| 1 | #!/usr/bin/env python |
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| 2 | # -*- coding: utf-8 -*- |
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| 3 | |
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| 4 | import sys |
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| 5 | import math |
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| 6 | from os.path import basename, dirname, join as joinpath |
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| 7 | import glob |
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| 8 | import datetime |
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| 9 | import traceback |
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| 10 | |
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| 11 | import numpy as np |
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| 12 | |
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| 13 | ROOT = dirname(__file__) |
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| 14 | sys.path.insert(0, ROOT) # Make sure sasmodels is first on the path |
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| 15 | |
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| 16 | |
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| 17 | from . import core |
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| 18 | from . import kerneldll |
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| 19 | from . import generate |
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| 20 | from .data import plot_theory, empty_data1D, empty_data2D |
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| 21 | from .direct_model import DirectModel |
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| 22 | from .convert import revert_model, constrain_new_to_old |
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| 23 | kerneldll.ALLOW_SINGLE_PRECISION_DLLS = True |
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| 24 | |
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| 25 | # List of available models |
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| 26 | MODELS = [basename(f)[:-3] |
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| 27 | for f in sorted(glob.glob(joinpath(ROOT,"models","[a-zA-Z]*.py")))] |
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| 28 | |
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| 29 | # CRUFT python 2.6 |
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| 30 | if not hasattr(datetime.timedelta, 'total_seconds'): |
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| 31 | def delay(dt): |
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| 32 | """Return number date-time delta as number seconds""" |
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| 33 | return dt.days * 86400 + dt.seconds + 1e-6 * dt.microseconds |
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| 34 | else: |
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| 35 | def delay(dt): |
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| 36 | """Return number date-time delta as number seconds""" |
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| 37 | return dt.total_seconds() |
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| 38 | |
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| 39 | |
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| 40 | def tic(): |
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| 41 | """ |
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| 42 | Timer function. |
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| 43 | |
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| 44 | Use "toc=tic()" to start the clock and "toc()" to measure |
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| 45 | a time interval. |
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| 46 | """ |
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| 47 | then = datetime.datetime.now() |
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| 48 | return lambda: delay(datetime.datetime.now() - then) |
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| 49 | |
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| 50 | |
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| 51 | def set_beam_stop(data, radius, outer=None): |
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| 52 | """ |
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| 53 | Add a beam stop of the given *radius*. If *outer*, make an annulus. |
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| 54 | |
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| 55 | Note: this function does not use the sasview package |
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| 56 | """ |
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| 57 | if hasattr(data, 'qx_data'): |
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| 58 | q = np.sqrt(data.qx_data**2 + data.qy_data**2) |
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| 59 | data.mask = (q < radius) |
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| 60 | if outer is not None: |
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| 61 | data.mask |= (q >= outer) |
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| 62 | else: |
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| 63 | data.mask = (data.x < radius) |
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| 64 | if outer is not None: |
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| 65 | data.mask |= (data.x >= outer) |
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| 66 | |
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| 67 | |
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| 68 | def parameter_range(p, v): |
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| 69 | """ |
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| 70 | Choose a parameter range based on parameter name and initial value. |
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| 71 | """ |
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| 72 | if p.endswith('_pd_n'): |
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| 73 | return [0, 100] |
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| 74 | elif p.endswith('_pd_nsigma'): |
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| 75 | return [0, 5] |
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| 76 | elif p.endswith('_pd_type'): |
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| 77 | return v |
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| 78 | elif any(s in p for s in ('theta','phi','psi')): |
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| 79 | # orientation in [-180,180], orientation pd in [0,45] |
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| 80 | if p.endswith('_pd'): |
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| 81 | return [0,45] |
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| 82 | else: |
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| 83 | return [-180, 180] |
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| 84 | elif 'sld' in p: |
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| 85 | return [-0.5, 10] |
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| 86 | elif p.endswith('_pd'): |
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| 87 | return [0, 1] |
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| 88 | elif p in ['background', 'scale']: |
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| 89 | return [0, 1e3] |
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| 90 | else: |
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| 91 | return [0, (2*v if v>0 else 1)] |
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| 92 | |
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| 93 | def _randomize_one(p, v): |
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| 94 | """ |
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| 95 | Randomizing parameter. |
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| 96 | """ |
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| 97 | if any(p.endswith(s) for s in ('_pd_n','_pd_nsigma','_pd_type')): |
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| 98 | return v |
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| 99 | else: |
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| 100 | return np.random.uniform(*parameter_range(p, v)) |
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| 101 | |
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| 102 | def randomize_pars(pars, seed=None): |
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| 103 | np.random.seed(seed) |
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| 104 | # Note: the sort guarantees order `of calls to random number generator |
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| 105 | pars = dict((p,_randomize_one(p,v)) |
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| 106 | for p,v in sorted(pars.items())) |
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| 107 | return pars |
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| 108 | |
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| 109 | def constrain_pars(model_definition, pars): |
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| 110 | """ |
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| 111 | Restrict parameters to valid values. |
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| 112 | """ |
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| 113 | name = model_definition.name |
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| 114 | if name == 'capped_cylinder' and pars['cap_radius'] < pars['radius']: |
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| 115 | pars['radius'],pars['cap_radius'] = pars['cap_radius'],pars['radius'] |
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| 116 | if name == 'barbell' and pars['bell_radius'] < pars['radius']: |
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| 117 | pars['radius'],pars['bell_radius'] = pars['bell_radius'],pars['radius'] |
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| 118 | |
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| 119 | # Limit guinier to an Rg such that Iq > 1e-30 (single precision cutoff) |
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| 120 | if name == 'guinier': |
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| 121 | #q_max = 0.2 # mid q maximum |
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| 122 | q_max = 1.0 # high q maximum |
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| 123 | rg_max = np.sqrt(90*np.log(10) + 3*np.log(pars['scale']))/q_max |
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| 124 | pars['rg'] = min(pars['rg'],rg_max) |
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| 125 | |
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| 126 | def parlist(pars): |
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| 127 | return "\n".join("%s: %s"%(p,v) for p,v in sorted(pars.items())) |
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| 128 | |
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| 129 | def suppress_pd(pars): |
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| 130 | """ |
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| 131 | Suppress theta_pd for now until the normalization is resolved. |
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| 132 | |
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| 133 | May also suppress complete polydispersity of the model to test |
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| 134 | models more quickly. |
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| 135 | """ |
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| 136 | pars = pars.copy() |
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| 137 | for p in pars: |
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| 138 | if p.endswith("_pd"): pars[p] = 0 |
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| 139 | return pars |
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| 140 | |
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| 141 | def eval_sasview(model_definition, data): |
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| 142 | # importing sas here so that the error message will be that sas failed to |
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| 143 | # import rather than the more obscure smear_selection not imported error |
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| 144 | import sas |
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| 145 | from sas.models.qsmearing import smear_selection |
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| 146 | |
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| 147 | # convert model parameters from sasmodel form to sasview form |
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| 148 | #print("old",sorted(pars.items())) |
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| 149 | modelname, pars = revert_model(model_definition, {}) |
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| 150 | #print("new",sorted(pars.items())) |
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| 151 | sas = __import__('sas.models.'+modelname) |
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| 152 | ModelClass = getattr(getattr(sas.models,modelname,None),modelname,None) |
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| 153 | if ModelClass is None: |
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| 154 | raise ValueError("could not find model %r in sas.models"%modelname) |
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| 155 | model = ModelClass() |
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| 156 | smearer = smear_selection(data, model=model) |
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| 157 | |
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| 158 | if hasattr(data, 'qx_data'): |
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| 159 | q = np.sqrt(data.qx_data**2 + data.qy_data**2) |
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| 160 | index = ((~data.mask) & (~np.isnan(data.data)) |
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| 161 | & (q >= data.qmin) & (q <= data.qmax)) |
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| 162 | if smearer is not None: |
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| 163 | smearer.model = model # because smear_selection has a bug |
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| 164 | smearer.accuracy = data.accuracy |
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| 165 | smearer.set_index(index) |
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| 166 | theory = lambda: smearer.get_value() |
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| 167 | else: |
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| 168 | theory = lambda: model.evalDistribution([data.qx_data[index], data.qy_data[index]]) |
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| 169 | elif smearer is not None: |
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| 170 | theory = lambda: smearer(model.evalDistribution(data.x)) |
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| 171 | else: |
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| 172 | theory = lambda: model.evalDistribution(data.x) |
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| 173 | |
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| 174 | def calculator(**pars): |
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| 175 | # paying for parameter conversion each time to keep life simple, if not fast |
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| 176 | _, pars = revert_model(model_definition, pars) |
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| 177 | for k,v in pars.items(): |
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| 178 | parts = k.split('.') # polydispersity components |
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| 179 | if len(parts) == 2: |
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| 180 | model.dispersion[parts[0]][parts[1]] = v |
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| 181 | else: |
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| 182 | model.setParam(k, v) |
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| 183 | return theory() |
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| 184 | |
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| 185 | calculator.engine = "sasview" |
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| 186 | return calculator |
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| 187 | |
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| 188 | DTYPE_MAP = { |
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| 189 | 'half': '16', |
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| 190 | 'fast': 'fast', |
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| 191 | 'single': '32', |
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| 192 | 'double': '64', |
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| 193 | 'quad': '128', |
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| 194 | 'f16': '16', |
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| 195 | 'f32': '32', |
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| 196 | 'f64': '64', |
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| 197 | 'longdouble': '128', |
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| 198 | } |
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| 199 | def eval_opencl(model_definition, data, dtype='single', cutoff=0.): |
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| 200 | try: |
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| 201 | model = core.load_model(model_definition, dtype=dtype, platform="ocl") |
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| 202 | except Exception as exc: |
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| 203 | print(exc) |
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| 204 | print("... trying again with single precision") |
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| 205 | dtype = 'single' |
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| 206 | model = core.load_model(model_definition, dtype=dtype, platform="ocl") |
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| 207 | calculator = DirectModel(data, model, cutoff=cutoff) |
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| 208 | calculator.engine = "OCL%s"%DTYPE_MAP[dtype] |
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| 209 | return calculator |
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| 210 | |
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| 211 | def eval_ctypes(model_definition, data, dtype='double', cutoff=0.): |
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| 212 | if dtype=='quad': |
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| 213 | dtype = 'longdouble' |
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| 214 | model = core.load_model(model_definition, dtype=dtype, platform="dll") |
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| 215 | calculator = DirectModel(data, model, cutoff=cutoff) |
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| 216 | calculator.engine = "OMP%s"%DTYPE_MAP[dtype] |
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| 217 | return calculator |
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| 218 | |
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| 219 | def time_calculation(calculator, pars, Nevals=1): |
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| 220 | # initialize the code so time is more accurate |
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| 221 | value = calculator(**suppress_pd(pars)) |
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| 222 | toc = tic() |
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| 223 | for _ in range(max(Nevals, 1)): # make sure there is at least one eval |
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| 224 | value = calculator(**pars) |
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| 225 | average_time = toc()*1000./Nevals |
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| 226 | return value, average_time |
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| 227 | |
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| 228 | def make_data(opts): |
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| 229 | qmax, nq, res = opts['qmax'], opts['nq'], opts['res'] |
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| 230 | if opts['is2d']: |
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| 231 | data = empty_data2D(np.linspace(-qmax, qmax, nq), resolution=res) |
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| 232 | data.accuracy = opts['accuracy'] |
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| 233 | set_beam_stop(data, 0.004) |
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| 234 | index = ~data.mask |
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| 235 | else: |
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| 236 | if opts['view'] == 'log': |
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| 237 | qmax = math.log10(qmax) |
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| 238 | q = np.logspace(qmax-3, qmax, nq) |
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| 239 | else: |
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| 240 | q = np.linspace(0.001*qmax, qmax, nq) |
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| 241 | data = empty_data1D(q, resolution=res) |
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| 242 | index = slice(None, None) |
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| 243 | return data, index |
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| 244 | |
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| 245 | def make_engine(model_definition, data, dtype, cutoff): |
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| 246 | if dtype == 'sasview': |
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| 247 | return eval_sasview(model_definition, data) |
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| 248 | elif dtype.endswith('!'): |
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| 249 | return eval_ctypes(model_definition, data, dtype=dtype[:-1], |
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| 250 | cutoff=cutoff) |
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| 251 | else: |
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| 252 | return eval_opencl(model_definition, data, dtype=dtype, |
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| 253 | cutoff=cutoff) |
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| 254 | |
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| 255 | def compare(opts): |
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| 256 | Nbase, Ncomp = opts['N1'], opts['N2'] |
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| 257 | pars = opts['pars'] |
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| 258 | data = opts['data'] |
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| 259 | |
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| 260 | # Base calculation |
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| 261 | if Nbase > 0: |
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| 262 | base = opts['engines'][0] |
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| 263 | try: |
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| 264 | base_value, base_time = time_calculation(base, pars, Nbase) |
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| 265 | print("%s t=%.1f ms, intensity=%.0f"%(base.engine, base_time, sum(base_value))) |
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| 266 | except ImportError: |
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| 267 | traceback.print_exc() |
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| 268 | Nbase = 0 |
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| 269 | |
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| 270 | # Comparison calculation |
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| 271 | if Ncomp > 0: |
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| 272 | comp = opts['engines'][1] |
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| 273 | try: |
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| 274 | comp_value, comp_time = time_calculation(comp, pars, Ncomp) |
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| 275 | print("%s t=%.1f ms, intensity=%.0f"%(comp.engine, comp_time, sum(comp_value))) |
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| 276 | except ImportError: |
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| 277 | traceback.print_exc() |
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| 278 | Ncomp = 0 |
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| 279 | |
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| 280 | # Compare, but only if computing both forms |
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| 281 | if Nbase > 0 and Ncomp > 0: |
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| 282 | #print("speedup %.2g"%(comp_time/base_time)) |
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| 283 | #print("max |base/comp|", max(abs(base_value/comp_value)), "%.15g"%max(abs(base_value)), "%.15g"%max(abs(comp_value))) |
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| 284 | #comp *= max(base_value/comp_value) |
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| 285 | resid = (base_value - comp_value) |
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| 286 | relerr = resid/comp_value |
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| 287 | _print_stats("|%s - %s|"%(base.engine,comp.engine)+(" "*(3+len(comp.engine))), resid) |
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| 288 | _print_stats("|(%s - %s) / %s|"%(base.engine,comp.engine,comp.engine), relerr) |
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| 289 | |
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| 290 | # Plot if requested |
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| 291 | if not opts['plot'] and not opts['explore']: return |
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| 292 | view = opts['view'] |
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| 293 | import matplotlib.pyplot as plt |
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| 294 | if Nbase > 0: |
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| 295 | if Ncomp > 0: plt.subplot(131) |
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| 296 | plot_theory(data, base_value, view=view, plot_data=False) |
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| 297 | plt.title("%s t=%.1f ms"%(base.engine, base_time)) |
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| 298 | #cbar_title = "log I" |
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| 299 | if Ncomp > 0: |
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| 300 | if Nbase > 0: plt.subplot(132) |
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| 301 | plot_theory(data, comp_value, view=view, plot_data=False) |
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| 302 | plt.title("%s t=%.1f ms"%(comp.engine,comp_time)) |
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| 303 | #cbar_title = "log I" |
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| 304 | if Ncomp > 0 and Nbase > 0: |
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| 305 | plt.subplot(133) |
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| 306 | if '-abs' in opts: |
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| 307 | err,errstr,errview = resid, "abs err", "linear" |
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| 308 | else: |
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| 309 | err,errstr,errview = abs(relerr), "rel err", "log" |
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| 310 | #err,errstr = base/comp,"ratio" |
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| 311 | plot_theory(data, None, resid=err, view=errview, plot_data=False) |
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| 312 | plt.title("max %s = %.3g"%(errstr, max(abs(err)))) |
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| 313 | #cbar_title = errstr if errview=="linear" else "log "+errstr |
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| 314 | #if is2D: |
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| 315 | # h = plt.colorbar() |
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| 316 | # h.ax.set_title(cbar_title) |
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| 317 | |
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| 318 | if Ncomp > 0 and Nbase > 0 and '-hist' in opts: |
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| 319 | plt.figure() |
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| 320 | v = relerr |
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| 321 | v[v==0] = 0.5*np.min(np.abs(v[v!=0])) |
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| 322 | plt.hist(np.log10(np.abs(v)), normed=1, bins=50); |
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| 323 | plt.xlabel('log10(err), err = |(%s - %s) / %s|'%(base.engine, comp.engine, comp.engine)); |
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| 324 | plt.ylabel('P(err)') |
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| 325 | plt.title('Distribution of relative error between calculation engines') |
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| 326 | |
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| 327 | if not opts['explore']: |
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| 328 | plt.show() |
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| 329 | |
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| 330 | def _print_stats(label, err): |
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| 331 | sorted_err = np.sort(abs(err)) |
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| 332 | p50 = int((len(err)-1)*0.50) |
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| 333 | p98 = int((len(err)-1)*0.98) |
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| 334 | data = [ |
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| 335 | "max:%.3e"%sorted_err[-1], |
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| 336 | "median:%.3e"%sorted_err[p50], |
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| 337 | "98%%:%.3e"%sorted_err[p98], |
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| 338 | "rms:%.3e"%np.sqrt(np.mean(err**2)), |
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| 339 | "zero-offset:%+.3e"%np.mean(err), |
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| 340 | ] |
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| 341 | print(label+" ".join(data)) |
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| 342 | |
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| 343 | |
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| 344 | |
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| 345 | # =========================================================================== |
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| 346 | # |
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| 347 | USAGE=""" |
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| 348 | usage: compare.py model N1 N2 [options...] [key=val] |
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| 349 | |
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| 350 | Compare the speed and value for a model between the SasView original and the |
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| 351 | sasmodels rewrite. |
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| 352 | |
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| 353 | model is the name of the model to compare (see below). |
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| 354 | N1 is the number of times to run sasmodels (default=1). |
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| 355 | N2 is the number times to run sasview (default=1). |
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| 356 | |
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| 357 | Options (* for default): |
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| 358 | |
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| 359 | -plot*/-noplot plots or suppress the plot of the model |
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| 360 | -lowq*/-midq/-highq/-exq use q values up to 0.05, 0.2, 1.0, 10.0 |
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| 361 | -nq=128 sets the number of Q points in the data set |
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| 362 | -1d*/-2d computes 1d or 2d data |
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| 363 | -preset*/-random[=seed] preset or random parameters |
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| 364 | -mono/-poly* force monodisperse/polydisperse |
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| 365 | -cutoff=1e-5* cutoff value for including a point in polydispersity |
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| 366 | -pars/-nopars* prints the parameter set or not |
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| 367 | -abs/-rel* plot relative or absolute error |
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| 368 | -linear/-log*/-q4 intensity scaling |
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| 369 | -hist/-nohist* plot histogram of relative error |
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| 370 | -res=0 sets the resolution width dQ/Q if calculating with resolution |
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| 371 | -accuracy=Low accuracy of the resolution calculation Low, Mid, High, Xhigh |
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| 372 | -edit starts the parameter explorer |
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| 373 | |
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| 374 | Any two calculation engines can be selected for comparison: |
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| 375 | |
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| 376 | -single/-double/-half/-fast sets an OpenCL calculation engine |
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| 377 | -single!/-double!/-quad! sets an OpenMP calculation engine |
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| 378 | -sasview sets the sasview calculation engine |
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| 379 | |
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| 380 | The default is -single -sasview. Note that the interpretation of quad |
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| 381 | precision depends on architecture, and may vary from 64-bit to 128-bit, |
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| 382 | with 80-bit floats being common (1e-19 precision). |
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| 383 | |
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| 384 | Key=value pairs allow you to set specific values for the model parameters. |
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| 385 | |
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| 386 | Available models: |
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| 387 | """ |
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| 388 | |
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| 389 | |
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| 390 | NAME_OPTIONS = set([ |
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| 391 | 'plot', 'noplot', |
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| 392 | 'half', 'fast', 'single', 'double', |
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| 393 | 'single!', 'double!', 'quad!', 'sasview', |
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| 394 | 'lowq', 'midq', 'highq', 'exq', |
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| 395 | '2d', '1d', |
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| 396 | 'preset', 'random', |
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| 397 | 'poly', 'mono', |
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| 398 | 'nopars', 'pars', |
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| 399 | 'rel', 'abs', |
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| 400 | 'linear', 'log', 'q4', |
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| 401 | 'hist', 'nohist', |
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| 402 | 'edit', |
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| 403 | ]) |
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| 404 | VALUE_OPTIONS = [ |
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| 405 | # Note: random is both a name option and a value option |
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| 406 | 'cutoff', 'random', 'nq', 'res', 'accuracy', |
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| 407 | ] |
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| 408 | |
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| 409 | def columnize(L, indent="", width=79): |
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| 410 | column_width = max(len(w) for w in L) + 1 |
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| 411 | num_columns = (width - len(indent)) // column_width |
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| 412 | num_rows = len(L) // num_columns |
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| 413 | L = L + [""] * (num_rows*num_columns - len(L)) |
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| 414 | columns = [L[k*num_rows:(k+1)*num_rows] for k in range(num_columns)] |
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| 415 | lines = [" ".join("%-*s"%(column_width, entry) for entry in row) |
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| 416 | for row in zip(*columns)] |
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| 417 | output = indent + ("\n"+indent).join(lines) |
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| 418 | return output |
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| 419 | |
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| 420 | |
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| 421 | def get_demo_pars(model_definition): |
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| 422 | info = generate.make_info(model_definition) |
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| 423 | # Get the default values for the parameters |
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| 424 | pars = dict((p[0],p[2]) for p in info['parameters']) |
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| 425 | |
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| 426 | # Fill in default values for the polydispersity parameters |
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| 427 | for p in info['parameters']: |
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| 428 | if p[4] in ('volume', 'orientation'): |
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| 429 | pars[p[0]+'_pd'] = 0.0 |
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| 430 | pars[p[0]+'_pd_n'] = 0 |
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| 431 | pars[p[0]+'_pd_nsigma'] = 3.0 |
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| 432 | pars[p[0]+'_pd_type'] = "gaussian" |
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| 433 | |
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| 434 | # Plug in values given in demo |
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| 435 | pars.update(info['demo']) |
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| 436 | return pars |
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| 437 | |
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| 438 | def parse_opts(): |
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| 439 | flags = [arg for arg in sys.argv[1:] if arg.startswith('-')] |
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| 440 | values = [arg for arg in sys.argv[1:] if not arg.startswith('-') and '=' in arg] |
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| 441 | args = [arg for arg in sys.argv[1:] if not arg.startswith('-') and '=' not in arg] |
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| 442 | models = "\n ".join("%-15s"%v for v in MODELS) |
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| 443 | if len(args) == 0: |
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| 444 | print(USAGE) |
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| 445 | print(columnize(MODELS, indent=" ")) |
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| 446 | sys.exit(1) |
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| 447 | if args[0] not in MODELS: |
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| 448 | print("Model %r not available. Use one of:\n %s"%(args[0],models)) |
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| 449 | sys.exit(1) |
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| 450 | if len(args) > 3: |
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| 451 | print("expected parameters: model Nopencl Nsasview") |
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| 452 | |
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| 453 | invalid = [o[1:] for o in flags |
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| 454 | if o[1:] not in NAME_OPTIONS |
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| 455 | and not any(o.startswith('-%s='%t) for t in VALUE_OPTIONS)] |
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| 456 | if invalid: |
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| 457 | print("Invalid options: %s"%(", ".join(invalid))) |
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| 458 | sys.exit(1) |
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| 459 | |
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| 460 | |
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| 461 | # Interpret the flags |
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| 462 | opts = { |
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| 463 | 'plot' : True, |
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| 464 | 'view' : 'log', |
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| 465 | 'is2d' : False, |
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| 466 | 'qmax' : 0.05, |
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| 467 | 'nq' : 128, |
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| 468 | 'res' : 0.0, |
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| 469 | 'accuracy' : 'Low', |
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| 470 | 'cutoff' : 1e-5, |
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| 471 | 'seed' : -1, # default to preset |
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| 472 | 'mono' : False, |
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| 473 | 'show_pars' : False, |
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| 474 | 'show_hist' : False, |
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| 475 | 'rel_err' : True, |
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| 476 | 'explore' : False, |
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| 477 | } |
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| 478 | engines = [] |
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| 479 | for arg in flags: |
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| 480 | if arg == '-noplot': opts['plot'] = False |
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| 481 | elif arg == '-plot': opts['plot'] = True |
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| 482 | elif arg == '-linear': opts['view'] = 'linear' |
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| 483 | elif arg == '-log': opts['view'] = 'log' |
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| 484 | elif arg == '-q4': opts['view'] = 'q4' |
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| 485 | elif arg == '-1d': opts['is2d'] = False |
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| 486 | elif arg == '-2d': opts['is2d'] = True |
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| 487 | elif arg == '-exq': opts['qmax'] = 10.0 |
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| 488 | elif arg == '-highq': opts['qmax'] = 1.0 |
|---|
| 489 | elif arg == '-midq': opts['qmax'] = 0.2 |
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| 490 | elif arg == '-loq': opts['qmax'] = 0.05 |
|---|
| 491 | elif arg.startswith('-nq='): opts['nq'] = int(arg[4:]) |
|---|
| 492 | elif arg.startswith('-res='): opts['res'] = float(arg[5:]) |
|---|
| 493 | elif arg.startswith('-accuracy='): opts['accuracy'] = arg[10:] |
|---|
| 494 | elif arg.startswith('-cutoff='): opts['cutoff'] = float(arg[8:]) |
|---|
| 495 | elif arg.startswith('-random='): opts['seed'] = int(arg[8:]) |
|---|
| 496 | elif arg == '-random': opts['seed'] = np.random.randint(1e6) |
|---|
| 497 | elif arg == '-preset': opts['seed'] = -1 |
|---|
| 498 | elif arg == '-mono': opts['mono'] = True |
|---|
| 499 | elif arg == '-poly': opts['mono'] = False |
|---|
| 500 | elif arg == '-pars': opts['show_pars'] = True |
|---|
| 501 | elif arg == '-nopars': opts['show_pars'] = False |
|---|
| 502 | elif arg == '-hist': opts['show_hist'] = True |
|---|
| 503 | elif arg == '-nohist': opts['show_hist'] = False |
|---|
| 504 | elif arg == '-rel': opts['rel_err'] = True |
|---|
| 505 | elif arg == '-abs': opts['rel_err'] = False |
|---|
| 506 | elif arg == '-half': engines.append(arg[1:]) |
|---|
| 507 | elif arg == '-fast': engines.append(arg[1:]) |
|---|
| 508 | elif arg == '-single': engines.append(arg[1:]) |
|---|
| 509 | elif arg == '-double': engines.append(arg[1:]) |
|---|
| 510 | elif arg == '-single!': engines.append(arg[1:]) |
|---|
| 511 | elif arg == '-double!': engines.append(arg[1:]) |
|---|
| 512 | elif arg == '-quad!': engines.append(arg[1:]) |
|---|
| 513 | elif arg == '-sasview': engines.append(arg[1:]) |
|---|
| 514 | elif arg == '-edit': opts['explore'] = True |
|---|
| 515 | |
|---|
| 516 | if len(engines) == 0: |
|---|
| 517 | engines.extend(['single','sasview']) |
|---|
| 518 | elif len(engines) == 1: |
|---|
| 519 | if engines[0][0] != 'sasview': |
|---|
| 520 | engines.append('sasview') |
|---|
| 521 | else: |
|---|
| 522 | engines.append('single') |
|---|
| 523 | elif len(engines) > 2: |
|---|
| 524 | del engines[2:] |
|---|
| 525 | |
|---|
| 526 | name = args[0] |
|---|
| 527 | model_definition = core.load_model_definition(name) |
|---|
| 528 | |
|---|
| 529 | N1 = int(args[1]) if len(args) > 1 else 1 |
|---|
| 530 | N2 = int(args[2]) if len(args) > 2 else 1 |
|---|
| 531 | |
|---|
| 532 | # Get demo parameters from model definition, or use default parameters |
|---|
| 533 | # if model does not define demo parameters |
|---|
| 534 | pars = get_demo_pars(model_definition) |
|---|
| 535 | |
|---|
| 536 | # Fill in parameters given on the command line |
|---|
| 537 | presets = {} |
|---|
| 538 | for arg in values: |
|---|
| 539 | k,v = arg.split('=',1) |
|---|
| 540 | if k not in pars: |
|---|
| 541 | # extract base name without polydispersity info |
|---|
| 542 | s = set(p.split('_pd')[0] for p in pars) |
|---|
| 543 | print("%r invalid; parameters are: %s"%(k,", ".join(sorted(s)))) |
|---|
| 544 | sys.exit(1) |
|---|
| 545 | presets[k] = float(v) if not k.endswith('type') else v |
|---|
| 546 | |
|---|
| 547 | # randomize parameters |
|---|
| 548 | #pars.update(set_pars) # set value before random to control range |
|---|
| 549 | if opts['seed'] > -1: |
|---|
| 550 | pars = randomize_pars(pars, seed=opts['seed']) |
|---|
| 551 | print("Randomize using -random=%i"%opts['seed']) |
|---|
| 552 | pars.update(presets) # set value after random to control value |
|---|
| 553 | constrain_pars(model_definition, pars) |
|---|
| 554 | constrain_new_to_old(model_definition, pars) |
|---|
| 555 | if opts['mono']: |
|---|
| 556 | pars = suppress_pd(pars) |
|---|
| 557 | if opts['show_pars']: |
|---|
| 558 | print("pars " + str(parlist(pars))) |
|---|
| 559 | |
|---|
| 560 | # Create the computational engines |
|---|
| 561 | data, _index = make_data(opts) |
|---|
| 562 | if N1: |
|---|
| 563 | base = make_engine(model_definition, data, engines[0], opts['cutoff']) |
|---|
| 564 | else: |
|---|
| 565 | base = None |
|---|
| 566 | if N2: |
|---|
| 567 | comp = make_engine(model_definition, data, engines[1], opts['cutoff']) |
|---|
| 568 | else: |
|---|
| 569 | comp = None |
|---|
| 570 | |
|---|
| 571 | # Remember it all |
|---|
| 572 | opts.update({ |
|---|
| 573 | 'name' : name, |
|---|
| 574 | 'def' : model_definition, |
|---|
| 575 | 'N1' : N1, |
|---|
| 576 | 'N2' : N2, |
|---|
| 577 | 'presets' : presets, |
|---|
| 578 | 'pars' : pars, |
|---|
| 579 | 'data' : data, |
|---|
| 580 | 'engines' : [base, comp], |
|---|
| 581 | }) |
|---|
| 582 | |
|---|
| 583 | return opts |
|---|
| 584 | |
|---|
| 585 | def main(): |
|---|
| 586 | opts = parse_opts() |
|---|
| 587 | if opts['explore']: |
|---|
| 588 | explore(opts) |
|---|
| 589 | else: |
|---|
| 590 | compare(opts) |
|---|
| 591 | |
|---|
| 592 | def explore(opts): |
|---|
| 593 | import wx |
|---|
| 594 | from bumps.names import FitProblem |
|---|
| 595 | from bumps.gui.app_frame import AppFrame |
|---|
| 596 | |
|---|
| 597 | problem = FitProblem(Explore(opts)) |
|---|
| 598 | isMac = "cocoa" in wx.version() |
|---|
| 599 | app = wx.App() |
|---|
| 600 | frame = AppFrame(parent=None, title="explore") |
|---|
| 601 | if not isMac: frame.Show() |
|---|
| 602 | frame.panel.set_model(model=problem) |
|---|
| 603 | frame.panel.Layout() |
|---|
| 604 | frame.panel.aui.Split(0, wx.TOP) |
|---|
| 605 | if isMac: frame.Show() |
|---|
| 606 | app.MainLoop() |
|---|
| 607 | |
|---|
| 608 | class Explore(object): |
|---|
| 609 | """ |
|---|
| 610 | Return a bumps wrapper for a SAS model comparison. |
|---|
| 611 | """ |
|---|
| 612 | def __init__(self, opts): |
|---|
| 613 | from bumps.cli import config_matplotlib |
|---|
| 614 | import bumps_model |
|---|
| 615 | config_matplotlib() |
|---|
| 616 | self.opts = opts |
|---|
| 617 | info = generate.make_info(opts['def']) |
|---|
| 618 | pars, pd_types = bumps_model.create_parameters(info, **opts['pars']) |
|---|
| 619 | if not opts['is2d']: |
|---|
| 620 | active = [base + ext |
|---|
| 621 | for base in info['partype']['pd-1d'] |
|---|
| 622 | for ext in ['','_pd','_pd_n','_pd_nsigma']] |
|---|
| 623 | active.extend(info['partype']['fixed-1d']) |
|---|
| 624 | for k in active: |
|---|
| 625 | v = pars[k] |
|---|
| 626 | v.range(*parameter_range(k, v.value)) |
|---|
| 627 | else: |
|---|
| 628 | for k, v in self.pars.items(): |
|---|
| 629 | v.range(*parameter_range(k, v.value)) |
|---|
| 630 | |
|---|
| 631 | self.pars = pars |
|---|
| 632 | self.pd_types = pd_types |
|---|
| 633 | |
|---|
| 634 | def numpoints(self): |
|---|
| 635 | """ |
|---|
| 636 | Return the number of points |
|---|
| 637 | """ |
|---|
| 638 | return len(self.pars) + 1 # so dof is 1 |
|---|
| 639 | |
|---|
| 640 | def parameters(self): |
|---|
| 641 | """ |
|---|
| 642 | Return a dictionary of parameters |
|---|
| 643 | """ |
|---|
| 644 | return self.pars |
|---|
| 645 | |
|---|
| 646 | def nllf(self): |
|---|
| 647 | return 0. # No nllf |
|---|
| 648 | |
|---|
| 649 | def plot(self, view='log'): |
|---|
| 650 | """ |
|---|
| 651 | Plot the data and residuals. |
|---|
| 652 | """ |
|---|
| 653 | pars = dict((k, v.value) for k,v in self.pars.items()) |
|---|
| 654 | pars.update(self.pd_types) |
|---|
| 655 | self.opts['pars'] = pars |
|---|
| 656 | compare(self.opts) |
|---|
| 657 | |
|---|
| 658 | |
|---|
| 659 | if __name__ == "__main__": |
|---|
| 660 | main() |
|---|
