source: sasmodels/doc/guide/scripting.rst @ 23df833

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Document direct call to Fq in user guide

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Scripting Interface

Need some basic details here of how to load models and data via script, evaluate them at given parameter values and run bumps fits.

The key functions are :func:`sasmodels.core.load_model` for loading the model definition and compiling the kernel and :func:`` for calling sasview to load the data.


Preparing data

Usually you will load data via the sasview loader, with the :func:`` function. For example:

from import load_data
data = load_data("sasmodels/example/093191_201.dat")

You may want to apply a data mask, such a beam stop, and trim high $q$:

from import set_beam_stop
set_beam_stop(data, qmin, qmax)

The :func:`` method simply sets the mask attribute for the data.


The data defines the resolution function and the q values to evaluate, so even if you simulating experiments prior to making measurements, you still need a data object for reference. Use :func:`` or :func:`` to create a container with a given $q$ and $Delta q/q$. For example:

import numpy as np
from import empty_data1D
# 120 points logarithmically spaced from 0.005 to 0.2, with dq/q = 5%
q = np.logspace(np.log10(5e-3), np.log10(2e-1), 120)
data = empty_data1D(q, resolution=0.05)

To use a more realistic model of resolution, or to load data from a file format not understood by SasView, you can use :class:`` or :class:`` directly. The 1D data uses x, y, dx and dy for $x = q$ and $y = I(q)$, and 2D data uses x, y, z, dx, dy, dz for $x, y = qx, qy$ and $z = I(qx, qy)$. [Note: internally, the Data2D object uses SasView conventions, qx_data, qy_data, data, dqx_data, dqy_data, and err_data.]


For USANS data, use 1D data, but set dxl and dxw attributes to indicate slit resolution:

data.dxl = 0.117

See :func:`sasmodels.resolution.slit_resolution` for details.


SESANS data is more complicated; if your SESANS format is not supported by SasView you need to define a number of attributes beyond x, y. For example:

SElength = np.linspace(0, 2400, 61) # [A]
data = np.ones_like(SElength)
err_data = np.ones_like(SElength)*0.03
class Source:
    wavelength = 6 # [A]
    wavelength_unit = "A"
class Sample:
    zacceptance = 0.1 # [A^-1]
    thickness = 0.2 # [cm]
class SESANSData1D:
    #q_zmax = 0.23 # [A^-1]
    lam = 0.2 # [nm]
    x = SElength
    y = data
    dy = err_data
    sample = Sample()
data = SESANSData1D()
x, y = ... # create or load sesans
data = smd.Data

The data module defines various data plotters as well.

Using sasmodels directly

Once you have a computational kernel and a data object, you can evaluate the model for various parameters using :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. For example:

import numpy as np
from import empty_data1D
from sasmodels.core import load_model
from sasmodels.direct_model import DirectModel
# 120 points logarithmically spaced from 0.005 to 0.2, with dq/q = 5%
q = np.logspace(np.log10(5e-3), np.log10(2e-1), 120)
data = empty_data1D(q, resolution=0.05)
kernel = load_model("ellipsoid)
f = DirectModel(data, kernel)
Iq = f(radius_polar=100)

Polydispersity information is set with special parameter names:

  • par_pd for polydispersity width, $Delta p/p$,
  • par_pd_n for the number of points in the distribution,
  • par_pd_type for the distribution type (as a string), and
  • par_pd_nsigmas for the limits of the distribution.

Using sasmodels through the bumps optimizer

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

import sys
from bumps.names import *
from sasmodels.core import load_model
from sasmodels.bumps_model import Model, Experiment
from import load_data, set_beam_stop, set_top
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,
    radius_polar=15, radius_equatorial=800,
    sld=.291, sld_solvent=7.105,
    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,
model.radius_polar.range(15, 1000)
model.radius_equatorial.range(15, 1000)
model.theta_pd.range(0, 360)
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/ --preview

Note that bumps and sasmodels are included as part of the SasView distribution. On windows, bumps can be called from the cmd prompt as follows:

SasViewCom bumps.cli example/ --preview

Calling the computation kernel

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. To accesses the underlying $<F(q)>$ and $<F^2(q)>$, use :func:`sasmodels.direct_model.call_Fq` instead.


The following example should help, example/

from numpy import logspace, sqrt
from matplotlib import pyplot as plt
from sasmodels.core import load_model
from sasmodels.direct_model import call_kernel, call_Fq
model = load_model('cylinder')
q = logspace(-3, -1, 200)
kernel = model.make_kernel([q])
pars = {'radius': 200, 'radius_pd': 0.1, 'scale': 2}
Iq = call_kernel(kernel, pars)
F, Fsq, Reff, V, Vratio = call_Fq(kernel, pars)
plt.loglog(q, Iq, label='2 I(q)')
plt.loglog(q, F**2/V, label='<F(q)>^2/V')
plt.loglog(q, Fsq/V, label='<F^2(q)>/V')
plt.xlabel('q (1/A)')
plt.ylabel('I(q) (1/cm)')
plt.title('Cylinder with radius 200.')

Comparison between $I(q)$, $<F(q)>$ and $<F^2(q)>$ for cylinder model.

This compares $I(q)$ with $<F(q)>$ and $<F^2(q)>$ for a cylinder with radius=200 +/- 20 and scale=2. Note that call_Fq does not include scale and background, nor does it normalize by the average volume. The definition of $F = rho V hat F$ scaled by the contrast and volume, compared to the canonical cylinder $hat F$, with $hat F(0) = 1$. Integrating over polydispersity and orientation, the returned values are $sum_{r,win N(r_o, r_o/10)} sum_theta w F(q,r_o,theta)sintheta$ and $sum_{r,win N(r_o, r_o/10)} sum_theta w F^2(q,r_o,theta)sintheta$.

On windows, this example can be called from the cmd prompt using sasview as as the python interpreter:

SasViewCom example/
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