invertor.py @ 4fbc93e

ESS_GUIESS_GUI_DocsESS_GUI_batch_fittingESS_GUI_bumps_abstractionESS_GUI_iss1116ESS_GUI_iss879ESS_GUI_iss959ESS_GUI_openclESS_GUI_orderingESS_GUI_sync_sascalccostrafo411magnetic_scattrelease-4.1.1release-4.1.2release-4.2.2release_4.0.1ticket-1009ticket-1094-headlessticket-1242-2d-resolutionticket-1243ticket-1249ticket885unittest-saveload

Last change on this file since 4fbc93e was 97d69d9, checked in by Mathieu Doucet <doucetm@…>, 15 years ago
pr_inversion: get rid of unnecessary imports
Property mode set to `100644`
File size: 25.6 KB

Line
1	"""
2	Module to perform P(r) inversion.
3	The module contains the Invertor class.
4	"""
5	from sans.pr.core.pr_inversion import Cinvertor
6	import numpy
7	import sys
8	import math, time
9	from numpy.linalg import lstsq
10
11	def help():
12	"""
13	Provide general online help text
14	Future work: extend this function to allow topic selection
15	"""
16	info_txt = "The inversion approach is based on Moore, J. Appl. Cryst. (1980) 13, 168-175.\n\n"
17	info_txt += "P(r) is set to be equal to an expansion of base functions of the type "
18	info_txt += "phi_n(r) = 2rsin(pinr/D_max). The coefficient of each base functions "
19	info_txt += "in the expansion is found by performing a least square fit with the "
20	info_txt += "following fit function:\n\n"
21	info_txt += "chi2 = sum_i[ I_meas(q_i) - I_th(q_i) ]2/error**2 + Reg_term\n\n"
22	info_txt += "where I_meas(q) is the measured scattering intensity and I_th(q) is "
23	info_txt += "the prediction from the Fourier transform of the P(r) expansion. "
24	info_txt += "The Reg_term term is a regularization term set to the second derivative "
25	info_txt += "d2P(r)/dr2 integrated over r. It is used to produce a smooth P(r) output.\n\n"
26	info_txt += "The following are user inputs:\n\n"
27	info_txt += " - Number of terms: the number of base functions in the P(r) expansion.\n\n"
28	info_txt += " - Regularization constant: a multiplicative constant to set the size of "
29	info_txt += "the regularization term.\n\n"
30	info_txt += " - Maximum distance: the maximum distance between any two points in the system.\n"
31
32	return info_txt
33
34
35	class Invertor(Cinvertor):
36	"""
37	Invertor class to perform P(r) inversion
38
39	The problem is solved by posing the problem as Ax = b,
40	where x is the set of coefficients we are looking for.
41
42	Npts is the number of points.
43
44	In the following i refers to the ith base function coefficient.
45	The matrix has its entries j in its first Npts rows set to
46	A[j][i] = (Fourier transformed base function for point j)
47
48	We them choose a number of r-points, n_r, to evaluate the second
49	derivative of P(r) at. This is used as our regularization term.
50	For a vector r of length n_r, the following n_r rows are set to
51	A[j+Npts][i] = (2nd derivative of P(r), d2(P(r))/d(r)2, evaluated at r[j])
52
53	The vector b has its first Npts entries set to
54	b[j] = (I(q) observed for point j)
55
56	The following n_r entries are set to zero.
57
58	The result is found by using scipy.linalg.basic.lstsq to invert
59	the matrix and find the coefficients x.
60
61	Methods inherited from Cinvertor:
62	- get_peaks(pars): returns the number of P(r) peaks
63	- oscillations(pars): returns the oscillation parameters for the output P(r)
64	- get_positive(pars): returns the fraction of P(r) that is above zero
65	- get_pos_err(pars): returns the fraction of P(r) that is 1-sigma above zero
66	"""
67	## Chisqr of the last computation
68	chi2 = 0
69	## Time elapsed for last computation
70	elapsed = 0
71	## Alpha to get the reg term the same size as the signal
72	suggested_alpha = 0
73	## Last number of base functions used
74	nfunc = 10
75	## Last output values
76	out = None
77	## Last errors on output values
78	cov = None
79	## Background value
80	background = 0
81	## Information dictionary for application use
82	info = {}
83
84
85	def __init__(self):
86	Cinvertor.__init__(self)
87
88	def __setattr__(self, name, value):
89	"""
90	Set the value of an attribute.
91	Access the parent class methods for
92	x, y, err, d_max, q_min, q_max and alpha
93	"""
94	if name=='x':
95	if 0.0 in value:
96	raise ValueError, "Invertor: one of your q-values is zero. Delete that entry before proceeding"
97	return self.set_x(value)
98	elif name=='y':
99	return self.set_y(value)
100	elif name=='err':
101	value2 = abs(value)
102	return self.set_err(value2)
103	elif name=='d_max':
104	return self.set_dmax(value)
105	elif name=='q_min':
106	if value==None:
107	return self.set_qmin(-1.0)
108	return self.set_qmin(value)
109	elif name=='q_max':
110	if value==None:
111	return self.set_qmax(-1.0)
112	return self.set_qmax(value)
113	elif name=='alpha':
114	return self.set_alpha(value)
115	elif name=='slit_height':
116	return self.set_slit_height(value)
117	elif name=='slit_width':
118	return self.set_slit_width(value)
119	elif name=='has_bck':
120	if value==True:
121	return self.set_has_bck(1)
122	elif value==False:
123	return self.set_has_bck(0)
124	else:
125	raise ValueError, "Invertor: has_bck can only be True or False"
126
127	return Cinvertor.__setattr__(self, name, value)
128
129	def __getattr__(self, name):
130	"""
131	Return the value of an attribute
132	"""
133	import numpy
134	if name=='x':
135	out = numpy.ones(self.get_nx())
136	self.get_x(out)
137	return out
138	elif name=='y':
139	out = numpy.ones(self.get_ny())
140	self.get_y(out)
141	return out
142	elif name=='err':
143	out = numpy.ones(self.get_nerr())
144	self.get_err(out)
145	return out
146	elif name=='d_max':
147	return self.get_dmax()
148	elif name=='q_min':
149	qmin = self.get_qmin()
150	if qmin<0:
151	return None
152	return qmin
153	elif name=='q_max':
154	qmax = self.get_qmax()
155	if qmax<0:
156	return None
157	return qmax
158	elif name=='alpha':
159	return self.get_alpha()
160	elif name=='slit_height':
161	return self.get_slit_height()
162	elif name=='slit_width':
163	return self.get_slit_width()
164	elif name=='has_bck':
165	value = self.get_has_bck()
166	if value==1:
167	return True
168	else:
169	return False
170	elif name in self.__dict__:
171	return self.__dict__[name]
172	return None
173
174	def clone(self):
175	"""
176	Return a clone of this instance
177	"""
178	import copy
179
180	invertor = Invertor()
181	invertor.chi2 = self.chi2
182	invertor.elapsed = self.elapsed
183	invertor.nfunc = self.nfunc
184	invertor.alpha = self.alpha
185	invertor.d_max = self.d_max
186	invertor.q_min = self.q_min
187	invertor.q_max = self.q_max
188
189	invertor.x = self.x
190	invertor.y = self.y
191	invertor.err = self.err
192	invertor.has_bck = self.has_bck
193	invertor.slit_height = self.slit_height
194	invertor.slit_width = self.slit_width
195
196	invertor.info = copy.deepcopy(self.info)
197
198	return invertor
199
200	def invert(self, nfunc=10, nr=20):
201	"""
202	Perform inversion to P(r)
203
204	The problem is solved by posing the problem as Ax = b,
205	where x is the set of coefficients we are looking for.
206
207	Npts is the number of points.
208
209	In the following i refers to the ith base function coefficient.
210	The matrix has its entries j in its first Npts rows set to
211	A[i][j] = (Fourier transformed base function for point j)
212
213	We them choose a number of r-points, n_r, to evaluate the second
214	derivative of P(r) at. This is used as our regularization term.
215	For a vector r of length n_r, the following n_r rows are set to
216	A[i+Npts][j] = (2nd derivative of P(r), d2(P(r))/d(r)2, evaluated at r[j])
217
218	The vector b has its first Npts entries set to
219	b[j] = (I(q) observed for point j)
220
221	The following n_r entries are set to zero.
222
223	The result is found by using scipy.linalg.basic.lstsq to invert
224	the matrix and find the coefficients x.
225
226	@param nfunc: number of base functions to use.
227	@param nr: number of r points to evaluate the 2nd derivative at for the reg. term.
228	@return: c_out, c_cov - the coefficients with covariance matrix
229	"""
230	# Reset the background value before proceeding
231	self.background = 0.0
232	return self.lstsq(nfunc, nr=nr)
233
234	def iq(self, out, q):
235	"""
236	Function to call to evaluate the scattering intensity
237	@param args: c-parameters, and q
238	@return: I(q)
239	"""
240	return Cinvertor.iq(self, out, q)+self.background
241
242	def invert_optimize(self, nfunc=10, nr=20):
243	"""
244	Slower version of the P(r) inversion that uses scipy.optimize.leastsq.
245
246	This probably produce more reliable results, but is much slower.
247	The minimization function is set to sum_i[ (I_obs(q_i) - I_theo(q_i))/err*2 ] + alpha reg_term,
248	where the reg_term is given by Svergun: it is the integral of the square of the first derivative
249	of P(r), d(P(r))/dr, integrated over the full range of r.
250
251	@param nfunc: number of base functions to use.
252	@param nr: number of r points to evaluate the 2nd derivative at for the reg. term.
253	@return: c_out, c_cov - the coefficients with covariance matrix
254	"""
255
256	from scipy import optimize
257	import time
258
259	self.nfunc = nfunc
260	# First, check that the current data is valid
261	if self.is_valid()<=0:
262	raise RuntimeError, "Invertor.invert: Data array are of different length"
263
264	p = numpy.ones(nfunc)
265	t_0 = time.time()
266	out, cov_x, info, mesg, success = optimize.leastsq(self.residuals, p, full_output=1, warning=True)
267
268	# Compute chi^2
269	res = self.residuals(out)
270	chisqr = 0
271	for i in range(len(res)):
272	chisqr += res[i]
273
274	self.chi2 = chisqr
275
276	# Store computation time
277	self.elapsed = time.time() - t_0
278
279	return out, cov_x
280
281	def pr_fit(self, nfunc=5):
282	"""
283	This is a direct fit to a given P(r). It assumes that the y data
284	is set to some P(r) distribution that we are trying to reproduce
285	with a set of base functions.
286
287	This method is provided as a test.
288	"""
289	from scipy import optimize
290
291	# First, check that the current data is valid
292	if self.is_valid()<=0:
293	raise RuntimeError, "Invertor.invert: Data arrays are of different length"
294
295	p = numpy.ones(nfunc)
296	t_0 = time.time()
297	out, cov_x, info, mesg, success = optimize.leastsq(self.pr_residuals, p, full_output=1, warning=True)
298
299	# Compute chi^2
300	res = self.pr_residuals(out)
301	chisqr = 0
302	for i in range(len(res)):
303	chisqr += res[i]
304
305	self.chisqr = chisqr
306
307	# Store computation time
308	self.elapsed = time.time() - t_0
309
310	return out, cov_x
311
312	def pr_err(self, c, c_cov, r):
313	"""
314	Returns the value of P(r) for a given r, and base function
315	coefficients, with error.
316
317	@param c: base function coefficients
318	@param c_cov: covariance matrice of the base function coefficients
319	@param r: r-value to evaluate P(r) at
320	@return: P(r)
321	"""
322	return self.get_pr_err(c, c_cov, r)
323
324	def _accept_q(self, q):
325	"""
326	Check q-value against user-defined range
327	"""
328	if not self.q_min==None and q<self.q_min:
329	return False
330	if not self.q_max==None and q>self.q_max:
331	return False
332	return True
333
334	def lstsq(self, nfunc=5, nr=20):
335	"""
336	The problem is solved by posing the problem as Ax = b,
337	where x is the set of coefficients we are looking for.
338
339	Npts is the number of points.
340
341	In the following i refers to the ith base function coefficient.
342	The matrix has its entries j in its first Npts rows set to
343	A[i][j] = (Fourier transformed base function for point j)
344
345	We them choose a number of r-points, n_r, to evaluate the second
346	derivative of P(r) at. This is used as our regularization term.
347	For a vector r of length n_r, the following n_r rows are set to
348	A[i+Npts][j] = (2nd derivative of P(r), d2(P(r))/d(r)2, evaluated at r[j])
349
350	The vector b has its first Npts entries set to
351	b[j] = (I(q) observed for point j)
352
353	The following n_r entries are set to zero.
354
355	The result is found by using scipy.linalg.basic.lstsq to invert
356	the matrix and find the coefficients x.
357
358	@param nfunc: number of base functions to use.
359	@param nr: number of r points to evaluate the 2nd derivative at for the reg. term.
360
361	If the result does not allow us to compute the covariance matrix,
362	a matrix filled with zeros will be returned.
363
364	"""
365	# Note: To make sure an array is contiguous:
366	# blah = numpy.ascontiguousarray(blah_original)
367	# ... before passing it to C
368
369	if self.is_valid()<0:
370	raise RuntimeError, "Invertor: invalid data; incompatible data lengths."
371
372	self.nfunc = nfunc
373	# a -- An M x N matrix.
374	# b -- An M x nrhs matrix or M vector.
375	npts = len(self.x)
376	nq = nr
377	sqrt_alpha = math.sqrt(math.fabs(self.alpha))
378	if sqrt_alpha<0.0:
379	nq = 0
380
381	# If we need to fit the background, add a term
382	if self.has_bck==True:
383	nfunc_0 = nfunc
384	nfunc += 1
385
386	a = numpy.zeros([npts+nq, nfunc])
387	b = numpy.zeros(npts+nq)
388	err = numpy.zeros([nfunc, nfunc])
389
390	# Construct the a matrix and b vector that represent the problem
391	t_0 = time.time()
392	self._get_matrix(nfunc, nq, a, b)
393
394	# Perform the inversion (least square fit)
395	c, chi2, rank, n = lstsq(a, b)
396	# Sanity check
397	try:
398	float(chi2)
399	except:
400	chi2 = -1.0
401	self.chi2 = chi2
402
403	inv_cov = numpy.zeros([nfunc,nfunc])
404	# Get the covariance matrix, defined as inv_cov = a_transposed * a
405	self._get_invcov_matrix(nfunc, nr, a, inv_cov)
406
407	# Compute the reg term size for the output
408	sum_sig, sum_reg = self._get_reg_size(nfunc, nr, a)
409
410	if math.fabs(self.alpha)>0:
411	new_alpha = sum_sig/(sum_reg/self.alpha)
412	else:
413	new_alpha = 0.0
414	self.suggested_alpha = new_alpha
415
416	try:
417	cov = numpy.linalg.pinv(inv_cov)
418	err = math.fabs(chi2/float(npts-nfunc)) * cov
419	except:
420	# We were not able to estimate the errors
421	# Return an empty error matrix
422	pass
423
424	# Keep a copy of the last output
425	if self.has_bck==False:
426	self.background = 0
427	self.out = c
428	self.cov = err
429	else:
430	self.background = c[0]
431
432	err_0 = numpy.zeros([nfunc, nfunc])
433	c_0 = numpy.zeros(nfunc)
434
435	for i in range(nfunc_0):
436	c_0[i] = c[i+1]
437	for j in range(nfunc_0):
438	err_0[i][j] = err[i+1][j+1]
439
440	self.out = c_0
441	self.cov = err_0
442
443	return self.out, self.cov
444
445	def estimate_numterms(self, isquit_func=None):
446	"""
447	Returns a reasonable guess for the
448	number of terms
449	@param isquit_func: reference to thread function to call to
450	check whether the computation needs to
451	be stopped.
452
453	@return: number of terms, alpha, message
454	"""
455	from num_term import Num_terms
456	estimator = Num_terms(self.clone())
457	try:
458	return estimator.num_terms(isquit_func)
459	except:
460	# If we fail, estimate alpha and return the default
461	# number of terms
462	best_alpha, message, elapsed =self.estimate_alpha(self.nfunc)
463	return self.nfunc, best_alpha, "Could not estimate number of terms"
464
465	def estimate_alpha(self, nfunc):
466	"""
467	Returns a reasonable guess for the
468	regularization constant alpha
469
470	@param nfunc: number of terms to use in the expansion.
471	@return: alpha, message, elapsed
472
473	where alpha is the estimate for alpha,
474	message is a message for the user,
475	elapsed is the computation time
476	"""
477	import time
478	try:
479	pr = self.clone()
480
481	# T_0 for computation time
482	starttime = time.time()
483	elapsed = 0
484
485	# If the current alpha is zero, try
486	# another value
487	if pr.alpha<=0:
488	pr.alpha = 0.0001
489
490	# Perform inversion to find the largest alpha
491	out, cov = pr.invert(nfunc)
492	elapsed = time.time()-starttime
493	initial_alpha = pr.alpha
494	initial_peaks = pr.get_peaks(out)
495
496	# Try the inversion with the estimated alpha
497	pr.alpha = pr.suggested_alpha
498	out, cov = pr.invert(nfunc)
499
500	npeaks = pr.get_peaks(out)
501	# if more than one peak to start with
502	# just return the estimate
503	if npeaks>1:
504	#message = "Your P(r) is not smooth, please check your inversion parameters"
505	message = None
506	return pr.suggested_alpha, message, elapsed
507	else:
508
509	# Look at smaller values
510	# We assume that for the suggested alpha, we have 1 peak
511	# if not, send a message to change parameters
512	alpha = pr.suggested_alpha
513	best_alpha = pr.suggested_alpha
514	found = False
515	for i in range(10):
516	pr.alpha = (0.33)*(i+1)alpha
517	out, cov = pr.invert(nfunc)
518
519	peaks = pr.get_peaks(out)
520	if peaks>1:
521	found = True
522	break
523	best_alpha = pr.alpha
524
525	# If we didn't find a turning point for alpha and
526	# the initial alpha already had only one peak,
527	# just return that
528	if not found and initial_peaks==1 and initial_alpha<best_alpha:
529	best_alpha = initial_alpha
530
531	# Check whether the size makes sense
532	message=''
533
534	if not found:
535	message = None
536	elif best_alpha>=0.5*pr.suggested_alpha:
537	# best alpha is too big, return a
538	# reasonable value
539	message = "The estimated alpha for your system is too large. "
540	message += "Try increasing your maximum distance."
541
542	return best_alpha, message, elapsed
543
544	except:
545	message = "Invertor.estimate_alpha: %s" % sys.exc_value
546	return 0, message, elapsed
547
548
549	def to_file(self, path, npts=100):
550	"""
551	Save the state to a file that will be readable
552	by SliceView.
553	@param path: path of the file to write
554	@param npts: number of P(r) points to be written
555	"""
556	file = open(path, 'w')
557	file.write("#d_max=%g\n" % self.d_max)
558	file.write("#nfunc=%g\n" % self.nfunc)
559	file.write("#alpha=%g\n" % self.alpha)
560	file.write("#chi2=%g\n" % self.chi2)
561	file.write("#elapsed=%g\n" % self.elapsed)
562	file.write("#qmin=%s\n" % str(self.q_min))
563	file.write("#qmax=%s\n" % str(self.q_max))
564	file.write("#slit_height=%g\n" % self.slit_height)
565	file.write("#slit_width=%g\n" % self.slit_width)
566	file.write("#background=%g\n" % self.background)
567	if self.has_bck==True:
568	file.write("#has_bck=1\n")
569	else:
570	file.write("#has_bck=0\n")
571	file.write("#alpha_estimate=%g\n" % self.suggested_alpha)
572	if not self.out==None:
573	if len(self.out)==len(self.cov):
574	for i in range(len(self.out)):
575	file.write("#C_%i=%s+-%s\n" % (i, str(self.out[i]), str(self.cov[i][i])))
576	file.write("<r> <Pr> <dPr>\n")
577	r = numpy.arange(0.0, self.d_max, self.d_max/npts)
578
579	for r_i in r:
580	(value, err) = self.pr_err(self.out, self.cov, r_i)
581	file.write("%g %g %g\n" % (r_i, value, err))
582
583	file.close()
584
585
586	def from_file(self, path):
587	"""
588	Load the state of the Invertor from a file,
589	to be able to generate P(r) from a set of
590	parameters.
591	@param path: path of the file to load
592	"""
593	import os
594	import re
595	if os.path.isfile(path):
596	try:
597	fd = open(path, 'r')
598
599	buff = fd.read()
600	lines = buff.split('\n')
601	for line in lines:
602	if line.startswith('#d_max='):
603	toks = line.split('=')
604	self.d_max = float(toks[1])
605	elif line.startswith('#nfunc='):
606	toks = line.split('=')
607	self.nfunc = int(toks[1])
608	self.out = numpy.zeros(self.nfunc)
609	self.cov = numpy.zeros([self.nfunc, self.nfunc])
610	elif line.startswith('#alpha='):
611	toks = line.split('=')
612	self.alpha = float(toks[1])
613	elif line.startswith('#chi2='):
614	toks = line.split('=')
615	self.chi2 = float(toks[1])
616	elif line.startswith('#elapsed='):
617	toks = line.split('=')
618	self.elapsed = float(toks[1])
619	elif line.startswith('#alpha_estimate='):
620	toks = line.split('=')
621	self.suggested_alpha = float(toks[1])
622	elif line.startswith('#qmin='):
623	toks = line.split('=')
624	try:
625	self.q_min = float(toks[1])
626	except:
627	self.q_min = None
628	elif line.startswith('#qmax='):
629	toks = line.split('=')
630	try:
631	self.q_max = float(toks[1])
632	except:
633	self.q_max = None
634	elif line.startswith('#slit_height='):
635	toks = line.split('=')
636	self.slit_height = float(toks[1])
637	elif line.startswith('#slit_width='):
638	toks = line.split('=')
639	self.slit_width = float(toks[1])
640	elif line.startswith('#background='):
641	toks = line.split('=')
642	self.background = float(toks[1])
643	elif line.startswith('#has_bck='):
644	toks = line.split('=')
645	if int(toks[1])==1:
646	self.has_bck=True
647	else:
648	self.has_bck=False
649
650	# Now read in the parameters
651	elif line.startswith('#C_'):
652	toks = line.split('=')
653	p = re.compile('#C_([0-9]+)')
654	m = p.search(toks[0])
655	toks2 = toks[1].split('+-')
656	i = int(m.group(1))
657	self.out[i] = float(toks2[0])
658
659	self.cov[i][i] = float(toks2[1])
660
661	except:
662	raise RuntimeError, "Invertor.from_file: corrupted file\n%s" % sys.exc_value
663	else:
664	raise RuntimeError, "Invertor.from_file: '%s' is not a file" % str(path)
665
666
667
668
669	if __name__ == "__main__":
670	o = Invertor()
671
672
673
674
675

Note: See TracBrowser for help on using the repository browser.

SasView

source: sasview/pr_inversion/invertor.py @ 4fbc93e

Download in other formats: