31. Sasview interface

sasmodels.sasview_model

Sasview model constructor.

Given a module defining an OpenCL kernel such as sasmodels.models.cylinder, create a sasview model class to run that kernel as follows:

from sasmodels.sasview_model import load_custom_model
CylinderModel = load_custom_model('sasmodels/models/cylinder.py')

sasmodels.sasview_model.MODELS: dict[str, Callable[[int], SasviewModel]] = {}

set of defined models (standard and custom)

sasmodels.sasview_model.MODEL_BY_PATH: dict[str, Callable[[int], SasviewModel]] = {}

custom model {path: model} mapping so we can check timestamps

sasmodels.sasview_model.MultiplicationModel(form_factor: SasviewModel, structure_factor: SasviewModel) → SasviewModel

Returns a constructed product model from form_factor and structure_factor.

class sasmodels.sasview_model.MultiplicityInfo(number, control, choices, x_axis_label)

Bases: tuple

__add__(value, /)

Return self+value.

__class__

alias of type

classmethod __class_getitem__()

See PEP 585

__contains__(key, /)

Return key in self.

__delattr__(name, /)

Implement delattr(self, name).

__dir__()

Default dir() implementation.

__doc__ = 'MultiplicityInfo(number, control, choices, x_axis_label)'

__eq__(value, /)

Return self==value.

__format__(format_spec, /)

Default object formatter.

__ge__(value, /)

Return self>=value.

__getattribute__(name, /)

Return getattr(self, name).

__getitem__(key, /)

Return self[key].

__getnewargs__()

Return self as a plain tuple. Used by copy and pickle.

__getstate__()

Helper for pickle.

__gt__(value, /)

Return self>value.

__hash__()

Return hash(self).

__init__()

classmethod __init_subclass__()

This method is called when a class is subclassed.

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

__iter__()

Implement iter(self).

__le__(value, /)

Return self<=value.

__len__()

Return len(self).

__lt__(value, /)

Return self<value.

__match_args__ = ('number', 'control', 'choices', 'x_axis_label')

__module__ = 'sasmodels.sasview_model'

__mul__(value, /)

Return self*value.

__ne__(value, /)

Return self!=value.

static __new__(_cls, number, control, choices, x_axis_label)

Create new instance of MultiplicityInfo(number, control, choices, x_axis_label)

__reduce__()

Helper for pickle.

__reduce_ex__(protocol, /)

Helper for pickle.

__repr__()

Return a nicely formatted representation string

__rmul__(value, /)

Return value*self.

__setattr__(name, value, /)

Implement setattr(self, name, value).

__sizeof__()

Size of object in memory, in bytes.

__slots__ = ()

__str__()

Return str(self).

classmethod __subclasshook__()

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).

_asdict()

Return a new dict which maps field names to their values.

_field_defaults = {}

_fields = ('number', 'control', 'choices', 'x_axis_label')

classmethod _make(iterable)

Make a new MultiplicityInfo object from a sequence or iterable

_replace(**kwds)

Return a new MultiplicityInfo object replacing specified fields with new values

choices

Alias for field number 2

control

Alias for field number 1

count(value, /)

Return number of occurrences of value.

index(value, start=0, stop=9223372036854775807, /)

Return first index of value.

Raises ValueError if the value is not present.

number

Alias for field number 0

x_axis_label

Alias for field number 3

sasmodels.sasview_model.MultiplicityInfoType

alias of MultiplicityInfo

sasmodels.sasview_model.SUPPORT_OLD_STYLE_PLUGINS = True

True if pre-existing plugins, with the old names and parameters, should continue to be supported.

class sasmodels.sasview_model.SasviewModel(multiplicity: int | None = None)

Bases: object

Sasview wrapper for opencl/ctypes model.

__class__

alias of type

__delattr__(name, /)

Implement delattr(self, name).

__dict__ = mappingproxy({'__module__': 'sasmodels.sasview_model', '__doc__': '\n    Sasview wrapper for opencl/ctypes model.\n    ', '_model': None, '_model_info': None, 'id': None, 'name': None, 'description': None, 'category': None, 'orientation_params': None, 'magnetic_params': None, 'fixed': None, 'input_name': 'Q', 'input_unit': 'A^{-1}', 'output_name': 'Intensity', 'output_unit': 'cm^{-1}', 'cutoff': 1e-05, 'non_fittable': (), 'is_structure_factor': False, 'is_form_factor': False, 'is_multiplicity_model': False, 'multiplicity_info': None, 'params': None, 'dispersion': None, 'details': None, 'multiplicity': None, '_persistency_dict': None, '__init__': <function SasviewModel.__init__>, '__get_state__': <function SasviewModel.__get_state__>, '__set_state__': <function SasviewModel.__set_state__>, '__str__': <function SasviewModel.__str__>, 'is_fittable': <function SasviewModel.is_fittable>, 'getProfile': <function SasviewModel.getProfile>, 'setParam': <function SasviewModel.setParam>, 'getParam': <function SasviewModel.getParam>, 'getParamList': <function SasviewModel.getParamList>, 'getDispParamList': <function SasviewModel.getDispParamList>, 'clone': <function SasviewModel.clone>, 'run': <function SasviewModel.run>, 'runXY': <function SasviewModel.runXY>, 'evalDistribution': <function SasviewModel.evalDistribution>, 'calc_composition_models': <function SasviewModel.calc_composition_models>, 'calculate_Iq': <function SasviewModel.calculate_Iq>, '_calculate_Iq': <function SasviewModel._calculate_Iq>, 'calculate_ER': <function SasviewModel.calculate_ER>, 'calculate_VR': <function SasviewModel.calculate_VR>, 'set_dispersion': <function SasviewModel.set_dispersion>, '_dispersion_mesh': <function SasviewModel._dispersion_mesh>, '_get_weights': <function SasviewModel._get_weights>, 'runTests': <classmethod(<function SasviewModel.runTests>)>, '__dict__': <attribute '__dict__' of 'SasviewModel' objects>, '__weakref__': <attribute '__weakref__' of 'SasviewModel' objects>, '__annotations__': {'_model': 'KernelModel', '_model_info': 'ModelInfo', 'id': 'str', 'name': 'str', 'description': 'str', 'category': 'str', 'orientation_params': 'list[str]', 'magnetic_params': 'list[str]', 'fixed': 'list[str]', 'non_fittable': 'Sequence[str]', 'multiplicity_info': 'MultiplicityInfoType', 'params': 'dict[str, float]', 'dispersion': 'dict[str, Any]', 'details': 'dict[str, Sequence[Any]]', 'multiplicity': 'Optional[int]', '_persistency_dict': 'dict[str, tuple[np.ndarray, np.ndarray]]'}})

__dir__()

Default dir() implementation.

__doc__ = '\n    Sasview wrapper for opencl/ctypes model.\n    '

__eq__(value, /)

Return self==value.

__format__(format_spec, /)

Default object formatter.

__ge__(value, /)

Return self>=value.

__get_state__() → dict[str, Any]

__getattribute__(name, /)

Return getattr(self, name).

__getstate__()

Helper for pickle.

__gt__(value, /)

Return self>value.

__hash__()

Return hash(self).

__init__(multiplicity: int | 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.sasview_model'

__ne__(value, /)

Return self!=value.

classmethod __new__(*args, **kwargs)

__reduce__()

Helper for pickle.

__reduce_ex__(protocol, /)

Helper for pickle.

__repr__()

Return repr(self).

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

__setattr__(name, value, /)

Implement setattr(self, name, value).

__sizeof__()

Size of object in memory, in bytes.

__str__() → str

Returns:

string representation

classmethod __subclasshook__()

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

_calculate_Iq(qx, qy=None)

_dispersion_mesh() → list[tuple[ndarray, ndarray]]

Create a mesh grid of dispersion parameters and weights.

Returns [p1,p2,…],w where pj is a vector of values for parameter j and w is a vector containing the products for weights for each parameter set in the vector.

_get_weights(par: Parameter) → tuple[ndarray, ndarray]

Return dispersion weights for parameter

_model: KernelModel = None

_model_info: ModelInfo = None

_persistency_dict: dict[str, tuple[ndarray, ndarray]] = None

memory for polydispersity array if using ArrayDispersion (used by sasview).

calc_composition_models(qx)

returns parts of the composition model or None if not a composition model.

calculate_ER(mode: int = 1) → float

Calculate the effective radius for P(q)*S(q)

mode is the R_eff type, which defaults to 1 to match the ER calculation for sasview models from version 3.x.

Returns:

the value of the effective radius

calculate_Iq(qx: Sequence[float], qy: Sequence[float] | None = None) → tuple[ndarray, Callable[[], OrderedDict[str, ndarray]]]

Calculate Iq for one set of q with the current parameters.

If the model is 1D, use q. If 2D, use qx, qy.

This should NOT be used for fitting since it copies the q vectors to the card for each evaluation.

The returned tuple contains the scattering intensity followed by a callable which returns a dictionary of intermediate data from ProductKernel.

calculate_VR() → float

Calculate the volf ratio for P(q)*S(q)

Returns:

the value of the form:shell volume ratio

category: str = None

default model category

clone() → SasviewModel

Return a identical copy of self

cutoff = 1e-05

default cutoff for polydispersity

description: str = None

short model description

details: dict[str, Sequence[Any]] = None

units and limits for each parameter

dispersion: dict[str, Any] = None

values for dispersion width, npts, nsigmas and type

evalDistribution(qdist: ndarray | tuple[ndarray, ndarray] | list[ndarray]) → ndarray

Evaluate a distribution of q-values.

Parameters:

qdist – array of q or a list of arrays [qx,qy]

• For 1D, a numpy array is expected as input

  evalDistribution(q)

where *q* is a numpy array.

• For 2D, a list of [qx,qy] is expected with 1D arrays as input

qx = [ qx[0], qx[1], qx[2], ....]
qy = [ qy[0], qy[1], qy[2], ....]

If the model is 1D only, then

\[q = \sqrt{q_x^2+q_y^2}\]

fixed: list[str] = None

names of the fittable parameters

getDispParamList() → Sequence[str]

Return a list of polydispersity parameters for the model

getParam(name: str) → float

Set the value of a model parameter

Parameters:

name – name of the parameter

getParamList() → Sequence[str]

Return a list of all available parameters for the model

getProfile() → tuple[ndarray, ndarray]

Get SLD profile

: return: (z, beta) where z is a list of depth of the transition points

beta is a list of the corresponding SLD values

id: str = None

load/save name for the model

input_name = 'Q'

input_unit = 'A^{-1}'

is_fittable(par_name: str) → bool

Check if a given parameter is fittable or not

Parameters:

par_name – the parameter name to check

is_form_factor = False

True if model should appear as a form factor

is_multiplicity_model = False

True if model has multiplicity

is_structure_factor = False

True if model should appear as a structure factor

magnetic_params: list[str] = None

names of the magnetic parameters in the order they appear

multiplicity: int | None = None

multiplicity value, or None if no multiplicity on the model

multiplicity_info: MultiplicityInfo = None

Multiplicity information

name: str = None

display name for the model

non_fittable: Sequence[str] = ()

parameters that are not fitted

orientation_params: list[str] = None

names of the orientation parameters in the order they appear

output_name = 'Intensity'

output_unit = 'cm^{-1}'

params: dict[str, float] = None

parameter {name: value} mapping

run(x: float | tuple[float, float] | Sequence[float] = 0.0) → float

Evaluate the model

Parameters:

x – input q, or [q,phi]

Returns:

scattering function P(q)

DEPRECATED: use calculate_Iq instead

classmethod runTests()

Run any tests built into the model and captures the test output.

Returns success flag and output

runXY(x: float | [<class 'float'>, <class 'float'>] | ~collections.abc.Sequence[float] = 0.0) → float

Evaluate the model in cartesian coordinates

Parameters:

x – input q, or [qx, qy]

Returns:

scattering function P(q)

DEPRECATED: use calculate_Iq instead

setParam(name: str, value: float) → None

Set the value of a model parameter

Parameters:

• name – name of the parameter

• value – value of the parameter

set_dispersion(parameter: str, dispersion: Dispersion) → None

Set the dispersion object for a model parameter

Parameters:

• parameter – name of the parameter [string]

• dispersion – dispersion object of type Dispersion

sasmodels.sasview_model._CACHED_MODULE: dict[str, ModuleType] = {}

Track modules that we have loaded so we can determine whether the model has changed since we last reloaded.

sasmodels.sasview_model._generate_model_attributes(model_info: ModelInfo) → dict[str, Any]

Generate the class attributes for the model.

This should include all the information necessary to query the model details so that you do not need to instantiate a model to query it.

All the attributes should be immutable to avoid accidents.

sasmodels.sasview_model._make_standard_model(name: str) → Callable[[int], SasviewModel]

Load the sasview model defined by name.

name can be a standard model name or a path to a custom model.

Returns a class that can be used directly as a sasview model.

sasmodels.sasview_model._register_old_models() → None

Place the new models into sasview under the old names.

Monkey patch sas.sascalc.fit as sas.models so that sas.models.pluginmodel is available to the plugin modules.

sasmodels.sasview_model.find_model(modelname: str) → Callable[[int], SasviewModel]

Find a model by name. If the model name ends in py, try loading it from custom models, otherwise look for it in the list of builtin models.

sasmodels.sasview_model.load_custom_model(path: str) → Callable[[int], SasviewModel]

Load a custom model given the model path.

sasmodels.sasview_model.load_standard_models() → list[Callable[[int], SasviewModel]]

Load and return the list of predefined models.

If there is an error loading a model, then a traceback is logged and the model is not returned.

sasmodels.sasview_model.magnetic_demo()

Demostrate call to magnetic model.

sasmodels.sasview_model.make_model_from_info(model_info: ModelInfo) → Callable[[int], SasviewModel]

Convert model_info into a SasView model wrapper.

sasmodels.sasview_model.reset_environment() → None

Clear the compute engine context so that the GUI can change devices.

This removes all compiled kernels, even those that are active on fit pages, but they will be restored the next time they are needed.

sasmodels.sasview_model.test_cylinder() → float

Test that the cylinder model runs, returning the value at [0.1,0.1].

sasmodels.sasview_model.test_empty_distribution() → None

Make sure that sasmodels returns NaN when there are no polydispersity points

sasmodels.sasview_model.test_model_list() → None

Make sure that all models build as sasview models

sasmodels.sasview_model.test_old_name() → None

Load and run cylinder model as sas.models.CylinderModel

sasmodels.sasview_model.test_product() → float

Test that 2-D hardsphere model runs and doesn’t produce NaN.

sasmodels.sasview_model.test_rpa() → float

Test that the 2-D RPA model runs

sasmodels.sasview_model.test_structure_factor() → float

Test that 2-D hardsphere model runs and doesn’t produce NaN.

sasmodels.sasview_model.test_structure_factor_background() → None

Check that sasview model and direct model match, with background=0.