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1 '''
1 '''
2
2
3 $Author: dsuarez $
3 $Author: dsuarez $
4 $Id: Processor.py 1 2012-11-12 18:56:07Z dsuarez $
4 $Id: Processor.py 1 2012-11-12 18:56:07Z dsuarez $
5 '''
5 '''
6 import os
6 import os
7 import numpy
7 import numpy
8 import datetime
8 import datetime
9 import time
9 import time
10
10
11 from jrodata import *
11 from jrodata import *
12 from jrodataIO import *
12 from jrodataIO import *
13 from jroplot import *
13 from jroplot import *
14
14
15 class ProcessingUnit:
15 class ProcessingUnit:
16
16
17 """
17 """
18 Esta es la clase base para el procesamiento de datos.
18 Esta es la clase base para el procesamiento de datos.
19
19
20 Contiene el metodo "call" para llamar operaciones. Las operaciones pueden ser:
20 Contiene el metodo "call" para llamar operaciones. Las operaciones pueden ser:
21 - Metodos internos (callMethod)
21 - Metodos internos (callMethod)
22 - Objetos del tipo Operation (callObject). Antes de ser llamados, estos objetos
22 - Objetos del tipo Operation (callObject). Antes de ser llamados, estos objetos
23 tienen que ser agreagados con el metodo "add".
23 tienen que ser agreagados con el metodo "add".
24
24
25 """
25 """
26 # objeto de datos de entrada (Voltage, Spectra o Correlation)
26 # objeto de datos de entrada (Voltage, Spectra o Correlation)
27 dataIn = None
27 dataIn = None
28
28
29 # objeto de datos de entrada (Voltage, Spectra o Correlation)
29 # objeto de datos de entrada (Voltage, Spectra o Correlation)
30 dataOut = None
30 dataOut = None
31
31
32
32
33 objectDict = None
33 objectDict = None
34
34
35 def __init__(self):
35 def __init__(self):
36
36
37 self.objectDict = {}
37 self.objectDict = {}
38
38
39 def init(self):
39 def init(self):
40
40
41 raise ValueError, "Not implemented"
41 raise ValueError, "Not implemented"
42
42
43 def addOperation(self, object, objId):
43 def addOperation(self, object, objId):
44
44
45 """
45 """
46 Agrega el objeto "object" a la lista de objetos "self.objectList" y retorna el
46 Agrega el objeto "object" a la lista de objetos "self.objectList" y retorna el
47 identificador asociado a este objeto.
47 identificador asociado a este objeto.
48
48
49 Input:
49 Input:
50
50
51 object : objeto de la clase "Operation"
51 object : objeto de la clase "Operation"
52
52
53 Return:
53 Return:
54
54
55 objId : identificador del objeto, necesario para ejecutar la operacion
55 objId : identificador del objeto, necesario para ejecutar la operacion
56 """
56 """
57
57
58 self.objectDict[objId] = object
58 self.objectDict[objId] = object
59
59
60 return objId
60 return objId
61
61
62 def operation(self, **kwargs):
62 def operation(self, **kwargs):
63
63
64 """
64 """
65 Operacion directa sobre la data (dataOut.data). Es necesario actualizar los valores de los
65 Operacion directa sobre la data (dataOut.data). Es necesario actualizar los valores de los
66 atributos del objeto dataOut
66 atributos del objeto dataOut
67
67
68 Input:
68 Input:
69
69
70 **kwargs : Diccionario de argumentos de la funcion a ejecutar
70 **kwargs : Diccionario de argumentos de la funcion a ejecutar
71 """
71 """
72
72
73 raise ValueError, "ImplementedError"
73 raise ValueError, "ImplementedError"
74
74
75 def callMethod(self, name, **kwargs):
75 def callMethod(self, name, **kwargs):
76
76
77 """
77 """
78 Ejecuta el metodo con el nombre "name" y con argumentos **kwargs de la propia clase.
78 Ejecuta el metodo con el nombre "name" y con argumentos **kwargs de la propia clase.
79
79
80 Input:
80 Input:
81 name : nombre del metodo a ejecutar
81 name : nombre del metodo a ejecutar
82
82
83 **kwargs : diccionario con los nombres y valores de la funcion a ejecutar.
83 **kwargs : diccionario con los nombres y valores de la funcion a ejecutar.
84
84
85 """
85 """
86 if name != 'run':
86 if name != 'run':
87
87
88 if name == 'init' and self.dataIn.isEmpty():
88 if name == 'init' and self.dataIn.isEmpty():
89 self.dataOut.flagNoData = True
89 self.dataOut.flagNoData = True
90 return False
90 return False
91
91
92 if name != 'init' and self.dataOut.isEmpty():
92 if name != 'init' and self.dataOut.isEmpty():
93 return False
93 return False
94
94
95 methodToCall = getattr(self, name)
95 methodToCall = getattr(self, name)
96
96
97 methodToCall(**kwargs)
97 methodToCall(**kwargs)
98
98
99 if name != 'run':
99 if name != 'run':
100 return True
100 return True
101
101
102 if self.dataOut.isEmpty():
102 if self.dataOut.isEmpty():
103 return False
103 return False
104
104
105 return True
105 return True
106
106
107 def callObject(self, objId, **kwargs):
107 def callObject(self, objId, **kwargs):
108
108
109 """
109 """
110 Ejecuta la operacion asociada al identificador del objeto "objId"
110 Ejecuta la operacion asociada al identificador del objeto "objId"
111
111
112 Input:
112 Input:
113
113
114 objId : identificador del objeto a ejecutar
114 objId : identificador del objeto a ejecutar
115
115
116 **kwargs : diccionario con los nombres y valores de la funcion a ejecutar.
116 **kwargs : diccionario con los nombres y valores de la funcion a ejecutar.
117
117
118 Return:
118 Return:
119
119
120 None
120 None
121 """
121 """
122
122
123 if self.dataOut.isEmpty():
123 if self.dataOut.isEmpty():
124 return False
124 return False
125
125
126 object = self.objectDict[objId]
126 object = self.objectDict[objId]
127
127
128 object.run(self.dataOut, **kwargs)
128 object.run(self.dataOut, **kwargs)
129
129
130 return True
130 return True
131
131
132 def call(self, operationConf, **kwargs):
132 def call(self, operationConf, **kwargs):
133
133
134 """
134 """
135 Return True si ejecuta la operacion "operationConf.name" con los
135 Return True si ejecuta la operacion "operationConf.name" con los
136 argumentos "**kwargs". False si la operacion no se ha ejecutado.
136 argumentos "**kwargs". False si la operacion no se ha ejecutado.
137 La operacion puede ser de dos tipos:
137 La operacion puede ser de dos tipos:
138
138
139 1. Un metodo propio de esta clase:
139 1. Un metodo propio de esta clase:
140
140
141 operation.type = "self"
141 operation.type = "self"
142
142
143 2. El metodo "run" de un objeto del tipo Operation o de un derivado de ella:
143 2. El metodo "run" de un objeto del tipo Operation o de un derivado de ella:
144 operation.type = "other".
144 operation.type = "other".
145
145
146 Este objeto de tipo Operation debe de haber sido agregado antes con el metodo:
146 Este objeto de tipo Operation debe de haber sido agregado antes con el metodo:
147 "addOperation" e identificado con el operation.id
147 "addOperation" e identificado con el operation.id
148
148
149
149
150 con el id de la operacion.
150 con el id de la operacion.
151
151
152 Input:
152 Input:
153
153
154 Operation : Objeto del tipo operacion con los atributos: name, type y id.
154 Operation : Objeto del tipo operacion con los atributos: name, type y id.
155
155
156 """
156 """
157
157
158 if operationConf.type == 'self':
158 if operationConf.type == 'self':
159 sts = self.callMethod(operationConf.name, **kwargs)
159 sts = self.callMethod(operationConf.name, **kwargs)
160
160
161 if operationConf.type == 'other':
161 if operationConf.type == 'other':
162 sts = self.callObject(operationConf.id, **kwargs)
162 sts = self.callObject(operationConf.id, **kwargs)
163
163
164 return sts
164 return sts
165
165
166 def setInput(self, dataIn):
166 def setInput(self, dataIn):
167
167
168 self.dataIn = dataIn
168 self.dataIn = dataIn
169
169
170 def getOutput(self):
170 def getOutput(self):
171
171
172 return self.dataOut
172 return self.dataOut
173
173
174 class Operation():
174 class Operation():
175
175
176 """
176 """
177 Clase base para definir las operaciones adicionales que se pueden agregar a la clase ProcessingUnit
177 Clase base para definir las operaciones adicionales que se pueden agregar a la clase ProcessingUnit
178 y necesiten acumular informacion previa de los datos a procesar. De preferencia usar un buffer de
178 y necesiten acumular informacion previa de los datos a procesar. De preferencia usar un buffer de
179 acumulacion dentro de esta clase
179 acumulacion dentro de esta clase
180
180
181 Ejemplo: Integraciones coherentes, necesita la informacion previa de los n perfiles anteriores (bufffer)
181 Ejemplo: Integraciones coherentes, necesita la informacion previa de los n perfiles anteriores (bufffer)
182
182
183 """
183 """
184
184
185 __buffer = None
185 __buffer = None
186 __isConfig = False
186 __isConfig = False
187
187
188 def __init__(self):
188 def __init__(self):
189
189
190 pass
190 pass
191
191
192 def run(self, dataIn, **kwargs):
192 def run(self, dataIn, **kwargs):
193
193
194 """
194 """
195 Realiza las operaciones necesarias sobre la dataIn.data y actualiza los atributos del objeto dataIn.
195 Realiza las operaciones necesarias sobre la dataIn.data y actualiza los atributos del objeto dataIn.
196
196
197 Input:
197 Input:
198
198
199 dataIn : objeto del tipo JROData
199 dataIn : objeto del tipo JROData
200
200
201 Return:
201 Return:
202
202
203 None
203 None
204
204
205 Affected:
205 Affected:
206 __buffer : buffer de recepcion de datos.
206 __buffer : buffer de recepcion de datos.
207
207
208 """
208 """
209
209
210 raise ValueError, "ImplementedError"
210 raise ValueError, "ImplementedError"
211
211
212 class VoltageProc(ProcessingUnit):
212 class VoltageProc(ProcessingUnit):
213
213
214
214
215 def __init__(self):
215 def __init__(self):
216
216
217 self.objectDict = {}
217 self.objectDict = {}
218 self.dataOut = Voltage()
218 self.dataOut = Voltage()
219 self.flip = 1
219 self.flip = 1
220
220
221 def init(self):
221 def init(self):
222
222
223 self.dataOut.copy(self.dataIn)
223 self.dataOut.copy(self.dataIn)
224 # No necesita copiar en cada init() los atributos de dataIn
224 # No necesita copiar en cada init() los atributos de dataIn
225 # la copia deberia hacerse por cada nuevo bloque de datos
225 # la copia deberia hacerse por cada nuevo bloque de datos
226
226
227 def selectChannels(self, channelList):
227 def selectChannels(self, channelList):
228
228
229 channelIndexList = []
229 channelIndexList = []
230
230
231 for channel in channelList:
231 for channel in channelList:
232 index = self.dataOut.channelList.index(channel)
232 index = self.dataOut.channelList.index(channel)
233 channelIndexList.append(index)
233 channelIndexList.append(index)
234
234
235 self.selectChannelsByIndex(channelIndexList)
235 self.selectChannelsByIndex(channelIndexList)
236
236
237 def selectChannelsByIndex(self, channelIndexList):
237 def selectChannelsByIndex(self, channelIndexList):
238 """
238 """
239 Selecciona un bloque de datos en base a canales segun el channelIndexList
239 Selecciona un bloque de datos en base a canales segun el channelIndexList
240
240
241 Input:
241 Input:
242 channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
242 channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
243
243
244 Affected:
244 Affected:
245 self.dataOut.data
245 self.dataOut.data
246 self.dataOut.channelIndexList
246 self.dataOut.channelIndexList
247 self.dataOut.nChannels
247 self.dataOut.nChannels
248 self.dataOut.m_ProcessingHeader.totalSpectra
248 self.dataOut.m_ProcessingHeader.totalSpectra
249 self.dataOut.systemHeaderObj.numChannels
249 self.dataOut.systemHeaderObj.numChannels
250 self.dataOut.m_ProcessingHeader.blockSize
250 self.dataOut.m_ProcessingHeader.blockSize
251
251
252 Return:
252 Return:
253 None
253 None
254 """
254 """
255
255
256 for channelIndex in channelIndexList:
256 for channelIndex in channelIndexList:
257 if channelIndex not in self.dataOut.channelIndexList:
257 if channelIndex not in self.dataOut.channelIndexList:
258 print channelIndexList
258 print channelIndexList
259 raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex
259 raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex
260
260
261 nChannels = len(channelIndexList)
261 nChannels = len(channelIndexList)
262
262
263 data = self.dataOut.data[channelIndexList,:]
263 data = self.dataOut.data[channelIndexList,:]
264
264
265 self.dataOut.data = data
265 self.dataOut.data = data
266 self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
266 self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
267 # self.dataOut.nChannels = nChannels
267 # self.dataOut.nChannels = nChannels
268
268
269 return 1
269 return 1
270
270
271 def selectHeights(self, minHei, maxHei):
271 def selectHeights(self, minHei, maxHei):
272 """
272 """
273 Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
273 Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
274 minHei <= height <= maxHei
274 minHei <= height <= maxHei
275
275
276 Input:
276 Input:
277 minHei : valor minimo de altura a considerar
277 minHei : valor minimo de altura a considerar
278 maxHei : valor maximo de altura a considerar
278 maxHei : valor maximo de altura a considerar
279
279
280 Affected:
280 Affected:
281 Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
281 Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
282
282
283 Return:
283 Return:
284 1 si el metodo se ejecuto con exito caso contrario devuelve 0
284 1 si el metodo se ejecuto con exito caso contrario devuelve 0
285 """
285 """
286 if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
286 if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
287 raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
287 raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
288
288
289 if (maxHei > self.dataOut.heightList[-1]):
289 if (maxHei > self.dataOut.heightList[-1]):
290 maxHei = self.dataOut.heightList[-1]
290 maxHei = self.dataOut.heightList[-1]
291 # raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
291 # raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
292
292
293 minIndex = 0
293 minIndex = 0
294 maxIndex = 0
294 maxIndex = 0
295 heights = self.dataOut.heightList
295 heights = self.dataOut.heightList
296
296
297 inda = numpy.where(heights >= minHei)
297 inda = numpy.where(heights >= minHei)
298 indb = numpy.where(heights <= maxHei)
298 indb = numpy.where(heights <= maxHei)
299
299
300 try:
300 try:
301 minIndex = inda[0][0]
301 minIndex = inda[0][0]
302 except:
302 except:
303 minIndex = 0
303 minIndex = 0
304
304
305 try:
305 try:
306 maxIndex = indb[0][-1]
306 maxIndex = indb[0][-1]
307 except:
307 except:
308 maxIndex = len(heights)
308 maxIndex = len(heights)
309
309
310 self.selectHeightsByIndex(minIndex, maxIndex)
310 self.selectHeightsByIndex(minIndex, maxIndex)
311
311
312 return 1
312 return 1
313
313
314
314
315 def selectHeightsByIndex(self, minIndex, maxIndex):
315 def selectHeightsByIndex(self, minIndex, maxIndex):
316 """
316 """
317 Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
317 Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
318 minIndex <= index <= maxIndex
318 minIndex <= index <= maxIndex
319
319
320 Input:
320 Input:
321 minIndex : valor de indice minimo de altura a considerar
321 minIndex : valor de indice minimo de altura a considerar
322 maxIndex : valor de indice maximo de altura a considerar
322 maxIndex : valor de indice maximo de altura a considerar
323
323
324 Affected:
324 Affected:
325 self.dataOut.data
325 self.dataOut.data
326 self.dataOut.heightList
326 self.dataOut.heightList
327
327
328 Return:
328 Return:
329 1 si el metodo se ejecuto con exito caso contrario devuelve 0
329 1 si el metodo se ejecuto con exito caso contrario devuelve 0
330 """
330 """
331
331
332 if (minIndex < 0) or (minIndex > maxIndex):
332 if (minIndex < 0) or (minIndex > maxIndex):
333 raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
333 raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
334
334
335 if (maxIndex >= self.dataOut.nHeights):
335 if (maxIndex >= self.dataOut.nHeights):
336 maxIndex = self.dataOut.nHeights-1
336 maxIndex = self.dataOut.nHeights-1
337 # raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
337 # raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
338
338
339 nHeights = maxIndex - minIndex + 1
339 nHeights = maxIndex - minIndex + 1
340
340
341 #voltage
341 #voltage
342 data = self.dataOut.data[:,minIndex:maxIndex+1]
342 data = self.dataOut.data[:,minIndex:maxIndex+1]
343
343
344 firstHeight = self.dataOut.heightList[minIndex]
344 firstHeight = self.dataOut.heightList[minIndex]
345
345
346 self.dataOut.data = data
346 self.dataOut.data = data
347 self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1]
347 self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1]
348
348
349 return 1
349 return 1
350
350
351
351
352 def filterByHeights(self, window):
352 def filterByHeights(self, window):
353 deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0]
353 deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0]
354
354
355 if window == None:
355 if window == None:
356 window = self.dataOut.radarControllerHeaderObj.txA / deltaHeight
356 window = self.dataOut.radarControllerHeaderObj.txA / deltaHeight
357
357
358 newdelta = deltaHeight * window
358 newdelta = deltaHeight * window
359 r = self.dataOut.data.shape[1] % window
359 r = self.dataOut.data.shape[1] % window
360 buffer = self.dataOut.data[:,0:self.dataOut.data.shape[1]-r]
360 buffer = self.dataOut.data[:,0:self.dataOut.data.shape[1]-r]
361 buffer = buffer.reshape(self.dataOut.data.shape[0],self.dataOut.data.shape[1]/window,window)
361 buffer = buffer.reshape(self.dataOut.data.shape[0],self.dataOut.data.shape[1]/window,window)
362 buffer = numpy.sum(buffer,2)
362 buffer = numpy.sum(buffer,2)
363 self.dataOut.data = buffer
363 self.dataOut.data = buffer
364 self.dataOut.heightList = numpy.arange(self.dataOut.heightList[0],newdelta*self.dataOut.nHeights/window-newdelta,newdelta)
364 self.dataOut.heightList = numpy.arange(self.dataOut.heightList[0],newdelta*self.dataOut.nHeights/window-newdelta,newdelta)
365 self.dataOut.windowOfFilter = window
365 self.dataOut.windowOfFilter = window
366
366
367 def deFlip(self):
367 def deFlip(self):
368 self.dataOut.data *= self.flip
368 self.dataOut.data *= self.flip
369 self.flip *= -1.
369 self.flip *= -1.
370
370
371
371
372 class CohInt(Operation):
372 class CohInt(Operation):
373
373
374 __isConfig = False
374 __isConfig = False
375
375
376 __profIndex = 0
376 __profIndex = 0
377 __withOverapping = False
377 __withOverapping = False
378
378
379 __byTime = False
379 __byTime = False
380 __initime = None
380 __initime = None
381 __lastdatatime = None
381 __lastdatatime = None
382 __integrationtime = None
382 __integrationtime = None
383
383
384 __buffer = None
384 __buffer = None
385
385
386 __dataReady = False
386 __dataReady = False
387
387
388 n = None
388 n = None
389
389
390
390
391 def __init__(self):
391 def __init__(self):
392
392
393 self.__isConfig = False
393 self.__isConfig = False
394
394
395 def setup(self, n=None, timeInterval=None, overlapping=False):
395 def setup(self, n=None, timeInterval=None, overlapping=False):
396 """
396 """
397 Set the parameters of the integration class.
397 Set the parameters of the integration class.
398
398
399 Inputs:
399 Inputs:
400
400
401 n : Number of coherent integrations
401 n : Number of coherent integrations
402 timeInterval : Time of integration. If the parameter "n" is selected this one does not work
402 timeInterval : Time of integration. If the parameter "n" is selected this one does not work
403 overlapping :
403 overlapping :
404
404
405 """
405 """
406
406
407 self.__initime = None
407 self.__initime = None
408 self.__lastdatatime = 0
408 self.__lastdatatime = 0
409 self.__buffer = None
409 self.__buffer = None
410 self.__dataReady = False
410 self.__dataReady = False
411
411
412
412
413 if n == None and timeInterval == None:
413 if n == None and timeInterval == None:
414 raise ValueError, "n or timeInterval should be specified ..."
414 raise ValueError, "n or timeInterval should be specified ..."
415
415
416 if n != None:
416 if n != None:
417 self.n = n
417 self.n = n
418 self.__byTime = False
418 self.__byTime = False
419 else:
419 else:
420 self.__integrationtime = timeInterval * 60. #if (type(timeInterval)!=integer) -> change this line
420 self.__integrationtime = timeInterval * 60. #if (type(timeInterval)!=integer) -> change this line
421 self.n = 9999
421 self.n = 9999
422 self.__byTime = True
422 self.__byTime = True
423
423
424 if overlapping:
424 if overlapping:
425 self.__withOverapping = True
425 self.__withOverapping = True
426 self.__buffer = None
426 self.__buffer = None
427 else:
427 else:
428 self.__withOverapping = False
428 self.__withOverapping = False
429 self.__buffer = 0
429 self.__buffer = 0
430
430
431 self.__profIndex = 0
431 self.__profIndex = 0
432
432
433 def putData(self, data):
433 def putData(self, data):
434
434
435 """
435 """
436 Add a profile to the __buffer and increase in one the __profileIndex
436 Add a profile to the __buffer and increase in one the __profileIndex
437
437
438 """
438 """
439
439
440 if not self.__withOverapping:
440 if not self.__withOverapping:
441 self.__buffer += data.copy()
441 self.__buffer += data.copy()
442 self.__profIndex += 1
442 self.__profIndex += 1
443 return
443 return
444
444
445 #Overlapping data
445 #Overlapping data
446 nChannels, nHeis = data.shape
446 nChannels, nHeis = data.shape
447 data = numpy.reshape(data, (1, nChannels, nHeis))
447 data = numpy.reshape(data, (1, nChannels, nHeis))
448
448
449 #If the buffer is empty then it takes the data value
449 #If the buffer is empty then it takes the data value
450 if self.__buffer == None:
450 if self.__buffer == None:
451 self.__buffer = data
451 self.__buffer = data
452 self.__profIndex += 1
452 self.__profIndex += 1
453 return
453 return
454
454
455 #If the buffer length is lower than n then stakcing the data value
455 #If the buffer length is lower than n then stakcing the data value
456 if self.__profIndex < self.n:
456 if self.__profIndex < self.n:
457 self.__buffer = numpy.vstack((self.__buffer, data))
457 self.__buffer = numpy.vstack((self.__buffer, data))
458 self.__profIndex += 1
458 self.__profIndex += 1
459 return
459 return
460
460
461 #If the buffer length is equal to n then replacing the last buffer value with the data value
461 #If the buffer length is equal to n then replacing the last buffer value with the data value
462 self.__buffer = numpy.roll(self.__buffer, -1, axis=0)
462 self.__buffer = numpy.roll(self.__buffer, -1, axis=0)
463 self.__buffer[self.n-1] = data
463 self.__buffer[self.n-1] = data
464 self.__profIndex = self.n
464 self.__profIndex = self.n
465 return
465 return
466
466
467
467
468 def pushData(self):
468 def pushData(self):
469 """
469 """
470 Return the sum of the last profiles and the profiles used in the sum.
470 Return the sum of the last profiles and the profiles used in the sum.
471
471
472 Affected:
472 Affected:
473
473
474 self.__profileIndex
474 self.__profileIndex
475
475
476 """
476 """
477
477
478 if not self.__withOverapping:
478 if not self.__withOverapping:
479 data = self.__buffer
479 data = self.__buffer
480 n = self.__profIndex
480 n = self.__profIndex
481
481
482 self.__buffer = 0
482 self.__buffer = 0
483 self.__profIndex = 0
483 self.__profIndex = 0
484
484
485 return data, n
485 return data, n
486
486
487 #Integration with Overlapping
487 #Integration with Overlapping
488 data = numpy.sum(self.__buffer, axis=0)
488 data = numpy.sum(self.__buffer, axis=0)
489 n = self.__profIndex
489 n = self.__profIndex
490
490
491 return data, n
491 return data, n
492
492
493 def byProfiles(self, data):
493 def byProfiles(self, data):
494
494
495 self.__dataReady = False
495 self.__dataReady = False
496 avgdata = None
496 avgdata = None
497 n = None
497 n = None
498
498
499 self.putData(data)
499 self.putData(data)
500
500
501 if self.__profIndex == self.n:
501 if self.__profIndex == self.n:
502
502
503 avgdata, n = self.pushData()
503 avgdata, n = self.pushData()
504 self.__dataReady = True
504 self.__dataReady = True
505
505
506 return avgdata
506 return avgdata
507
507
508 def byTime(self, data, datatime):
508 def byTime(self, data, datatime):
509
509
510 self.__dataReady = False
510 self.__dataReady = False
511 avgdata = None
511 avgdata = None
512 n = None
512 n = None
513
513
514 self.putData(data)
514 self.putData(data)
515
515
516 if (datatime - self.__initime) >= self.__integrationtime:
516 if (datatime - self.__initime) >= self.__integrationtime:
517 avgdata, n = self.pushData()
517 avgdata, n = self.pushData()
518 self.n = n
518 self.n = n
519 self.__dataReady = True
519 self.__dataReady = True
520
520
521 return avgdata
521 return avgdata
522
522
523 def integrate(self, data, datatime=None):
523 def integrate(self, data, datatime=None):
524
524
525 if self.__initime == None:
525 if self.__initime == None:
526 self.__initime = datatime
526 self.__initime = datatime
527
527
528 if self.__byTime:
528 if self.__byTime:
529 avgdata = self.byTime(data, datatime)
529 avgdata = self.byTime(data, datatime)
530 else:
530 else:
531 avgdata = self.byProfiles(data)
531 avgdata = self.byProfiles(data)
532
532
533
533
534 self.__lastdatatime = datatime
534 self.__lastdatatime = datatime
535
535
536 if avgdata == None:
536 if avgdata == None:
537 return None, None
537 return None, None
538
538
539 avgdatatime = self.__initime
539 avgdatatime = self.__initime
540
540
541 deltatime = datatime -self.__lastdatatime
541 deltatime = datatime -self.__lastdatatime
542
542
543 if not self.__withOverapping:
543 if not self.__withOverapping:
544 self.__initime = datatime
544 self.__initime = datatime
545 else:
545 else:
546 self.__initime += deltatime
546 self.__initime += deltatime
547
547
548 return avgdata, avgdatatime
548 return avgdata, avgdatatime
549
549
550 def run(self, dataOut, **kwargs):
550 def run(self, dataOut, **kwargs):
551
551
552 if not self.__isConfig:
552 if not self.__isConfig:
553 self.setup(**kwargs)
553 self.setup(**kwargs)
554 self.__isConfig = True
554 self.__isConfig = True
555
555
556 avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime)
556 avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime)
557
557
558 # dataOut.timeInterval *= n
558 # dataOut.timeInterval *= n
559 dataOut.flagNoData = True
559 dataOut.flagNoData = True
560
560
561 if self.__dataReady:
561 if self.__dataReady:
562 dataOut.data = avgdata
562 dataOut.data = avgdata
563 dataOut.nCohInt *= self.n
563 dataOut.nCohInt *= self.n
564 dataOut.utctime = avgdatatime
564 dataOut.utctime = avgdatatime
565 dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt
565 dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt
566 dataOut.flagNoData = False
566 dataOut.flagNoData = False
567
567
568
568
569 class Decoder(Operation):
569 class Decoder(Operation):
570
570
571 __isConfig = False
571 __isConfig = False
572 __profIndex = 0
572 __profIndex = 0
573
573
574 code = None
574 code = None
575
575
576 nCode = None
576 nCode = None
577 nBaud = None
577 nBaud = None
578
578
579 def __init__(self):
579 def __init__(self):
580
580
581 self.__isConfig = False
581 self.__isConfig = False
582
582
583 def setup(self, code):
583 def setup(self, code):
584
584
585 self.__profIndex = 0
585 self.__profIndex = 0
586
586
587 self.code = code
587 self.code = code
588
588
589 self.nCode = len(code)
589 self.nCode = len(code)
590 self.nBaud = len(code[0])
590 self.nBaud = len(code[0])
591
591
592 def convolutionInFreq(self, data):
592 def convolutionInFreq(self, data):
593
593
594 nchannel, ndata = data.shape
594 nchannel, ndata = data.shape
595 newcode = numpy.zeros(ndata)
595 newcode = numpy.zeros(ndata)
596 newcode[0:self.nBaud] = self.code[self.__profIndex]
596 newcode[0:self.nBaud] = self.code[self.__profIndex]
597
597
598 fft_data = numpy.fft.fft(data, axis=1)
598 fft_data = numpy.fft.fft(data, axis=1)
599 fft_code = numpy.conj(numpy.fft.fft(newcode))
599 fft_code = numpy.conj(numpy.fft.fft(newcode))
600 fft_code = fft_code.reshape(1,len(fft_code))
600 fft_code = fft_code.reshape(1,len(fft_code))
601
601
602 # conv = fft_data.copy()
602 # conv = fft_data.copy()
603 # conv.fill(0)
603 # conv.fill(0)
604
604
605 conv = fft_data*fft_code
605 conv = fft_data*fft_code
606
606
607 data = numpy.fft.ifft(conv,axis=1)
607 data = numpy.fft.ifft(conv,axis=1)
608
608
609 datadec = data[:,:-self.nBaud+1]
609 datadec = data[:,:-self.nBaud+1]
610 ndatadec = ndata - self.nBaud + 1
610 ndatadec = ndata - self.nBaud + 1
611
611
612 if self.__profIndex == self.nCode-1:
612 if self.__profIndex == self.nCode-1:
613 self.__profIndex = 0
613 self.__profIndex = 0
614 return ndatadec, datadec
614 return ndatadec, datadec
615
615
616 self.__profIndex += 1
616 self.__profIndex += 1
617
617
618 return ndatadec, datadec
618 return ndatadec, datadec
619
619
620
620
621 def convolutionInTime(self, data):
621 def convolutionInTime(self, data):
622
622
623 nchannel, ndata = data.shape
623 nchannel, ndata = data.shape
624 newcode = self.code[self.__profIndex]
624 newcode = self.code[self.__profIndex]
625 ndatadec = ndata - self.nBaud + 1
625 ndatadec = ndata - self.nBaud + 1
626
626
627 datadec = numpy.zeros((nchannel, ndatadec))
627 datadec = numpy.zeros((nchannel, ndatadec))
628
628
629 for i in range(nchannel):
629 for i in range(nchannel):
630 datadec[i,:] = numpy.correlate(data[i,:], newcode)
630 datadec[i,:] = numpy.correlate(data[i,:], newcode)
631
631
632 if self.__profIndex == self.nCode-1:
632 if self.__profIndex == self.nCode-1:
633 self.__profIndex = 0
633 self.__profIndex = 0
634 return ndatadec, datadec
634 return ndatadec, datadec
635
635
636 self.__profIndex += 1
636 self.__profIndex += 1
637
637
638 return ndatadec, datadec
638 return ndatadec, datadec
639
639
640 def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0):
640 def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0):
641
641
642 if not self.__isConfig:
642 if not self.__isConfig:
643 if code != None:
643 if code != None:
644 code = numpy.array(code).reshape(nCode,nBaud)
644 code = numpy.array(code).reshape(nCode,nBaud)
645 dataOut.code = code
645 dataOut.code = code
646 dataOut.nCode = nCode
646 dataOut.nCode = nCode
647 dataOut.nBaud = nBaud
647 dataOut.nBaud = nBaud
648 if code == None:
648 if code == None:
649 code = dataOut.code
649 code = dataOut.code
650
650
651 self.setup(code)
651 self.setup(code)
652 self.__isConfig = True
652 self.__isConfig = True
653
653
654 if mode == 0:
654 if mode == 0:
655 ndatadec, datadec = self.convolutionInFreq(dataOut.data)
655 ndatadec, datadec = self.convolutionInFreq(dataOut.data)
656
656
657 if mode == 1:
657 if mode == 1:
658 print "This function is not implemented"
658 print "This function is not implemented"
659 # ndatadec, datadec = self.convolutionInTime(dataOut.data)
659 # ndatadec, datadec = self.convolutionInTime(dataOut.data)
660
660
661 dataOut.data = datadec
661 dataOut.data = datadec
662
662
663 dataOut.heightList = dataOut.heightList[0:ndatadec]
663 dataOut.heightList = dataOut.heightList[0:ndatadec]
664
664
665 dataOut.flagDecodeData = True #asumo q la data no esta decodificada
665 dataOut.flagDecodeData = True #asumo q la data no esta decodificada
666
666
667 # dataOut.flagDeflipData = True #asumo q la data no esta sin flip
667 # dataOut.flagDeflipData = True #asumo q la data no esta sin flip
668
668
669
669
670 class SpectraProc(ProcessingUnit):
670 class SpectraProc(ProcessingUnit):
671
671
672 def __init__(self):
672 def __init__(self):
673
673
674 self.objectDict = {}
674 self.objectDict = {}
675 self.buffer = None
675 self.buffer = None
676 self.firstdatatime = None
676 self.firstdatatime = None
677 self.profIndex = 0
677 self.profIndex = 0
678 self.dataOut = Spectra()
678 self.dataOut = Spectra()
679
679
680 def __updateObjFromInput(self):
680 def __updateObjFromInput(self):
681
681
682 self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()
682 self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()
683 self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy()
683 self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy()
684 self.dataOut.channelList = self.dataIn.channelList
684 self.dataOut.channelList = self.dataIn.channelList
685 self.dataOut.heightList = self.dataIn.heightList
685 self.dataOut.heightList = self.dataIn.heightList
686 self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')])
686 self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')])
687 # self.dataOut.nHeights = self.dataIn.nHeights
687 # self.dataOut.nHeights = self.dataIn.nHeights
688 # self.dataOut.nChannels = self.dataIn.nChannels
688 # self.dataOut.nChannels = self.dataIn.nChannels
689 self.dataOut.nBaud = self.dataIn.nBaud
689 self.dataOut.nBaud = self.dataIn.nBaud
690 self.dataOut.nCode = self.dataIn.nCode
690 self.dataOut.nCode = self.dataIn.nCode
691 self.dataOut.code = self.dataIn.code
691 self.dataOut.code = self.dataIn.code
692 self.dataOut.nProfiles = self.dataOut.nFFTPoints
692 self.dataOut.nProfiles = self.dataOut.nFFTPoints
693 # self.dataOut.channelIndexList = self.dataIn.channelIndexList
693 # self.dataOut.channelIndexList = self.dataIn.channelIndexList
694 self.dataOut.flagTimeBlock = self.dataIn.flagTimeBlock
694 self.dataOut.flagTimeBlock = self.dataIn.flagTimeBlock
695 self.dataOut.utctime = self.firstdatatime
695 self.dataOut.utctime = self.firstdatatime
696 self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada
696 self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada
697 self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip
697 self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip
698 self.dataOut.flagShiftFFT = self.dataIn.flagShiftFFT
698 self.dataOut.flagShiftFFT = self.dataIn.flagShiftFFT
699 self.dataOut.nCohInt = self.dataIn.nCohInt
699 self.dataOut.nCohInt = self.dataIn.nCohInt
700 self.dataOut.nIncohInt = 1
700 self.dataOut.nIncohInt = 1
701 self.dataOut.ippSeconds = self.dataIn.ippSeconds
701 self.dataOut.ippSeconds = self.dataIn.ippSeconds
702 self.dataOut.windowOfFilter = self.dataIn.windowOfFilter
702 self.dataOut.windowOfFilter = self.dataIn.windowOfFilter
703
703
704 self.dataOut.timeInterval = self.dataIn.timeInterval*self.dataOut.nFFTPoints*self.dataOut.nIncohInt
704 self.dataOut.timeInterval = self.dataIn.timeInterval*self.dataOut.nFFTPoints*self.dataOut.nIncohInt
705
705
706 def __getFft(self):
706 def __getFft(self):
707 """
707 """
708 Convierte valores de Voltaje a Spectra
708 Convierte valores de Voltaje a Spectra
709
709
710 Affected:
710 Affected:
711 self.dataOut.data_spc
711 self.dataOut.data_spc
712 self.dataOut.data_cspc
712 self.dataOut.data_cspc
713 self.dataOut.data_dc
713 self.dataOut.data_dc
714 self.dataOut.heightList
714 self.dataOut.heightList
715 self.profIndex
715 self.profIndex
716 self.buffer
716 self.buffer
717 self.dataOut.flagNoData
717 self.dataOut.flagNoData
718 """
718 """
719 fft_volt = numpy.fft.fft(self.buffer,axis=1)
719 fft_volt = numpy.fft.fft(self.buffer,axis=1)
720 fft_volt = fft_volt.astype(numpy.dtype('complex'))
720 fft_volt = fft_volt.astype(numpy.dtype('complex'))
721 dc = fft_volt[:,0,:]
721 dc = fft_volt[:,0,:]
722
722
723 #calculo de self-spectra
723 #calculo de self-spectra
724 fft_volt = numpy.fft.fftshift(fft_volt,axes=(1,))
724 fft_volt = numpy.fft.fftshift(fft_volt,axes=(1,))
725 spc = fft_volt * numpy.conjugate(fft_volt)
725 spc = fft_volt * numpy.conjugate(fft_volt)
726 spc = spc.real
726 spc = spc.real
727
727
728 blocksize = 0
728 blocksize = 0
729 blocksize += dc.size
729 blocksize += dc.size
730 blocksize += spc.size
730 blocksize += spc.size
731
731
732 cspc = None
732 cspc = None
733 pairIndex = 0
733 pairIndex = 0
734 if self.dataOut.pairsList != None:
734 if self.dataOut.pairsList != None:
735 #calculo de cross-spectra
735 #calculo de cross-spectra
736 cspc = numpy.zeros((self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex')
736 cspc = numpy.zeros((self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex')
737 for pair in self.dataOut.pairsList:
737 for pair in self.dataOut.pairsList:
738 cspc[pairIndex,:,:] = fft_volt[pair[0],:,:] * numpy.conjugate(fft_volt[pair[1],:,:])
738 cspc[pairIndex,:,:] = fft_volt[pair[0],:,:] * numpy.conjugate(fft_volt[pair[1],:,:])
739 pairIndex += 1
739 pairIndex += 1
740 blocksize += cspc.size
740 blocksize += cspc.size
741
741
742 self.dataOut.data_spc = spc
742 self.dataOut.data_spc = spc
743 self.dataOut.data_cspc = cspc
743 self.dataOut.data_cspc = cspc
744 self.dataOut.data_dc = dc
744 self.dataOut.data_dc = dc
745 self.dataOut.blockSize = blocksize
745 self.dataOut.blockSize = blocksize
746
746
747 def init(self, nFFTPoints=None, pairsList=None):
747 def init(self, nFFTPoints=None, pairsList=None):
748
748
749 self.dataOut.flagNoData = True
749 self.dataOut.flagNoData = True
750
750
751 if self.dataIn.type == "Spectra":
751 if self.dataIn.type == "Spectra":
752 self.dataOut.copy(self.dataIn)
752 self.dataOut.copy(self.dataIn)
753 return
753 return
754
754
755 if self.dataIn.type == "Voltage":
755 if self.dataIn.type == "Voltage":
756
756
757 if nFFTPoints == None:
757 if nFFTPoints == None:
758 raise ValueError, "This SpectraProc.init() need nFFTPoints input variable"
758 raise ValueError, "This SpectraProc.init() need nFFTPoints input variable"
759
759
760 if pairsList == None:
760 if pairsList == None:
761 nPairs = 0
761 nPairs = 0
762 else:
762 else:
763 nPairs = len(pairsList)
763 nPairs = len(pairsList)
764
764
765 self.dataOut.nFFTPoints = nFFTPoints
765 self.dataOut.nFFTPoints = nFFTPoints
766 self.dataOut.pairsList = pairsList
766 self.dataOut.pairsList = pairsList
767 self.dataOut.nPairs = nPairs
767 self.dataOut.nPairs = nPairs
768
768
769 if self.buffer == None:
769 if self.buffer == None:
770 self.buffer = numpy.zeros((self.dataIn.nChannels,
770 self.buffer = numpy.zeros((self.dataIn.nChannels,
771 self.dataOut.nFFTPoints,
771 self.dataOut.nFFTPoints,
772 self.dataIn.nHeights),
772 self.dataIn.nHeights),
773 dtype='complex')
773 dtype='complex')
774
774
775
775
776 self.buffer[:,self.profIndex,:] = self.dataIn.data.copy()
776 self.buffer[:,self.profIndex,:] = self.dataIn.data.copy()
777 self.profIndex += 1
777 self.profIndex += 1
778
778
779 if self.firstdatatime == None:
779 if self.firstdatatime == None:
780 self.firstdatatime = self.dataIn.utctime
780 self.firstdatatime = self.dataIn.utctime
781
781
782 if self.profIndex == self.dataOut.nFFTPoints:
782 if self.profIndex == self.dataOut.nFFTPoints:
783 self.__updateObjFromInput()
783 self.__updateObjFromInput()
784 self.__getFft()
784 self.__getFft()
785
785
786 self.dataOut.flagNoData = False
786 self.dataOut.flagNoData = False
787
787
788 self.buffer = None
788 self.buffer = None
789 self.firstdatatime = None
789 self.firstdatatime = None
790 self.profIndex = 0
790 self.profIndex = 0
791
791
792 return
792 return
793
793
794 raise ValuError, "The type object %s is not valid"%(self.dataIn.type)
794 raise ValuError, "The type object %s is not valid"%(self.dataIn.type)
795
795
796 def selectChannels(self, channelList):
796 def selectChannels(self, channelList):
797
797
798 channelIndexList = []
798 channelIndexList = []
799
799
800 for channel in channelList:
800 for channel in channelList:
801 index = self.dataOut.channelList.index(channel)
801 index = self.dataOut.channelList.index(channel)
802 channelIndexList.append(index)
802 channelIndexList.append(index)
803
803
804 self.selectChannelsByIndex(channelIndexList)
804 self.selectChannelsByIndex(channelIndexList)
805
805
806 def selectChannelsByIndex(self, channelIndexList):
806 def selectChannelsByIndex(self, channelIndexList):
807 """
807 """
808 Selecciona un bloque de datos en base a canales segun el channelIndexList
808 Selecciona un bloque de datos en base a canales segun el channelIndexList
809
809
810 Input:
810 Input:
811 channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
811 channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
812
812
813 Affected:
813 Affected:
814 self.dataOut.data_spc
814 self.dataOut.data_spc
815 self.dataOut.channelIndexList
815 self.dataOut.channelIndexList
816 self.dataOut.nChannels
816 self.dataOut.nChannels
817
817
818 Return:
818 Return:
819 None
819 None
820 """
820 """
821
821
822 for channelIndex in channelIndexList:
822 for channelIndex in channelIndexList:
823 if channelIndex not in self.dataOut.channelIndexList:
823 if channelIndex not in self.dataOut.channelIndexList:
824 print channelIndexList
824 print channelIndexList
825 raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex
825 raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex
826
826
827 nChannels = len(channelIndexList)
827 nChannels = len(channelIndexList)
828
828
829 data_spc = self.dataOut.data_spc[channelIndexList,:]
829 data_spc = self.dataOut.data_spc[channelIndexList,:]
830
830
831 self.dataOut.data_spc = data_spc
831 self.dataOut.data_spc = data_spc
832 self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
832 self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
833 # self.dataOut.nChannels = nChannels
833 # self.dataOut.nChannels = nChannels
834
834
835 return 1
835 return 1
836
836
837 def selectHeights(self, minHei, maxHei):
837 def selectHeights(self, minHei, maxHei):
838 """
838 """
839 Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
839 Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
840 minHei <= height <= maxHei
840 minHei <= height <= maxHei
841
841
842 Input:
842 Input:
843 minHei : valor minimo de altura a considerar
843 minHei : valor minimo de altura a considerar
844 maxHei : valor maximo de altura a considerar
844 maxHei : valor maximo de altura a considerar
845
845
846 Affected:
846 Affected:
847 Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
847 Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
848
848
849 Return:
849 Return:
850 1 si el metodo se ejecuto con exito caso contrario devuelve 0
850 1 si el metodo se ejecuto con exito caso contrario devuelve 0
851 """
851 """
852 if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
852 if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
853 raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
853 raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
854
854
855 if (maxHei > self.dataOut.heightList[-1]):
855 if (maxHei > self.dataOut.heightList[-1]):
856 maxHei = self.dataOut.heightList[-1]
856 maxHei = self.dataOut.heightList[-1]
857 # raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
857 # raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
858
858
859 minIndex = 0
859 minIndex = 0
860 maxIndex = 0
860 maxIndex = 0
861 heights = self.dataOut.heightList
861 heights = self.dataOut.heightList
862
862
863 inda = numpy.where(heights >= minHei)
863 inda = numpy.where(heights >= minHei)
864 indb = numpy.where(heights <= maxHei)
864 indb = numpy.where(heights <= maxHei)
865
865
866 try:
866 try:
867 minIndex = inda[0][0]
867 minIndex = inda[0][0]
868 except:
868 except:
869 minIndex = 0
869 minIndex = 0
870
870
871 try:
871 try:
872 maxIndex = indb[0][-1]
872 maxIndex = indb[0][-1]
873 except:
873 except:
874 maxIndex = len(heights)
874 maxIndex = len(heights)
875
875
876 self.selectHeightsByIndex(minIndex, maxIndex)
876 self.selectHeightsByIndex(minIndex, maxIndex)
877
877
878 return 1
878 return 1
879
879
880
880
881 def selectHeightsByIndex(self, minIndex, maxIndex):
881 def selectHeightsByIndex(self, minIndex, maxIndex):
882 """
882 """
883 Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
883 Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
884 minIndex <= index <= maxIndex
884 minIndex <= index <= maxIndex
885
885
886 Input:
886 Input:
887 minIndex : valor de indice minimo de altura a considerar
887 minIndex : valor de indice minimo de altura a considerar
888 maxIndex : valor de indice maximo de altura a considerar
888 maxIndex : valor de indice maximo de altura a considerar
889
889
890 Affected:
890 Affected:
891 self.dataOut.data_spc
891 self.dataOut.data_spc
892 self.dataOut.data_cspc
892 self.dataOut.data_cspc
893 self.dataOut.data_dc
893 self.dataOut.data_dc
894 self.dataOut.heightList
894 self.dataOut.heightList
895
895
896 Return:
896 Return:
897 1 si el metodo se ejecuto con exito caso contrario devuelve 0
897 1 si el metodo se ejecuto con exito caso contrario devuelve 0
898 """
898 """
899
899
900 if (minIndex < 0) or (minIndex > maxIndex):
900 if (minIndex < 0) or (minIndex > maxIndex):
901 raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
901 raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
902
902
903 if (maxIndex >= self.dataOut.nHeights):
903 if (maxIndex >= self.dataOut.nHeights):
904 maxIndex = self.dataOut.nHeights-1
904 maxIndex = self.dataOut.nHeights-1
905 # raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
905 # raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
906
906
907 nHeights = maxIndex - minIndex + 1
907 nHeights = maxIndex - minIndex + 1
908
908
909 #Spectra
909 #Spectra
910 data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1]
910 data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1]
911
911
912 data_cspc = None
912 data_cspc = None
913 if self.dataOut.data_cspc != None:
913 if self.dataOut.data_cspc != None:
914 data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1]
914 data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1]
915
915
916 data_dc = None
916 data_dc = None
917 if self.dataOut.data_dc != None:
917 if self.dataOut.data_dc != None:
918 data_dc = self.dataOut.data_dc[:,:,minIndex:maxIndex+1]
918 data_dc = self.dataOut.data_dc[:,:,minIndex:maxIndex+1]
919
919
920 self.dataOut.data_spc = data_spc
920 self.dataOut.data_spc = data_spc
921 self.dataOut.data_cspc = data_cspc
921 self.dataOut.data_cspc = data_cspc
922 self.dataOut.data_dc = data_dc
922 self.dataOut.data_dc = data_dc
923
923
924 self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1]
924 self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1]
925
925
926 return 1
926 return 1
927
927
928 def removeDC(self, mode = 1):
928 def removeDC(self, mode = 1):
929
929
930 dc_index = 0
930 dc_index = 0
931 freq_index = numpy.array([-2,-1,1,2])
931 freq_index = numpy.array([-2,-1,1,2])
932 data_spc = self.dataOut.data_spc
932 data_spc = self.dataOut.data_spc
933 data_cspc = self.dataOut.data_cspc
933 data_cspc = self.dataOut.data_cspc
934 data_dc = self.dataOut.data_dc
934 data_dc = self.dataOut.data_dc
935
935
936 if self.dataOut.flagShiftFFT:
936 if self.dataOut.flagShiftFFT:
937 dc_index += self.dataOut.nFFTPoints/2
937 dc_index += self.dataOut.nFFTPoints/2
938 freq_index += self.dataOut.nFFTPoints/2
938 freq_index += self.dataOut.nFFTPoints/2
939
939
940 if mode == 1:
940 if mode == 1:
941 data_spc[dc_index] = (data_spc[:,freq_index[1],:] + data_spc[:,freq_index[2],:])/2
941 data_spc[dc_index] = (data_spc[:,freq_index[1],:] + data_spc[:,freq_index[2],:])/2
942 if data_cspc != None:
942 if data_cspc != None:
943 data_cspc[dc_index] = (data_cspc[:,freq_index[1],:] + data_cspc[:,freq_index[2],:])/2
943 data_cspc[dc_index] = (data_cspc[:,freq_index[1],:] + data_cspc[:,freq_index[2],:])/2
944 return 1
944 return 1
945
945
946 if mode == 2:
946 if mode == 2:
947 pass
947 pass
948
948
949 if mode == 3:
949 if mode == 3:
950 pass
950 pass
951
951
952 raise ValueError, "mode parameter has to be 1, 2 or 3"
952 raise ValueError, "mode parameter has to be 1, 2 or 3"
953
953
954 def removeInterference(self):
954 def removeInterference(self):
955
955
956 pass
956 pass
957
957
958
958
959 class IncohInt(Operation):
959 class IncohInt(Operation):
960
960
961
961
962 __profIndex = 0
962 __profIndex = 0
963 __withOverapping = False
963 __withOverapping = False
964
964
965 __byTime = False
965 __byTime = False
966 __initime = None
966 __initime = None
967 __lastdatatime = None
967 __lastdatatime = None
968 __integrationtime = None
968 __integrationtime = None
969
969
970 __buffer_spc = None
970 __buffer_spc = None
971 __buffer_cspc = None
971 __buffer_cspc = None
972 __buffer_dc = None
972 __buffer_dc = None
973
973
974 __dataReady = False
974 __dataReady = False
975
975
976 __timeInterval = None
977
976 n = None
978 n = None
977
979
978
980
981
979 def __init__(self):
982 def __init__(self):
980
983
981 self.__isConfig = False
984 self.__isConfig = False
982
985
983 def setup(self, n=None, timeInterval=None, overlapping=False):
986 def setup(self, n=None, timeInterval=None, overlapping=False):
984 """
987 """
985 Set the parameters of the integration class.
988 Set the parameters of the integration class.
986
989
987 Inputs:
990 Inputs:
988
991
989 n : Number of coherent integrations
992 n : Number of coherent integrations
990 timeInterval : Time of integration. If the parameter "n" is selected this one does not work
993 timeInterval : Time of integration. If the parameter "n" is selected this one does not work
991 overlapping :
994 overlapping :
992
995
993 """
996 """
994
997
995 self.__initime = None
998 self.__initime = None
996 self.__lastdatatime = 0
999 self.__lastdatatime = 0
997 self.__buffer_spc = None
1000 self.__buffer_spc = None
998 self.__buffer_cspc = None
1001 self.__buffer_cspc = None
999 self.__buffer_dc = None
1002 self.__buffer_dc = None
1000 self.__dataReady = False
1003 self.__dataReady = False
1001
1004
1002
1005
1003 if n == None and timeInterval == None:
1006 if n == None and timeInterval == None:
1004 raise ValueError, "n or timeInterval should be specified ..."
1007 raise ValueError, "n or timeInterval should be specified ..."
1005
1008
1006 if n != None:
1009 if n != None:
1007 self.n = n
1010 self.n = n
1008 self.__byTime = False
1011 self.__byTime = False
1009 else:
1012 else:
1010 self.__integrationtime = timeInterval * 60. #if (type(timeInterval)!=integer) -> change this line
1013 self.__integrationtime = timeInterval * 60. #if (type(timeInterval)!=integer) -> change this line
1011 self.n = 9999
1014 self.n = 9999
1012 self.__byTime = True
1015 self.__byTime = True
1013
1016
1014 if overlapping:
1017 if overlapping:
1015 self.__withOverapping = True
1018 self.__withOverapping = True
1016 else:
1019 else:
1017 self.__withOverapping = False
1020 self.__withOverapping = False
1018 self.__buffer_spc = 0
1021 self.__buffer_spc = 0
1019 self.__buffer_cspc = 0
1022 self.__buffer_cspc = 0
1020 self.__buffer_dc = 0
1023 self.__buffer_dc = 0
1021
1024
1022 self.__profIndex = 0
1025 self.__profIndex = 0
1023
1026
1024 def putData(self, data_spc, data_cspc, data_dc):
1027 def putData(self, data_spc, data_cspc, data_dc):
1025
1028
1026 """
1029 """
1027 Add a profile to the __buffer_spc and increase in one the __profileIndex
1030 Add a profile to the __buffer_spc and increase in one the __profileIndex
1028
1031
1029 """
1032 """
1030
1033
1031 if not self.__withOverapping:
1034 if not self.__withOverapping:
1032 self.__buffer_spc += data_spc
1035 self.__buffer_spc += data_spc
1033
1036
1034 if data_cspc == None:
1037 if data_cspc == None:
1035 self.__buffer_cspc = None
1038 self.__buffer_cspc = None
1036 else:
1039 else:
1037 self.__buffer_cspc += data_cspc
1040 self.__buffer_cspc += data_cspc
1038
1041
1039 if data_dc == None:
1042 if data_dc == None:
1040 self.__buffer_dc = None
1043 self.__buffer_dc = None
1041 else:
1044 else:
1042 self.__buffer_dc += data_dc
1045 self.__buffer_dc += data_dc
1043
1046
1044 self.__profIndex += 1
1047 self.__profIndex += 1
1045 return
1048 return
1046
1049
1047 #Overlapping data
1050 #Overlapping data
1048 nChannels, nFFTPoints, nHeis = data_spc.shape
1051 nChannels, nFFTPoints, nHeis = data_spc.shape
1049 data_spc = numpy.reshape(data_spc, (1, nChannels, nFFTPoints, nHeis))
1052 data_spc = numpy.reshape(data_spc, (1, nChannels, nFFTPoints, nHeis))
1050 if data_cspc != None:
1053 if data_cspc != None:
1051 data_cspc = numpy.reshape(data_cspc, (1, -1, nFFTPoints, nHeis))
1054 data_cspc = numpy.reshape(data_cspc, (1, -1, nFFTPoints, nHeis))
1052 if data_dc != None:
1055 if data_dc != None:
1053 data_dc = numpy.reshape(data_dc, (1, -1, nHeis))
1056 data_dc = numpy.reshape(data_dc, (1, -1, nHeis))
1054
1057
1055 #If the buffer is empty then it takes the data value
1058 #If the buffer is empty then it takes the data value
1056 if self.__buffer_spc == None:
1059 if self.__buffer_spc == None:
1057 self.__buffer_spc = data_spc
1060 self.__buffer_spc = data_spc
1058
1061
1059 if data_cspc == None:
1062 if data_cspc == None:
1060 self.__buffer_cspc = None
1063 self.__buffer_cspc = None
1061 else:
1064 else:
1062 self.__buffer_cspc += data_cspc
1065 self.__buffer_cspc += data_cspc
1063
1066
1064 if data_dc == None:
1067 if data_dc == None:
1065 self.__buffer_dc = None
1068 self.__buffer_dc = None
1066 else:
1069 else:
1067 self.__buffer_dc += data_dc
1070 self.__buffer_dc += data_dc
1068
1071
1069 self.__profIndex += 1
1072 self.__profIndex += 1
1070 return
1073 return
1071
1074
1072 #If the buffer length is lower than n then stakcing the data value
1075 #If the buffer length is lower than n then stakcing the data value
1073 if self.__profIndex < self.n:
1076 if self.__profIndex < self.n:
1074 self.__buffer_spc = numpy.vstack((self.__buffer_spc, data_spc))
1077 self.__buffer_spc = numpy.vstack((self.__buffer_spc, data_spc))
1075
1078
1076 if data_cspc != None:
1079 if data_cspc != None:
1077 self.__buffer_cspc = numpy.vstack((self.__buffer_cspc, data_cspc))
1080 self.__buffer_cspc = numpy.vstack((self.__buffer_cspc, data_cspc))
1078
1081
1079 if data_dc != None:
1082 if data_dc != None:
1080 self.__buffer_dc = numpy.vstack((self.__buffer_dc, data_dc))
1083 self.__buffer_dc = numpy.vstack((self.__buffer_dc, data_dc))
1081
1084
1082 self.__profIndex += 1
1085 self.__profIndex += 1
1083 return
1086 return
1084
1087
1085 #If the buffer length is equal to n then replacing the last buffer value with the data value
1088 #If the buffer length is equal to n then replacing the last buffer value with the data value
1086 self.__buffer_spc = numpy.roll(self.__buffer_spc, -1, axis=0)
1089 self.__buffer_spc = numpy.roll(self.__buffer_spc, -1, axis=0)
1087 self.__buffer_spc[self.n-1] = data_spc
1090 self.__buffer_spc[self.n-1] = data_spc
1088
1091
1089 if data_cspc != None:
1092 if data_cspc != None:
1090 self.__buffer_cspc = numpy.roll(self.__buffer_cspc, -1, axis=0)
1093 self.__buffer_cspc = numpy.roll(self.__buffer_cspc, -1, axis=0)
1091 self.__buffer_cspc[self.n-1] = data_cspc
1094 self.__buffer_cspc[self.n-1] = data_cspc
1092
1095
1093 if data_dc != None:
1096 if data_dc != None:
1094 self.__buffer_dc = numpy.roll(self.__buffer_dc, -1, axis=0)
1097 self.__buffer_dc = numpy.roll(self.__buffer_dc, -1, axis=0)
1095 self.__buffer_dc[self.n-1] = data_dc
1098 self.__buffer_dc[self.n-1] = data_dc
1096
1099
1097 self.__profIndex = self.n
1100 self.__profIndex = self.n
1098 return
1101 return
1099
1102
1100
1103
1101 def pushData(self):
1104 def pushData(self):
1102 """
1105 """
1103 Return the sum of the last profiles and the profiles used in the sum.
1106 Return the sum of the last profiles and the profiles used in the sum.
1104
1107
1105 Affected:
1108 Affected:
1106
1109
1107 self.__profileIndex
1110 self.__profileIndex
1108
1111
1109 """
1112 """
1110 data_spc = None
1113 data_spc = None
1111 data_cspc = None
1114 data_cspc = None
1112 data_dc = None
1115 data_dc = None
1113
1116
1114 if not self.__withOverapping:
1117 if not self.__withOverapping:
1115 data_spc = self.__buffer_spc
1118 data_spc = self.__buffer_spc
1116 data_cspc = self.__buffer_cspc
1119 data_cspc = self.__buffer_cspc
1117 data_dc = self.__buffer_dc
1120 data_dc = self.__buffer_dc
1118
1121
1119 n = self.__profIndex
1122 n = self.__profIndex
1120
1123
1121 self.__buffer_spc = 0
1124 self.__buffer_spc = 0
1122 self.__buffer_cspc = 0
1125 self.__buffer_cspc = 0
1123 self.__buffer_dc = 0
1126 self.__buffer_dc = 0
1124 self.__profIndex = 0
1127 self.__profIndex = 0
1125
1128
1126 return data_spc, data_cspc, data_dc, n
1129 return data_spc, data_cspc, data_dc, n
1127
1130
1128 #Integration with Overlapping
1131 #Integration with Overlapping
1129 data_spc = numpy.sum(self.__buffer_spc, axis=0)
1132 data_spc = numpy.sum(self.__buffer_spc, axis=0)
1130
1133
1131 if self.__buffer_cspc != None:
1134 if self.__buffer_cspc != None:
1132 data_cspc = numpy.sum(self.__buffer_cspc, axis=0)
1135 data_cspc = numpy.sum(self.__buffer_cspc, axis=0)
1133
1136
1134 if self.__buffer_dc != None:
1137 if self.__buffer_dc != None:
1135 data_dc = numpy.sum(self.__buffer_dc, axis=0)
1138 data_dc = numpy.sum(self.__buffer_dc, axis=0)
1136
1139
1137 n = self.__profIndex
1140 n = self.__profIndex
1138
1141
1139 return data_spc, data_cspc, data_dc, n
1142 return data_spc, data_cspc, data_dc, n
1140
1143
1141 def byProfiles(self, *args):
1144 def byProfiles(self, *args):
1142
1145
1143 self.__dataReady = False
1146 self.__dataReady = False
1144 avgdata_spc = None
1147 avgdata_spc = None
1145 avgdata_cspc = None
1148 avgdata_cspc = None
1146 avgdata_dc = None
1149 avgdata_dc = None
1147 n = None
1150 n = None
1148
1151
1149 self.putData(*args)
1152 self.putData(*args)
1150
1153
1151 if self.__profIndex == self.n:
1154 if self.__profIndex == self.n:
1152
1155
1153 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
1156 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
1154 self.__dataReady = True
1157 self.__dataReady = True
1155
1158
1156 return avgdata_spc, avgdata_cspc, avgdata_dc
1159 return avgdata_spc, avgdata_cspc, avgdata_dc
1157
1160
1158 def byTime(self, datatime, *args):
1161 def byTime(self, datatime, *args):
1159
1162
1160 self.__dataReady = False
1163 self.__dataReady = False
1161 avgdata_spc = None
1164 avgdata_spc = None
1162 avgdata_cspc = None
1165 avgdata_cspc = None
1163 avgdata_dc = None
1166 avgdata_dc = None
1164 n = None
1167 n = None
1165
1168
1166 self.putData(*args)
1169 self.putData(*args)
1167
1170
1168 if (datatime - self.__initime) >= self.__integrationtime:
1171 if (datatime - self.__initime) >= self.__integrationtime:
1169 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
1172 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
1170 self.n = n
1173 self.n = n
1171 self.__dataReady = True
1174 self.__dataReady = True
1172
1175
1173 return avgdata_spc, avgdata_cspc, avgdata_dc
1176 return avgdata_spc, avgdata_cspc, avgdata_dc
1174
1177
1175 def integrate(self, datatime, *args):
1178 def integrate(self, datatime, *args):
1176
1179
1177 if self.__initime == None:
1180 if self.__initime == None:
1178 self.__initime = datatime
1181 self.__initime = datatime
1179
1182
1180 if self.__byTime:
1183 if self.__byTime:
1181 avgdata_spc, avgdata_cspc, avgdata_dc = self.byTime(datatime, *args)
1184 avgdata_spc, avgdata_cspc, avgdata_dc = self.byTime(datatime, *args)
1182 else:
1185 else:
1183 avgdata_spc, avgdata_cspc, avgdata_dc = self.byProfiles(*args)
1186 avgdata_spc, avgdata_cspc, avgdata_dc = self.byProfiles(*args)
1184
1187
1185 self.__lastdatatime = datatime
1188 self.__lastdatatime = datatime
1186
1189
1187 if avgdata_spc == None:
1190 if avgdata_spc == None:
1188 return None, None, None, None
1191 return None, None, None, None
1189
1192
1190 avgdatatime = self.__initime
1193 avgdatatime = self.__initime
1194 self.__timeInterval = (self.__lastdatatime - self.__initime)/(self.n - 1)
1191
1195
1192 deltatime = datatime -self.__lastdatatime
1196 deltatime = datatime -self.__lastdatatime
1193
1197
1194 if not self.__withOverapping:
1198 if not self.__withOverapping:
1195 self.__initime = datatime
1199 self.__initime = datatime
1196 else:
1200 else:
1197 self.__initime += deltatime
1201 self.__initime += deltatime
1198
1202
1199 return avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc
1203 return avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc
1200
1204
1201 def run(self, dataOut, n=None, timeInterval=None, overlapping=False):
1205 def run(self, dataOut, n=None, timeInterval=None, overlapping=False):
1202
1206
1203 if not self.__isConfig:
1207 if not self.__isConfig:
1204 self.setup(n, timeInterval, overlapping)
1208 self.setup(n, timeInterval, overlapping)
1205 self.__isConfig = True
1209 self.__isConfig = True
1206
1210
1207 avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(dataOut.utctime,
1211 avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(dataOut.utctime,
1208 dataOut.data_spc,
1212 dataOut.data_spc,
1209 dataOut.data_cspc,
1213 dataOut.data_cspc,
1210 dataOut.data_dc)
1214 dataOut.data_dc)
1211
1215
1212 # dataOut.timeInterval *= n
1216 # dataOut.timeInterval *= n
1213 dataOut.flagNoData = True
1217 dataOut.flagNoData = True
1214
1218
1215 if self.__dataReady:
1219 if self.__dataReady:
1216
1220
1217 dataOut.data_spc = avgdata_spc
1221 dataOut.data_spc = avgdata_spc
1218 dataOut.data_cspc = avgdata_cspc
1222 dataOut.data_cspc = avgdata_cspc
1219 dataOut.data_dc = avgdata_dc
1223 dataOut.data_dc = avgdata_dc
1220
1224
1221 dataOut.nIncohInt *= self.n
1225 dataOut.nIncohInt *= self.n
1222 dataOut.utctime = avgdatatime
1226 dataOut.utctime = avgdatatime
1223 dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt * dataOut.nIncohInt * dataOut.nFFTPoints
1227 #dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt * dataOut.nIncohInt * dataOut.nFFTPoints
1228 dataOut.timeInterval = self.__timeInterval*self.n
1224 dataOut.flagNoData = False
1229 dataOut.flagNoData = False
1225
1230
1226 class ProfileSelector(Operation):
1231 class ProfileSelector(Operation):
1227
1232
1228 profileIndex = None
1233 profileIndex = None
1229 # Tamanho total de los perfiles
1234 # Tamanho total de los perfiles
1230 nProfiles = None
1235 nProfiles = None
1231
1236
1232 def __init__(self):
1237 def __init__(self):
1233
1238
1234 self.profileIndex = 0
1239 self.profileIndex = 0
1235
1240
1236 def incIndex(self):
1241 def incIndex(self):
1237 self.profileIndex += 1
1242 self.profileIndex += 1
1238
1243
1239 if self.profileIndex >= self.nProfiles:
1244 if self.profileIndex >= self.nProfiles:
1240 self.profileIndex = 0
1245 self.profileIndex = 0
1241
1246
1242 def isProfileInRange(self, minIndex, maxIndex):
1247 def isProfileInRange(self, minIndex, maxIndex):
1243
1248
1244 if self.profileIndex < minIndex:
1249 if self.profileIndex < minIndex:
1245 return False
1250 return False
1246
1251
1247 if self.profileIndex > maxIndex:
1252 if self.profileIndex > maxIndex:
1248 return False
1253 return False
1249
1254
1250 return True
1255 return True
1251
1256
1252 def isProfileInList(self, profileList):
1257 def isProfileInList(self, profileList):
1253
1258
1254 if self.profileIndex not in profileList:
1259 if self.profileIndex not in profileList:
1255 return False
1260 return False
1256
1261
1257 return True
1262 return True
1258
1263
1259 def run(self, dataOut, profileList=None, profileRangeList=None):
1264 def run(self, dataOut, profileList=None, profileRangeList=None):
1260
1265
1261 dataOut.flagNoData = True
1266 dataOut.flagNoData = True
1262 self.nProfiles = dataOut.nProfiles
1267 self.nProfiles = dataOut.nProfiles
1263
1268
1264 if profileList != None:
1269 if profileList != None:
1265 if self.isProfileInList(profileList):
1270 if self.isProfileInList(profileList):
1266 dataOut.flagNoData = False
1271 dataOut.flagNoData = False
1267
1272
1268 self.incIndex()
1273 self.incIndex()
1269 return 1
1274 return 1
1270
1275
1271
1276
1272 elif profileRangeList != None:
1277 elif profileRangeList != None:
1273 minIndex = profileRangeList[0]
1278 minIndex = profileRangeList[0]
1274 maxIndex = profileRangeList[1]
1279 maxIndex = profileRangeList[1]
1275 if self.isProfileInRange(minIndex, maxIndex):
1280 if self.isProfileInRange(minIndex, maxIndex):
1276 dataOut.flagNoData = False
1281 dataOut.flagNoData = False
1277
1282
1278 self.incIndex()
1283 self.incIndex()
1279 return 1
1284 return 1
1280
1285
1281 else:
1286 else:
1282 raise ValueError, "ProfileSelector needs profileList or profileRangeList"
1287 raise ValueError, "ProfileSelector needs profileList or profileRangeList"
1283
1288
1284 return 0
1289 return 0
1285
1290
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