.. _srmise-documentation-index: #################################################### SrMise Documentation #################################################### Software version |release|. Last updated |today|. Tool for unbiased peak extraction from atomic pair distribution functions. The diffpy.srmise package 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 diffpy.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 diffpy.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 removing (during a pruning step) 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, diffpy.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. =================== Disclaimer =================== .. literalinclude:: ../../../LICENSE.txt .. literalinclude:: ../../../LICENSE_PDFgui.txt ================ Acknowledgments ================ Developers ----------- diffpy.srmise is developed and maintained by .. literalinclude:: ../../../AUTHORS.txt The source code of *pdfdataset.py* was derived from diffpy.pdfgui. ====================================== Installation ====================================== See the `README.rst `_ file included with the distribution. ====================================== Where next? ====================================== .. toctree:: :maxdepth: 2 tutorial/index.rst extending.rst ====================================== API ====================================== Detailed API documentation will be available in a future version of diffpy.srmise. * :ref:`genindex` * :ref:`search`