source: sasmodels/doc/guide/scripting.rst @ 2015f02

.. _Scripting_Interface:

*******************
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:`sasmodels.data.load_data` for calling sasview to load the data.

Preparing data
==============

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

    from sasmodels.data 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 sasmodels.data import set_beam_stop
    set_beam_stop(data, qmin, qmax)

The :func:`sasmodels.data.set_beam_stop` 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:`sasmodels.data.empty_data1D`
or :func:`sasmodels.data.empty_data2D` to create a container with a
given $q$ and $\Delta q/q$.  For example::

    import numpy as np
    from sasmodels.data 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:`sasmodels.data.Data1D`
or :class:`sasmodels.data.Data2D` 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 sasmodels.data 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/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

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/model.py --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.  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()

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

    SasViewCom example/cylinder_eval.py