model_thread.py @ 378e808

ESS_GUIESS_GUI_DocsESS_GUI_batch_fittingESS_GUI_bumps_abstractionESS_GUI_iss1116ESS_GUI_iss879ESS_GUI_iss959ESS_GUI_openclESS_GUI_orderingESS_GUI_sync_sascalc

Last change on this file since 378e808 was fa81e94, checked in by Piotr Rozyczko <rozyczko@…>, 7 years ago
Initial commit of the P(r) inversion perspective. Code merged from Jeff Krzywon's ESS_GUI_Pr branch. Also, minor 2to3 mods to sascalc/sasgui to enble error free setup.
Property mode set to `100755`
File size: 11.6 KB

Line
1	"""
2	Calculation thread for modeling
3	"""
4
5	import time
6	import numpy as np
7	import math
8	from sas.sascalc.data_util.calcthread import CalcThread
9	from sas.sascalc.fit.MultiplicationModel import MultiplicationModel
10
11	class Calc2D(CalcThread):
12	"""
13	Compute 2D model
14	This calculation assumes a 2-fold symmetry of the model
15	where points are computed for one half of the detector
16	and I(qx, qy) = I(-qx, -qy) is assumed.
17	"""
18	def __init__(self, data, model, smearer, qmin, qmax, page_id,
19	state=None,
20	weight=None,
21	fid=None,
22	toggle_mode_on=False,
23	completefn=None,
24	updatefn=None,
25	update_chisqr=True,
26	source='model',
27	yieldtime=0.04,
28	worktime=0.04,
29	exception_handler=None,
30	):
31	CalcThread.__init__(self, completefn, updatefn, yieldtime, worktime,
32	exception_handler=exception_handler)
33	self.qmin = qmin
34	self.qmax = qmax
35	self.weight = weight
36	self.fid = fid
37	#self.qstep = qstep
38	self.toggle_mode_on = toggle_mode_on
39	self.data = data
40	self.page_id = page_id
41	self.state = None
42	# the model on to calculate
43	self.model = model
44	self.smearer = smearer
45	self.starttime = 0
46	self.update_chisqr = update_chisqr
47	self.source = source
48
49	def compute(self):
50	"""
51	Compute the data given a model function
52	"""
53	self.starttime = time.time()
54	# Determine appropriate q range
55	if self.qmin is None:
56	self.qmin = 0
57	if self.qmax is None:
58	if self.data is not None:
59	newx = math.pow(max(math.fabs(self.data.xmax),
60	math.fabs(self.data.xmin)), 2)
61	newy = math.pow(max(math.fabs(self.data.ymax),
62	math.fabs(self.data.ymin)), 2)
63	self.qmax = math.sqrt(newx + newy)
64
65	if self.data is None:
66	msg = "Compute Calc2D receive data = %s.\n" % str(self.data)
67	raise ValueError(msg)
68
69	# Define matrix where data will be plotted
70	radius = np.sqrt((self.data.qx_data * self.data.qx_data) + \
71	(self.data.qy_data * self.data.qy_data))
72
73	# For theory, qmax is based on 1d qmax
74	# so that must be mulitified by sqrt(2) to get actual max for 2d
75	index_model = (self.qmin <= radius) & (radius <= self.qmax)
76	index_model = index_model & self.data.mask
77	index_model = index_model & np.isfinite(self.data.data)
78
79	if self.smearer is not None:
80	# Set smearer w/ data, model and index.
81	fn = self.smearer
82	fn.set_model(self.model)
83	fn.set_index(index_model)
84	# Calculate smeared Intensity
85	#(by Gaussian averaging): DataLoader/smearing2d/Smearer2D()
86	value = fn.get_value()
87	else:
88	# calculation w/o smearing
89	value = self.model.evalDistribution([
90	self.data.qx_data[index_model],
91	self.data.qy_data[index_model]
92	])
93	output = np.zeros(len(self.data.qx_data))
94	# output default is None
95	# This method is to distinguish between masked
96	#point(nan) and data point = 0.
97	output = output / output
98	# set value for self.mask==True, else still None to Plottools
99	output[index_model] = value
100	elapsed = time.time() - self.starttime
101	#self.complete(image=output,
102	# data=self.data,
103	# page_id=self.page_id,
104	# model=self.model,
105	# state=self.state,
106	# toggle_mode_on=self.toggle_mode_on,
107	# elapsed=elapsed,
108	# index=index_model,
109	# fid=self.fid,
110	# qmin=self.qmin,
111	# qmax=self.qmax,
112	# weight=self.weight,
113	# #qstep=self.qstep,
114	# update_chisqr=self.update_chisqr,
115	# source=self.source)
116	return (output,
117	self.data,
118	self.page_id,
119	self.model,
120	self.state,
121	self.toggle_mode_on,
122	elapsed,
123	index_model,
124	self.fid,
125	self.qmin,
126	self.qmax,
127	self.weight,
128	self.update_chisqr,
129	self.source)
130
131
132	class Calc1D(CalcThread):
133	"""
134	Compute 1D data
135	"""
136	def __init__(self, model,
137	page_id,
138	data,
139	fid=None,
140	qmin=None,
141	qmax=None,
142	weight=None,
143	smearer=None,
144	toggle_mode_on=False,
145	state=None,
146	completefn=None,
147	update_chisqr=True,
148	source='model',
149	updatefn=None,
150	yieldtime=0.01,
151	worktime=0.01,
152	exception_handler=None,
153	):
154	"""
155	"""
156	CalcThread.__init__(self, completefn, updatefn, yieldtime, worktime,
157	exception_handler=exception_handler)
158	self.fid = fid
159	self.data = data
160	self.qmin = qmin
161	self.qmax = qmax
162	self.model = model
163	self.weight = weight
164	self.toggle_mode_on = toggle_mode_on
165	self.state = state
166	self.page_id = page_id
167	self.smearer = smearer
168	self.starttime = 0
169	self.update_chisqr = update_chisqr
170	self.source = source
171	self.out = None
172	self.index = None
173
174	def compute(self):
175	"""
176	Compute model 1d value given qmin , qmax , x value
177	"""
178	self.starttime = time.time()
179	output = np.zeros((len(self.data.x)))
180	index = (self.qmin <= self.data.x) & (self.data.x <= self.qmax)
181
182	# If we use a smearer, also return the unsmeared model
183	unsmeared_output = None
184	unsmeared_data = None
185	unsmeared_error = None
186	##smearer the ouput of the plot
187	if self.smearer is not None:
188	first_bin, last_bin = self.smearer.get_bin_range(self.qmin,
189	self.qmax)
190	mask = self.data.x[first_bin:last_bin+1]
191	unsmeared_output = np.zeros((len(self.data.x)))
192	unsmeared_output[first_bin:last_bin+1] = self.model.evalDistribution(mask)
193	self.smearer.model = self.model
194	output = self.smearer(unsmeared_output, first_bin, last_bin)
195
196	# Rescale data to unsmeared model
197	# Check that the arrays are compatible. If we only have a model but no data,
198	# the length of data.y will be zero.
199	if isinstance(self.data.y, np.ndarray) and output.shape == self.data.y.shape:
200	unsmeared_data = np.zeros((len(self.data.x)))
201	unsmeared_error = np.zeros((len(self.data.x)))
202	unsmeared_data[first_bin:last_bin+1] = self.data.y[first_bin:last_bin+1]\
203	* unsmeared_output[first_bin:last_bin+1]\
204	/ output[first_bin:last_bin+1]
205	unsmeared_error[first_bin:last_bin+1] = self.data.dy[first_bin:last_bin+1]\
206	* unsmeared_output[first_bin:last_bin+1]\
207	/ output[first_bin:last_bin+1]
208	unsmeared_output=unsmeared_output[index]
209	unsmeared_data=unsmeared_data[index]
210	unsmeared_error=unsmeared_error
211	else:
212	output[index] = self.model.evalDistribution(self.data.x[index])
213
214	sq_values = None
215	pq_values = None
216	s_model = None
217	p_model = None
218	if isinstance(self.model, MultiplicationModel):
219	s_model = self.model.s_model
220	p_model = self.model.p_model
221	elif hasattr(self.model, "get_composition_models"):
222	p_model, s_model = self.model.get_composition_models()
223
224	if p_model is not None and s_model is not None:
225	sq_values = np.zeros((len(self.data.x)))
226	pq_values = np.zeros((len(self.data.x)))
227	sq_values[index] = s_model.evalDistribution(self.data.x[index])
228	pq_values[index] = p_model.evalDistribution(self.data.x[index])
229
230	elapsed = time.time() - self.starttime
231
232	#self.complete(x=self.data.x[index], y=output[index],
233	# page_id=self.page_id,
234	# state=self.state,
235	# weight=self.weight,
236	# fid=self.fid,
237	# toggle_mode_on=self.toggle_mode_on,
238	# elapsed=elapsed, index=index, model=self.model,
239	# data=self.data,
240	# update_chisqr=self.update_chisqr,
241	# source=self.source)
242	return (self.data.x[index], output[index],
243	self.page_id,
244	self.state,
245	self.weight,
246	self.fid,
247	self.toggle_mode_on,
248	elapsed, index, self.model,
249	self.data,
250	self.update_chisqr,
251	self.source)
252
253	# TODO: as of 4.1, the output contains more items:
254	# unsmeared_* and pq_model/sq_model
255	# Need to add these too
256
257	#self.complete(x=self.data.x[index], y=output[index],
258	# page_id=self.page_id,
259	# state=self.state,
260	# weight=self.weight,
261	# fid=self.fid,
262	# toggle_mode_on=self.toggle_mode_on,
263	# elapsed=elapsed, index=index, model=self.model,
264	# data=self.data,
265	# update_chisqr=self.update_chisqr,
266	# source=self.source,
267	# unsmeared_model=unsmeared_output,
268	# unsmeared_data=unsmeared_data,
269	# unsmeared_error=unsmeared_error,
270	# pq_model=pq_values,
271	# sq_model=sq_values)
272
273	def results(self):
274	"""
275	Send resuts of the computation
276	"""
277	return [self.out, self.index]
278
279	"""
280	Example: ::
281
282	class CalcCommandline:
283	def __init__(self, n=20000):
284	#print thread.get_ident()
285	from sas.models.CylinderModel import CylinderModel
286
287	model = CylinderModel()
288
289
290	print model.runXY([0.01, 0.02])
291
292	qmax = 0.01
293	qstep = 0.0001
294	self.done = False
295
296	x = numpy.arange(-qmax, qmax+qstep*0.01, qstep)
297	y = numpy.arange(-qmax, qmax+qstep*0.01, qstep)
298
299
300	calc_thread_2D = Calc2D(x, y, None, model.clone(),None,
301	-qmax, qmax,qstep,
302	completefn=self.complete,
303	updatefn=self.update ,
304	yieldtime=0.0)
305
306	calc_thread_2D.queue()
307	calc_thread_2D.ready(2.5)
308
309	while not self.done:
310	time.sleep(1)
311
312	def update(self,output):
313	print "update"
314
315	def complete(self, image, data, model, elapsed, qmin, qmax,index, qstep ):
316	print "complete"
317	self.done = True
318
319	if __name__ == "__main__":
320	CalcCommandline()
321	"""

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source: sasview/src/sas/sasgui/perspectives/fitting/model_thread.py @ 378e808

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