Using sasmodels through bumps

With the data and the model, you can wrap it in a bumps model with class:sasmodels.bumps_model.Model and create an class:sasmodels.bump_model.Experiment that you can fit with the bumps interface. Here is an example from the example directory such as example/model.py:

import sys
from bumps.names import *
from sasmodels.core import load_model
from sasmodels.bumps_model import Model, Experiment
from sasmodels.data import load_data, set_beam_stop, set_top
""" IMPORT THE DATA USED """
radial_data = load_data('DEC07267.DAT')
set_beam_stop(radial_data, 0.00669, outer=0.025)
set_top(radial_data, -.0185)
kernel = load_model("ellipsoid")
model = Model(kernel,
    scale=0.08,
    radius_polar=15, radius_equatorial=800,
    sld=.291, sld_solvent=7.105,
    background=0,
    theta=90, phi=0,
    theta_pd=15, theta_pd_n=40, theta_pd_nsigma=3,
    radius_polar_pd=0.222296, radius_polar_pd_n=1, radius_polar_pd_nsigma=0,
    radius_equatorial_pd=.000128, radius_equatorial_pd_n=1, radius_equatorial_pd_nsigma=0,
    phi_pd=0, phi_pd_n=20, phi_pd_nsigma=3,
    )
# SET THE FITTING PARAMETERS
model.radius_polar.range(15, 1000)
model.radius_equatorial.range(15, 1000)
model.theta_pd.range(0, 360)
model.background.range(0,1000)
model.scale.range(0, 10)
#cutoff = 0     # no cutoff on polydisperisity loops
#cutoff = 1e-5  # default cutoff
cutoff = 1e-3  # low precision cutoff
M = Experiment(data=radial_data, model=model, cutoff=cutoff)
problem = FitProblem(M)

Assume that bumps has been installed and the bumps command is available. Maybe need to set the path to sasmodels/sasview using PYTHONPATH=path/to/sasmodels:path/to/sasview/src. To run the model use the bumps program:

$ bumps example/model.py --preview

Using sasmodels directly

Bumps has a notion of parameter boxes in which you can set and retrieve values. Instead of using bumps, you can create a directly callable function with :class:`sasmodels.direct_model.DirectModel`. The resulting object f will be callable as f(par=value, ...), returning the $I(q)$ for the $q$ values in the data. Polydisperse parameters use the same naming conventions as in the bumps model, with e.g., radius_pd being the polydispersity associated with radius.

Getting a simple function that you can call on a set of q values and return a result is not so simple. Since the time critical use case (fitting) involves calling the function over and over with identical $q$ values, we chose to optimize the call by only transfering the $q$ values to the GPU once at the start of the fit. We do this by creating a :class:`sasmodels.kernel.Kernel` object from the :class:`sasmodels.kernel.KernelModel` returned from :func:`sasmodels.core.load_model` using the :meth:`sasmodels.kernel.KernelModel.make_kernel` method. What it actual does depends on whether it is running as a DLL, as OpenCL or as a pure python kernel. Once the kernel is in hand, we can then marshal a set of parameters into a :class:`sasmodels.details.CallDetails` object and ship it to the kernel using the :func:`sansmodels.direct_model.call_kernel` function. An example should help, example/cylinder_eval.py:

from numpy import logspace
from matplotlib import pyplot as plt
from sasmodels.core import load_model
from sasmodels.direct_model import call_kernel
model = load_model('cylinder')
q = logspace(-3, -1, 200)
kernel = model.make_kernel([q])
Iq = call_kernel(kernel, dict(radius=200.))
plt.loglog(q, Iq)
plt.show()