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jroproc_voltage.py: ProfileSelector, Decoder, Reshape was tested with nTxs != 1 and getblock = True
Miguel Valdez -
r749:2d4c40d861d3
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1 import sys
1 import sys
2 import numpy
2 import numpy
3
3
4 from jroproc_base import ProcessingUnit, Operation
4 from jroproc_base import ProcessingUnit, Operation
5 from schainpy.model.data.jrodata import Voltage
5 from schainpy.model.data.jrodata import Voltage
6
6
7 class VoltageProc(ProcessingUnit):
7 class VoltageProc(ProcessingUnit):
8
8
9
9
10 def __init__(self):
10 def __init__(self):
11
11
12 ProcessingUnit.__init__(self)
12 ProcessingUnit.__init__(self)
13
13
14 # self.objectDict = {}
14 # self.objectDict = {}
15 self.dataOut = Voltage()
15 self.dataOut = Voltage()
16 self.flip = 1
16 self.flip = 1
17
17
18 def run(self):
18 def run(self):
19 if self.dataIn.type == 'AMISR':
19 if self.dataIn.type == 'AMISR':
20 self.__updateObjFromAmisrInput()
20 self.__updateObjFromAmisrInput()
21
21
22 if self.dataIn.type == 'Voltage':
22 if self.dataIn.type == 'Voltage':
23 self.dataOut.copy(self.dataIn)
23 self.dataOut.copy(self.dataIn)
24
24
25 # self.dataOut.copy(self.dataIn)
25 # self.dataOut.copy(self.dataIn)
26
26
27 def __updateObjFromAmisrInput(self):
27 def __updateObjFromAmisrInput(self):
28
28
29 self.dataOut.timeZone = self.dataIn.timeZone
29 self.dataOut.timeZone = self.dataIn.timeZone
30 self.dataOut.dstFlag = self.dataIn.dstFlag
30 self.dataOut.dstFlag = self.dataIn.dstFlag
31 self.dataOut.errorCount = self.dataIn.errorCount
31 self.dataOut.errorCount = self.dataIn.errorCount
32 self.dataOut.useLocalTime = self.dataIn.useLocalTime
32 self.dataOut.useLocalTime = self.dataIn.useLocalTime
33
33
34 self.dataOut.flagNoData = self.dataIn.flagNoData
34 self.dataOut.flagNoData = self.dataIn.flagNoData
35 self.dataOut.data = self.dataIn.data
35 self.dataOut.data = self.dataIn.data
36 self.dataOut.utctime = self.dataIn.utctime
36 self.dataOut.utctime = self.dataIn.utctime
37 self.dataOut.channelList = self.dataIn.channelList
37 self.dataOut.channelList = self.dataIn.channelList
38 # self.dataOut.timeInterval = self.dataIn.timeInterval
38 # self.dataOut.timeInterval = self.dataIn.timeInterval
39 self.dataOut.heightList = self.dataIn.heightList
39 self.dataOut.heightList = self.dataIn.heightList
40 self.dataOut.nProfiles = self.dataIn.nProfiles
40 self.dataOut.nProfiles = self.dataIn.nProfiles
41
41
42 self.dataOut.nCohInt = self.dataIn.nCohInt
42 self.dataOut.nCohInt = self.dataIn.nCohInt
43 self.dataOut.ippSeconds = self.dataIn.ippSeconds
43 self.dataOut.ippSeconds = self.dataIn.ippSeconds
44 self.dataOut.frequency = self.dataIn.frequency
44 self.dataOut.frequency = self.dataIn.frequency
45
45
46 self.dataOut.azimuth = self.dataIn.azimuth
46 self.dataOut.azimuth = self.dataIn.azimuth
47 self.dataOut.zenith = self.dataIn.zenith
47 self.dataOut.zenith = self.dataIn.zenith
48
48
49 self.dataOut.beam.codeList = self.dataIn.beam.codeList
49 self.dataOut.beam.codeList = self.dataIn.beam.codeList
50 self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList
50 self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList
51 self.dataOut.beam.zenithList = self.dataIn.beam.zenithList
51 self.dataOut.beam.zenithList = self.dataIn.beam.zenithList
52 #
52 #
53 # pass#
53 # pass#
54 #
54 #
55 # def init(self):
55 # def init(self):
56 #
56 #
57 #
57 #
58 # if self.dataIn.type == 'AMISR':
58 # if self.dataIn.type == 'AMISR':
59 # self.__updateObjFromAmisrInput()
59 # self.__updateObjFromAmisrInput()
60 #
60 #
61 # if self.dataIn.type == 'Voltage':
61 # if self.dataIn.type == 'Voltage':
62 # self.dataOut.copy(self.dataIn)
62 # self.dataOut.copy(self.dataIn)
63 # # No necesita copiar en cada init() los atributos de dataIn
63 # # No necesita copiar en cada init() los atributos de dataIn
64 # # la copia deberia hacerse por cada nuevo bloque de datos
64 # # la copia deberia hacerse por cada nuevo bloque de datos
65
65
66 def selectChannels(self, channelList):
66 def selectChannels(self, channelList):
67
67
68 channelIndexList = []
68 channelIndexList = []
69
69
70 for channel in channelList:
70 for channel in channelList:
71 if channel not in self.dataOut.channelList:
71 if channel not in self.dataOut.channelList:
72 raise ValueError, "Channel %d is not in %s" %(channel, str(self.dataOut.channelList))
72 raise ValueError, "Channel %d is not in %s" %(channel, str(self.dataOut.channelList))
73
73
74 index = self.dataOut.channelList.index(channel)
74 index = self.dataOut.channelList.index(channel)
75 channelIndexList.append(index)
75 channelIndexList.append(index)
76
76
77 self.selectChannelsByIndex(channelIndexList)
77 self.selectChannelsByIndex(channelIndexList)
78
78
79 def selectChannelsByIndex(self, channelIndexList):
79 def selectChannelsByIndex(self, channelIndexList):
80 """
80 """
81 Selecciona un bloque de datos en base a canales segun el channelIndexList
81 Selecciona un bloque de datos en base a canales segun el channelIndexList
82
82
83 Input:
83 Input:
84 channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
84 channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
85
85
86 Affected:
86 Affected:
87 self.dataOut.data
87 self.dataOut.data
88 self.dataOut.channelIndexList
88 self.dataOut.channelIndexList
89 self.dataOut.nChannels
89 self.dataOut.nChannels
90 self.dataOut.m_ProcessingHeader.totalSpectra
90 self.dataOut.m_ProcessingHeader.totalSpectra
91 self.dataOut.systemHeaderObj.numChannels
91 self.dataOut.systemHeaderObj.numChannels
92 self.dataOut.m_ProcessingHeader.blockSize
92 self.dataOut.m_ProcessingHeader.blockSize
93
93
94 Return:
94 Return:
95 None
95 None
96 """
96 """
97
97
98 for channelIndex in channelIndexList:
98 for channelIndex in channelIndexList:
99 if channelIndex not in self.dataOut.channelIndexList:
99 if channelIndex not in self.dataOut.channelIndexList:
100 print channelIndexList
100 print channelIndexList
101 raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex
101 raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex
102
102
103 if self.dataOut.flagDataAsBlock:
103 if self.dataOut.flagDataAsBlock:
104 """
104 """
105 Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
105 Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
106 """
106 """
107 data = self.dataOut.data[channelIndexList,:,:]
107 data = self.dataOut.data[channelIndexList,:,:]
108 else:
108 else:
109 data = self.dataOut.data[channelIndexList,:]
109 data = self.dataOut.data[channelIndexList,:]
110
110
111 self.dataOut.data = data
111 self.dataOut.data = data
112 self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
112 self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
113 # self.dataOut.nChannels = nChannels
113 # self.dataOut.nChannels = nChannels
114
114
115 return 1
115 return 1
116
116
117 def selectHeights(self, minHei=None, maxHei=None):
117 def selectHeights(self, minHei=None, maxHei=None):
118 """
118 """
119 Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
119 Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
120 minHei <= height <= maxHei
120 minHei <= height <= maxHei
121
121
122 Input:
122 Input:
123 minHei : valor minimo de altura a considerar
123 minHei : valor minimo de altura a considerar
124 maxHei : valor maximo de altura a considerar
124 maxHei : valor maximo de altura a considerar
125
125
126 Affected:
126 Affected:
127 Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
127 Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
128
128
129 Return:
129 Return:
130 1 si el metodo se ejecuto con exito caso contrario devuelve 0
130 1 si el metodo se ejecuto con exito caso contrario devuelve 0
131 """
131 """
132
132
133 if minHei == None:
133 if minHei == None:
134 minHei = self.dataOut.heightList[0]
134 minHei = self.dataOut.heightList[0]
135
135
136 if maxHei == None:
136 if maxHei == None:
137 maxHei = self.dataOut.heightList[-1]
137 maxHei = self.dataOut.heightList[-1]
138
138
139 if (minHei < self.dataOut.heightList[0]):
139 if (minHei < self.dataOut.heightList[0]):
140 minHei = self.dataOut.heightList[0]
140 minHei = self.dataOut.heightList[0]
141
141
142 if (maxHei > self.dataOut.heightList[-1]):
142 if (maxHei > self.dataOut.heightList[-1]):
143 maxHei = self.dataOut.heightList[-1]
143 maxHei = self.dataOut.heightList[-1]
144
144
145 minIndex = 0
145 minIndex = 0
146 maxIndex = 0
146 maxIndex = 0
147 heights = self.dataOut.heightList
147 heights = self.dataOut.heightList
148
148
149 inda = numpy.where(heights >= minHei)
149 inda = numpy.where(heights >= minHei)
150 indb = numpy.where(heights <= maxHei)
150 indb = numpy.where(heights <= maxHei)
151
151
152 try:
152 try:
153 minIndex = inda[0][0]
153 minIndex = inda[0][0]
154 except:
154 except:
155 minIndex = 0
155 minIndex = 0
156
156
157 try:
157 try:
158 maxIndex = indb[0][-1]
158 maxIndex = indb[0][-1]
159 except:
159 except:
160 maxIndex = len(heights)
160 maxIndex = len(heights)
161
161
162 self.selectHeightsByIndex(minIndex, maxIndex)
162 self.selectHeightsByIndex(minIndex, maxIndex)
163
163
164 return 1
164 return 1
165
165
166
166
167 def selectHeightsByIndex(self, minIndex, maxIndex):
167 def selectHeightsByIndex(self, minIndex, maxIndex):
168 """
168 """
169 Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
169 Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
170 minIndex <= index <= maxIndex
170 minIndex <= index <= maxIndex
171
171
172 Input:
172 Input:
173 minIndex : valor de indice minimo de altura a considerar
173 minIndex : valor de indice minimo de altura a considerar
174 maxIndex : valor de indice maximo de altura a considerar
174 maxIndex : valor de indice maximo de altura a considerar
175
175
176 Affected:
176 Affected:
177 self.dataOut.data
177 self.dataOut.data
178 self.dataOut.heightList
178 self.dataOut.heightList
179
179
180 Return:
180 Return:
181 1 si el metodo se ejecuto con exito caso contrario devuelve 0
181 1 si el metodo se ejecuto con exito caso contrario devuelve 0
182 """
182 """
183
183
184 if (minIndex < 0) or (minIndex > maxIndex):
184 if (minIndex < 0) or (minIndex > maxIndex):
185 raise ValueError, "Height index range (%d,%d) is not valid" % (minIndex, maxIndex)
185 raise ValueError, "Height index range (%d,%d) is not valid" % (minIndex, maxIndex)
186
186
187 if (maxIndex >= self.dataOut.nHeights):
187 if (maxIndex >= self.dataOut.nHeights):
188 maxIndex = self.dataOut.nHeights
188 maxIndex = self.dataOut.nHeights
189
189
190 #voltage
190 #voltage
191 if self.dataOut.flagDataAsBlock:
191 if self.dataOut.flagDataAsBlock:
192 """
192 """
193 Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
193 Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
194 """
194 """
195 data = self.dataOut.data[:,:, minIndex:maxIndex]
195 data = self.dataOut.data[:,:, minIndex:maxIndex]
196 else:
196 else:
197 data = self.dataOut.data[:, minIndex:maxIndex]
197 data = self.dataOut.data[:, minIndex:maxIndex]
198
198
199 # firstHeight = self.dataOut.heightList[minIndex]
199 # firstHeight = self.dataOut.heightList[minIndex]
200
200
201 self.dataOut.data = data
201 self.dataOut.data = data
202 self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex]
202 self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex]
203
203
204 if self.dataOut.nHeights <= 1:
204 if self.dataOut.nHeights <= 1:
205 raise ValueError, "selectHeights: Too few heights. Current number of heights is %d" %(self.dataOut.nHeights)
205 raise ValueError, "selectHeights: Too few heights. Current number of heights is %d" %(self.dataOut.nHeights)
206
206
207 return 1
207 return 1
208
208
209
209
210 def filterByHeights(self, window):
210 def filterByHeights(self, window):
211
211
212 deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0]
212 deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0]
213
213
214 if window == None:
214 if window == None:
215 window = (self.dataOut.radarControllerHeaderObj.txA/self.dataOut.radarControllerHeaderObj.nBaud) / deltaHeight
215 window = (self.dataOut.radarControllerHeaderObj.txA/self.dataOut.radarControllerHeaderObj.nBaud) / deltaHeight
216
216
217 newdelta = deltaHeight * window
217 newdelta = deltaHeight * window
218 r = self.dataOut.nHeights % window
218 r = self.dataOut.nHeights % window
219 newheights = (self.dataOut.nHeights-r)/window
219 newheights = (self.dataOut.nHeights-r)/window
220
220
221 if newheights <= 1:
221 if newheights <= 1:
222 raise ValueError, "filterByHeights: Too few heights. Current number of heights is %d and window is %d" %(self.dataOut.nHeights, window)
222 raise ValueError, "filterByHeights: Too few heights. Current number of heights is %d and window is %d" %(self.dataOut.nHeights, window)
223
223
224 if self.dataOut.flagDataAsBlock:
224 if self.dataOut.flagDataAsBlock:
225 """
225 """
226 Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
226 Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
227 """
227 """
228 buffer = self.dataOut.data[:, :, 0:self.dataOut.nHeights-r]
228 buffer = self.dataOut.data[:, :, 0:self.dataOut.nHeights-r]
229 buffer = buffer.reshape(self.dataOut.nChannels,self.dataOut.nProfiles,self.dataOut.nHeights/window,window)
229 buffer = buffer.reshape(self.dataOut.nChannels,self.dataOut.nProfiles,self.dataOut.nHeights/window,window)
230 buffer = numpy.sum(buffer,3)
230 buffer = numpy.sum(buffer,3)
231
231
232 else:
232 else:
233 buffer = self.dataOut.data[:,0:self.dataOut.nHeights-r]
233 buffer = self.dataOut.data[:,0:self.dataOut.nHeights-r]
234 buffer = buffer.reshape(self.dataOut.nChannels,self.dataOut.nHeights/window,window)
234 buffer = buffer.reshape(self.dataOut.nChannels,self.dataOut.nHeights/window,window)
235 buffer = numpy.sum(buffer,2)
235 buffer = numpy.sum(buffer,2)
236
236
237 self.dataOut.data = buffer
237 self.dataOut.data = buffer
238 self.dataOut.heightList = self.dataOut.heightList[0] + numpy.arange( newheights )*newdelta
238 self.dataOut.heightList = self.dataOut.heightList[0] + numpy.arange( newheights )*newdelta
239 self.dataOut.windowOfFilter = window
239 self.dataOut.windowOfFilter = window
240
240
241 def setH0(self, h0, deltaHeight = None):
241 def setH0(self, h0, deltaHeight = None):
242
242
243 if not deltaHeight:
243 if not deltaHeight:
244 deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0]
244 deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0]
245
245
246 nHeights = self.dataOut.nHeights
246 nHeights = self.dataOut.nHeights
247
247
248 newHeiRange = h0 + numpy.arange(nHeights)*deltaHeight
248 newHeiRange = h0 + numpy.arange(nHeights)*deltaHeight
249
249
250 self.dataOut.heightList = newHeiRange
250 self.dataOut.heightList = newHeiRange
251
251
252 def deFlip(self, channelList = []):
252 def deFlip(self, channelList = []):
253
253
254 data = self.dataOut.data.copy()
254 data = self.dataOut.data.copy()
255
255
256 if self.dataOut.flagDataAsBlock:
256 if self.dataOut.flagDataAsBlock:
257 flip = self.flip
257 flip = self.flip
258 profileList = range(self.dataOut.nProfiles)
258 profileList = range(self.dataOut.nProfiles)
259
259
260 if not channelList:
260 if not channelList:
261 for thisProfile in profileList:
261 for thisProfile in profileList:
262 data[:,thisProfile,:] = data[:,thisProfile,:]*flip
262 data[:,thisProfile,:] = data[:,thisProfile,:]*flip
263 flip *= -1.0
263 flip *= -1.0
264 else:
264 else:
265 for thisChannel in channelList:
265 for thisChannel in channelList:
266 if thisChannel not in self.dataOut.channelList:
266 if thisChannel not in self.dataOut.channelList:
267 continue
267 continue
268
268
269 for thisProfile in profileList:
269 for thisProfile in profileList:
270 data[thisChannel,thisProfile,:] = data[thisChannel,thisProfile,:]*flip
270 data[thisChannel,thisProfile,:] = data[thisChannel,thisProfile,:]*flip
271 flip *= -1.0
271 flip *= -1.0
272
272
273 self.flip = flip
273 self.flip = flip
274
274
275 else:
275 else:
276 if not channelList:
276 if not channelList:
277 data[:,:] = data[:,:]*self.flip
277 data[:,:] = data[:,:]*self.flip
278 else:
278 else:
279 for thisChannel in channelList:
279 for thisChannel in channelList:
280 if thisChannel not in self.dataOut.channelList:
280 if thisChannel not in self.dataOut.channelList:
281 continue
281 continue
282
282
283 data[thisChannel,:] = data[thisChannel,:]*self.flip
283 data[thisChannel,:] = data[thisChannel,:]*self.flip
284
284
285 self.flip *= -1.
285 self.flip *= -1.
286
286
287 self.dataOut.data = data
287 self.dataOut.data = data
288
288
289 def setRadarFrequency(self, frequency=None):
289 def setRadarFrequency(self, frequency=None):
290
290
291 if frequency != None:
291 if frequency != None:
292 self.dataOut.frequency = frequency
292 self.dataOut.frequency = frequency
293
293
294 return 1
294 return 1
295
295
296 class CohInt(Operation):
296 class CohInt(Operation):
297
297
298 isConfig = False
298 isConfig = False
299
299
300 __profIndex = 0
300 __profIndex = 0
301 __withOverapping = False
301 __withOverapping = False
302
302
303 __byTime = False
303 __byTime = False
304 __initime = None
304 __initime = None
305 __lastdatatime = None
305 __lastdatatime = None
306 __integrationtime = None
306 __integrationtime = None
307
307
308 __buffer = None
308 __buffer = None
309
309
310 __dataReady = False
310 __dataReady = False
311
311
312 n = None
312 n = None
313
313
314
314
315 def __init__(self):
315 def __init__(self):
316
316
317 Operation.__init__(self)
317 Operation.__init__(self)
318
318
319 # self.isConfig = False
319 # self.isConfig = False
320
320
321 def setup(self, n=None, timeInterval=None, overlapping=False, byblock=False):
321 def setup(self, n=None, timeInterval=None, overlapping=False, byblock=False):
322 """
322 """
323 Set the parameters of the integration class.
323 Set the parameters of the integration class.
324
324
325 Inputs:
325 Inputs:
326
326
327 n : Number of coherent integrations
327 n : Number of coherent integrations
328 timeInterval : Time of integration. If the parameter "n" is selected this one does not work
328 timeInterval : Time of integration. If the parameter "n" is selected this one does not work
329 overlapping :
329 overlapping :
330
330
331 """
331 """
332
332
333 self.__initime = None
333 self.__initime = None
334 self.__lastdatatime = 0
334 self.__lastdatatime = 0
335 self.__buffer = None
335 self.__buffer = None
336 self.__dataReady = False
336 self.__dataReady = False
337 self.byblock = byblock
337 self.byblock = byblock
338
338
339 if n == None and timeInterval == None:
339 if n == None and timeInterval == None:
340 raise ValueError, "n or timeInterval should be specified ..."
340 raise ValueError, "n or timeInterval should be specified ..."
341
341
342 if n != None:
342 if n != None:
343 self.n = n
343 self.n = n
344 self.__byTime = False
344 self.__byTime = False
345 else:
345 else:
346 self.__integrationtime = timeInterval #* 60. #if (type(timeInterval)!=integer) -> change this line
346 self.__integrationtime = timeInterval #* 60. #if (type(timeInterval)!=integer) -> change this line
347 self.n = 9999
347 self.n = 9999
348 self.__byTime = True
348 self.__byTime = True
349
349
350 if overlapping:
350 if overlapping:
351 self.__withOverapping = True
351 self.__withOverapping = True
352 self.__buffer = None
352 self.__buffer = None
353 else:
353 else:
354 self.__withOverapping = False
354 self.__withOverapping = False
355 self.__buffer = 0
355 self.__buffer = 0
356
356
357 self.__profIndex = 0
357 self.__profIndex = 0
358
358
359 def putData(self, data):
359 def putData(self, data):
360
360
361 """
361 """
362 Add a profile to the __buffer and increase in one the __profileIndex
362 Add a profile to the __buffer and increase in one the __profileIndex
363
363
364 """
364 """
365
365
366 if not self.__withOverapping:
366 if not self.__withOverapping:
367 self.__buffer += data.copy()
367 self.__buffer += data.copy()
368 self.__profIndex += 1
368 self.__profIndex += 1
369 return
369 return
370
370
371 #Overlapping data
371 #Overlapping data
372 nChannels, nHeis = data.shape
372 nChannels, nHeis = data.shape
373 data = numpy.reshape(data, (1, nChannels, nHeis))
373 data = numpy.reshape(data, (1, nChannels, nHeis))
374
374
375 #If the buffer is empty then it takes the data value
375 #If the buffer is empty then it takes the data value
376 if self.__buffer is None:
376 if self.__buffer is None:
377 self.__buffer = data
377 self.__buffer = data
378 self.__profIndex += 1
378 self.__profIndex += 1
379 return
379 return
380
380
381 #If the buffer length is lower than n then stakcing the data value
381 #If the buffer length is lower than n then stakcing the data value
382 if self.__profIndex < self.n:
382 if self.__profIndex < self.n:
383 self.__buffer = numpy.vstack((self.__buffer, data))
383 self.__buffer = numpy.vstack((self.__buffer, data))
384 self.__profIndex += 1
384 self.__profIndex += 1
385 return
385 return
386
386
387 #If the buffer length is equal to n then replacing the last buffer value with the data value
387 #If the buffer length is equal to n then replacing the last buffer value with the data value
388 self.__buffer = numpy.roll(self.__buffer, -1, axis=0)
388 self.__buffer = numpy.roll(self.__buffer, -1, axis=0)
389 self.__buffer[self.n-1] = data
389 self.__buffer[self.n-1] = data
390 self.__profIndex = self.n
390 self.__profIndex = self.n
391 return
391 return
392
392
393
393
394 def pushData(self):
394 def pushData(self):
395 """
395 """
396 Return the sum of the last profiles and the profiles used in the sum.
396 Return the sum of the last profiles and the profiles used in the sum.
397
397
398 Affected:
398 Affected:
399
399
400 self.__profileIndex
400 self.__profileIndex
401
401
402 """
402 """
403
403
404 if not self.__withOverapping:
404 if not self.__withOverapping:
405 data = self.__buffer
405 data = self.__buffer
406 n = self.__profIndex
406 n = self.__profIndex
407
407
408 self.__buffer = 0
408 self.__buffer = 0
409 self.__profIndex = 0
409 self.__profIndex = 0
410
410
411 return data, n
411 return data, n
412
412
413 #Integration with Overlapping
413 #Integration with Overlapping
414 data = numpy.sum(self.__buffer, axis=0)
414 data = numpy.sum(self.__buffer, axis=0)
415 n = self.__profIndex
415 n = self.__profIndex
416
416
417 return data, n
417 return data, n
418
418
419 def byProfiles(self, data):
419 def byProfiles(self, data):
420
420
421 self.__dataReady = False
421 self.__dataReady = False
422 avgdata = None
422 avgdata = None
423 # n = None
423 # n = None
424
424
425 self.putData(data)
425 self.putData(data)
426
426
427 if self.__profIndex == self.n:
427 if self.__profIndex == self.n:
428
428
429 avgdata, n = self.pushData()
429 avgdata, n = self.pushData()
430 self.__dataReady = True
430 self.__dataReady = True
431
431
432 return avgdata
432 return avgdata
433
433
434 def byTime(self, data, datatime):
434 def byTime(self, data, datatime):
435
435
436 self.__dataReady = False
436 self.__dataReady = False
437 avgdata = None
437 avgdata = None
438 n = None
438 n = None
439
439
440 self.putData(data)
440 self.putData(data)
441
441
442 if (datatime - self.__initime) >= self.__integrationtime:
442 if (datatime - self.__initime) >= self.__integrationtime:
443 avgdata, n = self.pushData()
443 avgdata, n = self.pushData()
444 self.n = n
444 self.n = n
445 self.__dataReady = True
445 self.__dataReady = True
446
446
447 return avgdata
447 return avgdata
448
448
449 def integrate(self, data, datatime=None):
449 def integrate(self, data, datatime=None):
450
450
451 if self.__initime == None:
451 if self.__initime == None:
452 self.__initime = datatime
452 self.__initime = datatime
453
453
454 if self.__byTime:
454 if self.__byTime:
455 avgdata = self.byTime(data, datatime)
455 avgdata = self.byTime(data, datatime)
456 else:
456 else:
457 avgdata = self.byProfiles(data)
457 avgdata = self.byProfiles(data)
458
458
459
459
460 self.__lastdatatime = datatime
460 self.__lastdatatime = datatime
461
461
462 if avgdata is None:
462 if avgdata is None:
463 return None, None
463 return None, None
464
464
465 avgdatatime = self.__initime
465 avgdatatime = self.__initime
466
466
467 deltatime = datatime -self.__lastdatatime
467 deltatime = datatime -self.__lastdatatime
468
468
469 if not self.__withOverapping:
469 if not self.__withOverapping:
470 self.__initime = datatime
470 self.__initime = datatime
471 else:
471 else:
472 self.__initime += deltatime
472 self.__initime += deltatime
473
473
474 return avgdata, avgdatatime
474 return avgdata, avgdatatime
475
475
476 def integrateByBlock(self, dataOut):
476 def integrateByBlock(self, dataOut):
477
477
478 times = int(dataOut.data.shape[1]/self.n)
478 times = int(dataOut.data.shape[1]/self.n)
479 avgdata = numpy.zeros((dataOut.nChannels, times, dataOut.nHeights), dtype=numpy.complex)
479 avgdata = numpy.zeros((dataOut.nChannels, times, dataOut.nHeights), dtype=numpy.complex)
480
480
481 id_min = 0
481 id_min = 0
482 id_max = self.n
482 id_max = self.n
483
483
484 for i in range(times):
484 for i in range(times):
485 junk = dataOut.data[:,id_min:id_max,:]
485 junk = dataOut.data[:,id_min:id_max,:]
486 avgdata[:,i,:] = junk.sum(axis=1)
486 avgdata[:,i,:] = junk.sum(axis=1)
487 id_min += self.n
487 id_min += self.n
488 id_max += self.n
488 id_max += self.n
489
489
490 timeInterval = dataOut.ippSeconds*self.n
490 timeInterval = dataOut.ippSeconds*self.n
491 avgdatatime = (times - 1) * timeInterval + dataOut.utctime
491 avgdatatime = (times - 1) * timeInterval + dataOut.utctime
492 self.__dataReady = True
492 self.__dataReady = True
493 return avgdata, avgdatatime
493 return avgdata, avgdatatime
494
494
495 def run(self, dataOut, **kwargs):
495 def run(self, dataOut, **kwargs):
496
496
497 if not self.isConfig:
497 if not self.isConfig:
498 self.setup(**kwargs)
498 self.setup(**kwargs)
499 self.isConfig = True
499 self.isConfig = True
500
500
501 if dataOut.flagDataAsBlock:
501 if dataOut.flagDataAsBlock:
502 """
502 """
503 Si la data es leida por bloques, dimension = [nChannels, nProfiles, nHeis]
503 Si la data es leida por bloques, dimension = [nChannels, nProfiles, nHeis]
504 """
504 """
505 avgdata, avgdatatime = self.integrateByBlock(dataOut)
505 avgdata, avgdatatime = self.integrateByBlock(dataOut)
506 dataOut.nProfiles /= self.n
506 dataOut.nProfiles /= self.n
507 else:
507 else:
508 avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime)
508 avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime)
509
509
510 # dataOut.timeInterval *= n
510 # dataOut.timeInterval *= n
511 dataOut.flagNoData = True
511 dataOut.flagNoData = True
512
512
513 if self.__dataReady:
513 if self.__dataReady:
514 dataOut.data = avgdata
514 dataOut.data = avgdata
515 dataOut.nCohInt *= self.n
515 dataOut.nCohInt *= self.n
516 dataOut.utctime = avgdatatime
516 dataOut.utctime = avgdatatime
517 # dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt
517 # dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt
518 dataOut.flagNoData = False
518 dataOut.flagNoData = False
519
519
520 class Decoder(Operation):
520 class Decoder(Operation):
521
521
522 isConfig = False
522 isConfig = False
523 __profIndex = 0
523 __profIndex = 0
524
524
525 code = None
525 code = None
526
526
527 nCode = None
527 nCode = None
528 nBaud = None
528 nBaud = None
529
529
530
530
531 def __init__(self):
531 def __init__(self):
532
532
533 Operation.__init__(self)
533 Operation.__init__(self)
534
534
535 self.times = None
535 self.times = None
536 self.osamp = None
536 self.osamp = None
537 # self.__setValues = False
537 # self.__setValues = False
538 self.isConfig = False
538 self.isConfig = False
539
539
540 def setup(self, code, osamp, dataOut):
540 def setup(self, code, osamp, dataOut):
541
541
542 self.__profIndex = 0
542 self.__profIndex = 0
543
543
544 self.code = code
544 self.code = code
545
545
546 self.nCode = len(code)
546 self.nCode = len(code)
547 self.nBaud = len(code[0])
547 self.nBaud = len(code[0])
548
548
549 if (osamp != None) and (osamp >1):
549 if (osamp != None) and (osamp >1):
550 self.osamp = osamp
550 self.osamp = osamp
551 self.code = numpy.repeat(code, repeats=self.osamp, axis=1)
551 self.code = numpy.repeat(code, repeats=self.osamp, axis=1)
552 self.nBaud = self.nBaud*self.osamp
552 self.nBaud = self.nBaud*self.osamp
553
553
554 self.__nChannels = dataOut.nChannels
554 self.__nChannels = dataOut.nChannels
555 self.__nProfiles = dataOut.nProfiles
555 self.__nProfiles = dataOut.nProfiles
556 self.__nHeis = dataOut.nHeights
556 self.__nHeis = dataOut.nHeights
557
557
558 if self.__nHeis < self.nBaud:
558 if self.__nHeis < self.nBaud:
559 raise ValueError, 'Number of heights (%d) should be greater than number of bauds (%d)' %(self.__nHeis, self.nBaud)
559 raise ValueError, 'Number of heights (%d) should be greater than number of bauds (%d)' %(self.__nHeis, self.nBaud)
560
560
561 #Frequency
561 #Frequency
562 __codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=numpy.complex)
562 __codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=numpy.complex)
563
563
564 __codeBuffer[:,0:self.nBaud] = self.code
564 __codeBuffer[:,0:self.nBaud] = self.code
565
565
566 self.fft_code = numpy.conj(numpy.fft.fft(__codeBuffer, axis=1))
566 self.fft_code = numpy.conj(numpy.fft.fft(__codeBuffer, axis=1))
567
567
568 if dataOut.flagDataAsBlock:
568 if dataOut.flagDataAsBlock:
569
569
570 self.ndatadec = self.__nHeis #- self.nBaud + 1
570 self.ndatadec = self.__nHeis #- self.nBaud + 1
571
571
572 self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=numpy.complex)
572 self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=numpy.complex)
573
573
574 else:
574 else:
575
575
576 #Time
576 #Time
577 self.ndatadec = self.__nHeis #- self.nBaud + 1
577 self.ndatadec = self.__nHeis #- self.nBaud + 1
578
578
579 self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=numpy.complex)
579 self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=numpy.complex)
580
580
581 def __convolutionInFreq(self, data):
581 def __convolutionInFreq(self, data):
582
582
583 fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
583 fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
584
584
585 fft_data = numpy.fft.fft(data, axis=1)
585 fft_data = numpy.fft.fft(data, axis=1)
586
586
587 conv = fft_data*fft_code
587 conv = fft_data*fft_code
588
588
589 data = numpy.fft.ifft(conv,axis=1)
589 data = numpy.fft.ifft(conv,axis=1)
590
590
591 return data
591 return data
592
592
593 def __convolutionInFreqOpt(self, data):
593 def __convolutionInFreqOpt(self, data):
594
594
595 raise NotImplementedError
595 raise NotImplementedError
596
596
597 def __convolutionInTime(self, data):
597 def __convolutionInTime(self, data):
598
598
599 code = self.code[self.__profIndex]
599 code = self.code[self.__profIndex]
600
600
601 for i in range(self.__nChannels):
601 for i in range(self.__nChannels):
602 self.datadecTime[i,:] = numpy.correlate(data[i,:], code, mode='full')[self.nBaud-1:]
602 self.datadecTime[i,:] = numpy.correlate(data[i,:], code, mode='full')[self.nBaud-1:]
603
603
604 return self.datadecTime
604 return self.datadecTime
605
605
606 def __convolutionByBlockInTime(self, data):
606 def __convolutionByBlockInTime(self, data):
607
607
608 repetitions = self.__nProfiles / self.nCode
608 repetitions = self.__nProfiles / self.nCode
609
609
610 junk = numpy.lib.stride_tricks.as_strided(self.code, (repetitions, self.code.size), (0, self.code.itemsize))
610 junk = numpy.lib.stride_tricks.as_strided(self.code, (repetitions, self.code.size), (0, self.code.itemsize))
611 junk = junk.flatten()
611 junk = junk.flatten()
612 code_block = numpy.reshape(junk, (self.nCode*repetitions, self.nBaud))
612 code_block = numpy.reshape(junk, (self.nCode*repetitions, self.nBaud))
613
613
614 for i in range(self.__nChannels):
614 for i in range(self.__nChannels):
615 for j in range(self.__nProfiles):
615 for j in range(self.__nProfiles):
616 self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:]
616 self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:]
617
617
618 return self.datadecTime
618 return self.datadecTime
619
619
620 def __convolutionByBlockInFreq(self, data):
620 def __convolutionByBlockInFreq(self, data):
621
621
622 raise NotImplementedError, "Decoder by frequency fro Blocks not implemented"
623
624
622 fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
625 fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
623
626
624 fft_data = numpy.fft.fft(data, axis=2)
627 fft_data = numpy.fft.fft(data, axis=2)
625
628
626 conv = fft_data*fft_code
629 conv = fft_data*fft_code
627
630
628 data = numpy.fft.ifft(conv,axis=2)
631 data = numpy.fft.ifft(conv,axis=2)
629
632
630 return data
633 return data
631
634
632 def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0, osamp=None, times=None):
635 def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0, osamp=None, times=None):
633
636
634 if dataOut.flagDecodeData:
637 if dataOut.flagDecodeData:
635 print "This data is already decoded, recoding again ..."
638 print "This data is already decoded, recoding again ..."
636
639
637 if not self.isConfig:
640 if not self.isConfig:
638
641
639 if code is None:
642 if code is None:
640 if dataOut.code is None:
643 if dataOut.code is None:
641 raise ValueError, "Code could not be read from %s instance. Enter a value in Code parameter" %dataOut.type
644 raise ValueError, "Code could not be read from %s instance. Enter a value in Code parameter" %dataOut.type
642
645
643 code = dataOut.code
646 code = dataOut.code
644 else:
647 else:
645 code = numpy.array(code).reshape(nCode,nBaud)
648 code = numpy.array(code).reshape(nCode,nBaud)
646
649
647 self.setup(code, osamp, dataOut)
650 self.setup(code, osamp, dataOut)
648
651
649 self.isConfig = True
652 self.isConfig = True
650
653
651 if mode == 3:
654 if mode == 3:
652 sys.stderr.write("Decoder Warning: mode=%d is not valid, using mode=0\n" %mode)
655 sys.stderr.write("Decoder Warning: mode=%d is not valid, using mode=0\n" %mode)
653
656
657 if times != None:
658 sys.stderr.write("Decoder Warning: Argument 'times' in not used anymore\n")
654
659
655 if self.code is None:
660 if self.code is None:
656 print "Fail decoding: Code is not defined."
661 print "Fail decoding: Code is not defined."
657 return
662 return
658
663
659 datadec = None
664 datadec = None
660
665 if mode == 3:
666 mode = 0
667
661 if dataOut.flagDataAsBlock:
668 if dataOut.flagDataAsBlock:
662 """
669 """
663 Decoding when data have been read as block,
670 Decoding when data have been read as block,
664 """
671 """
665 if mode == 3:
666 mode = 0
667
672
668 if mode == 0:
673 if mode == 0:
669 datadec = self.__convolutionByBlockInTime(dataOut.data)
674 datadec = self.__convolutionByBlockInTime(dataOut.data)
670 if mode == 1:
675 if mode == 1:
671 datadec = self.__convolutionByBlockInFreq(dataOut.data)
676 datadec = self.__convolutionByBlockInFreq(dataOut.data)
672 else:
677 else:
673 """
678 """
674 Decoding when data have been read profile by profile
679 Decoding when data have been read profile by profile
675 """
680 """
676 if mode == 0:
681 if mode == 0:
677 datadec = self.__convolutionInTime(dataOut.data)
682 datadec = self.__convolutionInTime(dataOut.data)
678
683
679 if mode == 1:
684 if mode == 1:
680 datadec = self.__convolutionInFreq(dataOut.data)
685 datadec = self.__convolutionInFreq(dataOut.data)
681
686
682 if mode == 2:
687 if mode == 2:
683 datadec = self.__convolutionInFreqOpt(dataOut.data)
688 datadec = self.__convolutionInFreqOpt(dataOut.data)
684
689
685 if datadec is None:
690 if datadec is None:
686 raise ValueError, "Codification mode selected is not valid: mode=%d. Try selecting 0 or 1" %mode
691 raise ValueError, "Codification mode selected is not valid: mode=%d. Try selecting 0 or 1" %mode
687
692
688 dataOut.code = self.code
693 dataOut.code = self.code
689 dataOut.nCode = self.nCode
694 dataOut.nCode = self.nCode
690 dataOut.nBaud = self.nBaud
695 dataOut.nBaud = self.nBaud
691
696
692 dataOut.data = datadec
697 dataOut.data = datadec
693
698
694 dataOut.heightList = dataOut.heightList[0:datadec.shape[-1]]
699 dataOut.heightList = dataOut.heightList[0:datadec.shape[-1]]
695
700
696 dataOut.flagDecodeData = True #asumo q la data esta decodificada
701 dataOut.flagDecodeData = True #asumo q la data esta decodificada
697
702
698 if self.__profIndex == self.nCode-1:
703 if self.__profIndex == self.nCode-1:
699 self.__profIndex = 0
704 self.__profIndex = 0
700 return 1
705 return 1
701
706
702 self.__profIndex += 1
707 self.__profIndex += 1
703
708
704 return 1
709 return 1
705 # dataOut.flagDeflipData = True #asumo q la data no esta sin flip
710 # dataOut.flagDeflipData = True #asumo q la data no esta sin flip
706
711
707
712
708 class ProfileConcat(Operation):
713 class ProfileConcat(Operation):
709
714
710 isConfig = False
715 isConfig = False
711 buffer = None
716 buffer = None
712
717
713 def __init__(self):
718 def __init__(self):
714
719
715 Operation.__init__(self)
720 Operation.__init__(self)
716 self.profileIndex = 0
721 self.profileIndex = 0
717
722
718 def reset(self):
723 def reset(self):
719 self.buffer = numpy.zeros_like(self.buffer)
724 self.buffer = numpy.zeros_like(self.buffer)
720 self.start_index = 0
725 self.start_index = 0
721 self.times = 1
726 self.times = 1
722
727
723 def setup(self, data, m, n=1):
728 def setup(self, data, m, n=1):
724 self.buffer = numpy.zeros((data.shape[0],data.shape[1]*m),dtype=type(data[0,0]))
729 self.buffer = numpy.zeros((data.shape[0],data.shape[1]*m),dtype=type(data[0,0]))
725 self.nHeights = data.nHeights
730 self.nHeights = data.nHeights
726 self.start_index = 0
731 self.start_index = 0
727 self.times = 1
732 self.times = 1
728
733
729 def concat(self, data):
734 def concat(self, data):
730
735
731 self.buffer[:,self.start_index:self.profiles*self.times] = data.copy()
736 self.buffer[:,self.start_index:self.profiles*self.times] = data.copy()
732 self.start_index = self.start_index + self.nHeights
737 self.start_index = self.start_index + self.nHeights
733
738
734 def run(self, dataOut, m):
739 def run(self, dataOut, m):
735
740
736 dataOut.flagNoData = True
741 dataOut.flagNoData = True
737
742
738 if not self.isConfig:
743 if not self.isConfig:
739 self.setup(dataOut.data, m, 1)
744 self.setup(dataOut.data, m, 1)
740 self.isConfig = True
745 self.isConfig = True
741
746
742 if dataOut.flagDataAsBlock:
747 if dataOut.flagDataAsBlock:
743 raise ValueError, "ProfileConcat can only be used when voltage have been read profile by profile, getBlock = False"
748 raise ValueError, "ProfileConcat can only be used when voltage have been read profile by profile, getBlock = False"
744
749
745 else:
750 else:
746 self.concat(dataOut.data)
751 self.concat(dataOut.data)
747 self.times += 1
752 self.times += 1
748 if self.times > m:
753 if self.times > m:
749 dataOut.data = self.buffer
754 dataOut.data = self.buffer
750 self.reset()
755 self.reset()
751 dataOut.flagNoData = False
756 dataOut.flagNoData = False
752 # se deben actualizar mas propiedades del header y del objeto dataOut, por ejemplo, las alturas
757 # se deben actualizar mas propiedades del header y del objeto dataOut, por ejemplo, las alturas
753 deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
758 deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
754 xf = dataOut.heightList[0] + dataOut.nHeights * deltaHeight * m
759 xf = dataOut.heightList[0] + dataOut.nHeights * deltaHeight * m
755 dataOut.heightList = numpy.arange(dataOut.heightList[0], xf, deltaHeight)
760 dataOut.heightList = numpy.arange(dataOut.heightList[0], xf, deltaHeight)
756 dataOut.ippSeconds *= m
761 dataOut.ippSeconds *= m
757
762
758 class ProfileSelector(Operation):
763 class ProfileSelector(Operation):
759
764
760 profileIndex = None
765 profileIndex = None
761 # Tamanho total de los perfiles
766 # Tamanho total de los perfiles
762 nProfiles = None
767 nProfiles = None
763
768
764 def __init__(self):
769 def __init__(self):
765
770
766 Operation.__init__(self)
771 Operation.__init__(self)
767 self.profileIndex = 0
772 self.profileIndex = 0
768
773
769 def incIndex(self):
774 def incIndex(self):
770
775
771 self.profileIndex += 1
776 self.profileIndex += 1
772
777
773 if self.profileIndex >= self.nProfiles:
778 if self.profileIndex >= self.nProfiles:
774 self.profileIndex = 0
779 self.profileIndex = 0
775
780
776 def isThisProfileInRange(self, profileIndex, minIndex, maxIndex):
781 def isThisProfileInRange(self, profileIndex, minIndex, maxIndex):
777
782
778 if profileIndex < minIndex:
783 if profileIndex < minIndex:
779 return False
784 return False
780
785
781 if profileIndex > maxIndex:
786 if profileIndex > maxIndex:
782 return False
787 return False
783
788
784 return True
789 return True
785
790
786 def isThisProfileInList(self, profileIndex, profileList):
791 def isThisProfileInList(self, profileIndex, profileList):
787
792
788 if profileIndex not in profileList:
793 if profileIndex not in profileList:
789 return False
794 return False
790
795
791 return True
796 return True
792
797
793 def run(self, dataOut, profileList=None, profileRangeList=None, beam=None, byblock=False, rangeList = None, nProfiles=None):
798 def run(self, dataOut, profileList=None, profileRangeList=None, beam=None, byblock=False, rangeList = None, nProfiles=None):
794
799
795 """
800 """
796 ProfileSelector:
801 ProfileSelector:
797
802
798 Inputs:
803 Inputs:
799 profileList : Index of profiles selected. Example: profileList = (0,1,2,7,8)
804 profileList : Index of profiles selected. Example: profileList = (0,1,2,7,8)
800
805
801 profileRangeList : Minimum and maximum profile indexes. Example: profileRangeList = (4, 30)
806 profileRangeList : Minimum and maximum profile indexes. Example: profileRangeList = (4, 30)
802
807
803 rangeList : List of profile ranges. Example: rangeList = ((4, 30), (32, 64), (128, 256))
808 rangeList : List of profile ranges. Example: rangeList = ((4, 30), (32, 64), (128, 256))
804
809
805 """
810 """
806
811
807 dataOut.flagNoData = True
812 dataOut.flagNoData = True
808
813
809 if dataOut.flagDataAsBlock:
814 if dataOut.flagDataAsBlock:
810 """
815 """
811 data dimension = [nChannels, nProfiles, nHeis]
816 data dimension = [nChannels, nProfiles, nHeis]
812 """
817 """
813 if profileList != None:
818 if profileList != None:
814 dataOut.data = dataOut.data[:,profileList,:]
819 dataOut.data = dataOut.data[:,profileList,:]
815 dataOut.nProfiles = len(profileList)
816 dataOut.profileIndex = dataOut.nProfiles - 1
817
820
818 if profileRangeList != None:
821 if profileRangeList != None:
819 minIndex = profileRangeList[0]
822 minIndex = profileRangeList[0]
820 maxIndex = profileRangeList[1]
823 maxIndex = profileRangeList[1]
821
824 profileList = range(minIndex, maxIndex+1)
825
822 dataOut.data = dataOut.data[:,minIndex:maxIndex+1,:]
826 dataOut.data = dataOut.data[:,minIndex:maxIndex+1,:]
823 dataOut.nProfiles = maxIndex - minIndex + 1
824 dataOut.profileIndex = dataOut.nProfiles - 1
825
827
826 if rangeList != None:
828 if rangeList != None:
827 raise ValueError, "Profile Selector: Invalid argument rangeList. Not implemented for getByBlock yet"
828
829
830 profileList = []
831
832 for thisRange in rangeList:
833 minIndex = thisRange[0]
834 maxIndex = thisRange[1]
835
836 profileList.extend(range(minIndex, maxIndex+1))
837
838 dataOut.data = dataOut.data[:,profileList,:]
839
840 dataOut.nProfiles = len(profileList)
841 dataOut.profileIndex = dataOut.nProfiles - 1
829 dataOut.flagNoData = False
842 dataOut.flagNoData = False
830
843
831 return True
844 return True
832
845
833 """
846 """
834 data dimension = [nChannels, nHeis]
847 data dimension = [nChannels, nHeis]
835 """
848 """
836
849
837 if nProfiles:
838 self.nProfiles = nProfiles
839 else:
840 self.nProfiles = dataOut.nProfiles
841
842 if profileList != None:
850 if profileList != None:
843
851
844 dataOut.nProfiles = len(profileList)
845
846 if self.isThisProfileInList(dataOut.profileIndex, profileList):
852 if self.isThisProfileInList(dataOut.profileIndex, profileList):
847 dataOut.flagNoData = False
853
854 self.nProfiles = len(profileList)
855 dataOut.nProfiles = self.nProfiles
848 dataOut.profileIndex = self.profileIndex
856 dataOut.profileIndex = self.profileIndex
857 dataOut.flagNoData = False
849
858
850 self.incIndex()
859 self.incIndex()
851 return True
860 return True
852
861
853 if profileRangeList != None:
862 if profileRangeList != None:
854
863
855 minIndex = profileRangeList[0]
864 minIndex = profileRangeList[0]
856 maxIndex = profileRangeList[1]
865 maxIndex = profileRangeList[1]
857
866
858 dataOut.nProfiles = maxIndex - minIndex + 1
859
860 if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex):
867 if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex):
861 dataOut.flagNoData = False
868
869 self.nProfiles = maxIndex - minIndex + 1
870 dataOut.nProfiles = self.nProfiles
862 dataOut.profileIndex = self.profileIndex
871 dataOut.profileIndex = self.profileIndex
872 dataOut.flagNoData = False
863
873
864 self.incIndex()
874 self.incIndex()
865 return True
875 return True
866
876
867 if rangeList != None:
877 if rangeList != None:
868
878
869 nProfiles = 0
879 nProfiles = 0
870
880
871 for thisRange in rangeList:
881 for thisRange in rangeList:
872 minIndex = thisRange[0]
882 minIndex = thisRange[0]
873 maxIndex = thisRange[1]
883 maxIndex = thisRange[1]
874
884
875 nProfiles += maxIndex - minIndex + 1
885 nProfiles += maxIndex - minIndex + 1
876
886
877 dataOut.nProfiles = nProfiles
878
879 for thisRange in rangeList:
887 for thisRange in rangeList:
880
888
881 minIndex = thisRange[0]
889 minIndex = thisRange[0]
882 maxIndex = thisRange[1]
890 maxIndex = thisRange[1]
883
891
884 if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex):
892 if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex):
885
893
886 # print "profileIndex = ", dataOut.profileIndex
894 self.nProfiles = nProfiles
887
895 dataOut.nProfiles = self.nProfiles
888 dataOut.flagNoData = False
889 dataOut.profileIndex = self.profileIndex
896 dataOut.profileIndex = self.profileIndex
890
897 dataOut.flagNoData = False
898
891 self.incIndex()
899 self.incIndex()
900
892 break
901 break
902
893 return True
903 return True
894
904
895
905
896 if beam != None: #beam is only for AMISR data
906 if beam != None: #beam is only for AMISR data
897 if self.isThisProfileInList(dataOut.profileIndex, dataOut.beamRangeDict[beam]):
907 if self.isThisProfileInList(dataOut.profileIndex, dataOut.beamRangeDict[beam]):
898 dataOut.flagNoData = False
908 dataOut.flagNoData = False
899 dataOut.profileIndex = self.profileIndex
909 dataOut.profileIndex = self.profileIndex
900
910
901 self.incIndex()
911 self.incIndex()
902
912
903 return True
913 return True
904
914
905 raise ValueError, "ProfileSelector needs profileList, profileRangeList or rangeList parameter"
915 raise ValueError, "ProfileSelector needs profileList, profileRangeList or rangeList parameter"
906
916
907 return False
917 return False
908
918
909
919
910
920
911 class Reshaper(Operation):
921 class Reshaper(Operation):
912
922
913 def __init__(self):
923 def __init__(self):
914
924
915 Operation.__init__(self)
925 Operation.__init__(self)
916 self.updateNewHeights = True
926
927 self.__buffer = None
928 self.__nitems = 0
929
930 def __appendProfile(self, dataOut, nTxs):
931
932 if self.__buffer is None:
933 shape = (dataOut.nChannels, int(dataOut.nHeights/nTxs) )
934 self.__buffer = numpy.empty(shape, dtype = dataOut.data.dtype)
935
936 ini = dataOut.nHeights * self.__nitems
937 end = ini + dataOut.nHeights
938
939 self.__buffer[:, ini:end] = dataOut.data
940
941 self.__nitems += 1
942
943 return int(self.__nitems*nTxs)
917
944
918 def run(self, dataOut, shape):
945 def __getBuffer(self):
919
946
920 if not dataOut.flagDataAsBlock:
947 if self.__nitems == int(1./self.__nTxs):
921 raise ValueError, "Reshaper can only be used when voltage have been read as Block, getBlock = True"
948
949 self.__nitems = 0
950
951 return self.__buffer.copy()
922
952
923 if len(shape) != 3:
953 return None
924 raise ValueError, "shape len should be equal to 3, (nChannels, nProfiles, nHeis)"
954
955 def __checkInputs(self, dataOut, shape, nTxs):
925
956
926 shape_tuple = tuple(shape)
957 if shape is None and nTxs is None:
927 dataOut.data = numpy.reshape(dataOut.data, shape_tuple)
958 raise ValueError, "Reshaper: shape of factor should be defined"
928 dataOut.flagNoData = False
929
959
930 if self.updateNewHeights:
960 if nTxs:
961 if nTxs < 0:
962 raise ValueError, "nTxs should be greater than 0"
931
963
932 old_nheights = dataOut.nHeights
964 if nTxs < 1 and dataOut.nProfiles % (1./nTxs) != 0:
933 new_nheights = dataOut.data.shape[2]
965 raise ValueError, "nProfiles= %d is not divisibled by (1./nTxs) = %f" %(dataOut.nProfiles, (1./nTxs))
934 factor = 1.0*new_nheights / old_nheights
935
966
936 deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
967 shape = [dataOut.nChannels, dataOut.nProfiles*nTxs, dataOut.nHeights/nTxs]
968
969 if len(shape) != 2 and len(shape) != 3:
970 raise ValueError, "shape dimension should be equal to 2 or 3. shape = (nProfiles, nHeis) or (nChannels, nProfiles, nHeis). Actually shape = (%d, %d, %d)" %(dataOut.nChannels, dataOut.nProfiles, dataOut.nHeights)
971
972 if len(shape) == 2:
973 shape_tuple = [dataOut.nChannels]
974 shape_tuple.extend(shape)
975 else:
976 shape_tuple = list(shape)
977
978 if not nTxs:
979 nTxs = int(shape_tuple[1]/dataOut.nProfiles)
937
980
938 xf = dataOut.heightList[0] + dataOut.nHeights * deltaHeight * factor
981 return shape_tuple, nTxs
982
983 def run(self, dataOut, shape=None, nTxs=None):
984
985 shape_tuple, self.__nTxs = self.__checkInputs(dataOut, shape, nTxs)
986
987 dataOut.flagNoData = True
988 profileIndex = None
989
990 if dataOut.flagDataAsBlock:
991
992 dataOut.data = numpy.reshape(dataOut.data, shape_tuple)
993 dataOut.flagNoData = False
939
994
940 dataOut.heightList = numpy.arange(dataOut.heightList[0], xf, deltaHeight)
995 profileIndex = int(dataOut.nProfiles*nTxs) - 1
941
996
942 dataOut.nProfiles = dataOut.data.shape[1]
997 else:
943
998
944 dataOut.ippSeconds *= factor
999 if self.__nTxs < 1:
1000
1001 self.__appendProfile(dataOut, self.__nTxs)
1002 new_data = self.__getBuffer()
1003
1004 if new_data is not None:
1005 dataOut.data = new_data
1006 dataOut.flagNoData = False
1007
1008 profileIndex = dataOut.profileIndex*nTxs
1009
1010 else:
1011 raise ValueError, "nTxs should be greater than 0 and lower than 1, or use VoltageReader(..., getblock=True)"
1012
1013 deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
1014
1015 dataOut.heightList = numpy.arange(dataOut.nHeights/self.__nTxs) * deltaHeight + dataOut.heightList[0]
1016
1017 dataOut.nProfiles = int(dataOut.nProfiles*self.__nTxs)
1018
1019 dataOut.profileIndex = profileIndex
1020
1021 dataOut.ippSeconds /= self.__nTxs
945 #
1022 #
946 # import collections
1023 # import collections
947 # from scipy.stats import mode
1024 # from scipy.stats import mode
948 #
1025 #
949 # class Synchronize(Operation):
1026 # class Synchronize(Operation):
950 #
1027 #
951 # isConfig = False
1028 # isConfig = False
952 # __profIndex = 0
1029 # __profIndex = 0
953 #
1030 #
954 # def __init__(self):
1031 # def __init__(self):
955 #
1032 #
956 # Operation.__init__(self)
1033 # Operation.__init__(self)
957 # # self.isConfig = False
1034 # # self.isConfig = False
958 # self.__powBuffer = None
1035 # self.__powBuffer = None
959 # self.__startIndex = 0
1036 # self.__startIndex = 0
960 # self.__pulseFound = False
1037 # self.__pulseFound = False
961 #
1038 #
962 # def __findTxPulse(self, dataOut, channel=0, pulse_with = None):
1039 # def __findTxPulse(self, dataOut, channel=0, pulse_with = None):
963 #
1040 #
964 # #Read data
1041 # #Read data
965 #
1042 #
966 # powerdB = dataOut.getPower(channel = channel)
1043 # powerdB = dataOut.getPower(channel = channel)
967 # noisedB = dataOut.getNoise(channel = channel)[0]
1044 # noisedB = dataOut.getNoise(channel = channel)[0]
968 #
1045 #
969 # self.__powBuffer.extend(powerdB.flatten())
1046 # self.__powBuffer.extend(powerdB.flatten())
970 #
1047 #
971 # dataArray = numpy.array(self.__powBuffer)
1048 # dataArray = numpy.array(self.__powBuffer)
972 #
1049 #
973 # filteredPower = numpy.correlate(dataArray, dataArray[0:self.__nSamples], "same")
1050 # filteredPower = numpy.correlate(dataArray, dataArray[0:self.__nSamples], "same")
974 #
1051 #
975 # maxValue = numpy.nanmax(filteredPower)
1052 # maxValue = numpy.nanmax(filteredPower)
976 #
1053 #
977 # if maxValue < noisedB + 10:
1054 # if maxValue < noisedB + 10:
978 # #No se encuentra ningun pulso de transmision
1055 # #No se encuentra ningun pulso de transmision
979 # return None
1056 # return None
980 #
1057 #
981 # maxValuesIndex = numpy.where(filteredPower > maxValue - 0.1*abs(maxValue))[0]
1058 # maxValuesIndex = numpy.where(filteredPower > maxValue - 0.1*abs(maxValue))[0]
982 #
1059 #
983 # if len(maxValuesIndex) < 2:
1060 # if len(maxValuesIndex) < 2:
984 # #Solo se encontro un solo pulso de transmision de un baudio, esperando por el siguiente TX
1061 # #Solo se encontro un solo pulso de transmision de un baudio, esperando por el siguiente TX
985 # return None
1062 # return None
986 #
1063 #
987 # phasedMaxValuesIndex = maxValuesIndex - self.__nSamples
1064 # phasedMaxValuesIndex = maxValuesIndex - self.__nSamples
988 #
1065 #
989 # #Seleccionar solo valores con un espaciamiento de nSamples
1066 # #Seleccionar solo valores con un espaciamiento de nSamples
990 # pulseIndex = numpy.intersect1d(maxValuesIndex, phasedMaxValuesIndex)
1067 # pulseIndex = numpy.intersect1d(maxValuesIndex, phasedMaxValuesIndex)
991 #
1068 #
992 # if len(pulseIndex) < 2:
1069 # if len(pulseIndex) < 2:
993 # #Solo se encontro un pulso de transmision con ancho mayor a 1
1070 # #Solo se encontro un pulso de transmision con ancho mayor a 1
994 # return None
1071 # return None
995 #
1072 #
996 # spacing = pulseIndex[1:] - pulseIndex[:-1]
1073 # spacing = pulseIndex[1:] - pulseIndex[:-1]
997 #
1074 #
998 # #remover senales que se distancien menos de 10 unidades o muestras
1075 # #remover senales que se distancien menos de 10 unidades o muestras
999 # #(No deberian existir IPP menor a 10 unidades)
1076 # #(No deberian existir IPP menor a 10 unidades)
1000 #
1077 #
1001 # realIndex = numpy.where(spacing > 10 )[0]
1078 # realIndex = numpy.where(spacing > 10 )[0]
1002 #
1079 #
1003 # if len(realIndex) < 2:
1080 # if len(realIndex) < 2:
1004 # #Solo se encontro un pulso de transmision con ancho mayor a 1
1081 # #Solo se encontro un pulso de transmision con ancho mayor a 1
1005 # return None
1082 # return None
1006 #
1083 #
1007 # #Eliminar pulsos anchos (deja solo la diferencia entre IPPs)
1084 # #Eliminar pulsos anchos (deja solo la diferencia entre IPPs)
1008 # realPulseIndex = pulseIndex[realIndex]
1085 # realPulseIndex = pulseIndex[realIndex]
1009 #
1086 #
1010 # period = mode(realPulseIndex[1:] - realPulseIndex[:-1])[0][0]
1087 # period = mode(realPulseIndex[1:] - realPulseIndex[:-1])[0][0]
1011 #
1088 #
1012 # print "IPP = %d samples" %period
1089 # print "IPP = %d samples" %period
1013 #
1090 #
1014 # self.__newNSamples = dataOut.nHeights #int(period)
1091 # self.__newNSamples = dataOut.nHeights #int(period)
1015 # self.__startIndex = int(realPulseIndex[0])
1092 # self.__startIndex = int(realPulseIndex[0])
1016 #
1093 #
1017 # return 1
1094 # return 1
1018 #
1095 #
1019 #
1096 #
1020 # def setup(self, nSamples, nChannels, buffer_size = 4):
1097 # def setup(self, nSamples, nChannels, buffer_size = 4):
1021 #
1098 #
1022 # self.__powBuffer = collections.deque(numpy.zeros( buffer_size*nSamples,dtype=numpy.float),
1099 # self.__powBuffer = collections.deque(numpy.zeros( buffer_size*nSamples,dtype=numpy.float),
1023 # maxlen = buffer_size*nSamples)
1100 # maxlen = buffer_size*nSamples)
1024 #
1101 #
1025 # bufferList = []
1102 # bufferList = []
1026 #
1103 #
1027 # for i in range(nChannels):
1104 # for i in range(nChannels):
1028 # bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=numpy.complex) + numpy.NAN,
1105 # bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=numpy.complex) + numpy.NAN,
1029 # maxlen = buffer_size*nSamples)
1106 # maxlen = buffer_size*nSamples)
1030 #
1107 #
1031 # bufferList.append(bufferByChannel)
1108 # bufferList.append(bufferByChannel)
1032 #
1109 #
1033 # self.__nSamples = nSamples
1110 # self.__nSamples = nSamples
1034 # self.__nChannels = nChannels
1111 # self.__nChannels = nChannels
1035 # self.__bufferList = bufferList
1112 # self.__bufferList = bufferList
1036 #
1113 #
1037 # def run(self, dataOut, channel = 0):
1114 # def run(self, dataOut, channel = 0):
1038 #
1115 #
1039 # if not self.isConfig:
1116 # if not self.isConfig:
1040 # nSamples = dataOut.nHeights
1117 # nSamples = dataOut.nHeights
1041 # nChannels = dataOut.nChannels
1118 # nChannels = dataOut.nChannels
1042 # self.setup(nSamples, nChannels)
1119 # self.setup(nSamples, nChannels)
1043 # self.isConfig = True
1120 # self.isConfig = True
1044 #
1121 #
1045 # #Append new data to internal buffer
1122 # #Append new data to internal buffer
1046 # for thisChannel in range(self.__nChannels):
1123 # for thisChannel in range(self.__nChannels):
1047 # bufferByChannel = self.__bufferList[thisChannel]
1124 # bufferByChannel = self.__bufferList[thisChannel]
1048 # bufferByChannel.extend(dataOut.data[thisChannel])
1125 # bufferByChannel.extend(dataOut.data[thisChannel])
1049 #
1126 #
1050 # if self.__pulseFound:
1127 # if self.__pulseFound:
1051 # self.__startIndex -= self.__nSamples
1128 # self.__startIndex -= self.__nSamples
1052 #
1129 #
1053 # #Finding Tx Pulse
1130 # #Finding Tx Pulse
1054 # if not self.__pulseFound:
1131 # if not self.__pulseFound:
1055 # indexFound = self.__findTxPulse(dataOut, channel)
1132 # indexFound = self.__findTxPulse(dataOut, channel)
1056 #
1133 #
1057 # if indexFound == None:
1134 # if indexFound == None:
1058 # dataOut.flagNoData = True
1135 # dataOut.flagNoData = True
1059 # return
1136 # return
1060 #
1137 #
1061 # self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = numpy.complex)
1138 # self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = numpy.complex)
1062 # self.__pulseFound = True
1139 # self.__pulseFound = True
1063 # self.__startIndex = indexFound
1140 # self.__startIndex = indexFound
1064 #
1141 #
1065 # #If pulse was found ...
1142 # #If pulse was found ...
1066 # for thisChannel in range(self.__nChannels):
1143 # for thisChannel in range(self.__nChannels):
1067 # bufferByChannel = self.__bufferList[thisChannel]
1144 # bufferByChannel = self.__bufferList[thisChannel]
1068 # #print self.__startIndex
1145 # #print self.__startIndex
1069 # x = numpy.array(bufferByChannel)
1146 # x = numpy.array(bufferByChannel)
1070 # self.__arrayBuffer[thisChannel] = x[self.__startIndex:self.__startIndex+self.__newNSamples]
1147 # self.__arrayBuffer[thisChannel] = x[self.__startIndex:self.__startIndex+self.__newNSamples]
1071 #
1148 #
1072 # deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
1149 # deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
1073 # dataOut.heightList = numpy.arange(self.__newNSamples)*deltaHeight
1150 # dataOut.heightList = numpy.arange(self.__newNSamples)*deltaHeight
1074 # # dataOut.ippSeconds = (self.__newNSamples / deltaHeight)/1e6
1151 # # dataOut.ippSeconds = (self.__newNSamples / deltaHeight)/1e6
1075 #
1152 #
1076 # dataOut.data = self.__arrayBuffer
1153 # dataOut.data = self.__arrayBuffer
1077 #
1154 #
1078 # self.__startIndex += self.__newNSamples
1155 # self.__startIndex += self.__newNSamples
1079 #
1156 #
1080 # return No newline at end of file
1157 # return
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