manipulations.py @ 0b12abb5

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 0b12abb5 was 0997158f, checked in by Gervaise Alina <gervyh@…>, 15 years ago
working on documentation
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1
2	#####################################################################
3	#This software was developed by the University of Tennessee as part of the
4	#Distributed Data Analysis of Neutron Scattering Experiments (DANSE)
5	#project funded by the US National Science Foundation.
6	#See the license text in license.txt
7	#copyright 2008, University of Tennessee
8	######################################################################
9
10	"""
11	Data manipulations for 2D data sets.
12	Using the meta data information, various types of averaging
13	are performed in Q-space
14	"""
15
16
17	#TODO: copy the meta data from the 2D object to the resulting 1D object
18
19	from data_info import plottable_2D, Data1D
20	import math
21	import numpy
22
23	def get_q(dx, dy, det_dist, wavelength):
24	"""
25	:param dx: x-distance from beam center [mm]
26	:param dy: y-distance from beam center [mm]
27
28	:return: q-value at the given position
29	"""
30	# Distance from beam center in the plane of detector
31	plane_dist = math.sqrt(dxdx + dydy)
32	# Half of the scattering angle
33	theta = 0.5*math.atan(plane_dist/det_dist)
34	return (4.0math.pi/wavelength)math.sin(theta)
35
36	def get_q_compo(dx, dy, det_dist, wavelength,compo=None):
37	"""
38	This reduces tiny error at very large q.
39	Implementation of this func is not started yet.<--ToDo
40	"""
41	if dy==0:
42	if dx>=0:
43	angle_xy=0
44	else:
45	angle_xy=math.pi
46	else:
47	angle_xy=math.atan(dx/dy)
48
49	if compo=="x":
50	out=get_q(dx, dy, det_dist, wavelength)*cos(angle_xy)
51	elif compo=="y":
52	out=get_q(dx, dy, det_dist, wavelength)*sin(angle_xy)
53	else:
54	out=get_q(dx, dy, det_dist, wavelength)
55	return out
56
57	def flip_phi(phi):
58	"""
59	Correct phi to within the 0 <= to <= 2pi range
60
61	:return: phi in >=0 and <=2Pi
62	"""
63	Pi = math.pi
64	if phi < 0:
65	phi_out = phi + 2*Pi
66	elif phi > 2*Pi:
67	phi_out = phi - 2*Pi
68	else:
69	phi_out = phi
70	return phi_out
71
72	def reader2D_converter(data2d=None):
73	"""
74	convert old 2d format opened by IhorReader or danse_reader to new Data2D format
75
76	:param data2d: 2d array of Data2D object
77
78	:return: 1d arrays of Data2D object
79
80	"""
81	if data2d.data==None or data2d.x_bins==None or data2d.y_bins==None:
82	raise ValueError,"Can't convert this data: data=None..."
83
84	from DataLoader.data_info import Data2D
85
86	new_x = numpy.tile(data2d.x_bins, (len(data2d.y_bins),1))
87	new_y = numpy.tile(data2d.y_bins, (len(data2d.x_bins),1))
88	new_y = new_y.swapaxes(0,1)
89
90	new_data = data2d.data.flatten()
91	qx_data = new_x.flatten()
92	qy_data = new_y.flatten()
93	q_data = numpy.sqrt(qx_dataqx_data+qy_dataqy_data)
94	if data2d.err_data == None or numpy.any(data2d.err_data<=0):
95	new_err_data = numpy.sqrt(numpy.abs(new_data))
96	else:
97	new_err_data = data2d.err_data.flatten()
98	mask = numpy.ones(len(new_data), dtype = bool)
99
100	output = Data2D()
101	output = data2d
102	output.data = new_data
103	output.err_data = new_err_data
104	output.qx_data = qx_data
105	output.qy_data = qy_data
106	output.q_data = q_data
107	output.mask = mask
108
109	return output
110
111	class _Slab(object):
112	"""
113	Compute average I(Q) for a region of interest
114	"""
115	def __init__(self, x_min=0.0, x_max=0.0, y_min=0.0, y_max=0.0, bin_width=0.001):
116	# Minimum Qx value [A-1]
117	self.x_min = x_min
118	# Maximum Qx value [A-1]
119	self.x_max = x_max
120	# Minimum Qy value [A-1]
121	self.y_min = y_min
122	# Maximum Qy value [A-1]
123	self.y_max = y_max
124	# Bin width (step size) [A-1]
125	self.bin_width = bin_width
126	# If True, I(\|Q\|) will be return, otherwise, negative q-values are allowed
127	self.fold = False
128
129	def __call__(self, data2D): return NotImplemented
130
131	def _avg(self, data2D, maj):
132	"""
133	Compute average I(Q_maj) for a region of interest.
134	The major axis is defined as the axis of Q_maj.
135	The minor axis is the axis that we average over.
136
137	:param data2D: Data2D object
138	:param maj_min: min value on the major axis
139
140	:return: Data1D object
141	"""
142	if len(data2D.detector) != 1:
143	raise RuntimeError, "_Slab._avg: invalid number of detectors: %g" % len(data2D.detector)
144
145	# Get data
146	data = data2D.data[numpy.isfinite(data2D.data)]
147	q_data = data2D.q_data[numpy.isfinite(data2D.data)]
148	err_data = data2D.err_data[numpy.isfinite(data2D.data)]
149	qx_data = data2D.qx_data[numpy.isfinite(data2D.data)]
150	qy_data = data2D.qy_data[numpy.isfinite(data2D.data)]
151
152	# Build array of Q intervals
153	if maj=='x':
154	if self.fold: x_min = 0
155	else: x_min = self.x_min
156	nbins = int(math.ceil((self.x_max-x_min)/self.bin_width))
157	qbins = self.bin_width*numpy.arange(nbins)+ x_min
158	elif maj=='y':
159	if self.fold: y_min = 0
160	else: y_min = self.y_min
161	nbins = int(math.ceil((self.y_max-y_min)/self.bin_width))
162	qbins = self.bin_width*numpy.arange(nbins)+ y_min
163	else:
164	raise RuntimeError, "_Slab._avg: unrecognized axis %s" % str(maj)
165
166	x = numpy.zeros(nbins)
167	y = numpy.zeros(nbins)
168	err_y = numpy.zeros(nbins)
169	y_counts = numpy.zeros(nbins)
170
171	# Average pixelsize in q space
172	for npts in range(len(data)):
173	# default frac
174	frac_x = 0
175	frac_y = 0
176	# get ROI
177	if self.x_min <= qx_data[npts] and self.x_max > qx_data[npts]:
178	frac_x = 1
179	if self.y_min <= qy_data[npts] and self.y_max > qy_data[npts]:
180	frac_y = 1
181
182	frac = frac_x * frac_y
183
184	if frac == 0: continue
185
186	# binning: find axis of q
187	if maj=='x':
188	q_value = qx_data[npts]
189	min = x_min
190	if maj=='y':
191	q_value = qy_data[npts]
192	min = y_min
193	if self.fold and q_value<0: q_value = -q_value
194
195	# bin
196	i_q = int(math.ceil((q_value-min)/self.bin_width)) - 1
197
198	# skip outside of max bins
199	if i_q<0 or i_q>=nbins: continue
200
201	# give it full weight
202	#frac = 1
203
204	#TODO: find better definition of x[i_q] based on q_data
205	x[i_q] = min +(i_q+1)*self.bin_width/2.0
206	y[i_q] += frac * data[npts]
207
208	if err_data == None or err_data[npts]==0.0:
209	if data[npts] <0: data[npts] = -data[npts]
210	err_y[i_q] += frac * frac * data[npts]
211	else:
212	err_y[i_q] += frac * frac * err_data[npts] * err_data[npts]
213	y_counts[i_q] += frac
214
215	# Average the sums
216	for n in range(nbins):
217	err_y[n] = math.sqrt(err_y[n])
218
219	err_y = err_y/y_counts
220	y = y/y_counts
221
222	idx = (numpy.isfinite(y)& numpy.isfinite(x))
223
224	if not idx.any():
225	raise ValueError, "Average Error: No points inside ROI to average..."
226	elif len(y[idx])!= nbins:
227	print "resulted",nbins- len(y[idx]),"empty bin(s) due to tight binning..."
228	return Data1D(x=x[idx], y=y[idx], dy=err_y[idx])
229
230	class SlabY(_Slab):
231	"""
232	Compute average I(Qy) for a region of interest
233	"""
234	def __call__(self, data2D):
235	"""
236	Compute average I(Qy) for a region of interest
237
238	:param data2D: Data2D object
239
240	:return: Data1D object
241	"""
242	return self._avg(data2D, 'y')
243
244	class SlabX(_Slab):
245	"""
246	Compute average I(Qx) for a region of interest
247	"""
248	def __call__(self, data2D):
249	"""
250	Compute average I(Qx) for a region of interest
251
252	:param data2D: Data2D object
253
254	:return: Data1D object
255
256	"""
257	return self._avg(data2D, 'x')
258
259	class Boxsum(object):
260	"""
261	Perform the sum of counts in a 2D region of interest.
262	"""
263	def __init__(self, x_min=0.0, x_max=0.0, y_min=0.0, y_max=0.0):
264	# Minimum Qx value [A-1]
265	self.x_min = x_min
266	# Maximum Qx value [A-1]
267	self.x_max = x_max
268	# Minimum Qy value [A-1]
269	self.y_min = y_min
270	# Maximum Qy value [A-1]
271	self.y_max = y_max
272
273	def __call__(self, data2D):
274	"""
275	Perform the sum in the region of interest
276
277	:param data2D: Data2D object
278
279	:return: number of counts, error on number of counts
280
281	"""
282	y, err_y, y_counts = self._sum(data2D)
283
284	# Average the sums
285	counts = 0 if y_counts==0 else y
286	error = 0 if y_counts==0 else math.sqrt(err_y)
287
288	return counts, error
289
290	def _sum(self, data2D):
291	"""
292	Perform the sum in the region of interest
293
294	:param data2D: Data2D object
295
296	:return: number of counts, error on number of counts, number of entries summed
297
298	"""
299	if len(data2D.detector) != 1:
300	raise RuntimeError, "Circular averaging: invalid number of detectors: %g" % len(data2D.detector)
301
302	# Get data
303	data = data2D.data[numpy.isfinite(data2D.data)]
304	q_data = data2D.q_data[numpy.isfinite(data2D.data)]
305	err_data = data2D.err_data[numpy.isfinite(data2D.data)]
306	qx_data = data2D.qx_data[numpy.isfinite(data2D.data)]
307	qy_data = data2D.qy_data[numpy.isfinite(data2D.data)]
308
309	y = 0.0
310	err_y = 0.0
311	y_counts = 0.0
312
313	# Average pixelsize in q space
314	for npts in range(len(data)):
315	# default frac
316	frac_x = 0
317	frac_y = 0
318
319	# get min and max at each points
320	qx = qx_data[npts]
321	qy = qy_data[npts]
322
323	# get the ROI
324	if self.x_min <= qx and self.x_max > qx:
325	frac_x = 1
326	if self.y_min <= qy and self.y_max > qy:
327	frac_y = 1
328
329	#Find the fraction along each directions
330	frac = frac_x * frac_y
331	if frac == 0: continue
332
333	y += frac * data[npts]
334	if err_data == None or err_data[npts]==0.0:
335	if data[npts] <0: data[npts] = -data[npts]
336	err_y += frac * frac * data[npts]
337	else:
338	err_y += frac * frac * err_data[npts] * err_data[npts]
339	y_counts += frac
340
341	return y, err_y, y_counts
342
343
344
345	class Boxavg(Boxsum):
346	"""
347	Perform the average of counts in a 2D region of interest.
348	"""
349	def __init__(self, x_min=0.0, x_max=0.0, y_min=0.0, y_max=0.0):
350	super(Boxavg, self).__init__(x_min=x_min, x_max=x_max, y_min=y_min, y_max=y_max)
351
352	def __call__(self, data2D):
353	"""
354	Perform the sum in the region of interest
355
356	:param data2D: Data2D object
357
358	:return: average counts, error on average counts
359
360	"""
361	y, err_y, y_counts = self._sum(data2D)
362
363	# Average the sums
364	counts = 0 if y_counts==0 else y/y_counts
365	error = 0 if y_counts==0 else math.sqrt(err_y)/y_counts
366
367	return counts, error
368
369	def get_pixel_fraction_square(x, xmin, xmax):
370	"""
371	Return the fraction of the length
372	from xmin to x.::
373
374
375	A B
376	+-----------+---------+
377	xmin x xmax
378
379	:param x: x-value
380	:param xmin: minimum x for the length considered
381	:param xmax: minimum x for the length considered
382
383	:return: (x-xmin)/(xmax-xmin) when xmin < x < xmax
384
385	"""
386	if x<=xmin:
387	return 0.0
388	if x>xmin and x<xmax:
389	return (x-xmin)/(xmax-xmin)
390	else:
391	return 1.0
392
393
394	class CircularAverage(object):
395	"""
396	Perform circular averaging on 2D data
397
398	The data returned is the distribution of counts
399	as a function of Q
400	"""
401	def __init__(self, r_min=0.0, r_max=0.0, bin_width=0.0005):
402	# Minimum radius included in the average [A-1]
403	self.r_min = r_min
404	# Maximum radius included in the average [A-1]
405	self.r_max = r_max
406	# Bin width (step size) [A-1]
407	self.bin_width = bin_width
408
409	def __call__(self, data2D):
410	"""
411	Perform circular averaging on the data
412
413	:param data2D: Data2D object
414
415	:return: Data1D object
416	"""
417	# Get data
418	data = data2D.data[numpy.isfinite(data2D.data)]
419	q_data = data2D.q_data[numpy.isfinite(data2D.data)]
420	err_data = data2D.err_data[numpy.isfinite(data2D.data)]
421
422	q_data_max = numpy.max(q_data)
423
424	if len(data2D.q_data) == None:
425	raise RuntimeError, "Circular averaging: invalid q_data: %g" % data2D.q_data
426
427	# Build array of Q intervals
428	nbins = int(math.ceil((self.r_max-self.r_min)/self.bin_width))
429	qbins = self.bin_width*numpy.arange(nbins)+self.r_min
430
431	x = numpy.zeros(nbins)
432	y = numpy.zeros(nbins)
433	err_y = numpy.zeros(nbins)
434	y_counts = numpy.zeros(nbins)
435
436	for npt in range(len(data)):
437	frac = 0
438
439	# q-value at the pixel (j,i)
440	q_value = q_data[npt]
441
442	data_n = data[npt]
443
444	## No need to calculate the frac when all data are within range
445	if self.r_min >= self.r_max:
446	raise ValueError, "Limit Error: min > max ???"
447
448	if self.r_min <= q_value and q_value <= self.r_max: frac = 1
449
450	if frac == 0: continue
451
452	i_q = int(math.floor((q_value-self.r_min)/self.bin_width))
453
454	# Take care of the edge case at phi = 2pi.
455	if i_q == nbins:
456	i_q = nbins -1
457
458	y[i_q] += frac * data_n
459
460	if err_data == None or err_data[npt]==0.0:
461	if data_n <0: data_n = -data_n
462	err_y[i_q] += frac * frac * data_n
463	else:
464	err_y[i_q] += frac * frac * err_data[npt] * err_data[npt]
465	y_counts[i_q] += frac
466
467	## x should be the center value of each bins
468	x = qbins+self.bin_width/2
469
470	# Average the sums
471	for n in range(nbins):
472	if err_y[n] <0: err_y[n] = -err_y[n]
473	err_y[n] = math.sqrt(err_y[n])
474
475	err_y = err_y/y_counts
476	y = y/y_counts
477	idx = (numpy.isfinite(y))&(numpy.isfinite(x))
478
479	if not idx.any():
480	raise ValueError, "Average Error: No points inside ROI to average..."
481	elif len(y[idx])!= nbins:
482	print "resulted",nbins- len(y[idx]),"empty bin(s) due to tight binning..."
483
484	return Data1D(x=x[idx], y=y[idx], dy=err_y[idx])
485
486
487	class Ring(object):
488	"""
489	Defines a ring on a 2D data set.
490	The ring is defined by r_min, r_max, and
491	the position of the center of the ring.
492
493	The data returned is the distribution of counts
494	around the ring as a function of phi.
495
496	Phi_min and phi_max should be defined between 0 and 2*pi
497	in anti-clockwise starting from the x- axis on the left-hand side
498	"""
499	#Todo: remove center.
500	def __init__(self, r_min=0, r_max=0, center_x=0, center_y=0,nbins=20 ):
501	# Minimum radius
502	self.r_min = r_min
503	# Maximum radius
504	self.r_max = r_max
505	# Center of the ring in x
506	self.center_x = center_x
507	# Center of the ring in y
508	self.center_y = center_y
509	# Number of angular bins
510	self.nbins_phi = nbins
511
512	def __call__(self, data2D):
513	"""
514	Apply the ring to the data set.
515	Returns the angular distribution for a given q range
516
517	:param data2D: Data2D object
518
519	:return: Data1D object
520	"""
521	if data2D.__class__.__name__ not in ["Data2D", "plottable_2D"]:
522	raise RuntimeError, "Ring averaging only take plottable_2D objects"
523
524	Pi = math.pi
525
526	# Get data
527	data = data2D.data[numpy.isfinite(data2D.data)]
528	q_data = data2D.q_data[numpy.isfinite(data2D.data)]
529	err_data = data2D.err_data[numpy.isfinite(data2D.data)]
530	qx_data = data2D.qx_data[numpy.isfinite(data2D.data)]
531	qy_data = data2D.qy_data[numpy.isfinite(data2D.data)]
532
533	q_data_max = numpy.max(q_data)
534
535	# Set space for 1d outputs
536	phi_bins = numpy.zeros(self.nbins_phi)
537	phi_counts = numpy.zeros(self.nbins_phi)
538	phi_values = numpy.zeros(self.nbins_phi)
539	phi_err = numpy.zeros(self.nbins_phi)
540
541	for npt in range(len(data)):
542	frac = 0
543
544	# q-value at the point (npt)
545	q_value = q_data[npt]
546
547	data_n = data[npt]
548
549	# phi-value at the point (npt)
550	phi_value=math.atan2(qy_data[npt],qx_data[npt])+Pi
551
552	if self.r_min <= q_value and q_value <= self.r_max: frac = 1
553
554	if frac == 0: continue
555
556	# binning
557	i_phi = int(math.floor((self.nbins_phi)phi_value/(2Pi)))
558
559	# Take care of the edge case at phi = 2pi.
560	if i_phi == self.nbins_phi:
561	i_phi = self.nbins_phi -1
562
563	phi_bins[i_phi] += frac * data[npt]
564
565	if err_data == None or err_data[npt] ==0.0:
566	if data_n <0: data_n = -data_n
567	phi_err[i_phi] += frac * frac * math.fabs(data_n)
568	else:
569	phi_err[i_phi] += frac * frac err_data[npt]err_data[npt]
570	phi_counts[i_phi] += frac
571
572	for i in range(self.nbins_phi):
573	phi_bins[i] = phi_bins[i] / phi_counts[i]
574	phi_err[i] = math.sqrt(phi_err[i]) / phi_counts[i]
575	phi_values[i] = 2.0math.pi/self.nbins_phi(1.0*i + 0.5)
576
577	idx = (numpy.isfinite(phi_bins))
578
579	if not idx.any():
580	raise ValueError, "Average Error: No points inside ROI to average..."
581	elif len(phi_bins[idx])!= self.nbins_phi:
582	print "resulted",self.nbins_phi- len(phi_bins[idx]),"empty bin(s) due to tight binning..."
583	return Data1D(x=phi_values[idx], y=phi_bins[idx], dy=phi_err[idx])
584
585	def get_pixel_fraction(qmax, q_00, q_01, q_10, q_11):
586	"""
587	Returns the fraction of the pixel defined by
588	the four corners (q_00, q_01, q_10, q_11) that
589	has q < qmax.::
590
591	q_01 q_11
592	y=1 +--------------+
593	\| \|
594	\| \|
595	\| \|
596	y=0 +--------------+
597	q_00 q_10
598
599	x=0 x=1
600
601	"""
602
603	# y side for x = minx
604	x_0 = get_intercept(qmax, q_00, q_01)
605	# y side for x = maxx
606	x_1 = get_intercept(qmax, q_10, q_11)
607
608	# x side for y = miny
609	y_0 = get_intercept(qmax, q_00, q_10)
610	# x side for y = maxy
611	y_1 = get_intercept(qmax, q_01, q_11)
612
613	# surface fraction for a 1x1 pixel
614	frac_max = 0
615
616	if x_0 and x_1:
617	frac_max = (x_0+x_1)/2.0
618
619	elif y_0 and y_1:
620	frac_max = (y_0+y_1)/2.0
621
622	elif x_0 and y_0:
623	if q_00 < q_10:
624	frac_max = x_0*y_0/2.0
625	else:
626	frac_max = 1.0-x_0*y_0/2.0
627
628	elif x_0 and y_1:
629	if q_00 < q_10:
630	frac_max = x_0*y_1/2.0
631	else:
632	frac_max = 1.0-x_0*y_1/2.0
633
634	elif x_1 and y_0:
635	if q_00 > q_10:
636	frac_max = x_1*y_0/2.0
637	else:
638	frac_max = 1.0-x_1*y_0/2.0
639
640	elif x_1 and y_1:
641	if q_00 < q_10:
642	frac_max = 1.0 - (1.0-x_1)*(1.0-y_1)/2.0
643	else:
644	frac_max = (1.0-x_1)*(1.0-y_1)/2.0
645
646	# If we make it here, there is no intercept between
647	# this pixel and the constant-q ring. We only need
648	# to know if we have to include it or exclude it.
649	elif (q_00+q_01+q_10+q_11)/4.0 < qmax:
650	frac_max = 1.0
651
652	return frac_max
653
654	def get_intercept(q, q_0, q_1):
655	"""
656	Returns the fraction of the side at which the
657	q-value intercept the pixel, None otherwise.
658	The values returned is the fraction ON THE SIDE
659	OF THE LOWEST Q. ::
660
661
662	A B
663	+-----------+--------+ <--- pixel size
664	0 1
665	Q_0 -------- Q ----- Q_1 <--- equivalent Q range
666	if Q_1 > Q_0, A is returned
667	if Q_1 < Q_0, B is returned
668	if Q is outside the range of [Q_0, Q_1], None is returned
669
670	"""
671	if q_1 > q_0:
672	if (q > q_0 and q <= q_1):
673	return (q-q_0)/(q_1 - q_0)
674	else:
675	if (q > q_1 and q <= q_0):
676	return (q-q_1)/(q_0 - q_1)
677	return None
678
679	class _Sector:
680	"""
681	Defines a sector region on a 2D data set.
682	The sector is defined by r_min, r_max, phi_min, phi_max,
683	and the position of the center of the ring
684	where phi_min and phi_max are defined by the right and left lines wrt central line
685	and phi_max could be less than phi_min.
686
687	Phi is defined between 0 and 2*pi in anti-clockwise starting from the x- axis on the left-hand side
688	"""
689	def __init__(self, r_min, r_max, phi_min=0, phi_max=2*math.pi,nbins=20):
690	self.r_min = r_min
691	self.r_max = r_max
692	self.phi_min = phi_min
693	self.phi_max = phi_max
694	self.nbins = nbins
695
696
697	def _agv(self, data2D, run='phi'):
698	"""
699	Perform sector averaging.
700
701	:param data2D: Data2D object
702	:param run: define the varying parameter ('phi' , 'q' , or 'q2')
703
704	:return: Data1D object
705	"""
706	if data2D.__class__.__name__ not in ["Data2D", "plottable_2D"]:
707	raise RuntimeError, "Ring averaging only take plottable_2D objects"
708	Pi = math.pi
709
710	# Get the all data & info
711	data = data2D.data[numpy.isfinite(data2D.data)]
712	q_data = data2D.q_data[numpy.isfinite(data2D.data)]
713	err_data = data2D.err_data[numpy.isfinite(data2D.data)]
714	qx_data = data2D.qx_data[numpy.isfinite(data2D.data)]
715	qy_data = data2D.qy_data[numpy.isfinite(data2D.data)]
716
717	#set space for 1d outputs
718	x = numpy.zeros(self.nbins)
719	y = numpy.zeros(self.nbins)
720	y_err = numpy.zeros(self.nbins)
721	y_counts = numpy.zeros(self.nbins)
722
723	# Get the min and max into the region: 0 <= phi < 2Pi
724	phi_min = flip_phi(self.phi_min)
725	phi_max = flip_phi(self.phi_max)
726
727	q_data_max = numpy.max(q_data)
728
729	for n in range(len(data)):
730	frac = 0
731
732	# q-value at the pixel (j,i)
733	q_value = q_data[n]
734
735
736	data_n = data[n]
737
738	# Is pixel within range?
739	is_in = False
740
741	# phi-value of the pixel (j,i)
742	phi_value=math.atan2(qy_data[n],qx_data[n])+Pi
743
744	## No need to calculate the frac when all data are within range
745	if self.r_min <= q_value and q_value <= self.r_max: frac = 1
746
747	if frac == 0: continue
748
749	#In case of two ROIs (symmetric major and minor regions)(for 'q2')
750	if run.lower()=='q2':
751	## For minor sector wing
752	# Calculate the minor wing phis
753	phi_min_minor = flip_phi(phi_min-Pi)
754	phi_max_minor = flip_phi(phi_max-Pi)
755	# Check if phis of the minor ring is within 0 to 2pi
756	if phi_min_minor > phi_max_minor:
757	is_in = (phi_value > phi_min_minor or phi_value < phi_max_minor)
758	else:
759	is_in = (phi_value > phi_min_minor and phi_value < phi_max_minor)
760
761	#For all cases(i.e.,for 'q', 'q2', and 'phi')
762	#Find pixels within ROI
763	if phi_min > phi_max:
764	is_in = is_in or (phi_value > phi_min or phi_value < phi_max)
765	else:
766	is_in = is_in or (phi_value>= phi_min and phi_value <phi_max)
767
768	if not is_in: frac = 0
769	if frac == 0: continue
770
771	# Check which type of averaging we need
772	if run.lower()=='phi':
773	i_bin = int(math.floor((self.nbins)*(phi_value-self.phi_min)\
774	/(self.phi_max-self.phi_min)))
775	else:
776	i_bin = int(math.floor((self.nbins)*(q_value-self.r_min)/(self.r_max-self.r_min)))
777
778	# Take care of the edge case at phi = 2pi.
779	if i_bin == self.nbins:
780	i_bin = self.nbins -1
781
782	## Get the total y
783	y[i_bin] += frac * data_n
784
785	if err_data == None or err_data[n] ==0.0:
786	if data_n<0: data_n= -data_n
787	y_err[i_bin] += frac * frac * data_n
788	else:
789	y_err[i_bin] += frac * frac * err_data[n]*err_data[n]
790	y_counts[i_bin] += frac
791
792	# Organize the results
793	for i in range(self.nbins):
794	y[i] = y[i] / y_counts[i]
795	y_err[i] = math.sqrt(y_err[i]) / y_counts[i]
796
797	# The type of averaging: phi,q2, or q
798	# Calculate x[i]should be at the center of the bin
799	if run.lower()=='phi':
800	x[i] = (self.phi_max-self.phi_min)/self.nbins(1.0i + 0.5)+self.phi_min
801	else:
802	x[i] = (self.r_max-self.r_min)/self.nbins(1.0i + 0.5)+self.r_min
803
804	idx = (numpy.isfinite(y)& numpy.isfinite(y_err))
805
806	if not idx.any():
807	raise ValueError, "Average Error: No points inside sector of ROI to average..."
808	elif len(y[idx])!= self.nbins:
809	print "resulted",self.nbins- len(y[idx]),"empty bin(s) due to tight binning..."
810	return Data1D(x=x[idx], y=y[idx], dy=y_err[idx])
811
812	class SectorPhi(_Sector):
813	"""
814	Sector average as a function of phi.
815	I(phi) is return and the data is averaged over Q.
816
817	A sector is defined by r_min, r_max, phi_min, phi_max.
818	The number of bin in phi also has to be defined.
819	"""
820	def __call__(self, data2D):
821	"""
822	Perform sector average and return I(phi).
823
824	:param data2D: Data2D object
825	:return: Data1D object
826	"""
827
828	return self._agv(data2D, 'phi')
829
830	class SectorQ(_Sector):
831	"""
832	Sector average as a function of Q for both symatric wings.
833	I(Q) is return and the data is averaged over phi.
834
835	A sector is defined by r_min, r_max, phi_min, phi_max.
836	r_min, r_max, phi_min, phi_max >0.
837	The number of bin in Q also has to be defined.
838	"""
839	def __call__(self, data2D):
840	"""
841	Perform sector average and return I(Q).
842
843	:param data2D: Data2D object
844
845	:return: Data1D object
846	"""
847	return self._agv(data2D, 'q2')
848
849	class Ringcut(object):
850	"""
851	Defines a ring on a 2D data set.
852	The ring is defined by r_min, r_max, and
853	the position of the center of the ring.
854
855	The data returned is the region inside the ring
856
857	Phi_min and phi_max should be defined between 0 and 2*pi
858	in anti-clockwise starting from the x- axis on the left-hand side
859	"""
860	def __init__(self, r_min=0, r_max=0, center_x=0, center_y=0 ):
861	# Minimum radius
862	self.r_min = r_min
863	# Maximum radius
864	self.r_max = r_max
865	# Center of the ring in x
866	self.center_x = center_x
867	# Center of the ring in y
868	self.center_y = center_y
869
870
871	def __call__(self, data2D):
872	"""
873	Apply the ring to the data set.
874	Returns the angular distribution for a given q range
875
876	:param data2D: Data2D object
877
878	:return: index array in the range
879	"""
880	if data2D.__class__.__name__ not in ["Data2D", "plottable_2D"]:
881	raise RuntimeError, "Ring cut only take plottable_2D objects"
882
883	# Get data
884	qx_data = data2D.qx_data
885	qy_data = data2D.qy_data
886	mask = data2D.mask
887	q_data = numpy.sqrt(qx_dataqx_data+qy_dataqy_data)
888	#q_data_max = numpy.max(q_data)
889
890	# check whether or not the data point is inside ROI
891	out = (self.r_min <= q_data) & (self.r_max >= q_data)
892
893	return (out)
894
895
896	class Boxcut(object):
897	"""
898	Find a rectangular 2D region of interest.
899	"""
900	def __init__(self, x_min=0.0, x_max=0.0, y_min=0.0, y_max=0.0):
901	# Minimum Qx value [A-1]
902	self.x_min = x_min
903	# Maximum Qx value [A-1]
904	self.x_max = x_max
905	# Minimum Qy value [A-1]
906	self.y_min = y_min
907	# Maximum Qy value [A-1]
908	self.y_max = y_max
909
910	def __call__(self, data2D):
911	"""
912	Find a rectangular 2D region of interest.
913
914	:param data2D: Data2D object
915	:return: mask, 1d array (len = len(data))
916	with Trues where the data points are inside ROI, otherwise False
917	"""
918	mask = self._find(data2D)
919
920	return mask
921
922	def _find(self, data2D):
923	"""
924	Find a rectangular 2D region of interest.
925
926	:param data2D: Data2D object
927
928	:return: out, 1d array (length = len(data))
929	with Trues where the data points are inside ROI, otherwise Falses
930	"""
931	if data2D.__class__.__name__ not in ["Data2D", "plottable_2D"]:
932	raise RuntimeError, "Boxcut take only plottable_2D objects"
933	# Get qx_ and qy_data
934	qx_data = data2D.qx_data
935	qy_data = data2D.qy_data
936	mask = data2D.mask
937
938	# check whether or not the data point is inside ROI
939	outx = (self.x_min <= qx_data) & (self.x_max > qx_data)
940	outy = (self.y_min <= qy_data) & (self.y_max > qy_data)
941
942	return (outx & outy)
943
944	class Sectorcut(object):
945	"""
946	Defines a sector (major + minor) region on a 2D data set.
947	The sector is defined by phi_min, phi_max,
948	where phi_min and phi_max are defined by the right and left lines wrt central line.
949
950	Phi_min and phi_max are given in units of radian
951	and (phi_max-phi_min) should not be larger than pi
952	"""
953	def __init__(self,phi_min=0, phi_max=math.pi):
954	self.phi_min = phi_min
955	self.phi_max = phi_max
956
957	def __call__(self, data2D):
958	"""
959	Find a rectangular 2D region of interest.
960
961	:param data2D: Data2D object
962
963	:return: mask, 1d array (len = len(data))
964
965	with Trues where the data points are inside ROI, otherwise False
966	"""
967	mask = self._find(data2D)
968
969	return mask
970
971	def _find(self, data2D):
972	"""
973	Find a rectangular 2D region of interest.
974
975	:param data2D: Data2D object
976
977	:return: out, 1d array (length = len(data))
978
979	with Trues where the data points are inside ROI, otherwise Falses
980	"""
981	if data2D.__class__.__name__ not in ["Data2D", "plottable_2D"]:
982	raise RuntimeError, "Sectorcut take only plottable_2D objects"
983	Pi = math.pi
984	# Get data
985	qx_data = data2D.qx_data
986	qy_data = data2D.qy_data
987	phi_data = numpy.zeros(len(qx_data))
988
989	# get phi from data
990	phi_data = numpy.arctan2(qy_data, qx_data)
991
992	# Get the min and max into the region: -pi <= phi < Pi
993	phi_min_major = flip_phi(self.phi_min+Pi)-Pi
994	phi_max_major = flip_phi(self.phi_max+Pi)-Pi
995	# check for major sector
996	if phi_min_major > phi_max_major:
997	out_major = (phi_min_major <= phi_data) + (phi_max_major > phi_data)
998	else:
999	out_major = (phi_min_major <= phi_data) & (phi_max_major > phi_data)
1000
1001	# minor sector
1002	# Get the min and max into the region: -pi <= phi < Pi
1003	phi_min_minor = flip_phi(self.phi_min)-Pi
1004	phi_max_minor = flip_phi(self.phi_max)-Pi
1005
1006	# check for minor sector
1007	if phi_min_minor > phi_max_minor:
1008	out_minor= (phi_min_minor <= phi_data) + (phi_max_minor>= phi_data)
1009	else:
1010	out_minor = (phi_min_minor <= phi_data) & (phi_max_minor >= phi_data)
1011	out = out_major + out_minor
1012
1013	return out
1014
1015	if __name__ == "__main__":
1016
1017	from loader import Loader
1018
1019
1020	d = Loader().load('test/MAR07232_rest.ASC')
1021	#d = Loader().load('test/MP_New.sans')
1022
1023
1024	r = SectorQ(r_min=.000001, r_max=.01, phi_min=0.0, phi_max=2*math.pi)
1025	o = r(d)
1026
1027	s = Ring(r_min=.000001, r_max=.01)
1028	p = s(d)
1029
1030	for i in range(len(o.x)):
1031	print o.x[i], o.y[i], o.dy[i]
1032
1033
1034

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SasView

source: sasview/DataLoader/manipulations.py @ 0b12abb5

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