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r"""
Definition
----------
This model is a trivial extension of the CoreShell function to a larger number
of shells. The scattering length density profile for the default sld values
(w/ 4 shells).
.. figure:: img/core_multi_shell_sld_default_profile.jpg
SLD profile of the core_multi_shell object from the center of sphere out
for the default SLDs.*
The 2D scattering intensity is the same as $P(q)$ above, regardless of the
orientation of the $\vec q$ vector which is defined as
.. math::
q = \sqrt{q_x^2 + q_y^2}
.. note:: **Be careful!** The SLDs and scale can be highly correlated. Hold as
many of these parameters fixed as possible.
.. note:: The outer most radius (= *radius* + *thickness*) is used as the
effective radius for $S(Q)$ when $P(Q)*S(Q)$ is applied.
For information about polarised and magnetic scattering, see
the :ref:`magnetism` documentation.
Our model uses the form factor calculations implemented in a C-library provided
by the NIST Center for Neutron Research [#Kline2006]_.
References
----------
Also see the :ref:`core-shell-sphere` model documentation and [#Feigin1987]_
.. [#Kline2006] S R Kline, *J Appl. Cryst.*, 39 (2006) 895
.. [#Feigin1987] L A Feigin and D I Svergun, *Structure Analysis by
Small-Angle X-Ray and Neutron Scattering*, Plenum Press, New York, 1987.
Authorship and Verification
----------------------------
* **Author:** NIST IGOR/DANSE **Date:** pre 2010
* **Last Modified by:** Paul Kienzle **Date:** September 12, 2016
* **Last Reviewed by:** Paul Kienzle **Date:** September 12, 2016
"""
from __future__ import division
import numpy as np
from numpy import inf
name = "core_multi_shell"
title = "This model provides the scattering from a spherical core with 1 to 10 \
concentric shell structures. The SLDs of the core and each shell are \
individually specified."
description = """\
Form factor for a core muti-shell sphere normalized by the volume.
Each of up to 10 shells can have a unique thickness and sld.
background:background,
rad_core0: radius of sphere(core)
thick_shell#:the thickness of the shell#
sld_core0: the SLD of the sphere
sld_solv: the SLD of the solvent
sld_shell: the SLD of the shell#
A_shell#: the coefficient in the exponential function
scale: 1.0 if data is on absolute scale
volfraction: volume fraction of spheres
radius: the radius of the core
sld: the SLD of the core
thick_shelli: the thickness of the i'th shell from the core
sld_shelli: the SLD of the i'th shell from the core
sld_solvent: the SLD of the solvent
background: incoherent background
"""
category = "shape:sphere"
# ["name", "units", default, [lower, upper], "type","description"],
parameters = [["sld_core", "1e-6/Ang^2", 1.0, [-inf, inf], "sld",
"Core scattering length density"],
["radius", "Ang", 200., [0, inf], "volume",
"Radius of the core"],
["sld_solvent", "1e-6/Ang^2", 6.4, [-inf, inf], "sld",
"Solvent scattering length density"],
["n", "", 1, [0, 10], "volume",
"number of shells"],
["sld[n]", "1e-6/Ang^2", 1.7, [-inf, inf], "sld",
"scattering length density of shell k"],
["thickness[n]", "Ang", 40., [0, inf], "volume",
"Thickness of shell k"],
]
source = ["lib/sas_3j1x_x.c", "core_multi_shell.c"]
have_Fq = True
radius_effective_modes = ["outer radius", "core radius"]
def random():
"""Return a random parameter set for the model."""
num_shells = np.minimum(np.random.poisson(3)+1, 10)
total_radius = 10**np.random.uniform(1.7, 4)
thickness = np.random.exponential(size=num_shells+1)
thickness *= total_radius/np.sum(thickness)
pars = dict(
#background=0,
n=num_shells,
radius=thickness[0],
)
for k, v in enumerate(thickness[1:]):
pars['thickness%d'%(k+1)] = v
return pars
def profile(sld_core, radius, sld_solvent, n, sld, thickness):
"""
Returns the SLD profile *r* (Ang), and *rho* (1e-6/Ang^2).
"""
n = int(n+0.5)
z = []
rho = []
# add in the core
z.append(0)
rho.append(sld_core)
z.append(radius)
rho.append(sld_core)
# add in the shells
for k in range(int(n)):
# Left side of each shells
z.append(z[-1])
rho.append(sld[k])
z.append(z[-1] + thickness[k])
rho.append(sld[k])
# add in the solvent
z.append(z[-1])
rho.append(sld_solvent)
z.append(z[-1]*1.25)
rho.append(sld_solvent)
return np.asarray(z), np.asarray(rho)
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