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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 = dataOut.code
645 else:
644 code = numpy.array(code).reshape(nCode,nBaud)
646 code = numpy.array(code).reshape(nCode,nBaud)
645 dataOut.code = code
647 dataOut.code = code
646 dataOut.nCode = nCode
648 dataOut.nCode = nCode
647 dataOut.nBaud = nBaud
649 dataOut.nBaud = nBaud
650
648 if code == None:
651 if code == None:
649 code = dataOut.code
652 return 1
650
653
651 self.setup(code)
654 self.setup(code)
652 self.__isConfig = True
655 self.__isConfig = True
653
656
654 if mode == 0:
657 if mode == 0:
655 ndatadec, datadec = self.convolutionInFreq(dataOut.data)
658 ndatadec, datadec = self.convolutionInFreq(dataOut.data)
656
659
657 if mode == 1:
660 if mode == 1:
658 print "This function is not implemented"
661 print "This function is not implemented"
659 # ndatadec, datadec = self.convolutionInTime(dataOut.data)
662 # ndatadec, datadec = self.convolutionInTime(dataOut.data)
660
663
661 dataOut.data = datadec
664 dataOut.data = datadec
662
665
663 dataOut.heightList = dataOut.heightList[0:ndatadec]
666 dataOut.heightList = dataOut.heightList[0:ndatadec]
664
667
665 dataOut.flagDecodeData = True #asumo q la data no esta decodificada
668 dataOut.flagDecodeData = True #asumo q la data no esta decodificada
666
669
667 # dataOut.flagDeflipData = True #asumo q la data no esta sin flip
670 # dataOut.flagDeflipData = True #asumo q la data no esta sin flip
668
671
669
672
670 class SpectraProc(ProcessingUnit):
673 class SpectraProc(ProcessingUnit):
671
674
672 def __init__(self):
675 def __init__(self):
673
676
674 self.objectDict = {}
677 self.objectDict = {}
675 self.buffer = None
678 self.buffer = None
676 self.firstdatatime = None
679 self.firstdatatime = None
677 self.profIndex = 0
680 self.profIndex = 0
678 self.dataOut = Spectra()
681 self.dataOut = Spectra()
679
682
680 def __updateObjFromInput(self):
683 def __updateObjFromInput(self):
681
684
682 self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()
685 self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()
683 self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy()
686 self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy()
684 self.dataOut.channelList = self.dataIn.channelList
687 self.dataOut.channelList = self.dataIn.channelList
685 self.dataOut.heightList = self.dataIn.heightList
688 self.dataOut.heightList = self.dataIn.heightList
686 self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')])
689 self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')])
687 # self.dataOut.nHeights = self.dataIn.nHeights
690 # self.dataOut.nHeights = self.dataIn.nHeights
688 # self.dataOut.nChannels = self.dataIn.nChannels
691 # self.dataOut.nChannels = self.dataIn.nChannels
689 self.dataOut.nBaud = self.dataIn.nBaud
692 self.dataOut.nBaud = self.dataIn.nBaud
690 self.dataOut.nCode = self.dataIn.nCode
693 self.dataOut.nCode = self.dataIn.nCode
691 self.dataOut.code = self.dataIn.code
694 self.dataOut.code = self.dataIn.code
692 self.dataOut.nProfiles = self.dataOut.nFFTPoints
695 self.dataOut.nProfiles = self.dataOut.nFFTPoints
693 # self.dataOut.channelIndexList = self.dataIn.channelIndexList
696 # self.dataOut.channelIndexList = self.dataIn.channelIndexList
694 self.dataOut.flagTimeBlock = self.dataIn.flagTimeBlock
697 self.dataOut.flagTimeBlock = self.dataIn.flagTimeBlock
695 self.dataOut.utctime = self.firstdatatime
698 self.dataOut.utctime = self.firstdatatime
696 self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada
699 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
700 self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip
698 self.dataOut.flagShiftFFT = self.dataIn.flagShiftFFT
701 self.dataOut.flagShiftFFT = self.dataIn.flagShiftFFT
699 self.dataOut.nCohInt = self.dataIn.nCohInt
702 self.dataOut.nCohInt = self.dataIn.nCohInt
700 self.dataOut.nIncohInt = 1
703 self.dataOut.nIncohInt = 1
701 self.dataOut.ippSeconds = self.dataIn.ippSeconds
704 self.dataOut.ippSeconds = self.dataIn.ippSeconds
702 self.dataOut.windowOfFilter = self.dataIn.windowOfFilter
705 self.dataOut.windowOfFilter = self.dataIn.windowOfFilter
703
706
704 self.dataOut.timeInterval = self.dataIn.timeInterval*self.dataOut.nFFTPoints*self.dataOut.nIncohInt
707 self.dataOut.timeInterval = self.dataIn.timeInterval*self.dataOut.nFFTPoints*self.dataOut.nIncohInt
705
708
706 def __getFft(self):
709 def __getFft(self):
707 """
710 """
708 Convierte valores de Voltaje a Spectra
711 Convierte valores de Voltaje a Spectra
709
712
710 Affected:
713 Affected:
711 self.dataOut.data_spc
714 self.dataOut.data_spc
712 self.dataOut.data_cspc
715 self.dataOut.data_cspc
713 self.dataOut.data_dc
716 self.dataOut.data_dc
714 self.dataOut.heightList
717 self.dataOut.heightList
715 self.profIndex
718 self.profIndex
716 self.buffer
719 self.buffer
717 self.dataOut.flagNoData
720 self.dataOut.flagNoData
718 """
721 """
719 fft_volt = numpy.fft.fft(self.buffer,axis=1)
722 fft_volt = numpy.fft.fft(self.buffer,axis=1)
720 fft_volt = fft_volt.astype(numpy.dtype('complex'))
723 fft_volt = fft_volt.astype(numpy.dtype('complex'))
721 dc = fft_volt[:,0,:]
724 dc = fft_volt[:,0,:]
722
725
723 #calculo de self-spectra
726 #calculo de self-spectra
724 fft_volt = numpy.fft.fftshift(fft_volt,axes=(1,))
727 fft_volt = numpy.fft.fftshift(fft_volt,axes=(1,))
725 spc = fft_volt * numpy.conjugate(fft_volt)
728 spc = fft_volt * numpy.conjugate(fft_volt)
726 spc = spc.real
729 spc = spc.real
727
730
728 blocksize = 0
731 blocksize = 0
729 blocksize += dc.size
732 blocksize += dc.size
730 blocksize += spc.size
733 blocksize += spc.size
731
734
732 cspc = None
735 cspc = None
733 pairIndex = 0
736 pairIndex = 0
734 if self.dataOut.pairsList != None:
737 if self.dataOut.pairsList != None:
735 #calculo de cross-spectra
738 #calculo de cross-spectra
736 cspc = numpy.zeros((self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex')
739 cspc = numpy.zeros((self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex')
737 for pair in self.dataOut.pairsList:
740 for pair in self.dataOut.pairsList:
738 cspc[pairIndex,:,:] = fft_volt[pair[0],:,:] * numpy.conjugate(fft_volt[pair[1],:,:])
741 cspc[pairIndex,:,:] = fft_volt[pair[0],:,:] * numpy.conjugate(fft_volt[pair[1],:,:])
739 pairIndex += 1
742 pairIndex += 1
740 blocksize += cspc.size
743 blocksize += cspc.size
741
744
742 self.dataOut.data_spc = spc
745 self.dataOut.data_spc = spc
743 self.dataOut.data_cspc = cspc
746 self.dataOut.data_cspc = cspc
744 self.dataOut.data_dc = dc
747 self.dataOut.data_dc = dc
745 self.dataOut.blockSize = blocksize
748 self.dataOut.blockSize = blocksize
746
749
747 def init(self, nFFTPoints=None, pairsList=None):
750 def init(self, nFFTPoints=None, pairsList=None):
748
751
749 self.dataOut.flagNoData = True
752 self.dataOut.flagNoData = True
750
753
751 if self.dataIn.type == "Spectra":
754 if self.dataIn.type == "Spectra":
752 self.dataOut.copy(self.dataIn)
755 self.dataOut.copy(self.dataIn)
753 return
756 return
754
757
755 if self.dataIn.type == "Voltage":
758 if self.dataIn.type == "Voltage":
756
759
757 if nFFTPoints == None:
760 if nFFTPoints == None:
758 raise ValueError, "This SpectraProc.init() need nFFTPoints input variable"
761 raise ValueError, "This SpectraProc.init() need nFFTPoints input variable"
759
762
760 if pairsList == None:
763 if pairsList == None:
761 nPairs = 0
764 nPairs = 0
762 else:
765 else:
763 nPairs = len(pairsList)
766 nPairs = len(pairsList)
764
767
765 self.dataOut.nFFTPoints = nFFTPoints
768 self.dataOut.nFFTPoints = nFFTPoints
766 self.dataOut.pairsList = pairsList
769 self.dataOut.pairsList = pairsList
767 self.dataOut.nPairs = nPairs
770 self.dataOut.nPairs = nPairs
768
771
769 if self.buffer == None:
772 if self.buffer == None:
770 self.buffer = numpy.zeros((self.dataIn.nChannels,
773 self.buffer = numpy.zeros((self.dataIn.nChannels,
771 self.dataOut.nFFTPoints,
774 self.dataOut.nFFTPoints,
772 self.dataIn.nHeights),
775 self.dataIn.nHeights),
773 dtype='complex')
776 dtype='complex')
774
777
775
778
776 self.buffer[:,self.profIndex,:] = self.dataIn.data.copy()
779 self.buffer[:,self.profIndex,:] = self.dataIn.data.copy()
777 self.profIndex += 1
780 self.profIndex += 1
778
781
779 if self.firstdatatime == None:
782 if self.firstdatatime == None:
780 self.firstdatatime = self.dataIn.utctime
783 self.firstdatatime = self.dataIn.utctime
781
784
782 if self.profIndex == self.dataOut.nFFTPoints:
785 if self.profIndex == self.dataOut.nFFTPoints:
783 self.__updateObjFromInput()
786 self.__updateObjFromInput()
784 self.__getFft()
787 self.__getFft()
785
788
786 self.dataOut.flagNoData = False
789 self.dataOut.flagNoData = False
787
790
788 self.buffer = None
791 self.buffer = None
789 self.firstdatatime = None
792 self.firstdatatime = None
790 self.profIndex = 0
793 self.profIndex = 0
791
794
792 return
795 return
793
796
794 raise ValuError, "The type object %s is not valid"%(self.dataIn.type)
797 raise ValuError, "The type object %s is not valid"%(self.dataIn.type)
795
798
796 def selectChannels(self, channelList):
799 def selectChannels(self, channelList):
797
800
798 channelIndexList = []
801 channelIndexList = []
799
802
800 for channel in channelList:
803 for channel in channelList:
801 index = self.dataOut.channelList.index(channel)
804 index = self.dataOut.channelList.index(channel)
802 channelIndexList.append(index)
805 channelIndexList.append(index)
803
806
804 self.selectChannelsByIndex(channelIndexList)
807 self.selectChannelsByIndex(channelIndexList)
805
808
806 def selectChannelsByIndex(self, channelIndexList):
809 def selectChannelsByIndex(self, channelIndexList):
807 """
810 """
808 Selecciona un bloque de datos en base a canales segun el channelIndexList
811 Selecciona un bloque de datos en base a canales segun el channelIndexList
809
812
810 Input:
813 Input:
811 channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
814 channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
812
815
813 Affected:
816 Affected:
814 self.dataOut.data_spc
817 self.dataOut.data_spc
815 self.dataOut.channelIndexList
818 self.dataOut.channelIndexList
816 self.dataOut.nChannels
819 self.dataOut.nChannels
817
820
818 Return:
821 Return:
819 None
822 None
820 """
823 """
821
824
822 for channelIndex in channelIndexList:
825 for channelIndex in channelIndexList:
823 if channelIndex not in self.dataOut.channelIndexList:
826 if channelIndex not in self.dataOut.channelIndexList:
824 print channelIndexList
827 print channelIndexList
825 raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex
828 raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex
826
829
827 nChannels = len(channelIndexList)
830 nChannels = len(channelIndexList)
828
831
829 data_spc = self.dataOut.data_spc[channelIndexList,:]
832 data_spc = self.dataOut.data_spc[channelIndexList,:]
830
833
831 self.dataOut.data_spc = data_spc
834 self.dataOut.data_spc = data_spc
832 self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
835 self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
833 # self.dataOut.nChannels = nChannels
836 # self.dataOut.nChannels = nChannels
834
837
835 return 1
838 return 1
836
839
837 def selectHeights(self, minHei, maxHei):
840 def selectHeights(self, minHei, maxHei):
838 """
841 """
839 Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
842 Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
840 minHei <= height <= maxHei
843 minHei <= height <= maxHei
841
844
842 Input:
845 Input:
843 minHei : valor minimo de altura a considerar
846 minHei : valor minimo de altura a considerar
844 maxHei : valor maximo de altura a considerar
847 maxHei : valor maximo de altura a considerar
845
848
846 Affected:
849 Affected:
847 Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
850 Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
848
851
849 Return:
852 Return:
850 1 si el metodo se ejecuto con exito caso contrario devuelve 0
853 1 si el metodo se ejecuto con exito caso contrario devuelve 0
851 """
854 """
852 if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
855 if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
853 raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
856 raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
854
857
855 if (maxHei > self.dataOut.heightList[-1]):
858 if (maxHei > self.dataOut.heightList[-1]):
856 maxHei = self.dataOut.heightList[-1]
859 maxHei = self.dataOut.heightList[-1]
857 # raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
860 # raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
858
861
859 minIndex = 0
862 minIndex = 0
860 maxIndex = 0
863 maxIndex = 0
861 heights = self.dataOut.heightList
864 heights = self.dataOut.heightList
862
865
863 inda = numpy.where(heights >= minHei)
866 inda = numpy.where(heights >= minHei)
864 indb = numpy.where(heights <= maxHei)
867 indb = numpy.where(heights <= maxHei)
865
868
866 try:
869 try:
867 minIndex = inda[0][0]
870 minIndex = inda[0][0]
868 except:
871 except:
869 minIndex = 0
872 minIndex = 0
870
873
871 try:
874 try:
872 maxIndex = indb[0][-1]
875 maxIndex = indb[0][-1]
873 except:
876 except:
874 maxIndex = len(heights)
877 maxIndex = len(heights)
875
878
876 self.selectHeightsByIndex(minIndex, maxIndex)
879 self.selectHeightsByIndex(minIndex, maxIndex)
877
880
878 return 1
881 return 1
879
882
880
883
881 def selectHeightsByIndex(self, minIndex, maxIndex):
884 def selectHeightsByIndex(self, minIndex, maxIndex):
882 """
885 """
883 Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
886 Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
884 minIndex <= index <= maxIndex
887 minIndex <= index <= maxIndex
885
888
886 Input:
889 Input:
887 minIndex : valor de indice minimo de altura a considerar
890 minIndex : valor de indice minimo de altura a considerar
888 maxIndex : valor de indice maximo de altura a considerar
891 maxIndex : valor de indice maximo de altura a considerar
889
892
890 Affected:
893 Affected:
891 self.dataOut.data_spc
894 self.dataOut.data_spc
892 self.dataOut.data_cspc
895 self.dataOut.data_cspc
893 self.dataOut.data_dc
896 self.dataOut.data_dc
894 self.dataOut.heightList
897 self.dataOut.heightList
895
898
896 Return:
899 Return:
897 1 si el metodo se ejecuto con exito caso contrario devuelve 0
900 1 si el metodo se ejecuto con exito caso contrario devuelve 0
898 """
901 """
899
902
900 if (minIndex < 0) or (minIndex > maxIndex):
903 if (minIndex < 0) or (minIndex > maxIndex):
901 raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
904 raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
902
905
903 if (maxIndex >= self.dataOut.nHeights):
906 if (maxIndex >= self.dataOut.nHeights):
904 maxIndex = self.dataOut.nHeights-1
907 maxIndex = self.dataOut.nHeights-1
905 # raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
908 # raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
906
909
907 nHeights = maxIndex - minIndex + 1
910 nHeights = maxIndex - minIndex + 1
908
911
909 #Spectra
912 #Spectra
910 data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1]
913 data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1]
911
914
912 data_cspc = None
915 data_cspc = None
913 if self.dataOut.data_cspc != None:
916 if self.dataOut.data_cspc != None:
914 data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1]
917 data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1]
915
918
916 data_dc = None
919 data_dc = None
917 if self.dataOut.data_dc != None:
920 if self.dataOut.data_dc != None:
918 data_dc = self.dataOut.data_dc[:,:,minIndex:maxIndex+1]
921 data_dc = self.dataOut.data_dc[:,:,minIndex:maxIndex+1]
919
922
920 self.dataOut.data_spc = data_spc
923 self.dataOut.data_spc = data_spc
921 self.dataOut.data_cspc = data_cspc
924 self.dataOut.data_cspc = data_cspc
922 self.dataOut.data_dc = data_dc
925 self.dataOut.data_dc = data_dc
923
926
924 self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1]
927 self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1]
925
928
926 return 1
929 return 1
927
930
928 def removeDC(self, mode = 1):
931 def removeDC(self, mode = 1):
929
932
930 dc_index = 0
933 dc_index = 0
931 freq_index = numpy.array([-2,-1,1,2])
934 freq_index = numpy.array([-2,-1,1,2])
932 data_spc = self.dataOut.data_spc
935 data_spc = self.dataOut.data_spc
933 data_cspc = self.dataOut.data_cspc
936 data_cspc = self.dataOut.data_cspc
934 data_dc = self.dataOut.data_dc
937 data_dc = self.dataOut.data_dc
935
938
936 if self.dataOut.flagShiftFFT:
939 if self.dataOut.flagShiftFFT:
937 dc_index += self.dataOut.nFFTPoints/2
940 dc_index += self.dataOut.nFFTPoints/2
938 freq_index += self.dataOut.nFFTPoints/2
941 freq_index += self.dataOut.nFFTPoints/2
939
942
940 if mode == 1:
943 if mode == 1:
941 data_spc[dc_index] = (data_spc[:,freq_index[1],:] + data_spc[:,freq_index[2],:])/2
944 data_spc[dc_index] = (data_spc[:,freq_index[1],:] + data_spc[:,freq_index[2],:])/2
942 if data_cspc != None:
945 if data_cspc != None:
943 data_cspc[dc_index] = (data_cspc[:,freq_index[1],:] + data_cspc[:,freq_index[2],:])/2
946 data_cspc[dc_index] = (data_cspc[:,freq_index[1],:] + data_cspc[:,freq_index[2],:])/2
944 return 1
947 return 1
945
948
946 if mode == 2:
949 if mode == 2:
947 pass
950 pass
948
951
949 if mode == 3:
952 if mode == 3:
950 pass
953 pass
951
954
952 raise ValueError, "mode parameter has to be 1, 2 or 3"
955 raise ValueError, "mode parameter has to be 1, 2 or 3"
953
956
954 def removeInterference(self):
957 def removeInterference(self):
955
958
956 pass
959 pass
957
960
958
961
959 class IncohInt(Operation):
962 class IncohInt(Operation):
960
963
961
964
962 __profIndex = 0
965 __profIndex = 0
963 __withOverapping = False
966 __withOverapping = False
964
967
965 __byTime = False
968 __byTime = False
966 __initime = None
969 __initime = None
967 __lastdatatime = None
970 __lastdatatime = None
968 __integrationtime = None
971 __integrationtime = None
969
972
970 __buffer_spc = None
973 __buffer_spc = None
971 __buffer_cspc = None
974 __buffer_cspc = None
972 __buffer_dc = None
975 __buffer_dc = None
973
976
974 __dataReady = False
977 __dataReady = False
975
978
976 __timeInterval = None
979 __timeInterval = None
977
980
978 n = None
981 n = None
979
982
980
983
981
984
982 def __init__(self):
985 def __init__(self):
983
986
984 self.__isConfig = False
987 self.__isConfig = False
985
988
986 def setup(self, n=None, timeInterval=None, overlapping=False):
989 def setup(self, n=None, timeInterval=None, overlapping=False):
987 """
990 """
988 Set the parameters of the integration class.
991 Set the parameters of the integration class.
989
992
990 Inputs:
993 Inputs:
991
994
992 n : Number of coherent integrations
995 n : Number of coherent integrations
993 timeInterval : Time of integration. If the parameter "n" is selected this one does not work
996 timeInterval : Time of integration. If the parameter "n" is selected this one does not work
994 overlapping :
997 overlapping :
995
998
996 """
999 """
997
1000
998 self.__initime = None
1001 self.__initime = None
999 self.__lastdatatime = 0
1002 self.__lastdatatime = 0
1000 self.__buffer_spc = None
1003 self.__buffer_spc = None
1001 self.__buffer_cspc = None
1004 self.__buffer_cspc = None
1002 self.__buffer_dc = None
1005 self.__buffer_dc = None
1003 self.__dataReady = False
1006 self.__dataReady = False
1004
1007
1005
1008
1006 if n == None and timeInterval == None:
1009 if n == None and timeInterval == None:
1007 raise ValueError, "n or timeInterval should be specified ..."
1010 raise ValueError, "n or timeInterval should be specified ..."
1008
1011
1009 if n != None:
1012 if n != None:
1010 self.n = n
1013 self.n = n
1011 self.__byTime = False
1014 self.__byTime = False
1012 else:
1015 else:
1013 self.__integrationtime = timeInterval * 60. #if (type(timeInterval)!=integer) -> change this line
1016 self.__integrationtime = timeInterval * 60. #if (type(timeInterval)!=integer) -> change this line
1014 self.n = 9999
1017 self.n = 9999
1015 self.__byTime = True
1018 self.__byTime = True
1016
1019
1017 if overlapping:
1020 if overlapping:
1018 self.__withOverapping = True
1021 self.__withOverapping = True
1019 else:
1022 else:
1020 self.__withOverapping = False
1023 self.__withOverapping = False
1021 self.__buffer_spc = 0
1024 self.__buffer_spc = 0
1022 self.__buffer_cspc = 0
1025 self.__buffer_cspc = 0
1023 self.__buffer_dc = 0
1026 self.__buffer_dc = 0
1024
1027
1025 self.__profIndex = 0
1028 self.__profIndex = 0
1026
1029
1027 def putData(self, data_spc, data_cspc, data_dc):
1030 def putData(self, data_spc, data_cspc, data_dc):
1028
1031
1029 """
1032 """
1030 Add a profile to the __buffer_spc and increase in one the __profileIndex
1033 Add a profile to the __buffer_spc and increase in one the __profileIndex
1031
1034
1032 """
1035 """
1033
1036
1034 if not self.__withOverapping:
1037 if not self.__withOverapping:
1035 self.__buffer_spc += data_spc
1038 self.__buffer_spc += data_spc
1036
1039
1037 if data_cspc == None:
1040 if data_cspc == None:
1038 self.__buffer_cspc = None
1041 self.__buffer_cspc = None
1039 else:
1042 else:
1040 self.__buffer_cspc += data_cspc
1043 self.__buffer_cspc += data_cspc
1041
1044
1042 if data_dc == None:
1045 if data_dc == None:
1043 self.__buffer_dc = None
1046 self.__buffer_dc = None
1044 else:
1047 else:
1045 self.__buffer_dc += data_dc
1048 self.__buffer_dc += data_dc
1046
1049
1047 self.__profIndex += 1
1050 self.__profIndex += 1
1048 return
1051 return
1049
1052
1050 #Overlapping data
1053 #Overlapping data
1051 nChannels, nFFTPoints, nHeis = data_spc.shape
1054 nChannels, nFFTPoints, nHeis = data_spc.shape
1052 data_spc = numpy.reshape(data_spc, (1, nChannels, nFFTPoints, nHeis))
1055 data_spc = numpy.reshape(data_spc, (1, nChannels, nFFTPoints, nHeis))
1053 if data_cspc != None:
1056 if data_cspc != None:
1054 data_cspc = numpy.reshape(data_cspc, (1, -1, nFFTPoints, nHeis))
1057 data_cspc = numpy.reshape(data_cspc, (1, -1, nFFTPoints, nHeis))
1055 if data_dc != None:
1058 if data_dc != None:
1056 data_dc = numpy.reshape(data_dc, (1, -1, nHeis))
1059 data_dc = numpy.reshape(data_dc, (1, -1, nHeis))
1057
1060
1058 #If the buffer is empty then it takes the data value
1061 #If the buffer is empty then it takes the data value
1059 if self.__buffer_spc == None:
1062 if self.__buffer_spc == None:
1060 self.__buffer_spc = data_spc
1063 self.__buffer_spc = data_spc
1061
1064
1062 if data_cspc == None:
1065 if data_cspc == None:
1063 self.__buffer_cspc = None
1066 self.__buffer_cspc = None
1064 else:
1067 else:
1065 self.__buffer_cspc += data_cspc
1068 self.__buffer_cspc += data_cspc
1066
1069
1067 if data_dc == None:
1070 if data_dc == None:
1068 self.__buffer_dc = None
1071 self.__buffer_dc = None
1069 else:
1072 else:
1070 self.__buffer_dc += data_dc
1073 self.__buffer_dc += data_dc
1071
1074
1072 self.__profIndex += 1
1075 self.__profIndex += 1
1073 return
1076 return
1074
1077
1075 #If the buffer length is lower than n then stakcing the data value
1078 #If the buffer length is lower than n then stakcing the data value
1076 if self.__profIndex < self.n:
1079 if self.__profIndex < self.n:
1077 self.__buffer_spc = numpy.vstack((self.__buffer_spc, data_spc))
1080 self.__buffer_spc = numpy.vstack((self.__buffer_spc, data_spc))
1078
1081
1079 if data_cspc != None:
1082 if data_cspc != None:
1080 self.__buffer_cspc = numpy.vstack((self.__buffer_cspc, data_cspc))
1083 self.__buffer_cspc = numpy.vstack((self.__buffer_cspc, data_cspc))
1081
1084
1082 if data_dc != None:
1085 if data_dc != None:
1083 self.__buffer_dc = numpy.vstack((self.__buffer_dc, data_dc))
1086 self.__buffer_dc = numpy.vstack((self.__buffer_dc, data_dc))
1084
1087
1085 self.__profIndex += 1
1088 self.__profIndex += 1
1086 return
1089 return
1087
1090
1088 #If the buffer length is equal to n then replacing the last buffer value with the data value
1091 #If the buffer length is equal to n then replacing the last buffer value with the data value
1089 self.__buffer_spc = numpy.roll(self.__buffer_spc, -1, axis=0)
1092 self.__buffer_spc = numpy.roll(self.__buffer_spc, -1, axis=0)
1090 self.__buffer_spc[self.n-1] = data_spc
1093 self.__buffer_spc[self.n-1] = data_spc
1091
1094
1092 if data_cspc != None:
1095 if data_cspc != None:
1093 self.__buffer_cspc = numpy.roll(self.__buffer_cspc, -1, axis=0)
1096 self.__buffer_cspc = numpy.roll(self.__buffer_cspc, -1, axis=0)
1094 self.__buffer_cspc[self.n-1] = data_cspc
1097 self.__buffer_cspc[self.n-1] = data_cspc
1095
1098
1096 if data_dc != None:
1099 if data_dc != None:
1097 self.__buffer_dc = numpy.roll(self.__buffer_dc, -1, axis=0)
1100 self.__buffer_dc = numpy.roll(self.__buffer_dc, -1, axis=0)
1098 self.__buffer_dc[self.n-1] = data_dc
1101 self.__buffer_dc[self.n-1] = data_dc
1099
1102
1100 self.__profIndex = self.n
1103 self.__profIndex = self.n
1101 return
1104 return
1102
1105
1103
1106
1104 def pushData(self):
1107 def pushData(self):
1105 """
1108 """
1106 Return the sum of the last profiles and the profiles used in the sum.
1109 Return the sum of the last profiles and the profiles used in the sum.
1107
1110
1108 Affected:
1111 Affected:
1109
1112
1110 self.__profileIndex
1113 self.__profileIndex
1111
1114
1112 """
1115 """
1113 data_spc = None
1116 data_spc = None
1114 data_cspc = None
1117 data_cspc = None
1115 data_dc = None
1118 data_dc = None
1116
1119
1117 if not self.__withOverapping:
1120 if not self.__withOverapping:
1118 data_spc = self.__buffer_spc
1121 data_spc = self.__buffer_spc
1119 data_cspc = self.__buffer_cspc
1122 data_cspc = self.__buffer_cspc
1120 data_dc = self.__buffer_dc
1123 data_dc = self.__buffer_dc
1121
1124
1122 n = self.__profIndex
1125 n = self.__profIndex
1123
1126
1124 self.__buffer_spc = 0
1127 self.__buffer_spc = 0
1125 self.__buffer_cspc = 0
1128 self.__buffer_cspc = 0
1126 self.__buffer_dc = 0
1129 self.__buffer_dc = 0
1127 self.__profIndex = 0
1130 self.__profIndex = 0
1128
1131
1129 return data_spc, data_cspc, data_dc, n
1132 return data_spc, data_cspc, data_dc, n
1130
1133
1131 #Integration with Overlapping
1134 #Integration with Overlapping
1132 data_spc = numpy.sum(self.__buffer_spc, axis=0)
1135 data_spc = numpy.sum(self.__buffer_spc, axis=0)
1133
1136
1134 if self.__buffer_cspc != None:
1137 if self.__buffer_cspc != None:
1135 data_cspc = numpy.sum(self.__buffer_cspc, axis=0)
1138 data_cspc = numpy.sum(self.__buffer_cspc, axis=0)
1136
1139
1137 if self.__buffer_dc != None:
1140 if self.__buffer_dc != None:
1138 data_dc = numpy.sum(self.__buffer_dc, axis=0)
1141 data_dc = numpy.sum(self.__buffer_dc, axis=0)
1139
1142
1140 n = self.__profIndex
1143 n = self.__profIndex
1141
1144
1142 return data_spc, data_cspc, data_dc, n
1145 return data_spc, data_cspc, data_dc, n
1143
1146
1144 def byProfiles(self, *args):
1147 def byProfiles(self, *args):
1145
1148
1146 self.__dataReady = False
1149 self.__dataReady = False
1147 avgdata_spc = None
1150 avgdata_spc = None
1148 avgdata_cspc = None
1151 avgdata_cspc = None
1149 avgdata_dc = None
1152 avgdata_dc = None
1150 n = None
1153 n = None
1151
1154
1152 self.putData(*args)
1155 self.putData(*args)
1153
1156
1154 if self.__profIndex == self.n:
1157 if self.__profIndex == self.n:
1155
1158
1156 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
1159 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
1157 self.__dataReady = True
1160 self.__dataReady = True
1158
1161
1159 return avgdata_spc, avgdata_cspc, avgdata_dc
1162 return avgdata_spc, avgdata_cspc, avgdata_dc
1160
1163
1161 def byTime(self, datatime, *args):
1164 def byTime(self, datatime, *args):
1162
1165
1163 self.__dataReady = False
1166 self.__dataReady = False
1164 avgdata_spc = None
1167 avgdata_spc = None
1165 avgdata_cspc = None
1168 avgdata_cspc = None
1166 avgdata_dc = None
1169 avgdata_dc = None
1167 n = None
1170 n = None
1168
1171
1169 self.putData(*args)
1172 self.putData(*args)
1170
1173
1171 if (datatime - self.__initime) >= self.__integrationtime:
1174 if (datatime - self.__initime) >= self.__integrationtime:
1172 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
1175 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
1173 self.n = n
1176 self.n = n
1174 self.__dataReady = True
1177 self.__dataReady = True
1175
1178
1176 return avgdata_spc, avgdata_cspc, avgdata_dc
1179 return avgdata_spc, avgdata_cspc, avgdata_dc
1177
1180
1178 def integrate(self, datatime, *args):
1181 def integrate(self, datatime, *args):
1179
1182
1180 if self.__initime == None:
1183 if self.__initime == None:
1181 self.__initime = datatime
1184 self.__initime = datatime
1182
1185
1183 if self.__byTime:
1186 if self.__byTime:
1184 avgdata_spc, avgdata_cspc, avgdata_dc = self.byTime(datatime, *args)
1187 avgdata_spc, avgdata_cspc, avgdata_dc = self.byTime(datatime, *args)
1185 else:
1188 else:
1186 avgdata_spc, avgdata_cspc, avgdata_dc = self.byProfiles(*args)
1189 avgdata_spc, avgdata_cspc, avgdata_dc = self.byProfiles(*args)
1187
1190
1188 self.__lastdatatime = datatime
1191 self.__lastdatatime = datatime
1189
1192
1190 if avgdata_spc == None:
1193 if avgdata_spc == None:
1191 return None, None, None, None
1194 return None, None, None, None
1192
1195
1193 avgdatatime = self.__initime
1196 avgdatatime = self.__initime
1194 self.__timeInterval = (self.__lastdatatime - self.__initime)/(self.n - 1)
1197 self.__timeInterval = (self.__lastdatatime - self.__initime)/(self.n - 1)
1195
1198
1196 deltatime = datatime -self.__lastdatatime
1199 deltatime = datatime -self.__lastdatatime
1197
1200
1198 if not self.__withOverapping:
1201 if not self.__withOverapping:
1199 self.__initime = datatime
1202 self.__initime = datatime
1200 else:
1203 else:
1201 self.__initime += deltatime
1204 self.__initime += deltatime
1202
1205
1203 return avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc
1206 return avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc
1204
1207
1205 def run(self, dataOut, n=None, timeInterval=None, overlapping=False):
1208 def run(self, dataOut, n=None, timeInterval=None, overlapping=False):
1206
1209
1207 if not self.__isConfig:
1210 if not self.__isConfig:
1208 self.setup(n, timeInterval, overlapping)
1211 self.setup(n, timeInterval, overlapping)
1209 self.__isConfig = True
1212 self.__isConfig = True
1210
1213
1211 avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(dataOut.utctime,
1214 avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(dataOut.utctime,
1212 dataOut.data_spc,
1215 dataOut.data_spc,
1213 dataOut.data_cspc,
1216 dataOut.data_cspc,
1214 dataOut.data_dc)
1217 dataOut.data_dc)
1215
1218
1216 # dataOut.timeInterval *= n
1219 # dataOut.timeInterval *= n
1217 dataOut.flagNoData = True
1220 dataOut.flagNoData = True
1218
1221
1219 if self.__dataReady:
1222 if self.__dataReady:
1220
1223
1221 dataOut.data_spc = avgdata_spc
1224 dataOut.data_spc = avgdata_spc
1222 dataOut.data_cspc = avgdata_cspc
1225 dataOut.data_cspc = avgdata_cspc
1223 dataOut.data_dc = avgdata_dc
1226 dataOut.data_dc = avgdata_dc
1224
1227
1225 dataOut.nIncohInt *= self.n
1228 dataOut.nIncohInt *= self.n
1226 dataOut.utctime = avgdatatime
1229 dataOut.utctime = avgdatatime
1227 #dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt * dataOut.nIncohInt * dataOut.nFFTPoints
1230 #dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt * dataOut.nIncohInt * dataOut.nFFTPoints
1228 dataOut.timeInterval = self.__timeInterval*self.n
1231 dataOut.timeInterval = self.__timeInterval*self.n
1229 dataOut.flagNoData = False
1232 dataOut.flagNoData = False
1230
1233
1231 class ProfileSelector(Operation):
1234 class ProfileSelector(Operation):
1232
1235
1233 profileIndex = None
1236 profileIndex = None
1234 # Tamanho total de los perfiles
1237 # Tamanho total de los perfiles
1235 nProfiles = None
1238 nProfiles = None
1236
1239
1237 def __init__(self):
1240 def __init__(self):
1238
1241
1239 self.profileIndex = 0
1242 self.profileIndex = 0
1240
1243
1241 def incIndex(self):
1244 def incIndex(self):
1242 self.profileIndex += 1
1245 self.profileIndex += 1
1243
1246
1244 if self.profileIndex >= self.nProfiles:
1247 if self.profileIndex >= self.nProfiles:
1245 self.profileIndex = 0
1248 self.profileIndex = 0
1246
1249
1247 def isProfileInRange(self, minIndex, maxIndex):
1250 def isProfileInRange(self, minIndex, maxIndex):
1248
1251
1249 if self.profileIndex < minIndex:
1252 if self.profileIndex < minIndex:
1250 return False
1253 return False
1251
1254
1252 if self.profileIndex > maxIndex:
1255 if self.profileIndex > maxIndex:
1253 return False
1256 return False
1254
1257
1255 return True
1258 return True
1256
1259
1257 def isProfileInList(self, profileList):
1260 def isProfileInList(self, profileList):
1258
1261
1259 if self.profileIndex not in profileList:
1262 if self.profileIndex not in profileList:
1260 return False
1263 return False
1261
1264
1262 return True
1265 return True
1263
1266
1264 def run(self, dataOut, profileList=None, profileRangeList=None):
1267 def run(self, dataOut, profileList=None, profileRangeList=None):
1265
1268
1266 dataOut.flagNoData = True
1269 dataOut.flagNoData = True
1267 self.nProfiles = dataOut.nProfiles
1270 self.nProfiles = dataOut.nProfiles
1268
1271
1269 if profileList != None:
1272 if profileList != None:
1270 if self.isProfileInList(profileList):
1273 if self.isProfileInList(profileList):
1271 dataOut.flagNoData = False
1274 dataOut.flagNoData = False
1272
1275
1273 self.incIndex()
1276 self.incIndex()
1274 return 1
1277 return 1
1275
1278
1276
1279
1277 elif profileRangeList != None:
1280 elif profileRangeList != None:
1278 minIndex = profileRangeList[0]
1281 minIndex = profileRangeList[0]
1279 maxIndex = profileRangeList[1]
1282 maxIndex = profileRangeList[1]
1280 if self.isProfileInRange(minIndex, maxIndex):
1283 if self.isProfileInRange(minIndex, maxIndex):
1281 dataOut.flagNoData = False
1284 dataOut.flagNoData = False
1282
1285
1283 self.incIndex()
1286 self.incIndex()
1284 return 1
1287 return 1
1285
1288
1286 else:
1289 else:
1287 raise ValueError, "ProfileSelector needs profileList or profileRangeList"
1290 raise ValueError, "ProfileSelector needs profileList or profileRangeList"
1288
1291
1289 return 0
1292 return 0
1290
1293
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