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36 | 36 | .. |Tracking| image:: https://img.shields.io/badge/issue_tracking-github-blue |
37 | 37 | :target: https://github.com/diffpy/diffpy.srmise/issues |
38 | 38 |
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39 | | -Peak extraction and peak fitting tool for atomic pair distribution functions. |
40 | | - |
41 | | -* LONGER DESCRIPTION HERE |
| 39 | +Implementation of the ParSCAPE algorithm for peak extraction from atomic pair distribution functions (PDFs) |
| 40 | + |
| 41 | +SrMise is an implementation of the `ParSCAPE algorithm |
| 42 | +<https://dx.doi.org/10.1107/S2053273315005276>`_ for peak extraction from |
| 43 | +atomic pair distribution functions (PDFs). It is designed to function even |
| 44 | +when *a priori* knowledge of the physical sample is limited, utilizing the |
| 45 | +Akaike Information Criterion (AIC) to estimate whether peaks are |
| 46 | +statistically justified relative to alternate models. Three basic use cases |
| 47 | +are anticipated for SrMise. The first is peak fitting a user-supplied |
| 48 | +collections of peaks. The second is peak extraction from a PDF with no (or |
| 49 | +only partial) user-supplied peaks. The third is an AIC-driven multimodeling |
| 50 | +analysis where the output of multiple SrMise trials are ranked. |
| 51 | + |
| 52 | +The framework for peak extraction defines peak-like clusters within the data, |
| 53 | +extracts a single peak within each cluster, and iteratively combines nearby |
| 54 | +clusters while performing a recursive search on the residual to identify |
| 55 | +occluded peaks. Eventually this results in a single global cluster |
| 56 | +containing many peaks fit over all the data. Over- and underfitting are |
| 57 | +discouraged by use of the AIC when adding or, during a pruning step, removing |
| 58 | +peaks. Termination effects, which can lead to physically spurious peaks in |
| 59 | +the PDF, are incorporated in the mathematical peak model and the pruning step |
| 60 | +attempts to remove peaks which are fit better as termination ripples due to |
| 61 | +another peak. |
| 62 | + |
| 63 | +Where possible, SrMise provides physically reasonable default values |
| 64 | +for extraction parameters. However, the PDF baseline should be estimated by |
| 65 | +the user before extraction, or by performing provisional peak extraction with |
| 66 | +varying baseline parameters. The package defines a linear (crystalline) |
| 67 | +baseline, arbitrary polynomial baseline, a spherical nanoparticle baseline, |
| 68 | +and an arbitrary baseline interpolated from a list of user-supplied values. |
| 69 | +In addition, PDFs with accurate experimentally-determined uncertainties are |
| 70 | +necessary to provide the most reliable results, but historically such PDFs |
| 71 | +are rare. In the absence of accurate uncertainties an *ad hoc* uncertainty |
| 72 | +must be specified. |
42 | 73 |
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43 | 74 | For more information about the diffpy.srmise library, please consult our `online documentation <https://diffpy.github.io/diffpy.srmise>`_. |
44 | 75 |
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45 | 76 | Citation |
46 | 77 | -------- |
47 | 78 |
|
48 | | -If you use diffpy.srmise in a scientific publication, we would like you to cite this package as |
| 79 | +If you use this program for a scientific research that leads |
| 80 | +to publication, we ask that you acknowledge use of the program |
| 81 | +by citing the following paper in your publication: |
49 | 82 |
|
50 | | - diffpy.srmise Package, https://github.com/diffpy/diffpy.srmise |
| 83 | + L. Granlund, S. J. L. Billinge and P. M. Duxbury, |
| 84 | + `Algorithm for systematic peak extraction from atomic pair distribution functions |
| 85 | + <http://dx.doi.org/10.1107/S2053273315005276>`__, |
| 86 | + *Acta Crystallogr. A* **4**, 392-409 (2015). |
51 | 87 |
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52 | 88 | Installation |
53 | 89 | ------------ |
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