Source code for sasmodels.bumps_model

Wrap sasmodels for direct use by bumps.

:class:`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.

:class:`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.

from __future__ import print_function

__all__ = ["Model", "Experiment"]

import numpy as np  # type: ignore

from .data import plot_theory
from .direct_model import DataMixin

# pylint: disable=unused-import
    from typing import Dict, Union, Tuple, Any, Optional
    from .data import Data1D, Data2D
    from .kernel import KernelModel
    from .modelinfo import ModelInfo
    from .resolution import Resolution
    Data = Union[Data1D, Data2D]
except ImportError:
# pylint: enable=unused-import

    # Optional import. This allows the doc builder and nosetests to run even
    # when bumps is not on the path.
    from bumps.names import Parameter # type: ignore
    from bumps.parameter import Reference # type: ignore
except ImportError:

def create_parameters(model_info, **kwargs):
    # type: (ModelInfo, Union[float, str, Parameter]) -> Tuple[Dict[str, Parameter], Dict[str, str]]
    Generate Bumps parameters from the model info.

    *model_info* is returned from :func:`.generate.model_info` on the
    model definition module.

    Any additional *key=value* pairs are initial values for the parameters
    to the models.  Uninitialized parameters will use the model default
    value.  The value can be a float, a bumps parameter, or in the case
    of the distribution type parameter, a string.

    Returns a dictionary of *{name: Parameter}* containing the bumps
    parameters for each model parameter, and a dictionary of
    *{name: str}* containing the polydispersity distribution types.
    pars = {}     # type: Dict[str, Parameter]
    pd_types = {} # type: Dict[str, str]
    for p in model_info.parameters.call_parameters:
        value = kwargs.pop(, p.default)
        pars[] = Parameter.default(value,, limits=p.limits)
        if p.polydisperse:
            for part, default, limits in [
                    ('_pd', 0., pars[].limits),
                    ('_pd_n', 35., (0, 1000)),
                    ('_pd_nsigma', 3., (0, 10)),
                name = + part
                value = kwargs.pop(name, default)
                pars[name] = Parameter.default(value, name=name, limits=limits)
            name = + '_pd_type'
            pd_types[name] = str(kwargs.pop(name, 'gaussian'))

    if kwargs:  # args not corresponding to parameters
        raise TypeError("unexpected parameters: %s"
                        % (", ".join(sorted(kwargs.keys()))))

    return pars, pd_types

[docs]class Model(object): """ Bumps wrapper for a SAS model. *model* is a runnable module as returned from :func:`.core.load_model`. *cutoff* is the polydispersity weight cutoff. Any additional *key=value* pairs are model dependent parameters. """ def __init__(self, model, **kwargs): # type: (KernelModel, **Dict[str, Union[float, Parameter]]) -> None self.sasmodel = model pars, pd_types = create_parameters(, **kwargs) for k, v in pars.items(): setattr(self, k, v) for k, v in pd_types.items(): setattr(self, k, v) self._parameter_names = list(pars.keys()) self._pd_type_names = list(pd_types.keys())
[docs] def parameters(self): # type: () -> Dict[str, Parameter] """ Return a dictionary of parameters objects for the parameters, excluding polydispersity distribution type. """ return dict((k, getattr(self, k)) for k in self._parameter_names)
[docs] def state(self): # type: () -> Dict[str, Union[Parameter, str]] """ Return a dictionary of current values for all the parameters, including polydispersity distribution type. """ pars = dict((k, getattr(self, k).value) for k in self._parameter_names) pars.update((k, getattr(self, k)) for k in self._pd_type_names) return pars
[docs]class Experiment(DataMixin): r""" Bumps wrapper for a SAS experiment. *data* is a :class:`.data.Data1D`, :class:`.data.Data2D` or :class:`.data.SesansData` object. Use :func:`.data.empty_data1D` or :func:`.data.empty_data2D` to define $q, \Delta q$ calculation points for displaying the SANS curve when there is no measured data. *model* is a :class:`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. """ _cache = None # type: Dict[str, np.ndarray] def __init__(self, data, model, cutoff=1e-5, name=None, extra_pars=None): # type: (Data, Model, float, Optional[str], Optional[Dict[str, Parameter]]) -> None # Allow resolution function to define fittable parameters. We do this # by creating reference parameters within the resolution object rather # than modifying the object itself to use bumps parameters. We need # to reset the parameters each time the object has changed. These # additional parameters need to be returned from the fitting engine. # To make them available to the user, they are added as top-level # attributes to the experiment object. The only change to the # resolution function is that it needs an optional 'fittable' attribute # which maps the internal name to the user visible name for the # for the parameter. self._resolution = None self._resolution_pars = {} # remember inputs so we can inspect from outside = data.filename if name is None else name self.model = model self.cutoff = cutoff self._interpret_data(data, model.sasmodel) self._cache = {} # CRUFT: no longer need extra parameters # Multiple scattering probability is now retrieved directly from the # multiple scattering resolution function. self.extra_pars = extra_pars
[docs] def update(self): # type: () -> None """ Call when model parameters have changed and theory needs to be recalculated. """ self._cache.clear()
[docs] def numpoints(self): # type: () -> float """ Return the number of data points """ return len(self.Iq)
@property def resolution(self): # type: () -> Union[None, Resolution] """ :class:`.resolution.Resolution` applied to the data, if any. """ return self._resolution @resolution.setter def resolution(self, value): # type: (Resolution) -> None """ :class:`.resolution.Resolution` applied to the data, if any. """ self._resolution = value # Remove old resolution fitting parameters from experiment for name in self._resolution_pars: delattr(self, name) # Create new resolution fitting parameters res_pars = getattr(self._resolution, 'fittable', {}) self._resolution_pars = { name: Reference(self._resolution, refname, name=name) for refname, name in res_pars.items() } # Add new resolution fitting parameters as experiment attributes for name, ref in self._resolution_pars.items(): setattr(self, name, ref)
[docs] def parameters(self): # type: () -> Dict[str, Parameter] """ Return a dictionary of parameters """ pars = self.model.parameters() if self.extra_pars is not None: pars.update(self.extra_pars) pars.update(self._resolution_pars) return pars
[docs] def theory(self): # type: () -> np.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. """ if 'theory' not in self._cache: pars = self.model.state() self._cache['theory'] = self._calc_theory(pars, cutoff=self.cutoff) return self._cache['theory']
[docs] def residuals(self): # type: () -> np.ndarray """ Return theory minus data normalized by uncertainty. """ #if np.any(self.err ==0): print("zeros in err") return (self.theory() - self.Iq) / self.dIq
[docs] def nllf(self): # type: () -> float """ Return the negative log likelihood of seeing data given the model parameters, up to a normalizing constant which depends on the data uncertainty. """ delta = self.residuals() #if np.any(np.isnan(R)): print("NaN in residuals") return 0.5 * np.sum(delta**2)
#def __call__(self): # return 2 * self.nllf() / self.dof
[docs] def plot(self, view=None): # type: (str) -> None """ Plot the data and residuals. """ data, theory, resid = self._data, self.theory(), self.residuals() # TODO: hack to display oriented usans 2-D pattern Iq_calc = self.Iq_calc if isinstance(self.Iq_calc, tuple) else None plot_theory(data, theory, resid, view, Iq_calc=Iq_calc)
[docs] def simulate_data(self, noise=None): # type: (float) -> None """ Generate simulated data. """ Iq = self.theory() self._set_data(Iq, noise)
[docs] def save(self, basename): # type: (str) -> None """ Save the model parameters and data into a file. Not Implemented except for sesans fits. """ if self.data_type == "sesans": np.savetxt(basename+".dat", np.array([self._data.x, self.theory()]).T)
def __getstate__(self): # type: () -> Dict[str, Any] # Can't pickle gpu functions, so instead make them lazy state = self.__dict__.copy() state['_kernel'] = None return state def __setstate__(self, state): # type: (Dict[str, Any]) -> None # pylint: disable=attribute-defined-outside-init self.__dict__ = state