1 | """ |
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2 | Wrap sasmodels for direct use by bumps. |
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3 | |
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4 | :class:`Model` is a wrapper for the sasmodels kernel which defines a |
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5 | bumps *Parameter* box for each kernel parameter. *Model* accepts keyword |
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6 | arguments to set the initial value for each parameter. |
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7 | |
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8 | :class:`Experiment` combines the *Model* function with a data file loaded by the |
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9 | sasview data loader. *Experiment* takes a *cutoff* parameter controlling |
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10 | how far the polydispersity integral extends. |
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11 | |
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12 | """ |
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13 | |
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14 | import warnings |
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15 | |
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16 | import numpy as np |
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17 | |
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18 | from .data import plot_theory |
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19 | from .direct_model import DataMixin |
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20 | |
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21 | # CRUFT: old style bumps wrapper which doesn't separate data and model |
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22 | def BumpsModel(data, model, cutoff=1e-5, **kw): |
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23 | warnings.warn("Use of BumpsModel is deprecated. Use bumps_model.Experiment instead.") |
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24 | model = Model(model, **kw) |
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25 | experiment = Experiment(data=data, model=model, cutoff=cutoff) |
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26 | for k in model._parameter_names: |
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27 | setattr(experiment, k, getattr(model, k)) |
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28 | return experiment |
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29 | |
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30 | def create_parameters(model_info, **kwargs): |
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31 | # lazy import; this allows the doc builder and nosetests to run even |
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32 | # when bumps is not on the path. |
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33 | from bumps.names import Parameter |
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34 | |
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35 | partype = model_info['partype'] |
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36 | |
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37 | pars = {} |
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38 | for p in model_info['parameters']: |
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39 | name, default, limits = p[0], p[2], p[3] |
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40 | value = kwargs.pop(name, default) |
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41 | pars[name] = Parameter.default(value, name=name, limits=limits) |
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42 | for name in partype['pd-2d']: |
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43 | for xpart, xdefault, xlimits in [ |
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44 | ('_pd', 0., limits), |
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45 | ('_pd_n', 35., (0, 1000)), |
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46 | ('_pd_nsigma', 3., (0, 10)), |
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47 | ]: |
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48 | xname = name + xpart |
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49 | xvalue = kwargs.pop(xname, xdefault) |
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50 | pars[xname] = Parameter.default(xvalue, name=xname, limits=xlimits) |
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51 | |
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52 | pd_types = {} |
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53 | for name in partype['pd-2d']: |
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54 | xname = name + '_pd_type' |
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55 | xvalue = kwargs.pop(xname, 'gaussian') |
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56 | pd_types[xname] = xvalue |
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57 | |
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58 | if kwargs: # args not corresponding to parameters |
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59 | raise TypeError("unexpected parameters: %s" |
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60 | % (", ".join(sorted(kwargs.keys())))) |
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61 | |
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62 | return pars, pd_types |
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63 | |
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64 | class Model(object): |
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65 | def __init__(self, model, **kwargs): |
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66 | self._sasmodel = model |
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67 | pars, pd_types = create_parameters(model.info, **kwargs) |
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68 | for k,v in pars.items(): |
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69 | setattr(self, k, v) |
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70 | for k,v in pd_types.items(): |
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71 | setattr(self, k, v) |
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72 | self._parameter_names = list(pars.keys()) |
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73 | self._pd_type_names = list(pd_types.keys()) |
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74 | |
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75 | def parameters(self): |
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76 | """ |
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77 | Return a dictionary of parameters |
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78 | """ |
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79 | return dict((k, getattr(self, k)) for k in self._parameter_names) |
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80 | |
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81 | def state(self): |
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82 | pars = dict((k, getattr(self, k).value) for k in self._parameter_names) |
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83 | pars.update((k, getattr(self, k)) for k in self._pd_type_names) |
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84 | return pars |
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85 | |
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86 | class Experiment(DataMixin): |
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87 | """ |
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88 | Return a bumps wrapper for a SAS model. |
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89 | |
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90 | *data* is the data to be fitted. |
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91 | |
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92 | *model* is the SAS model from :func:`core.load_model`. |
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93 | |
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94 | *cutoff* is the integration cutoff, which avoids computing the |
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95 | the SAS model where the polydispersity weight is low. |
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96 | |
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97 | Model parameters can be initialized with additional keyword |
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98 | arguments, or by assigning to model.parameter_name.value. |
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99 | |
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100 | The resulting bumps model can be used directly in a FitProblem call. |
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101 | """ |
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102 | def __init__(self, data, model, cutoff=1e-5): |
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103 | |
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104 | # remember inputs so we can inspect from outside |
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105 | self.model = model |
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106 | self.cutoff = cutoff |
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107 | self._interpret_data(data, model._sasmodel) |
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108 | self.update() |
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109 | |
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110 | def update(self): |
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111 | self._cache = {} |
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112 | |
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113 | def numpoints(self): |
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114 | """ |
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115 | Return the number of points |
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116 | """ |
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117 | return len(self.Iq) |
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118 | |
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119 | def parameters(self): |
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120 | """ |
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121 | Return a dictionary of parameters |
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122 | """ |
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123 | return self.model.parameters() |
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124 | |
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125 | def theory(self): |
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126 | if 'theory' not in self._cache: |
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127 | pars = self.model.state() |
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128 | self._cache['theory'] = self._calc_theory(pars, cutoff=self.cutoff) |
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129 | return self._cache['theory'] |
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130 | |
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131 | def residuals(self): |
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132 | #if np.any(self.err ==0): print("zeros in err") |
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133 | return (self.theory() - self.Iq) / self.dIq |
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134 | |
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135 | def nllf(self): |
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136 | delta = self.residuals() |
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137 | #if np.any(np.isnan(R)): print("NaN in residuals") |
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138 | return 0.5 * np.sum(delta ** 2) |
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139 | |
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140 | #def __call__(self): |
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141 | # return 2 * self.nllf() / self.dof |
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142 | |
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143 | def plot(self, view='log'): |
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144 | """ |
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145 | Plot the data and residuals. |
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146 | """ |
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147 | data, theory, resid = self._data, self.theory(), self.residuals() |
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148 | plot_theory(data, theory, resid, view) |
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149 | |
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150 | def simulate_data(self, noise=None): |
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151 | Iq = self.theory() |
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152 | self._set_data(Iq, noise) |
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153 | |
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154 | def save(self, basename): |
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155 | pass |
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156 | |
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157 | def __getstate__(self): |
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158 | # Can't pickle gpu functions, so instead make them lazy |
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159 | state = self.__dict__.copy() |
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160 | state['_kernel'] = None |
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161 | return state |
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162 | |
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163 | def __setstate__(self, state): |
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164 | # pylint: disable=attribute-defined-outside-init |
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165 | self.__dict__ = state |
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