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.

sasmodels.bumps_model.BumpsParameter

alias of Parameter

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

__class__

alias of type

__delattr__(name, /)

Implement delattr(self, name).

__dict__ = mappingproxy({'__module__': 'sasmodels.bumps_model', '__firstlineno__': 127, '__doc__': '\nBumps wrapper for a SAS experiment.\n\n*data* is a :class:`.data.Data1D`, :class:`.data.Data2D` or\n:class:`.data.SesansData` object.  Use :func:`.data.empty_data1D` or\n:func:`.data.empty_data2D` to define $q, \\Delta q$ calculation\npoints for displaying the SANS curve when there is no measured data.\n\n*model* is a :class:`Model` object.\n\n*cutoff* is the integration cutoff, which avoids computing the\nthe SAS model where the polydispersity weight is low.\n\nThe resulting model can be used directly in a Bumps FitProblem call.\n', '_cache': None, '__init__': <function Experiment.__init__>, 'update': <function Experiment.update>, 'numpoints': <function Experiment.numpoints>, 'resolution': <property object>, 'parameters': <function Experiment.parameters>, 'theory': <function Experiment.theory>, 'residuals': <function Experiment.residuals>, 'nllf': <function Experiment.nllf>, 'plot': <function Experiment.plot>, 'simulate_data': <function Experiment.simulate_data>, 'save': <function Experiment.save>, '__getstate__': <function Experiment.__getstate__>, '__setstate__': <function Experiment.__setstate__>, '__static_attributes__': ('__dict__', '_cache', '_resolution', '_resolution_pars', 'cutoff', 'extra_pars', 'model', 'name'), '__annotations__': {'_cache': 'dict[str, np.ndarray]'}})

__dir__()

Default dir() implementation.

__doc__ = '\nBumps wrapper for a SAS experiment.\n\n*data* is a :class:`.data.Data1D`, :class:`.data.Data2D` or\n:class:`.data.SesansData` object.  Use :func:`.data.empty_data1D` or\n:func:`.data.empty_data2D` to define $q, \\Delta q$ calculation\npoints for displaying the SANS curve when there is no measured data.\n\n*model* is a :class:`Model` object.\n\n*cutoff* is the integration cutoff, which avoids computing the\nthe SAS model where the polydispersity weight is low.\n\nThe resulting model can be used directly in a Bumps FitProblem call.\n'

__eq__(value, /)

Return self==value.

__firstlineno__ = 127

__format__(format_spec, /)

Default object formatter.

Return str(self) if format_spec is empty. Raise TypeError otherwise.

__ge__(value, /)

Return self>=value.

__getattribute__(name, /)

Return getattr(self, name).

__getstate__() → dict[str, Any]

Helper for pickle.

__gt__(value, /)

Return self>value.

__hash__()

Return hash(self).

__init__(data: Data1D | Data2D, model: Model, cutoff: float = 1e-05, name: str | None = None, extra_pars: dict[str, Parameter] | None = None) → None

classmethod __init_subclass__()

This method is called when a class is subclassed.

The default implementation does nothing. It may be overridden to extend subclasses.

__le__(value, /)

Return self<=value.

__lt__(value, /)

Return self<value.

__module__ = 'sasmodels.bumps_model'

__ne__(value, /)

Return self!=value.

classmethod __new__(*args, **kwargs)

__reduce__()

Helper for pickle.

__reduce_ex__(protocol, /)

Helper for pickle.

__repr__()

Return repr(self).

__setattr__(name, value, /)

Implement setattr(self, name, value).

__setstate__(state: dict[str, Any]) → None

__sizeof__()

Size of object in memory, in bytes.

__static_attributes__ = ('__dict__', '_cache', '_resolution', '_resolution_pars', 'cutoff', 'extra_pars', 'model', 'name')

__str__()

Return str(self).

classmethod __subclasshook__(object, /)

Abstract classes can override this to customize issubclass().

This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).

__weakref__

list of weak references to the object

_cache: dict[str, ndarray] = None

_calc_theory(pars: ParameterSet, cutoff: float = 0.0) → np.ndarray

_interpret_data(data: Data1D | Data2D | SesansData, model: KernelModel) → None

_set_data(Iq: np.ndarray, noise: float | None = None) → None

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, Parameter]

Return a dictionary of parameters

plot(view: str = None) → None

Plot the data and residuals.

residuals() → ndarray

Return theory minus data normalized by uncertainty.

property resolution: None | Resolution

resolution.Resolution applied to the data, if any.

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.

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

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.

__class__

alias of type

__delattr__(name, /)

Implement delattr(self, name).

__dict__ = mappingproxy({'__module__': 'sasmodels.bumps_model', '__firstlineno__': 88, '__doc__': '\nBumps wrapper for a SAS model.\n\n*model* is a runnable module as returned from :func:`.core.load_model`.\n\n*cutoff* is the polydispersity weight cutoff.\n\nAny additional *key=value* pairs are model dependent parameters.\n', '__init__': <function Model.__init__>, 'parameters': <function Model.parameters>, 'state': <function Model.state>, '__static_attributes__': ('_parameter_names', '_pd_type_names', 'sasmodel'), '__dict__': <attribute '__dict__' of 'Model' objects>, '__weakref__': <attribute '__weakref__' of 'Model' objects>, '__annotations__': {}})

__dir__()

Default dir() implementation.

__doc__ = '\nBumps wrapper for a SAS model.\n\n*model* is a runnable module as returned from :func:`.core.load_model`.\n\n*cutoff* is the polydispersity weight cutoff.\n\nAny additional *key=value* pairs are model dependent parameters.\n'

__eq__(value, /)

Return self==value.

__firstlineno__ = 88

__format__(format_spec, /)

Default object formatter.

Return str(self) if format_spec is empty. Raise TypeError otherwise.

__ge__(value, /)

Return self>=value.

__getattribute__(name, /)

Return getattr(self, name).

__getstate__()

Helper for pickle.

__gt__(value, /)

Return self>value.

__hash__()

Return hash(self).

__init__(model: KernelModel, **kwargs: dict[str, float | Parameter]) → None

classmethod __init_subclass__()

This method is called when a class is subclassed.

The default implementation does nothing. It may be overridden to extend subclasses.

__le__(value, /)

Return self<=value.

__lt__(value, /)

Return self<value.

__module__ = 'sasmodels.bumps_model'

__ne__(value, /)

Return self!=value.

classmethod __new__(*args, **kwargs)

__reduce__()

Helper for pickle.

__reduce_ex__(protocol, /)

Helper for pickle.

__repr__()

Return repr(self).

__setattr__(name, value, /)

Implement setattr(self, name, value).

__sizeof__()

Size of object in memory, in bytes.

__static_attributes__ = ('_parameter_names', '_pd_type_names', 'sasmodel')

__str__()

Return str(self).

classmethod __subclasshook__(object, /)

Abstract classes can override this to customize issubclass().

This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).

__weakref__

list of weak references to the object

parameters() → dict[str, Parameter]

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

state() → dict[str, Parameter | str]

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