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Codificacion optimizada
Miguel Valdez -
r303:2e8a2ff26882
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1 import numpy
1 import numpy
2 cimport numpy
2 cimport numpy
3
3
4 def decoder(numpy.ndarray[numpy.float32, ndim=2] fft_code, numpy.ndarray[numpy.complex, ndim=2] data):
4 def decoder(numpy.ndarray[numpy.float32_t, ndim=2] fft_code, numpy.ndarray[numpy.complex_t, ndim=2] data):
5
5
6 fft_data = numpy.fft.fft(data, axis=1)
6 fft_data = numpy.fft.fft(data, axis=1)
7 conv = fft_data*fft_code
7 conv = fft_data*fft_code
8 data = numpy.fft.ifft(conv, axis=1)
8 data = numpy.fft.ifft(conv, axis=1)
9
9
10 return data No newline at end of file
10 return data
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@@ -1,1319 +1,1322
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.float)
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
632
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
645
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 if code == None:
669 code = dataOut.code
669 code = dataOut.code
670 else:
670 else:
671 code = numpy.array(code).reshape(nCode,nBaud)
671 code = numpy.array(code).reshape(nCode,nBaud)
672 dataOut.code = code
672 dataOut.code = code
673 dataOut.nCode = nCode
673 dataOut.nCode = nCode
674 dataOut.nBaud = nBaud
674 dataOut.nBaud = nBaud
675
675
676 if code == None:
676 if code == None:
677 return 1
677 return 1
678
678
679 if not self.__isConfig:
679 if not self.__isConfig:
680 self.setup(code, dataOut.data.shape)
680 self.setup(code, dataOut.data.shape)
681 self.__isConfig = True
681 self.__isConfig = True
682
682
683 if mode == 0:
683 if mode == 0:
684 ndatadec, datadec = self.convolutionInFreq(dataOut.data)
684 ndatadec, datadec = self.convolutionInFreq(dataOut.data)
685
685
686 if mode == 1:
686 if mode == 1:
687 print "This function is not implemented"
687 print "This function is not implemented"
688 # ndatadec, datadec = self.convolutionInTime(dataOut.data)
688 # ndatadec, datadec = self.convolutionInTime(dataOut.data)
689
689
690 if mode == 3:
691 ndatadec, datadec = self.convolutionInFreqOpt(dataOut.data)
692
690 dataOut.data = datadec
693 dataOut.data = datadec
691
694
692 dataOut.heightList = dataOut.heightList[0:ndatadec]
695 dataOut.heightList = dataOut.heightList[0:ndatadec]
693
696
694 dataOut.flagDecodeData = True #asumo q la data no esta decodificada
697 dataOut.flagDecodeData = True #asumo q la data no esta decodificada
695
698
696 # dataOut.flagDeflipData = True #asumo q la data no esta sin flip
699 # dataOut.flagDeflipData = True #asumo q la data no esta sin flip
697
700
698
701
699 class SpectraProc(ProcessingUnit):
702 class SpectraProc(ProcessingUnit):
700
703
701 def __init__(self):
704 def __init__(self):
702
705
703 self.objectDict = {}
706 self.objectDict = {}
704 self.buffer = None
707 self.buffer = None
705 self.firstdatatime = None
708 self.firstdatatime = None
706 self.profIndex = 0
709 self.profIndex = 0
707 self.dataOut = Spectra()
710 self.dataOut = Spectra()
708
711
709 def __updateObjFromInput(self):
712 def __updateObjFromInput(self):
710
713
711 self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()
714 self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()
712 self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy()
715 self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy()
713 self.dataOut.channelList = self.dataIn.channelList
716 self.dataOut.channelList = self.dataIn.channelList
714 self.dataOut.heightList = self.dataIn.heightList
717 self.dataOut.heightList = self.dataIn.heightList
715 self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')])
718 self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')])
716 # self.dataOut.nHeights = self.dataIn.nHeights
719 # self.dataOut.nHeights = self.dataIn.nHeights
717 # self.dataOut.nChannels = self.dataIn.nChannels
720 # self.dataOut.nChannels = self.dataIn.nChannels
718 self.dataOut.nBaud = self.dataIn.nBaud
721 self.dataOut.nBaud = self.dataIn.nBaud
719 self.dataOut.nCode = self.dataIn.nCode
722 self.dataOut.nCode = self.dataIn.nCode
720 self.dataOut.code = self.dataIn.code
723 self.dataOut.code = self.dataIn.code
721 self.dataOut.nProfiles = self.dataOut.nFFTPoints
724 self.dataOut.nProfiles = self.dataOut.nFFTPoints
722 # self.dataOut.channelIndexList = self.dataIn.channelIndexList
725 # self.dataOut.channelIndexList = self.dataIn.channelIndexList
723 self.dataOut.flagTimeBlock = self.dataIn.flagTimeBlock
726 self.dataOut.flagTimeBlock = self.dataIn.flagTimeBlock
724 self.dataOut.utctime = self.firstdatatime
727 self.dataOut.utctime = self.firstdatatime
725 self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada
728 self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada
726 self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip
729 self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip
727 self.dataOut.flagShiftFFT = self.dataIn.flagShiftFFT
730 self.dataOut.flagShiftFFT = self.dataIn.flagShiftFFT
728 self.dataOut.nCohInt = self.dataIn.nCohInt
731 self.dataOut.nCohInt = self.dataIn.nCohInt
729 self.dataOut.nIncohInt = 1
732 self.dataOut.nIncohInt = 1
730 self.dataOut.ippSeconds = self.dataIn.ippSeconds
733 self.dataOut.ippSeconds = self.dataIn.ippSeconds
731 self.dataOut.windowOfFilter = self.dataIn.windowOfFilter
734 self.dataOut.windowOfFilter = self.dataIn.windowOfFilter
732
735
733 self.dataOut.timeInterval = self.dataIn.timeInterval*self.dataOut.nFFTPoints*self.dataOut.nIncohInt
736 self.dataOut.timeInterval = self.dataIn.timeInterval*self.dataOut.nFFTPoints*self.dataOut.nIncohInt
734
737
735 def __getFft(self):
738 def __getFft(self):
736 """
739 """
737 Convierte valores de Voltaje a Spectra
740 Convierte valores de Voltaje a Spectra
738
741
739 Affected:
742 Affected:
740 self.dataOut.data_spc
743 self.dataOut.data_spc
741 self.dataOut.data_cspc
744 self.dataOut.data_cspc
742 self.dataOut.data_dc
745 self.dataOut.data_dc
743 self.dataOut.heightList
746 self.dataOut.heightList
744 self.profIndex
747 self.profIndex
745 self.buffer
748 self.buffer
746 self.dataOut.flagNoData
749 self.dataOut.flagNoData
747 """
750 """
748 fft_volt = numpy.fft.fft(self.buffer,axis=1)
751 fft_volt = numpy.fft.fft(self.buffer,axis=1)
749 fft_volt = fft_volt.astype(numpy.dtype('complex'))
752 fft_volt = fft_volt.astype(numpy.dtype('complex'))
750 dc = fft_volt[:,0,:]
753 dc = fft_volt[:,0,:]
751
754
752 #calculo de self-spectra
755 #calculo de self-spectra
753 fft_volt = numpy.fft.fftshift(fft_volt,axes=(1,))
756 fft_volt = numpy.fft.fftshift(fft_volt,axes=(1,))
754 spc = fft_volt * numpy.conjugate(fft_volt)
757 spc = fft_volt * numpy.conjugate(fft_volt)
755 spc = spc.real
758 spc = spc.real
756
759
757 blocksize = 0
760 blocksize = 0
758 blocksize += dc.size
761 blocksize += dc.size
759 blocksize += spc.size
762 blocksize += spc.size
760
763
761 cspc = None
764 cspc = None
762 pairIndex = 0
765 pairIndex = 0
763 if self.dataOut.pairsList != None:
766 if self.dataOut.pairsList != None:
764 #calculo de cross-spectra
767 #calculo de cross-spectra
765 cspc = numpy.zeros((self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex')
768 cspc = numpy.zeros((self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex')
766 for pair in self.dataOut.pairsList:
769 for pair in self.dataOut.pairsList:
767 cspc[pairIndex,:,:] = fft_volt[pair[0],:,:] * numpy.conjugate(fft_volt[pair[1],:,:])
770 cspc[pairIndex,:,:] = fft_volt[pair[0],:,:] * numpy.conjugate(fft_volt[pair[1],:,:])
768 pairIndex += 1
771 pairIndex += 1
769 blocksize += cspc.size
772 blocksize += cspc.size
770
773
771 self.dataOut.data_spc = spc
774 self.dataOut.data_spc = spc
772 self.dataOut.data_cspc = cspc
775 self.dataOut.data_cspc = cspc
773 self.dataOut.data_dc = dc
776 self.dataOut.data_dc = dc
774 self.dataOut.blockSize = blocksize
777 self.dataOut.blockSize = blocksize
775
778
776 def init(self, nFFTPoints=None, pairsList=None):
779 def init(self, nFFTPoints=None, pairsList=None):
777
780
778 self.dataOut.flagNoData = True
781 self.dataOut.flagNoData = True
779
782
780 if self.dataIn.type == "Spectra":
783 if self.dataIn.type == "Spectra":
781 self.dataOut.copy(self.dataIn)
784 self.dataOut.copy(self.dataIn)
782 return
785 return
783
786
784 if self.dataIn.type == "Voltage":
787 if self.dataIn.type == "Voltage":
785
788
786 if nFFTPoints == None:
789 if nFFTPoints == None:
787 raise ValueError, "This SpectraProc.init() need nFFTPoints input variable"
790 raise ValueError, "This SpectraProc.init() need nFFTPoints input variable"
788
791
789 if pairsList == None:
792 if pairsList == None:
790 nPairs = 0
793 nPairs = 0
791 else:
794 else:
792 nPairs = len(pairsList)
795 nPairs = len(pairsList)
793
796
794 self.dataOut.nFFTPoints = nFFTPoints
797 self.dataOut.nFFTPoints = nFFTPoints
795 self.dataOut.pairsList = pairsList
798 self.dataOut.pairsList = pairsList
796 self.dataOut.nPairs = nPairs
799 self.dataOut.nPairs = nPairs
797
800
798 if self.buffer == None:
801 if self.buffer == None:
799 self.buffer = numpy.zeros((self.dataIn.nChannels,
802 self.buffer = numpy.zeros((self.dataIn.nChannels,
800 self.dataOut.nFFTPoints,
803 self.dataOut.nFFTPoints,
801 self.dataIn.nHeights),
804 self.dataIn.nHeights),
802 dtype='complex')
805 dtype='complex')
803
806
804
807
805 self.buffer[:,self.profIndex,:] = self.dataIn.data.copy()
808 self.buffer[:,self.profIndex,:] = self.dataIn.data.copy()
806 self.profIndex += 1
809 self.profIndex += 1
807
810
808 if self.firstdatatime == None:
811 if self.firstdatatime == None:
809 self.firstdatatime = self.dataIn.utctime
812 self.firstdatatime = self.dataIn.utctime
810
813
811 if self.profIndex == self.dataOut.nFFTPoints:
814 if self.profIndex == self.dataOut.nFFTPoints:
812 self.__updateObjFromInput()
815 self.__updateObjFromInput()
813 self.__getFft()
816 self.__getFft()
814
817
815 self.dataOut.flagNoData = False
818 self.dataOut.flagNoData = False
816
819
817 self.buffer = None
820 self.buffer = None
818 self.firstdatatime = None
821 self.firstdatatime = None
819 self.profIndex = 0
822 self.profIndex = 0
820
823
821 return
824 return
822
825
823 raise ValuError, "The type object %s is not valid"%(self.dataIn.type)
826 raise ValuError, "The type object %s is not valid"%(self.dataIn.type)
824
827
825 def selectChannels(self, channelList):
828 def selectChannels(self, channelList):
826
829
827 channelIndexList = []
830 channelIndexList = []
828
831
829 for channel in channelList:
832 for channel in channelList:
830 index = self.dataOut.channelList.index(channel)
833 index = self.dataOut.channelList.index(channel)
831 channelIndexList.append(index)
834 channelIndexList.append(index)
832
835
833 self.selectChannelsByIndex(channelIndexList)
836 self.selectChannelsByIndex(channelIndexList)
834
837
835 def selectChannelsByIndex(self, channelIndexList):
838 def selectChannelsByIndex(self, channelIndexList):
836 """
839 """
837 Selecciona un bloque de datos en base a canales segun el channelIndexList
840 Selecciona un bloque de datos en base a canales segun el channelIndexList
838
841
839 Input:
842 Input:
840 channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
843 channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
841
844
842 Affected:
845 Affected:
843 self.dataOut.data_spc
846 self.dataOut.data_spc
844 self.dataOut.channelIndexList
847 self.dataOut.channelIndexList
845 self.dataOut.nChannels
848 self.dataOut.nChannels
846
849
847 Return:
850 Return:
848 None
851 None
849 """
852 """
850
853
851 for channelIndex in channelIndexList:
854 for channelIndex in channelIndexList:
852 if channelIndex not in self.dataOut.channelIndexList:
855 if channelIndex not in self.dataOut.channelIndexList:
853 print channelIndexList
856 print channelIndexList
854 raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex
857 raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex
855
858
856 nChannels = len(channelIndexList)
859 nChannels = len(channelIndexList)
857
860
858 data_spc = self.dataOut.data_spc[channelIndexList,:]
861 data_spc = self.dataOut.data_spc[channelIndexList,:]
859
862
860 self.dataOut.data_spc = data_spc
863 self.dataOut.data_spc = data_spc
861 self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
864 self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
862 # self.dataOut.nChannels = nChannels
865 # self.dataOut.nChannels = nChannels
863
866
864 return 1
867 return 1
865
868
866 def selectHeights(self, minHei, maxHei):
869 def selectHeights(self, minHei, maxHei):
867 """
870 """
868 Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
871 Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
869 minHei <= height <= maxHei
872 minHei <= height <= maxHei
870
873
871 Input:
874 Input:
872 minHei : valor minimo de altura a considerar
875 minHei : valor minimo de altura a considerar
873 maxHei : valor maximo de altura a considerar
876 maxHei : valor maximo de altura a considerar
874
877
875 Affected:
878 Affected:
876 Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
879 Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
877
880
878 Return:
881 Return:
879 1 si el metodo se ejecuto con exito caso contrario devuelve 0
882 1 si el metodo se ejecuto con exito caso contrario devuelve 0
880 """
883 """
881 if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
884 if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
882 raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
885 raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
883
886
884 if (maxHei > self.dataOut.heightList[-1]):
887 if (maxHei > self.dataOut.heightList[-1]):
885 maxHei = self.dataOut.heightList[-1]
888 maxHei = self.dataOut.heightList[-1]
886 # raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
889 # raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
887
890
888 minIndex = 0
891 minIndex = 0
889 maxIndex = 0
892 maxIndex = 0
890 heights = self.dataOut.heightList
893 heights = self.dataOut.heightList
891
894
892 inda = numpy.where(heights >= minHei)
895 inda = numpy.where(heights >= minHei)
893 indb = numpy.where(heights <= maxHei)
896 indb = numpy.where(heights <= maxHei)
894
897
895 try:
898 try:
896 minIndex = inda[0][0]
899 minIndex = inda[0][0]
897 except:
900 except:
898 minIndex = 0
901 minIndex = 0
899
902
900 try:
903 try:
901 maxIndex = indb[0][-1]
904 maxIndex = indb[0][-1]
902 except:
905 except:
903 maxIndex = len(heights)
906 maxIndex = len(heights)
904
907
905 self.selectHeightsByIndex(minIndex, maxIndex)
908 self.selectHeightsByIndex(minIndex, maxIndex)
906
909
907 return 1
910 return 1
908
911
909
912
910 def selectHeightsByIndex(self, minIndex, maxIndex):
913 def selectHeightsByIndex(self, minIndex, maxIndex):
911 """
914 """
912 Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
915 Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
913 minIndex <= index <= maxIndex
916 minIndex <= index <= maxIndex
914
917
915 Input:
918 Input:
916 minIndex : valor de indice minimo de altura a considerar
919 minIndex : valor de indice minimo de altura a considerar
917 maxIndex : valor de indice maximo de altura a considerar
920 maxIndex : valor de indice maximo de altura a considerar
918
921
919 Affected:
922 Affected:
920 self.dataOut.data_spc
923 self.dataOut.data_spc
921 self.dataOut.data_cspc
924 self.dataOut.data_cspc
922 self.dataOut.data_dc
925 self.dataOut.data_dc
923 self.dataOut.heightList
926 self.dataOut.heightList
924
927
925 Return:
928 Return:
926 1 si el metodo se ejecuto con exito caso contrario devuelve 0
929 1 si el metodo se ejecuto con exito caso contrario devuelve 0
927 """
930 """
928
931
929 if (minIndex < 0) or (minIndex > maxIndex):
932 if (minIndex < 0) or (minIndex > maxIndex):
930 raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
933 raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
931
934
932 if (maxIndex >= self.dataOut.nHeights):
935 if (maxIndex >= self.dataOut.nHeights):
933 maxIndex = self.dataOut.nHeights-1
936 maxIndex = self.dataOut.nHeights-1
934 # raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
937 # raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
935
938
936 nHeights = maxIndex - minIndex + 1
939 nHeights = maxIndex - minIndex + 1
937
940
938 #Spectra
941 #Spectra
939 data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1]
942 data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1]
940
943
941 data_cspc = None
944 data_cspc = None
942 if self.dataOut.data_cspc != None:
945 if self.dataOut.data_cspc != None:
943 data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1]
946 data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1]
944
947
945 data_dc = None
948 data_dc = None
946 if self.dataOut.data_dc != None:
949 if self.dataOut.data_dc != None:
947 data_dc = self.dataOut.data_dc[:,minIndex:maxIndex+1]
950 data_dc = self.dataOut.data_dc[:,minIndex:maxIndex+1]
948
951
949 self.dataOut.data_spc = data_spc
952 self.dataOut.data_spc = data_spc
950 self.dataOut.data_cspc = data_cspc
953 self.dataOut.data_cspc = data_cspc
951 self.dataOut.data_dc = data_dc
954 self.dataOut.data_dc = data_dc
952
955
953 self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1]
956 self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1]
954
957
955 return 1
958 return 1
956
959
957 def removeDC(self, mode = 1):
960 def removeDC(self, mode = 1):
958
961
959 dc_index = 0
962 dc_index = 0
960 freq_index = numpy.array([-2,-1,1,2])
963 freq_index = numpy.array([-2,-1,1,2])
961 data_spc = self.dataOut.data_spc
964 data_spc = self.dataOut.data_spc
962 data_cspc = self.dataOut.data_cspc
965 data_cspc = self.dataOut.data_cspc
963 data_dc = self.dataOut.data_dc
966 data_dc = self.dataOut.data_dc
964
967
965 if self.dataOut.flagShiftFFT:
968 if self.dataOut.flagShiftFFT:
966 dc_index += self.dataOut.nFFTPoints/2
969 dc_index += self.dataOut.nFFTPoints/2
967 freq_index += self.dataOut.nFFTPoints/2
970 freq_index += self.dataOut.nFFTPoints/2
968
971
969 if mode == 1:
972 if mode == 1:
970 data_spc[dc_index] = (data_spc[:,freq_index[1],:] + data_spc[:,freq_index[2],:])/2
973 data_spc[dc_index] = (data_spc[:,freq_index[1],:] + data_spc[:,freq_index[2],:])/2
971 if data_cspc != None:
974 if data_cspc != None:
972 data_cspc[dc_index] = (data_cspc[:,freq_index[1],:] + data_cspc[:,freq_index[2],:])/2
975 data_cspc[dc_index] = (data_cspc[:,freq_index[1],:] + data_cspc[:,freq_index[2],:])/2
973 return 1
976 return 1
974
977
975 if mode == 2:
978 if mode == 2:
976 pass
979 pass
977
980
978 if mode == 3:
981 if mode == 3:
979 pass
982 pass
980
983
981 raise ValueError, "mode parameter has to be 1, 2 or 3"
984 raise ValueError, "mode parameter has to be 1, 2 or 3"
982
985
983 def removeInterference(self):
986 def removeInterference(self):
984
987
985 pass
988 pass
986
989
987
990
988 class IncohInt(Operation):
991 class IncohInt(Operation):
989
992
990
993
991 __profIndex = 0
994 __profIndex = 0
992 __withOverapping = False
995 __withOverapping = False
993
996
994 __byTime = False
997 __byTime = False
995 __initime = None
998 __initime = None
996 __lastdatatime = None
999 __lastdatatime = None
997 __integrationtime = None
1000 __integrationtime = None
998
1001
999 __buffer_spc = None
1002 __buffer_spc = None
1000 __buffer_cspc = None
1003 __buffer_cspc = None
1001 __buffer_dc = None
1004 __buffer_dc = None
1002
1005
1003 __dataReady = False
1006 __dataReady = False
1004
1007
1005 __timeInterval = None
1008 __timeInterval = None
1006
1009
1007 n = None
1010 n = None
1008
1011
1009
1012
1010
1013
1011 def __init__(self):
1014 def __init__(self):
1012
1015
1013 self.__isConfig = False
1016 self.__isConfig = False
1014
1017
1015 def setup(self, n=None, timeInterval=None, overlapping=False):
1018 def setup(self, n=None, timeInterval=None, overlapping=False):
1016 """
1019 """
1017 Set the parameters of the integration class.
1020 Set the parameters of the integration class.
1018
1021
1019 Inputs:
1022 Inputs:
1020
1023
1021 n : Number of coherent integrations
1024 n : Number of coherent integrations
1022 timeInterval : Time of integration. If the parameter "n" is selected this one does not work
1025 timeInterval : Time of integration. If the parameter "n" is selected this one does not work
1023 overlapping :
1026 overlapping :
1024
1027
1025 """
1028 """
1026
1029
1027 self.__initime = None
1030 self.__initime = None
1028 self.__lastdatatime = 0
1031 self.__lastdatatime = 0
1029 self.__buffer_spc = None
1032 self.__buffer_spc = None
1030 self.__buffer_cspc = None
1033 self.__buffer_cspc = None
1031 self.__buffer_dc = None
1034 self.__buffer_dc = None
1032 self.__dataReady = False
1035 self.__dataReady = False
1033
1036
1034
1037
1035 if n == None and timeInterval == None:
1038 if n == None and timeInterval == None:
1036 raise ValueError, "n or timeInterval should be specified ..."
1039 raise ValueError, "n or timeInterval should be specified ..."
1037
1040
1038 if n != None:
1041 if n != None:
1039 self.n = n
1042 self.n = n
1040 self.__byTime = False
1043 self.__byTime = False
1041 else:
1044 else:
1042 self.__integrationtime = timeInterval * 60. #if (type(timeInterval)!=integer) -> change this line
1045 self.__integrationtime = timeInterval * 60. #if (type(timeInterval)!=integer) -> change this line
1043 self.n = 9999
1046 self.n = 9999
1044 self.__byTime = True
1047 self.__byTime = True
1045
1048
1046 if overlapping:
1049 if overlapping:
1047 self.__withOverapping = True
1050 self.__withOverapping = True
1048 else:
1051 else:
1049 self.__withOverapping = False
1052 self.__withOverapping = False
1050 self.__buffer_spc = 0
1053 self.__buffer_spc = 0
1051 self.__buffer_cspc = 0
1054 self.__buffer_cspc = 0
1052 self.__buffer_dc = 0
1055 self.__buffer_dc = 0
1053
1056
1054 self.__profIndex = 0
1057 self.__profIndex = 0
1055
1058
1056 def putData(self, data_spc, data_cspc, data_dc):
1059 def putData(self, data_spc, data_cspc, data_dc):
1057
1060
1058 """
1061 """
1059 Add a profile to the __buffer_spc and increase in one the __profileIndex
1062 Add a profile to the __buffer_spc and increase in one the __profileIndex
1060
1063
1061 """
1064 """
1062
1065
1063 if not self.__withOverapping:
1066 if not self.__withOverapping:
1064 self.__buffer_spc += data_spc
1067 self.__buffer_spc += data_spc
1065
1068
1066 if data_cspc == None:
1069 if data_cspc == None:
1067 self.__buffer_cspc = None
1070 self.__buffer_cspc = None
1068 else:
1071 else:
1069 self.__buffer_cspc += data_cspc
1072 self.__buffer_cspc += data_cspc
1070
1073
1071 if data_dc == None:
1074 if data_dc == None:
1072 self.__buffer_dc = None
1075 self.__buffer_dc = None
1073 else:
1076 else:
1074 self.__buffer_dc += data_dc
1077 self.__buffer_dc += data_dc
1075
1078
1076 self.__profIndex += 1
1079 self.__profIndex += 1
1077 return
1080 return
1078
1081
1079 #Overlapping data
1082 #Overlapping data
1080 nChannels, nFFTPoints, nHeis = data_spc.shape
1083 nChannels, nFFTPoints, nHeis = data_spc.shape
1081 data_spc = numpy.reshape(data_spc, (1, nChannels, nFFTPoints, nHeis))
1084 data_spc = numpy.reshape(data_spc, (1, nChannels, nFFTPoints, nHeis))
1082 if data_cspc != None:
1085 if data_cspc != None:
1083 data_cspc = numpy.reshape(data_cspc, (1, -1, nFFTPoints, nHeis))
1086 data_cspc = numpy.reshape(data_cspc, (1, -1, nFFTPoints, nHeis))
1084 if data_dc != None:
1087 if data_dc != None:
1085 data_dc = numpy.reshape(data_dc, (1, -1, nHeis))
1088 data_dc = numpy.reshape(data_dc, (1, -1, nHeis))
1086
1089
1087 #If the buffer is empty then it takes the data value
1090 #If the buffer is empty then it takes the data value
1088 if self.__buffer_spc == None:
1091 if self.__buffer_spc == None:
1089 self.__buffer_spc = data_spc
1092 self.__buffer_spc = data_spc
1090
1093
1091 if data_cspc == None:
1094 if data_cspc == None:
1092 self.__buffer_cspc = None
1095 self.__buffer_cspc = None
1093 else:
1096 else:
1094 self.__buffer_cspc += data_cspc
1097 self.__buffer_cspc += data_cspc
1095
1098
1096 if data_dc == None:
1099 if data_dc == None:
1097 self.__buffer_dc = None
1100 self.__buffer_dc = None
1098 else:
1101 else:
1099 self.__buffer_dc += data_dc
1102 self.__buffer_dc += data_dc
1100
1103
1101 self.__profIndex += 1
1104 self.__profIndex += 1
1102 return
1105 return
1103
1106
1104 #If the buffer length is lower than n then stakcing the data value
1107 #If the buffer length is lower than n then stakcing the data value
1105 if self.__profIndex < self.n:
1108 if self.__profIndex < self.n:
1106 self.__buffer_spc = numpy.vstack((self.__buffer_spc, data_spc))
1109 self.__buffer_spc = numpy.vstack((self.__buffer_spc, data_spc))
1107
1110
1108 if data_cspc != None:
1111 if data_cspc != None:
1109 self.__buffer_cspc = numpy.vstack((self.__buffer_cspc, data_cspc))
1112 self.__buffer_cspc = numpy.vstack((self.__buffer_cspc, data_cspc))
1110
1113
1111 if data_dc != None:
1114 if data_dc != None:
1112 self.__buffer_dc = numpy.vstack((self.__buffer_dc, data_dc))
1115 self.__buffer_dc = numpy.vstack((self.__buffer_dc, data_dc))
1113
1116
1114 self.__profIndex += 1
1117 self.__profIndex += 1
1115 return
1118 return
1116
1119
1117 #If the buffer length is equal to n then replacing the last buffer value with the data value
1120 #If the buffer length is equal to n then replacing the last buffer value with the data value
1118 self.__buffer_spc = numpy.roll(self.__buffer_spc, -1, axis=0)
1121 self.__buffer_spc = numpy.roll(self.__buffer_spc, -1, axis=0)
1119 self.__buffer_spc[self.n-1] = data_spc
1122 self.__buffer_spc[self.n-1] = data_spc
1120
1123
1121 if data_cspc != None:
1124 if data_cspc != None:
1122 self.__buffer_cspc = numpy.roll(self.__buffer_cspc, -1, axis=0)
1125 self.__buffer_cspc = numpy.roll(self.__buffer_cspc, -1, axis=0)
1123 self.__buffer_cspc[self.n-1] = data_cspc
1126 self.__buffer_cspc[self.n-1] = data_cspc
1124
1127
1125 if data_dc != None:
1128 if data_dc != None:
1126 self.__buffer_dc = numpy.roll(self.__buffer_dc, -1, axis=0)
1129 self.__buffer_dc = numpy.roll(self.__buffer_dc, -1, axis=0)
1127 self.__buffer_dc[self.n-1] = data_dc
1130 self.__buffer_dc[self.n-1] = data_dc
1128
1131
1129 self.__profIndex = self.n
1132 self.__profIndex = self.n
1130 return
1133 return
1131
1134
1132
1135
1133 def pushData(self):
1136 def pushData(self):
1134 """
1137 """
1135 Return the sum of the last profiles and the profiles used in the sum.
1138 Return the sum of the last profiles and the profiles used in the sum.
1136
1139
1137 Affected:
1140 Affected:
1138
1141
1139 self.__profileIndex
1142 self.__profileIndex
1140
1143
1141 """
1144 """
1142 data_spc = None
1145 data_spc = None
1143 data_cspc = None
1146 data_cspc = None
1144 data_dc = None
1147 data_dc = None
1145
1148
1146 if not self.__withOverapping:
1149 if not self.__withOverapping:
1147 data_spc = self.__buffer_spc
1150 data_spc = self.__buffer_spc
1148 data_cspc = self.__buffer_cspc
1151 data_cspc = self.__buffer_cspc
1149 data_dc = self.__buffer_dc
1152 data_dc = self.__buffer_dc
1150
1153
1151 n = self.__profIndex
1154 n = self.__profIndex
1152
1155
1153 self.__buffer_spc = 0
1156 self.__buffer_spc = 0
1154 self.__buffer_cspc = 0
1157 self.__buffer_cspc = 0
1155 self.__buffer_dc = 0
1158 self.__buffer_dc = 0
1156 self.__profIndex = 0
1159 self.__profIndex = 0
1157
1160
1158 return data_spc, data_cspc, data_dc, n
1161 return data_spc, data_cspc, data_dc, n
1159
1162
1160 #Integration with Overlapping
1163 #Integration with Overlapping
1161 data_spc = numpy.sum(self.__buffer_spc, axis=0)
1164 data_spc = numpy.sum(self.__buffer_spc, axis=0)
1162
1165
1163 if self.__buffer_cspc != None:
1166 if self.__buffer_cspc != None:
1164 data_cspc = numpy.sum(self.__buffer_cspc, axis=0)
1167 data_cspc = numpy.sum(self.__buffer_cspc, axis=0)
1165
1168
1166 if self.__buffer_dc != None:
1169 if self.__buffer_dc != None:
1167 data_dc = numpy.sum(self.__buffer_dc, axis=0)
1170 data_dc = numpy.sum(self.__buffer_dc, axis=0)
1168
1171
1169 n = self.__profIndex
1172 n = self.__profIndex
1170
1173
1171 return data_spc, data_cspc, data_dc, n
1174 return data_spc, data_cspc, data_dc, n
1172
1175
1173 def byProfiles(self, *args):
1176 def byProfiles(self, *args):
1174
1177
1175 self.__dataReady = False
1178 self.__dataReady = False
1176 avgdata_spc = None
1179 avgdata_spc = None
1177 avgdata_cspc = None
1180 avgdata_cspc = None
1178 avgdata_dc = None
1181 avgdata_dc = None
1179 n = None
1182 n = None
1180
1183
1181 self.putData(*args)
1184 self.putData(*args)
1182
1185
1183 if self.__profIndex == self.n:
1186 if self.__profIndex == self.n:
1184
1187
1185 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
1188 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
1186 self.__dataReady = True
1189 self.__dataReady = True
1187
1190
1188 return avgdata_spc, avgdata_cspc, avgdata_dc
1191 return avgdata_spc, avgdata_cspc, avgdata_dc
1189
1192
1190 def byTime(self, datatime, *args):
1193 def byTime(self, datatime, *args):
1191
1194
1192 self.__dataReady = False
1195 self.__dataReady = False
1193 avgdata_spc = None
1196 avgdata_spc = None
1194 avgdata_cspc = None
1197 avgdata_cspc = None
1195 avgdata_dc = None
1198 avgdata_dc = None
1196 n = None
1199 n = None
1197
1200
1198 self.putData(*args)
1201 self.putData(*args)
1199
1202
1200 if (datatime - self.__initime) >= self.__integrationtime:
1203 if (datatime - self.__initime) >= self.__integrationtime:
1201 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
1204 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
1202 self.n = n
1205 self.n = n
1203 self.__dataReady = True
1206 self.__dataReady = True
1204
1207
1205 return avgdata_spc, avgdata_cspc, avgdata_dc
1208 return avgdata_spc, avgdata_cspc, avgdata_dc
1206
1209
1207 def integrate(self, datatime, *args):
1210 def integrate(self, datatime, *args):
1208
1211
1209 if self.__initime == None:
1212 if self.__initime == None:
1210 self.__initime = datatime
1213 self.__initime = datatime
1211
1214
1212 if self.__byTime:
1215 if self.__byTime:
1213 avgdata_spc, avgdata_cspc, avgdata_dc = self.byTime(datatime, *args)
1216 avgdata_spc, avgdata_cspc, avgdata_dc = self.byTime(datatime, *args)
1214 else:
1217 else:
1215 avgdata_spc, avgdata_cspc, avgdata_dc = self.byProfiles(*args)
1218 avgdata_spc, avgdata_cspc, avgdata_dc = self.byProfiles(*args)
1216
1219
1217 self.__lastdatatime = datatime
1220 self.__lastdatatime = datatime
1218
1221
1219 if avgdata_spc == None:
1222 if avgdata_spc == None:
1220 return None, None, None, None
1223 return None, None, None, None
1221
1224
1222 avgdatatime = self.__initime
1225 avgdatatime = self.__initime
1223 self.__timeInterval = (self.__lastdatatime - self.__initime)/(self.n - 1)
1226 self.__timeInterval = (self.__lastdatatime - self.__initime)/(self.n - 1)
1224
1227
1225 deltatime = datatime -self.__lastdatatime
1228 deltatime = datatime -self.__lastdatatime
1226
1229
1227 if not self.__withOverapping:
1230 if not self.__withOverapping:
1228 self.__initime = datatime
1231 self.__initime = datatime
1229 else:
1232 else:
1230 self.__initime += deltatime
1233 self.__initime += deltatime
1231
1234
1232 return avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc
1235 return avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc
1233
1236
1234 def run(self, dataOut, n=None, timeInterval=None, overlapping=False):
1237 def run(self, dataOut, n=None, timeInterval=None, overlapping=False):
1235
1238
1236 if not self.__isConfig:
1239 if not self.__isConfig:
1237 self.setup(n, timeInterval, overlapping)
1240 self.setup(n, timeInterval, overlapping)
1238 self.__isConfig = True
1241 self.__isConfig = True
1239
1242
1240 avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(dataOut.utctime,
1243 avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(dataOut.utctime,
1241 dataOut.data_spc,
1244 dataOut.data_spc,
1242 dataOut.data_cspc,
1245 dataOut.data_cspc,
1243 dataOut.data_dc)
1246 dataOut.data_dc)
1244
1247
1245 # dataOut.timeInterval *= n
1248 # dataOut.timeInterval *= n
1246 dataOut.flagNoData = True
1249 dataOut.flagNoData = True
1247
1250
1248 if self.__dataReady:
1251 if self.__dataReady:
1249
1252
1250 dataOut.data_spc = avgdata_spc
1253 dataOut.data_spc = avgdata_spc
1251 dataOut.data_cspc = avgdata_cspc
1254 dataOut.data_cspc = avgdata_cspc
1252 dataOut.data_dc = avgdata_dc
1255 dataOut.data_dc = avgdata_dc
1253
1256
1254 dataOut.nIncohInt *= self.n
1257 dataOut.nIncohInt *= self.n
1255 dataOut.utctime = avgdatatime
1258 dataOut.utctime = avgdatatime
1256 #dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt * dataOut.nIncohInt * dataOut.nFFTPoints
1259 #dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt * dataOut.nIncohInt * dataOut.nFFTPoints
1257 dataOut.timeInterval = self.__timeInterval*self.n
1260 dataOut.timeInterval = self.__timeInterval*self.n
1258 dataOut.flagNoData = False
1261 dataOut.flagNoData = False
1259
1262
1260 class ProfileSelector(Operation):
1263 class ProfileSelector(Operation):
1261
1264
1262 profileIndex = None
1265 profileIndex = None
1263 # Tamanho total de los perfiles
1266 # Tamanho total de los perfiles
1264 nProfiles = None
1267 nProfiles = None
1265
1268
1266 def __init__(self):
1269 def __init__(self):
1267
1270
1268 self.profileIndex = 0
1271 self.profileIndex = 0
1269
1272
1270 def incIndex(self):
1273 def incIndex(self):
1271 self.profileIndex += 1
1274 self.profileIndex += 1
1272
1275
1273 if self.profileIndex >= self.nProfiles:
1276 if self.profileIndex >= self.nProfiles:
1274 self.profileIndex = 0
1277 self.profileIndex = 0
1275
1278
1276 def isProfileInRange(self, minIndex, maxIndex):
1279 def isProfileInRange(self, minIndex, maxIndex):
1277
1280
1278 if self.profileIndex < minIndex:
1281 if self.profileIndex < minIndex:
1279 return False
1282 return False
1280
1283
1281 if self.profileIndex > maxIndex:
1284 if self.profileIndex > maxIndex:
1282 return False
1285 return False
1283
1286
1284 return True
1287 return True
1285
1288
1286 def isProfileInList(self, profileList):
1289 def isProfileInList(self, profileList):
1287
1290
1288 if self.profileIndex not in profileList:
1291 if self.profileIndex not in profileList:
1289 return False
1292 return False
1290
1293
1291 return True
1294 return True
1292
1295
1293 def run(self, dataOut, profileList=None, profileRangeList=None):
1296 def run(self, dataOut, profileList=None, profileRangeList=None):
1294
1297
1295 dataOut.flagNoData = True
1298 dataOut.flagNoData = True
1296 self.nProfiles = dataOut.nProfiles
1299 self.nProfiles = dataOut.nProfiles
1297
1300
1298 if profileList != None:
1301 if profileList != None:
1299 if self.isProfileInList(profileList):
1302 if self.isProfileInList(profileList):
1300 dataOut.flagNoData = False
1303 dataOut.flagNoData = False
1301
1304
1302 self.incIndex()
1305 self.incIndex()
1303 return 1
1306 return 1
1304
1307
1305
1308
1306 elif profileRangeList != None:
1309 elif profileRangeList != None:
1307 minIndex = profileRangeList[0]
1310 minIndex = profileRangeList[0]
1308 maxIndex = profileRangeList[1]
1311 maxIndex = profileRangeList[1]
1309 if self.isProfileInRange(minIndex, maxIndex):
1312 if self.isProfileInRange(minIndex, maxIndex):
1310 dataOut.flagNoData = False
1313 dataOut.flagNoData = False
1311
1314
1312 self.incIndex()
1315 self.incIndex()
1313 return 1
1316 return 1
1314
1317
1315 else:
1318 else:
1316 raise ValueError, "ProfileSelector needs profileList or profileRangeList"
1319 raise ValueError, "ProfileSelector needs profileList or profileRangeList"
1317
1320
1318 return 0
1321 return 0
1319
1322
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