1 | #!/usr/bin/env python |
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2 | """ |
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3 | Provide I(q) = A*pow(qval,-1.0*m1) for q<=qc |
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4 | =scale*pow(qval,-1.0*m2) for q>qc |
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5 | TwoPowerLaw function as a BaseComponent model |
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6 | """ |
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7 | |
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8 | from sans.models.BaseComponent import BaseComponent |
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9 | from numpy import power |
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10 | |
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11 | class TwoPowerLawModel(BaseComponent): |
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12 | """ |
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13 | Class that evaluates a TwoPowerLawModel. |
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14 | |
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15 | I(q) = coef_A*pow(qval,-1.0*power1) for q<=qc |
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16 | =C*pow(qval,-1.0*power2) for q>qc |
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17 | where C=coef_A*pow(qc,-1.0*power1)/pow(qc,-1.0*power2). |
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18 | List of default parameters: |
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19 | coef_A = coefficient |
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20 | power1 = (-) Power @ low Q |
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21 | power2 = (-) Power @ high Q |
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22 | qc = crossover Q-value |
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23 | background = incoherent background |
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24 | """ |
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25 | |
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26 | def __init__(self): |
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27 | """ Initialization """ |
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28 | |
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29 | # Initialize BaseComponent first, then sphere |
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30 | BaseComponent.__init__(self) |
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31 | |
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32 | ## Name of the model |
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33 | self.name = "TwoPowerLaw" |
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34 | self.description="""I(q) = coef_A*pow(qval,-1.0*power1) for q<=qc |
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35 | =C*pow(qval,-1.0*power2) for q>qc |
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36 | where C=coef_A*pow(qc,-1.0*power1)/pow(qc,-1.0*power2). |
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37 | List of default parameters: |
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38 | coef_A = coefficient |
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39 | power1 = (-) Power @ low Q |
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40 | power2 = (-) Power @ high Q |
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41 | qc = crossover Q-value |
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42 | background = incoherent background |
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43 | """ |
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44 | ## Define parameters |
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45 | self.params = {} |
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46 | self.params['coef_A'] = 1.0 |
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47 | self.params['power1'] = 1.0 |
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48 | self.params['power2'] = 4.0 |
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49 | self.params['qc'] = 0.04 |
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50 | self.params['background'] = 0.0 |
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51 | ## Parameter details [units, min, max] |
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52 | self.details = {} |
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53 | self.details['coef_A'] = ['', None, None] |
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54 | self.details['power1'] = ['', None, None] |
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55 | self.details['power2'] = ['', None, None] |
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56 | self.details['qc'] = ['1/A', None, None] |
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57 | self.details['background'] = ['[1/cm]', None, None] |
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58 | |
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59 | #list of parameter that cannot be fitted |
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60 | self.fixed= [] |
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61 | def _twopowerlaw(self, x): |
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62 | """ |
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63 | Model definition |
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64 | """ |
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65 | qc= self.params['qc'] |
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66 | if(x<=qc): |
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67 | inten = self.params['coef_A']*power(x,-1.0*self.params['power1']) |
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68 | else: |
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69 | scale = self.params['coef_A']*power(qc,-1.0*self.params['power1']) \ |
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70 | / power(qc,-1.0*self.params['power2']) |
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71 | inten = scale*power(x,-1.0*self.params['power2']) |
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72 | inten += self.params['background'] |
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73 | |
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74 | return inten |
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75 | |
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76 | def run(self, x = 0.0): |
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77 | """ Evaluate the model |
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78 | @param x: input q-value (float or [float, float] as [r, theta]) |
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79 | @return: (guinier value) |
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80 | """ |
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81 | if x.__class__.__name__ == 'list': |
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82 | return self._twopowerlaw(x[0]) |
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83 | elif x.__class__.__name__ == 'tuple': |
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84 | raise ValueError, "Tuples are not allowed as input to BaseComponent models" |
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85 | else: |
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86 | return self._twopowerlaw(x) |
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87 | |
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88 | def runXY(self, x = 0.0): |
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89 | """ Evaluate the model |
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90 | @param x: input q-value (float or [float, float] as [qx, qy]) |
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91 | @return: guinier value |
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92 | """ |
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93 | if x.__class__.__name__ == 'list': |
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94 | q = math.sqrt(x[0]**2 + x[1]**2) |
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95 | return self._twopowerlaw(q) |
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96 | elif x.__class__.__name__ == 'tuple': |
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97 | raise ValueError, "Tuples are not allowed as input to BaseComponent models" |
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98 | else: |
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99 | return self._twopowerlaw(x) |
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