def test_wrong_input_none_mask_radius_percent_to_nn(self):
sublattice = self.sublattice
with pytest.raises(ValueError):
afr.fit_atom_positions_gaussian(sublattice.atom_list,
sublattice.image,
percent_to_nn=None,
mask_radius=None)
def test_one_atoms(self):
sublattice = self.sublattice
atom_index = 55
atom_list = [sublattice.atom_list[atom_index]]
image_data = sublattice.image
afr.fit_atom_positions_gaussian(atom_list, image_data)
assert_allclose(sublattice.atom_list[atom_index].pixel_x,
self.x[atom_index],
rtol=1e-7)
assert_allclose(sublattice.atom_list[atom_index].pixel_y,
self.y[atom_index],
rtol=1e-7)
def test_nine_atoms(self):
sublattice = self.sublattice
atom_indices = [34, 35, 36, 44, 45, 46, 54, 55, 56]
atom_list = []
for index in atom_indices:
atom_list.append(sublattice.atom_list[index])
image_data = sublattice.image
afr.fit_atom_positions_gaussian(atom_list, image_data)
for atom_index in atom_indices:
assert_allclose(sublattice.atom_list[atom_index].pixel_x,
self.x[atom_index],
rtol=1e-7)
assert_allclose(sublattice.atom_list[atom_index].pixel_y,
self.y[atom_index],
rtol=1e-7)
def refine_position_gaussian(self, image=None, show_progressbar=True):
"""
Parameters
----------
image : NumPy 2D array, optional
show_progressbar : bool, default True
"""
if image is None:
image = self.image0
n_tot = len(self.sublattice_list[0].atom_list)
for i_atom in trange(
n_tot, desc="Gaussian fitting", disable=not show_progressbar):
atom_list = []
for sublattice in self.sublattice_list:
atom_list.append(sublattice.atom_list[i_atom])
fit_atom_positions_gaussian(
atom_list,
image)
def refine_position_using_2d_gaussian(
self,
image_data,
rotation_enabled=True,
percent_to_nn=0.40,
mask_radius=None,
centre_free=True):
"""
Use 2D Gaussian to refine the parameters of the atom position.
Parameters
----------
image_data : Numpy 2D array
rotation_enabled : bool, optional
If True, the Gaussian will be able to rotate.
Note, this can increase the chance of fitting failure.
Default True.
percent_to_nn : float, optional
The percent of the distance to the nearest neighbor atom
in the same sub lattice. The distance times this percentage
defines the mask around the atom where the Gaussian will be
fitted. A smaller value can reduce the effect from
neighboring atoms, but might also decrease the accuracy of
the fitting due to less data to fit to.
Default 0.4 (40%).
mask_radius : float, optional
Radius of the mask around each atom. If this is not set,
the radius will be the distance to the nearest atom in the
same sublattice times the `percent_to_nn` value.
Note: if `mask_radius` is not specified, the Atom_Position objects
must have a populated nearest_neighbor_list.
centre_free : bool, default True
If True, the centre parameter will be free, meaning that
the Gaussian can move.
"""
fit_atom_positions_gaussian(
[self],
image_data=image_data,
rotation_enabled=rotation_enabled,
percent_to_nn=percent_to_nn,
mask_radius=mask_radius,
centre_free=centre_free)
def refine_position_gaussian(self, image=None, show_progressbar=True,
percent_to_nn=0.40, mask_radius=None):
"""Fit several atoms at the same time.
For datasets where the atoms are too close together to do the fitting
individually.
Parameters
----------
image : NumPy 2D array, optional
show_progressbar : bool, default True
percent_to_nn : scalar
Default 0.4
mask_radius : float, optional
Radius of the mask around each atom. If this is not set,
the radius will be the distance to the nearest atom in the
same sublattice times the `percent_to_nn` value.
Note: if `mask_radius` is not specified, the Atom_Position objects
must have a populated nearest_neighbor_list. This is normally done
through the sublattice class, but can also be done manually.
Examples
--------
>>> dl = am.dummy_data.get_dumbbell_heterostructure_dumbbell_lattice()
>>> dl.refine_position_gaussian(show_progressbar=False)
"""
if image is None:
if self.original_image is None:
image = self.image
else:
image = self.original_image
n_tot = len(self.sublattice_list[0].atom_list)
for i_atom in progressbar(range(n_tot), desc="Gaussian fitting",
disable=not show_progressbar):
atom_list = []
for sublattice in self.sublattice_list:
atom_list.append(sublattice.atom_list[i_atom])
afr.fit_atom_positions_gaussian(
atom_list, image, percent_to_nn=percent_to_nn,
mask_radius=mask_radius)