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JRODATA: timeInterval is a property now
Miguel Valdez -
r528:de1409f843e0
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@@ -1,339 +1,339
1 1 import numpy
2 2
3 3 from jroproc_base import ProcessingUnit, Operation
4 4 from model.data.jrodata import SpectraHeis
5 5
6 6 class SpectraHeisProc(ProcessingUnit):
7 7
8 8 def __init__(self):
9 9
10 10 ProcessingUnit.__init__(self)
11 11
12 12 # self.buffer = None
13 13 # self.firstdatatime = None
14 14 # self.profIndex = 0
15 15 self.dataOut = SpectraHeis()
16 16
17 17 def __updateObjFromInput(self):
18 18
19 19 self.dataOut.timeZone = self.dataIn.timeZone
20 20 self.dataOut.dstFlag = self.dataIn.dstFlag
21 21 self.dataOut.errorCount = self.dataIn.errorCount
22 22 self.dataOut.useLocalTime = self.dataIn.useLocalTime
23 23
24 24 self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()#
25 25 self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy()#
26 26 self.dataOut.channelList = self.dataIn.channelList
27 27 self.dataOut.heightList = self.dataIn.heightList
28 28 # self.dataOut.dtype = self.dataIn.dtype
29 29 self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')])
30 30 # self.dataOut.nHeights = self.dataIn.nHeights
31 31 # self.dataOut.nChannels = self.dataIn.nChannels
32 32 self.dataOut.nBaud = self.dataIn.nBaud
33 33 self.dataOut.nCode = self.dataIn.nCode
34 34 self.dataOut.code = self.dataIn.code
35 35 # self.dataOut.nProfiles = 1
36 36 # self.dataOut.nProfiles = self.dataOut.nFFTPoints
37 37 self.dataOut.nFFTPoints = self.dataIn.nHeights
38 38 # self.dataOut.channelIndexList = self.dataIn.channelIndexList
39 39 # self.dataOut.flagNoData = self.dataIn.flagNoData
40 40 self.dataOut.flagTimeBlock = self.dataIn.flagTimeBlock
41 41 self.dataOut.utctime = self.dataIn.utctime
42 42 # self.dataOut.utctime = self.firstdatatime
43 43 self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada
44 44 self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip
45 45 # self.dataOut.flagShiftFFT = self.dataIn.flagShiftFFT
46 46 self.dataOut.nCohInt = self.dataIn.nCohInt
47 47 self.dataOut.nIncohInt = 1
48 48 # self.dataOut.ippSeconds= self.dataIn.ippSeconds
49 49 self.dataOut.windowOfFilter = self.dataIn.windowOfFilter
50 50
51 51 self.dataOut.timeInterval = self.dataIn.timeInterval*self.dataOut.nIncohInt
52 52 # self.dataOut.set=self.dataIn.set
53 53 # self.dataOut.deltaHeight=self.dataIn.deltaHeight
54 54
55 55
56 56 def __updateObjFromFits(self):
57 57 self.dataOut.utctime = self.dataIn.utctime
58 58 self.dataOut.channelIndexList = self.dataIn.channelIndexList
59 59
60 60 self.dataOut.channelList = self.dataIn.channelList
61 61 self.dataOut.heightList = self.dataIn.heightList
62 62 self.dataOut.data_spc = self.dataIn.data
63 63 self.dataOut.timeInterval = self.dataIn.timeInterval
64 64 self.dataOut.timeZone = self.dataIn.timeZone
65 65 self.dataOut.useLocalTime = True
66 66 # self.dataOut.
67 67 # self.dataOut.
68 68
69 69 def __getFft(self):
70 70
71 71 fft_volt = numpy.fft.fft(self.dataIn.data, axis=1)
72 72 fft_volt = numpy.fft.fftshift(fft_volt,axes=(1,))
73 73 spc = numpy.abs(fft_volt * numpy.conjugate(fft_volt))/(self.dataOut.nFFTPoints)
74 74 self.dataOut.data_spc = spc
75 75
76 76 def run(self):
77 77
78 78 self.dataOut.flagNoData = True
79 79
80 80 if self.dataIn.type == "Fits":
81 81 self.__updateObjFromFits()
82 82 self.dataOut.flagNoData = False
83 83 return
84 84
85 85 if self.dataIn.type == "SpectraHeis":
86 86 self.dataOut.copy(self.dataIn)
87 87 return
88 88
89 89 if self.dataIn.type == "Voltage":
90 90 self.__updateObjFromInput()
91 91 self.__getFft()
92 92 self.dataOut.flagNoData = False
93 93
94 94 return
95 95
96 96 raise ValueError, "The type object %s is not valid"%(self.dataIn.type)
97 97
98 98
99 99 def selectChannels(self, channelList):
100 100
101 101 channelIndexList = []
102 102
103 103 for channel in channelList:
104 104 index = self.dataOut.channelList.index(channel)
105 105 channelIndexList.append(index)
106 106
107 107 self.selectChannelsByIndex(channelIndexList)
108 108
109 109 def selectChannelsByIndex(self, channelIndexList):
110 110 """
111 111 Selecciona un bloque de datos en base a canales segun el channelIndexList
112 112
113 113 Input:
114 114 channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
115 115
116 116 Affected:
117 117 self.dataOut.data
118 118 self.dataOut.channelIndexList
119 119 self.dataOut.nChannels
120 120 self.dataOut.m_ProcessingHeader.totalSpectra
121 121 self.dataOut.systemHeaderObj.numChannels
122 122 self.dataOut.m_ProcessingHeader.blockSize
123 123
124 124 Return:
125 125 None
126 126 """
127 127
128 128 for channelIndex in channelIndexList:
129 129 if channelIndex not in self.dataOut.channelIndexList:
130 130 print channelIndexList
131 131 raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex
132 132
133 133 # nChannels = len(channelIndexList)
134 134
135 135 data_spc = self.dataOut.data_spc[channelIndexList,:]
136 136
137 137 self.dataOut.data_spc = data_spc
138 138 self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
139 139
140 140 return 1
141 141
142 142 class IncohInt4SpectraHeis(Operation):
143 143
144 144 isConfig = False
145 145
146 146 __profIndex = 0
147 147 __withOverapping = False
148 148
149 149 __byTime = False
150 150 __initime = None
151 151 __lastdatatime = None
152 152 __integrationtime = None
153 153
154 154 __buffer = None
155 155
156 156 __dataReady = False
157 157
158 158 n = None
159 159
160 160
161 161 def __init__(self):
162 162
163 163 Operation.__init__(self)
164 164 # self.isConfig = False
165 165
166 166 def setup(self, n=None, timeInterval=None, overlapping=False):
167 167 """
168 168 Set the parameters of the integration class.
169 169
170 170 Inputs:
171 171
172 172 n : Number of coherent integrations
173 173 timeInterval : Time of integration. If the parameter "n" is selected this one does not work
174 174 overlapping :
175 175
176 176 """
177 177
178 178 self.__initime = None
179 179 self.__lastdatatime = 0
180 180 self.__buffer = None
181 181 self.__dataReady = False
182 182
183 183
184 184 if n == None and timeInterval == None:
185 185 raise ValueError, "n or timeInterval should be specified ..."
186 186
187 187 if n != None:
188 188 self.n = n
189 189 self.__byTime = False
190 190 else:
191 191 self.__integrationtime = timeInterval #* 60. #if (type(timeInterval)!=integer) -> change this line
192 192 self.n = 9999
193 193 self.__byTime = True
194 194
195 195 if overlapping:
196 196 self.__withOverapping = True
197 197 self.__buffer = None
198 198 else:
199 199 self.__withOverapping = False
200 200 self.__buffer = 0
201 201
202 202 self.__profIndex = 0
203 203
204 204 def putData(self, data):
205 205
206 206 """
207 207 Add a profile to the __buffer and increase in one the __profileIndex
208 208
209 209 """
210 210
211 211 if not self.__withOverapping:
212 212 self.__buffer += data.copy()
213 213 self.__profIndex += 1
214 214 return
215 215
216 216 #Overlapping data
217 217 nChannels, nHeis = data.shape
218 218 data = numpy.reshape(data, (1, nChannels, nHeis))
219 219
220 220 #If the buffer is empty then it takes the data value
221 221 if self.__buffer == None:
222 222 self.__buffer = data
223 223 self.__profIndex += 1
224 224 return
225 225
226 226 #If the buffer length is lower than n then stakcing the data value
227 227 if self.__profIndex < self.n:
228 228 self.__buffer = numpy.vstack((self.__buffer, data))
229 229 self.__profIndex += 1
230 230 return
231 231
232 232 #If the buffer length is equal to n then replacing the last buffer value with the data value
233 233 self.__buffer = numpy.roll(self.__buffer, -1, axis=0)
234 234 self.__buffer[self.n-1] = data
235 235 self.__profIndex = self.n
236 236 return
237 237
238 238
239 239 def pushData(self):
240 240 """
241 241 Return the sum of the last profiles and the profiles used in the sum.
242 242
243 243 Affected:
244 244
245 245 self.__profileIndex
246 246
247 247 """
248 248
249 249 if not self.__withOverapping:
250 250 data = self.__buffer
251 251 n = self.__profIndex
252 252
253 253 self.__buffer = 0
254 254 self.__profIndex = 0
255 255
256 256 return data, n
257 257
258 258 #Integration with Overlapping
259 259 data = numpy.sum(self.__buffer, axis=0)
260 260 n = self.__profIndex
261 261
262 262 return data, n
263 263
264 264 def byProfiles(self, data):
265 265
266 266 self.__dataReady = False
267 267 avgdata = None
268 268 # n = None
269 269
270 270 self.putData(data)
271 271
272 272 if self.__profIndex == self.n:
273 273
274 274 avgdata, n = self.pushData()
275 275 self.__dataReady = True
276 276
277 277 return avgdata
278 278
279 279 def byTime(self, data, datatime):
280 280
281 281 self.__dataReady = False
282 282 avgdata = None
283 283 n = None
284 284
285 285 self.putData(data)
286 286
287 287 if (datatime - self.__initime) >= self.__integrationtime:
288 288 avgdata, n = self.pushData()
289 289 self.n = n
290 290 self.__dataReady = True
291 291
292 292 return avgdata
293 293
294 294 def integrate(self, data, datatime=None):
295 295
296 296 if self.__initime == None:
297 297 self.__initime = datatime
298 298
299 299 if self.__byTime:
300 300 avgdata = self.byTime(data, datatime)
301 301 else:
302 302 avgdata = self.byProfiles(data)
303 303
304 304
305 305 self.__lastdatatime = datatime
306 306
307 307 if avgdata == None:
308 308 return None, None
309 309
310 310 avgdatatime = self.__initime
311 311
312 312 deltatime = datatime -self.__lastdatatime
313 313
314 314 if not self.__withOverapping:
315 315 self.__initime = datatime
316 316 else:
317 317 self.__initime += deltatime
318 318
319 319 return avgdata, avgdatatime
320 320
321 321 def run(self, dataOut, **kwargs):
322 322
323 323 if not self.isConfig:
324 324 self.setup(**kwargs)
325 325 self.isConfig = True
326 326
327 327 avgdata, avgdatatime = self.integrate(dataOut.data_spc, dataOut.utctime)
328 328
329 329 # dataOut.timeInterval *= n
330 330 dataOut.flagNoData = True
331 331
332 332 if self.__dataReady:
333 333 dataOut.data_spc = avgdata
334 334 dataOut.nIncohInt *= self.n
335 335 # dataOut.nCohInt *= self.n
336 336 dataOut.utctime = avgdatatime
337 dataOut.timeInterval = dataOut.ippSeconds * dataOut.nIncohInt
337 # dataOut.timeInterval = dataOut.ippSeconds * dataOut.nIncohInt
338 338 # dataOut.timeInterval = self.__timeInterval*self.n
339 339 dataOut.flagNoData = False No newline at end of file
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@@ -1,935 +1,935
1 1 import numpy
2 2 import math
3 3
4 4 from jroproc_base import ProcessingUnit, Operation
5 5 from model.data.jrodata import Spectra
6 6 from model.data.jrodata import hildebrand_sekhon
7 7
8 8 class SpectraProc(ProcessingUnit):
9 9
10 10 def __init__(self):
11 11
12 12 ProcessingUnit.__init__(self)
13 13
14 14 self.buffer = None
15 15 self.firstdatatime = None
16 16 self.profIndex = 0
17 17 self.dataOut = Spectra()
18 18 self.id_min = None
19 19 self.id_max = None
20 20
21 21 def __updateObjFromInput(self):
22 22
23 23 self.dataOut.timeZone = self.dataIn.timeZone
24 24 self.dataOut.dstFlag = self.dataIn.dstFlag
25 25 self.dataOut.errorCount = self.dataIn.errorCount
26 26 self.dataOut.useLocalTime = self.dataIn.useLocalTime
27 27
28 28 self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()
29 29 self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy()
30 30 self.dataOut.channelList = self.dataIn.channelList
31 31 self.dataOut.heightList = self.dataIn.heightList
32 32 self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')])
33 33 # self.dataOut.nHeights = self.dataIn.nHeights
34 34 # self.dataOut.nChannels = self.dataIn.nChannels
35 35 self.dataOut.nBaud = self.dataIn.nBaud
36 36 self.dataOut.nCode = self.dataIn.nCode
37 37 self.dataOut.code = self.dataIn.code
38 38 self.dataOut.nProfiles = self.dataOut.nFFTPoints
39 39 # self.dataOut.channelIndexList = self.dataIn.channelIndexList
40 40 self.dataOut.flagTimeBlock = self.dataIn.flagTimeBlock
41 41 self.dataOut.utctime = self.firstdatatime
42 42 self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada
43 43 self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip
44 44 # self.dataOut.flagShiftFFT = self.dataIn.flagShiftFFT
45 45 self.dataOut.nCohInt = self.dataIn.nCohInt
46 46 self.dataOut.nIncohInt = 1
47 47 # self.dataOut.ippSeconds = self.dataIn.ippSeconds
48 48 self.dataOut.windowOfFilter = self.dataIn.windowOfFilter
49 49
50 self.dataOut.timeInterval = self.dataIn.timeInterval*self.dataOut.nFFTPoints*self.dataOut.nIncohInt
50 # self.dataOut.timeInterval = self.dataIn.timeInterval*self.dataOut.nFFTPoints*self.dataOut.nIncohInt
51 51 self.dataOut.frequency = self.dataIn.frequency
52 52 self.dataOut.realtime = self.dataIn.realtime
53 53
54 54 self.dataOut.azimuth = self.dataIn.azimuth
55 55 self.dataOut.zenith = self.dataIn.zenith
56 56
57 57 self.dataOut.beam.codeList = self.dataIn.beam.codeList
58 58 self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList
59 59 self.dataOut.beam.zenithList = self.dataIn.beam.zenithList
60 60
61 61 def __getFft(self):
62 62 """
63 63 Convierte valores de Voltaje a Spectra
64 64
65 65 Affected:
66 66 self.dataOut.data_spc
67 67 self.dataOut.data_cspc
68 68 self.dataOut.data_dc
69 69 self.dataOut.heightList
70 70 self.profIndex
71 71 self.buffer
72 72 self.dataOut.flagNoData
73 73 """
74 74 fft_volt = numpy.fft.fft(self.buffer,n=self.dataOut.nFFTPoints,axis=1)
75 75 fft_volt = fft_volt.astype(numpy.dtype('complex'))
76 76 dc = fft_volt[:,0,:]
77 77
78 78 #calculo de self-spectra
79 79 fft_volt = numpy.fft.fftshift(fft_volt,axes=(1,))
80 80 spc = fft_volt * numpy.conjugate(fft_volt)
81 81 spc = spc.real
82 82
83 83 blocksize = 0
84 84 blocksize += dc.size
85 85 blocksize += spc.size
86 86
87 87 cspc = None
88 88 pairIndex = 0
89 89 if self.dataOut.pairsList != None:
90 90 #calculo de cross-spectra
91 91 cspc = numpy.zeros((self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex')
92 92 for pair in self.dataOut.pairsList:
93 93 cspc[pairIndex,:,:] = fft_volt[pair[0],:,:] * numpy.conjugate(fft_volt[pair[1],:,:])
94 94 pairIndex += 1
95 95 blocksize += cspc.size
96 96
97 97 self.dataOut.data_spc = spc
98 98 self.dataOut.data_cspc = cspc
99 99 self.dataOut.data_dc = dc
100 100 self.dataOut.blockSize = blocksize
101 101 self.dataOut.flagShiftFFT = False
102 102
103 103 def run(self, nProfiles=None, nFFTPoints=None, pairsList=[], ippFactor=None):
104 104
105 105 self.dataOut.flagNoData = True
106 106
107 107 if self.dataIn.type == "Spectra":
108 108 self.dataOut.copy(self.dataIn)
109 109 return True
110 110
111 111 if self.dataIn.type == "Voltage":
112 112
113 113 if nFFTPoints == None:
114 114 raise ValueError, "This SpectraProc.run() need nFFTPoints input variable"
115 115
116 116 if nProfiles == None:
117 117 raise ValueError, "This SpectraProc.run() need nProfiles input variable"
118 118
119 119
120 120 if ippFactor == None:
121 121 ippFactor = 1
122 122 self.dataOut.ippFactor = ippFactor
123 123
124 124 self.dataOut.nFFTPoints = nFFTPoints
125 125 self.dataOut.pairsList = pairsList
126 126
127 127 if self.buffer == None:
128 128 self.buffer = numpy.zeros((self.dataIn.nChannels,
129 129 nProfiles,
130 130 self.dataIn.nHeights),
131 131 dtype='complex')
132 132 self.id_min = 0
133 133 self.id_max = self.dataIn.data.shape[1]
134 134
135 135 if len(self.dataIn.data.shape) == 2:
136 136 self.buffer[:,self.profIndex,:] = self.dataIn.data.copy()
137 137 self.profIndex += 1
138 138 else:
139 139 if self.dataIn.data.shape[1] == nProfiles:
140 140 self.buffer = self.dataIn.data.copy()
141 141 self.profIndex = nProfiles
142 142 elif self.dataIn.data.shape[1] < nProfiles:
143 143 self.buffer[:,self.id_min:self.id_max,:] = self.dataIn.data
144 144 self.profIndex += self.dataIn.data.shape[1]
145 145 self.id_min += self.dataIn.data.shape[1]
146 146 self.id_max += self.dataIn.data.shape[1]
147 147 else:
148 148 raise ValueError, "The type object %s has %d profiles, it should be equal to %d profiles"%(self.dataIn.type,self.dataIn.data.shape[1],nProfiles)
149 149 self.dataOut.flagNoData = True
150 150 return 0
151 151
152 152
153 153 if self.firstdatatime == None:
154 154 self.firstdatatime = self.dataIn.utctime
155 155
156 156 if self.profIndex == nProfiles:
157 157 self.__updateObjFromInput()
158 158 self.__getFft()
159 159
160 160 self.dataOut.flagNoData = False
161 161
162 162 self.buffer = None
163 163 self.firstdatatime = None
164 164 self.profIndex = 0
165 165
166 166 return True
167 167
168 168 raise ValueError, "The type object %s is not valid"%(self.dataIn.type)
169 169
170 170 def selectChannels(self, channelList):
171 171
172 172 channelIndexList = []
173 173
174 174 for channel in channelList:
175 175 index = self.dataOut.channelList.index(channel)
176 176 channelIndexList.append(index)
177 177
178 178 self.selectChannelsByIndex(channelIndexList)
179 179
180 180 def selectChannelsByIndex(self, channelIndexList):
181 181 """
182 182 Selecciona un bloque de datos en base a canales segun el channelIndexList
183 183
184 184 Input:
185 185 channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
186 186
187 187 Affected:
188 188 self.dataOut.data_spc
189 189 self.dataOut.channelIndexList
190 190 self.dataOut.nChannels
191 191
192 192 Return:
193 193 None
194 194 """
195 195
196 196 for channelIndex in channelIndexList:
197 197 if channelIndex not in self.dataOut.channelIndexList:
198 198 print channelIndexList
199 199 raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex
200 200
201 201 # nChannels = len(channelIndexList)
202 202
203 203 data_spc = self.dataOut.data_spc[channelIndexList,:]
204 204
205 205 self.dataOut.data_spc = data_spc
206 206 self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
207 207 # self.dataOut.nChannels = nChannels
208 208
209 209 return 1
210 210
211 211 def selectHeights(self, minHei, maxHei):
212 212 """
213 213 Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
214 214 minHei <= height <= maxHei
215 215
216 216 Input:
217 217 minHei : valor minimo de altura a considerar
218 218 maxHei : valor maximo de altura a considerar
219 219
220 220 Affected:
221 221 Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
222 222
223 223 Return:
224 224 1 si el metodo se ejecuto con exito caso contrario devuelve 0
225 225 """
226 226 if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
227 227 raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
228 228
229 229 if (maxHei > self.dataOut.heightList[-1]):
230 230 maxHei = self.dataOut.heightList[-1]
231 231 # raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
232 232
233 233 minIndex = 0
234 234 maxIndex = 0
235 235 heights = self.dataOut.heightList
236 236
237 237 inda = numpy.where(heights >= minHei)
238 238 indb = numpy.where(heights <= maxHei)
239 239
240 240 try:
241 241 minIndex = inda[0][0]
242 242 except:
243 243 minIndex = 0
244 244
245 245 try:
246 246 maxIndex = indb[0][-1]
247 247 except:
248 248 maxIndex = len(heights)
249 249
250 250 self.selectHeightsByIndex(minIndex, maxIndex)
251 251
252 252 return 1
253 253
254 254 def getBeaconSignal(self, tauindex = 0, channelindex = 0, hei_ref=None):
255 255 newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex])
256 256
257 257 if hei_ref != None:
258 258 newheis = numpy.where(self.dataOut.heightList>hei_ref)
259 259
260 260 minIndex = min(newheis[0])
261 261 maxIndex = max(newheis[0])
262 262 data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1]
263 263 heightList = self.dataOut.heightList[minIndex:maxIndex+1]
264 264
265 265 # determina indices
266 266 nheis = int(self.dataOut.radarControllerHeaderObj.txB/(self.dataOut.heightList[1]-self.dataOut.heightList[0]))
267 267 avg_dB = 10*numpy.log10(numpy.sum(data_spc[channelindex,:,:],axis=0))
268 268 beacon_dB = numpy.sort(avg_dB)[-nheis:]
269 269 beacon_heiIndexList = []
270 270 for val in avg_dB.tolist():
271 271 if val >= beacon_dB[0]:
272 272 beacon_heiIndexList.append(avg_dB.tolist().index(val))
273 273
274 274 #data_spc = data_spc[:,:,beacon_heiIndexList]
275 275 data_cspc = None
276 276 if self.dataOut.data_cspc != None:
277 277 data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1]
278 278 #data_cspc = data_cspc[:,:,beacon_heiIndexList]
279 279
280 280 data_dc = None
281 281 if self.dataOut.data_dc != None:
282 282 data_dc = self.dataOut.data_dc[:,minIndex:maxIndex+1]
283 283 #data_dc = data_dc[:,beacon_heiIndexList]
284 284
285 285 self.dataOut.data_spc = data_spc
286 286 self.dataOut.data_cspc = data_cspc
287 287 self.dataOut.data_dc = data_dc
288 288 self.dataOut.heightList = heightList
289 289 self.dataOut.beacon_heiIndexList = beacon_heiIndexList
290 290
291 291 return 1
292 292
293 293
294 294 def selectHeightsByIndex(self, minIndex, maxIndex):
295 295 """
296 296 Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
297 297 minIndex <= index <= maxIndex
298 298
299 299 Input:
300 300 minIndex : valor de indice minimo de altura a considerar
301 301 maxIndex : valor de indice maximo de altura a considerar
302 302
303 303 Affected:
304 304 self.dataOut.data_spc
305 305 self.dataOut.data_cspc
306 306 self.dataOut.data_dc
307 307 self.dataOut.heightList
308 308
309 309 Return:
310 310 1 si el metodo se ejecuto con exito caso contrario devuelve 0
311 311 """
312 312
313 313 if (minIndex < 0) or (minIndex > maxIndex):
314 314 raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
315 315
316 316 if (maxIndex >= self.dataOut.nHeights):
317 317 maxIndex = self.dataOut.nHeights-1
318 318 # raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
319 319
320 320 # nHeights = maxIndex - minIndex + 1
321 321
322 322 #Spectra
323 323 data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1]
324 324
325 325 data_cspc = None
326 326 if self.dataOut.data_cspc != None:
327 327 data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1]
328 328
329 329 data_dc = None
330 330 if self.dataOut.data_dc != None:
331 331 data_dc = self.dataOut.data_dc[:,minIndex:maxIndex+1]
332 332
333 333 self.dataOut.data_spc = data_spc
334 334 self.dataOut.data_cspc = data_cspc
335 335 self.dataOut.data_dc = data_dc
336 336
337 337 self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1]
338 338
339 339 return 1
340 340
341 341 def removeDC(self, mode = 2):
342 342 jspectra = self.dataOut.data_spc
343 343 jcspectra = self.dataOut.data_cspc
344 344
345 345
346 346 num_chan = jspectra.shape[0]
347 347 num_hei = jspectra.shape[2]
348 348
349 349 if jcspectra != None:
350 350 jcspectraExist = True
351 351 num_pairs = jcspectra.shape[0]
352 352 else: jcspectraExist = False
353 353
354 354 freq_dc = jspectra.shape[1]/2
355 355 ind_vel = numpy.array([-2,-1,1,2]) + freq_dc
356 356
357 357 if ind_vel[0]<0:
358 358 ind_vel[range(0,1)] = ind_vel[range(0,1)] + self.num_prof
359 359
360 360 if mode == 1:
361 361 jspectra[:,freq_dc,:] = (jspectra[:,ind_vel[1],:] + jspectra[:,ind_vel[2],:])/2 #CORRECCION
362 362
363 363 if jcspectraExist:
364 364 jcspectra[:,freq_dc,:] = (jcspectra[:,ind_vel[1],:] + jcspectra[:,ind_vel[2],:])/2
365 365
366 366 if mode == 2:
367 367
368 368 vel = numpy.array([-2,-1,1,2])
369 369 xx = numpy.zeros([4,4])
370 370
371 371 for fil in range(4):
372 372 xx[fil,:] = vel[fil]**numpy.asarray(range(4))
373 373
374 374 xx_inv = numpy.linalg.inv(xx)
375 375 xx_aux = xx_inv[0,:]
376 376
377 377 for ich in range(num_chan):
378 378 yy = jspectra[ich,ind_vel,:]
379 379 jspectra[ich,freq_dc,:] = numpy.dot(xx_aux,yy)
380 380
381 381 junkid = jspectra[ich,freq_dc,:]<=0
382 382 cjunkid = sum(junkid)
383 383
384 384 if cjunkid.any():
385 385 jspectra[ich,freq_dc,junkid.nonzero()] = (jspectra[ich,ind_vel[1],junkid] + jspectra[ich,ind_vel[2],junkid])/2
386 386
387 387 if jcspectraExist:
388 388 for ip in range(num_pairs):
389 389 yy = jcspectra[ip,ind_vel,:]
390 390 jcspectra[ip,freq_dc,:] = numpy.dot(xx_aux,yy)
391 391
392 392
393 393 self.dataOut.data_spc = jspectra
394 394 self.dataOut.data_cspc = jcspectra
395 395
396 396 return 1
397 397
398 398 def removeInterference(self, interf = 2,hei_interf = None, nhei_interf = None, offhei_interf = None):
399 399
400 400 jspectra = self.dataOut.data_spc
401 401 jcspectra = self.dataOut.data_cspc
402 402 jnoise = self.dataOut.getNoise()
403 403 num_incoh = self.dataOut.nIncohInt
404 404
405 405 num_channel = jspectra.shape[0]
406 406 num_prof = jspectra.shape[1]
407 407 num_hei = jspectra.shape[2]
408 408
409 409 #hei_interf
410 410 if hei_interf == None:
411 411 count_hei = num_hei/2 #Como es entero no importa
412 412 hei_interf = numpy.asmatrix(range(count_hei)) + num_hei - count_hei
413 413 hei_interf = numpy.asarray(hei_interf)[0]
414 414 #nhei_interf
415 415 if (nhei_interf == None):
416 416 nhei_interf = 5
417 417 if (nhei_interf < 1):
418 418 nhei_interf = 1
419 419 if (nhei_interf > count_hei):
420 420 nhei_interf = count_hei
421 421 if (offhei_interf == None):
422 422 offhei_interf = 0
423 423
424 424 ind_hei = range(num_hei)
425 425 # mask_prof = numpy.asarray(range(num_prof - 2)) + 1
426 426 # mask_prof[range(num_prof/2 - 1,len(mask_prof))] += 1
427 427 mask_prof = numpy.asarray(range(num_prof))
428 428 num_mask_prof = mask_prof.size
429 429 comp_mask_prof = [0, num_prof/2]
430 430
431 431
432 432 #noise_exist: Determina si la variable jnoise ha sido definida y contiene la informacion del ruido de cada canal
433 433 if (jnoise.size < num_channel or numpy.isnan(jnoise).any()):
434 434 jnoise = numpy.nan
435 435 noise_exist = jnoise[0] < numpy.Inf
436 436
437 437 #Subrutina de Remocion de la Interferencia
438 438 for ich in range(num_channel):
439 439 #Se ordena los espectros segun su potencia (menor a mayor)
440 440 power = jspectra[ich,mask_prof,:]
441 441 power = power[:,hei_interf]
442 442 power = power.sum(axis = 0)
443 443 psort = power.ravel().argsort()
444 444
445 445 #Se estima la interferencia promedio en los Espectros de Potencia empleando
446 446 junkspc_interf = jspectra[ich,:,hei_interf[psort[range(offhei_interf, nhei_interf + offhei_interf)]]]
447 447
448 448 if noise_exist:
449 449 # tmp_noise = jnoise[ich] / num_prof
450 450 tmp_noise = jnoise[ich]
451 451 junkspc_interf = junkspc_interf - tmp_noise
452 452 #junkspc_interf[:,comp_mask_prof] = 0
453 453
454 454 jspc_interf = junkspc_interf.sum(axis = 0) / nhei_interf
455 455 jspc_interf = jspc_interf.transpose()
456 456 #Calculando el espectro de interferencia promedio
457 457 noiseid = numpy.where(jspc_interf <= tmp_noise/ math.sqrt(num_incoh))
458 458 noiseid = noiseid[0]
459 459 cnoiseid = noiseid.size
460 460 interfid = numpy.where(jspc_interf > tmp_noise/ math.sqrt(num_incoh))
461 461 interfid = interfid[0]
462 462 cinterfid = interfid.size
463 463
464 464 if (cnoiseid > 0): jspc_interf[noiseid] = 0
465 465
466 466 #Expandiendo los perfiles a limpiar
467 467 if (cinterfid > 0):
468 468 new_interfid = (numpy.r_[interfid - 1, interfid, interfid + 1] + num_prof)%num_prof
469 469 new_interfid = numpy.asarray(new_interfid)
470 470 new_interfid = {x for x in new_interfid}
471 471 new_interfid = numpy.array(list(new_interfid))
472 472 new_cinterfid = new_interfid.size
473 473 else: new_cinterfid = 0
474 474
475 475 for ip in range(new_cinterfid):
476 476 ind = junkspc_interf[:,new_interfid[ip]].ravel().argsort()
477 477 jspc_interf[new_interfid[ip]] = junkspc_interf[ind[nhei_interf/2],new_interfid[ip]]
478 478
479 479
480 480 jspectra[ich,:,ind_hei] = jspectra[ich,:,ind_hei] - jspc_interf #Corregir indices
481 481
482 482 #Removiendo la interferencia del punto de mayor interferencia
483 483 ListAux = jspc_interf[mask_prof].tolist()
484 484 maxid = ListAux.index(max(ListAux))
485 485
486 486
487 487 if cinterfid > 0:
488 488 for ip in range(cinterfid*(interf == 2) - 1):
489 489 ind = (jspectra[ich,interfid[ip],:] < tmp_noise*(1 + 1/math.sqrt(num_incoh))).nonzero()
490 490 cind = len(ind)
491 491
492 492 if (cind > 0):
493 493 jspectra[ich,interfid[ip],ind] = tmp_noise*(1 + (numpy.random.uniform(cind) - 0.5)/math.sqrt(num_incoh))
494 494
495 495 ind = numpy.array([-2,-1,1,2])
496 496 xx = numpy.zeros([4,4])
497 497
498 498 for id1 in range(4):
499 499 xx[:,id1] = ind[id1]**numpy.asarray(range(4))
500 500
501 501 xx_inv = numpy.linalg.inv(xx)
502 502 xx = xx_inv[:,0]
503 503 ind = (ind + maxid + num_mask_prof)%num_mask_prof
504 504 yy = jspectra[ich,mask_prof[ind],:]
505 505 jspectra[ich,mask_prof[maxid],:] = numpy.dot(yy.transpose(),xx)
506 506
507 507
508 508 indAux = (jspectra[ich,:,:] < tmp_noise*(1-1/math.sqrt(num_incoh))).nonzero()
509 509 jspectra[ich,indAux[0],indAux[1]] = tmp_noise * (1 - 1/math.sqrt(num_incoh))
510 510
511 511 #Remocion de Interferencia en el Cross Spectra
512 512 if jcspectra == None: return jspectra, jcspectra
513 513 num_pairs = jcspectra.size/(num_prof*num_hei)
514 514 jcspectra = jcspectra.reshape(num_pairs, num_prof, num_hei)
515 515
516 516 for ip in range(num_pairs):
517 517
518 518 #-------------------------------------------
519 519
520 520 cspower = numpy.abs(jcspectra[ip,mask_prof,:])
521 521 cspower = cspower[:,hei_interf]
522 522 cspower = cspower.sum(axis = 0)
523 523
524 524 cspsort = cspower.ravel().argsort()
525 525 junkcspc_interf = jcspectra[ip,:,hei_interf[cspsort[range(offhei_interf, nhei_interf + offhei_interf)]]]
526 526 junkcspc_interf = junkcspc_interf.transpose()
527 527 jcspc_interf = junkcspc_interf.sum(axis = 1)/nhei_interf
528 528
529 529 ind = numpy.abs(jcspc_interf[mask_prof]).ravel().argsort()
530 530
531 531 median_real = numpy.median(numpy.real(junkcspc_interf[mask_prof[ind[range(3*num_prof/4)]],:]))
532 532 median_imag = numpy.median(numpy.imag(junkcspc_interf[mask_prof[ind[range(3*num_prof/4)]],:]))
533 533 junkcspc_interf[comp_mask_prof,:] = numpy.complex(median_real, median_imag)
534 534
535 535 for iprof in range(num_prof):
536 536 ind = numpy.abs(junkcspc_interf[iprof,:]).ravel().argsort()
537 537 jcspc_interf[iprof] = junkcspc_interf[iprof, ind[nhei_interf/2]]
538 538
539 539 #Removiendo la Interferencia
540 540 jcspectra[ip,:,ind_hei] = jcspectra[ip,:,ind_hei] - jcspc_interf
541 541
542 542 ListAux = numpy.abs(jcspc_interf[mask_prof]).tolist()
543 543 maxid = ListAux.index(max(ListAux))
544 544
545 545 ind = numpy.array([-2,-1,1,2])
546 546 xx = numpy.zeros([4,4])
547 547
548 548 for id1 in range(4):
549 549 xx[:,id1] = ind[id1]**numpy.asarray(range(4))
550 550
551 551 xx_inv = numpy.linalg.inv(xx)
552 552 xx = xx_inv[:,0]
553 553
554 554 ind = (ind + maxid + num_mask_prof)%num_mask_prof
555 555 yy = jcspectra[ip,mask_prof[ind],:]
556 556 jcspectra[ip,mask_prof[maxid],:] = numpy.dot(yy.transpose(),xx)
557 557
558 558 #Guardar Resultados
559 559 self.dataOut.data_spc = jspectra
560 560 self.dataOut.data_cspc = jcspectra
561 561
562 562 return 1
563 563
564 564 def setRadarFrequency(self, frequency=None):
565 565 if frequency != None:
566 566 self.dataOut.frequency = frequency
567 567
568 568 return 1
569 569
570 570 def getNoise(self, minHei=None, maxHei=None, minVel=None, maxVel=None):
571 571 #validacion de rango
572 572 if minHei == None:
573 573 minHei = self.dataOut.heightList[0]
574 574
575 575 if maxHei == None:
576 576 maxHei = self.dataOut.heightList[-1]
577 577
578 578 if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
579 579 print 'minHei: %.2f is out of the heights range'%(minHei)
580 580 print 'minHei is setting to %.2f'%(self.dataOut.heightList[0])
581 581 minHei = self.dataOut.heightList[0]
582 582
583 583 if (maxHei > self.dataOut.heightList[-1]) or (maxHei < minHei):
584 584 print 'maxHei: %.2f is out of the heights range'%(maxHei)
585 585 print 'maxHei is setting to %.2f'%(self.dataOut.heightList[-1])
586 586 maxHei = self.dataOut.heightList[-1]
587 587
588 588 # validacion de velocidades
589 589 velrange = self.dataOut.getVelRange(1)
590 590
591 591 if minVel == None:
592 592 minVel = velrange[0]
593 593
594 594 if maxVel == None:
595 595 maxVel = velrange[-1]
596 596
597 597 if (minVel < velrange[0]) or (minVel > maxVel):
598 598 print 'minVel: %.2f is out of the velocity range'%(minVel)
599 599 print 'minVel is setting to %.2f'%(velrange[0])
600 600 minVel = velrange[0]
601 601
602 602 if (maxVel > velrange[-1]) or (maxVel < minVel):
603 603 print 'maxVel: %.2f is out of the velocity range'%(maxVel)
604 604 print 'maxVel is setting to %.2f'%(velrange[-1])
605 605 maxVel = velrange[-1]
606 606
607 607 # seleccion de indices para rango
608 608 minIndex = 0
609 609 maxIndex = 0
610 610 heights = self.dataOut.heightList
611 611
612 612 inda = numpy.where(heights >= minHei)
613 613 indb = numpy.where(heights <= maxHei)
614 614
615 615 try:
616 616 minIndex = inda[0][0]
617 617 except:
618 618 minIndex = 0
619 619
620 620 try:
621 621 maxIndex = indb[0][-1]
622 622 except:
623 623 maxIndex = len(heights)
624 624
625 625 if (minIndex < 0) or (minIndex > maxIndex):
626 626 raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
627 627
628 628 if (maxIndex >= self.dataOut.nHeights):
629 629 maxIndex = self.dataOut.nHeights-1
630 630
631 631 # seleccion de indices para velocidades
632 632 indminvel = numpy.where(velrange >= minVel)
633 633 indmaxvel = numpy.where(velrange <= maxVel)
634 634 try:
635 635 minIndexVel = indminvel[0][0]
636 636 except:
637 637 minIndexVel = 0
638 638
639 639 try:
640 640 maxIndexVel = indmaxvel[0][-1]
641 641 except:
642 642 maxIndexVel = len(velrange)
643 643
644 644 #seleccion del espectro
645 645 data_spc = self.dataOut.data_spc[:,minIndexVel:maxIndexVel+1,minIndex:maxIndex+1]
646 646 #estimacion de ruido
647 647 noise = numpy.zeros(self.dataOut.nChannels)
648 648
649 649 for channel in range(self.dataOut.nChannels):
650 650 daux = data_spc[channel,:,:]
651 651 noise[channel] = hildebrand_sekhon(daux, self.dataOut.nIncohInt)
652 652
653 653 self.dataOut.noise_estimation = noise.copy()
654 654
655 655 return 1
656 656
657 657 class IncohInt(Operation):
658 658
659 659
660 660 __profIndex = 0
661 661 __withOverapping = False
662 662
663 663 __byTime = False
664 664 __initime = None
665 665 __lastdatatime = None
666 666 __integrationtime = None
667 667
668 668 __buffer_spc = None
669 669 __buffer_cspc = None
670 670 __buffer_dc = None
671 671
672 672 __dataReady = False
673 673
674 674 __timeInterval = None
675 675
676 676 n = None
677 677
678 678
679 679
680 680 def __init__(self):
681 681
682 682 Operation.__init__(self)
683 683 # self.isConfig = False
684 684
685 685 def setup(self, n=None, timeInterval=None, overlapping=False):
686 686 """
687 687 Set the parameters of the integration class.
688 688
689 689 Inputs:
690 690
691 691 n : Number of coherent integrations
692 692 timeInterval : Time of integration. If the parameter "n" is selected this one does not work
693 693 overlapping :
694 694
695 695 """
696 696
697 697 self.__initime = None
698 698 self.__lastdatatime = 0
699 699 self.__buffer_spc = None
700 700 self.__buffer_cspc = None
701 701 self.__buffer_dc = None
702 702 self.__dataReady = False
703 703
704 704
705 705 if n == None and timeInterval == None:
706 706 raise ValueError, "n or timeInterval should be specified ..."
707 707
708 708 if n != None:
709 709 self.n = n
710 710 self.__byTime = False
711 711 else:
712 712 self.__integrationtime = timeInterval #if (type(timeInterval)!=integer) -> change this line
713 713 self.n = 9999
714 714 self.__byTime = True
715 715
716 716 if overlapping:
717 717 self.__withOverapping = True
718 718 else:
719 719 self.__withOverapping = False
720 720 self.__buffer_spc = 0
721 721 self.__buffer_cspc = 0
722 722 self.__buffer_dc = 0
723 723
724 724 self.__profIndex = 0
725 725
726 726 def putData(self, data_spc, data_cspc, data_dc):
727 727
728 728 """
729 729 Add a profile to the __buffer_spc and increase in one the __profileIndex
730 730
731 731 """
732 732
733 733 if not self.__withOverapping:
734 734 self.__buffer_spc += data_spc
735 735
736 736 if data_cspc == None:
737 737 self.__buffer_cspc = None
738 738 else:
739 739 self.__buffer_cspc += data_cspc
740 740
741 741 if data_dc == None:
742 742 self.__buffer_dc = None
743 743 else:
744 744 self.__buffer_dc += data_dc
745 745
746 746 self.__profIndex += 1
747 747 return
748 748
749 749 #Overlapping data
750 750 nChannels, nFFTPoints, nHeis = data_spc.shape
751 751 data_spc = numpy.reshape(data_spc, (1, nChannels, nFFTPoints, nHeis))
752 752 if data_cspc != None:
753 753 data_cspc = numpy.reshape(data_cspc, (1, -1, nFFTPoints, nHeis))
754 754 if data_dc != None:
755 755 data_dc = numpy.reshape(data_dc, (1, -1, nHeis))
756 756
757 757 #If the buffer is empty then it takes the data value
758 758 if self.__buffer_spc == None:
759 759 self.__buffer_spc = data_spc
760 760
761 761 if data_cspc == None:
762 762 self.__buffer_cspc = None
763 763 else:
764 764 self.__buffer_cspc += data_cspc
765 765
766 766 if data_dc == None:
767 767 self.__buffer_dc = None
768 768 else:
769 769 self.__buffer_dc += data_dc
770 770
771 771 self.__profIndex += 1
772 772 return
773 773
774 774 #If the buffer length is lower than n then stakcing the data value
775 775 if self.__profIndex < self.n:
776 776 self.__buffer_spc = numpy.vstack((self.__buffer_spc, data_spc))
777 777
778 778 if data_cspc != None:
779 779 self.__buffer_cspc = numpy.vstack((self.__buffer_cspc, data_cspc))
780 780
781 781 if data_dc != None:
782 782 self.__buffer_dc = numpy.vstack((self.__buffer_dc, data_dc))
783 783
784 784 self.__profIndex += 1
785 785 return
786 786
787 787 #If the buffer length is equal to n then replacing the last buffer value with the data value
788 788 self.__buffer_spc = numpy.roll(self.__buffer_spc, -1, axis=0)
789 789 self.__buffer_spc[self.n-1] = data_spc
790 790
791 791 if data_cspc != None:
792 792 self.__buffer_cspc = numpy.roll(self.__buffer_cspc, -1, axis=0)
793 793 self.__buffer_cspc[self.n-1] = data_cspc
794 794
795 795 if data_dc != None:
796 796 self.__buffer_dc = numpy.roll(self.__buffer_dc, -1, axis=0)
797 797 self.__buffer_dc[self.n-1] = data_dc
798 798
799 799 self.__profIndex = self.n
800 800 return
801 801
802 802
803 803 def pushData(self):
804 804 """
805 805 Return the sum of the last profiles and the profiles used in the sum.
806 806
807 807 Affected:
808 808
809 809 self.__profileIndex
810 810
811 811 """
812 812 data_spc = None
813 813 data_cspc = None
814 814 data_dc = None
815 815
816 816 if not self.__withOverapping:
817 817 data_spc = self.__buffer_spc
818 818 data_cspc = self.__buffer_cspc
819 819 data_dc = self.__buffer_dc
820 820
821 821 n = self.__profIndex
822 822
823 823 self.__buffer_spc = 0
824 824 self.__buffer_cspc = 0
825 825 self.__buffer_dc = 0
826 826 self.__profIndex = 0
827 827
828 828 return data_spc, data_cspc, data_dc, n
829 829
830 830 #Integration with Overlapping
831 831 data_spc = numpy.sum(self.__buffer_spc, axis=0)
832 832
833 833 if self.__buffer_cspc != None:
834 834 data_cspc = numpy.sum(self.__buffer_cspc, axis=0)
835 835
836 836 if self.__buffer_dc != None:
837 837 data_dc = numpy.sum(self.__buffer_dc, axis=0)
838 838
839 839 n = self.__profIndex
840 840
841 841 return data_spc, data_cspc, data_dc, n
842 842
843 843 def byProfiles(self, *args):
844 844
845 845 self.__dataReady = False
846 846 avgdata_spc = None
847 847 avgdata_cspc = None
848 848 avgdata_dc = None
849 849 # n = None
850 850
851 851 self.putData(*args)
852 852
853 853 if self.__profIndex == self.n:
854 854
855 855 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
856 856 self.__dataReady = True
857 857
858 858 return avgdata_spc, avgdata_cspc, avgdata_dc
859 859
860 860 def byTime(self, datatime, *args):
861 861
862 862 self.__dataReady = False
863 863 avgdata_spc = None
864 864 avgdata_cspc = None
865 865 avgdata_dc = None
866 866 n = None
867 867
868 868 self.putData(*args)
869 869
870 870 if (datatime - self.__initime) >= self.__integrationtime:
871 871 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
872 872 self.n = n
873 873 self.__dataReady = True
874 874
875 875 return avgdata_spc, avgdata_cspc, avgdata_dc
876 876
877 877 def integrate(self, datatime, *args):
878 878
879 879 if self.__initime == None:
880 880 self.__initime = datatime
881 881
882 882 if self.__byTime:
883 883 avgdata_spc, avgdata_cspc, avgdata_dc = self.byTime(datatime, *args)
884 884 else:
885 885 avgdata_spc, avgdata_cspc, avgdata_dc = self.byProfiles(*args)
886 886
887 887 self.__lastdatatime = datatime
888 888
889 889 if avgdata_spc == None:
890 890 return None, None, None, None
891 891
892 892 avgdatatime = self.__initime
893 893 try:
894 894 self.__timeInterval = (self.__lastdatatime - self.__initime)/(self.n - 1)
895 895 except:
896 896 self.__timeInterval = self.__lastdatatime - self.__initime
897 897
898 898 deltatime = datatime -self.__lastdatatime
899 899
900 900 if not self.__withOverapping:
901 901 self.__initime = datatime
902 902 else:
903 903 self.__initime += deltatime
904 904
905 905 return avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc
906 906
907 907 def run(self, dataOut, n=None, timeInterval=None, overlapping=False):
908 908
909 909 if n==1:
910 910 dataOut.flagNoData = False
911 911 return
912 912
913 913 if not self.isConfig:
914 914 self.setup(n, timeInterval, overlapping)
915 915 self.isConfig = True
916 916
917 917 avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(dataOut.utctime,
918 918 dataOut.data_spc,
919 919 dataOut.data_cspc,
920 920 dataOut.data_dc)
921 921
922 922 # dataOut.timeInterval *= n
923 923 dataOut.flagNoData = True
924 924
925 925 if self.__dataReady:
926 926
927 927 dataOut.data_spc = avgdata_spc
928 928 dataOut.data_cspc = avgdata_cspc
929 929 dataOut.data_dc = avgdata_dc
930 930
931 931 dataOut.nIncohInt *= self.n
932 932 dataOut.utctime = avgdatatime
933 933 #dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt * dataOut.nIncohInt * dataOut.nFFTPoints
934 dataOut.timeInterval = self.__timeInterval*self.n
934 # dataOut.timeInterval = self.__timeInterval*self.n
935 935 dataOut.flagNoData = False
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