| @@ -1,937 +1,939 | |||||
| 1 | # Copyright (c) 2012-2020 Jicamarca Radio Observatory |
|
1 | # Copyright (c) 2012-2020 Jicamarca Radio Observatory | |
| 2 | # All rights reserved. |
|
2 | # All rights reserved. | |
| 3 | # |
|
3 | # | |
| 4 | # Distributed under the terms of the BSD 3-clause license. |
|
4 | # Distributed under the terms of the BSD 3-clause license. | |
| 5 | """Spectra processing Unit and operations |
|
5 | """Spectra processing Unit and operations | |
| 6 |
|
6 | |||
| 7 | Here you will find the processing unit `SpectraProc` and several operations |
|
7 | Here you will find the processing unit `SpectraProc` and several operations | |
| 8 | to work with Spectra data type |
|
8 | to work with Spectra data type | |
| 9 | """ |
|
9 | """ | |
| 10 |
|
10 | |||
| 11 | import time |
|
11 | import time | |
| 12 | import itertools |
|
12 | import itertools | |
| 13 | import numpy |
|
13 | import numpy | |
| 14 | # repositorio |
|
14 | # repositorio | |
| 15 | from schainpy.model.proc.jroproc_base import ProcessingUnit, MPDecorator, Operation |
|
15 | from schainpy.model.proc.jroproc_base import ProcessingUnit, MPDecorator, Operation | |
| 16 | from schainpy.model.data.jrodata import Spectra |
|
16 | from schainpy.model.data.jrodata import Spectra | |
| 17 | from schainpy.model.data.jrodata import hildebrand_sekhon |
|
17 | from schainpy.model.data.jrodata import hildebrand_sekhon | |
| 18 | from schainpy.utils import log |
|
18 | from schainpy.utils import log | |
| 19 |
|
19 | |||
| 20 |
|
20 | |||
| 21 | class SpectraProc(ProcessingUnit): |
|
21 | class SpectraProc(ProcessingUnit): | |
| 22 |
|
22 | |||
| 23 | def __init__(self): |
|
23 | def __init__(self): | |
| 24 |
|
24 | |||
| 25 | ProcessingUnit.__init__(self) |
|
25 | ProcessingUnit.__init__(self) | |
| 26 |
|
26 | |||
| 27 | self.buffer = None |
|
27 | self.buffer = None | |
| 28 | self.firstdatatime = None |
|
28 | self.firstdatatime = None | |
| 29 | self.profIndex = 0 |
|
29 | self.profIndex = 0 | |
| 30 | self.dataOut = Spectra() |
|
30 | self.dataOut = Spectra() | |
| 31 | self.id_min = None |
|
31 | self.id_min = None | |
| 32 | self.id_max = None |
|
32 | self.id_max = None | |
| 33 | self.setupReq = False #Agregar a todas las unidades de proc |
|
33 | self.setupReq = False #Agregar a todas las unidades de proc | |
| 34 |
|
34 | |||
| 35 | def __updateSpecFromVoltage(self): |
|
35 | def __updateSpecFromVoltage(self): | |
| 36 |
|
36 | |||
| 37 | self.dataOut.timeZone = self.dataIn.timeZone |
|
37 | self.dataOut.timeZone = self.dataIn.timeZone | |
| 38 | self.dataOut.dstFlag = self.dataIn.dstFlag |
|
38 | self.dataOut.dstFlag = self.dataIn.dstFlag | |
| 39 | self.dataOut.errorCount = self.dataIn.errorCount |
|
39 | self.dataOut.errorCount = self.dataIn.errorCount | |
| 40 | self.dataOut.useLocalTime = self.dataIn.useLocalTime |
|
40 | self.dataOut.useLocalTime = self.dataIn.useLocalTime | |
| 41 | try: |
|
41 | try: | |
| 42 | self.dataOut.processingHeaderObj = self.dataIn.processingHeaderObj.copy() |
|
42 | self.dataOut.processingHeaderObj = self.dataIn.processingHeaderObj.copy() | |
| 43 | except: |
|
43 | except: | |
| 44 | pass |
|
44 | pass | |
| 45 | self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() |
|
45 | self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() | |
| 46 | self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy() |
|
46 | self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy() | |
| 47 | self.dataOut.channelList = self.dataIn.channelList |
|
47 | self.dataOut.channelList = self.dataIn.channelList | |
| 48 | self.dataOut.heightList = self.dataIn.heightList |
|
48 | self.dataOut.heightList = self.dataIn.heightList | |
| 49 | self.dataOut.dtype = numpy.dtype([('real', '<f4'), ('imag', '<f4')]) |
|
49 | self.dataOut.dtype = numpy.dtype([('real', '<f4'), ('imag', '<f4')]) | |
| 50 | self.dataOut.nProfiles = self.dataOut.nFFTPoints |
|
50 | self.dataOut.nProfiles = self.dataOut.nFFTPoints | |
| 51 | self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock |
|
51 | self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock | |
| 52 | self.dataOut.utctime = self.firstdatatime |
|
52 | self.dataOut.utctime = self.firstdatatime | |
| 53 | self.dataOut.flagDecodeData = self.dataIn.flagDecodeData |
|
53 | self.dataOut.flagDecodeData = self.dataIn.flagDecodeData | |
| 54 | self.dataOut.flagDeflipData = self.dataIn.flagDeflipData |
|
54 | self.dataOut.flagDeflipData = self.dataIn.flagDeflipData | |
| 55 | self.dataOut.flagShiftFFT = False |
|
55 | self.dataOut.flagShiftFFT = False | |
| 56 | self.dataOut.nCohInt = self.dataIn.nCohInt |
|
56 | self.dataOut.nCohInt = self.dataIn.nCohInt | |
| 57 | self.dataOut.nIncohInt = 1 |
|
57 | self.dataOut.nIncohInt = 1 | |
| 58 | self.dataOut.windowOfFilter = self.dataIn.windowOfFilter |
|
58 | self.dataOut.windowOfFilter = self.dataIn.windowOfFilter | |
| 59 | self.dataOut.frequency = self.dataIn.frequency |
|
59 | self.dataOut.frequency = self.dataIn.frequency | |
| 60 | self.dataOut.realtime = self.dataIn.realtime |
|
60 | self.dataOut.realtime = self.dataIn.realtime | |
| 61 | self.dataOut.azimuth = self.dataIn.azimuth |
|
61 | self.dataOut.azimuth = self.dataIn.azimuth | |
| 62 | self.dataOut.zenith = self.dataIn.zenith |
|
62 | self.dataOut.zenith = self.dataIn.zenith | |
| 63 | self.dataOut.beam.codeList = self.dataIn.beam.codeList |
|
63 | self.dataOut.beam.codeList = self.dataIn.beam.codeList | |
| 64 | self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList |
|
64 | self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList | |
| 65 | self.dataOut.beam.zenithList = self.dataIn.beam.zenithList |
|
65 | self.dataOut.beam.zenithList = self.dataIn.beam.zenithList | |
| 66 | self.dataOut.runNextUnit = self.dataIn.runNextUnit |
|
66 | self.dataOut.runNextUnit = self.dataIn.runNextUnit | |
| 67 | try: |
|
67 | try: | |
| 68 | self.dataOut.step = self.dataIn.step |
|
68 | self.dataOut.step = self.dataIn.step | |
| 69 | except: |
|
69 | except: | |
| 70 | pass |
|
70 | pass | |
| 71 |
|
71 | |||
| 72 | def __getFft(self): |
|
72 | def __getFft(self): | |
| 73 | """ |
|
73 | """ | |
| 74 | Convierte valores de Voltaje a Spectra |
|
74 | Convierte valores de Voltaje a Spectra | |
| 75 |
|
75 | |||
| 76 | Affected: |
|
76 | Affected: | |
| 77 | self.dataOut.data_spc |
|
77 | self.dataOut.data_spc | |
| 78 | self.dataOut.data_cspc |
|
78 | self.dataOut.data_cspc | |
| 79 | self.dataOut.data_dc |
|
79 | self.dataOut.data_dc | |
| 80 | self.dataOut.heightList |
|
80 | self.dataOut.heightList | |
| 81 | self.profIndex |
|
81 | self.profIndex | |
| 82 | self.buffer |
|
82 | self.buffer | |
| 83 | self.dataOut.flagNoData |
|
83 | self.dataOut.flagNoData | |
| 84 | """ |
|
84 | """ | |
| 85 | fft_volt = numpy.fft.fft( |
|
85 | fft_volt = numpy.fft.fft( | |
| 86 | self.buffer, n=self.dataOut.nFFTPoints, axis=1) |
|
86 | self.buffer, n=self.dataOut.nFFTPoints, axis=1) | |
| 87 | fft_volt = fft_volt.astype(numpy.dtype('complex')) |
|
87 | fft_volt = fft_volt.astype(numpy.dtype('complex')) | |
| 88 | dc = fft_volt[:, 0, :] |
|
88 | dc = fft_volt[:, 0, :] | |
| 89 |
|
89 | |||
| 90 | # calculo de self-spectra |
|
90 | # calculo de self-spectra | |
| 91 | fft_volt = numpy.fft.fftshift(fft_volt, axes=(1,)) |
|
91 | fft_volt = numpy.fft.fftshift(fft_volt, axes=(1,)) | |
| 92 | spc = fft_volt * numpy.conjugate(fft_volt) |
|
92 | spc = fft_volt * numpy.conjugate(fft_volt) | |
| 93 | spc = spc.real |
|
93 | spc = spc.real | |
| 94 |
|
94 | |||
| 95 | blocksize = 0 |
|
95 | blocksize = 0 | |
| 96 | blocksize += dc.size |
|
96 | blocksize += dc.size | |
| 97 | blocksize += spc.size |
|
97 | blocksize += spc.size | |
| 98 |
|
98 | |||
| 99 | cspc = None |
|
99 | cspc = None | |
| 100 | pairIndex = 0 |
|
100 | pairIndex = 0 | |
| 101 | if self.dataOut.pairsList != None: |
|
101 | if self.dataOut.pairsList != None: | |
| 102 | # calculo de cross-spectra |
|
102 | # calculo de cross-spectra | |
| 103 | cspc = numpy.zeros( |
|
103 | cspc = numpy.zeros( | |
| 104 | (self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex') |
|
104 | (self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex') | |
| 105 | for pair in self.dataOut.pairsList: |
|
105 | for pair in self.dataOut.pairsList: | |
| 106 | if pair[0] not in self.dataOut.channelList: |
|
106 | if pair[0] not in self.dataOut.channelList: | |
| 107 | raise ValueError("Error getting CrossSpectra: pair 0 of %s is not in channelList = %s" % ( |
|
107 | raise ValueError("Error getting CrossSpectra: pair 0 of %s is not in channelList = %s" % ( | |
| 108 | str(pair), str(self.dataOut.channelList))) |
|
108 | str(pair), str(self.dataOut.channelList))) | |
| 109 | if pair[1] not in self.dataOut.channelList: |
|
109 | if pair[1] not in self.dataOut.channelList: | |
| 110 | raise ValueError("Error getting CrossSpectra: pair 1 of %s is not in channelList = %s" % ( |
|
110 | raise ValueError("Error getting CrossSpectra: pair 1 of %s is not in channelList = %s" % ( | |
| 111 | str(pair), str(self.dataOut.channelList))) |
|
111 | str(pair), str(self.dataOut.channelList))) | |
| 112 |
|
112 | |||
| 113 | cspc[pairIndex, :, :] = fft_volt[pair[0], :, :] * \ |
|
113 | cspc[pairIndex, :, :] = fft_volt[pair[0], :, :] * \ | |
| 114 | numpy.conjugate(fft_volt[pair[1], :, :]) |
|
114 | numpy.conjugate(fft_volt[pair[1], :, :]) | |
| 115 | pairIndex += 1 |
|
115 | pairIndex += 1 | |
| 116 | blocksize += cspc.size |
|
116 | blocksize += cspc.size | |
| 117 |
|
117 | |||
| 118 | self.dataOut.data_spc = spc |
|
118 | self.dataOut.data_spc = spc | |
| 119 | self.dataOut.data_cspc = cspc |
|
119 | self.dataOut.data_cspc = cspc | |
| 120 | self.dataOut.data_dc = dc |
|
120 | self.dataOut.data_dc = dc | |
| 121 | self.dataOut.blockSize = blocksize |
|
121 | self.dataOut.blockSize = blocksize | |
| 122 | self.dataOut.flagShiftFFT = False |
|
122 | self.dataOut.flagShiftFFT = False | |
| 123 |
|
123 | |||
| 124 | def run(self, nProfiles=None, nFFTPoints=None, pairsList=None, ippFactor=None, shift_fft=False, runNextUnit = 0): |
|
124 | def run(self, nProfiles=None, nFFTPoints=None, pairsList=None, ippFactor=None, shift_fft=False, runNextUnit = 0): | |
| 125 |
|
125 | |||
| 126 | self.dataIn.runNextUnit = runNextUnit |
|
126 | self.dataIn.runNextUnit = runNextUnit | |
| 127 | if self.dataIn.type == "Spectra": |
|
127 | if self.dataIn.type == "Spectra": | |
| 128 | self.dataOut.copy(self.dataIn) |
|
128 | self.dataOut.copy(self.dataIn) | |
| 129 | if shift_fft: |
|
129 | if shift_fft: | |
| 130 | #desplaza a la derecha en el eje 2 determinadas posiciones |
|
130 | #desplaza a la derecha en el eje 2 determinadas posiciones | |
| 131 | shift = int(self.dataOut.nFFTPoints/2) |
|
131 | shift = int(self.dataOut.nFFTPoints/2) | |
| 132 | self.dataOut.data_spc = numpy.roll(self.dataOut.data_spc, shift , axis=1) |
|
132 | self.dataOut.data_spc = numpy.roll(self.dataOut.data_spc, shift , axis=1) | |
| 133 |
|
133 | |||
| 134 | if self.dataOut.data_cspc is not None: |
|
134 | if self.dataOut.data_cspc is not None: | |
| 135 | #desplaza a la derecha en el eje 2 determinadas posiciones |
|
135 | #desplaza a la derecha en el eje 2 determinadas posiciones | |
| 136 | self.dataOut.data_cspc = numpy.roll(self.dataOut.data_cspc, shift, axis=1) |
|
136 | self.dataOut.data_cspc = numpy.roll(self.dataOut.data_cspc, shift, axis=1) | |
| 137 | if pairsList: |
|
137 | if pairsList: | |
| 138 | self.__selectPairs(pairsList) |
|
138 | self.__selectPairs(pairsList) | |
| 139 |
|
139 | |||
| 140 | elif self.dataIn.type == "Voltage": |
|
140 | elif self.dataIn.type == "Voltage": | |
| 141 |
|
141 | |||
| 142 | self.dataOut.flagNoData = True |
|
142 | self.dataOut.flagNoData = True | |
| 143 |
|
143 | |||
| 144 | if nFFTPoints == None: |
|
144 | if nFFTPoints == None: | |
| 145 | raise ValueError("This SpectraProc.run() need nFFTPoints input variable") |
|
145 | raise ValueError("This SpectraProc.run() need nFFTPoints input variable") | |
| 146 |
|
146 | |||
| 147 | if nProfiles == None: |
|
147 | if nProfiles == None: | |
| 148 | nProfiles = nFFTPoints |
|
148 | nProfiles = nFFTPoints | |
| 149 |
|
149 | |||
| 150 | if ippFactor == None: |
|
150 | if ippFactor == None: | |
| 151 | self.dataOut.ippFactor = 1 |
|
151 | self.dataOut.ippFactor = 1 | |
| 152 |
|
152 | |||
| 153 | self.dataOut.nFFTPoints = nFFTPoints |
|
153 | self.dataOut.nFFTPoints = nFFTPoints | |
| 154 |
|
154 | |||
| 155 | if self.buffer is None: |
|
155 | if self.buffer is None: | |
| 156 | self.buffer = numpy.zeros((self.dataIn.nChannels, |
|
156 | self.buffer = numpy.zeros((self.dataIn.nChannels, | |
| 157 | nProfiles, |
|
157 | nProfiles, | |
| 158 | self.dataIn.nHeights), |
|
158 | self.dataIn.nHeights), | |
| 159 | dtype='complex') |
|
159 | dtype='complex') | |
|
|
160 | print("W",self.buffer.shape) | |||
| 160 |
|
161 | |||
| 161 | if self.dataIn.flagDataAsBlock: |
|
162 | if self.dataIn.flagDataAsBlock: | |
| 162 | nVoltProfiles = self.dataIn.data.shape[1] |
|
163 | nVoltProfiles = self.dataIn.data.shape[1] | |
| 163 | if nVoltProfiles == nProfiles: |
|
164 | if nVoltProfiles == nProfiles: | |
| 164 | self.buffer = self.dataIn.data.copy() |
|
165 | self.buffer = self.dataIn.data.copy() | |
| 165 | self.profIndex = nVoltProfiles |
|
166 | self.profIndex = nVoltProfiles | |
| 166 |
|
167 | |||
| 167 | elif nVoltProfiles < nProfiles: |
|
168 | elif nVoltProfiles < nProfiles: | |
| 168 |
|
169 | |||
| 169 | if self.profIndex == 0: |
|
170 | if self.profIndex == 0: | |
| 170 | self.id_min = 0 |
|
171 | self.id_min = 0 | |
| 171 | self.id_max = nVoltProfiles |
|
172 | self.id_max = nVoltProfiles | |
| 172 |
|
173 | |||
| 173 | self.buffer[:, self.id_min:self.id_max, |
|
174 | self.buffer[:, self.id_min:self.id_max, | |
| 174 | :] = self.dataIn.data |
|
175 | :] = self.dataIn.data | |
| 175 | self.profIndex += nVoltProfiles |
|
176 | self.profIndex += nVoltProfiles | |
| 176 | self.id_min += nVoltProfiles |
|
177 | self.id_min += nVoltProfiles | |
| 177 | self.id_max += nVoltProfiles |
|
178 | self.id_max += nVoltProfiles | |
| 178 | elif nVoltProfiles > nProfiles: |
|
179 | elif nVoltProfiles > nProfiles: | |
| 179 | self.reader.bypass = True |
|
180 | self.reader.bypass = True | |
| 180 | if self.profIndex == 0: |
|
181 | if self.profIndex == 0: | |
| 181 | self.id_min = 0 |
|
182 | self.id_min = 0 | |
| 182 | self.id_max = nProfiles |
|
183 | self.id_max = nProfiles | |
| 183 |
|
184 | |||
| 184 | self.buffer = self.dataIn.data[:, self.id_min:self.id_max,:] |
|
185 | self.buffer = self.dataIn.data[:, self.id_min:self.id_max,:] | |
| 185 | self.profIndex += nProfiles |
|
186 | self.profIndex += nProfiles | |
| 186 | self.id_min += nProfiles |
|
187 | self.id_min += nProfiles | |
| 187 | self.id_max += nProfiles |
|
188 | self.id_max += nProfiles | |
| 188 | if self.id_max == nVoltProfiles: |
|
189 | if self.id_max == nVoltProfiles: | |
| 189 | self.reader.bypass = False |
|
190 | self.reader.bypass = False | |
| 190 |
|
191 | |||
| 191 | else: |
|
192 | else: | |
| 192 | raise ValueError("The type object %s has %d profiles, it should just has %d profiles" % ( |
|
193 | raise ValueError("The type object %s has %d profiles, it should just has %d profiles" % ( | |
| 193 | self.dataIn.type, self.dataIn.data.shape[1], nProfiles)) |
|
194 | self.dataIn.type, self.dataIn.data.shape[1], nProfiles)) | |
| 194 | self.dataOut.flagNoData = True |
|
195 | self.dataOut.flagNoData = True | |
| 195 | else: |
|
196 | else: | |
|
|
197 | print(self.profIndex,self.dataIn.data.copy().shape) | |||
| 196 | self.buffer[:, self.profIndex, :] = self.dataIn.data.copy() |
|
198 | self.buffer[:, self.profIndex, :] = self.dataIn.data.copy() | |
| 197 | self.profIndex += 1 |
|
199 | self.profIndex += 1 | |
| 198 |
|
200 | |||
| 199 | if self.firstdatatime == None: |
|
201 | if self.firstdatatime == None: | |
| 200 | self.firstdatatime = self.dataIn.utctime |
|
202 | self.firstdatatime = self.dataIn.utctime | |
| 201 |
|
203 | |||
| 202 | if self.profIndex == nProfiles: |
|
204 | if self.profIndex == nProfiles: | |
| 203 | self.__updateSpecFromVoltage() |
|
205 | self.__updateSpecFromVoltage() | |
| 204 | if pairsList == None: |
|
206 | if pairsList == None: | |
| 205 | self.dataOut.pairsList = [pair for pair in itertools.combinations(self.dataOut.channelList, 2)] |
|
207 | self.dataOut.pairsList = [pair for pair in itertools.combinations(self.dataOut.channelList, 2)] | |
| 206 | else: |
|
208 | else: | |
| 207 | self.dataOut.pairsList = pairsList |
|
209 | self.dataOut.pairsList = pairsList | |
| 208 | self.__getFft() |
|
210 | self.__getFft() | |
| 209 | self.dataOut.flagNoData = False |
|
211 | self.dataOut.flagNoData = False | |
| 210 | self.firstdatatime = None |
|
212 | self.firstdatatime = None | |
| 211 | if not self.reader.bypass: |
|
213 | #if not self.reader.bypass: | |
| 212 |
|
|
214 | self.profIndex = 0 | |
| 213 | else: |
|
215 | else: | |
| 214 | raise ValueError("The type of input object '%s' is not valid".format( |
|
216 | raise ValueError("The type of input object '%s' is not valid".format( | |
| 215 | self.dataIn.type)) |
|
217 | self.dataIn.type)) | |
| 216 |
|
218 | |||
| 217 | def __selectPairs(self, pairsList): |
|
219 | def __selectPairs(self, pairsList): | |
| 218 |
|
220 | |||
| 219 | if not pairsList: |
|
221 | if not pairsList: | |
| 220 | return |
|
222 | return | |
| 221 |
|
223 | |||
| 222 | pairs = [] |
|
224 | pairs = [] | |
| 223 | pairsIndex = [] |
|
225 | pairsIndex = [] | |
| 224 |
|
226 | |||
| 225 | for pair in pairsList: |
|
227 | for pair in pairsList: | |
| 226 | if pair[0] not in self.dataOut.channelList or pair[1] not in self.dataOut.channelList: |
|
228 | if pair[0] not in self.dataOut.channelList or pair[1] not in self.dataOut.channelList: | |
| 227 | continue |
|
229 | continue | |
| 228 | pairs.append(pair) |
|
230 | pairs.append(pair) | |
| 229 | pairsIndex.append(pairs.index(pair)) |
|
231 | pairsIndex.append(pairs.index(pair)) | |
| 230 |
|
232 | |||
| 231 | self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndex] |
|
233 | self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndex] | |
| 232 | self.dataOut.pairsList = pairs |
|
234 | self.dataOut.pairsList = pairs | |
| 233 |
|
235 | |||
| 234 | return |
|
236 | return | |
| 235 |
|
237 | |||
| 236 | def selectFFTs(self, minFFT, maxFFT ): |
|
238 | def selectFFTs(self, minFFT, maxFFT ): | |
| 237 | """ |
|
239 | """ | |
| 238 | Selecciona un bloque de datos en base a un grupo de valores de puntos FFTs segun el rango |
|
240 | Selecciona un bloque de datos en base a un grupo de valores de puntos FFTs segun el rango | |
| 239 | minFFT<= FFT <= maxFFT |
|
241 | minFFT<= FFT <= maxFFT | |
| 240 | """ |
|
242 | """ | |
| 241 |
|
243 | |||
| 242 | if (minFFT > maxFFT): |
|
244 | if (minFFT > maxFFT): | |
| 243 | raise ValueError("Error selecting heights: Height range (%d,%d) is not valid" % (minFFT, maxFFT)) |
|
245 | raise ValueError("Error selecting heights: Height range (%d,%d) is not valid" % (minFFT, maxFFT)) | |
| 244 |
|
246 | |||
| 245 | if (minFFT < self.dataOut.getFreqRange()[0]): |
|
247 | if (minFFT < self.dataOut.getFreqRange()[0]): | |
| 246 | minFFT = self.dataOut.getFreqRange()[0] |
|
248 | minFFT = self.dataOut.getFreqRange()[0] | |
| 247 |
|
249 | |||
| 248 | if (maxFFT > self.dataOut.getFreqRange()[-1]): |
|
250 | if (maxFFT > self.dataOut.getFreqRange()[-1]): | |
| 249 | maxFFT = self.dataOut.getFreqRange()[-1] |
|
251 | maxFFT = self.dataOut.getFreqRange()[-1] | |
| 250 |
|
252 | |||
| 251 | minIndex = 0 |
|
253 | minIndex = 0 | |
| 252 | maxIndex = 0 |
|
254 | maxIndex = 0 | |
| 253 | FFTs = self.dataOut.getFreqRange() |
|
255 | FFTs = self.dataOut.getFreqRange() | |
| 254 |
|
256 | |||
| 255 | inda = numpy.where(FFTs >= minFFT) |
|
257 | inda = numpy.where(FFTs >= minFFT) | |
| 256 | indb = numpy.where(FFTs <= maxFFT) |
|
258 | indb = numpy.where(FFTs <= maxFFT) | |
| 257 |
|
259 | |||
| 258 | try: |
|
260 | try: | |
| 259 | minIndex = inda[0][0] |
|
261 | minIndex = inda[0][0] | |
| 260 | except: |
|
262 | except: | |
| 261 | minIndex = 0 |
|
263 | minIndex = 0 | |
| 262 |
|
264 | |||
| 263 | try: |
|
265 | try: | |
| 264 | maxIndex = indb[0][-1] |
|
266 | maxIndex = indb[0][-1] | |
| 265 | except: |
|
267 | except: | |
| 266 | maxIndex = len(FFTs) |
|
268 | maxIndex = len(FFTs) | |
| 267 |
|
269 | |||
| 268 | self.selectFFTsByIndex(minIndex, maxIndex) |
|
270 | self.selectFFTsByIndex(minIndex, maxIndex) | |
| 269 |
|
271 | |||
| 270 | return 1 |
|
272 | return 1 | |
| 271 |
|
273 | |||
| 272 | def getBeaconSignal(self, tauindex=0, channelindex=0, hei_ref=None): |
|
274 | def getBeaconSignal(self, tauindex=0, channelindex=0, hei_ref=None): | |
| 273 | newheis = numpy.where( |
|
275 | newheis = numpy.where( | |
| 274 | self.dataOut.heightList > self.dataOut.radarControllerHeaderObj.Taus[tauindex]) |
|
276 | self.dataOut.heightList > self.dataOut.radarControllerHeaderObj.Taus[tauindex]) | |
| 275 |
|
277 | |||
| 276 | if hei_ref != None: |
|
278 | if hei_ref != None: | |
| 277 | newheis = numpy.where(self.dataOut.heightList > hei_ref) |
|
279 | newheis = numpy.where(self.dataOut.heightList > hei_ref) | |
| 278 |
|
280 | |||
| 279 | minIndex = min(newheis[0]) |
|
281 | minIndex = min(newheis[0]) | |
| 280 | maxIndex = max(newheis[0]) |
|
282 | maxIndex = max(newheis[0]) | |
| 281 | data_spc = self.dataOut.data_spc[:, :, minIndex:maxIndex + 1] |
|
283 | data_spc = self.dataOut.data_spc[:, :, minIndex:maxIndex + 1] | |
| 282 | heightList = self.dataOut.heightList[minIndex:maxIndex + 1] |
|
284 | heightList = self.dataOut.heightList[minIndex:maxIndex + 1] | |
| 283 |
|
285 | |||
| 284 | # determina indices |
|
286 | # determina indices | |
| 285 | nheis = int(self.dataOut.radarControllerHeaderObj.txB / |
|
287 | nheis = int(self.dataOut.radarControllerHeaderObj.txB / | |
| 286 | (self.dataOut.heightList[1] - self.dataOut.heightList[0])) |
|
288 | (self.dataOut.heightList[1] - self.dataOut.heightList[0])) | |
| 287 | avg_dB = 10 * \ |
|
289 | avg_dB = 10 * \ | |
| 288 | numpy.log10(numpy.sum(data_spc[channelindex, :, :], axis=0)) |
|
290 | numpy.log10(numpy.sum(data_spc[channelindex, :, :], axis=0)) | |
| 289 | beacon_dB = numpy.sort(avg_dB)[-nheis:] |
|
291 | beacon_dB = numpy.sort(avg_dB)[-nheis:] | |
| 290 | beacon_heiIndexList = [] |
|
292 | beacon_heiIndexList = [] | |
| 291 | for val in avg_dB.tolist(): |
|
293 | for val in avg_dB.tolist(): | |
| 292 | if val >= beacon_dB[0]: |
|
294 | if val >= beacon_dB[0]: | |
| 293 | beacon_heiIndexList.append(avg_dB.tolist().index(val)) |
|
295 | beacon_heiIndexList.append(avg_dB.tolist().index(val)) | |
| 294 |
|
296 | |||
| 295 | data_cspc = None |
|
297 | data_cspc = None | |
| 296 | if self.dataOut.data_cspc is not None: |
|
298 | if self.dataOut.data_cspc is not None: | |
| 297 | data_cspc = self.dataOut.data_cspc[:, :, minIndex:maxIndex + 1] |
|
299 | data_cspc = self.dataOut.data_cspc[:, :, minIndex:maxIndex + 1] | |
| 298 |
|
300 | |||
| 299 | data_dc = None |
|
301 | data_dc = None | |
| 300 | if self.dataOut.data_dc is not None: |
|
302 | if self.dataOut.data_dc is not None: | |
| 301 | data_dc = self.dataOut.data_dc[:, minIndex:maxIndex + 1] |
|
303 | data_dc = self.dataOut.data_dc[:, minIndex:maxIndex + 1] | |
| 302 |
|
304 | |||
| 303 | self.dataOut.data_spc = data_spc |
|
305 | self.dataOut.data_spc = data_spc | |
| 304 | self.dataOut.data_cspc = data_cspc |
|
306 | self.dataOut.data_cspc = data_cspc | |
| 305 | self.dataOut.data_dc = data_dc |
|
307 | self.dataOut.data_dc = data_dc | |
| 306 | self.dataOut.heightList = heightList |
|
308 | self.dataOut.heightList = heightList | |
| 307 | self.dataOut.beacon_heiIndexList = beacon_heiIndexList |
|
309 | self.dataOut.beacon_heiIndexList = beacon_heiIndexList | |
| 308 |
|
310 | |||
| 309 | return 1 |
|
311 | return 1 | |
| 310 |
|
312 | |||
| 311 | def selectFFTsByIndex(self, minIndex, maxIndex): |
|
313 | def selectFFTsByIndex(self, minIndex, maxIndex): | |
| 312 | """ |
|
314 | """ | |
| 313 |
|
315 | |||
| 314 | """ |
|
316 | """ | |
| 315 |
|
317 | |||
| 316 | if (minIndex < 0) or (minIndex > maxIndex): |
|
318 | if (minIndex < 0) or (minIndex > maxIndex): | |
| 317 | raise ValueError("Error selecting heights: Index range (%d,%d) is not valid" % (minIndex, maxIndex)) |
|
319 | raise ValueError("Error selecting heights: Index range (%d,%d) is not valid" % (minIndex, maxIndex)) | |
| 318 |
|
320 | |||
| 319 | if (maxIndex >= self.dataOut.nProfiles): |
|
321 | if (maxIndex >= self.dataOut.nProfiles): | |
| 320 | maxIndex = self.dataOut.nProfiles-1 |
|
322 | maxIndex = self.dataOut.nProfiles-1 | |
| 321 |
|
323 | |||
| 322 | #Spectra |
|
324 | #Spectra | |
| 323 | data_spc = self.dataOut.data_spc[:,minIndex:maxIndex+1,:] |
|
325 | data_spc = self.dataOut.data_spc[:,minIndex:maxIndex+1,:] | |
| 324 |
|
326 | |||
| 325 | data_cspc = None |
|
327 | data_cspc = None | |
| 326 | if self.dataOut.data_cspc is not None: |
|
328 | if self.dataOut.data_cspc is not None: | |
| 327 | data_cspc = self.dataOut.data_cspc[:,minIndex:maxIndex+1,:] |
|
329 | data_cspc = self.dataOut.data_cspc[:,minIndex:maxIndex+1,:] | |
| 328 |
|
330 | |||
| 329 | data_dc = None |
|
331 | data_dc = None | |
| 330 | if self.dataOut.data_dc is not None: |
|
332 | if self.dataOut.data_dc is not None: | |
| 331 | data_dc = self.dataOut.data_dc[minIndex:maxIndex+1,:] |
|
333 | data_dc = self.dataOut.data_dc[minIndex:maxIndex+1,:] | |
| 332 |
|
334 | |||
| 333 | self.dataOut.data_spc = data_spc |
|
335 | self.dataOut.data_spc = data_spc | |
| 334 | self.dataOut.data_cspc = data_cspc |
|
336 | self.dataOut.data_cspc = data_cspc | |
| 335 | self.dataOut.data_dc = data_dc |
|
337 | self.dataOut.data_dc = data_dc | |
| 336 |
|
338 | |||
| 337 | self.dataOut.ippSeconds = self.dataOut.ippSeconds*(self.dataOut.nFFTPoints / numpy.shape(data_cspc)[1]) |
|
339 | self.dataOut.ippSeconds = self.dataOut.ippSeconds*(self.dataOut.nFFTPoints / numpy.shape(data_cspc)[1]) | |
| 338 | self.dataOut.nFFTPoints = numpy.shape(data_cspc)[1] |
|
340 | self.dataOut.nFFTPoints = numpy.shape(data_cspc)[1] | |
| 339 | self.dataOut.profilesPerBlock = numpy.shape(data_cspc)[1] |
|
341 | self.dataOut.profilesPerBlock = numpy.shape(data_cspc)[1] | |
| 340 |
|
342 | |||
| 341 | return 1 |
|
343 | return 1 | |
| 342 |
|
344 | |||
| 343 | def getNoise(self, minHei=None, maxHei=None, minVel=None, maxVel=None): |
|
345 | def getNoise(self, minHei=None, maxHei=None, minVel=None, maxVel=None): | |
| 344 | # validacion de rango |
|
346 | # validacion de rango | |
| 345 | if minHei == None: |
|
347 | if minHei == None: | |
| 346 | minHei = self.dataOut.heightList[0] |
|
348 | minHei = self.dataOut.heightList[0] | |
| 347 |
|
349 | |||
| 348 | if maxHei == None: |
|
350 | if maxHei == None: | |
| 349 | maxHei = self.dataOut.heightList[-1] |
|
351 | maxHei = self.dataOut.heightList[-1] | |
| 350 |
|
352 | |||
| 351 | if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei): |
|
353 | if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei): | |
| 352 | print('minHei: %.2f is out of the heights range' % (minHei)) |
|
354 | print('minHei: %.2f is out of the heights range' % (minHei)) | |
| 353 | print('minHei is setting to %.2f' % (self.dataOut.heightList[0])) |
|
355 | print('minHei is setting to %.2f' % (self.dataOut.heightList[0])) | |
| 354 | minHei = self.dataOut.heightList[0] |
|
356 | minHei = self.dataOut.heightList[0] | |
| 355 |
|
357 | |||
| 356 | if (maxHei > self.dataOut.heightList[-1]) or (maxHei < minHei): |
|
358 | if (maxHei > self.dataOut.heightList[-1]) or (maxHei < minHei): | |
| 357 | print('maxHei: %.2f is out of the heights range' % (maxHei)) |
|
359 | print('maxHei: %.2f is out of the heights range' % (maxHei)) | |
| 358 | print('maxHei is setting to %.2f' % (self.dataOut.heightList[-1])) |
|
360 | print('maxHei is setting to %.2f' % (self.dataOut.heightList[-1])) | |
| 359 | maxHei = self.dataOut.heightList[-1] |
|
361 | maxHei = self.dataOut.heightList[-1] | |
| 360 |
|
362 | |||
| 361 | # validacion de velocidades |
|
363 | # validacion de velocidades | |
| 362 | velrange = self.dataOut.getVelRange(1) |
|
364 | velrange = self.dataOut.getVelRange(1) | |
| 363 |
|
365 | |||
| 364 | if minVel == None: |
|
366 | if minVel == None: | |
| 365 | minVel = velrange[0] |
|
367 | minVel = velrange[0] | |
| 366 |
|
368 | |||
| 367 | if maxVel == None: |
|
369 | if maxVel == None: | |
| 368 | maxVel = velrange[-1] |
|
370 | maxVel = velrange[-1] | |
| 369 |
|
371 | |||
| 370 | if (minVel < velrange[0]) or (minVel > maxVel): |
|
372 | if (minVel < velrange[0]) or (minVel > maxVel): | |
| 371 | print('minVel: %.2f is out of the velocity range' % (minVel)) |
|
373 | print('minVel: %.2f is out of the velocity range' % (minVel)) | |
| 372 | print('minVel is setting to %.2f' % (velrange[0])) |
|
374 | print('minVel is setting to %.2f' % (velrange[0])) | |
| 373 | minVel = velrange[0] |
|
375 | minVel = velrange[0] | |
| 374 |
|
376 | |||
| 375 | if (maxVel > velrange[-1]) or (maxVel < minVel): |
|
377 | if (maxVel > velrange[-1]) or (maxVel < minVel): | |
| 376 | print('maxVel: %.2f is out of the velocity range' % (maxVel)) |
|
378 | print('maxVel: %.2f is out of the velocity range' % (maxVel)) | |
| 377 | print('maxVel is setting to %.2f' % (velrange[-1])) |
|
379 | print('maxVel is setting to %.2f' % (velrange[-1])) | |
| 378 | maxVel = velrange[-1] |
|
380 | maxVel = velrange[-1] | |
| 379 |
|
381 | |||
| 380 | # seleccion de indices para rango |
|
382 | # seleccion de indices para rango | |
| 381 | minIndex = 0 |
|
383 | minIndex = 0 | |
| 382 | maxIndex = 0 |
|
384 | maxIndex = 0 | |
| 383 | heights = self.dataOut.heightList |
|
385 | heights = self.dataOut.heightList | |
| 384 |
|
386 | |||
| 385 | inda = numpy.where(heights >= minHei) |
|
387 | inda = numpy.where(heights >= minHei) | |
| 386 | indb = numpy.where(heights <= maxHei) |
|
388 | indb = numpy.where(heights <= maxHei) | |
| 387 |
|
389 | |||
| 388 | try: |
|
390 | try: | |
| 389 | minIndex = inda[0][0] |
|
391 | minIndex = inda[0][0] | |
| 390 | except: |
|
392 | except: | |
| 391 | minIndex = 0 |
|
393 | minIndex = 0 | |
| 392 |
|
394 | |||
| 393 | try: |
|
395 | try: | |
| 394 | maxIndex = indb[0][-1] |
|
396 | maxIndex = indb[0][-1] | |
| 395 | except: |
|
397 | except: | |
| 396 | maxIndex = len(heights) |
|
398 | maxIndex = len(heights) | |
| 397 |
|
399 | |||
| 398 | if (minIndex < 0) or (minIndex > maxIndex): |
|
400 | if (minIndex < 0) or (minIndex > maxIndex): | |
| 399 | raise ValueError("some value in (%d,%d) is not valid" % ( |
|
401 | raise ValueError("some value in (%d,%d) is not valid" % ( | |
| 400 | minIndex, maxIndex)) |
|
402 | minIndex, maxIndex)) | |
| 401 |
|
403 | |||
| 402 | if (maxIndex >= self.dataOut.nHeights): |
|
404 | if (maxIndex >= self.dataOut.nHeights): | |
| 403 | maxIndex = self.dataOut.nHeights - 1 |
|
405 | maxIndex = self.dataOut.nHeights - 1 | |
| 404 |
|
406 | |||
| 405 | # seleccion de indices para velocidades |
|
407 | # seleccion de indices para velocidades | |
| 406 | indminvel = numpy.where(velrange >= minVel) |
|
408 | indminvel = numpy.where(velrange >= minVel) | |
| 407 | indmaxvel = numpy.where(velrange <= maxVel) |
|
409 | indmaxvel = numpy.where(velrange <= maxVel) | |
| 408 | try: |
|
410 | try: | |
| 409 | minIndexVel = indminvel[0][0] |
|
411 | minIndexVel = indminvel[0][0] | |
| 410 | except: |
|
412 | except: | |
| 411 | minIndexVel = 0 |
|
413 | minIndexVel = 0 | |
| 412 |
|
414 | |||
| 413 | try: |
|
415 | try: | |
| 414 | maxIndexVel = indmaxvel[0][-1] |
|
416 | maxIndexVel = indmaxvel[0][-1] | |
| 415 | except: |
|
417 | except: | |
| 416 | maxIndexVel = len(velrange) |
|
418 | maxIndexVel = len(velrange) | |
| 417 |
|
419 | |||
| 418 | # seleccion del espectro |
|
420 | # seleccion del espectro | |
| 419 | data_spc = self.dataOut.data_spc[:, |
|
421 | data_spc = self.dataOut.data_spc[:, | |
| 420 | minIndexVel:maxIndexVel + 1, minIndex:maxIndex + 1] |
|
422 | minIndexVel:maxIndexVel + 1, minIndex:maxIndex + 1] | |
| 421 | # estimacion de ruido |
|
423 | # estimacion de ruido | |
| 422 | noise = numpy.zeros(self.dataOut.nChannels) |
|
424 | noise = numpy.zeros(self.dataOut.nChannels) | |
| 423 |
|
425 | |||
| 424 | for channel in range(self.dataOut.nChannels): |
|
426 | for channel in range(self.dataOut.nChannels): | |
| 425 | daux = data_spc[channel, :, :] |
|
427 | daux = data_spc[channel, :, :] | |
| 426 | sortdata = numpy.sort(daux, axis=None) |
|
428 | sortdata = numpy.sort(daux, axis=None) | |
| 427 | noise[channel] = hildebrand_sekhon(sortdata, self.dataOut.nIncohInt) |
|
429 | noise[channel] = hildebrand_sekhon(sortdata, self.dataOut.nIncohInt) | |
| 428 |
|
430 | |||
| 429 | self.dataOut.noise_estimation = noise.copy() |
|
431 | self.dataOut.noise_estimation = noise.copy() | |
| 430 |
|
432 | |||
| 431 | return 1 |
|
433 | return 1 | |
| 432 |
|
434 | |||
| 433 | class removeDC(Operation): |
|
435 | class removeDC(Operation): | |
| 434 |
|
436 | |||
| 435 | def run(self, dataOut, mode=2): |
|
437 | def run(self, dataOut, mode=2): | |
| 436 | self.dataOut = dataOut |
|
438 | self.dataOut = dataOut | |
| 437 | jspectra = self.dataOut.data_spc |
|
439 | jspectra = self.dataOut.data_spc | |
| 438 | jcspectra = self.dataOut.data_cspc |
|
440 | jcspectra = self.dataOut.data_cspc | |
| 439 |
|
441 | |||
| 440 | num_chan = jspectra.shape[0] |
|
442 | num_chan = jspectra.shape[0] | |
| 441 | num_hei = jspectra.shape[2] |
|
443 | num_hei = jspectra.shape[2] | |
| 442 |
|
444 | |||
| 443 | if jcspectra is not None: |
|
445 | if jcspectra is not None: | |
| 444 | jcspectraExist = True |
|
446 | jcspectraExist = True | |
| 445 | num_pairs = jcspectra.shape[0] |
|
447 | num_pairs = jcspectra.shape[0] | |
| 446 | else: |
|
448 | else: | |
| 447 | jcspectraExist = False |
|
449 | jcspectraExist = False | |
| 448 |
|
450 | |||
| 449 | freq_dc = int(jspectra.shape[1] / 2) |
|
451 | freq_dc = int(jspectra.shape[1] / 2) | |
| 450 | ind_vel = numpy.array([-2, -1, 1, 2]) + freq_dc |
|
452 | ind_vel = numpy.array([-2, -1, 1, 2]) + freq_dc | |
| 451 | ind_vel = ind_vel.astype(int) |
|
453 | ind_vel = ind_vel.astype(int) | |
| 452 |
|
454 | |||
| 453 | if ind_vel[0] < 0: |
|
455 | if ind_vel[0] < 0: | |
| 454 | ind_vel[list(range(0, 1))] = ind_vel[list(range(0, 1))] + self.num_prof |
|
456 | ind_vel[list(range(0, 1))] = ind_vel[list(range(0, 1))] + self.num_prof | |
| 455 |
|
457 | |||
| 456 | if mode == 1: |
|
458 | if mode == 1: | |
| 457 | jspectra[:, freq_dc, :] = ( |
|
459 | jspectra[:, freq_dc, :] = ( | |
| 458 | jspectra[:, ind_vel[1], :] + jspectra[:, ind_vel[2], :]) / 2 # CORRECCION |
|
460 | jspectra[:, ind_vel[1], :] + jspectra[:, ind_vel[2], :]) / 2 # CORRECCION | |
| 459 |
|
461 | |||
| 460 | if jcspectraExist: |
|
462 | if jcspectraExist: | |
| 461 | jcspectra[:, freq_dc, :] = ( |
|
463 | jcspectra[:, freq_dc, :] = ( | |
| 462 | jcspectra[:, ind_vel[1], :] + jcspectra[:, ind_vel[2], :]) / 2 |
|
464 | jcspectra[:, ind_vel[1], :] + jcspectra[:, ind_vel[2], :]) / 2 | |
| 463 |
|
465 | |||
| 464 | if mode == 2: |
|
466 | if mode == 2: | |
| 465 |
|
467 | |||
| 466 | vel = numpy.array([-2, -1, 1, 2]) |
|
468 | vel = numpy.array([-2, -1, 1, 2]) | |
| 467 | xx = numpy.zeros([4, 4]) |
|
469 | xx = numpy.zeros([4, 4]) | |
| 468 |
|
470 | |||
| 469 | for fil in range(4): |
|
471 | for fil in range(4): | |
| 470 | xx[fil, :] = vel[fil]**numpy.asarray(list(range(4))) |
|
472 | xx[fil, :] = vel[fil]**numpy.asarray(list(range(4))) | |
| 471 |
|
473 | |||
| 472 | xx_inv = numpy.linalg.inv(xx) |
|
474 | xx_inv = numpy.linalg.inv(xx) | |
| 473 | xx_aux = xx_inv[0, :] |
|
475 | xx_aux = xx_inv[0, :] | |
| 474 |
|
476 | |||
| 475 | for ich in range(num_chan): |
|
477 | for ich in range(num_chan): | |
| 476 | yy = jspectra[ich, ind_vel, :] |
|
478 | yy = jspectra[ich, ind_vel, :] | |
| 477 | jspectra[ich, freq_dc, :] = numpy.dot(xx_aux, yy) |
|
479 | jspectra[ich, freq_dc, :] = numpy.dot(xx_aux, yy) | |
| 478 |
|
480 | |||
| 479 | junkid = jspectra[ich, freq_dc, :] <= 0 |
|
481 | junkid = jspectra[ich, freq_dc, :] <= 0 | |
| 480 | cjunkid = sum(junkid) |
|
482 | cjunkid = sum(junkid) | |
| 481 |
|
483 | |||
| 482 | if cjunkid.any(): |
|
484 | if cjunkid.any(): | |
| 483 | jspectra[ich, freq_dc, junkid.nonzero()] = ( |
|
485 | jspectra[ich, freq_dc, junkid.nonzero()] = ( | |
| 484 | jspectra[ich, ind_vel[1], junkid] + jspectra[ich, ind_vel[2], junkid]) / 2 |
|
486 | jspectra[ich, ind_vel[1], junkid] + jspectra[ich, ind_vel[2], junkid]) / 2 | |
| 485 |
|
487 | |||
| 486 | if jcspectraExist: |
|
488 | if jcspectraExist: | |
| 487 | for ip in range(num_pairs): |
|
489 | for ip in range(num_pairs): | |
| 488 | yy = jcspectra[ip, ind_vel, :] |
|
490 | yy = jcspectra[ip, ind_vel, :] | |
| 489 | jcspectra[ip, freq_dc, :] = numpy.dot(xx_aux, yy) |
|
491 | jcspectra[ip, freq_dc, :] = numpy.dot(xx_aux, yy) | |
| 490 |
|
492 | |||
| 491 | self.dataOut.data_spc = jspectra |
|
493 | self.dataOut.data_spc = jspectra | |
| 492 | self.dataOut.data_cspc = jcspectra |
|
494 | self.dataOut.data_cspc = jcspectra | |
| 493 |
|
495 | |||
| 494 | return self.dataOut |
|
496 | return self.dataOut | |
| 495 |
|
497 | |||
| 496 | class removeInterference(Operation): |
|
498 | class removeInterference(Operation): | |
| 497 |
|
499 | |||
| 498 | def removeInterference2(self): |
|
500 | def removeInterference2(self): | |
| 499 |
|
501 | |||
| 500 | cspc = self.dataOut.data_cspc |
|
502 | cspc = self.dataOut.data_cspc | |
| 501 | spc = self.dataOut.data_spc |
|
503 | spc = self.dataOut.data_spc | |
| 502 | Heights = numpy.arange(cspc.shape[2]) |
|
504 | Heights = numpy.arange(cspc.shape[2]) | |
| 503 | realCspc = numpy.abs(cspc) |
|
505 | realCspc = numpy.abs(cspc) | |
| 504 |
|
506 | |||
| 505 | for i in range(cspc.shape[0]): |
|
507 | for i in range(cspc.shape[0]): | |
| 506 | LinePower= numpy.sum(realCspc[i], axis=0) |
|
508 | LinePower= numpy.sum(realCspc[i], axis=0) | |
| 507 | Threshold = numpy.amax(LinePower)-numpy.sort(LinePower)[len(Heights)-int(len(Heights)*0.1)] |
|
509 | Threshold = numpy.amax(LinePower)-numpy.sort(LinePower)[len(Heights)-int(len(Heights)*0.1)] | |
| 508 | SelectedHeights = Heights[ numpy.where(LinePower < Threshold) ] |
|
510 | SelectedHeights = Heights[ numpy.where(LinePower < Threshold) ] | |
| 509 | InterferenceSum = numpy.sum(realCspc[i,:,SelectedHeights],axis=0) |
|
511 | InterferenceSum = numpy.sum(realCspc[i,:,SelectedHeights],axis=0) | |
| 510 | InterferenceThresholdMin = numpy.sort(InterferenceSum)[int(len(InterferenceSum)*0.98)] |
|
512 | InterferenceThresholdMin = numpy.sort(InterferenceSum)[int(len(InterferenceSum)*0.98)] | |
| 511 | InterferenceThresholdMax = numpy.sort(InterferenceSum)[int(len(InterferenceSum)*0.99)] |
|
513 | InterferenceThresholdMax = numpy.sort(InterferenceSum)[int(len(InterferenceSum)*0.99)] | |
| 512 |
|
514 | |||
| 513 |
|
515 | |||
| 514 | InterferenceRange = numpy.where(([InterferenceSum > InterferenceThresholdMin]))# , InterferenceSum < InterferenceThresholdMax]) ) |
|
516 | InterferenceRange = numpy.where(([InterferenceSum > InterferenceThresholdMin]))# , InterferenceSum < InterferenceThresholdMax]) ) | |
| 515 | #InterferenceRange = numpy.where( ([InterferenceRange < InterferenceThresholdMax])) |
|
517 | #InterferenceRange = numpy.where( ([InterferenceRange < InterferenceThresholdMax])) | |
| 516 | if len(InterferenceRange)<int(cspc.shape[1]*0.3): |
|
518 | if len(InterferenceRange)<int(cspc.shape[1]*0.3): | |
| 517 | cspc[i,InterferenceRange,:] = numpy.NaN |
|
519 | cspc[i,InterferenceRange,:] = numpy.NaN | |
| 518 |
|
520 | |||
| 519 | self.dataOut.data_cspc = cspc |
|
521 | self.dataOut.data_cspc = cspc | |
| 520 |
|
522 | |||
| 521 | def removeInterference(self, interf=2, hei_interf=None, nhei_interf=None, offhei_interf=None): |
|
523 | def removeInterference(self, interf=2, hei_interf=None, nhei_interf=None, offhei_interf=None): | |
| 522 |
|
524 | |||
| 523 | jspectra = self.dataOut.data_spc |
|
525 | jspectra = self.dataOut.data_spc | |
| 524 | jcspectra = self.dataOut.data_cspc |
|
526 | jcspectra = self.dataOut.data_cspc | |
| 525 | jnoise = self.dataOut.getNoise() |
|
527 | jnoise = self.dataOut.getNoise() | |
| 526 | num_incoh = self.dataOut.nIncohInt |
|
528 | num_incoh = self.dataOut.nIncohInt | |
| 527 |
|
529 | |||
| 528 | num_channel = jspectra.shape[0] |
|
530 | num_channel = jspectra.shape[0] | |
| 529 | num_prof = jspectra.shape[1] |
|
531 | num_prof = jspectra.shape[1] | |
| 530 | num_hei = jspectra.shape[2] |
|
532 | num_hei = jspectra.shape[2] | |
| 531 |
|
533 | |||
| 532 | # hei_interf |
|
534 | # hei_interf | |
| 533 | if hei_interf is None: |
|
535 | if hei_interf is None: | |
| 534 | count_hei = int(num_hei / 2) |
|
536 | count_hei = int(num_hei / 2) | |
| 535 | hei_interf = numpy.asmatrix(list(range(count_hei))) + num_hei - count_hei |
|
537 | hei_interf = numpy.asmatrix(list(range(count_hei))) + num_hei - count_hei | |
| 536 | hei_interf = numpy.asarray(hei_interf)[0] |
|
538 | hei_interf = numpy.asarray(hei_interf)[0] | |
| 537 | # nhei_interf |
|
539 | # nhei_interf | |
| 538 | if (nhei_interf == None): |
|
540 | if (nhei_interf == None): | |
| 539 | nhei_interf = 5 |
|
541 | nhei_interf = 5 | |
| 540 | if (nhei_interf < 1): |
|
542 | if (nhei_interf < 1): | |
| 541 | nhei_interf = 1 |
|
543 | nhei_interf = 1 | |
| 542 | if (nhei_interf > count_hei): |
|
544 | if (nhei_interf > count_hei): | |
| 543 | nhei_interf = count_hei |
|
545 | nhei_interf = count_hei | |
| 544 | if (offhei_interf == None): |
|
546 | if (offhei_interf == None): | |
| 545 | offhei_interf = 0 |
|
547 | offhei_interf = 0 | |
| 546 |
|
548 | |||
| 547 | ind_hei = list(range(num_hei)) |
|
549 | ind_hei = list(range(num_hei)) | |
| 548 | # mask_prof = numpy.asarray(range(num_prof - 2)) + 1 |
|
550 | # mask_prof = numpy.asarray(range(num_prof - 2)) + 1 | |
| 549 | # mask_prof[range(num_prof/2 - 1,len(mask_prof))] += 1 |
|
551 | # mask_prof[range(num_prof/2 - 1,len(mask_prof))] += 1 | |
| 550 | mask_prof = numpy.asarray(list(range(num_prof))) |
|
552 | mask_prof = numpy.asarray(list(range(num_prof))) | |
| 551 | num_mask_prof = mask_prof.size |
|
553 | num_mask_prof = mask_prof.size | |
| 552 | comp_mask_prof = [0, num_prof / 2] |
|
554 | comp_mask_prof = [0, num_prof / 2] | |
| 553 |
|
555 | |||
| 554 | # noise_exist: Determina si la variable jnoise ha sido definida y contiene la informacion del ruido de cada canal |
|
556 | # noise_exist: Determina si la variable jnoise ha sido definida y contiene la informacion del ruido de cada canal | |
| 555 | if (jnoise.size < num_channel or numpy.isnan(jnoise).any()): |
|
557 | if (jnoise.size < num_channel or numpy.isnan(jnoise).any()): | |
| 556 | jnoise = numpy.nan |
|
558 | jnoise = numpy.nan | |
| 557 | noise_exist = jnoise[0] < numpy.Inf |
|
559 | noise_exist = jnoise[0] < numpy.Inf | |
| 558 |
|
560 | |||
| 559 | # Subrutina de Remocion de la Interferencia |
|
561 | # Subrutina de Remocion de la Interferencia | |
| 560 | for ich in range(num_channel): |
|
562 | for ich in range(num_channel): | |
| 561 | # Se ordena los espectros segun su potencia (menor a mayor) |
|
563 | # Se ordena los espectros segun su potencia (menor a mayor) | |
| 562 | power = jspectra[ich, mask_prof, :] |
|
564 | power = jspectra[ich, mask_prof, :] | |
| 563 | power = power[:, hei_interf] |
|
565 | power = power[:, hei_interf] | |
| 564 | power = power.sum(axis=0) |
|
566 | power = power.sum(axis=0) | |
| 565 | psort = power.ravel().argsort() |
|
567 | psort = power.ravel().argsort() | |
| 566 |
|
568 | |||
| 567 | # Se estima la interferencia promedio en los Espectros de Potencia empleando |
|
569 | # Se estima la interferencia promedio en los Espectros de Potencia empleando | |
| 568 | junkspc_interf = jspectra[ich, :, hei_interf[psort[list(range( |
|
570 | junkspc_interf = jspectra[ich, :, hei_interf[psort[list(range( | |
| 569 | offhei_interf, nhei_interf + offhei_interf))]]] |
|
571 | offhei_interf, nhei_interf + offhei_interf))]]] | |
| 570 |
|
572 | |||
| 571 | if noise_exist: |
|
573 | if noise_exist: | |
| 572 | # tmp_noise = jnoise[ich] / num_prof |
|
574 | # tmp_noise = jnoise[ich] / num_prof | |
| 573 | tmp_noise = jnoise[ich] |
|
575 | tmp_noise = jnoise[ich] | |
| 574 | junkspc_interf = junkspc_interf - tmp_noise |
|
576 | junkspc_interf = junkspc_interf - tmp_noise | |
| 575 | #junkspc_interf[:,comp_mask_prof] = 0 |
|
577 | #junkspc_interf[:,comp_mask_prof] = 0 | |
| 576 |
|
578 | |||
| 577 | jspc_interf = junkspc_interf.sum(axis=0) / nhei_interf |
|
579 | jspc_interf = junkspc_interf.sum(axis=0) / nhei_interf | |
| 578 | jspc_interf = jspc_interf.transpose() |
|
580 | jspc_interf = jspc_interf.transpose() | |
| 579 | # Calculando el espectro de interferencia promedio |
|
581 | # Calculando el espectro de interferencia promedio | |
| 580 | noiseid = numpy.where( |
|
582 | noiseid = numpy.where( | |
| 581 | jspc_interf <= tmp_noise / numpy.sqrt(num_incoh)) |
|
583 | jspc_interf <= tmp_noise / numpy.sqrt(num_incoh)) | |
| 582 | noiseid = noiseid[0] |
|
584 | noiseid = noiseid[0] | |
| 583 | cnoiseid = noiseid.size |
|
585 | cnoiseid = noiseid.size | |
| 584 | interfid = numpy.where( |
|
586 | interfid = numpy.where( | |
| 585 | jspc_interf > tmp_noise / numpy.sqrt(num_incoh)) |
|
587 | jspc_interf > tmp_noise / numpy.sqrt(num_incoh)) | |
| 586 | interfid = interfid[0] |
|
588 | interfid = interfid[0] | |
| 587 | cinterfid = interfid.size |
|
589 | cinterfid = interfid.size | |
| 588 |
|
590 | |||
| 589 | if (cnoiseid > 0): |
|
591 | if (cnoiseid > 0): | |
| 590 | jspc_interf[noiseid] = 0 |
|
592 | jspc_interf[noiseid] = 0 | |
| 591 |
|
593 | |||
| 592 | # Expandiendo los perfiles a limpiar |
|
594 | # Expandiendo los perfiles a limpiar | |
| 593 | if (cinterfid > 0): |
|
595 | if (cinterfid > 0): | |
| 594 | new_interfid = ( |
|
596 | new_interfid = ( | |
| 595 | numpy.r_[interfid - 1, interfid, interfid + 1] + num_prof) % num_prof |
|
597 | numpy.r_[interfid - 1, interfid, interfid + 1] + num_prof) % num_prof | |
| 596 | new_interfid = numpy.asarray(new_interfid) |
|
598 | new_interfid = numpy.asarray(new_interfid) | |
| 597 | new_interfid = {x for x in new_interfid} |
|
599 | new_interfid = {x for x in new_interfid} | |
| 598 | new_interfid = numpy.array(list(new_interfid)) |
|
600 | new_interfid = numpy.array(list(new_interfid)) | |
| 599 | new_cinterfid = new_interfid.size |
|
601 | new_cinterfid = new_interfid.size | |
| 600 | else: |
|
602 | else: | |
| 601 | new_cinterfid = 0 |
|
603 | new_cinterfid = 0 | |
| 602 |
|
604 | |||
| 603 | for ip in range(new_cinterfid): |
|
605 | for ip in range(new_cinterfid): | |
| 604 | ind = junkspc_interf[:, new_interfid[ip]].ravel().argsort() |
|
606 | ind = junkspc_interf[:, new_interfid[ip]].ravel().argsort() | |
| 605 | jspc_interf[new_interfid[ip] |
|
607 | jspc_interf[new_interfid[ip] | |
| 606 | ] = junkspc_interf[ind[nhei_interf // 2], new_interfid[ip]] |
|
608 | ] = junkspc_interf[ind[nhei_interf // 2], new_interfid[ip]] | |
| 607 |
|
609 | |||
| 608 | jspectra[ich, :, ind_hei] = jspectra[ich, :, |
|
610 | jspectra[ich, :, ind_hei] = jspectra[ich, :, | |
| 609 | ind_hei] - jspc_interf # Corregir indices |
|
611 | ind_hei] - jspc_interf # Corregir indices | |
| 610 |
|
612 | |||
| 611 | # Removiendo la interferencia del punto de mayor interferencia |
|
613 | # Removiendo la interferencia del punto de mayor interferencia | |
| 612 | ListAux = jspc_interf[mask_prof].tolist() |
|
614 | ListAux = jspc_interf[mask_prof].tolist() | |
| 613 | maxid = ListAux.index(max(ListAux)) |
|
615 | maxid = ListAux.index(max(ListAux)) | |
| 614 |
|
616 | |||
| 615 | if cinterfid > 0: |
|
617 | if cinterfid > 0: | |
| 616 | for ip in range(cinterfid * (interf == 2) - 1): |
|
618 | for ip in range(cinterfid * (interf == 2) - 1): | |
| 617 | ind = (jspectra[ich, interfid[ip], :] < tmp_noise * |
|
619 | ind = (jspectra[ich, interfid[ip], :] < tmp_noise * | |
| 618 | (1 + 1 / numpy.sqrt(num_incoh))).nonzero() |
|
620 | (1 + 1 / numpy.sqrt(num_incoh))).nonzero() | |
| 619 | cind = len(ind) |
|
621 | cind = len(ind) | |
| 620 |
|
622 | |||
| 621 | if (cind > 0): |
|
623 | if (cind > 0): | |
| 622 | jspectra[ich, interfid[ip], ind] = tmp_noise * \ |
|
624 | jspectra[ich, interfid[ip], ind] = tmp_noise * \ | |
| 623 | (1 + (numpy.random.uniform(cind) - 0.5) / |
|
625 | (1 + (numpy.random.uniform(cind) - 0.5) / | |
| 624 | numpy.sqrt(num_incoh)) |
|
626 | numpy.sqrt(num_incoh)) | |
| 625 |
|
627 | |||
| 626 | ind = numpy.array([-2, -1, 1, 2]) |
|
628 | ind = numpy.array([-2, -1, 1, 2]) | |
| 627 | xx = numpy.zeros([4, 4]) |
|
629 | xx = numpy.zeros([4, 4]) | |
| 628 |
|
630 | |||
| 629 | for id1 in range(4): |
|
631 | for id1 in range(4): | |
| 630 | xx[:, id1] = ind[id1]**numpy.asarray(list(range(4))) |
|
632 | xx[:, id1] = ind[id1]**numpy.asarray(list(range(4))) | |
| 631 |
|
633 | |||
| 632 | xx_inv = numpy.linalg.inv(xx) |
|
634 | xx_inv = numpy.linalg.inv(xx) | |
| 633 | xx = xx_inv[:, 0] |
|
635 | xx = xx_inv[:, 0] | |
| 634 | ind = (ind + maxid + num_mask_prof) % num_mask_prof |
|
636 | ind = (ind + maxid + num_mask_prof) % num_mask_prof | |
| 635 | yy = jspectra[ich, mask_prof[ind], :] |
|
637 | yy = jspectra[ich, mask_prof[ind], :] | |
| 636 | jspectra[ich, mask_prof[maxid], :] = numpy.dot( |
|
638 | jspectra[ich, mask_prof[maxid], :] = numpy.dot( | |
| 637 | yy.transpose(), xx) |
|
639 | yy.transpose(), xx) | |
| 638 |
|
640 | |||
| 639 | indAux = (jspectra[ich, :, :] < tmp_noise * |
|
641 | indAux = (jspectra[ich, :, :] < tmp_noise * | |
| 640 | (1 - 1 / numpy.sqrt(num_incoh))).nonzero() |
|
642 | (1 - 1 / numpy.sqrt(num_incoh))).nonzero() | |
| 641 | jspectra[ich, indAux[0], indAux[1]] = tmp_noise * \ |
|
643 | jspectra[ich, indAux[0], indAux[1]] = tmp_noise * \ | |
| 642 | (1 - 1 / numpy.sqrt(num_incoh)) |
|
644 | (1 - 1 / numpy.sqrt(num_incoh)) | |
| 643 |
|
645 | |||
| 644 | # Remocion de Interferencia en el Cross Spectra |
|
646 | # Remocion de Interferencia en el Cross Spectra | |
| 645 | if jcspectra is None: |
|
647 | if jcspectra is None: | |
| 646 | return jspectra, jcspectra |
|
648 | return jspectra, jcspectra | |
| 647 | num_pairs = int(jcspectra.size / (num_prof * num_hei)) |
|
649 | num_pairs = int(jcspectra.size / (num_prof * num_hei)) | |
| 648 | jcspectra = jcspectra.reshape(num_pairs, num_prof, num_hei) |
|
650 | jcspectra = jcspectra.reshape(num_pairs, num_prof, num_hei) | |
| 649 |
|
651 | |||
| 650 | for ip in range(num_pairs): |
|
652 | for ip in range(num_pairs): | |
| 651 |
|
653 | |||
| 652 | #------------------------------------------- |
|
654 | #------------------------------------------- | |
| 653 |
|
655 | |||
| 654 | cspower = numpy.abs(jcspectra[ip, mask_prof, :]) |
|
656 | cspower = numpy.abs(jcspectra[ip, mask_prof, :]) | |
| 655 | cspower = cspower[:, hei_interf] |
|
657 | cspower = cspower[:, hei_interf] | |
| 656 | cspower = cspower.sum(axis=0) |
|
658 | cspower = cspower.sum(axis=0) | |
| 657 |
|
659 | |||
| 658 | cspsort = cspower.ravel().argsort() |
|
660 | cspsort = cspower.ravel().argsort() | |
| 659 | junkcspc_interf = jcspectra[ip, :, hei_interf[cspsort[list(range( |
|
661 | junkcspc_interf = jcspectra[ip, :, hei_interf[cspsort[list(range( | |
| 660 | offhei_interf, nhei_interf + offhei_interf))]]] |
|
662 | offhei_interf, nhei_interf + offhei_interf))]]] | |
| 661 | junkcspc_interf = junkcspc_interf.transpose() |
|
663 | junkcspc_interf = junkcspc_interf.transpose() | |
| 662 | jcspc_interf = junkcspc_interf.sum(axis=1) / nhei_interf |
|
664 | jcspc_interf = junkcspc_interf.sum(axis=1) / nhei_interf | |
| 663 |
|
665 | |||
| 664 | ind = numpy.abs(jcspc_interf[mask_prof]).ravel().argsort() |
|
666 | ind = numpy.abs(jcspc_interf[mask_prof]).ravel().argsort() | |
| 665 |
|
667 | |||
| 666 | median_real = int(numpy.median(numpy.real( |
|
668 | median_real = int(numpy.median(numpy.real( | |
| 667 | junkcspc_interf[mask_prof[ind[list(range(3 * num_prof // 4))]], :]))) |
|
669 | junkcspc_interf[mask_prof[ind[list(range(3 * num_prof // 4))]], :]))) | |
| 668 | median_imag = int(numpy.median(numpy.imag( |
|
670 | median_imag = int(numpy.median(numpy.imag( | |
| 669 | junkcspc_interf[mask_prof[ind[list(range(3 * num_prof // 4))]], :]))) |
|
671 | junkcspc_interf[mask_prof[ind[list(range(3 * num_prof // 4))]], :]))) | |
| 670 | comp_mask_prof = [int(e) for e in comp_mask_prof] |
|
672 | comp_mask_prof = [int(e) for e in comp_mask_prof] | |
| 671 | junkcspc_interf[comp_mask_prof, :] = numpy.complex( |
|
673 | junkcspc_interf[comp_mask_prof, :] = numpy.complex( | |
| 672 | median_real, median_imag) |
|
674 | median_real, median_imag) | |
| 673 |
|
675 | |||
| 674 | for iprof in range(num_prof): |
|
676 | for iprof in range(num_prof): | |
| 675 | ind = numpy.abs(junkcspc_interf[iprof, :]).ravel().argsort() |
|
677 | ind = numpy.abs(junkcspc_interf[iprof, :]).ravel().argsort() | |
| 676 | jcspc_interf[iprof] = junkcspc_interf[iprof, ind[nhei_interf // 2]] |
|
678 | jcspc_interf[iprof] = junkcspc_interf[iprof, ind[nhei_interf // 2]] | |
| 677 |
|
679 | |||
| 678 | # Removiendo la Interferencia |
|
680 | # Removiendo la Interferencia | |
| 679 | jcspectra[ip, :, ind_hei] = jcspectra[ip, |
|
681 | jcspectra[ip, :, ind_hei] = jcspectra[ip, | |
| 680 | :, ind_hei] - jcspc_interf |
|
682 | :, ind_hei] - jcspc_interf | |
| 681 |
|
683 | |||
| 682 | ListAux = numpy.abs(jcspc_interf[mask_prof]).tolist() |
|
684 | ListAux = numpy.abs(jcspc_interf[mask_prof]).tolist() | |
| 683 | maxid = ListAux.index(max(ListAux)) |
|
685 | maxid = ListAux.index(max(ListAux)) | |
| 684 |
|
686 | |||
| 685 | ind = numpy.array([-2, -1, 1, 2]) |
|
687 | ind = numpy.array([-2, -1, 1, 2]) | |
| 686 | xx = numpy.zeros([4, 4]) |
|
688 | xx = numpy.zeros([4, 4]) | |
| 687 |
|
689 | |||
| 688 | for id1 in range(4): |
|
690 | for id1 in range(4): | |
| 689 | xx[:, id1] = ind[id1]**numpy.asarray(list(range(4))) |
|
691 | xx[:, id1] = ind[id1]**numpy.asarray(list(range(4))) | |
| 690 |
|
692 | |||
| 691 | xx_inv = numpy.linalg.inv(xx) |
|
693 | xx_inv = numpy.linalg.inv(xx) | |
| 692 | xx = xx_inv[:, 0] |
|
694 | xx = xx_inv[:, 0] | |
| 693 |
|
695 | |||
| 694 | ind = (ind + maxid + num_mask_prof) % num_mask_prof |
|
696 | ind = (ind + maxid + num_mask_prof) % num_mask_prof | |
| 695 | yy = jcspectra[ip, mask_prof[ind], :] |
|
697 | yy = jcspectra[ip, mask_prof[ind], :] | |
| 696 | jcspectra[ip, mask_prof[maxid], :] = numpy.dot(yy.transpose(), xx) |
|
698 | jcspectra[ip, mask_prof[maxid], :] = numpy.dot(yy.transpose(), xx) | |
| 697 |
|
699 | |||
| 698 | # Guardar Resultados |
|
700 | # Guardar Resultados | |
| 699 | self.dataOut.data_spc = jspectra |
|
701 | self.dataOut.data_spc = jspectra | |
| 700 | self.dataOut.data_cspc = jcspectra |
|
702 | self.dataOut.data_cspc = jcspectra | |
| 701 |
|
703 | |||
| 702 | return 1 |
|
704 | return 1 | |
| 703 |
|
705 | |||
| 704 | def run(self, dataOut, interf=2,hei_interf=None, nhei_interf=None, offhei_interf=None, mode=1): |
|
706 | def run(self, dataOut, interf=2,hei_interf=None, nhei_interf=None, offhei_interf=None, mode=1): | |
| 705 |
|
707 | |||
| 706 | self.dataOut = dataOut |
|
708 | self.dataOut = dataOut | |
| 707 |
|
709 | |||
| 708 | if mode == 1: |
|
710 | if mode == 1: | |
| 709 | self.removeInterference(interf=2,hei_interf=None, nhei_interf=None, offhei_interf=None) |
|
711 | self.removeInterference(interf=2,hei_interf=None, nhei_interf=None, offhei_interf=None) | |
| 710 | elif mode == 2: |
|
712 | elif mode == 2: | |
| 711 | self.removeInterference2() |
|
713 | self.removeInterference2() | |
| 712 |
|
714 | |||
| 713 | return self.dataOut |
|
715 | return self.dataOut | |
| 714 |
|
716 | |||
| 715 |
|
717 | |||
| 716 | class deflip(Operation): |
|
718 | class deflip(Operation): | |
| 717 |
|
719 | |||
| 718 | def run(self, dataOut): |
|
720 | def run(self, dataOut): | |
| 719 | # arreglo 1: (num_chan, num_profiles, num_heights) |
|
721 | # arreglo 1: (num_chan, num_profiles, num_heights) | |
| 720 | self.dataOut = dataOut |
|
722 | self.dataOut = dataOut | |
| 721 |
|
723 | |||
| 722 | # JULIA-oblicua, indice 2 |
|
724 | # JULIA-oblicua, indice 2 | |
| 723 | # arreglo 2: (num_profiles, num_heights) |
|
725 | # arreglo 2: (num_profiles, num_heights) | |
| 724 | jspectra = self.dataOut.data_spc[2] |
|
726 | jspectra = self.dataOut.data_spc[2] | |
| 725 | jspectra_tmp=numpy.zeros(jspectra.shape) |
|
727 | jspectra_tmp=numpy.zeros(jspectra.shape) | |
| 726 | num_profiles=jspectra.shape[0] |
|
728 | num_profiles=jspectra.shape[0] | |
| 727 | freq_dc = int(num_profiles / 2) |
|
729 | freq_dc = int(num_profiles / 2) | |
| 728 | # Flip con for |
|
730 | # Flip con for | |
| 729 | for j in range(num_profiles): |
|
731 | for j in range(num_profiles): | |
| 730 | jspectra_tmp[num_profiles-j-1]= jspectra[j] |
|
732 | jspectra_tmp[num_profiles-j-1]= jspectra[j] | |
| 731 | # Intercambio perfil de DC con perfil inmediato anterior |
|
733 | # Intercambio perfil de DC con perfil inmediato anterior | |
| 732 | jspectra_tmp[freq_dc-1]= jspectra[freq_dc-1] |
|
734 | jspectra_tmp[freq_dc-1]= jspectra[freq_dc-1] | |
| 733 | jspectra_tmp[freq_dc]= jspectra[freq_dc] |
|
735 | jspectra_tmp[freq_dc]= jspectra[freq_dc] | |
| 734 | # canal modificado es re-escrito en el arreglo de canales |
|
736 | # canal modificado es re-escrito en el arreglo de canales | |
| 735 | self.dataOut.data_spc[2] = jspectra_tmp |
|
737 | self.dataOut.data_spc[2] = jspectra_tmp | |
| 736 |
|
738 | |||
| 737 | return self.dataOut |
|
739 | return self.dataOut | |
| 738 |
|
740 | |||
| 739 |
|
741 | |||
| 740 | class IncohInt(Operation): |
|
742 | class IncohInt(Operation): | |
| 741 |
|
743 | |||
| 742 | __profIndex = 0 |
|
744 | __profIndex = 0 | |
| 743 | __withOverapping = False |
|
745 | __withOverapping = False | |
| 744 |
|
746 | |||
| 745 | __byTime = False |
|
747 | __byTime = False | |
| 746 | __initime = None |
|
748 | __initime = None | |
| 747 | __lastdatatime = None |
|
749 | __lastdatatime = None | |
| 748 | __integrationtime = None |
|
750 | __integrationtime = None | |
| 749 |
|
751 | |||
| 750 | __buffer_spc = None |
|
752 | __buffer_spc = None | |
| 751 | __buffer_cspc = None |
|
753 | __buffer_cspc = None | |
| 752 | __buffer_dc = None |
|
754 | __buffer_dc = None | |
| 753 |
|
755 | |||
| 754 | __dataReady = False |
|
756 | __dataReady = False | |
| 755 |
|
757 | |||
| 756 | __timeInterval = None |
|
758 | __timeInterval = None | |
| 757 |
|
759 | |||
| 758 | n = None |
|
760 | n = None | |
| 759 |
|
761 | |||
| 760 | def __init__(self): |
|
762 | def __init__(self): | |
| 761 |
|
763 | |||
| 762 | Operation.__init__(self) |
|
764 | Operation.__init__(self) | |
| 763 |
|
765 | |||
| 764 | def setup(self, n=None, timeInterval=None, overlapping=False): |
|
766 | def setup(self, n=None, timeInterval=None, overlapping=False): | |
| 765 | """ |
|
767 | """ | |
| 766 | Set the parameters of the integration class. |
|
768 | Set the parameters of the integration class. | |
| 767 |
|
769 | |||
| 768 | Inputs: |
|
770 | Inputs: | |
| 769 |
|
771 | |||
| 770 | n : Number of coherent integrations |
|
772 | n : Number of coherent integrations | |
| 771 | timeInterval : Time of integration. If the parameter "n" is selected this one does not work |
|
773 | timeInterval : Time of integration. If the parameter "n" is selected this one does not work | |
| 772 | overlapping : |
|
774 | overlapping : | |
| 773 |
|
775 | |||
| 774 | """ |
|
776 | """ | |
| 775 |
|
777 | |||
| 776 | self.__initime = None |
|
778 | self.__initime = None | |
| 777 | self.__lastdatatime = 0 |
|
779 | self.__lastdatatime = 0 | |
| 778 |
|
780 | |||
| 779 | self.__buffer_spc = 0 |
|
781 | self.__buffer_spc = 0 | |
| 780 | self.__buffer_cspc = 0 |
|
782 | self.__buffer_cspc = 0 | |
| 781 | self.__buffer_dc = 0 |
|
783 | self.__buffer_dc = 0 | |
| 782 |
|
784 | |||
| 783 | self.__profIndex = 0 |
|
785 | self.__profIndex = 0 | |
| 784 | self.__dataReady = False |
|
786 | self.__dataReady = False | |
| 785 | self.__byTime = False |
|
787 | self.__byTime = False | |
| 786 |
|
788 | |||
| 787 | if n is None and timeInterval is None: |
|
789 | if n is None and timeInterval is None: | |
| 788 | raise ValueError("n or timeInterval should be specified ...") |
|
790 | raise ValueError("n or timeInterval should be specified ...") | |
| 789 |
|
791 | |||
| 790 | if n is not None: |
|
792 | if n is not None: | |
| 791 | self.n = int(n) |
|
793 | self.n = int(n) | |
| 792 | else: |
|
794 | else: | |
| 793 |
|
795 | |||
| 794 | self.__integrationtime = int(timeInterval) |
|
796 | self.__integrationtime = int(timeInterval) | |
| 795 | self.n = None |
|
797 | self.n = None | |
| 796 | self.__byTime = True |
|
798 | self.__byTime = True | |
| 797 |
|
799 | |||
| 798 | def putData(self, data_spc, data_cspc, data_dc): |
|
800 | def putData(self, data_spc, data_cspc, data_dc): | |
| 799 | """ |
|
801 | """ | |
| 800 | Add a profile to the __buffer_spc and increase in one the __profileIndex |
|
802 | Add a profile to the __buffer_spc and increase in one the __profileIndex | |
| 801 |
|
803 | |||
| 802 | """ |
|
804 | """ | |
| 803 |
|
805 | |||
| 804 | self.__buffer_spc += data_spc |
|
806 | self.__buffer_spc += data_spc | |
| 805 |
|
807 | |||
| 806 | if data_cspc is None: |
|
808 | if data_cspc is None: | |
| 807 | self.__buffer_cspc = None |
|
809 | self.__buffer_cspc = None | |
| 808 | else: |
|
810 | else: | |
| 809 | self.__buffer_cspc += data_cspc |
|
811 | self.__buffer_cspc += data_cspc | |
| 810 |
|
812 | |||
| 811 | if data_dc is None: |
|
813 | if data_dc is None: | |
| 812 | self.__buffer_dc = None |
|
814 | self.__buffer_dc = None | |
| 813 | else: |
|
815 | else: | |
| 814 | self.__buffer_dc += data_dc |
|
816 | self.__buffer_dc += data_dc | |
| 815 |
|
817 | |||
| 816 | self.__profIndex += 1 |
|
818 | self.__profIndex += 1 | |
| 817 |
|
819 | |||
| 818 | return |
|
820 | return | |
| 819 |
|
821 | |||
| 820 | def pushData(self): |
|
822 | def pushData(self): | |
| 821 | """ |
|
823 | """ | |
| 822 | Return the sum of the last profiles and the profiles used in the sum. |
|
824 | Return the sum of the last profiles and the profiles used in the sum. | |
| 823 |
|
825 | |||
| 824 | Affected: |
|
826 | Affected: | |
| 825 |
|
827 | |||
| 826 | self.__profileIndex |
|
828 | self.__profileIndex | |
| 827 |
|
829 | |||
| 828 | """ |
|
830 | """ | |
| 829 |
|
831 | |||
| 830 | data_spc = self.__buffer_spc |
|
832 | data_spc = self.__buffer_spc | |
| 831 | data_cspc = self.__buffer_cspc |
|
833 | data_cspc = self.__buffer_cspc | |
| 832 | data_dc = self.__buffer_dc |
|
834 | data_dc = self.__buffer_dc | |
| 833 | n = self.__profIndex |
|
835 | n = self.__profIndex | |
| 834 |
|
836 | |||
| 835 | self.__buffer_spc = 0 |
|
837 | self.__buffer_spc = 0 | |
| 836 | self.__buffer_cspc = 0 |
|
838 | self.__buffer_cspc = 0 | |
| 837 | self.__buffer_dc = 0 |
|
839 | self.__buffer_dc = 0 | |
| 838 | self.__profIndex = 0 |
|
840 | self.__profIndex = 0 | |
| 839 |
|
841 | |||
| 840 | return data_spc, data_cspc, data_dc, n |
|
842 | return data_spc, data_cspc, data_dc, n | |
| 841 |
|
843 | |||
| 842 | def byProfiles(self, *args): |
|
844 | def byProfiles(self, *args): | |
| 843 |
|
845 | |||
| 844 | self.__dataReady = False |
|
846 | self.__dataReady = False | |
| 845 | avgdata_spc = None |
|
847 | avgdata_spc = None | |
| 846 | avgdata_cspc = None |
|
848 | avgdata_cspc = None | |
| 847 | avgdata_dc = None |
|
849 | avgdata_dc = None | |
| 848 |
|
850 | |||
| 849 | self.putData(*args) |
|
851 | self.putData(*args) | |
| 850 |
|
852 | |||
| 851 | if self.__profIndex == self.n: |
|
853 | if self.__profIndex == self.n: | |
| 852 |
|
854 | |||
| 853 | avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData() |
|
855 | avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData() | |
| 854 | self.n = n |
|
856 | self.n = n | |
| 855 | self.__dataReady = True |
|
857 | self.__dataReady = True | |
| 856 |
|
858 | |||
| 857 | return avgdata_spc, avgdata_cspc, avgdata_dc |
|
859 | return avgdata_spc, avgdata_cspc, avgdata_dc | |
| 858 |
|
860 | |||
| 859 | def byTime(self, datatime, *args): |
|
861 | def byTime(self, datatime, *args): | |
| 860 |
|
862 | |||
| 861 | self.__dataReady = False |
|
863 | self.__dataReady = False | |
| 862 | avgdata_spc = None |
|
864 | avgdata_spc = None | |
| 863 | avgdata_cspc = None |
|
865 | avgdata_cspc = None | |
| 864 | avgdata_dc = None |
|
866 | avgdata_dc = None | |
| 865 |
|
867 | |||
| 866 | self.putData(*args) |
|
868 | self.putData(*args) | |
| 867 |
|
869 | |||
| 868 | if (datatime - self.__initime) >= self.__integrationtime: |
|
870 | if (datatime - self.__initime) >= self.__integrationtime: | |
| 869 | avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData() |
|
871 | avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData() | |
| 870 | self.n = n |
|
872 | self.n = n | |
| 871 | self.__dataReady = True |
|
873 | self.__dataReady = True | |
| 872 |
|
874 | |||
| 873 | return avgdata_spc, avgdata_cspc, avgdata_dc |
|
875 | return avgdata_spc, avgdata_cspc, avgdata_dc | |
| 874 |
|
876 | |||
| 875 | def integrate(self, datatime, *args): |
|
877 | def integrate(self, datatime, *args): | |
| 876 |
|
878 | |||
| 877 | if self.__profIndex == 0: |
|
879 | if self.__profIndex == 0: | |
| 878 | self.__initime = datatime |
|
880 | self.__initime = datatime | |
| 879 |
|
881 | |||
| 880 | if self.__byTime: |
|
882 | if self.__byTime: | |
| 881 | avgdata_spc, avgdata_cspc, avgdata_dc = self.byTime( |
|
883 | avgdata_spc, avgdata_cspc, avgdata_dc = self.byTime( | |
| 882 | datatime, *args) |
|
884 | datatime, *args) | |
| 883 | else: |
|
885 | else: | |
| 884 | avgdata_spc, avgdata_cspc, avgdata_dc = self.byProfiles(*args) |
|
886 | avgdata_spc, avgdata_cspc, avgdata_dc = self.byProfiles(*args) | |
| 885 |
|
887 | |||
| 886 | if not self.__dataReady: |
|
888 | if not self.__dataReady: | |
| 887 | return None, None, None, None |
|
889 | return None, None, None, None | |
| 888 |
|
890 | |||
| 889 | return self.__initime, avgdata_spc, avgdata_cspc, avgdata_dc |
|
891 | return self.__initime, avgdata_spc, avgdata_cspc, avgdata_dc | |
| 890 |
|
892 | |||
| 891 | def run(self, dataOut, n=None, timeInterval=None, overlapping=False): |
|
893 | def run(self, dataOut, n=None, timeInterval=None, overlapping=False): | |
| 892 | if n == 1: |
|
894 | if n == 1: | |
| 893 | return dataOut |
|
895 | return dataOut | |
| 894 |
|
896 | |||
| 895 | dataOut.flagNoData = True |
|
897 | dataOut.flagNoData = True | |
| 896 |
|
898 | |||
| 897 | if not self.isConfig: |
|
899 | if not self.isConfig: | |
| 898 | self.setup(n, timeInterval, overlapping) |
|
900 | self.setup(n, timeInterval, overlapping) | |
| 899 | self.isConfig = True |
|
901 | self.isConfig = True | |
| 900 |
|
902 | |||
| 901 | avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(dataOut.utctime, |
|
903 | avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(dataOut.utctime, | |
| 902 | dataOut.data_spc, |
|
904 | dataOut.data_spc, | |
| 903 | dataOut.data_cspc, |
|
905 | dataOut.data_cspc, | |
| 904 | dataOut.data_dc) |
|
906 | dataOut.data_dc) | |
| 905 |
|
907 | |||
| 906 | if self.__dataReady: |
|
908 | if self.__dataReady: | |
| 907 |
|
909 | |||
| 908 | dataOut.data_spc = avgdata_spc |
|
910 | dataOut.data_spc = avgdata_spc | |
| 909 | dataOut.data_cspc = avgdata_cspc |
|
911 | dataOut.data_cspc = avgdata_cspc | |
| 910 | dataOut.data_dc = avgdata_dc |
|
912 | dataOut.data_dc = avgdata_dc | |
| 911 | dataOut.nIncohInt *= self.n |
|
913 | dataOut.nIncohInt *= self.n | |
| 912 | dataOut.utctime = avgdatatime |
|
914 | dataOut.utctime = avgdatatime | |
| 913 | dataOut.flagNoData = False |
|
915 | dataOut.flagNoData = False | |
| 914 |
|
916 | |||
| 915 | return dataOut |
|
917 | return dataOut | |
| 916 |
|
918 | |||
| 917 | class dopplerFlip(Operation): |
|
919 | class dopplerFlip(Operation): | |
| 918 |
|
920 | |||
| 919 | def run(self, dataOut): |
|
921 | def run(self, dataOut): | |
| 920 | # arreglo 1: (num_chan, num_profiles, num_heights) |
|
922 | # arreglo 1: (num_chan, num_profiles, num_heights) | |
| 921 | self.dataOut = dataOut |
|
923 | self.dataOut = dataOut | |
| 922 | # JULIA-oblicua, indice 2 |
|
924 | # JULIA-oblicua, indice 2 | |
| 923 | # arreglo 2: (num_profiles, num_heights) |
|
925 | # arreglo 2: (num_profiles, num_heights) | |
| 924 | jspectra = self.dataOut.data_spc[2] |
|
926 | jspectra = self.dataOut.data_spc[2] | |
| 925 | jspectra_tmp = numpy.zeros(jspectra.shape) |
|
927 | jspectra_tmp = numpy.zeros(jspectra.shape) | |
| 926 | num_profiles = jspectra.shape[0] |
|
928 | num_profiles = jspectra.shape[0] | |
| 927 | freq_dc = int(num_profiles / 2) |
|
929 | freq_dc = int(num_profiles / 2) | |
| 928 | # Flip con for |
|
930 | # Flip con for | |
| 929 | for j in range(num_profiles): |
|
931 | for j in range(num_profiles): | |
| 930 | jspectra_tmp[num_profiles-j-1]= jspectra[j] |
|
932 | jspectra_tmp[num_profiles-j-1]= jspectra[j] | |
| 931 | # Intercambio perfil de DC con perfil inmediato anterior |
|
933 | # Intercambio perfil de DC con perfil inmediato anterior | |
| 932 | jspectra_tmp[freq_dc-1]= jspectra[freq_dc-1] |
|
934 | jspectra_tmp[freq_dc-1]= jspectra[freq_dc-1] | |
| 933 | jspectra_tmp[freq_dc]= jspectra[freq_dc] |
|
935 | jspectra_tmp[freq_dc]= jspectra[freq_dc] | |
| 934 | # canal modificado es re-escrito en el arreglo de canales |
|
936 | # canal modificado es re-escrito en el arreglo de canales | |
| 935 | self.dataOut.data_spc[2] = jspectra_tmp |
|
937 | self.dataOut.data_spc[2] = jspectra_tmp | |
| 936 |
|
938 | |||
| 937 | return self.dataOut No newline at end of file |
|
939 | return self.dataOut | |
| @@ -1,1627 +1,1625 | |||||
| 1 | import sys |
|
1 | import sys | |
| 2 | import numpy,math |
|
2 | import numpy,math | |
| 3 | from scipy import interpolate |
|
3 | from scipy import interpolate | |
| 4 | from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation, MPDecorator |
|
4 | from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation, MPDecorator | |
| 5 | from schainpy.model.data.jrodata import Voltage,hildebrand_sekhon |
|
5 | from schainpy.model.data.jrodata import Voltage,hildebrand_sekhon | |
| 6 | from schainpy.utils import log |
|
6 | from schainpy.utils import log | |
| 7 | from time import time |
|
7 | from time import time | |
| 8 | # voltage proc master |
|
8 | # voltage proc master | |
| 9 |
|
9 | |||
| 10 | class VoltageProc(ProcessingUnit): |
|
10 | class VoltageProc(ProcessingUnit): | |
| 11 |
|
11 | |||
| 12 | def __init__(self): |
|
12 | def __init__(self): | |
| 13 |
|
13 | |||
| 14 | ProcessingUnit.__init__(self) |
|
14 | ProcessingUnit.__init__(self) | |
| 15 |
|
15 | |||
| 16 | self.dataOut = Voltage() |
|
16 | self.dataOut = Voltage() | |
| 17 | self.flip = 1 |
|
17 | self.flip = 1 | |
| 18 | self.setupReq = False |
|
18 | self.setupReq = False | |
| 19 |
|
19 | |||
| 20 | def run(self, runNextUnit = 0): |
|
20 | def run(self, runNextUnit = 0): | |
| 21 |
|
21 | |||
| 22 | if self.dataIn.type == 'AMISR': |
|
22 | if self.dataIn.type == 'AMISR': | |
| 23 | self.__updateObjFromAmisrInput() |
|
23 | self.__updateObjFromAmisrInput() | |
| 24 |
|
24 | |||
| 25 | if self.dataIn.type == 'Voltage': |
|
25 | if self.dataIn.type == 'Voltage': | |
| 26 | self.dataOut.copy(self.dataIn) |
|
26 | self.dataOut.copy(self.dataIn) | |
| 27 | self.dataOut.runNextUnit = runNextUnit |
|
27 | self.dataOut.runNextUnit = runNextUnit | |
| 28 |
|
28 | |||
| 29 |
|
29 | |||
| 30 | def __updateObjFromAmisrInput(self): |
|
30 | def __updateObjFromAmisrInput(self): | |
| 31 |
|
31 | |||
| 32 | self.dataOut.timeZone = self.dataIn.timeZone |
|
32 | self.dataOut.timeZone = self.dataIn.timeZone | |
| 33 | self.dataOut.dstFlag = self.dataIn.dstFlag |
|
33 | self.dataOut.dstFlag = self.dataIn.dstFlag | |
| 34 | self.dataOut.errorCount = self.dataIn.errorCount |
|
34 | self.dataOut.errorCount = self.dataIn.errorCount | |
| 35 | self.dataOut.useLocalTime = self.dataIn.useLocalTime |
|
35 | self.dataOut.useLocalTime = self.dataIn.useLocalTime | |
| 36 |
|
36 | |||
| 37 | self.dataOut.flagNoData = self.dataIn.flagNoData |
|
37 | self.dataOut.flagNoData = self.dataIn.flagNoData | |
| 38 | self.dataOut.data = self.dataIn.data |
|
38 | self.dataOut.data = self.dataIn.data | |
| 39 | self.dataOut.utctime = self.dataIn.utctime |
|
39 | self.dataOut.utctime = self.dataIn.utctime | |
| 40 | self.dataOut.channelList = self.dataIn.channelList |
|
40 | self.dataOut.channelList = self.dataIn.channelList | |
| 41 | #self.dataOut.timeInterval = self.dataIn.timeInterval |
|
41 | #self.dataOut.timeInterval = self.dataIn.timeInterval | |
| 42 | self.dataOut.heightList = self.dataIn.heightList |
|
42 | self.dataOut.heightList = self.dataIn.heightList | |
| 43 | self.dataOut.nProfiles = self.dataIn.nProfiles |
|
43 | self.dataOut.nProfiles = self.dataIn.nProfiles | |
| 44 |
|
44 | |||
| 45 | self.dataOut.nCohInt = self.dataIn.nCohInt |
|
45 | self.dataOut.nCohInt = self.dataIn.nCohInt | |
| 46 | self.dataOut.ippSeconds = self.dataIn.ippSeconds |
|
46 | self.dataOut.ippSeconds = self.dataIn.ippSeconds | |
| 47 | self.dataOut.frequency = self.dataIn.frequency |
|
47 | self.dataOut.frequency = self.dataIn.frequency | |
| 48 |
|
48 | |||
| 49 | self.dataOut.azimuth = self.dataIn.azimuth |
|
49 | self.dataOut.azimuth = self.dataIn.azimuth | |
| 50 | self.dataOut.zenith = self.dataIn.zenith |
|
50 | self.dataOut.zenith = self.dataIn.zenith | |
| 51 |
|
51 | |||
| 52 | self.dataOut.beam.codeList = self.dataIn.beam.codeList |
|
52 | self.dataOut.beam.codeList = self.dataIn.beam.codeList | |
| 53 | self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList |
|
53 | self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList | |
| 54 | self.dataOut.beam.zenithList = self.dataIn.beam.zenithList |
|
54 | self.dataOut.beam.zenithList = self.dataIn.beam.zenithList | |
| 55 |
|
55 | |||
| 56 |
|
56 | |||
| 57 | class selectChannels(Operation): |
|
57 | class selectChannels(Operation): | |
| 58 |
|
58 | |||
| 59 | def run(self, dataOut, channelList): |
|
59 | def run(self, dataOut, channelList): | |
| 60 |
|
60 | |||
| 61 | channelIndexList = [] |
|
61 | channelIndexList = [] | |
| 62 | self.dataOut = dataOut |
|
62 | self.dataOut = dataOut | |
| 63 | for channel in channelList: |
|
63 | for channel in channelList: | |
| 64 | if channel not in self.dataOut.channelList: |
|
64 | if channel not in self.dataOut.channelList: | |
| 65 | raise ValueError("Channel %d is not in %s" %(channel, str(self.dataOut.channelList))) |
|
65 | raise ValueError("Channel %d is not in %s" %(channel, str(self.dataOut.channelList))) | |
| 66 |
|
66 | |||
| 67 | index = self.dataOut.channelList.index(channel) |
|
67 | index = self.dataOut.channelList.index(channel) | |
| 68 | channelIndexList.append(index) |
|
68 | channelIndexList.append(index) | |
| 69 | self.selectChannelsByIndex(channelIndexList) |
|
69 | self.selectChannelsByIndex(channelIndexList) | |
| 70 | return self.dataOut |
|
70 | return self.dataOut | |
| 71 |
|
71 | |||
| 72 | def selectChannelsByIndex(self, channelIndexList): |
|
72 | def selectChannelsByIndex(self, channelIndexList): | |
| 73 | """ |
|
73 | """ | |
| 74 | Selecciona un bloque de datos en base a canales segun el channelIndexList |
|
74 | Selecciona un bloque de datos en base a canales segun el channelIndexList | |
| 75 |
|
75 | |||
| 76 | Input: |
|
76 | Input: | |
| 77 | channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7] |
|
77 | channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7] | |
| 78 |
|
78 | |||
| 79 | Affected: |
|
79 | Affected: | |
| 80 | self.dataOut.data |
|
80 | self.dataOut.data | |
| 81 | self.dataOut.channelIndexList |
|
81 | self.dataOut.channelIndexList | |
| 82 | self.dataOut.nChannels |
|
82 | self.dataOut.nChannels | |
| 83 | self.dataOut.m_ProcessingHeader.totalSpectra |
|
83 | self.dataOut.m_ProcessingHeader.totalSpectra | |
| 84 | self.dataOut.systemHeaderObj.numChannels |
|
84 | self.dataOut.systemHeaderObj.numChannels | |
| 85 | self.dataOut.m_ProcessingHeader.blockSize |
|
85 | self.dataOut.m_ProcessingHeader.blockSize | |
| 86 |
|
86 | |||
| 87 | Return: |
|
87 | Return: | |
| 88 | None |
|
88 | None | |
| 89 | """ |
|
89 | """ | |
| 90 |
|
90 | |||
| 91 | for channelIndex in channelIndexList: |
|
91 | for channelIndex in channelIndexList: | |
| 92 | if channelIndex not in self.dataOut.channelIndexList: |
|
92 | if channelIndex not in self.dataOut.channelIndexList: | |
| 93 | raise ValueError("The value %d in channelIndexList is not valid" %channelIndex) |
|
93 | raise ValueError("The value %d in channelIndexList is not valid" %channelIndex) | |
| 94 |
|
94 | |||
| 95 | if self.dataOut.type == 'Voltage': |
|
95 | if self.dataOut.type == 'Voltage': | |
| 96 | if self.dataOut.flagDataAsBlock: |
|
96 | if self.dataOut.flagDataAsBlock: | |
| 97 | """ |
|
97 | """ | |
| 98 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
98 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] | |
| 99 | """ |
|
99 | """ | |
| 100 | data = self.dataOut.data[channelIndexList,:,:] |
|
100 | data = self.dataOut.data[channelIndexList,:,:] | |
| 101 | else: |
|
101 | else: | |
| 102 | data = self.dataOut.data[channelIndexList,:] |
|
102 | data = self.dataOut.data[channelIndexList,:] | |
| 103 |
|
103 | |||
| 104 | self.dataOut.data = data |
|
104 | self.dataOut.data = data | |
| 105 | # self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList] |
|
105 | # self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList] | |
| 106 | self.dataOut.channelList = range(len(channelIndexList)) |
|
106 | self.dataOut.channelList = range(len(channelIndexList)) | |
| 107 |
|
107 | |||
| 108 | elif self.dataOut.type == 'Spectra': |
|
108 | elif self.dataOut.type == 'Spectra': | |
| 109 | data_spc = self.dataOut.data_spc[channelIndexList, :] |
|
109 | data_spc = self.dataOut.data_spc[channelIndexList, :] | |
| 110 | data_dc = self.dataOut.data_dc[channelIndexList, :] |
|
110 | data_dc = self.dataOut.data_dc[channelIndexList, :] | |
| 111 |
|
111 | |||
| 112 | self.dataOut.data_spc = data_spc |
|
112 | self.dataOut.data_spc = data_spc | |
| 113 | self.dataOut.data_dc = data_dc |
|
113 | self.dataOut.data_dc = data_dc | |
| 114 |
|
114 | |||
| 115 | # self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList] |
|
115 | # self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList] | |
| 116 | self.dataOut.channelList = range(len(channelIndexList)) |
|
116 | self.dataOut.channelList = range(len(channelIndexList)) | |
| 117 | self.__selectPairsByChannel(channelIndexList) |
|
117 | self.__selectPairsByChannel(channelIndexList) | |
| 118 |
|
118 | |||
| 119 | return 1 |
|
119 | return 1 | |
| 120 |
|
120 | |||
| 121 | def __selectPairsByChannel(self, channelList=None): |
|
121 | def __selectPairsByChannel(self, channelList=None): | |
| 122 |
|
122 | |||
| 123 | if channelList == None: |
|
123 | if channelList == None: | |
| 124 | return |
|
124 | return | |
| 125 |
|
125 | |||
| 126 | pairsIndexListSelected = [] |
|
126 | pairsIndexListSelected = [] | |
| 127 | for pairIndex in self.dataOut.pairsIndexList: |
|
127 | for pairIndex in self.dataOut.pairsIndexList: | |
| 128 | # First pair |
|
128 | # First pair | |
| 129 | if self.dataOut.pairsList[pairIndex][0] not in channelList: |
|
129 | if self.dataOut.pairsList[pairIndex][0] not in channelList: | |
| 130 | continue |
|
130 | continue | |
| 131 | # Second pair |
|
131 | # Second pair | |
| 132 | if self.dataOut.pairsList[pairIndex][1] not in channelList: |
|
132 | if self.dataOut.pairsList[pairIndex][1] not in channelList: | |
| 133 | continue |
|
133 | continue | |
| 134 |
|
134 | |||
| 135 | pairsIndexListSelected.append(pairIndex) |
|
135 | pairsIndexListSelected.append(pairIndex) | |
| 136 |
|
136 | |||
| 137 | if not pairsIndexListSelected: |
|
137 | if not pairsIndexListSelected: | |
| 138 | self.dataOut.data_cspc = None |
|
138 | self.dataOut.data_cspc = None | |
| 139 | self.dataOut.pairsList = [] |
|
139 | self.dataOut.pairsList = [] | |
| 140 | return |
|
140 | return | |
| 141 |
|
141 | |||
| 142 | self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected] |
|
142 | self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected] | |
| 143 | self.dataOut.pairsList = [self.dataOut.pairsList[i] |
|
143 | self.dataOut.pairsList = [self.dataOut.pairsList[i] | |
| 144 | for i in pairsIndexListSelected] |
|
144 | for i in pairsIndexListSelected] | |
| 145 |
|
145 | |||
| 146 | return |
|
146 | return | |
| 147 |
|
147 | |||
| 148 | class selectHeights(Operation): |
|
148 | class selectHeights(Operation): | |
| 149 |
|
149 | |||
| 150 | def run(self, dataOut, minHei=None, maxHei=None, minIndex=None, maxIndex=None): |
|
150 | def run(self, dataOut, minHei=None, maxHei=None, minIndex=None, maxIndex=None): | |
| 151 | """ |
|
151 | """ | |
| 152 | Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango |
|
152 | Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango | |
| 153 | minHei <= height <= maxHei |
|
153 | minHei <= height <= maxHei | |
| 154 |
|
154 | |||
| 155 | Input: |
|
155 | Input: | |
| 156 | minHei : valor minimo de altura a considerar |
|
156 | minHei : valor minimo de altura a considerar | |
| 157 | maxHei : valor maximo de altura a considerar |
|
157 | maxHei : valor maximo de altura a considerar | |
| 158 |
|
158 | |||
| 159 | Affected: |
|
159 | Affected: | |
| 160 | Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex |
|
160 | Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex | |
| 161 |
|
161 | |||
| 162 | Return: |
|
162 | Return: | |
| 163 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 |
|
163 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 | |
| 164 | """ |
|
164 | """ | |
| 165 |
|
165 | |||
| 166 | self.dataOut = dataOut |
|
166 | self.dataOut = dataOut | |
| 167 |
|
167 | |||
| 168 | if type(minHei) == int or type(minHei) == float: |
|
168 | if type(minHei) == int or type(minHei) == float: | |
| 169 | v_minHei= True |
|
169 | v_minHei= True | |
| 170 | else: |
|
170 | else: | |
| 171 | v_minHei= False |
|
171 | v_minHei= False | |
| 172 |
|
172 | |||
| 173 | if v_minHei and maxHei: |
|
173 | if v_minHei and maxHei: | |
| 174 | if (minHei < self.dataOut.heightList[0]): |
|
174 | if (minHei < self.dataOut.heightList[0]): | |
| 175 | minHei = self.dataOut.heightList[0] |
|
175 | minHei = self.dataOut.heightList[0] | |
| 176 |
|
176 | |||
| 177 | if (maxHei > self.dataOut.heightList[-1]): |
|
177 | if (maxHei > self.dataOut.heightList[-1]): | |
| 178 | maxHei = self.dataOut.heightList[-1] |
|
178 | maxHei = self.dataOut.heightList[-1] | |
| 179 |
|
179 | |||
| 180 | minIndex = 0 |
|
180 | minIndex = 0 | |
| 181 | maxIndex = 0 |
|
181 | maxIndex = 0 | |
| 182 | heights = self.dataOut.heightList |
|
182 | heights = self.dataOut.heightList | |
| 183 | inda = numpy.where(heights >= minHei) |
|
183 | inda = numpy.where(heights >= minHei) | |
| 184 | indb = numpy.where(heights <= maxHei) |
|
184 | indb = numpy.where(heights <= maxHei) | |
| 185 |
|
185 | |||
| 186 | try: |
|
186 | try: | |
| 187 | minIndex = inda[0][0] |
|
187 | minIndex = inda[0][0] | |
| 188 | except: |
|
188 | except: | |
| 189 | minIndex = 0 |
|
189 | minIndex = 0 | |
| 190 |
|
190 | |||
| 191 | try: |
|
191 | try: | |
| 192 | maxIndex = indb[0][-1] |
|
192 | maxIndex = indb[0][-1] | |
| 193 | except: |
|
193 | except: | |
| 194 | maxIndex = len(heights) |
|
194 | maxIndex = len(heights) | |
| 195 | self.selectHeightsByIndex(minIndex, maxIndex) |
|
195 | self.selectHeightsByIndex(minIndex, maxIndex) | |
| 196 |
|
196 | |||
| 197 | return self.dataOut |
|
197 | return self.dataOut | |
| 198 |
|
198 | |||
| 199 | def selectHeightsByIndex(self, minIndex, maxIndex): |
|
199 | def selectHeightsByIndex(self, minIndex, maxIndex): | |
| 200 | """ |
|
200 | """ | |
| 201 | Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango |
|
201 | Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango | |
| 202 | minIndex <= index <= maxIndex |
|
202 | minIndex <= index <= maxIndex | |
| 203 |
|
203 | |||
| 204 | Input: |
|
204 | Input: | |
| 205 | minIndex : valor de indice minimo de altura a considerar |
|
205 | minIndex : valor de indice minimo de altura a considerar | |
| 206 | maxIndex : valor de indice maximo de altura a considerar |
|
206 | maxIndex : valor de indice maximo de altura a considerar | |
| 207 |
|
207 | |||
| 208 | Affected: |
|
208 | Affected: | |
| 209 | self.dataOut.data |
|
209 | self.dataOut.data | |
| 210 | self.dataOut.heightList |
|
210 | self.dataOut.heightList | |
| 211 |
|
211 | |||
| 212 | Return: |
|
212 | Return: | |
| 213 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 |
|
213 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 | |
| 214 | """ |
|
214 | """ | |
| 215 |
|
215 | |||
| 216 | if self.dataOut.type == 'Voltage': |
|
216 | if self.dataOut.type == 'Voltage': | |
| 217 | print(minIndex) |
|
|||
| 218 | print(maxIndex) |
|
|||
| 219 | if (minIndex < 0) or (minIndex > maxIndex): |
|
217 | if (minIndex < 0) or (minIndex > maxIndex): | |
| 220 | raise ValueError("Height index range (%d,%d) is not valid" % (minIndex, maxIndex)) |
|
218 | raise ValueError("Height index range (%d,%d) is not valid" % (minIndex, maxIndex)) | |
| 221 |
|
219 | |||
| 222 | if (maxIndex >= self.dataOut.nHeights): |
|
220 | if (maxIndex >= self.dataOut.nHeights): | |
| 223 | maxIndex = self.dataOut.nHeights |
|
221 | maxIndex = self.dataOut.nHeights | |
| 224 |
|
222 | |||
| 225 | #voltage |
|
223 | #voltage | |
| 226 | if self.dataOut.flagDataAsBlock: |
|
224 | if self.dataOut.flagDataAsBlock: | |
| 227 | """ |
|
225 | """ | |
| 228 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
226 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] | |
| 229 | """ |
|
227 | """ | |
| 230 | data = self.dataOut.data[:,:, minIndex:maxIndex] |
|
228 | data = self.dataOut.data[:,:, minIndex:maxIndex] | |
| 231 | else: |
|
229 | else: | |
| 232 | data = self.dataOut.data[:, minIndex:maxIndex] |
|
230 | data = self.dataOut.data[:, minIndex:maxIndex] | |
| 233 |
|
231 | |||
| 234 | # firstHeight = self.dataOut.heightList[minIndex] |
|
232 | # firstHeight = self.dataOut.heightList[minIndex] | |
| 235 |
|
233 | |||
| 236 | self.dataOut.data = data |
|
234 | self.dataOut.data = data | |
| 237 | self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex] |
|
235 | self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex] | |
| 238 |
|
236 | |||
| 239 | if self.dataOut.nHeights <= 1: |
|
237 | if self.dataOut.nHeights <= 1: | |
| 240 | raise ValueError("selectHeights: Too few heights. Current number of heights is %d" %(self.dataOut.nHeights)) |
|
238 | raise ValueError("selectHeights: Too few heights. Current number of heights is %d" %(self.dataOut.nHeights)) | |
| 241 | elif self.dataOut.type == 'Spectra': |
|
239 | elif self.dataOut.type == 'Spectra': | |
| 242 | if (minIndex < 0) or (minIndex > maxIndex): |
|
240 | if (minIndex < 0) or (minIndex > maxIndex): | |
| 243 | raise ValueError("Error selecting heights: Index range (%d,%d) is not valid" % ( |
|
241 | raise ValueError("Error selecting heights: Index range (%d,%d) is not valid" % ( | |
| 244 | minIndex, maxIndex)) |
|
242 | minIndex, maxIndex)) | |
| 245 |
|
243 | |||
| 246 | if (maxIndex >= self.dataOut.nHeights): |
|
244 | if (maxIndex >= self.dataOut.nHeights): | |
| 247 | maxIndex = self.dataOut.nHeights - 1 |
|
245 | maxIndex = self.dataOut.nHeights - 1 | |
| 248 |
|
246 | |||
| 249 | # Spectra |
|
247 | # Spectra | |
| 250 | data_spc = self.dataOut.data_spc[:, :, minIndex:maxIndex + 1] |
|
248 | data_spc = self.dataOut.data_spc[:, :, minIndex:maxIndex + 1] | |
| 251 |
|
249 | |||
| 252 | data_cspc = None |
|
250 | data_cspc = None | |
| 253 | if self.dataOut.data_cspc is not None: |
|
251 | if self.dataOut.data_cspc is not None: | |
| 254 | data_cspc = self.dataOut.data_cspc[:, :, minIndex:maxIndex + 1] |
|
252 | data_cspc = self.dataOut.data_cspc[:, :, minIndex:maxIndex + 1] | |
| 255 |
|
253 | |||
| 256 | data_dc = None |
|
254 | data_dc = None | |
| 257 | if self.dataOut.data_dc is not None: |
|
255 | if self.dataOut.data_dc is not None: | |
| 258 | data_dc = self.dataOut.data_dc[:, minIndex:maxIndex + 1] |
|
256 | data_dc = self.dataOut.data_dc[:, minIndex:maxIndex + 1] | |
| 259 |
|
257 | |||
| 260 | self.dataOut.data_spc = data_spc |
|
258 | self.dataOut.data_spc = data_spc | |
| 261 | self.dataOut.data_cspc = data_cspc |
|
259 | self.dataOut.data_cspc = data_cspc | |
| 262 | self.dataOut.data_dc = data_dc |
|
260 | self.dataOut.data_dc = data_dc | |
| 263 |
|
261 | |||
| 264 | self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex + 1] |
|
262 | self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex + 1] | |
| 265 |
|
263 | |||
| 266 | return 1 |
|
264 | return 1 | |
| 267 |
|
265 | |||
| 268 |
|
266 | |||
| 269 | class filterByHeights(Operation): |
|
267 | class filterByHeights(Operation): | |
| 270 |
|
268 | |||
| 271 | def run(self, dataOut, window): |
|
269 | def run(self, dataOut, window): | |
| 272 |
|
270 | |||
| 273 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
271 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] | |
| 274 |
|
272 | |||
| 275 | if window == None: |
|
273 | if window == None: | |
| 276 | window = (dataOut.radarControllerHeaderObj.txA/dataOut.radarControllerHeaderObj.nBaud) / deltaHeight |
|
274 | window = (dataOut.radarControllerHeaderObj.txA/dataOut.radarControllerHeaderObj.nBaud) / deltaHeight | |
| 277 |
|
275 | |||
| 278 | newdelta = deltaHeight * window |
|
276 | newdelta = deltaHeight * window | |
| 279 | r = dataOut.nHeights % window |
|
277 | r = dataOut.nHeights % window | |
| 280 | newheights = (dataOut.nHeights-r)/window |
|
278 | newheights = (dataOut.nHeights-r)/window | |
| 281 |
|
279 | |||
| 282 | if newheights <= 1: |
|
280 | if newheights <= 1: | |
| 283 | raise ValueError("filterByHeights: Too few heights. Current number of heights is %d and window is %d" %(dataOut.nHeights, window)) |
|
281 | raise ValueError("filterByHeights: Too few heights. Current number of heights is %d and window is %d" %(dataOut.nHeights, window)) | |
| 284 |
|
282 | |||
| 285 | if dataOut.flagDataAsBlock: |
|
283 | if dataOut.flagDataAsBlock: | |
| 286 | """ |
|
284 | """ | |
| 287 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
285 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] | |
| 288 | """ |
|
286 | """ | |
| 289 | buffer = dataOut.data[:, :, 0:int(dataOut.nHeights-r)] |
|
287 | buffer = dataOut.data[:, :, 0:int(dataOut.nHeights-r)] | |
| 290 | buffer = buffer.reshape(dataOut.nChannels, dataOut.nProfiles, int(dataOut.nHeights/window), window) |
|
288 | buffer = buffer.reshape(dataOut.nChannels, dataOut.nProfiles, int(dataOut.nHeights/window), window) | |
| 291 | buffer = numpy.sum(buffer,3) |
|
289 | buffer = numpy.sum(buffer,3) | |
| 292 |
|
290 | |||
| 293 | else: |
|
291 | else: | |
| 294 | buffer = dataOut.data[:,0:int(dataOut.nHeights-r)] |
|
292 | buffer = dataOut.data[:,0:int(dataOut.nHeights-r)] | |
| 295 | buffer = buffer.reshape(dataOut.nChannels,int(dataOut.nHeights/window),int(window)) |
|
293 | buffer = buffer.reshape(dataOut.nChannels,int(dataOut.nHeights/window),int(window)) | |
| 296 | buffer = numpy.sum(buffer,2) |
|
294 | buffer = numpy.sum(buffer,2) | |
| 297 |
|
295 | |||
| 298 | dataOut.data = buffer |
|
296 | dataOut.data = buffer | |
| 299 | dataOut.heightList = dataOut.heightList[0] + numpy.arange( newheights )*newdelta |
|
297 | dataOut.heightList = dataOut.heightList[0] + numpy.arange( newheights )*newdelta | |
| 300 | dataOut.windowOfFilter = window |
|
298 | dataOut.windowOfFilter = window | |
| 301 |
|
299 | |||
| 302 | return dataOut |
|
300 | return dataOut | |
| 303 |
|
301 | |||
| 304 |
|
302 | |||
| 305 | class setH0(Operation): |
|
303 | class setH0(Operation): | |
| 306 |
|
304 | |||
| 307 | def run(self, dataOut, h0, deltaHeight = None): |
|
305 | def run(self, dataOut, h0, deltaHeight = None): | |
| 308 |
|
306 | |||
| 309 | if not deltaHeight: |
|
307 | if not deltaHeight: | |
| 310 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
308 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] | |
| 311 |
|
309 | |||
| 312 | nHeights = dataOut.nHeights |
|
310 | nHeights = dataOut.nHeights | |
| 313 |
|
311 | |||
| 314 | newHeiRange = h0 + numpy.arange(nHeights)*deltaHeight |
|
312 | newHeiRange = h0 + numpy.arange(nHeights)*deltaHeight | |
| 315 |
|
313 | |||
| 316 | dataOut.heightList = newHeiRange |
|
314 | dataOut.heightList = newHeiRange | |
| 317 |
|
315 | |||
| 318 | return dataOut |
|
316 | return dataOut | |
| 319 |
|
317 | |||
| 320 |
|
318 | |||
| 321 | class deFlip(Operation): |
|
319 | class deFlip(Operation): | |
| 322 |
|
320 | |||
| 323 | def run(self, dataOut, channelList = []): |
|
321 | def run(self, dataOut, channelList = []): | |
| 324 |
|
322 | |||
| 325 | data = dataOut.data.copy() |
|
323 | data = dataOut.data.copy() | |
| 326 |
|
324 | |||
| 327 | if dataOut.flagDataAsBlock: |
|
325 | if dataOut.flagDataAsBlock: | |
| 328 | flip = self.flip |
|
326 | flip = self.flip | |
| 329 | profileList = list(range(dataOut.nProfiles)) |
|
327 | profileList = list(range(dataOut.nProfiles)) | |
| 330 |
|
328 | |||
| 331 | if not channelList: |
|
329 | if not channelList: | |
| 332 | for thisProfile in profileList: |
|
330 | for thisProfile in profileList: | |
| 333 | data[:,thisProfile,:] = data[:,thisProfile,:]*flip |
|
331 | data[:,thisProfile,:] = data[:,thisProfile,:]*flip | |
| 334 | flip *= -1.0 |
|
332 | flip *= -1.0 | |
| 335 | else: |
|
333 | else: | |
| 336 | for thisChannel in channelList: |
|
334 | for thisChannel in channelList: | |
| 337 | if thisChannel not in dataOut.channelList: |
|
335 | if thisChannel not in dataOut.channelList: | |
| 338 | continue |
|
336 | continue | |
| 339 |
|
337 | |||
| 340 | for thisProfile in profileList: |
|
338 | for thisProfile in profileList: | |
| 341 | data[thisChannel,thisProfile,:] = data[thisChannel,thisProfile,:]*flip |
|
339 | data[thisChannel,thisProfile,:] = data[thisChannel,thisProfile,:]*flip | |
| 342 | flip *= -1.0 |
|
340 | flip *= -1.0 | |
| 343 |
|
341 | |||
| 344 | self.flip = flip |
|
342 | self.flip = flip | |
| 345 |
|
343 | |||
| 346 | else: |
|
344 | else: | |
| 347 | if not channelList: |
|
345 | if not channelList: | |
| 348 | data[:,:] = data[:,:]*self.flip |
|
346 | data[:,:] = data[:,:]*self.flip | |
| 349 | else: |
|
347 | else: | |
| 350 | for thisChannel in channelList: |
|
348 | for thisChannel in channelList: | |
| 351 | if thisChannel not in dataOut.channelList: |
|
349 | if thisChannel not in dataOut.channelList: | |
| 352 | continue |
|
350 | continue | |
| 353 |
|
351 | |||
| 354 | data[thisChannel,:] = data[thisChannel,:]*self.flip |
|
352 | data[thisChannel,:] = data[thisChannel,:]*self.flip | |
| 355 |
|
353 | |||
| 356 | self.flip *= -1. |
|
354 | self.flip *= -1. | |
| 357 |
|
355 | |||
| 358 | dataOut.data = data |
|
356 | dataOut.data = data | |
| 359 |
|
357 | |||
| 360 | return dataOut |
|
358 | return dataOut | |
| 361 |
|
359 | |||
| 362 |
|
360 | |||
| 363 | class setAttribute(Operation): |
|
361 | class setAttribute(Operation): | |
| 364 | ''' |
|
362 | ''' | |
| 365 | Set an arbitrary attribute(s) to dataOut |
|
363 | Set an arbitrary attribute(s) to dataOut | |
| 366 | ''' |
|
364 | ''' | |
| 367 |
|
365 | |||
| 368 | def __init__(self): |
|
366 | def __init__(self): | |
| 369 |
|
367 | |||
| 370 | Operation.__init__(self) |
|
368 | Operation.__init__(self) | |
| 371 | self._ready = False |
|
369 | self._ready = False | |
| 372 |
|
370 | |||
| 373 | def run(self, dataOut, **kwargs): |
|
371 | def run(self, dataOut, **kwargs): | |
| 374 |
|
372 | |||
| 375 | for key, value in kwargs.items(): |
|
373 | for key, value in kwargs.items(): | |
| 376 | setattr(dataOut, key, value) |
|
374 | setattr(dataOut, key, value) | |
| 377 |
|
375 | |||
| 378 | return dataOut |
|
376 | return dataOut | |
| 379 |
|
377 | |||
| 380 |
|
378 | |||
| 381 | @MPDecorator |
|
379 | @MPDecorator | |
| 382 | class printAttribute(Operation): |
|
380 | class printAttribute(Operation): | |
| 383 | ''' |
|
381 | ''' | |
| 384 | Print an arbitrary attribute of dataOut |
|
382 | Print an arbitrary attribute of dataOut | |
| 385 | ''' |
|
383 | ''' | |
| 386 |
|
384 | |||
| 387 | def __init__(self): |
|
385 | def __init__(self): | |
| 388 |
|
386 | |||
| 389 | Operation.__init__(self) |
|
387 | Operation.__init__(self) | |
| 390 |
|
388 | |||
| 391 | def run(self, dataOut, attributes): |
|
389 | def run(self, dataOut, attributes): | |
| 392 |
|
390 | |||
| 393 | if isinstance(attributes, str): |
|
391 | if isinstance(attributes, str): | |
| 394 | attributes = [attributes] |
|
392 | attributes = [attributes] | |
| 395 | for attr in attributes: |
|
393 | for attr in attributes: | |
| 396 | if hasattr(dataOut, attr): |
|
394 | if hasattr(dataOut, attr): | |
| 397 | log.log(getattr(dataOut, attr), attr) |
|
395 | log.log(getattr(dataOut, attr), attr) | |
| 398 |
|
396 | |||
| 399 |
|
397 | |||
| 400 | class interpolateHeights(Operation): |
|
398 | class interpolateHeights(Operation): | |
| 401 |
|
399 | |||
| 402 | def run(self, dataOut, topLim, botLim): |
|
400 | def run(self, dataOut, topLim, botLim): | |
| 403 | #69 al 72 para julia |
|
401 | #69 al 72 para julia | |
| 404 | #82-84 para meteoros |
|
402 | #82-84 para meteoros | |
| 405 | if len(numpy.shape(dataOut.data))==2: |
|
403 | if len(numpy.shape(dataOut.data))==2: | |
| 406 | sampInterp = (dataOut.data[:,botLim-1] + dataOut.data[:,topLim+1])/2 |
|
404 | sampInterp = (dataOut.data[:,botLim-1] + dataOut.data[:,topLim+1])/2 | |
| 407 | sampInterp = numpy.transpose(numpy.tile(sampInterp,(topLim-botLim + 1,1))) |
|
405 | sampInterp = numpy.transpose(numpy.tile(sampInterp,(topLim-botLim + 1,1))) | |
| 408 | #dataOut.data[:,botLim:limSup+1] = sampInterp |
|
406 | #dataOut.data[:,botLim:limSup+1] = sampInterp | |
| 409 | dataOut.data[:,botLim:topLim+1] = sampInterp |
|
407 | dataOut.data[:,botLim:topLim+1] = sampInterp | |
| 410 | else: |
|
408 | else: | |
| 411 | nHeights = dataOut.data.shape[2] |
|
409 | nHeights = dataOut.data.shape[2] | |
| 412 | x = numpy.hstack((numpy.arange(botLim),numpy.arange(topLim+1,nHeights))) |
|
410 | x = numpy.hstack((numpy.arange(botLim),numpy.arange(topLim+1,nHeights))) | |
| 413 | y = dataOut.data[:,:,list(range(botLim))+list(range(topLim+1,nHeights))] |
|
411 | y = dataOut.data[:,:,list(range(botLim))+list(range(topLim+1,nHeights))] | |
| 414 | f = interpolate.interp1d(x, y, axis = 2) |
|
412 | f = interpolate.interp1d(x, y, axis = 2) | |
| 415 | xnew = numpy.arange(botLim,topLim+1) |
|
413 | xnew = numpy.arange(botLim,topLim+1) | |
| 416 | ynew = f(xnew) |
|
414 | ynew = f(xnew) | |
| 417 | dataOut.data[:,:,botLim:topLim+1] = ynew |
|
415 | dataOut.data[:,:,botLim:topLim+1] = ynew | |
| 418 |
|
416 | |||
| 419 | return dataOut |
|
417 | return dataOut | |
| 420 |
|
418 | |||
| 421 |
|
419 | |||
| 422 | class CohInt(Operation): |
|
420 | class CohInt(Operation): | |
| 423 |
|
421 | |||
| 424 | isConfig = False |
|
422 | isConfig = False | |
| 425 | __profIndex = 0 |
|
423 | __profIndex = 0 | |
| 426 | __byTime = False |
|
424 | __byTime = False | |
| 427 | __initime = None |
|
425 | __initime = None | |
| 428 | __lastdatatime = None |
|
426 | __lastdatatime = None | |
| 429 | __integrationtime = None |
|
427 | __integrationtime = None | |
| 430 | __buffer = None |
|
428 | __buffer = None | |
| 431 | __bufferStride = [] |
|
429 | __bufferStride = [] | |
| 432 | __dataReady = False |
|
430 | __dataReady = False | |
| 433 | __profIndexStride = 0 |
|
431 | __profIndexStride = 0 | |
| 434 | __dataToPutStride = False |
|
432 | __dataToPutStride = False | |
| 435 | n = None |
|
433 | n = None | |
| 436 |
|
434 | |||
| 437 | def __init__(self, **kwargs): |
|
435 | def __init__(self, **kwargs): | |
| 438 |
|
436 | |||
| 439 | Operation.__init__(self, **kwargs) |
|
437 | Operation.__init__(self, **kwargs) | |
| 440 |
|
438 | |||
| 441 | def setup(self, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False): |
|
439 | def setup(self, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False): | |
| 442 | """ |
|
440 | """ | |
| 443 | Set the parameters of the integration class. |
|
441 | Set the parameters of the integration class. | |
| 444 |
|
442 | |||
| 445 | Inputs: |
|
443 | Inputs: | |
| 446 |
|
444 | |||
| 447 | n : Number of coherent integrations |
|
445 | n : Number of coherent integrations | |
| 448 | timeInterval : Time of integration. If the parameter "n" is selected this one does not work |
|
446 | timeInterval : Time of integration. If the parameter "n" is selected this one does not work | |
| 449 | overlapping : |
|
447 | overlapping : | |
| 450 | """ |
|
448 | """ | |
| 451 |
|
449 | |||
| 452 | self.__initime = None |
|
450 | self.__initime = None | |
| 453 | self.__lastdatatime = 0 |
|
451 | self.__lastdatatime = 0 | |
| 454 | self.__buffer = None |
|
452 | self.__buffer = None | |
| 455 | self.__dataReady = False |
|
453 | self.__dataReady = False | |
| 456 | self.byblock = byblock |
|
454 | self.byblock = byblock | |
| 457 | self.stride = stride |
|
455 | self.stride = stride | |
| 458 |
|
456 | |||
| 459 | if n == None and timeInterval == None: |
|
457 | if n == None and timeInterval == None: | |
| 460 | raise ValueError("n or timeInterval should be specified ...") |
|
458 | raise ValueError("n or timeInterval should be specified ...") | |
| 461 |
|
459 | |||
| 462 | if n != None: |
|
460 | if n != None: | |
| 463 | self.n = n |
|
461 | self.n = n | |
| 464 | self.__byTime = False |
|
462 | self.__byTime = False | |
| 465 | else: |
|
463 | else: | |
| 466 | self.__integrationtime = timeInterval #* 60. #if (type(timeInterval)!=integer) -> change this line |
|
464 | self.__integrationtime = timeInterval #* 60. #if (type(timeInterval)!=integer) -> change this line | |
| 467 | self.n = 9999 |
|
465 | self.n = 9999 | |
| 468 | self.__byTime = True |
|
466 | self.__byTime = True | |
| 469 |
|
467 | |||
| 470 | if overlapping: |
|
468 | if overlapping: | |
| 471 | self.__withOverlapping = True |
|
469 | self.__withOverlapping = True | |
| 472 | self.__buffer = None |
|
470 | self.__buffer = None | |
| 473 | else: |
|
471 | else: | |
| 474 | self.__withOverlapping = False |
|
472 | self.__withOverlapping = False | |
| 475 | self.__buffer = 0 |
|
473 | self.__buffer = 0 | |
| 476 |
|
474 | |||
| 477 | self.__profIndex = 0 |
|
475 | self.__profIndex = 0 | |
| 478 |
|
476 | |||
| 479 | def putData(self, data): |
|
477 | def putData(self, data): | |
| 480 |
|
478 | |||
| 481 | """ |
|
479 | """ | |
| 482 | Add a profile to the __buffer and increase in one the __profileIndex |
|
480 | Add a profile to the __buffer and increase in one the __profileIndex | |
| 483 |
|
481 | |||
| 484 | """ |
|
482 | """ | |
| 485 |
|
483 | |||
| 486 | if not self.__withOverlapping: |
|
484 | if not self.__withOverlapping: | |
| 487 | self.__buffer += data.copy() |
|
485 | self.__buffer += data.copy() | |
| 488 | self.__profIndex += 1 |
|
486 | self.__profIndex += 1 | |
| 489 | return |
|
487 | return | |
| 490 |
|
488 | |||
| 491 | #Overlapping data |
|
489 | #Overlapping data | |
| 492 | nChannels, nHeis = data.shape |
|
490 | nChannels, nHeis = data.shape | |
| 493 | data = numpy.reshape(data, (1, nChannels, nHeis)) |
|
491 | data = numpy.reshape(data, (1, nChannels, nHeis)) | |
| 494 |
|
492 | |||
| 495 | #If the buffer is empty then it takes the data value |
|
493 | #If the buffer is empty then it takes the data value | |
| 496 | if self.__buffer is None: |
|
494 | if self.__buffer is None: | |
| 497 | self.__buffer = data |
|
495 | self.__buffer = data | |
| 498 | self.__profIndex += 1 |
|
496 | self.__profIndex += 1 | |
| 499 | return |
|
497 | return | |
| 500 |
|
498 | |||
| 501 | #If the buffer length is lower than n then stakcing the data value |
|
499 | #If the buffer length is lower than n then stakcing the data value | |
| 502 | if self.__profIndex < self.n: |
|
500 | if self.__profIndex < self.n: | |
| 503 | self.__buffer = numpy.vstack((self.__buffer, data)) |
|
501 | self.__buffer = numpy.vstack((self.__buffer, data)) | |
| 504 | self.__profIndex += 1 |
|
502 | self.__profIndex += 1 | |
| 505 | return |
|
503 | return | |
| 506 |
|
504 | |||
| 507 | #If the buffer length is equal to n then replacing the last buffer value with the data value |
|
505 | #If the buffer length is equal to n then replacing the last buffer value with the data value | |
| 508 | self.__buffer = numpy.roll(self.__buffer, -1, axis=0) |
|
506 | self.__buffer = numpy.roll(self.__buffer, -1, axis=0) | |
| 509 | self.__buffer[self.n-1] = data |
|
507 | self.__buffer[self.n-1] = data | |
| 510 | self.__profIndex = self.n |
|
508 | self.__profIndex = self.n | |
| 511 | return |
|
509 | return | |
| 512 |
|
510 | |||
| 513 |
|
511 | |||
| 514 | def pushData(self): |
|
512 | def pushData(self): | |
| 515 | """ |
|
513 | """ | |
| 516 | Return the sum of the last profiles and the profiles used in the sum. |
|
514 | Return the sum of the last profiles and the profiles used in the sum. | |
| 517 |
|
515 | |||
| 518 | Affected: |
|
516 | Affected: | |
| 519 |
|
517 | |||
| 520 | self.__profileIndex |
|
518 | self.__profileIndex | |
| 521 |
|
519 | |||
| 522 | """ |
|
520 | """ | |
| 523 |
|
521 | |||
| 524 | if not self.__withOverlapping: |
|
522 | if not self.__withOverlapping: | |
| 525 | data = self.__buffer |
|
523 | data = self.__buffer | |
| 526 | n = self.__profIndex |
|
524 | n = self.__profIndex | |
| 527 |
|
525 | |||
| 528 | self.__buffer = 0 |
|
526 | self.__buffer = 0 | |
| 529 | self.__profIndex = 0 |
|
527 | self.__profIndex = 0 | |
| 530 |
|
528 | |||
| 531 | return data, n |
|
529 | return data, n | |
| 532 |
|
530 | |||
| 533 | #Integration with Overlapping |
|
531 | #Integration with Overlapping | |
| 534 | data = numpy.sum(self.__buffer, axis=0) |
|
532 | data = numpy.sum(self.__buffer, axis=0) | |
| 535 | # print data |
|
533 | # print data | |
| 536 | # raise |
|
534 | # raise | |
| 537 | n = self.__profIndex |
|
535 | n = self.__profIndex | |
| 538 |
|
536 | |||
| 539 | return data, n |
|
537 | return data, n | |
| 540 |
|
538 | |||
| 541 | def byProfiles(self, data): |
|
539 | def byProfiles(self, data): | |
| 542 |
|
540 | |||
| 543 | self.__dataReady = False |
|
541 | self.__dataReady = False | |
| 544 | avgdata = None |
|
542 | avgdata = None | |
| 545 | # n = None |
|
543 | # n = None | |
| 546 | # print data |
|
544 | # print data | |
| 547 | # raise |
|
545 | # raise | |
| 548 | self.putData(data) |
|
546 | self.putData(data) | |
| 549 |
|
547 | |||
| 550 | if self.__profIndex == self.n: |
|
548 | if self.__profIndex == self.n: | |
| 551 | avgdata, n = self.pushData() |
|
549 | avgdata, n = self.pushData() | |
| 552 | self.__dataReady = True |
|
550 | self.__dataReady = True | |
| 553 |
|
551 | |||
| 554 | return avgdata |
|
552 | return avgdata | |
| 555 |
|
553 | |||
| 556 | def byTime(self, data, datatime): |
|
554 | def byTime(self, data, datatime): | |
| 557 |
|
555 | |||
| 558 | self.__dataReady = False |
|
556 | self.__dataReady = False | |
| 559 | avgdata = None |
|
557 | avgdata = None | |
| 560 | n = None |
|
558 | n = None | |
| 561 |
|
559 | |||
| 562 | self.putData(data) |
|
560 | self.putData(data) | |
| 563 |
|
561 | |||
| 564 | if (datatime - self.__initime) >= self.__integrationtime: |
|
562 | if (datatime - self.__initime) >= self.__integrationtime: | |
| 565 | avgdata, n = self.pushData() |
|
563 | avgdata, n = self.pushData() | |
| 566 | self.n = n |
|
564 | self.n = n | |
| 567 | self.__dataReady = True |
|
565 | self.__dataReady = True | |
| 568 |
|
566 | |||
| 569 | return avgdata |
|
567 | return avgdata | |
| 570 |
|
568 | |||
| 571 | def integrateByStride(self, data, datatime): |
|
569 | def integrateByStride(self, data, datatime): | |
| 572 | # print data |
|
570 | # print data | |
| 573 | if self.__profIndex == 0: |
|
571 | if self.__profIndex == 0: | |
| 574 | self.__buffer = [[data.copy(), datatime]] |
|
572 | self.__buffer = [[data.copy(), datatime]] | |
| 575 | else: |
|
573 | else: | |
| 576 | self.__buffer.append([data.copy(),datatime]) |
|
574 | self.__buffer.append([data.copy(),datatime]) | |
| 577 | self.__profIndex += 1 |
|
575 | self.__profIndex += 1 | |
| 578 | self.__dataReady = False |
|
576 | self.__dataReady = False | |
| 579 |
|
577 | |||
| 580 | if self.__profIndex == self.n * self.stride : |
|
578 | if self.__profIndex == self.n * self.stride : | |
| 581 | self.__dataToPutStride = True |
|
579 | self.__dataToPutStride = True | |
| 582 | self.__profIndexStride = 0 |
|
580 | self.__profIndexStride = 0 | |
| 583 | self.__profIndex = 0 |
|
581 | self.__profIndex = 0 | |
| 584 | self.__bufferStride = [] |
|
582 | self.__bufferStride = [] | |
| 585 | for i in range(self.stride): |
|
583 | for i in range(self.stride): | |
| 586 | current = self.__buffer[i::self.stride] |
|
584 | current = self.__buffer[i::self.stride] | |
| 587 | data = numpy.sum([t[0] for t in current], axis=0) |
|
585 | data = numpy.sum([t[0] for t in current], axis=0) | |
| 588 | avgdatatime = numpy.average([t[1] for t in current]) |
|
586 | avgdatatime = numpy.average([t[1] for t in current]) | |
| 589 | # print data |
|
587 | # print data | |
| 590 | self.__bufferStride.append((data, avgdatatime)) |
|
588 | self.__bufferStride.append((data, avgdatatime)) | |
| 591 |
|
589 | |||
| 592 | if self.__dataToPutStride: |
|
590 | if self.__dataToPutStride: | |
| 593 | self.__dataReady = True |
|
591 | self.__dataReady = True | |
| 594 | self.__profIndexStride += 1 |
|
592 | self.__profIndexStride += 1 | |
| 595 | if self.__profIndexStride == self.stride: |
|
593 | if self.__profIndexStride == self.stride: | |
| 596 | self.__dataToPutStride = False |
|
594 | self.__dataToPutStride = False | |
| 597 | # print self.__bufferStride[self.__profIndexStride - 1] |
|
595 | # print self.__bufferStride[self.__profIndexStride - 1] | |
| 598 | # raise |
|
596 | # raise | |
| 599 | return self.__bufferStride[self.__profIndexStride - 1] |
|
597 | return self.__bufferStride[self.__profIndexStride - 1] | |
| 600 |
|
598 | |||
| 601 |
|
599 | |||
| 602 | return None, None |
|
600 | return None, None | |
| 603 |
|
601 | |||
| 604 | def integrate(self, data, datatime=None): |
|
602 | def integrate(self, data, datatime=None): | |
| 605 |
|
603 | |||
| 606 | if self.__initime == None: |
|
604 | if self.__initime == None: | |
| 607 | self.__initime = datatime |
|
605 | self.__initime = datatime | |
| 608 |
|
606 | |||
| 609 | if self.__byTime: |
|
607 | if self.__byTime: | |
| 610 | avgdata = self.byTime(data, datatime) |
|
608 | avgdata = self.byTime(data, datatime) | |
| 611 | else: |
|
609 | else: | |
| 612 | avgdata = self.byProfiles(data) |
|
610 | avgdata = self.byProfiles(data) | |
| 613 |
|
611 | |||
| 614 |
|
612 | |||
| 615 | self.__lastdatatime = datatime |
|
613 | self.__lastdatatime = datatime | |
| 616 |
|
614 | |||
| 617 | if avgdata is None: |
|
615 | if avgdata is None: | |
| 618 | return None, None |
|
616 | return None, None | |
| 619 |
|
617 | |||
| 620 | avgdatatime = self.__initime |
|
618 | avgdatatime = self.__initime | |
| 621 |
|
619 | |||
| 622 | deltatime = datatime - self.__lastdatatime |
|
620 | deltatime = datatime - self.__lastdatatime | |
| 623 |
|
621 | |||
| 624 | if not self.__withOverlapping: |
|
622 | if not self.__withOverlapping: | |
| 625 | self.__initime = datatime |
|
623 | self.__initime = datatime | |
| 626 | else: |
|
624 | else: | |
| 627 | self.__initime += deltatime |
|
625 | self.__initime += deltatime | |
| 628 |
|
626 | |||
| 629 | return avgdata, avgdatatime |
|
627 | return avgdata, avgdatatime | |
| 630 |
|
628 | |||
| 631 | def integrateByBlock(self, dataOut): |
|
629 | def integrateByBlock(self, dataOut): | |
| 632 |
|
630 | |||
| 633 | times = int(dataOut.data.shape[1]/self.n) |
|
631 | times = int(dataOut.data.shape[1]/self.n) | |
| 634 | avgdata = numpy.zeros((dataOut.nChannels, times, dataOut.nHeights), dtype=numpy.complex) |
|
632 | avgdata = numpy.zeros((dataOut.nChannels, times, dataOut.nHeights), dtype=numpy.complex) | |
| 635 |
|
633 | |||
| 636 | id_min = 0 |
|
634 | id_min = 0 | |
| 637 | id_max = self.n |
|
635 | id_max = self.n | |
| 638 |
|
636 | |||
| 639 | for i in range(times): |
|
637 | for i in range(times): | |
| 640 | junk = dataOut.data[:,id_min:id_max,:] |
|
638 | junk = dataOut.data[:,id_min:id_max,:] | |
| 641 | avgdata[:,i,:] = junk.sum(axis=1) |
|
639 | avgdata[:,i,:] = junk.sum(axis=1) | |
| 642 | id_min += self.n |
|
640 | id_min += self.n | |
| 643 | id_max += self.n |
|
641 | id_max += self.n | |
| 644 |
|
642 | |||
| 645 | timeInterval = dataOut.ippSeconds*self.n |
|
643 | timeInterval = dataOut.ippSeconds*self.n | |
| 646 | avgdatatime = (times - 1) * timeInterval + dataOut.utctime |
|
644 | avgdatatime = (times - 1) * timeInterval + dataOut.utctime | |
| 647 | self.__dataReady = True |
|
645 | self.__dataReady = True | |
| 648 | return avgdata, avgdatatime |
|
646 | return avgdata, avgdatatime | |
| 649 |
|
647 | |||
| 650 | def run(self, dataOut, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False, **kwargs): |
|
648 | def run(self, dataOut, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False, **kwargs): | |
| 651 |
|
649 | |||
| 652 | if not self.isConfig: |
|
650 | if not self.isConfig: | |
| 653 | self.setup(n=n, stride=stride, timeInterval=timeInterval, overlapping=overlapping, byblock=byblock, **kwargs) |
|
651 | self.setup(n=n, stride=stride, timeInterval=timeInterval, overlapping=overlapping, byblock=byblock, **kwargs) | |
| 654 | self.isConfig = True |
|
652 | self.isConfig = True | |
| 655 | if dataOut.flagDataAsBlock: |
|
653 | if dataOut.flagDataAsBlock: | |
| 656 | """ |
|
654 | """ | |
| 657 | Si la data es leida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
655 | Si la data es leida por bloques, dimension = [nChannels, nProfiles, nHeis] | |
| 658 | """ |
|
656 | """ | |
| 659 | avgdata, avgdatatime = self.integrateByBlock(dataOut) |
|
657 | avgdata, avgdatatime = self.integrateByBlock(dataOut) | |
| 660 | dataOut.nProfiles /= self.n |
|
658 | dataOut.nProfiles /= self.n | |
| 661 | else: |
|
659 | else: | |
| 662 | if stride is None: |
|
660 | if stride is None: | |
| 663 | avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime) |
|
661 | avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime) | |
| 664 | else: |
|
662 | else: | |
| 665 | avgdata, avgdatatime = self.integrateByStride(dataOut.data, dataOut.utctime) |
|
663 | avgdata, avgdatatime = self.integrateByStride(dataOut.data, dataOut.utctime) | |
| 666 |
|
664 | |||
| 667 |
|
665 | |||
| 668 | # dataOut.timeInterval *= n |
|
666 | # dataOut.timeInterval *= n | |
| 669 | dataOut.flagNoData = True |
|
667 | dataOut.flagNoData = True | |
| 670 |
|
668 | |||
| 671 | if self.__dataReady: |
|
669 | if self.__dataReady: | |
| 672 | dataOut.data = avgdata |
|
670 | dataOut.data = avgdata | |
| 673 | if not dataOut.flagCohInt: |
|
671 | if not dataOut.flagCohInt: | |
| 674 | dataOut.nCohInt *= self.n |
|
672 | dataOut.nCohInt *= self.n | |
| 675 | dataOut.flagCohInt = True |
|
673 | dataOut.flagCohInt = True | |
| 676 | dataOut.utctime = avgdatatime |
|
674 | dataOut.utctime = avgdatatime | |
| 677 | # print avgdata, avgdatatime |
|
675 | # print avgdata, avgdatatime | |
| 678 | # raise |
|
676 | # raise | |
| 679 | # dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt |
|
677 | # dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt | |
| 680 | dataOut.flagNoData = False |
|
678 | dataOut.flagNoData = False | |
| 681 | return dataOut |
|
679 | return dataOut | |
| 682 |
|
680 | |||
| 683 | class Decoder(Operation): |
|
681 | class Decoder(Operation): | |
| 684 |
|
682 | |||
| 685 | isConfig = False |
|
683 | isConfig = False | |
| 686 | __profIndex = 0 |
|
684 | __profIndex = 0 | |
| 687 |
|
685 | |||
| 688 | code = None |
|
686 | code = None | |
| 689 |
|
687 | |||
| 690 | nCode = None |
|
688 | nCode = None | |
| 691 | nBaud = None |
|
689 | nBaud = None | |
| 692 |
|
690 | |||
| 693 | def __init__(self, **kwargs): |
|
691 | def __init__(self, **kwargs): | |
| 694 |
|
692 | |||
| 695 | Operation.__init__(self, **kwargs) |
|
693 | Operation.__init__(self, **kwargs) | |
| 696 |
|
694 | |||
| 697 | self.times = None |
|
695 | self.times = None | |
| 698 | self.osamp = None |
|
696 | self.osamp = None | |
| 699 | # self.__setValues = False |
|
697 | # self.__setValues = False | |
| 700 | self.isConfig = False |
|
698 | self.isConfig = False | |
| 701 | self.setupReq = False |
|
699 | self.setupReq = False | |
| 702 | def setup(self, code, osamp, dataOut): |
|
700 | def setup(self, code, osamp, dataOut): | |
| 703 |
|
701 | |||
| 704 | self.__profIndex = 0 |
|
702 | self.__profIndex = 0 | |
| 705 |
|
703 | |||
| 706 | self.code = code |
|
704 | self.code = code | |
| 707 |
|
705 | |||
| 708 | self.nCode = len(code) |
|
706 | self.nCode = len(code) | |
| 709 | self.nBaud = len(code[0]) |
|
707 | self.nBaud = len(code[0]) | |
| 710 |
|
708 | |||
| 711 | if (osamp != None) and (osamp >1): |
|
709 | if (osamp != None) and (osamp >1): | |
| 712 | self.osamp = osamp |
|
710 | self.osamp = osamp | |
| 713 | self.code = numpy.repeat(code, repeats=self.osamp, axis=1) |
|
711 | self.code = numpy.repeat(code, repeats=self.osamp, axis=1) | |
| 714 | self.nBaud = self.nBaud*self.osamp |
|
712 | self.nBaud = self.nBaud*self.osamp | |
| 715 |
|
713 | |||
| 716 | self.__nChannels = dataOut.nChannels |
|
714 | self.__nChannels = dataOut.nChannels | |
| 717 | self.__nProfiles = dataOut.nProfiles |
|
715 | self.__nProfiles = dataOut.nProfiles | |
| 718 | self.__nHeis = dataOut.nHeights |
|
716 | self.__nHeis = dataOut.nHeights | |
| 719 |
|
717 | |||
| 720 | if self.__nHeis < self.nBaud: |
|
718 | if self.__nHeis < self.nBaud: | |
| 721 | raise ValueError('Number of heights (%d) should be greater than number of bauds (%d)' %(self.__nHeis, self.nBaud)) |
|
719 | raise ValueError('Number of heights (%d) should be greater than number of bauds (%d)' %(self.__nHeis, self.nBaud)) | |
| 722 |
|
720 | |||
| 723 | #Frequency |
|
721 | #Frequency | |
| 724 | __codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=numpy.complex) |
|
722 | __codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=numpy.complex) | |
| 725 |
|
723 | |||
| 726 | __codeBuffer[:,0:self.nBaud] = self.code |
|
724 | __codeBuffer[:,0:self.nBaud] = self.code | |
| 727 |
|
725 | |||
| 728 | self.fft_code = numpy.conj(numpy.fft.fft(__codeBuffer, axis=1)) |
|
726 | self.fft_code = numpy.conj(numpy.fft.fft(__codeBuffer, axis=1)) | |
| 729 |
|
727 | |||
| 730 | if dataOut.flagDataAsBlock: |
|
728 | if dataOut.flagDataAsBlock: | |
| 731 |
|
729 | |||
| 732 | self.ndatadec = self.__nHeis #- self.nBaud + 1 |
|
730 | self.ndatadec = self.__nHeis #- self.nBaud + 1 | |
| 733 |
|
731 | |||
| 734 | self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=numpy.complex) |
|
732 | self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=numpy.complex) | |
| 735 |
|
733 | |||
| 736 | else: |
|
734 | else: | |
| 737 |
|
735 | |||
| 738 | #Time |
|
736 | #Time | |
| 739 | self.ndatadec = self.__nHeis #- self.nBaud + 1 |
|
737 | self.ndatadec = self.__nHeis #- self.nBaud + 1 | |
| 740 |
|
738 | |||
| 741 | self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=numpy.complex) |
|
739 | self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=numpy.complex) | |
| 742 |
|
740 | |||
| 743 | def __convolutionInFreq(self, data): |
|
741 | def __convolutionInFreq(self, data): | |
| 744 |
|
742 | |||
| 745 | fft_code = self.fft_code[self.__profIndex].reshape(1,-1) |
|
743 | fft_code = self.fft_code[self.__profIndex].reshape(1,-1) | |
| 746 |
|
744 | |||
| 747 | fft_data = numpy.fft.fft(data, axis=1) |
|
745 | fft_data = numpy.fft.fft(data, axis=1) | |
| 748 |
|
746 | |||
| 749 | conv = fft_data*fft_code |
|
747 | conv = fft_data*fft_code | |
| 750 |
|
748 | |||
| 751 | data = numpy.fft.ifft(conv,axis=1) |
|
749 | data = numpy.fft.ifft(conv,axis=1) | |
| 752 |
|
750 | |||
| 753 | return data |
|
751 | return data | |
| 754 |
|
752 | |||
| 755 | def __convolutionInFreqOpt(self, data): |
|
753 | def __convolutionInFreqOpt(self, data): | |
| 756 |
|
754 | |||
| 757 | raise NotImplementedError |
|
755 | raise NotImplementedError | |
| 758 |
|
756 | |||
| 759 | def __convolutionInTime(self, data): |
|
757 | def __convolutionInTime(self, data): | |
| 760 |
|
758 | |||
| 761 | code = self.code[self.__profIndex] |
|
759 | code = self.code[self.__profIndex] | |
| 762 | for i in range(self.__nChannels): |
|
760 | for i in range(self.__nChannels): | |
| 763 | self.datadecTime[i,:] = numpy.correlate(data[i,:], code, mode='full')[self.nBaud-1:] |
|
761 | self.datadecTime[i,:] = numpy.correlate(data[i,:], code, mode='full')[self.nBaud-1:] | |
| 764 |
|
762 | |||
| 765 | return self.datadecTime |
|
763 | return self.datadecTime | |
| 766 |
|
764 | |||
| 767 | def __convolutionByBlockInTime(self, data): |
|
765 | def __convolutionByBlockInTime(self, data): | |
| 768 |
|
766 | |||
| 769 | repetitions = int(self.__nProfiles / self.nCode) |
|
767 | repetitions = int(self.__nProfiles / self.nCode) | |
| 770 | junk = numpy.lib.stride_tricks.as_strided(self.code, (repetitions, self.code.size), (0, self.code.itemsize)) |
|
768 | junk = numpy.lib.stride_tricks.as_strided(self.code, (repetitions, self.code.size), (0, self.code.itemsize)) | |
| 771 | junk = junk.flatten() |
|
769 | junk = junk.flatten() | |
| 772 | code_block = numpy.reshape(junk, (self.nCode*repetitions, self.nBaud)) |
|
770 | code_block = numpy.reshape(junk, (self.nCode*repetitions, self.nBaud)) | |
| 773 | profilesList = range(self.__nProfiles) |
|
771 | profilesList = range(self.__nProfiles) | |
| 774 |
|
772 | |||
| 775 | for i in range(self.__nChannels): |
|
773 | for i in range(self.__nChannels): | |
| 776 | for j in profilesList: |
|
774 | for j in profilesList: | |
| 777 | self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:] |
|
775 | self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:] | |
| 778 | return self.datadecTime |
|
776 | return self.datadecTime | |
| 779 |
|
777 | |||
| 780 | def __convolutionByBlockInFreq(self, data): |
|
778 | def __convolutionByBlockInFreq(self, data): | |
| 781 |
|
779 | |||
| 782 | raise NotImplementedError("Decoder by frequency fro Blocks not implemented") |
|
780 | raise NotImplementedError("Decoder by frequency fro Blocks not implemented") | |
| 783 |
|
781 | |||
| 784 |
|
782 | |||
| 785 | fft_code = self.fft_code[self.__profIndex].reshape(1,-1) |
|
783 | fft_code = self.fft_code[self.__profIndex].reshape(1,-1) | |
| 786 |
|
784 | |||
| 787 | fft_data = numpy.fft.fft(data, axis=2) |
|
785 | fft_data = numpy.fft.fft(data, axis=2) | |
| 788 |
|
786 | |||
| 789 | conv = fft_data*fft_code |
|
787 | conv = fft_data*fft_code | |
| 790 |
|
788 | |||
| 791 | data = numpy.fft.ifft(conv,axis=2) |
|
789 | data = numpy.fft.ifft(conv,axis=2) | |
| 792 |
|
790 | |||
| 793 | return data |
|
791 | return data | |
| 794 |
|
792 | |||
| 795 |
|
793 | |||
| 796 | def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0, osamp=None, times=None): |
|
794 | def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0, osamp=None, times=None): | |
| 797 |
|
795 | |||
| 798 | if dataOut.flagDecodeData: |
|
796 | if dataOut.flagDecodeData: | |
| 799 | print("This data is already decoded, recoding again ...") |
|
797 | print("This data is already decoded, recoding again ...") | |
| 800 |
|
798 | |||
| 801 | if not self.isConfig: |
|
799 | if not self.isConfig: | |
| 802 |
|
800 | |||
| 803 | if code is None: |
|
801 | if code is None: | |
| 804 | if dataOut.code is None: |
|
802 | if dataOut.code is None: | |
| 805 | raise ValueError("Code could not be read from %s instance. Enter a value in Code parameter" %dataOut.type) |
|
803 | raise ValueError("Code could not be read from %s instance. Enter a value in Code parameter" %dataOut.type) | |
| 806 |
|
804 | |||
| 807 | code = dataOut.code |
|
805 | code = dataOut.code | |
| 808 | else: |
|
806 | else: | |
| 809 | code = numpy.array(code).reshape(nCode,nBaud) |
|
807 | code = numpy.array(code).reshape(nCode,nBaud) | |
| 810 | self.setup(code, osamp, dataOut) |
|
808 | self.setup(code, osamp, dataOut) | |
| 811 |
|
809 | |||
| 812 | self.isConfig = True |
|
810 | self.isConfig = True | |
| 813 |
|
811 | |||
| 814 | if mode == 3: |
|
812 | if mode == 3: | |
| 815 | sys.stderr.write("Decoder Warning: mode=%d is not valid, using mode=0\n" %mode) |
|
813 | sys.stderr.write("Decoder Warning: mode=%d is not valid, using mode=0\n" %mode) | |
| 816 |
|
814 | |||
| 817 | if times != None: |
|
815 | if times != None: | |
| 818 | sys.stderr.write("Decoder Warning: Argument 'times' in not used anymore\n") |
|
816 | sys.stderr.write("Decoder Warning: Argument 'times' in not used anymore\n") | |
| 819 |
|
817 | |||
| 820 | if self.code is None: |
|
818 | if self.code is None: | |
| 821 | print("Fail decoding: Code is not defined.") |
|
819 | print("Fail decoding: Code is not defined.") | |
| 822 | return |
|
820 | return | |
| 823 |
|
821 | |||
| 824 | self.__nProfiles = dataOut.nProfiles |
|
822 | self.__nProfiles = dataOut.nProfiles | |
| 825 | datadec = None |
|
823 | datadec = None | |
| 826 |
|
824 | |||
| 827 | if mode == 3: |
|
825 | if mode == 3: | |
| 828 | mode = 0 |
|
826 | mode = 0 | |
| 829 |
|
827 | |||
| 830 | if dataOut.flagDataAsBlock: |
|
828 | if dataOut.flagDataAsBlock: | |
| 831 | """ |
|
829 | """ | |
| 832 | Decoding when data have been read as block, |
|
830 | Decoding when data have been read as block, | |
| 833 | """ |
|
831 | """ | |
| 834 |
|
832 | |||
| 835 | if mode == 0: |
|
833 | if mode == 0: | |
| 836 | datadec = self.__convolutionByBlockInTime(dataOut.data) |
|
834 | datadec = self.__convolutionByBlockInTime(dataOut.data) | |
| 837 | if mode == 1: |
|
835 | if mode == 1: | |
| 838 | datadec = self.__convolutionByBlockInFreq(dataOut.data) |
|
836 | datadec = self.__convolutionByBlockInFreq(dataOut.data) | |
| 839 | else: |
|
837 | else: | |
| 840 | """ |
|
838 | """ | |
| 841 | Decoding when data have been read profile by profile |
|
839 | Decoding when data have been read profile by profile | |
| 842 | """ |
|
840 | """ | |
| 843 | if mode == 0: |
|
841 | if mode == 0: | |
| 844 | datadec = self.__convolutionInTime(dataOut.data) |
|
842 | datadec = self.__convolutionInTime(dataOut.data) | |
| 845 |
|
843 | |||
| 846 | if mode == 1: |
|
844 | if mode == 1: | |
| 847 | datadec = self.__convolutionInFreq(dataOut.data) |
|
845 | datadec = self.__convolutionInFreq(dataOut.data) | |
| 848 |
|
846 | |||
| 849 | if mode == 2: |
|
847 | if mode == 2: | |
| 850 | datadec = self.__convolutionInFreqOpt(dataOut.data) |
|
848 | datadec = self.__convolutionInFreqOpt(dataOut.data) | |
| 851 |
|
849 | |||
| 852 | if datadec is None: |
|
850 | if datadec is None: | |
| 853 | raise ValueError("Codification mode selected is not valid: mode=%d. Try selecting 0 or 1" %mode) |
|
851 | raise ValueError("Codification mode selected is not valid: mode=%d. Try selecting 0 or 1" %mode) | |
| 854 |
|
852 | |||
| 855 | dataOut.code = self.code |
|
853 | dataOut.code = self.code | |
| 856 | dataOut.nCode = self.nCode |
|
854 | dataOut.nCode = self.nCode | |
| 857 | dataOut.nBaud = self.nBaud |
|
855 | dataOut.nBaud = self.nBaud | |
| 858 |
|
856 | |||
| 859 | dataOut.data = datadec |
|
857 | dataOut.data = datadec | |
| 860 | dataOut.heightList = dataOut.heightList[0:datadec.shape[-1]] |
|
858 | dataOut.heightList = dataOut.heightList[0:datadec.shape[-1]] | |
| 861 |
|
859 | |||
| 862 | dataOut.flagDecodeData = True #asumo q la data esta decodificada |
|
860 | dataOut.flagDecodeData = True #asumo q la data esta decodificada | |
| 863 |
|
861 | |||
| 864 | if self.__profIndex == self.nCode-1: |
|
862 | if self.__profIndex == self.nCode-1: | |
| 865 | self.__profIndex = 0 |
|
863 | self.__profIndex = 0 | |
| 866 | return dataOut |
|
864 | return dataOut | |
| 867 |
|
865 | |||
| 868 | self.__profIndex += 1 |
|
866 | self.__profIndex += 1 | |
| 869 |
|
867 | |||
| 870 | return dataOut |
|
868 | return dataOut | |
| 871 |
|
869 | |||
| 872 |
|
870 | |||
| 873 | class ProfileConcat(Operation): |
|
871 | class ProfileConcat(Operation): | |
| 874 |
|
872 | |||
| 875 | isConfig = False |
|
873 | isConfig = False | |
| 876 | buffer = None |
|
874 | buffer = None | |
| 877 |
|
875 | |||
| 878 | def __init__(self, **kwargs): |
|
876 | def __init__(self, **kwargs): | |
| 879 |
|
877 | |||
| 880 | Operation.__init__(self, **kwargs) |
|
878 | Operation.__init__(self, **kwargs) | |
| 881 | self.profileIndex = 0 |
|
879 | self.profileIndex = 0 | |
| 882 |
|
880 | |||
| 883 | def reset(self): |
|
881 | def reset(self): | |
| 884 | self.buffer = numpy.zeros_like(self.buffer) |
|
882 | self.buffer = numpy.zeros_like(self.buffer) | |
| 885 | self.start_index = 0 |
|
883 | self.start_index = 0 | |
| 886 | self.times = 1 |
|
884 | self.times = 1 | |
| 887 |
|
885 | |||
| 888 | def setup(self, data, m, n=1): |
|
886 | def setup(self, data, m, n=1): | |
| 889 | self.buffer = numpy.zeros((data.shape[0],data.shape[1]*m),dtype=type(data[0,0])) |
|
887 | self.buffer = numpy.zeros((data.shape[0],data.shape[1]*m),dtype=type(data[0,0])) | |
| 890 | self.nHeights = data.shape[1]#.nHeights |
|
888 | self.nHeights = data.shape[1]#.nHeights | |
| 891 | self.start_index = 0 |
|
889 | self.start_index = 0 | |
| 892 | self.times = 1 |
|
890 | self.times = 1 | |
| 893 |
|
891 | |||
| 894 | def concat(self, data): |
|
892 | def concat(self, data): | |
| 895 |
|
893 | |||
| 896 | self.buffer[:,self.start_index:self.nHeights*self.times] = data.copy() |
|
894 | self.buffer[:,self.start_index:self.nHeights*self.times] = data.copy() | |
| 897 | self.start_index = self.start_index + self.nHeights |
|
895 | self.start_index = self.start_index + self.nHeights | |
| 898 |
|
896 | |||
| 899 | def run(self, dataOut, m): |
|
897 | def run(self, dataOut, m): | |
| 900 | dataOut.flagNoData = True |
|
898 | dataOut.flagNoData = True | |
| 901 |
|
899 | |||
| 902 | if not self.isConfig: |
|
900 | if not self.isConfig: | |
| 903 | self.setup(dataOut.data, m, 1) |
|
901 | self.setup(dataOut.data, m, 1) | |
| 904 | self.isConfig = True |
|
902 | self.isConfig = True | |
| 905 |
|
903 | |||
| 906 | if dataOut.flagDataAsBlock: |
|
904 | if dataOut.flagDataAsBlock: | |
| 907 | raise ValueError("ProfileConcat can only be used when voltage have been read profile by profile, getBlock = False") |
|
905 | raise ValueError("ProfileConcat can only be used when voltage have been read profile by profile, getBlock = False") | |
| 908 |
|
906 | |||
| 909 | else: |
|
907 | else: | |
| 910 | self.concat(dataOut.data) |
|
908 | self.concat(dataOut.data) | |
| 911 | self.times += 1 |
|
909 | self.times += 1 | |
| 912 | if self.times > m: |
|
910 | if self.times > m: | |
| 913 | dataOut.data = self.buffer |
|
911 | dataOut.data = self.buffer | |
| 914 | self.reset() |
|
912 | self.reset() | |
| 915 | dataOut.flagNoData = False |
|
913 | dataOut.flagNoData = False | |
| 916 | # se deben actualizar mas propiedades del header y del objeto dataOut, por ejemplo, las alturas |
|
914 | # se deben actualizar mas propiedades del header y del objeto dataOut, por ejemplo, las alturas | |
| 917 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
915 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] | |
| 918 | xf = dataOut.heightList[0] + dataOut.nHeights * deltaHeight * m |
|
916 | xf = dataOut.heightList[0] + dataOut.nHeights * deltaHeight * m | |
| 919 | dataOut.heightList = numpy.arange(dataOut.heightList[0], xf, deltaHeight) |
|
917 | dataOut.heightList = numpy.arange(dataOut.heightList[0], xf, deltaHeight) | |
| 920 | dataOut.ippSeconds *= m |
|
918 | dataOut.ippSeconds *= m | |
| 921 | return dataOut |
|
919 | return dataOut | |
| 922 |
|
920 | |||
| 923 | class ProfileSelector(Operation): |
|
921 | class ProfileSelector(Operation): | |
| 924 |
|
922 | |||
| 925 | profileIndex = None |
|
923 | profileIndex = None | |
| 926 | # Tamanho total de los perfiles |
|
924 | # Tamanho total de los perfiles | |
| 927 | nProfiles = None |
|
925 | nProfiles = None | |
| 928 |
|
926 | |||
| 929 | def __init__(self, **kwargs): |
|
927 | def __init__(self, **kwargs): | |
| 930 |
|
928 | |||
| 931 | Operation.__init__(self, **kwargs) |
|
929 | Operation.__init__(self, **kwargs) | |
| 932 | self.profileIndex = 0 |
|
930 | self.profileIndex = 0 | |
| 933 |
|
931 | |||
| 934 | def incProfileIndex(self): |
|
932 | def incProfileIndex(self): | |
| 935 |
|
933 | |||
| 936 | self.profileIndex += 1 |
|
934 | self.profileIndex += 1 | |
| 937 |
|
935 | |||
| 938 | if self.profileIndex >= self.nProfiles: |
|
936 | if self.profileIndex >= self.nProfiles: | |
| 939 | self.profileIndex = 0 |
|
937 | self.profileIndex = 0 | |
| 940 |
|
938 | |||
| 941 | def isThisProfileInRange(self, profileIndex, minIndex, maxIndex): |
|
939 | def isThisProfileInRange(self, profileIndex, minIndex, maxIndex): | |
| 942 |
|
940 | |||
| 943 | if profileIndex < minIndex: |
|
941 | if profileIndex < minIndex: | |
| 944 | return False |
|
942 | return False | |
| 945 |
|
943 | |||
| 946 | if profileIndex > maxIndex: |
|
944 | if profileIndex > maxIndex: | |
| 947 | return False |
|
945 | return False | |
| 948 |
|
946 | |||
| 949 | return True |
|
947 | return True | |
| 950 |
|
948 | |||
| 951 | def isThisProfileInList(self, profileIndex, profileList): |
|
949 | def isThisProfileInList(self, profileIndex, profileList): | |
| 952 |
|
950 | |||
| 953 | if profileIndex not in profileList: |
|
951 | if profileIndex not in profileList: | |
| 954 | return False |
|
952 | return False | |
| 955 |
|
953 | |||
| 956 | return True |
|
954 | return True | |
| 957 |
|
955 | |||
| 958 | def run(self, dataOut, profileList=None, profileRangeList=None, beam=None, byblock=False, rangeList = None, nProfiles=None): |
|
956 | def run(self, dataOut, profileList=None, profileRangeList=None, beam=None, byblock=False, rangeList = None, nProfiles=None): | |
| 959 |
|
957 | |||
| 960 | """ |
|
958 | """ | |
| 961 | ProfileSelector: |
|
959 | ProfileSelector: | |
| 962 |
|
960 | |||
| 963 | Inputs: |
|
961 | Inputs: | |
| 964 | profileList : Index of profiles selected. Example: profileList = (0,1,2,7,8) |
|
962 | profileList : Index of profiles selected. Example: profileList = (0,1,2,7,8) | |
| 965 |
|
963 | |||
| 966 | profileRangeList : Minimum and maximum profile indexes. Example: profileRangeList = (4, 30) |
|
964 | profileRangeList : Minimum and maximum profile indexes. Example: profileRangeList = (4, 30) | |
| 967 |
|
965 | |||
| 968 | rangeList : List of profile ranges. Example: rangeList = ((4, 30), (32, 64), (128, 256)) |
|
966 | rangeList : List of profile ranges. Example: rangeList = ((4, 30), (32, 64), (128, 256)) | |
| 969 |
|
967 | |||
| 970 | """ |
|
968 | """ | |
| 971 |
|
969 | |||
| 972 | if rangeList is not None: |
|
970 | if rangeList is not None: | |
| 973 | if type(rangeList[0]) not in (tuple, list): |
|
971 | if type(rangeList[0]) not in (tuple, list): | |
| 974 | rangeList = [rangeList] |
|
972 | rangeList = [rangeList] | |
| 975 |
|
973 | |||
| 976 | dataOut.flagNoData = True |
|
974 | dataOut.flagNoData = True | |
| 977 |
|
975 | |||
| 978 | if dataOut.flagDataAsBlock: |
|
976 | if dataOut.flagDataAsBlock: | |
| 979 | """ |
|
977 | """ | |
| 980 | data dimension = [nChannels, nProfiles, nHeis] |
|
978 | data dimension = [nChannels, nProfiles, nHeis] | |
| 981 | """ |
|
979 | """ | |
| 982 | if profileList != None: |
|
980 | if profileList != None: | |
| 983 | dataOut.data = dataOut.data[:,profileList,:] |
|
981 | dataOut.data = dataOut.data[:,profileList,:] | |
| 984 |
|
982 | |||
| 985 | if profileRangeList != None: |
|
983 | if profileRangeList != None: | |
| 986 | minIndex = profileRangeList[0] |
|
984 | minIndex = profileRangeList[0] | |
| 987 | maxIndex = profileRangeList[1] |
|
985 | maxIndex = profileRangeList[1] | |
| 988 | profileList = list(range(minIndex, maxIndex+1)) |
|
986 | profileList = list(range(minIndex, maxIndex+1)) | |
| 989 |
|
987 | |||
| 990 | dataOut.data = dataOut.data[:,minIndex:maxIndex+1,:] |
|
988 | dataOut.data = dataOut.data[:,minIndex:maxIndex+1,:] | |
| 991 |
|
989 | |||
| 992 | if rangeList != None: |
|
990 | if rangeList != None: | |
| 993 |
|
991 | |||
| 994 | profileList = [] |
|
992 | profileList = [] | |
| 995 |
|
993 | |||
| 996 | for thisRange in rangeList: |
|
994 | for thisRange in rangeList: | |
| 997 | minIndex = thisRange[0] |
|
995 | minIndex = thisRange[0] | |
| 998 | maxIndex = thisRange[1] |
|
996 | maxIndex = thisRange[1] | |
| 999 |
|
997 | |||
| 1000 | profileList.extend(list(range(minIndex, maxIndex+1))) |
|
998 | profileList.extend(list(range(minIndex, maxIndex+1))) | |
| 1001 |
|
999 | |||
| 1002 | dataOut.data = dataOut.data[:,profileList,:] |
|
1000 | dataOut.data = dataOut.data[:,profileList,:] | |
| 1003 |
|
1001 | |||
| 1004 | dataOut.nProfiles = len(profileList) |
|
1002 | dataOut.nProfiles = len(profileList) | |
| 1005 | dataOut.profileIndex = dataOut.nProfiles - 1 |
|
1003 | dataOut.profileIndex = dataOut.nProfiles - 1 | |
| 1006 | dataOut.flagNoData = False |
|
1004 | dataOut.flagNoData = False | |
| 1007 |
|
1005 | |||
| 1008 | return dataOut |
|
1006 | return dataOut | |
| 1009 |
|
1007 | |||
| 1010 | """ |
|
1008 | """ | |
| 1011 | data dimension = [nChannels, nHeis] |
|
1009 | data dimension = [nChannels, nHeis] | |
| 1012 | """ |
|
1010 | """ | |
| 1013 |
|
1011 | |||
| 1014 | if profileList != None: |
|
1012 | if profileList != None: | |
| 1015 |
|
1013 | |||
| 1016 | if self.isThisProfileInList(dataOut.profileIndex, profileList): |
|
1014 | if self.isThisProfileInList(dataOut.profileIndex, profileList): | |
| 1017 |
|
1015 | |||
| 1018 | self.nProfiles = len(profileList) |
|
1016 | self.nProfiles = len(profileList) | |
| 1019 | dataOut.nProfiles = self.nProfiles |
|
1017 | dataOut.nProfiles = self.nProfiles | |
| 1020 | dataOut.profileIndex = self.profileIndex |
|
1018 | dataOut.profileIndex = self.profileIndex | |
| 1021 | dataOut.flagNoData = False |
|
1019 | dataOut.flagNoData = False | |
| 1022 |
|
1020 | |||
| 1023 | self.incProfileIndex() |
|
1021 | self.incProfileIndex() | |
| 1024 | return dataOut |
|
1022 | return dataOut | |
| 1025 |
|
1023 | |||
| 1026 | if profileRangeList != None: |
|
1024 | if profileRangeList != None: | |
| 1027 |
|
1025 | |||
| 1028 | minIndex = profileRangeList[0] |
|
1026 | minIndex = profileRangeList[0] | |
| 1029 | maxIndex = profileRangeList[1] |
|
1027 | maxIndex = profileRangeList[1] | |
| 1030 |
|
1028 | |||
| 1031 | if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex): |
|
1029 | if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex): | |
| 1032 |
|
1030 | |||
| 1033 | self.nProfiles = maxIndex - minIndex + 1 |
|
1031 | self.nProfiles = maxIndex - minIndex + 1 | |
| 1034 | dataOut.nProfiles = self.nProfiles |
|
1032 | dataOut.nProfiles = self.nProfiles | |
| 1035 | dataOut.profileIndex = self.profileIndex |
|
1033 | dataOut.profileIndex = self.profileIndex | |
| 1036 | dataOut.flagNoData = False |
|
1034 | dataOut.flagNoData = False | |
| 1037 |
|
1035 | |||
| 1038 | self.incProfileIndex() |
|
1036 | self.incProfileIndex() | |
| 1039 | return dataOut |
|
1037 | return dataOut | |
| 1040 |
|
1038 | |||
| 1041 | if rangeList != None: |
|
1039 | if rangeList != None: | |
| 1042 |
|
1040 | |||
| 1043 | nProfiles = 0 |
|
1041 | nProfiles = 0 | |
| 1044 |
|
1042 | |||
| 1045 | for thisRange in rangeList: |
|
1043 | for thisRange in rangeList: | |
| 1046 | minIndex = thisRange[0] |
|
1044 | minIndex = thisRange[0] | |
| 1047 | maxIndex = thisRange[1] |
|
1045 | maxIndex = thisRange[1] | |
| 1048 |
|
1046 | |||
| 1049 | nProfiles += maxIndex - minIndex + 1 |
|
1047 | nProfiles += maxIndex - minIndex + 1 | |
| 1050 |
|
1048 | |||
| 1051 | for thisRange in rangeList: |
|
1049 | for thisRange in rangeList: | |
| 1052 |
|
1050 | |||
| 1053 | minIndex = thisRange[0] |
|
1051 | minIndex = thisRange[0] | |
| 1054 | maxIndex = thisRange[1] |
|
1052 | maxIndex = thisRange[1] | |
| 1055 |
|
1053 | |||
| 1056 | if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex): |
|
1054 | if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex): | |
| 1057 |
|
1055 | |||
| 1058 | self.nProfiles = nProfiles |
|
1056 | self.nProfiles = nProfiles | |
| 1059 | dataOut.nProfiles = self.nProfiles |
|
1057 | dataOut.nProfiles = self.nProfiles | |
| 1060 | dataOut.profileIndex = self.profileIndex |
|
1058 | dataOut.profileIndex = self.profileIndex | |
| 1061 | dataOut.flagNoData = False |
|
1059 | dataOut.flagNoData = False | |
| 1062 |
|
1060 | |||
| 1063 | self.incProfileIndex() |
|
1061 | self.incProfileIndex() | |
| 1064 |
|
1062 | |||
| 1065 | break |
|
1063 | break | |
| 1066 |
|
1064 | |||
| 1067 | return dataOut |
|
1065 | return dataOut | |
| 1068 |
|
1066 | |||
| 1069 |
|
1067 | |||
| 1070 | if beam != None: #beam is only for AMISR data |
|
1068 | if beam != None: #beam is only for AMISR data | |
| 1071 | if self.isThisProfileInList(dataOut.profileIndex, dataOut.beamRangeDict[beam]): |
|
1069 | if self.isThisProfileInList(dataOut.profileIndex, dataOut.beamRangeDict[beam]): | |
| 1072 | dataOut.flagNoData = False |
|
1070 | dataOut.flagNoData = False | |
| 1073 | dataOut.profileIndex = self.profileIndex |
|
1071 | dataOut.profileIndex = self.profileIndex | |
| 1074 |
|
1072 | |||
| 1075 | self.incProfileIndex() |
|
1073 | self.incProfileIndex() | |
| 1076 |
|
1074 | |||
| 1077 | return dataOut |
|
1075 | return dataOut | |
| 1078 |
|
1076 | |||
| 1079 | raise ValueError("ProfileSelector needs profileList, profileRangeList or rangeList parameter") |
|
1077 | raise ValueError("ProfileSelector needs profileList, profileRangeList or rangeList parameter") | |
| 1080 |
|
1078 | |||
| 1081 |
|
1079 | |||
| 1082 | class Reshaper(Operation): |
|
1080 | class Reshaper(Operation): | |
| 1083 |
|
1081 | |||
| 1084 | def __init__(self, **kwargs): |
|
1082 | def __init__(self, **kwargs): | |
| 1085 |
|
1083 | |||
| 1086 | Operation.__init__(self, **kwargs) |
|
1084 | Operation.__init__(self, **kwargs) | |
| 1087 |
|
1085 | |||
| 1088 | self.__buffer = None |
|
1086 | self.__buffer = None | |
| 1089 | self.__nitems = 0 |
|
1087 | self.__nitems = 0 | |
| 1090 |
|
1088 | |||
| 1091 | def __appendProfile(self, dataOut, nTxs): |
|
1089 | def __appendProfile(self, dataOut, nTxs): | |
| 1092 |
|
1090 | |||
| 1093 | if self.__buffer is None: |
|
1091 | if self.__buffer is None: | |
| 1094 | shape = (dataOut.nChannels, int(dataOut.nHeights/nTxs) ) |
|
1092 | shape = (dataOut.nChannels, int(dataOut.nHeights/nTxs) ) | |
| 1095 | self.__buffer = numpy.empty(shape, dtype = dataOut.data.dtype) |
|
1093 | self.__buffer = numpy.empty(shape, dtype = dataOut.data.dtype) | |
| 1096 |
|
1094 | |||
| 1097 | ini = dataOut.nHeights * self.__nitems |
|
1095 | ini = dataOut.nHeights * self.__nitems | |
| 1098 | end = ini + dataOut.nHeights |
|
1096 | end = ini + dataOut.nHeights | |
| 1099 |
|
1097 | |||
| 1100 | self.__buffer[:, ini:end] = dataOut.data |
|
1098 | self.__buffer[:, ini:end] = dataOut.data | |
| 1101 |
|
1099 | |||
| 1102 | self.__nitems += 1 |
|
1100 | self.__nitems += 1 | |
| 1103 |
|
1101 | |||
| 1104 | return int(self.__nitems*nTxs) |
|
1102 | return int(self.__nitems*nTxs) | |
| 1105 |
|
1103 | |||
| 1106 | def __getBuffer(self): |
|
1104 | def __getBuffer(self): | |
| 1107 |
|
1105 | |||
| 1108 | if self.__nitems == int(1./self.__nTxs): |
|
1106 | if self.__nitems == int(1./self.__nTxs): | |
| 1109 |
|
1107 | |||
| 1110 | self.__nitems = 0 |
|
1108 | self.__nitems = 0 | |
| 1111 |
|
1109 | |||
| 1112 | return self.__buffer.copy() |
|
1110 | return self.__buffer.copy() | |
| 1113 |
|
1111 | |||
| 1114 | return None |
|
1112 | return None | |
| 1115 |
|
1113 | |||
| 1116 | def __checkInputs(self, dataOut, shape, nTxs): |
|
1114 | def __checkInputs(self, dataOut, shape, nTxs): | |
| 1117 |
|
1115 | |||
| 1118 | if shape is None and nTxs is None: |
|
1116 | if shape is None and nTxs is None: | |
| 1119 | raise ValueError("Reshaper: shape of factor should be defined") |
|
1117 | raise ValueError("Reshaper: shape of factor should be defined") | |
| 1120 |
|
1118 | |||
| 1121 | if nTxs: |
|
1119 | if nTxs: | |
| 1122 | if nTxs < 0: |
|
1120 | if nTxs < 0: | |
| 1123 | raise ValueError("nTxs should be greater than 0") |
|
1121 | raise ValueError("nTxs should be greater than 0") | |
| 1124 |
|
1122 | |||
| 1125 | if nTxs < 1 and dataOut.nProfiles % (1./nTxs) != 0: |
|
1123 | if nTxs < 1 and dataOut.nProfiles % (1./nTxs) != 0: | |
| 1126 | raise ValueError("nProfiles= %d is not divisibled by (1./nTxs) = %f" %(dataOut.nProfiles, (1./nTxs))) |
|
1124 | raise ValueError("nProfiles= %d is not divisibled by (1./nTxs) = %f" %(dataOut.nProfiles, (1./nTxs))) | |
| 1127 |
|
1125 | |||
| 1128 | shape = [dataOut.nChannels, dataOut.nProfiles*nTxs, dataOut.nHeights/nTxs] |
|
1126 | shape = [dataOut.nChannels, dataOut.nProfiles*nTxs, dataOut.nHeights/nTxs] | |
| 1129 |
|
1127 | |||
| 1130 | return shape, nTxs |
|
1128 | return shape, nTxs | |
| 1131 |
|
1129 | |||
| 1132 | if len(shape) != 2 and len(shape) != 3: |
|
1130 | if len(shape) != 2 and len(shape) != 3: | |
| 1133 | 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)) |
|
1131 | 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)) | |
| 1134 |
|
1132 | |||
| 1135 | if len(shape) == 2: |
|
1133 | if len(shape) == 2: | |
| 1136 | shape_tuple = [dataOut.nChannels] |
|
1134 | shape_tuple = [dataOut.nChannels] | |
| 1137 | shape_tuple.extend(shape) |
|
1135 | shape_tuple.extend(shape) | |
| 1138 | else: |
|
1136 | else: | |
| 1139 | shape_tuple = list(shape) |
|
1137 | shape_tuple = list(shape) | |
| 1140 |
|
1138 | |||
| 1141 | nTxs = 1.0*shape_tuple[1]/dataOut.nProfiles |
|
1139 | nTxs = 1.0*shape_tuple[1]/dataOut.nProfiles | |
| 1142 |
|
1140 | |||
| 1143 | return shape_tuple, nTxs |
|
1141 | return shape_tuple, nTxs | |
| 1144 |
|
1142 | |||
| 1145 | def run(self, dataOut, shape=None, nTxs=None): |
|
1143 | def run(self, dataOut, shape=None, nTxs=None): | |
| 1146 |
|
1144 | |||
| 1147 | shape_tuple, self.__nTxs = self.__checkInputs(dataOut, shape, nTxs) |
|
1145 | shape_tuple, self.__nTxs = self.__checkInputs(dataOut, shape, nTxs) | |
| 1148 |
|
1146 | |||
| 1149 | dataOut.flagNoData = True |
|
1147 | dataOut.flagNoData = True | |
| 1150 | profileIndex = None |
|
1148 | profileIndex = None | |
| 1151 |
|
1149 | |||
| 1152 | if dataOut.flagDataAsBlock: |
|
1150 | if dataOut.flagDataAsBlock: | |
| 1153 |
|
1151 | |||
| 1154 | dataOut.data = numpy.reshape(dataOut.data, shape_tuple) |
|
1152 | dataOut.data = numpy.reshape(dataOut.data, shape_tuple) | |
| 1155 | dataOut.flagNoData = False |
|
1153 | dataOut.flagNoData = False | |
| 1156 |
|
1154 | |||
| 1157 | profileIndex = int(dataOut.nProfiles*self.__nTxs) - 1 |
|
1155 | profileIndex = int(dataOut.nProfiles*self.__nTxs) - 1 | |
| 1158 |
|
1156 | |||
| 1159 | else: |
|
1157 | else: | |
| 1160 |
|
1158 | |||
| 1161 | if self.__nTxs < 1: |
|
1159 | if self.__nTxs < 1: | |
| 1162 |
|
1160 | |||
| 1163 | self.__appendProfile(dataOut, self.__nTxs) |
|
1161 | self.__appendProfile(dataOut, self.__nTxs) | |
| 1164 | new_data = self.__getBuffer() |
|
1162 | new_data = self.__getBuffer() | |
| 1165 |
|
1163 | |||
| 1166 | if new_data is not None: |
|
1164 | if new_data is not None: | |
| 1167 | dataOut.data = new_data |
|
1165 | dataOut.data = new_data | |
| 1168 | dataOut.flagNoData = False |
|
1166 | dataOut.flagNoData = False | |
| 1169 |
|
1167 | |||
| 1170 | profileIndex = dataOut.profileIndex*nTxs |
|
1168 | profileIndex = dataOut.profileIndex*nTxs | |
| 1171 |
|
1169 | |||
| 1172 | else: |
|
1170 | else: | |
| 1173 | raise ValueError("nTxs should be greater than 0 and lower than 1, or use VoltageReader(..., getblock=True)") |
|
1171 | raise ValueError("nTxs should be greater than 0 and lower than 1, or use VoltageReader(..., getblock=True)") | |
| 1174 |
|
1172 | |||
| 1175 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
1173 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] | |
| 1176 |
|
1174 | |||
| 1177 | dataOut.heightList = numpy.arange(dataOut.nHeights/self.__nTxs) * deltaHeight + dataOut.heightList[0] |
|
1175 | dataOut.heightList = numpy.arange(dataOut.nHeights/self.__nTxs) * deltaHeight + dataOut.heightList[0] | |
| 1178 |
|
1176 | |||
| 1179 | dataOut.nProfiles = int(dataOut.nProfiles*self.__nTxs) |
|
1177 | dataOut.nProfiles = int(dataOut.nProfiles*self.__nTxs) | |
| 1180 |
|
1178 | |||
| 1181 | dataOut.profileIndex = profileIndex |
|
1179 | dataOut.profileIndex = profileIndex | |
| 1182 |
|
1180 | |||
| 1183 | dataOut.ippSeconds /= self.__nTxs |
|
1181 | dataOut.ippSeconds /= self.__nTxs | |
| 1184 |
|
1182 | |||
| 1185 | return dataOut |
|
1183 | return dataOut | |
| 1186 |
|
1184 | |||
| 1187 | class SplitProfiles(Operation): |
|
1185 | class SplitProfiles(Operation): | |
| 1188 |
|
1186 | |||
| 1189 | def __init__(self, **kwargs): |
|
1187 | def __init__(self, **kwargs): | |
| 1190 |
|
1188 | |||
| 1191 | Operation.__init__(self, **kwargs) |
|
1189 | Operation.__init__(self, **kwargs) | |
| 1192 |
|
1190 | |||
| 1193 | def run(self, dataOut, n): |
|
1191 | def run(self, dataOut, n): | |
| 1194 |
|
1192 | |||
| 1195 | dataOut.flagNoData = True |
|
1193 | dataOut.flagNoData = True | |
| 1196 | profileIndex = None |
|
1194 | profileIndex = None | |
| 1197 |
|
1195 | |||
| 1198 | if dataOut.flagDataAsBlock: |
|
1196 | if dataOut.flagDataAsBlock: | |
| 1199 |
|
1197 | |||
| 1200 | #nchannels, nprofiles, nsamples |
|
1198 | #nchannels, nprofiles, nsamples | |
| 1201 | shape = dataOut.data.shape |
|
1199 | shape = dataOut.data.shape | |
| 1202 |
|
1200 | |||
| 1203 | if shape[2] % n != 0: |
|
1201 | if shape[2] % n != 0: | |
| 1204 | raise ValueError("Could not split the data, n=%d has to be multiple of %d" %(n, shape[2])) |
|
1202 | raise ValueError("Could not split the data, n=%d has to be multiple of %d" %(n, shape[2])) | |
| 1205 |
|
1203 | |||
| 1206 | new_shape = shape[0], shape[1]*n, int(shape[2]/n) |
|
1204 | new_shape = shape[0], shape[1]*n, int(shape[2]/n) | |
| 1207 |
|
1205 | |||
| 1208 | dataOut.data = numpy.reshape(dataOut.data, new_shape) |
|
1206 | dataOut.data = numpy.reshape(dataOut.data, new_shape) | |
| 1209 | dataOut.flagNoData = False |
|
1207 | dataOut.flagNoData = False | |
| 1210 |
|
1208 | |||
| 1211 | profileIndex = int(dataOut.nProfiles/n) - 1 |
|
1209 | profileIndex = int(dataOut.nProfiles/n) - 1 | |
| 1212 |
|
1210 | |||
| 1213 | else: |
|
1211 | else: | |
| 1214 |
|
1212 | |||
| 1215 | raise ValueError("Could not split the data when is read Profile by Profile. Use VoltageReader(..., getblock=True)") |
|
1213 | raise ValueError("Could not split the data when is read Profile by Profile. Use VoltageReader(..., getblock=True)") | |
| 1216 |
|
1214 | |||
| 1217 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
1215 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] | |
| 1218 |
|
1216 | |||
| 1219 | dataOut.heightList = numpy.arange(dataOut.nHeights/n) * deltaHeight + dataOut.heightList[0] |
|
1217 | dataOut.heightList = numpy.arange(dataOut.nHeights/n) * deltaHeight + dataOut.heightList[0] | |
| 1220 |
|
1218 | |||
| 1221 | dataOut.nProfiles = int(dataOut.nProfiles*n) |
|
1219 | dataOut.nProfiles = int(dataOut.nProfiles*n) | |
| 1222 |
|
1220 | |||
| 1223 | dataOut.profileIndex = profileIndex |
|
1221 | dataOut.profileIndex = profileIndex | |
| 1224 |
|
1222 | |||
| 1225 | dataOut.ippSeconds /= n |
|
1223 | dataOut.ippSeconds /= n | |
| 1226 |
|
1224 | |||
| 1227 | return dataOut |
|
1225 | return dataOut | |
| 1228 |
|
1226 | |||
| 1229 | class CombineProfiles(Operation): |
|
1227 | class CombineProfiles(Operation): | |
| 1230 | def __init__(self, **kwargs): |
|
1228 | def __init__(self, **kwargs): | |
| 1231 |
|
1229 | |||
| 1232 | Operation.__init__(self, **kwargs) |
|
1230 | Operation.__init__(self, **kwargs) | |
| 1233 |
|
1231 | |||
| 1234 | self.__remData = None |
|
1232 | self.__remData = None | |
| 1235 | self.__profileIndex = 0 |
|
1233 | self.__profileIndex = 0 | |
| 1236 |
|
1234 | |||
| 1237 | def run(self, dataOut, n): |
|
1235 | def run(self, dataOut, n): | |
| 1238 |
|
1236 | |||
| 1239 | dataOut.flagNoData = True |
|
1237 | dataOut.flagNoData = True | |
| 1240 | profileIndex = None |
|
1238 | profileIndex = None | |
| 1241 |
|
1239 | |||
| 1242 | if dataOut.flagDataAsBlock: |
|
1240 | if dataOut.flagDataAsBlock: | |
| 1243 |
|
1241 | |||
| 1244 | #nchannels, nprofiles, nsamples |
|
1242 | #nchannels, nprofiles, nsamples | |
| 1245 | shape = dataOut.data.shape |
|
1243 | shape = dataOut.data.shape | |
| 1246 | new_shape = shape[0], shape[1]/n, shape[2]*n |
|
1244 | new_shape = shape[0], shape[1]/n, shape[2]*n | |
| 1247 |
|
1245 | |||
| 1248 | if shape[1] % n != 0: |
|
1246 | if shape[1] % n != 0: | |
| 1249 | raise ValueError("Could not split the data, n=%d has to be multiple of %d" %(n, shape[1])) |
|
1247 | raise ValueError("Could not split the data, n=%d has to be multiple of %d" %(n, shape[1])) | |
| 1250 |
|
1248 | |||
| 1251 | dataOut.data = numpy.reshape(dataOut.data, new_shape) |
|
1249 | dataOut.data = numpy.reshape(dataOut.data, new_shape) | |
| 1252 | dataOut.flagNoData = False |
|
1250 | dataOut.flagNoData = False | |
| 1253 |
|
1251 | |||
| 1254 | profileIndex = int(dataOut.nProfiles*n) - 1 |
|
1252 | profileIndex = int(dataOut.nProfiles*n) - 1 | |
| 1255 |
|
1253 | |||
| 1256 | else: |
|
1254 | else: | |
| 1257 |
|
1255 | |||
| 1258 | #nchannels, nsamples |
|
1256 | #nchannels, nsamples | |
| 1259 | if self.__remData is None: |
|
1257 | if self.__remData is None: | |
| 1260 | newData = dataOut.data |
|
1258 | newData = dataOut.data | |
| 1261 | else: |
|
1259 | else: | |
| 1262 | newData = numpy.concatenate((self.__remData, dataOut.data), axis=1) |
|
1260 | newData = numpy.concatenate((self.__remData, dataOut.data), axis=1) | |
| 1263 |
|
1261 | |||
| 1264 | self.__profileIndex += 1 |
|
1262 | self.__profileIndex += 1 | |
| 1265 |
|
1263 | |||
| 1266 | if self.__profileIndex < n: |
|
1264 | if self.__profileIndex < n: | |
| 1267 | self.__remData = newData |
|
1265 | self.__remData = newData | |
| 1268 | #continue |
|
1266 | #continue | |
| 1269 | return |
|
1267 | return | |
| 1270 |
|
1268 | |||
| 1271 | self.__profileIndex = 0 |
|
1269 | self.__profileIndex = 0 | |
| 1272 | self.__remData = None |
|
1270 | self.__remData = None | |
| 1273 |
|
1271 | |||
| 1274 | dataOut.data = newData |
|
1272 | dataOut.data = newData | |
| 1275 | dataOut.flagNoData = False |
|
1273 | dataOut.flagNoData = False | |
| 1276 |
|
1274 | |||
| 1277 | profileIndex = dataOut.profileIndex/n |
|
1275 | profileIndex = dataOut.profileIndex/n | |
| 1278 |
|
1276 | |||
| 1279 |
|
1277 | |||
| 1280 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
1278 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] | |
| 1281 |
|
1279 | |||
| 1282 | dataOut.heightList = numpy.arange(dataOut.nHeights*n) * deltaHeight + dataOut.heightList[0] |
|
1280 | dataOut.heightList = numpy.arange(dataOut.nHeights*n) * deltaHeight + dataOut.heightList[0] | |
| 1283 |
|
1281 | |||
| 1284 | dataOut.nProfiles = int(dataOut.nProfiles/n) |
|
1282 | dataOut.nProfiles = int(dataOut.nProfiles/n) | |
| 1285 |
|
1283 | |||
| 1286 | dataOut.profileIndex = profileIndex |
|
1284 | dataOut.profileIndex = profileIndex | |
| 1287 |
|
1285 | |||
| 1288 | dataOut.ippSeconds *= n |
|
1286 | dataOut.ippSeconds *= n | |
| 1289 |
|
1287 | |||
| 1290 | return dataOut |
|
1288 | return dataOut | |
| 1291 |
|
1289 | |||
| 1292 | class PulsePairVoltage(Operation): |
|
1290 | class PulsePairVoltage(Operation): | |
| 1293 | ''' |
|
1291 | ''' | |
| 1294 | Function PulsePair(Signal Power, Velocity) |
|
1292 | Function PulsePair(Signal Power, Velocity) | |
| 1295 | The real component of Lag[0] provides Intensity Information |
|
1293 | The real component of Lag[0] provides Intensity Information | |
| 1296 | The imag component of Lag[1] Phase provides Velocity Information |
|
1294 | The imag component of Lag[1] Phase provides Velocity Information | |
| 1297 |
|
1295 | |||
| 1298 | Configuration Parameters: |
|
1296 | Configuration Parameters: | |
| 1299 | nPRF = Number of Several PRF |
|
1297 | nPRF = Number of Several PRF | |
| 1300 | theta = Degree Azimuth angel Boundaries |
|
1298 | theta = Degree Azimuth angel Boundaries | |
| 1301 |
|
1299 | |||
| 1302 | Input: |
|
1300 | Input: | |
| 1303 | self.dataOut |
|
1301 | self.dataOut | |
| 1304 | lag[N] |
|
1302 | lag[N] | |
| 1305 | Affected: |
|
1303 | Affected: | |
| 1306 | self.dataOut.spc |
|
1304 | self.dataOut.spc | |
| 1307 | ''' |
|
1305 | ''' | |
| 1308 | isConfig = False |
|
1306 | isConfig = False | |
| 1309 | __profIndex = 0 |
|
1307 | __profIndex = 0 | |
| 1310 | __initime = None |
|
1308 | __initime = None | |
| 1311 | __lastdatatime = None |
|
1309 | __lastdatatime = None | |
| 1312 | __buffer = None |
|
1310 | __buffer = None | |
| 1313 | noise = None |
|
1311 | noise = None | |
| 1314 | __dataReady = False |
|
1312 | __dataReady = False | |
| 1315 | n = None |
|
1313 | n = None | |
| 1316 | __nch = 0 |
|
1314 | __nch = 0 | |
| 1317 | __nHeis = 0 |
|
1315 | __nHeis = 0 | |
| 1318 | removeDC = False |
|
1316 | removeDC = False | |
| 1319 | ipp = None |
|
1317 | ipp = None | |
| 1320 | lambda_ = 0 |
|
1318 | lambda_ = 0 | |
| 1321 |
|
1319 | |||
| 1322 | def __init__(self,**kwargs): |
|
1320 | def __init__(self,**kwargs): | |
| 1323 | Operation.__init__(self,**kwargs) |
|
1321 | Operation.__init__(self,**kwargs) | |
| 1324 |
|
1322 | |||
| 1325 | def setup(self, dataOut, n = None, removeDC=False): |
|
1323 | def setup(self, dataOut, n = None, removeDC=False): | |
| 1326 | ''' |
|
1324 | ''' | |
| 1327 | n= Numero de PRF's de entrada |
|
1325 | n= Numero de PRF's de entrada | |
| 1328 | ''' |
|
1326 | ''' | |
| 1329 | self.__initime = None |
|
1327 | self.__initime = None | |
| 1330 | self.__lastdatatime = 0 |
|
1328 | self.__lastdatatime = 0 | |
| 1331 | self.__dataReady = False |
|
1329 | self.__dataReady = False | |
| 1332 | self.__buffer = 0 |
|
1330 | self.__buffer = 0 | |
| 1333 | self.__profIndex = 0 |
|
1331 | self.__profIndex = 0 | |
| 1334 | self.noise = None |
|
1332 | self.noise = None | |
| 1335 | self.__nch = dataOut.nChannels |
|
1333 | self.__nch = dataOut.nChannels | |
| 1336 | self.__nHeis = dataOut.nHeights |
|
1334 | self.__nHeis = dataOut.nHeights | |
| 1337 | self.removeDC = removeDC |
|
1335 | self.removeDC = removeDC | |
| 1338 | self.lambda_ = 3.0e8/(9345.0e6) |
|
1336 | self.lambda_ = 3.0e8/(9345.0e6) | |
| 1339 | self.ippSec = dataOut.ippSeconds |
|
1337 | self.ippSec = dataOut.ippSeconds | |
| 1340 | self.nCohInt = dataOut.nCohInt |
|
1338 | self.nCohInt = dataOut.nCohInt | |
| 1341 | print("IPPseconds",dataOut.ippSeconds) |
|
1339 | print("IPPseconds",dataOut.ippSeconds) | |
| 1342 |
|
1340 | |||
| 1343 | print("ELVALOR DE n es:", n) |
|
1341 | print("ELVALOR DE n es:", n) | |
| 1344 | if n == None: |
|
1342 | if n == None: | |
| 1345 | raise ValueError("n should be specified.") |
|
1343 | raise ValueError("n should be specified.") | |
| 1346 |
|
1344 | |||
| 1347 | if n != None: |
|
1345 | if n != None: | |
| 1348 | if n<2: |
|
1346 | if n<2: | |
| 1349 | raise ValueError("n should be greater than 2") |
|
1347 | raise ValueError("n should be greater than 2") | |
| 1350 |
|
1348 | |||
| 1351 | self.n = n |
|
1349 | self.n = n | |
| 1352 | self.__nProf = n |
|
1350 | self.__nProf = n | |
| 1353 |
|
1351 | |||
| 1354 | self.__buffer = numpy.zeros((dataOut.nChannels, |
|
1352 | self.__buffer = numpy.zeros((dataOut.nChannels, | |
| 1355 | n, |
|
1353 | n, | |
| 1356 | dataOut.nHeights), |
|
1354 | dataOut.nHeights), | |
| 1357 | dtype='complex') |
|
1355 | dtype='complex') | |
| 1358 |
|
1356 | |||
| 1359 | def putData(self,data): |
|
1357 | def putData(self,data): | |
| 1360 | ''' |
|
1358 | ''' | |
| 1361 | Add a profile to he __buffer and increase in one the __profiel Index |
|
1359 | Add a profile to he __buffer and increase in one the __profiel Index | |
| 1362 | ''' |
|
1360 | ''' | |
| 1363 | self.__buffer[:,self.__profIndex,:]= data |
|
1361 | self.__buffer[:,self.__profIndex,:]= data | |
| 1364 | self.__profIndex += 1 |
|
1362 | self.__profIndex += 1 | |
| 1365 | return |
|
1363 | return | |
| 1366 |
|
1364 | |||
| 1367 | def pushData(self,dataOut): |
|
1365 | def pushData(self,dataOut): | |
| 1368 | ''' |
|
1366 | ''' | |
| 1369 | Return the PULSEPAIR and the profiles used in the operation |
|
1367 | Return the PULSEPAIR and the profiles used in the operation | |
| 1370 | Affected : self.__profileIndex |
|
1368 | Affected : self.__profileIndex | |
| 1371 | ''' |
|
1369 | ''' | |
| 1372 | #----------------- Remove DC----------------------------------- |
|
1370 | #----------------- Remove DC----------------------------------- | |
| 1373 | if self.removeDC==True: |
|
1371 | if self.removeDC==True: | |
| 1374 | mean = numpy.mean(self.__buffer,1) |
|
1372 | mean = numpy.mean(self.__buffer,1) | |
| 1375 | tmp = mean.reshape(self.__nch,1,self.__nHeis) |
|
1373 | tmp = mean.reshape(self.__nch,1,self.__nHeis) | |
| 1376 | dc= numpy.tile(tmp,[1,self.__nProf,1]) |
|
1374 | dc= numpy.tile(tmp,[1,self.__nProf,1]) | |
| 1377 | self.__buffer = self.__buffer - dc |
|
1375 | self.__buffer = self.__buffer - dc | |
| 1378 | #------------------Calculo de Potencia ------------------------ |
|
1376 | #------------------Calculo de Potencia ------------------------ | |
| 1379 | pair0 = self.__buffer*numpy.conj(self.__buffer) |
|
1377 | pair0 = self.__buffer*numpy.conj(self.__buffer) | |
| 1380 | pair0 = pair0.real |
|
1378 | pair0 = pair0.real | |
| 1381 | lag_0 = numpy.sum(pair0,1) |
|
1379 | lag_0 = numpy.sum(pair0,1) | |
| 1382 | #------------------Calculo de Ruido x canal-------------------- |
|
1380 | #------------------Calculo de Ruido x canal-------------------- | |
| 1383 | self.noise = numpy.zeros(self.__nch) |
|
1381 | self.noise = numpy.zeros(self.__nch) | |
| 1384 | for i in range(self.__nch): |
|
1382 | for i in range(self.__nch): | |
| 1385 | daux = numpy.sort(pair0[i,:,:],axis= None) |
|
1383 | daux = numpy.sort(pair0[i,:,:],axis= None) | |
| 1386 | self.noise[i]=hildebrand_sekhon( daux ,self.nCohInt) |
|
1384 | self.noise[i]=hildebrand_sekhon( daux ,self.nCohInt) | |
| 1387 |
|
1385 | |||
| 1388 | self.noise = self.noise.reshape(self.__nch,1) |
|
1386 | self.noise = self.noise.reshape(self.__nch,1) | |
| 1389 | self.noise = numpy.tile(self.noise,[1,self.__nHeis]) |
|
1387 | self.noise = numpy.tile(self.noise,[1,self.__nHeis]) | |
| 1390 | noise_buffer = self.noise.reshape(self.__nch,1,self.__nHeis) |
|
1388 | noise_buffer = self.noise.reshape(self.__nch,1,self.__nHeis) | |
| 1391 | noise_buffer = numpy.tile(noise_buffer,[1,self.__nProf,1]) |
|
1389 | noise_buffer = numpy.tile(noise_buffer,[1,self.__nProf,1]) | |
| 1392 | #------------------ Potencia recibida= P , Potencia senal = S , Ruido= N-- |
|
1390 | #------------------ Potencia recibida= P , Potencia senal = S , Ruido= N-- | |
| 1393 | #------------------ P= S+N ,P=lag_0/N --------------------------------- |
|
1391 | #------------------ P= S+N ,P=lag_0/N --------------------------------- | |
| 1394 | #-------------------- Power -------------------------------------------------- |
|
1392 | #-------------------- Power -------------------------------------------------- | |
| 1395 | data_power = lag_0/(self.n*self.nCohInt) |
|
1393 | data_power = lag_0/(self.n*self.nCohInt) | |
| 1396 | #------------------ Senal --------------------------------------------------- |
|
1394 | #------------------ Senal --------------------------------------------------- | |
| 1397 | data_intensity = pair0 - noise_buffer |
|
1395 | data_intensity = pair0 - noise_buffer | |
| 1398 | data_intensity = numpy.sum(data_intensity,axis=1)*(self.n*self.nCohInt)#*self.nCohInt) |
|
1396 | data_intensity = numpy.sum(data_intensity,axis=1)*(self.n*self.nCohInt)#*self.nCohInt) | |
| 1399 | #data_intensity = (lag_0-self.noise*self.n)*(self.n*self.nCohInt) |
|
1397 | #data_intensity = (lag_0-self.noise*self.n)*(self.n*self.nCohInt) | |
| 1400 | for i in range(self.__nch): |
|
1398 | for i in range(self.__nch): | |
| 1401 | for j in range(self.__nHeis): |
|
1399 | for j in range(self.__nHeis): | |
| 1402 | if data_intensity[i][j] < 0: |
|
1400 | if data_intensity[i][j] < 0: | |
| 1403 | data_intensity[i][j] = numpy.min(numpy.absolute(data_intensity[i][j])) |
|
1401 | data_intensity[i][j] = numpy.min(numpy.absolute(data_intensity[i][j])) | |
| 1404 |
|
1402 | |||
| 1405 | #----------------- Calculo de Frecuencia y Velocidad doppler-------- |
|
1403 | #----------------- Calculo de Frecuencia y Velocidad doppler-------- | |
| 1406 | pair1 = self.__buffer[:,:-1,:]*numpy.conjugate(self.__buffer[:,1:,:]) |
|
1404 | pair1 = self.__buffer[:,:-1,:]*numpy.conjugate(self.__buffer[:,1:,:]) | |
| 1407 | lag_1 = numpy.sum(pair1,1) |
|
1405 | lag_1 = numpy.sum(pair1,1) | |
| 1408 | data_freq = (-1/(2.0*math.pi*self.ippSec*self.nCohInt))*numpy.angle(lag_1) |
|
1406 | data_freq = (-1/(2.0*math.pi*self.ippSec*self.nCohInt))*numpy.angle(lag_1) | |
| 1409 | data_velocity = (self.lambda_/2.0)*data_freq |
|
1407 | data_velocity = (self.lambda_/2.0)*data_freq | |
| 1410 |
|
1408 | |||
| 1411 | #---------------- Potencia promedio estimada de la Senal----------- |
|
1409 | #---------------- Potencia promedio estimada de la Senal----------- | |
| 1412 | lag_0 = lag_0/self.n |
|
1410 | lag_0 = lag_0/self.n | |
| 1413 | S = lag_0-self.noise |
|
1411 | S = lag_0-self.noise | |
| 1414 |
|
1412 | |||
| 1415 | #---------------- Frecuencia Doppler promedio --------------------- |
|
1413 | #---------------- Frecuencia Doppler promedio --------------------- | |
| 1416 | lag_1 = lag_1/(self.n-1) |
|
1414 | lag_1 = lag_1/(self.n-1) | |
| 1417 | R1 = numpy.abs(lag_1) |
|
1415 | R1 = numpy.abs(lag_1) | |
| 1418 |
|
1416 | |||
| 1419 | #---------------- Calculo del SNR---------------------------------- |
|
1417 | #---------------- Calculo del SNR---------------------------------- | |
| 1420 | data_snrPP = S/self.noise |
|
1418 | data_snrPP = S/self.noise | |
| 1421 | for i in range(self.__nch): |
|
1419 | for i in range(self.__nch): | |
| 1422 | for j in range(self.__nHeis): |
|
1420 | for j in range(self.__nHeis): | |
| 1423 | if data_snrPP[i][j] < 1.e-20: |
|
1421 | if data_snrPP[i][j] < 1.e-20: | |
| 1424 | data_snrPP[i][j] = 1.e-20 |
|
1422 | data_snrPP[i][j] = 1.e-20 | |
| 1425 |
|
1423 | |||
| 1426 | #----------------- Calculo del ancho espectral ---------------------- |
|
1424 | #----------------- Calculo del ancho espectral ---------------------- | |
| 1427 | L = S/R1 |
|
1425 | L = S/R1 | |
| 1428 | L = numpy.where(L<0,1,L) |
|
1426 | L = numpy.where(L<0,1,L) | |
| 1429 | L = numpy.log(L) |
|
1427 | L = numpy.log(L) | |
| 1430 | tmp = numpy.sqrt(numpy.absolute(L)) |
|
1428 | tmp = numpy.sqrt(numpy.absolute(L)) | |
| 1431 | data_specwidth = (self.lambda_/(2*math.sqrt(2)*math.pi*self.ippSec*self.nCohInt))*tmp*numpy.sign(L) |
|
1429 | data_specwidth = (self.lambda_/(2*math.sqrt(2)*math.pi*self.ippSec*self.nCohInt))*tmp*numpy.sign(L) | |
| 1432 | n = self.__profIndex |
|
1430 | n = self.__profIndex | |
| 1433 |
|
1431 | |||
| 1434 | self.__buffer = numpy.zeros((self.__nch, self.__nProf,self.__nHeis), dtype='complex') |
|
1432 | self.__buffer = numpy.zeros((self.__nch, self.__nProf,self.__nHeis), dtype='complex') | |
| 1435 | self.__profIndex = 0 |
|
1433 | self.__profIndex = 0 | |
| 1436 | return data_power,data_intensity,data_velocity,data_snrPP,data_specwidth,n |
|
1434 | return data_power,data_intensity,data_velocity,data_snrPP,data_specwidth,n | |
| 1437 |
|
1435 | |||
| 1438 |
|
1436 | |||
| 1439 | def pulsePairbyProfiles(self,dataOut): |
|
1437 | def pulsePairbyProfiles(self,dataOut): | |
| 1440 |
|
1438 | |||
| 1441 | self.__dataReady = False |
|
1439 | self.__dataReady = False | |
| 1442 | data_power = None |
|
1440 | data_power = None | |
| 1443 | data_intensity = None |
|
1441 | data_intensity = None | |
| 1444 | data_velocity = None |
|
1442 | data_velocity = None | |
| 1445 | data_specwidth = None |
|
1443 | data_specwidth = None | |
| 1446 | data_snrPP = None |
|
1444 | data_snrPP = None | |
| 1447 | self.putData(data=dataOut.data) |
|
1445 | self.putData(data=dataOut.data) | |
| 1448 | if self.__profIndex == self.n: |
|
1446 | if self.__profIndex == self.n: | |
| 1449 | data_power,data_intensity, data_velocity,data_snrPP,data_specwidth, n = self.pushData(dataOut=dataOut) |
|
1447 | data_power,data_intensity, data_velocity,data_snrPP,data_specwidth, n = self.pushData(dataOut=dataOut) | |
| 1450 | self.__dataReady = True |
|
1448 | self.__dataReady = True | |
| 1451 |
|
1449 | |||
| 1452 | return data_power, data_intensity, data_velocity, data_snrPP, data_specwidth |
|
1450 | return data_power, data_intensity, data_velocity, data_snrPP, data_specwidth | |
| 1453 |
|
1451 | |||
| 1454 |
|
1452 | |||
| 1455 | def pulsePairOp(self, dataOut, datatime= None): |
|
1453 | def pulsePairOp(self, dataOut, datatime= None): | |
| 1456 |
|
1454 | |||
| 1457 | if self.__initime == None: |
|
1455 | if self.__initime == None: | |
| 1458 | self.__initime = datatime |
|
1456 | self.__initime = datatime | |
| 1459 | data_power, data_intensity, data_velocity, data_snrPP, data_specwidth = self.pulsePairbyProfiles(dataOut) |
|
1457 | data_power, data_intensity, data_velocity, data_snrPP, data_specwidth = self.pulsePairbyProfiles(dataOut) | |
| 1460 | self.__lastdatatime = datatime |
|
1458 | self.__lastdatatime = datatime | |
| 1461 |
|
1459 | |||
| 1462 | if data_power is None: |
|
1460 | if data_power is None: | |
| 1463 | return None, None, None,None,None,None |
|
1461 | return None, None, None,None,None,None | |
| 1464 |
|
1462 | |||
| 1465 | avgdatatime = self.__initime |
|
1463 | avgdatatime = self.__initime | |
| 1466 | deltatime = datatime - self.__lastdatatime |
|
1464 | deltatime = datatime - self.__lastdatatime | |
| 1467 | self.__initime = datatime |
|
1465 | self.__initime = datatime | |
| 1468 |
|
1466 | |||
| 1469 | return data_power, data_intensity, data_velocity, data_snrPP, data_specwidth, avgdatatime |
|
1467 | return data_power, data_intensity, data_velocity, data_snrPP, data_specwidth, avgdatatime | |
| 1470 |
|
1468 | |||
| 1471 | def run(self, dataOut,n = None,removeDC= False, overlapping= False,**kwargs): |
|
1469 | def run(self, dataOut,n = None,removeDC= False, overlapping= False,**kwargs): | |
| 1472 |
|
1470 | |||
| 1473 | if not self.isConfig: |
|
1471 | if not self.isConfig: | |
| 1474 | self.setup(dataOut = dataOut, n = n , removeDC=removeDC , **kwargs) |
|
1472 | self.setup(dataOut = dataOut, n = n , removeDC=removeDC , **kwargs) | |
| 1475 | self.isConfig = True |
|
1473 | self.isConfig = True | |
| 1476 | data_power, data_intensity, data_velocity,data_snrPP,data_specwidth, avgdatatime = self.pulsePairOp(dataOut, dataOut.utctime) |
|
1474 | data_power, data_intensity, data_velocity,data_snrPP,data_specwidth, avgdatatime = self.pulsePairOp(dataOut, dataOut.utctime) | |
| 1477 | dataOut.flagNoData = True |
|
1475 | dataOut.flagNoData = True | |
| 1478 |
|
1476 | |||
| 1479 | if self.__dataReady: |
|
1477 | if self.__dataReady: | |
| 1480 | dataOut.nCohInt *= self.n |
|
1478 | dataOut.nCohInt *= self.n | |
| 1481 | dataOut.dataPP_POW = data_intensity # S |
|
1479 | dataOut.dataPP_POW = data_intensity # S | |
| 1482 | dataOut.dataPP_POWER = data_power # P |
|
1480 | dataOut.dataPP_POWER = data_power # P | |
| 1483 | dataOut.dataPP_DOP = data_velocity |
|
1481 | dataOut.dataPP_DOP = data_velocity | |
| 1484 | dataOut.dataPP_SNR = data_snrPP |
|
1482 | dataOut.dataPP_SNR = data_snrPP | |
| 1485 | dataOut.dataPP_WIDTH = data_specwidth |
|
1483 | dataOut.dataPP_WIDTH = data_specwidth | |
| 1486 | dataOut.PRFbyAngle = self.n #numero de PRF*cada angulo rotado que equivale a un tiempo. |
|
1484 | dataOut.PRFbyAngle = self.n #numero de PRF*cada angulo rotado que equivale a un tiempo. | |
| 1487 | dataOut.utctime = avgdatatime |
|
1485 | dataOut.utctime = avgdatatime | |
| 1488 | dataOut.flagNoData = False |
|
1486 | dataOut.flagNoData = False | |
| 1489 | return dataOut |
|
1487 | return dataOut | |
| 1490 |
|
1488 | |||
| 1491 |
|
1489 | |||
| 1492 |
|
1490 | |||
| 1493 | # import collections |
|
1491 | # import collections | |
| 1494 | # from scipy.stats import mode |
|
1492 | # from scipy.stats import mode | |
| 1495 | # |
|
1493 | # | |
| 1496 | # class Synchronize(Operation): |
|
1494 | # class Synchronize(Operation): | |
| 1497 | # |
|
1495 | # | |
| 1498 | # isConfig = False |
|
1496 | # isConfig = False | |
| 1499 | # __profIndex = 0 |
|
1497 | # __profIndex = 0 | |
| 1500 | # |
|
1498 | # | |
| 1501 | # def __init__(self, **kwargs): |
|
1499 | # def __init__(self, **kwargs): | |
| 1502 | # |
|
1500 | # | |
| 1503 | # Operation.__init__(self, **kwargs) |
|
1501 | # Operation.__init__(self, **kwargs) | |
| 1504 | # # self.isConfig = False |
|
1502 | # # self.isConfig = False | |
| 1505 | # self.__powBuffer = None |
|
1503 | # self.__powBuffer = None | |
| 1506 | # self.__startIndex = 0 |
|
1504 | # self.__startIndex = 0 | |
| 1507 | # self.__pulseFound = False |
|
1505 | # self.__pulseFound = False | |
| 1508 | # |
|
1506 | # | |
| 1509 | # def __findTxPulse(self, dataOut, channel=0, pulse_with = None): |
|
1507 | # def __findTxPulse(self, dataOut, channel=0, pulse_with = None): | |
| 1510 | # |
|
1508 | # | |
| 1511 | # #Read data |
|
1509 | # #Read data | |
| 1512 | # |
|
1510 | # | |
| 1513 | # powerdB = dataOut.getPower(channel = channel) |
|
1511 | # powerdB = dataOut.getPower(channel = channel) | |
| 1514 | # noisedB = dataOut.getNoise(channel = channel)[0] |
|
1512 | # noisedB = dataOut.getNoise(channel = channel)[0] | |
| 1515 | # |
|
1513 | # | |
| 1516 | # self.__powBuffer.extend(powerdB.flatten()) |
|
1514 | # self.__powBuffer.extend(powerdB.flatten()) | |
| 1517 | # |
|
1515 | # | |
| 1518 | # dataArray = numpy.array(self.__powBuffer) |
|
1516 | # dataArray = numpy.array(self.__powBuffer) | |
| 1519 | # |
|
1517 | # | |
| 1520 | # filteredPower = numpy.correlate(dataArray, dataArray[0:self.__nSamples], "same") |
|
1518 | # filteredPower = numpy.correlate(dataArray, dataArray[0:self.__nSamples], "same") | |
| 1521 | # |
|
1519 | # | |
| 1522 | # maxValue = numpy.nanmax(filteredPower) |
|
1520 | # maxValue = numpy.nanmax(filteredPower) | |
| 1523 | # |
|
1521 | # | |
| 1524 | # if maxValue < noisedB + 10: |
|
1522 | # if maxValue < noisedB + 10: | |
| 1525 | # #No se encuentra ningun pulso de transmision |
|
1523 | # #No se encuentra ningun pulso de transmision | |
| 1526 | # return None |
|
1524 | # return None | |
| 1527 | # |
|
1525 | # | |
| 1528 | # maxValuesIndex = numpy.where(filteredPower > maxValue - 0.1*abs(maxValue))[0] |
|
1526 | # maxValuesIndex = numpy.where(filteredPower > maxValue - 0.1*abs(maxValue))[0] | |
| 1529 | # |
|
1527 | # | |
| 1530 | # if len(maxValuesIndex) < 2: |
|
1528 | # if len(maxValuesIndex) < 2: | |
| 1531 | # #Solo se encontro un solo pulso de transmision de un baudio, esperando por el siguiente TX |
|
1529 | # #Solo se encontro un solo pulso de transmision de un baudio, esperando por el siguiente TX | |
| 1532 | # return None |
|
1530 | # return None | |
| 1533 | # |
|
1531 | # | |
| 1534 | # phasedMaxValuesIndex = maxValuesIndex - self.__nSamples |
|
1532 | # phasedMaxValuesIndex = maxValuesIndex - self.__nSamples | |
| 1535 | # |
|
1533 | # | |
| 1536 | # #Seleccionar solo valores con un espaciamiento de nSamples |
|
1534 | # #Seleccionar solo valores con un espaciamiento de nSamples | |
| 1537 | # pulseIndex = numpy.intersect1d(maxValuesIndex, phasedMaxValuesIndex) |
|
1535 | # pulseIndex = numpy.intersect1d(maxValuesIndex, phasedMaxValuesIndex) | |
| 1538 | # |
|
1536 | # | |
| 1539 | # if len(pulseIndex) < 2: |
|
1537 | # if len(pulseIndex) < 2: | |
| 1540 | # #Solo se encontro un pulso de transmision con ancho mayor a 1 |
|
1538 | # #Solo se encontro un pulso de transmision con ancho mayor a 1 | |
| 1541 | # return None |
|
1539 | # return None | |
| 1542 | # |
|
1540 | # | |
| 1543 | # spacing = pulseIndex[1:] - pulseIndex[:-1] |
|
1541 | # spacing = pulseIndex[1:] - pulseIndex[:-1] | |
| 1544 | # |
|
1542 | # | |
| 1545 | # #remover senales que se distancien menos de 10 unidades o muestras |
|
1543 | # #remover senales que se distancien menos de 10 unidades o muestras | |
| 1546 | # #(No deberian existir IPP menor a 10 unidades) |
|
1544 | # #(No deberian existir IPP menor a 10 unidades) | |
| 1547 | # |
|
1545 | # | |
| 1548 | # realIndex = numpy.where(spacing > 10 )[0] |
|
1546 | # realIndex = numpy.where(spacing > 10 )[0] | |
| 1549 | # |
|
1547 | # | |
| 1550 | # if len(realIndex) < 2: |
|
1548 | # if len(realIndex) < 2: | |
| 1551 | # #Solo se encontro un pulso de transmision con ancho mayor a 1 |
|
1549 | # #Solo se encontro un pulso de transmision con ancho mayor a 1 | |
| 1552 | # return None |
|
1550 | # return None | |
| 1553 | # |
|
1551 | # | |
| 1554 | # #Eliminar pulsos anchos (deja solo la diferencia entre IPPs) |
|
1552 | # #Eliminar pulsos anchos (deja solo la diferencia entre IPPs) | |
| 1555 | # realPulseIndex = pulseIndex[realIndex] |
|
1553 | # realPulseIndex = pulseIndex[realIndex] | |
| 1556 | # |
|
1554 | # | |
| 1557 | # period = mode(realPulseIndex[1:] - realPulseIndex[:-1])[0][0] |
|
1555 | # period = mode(realPulseIndex[1:] - realPulseIndex[:-1])[0][0] | |
| 1558 | # |
|
1556 | # | |
| 1559 | # print "IPP = %d samples" %period |
|
1557 | # print "IPP = %d samples" %period | |
| 1560 | # |
|
1558 | # | |
| 1561 | # self.__newNSamples = dataOut.nHeights #int(period) |
|
1559 | # self.__newNSamples = dataOut.nHeights #int(period) | |
| 1562 | # self.__startIndex = int(realPulseIndex[0]) |
|
1560 | # self.__startIndex = int(realPulseIndex[0]) | |
| 1563 | # |
|
1561 | # | |
| 1564 | # return 1 |
|
1562 | # return 1 | |
| 1565 | # |
|
1563 | # | |
| 1566 | # |
|
1564 | # | |
| 1567 | # def setup(self, nSamples, nChannels, buffer_size = 4): |
|
1565 | # def setup(self, nSamples, nChannels, buffer_size = 4): | |
| 1568 | # |
|
1566 | # | |
| 1569 | # self.__powBuffer = collections.deque(numpy.zeros( buffer_size*nSamples,dtype=numpy.float), |
|
1567 | # self.__powBuffer = collections.deque(numpy.zeros( buffer_size*nSamples,dtype=numpy.float), | |
| 1570 | # maxlen = buffer_size*nSamples) |
|
1568 | # maxlen = buffer_size*nSamples) | |
| 1571 | # |
|
1569 | # | |
| 1572 | # bufferList = [] |
|
1570 | # bufferList = [] | |
| 1573 | # |
|
1571 | # | |
| 1574 | # for i in range(nChannels): |
|
1572 | # for i in range(nChannels): | |
| 1575 | # bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=numpy.complex) + numpy.NAN, |
|
1573 | # bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=numpy.complex) + numpy.NAN, | |
| 1576 | # maxlen = buffer_size*nSamples) |
|
1574 | # maxlen = buffer_size*nSamples) | |
| 1577 | # |
|
1575 | # | |
| 1578 | # bufferList.append(bufferByChannel) |
|
1576 | # bufferList.append(bufferByChannel) | |
| 1579 | # |
|
1577 | # | |
| 1580 | # self.__nSamples = nSamples |
|
1578 | # self.__nSamples = nSamples | |
| 1581 | # self.__nChannels = nChannels |
|
1579 | # self.__nChannels = nChannels | |
| 1582 | # self.__bufferList = bufferList |
|
1580 | # self.__bufferList = bufferList | |
| 1583 | # |
|
1581 | # | |
| 1584 | # def run(self, dataOut, channel = 0): |
|
1582 | # def run(self, dataOut, channel = 0): | |
| 1585 | # |
|
1583 | # | |
| 1586 | # if not self.isConfig: |
|
1584 | # if not self.isConfig: | |
| 1587 | # nSamples = dataOut.nHeights |
|
1585 | # nSamples = dataOut.nHeights | |
| 1588 | # nChannels = dataOut.nChannels |
|
1586 | # nChannels = dataOut.nChannels | |
| 1589 | # self.setup(nSamples, nChannels) |
|
1587 | # self.setup(nSamples, nChannels) | |
| 1590 | # self.isConfig = True |
|
1588 | # self.isConfig = True | |
| 1591 | # |
|
1589 | # | |
| 1592 | # #Append new data to internal buffer |
|
1590 | # #Append new data to internal buffer | |
| 1593 | # for thisChannel in range(self.__nChannels): |
|
1591 | # for thisChannel in range(self.__nChannels): | |
| 1594 | # bufferByChannel = self.__bufferList[thisChannel] |
|
1592 | # bufferByChannel = self.__bufferList[thisChannel] | |
| 1595 | # bufferByChannel.extend(dataOut.data[thisChannel]) |
|
1593 | # bufferByChannel.extend(dataOut.data[thisChannel]) | |
| 1596 | # |
|
1594 | # | |
| 1597 | # if self.__pulseFound: |
|
1595 | # if self.__pulseFound: | |
| 1598 | # self.__startIndex -= self.__nSamples |
|
1596 | # self.__startIndex -= self.__nSamples | |
| 1599 | # |
|
1597 | # | |
| 1600 | # #Finding Tx Pulse |
|
1598 | # #Finding Tx Pulse | |
| 1601 | # if not self.__pulseFound: |
|
1599 | # if not self.__pulseFound: | |
| 1602 | # indexFound = self.__findTxPulse(dataOut, channel) |
|
1600 | # indexFound = self.__findTxPulse(dataOut, channel) | |
| 1603 | # |
|
1601 | # | |
| 1604 | # if indexFound == None: |
|
1602 | # if indexFound == None: | |
| 1605 | # dataOut.flagNoData = True |
|
1603 | # dataOut.flagNoData = True | |
| 1606 | # return |
|
1604 | # return | |
| 1607 | # |
|
1605 | # | |
| 1608 | # self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = numpy.complex) |
|
1606 | # self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = numpy.complex) | |
| 1609 | # self.__pulseFound = True |
|
1607 | # self.__pulseFound = True | |
| 1610 | # self.__startIndex = indexFound |
|
1608 | # self.__startIndex = indexFound | |
| 1611 | # |
|
1609 | # | |
| 1612 | # #If pulse was found ... |
|
1610 | # #If pulse was found ... | |
| 1613 | # for thisChannel in range(self.__nChannels): |
|
1611 | # for thisChannel in range(self.__nChannels): | |
| 1614 | # bufferByChannel = self.__bufferList[thisChannel] |
|
1612 | # bufferByChannel = self.__bufferList[thisChannel] | |
| 1615 | # #print self.__startIndex |
|
1613 | # #print self.__startIndex | |
| 1616 | # x = numpy.array(bufferByChannel) |
|
1614 | # x = numpy.array(bufferByChannel) | |
| 1617 | # self.__arrayBuffer[thisChannel] = x[self.__startIndex:self.__startIndex+self.__newNSamples] |
|
1615 | # self.__arrayBuffer[thisChannel] = x[self.__startIndex:self.__startIndex+self.__newNSamples] | |
| 1618 | # |
|
1616 | # | |
| 1619 | # deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
1617 | # deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] | |
| 1620 | # dataOut.heightList = numpy.arange(self.__newNSamples)*deltaHeight |
|
1618 | # dataOut.heightList = numpy.arange(self.__newNSamples)*deltaHeight | |
| 1621 | # # dataOut.ippSeconds = (self.__newNSamples / deltaHeight)/1e6 |
|
1619 | # # dataOut.ippSeconds = (self.__newNSamples / deltaHeight)/1e6 | |
| 1622 | # |
|
1620 | # | |
| 1623 | # dataOut.data = self.__arrayBuffer |
|
1621 | # dataOut.data = self.__arrayBuffer | |
| 1624 | # |
|
1622 | # | |
| 1625 | # self.__startIndex += self.__newNSamples |
|
1623 | # self.__startIndex += self.__newNSamples | |
| 1626 | # |
|
1624 | # | |
| 1627 | # return No newline at end of file |
|
1625 | # return | |
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