Commit 43886ebe authored by Nicola Vigano's avatar Nicola Vigano
Browse files

Alignment: detector translation: simplified function


Signed-off-by: Nicola Vigano's avatarNicola VIGANÒ <nicola.vigano@esrf.fr>
parent f30cb49b
......@@ -481,27 +481,6 @@ class DetectorTranslationAlongBeam(AlignmentBase):
% (" ".join(("%d" % x for x in shape_stack)), " ".join(("%d" % x for x in shape_pos)),)
)
@staticmethod
def _check_equispaced(img_pos, supposed_equispaced):
img_pos_incrs = np.diff(img_pos)
detected_equispaced = np.all(np.isclose(img_pos_incrs[0], img_pos_incrs[1:]))
if detected_equispaced and not supposed_equispaced:
logging.getLogger(__name__).warning(
"The image position increments were supposed to NOT be equispaced, "
+ "but they seem to be equispaced: "
+ " ".join(("%f" % x for x in img_pos))
+ ". Forcing behavior according to the detected condition."
)
if not detected_equispaced and supposed_equispaced:
logging.getLogger(__name__).warning(
"The image position increments were supposed to be equispaced, "
+ "but they seem to NOT be equispaced: "
+ " ".join(("%f" % x for x in img_pos))
+ ". Forcing behavior according to the detected condition."
)
return detected_equispaced
def find_shift(
self,
img_stack: np.ndarray,
......@@ -514,6 +493,7 @@ class DetectorTranslationAlongBeam(AlignmentBase):
low_pass=None,
equispaced_increments=True,
return_shifts=False,
use_adjacent_imgs=False,
):
"""Find the deviation of the translation axis of the area detector
along the beam propagation direction.
......@@ -551,19 +531,12 @@ class DetectorTranslationAlongBeam(AlignmentBase):
Low-pass filter properties, as described in `nabu.misc.fourier_filters`.
high_pass: float or sequence of two floats
High-pass filter properties, as described in `nabu.misc.fourier_filters`.
equispaced_increments: boolean, optional
Tells whether the position increments are equispaced or not. If
equispaced increments are used, we have to compute the correlation
images with respect to the first image, otherwise we can do it
against adjacent images.
The advantage of doing it between adjacent images is that we do not
build up large displacements in the correlation.
However, if this is done for equispaced images, the linear fitting
becomes unstable.
found_shifts_list: list, optional
if a list is given in input, it will be populated
with the found shifts. For each given image of the stack a tuple of shifts will be
appended to the list. This slot is intended for introspection.
use_adjacent_imgs: boolean, optional
Compute correlation between adjacent images. It is better when dealing with large shifts.
Returns
-------
......@@ -576,7 +549,6 @@ class DetectorTranslationAlongBeam(AlignmentBase):
TODO: Add examples here!
"""
self._check_img_sizes(img_stack, img_pos)
equispaced_increments = self._check_equispaced(img_pos, equispaced_increments)
if peak_fit_radius < 1:
logging.getLogger(__name__).warning(
......@@ -596,7 +568,7 @@ class DetectorTranslationAlongBeam(AlignmentBase):
# do correlations
ccs = [
self._compute_correlation_fft(
img_stack[0 if equispaced_increments else ii - 1, ...],
img_stack[ii - 1 if use_adjacent_imgs else 0, ...],
img_stack[ii, ...],
padding_mode,
high_pass=high_pass,
......@@ -610,10 +582,9 @@ class DetectorTranslationAlongBeam(AlignmentBase):
(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)
if equispaced_increments:
img_pos_increments = img_pos[1:] - img_pos[0]
else:
img_pos_increments = np.diff(img_pos)
if use_adjacent_imgs:
shifts_vh = np.cumsum(shifts_vh, axis=0)
img_pos_increments = img_pos[1:] - img_pos[0]
# Polynomial.fit is supposed to be more numerically stable than polyfit
# (according to numpy)
......@@ -621,9 +592,6 @@ class DetectorTranslationAlongBeam(AlignmentBase):
coeffs_h = Polynomial.fit(img_pos_increments, shifts_vh[:, 1], deg=1).convert().coef
if return_shifts:
r_shifts = []
for vh in shifts_vh:
r_shifts.append(vh)
return coeffs_v[1], coeffs_h[1], r_shifts
return coeffs_v[1], coeffs_h[1], shifts_vh
else:
return coeffs_v[1], coeffs_h[1]
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