[8f20419d] | 1 | #!/usr/bin/env python |
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| 2 | """ |
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| 3 | TwoLorentzianModel function as a BaseComponent model |
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| 4 | """ |
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| 5 | |
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[79492222] | 6 | from sas.models.BaseComponent import BaseComponent |
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[18695bf] | 7 | from numpy import power |
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[411d8bf] | 8 | import math |
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[8f20419d] | 9 | |
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| 10 | class TwoLorentzianModel(BaseComponent): |
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| 11 | """ |
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| 12 | Class that evaluates a TwoLorentzianModel. |
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| 13 | I(q) = II(q) = scale_1/(1.0 + pow((q*length_1),exponent_1)) |
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| 14 | + scale_2/(1.0 + pow((q*length_2),exponent_2) )+ background |
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| 15 | """ |
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| 16 | |
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| 17 | def __init__(self): |
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| 18 | """ Initialization """ |
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| 19 | |
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| 20 | # Initialize BaseComponent first, then sphere |
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| 21 | BaseComponent.__init__(self) |
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| 22 | |
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| 23 | ## Name of the model |
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| 24 | self.name = "TwoLorentzian" |
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[411d8bf] | 25 | self.description = """I(q) = scale_1/(1.0 + pow((q*length_1),exponent_1)) |
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[8f20419d] | 26 | + scale_2/(1.0 + pow((q*length_2),exponent_2) )+ background |
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| 27 | List of default parameters: |
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| 28 | scale_1 = Lorentzian term scaling #1 |
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| 29 | length_1 = Lorentzian screening length #1 [A] |
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| 30 | exponent_1 = Lorentzian exponent #1 |
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| 31 | scale_2 = Lorentzian term scaling #2 |
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| 32 | length_2 = Lorentzian screening length #2 [A] |
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| 33 | exponent_2 = Lorentzian exponent #2 |
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| 34 | background = Incoherent background |
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| 35 | """ |
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| 36 | ## Define parameters |
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| 37 | self.params = {} |
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| 38 | self.params['scale_1'] = 10.0 |
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| 39 | self.params['length_1'] = 100.0 |
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| 40 | self.params['exponent_1'] = 3.0 |
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| 41 | self.params['scale_2'] = 1.0 |
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| 42 | self.params['length_2'] = 10.0 |
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| 43 | self.params['exponent_2'] = 2.0 |
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| 44 | self.params['background'] = 0.1 |
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| 45 | ## Parameter details [units, min, max] |
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| 46 | self.details = {} |
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| 47 | self.details['scale_1'] = ['', None, None] |
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| 48 | self.details['length_1'] = ['A', None, None] |
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| 49 | self.details['exponent_1'] = ['', None, None] |
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| 50 | self.details['scale_2'] = ['', None, None] |
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| 51 | self.details['length_2'] = ['A', None, None] |
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| 52 | self.details['exponent_2'] = ['', None, None] |
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| 53 | self.details['background'] = ['[1/cm]', None, None] |
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| 54 | |
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| 55 | #list of parameter that cannot be fitted |
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[411d8bf] | 56 | self.fixed = [] |
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[8f20419d] | 57 | def _twolorentzian(self, x): |
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| 58 | """ |
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| 59 | Model definition |
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| 60 | """ |
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| 61 | inten = self.params['scale_1']/(1.0 + \ |
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[18695bf] | 62 | power((x*self.params['length_1']),self.params['exponent_1'])) |
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[8f20419d] | 63 | inten += self.params['scale_2']/(1.0 + \ |
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[18695bf] | 64 | power((x*self.params['length_2']),self.params['exponent_2'])) |
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[8f20419d] | 65 | inten += self.params['background'] |
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| 66 | |
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| 67 | return inten |
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| 68 | |
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| 69 | def run(self, x = 0.0): |
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| 70 | """ |
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| 71 | Evaluate the model |
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| 72 | |
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| 73 | param x: input q-value (float or [float, float] as [r, theta]) |
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| 74 | return: (scattering value) |
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| 75 | """ |
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| 76 | if x.__class__.__name__ == 'list': |
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| 77 | return self._twolorentzian(x[0]) |
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| 78 | elif x.__class__.__name__ == 'tuple': |
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| 79 | raise ValueError, "Tuples are not allowed as input to BaseComponent models" |
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| 80 | else: |
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| 81 | return self._twolorentzian(x) |
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| 82 | |
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| 83 | def runXY(self, x = 0.0): |
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| 84 | """ |
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| 85 | Evaluate the model |
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| 86 | |
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| 87 | param x: input q-value (float or [float, float] as [qx, qy]) |
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| 88 | return: scattering value |
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| 89 | """ |
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| 90 | if x.__class__.__name__ == 'list': |
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| 91 | q = math.sqrt(x[0]**2 + x[1]**2) |
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| 92 | return self._twolorentzian(q) |
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| 93 | elif x.__class__.__name__ == 'tuple': |
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| 94 | raise ValueError, "Tuples are not allowed as input to BaseComponent models" |
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| 95 | else: |
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| 96 | return self._twolorentzian(x) |
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