1 | """ |
---|
2 | Sasmodels core. |
---|
3 | """ |
---|
4 | import sys, os |
---|
5 | import datetime |
---|
6 | |
---|
7 | from sasmodels import sesans |
---|
8 | |
---|
9 | # CRUFT python 2.6 |
---|
10 | if not hasattr(datetime.timedelta, 'total_seconds'): |
---|
11 | def delay(dt): return dt.days*86400 + dt.seconds + 1e-6*dt.microseconds |
---|
12 | else: |
---|
13 | def delay(dt): return dt.total_seconds() |
---|
14 | |
---|
15 | import numpy as np |
---|
16 | |
---|
17 | try: |
---|
18 | from .kernelcl import load_model as _loader |
---|
19 | except RuntimeError,exc: |
---|
20 | import warnings |
---|
21 | warnings.warn(str(exc)) |
---|
22 | warnings.warn("OpenCL not available --- using ctypes instead") |
---|
23 | from .kerneldll import load_model as _loader |
---|
24 | |
---|
25 | def load_model(modelname, dtype='single'): |
---|
26 | """ |
---|
27 | Load model by name. |
---|
28 | """ |
---|
29 | sasmodels = __import__('sasmodels.models.'+modelname) |
---|
30 | module = getattr(sasmodels.models, modelname, None) |
---|
31 | model = _loader(module, dtype=dtype) |
---|
32 | return model |
---|
33 | |
---|
34 | |
---|
35 | def tic(): |
---|
36 | """ |
---|
37 | Timer function. |
---|
38 | |
---|
39 | Use "toc=tic()" to start the clock and "toc()" to measure |
---|
40 | a time interval. |
---|
41 | """ |
---|
42 | then = datetime.datetime.now() |
---|
43 | return lambda: delay(datetime.datetime.now()-then) |
---|
44 | |
---|
45 | |
---|
46 | def load_data(filename): |
---|
47 | """ |
---|
48 | Load data using a sasview loader. |
---|
49 | """ |
---|
50 | from sas.dataloader.loader import Loader |
---|
51 | loader = Loader() |
---|
52 | data = loader.load(filename) |
---|
53 | if data is None: |
---|
54 | raise IOError("Data %r could not be loaded"%filename) |
---|
55 | return data |
---|
56 | |
---|
57 | |
---|
58 | def empty_data1D(q): |
---|
59 | """ |
---|
60 | Create empty 1D data using the given *q* as the x value. |
---|
61 | |
---|
62 | Resolutions dq/q is 5%. |
---|
63 | """ |
---|
64 | |
---|
65 | from sas.dataloader.data_info import Data1D |
---|
66 | |
---|
67 | Iq = 100*np.ones_like(q) |
---|
68 | dIq = np.sqrt(Iq) |
---|
69 | data = Data1D(q, Iq, dx=0.05*q, dy=dIq) |
---|
70 | data.filename = "fake data" |
---|
71 | data.qmin, data.qmax = q.min(), q.max() |
---|
72 | return data |
---|
73 | |
---|
74 | |
---|
75 | def empty_data2D(qx, qy=None): |
---|
76 | """ |
---|
77 | Create empty 2D data using the given mesh. |
---|
78 | |
---|
79 | If *qy* is missing, create a square mesh with *qy=qx*. |
---|
80 | |
---|
81 | Resolution dq/q is 5%. |
---|
82 | """ |
---|
83 | from sas.dataloader.data_info import Data2D, Detector |
---|
84 | |
---|
85 | if qy is None: |
---|
86 | qy = qx |
---|
87 | Qx,Qy = np.meshgrid(qx,qy) |
---|
88 | Qx,Qy = Qx.flatten(), Qy.flatten() |
---|
89 | Iq = 100*np.ones_like(Qx) |
---|
90 | dIq = np.sqrt(Iq) |
---|
91 | mask = np.ones(len(Iq), dtype='bool') |
---|
92 | |
---|
93 | data = Data2D() |
---|
94 | data.filename = "fake data" |
---|
95 | data.qx_data = Qx |
---|
96 | data.qy_data = Qy |
---|
97 | data.data = Iq |
---|
98 | data.err_data = dIq |
---|
99 | data.mask = mask |
---|
100 | |
---|
101 | # 5% dQ/Q resolution |
---|
102 | data.dqx_data = 0.05*Qx |
---|
103 | data.dqy_data = 0.05*Qy |
---|
104 | |
---|
105 | detector = Detector() |
---|
106 | detector.pixel_size.x = 5 # mm |
---|
107 | detector.pixel_size.y = 5 # mm |
---|
108 | detector.distance = 4 # m |
---|
109 | data.detector.append(detector) |
---|
110 | data.xbins = qx |
---|
111 | data.ybins = qy |
---|
112 | data.source.wavelength = 5 # angstroms |
---|
113 | data.source.wavelength_unit = "A" |
---|
114 | data.Q_unit = "1/A" |
---|
115 | data.I_unit = "1/cm" |
---|
116 | data.q_data = np.sqrt(Qx**2 + Qy**2) |
---|
117 | data.xaxis("Q_x", "A^{-1}") |
---|
118 | data.yaxis("Q_y", "A^{-1}") |
---|
119 | data.zaxis("Intensity", r"\text{cm}^{-1}") |
---|
120 | return data |
---|
121 | |
---|
122 | |
---|
123 | def set_beam_stop(data, radius, outer=None): |
---|
124 | """ |
---|
125 | Add a beam stop of the given *radius*. If *outer*, make an annulus. |
---|
126 | """ |
---|
127 | from sas.dataloader.manipulations import Ringcut |
---|
128 | if hasattr(data, 'qx_data'): |
---|
129 | data.mask = Ringcut(0, radius)(data) |
---|
130 | if outer is not None: |
---|
131 | data.mask += Ringcut(outer,np.inf)(data) |
---|
132 | else: |
---|
133 | data.mask = (data.x>=radius) |
---|
134 | if outer is not None: |
---|
135 | data.mask &= (data.x<outer) |
---|
136 | |
---|
137 | |
---|
138 | def set_half(data, half): |
---|
139 | """ |
---|
140 | Select half of the data, either "right" or "left". |
---|
141 | """ |
---|
142 | from sas.dataloader.manipulations import Boxcut |
---|
143 | if half == 'right': |
---|
144 | data.mask += Boxcut(x_min=-np.inf, x_max=0.0, y_min=-np.inf, y_max=np.inf)(data) |
---|
145 | if half == 'left': |
---|
146 | data.mask += Boxcut(x_min=0.0, x_max=np.inf, y_min=-np.inf, y_max=np.inf)(data) |
---|
147 | |
---|
148 | |
---|
149 | def set_top(data, max): |
---|
150 | """ |
---|
151 | Chop the top off the data, above *max*. |
---|
152 | """ |
---|
153 | from sas.dataloader.manipulations import Boxcut |
---|
154 | data.mask += Boxcut(x_min=-np.inf, x_max=np.inf, y_min=-np.inf, y_max=max)(data) |
---|
155 | |
---|
156 | |
---|
157 | def plot_data(data, iq, vmin=None, vmax=None, scale='log'): |
---|
158 | """ |
---|
159 | Plot the target value for the data. This could be the data itself, |
---|
160 | the theory calculation, or the residuals. |
---|
161 | |
---|
162 | *scale* can be 'log' for log scale data, or 'linear'. |
---|
163 | """ |
---|
164 | from numpy.ma import masked_array, masked |
---|
165 | import matplotlib.pyplot as plt |
---|
166 | if hasattr(data, 'qx_data'): |
---|
167 | iq = iq[:] |
---|
168 | valid = np.isfinite(iq) |
---|
169 | if scale == 'log': |
---|
170 | valid[valid] = (iq[valid] > 0) |
---|
171 | iq[valid] = np.log10(iq[valid]) |
---|
172 | iq[~valid|data.mask] = 0 |
---|
173 | #plottable = iq |
---|
174 | plottable = masked_array(iq, ~valid|data.mask) |
---|
175 | xmin, xmax = min(data.qx_data), max(data.qx_data) |
---|
176 | ymin, ymax = min(data.qy_data), max(data.qy_data) |
---|
177 | if vmin is None: vmin = iq[valid&~data.mask].min() |
---|
178 | if vmax is None: vmax = iq[valid&~data.mask].max() |
---|
179 | plt.imshow(plottable.reshape(128,128), |
---|
180 | interpolation='nearest', aspect=1, origin='upper', |
---|
181 | extent=[xmin, xmax, ymin, ymax], vmin=vmin, vmax=vmax) |
---|
182 | else: # 1D data |
---|
183 | if scale == 'linear': |
---|
184 | idx = np.isfinite(iq) |
---|
185 | plt.plot(data.x[idx], iq[idx]) |
---|
186 | else: |
---|
187 | idx = np.isfinite(iq) |
---|
188 | idx[idx] = (iq[idx]>0) |
---|
189 | plt.loglog(data.x[idx], iq[idx]) |
---|
190 | |
---|
191 | |
---|
192 | def _plot_result1D(data, theory, view): |
---|
193 | """ |
---|
194 | Plot the data and residuals for 1D data. |
---|
195 | """ |
---|
196 | import matplotlib.pyplot as plt |
---|
197 | from numpy.ma import masked_array, masked |
---|
198 | #print "not a number",sum(np.isnan(data.y)) |
---|
199 | #data.y[data.y<0.05] = 0.5 |
---|
200 | mdata = masked_array(data.y, data.mask) |
---|
201 | mdata[np.isnan(mdata)] = masked |
---|
202 | if view is 'log': |
---|
203 | mdata[mdata <= 0] = masked |
---|
204 | mtheory = masked_array(theory, mdata.mask) |
---|
205 | mresid = masked_array((theory-data.y)/data.dy, mdata.mask) |
---|
206 | |
---|
207 | plt.subplot(121) |
---|
208 | plt.errorbar(data.x, mdata, yerr=data.dy) |
---|
209 | plt.plot(data.x, mtheory, '-', hold=True) |
---|
210 | plt.yscale(view) |
---|
211 | plt.subplot(122) |
---|
212 | plt.plot(data.x, mresid, 'x') |
---|
213 | #plt.axhline(1, color='black', ls='--',lw=1, hold=True) |
---|
214 | #plt.axhline(0, color='black', lw=1, hold=True) |
---|
215 | #plt.axhline(-1, color='black', ls='--',lw=1, hold=True) |
---|
216 | |
---|
217 | def _plot_sesans(data, theory, view): |
---|
218 | import matplotlib.pyplot as plt |
---|
219 | resid = (theory - data.y)/data.dy |
---|
220 | plt.subplot(121) |
---|
221 | plt.errorbar(data.x, data.y, yerr=data.dy) |
---|
222 | plt.plot(data.x, theory, '-', hold=True) |
---|
223 | plt.xlabel('spin echo length (nm)') |
---|
224 | plt.ylabel('polarization (P/P0)') |
---|
225 | plt.subplot(122) |
---|
226 | plt.plot(data.x, resid, 'x') |
---|
227 | plt.xlabel('spin echo length (nm)') |
---|
228 | plt.ylabel('residuals (P/P0)') |
---|
229 | |
---|
230 | def _plot_result2D(data, theory, view): |
---|
231 | """ |
---|
232 | Plot the data and residuals for 2D data. |
---|
233 | """ |
---|
234 | import matplotlib.pyplot as plt |
---|
235 | resid = (theory-data.data)/data.err_data |
---|
236 | plt.subplot(131) |
---|
237 | plot_data(data, data.data, scale=view) |
---|
238 | plt.colorbar() |
---|
239 | plt.subplot(132) |
---|
240 | plot_data(data, theory, scale=view) |
---|
241 | plt.colorbar() |
---|
242 | plt.subplot(133) |
---|
243 | plot_data(data, resid, scale='linear') |
---|
244 | plt.colorbar() |
---|
245 | |
---|
246 | class BumpsModel(object): |
---|
247 | """ |
---|
248 | Return a bumps wrapper for a SAS model. |
---|
249 | |
---|
250 | *data* is the data to be fitted. |
---|
251 | |
---|
252 | *model* is the SAS model, e.g., from :func:`gen.opencl_model`. |
---|
253 | |
---|
254 | *cutoff* is the integration cutoff, which avoids computing the |
---|
255 | the SAS model where the polydispersity weight is low. |
---|
256 | |
---|
257 | Model parameters can be initialized with additional keyword |
---|
258 | arguments, or by assigning to model.parameter_name.value. |
---|
259 | |
---|
260 | The resulting bumps model can be used directly in a FitProblem call. |
---|
261 | """ |
---|
262 | def __init__(self, data, model, cutoff=1e-5, **kw): |
---|
263 | from bumps.names import Parameter |
---|
264 | |
---|
265 | # remember inputs so we can inspect from outside |
---|
266 | self.data = data |
---|
267 | self.model = model |
---|
268 | self.cutoff = cutoff |
---|
269 | # TODO if isinstance(data,SESANSData1D) |
---|
270 | if hasattr(data, 'lam'): |
---|
271 | self.data_type = 'sesans' |
---|
272 | elif hasattr(data, 'qx_data'): |
---|
273 | self.data_type = 'Iqxy' |
---|
274 | else: |
---|
275 | self.data_type = 'Iq' |
---|
276 | |
---|
277 | partype = model.info['partype'] |
---|
278 | |
---|
279 | # interpret data |
---|
280 | if self.data_type == 'sesans': |
---|
281 | q = sesans.make_q(data.sample.zacceptance, data.Rmax) |
---|
282 | self.index = slice(None,None) |
---|
283 | self.iq = data.y |
---|
284 | self.diq = data.dy |
---|
285 | self._theory = np.zeros_like(q) |
---|
286 | q_vectors = [q] |
---|
287 | elif self.data_type == 'Iqxy': |
---|
288 | self.index = (data.mask==0) & (~np.isnan(data.data)) |
---|
289 | self.iq = data.data[self.index] |
---|
290 | self.diq = data.err_data[self.index] |
---|
291 | self._theory = np.zeros_like(data.data) |
---|
292 | if not partype['orientation'] and not partype['magnetic']: |
---|
293 | q_vectors = [np.sqrt(data.qx_data**2+data.qy_data**2)] |
---|
294 | else: |
---|
295 | q_vectors = [data.qx_data, data.qy_data] |
---|
296 | elif self.data_type == 'Iq': |
---|
297 | self.index = (data.x>=data.qmin) & (data.x<=data.qmax) & ~np.isnan(data.y) |
---|
298 | self.iq = data.y[self.index] |
---|
299 | self.diq = data.dy[self.index] |
---|
300 | self._theory = np.zeros_like(data.y) |
---|
301 | q_vectors = [data.x] |
---|
302 | else: |
---|
303 | raise ValueError("Unknown data type") # never gets here |
---|
304 | |
---|
305 | # Remember function inputs so we can delay loading the function and |
---|
306 | # so we can save/restore state |
---|
307 | self._fn_inputs = [v[self.index] for v in q_vectors] |
---|
308 | self._fn = None |
---|
309 | |
---|
310 | # define bumps parameters |
---|
311 | pars = [] |
---|
312 | for p in model.info['parameters']: |
---|
313 | name, default, limits, ptype = p[0], p[2], p[3], p[4] |
---|
314 | value = kw.pop(name, default) |
---|
315 | setattr(self, name, Parameter.default(value, name=name, limits=limits)) |
---|
316 | pars.append(name) |
---|
317 | for name in partype['pd-2d']: |
---|
318 | for xpart,xdefault,xlimits in [ |
---|
319 | ('_pd', 0, limits), |
---|
320 | ('_pd_n', 35, (0,1000)), |
---|
321 | ('_pd_nsigma', 3, (0, 10)), |
---|
322 | ('_pd_type', 'gaussian', None), |
---|
323 | ]: |
---|
324 | xname = name+xpart |
---|
325 | xvalue = kw.pop(xname, xdefault) |
---|
326 | if xlimits is not None: |
---|
327 | xvalue = Parameter.default(xvalue, name=xname, limits=xlimits) |
---|
328 | pars.append(xname) |
---|
329 | setattr(self, xname, xvalue) |
---|
330 | self._parameter_names = pars |
---|
331 | if kw: |
---|
332 | raise TypeError("unexpected parameters: %s"%(", ".join(sorted(kw.keys())))) |
---|
333 | self.update() |
---|
334 | |
---|
335 | def update(self): |
---|
336 | self._cache = {} |
---|
337 | |
---|
338 | def numpoints(self): |
---|
339 | return len(self.iq) |
---|
340 | |
---|
341 | def parameters(self): |
---|
342 | return dict((k,getattr(self,k)) for k in self._parameter_names) |
---|
343 | |
---|
344 | def theory(self): |
---|
345 | if 'theory' not in self._cache: |
---|
346 | if self._fn is None: |
---|
347 | input = self.model.make_input(self._fn_inputs) |
---|
348 | self._fn = self.model(input) |
---|
349 | |
---|
350 | pars = [getattr(self,p).value for p in self._fn.fixed_pars] |
---|
351 | pd_pars = [self._get_weights(p) for p in self._fn.pd_pars] |
---|
352 | #print pars |
---|
353 | self._theory[self.index] = self._fn(pars, pd_pars, self.cutoff) |
---|
354 | #self._theory[:] = self._fn.eval(pars, pd_pars) |
---|
355 | if self.data_type == 'sesans': |
---|
356 | P = sesans.hankel(self.data.x, self.data.lam*1e-9, |
---|
357 | self.data.sample.thickness/10, self._fn_inputs[0], |
---|
358 | self._theory) |
---|
359 | self._cache['theory'] = P |
---|
360 | else: |
---|
361 | self._cache['theory'] = self._theory |
---|
362 | return self._cache['theory'] |
---|
363 | |
---|
364 | def residuals(self): |
---|
365 | #if np.any(self.err ==0): print "zeros in err" |
---|
366 | return (self.theory()[self.index]-self.iq)/self.diq |
---|
367 | |
---|
368 | def nllf(self): |
---|
369 | R = self.residuals() |
---|
370 | #if np.any(np.isnan(R)): print "NaN in residuals" |
---|
371 | return 0.5*np.sum(R**2) |
---|
372 | |
---|
373 | def __call__(self): |
---|
374 | return 2*self.nllf()/self.dof |
---|
375 | |
---|
376 | def plot(self, view='log'): |
---|
377 | """ |
---|
378 | Plot the data and residuals. |
---|
379 | """ |
---|
380 | data, theory = self.data, self.theory() |
---|
381 | if self.data_type == 'Iq': |
---|
382 | _plot_result1D(data, theory, view) |
---|
383 | elif self.data_type == 'Iqxy': |
---|
384 | _plot_result2D(data, theory, view) |
---|
385 | elif self.data_type == 'sesans': |
---|
386 | _plot_sesans(data, theory, view) |
---|
387 | else: |
---|
388 | raise ValueError("Unknown data type") |
---|
389 | |
---|
390 | def simulate_data(self, noise=None): |
---|
391 | print "noise", noise |
---|
392 | if noise is None: |
---|
393 | noise = self.diq[self.index] |
---|
394 | else: |
---|
395 | noise = 0.01*noise |
---|
396 | self.diq[self.index] = noise |
---|
397 | y = self.theory() |
---|
398 | y += y*np.random.randn(*y.shape)*noise |
---|
399 | if self.data_type == 'Iq': |
---|
400 | self.data.y[self.index] = y |
---|
401 | elif self.data_type == 'Iqxy': |
---|
402 | self.data.data[self.index] = y |
---|
403 | elif self.data_type == 'sesans': |
---|
404 | self.data.y[self.index] = y |
---|
405 | else: |
---|
406 | raise ValueError("Unknown model") |
---|
407 | |
---|
408 | def save(self, basename): |
---|
409 | pass |
---|
410 | |
---|
411 | def _get_weights(self, par): |
---|
412 | from . import weights |
---|
413 | |
---|
414 | relative = self.model.info['partype']['pd-rel'] |
---|
415 | limits = self.model.info['limits'] |
---|
416 | disperser,value,npts,width,nsigma = [getattr(self, par+ext) |
---|
417 | for ext in ('_pd_type','','_pd_n','_pd','_pd_nsigma')] |
---|
418 | v,w = weights.get_weights( |
---|
419 | disperser, int(npts.value), width.value, nsigma.value, |
---|
420 | value.value, limits[par], par in relative) |
---|
421 | return v,w/w.max() |
---|
422 | |
---|
423 | def __getstate__(self): |
---|
424 | # Can't pickle gpu functions, so instead make them lazy |
---|
425 | state = self.__dict__.copy() |
---|
426 | state['_fn'] = None |
---|
427 | return state |
---|
428 | |
---|
429 | def __setstate__(self, state): |
---|
430 | self.__dict__ = state |
---|
431 | |
---|
432 | |
---|
433 | def demo(): |
---|
434 | data = load_data('DEC07086.DAT') |
---|
435 | set_beam_stop(data, 0.004) |
---|
436 | plot_data(data) |
---|
437 | import matplotlib.pyplot as plt; plt.show() |
---|
438 | |
---|
439 | |
---|
440 | if __name__ == "__main__": |
---|
441 | demo() |
---|