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 kernelcuda |
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24 | from . import kernelcl |
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25 | from . import kerneldll |
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26 | from . import custom |
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27 | |
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28 | # pylint: disable=unused-import |
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29 | try: |
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30 | from typing import List, Union, Optional, Any |
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31 | from .kernel import KernelModel |
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32 | from .modelinfo import ModelInfo |
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33 | except ImportError: |
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34 | pass |
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35 | # pylint: enable=unused-import |
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36 | |
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37 | CUSTOM_MODEL_PATH = os.environ.get('SAS_MODELPATH', "") |
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38 | if CUSTOM_MODEL_PATH == "": |
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39 | CUSTOM_MODEL_PATH = joinpath(os.path.expanduser("~"), ".sasmodels", "custom_models") |
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40 | #if not os.path.isdir(CUSTOM_MODEL_PATH): |
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41 | # os.makedirs(CUSTOM_MODEL_PATH) |
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42 | |
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43 | # TODO: refactor composite model support |
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44 | # The current load_model_info/build_model does not reuse existing model |
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45 | # definitions when loading a composite model, instead reloading and |
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46 | # rebuilding the kernel for each component model in the expression. This |
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47 | # is fine in a scripting environment where the model is built when the script |
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48 | # starts and is thrown away when the script ends, but may not be the best |
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49 | # solution in a long-lived application. This affects the following functions: |
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50 | # |
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51 | # load_model |
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52 | # load_model_info |
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53 | # build_model |
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54 | |
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55 | KINDS = ("all", "py", "c", "double", "single", "opencl", "1d", "2d", |
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56 | "nonmagnetic", "magnetic") |
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57 | def list_models(kind=None): |
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58 | # type: (str) -> List[str] |
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59 | """ |
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60 | Return the list of available models on the model path. |
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61 | |
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62 | *kind* can be one of the following: |
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63 | |
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64 | * all: all models |
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65 | * py: python models only |
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66 | * c: c models only |
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67 | * single: c models which support single precision |
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68 | * double: c models which require double precision |
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69 | * opencl: c models which run in opencl |
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70 | * dll: c models which do not run in opencl |
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71 | * 1d: models without orientation |
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72 | * 2d: models with orientation |
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73 | * magnetic: models supporting magnetic sld |
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74 | * nommagnetic: models without magnetic parameter |
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75 | |
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76 | For multiple conditions, combine with plus. For example, *c+single+2d* |
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77 | would return all oriented models implemented in C which can be computed |
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78 | accurately with single precision arithmetic. |
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79 | """ |
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80 | if kind and any(k not in KINDS for k in kind.split('+')): |
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81 | raise ValueError("kind not in " + ", ".join(KINDS)) |
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82 | files = sorted(glob(joinpath(generate.MODEL_PATH, "[a-zA-Z]*.py"))) |
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83 | available_models = [basename(f)[:-3] for f in files] |
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84 | if kind and '+' in kind: |
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85 | all_kinds = kind.split('+') |
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86 | condition = lambda name: all(_matches(name, k) for k in all_kinds) |
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87 | else: |
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88 | condition = lambda name: _matches(name, kind) |
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89 | selected = [name for name in available_models if condition(name)] |
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90 | |
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91 | return selected |
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92 | |
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93 | def _matches(name, kind): |
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94 | if kind is None or kind == "all": |
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95 | return True |
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96 | info = load_model_info(name) |
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97 | pars = info.parameters.kernel_parameters |
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98 | # TODO: may be adding Fq to the list at some point |
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99 | is_pure_py = callable(info.Iq) |
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100 | if kind == "py": |
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101 | return is_pure_py |
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102 | elif kind == "c": |
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103 | return not is_pure_py |
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104 | elif kind == "double": |
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105 | return not info.single and not is_pure_py |
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106 | elif kind == "single": |
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107 | return info.single and not is_pure_py |
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108 | elif kind == "opencl": |
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109 | return info.opencl |
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110 | elif kind == "dll": |
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111 | return not info.opencl and not is_pure_py |
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112 | elif kind == "2d": |
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113 | return any(p.type == 'orientation' for p in pars) |
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114 | elif kind == "1d": |
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115 | return all(p.type != 'orientation' for p in pars) |
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116 | elif kind == "magnetic": |
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117 | return any(p.type == 'sld' for p in pars) |
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118 | elif kind == "nonmagnetic": |
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119 | return not any(p.type == 'sld' for p in pars) |
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120 | return False |
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121 | |
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122 | def load_model(model_name, dtype=None, platform='ocl'): |
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123 | # type: (str, str, str) -> KernelModel |
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124 | """ |
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125 | Load model info and build model. |
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126 | |
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127 | *model_name* is the name of the model, or perhaps a model expression |
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128 | such as sphere*hardsphere or sphere+cylinder. |
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129 | |
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130 | *dtype* and *platform* are given by :func:`build_model`. |
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131 | """ |
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132 | return build_model(load_model_info(model_name), |
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133 | dtype=dtype, platform=platform) |
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134 | |
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135 | def load_model_info(model_string): |
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136 | # type: (str) -> modelinfo.ModelInfo |
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137 | """ |
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138 | Load a model definition given the model name. |
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139 | |
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140 | *model_string* is the name of the model, or perhaps a model expression |
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141 | such as sphere*cylinder or sphere+cylinder. Use '@' for a structure |
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142 | factor product, e.g. sphere@hardsphere. Custom models can be specified by |
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143 | prefixing the model name with 'custom.', e.g. 'custom.MyModel+sphere'. |
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144 | |
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145 | This returns a handle to the module defining the model. This can be |
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146 | used with functions in generate to build the docs or extract model info. |
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147 | """ |
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148 | if "+" in model_string: |
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149 | parts = [load_model_info(part) |
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150 | for part in model_string.split("+")] |
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151 | return mixture.make_mixture_info(parts, operation='+') |
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152 | elif "*" in model_string: |
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153 | parts = [load_model_info(part) |
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154 | for part in model_string.split("*")] |
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155 | return mixture.make_mixture_info(parts, operation='*') |
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156 | elif "@" in model_string: |
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157 | p_info, q_info = [load_model_info(part) |
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158 | for part in model_string.split("@")] |
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159 | return product.make_product_info(p_info, q_info) |
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160 | # We are now dealing with a pure model |
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161 | elif "custom." in model_string: |
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162 | pattern = "custom.([A-Za-z0-9_-]+)" |
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163 | result = re.match(pattern, model_string) |
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164 | if result is None: |
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165 | raise ValueError("Model name in invalid format: " + model_string) |
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166 | model_name = result.group(1) |
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167 | # Use ModelName to find the path to the custom model file |
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168 | model_path = joinpath(CUSTOM_MODEL_PATH, model_name + ".py") |
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169 | if not os.path.isfile(model_path): |
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170 | raise ValueError("The model file {} doesn't exist".format(model_path)) |
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171 | kernel_module = custom.load_custom_kernel_module(model_path) |
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172 | return modelinfo.make_model_info(kernel_module) |
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173 | kernel_module = generate.load_kernel_module(model_string) |
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174 | return modelinfo.make_model_info(kernel_module) |
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175 | |
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176 | |
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177 | def build_model(model_info, dtype=None, platform="ocl"): |
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178 | # type: (modelinfo.ModelInfo, str, str) -> KernelModel |
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179 | """ |
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180 | Prepare the model for the default execution platform. |
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181 | |
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182 | This will return an OpenCL model, a DLL model or a python model depending |
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183 | on the model and the computing platform. |
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184 | |
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185 | *model_info* is the model definition structure returned from |
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186 | :func:`load_model_info`. |
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187 | |
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188 | *dtype* indicates whether the model should use single or double precision |
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189 | for the calculation. Choices are 'single', 'double', 'quad', 'half', |
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190 | or 'fast'. If *dtype* ends with '!', then force the use of the DLL rather |
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191 | than OpenCL for the calculation. |
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192 | |
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193 | *platform* should be "dll" to force the dll to be used for C models, |
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194 | otherwise it uses the default "ocl". |
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195 | """ |
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196 | composition = model_info.composition |
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197 | if composition is not None: |
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198 | composition_type, parts = composition |
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199 | models = [build_model(p, dtype=dtype, platform=platform) for p in parts] |
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200 | if composition_type == 'mixture': |
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201 | return mixture.MixtureModel(model_info, models) |
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202 | elif composition_type == 'product': |
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203 | P, S = models |
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204 | return product.ProductModel(model_info, P, S) |
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205 | else: |
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206 | raise ValueError('unknown mixture type %s'%composition_type) |
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207 | |
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208 | # If it is a python model, return it immediately |
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209 | if callable(model_info.Iq): |
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210 | return kernelpy.PyModel(model_info) |
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211 | |
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212 | numpy_dtype, fast, platform = parse_dtype(model_info, dtype, platform) |
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213 | source = generate.make_source(model_info) |
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214 | if platform == "dll": |
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215 | #print("building dll", numpy_dtype) |
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216 | return kerneldll.load_dll(source['dll'], model_info, numpy_dtype) |
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217 | elif platform == "cuda": |
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218 | return kernelcuda.GpuModel(source, model_info, numpy_dtype, fast=fast) |
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219 | else: |
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220 | #print("building ocl", numpy_dtype) |
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221 | return kernelcl.GpuModel(source, model_info, numpy_dtype, fast=fast) |
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222 | |
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223 | def precompile_dlls(path, dtype="double"): |
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224 | # type: (str, str) -> List[str] |
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225 | """ |
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226 | Precompile the dlls for all builtin models, returning a list of dll paths. |
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227 | |
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228 | *path* is the directory in which to save the dlls. It will be created if |
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229 | it does not already exist. |
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230 | |
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231 | This can be used when build the windows distribution of sasmodels |
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232 | which may be missing the OpenCL driver and the dll compiler. |
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233 | """ |
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234 | numpy_dtype = np.dtype(dtype) |
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235 | if not os.path.exists(path): |
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236 | os.makedirs(path) |
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237 | compiled_dlls = [] |
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238 | for model_name in list_models(): |
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239 | model_info = load_model_info(model_name) |
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240 | if not callable(model_info.Iq): |
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241 | source = generate.make_source(model_info)['dll'] |
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242 | old_path = kerneldll.SAS_DLL_PATH |
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243 | try: |
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244 | kerneldll.SAS_DLL_PATH = path |
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245 | dll = kerneldll.make_dll(source, model_info, dtype=numpy_dtype) |
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246 | finally: |
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247 | kerneldll.SAS_DLL_PATH = old_path |
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248 | compiled_dlls.append(dll) |
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249 | return compiled_dlls |
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250 | |
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251 | def parse_dtype(model_info, dtype=None, platform=None): |
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252 | # type: (ModelInfo, str, str) -> (np.dtype, bool, str) |
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253 | """ |
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254 | Interpret dtype string, returning np.dtype, fast flag and platform. |
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255 | |
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256 | Possible types include 'half', 'single', 'double' and 'quad'. If the |
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257 | type is 'fast', then this is equivalent to dtype 'single' but using |
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258 | fast native functions rather than those with the precision level |
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259 | guaranteed by the OpenCL standard. 'default' will choose the appropriate |
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260 | default for the model and platform. |
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261 | |
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262 | Platform preference can be specfied ("ocl", "cuda", "dll"), with the |
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263 | default being OpenCL or CUDA if available, otherwise DLL. If the dtype |
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264 | name ends with '!' then platform is forced to be DLL rather than GPU. |
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265 | The default platform is set by the environment variable SAS_OPENCL, |
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266 | SAS_OPENCL=driver:device for OpenCL, SAS_OPENCL=cuda:device for CUDA |
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267 | or SAS_OPENCL=none for DLL. |
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268 | |
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269 | This routine ignores the preferences within the model definition. This |
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270 | is by design. It allows us to test models in single precision even when |
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271 | we have flagged them as requiring double precision so we can easily check |
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272 | the performance on different platforms without having to change the model |
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273 | definition. |
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274 | """ |
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275 | # Assign default platform, overriding ocl with dll if OpenCL is unavailable |
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276 | # If opencl=False OpenCL is switched off |
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277 | if platform is None: |
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278 | platform = "ocl" |
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279 | |
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280 | # Check if type indicates dll regardless of which platform is given |
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281 | if dtype is not None and dtype.endswith('!'): |
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282 | platform = "dll" |
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283 | dtype = dtype[:-1] |
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284 | |
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285 | # Make sure model allows opencl/gpu |
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286 | if not model_info.opencl: |
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287 | platform = "dll" |
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288 | |
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289 | # Make sure opencl is available, or fallback to cuda then to dll |
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290 | if platform == "ocl" and not kernelcl.use_opencl(): |
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291 | platform = "cuda" if kernelcuda.use_cuda() else "dll" |
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292 | |
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293 | # Convert special type names "half", "fast", and "quad" |
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294 | fast = (dtype == "fast") |
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295 | if fast: |
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296 | dtype = "single" |
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297 | elif dtype == "quad": |
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298 | dtype = "longdouble" |
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299 | elif dtype == "half": |
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300 | dtype = "float16" |
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301 | |
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302 | # Convert dtype string to numpy dtype. Use single precision for GPU |
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303 | # if model allows it, otherwise use double precision. |
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304 | if dtype is None or dtype == "default": |
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305 | numpy_dtype = (generate.F32 if model_info.single and platform in ("ocl", "cuda") |
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306 | else generate.F64) |
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307 | else: |
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308 | numpy_dtype = np.dtype(dtype) |
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309 | |
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310 | # Make sure that the type is supported by GPU, otherwise use dll |
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311 | if platform == "ocl": |
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312 | env = kernelcl.environment() |
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313 | elif platform == "cuda": |
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314 | env = kernelcuda.environment() |
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315 | else: |
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316 | env = None |
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317 | if env is not None and not env.has_type(numpy_dtype): |
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318 | platform = "dll" |
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319 | if dtype is None: |
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320 | numpy_dtype = generate.F64 |
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321 | |
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322 | return numpy_dtype, fast, platform |
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323 | |
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324 | def test_composite_order(): |
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325 | def test_models(fst, snd): |
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326 | """Confirm that two models produce the same parameters""" |
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327 | fst = load_model(fst) |
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328 | snd = load_model(snd) |
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329 | # Un-disambiguate parameter names so that we can check if the same |
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330 | # parameters are in a pair of composite models. Since each parameter in |
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331 | # the mixture model is tagged as e.g., A_sld, we ought to use a |
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332 | # regex subsitution s/^[A-Z]+_/_/, but removing all uppercase letters |
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333 | # is good enough. |
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334 | fst = [[x for x in p.name if x == x.lower()] for p in fst.info.parameters.kernel_parameters] |
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335 | snd = [[x for x in p.name if x == x.lower()] for p in snd.info.parameters.kernel_parameters] |
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336 | assert sorted(fst) == sorted(snd), "{} != {}".format(fst, snd) |
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337 | |
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338 | def build_test(first, second): |
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339 | test = lambda description: test_models(first, second) |
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340 | description = first + " vs. " + second |
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341 | return test, description |
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342 | |
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343 | yield build_test( |
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344 | "cylinder+sphere", |
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345 | "sphere+cylinder") |
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346 | yield build_test( |
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347 | "cylinder*sphere", |
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348 | "sphere*cylinder") |
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349 | yield build_test( |
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350 | "cylinder@hardsphere*sphere", |
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351 | "sphere*cylinder@hardsphere") |
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352 | yield build_test( |
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353 | "barbell+sphere*cylinder@hardsphere", |
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354 | "sphere*cylinder@hardsphere+barbell") |
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355 | yield build_test( |
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356 | "barbell+cylinder@hardsphere*sphere", |
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357 | "cylinder@hardsphere*sphere+barbell") |
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358 | yield build_test( |
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359 | "barbell+sphere*cylinder@hardsphere", |
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360 | "barbell+cylinder@hardsphere*sphere") |
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361 | yield build_test( |
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362 | "sphere*cylinder@hardsphere+barbell", |
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363 | "cylinder@hardsphere*sphere+barbell") |
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364 | yield build_test( |
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365 | "barbell+sphere*cylinder@hardsphere", |
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366 | "cylinder@hardsphere*sphere+barbell") |
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367 | yield build_test( |
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368 | "barbell+cylinder@hardsphere*sphere", |
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369 | "sphere*cylinder@hardsphere+barbell") |
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370 | |
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371 | def test_composite(): |
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372 | # type: () -> None |
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373 | """Check that model load works""" |
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374 | #Test the the model produces the parameters that we would expect |
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375 | model = load_model("cylinder@hardsphere*sphere") |
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376 | actual = [p.name for p in model.info.parameters.kernel_parameters] |
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377 | target = ("sld sld_solvent radius length theta phi" |
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378 | " radius_effective volfraction " |
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379 | " structure_factor_mode radius_effective_mode" |
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380 | " A_sld A_sld_solvent A_radius").split() |
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381 | assert target == actual, "%s != %s"%(target, actual) |
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382 | |
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383 | def list_models_main(): |
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384 | # type: () -> None |
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385 | """ |
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386 | Run list_models as a main program. See :func:`list_models` for the |
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387 | kinds of models that can be requested on the command line. |
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388 | """ |
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389 | import sys |
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390 | kind = sys.argv[1] if len(sys.argv) > 1 else "all" |
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391 | try: |
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392 | models = list_models(kind) |
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393 | except Exception as exc: |
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394 | print(list_models.__doc__) |
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395 | return 1 |
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396 | |
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397 | print("\n".join(list_models(kind))) |
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398 | |
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399 | if __name__ == "__main__": |
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400 | list_models_main() |
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