main functions for producing plots

xppl37_spectra.py

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+import numpy as np
+import matplotlib.pyplot as plt
+import copy
+import argparse
+import collections
+import xanes_analyzeRun
+
+parser = argparse.ArgumentParser(description='Process argv')
+
+parser.add_argument('--run', type=int,default=82,help='which run to analyze')
+parser.add_argument('--force', action="store_true",help='force calculation')
+
+
+args = parser.parse_args()
+
+nice_colors = ["#1b9e77", "#d95f02", "#7570b3"]
+
+def calcProfiles(run=82,refCalib=0,force=False):
+  r = xanes_analyzeRun.AnalyzeRun(run)
+  fname = "thisfiledoesnotexists" if force else "auto"
+  try:
+    r.load(fname)
+    print("Loading previously saved results")
+  except FileNotFoundError:
+    r.doShots(shots=slice(20),calib=refCalib,doFit=True)
+    r.saveTransform()
+    ret  = r.analyzeScan(shots="all",nImagesToFit=0,nSaveImg=4)
+    r.save(overwrite=True)
+  return r
+
+def calcRef(run,refCalibs=(0,2,4,6,8),threshold=0.05):
+  r = calcProfiles(run=run,refCalib=refCalibs[0])
+  r1 = [r.results[calib].p1 for calib in refCalibs]
+  r2 = [r.results[calib].p2 for calib in refCalibs]
+  out = collections.OrderedDict()
+  out["ratioOfAverage"] = dict()
+  out["medianOfRatios"] = dict()
+  for p1,p2,n in zip(r1,r2,refCalibs):
+    out["ratioOfAverage"][n] = xanes_analyzeRun.ratioOfAverage(p1,p2,threshold=threshold)
+    out["medianOfRatios"][n] = xanes_analyzeRun.medianRatio(p1,p2,threshold=threshold)
+  # add curves with all calib together
+  p1 = np.vstack(r1)
+  p2 = np.vstack(r2)
+  n  = ",".join(map(str,refCalibs))
+  ref1 = xanes_analyzeRun.ratioOfAverage(p1,p2,threshold=threshold)
+  ref2 = xanes_analyzeRun.medianRatio(p1,p2,threshold=threshold)
+  out["ratioOfAverage"][n] = xanes_analyzeRun.ratioOfAverage(p1,p2,threshold=threshold)
+  out["medianOfRatios"][n] = xanes_analyzeRun.medianRatio(p1,p2,threshold=threshold)
+  return r.E,out
+
+def showDifferentRefs(run=82,refCalibs=(0,2,4,6,8),threshold=0.05):
+  """ example plots showing how stable are the different ways of taking spectra """
+  E,refs = calcRef(run=run,refCalibs=refCalibs,threshold=threshold)
+  kind_of_av = list(refs.keys())
+  fig,ax=plt.subplots(len(kind_of_av)+1,1,sharex=True,sharey=True)
+  calibs = list(refs[kind_of_av[0]].keys())
+  for ikind,kind in enumerate(kind_of_av):
+    for calib in calibs:
+      if isinstance(calib,int):
+        ax[ikind].plot(E,refs[kind][calib],label="calib %s"%calib)
+      else:
+        ax[ikind].plot(E,refs[kind][calib],label="calib %s"%calib,lw=2,color='k',alpha=0.7)
+        ax[-1].plot(E,refs[kind][calib],label="calib all, %s"%kind,lw=1.5,color=nice_colors[ikind],alpha=0.8)
+  for ikind,kind in enumerate(kind_of_av): ax[ikind].set_title("Run %d, %s"%(run,kind)) 
+  ax[0].set_ylim(0.88,1.12)
+  ax[0].set_ylim(0.88,1.12)
+  ax[-2].legend()
+  ax[-1].legend()
+  for a in ax: a.grid()
+
+def main(run,refCalib=0,force=False):
+  r = calcProfiles(run,refCalib=refCalib,force=force)
+
+
+if __name__ == "__main__":
+  main(args.run,force=args.force)

xppl37_theta_scan.py

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+import xanes_analyzeRun
+import sys
+def doRun(run=56):
+  r = xanes_analyzeRun.AnalyzeRun(run,initAlign=56)
+  ret = r.analyzeScan(shots="all",nImagesToFit=5)
+  r.save(overwrite=True)
+  
+run = 56
+if len(sys.argv) > 1: run = int(sys.argv[1])
+doRun(run)