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Python package for analysis of neutron backscattering data

Project description

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nPDyn

nPDyn is a Python based API for analysis of neutron backscattering data.

The API aims at providing a lightweight, user-friendly and modular tool to process and analyze quasi-elastic neutron scattering (QENS) and fixed-window scans (FWS) obtained with backscattering spectroscopy.

nPDyn can be used in combination with other software for neutron data analysis such as Mantid. The API provides an interface to Mantid workspaces for that.

An important feature of nPDyn is the modelling interface, which is designed to be highly versatile and intuitive for multidimensional dataset with global and non-global parameters. The modelling in nPDyn is provided by builtin classes, params.Parameters, model.Model and model.Component. nPDyn provides also some helper functions to use lmfit as modelling backend. See Fit data section in documentation for details.

Eventually, some plotting methods are available to examine processed data, model fitting and optimized parameters.

Installation:

Prior to building on Windows, the path to Gnu Scientific Library (GSL) should be given in setup.cfg file (required by libabsco)

If not, the package can still be installed but paalman-ping corrections won’t work.

Unix and Windows

For installation within your python framework, use:

make install

or

python3 setup.py install

Full documentation

See https://npdyn.readthedocs.io/en/latest/

Support

A google group is available for any question, discussion on features or comment.

In case of bugs or obvious change to be done in the code use GitHub Issues.

Contributions

See contributing.

Getting started

The nPDyn API is organized around a dataset.Dataset class. This class has a Dataset.dataList attribute used to store the experimental data. Each measurement in Dataset.dataList consists in a class that inherits from baseType.BaseType.

In a neutron backscattering experiment, there is not only the measurement of samples but also some calibration measurements like vanadium, empty cell and solvent signal (often D2O). The dataset.Dataset can handle these in the special attributes Dataset.resData, Dataset.ECData and Dataset.D2OData, respectively. Each data in Dataset.dataList can have some calibration data associated with it in the BaseType.resData, BaseType.ECData and BaseType.D2OData attributes.

In the current state of nPDyn, only one file can be loaded for empty cell and solvent calibration measurements. For the resolution function, the Dataset.resData attribute is actually a list that can contain several measurements. The reason for this is that the resolution function can be obtained by measuring the samples at very low temperature instead of using a single vanadium measurement. Hence, each data in Dataset.dataList can be associated with a resolution measurement in Dataset.resData.

The aforementioned structure of the API is sketched below for two samples, measured at temperatures t1 and t2 each, with a measurement for the resolution function at 10K for each sample, one measurement of empty cell and one of D2O background:

docs/fig/sketch_structure_01.png

Details regarding importation of data are available in the documentation section of the documentation.

The baseType.BaseType base class and its derivatives qensType.QENSType and fwsType.FWSType contain several methods for data processing (see Process data in documentation) and fitting (see Fit data section in documentation). In addition the class dataset.Dataset contains some shortcut methods to apply data processing and fitting algorithm quickly on the sample and calibration data. It also contains plotting methods to examine data and the fitted model and its optimized parameters.

Importantly, nPDyn provides versatile tools for model building and fitting to the data. See the section Fit data in documentation for details.

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