Metadata-Version: 2.1
Name: pygix
Version: 2021.10.7
Summary: Software library for grazing-incidence/fibre X-Ray data analysis
Home-page: https://github.com/tgdane/pygix
Author: Thomas Dane
Author-email: thomasgdane@gmail.com
License: UNKNOWN
Description: pygix
        ====
        
        Pygix is a generic python library for performing reduction of 
        grazing-incidence and fiber X-ray scattering data.
        
        ----
        
        The pygix library has been developed to reduce 2D X-ray scattering data for
        experiments recorded in grazing-incidence (GISAXS, GIWAXS, GIXRD, GIXD,
        collectively referred to as GIXS) and fiber diffraction modes. Both 2D image
        projections and 1D line profile extraction are handled. The package is designed
        to be as generic as possible, i.e., it makes no assumptions about the data
        (passed by the user as numpy arrays) and all detector and geometric corrections
        can be handled at the point of data reduction.
        
        ----
        
        Please refer to the [wiki](https://github.com/tgdane/pygix/wiki>) for more
        detailed discussion!
        
        ----
        
        Example usage:
        
        ```python
        
            import pygix
            import pygix.plotting as pp
        
            pg = pygix.Transform()
            pg.load('detector_calibration.poni')
            pg.indcident_angle = 0.2
        
            # transform image into reciprocal space:
            i, qxy, qz = pg.transform_reciprocal(data)
        
            pp.implot(i, qxy, qz, xlim=(-5, 28), ylim=(-.5, 32), mode='rsm')
        ```
        
        Which will generate the following image:
        ![alt text](http://i.imgur.com/Wvy8Efh.png "Example transformed image")
        
        
        Pygix uses the fiber transformation originally described by Stribeck [1] (based
        on earlier work by Polayni [2]), which has recently been formulated for the case
        of grazing-incidence scattering [3]. The reciprocal space transformation for the
        fiber and grazing-incidence X-ray scattering are thus equivalent, meaning this
        python library is generic for both classes of experiments.
        
        Pygix is heavily based on the pyFAI library for conventional 2D transmission
        azimuthal integration [4].
        
        
        References
        ----
        1.    Stribeck and Nöchel, J. Appl. Crystallogr., (2009), 42, 295–301
        2.    Polanyi, Z. Physik, (1921), 7, 149-180
        3.    Lilliu and Dane, 	arXiv:1511.06224 [cond-mat.soft]
        4.    Ashiotis, Deschildre, Nawaz, Wright, Karkoulis,
              Picca and Kieffer, J. Appl. Crystallogr., 2015, 48, 510–519
              (https://github.com/silx-kit/pyFAI/)
        
        Installation
        ----
        In the near future, pygix will be available via PIP. In the meantime, download
        the source code in .zip format from the github
        [repository](https://github.com/tgdane/pygix/archive/master.zip) and unpack it.
        
        ```
            unzip pygix-master.zip
        ```
        
        Go to the `pygix-master` directory, build and install the package:
        
        ```
            cd pygix-master
            python setup.py build install
        ```
        
        Credits
        ----
        * pygix was written by Thomas Dane with contributions and assistance from [Jerome Kieffer](https://github.com/kif).
        * The derivation of the reciprocal space transformation was done in collaboration with Samuele Lilliu.
        * pygix relies heavily on the [pyFAI](https://github.com/silx-kit/pyFAI/) library.
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Programming Language :: Cython
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Environment :: Console
Classifier: Environment :: MacOS X
Classifier: Environment :: Win32 (MS Windows)
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: Unix
Classifier: Operating System :: POSIX
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Topic :: Documentation :: Sphinx
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
