1 | r""" |
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2 | This model describes a Lorentzian shaped peak on a flat background. |
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3 | |
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4 | Definition |
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5 | ---------- |
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6 | |
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7 | The scattering intensity $I(q)$ is calculated as |
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8 | |
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9 | .. math:: |
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10 | |
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11 | I(q) = \frac{scale}{\bigl(1+\bigl(\frac{q-q_0}{B}\bigr)^2\bigr)} + background |
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12 | |
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13 | with the peak having height of $I_0$ centered at $q_0$ and having |
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14 | a HWHM (half-width half-maximum) of B. |
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15 | |
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16 | For 2D data the scattering intensity is calculated in the same way as 1D, |
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17 | where the $q$ vector is defined as |
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18 | |
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19 | .. math:: |
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20 | |
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21 | q = \sqrt{q_x^2 + q_y^2} |
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22 | |
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23 | |
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24 | References |
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25 | ---------- |
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26 | |
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27 | None. |
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28 | |
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29 | Source |
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30 | ------ |
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31 | |
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32 | `peak_lorentz.py <https://github.com/SasView/sasmodels/blob/master/sasmodels/models/peak_lorentz.py>`_ |
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33 | |
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34 | `peak_lorentz.c <https://github.com/SasView/sasmodels/blob/master/sasmodels/models/peak_lorentz.c>`_ |
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35 | |
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36 | Authorship and Verification |
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37 | ---------------------------- |
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38 | |
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39 | * **Author:** |
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40 | * **Last Modified by:** |
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41 | * **Last Reviewed by:** |
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42 | * **Source added by :** Steve King **Date:** March 25, 2019 |
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43 | """ |
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44 | |
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45 | import numpy as np |
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46 | from numpy import inf |
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47 | |
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48 | name = "peak_lorentz" |
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49 | title = "A Lorentzian peak on a flat background" |
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50 | description = """\ |
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51 | Class that evaluates a lorentzian shaped peak. |
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52 | |
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53 | F(q) = scale/(1+[(q-q0)/B]^2 ) + background |
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54 | |
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55 | The model has three parameters: |
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56 | scale = scale |
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57 | peak_pos = peak position |
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58 | peak_hwhm = half-width-half-maximum of peak |
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59 | background= incoherent background""" |
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60 | |
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61 | category = "shape-independent" |
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62 | |
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63 | # ["name", "units", default, [lower, upper], "type", "description"], |
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64 | parameters = [["peak_pos", "1/Ang", 0.05, [-inf, inf], "", "Peak postion in q"], |
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65 | ["peak_hwhm", "1/Ang", 0.005, [-inf, inf], "", "HWHM of peak"], |
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66 | ] |
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67 | |
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68 | def Iq(q, peak_pos, peak_hwhm): |
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69 | """ |
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70 | Return I(q) |
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71 | """ |
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72 | inten = (1/(1+((q-peak_pos)/peak_hwhm)**2)) |
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73 | return inten |
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74 | Iq.vectorized = True # Iq accepts an array of q values |
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75 | |
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76 | def random(): |
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77 | """Return a random parameter set for the model.""" |
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78 | peak_pos = 10**np.random.uniform(-3, -1) |
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79 | peak_hwhm = peak_pos * 10**np.random.uniform(-3, 0) |
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80 | pars = dict( |
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81 | #background=0, |
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82 | scale=10**np.random.uniform(2, 6), |
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83 | peak_pos=peak_pos, |
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84 | peak_hwhm=peak_hwhm, |
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85 | ) |
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86 | return pars |
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87 | |
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88 | demo = dict(scale=100, background=1.0, |
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89 | peak_pos=0.05, peak_hwhm=0.005) |
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90 | |
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91 | tests = [[{'scale':100.0, 'background':1.0}, 0.001, 2.0305]] |
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