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Bug fixed selecting heis: Dc array has 2 dimensions, not 3
Miguel Valdez -
r291:5df7dc63df70
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1 '''
1 '''
2
2
3 $Author: dsuarez $
3 $Author: dsuarez $
4 $Id: Processor.py 1 2012-11-12 18:56:07Z dsuarez $
4 $Id: Processor.py 1 2012-11-12 18:56:07Z dsuarez $
5 '''
5 '''
6 import os
6 import os
7 import numpy
7 import numpy
8 import datetime
8 import datetime
9 import time
9 import time
10
10
11 from jrodata import *
11 from jrodata import *
12 from jrodataIO import *
12 from jrodataIO import *
13 from jroplot import *
13 from jroplot import *
14
14
15 class ProcessingUnit:
15 class ProcessingUnit:
16
16
17 """
17 """
18 Esta es la clase base para el procesamiento de datos.
18 Esta es la clase base para el procesamiento de datos.
19
19
20 Contiene el metodo "call" para llamar operaciones. Las operaciones pueden ser:
20 Contiene el metodo "call" para llamar operaciones. Las operaciones pueden ser:
21 - Metodos internos (callMethod)
21 - Metodos internos (callMethod)
22 - Objetos del tipo Operation (callObject). Antes de ser llamados, estos objetos
22 - Objetos del tipo Operation (callObject). Antes de ser llamados, estos objetos
23 tienen que ser agreagados con el metodo "add".
23 tienen que ser agreagados con el metodo "add".
24
24
25 """
25 """
26 # objeto de datos de entrada (Voltage, Spectra o Correlation)
26 # objeto de datos de entrada (Voltage, Spectra o Correlation)
27 dataIn = None
27 dataIn = None
28
28
29 # objeto de datos de entrada (Voltage, Spectra o Correlation)
29 # objeto de datos de entrada (Voltage, Spectra o Correlation)
30 dataOut = None
30 dataOut = None
31
31
32
32
33 objectDict = None
33 objectDict = None
34
34
35 def __init__(self):
35 def __init__(self):
36
36
37 self.objectDict = {}
37 self.objectDict = {}
38
38
39 def init(self):
39 def init(self):
40
40
41 raise ValueError, "Not implemented"
41 raise ValueError, "Not implemented"
42
42
43 def addOperation(self, object, objId):
43 def addOperation(self, object, objId):
44
44
45 """
45 """
46 Agrega el objeto "object" a la lista de objetos "self.objectList" y retorna el
46 Agrega el objeto "object" a la lista de objetos "self.objectList" y retorna el
47 identificador asociado a este objeto.
47 identificador asociado a este objeto.
48
48
49 Input:
49 Input:
50
50
51 object : objeto de la clase "Operation"
51 object : objeto de la clase "Operation"
52
52
53 Return:
53 Return:
54
54
55 objId : identificador del objeto, necesario para ejecutar la operacion
55 objId : identificador del objeto, necesario para ejecutar la operacion
56 """
56 """
57
57
58 self.objectDict[objId] = object
58 self.objectDict[objId] = object
59
59
60 return objId
60 return objId
61
61
62 def operation(self, **kwargs):
62 def operation(self, **kwargs):
63
63
64 """
64 """
65 Operacion directa sobre la data (dataOut.data). Es necesario actualizar los valores de los
65 Operacion directa sobre la data (dataOut.data). Es necesario actualizar los valores de los
66 atributos del objeto dataOut
66 atributos del objeto dataOut
67
67
68 Input:
68 Input:
69
69
70 **kwargs : Diccionario de argumentos de la funcion a ejecutar
70 **kwargs : Diccionario de argumentos de la funcion a ejecutar
71 """
71 """
72
72
73 raise ValueError, "ImplementedError"
73 raise ValueError, "ImplementedError"
74
74
75 def callMethod(self, name, **kwargs):
75 def callMethod(self, name, **kwargs):
76
76
77 """
77 """
78 Ejecuta el metodo con el nombre "name" y con argumentos **kwargs de la propia clase.
78 Ejecuta el metodo con el nombre "name" y con argumentos **kwargs de la propia clase.
79
79
80 Input:
80 Input:
81 name : nombre del metodo a ejecutar
81 name : nombre del metodo a ejecutar
82
82
83 **kwargs : diccionario con los nombres y valores de la funcion a ejecutar.
83 **kwargs : diccionario con los nombres y valores de la funcion a ejecutar.
84
84
85 """
85 """
86 if name != 'run':
86 if name != 'run':
87
87
88 if name == 'init' and self.dataIn.isEmpty():
88 if name == 'init' and self.dataIn.isEmpty():
89 self.dataOut.flagNoData = True
89 self.dataOut.flagNoData = True
90 return False
90 return False
91
91
92 if name != 'init' and self.dataOut.isEmpty():
92 if name != 'init' and self.dataOut.isEmpty():
93 return False
93 return False
94
94
95 methodToCall = getattr(self, name)
95 methodToCall = getattr(self, name)
96
96
97 methodToCall(**kwargs)
97 methodToCall(**kwargs)
98
98
99 if name != 'run':
99 if name != 'run':
100 return True
100 return True
101
101
102 if self.dataOut.isEmpty():
102 if self.dataOut.isEmpty():
103 return False
103 return False
104
104
105 return True
105 return True
106
106
107 def callObject(self, objId, **kwargs):
107 def callObject(self, objId, **kwargs):
108
108
109 """
109 """
110 Ejecuta la operacion asociada al identificador del objeto "objId"
110 Ejecuta la operacion asociada al identificador del objeto "objId"
111
111
112 Input:
112 Input:
113
113
114 objId : identificador del objeto a ejecutar
114 objId : identificador del objeto a ejecutar
115
115
116 **kwargs : diccionario con los nombres y valores de la funcion a ejecutar.
116 **kwargs : diccionario con los nombres y valores de la funcion a ejecutar.
117
117
118 Return:
118 Return:
119
119
120 None
120 None
121 """
121 """
122
122
123 if self.dataOut.isEmpty():
123 if self.dataOut.isEmpty():
124 return False
124 return False
125
125
126 object = self.objectDict[objId]
126 object = self.objectDict[objId]
127
127
128 object.run(self.dataOut, **kwargs)
128 object.run(self.dataOut, **kwargs)
129
129
130 return True
130 return True
131
131
132 def call(self, operationConf, **kwargs):
132 def call(self, operationConf, **kwargs):
133
133
134 """
134 """
135 Return True si ejecuta la operacion "operationConf.name" con los
135 Return True si ejecuta la operacion "operationConf.name" con los
136 argumentos "**kwargs". False si la operacion no se ha ejecutado.
136 argumentos "**kwargs". False si la operacion no se ha ejecutado.
137 La operacion puede ser de dos tipos:
137 La operacion puede ser de dos tipos:
138
138
139 1. Un metodo propio de esta clase:
139 1. Un metodo propio de esta clase:
140
140
141 operation.type = "self"
141 operation.type = "self"
142
142
143 2. El metodo "run" de un objeto del tipo Operation o de un derivado de ella:
143 2. El metodo "run" de un objeto del tipo Operation o de un derivado de ella:
144 operation.type = "other".
144 operation.type = "other".
145
145
146 Este objeto de tipo Operation debe de haber sido agregado antes con el metodo:
146 Este objeto de tipo Operation debe de haber sido agregado antes con el metodo:
147 "addOperation" e identificado con el operation.id
147 "addOperation" e identificado con el operation.id
148
148
149
149
150 con el id de la operacion.
150 con el id de la operacion.
151
151
152 Input:
152 Input:
153
153
154 Operation : Objeto del tipo operacion con los atributos: name, type y id.
154 Operation : Objeto del tipo operacion con los atributos: name, type y id.
155
155
156 """
156 """
157
157
158 if operationConf.type == 'self':
158 if operationConf.type == 'self':
159 sts = self.callMethod(operationConf.name, **kwargs)
159 sts = self.callMethod(operationConf.name, **kwargs)
160
160
161 if operationConf.type == 'other':
161 if operationConf.type == 'other':
162 sts = self.callObject(operationConf.id, **kwargs)
162 sts = self.callObject(operationConf.id, **kwargs)
163
163
164 return sts
164 return sts
165
165
166 def setInput(self, dataIn):
166 def setInput(self, dataIn):
167
167
168 self.dataIn = dataIn
168 self.dataIn = dataIn
169
169
170 def getOutput(self):
170 def getOutput(self):
171
171
172 return self.dataOut
172 return self.dataOut
173
173
174 class Operation():
174 class Operation():
175
175
176 """
176 """
177 Clase base para definir las operaciones adicionales que se pueden agregar a la clase ProcessingUnit
177 Clase base para definir las operaciones adicionales que se pueden agregar a la clase ProcessingUnit
178 y necesiten acumular informacion previa de los datos a procesar. De preferencia usar un buffer de
178 y necesiten acumular informacion previa de los datos a procesar. De preferencia usar un buffer de
179 acumulacion dentro de esta clase
179 acumulacion dentro de esta clase
180
180
181 Ejemplo: Integraciones coherentes, necesita la informacion previa de los n perfiles anteriores (bufffer)
181 Ejemplo: Integraciones coherentes, necesita la informacion previa de los n perfiles anteriores (bufffer)
182
182
183 """
183 """
184
184
185 __buffer = None
185 __buffer = None
186 __isConfig = False
186 __isConfig = False
187
187
188 def __init__(self):
188 def __init__(self):
189
189
190 pass
190 pass
191
191
192 def run(self, dataIn, **kwargs):
192 def run(self, dataIn, **kwargs):
193
193
194 """
194 """
195 Realiza las operaciones necesarias sobre la dataIn.data y actualiza los atributos del objeto dataIn.
195 Realiza las operaciones necesarias sobre la dataIn.data y actualiza los atributos del objeto dataIn.
196
196
197 Input:
197 Input:
198
198
199 dataIn : objeto del tipo JROData
199 dataIn : objeto del tipo JROData
200
200
201 Return:
201 Return:
202
202
203 None
203 None
204
204
205 Affected:
205 Affected:
206 __buffer : buffer de recepcion de datos.
206 __buffer : buffer de recepcion de datos.
207
207
208 """
208 """
209
209
210 raise ValueError, "ImplementedError"
210 raise ValueError, "ImplementedError"
211
211
212 class VoltageProc(ProcessingUnit):
212 class VoltageProc(ProcessingUnit):
213
213
214
214
215 def __init__(self):
215 def __init__(self):
216
216
217 self.objectDict = {}
217 self.objectDict = {}
218 self.dataOut = Voltage()
218 self.dataOut = Voltage()
219 self.flip = 1
219 self.flip = 1
220
220
221 def init(self):
221 def init(self):
222
222
223 self.dataOut.copy(self.dataIn)
223 self.dataOut.copy(self.dataIn)
224 # No necesita copiar en cada init() los atributos de dataIn
224 # No necesita copiar en cada init() los atributos de dataIn
225 # la copia deberia hacerse por cada nuevo bloque de datos
225 # la copia deberia hacerse por cada nuevo bloque de datos
226
226
227 def selectChannels(self, channelList):
227 def selectChannels(self, channelList):
228
228
229 channelIndexList = []
229 channelIndexList = []
230
230
231 for channel in channelList:
231 for channel in channelList:
232 index = self.dataOut.channelList.index(channel)
232 index = self.dataOut.channelList.index(channel)
233 channelIndexList.append(index)
233 channelIndexList.append(index)
234
234
235 self.selectChannelsByIndex(channelIndexList)
235 self.selectChannelsByIndex(channelIndexList)
236
236
237 def selectChannelsByIndex(self, channelIndexList):
237 def selectChannelsByIndex(self, channelIndexList):
238 """
238 """
239 Selecciona un bloque de datos en base a canales segun el channelIndexList
239 Selecciona un bloque de datos en base a canales segun el channelIndexList
240
240
241 Input:
241 Input:
242 channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
242 channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
243
243
244 Affected:
244 Affected:
245 self.dataOut.data
245 self.dataOut.data
246 self.dataOut.channelIndexList
246 self.dataOut.channelIndexList
247 self.dataOut.nChannels
247 self.dataOut.nChannels
248 self.dataOut.m_ProcessingHeader.totalSpectra
248 self.dataOut.m_ProcessingHeader.totalSpectra
249 self.dataOut.systemHeaderObj.numChannels
249 self.dataOut.systemHeaderObj.numChannels
250 self.dataOut.m_ProcessingHeader.blockSize
250 self.dataOut.m_ProcessingHeader.blockSize
251
251
252 Return:
252 Return:
253 None
253 None
254 """
254 """
255
255
256 for channelIndex in channelIndexList:
256 for channelIndex in channelIndexList:
257 if channelIndex not in self.dataOut.channelIndexList:
257 if channelIndex not in self.dataOut.channelIndexList:
258 print channelIndexList
258 print channelIndexList
259 raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex
259 raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex
260
260
261 nChannels = len(channelIndexList)
261 nChannels = len(channelIndexList)
262
262
263 data = self.dataOut.data[channelIndexList,:]
263 data = self.dataOut.data[channelIndexList,:]
264
264
265 self.dataOut.data = data
265 self.dataOut.data = data
266 self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
266 self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
267 # self.dataOut.nChannels = nChannels
267 # self.dataOut.nChannels = nChannels
268
268
269 return 1
269 return 1
270
270
271 def selectHeights(self, minHei, maxHei):
271 def selectHeights(self, minHei, maxHei):
272 """
272 """
273 Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
273 Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
274 minHei <= height <= maxHei
274 minHei <= height <= maxHei
275
275
276 Input:
276 Input:
277 minHei : valor minimo de altura a considerar
277 minHei : valor minimo de altura a considerar
278 maxHei : valor maximo de altura a considerar
278 maxHei : valor maximo de altura a considerar
279
279
280 Affected:
280 Affected:
281 Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
281 Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
282
282
283 Return:
283 Return:
284 1 si el metodo se ejecuto con exito caso contrario devuelve 0
284 1 si el metodo se ejecuto con exito caso contrario devuelve 0
285 """
285 """
286 if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
286 if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
287 raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
287 raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
288
288
289 if (maxHei > self.dataOut.heightList[-1]):
289 if (maxHei > self.dataOut.heightList[-1]):
290 maxHei = self.dataOut.heightList[-1]
290 maxHei = self.dataOut.heightList[-1]
291 # raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
291 # raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
292
292
293 minIndex = 0
293 minIndex = 0
294 maxIndex = 0
294 maxIndex = 0
295 heights = self.dataOut.heightList
295 heights = self.dataOut.heightList
296
296
297 inda = numpy.where(heights >= minHei)
297 inda = numpy.where(heights >= minHei)
298 indb = numpy.where(heights <= maxHei)
298 indb = numpy.where(heights <= maxHei)
299
299
300 try:
300 try:
301 minIndex = inda[0][0]
301 minIndex = inda[0][0]
302 except:
302 except:
303 minIndex = 0
303 minIndex = 0
304
304
305 try:
305 try:
306 maxIndex = indb[0][-1]
306 maxIndex = indb[0][-1]
307 except:
307 except:
308 maxIndex = len(heights)
308 maxIndex = len(heights)
309
309
310 self.selectHeightsByIndex(minIndex, maxIndex)
310 self.selectHeightsByIndex(minIndex, maxIndex)
311
311
312 return 1
312 return 1
313
313
314
314
315 def selectHeightsByIndex(self, minIndex, maxIndex):
315 def selectHeightsByIndex(self, minIndex, maxIndex):
316 """
316 """
317 Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
317 Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
318 minIndex <= index <= maxIndex
318 minIndex <= index <= maxIndex
319
319
320 Input:
320 Input:
321 minIndex : valor de indice minimo de altura a considerar
321 minIndex : valor de indice minimo de altura a considerar
322 maxIndex : valor de indice maximo de altura a considerar
322 maxIndex : valor de indice maximo de altura a considerar
323
323
324 Affected:
324 Affected:
325 self.dataOut.data
325 self.dataOut.data
326 self.dataOut.heightList
326 self.dataOut.heightList
327
327
328 Return:
328 Return:
329 1 si el metodo se ejecuto con exito caso contrario devuelve 0
329 1 si el metodo se ejecuto con exito caso contrario devuelve 0
330 """
330 """
331
331
332 if (minIndex < 0) or (minIndex > maxIndex):
332 if (minIndex < 0) or (minIndex > maxIndex):
333 raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
333 raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
334
334
335 if (maxIndex >= self.dataOut.nHeights):
335 if (maxIndex >= self.dataOut.nHeights):
336 maxIndex = self.dataOut.nHeights-1
336 maxIndex = self.dataOut.nHeights-1
337 # raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
337 # raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
338
338
339 nHeights = maxIndex - minIndex + 1
339 nHeights = maxIndex - minIndex + 1
340
340
341 #voltage
341 #voltage
342 data = self.dataOut.data[:,minIndex:maxIndex+1]
342 data = self.dataOut.data[:,minIndex:maxIndex+1]
343
343
344 firstHeight = self.dataOut.heightList[minIndex]
344 firstHeight = self.dataOut.heightList[minIndex]
345
345
346 self.dataOut.data = data
346 self.dataOut.data = data
347 self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1]
347 self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1]
348
348
349 return 1
349 return 1
350
350
351
351
352 def filterByHeights(self, window):
352 def filterByHeights(self, window):
353 deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0]
353 deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0]
354
354
355 if window == None:
355 if window == None:
356 window = self.dataOut.radarControllerHeaderObj.txA / deltaHeight
356 window = self.dataOut.radarControllerHeaderObj.txA / deltaHeight
357
357
358 newdelta = deltaHeight * window
358 newdelta = deltaHeight * window
359 r = self.dataOut.data.shape[1] % window
359 r = self.dataOut.data.shape[1] % window
360 buffer = self.dataOut.data[:,0:self.dataOut.data.shape[1]-r]
360 buffer = self.dataOut.data[:,0:self.dataOut.data.shape[1]-r]
361 buffer = buffer.reshape(self.dataOut.data.shape[0],self.dataOut.data.shape[1]/window,window)
361 buffer = buffer.reshape(self.dataOut.data.shape[0],self.dataOut.data.shape[1]/window,window)
362 buffer = numpy.sum(buffer,2)
362 buffer = numpy.sum(buffer,2)
363 self.dataOut.data = buffer
363 self.dataOut.data = buffer
364 self.dataOut.heightList = numpy.arange(self.dataOut.heightList[0],newdelta*self.dataOut.nHeights/window-newdelta,newdelta)
364 self.dataOut.heightList = numpy.arange(self.dataOut.heightList[0],newdelta*self.dataOut.nHeights/window-newdelta,newdelta)
365 self.dataOut.windowOfFilter = window
365 self.dataOut.windowOfFilter = window
366
366
367 def deFlip(self):
367 def deFlip(self):
368 self.dataOut.data *= self.flip
368 self.dataOut.data *= self.flip
369 self.flip *= -1.
369 self.flip *= -1.
370
370
371
371
372 class CohInt(Operation):
372 class CohInt(Operation):
373
373
374 __isConfig = False
374 __isConfig = False
375
375
376 __profIndex = 0
376 __profIndex = 0
377 __withOverapping = False
377 __withOverapping = False
378
378
379 __byTime = False
379 __byTime = False
380 __initime = None
380 __initime = None
381 __lastdatatime = None
381 __lastdatatime = None
382 __integrationtime = None
382 __integrationtime = None
383
383
384 __buffer = None
384 __buffer = None
385
385
386 __dataReady = False
386 __dataReady = False
387
387
388 n = None
388 n = None
389
389
390
390
391 def __init__(self):
391 def __init__(self):
392
392
393 self.__isConfig = False
393 self.__isConfig = False
394
394
395 def setup(self, n=None, timeInterval=None, overlapping=False):
395 def setup(self, n=None, timeInterval=None, overlapping=False):
396 """
396 """
397 Set the parameters of the integration class.
397 Set the parameters of the integration class.
398
398
399 Inputs:
399 Inputs:
400
400
401 n : Number of coherent integrations
401 n : Number of coherent integrations
402 timeInterval : Time of integration. If the parameter "n" is selected this one does not work
402 timeInterval : Time of integration. If the parameter "n" is selected this one does not work
403 overlapping :
403 overlapping :
404
404
405 """
405 """
406
406
407 self.__initime = None
407 self.__initime = None
408 self.__lastdatatime = 0
408 self.__lastdatatime = 0
409 self.__buffer = None
409 self.__buffer = None
410 self.__dataReady = False
410 self.__dataReady = False
411
411
412
412
413 if n == None and timeInterval == None:
413 if n == None and timeInterval == None:
414 raise ValueError, "n or timeInterval should be specified ..."
414 raise ValueError, "n or timeInterval should be specified ..."
415
415
416 if n != None:
416 if n != None:
417 self.n = n
417 self.n = n
418 self.__byTime = False
418 self.__byTime = False
419 else:
419 else:
420 self.__integrationtime = timeInterval * 60. #if (type(timeInterval)!=integer) -> change this line
420 self.__integrationtime = timeInterval * 60. #if (type(timeInterval)!=integer) -> change this line
421 self.n = 9999
421 self.n = 9999
422 self.__byTime = True
422 self.__byTime = True
423
423
424 if overlapping:
424 if overlapping:
425 self.__withOverapping = True
425 self.__withOverapping = True
426 self.__buffer = None
426 self.__buffer = None
427 else:
427 else:
428 self.__withOverapping = False
428 self.__withOverapping = False
429 self.__buffer = 0
429 self.__buffer = 0
430
430
431 self.__profIndex = 0
431 self.__profIndex = 0
432
432
433 def putData(self, data):
433 def putData(self, data):
434
434
435 """
435 """
436 Add a profile to the __buffer and increase in one the __profileIndex
436 Add a profile to the __buffer and increase in one the __profileIndex
437
437
438 """
438 """
439
439
440 if not self.__withOverapping:
440 if not self.__withOverapping:
441 self.__buffer += data.copy()
441 self.__buffer += data.copy()
442 self.__profIndex += 1
442 self.__profIndex += 1
443 return
443 return
444
444
445 #Overlapping data
445 #Overlapping data
446 nChannels, nHeis = data.shape
446 nChannels, nHeis = data.shape
447 data = numpy.reshape(data, (1, nChannels, nHeis))
447 data = numpy.reshape(data, (1, nChannels, nHeis))
448
448
449 #If the buffer is empty then it takes the data value
449 #If the buffer is empty then it takes the data value
450 if self.__buffer == None:
450 if self.__buffer == None:
451 self.__buffer = data
451 self.__buffer = data
452 self.__profIndex += 1
452 self.__profIndex += 1
453 return
453 return
454
454
455 #If the buffer length is lower than n then stakcing the data value
455 #If the buffer length is lower than n then stakcing the data value
456 if self.__profIndex < self.n:
456 if self.__profIndex < self.n:
457 self.__buffer = numpy.vstack((self.__buffer, data))
457 self.__buffer = numpy.vstack((self.__buffer, data))
458 self.__profIndex += 1
458 self.__profIndex += 1
459 return
459 return
460
460
461 #If the buffer length is equal to n then replacing the last buffer value with the data value
461 #If the buffer length is equal to n then replacing the last buffer value with the data value
462 self.__buffer = numpy.roll(self.__buffer, -1, axis=0)
462 self.__buffer = numpy.roll(self.__buffer, -1, axis=0)
463 self.__buffer[self.n-1] = data
463 self.__buffer[self.n-1] = data
464 self.__profIndex = self.n
464 self.__profIndex = self.n
465 return
465 return
466
466
467
467
468 def pushData(self):
468 def pushData(self):
469 """
469 """
470 Return the sum of the last profiles and the profiles used in the sum.
470 Return the sum of the last profiles and the profiles used in the sum.
471
471
472 Affected:
472 Affected:
473
473
474 self.__profileIndex
474 self.__profileIndex
475
475
476 """
476 """
477
477
478 if not self.__withOverapping:
478 if not self.__withOverapping:
479 data = self.__buffer
479 data = self.__buffer
480 n = self.__profIndex
480 n = self.__profIndex
481
481
482 self.__buffer = 0
482 self.__buffer = 0
483 self.__profIndex = 0
483 self.__profIndex = 0
484
484
485 return data, n
485 return data, n
486
486
487 #Integration with Overlapping
487 #Integration with Overlapping
488 data = numpy.sum(self.__buffer, axis=0)
488 data = numpy.sum(self.__buffer, axis=0)
489 n = self.__profIndex
489 n = self.__profIndex
490
490
491 return data, n
491 return data, n
492
492
493 def byProfiles(self, data):
493 def byProfiles(self, data):
494
494
495 self.__dataReady = False
495 self.__dataReady = False
496 avgdata = None
496 avgdata = None
497 n = None
497 n = None
498
498
499 self.putData(data)
499 self.putData(data)
500
500
501 if self.__profIndex == self.n:
501 if self.__profIndex == self.n:
502
502
503 avgdata, n = self.pushData()
503 avgdata, n = self.pushData()
504 self.__dataReady = True
504 self.__dataReady = True
505
505
506 return avgdata
506 return avgdata
507
507
508 def byTime(self, data, datatime):
508 def byTime(self, data, datatime):
509
509
510 self.__dataReady = False
510 self.__dataReady = False
511 avgdata = None
511 avgdata = None
512 n = None
512 n = None
513
513
514 self.putData(data)
514 self.putData(data)
515
515
516 if (datatime - self.__initime) >= self.__integrationtime:
516 if (datatime - self.__initime) >= self.__integrationtime:
517 avgdata, n = self.pushData()
517 avgdata, n = self.pushData()
518 self.n = n
518 self.n = n
519 self.__dataReady = True
519 self.__dataReady = True
520
520
521 return avgdata
521 return avgdata
522
522
523 def integrate(self, data, datatime=None):
523 def integrate(self, data, datatime=None):
524
524
525 if self.__initime == None:
525 if self.__initime == None:
526 self.__initime = datatime
526 self.__initime = datatime
527
527
528 if self.__byTime:
528 if self.__byTime:
529 avgdata = self.byTime(data, datatime)
529 avgdata = self.byTime(data, datatime)
530 else:
530 else:
531 avgdata = self.byProfiles(data)
531 avgdata = self.byProfiles(data)
532
532
533
533
534 self.__lastdatatime = datatime
534 self.__lastdatatime = datatime
535
535
536 if avgdata == None:
536 if avgdata == None:
537 return None, None
537 return None, None
538
538
539 avgdatatime = self.__initime
539 avgdatatime = self.__initime
540
540
541 deltatime = datatime -self.__lastdatatime
541 deltatime = datatime -self.__lastdatatime
542
542
543 if not self.__withOverapping:
543 if not self.__withOverapping:
544 self.__initime = datatime
544 self.__initime = datatime
545 else:
545 else:
546 self.__initime += deltatime
546 self.__initime += deltatime
547
547
548 return avgdata, avgdatatime
548 return avgdata, avgdatatime
549
549
550 def run(self, dataOut, **kwargs):
550 def run(self, dataOut, **kwargs):
551
551
552 if not self.__isConfig:
552 if not self.__isConfig:
553 self.setup(**kwargs)
553 self.setup(**kwargs)
554 self.__isConfig = True
554 self.__isConfig = True
555
555
556 avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime)
556 avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime)
557
557
558 # dataOut.timeInterval *= n
558 # dataOut.timeInterval *= n
559 dataOut.flagNoData = True
559 dataOut.flagNoData = True
560
560
561 if self.__dataReady:
561 if self.__dataReady:
562 dataOut.data = avgdata
562 dataOut.data = avgdata
563 dataOut.nCohInt *= self.n
563 dataOut.nCohInt *= self.n
564 dataOut.utctime = avgdatatime
564 dataOut.utctime = avgdatatime
565 dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt
565 dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt
566 dataOut.flagNoData = False
566 dataOut.flagNoData = False
567
567
568
568
569 class Decoder(Operation):
569 class Decoder(Operation):
570
570
571 __isConfig = False
571 __isConfig = False
572 __profIndex = 0
572 __profIndex = 0
573
573
574 code = None
574 code = None
575
575
576 nCode = None
576 nCode = None
577 nBaud = None
577 nBaud = None
578
578
579 def __init__(self):
579 def __init__(self):
580
580
581 self.__isConfig = False
581 self.__isConfig = False
582
582
583 def setup(self, code):
583 def setup(self, code):
584
584
585 self.__profIndex = 0
585 self.__profIndex = 0
586
586
587 self.code = code
587 self.code = code
588
588
589 self.nCode = len(code)
589 self.nCode = len(code)
590 self.nBaud = len(code[0])
590 self.nBaud = len(code[0])
591
591
592 def convolutionInFreq(self, data):
592 def convolutionInFreq(self, data):
593
593
594 nchannel, ndata = data.shape
594 nchannel, ndata = data.shape
595 newcode = numpy.zeros(ndata)
595 newcode = numpy.zeros(ndata)
596 newcode[0:self.nBaud] = self.code[self.__profIndex]
596 newcode[0:self.nBaud] = self.code[self.__profIndex]
597
597
598 fft_data = numpy.fft.fft(data, axis=1)
598 fft_data = numpy.fft.fft(data, axis=1)
599 fft_code = numpy.conj(numpy.fft.fft(newcode))
599 fft_code = numpy.conj(numpy.fft.fft(newcode))
600 fft_code = fft_code.reshape(1,len(fft_code))
600 fft_code = fft_code.reshape(1,len(fft_code))
601
601
602 # conv = fft_data.copy()
602 # conv = fft_data.copy()
603 # conv.fill(0)
603 # conv.fill(0)
604
604
605 conv = fft_data*fft_code
605 conv = fft_data*fft_code
606
606
607 data = numpy.fft.ifft(conv,axis=1)
607 data = numpy.fft.ifft(conv,axis=1)
608
608
609 datadec = data[:,:-self.nBaud+1]
609 datadec = data[:,:-self.nBaud+1]
610 ndatadec = ndata - self.nBaud + 1
610 ndatadec = ndata - self.nBaud + 1
611
611
612 if self.__profIndex == self.nCode-1:
612 if self.__profIndex == self.nCode-1:
613 self.__profIndex = 0
613 self.__profIndex = 0
614 return ndatadec, datadec
614 return ndatadec, datadec
615
615
616 self.__profIndex += 1
616 self.__profIndex += 1
617
617
618 return ndatadec, datadec
618 return ndatadec, datadec
619
619
620
620
621 def convolutionInTime(self, data):
621 def convolutionInTime(self, data):
622
622
623 nchannel, ndata = data.shape
623 nchannel, ndata = data.shape
624 newcode = self.code[self.__profIndex]
624 newcode = self.code[self.__profIndex]
625 ndatadec = ndata - self.nBaud + 1
625 ndatadec = ndata - self.nBaud + 1
626
626
627 datadec = numpy.zeros((nchannel, ndatadec))
627 datadec = numpy.zeros((nchannel, ndatadec))
628
628
629 for i in range(nchannel):
629 for i in range(nchannel):
630 datadec[i,:] = numpy.correlate(data[i,:], newcode)
630 datadec[i,:] = numpy.correlate(data[i,:], newcode)
631
631
632 if self.__profIndex == self.nCode-1:
632 if self.__profIndex == self.nCode-1:
633 self.__profIndex = 0
633 self.__profIndex = 0
634 return ndatadec, datadec
634 return ndatadec, datadec
635
635
636 self.__profIndex += 1
636 self.__profIndex += 1
637
637
638 return ndatadec, datadec
638 return ndatadec, datadec
639
639
640 def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0):
640 def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0):
641 if code == None:
641 if code == None:
642 code = dataOut.code
642 code = dataOut.code
643 else:
643 else:
644 code = numpy.array(code).reshape(nCode,nBaud)
644 code = numpy.array(code).reshape(nCode,nBaud)
645 dataOut.code = code
645 dataOut.code = code
646 dataOut.nCode = nCode
646 dataOut.nCode = nCode
647 dataOut.nBaud = nBaud
647 dataOut.nBaud = nBaud
648
648
649 if code == None:
649 if code == None:
650 return 1
650 return 1
651
651
652 if not self.__isConfig:
652 if not self.__isConfig:
653 self.setup(code)
653 self.setup(code)
654 self.__isConfig = True
654 self.__isConfig = True
655
655
656 if mode == 0:
656 if mode == 0:
657 ndatadec, datadec = self.convolutionInFreq(dataOut.data)
657 ndatadec, datadec = self.convolutionInFreq(dataOut.data)
658
658
659 if mode == 1:
659 if mode == 1:
660 print "This function is not implemented"
660 print "This function is not implemented"
661 # ndatadec, datadec = self.convolutionInTime(dataOut.data)
661 # ndatadec, datadec = self.convolutionInTime(dataOut.data)
662
662
663 dataOut.data = datadec
663 dataOut.data = datadec
664
664
665 dataOut.heightList = dataOut.heightList[0:ndatadec]
665 dataOut.heightList = dataOut.heightList[0:ndatadec]
666
666
667 dataOut.flagDecodeData = True #asumo q la data no esta decodificada
667 dataOut.flagDecodeData = True #asumo q la data no esta decodificada
668
668
669 # dataOut.flagDeflipData = True #asumo q la data no esta sin flip
669 # dataOut.flagDeflipData = True #asumo q la data no esta sin flip
670
670
671
671
672 class SpectraProc(ProcessingUnit):
672 class SpectraProc(ProcessingUnit):
673
673
674 def __init__(self):
674 def __init__(self):
675
675
676 self.objectDict = {}
676 self.objectDict = {}
677 self.buffer = None
677 self.buffer = None
678 self.firstdatatime = None
678 self.firstdatatime = None
679 self.profIndex = 0
679 self.profIndex = 0
680 self.dataOut = Spectra()
680 self.dataOut = Spectra()
681
681
682 def __updateObjFromInput(self):
682 def __updateObjFromInput(self):
683
683
684 self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()
684 self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()
685 self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy()
685 self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy()
686 self.dataOut.channelList = self.dataIn.channelList
686 self.dataOut.channelList = self.dataIn.channelList
687 self.dataOut.heightList = self.dataIn.heightList
687 self.dataOut.heightList = self.dataIn.heightList
688 self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')])
688 self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')])
689 # self.dataOut.nHeights = self.dataIn.nHeights
689 # self.dataOut.nHeights = self.dataIn.nHeights
690 # self.dataOut.nChannels = self.dataIn.nChannels
690 # self.dataOut.nChannels = self.dataIn.nChannels
691 self.dataOut.nBaud = self.dataIn.nBaud
691 self.dataOut.nBaud = self.dataIn.nBaud
692 self.dataOut.nCode = self.dataIn.nCode
692 self.dataOut.nCode = self.dataIn.nCode
693 self.dataOut.code = self.dataIn.code
693 self.dataOut.code = self.dataIn.code
694 self.dataOut.nProfiles = self.dataOut.nFFTPoints
694 self.dataOut.nProfiles = self.dataOut.nFFTPoints
695 # self.dataOut.channelIndexList = self.dataIn.channelIndexList
695 # self.dataOut.channelIndexList = self.dataIn.channelIndexList
696 self.dataOut.flagTimeBlock = self.dataIn.flagTimeBlock
696 self.dataOut.flagTimeBlock = self.dataIn.flagTimeBlock
697 self.dataOut.utctime = self.firstdatatime
697 self.dataOut.utctime = self.firstdatatime
698 self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada
698 self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada
699 self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip
699 self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip
700 self.dataOut.flagShiftFFT = self.dataIn.flagShiftFFT
700 self.dataOut.flagShiftFFT = self.dataIn.flagShiftFFT
701 self.dataOut.nCohInt = self.dataIn.nCohInt
701 self.dataOut.nCohInt = self.dataIn.nCohInt
702 self.dataOut.nIncohInt = 1
702 self.dataOut.nIncohInt = 1
703 self.dataOut.ippSeconds = self.dataIn.ippSeconds
703 self.dataOut.ippSeconds = self.dataIn.ippSeconds
704 self.dataOut.windowOfFilter = self.dataIn.windowOfFilter
704 self.dataOut.windowOfFilter = self.dataIn.windowOfFilter
705
705
706 self.dataOut.timeInterval = self.dataIn.timeInterval*self.dataOut.nFFTPoints*self.dataOut.nIncohInt
706 self.dataOut.timeInterval = self.dataIn.timeInterval*self.dataOut.nFFTPoints*self.dataOut.nIncohInt
707
707
708 def __getFft(self):
708 def __getFft(self):
709 """
709 """
710 Convierte valores de Voltaje a Spectra
710 Convierte valores de Voltaje a Spectra
711
711
712 Affected:
712 Affected:
713 self.dataOut.data_spc
713 self.dataOut.data_spc
714 self.dataOut.data_cspc
714 self.dataOut.data_cspc
715 self.dataOut.data_dc
715 self.dataOut.data_dc
716 self.dataOut.heightList
716 self.dataOut.heightList
717 self.profIndex
717 self.profIndex
718 self.buffer
718 self.buffer
719 self.dataOut.flagNoData
719 self.dataOut.flagNoData
720 """
720 """
721 fft_volt = numpy.fft.fft(self.buffer,axis=1)
721 fft_volt = numpy.fft.fft(self.buffer,axis=1)
722 fft_volt = fft_volt.astype(numpy.dtype('complex'))
722 fft_volt = fft_volt.astype(numpy.dtype('complex'))
723 dc = fft_volt[:,0,:]
723 dc = fft_volt[:,0,:]
724
724
725 #calculo de self-spectra
725 #calculo de self-spectra
726 fft_volt = numpy.fft.fftshift(fft_volt,axes=(1,))
726 fft_volt = numpy.fft.fftshift(fft_volt,axes=(1,))
727 spc = fft_volt * numpy.conjugate(fft_volt)
727 spc = fft_volt * numpy.conjugate(fft_volt)
728 spc = spc.real
728 spc = spc.real
729
729
730 blocksize = 0
730 blocksize = 0
731 blocksize += dc.size
731 blocksize += dc.size
732 blocksize += spc.size
732 blocksize += spc.size
733
733
734 cspc = None
734 cspc = None
735 pairIndex = 0
735 pairIndex = 0
736 if self.dataOut.pairsList != None:
736 if self.dataOut.pairsList != None:
737 #calculo de cross-spectra
737 #calculo de cross-spectra
738 cspc = numpy.zeros((self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex')
738 cspc = numpy.zeros((self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex')
739 for pair in self.dataOut.pairsList:
739 for pair in self.dataOut.pairsList:
740 cspc[pairIndex,:,:] = fft_volt[pair[0],:,:] * numpy.conjugate(fft_volt[pair[1],:,:])
740 cspc[pairIndex,:,:] = fft_volt[pair[0],:,:] * numpy.conjugate(fft_volt[pair[1],:,:])
741 pairIndex += 1
741 pairIndex += 1
742 blocksize += cspc.size
742 blocksize += cspc.size
743
743
744 self.dataOut.data_spc = spc
744 self.dataOut.data_spc = spc
745 self.dataOut.data_cspc = cspc
745 self.dataOut.data_cspc = cspc
746 self.dataOut.data_dc = dc
746 self.dataOut.data_dc = dc
747 self.dataOut.blockSize = blocksize
747 self.dataOut.blockSize = blocksize
748
748
749 def init(self, nFFTPoints=None, pairsList=None):
749 def init(self, nFFTPoints=None, pairsList=None):
750
750
751 self.dataOut.flagNoData = True
751 self.dataOut.flagNoData = True
752
752
753 if self.dataIn.type == "Spectra":
753 if self.dataIn.type == "Spectra":
754 self.dataOut.copy(self.dataIn)
754 self.dataOut.copy(self.dataIn)
755 return
755 return
756
756
757 if self.dataIn.type == "Voltage":
757 if self.dataIn.type == "Voltage":
758
758
759 if nFFTPoints == None:
759 if nFFTPoints == None:
760 raise ValueError, "This SpectraProc.init() need nFFTPoints input variable"
760 raise ValueError, "This SpectraProc.init() need nFFTPoints input variable"
761
761
762 if pairsList == None:
762 if pairsList == None:
763 nPairs = 0
763 nPairs = 0
764 else:
764 else:
765 nPairs = len(pairsList)
765 nPairs = len(pairsList)
766
766
767 self.dataOut.nFFTPoints = nFFTPoints
767 self.dataOut.nFFTPoints = nFFTPoints
768 self.dataOut.pairsList = pairsList
768 self.dataOut.pairsList = pairsList
769 self.dataOut.nPairs = nPairs
769 self.dataOut.nPairs = nPairs
770
770
771 if self.buffer == None:
771 if self.buffer == None:
772 self.buffer = numpy.zeros((self.dataIn.nChannels,
772 self.buffer = numpy.zeros((self.dataIn.nChannels,
773 self.dataOut.nFFTPoints,
773 self.dataOut.nFFTPoints,
774 self.dataIn.nHeights),
774 self.dataIn.nHeights),
775 dtype='complex')
775 dtype='complex')
776
776
777
777
778 self.buffer[:,self.profIndex,:] = self.dataIn.data.copy()
778 self.buffer[:,self.profIndex,:] = self.dataIn.data.copy()
779 self.profIndex += 1
779 self.profIndex += 1
780
780
781 if self.firstdatatime == None:
781 if self.firstdatatime == None:
782 self.firstdatatime = self.dataIn.utctime
782 self.firstdatatime = self.dataIn.utctime
783
783
784 if self.profIndex == self.dataOut.nFFTPoints:
784 if self.profIndex == self.dataOut.nFFTPoints:
785 self.__updateObjFromInput()
785 self.__updateObjFromInput()
786 self.__getFft()
786 self.__getFft()
787
787
788 self.dataOut.flagNoData = False
788 self.dataOut.flagNoData = False
789
789
790 self.buffer = None
790 self.buffer = None
791 self.firstdatatime = None
791 self.firstdatatime = None
792 self.profIndex = 0
792 self.profIndex = 0
793
793
794 return
794 return
795
795
796 raise ValuError, "The type object %s is not valid"%(self.dataIn.type)
796 raise ValuError, "The type object %s is not valid"%(self.dataIn.type)
797
797
798 def selectChannels(self, channelList):
798 def selectChannels(self, channelList):
799
799
800 channelIndexList = []
800 channelIndexList = []
801
801
802 for channel in channelList:
802 for channel in channelList:
803 index = self.dataOut.channelList.index(channel)
803 index = self.dataOut.channelList.index(channel)
804 channelIndexList.append(index)
804 channelIndexList.append(index)
805
805
806 self.selectChannelsByIndex(channelIndexList)
806 self.selectChannelsByIndex(channelIndexList)
807
807
808 def selectChannelsByIndex(self, channelIndexList):
808 def selectChannelsByIndex(self, channelIndexList):
809 """
809 """
810 Selecciona un bloque de datos en base a canales segun el channelIndexList
810 Selecciona un bloque de datos en base a canales segun el channelIndexList
811
811
812 Input:
812 Input:
813 channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
813 channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
814
814
815 Affected:
815 Affected:
816 self.dataOut.data_spc
816 self.dataOut.data_spc
817 self.dataOut.channelIndexList
817 self.dataOut.channelIndexList
818 self.dataOut.nChannels
818 self.dataOut.nChannels
819
819
820 Return:
820 Return:
821 None
821 None
822 """
822 """
823
823
824 for channelIndex in channelIndexList:
824 for channelIndex in channelIndexList:
825 if channelIndex not in self.dataOut.channelIndexList:
825 if channelIndex not in self.dataOut.channelIndexList:
826 print channelIndexList
826 print channelIndexList
827 raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex
827 raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex
828
828
829 nChannels = len(channelIndexList)
829 nChannels = len(channelIndexList)
830
830
831 data_spc = self.dataOut.data_spc[channelIndexList,:]
831 data_spc = self.dataOut.data_spc[channelIndexList,:]
832
832
833 self.dataOut.data_spc = data_spc
833 self.dataOut.data_spc = data_spc
834 self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
834 self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
835 # self.dataOut.nChannels = nChannels
835 # self.dataOut.nChannels = nChannels
836
836
837 return 1
837 return 1
838
838
839 def selectHeights(self, minHei, maxHei):
839 def selectHeights(self, minHei, maxHei):
840 """
840 """
841 Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
841 Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
842 minHei <= height <= maxHei
842 minHei <= height <= maxHei
843
843
844 Input:
844 Input:
845 minHei : valor minimo de altura a considerar
845 minHei : valor minimo de altura a considerar
846 maxHei : valor maximo de altura a considerar
846 maxHei : valor maximo de altura a considerar
847
847
848 Affected:
848 Affected:
849 Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
849 Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
850
850
851 Return:
851 Return:
852 1 si el metodo se ejecuto con exito caso contrario devuelve 0
852 1 si el metodo se ejecuto con exito caso contrario devuelve 0
853 """
853 """
854 if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
854 if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
855 raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
855 raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
856
856
857 if (maxHei > self.dataOut.heightList[-1]):
857 if (maxHei > self.dataOut.heightList[-1]):
858 maxHei = self.dataOut.heightList[-1]
858 maxHei = self.dataOut.heightList[-1]
859 # raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
859 # raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
860
860
861 minIndex = 0
861 minIndex = 0
862 maxIndex = 0
862 maxIndex = 0
863 heights = self.dataOut.heightList
863 heights = self.dataOut.heightList
864
864
865 inda = numpy.where(heights >= minHei)
865 inda = numpy.where(heights >= minHei)
866 indb = numpy.where(heights <= maxHei)
866 indb = numpy.where(heights <= maxHei)
867
867
868 try:
868 try:
869 minIndex = inda[0][0]
869 minIndex = inda[0][0]
870 except:
870 except:
871 minIndex = 0
871 minIndex = 0
872
872
873 try:
873 try:
874 maxIndex = indb[0][-1]
874 maxIndex = indb[0][-1]
875 except:
875 except:
876 maxIndex = len(heights)
876 maxIndex = len(heights)
877
877
878 self.selectHeightsByIndex(minIndex, maxIndex)
878 self.selectHeightsByIndex(minIndex, maxIndex)
879
879
880 return 1
880 return 1
881
881
882
882
883 def selectHeightsByIndex(self, minIndex, maxIndex):
883 def selectHeightsByIndex(self, minIndex, maxIndex):
884 """
884 """
885 Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
885 Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
886 minIndex <= index <= maxIndex
886 minIndex <= index <= maxIndex
887
887
888 Input:
888 Input:
889 minIndex : valor de indice minimo de altura a considerar
889 minIndex : valor de indice minimo de altura a considerar
890 maxIndex : valor de indice maximo de altura a considerar
890 maxIndex : valor de indice maximo de altura a considerar
891
891
892 Affected:
892 Affected:
893 self.dataOut.data_spc
893 self.dataOut.data_spc
894 self.dataOut.data_cspc
894 self.dataOut.data_cspc
895 self.dataOut.data_dc
895 self.dataOut.data_dc
896 self.dataOut.heightList
896 self.dataOut.heightList
897
897
898 Return:
898 Return:
899 1 si el metodo se ejecuto con exito caso contrario devuelve 0
899 1 si el metodo se ejecuto con exito caso contrario devuelve 0
900 """
900 """
901
901
902 if (minIndex < 0) or (minIndex > maxIndex):
902 if (minIndex < 0) or (minIndex > maxIndex):
903 raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
903 raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
904
904
905 if (maxIndex >= self.dataOut.nHeights):
905 if (maxIndex >= self.dataOut.nHeights):
906 maxIndex = self.dataOut.nHeights-1
906 maxIndex = self.dataOut.nHeights-1
907 # raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
907 # raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
908
908
909 nHeights = maxIndex - minIndex + 1
909 nHeights = maxIndex - minIndex + 1
910
910
911 #Spectra
911 #Spectra
912 data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1]
912 data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1]
913
913
914 data_cspc = None
914 data_cspc = None
915 if self.dataOut.data_cspc != None:
915 if self.dataOut.data_cspc != None:
916 data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1]
916 data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1]
917
917
918 data_dc = None
918 data_dc = None
919 if self.dataOut.data_dc != None:
919 if self.dataOut.data_dc != None:
920 data_dc = self.dataOut.data_dc[:,:,minIndex:maxIndex+1]
920 data_dc = self.dataOut.data_dc[:,minIndex:maxIndex+1]
921
921
922 self.dataOut.data_spc = data_spc
922 self.dataOut.data_spc = data_spc
923 self.dataOut.data_cspc = data_cspc
923 self.dataOut.data_cspc = data_cspc
924 self.dataOut.data_dc = data_dc
924 self.dataOut.data_dc = data_dc
925
925
926 self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1]
926 self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1]
927
927
928 return 1
928 return 1
929
929
930 def removeDC(self, mode = 1):
930 def removeDC(self, mode = 1):
931
931
932 dc_index = 0
932 dc_index = 0
933 freq_index = numpy.array([-2,-1,1,2])
933 freq_index = numpy.array([-2,-1,1,2])
934 data_spc = self.dataOut.data_spc
934 data_spc = self.dataOut.data_spc
935 data_cspc = self.dataOut.data_cspc
935 data_cspc = self.dataOut.data_cspc
936 data_dc = self.dataOut.data_dc
936 data_dc = self.dataOut.data_dc
937
937
938 if self.dataOut.flagShiftFFT:
938 if self.dataOut.flagShiftFFT:
939 dc_index += self.dataOut.nFFTPoints/2
939 dc_index += self.dataOut.nFFTPoints/2
940 freq_index += self.dataOut.nFFTPoints/2
940 freq_index += self.dataOut.nFFTPoints/2
941
941
942 if mode == 1:
942 if mode == 1:
943 data_spc[dc_index] = (data_spc[:,freq_index[1],:] + data_spc[:,freq_index[2],:])/2
943 data_spc[dc_index] = (data_spc[:,freq_index[1],:] + data_spc[:,freq_index[2],:])/2
944 if data_cspc != None:
944 if data_cspc != None:
945 data_cspc[dc_index] = (data_cspc[:,freq_index[1],:] + data_cspc[:,freq_index[2],:])/2
945 data_cspc[dc_index] = (data_cspc[:,freq_index[1],:] + data_cspc[:,freq_index[2],:])/2
946 return 1
946 return 1
947
947
948 if mode == 2:
948 if mode == 2:
949 pass
949 pass
950
950
951 if mode == 3:
951 if mode == 3:
952 pass
952 pass
953
953
954 raise ValueError, "mode parameter has to be 1, 2 or 3"
954 raise ValueError, "mode parameter has to be 1, 2 or 3"
955
955
956 def removeInterference(self):
956 def removeInterference(self):
957
957
958 pass
958 pass
959
959
960
960
961 class IncohInt(Operation):
961 class IncohInt(Operation):
962
962
963
963
964 __profIndex = 0
964 __profIndex = 0
965 __withOverapping = False
965 __withOverapping = False
966
966
967 __byTime = False
967 __byTime = False
968 __initime = None
968 __initime = None
969 __lastdatatime = None
969 __lastdatatime = None
970 __integrationtime = None
970 __integrationtime = None
971
971
972 __buffer_spc = None
972 __buffer_spc = None
973 __buffer_cspc = None
973 __buffer_cspc = None
974 __buffer_dc = None
974 __buffer_dc = None
975
975
976 __dataReady = False
976 __dataReady = False
977
977
978 __timeInterval = None
978 __timeInterval = None
979
979
980 n = None
980 n = None
981
981
982
982
983
983
984 def __init__(self):
984 def __init__(self):
985
985
986 self.__isConfig = False
986 self.__isConfig = False
987
987
988 def setup(self, n=None, timeInterval=None, overlapping=False):
988 def setup(self, n=None, timeInterval=None, overlapping=False):
989 """
989 """
990 Set the parameters of the integration class.
990 Set the parameters of the integration class.
991
991
992 Inputs:
992 Inputs:
993
993
994 n : Number of coherent integrations
994 n : Number of coherent integrations
995 timeInterval : Time of integration. If the parameter "n" is selected this one does not work
995 timeInterval : Time of integration. If the parameter "n" is selected this one does not work
996 overlapping :
996 overlapping :
997
997
998 """
998 """
999
999
1000 self.__initime = None
1000 self.__initime = None
1001 self.__lastdatatime = 0
1001 self.__lastdatatime = 0
1002 self.__buffer_spc = None
1002 self.__buffer_spc = None
1003 self.__buffer_cspc = None
1003 self.__buffer_cspc = None
1004 self.__buffer_dc = None
1004 self.__buffer_dc = None
1005 self.__dataReady = False
1005 self.__dataReady = False
1006
1006
1007
1007
1008 if n == None and timeInterval == None:
1008 if n == None and timeInterval == None:
1009 raise ValueError, "n or timeInterval should be specified ..."
1009 raise ValueError, "n or timeInterval should be specified ..."
1010
1010
1011 if n != None:
1011 if n != None:
1012 self.n = n
1012 self.n = n
1013 self.__byTime = False
1013 self.__byTime = False
1014 else:
1014 else:
1015 self.__integrationtime = timeInterval * 60. #if (type(timeInterval)!=integer) -> change this line
1015 self.__integrationtime = timeInterval * 60. #if (type(timeInterval)!=integer) -> change this line
1016 self.n = 9999
1016 self.n = 9999
1017 self.__byTime = True
1017 self.__byTime = True
1018
1018
1019 if overlapping:
1019 if overlapping:
1020 self.__withOverapping = True
1020 self.__withOverapping = True
1021 else:
1021 else:
1022 self.__withOverapping = False
1022 self.__withOverapping = False
1023 self.__buffer_spc = 0
1023 self.__buffer_spc = 0
1024 self.__buffer_cspc = 0
1024 self.__buffer_cspc = 0
1025 self.__buffer_dc = 0
1025 self.__buffer_dc = 0
1026
1026
1027 self.__profIndex = 0
1027 self.__profIndex = 0
1028
1028
1029 def putData(self, data_spc, data_cspc, data_dc):
1029 def putData(self, data_spc, data_cspc, data_dc):
1030
1030
1031 """
1031 """
1032 Add a profile to the __buffer_spc and increase in one the __profileIndex
1032 Add a profile to the __buffer_spc and increase in one the __profileIndex
1033
1033
1034 """
1034 """
1035
1035
1036 if not self.__withOverapping:
1036 if not self.__withOverapping:
1037 self.__buffer_spc += data_spc
1037 self.__buffer_spc += data_spc
1038
1038
1039 if data_cspc == None:
1039 if data_cspc == None:
1040 self.__buffer_cspc = None
1040 self.__buffer_cspc = None
1041 else:
1041 else:
1042 self.__buffer_cspc += data_cspc
1042 self.__buffer_cspc += data_cspc
1043
1043
1044 if data_dc == None:
1044 if data_dc == None:
1045 self.__buffer_dc = None
1045 self.__buffer_dc = None
1046 else:
1046 else:
1047 self.__buffer_dc += data_dc
1047 self.__buffer_dc += data_dc
1048
1048
1049 self.__profIndex += 1
1049 self.__profIndex += 1
1050 return
1050 return
1051
1051
1052 #Overlapping data
1052 #Overlapping data
1053 nChannels, nFFTPoints, nHeis = data_spc.shape
1053 nChannels, nFFTPoints, nHeis = data_spc.shape
1054 data_spc = numpy.reshape(data_spc, (1, nChannels, nFFTPoints, nHeis))
1054 data_spc = numpy.reshape(data_spc, (1, nChannels, nFFTPoints, nHeis))
1055 if data_cspc != None:
1055 if data_cspc != None:
1056 data_cspc = numpy.reshape(data_cspc, (1, -1, nFFTPoints, nHeis))
1056 data_cspc = numpy.reshape(data_cspc, (1, -1, nFFTPoints, nHeis))
1057 if data_dc != None:
1057 if data_dc != None:
1058 data_dc = numpy.reshape(data_dc, (1, -1, nHeis))
1058 data_dc = numpy.reshape(data_dc, (1, -1, nHeis))
1059
1059
1060 #If the buffer is empty then it takes the data value
1060 #If the buffer is empty then it takes the data value
1061 if self.__buffer_spc == None:
1061 if self.__buffer_spc == None:
1062 self.__buffer_spc = data_spc
1062 self.__buffer_spc = data_spc
1063
1063
1064 if data_cspc == None:
1064 if data_cspc == None:
1065 self.__buffer_cspc = None
1065 self.__buffer_cspc = None
1066 else:
1066 else:
1067 self.__buffer_cspc += data_cspc
1067 self.__buffer_cspc += data_cspc
1068
1068
1069 if data_dc == None:
1069 if data_dc == None:
1070 self.__buffer_dc = None
1070 self.__buffer_dc = None
1071 else:
1071 else:
1072 self.__buffer_dc += data_dc
1072 self.__buffer_dc += data_dc
1073
1073
1074 self.__profIndex += 1
1074 self.__profIndex += 1
1075 return
1075 return
1076
1076
1077 #If the buffer length is lower than n then stakcing the data value
1077 #If the buffer length is lower than n then stakcing the data value
1078 if self.__profIndex < self.n:
1078 if self.__profIndex < self.n:
1079 self.__buffer_spc = numpy.vstack((self.__buffer_spc, data_spc))
1079 self.__buffer_spc = numpy.vstack((self.__buffer_spc, data_spc))
1080
1080
1081 if data_cspc != None:
1081 if data_cspc != None:
1082 self.__buffer_cspc = numpy.vstack((self.__buffer_cspc, data_cspc))
1082 self.__buffer_cspc = numpy.vstack((self.__buffer_cspc, data_cspc))
1083
1083
1084 if data_dc != None:
1084 if data_dc != None:
1085 self.__buffer_dc = numpy.vstack((self.__buffer_dc, data_dc))
1085 self.__buffer_dc = numpy.vstack((self.__buffer_dc, data_dc))
1086
1086
1087 self.__profIndex += 1
1087 self.__profIndex += 1
1088 return
1088 return
1089
1089
1090 #If the buffer length is equal to n then replacing the last buffer value with the data value
1090 #If the buffer length is equal to n then replacing the last buffer value with the data value
1091 self.__buffer_spc = numpy.roll(self.__buffer_spc, -1, axis=0)
1091 self.__buffer_spc = numpy.roll(self.__buffer_spc, -1, axis=0)
1092 self.__buffer_spc[self.n-1] = data_spc
1092 self.__buffer_spc[self.n-1] = data_spc
1093
1093
1094 if data_cspc != None:
1094 if data_cspc != None:
1095 self.__buffer_cspc = numpy.roll(self.__buffer_cspc, -1, axis=0)
1095 self.__buffer_cspc = numpy.roll(self.__buffer_cspc, -1, axis=0)
1096 self.__buffer_cspc[self.n-1] = data_cspc
1096 self.__buffer_cspc[self.n-1] = data_cspc
1097
1097
1098 if data_dc != None:
1098 if data_dc != None:
1099 self.__buffer_dc = numpy.roll(self.__buffer_dc, -1, axis=0)
1099 self.__buffer_dc = numpy.roll(self.__buffer_dc, -1, axis=0)
1100 self.__buffer_dc[self.n-1] = data_dc
1100 self.__buffer_dc[self.n-1] = data_dc
1101
1101
1102 self.__profIndex = self.n
1102 self.__profIndex = self.n
1103 return
1103 return
1104
1104
1105
1105
1106 def pushData(self):
1106 def pushData(self):
1107 """
1107 """
1108 Return the sum of the last profiles and the profiles used in the sum.
1108 Return the sum of the last profiles and the profiles used in the sum.
1109
1109
1110 Affected:
1110 Affected:
1111
1111
1112 self.__profileIndex
1112 self.__profileIndex
1113
1113
1114 """
1114 """
1115 data_spc = None
1115 data_spc = None
1116 data_cspc = None
1116 data_cspc = None
1117 data_dc = None
1117 data_dc = None
1118
1118
1119 if not self.__withOverapping:
1119 if not self.__withOverapping:
1120 data_spc = self.__buffer_spc
1120 data_spc = self.__buffer_spc
1121 data_cspc = self.__buffer_cspc
1121 data_cspc = self.__buffer_cspc
1122 data_dc = self.__buffer_dc
1122 data_dc = self.__buffer_dc
1123
1123
1124 n = self.__profIndex
1124 n = self.__profIndex
1125
1125
1126 self.__buffer_spc = 0
1126 self.__buffer_spc = 0
1127 self.__buffer_cspc = 0
1127 self.__buffer_cspc = 0
1128 self.__buffer_dc = 0
1128 self.__buffer_dc = 0
1129 self.__profIndex = 0
1129 self.__profIndex = 0
1130
1130
1131 return data_spc, data_cspc, data_dc, n
1131 return data_spc, data_cspc, data_dc, n
1132
1132
1133 #Integration with Overlapping
1133 #Integration with Overlapping
1134 data_spc = numpy.sum(self.__buffer_spc, axis=0)
1134 data_spc = numpy.sum(self.__buffer_spc, axis=0)
1135
1135
1136 if self.__buffer_cspc != None:
1136 if self.__buffer_cspc != None:
1137 data_cspc = numpy.sum(self.__buffer_cspc, axis=0)
1137 data_cspc = numpy.sum(self.__buffer_cspc, axis=0)
1138
1138
1139 if self.__buffer_dc != None:
1139 if self.__buffer_dc != None:
1140 data_dc = numpy.sum(self.__buffer_dc, axis=0)
1140 data_dc = numpy.sum(self.__buffer_dc, axis=0)
1141
1141
1142 n = self.__profIndex
1142 n = self.__profIndex
1143
1143
1144 return data_spc, data_cspc, data_dc, n
1144 return data_spc, data_cspc, data_dc, n
1145
1145
1146 def byProfiles(self, *args):
1146 def byProfiles(self, *args):
1147
1147
1148 self.__dataReady = False
1148 self.__dataReady = False
1149 avgdata_spc = None
1149 avgdata_spc = None
1150 avgdata_cspc = None
1150 avgdata_cspc = None
1151 avgdata_dc = None
1151 avgdata_dc = None
1152 n = None
1152 n = None
1153
1153
1154 self.putData(*args)
1154 self.putData(*args)
1155
1155
1156 if self.__profIndex == self.n:
1156 if self.__profIndex == self.n:
1157
1157
1158 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
1158 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
1159 self.__dataReady = True
1159 self.__dataReady = True
1160
1160
1161 return avgdata_spc, avgdata_cspc, avgdata_dc
1161 return avgdata_spc, avgdata_cspc, avgdata_dc
1162
1162
1163 def byTime(self, datatime, *args):
1163 def byTime(self, datatime, *args):
1164
1164
1165 self.__dataReady = False
1165 self.__dataReady = False
1166 avgdata_spc = None
1166 avgdata_spc = None
1167 avgdata_cspc = None
1167 avgdata_cspc = None
1168 avgdata_dc = None
1168 avgdata_dc = None
1169 n = None
1169 n = None
1170
1170
1171 self.putData(*args)
1171 self.putData(*args)
1172
1172
1173 if (datatime - self.__initime) >= self.__integrationtime:
1173 if (datatime - self.__initime) >= self.__integrationtime:
1174 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
1174 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
1175 self.n = n
1175 self.n = n
1176 self.__dataReady = True
1176 self.__dataReady = True
1177
1177
1178 return avgdata_spc, avgdata_cspc, avgdata_dc
1178 return avgdata_spc, avgdata_cspc, avgdata_dc
1179
1179
1180 def integrate(self, datatime, *args):
1180 def integrate(self, datatime, *args):
1181
1181
1182 if self.__initime == None:
1182 if self.__initime == None:
1183 self.__initime = datatime
1183 self.__initime = datatime
1184
1184
1185 if self.__byTime:
1185 if self.__byTime:
1186 avgdata_spc, avgdata_cspc, avgdata_dc = self.byTime(datatime, *args)
1186 avgdata_spc, avgdata_cspc, avgdata_dc = self.byTime(datatime, *args)
1187 else:
1187 else:
1188 avgdata_spc, avgdata_cspc, avgdata_dc = self.byProfiles(*args)
1188 avgdata_spc, avgdata_cspc, avgdata_dc = self.byProfiles(*args)
1189
1189
1190 self.__lastdatatime = datatime
1190 self.__lastdatatime = datatime
1191
1191
1192 if avgdata_spc == None:
1192 if avgdata_spc == None:
1193 return None, None, None, None
1193 return None, None, None, None
1194
1194
1195 avgdatatime = self.__initime
1195 avgdatatime = self.__initime
1196 self.__timeInterval = (self.__lastdatatime - self.__initime)/(self.n - 1)
1196 self.__timeInterval = (self.__lastdatatime - self.__initime)/(self.n - 1)
1197
1197
1198 deltatime = datatime -self.__lastdatatime
1198 deltatime = datatime -self.__lastdatatime
1199
1199
1200 if not self.__withOverapping:
1200 if not self.__withOverapping:
1201 self.__initime = datatime
1201 self.__initime = datatime
1202 else:
1202 else:
1203 self.__initime += deltatime
1203 self.__initime += deltatime
1204
1204
1205 return avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc
1205 return avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc
1206
1206
1207 def run(self, dataOut, n=None, timeInterval=None, overlapping=False):
1207 def run(self, dataOut, n=None, timeInterval=None, overlapping=False):
1208
1208
1209 if not self.__isConfig:
1209 if not self.__isConfig:
1210 self.setup(n, timeInterval, overlapping)
1210 self.setup(n, timeInterval, overlapping)
1211 self.__isConfig = True
1211 self.__isConfig = True
1212
1212
1213 avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(dataOut.utctime,
1213 avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(dataOut.utctime,
1214 dataOut.data_spc,
1214 dataOut.data_spc,
1215 dataOut.data_cspc,
1215 dataOut.data_cspc,
1216 dataOut.data_dc)
1216 dataOut.data_dc)
1217
1217
1218 # dataOut.timeInterval *= n
1218 # dataOut.timeInterval *= n
1219 dataOut.flagNoData = True
1219 dataOut.flagNoData = True
1220
1220
1221 if self.__dataReady:
1221 if self.__dataReady:
1222
1222
1223 dataOut.data_spc = avgdata_spc
1223 dataOut.data_spc = avgdata_spc
1224 dataOut.data_cspc = avgdata_cspc
1224 dataOut.data_cspc = avgdata_cspc
1225 dataOut.data_dc = avgdata_dc
1225 dataOut.data_dc = avgdata_dc
1226
1226
1227 dataOut.nIncohInt *= self.n
1227 dataOut.nIncohInt *= self.n
1228 dataOut.utctime = avgdatatime
1228 dataOut.utctime = avgdatatime
1229 #dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt * dataOut.nIncohInt * dataOut.nFFTPoints
1229 #dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt * dataOut.nIncohInt * dataOut.nFFTPoints
1230 dataOut.timeInterval = self.__timeInterval*self.n
1230 dataOut.timeInterval = self.__timeInterval*self.n
1231 dataOut.flagNoData = False
1231 dataOut.flagNoData = False
1232
1232
1233 class ProfileSelector(Operation):
1233 class ProfileSelector(Operation):
1234
1234
1235 profileIndex = None
1235 profileIndex = None
1236 # Tamanho total de los perfiles
1236 # Tamanho total de los perfiles
1237 nProfiles = None
1237 nProfiles = None
1238
1238
1239 def __init__(self):
1239 def __init__(self):
1240
1240
1241 self.profileIndex = 0
1241 self.profileIndex = 0
1242
1242
1243 def incIndex(self):
1243 def incIndex(self):
1244 self.profileIndex += 1
1244 self.profileIndex += 1
1245
1245
1246 if self.profileIndex >= self.nProfiles:
1246 if self.profileIndex >= self.nProfiles:
1247 self.profileIndex = 0
1247 self.profileIndex = 0
1248
1248
1249 def isProfileInRange(self, minIndex, maxIndex):
1249 def isProfileInRange(self, minIndex, maxIndex):
1250
1250
1251 if self.profileIndex < minIndex:
1251 if self.profileIndex < minIndex:
1252 return False
1252 return False
1253
1253
1254 if self.profileIndex > maxIndex:
1254 if self.profileIndex > maxIndex:
1255 return False
1255 return False
1256
1256
1257 return True
1257 return True
1258
1258
1259 def isProfileInList(self, profileList):
1259 def isProfileInList(self, profileList):
1260
1260
1261 if self.profileIndex not in profileList:
1261 if self.profileIndex not in profileList:
1262 return False
1262 return False
1263
1263
1264 return True
1264 return True
1265
1265
1266 def run(self, dataOut, profileList=None, profileRangeList=None):
1266 def run(self, dataOut, profileList=None, profileRangeList=None):
1267
1267
1268 dataOut.flagNoData = True
1268 dataOut.flagNoData = True
1269 self.nProfiles = dataOut.nProfiles
1269 self.nProfiles = dataOut.nProfiles
1270
1270
1271 if profileList != None:
1271 if profileList != None:
1272 if self.isProfileInList(profileList):
1272 if self.isProfileInList(profileList):
1273 dataOut.flagNoData = False
1273 dataOut.flagNoData = False
1274
1274
1275 self.incIndex()
1275 self.incIndex()
1276 return 1
1276 return 1
1277
1277
1278
1278
1279 elif profileRangeList != None:
1279 elif profileRangeList != None:
1280 minIndex = profileRangeList[0]
1280 minIndex = profileRangeList[0]
1281 maxIndex = profileRangeList[1]
1281 maxIndex = profileRangeList[1]
1282 if self.isProfileInRange(minIndex, maxIndex):
1282 if self.isProfileInRange(minIndex, maxIndex):
1283 dataOut.flagNoData = False
1283 dataOut.flagNoData = False
1284
1284
1285 self.incIndex()
1285 self.incIndex()
1286 return 1
1286 return 1
1287
1287
1288 else:
1288 else:
1289 raise ValueError, "ProfileSelector needs profileList or profileRangeList"
1289 raise ValueError, "ProfileSelector needs profileList or profileRangeList"
1290
1290
1291 return 0
1291 return 0
1292
1292
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