Welcome to Atomap’s documentation!

News

2018-03-25: Atomap 0.1.1 released!

The major new features are methods for integrating atomic column intensity and quantifying this intensity in ADF STEM images, see Integration of atomic columns and Absolute Integrator for more info. Thanks to Katherine E. MacArthur for adding this! Other features include tools for rotating atomic positions for plotting purposes (Rotating image and points), and reduced memory use when processing data with many atoms.

2017-11-16: Atomap 0.1.0 released!

We are happy to announce a new Atomap release. It includes a major makeover of the tutorial, start with Finding the atom lattice. New features in this release are methods for finding atomic column intensity, new and simple plotting tools and a module for generating test data.

About Atomap

Atomap is a Python library for analysing atomic resolution scanning transmission electron microscopy images. It relies in fitting 2-D Gaussian functions to every atomic column in an image, and automatically find all major symmetry axes. The full procedure is explained in the article Atomap: a new software tool for the automated analysis of atomic resolution images using two-dimensional Gaussian fitting.

_images/elli_figure.jpg

Measuring the ellipticity of atomic columns. More info

_images/oxygen_superstructure_figure.jpg

Mapping the variation in distance between oxygen columns. More information

Atomap is under development and still in alpha, so bugs and errors can be expected. Bug reports and feature requests are welcome on the issue tracker. Contributors are welcome too!

If you publish scientific articles using Atomap, please consider citing the article Atomap: a new software tool for the automated analysis of atomic resolution images using two-dimensional Gaussian fitting. (M. Nord et al, Advanced Structural and Chemical Imaging 2017 3:9)

Atomap is available under the GNU GPL v3 license. The source code is found in the GitLab repository.

Old news

2017-07-03: version 0.0.8 released!

New features: ability to process dumbbell structures, fitting of multiple 2D Gaussians at the same time, improved background subtraction during 2D Gaussian fitting, and processing of nanoparticles.