diff --git a/alignment.py b/alignment.py
deleted file mode 100644
index 30f0bea..0000000
--- a/alignment.py
+++ /dev/null
@@ -1,648 +0,0 @@
-from __future__ import print_function,division
-from skimage import transform as tf
-from scipy.ndimage import gaussian_filter1d as g1d
-import mcutils as mc
-import joblib
-import collections
-import numpy as np
-import matplotlib.pyplot as plt
-import time
-
-# /--------\
-# |        |
-# | UTILS  |
-# |        |
-# \--------/
-
-#defaultE = np.arange(1024)*0.12+7060
-defaultE = (np.arange(1024)-512)*0.189+7123
-defaultE = 7063.5+0.1295*np.arange(1024); # done on nov 30 2016, based on xppl3716:r77
-
-__i = np.arange(1024)
-__x = (__i-512)/512
-
-g_fit_default_kw = dict(
-    intensity        = 1.,
-    error_intensity  = 0.02,
-    transx           = 0,
-    error_transx     = 3,
-    limit_transx     = ( -400,400 ),
-    transy           = 0,
-    error_transy     = 3,
-    limit_transy     = ( -50,50 ),
-    rotation         = 0.00,
-    error_rotation   = 0.005,
-    limit_rotation   = (-0.06,0.06),
-    scalex           = 1,
-    limit_scalex     = (0.4,1.2),
-    error_scalex     = 0.05,
-    scaley           = 1,
-    error_scaley     = 0.05,
-    limit_scaley     = (0.8,1.2),
-    shear            = 0.00,
-    error_shear      = 0.001,
-    limit_shear      = (-0.2,0.2),
-    igauss1cen       = 512,
-    error_igauss1cen = 2.,
-    fix_igauss1cen   = True,
-    igauss1sig       = 4000.,
-    error_igauss1sig = 2.,
-    fix_igauss1sig   = True,
-    igauss2cen       = 512,
-    error_igauss2cen = 2.,
-    fix_igauss2cen   = True,
-    igauss2sig       = 4000.,
-    error_igauss2sig = 2.,
-    fix_igauss2sig   = True,
-    iblur1           = 0,
-    limit_iblur1     = (0,20),
-    error_iblur1     = 0.02,
-    fix_iblur1       = True,
-    iblur2           = 0,
-    limit_iblur2     = (0,20),
-    error_iblur2     = 0.02,
-    fix_iblur2       = True
-
-)
-
-
-def rebin1D(a,shape):
-  n0 = a.shape[0]//shape
-  sh = shape,n0
-  return a[:n0*shape].reshape(sh).mean(1)
-
-cmap = plt.cm.viridis if hasattr(plt.cm,"viridis") else plt.cm.gray
-kw_2dplot = dict(
-  interpolation = "none",
-  aspect        = "auto",
-  cmap          = cmap
-  )
-
-fit_ret = collections.namedtuple("fit_ret",["init_pars","final_pars",\
-  "final_transform1","final_transform2","im1","im2","E","p1","p1_sum","p2","p2_sum","fom","ratio","tneeded"] )
-
-def calcFOM(p1,p2,ratio):
-  idx = ( p1>p1.max()/10 )# & (p2>p2.max()/10)
-  ratio = ratio[idx]
-  return ratio.std()/np.abs(ratio.mean())
-  
-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
-
-def getCenterOfMax(img,axis=0,threshold=0.05):
-  img = img.copy()
-  img[img<img.max()*threshold] = 0
-  if axis == 1: img=img.T
-  p   = img.mean(1)
-  x   = np.arange(img.shape[0])
-  return np.sum(x*p)/np.sum(p)
-
-
-def findRoi(img,height=100,axis=0):
-  c = int( getCenterOfMax(img,axis=axis) )
-  roi = slice(c-height//2,c+height//2)
-  return roi
-
-# /--------------------\
-# |                    |
-# |     PLOTS & CO.    |
-# |                    |
-# \--------------------/
-
-def plotShot(im1,im2,transf1=None,transf2=None,fig=None,ax=None,res=None,E=defaultE,save=None):
-  if transf1 is not None: im1 = transf1.transformImage(im1)
-  if transf2 is not None: im2 = transf2.transformImage(im2)
-  if fig is None and ax is None:
-    fig = plt.subplots(2,3,figsize=[7,5],sharex=True)[0]
-    ax = fig.axes
-  elif fig is not None:
-    ax = fig.axes
-  if E is None: E=np.arange(im1.shape[1])
-  n = im1.shape[0]
-  ax[0].imshow(im1,extent=(E[0],E[-1],0,n),**kw_2dplot)
-  ax[1].imshow(im2,extent=(E[0],E[-1],0,n),**kw_2dplot)
-  ax[2].imshow(im1-im2,extent=(E[0],E[-1],0,n),**kw_2dplot)
-  if res is None:
-    p1 = np.nansum(im1,axis=0)
-    p2 = np.nansum(im2,axis=0)
-    pr = p2/p1
-  else:
-    p1 = res.p1; p2 = res.p2; pr = res.ratio
-  ax[3].plot(E,p1,lw=3)
-  ax[4].plot(E,p1,lw=1)
-  ax[4].plot(E,p2,lw=3)
-  idx = (p1>p1.max()/10.)
-  ax[5].plot(E[idx],pr[idx])
-  if res is not None:
-    ax[5].set_title("FOM: %.2f"%res.fom)
-  else:
-    ax[5].set_title("FOM: %.2f"% calcFOM(p1,p2,pr))
-  if (save is not None) and (save is not False): plt.savefig(save,transparent=True,dpi=500)
-  return fig
-
-def plotRatios(r,shot='random',fig=None,E=defaultE,save=None):
-  if fig is None: fig = plt.subplots(2,1,sharex=True)[0]
-  ax = fig.axes
-  n = r.shape[0]
-  i = ax[0].imshow(r,extent=(E[0],E[-1],0,n),**kw_2dplot)
-  i.set_clim(0,1.2)
-  if shot == 'random' : shot = np.random.random_integers(0,n-1)
-  ax[1].plot(E,r[shot],label="Shot n %d"%shot)
-  ax[1].plot(E,np.nanmedian(r[:10],axis=0),label="median 10 shots")
-  ax[1].plot(E,np.nanmedian(r,axis=0),label="all shots")
-  ax[1].legend()
-  ax[1].set_ylim(0,1.5)
-  ax[1].set_xlabel("Energy")
-  ax[1].set_ylabel("Transmission")
-  ax[0].set_ylabel("Shot num")
-  if (save is not None) and (save is not False): plt.savefig(save,transparent=True,dpi=500)
-
-def plotSingleShots(r,nShots=10,fig=None,E=defaultE,save=None,ErangeForStd=(7090,7150)):
-  if fig is None: fig = plt.subplots(2,1,sharex=True)[0]
-  ax = fig.axes
-  for i in range(nShots):
-    ax[0].plot(E,r[i]+i)
-  ax[0].set_ylim(0,nShots+0.5)
-  av = (1,3,10,30,100)
-  good = np.nanmedian(r,0)
-  for i,a in enumerate(av):
-   m = np.nanmedian(r[:a],0)
-   idx = (E>ErangeForStd[0]) & (E<ErangeForStd[1])
-   fom = np.nanstd( m[idx]/good[idx] ) 
-   print("n shots %d, std %.2f"%(a,fom) )
-   ax[1].plot(E,m+i,label="%d shots, std :%.2f"%(a,fom))
-  ax[1].legend()
-  ax[1].set_ylim(0,len(av)+0.5)
-  ax[1].set_xlabel("Energy")
-  ax[1].set_ylabel("Transmission")
-  if (save is not None) and (save is not False): plt.savefig(save,transparent=True,dpi=500)
-
-
-# /--------------------\
-# |                    |
-# |  TRANSFORM & CO.   |
-# |                    |
-# \--------------------/
-
-
-def transformImage(img,transform=None,iblur=None,intensity=1,\
-    igauss=None,orderRotation=1,show=False):
-  """ Transform is the geometrical (affine) transform)
-      blur is a tuple (blurx,blury); if a single value used for energy axis
-      i    is to correct the itensity
-      igauss is to multiply the intensity with a gaussian along the energy axis (=0); if a single assumed centered at center of image, or has to be a tuple (cen,witdth)
-  """
-  if transform is not None and not np.all(transform.params==np.eye(3)) :
-    try:
-      t = np.linalg.inv(transform.params)
-      i = tf._warps_cy._warp_fast(img,t,order=orderRotation)
-    except np.linalg.LinAlgError:
-      print("Image transformation failed, returning original image")
-      i = img.copy()
-  else:
-    i = img.copy()
-  i *= intensity
-  if iblur is not None:
-    if isinstance(iblur,(int,float)):
-      iblur = (iblur,None)
-    if (iblur[0] is not None) and (iblur[0]>0): i = g1d(i,iblur[0],axis=1)
-    if (iblur[1] is not None) and (iblur[1]>0): i = g1d(i,iblur[1],axis=0)
-  if igauss is not None:
-    if isinstance(igauss,(int,float)):
-      igauss = (__i[-1]/2,igauss); # if one parameter only, assume it is centered on image
-    g = mc.gaussian(__i,x0=igauss[0],sig=igauss[1],normalize=False)
-    i *= g
-  if show: plotShot(img,i)
-  return i
-
-
-class SpecrometerTransformation(object):
-  def __init__(self,translation=(0,0),scale=(1,1),rotation=0,shear=0,
-               intensity=1,igauss=None,iblur=None):
-    self.affineTransform = getTransform(translation=translation,
-      scale = scale,rotation=rotation,shear=shear)
-    self.intensity=intensity
-    self.igauss = igauss
-    self.iblur   = iblur
-
-  def update(self,**kw):
-    # current transformation, necessary because skimage transformation do not support
-    # setting of attributes
-    names = ["translation","scale","rotation","shear"]
-    trans_dict = dict( [(n,getattr(self.affineTransform,n)) for n in names] )
-    for k,v in kw.items():
-      if hasattr(self,k):
-        setattr(self,k,v)
-      elif k in names:
-        trans_dict[k] = v
-    self.affineTransform = getTransform(**trans_dict)
-
-  def transformImage(self,img,orderRotation=1,show=False):
-    return transformImage(img,self.affineTransform,iblur=self.iblur,\
-      intensity=self.intensity,igauss=self.igauss,orderRotation=orderRotation,\
-      show=show)
-
-def saveAlignment(fname,transform,roi1,roi2):
-  np.save(fname, dict( transform = transform, roi1=roi1, roi2 = roi2 ) )
-
-def loadAlignment(fname):
-  return np.load(fname).item()
-
-def getBestTransform(results):
-  # try to see if it is unravelled
-  if not hasattr(results,"_fields"): results = unravel_results(results)
-  # find best based on FOM
-  idx = np.nanargmin(np.abs(results.fom))
-  parnames = results.init_pars.keys()
-  bestPars = dict()
-  for n in parnames:
-    bestPars[n] = results.final_pars[n][idx]
-    if n.find("limit_") == 0:
-      if bestPars[n][0] is None:
-        bestPars[n] = None
-      else:
-        bestPars[n] = tuple(bestPars[n])
-  return bestPars,np.nanmin(np.abs(results.fom))
-  
-  
-  
-  
-
-def unravel_results(res,nSaveImg='all'):
-  final_pars = dict()
-  # res[0].fit_result is a list if we are trying to unravel retult from getShots
-  if isinstance(res[0].init_pars,list):
-    parnames = res[0].init_pars[0].get_keys()
-  else:
-    parnames = res[0].init_pars.keys()
-  final_pars = dict()
-  init_pars  = dict()
-  for n in parnames:
-    if n.find("limit_") == 0:
-      final_pars[n] = np.vstack( [r.final_pars[n] for r in res])
-      init_pars[n]  = np.vstack( [r.init_pars[n] for r in res] )
-    else:
-      final_pars[n] = np.hstack( [r.final_pars[n] for r in res])
-      init_pars[n]  = np.hstack( [r.init_pars[n] for r in res] )
-  im1  = np.asarray( [r.im1 for r in res if r.im1.shape[0] != 0])
-  im2  = np.asarray( [r.im2 for r in res if r.im2.shape[0] != 0])
-  if nSaveImg != 'all':
-    im1 = im1[:nSaveImg].copy(); # copy is necessary to free the original memory
-    im2 = im2[:nSaveImg].copy(); # of original array
-  return fit_ret(
-    init_pars    = init_pars,
-    final_pars   = final_pars,
-    final_transform1 = [r.final_transform1 for r in res],
-    final_transform2 = [r.final_transform2 for r in res],
-    im1          = im1,
-    im2          = im2,
-    E            = defaultE,
-    p1           = np.vstack( [r.p1 for r in res]),
-    p1_sum       = np.hstack( [r.p1_sum for r in res]),
-    p2           = np.vstack( [r.p2 for r in res]),
-    p2_sum       = np.hstack( [r.p2_sum for r in res]),
-    ratio        = np.vstack( [r.ratio for r in res]),
-    fom          = np.hstack( [r.fom for r in res] ),
-    tneeded      = np.hstack( [r.tneeded for r in res])
-  )
-
-
-def getTransform(translation=(0,0),scale=(1,1),rotation=0,shear=0):
-  t= tf.AffineTransform(scale=scale,rotation=rotation,shear=shear,\
-         translation=translation)
-  return t
-
-def findTransform(p1,p2,ttype="affine"):
-  return tf.estimate_transform(ttype,p1,p2)
-
-
-#__i = np.arange(2**12)/2**11
-#def _transformToIntensitylDependence(transform):
-#  c = 1.
-#  if hasattr(transform,"i_a"):
-#    c += transform.i_a*_i
-#  return c
-
-
-
-# /--------\
-# |        |
-# |   FIT  |
-# |        |
-# \--------/
-
-
-def transformIminuit(im1,im2,init_transform=dict(),show=False,verbose=True,zeroThreshold=0.05,doFit=True):
-  import iminuit
-  assert im1.dtype == im2.dtype
-  t0 = time.time()
-  # create local copy we can mess up with
-  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)
-  im1_toFit[im1_toFit<im1.max()*zeroThreshold] = 0
-  im2_toFit[im2_toFit<im2.max()*zeroThreshold] = 0
-  p1 = im1.mean(0)
-  p2 = im2.mean(0)
-
-  # set errorbar
-  err = 3.
-
-  def transforms(intensity,
-    igauss1cen,igauss1sig,iblur1,
-    scalex,scaley,rotation,transx,transy,shear,
-    igauss2cen,igauss2sig,iblur2):
-    t1 = SpecrometerTransformation(translation=(transx,transy),scale=(scalex,scaley),\
-         rotation=rotation,shear=shear,intensity=intensity,igauss=(igauss1cen,igauss1sig),\
-         iblur=iblur1)
-    t2 = SpecrometerTransformation(igauss=(igauss2cen,igauss2sig),iblur=iblur2)
-    return t1,t2
-
-  def model(intensity,
-    igauss1cen,igauss1sig,iblur1,
-    scalex,scaley,rotation,transx,transy,shear,
-    igauss2cen,igauss2sig,iblur2):
-
-    t1,t2 = transforms(intensity,
-    igauss1cen,igauss1sig,iblur1,
-    scalex,scaley,rotation,transx,transy,shear,
-    igauss2cen,igauss2sig,iblur2)
-    return t1.transformImage(im1_toFit),t2.transformImage(im2_toFit)
-  
-  def chi2(intensity,
-    igauss1cen,igauss1sig,iblur1,
-    scalex,scaley,rotation,transx,transy,shear,
-    igauss2cen,igauss2sig,iblur2):
-
-    i1,i2 = model(intensity,     \
-    igauss1cen,igauss1sig,iblur1, \
-    scalex,scaley,rotation,transx,transy,shear, \
-    igauss2cen,igauss2sig,iblur2)
-    d = (i1-i2)/err
-    return np.sum(d*d)
-
-  # set default initial stepsize and limits
-  r = im2.mean(0).sum()/im1.mean(0).sum()
-
-  default_kw = g_fit_default_kw.copy()
-  default_kw["intensity"] = r
-
-  init_kw = dict()
-  if isinstance(init_transform,dict):
-    init_kw = init_transform.copy()
-  elif isinstance(init_transform,iminuit._libiminuit.Minuit):
-    init_kw = init_transform.fitarg.copy()
-  elif isinstance(init_transform,tf.AffineTransform):
-    init_kw["transx"],init_kw["transy"] = init_transform.translation
-    init_kw["scalex"],init_kw["scaley"] = init_transform.scale
-    init_kw["rotation"] = init_transform.rotation
-    init_kw["shear"] = init_transform.shear
-  if "intensity" in init_kw and init_kw["intensity"] == "auto":
-    r = im2.mean(0).sum()/im1.mean(0).sum()
-    init_kw["intensity"]= r
-
-  kw = default_kw.copy()
-  kw.update(init_kw)
-  kw["fix_shear"]=True
-
-
-  tofix = ("scalex","scaley","rotation","shear")
-  kw_tofix = dict( [("fix_%s"%p,True) for p in tofix] )
-  kw.update(kw_tofix)
-  imin  = iminuit.Minuit(chi2,errordef=1.,**kw)
-  imin.set_strategy(1)
-  init_params = imin.fitarg.copy()
-
-  if show:
-    i1,i2 = model(*imin.args)
-    plotShot(i1,i2)
-    fig = plt.gcf()
-    fig.text(0.5,0.9,"Initial Pars")
-    input("Enter to start fit")
-
-  if doFit: imin.migrad()
-
-  pars = imin.fitarg.copy()
-  kw_tofree = dict( [("fix_%s"%p,False) for p in tofix] )
-  pars.update(kw_tofree)
-  imin  = iminuit.Minuit(chi2,errordef=1.,**pars)
-  imin.set_strategy(1)
-
-  if doFit: imin.migrad()
-
-  final_params = imin.fitarg.copy()
-  t1,t2 = transforms(*imin.args)
-  i1,i2 = model(*imin.args)
-
-  i1 = t1.transformImage(im1)
-  i2 = t2.transformImage(im2)
-  if show:
-    plotShot(i1,i2)
-    fig = plt.gcf()
-    fig.text(0.5,0.9,"Final Pars")
-
-  p1 = np.nansum(i1,axis=0)
-  p2 = np.nansum(i2,axis=0)
-  r  = p2/p1
-  idx = p1>np.nanmax(p1)/10.
-  fom = calcFOM(p1,p2,r)
-  return fit_ret(
-    init_pars    = init_params,
-    final_pars    = final_params,
-    final_transform1 = t1,
-    final_transform2 = t2,
-    im1          = i1,
-    im2          = i2,
-    E            = defaultE,
-    p1           = p1,
-    p1_sum       = p1.sum(),
-    p2           = p2,
-    p2_sum       = p2.sum(),
-    ratio        = r,
-    fom          = fom,
-    tneeded      = time.time()-t0
-  )
-
- 
-# /--------\
-# |        |
-# | MANUAL |
-# | ALIGN  |
-# |        |
-# \--------/
-class GuiAlignment(object):
-  def __init__(self,im1,im2,autostart=False):
-    self.im1 = im1
-    self.im2 = im2
-    self.f,self.ax=plt.subplots(1,2)
-    self.ax[0].imshow(im1,aspect="auto")
-    self.ax[1].imshow(im2,aspect="auto")
-    self.transform = None
-    if autostart:
-      return self.start()
-    else:
-      print("Zoom first then use the .start method")
-
-  def OnClick(self,event):
-    if event.button == 1:
-      if self._count % 2 == 0:
-        self.im1_p.append( (event.xdata,event.ydata) )
-        print("Added",event.xdata,event.ydata,"to im1")
-      else:
-        self.im2_p.append( (event.xdata,event.ydata) )
-        print("Added",event.xdata,event.ydata,"to im2")
-      self._count += 1
-    elif event.button == 2:
-      # neglect middle click
-      return
-    elif event.button == 3:
-      self.done = True
-      return
-    else:
-      return
-
-  def start(self):
-    self._nP = 0
-    self._count = 0
-    self.im1_p = []
-    self.im2_p = []
-    self.done = False
-    cid_up = self.f.canvas.mpl_connect('button_press_event', self.OnClick)
-    print("Press right button to finish")
-    while not self.done:
-      if self._count % 2 == 0:
-        print("Select point %d for left image"%self._nP)
-      else:
-        print("Select point %d for right image"%self._nP)
-      self._nP = self._count //2
-      plt.waitforbuttonpress()
-    self.im1_p = np.asarray(self.im1_p)
-    self.im2_p = np.asarray(self.im2_p)
-    self.transform = findTransform(self.im1_p,self.im2_p)
-    self.transform.intensity = 1.
-    return self.transform
-
-  def show(self):
-    if self.transform is None:
-      print("Do the alignment first (with .start()")
-      return
-    # just to save some tipying
-    im1 = self.im1; im2 = self.im2
-    im1_new = transformImage(im1,self.transform)
-    f,ax=plt.subplots(1,3)
-    ax[0].imshow(im1,aspect="auto")
-    ax[1].imshow(im1_new,aspect="auto")
-    ax[2].imshow(im2,aspect="auto")
-
-  def save(self,fname):
-    if self.transform is None:
-      print("Do the alignment first (with .start()")
-      return
-    else:
-      np.save(fname,self.transform)
-
-  def load(self,fname):
-    self.transform = np.load(fname).item()
-
-
-def getAverageTransformation(out):
-  res = append_results(out)
-  # get average parameters
-  tx = np.median(res["transx"])
-  ty = np.median(res["transy"])
-  sx = np.median(res["scalex"])
-  sy = np.median(res["scaley"])
-  r  = np.median(res["rotation"])
-  sh = np.median(res["shear"])
-  inten  = np.median(res["intensity"])
-  t  = getTransform( (tx,ty),(sx,sy),r,sh,intensity=inten )
-  return t
-
-def checkAverageTransformation(out,imgs1):
-  t,inten = getAverageTransformation(out)
-  res["ratio_av"] = []
-  for shot,values in out.items():
-    i = transformImage(imgs1[shot],t)
-    p1 = np.nansum(i,axis=0)
-    r  = p1/values.p2
-    res["ratio_av"].append( r )
-  res["ratio_av"] = np.asarray(res["ratio_av"])
-  return res
- 
-
-g_lastpars = None
-
-def clearCache():
-  globals()["g_lastpars"]=None
-
-def doShot(i1,i2,init_pars,doFit=True,show=False):
-  #if g_lastpars is not None: init_pars = g_lastpars
-  r = transformIminuit(i1,i2,init_pars,show=show,verbose=False,doFit=doFit)
-  return r
-
-
-def doShots(imgs1,imgs2,initpars,nJobs=16,doFit=False,returnBestTransform=False,nSaveImg='all'):
-  clearCache()
-  N = imgs1.shape[0]
-  pool = joblib.Parallel(backend="threading",n_jobs=nJobs) \
-    (joblib.delayed(doShot)(imgs1[i],imgs2[i],initpars,doFit=doFit) for i in range(N))
-  res = unravel_results(pool,nSaveImg=nSaveImg)
-  if returnBestTransform:
-    bestT,fom = getBestTransform(res)
-    print("FOM for best alignment %.2f"%fom)
-    return res,bestT
-  else:
-    return res
-#  out = collections.OrderedDict( enumerate(pool) )
-#  return out
-    
-# /--------\
-# |        |
-# | TEST   |
-# | ALIGN  |
-# |        |
-# \--------/
-
-
-
-def testDiling(N=100,roi=slice(350,680),doGUIalignment=False,nJobs=4,useIminuit=True):
-  import xppll37_mc
-  globals()["g_lastpars"]=None
-  d = xppll37_mc.readDilingDataset()
-  im1 = xppll37_mc.subtractBkg(d.opal1[:N])[:,roi,:]
-  im2 = xppll37_mc.subtractBkg(d.opal2[:N])[:,roi,:]
-  N   = im1.shape[0]; # redefie N in case it reads less than N
-  # to manual alignment
-  if doGUIalignment:
-    a = GuiAlignment(im1[0],im2[0])
-    input("Ok to start")
-    init_pars = a.start()
-    np.save("gui_align_transform.npy",init_pars)
-  else:
-    init_pars = np.load("gui_align_transform.npy").item()
-  pool = joblib.Parallel(backend="threading",n_jobs=nJobs,verbose=20) \
-    (joblib.delayed(doShot)(im1[i],im2[i],init_pars,useIminuit=useIminuit) for i in range(N))
-  out = collections.OrderedDict( enumerate(pool) )
-  return out
-
-if __name__ == '__main__':
-  pass