Library to help write data analysis tools for experimental condensed matter physics.
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, churn through large numbers of small text data files and work with certain types of scientific image files.
For a general introduction, users are referred to the Users Guide, which is part of the online documentation along with the API Reference guide. The github repository also contains some example scripts.
Getting this Code
The Stoner package requires numpy >=1.8, scipy >=0.14, matplotlib >=1.5, h5py, lmfit,Pillow and has a number of optional dependencies on blist, filemagic, npTDMS, imreg_dft and numba.
Ananconda Python (and probably other scientific Python distributions) include nearly all of the dependencies, and the remaining dependencies are collected together in the phygbu repositry on anaconda cloud. The easiest way to install the Stoner package is, therefore, to install the most recent Anaconda Python distribution (Python 3.7, 3.6, 3.5 or 2.7 should work) and then to install the Stoner package via:
conda install -c phygbu Stoner
If you are not using Anaconda python, then pip should also work:
pip install Stoner
This will install the Stoner package and any missing dependencies 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/stonerlab/Stoner-PythonCode
The codebase is compatible with Python 2.7 and Python 3.5+, at present we still develop primarily in Python 3.6 and 3.7 but test with 2.7 as well. NB Python 3.7 is only supported in version 0.9x onwards and is known to not work with version 0.8.x.
The main part of 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.
Data and Friends
Stoner.Data is the core class for representing individual experimental data sets. It is actually composed of several mixin classes that provide different functionality, with methods to examine and manipulate data, manage metadata, load and save data files, plot results and carry out various analysis tasks. It has a large number of sub classes - most of these are in Stoner.formats and are used to handle the loading of specific file formats.
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. A key feature of DataFolder is its ability to work with the collated metadata from the individual files that are held in the DataFolder. In combination with its ability to walk through a complete heirarchy of groups of Data objects, the handling of the common metadata provides powerful tools for quickly writing data reduction scripts.
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. The module also provides some classes to support loading some other HDF5 flavoured files into a DataFile.
The Stoner.Zip module provides a similar set of classes to Stoner.HDF5 but working with the ubiquitous zip compressed file format.
The Stoner.Image package is a new feature of recent versions of the package and provides dedicated classes for working with image data, and in particular for analysing Kerr Microscope image files. It provides an ImageFile class that is functionally similar to DataFile except that the numerical data is understood to represent image data and additional methods are incorporated to facilitate processing. The ImageFolder and ImageStack classes provide similar functionality to DataFolder but with additional methods specific to handling collections of images. ImageStack uses a 3D numpy array as it’s primary image store which permits faster access (at the expense of a larger memory footprint) than the lazy loading ordered dictionary of ImageFolder
Included in the github repository 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 as part of this documentation, along with a doc(complete API reference <Stoner>)
Contact and Licensing
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 2008-2017 unless otherwise indficated in the source file. The contents of this package are licensed under the terms of the GNU Public License v3
Current PyPi Version
The current version of the package on PyPi will be the stable branch until the development branch enters beta testing, when we start making beta packages available.
The current development version is hosted in the master branch of the repository and will become version 0.10. There is no definitive list of features at this time. Better integration with pandas and xarray are under consideration as is depricating some of the less optimal parts of the api.
Version 0.7 onwards are tested using the Travis-CI services with unit test coverage assessed by Coveralls.
Version 0.9 is tested with Python 2.7, 3.5, 3.6,
The development version - which will be 0.10 will be tested with Python 3.6 and Python 3.7 only until Python 3.8 becomes stable.
Citing the Stoner Package
We maintain a digital object identifier (doi) for this package (linked to on the status bar at the top of this readme) and encourage any users to cite this package via that doi.
Version 0.9 is the current stable version. This is the last version to support Python 2 and 3<3.6. Features of this release are:
- Refactoring of the package into a more granual core, plot, formats, folders packages with submodules
- Overhaul of the documentation and user guide
- Dropping support for the older Stoner.Image.stack.ImageStack class
- Droppping support for matplotlib<2.0
- Support for Python 3.7
- Unit tests now > 80% coverage across the package.
Online documentation for all versions can be found on the ReadTheDocs pages online documentation
Version 0.8 is the previous stable release. The main new features were:
- Reworking of the ImageArray, ImageFile and ImageFolder with many updates and new features.
- New mixin based ImageStack2 that can manipulate a large number of images in a 3D numpy array
- Continued re-factoring of DataFolder using the mixin approach
- Further increases to unit-test coverage, bug fixes and refactoring of some parts of the code.
- _setas objects implement a more complete MutableMapping interface and also support +/- operators.
- conda packages now being prepared as the preferred package format
0.8.2 was the final release of the 0.8.0 branch
The old stable version is 0.7.2. Features of 0.7.2 include
- Replace older AnalyseFile and PlotFile with mixin based versions AnalysisMixin and PlotMixin
- Addition of Stoner.Image package to handle image analysis
- Refactor DataFolder to use Mixin classes
- DataFolder now defaults to using Stoner.Core.Data
- DataFolder has an options to skip iterating over empty Data files
- Further improvements to Stoner.Core.DataFile.setas handline.
No further relases will be made to 0.7.x.
0.6, 0.7 should work on Python 2.7 and 3.5 0.8 is also tested on Python 3.6
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