[ae3ce4e] | 1 | #!/usr/bin/env python |
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[95986b5] | 2 | |
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[79ac6f8] | 3 | ############################################################################## |
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| 4 | # This software was developed by the University of Tennessee as part of the |
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| 5 | # Distributed Data Analysis of Neutron Scattering Experiments (DANSE) |
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| 6 | # project funded by the US National Science Foundation. |
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| 7 | # |
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| 8 | # If you use DANSE applications to do scientific research that leads to |
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| 9 | # publication, we ask that you acknowledge the use of the software with the |
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| 10 | # following sentence: |
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| 11 | # |
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| 12 | # "This work benefited from DANSE software developed under NSF award DMR-0520547." |
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| 13 | # |
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| 14 | # copyright 2008, University of Tennessee |
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| 15 | ############################################################################## |
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[95986b5] | 16 | |
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| 17 | |
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[79ac6f8] | 18 | """ |
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| 19 | Provide functionality for a C extension model |
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[ae3ce4e] | 20 | |
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[79ac6f8] | 21 | :WARNING: THIS FILE WAS GENERATED BY WRAPPERGENERATOR.PY |
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| 22 | DO NOT MODIFY THIS FILE, MODIFY ..\c_extensions\gaussian.h |
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| 23 | AND RE-RUN THE GENERATOR SCRIPT |
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[ae3ce4e] | 24 | |
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| 25 | """ |
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| 26 | |
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| 27 | from sans.models.BaseComponent import BaseComponent |
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| 28 | from sans_extension.c_models import CGaussian |
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| 29 | import copy |
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| 30 | |
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| 31 | class Gaussian(CGaussian, BaseComponent): |
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[79ac6f8] | 32 | """ |
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| 33 | Class that evaluates a Gaussian model. |
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| 34 | This file was auto-generated from ..\c_extensions\gaussian.h. |
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| 35 | Refer to that file and the structure it contains |
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| 36 | for details of the model. |
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| 37 | List of default parameters: |
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[ae3ce4e] | 38 | scale = 1.0 |
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| 39 | sigma = 1.0 |
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| 40 | center = 0.0 |
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| 41 | |
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| 42 | """ |
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| 43 | |
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| 44 | def __init__(self): |
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| 45 | """ Initialization """ |
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| 46 | |
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| 47 | # Initialize BaseComponent first, then sphere |
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| 48 | BaseComponent.__init__(self) |
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| 49 | CGaussian.__init__(self) |
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| 50 | |
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| 51 | ## Name of the model |
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| 52 | self.name = "Gaussian" |
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[9bd69098] | 53 | ## Model description |
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[95986b5] | 54 | self.description ="""f(x)=scale * 1/(sigma^2*2pi)e^(-(x-mu)^2/2sigma^2)""" |
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[9bd69098] | 55 | |
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[fe9c19b4] | 56 | ## Parameter details [units, min, max] |
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[ae3ce4e] | 57 | self.details = {} |
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| 58 | self.details['scale'] = ['', None, None] |
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| 59 | self.details['sigma'] = ['', None, None] |
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| 60 | self.details['center'] = ['', None, None] |
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[9bd69098] | 61 | |
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[fe9c19b4] | 62 | ## fittable parameters |
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[9bd69098] | 63 | self.fixed=[] |
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| 64 | |
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| 65 | ## parameters with orientation |
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| 66 | self.orientation_params =[] |
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[ae3ce4e] | 67 | |
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| 68 | def clone(self): |
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| 69 | """ Return a identical copy of self """ |
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[95986b5] | 70 | return self._clone(Gaussian()) |
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[fe9c19b4] | 71 | |
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| 72 | def __getstate__(self): |
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[79ac6f8] | 73 | """ |
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| 74 | return object state for pickling and copying |
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| 75 | """ |
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[fe9c19b4] | 76 | model_state = {'params': self.params, 'dispersion': self.dispersion, 'log': self.log} |
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| 77 | |
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| 78 | return self.__dict__, model_state |
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| 79 | |
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| 80 | def __setstate__(self, state): |
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[79ac6f8] | 81 | """ |
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| 82 | create object from pickled state |
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| 83 | |
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| 84 | :param state: the state of the current model |
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| 85 | |
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| 86 | """ |
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[fe9c19b4] | 87 | |
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| 88 | self.__dict__, model_state = state |
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| 89 | self.params = model_state['params'] |
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| 90 | self.dispersion = model_state['dispersion'] |
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| 91 | self.log = model_state['log'] |
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| 92 | |
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[ae3ce4e] | 93 | |
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[79ac6f8] | 94 | def run(self, x=0.0): |
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| 95 | """ |
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| 96 | Evaluate the model |
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| 97 | |
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| 98 | :param x: input q, or [q,phi] |
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| 99 | |
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| 100 | :return: scattering function P(q) |
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| 101 | |
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[ae3ce4e] | 102 | """ |
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| 103 | |
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| 104 | return CGaussian.run(self, x) |
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| 105 | |
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[79ac6f8] | 106 | def runXY(self, x=0.0): |
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| 107 | """ |
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| 108 | Evaluate the model in cartesian coordinates |
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| 109 | |
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| 110 | :param x: input q, or [qx, qy] |
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| 111 | |
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| 112 | :return: scattering function P(q) |
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| 113 | |
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[ae3ce4e] | 114 | """ |
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| 115 | |
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| 116 | return CGaussian.runXY(self, x) |
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[95986b5] | 117 | |
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[79ac6f8] | 118 | def evalDistribution(self, x=[]): |
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| 119 | """ |
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| 120 | Evaluate the model in cartesian coordinates |
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| 121 | |
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| 122 | :param x: input q[], or [qx[], qy[]] |
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| 123 | |
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| 124 | :return: scattering function P(q[]) |
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| 125 | |
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[9bd69098] | 126 | """ |
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[f9a1279] | 127 | return CGaussian.evalDistribution(self, x) |
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[9bd69098] | 128 | |
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[5eb9154] | 129 | def calculate_ER(self): |
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[79ac6f8] | 130 | """ |
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| 131 | Calculate the effective radius for P(q)*S(q) |
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| 132 | |
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| 133 | :return: the value of the effective radius |
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| 134 | |
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[5eb9154] | 135 | """ |
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| 136 | return CGaussian.calculate_ER(self) |
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| 137 | |
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[95986b5] | 138 | def set_dispersion(self, parameter, dispersion): |
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| 139 | """ |
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[79ac6f8] | 140 | Set the dispersion object for a model parameter |
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| 141 | |
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| 142 | :param parameter: name of the parameter [string] |
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| 143 | :param dispersion: dispersion object of type DispersionModel |
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| 144 | |
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[95986b5] | 145 | """ |
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| 146 | return CGaussian.set_dispersion(self, parameter, dispersion.cdisp) |
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| 147 | |
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[ae3ce4e] | 148 | |
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| 149 | # End of file |
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