source: sasview/sansmodels/src/sans/models/Gaussian.py @ 0b12abb5

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Last change on this file since 0b12abb5 was 79ac6f8, checked in by Gervaise Alina <gervyh@…>, 15 years ago

working on documentation

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