Squarewell - squarewell.py

    # Note: model title and parameter table are inserted automatically
r"""
Calculates the interparticle structure factor for a hard sphere fluid
with a narrow, attractive, square well potential. **The Mean Spherical
Approximation (MSA) closure relationship is used, but it is not the most
appropriate closure for an attractive interparticle potential.** However,
the solution has been compared to Monte Carlo simulations for a square
well fluid and these show the MSA calculation to be limited to well
depths $epsilon < 1.5$ kT and volume fractions $phi < 0.08$.

Positive well depths correspond to an attractive potential well. Negative
well depths correspond to a potential "shoulder", which may or may not be
physically reasonable. The :ref:`stickyhardsphere` model may be a better
choice in some circumstances.

Computed values may behave badly at extremely small $qR$.

.. note::

   Earlier versions of SasView did not incorporate the so-called
   $eta(q)$ ("beta") correction [2] for polydispersity and non-sphericity.
   This is only available in SasView versions 5.0 and higher.

The well width $(lambda)$ is defined as multiples of the particle diameter
$(2 R)$.

The interaction potential is:

.. math::

    U(r) = egin{cases}
    infty & r < 2R \
    -epsilon & 2R leq r < 2Rlambda \
    0 & r geq 2Rlambda
    end{cases}

where $r$ is the distance from the center of a sphere of a radius $R$.

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 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
----------

.. [#] R V Sharma, K C Sharma, *Physica*, 89A (1977) 213

.. [#] M Kotlarchyk and S-H Chen, *J. Chem. Phys.*, 79 (1983) 2461-2469

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

* **Author:**
* **Last Modified by:**
* **Last Reviewed by:** Steve King **Date:** March 27, 2019
"""

import numpy as np
from numpy import inf

name = "squarewell"
title = "Square well structure factor with Mean Spherical Approximation closure"
description = """
    [Square well structure factor, with MSA closure]
        Interparticle structure factor S(Q) for a hard sphere fluid
    with a narrow attractive well. Fits are prone to deliver non-
    physical parameters; use with care and read the references in
    the model documentation.The "beta(q)" correction is available
    in versions 4.2.2 and higher.
"""
category = "structure-factor"
structure_factor = True
single = False

#single = False
#             ["name", "units", default, [lower, upper], "type","description"],
parameters = [
    #   [ "name", "units", default, [lower, upper], "type",
    #     "description" ],
    ["radius_effective", "Ang", 50.0, [0, inf], "volume",
     "effective radius of hard sphere"],
    ["volfraction", "", 0.04, [0, 0.08], "",
     "volume fraction of spheres"],
    ["welldepth", "kT", 1.5, [0.0, 1.5], "",
     "depth of well, epsilon"],
    ["wellwidth", "diameters", 1.2, [1.0, inf], "",
     "width of well in diameters (=2R) units, must be > 1"],
    ]

# No volume normalization despite having a volume parameter
# This should perhaps be volume normalized?
form_volume = """
    return 1.0;
    """

Iq = """
// single precision is very poor at extreme small Q, would need a Taylor series
	double req,phis,edibkb,lambda,struc;
	double sigma,eta,eta2,eta3,eta4,etam1,etam14,alpha,beta,gamm;
	double x,sk,sk2,sk3,sk4,t1,t2,t3,t4,ck;
	double S,C,SL,CL;
	x= q;

	req = radius_effective;
	phis = volfraction;
	edibkb = welldepth;
	lambda = wellwidth;

	sigma = req*2.;
	eta = phis;
	eta2 = eta*eta;
	eta3 = eta*eta2;
	eta4 = eta*eta3;
	etam1 = 1. - eta;
	etam14 = etam1*etam1*etam1*etam1;
	// temp borrow sk for an intermediate calc
	sk = 1.0 +2.0*eta;
	sk *= sk;
	alpha = (  sk + eta3*( eta-4.0 )  )/etam14;
	beta = -(eta/3.0) * ( 18. + 20.*eta - 12.*eta2 + eta4 )/etam14;
	gamm = 0.5*eta*( sk + eta3*(eta-4.) )/etam14;

	//  calculate the structure factor

	sk = x*sigma;
	sk2 = sk*sk;
	sk3 = sk*sk2;
	sk4 = sk3*sk;
	SINCOS(sk,S,C);
	SINCOS(lambda*sk,SL,CL);
	t1 = alpha * sk3 * ( S - sk * C );
	t2 = beta * sk2 * 2.0*( sk*S - (0.5*sk2 - 1.)*C - 1.0 );
	t3 = gamm*( ( 4.0*sk3 - 24.*sk ) * S - ( sk4 - 12.0*sk2 + 24.0 )*C + 24.0 );
	t4 = -edibkb*sk3*(SL +sk*(C - lambda*CL) - S );
	ck =  -24.0*eta*( t1 + t2 + t3 + t4 )/sk3/sk3;
	struc  = 1./(1.-ck);

	return(struc);
"""

def random():
    """Return a random parameter set for the model."""
    pars = dict(
        scale=1, background=0,
        radius_effective=10**np.random.uniform(1, 4.7),
        volfraction=np.random.uniform(0.00001, 0.08),
        welldepth=np.random.uniform(0, 1.5),
        wellwidth=np.random.uniform(1, 1.2),
    )
    return pars

demo = dict(radius_effective=50, volfraction=0.04, welldepth=1.5,
            wellwidth=1.2, radius_effective_pd=0, radius_effective_pd_n=0)
#
tests = [
    [{'scale': 1.0, 'background': 0.0, 'radius_effective': 50.0,
      'volfraction': 0.04, 'welldepth': 1.5, 'wellwidth': 1.2,
      'radius_effective_pd': 0}, [0.001], [0.97665742]],
    ]
# ADDED by: converting from sasview RKH  ON: 16Mar2016
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