The diverse spectrum of material characteristics including band gap, mechanical moduli, color, phonon and electronic density of states, along with catalytic and surface properties are intricately intertwined with the atomic structure and the corresponding interatomic bond-lengths. This interconnection extends to the manifestation of interplanar spacings within a crystalline lattice. Analysis of these interplanar spacings and the comprehension of any deviations, whether it be lattice compression or expansion, commonly referred to as strain, hold paramount significance in unraveling various unknowns within the field. Transmission Electron Microscopy (TEM) is widely used to capture atomic-scale ordering, facilitating direct investigation of interplanar spacings. However, creating critical contour maps for visualizing and interpreting lattice stresses in TEM images remains a challenging task. Here we developed a Python code for TEM image processing that can handle a wide range of materials including nanoparticles, 2D materials, pure crystals and solid solutions. This algorithm converts local differences in interplanar spacings into contour maps allowing for a visual representation of lattice expansion and compression. The tool is very generic and can significantly aid in analyzing material properties using TEM images, allowing for a more in-depth exploration of the underlying science behind strain engineering via strain contour maps at the atomic level.

翻译：材料的多种特性，包括带隙、力学模量、颜色、声子与电子态密度，以及催化和表面性质，都与原子结构及相应的原子间键长密切相关。这种关联延伸到晶格中晶面间距的体现。分析这些晶面间距并理解任何偏差（无论是晶格压缩还是膨胀，通常称为应变），对于揭示该领域内的各种未知因素具有至关重要的意义。透射电子显微镜（TEM）被广泛用于捕捉原子尺度有序性，从而促进对晶面间距的直接研究。然而，在TEM图像中创建用于可视化和解释晶格应力的关键等高线图仍然是一项具有挑战性的任务。在此，我们开发了一个用于TEM图像处理的Python代码，可处理多种材料，包括纳米颗粒、二维材料、纯晶体和固溶体。该算法将晶面间距的局部差异转换为等高线图，从而实现晶格膨胀和压缩的可视化呈现。该工具非常通用，能够显著辅助利用TEM图像分析材料特性，通过原子级别的应变等高线图，更深入地探索应变工程背后的基础科学。