source: sasmodels/sasmodels/product.py @ 21c93c3

core_shell_microgelsmagnetic_modelticket-1257-vesicle-productticket_1156ticket_1265_superballticket_822_more_unit_tests
Last change on this file since 21c93c3 was b171acd, checked in by Paul Kienzle <pkienzle@…>, 5 years ago

move 'multiplicity' handling into sasview model. Refs #1022.

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Line 
1"""
2Product model
3-------------
4
5The product model multiplies the structure factor by the form factor,
6modulated by the effective radius of the form.  The resulting model
7has a attributes of both the model description (with parameters, etc.)
8and the module evaluator (with call, release, etc.).
9
10To use it, first load form factor P and structure factor S, then create
11*make_product_info(P, S)*.
12"""
13from __future__ import print_function, division
14
15from copy import copy
16import numpy as np  # type: ignore
17
18from .modelinfo import ParameterTable, ModelInfo
19from .kernel import KernelModel, Kernel
20from .details import make_details, dispersion_mesh
21
22# pylint: disable=unused-import
23try:
24    from typing import Tuple
25except ImportError:
26    pass
27else:
28    from .modelinfo import ParameterSet
29# pylint: enable=unused-import
30
31# TODO: make estimates available to constraints
32#ESTIMATED_PARAMETERS = [
33#    ["est_radius_effective", "A", 0.0, [0, np.inf], "", "Estimated effective radius"],
34#    ["est_volume_ratio", "", 1.0, [0, np.inf], "", "Estimated volume ratio"],
35#]
36
37ER_ID = "radius_effective"
38VF_ID = "volfraction"
39
40# TODO: core_shell_sphere model has suppressed the volume ratio calculation
41# revert it after making VR and ER available at run time as constraints.
42def make_product_info(p_info, s_info):
43    # type: (ModelInfo, ModelInfo) -> ModelInfo
44    """
45    Create info block for product model.
46    """
47    # Make sure effective radius is the first parameter and
48    # make sure volume fraction is the second parameter of the
49    # structure factor calculator.  Structure factors should not
50    # have any magnetic parameters
51    if not len(s_info.parameters.kernel_parameters) >= 2:
52        raise TypeError("S needs {} and {} as its first parameters".format(ER_ID, VF_ID))
53    if not s_info.parameters.kernel_parameters[0].id == ER_ID:
54        raise TypeError("S needs {} as first parameter".format(ER_ID))
55    if not s_info.parameters.kernel_parameters[1].id == VF_ID:
56        raise TypeError("S needs {} as second parameter".format(VF_ID))
57    if not s_info.parameters.magnetism_index == []:
58        raise TypeError("S should not have SLD parameters")
59    p_id, p_name, p_pars = p_info.id, p_info.name, p_info.parameters
60    s_id, s_name, s_pars = s_info.id, s_info.name, s_info.parameters
61
62    # Create list of parameters for the combined model.  Skip the first
63    # parameter of S, which we verified above is effective radius.  If there
64    # are any names in P that overlap with those in S, modify the name in S
65    # to distinguish it.
66    p_set = set(p.id for p in p_pars.kernel_parameters)
67    s_list = [(_tag_parameter(par) if par.id in p_set else par)
68              for par in s_pars.kernel_parameters[1:]]
69    # Check if still a collision after renaming.  This could happen if for
70    # example S has volfrac and P has both volfrac and volfrac_S.
71    if any(p.id in p_set for p in s_list):
72        raise TypeError("name collision: P has P.name and P.name_S while S has S.name")
73
74    translate_name = dict((old.id, new.id) for old, new
75                          in zip(s_pars.kernel_parameters[1:], s_list))
76    combined_pars = p_pars.kernel_parameters + s_list
77    parameters = ParameterTable(combined_pars)
78    parameters.max_pd = p_pars.max_pd + s_pars.max_pd
79    def random():
80        combined_pars = p_info.random()
81        s_names = set(par.id for par in s_pars.kernel_parameters[1:])
82        combined_pars.update((translate_name[k], v)
83                             for k, v in s_info.random().items()
84                             if k in s_names)
85        return combined_pars
86
87    model_info = ModelInfo()
88    model_info.id = '@'.join((p_id, s_id))
89    model_info.name = '@'.join((p_name, s_name))
90    model_info.filename = None
91    model_info.title = 'Product of %s and %s'%(p_name, s_name)
92    model_info.description = model_info.title
93    model_info.docs = model_info.title
94    model_info.category = "custom"
95    model_info.parameters = parameters
96    model_info.random = random
97    #model_info.single = p_info.single and s_info.single
98    model_info.structure_factor = False
99    model_info.variant_info = None
100    #model_info.tests = []
101    #model_info.source = []
102    # Iq, Iqxy, form_volume, ER, VR and sesans
103    # Remember the component info blocks so we can build the model
104    model_info.composition = ('product', [p_info, s_info])
105    model_info.hidden = p_info.hidden
106    if getattr(p_info, 'profile', None) is not None:
107        profile_pars = set(p.id for p in p_info.parameters.kernel_parameters)
108        def profile(**kwargs):
109            # extract the profile args
110            kwargs = dict((k, v) for k, v in kwargs.items() if k in profile_pars)
111            return p_info.profile(**kwargs)
112    else:
113        profile = None
114    model_info.profile = profile
115    model_info.profile_axes = p_info.profile_axes
116
117    # TODO: delegate random to p_info, s_info
118    #model_info.random = lambda: {}
119
120    ## Show the parameter table
121    #from .compare import get_pars, parlist
122    #print("==== %s ====="%model_info.name)
123    #values = get_pars(model_info)
124    #print(parlist(model_info, values, is2d=True))
125    return model_info
126
127def _tag_parameter(par):
128    """
129    Tag the parameter name with _S to indicate that the parameter comes from
130    the structure factor parameter set.  This is only necessary if the
131    form factor model includes a parameter of the same name as a parameter
132    in the structure factor.
133    """
134    par = copy(par)
135    # Protect against a vector parameter in S by appending the vector length
136    # to the renamed parameter.  Note: haven't tested this since no existing
137    # structure factor models contain vector parameters.
138    vector_length = par.name[len(par.id):]
139    par.id = par.id + "_S"
140    par.name = par.id + vector_length
141    return par
142
143class ProductModel(KernelModel):
144    def __init__(self, model_info, P, S):
145        # type: (ModelInfo, KernelModel, KernelModel) -> None
146        #: Combined info plock for the product model
147        self.info = model_info
148        #: Form factor modelling individual particles.
149        self.P = P
150        #: Structure factor modelling interaction between particles.
151        self.S = S
152        #: Model precision. This is not really relevant, since it is the
153        #: individual P and S models that control the effective dtype,
154        #: converting the q-vectors to the correct type when the kernels
155        #: for each are created. Ideally this should be set to the more
156        #: precise type to avoid loss of precision, but precision in q is
157        #: not critical (single is good enough for our purposes), so it just
158        #: uses the precision of the form factor.
159        self.dtype = P.dtype  # type: np.dtype
160
161    def make_kernel(self, q_vectors):
162        # type: (List[np.ndarray]) -> Kernel
163        # Note: may be sending the q_vectors to the GPU twice even though they
164        # are only needed once.  It would mess up modularity quite a bit to
165        # handle this optimally, especially since there are many cases where
166        # separate q vectors are needed (e.g., form in python and structure
167        # in opencl; or both in opencl, but one in single precision and the
168        # other in double precision).
169        p_kernel = self.P.make_kernel(q_vectors)
170        s_kernel = self.S.make_kernel(q_vectors)
171        return ProductKernel(self.info, p_kernel, s_kernel)
172
173    def release(self):
174        # type: (None) -> None
175        """
176        Free resources associated with the model.
177        """
178        self.P.release()
179        self.S.release()
180
181
182class ProductKernel(Kernel):
183    def __init__(self, model_info, p_kernel, s_kernel):
184        # type: (ModelInfo, Kernel, Kernel) -> None
185        self.info = model_info
186        self.p_kernel = p_kernel
187        self.s_kernel = s_kernel
188        self.dtype = p_kernel.dtype
189        self.results = []  # type: List[np.ndarray]
190
191    def __call__(self, call_details, values, cutoff, magnetic):
192        # type: (CallDetails, np.ndarray, float, bool) -> np.ndarray
193        p_info, s_info = self.info.composition[1]
194
195        # if there are magnetic parameters, they will only be on the
196        # form factor P, not the structure factor S.
197        nmagnetic = len(self.info.parameters.magnetism_index)
198        if nmagnetic:
199            spin_index = self.info.parameters.npars + 2
200            magnetism = values[spin_index: spin_index+3+3*nmagnetic]
201        else:
202            magnetism = []
203        nvalues = self.info.parameters.nvalues
204        nweights = call_details.num_weights
205        weights = values[nvalues:nvalues + 2*nweights]
206
207        # Construct the calling parameters for P.
208        p_npars = p_info.parameters.npars
209        p_length = call_details.length[:p_npars]
210        p_offset = call_details.offset[:p_npars]
211        p_details = make_details(p_info, p_length, p_offset, nweights)
212        # Set p scale to the volume fraction in s, which is the first of the
213        # 'S' parameters in the parameter list, or 2+np in 0-origin.
214        volfrac = values[2+p_npars]
215        p_values = [[volfrac, 0.0], values[2:2+p_npars], magnetism, weights]
216        spacer = (32 - sum(len(v) for v in p_values)%32)%32
217        p_values.append([0.]*spacer)
218        p_values = np.hstack(p_values).astype(self.p_kernel.dtype)
219
220        # Call ER and VR for P since these are needed for S.
221        p_er, p_vr = calc_er_vr(p_info, p_details, p_values)
222        s_vr = (volfrac/p_vr if p_vr != 0. else volfrac)
223        #print("volfrac:%g p_er:%g p_vr:%g s_vr:%g"%(volfrac,p_er,p_vr,s_vr))
224
225        # Construct the calling parameters for S.
226        # The  effective radius is not in the combined parameter list, so
227        # the number of 'S' parameters is one less than expected.  The
228        # computed effective radius needs to be added into the weights
229        # vector, especially since it is a polydisperse parameter in the
230        # stand-alone structure factor models.  We will added it at the
231        # end so the remaining offsets don't need to change.
232        s_npars = s_info.parameters.npars-1
233        s_length = call_details.length[p_npars:p_npars+s_npars]
234        s_offset = call_details.offset[p_npars:p_npars+s_npars]
235        s_length = np.hstack((1, s_length))
236        s_offset = np.hstack((nweights, s_offset))
237        s_details = make_details(s_info, s_length, s_offset, nweights+1)
238        v, w = weights[:nweights], weights[nweights:]
239        s_values = [
240            # scale=1, background=0, radius_effective=p_er, volfraction=s_vr
241            [1., 0., p_er, s_vr],
242            # structure factor parameters start after scale, background and
243            # all the form factor parameters.  Skip the volfraction parameter
244            # as well, since it is computed elsewhere, and go to the end of the
245            # parameter list.
246            values[2+p_npars+1:2+p_npars+s_npars],
247            # no magnetism parameters to include for S
248            # add er into the (value, weights) pairs
249            v, [p_er], w, [1.0]
250        ]
251        spacer = (32 - sum(len(v) for v in s_values)%32)%32
252        s_values.append([0.]*spacer)
253        s_values = np.hstack(s_values).astype(self.s_kernel.dtype)
254
255        # Call the kernels
256        p_result = self.p_kernel(p_details, p_values, cutoff, magnetic)
257        s_result = self.s_kernel(s_details, s_values, cutoff, False)
258
259        #print("p_npars",p_npars,s_npars,p_er,s_vr,values[2+p_npars+1:2+p_npars+s_npars])
260        #call_details.show(values)
261        #print("values", values)
262        #p_details.show(p_values)
263        #print("=>", p_result)
264        #s_details.show(s_values)
265        #print("=>", s_result)
266
267        # remember the parts for plotting later
268        self.results = [p_result, s_result]
269
270        #import pylab as plt
271        #plt.subplot(211); plt.loglog(self.p_kernel.q_input.q, p_result, '-')
272        #plt.subplot(212); plt.loglog(self.s_kernel.q_input.q, s_result, '-')
273        #plt.figure()
274
275        return values[0]*(p_result*s_result) + values[1]
276
277    def release(self):
278        # type: () -> None
279        self.p_kernel.release()
280        self.s_kernel.release()
281
282
283def calc_er_vr(model_info, call_details, values):
284    # type: (ModelInfo, ParameterSet) -> Tuple[float, float]
285
286    if model_info.ER is None and model_info.VR is None:
287        return 1.0, 1.0
288
289    nvalues = model_info.parameters.nvalues
290    value = values[nvalues:nvalues + call_details.num_weights]
291    weight = values[nvalues + call_details.num_weights: nvalues + 2*call_details.num_weights]
292    npars = model_info.parameters.npars
293    # Note: changed from pairs ([v], [w]) to triples (p, [v], [w]), but the
294    # dispersion mesh code doesn't actually care about the nominal parameter
295    # value, p, so set it to None.
296    pairs = [(None, value[offset:offset+length], weight[offset:offset+length])
297             for p, offset, length
298             in zip(model_info.parameters.call_parameters[2:2+npars],
299                    call_details.offset,
300                    call_details.length)
301             if p.type == 'volume']
302    value, weight = dispersion_mesh(model_info, pairs)
303
304    if model_info.ER is not None:
305        individual_radii = model_info.ER(*value)
306        radius_effective = np.sum(weight*individual_radii) / np.sum(weight)
307    else:
308        radius_effective = 1.0
309
310    if model_info.VR is not None:
311        whole, part = model_info.VR(*value)
312        volume_ratio = np.sum(weight*part)/np.sum(weight*whole)
313    else:
314        volume_ratio = 1.0
315
316    return radius_effective, volume_ratio
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