Metadata-Version: 1.2
Name: fabio
Version: 0.11.0
Summary: Image IO for fable
Home-page: http://github.com/silx-kit/fabio
Author: Henning Sorensen, Erik Knudsen, Jon Wright, Regis Perdreau, Jérôme Kieffer, Gael Goret, Brian Pauw, Valentin Valls
Author-email: fable-talk@lists.sourceforge.net
License: MIT
Download-URL: https://github.com/silx-kit/fabio/releases
Description: FabIO: Fable Input/Output library
        =================================
        
        Main websites:
        
         * https://github.com/silx-kit/fabio
         * http://fable.sf.net (historical)
        
        
        |Build Status| |Appveyor Status|
        
        ----
        
        FabIO is an I/O library for images produced by 2D X-ray detectors and written in Python.
        FabIO support images detectors from a dozen of companies (including Mar, Dectris, ADSC, Hamamatsu, Oxford, ...),
        for a total of 30 different file formats (like CBF, EDF, TIFF, ...) and offers an unified interface to their
        headers (as a python dictionary) and datasets (as a numpy ndarray of integers or floats)
        
        Getting FabIO
        -------------
        
        FabIO is available from `PyPI <https://pypi.python.org/pypi/fabio>`_.
        
        `Debian/Ubuntu packages <http://www.silx.org/pub/debian/binary/>`_, and
        `wheels <http://www.silx.org/pub/wheelhouse/>`_ are available
        for windows, linux and MacOSX from the silx repository:
        
        Documentation is available at `silx <http://www.silx.org/doc/fabio/>`_. 
        
        Citation:
        ---------
        
        The general philosophy of the library is described in:
        `FabIO: easy access to two-dimensional X-ray detector images in Python; E. B. Knudsen, H. O. Sørensen, J. P. Wright, G. Goret and J. Kieffer Journal of Applied Crystallography, Volume 46, Part 2, pages 537-539. <http://dx.doi.org/10.1107/S0021889813000150>`_
        
        Transparent handling of compressed files
        ----------------------------------------
        
        FabIO is expected to handle gzip and bzip2 compressed files transparently.
        Following a query about the performance of reading compressed data, some
        benchmarking details have been collected at fabio_compressed_speed.
        This means that when your python was configured and built you needed the
        bzip and gzip modules to be present (eg libbz2-dev package for ubuntu)
        Using fabio in your own python programs
        Example::
        
          >>> import fabio
          >>> obj = fabio.edfimage.EdfImage("mydata0000.edf")
          >>> obj.data.shape
          (2048, 2048)
          >>> obj.header["Omega"]
          23.5
        
        
        Design Specifications
        ---------------------
        
        Name: 
        .....
        
        FabIO = Fable Input/Output
        
        Idea:
        .....
        
        Have a base class for all our 2D diffraction greyscale images.
        This consists of a 2D array (numpy ndarray)
        and a python dictionary (actually an ordered dict) of header information in (string key, string value) pairs.
        
        Class FabioImage
        ................
        
        Needs a name which will not to be confused with an RGB color image.
        
        Class attributes, often exposed as properties:
        
        * data   					-> 2D array
        * header 					-> ordered dictionary
        * rows, columns, dim1, dim2 -> data.shape (propertiy)
        * header_keys               -> property for list(header.keys()), formerly used to retain the order of the header
        * bytecode                 	-> data.typecode() (property)
        * m, minval, maxval, stddev	-> image statistics, could add others, eg roi[slice]
        
        Class methods (functions):
        
        * integrate_area()      -> return sum(self.data) within slice
        * rebin(fact)           -> rebins data, adjusts dims
        * toPIL16()             -> returns a PILimage
        * getheader()           -> returns self.header
        * resetvals()           -> resets the statistics
        * getmean()             -> (computes) returns self.m
        * getmin()              -> (computes) returns self.minval
        * getmax()              -> (computes) returns self.maxval
        * getstddev()           -> (computes) returns self.stddev
        * read()        		-> read image from file [or stream, or shared memory]
        * write()       		-> write image to file  [or stream, or shared memory]
        * readheader()          -> read only the header [much faster for scanning files]
        
        Each individual file format would then inherit all the functionality of this class and just make new read and write methods.
        
        There are also fileseries related methods (next(), previous(), ...) which returns a FabioImage instance of the next/previous frame in a fileserie
        
        Other feature:
        
        * possibility for using on-the-fly external compression - i.e. if files are
          stored as something as .gz, .bz2 etc could decompress them, using an external
          compression mechanism (if available). 
        
        
        Supported file formats
        ----------------------
        
        * ADSC:
        
          + AdscImage
        
        * Bruker:
        
          + BrukerImage
          + Bruker100Image
          + KcdImage: Nonius KappaCCD diffractometer
        
        * D3M
        
          + D3mImage
        
        * Dectris:
        
          + CbfImage (implements a fast byte offset de/compression scheme in python/cython)
          + PilatusImage (fileformat derived from Tiff)
          + EigerImage (derived from HDF5/NeXus format, depends on `h5py`)
        
        * ESRF:
        
          + EdfImage: The ESRF data Format
          + XsdImage: XML serialized image from EDNA
          + Fit2dImage: Fit2d binary format
          + Fit2dmaskImage: Fit2d Mask format
          + Fit2dSpreadsheetImage: Fit2d ascii tables (spread-sheet)
          + LimaImage: image stacks written by the LImA aquisition system
          + SparseImage: single crystal diffractions images written by pyFAI
        
        * General Electrics 
        
          + GEimage (including support for variant used at APS) 
        
        * Hamamatsu
        
          + HiPiCImage
        
        * HDF5: generic format for stack of images based on h5py
        
          + Hdf5Image
          + EigerImage
          + LimaImage
          + SparseImage
        
        * JPEG image format:
          
          + JPEG using PIL
          + JPEG 2000 using Glymur 
          
        * Mar Research:
        
          + MarccdImage (fileformat derived from Tiff)
          + Mar345Image imaging plate with PCK compression
        
        * MPA multiwire 
        
          +	MpaImage
        
        * Medical Research Council file format for 3D electron density and 2D images
        
          + MrcImage
        
        * Nonius -> now owned by Bruker
          
          + KcdImage 
        
        * Numpy: generic reader for 2D arrays saved
        
          + NumpyImage 
        
        * Oxford Diffraction Sapphire 3
        
          + OXDimage uncompressed or with TY1 or TY5 compression scheme
          + Esperanto format (with bitfield compression)
        
        * Pixirad Imaging
        
          + PixiImage
           
        * PNM
        
          + PnmImage
        
        * Princeton Instrument SPE
        
          + SpeImage
        
        * Raw Binary without compression
        
        * Rigaku
        
          + RaxisImage
          + DtrekImage
          
        * Tiff
        
          + TifImage using either:
          	- Pillow (external dependency)
          	- TiffIO taken from PyMca
        
        
        Installation
        ------------
        
        Please see doc/source/INSTALL.rst
        
        Changelog
        ---------
        
        Please see doc/source/Changelog.rst
        
        .. |Build Status| image:: https://travis-ci.org/silx-kit/fabio.svg?branch=master
           :target: https://travis-ci.org/silx-kit/fabio
        .. |Appveyor Status| image:: https://ci.appveyor.com/api/projects/status/4k6lol1vq30qhf66/branch/master?svg=true
           :target: https://ci.appveyor.com/project/ESRF/fabio/branch/master
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Programming Language :: C
Classifier: Programming Language :: Cython
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.5
