Stoner 0.5.0

Classes to represent simple scientific data sets and write analysis codes, developed for the University of Leeds Condensed Matter Physics Group

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*.

easy\_install lmfit

The easiest way to install the Stoner package is via seuptools’ easy_install

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 <<[e-mail]>> <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.