more cleanup and add funciton for showing abs spectra with vernier sweep
This commit is contained in:
Marco Cammarata
parent
327d2825c6
commit
0df7c7b2f1
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@ -0,0 +1 @@
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dispersiveXanes_alignment.py
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@ -68,77 +68,3 @@ def medianRatio(p1,p2,threshold=0.03):
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return np.ma.average(ratio,axis=0,weights=p1)
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# /--------------------\
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# | |
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# | PLOTS & CO. |
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# | |
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# \--------------------/
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def plotShot(im1,im2,transf1=None,transf2=None,fig=None,ax=None,res=None,E=defaultE,save=None):
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if transf1 is not None: im1 = transf1.transformImage(im1)
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if transf2 is not None: im2 = transf2.transformImage(im2)
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if fig is None and ax is None:
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fig = plt.subplots(2,3,figsize=[7,5],sharex=True)[0]
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ax = fig.axes
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elif fig is not None:
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ax = fig.axes
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if E is None: E=np.arange(im1.shape[1])
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n = im1.shape[0]
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ax[0].imshow(im1,extent=(E[0],E[-1],0,n),**kw_2dplot)
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ax[1].imshow(im2,extent=(E[0],E[-1],0,n),**kw_2dplot)
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ax[2].imshow(im1-im2,extent=(E[0],E[-1],0,n),**kw_2dplot)
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if res is None:
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p1 = np.nansum(im1,axis=0)
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p2 = np.nansum(im2,axis=0)
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pr = p2/p1
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else:
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p1 = res.p1; p2 = res.p2; pr = res.ratio
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ax[3].plot(E,p1,lw=3)
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ax[4].plot(E,p1,lw=1)
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ax[4].plot(E,p2,lw=3)
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idx = (p1>p1.max()/10.)
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ax[5].plot(E[idx],pr[idx])
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if res is not None:
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ax[5].set_title("FOM: %.2f"%res.fom)
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else:
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ax[5].set_title("FOM: %.2f"% calcFOM(p1,p2,pr))
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if (save is not None) and (save is not False): plt.savefig(save,transparent=True,dpi=500)
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return fig
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def plotRatios(r,shot='random',fig=None,E=defaultE,save=None):
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if fig is None: fig = plt.subplots(2,1,sharex=True)[0]
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ax = fig.axes
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n = r.shape[0]
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i = ax[0].imshow(r,extent=(E[0],E[-1],0,n),**kw_2dplot)
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i.set_clim(0,1.2)
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if shot == 'random' : shot = np.random.random_integers(0,n-1)
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ax[1].plot(E,r[shot],label="Shot n %d"%shot)
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ax[1].plot(E,np.nanmedian(r[:10],axis=0),label="median 10 shots")
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ax[1].plot(E,np.nanmedian(r,axis=0),label="all shots")
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ax[1].legend()
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ax[1].set_ylim(0,1.5)
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ax[1].set_xlabel("Energy")
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ax[1].set_ylabel("Transmission")
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ax[0].set_ylabel("Shot num")
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if (save is not None) and (save is not False): plt.savefig(save,transparent=True,dpi=500)
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def plotSingleShots(r,nShots=10,fig=None,E=defaultE,save=None,ErangeForStd=(7090,7150)):
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if fig is None: fig = plt.subplots(2,1,sharex=True)[0]
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ax = fig.axes
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for i in range(nShots):
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ax[0].plot(E,r[i]+i)
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ax[0].set_ylim(0,nShots+0.5)
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av = (1,3,10,30,100)
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good = np.nanmedian(r,0)
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for i,a in enumerate(av):
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m = np.nanmedian(r[:a],0)
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idx = (E>ErangeForStd[0]) & (E<ErangeForStd[1])
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fom = np.nanstd( m[idx]/good[idx] )
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print("n shots %d, std %.2f"%(a,fom) )
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ax[1].plot(E,m+i,label="%d shots, std :%.2f"%(a,fom))
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ax[1].legend()
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ax[1].set_ylim(0,len(av)+0.5)
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ax[1].set_xlabel("Energy")
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ax[1].set_ylabel("Transmission")
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if (save is not None) and (save is not False): plt.savefig(save,transparent=True,dpi=500)
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@ -4,6 +4,7 @@ import copy
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import argparse
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import collections
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import dispersiveXanes_alignment as alignment
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import dispersiveXanes_utils as utils
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import xanes_analyzeRun
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parser = argparse.ArgumentParser(description='Process argv')
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@ -102,16 +103,16 @@ def calcRef(r1,r2,calibs=None,threshold=0.05):
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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] = alignment.ratioOfAverage(p1,p2,threshold=threshold)
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out["medianOfRatios"][n] = alignment.medianRatio(p1,p2,threshold=threshold)
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out["ratioOfAverage"][n] = utils.ratioOfAverage(p1,p2,threshold=threshold)
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out["medianOfRatios"][n] = utils.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] = alignment.ratioOfAverage(p1,p2,threshold=threshold)
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out["medianOfRatios"][n] = alignment.medianRatio(p1,p2,threshold=threshold)
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ref1 = utils.ratioOfAverage(p1,p2,threshold=threshold)
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ref2 = utils.medianRatio(p1,p2,threshold=threshold)
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out["ratioOfAverage"][n] = utils.ratioOfAverage(p1,p2,threshold=threshold)
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out["medianOfRatios"][n] = utils.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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@ -150,7 +151,7 @@ def calcSampleAbs(run=82,refCalibs=slice(None,None,2),threshold=0.05,refKind="me
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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 = alignment.maskLowIntensity(p1,p2,threshold=threshold)
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p1,p2 = utils.maskLowIntensity(p1,p2,threshold=threshold)
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ratio = p2/p1
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ratio = ratio/ref
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return p1,p2,-np.log10(ratio)
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@ -185,13 +186,26 @@ def showSpectra(run=82,shots=slice(5),calibs=0,averageEachCalib=False,
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ax[0][0].set_title("Run %d"%run)
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if not averageEachCalib: ax[0][0].set_ylim(0,shifty*(len(spectra_norm)))
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def showAbs(run=82,shots=slice(5),normalization="auto",shifty=1,xlim=(7080,7180),showAv=True,smoothWidth=0,threshold=0.01):
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color_ss = '#08519c'
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color_av = '#238b45'
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color_av_all = '#d95f0e'
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def showAbs(run=82,shots=slice(5),normalization="auto",shifty=1,xlim=(7080,7180),showAv=True,showAvOverAll=True,smoothWidth=0,threshold=0.01,filterShot=0.1):
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""" normalization: if "auto", the max of the spectra that will be plotted will be used
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filterShot = means that it filters out the filterShot*100 percentile
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"""
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E = alignment.defaultE
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p1,p2,abs = calcSampleAbs(run=run,threshold=threshold)
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p1_sum = p1.sum(-1)
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if filterShot>0:
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idx = p1_sum>np.percentile(p1_sum,filterShot*100)
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p1 = p1[idx]
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p2 = p2[idx]
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abs = abs[idx]
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p1_av = np.nanmean(p1,axis=0)
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p2_av = np.nanmean(p2,axis=0)
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# somehow nanmedian screws up when array is too big ... so using nanmean
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abs_av = np.nanmean(abs,axis=0)
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p1 = p1[shots]; p2=p2[shots]; abs = abs[shots]
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if smoothWidth > 0: abs = smoothSpectra(E,abs,res=smoothWidth)
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@ -204,17 +218,26 @@ def showAbs(run=82,shots=slice(5),normalization="auto",shifty=1,xlim=(7080,7180)
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color = gradual_colors[ishot%len(gradual_colors)]
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ax[0].axhline(ishot*shifty,ls='--',color=color)
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ax[1].axhline(ishot*shifty,ls='--',color=color)
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if showAvOverAll:
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if ishot == 0: ax[0].fill_between(E,ishot*shifty,p1_av/normalization+ishot*shifty,color=color_av_all,alpha=0.6)
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ax[1].plot(E,abs_av+ishot*shifty,color=color_av_all,lw=2,zorder=20)
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if showAv:
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if ishot == 0: ax[0].fill_between(E,ishot*shifty,p1_av/normalization+ishot*shifty,color='#d95f0e',alpha=0.6)
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ax[1].plot(E,abs_av+ishot*shifty,color='#d95f0e',lw=2,zorder=20)
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ax[1].plot(E,np.nanmedian(abs,0)+ishot*shifty,color=color_av,lw=2,zorder=10)
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ax[0].plot(E,s1_norm+ishot*shifty,ls = '-' ,color='0.8',lw=2)
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ax[0].plot(E,s2_norm+ishot*shifty,ls = '-' ,color='0.3',lw=2)
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ax[1].plot(E,a+ishot*shifty,color=color,lw=2)
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ax[1].plot(E,a+ishot*shifty,color=color_ss,lw=2)
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ax[0].set_xlim(*xlim)
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ax[0].set_title("Run %d"%run)
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ax[0].set_title("Run %s"%str(run))
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ax[1].set_ylabel("Sample Absorption")
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ax[0].set_ylabel("Normalized Spectra")
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ax[0].set_ylim(0,shifty*(p1.shape[0]))
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print("STD of (average over shown shots) - (average over all): %.3f"%np.nanstd(np.nanmedian(abs,0)-abs_av))
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ax[1].set_title("STD of (average_{shown}) - (average_{all}): %.3f"%np.nanstd(np.nanmedian(abs,0)-abs_av))
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def showAbsWithSweep(run=(155,156),first=0,period=150,nSpectra=10,**kwards):
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shots = slice(first,first+period,int(period/nSpectra))
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showAbs(run=run,shots=shots,**kwards)
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def smoothSpectra(E,abs_spectra,res=0.5):
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from scipy import integrate
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