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SrMise

DiffPy project tool for unbiased peak extraction from atomic pair distribution functions.

SrMise is an implementation of the ParSCAPE algorithm for peak extraction from atomic pair distribution functions (PDFs). It is designed to function even when a priori knowledge of the physical sample is limited, utilizing the Akaike Information Criterion (AIC) to estimate whether peaks are statistically justified relative to alternate models. Three basic use cases are anticipated for SrMise. The first is peak fitting a user-supplied collections of peaks. The second is peak extraction from a PDF with no (or only partial) user-supplied peaks. The third is an AIC-driven multimodeling analysis where the output of multiple SrMise trials are ranked.

The framework for peak extraction defines peak-like clusters within the data, extracts a single peak within each cluster, and iteratively combines nearby clusters while performing a recursive search on the residual to identify occluded peaks. Eventually this results in a single global cluster containing many peaks fit over all the data. Over- and underfitting are discouraged by use of the AIC when adding or, during a pruning step, removing peaks. Termination effects, which can lead to physically spurious peaks in the PDF, are incorporated in the mathematical peak model and the pruning step attempts to remove peaks which are fit better as termination ripples due to another peak.

Where possible, SrMise provides physically reasonable default values for extraction parameters. However, the PDF baseline should be estimated by the user before extraction, or by performing provisional peak extraction with varying baseline parameters. The package defines a linear (crystalline) baseline, arbitrary polynomial baseline, a spherical nanoparticle baseline, and an arbitrary baseline interpolated from a list of user-supplied values. In addition, PDFs with accurate experimentally-determined uncertainties are necessary to provide the most reliable results, but historically such PDFs are rare. In the absence of accurate uncertainties an ad hoc uncertainty must be specified.

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

Getting Started

The diffpy.srmise package requires Python 2.6 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

See the SrMise license for terms and conditions of use. Detailed installation instructions for the Windows, Mac OS X, and Linux platforms follow.

Windows

Several prebuilt Python distributions for Windows include all the prerequisite software required to run SrMise, and installing one of these is the simplest way to get started. These distributions are usually free for individual and/or academic use, but some also have commercial version. Links to executables, installation instructions, and licensing information for some popular options are listed below.

Alternately, individual Windows executables for Python and the required components can be downloaded and installed. The official Windows releases of Numpy and SciPy do not currently support 64-bit Python installations, so be sure to download the 32-bit versions of these packages.

After installing Python and the required packages, the simplest way to obtain SrMise is using pip to download and install the latest release from the Python Package Index (PyPI). Open a command window by running cmd from the Start Menu's application search box (Windows 7/8/10) or Run command (Windows Vista and earlier). Verify that the pip program is installed by running

pip --version

If this command is not found, download and run get-pip.py, which will install both it and setuptools. For example, if the file were downloaded to the desktop, a Windows user named MyName should run the following from the command line:

cd C:\Users\MyName\Desktop
python get-pip.py

Finally, install the latest version of SrMise by running

pip install diffpy.srmise

Mac OS X

For Mac OS X systems with the MacPorts package manager, the required software can be installed with

sudo port install \
   python27 py27-setuptools py27-numpy py27-scipy py27-matplotlib

When installing for MacPorts, make sure the MacPorts bin directory is the first in the system PATH and that python27 is selected as the default Python version in MacPorts:

sudo port select --set python python27

The simplest way to obtain diffpy.srmise on Mac OS X systems is using pip to download and install the latest release from PyPI.

sudo pip install diffpy.srmise

Those who prefer to install from sources may download them from the GitHub or PyPI pages for SrMise. Uncompress them to a directory, and from that directory run

sudo python setup.py install

This installs diffpy.srmise for all users in the default system location. If administrator (root) access is not available, see the usage info from python setup.py install --help for options to install to user-writable directories.

Linux

On Ubuntu and Debian Linux, the required software can easily be installed using the system package manager:

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

Similarly, on Fedora:

sudo yum install python-setuptools numpy scipy python-matplotlib

For other Linux distributions consult the appropriate package manager.

The simplest way to obtain diffpy.srmise on Linux systems is using pip to download and install the latest release from the PyPI.

sudo pip install diffpy.srmise

Those who prefer to install from sources may download them from the GitHub or PyPI pages for SrMise. Uncompress them to a directory, and from that directory run

sudo python setup.py install

This installs diffpy.srmise for all users in the default system location. If administrator (root) access is not available, see the usage info from python setup.py install --help for options to install to user-writable directories.

DEVELOPMENT

diffpy.srmise is open-source software developed with support of the Center of Research Excellence in Complex Materials at Michigan State University, in cooperation with the DiffPy-CMI complex modeling initiative at the Brookhaven National Laboratory. The diffpy.srmise sources are hosted at https://github.com/diffpy/diffpy.srmise.

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

python setup.py develop --user

ACKNOWLEDGEMENT

The source code of pdfdataset.py was derived from diffpy.pdfgui.

CONTACTS

For more information on SrMise please visit the DiffPy project web-page

http://www.diffpy.org/

or email Prof. Simon Billinge at sb2896@columbia.edu.