import atomap.api as am
import hyperspy.api as hs
import numpy as np
import os
path_to_data = os.path.join(os.path.dirname(__file__), "data")
os.chdir(path_to_data)
# Open the original PTO Junction dataset
image = hs.load("filtered_PTO_HAADF_STEM.hspy")
sampling = image.axes_manager[-1].scale # nm/pix
units = image.axes_manager[-1].units
image.plot()
# Open the pre-made PTO atom lattice.
atom_lattice = am.load_atom_lattice_from_hdf5("Atom_Lattice.hdf5", False)
sublattice1 = atom_lattice.sublattice_list[0] # Pb Sublattice
sublattice2 = atom_lattice.sublattice_list[1] # Ti Sublattice
sublattice1.construct_zone_axes(atom_plane_tolerance=1)
# Set up parameters for plotting the strain, rotation, and c/a ratio maps:
zone_vector_index_A = 0
zone_vector_index_B = 1
# Note that sometimes the 0 and 1 axes are constructed first or second,
# so you may have to swap them.
filename = None # Set to a string if you want to save the map
'''
You can use return_x_y_z=True to get the x,y, and strain/rotation/ratio
values also!
Check the documentation here: temul-toolkit.readthedocs.io
my_path, 'line_monolayer.png'),
overwrite=True)
def dd_line():
zone = sublattice_B.zones_axis_average_distances[1]
plane = sublattice_B.atom_planes_by_zone_vector[zone][-1]
s_line = sublattice_B.get_atom_distance_difference_line_profile(
zone, plane)
s_line.plot()
s_line._plot.signal_plot.figure.savefig(os.path.join(
my_path, 'line_dd.png'),
overwrite=True)
atom_lattice = am.load_atom_lattice_from_hdf5(
os.path.join(my_path, 'fantasite.hdf5'))
sublattice_A = atom_lattice.sublattice_list[0]
sublattice_B = atom_lattice.sublattice_list[1]
plot_elli_maps()
plot_monolayer_map()
plot_atom_plane_monolayer_map()
plot_atom_dd()
plot_dd_plane()
plot_voronoi_integration(atom_lattice)
plot_watershed_integration(atom_lattice)
plot_angle_figs()
plot_al_zoom()
elli_line()
elli_line_errorbar()
plot_line_plane()
import atomap.api as am
import hyperspy.api as hs
import os
path_to_data = os.path.join(os.path.dirname(__file__), "data")
os.chdir(path_to_data)
# Open the PTO/SRO dataset
image = hs.load('Cropped_PTO-SRO_Aligned.hspy')
sampling = image.axes_manager[-1].scale # nm/pix
units = image.axes_manager[-1].units
image.plot()
# Open the pre-made PTO-SRO atom lattice.
atom_lattice = am.load_atom_lattice_from_hdf5("Atom_Lattice_crop.hdf5")
sublattice1 = atom_lattice.sublattice_list[0] # Pb-Sr Sublattice
sublattice2 = atom_lattice.sublattice_list[1] # Ti-Ru Sublattice
# Plot the sublattice planes to see which zone_vector_index we use
sublattice2.construct_zone_axes(atom_plane_tolerance=1)
# sublattice2.plot_planes()
# Set up parameters for calculate_atom_plane_curvature
zone_vector_index = 0
atom_planes = (2, 6) # chooses the starting and ending atom planes
vmin, vmax = 1, 2
cmap = 'bwr' # see matplotlib and colorcet for more colormaps
title = 'Curvature Map'
filename = None # Set to a string if you want to save the map
transparent=True,
frameon=False,
bbox_inches='tight',
pad_inches=None,
dpi=300,
labels=False)
plt.close()
from temul.model_refiner import Model_Refiner
import atomap.api as am
import temul.example_data as example_data
s_original = example_data.load_Se_implanted_MoS2_data()
real_sampling = s_original.axes_manager[-1].scale
atom_lattice = am.load_atom_lattice_from_hdf5('Atom_Lattice_max.hdf5')
sub1 = atom_lattice.sublattice_list[0]
sub2 = atom_lattice.sublattice_list[1]
sub3 = atom_lattice.sublattice_list[2]
''' Refine the Sublattice elements '''
element_list_sub1 = ['Mo_0', 'Mo_1', 'Mo_1.S_1', 'Mo_1.Se_1', 'Mo_2']
element_list_sub2 = ['S_0', 'S_1', 'S_2', 'Se_1', 'Se_1.S_1', 'Se_2']
element_list_sub3 = [
'H_0',
'S_1',
'Se_1',
'Mo_1',
]
sub_dict = {
sub1: element_list_sub1,