py4DSTEM 0.8.0

An open source python package for processing and analysis of 4D STEM data.

py4DSTEM: open source processing and analysis of 4D-STEM data

py4DSTEM is a python tool for analysis of four-dimensional scanning transimission electron microscopy (4D-STEM) data. It is open source software distributed under a GPLv3 license. It is free to use, alter, or build on, provided that any work derived from py4DSTEM is also kept free and open.

What is 4D-STEM?

In a traditional STEM experiment, a beam of high energy electrons is focused to a very fine probe - on the order of or, often, smaller than the atomic lattice spacings - and rastered across the surface of the sample. A two-dimensional image is then formed by populating the value of each pixel with the electron flux through a detector at the corresponding beam position. In 4D-STEM a pixellated detector is used, such that a 2D image of the diffraction plane is acquired at every raster position of the electron beam. A 4D-STEM scan thus results in a 4D data array.

4D-STEM data is information rich. A datacube can be collapsed in real space to yield information comparable to nanobeam electron diffraction experiment, or in diffraction space to yield a variety of virtual images, corresponding to both traditional STEM imaging modes as well as more exotic virtual imaging modalities. The structure, symmetries, and spacings of Bragg disks can be used to extract spatially resolved maps of crystallinity, grain orientations, and lattice strain. Redundant information in overlapping Bragg disks can be leveraged to calculate the sample potential. Structure in the diffracted halos of amorphous systems can be used to describe the short and medium range order.

For more information, see https://arxiv.org/abs/2003.09523.

Using py4DSTEM

This sections describes:

• Installation
• Running the GUI
• Running from a python interpretter
• Sample jupter notebooks
• Sample scripts
• For developers

Installation

The recommended installation for py4DSTEM uses the Anaconda python distribution. First, download and install Anaconda. Instructions can be found at www.anaconda.com/download. Then open a terminal and run

conda updata conda
conda create -n py4dstem
conda activate py4dstem
conda install pip
pip install py4dstem

In order, these commands

• ensure your anaconda is up-to-date
• make a virtual environment to install the package into
• enter the environment
• make sure your new environment talks nicely to pip, the Python package installer
• use pip to install the py4DSTEM package

Note that directions above only install py4DSTEM in the py4dstem conda environment. This means you'll need to activate the environment again each time you want to use py4DSTEM.

Running the GUI

From a terminal, run

conda activate py4dstem
py4dstem

Running from a python interpreter

From any python interpreter inside the py4dstem conda environment, py4DSTEM can be imported in the usual way:

import py4DSTEM

Sample jupter notebooks

TODO -- apologies, we are under construction ;)

Sample scripts

TODO -- lolcats

For contributors

To contribute to py4DSTEM, first fork the repository.

Set up and activate a new environment, e.g. in anaconda use

conda create -n py4dstem_dev
conda activate py4dstem_dev

Next, navigate to the directory where you want to put the project, and type

git clone your_fork_url.git

where your_fork_url is the url to the fork you created. Navigate into the new py4DSTEM directory which contains the setup.py file, and run

conda install pip
pip install -e .

where the -e option installs the package in 'editable' mode, so that changes within this project directory will be reflected in py4DSTEM imports from Python interpreters within this environment.

Create a new branch, and make any edits. To merge back in to the main repository, submit a pull request to the dev branch of github.com/py4dstem/py4DSTEM.

More information

Dependencies

• numpy
• scipy
• h5py
• scikit-image
• scikit-learn
• tqdm
• ncempy
• hyperspy
• PyQt5
• pyqtgraph
• qtconsole
• ipywidgets

Versioning

v. 0.8.0

License

GNU GPLv3

Acknowledgements

The developers gratefully acknowledge the financial support of the Toyota Research Institute for the research and developement time which made this project possible.