various changes: some naming, added function for averaging and changed doShots to accept list of calibs
This commit is contained in:
Marco Cammarata
parent
b0914f15f8
commit
a2c508fa5d
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@ -101,6 +101,54 @@ def showShots(im1,im2):
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a[0].plot(p1)
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a[1].plot(p2)
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def ratioOfAverage(p1,p2,threshold=0.03):
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"""
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p1 and p2 are the energy spectrum. if 2D the first index has to be the shot number
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calculate median ratio taking into account only regions where p1 and p2 are > 5% of the max """
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# check if they are 2D
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if p1.ndim == 1:
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p1 = p1[np.newaxis,:]
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p2 = p2[np.newaxis,:]
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# w1 and w2 are the weights
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w1 = p1.copy(); w2 = p2.copy()
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if threshold is not None:
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# weights will be set to zero if intensity is smaller than 5% of max
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# for each shots, get maximum
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m1 = np.nanmax(p1,axis=1); m2 = np.nanmax(p2,axis=1)
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# find where each spectrum is smaller than threshold*max_for_that_shot; they will be masked out
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idx1 = p1 < (m1[:,np.newaxis]*threshold)
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idx2 = p2 < (m2[:,np.newaxis]*threshold)
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w1[idx1]=0
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w2[idx2]=0
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# using masked array because some pixel will have zero shots contributing
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av1 = np.ma.average(p1,axis=0,weights=w1)
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av1[av1.mask] = np.nan
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av2 = np.ma.average(p2,axis=0,weights=w2)
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av2[av2.mask] = np.nan
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return av2/av1
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def medianRatio(p1,p2,threshold=0.03):
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"""
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p1 and p2 are the energy spectrum. if 2D the first index has to be the shot number
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calculate median ratio taking into account only regions where p1 and p2 are > 5% of the max """
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# check if they are 2D
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if p1.ndim == 1:
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p1 = p1[np.newaxis,:]
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p2 = p2[np.newaxis,:]
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p1 = np.ma.asarray( p1.copy() )
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p2 = np.ma.asarray( p2.copy() )
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if threshold is not None:
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m1 = np.nanmax(p1,axis=1); m2 = np.nanmax(p2,axis=1)
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# find where each spectrum is smaller than threshold*max_for_that_shot; they will be masked out
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idx1 = p1 < (m1[:,np.newaxis]*threshold)
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idx2 = p2 < (m2[:,np.newaxis]*threshold)
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idx = idx1 & idx2
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p1.mask = idx
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p2.mask = idx
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ratio = p2/p1
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return np.ma.average(ratio,axis=0,weights=p1)
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class AnalyzeRun(object):
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def __init__(self,run,initAlign="auto",swapx=g_swapx,swapy=g_swapy):
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@ -125,7 +173,8 @@ class AnalyzeRun(object):
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d = self.data
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self.spec1 = d.spec1 ; # spec1 is the one that is moved
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self.spec2 = d.spec2 ;
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self.spec2 = d.spec2 ;
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self.E = alignment.defaultE
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try:
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self.loadTransform(initAlign)
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@ -136,10 +185,15 @@ class AnalyzeRun(object):
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else:
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self.initAlign = initAlign
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def getShot(self,shot=0,calib=None,bkgSub="line",roi=g_roi_height):
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def getShots(self,shots=0,calib=None,bkgSub="line",roi=g_roi_height):
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if shots == "all":
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if calib != None:
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shots = slice(0,self.nShotsPerCalib[calib])
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else:
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shots = slice(0,self.data.spec1.nShots)
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# read data
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im1 = self.spec1.getShots(shot,calib=calib)
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im2 = self.spec2.getShots(shot,calib=calib)
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im1 = self.spec1.getShots(shots,calib=calib)
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im2 = self.spec2.getShots(shots,calib=calib)
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# subtractBkg bkg
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im1 = alignment.subtractBkg(im1,bkg_type=bkgSub)
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im2 = alignment.subtractBkg(im2,bkg_type=bkgSub)
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@ -174,31 +228,40 @@ class AnalyzeRun(object):
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gui.save(fname)
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def analyzeScan(self,initpars=None,nShotsPerCalib=20,nC=None,doFit=False,fitEveryCalib=False,nSaveImg=5):
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""" this is a comment """
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def analyzeScan(self,initpars=None,shots=slice(0,30),calibs="all",nImagesToFit=0,nSaveImg=5):
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""" nImagesToFit: number of images to Fit per calibcycle, (int or "all") """
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if initpars is None: initpars= self.initAlign
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if nC is None: nC = self.nCalib
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out = []
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for i in range(nC):
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if nShotsPerCalib == 'all':
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shots = slice(self.nShotsPerCalib[i])
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if calibs == "all": calibs=list(range(self.nCalib))
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if isinstance(calibs,slice): calibs=list(range(self.nCalib))[calibs]
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nC = len(calibs)
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for ic,calib in enumerate(calibs):
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if nImagesToFit == "all":
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nToFit = self.nShotsPerCalib[calib]
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else:
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shots = slice(nShotsPerCalib)
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s1,s2 = self.getShot(shots,calib=i)
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if fitEveryCalib is not False:
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ret,bestTransf = alignment.doShots(s1[:fitEveryCalib],s2[:fitEveryCalib],doFit=True,\
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nToFit = nImagesToFit
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#print("Memory available 1",x3py.toolsOS.memAvailable())
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s1,s2 = self.getShots(shots,calib=calib)
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#print("Memory available 2",x3py.toolsOS.memAvailable())
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if nImagesToFit > 0:
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ret,bestTransf = alignment.doShots(s1[:nToFit],s2[:nToFit],doFit=True,\
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initpars=initpars,nSaveImg=nSaveImg,returnBestTransform=True);
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initpars = bestTransf; self.initAlign=bestTransf
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ret = alignment.doShots(s1,s2,initpars=initpars,doFit=doFit,nSaveImg=nSaveImg)
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self.results[i] = ret
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print("Calib cycle %d -> %.3f (best FOM: %.2f)" % (i,self.scanpos[i],np.min(ret.fom)))
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out.append(ret)
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return out
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if nToFit < s1.shape[0]:
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ret2 = alignment.doShots(s1[nToFit:],s2[nToFit:],initpars=initpars,doFit=False,nSaveImg=0)
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if ret is None:
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ret = ret2
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else:
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ret = alignment.unravel_results( (ret,ret2) )
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#print("Memory available 3",x3py.toolsOS.memAvailable())
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self.results[calib] = ret
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#print("Memory available 4",x3py.toolsOS.memAvailable())
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print("Calib cycle %d/%d -> %.3f (best FOM: %.2f)" % (ic,nC,self.scanpos[ic],np.nanmin(ret.fom)))
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return [self.results[c] for c in calibs]
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def doShot(self,shot=0,calib=None,initpars=None,im1=None,im2=None,doFit=True,show=False,showInit=False,save=False,savePlot="auto"):
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if initpars is None: initpars= self.initAlign
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if (im1 is None) or (im2 is None):
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im1,im2 = self.getShot(shot,calib=calib); im1=im1[0]; im2=im2[0]
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im1,im2 = self.getShots(shot,calib=calib); im1=im1[0]; im2=im2[0]
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r = alignment.doShot(im1,im2,initpars,doFit=doFit,show=showInit)
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im1 = r.im1
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im2 = r.im2
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@ -219,7 +282,7 @@ class AnalyzeRun(object):
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"""
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if initpars is None: initpars= self.initAlign
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if shots == "all": shots = slice(self.nShotsPerCalib[calib])
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s1,s2 = self.getShot(shots,calib=calib)
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s1,s2 = self.getShots(shots,calib=calib)
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ret,transformForBestFit = alignment.doShots(s1,s2,initpars=initpars,doFit=doFit,\
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returnBestTransform=True,nSaveImg=nSaveImg)
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if doFit: self.initAlign = transformForBestFit
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@ -234,48 +297,34 @@ class AnalyzeRun(object):
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if len(self.results) == 0: print("self.results are empty, returning without saving")
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if not os.path.isdir(g_folder_out): os.makedirs(g_folder_out)
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if fname == "auto":
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fname = g_folder_out+"/run%04d_analysis.h5" % self.run
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fname = g_folder_out+"/run%04d_analysis" % self.run
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if os.path.exists(fname) and not overwrite:
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print("File %s exists, **NOT** saving, use overwrite=True is you want ..."%fname)
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return
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if os.path.exists(fname) and overwrite: os.unlink(fname)
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print("Saving results to %s"%fname)
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h = h5py.File(fname)
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h["roi1"] = (self.roi1.start,self.roi1.stop)
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h["roi2"] = (self.roi2.start,self.roi2.stop)
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h["scanmot0"] = self.data.scan.scanmotor0
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h["scanpos0"] = self.data.scan.scanmotor0_values
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if hasattr(self.data.scan,"scanmotor1"):
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h["scanmot1"] = self.data.scan.scanmotor1
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h["scanpos1"] = self.data.scan.scanmotor1_values
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#h["transform"] = self.initAlign
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for (c,v) in self.results.items():
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cname = "calib%04d/" % c if isinstance(c,int) else "calib%s/" % c
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for p,vv in mc.objToDict(v).items():
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# cannot save in hfd5 certain python objects
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if p == "fit_result" or p.find("final_transform")==0:
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continue
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if isinstance(vv,dict):
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for pname,parray in vv.items():
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name = cname + p + "/" + pname
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h[name] = parray
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else:
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h[cname + p] = vv
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h.close()
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def load(self,fname="auto"):
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if fname == "auto": fname = g_folder_out+"/run%04d_analysis.npz" % self.run
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temp = np.load(fname)
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self.results = temp["results"].item()
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temp.close()
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def saveTransform(self,fname="auto",transform=None):
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def _auto_transform_name(self,run=None,calib=None):
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if run is None: run = self.run
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fname = g_folder_init+"/run%04d_transform" % run
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if calib is not None:
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fname = fname + "_c%03d" % calib
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return fname + ".npy"
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def saveTransform(self,fname="auto",calib=None,transform=None):
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if transform is None: transform = self.initAlign
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if fname == "auto":
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fname = g_folder_init+"/run%04d_transform.npy" % self.run
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if fname == "auto": fname = self._auto_transform_name(calib=calib)
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print("Saving roi and transformation parameter to %s"%fname)
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alignment.saveAlignment(fname,self.initAlign,self.roi1,self.roi2)
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def loadTransform(self,fname="auto"):
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def loadTransform(self,fname="auto", calib=None):
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if isinstance(fname,dict): raise FileNotFoundError
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if fname == "auto":
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fname = g_folder_init+"/run%04d_transform.npy" % self.run
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elif isinstance(fname,int):
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if fname == "auto": fname = self._auto_transform_name(calib=calib)
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if isinstance(fname,int):
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fname = g_folder_init+"/run%04d_transform.npy" % fname
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if not os.path.exists(fname): print("Asked to read %s, but it does not exist"%fname)
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temp = np.load(fname).item()
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