source: sasmodels/sasmodels/models/sphere.py @ a34b811

r"""
For information about polarised and magnetic scattering, see
the :ref:`magnetism` documentation.

Definition
----------

The 1D scattering intensity is calculated in the following way (Guinier, 1955)

.. math::

    I(q) = \frac{\text{scale}}{V} \cdot \left[
        3V(\Delta\rho) \cdot \frac{\sin(qr) - qr\cos(qr))}{(qr)^3}
        \right]^2 + \text{background}

where *scale* is a volume fraction, $V$ is the volume of the scatterer,
$r$ is the radius of the sphere and *background* is the background level.
*sld* and *sld_solvent* are the scattering length densities (SLDs) of the
scatterer and the solvent respectively, whose difference is $\Delta\rho$.

Note that if your data is in absolute scale, the *scale* should represent
the volume fraction (which is unitless) if you have a good fit. If not,
it should represent the volume fraction times a factor (by which your data
might need to be rescaled).

The 2D scattering intensity is the same as above, regardless of the
orientation of $\vec q$.

Validation
----------

Validation of our code was done by comparing the output of the 1D model
to the output of the software provided by the NIST (Kline, 2006).


References
----------

.. [#] A Guinier and G. Fournet, *Small-Angle Scattering of X-Rays*, John Wiley and Sons, New York, (1955)

Source
------

`sphere.py <https://github.com/SasView/sasmodels/blob/master/sasmodels/models/sphere.py>`_

`sphere.c <https://github.com/SasView/sasmodels/blob/master/sasmodels/models/sphere.c>`_

Authorship and Verification
----------------------------

* **Author:**
* **Last Modified by:**
* **Last Reviewed by:** S King and P Parker **Date:** 2013/09/09 and 2014/01/06
* **Source added by :** Steve King **Date:** March 25, 2019
"""

import numpy as np
from numpy import inf

name = "sphere"
title = "Spheres with uniform scattering length density"
description = """\
P(q)=(scale/V)*[3V(sld-sld_solvent)*(sin(qr)-qr cos(qr))
                /(qr)^3]^2 + background
    r: radius of sphere
    V: The volume of the scatter
    sld: the SLD of the sphere
    sld_solvent: the SLD of the solvent
"""
category = "shape:sphere"

#             ["name", "units", default, [lower, upper], "type","description"],
parameters = [["sld", "1e-6/Ang^2", 1, [-inf, inf], "sld",
               "Layer scattering length density"],
              ["sld_solvent", "1e-6/Ang^2", 6, [-inf, inf], "sld",
               "Solvent scattering length density"],
              ["radius", "Ang", 50, [0, inf], "volume",
               "Sphere radius"],
             ]

source = ["lib/sas_3j1x_x.c", "sphere.c"]
have_Fq = True
radius_effective_modes = ["radius"]

def random():
    """Return a random parameter set for the model."""
    radius = 10**np.random.uniform(1.3, 4)
    pars = dict(
        radius=radius,
    )
    return pars

tests = [
     [{}, 0.2, 0.726362], # each test starts with default parameter values inside { }, unless modified. Then Q and expected value of I(Q)
     [{"scale": 1., "background": 0., "sld": 6., "sld_solvent": 1.,
       "radius": 120., "radius_pd": 0.2, "radius_pd_n":45},
      0.2, 0.2288431],
    [{"radius": 120., "radius_pd": 0.02, "radius_pd_n":45},
      0.2, 792.0646662454202, 1166737.0473152, 120.0, 7246723.820358589, 1.0], # the longer list here checks  F1, F2, R_eff, volume, volume_ratio = call_Fq(kernel, pars)
   #  But note P(Q) = F2/volume
   #  F and F^2 are "unscaled", with for  n <F F*>S(q) or for beta approx I(q) = n [<F F*> + <F><F*> (S(q) - 1)]
   #  for n the number density and <.> the orientation average, and F = integral rho(r) exp(i q . r) dr.
   #  The number density is volume fraction divided by particle volume.
   #  Effectively, this leaves F = V drho form, where form is the usual 3 j1(qr)/(qr) or whatever depending on the shape.
   # [{"@S": "hardsphere"},
   #    0.01, 55.881884232102124], # this is current value, not verified elsewhere yet
   # [{"radius": 120., "radius_pd": 0.2, "radius_pd_n":45},
   #   0.2, 1.233304061, [1850806.119736], 120.0, 8087664.1226, 1.0], # the longer list here checks  F1, F2, R_eff, volume, volume_ratio = call_Fq(kernel, pars)
   # [{"@S": "hardsphere"},
   #     0.2, 0.14730859242492958], #  this is current value, not verified elsewhere yet
    # [{"@S": "hardsphere"},
    #    0.1, 0.7940350343811906], #  this is current value, not verified elsewhere yet
    [{"@S": "hardsphere",
     "radius": 120., "radius_pd": 0.2, "radius_pd_n":45,
     "volfraction":0.2,
     "radius_effective":45.0,     # uses this (check)
     "structure_factor_mode": 1,  # 0 = normal decoupling approximation, 1 = beta(Q) approx
     "radius_effective_mode": 0   # equivalent sphere, there is only one valid mode for sphere. says -this used r_eff =0 or default 50?
     }, 0.01, 1316.2990966463444 ],
    [{"@S": "hardsphere",
     "radius": 120., "radius_pd": 0.2, "radius_pd_n":45,
     "volfraction":0.2,
     "radius_effective":50.0,        # hard sphere structure factor
     "structure_factor_mode": 1,  # 0 = normal decoupling approximation, 1 = beta(Q) approx
     "radius_effective_mode": 0   # this used r_eff =0 or default 50?
     }, 0.01, 1324.7375636587356 ],
    [{"@S": "hardsphere",
     "radius": 120., "radius_pd": 0.2, "radius_pd_n":45,
     "volfraction":0.2,
     "radius_effective":50.0,        # hard sphere structure factor
     "structure_factor_mode": 1,  # 0 = normal decoupling approximation, 1 = beta(Q) approx
     "radius_effective_mode": 1   # this used 120 ???
     }, 0.01, 1579.2858949296565 ]
]
# putting None for expected result will pass the test if there are no errors from the routine, but without any check on the value of the result