various changes: some naming, added function for averaging and changed doShots to accept list of calibs

xanes_analyzeRun.py

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