1 | """ |
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2 | This software was developed by the University of Tennessee as part of the |
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3 | Distributed Data Analysis of Neutron Scattering Experiments (DANSE) |
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4 | project funded by the US National Science Foundation. |
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5 | |
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6 | If you use DANSE applications to do scientific research that leads to |
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7 | publication, we ask that you acknowledge the use of the software with the |
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8 | following sentence: |
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9 | |
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10 | "This work benefited from DANSE software developed under NSF award DMR-0520547." |
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11 | |
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12 | copyright 2008, University of Tennessee |
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13 | """ |
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14 | |
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15 | """ |
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16 | Class definitions for python dispersion model for |
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17 | model parameters. These classes are bridges to the C++ |
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18 | dispersion object. |
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19 | |
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20 | The ArrayDispersion class takes in numpy arrays only. |
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21 | |
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22 | Usage: |
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23 | These classes can be used to set the dispersion model of a SANS model |
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24 | parameter: |
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25 | |
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26 | cyl = CylinderModel() |
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27 | cyl.set_dispersion('radius', GaussianDispersion()) |
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28 | |
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29 | |
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30 | After the dispersion model is set, you can access it's |
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31 | parameter through the dispersion dictionary: |
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32 | |
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33 | cyl.dispersion['radius']['width'] = 5.0 |
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34 | |
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35 | TODO: For backward compatibility, the model parameters are still kept in |
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36 | a dictionary. The next iteration of refactoring work should involve moving |
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37 | away from value-based parameters to object-based parameter. We want to |
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38 | store parameters as objects so that we can unify the 'params' and 'dispersion' |
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39 | dictionaries into a single dictionary of parameter objects that hold the |
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40 | complete information about the parameter (units, limits, dispersion model, etc...). |
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41 | |
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42 | |
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43 | """ |
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44 | import sans_extension.c_models as c_models |
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45 | |
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46 | |
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47 | |
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48 | class DispersionModel: |
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49 | """ |
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50 | Python bridge class for a basic dispersion model |
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51 | class with a constant parameter value distribution |
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52 | """ |
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53 | def __init__(self): |
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54 | self.cdisp = c_models.new_dispersion_model() |
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55 | |
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56 | def set_weights(self, values, weights): |
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57 | """ |
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58 | Set the weights of an array dispersion |
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59 | """ |
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60 | message = "set_weights is not available for DispersionModel.\n" |
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61 | message += " Solution: Use an ArrayDispersion object" |
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62 | raise "RuntimeError", message |
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63 | |
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64 | class GaussianDispersion(DispersionModel): |
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65 | """ |
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66 | Python bridge class for a dispersion model based |
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67 | on a Gaussian distribution. |
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68 | """ |
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69 | def __init__(self): |
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70 | self.cdisp = c_models.new_gaussian_model() |
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71 | |
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72 | def set_weights(self, values, weights): |
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73 | """ |
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74 | Set the weights of an array dispersion |
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75 | """ |
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76 | message = "set_weights is not available for GaussiantDispersion.\n" |
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77 | message += " Solution: Use an ArrayDispersion object" |
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78 | raise "RuntimeError", message |
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79 | |
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80 | class ArrayDispersion(DispersionModel): |
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81 | """ |
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82 | Python bridge class for a dispersion model based on arrays. |
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83 | The user has to set a weight distribution that |
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84 | will be used in the averaging the model parameter |
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85 | it is applied to. |
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86 | """ |
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87 | def __init__(self): |
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88 | self.cdisp = c_models.new_array_model() |
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89 | |
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90 | def set_weights(self, values, weights): |
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91 | """ |
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92 | Set the weights of an array dispersion |
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93 | Only accept numpy arrays. |
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94 | @param values: numpy array of values |
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95 | @param weights: numpy array of weights for each value entry |
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96 | """ |
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97 | if len(values) != len(weights): |
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98 | raise ValueError, "ArrayDispersion.set_weights: given arrays are of different lengths" |
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99 | |
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100 | c_models.set_dispersion_weights(self.cdisp, values, weights) |
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101 | |
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102 | models = {GaussianDispersion:"GaussianModel", ArrayDispersion:"MyModel"} |
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103 | |
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