break

nabu/preproc/alignment.py

@@ -604,7 +604,7 @@ class CenterOfRotation(AlignmentBase):
 class CenterOfRotationAdaptiveSearch(CenterOfRotation):
     """ This adaptive method works by applying a gaussian which highlights, by apodisation, a region 
         which can possibly contain the good center of rotation.
-        The whole image is spanned during several applications of the apodisation, at each application
+        The whole image is spanned during several applications of the apodisation. At each application
         the apodisation function, which is a gaussian, is moved to a new guess position.
         The lenght of the step, by which the gaussian is moved, and its sigma are
         obtained by multiplying the shortest distance to the left or right border with
@@ -738,12 +738,12 @@ class CenterOfRotationAdaptiveSearch(CenterOfRotation):
         Xcor = lim1
         while Xcor < lim2:
 
-            tmpsigma = min(
+            tmp_sigma = min(
                 (img_1.shape[1] - Xcor) ,
                 (Xcor) ,
             )* self.sigma_fraction
 
-            tmpx = (np.arange(img_1.shape[1]) - Xcor) / tmpsigma
+            tmpx = (np.arange(img_1.shape[1]) - Xcor) / tmp_sigma
             apodis = np.exp(-tmpx * tmpx / 2.0)
             
             Xcor_rel = Xcor - (img_1.shape[1] // 2)
@@ -771,13 +771,13 @@ class CenterOfRotationAdaptiveSearch(CenterOfRotation):
                 cor_position = p2 / 2
             
             cor_in_img = img_1.shape[1] // 2 + cor_position
-            tmpsigma = min(
+            tmp_sigma = min(
                 (img_1.shape[1] - cor_in_img) ,
                 (cor_in_img) ,
             )* self.sigma_fraction
             
-            M1 = int(round(cor_position + img_1.shape[1] // 2)) - int(round(tmpsigma))
-            M2 = int(round(cor_position + img_1.shape[1] // 2)) + int(round(tmpsigma))
+            M1 = int(round(cor_position + img_1.shape[1] // 2)) - int(round(tmp_sigma))
+            M2 = int(round(cor_position + img_1.shape[1] // 2)) + int(round(tmp_sigma))
 
             piece1 = img_1[:, M1:M2]
             piece2 = img_2[:, img_1.shape[1] - M2 : img_1.shape[1] - M1]