarray(p2)
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ref = np.nanmedian( p2 ,axis=0)
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# normalize p2
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p2 = p2/ref
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# remove non-flat part
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_x = np.arange(p2.shape[1],dtype=float)
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p2 = [s-mc.poly_approximant(_x,s,order=20)(_x) for s in p2]
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p2 = np.asarray(p2)
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x = np.asarray( [_x for _ in p2])
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x[p2>-0.15] = np.nan
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x[x<100] = np.nan; # below pixel 200 is noise
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pos = np.nanmean(x,axis=1)
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fig,ax=plt.subplots(1,2,sharey=True)
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xshift = 0.2
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pcalib = []
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for icalib,(xi,p2i) in enumerate(zip(x,p2)):
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ppar = dict ( color = myc(icalib) )
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ax[0].plot(p2i+icalib*xshift,_x,'--',lw=1,alpha=0.5,**ppar)
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ax[0].plot(p2i+icalib*xshift,xi,lw=2,**ppar)
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pcalib.append( np.nanmedian(xi) )
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ax[1].plot(r.scanpos[icalib],pcalib[icalib],'o',**ppar)
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polycal = np.polyfit(pcalib,r.scanpos,1)
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ax[1].plot(np.polyval(polycal,_x),_x)
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print("polynomial calibration",polycal)
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