1 | #!/usr/bin/env python |
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2 | # -*- coding: utf-8 -*- |
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3 | """ |
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4 | Program to compare models using different compute engines. |
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5 | |
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6 | This program lets you compare results between OpenCL and DLL versions |
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7 | of the code and between precision (half, fast, single, double, quad), |
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8 | where fast precision is single precision using native functions for |
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9 | trig, etc., and may not be completely IEEE 754 compliant. This lets |
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10 | make sure that the model calculations are stable, or if you need to |
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11 | tag the model as double precision only. |
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12 | |
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13 | Run using ./compare.sh (Linux, Mac) or compare.bat (Windows) in the |
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14 | sasmodels root to see the command line options. |
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15 | |
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16 | Note that there is no way within sasmodels to select between an |
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17 | OpenCL CPU device and a GPU device, but you can do so by setting the |
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18 | PYOPENCL_CTX environment variable ahead of time. Start a python |
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19 | interpreter and enter:: |
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20 | |
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21 | import pyopencl as cl |
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22 | cl.create_some_context() |
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23 | |
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24 | This will prompt you to select from the available OpenCL devices |
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25 | and tell you which string to use for the PYOPENCL_CTX variable. |
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26 | On Windows you will need to remove the quotes. |
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27 | """ |
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28 | |
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29 | from __future__ import print_function |
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30 | |
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31 | import sys |
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32 | import math |
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33 | import datetime |
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34 | import traceback |
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35 | |
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36 | import numpy as np # type: ignore |
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37 | |
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38 | from . import core |
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39 | from . import kerneldll |
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40 | from . import weights |
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41 | from .data import plot_theory, empty_data1D, empty_data2D |
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42 | from .direct_model import DirectModel |
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43 | from .convert import revert_name, revert_pars, constrain_new_to_old |
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44 | |
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45 | try: |
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46 | from typing import Optional, Dict, Any, Callable, Tuple |
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47 | except: |
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48 | pass |
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49 | else: |
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50 | from .modelinfo import ModelInfo, Parameter, ParameterSet |
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51 | from .data import Data |
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52 | Calculator = Callable[[float], np.ndarray] |
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53 | |
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54 | USAGE = """ |
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55 | usage: compare.py model N1 N2 [options...] [key=val] |
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56 | |
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57 | Compare the speed and value for a model between the SasView original and the |
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58 | sasmodels rewrite. |
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59 | |
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60 | model is the name of the model to compare (see below). |
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61 | N1 is the number of times to run sasmodels (default=1). |
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62 | N2 is the number times to run sasview (default=1). |
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63 | |
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64 | Options (* for default): |
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65 | |
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66 | -plot*/-noplot plots or suppress the plot of the model |
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67 | -lowq*/-midq/-highq/-exq use q values up to 0.05, 0.2, 1.0, 10.0 |
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68 | -nq=128 sets the number of Q points in the data set |
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69 | -zero indicates that q=0 should be included |
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70 | -1d*/-2d computes 1d or 2d data |
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71 | -preset*/-random[=seed] preset or random parameters |
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72 | -mono/-poly* force monodisperse/polydisperse |
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73 | -cutoff=1e-5* cutoff value for including a point in polydispersity |
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74 | -pars/-nopars* prints the parameter set or not |
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75 | -abs/-rel* plot relative or absolute error |
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76 | -linear/-log*/-q4 intensity scaling |
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77 | -hist/-nohist* plot histogram of relative error |
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78 | -res=0 sets the resolution width dQ/Q if calculating with resolution |
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79 | -accuracy=Low accuracy of the resolution calculation Low, Mid, High, Xhigh |
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80 | -edit starts the parameter explorer |
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81 | -default/-demo* use demo vs default parameters |
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82 | |
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83 | Any two calculation engines can be selected for comparison: |
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84 | |
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85 | -single/-double/-half/-fast sets an OpenCL calculation engine |
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86 | -single!/-double!/-quad! sets an OpenMP calculation engine |
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87 | -sasview sets the sasview calculation engine |
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88 | |
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89 | The default is -single -sasview. Note that the interpretation of quad |
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90 | precision depends on architecture, and may vary from 64-bit to 128-bit, |
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91 | with 80-bit floats being common (1e-19 precision). |
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92 | |
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93 | Key=value pairs allow you to set specific values for the model parameters. |
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94 | """ |
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95 | |
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96 | # Update docs with command line usage string. This is separate from the usual |
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97 | # doc string so that we can display it at run time if there is an error. |
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98 | # lin |
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99 | __doc__ = (__doc__ # pylint: disable=redefined-builtin |
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100 | + """ |
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101 | Program description |
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102 | ------------------- |
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103 | |
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104 | """ |
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105 | + USAGE) |
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106 | |
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107 | kerneldll.ALLOW_SINGLE_PRECISION_DLLS = True |
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108 | |
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109 | # CRUFT python 2.6 |
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110 | if not hasattr(datetime.timedelta, 'total_seconds'): |
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111 | def delay(dt): |
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112 | """Return number date-time delta as number seconds""" |
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113 | return dt.days * 86400 + dt.seconds + 1e-6 * dt.microseconds |
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114 | else: |
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115 | def delay(dt): |
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116 | """Return number date-time delta as number seconds""" |
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117 | return dt.total_seconds() |
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118 | |
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119 | |
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120 | class push_seed(object): |
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121 | """ |
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122 | Set the seed value for the random number generator. |
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123 | |
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124 | When used in a with statement, the random number generator state is |
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125 | restored after the with statement is complete. |
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126 | |
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127 | :Parameters: |
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128 | |
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129 | *seed* : int or array_like, optional |
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130 | Seed for RandomState |
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131 | |
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132 | :Example: |
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133 | |
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134 | Seed can be used directly to set the seed:: |
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135 | |
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136 | >>> from numpy.random import randint |
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137 | >>> push_seed(24) |
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138 | <...push_seed object at...> |
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139 | >>> print(randint(0,1000000,3)) |
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140 | [242082 899 211136] |
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141 | |
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142 | Seed can also be used in a with statement, which sets the random |
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143 | number generator state for the enclosed computations and restores |
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144 | it to the previous state on completion:: |
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145 | |
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146 | >>> with push_seed(24): |
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147 | ... print(randint(0,1000000,3)) |
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148 | [242082 899 211136] |
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149 | |
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150 | Using nested contexts, we can demonstrate that state is indeed |
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151 | restored after the block completes:: |
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152 | |
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153 | >>> with push_seed(24): |
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154 | ... print(randint(0,1000000)) |
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155 | ... with push_seed(24): |
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156 | ... print(randint(0,1000000,3)) |
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157 | ... print(randint(0,1000000)) |
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158 | 242082 |
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159 | [242082 899 211136] |
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160 | 899 |
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161 | |
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162 | The restore step is protected against exceptions in the block:: |
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163 | |
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164 | >>> with push_seed(24): |
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165 | ... print(randint(0,1000000)) |
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166 | ... try: |
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167 | ... with push_seed(24): |
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168 | ... print(randint(0,1000000,3)) |
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169 | ... raise Exception() |
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170 | ... except Exception: |
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171 | ... print("Exception raised") |
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172 | ... print(randint(0,1000000)) |
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173 | 242082 |
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174 | [242082 899 211136] |
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175 | Exception raised |
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176 | 899 |
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177 | """ |
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178 | def __init__(self, seed=None): |
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179 | # type: (Optional[int]) -> None |
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180 | self._state = np.random.get_state() |
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181 | np.random.seed(seed) |
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182 | |
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183 | def __enter__(self): |
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184 | # type: () -> None |
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185 | pass |
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186 | |
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187 | def __exit__(self, exc_type, exc_value, traceback): |
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188 | # type: (Any, BaseException, Any) -> None |
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189 | # TODO: better typing for __exit__ method |
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190 | np.random.set_state(self._state) |
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191 | |
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192 | def tic(): |
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193 | # type: () -> Callable[[], float] |
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194 | """ |
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195 | Timer function. |
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196 | |
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197 | Use "toc=tic()" to start the clock and "toc()" to measure |
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198 | a time interval. |
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199 | """ |
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200 | then = datetime.datetime.now() |
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201 | return lambda: delay(datetime.datetime.now() - then) |
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202 | |
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203 | |
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204 | def set_beam_stop(data, radius, outer=None): |
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205 | # type: (Data, float, float) -> None |
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206 | """ |
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207 | Add a beam stop of the given *radius*. If *outer*, make an annulus. |
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208 | |
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209 | Note: this function does not require sasview |
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210 | """ |
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211 | if hasattr(data, 'qx_data'): |
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212 | q = np.sqrt(data.qx_data**2 + data.qy_data**2) |
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213 | data.mask = (q < radius) |
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214 | if outer is not None: |
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215 | data.mask |= (q >= outer) |
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216 | else: |
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217 | data.mask = (data.x < radius) |
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218 | if outer is not None: |
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219 | data.mask |= (data.x >= outer) |
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220 | |
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221 | |
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222 | def parameter_range(p, v): |
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223 | # type: (str, float) -> Tuple[float, float] |
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224 | """ |
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225 | Choose a parameter range based on parameter name and initial value. |
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226 | """ |
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227 | # process the polydispersity options |
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228 | if p.endswith('_pd_n'): |
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229 | return 0., 100. |
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230 | elif p.endswith('_pd_nsigma'): |
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231 | return 0., 5. |
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232 | elif p.endswith('_pd_type'): |
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233 | raise ValueError("Cannot return a range for a string value") |
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234 | elif any(s in p for s in ('theta', 'phi', 'psi')): |
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235 | # orientation in [-180,180], orientation pd in [0,45] |
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236 | if p.endswith('_pd'): |
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237 | return 0., 45. |
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238 | else: |
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239 | return -180., 180. |
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240 | elif p.endswith('_pd'): |
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241 | return 0., 1. |
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242 | elif 'sld' in p: |
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243 | return -0.5, 10. |
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244 | elif p == 'background': |
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245 | return 0., 10. |
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246 | elif p == 'scale': |
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247 | return 0., 1.e3 |
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248 | elif v < 0.: |
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249 | return 2.*v, -2.*v |
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250 | else: |
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251 | return 0., (2.*v if v > 0. else 1.) |
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252 | |
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253 | |
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254 | def _randomize_one(model_info, p, v): |
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255 | # type: (ModelInfo, str, float) -> float |
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256 | # type: (ModelInfo, str, str) -> str |
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257 | """ |
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258 | Randomize a single parameter. |
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259 | """ |
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260 | if any(p.endswith(s) for s in ('_pd', '_pd_n', '_pd_nsigma', '_pd_type')): |
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261 | return v |
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262 | |
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263 | # Find the parameter definition |
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264 | for par in model_info.parameters.call_parameters: |
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265 | if par.name == p: |
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266 | break |
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267 | else: |
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268 | raise ValueError("unknown parameter %r"%p) |
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269 | |
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270 | if len(par.limits) > 2: # choice list |
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271 | return np.random.randint(len(par.limits)) |
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272 | |
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273 | limits = par.limits |
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274 | if not np.isfinite(limits).all(): |
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275 | low, high = parameter_range(p, v) |
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276 | limits = (max(limits[0], low), min(limits[1], high)) |
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277 | |
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278 | return np.random.uniform(*limits) |
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279 | |
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280 | |
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281 | def randomize_pars(model_info, pars, seed=None): |
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282 | # type: (ModelInfo, ParameterSet, int) -> ParameterSet |
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283 | """ |
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284 | Generate random values for all of the parameters. |
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285 | |
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286 | Valid ranges for the random number generator are guessed from the name of |
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287 | the parameter; this will not account for constraints such as cap radius |
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288 | greater than cylinder radius in the capped_cylinder model, so |
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289 | :func:`constrain_pars` needs to be called afterward.. |
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290 | """ |
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291 | with push_seed(seed): |
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292 | # Note: the sort guarantees order `of calls to random number generator |
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293 | random_pars = dict((p, _randomize_one(model_info, p, v)) |
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294 | for p, v in sorted(pars.items())) |
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295 | return random_pars |
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296 | |
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297 | def constrain_pars(model_info, pars): |
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298 | # type: (ModelInfo, ParameterSet) -> None |
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299 | """ |
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300 | Restrict parameters to valid values. |
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301 | |
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302 | This includes model specific code for models such as capped_cylinder |
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303 | which need to support within model constraints (cap radius more than |
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304 | cylinder radius in this case). |
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305 | |
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306 | Warning: this updates the *pars* dictionary in place. |
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307 | """ |
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308 | name = model_info.id |
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309 | # if it is a product model, then just look at the form factor since |
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310 | # none of the structure factors need any constraints. |
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311 | if '*' in name: |
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312 | name = name.split('*')[0] |
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313 | |
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314 | if name == 'capped_cylinder' and pars['cap_radius'] < pars['radius']: |
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315 | pars['radius'], pars['cap_radius'] = pars['cap_radius'], pars['radius'] |
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316 | if name == 'barbell' and pars['bell_radius'] < pars['radius']: |
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317 | pars['radius'], pars['bell_radius'] = pars['bell_radius'], pars['radius'] |
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318 | |
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319 | # Limit guinier to an Rg such that Iq > 1e-30 (single precision cutoff) |
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320 | if name == 'guinier': |
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321 | #q_max = 0.2 # mid q maximum |
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322 | q_max = 1.0 # high q maximum |
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323 | rg_max = np.sqrt(90*np.log(10) + 3*np.log(pars['scale']))/q_max |
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324 | pars['rg'] = min(pars['rg'], rg_max) |
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325 | |
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326 | if name == 'rpa': |
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327 | # Make sure phi sums to 1.0 |
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328 | if pars['case_num'] < 2: |
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329 | pars['Phi1'] = 0. |
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330 | pars['Phi2'] = 0. |
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331 | elif pars['case_num'] < 5: |
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332 | pars['Phi1'] = 0. |
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333 | total = sum(pars['Phi'+c] for c in '1234') |
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334 | for c in '1234': |
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335 | pars['Phi'+c] /= total |
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336 | |
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337 | def parlist(model_info, pars, is2d): |
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338 | # type: (ModelInfo, ParameterSet, bool) -> str |
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339 | """ |
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340 | Format the parameter list for printing. |
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341 | """ |
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342 | lines = [] |
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343 | parameters = model_info.parameters |
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344 | for p in parameters.user_parameters(pars, is2d): |
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345 | fields = dict( |
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346 | value=pars.get(p.id, p.default), |
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347 | pd=pars.get(p.id+"_pd", 0.), |
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348 | n=int(pars.get(p.id+"_pd_n", 0)), |
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349 | nsigma=pars.get(p.id+"_pd_nsgima", 3.), |
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350 | pdtype=pars.get(p.id+"_pd_type", 'gaussian'), |
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351 | relative_pd=p.relative_pd, |
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352 | ) |
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353 | lines.append(_format_par(p.name, **fields)) |
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354 | return "\n".join(lines) |
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355 | |
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356 | #return "\n".join("%s: %s"%(p, v) for p, v in sorted(pars.items())) |
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357 | |
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358 | def _format_par(name, value=0., pd=0., n=0, nsigma=3., pdtype='gaussian', |
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359 | relative_pd=False): |
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360 | # type: (str, float, float, int, float, str) -> str |
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361 | line = "%s: %g"%(name, value) |
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362 | if pd != 0. and n != 0: |
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363 | if relative_pd: |
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364 | pd *= value |
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365 | line += " +/- %g (%d points in [-%g,%g] sigma %s)"\ |
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366 | % (pd, n, nsigma, nsigma, pdtype) |
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367 | return line |
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368 | |
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369 | def suppress_pd(pars): |
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370 | # type: (ParameterSet) -> ParameterSet |
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371 | """ |
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372 | Suppress theta_pd for now until the normalization is resolved. |
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373 | |
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374 | May also suppress complete polydispersity of the model to test |
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375 | models more quickly. |
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376 | """ |
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377 | pars = pars.copy() |
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378 | for p in pars: |
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379 | if p.endswith("_pd_n"): pars[p] = 0 |
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380 | return pars |
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381 | |
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382 | def eval_sasview(model_info, data): |
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383 | # type: (Modelinfo, Data) -> Calculator |
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384 | """ |
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385 | Return a model calculator using the pre-4.0 SasView models. |
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386 | """ |
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387 | # importing sas here so that the error message will be that sas failed to |
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388 | # import rather than the more obscure smear_selection not imported error |
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389 | import sas |
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390 | import sas.models |
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391 | from sas.models.qsmearing import smear_selection |
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392 | from sas.models.MultiplicationModel import MultiplicationModel |
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393 | from sas.models.dispersion_models import models as dispersers |
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394 | |
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395 | def get_model_class(name): |
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396 | # type: (str) -> "sas.models.BaseComponent" |
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397 | #print("new",sorted(_pars.items())) |
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398 | __import__('sas.models.' + name) |
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399 | ModelClass = getattr(getattr(sas.models, name, None), name, None) |
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400 | if ModelClass is None: |
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401 | raise ValueError("could not find model %r in sas.models"%name) |
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402 | return ModelClass |
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403 | |
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404 | # WARNING: ugly hack when handling model! |
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405 | # Sasview models with multiplicity need to be created with the target |
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406 | # multiplicity, so we cannot create the target model ahead of time for |
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407 | # for multiplicity models. Instead we store the model in a list and |
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408 | # update the first element of that list with the new multiplicity model |
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409 | # every time we evaluate. |
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410 | |
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411 | # grab the sasview model, or create it if it is a product model |
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412 | if model_info.composition: |
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413 | composition_type, parts = model_info.composition |
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414 | if composition_type == 'product': |
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415 | P, S = [get_model_class(revert_name(p))() for p in parts] |
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416 | model = [MultiplicationModel(P, S)] |
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417 | else: |
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418 | raise ValueError("sasview mixture models not supported by compare") |
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419 | else: |
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420 | old_name = revert_name(model_info) |
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421 | if old_name is None: |
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422 | raise ValueError("model %r does not exist in old sasview" |
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423 | % model_info.id) |
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424 | ModelClass = get_model_class(old_name) |
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425 | model = [ModelClass()] |
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426 | model[0].disperser_handles = {} |
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427 | |
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428 | # build a smearer with which to call the model, if necessary |
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429 | smearer = smear_selection(data, model=model) |
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430 | if hasattr(data, 'qx_data'): |
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431 | q = np.sqrt(data.qx_data**2 + data.qy_data**2) |
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432 | index = ((~data.mask) & (~np.isnan(data.data)) |
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433 | & (q >= data.qmin) & (q <= data.qmax)) |
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434 | if smearer is not None: |
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435 | smearer.model = model # because smear_selection has a bug |
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436 | smearer.accuracy = data.accuracy |
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437 | smearer.set_index(index) |
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438 | def _call_smearer(): |
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439 | smearer.model = model[0] |
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440 | return smearer.get_value() |
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441 | theory = _call_smearer |
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442 | else: |
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443 | theory = lambda: model[0].evalDistribution([data.qx_data[index], |
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444 | data.qy_data[index]]) |
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445 | elif smearer is not None: |
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446 | theory = lambda: smearer(model[0].evalDistribution(data.x)) |
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447 | else: |
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448 | theory = lambda: model[0].evalDistribution(data.x) |
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449 | |
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450 | def calculator(**pars): |
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451 | # type: (float, ...) -> np.ndarray |
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452 | """ |
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453 | Sasview calculator for model. |
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454 | """ |
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455 | oldpars = revert_pars(model_info, pars) |
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456 | # For multiplicity models, create a model with the correct multiplicity |
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457 | control = oldpars.pop("CONTROL", None) |
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458 | if control is not None: |
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459 | # sphericalSLD has one fewer multiplicity. This update should |
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460 | # happen in revert_pars, but it hasn't been called yet. |
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461 | model[0] = ModelClass(control) |
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462 | # paying for parameter conversion each time to keep life simple, if not fast |
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463 | for k, v in oldpars.items(): |
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464 | if k.endswith('.type'): |
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465 | par = k[:-5] |
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466 | cls = dispersers[v if v != 'rectangle' else 'rectangula'] |
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467 | handle = cls() |
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468 | model[0].disperser_handles[par] = handle |
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469 | model[0].set_dispersion(par, handle) |
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470 | |
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471 | #print("sasview pars",oldpars) |
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472 | for k, v in oldpars.items(): |
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473 | name_attr = k.split('.') # polydispersity components |
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474 | if len(name_attr) == 2: |
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475 | par, disp_par = name_attr |
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476 | model[0].dispersion[par][disp_par] = v |
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477 | else: |
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478 | model[0].setParam(k, v) |
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479 | return theory() |
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480 | |
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481 | calculator.engine = "sasview" |
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482 | return calculator |
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483 | |
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484 | DTYPE_MAP = { |
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485 | 'half': '16', |
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486 | 'fast': 'fast', |
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487 | 'single': '32', |
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488 | 'double': '64', |
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489 | 'quad': '128', |
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490 | 'f16': '16', |
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491 | 'f32': '32', |
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492 | 'f64': '64', |
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493 | 'longdouble': '128', |
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494 | } |
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495 | def eval_opencl(model_info, data, dtype='single', cutoff=0.): |
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496 | # type: (ModelInfo, Data, str, float) -> Calculator |
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497 | """ |
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498 | Return a model calculator using the OpenCL calculation engine. |
---|
499 | """ |
---|
500 | if not core.HAVE_OPENCL: |
---|
501 | raise RuntimeError("OpenCL not available") |
---|
502 | model = core.build_model(model_info, dtype=dtype, platform="ocl") |
---|
503 | calculator = DirectModel(data, model, cutoff=cutoff) |
---|
504 | calculator.engine = "OCL%s"%DTYPE_MAP[dtype] |
---|
505 | return calculator |
---|
506 | |
---|
507 | def eval_ctypes(model_info, data, dtype='double', cutoff=0.): |
---|
508 | # type: (ModelInfo, Data, str, float) -> Calculator |
---|
509 | """ |
---|
510 | Return a model calculator using the DLL calculation engine. |
---|
511 | """ |
---|
512 | model = core.build_model(model_info, dtype=dtype, platform="dll") |
---|
513 | calculator = DirectModel(data, model, cutoff=cutoff) |
---|
514 | calculator.engine = "OMP%s"%DTYPE_MAP[dtype] |
---|
515 | return calculator |
---|
516 | |
---|
517 | def time_calculation(calculator, pars, evals=1): |
---|
518 | # type: (Calculator, ParameterSet, int) -> Tuple[np.ndarray, float] |
---|
519 | """ |
---|
520 | Compute the average calculation time over N evaluations. |
---|
521 | |
---|
522 | An additional call is generated without polydispersity in order to |
---|
523 | initialize the calculation engine, and make the average more stable. |
---|
524 | """ |
---|
525 | # initialize the code so time is more accurate |
---|
526 | if evals > 1: |
---|
527 | calculator(**suppress_pd(pars)) |
---|
528 | toc = tic() |
---|
529 | # make sure there is at least one eval |
---|
530 | value = calculator(**pars) |
---|
531 | for _ in range(evals-1): |
---|
532 | value = calculator(**pars) |
---|
533 | average_time = toc()*1000. / evals |
---|
534 | #print("I(q)",value) |
---|
535 | return value, average_time |
---|
536 | |
---|
537 | def make_data(opts): |
---|
538 | # type: (Dict[str, Any]) -> Tuple[Data, np.ndarray] |
---|
539 | """ |
---|
540 | Generate an empty dataset, used with the model to set Q points |
---|
541 | and resolution. |
---|
542 | |
---|
543 | *opts* contains the options, with 'qmax', 'nq', 'res', |
---|
544 | 'accuracy', 'is2d' and 'view' parsed from the command line. |
---|
545 | """ |
---|
546 | qmax, nq, res = opts['qmax'], opts['nq'], opts['res'] |
---|
547 | if opts['is2d']: |
---|
548 | q = np.linspace(-qmax, qmax, nq) # type: np.ndarray |
---|
549 | data = empty_data2D(q, resolution=res) |
---|
550 | data.accuracy = opts['accuracy'] |
---|
551 | set_beam_stop(data, 0.0004) |
---|
552 | index = ~data.mask |
---|
553 | else: |
---|
554 | if opts['view'] == 'log' and not opts['zero']: |
---|
555 | qmax = math.log10(qmax) |
---|
556 | q = np.logspace(qmax-3, qmax, nq) |
---|
557 | else: |
---|
558 | q = np.linspace(0.001*qmax, qmax, nq) |
---|
559 | if opts['zero']: |
---|
560 | q = np.hstack((0, q)) |
---|
561 | data = empty_data1D(q, resolution=res) |
---|
562 | index = slice(None, None) |
---|
563 | return data, index |
---|
564 | |
---|
565 | def make_engine(model_info, data, dtype, cutoff): |
---|
566 | # type: (ModelInfo, Data, str, float) -> Calculator |
---|
567 | """ |
---|
568 | Generate the appropriate calculation engine for the given datatype. |
---|
569 | |
---|
570 | Datatypes with '!' appended are evaluated using external C DLLs rather |
---|
571 | than OpenCL. |
---|
572 | """ |
---|
573 | if dtype == 'sasview': |
---|
574 | return eval_sasview(model_info, data) |
---|
575 | elif dtype.endswith('!'): |
---|
576 | return eval_ctypes(model_info, data, dtype=dtype[:-1], cutoff=cutoff) |
---|
577 | else: |
---|
578 | return eval_opencl(model_info, data, dtype=dtype, cutoff=cutoff) |
---|
579 | |
---|
580 | def _show_invalid(data, theory): |
---|
581 | # type: (Data, np.ma.ndarray) -> None |
---|
582 | """ |
---|
583 | Display a list of the non-finite values in theory. |
---|
584 | """ |
---|
585 | if not theory.mask.any(): |
---|
586 | return |
---|
587 | |
---|
588 | if hasattr(data, 'x'): |
---|
589 | bad = zip(data.x[theory.mask], theory[theory.mask]) |
---|
590 | print(" *** ", ", ".join("I(%g)=%g"%(x, y) for x, y in bad)) |
---|
591 | |
---|
592 | |
---|
593 | def compare(opts, limits=None): |
---|
594 | # type: (Dict[str, Any], Optional[Tuple[float, float]]) -> Tuple[float, float] |
---|
595 | """ |
---|
596 | Preform a comparison using options from the command line. |
---|
597 | |
---|
598 | *limits* are the limits on the values to use, either to set the y-axis |
---|
599 | for 1D or to set the colormap scale for 2D. If None, then they are |
---|
600 | inferred from the data and returned. When exploring using Bumps, |
---|
601 | the limits are set when the model is initially called, and maintained |
---|
602 | as the values are adjusted, making it easier to see the effects of the |
---|
603 | parameters. |
---|
604 | """ |
---|
605 | n_base, n_comp = opts['n1'], opts['n2'] |
---|
606 | pars = opts['pars'] |
---|
607 | data = opts['data'] |
---|
608 | |
---|
609 | # silence the linter |
---|
610 | base = opts['engines'][0] if n_base else None |
---|
611 | comp = opts['engines'][1] if n_comp else None |
---|
612 | base_time = comp_time = None |
---|
613 | base_value = comp_value = resid = relerr = None |
---|
614 | |
---|
615 | # Base calculation |
---|
616 | if n_base > 0: |
---|
617 | try: |
---|
618 | base_raw, base_time = time_calculation(base, pars, n_base) |
---|
619 | base_value = np.ma.masked_invalid(base_raw) |
---|
620 | print("%s t=%.2f ms, intensity=%.0f" |
---|
621 | % (base.engine, base_time, base_value.sum())) |
---|
622 | _show_invalid(data, base_value) |
---|
623 | except ImportError: |
---|
624 | traceback.print_exc() |
---|
625 | n_base = 0 |
---|
626 | |
---|
627 | # Comparison calculation |
---|
628 | if n_comp > 0: |
---|
629 | try: |
---|
630 | comp_raw, comp_time = time_calculation(comp, pars, n_comp) |
---|
631 | comp_value = np.ma.masked_invalid(comp_raw) |
---|
632 | print("%s t=%.2f ms, intensity=%.0f" |
---|
633 | % (comp.engine, comp_time, comp_value.sum())) |
---|
634 | _show_invalid(data, comp_value) |
---|
635 | except ImportError: |
---|
636 | traceback.print_exc() |
---|
637 | n_comp = 0 |
---|
638 | |
---|
639 | # Compare, but only if computing both forms |
---|
640 | if n_base > 0 and n_comp > 0: |
---|
641 | resid = (base_value - comp_value) |
---|
642 | relerr = resid/np.where(comp_value != 0., abs(comp_value), 1.0) |
---|
643 | _print_stats("|%s-%s|" |
---|
644 | % (base.engine, comp.engine) + (" "*(3+len(comp.engine))), |
---|
645 | resid) |
---|
646 | _print_stats("|(%s-%s)/%s|" |
---|
647 | % (base.engine, comp.engine, comp.engine), |
---|
648 | relerr) |
---|
649 | |
---|
650 | # Plot if requested |
---|
651 | if not opts['plot'] and not opts['explore']: return |
---|
652 | view = opts['view'] |
---|
653 | import matplotlib.pyplot as plt |
---|
654 | if limits is None: |
---|
655 | vmin, vmax = np.Inf, -np.Inf |
---|
656 | if n_base > 0: |
---|
657 | vmin = min(vmin, base_value.min()) |
---|
658 | vmax = max(vmax, base_value.max()) |
---|
659 | if n_comp > 0: |
---|
660 | vmin = min(vmin, comp_value.min()) |
---|
661 | vmax = max(vmax, comp_value.max()) |
---|
662 | limits = vmin, vmax |
---|
663 | |
---|
664 | if n_base > 0: |
---|
665 | if n_comp > 0: plt.subplot(131) |
---|
666 | plot_theory(data, base_value, view=view, use_data=False, limits=limits) |
---|
667 | plt.title("%s t=%.2f ms"%(base.engine, base_time)) |
---|
668 | #cbar_title = "log I" |
---|
669 | if n_comp > 0: |
---|
670 | if n_base > 0: plt.subplot(132) |
---|
671 | plot_theory(data, comp_value, view=view, use_data=False, limits=limits) |
---|
672 | plt.title("%s t=%.2f ms"%(comp.engine, comp_time)) |
---|
673 | #cbar_title = "log I" |
---|
674 | if n_comp > 0 and n_base > 0: |
---|
675 | plt.subplot(133) |
---|
676 | if not opts['rel_err']: |
---|
677 | err, errstr, errview = resid, "abs err", "linear" |
---|
678 | else: |
---|
679 | err, errstr, errview = abs(relerr), "rel err", "log" |
---|
680 | #err,errstr = base/comp,"ratio" |
---|
681 | plot_theory(data, None, resid=err, view=errview, use_data=False) |
---|
682 | if view == 'linear': |
---|
683 | plt.xscale('linear') |
---|
684 | plt.title("max %s = %.3g"%(errstr, abs(err).max())) |
---|
685 | #cbar_title = errstr if errview=="linear" else "log "+errstr |
---|
686 | #if is2D: |
---|
687 | # h = plt.colorbar() |
---|
688 | # h.ax.set_title(cbar_title) |
---|
689 | fig = plt.gcf() |
---|
690 | fig.suptitle(opts['name']) |
---|
691 | |
---|
692 | if n_comp > 0 and n_base > 0 and '-hist' in opts: |
---|
693 | plt.figure() |
---|
694 | v = relerr |
---|
695 | v[v == 0] = 0.5*np.min(np.abs(v[v != 0])) |
---|
696 | plt.hist(np.log10(np.abs(v)), normed=1, bins=50) |
---|
697 | plt.xlabel('log10(err), err = |(%s - %s) / %s|' |
---|
698 | % (base.engine, comp.engine, comp.engine)) |
---|
699 | plt.ylabel('P(err)') |
---|
700 | plt.title('Distribution of relative error between calculation engines') |
---|
701 | |
---|
702 | if not opts['explore']: |
---|
703 | plt.show() |
---|
704 | |
---|
705 | return limits |
---|
706 | |
---|
707 | def _print_stats(label, err): |
---|
708 | # type: (str, np.ma.ndarray) -> None |
---|
709 | # work with trimmed data, not the full set |
---|
710 | sorted_err = np.sort(abs(err.compressed())) |
---|
711 | p50 = int((len(sorted_err)-1)*0.50) |
---|
712 | p98 = int((len(sorted_err)-1)*0.98) |
---|
713 | data = [ |
---|
714 | "max:%.3e"%sorted_err[-1], |
---|
715 | "median:%.3e"%sorted_err[p50], |
---|
716 | "98%%:%.3e"%sorted_err[p98], |
---|
717 | "rms:%.3e"%np.sqrt(np.mean(sorted_err**2)), |
---|
718 | "zero-offset:%+.3e"%np.mean(sorted_err), |
---|
719 | ] |
---|
720 | print(label+" "+" ".join(data)) |
---|
721 | |
---|
722 | |
---|
723 | |
---|
724 | # =========================================================================== |
---|
725 | # |
---|
726 | NAME_OPTIONS = set([ |
---|
727 | 'plot', 'noplot', |
---|
728 | 'half', 'fast', 'single', 'double', |
---|
729 | 'single!', 'double!', 'quad!', 'sasview', |
---|
730 | 'lowq', 'midq', 'highq', 'exq', 'zero', |
---|
731 | '2d', '1d', |
---|
732 | 'preset', 'random', |
---|
733 | 'poly', 'mono', |
---|
734 | 'nopars', 'pars', |
---|
735 | 'rel', 'abs', |
---|
736 | 'linear', 'log', 'q4', |
---|
737 | 'hist', 'nohist', |
---|
738 | 'edit', |
---|
739 | 'demo', 'default', |
---|
740 | ]) |
---|
741 | VALUE_OPTIONS = [ |
---|
742 | # Note: random is both a name option and a value option |
---|
743 | 'cutoff', 'random', 'nq', 'res', 'accuracy', |
---|
744 | ] |
---|
745 | |
---|
746 | def columnize(items, indent="", width=79): |
---|
747 | # type: (List[str], str, int) -> str |
---|
748 | """ |
---|
749 | Format a list of strings into columns. |
---|
750 | |
---|
751 | Returns a string with carriage returns ready for printing. |
---|
752 | """ |
---|
753 | column_width = max(len(w) for w in items) + 1 |
---|
754 | num_columns = (width - len(indent)) // column_width |
---|
755 | num_rows = len(items) // num_columns |
---|
756 | items = items + [""] * (num_rows * num_columns - len(items)) |
---|
757 | columns = [items[k*num_rows:(k+1)*num_rows] for k in range(num_columns)] |
---|
758 | lines = [" ".join("%-*s"%(column_width, entry) for entry in row) |
---|
759 | for row in zip(*columns)] |
---|
760 | output = indent + ("\n"+indent).join(lines) |
---|
761 | return output |
---|
762 | |
---|
763 | |
---|
764 | def get_pars(model_info, use_demo=False): |
---|
765 | # type: (ModelInfo, bool) -> ParameterSet |
---|
766 | """ |
---|
767 | Extract demo parameters from the model definition. |
---|
768 | """ |
---|
769 | # Get the default values for the parameters |
---|
770 | pars = {} |
---|
771 | for p in model_info.parameters.call_parameters: |
---|
772 | parts = [('', p.default)] |
---|
773 | if p.polydisperse: |
---|
774 | parts.append(('_pd', 0.0)) |
---|
775 | parts.append(('_pd_n', 0)) |
---|
776 | parts.append(('_pd_nsigma', 3.0)) |
---|
777 | parts.append(('_pd_type', "gaussian")) |
---|
778 | for ext, val in parts: |
---|
779 | if p.length > 1: |
---|
780 | dict(("%s%d%s" % (p.id, k, ext), val) |
---|
781 | for k in range(1, p.length+1)) |
---|
782 | else: |
---|
783 | pars[p.id + ext] = val |
---|
784 | |
---|
785 | # Plug in values given in demo |
---|
786 | if use_demo: |
---|
787 | pars.update(model_info.demo) |
---|
788 | return pars |
---|
789 | |
---|
790 | |
---|
791 | def parse_opts(argv): |
---|
792 | # type: (List[str]) -> Dict[str, Any] |
---|
793 | """ |
---|
794 | Parse command line options. |
---|
795 | """ |
---|
796 | MODELS = core.list_models() |
---|
797 | flags = [arg for arg in argv |
---|
798 | if arg.startswith('-')] |
---|
799 | values = [arg for arg in argv |
---|
800 | if not arg.startswith('-') and '=' in arg] |
---|
801 | positional_args = [arg for arg in argv |
---|
802 | if not arg.startswith('-') and '=' not in arg] |
---|
803 | models = "\n ".join("%-15s"%v for v in MODELS) |
---|
804 | if len(positional_args) == 0: |
---|
805 | print(USAGE) |
---|
806 | print("\nAvailable models:") |
---|
807 | print(columnize(MODELS, indent=" ")) |
---|
808 | return None |
---|
809 | if len(positional_args) > 3: |
---|
810 | print("expected parameters: model N1 N2") |
---|
811 | |
---|
812 | name = positional_args[0] |
---|
813 | try: |
---|
814 | model_info = core.load_model_info(name) |
---|
815 | except ImportError as exc: |
---|
816 | print(str(exc)) |
---|
817 | print("Could not find model; use one of:\n " + models) |
---|
818 | return None |
---|
819 | |
---|
820 | invalid = [o[1:] for o in flags |
---|
821 | if o[1:] not in NAME_OPTIONS |
---|
822 | and not any(o.startswith('-%s='%t) for t in VALUE_OPTIONS)] |
---|
823 | if invalid: |
---|
824 | print("Invalid options: %s"%(", ".join(invalid))) |
---|
825 | return None |
---|
826 | |
---|
827 | |
---|
828 | # pylint: disable=bad-whitespace |
---|
829 | # Interpret the flags |
---|
830 | opts = { |
---|
831 | 'plot' : True, |
---|
832 | 'view' : 'log', |
---|
833 | 'is2d' : False, |
---|
834 | 'qmax' : 0.05, |
---|
835 | 'nq' : 128, |
---|
836 | 'res' : 0.0, |
---|
837 | 'accuracy' : 'Low', |
---|
838 | 'cutoff' : 0.0, |
---|
839 | 'seed' : -1, # default to preset |
---|
840 | 'mono' : False, |
---|
841 | 'show_pars' : False, |
---|
842 | 'show_hist' : False, |
---|
843 | 'rel_err' : True, |
---|
844 | 'explore' : False, |
---|
845 | 'use_demo' : True, |
---|
846 | 'zero' : False, |
---|
847 | } |
---|
848 | engines = [] |
---|
849 | for arg in flags: |
---|
850 | if arg == '-noplot': opts['plot'] = False |
---|
851 | elif arg == '-plot': opts['plot'] = True |
---|
852 | elif arg == '-linear': opts['view'] = 'linear' |
---|
853 | elif arg == '-log': opts['view'] = 'log' |
---|
854 | elif arg == '-q4': opts['view'] = 'q4' |
---|
855 | elif arg == '-1d': opts['is2d'] = False |
---|
856 | elif arg == '-2d': opts['is2d'] = True |
---|
857 | elif arg == '-exq': opts['qmax'] = 10.0 |
---|
858 | elif arg == '-highq': opts['qmax'] = 1.0 |
---|
859 | elif arg == '-midq': opts['qmax'] = 0.2 |
---|
860 | elif arg == '-lowq': opts['qmax'] = 0.05 |
---|
861 | elif arg == '-zero': opts['zero'] = True |
---|
862 | elif arg.startswith('-nq='): opts['nq'] = int(arg[4:]) |
---|
863 | elif arg.startswith('-res='): opts['res'] = float(arg[5:]) |
---|
864 | elif arg.startswith('-accuracy='): opts['accuracy'] = arg[10:] |
---|
865 | elif arg.startswith('-cutoff='): opts['cutoff'] = float(arg[8:]) |
---|
866 | elif arg.startswith('-random='): opts['seed'] = int(arg[8:]) |
---|
867 | elif arg == '-random': opts['seed'] = np.random.randint(1000000) |
---|
868 | elif arg == '-preset': opts['seed'] = -1 |
---|
869 | elif arg == '-mono': opts['mono'] = True |
---|
870 | elif arg == '-poly': opts['mono'] = False |
---|
871 | elif arg == '-pars': opts['show_pars'] = True |
---|
872 | elif arg == '-nopars': opts['show_pars'] = False |
---|
873 | elif arg == '-hist': opts['show_hist'] = True |
---|
874 | elif arg == '-nohist': opts['show_hist'] = False |
---|
875 | elif arg == '-rel': opts['rel_err'] = True |
---|
876 | elif arg == '-abs': opts['rel_err'] = False |
---|
877 | elif arg == '-half': engines.append(arg[1:]) |
---|
878 | elif arg == '-fast': engines.append(arg[1:]) |
---|
879 | elif arg == '-single': engines.append(arg[1:]) |
---|
880 | elif arg == '-double': engines.append(arg[1:]) |
---|
881 | elif arg == '-single!': engines.append(arg[1:]) |
---|
882 | elif arg == '-double!': engines.append(arg[1:]) |
---|
883 | elif arg == '-quad!': engines.append(arg[1:]) |
---|
884 | elif arg == '-sasview': engines.append(arg[1:]) |
---|
885 | elif arg == '-edit': opts['explore'] = True |
---|
886 | elif arg == '-demo': opts['use_demo'] = True |
---|
887 | elif arg == '-default': opts['use_demo'] = False |
---|
888 | # pylint: enable=bad-whitespace |
---|
889 | |
---|
890 | if len(engines) == 0: |
---|
891 | engines.extend(['single', 'double']) |
---|
892 | elif len(engines) == 1: |
---|
893 | if engines[0][0] == 'double': |
---|
894 | engines.append('single') |
---|
895 | else: |
---|
896 | engines.append('double') |
---|
897 | elif len(engines) > 2: |
---|
898 | del engines[2:] |
---|
899 | |
---|
900 | n1 = int(positional_args[1]) if len(positional_args) > 1 else 1 |
---|
901 | n2 = int(positional_args[2]) if len(positional_args) > 2 else 1 |
---|
902 | use_sasview = any(engine == 'sasview' and count > 0 |
---|
903 | for engine, count in zip(engines, [n1, n2])) |
---|
904 | |
---|
905 | # Get demo parameters from model definition, or use default parameters |
---|
906 | # if model does not define demo parameters |
---|
907 | pars = get_pars(model_info, opts['use_demo']) |
---|
908 | |
---|
909 | |
---|
910 | # Fill in parameters given on the command line |
---|
911 | presets = {} |
---|
912 | for arg in values: |
---|
913 | k, v = arg.split('=', 1) |
---|
914 | if k not in pars: |
---|
915 | # extract base name without polydispersity info |
---|
916 | s = set(p.split('_pd')[0] for p in pars) |
---|
917 | print("%r invalid; parameters are: %s"%(k, ", ".join(sorted(s)))) |
---|
918 | return None |
---|
919 | presets[k] = float(v) if not k.endswith('type') else v |
---|
920 | |
---|
921 | # randomize parameters |
---|
922 | #pars.update(set_pars) # set value before random to control range |
---|
923 | if opts['seed'] > -1: |
---|
924 | pars = randomize_pars(model_info, pars, seed=opts['seed']) |
---|
925 | print("Randomize using -random=%i"%opts['seed']) |
---|
926 | if opts['mono']: |
---|
927 | pars = suppress_pd(pars) |
---|
928 | pars.update(presets) # set value after random to control value |
---|
929 | #import pprint; pprint.pprint(model_info) |
---|
930 | constrain_pars(model_info, pars) |
---|
931 | if use_sasview: |
---|
932 | constrain_new_to_old(model_info, pars) |
---|
933 | if opts['show_pars']: |
---|
934 | print(str(parlist(model_info, pars, opts['is2d']))) |
---|
935 | |
---|
936 | # Create the computational engines |
---|
937 | data, _ = make_data(opts) |
---|
938 | if n1: |
---|
939 | base = make_engine(model_info, data, engines[0], opts['cutoff']) |
---|
940 | else: |
---|
941 | base = None |
---|
942 | if n2: |
---|
943 | comp = make_engine(model_info, data, engines[1], opts['cutoff']) |
---|
944 | else: |
---|
945 | comp = None |
---|
946 | |
---|
947 | # pylint: disable=bad-whitespace |
---|
948 | # Remember it all |
---|
949 | opts.update({ |
---|
950 | 'name' : name, |
---|
951 | 'def' : model_info, |
---|
952 | 'n1' : n1, |
---|
953 | 'n2' : n2, |
---|
954 | 'presets' : presets, |
---|
955 | 'pars' : pars, |
---|
956 | 'data' : data, |
---|
957 | 'engines' : [base, comp], |
---|
958 | }) |
---|
959 | # pylint: enable=bad-whitespace |
---|
960 | |
---|
961 | return opts |
---|
962 | |
---|
963 | def explore(opts): |
---|
964 | # type: (Dict[str, Any]) -> None |
---|
965 | """ |
---|
966 | Explore the model using the Bumps GUI. |
---|
967 | """ |
---|
968 | import wx # type: ignore |
---|
969 | from bumps.names import FitProblem # type: ignore |
---|
970 | from bumps.gui.app_frame import AppFrame # type: ignore |
---|
971 | |
---|
972 | is_mac = "cocoa" in wx.version() |
---|
973 | # Create an app if not running embedded |
---|
974 | app = wx.App() if wx.GetApp() is None else None |
---|
975 | problem = FitProblem(Explore(opts)) |
---|
976 | frame = AppFrame(parent=None, title="explore", size=(1000,700)) |
---|
977 | if not is_mac: frame.Show() |
---|
978 | frame.panel.set_model(model=problem) |
---|
979 | frame.panel.Layout() |
---|
980 | frame.panel.aui.Split(0, wx.TOP) |
---|
981 | if is_mac: frame.Show() |
---|
982 | # If running withing an app, start the main loop |
---|
983 | if app: app.MainLoop() |
---|
984 | |
---|
985 | class Explore(object): |
---|
986 | """ |
---|
987 | Bumps wrapper for a SAS model comparison. |
---|
988 | |
---|
989 | The resulting object can be used as a Bumps fit problem so that |
---|
990 | parameters can be adjusted in the GUI, with plots updated on the fly. |
---|
991 | """ |
---|
992 | def __init__(self, opts): |
---|
993 | # type: (Dict[str, Any]) -> None |
---|
994 | from bumps.cli import config_matplotlib # type: ignore |
---|
995 | from . import bumps_model |
---|
996 | config_matplotlib() |
---|
997 | self.opts = opts |
---|
998 | model_info = opts['def'] |
---|
999 | pars, pd_types = bumps_model.create_parameters(model_info, **opts['pars']) |
---|
1000 | # Initialize parameter ranges, fixing the 2D parameters for 1D data. |
---|
1001 | if not opts['is2d']: |
---|
1002 | for p in model_info.parameters.user_parameters(is2d=False): |
---|
1003 | for ext in ['', '_pd', '_pd_n', '_pd_nsigma']: |
---|
1004 | k = p.name+ext |
---|
1005 | v = pars.get(k, None) |
---|
1006 | if v is not None: |
---|
1007 | v.range(*parameter_range(k, v.value)) |
---|
1008 | else: |
---|
1009 | for k, v in pars.items(): |
---|
1010 | v.range(*parameter_range(k, v.value)) |
---|
1011 | |
---|
1012 | self.pars = pars |
---|
1013 | self.pd_types = pd_types |
---|
1014 | self.limits = None |
---|
1015 | |
---|
1016 | def numpoints(self): |
---|
1017 | # type: () -> int |
---|
1018 | """ |
---|
1019 | Return the number of points. |
---|
1020 | """ |
---|
1021 | return len(self.pars) + 1 # so dof is 1 |
---|
1022 | |
---|
1023 | def parameters(self): |
---|
1024 | # type: () -> Any # Dict/List hierarchy of parameters |
---|
1025 | """ |
---|
1026 | Return a dictionary of parameters. |
---|
1027 | """ |
---|
1028 | return self.pars |
---|
1029 | |
---|
1030 | def nllf(self): |
---|
1031 | # type: () -> float |
---|
1032 | """ |
---|
1033 | Return cost. |
---|
1034 | """ |
---|
1035 | # pylint: disable=no-self-use |
---|
1036 | return 0. # No nllf |
---|
1037 | |
---|
1038 | def plot(self, view='log'): |
---|
1039 | # type: (str) -> None |
---|
1040 | """ |
---|
1041 | Plot the data and residuals. |
---|
1042 | """ |
---|
1043 | pars = dict((k, v.value) for k, v in self.pars.items()) |
---|
1044 | pars.update(self.pd_types) |
---|
1045 | self.opts['pars'] = pars |
---|
1046 | limits = compare(self.opts, limits=self.limits) |
---|
1047 | if self.limits is None: |
---|
1048 | vmin, vmax = limits |
---|
1049 | self.limits = vmax*1e-7, 1.3*vmax |
---|
1050 | |
---|
1051 | |
---|
1052 | def main(*argv): |
---|
1053 | # type: (*str) -> None |
---|
1054 | """ |
---|
1055 | Main program. |
---|
1056 | """ |
---|
1057 | opts = parse_opts(argv) |
---|
1058 | if opts is not None: |
---|
1059 | if opts['explore']: |
---|
1060 | explore(opts) |
---|
1061 | else: |
---|
1062 | compare(opts) |
---|
1063 | |
---|
1064 | if __name__ == "__main__": |
---|
1065 | main(*sys.argv[1:]) |
---|