Fix for the BOZ analysis

• in inspecting the flat field, use np.roll to avoid issues with rois of different size
• make find roi a bit more robust by dividing the used sensor in quarters within which the rois boundaries will be found
• fitting non-linearity of UP2589 did not work. The issue could be with not ideal dark, affecting certain data point at low intensity. Here we use weighted mean and standard deviation of both p/0 and n/0 orders for the fit instead of deviation from a transmission of 1.

src/toolbox_scs/routines/boz.py

@@ -795,26 +795,24 @@ def inspect_plane_fitting(avg, rois, vmin=None, vmax=None):
     fig, axs = plt.subplots(2, 3, sharex=True, figsize=(6, 6))
 
     img_rois = {}
+    centers = {}
+
+    for k, r in enumerate(['n', '0', 'p']):
+        roi = rois[r]
+        centers[r] = np.array([(roi['yl'] + roi['yh'])//2,
+                      (roi['xl'] + roi['xh'])//2])
 
+    d = '0'
+    roi = rois[d]
     for k, r in enumerate(['n', '0', 'p']):
-        img_rois[r] = avg[rois[r]['yl']:rois[r]['yh'],
-                          rois[r]['xl']:rois[r]['xh']]
+        img_rois[r] = np.roll(avg, tuple(centers[r] - centers[d]))[
+        roi['yl']:roi['yh'], roi['xl']:roi['xh']]
         im = axs[0, k].imshow(img_rois[r],
                               vmin=vmin,
                               vmax=vmax)
 
     for k, r in enumerate(['n', '0', 'p']):
-        if img_rois[r].shape[1] != img_rois['0'].shape[1]:
-            if k == 0:
-                n1 = img_rois[r].shape[1]
-                n = img_rois['0'].shape[1]
-                v = img_rois[r][:, (n1-n):]/img_rois['0']
-            else:
-                n1 = img_rois[r].shape[1]
-                n = img_rois['0'].shape[1]
-                v = img_rois[r][:, :-(n1-n)]/img_rois['0']
-        else:
-            v = img_rois[r]/img_rois['0']
+        v = img_rois[r]/img_rois['0']
         im2 = axs[1, k].imshow(v, vmin=0.2, vmax=1.1, cmap='RdBu_r')
 
     cbar = fig.colorbar(im, ax=axs[0, :], orientation="horizontal")