0.1.0 (2020-01-04) 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.readthedocs.io 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 connection, 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/user_guide/tools.html) for details. Contributors to this release (alphabetical by first name) Håkon Wiik Ånes Tina Bergh