Create a TEM-like image (HAADF) from a cif file using ASE and HyperSpy

cif_to_STEM.py

import hyperspy.api as hs
import numpy as np
import matplotlib.pyplot as plt

def cell2sig(cell, pixel_size = 0.05, sigma=0.25):
    Signal2D = hs.signals.Signal2D
    Gaussian2D = hs.model.components2D.Gaussian2D


    XLEN, YLEN = (cell.cell.diagonal()[:2] // pixel_size).astype(int)
    ax0 = {
        'name':'y',
        'size': YLEN,
        'offset':0,
        'scale':pixel_size, 
        'units':'Å',
    }
    ax1 = {
        'name':'x', 
        'size': XLEN, 
        'offset':0,
        'scale':pixel_size, 
        'units':'Å',
    }

    axes = [ax0, ax1]
    s = Signal2D(np.zeros((YLEN, XLEN)), axes=axes)

    m = s.create_model()

    # Add equivalent unit cells around the image, so that any gaussians that extend into the image, do so.

    shifts = [(-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 0), (0, 1), (1, -1), (1, 0), (1, 1)]

    cell_center = np.array([(ax.high_value + ax.scale + ax.low_value)/2 for ax in s.axes_manager.signal_axes])
    diagonal_radius = np.array([ax.high_value for ax in s.axes_manager.signal_axes])

    for atom in cell:
        for offset in shifts:
            xyposition = atom.position[:2] + cell.cell.diagonal()[:2] * offset
            if np.abs(np.linalg.norm(xyposition - cell_center)) > np.linalg.norm(cell_center - diagonal_radius) + 1:
                continue
            A = atom.number**2
            x, y = xyposition
            g = Gaussian2D(A, sigma, sigma, x, y )
            m.append(g)
    s2 = m.as_signal()
    return s2