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 | import re |
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36 | |
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37 | import numpy as np # type: ignore |
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38 | |
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39 | from . import core |
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40 | from . import kerneldll |
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41 | from . import exception |
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42 | from .data import plot_theory, empty_data1D, empty_data2D |
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43 | from .direct_model import DirectModel |
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44 | from .convert import revert_name, revert_pars, constrain_new_to_old |
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45 | from .generate import FLOAT_RE |
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46 | |
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47 | try: |
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48 | from typing import Optional, Dict, Any, Callable, Tuple |
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49 | except Exception: |
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50 | pass |
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51 | else: |
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52 | from .modelinfo import ModelInfo, Parameter, ParameterSet |
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53 | from .data import Data |
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54 | Calculator = Callable[[float], np.ndarray] |
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55 | |
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56 | USAGE = """ |
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57 | usage: compare.py model N1 N2 [options...] [key=val] |
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58 | |
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59 | Compare the speed and value for a model between the SasView original and the |
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60 | sasmodels rewrite. |
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61 | |
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62 | model or model1,model2 are the names of the models to compare (see below). |
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63 | N1 is the number of times to run sasmodels (default=1). |
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64 | N2 is the number times to run sasview (default=1). |
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65 | |
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66 | Options (* for default): |
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67 | |
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68 | -plot*/-noplot plots or suppress the plot of the model |
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69 | -lowq*/-midq/-highq/-exq use q values up to 0.05, 0.2, 1.0, 10.0 |
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70 | -nq=128 sets the number of Q points in the data set |
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71 | -zero indicates that q=0 should be included |
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72 | -1d*/-2d computes 1d or 2d data |
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73 | -preset*/-random[=seed] preset or random parameters |
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74 | -mono/-poly* force monodisperse/polydisperse |
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75 | -magnetic/-nonmagnetic* suppress magnetism |
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76 | -cutoff=1e-5* cutoff value for including a point in polydispersity |
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77 | -pars/-nopars* prints the parameter set or not |
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78 | -abs/-rel* plot relative or absolute error |
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79 | -linear/-log*/-q4 intensity scaling |
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80 | -hist/-nohist* plot histogram of relative error |
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81 | -res=0 sets the resolution width dQ/Q if calculating with resolution |
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82 | -accuracy=Low accuracy of the resolution calculation Low, Mid, High, Xhigh |
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83 | -edit starts the parameter explorer |
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84 | -default/-demo* use demo vs default parameters |
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85 | -html shows the model docs instead of running the model |
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86 | -title="note" adds note to the plot title, after the model name |
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87 | |
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88 | Any two calculation engines can be selected for comparison: |
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89 | |
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90 | -single/-double/-half/-fast sets an OpenCL calculation engine |
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91 | -single!/-double!/-quad! sets an OpenMP calculation engine |
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92 | -sasview sets the sasview calculation engine |
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93 | |
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94 | The default is -single -double. Note that the interpretation of quad |
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95 | precision depends on architecture, and may vary from 64-bit to 128-bit, |
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96 | with 80-bit floats being common (1e-19 precision). |
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97 | |
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98 | Key=value pairs allow you to set specific values for the model parameters. |
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99 | Key=value1,value2 to compare different values of the same parameter. |
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100 | value can be an expression including other parameters |
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101 | """ |
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102 | |
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103 | # Update docs with command line usage string. This is separate from the usual |
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104 | # doc string so that we can display it at run time if there is an error. |
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105 | # lin |
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106 | __doc__ = (__doc__ # pylint: disable=redefined-builtin |
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107 | + """ |
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108 | Program description |
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109 | ------------------- |
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110 | |
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111 | """ |
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112 | + USAGE) |
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113 | |
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114 | kerneldll.ALLOW_SINGLE_PRECISION_DLLS = True |
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115 | |
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116 | # list of math functions for use in evaluating parameters |
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117 | MATH = dict((k,getattr(math, k)) for k in dir(math) if not k.startswith('_')) |
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118 | |
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119 | # CRUFT python 2.6 |
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120 | if not hasattr(datetime.timedelta, 'total_seconds'): |
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121 | def delay(dt): |
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122 | """Return number date-time delta as number seconds""" |
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123 | return dt.days * 86400 + dt.seconds + 1e-6 * dt.microseconds |
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124 | else: |
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125 | def delay(dt): |
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126 | """Return number date-time delta as number seconds""" |
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127 | return dt.total_seconds() |
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128 | |
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129 | |
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130 | class push_seed(object): |
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131 | """ |
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132 | Set the seed value for the random number generator. |
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133 | |
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134 | When used in a with statement, the random number generator state is |
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135 | restored after the with statement is complete. |
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136 | |
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137 | :Parameters: |
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138 | |
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139 | *seed* : int or array_like, optional |
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140 | Seed for RandomState |
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141 | |
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142 | :Example: |
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143 | |
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144 | Seed can be used directly to set the seed:: |
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145 | |
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146 | >>> from numpy.random import randint |
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147 | >>> push_seed(24) |
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148 | <...push_seed object at...> |
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149 | >>> print(randint(0,1000000,3)) |
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150 | [242082 899 211136] |
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151 | |
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152 | Seed can also be used in a with statement, which sets the random |
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153 | number generator state for the enclosed computations and restores |
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154 | it to the previous state on completion:: |
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155 | |
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156 | >>> with push_seed(24): |
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157 | ... print(randint(0,1000000,3)) |
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158 | [242082 899 211136] |
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159 | |
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160 | Using nested contexts, we can demonstrate that state is indeed |
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161 | restored after the block completes:: |
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162 | |
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163 | >>> with push_seed(24): |
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164 | ... print(randint(0,1000000)) |
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165 | ... with push_seed(24): |
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166 | ... print(randint(0,1000000,3)) |
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167 | ... print(randint(0,1000000)) |
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168 | 242082 |
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169 | [242082 899 211136] |
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170 | 899 |
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171 | |
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172 | The restore step is protected against exceptions in the block:: |
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173 | |
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174 | >>> with push_seed(24): |
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175 | ... print(randint(0,1000000)) |
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176 | ... try: |
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177 | ... with push_seed(24): |
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178 | ... print(randint(0,1000000,3)) |
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179 | ... raise Exception() |
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180 | ... except Exception: |
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181 | ... print("Exception raised") |
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182 | ... print(randint(0,1000000)) |
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183 | 242082 |
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184 | [242082 899 211136] |
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185 | Exception raised |
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186 | 899 |
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187 | """ |
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188 | def __init__(self, seed=None): |
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189 | # type: (Optional[int]) -> None |
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190 | self._state = np.random.get_state() |
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191 | np.random.seed(seed) |
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192 | |
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193 | def __enter__(self): |
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194 | # type: () -> None |
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195 | pass |
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196 | |
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197 | def __exit__(self, exc_type, exc_value, traceback): |
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198 | # type: (Any, BaseException, Any) -> None |
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199 | # TODO: better typing for __exit__ method |
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200 | np.random.set_state(self._state) |
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201 | |
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202 | def tic(): |
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203 | # type: () -> Callable[[], float] |
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204 | """ |
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205 | Timer function. |
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206 | |
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207 | Use "toc=tic()" to start the clock and "toc()" to measure |
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208 | a time interval. |
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209 | """ |
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210 | then = datetime.datetime.now() |
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211 | return lambda: delay(datetime.datetime.now() - then) |
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212 | |
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213 | |
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214 | def set_beam_stop(data, radius, outer=None): |
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215 | # type: (Data, float, float) -> None |
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216 | """ |
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217 | Add a beam stop of the given *radius*. If *outer*, make an annulus. |
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218 | |
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219 | Note: this function does not require sasview |
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220 | """ |
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221 | if hasattr(data, 'qx_data'): |
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222 | q = np.sqrt(data.qx_data**2 + data.qy_data**2) |
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223 | data.mask = (q < radius) |
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224 | if outer is not None: |
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225 | data.mask |= (q >= outer) |
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226 | else: |
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227 | data.mask = (data.x < radius) |
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228 | if outer is not None: |
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229 | data.mask |= (data.x >= outer) |
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230 | |
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231 | |
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232 | def parameter_range(p, v): |
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233 | # type: (str, float) -> Tuple[float, float] |
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234 | """ |
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235 | Choose a parameter range based on parameter name and initial value. |
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236 | """ |
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237 | # process the polydispersity options |
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238 | if p.endswith('_pd_n'): |
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239 | return 0., 100. |
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240 | elif p.endswith('_pd_nsigma'): |
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241 | return 0., 5. |
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242 | elif p.endswith('_pd_type'): |
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243 | raise ValueError("Cannot return a range for a string value") |
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244 | elif any(s in p for s in ('theta', 'phi', 'psi')): |
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245 | # orientation in [-180,180], orientation pd in [0,45] |
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246 | if p.endswith('_pd'): |
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247 | return 0., 45. |
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248 | else: |
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249 | return -180., 180. |
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250 | elif p.endswith('_pd'): |
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251 | return 0., 1. |
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252 | elif 'sld' in p: |
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253 | return -0.5, 10. |
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254 | elif p == 'background': |
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255 | return 0., 10. |
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256 | elif p == 'scale': |
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257 | return 0., 1.e3 |
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258 | elif v < 0.: |
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259 | return 2.*v, -2.*v |
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260 | else: |
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261 | return 0., (2.*v if v > 0. else 1.) |
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262 | |
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263 | |
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264 | def _randomize_one(model_info, p, v): |
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265 | # type: (ModelInfo, str, float) -> float |
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266 | # type: (ModelInfo, str, str) -> str |
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267 | """ |
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268 | Randomize a single parameter. |
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269 | """ |
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270 | if any(p.endswith(s) for s in ('_pd', '_pd_n', '_pd_nsigma', '_pd_type')): |
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271 | return v |
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272 | |
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273 | # Find the parameter definition |
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274 | for par in model_info.parameters.call_parameters: |
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275 | if par.name == p: |
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276 | break |
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277 | else: |
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278 | raise ValueError("unknown parameter %r"%p) |
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279 | |
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280 | if len(par.limits) > 2: # choice list |
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281 | return np.random.randint(len(par.limits)) |
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282 | |
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283 | limits = par.limits |
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284 | if not np.isfinite(limits).all(): |
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285 | low, high = parameter_range(p, v) |
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286 | limits = (max(limits[0], low), min(limits[1], high)) |
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287 | |
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288 | return np.random.uniform(*limits) |
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289 | |
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290 | |
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291 | def randomize_pars(model_info, pars, seed=None): |
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292 | # type: (ModelInfo, ParameterSet, int) -> ParameterSet |
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293 | """ |
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294 | Generate random values for all of the parameters. |
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295 | |
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296 | Valid ranges for the random number generator are guessed from the name of |
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297 | the parameter; this will not account for constraints such as cap radius |
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298 | greater than cylinder radius in the capped_cylinder model, so |
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299 | :func:`constrain_pars` needs to be called afterward.. |
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300 | """ |
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301 | with push_seed(seed): |
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302 | # Note: the sort guarantees order `of calls to random number generator |
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303 | random_pars = dict((p, _randomize_one(model_info, p, v)) |
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304 | for p, v in sorted(pars.items())) |
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305 | return random_pars |
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306 | |
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307 | def constrain_pars(model_info, pars): |
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308 | # type: (ModelInfo, ParameterSet) -> None |
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309 | """ |
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310 | Restrict parameters to valid values. |
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311 | |
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312 | This includes model specific code for models such as capped_cylinder |
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313 | which need to support within model constraints (cap radius more than |
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314 | cylinder radius in this case). |
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315 | |
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316 | Warning: this updates the *pars* dictionary in place. |
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317 | """ |
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318 | name = model_info.id |
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319 | # if it is a product model, then just look at the form factor since |
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320 | # none of the structure factors need any constraints. |
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321 | if '*' in name: |
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322 | name = name.split('*')[0] |
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323 | |
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324 | if name == 'barbell': |
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325 | if pars['radius_bell'] < pars['radius']: |
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326 | pars['radius'], pars['radius_bell'] = pars['radius_bell'], pars['radius'] |
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327 | |
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328 | elif name == 'capped_cylinder': |
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329 | if pars['radius_cap'] < pars['radius']: |
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330 | pars['radius'], pars['radius_cap'] = pars['radius_cap'], pars['radius'] |
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331 | |
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332 | elif name == 'guinier': |
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333 | # Limit guinier to an Rg such that Iq > 1e-30 (single precision cutoff) |
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334 | #q_max = 0.2 # mid q maximum |
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335 | q_max = 1.0 # high q maximum |
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336 | rg_max = np.sqrt(90*np.log(10) + 3*np.log(pars['scale']))/q_max |
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337 | pars['rg'] = min(pars['rg'], rg_max) |
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338 | |
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339 | elif name == 'pearl_necklace': |
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340 | if pars['radius'] < pars['thick_string']: |
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341 | pars['radius'], pars['thick_string'] = pars['thick_string'], pars['radius'] |
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342 | pass |
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343 | |
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344 | elif name == 'rpa': |
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345 | # Make sure phi sums to 1.0 |
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346 | if pars['case_num'] < 2: |
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347 | pars['Phi1'] = 0. |
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348 | pars['Phi2'] = 0. |
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349 | elif pars['case_num'] < 5: |
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350 | pars['Phi1'] = 0. |
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351 | total = sum(pars['Phi'+c] for c in '1234') |
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352 | for c in '1234': |
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353 | pars['Phi'+c] /= total |
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354 | |
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355 | def parlist(model_info, pars, is2d): |
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356 | # type: (ModelInfo, ParameterSet, bool) -> str |
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357 | """ |
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358 | Format the parameter list for printing. |
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359 | """ |
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360 | lines = [] |
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361 | parameters = model_info.parameters |
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362 | magnetic = False |
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363 | for p in parameters.user_parameters(pars, is2d): |
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364 | if any(p.id.startswith(x) for x in ('M0:', 'mtheta:', 'mphi:')): |
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365 | continue |
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366 | if p.id.startswith('up:') and not magnetic: |
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367 | continue |
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368 | fields = dict( |
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369 | value=pars.get(p.id, p.default), |
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370 | pd=pars.get(p.id+"_pd", 0.), |
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371 | n=int(pars.get(p.id+"_pd_n", 0)), |
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372 | nsigma=pars.get(p.id+"_pd_nsgima", 3.), |
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373 | pdtype=pars.get(p.id+"_pd_type", 'gaussian'), |
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374 | relative_pd=p.relative_pd, |
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375 | M0=pars.get('M0:'+p.id, 0.), |
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376 | mphi=pars.get('mphi:'+p.id, 0.), |
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377 | mtheta=pars.get('mtheta:'+p.id, 0.), |
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378 | ) |
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379 | lines.append(_format_par(p.name, **fields)) |
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380 | magnetic = magnetic or fields['M0'] != 0. |
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381 | return "\n".join(lines) |
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382 | |
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383 | #return "\n".join("%s: %s"%(p, v) for p, v in sorted(pars.items())) |
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384 | |
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385 | def _format_par(name, value=0., pd=0., n=0, nsigma=3., pdtype='gaussian', |
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386 | relative_pd=False, M0=0., mphi=0., mtheta=0.): |
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387 | # type: (str, float, float, int, float, str) -> str |
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388 | line = "%s: %g"%(name, value) |
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389 | if pd != 0. and n != 0: |
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390 | if relative_pd: |
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391 | pd *= value |
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392 | line += " +/- %g (%d points in [-%g,%g] sigma %s)"\ |
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393 | % (pd, n, nsigma, nsigma, pdtype) |
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394 | if M0 != 0.: |
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395 | line += " M0:%.3f mphi:%.1f mtheta:%.1f" % (M0, mphi, mtheta) |
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396 | return line |
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397 | |
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398 | def suppress_pd(pars): |
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399 | # type: (ParameterSet) -> ParameterSet |
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400 | """ |
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401 | Suppress theta_pd for now until the normalization is resolved. |
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402 | |
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403 | May also suppress complete polydispersity of the model to test |
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404 | models more quickly. |
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405 | """ |
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406 | pars = pars.copy() |
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407 | for p in pars: |
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408 | if p.endswith("_pd_n"): pars[p] = 0 |
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409 | return pars |
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410 | |
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411 | def suppress_magnetism(pars): |
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412 | # type: (ParameterSet) -> ParameterSet |
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413 | """ |
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414 | Suppress theta_pd for now until the normalization is resolved. |
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415 | |
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416 | May also suppress complete polydispersity of the model to test |
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417 | models more quickly. |
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418 | """ |
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419 | pars = pars.copy() |
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420 | for p in pars: |
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421 | if p.startswith("M0:"): pars[p] = 0 |
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422 | return pars |
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423 | |
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424 | def eval_sasview(model_info, data): |
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425 | # type: (Modelinfo, Data) -> Calculator |
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426 | """ |
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427 | Return a model calculator using the pre-4.0 SasView models. |
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428 | """ |
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429 | # importing sas here so that the error message will be that sas failed to |
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430 | # import rather than the more obscure smear_selection not imported error |
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431 | import sas |
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432 | import sas.models |
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433 | from sas.models.qsmearing import smear_selection |
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434 | from sas.models.MultiplicationModel import MultiplicationModel |
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435 | from sas.models.dispersion_models import models as dispersers |
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436 | |
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437 | def get_model_class(name): |
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438 | # type: (str) -> "sas.models.BaseComponent" |
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439 | #print("new",sorted(_pars.items())) |
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440 | __import__('sas.models.' + name) |
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441 | ModelClass = getattr(getattr(sas.models, name, None), name, None) |
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442 | if ModelClass is None: |
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443 | raise ValueError("could not find model %r in sas.models"%name) |
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444 | return ModelClass |
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445 | |
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446 | # WARNING: ugly hack when handling model! |
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447 | # Sasview models with multiplicity need to be created with the target |
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448 | # multiplicity, so we cannot create the target model ahead of time for |
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449 | # for multiplicity models. Instead we store the model in a list and |
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450 | # update the first element of that list with the new multiplicity model |
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451 | # every time we evaluate. |
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452 | |
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453 | # grab the sasview model, or create it if it is a product model |
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454 | if model_info.composition: |
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455 | composition_type, parts = model_info.composition |
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456 | if composition_type == 'product': |
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457 | P, S = [get_model_class(revert_name(p))() for p in parts] |
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458 | model = [MultiplicationModel(P, S)] |
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459 | else: |
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460 | raise ValueError("sasview mixture models not supported by compare") |
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461 | else: |
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462 | old_name = revert_name(model_info) |
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463 | if old_name is None: |
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464 | raise ValueError("model %r does not exist in old sasview" |
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465 | % model_info.id) |
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466 | ModelClass = get_model_class(old_name) |
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467 | model = [ModelClass()] |
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468 | model[0].disperser_handles = {} |
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469 | |
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470 | # build a smearer with which to call the model, if necessary |
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471 | smearer = smear_selection(data, model=model) |
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472 | if hasattr(data, 'qx_data'): |
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473 | q = np.sqrt(data.qx_data**2 + data.qy_data**2) |
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474 | index = ((~data.mask) & (~np.isnan(data.data)) |
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475 | & (q >= data.qmin) & (q <= data.qmax)) |
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476 | if smearer is not None: |
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477 | smearer.model = model # because smear_selection has a bug |
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478 | smearer.accuracy = data.accuracy |
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479 | smearer.set_index(index) |
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480 | def _call_smearer(): |
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481 | smearer.model = model[0] |
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482 | return smearer.get_value() |
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483 | theory = _call_smearer |
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484 | else: |
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485 | theory = lambda: model[0].evalDistribution([data.qx_data[index], |
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486 | data.qy_data[index]]) |
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487 | elif smearer is not None: |
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488 | theory = lambda: smearer(model[0].evalDistribution(data.x)) |
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489 | else: |
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490 | theory = lambda: model[0].evalDistribution(data.x) |
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491 | |
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492 | def calculator(**pars): |
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493 | # type: (float, ...) -> np.ndarray |
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494 | """ |
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495 | Sasview calculator for model. |
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496 | """ |
---|
497 | oldpars = revert_pars(model_info, pars) |
---|
498 | # For multiplicity models, create a model with the correct multiplicity |
---|
499 | control = oldpars.pop("CONTROL", None) |
---|
500 | if control is not None: |
---|
501 | # sphericalSLD has one fewer multiplicity. This update should |
---|
502 | # happen in revert_pars, but it hasn't been called yet. |
---|
503 | model[0] = ModelClass(control) |
---|
504 | # paying for parameter conversion each time to keep life simple, if not fast |
---|
505 | for k, v in oldpars.items(): |
---|
506 | if k.endswith('.type'): |
---|
507 | par = k[:-5] |
---|
508 | if v == 'gaussian': continue |
---|
509 | cls = dispersers[v if v != 'rectangle' else 'rectangula'] |
---|
510 | handle = cls() |
---|
511 | model[0].disperser_handles[par] = handle |
---|
512 | try: |
---|
513 | model[0].set_dispersion(par, handle) |
---|
514 | except Exception: |
---|
515 | exception.annotate_exception("while setting %s to %r" |
---|
516 | %(par, v)) |
---|
517 | raise |
---|
518 | |
---|
519 | |
---|
520 | #print("sasview pars",oldpars) |
---|
521 | for k, v in oldpars.items(): |
---|
522 | name_attr = k.split('.') # polydispersity components |
---|
523 | if len(name_attr) == 2: |
---|
524 | par, disp_par = name_attr |
---|
525 | model[0].dispersion[par][disp_par] = v |
---|
526 | else: |
---|
527 | model[0].setParam(k, v) |
---|
528 | return theory() |
---|
529 | |
---|
530 | calculator.engine = "sasview" |
---|
531 | return calculator |
---|
532 | |
---|
533 | DTYPE_MAP = { |
---|
534 | 'half': '16', |
---|
535 | 'fast': 'fast', |
---|
536 | 'single': '32', |
---|
537 | 'double': '64', |
---|
538 | 'quad': '128', |
---|
539 | 'f16': '16', |
---|
540 | 'f32': '32', |
---|
541 | 'f64': '64', |
---|
542 | 'longdouble': '128', |
---|
543 | } |
---|
544 | def eval_opencl(model_info, data, dtype='single', cutoff=0.): |
---|
545 | # type: (ModelInfo, Data, str, float) -> Calculator |
---|
546 | """ |
---|
547 | Return a model calculator using the OpenCL calculation engine. |
---|
548 | """ |
---|
549 | if not core.HAVE_OPENCL: |
---|
550 | raise RuntimeError("OpenCL not available") |
---|
551 | model = core.build_model(model_info, dtype=dtype, platform="ocl") |
---|
552 | calculator = DirectModel(data, model, cutoff=cutoff) |
---|
553 | calculator.engine = "OCL%s"%DTYPE_MAP[dtype] |
---|
554 | return calculator |
---|
555 | |
---|
556 | def eval_ctypes(model_info, data, dtype='double', cutoff=0.): |
---|
557 | # type: (ModelInfo, Data, str, float) -> Calculator |
---|
558 | """ |
---|
559 | Return a model calculator using the DLL calculation engine. |
---|
560 | """ |
---|
561 | model = core.build_model(model_info, dtype=dtype, platform="dll") |
---|
562 | calculator = DirectModel(data, model, cutoff=cutoff) |
---|
563 | calculator.engine = "OMP%s"%DTYPE_MAP[dtype] |
---|
564 | return calculator |
---|
565 | |
---|
566 | def time_calculation(calculator, pars, evals=1): |
---|
567 | # type: (Calculator, ParameterSet, int) -> Tuple[np.ndarray, float] |
---|
568 | """ |
---|
569 | Compute the average calculation time over N evaluations. |
---|
570 | |
---|
571 | An additional call is generated without polydispersity in order to |
---|
572 | initialize the calculation engine, and make the average more stable. |
---|
573 | """ |
---|
574 | # initialize the code so time is more accurate |
---|
575 | if evals > 1: |
---|
576 | calculator(**suppress_pd(pars)) |
---|
577 | toc = tic() |
---|
578 | # make sure there is at least one eval |
---|
579 | value = calculator(**pars) |
---|
580 | for _ in range(evals-1): |
---|
581 | value = calculator(**pars) |
---|
582 | average_time = toc()*1000. / evals |
---|
583 | #print("I(q)",value) |
---|
584 | return value, average_time |
---|
585 | |
---|
586 | def make_data(opts): |
---|
587 | # type: (Dict[str, Any]) -> Tuple[Data, np.ndarray] |
---|
588 | """ |
---|
589 | Generate an empty dataset, used with the model to set Q points |
---|
590 | and resolution. |
---|
591 | |
---|
592 | *opts* contains the options, with 'qmax', 'nq', 'res', |
---|
593 | 'accuracy', 'is2d' and 'view' parsed from the command line. |
---|
594 | """ |
---|
595 | qmax, nq, res = opts['qmax'], opts['nq'], opts['res'] |
---|
596 | if opts['is2d']: |
---|
597 | q = np.linspace(-qmax, qmax, nq) # type: np.ndarray |
---|
598 | data = empty_data2D(q, resolution=res) |
---|
599 | data.accuracy = opts['accuracy'] |
---|
600 | set_beam_stop(data, 0.0004) |
---|
601 | index = ~data.mask |
---|
602 | else: |
---|
603 | if opts['view'] == 'log' and not opts['zero']: |
---|
604 | qmax = math.log10(qmax) |
---|
605 | q = np.logspace(qmax-3, qmax, nq) |
---|
606 | else: |
---|
607 | q = np.linspace(0.001*qmax, qmax, nq) |
---|
608 | if opts['zero']: |
---|
609 | q = np.hstack((0, q)) |
---|
610 | data = empty_data1D(q, resolution=res) |
---|
611 | index = slice(None, None) |
---|
612 | return data, index |
---|
613 | |
---|
614 | def make_engine(model_info, data, dtype, cutoff): |
---|
615 | # type: (ModelInfo, Data, str, float) -> Calculator |
---|
616 | """ |
---|
617 | Generate the appropriate calculation engine for the given datatype. |
---|
618 | |
---|
619 | Datatypes with '!' appended are evaluated using external C DLLs rather |
---|
620 | than OpenCL. |
---|
621 | """ |
---|
622 | if dtype == 'sasview': |
---|
623 | return eval_sasview(model_info, data) |
---|
624 | elif dtype.endswith('!'): |
---|
625 | return eval_ctypes(model_info, data, dtype=dtype[:-1], cutoff=cutoff) |
---|
626 | else: |
---|
627 | return eval_opencl(model_info, data, dtype=dtype, cutoff=cutoff) |
---|
628 | |
---|
629 | def _show_invalid(data, theory): |
---|
630 | # type: (Data, np.ma.ndarray) -> None |
---|
631 | """ |
---|
632 | Display a list of the non-finite values in theory. |
---|
633 | """ |
---|
634 | if not theory.mask.any(): |
---|
635 | return |
---|
636 | |
---|
637 | if hasattr(data, 'x'): |
---|
638 | bad = zip(data.x[theory.mask], theory[theory.mask]) |
---|
639 | print(" *** ", ", ".join("I(%g)=%g"%(x, y) for x, y in bad)) |
---|
640 | |
---|
641 | |
---|
642 | def compare(opts, limits=None): |
---|
643 | # type: (Dict[str, Any], Optional[Tuple[float, float]]) -> Tuple[float, float] |
---|
644 | """ |
---|
645 | Preform a comparison using options from the command line. |
---|
646 | |
---|
647 | *limits* are the limits on the values to use, either to set the y-axis |
---|
648 | for 1D or to set the colormap scale for 2D. If None, then they are |
---|
649 | inferred from the data and returned. When exploring using Bumps, |
---|
650 | the limits are set when the model is initially called, and maintained |
---|
651 | as the values are adjusted, making it easier to see the effects of the |
---|
652 | parameters. |
---|
653 | """ |
---|
654 | result = run_models(opts, verbose=True) |
---|
655 | if opts['plot']: # Note: never called from explore |
---|
656 | plot_models(opts, result, limits=limits) |
---|
657 | |
---|
658 | def run_models(opts, verbose=False): |
---|
659 | # type: (Dict[str, Any]) -> Dict[str, Any] |
---|
660 | |
---|
661 | n_base, n_comp = opts['count'] |
---|
662 | pars, pars2 = opts['pars'] |
---|
663 | data = opts['data'] |
---|
664 | |
---|
665 | # silence the linter |
---|
666 | base = opts['engines'][0] if n_base else None |
---|
667 | comp = opts['engines'][1] if n_comp else None |
---|
668 | |
---|
669 | base_time = comp_time = None |
---|
670 | base_value = comp_value = resid = relerr = None |
---|
671 | |
---|
672 | # Base calculation |
---|
673 | if n_base > 0: |
---|
674 | try: |
---|
675 | base_raw, base_time = time_calculation(base, pars, n_base) |
---|
676 | base_value = np.ma.masked_invalid(base_raw) |
---|
677 | if verbose: |
---|
678 | print("%s t=%.2f ms, intensity=%.0f" |
---|
679 | % (base.engine, base_time, base_value.sum())) |
---|
680 | _show_invalid(data, base_value) |
---|
681 | except ImportError: |
---|
682 | traceback.print_exc() |
---|
683 | n_base = 0 |
---|
684 | |
---|
685 | # Comparison calculation |
---|
686 | if n_comp > 0: |
---|
687 | try: |
---|
688 | comp_raw, comp_time = time_calculation(comp, pars2, n_comp) |
---|
689 | comp_value = np.ma.masked_invalid(comp_raw) |
---|
690 | if verbose: |
---|
691 | print("%s t=%.2f ms, intensity=%.0f" |
---|
692 | % (comp.engine, comp_time, comp_value.sum())) |
---|
693 | _show_invalid(data, comp_value) |
---|
694 | except ImportError: |
---|
695 | traceback.print_exc() |
---|
696 | n_comp = 0 |
---|
697 | |
---|
698 | # Compare, but only if computing both forms |
---|
699 | if n_base > 0 and n_comp > 0: |
---|
700 | resid = (base_value - comp_value) |
---|
701 | relerr = resid/np.where(comp_value != 0., abs(comp_value), 1.0) |
---|
702 | if verbose: |
---|
703 | _print_stats("|%s-%s|" |
---|
704 | % (base.engine, comp.engine) + (" "*(3+len(comp.engine))), |
---|
705 | resid) |
---|
706 | _print_stats("|(%s-%s)/%s|" |
---|
707 | % (base.engine, comp.engine, comp.engine), |
---|
708 | relerr) |
---|
709 | |
---|
710 | return dict(base_value=base_value, comp_value=comp_value, |
---|
711 | base_time=base_time, comp_time=comp_time, |
---|
712 | resid=resid, relerr=relerr) |
---|
713 | |
---|
714 | |
---|
715 | def _print_stats(label, err): |
---|
716 | # type: (str, np.ma.ndarray) -> None |
---|
717 | # work with trimmed data, not the full set |
---|
718 | sorted_err = np.sort(abs(err.compressed())) |
---|
719 | if len(sorted_err) == 0.: |
---|
720 | print(label + " no valid values") |
---|
721 | return |
---|
722 | |
---|
723 | p50 = int((len(sorted_err)-1)*0.50) |
---|
724 | p98 = int((len(sorted_err)-1)*0.98) |
---|
725 | data = [ |
---|
726 | "max:%.3e"%sorted_err[-1], |
---|
727 | "median:%.3e"%sorted_err[p50], |
---|
728 | "98%%:%.3e"%sorted_err[p98], |
---|
729 | "rms:%.3e"%np.sqrt(np.mean(sorted_err**2)), |
---|
730 | "zero-offset:%+.3e"%np.mean(sorted_err), |
---|
731 | ] |
---|
732 | print(label+" "+" ".join(data)) |
---|
733 | |
---|
734 | |
---|
735 | def plot_models(opts, result, limits=None): |
---|
736 | # type: (Dict[str, Any], Dict[str, Any], Optional[Tuple[float, float]]) -> Tuple[float, float] |
---|
737 | base_value, comp_value= result['base_value'], result['comp_value'] |
---|
738 | base_time, comp_time = result['base_time'], result['comp_time'] |
---|
739 | resid, relerr = result['resid'], result['relerr'] |
---|
740 | |
---|
741 | have_base, have_comp = (base_value is not None), (comp_value is not None) |
---|
742 | base = opts['engines'][0] if have_base else None |
---|
743 | comp = opts['engines'][1] if have_comp else None |
---|
744 | data = opts['data'] |
---|
745 | |
---|
746 | # Plot if requested |
---|
747 | view = opts['view'] |
---|
748 | import matplotlib.pyplot as plt |
---|
749 | if limits is None: |
---|
750 | vmin, vmax = np.Inf, -np.Inf |
---|
751 | if have_base: |
---|
752 | vmin = min(vmin, base_value.min()) |
---|
753 | vmax = max(vmax, base_value.max()) |
---|
754 | if have_comp: |
---|
755 | vmin = min(vmin, comp_value.min()) |
---|
756 | vmax = max(vmax, comp_value.max()) |
---|
757 | limits = vmin, vmax |
---|
758 | |
---|
759 | if have_base: |
---|
760 | if have_comp: plt.subplot(131) |
---|
761 | plot_theory(data, base_value, view=view, use_data=False, limits=limits) |
---|
762 | plt.title("%s t=%.2f ms"%(base.engine, base_time)) |
---|
763 | #cbar_title = "log I" |
---|
764 | if have_comp: |
---|
765 | if have_base: plt.subplot(132) |
---|
766 | if not opts['is2d'] and have_base: |
---|
767 | plot_theory(data, base_value, view=view, use_data=False, limits=limits) |
---|
768 | plot_theory(data, comp_value, view=view, use_data=False, limits=limits) |
---|
769 | plt.title("%s t=%.2f ms"%(comp.engine, comp_time)) |
---|
770 | #cbar_title = "log I" |
---|
771 | if have_base and have_comp: |
---|
772 | plt.subplot(133) |
---|
773 | if not opts['rel_err']: |
---|
774 | err, errstr, errview = resid, "abs err", "linear" |
---|
775 | else: |
---|
776 | err, errstr, errview = abs(relerr), "rel err", "log" |
---|
777 | if 0: # 95% cutoff |
---|
778 | sorted = np.sort(err.flatten()) |
---|
779 | cutoff = sorted[int(sorted.size*0.95)] |
---|
780 | err[err>cutoff] = cutoff |
---|
781 | #err,errstr = base/comp,"ratio" |
---|
782 | plot_theory(data, None, resid=err, view=errview, use_data=False) |
---|
783 | if view == 'linear': |
---|
784 | plt.xscale('linear') |
---|
785 | plt.title("max %s = %.3g"%(errstr, abs(err).max())) |
---|
786 | #cbar_title = errstr if errview=="linear" else "log "+errstr |
---|
787 | #if is2D: |
---|
788 | # h = plt.colorbar() |
---|
789 | # h.ax.set_title(cbar_title) |
---|
790 | fig = plt.gcf() |
---|
791 | extra_title = ' '+opts['title'] if opts['title'] else '' |
---|
792 | fig.suptitle(":".join(opts['name']) + extra_title) |
---|
793 | |
---|
794 | if have_base and have_comp and opts['show_hist']: |
---|
795 | plt.figure() |
---|
796 | v = relerr |
---|
797 | v[v == 0] = 0.5*np.min(np.abs(v[v != 0])) |
---|
798 | plt.hist(np.log10(np.abs(v)), normed=1, bins=50) |
---|
799 | plt.xlabel('log10(err), err = |(%s - %s) / %s|' |
---|
800 | % (base.engine, comp.engine, comp.engine)) |
---|
801 | plt.ylabel('P(err)') |
---|
802 | plt.title('Distribution of relative error between calculation engines') |
---|
803 | |
---|
804 | if not opts['explore']: |
---|
805 | plt.show() |
---|
806 | |
---|
807 | return limits |
---|
808 | |
---|
809 | |
---|
810 | |
---|
811 | |
---|
812 | # =========================================================================== |
---|
813 | # |
---|
814 | NAME_OPTIONS = set([ |
---|
815 | 'plot', 'noplot', |
---|
816 | 'half', 'fast', 'single', 'double', |
---|
817 | 'single!', 'double!', 'quad!', 'sasview', |
---|
818 | 'lowq', 'midq', 'highq', 'exq', 'zero', |
---|
819 | '2d', '1d', |
---|
820 | 'preset', 'random', |
---|
821 | 'poly', 'mono', |
---|
822 | 'magnetic', 'nonmagnetic', |
---|
823 | 'nopars', 'pars', |
---|
824 | 'rel', 'abs', |
---|
825 | 'linear', 'log', 'q4', |
---|
826 | 'hist', 'nohist', |
---|
827 | 'edit', 'html', |
---|
828 | 'demo', 'default', |
---|
829 | ]) |
---|
830 | VALUE_OPTIONS = [ |
---|
831 | # Note: random is both a name option and a value option |
---|
832 | 'cutoff', 'random', 'nq', 'res', 'accuracy', 'title', |
---|
833 | ] |
---|
834 | |
---|
835 | def columnize(items, indent="", width=79): |
---|
836 | # type: (List[str], str, int) -> str |
---|
837 | """ |
---|
838 | Format a list of strings into columns. |
---|
839 | |
---|
840 | Returns a string with carriage returns ready for printing. |
---|
841 | """ |
---|
842 | column_width = max(len(w) for w in items) + 1 |
---|
843 | num_columns = (width - len(indent)) // column_width |
---|
844 | num_rows = len(items) // num_columns |
---|
845 | items = items + [""] * (num_rows * num_columns - len(items)) |
---|
846 | columns = [items[k*num_rows:(k+1)*num_rows] for k in range(num_columns)] |
---|
847 | lines = [" ".join("%-*s"%(column_width, entry) for entry in row) |
---|
848 | for row in zip(*columns)] |
---|
849 | output = indent + ("\n"+indent).join(lines) |
---|
850 | return output |
---|
851 | |
---|
852 | |
---|
853 | def get_pars(model_info, use_demo=False): |
---|
854 | # type: (ModelInfo, bool) -> ParameterSet |
---|
855 | """ |
---|
856 | Extract demo parameters from the model definition. |
---|
857 | """ |
---|
858 | # Get the default values for the parameters |
---|
859 | pars = {} |
---|
860 | for p in model_info.parameters.call_parameters: |
---|
861 | parts = [('', p.default)] |
---|
862 | if p.polydisperse: |
---|
863 | parts.append(('_pd', 0.0)) |
---|
864 | parts.append(('_pd_n', 0)) |
---|
865 | parts.append(('_pd_nsigma', 3.0)) |
---|
866 | parts.append(('_pd_type', "gaussian")) |
---|
867 | for ext, val in parts: |
---|
868 | if p.length > 1: |
---|
869 | dict(("%s%d%s" % (p.id, k, ext), val) |
---|
870 | for k in range(1, p.length+1)) |
---|
871 | else: |
---|
872 | pars[p.id + ext] = val |
---|
873 | |
---|
874 | # Plug in values given in demo |
---|
875 | if use_demo: |
---|
876 | pars.update(model_info.demo) |
---|
877 | return pars |
---|
878 | |
---|
879 | INTEGER_RE = re.compile("^[+-]?[1-9][0-9]*$") |
---|
880 | def isnumber(str): |
---|
881 | match = FLOAT_RE.match(str) |
---|
882 | isfloat = (match and not str[match.end():]) |
---|
883 | return isfloat or INTEGER_RE.match(str) |
---|
884 | |
---|
885 | # For distinguishing pairs of models for comparison |
---|
886 | # key-value pair separator = |
---|
887 | # shell characters | & ; <> $ % ' " \ # ` |
---|
888 | # model and parameter names _ |
---|
889 | # parameter expressions - + * / . ( ) |
---|
890 | # path characters including tilde expansion and windows drive ~ / : |
---|
891 | # not sure about brackets [] {} |
---|
892 | # maybe one of the following @ ? ^ ! , |
---|
893 | MODEL_SPLIT = ',' |
---|
894 | def parse_opts(argv): |
---|
895 | # type: (List[str]) -> Dict[str, Any] |
---|
896 | """ |
---|
897 | Parse command line options. |
---|
898 | """ |
---|
899 | MODELS = core.list_models() |
---|
900 | flags = [arg for arg in argv |
---|
901 | if arg.startswith('-')] |
---|
902 | values = [arg for arg in argv |
---|
903 | if not arg.startswith('-') and '=' in arg] |
---|
904 | positional_args = [arg for arg in argv |
---|
905 | if not arg.startswith('-') and '=' not in arg] |
---|
906 | models = "\n ".join("%-15s"%v for v in MODELS) |
---|
907 | if len(positional_args) == 0: |
---|
908 | print(USAGE) |
---|
909 | print("\nAvailable models:") |
---|
910 | print(columnize(MODELS, indent=" ")) |
---|
911 | return None |
---|
912 | if len(positional_args) > 3: |
---|
913 | print("expected parameters: model N1 N2") |
---|
914 | |
---|
915 | invalid = [o[1:] for o in flags |
---|
916 | if o[1:] not in NAME_OPTIONS |
---|
917 | and not any(o.startswith('-%s='%t) for t in VALUE_OPTIONS)] |
---|
918 | if invalid: |
---|
919 | print("Invalid options: %s"%(", ".join(invalid))) |
---|
920 | return None |
---|
921 | |
---|
922 | name = positional_args[0] |
---|
923 | n1 = int(positional_args[1]) if len(positional_args) > 1 else 1 |
---|
924 | n2 = int(positional_args[2]) if len(positional_args) > 2 else 1 |
---|
925 | |
---|
926 | # pylint: disable=bad-whitespace |
---|
927 | # Interpret the flags |
---|
928 | opts = { |
---|
929 | 'plot' : True, |
---|
930 | 'view' : 'log', |
---|
931 | 'is2d' : False, |
---|
932 | 'qmax' : 0.05, |
---|
933 | 'nq' : 128, |
---|
934 | 'res' : 0.0, |
---|
935 | 'accuracy' : 'Low', |
---|
936 | 'cutoff' : 0.0, |
---|
937 | 'seed' : -1, # default to preset |
---|
938 | 'mono' : False, |
---|
939 | # Default to magnetic a magnetic moment is set on the command line |
---|
940 | 'magnetic' : False, |
---|
941 | 'show_pars' : False, |
---|
942 | 'show_hist' : False, |
---|
943 | 'rel_err' : True, |
---|
944 | 'explore' : False, |
---|
945 | 'use_demo' : True, |
---|
946 | 'zero' : False, |
---|
947 | 'html' : False, |
---|
948 | 'title' : None, |
---|
949 | } |
---|
950 | engines = [] |
---|
951 | for arg in flags: |
---|
952 | if arg == '-noplot': opts['plot'] = False |
---|
953 | elif arg == '-plot': opts['plot'] = True |
---|
954 | elif arg == '-linear': opts['view'] = 'linear' |
---|
955 | elif arg == '-log': opts['view'] = 'log' |
---|
956 | elif arg == '-q4': opts['view'] = 'q4' |
---|
957 | elif arg == '-1d': opts['is2d'] = False |
---|
958 | elif arg == '-2d': opts['is2d'] = True |
---|
959 | elif arg == '-exq': opts['qmax'] = 10.0 |
---|
960 | elif arg == '-highq': opts['qmax'] = 1.0 |
---|
961 | elif arg == '-midq': opts['qmax'] = 0.2 |
---|
962 | elif arg == '-lowq': opts['qmax'] = 0.05 |
---|
963 | elif arg == '-zero': opts['zero'] = True |
---|
964 | elif arg.startswith('-nq='): opts['nq'] = int(arg[4:]) |
---|
965 | elif arg.startswith('-res='): opts['res'] = float(arg[5:]) |
---|
966 | elif arg.startswith('-accuracy='): opts['accuracy'] = arg[10:] |
---|
967 | elif arg.startswith('-cutoff='): opts['cutoff'] = float(arg[8:]) |
---|
968 | elif arg.startswith('-random='): opts['seed'] = int(arg[8:]) |
---|
969 | elif arg.startswith('-title'): opts['title'] = arg[7:] |
---|
970 | elif arg == '-random': opts['seed'] = np.random.randint(1000000) |
---|
971 | elif arg == '-preset': opts['seed'] = -1 |
---|
972 | elif arg == '-mono': opts['mono'] = True |
---|
973 | elif arg == '-poly': opts['mono'] = False |
---|
974 | elif arg == '-magnetic': opts['magnetic'] = True |
---|
975 | elif arg == '-nonmagnetic': opts['magnetic'] = False |
---|
976 | elif arg == '-pars': opts['show_pars'] = True |
---|
977 | elif arg == '-nopars': opts['show_pars'] = False |
---|
978 | elif arg == '-hist': opts['show_hist'] = True |
---|
979 | elif arg == '-nohist': opts['show_hist'] = False |
---|
980 | elif arg == '-rel': opts['rel_err'] = True |
---|
981 | elif arg == '-abs': opts['rel_err'] = False |
---|
982 | elif arg == '-half': engines.append(arg[1:]) |
---|
983 | elif arg == '-fast': engines.append(arg[1:]) |
---|
984 | elif arg == '-single': engines.append(arg[1:]) |
---|
985 | elif arg == '-double': engines.append(arg[1:]) |
---|
986 | elif arg == '-single!': engines.append(arg[1:]) |
---|
987 | elif arg == '-double!': engines.append(arg[1:]) |
---|
988 | elif arg == '-quad!': engines.append(arg[1:]) |
---|
989 | elif arg == '-sasview': engines.append(arg[1:]) |
---|
990 | elif arg == '-edit': opts['explore'] = True |
---|
991 | elif arg == '-demo': opts['use_demo'] = True |
---|
992 | elif arg == '-default': opts['use_demo'] = False |
---|
993 | elif arg == '-html': opts['html'] = True |
---|
994 | # pylint: enable=bad-whitespace |
---|
995 | |
---|
996 | if MODEL_SPLIT in name: |
---|
997 | name, name2 = name.split(MODEL_SPLIT, 2) |
---|
998 | else: |
---|
999 | name2 = name |
---|
1000 | try: |
---|
1001 | model_info = core.load_model_info(name) |
---|
1002 | model_info2 = core.load_model_info(name2) if name2 != name else model_info |
---|
1003 | except ImportError as exc: |
---|
1004 | print(str(exc)) |
---|
1005 | print("Could not find model; use one of:\n " + models) |
---|
1006 | return None |
---|
1007 | |
---|
1008 | # Get demo parameters from model definition, or use default parameters |
---|
1009 | # if model does not define demo parameters |
---|
1010 | pars = get_pars(model_info, opts['use_demo']) |
---|
1011 | pars2 = get_pars(model_info2, opts['use_demo']) |
---|
1012 | pars2.update((k, v) for k, v in pars.items() if k in pars2) |
---|
1013 | # randomize parameters |
---|
1014 | #pars.update(set_pars) # set value before random to control range |
---|
1015 | if opts['seed'] > -1: |
---|
1016 | pars = randomize_pars(model_info, pars, seed=opts['seed']) |
---|
1017 | if model_info != model_info2: |
---|
1018 | pars2 = randomize_pars(model_info2, pars2, seed=opts['seed']) |
---|
1019 | # Share values for parameters with the same name |
---|
1020 | for k, v in pars.items(): |
---|
1021 | if k in pars2: |
---|
1022 | pars2[k] = v |
---|
1023 | else: |
---|
1024 | pars2 = pars.copy() |
---|
1025 | constrain_pars(model_info, pars) |
---|
1026 | constrain_pars(model_info2, pars2) |
---|
1027 | print("Randomize using -random=%i"%opts['seed']) |
---|
1028 | if opts['mono']: |
---|
1029 | pars = suppress_pd(pars) |
---|
1030 | pars2 = suppress_pd(pars2) |
---|
1031 | if not opts['magnetic']: |
---|
1032 | pars = suppress_magnetism(pars) |
---|
1033 | pars2 = suppress_magnetism(pars2) |
---|
1034 | |
---|
1035 | # Fill in parameters given on the command line |
---|
1036 | presets = {} |
---|
1037 | presets2 = {} |
---|
1038 | for arg in values: |
---|
1039 | k, v = arg.split('=', 1) |
---|
1040 | if k not in pars and k not in pars2: |
---|
1041 | # extract base name without polydispersity info |
---|
1042 | s = set(p.split('_pd')[0] for p in pars) |
---|
1043 | print("%r invalid; parameters are: %s"%(k, ", ".join(sorted(s)))) |
---|
1044 | return None |
---|
1045 | v1, v2 = v.split(MODEL_SPLIT, 2) if MODEL_SPLIT in v else (v,v) |
---|
1046 | if v1 and k in pars: |
---|
1047 | presets[k] = float(v1) if isnumber(v1) else v1 |
---|
1048 | if v2 and k in pars2: |
---|
1049 | presets2[k] = float(v2) if isnumber(v2) else v2 |
---|
1050 | |
---|
1051 | # If pd given on the command line, default pd_n to 35 |
---|
1052 | for k, v in list(presets.items()): |
---|
1053 | if k.endswith('_pd'): |
---|
1054 | presets.setdefault(k+'_n', 35.) |
---|
1055 | for k, v in list(presets2.items()): |
---|
1056 | if k.endswith('_pd'): |
---|
1057 | presets2.setdefault(k+'_n', 35.) |
---|
1058 | |
---|
1059 | # Evaluate preset parameter expressions |
---|
1060 | context = MATH.copy() |
---|
1061 | context['np'] = np |
---|
1062 | context.update(pars) |
---|
1063 | context.update((k,v) for k,v in presets.items() if isinstance(v, float)) |
---|
1064 | for k, v in presets.items(): |
---|
1065 | if not isinstance(v, float) and not k.endswith('_type'): |
---|
1066 | presets[k] = eval(v, context) |
---|
1067 | context.update(presets) |
---|
1068 | context.update((k,v) for k,v in presets2.items() if isinstance(v, float)) |
---|
1069 | for k, v in presets2.items(): |
---|
1070 | if not isinstance(v, float) and not k.endswith('_type'): |
---|
1071 | presets2[k] = eval(v, context) |
---|
1072 | |
---|
1073 | # update parameters with presets |
---|
1074 | pars.update(presets) # set value after random to control value |
---|
1075 | pars2.update(presets2) # set value after random to control value |
---|
1076 | #import pprint; pprint.pprint(model_info) |
---|
1077 | |
---|
1078 | same_model = name == name2 and pars == pars |
---|
1079 | if len(engines) == 0: |
---|
1080 | if same_model: |
---|
1081 | engines.extend(['single', 'double']) |
---|
1082 | else: |
---|
1083 | engines.extend(['single', 'single']) |
---|
1084 | elif len(engines) == 1: |
---|
1085 | if not same_model: |
---|
1086 | engines.append(engines[0]) |
---|
1087 | elif engines[0] == 'double': |
---|
1088 | engines.append('single') |
---|
1089 | else: |
---|
1090 | engines.append('double') |
---|
1091 | elif len(engines) > 2: |
---|
1092 | del engines[2:] |
---|
1093 | |
---|
1094 | use_sasview = any(engine == 'sasview' and count > 0 |
---|
1095 | for engine, count in zip(engines, [n1, n2])) |
---|
1096 | if use_sasview: |
---|
1097 | constrain_new_to_old(model_info, pars) |
---|
1098 | constrain_new_to_old(model_info2, pars2) |
---|
1099 | |
---|
1100 | if opts['show_pars']: |
---|
1101 | if not same_model: |
---|
1102 | print("==== %s ====="%model_info.name) |
---|
1103 | print(str(parlist(model_info, pars, opts['is2d']))) |
---|
1104 | print("==== %s ====="%model_info2.name) |
---|
1105 | print(str(parlist(model_info2, pars2, opts['is2d']))) |
---|
1106 | else: |
---|
1107 | print(str(parlist(model_info, pars, opts['is2d']))) |
---|
1108 | |
---|
1109 | # Create the computational engines |
---|
1110 | data, _ = make_data(opts) |
---|
1111 | if n1: |
---|
1112 | base = make_engine(model_info, data, engines[0], opts['cutoff']) |
---|
1113 | else: |
---|
1114 | base = None |
---|
1115 | if n2: |
---|
1116 | comp = make_engine(model_info2, data, engines[1], opts['cutoff']) |
---|
1117 | else: |
---|
1118 | comp = None |
---|
1119 | |
---|
1120 | # pylint: disable=bad-whitespace |
---|
1121 | # Remember it all |
---|
1122 | opts.update({ |
---|
1123 | 'data' : data, |
---|
1124 | 'name' : [name, name2], |
---|
1125 | 'def' : [model_info, model_info2], |
---|
1126 | 'count' : [n1, n2], |
---|
1127 | 'presets' : [presets, presets2], |
---|
1128 | 'pars' : [pars, pars2], |
---|
1129 | 'engines' : [base, comp], |
---|
1130 | }) |
---|
1131 | # pylint: enable=bad-whitespace |
---|
1132 | |
---|
1133 | return opts |
---|
1134 | |
---|
1135 | def show_docs(opts): |
---|
1136 | # type: (Dict[str, Any]) -> None |
---|
1137 | """ |
---|
1138 | show html docs for the model |
---|
1139 | """ |
---|
1140 | import wx # type: ignore |
---|
1141 | from .generate import view_html_from_info |
---|
1142 | app = wx.App() if wx.GetApp() is None else None |
---|
1143 | view_html_from_info(opts['def'][0]) |
---|
1144 | if app: app.MainLoop() |
---|
1145 | |
---|
1146 | |
---|
1147 | def explore(opts): |
---|
1148 | # type: (Dict[str, Any]) -> None |
---|
1149 | """ |
---|
1150 | explore the model using the bumps gui. |
---|
1151 | """ |
---|
1152 | import wx # type: ignore |
---|
1153 | from bumps.names import FitProblem # type: ignore |
---|
1154 | from bumps.gui.app_frame import AppFrame # type: ignore |
---|
1155 | from bumps.gui import signal |
---|
1156 | |
---|
1157 | is_mac = "cocoa" in wx.version() |
---|
1158 | # Create an app if not running embedded |
---|
1159 | app = wx.App() if wx.GetApp() is None else None |
---|
1160 | model = Explore(opts) |
---|
1161 | problem = FitProblem(model) |
---|
1162 | frame = AppFrame(parent=None, title="explore", size=(1000,700)) |
---|
1163 | if not is_mac: frame.Show() |
---|
1164 | frame.panel.set_model(model=problem) |
---|
1165 | frame.panel.Layout() |
---|
1166 | frame.panel.aui.Split(0, wx.TOP) |
---|
1167 | def reset_parameters(event): |
---|
1168 | model.revert_values() |
---|
1169 | signal.update_parameters(problem) |
---|
1170 | frame.Bind(wx.EVT_TOOL, reset_parameters, frame.ToolBar.GetToolByPos(1)) |
---|
1171 | if is_mac: frame.Show() |
---|
1172 | # If running withing an app, start the main loop |
---|
1173 | if app: app.MainLoop() |
---|
1174 | |
---|
1175 | class Explore(object): |
---|
1176 | """ |
---|
1177 | Bumps wrapper for a SAS model comparison. |
---|
1178 | |
---|
1179 | The resulting object can be used as a Bumps fit problem so that |
---|
1180 | parameters can be adjusted in the GUI, with plots updated on the fly. |
---|
1181 | """ |
---|
1182 | def __init__(self, opts): |
---|
1183 | # type: (Dict[str, Any]) -> None |
---|
1184 | from bumps.cli import config_matplotlib # type: ignore |
---|
1185 | from . import bumps_model |
---|
1186 | config_matplotlib() |
---|
1187 | self.opts = opts |
---|
1188 | p1, p2 = opts['pars'] |
---|
1189 | m1, m2 = opts['def'] |
---|
1190 | self.fix_p2 = m1 != m2 or p1 != p2 |
---|
1191 | model_info = m1 |
---|
1192 | pars, pd_types = bumps_model.create_parameters(model_info, **p1) |
---|
1193 | # Initialize parameter ranges, fixing the 2D parameters for 1D data. |
---|
1194 | if not opts['is2d']: |
---|
1195 | for p in model_info.parameters.user_parameters({}, is2d=False): |
---|
1196 | for ext in ['', '_pd', '_pd_n', '_pd_nsigma']: |
---|
1197 | k = p.name+ext |
---|
1198 | v = pars.get(k, None) |
---|
1199 | if v is not None: |
---|
1200 | v.range(*parameter_range(k, v.value)) |
---|
1201 | else: |
---|
1202 | for k, v in pars.items(): |
---|
1203 | v.range(*parameter_range(k, v.value)) |
---|
1204 | |
---|
1205 | self.pars = pars |
---|
1206 | self.starting_values = dict((k, v.value) for k, v in pars.items()) |
---|
1207 | self.pd_types = pd_types |
---|
1208 | self.limits = None |
---|
1209 | |
---|
1210 | def revert_values(self): |
---|
1211 | for k, v in self.starting_values.items(): |
---|
1212 | self.pars[k].value = v |
---|
1213 | |
---|
1214 | def model_update(self): |
---|
1215 | pass |
---|
1216 | |
---|
1217 | def numpoints(self): |
---|
1218 | # type: () -> int |
---|
1219 | """ |
---|
1220 | Return the number of points. |
---|
1221 | """ |
---|
1222 | return len(self.pars) + 1 # so dof is 1 |
---|
1223 | |
---|
1224 | def parameters(self): |
---|
1225 | # type: () -> Any # Dict/List hierarchy of parameters |
---|
1226 | """ |
---|
1227 | Return a dictionary of parameters. |
---|
1228 | """ |
---|
1229 | return self.pars |
---|
1230 | |
---|
1231 | def nllf(self): |
---|
1232 | # type: () -> float |
---|
1233 | """ |
---|
1234 | Return cost. |
---|
1235 | """ |
---|
1236 | # pylint: disable=no-self-use |
---|
1237 | return 0. # No nllf |
---|
1238 | |
---|
1239 | def plot(self, view='log'): |
---|
1240 | # type: (str) -> None |
---|
1241 | """ |
---|
1242 | Plot the data and residuals. |
---|
1243 | """ |
---|
1244 | pars = dict((k, v.value) for k, v in self.pars.items()) |
---|
1245 | pars.update(self.pd_types) |
---|
1246 | self.opts['pars'][0] = pars |
---|
1247 | if not self.fix_p2: |
---|
1248 | self.opts['pars'][1] = pars |
---|
1249 | result = run_models(self.opts) |
---|
1250 | limits = plot_models(self.opts, result, limits=self.limits) |
---|
1251 | if self.limits is None: |
---|
1252 | vmin, vmax = limits |
---|
1253 | self.limits = vmax*1e-7, 1.3*vmax |
---|
1254 | import pylab; pylab.clf() |
---|
1255 | plot_models(self.opts, result, limits=self.limits) |
---|
1256 | |
---|
1257 | |
---|
1258 | def main(*argv): |
---|
1259 | # type: (*str) -> None |
---|
1260 | """ |
---|
1261 | Main program. |
---|
1262 | """ |
---|
1263 | opts = parse_opts(argv) |
---|
1264 | if opts is not None: |
---|
1265 | if opts['html']: |
---|
1266 | show_docs(opts) |
---|
1267 | elif opts['explore']: |
---|
1268 | explore(opts) |
---|
1269 | else: |
---|
1270 | compare(opts) |
---|
1271 | |
---|
1272 | if __name__ == "__main__": |
---|
1273 | main(*sys.argv[1:]) |
---|