it works correctly

nabu/preproc/alignment.py

@@ -607,8 +607,8 @@ class CenterOfRotationAdaptiveSearch(CenterOfRotation):
         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
-        step_fraction and  sigma_fraction factors
+        obtained by multiplying the shortest distance from the left or right border with
+        a self.step_fraction and  self.sigma_fraction factors which ensure global overlapping
     """
     sigma_fraction = 1.0/4.0
     step_fraction  = 1.0/6.0
@@ -673,7 +673,6 @@ class CenterOfRotationAdaptiveSearch(CenterOfRotation):
             if margins is None or in the form of  (margin1,margin2) the search is done between margin1 and  dim_x-1-margin2.
             If left to None then by default (margin1,margin2)  = ( 10, 10 )
             
-
         Raises
         ------
         ValueError
@@ -789,7 +788,7 @@ class CenterOfRotationAdaptiveSearch(CenterOfRotation):
             if not np.isnan(cost):
                 found_centers.append([cost, abs(Xcor_rel - cor_position), cor_position, energy])
 
-            Xcor = min(Xcor + Xcor  , Xcor + (dim_radio - Xcor) )* self.step_fraction
+            Xcor = min(Xcor + Xcor* self.step_fraction  , Xcor + (dim_radio - Xcor)* self.step_fraction )
 
         found_centers.sort()
         cor_position = found_centers[0][2]

nabu/preproc/tests/test_alignment.py

@@ -158,12 +158,12 @@ def get_focus_data(*dataset_path):
     return data, img_pos, pixel_size, (calib_data_std, calib_data_angle)
 
 
-def _peaks2im(possY, possX, widths, NY, NX):
+def _peaks2im(poss_y, poss_x, widths, ny, nx):
     """Given a set of positions, widths and the image shape creates an image with spots"""
-    res = np.zeros([NY, NX], "f")
-    ys = np.arange(NY)
-    xs = np.arange(NX)
-    for py, px, w in zip(possY, possX, widths):
+    res = np.zeros([ny, nx], "f")
+    ys = np.arange(ny)
+    xs = np.arange(nx)
+    for py, px, w in zip(poss_y, poss_x, widths):
         y = ys - py
         x = xs - px
         add = np.exp(-((y * y)[:, None] + x * x) / (2.0 * w * w))
@@ -176,49 +176,49 @@ def _get_challenging_ImsCouple_for_halftomo_cor():
 
     np.random.seed(0)
 
-    NY = 400
-    NX = 500
+    ny = 400
+    nx = 500
 
     npoints = 2000
 
-    possX = -NX / 2.0 + 2 * NX * np.random.random(npoints)
-    possY = -NY / 2.0 + 2 * NY * np.random.random(npoints)
+    poss_x = -nx / 2.0 + 2 * nx * np.random.random(npoints)
+    poss_y = -ny / 2.0 + 2 * ny * np.random.random(npoints)
     widths = 3 * np.random.random(npoints)
 
-    im1 = _peaks2im(possY, possX, widths, NY, NX)
+    im1 = _peaks2im(poss_y, poss_x, widths, ny, nx)
 
     CHT = 201.67
 
     CenterX = 400.123
     shiftY = 3.1415
 
-    possX_2 = 2 * CenterX - possX
-    possY_2 = shiftY + possY
+    poss_x_2 = 2 * CenterX - poss_x
+    poss_y_2 = shiftY + poss_y
 
-    im2 = _peaks2im(possY_2, possX_2, widths, NY, NX)
+    im2 = _peaks2im(poss_y_2, poss_x_2, widths, ny, nx)
 
     npoints_spurious = 200
 
-    spurious_possX = NX * np.random.random(npoints_spurious)
-    spurious_possY = NY * np.random.random(npoints_spurious)
+    spurious_poss_x = nx * np.random.random(npoints_spurious)
+    spurious_poss_y = ny * np.random.random(npoints_spurious)
     widths = [20.0] * npoints_spurious
-    spurious_im = _peaks2im(spurious_possY, spurious_possX, widths, NY, NX)
+    spurious_im = _peaks2im(spurious_poss_y, spurious_poss_x, widths, ny, nx)
     im1[:] += spurious_im * 0.1
 
-    spurious_possX = NX * np.random.random(npoints_spurious)
-    spurious_possY = NY * np.random.random(npoints_spurious)
+    spurious_poss_x = nx * np.random.random(npoints_spurious)
+    spurious_poss_y = ny * np.random.random(npoints_spurious)
     widths = [20.0] * npoints_spurious
-    spurious_im = _peaks2im(spurious_possY, spurious_possX, widths, NY, NX)
+    spurious_im = _peaks2im(spurious_poss_y, spurious_poss_x, widths, ny, nx)
     im2[:] += spurious_im * 0.1
 
     noise_level = 0.2
-    noise_ima1 = np.random.normal(0.0, size=[NY, NX]) * noise_level
-    noise_ima2 = np.random.normal(0.0, size=[NY, NX]) * noise_level
+    noise_ima1 = np.random.normal(0.0, size=[ny, nx]) * noise_level
+    noise_ima2 = np.random.normal(0.0, size=[ny, nx]) * noise_level
 
     im1[:] += noise_ima1
     im2[:] += noise_ima2
 
-    return im1, im2, (CenterX - (NX - 1) / 2.0)
+    return im1, im2, (CenterX - (nx - 1) / 2.0)
 
 
 @pytest.mark.usefixtures("bootstrap_base")
@@ -365,10 +365,10 @@ class TestCor(object):
         radios = nabu_get_data("ha_autocor_radios.npz")
         radio1 = radios["radio1"]
         radio2 = radios["radio2"]
+        cor_pos = radios["cor_pos"]
 
         radio2 = np.fliplr(radio2)
 
-        cor_pos = 983.107
 
         CoR_calc = alignment.CenterOfRotationAdaptiveSearch()