##// END OF EJS Templates
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Feature added to jroproc_voltage.ProfileSelector(): rangeList replaces to profileRangeList. This parameter will be eliminated in future versions.
Miguel Valdez -
r756:ca0956eb2236
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@@ -1,1157 +1,1161
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"
622 raise NotImplementedError, "Decoder by frequency fro Blocks not implemented"
623
623
624
624
625 fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
625 fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
626
626
627 fft_data = numpy.fft.fft(data, axis=2)
627 fft_data = numpy.fft.fft(data, axis=2)
628
628
629 conv = fft_data*fft_code
629 conv = fft_data*fft_code
630
630
631 data = numpy.fft.ifft(conv,axis=2)
631 data = numpy.fft.ifft(conv,axis=2)
632
632
633 return data
633 return data
634
634
635 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):
636
636
637 if dataOut.flagDecodeData:
637 if dataOut.flagDecodeData:
638 print "This data is already decoded, recoding again ..."
638 print "This data is already decoded, recoding again ..."
639
639
640 if not self.isConfig:
640 if not self.isConfig:
641
641
642 if code is None:
642 if code is None:
643 if dataOut.code is None:
643 if dataOut.code is None:
644 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
645
645
646 code = dataOut.code
646 code = dataOut.code
647 else:
647 else:
648 code = numpy.array(code).reshape(nCode,nBaud)
648 code = numpy.array(code).reshape(nCode,nBaud)
649
649
650 self.setup(code, osamp, dataOut)
650 self.setup(code, osamp, dataOut)
651
651
652 self.isConfig = True
652 self.isConfig = True
653
653
654 if mode == 3:
654 if mode == 3:
655 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)
656
656
657 if times != None:
657 if times != None:
658 sys.stderr.write("Decoder Warning: Argument 'times' in not used anymore\n")
658 sys.stderr.write("Decoder Warning: Argument 'times' in not used anymore\n")
659
659
660 if self.code is None:
660 if self.code is None:
661 print "Fail decoding: Code is not defined."
661 print "Fail decoding: Code is not defined."
662 return
662 return
663
663
664 datadec = None
664 datadec = None
665 if mode == 3:
665 if mode == 3:
666 mode = 0
666 mode = 0
667
667
668 if dataOut.flagDataAsBlock:
668 if dataOut.flagDataAsBlock:
669 """
669 """
670 Decoding when data have been read as block,
670 Decoding when data have been read as block,
671 """
671 """
672
672
673 if mode == 0:
673 if mode == 0:
674 datadec = self.__convolutionByBlockInTime(dataOut.data)
674 datadec = self.__convolutionByBlockInTime(dataOut.data)
675 if mode == 1:
675 if mode == 1:
676 datadec = self.__convolutionByBlockInFreq(dataOut.data)
676 datadec = self.__convolutionByBlockInFreq(dataOut.data)
677 else:
677 else:
678 """
678 """
679 Decoding when data have been read profile by profile
679 Decoding when data have been read profile by profile
680 """
680 """
681 if mode == 0:
681 if mode == 0:
682 datadec = self.__convolutionInTime(dataOut.data)
682 datadec = self.__convolutionInTime(dataOut.data)
683
683
684 if mode == 1:
684 if mode == 1:
685 datadec = self.__convolutionInFreq(dataOut.data)
685 datadec = self.__convolutionInFreq(dataOut.data)
686
686
687 if mode == 2:
687 if mode == 2:
688 datadec = self.__convolutionInFreqOpt(dataOut.data)
688 datadec = self.__convolutionInFreqOpt(dataOut.data)
689
689
690 if datadec is None:
690 if datadec is None:
691 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
692
692
693 dataOut.code = self.code
693 dataOut.code = self.code
694 dataOut.nCode = self.nCode
694 dataOut.nCode = self.nCode
695 dataOut.nBaud = self.nBaud
695 dataOut.nBaud = self.nBaud
696
696
697 dataOut.data = datadec
697 dataOut.data = datadec
698
698
699 dataOut.heightList = dataOut.heightList[0:datadec.shape[-1]]
699 dataOut.heightList = dataOut.heightList[0:datadec.shape[-1]]
700
700
701 dataOut.flagDecodeData = True #asumo q la data esta decodificada
701 dataOut.flagDecodeData = True #asumo q la data esta decodificada
702
702
703 if self.__profIndex == self.nCode-1:
703 if self.__profIndex == self.nCode-1:
704 self.__profIndex = 0
704 self.__profIndex = 0
705 return 1
705 return 1
706
706
707 self.__profIndex += 1
707 self.__profIndex += 1
708
708
709 return 1
709 return 1
710 # dataOut.flagDeflipData = True #asumo q la data no esta sin flip
710 # dataOut.flagDeflipData = True #asumo q la data no esta sin flip
711
711
712
712
713 class ProfileConcat(Operation):
713 class ProfileConcat(Operation):
714
714
715 isConfig = False
715 isConfig = False
716 buffer = None
716 buffer = None
717
717
718 def __init__(self):
718 def __init__(self):
719
719
720 Operation.__init__(self)
720 Operation.__init__(self)
721 self.profileIndex = 0
721 self.profileIndex = 0
722
722
723 def reset(self):
723 def reset(self):
724 self.buffer = numpy.zeros_like(self.buffer)
724 self.buffer = numpy.zeros_like(self.buffer)
725 self.start_index = 0
725 self.start_index = 0
726 self.times = 1
726 self.times = 1
727
727
728 def setup(self, data, m, n=1):
728 def setup(self, data, m, n=1):
729 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]))
730 self.nHeights = data.nHeights
730 self.nHeights = data.nHeights
731 self.start_index = 0
731 self.start_index = 0
732 self.times = 1
732 self.times = 1
733
733
734 def concat(self, data):
734 def concat(self, data):
735
735
736 self.buffer[:,self.start_index:self.profiles*self.times] = data.copy()
736 self.buffer[:,self.start_index:self.profiles*self.times] = data.copy()
737 self.start_index = self.start_index + self.nHeights
737 self.start_index = self.start_index + self.nHeights
738
738
739 def run(self, dataOut, m):
739 def run(self, dataOut, m):
740
740
741 dataOut.flagNoData = True
741 dataOut.flagNoData = True
742
742
743 if not self.isConfig:
743 if not self.isConfig:
744 self.setup(dataOut.data, m, 1)
744 self.setup(dataOut.data, m, 1)
745 self.isConfig = True
745 self.isConfig = True
746
746
747 if dataOut.flagDataAsBlock:
747 if dataOut.flagDataAsBlock:
748 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"
749
749
750 else:
750 else:
751 self.concat(dataOut.data)
751 self.concat(dataOut.data)
752 self.times += 1
752 self.times += 1
753 if self.times > m:
753 if self.times > m:
754 dataOut.data = self.buffer
754 dataOut.data = self.buffer
755 self.reset()
755 self.reset()
756 dataOut.flagNoData = False
756 dataOut.flagNoData = False
757 # 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
758 deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
758 deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
759 xf = dataOut.heightList[0] + dataOut.nHeights * deltaHeight * m
759 xf = dataOut.heightList[0] + dataOut.nHeights * deltaHeight * m
760 dataOut.heightList = numpy.arange(dataOut.heightList[0], xf, deltaHeight)
760 dataOut.heightList = numpy.arange(dataOut.heightList[0], xf, deltaHeight)
761 dataOut.ippSeconds *= m
761 dataOut.ippSeconds *= m
762
762
763 class ProfileSelector(Operation):
763 class ProfileSelector(Operation):
764
764
765 profileIndex = None
765 profileIndex = None
766 # Tamanho total de los perfiles
766 # Tamanho total de los perfiles
767 nProfiles = None
767 nProfiles = None
768
768
769 def __init__(self):
769 def __init__(self):
770
770
771 Operation.__init__(self)
771 Operation.__init__(self)
772 self.profileIndex = 0
772 self.profileIndex = 0
773
773
774 def incProfileIndex(self):
774 def incProfileIndex(self):
775
775
776 self.profileIndex += 1
776 self.profileIndex += 1
777
777
778 if self.profileIndex >= self.nProfiles:
778 if self.profileIndex >= self.nProfiles:
779 self.profileIndex = 0
779 self.profileIndex = 0
780
780
781 def isThisProfileInRange(self, profileIndex, minIndex, maxIndex):
781 def isThisProfileInRange(self, profileIndex, minIndex, maxIndex):
782
782
783 if profileIndex < minIndex:
783 if profileIndex < minIndex:
784 return False
784 return False
785
785
786 if profileIndex > maxIndex:
786 if profileIndex > maxIndex:
787 return False
787 return False
788
788
789 return True
789 return True
790
790
791 def isThisProfileInList(self, profileIndex, profileList):
791 def isThisProfileInList(self, profileIndex, profileList):
792
792
793 if profileIndex not in profileList:
793 if profileIndex not in profileList:
794 return False
794 return False
795
795
796 return True
796 return True
797
797
798 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):
799
799
800 """
800 """
801 ProfileSelector:
801 ProfileSelector:
802
802
803 Inputs:
803 Inputs:
804 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)
805
805
806 profileRangeList : Minimum and maximum profile indexes. Example: profileRangeList = (4, 30)
806 profileRangeList : Minimum and maximum profile indexes. Example: profileRangeList = (4, 30)
807
807
808 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))
809
809
810 """
810 """
811
811
812 if rangeList is not None:
813 if type(rangeList[0]) not in (tuple, list):
814 rangeList = [rangeList]
815
812 dataOut.flagNoData = True
816 dataOut.flagNoData = True
813
817
814 if dataOut.flagDataAsBlock:
818 if dataOut.flagDataAsBlock:
815 """
819 """
816 data dimension = [nChannels, nProfiles, nHeis]
820 data dimension = [nChannels, nProfiles, nHeis]
817 """
821 """
818 if profileList != None:
822 if profileList != None:
819 dataOut.data = dataOut.data[:,profileList,:]
823 dataOut.data = dataOut.data[:,profileList,:]
820
824
821 if profileRangeList != None:
825 if profileRangeList != None:
822 minIndex = profileRangeList[0]
826 minIndex = profileRangeList[0]
823 maxIndex = profileRangeList[1]
827 maxIndex = profileRangeList[1]
824 profileList = range(minIndex, maxIndex+1)
828 profileList = range(minIndex, maxIndex+1)
825
829
826 dataOut.data = dataOut.data[:,minIndex:maxIndex+1,:]
830 dataOut.data = dataOut.data[:,minIndex:maxIndex+1,:]
827
831
828 if rangeList != None:
832 if rangeList != None:
829
833
830 profileList = []
834 profileList = []
831
835
832 for thisRange in rangeList:
836 for thisRange in rangeList:
833 minIndex = thisRange[0]
837 minIndex = thisRange[0]
834 maxIndex = thisRange[1]
838 maxIndex = thisRange[1]
835
839
836 profileList.extend(range(minIndex, maxIndex+1))
840 profileList.extend(range(minIndex, maxIndex+1))
837
841
838 dataOut.data = dataOut.data[:,profileList,:]
842 dataOut.data = dataOut.data[:,profileList,:]
839
843
840 dataOut.nProfiles = len(profileList)
844 dataOut.nProfiles = len(profileList)
841 dataOut.profileIndex = dataOut.nProfiles - 1
845 dataOut.profileIndex = dataOut.nProfiles - 1
842 dataOut.flagNoData = False
846 dataOut.flagNoData = False
843
847
844 return True
848 return True
845
849
846 """
850 """
847 data dimension = [nChannels, nHeis]
851 data dimension = [nChannels, nHeis]
848 """
852 """
849
853
850 if profileList != None:
854 if profileList != None:
851
855
852 if self.isThisProfileInList(dataOut.profileIndex, profileList):
856 if self.isThisProfileInList(dataOut.profileIndex, profileList):
853
857
854 self.nProfiles = len(profileList)
858 self.nProfiles = len(profileList)
855 dataOut.nProfiles = self.nProfiles
859 dataOut.nProfiles = self.nProfiles
856 dataOut.profileIndex = self.profileIndex
860 dataOut.profileIndex = self.profileIndex
857 dataOut.flagNoData = False
861 dataOut.flagNoData = False
858
862
859 self.incProfileIndex()
863 self.incProfileIndex()
860 return True
864 return True
861
865
862 if profileRangeList != None:
866 if profileRangeList != None:
863
867
864 minIndex = profileRangeList[0]
868 minIndex = profileRangeList[0]
865 maxIndex = profileRangeList[1]
869 maxIndex = profileRangeList[1]
866
870
867 if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex):
871 if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex):
868
872
869 self.nProfiles = maxIndex - minIndex + 1
873 self.nProfiles = maxIndex - minIndex + 1
870 dataOut.nProfiles = self.nProfiles
874 dataOut.nProfiles = self.nProfiles
871 dataOut.profileIndex = self.profileIndex
875 dataOut.profileIndex = self.profileIndex
872 dataOut.flagNoData = False
876 dataOut.flagNoData = False
873
877
874 self.incProfileIndex()
878 self.incProfileIndex()
875 return True
879 return True
876
880
877 if rangeList != None:
881 if rangeList != None:
878
882
879 nProfiles = 0
883 nProfiles = 0
880
884
881 for thisRange in rangeList:
885 for thisRange in rangeList:
882 minIndex = thisRange[0]
886 minIndex = thisRange[0]
883 maxIndex = thisRange[1]
887 maxIndex = thisRange[1]
884
888
885 nProfiles += maxIndex - minIndex + 1
889 nProfiles += maxIndex - minIndex + 1
886
890
887 for thisRange in rangeList:
891 for thisRange in rangeList:
888
892
889 minIndex = thisRange[0]
893 minIndex = thisRange[0]
890 maxIndex = thisRange[1]
894 maxIndex = thisRange[1]
891
895
892 if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex):
896 if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex):
893
897
894 self.nProfiles = nProfiles
898 self.nProfiles = nProfiles
895 dataOut.nProfiles = self.nProfiles
899 dataOut.nProfiles = self.nProfiles
896 dataOut.profileIndex = self.profileIndex
900 dataOut.profileIndex = self.profileIndex
897 dataOut.flagNoData = False
901 dataOut.flagNoData = False
898
902
899 self.incProfileIndex()
903 self.incProfileIndex()
900
904
901 break
905 break
902
906
903 return True
907 return True
904
908
905
909
906 if beam != None: #beam is only for AMISR data
910 if beam != None: #beam is only for AMISR data
907 if self.isThisProfileInList(dataOut.profileIndex, dataOut.beamRangeDict[beam]):
911 if self.isThisProfileInList(dataOut.profileIndex, dataOut.beamRangeDict[beam]):
908 dataOut.flagNoData = False
912 dataOut.flagNoData = False
909 dataOut.profileIndex = self.profileIndex
913 dataOut.profileIndex = self.profileIndex
910
914
911 self.incProfileIndex()
915 self.incProfileIndex()
912
916
913 return True
917 return True
914
918
915 raise ValueError, "ProfileSelector needs profileList, profileRangeList or rangeList parameter"
919 raise ValueError, "ProfileSelector needs profileList, profileRangeList or rangeList parameter"
916
920
917 return False
921 return False
918
922
919
923
920
924
921 class Reshaper(Operation):
925 class Reshaper(Operation):
922
926
923 def __init__(self):
927 def __init__(self):
924
928
925 Operation.__init__(self)
929 Operation.__init__(self)
926
930
927 self.__buffer = None
931 self.__buffer = None
928 self.__nitems = 0
932 self.__nitems = 0
929
933
930 def __appendProfile(self, dataOut, nTxs):
934 def __appendProfile(self, dataOut, nTxs):
931
935
932 if self.__buffer is None:
936 if self.__buffer is None:
933 shape = (dataOut.nChannels, int(dataOut.nHeights/nTxs) )
937 shape = (dataOut.nChannels, int(dataOut.nHeights/nTxs) )
934 self.__buffer = numpy.empty(shape, dtype = dataOut.data.dtype)
938 self.__buffer = numpy.empty(shape, dtype = dataOut.data.dtype)
935
939
936 ini = dataOut.nHeights * self.__nitems
940 ini = dataOut.nHeights * self.__nitems
937 end = ini + dataOut.nHeights
941 end = ini + dataOut.nHeights
938
942
939 self.__buffer[:, ini:end] = dataOut.data
943 self.__buffer[:, ini:end] = dataOut.data
940
944
941 self.__nitems += 1
945 self.__nitems += 1
942
946
943 return int(self.__nitems*nTxs)
947 return int(self.__nitems*nTxs)
944
948
945 def __getBuffer(self):
949 def __getBuffer(self):
946
950
947 if self.__nitems == int(1./self.__nTxs):
951 if self.__nitems == int(1./self.__nTxs):
948
952
949 self.__nitems = 0
953 self.__nitems = 0
950
954
951 return self.__buffer.copy()
955 return self.__buffer.copy()
952
956
953 return None
957 return None
954
958
955 def __checkInputs(self, dataOut, shape, nTxs):
959 def __checkInputs(self, dataOut, shape, nTxs):
956
960
957 if shape is None and nTxs is None:
961 if shape is None and nTxs is None:
958 raise ValueError, "Reshaper: shape of factor should be defined"
962 raise ValueError, "Reshaper: shape of factor should be defined"
959
963
960 if nTxs:
964 if nTxs:
961 if nTxs < 0:
965 if nTxs < 0:
962 raise ValueError, "nTxs should be greater than 0"
966 raise ValueError, "nTxs should be greater than 0"
963
967
964 if nTxs < 1 and dataOut.nProfiles % (1./nTxs) != 0:
968 if nTxs < 1 and dataOut.nProfiles % (1./nTxs) != 0:
965 raise ValueError, "nProfiles= %d is not divisibled by (1./nTxs) = %f" %(dataOut.nProfiles, (1./nTxs))
969 raise ValueError, "nProfiles= %d is not divisibled by (1./nTxs) = %f" %(dataOut.nProfiles, (1./nTxs))
966
970
967 shape = [dataOut.nChannels, dataOut.nProfiles*nTxs, dataOut.nHeights/nTxs]
971 shape = [dataOut.nChannels, dataOut.nProfiles*nTxs, dataOut.nHeights/nTxs]
968
972
969 if len(shape) != 2 and len(shape) != 3:
973 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)
974 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
975
972 if len(shape) == 2:
976 if len(shape) == 2:
973 shape_tuple = [dataOut.nChannels]
977 shape_tuple = [dataOut.nChannels]
974 shape_tuple.extend(shape)
978 shape_tuple.extend(shape)
975 else:
979 else:
976 shape_tuple = list(shape)
980 shape_tuple = list(shape)
977
981
978 if not nTxs:
982 if not nTxs:
979 nTxs = int(shape_tuple[1]/dataOut.nProfiles)
983 nTxs = int(shape_tuple[1]/dataOut.nProfiles)
980
984
981 return shape_tuple, nTxs
985 return shape_tuple, nTxs
982
986
983 def run(self, dataOut, shape=None, nTxs=None):
987 def run(self, dataOut, shape=None, nTxs=None):
984
988
985 shape_tuple, self.__nTxs = self.__checkInputs(dataOut, shape, nTxs)
989 shape_tuple, self.__nTxs = self.__checkInputs(dataOut, shape, nTxs)
986
990
987 dataOut.flagNoData = True
991 dataOut.flagNoData = True
988 profileIndex = None
992 profileIndex = None
989
993
990 if dataOut.flagDataAsBlock:
994 if dataOut.flagDataAsBlock:
991
995
992 dataOut.data = numpy.reshape(dataOut.data, shape_tuple)
996 dataOut.data = numpy.reshape(dataOut.data, shape_tuple)
993 dataOut.flagNoData = False
997 dataOut.flagNoData = False
994
998
995 profileIndex = int(dataOut.nProfiles*nTxs) - 1
999 profileIndex = int(dataOut.nProfiles*nTxs) - 1
996
1000
997 else:
1001 else:
998
1002
999 if self.__nTxs < 1:
1003 if self.__nTxs < 1:
1000
1004
1001 self.__appendProfile(dataOut, self.__nTxs)
1005 self.__appendProfile(dataOut, self.__nTxs)
1002 new_data = self.__getBuffer()
1006 new_data = self.__getBuffer()
1003
1007
1004 if new_data is not None:
1008 if new_data is not None:
1005 dataOut.data = new_data
1009 dataOut.data = new_data
1006 dataOut.flagNoData = False
1010 dataOut.flagNoData = False
1007
1011
1008 profileIndex = dataOut.profileIndex*nTxs
1012 profileIndex = dataOut.profileIndex*nTxs
1009
1013
1010 else:
1014 else:
1011 raise ValueError, "nTxs should be greater than 0 and lower than 1, or use VoltageReader(..., getblock=True)"
1015 raise ValueError, "nTxs should be greater than 0 and lower than 1, or use VoltageReader(..., getblock=True)"
1012
1016
1013 deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
1017 deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
1014
1018
1015 dataOut.heightList = numpy.arange(dataOut.nHeights/self.__nTxs) * deltaHeight + dataOut.heightList[0]
1019 dataOut.heightList = numpy.arange(dataOut.nHeights/self.__nTxs) * deltaHeight + dataOut.heightList[0]
1016
1020
1017 dataOut.nProfiles = int(dataOut.nProfiles*self.__nTxs)
1021 dataOut.nProfiles = int(dataOut.nProfiles*self.__nTxs)
1018
1022
1019 dataOut.profileIndex = profileIndex
1023 dataOut.profileIndex = profileIndex
1020
1024
1021 dataOut.ippSeconds /= self.__nTxs
1025 dataOut.ippSeconds /= self.__nTxs
1022 #
1026 #
1023 # import collections
1027 # import collections
1024 # from scipy.stats import mode
1028 # from scipy.stats import mode
1025 #
1029 #
1026 # class Synchronize(Operation):
1030 # class Synchronize(Operation):
1027 #
1031 #
1028 # isConfig = False
1032 # isConfig = False
1029 # __profIndex = 0
1033 # __profIndex = 0
1030 #
1034 #
1031 # def __init__(self):
1035 # def __init__(self):
1032 #
1036 #
1033 # Operation.__init__(self)
1037 # Operation.__init__(self)
1034 # # self.isConfig = False
1038 # # self.isConfig = False
1035 # self.__powBuffer = None
1039 # self.__powBuffer = None
1036 # self.__startIndex = 0
1040 # self.__startIndex = 0
1037 # self.__pulseFound = False
1041 # self.__pulseFound = False
1038 #
1042 #
1039 # def __findTxPulse(self, dataOut, channel=0, pulse_with = None):
1043 # def __findTxPulse(self, dataOut, channel=0, pulse_with = None):
1040 #
1044 #
1041 # #Read data
1045 # #Read data
1042 #
1046 #
1043 # powerdB = dataOut.getPower(channel = channel)
1047 # powerdB = dataOut.getPower(channel = channel)
1044 # noisedB = dataOut.getNoise(channel = channel)[0]
1048 # noisedB = dataOut.getNoise(channel = channel)[0]
1045 #
1049 #
1046 # self.__powBuffer.extend(powerdB.flatten())
1050 # self.__powBuffer.extend(powerdB.flatten())
1047 #
1051 #
1048 # dataArray = numpy.array(self.__powBuffer)
1052 # dataArray = numpy.array(self.__powBuffer)
1049 #
1053 #
1050 # filteredPower = numpy.correlate(dataArray, dataArray[0:self.__nSamples], "same")
1054 # filteredPower = numpy.correlate(dataArray, dataArray[0:self.__nSamples], "same")
1051 #
1055 #
1052 # maxValue = numpy.nanmax(filteredPower)
1056 # maxValue = numpy.nanmax(filteredPower)
1053 #
1057 #
1054 # if maxValue < noisedB + 10:
1058 # if maxValue < noisedB + 10:
1055 # #No se encuentra ningun pulso de transmision
1059 # #No se encuentra ningun pulso de transmision
1056 # return None
1060 # return None
1057 #
1061 #
1058 # maxValuesIndex = numpy.where(filteredPower > maxValue - 0.1*abs(maxValue))[0]
1062 # maxValuesIndex = numpy.where(filteredPower > maxValue - 0.1*abs(maxValue))[0]
1059 #
1063 #
1060 # if len(maxValuesIndex) < 2:
1064 # if len(maxValuesIndex) < 2:
1061 # #Solo se encontro un solo pulso de transmision de un baudio, esperando por el siguiente TX
1065 # #Solo se encontro un solo pulso de transmision de un baudio, esperando por el siguiente TX
1062 # return None
1066 # return None
1063 #
1067 #
1064 # phasedMaxValuesIndex = maxValuesIndex - self.__nSamples
1068 # phasedMaxValuesIndex = maxValuesIndex - self.__nSamples
1065 #
1069 #
1066 # #Seleccionar solo valores con un espaciamiento de nSamples
1070 # #Seleccionar solo valores con un espaciamiento de nSamples
1067 # pulseIndex = numpy.intersect1d(maxValuesIndex, phasedMaxValuesIndex)
1071 # pulseIndex = numpy.intersect1d(maxValuesIndex, phasedMaxValuesIndex)
1068 #
1072 #
1069 # if len(pulseIndex) < 2:
1073 # if len(pulseIndex) < 2:
1070 # #Solo se encontro un pulso de transmision con ancho mayor a 1
1074 # #Solo se encontro un pulso de transmision con ancho mayor a 1
1071 # return None
1075 # return None
1072 #
1076 #
1073 # spacing = pulseIndex[1:] - pulseIndex[:-1]
1077 # spacing = pulseIndex[1:] - pulseIndex[:-1]
1074 #
1078 #
1075 # #remover senales que se distancien menos de 10 unidades o muestras
1079 # #remover senales que se distancien menos de 10 unidades o muestras
1076 # #(No deberian existir IPP menor a 10 unidades)
1080 # #(No deberian existir IPP menor a 10 unidades)
1077 #
1081 #
1078 # realIndex = numpy.where(spacing > 10 )[0]
1082 # realIndex = numpy.where(spacing > 10 )[0]
1079 #
1083 #
1080 # if len(realIndex) < 2:
1084 # if len(realIndex) < 2:
1081 # #Solo se encontro un pulso de transmision con ancho mayor a 1
1085 # #Solo se encontro un pulso de transmision con ancho mayor a 1
1082 # return None
1086 # return None
1083 #
1087 #
1084 # #Eliminar pulsos anchos (deja solo la diferencia entre IPPs)
1088 # #Eliminar pulsos anchos (deja solo la diferencia entre IPPs)
1085 # realPulseIndex = pulseIndex[realIndex]
1089 # realPulseIndex = pulseIndex[realIndex]
1086 #
1090 #
1087 # period = mode(realPulseIndex[1:] - realPulseIndex[:-1])[0][0]
1091 # period = mode(realPulseIndex[1:] - realPulseIndex[:-1])[0][0]
1088 #
1092 #
1089 # print "IPP = %d samples" %period
1093 # print "IPP = %d samples" %period
1090 #
1094 #
1091 # self.__newNSamples = dataOut.nHeights #int(period)
1095 # self.__newNSamples = dataOut.nHeights #int(period)
1092 # self.__startIndex = int(realPulseIndex[0])
1096 # self.__startIndex = int(realPulseIndex[0])
1093 #
1097 #
1094 # return 1
1098 # return 1
1095 #
1099 #
1096 #
1100 #
1097 # def setup(self, nSamples, nChannels, buffer_size = 4):
1101 # def setup(self, nSamples, nChannels, buffer_size = 4):
1098 #
1102 #
1099 # self.__powBuffer = collections.deque(numpy.zeros( buffer_size*nSamples,dtype=numpy.float),
1103 # self.__powBuffer = collections.deque(numpy.zeros( buffer_size*nSamples,dtype=numpy.float),
1100 # maxlen = buffer_size*nSamples)
1104 # maxlen = buffer_size*nSamples)
1101 #
1105 #
1102 # bufferList = []
1106 # bufferList = []
1103 #
1107 #
1104 # for i in range(nChannels):
1108 # for i in range(nChannels):
1105 # bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=numpy.complex) + numpy.NAN,
1109 # bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=numpy.complex) + numpy.NAN,
1106 # maxlen = buffer_size*nSamples)
1110 # maxlen = buffer_size*nSamples)
1107 #
1111 #
1108 # bufferList.append(bufferByChannel)
1112 # bufferList.append(bufferByChannel)
1109 #
1113 #
1110 # self.__nSamples = nSamples
1114 # self.__nSamples = nSamples
1111 # self.__nChannels = nChannels
1115 # self.__nChannels = nChannels
1112 # self.__bufferList = bufferList
1116 # self.__bufferList = bufferList
1113 #
1117 #
1114 # def run(self, dataOut, channel = 0):
1118 # def run(self, dataOut, channel = 0):
1115 #
1119 #
1116 # if not self.isConfig:
1120 # if not self.isConfig:
1117 # nSamples = dataOut.nHeights
1121 # nSamples = dataOut.nHeights
1118 # nChannels = dataOut.nChannels
1122 # nChannels = dataOut.nChannels
1119 # self.setup(nSamples, nChannels)
1123 # self.setup(nSamples, nChannels)
1120 # self.isConfig = True
1124 # self.isConfig = True
1121 #
1125 #
1122 # #Append new data to internal buffer
1126 # #Append new data to internal buffer
1123 # for thisChannel in range(self.__nChannels):
1127 # for thisChannel in range(self.__nChannels):
1124 # bufferByChannel = self.__bufferList[thisChannel]
1128 # bufferByChannel = self.__bufferList[thisChannel]
1125 # bufferByChannel.extend(dataOut.data[thisChannel])
1129 # bufferByChannel.extend(dataOut.data[thisChannel])
1126 #
1130 #
1127 # if self.__pulseFound:
1131 # if self.__pulseFound:
1128 # self.__startIndex -= self.__nSamples
1132 # self.__startIndex -= self.__nSamples
1129 #
1133 #
1130 # #Finding Tx Pulse
1134 # #Finding Tx Pulse
1131 # if not self.__pulseFound:
1135 # if not self.__pulseFound:
1132 # indexFound = self.__findTxPulse(dataOut, channel)
1136 # indexFound = self.__findTxPulse(dataOut, channel)
1133 #
1137 #
1134 # if indexFound == None:
1138 # if indexFound == None:
1135 # dataOut.flagNoData = True
1139 # dataOut.flagNoData = True
1136 # return
1140 # return
1137 #
1141 #
1138 # self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = numpy.complex)
1142 # self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = numpy.complex)
1139 # self.__pulseFound = True
1143 # self.__pulseFound = True
1140 # self.__startIndex = indexFound
1144 # self.__startIndex = indexFound
1141 #
1145 #
1142 # #If pulse was found ...
1146 # #If pulse was found ...
1143 # for thisChannel in range(self.__nChannels):
1147 # for thisChannel in range(self.__nChannels):
1144 # bufferByChannel = self.__bufferList[thisChannel]
1148 # bufferByChannel = self.__bufferList[thisChannel]
1145 # #print self.__startIndex
1149 # #print self.__startIndex
1146 # x = numpy.array(bufferByChannel)
1150 # x = numpy.array(bufferByChannel)
1147 # self.__arrayBuffer[thisChannel] = x[self.__startIndex:self.__startIndex+self.__newNSamples]
1151 # self.__arrayBuffer[thisChannel] = x[self.__startIndex:self.__startIndex+self.__newNSamples]
1148 #
1152 #
1149 # deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
1153 # deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
1150 # dataOut.heightList = numpy.arange(self.__newNSamples)*deltaHeight
1154 # dataOut.heightList = numpy.arange(self.__newNSamples)*deltaHeight
1151 # # dataOut.ippSeconds = (self.__newNSamples / deltaHeight)/1e6
1155 # # dataOut.ippSeconds = (self.__newNSamples / deltaHeight)/1e6
1152 #
1156 #
1153 # dataOut.data = self.__arrayBuffer
1157 # dataOut.data = self.__arrayBuffer
1154 #
1158 #
1155 # self.__startIndex += self.__newNSamples
1159 # self.__startIndex += self.__newNSamples
1156 #
1160 #
1157 # return No newline at end of file
1161 # return
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