def get_virtual_electron_diffraction(self, calibration, shape, sigma):
"""Obtain a virtual electron diffraction signal that consists
of one virtual diffraction pattern for each segment. The virtual
diffraction pattern is composed of Gaussians centered at each
vector position. If given, the integrated intensities of each
vector will be taken into account by multiplication with the
Gaussians.
Parameters
----------
calibration : float
Reciprocal space calibration in inverse Angstrom per pixel.
shape : tuple
Shape of the signal, (shape_x, shape_y) in pixels, where
shape_x and shape_y are integers.
sigma : float
The standard deviation of the Gaussians in inverse Angstrom
per pixel. 'calibration' is a decent starting value.
Returns
-------
virtual_ed : ElectronDiffraction2D
Virtual electron diffraction signal consisting of one
virtual diffraction pattern for each segment.
"""
vectors = self.vectors_of_segments.data
segments = self.segments.data
num_segments = np.shape(segments)[0]
if self.intensities is None:
raise ValueError("The VDFSegment does not have the attribute "
"intensities, required for this method.")
else:
intensities = self.intensities
# TODO: Refactor this to use the diffsims simulation to plot functionality
size_x, size_y = shape[0], shape[1]
cx, cy = -size_x / 2 * calibration, -size_y / 2 * calibration
x, y = np.indices((size_x, size_y))
x, y = x * calibration + cx, y * calibration + cy
virtual_ed = np.zeros((size_x, size_y, num_segments))
for i in range(num_segments):
# Allow plotting for segments that are only associated with
# one vector.
if np.shape(np.shape(vectors[i]))[0] <= 1:
virtual_ed[..., i] = get_gaussian2d(
intensities[i],
vectors[i][..., 0],
vectors[i][..., 1],
x=x,
y=y,
sigma=sigma,
)
# Allow plotting for segments associated with several vectors.
else:
virtual_ed[..., i] = sum(
list(
map(
lambda a, xo, yo: get_gaussian2d(
a, xo, yo, x=x, y=y, sigma=sigma),
intensities[i],
vectors[i][..., 0],
vectors[i][..., 1],
)))
virtual_ed = ElectronDiffraction2D(virtual_ed.T)
return virtual_ed
def test_get_gaussian2d(a, xo, yo, x, y, sigma, gauss_shape_expt):
gauss = get_gaussian2d(a, xo, yo, x, y, sigma)
assert isinstance(gauss, np.ndarray)
assert gauss.dtype == float
np.testing.assert_equal(gauss.shape, gauss_shape_expt)
