source: sasmodels/sasmodels/models/spherical_sld.py @ eb97b11

r"""
This model calculates an empirical functional form for SAS data using SpericalSLD profile

Similarly to the OnionExpShellModel, this model provides the form factor, P(q), for a multi-shell sphere,
where the interface between the each neighboring shells can be described by one of a number of functions
including error, power-law, and exponential functions. This model is to calculate the scattering intensity
by building a continuous custom SLD profile against the radius of the particle. The SLD profile is composed
of a flat core, a flat solvent, a number (up to 9 ) flat shells, and the interfacial layers between
the adjacent flat shells (or core, and solvent) (see below).

.. figure:: img/spherical_sld_profile.gif

    Exemplary SLD profile

Unlike the <onion> model (using an analytical integration),
the interfacial layers here are sub-divided and numerically integrated assuming each of the sub-layers are described
by a line function. The number of the sub-layer can be given by users by setting the integer values of npts_inter.
The form factor is normalized by the total volume of the sphere.

Definition
----------

The form factor $P(q)$ in 1D is calculated by:

.. math::

    P(q) = \frac{f^2}{V_\text{particle}} \text{ where }
    f = f_\text{core} + \sum_{\text{inter}_i=0}^N f_{\text{inter}_i} +
    \sum_{\text{flat}_i=0}^N f_{\text{flat}_i} +f_\text{solvent}

For a spherically symmetric particle with a particle density $\rho_x(r)$ the sld function can be defined as:

.. math::

    f_x = 4 \pi \int_{0}^{\infty} \rho_x(r)  \frac{\sin(qr)} {qr^2} r^2 dr


so that individual terms can be calcualted as follows:

.. math::
    f_\text{core} = 4 \pi \int_{0}^{r_\text{core}} \rho_\text{core} \frac{\sin(qr)} {qr} r^2 dr =
    3 \rho_\text{core} V(r_\text{core})
    \Big[ \frac{\sin(qr_\text{core}) - qr_\text{core} \cos(qr_\text{core})} {qr_\text{core}^3} \Big]

    f_{\text{inter}_i} = 4 \pi \int_{\Delta t_{ \text{inter}_i } } \rho_{ \text{inter}_i } \frac{\sin(qr)} {qr} r^2 dr

    f_{\text{shell}_i} = 4 \pi \int_{\Delta t_{ \text{inter}_i } } \rho_{ \text{flat}_i } \frac{\sin(qr)} {qr} r^2 dr =
    3 \rho_{ \text{flat}_i } V ( r_{ \text{inter}_i } + \Delta t_{ \text{inter}_i } )
    \Big[ \frac{\sin(qr_{\text{inter}_i} + \Delta t_{ \text{inter}_i } ) - q (r_{\text{inter}_i} +
    \Delta t_{ \text{inter}_i }) \cos(q( r_{\text{inter}_i} + \Delta t_{ \text{inter}_i } ) ) }
    {q ( r_{\text{inter}_i} + \Delta t_{ \text{inter}_i } )^3 }  \Big]
    -3 \rho_{ \text{flat}_i } V(r_{ \text{inter}_i })
    \Big[ \frac{\sin(qr_{\text{inter}_i}) - qr_{\text{flat}_i} \cos(qr_{\text{inter}_i}) } {qr_{\text{inter}_i}^3} \Big]

    f_\text{solvent} = 4 \pi \int_{r_N}^{\infty} \rho_\text{solvent} \frac{\sin(qr)} {qr} r^2 dr =
    3 \rho_\text{solvent} V(r_N)
    \Big[ \frac{\sin(qr_N) - qr_N \cos(qr_N)} {qr_N^3} \Big]


Here we assumed that the SLDs of the core and solvent are constant against $r$.
The SLD at the interface between shells, $\rho_{\text {inter}_i}$
is calculated with a function chosen by an user, where the functions are

Exp:

.. math::
    \rho_{{inter}_i} (r) = \begin{cases}
    B \exp\Big( \frac {\pm A(r - r_{\text{flat}_i})} {\Delta t_{ \text{inter}_i }} \Big) +C  & \text{for} A \neq 0 \\
    B \Big( \frac {(r - r_{\text{flat}_i})} {\Delta t_{ \text{inter}_i }} \Big) +C  & \text{for} A = 0 \\
    \end{cases}

Power-Law

.. math::
    \rho_{{inter}_i} (r) = \begin{cases}
    \pm B \Big( \frac {(r - r_{\text{flat}_i} )} {\Delta t_{ \text{inter}_i }} \Big) ^A  +C  & \text{for} A \neq 0 \\
    \rho_{\text{flat}_{i+1}}  & \text{for} A = 0 \\
    \end{cases}

Erf:

.. math::
    \rho_{{inter}_i} (r) = \begin{cases}
    B \text{erf} \Big( \frac { A(r - r_{\text{flat}_i})} {\sqrt{2} \Delta t_{ \text{inter}_i }} \Big) +C  & \text{for} A \neq 0 \\
    B \Big( \frac {(r - r_{\text{flat}_i} )} {\Delta t_{ \text{inter}_i }} \Big)  +C  & \text{for} A = 0 \\
    \end{cases}

The functions are normalized so that they vary between 0 and 1, and they are constrained such that the SLD
is continuous at the boundaries of the interface as well as each sub-layers. Thus B and C are determined.

Once $\rho_{\text{inter}_i}$ is found at the boundary of the sub-layer of the interface, we can find its contribution
to the form factor $P(q)$

.. math::
    f_{\text{inter}_i} = 4 \pi \int_{\Delta t_{ \text{inter}_i } } \rho_{ \text{inter}_i } \frac{\sin(qr)} {qr} r^2 dr =
    4 \pi \sum_{j=0}^{npts_{\text{inter}_i} -1 }
    \int_{r_j}^{r_{j+1}} \rho_{ \text{inter}_i } (r_j) \frac{\sin(qr)} {qr} r^2 dr \approx

    4 \pi \sum_{j=0}^{npts_{\text{inter}_i} -1 } \Big[
    3 ( \rho_{ \text{inter}_i } ( r_{j+1} ) - \rho_{ \text{inter}_i } ( r_{j} ) V ( r_{ \text{sublayer}_j } )
    \Big[ \frac {r_j^2 \beta_\text{out}^2 \sin(\beta_\text{out}) - (\beta_\text{out}^2-2) \cos(\beta_\text{out}) }
    {\beta_\text{out}^4 } \Big]

    - 3 ( \rho_{ \text{inter}_i } ( r_{j+1} ) - \rho_{ \text{inter}_i } ( r_{j} ) V ( r_{ \text{sublayer}_j-1 } )
    \Big[ \frac {r_{j-1}^2 \sin(\beta_\text{in}) - (\beta_\text{in}^2-2) \cos(\beta_\text{in}) }
    {\beta_\text{in}^4 } \Big]

    + 3 \rho_{ \text{inter}_i } ( r_{j+1} )  V ( r_j )
    \Big[ \frac {\sin(\beta_\text{out}) - \cos(\beta_\text{out}) }
    {\beta_\text{out}^4 } \Big]

    - 3 \rho_{ \text{inter}_i } ( r_{j} )  V ( r_j )
    \Big[ \frac {\sin(\beta_\text{in}) - \cos(\beta_\text{in}) }
    {\beta_\text{in}^4 } \Big]
    \Big]

where

.. math::
    V(a) = \frac {4\pi}{3}a^3

    a_\text{in} ~ \frac{r_j}{r_{j+1} -r_j} \text{, } a_\text{out} ~ \frac{r_{j+1}}{r_{j+1} -r_j}

    \beta_\text{in} = qr_j \text{, } \beta_\text{out} = qr_{j+1}


We assume the $\rho_{\text{inter}_i} (r)$ can be approximately linear within a sub-layer $j$

Finally form factor can be calculated by

.. math::

    P(q) = \frac{[f]^2} {V_\text{particle}} \text{where} V_\text{particle} = V(r_{\text{shell}_N})

For 2D data the 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}


.. figure:: img/spherical_sld_1d.jpg

    1D plot using the default values (w/400 data point).

.. figure:: img/spherical_sld_default_profile.jpg

    SLD profile from the default values.

.. note::
    The outer most radius is used as the effective radius for S(Q) when $P(Q) * S(Q)$ is applied.

References
----------
L A Feigin and D I Svergun, Structure Analysis by Small-Angle X-Ray and Neutron Scattering, Plenum Press, New York, (1987)

"""

from numpy import inf

name = "spherical_sld"
title = "Sperical SLD intensity calculation"
description = """
            I(q) =
               background = Incoherent background [1/cm]
        """
category = "sphere-based"

# pylint: disable=bad-whitespace, line-too-long
#            ["name", "units", default, [lower, upper], "type", "description"],
parameters = [["n_shells",                "",               1,      [0, 9],         "", "number of shells"],
              ["radius_core",      "Ang",            50.0,   [0, inf],       "", "intern layer thickness"],
              ["sld_core",         "1e-6/Ang^2",     2.07,   [-inf, inf],    "", "sld function flat"],
              ["sld_solvent",      "1e-6/Ang^2",     1.0,    [-inf, inf],    "", "sld function solvent"],
              ["sld_flat[n]",      "1e-6/Ang^2",     4.06,   [-inf, inf],    "", "sld function flat"],
              ["thick_flat[n]",    "Ang",            100.0,  [0, inf],       "", "flat layer_thickness"],
              ["func_inter[n]",    "",               0,      [0, 4],         "", "Erf:0, RPower:1, LPower:2, RExp:3, LExp:4"],
              ["thick_inter[n]",   "Ang",            50.0,   [0, inf],       "", "intern layer thickness"],
              ["nu_inter[n]",      "",               2.5,    [-inf, inf],    "", "steepness parameter"],
              ["npts_inter",       "",               35,     [0, 35],        "", "number of points in each sublayer Must be odd number"],
              ]
# pylint: enable=bad-whitespace, line-too-long
source = ["lib/librefl.c",  "lib/sph_j1c.c", "spherical_sld.c"]

#def Iq(q, *args, **kw):
#    return q

#def Iqxy(qx, *args, **kw):
#    return qx

def profile(n_shells, radius_core,  sld_core,  sld_solvent, sld_flat,
            thick_flat, func_inter, thick_inter, nu_inter, npts_inter):
    """
    Returns shape profile with x=radius, y=SLD.
    """

    #total_radius = 1.25*(sum(thickness[:n_shells]) + core_radius + 1)
    #dr = total_radius/400  # 400 points for a smooth plot

    z = []
    beta = []
    z0 = 0
    # two sld points for core
    z.append(0)
    beta.append(sld_core)
    z.append(radius_core)
    beta.append(sld_core)
    z0 += radius_core

    for i in range(1, n_shells+2):
        dz = thick_inter[i-1]/npts_inter
        # j=0 for interface, j=1 for flat layer
        for j in range(0, 2):
            # interation for sub-layers
            for n_s in range(0, npts_inter+1):
                if j == 1:
                    if i == n_shells+1:
                        break
                    # shift half sub thickness for the first point
                    z0 -= dz#/2.0
                        z.append(z0)
                        #z0 -= dz/2.0
                        z0 += thick_flat[i]
                        sld_i = sld_flat[i]
                        beta.append(sld_flat[i])
                        dz = 0
                    else:
                        nu = nu_inter[i-1]
                        # decide which sld is which, sld_r or sld_l
                        if i == 1:
                            sld_l = sld_core
                        else:
                            sld_l = sld_flat[i-1]
                        if i == n_shells+1:
                            sld_r = sld_solvent
                        else:
                            sld_r = sld_flat[i]
                        # get function type
                        func_idx = func_inter[i-1]
                        # calculate the sld
                        sld_i = intersldfunc(func_idx, npts_inter, n_s, nu,
                                            sld_l, sld_r)
                    # append to the list
                    z.append(z0)
                    beta.append(sld_i)
                    z0 += dz
                    if j == 1:
                        break
    # put sld of solvent
    z.append(z0)
    beta.append(sld_solvent)
    z_ext = z0/5.0
    z.append(z0+z_ext)
    beta.append(sld_solvent)
    # return sld profile (r, beta)
    
    return np.asarray(z), np.asarray(beta)*1e-6

def ER(core_radius, n, thickness):
    return np.sum(thickness[:n[0]], axis=0) + core_radius

def VR(core_radius, n, thickness):
    return 1.0, 1.0


demo = dict(
    n=4,
    scale=1.0,
    solvent_sld=1.0,
    background=0.0,
    npts_inter=35.0,
    )

#TODO: Not working yet
tests = [
    # Accuracy tests based on content in test/utest_extra_models.py
    [{'npts_iter':35,
        'sld_solv':1,
        'radius_core':50.0,
        'sld_core':2.07,
        'func_inter2':0.0,
        'thick_inter2':50,
        'nu_inter2':2.5,
        'sld_flat2':4,
        'thick_flat2':100,
        'func_inter1':0.0,
        'thick_inter1':50,
        'nu_inter1':2.5,
        'background': 0.0,
    }, 0.001, 0.001],
]