[a36c6d3] | 1 | r""" |
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| 2 | Definition |
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| 3 | ---------- |
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| 4 | |
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| 5 | The scattering intensity $I(q)$ is calculated as |
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| 6 | |
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| 7 | .. math:: |
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| 8 | |
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[8115d82] | 9 | I(q) = \begin{cases} |
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[c6652bb] | 10 | A q^{-m1} + \text{background} & q <= q_c \\ |
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| 11 | C q^{-m2} + \text{background} & q > q_c |
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[8115d82] | 12 | \end{cases} |
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[a36c6d3] | 13 | |
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[c6652bb] | 14 | where $q_c$ = the location of the crossover from one slope to the other, |
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| 15 | $A$ = the scaling coefficent that sets the overall intensity of the lower Q |
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[40a87fa] | 16 | power law region, $m1$ = power law exponent at low Q, and $m2$ = power law |
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[c6652bb] | 17 | exponent at high Q. The scaling of the second power law region (coefficent C) |
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| 18 | is then automatically scaled to match the first by following formula: |
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[8115d82] | 19 | |
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| 20 | .. math:: |
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[c6652bb] | 21 | C = \frac{A q_c^{m2}}{q_c^{m1}} |
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[8115d82] | 22 | |
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[34d6cab] | 23 | .. note:: |
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[8115d82] | 24 | Be sure to enter the power law exponents as positive values! |
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[a36c6d3] | 25 | |
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| 26 | For 2D data the scattering intensity is calculated in the same way as 1D, |
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| 27 | where the $q$ vector is defined as |
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| 28 | |
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| 29 | .. math:: |
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| 30 | |
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| 31 | q = \sqrt{q_x^2 + q_y^2} |
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| 32 | |
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| 33 | |
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| 34 | References |
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| 35 | ---------- |
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| 36 | |
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| 37 | None. |
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| 38 | |
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[c6652bb] | 39 | **Author:** NIST IGOR/DANSE **on:** pre 2010 |
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| 40 | |
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| 41 | **Last Modified by:** Wojciech Wpotrzebowski **on:** February 18, 2016 |
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| 42 | |
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| 43 | **Last Reviewed by:** Paul Butler **on:** March 21, 2016 |
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[a36c6d3] | 44 | """ |
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| 45 | |
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[2c74c11] | 46 | from numpy import inf, power, empty, errstate |
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[8115d82] | 47 | |
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| 48 | name = "two_power_law" |
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[c6652bb] | 49 | title = "This model calculates an empirical functional form for SAS data \ |
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| 50 | characterized by two power laws." |
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[8115d82] | 51 | description = """ |
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[c6652bb] | 52 | I(q) = coef_A*pow(qval,-1.0*power1) + background for q<=q_c |
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| 53 | =C*pow(qval,-1.0*power2) + background for q>q_c |
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| 54 | where C=coef_A*pow(q_c,-1.0*power1)/pow(q_c,-1.0*power2). |
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[8115d82] | 55 | |
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| 56 | coef_A = scaling coefficent |
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[c6652bb] | 57 | q_c = crossover location [1/A] |
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[8115d82] | 58 | power_1 (=m1) = power law exponent at low Q |
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| 59 | power_2 (=m2) = power law exponent at high Q |
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| 60 | background = Incoherent background [1/cm] |
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[a36c6d3] | 61 | """ |
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[8115d82] | 62 | category = "shape-independent" |
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| 63 | |
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[40a87fa] | 64 | # pylint: disable=bad-whitespace, line-too-long |
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| 65 | # ["name", "units", default, [lower, upper], "type", "description"], |
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| 66 | parameters = [ |
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| 67 | ["coefficent_1", "", 1.0, [-inf, inf], "", "coefficent A in low Q region"], |
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| 68 | ["crossover", "1/Ang", 0.04,[0, inf], "", "crossover location"], |
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| 69 | ["power_1", "", 1.0, [0, inf], "", "power law exponent at low Q"], |
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| 70 | ["power_2", "", 4.0, [0, inf], "", "power law exponent at high Q"], |
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| 71 | ] |
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| 72 | # pylint: enable=bad-whitespace, line-too-long |
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[8115d82] | 73 | |
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[7e1d090] | 74 | |
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[8115d82] | 75 | def Iq(q, |
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[34d6cab] | 76 | coefficent_1=1.0, |
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| 77 | crossover=0.04, |
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[8115d82] | 78 | power_1=1.0, |
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| 79 | power_2=4.0, |
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[7e1d090] | 80 | ): |
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[8115d82] | 81 | |
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| 82 | """ |
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| 83 | :param q: Input q-value (float or [float, float]) |
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[7e1d090] | 84 | :param coefficent_1: Scaling coefficent at low Q |
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[34d6cab] | 85 | :param crossover: Crossover location |
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[8115d82] | 86 | :param power_1: Exponent of power law function at low Q |
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| 87 | :param power_2: Exponent of power law function at high Q |
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| 88 | :return: Calculated intensity |
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| 89 | """ |
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[40a87fa] | 90 | result= empty(q.shape, 'd') |
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| 91 | index = (q <= crossover) |
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[2c74c11] | 92 | with errstate(divide='ignore'): |
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| 93 | coefficent_2 = coefficent_1 * power(crossover, power_2 - power_1) |
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[40a87fa] | 94 | result[index] = coefficent_1 * power(q[index], -power_1) |
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| 95 | result[~index] = coefficent_2 * power(q[~index], -power_2) |
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| 96 | return result |
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[8115d82] | 97 | |
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[3eb3312] | 98 | Iq.vectorized = True # Iq accepts an array of q values |
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[8115d82] | 99 | |
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[48462b0] | 100 | def random(): |
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| 101 | import numpy as np |
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| 102 | coefficient_1 = 1 |
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| 103 | crossover = 10**np.random.uniform(-3, -1) |
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| 104 | power_1 = np.random.uniform(1, 6) |
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| 105 | power_2 = np.random.uniform(1, 6) |
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| 106 | pars = dict( |
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| 107 | scale=1, #background=0, |
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| 108 | coefficient_1=coefficient_1, |
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| 109 | crossover=crossover, |
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| 110 | power_1=power_1, |
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| 111 | power_2=power_2, |
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| 112 | ) |
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| 113 | return pars |
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| 114 | |
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[7e1d090] | 115 | demo = dict(scale=1, background=0.0, |
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[34d6cab] | 116 | coefficent_1=1.0, |
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| 117 | crossover=0.04, |
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[8115d82] | 118 | power_1=1.0, |
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| 119 | power_2=4.0) |
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| 120 | |
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| 121 | tests = [ |
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| 122 | # Accuracy tests based on content in test/utest_extra_models.py |
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[34d6cab] | 123 | [{'coefficent_1': 1.0, |
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| 124 | 'crossover': 0.04, |
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[8115d82] | 125 | 'power_1': 1.0, |
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| 126 | 'power_2': 4.0, |
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[34d6cab] | 127 | 'background': 0.0, |
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[7e1d090] | 128 | }, 0.001, 1000], |
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[8115d82] | 129 | |
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[34d6cab] | 130 | [{'coefficent_1': 1.0, |
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| 131 | 'crossover': 0.04, |
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[8115d82] | 132 | 'power_1': 1.0, |
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| 133 | 'power_2': 4.0, |
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[34d6cab] | 134 | 'background': 0.0, |
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[7e1d090] | 135 | }, 0.150141, 0.125945], |
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[8115d82] | 136 | |
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[bb4b509] | 137 | [{'coefficent_1': 1.0, |
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[34d6cab] | 138 | 'crossover': 0.04, |
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[8115d82] | 139 | 'power_1': 1.0, |
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| 140 | 'power_2': 4.0, |
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[34d6cab] | 141 | 'background': 0.0, |
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[7e1d090] | 142 | }, 0.442528, 0.00166884], |
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[3eb3312] | 143 | |
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[bb4b509] | 144 | [{'coefficent_1': 1.0, |
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[3eb3312] | 145 | 'crossover': 0.04, |
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| 146 | 'power_1': 1.0, |
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| 147 | 'power_2': 4.0, |
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| 148 | 'background': 0.0, |
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[7e1d090] | 149 | }, (0.442528, 0.00166884), 0.00166884], |
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[3eb3312] | 150 | |
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[34d6cab] | 151 | ] |
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