-
Notifications
You must be signed in to change notification settings - Fork 122
/
RefLReduction.py
397 lines (334 loc) · 21.8 KB
/
RefLReduction.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
#pylint: disable=no-init,invalid-name
from __future__ import (absolute_import, division, print_function)
from mantid.api import *
from mantid.simpleapi import *
from mantid.kernel import *
# import sfCalculator
import sys
import os
sys.path.insert(0,os.path.dirname(__file__))
import sfCalculator # noqa
sys.path.pop(0)
class RefLReduction(PythonAlgorithm):
def category(self):
return "Reflectometry\\SNS"
def name(self):
return "RefLReduction"
def version(self):
return 1
def summary(self):
return "Liquids Reflectometer (REFL) reduction"
def PyInit(self):
self.declareProperty(IntArrayProperty("RunNumbers"), "List of run numbers to process")
self.declareProperty("NormalizationRunNumber", 0, "Run number of the normalization run to use")
self.declareProperty(IntArrayProperty("SignalPeakPixelRange"), "Pixel range defining the data peak")
self.declareProperty("SubtractSignalBackground", True,
doc='If true, the background will be subtracted from the data peak')
self.declareProperty(IntArrayProperty("SignalBackgroundPixelRange", [123, 137],
IntArrayLengthValidator(2), direction=Direction.Input),
"Pixelrange defining the background. Default:(123,137)")
self.declareProperty("NormFlag", True, doc="If true, the data will be normalized")
self.declareProperty(IntArrayProperty("NormPeakPixelRange", [127,133],
IntArrayLengthValidator(2), direction=Direction.Input),
"Pixel range defining the normalization peak")
self.declareProperty("SubtractNormBackground", True,
doc="If true, the background will be subtracted from the normalization peak")
self.declareProperty(IntArrayProperty("NormBackgroundPixelRange", [127,137],
IntArrayLengthValidator(2), direction=Direction.Input),
"Pixel range defining the background for the normalization")
self.declareProperty("LowResDataAxisPixelRangeFlag", True,
doc="If true, the low resolution direction of the data will be cropped according "+
"to the lowResDataAxisPixelRange property")
self.declareProperty(IntArrayProperty("LowResDataAxisPixelRange", [115,210],
IntArrayLengthValidator(2), direction=Direction.Input),
"Pixel range to use in the low resolution direction of the data")
self.declareProperty("LowResNormAxisPixelRangeFlag", True,
doc="If true, the low resolution direction of the normalization run will be cropped "+
"according to the LowResNormAxisPixelRange property")
self.declareProperty(IntArrayProperty("LowResNormAxisPixelRange", [115,210],
IntArrayLengthValidator(2), direction=Direction.Input),
"Pixel range to use in the low resolution direction of the normalizaion run")
self.declareProperty(FloatArrayProperty("TOFRange", [9000., 23600.],
FloatArrayLengthValidator(2), direction=Direction.Input),
"TOF range to use")
self.declareProperty("TofRangeFlag", True,
doc="If true, the TOF will be cropped according to the TOF range property")
self.declareProperty("QMin", 0.05, doc="Mnimum Q-value")
self.declareProperty("QStep", 0.02, doc="Step size in Q. Enter a negative value to get a log scale")
self.declareProperty("AngleOffset", 0.0, doc="angle offset (degrees)")
self.declareProperty("AngleOffsetError", 0.0, doc="Angle offset error (degrees)")
self.declareProperty(MatrixWorkspaceProperty("OutputWorkspace", "", Direction.Output), "Output workspace")
# self.declareProperty("", True,
# doc="Use Scaling Factor configuration file")
self.declareProperty("ScalingFactorFile", "", doc="Scaling factor configuration file")
self.declareProperty("SlitsWidthFlag", True,
doc="Looking for perfect match of slits width when using Scaling Factor file")
self.declareProperty("IncidentMediumSelected", "", doc="Incident medium used for those runs")
self.declareProperty("GeometryCorrectionFlag", False, doc="Use or not the geometry correction")
#pylint: disable=too-many-locals, too-many-branches
def PyExec(self):
print('-- > starting new Reflectometer Reduction ...')
from reduction.instruments.reflectometer import wks_utility
#remove all previous workspaces
list_mt = mtd.getObjectNames()
for _mt in list_mt:
if _mt.find('_scaled') != -1:
DeleteWorkspace(_mt)
if _mt.find('_reflectivity') != -1:
DeleteWorkspace(_mt)
# retrieve settings from GUI
print('-> Retrieving settings from GUI')
#print 'RunNumbers: ' + str(self.getProperty("RunNumbers").value)
#print 'NormalizationRunNumber: ' + str(self.getProperty("NormalizationRunNumber").value)
#print 'SignalPeakPixelRange: ' + str(self.getProperty("SignalPeakPixelRange").value)
#print 'SubtractSignalBackground: ' + str(self.getProperty("SubtractSignalBackground").value)
#print 'SignalBackgroundPixelRange: ' + str(self.getProperty("SignalBackgroundPixelRange").value)
#print "NormFlag: " + str(self.getProperty("NormFlag").value)
#print "NormPeakPixelRange: " + str(self.getProperty("NormPeakPixelRange").value)
#print "NormBackgroundPixelRange: " + str(self.getProperty("NormBackgroundPixelRange").value)
#print "SubtractNormBackground: " + str(self.getProperty("SubtractNormBackground").value)
#print "LowResDataAxisPixelRangeFlag: " + str(self.getProperty("LowResDataAxisPixelRangeFlag").value)
#print "LowResDataAxisPixelRange: " + str(self.getProperty("LowResDataAxisPixelRange").value)
#print "LowResNormAxisPixelRangeFlag: " + str(self.getProperty("LowResNormAxisPixelRangeFlag").value)
#print "LowResNormAxisPixelRange: " + str(self.getProperty("LowResNormAxisPixelRange").value)
#print "TOFRange: " + str(self.getProperty("TOFRange").value)
#print "IncidentMediumSelected: " + str(self.getProperty("incidentMediumSelected").value)
#print "GeometryCorrectionFlag: " + str(self.getProperty("GeometryCorrectionFlag").value)
#print "QMin: " + str(self.getProperty("QMin").value)
#print "QStep: " + str(self.getProperty("QStep").value)
#print "ScalingFactorFile: " + str(self.getProperty("ScalingFactorFile").value)
#print "SlitsWidthFlag: " + str(self.getProperty("SlitsWidthFlag").value)
#print "OutputWorkspace: " + str(self.getProperty("OutputWorkspace").value)
# DATA
dataRunNumbers = self.getProperty("RunNumbers").value
dataPeakRange = self.getProperty("SignalPeakPixelRange").value
dataBackRange = self.getProperty("SignalBackgroundPixelRange").value
dataBackFlag = self.getProperty("SubtractSignalBackground").value
#Due to the frame effect, it's sometimes necessary to narrow the range
#over which we add all the pixels along the low resolution
#Parameter
dataLowResFlag = self.getProperty("LowResDataAxisPixelRangeFlag")
if dataLowResFlag:
dataLowResRange = self.getProperty("LowResDataAxisPixelRange").value
else:
dataLowResRange = [0,maxX-1]
# NORM
normalizationRunNumber = self.getProperty("NormalizationRunNumber").value
# normFlag = self.getProperty("NormFlag")
normBackRange = self.getProperty("NormBackgroundPixelRange").value
normPeakRange = self.getProperty("NormPeakPixelRange").value
normBackFlag = self.getProperty("SubtractNormBackground").value
#Due to the frame effect, it's sometimes necessary to narrow the range
#over which we add all the pixels along the low resolution
#Parameter
normLowResFlag = self.getProperty("LowResNormAxisPixelRangeFlag")
if normLowResFlag:
normLowResRange = self.getProperty("LowResNormAxisPixelRange").value
else:
normLowResRange = [0,maxX-1]
#GENERAL
TOFrangeFlag = self.getProperty("TofRangeFlag")
if TOFrangeFlag:
TOFrange = self.getProperty("TOFRange").value #microS
else:
TOFrange = [0, 200000]
# TOF binning parameters
binTOFrange = [0, 200000]
binTOFsteps = 40
# geometry correction
geometryCorrectionFlag = self.getProperty("GeometryCorrectionFlag").value
qMin = self.getProperty("QMin").value
qStep = self.getProperty("QStep").value
if qStep > 0: #force logarithmic binning
qStep = -qStep
# angle offset
angleOffsetDeg = self.getProperty("AngleOffset").value
# sfCalculator settings
slitsValuePrecision = sfCalculator.PRECISION
sfFile = self.getProperty("ScalingFactorFile").value
incidentMedium = self.getProperty("IncidentMediumSelected").value
slitsWidthFlag = self.getProperty("SlitsWidthFlag").value
# ==== done retrievin the settings =====
# ==== start reduction ====
# work with data
# load data
ws_event_data = wks_utility.loadNeXus(dataRunNumbers, 'data')
is_nexus_detector_rotated_flag = wks_utility.isNexusTakeAfterRefDate(ws_event_data.getRun().getProperty('run_start').value)
print('-> is NeXus taken with new detector geometry: ' + str(is_nexus_detector_rotated_flag))
#dimension of the detector (256 by 304 pixels)
if is_nexus_detector_rotated_flag:
maxX = 256
maxY = 304
else:
maxX = 304
maxY = 256
## retrieve general informations
# calculate the central pixel (using weighted average)
print('-> retrieving general informations')
data_central_pixel = wks_utility.getCentralPixel(ws_event_data,
dataPeakRange,
is_nexus_detector_rotated_flag)
# get the distance moderator-detector and sample-detector
[dMD, dSD] = wks_utility.getDistances(ws_event_data)
# get theta
theta = wks_utility.getTheta(ws_event_data, angleOffsetDeg)
# get proton charge
pc = wks_utility.getProtonCharge(ws_event_data)
error_0 = 1. / pc
# rebin data
ws_histo_data = wks_utility.rebinNeXus(ws_event_data,
[binTOFrange[0], binTOFsteps, binTOFrange[1]],
'data')
# get q range
q_range = wks_utility.getQrange(ws_histo_data, theta, dMD, qMin, qStep)
# slit size
[first_slit_size, last_slit_size] = wks_utility.getSlitsSize(ws_histo_data)
# keep only TOF range
ws_histo_data = wks_utility.cropTOF(ws_histo_data,
TOFrange[0],
TOFrange[1],
'data')
# normalize by current proton charge
ws_histo_data = wks_utility.normalizeNeXus(ws_histo_data, 'data')
# integrate over low resolution range
[data_tof_axis, data_y_axis, data_y_error_axis] = wks_utility.integrateOverLowResRange(ws_histo_data,
dataLowResRange,
'data',
is_nexus_detector_rotated_flag)
# #DEBUG ONLY
# wks_utility.ouput_big_ascii_file(
#'/mnt/hgfs/j35/Matlab/DebugMantid/Strange0ValuesToData/data_file_after_low_resolution_integration.txt',
# data_tof_axis,
# data_y_axis,
# data_y_error_axis)
tof_axis = data_tof_axis[0:-1].copy()
tof_axis_full = data_tof_axis.copy()
# data_tof_axis.shape -> (62,)
# data_y_axis.shape -> (256,61)
#substract background
[data_y_axis, data_y_error_axis] = wks_utility.substractBackground(tof_axis ,
data_y_axis,
data_y_error_axis,
dataPeakRange,
dataBackFlag,
dataBackRange,
error_0,
'data')
# #DEBUG ONLY
# wks_utility.ouput_big_ascii_file('/mnt/hgfs/j35/Matlab/DebugMantid/Strange0ValuesToData/data_file_back_sub_not_integrated.txt',
# data_tof_axis,
# data_y_axis,
# data_y_error_axis)
# work with normalization
# load normalization
ws_event_norm = wks_utility.loadNeXus(int(normalizationRunNumber), 'normalization')
# get proton charge
pc = wks_utility.getProtonCharge(ws_event_norm)
error_0 = 1. / pc
# rebin normalization
ws_histo_norm = wks_utility.rebinNeXus(ws_event_norm,
[binTOFrange[0], binTOFsteps, binTOFrange[1]],
'normalization')
# keep only TOF range
ws_histo_norm = wks_utility.cropTOF(ws_histo_norm,
TOFrange[0],
TOFrange[1],
'normalization')
# normalize by current proton charge
ws_histo_norm = wks_utility.normalizeNeXus(ws_histo_norm, 'normalization')
# integrate over low resolution range
[norm_tof_axis, norm_y_axis, norm_y_error_axis] = wks_utility.integrateOverLowResRange(ws_histo_norm,
normLowResRange,
'normalization',
is_nexus_detector_rotated_flag)
# substract background
[norm_y_axis, norm_y_error_axis] = wks_utility.substractBackground(norm_tof_axis[0:-1],
norm_y_axis,
norm_y_error_axis,
normPeakRange,
normBackFlag,
normBackRange,
error_0,
'normalization')
[av_norm, av_norm_error] = wks_utility.fullSumWithError(norm_y_axis,
norm_y_error_axis)
# ## DEBUGGING ONLY
# wks_utility.ouput_ascii_file('/mnt/hgfs/j35/Matlab/DebugMantid/Strange0ValuesToData/norm_file_back_sub_not_integrated.txt',
# norm_tof_axis,
# av_norm,
# av_norm_error)
[final_data_y_axis, final_data_y_error_axis] = wks_utility.divideDataByNormalization(data_y_axis,
data_y_error_axis,
av_norm,
av_norm_error)
# #DEBUG ONLY
# wks_utility.ouput_big_ascii_file('/mnt/hgfs/j35/Matlab/DebugMantid/Strange0ValuesToData/data_divided_by_norm_not_integrated.txt',
# data_tof_axis,
# final_data_y_axis,
# final_data_y_error_axis)
# apply Scaling factor
[tof_axis_full, y_axis, y_error_axis, isSFfound] = wks_utility.applyScalingFactor(tof_axis_full,
final_data_y_axis,
final_data_y_error_axis,
incidentMedium,
sfFile,
slitsValuePrecision,
slitsWidthFlag)
# #DEBUG ONLY
# wks_utility.ouput_big_ascii_file('/mnt/hgfs/j35/Matlab/DebugMantid/Strange0ValuesToData/after_applying_scaling_factor.txt',
# data_tof_axis,
# y_axis,
# y_error_axis)
if geometryCorrectionFlag: # convert To Q with correction
[q_axis, y_axis, y_error_axis] = wks_utility.convertToQ(tof_axis_full,
y_axis,
y_error_axis,
peak_range = dataPeakRange,
central_pixel = data_central_pixel,
source_to_detector_distance = dMD,
sample_to_detector_distance = dSD,
theta = theta,
first_slit_size = first_slit_size,
last_slit_size = last_slit_size)
else: # convert to Q without correction
[q_axis, y_axis, y_error_axis] = wks_utility.convertToQWithoutCorrection(tof_axis_full,
y_axis,
y_error_axis,
peak_range = dataPeakRange,
source_to_detector_distance = dMD,
sample_to_detector_distance = dSD,
theta = theta,
first_slit_size = first_slit_size,
last_slit_size = last_slit_size)
# wks_utility.ouput_big_Q_ascii_file('/mnt/hgfs/j35/Matlab/DebugMantid/Strange0ValuesToData/after_conversion_to_q.txt',
# q_axis,
# y_axis,
# y_error_axis)
# create workspace
q_workspace = wks_utility.createQworkspace(q_axis, y_axis, y_error_axis)
q_rebin = Rebin(InputWorkspace=q_workspace,
Params=q_range,
PreserveEvents=True)
# keep only the q values that have non zero counts
nonzero_q_rebin_wks = wks_utility.cropAxisToOnlyNonzeroElements(q_rebin,
dataPeakRange)
# integrate spectra (normal mean) and remove first and last Q value
[final_x_axis, final_y_axis, final_error_axis] = wks_utility.integrateOverPeakRange(nonzero_q_rebin_wks, dataPeakRange)
# cleanup data
[final_y_axis, final_y_error_axis] = wks_utility.cleanupData1D(final_y_axis,
final_error_axis)
# create final workspace
import time
_time = int(time.time())
name_output_ws = self.getPropertyValue("OutputWorkspace")
name_output_ws = name_output_ws + '_#' + str(_time) + 'ts'
wks_utility.createFinalWorkspace(final_x_axis,
final_y_axis,
final_y_error_axis,
name_output_ws,
ws_event_data)
AddSampleLog(Workspace=name_output_ws,
LogName='isSFfound',
LOgText=str(isSFfound))
self.setProperty('OutputWorkspace', mtd[name_output_ws])
AlgorithmFactory.subscribe(RefLReduction)