Metadata-Version: 2.1
Name: fpd
Version: 0.2.0
Summary: FPD: Fast pixelated detector data storage, analysis and visualisation.
Home-page: http://gitlab.com/fpdpy/fpd
Author: Gary Paterson
Author-email: dr.gary.paterson@gmail.com
License: GPL v3
Description: 
        News
        ====
        **25 March 2021** - Release 0.2.0 with automatic threading control and several updates and improvements (see [changelog](https://fpdpy.gitlab.io/fpd/changelog.html) for details).
        
        **23 January 2021** - Release 0.1.13 with several updates and improvements (see [changelog](https://fpdpy.gitlab.io/fpd/changelog.html) for details).
        
        **21 December 2020** - Release 0.1.12 with several updates and improvements (see [changelog](https://fpdpy.gitlab.io/fpd/changelog.html) for details).
        
        **1 November 2020** - Release 0.1.11 with lots of new features and improvements (see [changelog](https://fpdpy.gitlab.io/fpd/changelog.html) for details).
        
        **4 September 2020** - Publication of **Fast Pixelated Detectors in Scanning Transmission Electron Microscopy. Part II: Post Acquisition Data Processing, Visualisation, and Structural Characterisation**, [Microsc. Microanal. 26, 944 (2020)](https://doi.org/10.1017/S1431927620024307), [arXiv (2020)](https://arxiv.org/abs/2004.02777).
        
        See https://fpdpy.gitlab.io/fpd/news.html for earlier news.
        
        
        FPD package
        ===========
        The fpd package provides code for the storage, analysis and visualisation
        of data from fast pixelated detectors. The data storage uses the hdf5 based 
        EMD file format, and the conversion currently supports the Merlin readout from 
        Medipix3 detectors. Differential phase contrast imaging and several other common
        data analyses, like radial distributions, virtual apertures, and lattice analysis,
        are also implemented, along with many utilities and general electron microscopy
        related tools.
        
        The package is relatively lightweight, with most of its few dependencies being
        standard scientific libraries. All calculations run on CPUs and many use 
        out-of-core processing, allowing data to be visualised and processed on anything
        from very modest to powerful hardware.
        
        A degree of optimisation through parallelisation has been implemented. The 
        development environment is Linux; the efficiency should be similar across all
        operating systems. If you are on Windows 10 but want all the other benefits of a
        Linux environment, the Linux subsystem has been reported to work well.
        
        
        Citing
        ------
        If you find this software useful and use it to produce results in a 
        puplication, please consider citing the website or related paper(s).
        
        An example bibtex entry with the date in the note field yet to be specified:
        
        ```
        @Misc{fpd,
            Title                    = {{FPD: Fast pixelated detector data storage, analysis and visualisation}},
            howpublished             = {\url{https://gitlab.com/fpdpy/fpd}},
            note                     = {{Accessed} todays date}
        }
        ```
        
        Aspects of the library are covered in papers:
        
        - Fast Pixelated Detectors in Scanning Transmission Electron Microscopy. Part I: Data Acquisition, Live Processing and Storage,\
        [arXiv (2019)](https://arxiv.org/abs/1911.11560), [Microsc. Microanal. 26, 653 (2020)](https://doi.org/10.1017/S1431927620001713).
        [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3479124.svg)](https://doi.org/10.5281/zenodo.3479124)
        
        - Fast Pixelated Detectors in Scanning Transmission Electron Microscopy. Part II: Post Acquisition Data Processing, Visualisation, and Structural Characterisation,\
        [Microsc. Microanal. 26, 944 (2020)](https://doi.org/10.1017/S1431927620024307), [arXiv (2020)](https://arxiv.org/abs/2004.02777).
        [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3903517.svg)](https://doi.org/10.5281/zenodo.3903517)
        
        
        Publications
        ------------
        Some of the known scientific papers that used the fpd library are listed below.
        If you use the library for results contributing to a publication, please pass
        the paper details to developers for inclusion in this list.
        
        - Correlative chemical and structural nanocharacterization of a pseudo-binary 0.75Bi(Fe0.97Ti0.03)O3-0.25BaTiO3 ceramic,\
        [J. Am. Ceram. Soc. 104, 2388 (2021)](https://doi.org/10.1111/jace.17599), [arXiv (2020)](https://arxiv.org/abs/2010.10975).
        
        - Formations of narrow stripes and vortex-antivortex pairs in a quasi-two-dimensional ferromagnet K2CuF4,\
        [J. Phys. Soc. Jpn. 90, 014702 (2021)](https://doi.org/10.7566/JPSJ.90.014702), [Enlighten: Publications (2020)](http://eprints.gla.ac.uk/224185/).
        
        - Tunable superconductivity in Fe-pnictide heterointerfaces by diffusion control,\
        [arXiv (2020)](https://arxiv.org/abs/2009.04799).
        
        - Fast Pixelated Detectors in Scanning Transmission Electron Microscopy. Part II: Post Acquisition Data Processing, Visualisation, and Structural Characterisation,\
        [Microsc. Microanal. 26, 944 (2020)](https://doi.org/10.1017/S1431927620024307), [arXiv (2020)](https://arxiv.org/abs/2004.02777).
        
        - Fast Pixelated Detectors in Scanning Transmission Electron Microscopy. Part I: Data Acquisition, Live Processing and Storage,\
        [arXiv (2019)](https://arxiv.org/abs/1911.11560), [Microsc. Microanal. 26, 653 (2020)](https://doi.org/10.1017/S1431927620001713).
        
        - Spontaneous creation and annihilation dynamics and strain-limited stability of magnetic skyrmions,\
        [arXiv (2019)](https://arxiv.org/abs/1911.10094), [Nat. Commun. 11, 3536 (2020)](https://doi.org/10.1038/s41467-020-17338-7).
        
        - Tensile deformations of the magnetic chiral soliton lattice probed by Lorentz transmission electron microscopy,\
        [arXiv (2019)](https://arxiv.org/abs/1911.09634), [Phys. Rev. B 101, 184424 (2020)](https://dx.doi.org/10.1103/PhysRevB.101.184424).
        
        - Sub-100 nanosecond temporally resolved imaging with the Medipix3 direct electron detector,\
        [arXiv (2019)](https://arxiv.org/abs/1905.11884), [Ultramicroscopy, 210, 112917 (2020)](https://doi.org/10.1016/j.ultramic.2019.112917).
        
        - Strain Anisotropy and Magnetic Domains in Embedded Nanomagnets,\
        [Small, 1904738 (2019)](https://doi.org/10.1002/smll.201904738).
        
        - Heisenberg pseudo-exchange and emergent anisotropies in field-driven pinwheel artificial spin ice,\
        [arXiv (2019)](https://arxiv.org/abs/1908.10626), [Phys. Rev. B 100, 174410 (2019)](https://doi.org/10.1103/PhysRevB.100.174410).
        
        - Order and disorder in the magnetization of the chiral crystal CrNb3S6,\
        [arXiv (2019)](https://arxiv.org/abs/1903.09519), [Phys. Rev. B 99, 224429 (2019)](https://doi.org/10.1103/PhysRevB.99.224429).
        
        
        Installation
        ------------
        The package currently supports both python versions 2.7 and 3.x. Hyperspy is
        used in a few places but most of the fpd module can be used without it being 
        installed (simply install the package dependencies manually and ignore them when
        using pip by adding ``--no-deps`` to the install command).
        
        Installation from source:
        
        ```bash
        pip3 install --user .
        ```
        
        Instalation from PyPI (https://pypi.org/project/fpd/):
        
        ```bash
        pip3 install --user fpd
        ```
        
        ``-U`` can be added to force an upgrade / reinstall; in combination with ``--no-deps``,
        only the ``fpd`` package will be reinstalled.
        
        The package can be removed with:
        
        ```bash
        pip3 uninstall fpd
        ```
        
        
        Usage
        -----
        In python or ipython:
        
        ```python
        import fpd
        d = fpd.DPC_Explorer(-64)
        ```
        
        ```python
        import fpd.fpd_processing as fpdp
        rtn = fpdp.phase_correlation(data, 32, 32)
        ```
        where `data` is any array-like object. For example, this can be an in-memory 
        numpy array, an hdf5 object on disk, or a dask array, such as that used in 
        'lazy' hyperspy signals.
        
        All functions and classes are documented and can be read, for example, in `ipython`
        by appending a `?` to the object. E.g.:
        
        ```python
        import fpd
        fpd.DPC_Explorer?
        ```
        
        Documentation
        -------------
        Release: https://fpdpy.gitlab.io/fpd/
        
        Development version: https://gitlab.com/fpdpy/fpd/builds/artifacts/master/file/pages_development/index.html?job=pages_development
        
        Notebook demos: https://gitlab.com/fpdpy/fpd-demos.
        
        Further documentation and examples will be made available over time.
        
        
        Related projects
        ----------------
        
        https://www.gla.ac.uk/schools/physics/research/groups/mcmp/researchareas/pixstem/
        
        http://quantumdetectors.com/stem/
        
        https://gitlab.com/fast_pixelated_detectors/merlin_interface
        
        https://gitlab.com/fast_pixelated_detectors/fpd_live_imaging
        
        https://gitlab.com/pixstem/pixstem
        
        https://emdatasets.com/format
        
        http://hyperspy.org/
        
        http://gwyddion.net/
        
        More packages will be added to the https://gitlab.com/fast_pixelated_detectors
        group as they develop.
        
        
        Coverage
        --------
        [master coverage](https://gitlab.com/fpdpy/fpd/builds/artifacts/master/file/coverage.txt?job=test:p3)
        
        
        
Keywords: microscopy,STEM,TEM,fast pixelated detector,fpd,data storage,EMD,hdf5,data analysis,differential phase contrast,DPC,segmented DPC,pixelated DPC,virtual detector,lattice analyis,strain analysis,non-rigid image alignment
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Physics
Description-Content-Type: text/markdown
