Welcome to pixStem’s documentation!

News

2020-2-19: pixStem 0.4.0 released!

About pixStem

_images/stem_diffraction.jpg _images/nbed_example.jpg _images/dpc_dummy_data.jpg

Library for processing data acquired on a fast pixelated electron detector, acquired using scanning transmission electron microscopy (STEM). For more information about this technique and how do analyse this type of data, see the paper Fast Pixelated Detectors in Scanning Transmission Electron Microscopy. Part I: Data Acquisition, Live Processing and Storage at arXiv.

Install instructions: Installing.

pixStem is available under the GNU GPL v3 license, and the source code is found at GitLab repository.

Old news

2019-3-5: pixStem 0.3.3 released!

This release includes:

2018-12-3: pixStem 0.3.2 released!

This release includes:

All these rely on the dask library, so they can be performed on very large datasets.

2018-6-28: pixStem 0.3.1 released!

This is a minor release which includes functionality for correcting both dead and hot pixels through find_dead_pixels(), find_hot_pixels() and correct_bad_pixels(). Featurewise it also includes a simple function for loading binary signals: pixstem.io_tools.load_binary_merlin_signal(). Unit testing of docstrings has been improved, with a combined setup and teardown function, and introduction of pixstem and numpy into the docstring’s namespace.

2018-6-13: pixStem 0.3.0 released!

fpd_data_processing has been renamed pixStem! This release also includes greatly improved center of mass, virtual annular dark field and virtual bright field functions, which now uses only dask array operations. This means they can return lazy signal, which makes it much easier to work with large datasets on computers with limited memory. It also includes several backend improvements to DPCSignal.correct_ramp and utility functions for getting diffraction values. A Jupyter Notebook giving a basic introduction to pixStem’s features is available at pixStem demos.

2018-2-18: fpd_data_processing 0.2.1 released!

This release includes a major improvement for lazy signal processing with center of mass and radial integration. It also includes a new method for shifting diffraction patterns in the PixelatedSTEM class.

Acknowledgement

Initial work from Magnus Nord funded by EPSRC via the project “Fast Pixel Detectors: a paradigm shift in STEM imaging” (Grant reference EP/M009963/1).

Indices and tables