| 1 | #!/usr/bin/env python |
|---|
| 2 | # -*- coding: utf-8 -*- |
|---|
| 3 | |
|---|
| 4 | import sys |
|---|
| 5 | import math |
|---|
| 6 | from os.path import basename, dirname, join as joinpath |
|---|
| 7 | import glob |
|---|
| 8 | import datetime |
|---|
| 9 | |
|---|
| 10 | import numpy as np |
|---|
| 11 | |
|---|
| 12 | ROOT = dirname(__file__) |
|---|
| 13 | sys.path.insert(0, ROOT) # Make sure sasmodels is first on the path |
|---|
| 14 | |
|---|
| 15 | |
|---|
| 16 | from sasmodels import core |
|---|
| 17 | from sasmodels import kerneldll |
|---|
| 18 | from sasmodels.data import plot_theory, empty_data1D, empty_data2D |
|---|
| 19 | from sasmodels.direct_model import DirectModel |
|---|
| 20 | from sasmodels.convert import revert_model |
|---|
| 21 | kerneldll.ALLOW_SINGLE_PRECISION_DLLS = True |
|---|
| 22 | |
|---|
| 23 | # List of available models |
|---|
| 24 | MODELS = [basename(f)[:-3] |
|---|
| 25 | for f in sorted(glob.glob(joinpath(ROOT,"sasmodels","models","[a-zA-Z]*.py")))] |
|---|
| 26 | |
|---|
| 27 | # CRUFT python 2.6 |
|---|
| 28 | if not hasattr(datetime.timedelta, 'total_seconds'): |
|---|
| 29 | def delay(dt): |
|---|
| 30 | """Return number date-time delta as number seconds""" |
|---|
| 31 | return dt.days * 86400 + dt.seconds + 1e-6 * dt.microseconds |
|---|
| 32 | else: |
|---|
| 33 | def delay(dt): |
|---|
| 34 | """Return number date-time delta as number seconds""" |
|---|
| 35 | return dt.total_seconds() |
|---|
| 36 | |
|---|
| 37 | |
|---|
| 38 | def tic(): |
|---|
| 39 | """ |
|---|
| 40 | Timer function. |
|---|
| 41 | |
|---|
| 42 | Use "toc=tic()" to start the clock and "toc()" to measure |
|---|
| 43 | a time interval. |
|---|
| 44 | """ |
|---|
| 45 | then = datetime.datetime.now() |
|---|
| 46 | return lambda: delay(datetime.datetime.now() - then) |
|---|
| 47 | |
|---|
| 48 | |
|---|
| 49 | def set_beam_stop(data, radius, outer=None): |
|---|
| 50 | """ |
|---|
| 51 | Add a beam stop of the given *radius*. If *outer*, make an annulus. |
|---|
| 52 | |
|---|
| 53 | Note: this function does not use the sasview package |
|---|
| 54 | """ |
|---|
| 55 | if hasattr(data, 'qx_data'): |
|---|
| 56 | q = np.sqrt(data.qx_data**2 + data.qy_data**2) |
|---|
| 57 | data.mask = (q < radius) |
|---|
| 58 | if outer is not None: |
|---|
| 59 | data.mask |= (q >= outer) |
|---|
| 60 | else: |
|---|
| 61 | data.mask = (data.x < radius) |
|---|
| 62 | if outer is not None: |
|---|
| 63 | data.mask |= (data.x >= outer) |
|---|
| 64 | |
|---|
| 65 | |
|---|
| 66 | def sasview_model(model_definition, **pars): |
|---|
| 67 | """ |
|---|
| 68 | Load a sasview model given the model name. |
|---|
| 69 | """ |
|---|
| 70 | # convert model parameters from sasmodel form to sasview form |
|---|
| 71 | #print "old",sorted(pars.items()) |
|---|
| 72 | modelname, pars = revert_model(model_definition, pars) |
|---|
| 73 | #print "new",sorted(pars.items()) |
|---|
| 74 | sas = __import__('sas.models.'+modelname) |
|---|
| 75 | ModelClass = getattr(getattr(sas.models,modelname,None),modelname,None) |
|---|
| 76 | if ModelClass is None: |
|---|
| 77 | raise ValueError("could not find model %r in sas.models"%modelname) |
|---|
| 78 | model = ModelClass() |
|---|
| 79 | |
|---|
| 80 | for k,v in pars.items(): |
|---|
| 81 | if k.endswith("_pd"): |
|---|
| 82 | model.dispersion[k[:-3]]['width'] = v |
|---|
| 83 | elif k.endswith("_pd_n"): |
|---|
| 84 | model.dispersion[k[:-5]]['npts'] = v |
|---|
| 85 | elif k.endswith("_pd_nsigma"): |
|---|
| 86 | model.dispersion[k[:-10]]['nsigmas'] = v |
|---|
| 87 | elif k.endswith("_pd_type"): |
|---|
| 88 | model.dispersion[k[:-8]]['type'] = v |
|---|
| 89 | else: |
|---|
| 90 | model.setParam(k, v) |
|---|
| 91 | return model |
|---|
| 92 | |
|---|
| 93 | def randomize(p, v): |
|---|
| 94 | """ |
|---|
| 95 | Randomizing parameter. |
|---|
| 96 | |
|---|
| 97 | Guess the parameter type from name. |
|---|
| 98 | """ |
|---|
| 99 | if any(p.endswith(s) for s in ('_pd_n','_pd_nsigma','_pd_type')): |
|---|
| 100 | return v |
|---|
| 101 | elif any(s in p for s in ('theta','phi','psi')): |
|---|
| 102 | # orientation in [-180,180], orientation pd in [0,45] |
|---|
| 103 | if p.endswith('_pd'): |
|---|
| 104 | return 45*np.random.rand() |
|---|
| 105 | else: |
|---|
| 106 | return 360*np.random.rand() - 180 |
|---|
| 107 | elif 'sld' in p: |
|---|
| 108 | # sld in in [-0.5,10] |
|---|
| 109 | return 10.5*np.random.rand() - 0.5 |
|---|
| 110 | elif p.endswith('_pd'): |
|---|
| 111 | # length pd in [0,1] |
|---|
| 112 | return np.random.rand() |
|---|
| 113 | else: |
|---|
| 114 | # values from 0 to 2*x for all other parameters |
|---|
| 115 | return 2*np.random.rand()*(v if v != 0 else 1) |
|---|
| 116 | |
|---|
| 117 | def randomize_model(name, pars, seed=None): |
|---|
| 118 | if seed is None: |
|---|
| 119 | seed = np.random.randint(1e9) |
|---|
| 120 | np.random.seed(seed) |
|---|
| 121 | # Note: the sort guarantees order of calls to random number generator |
|---|
| 122 | pars = dict((p,randomize(p,v)) for p,v in sorted(pars.items())) |
|---|
| 123 | # The capped cylinder model has a constraint on its parameters |
|---|
| 124 | if name == 'capped_cylinder' and pars['cap_radius'] < pars['radius']: |
|---|
| 125 | pars['radius'],pars['cap_radius'] = pars['cap_radius'],pars['radius'] |
|---|
| 126 | return pars, seed |
|---|
| 127 | |
|---|
| 128 | def parlist(pars): |
|---|
| 129 | return "\n".join("%s: %s"%(p,v) for p,v in sorted(pars.items())) |
|---|
| 130 | |
|---|
| 131 | def suppress_pd(pars): |
|---|
| 132 | """ |
|---|
| 133 | Suppress theta_pd for now until the normalization is resolved. |
|---|
| 134 | |
|---|
| 135 | May also suppress complete polydispersity of the model to test |
|---|
| 136 | models more quickly. |
|---|
| 137 | """ |
|---|
| 138 | for p in pars: |
|---|
| 139 | if p.endswith("_pd"): pars[p] = 0 |
|---|
| 140 | |
|---|
| 141 | def eval_sasview(model_definition, pars, data, Nevals=1): |
|---|
| 142 | from sas.models.qsmearing import smear_selection |
|---|
| 143 | model = sasview_model(model_definition, **pars) |
|---|
| 144 | smearer = smear_selection(data, model=model) |
|---|
| 145 | value = None # silence the linter |
|---|
| 146 | toc = tic() |
|---|
| 147 | for _ in range(max(Nevals, 1)): # make sure there is at least one eval |
|---|
| 148 | if hasattr(data, 'qx_data'): |
|---|
| 149 | q = np.sqrt(data.qx_data**2 + data.qy_data**2) |
|---|
| 150 | index = ((~data.mask) & (~np.isnan(data.data)) |
|---|
| 151 | & (q >= data.qmin) & (q <= data.qmax)) |
|---|
| 152 | if smearer is not None: |
|---|
| 153 | smearer.model = model # because smear_selection has a bug |
|---|
| 154 | smearer.accuracy = data.accuracy |
|---|
| 155 | smearer.set_index(index) |
|---|
| 156 | value = smearer.get_value() |
|---|
| 157 | else: |
|---|
| 158 | value = model.evalDistribution([data.qx_data[index], data.qy_data[index]]) |
|---|
| 159 | else: |
|---|
| 160 | value = model.evalDistribution(data.x) |
|---|
| 161 | if smearer is not None: |
|---|
| 162 | value = smearer(value) |
|---|
| 163 | average_time = toc()*1000./Nevals |
|---|
| 164 | return value, average_time |
|---|
| 165 | |
|---|
| 166 | def eval_opencl(model_definition, pars, data, dtype='single', Nevals=1, cutoff=0.): |
|---|
| 167 | try: |
|---|
| 168 | model = core.load_model(model_definition, dtype=dtype, platform="ocl") |
|---|
| 169 | except Exception,exc: |
|---|
| 170 | print exc |
|---|
| 171 | print "... trying again with single precision" |
|---|
| 172 | model = core.load_model(model_definition, dtype='single', platform="ocl") |
|---|
| 173 | calculator = DirectModel(data, model, cutoff=cutoff) |
|---|
| 174 | value = None # silence the linter |
|---|
| 175 | toc = tic() |
|---|
| 176 | for _ in range(max(Nevals, 1)): # force at least one eval |
|---|
| 177 | value = calculator(**pars) |
|---|
| 178 | average_time = toc()*1000./Nevals |
|---|
| 179 | return value, average_time |
|---|
| 180 | |
|---|
| 181 | |
|---|
| 182 | def eval_ctypes(model_definition, pars, data, dtype='double', Nevals=1, cutoff=0.): |
|---|
| 183 | model = core.load_model(model_definition, dtype=dtype, platform="dll") |
|---|
| 184 | calculator = DirectModel(data, model, cutoff=cutoff) |
|---|
| 185 | value = None # silence the linter |
|---|
| 186 | toc = tic() |
|---|
| 187 | for _ in range(max(Nevals, 1)): # force at least one eval |
|---|
| 188 | value = calculator(**pars) |
|---|
| 189 | average_time = toc()*1000./Nevals |
|---|
| 190 | return value, average_time |
|---|
| 191 | |
|---|
| 192 | |
|---|
| 193 | def make_data(qmax, is2D, Nq=128, resolution=0.0, accuracy='Low', view='log'): |
|---|
| 194 | if is2D: |
|---|
| 195 | data = empty_data2D(np.linspace(-qmax, qmax, Nq), resolution=resolution) |
|---|
| 196 | data.accuracy = accuracy |
|---|
| 197 | set_beam_stop(data, 0.004) |
|---|
| 198 | index = ~data.mask |
|---|
| 199 | else: |
|---|
| 200 | if view == 'log': |
|---|
| 201 | qmax = math.log10(qmax) |
|---|
| 202 | q = np.logspace(qmax-3, qmax, Nq) |
|---|
| 203 | else: |
|---|
| 204 | q = np.linspace(0.001*qmax, qmax, Nq) |
|---|
| 205 | data = empty_data1D(q, resolution=resolution) |
|---|
| 206 | index = slice(None, None) |
|---|
| 207 | return data, index |
|---|
| 208 | |
|---|
| 209 | def compare(name, pars, Ncpu, Nocl, opts, set_pars): |
|---|
| 210 | view = 'linear' if '-linear' in opts else 'log' if '-log' in opts else 'q4' if '-q4' in opts else 'log' |
|---|
| 211 | |
|---|
| 212 | opt_values = dict(split |
|---|
| 213 | for s in opts for split in ((s.split('='),)) |
|---|
| 214 | if len(split) == 2) |
|---|
| 215 | # Sort out data |
|---|
| 216 | qmax = 10.0 if '-exq' in opts else 1.0 if '-highq' in opts else 0.2 if '-midq' in opts else 0.05 |
|---|
| 217 | Nq = int(opt_values.get('-Nq', '128')) |
|---|
| 218 | res = float(opt_values.get('-res', '0')) |
|---|
| 219 | accuracy = opt_values.get('-accuracy', 'Low') |
|---|
| 220 | is2D = not "-1d" in opts |
|---|
| 221 | data, index = make_data(qmax, is2D, Nq, res, accuracy, view=view) |
|---|
| 222 | |
|---|
| 223 | |
|---|
| 224 | # modelling accuracy is determined by dtype and cutoff |
|---|
| 225 | dtype = 'double' if '-double' in opts else 'single' |
|---|
| 226 | cutoff = float(opt_values.get('-cutoff','1e-5')) |
|---|
| 227 | |
|---|
| 228 | # randomize parameters |
|---|
| 229 | #pars.update(set_pars) # set value before random to control range |
|---|
| 230 | if '-random' in opts or '-random' in opt_values: |
|---|
| 231 | seed = int(opt_values['-random']) if '-random' in opt_values else None |
|---|
| 232 | pars, seed = randomize_model(name, pars, seed=seed) |
|---|
| 233 | print "Randomize using -random=%i"%seed |
|---|
| 234 | pars.update(set_pars) # set value after random to control value |
|---|
| 235 | |
|---|
| 236 | # parameter selection |
|---|
| 237 | if '-mono' in opts: |
|---|
| 238 | suppress_pd(pars) |
|---|
| 239 | if '-pars' in opts: |
|---|
| 240 | print "pars",parlist(pars) |
|---|
| 241 | |
|---|
| 242 | model_definition = core.load_model_definition(name) |
|---|
| 243 | # OpenCl calculation |
|---|
| 244 | if Nocl > 0: |
|---|
| 245 | ocl, ocl_time = eval_opencl(model_definition, pars, data, |
|---|
| 246 | dtype=dtype, cutoff=cutoff, Nevals=Nocl) |
|---|
| 247 | print "opencl t=%.1f ms, intensity=%.0f"%(ocl_time, sum(ocl)) |
|---|
| 248 | #print max(ocl), min(ocl) |
|---|
| 249 | |
|---|
| 250 | # ctypes/sasview calculation |
|---|
| 251 | if Ncpu > 0 and "-ctypes" in opts: |
|---|
| 252 | cpu, cpu_time = eval_ctypes(model_definition, pars, data, |
|---|
| 253 | dtype=dtype, cutoff=cutoff, Nevals=Ncpu) |
|---|
| 254 | comp = "ctypes" |
|---|
| 255 | print "ctypes t=%.1f ms, intensity=%.0f"%(cpu_time, sum(cpu)) |
|---|
| 256 | elif Ncpu > 0: |
|---|
| 257 | try: |
|---|
| 258 | cpu, cpu_time = eval_sasview(model_definition, pars, data, Ncpu) |
|---|
| 259 | comp = "sasview" |
|---|
| 260 | #print "ocl/sasview", (ocl-pars['background'])/(cpu-pars['background']) |
|---|
| 261 | print "sasview t=%.1f ms, intensity=%.0f"%(cpu_time, sum(cpu)) |
|---|
| 262 | except ImportError: |
|---|
| 263 | Ncpu = 0 |
|---|
| 264 | |
|---|
| 265 | # Compare, but only if computing both forms |
|---|
| 266 | if Nocl > 0 and Ncpu > 0: |
|---|
| 267 | #print "speedup %.2g"%(cpu_time/ocl_time) |
|---|
| 268 | #print "max |ocl/cpu|", max(abs(ocl/cpu)), "%.15g"%max(abs(ocl)), "%.15g"%max(abs(cpu)) |
|---|
| 269 | #cpu *= max(ocl/cpu) |
|---|
| 270 | resid = (ocl - cpu) |
|---|
| 271 | relerr = resid/cpu |
|---|
| 272 | #bad = (relerr>1e-4) |
|---|
| 273 | #print relerr[bad],cpu[bad],ocl[bad],data.qx_data[bad],data.qy_data[bad] |
|---|
| 274 | _print_stats("|ocl-%s|"%comp+(" "*(3+len(comp))), resid) |
|---|
| 275 | _print_stats("|(ocl-%s)/%s|"%(comp,comp), relerr) |
|---|
| 276 | |
|---|
| 277 | # Plot if requested |
|---|
| 278 | if '-noplot' in opts: return |
|---|
| 279 | import matplotlib.pyplot as plt |
|---|
| 280 | if Ncpu > 0: |
|---|
| 281 | if Nocl > 0: plt.subplot(131) |
|---|
| 282 | plot_theory(data, cpu, view=view, plot_data=False) |
|---|
| 283 | plt.title("%s t=%.1f ms"%(comp,cpu_time)) |
|---|
| 284 | #cbar_title = "log I" |
|---|
| 285 | if Nocl > 0: |
|---|
| 286 | if Ncpu > 0: plt.subplot(132) |
|---|
| 287 | plot_theory(data, ocl, view=view, plot_data=False) |
|---|
| 288 | plt.title("opencl t=%.1f ms"%ocl_time) |
|---|
| 289 | #cbar_title = "log I" |
|---|
| 290 | if Ncpu > 0 and Nocl > 0: |
|---|
| 291 | plt.subplot(133) |
|---|
| 292 | if '-abs' in opts: |
|---|
| 293 | err,errstr,errview = resid, "abs err", "linear" |
|---|
| 294 | else: |
|---|
| 295 | err,errstr,errview = abs(relerr), "rel err", "log" |
|---|
| 296 | #err,errstr = ocl/cpu,"ratio" |
|---|
| 297 | plot_theory(data, None, resid=err, view=errview, plot_data=False) |
|---|
| 298 | plt.title("max %s = %.3g"%(errstr, max(abs(err)))) |
|---|
| 299 | #cbar_title = errstr if errview=="linear" else "log "+errstr |
|---|
| 300 | #if is2D: |
|---|
| 301 | # h = plt.colorbar() |
|---|
| 302 | # h.ax.set_title(cbar_title) |
|---|
| 303 | |
|---|
| 304 | if Ncpu > 0 and Nocl > 0 and '-hist' in opts: |
|---|
| 305 | plt.figure() |
|---|
| 306 | v = relerr |
|---|
| 307 | v[v==0] = 0.5*np.min(np.abs(v[v!=0])) |
|---|
| 308 | plt.hist(np.log10(np.abs(v)), normed=1, bins=50); |
|---|
| 309 | plt.xlabel('log10(err), err = | F(q) single - F(q) double| / | F(q) double |'); |
|---|
| 310 | plt.ylabel('P(err)') |
|---|
| 311 | plt.title('Comparison of single and double precision models for %s'%name) |
|---|
| 312 | |
|---|
| 313 | plt.show() |
|---|
| 314 | |
|---|
| 315 | def _print_stats(label, err): |
|---|
| 316 | sorted_err = np.sort(abs(err)) |
|---|
| 317 | p50 = int((len(err)-1)*0.50) |
|---|
| 318 | p98 = int((len(err)-1)*0.98) |
|---|
| 319 | data = [ |
|---|
| 320 | "max:%.3e"%sorted_err[-1], |
|---|
| 321 | "median:%.3e"%sorted_err[p50], |
|---|
| 322 | "98%%:%.3e"%sorted_err[p98], |
|---|
| 323 | "rms:%.3e"%np.sqrt(np.mean(err**2)), |
|---|
| 324 | "zero-offset:%+.3e"%np.mean(err), |
|---|
| 325 | ] |
|---|
| 326 | print label," ".join(data) |
|---|
| 327 | |
|---|
| 328 | |
|---|
| 329 | |
|---|
| 330 | # =========================================================================== |
|---|
| 331 | # |
|---|
| 332 | USAGE=""" |
|---|
| 333 | usage: compare.py model [Nopencl] [Nsasview] [options...] [key=val] |
|---|
| 334 | |
|---|
| 335 | Compare the speed and value for a model between the SasView original and the |
|---|
| 336 | OpenCL rewrite. |
|---|
| 337 | |
|---|
| 338 | model is the name of the model to compare (see below). |
|---|
| 339 | Nopencl is the number of times to run the OpenCL model (default=5) |
|---|
| 340 | Nsasview is the number of times to run the Sasview model (default=1) |
|---|
| 341 | |
|---|
| 342 | Options (* for default): |
|---|
| 343 | |
|---|
| 344 | -plot*/-noplot plots or suppress the plot of the model |
|---|
| 345 | -single*/-double uses double precision for comparison |
|---|
| 346 | -lowq*/-midq/-highq/-exq use q values up to 0.05, 0.2, 1.0, 10.0 |
|---|
| 347 | -Nq=128 sets the number of Q points in the data set |
|---|
| 348 | -1d/-2d* computes 1d or 2d data |
|---|
| 349 | -preset*/-random[=seed] preset or random parameters |
|---|
| 350 | -mono/-poly* force monodisperse/polydisperse |
|---|
| 351 | -ctypes/-sasview* whether cpu is tested using sasview or ctypes |
|---|
| 352 | -cutoff=1e-5* cutoff value for including a point in polydispersity |
|---|
| 353 | -pars/-nopars* prints the parameter set or not |
|---|
| 354 | -abs/-rel* plot relative or absolute error |
|---|
| 355 | -linear/-log/-q4 intensity scaling |
|---|
| 356 | -hist/-nohist* plot histogram of relative error |
|---|
| 357 | -res=0 sets the resolution width dQ/Q if calculating with resolution |
|---|
| 358 | -accuracy=Low resolution accuracy Low, Mid, High, Xhigh |
|---|
| 359 | |
|---|
| 360 | Key=value pairs allow you to set specific values to any of the model |
|---|
| 361 | parameters. |
|---|
| 362 | |
|---|
| 363 | Available models: |
|---|
| 364 | """ |
|---|
| 365 | |
|---|
| 366 | |
|---|
| 367 | NAME_OPTIONS = set([ |
|---|
| 368 | 'plot','noplot', |
|---|
| 369 | 'single','double', |
|---|
| 370 | 'lowq','midq','highq','exq', |
|---|
| 371 | '2d','1d', |
|---|
| 372 | 'preset','random', |
|---|
| 373 | 'poly','mono', |
|---|
| 374 | 'sasview','ctypes', |
|---|
| 375 | 'nopars','pars', |
|---|
| 376 | 'rel','abs', |
|---|
| 377 | 'linear', 'log', 'q4', |
|---|
| 378 | 'hist','nohist', |
|---|
| 379 | ]) |
|---|
| 380 | VALUE_OPTIONS = [ |
|---|
| 381 | # Note: random is both a name option and a value option |
|---|
| 382 | 'cutoff', 'random', 'Nq', 'res', 'accuracy', |
|---|
| 383 | ] |
|---|
| 384 | |
|---|
| 385 | def columnize(L, indent="", width=79): |
|---|
| 386 | column_width = max(len(w) for w in L) + 1 |
|---|
| 387 | num_columns = (width - len(indent)) // column_width |
|---|
| 388 | num_rows = len(L) // num_columns |
|---|
| 389 | L = L + [""] * (num_rows*num_columns - len(L)) |
|---|
| 390 | columns = [L[k*num_rows:(k+1)*num_rows] for k in range(num_columns)] |
|---|
| 391 | lines = [" ".join("%-*s"%(column_width, entry) for entry in row) |
|---|
| 392 | for row in zip(*columns)] |
|---|
| 393 | output = indent + ("\n"+indent).join(lines) |
|---|
| 394 | return output |
|---|
| 395 | |
|---|
| 396 | |
|---|
| 397 | def get_demo_pars(name): |
|---|
| 398 | import sasmodels.models |
|---|
| 399 | __import__('sasmodels.models.'+name) |
|---|
| 400 | model = getattr(sasmodels.models, name) |
|---|
| 401 | pars = getattr(model, 'demo', None) |
|---|
| 402 | if pars is None: pars = dict((p[0],p[2]) for p in model.parameters) |
|---|
| 403 | return pars |
|---|
| 404 | |
|---|
| 405 | def main(): |
|---|
| 406 | opts = [arg for arg in sys.argv[1:] if arg.startswith('-')] |
|---|
| 407 | args = [arg for arg in sys.argv[1:] if not arg.startswith('-')] |
|---|
| 408 | models = "\n ".join("%-15s"%v for v in MODELS) |
|---|
| 409 | if len(args) == 0: |
|---|
| 410 | print(USAGE) |
|---|
| 411 | print(columnize(MODELS, indent=" ")) |
|---|
| 412 | sys.exit(1) |
|---|
| 413 | if args[0] not in MODELS: |
|---|
| 414 | print "Model %r not available. Use one of:\n %s"%(args[0],models) |
|---|
| 415 | sys.exit(1) |
|---|
| 416 | |
|---|
| 417 | invalid = [o[1:] for o in opts |
|---|
| 418 | if o[1:] not in NAME_OPTIONS |
|---|
| 419 | and not any(o.startswith('-%s='%t) for t in VALUE_OPTIONS)] |
|---|
| 420 | if invalid: |
|---|
| 421 | print "Invalid options: %s"%(", ".join(invalid)) |
|---|
| 422 | sys.exit(1) |
|---|
| 423 | |
|---|
| 424 | # Get demo parameters from model definition, or use default parameters |
|---|
| 425 | # if model does not define demo parameters |
|---|
| 426 | name = args[0] |
|---|
| 427 | pars = get_demo_pars(name) |
|---|
| 428 | |
|---|
| 429 | Nopencl = int(args[1]) if len(args) > 1 else 5 |
|---|
| 430 | Nsasview = int(args[2]) if len(args) > 2 else 1 |
|---|
| 431 | |
|---|
| 432 | # Fill in default polydispersity parameters |
|---|
| 433 | pds = set(p.split('_pd')[0] for p in pars if p.endswith('_pd')) |
|---|
| 434 | for p in pds: |
|---|
| 435 | if p+"_pd_nsigma" not in pars: pars[p+"_pd_nsigma"] = 3 |
|---|
| 436 | if p+"_pd_type" not in pars: pars[p+"_pd_type"] = "gaussian" |
|---|
| 437 | |
|---|
| 438 | # Fill in parameters given on the command line |
|---|
| 439 | set_pars = {} |
|---|
| 440 | for arg in args[3:]: |
|---|
| 441 | k,v = arg.split('=') |
|---|
| 442 | if k not in pars: |
|---|
| 443 | # extract base name without distribution |
|---|
| 444 | s = set(p.split('_pd')[0] for p in pars) |
|---|
| 445 | print "%r invalid; parameters are: %s"%(k,", ".join(sorted(s))) |
|---|
| 446 | sys.exit(1) |
|---|
| 447 | set_pars[k] = float(v) if not v.endswith('type') else v |
|---|
| 448 | |
|---|
| 449 | compare(name, pars, Nsasview, Nopencl, opts, set_pars) |
|---|
| 450 | |
|---|
| 451 | if __name__ == "__main__": |
|---|
| 452 | main() |
|---|
