DetectorTranslationAlongBeam: return complete list of shifts (with initial zeros)

Signed-off-by: Nicola VIGANÒ <nicola.vigano@esrf.fr>

nabu/preproc/alignment.py

@@ -717,27 +717,25 @@ class DetectorTranslationAlongBeam(AlignmentBase):
         cc_vs = np.fft.fftfreq(img_shape[-2], 1 / img_shape[-2])
         cc_hs = np.fft.fftfreq(img_shape[-1], 1 / img_shape[-1])
 
-        shifts_vh = np.empty((num_imgs - 1, 2))
+        shifts_vh = np.empty((num_imgs, 2))
         for ii, cc in enumerate(ccs):
             (f_vals, fv, fh) = self.extract_peak_region_2d(cc, peak_radius=peak_fit_radius, cc_vs=cc_vs, cc_hs=cc_hs)
-            shifts_vh[ii, :] = self.refine_max_position_2d(f_vals, fv, fh)
+            shifts_vh[ii+1, :] = self.refine_max_position_2d(f_vals, fv, fh)
 
         if use_adjacent_imgs:
             shifts_vh = np.cumsum(shifts_vh, axis=0)
 
-        img_shifts_vh = np.concatenate(([[0, 0]], shifts_vh), axis=0)
-
         # Polynomial.fit is supposed to be more numerically stable than polyfit
         # (according to numpy)
-        coeffs_v = Polynomial.fit(img_pos, img_shifts_vh[:, 0], deg=1).convert().coef
-        coeffs_h = Polynomial.fit(img_pos, img_shifts_vh[:, 1], deg=1).convert().coef
+        coeffs_v = Polynomial.fit(img_pos, shifts_vh[:, 0], deg=1).convert().coef
+        coeffs_h = Polynomial.fit(img_pos, shifts_vh[:, 1], deg=1).convert().coef
 
         if self.verbose:
             f, axs = plt.subplots(1, 2)
-            axs[0].scatter(img_pos, img_shifts_vh[:, 0])
+            axs[0].scatter(img_pos, shifts_vh[:, 0])
             axs[0].plot(img_pos, polyval(img_pos, coeffs_v))
             axs[0].set_title("Vertical shifts")
-            axs[1].scatter(img_pos, img_shifts_vh[:, 1])
+            axs[1].scatter(img_pos, shifts_vh[:, 1])
             axs[1].plot(img_pos, polyval(img_pos, coeffs_h))
             axs[1].set_title("Horizontal shifts")
             plt.show(block=False)

nabu/preproc/tests/test_alignment.py

@@ -38,6 +38,7 @@ def bootstrap_dtr(request):
     cls.expected_shifts_vh = np.array((129.93, 353.18))
 
     cls.reference_shifts_list = [
+        [0, 0],
         [-9.39, 11.29],
         [-5.02, 3.81],
         [-3.67, 9.73],