Python package for structure refinement from diffraction data.

Configurable code for solving atomic structures.

The diffpy.srfit package provides the framework for building a global optimizer on the fly from components such as function calculators (that calculate different data spectra), regression algorithms and structure models. The software is capable of co-refinement using multiple information sources or models. It provides a uniform interface for various regression algorithms. The target function being optimized can be specified by the user according to the data available.

Within the diffpy.srfit framework, any parameter used in describing the structure of a material can be passed as a refinable variable to the global optimizer. Once parameters are declared as variables they can easily be turned "on" or "off", i.e. fixed or allowed to vary. Additionally, variables may be constrained to obey mathematical relationships with other parameters or variables used in the structural model. Restraints can be applied to variables, which adds a penalty to the refinement process commensurate with the deviation from the known value or range. The cost function can also be customized by the user. If the refinement contains multiple models, each model can have its own cost function which will be properly weighted and combined to obtain the total cost function. Additionally, diffpy.srfit is designed to be extensible, allowing the user to integrate external calculators to perform co-refinements with other techniques.

For more information about the diffpy.srfit library, see the users manual at http://diffpy.github.io/diffpy.srfit.

If you use this program for a scientific research that leads to publication, we ask that you acknowledge use of the program by citing the following paper in your publication:

P. Juhás, C. L. Farrow, X. Yang, K. R. Knox and S. J. L. Billinge, Complex modeling: a strategy and software program for combining multiple information sources to solve ill posed structure and nanostructure inverse problems, Acta Crystallogr. A 71, 562-568 (2015).

The diffpy.srfit package requires Python 3.5 or later or 2.7 and the following software:

• setuptools - software distribution tools for Python
• NumPy - numerical mathematics and fast array operations for Python
• SciPy - scientific libraries for Python
• matplotlib - python plotting library

Recommended software:

Optimizations involving crystal structures or molecules require

• diffpy.structure - crystal structure container and parsers, https://github.com/diffpy/diffpy.structure
• pyobjcryst - Crystal and Molecule storage, rigid units, bond length and bond angle restraints, https://github.com/diffpy/pyobjcryst

Optimizations involving pair distribution functions PDF or bond valence sums require

• diffpy.srreal - python library for PDF calculation, https://github.com/diffpy/diffpy.srreal

Optimizations involving small angle scattering or shape characteristic functions from the diffpy.srfit.sas module require

• sas - module for calculation of P(R) in small-angle scattering from the SasView project, http://www.sasview.org

We recommend to use Anaconda Python as it allows to install all software dependencies together with diffpy.srfit. For other Python distributions it is necessary to install the required software separately. As an example, on Ubuntu Linux some of the required software can be installed using

sudo apt-get install \
   python3-setuptools python3-numpy python3-scipy python3-matplotlib

For other required packages see their respective web pages for installation instructions.

The preferred method is to use Miniconda Python and install from the "conda-forge" channel of Conda packages.

To add "conda-forge" to the conda channels, run the following in a terminal.

conda config --add channels conda-forge

We want to install our packages in a suitable conda environment. The following creates and activates a new environment named diffpy.srfit_env

conda create -n diffpy.srfit_env python=3
conda activate diffpy.srfit_env

Then, to fully install diffpy.srfit in our active environment, run

conda install diffpy.srfit

Another option is to use pip to download and install the latest release from Python Package Index. To install using pip into your diffpy.srfit_env environment, type

pip install diffpy.srfit

If you prefer to install from sources, after installing the dependencies, obtain the source archive from GitHub. Once installed, cd into your diffpy.srfit directory and run the following

pip install .

Diffpy user group is the discussion forum for general questions and discussions about the use of diffpy.srfit. Please join the diffpy.srfit users community by joining the Google group. The diffpy.srfit project welcomes your expertise and enthusiasm!

If you see a bug or want to request a feature, please report it as an issue and/or submit a fix as a PR. You can also post it to the Diffpy user group.

diffpy.srfit is an open-source software developed as a part of the DiffPy-CMI complex modeling initiative at Columbia University.

Feel free to fork the project and contribute. To install diffpy.srfit in a development mode, with its sources being directly used by Python rather than copied to a package directory, use the following in the root directory

pip install -e .

To ensure code quality and to prevent accidental commits into the default branch, please set up the use of our pre-commit hooks.

1. Install pre-commit in your working environment by running conda install pre-commit.
2. Initialize pre-commit (one time only) pre-commit install.

Thereafter your code will be linted by black and isort and checked against flake8 before you can commit. If it fails by black or isort, just rerun and it should pass (black and isort will modify the files so should pass after they are modified). If the flake8 test fails please see the error messages and fix them manually before trying to commit again.

Improvements and fixes are always appreciated.

Before contribuing, please read our Code of Conduct.

The source code in observable.py was derived from the 1.0 version of the Caltech "Pyre" project.

For more information on diffpy.srfit please visit the project web-page or email Prof. Simon Billinge at sb2896@columbia.edu.