source: sasmodels/sasmodels/models/sphere.py @ a430f5f

ticket-1257-vesicle-productticket_1156ticket_822_more_unit_tests
Last change on this file since a430f5f was a430f5f, checked in by richardh, 7 months ago

more progress with model_test, see sphere.py

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File size: 4.7 KB
Line 
1r"""
2For information about polarised and magnetic scattering, see
3the :ref:`magnetism` documentation.
4
5Definition
6----------
7
8The 1D scattering intensity is calculated in the following way (Guinier, 1955)
9
10.. math::
11
12    I(q) = \frac{\text{scale}}{V} \cdot \left[
13        3V(\Delta\rho) \cdot \frac{\sin(qr) - qr\cos(qr))}{(qr)^3}
14        \right]^2 + \text{background}
15
16where *scale* is a volume fraction, $V$ is the volume of the scatterer,
17$r$ is the radius of the sphere and *background* is the background level.
18*sld* and *sld_solvent* are the scattering length densities (SLDs) of the
19scatterer and the solvent respectively, whose difference is $\Delta\rho$.
20
21Note that if your data is in absolute scale, the *scale* should represent
22the volume fraction (which is unitless) if you have a good fit. If not,
23it should represent the volume fraction times a factor (by which your data
24might need to be rescaled).
25
26The 2D scattering intensity is the same as above, regardless of the
27orientation of $\vec q$.
28
29Validation
30----------
31
32Validation of our code was done by comparing the output of the 1D model
33to the output of the software provided by the NIST (Kline, 2006).
34
35
36References
37----------
38
39.. [#] A Guinier and G. Fournet, *Small-Angle Scattering of X-Rays*, John Wiley and Sons, New York, (1955)
40
41Source
42------
43
44`sphere.py <https://github.com/SasView/sasmodels/blob/master/sasmodels/models/sphere.py>`_
45
46`sphere.c <https://github.com/SasView/sasmodels/blob/master/sasmodels/models/sphere.c>`_
47
48Authorship and Verification
49----------------------------
50
51* **Author:**
52* **Last Modified by:**
53* **Last Reviewed by:** S King and P Parker **Date:** 2013/09/09 and 2014/01/06
54* **Source added by :** Steve King **Date:** March 25, 2019
55"""
56
57import numpy as np
58from numpy import inf
59
60name = "sphere"
61title = "Spheres with uniform scattering length density"
62description = """\
63P(q)=(scale/V)*[3V(sld-sld_solvent)*(sin(qr)-qr cos(qr))
64                /(qr)^3]^2 + background
65    r: radius of sphere
66    V: The volume of the scatter
67    sld: the SLD of the sphere
68    sld_solvent: the SLD of the solvent
69"""
70category = "shape:sphere"
71
72#             ["name", "units", default, [lower, upper], "type","description"],
73parameters = [["sld", "1e-6/Ang^2", 1, [-inf, inf], "sld",
74               "Layer scattering length density"],
75              ["sld_solvent", "1e-6/Ang^2", 6, [-inf, inf], "sld",
76               "Solvent scattering length density"],
77              ["radius", "Ang", 50, [0, inf], "volume",
78               "Sphere radius"],
79             ]
80
81source = ["lib/sas_3j1x_x.c", "sphere.c"]
82have_Fq = True
83effective_radius_type = ["radius"]
84
85def random():
86    """Return a random parameter set for the model."""
87    radius = 10**np.random.uniform(1.3, 4)
88    pars = dict(
89        radius=radius,
90    )
91    return pars
92
93tests = [
94    [{}, 0.2, 0.726362],
95    [{"scale": 1., "background": 0., "sld": 6., "sld_solvent": 1.,
96      "radius": 120., "radius_pd": 0.2, "radius_pd_n":45},
97     0.2, 0.2288431],
98    [{"radius": 120., "radius_pd": 0.02, "radius_pd_n":45},
99     0.2, 792.0646662454202, [1166737.0473152], 120.0, 7246723.820358589, 1.0], # the longer list here checks  F1, F2, R_eff, volume, volume_ratio = call_Fq(kernel, pars)
100    #          But note P(Q) = F2/volume,  F1 and F2 are vectors, for some reason only F2 needs square brackets
101    #          BUT what is scaling of F1 ???  At low Pd F2 ~ F1^2 ?
102   [{"radius": 120., "radius_pd": 0.2, "radius_pd_n":45},
103     0.2, 1.233304061, [1850806.119736], 120.0, 8087664.1226, 1.0], # the longer list here checks  F1, F2, R_eff, volume, volume_ratio = call_Fq(kernel, pars)
104    [{"@S": "hardsphere"},
105       0.01, 55.881884232102124], # this is current value, not verified elsewhere yet
106    [{"@S": "hardsphere"},
107       0.2, 0.14730859242492958], #  this is current value, not verified elsewhere yet
108    [{"@S": "hardsphere"},
109       0.1, 0.7940350343811906], #  this is current value, not verified elsewhere yet
110        [{"@S": "hardsphere",          # hard sphere structure factor
111     "structure_factor_mode": 1,  # decoupling approximation
112     "effective_radius_type": 1, "radius_effective":27.0 # equivalent sphere   Currently have hardwired model_test to accept radius_effective
113     # direct_model has the name & value BUT does it get passed to S(Q)???  What about volfracion, plus the many parameters used by other S(Q) ?
114     # effective_radius_type does NOT appear in the list, has it been stripped out???
115         }, 0.1, 0.7940350343881906],
116#       [{"@S": "hardsphere",          # hard sphere structure factor
117#     "structure_factor_mode": 3,  #  -  WHY same result?
118#     "effective_radius_type": 3, "radius_effective":23.0    #
119#        }, 0.1, 0.7940350343881906]
120]
121# putting None for expected result will pass the test if there are no errors from the routine, but without any check on the value of the result
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