Hardsphere - hardsphere.py
# Note: model title and parameter table are inserted automatically
r"""
Calculates the interparticle structure factor for monodisperse
spherical particles interacting through hard sphere (excluded volume)
interactions. This $S(q)$ may also be a reasonable approximation for
other particle shapes that freely rotate (but see the note below),
and for moderately polydisperse systems.
.. note::
This routine is intended for uncharged particles! For charged
particles try using the :ref:`hayter-msa` $S(q)$ instead.
.. note::
Earlier versions of SasView did not incorporate the so-called
$\beta(q)$ ("beta") correction [1] for polydispersity and non-sphericity.
This is only available in SasView versions 5.0 and higher.
radius_effective is the effective hard sphere radius.
volfraction is the volume fraction occupied by the spheres.
In SasView the effective radius may be calculated from the parameters
used in the form factor $P(q)$ that this $S(q)$ is combined with.
For numerical stability the computation uses a Taylor series expansion
at very small $qR$, but there may be a very minor glitch at the
transition point in some circumstances.
This S(q) uses the Percus-Yevick closure relationship [2] where the
interparticle potential $U(r)$ is
.. math::
U(r) = \begin{cases}
\infty & r < 2R \\
0 & r \geq 2R
\end{cases}
where $r$ is the distance from the center of a sphere of a radius $R$.
For a 2D plot, the wave transfer is defined as
.. math::
q = \sqrt{q_x^2 + q_y^2}
References
----------
.. [#] M Kotlarchyk & S-H Chen, *J. Chem. Phys.*, 79 (1983) 2461-2469
.. [#] J K Percus, J Yevick, *J. Phys. Rev.*, 110, (1958) 1
Authorship and Verification
----------------------------
* **Author:**
* **Last Modified by:**
* **Last Reviewed by:**
"""
import numpy as np
from numpy import inf
name = "hardsphere"
title = "Hard sphere structure factor, with Percus-Yevick closure"
description = """\
[Hard sphere structure factor, with Percus-Yevick closure]
Interparticle S(Q) for random, non-interacting spheres.
May be a reasonable approximation for other particle shapes
that freely rotate, and for moderately polydisperse systems
. The "beta(q)" correction is available in versions 4.2.2
and higher.
radius_effective is the hard sphere radius
volfraction is the volume fraction occupied by the spheres.
"""
category = "structure-factor"
structure_factor = True
single = False # TODO: check
# ["name", "units", default, [lower, upper], "type","description"],
parameters = [["radius_effective", "Ang", 50.0, [0, inf], "",
"effective radius of hard sphere"],
["volfraction", "", 0.2, [0, 0.74], "",
"volume fraction of hard spheres"],
]
Iq = r"""
double D,A,B,G,X,X2,X4,S,C,FF,HARDSPH;
// these are c compiler instructions, can also put normal code inside the "if else" structure
#if FLOAT_SIZE > 4
// double precision
// orig had 0.2, don't call the variable cutoff as PAK already has one called that!
// Must use UPPERCASE name please.
// 0.05 better, 0.1 OK
#define CUTOFFHS 0.05
#else
// 0.1 bad, 0.2 OK, 0.3 good, 0.4 better, 0.8 no good
#define CUTOFFHS 0.4
#endif
if(fabs(radius_effective) < 1.E-12) {
HARDSPH=1.0;
//printf("HS1 %g: %g\n",q,HARDSPH);
return(HARDSPH);
}
// removing use of pow(xxx,2) and rearranging the calcs
// of A, B & G cut ~40% off execution time ( 0.5 to 0.3 msec)
X = 1.0/( 1.0 -volfraction);
D= X*X;
A= (1.+2.*volfraction)*D;
A *=A;
X=fabs(q*radius_effective*2.0);
if(X < 5.E-06) {
HARDSPH=1./A;
//printf("HS2 %g: %g\n",q,HARDSPH);
return(HARDSPH);
}
X2 =X*X;
B = (1.0 +0.5*volfraction)*D;
B *= B;
B *= -6.*volfraction;
G=0.5*volfraction*A;
if(X < CUTOFFHS) {
// RKH Feb 2016, use Taylor series expansion for small X
// else no obvious way to rearrange the equations to avoid
// needing a very high number of significant figures.
// Series expansion found using Mathematica software. Numerical test
// in .xls showed terms to X^2 are sufficient
// for 5 or 6 significant figures, but I put the X^4 one in anyway
//FF = 8*A +6*B + 4*G - (0.8*A +2.0*B/3.0 +0.5*G)*X2 +(A/35. +B/40. +G/50.)*X4;
// refactoring the polynomial makes it very slightly faster (0.5 not 0.6 msec)
//FF = 8*A +6*B + 4*G + ( -0.8*A -2.0*B/3.0 -0.5*G +(A/35. +B/40. +G/50.)*X2)*X2;
FF = 8.0*A +6.0*B + 4.0*G + ( -0.8*A -B/1.5 -0.5*G +(A/35. +0.0125*B +0.02*G)*X2)*X2;
// combining the terms makes things worse at smallest Q in single precision
//FF = (8-0.8*X2)*A +(3.0-X2/3.)*2*B + (4+0.5*X2)*G +(A/35. +B/40. +G/50.)*X4;
// note that G = -volfraction*A/2, combining this makes no further difference at smallest Q
//FF = (8 +2.*volfraction + ( volfraction/4. -0.8 +(volfraction/100. -1./35.)*X2 )*X2 )*A + (3.0 -X2/3. +X4/40.)*2.*B;
HARDSPH= 1./(1. + volfraction*FF );
//printf("HS3 %g: %g\n",q,HARDSPH);
return(HARDSPH);
}
X4=X2*X2;
SINCOS(X,S,C);
// RKH Feb 2016, use version FISH code as is better than original sasview one
// at small Q in single precision, and more than twice as fast in double.
//FF=A*(S-X*C)/X + B*(2.*X*S -(X2-2.)*C -2.)/X2 + G*( (4.*X2*X -24.*X)*S -(X4 -12.*X2 +24.)*C +24. )/X4;
// refactoring the polynomial here & above makes it slightly faster
FF= (( G*( (4.*X2 -24.)*X*S -(X4 -12.*X2 +24.)*C +24. )/X2 + B*(2.*X*S -(X2-2.)*C -2.) )/X + A*(S-X*C))/X ;
HARDSPH= 1./(1. + 24.*volfraction*FF/X2 );
// changing /X and /X2 to *MX1 and *MX2, no significantg difference?
//MX=1.0/X;
//MX2=MX*MX;
//FF= (( G*( (4.*X2 -24.)*X*S -(X4 -12.*X2 +24.)*C +24. )*MX2 + B*(2.*X*S -(X2-2.)*C -2.) )*MX + A*(S-X*C)) ;
//HARDSPH= 1./(1. + 24.*volfraction*FF*MX2*MX );
// grouping the terms, was about same as sasmodels for single precision issues
// FF=A*(S/X-C) + B*(2.*S/X - C +2.0*(C-1.0)/X2) + G*( (4./X -24./X3)*S -(1.0 -12./X2 +24./X4)*C +24./X4 );
// HARDSPH= 1./(1. + 24.*volfraction*FF/X2 );
// remove 1/X2 from final line, take more powers of X inside the brackets, stil bad
// FF=A*(S/X3-C/X2) + B*(2.*S/X3 - C/X2 +2.0*(C-1.0)/X4) + G*( (4./X -24./X3)*S -(1.0 -12./X2 +24./X4)*C +24./X4 )/X2;
// HARDSPH= 1./(1. + 24.*volfraction*FF );
//printf("HS4 %g: %g\n",q,HARDSPH);
return(HARDSPH);
"""
def random():
"""Return a random parameter set for the model."""
pars = dict(
scale=1, background=0,
radius_effective=10**np.random.uniform(1, 4),
volfraction=10**np.random.uniform(-2, 0), # high volume fraction
)
return pars
# Q=0.001 is in the Taylor series, low Q part, so add Q=0.1,
# assuming double precision sasview is correct
tests = [
[{'scale': 1.0, 'background' : 0.0, 'radius_effective' : 50.0,
'volfraction' : 0.2, 'radius_effective_pd' : 0},
[0.001, 0.1], [0.209128, 0.930587]],
]
# ADDED by: RKH ON: 16Mar2016 using equations from FISH as better than
# orig sasview, see notes above. Added Taylor expansions at small Q.
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