resins 0.1.0

Python library for working with resolution functions of neutron instruments

ResINS

Python library for working with resolution functions of inelastic neutron scattering (INS) instruments. This package exists to centralise all things related to resolution of INS instruments and make it easier to work with. It pools related code from existing projects, namely AbINS and PyChop, as well as implementing code published in literature. The main purposes are:

1. Provide one, central place implementing various models for INS instruments (resolution functions)
2. Provide a simple way to obtain the broadening at a given frequency, for a given instrument and settings
3. Provide a way to apply broadening to a spectrum

See the main documentation for more detail.

Note on API stability

While we are on 0.x.y versions, there may be breaking API changes between minor (x) versions, while bugfix (y) versions may contain fixes, trivial enhancements and development/deployment/documentation tweaks. If you are using ResINS it is highly recommended to pin to a specific minor version e.g.

dependencies = [ "resins>=0.1.1,<0.2"]

With version 1.0 the project will move to stable semantic versioning, and downstream projects will be able to pin to the major version.

Quick Start

The resins library can be installed with pip (see Installation). To start, import the main Instrument class and get the instrument object of your choice:

>>> from resins import Instrument
>>> maps = Instrument.from_default('MAPS', 'MAPS')
>>> print(maps)
Instrument(name=MAPS, version=MAPS)

To get the resolution function, call the get_resolution_function method (providing all your choices for the required settings and configurations), which returns a callable that can be called to broaden the data.

>>> # The available models for a given instrument can be queried:
>>> maps.available_models
['PyChop_fit']
>>> # There are multiple ways of querying the model-specific parameters, but the most comprehensive is
>>> maps.get_model_signature('PyChop_fit')
<Signature (model_name: Optional[str] = 'PyChop_fit_v1', *, chopper_package: Literal['A', 'B', 'S'] = 'A', e_init: Annotated[ForwardRef('Optional[float]'), 'restriction=[0, 2000]'] = 500, chopper_frequency: Annotated[ForwardRef('Optional[int]'), 'restriction=[50, 601, 50]'] = 400, fitting_order: 'int' = 4, _) -> resins.models.pychop.PyChopModelFermi>
>>> # Now we can get the resolution function
>>> pychop = maps.get_resolution_function('PyChop_fit', chopper_package='B', e_init=500, chopper_frequency=300)
>>> print(pychop)
PyChopModelFermi(citation=[''])

Calling the model (like a function) broadens the data at the provided combinations of energy transfer and momentum ([w, Q]), using a mesh and the corresponding data:

>>> import numpy as np
>>> energy_transfer = np.array([100, 200, 300])[:, np.newaxis]
>>> data = np.array([0.6, 1.5, 0.9])
>>> mesh = np.linspace(0, 500, 1000)
>>> pychop(energy_transfer, data, mesh)
array([3.43947518e-028, ... 5.99877942e-002, ... 7.31766110e-249])

However, the model also provides methods that go lower;

• get_kernel computes the broadening kernel at each [w, Q] (centered on 0)
• get_peak computes the broadening peak at each [w, Q] (centered on the [w, Q])
• get_characteristics returns only the characteristic parameters of the kernel at each [w, Q] (such as the standard deviation of the normal distribution)

>>> peaks = pychop.get_peak(energy_transfer, mesh)
>>> mesh_centered_on_0 = np.linspace(-100, 100, 1000)
>>> kernels = pychop.get_kernel(energy_transfer, mesh_centered_on_0)
>>> pychop.get_characteristics(energy_transfer)
{'sigma': array([9.15987016, 7.38868127, 5.93104319])}

Installation

This package can be installed using pip, though it is not yet on PyPI, so it has to be installed directly from GitHub:

pip install git+https://github.com/pace-neutrons/resins.git

or from a local copy:

git clone https://github.com/pace-neutrons/resins.git
pip install resins