[a84a0ca] | 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 |
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| 9 | the 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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[7ae2b7f] | 16 | import numpy as np # type: ignore |
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[a84a0ca] | 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 | __all__ = [ |
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| 22 | "Model", "Experiment", |
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| 23 | ] |
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| 24 | |
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| 25 | # CRUFT: old style bumps wrapper which doesn't separate data and model |
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| 26 | # pylint: disable=invalid-name |
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| 27 | def BumpsModel(data, model, cutoff=1e-5, **kw): |
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| 28 | r""" |
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| 29 | Bind a model to data, along with a polydispersity cutoff. |
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| 30 | |
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| 31 | *data* is a :class:`data.Data1D`, :class:`data.Data2D` or |
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| 32 | :class:`data.Sesans` object. Use :func:`data.empty_data1D` or |
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| 33 | :func:`data.empty_data2D` to define $q, \Delta q$ calculation |
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| 34 | points for displaying the SANS curve when there is no measured data. |
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| 35 | |
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| 36 | *model* is a runnable module as returned from :func:`core.load_model`. |
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| 37 | |
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| 38 | *cutoff* is the polydispersity weight cutoff. |
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| 39 | |
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| 40 | Any additional *key=value* pairs are model dependent parameters. |
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| 41 | |
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| 42 | Returns an :class:`Experiment` object. |
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| 43 | |
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| 44 | Note that the usual Bumps semantics is not fully supported, since |
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| 45 | assigning *M.name = parameter* on the returned experiment object |
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| 46 | does not set that parameter in the model. Range setting will still |
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| 47 | work as expected though. |
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| 48 | |
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| 49 | .. deprecated:: 0.1 |
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| 50 | Use :class:`Experiment` instead. |
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| 51 | """ |
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| 52 | warnings.warn("Use of BumpsModel is deprecated. Use bumps_model.Experiment instead.") |
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| 53 | |
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| 54 | # Create the model and experiment |
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| 55 | model = Model(model, **kw) |
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| 56 | experiment = Experiment(data=data, model=model, cutoff=cutoff) |
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| 57 | |
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| 58 | # Copy the model parameters up to the experiment object. |
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| 59 | for k, v in model.parameters().items(): |
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| 60 | setattr(experiment, k, v) |
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| 61 | return experiment |
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| 62 | |
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| 63 | |
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| 64 | def create_parameters(model_info, **kwargs): |
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| 65 | """ |
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| 66 | Generate Bumps parameters from the model info. |
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| 67 | |
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| 68 | *model_info* is returned from :func:`generate.model_info` on the |
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| 69 | model definition module. |
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| 70 | |
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| 71 | Any additional *key=value* pairs are initial values for the parameters |
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| 72 | to the models. Uninitialized parameters will use the model default |
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| 73 | value. |
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| 74 | |
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| 75 | Returns a dictionary of *{name: Parameter}* containing the bumps |
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| 76 | parameters for each model parameter, and a dictionary of |
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| 77 | *{name: str}* containing the polydispersity distribution types. |
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| 78 | """ |
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| 79 | # lazy import; this allows the doc builder and nosetests to run even |
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| 80 | # when bumps is not on the path. |
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[7ae2b7f] | 81 | from bumps.names import Parameter # type: ignore |
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[a84a0ca] | 82 | |
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[d19962c] | 83 | pars = {} # floating point parameters |
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| 84 | pd_types = {} # distribution names |
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[6d6508e] | 85 | for p in model_info.parameters.call_parameters: |
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[a84a0ca] | 86 | value = kwargs.pop(p.name, p.default) |
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| 87 | pars[p.name] = Parameter.default(value, name=p.name, limits=p.limits) |
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[d19962c] | 88 | if p.polydisperse: |
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| 89 | for part, default, limits in [ |
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| 90 | ('_pd', 0., pars[p.name].limits), |
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| 91 | ('_pd_n', 35., (0, 1000)), |
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| 92 | ('_pd_nsigma', 3., (0, 10)), |
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| 93 | ]: |
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| 94 | name = p.name + part |
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| 95 | value = kwargs.pop(name, default) |
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| 96 | pars[name] = Parameter.default(value, name=name, limits=limits) |
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[21b116f] | 97 | name = p.name + '_pd_type' |
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| 98 | pd_types[name] = kwargs.pop(name, 'gaussian') |
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[a84a0ca] | 99 | |
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| 100 | if kwargs: # args not corresponding to parameters |
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| 101 | raise TypeError("unexpected parameters: %s" |
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| 102 | % (", ".join(sorted(kwargs.keys())))) |
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| 103 | |
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| 104 | return pars, pd_types |
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| 105 | |
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| 106 | class Model(object): |
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| 107 | """ |
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| 108 | Bumps wrapper for a SAS model. |
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| 109 | |
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| 110 | *model* is a runnable module as returned from :func:`core.load_model`. |
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| 111 | |
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| 112 | *cutoff* is the polydispersity weight cutoff. |
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| 113 | |
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| 114 | Any additional *key=value* pairs are model dependent parameters. |
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| 115 | """ |
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| 116 | def __init__(self, model, **kwargs): |
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| 117 | self._sasmodel = model |
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| 118 | pars, pd_types = create_parameters(model.info, **kwargs) |
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| 119 | for k, v in pars.items(): |
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| 120 | setattr(self, k, v) |
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| 121 | for k, v in pd_types.items(): |
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| 122 | setattr(self, k, v) |
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| 123 | self._parameter_names = list(pars.keys()) |
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| 124 | self._pd_type_names = list(pd_types.keys()) |
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| 125 | |
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| 126 | def parameters(self): |
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| 127 | """ |
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| 128 | Return a dictionary of parameters objects for the parameters, |
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| 129 | excluding polydispersity distribution type. |
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| 130 | """ |
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| 131 | return dict((k, getattr(self, k)) for k in self._parameter_names) |
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| 132 | |
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| 133 | def state(self): |
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| 134 | """ |
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| 135 | Return a dictionary of current values for all the parameters, |
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| 136 | including polydispersity distribution type. |
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| 137 | """ |
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| 138 | pars = dict((k, getattr(self, k).value) for k in self._parameter_names) |
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| 139 | pars.update((k, getattr(self, k)) for k in self._pd_type_names) |
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| 140 | return pars |
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| 141 | |
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| 142 | class Experiment(DataMixin): |
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| 143 | r""" |
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| 144 | Bumps wrapper for a SAS experiment. |
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| 145 | |
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| 146 | *data* is a :class:`data.Data1D`, :class:`data.Data2D` or |
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| 147 | :class:`data.Sesans` object. Use :func:`data.empty_data1D` or |
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| 148 | :func:`data.empty_data2D` to define $q, \Delta q$ calculation |
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| 149 | points for displaying the SANS curve when there is no measured data. |
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| 150 | |
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| 151 | *model* is a :class:`Model` object. |
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| 152 | |
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| 153 | *cutoff* is the integration cutoff, which avoids computing the |
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| 154 | the SAS model where the polydispersity weight is low. |
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| 155 | |
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| 156 | The resulting model can be used directly in a Bumps FitProblem call. |
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| 157 | """ |
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| 158 | def __init__(self, data, model, cutoff=1e-5): |
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| 159 | |
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| 160 | # remember inputs so we can inspect from outside |
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| 161 | self.model = model |
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| 162 | self.cutoff = cutoff |
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| 163 | self._interpret_data(data, model._sasmodel) |
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| 164 | self.update() |
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| 165 | |
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| 166 | def update(self): |
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| 167 | """ |
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| 168 | Call when model parameters have changed and theory needs to be |
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| 169 | recalculated. |
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| 170 | """ |
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| 171 | self._cache = {} |
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| 172 | |
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| 173 | def numpoints(self): |
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| 174 | """ |
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| 175 | Return the number of data points |
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| 176 | """ |
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| 177 | return len(self.Iq) |
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| 178 | |
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| 179 | def parameters(self): |
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| 180 | """ |
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| 181 | Return a dictionary of parameters |
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| 182 | """ |
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| 183 | return self.model.parameters() |
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| 184 | |
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| 185 | def theory(self): |
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| 186 | """ |
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| 187 | Return the theory corresponding to the model parameters. |
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| 188 | |
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| 189 | This method uses lazy evaluation, and requires model.update() to be |
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| 190 | called when the parameters have changed. |
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| 191 | """ |
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| 192 | if 'theory' not in self._cache: |
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| 193 | pars = self.model.state() |
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| 194 | self._cache['theory'] = self._calc_theory(pars, cutoff=self.cutoff) |
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| 195 | return self._cache['theory'] |
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| 196 | |
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| 197 | def residuals(self): |
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| 198 | """ |
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| 199 | Return theory minus data normalized by uncertainty. |
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| 200 | """ |
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| 201 | #if np.any(self.err ==0): print("zeros in err") |
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| 202 | return (self.theory() - self.Iq) / self.dIq |
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| 203 | |
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| 204 | def nllf(self): |
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| 205 | """ |
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| 206 | Return the negative log likelihood of seeing data given the model |
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| 207 | parameters, up to a normalizing constant which depends on the data |
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| 208 | uncertainty. |
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| 209 | """ |
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| 210 | delta = self.residuals() |
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| 211 | #if np.any(np.isnan(R)): print("NaN in residuals") |
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| 212 | return 0.5 * np.sum(delta ** 2) |
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| 213 | |
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| 214 | #def __call__(self): |
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| 215 | # return 2 * self.nllf() / self.dof |
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| 216 | |
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| 217 | def plot(self, view='log'): |
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| 218 | """ |
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| 219 | Plot the data and residuals. |
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| 220 | """ |
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| 221 | data, theory, resid = self._data, self.theory(), self.residuals() |
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[ea75043] | 222 | plot_theory(data, theory, resid, view, Iq_calc = self.Iq_calc) |
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[a84a0ca] | 223 | |
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| 224 | def simulate_data(self, noise=None): |
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| 225 | """ |
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| 226 | Generate simulated data. |
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| 227 | """ |
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| 228 | Iq = self.theory() |
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| 229 | self._set_data(Iq, noise) |
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| 230 | |
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| 231 | def save(self, basename): |
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| 232 | """ |
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| 233 | Save the model parameters and data into a file. |
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| 234 | |
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| 235 | Not Implemented. |
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| 236 | """ |
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[9217ef8] | 237 | if self.data_type == "sesans": |
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| 238 | np.savetxt(basename+".dat", np.array([self._data.x, self.theory()]).T) |
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[a84a0ca] | 239 | pass |
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| 240 | |
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| 241 | def __getstate__(self): |
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| 242 | # Can't pickle gpu functions, so instead make them lazy |
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| 243 | state = self.__dict__.copy() |
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| 244 | state['_kernel'] = None |
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| 245 | return state |
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| 246 | |
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| 247 | def __setstate__(self, state): |
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| 248 | # pylint: disable=attribute-defined-outside-init |
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| 249 | self.__dict__ = state |
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