146 lines
5.4 KiB
Python
146 lines
5.4 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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import copy
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import argparse
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import collections
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import xanes_analyzeRun
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parser = argparse.ArgumentParser(description='Process argv')
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parser.add_argument('--run', type=int,default=82,help='which run to analyze')
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parser.add_argument('--force', action="store_true",help='force calculation')
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args = parser.parse_args()
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profile_ret = collections.namedtuple("profile_ret",["run","p1","p2","calibs"])
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nice_colors = ["#1b9e77", "#d95f02", "#7570b3"]
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def calcProfilesForRun(run=82,calibs="all",realign=False,init="auto",alignCalib=0):
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""" init and alignCalib are used only if realignment is performed:
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init = run for initial alignment, use auto is you want to use same run
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alignCalib = calibcycle for alignment
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"""
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if init=="auto": init=run
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if isinstance(run,int):
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r = xanes_analyzeRun.AnalyzeRun(run,initAlign=init)
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else:
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r = run
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if realign:
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r.doShots(slice(20),calib=refCalib,doFit=True)
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r.saveTransform()
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# next line is used to force calculations in case of realignment
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fname = 'auto' if not realign else "thisfiledoesnotexists"
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if len(r.results) == 0:
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try:
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r.load(fname)
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print("Loading previously saved results")
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except FileNotFoundError:
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r.analyzeScan(shots=slice(600),calibs=calibs,nImagesToFit=0,nSaveImg=4)
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r.save(overwrite=True)
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# cannot take the output from r.results because it might have been calculated for
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# a bigger range than asked for.
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if isinstance(calibs,int):
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calibsForOut = (calibs,)
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elif isinstance(calibs,slice):
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calibsForOut = list(range(r.nCalib))[calibs]
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elif calibs == "all":
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calibsForOut = list(range(r.nCalib))
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else:
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calibsForOut = calibs
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p1 = [r.results[calib].p1 for calib in calibsForOut]
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p2 = [r.results[calib].p2 for calib in calibsForOut]
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return profile_ret( run =r, p1 =p1, p2=p2,calibs=calibsForOut)
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def calcProfilesForRefAndSample(run=82,refCalibs=0,force=False):
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if isinstance(run,int):
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refRun = xanes_analyzeRun.AnalyzeRun(run)
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sampleRun = refRun
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if isinstance(refCalibs,slice): refCalibs = list(range(refRun.nCalib))[refCalibs]
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if isinstance(refCalibs,int): refCalibs = (refCalibs,)
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sampleCalibs = [c+1 for c in refCalibs]
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elif isinstance(run,(list,tuple)):
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refRun = xanes_analyzeRun.AnalyzeRun(run[0])
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sampleRun = xanes_analyzeRun.AnalyzeRun(run[1],initAlign=run[0])
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refCalibs = [0,]
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sampleCalibs = [0,]
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if refRun == sampleRun:
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# otherwise same does not save them both
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temp = calcProfilesForRun(refRun,calibs=sampleCalibs+refCalibs);
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ref = calcProfilesForRun(refRun,calibs=refCalibs)
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sample = calcProfilesForRun(sampleRun,calibs=sampleCalibs)
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return ref,sample
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def calcRef(r1,r2,calibs=None,threshold=0.05):
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""" r1 and r2 are list of 2d arrays (nShots,nPixels) for each calibcycle """
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if calibs is None: calibs = list(range(len(r1)))
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out = collections.OrderedDict()
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out["ratioOfAverage"] = dict()
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out["medianOfRatios"] = dict()
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for p1,p2,n in zip(r1,r2,calibs):
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out["ratioOfAverage"][n] = xanes_analyzeRun.ratioOfAverage(p1,p2,threshold=threshold)
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out["medianOfRatios"][n] = xanes_analyzeRun.medianRatio(p1,p2,threshold=threshold)
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# add curves with all calib together
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p1 = np.vstack(r1)
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p2 = np.vstack(r2)
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n = ",".join(map(str,calibs))
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ref1 = xanes_analyzeRun.ratioOfAverage(p1,p2,threshold=threshold)
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ref2 = xanes_analyzeRun.medianRatio(p1,p2,threshold=threshold)
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out["ratioOfAverage"][n] = xanes_analyzeRun.ratioOfAverage(p1,p2,threshold=threshold)
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out["medianOfRatios"][n] = xanes_analyzeRun.medianRatio(p1,p2,threshold=threshold)
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out["ratioOfAverage"]['all'] = out["ratioOfAverage"][n]
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out["medianOfRatios"]['all'] = out["medianOfRatios"][n]
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return out
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def showDifferentRefs(run=82,refCalibs=slice(None,None,2),threshold=0.05):
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""" example plots showing how stable are the different ways of taking spectra """
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prof = calcProfilesForRun(run,calibs=refCalibs)
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refs = calcRef(prof.p1,prof.p2,calibs=prof.calibs)
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kind_of_av = list(refs.keys())
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fig,ax=plt.subplots(len(kind_of_av)+1,1,sharex=True,sharey=True)
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E = prof.run.E
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calibs = list(refs[kind_of_av[0]].keys())
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for ikind,kind in enumerate(kind_of_av):
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for calib in calibs:
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if isinstance(calib,int):
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ax[ikind].plot(E,refs[kind][calib],label="calib %s"%calib)
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else:
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if calibs == 'all': continue
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ax[ikind].plot(E,refs[kind][calib],label="calib %s"%calib,lw=2,color='k',alpha=0.7)
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ax[-1].plot(E,refs[kind][calib],label="calib all, %s"%kind,lw=1.5,color=nice_colors[ikind],alpha=0.8)
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for ikind,kind in enumerate(kind_of_av): ax[ikind].set_title("Run %d, %s"%(run,kind))
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ax[0].set_ylim(0.88,1.12)
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ax[0].set_ylim(0.88,1.12)
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ax[-2].legend()
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ax[-1].legend()
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for a in ax: a.grid()
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def calcSampleAbs(run=82,refCalibs=slice(None,None,2),threshold=0.05,refKind="medianOfRatios"):
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""" example of use
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ratio = calcSampleAbs(82)
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ratio = calcSampleAbs( (155,156) )
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"""
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ref,sample = calcProfilesForRefAndSample(run,refCalibs=refCalibs)
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temp = calcRef(ref.p1,ref.p2,calibs=ref.calibs,threshold=threshold)
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ref = temp[refKind]['all']
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p1 = np.vstack( sample.p1 )
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p2 = np.vstack( sample.p2 )
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print(p1.shape)
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p1,p2 = xanes_analyzeRun.maskLowIntensity(p1,p2,threshold=0.1)
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ratio = p2/p1
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ratio = ratio/ref
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return ratio
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def main(run,refCalib=0,force=False):
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pass
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#r = calcProfiles(run,refCalibs=refCalib,force=force)
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if __name__ == "__main__":
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main(args.run,force=args.force)
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