[8a20be5] | 1 | #!/usr/bin/env python |
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| 2 | # -*- coding: utf-8 -*- |
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[caeb06d] | 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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[9cfcac8] | 11 | tag the model as double precision only. |
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[caeb06d] | 12 | |
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[9cfcac8] | 13 | Run using ./compare.sh (Linux, Mac) or compare.bat (Windows) in the |
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[caeb06d] | 14 | sasmodels root to see the command line options. |
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| 15 | |
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[9cfcac8] | 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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[caeb06d] | 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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[190fc2b] | 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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[caeb06d] | 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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[d15a908] | 90 | __doc__ = (__doc__ # pylint: disable=redefined-builtin |
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| 91 | + """ |
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[caeb06d] | 92 | Program description |
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| 93 | ------------------- |
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| 94 | |
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[d15a908] | 95 | """ |
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| 96 | + USAGE) |
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[caeb06d] | 97 | |
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[750ffa5] | 98 | kerneldll.ALLOW_SINGLE_PRECISION_DLLS = True |
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[87985ca] | 99 | |
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[d547f16] | 100 | # List of available models |
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[190fc2b] | 101 | ROOT = dirname(__file__) |
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[d547f16] | 102 | MODELS = [basename(f)[:-3] |
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[caeb06d] | 103 | for f in sorted(glob.glob(joinpath(ROOT, "models", "[a-zA-Z]*.py")))] |
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[d547f16] | 104 | |
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[7cf2cfd] | 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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[4f2478e] | 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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[7cf2cfd] | 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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[8a20be5] | 211 | |
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[ec7e360] | 212 | def parameter_range(p, v): |
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[87985ca] | 213 | """ |
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[ec7e360] | 214 | Choose a parameter range based on parameter name and initial value. |
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[87985ca] | 215 | """ |
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[ec7e360] | 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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[87985ca] | 221 | return v |
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[caeb06d] | 222 | elif any(s in p for s in ('theta', 'phi', 'psi')): |
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[87985ca] | 223 | # orientation in [-180,180], orientation pd in [0,45] |
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| 224 | if p.endswith('_pd'): |
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[caeb06d] | 225 | return [0, 45] |
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[87985ca] | 226 | else: |
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[ec7e360] | 227 | return [-180, 180] |
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[87985ca] | 228 | elif 'sld' in p: |
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[ec7e360] | 229 | return [-0.5, 10] |
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[87985ca] | 230 | elif p.endswith('_pd'): |
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[ec7e360] | 231 | return [0, 1] |
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[eb46451] | 232 | elif p == 'background': |
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| 233 | return [0, 10] |
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| 234 | elif p == 'scale': |
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[ec7e360] | 235 | return [0, 1e3] |
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[eb46451] | 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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[87985ca] | 242 | else: |
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[caeb06d] | 243 | return [0, (2*v if v > 0 else 1)] |
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[87985ca] | 244 | |
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[4f2478e] | 245 | |
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[ec7e360] | 246 | def _randomize_one(p, v): |
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| 247 | """ |
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[caeb06d] | 248 | Randomize a single parameter. |
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[ec7e360] | 249 | """ |
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[caeb06d] | 250 | if any(p.endswith(s) for s in ('_pd_n', '_pd_nsigma', '_pd_type')): |
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[ec7e360] | 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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[cd3dba0] | 254 | |
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[4f2478e] | 255 | |
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[ec7e360] | 256 | def randomize_pars(pars, seed=None): |
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[caeb06d] | 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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[4f2478e] | 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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[ec7e360] | 269 | return pars |
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[cd3dba0] | 270 | |
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| 271 | def constrain_pars(model_definition, pars): |
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[9a66e65] | 272 | """ |
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| 273 | Restrict parameters to valid values. |
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[caeb06d] | 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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[9a66e65] | 278 | """ |
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[cd3dba0] | 279 | name = model_definition.name |
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[216a9e1] | 280 | if name == 'capped_cylinder' and pars['cap_radius'] < pars['radius']: |
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[caeb06d] | 281 | pars['radius'], pars['cap_radius'] = pars['cap_radius'], pars['radius'] |
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[b514adf] | 282 | if name == 'barbell' and pars['bell_radius'] < pars['radius']: |
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[caeb06d] | 283 | pars['radius'], pars['bell_radius'] = pars['bell_radius'], pars['radius'] |
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[b514adf] | 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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[caeb06d] | 290 | pars['rg'] = min(pars['rg'], rg_max) |
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[cd3dba0] | 291 | |
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[82c299f] | 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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[87985ca] | 303 | def parlist(pars): |
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[caeb06d] | 304 | """ |
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| 305 | Format the parameter list for printing. |
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| 306 | """ |
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[a4a7308] | 307 | active = None |
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| 308 | fields = {} |
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| 309 | lines = [] |
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| 310 | for k, v in sorted(pars.items()): |
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| 311 | parts = k.split('_pd') |
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| 312 | #print(k, active, parts) |
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| 313 | if len(parts) == 1: |
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| 314 | if active: lines.append(_format_par(active, **fields)) |
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| 315 | active = k |
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| 316 | fields = {'value': v} |
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| 317 | else: |
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| 318 | assert parts[0] == active |
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| 319 | if parts[1]: |
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| 320 | fields[parts[1][1:]] = v |
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| 321 | else: |
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| 322 | fields['pd'] = v |
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| 323 | if active: lines.append(_format_par(active, **fields)) |
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| 324 | return "\n".join(lines) |
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| 325 | |
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| 326 | #return "\n".join("%s: %s"%(p, v) for p, v in sorted(pars.items())) |
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| 327 | |
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| 328 | def _format_par(name, value=0., pd=0., n=0, nsigma=3., type='gaussian'): |
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| 329 | line = "%s: %g"%(name, value) |
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| 330 | if pd != 0. and n != 0: |
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| 331 | line += " +/- %g (%d points in [-%g,%g] sigma %s)"\ |
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| 332 | % (pd, n, nsigma, nsigma, type) |
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| 333 | return line |
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[87985ca] | 334 | |
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| 335 | def suppress_pd(pars): |
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| 336 | """ |
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| 337 | Suppress theta_pd for now until the normalization is resolved. |
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| 338 | |
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| 339 | May also suppress complete polydispersity of the model to test |
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| 340 | models more quickly. |
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| 341 | """ |
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[f4f3919] | 342 | pars = pars.copy() |
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[87985ca] | 343 | for p in pars: |
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[8b25ee1] | 344 | if p.endswith("_pd_n"): pars[p] = 0 |
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[f4f3919] | 345 | return pars |
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[87985ca] | 346 | |
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[ec7e360] | 347 | def eval_sasview(model_definition, data): |
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[caeb06d] | 348 | """ |
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| 349 | Return a model calculator using the SasView fitting engine. |
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| 350 | """ |
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[dc056b9] | 351 | # importing sas here so that the error message will be that sas failed to |
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| 352 | # import rather than the more obscure smear_selection not imported error |
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[2bebe2b] | 353 | import sas |
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[346bc88] | 354 | from sas.models.qsmearing import smear_selection |
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[ec7e360] | 355 | |
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| 356 | # convert model parameters from sasmodel form to sasview form |
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| 357 | #print("old",sorted(pars.items())) |
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[9cfcac8] | 358 | modelname, _ = revert_model(model_definition, {}) |
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[caeb06d] | 359 | #print("new",sorted(_pars.items())) |
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[ec7e360] | 360 | sas = __import__('sas.models.'+modelname) |
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[caeb06d] | 361 | ModelClass = getattr(getattr(sas.models, modelname, None), modelname, None) |
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[ec7e360] | 362 | if ModelClass is None: |
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| 363 | raise ValueError("could not find model %r in sas.models"%modelname) |
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| 364 | model = ModelClass() |
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[346bc88] | 365 | smearer = smear_selection(data, model=model) |
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[216a9e1] | 366 | |
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[ec7e360] | 367 | if hasattr(data, 'qx_data'): |
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| 368 | q = np.sqrt(data.qx_data**2 + data.qy_data**2) |
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| 369 | index = ((~data.mask) & (~np.isnan(data.data)) |
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| 370 | & (q >= data.qmin) & (q <= data.qmax)) |
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| 371 | if smearer is not None: |
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| 372 | smearer.model = model # because smear_selection has a bug |
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| 373 | smearer.accuracy = data.accuracy |
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| 374 | smearer.set_index(index) |
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| 375 | theory = lambda: smearer.get_value() |
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| 376 | else: |
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[d15a908] | 377 | theory = lambda: model.evalDistribution([data.qx_data[index], |
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| 378 | data.qy_data[index]]) |
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[ec7e360] | 379 | elif smearer is not None: |
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| 380 | theory = lambda: smearer(model.evalDistribution(data.x)) |
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| 381 | else: |
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| 382 | theory = lambda: model.evalDistribution(data.x) |
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| 383 | |
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| 384 | def calculator(**pars): |
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[caeb06d] | 385 | """ |
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| 386 | Sasview calculator for model. |
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| 387 | """ |
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[ec7e360] | 388 | # paying for parameter conversion each time to keep life simple, if not fast |
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| 389 | _, pars = revert_model(model_definition, pars) |
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[caeb06d] | 390 | for k, v in pars.items(): |
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[ec7e360] | 391 | parts = k.split('.') # polydispersity components |
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| 392 | if len(parts) == 2: |
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| 393 | model.dispersion[parts[0]][parts[1]] = v |
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| 394 | else: |
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| 395 | model.setParam(k, v) |
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| 396 | return theory() |
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| 397 | |
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| 398 | calculator.engine = "sasview" |
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| 399 | return calculator |
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| 400 | |
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| 401 | DTYPE_MAP = { |
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| 402 | 'half': '16', |
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| 403 | 'fast': 'fast', |
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| 404 | 'single': '32', |
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| 405 | 'double': '64', |
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| 406 | 'quad': '128', |
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| 407 | 'f16': '16', |
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| 408 | 'f32': '32', |
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| 409 | 'f64': '64', |
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| 410 | 'longdouble': '128', |
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| 411 | } |
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| 412 | def eval_opencl(model_definition, data, dtype='single', cutoff=0.): |
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[caeb06d] | 413 | """ |
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| 414 | Return a model calculator using the OpenCL calculation engine. |
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| 415 | """ |
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[216a9e1] | 416 | try: |
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[ec7e360] | 417 | model = core.load_model(model_definition, dtype=dtype, platform="ocl") |
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[9404dd3] | 418 | except Exception as exc: |
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| 419 | print(exc) |
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| 420 | print("... trying again with single precision") |
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[ec7e360] | 421 | dtype = 'single' |
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| 422 | model = core.load_model(model_definition, dtype=dtype, platform="ocl") |
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[7cf2cfd] | 423 | calculator = DirectModel(data, model, cutoff=cutoff) |
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[ec7e360] | 424 | calculator.engine = "OCL%s"%DTYPE_MAP[dtype] |
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| 425 | return calculator |
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[216a9e1] | 426 | |
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[ec7e360] | 427 | def eval_ctypes(model_definition, data, dtype='double', cutoff=0.): |
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[9cfcac8] | 428 | """ |
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| 429 | Return a model calculator using the DLL calculation engine. |
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| 430 | """ |
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[caeb06d] | 431 | if dtype == 'quad': |
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[ec7e360] | 432 | dtype = 'longdouble' |
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[aa4946b] | 433 | model = core.load_model(model_definition, dtype=dtype, platform="dll") |
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[7cf2cfd] | 434 | calculator = DirectModel(data, model, cutoff=cutoff) |
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[ec7e360] | 435 | calculator.engine = "OMP%s"%DTYPE_MAP[dtype] |
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| 436 | return calculator |
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| 437 | |
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| 438 | def time_calculation(calculator, pars, Nevals=1): |
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[caeb06d] | 439 | """ |
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| 440 | Compute the average calculation time over N evaluations. |
---|
| 441 | |
---|
| 442 | An additional call is generated without polydispersity in order to |
---|
| 443 | initialize the calculation engine, and make the average more stable. |
---|
| 444 | """ |
---|
[ec7e360] | 445 | # initialize the code so time is more accurate |
---|
[f4f3919] | 446 | value = calculator(**suppress_pd(pars)) |
---|
[216a9e1] | 447 | toc = tic() |
---|
[ec7e360] | 448 | for _ in range(max(Nevals, 1)): # make sure there is at least one eval |
---|
[7cf2cfd] | 449 | value = calculator(**pars) |
---|
[216a9e1] | 450 | average_time = toc()*1000./Nevals |
---|
| 451 | return value, average_time |
---|
| 452 | |
---|
[ec7e360] | 453 | def make_data(opts): |
---|
[caeb06d] | 454 | """ |
---|
| 455 | Generate an empty dataset, used with the model to set Q points |
---|
| 456 | and resolution. |
---|
| 457 | |
---|
| 458 | *opts* contains the options, with 'qmax', 'nq', 'res', |
---|
| 459 | 'accuracy', 'is2d' and 'view' parsed from the command line. |
---|
| 460 | """ |
---|
[ec7e360] | 461 | qmax, nq, res = opts['qmax'], opts['nq'], opts['res'] |
---|
| 462 | if opts['is2d']: |
---|
| 463 | data = empty_data2D(np.linspace(-qmax, qmax, nq), resolution=res) |
---|
| 464 | data.accuracy = opts['accuracy'] |
---|
[87985ca] | 465 | set_beam_stop(data, 0.004) |
---|
| 466 | index = ~data.mask |
---|
[216a9e1] | 467 | else: |
---|
[ec7e360] | 468 | if opts['view'] == 'log': |
---|
[b89f519] | 469 | qmax = math.log10(qmax) |
---|
[ec7e360] | 470 | q = np.logspace(qmax-3, qmax, nq) |
---|
[b89f519] | 471 | else: |
---|
[ec7e360] | 472 | q = np.linspace(0.001*qmax, qmax, nq) |
---|
| 473 | data = empty_data1D(q, resolution=res) |
---|
[216a9e1] | 474 | index = slice(None, None) |
---|
| 475 | return data, index |
---|
| 476 | |
---|
[ec7e360] | 477 | def make_engine(model_definition, data, dtype, cutoff): |
---|
[caeb06d] | 478 | """ |
---|
| 479 | Generate the appropriate calculation engine for the given datatype. |
---|
| 480 | |
---|
| 481 | Datatypes with '!' appended are evaluated using external C DLLs rather |
---|
| 482 | than OpenCL. |
---|
| 483 | """ |
---|
[ec7e360] | 484 | if dtype == 'sasview': |
---|
| 485 | return eval_sasview(model_definition, data) |
---|
| 486 | elif dtype.endswith('!'): |
---|
| 487 | return eval_ctypes(model_definition, data, dtype=dtype[:-1], |
---|
| 488 | cutoff=cutoff) |
---|
| 489 | else: |
---|
| 490 | return eval_opencl(model_definition, data, dtype=dtype, |
---|
| 491 | cutoff=cutoff) |
---|
[87985ca] | 492 | |
---|
[013adb7] | 493 | def compare(opts, limits=None): |
---|
[caeb06d] | 494 | """ |
---|
| 495 | Preform a comparison using options from the command line. |
---|
| 496 | |
---|
| 497 | *limits* are the limits on the values to use, either to set the y-axis |
---|
| 498 | for 1D or to set the colormap scale for 2D. If None, then they are |
---|
| 499 | inferred from the data and returned. When exploring using Bumps, |
---|
| 500 | the limits are set when the model is initially called, and maintained |
---|
| 501 | as the values are adjusted, making it easier to see the effects of the |
---|
| 502 | parameters. |
---|
| 503 | """ |
---|
[9cfcac8] | 504 | Nbase, Ncomp = opts['n1'], opts['n2'] |
---|
[ec7e360] | 505 | pars = opts['pars'] |
---|
| 506 | data = opts['data'] |
---|
[87985ca] | 507 | |
---|
[4b41184] | 508 | # Base calculation |
---|
[ec7e360] | 509 | if Nbase > 0: |
---|
| 510 | base = opts['engines'][0] |
---|
[319ab14] | 511 | try: |
---|
[ec7e360] | 512 | base_value, base_time = time_calculation(base, pars, Nbase) |
---|
[d15a908] | 513 | print("%s t=%.1f ms, intensity=%.0f" |
---|
| 514 | % (base.engine, base_time, sum(base_value))) |
---|
[319ab14] | 515 | except ImportError: |
---|
| 516 | traceback.print_exc() |
---|
[1ec7efa] | 517 | Nbase = 0 |
---|
[4b41184] | 518 | |
---|
| 519 | # Comparison calculation |
---|
[ec7e360] | 520 | if Ncomp > 0: |
---|
| 521 | comp = opts['engines'][1] |
---|
[7cf2cfd] | 522 | try: |
---|
[ec7e360] | 523 | comp_value, comp_time = time_calculation(comp, pars, Ncomp) |
---|
[d15a908] | 524 | print("%s t=%.1f ms, intensity=%.0f" |
---|
| 525 | % (comp.engine, comp_time, sum(comp_value))) |
---|
[7cf2cfd] | 526 | except ImportError: |
---|
[5753e4e] | 527 | traceback.print_exc() |
---|
[4b41184] | 528 | Ncomp = 0 |
---|
[87985ca] | 529 | |
---|
| 530 | # Compare, but only if computing both forms |
---|
[4b41184] | 531 | if Nbase > 0 and Ncomp > 0: |
---|
[ec7e360] | 532 | resid = (base_value - comp_value) |
---|
| 533 | relerr = resid/comp_value |
---|
[d15a908] | 534 | _print_stats("|%s-%s|" |
---|
| 535 | % (base.engine, comp.engine) + (" "*(3+len(comp.engine))), |
---|
[caeb06d] | 536 | resid) |
---|
[d15a908] | 537 | _print_stats("|(%s-%s)/%s|" |
---|
| 538 | % (base.engine, comp.engine, comp.engine), |
---|
[caeb06d] | 539 | relerr) |
---|
[87985ca] | 540 | |
---|
| 541 | # Plot if requested |
---|
[ec7e360] | 542 | if not opts['plot'] and not opts['explore']: return |
---|
| 543 | view = opts['view'] |
---|
[1726b21] | 544 | import matplotlib.pyplot as plt |
---|
[013adb7] | 545 | if limits is None: |
---|
| 546 | vmin, vmax = np.Inf, -np.Inf |
---|
| 547 | if Nbase > 0: |
---|
| 548 | vmin = min(vmin, min(base_value)) |
---|
| 549 | vmax = max(vmax, max(base_value)) |
---|
| 550 | if Ncomp > 0: |
---|
| 551 | vmin = min(vmin, min(comp_value)) |
---|
| 552 | vmax = max(vmax, max(comp_value)) |
---|
| 553 | limits = vmin, vmax |
---|
| 554 | |
---|
[4b41184] | 555 | if Nbase > 0: |
---|
[ec7e360] | 556 | if Ncomp > 0: plt.subplot(131) |
---|
[841753c] | 557 | plot_theory(data, base_value, view=view, use_data=False, limits=limits) |
---|
[ec7e360] | 558 | plt.title("%s t=%.1f ms"%(base.engine, base_time)) |
---|
| 559 | #cbar_title = "log I" |
---|
| 560 | if Ncomp > 0: |
---|
| 561 | if Nbase > 0: plt.subplot(132) |
---|
[841753c] | 562 | plot_theory(data, comp_value, view=view, use_data=False, limits=limits) |
---|
[caeb06d] | 563 | plt.title("%s t=%.1f ms"%(comp.engine, comp_time)) |
---|
[7cf2cfd] | 564 | #cbar_title = "log I" |
---|
[4b41184] | 565 | if Ncomp > 0 and Nbase > 0: |
---|
[87985ca] | 566 | plt.subplot(133) |
---|
[d5e650d] | 567 | if not opts['rel_err']: |
---|
[caeb06d] | 568 | err, errstr, errview = resid, "abs err", "linear" |
---|
[29f5536] | 569 | else: |
---|
[caeb06d] | 570 | err, errstr, errview = abs(relerr), "rel err", "log" |
---|
[4b41184] | 571 | #err,errstr = base/comp,"ratio" |
---|
[841753c] | 572 | plot_theory(data, None, resid=err, view=errview, use_data=False) |
---|
[d5e650d] | 573 | if view == 'linear': |
---|
| 574 | plt.xscale('linear') |
---|
[346bc88] | 575 | plt.title("max %s = %.3g"%(errstr, max(abs(err)))) |
---|
[7cf2cfd] | 576 | #cbar_title = errstr if errview=="linear" else "log "+errstr |
---|
| 577 | #if is2D: |
---|
| 578 | # h = plt.colorbar() |
---|
| 579 | # h.ax.set_title(cbar_title) |
---|
[ba69383] | 580 | |
---|
[4b41184] | 581 | if Ncomp > 0 and Nbase > 0 and '-hist' in opts: |
---|
[ba69383] | 582 | plt.figure() |
---|
[346bc88] | 583 | v = relerr |
---|
[caeb06d] | 584 | v[v == 0] = 0.5*np.min(np.abs(v[v != 0])) |
---|
| 585 | plt.hist(np.log10(np.abs(v)), normed=1, bins=50) |
---|
| 586 | plt.xlabel('log10(err), err = |(%s - %s) / %s|' |
---|
| 587 | % (base.engine, comp.engine, comp.engine)) |
---|
[ba69383] | 588 | plt.ylabel('P(err)') |
---|
[ec7e360] | 589 | plt.title('Distribution of relative error between calculation engines') |
---|
[ba69383] | 590 | |
---|
[ec7e360] | 591 | if not opts['explore']: |
---|
| 592 | plt.show() |
---|
[8a20be5] | 593 | |
---|
[013adb7] | 594 | return limits |
---|
| 595 | |
---|
[0763009] | 596 | def _print_stats(label, err): |
---|
| 597 | sorted_err = np.sort(abs(err)) |
---|
| 598 | p50 = int((len(err)-1)*0.50) |
---|
| 599 | p98 = int((len(err)-1)*0.98) |
---|
| 600 | data = [ |
---|
| 601 | "max:%.3e"%sorted_err[-1], |
---|
| 602 | "median:%.3e"%sorted_err[p50], |
---|
| 603 | "98%%:%.3e"%sorted_err[p98], |
---|
| 604 | "rms:%.3e"%np.sqrt(np.mean(err**2)), |
---|
| 605 | "zero-offset:%+.3e"%np.mean(err), |
---|
| 606 | ] |
---|
[caeb06d] | 607 | print(label+" "+" ".join(data)) |
---|
[0763009] | 608 | |
---|
| 609 | |
---|
| 610 | |
---|
[87985ca] | 611 | # =========================================================================== |
---|
| 612 | # |
---|
[216a9e1] | 613 | NAME_OPTIONS = set([ |
---|
[5d316e9] | 614 | 'plot', 'noplot', |
---|
[ec7e360] | 615 | 'half', 'fast', 'single', 'double', |
---|
| 616 | 'single!', 'double!', 'quad!', 'sasview', |
---|
[5d316e9] | 617 | 'lowq', 'midq', 'highq', 'exq', |
---|
| 618 | '2d', '1d', |
---|
| 619 | 'preset', 'random', |
---|
| 620 | 'poly', 'mono', |
---|
| 621 | 'nopars', 'pars', |
---|
| 622 | 'rel', 'abs', |
---|
[b89f519] | 623 | 'linear', 'log', 'q4', |
---|
[5d316e9] | 624 | 'hist', 'nohist', |
---|
[ec7e360] | 625 | 'edit', |
---|
[216a9e1] | 626 | ]) |
---|
| 627 | VALUE_OPTIONS = [ |
---|
| 628 | # Note: random is both a name option and a value option |
---|
[ec7e360] | 629 | 'cutoff', 'random', 'nq', 'res', 'accuracy', |
---|
[87985ca] | 630 | ] |
---|
| 631 | |
---|
[7cf2cfd] | 632 | def columnize(L, indent="", width=79): |
---|
[caeb06d] | 633 | """ |
---|
[1d4017a] | 634 | Format a list of strings into columns. |
---|
| 635 | |
---|
| 636 | Returns a string with carriage returns ready for printing. |
---|
[caeb06d] | 637 | """ |
---|
[7cf2cfd] | 638 | column_width = max(len(w) for w in L) + 1 |
---|
| 639 | num_columns = (width - len(indent)) // column_width |
---|
| 640 | num_rows = len(L) // num_columns |
---|
| 641 | L = L + [""] * (num_rows*num_columns - len(L)) |
---|
| 642 | columns = [L[k*num_rows:(k+1)*num_rows] for k in range(num_columns)] |
---|
| 643 | lines = [" ".join("%-*s"%(column_width, entry) for entry in row) |
---|
| 644 | for row in zip(*columns)] |
---|
| 645 | output = indent + ("\n"+indent).join(lines) |
---|
| 646 | return output |
---|
| 647 | |
---|
| 648 | |
---|
[cd3dba0] | 649 | def get_demo_pars(model_definition): |
---|
[caeb06d] | 650 | """ |
---|
| 651 | Extract demo parameters from the model definition. |
---|
| 652 | """ |
---|
[cd3dba0] | 653 | info = generate.make_info(model_definition) |
---|
[ec7e360] | 654 | # Get the default values for the parameters |
---|
[9cfcac8] | 655 | pars = dict((p[0], p[2]) for p in info['parameters']) |
---|
[ec7e360] | 656 | |
---|
| 657 | # Fill in default values for the polydispersity parameters |
---|
| 658 | for p in info['parameters']: |
---|
| 659 | if p[4] in ('volume', 'orientation'): |
---|
| 660 | pars[p[0]+'_pd'] = 0.0 |
---|
| 661 | pars[p[0]+'_pd_n'] = 0 |
---|
| 662 | pars[p[0]+'_pd_nsigma'] = 3.0 |
---|
| 663 | pars[p[0]+'_pd_type'] = "gaussian" |
---|
| 664 | |
---|
| 665 | # Plug in values given in demo |
---|
[cd3dba0] | 666 | pars.update(info['demo']) |
---|
[373d1b6] | 667 | return pars |
---|
| 668 | |
---|
[ec7e360] | 669 | def parse_opts(): |
---|
[caeb06d] | 670 | """ |
---|
| 671 | Parse command line options. |
---|
| 672 | """ |
---|
| 673 | flags = [arg for arg in sys.argv[1:] |
---|
| 674 | if arg.startswith('-')] |
---|
| 675 | values = [arg for arg in sys.argv[1:] |
---|
| 676 | if not arg.startswith('-') and '=' in arg] |
---|
| 677 | args = [arg for arg in sys.argv[1:] |
---|
| 678 | if not arg.startswith('-') and '=' not in arg] |
---|
[d547f16] | 679 | models = "\n ".join("%-15s"%v for v in MODELS) |
---|
[87985ca] | 680 | if len(args) == 0: |
---|
[7cf2cfd] | 681 | print(USAGE) |
---|
[caeb06d] | 682 | print("\nAvailable models:") |
---|
[7cf2cfd] | 683 | print(columnize(MODELS, indent=" ")) |
---|
[87985ca] | 684 | sys.exit(1) |
---|
| 685 | if args[0] not in MODELS: |
---|
[caeb06d] | 686 | print("Model %r not available. Use one of:\n %s"%(args[0], models)) |
---|
[87985ca] | 687 | sys.exit(1) |
---|
[319ab14] | 688 | if len(args) > 3: |
---|
[9cfcac8] | 689 | print("expected parameters: model N1 N2") |
---|
[87985ca] | 690 | |
---|
[ec7e360] | 691 | invalid = [o[1:] for o in flags |
---|
[216a9e1] | 692 | if o[1:] not in NAME_OPTIONS |
---|
[d15a908] | 693 | and not any(o.startswith('-%s='%t) for t in VALUE_OPTIONS)] |
---|
[87985ca] | 694 | if invalid: |
---|
[9404dd3] | 695 | print("Invalid options: %s"%(", ".join(invalid))) |
---|
[87985ca] | 696 | sys.exit(1) |
---|
| 697 | |
---|
[ec7e360] | 698 | |
---|
[d15a908] | 699 | # pylint: disable=bad-whitespace |
---|
[ec7e360] | 700 | # Interpret the flags |
---|
| 701 | opts = { |
---|
| 702 | 'plot' : True, |
---|
| 703 | 'view' : 'log', |
---|
| 704 | 'is2d' : False, |
---|
| 705 | 'qmax' : 0.05, |
---|
| 706 | 'nq' : 128, |
---|
| 707 | 'res' : 0.0, |
---|
| 708 | 'accuracy' : 'Low', |
---|
| 709 | 'cutoff' : 1e-5, |
---|
| 710 | 'seed' : -1, # default to preset |
---|
| 711 | 'mono' : False, |
---|
| 712 | 'show_pars' : False, |
---|
| 713 | 'show_hist' : False, |
---|
| 714 | 'rel_err' : True, |
---|
| 715 | 'explore' : False, |
---|
| 716 | } |
---|
| 717 | engines = [] |
---|
| 718 | for arg in flags: |
---|
| 719 | if arg == '-noplot': opts['plot'] = False |
---|
| 720 | elif arg == '-plot': opts['plot'] = True |
---|
| 721 | elif arg == '-linear': opts['view'] = 'linear' |
---|
| 722 | elif arg == '-log': opts['view'] = 'log' |
---|
| 723 | elif arg == '-q4': opts['view'] = 'q4' |
---|
| 724 | elif arg == '-1d': opts['is2d'] = False |
---|
| 725 | elif arg == '-2d': opts['is2d'] = True |
---|
| 726 | elif arg == '-exq': opts['qmax'] = 10.0 |
---|
| 727 | elif arg == '-highq': opts['qmax'] = 1.0 |
---|
| 728 | elif arg == '-midq': opts['qmax'] = 0.2 |
---|
[ce0b154] | 729 | elif arg == '-lowq': opts['qmax'] = 0.05 |
---|
[ec7e360] | 730 | elif arg.startswith('-nq='): opts['nq'] = int(arg[4:]) |
---|
| 731 | elif arg.startswith('-res='): opts['res'] = float(arg[5:]) |
---|
| 732 | elif arg.startswith('-accuracy='): opts['accuracy'] = arg[10:] |
---|
| 733 | elif arg.startswith('-cutoff='): opts['cutoff'] = float(arg[8:]) |
---|
| 734 | elif arg.startswith('-random='): opts['seed'] = int(arg[8:]) |
---|
| 735 | elif arg == '-random': opts['seed'] = np.random.randint(1e6) |
---|
| 736 | elif arg == '-preset': opts['seed'] = -1 |
---|
| 737 | elif arg == '-mono': opts['mono'] = True |
---|
| 738 | elif arg == '-poly': opts['mono'] = False |
---|
| 739 | elif arg == '-pars': opts['show_pars'] = True |
---|
| 740 | elif arg == '-nopars': opts['show_pars'] = False |
---|
| 741 | elif arg == '-hist': opts['show_hist'] = True |
---|
| 742 | elif arg == '-nohist': opts['show_hist'] = False |
---|
| 743 | elif arg == '-rel': opts['rel_err'] = True |
---|
| 744 | elif arg == '-abs': opts['rel_err'] = False |
---|
| 745 | elif arg == '-half': engines.append(arg[1:]) |
---|
| 746 | elif arg == '-fast': engines.append(arg[1:]) |
---|
| 747 | elif arg == '-single': engines.append(arg[1:]) |
---|
| 748 | elif arg == '-double': engines.append(arg[1:]) |
---|
| 749 | elif arg == '-single!': engines.append(arg[1:]) |
---|
| 750 | elif arg == '-double!': engines.append(arg[1:]) |
---|
| 751 | elif arg == '-quad!': engines.append(arg[1:]) |
---|
| 752 | elif arg == '-sasview': engines.append(arg[1:]) |
---|
| 753 | elif arg == '-edit': opts['explore'] = True |
---|
[d15a908] | 754 | # pylint: enable=bad-whitespace |
---|
[ec7e360] | 755 | |
---|
| 756 | if len(engines) == 0: |
---|
[9cfcac8] | 757 | engines.extend(['single', 'sasview']) |
---|
[ec7e360] | 758 | elif len(engines) == 1: |
---|
| 759 | if engines[0][0] != 'sasview': |
---|
| 760 | engines.append('sasview') |
---|
| 761 | else: |
---|
| 762 | engines.append('single') |
---|
| 763 | elif len(engines) > 2: |
---|
| 764 | del engines[2:] |
---|
| 765 | |
---|
[d547f16] | 766 | name = args[0] |
---|
[cd3dba0] | 767 | model_definition = core.load_model_definition(name) |
---|
[d547f16] | 768 | |
---|
[9cfcac8] | 769 | n1 = int(args[1]) if len(args) > 1 else 1 |
---|
| 770 | n2 = int(args[2]) if len(args) > 2 else 1 |
---|
[87985ca] | 771 | |
---|
[ec7e360] | 772 | # Get demo parameters from model definition, or use default parameters |
---|
| 773 | # if model does not define demo parameters |
---|
| 774 | pars = get_demo_pars(model_definition) |
---|
[87985ca] | 775 | |
---|
| 776 | # Fill in parameters given on the command line |
---|
[ec7e360] | 777 | presets = {} |
---|
| 778 | for arg in values: |
---|
[d15a908] | 779 | k, v = arg.split('=', 1) |
---|
[87985ca] | 780 | if k not in pars: |
---|
[ec7e360] | 781 | # extract base name without polydispersity info |
---|
[87985ca] | 782 | s = set(p.split('_pd')[0] for p in pars) |
---|
[d15a908] | 783 | print("%r invalid; parameters are: %s"%(k, ", ".join(sorted(s)))) |
---|
[87985ca] | 784 | sys.exit(1) |
---|
[ec7e360] | 785 | presets[k] = float(v) if not k.endswith('type') else v |
---|
| 786 | |
---|
| 787 | # randomize parameters |
---|
| 788 | #pars.update(set_pars) # set value before random to control range |
---|
| 789 | if opts['seed'] > -1: |
---|
| 790 | pars = randomize_pars(pars, seed=opts['seed']) |
---|
| 791 | print("Randomize using -random=%i"%opts['seed']) |
---|
[8b25ee1] | 792 | if opts['mono']: |
---|
| 793 | pars = suppress_pd(pars) |
---|
[ec7e360] | 794 | pars.update(presets) # set value after random to control value |
---|
| 795 | constrain_pars(model_definition, pars) |
---|
| 796 | constrain_new_to_old(model_definition, pars) |
---|
| 797 | if opts['show_pars']: |
---|
[a4a7308] | 798 | print(str(parlist(pars))) |
---|
[ec7e360] | 799 | |
---|
| 800 | # Create the computational engines |
---|
[d15a908] | 801 | data, _ = make_data(opts) |
---|
[9cfcac8] | 802 | if n1: |
---|
[ec7e360] | 803 | base = make_engine(model_definition, data, engines[0], opts['cutoff']) |
---|
| 804 | else: |
---|
| 805 | base = None |
---|
[9cfcac8] | 806 | if n2: |
---|
[ec7e360] | 807 | comp = make_engine(model_definition, data, engines[1], opts['cutoff']) |
---|
| 808 | else: |
---|
| 809 | comp = None |
---|
| 810 | |
---|
[d15a908] | 811 | # pylint: disable=bad-whitespace |
---|
[ec7e360] | 812 | # Remember it all |
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| 813 | opts.update({ |
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| 814 | 'name' : name, |
---|
| 815 | 'def' : model_definition, |
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[9cfcac8] | 816 | 'n1' : n1, |
---|
| 817 | 'n2' : n2, |
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[ec7e360] | 818 | 'presets' : presets, |
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| 819 | 'pars' : pars, |
---|
| 820 | 'data' : data, |
---|
| 821 | 'engines' : [base, comp], |
---|
| 822 | }) |
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[d15a908] | 823 | # pylint: enable=bad-whitespace |
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[ec7e360] | 824 | |
---|
| 825 | return opts |
---|
| 826 | |
---|
| 827 | def explore(opts): |
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[d15a908] | 828 | """ |
---|
| 829 | Explore the model using the Bumps GUI. |
---|
| 830 | """ |
---|
[ec7e360] | 831 | import wx |
---|
| 832 | from bumps.names import FitProblem |
---|
| 833 | from bumps.gui.app_frame import AppFrame |
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| 834 | |
---|
| 835 | problem = FitProblem(Explore(opts)) |
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[d15a908] | 836 | is_mac = "cocoa" in wx.version() |
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[ec7e360] | 837 | app = wx.App() |
---|
| 838 | frame = AppFrame(parent=None, title="explore") |
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[d15a908] | 839 | if not is_mac: frame.Show() |
---|
[ec7e360] | 840 | frame.panel.set_model(model=problem) |
---|
| 841 | frame.panel.Layout() |
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| 842 | frame.panel.aui.Split(0, wx.TOP) |
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[d15a908] | 843 | if is_mac: frame.Show() |
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[ec7e360] | 844 | app.MainLoop() |
---|
| 845 | |
---|
| 846 | class Explore(object): |
---|
| 847 | """ |
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[d15a908] | 848 | Bumps wrapper for a SAS model comparison. |
---|
| 849 | |
---|
| 850 | The resulting object can be used as a Bumps fit problem so that |
---|
| 851 | parameters can be adjusted in the GUI, with plots updated on the fly. |
---|
[ec7e360] | 852 | """ |
---|
| 853 | def __init__(self, opts): |
---|
| 854 | from bumps.cli import config_matplotlib |
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[608e31e] | 855 | from . import bumps_model |
---|
[ec7e360] | 856 | config_matplotlib() |
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| 857 | self.opts = opts |
---|
| 858 | info = generate.make_info(opts['def']) |
---|
| 859 | pars, pd_types = bumps_model.create_parameters(info, **opts['pars']) |
---|
| 860 | if not opts['is2d']: |
---|
| 861 | active = [base + ext |
---|
| 862 | for base in info['partype']['pd-1d'] |
---|
[608e31e] | 863 | for ext in ['', '_pd', '_pd_n', '_pd_nsigma']] |
---|
[ec7e360] | 864 | active.extend(info['partype']['fixed-1d']) |
---|
| 865 | for k in active: |
---|
| 866 | v = pars[k] |
---|
| 867 | v.range(*parameter_range(k, v.value)) |
---|
| 868 | else: |
---|
[013adb7] | 869 | for k, v in pars.items(): |
---|
[ec7e360] | 870 | v.range(*parameter_range(k, v.value)) |
---|
| 871 | |
---|
| 872 | self.pars = pars |
---|
| 873 | self.pd_types = pd_types |
---|
[013adb7] | 874 | self.limits = None |
---|
[ec7e360] | 875 | |
---|
| 876 | def numpoints(self): |
---|
| 877 | """ |
---|
[608e31e] | 878 | Return the number of points. |
---|
[ec7e360] | 879 | """ |
---|
| 880 | return len(self.pars) + 1 # so dof is 1 |
---|
| 881 | |
---|
| 882 | def parameters(self): |
---|
| 883 | """ |
---|
[608e31e] | 884 | Return a dictionary of parameters. |
---|
[ec7e360] | 885 | """ |
---|
| 886 | return self.pars |
---|
| 887 | |
---|
| 888 | def nllf(self): |
---|
[608e31e] | 889 | """ |
---|
| 890 | Return cost. |
---|
| 891 | """ |
---|
[d15a908] | 892 | # pylint: disable=no-self-use |
---|
[ec7e360] | 893 | return 0. # No nllf |
---|
| 894 | |
---|
| 895 | def plot(self, view='log'): |
---|
| 896 | """ |
---|
| 897 | Plot the data and residuals. |
---|
| 898 | """ |
---|
[608e31e] | 899 | pars = dict((k, v.value) for k, v in self.pars.items()) |
---|
[ec7e360] | 900 | pars.update(self.pd_types) |
---|
| 901 | self.opts['pars'] = pars |
---|
[013adb7] | 902 | limits = compare(self.opts, limits=self.limits) |
---|
| 903 | if self.limits is None: |
---|
| 904 | vmin, vmax = limits |
---|
| 905 | vmax = 1.3*vmax |
---|
| 906 | vmin = vmax*1e-7 |
---|
| 907 | self.limits = vmin, vmax |
---|
[87985ca] | 908 | |
---|
| 909 | |
---|
[d15a908] | 910 | def main(): |
---|
| 911 | """ |
---|
| 912 | Main program. |
---|
| 913 | """ |
---|
| 914 | opts = parse_opts() |
---|
| 915 | if opts['explore']: |
---|
| 916 | explore(opts) |
---|
| 917 | else: |
---|
| 918 | compare(opts) |
---|
| 919 | |
---|
[8a20be5] | 920 | if __name__ == "__main__": |
---|
[87985ca] | 921 | main() |
---|