[27fea3f] | 1 | #!/usr/bin/env python |
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| 2 | """ |
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| 3 | Model describes a Gaussian shaped peak including a flat background |
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| 4 | Provide F(q) = scale*exp( -1/2 *[(q-q0)/B]^2 )+ background |
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| 5 | PeakGaussModel 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 | import math |
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| 10 | |
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| 11 | class PeakGaussModel(BaseComponent): |
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| 12 | |
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| 13 | """ |
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| 14 | Class that evaluates a gaussian shaped peak. |
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| 15 | |
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| 16 | F(q) = scale*exp( -1/2 *[(q-qo)/B]^2 )+ background |
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| 17 | |
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| 18 | The model has three parameters: |
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| 19 | scale = scale |
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| 20 | q0 = peak position |
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| 21 | B = standard deviation |
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| 22 | background= incoherent background |
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| 23 | """ |
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| 24 | |
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| 25 | def __init__(self): |
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| 26 | """ Initialization """ |
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| 27 | |
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| 28 | # Initialize BaseComponent first, then sphere |
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| 29 | BaseComponent.__init__(self) |
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| 30 | |
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| 31 | ## Name of the model |
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| 32 | self.name = "Peak Gauss Model" |
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| 33 | self.description=""" F(q) = scale*exp( -1/2 *[(q-q0)/B]^2 )+ background |
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| 34 | |
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| 35 | The model has three parameters: |
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| 36 | scale = scale |
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| 37 | q0 = peak position |
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| 38 | B = standard deviation |
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| 39 | background= incoherent background""" |
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| 40 | ## Define parameters |
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| 41 | self.params = {} |
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| 42 | self.params['scale'] = 100.0 |
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| 43 | self.params['q0'] = 0.05 |
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| 44 | self.params['B'] = 0.005 |
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| 45 | self.params['background'] = 1.0 |
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| 46 | |
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| 47 | ## Parameter details [units, min, max] |
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| 48 | self.details = {} |
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| 49 | self.details['q0'] = ['[1/A]', None, None] |
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| 50 | self.details['scale'] = ['', 0, None] |
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| 51 | self.details['B'] = ['[1/A]', None, None] |
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| 52 | self.details['background'] = ['[1/cm]', None, None] |
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| 53 | #list of parameter that cannot be fitted |
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| 54 | self.fixed= [] |
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| 55 | |
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| 56 | def _PeakGauss(self, x): |
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| 57 | """ |
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| 58 | Evaluate F(x) = scale*exp( -1/2 *[(x-q0)/B]^2 )+ background |
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| 59 | |
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| 60 | """ |
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| 61 | return self.params['scale']*math.exp(-1/2 *\ |
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| 62 | math.pow((x - self.params['q0'])/self.params['B'],2)) \ |
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| 63 | + self.params['background'] |
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| 64 | |
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| 65 | |
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| 66 | def run(self, x = 0.0): |
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| 67 | """ Evaluate the model |
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| 68 | @param x: input q-value (float or [float, float] as [r, theta]) |
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| 69 | @return: (Peak Gaussian value) |
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| 70 | """ |
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| 71 | if x.__class__.__name__ == 'list': |
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| 72 | return self._PeakGauss(x[0]) |
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| 73 | elif x.__class__.__name__ == 'tuple': |
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| 74 | raise ValueError, "Tuples are not allowed as input to BaseComponent models" |
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| 75 | else: |
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| 76 | return self._PeakGauss(x) |
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| 77 | |
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| 78 | def runXY(self, x = 0.0): |
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| 79 | """ Evaluate the model |
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| 80 | @param x: input q-value (float or [float, float] as [qx, qy]) |
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| 81 | @return: Peak Gaussian value |
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| 82 | """ |
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| 83 | if x.__class__.__name__ == 'list': |
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| 84 | q = math.sqrt(x[0]**2 + x[1]**2) |
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| 85 | return self._PeakGauss(q) |
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| 86 | elif x.__class__.__name__ == 'tuple': |
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| 87 | raise ValueError, "Tuples are not allowed as input to BaseComponent models" |
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| 88 | else: |
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| 89 | return self._PeakGauss(x) |
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