Improved BOZ flat field

• adds low and high threshold on product and ratio of ROIs to calculate flat field correction
• adds default flat field guess for the fit as parameters
• implement hexagonal lattice for DSSC pixels
• breaks left/right symmetry for flat field fitting since zone plate can be slightly rotated
• implements a refine fit with regularization for flat field calculation
• implements dask auto rechunking

Closes #53 (closed) #55 (closed)

src/toolbox_scs/routines/boz.py

@@ -1054,16 +1054,17 @@ def ff_refine_crit(p, alpha, params, arr_dark, arr, tid, rois,
     # drop saturated shots
     d = data.where(data['sat_sat'] == False, drop=True)
     
-    rn = xas(d, 40, Iokey='0', Itkey='n', nrjkey='0')
-    rp = xas(d, 40, Iokey='0', Itkey='p', nrjkey='0')
-    rd = xas(d, 40, Iokey='p', Itkey='n', nrjkey='0')
+    rn = xas(d, 40, Iokey='0', Itkey='n', nrjkey='0', fluorescence=True)
+    rp = xas(d, 40, Iokey='0', Itkey='p', nrjkey='0', fluorescence=True)
+    rd = xas(d, 40, Iokey='p', Itkey='n', nrjkey='0', fluorescence=True)
 
-    err = np.nansum(rn['sigmaA']) + np.nansum(rp['sigmaA']) + np.nansum(rd['sigmaA'])
-    mean = ((1.0 - np.nanmean(rn['muA']))**2 +
-            (1.0 - np.nanmean(rp['muA']))**2 +
-            (1.0 - np.nanmean(rd['muA']))**2)
+    err_sigma = (np.nansum(rn['sigmaA']) + np.nansum(rp['sigmaA'])
+                 + np.nansum(rd['sigmaA']))
+    err_mean = ((1.0 - np.nanmean(rn['muA']))**2 +
+                (1.0 - np.nanmean(rp['muA']))**2 +
+                (1.0 - np.nanmean(rd['muA']))**2)
 
-    return 1e3*(err*alpha + (1-alpha)*mean)
+    return 1e3*(alpha*err_sigma + (1-alpha)*err_mean)
 
 
 def ff_refine_fit(params):