def get_model_refiner_one_sublattice_12_vacancies(
image_noise=True, test_element='Ti_2'):
# make one with more vacancies maybe
sublattice = get_simple_cubic_sublattice(
image_noise=image_noise,
with_vacancies=False)
sub1_atom_positions = np.array(sublattice.atom_positions).T
sub1_image = get_simple_cubic_signal(
image_noise=image_noise,
with_vacancies=True)
sub1 = am.Sublattice(sub1_atom_positions, sub1_image)
for i in range(0, len(sub1.atom_list)):
sub1.atom_list[i].elements = test_element
test_element_info = split_and_sort_element(test_element)
sub1_element_list = auto_generate_sublattice_element_list(
material_type='single_element_column',
elements=test_element_info[0][1],
max_number_atoms_z=test_element_info[0][2] + 3)
refiner_dict = {sub1: sub1_element_list}
comparison_image = sublattice.signal
refiner = Model_Refiner(refiner_dict, comparison_image)
return refiner
def _auto_mask_radii_init(self):
'''
Automatically set the mask_radius for each sublattice.
Use mask_radius='auto' when calling a Model Refiner method.
Ideally you could have the mask_radius/percent_to_nn change depending
on the atom, but for now depending on the sublattice is okay.
'''
mask_radii = []
for sublattice in self.sublattice_list:
element_config = get_most_common_sublattice_element(
sublattice, info='element')
chemical = split_and_sort_element(element_config)[0][1]
radius = atomic_radii_in_pixels(self.sampling, chemical)
mask_radii.append(radius)
self.auto_mask_radius = mask_radii
def get_model_refiner_one_sublattice_varying_amp(
image_noise=False, amplitude=[0, 5],
test_element='Ti_2'):
sub1 = get_simple_cubic_sublattice(image_noise=image_noise,
amplitude=amplitude)
for i in range(0, len(sub1.atom_list)):
sub1.atom_list[i].elements = test_element
test_element_info = split_and_sort_element(test_element)
sub1_element_list = auto_generate_sublattice_element_list(
material_type='single_element_column',
elements=test_element_info[0][1],
max_number_atoms_z=np.max(amplitude))
refiner_dict = {sub1: sub1_element_list}
comparison_image = get_simple_cubic_signal(
image_noise=image_noise,
amplitude=test_element_info[0][2])
refiner = Model_Refiner(refiner_dict, comparison_image)
return refiner
def get_model_refiner_one_sublattice_3_vacancies(
image_noise=True, test_element='Ti_2'):
# make one with more vacancies maybe
sub1 = am_dev.dummy_data.get_simple_cubic_with_vacancies_sublattice(
image_noise=image_noise)
for i in range(0, len(sub1.atom_list)):
sub1.atom_list[i].elements = test_element
test_element_info = split_and_sort_element(test_element)
sub1_element_list = auto_generate_sublattice_element_list(
material_type='single_element_column',
elements=test_element_info[0][1],
max_number_atoms_z=test_element_info[0][2] + 3)
refiner_dict = {sub1: sub1_element_list}
comparison_image = am_dev.dummy_data.get_simple_cubic_signal(
image_noise=image_noise)
refiner = Model_Refiner(refiner_dict, comparison_image)
refiner.auto_mask_radius = [4]
return refiner
def create_dataframe_for_xyz(sublattice_list,
element_list,
x_size,
y_size,
z_size,
filename,
header_comment='top_level_comment'):
"""
Creates a Pandas Dataframe and a .xyz file (usable with Prismatic) from the
inputted sublattice(s).
Parameters
----------
sublattice_list : list of Atomap Sublattice objects
element_list : list of strings
Each string must be an element symbol from the periodic table.
x_size, y_size, z_size : floats
Dimensions of the x,y,z axes in Angstrom.
filename : string
Name with which the .xyz file will be saved.
header_comment : string, default 'top_level_comment'
Example
-------
>>> import temul.external.atomap_devel_012.dummy_data as dummy_data
>>> sublattice = dummy_data.get_simple_cubic_sublattice()
>>> for i in range(0, len(sublattice.atom_list)):
... sublattice.atom_list[i].elements = 'Mo_1'
... sublattice.atom_list[i].z_height = '0.5'
>>> element_list = ['Mo_0', 'Mo_1', 'Mo_2']
>>> x_size, y_size = 50, 50
>>> z_size = 5
>>> dataframe = create_dataframe_for_xyz([sublattice], element_list,
... x_size, y_size, z_size,
... filename='dataframe',
... header_comment='Here is an Example')
"""
df_xyz = pd.DataFrame(columns=[
'_atom_site_Z_number', '_atom_site_fract_x', '_atom_site_fract_y',
'_atom_site_fract_z', '_atom_site_occupancy',
'_atom_site_RMS_thermal_vib'
])
# add header sentence
df_xyz = df_xyz.append(
{
'_atom_site_Z_number': header_comment,
'_atom_site_fract_x': '',
'_atom_site_fract_y': '',
'_atom_site_fract_z': '',
'_atom_site_occupancy': '',
'_atom_site_RMS_thermal_vib': ''
},
ignore_index=True)
# add unit cell dimensions
df_xyz = df_xyz.append(
{
'_atom_site_Z_number': '',
'_atom_site_fract_x': format(x_size, '.6f'),
'_atom_site_fract_y': format(y_size, '.6f'),
'_atom_site_fract_z': format(z_size, '.6f'),
'_atom_site_occupancy': '',
'_atom_site_RMS_thermal_vib': ''
},
ignore_index=True)
for sublattice in sublattice_list:
# denomiator could also be: sublattice.signal.axes_manager[0].size
for i in range(0, len(sublattice.atom_list)):
if sublattice.atom_list[i].elements in element_list:
# value = 0
# this loop cycles through the length of the split element eg,
# 2 for 'Se_1.S_1' and
# outputs an atom label for each
for k in range(
0,
len(
split_and_sort_element(
sublattice.atom_list[i].elements))):
if split_and_sort_element(
sublattice.atom_list[i].elements)[k][2] >= 1:
atomic_number = split_and_sort_element(
sublattice.atom_list[i].elements)[k][3]
if "," in sublattice.atom_list[i].z_height:
atom_z_height = float(
sublattice.atom_list[i].z_height.split(",")[k])
else:
atom_z_height = float(
sublattice.atom_list[i].z_height)
# this loop controls the z_height
# len(sublattice.atom_list[i].z_height)):
for p in range(
0,
split_and_sort_element(
sublattice.atom_list[i].elements)[k][2]):
# could use ' ' + value to get an extra space
# between columns!
# nans could be better than ''
# (len(sublattice.image)-
if "," in sublattice.atom_list[
i].z_height and split_and_sort_element(
sublattice.atom_list[i].elements
)[k][2] > 1:
atom_z_height = float(
sublattice.atom_list[i].z_height.split(
",")[p])
else:
pass
df_xyz = df_xyz.append(
{
'_atom_site_Z_number':
atomic_number,
'_atom_site_fract_x':
format(
sublattice.atom_list[i].pixel_x *
(x_size / len(sublattice.image[0, :])),
'.6f'),
'_atom_site_fract_y':
format(
sublattice.atom_list[i].pixel_y *
(y_size / len(sublattice.image[:, 0])),
'.6f'),
# this is a fraction already, which is why we
# don't divide as in x and y
'_atom_site_fract_z':
format(atom_z_height * z_size, '.6f'),
'_atom_site_occupancy':
1.0,
'_atom_site_RMS_thermal_vib':
0.1
},
ignore_index=True) # insert row
df_xyz = df_xyz.append(
{
'_atom_site_Z_number': int(-1),
'_atom_site_fract_x': '',
'_atom_site_fract_y': '',
'_atom_site_fract_z': '',
'_atom_site_occupancy': '',
'_atom_site_RMS_thermal_vib': ''
},
ignore_index=True)
if filename is not None:
df_xyz.to_csv(filename + '.xyz', sep=' ', header=False, index=False)
return (df_xyz)
def create_dataframe_for_xyz(sublattice_list,
element_list,
x_size,
y_size,
z_size,
filename,
header_comment='top_level_comment'):
"""
Parameters
----------
Example
-------
>>> import atomap.api as am
>>> sublattice = am.dummy_data.get_simple_cubic_sublattice()
>>> for i in range(0, len(sublattice.atom_list)):
... sublattice.atom_list[i].elements = 'Mo_1'
... sublattice.atom_list[i].z_height = '0.5'
>>> element_list = ['Mo_0', 'Mo_1', 'Mo_2']
>>> x_size, y_size = 50, 50
>>> z_size = 5
>>> dataframe = create_dataframe_for_xyz([sublattice], element_list,
... x_size, y_size, z_size,
... filename='dataframe',
... header_comment='Here is an Example')
"""
df_xyz = pd.DataFrame(columns=[
'_atom_site_Z_number', '_atom_site_fract_x', '_atom_site_fract_y',
'_atom_site_fract_z', '_atom_site_occupancy',
'_atom_site_RMS_thermal_vib'
])
# add header sentence
df_xyz = df_xyz.append(
{
'_atom_site_Z_number': header_comment,
'_atom_site_fract_x': '',
'_atom_site_fract_y': '',
'_atom_site_fract_z': '',
'_atom_site_occupancy': '',
'_atom_site_RMS_thermal_vib': ''
},
ignore_index=True)
# add unit cell dimensions
df_xyz = df_xyz.append(
{
'_atom_site_Z_number': '',
'_atom_site_fract_x': format(x_size, '.6f'),
'_atom_site_fract_y': format(y_size, '.6f'),
'_atom_site_fract_z': format(z_size, '.6f'),
'_atom_site_occupancy': '',
'_atom_site_RMS_thermal_vib': ''
},
ignore_index=True)
for sublattice in sublattice_list:
# denomiator could also be: sublattice.signal.axes_manager[0].size
for i in range(0, len(sublattice.atom_list)):
if sublattice.atom_list[i].elements in element_list:
# value = 0
# this loop cycles through the length of the split element eg,
# 2 for 'Se_1.S_1' and
# outputs an atom label for each
for k in range(
0,
len(
split_and_sort_element(
sublattice.atom_list[i].elements))):
if split_and_sort_element(
sublattice.atom_list[i].elements)[k][2] >= 1:
atomic_number = split_and_sort_element(
sublattice.atom_list[i].elements)[k][3]
if "," in sublattice.atom_list[i].z_height:
atom_z_height = float(
sublattice.atom_list[i].z_height.split(",")[k])
else:
atom_z_height = float(
sublattice.atom_list[i].z_height)
# this loop controls the z_height
# len(sublattice.atom_list[i].z_height)):
for p in range(
0,
split_and_sort_element(
sublattice.atom_list[i].elements)[k][2]):
# could use ' ' + value to get an extra space
# between columns!
# nans could be better than ''
# (len(sublattice.image)-
if "," in sublattice.atom_list[
i].z_height and split_and_sort_element(
sublattice.atom_list[i].elements
)[k][2] > 1:
atom_z_height = float(
sublattice.atom_list[i].z_height.split(
",")[p])
else:
pass
df_xyz = df_xyz.append(
{
'_atom_site_Z_number':
atomic_number,
'_atom_site_fract_x':
format(
sublattice.atom_list[i].pixel_x *
(x_size / len(sublattice.image[0, :])),
'.6f'),
'_atom_site_fract_y':
format(
sublattice.atom_list[i].pixel_y *
(y_size / len(sublattice.image[:, 0])),
'.6f'),
# this is a fraction already, which is why we don't divide as in x and y
'_atom_site_fract_z':
format(atom_z_height * z_size, '.6f'),
'_atom_site_occupancy':
1.0, # might need to loop through the vancancies here?
'_atom_site_RMS_thermal_vib':
0.1
},
ignore_index=True) # insert row
df_xyz = df_xyz.append(
{
'_atom_site_Z_number': int(-1),
'_atom_site_fract_x': '',
'_atom_site_fract_y': '',
'_atom_site_fract_z': '',
'_atom_site_occupancy': '',
'_atom_site_RMS_thermal_vib': ''
},
ignore_index=True)
if filename is not None:
df_xyz.to_csv(filename + '.xyz', sep=' ', header=False, index=False)
return (df_xyz)