1 | """ |
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2 | Core model handling routines. |
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3 | """ |
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4 | __all__ = ["list_models", "load_model_definition", "precompile_dll", |
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5 | "load_model", "make_kernel", "call_kernel", "call_ER", "call_VR" ] |
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6 | |
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7 | from os.path import basename, dirname, join as joinpath |
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8 | from glob import glob |
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9 | |
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10 | import numpy as np |
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11 | |
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12 | from . import models |
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13 | from . import weights |
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14 | from . import generate |
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15 | |
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16 | from . import kernelpy |
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17 | from . import kerneldll |
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18 | try: |
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19 | from . import kernelcl |
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20 | HAVE_OPENCL = True |
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21 | except: |
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22 | HAVE_OPENCL = False |
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23 | |
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24 | |
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25 | def list_models(): |
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26 | """ |
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27 | Return the list of available models on the model path. |
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28 | """ |
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29 | root = dirname(__file__) |
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30 | files = sorted(glob(joinpath(root, 'models', "[a-zA-Z]*.py"))) |
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31 | available_models = [basename(f)[:-3] for f in files] |
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32 | return available_models |
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33 | |
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34 | |
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35 | def load_model_definition(model_name): |
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36 | """ |
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37 | Load a model definition given the model name. |
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38 | """ |
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39 | __import__('sasmodels.models.'+model_name) |
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40 | model_definition = getattr(models, model_name, None) |
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41 | return model_definition |
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42 | |
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43 | |
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44 | def precompile_dll(model_name, dtype="double"): |
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45 | """ |
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46 | Precompile the dll for a model. |
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47 | |
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48 | Returns the path to the compiled model. |
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49 | |
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50 | This can be used when build the windows distribution of sasmodels |
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51 | (which may be missing the OpenCL driver and the dll compiler), or |
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52 | otherwise sharing models with windows users who do not have a compiler. |
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53 | |
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54 | See :func:`sasmodels.kerneldll.make_dll` for details on controlling the |
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55 | dll path and the allowed floating point precision. |
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56 | """ |
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57 | model_definition = load_model_definition(model_name) |
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58 | source, info = generate.make(model_definition) |
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59 | return kerneldll.make_dll(source, info, dtype=dtype) |
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60 | |
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61 | |
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62 | def isstr(s): |
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63 | try: s + '' |
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64 | except: return False |
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65 | return True |
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66 | |
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67 | def load_model(model_definition, dtype="single", platform="ocl", fast=False): |
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68 | """ |
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69 | Prepare the model for the default execution platform. |
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70 | |
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71 | This will return an OpenCL model, a DLL model or a python model depending |
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72 | on the model and the computing platform. |
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73 | |
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74 | *model_definition* is the python module which defines the model. If the |
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75 | model name is given instead, then :func:`load_model_definition` will be |
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76 | called with the model name. |
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77 | |
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78 | *dtype* indicates whether the model should use single or double precision |
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79 | for the calculation. Any valid numpy single or double precision identifier |
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80 | is valid, such as 'single', 'f', 'f32', or np.float32 for single, or |
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81 | 'double', 'd', 'f64' and np.float64 for double. |
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82 | |
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83 | *platform* should be "dll" to force the dll to be used for C models, |
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84 | otherwise it uses the default "ocl". |
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85 | |
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86 | *fast* is True if fast inaccurate math is acceptable (40% speed increase). |
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87 | """ |
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88 | if isstr(model_definition): |
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89 | model_definition = load_model_definition(model_definition) |
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90 | source, info = generate.make(model_definition) |
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91 | if callable(info.get('Iq', None)): |
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92 | return kernelpy.PyModel(info) |
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93 | |
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94 | ## for debugging: |
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95 | ## 1. uncomment open().write so that the source will be saved next time |
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96 | ## 2. run "python -m sasmodels.direct_model $MODELNAME" to save the source |
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97 | ## 3. recomment the open.write() and uncomment open().read() |
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98 | ## 4. rerun "python -m sasmodels.direct_model $MODELNAME" |
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99 | ## 5. uncomment open().read() so that source will be regenerated from model |
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100 | # open(info['name']+'.c','w').write(source) |
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101 | # source = open(info['name']+'.cl','r').read() |
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102 | |
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103 | dtype = np.dtype(dtype) |
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104 | if (platform=="dll" |
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105 | or not HAVE_OPENCL |
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106 | or not kernelcl.environment().has_type(dtype)): |
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107 | return kerneldll.load_dll(source, info, dtype) |
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108 | else: |
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109 | return kernelcl.GpuModel(source, info, dtype, fast) |
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110 | |
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111 | def make_kernel(model, q_vectors): |
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112 | """ |
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113 | Return a computation kernel from the model definition and the q input. |
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114 | """ |
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115 | model_input = model.make_input(q_vectors) |
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116 | return model(model_input) |
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117 | |
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118 | def get_weights(info, pars, name): |
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119 | """ |
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120 | Generate the distribution for parameter *name* given the parameter values |
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121 | in *pars*. |
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122 | |
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123 | Uses "name", "name_pd", "name_pd_type", "name_pd_n", "name_pd_sigma" |
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124 | from the *pars* dictionary for parameter value and parameter dispersion. |
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125 | """ |
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126 | relative = name in info['partype']['pd-rel'] |
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127 | limits = info['limits'][name] |
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128 | disperser = pars.get(name+'_pd_type', 'gaussian') |
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129 | value = pars.get(name, info['defaults'][name]) |
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130 | npts = pars.get(name+'_pd_n', 0) |
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131 | width = pars.get(name+'_pd', 0.0) |
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132 | nsigma = pars.get(name+'_pd_nsigma', 3.0) |
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133 | value,weight = weights.get_weights( |
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134 | disperser, npts, width, nsigma, value, limits, relative) |
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135 | return value, weight / np.sum(weight) |
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136 | |
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137 | def dispersion_mesh(pars): |
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138 | """ |
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139 | Create a mesh grid of dispersion parameters and weights. |
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140 | |
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141 | Returns [p1,p2,...],w where pj is a vector of values for parameter j |
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142 | and w is a vector containing the products for weights for each |
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143 | parameter set in the vector. |
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144 | """ |
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145 | value, weight = zip(*pars) |
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146 | if len(value) > 1: |
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147 | value = [v.flatten() for v in np.meshgrid(*value)] |
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148 | weight = np.vstack([v.flatten() for v in np.meshgrid(*weight)]) |
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149 | weight = np.prod(weight, axis=0) |
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150 | return value, weight |
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151 | |
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152 | def call_kernel(kernel, pars, cutoff=0): |
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153 | """ |
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154 | Call *kernel* returned from :func:`make_kernel` with parameters *pars*. |
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155 | |
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156 | *cutoff* is the limiting value for the product of dispersion weights used |
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157 | to perform the multidimensional dispersion calculation more quickly at a |
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158 | slight cost to accuracy. The default value of *cutoff=0* integrates over |
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159 | the entire dispersion cube. Using *cutoff=1e-5* can be 50% faster, but |
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160 | with an error of about 1%, which is usually less than the measurement |
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161 | uncertainty. |
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162 | """ |
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163 | fixed_pars = [pars.get(name, kernel.info['defaults'][name]) |
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164 | for name in kernel.fixed_pars] |
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165 | pd_pars = [get_weights(kernel.info, pars, name) for name in kernel.pd_pars] |
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166 | return kernel(fixed_pars, pd_pars, cutoff=cutoff) |
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167 | |
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168 | def call_ER(info, pars): |
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169 | """ |
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170 | Call the model ER function using *pars*. |
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171 | |
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172 | *info* is either *model.info* if you have a loaded model, or *kernel.info* |
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173 | if you have a model kernel prepared for evaluation. |
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174 | """ |
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175 | ER = info.get('ER', None) |
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176 | if ER is None: |
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177 | return 1.0 |
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178 | else: |
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179 | vol_pars = [get_weights(info, pars, name) |
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180 | for name in info['partype']['volume']] |
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181 | value, weight = dispersion_mesh(vol_pars) |
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182 | individual_radii = ER(*value) |
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183 | #print(values[0].shape, weights.shape, fv.shape) |
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184 | return np.sum(weight*individual_radii) / np.sum(weight) |
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185 | |
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186 | def call_VR(info, pars): |
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187 | """ |
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188 | Call the model VR function using *pars*. |
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189 | |
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190 | *info* is either *model.info* if you have a loaded model, or *kernel.info* |
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191 | if you have a model kernel prepared for evaluation. |
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192 | """ |
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193 | VR = info.get('VR', None) |
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194 | if VR is None: |
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195 | return 1.0 |
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196 | else: |
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197 | vol_pars = [get_weights(info, pars, name) |
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198 | for name in info['partype']['volume']] |
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199 | value, weight = dispersion_mesh(vol_pars) |
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200 | whole,part = VR(*value) |
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201 | return np.sum(weight*part)/np.sum(weight*whole) |
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202 | |
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