Alignment: updated docstring of DetectorTranslationAlongBeam

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

nabu/preproc/alignment.py

@@ -630,9 +630,8 @@ class DetectorTranslationAlongBeam(AlignmentBase):
         high_pass: float or sequence of two floats
             High-pass filter properties, as described in `nabu.misc.fourier_filters`.
         return_shifts: boolean, optional
-            if a True a list, containing for each given image of the stack a tuple of shifts, will
-            be return together with  the increment per unit-distance.
-            This slot is intended for introspection.
+            Adds a third returned argument, containing the pixel shifts of each
+            image with respect to the first one in the stack. Defaults to False.
         use_adjacent_imgs: boolean, optional
             Compute correlation between adjacent images.
             It can be used when dealing with large shifts, to avoid overflowing the shift.
@@ -641,15 +640,16 @@ class DetectorTranslationAlongBeam(AlignmentBase):
 
         Returns
         -------
-        tuple(float, float)
-            Estimated (vertical, horizontal) increment per unit-distance of the
-            ratio between pixel-size and detector translation.
-            Optionally a tuple containing the shifts per each image are found if optional argument
-            return_shifts is true
+        coeff_v: float
+            Estimated vertical shift in pixel per unit-distance of the detector translation.
+        coeff_h: float
+            Estimated horizontal shift in pixel per unit-distance of the detector translation.
+        shifts_vh: list, optional
+            The pixel shifts of each image with respect to the first image in the stack.
+            Activated if return_shifts is True.
 
         Examples
         --------
-
         The example below builds first a stack of four imagew, each one correspondng to a motor step.
         At each step the same feature is shifted by 2.468, 1.234 pixels (vertical, horizontal).
         Moreover, to show the filtering option, a spurious low frequency feature is added
@@ -659,7 +659,6 @@ class DetectorTranslationAlongBeam(AlignmentBase):
         second call argument. Here we use a high-pass filter, due to the presence of some low-frequency
         noise component.
 
-
         >>> import numpy as np
         ... import scipy as sp
         ... import scipy.ndimage