def test_oval_radial_background_symmetry():
radius, angle = m.polar_map(centerX=10,
centerY=10,
imageSizeX=21,
imageSizeY=21,
stretchY=1.5,
angle=0.)
radius2, angle2 = m.polar_map(centerX=10,
centerY=10,
imageSizeX=21,
imageSizeY=21,
stretchY=1.5,
angle=np.pi)
radius3, angle3 = m.polar_map(centerX=10,
centerY=10,
imageSizeX=21,
imageSizeY=21,
stretchY=1.5,
angle=np.pi / 4)
radius4, angle4 = m.polar_map(centerX=10,
centerY=10,
imageSizeX=21,
imageSizeY=21,
stretchY=1.5,
angle=-np.pi / 4)
assert np.allclose(radius, radius2)
assert np.allclose(radius3, np.flip(radius4, axis=1))
def stack():
rings = masks.radial_bins(centerX=cx,
centerY=cy,
imageSizeX=detector_x,
imageSizeY=detector_y,
radius=ro,
radius_inner=ri,
n_bins=n_bins,
use_sparse=use_sparse,
dtype=np.complex64)
orders = np.arange(max_order + 1)
r, phi = masks.polar_map(centerX=cx,
centerY=cy,
imageSizeX=detector_x,
imageSizeY=detector_y)
modulator = np.exp(phi * orders[:, np.newaxis, np.newaxis] * 1j)
if use_sparse:
rings = rings.reshape((rings.shape[0], 1, *rings.shape[1:]))
ring_stack = [rings] * len(orders)
ring_stack = sparse.concatenate(ring_stack, axis=1)
ring_stack *= modulator
else:
ring_stack = rings[:, np.newaxis, ...] * modulator
return ring_stack.reshape((-1, detector_y, detector_x))
def test_oval_radial_background_balance():
radius, angle = m.polar_map(centerX=10,
centerY=10,
imageSizeX=21,
imageSizeY=21,
stretchY=1.5,
angle=np.pi / 4)
template = m.radial_gradient_background_subtraction(r=radius,
r0=5,
r_outer=7)
template = m.balance(template)
outside = radius > 7
inside = (radius < 5) * (radius > 0)
assert np.allclose(template.sum(), 0)
assert np.allclose(template[outside], 0)
assert np.all(template[inside] > 0)
assert np.allclose(template[0, :], 0)
assert np.allclose(template[20, :], 0)
assert np.allclose(template[:, 0], 0)
assert np.allclose(template[:, 20], 0)
diag = int(np.sqrt(5))
# Confirm stretched gradient in 45 diagonal
assert template[10 + diag, 10 + diag] > 0
assert template[10 - diag, 10 + diag] < template[10 + diag, 10 + diag]
def __init__(self,
radius,
search=None,
radius_outer=None,
delta=1,
radial_map=None):
'''
See :meth:`~libertem.masks.radial_gradient_background_subtraction` for details.
Parameters
----------
radius : float
Radius of the circular pattern in px
search : float, optional
Range from the center point in px to include in the correlation.
:code:`max(2*radius, radius_outer)` by default
Defining the size of the square correlation pattern.
radius_outer : float, optional
Radius of the negative region in px. 1.5x radius by default.
delta : float, optional
Width of the transition region between positive and negative in px
radial_map : numpy.ndarray, optional
Radius value of each pixel in px. This can be used to distort the shape as needed
or work in physical coordinates instead of pixels.
A suitable map can be generated with :meth:`libertem.masks.polar_map`.
Example
-------
>>> import matplotlib.pyplot as plt
>>> (radius, phi) = libertem.masks.polar_map(
... centerX=64, centerY=64,
... imageSizeX=128, imageSizeY=128,
... stretchY=2., angle=np.pi/4
... )
>>> template = RadialGradientBackgroundSubtraction(
... radius=30, radial_map=radius)
>>> # This shows an elliptical template that is stretched
>>> # along the 45 ° bottom-left top-right diagonal
>>> plt.imshow(template.get_mask(sig_shape=(128, 128)))
<matplotlib.image.AxesImage object at ...>
>>> plt.show() # doctest: +SKIP
'''
if radius_outer is None:
radius_outer = radius * 1.5
if search is None:
search = max(2 * radius, radius_outer)
if radial_map is None:
r = max(radius, radius_outer)
radial_map, _ = masks.polar_map(
centerX=r + 1,
centerY=r + 1,
imageSizeX=int(np.ceil(2 * r + 2)),
imageSizeY=int(np.ceil(2 * r + 2)),
)
self.radius = radius
self.radius_outer = radius_outer
self.delta = delta
self.radial_map = radial_map
template = masks.radial_gradient_background_subtraction(
r=self.radial_map,
r0=self.radius,
r_outer=self.radius_outer,
delta=self.delta)
super().__init__(template=template, search=search)
