[8f20419d] | 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 math import pow |
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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']*pow(x,-1.0*self.params['power1']) |
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| 68 | else: |
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| 69 | scale = self.params['coef_A']*pow(qc,-1.0*self.params['power1']) \ |
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| 70 | / pow(qc,-1.0*self.params['power2']) |
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| 71 | inten = scale*pow(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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