1 | """ |
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2 | Core model handling routines. |
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3 | """ |
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4 | from __future__ import print_function |
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5 | |
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6 | __all__ = [ |
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7 | "list_models", "load_model", "load_model_info", |
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8 | "build_model", "precompile_dlls", |
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9 | ] |
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10 | |
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11 | import os |
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12 | from os.path import basename, join as joinpath |
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13 | from glob import glob |
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14 | import re |
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15 | |
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16 | import numpy as np # type: ignore |
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17 | |
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18 | from . import generate |
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19 | from . import modelinfo |
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20 | from . import product |
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21 | from . import mixture |
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22 | from . import kernelpy |
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23 | from . import kernelcl |
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24 | from . import kerneldll |
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25 | from . import custom |
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26 | |
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27 | # pylint: disable=unused-import |
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28 | try: |
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29 | from typing import List, Union, Optional, Any |
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30 | from .kernel import KernelModel |
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31 | from .modelinfo import ModelInfo |
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32 | except ImportError: |
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33 | pass |
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34 | # pylint: enable=unused-import |
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35 | |
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36 | CUSTOM_MODEL_PATH = os.environ.get('SAS_MODELPATH', "") |
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37 | if CUSTOM_MODEL_PATH == "": |
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38 | CUSTOM_MODEL_PATH = joinpath(os.path.expanduser("~"), ".sasmodels", "custom_models") |
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39 | if not os.path.isdir(CUSTOM_MODEL_PATH): |
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40 | os.makedirs(CUSTOM_MODEL_PATH) |
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41 | |
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42 | # TODO: refactor composite model support |
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43 | # The current load_model_info/build_model does not reuse existing model |
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44 | # definitions when loading a composite model, instead reloading and |
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45 | # rebuilding the kernel for each component model in the expression. This |
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46 | # is fine in a scripting environment where the model is built when the script |
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47 | # starts and is thrown away when the script ends, but may not be the best |
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48 | # solution in a long-lived application. This affects the following functions: |
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49 | # |
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50 | # load_model |
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51 | # load_model_info |
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52 | # build_model |
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53 | |
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54 | KINDS = ("all", "py", "c", "double", "single", "opencl", "1d", "2d", |
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55 | "nonmagnetic", "magnetic") |
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56 | def list_models(kind=None): |
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57 | # type: (str) -> List[str] |
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58 | """ |
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59 | Return the list of available models on the model path. |
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60 | |
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61 | *kind* can be one of the following: |
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62 | |
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63 | * all: all models |
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64 | * py: python models only |
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65 | * c: compiled models only |
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66 | * single: models which support single precision |
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67 | * double: models which require double precision |
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68 | * opencl: controls if OpenCL is supperessed |
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69 | * 1d: models which are 1D only, or 2D using abs(q) |
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70 | * 2d: models which can be 2D |
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71 | * magnetic: models with an sld |
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72 | * nommagnetic: models without an sld |
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73 | |
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74 | For multiple conditions, combine with plus. For example, *c+single+2d* |
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75 | would return all oriented models implemented in C which can be computed |
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76 | accurately with single precision arithmetic. |
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77 | """ |
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78 | if kind and any(k not in KINDS for k in kind.split('+')): |
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79 | raise ValueError("kind not in " + ", ".join(KINDS)) |
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80 | files = sorted(glob(joinpath(generate.MODEL_PATH, "[a-zA-Z]*.py"))) |
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81 | available_models = [basename(f)[:-3] for f in files] |
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82 | if kind and '+' in kind: |
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83 | all_kinds = kind.split('+') |
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84 | condition = lambda name: all(_matches(name, k) for k in all_kinds) |
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85 | else: |
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86 | condition = lambda name: _matches(name, kind) |
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87 | selected = [name for name in available_models if condition(name)] |
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88 | |
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89 | return selected |
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90 | |
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91 | def _matches(name, kind): |
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92 | if kind is None or kind == "all": |
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93 | return True |
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94 | info = load_model_info(name) |
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95 | pars = info.parameters.kernel_parameters |
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96 | if kind == "py" and callable(info.Iq): |
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97 | return True |
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98 | elif kind == "c" and not callable(info.Iq): |
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99 | return True |
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100 | elif kind == "double" and not info.single: |
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101 | return True |
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102 | elif kind == "single" and info.single: |
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103 | return True |
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104 | elif kind == "opencl" and info.opencl: |
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105 | return True |
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106 | elif kind == "2d" and any(p.type == 'orientation' for p in pars): |
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107 | return True |
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108 | elif kind == "1d" and all(p.type != 'orientation' for p in pars): |
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109 | return True |
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110 | elif kind == "magnetic" and any(p.type == 'sld' for p in pars): |
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111 | return True |
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112 | elif kind == "nonmagnetic" and any(p.type != 'sld' for p in pars): |
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113 | return True |
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114 | return False |
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115 | |
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116 | def load_model(model_name, dtype=None, platform='ocl'): |
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117 | # type: (str, str, str) -> KernelModel |
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118 | """ |
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119 | Load model info and build model. |
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120 | |
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121 | *model_name* is the name of the model, or perhaps a model expression |
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122 | such as sphere*hardsphere or sphere+cylinder. |
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123 | |
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124 | *dtype* and *platform* are given by :func:`build_model`. |
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125 | """ |
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126 | return build_model(load_model_info(model_name), |
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127 | dtype=dtype, platform=platform) |
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128 | |
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129 | def load_model_info(model_string): |
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130 | # type: (str) -> modelinfo.ModelInfo |
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131 | """ |
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132 | Load a model definition given the model name. |
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133 | |
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134 | *model_string* is the name of the model, or perhaps a model expression |
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135 | such as sphere*cylinder or sphere+cylinder. Use '@' for a structure |
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136 | factor product, e.g. sphere@hardsphere. Custom models can be specified by |
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137 | prefixing the model name with 'custom.', e.g. 'custom.MyModel+sphere'. |
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138 | |
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139 | This returns a handle to the module defining the model. This can be |
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140 | used with functions in generate to build the docs or extract model info. |
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141 | """ |
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142 | if "+" in model_string: |
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143 | parts = [load_model_info(part) |
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144 | for part in model_string.split("+")] |
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145 | return mixture.make_mixture_info(parts, operation='+') |
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146 | elif "*" in model_string: |
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147 | parts = [load_model_info(part) |
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148 | for part in model_string.split("*")] |
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149 | return mixture.make_mixture_info(parts, operation='*') |
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150 | elif "@" in model_string: |
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151 | p_info, q_info = [load_model_info(part) |
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152 | for part in model_string.split("@")] |
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153 | return product.make_product_info(p_info, q_info) |
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154 | # We are now dealing with a pure model |
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155 | elif "custom." in model_string: |
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156 | pattern = "custom.([A-Za-z0-9_-]+)" |
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157 | result = re.match(pattern, model_string) |
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158 | if result is None: |
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159 | raise ValueError("Model name in invalid format: " + model_string) |
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160 | model_name = result.group(1) |
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161 | # Use ModelName to find the path to the custom model file |
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162 | model_path = joinpath(CUSTOM_MODEL_PATH, model_name + ".py") |
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163 | if not os.path.isfile(model_path): |
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164 | raise ValueError("The model file {} doesn't exist".format(model_path)) |
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165 | kernel_module = custom.load_custom_kernel_module(model_path) |
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166 | return modelinfo.make_model_info(kernel_module) |
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167 | kernel_module = generate.load_kernel_module(model_string) |
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168 | return modelinfo.make_model_info(kernel_module) |
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169 | |
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170 | |
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171 | def build_model(model_info, dtype=None, platform="ocl"): |
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172 | # type: (modelinfo.ModelInfo, str, str) -> KernelModel |
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173 | """ |
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174 | Prepare the model for the default execution platform. |
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175 | |
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176 | This will return an OpenCL model, a DLL model or a python model depending |
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177 | on the model and the computing platform. |
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178 | |
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179 | *model_info* is the model definition structure returned from |
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180 | :func:`load_model_info`. |
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181 | |
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182 | *dtype* indicates whether the model should use single or double precision |
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183 | for the calculation. Choices are 'single', 'double', 'quad', 'half', |
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184 | or 'fast'. If *dtype* ends with '!', then force the use of the DLL rather |
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185 | than OpenCL for the calculation. |
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186 | |
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187 | *platform* should be "dll" to force the dll to be used for C models, |
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188 | otherwise it uses the default "ocl". |
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189 | """ |
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190 | composition = model_info.composition |
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191 | if composition is not None: |
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192 | composition_type, parts = composition |
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193 | models = [build_model(p, dtype=dtype, platform=platform) for p in parts] |
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194 | if composition_type == 'mixture': |
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195 | return mixture.MixtureModel(model_info, models) |
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196 | elif composition_type == 'product': |
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197 | P, S = models |
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198 | return product.ProductModel(model_info, P, S) |
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199 | else: |
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200 | raise ValueError('unknown mixture type %s'%composition_type) |
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201 | |
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202 | # If it is a python model, return it immediately |
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203 | if callable(model_info.Iq): |
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204 | return kernelpy.PyModel(model_info) |
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205 | |
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206 | numpy_dtype, fast, platform = parse_dtype(model_info, dtype, platform) |
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207 | |
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208 | source = generate.make_source(model_info) |
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209 | if platform == "dll": |
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210 | #print("building dll", numpy_dtype) |
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211 | return kerneldll.load_dll(source['dll'], model_info, numpy_dtype) |
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212 | else: |
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213 | #print("building ocl", numpy_dtype) |
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214 | return kernelcl.GpuModel(source, model_info, numpy_dtype, fast=fast) |
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215 | |
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216 | def precompile_dlls(path, dtype="double"): |
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217 | # type: (str, str) -> List[str] |
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218 | """ |
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219 | Precompile the dlls for all builtin models, returning a list of dll paths. |
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220 | |
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221 | *path* is the directory in which to save the dlls. It will be created if |
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222 | it does not already exist. |
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223 | |
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224 | This can be used when build the windows distribution of sasmodels |
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225 | which may be missing the OpenCL driver and the dll compiler. |
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226 | """ |
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227 | numpy_dtype = np.dtype(dtype) |
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228 | if not os.path.exists(path): |
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229 | os.makedirs(path) |
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230 | compiled_dlls = [] |
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231 | for model_name in list_models(): |
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232 | model_info = load_model_info(model_name) |
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233 | if not callable(model_info.Iq): |
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234 | source = generate.make_source(model_info)['dll'] |
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235 | old_path = kerneldll.DLL_PATH |
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236 | try: |
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237 | kerneldll.DLL_PATH = path |
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238 | dll = kerneldll.make_dll(source, model_info, dtype=numpy_dtype) |
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239 | finally: |
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240 | kerneldll.DLL_PATH = old_path |
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241 | compiled_dlls.append(dll) |
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242 | return compiled_dlls |
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243 | |
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244 | def parse_dtype(model_info, dtype=None, platform=None): |
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245 | # type: (ModelInfo, str, str) -> (np.dtype, bool, str) |
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246 | """ |
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247 | Interpret dtype string, returning np.dtype and fast flag. |
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248 | |
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249 | Possible types include 'half', 'single', 'double' and 'quad'. If the |
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250 | type is 'fast', then this is equivalent to dtype 'single' but using |
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251 | fast native functions rather than those with the precision level |
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252 | guaranteed by the OpenCL standard. 'default' will choose the appropriate |
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253 | default for the model and platform. |
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254 | |
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255 | Platform preference can be specfied ("ocl" vs "dll"), with the default |
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256 | being OpenCL if it is availabe. If the dtype name ends with '!' then |
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257 | platform is forced to be DLL rather than OpenCL. |
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258 | |
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259 | This routine ignores the preferences within the model definition. This |
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260 | is by design. It allows us to test models in single precision even when |
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261 | we have flagged them as requiring double precision so we can easily check |
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262 | the performance on different platforms without having to change the model |
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263 | definition. |
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264 | """ |
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265 | # Assign default platform, overriding ocl with dll if OpenCL is unavailable |
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266 | # If opencl=False OpenCL is switched off |
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267 | |
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268 | if platform is None: |
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269 | platform = "ocl" |
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270 | if not kernelcl.use_opencl() or not model_info.opencl: |
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271 | platform = "dll" |
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272 | |
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273 | # Check if type indicates dll regardless of which platform is given |
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274 | if dtype is not None and dtype.endswith('!'): |
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275 | platform = "dll" |
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276 | dtype = dtype[:-1] |
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277 | |
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278 | # Convert special type names "half", "fast", and "quad" |
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279 | fast = (dtype == "fast") |
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280 | if fast: |
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281 | dtype = "single" |
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282 | elif dtype == "quad": |
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283 | dtype = "longdouble" |
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284 | elif dtype == "half": |
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285 | dtype = "float16" |
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286 | |
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287 | # Convert dtype string to numpy dtype. |
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288 | if dtype is None or dtype == "default": |
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289 | numpy_dtype = (generate.F32 if platform == "ocl" and model_info.single |
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290 | else generate.F64) |
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291 | else: |
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292 | numpy_dtype = np.dtype(dtype) |
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293 | |
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294 | # Make sure that the type is supported by opencl, otherwise use dll |
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295 | if platform == "ocl": |
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296 | env = kernelcl.environment() |
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297 | if not env.has_type(numpy_dtype): |
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298 | platform = "dll" |
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299 | if dtype is None: |
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300 | numpy_dtype = generate.F64 |
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301 | |
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302 | return numpy_dtype, fast, platform |
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303 | |
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304 | def list_models_main(): |
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305 | # type: () -> None |
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306 | """ |
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307 | Run list_models as a main program. See :func:`list_models` for the |
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308 | kinds of models that can be requested on the command line. |
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309 | """ |
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310 | import sys |
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311 | kind = sys.argv[1] if len(sys.argv) > 1 else "all" |
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312 | print("\n".join(list_models(kind))) |
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313 | |
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314 | def test_composite_order(): |
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315 | def test_models(fst, snd): |
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316 | """Confirm that two models produce the same parameters""" |
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317 | fst = load_model(fst) |
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318 | snd = load_model(snd) |
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319 | # Un-disambiguate parameter names so that we can check if the same |
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320 | # parameters are in a pair of composite models. Since each parameter in |
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321 | # the mixture model is tagged as e.g., A_sld, we ought to use a |
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322 | # regex subsitution s/^[A-Z]+_/_/, but removing all uppercase letters |
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323 | # is good enough. |
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324 | fst = [[x for x in p.name if x == x.lower()] for p in fst.info.parameters.kernel_parameters] |
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325 | snd = [[x for x in p.name if x == x.lower()] for p in snd.info.parameters.kernel_parameters] |
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326 | assert sorted(fst) == sorted(snd), "{} != {}".format(fst, snd) |
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327 | |
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328 | def build_test(first, second): |
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329 | test = lambda description: test_models(first, second) |
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330 | description = first + " vs. " + second |
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331 | return test, description |
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332 | |
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333 | yield build_test( |
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334 | "cylinder+sphere", |
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335 | "sphere+cylinder") |
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336 | yield build_test( |
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337 | "cylinder*sphere", |
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338 | "sphere*cylinder") |
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339 | yield build_test( |
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340 | "cylinder@hardsphere*sphere", |
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341 | "sphere*cylinder@hardsphere") |
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342 | yield build_test( |
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343 | "barbell+sphere*cylinder@hardsphere", |
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344 | "sphere*cylinder@hardsphere+barbell") |
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345 | yield build_test( |
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346 | "barbell+cylinder@hardsphere*sphere", |
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347 | "cylinder@hardsphere*sphere+barbell") |
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348 | yield build_test( |
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349 | "barbell+sphere*cylinder@hardsphere", |
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350 | "barbell+cylinder@hardsphere*sphere") |
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351 | yield build_test( |
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352 | "sphere*cylinder@hardsphere+barbell", |
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353 | "cylinder@hardsphere*sphere+barbell") |
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354 | yield build_test( |
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355 | "barbell+sphere*cylinder@hardsphere", |
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356 | "cylinder@hardsphere*sphere+barbell") |
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357 | yield build_test( |
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358 | "barbell+cylinder@hardsphere*sphere", |
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359 | "sphere*cylinder@hardsphere+barbell") |
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360 | |
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361 | def test_composite(): |
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362 | # type: () -> None |
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363 | """Check that model load works""" |
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364 | #Test the the model produces the parameters that we would expect |
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365 | model = load_model("cylinder@hardsphere*sphere") |
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366 | actual = [p.name for p in model.info.parameters.kernel_parameters] |
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367 | target = ("sld sld_solvent radius length theta phi volfraction" |
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368 | " A_sld A_sld_solvent A_radius").split() |
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369 | assert target == actual, "%s != %s"%(target, actual) |
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370 | |
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371 | if __name__ == "__main__": |
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372 | list_models_main() |
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