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We're happy to announce the release of kikuchipy v0.1.0!
kikuchipy is an open-source Python library for processing and analysis of electron
backscatter diffraction (EBSD) patterns. The library builds upon the tools for
multi-dimensional data analysis provided by the HyperSpy library.
For more information, a user guide, and the full reference API documentation, please
visit: https://kikuchipy.org.
This is the initial pre-release, where things start to get serious... seriously fun!
Features
--------
- Load EBSD patterns and metadata from the NORDIF binary format (.dat), or Bruker Nano's
or EDAX TSL's h5ebsd formats (.h5) into an ``EBSD`` object, e.g. ``s``, based upon
HyperSpy's `Signal2D` class, using ``s = kp.load()``. This ensures easy access to
patterns and metadata in the attributes ``s.data`` and ``s.metadata``, respectively.
- Save EBSD patterns to the NORDIF binary format (.dat) and our own h5ebsd format (.h5),
using ``s.save()``. Both formats are readable by EMsoft's NORDIF and EMEBSD readers,
respectively.
- All functionality in kikuchipy can be performed both directly and lazily (except some
multivariate analysis algorithms). The latter means that all operations on a scan,
including plotting, can be done by loading only necessary parts of the scan into
memory at a time. Ultimately, this lets us operate on scans larger than memory using
all of our cores.
- Visualize patterns easily with HyperSpy's powerful and versatile ``s.plot()``. Any
image of the same navigation size, e.g. a virtual backscatter electron image, quality
map, phase map, or orientation map, can be used to navigate in. Multiple scans of the
same size, e.g. a scan of experimental patterns and the best matching simulated
patterns to that scan, can be plotted simultaneously with HyperSpy's
``plot_signals()``.
- Virtual backscatter electron (VBSE) imaging is easily performed with
``s.virtual_backscatter_electron_imaging()`` based upon similar functionality in
pyXem. Arbitrary regions of interests can be used, and the corresponding VBSE image
can be inspected interactively. Finally, the VBSE image can be obtained in a new
``EBSD`` object with ``vbse = s.get_virtual_image()``, before writing the data to an
image file in your desired format with matplotlib's
``imsave('filename.png', vbse.data)``.
- Change scan and pattern size, e.g. by cropping on the detector or extracting a region
of interest, by using ``s.isig`` or ``s.inav``, respectively. Patterns can be binned
(upscaled or downscaled) using ``s.rebin``. These methods are provided by HyperSpy.
- Perform static and dynamic background correction by subtraction or division with
``s.static_background_correction()`` and ``s.dynamic_background_correction()``. For
the former correction, relative intensities between patterns can be kept if desired.
- Perform adaptive histogram equalization by setting an appropriate contextual region
(kernel size) with ``s.adaptive_histogram_equalization()``.
- Rescale pattern intensities to desired data type and range using
``s.rescale_intensities()``.
- Multivariate statistical analysis, like principal component analysis and many other
decomposition algorithms, can be easily performed with ``s.decomposition()``, provided
by HyperSpy.
- Since the ``EBSD`` class is based upon HyperSpy's ``Signal2D`` class, which itself is
based upon their ``BaseSignal`` class, all functionality available to ``Signal2D`` is
also available to the ``EBSD`` class. See HyperSpy's user guide
(http://hyperspy.org/hyperspy-doc/current/index.html) for details.
Contributors
------------
- Håkon Wiik Ånes
- Tina Bergh