1 | """ |
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2 | Sasview model constructor. |
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3 | |
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4 | Given a module defining an OpenCL kernel such as sasmodels.models.cylinder, |
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5 | create a sasview model class to run that kernel as follows:: |
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6 | |
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7 | from sasmodels.sasview_model import load_custom_model |
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8 | CylinderModel = load_custom_model('sasmodels/models/cylinder.py') |
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9 | """ |
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10 | from __future__ import print_function |
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11 | |
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12 | import math |
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13 | from copy import deepcopy |
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14 | import collections |
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15 | import traceback |
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16 | import logging |
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17 | from os.path import basename, splitext, abspath |
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18 | try: |
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19 | import _thread as thread |
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20 | except ImportError: |
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21 | import thread |
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22 | |
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23 | import numpy as np # type: ignore |
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24 | |
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25 | from . import core |
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26 | from . import custom |
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27 | from . import kernelcl |
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28 | from . import product |
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29 | from . import generate |
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30 | from . import weights |
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31 | from . import modelinfo |
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32 | from .details import make_kernel_args, dispersion_mesh |
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33 | |
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34 | # pylint: disable=unused-import |
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35 | try: |
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36 | from typing import (Dict, Mapping, Any, Sequence, Tuple, NamedTuple, |
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37 | List, Optional, Union, Callable) |
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38 | from .modelinfo import ModelInfo, Parameter |
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39 | from .kernel import KernelModel |
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40 | MultiplicityInfoType = NamedTuple( |
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41 | 'MultiplicityInfo', |
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42 | [("number", int), ("control", str), ("choices", List[str]), |
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43 | ("x_axis_label", str)]) |
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44 | SasviewModelType = Callable[[int], "SasviewModel"] |
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45 | except ImportError: |
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46 | pass |
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47 | # pylint: enable=unused-import |
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48 | |
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49 | logger = logging.getLogger(__name__) |
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50 | |
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51 | calculation_lock = thread.allocate_lock() |
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52 | |
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53 | #: True if pre-existing plugins, with the old names and parameters, should |
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54 | #: continue to be supported. |
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55 | SUPPORT_OLD_STYLE_PLUGINS = True |
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56 | |
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57 | # TODO: separate x_axis_label from multiplicity info |
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58 | MultiplicityInfo = collections.namedtuple( |
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59 | 'MultiplicityInfo', |
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60 | ["number", "control", "choices", "x_axis_label"], |
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61 | ) |
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62 | |
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63 | #: set of defined models (standard and custom) |
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64 | MODELS = {} # type: Dict[str, SasviewModelType] |
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65 | # TODO: remove unused MODEL_BY_PATH cache once sasview no longer references it |
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66 | #: custom model {path: model} mapping so we can check timestamps |
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67 | MODEL_BY_PATH = {} # type: Dict[str, SasviewModelType] |
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68 | #: Track modules that we have loaded so we can determine whether the model |
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69 | #: has changed since we last reloaded. |
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70 | _CACHED_MODULE = {} # type: Dict[str, "module"] |
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71 | |
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72 | def reset_environment(): |
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73 | # type: () -> None |
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74 | """ |
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75 | Clear the compute engine context so that the GUI can change devices. |
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76 | |
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77 | This removes all compiled kernels, even those that are active on fit |
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78 | pages, but they will be restored the next time they are needed. |
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79 | """ |
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80 | kernelcl.reset_environment() |
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81 | for model in MODELS.values(): |
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82 | model._model = None |
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83 | |
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84 | def find_model(modelname): |
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85 | # type: (str) -> SasviewModelType |
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86 | """ |
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87 | Find a model by name. If the model name ends in py, try loading it from |
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88 | custom models, otherwise look for it in the list of builtin models. |
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89 | """ |
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90 | # TODO: used by sum/product model to load an existing model |
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91 | # TODO: doesn't handle custom models properly |
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92 | if modelname.endswith('.py'): |
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93 | return load_custom_model(modelname) |
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94 | elif modelname in MODELS: |
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95 | return MODELS[modelname] |
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96 | else: |
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97 | raise ValueError("unknown model %r"%modelname) |
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98 | |
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99 | |
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100 | # TODO: figure out how to say that the return type is a subclass |
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101 | def load_standard_models(): |
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102 | # type: () -> List[SasviewModelType] |
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103 | """ |
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104 | Load and return the list of predefined models. |
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105 | |
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106 | If there is an error loading a model, then a traceback is logged and the |
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107 | model is not returned. |
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108 | """ |
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109 | for name in core.list_models(): |
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110 | try: |
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111 | MODELS[name] = _make_standard_model(name) |
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112 | except Exception: |
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113 | logger.error(traceback.format_exc()) |
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114 | if SUPPORT_OLD_STYLE_PLUGINS: |
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115 | _register_old_models() |
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116 | |
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117 | return list(MODELS.values()) |
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118 | |
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119 | |
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120 | def load_custom_model(path): |
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121 | # type: (str) -> SasviewModelType |
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122 | """ |
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123 | Load a custom model given the model path. |
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124 | """ |
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125 | #logger.info("Loading model %s", path) |
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126 | |
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127 | # Load the kernel module. This may already be cached by the loader, so |
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128 | # only requires checking the timestamps of the dependents. |
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129 | kernel_module = custom.load_custom_kernel_module(path) |
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130 | |
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131 | # Check if the module has changed since we last looked. |
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132 | reloaded = kernel_module != _CACHED_MODULE.get(path, None) |
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133 | _CACHED_MODULE[path] = kernel_module |
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134 | |
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135 | # Turn the module into a model. We need to do this in even if the |
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136 | # model has already been loaded so that we can determine the model |
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137 | # name and retrieve it from the MODELS cache. |
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138 | model = getattr(kernel_module, 'Model', None) |
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139 | if model is not None: |
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140 | # Old style models do not set the name in the class attributes, so |
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141 | # set it here; this name will be overridden when the object is created |
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142 | # with an instance variable that has the same value. |
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143 | if model.name == "": |
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144 | model.name = splitext(basename(path))[0] |
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145 | if not hasattr(model, 'filename'): |
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146 | model.filename = abspath(kernel_module.__file__).replace('.pyc', '.py') |
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147 | if not hasattr(model, 'id'): |
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148 | model.id = splitext(basename(model.filename))[0] |
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149 | else: |
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150 | model_info = modelinfo.make_model_info(kernel_module) |
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151 | model = make_model_from_info(model_info) |
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152 | |
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153 | # If a model name already exists and we are loading a different model, |
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154 | # use the model file name as the model name. |
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155 | if model.name in MODELS and not model.filename == MODELS[model.name].filename: |
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156 | _previous_name = model.name |
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157 | model.name = model.id |
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158 | |
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159 | # If the new model name is still in the model list (for instance, |
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160 | # if we put a cylinder.py in our plug-in directory), then append |
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161 | # an identifier. |
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162 | if model.name in MODELS and not model.filename == MODELS[model.name].filename: |
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163 | model.name = model.id + '_user' |
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164 | logger.info("Model %s already exists: using %s [%s]", |
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165 | _previous_name, model.name, model.filename) |
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166 | |
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167 | # Only update the model if the module has changed |
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168 | if reloaded or model.name not in MODELS: |
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169 | MODELS[model.name] = model |
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170 | |
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171 | return MODELS[model.name] |
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172 | |
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173 | |
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174 | def make_model_from_info(model_info): |
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175 | # type: (ModelInfo) -> SasviewModelType |
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176 | """ |
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177 | Convert *model_info* into a SasView model wrapper. |
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178 | """ |
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179 | def __init__(self, multiplicity=None): |
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180 | SasviewModel.__init__(self, multiplicity=multiplicity) |
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181 | attrs = _generate_model_attributes(model_info) |
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182 | attrs['__init__'] = __init__ |
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183 | attrs['filename'] = model_info.filename |
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184 | ConstructedModel = type(model_info.name, (SasviewModel,), attrs) # type: SasviewModelType |
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185 | return ConstructedModel |
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186 | |
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187 | |
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188 | def _make_standard_model(name): |
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189 | # type: (str) -> SasviewModelType |
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190 | """ |
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191 | Load the sasview model defined by *name*. |
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192 | |
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193 | *name* can be a standard model name or a path to a custom model. |
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194 | |
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195 | Returns a class that can be used directly as a sasview model. |
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196 | """ |
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197 | kernel_module = generate.load_kernel_module(name) |
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198 | model_info = modelinfo.make_model_info(kernel_module) |
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199 | return make_model_from_info(model_info) |
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200 | |
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201 | |
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202 | def _register_old_models(): |
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203 | # type: () -> None |
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204 | """ |
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205 | Place the new models into sasview under the old names. |
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206 | |
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207 | Monkey patch sas.sascalc.fit as sas.models so that sas.models.pluginmodel |
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208 | is available to the plugin modules. |
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209 | """ |
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210 | import sys |
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211 | import sas # needed in order to set sas.models |
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212 | import sas.sascalc.fit |
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213 | sys.modules['sas.models'] = sas.sascalc.fit |
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214 | sas.models = sas.sascalc.fit |
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215 | import sas.models |
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216 | from sasmodels.conversion_table import CONVERSION_TABLE |
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217 | |
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218 | for new_name, conversion in CONVERSION_TABLE.get((3, 1, 2), {}).items(): |
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219 | # CoreShellEllipsoidModel => core_shell_ellipsoid:1 |
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220 | new_name = new_name.split(':')[0] |
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221 | old_name = conversion[0] if len(conversion) < 3 else conversion[2] |
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222 | module_attrs = {old_name: find_model(new_name)} |
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223 | ConstructedModule = type(old_name, (), module_attrs) |
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224 | old_path = 'sas.models.' + old_name |
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225 | setattr(sas.models, old_path, ConstructedModule) |
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226 | sys.modules[old_path] = ConstructedModule |
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227 | |
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228 | |
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229 | def MultiplicationModel(form_factor, structure_factor): |
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230 | # type: ("SasviewModel", "SasviewModel") -> "SasviewModel" |
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231 | """ |
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232 | Returns a constructed product model from form_factor and structure_factor. |
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233 | """ |
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234 | model_info = product.make_product_info(form_factor._model_info, |
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235 | structure_factor._model_info) |
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236 | ConstructedModel = make_model_from_info(model_info) |
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237 | return ConstructedModel(form_factor.multiplicity) |
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238 | |
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239 | |
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240 | def _generate_model_attributes(model_info): |
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241 | # type: (ModelInfo) -> Dict[str, Any] |
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242 | """ |
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243 | Generate the class attributes for the model. |
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244 | |
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245 | This should include all the information necessary to query the model |
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246 | details so that you do not need to instantiate a model to query it. |
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247 | |
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248 | All the attributes should be immutable to avoid accidents. |
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249 | """ |
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250 | |
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251 | # TODO: allow model to override axis labels input/output name/unit |
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252 | |
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253 | # Process multiplicity |
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254 | control_pars = [p.id for p in model_info.parameters.kernel_parameters |
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255 | if p.is_control] |
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256 | control_id = control_pars[0] if control_pars else None |
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257 | non_fittable = [] # type: List[str] |
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258 | xlabel = model_info.profile_axes[0] if model_info.profile is not None else "" |
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259 | variants = MultiplicityInfo(0, "", [], xlabel) |
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260 | for p in model_info.parameters.kernel_parameters: |
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261 | if p.id == control_id: |
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262 | non_fittable.append(p.name) |
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263 | variants = MultiplicityInfo( |
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264 | len(p.choices) if p.choices else int(p.limits[1]), |
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265 | p.name, p.choices, xlabel |
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266 | ) |
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267 | break |
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268 | |
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269 | # Only a single drop-down list parameter available |
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270 | fun_list = [] |
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271 | for p in model_info.parameters.kernel_parameters: |
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272 | if p.choices: |
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273 | fun_list = p.choices |
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274 | if p.length > 1: |
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275 | non_fittable.extend(p.id+str(k) for k in range(1, p.length+1)) |
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276 | break |
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277 | |
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278 | # Organize parameter sets |
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279 | orientation_params = [] |
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280 | magnetic_params = [] |
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281 | fixed = [] |
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282 | for p in model_info.parameters.user_parameters({}, is2d=True): |
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283 | if p.type == 'orientation': |
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284 | orientation_params.append(p.name) |
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285 | orientation_params.append(p.name+".width") |
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286 | fixed.append(p.name+".width") |
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287 | elif p.type == 'magnetic': |
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288 | orientation_params.append(p.name) |
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289 | magnetic_params.append(p.name) |
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290 | fixed.append(p.name+".width") |
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291 | |
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292 | |
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293 | # Build class dictionary |
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294 | attrs = {} # type: Dict[str, Any] |
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295 | attrs['_model_info'] = model_info |
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296 | attrs['name'] = model_info.name |
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297 | attrs['id'] = model_info.id |
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298 | attrs['description'] = model_info.description |
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299 | attrs['category'] = model_info.category |
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300 | attrs['is_structure_factor'] = model_info.structure_factor |
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301 | attrs['is_form_factor'] = model_info.effective_radius_type is not None |
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302 | attrs['is_multiplicity_model'] = variants[0] > 1 |
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303 | attrs['multiplicity_info'] = variants |
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304 | attrs['orientation_params'] = tuple(orientation_params) |
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305 | attrs['magnetic_params'] = tuple(magnetic_params) |
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306 | attrs['fixed'] = tuple(fixed) |
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307 | attrs['non_fittable'] = tuple(non_fittable) |
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308 | attrs['fun_list'] = tuple(fun_list) |
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309 | |
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310 | return attrs |
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311 | |
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312 | class SasviewModel(object): |
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313 | """ |
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314 | Sasview wrapper for opencl/ctypes model. |
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315 | """ |
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316 | # Model parameters for the specific model are set in the class constructor |
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317 | # via the _generate_model_attributes function, which subclasses |
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318 | # SasviewModel. They are included here for typing and documentation |
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319 | # purposes. |
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320 | _model = None # type: KernelModel |
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321 | _model_info = None # type: ModelInfo |
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322 | #: load/save name for the model |
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323 | id = None # type: str |
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324 | #: display name for the model |
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325 | name = None # type: str |
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326 | #: short model description |
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327 | description = None # type: str |
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328 | #: default model category |
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329 | category = None # type: str |
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330 | |
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331 | #: names of the orientation parameters in the order they appear |
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332 | orientation_params = None # type: List[str] |
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333 | #: names of the magnetic parameters in the order they appear |
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334 | magnetic_params = None # type: List[str] |
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335 | #: names of the fittable parameters |
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336 | fixed = None # type: List[str] |
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337 | # TODO: the attribute fixed is ill-named |
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338 | |
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339 | # Axis labels |
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340 | input_name = "Q" |
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341 | input_unit = "A^{-1}" |
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342 | output_name = "Intensity" |
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343 | output_unit = "cm^{-1}" |
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344 | |
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345 | #: default cutoff for polydispersity |
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346 | cutoff = 1e-5 |
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347 | |
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348 | # Note: Use non-mutable values for class attributes to avoid errors |
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349 | #: parameters that are not fitted |
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350 | non_fittable = () # type: Sequence[str] |
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351 | |
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352 | #: True if model should appear as a structure factor |
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353 | is_structure_factor = False |
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354 | #: True if model should appear as a form factor |
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355 | is_form_factor = False |
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356 | #: True if model has multiplicity |
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357 | is_multiplicity_model = False |
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358 | #: Multiplicity information |
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359 | multiplicity_info = None # type: MultiplicityInfoType |
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360 | |
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361 | # Per-instance variables |
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362 | #: parameter {name: value} mapping |
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363 | params = None # type: Dict[str, float] |
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364 | #: values for dispersion width, npts, nsigmas and type |
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365 | dispersion = None # type: Dict[str, Any] |
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366 | #: units and limits for each parameter |
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367 | details = None # type: Dict[str, Sequence[Any]] |
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368 | # # actual type is Dict[str, List[str, float, float]] |
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369 | #: multiplicity value, or None if no multiplicity on the model |
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370 | multiplicity = None # type: Optional[int] |
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371 | #: memory for polydispersity array if using ArrayDispersion (used by sasview). |
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372 | _persistency_dict = None # type: Dict[str, Tuple[np.ndarray, np.ndarray]] |
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373 | |
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374 | def __init__(self, multiplicity=None): |
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375 | # type: (Optional[int]) -> None |
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376 | |
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377 | # TODO: _persistency_dict to persistency_dict throughout sasview |
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378 | # TODO: refactor multiplicity to encompass variants |
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379 | # TODO: dispersion should be a class |
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380 | # TODO: refactor multiplicity info |
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381 | # TODO: separate profile view from multiplicity |
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382 | # The button label, x and y axis labels and scale need to be under |
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383 | # the control of the model, not the fit page. Maximum flexibility, |
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384 | # the fit page would supply the canvas and the profile could plot |
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385 | # how it wants, but this assumes matplotlib. Next level is that |
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386 | # we provide some sort of data description including title, labels |
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387 | # and lines to plot. |
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388 | |
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389 | # Get the list of hidden parameters given the multiplicity |
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390 | # Don't include multiplicity in the list of parameters |
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391 | self.multiplicity = multiplicity |
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392 | if multiplicity is not None: |
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393 | hidden = self._model_info.get_hidden_parameters(multiplicity) |
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394 | hidden |= set([self.multiplicity_info.control]) |
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395 | else: |
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396 | hidden = set() |
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397 | if self._model_info.structure_factor: |
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398 | hidden.add('scale') |
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399 | hidden.add('background') |
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400 | |
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401 | # Update the parameter lists to exclude any hidden parameters |
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402 | self.magnetic_params = tuple(pname for pname in self.magnetic_params |
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403 | if pname not in hidden) |
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404 | self.orientation_params = tuple(pname for pname in self.orientation_params |
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405 | if pname not in hidden) |
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406 | |
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407 | self._persistency_dict = {} |
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408 | self.params = collections.OrderedDict() |
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409 | self.dispersion = collections.OrderedDict() |
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410 | self.details = {} |
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411 | for p in self._model_info.parameters.user_parameters({}, is2d=True): |
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412 | if p.name in hidden: |
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413 | continue |
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414 | self.params[p.name] = p.default |
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415 | self.details[p.id] = [p.units, p.limits[0], p.limits[1]] |
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416 | if p.polydisperse: |
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417 | self.details[p.id+".width"] = [ |
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418 | "", 0.0, 1.0 if p.relative_pd else np.inf |
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419 | ] |
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420 | self.dispersion[p.name] = { |
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421 | 'width': 0, |
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422 | 'npts': 35, |
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423 | 'nsigmas': 3, |
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424 | 'type': 'gaussian', |
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425 | } |
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426 | |
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427 | def __get_state__(self): |
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428 | # type: () -> Dict[str, Any] |
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429 | state = self.__dict__.copy() |
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430 | state.pop('_model') |
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431 | # May need to reload model info on set state since it has pointers |
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432 | # to python implementations of Iq, etc. |
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433 | #state.pop('_model_info') |
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434 | return state |
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435 | |
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436 | def __set_state__(self, state): |
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437 | # type: (Dict[str, Any]) -> None |
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438 | self.__dict__ = state |
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439 | self._model = None |
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440 | |
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441 | def __str__(self): |
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442 | # type: () -> str |
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443 | """ |
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444 | :return: string representation |
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445 | """ |
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446 | return self.name |
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447 | |
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448 | def is_fittable(self, par_name): |
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449 | # type: (str) -> bool |
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450 | """ |
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451 | Check if a given parameter is fittable or not |
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452 | |
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453 | :param par_name: the parameter name to check |
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454 | """ |
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455 | return par_name in self.fixed |
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456 | #For the future |
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457 | #return self.params[str(par_name)].is_fittable() |
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458 | |
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459 | |
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460 | def getProfile(self): |
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461 | # type: () -> (np.ndarray, np.ndarray) |
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462 | """ |
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463 | Get SLD profile |
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464 | |
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465 | : return: (z, beta) where z is a list of depth of the transition points |
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466 | beta is a list of the corresponding SLD values |
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467 | """ |
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468 | args = {} # type: Dict[str, Any] |
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469 | for p in self._model_info.parameters.kernel_parameters: |
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470 | if p.id == self.multiplicity_info.control: |
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471 | value = float(self.multiplicity) |
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472 | elif p.length == 1: |
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473 | value = self.params.get(p.id, np.NaN) |
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474 | else: |
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475 | value = np.array([self.params.get(p.id+str(k), np.NaN) |
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476 | for k in range(1, p.length+1)]) |
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477 | args[p.id] = value |
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478 | |
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479 | x, y = self._model_info.profile(**args) |
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480 | return x, 1e-6*y |
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481 | |
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482 | def setParam(self, name, value): |
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483 | # type: (str, float) -> None |
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484 | """ |
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485 | Set the value of a model parameter |
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486 | |
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487 | :param name: name of the parameter |
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488 | :param value: value of the parameter |
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489 | |
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490 | """ |
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491 | # Look for dispersion parameters |
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492 | toks = name.split('.') |
---|
493 | if len(toks) == 2: |
---|
494 | for item in self.dispersion.keys(): |
---|
495 | if item == toks[0]: |
---|
496 | for par in self.dispersion[item]: |
---|
497 | if par == toks[1]: |
---|
498 | self.dispersion[item][par] = value |
---|
499 | return |
---|
500 | else: |
---|
501 | # Look for standard parameter |
---|
502 | for item in self.params.keys(): |
---|
503 | if item == name: |
---|
504 | self.params[item] = value |
---|
505 | return |
---|
506 | |
---|
507 | raise ValueError("Model does not contain parameter %s" % name) |
---|
508 | |
---|
509 | def getParam(self, name): |
---|
510 | # type: (str) -> float |
---|
511 | """ |
---|
512 | Set the value of a model parameter |
---|
513 | |
---|
514 | :param name: name of the parameter |
---|
515 | |
---|
516 | """ |
---|
517 | # Look for dispersion parameters |
---|
518 | toks = name.split('.') |
---|
519 | if len(toks) == 2: |
---|
520 | for item in self.dispersion.keys(): |
---|
521 | if item == toks[0]: |
---|
522 | for par in self.dispersion[item]: |
---|
523 | if par == toks[1]: |
---|
524 | return self.dispersion[item][par] |
---|
525 | else: |
---|
526 | # Look for standard parameter |
---|
527 | for item in self.params.keys(): |
---|
528 | if item == name: |
---|
529 | return self.params[item] |
---|
530 | |
---|
531 | raise ValueError("Model does not contain parameter %s" % name) |
---|
532 | |
---|
533 | def getParamList(self): |
---|
534 | # type: () -> Sequence[str] |
---|
535 | """ |
---|
536 | Return a list of all available parameters for the model |
---|
537 | """ |
---|
538 | param_list = list(self.params.keys()) |
---|
539 | # WARNING: Extending the list with the dispersion parameters |
---|
540 | param_list.extend(self.getDispParamList()) |
---|
541 | return param_list |
---|
542 | |
---|
543 | def getDispParamList(self): |
---|
544 | # type: () -> Sequence[str] |
---|
545 | """ |
---|
546 | Return a list of polydispersity parameters for the model |
---|
547 | """ |
---|
548 | # TODO: fix test so that parameter order doesn't matter |
---|
549 | ret = ['%s.%s' % (p_name, ext) |
---|
550 | for p_name in self.dispersion.keys() |
---|
551 | for ext in ('npts', 'nsigmas', 'width')] |
---|
552 | #print(ret) |
---|
553 | return ret |
---|
554 | |
---|
555 | def clone(self): |
---|
556 | # type: () -> "SasviewModel" |
---|
557 | """ Return a identical copy of self """ |
---|
558 | return deepcopy(self) |
---|
559 | |
---|
560 | def run(self, x=0.0): |
---|
561 | # type: (Union[float, (float, float), List[float]]) -> float |
---|
562 | """ |
---|
563 | Evaluate the model |
---|
564 | |
---|
565 | :param x: input q, or [q,phi] |
---|
566 | |
---|
567 | :return: scattering function P(q) |
---|
568 | |
---|
569 | **DEPRECATED**: use calculate_Iq instead |
---|
570 | """ |
---|
571 | if isinstance(x, (list, tuple)): |
---|
572 | # pylint: disable=unpacking-non-sequence |
---|
573 | q, phi = x |
---|
574 | result, _ = self.calculate_Iq([q*math.cos(phi)], [q*math.sin(phi)]) |
---|
575 | return result[0] |
---|
576 | else: |
---|
577 | result, _ = self.calculate_Iq([x]) |
---|
578 | return result[0] |
---|
579 | |
---|
580 | |
---|
581 | def runXY(self, x=0.0): |
---|
582 | # type: (Union[float, (float, float), List[float]]) -> float |
---|
583 | """ |
---|
584 | Evaluate the model in cartesian coordinates |
---|
585 | |
---|
586 | :param x: input q, or [qx, qy] |
---|
587 | |
---|
588 | :return: scattering function P(q) |
---|
589 | |
---|
590 | **DEPRECATED**: use calculate_Iq instead |
---|
591 | """ |
---|
592 | if isinstance(x, (list, tuple)): |
---|
593 | result, _ = self.calculate_Iq([x[0]], [x[1]]) |
---|
594 | return result[0] |
---|
595 | else: |
---|
596 | result, _ = self.calculate_Iq([x]) |
---|
597 | return result[0] |
---|
598 | |
---|
599 | def evalDistribution(self, qdist): |
---|
600 | # type: (Union[np.ndarray, Tuple[np.ndarray, np.ndarray], List[np.ndarray]]) -> np.ndarray |
---|
601 | r""" |
---|
602 | Evaluate a distribution of q-values. |
---|
603 | |
---|
604 | :param qdist: array of q or a list of arrays [qx,qy] |
---|
605 | |
---|
606 | * For 1D, a numpy array is expected as input |
---|
607 | |
---|
608 | :: |
---|
609 | |
---|
610 | evalDistribution(q) |
---|
611 | |
---|
612 | where *q* is a numpy array. |
---|
613 | |
---|
614 | * For 2D, a list of *[qx,qy]* is expected with 1D arrays as input |
---|
615 | |
---|
616 | :: |
---|
617 | |
---|
618 | qx = [ qx[0], qx[1], qx[2], ....] |
---|
619 | qy = [ qy[0], qy[1], qy[2], ....] |
---|
620 | |
---|
621 | If the model is 1D only, then |
---|
622 | |
---|
623 | .. math:: |
---|
624 | |
---|
625 | q = \sqrt{q_x^2+q_y^2} |
---|
626 | |
---|
627 | """ |
---|
628 | if isinstance(qdist, (list, tuple)): |
---|
629 | # Check whether we have a list of ndarrays [qx,qy] |
---|
630 | qx, qy = qdist |
---|
631 | result, _ = self.calculate_Iq(qx, qy) |
---|
632 | return result |
---|
633 | |
---|
634 | elif isinstance(qdist, np.ndarray): |
---|
635 | # We have a simple 1D distribution of q-values |
---|
636 | result, _ = self.calculate_Iq(qdist) |
---|
637 | return result |
---|
638 | |
---|
639 | else: |
---|
640 | raise TypeError("evalDistribution expects q or [qx, qy], not %r" |
---|
641 | % type(qdist)) |
---|
642 | |
---|
643 | def calc_composition_models(self, qx): |
---|
644 | """ |
---|
645 | returns parts of the composition model or None if not a composition |
---|
646 | model. |
---|
647 | """ |
---|
648 | # TODO: have calculate_Iq return the intermediates. |
---|
649 | # |
---|
650 | # The current interface causes calculate_Iq() to be called twice, |
---|
651 | # once to get the combined result and again to get the intermediate |
---|
652 | # results. This is necessary for now. |
---|
653 | # Long term, the solution is to change the interface to calculate_Iq |
---|
654 | # so that it returns a results object containing all the bits: |
---|
655 | # the A, B, C, ... of the composition model (and any subcomponents?) |
---|
656 | # the P and S of the product model |
---|
657 | # the combined model before resolution smearing, |
---|
658 | # the sasmodel before sesans conversion, |
---|
659 | # the oriented 2D model used to fit oriented usans data, |
---|
660 | # the final I(q), |
---|
661 | # ... |
---|
662 | # |
---|
663 | # Have the model calculator add all of these blindly to the data |
---|
664 | # tree, and update the graphs which contain them. The fitter |
---|
665 | # needs to be updated to use the I(q) value only, ignoring the rest. |
---|
666 | # |
---|
667 | # The simple fix of returning the existing intermediate results |
---|
668 | # will not work for a couple of reasons: (1) another thread may |
---|
669 | # sneak in to compute its own results before calc_composition_models |
---|
670 | # is called, and (2) calculate_Iq is currently called three times: |
---|
671 | # once with q, once with q values before qmin and once with q values |
---|
672 | # after q max. Both of these should be addressed before |
---|
673 | # replacing this code. |
---|
674 | composition = self._model_info.composition |
---|
675 | if composition and composition[0] == 'product': # only P*S for now |
---|
676 | with calculation_lock: |
---|
677 | _, lazy_results = self._calculate_Iq(qx) |
---|
678 | # for compatibility with sasview 4.x |
---|
679 | results = lazy_results() |
---|
680 | pq_data = results.get("P(Q)") |
---|
681 | sq_data = results.get("S(Q)") |
---|
682 | return pq_data, sq_data |
---|
683 | else: |
---|
684 | return None |
---|
685 | |
---|
686 | def calculate_Iq(self, |
---|
687 | qx, # type: Sequence[float] |
---|
688 | qy=None # type: Optional[Sequence[float]] |
---|
689 | ): |
---|
690 | # type: (...) -> Tuple[np.ndarray, Callable[[], collections.OrderedDict[str, np.ndarray]]] |
---|
691 | """ |
---|
692 | Calculate Iq for one set of q with the current parameters. |
---|
693 | |
---|
694 | If the model is 1D, use *q*. If 2D, use *qx*, *qy*. |
---|
695 | |
---|
696 | This should NOT be used for fitting since it copies the *q* vectors |
---|
697 | to the card for each evaluation. |
---|
698 | |
---|
699 | The returned tuple contains the scattering intensity followed by a |
---|
700 | callable which returns a dictionary of intermediate data from |
---|
701 | ProductKernel. |
---|
702 | """ |
---|
703 | ## uncomment the following when trying to debug the uncoordinated calls |
---|
704 | ## to calculate_Iq |
---|
705 | #if calculation_lock.locked(): |
---|
706 | # logger.info("calculation waiting for another thread to complete") |
---|
707 | # logger.info("\n".join(traceback.format_stack())) |
---|
708 | |
---|
709 | with calculation_lock: |
---|
710 | return self._calculate_Iq(qx, qy) |
---|
711 | |
---|
712 | def _calculate_Iq(self, qx, qy=None): |
---|
713 | if self._model is None: |
---|
714 | # Only need one copy of the compiled kernel regardless of how many |
---|
715 | # times it is used, so store it in the class. Also, to reset the |
---|
716 | # compute engine, need to clear out all existing compiled kernels, |
---|
717 | # which is much easier to do if we store them in the class. |
---|
718 | self.__class__._model = core.build_model(self._model_info) |
---|
719 | if qy is not None: |
---|
720 | q_vectors = [np.asarray(qx), np.asarray(qy)] |
---|
721 | else: |
---|
722 | q_vectors = [np.asarray(qx)] |
---|
723 | calculator = self._model.make_kernel(q_vectors) |
---|
724 | parameters = self._model_info.parameters |
---|
725 | pairs = [self._get_weights(p) for p in parameters.call_parameters] |
---|
726 | #weights.plot_weights(self._model_info, pairs) |
---|
727 | call_details, values, is_magnetic = make_kernel_args(calculator, pairs) |
---|
728 | #call_details.show() |
---|
729 | #print("================ parameters ==================") |
---|
730 | #for p, v in zip(parameters.call_parameters, pairs): print(p.name, v[0]) |
---|
731 | #for k, p in enumerate(self._model_info.parameters.call_parameters): |
---|
732 | # print(k, p.name, *pairs[k]) |
---|
733 | #print("params", self.params) |
---|
734 | #print("values", values) |
---|
735 | #print("is_mag", is_magnetic) |
---|
736 | result = calculator(call_details, values, cutoff=self.cutoff, |
---|
737 | magnetic=is_magnetic) |
---|
738 | lazy_results = getattr(calculator, 'results', |
---|
739 | lambda: collections.OrderedDict()) |
---|
740 | #print("result", result) |
---|
741 | |
---|
742 | calculator.release() |
---|
743 | #self._model.release() |
---|
744 | |
---|
745 | return result, lazy_results |
---|
746 | |
---|
747 | |
---|
748 | def calculate_ER(self, mode=1): |
---|
749 | # type: () -> float |
---|
750 | """ |
---|
751 | Calculate the effective radius for P(q)*S(q) |
---|
752 | |
---|
753 | *mode* is the R_eff type, which defaults to 1 to match the ER |
---|
754 | calculation for sasview models from version 3.x. |
---|
755 | |
---|
756 | :return: the value of the effective radius |
---|
757 | """ |
---|
758 | # ER and VR are only needed for old multiplication models, based on |
---|
759 | # sas.sascalc.fit.MultiplicationModel. Fail for now. If we want to |
---|
760 | # continue supporting them then add some test cases so that the code |
---|
761 | # is exercised. We can access ER/VR using the kernel Fq function by |
---|
762 | # extending _calculate_Iq so that it calls: |
---|
763 | # if er_mode > 0: |
---|
764 | # res = calculator.Fq(call_details, values, cutoff=self.cutoff, |
---|
765 | # magnetic=False, effective_radius_type=mode) |
---|
766 | # R_eff, form_shell_ratio = res[2], res[4] |
---|
767 | # return R_eff, form_shell_ratio |
---|
768 | # Then use the following in calculate_ER: |
---|
769 | # ER, VR = self._calculate_Iq(q=[0.1], er_mode=mode) |
---|
770 | # return ER |
---|
771 | # Similarly, for calculate_VR: |
---|
772 | # ER, VR = self._calculate_Iq(q=[0.1], er_mode=1) |
---|
773 | # return VR |
---|
774 | # Obviously a combined calculate_ER_VR method would be better, but |
---|
775 | # we only need them to support very old models, so ignore the 2x |
---|
776 | # performance hit. |
---|
777 | raise NotImplementedError("ER function is no longer available.") |
---|
778 | |
---|
779 | def calculate_VR(self): |
---|
780 | # type: () -> float |
---|
781 | """ |
---|
782 | Calculate the volf ratio for P(q)*S(q) |
---|
783 | |
---|
784 | :return: the value of the form:shell volume ratio |
---|
785 | """ |
---|
786 | # See comments in calculate_ER. |
---|
787 | raise NotImplementedError("VR function is no longer available.") |
---|
788 | |
---|
789 | def set_dispersion(self, parameter, dispersion): |
---|
790 | # type: (str, weights.Dispersion) -> None |
---|
791 | """ |
---|
792 | Set the dispersion object for a model parameter |
---|
793 | |
---|
794 | :param parameter: name of the parameter [string] |
---|
795 | :param dispersion: dispersion object of type Dispersion |
---|
796 | """ |
---|
797 | if parameter in self.params: |
---|
798 | # TODO: Store the disperser object directly in the model. |
---|
799 | # The current method of relying on the sasview GUI to |
---|
800 | # remember them is kind of funky. |
---|
801 | # Note: can't seem to get disperser parameters from sasview |
---|
802 | # (1) Could create a sasview model that has not yet been |
---|
803 | # converted, assign the disperser to one of its polydisperse |
---|
804 | # parameters, then retrieve the disperser parameters from the |
---|
805 | # sasview model. |
---|
806 | # (2) Could write a disperser parameter retriever in sasview. |
---|
807 | # (3) Could modify sasview to use sasmodels.weights dispersers. |
---|
808 | # For now, rely on the fact that the sasview only ever uses |
---|
809 | # new dispersers in the set_dispersion call and create a new |
---|
810 | # one instead of trying to assign parameters. |
---|
811 | self.dispersion[parameter] = dispersion.get_pars() |
---|
812 | else: |
---|
813 | raise ValueError("%r is not a dispersity or orientation parameter" |
---|
814 | % parameter) |
---|
815 | |
---|
816 | def _dispersion_mesh(self): |
---|
817 | # type: () -> List[Tuple[np.ndarray, np.ndarray]] |
---|
818 | """ |
---|
819 | Create a mesh grid of dispersion parameters and weights. |
---|
820 | |
---|
821 | Returns [p1,p2,...],w where pj is a vector of values for parameter j |
---|
822 | and w is a vector containing the products for weights for each |
---|
823 | parameter set in the vector. |
---|
824 | """ |
---|
825 | pars = [self._get_weights(p) |
---|
826 | for p in self._model_info.parameters.call_parameters |
---|
827 | if p.type == 'volume'] |
---|
828 | return dispersion_mesh(self._model_info, pars) |
---|
829 | |
---|
830 | def _get_weights(self, par): |
---|
831 | # type: (Parameter) -> Tuple[np.ndarray, np.ndarray] |
---|
832 | """ |
---|
833 | Return dispersion weights for parameter |
---|
834 | """ |
---|
835 | if par.name not in self.params: |
---|
836 | if par.id == self.multiplicity_info.control: |
---|
837 | return self.multiplicity, [self.multiplicity], [1.0] |
---|
838 | else: |
---|
839 | # For hidden parameters use default values. This sets |
---|
840 | # scale=1 and background=0 for structure factors |
---|
841 | default = self._model_info.parameters.defaults.get(par.name, np.NaN) |
---|
842 | return default, [default], [1.0] |
---|
843 | elif par.polydisperse: |
---|
844 | value = self.params[par.name] |
---|
845 | dis = self.dispersion[par.name] |
---|
846 | if dis['type'] == 'array': |
---|
847 | dispersity, weight = dis['values'], dis['weights'] |
---|
848 | else: |
---|
849 | dispersity, weight = weights.get_weights( |
---|
850 | dis['type'], dis['npts'], dis['width'], dis['nsigmas'], |
---|
851 | value, par.limits, par.relative_pd) |
---|
852 | return value, dispersity, weight |
---|
853 | else: |
---|
854 | value = self.params[par.name] |
---|
855 | return value, [value], [1.0] |
---|
856 | |
---|
857 | @classmethod |
---|
858 | def runTests(cls): |
---|
859 | """ |
---|
860 | Run any tests built into the model and captures the test output. |
---|
861 | |
---|
862 | Returns success flag and output |
---|
863 | """ |
---|
864 | from .model_test import check_model |
---|
865 | return check_model(cls._model_info) |
---|
866 | |
---|
867 | def test_cylinder(): |
---|
868 | # type: () -> float |
---|
869 | """ |
---|
870 | Test that the cylinder model runs, returning the value at [0.1,0.1]. |
---|
871 | """ |
---|
872 | Cylinder = _make_standard_model('cylinder') |
---|
873 | cylinder = Cylinder() |
---|
874 | return cylinder.evalDistribution([0.1, 0.1]) |
---|
875 | |
---|
876 | def test_structure_factor(): |
---|
877 | # type: () -> float |
---|
878 | """ |
---|
879 | Test that 2-D hardsphere model runs and doesn't produce NaN. |
---|
880 | """ |
---|
881 | Model = _make_standard_model('hardsphere') |
---|
882 | model = Model() |
---|
883 | value2d = model.evalDistribution([0.1, 0.1]) |
---|
884 | value1d = model.evalDistribution(np.array([0.1*np.sqrt(2)])) |
---|
885 | #print("hardsphere", value1d, value2d) |
---|
886 | if np.isnan(value1d) or np.isnan(value2d): |
---|
887 | raise ValueError("hardsphere returns nan") |
---|
888 | |
---|
889 | def test_product(): |
---|
890 | # type: () -> float |
---|
891 | """ |
---|
892 | Test that 2-D hardsphere model runs and doesn't produce NaN. |
---|
893 | """ |
---|
894 | S = _make_standard_model('hayter_msa')() |
---|
895 | P = _make_standard_model('cylinder')() |
---|
896 | model = MultiplicationModel(P, S) |
---|
897 | model.setParam(product.RADIUS_MODE_ID, 1.0) |
---|
898 | value = model.evalDistribution([0.1, 0.1]) |
---|
899 | if np.isnan(value): |
---|
900 | raise ValueError("cylinder*hatyer_msa returns null") |
---|
901 | |
---|
902 | def test_rpa(): |
---|
903 | # type: () -> float |
---|
904 | """ |
---|
905 | Test that the 2-D RPA model runs |
---|
906 | """ |
---|
907 | RPA = _make_standard_model('rpa') |
---|
908 | rpa = RPA(3) |
---|
909 | return rpa.evalDistribution([0.1, 0.1]) |
---|
910 | |
---|
911 | def test_empty_distribution(): |
---|
912 | # type: () -> None |
---|
913 | """ |
---|
914 | Make sure that sasmodels returns NaN when there are no polydispersity points |
---|
915 | """ |
---|
916 | Cylinder = _make_standard_model('cylinder') |
---|
917 | cylinder = Cylinder() |
---|
918 | cylinder.setParam('radius', -1.0) |
---|
919 | cylinder.setParam('background', 0.) |
---|
920 | Iq = cylinder.evalDistribution(np.asarray([0.1])) |
---|
921 | assert Iq[0] == 0., "empty distribution fails" |
---|
922 | |
---|
923 | def test_model_list(): |
---|
924 | # type: () -> None |
---|
925 | """ |
---|
926 | Make sure that all models build as sasview models |
---|
927 | """ |
---|
928 | from .exception import annotate_exception |
---|
929 | for name in core.list_models(): |
---|
930 | try: |
---|
931 | _make_standard_model(name) |
---|
932 | except: |
---|
933 | annotate_exception("when loading "+name) |
---|
934 | raise |
---|
935 | |
---|
936 | def test_old_name(): |
---|
937 | # type: () -> None |
---|
938 | """ |
---|
939 | Load and run cylinder model as sas.models.CylinderModel |
---|
940 | """ |
---|
941 | if not SUPPORT_OLD_STYLE_PLUGINS: |
---|
942 | return |
---|
943 | try: |
---|
944 | # if sasview is not on the path then don't try to test it |
---|
945 | import sas |
---|
946 | except ImportError: |
---|
947 | return |
---|
948 | load_standard_models() |
---|
949 | from sas.models.CylinderModel import CylinderModel |
---|
950 | CylinderModel().evalDistribution([0.1, 0.1]) |
---|
951 | |
---|
952 | def test_structure_factor_background(): |
---|
953 | # type: () -> None |
---|
954 | """ |
---|
955 | Check that sasview model and direct model match, with background=0. |
---|
956 | """ |
---|
957 | from .data import empty_data1D |
---|
958 | from .core import load_model_info, build_model |
---|
959 | from .direct_model import DirectModel |
---|
960 | |
---|
961 | model_name = "hardsphere" |
---|
962 | q = [0.0] |
---|
963 | |
---|
964 | sasview_model = _make_standard_model(model_name)() |
---|
965 | sasview_value = sasview_model.evalDistribution(np.array(q))[0] |
---|
966 | |
---|
967 | data = empty_data1D(q) |
---|
968 | model_info = load_model_info(model_name) |
---|
969 | model = build_model(model_info) |
---|
970 | direct_model = DirectModel(data, model) |
---|
971 | direct_value_zero_background = direct_model(background=0.0) |
---|
972 | |
---|
973 | assert sasview_value == direct_value_zero_background |
---|
974 | |
---|
975 | # Additionally check that direct value background defaults to zero |
---|
976 | direct_value_default = direct_model() |
---|
977 | assert sasview_value == direct_value_default |
---|
978 | |
---|
979 | |
---|
980 | def magnetic_demo(): |
---|
981 | """ |
---|
982 | Demostrate call to magnetic model. |
---|
983 | """ |
---|
984 | Model = _make_standard_model('sphere') |
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985 | model = Model() |
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986 | model.setParam('sld_M0', 8) |
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987 | q = np.linspace(-0.35, 0.35, 500) |
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988 | qx, qy = np.meshgrid(q, q) |
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989 | result, _ = model.calculate_Iq(qx.flatten(), qy.flatten()) |
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990 | result = result.reshape(qx.shape) |
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991 | |
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992 | import pylab |
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993 | pylab.imshow(np.log(result + 0.001)) |
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994 | pylab.show() |
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995 | |
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996 | if __name__ == "__main__": |
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997 | print("cylinder(0.1,0.1)=%g"%test_cylinder()) |
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998 | #magnetic_demo() |
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999 | #test_product() |
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1000 | #test_structure_factor() |
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1001 | #print("rpa:", test_rpa()) |
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1002 | #test_empty_distribution() |
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1003 | #test_structure_factor_background() |
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