1 | """ |
---|
2 | Sasview model constructor. |
---|
3 | |
---|
4 | Given a module defining an OpenCL kernel such as sasmodels.models.cylinder, |
---|
5 | create a sasview model class to run that kernel as follows:: |
---|
6 | |
---|
7 | from sasmodels.sasview_model import make_class |
---|
8 | from sasmodels.models import cylinder |
---|
9 | CylinderModel = make_class(cylinder, dtype='single') |
---|
10 | |
---|
11 | The model parameters for sasmodels are different from those in sasview. |
---|
12 | When reloading previously saved models, the parameters should be converted |
---|
13 | using :func:`sasmodels.convert.convert`. |
---|
14 | """ |
---|
15 | from __future__ import print_function |
---|
16 | |
---|
17 | import math |
---|
18 | from copy import deepcopy |
---|
19 | import collections |
---|
20 | import traceback |
---|
21 | import logging |
---|
22 | |
---|
23 | import numpy as np |
---|
24 | |
---|
25 | from . import core |
---|
26 | from . import custom |
---|
27 | from . import generate |
---|
28 | from . import weights |
---|
29 | |
---|
30 | def load_standard_models(): |
---|
31 | """ |
---|
32 | Load and return the list of predefined models. |
---|
33 | |
---|
34 | If there is an error loading a model, then a traceback is logged and the |
---|
35 | model is not returned. |
---|
36 | """ |
---|
37 | models = [] |
---|
38 | for name in core.list_models(): |
---|
39 | try: |
---|
40 | models.append(_make_standard_model(name)) |
---|
41 | except: |
---|
42 | logging.error(traceback.format_exc()) |
---|
43 | return models |
---|
44 | |
---|
45 | |
---|
46 | def load_custom_model(path): |
---|
47 | """ |
---|
48 | Load a custom model given the model path. |
---|
49 | """ |
---|
50 | kernel_module = custom.load_custom_kernel_module(path) |
---|
51 | model_info = generate.make_model_info(kernel_module) |
---|
52 | return _make_model_from_info(model_info) |
---|
53 | |
---|
54 | |
---|
55 | def _make_standard_model(name): |
---|
56 | """ |
---|
57 | Load the sasview model defined by *name*. |
---|
58 | |
---|
59 | *name* can be a standard model name or a path to a custom model. |
---|
60 | |
---|
61 | Returns a class that can be used directly as a sasview model. |
---|
62 | """ |
---|
63 | kernel_module = generate.load_kernel_module(name) |
---|
64 | model_info = generate.make_model_info(kernel_module) |
---|
65 | return _make_model_from_info(model_info) |
---|
66 | |
---|
67 | |
---|
68 | def _make_model_from_info(model_info): |
---|
69 | """ |
---|
70 | Convert *model_info* into a SasView model wrapper. |
---|
71 | """ |
---|
72 | def __init__(self, multfactor=1): |
---|
73 | SasviewModel.__init__(self) |
---|
74 | attrs = dict(__init__=__init__, _model_info=model_info) |
---|
75 | ConstructedModel = type(model_info['name'], (SasviewModel,), attrs) |
---|
76 | return ConstructedModel |
---|
77 | |
---|
78 | |
---|
79 | class SasviewModel(object): |
---|
80 | """ |
---|
81 | Sasview wrapper for opencl/ctypes model. |
---|
82 | """ |
---|
83 | _model_info = {} |
---|
84 | def __init__(self): |
---|
85 | self._model = None |
---|
86 | model_info = self._model_info |
---|
87 | parameters = model_info['parameters'] |
---|
88 | |
---|
89 | self.name = model_info['name'] |
---|
90 | self.description = model_info['description'] |
---|
91 | self.category = None |
---|
92 | #self.is_multifunc = False |
---|
93 | for p in parameters.kernel_parameters: |
---|
94 | if p.is_control: |
---|
95 | profile_axes = model_info['profile_axes'] |
---|
96 | self.multiplicity_info = [ |
---|
97 | p.limits[1], p.name, p.choices, profile_axes[0] |
---|
98 | ] |
---|
99 | break |
---|
100 | else: |
---|
101 | self.multiplicity_info = [] |
---|
102 | |
---|
103 | ## interpret the parameters |
---|
104 | ## TODO: reorganize parameter handling |
---|
105 | self.details = dict() |
---|
106 | self.params = collections.OrderedDict() |
---|
107 | self.dispersion = dict() |
---|
108 | |
---|
109 | self.orientation_params = [] |
---|
110 | self.magnetic_params = [] |
---|
111 | self.fixed = [] |
---|
112 | for p in parameters.user_parameters(): |
---|
113 | self.params[p.name] = p.default |
---|
114 | self.details[p.name] = [p.units] + p.limits |
---|
115 | if p.polydisperse: |
---|
116 | self.dispersion[p.name] = { |
---|
117 | 'width': 0, |
---|
118 | 'npts': 35, |
---|
119 | 'nsigmas': 3, |
---|
120 | 'type': 'gaussian', |
---|
121 | } |
---|
122 | if p.type == 'orientation': |
---|
123 | self.orientation_params.append(p.name) |
---|
124 | self.orientation_params.append(p.name+".width") |
---|
125 | self.fixed.append(p.name+".width") |
---|
126 | if p.type == 'magnetic': |
---|
127 | self.orientation_params.append(p.name) |
---|
128 | self.magnetic_params.append(p.name) |
---|
129 | self.fixed.append(p.name+".width") |
---|
130 | |
---|
131 | self.non_fittable = [] |
---|
132 | |
---|
133 | ## independent parameter name and unit [string] |
---|
134 | self.input_name = model_info.get("input_name", "Q") |
---|
135 | self.input_unit = model_info.get("input_unit", "A^{-1}") |
---|
136 | self.output_name = model_info.get("output_name", "Intensity") |
---|
137 | self.output_unit = model_info.get("output_unit", "cm^{-1}") |
---|
138 | |
---|
139 | ## _persistency_dict is used by sas.perspectives.fitting.basepage |
---|
140 | ## to store dispersity reference. |
---|
141 | ## TODO: _persistency_dict to persistency_dict throughout sasview |
---|
142 | self._persistency_dict = {} |
---|
143 | |
---|
144 | ## New fields introduced for opencl rewrite |
---|
145 | self.cutoff = 1e-5 |
---|
146 | |
---|
147 | def __get_state__(self): |
---|
148 | state = self.__dict__.copy() |
---|
149 | state.pop('_model') |
---|
150 | # May need to reload model info on set state since it has pointers |
---|
151 | # to python implementations of Iq, etc. |
---|
152 | #state.pop('_model_info') |
---|
153 | return state |
---|
154 | |
---|
155 | def __set_state__(self, state): |
---|
156 | self.__dict__ = state |
---|
157 | self._model = None |
---|
158 | |
---|
159 | def __str__(self): |
---|
160 | """ |
---|
161 | :return: string representation |
---|
162 | """ |
---|
163 | return self.name |
---|
164 | |
---|
165 | def is_fittable(self, par_name): |
---|
166 | """ |
---|
167 | Check if a given parameter is fittable or not |
---|
168 | |
---|
169 | :param par_name: the parameter name to check |
---|
170 | """ |
---|
171 | return par_name.lower() in self.fixed |
---|
172 | #For the future |
---|
173 | #return self.params[str(par_name)].is_fittable() |
---|
174 | |
---|
175 | |
---|
176 | # pylint: disable=no-self-use |
---|
177 | def getProfile(self): |
---|
178 | """ |
---|
179 | Get SLD profile |
---|
180 | |
---|
181 | : return: (z, beta) where z is a list of depth of the transition points |
---|
182 | beta is a list of the corresponding SLD values |
---|
183 | """ |
---|
184 | return None, None |
---|
185 | |
---|
186 | def setParam(self, name, value): |
---|
187 | """ |
---|
188 | Set the value of a model parameter |
---|
189 | |
---|
190 | :param name: name of the parameter |
---|
191 | :param value: value of the parameter |
---|
192 | |
---|
193 | """ |
---|
194 | # Look for dispersion parameters |
---|
195 | toks = name.split('.') |
---|
196 | if len(toks) == 2: |
---|
197 | for item in self.dispersion.keys(): |
---|
198 | if item.lower() == toks[0].lower(): |
---|
199 | for par in self.dispersion[item]: |
---|
200 | if par.lower() == toks[1].lower(): |
---|
201 | self.dispersion[item][par] = value |
---|
202 | return |
---|
203 | else: |
---|
204 | # Look for standard parameter |
---|
205 | for item in self.params.keys(): |
---|
206 | if item.lower() == name.lower(): |
---|
207 | self.params[item] = value |
---|
208 | return |
---|
209 | |
---|
210 | raise ValueError("Model does not contain parameter %s" % name) |
---|
211 | |
---|
212 | def getParam(self, name): |
---|
213 | """ |
---|
214 | Set the value of a model parameter |
---|
215 | |
---|
216 | :param name: name of the parameter |
---|
217 | |
---|
218 | """ |
---|
219 | # Look for dispersion parameters |
---|
220 | toks = name.split('.') |
---|
221 | if len(toks) == 2: |
---|
222 | for item in self.dispersion.keys(): |
---|
223 | if item.lower() == toks[0].lower(): |
---|
224 | for par in self.dispersion[item]: |
---|
225 | if par.lower() == toks[1].lower(): |
---|
226 | return self.dispersion[item][par] |
---|
227 | else: |
---|
228 | # Look for standard parameter |
---|
229 | for item in self.params.keys(): |
---|
230 | if item.lower() == name.lower(): |
---|
231 | return self.params[item] |
---|
232 | |
---|
233 | raise ValueError("Model does not contain parameter %s" % name) |
---|
234 | |
---|
235 | def getParamList(self): |
---|
236 | """ |
---|
237 | Return a list of all available parameters for the model |
---|
238 | """ |
---|
239 | param_list = self.params.keys() |
---|
240 | # WARNING: Extending the list with the dispersion parameters |
---|
241 | param_list.extend(self.getDispParamList()) |
---|
242 | return param_list |
---|
243 | |
---|
244 | def getDispParamList(self): |
---|
245 | """ |
---|
246 | Return a list of polydispersity parameters for the model |
---|
247 | """ |
---|
248 | # TODO: fix test so that parameter order doesn't matter |
---|
249 | ret = ['%s.%s' % (p.name.lower(), ext) |
---|
250 | for p in self._model_info['parameters'].user_parameters() |
---|
251 | for ext in ('npts', 'nsigmas', 'width') |
---|
252 | if p.polydisperse] |
---|
253 | #print(ret) |
---|
254 | return ret |
---|
255 | |
---|
256 | def clone(self): |
---|
257 | """ Return a identical copy of self """ |
---|
258 | return deepcopy(self) |
---|
259 | |
---|
260 | def run(self, x=0.0): |
---|
261 | """ |
---|
262 | Evaluate the model |
---|
263 | |
---|
264 | :param x: input q, or [q,phi] |
---|
265 | |
---|
266 | :return: scattering function P(q) |
---|
267 | |
---|
268 | **DEPRECATED**: use calculate_Iq instead |
---|
269 | """ |
---|
270 | if isinstance(x, (list, tuple)): |
---|
271 | # pylint: disable=unpacking-non-sequence |
---|
272 | q, phi = x |
---|
273 | return self.calculate_Iq([q * math.cos(phi)], |
---|
274 | [q * math.sin(phi)])[0] |
---|
275 | else: |
---|
276 | return self.calculate_Iq([float(x)])[0] |
---|
277 | |
---|
278 | |
---|
279 | def runXY(self, x=0.0): |
---|
280 | """ |
---|
281 | Evaluate the model in cartesian coordinates |
---|
282 | |
---|
283 | :param x: input q, or [qx, qy] |
---|
284 | |
---|
285 | :return: scattering function P(q) |
---|
286 | |
---|
287 | **DEPRECATED**: use calculate_Iq instead |
---|
288 | """ |
---|
289 | if isinstance(x, (list, tuple)): |
---|
290 | return self.calculate_Iq([float(x[0])], [float(x[1])])[0] |
---|
291 | else: |
---|
292 | return self.calculate_Iq([float(x)])[0] |
---|
293 | |
---|
294 | def evalDistribution(self, qdist): |
---|
295 | r""" |
---|
296 | Evaluate a distribution of q-values. |
---|
297 | |
---|
298 | :param qdist: array of q or a list of arrays [qx,qy] |
---|
299 | |
---|
300 | * For 1D, a numpy array is expected as input |
---|
301 | |
---|
302 | :: |
---|
303 | |
---|
304 | evalDistribution(q) |
---|
305 | |
---|
306 | where *q* is a numpy array. |
---|
307 | |
---|
308 | * For 2D, a list of *[qx,qy]* is expected with 1D arrays as input |
---|
309 | |
---|
310 | :: |
---|
311 | |
---|
312 | qx = [ qx[0], qx[1], qx[2], ....] |
---|
313 | qy = [ qy[0], qy[1], qy[2], ....] |
---|
314 | |
---|
315 | If the model is 1D only, then |
---|
316 | |
---|
317 | .. math:: |
---|
318 | |
---|
319 | q = \sqrt{q_x^2+q_y^2} |
---|
320 | |
---|
321 | """ |
---|
322 | if isinstance(qdist, (list, tuple)): |
---|
323 | # Check whether we have a list of ndarrays [qx,qy] |
---|
324 | qx, qy = qdist |
---|
325 | if not self._model_info['parameters'].has_2d: |
---|
326 | return self.calculate_Iq(np.sqrt(qx ** 2 + qy ** 2)) |
---|
327 | else: |
---|
328 | return self.calculate_Iq(qx, qy) |
---|
329 | |
---|
330 | elif isinstance(qdist, np.ndarray): |
---|
331 | # We have a simple 1D distribution of q-values |
---|
332 | return self.calculate_Iq(qdist) |
---|
333 | |
---|
334 | else: |
---|
335 | raise TypeError("evalDistribution expects q or [qx, qy], not %r" |
---|
336 | % type(qdist)) |
---|
337 | |
---|
338 | def calculate_Iq(self, *args): |
---|
339 | """ |
---|
340 | Calculate Iq for one set of q with the current parameters. |
---|
341 | |
---|
342 | If the model is 1D, use *q*. If 2D, use *qx*, *qy*. |
---|
343 | |
---|
344 | This should NOT be used for fitting since it copies the *q* vectors |
---|
345 | to the card for each evaluation. |
---|
346 | """ |
---|
347 | if self._model is None: |
---|
348 | self._model = core.build_model(self._model_info, platform='dll') |
---|
349 | q_vectors = [np.asarray(q) for q in args] |
---|
350 | kernel = self._model.make_kernel(q_vectors) |
---|
351 | pairs = [self._get_weights(p) |
---|
352 | for p in self._model_info['parameters'].call_parameters] |
---|
353 | details, weights, values = core.build_details(kernel, pairs) |
---|
354 | return kernel(details, weights, values, cutoff=self.cutoff) |
---|
355 | kernel.q_input.release() |
---|
356 | kernel.release() |
---|
357 | return result |
---|
358 | |
---|
359 | def calculate_ER(self): |
---|
360 | """ |
---|
361 | Calculate the effective radius for P(q)*S(q) |
---|
362 | |
---|
363 | :return: the value of the effective radius |
---|
364 | """ |
---|
365 | ER = self._model_info.get('ER', None) |
---|
366 | if ER is None: |
---|
367 | return 1.0 |
---|
368 | else: |
---|
369 | values, weights = self._dispersion_mesh() |
---|
370 | fv = ER(*values) |
---|
371 | #print(values[0].shape, weights.shape, fv.shape) |
---|
372 | return np.sum(weights * fv) / np.sum(weights) |
---|
373 | |
---|
374 | def calculate_VR(self): |
---|
375 | """ |
---|
376 | Calculate the volf ratio for P(q)*S(q) |
---|
377 | |
---|
378 | :return: the value of the volf ratio |
---|
379 | """ |
---|
380 | VR = self._model_info.get('VR', None) |
---|
381 | if VR is None: |
---|
382 | return 1.0 |
---|
383 | else: |
---|
384 | values, weights = self._dispersion_mesh() |
---|
385 | whole, part = VR(*values) |
---|
386 | return np.sum(weights * part) / np.sum(weights * whole) |
---|
387 | |
---|
388 | def set_dispersion(self, parameter, dispersion): |
---|
389 | """ |
---|
390 | Set the dispersion object for a model parameter |
---|
391 | |
---|
392 | :param parameter: name of the parameter [string] |
---|
393 | :param dispersion: dispersion object of type Dispersion |
---|
394 | """ |
---|
395 | if parameter.lower() in (s.lower() for s in self.params.keys()): |
---|
396 | # TODO: Store the disperser object directly in the model. |
---|
397 | # The current method of creating one on the fly whenever it is |
---|
398 | # needed is kind of funky. |
---|
399 | # Note: can't seem to get disperser parameters from sasview |
---|
400 | # (1) Could create a sasview model that has not yet # been |
---|
401 | # converted, assign the disperser to one of its polydisperse |
---|
402 | # parameters, then retrieve the disperser parameters from the |
---|
403 | # sasview model. (2) Could write a disperser parameter retriever |
---|
404 | # in sasview. (3) Could modify sasview to use sasmodels.weights |
---|
405 | # dispersers. |
---|
406 | # For now, rely on the fact that the sasview only ever uses |
---|
407 | # new dispersers in the set_dispersion call and create a new |
---|
408 | # one instead of trying to assign parameters. |
---|
409 | from . import weights |
---|
410 | disperser = weights.dispersers[dispersion.__class__.__name__] |
---|
411 | dispersion = weights.models[disperser]() |
---|
412 | self.dispersion[parameter] = dispersion.get_pars() |
---|
413 | else: |
---|
414 | raise ValueError("%r is not a dispersity or orientation parameter") |
---|
415 | |
---|
416 | def _dispersion_mesh(self): |
---|
417 | """ |
---|
418 | Create a mesh grid of dispersion parameters and weights. |
---|
419 | |
---|
420 | Returns [p1,p2,...],w where pj is a vector of values for parameter j |
---|
421 | and w is a vector containing the products for weights for each |
---|
422 | parameter set in the vector. |
---|
423 | """ |
---|
424 | pars = self._model_info['partype']['volume'] |
---|
425 | return core.dispersion_mesh([self._get_weights(p) for p in pars]) |
---|
426 | |
---|
427 | def _get_weights(self, par): |
---|
428 | """ |
---|
429 | Return dispersion weights for parameter |
---|
430 | """ |
---|
431 | if par.polydisperse: |
---|
432 | dis = self.dispersion[par.name] |
---|
433 | value, weight = weights.get_weights( |
---|
434 | dis['type'], dis['npts'], dis['width'], dis['nsigmas'], |
---|
435 | self.params[par.name], par.limits, par.relative_pd) |
---|
436 | return value, weight / np.sum(weight) |
---|
437 | else: |
---|
438 | return [self.params[par.name]], [] |
---|
439 | |
---|
440 | def test_model(): |
---|
441 | """ |
---|
442 | Test that a sasview model (cylinder) can be run. |
---|
443 | """ |
---|
444 | Cylinder = _make_standard_model('cylinder') |
---|
445 | cylinder = Cylinder() |
---|
446 | return cylinder.evalDistribution([0.1,0.1]) |
---|
447 | |
---|
448 | |
---|
449 | def test_model_list(): |
---|
450 | """ |
---|
451 | Make sure that all models build as sasview models. |
---|
452 | """ |
---|
453 | from .exception import annotate_exception |
---|
454 | for name in core.list_models(): |
---|
455 | try: |
---|
456 | _make_standard_model(name) |
---|
457 | except: |
---|
458 | annotate_exception("when loading "+name) |
---|
459 | raise |
---|
460 | |
---|
461 | if __name__ == "__main__": |
---|
462 | print("cylinder(0.1,0.1)=%g"%test_model()) |
---|