source: sasmodels/sasmodels/models/two_lorentzian.py @ 0507e09

core_shell_microgelsmagnetic_modelticket-1257-vesicle-productticket_1156ticket_1265_superballticket_822_more_unit_tests
Last change on this file since 0507e09 was 0507e09, checked in by smk78, 3 months ago

Added link to source code to each model. Closes #883

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Line 
1r"""
2Definition
3----------
4
5The scattering intensity $I(q)$ is calculated as
6
7.. math::
8
9    I(q) = \frac{A}{1 +(Q\xi_1)^n} + \frac{C}{1 +(Q\xi_2)^m} + \text{B}
10
11where $A$ = Lorentzian scale factor #1, $C$ = Lorentzian scale #2,
12$\xi_1$ and $\xi_2$ are the corresponding correlation lengths, and $n$ and
13$m$ are the respective power law exponents (set $n = m = 2$ for
14Ornstein-Zernicke behaviour).
15
16For 2D data the scattering intensity is calculated in the same way as 1D,
17where the $q$ vector is defined as
18
19.. math::
20
21    q = \sqrt{q_x^2 + q_y^2}
22
23
24References
25----------
26
27None.
28
29Source
30------
31
32`two_lorentzian.py <https://github.com/SasView/sasmodels/blob/master/sasmodels/models/two_lorentzian.py>`_
33
34Authorship and Verification
35----------------------------
36
37* **Author:** NIST IGOR/DANSE **Date:** pre 2010
38* **Last Modified by:** Piotr rozyczko **Date:** January 29, 2016
39* **Last Reviewed by:** Paul Butler **Date:** March 21, 2016
40* **Source added by :** Steve King **Date:** March 25, 2019
41"""
42
43import numpy as np
44from numpy import inf, power
45
46name = "two_lorentzian"
47title = "This model calculates an empirical functional form for SAS data \
48characterized by two Lorentzian-type functions."
49description = """I(q) = scale_1/(1.0 + pow((q*length_1),exponent_1))
50             + scale_2/(1.0 + pow((q*length_2),exponent_2) )+ background
51
52             scale_1    = Lorentzian term scaling #1
53             length_1   = Lorentzian screening length #1 [A]
54             exponent_1 = Lorentzian exponent #1
55             scale_2    = Lorentzian term scaling #2
56             length_2   = Lorentzian screening length #2 [A]
57             exponent_2 = Lorentzian exponent #2
58             background = Incoherent background
59        """
60category = "shape-independent"
61
62# pylint: disable=bad-whitespace, line-too-long
63#            ["name", "units", default, [lower, upper], "type", "description"],
64parameters = [["lorentz_scale_1",  "",     10.0, [-inf, inf], "", "First power law scale factor"],
65              ["lorentz_length_1", "Ang", 100.0, [-inf, inf], "", "First Lorentzian screening length"],
66              ["lorentz_exp_1",    "",      3.0, [-inf, inf], "", "First exponent of power law"],
67              ["lorentz_scale_2",  "",      1.0, [-inf, inf], "", "Second scale factor for broad Lorentzian peak"],
68              ["lorentz_length_2", "Ang",  10.0, [-inf, inf], "", "Second Lorentzian screening length"],
69              ["lorentz_exp_2",    "",      2.0, [-inf, inf], "", "Second exponent of power law"],
70             ]
71# pylint: enable=bad-whitespace, line-too-long
72
73
74def Iq(q,
75       lorentz_scale_1=10.0,
76       lorentz_length_1=100.0,
77       lorentz_exp_1=3.0,
78       lorentz_scale_2=1.0,
79       lorentz_length_2=10.0,
80       lorentz_exp_2=2.0):
81
82    """
83    :param q:                   Input q-value (float or [float, float])
84    :param lorentz_scale_1:     Second scale factor for broad Lorentzian peak
85    :param lorentz_length_1:    First Lorentzian screening length
86    :param lorentz_exp_1:       Exponent of the second Lorentz function
87    :param lorentz_scale_2:     Second scale factor for broad Lorentzian peak
88    :param lorentz_length_2:    Second Lorentzian screening length
89    :param lorentz_exp_2:       Exponent of the second Lorentz function
90    :return:                    Calculated intensity
91    """
92# pylint: disable=bad-whitespace
93    intensity  = lorentz_scale_1/(1.0 +
94                                  power(q*lorentz_length_1, lorentz_exp_1))
95    intensity += lorentz_scale_2/(1.0 +
96                                  power(q*lorentz_length_2, lorentz_exp_2))
97# pylint: enable=bad-whitespace
98    return intensity
99
100Iq.vectorized = True  # Iq accepts an array of q values
101
102def random():
103    """Return a random parameter set for the model."""
104    scale = 10**np.random.uniform(0, 4, 2)
105    length = 10**np.random.uniform(1, 4, 2)
106    expon = np.random.uniform(1, 6, 2)
107
108    pars = dict(
109        #background=0,
110        scale=1, # scale provided in model
111        lorentz_scale_1=scale[0],
112        lorentz_length_1=length[0],
113        lorentz_exp_1=expon[0],
114        lorentz_scale_2=scale[1],
115        lorentz_length_2=length[1],
116        lorentz_exp_2=expon[1],
117    )
118    return pars
119
120
121demo = dict(scale=1, background=0.1,
122            lorentz_scale_1=10,
123            lorentz_length_1=100.0,
124            lorentz_exp_1=3.0,
125            lorentz_scale_2=1,
126            lorentz_length_2=10,
127            lorentz_exp_2=2.0)
128
129tests = [
130
131    # Accuracy tests based on content in test/utest_extra_models.py
132    [{'lorentz_scale_1':   10.0,
133      'lorentz_length_1': 100.0,
134      'lorentz_exp_1':      3.0,
135      'lorentz_scale_2':    1.0,
136      'lorentz_length_2':  10.0,
137      'lorentz_exp_2':      2.0,
138      'background':         0.1,
139     }, 0.001, 11.08991],
140
141    [{'lorentz_scale_1':   10.0,
142      'lorentz_length_1': 100.0,
143      'lorentz_exp_1':      3.0,
144      'lorentz_scale_2':    1.0,
145      'lorentz_length_2':  10.0,
146      'lorentz_exp_2':      2.0,
147      'background':         0.1,
148     }, 0.150141, 0.410245],
149
150    [{'lorentz_scale_1':   10.0,
151      'lorentz_length_1': 100.0,
152      'lorentz_exp_1':      3.0,
153      'lorentz_scale_2':    1.0,
154      'lorentz_length_2':  10.0,
155      'lorentz_exp_2':      2.0,
156      'background':         0.1,
157     }, 0.442528, 0.148699],
158
159    # Additional tests with larger range of parameters
160    [{'lorentz_scale_1':   10.0,
161      'lorentz_length_1': 100.0,
162      'lorentz_exp_1':      3.0,
163      'lorentz_scale_2':    1.0,
164      'lorentz_length_2':  10.0,
165      'lorentz_exp_2':      2.0,
166     }, 0.000332070182643, 10.9996228107],
167
168    [{'lorentz_scale_1':  0.0,
169      'lorentz_length_1': 0.0,
170      'lorentz_exp_1':    0.0,
171      'lorentz_scale_2':  0.0,
172      'lorentz_length_2': 0.0,
173      'lorentz_exp_2':    0.0,
174      'background':     100.0
175     }, 5.0, 100.0],
176
177    [{'lorentz_scale_1': 200.0,
178      'lorentz_length_1': 10.0,
179      'lorentz_exp_1':     0.1,
180      'lorentz_scale_2':   0.1,
181      'lorentz_length_2':  5.0,
182      'lorentz_exp_2':     2.0
183     }, 20000., 45.5659201896],
184    ]
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