2. Bumps interface

sasmodels.bumps_model

Wrap sasmodels for direct use by bumps.

Model is a wrapper for the sasmodels kernel which defines a bumps Parameter box for each kernel parameter. Model accepts keyword arguments to set the initial value for each parameter.

Experiment combines the Model function with a data file loaded by the sasview data loader. Experiment takes a cutoff parameter controlling how far the polydispersity integral extends.

class sasmodels.bumps_model.BumpsParameter

Bases: object

See parameter.Parameter in the bumps documentation.

class sasmodels.bumps_model.Experiment(data: Data1D | Data2D, model: Model, cutoff: float = 1e-05, name: str | None = None, extra_pars: dict[str, BumpsParameter] | None = None)

Bases: DataMixin

Bumps wrapper for a SAS experiment.

data is a data.Data1D, data.Data2D or data.SesansData object. Use data.empty_data1D() or data.empty_data2D() to define \(q, \Delta q\) calculation points for displaying the SANS curve when there is no measured data.

model is a Model object.

cutoff is the integration cutoff, which avoids computing the the SAS model where the polydispersity weight is low.

The resulting model can be used directly in a Bumps FitProblem call.

nllf() → float

Return the negative log likelihood of seeing data given the model parameters, up to a normalizing constant which depends on the data uncertainty.

numpoints() → float

Return the number of data points

parameters() → dict[str, BumpsParameter]

Return a dictionary of parameters

plot(view: str = None) → None

Plot the data and residuals.

residuals() → ndarray

Return theory minus data normalized by uncertainty.

save(basename: str) → None

Save the model parameters and data into a file.

Not Implemented except for sesans fits.

simulate_data(noise: float = None) → None

Generate simulated data. noise is the fractional uncertainty in percent.

theory() → ndarray

Return the theory corresponding to the model parameters.

This method uses lazy evaluation, and requires model.update() to be called when the parameters have changed.

update() → None

Call when model parameters have changed and theory needs to be recalculated.

property resolution: None | Resolution

resolution.Resolution applied to the data, if any.

class sasmodels.bumps_model.Model(model: KernelModel, **kwargs: dict[str, float | BumpsParameter])

Bases: object

Bumps wrapper for a SAS model.

model is a runnable module as returned from core.load_model().

cutoff is the polydispersity weight cutoff.

Any additional key=value pairs are model dependent parameters.

parameters() → dict[str, BumpsParameter]

Return a dictionary of parameters objects for the parameters, excluding polydispersity distribution type.

state() → dict[str, BumpsParameter | str]

Return a dictionary of current values for all the parameters, including polydispersity distribution type.