source: sasmodels/sasmodels/models/ellipsoid.py @ 19dcb933

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Last change on this file since 19dcb933 was 19dcb933, checked in by Paul Kienzle <pkienzle@…>, 10 years ago

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1# ellipsoid model
2# Note: model title and parameter table are inserted automatically
3r"""
4The form factor is normalized by the particle volume.
5
6Definition
7----------
8
9The output of the 2D scattering intensity function for oriented ellipsoids
10is given by (Feigin, 1987)
11
12.. math::
13
14    P(Q,\alpha) = {\text{scale} \over V} F^2(Q) + \text{background}
15
16where
17
18.. math::
19
20    F(Q) = {3 (\Delta rho)) V (\sin[Qr(R_p,R_e,\alpha)]
21                - \cos[Qr(R_p,R_e,\alpha)])
22            \over [Qr(R_p,R_e,\alpha)]^3 }
23
24and
25
26.. math::
27
28    r(R_p,R_e,\alpha) = \left[ R_e^2 \sin^2 \alpha
29        + R_p^2 \cos^2 \alpha \right]^{1/2}
30
31
32$\alpha$ is the angle between the axis of the ellipsoid and $\vec q$,
33$V$ is the volume of the ellipsoid, $R_p$ is the polar radius along the
34rotational axis of the ellipsoid, $R_e$ is the equatorial radius perpendicular
35to the rotational axis of the ellipsoid and $\Delta \rho$ (contrast) is the
36scattering length density difference between the scatterer and the solvent.
37
38To provide easy access to the orientation of the ellipsoid, we define
39the rotation axis of the ellipsoid using two angles $\theta$ and $\phi$.
40These angles are defined in the
41:ref:`cylinder orientation figure <cylinder-orientation>`.
42For the ellipsoid, $\theta$ is the angle between the rotational axis
43and the $z$-axis.
44
45NB: The 2nd virial coefficient of the solid ellipsoid is calculated based
46on the $R_p$ and $R_e$ values, and used as the effective radius for
47$S(Q)$ when $P(Q) \cdot S(Q)$ is applied.
48
49.. _ellipsoid-1d:
50
51.. figure:: img/ellipsoid_1d.JPG
52
53    The output of the 1D scattering intensity function for randomly oriented
54    ellipsoids given by the equation above.
55
56
57The $\theta$ and $\phi$ parameters are not used for the 1D output. Our
58implementation of the scattering kernel and the 1D scattering intensity
59use the c-library from NIST.
60
61.. _ellipsoid-geometry:
62
63.. figure:: img/ellipsoid_geometry.JPG
64
65    The angles for oriented ellipsoid.
66
67Validation
68----------
69
70Validation of our code was done by comparing the output of the 1D model
71to the output of the software provided by the NIST (Kline, 2006).
72:num:`Figure ellipsoid-comparison-1d` below shows a comparison of
73the 1D output of our model and the output of the NIST software.
74
75.. _ellipsoid-comparison-1d:
76
77.. figure:: img/ellipsoid_comparison_1d.jpg
78
79    Comparison of the SasView scattering intensity for an ellipsoid
80    with the output of the NIST SANS analysis software.  The parameters
81    were set to: *scale* = 1.0, *rpolar* = 20 |Ang|,
82    *requatorial* =400 |Ang|, *contrast* = 3e-6 |Ang^-2|,
83    and *background* = 0.01 |cm^-1|.
84
85Averaging over a distribution of orientation is done by evaluating the
86equation above. Since we have no other software to compare the
87implementation of the intensity for fully oriented ellipsoids, we can
88compare the result of averaging our 2D output using a uniform distribution
89$p(\theta,\phi) = 1.0$.  :num:`Figure #ellipsoid-comparison-2d`
90shows the result of such a cross-check.
91
92.. _ellipsoid-comparison-2d:
93
94.. figure:: img/ellipsoid_comparison_2d.jpg
95
96    Comparison of the intensity for uniformly distributed ellipsoids
97    calculated from our 2D model and the intensity from the NIST SANS
98    analysis software. The parameters used were: *scale* = 1.0,
99    *rpolar* = 20 |Ang|, *requatorial* = 400 |Ang|,
100    *contrast* = 3e-6 |Ang^-2|, and *background* = 0.0 |cm^-1|.
101
102The discrepancy above *q* = 0.3 |cm^-1| is due to the way the form factors
103are calculated in the c-library provided by NIST. A numerical integration
104has to be performed to obtain $P(Q)$ for randomly oriented particles.
105The NIST software performs that integration with a 76-point Gaussian
106quadrature rule, which will become imprecise at high $Q$ where the amplitude
107varies quickly as a function of $Q$. The SasView result shown has been
108obtained by summing over 501 equidistant points. Our result was found
109to be stable over the range of $Q$ shown for a number of points higher
110than 500.
111
112REFERENCE
113
114L A Feigin and D I Svergun. *Structure Analysis by Small-Angle X-Ray and Neutron Scattering*, Plenum,
115New York, 1987.
116"""
117
118from numpy import pi, inf
119
120name = "ellipsoid"
121title = "Ellipsoid of revolution with uniform scattering length density."
122
123description = """\
124P(q.alpha)= scale*f(q)^2 + background, where f(q)= 3*(sld
125                - solvent_sld)*V*[sin(q*r(Rp,Re,alpha))
126                -q*r*cos(qr(Rp,Re,alpha))]
127                /[qr(Rp,Re,alpha)]^3"
128
129     r(Rp,Re,alpha)= [Re^(2)*(sin(alpha))^2
130                + Rp^(2)*(cos(alpha))^2]^(1/2)
131
132                sld: SLD of the ellipsoid
133                solvent_sld: SLD of the solvent
134                V: volume of the ellipsoid
135                Rp: polar radius of the ellipsoid
136                Re: equatorial radius of the ellipsoid
137"""
138
139parameters = [
140#   [ "name", "units", default, [lower, upper], "type",
141#     "description" ],
142    [ "sld", "1e-6/Ang^2", 4, [-inf,inf], "",
143      "Ellipsoid scattering length density" ],
144    [ "solvent_sld", "1e-6/Ang^2", 1, [-inf,inf], "",
145      "Solvent scattering length density" ],
146    [ "rpolar", "Ang",  20, [0, inf], "volume",
147      "Polar radius" ],
148    [ "requatorial", "Ang",  400, [0, inf], "volume",
149      "Equatorial radius" ],
150    [ "theta", "degrees", 60, [-inf, inf], "orientation",
151      "In plane angle" ],
152    [ "phi", "degrees", 60, [-inf, inf], "orientation",
153      "Out of plane angle" ],
154    ]
155
156source = [ "lib/J1.c", "lib/gauss76.c", "ellipsoid.c"]
157
158def ER(rpolar, requatorial):
159    import numpy as np
160
161    ee = np.empty_like(rpolar)
162    idx = rpolar > requatorial
163    ee[idx] = (rpolar[idx]**2 - requatorial[idx]**2)/rpolar[idx]**2
164    idx = rpolar < requatorial
165    ee[idx] = (requatorial[idx]**2 - rpolar[idx]**2)/requatorial[idx]**2
166    idx = rpolar == requatorial
167    ee[idx] = 2*rpolar[idx]
168    valid = (rpolar*requatorial != 0)
169    bd = 1.0-ee[valid]
170    e1 = np.sqrt(ee[valid])
171    b1 = 1.0 + np.arcsin(e1)/(e1*np.sqrt(bd))
172    bL = (1.0+e1)/(1.0-e1)
173    b2 = 1.0 + bd/2/e1*np.log(bL)
174    delta = 0.75*b1*b2
175
176    ddd = np.zeros_like(rpolar)
177    ddd[valid] = 2.0*(delta+1.0)*rpolar*requatorial**2
178    return 0.5*ddd**(1.0/3.0)
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