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Commit bb7390cd authored by Pierre Paleo's avatar Pierre Paleo
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Apply black -l 128 on files

parent bed0fd0f
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nabu/preproc/alignment.py
View file @ bb7390cd
......@@ -38,7 +38,11 @@ except ImportError:
class AlignmentBase(object):
def __init__(
self, vert_fft_width=False, horz_fft_width=False, verbose=False, data_type=np.float32,
self,
vert_fft_width=False,
horz_fft_width=False,
verbose=False,
data_type=np.float32,
):
"""
Alignment basic functions.
......@@ -89,7 +93,10 @@ class AlignmentBase(object):
raise ValueError(
"The same number of images and positions is required."
+ " Shape of stack: %s, shape of positions variable: %s"
% (" ".join(("%d" % x for x in shape_stack)), " ".join(("%d" % x for x in shape_pos)),)
% (
" ".join(("%d" % x for x in shape_stack)),
" ".join(("%d" % x for x in shape_pos)),
)
)
@staticmethod
......@@ -103,7 +110,10 @@ class AlignmentBase(object):
if not np.all(shape_1 == shape_2):
raise ValueError(
"Images need to be of the same shape. Shape of image #1: %s, image #2: %s"
% (" ".join(("%d" % x for x in shape_1)), " ".join(("%d" % x for x in shape_2)),)
% (
" ".join(("%d" % x for x in shape_1)),
" ".join(("%d" % x for x in shape_2)),
)
)
@staticmethod
......@@ -172,7 +182,12 @@ class AlignmentBase(object):
if np.any(vertex_yx < vertex_min_yx) or np.any(vertex_yx > vertex_max_yx):
raise ValueError(
"Fitted (y: {}, x: {}) positions are outide the input margins y: [{}, {}], and x: [{}, {}]".format(
vertex_yx[0], vertex_yx[1], vertex_min_yx[0], vertex_max_yx[0], vertex_min_yx[1], vertex_max_yx[1],
vertex_yx[0],
vertex_yx[1],
vertex_min_yx[0],
vertex_max_yx[0],
vertex_min_yx[1],
vertex_max_yx[1],
)
)
return vertex_yx
......@@ -241,7 +256,10 @@ class AlignmentBase(object):
message = "Fitted position {} is outide the input margins [{}, {}]".format(vertex_x, vertex_min_x, vertex_max_x)
else:
message = "Fitted positions outide the input margins [{}, {}]: %d below and %d above".format(
vertex_min_x, vertex_max_x, np.sum(1 - lower_bound_ok), np.sum(1 - upper_bound_ok),
vertex_min_x,
vertex_max_x,
np.sum(1 - lower_bound_ok),
np.sum(1 - upper_bound_ok),
)
raise ValueError(message)
if return_vertex_val:
......@@ -362,7 +380,13 @@ class AlignmentBase(object):
return roi_yxhw
def _prepare_image(
self, img, invalid_val=1e-5, roi_yxhw=None, median_filt_shape=None, low_pass=None, high_pass=None,
self,
img,
invalid_val=1e-5,
roi_yxhw=None,
median_filt_shape=None,
low_pass=None,
high_pass=None,
):
"""
Prepare and returns a cropped and filtered image, or array of filtered images if the input is an array of images.
......@@ -390,7 +414,9 @@ class AlignmentBase(object):
if roi_yxhw is not None:
img = img[
..., roi_yxhw[0] : roi_yxhw[0] + roi_yxhw[2], roi_yxhw[1] : roi_yxhw[1] + roi_yxhw[3],
...,
roi_yxhw[0] : roi_yxhw[0] + roi_yxhw[2],
roi_yxhw[1] : roi_yxhw[1] + roi_yxhw[3],
]
img = img.copy()
......@@ -415,7 +441,10 @@ class AlignmentBase(object):
# expanding filter shape with ones, to cover the stack of images
# but disabling inter-image filtering
median_filt_shape = np.concatenate(
(np.ones((len(img_shape) - len(median_filt_shape),), dtype=np.int), median_filt_shape,)
(
np.ones((len(img_shape) - len(median_filt_shape),), dtype=np.int),
median_filt_shape,
)
)
img = median_filter(img, size=median_filt_shape)
else:
......@@ -510,7 +539,12 @@ class CenterOfRotation(AlignmentBase):
while Xcor < lim2:
Xcor_rel = Xcor - (img_1.shape[1] // 2)
cor_position, merit, energy = self.find_shift(
img_1, img_2, low_pass=low_pass, high_pass=high_pass, half_tomo_cor_guess=Xcor_rel, half_tomo_return_stats=True,
img_1,
img_2,
low_pass=low_pass,
high_pass=high_pass,
half_tomo_cor_guess=Xcor_rel,
half_tomo_return_stats=True,
)
if not np.isnan(merit):
found_centers.append([merit, abs(Xcor_rel - cor_position), cor_position, energy])
......@@ -637,7 +671,10 @@ class CenterOfRotation(AlignmentBase):
if half_tomo_cor_guess is not None:
cor_in_img = img_1.shape[1] // 2 + half_tomo_cor_guess
tmpsigma = min((img_1.shape[1] - cor_in_img) / 4.0, (cor_in_img) / 4.0,)
tmpsigma = min(
(img_1.shape[1] - cor_in_img) / 4.0,
(cor_in_img) / 4.0,
)
tmpx = (np.arange(img_1.shape[1]) - cor_in_img) / tmpsigma
apodis = np.exp(-tmpx * tmpx / 2.0)
......@@ -671,7 +708,10 @@ class CenterOfRotation(AlignmentBase):
if half_tomo_return_stats:
cor_in_img = img_1.shape[1] // 2 + return_value
tmpsigma = min((img_1.shape[1] - cor_in_img) / 4.0, (cor_in_img) / 4.0,)
tmpsigma = min(
(img_1.shape[1] - cor_in_img) / 4.0,
(cor_in_img) / 4.0,
)
M1 = int(round(return_value + img_1.shape[1] // 2)) - int(round(tmpsigma))
M2 = int(round(return_value + img_1.shape[1] // 2)) + int(round(tmpsigma))
......@@ -964,7 +1004,14 @@ class CameraTilt(CenterOfRotation):
img_1 = self._prepare_image(img_1, roi_yxhw=roi_yxhw, median_filt_shape=median_filt_shape)
img_2 = self._prepare_image(img_2, roi_yxhw=roi_yxhw, median_filt_shape=median_filt_shape)
cc = self._compute_correlation_fft(img_1, img_2, padding_mode, axes=(-1,), high_pass=high_pass, low_pass=low_pass,)
cc = self._compute_correlation_fft(
img_1,
img_2,
padding_mode,
axes=(-1,),
high_pass=high_pass,
low_pass=low_pass,
)
img_shape = img_2.shape
cc_h_coords = np.fft.fftfreq(img_shape[-1], 1 / img_shape[-1])
......@@ -988,7 +1035,10 @@ class CameraTilt(CenterOfRotation):
if self.verbose:
print(
"Fitted center of rotation (pixels):", cor_offset_pix, "and camera tilt (degrees):", tilt_deg,
"Fitted center of rotation (pixels):",
cor_offset_pix,
"and camera tilt (degrees):",
tilt_deg,
)
f, ax = plt.subplots(1, 1)
ax.scatter(cc_v_coords, fitted_shifts_h)
......@@ -1083,7 +1133,11 @@ class CameraFocus(CenterOfRotation):
roi_yxhw = self._determine_roi(img_shape, roi_yxhw)
img_stack = self._prepare_image(
img_stack, roi_yxhw=roi_yxhw, median_filt_shape=median_filt_shape, low_pass=low_pass, high_pass=high_pass,
img_stack,
roi_yxhw=roi_yxhw,
median_filt_shape=median_filt_shape,
low_pass=low_pass,
high_pass=high_pass,
)
img_stds = np.std(img_stack, axis=(-2, -1)) / np.mean(img_stack, axis=(-2, -1))
......@@ -1101,7 +1155,10 @@ class CameraFocus(CenterOfRotation):
if self.verbose:
print(
"Fitted focus motor position:", focus_pos, "and corresponding image position:", focus_ind,
"Fitted focus motor position:",
focus_pos,
"and corresponding image position:",
focus_ind,
)
f, ax = plt.subplots(1, 1)
ax.stem(img_pos, img_stds)
......@@ -1282,12 +1339,19 @@ class CameraFocus(CenterOfRotation):
self._check_img_block_size(roi_yxhw[2:], regions_number, suggest_new_shape=False)
img_stack = self._prepare_image(
img_stack, roi_yxhw=roi_yxhw, median_filt_shape=median_filt_shape, low_pass=low_pass, high_pass=high_pass,
img_stack,
roi_yxhw=roi_yxhw,
median_filt_shape=median_filt_shape,
low_pass=low_pass,
high_pass=high_pass,
)
img_shape = img_stack.shape[-2:]
block_size = np.array(img_shape) / regions_number
block_stack_size = np.array([num_imgs, regions_number, block_size[-2], regions_number, block_size[-1]], dtype=np.int,)
block_stack_size = np.array(
[num_imgs, regions_number, block_size[-2], regions_number, block_size[-1]],
dtype=np.int,
)
img_stack = np.reshape(img_stack, block_stack_size)
img_stds = np.std(img_stack, axis=(-3, -1)) / np.mean(img_stack, axis=(-3, -1))
......@@ -1315,7 +1379,10 @@ class CameraFocus(CenterOfRotation):
if self.verbose:
print(
"Fitted focus motor position:", focus_pos, "and corresponding image position:", focus_ind,
"Fitted focus motor position:",
focus_pos,
"and corresponding image position:",
focus_ind,
)
print("Fitted tilts (to be divided by pixel size, and converted to deg): (v, h) %s" % tilts_vh)
fig = plt.figure()
......@@ -1324,10 +1391,16 @@ class CameraFocus(CenterOfRotation):
regions_half_shape = (regions_number - 1) / 2
base_points = np.linspace(-regions_half_shape, regions_half_shape, regions_number)
ax.plot(
np.zeros((regions_number,)), base_points, np.polyval([tg_v, focus_pos], base_points), "C2",
np.zeros((regions_number,)),
base_points,
np.polyval([tg_v, focus_pos], base_points),
"C2",
)
ax.plot(
base_points, np.zeros((regions_number,)), np.polyval([tg_h, focus_pos], base_points), "C2",
base_points,
np.zeros((regions_number,)),
np.polyval([tg_h, focus_pos], base_points),
"C2",
)
ax.scatter(0, 0, focus_pos, marker="o", c="C1")
ax.set_title("Images std")
......
nabu/preproc/tests/test_alignment.py
View file @ bb7390cd
......@@ -48,8 +48,18 @@ def bootstrap_fcs(request):
cls.abs_tol_dist = 1e-2
cls.abs_tol_tilt = 2.5e-4
(cls.data, cls.img_pos, cls.pixel_size, (calib_data_std, calib_data_angle),) = get_focus_data("test_alignment_focus.h5")
(cls.angle_best_ind, cls.angle_best_pos, cls.angle_tilt_v, cls.angle_tilt_h,) = calib_data_angle
(
cls.data,
cls.img_pos,
cls.pixel_size,
(calib_data_std, calib_data_angle),
) = get_focus_data("test_alignment_focus.h5")
(
cls.angle_best_ind,
cls.angle_best_pos,
cls.angle_tilt_v,
cls.angle_tilt_h,
) = calib_data_angle
cls.std_best_ind, cls.std_best_pos = calib_data_std
......@@ -73,8 +83,7 @@ def get_cor_data_h5(*dataset_path):
def get_cor_data_half_tomo():
""" Obtains two weakly overlapping images with features plus spurious noise for a challenging test of cor retrieval for half tomo.
"""
"""Obtains two weakly overlapping images with features plus spurious noise for a challenging test of cor retrieval for half tomo."""
datasource = ExternalResources(project="nabu", url_base=None)
myfile = os.path.join(datasource.data_home, "data_for_ht_cor.h5")
......@@ -130,7 +139,10 @@ def get_focus_data(*dataset_path):
img_pos = hf["/entry/instrument/detector/distance"][()]
pixel_size = np.mean(
[hf["/entry/instrument/detector/x_pixel_size"][()], hf["/entry/instrument/detector/y_pixel_size"][()],]
[
hf["/entry/instrument/detector/x_pixel_size"][()],
hf["/entry/instrument/detector/y_pixel_size"][()],
]
)
angle_best_ind = hf["/calibration/focus/angle/best_img"][()]
......@@ -147,8 +159,7 @@ def get_focus_data(*dataset_path):
def _peaks2im(possY, possX, widths, NY, NX):
""" Given a set of positions, widths and the image shape creates an image with spots
"""
"""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)
......@@ -161,8 +172,7 @@ def _peaks2im(possY, possX, widths, NY, NX):
def _get_challenging_ImsCouple_for_halftomo_cor():
""" Creates two weakly overlapping images with features plus spurious noise for a challenging test of cor retrieval for half tomo.
"""
"""Creates two weakly overlapping images with features plus spurious noise for a challenging test of cor retrieval for half tomo."""
np.random.seed(0)
......@@ -258,7 +268,10 @@ class TestAlignmentBase(object):
cc_coords = np.arange(0, 8)
(found_peaks_val, found_peaks_pos,) = alignment.AlignmentBase.extract_peak_regions_1d(img, axis=-1, cc_coords=cc_coords)
(
found_peaks_val,
found_peaks_pos,
) = alignment.AlignmentBase.extract_peak_regions_1d(img, axis=-1, cc_coords=cc_coords)
message = "The found peak positions do not correspond to the expected peak positions:\n Expected: %s\n Found: %s" % (
peaks_pos,
found_peaks_pos[1, :],
......@@ -293,8 +306,7 @@ class TestCor(object):
@pytest.mark.skipif(not (__has_scipy__), reason="need scipy for this test")
def test_noisyHF_cor_posx(self):
""" test with noise at high frequencies
"""
"""test with noise at high frequencies"""
radio1 = self.data[0, :, :]
radio2 = np.fliplr(self.data[1, :, :])
......@@ -317,8 +329,7 @@ class TestCor(object):
assert np.isclose(self.cor_gl_pix, cor_position, atol=self.abs_tol), message
def test_half_tomo_cor_Err(self):
""" test that the test is challenging enough
"""
"""test that the test is challenging enough"""
radio1 = self.ht_im1
radio2 = np.fliplr(self.ht_im2)
cor_pos = self.ht_cor
......@@ -334,8 +345,7 @@ class TestCor(object):
assert not np.isclose(cor_pos, cor_position, atol=self.abs_tol), message
def test_half_tomo_cor(self):
""" test that the half tomo algorithm is precise enough
"""
"""test that the half tomo algorithm is precise enough"""
radio1 = self.ht_im1
radio2 = np.fliplr(self.ht_im2)
cor_pos = self.ht_cor
......@@ -350,8 +360,7 @@ class TestCor(object):
assert np.isclose(cor_pos, cor_position, atol=self.abs_tol), message
def test_half_tomo_cor_exp(self):
""" test the hal_tomo algorithm on experimental data and global search
"""
"""test the hal_tomo algorithm on experimental data and global search"""
radios = nabu_get_data("ha_autocor_radios.npz")
radio1 = radios["radio1"]
......@@ -374,8 +383,7 @@ class TestCor(object):
assert np.isclose(cor_pos, cor_position, atol=self.abs_tol), message
def test_half_tomo_cor_exp_limited(self):
""" test the hal_tomo algorithm on experimental data and global search with limits
"""
"""test the hal_tomo algorithm on experimental data and global search with limits"""
radios = nabu_get_data("ha_autocor_radios.npz")
radio1 = radios["radio1"]
......@@ -478,7 +486,10 @@ class TestDetectorTranslation(object):
)
assert np.all(np.isclose(self.expected_shifts_vh, [shifts_v, shifts_h], atol=self.abs_tol)), message
message = "Computed shifts %s and expected %s do not coincide" % (found_shifts_list, self.all_shifts_vh,)
message = "Computed shifts %s and expected %s do not coincide" % (
found_shifts_list,
self.all_shifts_vh,
)
assert np.all(np.isclose(found_shifts_list, self.all_shifts_vh, atol=self.abs_tol)), message
@pytest.mark.skipif(not (__has_scipy__), reason="need scipy for this test")
......@@ -498,7 +509,10 @@ class TestDetectorTranslation(object):
stack, np.array([0.0, 1, 2, 3]), high_pass=1.0, return_shifts=True
)
message = "Found shifts per units %s and reference %s do not coincide" % ((shifts_v, shifts_h), (-1.234 * 2, -1.234),)
message = "Found shifts per units %s and reference %s do not coincide" % (
(shifts_v, shifts_h),
(-1.234 * 2, -1.234),
)
assert np.all(np.isclose((shifts_v, shifts_h), (-1.234 * 2, -1.234), atol=self.abs_tol)), message
......@@ -527,7 +541,10 @@ class TestFocus(object):
expected_tilts_vh = np.squeeze(np.array([self.angle_tilt_v, self.angle_tilt_h]))
computed_tilts_vh = -tilts_vh / (self.pixel_size / 1000)
message = "Computed tilts %s and expected %s do not coincide" % (computed_tilts_vh, expected_tilts_vh,)
message = "Computed tilts %s and expected %s do not coincide" % (
computed_tilts_vh,
expected_tilts_vh,
)
assert np.all(np.isclose(computed_tilts_vh, expected_tilts_vh, atol=self.abs_tol_tilt)), message
def test_size_determination(self):
......
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