def get_generic_results(self, img_sum, img_y, img_x):
ref_x = self.parameters["cx"]
ref_y = self.parameters["cy"]
y_centers_raw, x_centers_raw = center_shifts(img_sum, img_y, img_x, ref_y, ref_x)
shape = y_centers_raw.shape
if self.parameters["flip_y"]:
transform = flip_y()
else:
transform = identity()
# Transformations are applied right to left
transform = rotate_deg(self.parameters["scan_rotation"]) @ transform
y_centers, x_centers = transform @ (y_centers_raw.reshape(-1), x_centers_raw.reshape(-1))
y_centers = y_centers.reshape(shape)
x_centers = x_centers.reshape(shape)
if img_sum.dtype.kind == 'c':
x_real, x_imag = np.real(x_centers), np.imag(x_centers)
y_real, y_imag = np.real(y_centers), np.imag(y_centers)
return COMResultSet([
AnalysisResult(raw_data=x_real, visualized=visualize_simple(x_real),
key="x_real", title="x [real]", desc="x component of the center"),
AnalysisResult(raw_data=y_real, visualized=visualize_simple(y_real),
key="y_real", title="y [real]", desc="y component of the center"),
AnalysisResult(raw_data=x_imag, visualized=visualize_simple(x_imag),
key="x_imag", title="x [imag]", desc="x component of the center"),
AnalysisResult(raw_data=y_imag, visualized=visualize_simple(y_imag),
key="y_imag", title="y [imag]", desc="y component of the center"),
])
else:
f = CMAP_CIRCULAR_DEFAULT.rgb_from_vector((y_centers, x_centers))
d = divergence(y_centers, x_centers)
c = curl_2d(y_centers, x_centers)
m = magnitude(y_centers, x_centers)
return COMResultSet([
AnalysisResult(raw_data=(x_centers, y_centers), visualized=f,
key="field", title="field", desc="cubehelix colorwheel visualization",
include_in_download=False),
AnalysisResult(raw_data=m, visualized=visualize_simple(m),
key="magnitude", title="magnitude", desc="magnitude of the vector field"),
AnalysisResult(raw_data=d, visualized=visualize_simple(d),
key="divergence", title="divergence", desc="divergence of the vector field"),
AnalysisResult(raw_data=c, visualized=visualize_simple(c),
key="curl", title="curl", desc="curl of the 2D vector field"),
AnalysisResult(raw_data=x_centers, visualized=visualize_simple(x_centers),
key="x", title="x", desc="x component of the center"),
AnalysisResult(raw_data=y_centers, visualized=visualize_simple(y_centers),
key="y", title="y", desc="y component of the center"),
])
def apply_correction(y_centers,
x_centers,
scan_rotation,
flip_y,
forward=True):
shape = y_centers.shape
if flip_y:
transform = coordinates.flip_y()
else:
transform = coordinates.identity()
# Transformations are applied right to left
transform = coordinates.rotate_deg(scan_rotation) @ transform
y_centers = y_centers.reshape(-1)
x_centers = x_centers.reshape(-1)
if not forward:
transform = np.linalg.inv(transform)
y_transformed, x_transformed = transform @ (y_centers, x_centers)
y_transformed = y_transformed.reshape(shape)
x_transformed = x_transformed.reshape(shape)
return (y_transformed, x_transformed)
def test_flip_y():
y, x = np.random.random((2, 7))
r_y, r_x = c.flip_y() @ (y, x)
assert np.allclose(r_y, -y)
assert np.allclose(r_x, x)
def get_generic_results(self, img_sum, img_y, img_x, damage):
from libertem.viz import CMAP_CIRCULAR_DEFAULT, visualize_simple
ref_x = self.parameters["cx"]
ref_y = self.parameters["cy"]
y_centers_raw, x_centers_raw = center_shifts(img_sum, img_y, img_x,
ref_y, ref_x)
shape = y_centers_raw.shape
if self.parameters["flip_y"]:
transform = flip_y()
else:
transform = identity()
# Transformations are applied right to left
transform = rotate_deg(self.parameters["scan_rotation"]) @ transform
y_centers, x_centers = transform @ (y_centers_raw.reshape(-1),
x_centers_raw.reshape(-1))
y_centers = y_centers.reshape(shape)
x_centers = x_centers.reshape(shape)
if img_sum.dtype.kind == 'c':
x_real, x_imag = np.real(x_centers), np.imag(x_centers)
y_real, y_imag = np.real(y_centers), np.imag(y_centers)
return COMResultSet([
AnalysisResult(raw_data=x_real,
visualized=visualize_simple(x_real,
damage=damage),
key="x_real",
title="x [real]",
desc="x component of the center"),
AnalysisResult(raw_data=y_real,
visualized=visualize_simple(y_real,
damage=damage),
key="y_real",
title="y [real]",
desc="y component of the center"),
AnalysisResult(raw_data=x_imag,
visualized=visualize_simple(x_imag,
damage=damage),
key="x_imag",
title="x [imag]",
desc="x component of the center"),
AnalysisResult(raw_data=y_imag,
visualized=visualize_simple(y_imag,
damage=damage),
key="y_imag",
title="y [imag]",
desc="y component of the center"),
])
else:
damage = damage & np.isfinite(x_centers) & np.isfinite(y_centers)
# Make sure that an all-False `damage` is handled since np.max()
# trips on an empty array.
# As a remark -- the NumPy error message
# "zero-size array to reduction operation maximum which has no identity"
# is probably wrong since -np.inf is the identity element for maximum on
# floating point numbers and should be returned here.
if np.count_nonzero(damage) > 0:
vmax = np.sqrt(
np.max(x_centers[damage]**2 + y_centers[damage]**2))
else:
vmax = 1
f = CMAP_CIRCULAR_DEFAULT.rgb_from_vector(
(x_centers, y_centers, 0), vmax=vmax)
d = divergence(y_centers, x_centers)
c = curl_2d(y_centers, x_centers)
m = magnitude(y_centers, x_centers)
return COMResultSet([
AnalysisResult(raw_data=(x_centers, y_centers),
visualized=f,
key="field",
title="field",
desc="cubehelix colorwheel visualization",
include_in_download=False),
AnalysisResult(raw_data=m,
visualized=visualize_simple(m, damage=damage),
key="magnitude",
title="magnitude",
desc="magnitude of the vector field"),
AnalysisResult(raw_data=d,
visualized=visualize_simple(d, damage=damage),
key="divergence",
title="divergence",
desc="divergence of the vector field"),
AnalysisResult(raw_data=c,
visualized=visualize_simple(c, damage=damage),
key="curl",
title="curl",
desc="curl of the 2D vector field"),
AnalysisResult(raw_data=x_centers,
visualized=visualize_simple(x_centers,
damage=damage),
key="x",
title="x",
desc="x component of the center"),
AnalysisResult(raw_data=y_centers,
visualized=visualize_simple(y_centers,
damage=damage),
key="y",
title="y",
desc="y component of the center"),
])
