few more pyFAI related functions includind a neat way to quickly find the center and some utility (pyFAI_dict)

mcutils.py

@@ -1220,6 +1220,24 @@ def insertInSortedArray(a,v):
   a[idx]=v
   return a
 
+##### X-ray images #############
+
+def pyFAIread(fname):
+  import fabio
+  f = fabio.open(fname)
+  data = f.data
+  del f
+  return data
+
+def pyFAI_dict(ai):
+  """ ai is a pyFAI azimuthal intagrator"""
+  methods = dir(ai)
+  methods = [m for m in methods if m.find("get_") == 0]
+  names   = [m[4:] for m in methods]
+  values  = [getattr(ai,m)() for m in methods]
+  ret = dict( zip(names,values) )
+  return ret
+
 def pyFAI1d(ai, imgs, mask = None, npt_radial = 600, method = 'csr',safe=True,dark=10., polCorr = 1):
     """ ai is a pyFAI azimuthal intagrator 
               it can be defined with pyFAI.load(ponifile)
@@ -1252,6 +1270,59 @@ def pyFAI2d(ai, imgs, mask = None, npt_radial = 600, npt_azim=360,method = 'csr'
       out[_i] = i2d
     return q,azTheta,np.squeeze(out)
 
+def _calc_R(x,y, xc, yc):
+  """ calculate the distance of each 2D points from the center (xc, yc) """
+  return np.sqrt((x-xc)**2 + (y-yc)**2)
+
+def _chi2(c, x, y):
+  """ calculate the algebraic distance between the data points and the mean
+      circle centered at c=(xc, yc) """
+  Ri = _calc_R(x, y, *c)
+  return Ri - Ri.mean()
+
+def leastsq_circle(x,y):
+  from scipy import optimize
+  # coordinates of the barycenter
+  center_estimate = np.nanmean(x), np.nanmean(y)
+  center, ier = optimize.leastsq(_chi2, center_estimate, args=(x,y))
+  xc, yc = center
+  Ri       = _calc_R(x, y, *center)
+  R        = Ri.mean()
+  residu   = np.sum((Ri - R)**2)
+  return xc, yc, R
+
+def pyFAI_find_center(img,psize=100e-6,dist=0.1,wavelength=0.8e-10,**kwargs):
+  import pyFAI
+  plt.ion()
+  kw = dict( pixel1 = psize, pixel2 = psize, dist = dist,wavelength=wavelength )
+  kw.update(kwargs)
+  ai =  pyFAI.azimuthalIntegrator.AzimuthalIntegrator(**kw)
+  fig_img,ax_img = plt.subplots(1,1)
+  fig_pyfai,ax_pyfai = plt.subplots(1,1)
+  fig_pyfai = plt.figure(2)
+  ax_img.imshow(img)
+  plt.sca(ax_img); # set figure to use for mouse interaction
+  ans = ""
+  print("Enter 'end' when done")
+  while ans != "end":
+    if ans == "":
+      print("Click on beam center:")
+      plt.sca(ax_img); # set figure to use for mouse interaction
+      xc,yc = plt.ginput()[0]
+    else:
+      xc,yc = map(float,ans.split(","))
+    print("Selected center:",xc,yc)
+    ai.set_poni1(xc*psize)
+    ai.set_poni2(yc*psize)
+    q,az,i = pyFAI2d(ai,img)
+    ax_pyfai.pcolormesh(q,az,i)
+    ax_pyfai.set_title(str( (xc,yc) ))
+    plt.pause(0.01)
+    plt.draw()
+    ans=input("Enter to continue with clinking or enter xc,yc values")
+  print("Final values: (in pixels) %.3f %.3f"%(xc,yc))
+  return ai
+  
 
 ### Objects ###