[8f20419d] | 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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