[Neutron] DiffPy-CMI - new software suite for structure modeling from diffraction data

Pavol Juhas pavol.juhas at gmail.com
Sun Mar 23 18:19:54 CET 2014

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We are very excited to announce the first public release of our DiffPy
Complex Modeling Initiative (DiffPy-CMI) project. This includes the SrFit
complex modeling framework, a powerful configurable extensible program for
fitting structure models to pair distribution functions (PDFs), small angle
scattering (SAS) data, and more. In more detail, DiffPy-CMI is a collection
of Python modules for storage and manipulation of structure data,
calculation of structure-based quantities, such as PDF, SAS, bond valence
sums, atom overlaps, bond lengths, coordinations, and a fitting framework
for combining multiple experimental inputs in a single optimization
problem. A complete list of the included Python modules is available
at the DiffPy-CMI
page <http://diffpy.org/products/diffpycmi/contents.html>.

The software has already been used in a number of publications (see
diffpy.org), allowing fitting of small nanoparticles, molecules, applying
multiple constraints and so on.
Who should download the release?

This is an early version in a series of releases. The code is stable, but
the emphasis of our development to date has been functionality and not
interface -- the code thus require some Python proficiency to use. The
purpose of these codes is to support advanced experimental modeling. If you
are a PDF beginner or if you are not familiar with Python you might be
happier with the PDFgui program (also available from diffpy.org).

The features and flexibility offered by this software come at the expense
of a steeper learning curve. To make it easier to get started with this
software, we have created an open-source project
cmi_exchange<https://github.com/diffpy/cmi_exchange>for tutorials,
example fits and useful Python plugins. To begin with
DiffPy-CMI we recommend you to browse the examples in DiffPy-CMI and try to
run them or adapt them for your needs.
What platforms are supported?

This release is for various flavors of 32 and 64 bit Linux distributions
and for Mac OS X. If you are a Windows user we ask that you please stay
tuned and wait for a future release.
Is the software free?

DiffPy-CMI is open source and free to use. As usual, we ask that you cite
the paper describing the program, which will be submitted shortly (we
didn’t want to hold up the release for that!). Also, we hope that you will
contribute script templates to the
cmi_exchange<https://github.com/diffpy/cmi_exchange>for use by other
members of the community.
How do I get the code?

Please visit www.diffpy.org and follow the instructions.

Please enjoy the code. As usual, post questions, problems, and requests to
the diffpy-users <https://groups.google.com/d/forum/diffpy-users> Google
group and bug-reports to the
diffpy-dev<https://groups.google.com/d/forum/diffpy-dev>group or email
Prof. Billinge directly at
sb2896 at columbia.edu.

DiffPy developers

Pavol Juhas, Kevin Knox, Xiaohao Yang, Michael McKerns, Christopher L.
Farrow, and Simon J. L. Billinge

Full Credits <http://www.diffpy.org/acknowledgements.html>

Best wishes from the Diffpy Team, and  Happy modeling!

The DiffPy project is currently supported by Laboratory Directed Research
and Development (LDRD) Program 12-007 (Complex Modeling) at Brookhaven
National Laboratory (BNL). BNL is funded by the US Department of Energy
Office of Science, Office of Basic Energy Sciences under contract
DE-AC02-98CH10886. Previous funding for DiffPy was provided by the
Distributed Data Analysis of Neutron Scattering Experiments (DANSE) project
funded by the US National Science Foundation under award DMR-0520547.
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