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modo 2 decodificacion
Miguel Valdez -
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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 try:
15 try:
16 import cfunctions
16 import cfunctions
17 except:
17 except:
18 pass
18 pass
19
19
20 class ProcessingUnit:
20 class ProcessingUnit:
21
21
22 """
22 """
23 Esta es la clase base para el procesamiento de datos.
23 Esta es la clase base para el procesamiento de datos.
24
24
25 Contiene el metodo "call" para llamar operaciones. Las operaciones pueden ser:
25 Contiene el metodo "call" para llamar operaciones. Las operaciones pueden ser:
26 - Metodos internos (callMethod)
26 - Metodos internos (callMethod)
27 - Objetos del tipo Operation (callObject). Antes de ser llamados, estos objetos
27 - Objetos del tipo Operation (callObject). Antes de ser llamados, estos objetos
28 tienen que ser agreagados con el metodo "add".
28 tienen que ser agreagados con el metodo "add".
29
29
30 """
30 """
31 # objeto de datos de entrada (Voltage, Spectra o Correlation)
31 # objeto de datos de entrada (Voltage, Spectra o Correlation)
32 dataIn = None
32 dataIn = None
33
33
34 # objeto de datos de entrada (Voltage, Spectra o Correlation)
34 # objeto de datos de entrada (Voltage, Spectra o Correlation)
35 dataOut = None
35 dataOut = None
36
36
37
37
38 objectDict = None
38 objectDict = None
39
39
40 def __init__(self):
40 def __init__(self):
41
41
42 self.objectDict = {}
42 self.objectDict = {}
43
43
44 def init(self):
44 def init(self):
45
45
46 raise ValueError, "Not implemented"
46 raise ValueError, "Not implemented"
47
47
48 def addOperation(self, object, objId):
48 def addOperation(self, object, objId):
49
49
50 """
50 """
51 Agrega el objeto "object" a la lista de objetos "self.objectList" y retorna el
51 Agrega el objeto "object" a la lista de objetos "self.objectList" y retorna el
52 identificador asociado a este objeto.
52 identificador asociado a este objeto.
53
53
54 Input:
54 Input:
55
55
56 object : objeto de la clase "Operation"
56 object : objeto de la clase "Operation"
57
57
58 Return:
58 Return:
59
59
60 objId : identificador del objeto, necesario para ejecutar la operacion
60 objId : identificador del objeto, necesario para ejecutar la operacion
61 """
61 """
62
62
63 self.objectDict[objId] = object
63 self.objectDict[objId] = object
64
64
65 return objId
65 return objId
66
66
67 def operation(self, **kwargs):
67 def operation(self, **kwargs):
68
68
69 """
69 """
70 Operacion directa sobre la data (dataOut.data). Es necesario actualizar los valores de los
70 Operacion directa sobre la data (dataOut.data). Es necesario actualizar los valores de los
71 atributos del objeto dataOut
71 atributos del objeto dataOut
72
72
73 Input:
73 Input:
74
74
75 **kwargs : Diccionario de argumentos de la funcion a ejecutar
75 **kwargs : Diccionario de argumentos de la funcion a ejecutar
76 """
76 """
77
77
78 raise ValueError, "ImplementedError"
78 raise ValueError, "ImplementedError"
79
79
80 def callMethod(self, name, **kwargs):
80 def callMethod(self, name, **kwargs):
81
81
82 """
82 """
83 Ejecuta el metodo con el nombre "name" y con argumentos **kwargs de la propia clase.
83 Ejecuta el metodo con el nombre "name" y con argumentos **kwargs de la propia clase.
84
84
85 Input:
85 Input:
86 name : nombre del metodo a ejecutar
86 name : nombre del metodo a ejecutar
87
87
88 **kwargs : diccionario con los nombres y valores de la funcion a ejecutar.
88 **kwargs : diccionario con los nombres y valores de la funcion a ejecutar.
89
89
90 """
90 """
91 if name != 'run':
91 if name != 'run':
92
92
93 if name == 'init' and self.dataIn.isEmpty():
93 if name == 'init' and self.dataIn.isEmpty():
94 self.dataOut.flagNoData = True
94 self.dataOut.flagNoData = True
95 return False
95 return False
96
96
97 if name != 'init' and self.dataOut.isEmpty():
97 if name != 'init' and self.dataOut.isEmpty():
98 return False
98 return False
99
99
100 methodToCall = getattr(self, name)
100 methodToCall = getattr(self, name)
101
101
102 methodToCall(**kwargs)
102 methodToCall(**kwargs)
103
103
104 if name != 'run':
104 if name != 'run':
105 return True
105 return True
106
106
107 if self.dataOut.isEmpty():
107 if self.dataOut.isEmpty():
108 return False
108 return False
109
109
110 return True
110 return True
111
111
112 def callObject(self, objId, **kwargs):
112 def callObject(self, objId, **kwargs):
113
113
114 """
114 """
115 Ejecuta la operacion asociada al identificador del objeto "objId"
115 Ejecuta la operacion asociada al identificador del objeto "objId"
116
116
117 Input:
117 Input:
118
118
119 objId : identificador del objeto a ejecutar
119 objId : identificador del objeto a ejecutar
120
120
121 **kwargs : diccionario con los nombres y valores de la funcion a ejecutar.
121 **kwargs : diccionario con los nombres y valores de la funcion a ejecutar.
122
122
123 Return:
123 Return:
124
124
125 None
125 None
126 """
126 """
127
127
128 if self.dataOut.isEmpty():
128 if self.dataOut.isEmpty():
129 return False
129 return False
130
130
131 object = self.objectDict[objId]
131 object = self.objectDict[objId]
132
132
133 object.run(self.dataOut, **kwargs)
133 object.run(self.dataOut, **kwargs)
134
134
135 return True
135 return True
136
136
137 def call(self, operationConf, **kwargs):
137 def call(self, operationConf, **kwargs):
138
138
139 """
139 """
140 Return True si ejecuta la operacion "operationConf.name" con los
140 Return True si ejecuta la operacion "operationConf.name" con los
141 argumentos "**kwargs". False si la operacion no se ha ejecutado.
141 argumentos "**kwargs". False si la operacion no se ha ejecutado.
142 La operacion puede ser de dos tipos:
142 La operacion puede ser de dos tipos:
143
143
144 1. Un metodo propio de esta clase:
144 1. Un metodo propio de esta clase:
145
145
146 operation.type = "self"
146 operation.type = "self"
147
147
148 2. El metodo "run" de un objeto del tipo Operation o de un derivado de ella:
148 2. El metodo "run" de un objeto del tipo Operation o de un derivado de ella:
149 operation.type = "other".
149 operation.type = "other".
150
150
151 Este objeto de tipo Operation debe de haber sido agregado antes con el metodo:
151 Este objeto de tipo Operation debe de haber sido agregado antes con el metodo:
152 "addOperation" e identificado con el operation.id
152 "addOperation" e identificado con el operation.id
153
153
154
154
155 con el id de la operacion.
155 con el id de la operacion.
156
156
157 Input:
157 Input:
158
158
159 Operation : Objeto del tipo operacion con los atributos: name, type y id.
159 Operation : Objeto del tipo operacion con los atributos: name, type y id.
160
160
161 """
161 """
162
162
163 if operationConf.type == 'self':
163 if operationConf.type == 'self':
164 sts = self.callMethod(operationConf.name, **kwargs)
164 sts = self.callMethod(operationConf.name, **kwargs)
165
165
166 if operationConf.type == 'other':
166 if operationConf.type == 'other':
167 sts = self.callObject(operationConf.id, **kwargs)
167 sts = self.callObject(operationConf.id, **kwargs)
168
168
169 return sts
169 return sts
170
170
171 def setInput(self, dataIn):
171 def setInput(self, dataIn):
172
172
173 self.dataIn = dataIn
173 self.dataIn = dataIn
174
174
175 def getOutput(self):
175 def getOutput(self):
176
176
177 return self.dataOut
177 return self.dataOut
178
178
179 class Operation():
179 class Operation():
180
180
181 """
181 """
182 Clase base para definir las operaciones adicionales que se pueden agregar a la clase ProcessingUnit
182 Clase base para definir las operaciones adicionales que se pueden agregar a la clase ProcessingUnit
183 y necesiten acumular informacion previa de los datos a procesar. De preferencia usar un buffer de
183 y necesiten acumular informacion previa de los datos a procesar. De preferencia usar un buffer de
184 acumulacion dentro de esta clase
184 acumulacion dentro de esta clase
185
185
186 Ejemplo: Integraciones coherentes, necesita la informacion previa de los n perfiles anteriores (bufffer)
186 Ejemplo: Integraciones coherentes, necesita la informacion previa de los n perfiles anteriores (bufffer)
187
187
188 """
188 """
189
189
190 __buffer = None
190 __buffer = None
191 __isConfig = False
191 __isConfig = False
192
192
193 def __init__(self):
193 def __init__(self):
194
194
195 pass
195 pass
196
196
197 def run(self, dataIn, **kwargs):
197 def run(self, dataIn, **kwargs):
198
198
199 """
199 """
200 Realiza las operaciones necesarias sobre la dataIn.data y actualiza los atributos del objeto dataIn.
200 Realiza las operaciones necesarias sobre la dataIn.data y actualiza los atributos del objeto dataIn.
201
201
202 Input:
202 Input:
203
203
204 dataIn : objeto del tipo JROData
204 dataIn : objeto del tipo JROData
205
205
206 Return:
206 Return:
207
207
208 None
208 None
209
209
210 Affected:
210 Affected:
211 __buffer : buffer de recepcion de datos.
211 __buffer : buffer de recepcion de datos.
212
212
213 """
213 """
214
214
215 raise ValueError, "ImplementedError"
215 raise ValueError, "ImplementedError"
216
216
217 class VoltageProc(ProcessingUnit):
217 class VoltageProc(ProcessingUnit):
218
218
219
219
220 def __init__(self):
220 def __init__(self):
221
221
222 self.objectDict = {}
222 self.objectDict = {}
223 self.dataOut = Voltage()
223 self.dataOut = Voltage()
224 self.flip = 1
224 self.flip = 1
225
225
226 def init(self):
226 def init(self):
227
227
228 self.dataOut.copy(self.dataIn)
228 self.dataOut.copy(self.dataIn)
229 # No necesita copiar en cada init() los atributos de dataIn
229 # No necesita copiar en cada init() los atributos de dataIn
230 # la copia deberia hacerse por cada nuevo bloque de datos
230 # la copia deberia hacerse por cada nuevo bloque de datos
231
231
232 def selectChannels(self, channelList):
232 def selectChannels(self, channelList):
233
233
234 channelIndexList = []
234 channelIndexList = []
235
235
236 for channel in channelList:
236 for channel in channelList:
237 index = self.dataOut.channelList.index(channel)
237 index = self.dataOut.channelList.index(channel)
238 channelIndexList.append(index)
238 channelIndexList.append(index)
239
239
240 self.selectChannelsByIndex(channelIndexList)
240 self.selectChannelsByIndex(channelIndexList)
241
241
242 def selectChannelsByIndex(self, channelIndexList):
242 def selectChannelsByIndex(self, channelIndexList):
243 """
243 """
244 Selecciona un bloque de datos en base a canales segun el channelIndexList
244 Selecciona un bloque de datos en base a canales segun el channelIndexList
245
245
246 Input:
246 Input:
247 channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
247 channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
248
248
249 Affected:
249 Affected:
250 self.dataOut.data
250 self.dataOut.data
251 self.dataOut.channelIndexList
251 self.dataOut.channelIndexList
252 self.dataOut.nChannels
252 self.dataOut.nChannels
253 self.dataOut.m_ProcessingHeader.totalSpectra
253 self.dataOut.m_ProcessingHeader.totalSpectra
254 self.dataOut.systemHeaderObj.numChannels
254 self.dataOut.systemHeaderObj.numChannels
255 self.dataOut.m_ProcessingHeader.blockSize
255 self.dataOut.m_ProcessingHeader.blockSize
256
256
257 Return:
257 Return:
258 None
258 None
259 """
259 """
260
260
261 for channelIndex in channelIndexList:
261 for channelIndex in channelIndexList:
262 if channelIndex not in self.dataOut.channelIndexList:
262 if channelIndex not in self.dataOut.channelIndexList:
263 print channelIndexList
263 print channelIndexList
264 raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex
264 raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex
265
265
266 nChannels = len(channelIndexList)
266 nChannels = len(channelIndexList)
267
267
268 data = self.dataOut.data[channelIndexList,:]
268 data = self.dataOut.data[channelIndexList,:]
269
269
270 self.dataOut.data = data
270 self.dataOut.data = data
271 self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
271 self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
272 # self.dataOut.nChannels = nChannels
272 # self.dataOut.nChannels = nChannels
273
273
274 return 1
274 return 1
275
275
276 def selectHeights(self, minHei, maxHei):
276 def selectHeights(self, minHei, maxHei):
277 """
277 """
278 Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
278 Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
279 minHei <= height <= maxHei
279 minHei <= height <= maxHei
280
280
281 Input:
281 Input:
282 minHei : valor minimo de altura a considerar
282 minHei : valor minimo de altura a considerar
283 maxHei : valor maximo de altura a considerar
283 maxHei : valor maximo de altura a considerar
284
284
285 Affected:
285 Affected:
286 Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
286 Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
287
287
288 Return:
288 Return:
289 1 si el metodo se ejecuto con exito caso contrario devuelve 0
289 1 si el metodo se ejecuto con exito caso contrario devuelve 0
290 """
290 """
291 if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
291 if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
292 raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
292 raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
293
293
294 if (maxHei > self.dataOut.heightList[-1]):
294 if (maxHei > self.dataOut.heightList[-1]):
295 maxHei = self.dataOut.heightList[-1]
295 maxHei = self.dataOut.heightList[-1]
296 # raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
296 # raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
297
297
298 minIndex = 0
298 minIndex = 0
299 maxIndex = 0
299 maxIndex = 0
300 heights = self.dataOut.heightList
300 heights = self.dataOut.heightList
301
301
302 inda = numpy.where(heights >= minHei)
302 inda = numpy.where(heights >= minHei)
303 indb = numpy.where(heights <= maxHei)
303 indb = numpy.where(heights <= maxHei)
304
304
305 try:
305 try:
306 minIndex = inda[0][0]
306 minIndex = inda[0][0]
307 except:
307 except:
308 minIndex = 0
308 minIndex = 0
309
309
310 try:
310 try:
311 maxIndex = indb[0][-1]
311 maxIndex = indb[0][-1]
312 except:
312 except:
313 maxIndex = len(heights)
313 maxIndex = len(heights)
314
314
315 self.selectHeightsByIndex(minIndex, maxIndex)
315 self.selectHeightsByIndex(minIndex, maxIndex)
316
316
317 return 1
317 return 1
318
318
319
319
320 def selectHeightsByIndex(self, minIndex, maxIndex):
320 def selectHeightsByIndex(self, minIndex, maxIndex):
321 """
321 """
322 Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
322 Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
323 minIndex <= index <= maxIndex
323 minIndex <= index <= maxIndex
324
324
325 Input:
325 Input:
326 minIndex : valor de indice minimo de altura a considerar
326 minIndex : valor de indice minimo de altura a considerar
327 maxIndex : valor de indice maximo de altura a considerar
327 maxIndex : valor de indice maximo de altura a considerar
328
328
329 Affected:
329 Affected:
330 self.dataOut.data
330 self.dataOut.data
331 self.dataOut.heightList
331 self.dataOut.heightList
332
332
333 Return:
333 Return:
334 1 si el metodo se ejecuto con exito caso contrario devuelve 0
334 1 si el metodo se ejecuto con exito caso contrario devuelve 0
335 """
335 """
336
336
337 if (minIndex < 0) or (minIndex > maxIndex):
337 if (minIndex < 0) or (minIndex > maxIndex):
338 raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
338 raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
339
339
340 if (maxIndex >= self.dataOut.nHeights):
340 if (maxIndex >= self.dataOut.nHeights):
341 maxIndex = self.dataOut.nHeights-1
341 maxIndex = self.dataOut.nHeights-1
342 # raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
342 # raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
343
343
344 nHeights = maxIndex - minIndex + 1
344 nHeights = maxIndex - minIndex + 1
345
345
346 #voltage
346 #voltage
347 data = self.dataOut.data[:,minIndex:maxIndex+1]
347 data = self.dataOut.data[:,minIndex:maxIndex+1]
348
348
349 firstHeight = self.dataOut.heightList[minIndex]
349 firstHeight = self.dataOut.heightList[minIndex]
350
350
351 self.dataOut.data = data
351 self.dataOut.data = data
352 self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1]
352 self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1]
353
353
354 return 1
354 return 1
355
355
356
356
357 def filterByHeights(self, window):
357 def filterByHeights(self, window):
358 deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0]
358 deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0]
359
359
360 if window == None:
360 if window == None:
361 window = self.dataOut.radarControllerHeaderObj.txA / deltaHeight
361 window = self.dataOut.radarControllerHeaderObj.txA / deltaHeight
362
362
363 newdelta = deltaHeight * window
363 newdelta = deltaHeight * window
364 r = self.dataOut.data.shape[1] % window
364 r = self.dataOut.data.shape[1] % window
365 buffer = self.dataOut.data[:,0:self.dataOut.data.shape[1]-r]
365 buffer = self.dataOut.data[:,0:self.dataOut.data.shape[1]-r]
366 buffer = buffer.reshape(self.dataOut.data.shape[0],self.dataOut.data.shape[1]/window,window)
366 buffer = buffer.reshape(self.dataOut.data.shape[0],self.dataOut.data.shape[1]/window,window)
367 buffer = numpy.sum(buffer,2)
367 buffer = numpy.sum(buffer,2)
368 self.dataOut.data = buffer
368 self.dataOut.data = buffer
369 self.dataOut.heightList = numpy.arange(self.dataOut.heightList[0],newdelta*self.dataOut.nHeights/window-newdelta,newdelta)
369 self.dataOut.heightList = numpy.arange(self.dataOut.heightList[0],newdelta*self.dataOut.nHeights/window-newdelta,newdelta)
370 self.dataOut.windowOfFilter = window
370 self.dataOut.windowOfFilter = window
371
371
372 def deFlip(self):
372 def deFlip(self):
373 self.dataOut.data *= self.flip
373 self.dataOut.data *= self.flip
374 self.flip *= -1.
374 self.flip *= -1.
375
375
376
376
377 class CohInt(Operation):
377 class CohInt(Operation):
378
378
379 __isConfig = False
379 __isConfig = False
380
380
381 __profIndex = 0
381 __profIndex = 0
382 __withOverapping = False
382 __withOverapping = False
383
383
384 __byTime = False
384 __byTime = False
385 __initime = None
385 __initime = None
386 __lastdatatime = None
386 __lastdatatime = None
387 __integrationtime = None
387 __integrationtime = None
388
388
389 __buffer = None
389 __buffer = None
390
390
391 __dataReady = False
391 __dataReady = False
392
392
393 n = None
393 n = None
394
394
395
395
396 def __init__(self):
396 def __init__(self):
397
397
398 self.__isConfig = False
398 self.__isConfig = False
399
399
400 def setup(self, n=None, timeInterval=None, overlapping=False):
400 def setup(self, n=None, timeInterval=None, overlapping=False):
401 """
401 """
402 Set the parameters of the integration class.
402 Set the parameters of the integration class.
403
403
404 Inputs:
404 Inputs:
405
405
406 n : Number of coherent integrations
406 n : Number of coherent integrations
407 timeInterval : Time of integration. If the parameter "n" is selected this one does not work
407 timeInterval : Time of integration. If the parameter "n" is selected this one does not work
408 overlapping :
408 overlapping :
409
409
410 """
410 """
411
411
412 self.__initime = None
412 self.__initime = None
413 self.__lastdatatime = 0
413 self.__lastdatatime = 0
414 self.__buffer = None
414 self.__buffer = None
415 self.__dataReady = False
415 self.__dataReady = False
416
416
417
417
418 if n == None and timeInterval == None:
418 if n == None and timeInterval == None:
419 raise ValueError, "n or timeInterval should be specified ..."
419 raise ValueError, "n or timeInterval should be specified ..."
420
420
421 if n != None:
421 if n != None:
422 self.n = n
422 self.n = n
423 self.__byTime = False
423 self.__byTime = False
424 else:
424 else:
425 self.__integrationtime = timeInterval * 60. #if (type(timeInterval)!=integer) -> change this line
425 self.__integrationtime = timeInterval * 60. #if (type(timeInterval)!=integer) -> change this line
426 self.n = 9999
426 self.n = 9999
427 self.__byTime = True
427 self.__byTime = True
428
428
429 if overlapping:
429 if overlapping:
430 self.__withOverapping = True
430 self.__withOverapping = True
431 self.__buffer = None
431 self.__buffer = None
432 else:
432 else:
433 self.__withOverapping = False
433 self.__withOverapping = False
434 self.__buffer = 0
434 self.__buffer = 0
435
435
436 self.__profIndex = 0
436 self.__profIndex = 0
437
437
438 def putData(self, data):
438 def putData(self, data):
439
439
440 """
440 """
441 Add a profile to the __buffer and increase in one the __profileIndex
441 Add a profile to the __buffer and increase in one the __profileIndex
442
442
443 """
443 """
444
444
445 if not self.__withOverapping:
445 if not self.__withOverapping:
446 self.__buffer += data.copy()
446 self.__buffer += data.copy()
447 self.__profIndex += 1
447 self.__profIndex += 1
448 return
448 return
449
449
450 #Overlapping data
450 #Overlapping data
451 nChannels, nHeis = data.shape
451 nChannels, nHeis = data.shape
452 data = numpy.reshape(data, (1, nChannels, nHeis))
452 data = numpy.reshape(data, (1, nChannels, nHeis))
453
453
454 #If the buffer is empty then it takes the data value
454 #If the buffer is empty then it takes the data value
455 if self.__buffer == None:
455 if self.__buffer == None:
456 self.__buffer = data
456 self.__buffer = data
457 self.__profIndex += 1
457 self.__profIndex += 1
458 return
458 return
459
459
460 #If the buffer length is lower than n then stakcing the data value
460 #If the buffer length is lower than n then stakcing the data value
461 if self.__profIndex < self.n:
461 if self.__profIndex < self.n:
462 self.__buffer = numpy.vstack((self.__buffer, data))
462 self.__buffer = numpy.vstack((self.__buffer, data))
463 self.__profIndex += 1
463 self.__profIndex += 1
464 return
464 return
465
465
466 #If the buffer length is equal to n then replacing the last buffer value with the data value
466 #If the buffer length is equal to n then replacing the last buffer value with the data value
467 self.__buffer = numpy.roll(self.__buffer, -1, axis=0)
467 self.__buffer = numpy.roll(self.__buffer, -1, axis=0)
468 self.__buffer[self.n-1] = data
468 self.__buffer[self.n-1] = data
469 self.__profIndex = self.n
469 self.__profIndex = self.n
470 return
470 return
471
471
472
472
473 def pushData(self):
473 def pushData(self):
474 """
474 """
475 Return the sum of the last profiles and the profiles used in the sum.
475 Return the sum of the last profiles and the profiles used in the sum.
476
476
477 Affected:
477 Affected:
478
478
479 self.__profileIndex
479 self.__profileIndex
480
480
481 """
481 """
482
482
483 if not self.__withOverapping:
483 if not self.__withOverapping:
484 data = self.__buffer
484 data = self.__buffer
485 n = self.__profIndex
485 n = self.__profIndex
486
486
487 self.__buffer = 0
487 self.__buffer = 0
488 self.__profIndex = 0
488 self.__profIndex = 0
489
489
490 return data, n
490 return data, n
491
491
492 #Integration with Overlapping
492 #Integration with Overlapping
493 data = numpy.sum(self.__buffer, axis=0)
493 data = numpy.sum(self.__buffer, axis=0)
494 n = self.__profIndex
494 n = self.__profIndex
495
495
496 return data, n
496 return data, n
497
497
498 def byProfiles(self, data):
498 def byProfiles(self, data):
499
499
500 self.__dataReady = False
500 self.__dataReady = False
501 avgdata = None
501 avgdata = None
502 n = None
502 n = None
503
503
504 self.putData(data)
504 self.putData(data)
505
505
506 if self.__profIndex == self.n:
506 if self.__profIndex == self.n:
507
507
508 avgdata, n = self.pushData()
508 avgdata, n = self.pushData()
509 self.__dataReady = True
509 self.__dataReady = True
510
510
511 return avgdata
511 return avgdata
512
512
513 def byTime(self, data, datatime):
513 def byTime(self, data, datatime):
514
514
515 self.__dataReady = False
515 self.__dataReady = False
516 avgdata = None
516 avgdata = None
517 n = None
517 n = None
518
518
519 self.putData(data)
519 self.putData(data)
520
520
521 if (datatime - self.__initime) >= self.__integrationtime:
521 if (datatime - self.__initime) >= self.__integrationtime:
522 avgdata, n = self.pushData()
522 avgdata, n = self.pushData()
523 self.n = n
523 self.n = n
524 self.__dataReady = True
524 self.__dataReady = True
525
525
526 return avgdata
526 return avgdata
527
527
528 def integrate(self, data, datatime=None):
528 def integrate(self, data, datatime=None):
529
529
530 if self.__initime == None:
530 if self.__initime == None:
531 self.__initime = datatime
531 self.__initime = datatime
532
532
533 if self.__byTime:
533 if self.__byTime:
534 avgdata = self.byTime(data, datatime)
534 avgdata = self.byTime(data, datatime)
535 else:
535 else:
536 avgdata = self.byProfiles(data)
536 avgdata = self.byProfiles(data)
537
537
538
538
539 self.__lastdatatime = datatime
539 self.__lastdatatime = datatime
540
540
541 if avgdata == None:
541 if avgdata == None:
542 return None, None
542 return None, None
543
543
544 avgdatatime = self.__initime
544 avgdatatime = self.__initime
545
545
546 deltatime = datatime -self.__lastdatatime
546 deltatime = datatime -self.__lastdatatime
547
547
548 if not self.__withOverapping:
548 if not self.__withOverapping:
549 self.__initime = datatime
549 self.__initime = datatime
550 else:
550 else:
551 self.__initime += deltatime
551 self.__initime += deltatime
552
552
553 return avgdata, avgdatatime
553 return avgdata, avgdatatime
554
554
555 def run(self, dataOut, **kwargs):
555 def run(self, dataOut, **kwargs):
556
556
557 if not self.__isConfig:
557 if not self.__isConfig:
558 self.setup(**kwargs)
558 self.setup(**kwargs)
559 self.__isConfig = True
559 self.__isConfig = True
560
560
561 avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime)
561 avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime)
562
562
563 # dataOut.timeInterval *= n
563 # dataOut.timeInterval *= n
564 dataOut.flagNoData = True
564 dataOut.flagNoData = True
565
565
566 if self.__dataReady:
566 if self.__dataReady:
567 dataOut.data = avgdata
567 dataOut.data = avgdata
568 dataOut.nCohInt *= self.n
568 dataOut.nCohInt *= self.n
569 dataOut.utctime = avgdatatime
569 dataOut.utctime = avgdatatime
570 dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt
570 dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt
571 dataOut.flagNoData = False
571 dataOut.flagNoData = False
572
572
573
573
574 class Decoder(Operation):
574 class Decoder(Operation):
575
575
576 __isConfig = False
576 __isConfig = False
577 __profIndex = 0
577 __profIndex = 0
578
578
579 code = None
579 code = None
580
580
581 nCode = None
581 nCode = None
582 nBaud = None
582 nBaud = None
583
583
584 def __init__(self):
584 def __init__(self):
585
585
586 self.__isConfig = False
586 self.__isConfig = False
587
587
588 def setup(self, code, shape):
588 def setup(self, code, shape):
589
589
590 self.__profIndex = 0
590 self.__profIndex = 0
591
591
592 self.code = code
592 self.code = code
593
593
594 self.nCode = len(code)
594 self.nCode = len(code)
595 self.nBaud = len(code[0])
595 self.nBaud = len(code[0])
596
596
597 self.__nChannels, self.__nHeis = shape
597 self.__nChannels, self.__nHeis = shape
598
598
599 self.__codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=numpy.float32)
599 self.__codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=numpy.float32)
600
600
601 self.__codeBuffer[:,0:self.nBaud] = self.code
601 self.__codeBuffer[:,0:self.nBaud] = self.code
602
602
603 self.fft_code = numpy.conj(numpy.fft.fft(self.__codeBuffer, axis=1))
603 self.fft_code = numpy.conj(numpy.fft.fft(self.__codeBuffer, axis=1))
604
604
605
605
606 def convolutionInFreq(self, data):
606 def convolutionInFreq(self, data):
607
607
608 fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
608 fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
609
609
610 fft_data = numpy.fft.fft(data, axis=1)
610 fft_data = numpy.fft.fft(data, axis=1)
611
611
612
612
613 # conv = fft_data.copy()
613 # conv = fft_data.copy()
614 # conv.fill(0)
614 # conv.fill(0)
615
615
616 conv = fft_data*fft_code
616 conv = fft_data*fft_code
617
617
618 data = numpy.fft.ifft(conv,axis=1)
618 data = numpy.fft.ifft(conv,axis=1)
619
619
620 datadec = data[:,:-self.nBaud+1]
620 datadec = data[:,:-self.nBaud+1]
621 ndatadec = self.__nHeis - self.nBaud + 1
621 ndatadec = self.__nHeis - self.nBaud + 1
622
622
623 if self.__profIndex == self.nCode-1:
623 if self.__profIndex == self.nCode-1:
624 self.__profIndex = 0
624 self.__profIndex = 0
625 return ndatadec, datadec
625 return ndatadec, datadec
626
626
627 self.__profIndex += 1
627 self.__profIndex += 1
628
628
629 return ndatadec, datadec
629 return ndatadec, datadec
630
630
631 def convolutionInFreqOpt(self, data):
631 def convolutionInFreqOpt(self, data):
632 ini = time.time()
632 ini = time.time()
633 fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
633 fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
634
634
635 data = cfunctions.decoder(fft_code, data)
635 data = cfunctions.decoder(fft_code, data)
636
636
637 datadec = data[:,:-self.nBaud+1]
637 datadec = data[:,:-self.nBaud+1]
638 ndatadec = self.__nHeis - self.nBaud + 1
638 ndatadec = self.__nHeis - self.nBaud + 1
639
639
640 if self.__profIndex == self.nCode-1:
640 if self.__profIndex == self.nCode-1:
641 self.__profIndex = 0
641 self.__profIndex = 0
642 return ndatadec, datadec
642 return ndatadec, datadec
643
643
644 self.__profIndex += 1
644 self.__profIndex += 1
645 print time.time() - ini
645 print time.time() - ini
646 return ndatadec, datadec
646 return ndatadec, datadec
647
647
648 def convolutionInTime(self, data):
648 def convolutionInTime(self, data):
649
649
650 self.__nChannels, self.__nHeis = data.shape
650 self.__nChannels, self.__nHeis = data.shape
651 self.__codeBuffer = self.code[self.__profIndex]
651 self.__codeBuffer = self.code[self.__profIndex]
652 ndatadec = self.__nHeis - self.nBaud + 1
652 ndatadec = self.__nHeis - self.nBaud + 1
653
653
654 datadec = numpy.zeros((self.__nChannels, ndatadec))
654 datadec = numpy.zeros((self.__nChannels, ndatadec))
655
655
656 for i in range(self.__nChannels):
656 for i in range(self.__nChannels):
657 datadec[i,:] = numpy.correlate(data[i,:], self.__codeBuffer)
657 datadec[i,:] = numpy.correlate(data[i,:], self.__codeBuffer)
658
658
659 if self.__profIndex == self.nCode-1:
659 if self.__profIndex == self.nCode-1:
660 self.__profIndex = 0
660 self.__profIndex = 0
661 return ndatadec, datadec
661 return ndatadec, datadec
662
662
663 self.__profIndex += 1
663 self.__profIndex += 1
664
664
665 return ndatadec, datadec
665 return ndatadec, datadec
666
666
667 def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0):
667 def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0):
668 if code == None:
668
669 code = dataOut.code
669 if not self.__isConfig:
670 else:
671 code = numpy.array(code).reshape(nCode,nBaud)
672 dataOut.code = code
673 dataOut.nCode = nCode
674 dataOut.nBaud = nBaud
675
670
676 if code == None:
671 if code == None:
677 return 1
672 code = dataOut.code
673 else:
674 code = numpy.array(code).reshape(nCode,nBaud)
675 dataOut.code = code
676 dataOut.nCode = nCode
677 dataOut.nBaud = nBaud
678
679 if code == None:
680 return 1
678
681
679 if not self.__isConfig:
680 self.setup(code, dataOut.data.shape)
682 self.setup(code, dataOut.data.shape)
681 self.__isConfig = True
683 self.__isConfig = True
682
684
683 if mode == 0:
685 if mode == 0:
684 ndatadec, datadec = self.convolutionInFreq(dataOut.data)
686 ndatadec, datadec = self.convolutionInFreq(dataOut.data)
685
687
686 if mode == 1:
688 if mode == 1:
687 print "This function is not implemented"
689 print "This function is not implemented"
688 # ndatadec, datadec = self.convolutionInTime(dataOut.data)
690 # ndatadec, datadec = self.convolutionInTime(dataOut.data)
689
691
690 if mode == 3:
692 if mode == 2:
691 ndatadec, datadec = self.convolutionInFreqOpt(dataOut.data)
693 ndatadec, datadec = self.convolutionInFreqOpt(dataOut.data)
692
694
693 dataOut.data = datadec
695 dataOut.data = datadec
694
696
695 dataOut.heightList = dataOut.heightList[0:ndatadec]
697 dataOut.heightList = dataOut.heightList[0:ndatadec]
696
698
697 dataOut.flagDecodeData = True #asumo q la data no esta decodificada
699 dataOut.flagDecodeData = True #asumo q la data no esta decodificada
698
700
699 # dataOut.flagDeflipData = True #asumo q la data no esta sin flip
701 # dataOut.flagDeflipData = True #asumo q la data no esta sin flip
700
702
701
703
702 class SpectraProc(ProcessingUnit):
704 class SpectraProc(ProcessingUnit):
703
705
704 def __init__(self):
706 def __init__(self):
705
707
706 self.objectDict = {}
708 self.objectDict = {}
707 self.buffer = None
709 self.buffer = None
708 self.firstdatatime = None
710 self.firstdatatime = None
709 self.profIndex = 0
711 self.profIndex = 0
710 self.dataOut = Spectra()
712 self.dataOut = Spectra()
711
713
712 def __updateObjFromInput(self):
714 def __updateObjFromInput(self):
713
715
714 self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()
716 self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()
715 self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy()
717 self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy()
716 self.dataOut.channelList = self.dataIn.channelList
718 self.dataOut.channelList = self.dataIn.channelList
717 self.dataOut.heightList = self.dataIn.heightList
719 self.dataOut.heightList = self.dataIn.heightList
718 self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')])
720 self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')])
719 # self.dataOut.nHeights = self.dataIn.nHeights
721 # self.dataOut.nHeights = self.dataIn.nHeights
720 # self.dataOut.nChannels = self.dataIn.nChannels
722 # self.dataOut.nChannels = self.dataIn.nChannels
721 self.dataOut.nBaud = self.dataIn.nBaud
723 self.dataOut.nBaud = self.dataIn.nBaud
722 self.dataOut.nCode = self.dataIn.nCode
724 self.dataOut.nCode = self.dataIn.nCode
723 self.dataOut.code = self.dataIn.code
725 self.dataOut.code = self.dataIn.code
724 self.dataOut.nProfiles = self.dataOut.nFFTPoints
726 self.dataOut.nProfiles = self.dataOut.nFFTPoints
725 # self.dataOut.channelIndexList = self.dataIn.channelIndexList
727 # self.dataOut.channelIndexList = self.dataIn.channelIndexList
726 self.dataOut.flagTimeBlock = self.dataIn.flagTimeBlock
728 self.dataOut.flagTimeBlock = self.dataIn.flagTimeBlock
727 self.dataOut.utctime = self.firstdatatime
729 self.dataOut.utctime = self.firstdatatime
728 self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada
730 self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada
729 self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip
731 self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip
730 self.dataOut.flagShiftFFT = self.dataIn.flagShiftFFT
732 self.dataOut.flagShiftFFT = self.dataIn.flagShiftFFT
731 self.dataOut.nCohInt = self.dataIn.nCohInt
733 self.dataOut.nCohInt = self.dataIn.nCohInt
732 self.dataOut.nIncohInt = 1
734 self.dataOut.nIncohInt = 1
733 self.dataOut.ippSeconds = self.dataIn.ippSeconds
735 self.dataOut.ippSeconds = self.dataIn.ippSeconds
734 self.dataOut.windowOfFilter = self.dataIn.windowOfFilter
736 self.dataOut.windowOfFilter = self.dataIn.windowOfFilter
735
737
736 self.dataOut.timeInterval = self.dataIn.timeInterval*self.dataOut.nFFTPoints*self.dataOut.nIncohInt
738 self.dataOut.timeInterval = self.dataIn.timeInterval*self.dataOut.nFFTPoints*self.dataOut.nIncohInt
737
739
738 def __getFft(self):
740 def __getFft(self):
739 """
741 """
740 Convierte valores de Voltaje a Spectra
742 Convierte valores de Voltaje a Spectra
741
743
742 Affected:
744 Affected:
743 self.dataOut.data_spc
745 self.dataOut.data_spc
744 self.dataOut.data_cspc
746 self.dataOut.data_cspc
745 self.dataOut.data_dc
747 self.dataOut.data_dc
746 self.dataOut.heightList
748 self.dataOut.heightList
747 self.profIndex
749 self.profIndex
748 self.buffer
750 self.buffer
749 self.dataOut.flagNoData
751 self.dataOut.flagNoData
750 """
752 """
751 fft_volt = numpy.fft.fft(self.buffer,axis=1)
753 fft_volt = numpy.fft.fft(self.buffer,axis=1)
752 fft_volt = fft_volt.astype(numpy.dtype('complex'))
754 fft_volt = fft_volt.astype(numpy.dtype('complex'))
753 dc = fft_volt[:,0,:]
755 dc = fft_volt[:,0,:]
754
756
755 #calculo de self-spectra
757 #calculo de self-spectra
756 fft_volt = numpy.fft.fftshift(fft_volt,axes=(1,))
758 fft_volt = numpy.fft.fftshift(fft_volt,axes=(1,))
757 spc = fft_volt * numpy.conjugate(fft_volt)
759 spc = fft_volt * numpy.conjugate(fft_volt)
758 spc = spc.real
760 spc = spc.real
759
761
760 blocksize = 0
762 blocksize = 0
761 blocksize += dc.size
763 blocksize += dc.size
762 blocksize += spc.size
764 blocksize += spc.size
763
765
764 cspc = None
766 cspc = None
765 pairIndex = 0
767 pairIndex = 0
766 if self.dataOut.pairsList != None:
768 if self.dataOut.pairsList != None:
767 #calculo de cross-spectra
769 #calculo de cross-spectra
768 cspc = numpy.zeros((self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex')
770 cspc = numpy.zeros((self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex')
769 for pair in self.dataOut.pairsList:
771 for pair in self.dataOut.pairsList:
770 cspc[pairIndex,:,:] = fft_volt[pair[0],:,:] * numpy.conjugate(fft_volt[pair[1],:,:])
772 cspc[pairIndex,:,:] = fft_volt[pair[0],:,:] * numpy.conjugate(fft_volt[pair[1],:,:])
771 pairIndex += 1
773 pairIndex += 1
772 blocksize += cspc.size
774 blocksize += cspc.size
773
775
774 self.dataOut.data_spc = spc
776 self.dataOut.data_spc = spc
775 self.dataOut.data_cspc = cspc
777 self.dataOut.data_cspc = cspc
776 self.dataOut.data_dc = dc
778 self.dataOut.data_dc = dc
777 self.dataOut.blockSize = blocksize
779 self.dataOut.blockSize = blocksize
778
780
779 def init(self, nFFTPoints=None, pairsList=None):
781 def init(self, nFFTPoints=None, pairsList=None):
780
782
781 self.dataOut.flagNoData = True
783 self.dataOut.flagNoData = True
782
784
783 if self.dataIn.type == "Spectra":
785 if self.dataIn.type == "Spectra":
784 self.dataOut.copy(self.dataIn)
786 self.dataOut.copy(self.dataIn)
785 return
787 return
786
788
787 if self.dataIn.type == "Voltage":
789 if self.dataIn.type == "Voltage":
788
790
789 if nFFTPoints == None:
791 if nFFTPoints == None:
790 raise ValueError, "This SpectraProc.init() need nFFTPoints input variable"
792 raise ValueError, "This SpectraProc.init() need nFFTPoints input variable"
791
793
792 if pairsList == None:
794 if pairsList == None:
793 nPairs = 0
795 nPairs = 0
794 else:
796 else:
795 nPairs = len(pairsList)
797 nPairs = len(pairsList)
796
798
797 self.dataOut.nFFTPoints = nFFTPoints
799 self.dataOut.nFFTPoints = nFFTPoints
798 self.dataOut.pairsList = pairsList
800 self.dataOut.pairsList = pairsList
799 self.dataOut.nPairs = nPairs
801 self.dataOut.nPairs = nPairs
800
802
801 if self.buffer == None:
803 if self.buffer == None:
802 self.buffer = numpy.zeros((self.dataIn.nChannels,
804 self.buffer = numpy.zeros((self.dataIn.nChannels,
803 self.dataOut.nFFTPoints,
805 self.dataOut.nFFTPoints,
804 self.dataIn.nHeights),
806 self.dataIn.nHeights),
805 dtype='complex')
807 dtype='complex')
806
808
807
809
808 self.buffer[:,self.profIndex,:] = self.dataIn.data.copy()
810 self.buffer[:,self.profIndex,:] = self.dataIn.data.copy()
809 self.profIndex += 1
811 self.profIndex += 1
810
812
811 if self.firstdatatime == None:
813 if self.firstdatatime == None:
812 self.firstdatatime = self.dataIn.utctime
814 self.firstdatatime = self.dataIn.utctime
813
815
814 if self.profIndex == self.dataOut.nFFTPoints:
816 if self.profIndex == self.dataOut.nFFTPoints:
815 self.__updateObjFromInput()
817 self.__updateObjFromInput()
816 self.__getFft()
818 self.__getFft()
817
819
818 self.dataOut.flagNoData = False
820 self.dataOut.flagNoData = False
819
821
820 self.buffer = None
822 self.buffer = None
821 self.firstdatatime = None
823 self.firstdatatime = None
822 self.profIndex = 0
824 self.profIndex = 0
823
825
824 return
826 return
825
827
826 raise ValuError, "The type object %s is not valid"%(self.dataIn.type)
828 raise ValuError, "The type object %s is not valid"%(self.dataIn.type)
827
829
828 def selectChannels(self, channelList):
830 def selectChannels(self, channelList):
829
831
830 channelIndexList = []
832 channelIndexList = []
831
833
832 for channel in channelList:
834 for channel in channelList:
833 index = self.dataOut.channelList.index(channel)
835 index = self.dataOut.channelList.index(channel)
834 channelIndexList.append(index)
836 channelIndexList.append(index)
835
837
836 self.selectChannelsByIndex(channelIndexList)
838 self.selectChannelsByIndex(channelIndexList)
837
839
838 def selectChannelsByIndex(self, channelIndexList):
840 def selectChannelsByIndex(self, channelIndexList):
839 """
841 """
840 Selecciona un bloque de datos en base a canales segun el channelIndexList
842 Selecciona un bloque de datos en base a canales segun el channelIndexList
841
843
842 Input:
844 Input:
843 channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
845 channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
844
846
845 Affected:
847 Affected:
846 self.dataOut.data_spc
848 self.dataOut.data_spc
847 self.dataOut.channelIndexList
849 self.dataOut.channelIndexList
848 self.dataOut.nChannels
850 self.dataOut.nChannels
849
851
850 Return:
852 Return:
851 None
853 None
852 """
854 """
853
855
854 for channelIndex in channelIndexList:
856 for channelIndex in channelIndexList:
855 if channelIndex not in self.dataOut.channelIndexList:
857 if channelIndex not in self.dataOut.channelIndexList:
856 print channelIndexList
858 print channelIndexList
857 raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex
859 raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex
858
860
859 nChannels = len(channelIndexList)
861 nChannels = len(channelIndexList)
860
862
861 data_spc = self.dataOut.data_spc[channelIndexList,:]
863 data_spc = self.dataOut.data_spc[channelIndexList,:]
862
864
863 self.dataOut.data_spc = data_spc
865 self.dataOut.data_spc = data_spc
864 self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
866 self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
865 # self.dataOut.nChannels = nChannels
867 # self.dataOut.nChannels = nChannels
866
868
867 return 1
869 return 1
868
870
869 def selectHeights(self, minHei, maxHei):
871 def selectHeights(self, minHei, maxHei):
870 """
872 """
871 Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
873 Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
872 minHei <= height <= maxHei
874 minHei <= height <= maxHei
873
875
874 Input:
876 Input:
875 minHei : valor minimo de altura a considerar
877 minHei : valor minimo de altura a considerar
876 maxHei : valor maximo de altura a considerar
878 maxHei : valor maximo de altura a considerar
877
879
878 Affected:
880 Affected:
879 Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
881 Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
880
882
881 Return:
883 Return:
882 1 si el metodo se ejecuto con exito caso contrario devuelve 0
884 1 si el metodo se ejecuto con exito caso contrario devuelve 0
883 """
885 """
884 if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
886 if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
885 raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
887 raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
886
888
887 if (maxHei > self.dataOut.heightList[-1]):
889 if (maxHei > self.dataOut.heightList[-1]):
888 maxHei = self.dataOut.heightList[-1]
890 maxHei = self.dataOut.heightList[-1]
889 # raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
891 # raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
890
892
891 minIndex = 0
893 minIndex = 0
892 maxIndex = 0
894 maxIndex = 0
893 heights = self.dataOut.heightList
895 heights = self.dataOut.heightList
894
896
895 inda = numpy.where(heights >= minHei)
897 inda = numpy.where(heights >= minHei)
896 indb = numpy.where(heights <= maxHei)
898 indb = numpy.where(heights <= maxHei)
897
899
898 try:
900 try:
899 minIndex = inda[0][0]
901 minIndex = inda[0][0]
900 except:
902 except:
901 minIndex = 0
903 minIndex = 0
902
904
903 try:
905 try:
904 maxIndex = indb[0][-1]
906 maxIndex = indb[0][-1]
905 except:
907 except:
906 maxIndex = len(heights)
908 maxIndex = len(heights)
907
909
908 self.selectHeightsByIndex(minIndex, maxIndex)
910 self.selectHeightsByIndex(minIndex, maxIndex)
909
911
910 return 1
912 return 1
911
913
912
914
913 def selectHeightsByIndex(self, minIndex, maxIndex):
915 def selectHeightsByIndex(self, minIndex, maxIndex):
914 """
916 """
915 Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
917 Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
916 minIndex <= index <= maxIndex
918 minIndex <= index <= maxIndex
917
919
918 Input:
920 Input:
919 minIndex : valor de indice minimo de altura a considerar
921 minIndex : valor de indice minimo de altura a considerar
920 maxIndex : valor de indice maximo de altura a considerar
922 maxIndex : valor de indice maximo de altura a considerar
921
923
922 Affected:
924 Affected:
923 self.dataOut.data_spc
925 self.dataOut.data_spc
924 self.dataOut.data_cspc
926 self.dataOut.data_cspc
925 self.dataOut.data_dc
927 self.dataOut.data_dc
926 self.dataOut.heightList
928 self.dataOut.heightList
927
929
928 Return:
930 Return:
929 1 si el metodo se ejecuto con exito caso contrario devuelve 0
931 1 si el metodo se ejecuto con exito caso contrario devuelve 0
930 """
932 """
931
933
932 if (minIndex < 0) or (minIndex > maxIndex):
934 if (minIndex < 0) or (minIndex > maxIndex):
933 raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
935 raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
934
936
935 if (maxIndex >= self.dataOut.nHeights):
937 if (maxIndex >= self.dataOut.nHeights):
936 maxIndex = self.dataOut.nHeights-1
938 maxIndex = self.dataOut.nHeights-1
937 # raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
939 # raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
938
940
939 nHeights = maxIndex - minIndex + 1
941 nHeights = maxIndex - minIndex + 1
940
942
941 #Spectra
943 #Spectra
942 data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1]
944 data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1]
943
945
944 data_cspc = None
946 data_cspc = None
945 if self.dataOut.data_cspc != None:
947 if self.dataOut.data_cspc != None:
946 data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1]
948 data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1]
947
949
948 data_dc = None
950 data_dc = None
949 if self.dataOut.data_dc != None:
951 if self.dataOut.data_dc != None:
950 data_dc = self.dataOut.data_dc[:,minIndex:maxIndex+1]
952 data_dc = self.dataOut.data_dc[:,minIndex:maxIndex+1]
951
953
952 self.dataOut.data_spc = data_spc
954 self.dataOut.data_spc = data_spc
953 self.dataOut.data_cspc = data_cspc
955 self.dataOut.data_cspc = data_cspc
954 self.dataOut.data_dc = data_dc
956 self.dataOut.data_dc = data_dc
955
957
956 self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1]
958 self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1]
957
959
958 return 1
960 return 1
959
961
960 def removeDC(self, mode = 1):
962 def removeDC(self, mode = 1):
961
963
962 dc_index = 0
964 dc_index = 0
963 freq_index = numpy.array([-2,-1,1,2])
965 freq_index = numpy.array([-2,-1,1,2])
964 data_spc = self.dataOut.data_spc
966 data_spc = self.dataOut.data_spc
965 data_cspc = self.dataOut.data_cspc
967 data_cspc = self.dataOut.data_cspc
966 data_dc = self.dataOut.data_dc
968 data_dc = self.dataOut.data_dc
967
969
968 if self.dataOut.flagShiftFFT:
970 if self.dataOut.flagShiftFFT:
969 dc_index += self.dataOut.nFFTPoints/2
971 dc_index += self.dataOut.nFFTPoints/2
970 freq_index += self.dataOut.nFFTPoints/2
972 freq_index += self.dataOut.nFFTPoints/2
971
973
972 if mode == 1:
974 if mode == 1:
973 data_spc[dc_index] = (data_spc[:,freq_index[1],:] + data_spc[:,freq_index[2],:])/2
975 data_spc[dc_index] = (data_spc[:,freq_index[1],:] + data_spc[:,freq_index[2],:])/2
974 if data_cspc != None:
976 if data_cspc != None:
975 data_cspc[dc_index] = (data_cspc[:,freq_index[1],:] + data_cspc[:,freq_index[2],:])/2
977 data_cspc[dc_index] = (data_cspc[:,freq_index[1],:] + data_cspc[:,freq_index[2],:])/2
976 return 1
978 return 1
977
979
978 if mode == 2:
980 if mode == 2:
979 pass
981 pass
980
982
981 if mode == 3:
983 if mode == 3:
982 pass
984 pass
983
985
984 raise ValueError, "mode parameter has to be 1, 2 or 3"
986 raise ValueError, "mode parameter has to be 1, 2 or 3"
985
987
986 def removeInterference(self):
988 def removeInterference(self):
987
989
988 pass
990 pass
989
991
990
992
991 class IncohInt(Operation):
993 class IncohInt(Operation):
992
994
993
995
994 __profIndex = 0
996 __profIndex = 0
995 __withOverapping = False
997 __withOverapping = False
996
998
997 __byTime = False
999 __byTime = False
998 __initime = None
1000 __initime = None
999 __lastdatatime = None
1001 __lastdatatime = None
1000 __integrationtime = None
1002 __integrationtime = None
1001
1003
1002 __buffer_spc = None
1004 __buffer_spc = None
1003 __buffer_cspc = None
1005 __buffer_cspc = None
1004 __buffer_dc = None
1006 __buffer_dc = None
1005
1007
1006 __dataReady = False
1008 __dataReady = False
1007
1009
1008 __timeInterval = None
1010 __timeInterval = None
1009
1011
1010 n = None
1012 n = None
1011
1013
1012
1014
1013
1015
1014 def __init__(self):
1016 def __init__(self):
1015
1017
1016 self.__isConfig = False
1018 self.__isConfig = False
1017
1019
1018 def setup(self, n=None, timeInterval=None, overlapping=False):
1020 def setup(self, n=None, timeInterval=None, overlapping=False):
1019 """
1021 """
1020 Set the parameters of the integration class.
1022 Set the parameters of the integration class.
1021
1023
1022 Inputs:
1024 Inputs:
1023
1025
1024 n : Number of coherent integrations
1026 n : Number of coherent integrations
1025 timeInterval : Time of integration. If the parameter "n" is selected this one does not work
1027 timeInterval : Time of integration. If the parameter "n" is selected this one does not work
1026 overlapping :
1028 overlapping :
1027
1029
1028 """
1030 """
1029
1031
1030 self.__initime = None
1032 self.__initime = None
1031 self.__lastdatatime = 0
1033 self.__lastdatatime = 0
1032 self.__buffer_spc = None
1034 self.__buffer_spc = None
1033 self.__buffer_cspc = None
1035 self.__buffer_cspc = None
1034 self.__buffer_dc = None
1036 self.__buffer_dc = None
1035 self.__dataReady = False
1037 self.__dataReady = False
1036
1038
1037
1039
1038 if n == None and timeInterval == None:
1040 if n == None and timeInterval == None:
1039 raise ValueError, "n or timeInterval should be specified ..."
1041 raise ValueError, "n or timeInterval should be specified ..."
1040
1042
1041 if n != None:
1043 if n != None:
1042 self.n = n
1044 self.n = n
1043 self.__byTime = False
1045 self.__byTime = False
1044 else:
1046 else:
1045 self.__integrationtime = timeInterval * 60. #if (type(timeInterval)!=integer) -> change this line
1047 self.__integrationtime = timeInterval * 60. #if (type(timeInterval)!=integer) -> change this line
1046 self.n = 9999
1048 self.n = 9999
1047 self.__byTime = True
1049 self.__byTime = True
1048
1050
1049 if overlapping:
1051 if overlapping:
1050 self.__withOverapping = True
1052 self.__withOverapping = True
1051 else:
1053 else:
1052 self.__withOverapping = False
1054 self.__withOverapping = False
1053 self.__buffer_spc = 0
1055 self.__buffer_spc = 0
1054 self.__buffer_cspc = 0
1056 self.__buffer_cspc = 0
1055 self.__buffer_dc = 0
1057 self.__buffer_dc = 0
1056
1058
1057 self.__profIndex = 0
1059 self.__profIndex = 0
1058
1060
1059 def putData(self, data_spc, data_cspc, data_dc):
1061 def putData(self, data_spc, data_cspc, data_dc):
1060
1062
1061 """
1063 """
1062 Add a profile to the __buffer_spc and increase in one the __profileIndex
1064 Add a profile to the __buffer_spc and increase in one the __profileIndex
1063
1065
1064 """
1066 """
1065
1067
1066 if not self.__withOverapping:
1068 if not self.__withOverapping:
1067 self.__buffer_spc += data_spc
1069 self.__buffer_spc += data_spc
1068
1070
1069 if data_cspc == None:
1071 if data_cspc == None:
1070 self.__buffer_cspc = None
1072 self.__buffer_cspc = None
1071 else:
1073 else:
1072 self.__buffer_cspc += data_cspc
1074 self.__buffer_cspc += data_cspc
1073
1075
1074 if data_dc == None:
1076 if data_dc == None:
1075 self.__buffer_dc = None
1077 self.__buffer_dc = None
1076 else:
1078 else:
1077 self.__buffer_dc += data_dc
1079 self.__buffer_dc += data_dc
1078
1080
1079 self.__profIndex += 1
1081 self.__profIndex += 1
1080 return
1082 return
1081
1083
1082 #Overlapping data
1084 #Overlapping data
1083 nChannels, nFFTPoints, nHeis = data_spc.shape
1085 nChannels, nFFTPoints, nHeis = data_spc.shape
1084 data_spc = numpy.reshape(data_spc, (1, nChannels, nFFTPoints, nHeis))
1086 data_spc = numpy.reshape(data_spc, (1, nChannels, nFFTPoints, nHeis))
1085 if data_cspc != None:
1087 if data_cspc != None:
1086 data_cspc = numpy.reshape(data_cspc, (1, -1, nFFTPoints, nHeis))
1088 data_cspc = numpy.reshape(data_cspc, (1, -1, nFFTPoints, nHeis))
1087 if data_dc != None:
1089 if data_dc != None:
1088 data_dc = numpy.reshape(data_dc, (1, -1, nHeis))
1090 data_dc = numpy.reshape(data_dc, (1, -1, nHeis))
1089
1091
1090 #If the buffer is empty then it takes the data value
1092 #If the buffer is empty then it takes the data value
1091 if self.__buffer_spc == None:
1093 if self.__buffer_spc == None:
1092 self.__buffer_spc = data_spc
1094 self.__buffer_spc = data_spc
1093
1095
1094 if data_cspc == None:
1096 if data_cspc == None:
1095 self.__buffer_cspc = None
1097 self.__buffer_cspc = None
1096 else:
1098 else:
1097 self.__buffer_cspc += data_cspc
1099 self.__buffer_cspc += data_cspc
1098
1100
1099 if data_dc == None:
1101 if data_dc == None:
1100 self.__buffer_dc = None
1102 self.__buffer_dc = None
1101 else:
1103 else:
1102 self.__buffer_dc += data_dc
1104 self.__buffer_dc += data_dc
1103
1105
1104 self.__profIndex += 1
1106 self.__profIndex += 1
1105 return
1107 return
1106
1108
1107 #If the buffer length is lower than n then stakcing the data value
1109 #If the buffer length is lower than n then stakcing the data value
1108 if self.__profIndex < self.n:
1110 if self.__profIndex < self.n:
1109 self.__buffer_spc = numpy.vstack((self.__buffer_spc, data_spc))
1111 self.__buffer_spc = numpy.vstack((self.__buffer_spc, data_spc))
1110
1112
1111 if data_cspc != None:
1113 if data_cspc != None:
1112 self.__buffer_cspc = numpy.vstack((self.__buffer_cspc, data_cspc))
1114 self.__buffer_cspc = numpy.vstack((self.__buffer_cspc, data_cspc))
1113
1115
1114 if data_dc != None:
1116 if data_dc != None:
1115 self.__buffer_dc = numpy.vstack((self.__buffer_dc, data_dc))
1117 self.__buffer_dc = numpy.vstack((self.__buffer_dc, data_dc))
1116
1118
1117 self.__profIndex += 1
1119 self.__profIndex += 1
1118 return
1120 return
1119
1121
1120 #If the buffer length is equal to n then replacing the last buffer value with the data value
1122 #If the buffer length is equal to n then replacing the last buffer value with the data value
1121 self.__buffer_spc = numpy.roll(self.__buffer_spc, -1, axis=0)
1123 self.__buffer_spc = numpy.roll(self.__buffer_spc, -1, axis=0)
1122 self.__buffer_spc[self.n-1] = data_spc
1124 self.__buffer_spc[self.n-1] = data_spc
1123
1125
1124 if data_cspc != None:
1126 if data_cspc != None:
1125 self.__buffer_cspc = numpy.roll(self.__buffer_cspc, -1, axis=0)
1127 self.__buffer_cspc = numpy.roll(self.__buffer_cspc, -1, axis=0)
1126 self.__buffer_cspc[self.n-1] = data_cspc
1128 self.__buffer_cspc[self.n-1] = data_cspc
1127
1129
1128 if data_dc != None:
1130 if data_dc != None:
1129 self.__buffer_dc = numpy.roll(self.__buffer_dc, -1, axis=0)
1131 self.__buffer_dc = numpy.roll(self.__buffer_dc, -1, axis=0)
1130 self.__buffer_dc[self.n-1] = data_dc
1132 self.__buffer_dc[self.n-1] = data_dc
1131
1133
1132 self.__profIndex = self.n
1134 self.__profIndex = self.n
1133 return
1135 return
1134
1136
1135
1137
1136 def pushData(self):
1138 def pushData(self):
1137 """
1139 """
1138 Return the sum of the last profiles and the profiles used in the sum.
1140 Return the sum of the last profiles and the profiles used in the sum.
1139
1141
1140 Affected:
1142 Affected:
1141
1143
1142 self.__profileIndex
1144 self.__profileIndex
1143
1145
1144 """
1146 """
1145 data_spc = None
1147 data_spc = None
1146 data_cspc = None
1148 data_cspc = None
1147 data_dc = None
1149 data_dc = None
1148
1150
1149 if not self.__withOverapping:
1151 if not self.__withOverapping:
1150 data_spc = self.__buffer_spc
1152 data_spc = self.__buffer_spc
1151 data_cspc = self.__buffer_cspc
1153 data_cspc = self.__buffer_cspc
1152 data_dc = self.__buffer_dc
1154 data_dc = self.__buffer_dc
1153
1155
1154 n = self.__profIndex
1156 n = self.__profIndex
1155
1157
1156 self.__buffer_spc = 0
1158 self.__buffer_spc = 0
1157 self.__buffer_cspc = 0
1159 self.__buffer_cspc = 0
1158 self.__buffer_dc = 0
1160 self.__buffer_dc = 0
1159 self.__profIndex = 0
1161 self.__profIndex = 0
1160
1162
1161 return data_spc, data_cspc, data_dc, n
1163 return data_spc, data_cspc, data_dc, n
1162
1164
1163 #Integration with Overlapping
1165 #Integration with Overlapping
1164 data_spc = numpy.sum(self.__buffer_spc, axis=0)
1166 data_spc = numpy.sum(self.__buffer_spc, axis=0)
1165
1167
1166 if self.__buffer_cspc != None:
1168 if self.__buffer_cspc != None:
1167 data_cspc = numpy.sum(self.__buffer_cspc, axis=0)
1169 data_cspc = numpy.sum(self.__buffer_cspc, axis=0)
1168
1170
1169 if self.__buffer_dc != None:
1171 if self.__buffer_dc != None:
1170 data_dc = numpy.sum(self.__buffer_dc, axis=0)
1172 data_dc = numpy.sum(self.__buffer_dc, axis=0)
1171
1173
1172 n = self.__profIndex
1174 n = self.__profIndex
1173
1175
1174 return data_spc, data_cspc, data_dc, n
1176 return data_spc, data_cspc, data_dc, n
1175
1177
1176 def byProfiles(self, *args):
1178 def byProfiles(self, *args):
1177
1179
1178 self.__dataReady = False
1180 self.__dataReady = False
1179 avgdata_spc = None
1181 avgdata_spc = None
1180 avgdata_cspc = None
1182 avgdata_cspc = None
1181 avgdata_dc = None
1183 avgdata_dc = None
1182 n = None
1184 n = None
1183
1185
1184 self.putData(*args)
1186 self.putData(*args)
1185
1187
1186 if self.__profIndex == self.n:
1188 if self.__profIndex == self.n:
1187
1189
1188 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
1190 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
1189 self.__dataReady = True
1191 self.__dataReady = True
1190
1192
1191 return avgdata_spc, avgdata_cspc, avgdata_dc
1193 return avgdata_spc, avgdata_cspc, avgdata_dc
1192
1194
1193 def byTime(self, datatime, *args):
1195 def byTime(self, datatime, *args):
1194
1196
1195 self.__dataReady = False
1197 self.__dataReady = False
1196 avgdata_spc = None
1198 avgdata_spc = None
1197 avgdata_cspc = None
1199 avgdata_cspc = None
1198 avgdata_dc = None
1200 avgdata_dc = None
1199 n = None
1201 n = None
1200
1202
1201 self.putData(*args)
1203 self.putData(*args)
1202
1204
1203 if (datatime - self.__initime) >= self.__integrationtime:
1205 if (datatime - self.__initime) >= self.__integrationtime:
1204 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
1206 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
1205 self.n = n
1207 self.n = n
1206 self.__dataReady = True
1208 self.__dataReady = True
1207
1209
1208 return avgdata_spc, avgdata_cspc, avgdata_dc
1210 return avgdata_spc, avgdata_cspc, avgdata_dc
1209
1211
1210 def integrate(self, datatime, *args):
1212 def integrate(self, datatime, *args):
1211
1213
1212 if self.__initime == None:
1214 if self.__initime == None:
1213 self.__initime = datatime
1215 self.__initime = datatime
1214
1216
1215 if self.__byTime:
1217 if self.__byTime:
1216 avgdata_spc, avgdata_cspc, avgdata_dc = self.byTime(datatime, *args)
1218 avgdata_spc, avgdata_cspc, avgdata_dc = self.byTime(datatime, *args)
1217 else:
1219 else:
1218 avgdata_spc, avgdata_cspc, avgdata_dc = self.byProfiles(*args)
1220 avgdata_spc, avgdata_cspc, avgdata_dc = self.byProfiles(*args)
1219
1221
1220 self.__lastdatatime = datatime
1222 self.__lastdatatime = datatime
1221
1223
1222 if avgdata_spc == None:
1224 if avgdata_spc == None:
1223 return None, None, None, None
1225 return None, None, None, None
1224
1226
1225 avgdatatime = self.__initime
1227 avgdatatime = self.__initime
1226 self.__timeInterval = (self.__lastdatatime - self.__initime)/(self.n - 1)
1228 self.__timeInterval = (self.__lastdatatime - self.__initime)/(self.n - 1)
1227
1229
1228 deltatime = datatime -self.__lastdatatime
1230 deltatime = datatime -self.__lastdatatime
1229
1231
1230 if not self.__withOverapping:
1232 if not self.__withOverapping:
1231 self.__initime = datatime
1233 self.__initime = datatime
1232 else:
1234 else:
1233 self.__initime += deltatime
1235 self.__initime += deltatime
1234
1236
1235 return avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc
1237 return avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc
1236
1238
1237 def run(self, dataOut, n=None, timeInterval=None, overlapping=False):
1239 def run(self, dataOut, n=None, timeInterval=None, overlapping=False):
1238
1240
1239 if not self.__isConfig:
1241 if not self.__isConfig:
1240 self.setup(n, timeInterval, overlapping)
1242 self.setup(n, timeInterval, overlapping)
1241 self.__isConfig = True
1243 self.__isConfig = True
1242
1244
1243 avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(dataOut.utctime,
1245 avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(dataOut.utctime,
1244 dataOut.data_spc,
1246 dataOut.data_spc,
1245 dataOut.data_cspc,
1247 dataOut.data_cspc,
1246 dataOut.data_dc)
1248 dataOut.data_dc)
1247
1249
1248 # dataOut.timeInterval *= n
1250 # dataOut.timeInterval *= n
1249 dataOut.flagNoData = True
1251 dataOut.flagNoData = True
1250
1252
1251 if self.__dataReady:
1253 if self.__dataReady:
1252
1254
1253 dataOut.data_spc = avgdata_spc
1255 dataOut.data_spc = avgdata_spc
1254 dataOut.data_cspc = avgdata_cspc
1256 dataOut.data_cspc = avgdata_cspc
1255 dataOut.data_dc = avgdata_dc
1257 dataOut.data_dc = avgdata_dc
1256
1258
1257 dataOut.nIncohInt *= self.n
1259 dataOut.nIncohInt *= self.n
1258 dataOut.utctime = avgdatatime
1260 dataOut.utctime = avgdatatime
1259 #dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt * dataOut.nIncohInt * dataOut.nFFTPoints
1261 #dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt * dataOut.nIncohInt * dataOut.nFFTPoints
1260 dataOut.timeInterval = self.__timeInterval*self.n
1262 dataOut.timeInterval = self.__timeInterval*self.n
1261 dataOut.flagNoData = False
1263 dataOut.flagNoData = False
1262
1264
1263 class ProfileSelector(Operation):
1265 class ProfileSelector(Operation):
1264
1266
1265 profileIndex = None
1267 profileIndex = None
1266 # Tamanho total de los perfiles
1268 # Tamanho total de los perfiles
1267 nProfiles = None
1269 nProfiles = None
1268
1270
1269 def __init__(self):
1271 def __init__(self):
1270
1272
1271 self.profileIndex = 0
1273 self.profileIndex = 0
1272
1274
1273 def incIndex(self):
1275 def incIndex(self):
1274 self.profileIndex += 1
1276 self.profileIndex += 1
1275
1277
1276 if self.profileIndex >= self.nProfiles:
1278 if self.profileIndex >= self.nProfiles:
1277 self.profileIndex = 0
1279 self.profileIndex = 0
1278
1280
1279 def isProfileInRange(self, minIndex, maxIndex):
1281 def isProfileInRange(self, minIndex, maxIndex):
1280
1282
1281 if self.profileIndex < minIndex:
1283 if self.profileIndex < minIndex:
1282 return False
1284 return False
1283
1285
1284 if self.profileIndex > maxIndex:
1286 if self.profileIndex > maxIndex:
1285 return False
1287 return False
1286
1288
1287 return True
1289 return True
1288
1290
1289 def isProfileInList(self, profileList):
1291 def isProfileInList(self, profileList):
1290
1292
1291 if self.profileIndex not in profileList:
1293 if self.profileIndex not in profileList:
1292 return False
1294 return False
1293
1295
1294 return True
1296 return True
1295
1297
1296 def run(self, dataOut, profileList=None, profileRangeList=None):
1298 def run(self, dataOut, profileList=None, profileRangeList=None):
1297
1299
1298 dataOut.flagNoData = True
1300 dataOut.flagNoData = True
1299 self.nProfiles = dataOut.nProfiles
1301 self.nProfiles = dataOut.nProfiles
1300
1302
1301 if profileList != None:
1303 if profileList != None:
1302 if self.isProfileInList(profileList):
1304 if self.isProfileInList(profileList):
1303 dataOut.flagNoData = False
1305 dataOut.flagNoData = False
1304
1306
1305 self.incIndex()
1307 self.incIndex()
1306 return 1
1308 return 1
1307
1309
1308
1310
1309 elif profileRangeList != None:
1311 elif profileRangeList != None:
1310 minIndex = profileRangeList[0]
1312 minIndex = profileRangeList[0]
1311 maxIndex = profileRangeList[1]
1313 maxIndex = profileRangeList[1]
1312 if self.isProfileInRange(minIndex, maxIndex):
1314 if self.isProfileInRange(minIndex, maxIndex):
1313 dataOut.flagNoData = False
1315 dataOut.flagNoData = False
1314
1316
1315 self.incIndex()
1317 self.incIndex()
1316 return 1
1318 return 1
1317
1319
1318 else:
1320 else:
1319 raise ValueError, "ProfileSelector needs profileList or profileRangeList"
1321 raise ValueError, "ProfileSelector needs profileList or profileRangeList"
1320
1322
1321 return 0
1323 return 0
1322
1324
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