source: sasmodels/sasmodels/models/fuzzy_sphere.py @ 0cc31e1

r"""
For information about polarised and magnetic scattering, click here_.

.. _here: polar_mag_help.html

Definition
----------

The scattering intensity *I(q)* is calculated as:

.. math::
    I(q) = \frac{scale}{V}(\Delta \rho)^2 A^2(q) S(q) +\text{background}


where the amplitude *A(q)* is given as the typical sphere scattering convoluted
with a Gaussian to get a gradual drop-off in the scattering length density:

.. math::

    A(q) = \frac{3\left[\sin(qR) - qR \cos(qR)\right]}{(qR)^3}
           \exp\left(\frac{-(\sigma_{fuzzy}q)^2}{2}\right)

Here *|A(q)|*:sup:`2`\  is the form factor, *P(q)*. The scale is equivalent to the
volume fraction of spheres, each of volume, *V*\. Contrast (|drho|) is the
difference of scattering length densities of the sphere and the surrounding
solvent.

Poly-dispersion in radius and in fuzziness is provided for, though the fuzziness
must be kept much smaller than the sphere radius for meaningful results.



From the reference:

  The "fuzziness" of the interface is defined by the parameter
  |sigma| :sub:`fuzzy`\ . The particle radius *R* represents the radius of the
  particle where the scattering length density profile decreased to 1/2 of the
  core density. The |sigma| :sub:`fuzzy`\ is the width of the smeared particle
  surface; i.e., the standard deviation from the average height of the fuzzy
  interface. The inner regions of the microgel that display a higher density
  are described by the radial box profile extending to a radius of
  approximately *Rbox* ~ *R* - 2\ |sigma|\ . The profile approaches zero as
  *Rsans* ~ *R* + 2\ |sigma|\ .

For 2D data: The 2D scattering intensity is calculated in the same way as 1D,
where the *q* vector is defined as

.. math::

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


References
----------

M Stieger, J. S Pedersen, P Lindner, W Richtering, *Langmuir*,
20 (2004) 7283-7292
"""

from numpy import inf

name = "fuzzy_sphere"
title = "Scattering from spherical particles with a fuzzy surface."
description = """\
scale: scale factor times volume fraction,
or just volume fraction for absolute scale data
radius: radius of the solid sphere
fuzziness = the standard deviation of the fuzzy interfacial
thickness (ie., so-called interfacial roughness)
sld: the SLD of the sphere
solvend_sld: the SLD of the solvent
background: incoherent background
Note: By definition, this function works only when fuzziness << radius.
"""
category = "shape:sphere"

# pylint: disable=bad-whitespace,line-too-long
# ["name", "units", default, [lower, upper], "type","description"],
parameters = [["sld",         "1e-6/Ang^2",  1, [-inf, inf], "",       "Particle scattering length density"],
              ["sld_solvent", "1e-6/Ang^2",  3, [-inf, inf], "",       "Solvent scattering length density"],
              ["radius",      "Ang",        60, [0, inf],    "volume", "Sphere radius"],
              ["fuzziness",   "Ang",        10, [0, inf],    "",       "std deviation of Gaussian convolution for interface (must be << radius)"],
             ]
# pylint: enable=bad-whitespace,line-too-long

source = ["lib/sph_j1c.c"]

# No volume normalization despite having a volume parameter
# This should perhaps be volume normalized?
form_volume = """
    return 1.333333333333333*M_PI*radius*radius*radius;
    """

Iq = """
    const double qr = q*radius;
    const double bes = sph_j1c(qr);
    const double qf = q*fuzziness;
    const double fq = bes * (sld - sld_solvent) * form_volume(radius) * exp(-0.5*qf*qf);
    return 1.0e-4*fq*fq;
    """

Iqxy = """
    // never called since no orientation or magnetic parameters.
    //return -1.0;
    return Iq(sqrt(qx*qx + qy*qy), sld, sld_solvent, radius, fuzziness);
    """

def ER(radius):
    """
    Return radius
    """
    return radius

# VR defaults to 1.0

demo = dict(scale=1, background=0.001,
            sld=1, sld_solvent=3,
            radius=60,
            fuzziness=10,
            radius_pd=.2, radius_pd_n=45,
            fuzziness_pd=.2, fuzziness_pd_n=0)

oldname = "FuzzySphereModel"
oldpars = dict(sld='sldSph', sld_solvent='sldSolv', radius='radius', fuzziness='fuzziness')


tests = [
    # Accuracy tests based on content in test/utest_models_new1_3.py
    #[{'background': 0.001}, 1.0, 0.001],

    [{}, 0.00301005, 359.2315],

    ]