new quick-and-dirty script for calculating energy calibration

xppl37_calibration.py

@@ -0,0 +1,42 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+import xanes_analyzeRun
+import mcutils as mc
+
+colors = "#a6cee3 #1f78b4 #b2df8a #33a02c #fb9a99 #e31a1c #fdbf6f #ff7f00 #cab2d6 #6a3d9a #ffff99 #b15928".split()
+
+def myc(i):
+  return colors[i%len(colors)]
+
+def doRun(run=77):
+  """ Since p1 is transformed, for the calibration look at p2 """
+  r = xanes_analyzeRun.AnalyzeRun(run)
+  r.load()
+  calibs = list(r.results.keys())
+  calibs.sort()
+  p2 = [ np.nanmedian(r.results[c].p1,axis=0) for c in calibs ]
+  p2 = np.asarray(p2)
+  ref = np.nanmedian( p2 ,axis=0)
+  # normalize p2
+  p2 = p2/ref
+  # remove non-flat part
+  _x = np.arange(p2.shape[1],dtype=float)
+  p2 = [s-mc.poly_approximant(_x,s,order=20)(_x) for s in p2]
+  p2 = np.asarray(p2)
+  x = np.asarray( [_x for _ in p2])
+  x[p2>-0.15] = np.nan
+  x[x<100] = np.nan; # below pixel 200 is noise
+  pos = np.nanmean(x,axis=1)
+  fig,ax=plt.subplots(1,2,sharey=True)
+  xshift = 0.2
+  pcalib = []
+  for icalib,(xi,p2i) in enumerate(zip(x,p2)):
+    ppar = dict ( color = myc(icalib) )
+    ax[0].plot(p2i+icalib*xshift,_x,'--',lw=1,alpha=0.5,**ppar)
+    ax[0].plot(p2i+icalib*xshift,xi,lw=2,**ppar)
+    pcalib.append( np.nanmedian(xi) )
+    ax[1].plot(r.scanpos[icalib],pcalib[icalib],'o',**ppar)
+  polycal = np.polyfit(pcalib,r.scanpos,1)
+  ax[1].plot(np.polyval(polycal,_x),_x)
+  print("polynomial calibration",polycal)