source: sasview/sansmodels/src/sans/models/LogNormal.py @ dfa8832

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Last change on this file since dfa8832 was c451be9, checked in by Jae Cho <jhjcho@…>, 15 years ago

corrections on the definition of polydispersity as suggested by steve K: should be normalized by average volume

  • Property mode set to 100644
File size: 4.0 KB
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[870f131]1#!/usr/bin/env python
2"""
3        This software was developed by the University of Tennessee as part of the
4        Distributed Data Analysis of Neutron Scattering Experiments (DANSE)
5        project funded by the US National Science Foundation.
6
7        If you use DANSE applications to do scientific research that leads to
8        publication, we ask that you acknowledge the use of the software with the
9        following sentence:
10
11        "This work benefited from DANSE software developed under NSF award DMR-0520547."
12
13        copyright 2008, University of Tennessee
14"""
15
16""" Provide functionality for a C extension model
17
18        WARNING: THIS FILE WAS GENERATED BY WRAPPERGENERATOR.PY
[9ce41c6]19                 DO NOT MODIFY THIS FILE, MODIFY ..\c_extensions\logNormal.h
[870f131]20                 AND RE-RUN THE GENERATOR SCRIPT
21
22"""
23
24from sans.models.BaseComponent import BaseComponent
25from sans_extension.c_models import CLogNormal
26import copy   
27   
28class LogNormal(CLogNormal, BaseComponent):
29    """ Class that evaluates a LogNormal model.
[fe9c19b4]30        This file was auto-generated from ..\c_extensions\logNormal.h.
31        Refer to that file and the structure it contains
32        for details of the model.
33        List of default parameters:
[870f131]34         scale           = 1.0
35         sigma           = 1.0
36         center          = 0.0
37
38    """
39       
40    def __init__(self):
41        """ Initialization """
42       
43        # Initialize BaseComponent first, then sphere
44        BaseComponent.__init__(self)
45        CLogNormal.__init__(self)
46       
47        ## Name of the model
48        self.name = "LogNormal"
49        ## Model description
50        self.description ="""f(x)=scale * 1/(sigma*math.sqrt(2pi))e^(-1/2*((math.log(x)-mu)/sigma)^2)"""
51       
[fe9c19b4]52        ## Parameter details [units, min, max]
[870f131]53        self.details = {}
54        self.details['scale'] = ['', None, None]
55        self.details['sigma'] = ['', None, None]
56        self.details['center'] = ['', None, None]
57
[fe9c19b4]58        ## fittable parameters
[870f131]59        self.fixed=[]
60       
61        ## parameters with orientation
62        self.orientation_params =[]
63   
64    def clone(self):
65        """ Return a identical copy of self """
66        return self._clone(LogNormal())   
[fe9c19b4]67       
68    def __getstate__(self):
69        """ return object state for pickling and copying """
70        model_state = {'params': self.params, 'dispersion': self.dispersion, 'log': self.log}
71       
72        return self.__dict__, model_state
73       
74    def __setstate__(self, state):
75        """ create object from pickled state """
76       
77        self.__dict__, model_state = state
78        self.params = model_state['params']
79        self.dispersion = model_state['dispersion']
80        self.log = model_state['log']
81       
[870f131]82   
83    def run(self, x = 0.0):
84        """ Evaluate the model
85            @param x: input q, or [q,phi]
86            @return: scattering function P(q)
87        """
88       
89        return CLogNormal.run(self, x)
90   
91    def runXY(self, x = 0.0):
92        """ Evaluate the model in cartesian coordinates
93            @param x: input q, or [qx, qy]
94            @return: scattering function P(q)
95        """
96       
97        return CLogNormal.runXY(self, x)
98       
99    def evalDistribition(self, x = []):
100        """ Evaluate the model in cartesian coordinates
101            @param x: input q[], or [qx[], qy[]]
102            @return: scattering function P(q[])
103        """
104        return CLogNormal.evalDistribition(self, x)
105       
[5eb9154]106    def calculate_ER(self):
107        """ Calculate the effective radius for P(q)*S(q)
108            @return: the value of the effective radius
109        """       
110        return CLogNormal.calculate_ER(self)
111       
[870f131]112    def set_dispersion(self, parameter, dispersion):
113        """
114            Set the dispersion object for a model parameter
115            @param parameter: name of the parameter [string]
116            @dispersion: dispersion object of type DispersionModel
117        """
118        return CLogNormal.set_dispersion(self, parameter, dispersion.cdisp)
119       
120   
121# End of file
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