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 | from os.path import basename, dirname, join as joinpath |
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34 | import glob |
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35 | import datetime |
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36 | import traceback |
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37 | |
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38 | import numpy as np |
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39 | |
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40 | from . import core |
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41 | from . import kerneldll |
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42 | from . import generate |
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43 | from .data import plot_theory, empty_data1D, empty_data2D |
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44 | from .direct_model import DirectModel |
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45 | from .convert import revert_model, constrain_new_to_old |
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46 | |
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47 | USAGE = """ |
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48 | usage: compare.py model N1 N2 [options...] [key=val] |
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49 | |
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50 | Compare the speed and value for a model between the SasView original and the |
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51 | sasmodels rewrite. |
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52 | |
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53 | model is the name of the model to compare (see below). |
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54 | N1 is the number of times to run sasmodels (default=1). |
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55 | N2 is the number times to run sasview (default=1). |
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56 | |
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57 | Options (* for default): |
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58 | |
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59 | -plot*/-noplot plots or suppress the plot of the model |
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60 | -lowq*/-midq/-highq/-exq use q values up to 0.05, 0.2, 1.0, 10.0 |
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61 | -nq=128 sets the number of Q points in the data set |
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62 | -1d*/-2d computes 1d or 2d data |
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63 | -preset*/-random[=seed] preset or random parameters |
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64 | -mono/-poly* force monodisperse/polydisperse |
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65 | -cutoff=1e-5* cutoff value for including a point in polydispersity |
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66 | -pars/-nopars* prints the parameter set or not |
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67 | -abs/-rel* plot relative or absolute error |
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68 | -linear/-log*/-q4 intensity scaling |
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69 | -hist/-nohist* plot histogram of relative error |
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70 | -res=0 sets the resolution width dQ/Q if calculating with resolution |
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71 | -accuracy=Low accuracy of the resolution calculation Low, Mid, High, Xhigh |
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72 | -edit starts the parameter explorer |
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73 | |
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74 | Any two calculation engines can be selected for comparison: |
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75 | |
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76 | -single/-double/-half/-fast sets an OpenCL calculation engine |
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77 | -single!/-double!/-quad! sets an OpenMP calculation engine |
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78 | -sasview sets the sasview calculation engine |
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79 | |
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80 | The default is -single -sasview. Note that the interpretation of quad |
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81 | precision depends on architecture, and may vary from 64-bit to 128-bit, |
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82 | with 80-bit floats being common (1e-19 precision). |
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83 | |
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84 | Key=value pairs allow you to set specific values for the model parameters. |
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85 | """ |
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86 | |
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87 | # Update docs with command line usage string. This is separate from the usual |
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88 | # doc string so that we can display it at run time if there is an error. |
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89 | # lin |
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90 | __doc__ = (__doc__ # pylint: disable=redefined-builtin |
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91 | + """ |
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92 | Program description |
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93 | ------------------- |
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94 | |
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95 | """ |
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96 | + USAGE) |
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97 | |
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98 | kerneldll.ALLOW_SINGLE_PRECISION_DLLS = True |
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99 | |
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100 | # List of available models |
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101 | ROOT = dirname(__file__) |
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102 | MODELS = [basename(f)[:-3] |
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103 | for f in sorted(glob.glob(joinpath(ROOT, "models", "[a-zA-Z]*.py")))] |
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104 | |
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105 | # CRUFT python 2.6 |
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106 | if not hasattr(datetime.timedelta, 'total_seconds'): |
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107 | def delay(dt): |
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108 | """Return number date-time delta as number seconds""" |
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109 | return dt.days * 86400 + dt.seconds + 1e-6 * dt.microseconds |
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110 | else: |
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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.total_seconds() |
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114 | |
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115 | |
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116 | class push_seed(object): |
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117 | """ |
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118 | Set the seed value for the random number generator. |
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119 | |
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120 | When used in a with statement, the random number generator state is |
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121 | restored after the with statement is complete. |
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122 | |
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123 | :Parameters: |
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124 | |
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125 | *seed* : int or array_like, optional |
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126 | Seed for RandomState |
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127 | |
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128 | :Example: |
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129 | |
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130 | Seed can be used directly to set the seed:: |
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131 | |
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132 | >>> from numpy.random import randint |
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133 | >>> push_seed(24) |
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134 | <...push_seed object at...> |
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135 | >>> print(randint(0,1000000,3)) |
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136 | [242082 899 211136] |
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137 | |
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138 | Seed can also be used in a with statement, which sets the random |
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139 | number generator state for the enclosed computations and restores |
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140 | it to the previous state on completion:: |
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141 | |
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142 | >>> with push_seed(24): |
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143 | ... print(randint(0,1000000,3)) |
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144 | [242082 899 211136] |
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145 | |
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146 | Using nested contexts, we can demonstrate that state is indeed |
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147 | restored after the block completes:: |
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148 | |
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149 | >>> with push_seed(24): |
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150 | ... print(randint(0,1000000)) |
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151 | ... with push_seed(24): |
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152 | ... print(randint(0,1000000,3)) |
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153 | ... print(randint(0,1000000)) |
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154 | 242082 |
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155 | [242082 899 211136] |
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156 | 899 |
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157 | |
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158 | The restore step is protected against exceptions in the block:: |
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159 | |
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160 | >>> with push_seed(24): |
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161 | ... print(randint(0,1000000)) |
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162 | ... try: |
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163 | ... with push_seed(24): |
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164 | ... print(randint(0,1000000,3)) |
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165 | ... raise Exception() |
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166 | ... except: |
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167 | ... print("Exception raised") |
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168 | ... print(randint(0,1000000)) |
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169 | 242082 |
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170 | [242082 899 211136] |
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171 | Exception raised |
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172 | 899 |
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173 | """ |
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174 | def __init__(self, seed=None): |
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175 | self._state = np.random.get_state() |
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176 | np.random.seed(seed) |
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177 | |
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178 | def __enter__(self): |
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179 | return None |
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180 | |
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181 | def __exit__(self, *args): |
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182 | np.random.set_state(self._state) |
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183 | |
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184 | def tic(): |
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185 | """ |
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186 | Timer function. |
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187 | |
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188 | Use "toc=tic()" to start the clock and "toc()" to measure |
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189 | a time interval. |
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190 | """ |
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191 | then = datetime.datetime.now() |
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192 | return lambda: delay(datetime.datetime.now() - then) |
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193 | |
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194 | |
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195 | def set_beam_stop(data, radius, outer=None): |
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196 | """ |
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197 | Add a beam stop of the given *radius*. If *outer*, make an annulus. |
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198 | |
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199 | Note: this function does not use the sasview package |
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200 | """ |
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201 | if hasattr(data, 'qx_data'): |
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202 | q = np.sqrt(data.qx_data**2 + data.qy_data**2) |
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203 | data.mask = (q < radius) |
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204 | if outer is not None: |
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205 | data.mask |= (q >= outer) |
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206 | else: |
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207 | data.mask = (data.x < radius) |
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208 | if outer is not None: |
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209 | data.mask |= (data.x >= outer) |
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210 | |
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211 | |
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212 | def parameter_range(p, v): |
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213 | """ |
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214 | Choose a parameter range based on parameter name and initial value. |
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215 | """ |
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216 | if p.endswith('_pd_n'): |
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217 | return [0, 100] |
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218 | elif p.endswith('_pd_nsigma'): |
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219 | return [0, 5] |
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220 | elif p.endswith('_pd_type'): |
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221 | return v |
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222 | elif any(s in p for s in ('theta', 'phi', 'psi')): |
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223 | # orientation in [-180,180], orientation pd in [0,45] |
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224 | if p.endswith('_pd'): |
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225 | return [0, 45] |
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226 | else: |
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227 | return [-180, 180] |
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228 | elif 'sld' in p: |
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229 | return [-0.5, 10] |
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230 | elif p.endswith('_pd'): |
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231 | return [0, 1] |
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232 | elif p == 'background': |
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233 | return [0, 10] |
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234 | elif p == 'scale': |
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235 | return [0, 1e3] |
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236 | elif p == 'case_num': |
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237 | # RPA hack |
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238 | return [0, 10] |
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239 | elif v < 0: |
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240 | # Kxy parameters in rpa model can be negative |
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241 | return [2*v, -2*v] |
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242 | else: |
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243 | return [0, (2*v if v > 0 else 1)] |
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244 | |
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245 | |
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246 | def _randomize_one(p, v): |
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247 | """ |
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248 | Randomize a single parameter. |
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249 | """ |
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250 | if any(p.endswith(s) for s in ('_pd_n', '_pd_nsigma', '_pd_type')): |
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251 | return v |
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252 | else: |
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253 | return np.random.uniform(*parameter_range(p, v)) |
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254 | |
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255 | |
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256 | def randomize_pars(pars, seed=None): |
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257 | """ |
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258 | Generate random values for all of the parameters. |
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259 | |
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260 | Valid ranges for the random number generator are guessed from the name of |
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261 | the parameter; this will not account for constraints such as cap radius |
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262 | greater than cylinder radius in the capped_cylinder model, so |
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263 | :func:`constrain_pars` needs to be called afterward.. |
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264 | """ |
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265 | with push_seed(seed): |
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266 | # Note: the sort guarantees order `of calls to random number generator |
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267 | pars = dict((p, _randomize_one(p, v)) |
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268 | for p, v in sorted(pars.items())) |
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269 | return pars |
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270 | |
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271 | def constrain_pars(model_definition, pars): |
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272 | """ |
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273 | Restrict parameters to valid values. |
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274 | |
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275 | This includes model specific code for models such as capped_cylinder |
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276 | which need to support within model constraints (cap radius more than |
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277 | cylinder radius in this case). |
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278 | """ |
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279 | name = model_definition.name |
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280 | if name == 'capped_cylinder' and pars['cap_radius'] < pars['radius']: |
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281 | pars['radius'], pars['cap_radius'] = pars['cap_radius'], pars['radius'] |
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282 | if name == 'barbell' and pars['bell_radius'] < pars['radius']: |
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283 | pars['radius'], pars['bell_radius'] = pars['bell_radius'], pars['radius'] |
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284 | |
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285 | # Limit guinier to an Rg such that Iq > 1e-30 (single precision cutoff) |
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286 | if name == 'guinier': |
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287 | #q_max = 0.2 # mid q maximum |
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288 | q_max = 1.0 # high q maximum |
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289 | rg_max = np.sqrt(90*np.log(10) + 3*np.log(pars['scale']))/q_max |
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290 | pars['rg'] = min(pars['rg'], rg_max) |
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291 | |
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292 | if name == 'rpa': |
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293 | # Make sure phi sums to 1.0 |
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294 | if pars['case_num'] < 2: |
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295 | pars['Phia'] = 0. |
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296 | pars['Phib'] = 0. |
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297 | elif pars['case_num'] < 5: |
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298 | pars['Phia'] = 0. |
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299 | total = sum(pars['Phi'+c] for c in 'abcd') |
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300 | for c in 'abcd': |
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301 | pars['Phi'+c] /= total |
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302 | |
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303 | def parlist(pars): |
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304 | """ |
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305 | Format the parameter list for printing. |
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306 | """ |
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307 | return "\n".join("%s: %s"%(p, v) for p, v in sorted(pars.items())) |
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308 | |
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309 | def suppress_pd(pars): |
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310 | """ |
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311 | Suppress theta_pd for now until the normalization is resolved. |
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312 | |
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313 | May also suppress complete polydispersity of the model to test |
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314 | models more quickly. |
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315 | """ |
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316 | pars = pars.copy() |
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317 | for p in pars: |
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318 | if p.endswith("_pd_n"): pars[p] = 0 |
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319 | return pars |
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320 | |
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321 | def eval_sasview(model_definition, data): |
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322 | """ |
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323 | Return a model calculator using the SasView fitting engine. |
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324 | """ |
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325 | # importing sas here so that the error message will be that sas failed to |
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326 | # import rather than the more obscure smear_selection not imported error |
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327 | import sas |
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328 | from sas.models.qsmearing import smear_selection |
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329 | |
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330 | # convert model parameters from sasmodel form to sasview form |
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331 | #print("old",sorted(pars.items())) |
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332 | modelname, _ = revert_model(model_definition, {}) |
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333 | #print("new",sorted(_pars.items())) |
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334 | sas = __import__('sas.models.'+modelname) |
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335 | ModelClass = getattr(getattr(sas.models, modelname, None), modelname, None) |
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336 | if ModelClass is None: |
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337 | raise ValueError("could not find model %r in sas.models"%modelname) |
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338 | model = ModelClass() |
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339 | smearer = smear_selection(data, model=model) |
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340 | |
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341 | if hasattr(data, 'qx_data'): |
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342 | q = np.sqrt(data.qx_data**2 + data.qy_data**2) |
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343 | index = ((~data.mask) & (~np.isnan(data.data)) |
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344 | & (q >= data.qmin) & (q <= data.qmax)) |
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345 | if smearer is not None: |
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346 | smearer.model = model # because smear_selection has a bug |
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347 | smearer.accuracy = data.accuracy |
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348 | smearer.set_index(index) |
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349 | theory = lambda: smearer.get_value() |
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350 | else: |
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351 | theory = lambda: model.evalDistribution([data.qx_data[index], |
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352 | data.qy_data[index]]) |
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353 | elif smearer is not None: |
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354 | theory = lambda: smearer(model.evalDistribution(data.x)) |
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355 | else: |
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356 | theory = lambda: model.evalDistribution(data.x) |
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357 | |
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358 | def calculator(**pars): |
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359 | """ |
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360 | Sasview calculator for model. |
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361 | """ |
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362 | # paying for parameter conversion each time to keep life simple, if not fast |
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363 | _, pars = revert_model(model_definition, pars) |
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364 | for k, v in pars.items(): |
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365 | parts = k.split('.') # polydispersity components |
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366 | if len(parts) == 2: |
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367 | model.dispersion[parts[0]][parts[1]] = v |
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368 | else: |
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369 | model.setParam(k, v) |
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370 | return theory() |
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371 | |
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372 | calculator.engine = "sasview" |
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373 | return calculator |
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374 | |
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375 | DTYPE_MAP = { |
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376 | 'half': '16', |
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377 | 'fast': 'fast', |
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378 | 'single': '32', |
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379 | 'double': '64', |
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380 | 'quad': '128', |
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381 | 'f16': '16', |
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382 | 'f32': '32', |
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383 | 'f64': '64', |
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384 | 'longdouble': '128', |
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385 | } |
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386 | def eval_opencl(model_definition, data, dtype='single', cutoff=0.): |
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387 | """ |
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388 | Return a model calculator using the OpenCL calculation engine. |
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389 | """ |
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390 | try: |
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391 | model = core.load_model(model_definition, dtype=dtype, platform="ocl") |
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392 | except Exception as exc: |
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393 | print(exc) |
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394 | print("... trying again with single precision") |
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395 | dtype = 'single' |
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396 | model = core.load_model(model_definition, dtype=dtype, platform="ocl") |
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397 | calculator = DirectModel(data, model, cutoff=cutoff) |
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398 | calculator.engine = "OCL%s"%DTYPE_MAP[dtype] |
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399 | return calculator |
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400 | |
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401 | def eval_ctypes(model_definition, data, dtype='double', cutoff=0.): |
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402 | """ |
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403 | Return a model calculator using the DLL calculation engine. |
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404 | """ |
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405 | if dtype == 'quad': |
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406 | dtype = 'longdouble' |
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407 | model = core.load_model(model_definition, dtype=dtype, platform="dll") |
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408 | calculator = DirectModel(data, model, cutoff=cutoff) |
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409 | calculator.engine = "OMP%s"%DTYPE_MAP[dtype] |
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410 | return calculator |
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411 | |
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412 | def time_calculation(calculator, pars, Nevals=1): |
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413 | """ |
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414 | Compute the average calculation time over N evaluations. |
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415 | |
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416 | An additional call is generated without polydispersity in order to |
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417 | initialize the calculation engine, and make the average more stable. |
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418 | """ |
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419 | # initialize the code so time is more accurate |
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420 | value = calculator(**suppress_pd(pars)) |
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421 | toc = tic() |
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422 | for _ in range(max(Nevals, 1)): # make sure there is at least one eval |
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423 | value = calculator(**pars) |
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424 | average_time = toc()*1000./Nevals |
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425 | return value, average_time |
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426 | |
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427 | def make_data(opts): |
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428 | """ |
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429 | Generate an empty dataset, used with the model to set Q points |
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430 | and resolution. |
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431 | |
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432 | *opts* contains the options, with 'qmax', 'nq', 'res', |
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433 | 'accuracy', 'is2d' and 'view' parsed from the command line. |
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434 | """ |
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435 | qmax, nq, res = opts['qmax'], opts['nq'], opts['res'] |
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436 | if opts['is2d']: |
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437 | data = empty_data2D(np.linspace(-qmax, qmax, nq), resolution=res) |
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438 | data.accuracy = opts['accuracy'] |
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439 | set_beam_stop(data, 0.004) |
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440 | index = ~data.mask |
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441 | else: |
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442 | if opts['view'] == 'log': |
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443 | qmax = math.log10(qmax) |
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444 | q = np.logspace(qmax-3, qmax, nq) |
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445 | else: |
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446 | q = np.linspace(0.001*qmax, qmax, nq) |
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447 | data = empty_data1D(q, resolution=res) |
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448 | index = slice(None, None) |
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449 | return data, index |
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450 | |
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451 | def make_engine(model_definition, data, dtype, cutoff): |
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452 | """ |
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453 | Generate the appropriate calculation engine for the given datatype. |
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454 | |
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455 | Datatypes with '!' appended are evaluated using external C DLLs rather |
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456 | than OpenCL. |
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457 | """ |
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458 | if dtype == 'sasview': |
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459 | return eval_sasview(model_definition, data) |
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460 | elif dtype.endswith('!'): |
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461 | return eval_ctypes(model_definition, data, dtype=dtype[:-1], |
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462 | cutoff=cutoff) |
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463 | else: |
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464 | return eval_opencl(model_definition, data, dtype=dtype, |
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465 | cutoff=cutoff) |
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466 | |
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467 | def compare(opts, limits=None): |
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468 | """ |
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469 | Preform a comparison using options from the command line. |
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470 | |
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471 | *limits* are the limits on the values to use, either to set the y-axis |
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472 | for 1D or to set the colormap scale for 2D. If None, then they are |
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473 | inferred from the data and returned. When exploring using Bumps, |
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474 | the limits are set when the model is initially called, and maintained |
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475 | as the values are adjusted, making it easier to see the effects of the |
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476 | parameters. |
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477 | """ |
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478 | Nbase, Ncomp = opts['n1'], opts['n2'] |
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479 | pars = opts['pars'] |
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480 | data = opts['data'] |
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481 | |
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482 | # Base calculation |
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483 | if Nbase > 0: |
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484 | base = opts['engines'][0] |
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485 | try: |
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486 | base_value, base_time = time_calculation(base, pars, Nbase) |
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487 | print("%s t=%.1f ms, intensity=%.0f" |
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488 | % (base.engine, base_time, sum(base_value))) |
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489 | except ImportError: |
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490 | traceback.print_exc() |
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491 | Nbase = 0 |
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492 | |
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493 | # Comparison calculation |
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494 | if Ncomp > 0: |
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495 | comp = opts['engines'][1] |
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496 | try: |
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497 | comp_value, comp_time = time_calculation(comp, pars, Ncomp) |
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498 | print("%s t=%.1f ms, intensity=%.0f" |
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499 | % (comp.engine, comp_time, sum(comp_value))) |
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500 | except ImportError: |
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501 | traceback.print_exc() |
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502 | Ncomp = 0 |
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503 | |
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504 | # Compare, but only if computing both forms |
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505 | if Nbase > 0 and Ncomp > 0: |
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506 | resid = (base_value - comp_value) |
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507 | relerr = resid/comp_value |
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508 | _print_stats("|%s-%s|" |
---|
509 | % (base.engine, comp.engine) + (" "*(3+len(comp.engine))), |
---|
510 | resid) |
---|
511 | _print_stats("|(%s-%s)/%s|" |
---|
512 | % (base.engine, comp.engine, comp.engine), |
---|
513 | relerr) |
---|
514 | |
---|
515 | # Plot if requested |
---|
516 | if not opts['plot'] and not opts['explore']: return |
---|
517 | view = opts['view'] |
---|
518 | import matplotlib.pyplot as plt |
---|
519 | if limits is None: |
---|
520 | vmin, vmax = np.Inf, -np.Inf |
---|
521 | if Nbase > 0: |
---|
522 | vmin = min(vmin, min(base_value)) |
---|
523 | vmax = max(vmax, max(base_value)) |
---|
524 | if Ncomp > 0: |
---|
525 | vmin = min(vmin, min(comp_value)) |
---|
526 | vmax = max(vmax, max(comp_value)) |
---|
527 | limits = vmin, vmax |
---|
528 | |
---|
529 | if Nbase > 0: |
---|
530 | if Ncomp > 0: plt.subplot(131) |
---|
531 | plot_theory(data, base_value, view=view, use_data=False, limits=limits) |
---|
532 | plt.title("%s t=%.1f ms"%(base.engine, base_time)) |
---|
533 | #cbar_title = "log I" |
---|
534 | if Ncomp > 0: |
---|
535 | if Nbase > 0: plt.subplot(132) |
---|
536 | plot_theory(data, comp_value, view=view, use_data=False, limits=limits) |
---|
537 | plt.title("%s t=%.1f ms"%(comp.engine, comp_time)) |
---|
538 | #cbar_title = "log I" |
---|
539 | if Ncomp > 0 and Nbase > 0: |
---|
540 | plt.subplot(133) |
---|
541 | if not opts['rel_err']: |
---|
542 | err, errstr, errview = resid, "abs err", "linear" |
---|
543 | else: |
---|
544 | err, errstr, errview = abs(relerr), "rel err", "log" |
---|
545 | #err,errstr = base/comp,"ratio" |
---|
546 | plot_theory(data, None, resid=err, view=errview, use_data=False) |
---|
547 | if view == 'linear': |
---|
548 | plt.xscale('linear') |
---|
549 | plt.title("max %s = %.3g"%(errstr, max(abs(err)))) |
---|
550 | #cbar_title = errstr if errview=="linear" else "log "+errstr |
---|
551 | #if is2D: |
---|
552 | # h = plt.colorbar() |
---|
553 | # h.ax.set_title(cbar_title) |
---|
554 | |
---|
555 | if Ncomp > 0 and Nbase > 0 and '-hist' in opts: |
---|
556 | plt.figure() |
---|
557 | v = relerr |
---|
558 | v[v == 0] = 0.5*np.min(np.abs(v[v != 0])) |
---|
559 | plt.hist(np.log10(np.abs(v)), normed=1, bins=50) |
---|
560 | plt.xlabel('log10(err), err = |(%s - %s) / %s|' |
---|
561 | % (base.engine, comp.engine, comp.engine)) |
---|
562 | plt.ylabel('P(err)') |
---|
563 | plt.title('Distribution of relative error between calculation engines') |
---|
564 | |
---|
565 | if not opts['explore']: |
---|
566 | plt.show() |
---|
567 | |
---|
568 | return limits |
---|
569 | |
---|
570 | def _print_stats(label, err): |
---|
571 | sorted_err = np.sort(abs(err)) |
---|
572 | p50 = int((len(err)-1)*0.50) |
---|
573 | p98 = int((len(err)-1)*0.98) |
---|
574 | data = [ |
---|
575 | "max:%.3e"%sorted_err[-1], |
---|
576 | "median:%.3e"%sorted_err[p50], |
---|
577 | "98%%:%.3e"%sorted_err[p98], |
---|
578 | "rms:%.3e"%np.sqrt(np.mean(err**2)), |
---|
579 | "zero-offset:%+.3e"%np.mean(err), |
---|
580 | ] |
---|
581 | print(label+" "+" ".join(data)) |
---|
582 | |
---|
583 | |
---|
584 | |
---|
585 | # =========================================================================== |
---|
586 | # |
---|
587 | NAME_OPTIONS = set([ |
---|
588 | 'plot', 'noplot', |
---|
589 | 'half', 'fast', 'single', 'double', |
---|
590 | 'single!', 'double!', 'quad!', 'sasview', |
---|
591 | 'lowq', 'midq', 'highq', 'exq', |
---|
592 | '2d', '1d', |
---|
593 | 'preset', 'random', |
---|
594 | 'poly', 'mono', |
---|
595 | 'nopars', 'pars', |
---|
596 | 'rel', 'abs', |
---|
597 | 'linear', 'log', 'q4', |
---|
598 | 'hist', 'nohist', |
---|
599 | 'edit', |
---|
600 | ]) |
---|
601 | VALUE_OPTIONS = [ |
---|
602 | # Note: random is both a name option and a value option |
---|
603 | 'cutoff', 'random', 'nq', 'res', 'accuracy', |
---|
604 | ] |
---|
605 | |
---|
606 | def columnize(L, indent="", width=79): |
---|
607 | """ |
---|
608 | Format a list of strings into columns. |
---|
609 | |
---|
610 | Returns a string with carriage returns ready for printing. |
---|
611 | """ |
---|
612 | column_width = max(len(w) for w in L) + 1 |
---|
613 | num_columns = (width - len(indent)) // column_width |
---|
614 | num_rows = len(L) // num_columns |
---|
615 | L = L + [""] * (num_rows*num_columns - len(L)) |
---|
616 | columns = [L[k*num_rows:(k+1)*num_rows] for k in range(num_columns)] |
---|
617 | lines = [" ".join("%-*s"%(column_width, entry) for entry in row) |
---|
618 | for row in zip(*columns)] |
---|
619 | output = indent + ("\n"+indent).join(lines) |
---|
620 | return output |
---|
621 | |
---|
622 | |
---|
623 | def get_demo_pars(model_definition): |
---|
624 | """ |
---|
625 | Extract demo parameters from the model definition. |
---|
626 | """ |
---|
627 | info = generate.make_info(model_definition) |
---|
628 | # Get the default values for the parameters |
---|
629 | pars = dict((p[0], p[2]) for p in info['parameters']) |
---|
630 | |
---|
631 | # Fill in default values for the polydispersity parameters |
---|
632 | for p in info['parameters']: |
---|
633 | if p[4] in ('volume', 'orientation'): |
---|
634 | pars[p[0]+'_pd'] = 0.0 |
---|
635 | pars[p[0]+'_pd_n'] = 0 |
---|
636 | pars[p[0]+'_pd_nsigma'] = 3.0 |
---|
637 | pars[p[0]+'_pd_type'] = "gaussian" |
---|
638 | |
---|
639 | # Plug in values given in demo |
---|
640 | pars.update(info['demo']) |
---|
641 | return pars |
---|
642 | |
---|
643 | def parse_opts(): |
---|
644 | """ |
---|
645 | Parse command line options. |
---|
646 | """ |
---|
647 | flags = [arg for arg in sys.argv[1:] |
---|
648 | if arg.startswith('-')] |
---|
649 | values = [arg for arg in sys.argv[1:] |
---|
650 | if not arg.startswith('-') and '=' in arg] |
---|
651 | args = [arg for arg in sys.argv[1:] |
---|
652 | if not arg.startswith('-') and '=' not in arg] |
---|
653 | models = "\n ".join("%-15s"%v for v in MODELS) |
---|
654 | if len(args) == 0: |
---|
655 | print(USAGE) |
---|
656 | print("\nAvailable models:") |
---|
657 | print(columnize(MODELS, indent=" ")) |
---|
658 | sys.exit(1) |
---|
659 | if args[0] not in MODELS: |
---|
660 | print("Model %r not available. Use one of:\n %s"%(args[0], models)) |
---|
661 | sys.exit(1) |
---|
662 | if len(args) > 3: |
---|
663 | print("expected parameters: model N1 N2") |
---|
664 | |
---|
665 | invalid = [o[1:] for o in flags |
---|
666 | if o[1:] not in NAME_OPTIONS |
---|
667 | and not any(o.startswith('-%s='%t) for t in VALUE_OPTIONS)] |
---|
668 | if invalid: |
---|
669 | print("Invalid options: %s"%(", ".join(invalid))) |
---|
670 | sys.exit(1) |
---|
671 | |
---|
672 | |
---|
673 | # pylint: disable=bad-whitespace |
---|
674 | # Interpret the flags |
---|
675 | opts = { |
---|
676 | 'plot' : True, |
---|
677 | 'view' : 'log', |
---|
678 | 'is2d' : False, |
---|
679 | 'qmax' : 0.05, |
---|
680 | 'nq' : 128, |
---|
681 | 'res' : 0.0, |
---|
682 | 'accuracy' : 'Low', |
---|
683 | 'cutoff' : 1e-5, |
---|
684 | 'seed' : -1, # default to preset |
---|
685 | 'mono' : False, |
---|
686 | 'show_pars' : False, |
---|
687 | 'show_hist' : False, |
---|
688 | 'rel_err' : True, |
---|
689 | 'explore' : False, |
---|
690 | } |
---|
691 | engines = [] |
---|
692 | for arg in flags: |
---|
693 | if arg == '-noplot': opts['plot'] = False |
---|
694 | elif arg == '-plot': opts['plot'] = True |
---|
695 | elif arg == '-linear': opts['view'] = 'linear' |
---|
696 | elif arg == '-log': opts['view'] = 'log' |
---|
697 | elif arg == '-q4': opts['view'] = 'q4' |
---|
698 | elif arg == '-1d': opts['is2d'] = False |
---|
699 | elif arg == '-2d': opts['is2d'] = True |
---|
700 | elif arg == '-exq': opts['qmax'] = 10.0 |
---|
701 | elif arg == '-highq': opts['qmax'] = 1.0 |
---|
702 | elif arg == '-midq': opts['qmax'] = 0.2 |
---|
703 | elif arg == '-loq': opts['qmax'] = 0.05 |
---|
704 | elif arg.startswith('-nq='): opts['nq'] = int(arg[4:]) |
---|
705 | elif arg.startswith('-res='): opts['res'] = float(arg[5:]) |
---|
706 | elif arg.startswith('-accuracy='): opts['accuracy'] = arg[10:] |
---|
707 | elif arg.startswith('-cutoff='): opts['cutoff'] = float(arg[8:]) |
---|
708 | elif arg.startswith('-random='): opts['seed'] = int(arg[8:]) |
---|
709 | elif arg == '-random': opts['seed'] = np.random.randint(1e6) |
---|
710 | elif arg == '-preset': opts['seed'] = -1 |
---|
711 | elif arg == '-mono': opts['mono'] = True |
---|
712 | elif arg == '-poly': opts['mono'] = False |
---|
713 | elif arg == '-pars': opts['show_pars'] = True |
---|
714 | elif arg == '-nopars': opts['show_pars'] = False |
---|
715 | elif arg == '-hist': opts['show_hist'] = True |
---|
716 | elif arg == '-nohist': opts['show_hist'] = False |
---|
717 | elif arg == '-rel': opts['rel_err'] = True |
---|
718 | elif arg == '-abs': opts['rel_err'] = False |
---|
719 | elif arg == '-half': engines.append(arg[1:]) |
---|
720 | elif arg == '-fast': engines.append(arg[1:]) |
---|
721 | elif arg == '-single': engines.append(arg[1:]) |
---|
722 | elif arg == '-double': engines.append(arg[1:]) |
---|
723 | elif arg == '-single!': engines.append(arg[1:]) |
---|
724 | elif arg == '-double!': engines.append(arg[1:]) |
---|
725 | elif arg == '-quad!': engines.append(arg[1:]) |
---|
726 | elif arg == '-sasview': engines.append(arg[1:]) |
---|
727 | elif arg == '-edit': opts['explore'] = True |
---|
728 | # pylint: enable=bad-whitespace |
---|
729 | |
---|
730 | if len(engines) == 0: |
---|
731 | engines.extend(['single', 'sasview']) |
---|
732 | elif len(engines) == 1: |
---|
733 | if engines[0][0] != 'sasview': |
---|
734 | engines.append('sasview') |
---|
735 | else: |
---|
736 | engines.append('single') |
---|
737 | elif len(engines) > 2: |
---|
738 | del engines[2:] |
---|
739 | |
---|
740 | name = args[0] |
---|
741 | model_definition = core.load_model_definition(name) |
---|
742 | |
---|
743 | n1 = int(args[1]) if len(args) > 1 else 1 |
---|
744 | n2 = int(args[2]) if len(args) > 2 else 1 |
---|
745 | |
---|
746 | # Get demo parameters from model definition, or use default parameters |
---|
747 | # if model does not define demo parameters |
---|
748 | pars = get_demo_pars(model_definition) |
---|
749 | |
---|
750 | # Fill in parameters given on the command line |
---|
751 | presets = {} |
---|
752 | for arg in values: |
---|
753 | k, v = arg.split('=', 1) |
---|
754 | if k not in pars: |
---|
755 | # extract base name without polydispersity info |
---|
756 | s = set(p.split('_pd')[0] for p in pars) |
---|
757 | print("%r invalid; parameters are: %s"%(k, ", ".join(sorted(s)))) |
---|
758 | sys.exit(1) |
---|
759 | presets[k] = float(v) if not k.endswith('type') else v |
---|
760 | |
---|
761 | # randomize parameters |
---|
762 | #pars.update(set_pars) # set value before random to control range |
---|
763 | if opts['seed'] > -1: |
---|
764 | pars = randomize_pars(pars, seed=opts['seed']) |
---|
765 | print("Randomize using -random=%i"%opts['seed']) |
---|
766 | if opts['mono']: |
---|
767 | pars = suppress_pd(pars) |
---|
768 | pars.update(presets) # set value after random to control value |
---|
769 | constrain_pars(model_definition, pars) |
---|
770 | constrain_new_to_old(model_definition, pars) |
---|
771 | if opts['show_pars']: |
---|
772 | print("pars " + str(parlist(pars))) |
---|
773 | |
---|
774 | # Create the computational engines |
---|
775 | data, _ = make_data(opts) |
---|
776 | if n1: |
---|
777 | base = make_engine(model_definition, data, engines[0], opts['cutoff']) |
---|
778 | else: |
---|
779 | base = None |
---|
780 | if n2: |
---|
781 | comp = make_engine(model_definition, data, engines[1], opts['cutoff']) |
---|
782 | else: |
---|
783 | comp = None |
---|
784 | |
---|
785 | # pylint: disable=bad-whitespace |
---|
786 | # Remember it all |
---|
787 | opts.update({ |
---|
788 | 'name' : name, |
---|
789 | 'def' : model_definition, |
---|
790 | 'n1' : n1, |
---|
791 | 'n2' : n2, |
---|
792 | 'presets' : presets, |
---|
793 | 'pars' : pars, |
---|
794 | 'data' : data, |
---|
795 | 'engines' : [base, comp], |
---|
796 | }) |
---|
797 | # pylint: enable=bad-whitespace |
---|
798 | |
---|
799 | return opts |
---|
800 | |
---|
801 | def explore(opts): |
---|
802 | """ |
---|
803 | Explore the model using the Bumps GUI. |
---|
804 | """ |
---|
805 | import wx |
---|
806 | from bumps.names import FitProblem |
---|
807 | from bumps.gui.app_frame import AppFrame |
---|
808 | |
---|
809 | problem = FitProblem(Explore(opts)) |
---|
810 | is_mac = "cocoa" in wx.version() |
---|
811 | app = wx.App() |
---|
812 | frame = AppFrame(parent=None, title="explore") |
---|
813 | if not is_mac: frame.Show() |
---|
814 | frame.panel.set_model(model=problem) |
---|
815 | frame.panel.Layout() |
---|
816 | frame.panel.aui.Split(0, wx.TOP) |
---|
817 | if is_mac: frame.Show() |
---|
818 | app.MainLoop() |
---|
819 | |
---|
820 | class Explore(object): |
---|
821 | """ |
---|
822 | Bumps wrapper for a SAS model comparison. |
---|
823 | |
---|
824 | The resulting object can be used as a Bumps fit problem so that |
---|
825 | parameters can be adjusted in the GUI, with plots updated on the fly. |
---|
826 | """ |
---|
827 | def __init__(self, opts): |
---|
828 | from bumps.cli import config_matplotlib |
---|
829 | from . import bumps_model |
---|
830 | config_matplotlib() |
---|
831 | self.opts = opts |
---|
832 | info = generate.make_info(opts['def']) |
---|
833 | pars, pd_types = bumps_model.create_parameters(info, **opts['pars']) |
---|
834 | if not opts['is2d']: |
---|
835 | active = [base + ext |
---|
836 | for base in info['partype']['pd-1d'] |
---|
837 | for ext in ['', '_pd', '_pd_n', '_pd_nsigma']] |
---|
838 | active.extend(info['partype']['fixed-1d']) |
---|
839 | for k in active: |
---|
840 | v = pars[k] |
---|
841 | v.range(*parameter_range(k, v.value)) |
---|
842 | else: |
---|
843 | for k, v in pars.items(): |
---|
844 | v.range(*parameter_range(k, v.value)) |
---|
845 | |
---|
846 | self.pars = pars |
---|
847 | self.pd_types = pd_types |
---|
848 | self.limits = None |
---|
849 | |
---|
850 | def numpoints(self): |
---|
851 | """ |
---|
852 | Return the number of points. |
---|
853 | """ |
---|
854 | return len(self.pars) + 1 # so dof is 1 |
---|
855 | |
---|
856 | def parameters(self): |
---|
857 | """ |
---|
858 | Return a dictionary of parameters. |
---|
859 | """ |
---|
860 | return self.pars |
---|
861 | |
---|
862 | def nllf(self): |
---|
863 | """ |
---|
864 | Return cost. |
---|
865 | """ |
---|
866 | # pylint: disable=no-self-use |
---|
867 | return 0. # No nllf |
---|
868 | |
---|
869 | def plot(self, view='log'): |
---|
870 | """ |
---|
871 | Plot the data and residuals. |
---|
872 | """ |
---|
873 | pars = dict((k, v.value) for k, v in self.pars.items()) |
---|
874 | pars.update(self.pd_types) |
---|
875 | self.opts['pars'] = pars |
---|
876 | limits = compare(self.opts, limits=self.limits) |
---|
877 | if self.limits is None: |
---|
878 | vmin, vmax = limits |
---|
879 | vmax = 1.3*vmax |
---|
880 | vmin = vmax*1e-7 |
---|
881 | self.limits = vmin, vmax |
---|
882 | |
---|
883 | |
---|
884 | def main(): |
---|
885 | """ |
---|
886 | Main program. |
---|
887 | """ |
---|
888 | opts = parse_opts() |
---|
889 | if opts['explore']: |
---|
890 | explore(opts) |
---|
891 | else: |
---|
892 | compare(opts) |
---|
893 | |
---|
894 | if __name__ == "__main__": |
---|
895 | main() |
---|