Welcome to pixStem’s documentation!¶
2020-2-19: pixStem 0.4.0 released!
- Several functions for analysing low convergence STEM diffraction data, like nanobeam electron diffraction:
intensity_peaks()and a Laplacian of Gaussian (LoG) method to
find_peaks(). See Analysing NBED data or the Jupyter Notebook for an extended example. Thanks to Daen Jannis for the contribution!
- Improvements to Template matching with a disk or ring and Peak finding, so they now work with any number of navigation dimensions, from single diffraction images, line scans and larger. This will be expanded to the other functions in the future, so they will allow for any data dimensionality.
- Add two new template matching functions: template_match_ring and Template matching with any binary image
- Added two new Jupyter Notebooks using data from journal articles with open data: magnetic characterization with 4-D STEM-DPC and structural characterization with 4-D STEM.
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.
- Start pixStem
- Loading data
- Using the pixelated STEM class
- Analysing HOLZ datasets
- Analysing NBED data
- Analysing differential phase contrast data
- Fluctuation electron microscopy analysis
- Open datasets
- Generating test data
- Miscellaneous functions
- Related projects
- API documentation
2019-3-5: pixStem 0.3.3 released!
This release includes:
- Improvements to template_match_disk and Peak finding, so they now work with any number of navigation dimensions, from single diffraction images, line scans and larger. This will be expanded to the other functions in the future, so they will allow for any data dimensionality.
- Improved documentation for the fluctuation electron microscopy analysis. Thanks to Andrew Herzing for this contribution!
- Several minor bug-fixes.
2018-12-3: pixStem 0.3.2 released!
This release includes:
- Data analysis functionality for analysing fluctuation electron microscopy data, thanks to Andrew Herzing for the contribution!
- A new method in the
PixelatedSTEMclass for template matching a disk with STEM diffraction data: Template matching with a disk or ring
- Another method for locating diffraction spots in STEM diffraction data, using peak finding functionality from
skimage: Peak finding
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
Featurewise it also includes a simple function for loading binary signals:
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.
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).