1 | #!/usr/bin/env python |
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2 | """ |
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3 | BroadPeakModel function as a BaseComponent model |
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4 | """ |
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5 | |
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6 | from sans.models.BaseComponent import BaseComponent |
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7 | from math import pow, fabs |
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8 | |
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9 | class BroadPeakModel(BaseComponent): |
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10 | """ |
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11 | Class that evaluates a BroadPeakModel. |
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12 | I(q) = I(q) = scale_p/pow(qval,exponent)+scale_l/ |
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13 | (1.0 + pow((qval*length_l),exponent_l) )+ background |
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14 | """ |
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15 | |
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16 | def __init__(self): |
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17 | """ Initialization """ |
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18 | |
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19 | # Initialize BaseComponent first, then sphere |
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20 | BaseComponent.__init__(self) |
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21 | |
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22 | ## Name of the model |
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23 | self.name = "BroadPeak" |
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24 | self.description="""I(q) = scale_p/pow(q,exponent)+scale_l/ |
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25 | (1.0 + pow((fabs(q-q_peak)*length_l),exponent_l) )+ background |
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26 | List of default parameters: |
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27 | scale_p = Porod term scaling |
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28 | exponent_p = Porod exponent |
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29 | scale_l = Lorentzian term scaling |
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30 | length_l = Lorentzian screening length [A] |
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31 | q_peak = peak location [1/A] |
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32 | exponent_l = Lorentzian exponent |
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33 | background = Incoherent background |
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34 | """ |
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35 | ## Define parameters |
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36 | self.params = {} |
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37 | self.params['scale_p'] = 1.0e-05 |
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38 | self.params['exponent_p'] = 3.0 |
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39 | self.params['scale_l'] = 10.0 |
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40 | self.params['length_l'] = 50.0 |
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41 | self.params['q_peak'] = 0.1 |
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42 | self.params['exponent_l'] = 2.0 |
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43 | self.params['background'] = 0.1 |
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44 | ## Parameter details [units, min, max] |
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45 | self.details = {} |
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46 | self.details['scale_p'] = ['', None, None] |
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47 | self.details['exponent_p'] = ['', None, None] |
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48 | self.details['scale_l'] = ['', None, None] |
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49 | self.details['length_l'] = ['A', None, None] |
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50 | self.details['q_peak'] = ['1/A', None, None] |
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51 | self.details['exponent_l'] = ['', None, None] |
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52 | self.details['background'] = ['[1/cm]', None, None] |
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53 | |
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54 | #list of parameter that cannot be fitted |
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55 | self.fixed= [] |
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56 | def _broadpeak(self, x): |
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57 | """ |
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58 | Model definition |
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59 | """ |
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60 | inten = self.params['scale_p']/pow(x,self.params['exponent_p']) |
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61 | inten += self.params['scale_l']/(1.0 + \ |
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62 | pow((fabs(x-self.params['q_peak'])*self.params['length_l']),self.params['exponent_l'])) |
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63 | inten += self.params['background'] |
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64 | |
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65 | return inten |
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66 | |
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67 | def run(self, x = 0.0): |
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68 | """ |
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69 | Evaluate the model |
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70 | |
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71 | param x: input q-value (float or [float, float] as [r, theta]) |
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72 | return: (scattering value) |
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73 | """ |
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74 | if x.__class__.__name__ == 'list': |
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75 | return self._broadpeak(x[0]) |
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76 | elif x.__class__.__name__ == 'tuple': |
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77 | raise ValueError, "Tuples are not allowed as input to BaseComponent models" |
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78 | else: |
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79 | return self._broadpeak(x) |
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80 | |
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81 | def runXY(self, x = 0.0): |
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82 | """ |
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83 | Evaluate the model |
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84 | |
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85 | param x: input q-value (float or [float, float] as [qx, qy]) |
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86 | return: scattering value |
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87 | """ |
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88 | if x.__class__.__name__ == 'list': |
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89 | q = math.sqrt(x[0]**2 + x[1]**2) |
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90 | return self._broadpeak(q) |
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91 | elif x.__class__.__name__ == 'tuple': |
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92 | raise ValueError, "Tuples are not allowed as input to BaseComponent models" |
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93 | else: |
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94 | return self._broadpeak(x) |
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95 | |
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96 | |
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