during marion visit

README.md

@@ -19,9 +19,12 @@
 # 2. tell python to use look for modules in the folder
 # 3. import sys; sys.path.insert(0,"~/my_folder")
 
-# there are two files:
+# these are the important files:
 # 1. alignment.py          (deals with images)
 # 2. xanes_analyzeRun.py   (deals with run and images reading)
+# 3. xppl37_calibration.py (deals with spectrometer calibration)
+# 4. xppl37_theta_scan.py  (deals with theta scans)
+# 5. xppl37_spectra.py     (deals with calculation of absorption spectra for IN/OUT scans, etc.
 
 %matplotlib nbagg
 import matplotlib
@@ -45,6 +48,8 @@ pars = dict( scalex = 0.6, intensity = 0.1, iblur1=2,fix_iblur1 = False )
 
 # define the run object
 #### NOTE : for mec run swapx=True,swapy=False
+#### NOTE : for xpp un-focused run: swapx=False,swapy=False
+#### NOTE : for xpp    focused run: swapx=False,swapy=True
 r = xanes_analyzeRun.AnalyzeRun(190,initAlign=pars,swapx=True,swapy=False)
 
 # data are d.spec1 and d.spec2 (spec1 is the one **upbeam**)

dispersiveXanes_alignment.py

@@ -84,6 +84,27 @@ def findRoi(img,height=100,axis=0):
   return roi
 
 
+def subtractBkg(imgs,nPix=100,bkg_type="line"):
+  if imgs.ndim == 2: imgs = imgs[np.newaxis,:]
+  imgs = imgs.astype(np.float)
+  if bkg_type == "line":
+    bkg = np.median(imgs[:,:nPix,:],axis=1)
+    imgs = imgs-bkg[:,np.newaxis,:]
+  elif bkg_type == "corner":
+    q1 = imgs[:,:nPix,:nPix].mean(-1).mean(-1)
+    imgs[:,:512,:512]-=q1[:,np.newaxis,np.newaxis]
+    q2 = imgs[:,:nPix,-nPix:].mean(-1).mean(-1)
+    imgs[:,:512,-512:]-=q2[:,np.newaxis,np.newaxis]
+    q3 = imgs[:,-nPix:,-nPix:].mean(-1).mean(-1)
+    imgs[:,-512:,-512:]-=q3[:,np.newaxis,np.newaxis]
+    q4 = imgs[:,-nPix:,:nPix].mean(-1).mean(-1)
+    imgs[:,-512:,:512]-=q4[:,np.newaxis,np.newaxis]
+  elif bkg_type is None:
+    if imgs.ndim == 2: imgs = imgs[np.newaxis,:].astype(np.float)
+  else:
+    print("Background subtraction '%s' Not impleted"%bkg_type)
+  return imgs
+
 # /--------------------\
 # |                    |
 # |     PLOTS & CO.    |
@@ -313,7 +334,7 @@ def findTransform(p1,p2,ttype="affine"):
 # \--------/
 
 
-def transformIminuit(im1,im2,init_transform=dict(),show=False,verbose=True,zeroThreshold=0.05,doFit=True):
+def transformIminuit(im1,im2,init_transform=dict(),show=False,verbose=True,zeroThreshold=0.0,doFit=True):
   import iminuit
   assert im1.dtype == im2.dtype
   t0 = time.time()
@@ -321,7 +342,7 @@ def transformIminuit(im1,im2,init_transform=dict(),show=False,verbose=True,zeroT
   im1_toFit = im1.copy() 
   im2_toFit = im2.copy() 
 
-  # set anything below the 5% of the max to zero (one of the two opal is noisy)
+  # set anything below the zeroThreshold of the max to zero (one of the two opal is noisy)
   im1_toFit[im1_toFit<im1.max()*zeroThreshold] = 0
   im2_toFit[im2_toFit<im2.max()*zeroThreshold] = 0
   p1 = im1.mean(0)
@@ -365,8 +386,8 @@ def transformIminuit(im1,im2,init_transform=dict(),show=False,verbose=True,zeroT
 
   # set default initial stepsize and limits
   r = im2.mean(0).sum()/im1.mean(0).sum()
-
   default_kw = g_fit_default_kw.copy()
+  # will be used only if not in initpars
   default_kw["intensity"] = r
 
   init_kw = dict()
@@ -400,6 +421,7 @@ def transformIminuit(im1,im2,init_transform=dict(),show=False,verbose=True,zeroT
     plotShot(i1,i2)
     fig = plt.gcf()
     fig.text(0.5,0.9,"Initial Pars")
+    plt.draw()
     input("Enter to start fit")
 
   if doFit: imin.migrad()

xanes_analyzeRun.py

@@ -156,10 +156,8 @@ class AnalyzeRun(object):
     # rebin and swap im1 if necessary
     if im1.shape[-1] != 1024:
       im1 = mc.rebin(im1, (im1.shape[0],im1.shape[1],1024) )
-    if self.swap[0]:
+    if self.swap[0]: im1 = im1[:,:,::-1]
-      im1 = im1[:,:,::-1]
+    if self.swap[1]: im1 = im1[:,::-1,:]
-    if self.swap[1]:
-      im1 = im1[:,::-1,:]
     if roi is None:
       pass
     elif isinstance(roi,slice):
@@ -268,6 +266,7 @@ class AnalyzeRun(object):
   def save(self,fname="auto",overwrite=False):
     if len(self.results) == 0:
       print("self.results are empty, returning without saving")
+      return
     if not os.path.isdir(g_folder_out): os.makedirs(g_folder_out)
     if fname == "auto":
       fname = g_folder_out+"/run%04d_analysis.npz" % self.run

xppl37_spectra.py

@@ -22,8 +22,11 @@ gradual_colors = ['#014636', '#016c59', '#02818a', '#3690c0', '#67a9cf', '#a6bdd
 
 
 def calcSpectraForRun(run=82,calibs="all",realign=False,init="auto",alignCalib=0,force=False):
-  """ init and alignCalib are used only if realignment is performed:
+  """ Calculate spectra (spec1, spec2) for run based on alignment.
-      init = run for initial alignment, use auto is you want to use same run
+
+      Alignment can be 'forced' with realign=True.
+      In such a case 
+        init = run for initial alignment
         alignCalib = calibcycle for alignment
   """
   if init=="auto": init=run
@@ -51,11 +54,17 @@ def calcSpectraForRun(run=82,calibs="all",realign=False,init="auto",alignCalib=0
   elif isinstance(calibs,slice):
     calibsForOut = list(range(r.nCalib))[calibs]
   elif calibs == "all":
-    calibsForOut = list(range(r.nCalib))
+    calibsForOut = r.results.keys()
   else:
     calibsForOut = calibs
+  # focused data have one single calibcycle ...
+  if len(r.results) > 1:
     p1 = [r.results[calib].p1 for calib in calibsForOut]
     p2 = [r.results[calib].p2 for calib in calibsForOut]
+  else:
+    idx = r.results[None].fom < 0.5
+    p1 = [ r.results[None].p1[idx], r.results[None].p1[~idx] ]
+    p2 = [ r.results[None].p2[idx], r.results[None].p2[~idx] ]
   return profile_ret( run =r, p1 =p1, p2=p2,calibs=calibsForOut)
 
 
@@ -80,9 +89,13 @@ def calcSpectraForRefAndSample(run=82,refCalibs=slice(None,None,2),forceSpectraC
     if isinstance(refCalibs,int):   refCalibs  = [refCalibs,]
     sampleCalibs = [c+1 for c in refCalibs]
     # need a single call (for sample and ref) to save all calibcycles
-    calcSpectraForRun(run,calibs=refCalibs+sampleCalibs,\
+    data= calcSpectraForRun(run,calibs=refCalibs+sampleCalibs,\
                       force=forceSpectraCalculation);
-    
+    # for focused runs
+    if len(data.run.results) == 1:
+      ref    = profile_ret(run=data.run,p1=[data.p1[0],],p2=[data.p2[0],],calibs=[0,])
+      sample = profile_ret(run=data.run,p1=[data.p1[1],],p2=[data.p2[1],],calibs=[0,])
+    else:
       ref = calcSpectraForRun(run,calibs=refCalibs)
       sample  = calcSpectraForRun(run,calibs=sampleCalibs)
   elif isinstance(run,(list,tuple)):