resins.models.mixins

Mixins providing generic implementations for InstrumentModel methods.

The classes defined here are mixins to be used by specific models via multiple inheritance, allowing common code to be shared between models. Please note, however, that when doing this, the mixin must be the first base class (i.e. class Foo(Mixin, InstrumentModel)) so that its implementation of a method overrides the abstract declaration in InstrumentModel.

class resins.models.mixins.GaussianKernel1DMixin

Bases: object

A mixin providing the implementation for the Gaussian kernel get_kernel method.

Implements resins.models.model_base.InstrumentModel.get_kernel method of models whose broadening can be represented by a 1D Gaussian distribution. Any model that satisfies this condition should inherit the get_kernel method from this mixin instead of writing its own implementation.

Technically, any model that implements the resins.models.model_base.InstrumentModel.get_characteristics method and which returns the sigma parameter in its dictionary can use this mixin to inherit the Gaussian get_kernel method. However, it is recommended that only models that actually model a Gaussian kernel should use this mixin.

Methods

`get_kernel`(points, mesh)	Computes the Gaussian kernel centered on zero on the provided `mesh` at each (energy transfer or momentum scalar) point in `points`.
`get_peak`(points, mesh)	Computes the Gaussian broadening peak on the provided `mesh` at each (energy transfer or momentum scalar) point in `points`.

get_kernel(points: Float[np.ndarray, 'sample dimension=1'], mesh: Float[np.ndarray, 'mesh']) → Float[np.ndarray, 'sample mesh']

Computes the Gaussian kernel centered on zero on the provided mesh at each (energy transfer or momentum scalar) point in points.

Parameters:

points: The energy transfer or momentum scalar for which to compute the kernel. This must be a Nx1 2D array where N is the number of w/Q values.
mesh: The mesh on which to evaluate the kernel. A 1D array which must encompass 0.

Returns:

kernels: The Gaussian kernel at w/Q point as given by this model, computed on the mesh and centered on zero. This is a 2D N x M array where N is the number of w/Q values and M is the length of the mesh array.

get_peak(points: Float[np.ndarray, 'sample dimension=1'], mesh: Float[np.ndarray, 'mesh']) → Float[np.ndarray, 'sample mesh']

Computes the Gaussian broadening peak on the provided mesh at each (energy transfer or momentum scalar) point in points.

Parameters:

points: The energy transfer or momentum scalar for which to compute the peak. This must be a Nx1 2D array where N is the number of w/Q values.
mesh: The mesh on which to evaluate the kernel. This is a 1D array which must span the points wQ space of interest.

Returns:

peaks: The Gaussian peak at each w/Q point in points as given by this model, computed on the mesh and centered on the corresponding w/Q. This is a 2D N x M array where N is the number of w/Q values and M is the length of the mesh array.

class resins.models.mixins.SimpleBroaden1DMixin

Bases: object

A mixin providing the most simple implementation for the broaden method.

Implements resins.models.model_base.InstrumentModel.convolve method in the most simple and basic way - the dot product between the matrix of kernels (obtained from the get_kernel method) and the intensities.

This implementation should be mostly used as a reference method given that it is correct but inefficient. It should be able to work with any model, so it may be used when other implementations are unavailable.

Methods

broaden(points, data, mesh)

Broadens the data on the full mesh using the straightforward scheme.

broaden(points: Float[np.ndarray, 'sample dimension=1'], data: Float[np.ndarray, 'data'], mesh: Float[np.ndarray, 'mesh']) → Float[np.ndarray, 'spectrum']

Broadens the data on the full mesh using the straightforward scheme.

Parameters:

points: The independent variable (energy transfer or momentum scalar) whose data to broaden. This must be a sample x 1 2D array where sample is the number of w/Q values for which there is data. Therefore, the sample dimension must match the length of the data array.
data: The intensities at the w/Q points.
mesh: The mesh to use for the broadening. This is a 1D array which must span the entire points space of interest.

Returns:

spectrum: The broadened spectrum. This is a 1D array of the same length as mesh.