Metadata-Version: 1.1
Name: Stoner
Version: 0.5.0rc4
Summary: Classes to represent simple scientific data sets and write analysis codes, developed for the University of Leeds Condensed Matter Physics Group
Home-page: http://github.com/~gb119/Stoner-PythonCode
Author: Gavin Burnell
Author-email: g.burnell@leeds.ac.uk
License: GPLv3
Description: Introduction
        ============
        
        The *Stoner* Python package is a set of utility classes for writing data
        analysis code. It was written within the Condensed Matter Physics group
        at the University of Leeds as a shared resource for quickly writing
        simple programs to do things like fitting functions to data, extract
        curve parameters and churn through large numbers of small text data
        files.
        
        For a general introduction, users are referred to the Users Guide, which
        is part of the online documentation
        \<http://pythonhosted.org/Stoner/\> along with the API Reference guide.
        The github repository
        \<http://www.github.com/gb119/Stoner-PythonCode/\> also contains some example scripts.
        
        Getting this Code
        ==================
        
        The \*Stoner\* package requires numpy \>=1.8, scipy \>=0.14, matplotlib \>=1.4, h5py, numba  and lmfit. Experimental code also makes use of
        the Enthought Tools Suite packages.
        
        Ananconda Python (and probably other scientific Python distributions) include nearly all of the dependencies, aprt from lmfit.
        However, this can by installed with the usual tools such as \*easy\_install\* or \*pip\*.
        
        .. code-block:: sh
        
           easy\_install lmfit
        
        The easiest way to install the Stoner package is via seuptools' easy\_install
        
        .. code-block:: sh
        
           easy\_install Stoner
        
        This will install the Stoner package into your current Python environment. Since the package is under fairly
        constant updates, you might want to follow the development with git. The source code, along with example scripts
        and some sample data files can be obtained from the github repository: https://github.com/gb119/Stoner-PythonCode
        
        The codebase is largely compatible with Python 3.4, with the expception of the 3D vector map plots which make use of
        Enthought's \*mayavi\* package which is still only Python 2 compatible due to the underlying Vtk toolkit. Other issues of
        broken 3.4 code are bugs to be squashed.
        
        Overview
        ========
        The \*\*Stoner\*\* package provides two basic top-level classes that describe an individual file of experimental data and a
        list (such as a directory tree on disc) of many experimental files. For our research, a typical single experimental data file
        is essentially a single 2D table of floating point numbers with associated metadata, usually saved in some
        ASCII text format. This seems to cover most experiments in the physical sciences, but it you need a more complex
        format with more dimensions of data, we suggest you look elsewhere.
        
        DataFile and Friends
        --------------------
        
        \*\*Stoner.Core.DataFile\*\* is the base class for representing individual experimental data sets.
        It provides basic methods to examine and manipulate data, manage metadata and load and save data files.
        It has a large number of sub classes - most of these are in Stoner.FileFormats and are used to handle the loading of specific
        file formats. Two, however, contain additional functionality for writing analysis programs.
        
        \*   \*\*Stoner.Analysis.AnalyseFile\*\* provides additional methods for curve-fitting, differentiating, smoothing and carrying out
                basic calculations on data.
        
        \* \*\*Stoner.Plot.PlotFile\*\* provides additional routines for plotting data on 2D or 3D plots.
        
        As mentioned above, there are subclasses of \*\*DataFile\*\* in the \*\*Stoner.FileFormats\*\* module that support
        loading many of the common file formats used in our research.
        
        For rapid development of small scripts, we would recommend the \*\*Stoner.Data\*\* class which is a superclass of the above,
        and provides a 'kitchen-sink' one stop shop for most of the package's functionality.
        
        DataFolder
        ----------
        
        \*\*Stoner.Folders.DataFolder\*\* is a class for assisting with the work of processing lots of files in a common directory
        structure. It provides methods to list. filter and group data according to filename patterns or metadata and then to execute
        a function on each file or group of files.
        
        The \*\*Stoner.HDF5\*\* module provides some experimental classes to manipulate \*DataFile\* and \*DataFolder\* objects within HDF5
        format files. These are not a way to handle arbitary HDF5 files - the format is much to complex and flexible to make that
        an easy task, rather it is a way to work with large numbers of experimental sets using just a single file which may be less
        brutal to your computer's OS than having directory trees with millions of individual files.
        
        Resources
        ==========
        
        Included in the github repository
        \<<http://www.github.com/gb119/Stoner-PythonCode/>\> are a (small)
        collection of sample scripts for carrying out various operations and
        some sample data files for testing the loading and processing of data.
        There is also a user guide \<UserGuide/ugindex\> as part of this
        documentation, along with a complete API reference \<Stoner\>
        
        Contact and Licensing
        =====================
        
        The lead developer for this code is Dr Gavin Burnell
        \<<g.burnell@leeds.ac.uk>\> <http://www.stoner.leeds.ac.uk/people/gb>.
        The User Guide gives the current list of other contributors to the
        project.
        
        This code and the sample data are all (C) The University of Leeds
        2008-2015 unless otherwise indficated in the source file. The contents
        of this package are licensed under the terms of the GNU Public License
        v3
        
        Recent Changes
        ==============
        
        Version 0.4.x
        -------------
        
        Refactored the PlotFormats sub module to use Matplotlib 1.4 stylesheets.
        Additional features in Plot and new outlier detection routines in
        AnalyseFile. New operators added to DataFile to make some column and row
        operations more compact. Number of bugs squashed.
        
        Version 0.3.0
        -------------
        
        Refactorise the setas attribute again to remove circular references. Get
        writing to the setas column attributes working.
        
        Version 0.2.5
        -------------
        
        Add a MokeFile class for loading Leeds MOKE system files.
        
        Version 0.2.4
        -------------
        
        Refactored the setas attribute, improvments to loading some file
        formats, new Engineering formatting for plots (optional)
        
        Version 0.2.0
        -------------
        
        Added the dependency on lmfit and depricated mpfit for doing bounded
        least-squares fitting of complex data functions.
        
Keywords: Data-Analysis Physics
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Programming Language :: Python :: 2.7
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
