def test_initial_position_outside_mask_y(self):
sublattice = self.sublattice
atom = [sublattice.atom_list[6]]
atom[0].pixel_y -= 4
g = afr._fit_atom_positions_with_gaussian_model(atom,
sublattice.image,
mask_radius=2)
assert not g
def test_initial_position_inside_mask_x(self):
sublattice = self.sublattice
atom = [sublattice.atom_list[6]]
x0 = atom[0].pixel_x
atom[0].pixel_x += 2
g = afr._fit_atom_positions_with_gaussian_model(atom,
sublattice.image,
mask_radius=4)
assert_allclose(g[0].centre_x.value, x0, rtol=1e-2)
def test_outside_high_x(self):
im_x, im_y = 90, 100
test_data = MakeTestData(im_x, im_y)
test_data.add_atom(100, 50, amplitude=200, sigma_x=10, sigma_y=10)
image = test_data.signal.data
atom_position = Atom_Position(80, 50)
gaussian_list = afr._fit_atom_positions_with_gaussian_model(
[atom_position], image, mask_radius=30)
if gaussian_list is not False:
gaussian = gaussian_list[0]
assert gaussian.centre_x.value > 0
assert gaussian.centre_x.value < im_x
assert gaussian.centre_y.value > 0
assert gaussian.centre_y.value < im_y
def remove_atoms_from_image_using_2d_gaussian(
image, sublattice,
percent_to_nn=0.40,
show_progressbar=True):
"""
Parameters
----------
image : NumPy 2D array
sublattice : Atomap sublattice object
percent_to_nn : float
Percent to nearest neighbor. The function will find the closest
nearest neighbor to the current atom position, and
this value times percent_to_nn will be the radius of the mask
centered on the atom position. Value should be somewhere
between 0.01 (1%) and 1 (100%). Having a too low value might
lead to bad fitting.
show_progressbar : bool, default True
Returns
-------
subtracted_image : NumPy 2D array
Examples
--------
>>> atom_lattice = am.dummy_data.get_simple_atom_lattice_two_sublattices()
>>> sublattice0 = atom_lattice.sublattice_list[0]
>>> sublattice0.find_nearest_neighbors()
>>> import atomap.tools as at
>>> image_subtracted = at.remove_atoms_from_image_using_2d_gaussian(
... image=atom_lattice.image, sublattice=sublattice0,
... show_progressbar=False)
>>> import hyperspy.api as hs
>>> s = hs.signals.Signal2D(image_subtracted)
>>> s.plot()
Decrease percent_to_nn, to reduce the effect of overlapping atoms.
For this dataset it won't change much, but might be very useful for
real datasets.
>>> image_subtracted = at.remove_atoms_from_image_using_2d_gaussian(
... image=atom_lattice.image, sublattice=sublattice0,
... percent_to_nn=0.2, show_progressbar=False)
"""
if sublattice.atom_list[0].nearest_neighbor_list is None:
raise ValueError(
"The atom_position objects does not seem to have a "
"populated nearest neighbor list. "
"Has sublattice.find_nearest_neighbors() been called?")
model_image = np.zeros(image.shape)
X, Y = np.meshgrid(np.arange(
model_image.shape[1]), np.arange(model_image.shape[0]))
for atom in tqdm(
sublattice.atom_list, desc='Subtracting atoms',
disable=not show_progressbar):
percent_distance = percent_to_nn
for i in range(10):
g_list = _fit_atom_positions_with_gaussian_model(
[atom],
image,
rotation_enabled=True,
percent_to_nn=percent_distance)
if g_list is False:
if i == 9:
break
else:
percent_distance *= 0.95
else:
g = g_list[0]
model_image += g.function(X, Y)
break
subtracted_image = copy.deepcopy(image) - model_image
return(subtracted_image)
def test_too_small_percent_to_nn(self):
sublattice = self.sublattice
afr._fit_atom_positions_with_gaussian_model([sublattice.atom_list[6]],
sublattice.image,
percent_to_nn=0.01)
def test_wrong_input_1(self):
sublattice = self.sublattice
with pytest.raises(TypeError):
afr._fit_atom_positions_with_gaussian_model(
[sublattice.atom_list[5:7]], sublattice.image)
def test_5_atom(self):
sublattice = self.sublattice
g_list = afr._fit_atom_positions_with_gaussian_model(
sublattice.atom_list[5:10], sublattice.image)
assert len(g_list) == 5