| 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 | |
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| 9 | import numpy as np |
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| 10 | |
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| 11 | from sasmodels.bumps_model import Model, Experiment, plot_theory, tic |
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| 12 | from sasmodels import core |
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| 13 | from sasmodels import kerneldll |
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| 14 | from sasmodels.convert import revert_model |
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| 15 | kerneldll.ALLOW_SINGLE_PRECISION_DLLS = True |
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| 16 | |
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| 17 | # List of available models |
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| 18 | ROOT = dirname(__file__) |
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| 19 | MODELS = [basename(f)[:-3] |
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| 20 | for f in sorted(glob.glob(joinpath(ROOT,"sasmodels","models","[a-zA-Z]*.py")))] |
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| 21 | |
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| 22 | |
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| 23 | def sasview_model(model_definition, **pars): |
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| 24 | """ |
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| 25 | Load a sasview model given the model name. |
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| 26 | """ |
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| 27 | # convert model parameters from sasmodel form to sasview form |
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| 28 | #print "old",sorted(pars.items()) |
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| 29 | modelname, pars = revert_model(model_definition, pars) |
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| 30 | #print "new",sorted(pars.items()) |
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| 31 | sas = __import__('sas.models.'+modelname) |
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| 32 | ModelClass = getattr(getattr(sas.models,modelname,None),modelname,None) |
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| 33 | if ModelClass is None: |
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| 34 | raise ValueError("could not find model %r in sas.models"%modelname) |
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| 35 | model = ModelClass() |
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| 36 | |
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| 37 | for k,v in pars.items(): |
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| 38 | if k.endswith("_pd"): |
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| 39 | model.dispersion[k[:-3]]['width'] = v |
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| 40 | elif k.endswith("_pd_n"): |
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| 41 | model.dispersion[k[:-5]]['npts'] = v |
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| 42 | elif k.endswith("_pd_nsigma"): |
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| 43 | model.dispersion[k[:-10]]['nsigmas'] = v |
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| 44 | elif k.endswith("_pd_type"): |
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| 45 | model.dispersion[k[:-8]]['type'] = v |
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| 46 | else: |
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| 47 | model.setParam(k, v) |
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| 48 | return model |
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| 49 | |
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| 50 | def randomize(p, v): |
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| 51 | """ |
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| 52 | Randomizing parameter. |
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| 53 | |
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| 54 | Guess the parameter type from name. |
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| 55 | """ |
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| 56 | if any(p.endswith(s) for s in ('_pd_n','_pd_nsigma','_pd_type')): |
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| 57 | return v |
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| 58 | elif any(s in p for s in ('theta','phi','psi')): |
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| 59 | # orientation in [-180,180], orientation pd in [0,45] |
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| 60 | if p.endswith('_pd'): |
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| 61 | return 45*np.random.rand() |
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| 62 | else: |
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| 63 | return 360*np.random.rand() - 180 |
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| 64 | elif 'sld' in p: |
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| 65 | # sld in in [-0.5,10] |
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| 66 | return 10.5*np.random.rand() - 0.5 |
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| 67 | elif p.endswith('_pd'): |
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| 68 | # length pd in [0,1] |
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| 69 | return np.random.rand() |
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| 70 | else: |
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| 71 | # values from 0 to 2*x for all other parameters |
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| 72 | return 2*np.random.rand()*(v if v != 0 else 1) |
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| 73 | |
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| 74 | def randomize_model(name, pars, seed=None): |
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| 75 | if seed is None: |
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| 76 | seed = np.random.randint(1e9) |
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| 77 | np.random.seed(seed) |
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| 78 | # Note: the sort guarantees order of calls to random number generator |
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| 79 | pars = dict((p,randomize(p,v)) for p,v in sorted(pars.items())) |
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| 80 | # The capped cylinder model has a constraint on its parameters |
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| 81 | if name == 'capped_cylinder' and pars['cap_radius'] < pars['radius']: |
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| 82 | pars['radius'],pars['cap_radius'] = pars['cap_radius'],pars['radius'] |
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| 83 | return pars, seed |
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| 84 | |
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| 85 | def parlist(pars): |
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| 86 | return "\n".join("%s: %s"%(p,v) for p,v in sorted(pars.items())) |
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| 87 | |
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| 88 | def suppress_pd(pars): |
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| 89 | """ |
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| 90 | Suppress theta_pd for now until the normalization is resolved. |
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| 91 | |
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| 92 | May also suppress complete polydispersity of the model to test |
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| 93 | models more quickly. |
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| 94 | """ |
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| 95 | for p in pars: |
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| 96 | if p.endswith("_pd"): pars[p] = 0 |
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| 97 | |
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| 98 | def eval_sasview(name, pars, data, Nevals=1): |
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| 99 | from sas.models.qsmearing import smear_selection |
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| 100 | model = sasview_model(name, **pars) |
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| 101 | smearer = smear_selection(data, model=model) |
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| 102 | toc = tic() |
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| 103 | for _ in range(Nevals): |
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| 104 | if hasattr(data, 'qx_data'): |
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| 105 | q = np.sqrt(data.qx_data**2 + data.qy_data**2) |
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| 106 | index = ((~data.mask) & (~np.isnan(data.data)) |
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| 107 | & (q >= data.qmin) & (q <= data.qmax)) |
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| 108 | if smearer is not None: |
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| 109 | smearer.model = model # because smear_selection has a bug |
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| 110 | smearer.accuracy = data.accuracy |
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| 111 | smearer.set_index(index) |
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| 112 | value = smearer.get_value() |
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| 113 | else: |
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| 114 | value = model.evalDistribution([data.qx_data[index], data.qy_data[index]]) |
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| 115 | else: |
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| 116 | value = model.evalDistribution(data.x) |
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| 117 | if smearer is not None: |
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| 118 | value = smearer(value) |
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| 119 | average_time = toc()*1000./Nevals |
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| 120 | return value, average_time |
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| 121 | |
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| 122 | def eval_opencl(model_definition, pars, data, dtype='single', Nevals=1, cutoff=0): |
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| 123 | try: |
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| 124 | model = core.load_model(model_definition, dtype=dtype, platform="ocl") |
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| 125 | except Exception,exc: |
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| 126 | print exc |
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| 127 | print "... trying again with single precision" |
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| 128 | model = core.load_model(model_definition, dtype='single', platform="ocl") |
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| 129 | problem = Experiment(data, Model(model, **pars), cutoff=cutoff) |
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| 130 | toc = tic() |
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| 131 | for _ in range(Nevals): |
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| 132 | #pars['scale'] = np.random.rand() |
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| 133 | problem.update() |
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| 134 | value = problem.theory() |
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| 135 | average_time = toc()*1000./Nevals |
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| 136 | return value, average_time |
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| 137 | |
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| 138 | def eval_ctypes(model_definition, pars, data, dtype='double', Nevals=1, cutoff=0): |
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| 139 | model = core.load_model(model_definition, dtype=dtype, platform="dll") |
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| 140 | problem = Experiment(data, Model(model, **pars), cutoff=cutoff) |
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| 141 | toc = tic() |
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| 142 | for _ in range(Nevals): |
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| 143 | problem.update() |
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| 144 | value = problem.theory() |
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| 145 | average_time = toc()*1000./Nevals |
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| 146 | return value, average_time |
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| 147 | |
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| 148 | def make_data(qmax, is2D, Nq=128, resolution=0.0, accuracy='Low', view='log'): |
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| 149 | if is2D: |
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| 150 | from sasmodels.bumps_model import empty_data2D, set_beam_stop |
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| 151 | data = empty_data2D(np.linspace(-qmax, qmax, Nq), resolution=resolution) |
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| 152 | data.accuracy = accuracy |
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| 153 | set_beam_stop(data, 0.004) |
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| 154 | index = ~data.mask |
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| 155 | else: |
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| 156 | from sasmodels.bumps_model import empty_data1D |
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| 157 | if view == 'log': |
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| 158 | qmax = math.log10(qmax) |
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| 159 | q = np.logspace(qmax-3, qmax, Nq) |
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| 160 | else: |
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| 161 | q = np.linspace(0.001*qmax, qmax, Nq) |
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| 162 | data = empty_data1D(q, resolution=resolution) |
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| 163 | index = slice(None, None) |
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| 164 | return data, index |
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| 165 | |
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| 166 | def compare(name, pars, Ncpu, Nocl, opts, set_pars): |
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| 167 | view = 'linear' if '-linear' in opts else 'log' if '-log' in opts else 'q4' if '-q4' in opts else 'log' |
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| 168 | |
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| 169 | opt_values = dict(split |
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| 170 | for s in opts for split in ((s.split('='),)) |
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| 171 | if len(split) == 2) |
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| 172 | # Sort out data |
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| 173 | qmax = 10.0 if '-exq' in opts else 1.0 if '-highq' in opts else 0.2 if '-midq' in opts else 0.05 |
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| 174 | Nq = int(opt_values.get('-Nq', '128')) |
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| 175 | res = float(opt_values.get('-res', '0')) |
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| 176 | accuracy = opt_values.get('-accuracy', 'Low') |
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| 177 | is2D = not "-1d" in opts |
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| 178 | data, index = make_data(qmax, is2D, Nq, res, accuracy, view=view) |
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| 179 | |
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| 180 | |
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| 181 | # modelling accuracy is determined by dtype and cutoff |
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| 182 | dtype = 'double' if '-double' in opts else 'single' |
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| 183 | cutoff = float(opt_values.get('-cutoff','1e-5')) |
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| 184 | |
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| 185 | # randomize parameters |
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| 186 | pars.update(set_pars) |
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| 187 | if '-random' in opts or '-random' in opt_values: |
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| 188 | seed = int(opt_values['-random']) if '-random' in opt_values else None |
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| 189 | pars, seed = randomize_model(name, pars, seed=seed) |
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| 190 | print "Randomize using -random=%i"%seed |
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| 191 | |
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| 192 | # parameter selection |
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| 193 | if '-mono' in opts: |
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| 194 | suppress_pd(pars) |
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| 195 | if '-pars' in opts: |
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| 196 | print "pars",parlist(pars) |
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| 197 | |
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| 198 | model_definition = core.load_model_definition(name) |
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| 199 | # OpenCl calculation |
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| 200 | if Nocl > 0: |
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| 201 | ocl, ocl_time = eval_opencl(model_definition, pars, data, |
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| 202 | dtype=dtype, cutoff=cutoff, Nevals=Nocl) |
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| 203 | print "opencl t=%.1f ms, intensity=%.0f"%(ocl_time, sum(ocl)) |
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| 204 | #print max(ocl), min(ocl) |
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| 205 | |
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| 206 | # ctypes/sasview calculation |
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| 207 | if Ncpu > 0 and "-ctypes" in opts: |
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| 208 | cpu, cpu_time = eval_ctypes(model_definition, pars, data, |
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| 209 | dtype=dtype, cutoff=cutoff, Nevals=Ncpu) |
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| 210 | comp = "ctypes" |
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| 211 | print "ctypes t=%.1f ms, intensity=%.0f"%(cpu_time, sum(cpu)) |
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| 212 | elif Ncpu > 0: |
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| 213 | cpu, cpu_time = eval_sasview(model_definition, pars, data, Ncpu) |
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| 214 | comp = "sasview" |
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| 215 | print "sasview t=%.1f ms, intensity=%.0f"%(cpu_time, sum(cpu)) |
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| 216 | |
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| 217 | # Compare, but only if computing both forms |
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| 218 | if Nocl > 0 and Ncpu > 0: |
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| 219 | #print "speedup %.2g"%(cpu_time/ocl_time) |
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| 220 | #print "max |ocl/cpu|", max(abs(ocl/cpu)), "%.15g"%max(abs(ocl)), "%.15g"%max(abs(cpu)) |
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| 221 | #cpu *= max(ocl/cpu) |
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| 222 | resid = (ocl - cpu) |
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| 223 | relerr = resid/cpu |
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| 224 | #bad = (relerr>1e-4) |
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| 225 | #print relerr[bad],cpu[bad],ocl[bad],data.qx_data[bad],data.qy_data[bad] |
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| 226 | def stats(label,err): |
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| 227 | sorted_err = np.sort(abs(err)) |
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| 228 | p50 = int((len(err)-1)*0.50) |
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| 229 | p98 = int((len(err)-1)*0.98) |
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| 230 | data = [ |
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| 231 | "max:%.3e"%sorted_err[-1], |
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| 232 | "median:%.3e"%sorted_err[p50], |
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| 233 | "98%%:%.3e"%sorted_err[p98], |
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| 234 | "rms:%.3e"%np.sqrt(np.mean(err**2)), |
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| 235 | "zero-offset:%+.3e"%np.mean(err), |
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| 236 | ] |
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| 237 | print label," ".join(data) |
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| 238 | stats("|ocl-%s|"%comp+(" "*(3+len(comp))), resid) |
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| 239 | stats("|(ocl-%s)/%s|"%(comp,comp), relerr) |
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| 240 | |
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| 241 | # Plot if requested |
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| 242 | if '-noplot' in opts: return |
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| 243 | import matplotlib.pyplot as plt |
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| 244 | if Ncpu > 0: |
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| 245 | if Nocl > 0: plt.subplot(131) |
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| 246 | plot_theory(data, cpu, view=view) |
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| 247 | plt.title("%s t=%.1f ms"%(comp,cpu_time)) |
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| 248 | cbar_title = "log I" |
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| 249 | if Nocl > 0: |
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| 250 | if Ncpu > 0: plt.subplot(132) |
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| 251 | plot_theory(data, ocl, view=view) |
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| 252 | plt.title("opencl t=%.1f ms"%ocl_time) |
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| 253 | cbar_title = "log I" |
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| 254 | if Ncpu > 0 and Nocl > 0: |
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| 255 | plt.subplot(133) |
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| 256 | if '-abs' in opts: |
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| 257 | err,errstr,errview = resid, "abs err", "linear" |
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| 258 | else: |
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| 259 | err,errstr,errview = abs(relerr), "rel err", "log" |
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| 260 | #err,errstr = ocl/cpu,"ratio" |
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| 261 | plot_theory(data, err, view=errview) |
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| 262 | plt.title("max %s = %.3g"%(errstr, max(abs(err)))) |
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| 263 | cbar_title = errstr if errview=="linear" else "log "+errstr |
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| 264 | if is2D: |
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| 265 | h = plt.colorbar() |
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| 266 | h.ax.set_title(cbar_title) |
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| 267 | |
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| 268 | if Ncpu > 0 and Nocl > 0 and '-hist' in opts: |
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| 269 | plt.figure() |
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| 270 | v = relerr |
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| 271 | v[v==0] = 0.5*np.min(np.abs(v[v!=0])) |
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| 272 | plt.hist(np.log10(np.abs(v)), normed=1, bins=50); |
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| 273 | plt.xlabel('log10(err), err = | F(q) single - F(q) double| / | F(q) double |'); |
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| 274 | plt.ylabel('P(err)') |
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| 275 | plt.title('Comparison of single and double precision models for %s'%name) |
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| 276 | |
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| 277 | plt.show() |
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| 278 | |
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| 279 | # =========================================================================== |
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| 280 | # |
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| 281 | USAGE=""" |
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| 282 | usage: compare.py model [Nopencl] [Nsasview] [options...] [key=val] |
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| 283 | |
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| 284 | Compare the speed and value for a model between the SasView original and the |
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| 285 | OpenCL rewrite. |
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| 286 | |
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| 287 | model is the name of the model to compare (see below). |
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| 288 | Nopencl is the number of times to run the OpenCL model (default=5) |
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| 289 | Nsasview is the number of times to run the Sasview model (default=1) |
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| 290 | |
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| 291 | Options (* for default): |
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| 292 | |
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| 293 | -plot*/-noplot plots or suppress the plot of the model |
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| 294 | -single*/-double uses double precision for comparison |
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| 295 | -lowq*/-midq/-highq/-exq use q values up to 0.05, 0.2, 1.0, 10.0 |
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| 296 | -Nq=128 sets the number of Q points in the data set |
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| 297 | -1d/-2d* computes 1d or 2d data |
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| 298 | -preset*/-random[=seed] preset or random parameters |
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| 299 | -mono/-poly* force monodisperse/polydisperse |
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| 300 | -ctypes/-sasview* whether cpu is tested using sasview or ctypes |
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| 301 | -cutoff=1e-5* cutoff value for including a point in polydispersity |
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| 302 | -pars/-nopars* prints the parameter set or not |
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| 303 | -abs/-rel* plot relative or absolute error |
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| 304 | -linear/-log/-q4 intensity scaling |
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| 305 | -hist/-nohist* plot histogram of relative error |
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| 306 | -res=0 sets the resolution width dQ/Q if calculating with resolution |
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| 307 | -accuracy=Low resolution accuracy Low, Mid, High, Xhigh |
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| 308 | |
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| 309 | Key=value pairs allow you to set specific values to any of the model |
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| 310 | parameters. |
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| 311 | |
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| 312 | Available models: |
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| 313 | |
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| 314 | %s |
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| 315 | """ |
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| 316 | |
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| 317 | NAME_OPTIONS = set([ |
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| 318 | 'plot','noplot', |
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| 319 | 'single','double', |
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| 320 | 'lowq','midq','highq','exq', |
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| 321 | '2d','1d', |
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| 322 | 'preset','random', |
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| 323 | 'poly','mono', |
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| 324 | 'sasview','ctypes', |
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| 325 | 'nopars','pars', |
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| 326 | 'rel','abs', |
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| 327 | 'linear', 'log', 'q4', |
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| 328 | 'hist','nohist', |
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| 329 | ]) |
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| 330 | VALUE_OPTIONS = [ |
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| 331 | # Note: random is both a name option and a value option |
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| 332 | 'cutoff', 'random', 'Nq', 'res', 'accuracy', |
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| 333 | ] |
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| 334 | |
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| 335 | def get_demo_pars(name): |
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| 336 | import sasmodels.models |
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| 337 | __import__('sasmodels.models.'+name) |
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| 338 | model = getattr(sasmodels.models, name) |
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| 339 | pars = getattr(model, 'demo', None) |
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| 340 | if pars is None: pars = dict((p[0],p[2]) for p in model.parameters) |
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| 341 | return pars |
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| 342 | |
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| 343 | def main(): |
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| 344 | opts = [arg for arg in sys.argv[1:] if arg.startswith('-')] |
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| 345 | args = [arg for arg in sys.argv[1:] if not arg.startswith('-')] |
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| 346 | models = "\n ".join("%-15s"%v for v in MODELS) |
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| 347 | if len(args) == 0: |
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| 348 | print(USAGE%models) |
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| 349 | sys.exit(1) |
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| 350 | if args[0] not in MODELS: |
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| 351 | print "Model %r not available. Use one of:\n %s"%(args[0],models) |
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| 352 | sys.exit(1) |
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| 353 | |
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| 354 | invalid = [o[1:] for o in opts |
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| 355 | if o[1:] not in NAME_OPTIONS |
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| 356 | and not any(o.startswith('-%s='%t) for t in VALUE_OPTIONS)] |
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| 357 | if invalid: |
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| 358 | print "Invalid options: %s"%(", ".join(invalid)) |
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| 359 | sys.exit(1) |
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| 360 | |
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| 361 | # Get demo parameters from model definition, or use default parameters |
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| 362 | # if model does not define demo parameters |
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| 363 | name = args[0] |
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| 364 | pars = get_demo_pars(name) |
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| 365 | |
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| 366 | Nopencl = int(args[1]) if len(args) > 1 else 5 |
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| 367 | Nsasview = int(args[2]) if len(args) > 2 else 1 |
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| 368 | |
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| 369 | # Fill in default polydispersity parameters |
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| 370 | pds = set(p.split('_pd')[0] for p in pars if p.endswith('_pd')) |
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| 371 | for p in pds: |
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| 372 | if p+"_pd_nsigma" not in pars: pars[p+"_pd_nsigma"] = 3 |
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| 373 | if p+"_pd_type" not in pars: pars[p+"_pd_type"] = "gaussian" |
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| 374 | |
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| 375 | # Fill in parameters given on the command line |
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| 376 | set_pars = {} |
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| 377 | for arg in args[3:]: |
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| 378 | k,v = arg.split('=') |
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| 379 | if k not in pars: |
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| 380 | # extract base name without distribution |
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| 381 | s = set(p.split('_pd')[0] for p in pars) |
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| 382 | print "%r invalid; parameters are: %s"%(k,", ".join(sorted(s))) |
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| 383 | sys.exit(1) |
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| 384 | set_pars[k] = float(v) if not v.endswith('type') else v |
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| 385 | |
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| 386 | compare(name, pars, Nsasview, Nopencl, opts, set_pars) |
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| 387 | |
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| 388 | if __name__ == "__main__": |
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| 389 | main() |
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