| @@ -1,6939 +1,6944 | |||||
| 1 |
|
1 | |||
| 2 | import os |
|
2 | import os | |
| 3 | import sys |
|
3 | import sys | |
| 4 | import numpy, math |
|
4 | import numpy, math | |
| 5 | from scipy import interpolate |
|
5 | from scipy import interpolate | |
| 6 | from scipy.optimize import nnls |
|
6 | from scipy.optimize import nnls | |
| 7 | from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation, MPDecorator |
|
7 | from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation, MPDecorator | |
| 8 | from schainpy.model.data.jrodata import Voltage, hildebrand_sekhon |
|
8 | from schainpy.model.data.jrodata import Voltage, hildebrand_sekhon | |
| 9 | from schainpy.utils import log |
|
9 | from schainpy.utils import log | |
| 10 | from time import time, mktime, strptime, gmtime, ctime |
|
10 | from time import time, mktime, strptime, gmtime, ctime | |
| 11 | from scipy.optimize import least_squares |
|
11 | from scipy.optimize import least_squares | |
| 12 | import datetime |
|
12 | import datetime | |
| 13 | import csv |
|
13 | import csv | |
| 14 |
|
14 | |||
| 15 | try: |
|
15 | try: | |
| 16 | from schainpy.model.proc import fitacf_guess |
|
16 | from schainpy.model.proc import fitacf_guess | |
| 17 | from schainpy.model.proc import fitacf_fit_short |
|
17 | from schainpy.model.proc import fitacf_fit_short | |
| 18 | from schainpy.model.proc import fitacf_acf2 |
|
18 | from schainpy.model.proc import fitacf_acf2 | |
| 19 | from schainpy.model.proc import full_profile_profile |
|
19 | from schainpy.model.proc import full_profile_profile | |
| 20 | except: |
|
20 | except: | |
| 21 | log.warning('Missing Faraday fortran libs') |
|
21 | log.warning('Missing Faraday fortran libs') | |
| 22 |
|
22 | |||
| 23 | class VoltageProc(ProcessingUnit): |
|
23 | class VoltageProc(ProcessingUnit): | |
| 24 |
|
24 | |||
| 25 | def __init__(self): |
|
25 | def __init__(self): | |
| 26 |
|
26 | |||
| 27 | ProcessingUnit.__init__(self) |
|
27 | ProcessingUnit.__init__(self) | |
| 28 |
|
28 | |||
| 29 | self.dataOut = Voltage() |
|
29 | self.dataOut = Voltage() | |
| 30 | self.flip = 1 |
|
30 | self.flip = 1 | |
| 31 | self.setupReq = False |
|
31 | self.setupReq = False | |
| 32 | #self.dataOut.test=1 |
|
32 | #self.dataOut.test=1 | |
| 33 |
|
33 | |||
| 34 |
|
34 | |||
| 35 | def run(self, runNextUnit = 0): |
|
35 | def run(self, runNextUnit = 0): | |
| 36 | #import time |
|
36 | #import time | |
| 37 | #time.sleep(3) |
|
37 | #time.sleep(3) | |
| 38 |
|
38 | |||
| 39 | if self.dataIn.type == 'AMISR': |
|
39 | if self.dataIn.type == 'AMISR': | |
| 40 | self.__updateObjFromAmisrInput() |
|
40 | self.__updateObjFromAmisrInput() | |
| 41 |
|
41 | |||
| 42 | if self.dataIn.type == 'Voltage': |
|
42 | if self.dataIn.type == 'Voltage': | |
| 43 | self.dataOut.copy(self.dataIn) |
|
43 | self.dataOut.copy(self.dataIn) | |
| 44 | self.dataOut.runNextUnit = runNextUnit |
|
44 | self.dataOut.runNextUnit = runNextUnit | |
| 45 |
|
45 | |||
| 46 |
|
46 | |||
| 47 | #self.dataOut.flagNoData=True |
|
47 | #self.dataOut.flagNoData=True | |
| 48 | #print(self.dataOut.data[-1,:]) |
|
48 | #print(self.dataOut.data[-1,:]) | |
| 49 | #print(ctime(self.dataOut.utctime)) |
|
49 | #print(ctime(self.dataOut.utctime)) | |
| 50 | #print(self.dataOut.heightList) |
|
50 | #print(self.dataOut.heightList) | |
| 51 | #print(self.dataOut.nHeights) |
|
51 | #print(self.dataOut.nHeights) | |
| 52 | #exit(1) |
|
52 | #exit(1) | |
| 53 | #print(self.dataOut.data[6,:32]) |
|
53 | #print(self.dataOut.data[6,:32]) | |
| 54 | #print(self.dataOut.data[0,320-5:320+5-5]) |
|
54 | #print(self.dataOut.data[0,320-5:320+5-5]) | |
| 55 | ##print(self.dataOut.heightList[-20:]) |
|
55 | ##print(self.dataOut.heightList[-20:]) | |
| 56 | #print(numpy.shape(self.dataOut.data)) |
|
56 | #print(numpy.shape(self.dataOut.data)) | |
| 57 | #print(self.dataOut.code) |
|
57 | #print(self.dataOut.code) | |
| 58 | #print(numpy.shape(self.dataOut.code)) |
|
58 | #print(numpy.shape(self.dataOut.code)) | |
| 59 | #exit(1) |
|
59 | #exit(1) | |
| 60 | #print(self.dataOut.CurrentBlock) |
|
60 | #print(self.dataOut.CurrentBlock) | |
| 61 | #print(self.dataOut.data[0,:,0]) |
|
61 | #print(self.dataOut.data[0,:,0]) | |
| 62 |
|
62 | |||
| 63 | #print(numpy.shape(self.dataOut.data)) |
|
63 | #print(numpy.shape(self.dataOut.data)) | |
| 64 | #print(self.dataOut.data[0,:,1666:1666+320]) |
|
64 | #print(self.dataOut.data[0,:,1666:1666+320]) | |
| 65 | #exit(1) |
|
65 | #exit(1) | |
| 66 |
|
66 | |||
| 67 | #print(self.dataOut.utctime) |
|
67 | #print(self.dataOut.utctime) | |
| 68 | #self.dataOut.test+=1 |
|
68 | #self.dataOut.test+=1 | |
| 69 |
|
69 | |||
| 70 |
|
70 | |||
| 71 | def __updateObjFromAmisrInput(self): |
|
71 | def __updateObjFromAmisrInput(self): | |
| 72 |
|
72 | |||
| 73 | self.dataOut.timeZone = self.dataIn.timeZone |
|
73 | self.dataOut.timeZone = self.dataIn.timeZone | |
| 74 | self.dataOut.dstFlag = self.dataIn.dstFlag |
|
74 | self.dataOut.dstFlag = self.dataIn.dstFlag | |
| 75 | self.dataOut.errorCount = self.dataIn.errorCount |
|
75 | self.dataOut.errorCount = self.dataIn.errorCount | |
| 76 | self.dataOut.useLocalTime = self.dataIn.useLocalTime |
|
76 | self.dataOut.useLocalTime = self.dataIn.useLocalTime | |
| 77 |
|
77 | |||
| 78 | self.dataOut.flagNoData = self.dataIn.flagNoData |
|
78 | self.dataOut.flagNoData = self.dataIn.flagNoData | |
| 79 | self.dataOut.data = self.dataIn.data |
|
79 | self.dataOut.data = self.dataIn.data | |
| 80 | self.dataOut.utctime = self.dataIn.utctime |
|
80 | self.dataOut.utctime = self.dataIn.utctime | |
| 81 | self.dataOut.channelList = self.dataIn.channelList |
|
81 | self.dataOut.channelList = self.dataIn.channelList | |
| 82 | # self.dataOut.timeInterval = self.dataIn.timeInterval |
|
82 | # self.dataOut.timeInterval = self.dataIn.timeInterval | |
| 83 | self.dataOut.heightList = self.dataIn.heightList |
|
83 | self.dataOut.heightList = self.dataIn.heightList | |
| 84 | self.dataOut.nProfiles = self.dataIn.nProfiles |
|
84 | self.dataOut.nProfiles = self.dataIn.nProfiles | |
| 85 |
|
85 | |||
| 86 | self.dataOut.nCohInt = self.dataIn.nCohInt |
|
86 | self.dataOut.nCohInt = self.dataIn.nCohInt | |
| 87 | self.dataOut.ippSeconds = self.dataIn.ippSeconds |
|
87 | self.dataOut.ippSeconds = self.dataIn.ippSeconds | |
| 88 | self.dataOut.frequency = self.dataIn.frequency |
|
88 | self.dataOut.frequency = self.dataIn.frequency | |
| 89 |
|
89 | |||
| 90 | self.dataOut.azimuth = self.dataIn.azimuth |
|
90 | self.dataOut.azimuth = self.dataIn.azimuth | |
| 91 | self.dataOut.zenith = self.dataIn.zenith |
|
91 | self.dataOut.zenith = self.dataIn.zenith | |
| 92 |
|
92 | |||
| 93 | self.dataOut.beam.codeList = self.dataIn.beam.codeList |
|
93 | self.dataOut.beam.codeList = self.dataIn.beam.codeList | |
| 94 | self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList |
|
94 | self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList | |
| 95 | self.dataOut.beam.zenithList = self.dataIn.beam.zenithList |
|
95 | self.dataOut.beam.zenithList = self.dataIn.beam.zenithList | |
| 96 |
|
96 | |||
| 97 | class selectChannels(Operation): |
|
97 | class selectChannels(Operation): | |
| 98 |
|
98 | |||
| 99 | def run(self, dataOut, channelList): |
|
99 | def run(self, dataOut, channelList): | |
| 100 |
|
100 | |||
| 101 |
|
101 | |||
| 102 |
|
102 | |||
| 103 |
|
103 | |||
| 104 | channelIndexList = [] |
|
104 | channelIndexList = [] | |
| 105 | self.dataOut = dataOut |
|
105 | self.dataOut = dataOut | |
| 106 | for channel in channelList: |
|
106 | for channel in channelList: | |
| 107 | if channel not in self.dataOut.channelList: |
|
107 | if channel not in self.dataOut.channelList: | |
| 108 | raise ValueError("Channel %d is not in %s" % (channel, str(self.dataOut.channelList))) |
|
108 | raise ValueError("Channel %d is not in %s" % (channel, str(self.dataOut.channelList))) | |
| 109 |
|
109 | |||
| 110 | index = self.dataOut.channelList.index(channel) |
|
110 | index = self.dataOut.channelList.index(channel) | |
| 111 | channelIndexList.append(index) |
|
111 | channelIndexList.append(index) | |
| 112 | self.selectChannelsByIndex(channelIndexList) |
|
112 | self.selectChannelsByIndex(channelIndexList) | |
| 113 |
|
113 | |||
| 114 | return self.dataOut |
|
114 | return self.dataOut | |
| 115 |
|
115 | |||
| 116 |
|
116 | |||
| 117 | def selectChannelsByIndex(self, channelIndexList): |
|
117 | def selectChannelsByIndex(self, channelIndexList): | |
| 118 | """ |
|
118 | """ | |
| 119 | Selecciona un bloque de datos en base a canales segun el channelIndexList |
|
119 | Selecciona un bloque de datos en base a canales segun el channelIndexList | |
| 120 |
|
120 | |||
| 121 | Input: |
|
121 | Input: | |
| 122 | channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7] |
|
122 | channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7] | |
| 123 |
|
123 | |||
| 124 | Affected: |
|
124 | Affected: | |
| 125 | self.dataOut.data |
|
125 | self.dataOut.data | |
| 126 | self.dataOut.channelIndexList |
|
126 | self.dataOut.channelIndexList | |
| 127 | self.dataOut.nChannels |
|
127 | self.dataOut.nChannels | |
| 128 | self.dataOut.m_ProcessingHeader.totalSpectra |
|
128 | self.dataOut.m_ProcessingHeader.totalSpectra | |
| 129 | self.dataOut.systemHeaderObj.numChannels |
|
129 | self.dataOut.systemHeaderObj.numChannels | |
| 130 | self.dataOut.m_ProcessingHeader.blockSize |
|
130 | self.dataOut.m_ProcessingHeader.blockSize | |
| 131 |
|
131 | |||
| 132 | Return: |
|
132 | Return: | |
| 133 | None |
|
133 | None | |
| 134 | """ |
|
134 | """ | |
| 135 |
|
135 | |||
| 136 | for channelIndex in channelIndexList: |
|
136 | for channelIndex in channelIndexList: | |
| 137 | if channelIndex not in self.dataOut.channelIndexList: |
|
137 | if channelIndex not in self.dataOut.channelIndexList: | |
| 138 | raise ValueError("The value %d in channelIndexList is not valid" % channelIndex) |
|
138 | raise ValueError("The value %d in channelIndexList is not valid" % channelIndex) | |
| 139 |
|
139 | |||
| 140 | if self.dataOut.type == 'Voltage': |
|
140 | if self.dataOut.type == 'Voltage': | |
| 141 | if self.dataOut.flagDataAsBlock: |
|
141 | if self.dataOut.flagDataAsBlock: | |
| 142 | """ |
|
142 | """ | |
| 143 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
143 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] | |
| 144 | """ |
|
144 | """ | |
| 145 | data = self.dataOut.data[channelIndexList, :, :] |
|
145 | data = self.dataOut.data[channelIndexList, :, :] | |
| 146 | else: |
|
146 | else: | |
| 147 | data = self.dataOut.data[channelIndexList, :] |
|
147 | data = self.dataOut.data[channelIndexList, :] | |
| 148 |
|
148 | |||
| 149 | self.dataOut.data = data |
|
149 | self.dataOut.data = data | |
| 150 | # self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList] |
|
150 | # self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList] | |
| 151 | self.dataOut.channelList = range(len(channelIndexList)) |
|
151 | self.dataOut.channelList = range(len(channelIndexList)) | |
| 152 |
|
152 | |||
| 153 | elif self.dataOut.type == 'Spectra': |
|
153 | elif self.dataOut.type == 'Spectra': | |
| 154 | data_spc = self.dataOut.data_spc[channelIndexList, :] |
|
154 | data_spc = self.dataOut.data_spc[channelIndexList, :] | |
| 155 | data_dc = self.dataOut.data_dc[channelIndexList, :] |
|
155 | data_dc = self.dataOut.data_dc[channelIndexList, :] | |
| 156 |
|
156 | |||
| 157 | self.dataOut.data_spc = data_spc |
|
157 | self.dataOut.data_spc = data_spc | |
| 158 | self.dataOut.data_dc = data_dc |
|
158 | self.dataOut.data_dc = data_dc | |
| 159 |
|
159 | |||
| 160 | # self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList] |
|
160 | # self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList] | |
| 161 | self.dataOut.channelList = range(len(channelIndexList)) |
|
161 | self.dataOut.channelList = range(len(channelIndexList)) | |
| 162 | self.__selectPairsByChannel(channelIndexList) |
|
162 | self.__selectPairsByChannel(channelIndexList) | |
| 163 |
|
163 | |||
| 164 | return 1 |
|
164 | return 1 | |
| 165 |
|
165 | |||
| 166 | def __selectPairsByChannel(self, channelList=None): |
|
166 | def __selectPairsByChannel(self, channelList=None): | |
| 167 |
|
167 | |||
| 168 | if channelList == None: |
|
168 | if channelList == None: | |
| 169 | return |
|
169 | return | |
| 170 |
|
170 | |||
| 171 | pairsIndexListSelected = [] |
|
171 | pairsIndexListSelected = [] | |
| 172 | for pairIndex in self.dataOut.pairsIndexList: |
|
172 | for pairIndex in self.dataOut.pairsIndexList: | |
| 173 | # First pair |
|
173 | # First pair | |
| 174 | if self.dataOut.pairsList[pairIndex][0] not in channelList: |
|
174 | if self.dataOut.pairsList[pairIndex][0] not in channelList: | |
| 175 | continue |
|
175 | continue | |
| 176 | # Second pair |
|
176 | # Second pair | |
| 177 | if self.dataOut.pairsList[pairIndex][1] not in channelList: |
|
177 | if self.dataOut.pairsList[pairIndex][1] not in channelList: | |
| 178 | continue |
|
178 | continue | |
| 179 |
|
179 | |||
| 180 | pairsIndexListSelected.append(pairIndex) |
|
180 | pairsIndexListSelected.append(pairIndex) | |
| 181 |
|
181 | |||
| 182 | if not pairsIndexListSelected: |
|
182 | if not pairsIndexListSelected: | |
| 183 | self.dataOut.data_cspc = None |
|
183 | self.dataOut.data_cspc = None | |
| 184 | self.dataOut.pairsList = [] |
|
184 | self.dataOut.pairsList = [] | |
| 185 | return |
|
185 | return | |
| 186 |
|
186 | |||
| 187 | self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected] |
|
187 | self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected] | |
| 188 | self.dataOut.pairsList = [self.dataOut.pairsList[i] |
|
188 | self.dataOut.pairsList = [self.dataOut.pairsList[i] | |
| 189 | for i in pairsIndexListSelected] |
|
189 | for i in pairsIndexListSelected] | |
| 190 |
|
190 | |||
| 191 | return |
|
191 | return | |
| 192 |
|
192 | |||
| 193 | class selectHeights(Operation): |
|
193 | class selectHeights(Operation): | |
| 194 |
|
194 | |||
| 195 | def run(self, dataOut, minHei=None, maxHei=None, minIndex=None, maxIndex=None): |
|
195 | def run(self, dataOut, minHei=None, maxHei=None, minIndex=None, maxIndex=None): | |
| 196 | """ |
|
196 | """ | |
| 197 | Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango |
|
197 | Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango | |
| 198 | minHei <= height <= maxHei |
|
198 | minHei <= height <= maxHei | |
| 199 |
|
199 | |||
| 200 | Input: |
|
200 | Input: | |
| 201 | minHei : valor minimo de altura a considerar |
|
201 | minHei : valor minimo de altura a considerar | |
| 202 | maxHei : valor maximo de altura a considerar |
|
202 | maxHei : valor maximo de altura a considerar | |
| 203 |
|
203 | |||
| 204 | Affected: |
|
204 | Affected: | |
| 205 | Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex |
|
205 | Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex | |
| 206 |
|
206 | |||
| 207 | Return: |
|
207 | Return: | |
| 208 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 |
|
208 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 | |
| 209 | """ |
|
209 | """ | |
| 210 |
|
210 | |||
| 211 | self.dataOut = dataOut |
|
211 | self.dataOut = dataOut | |
| 212 |
|
212 | |||
| 213 | #if minHei and maxHei: |
|
213 | #if minHei and maxHei: | |
| 214 | if 1: |
|
214 | if 1: | |
| 215 | if minHei == None: |
|
215 | if minHei == None: | |
| 216 | minHei = self.dataOut.heightList[0] |
|
216 | minHei = self.dataOut.heightList[0] | |
| 217 |
|
217 | |||
| 218 | if maxHei == None: |
|
218 | if maxHei == None: | |
| 219 | maxHei = self.dataOut.heightList[-1] |
|
219 | maxHei = self.dataOut.heightList[-1] | |
| 220 |
|
220 | |||
| 221 | if (minHei < self.dataOut.heightList[0]): |
|
221 | if (minHei < self.dataOut.heightList[0]): | |
| 222 | minHei = self.dataOut.heightList[0] |
|
222 | minHei = self.dataOut.heightList[0] | |
| 223 |
|
223 | |||
| 224 | if (maxHei > self.dataOut.heightList[-1]): |
|
224 | if (maxHei > self.dataOut.heightList[-1]): | |
| 225 | maxHei = self.dataOut.heightList[-1] |
|
225 | maxHei = self.dataOut.heightList[-1] | |
| 226 |
|
226 | |||
| 227 | minIndex = 0 |
|
227 | minIndex = 0 | |
| 228 | maxIndex = 0 |
|
228 | maxIndex = 0 | |
| 229 | heights = self.dataOut.heightList |
|
229 | heights = self.dataOut.heightList | |
| 230 |
|
230 | |||
| 231 | inda = numpy.where(heights >= minHei) |
|
231 | inda = numpy.where(heights >= minHei) | |
| 232 | indb = numpy.where(heights <= maxHei) |
|
232 | indb = numpy.where(heights <= maxHei) | |
| 233 |
|
233 | |||
| 234 | try: |
|
234 | try: | |
| 235 | minIndex = inda[0][0] |
|
235 | minIndex = inda[0][0] | |
| 236 | except: |
|
236 | except: | |
| 237 | minIndex = 0 |
|
237 | minIndex = 0 | |
| 238 |
|
238 | |||
| 239 | try: |
|
239 | try: | |
| 240 | maxIndex = indb[0][-1] |
|
240 | maxIndex = indb[0][-1] | |
| 241 | except: |
|
241 | except: | |
| 242 | maxIndex = len(heights) |
|
242 | maxIndex = len(heights) | |
| 243 |
|
243 | |||
| 244 | self.selectHeightsByIndex(minIndex, maxIndex) |
|
244 | self.selectHeightsByIndex(minIndex, maxIndex) | |
| 245 | #print(self.dataOut.nHeights) |
|
245 | #print(self.dataOut.nHeights) | |
| 246 |
|
246 | |||
| 247 |
|
247 | |||
| 248 | return self.dataOut |
|
248 | return self.dataOut | |
| 249 |
|
249 | |||
| 250 | def selectHeightsByIndex(self, minIndex, maxIndex): |
|
250 | def selectHeightsByIndex(self, minIndex, maxIndex): | |
| 251 | """ |
|
251 | """ | |
| 252 | Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango |
|
252 | Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango | |
| 253 | minIndex <= index <= maxIndex |
|
253 | minIndex <= index <= maxIndex | |
| 254 |
|
254 | |||
| 255 | Input: |
|
255 | Input: | |
| 256 | minIndex : valor de indice minimo de altura a considerar |
|
256 | minIndex : valor de indice minimo de altura a considerar | |
| 257 | maxIndex : valor de indice maximo de altura a considerar |
|
257 | maxIndex : valor de indice maximo de altura a considerar | |
| 258 |
|
258 | |||
| 259 | Affected: |
|
259 | Affected: | |
| 260 | self.dataOut.data |
|
260 | self.dataOut.data | |
| 261 | self.dataOut.heightList |
|
261 | self.dataOut.heightList | |
| 262 |
|
262 | |||
| 263 | Return: |
|
263 | Return: | |
| 264 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 |
|
264 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 | |
| 265 | """ |
|
265 | """ | |
| 266 |
|
266 | |||
| 267 | if self.dataOut.type == 'Voltage': |
|
267 | if self.dataOut.type == 'Voltage': | |
| 268 | if (minIndex < 0) or (minIndex > maxIndex): |
|
268 | if (minIndex < 0) or (minIndex > maxIndex): | |
| 269 | raise ValueError("Height index range (%d,%d) is not valid" % (minIndex, maxIndex)) |
|
269 | raise ValueError("Height index range (%d,%d) is not valid" % (minIndex, maxIndex)) | |
| 270 |
|
270 | |||
| 271 | if (maxIndex >= self.dataOut.nHeights): |
|
271 | if (maxIndex >= self.dataOut.nHeights): | |
| 272 | maxIndex = self.dataOut.nHeights |
|
272 | maxIndex = self.dataOut.nHeights | |
| 273 |
|
273 | |||
| 274 | # voltage |
|
274 | # voltage | |
| 275 | if self.dataOut.flagDataAsBlock: |
|
275 | if self.dataOut.flagDataAsBlock: | |
| 276 | """ |
|
276 | """ | |
| 277 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
277 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] | |
| 278 | """ |
|
278 | """ | |
| 279 | data = self.dataOut.data[:, :, minIndex:maxIndex] |
|
279 | data = self.dataOut.data[:, :, minIndex:maxIndex] | |
| 280 | else: |
|
280 | else: | |
| 281 | data = self.dataOut.data[:, minIndex:maxIndex] |
|
281 | data = self.dataOut.data[:, minIndex:maxIndex] | |
| 282 |
|
282 | |||
| 283 | # firstHeight = self.dataOut.heightList[minIndex] |
|
283 | # firstHeight = self.dataOut.heightList[minIndex] | |
| 284 |
|
284 | |||
| 285 | self.dataOut.data = data |
|
285 | self.dataOut.data = data | |
| 286 | self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex] |
|
286 | self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex] | |
| 287 |
|
287 | |||
| 288 | if self.dataOut.nHeights <= 1: |
|
288 | if self.dataOut.nHeights <= 1: | |
| 289 | raise ValueError("selectHeights: Too few heights. Current number of heights is %d" % (self.dataOut.nHeights)) |
|
289 | raise ValueError("selectHeights: Too few heights. Current number of heights is %d" % (self.dataOut.nHeights)) | |
| 290 | elif self.dataOut.type == 'Spectra': |
|
290 | elif self.dataOut.type == 'Spectra': | |
| 291 | if (minIndex < 0) or (minIndex > maxIndex): |
|
291 | if (minIndex < 0) or (minIndex > maxIndex): | |
| 292 | raise ValueError("Error selecting heights: Index range (%d,%d) is not valid" % ( |
|
292 | raise ValueError("Error selecting heights: Index range (%d,%d) is not valid" % ( | |
| 293 | minIndex, maxIndex)) |
|
293 | minIndex, maxIndex)) | |
| 294 |
|
294 | |||
| 295 | if (maxIndex >= self.dataOut.nHeights): |
|
295 | if (maxIndex >= self.dataOut.nHeights): | |
| 296 | maxIndex = self.dataOut.nHeights - 1 |
|
296 | maxIndex = self.dataOut.nHeights - 1 | |
| 297 |
|
297 | |||
| 298 | # Spectra |
|
298 | # Spectra | |
| 299 | data_spc = self.dataOut.data_spc[:, :, minIndex:maxIndex + 1] |
|
299 | data_spc = self.dataOut.data_spc[:, :, minIndex:maxIndex + 1] | |
| 300 |
|
300 | |||
| 301 | data_cspc = None |
|
301 | data_cspc = None | |
| 302 | if self.dataOut.data_cspc is not None: |
|
302 | if self.dataOut.data_cspc is not None: | |
| 303 | data_cspc = self.dataOut.data_cspc[:, :, minIndex:maxIndex + 1] |
|
303 | data_cspc = self.dataOut.data_cspc[:, :, minIndex:maxIndex + 1] | |
| 304 |
|
304 | |||
| 305 | data_dc = None |
|
305 | data_dc = None | |
| 306 | if self.dataOut.data_dc is not None: |
|
306 | if self.dataOut.data_dc is not None: | |
| 307 | data_dc = self.dataOut.data_dc[:, minIndex:maxIndex + 1] |
|
307 | data_dc = self.dataOut.data_dc[:, minIndex:maxIndex + 1] | |
| 308 |
|
308 | |||
| 309 | self.dataOut.data_spc = data_spc |
|
309 | self.dataOut.data_spc = data_spc | |
| 310 | self.dataOut.data_cspc = data_cspc |
|
310 | self.dataOut.data_cspc = data_cspc | |
| 311 | self.dataOut.data_dc = data_dc |
|
311 | self.dataOut.data_dc = data_dc | |
| 312 |
|
312 | |||
| 313 | self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex + 1] |
|
313 | self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex + 1] | |
| 314 |
|
314 | |||
| 315 | return 1 |
|
315 | return 1 | |
| 316 |
|
316 | |||
| 317 |
|
317 | |||
| 318 | class filterByHeights(Operation): |
|
318 | class filterByHeights(Operation): | |
| 319 |
|
319 | |||
| 320 | def run(self, dataOut, window): |
|
320 | def run(self, dataOut, window): | |
| 321 |
|
321 | |||
| 322 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
322 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] | |
| 323 |
|
323 | |||
| 324 | if window == None: |
|
324 | if window == None: | |
| 325 | window = (dataOut.radarControllerHeaderObj.txA / dataOut.radarControllerHeaderObj.nBaud) / deltaHeight |
|
325 | window = (dataOut.radarControllerHeaderObj.txA / dataOut.radarControllerHeaderObj.nBaud) / deltaHeight | |
| 326 |
|
326 | |||
| 327 | newdelta = deltaHeight * window |
|
327 | newdelta = deltaHeight * window | |
| 328 | r = dataOut.nHeights % window |
|
328 | r = dataOut.nHeights % window | |
| 329 | newheights = (dataOut.nHeights - r) / window |
|
329 | newheights = (dataOut.nHeights - r) / window | |
| 330 |
|
330 | |||
| 331 | if newheights <= 1: |
|
331 | if newheights <= 1: | |
| 332 | raise ValueError("filterByHeights: Too few heights. Current number of heights is %d and window is %d" % (dataOut.nHeights, window)) |
|
332 | raise ValueError("filterByHeights: Too few heights. Current number of heights is %d and window is %d" % (dataOut.nHeights, window)) | |
| 333 |
|
333 | |||
| 334 | if dataOut.flagDataAsBlock: |
|
334 | if dataOut.flagDataAsBlock: | |
| 335 | """ |
|
335 | """ | |
| 336 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
336 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] | |
| 337 | """ |
|
337 | """ | |
| 338 | buffer = dataOut.data[:, :, 0:int(dataOut.nHeights - r)] |
|
338 | buffer = dataOut.data[:, :, 0:int(dataOut.nHeights - r)] | |
| 339 | buffer = buffer.reshape(dataOut.nChannels, dataOut.nProfiles, int(dataOut.nHeights / window), window) |
|
339 | buffer = buffer.reshape(dataOut.nChannels, dataOut.nProfiles, int(dataOut.nHeights / window), window) | |
| 340 | buffer = numpy.sum(buffer, 3) |
|
340 | buffer = numpy.sum(buffer, 3) | |
| 341 |
|
341 | |||
| 342 | else: |
|
342 | else: | |
| 343 | buffer = dataOut.data[:, 0:int(dataOut.nHeights - r)] |
|
343 | buffer = dataOut.data[:, 0:int(dataOut.nHeights - r)] | |
| 344 | buffer = buffer.reshape(dataOut.nChannels, int(dataOut.nHeights / window), int(window)) |
|
344 | buffer = buffer.reshape(dataOut.nChannels, int(dataOut.nHeights / window), int(window)) | |
| 345 | buffer = numpy.sum(buffer, 2) |
|
345 | buffer = numpy.sum(buffer, 2) | |
| 346 |
|
346 | |||
| 347 | dataOut.data = buffer |
|
347 | dataOut.data = buffer | |
| 348 | dataOut.heightList = dataOut.heightList[0] + numpy.arange(newheights) * newdelta |
|
348 | dataOut.heightList = dataOut.heightList[0] + numpy.arange(newheights) * newdelta | |
| 349 | dataOut.windowOfFilter = window |
|
349 | dataOut.windowOfFilter = window | |
| 350 |
|
350 | |||
| 351 | return dataOut |
|
351 | return dataOut | |
| 352 |
|
352 | |||
| 353 | class setOffset(Operation): |
|
353 | class setOffset(Operation): | |
| 354 |
|
354 | |||
| 355 | def run(self, dataOut, offset=None): |
|
355 | def run(self, dataOut, offset=None): | |
| 356 |
|
356 | |||
| 357 | if not offset: |
|
357 | if not offset: | |
| 358 | offset = 0.0 |
|
358 | offset = 0.0 | |
| 359 |
|
359 | |||
| 360 | newHeiRange = dataOut.heightList - offset |
|
360 | newHeiRange = dataOut.heightList - offset | |
| 361 |
|
361 | |||
| 362 | dataOut.heightList = newHeiRange |
|
362 | dataOut.heightList = newHeiRange | |
| 363 |
|
363 | |||
| 364 | return dataOut |
|
364 | return dataOut | |
| 365 |
|
365 | |||
| 366 | class setH0(Operation): |
|
366 | class setH0(Operation): | |
| 367 |
|
367 | |||
| 368 | def run(self, dataOut, h0, deltaHeight=None): |
|
368 | def run(self, dataOut, h0, deltaHeight=None): | |
| 369 |
|
369 | |||
| 370 | if not deltaHeight: |
|
370 | if not deltaHeight: | |
| 371 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
371 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] | |
| 372 |
|
372 | |||
| 373 | nHeights = dataOut.nHeights |
|
373 | nHeights = dataOut.nHeights | |
| 374 |
|
374 | |||
| 375 | newHeiRange = h0 + numpy.arange(nHeights) * deltaHeight |
|
375 | newHeiRange = h0 + numpy.arange(nHeights) * deltaHeight | |
| 376 |
|
376 | |||
| 377 | dataOut.heightList = newHeiRange |
|
377 | dataOut.heightList = newHeiRange | |
| 378 |
|
378 | |||
| 379 | return dataOut |
|
379 | return dataOut | |
| 380 |
|
380 | |||
| 381 |
|
381 | |||
| 382 | class deFlip(Operation): |
|
382 | class deFlip(Operation): | |
| 383 | def __init__(self): |
|
383 | def __init__(self): | |
| 384 |
|
384 | |||
| 385 | self.flip = 1 |
|
385 | self.flip = 1 | |
| 386 |
|
386 | |||
| 387 | def run(self, dataOut, channelList=[]): |
|
387 | def run(self, dataOut, channelList=[]): | |
| 388 |
|
388 | |||
| 389 | data = dataOut.data.copy() |
|
389 | data = dataOut.data.copy() | |
| 390 | #print(dataOut.channelList) |
|
390 | #print(dataOut.channelList) | |
| 391 | #exit() |
|
391 | #exit() | |
| 392 |
|
392 | |||
| 393 | if channelList==1: #PARCHE |
|
393 | if channelList==1: #PARCHE | |
| 394 | channelList=[1] |
|
394 | channelList=[1] | |
| 395 |
|
395 | |||
| 396 |
|
396 | |||
| 397 | dataOut.FlipChannels=channelList |
|
397 | dataOut.FlipChannels=channelList | |
| 398 | if dataOut.flagDataAsBlock: |
|
398 | if dataOut.flagDataAsBlock: | |
| 399 | flip = self.flip |
|
399 | flip = self.flip | |
| 400 | profileList = list(range(dataOut.nProfiles)) |
|
400 | profileList = list(range(dataOut.nProfiles)) | |
| 401 |
|
401 | |||
| 402 | if not channelList: |
|
402 | if not channelList: | |
| 403 | for thisProfile in profileList: |
|
403 | for thisProfile in profileList: | |
| 404 | data[:, thisProfile, :] = data[:, thisProfile, :] * flip |
|
404 | data[:, thisProfile, :] = data[:, thisProfile, :] * flip | |
| 405 | flip *= -1.0 |
|
405 | flip *= -1.0 | |
| 406 | else: |
|
406 | else: | |
| 407 | for thisChannel in channelList: |
|
407 | for thisChannel in channelList: | |
| 408 | if thisChannel not in dataOut.channelList: |
|
408 | if thisChannel not in dataOut.channelList: | |
| 409 | continue |
|
409 | continue | |
| 410 |
|
410 | |||
| 411 | for thisProfile in profileList: |
|
411 | for thisProfile in profileList: | |
| 412 | data[thisChannel, thisProfile, :] = data[thisChannel, thisProfile, :] * flip |
|
412 | data[thisChannel, thisProfile, :] = data[thisChannel, thisProfile, :] * flip | |
| 413 | flip *= -1.0 |
|
413 | flip *= -1.0 | |
| 414 |
|
414 | |||
| 415 | self.flip = flip |
|
415 | self.flip = flip | |
| 416 |
|
416 | |||
| 417 | else: |
|
417 | else: | |
| 418 | if not channelList: |
|
418 | if not channelList: | |
| 419 | data[:, :] = data[:, :] * self.flip |
|
419 | data[:, :] = data[:, :] * self.flip | |
| 420 | else: |
|
420 | else: | |
| 421 | #channelList=[1] |
|
421 | #channelList=[1] | |
| 422 | #print(self.flip) |
|
422 | #print(self.flip) | |
| 423 | for thisChannel in channelList: |
|
423 | for thisChannel in channelList: | |
| 424 | if thisChannel not in dataOut.channelList: |
|
424 | if thisChannel not in dataOut.channelList: | |
| 425 | continue |
|
425 | continue | |
| 426 |
|
426 | |||
| 427 | data[thisChannel, :] = data[thisChannel, :] * self.flip |
|
427 | data[thisChannel, :] = data[thisChannel, :] * self.flip | |
| 428 |
|
428 | |||
| 429 | self.flip *= -1. |
|
429 | self.flip *= -1. | |
| 430 |
|
430 | |||
| 431 | dataOut.data = data |
|
431 | dataOut.data = data | |
| 432 |
|
432 | |||
| 433 | return dataOut |
|
433 | return dataOut | |
| 434 |
|
434 | |||
| 435 | class deFlipHP(Operation): |
|
435 | class deFlipHP(Operation): | |
| 436 | ''' |
|
436 | ''' | |
| 437 | Written by R. Flores |
|
437 | Written by R. Flores | |
| 438 | ''' |
|
438 | ''' | |
| 439 | def __init__(self): |
|
439 | def __init__(self): | |
| 440 |
|
440 | |||
| 441 | self.flip = 1 |
|
441 | self.flip = 1 | |
| 442 |
|
442 | |||
| 443 | def run(self, dataOut, byHeights = False, channelList = [], HeiRangeList = None): |
|
443 | def run(self, dataOut, byHeights = False, channelList = [], HeiRangeList = None): | |
| 444 |
|
444 | |||
| 445 | data = dataOut.data.copy() |
|
445 | data = dataOut.data.copy() | |
| 446 |
|
446 | |||
| 447 | firstHeight = HeiRangeList[0] |
|
447 | firstHeight = HeiRangeList[0] | |
| 448 | lastHeight = HeiRangeList[1]+1 |
|
448 | lastHeight = HeiRangeList[1]+1 | |
| 449 |
|
449 | |||
| 450 | #if channelList==1: #PARCHE #Lista de un solo canal produce error |
|
450 | #if channelList==1: #PARCHE #Lista de un solo canal produce error | |
| 451 | #channelList=[1] |
|
451 | #channelList=[1] | |
| 452 |
|
452 | |||
| 453 | dataOut.FlipChannels=channelList |
|
453 | dataOut.FlipChannels=channelList | |
| 454 | if dataOut.flagDataAsBlock: |
|
454 | if dataOut.flagDataAsBlock: | |
| 455 | flip = self.flip |
|
455 | flip = self.flip | |
| 456 | profileList = list(range(dataOut.nProfiles)) |
|
456 | profileList = list(range(dataOut.nProfiles)) | |
| 457 |
|
457 | |||
| 458 | if not channelList: |
|
458 | if not channelList: | |
| 459 | for thisProfile in profileList: |
|
459 | for thisProfile in profileList: | |
| 460 | data[:,thisProfile,:] = data[:,thisProfile,:]*flip |
|
460 | data[:,thisProfile,:] = data[:,thisProfile,:]*flip | |
| 461 | flip *= -1.0 |
|
461 | flip *= -1.0 | |
| 462 | else: |
|
462 | else: | |
| 463 | for thisChannel in channelList: |
|
463 | for thisChannel in channelList: | |
| 464 | if thisChannel not in dataOut.channelList: |
|
464 | if thisChannel not in dataOut.channelList: | |
| 465 | continue |
|
465 | continue | |
| 466 | if not byHeights: |
|
466 | if not byHeights: | |
| 467 | for thisProfile in profileList: |
|
467 | for thisProfile in profileList: | |
| 468 | data[thisChannel,thisProfile,:] = data[thisChannel,thisProfile,:]*flip |
|
468 | data[thisChannel,thisProfile,:] = data[thisChannel,thisProfile,:]*flip | |
| 469 | flip *= -1.0 |
|
469 | flip *= -1.0 | |
| 470 |
|
470 | |||
| 471 | else: |
|
471 | else: | |
| 472 | firstHeight = HeiRangeList[0] |
|
472 | firstHeight = HeiRangeList[0] | |
| 473 | lastHeight = HeiRangeList[1]+1 |
|
473 | lastHeight = HeiRangeList[1]+1 | |
| 474 | flip = -1.0 |
|
474 | flip = -1.0 | |
| 475 | data[thisChannel,:,firstHeight:lastHeight] = data[thisChannel,:,firstHeight:lastHeight]*flip |
|
475 | data[thisChannel,:,firstHeight:lastHeight] = data[thisChannel,:,firstHeight:lastHeight]*flip | |
| 476 |
|
476 | |||
| 477 |
|
477 | |||
| 478 | self.flip = flip |
|
478 | self.flip = flip | |
| 479 |
|
479 | |||
| 480 | else: |
|
480 | else: | |
| 481 | if not channelList: |
|
481 | if not channelList: | |
| 482 | data[:,:] = data[:,:]*self.flip |
|
482 | data[:,:] = data[:,:]*self.flip | |
| 483 | else: |
|
483 | else: | |
| 484 | #channelList=[1] |
|
484 | #channelList=[1] | |
| 485 |
|
485 | |||
| 486 | for thisChannel in channelList: |
|
486 | for thisChannel in channelList: | |
| 487 | if thisChannel not in dataOut.channelList: |
|
487 | if thisChannel not in dataOut.channelList: | |
| 488 | continue |
|
488 | continue | |
| 489 |
|
489 | |||
| 490 | if not byHeights: |
|
490 | if not byHeights: | |
| 491 | data[thisChannel,:] = data[thisChannel,:]*flip |
|
491 | data[thisChannel,:] = data[thisChannel,:]*flip | |
| 492 |
|
492 | |||
| 493 | else: |
|
493 | else: | |
| 494 | firstHeight = HeiRangeList[0] |
|
494 | firstHeight = HeiRangeList[0] | |
| 495 | lastHeight = HeiRangeList[1]+1 |
|
495 | lastHeight = HeiRangeList[1]+1 | |
| 496 | flip = -1.0 |
|
496 | flip = -1.0 | |
| 497 | data[thisChannel,firstHeight:lastHeight] = data[thisChannel,firstHeight:lastHeight]*flip |
|
497 | data[thisChannel,firstHeight:lastHeight] = data[thisChannel,firstHeight:lastHeight]*flip | |
| 498 |
|
498 | |||
| 499 | #data[thisChannel,:] = data[thisChannel,:]*self.flip |
|
499 | #data[thisChannel,:] = data[thisChannel,:]*self.flip | |
| 500 |
|
500 | |||
| 501 | self.flip *= -1. |
|
501 | self.flip *= -1. | |
| 502 |
|
502 | |||
| 503 | #print(dataOut.data[0,:12,1066+2]) |
|
503 | #print(dataOut.data[0,:12,1066+2]) | |
| 504 | #print(dataOut.data[1,:12,1066+2]) |
|
504 | #print(dataOut.data[1,:12,1066+2]) | |
| 505 | dataOut.data =data |
|
505 | dataOut.data =data | |
| 506 | #print(dataOut.data[0,:12,1066+2]) |
|
506 | #print(dataOut.data[0,:12,1066+2]) | |
| 507 | #print(dataOut.data[1,:12,1066+2]) |
|
507 | #print(dataOut.data[1,:12,1066+2]) | |
| 508 | #exit(1) |
|
508 | #exit(1) | |
| 509 |
|
509 | |||
| 510 | return dataOut |
|
510 | return dataOut | |
| 511 |
|
511 | |||
| 512 | class setAttribute(Operation): |
|
512 | class setAttribute(Operation): | |
| 513 | ''' |
|
513 | ''' | |
| 514 | Set an arbitrary attribute(s) to dataOut |
|
514 | Set an arbitrary attribute(s) to dataOut | |
| 515 | ''' |
|
515 | ''' | |
| 516 |
|
516 | |||
| 517 | def __init__(self): |
|
517 | def __init__(self): | |
| 518 |
|
518 | |||
| 519 | Operation.__init__(self) |
|
519 | Operation.__init__(self) | |
| 520 | self._ready = False |
|
520 | self._ready = False | |
| 521 |
|
521 | |||
| 522 | def run(self, dataOut, **kwargs): |
|
522 | def run(self, dataOut, **kwargs): | |
| 523 |
|
523 | |||
| 524 | for key, value in kwargs.items(): |
|
524 | for key, value in kwargs.items(): | |
| 525 | setattr(dataOut, key, value) |
|
525 | setattr(dataOut, key, value) | |
| 526 |
|
526 | |||
| 527 | return dataOut |
|
527 | return dataOut | |
| 528 |
|
528 | |||
| 529 |
|
529 | |||
| 530 | @MPDecorator |
|
530 | @MPDecorator | |
| 531 | class printAttribute(Operation): |
|
531 | class printAttribute(Operation): | |
| 532 | ''' |
|
532 | ''' | |
| 533 | Print an arbitrary attribute of dataOut |
|
533 | Print an arbitrary attribute of dataOut | |
| 534 | ''' |
|
534 | ''' | |
| 535 |
|
535 | |||
| 536 | def __init__(self): |
|
536 | def __init__(self): | |
| 537 |
|
537 | |||
| 538 | Operation.__init__(self) |
|
538 | Operation.__init__(self) | |
| 539 |
|
539 | |||
| 540 | def run(self, dataOut, attributes): |
|
540 | def run(self, dataOut, attributes): | |
| 541 |
|
541 | |||
| 542 | if isinstance(attributes, str): |
|
542 | if isinstance(attributes, str): | |
| 543 | attributes = [attributes] |
|
543 | attributes = [attributes] | |
| 544 | for attr in attributes: |
|
544 | for attr in attributes: | |
| 545 | if hasattr(dataOut, attr): |
|
545 | if hasattr(dataOut, attr): | |
| 546 | log.log(getattr(dataOut, attr), attr) |
|
546 | log.log(getattr(dataOut, attr), attr) | |
| 547 |
|
547 | |||
| 548 |
|
548 | |||
| 549 | class interpolateHeights(Operation): |
|
549 | class interpolateHeights(Operation): | |
| 550 |
|
550 | |||
| 551 | def run(self, dataOut, topLim, botLim): |
|
551 | def run(self, dataOut, topLim, botLim): | |
| 552 | # 69 al 72 para julia |
|
552 | # 69 al 72 para julia | |
| 553 | # 82-84 para meteoros |
|
553 | # 82-84 para meteoros | |
| 554 | if len(numpy.shape(dataOut.data)) == 2: |
|
554 | if len(numpy.shape(dataOut.data)) == 2: | |
| 555 | sampInterp = (dataOut.data[:, botLim - 1] + dataOut.data[:, topLim + 1]) / 2 |
|
555 | sampInterp = (dataOut.data[:, botLim - 1] + dataOut.data[:, topLim + 1]) / 2 | |
| 556 | sampInterp = numpy.transpose(numpy.tile(sampInterp, (topLim - botLim + 1, 1))) |
|
556 | sampInterp = numpy.transpose(numpy.tile(sampInterp, (topLim - botLim + 1, 1))) | |
| 557 | # dataOut.data[:,botLim:limSup+1] = sampInterp |
|
557 | # dataOut.data[:,botLim:limSup+1] = sampInterp | |
| 558 | dataOut.data[:, botLim:topLim + 1] = sampInterp |
|
558 | dataOut.data[:, botLim:topLim + 1] = sampInterp | |
| 559 | else: |
|
559 | else: | |
| 560 | nHeights = dataOut.data.shape[2] |
|
560 | nHeights = dataOut.data.shape[2] | |
| 561 | x = numpy.hstack((numpy.arange(botLim), numpy.arange(topLim + 1, nHeights))) |
|
561 | x = numpy.hstack((numpy.arange(botLim), numpy.arange(topLim + 1, nHeights))) | |
| 562 | y = dataOut.data[:, :, list(range(botLim)) + list(range(topLim + 1, nHeights))] |
|
562 | y = dataOut.data[:, :, list(range(botLim)) + list(range(topLim + 1, nHeights))] | |
| 563 | f = interpolate.interp1d(x, y, axis=2) |
|
563 | f = interpolate.interp1d(x, y, axis=2) | |
| 564 | xnew = numpy.arange(botLim, topLim + 1) |
|
564 | xnew = numpy.arange(botLim, topLim + 1) | |
| 565 | ynew = f(xnew) |
|
565 | ynew = f(xnew) | |
| 566 | dataOut.data[:, :, botLim:topLim + 1] = ynew |
|
566 | dataOut.data[:, :, botLim:topLim + 1] = ynew | |
| 567 |
|
567 | |||
| 568 | return dataOut |
|
568 | return dataOut | |
| 569 |
|
569 | |||
| 570 |
|
570 | |||
| 571 | class LagsReshape(Operation): |
|
571 | class LagsReshape(Operation): | |
| 572 | ''' |
|
572 | ''' | |
| 573 | Written by R. Flores |
|
573 | Written by R. Flores | |
| 574 | ''' |
|
574 | ''' | |
| 575 | """Operation to reshape input data into (Channels,Profiles(with same lag),Heights,Lags) and heights reconstruction. |
|
575 | """Operation to reshape input data into (Channels,Profiles(with same lag),Heights,Lags) and heights reconstruction. | |
| 576 |
|
576 | |||
| 577 | Parameters: |
|
577 | Parameters: | |
| 578 | ----------- |
|
578 | ----------- | |
| 579 |
|
579 | |||
| 580 |
|
580 | |||
| 581 | Example |
|
581 | Example | |
| 582 | -------- |
|
582 | -------- | |
| 583 |
|
583 | |||
| 584 | op = proc_unit.addOperation(name='LagsReshape') |
|
584 | op = proc_unit.addOperation(name='LagsReshape') | |
| 585 |
|
585 | |||
| 586 |
|
586 | |||
| 587 | """ |
|
587 | """ | |
| 588 |
|
588 | |||
| 589 | def __init__(self, **kwargs): |
|
589 | def __init__(self, **kwargs): | |
| 590 |
|
590 | |||
| 591 | Operation.__init__(self, **kwargs) |
|
591 | Operation.__init__(self, **kwargs) | |
| 592 |
|
592 | |||
| 593 | self.buffer=None |
|
593 | self.buffer=None | |
| 594 | self.buffer_HR=None |
|
594 | self.buffer_HR=None | |
| 595 | self.buffer_HRonelag=None |
|
595 | self.buffer_HRonelag=None | |
| 596 |
|
596 | |||
| 597 | def LagDistribution(self,dataOut): |
|
597 | def LagDistribution(self,dataOut): | |
| 598 |
|
598 | |||
| 599 | dataOut.datapure=numpy.copy(dataOut.data[:,0:dataOut.NSCAN,:]) |
|
599 | dataOut.datapure=numpy.copy(dataOut.data[:,0:dataOut.NSCAN,:]) | |
| 600 | self.buffer = numpy.zeros((dataOut.nChannels, |
|
600 | self.buffer = numpy.zeros((dataOut.nChannels, | |
| 601 | int(dataOut.NSCAN/dataOut.DPL), |
|
601 | int(dataOut.NSCAN/dataOut.DPL), | |
| 602 | dataOut.nHeights,dataOut.DPL), |
|
602 | dataOut.nHeights,dataOut.DPL), | |
| 603 | dtype='complex') |
|
603 | dtype='complex') | |
| 604 |
|
604 | |||
| 605 | for j in range(int(self.buffer.shape[1]/2)): |
|
605 | for j in range(int(self.buffer.shape[1]/2)): | |
| 606 | for i in range(dataOut.DPL): |
|
606 | for i in range(dataOut.DPL): | |
| 607 | if j+1==int(self.buffer.shape[1]/2) and i+1==dataOut.DPL: |
|
607 | if j+1==int(self.buffer.shape[1]/2) and i+1==dataOut.DPL: | |
| 608 | self.buffer[:,2*j:,:,i]=dataOut.datapure[:,2*i+int(2*j*dataOut.DPL):,:] |
|
608 | self.buffer[:,2*j:,:,i]=dataOut.datapure[:,2*i+int(2*j*dataOut.DPL):,:] | |
| 609 | else: |
|
609 | else: | |
| 610 | self.buffer[:,2*j:2*(j+1),:,i]=dataOut.datapure[:,2*i+int(2*j*dataOut.DPL):2*(i+1)+int(2*j*dataOut.DPL),:] |
|
610 | self.buffer[:,2*j:2*(j+1),:,i]=dataOut.datapure[:,2*i+int(2*j*dataOut.DPL):2*(i+1)+int(2*j*dataOut.DPL),:] | |
| 611 |
|
611 | |||
| 612 | return self.buffer |
|
612 | return self.buffer | |
| 613 |
|
613 | |||
| 614 | def HeightReconstruction(self,dataOut): |
|
614 | def HeightReconstruction(self,dataOut): | |
| 615 |
|
615 | |||
| 616 | self.buffer_HR = numpy.zeros((int(dataOut.NSCAN/dataOut.DPL), |
|
616 | self.buffer_HR = numpy.zeros((int(dataOut.NSCAN/dataOut.DPL), | |
| 617 | dataOut.nHeights,dataOut.DPL), |
|
617 | dataOut.nHeights,dataOut.DPL), | |
| 618 | dtype='complex') |
|
618 | dtype='complex') | |
| 619 |
|
619 | |||
| 620 | for i in range(int(dataOut.DPL)): #Only channel B |
|
620 | for i in range(int(dataOut.DPL)): #Only channel B | |
| 621 | if i==0: |
|
621 | if i==0: | |
| 622 | self.buffer_HR[:,:,i]=dataOut.datalags[1,:,:,i] |
|
622 | self.buffer_HR[:,:,i]=dataOut.datalags[1,:,:,i] | |
| 623 | else: |
|
623 | else: | |
| 624 | self.buffer_HR[:,:,i]=self.HRonelag(dataOut,i) |
|
624 | self.buffer_HR[:,:,i]=self.HRonelag(dataOut,i) | |
| 625 |
|
625 | |||
| 626 | return self.buffer_HR |
|
626 | return self.buffer_HR | |
| 627 |
|
627 | |||
| 628 |
|
628 | |||
| 629 | def HRonelag(self,dataOut,whichlag): |
|
629 | def HRonelag(self,dataOut,whichlag): | |
| 630 | self.buffer_HRonelag = numpy.zeros((int(dataOut.NSCAN/dataOut.DPL), |
|
630 | self.buffer_HRonelag = numpy.zeros((int(dataOut.NSCAN/dataOut.DPL), | |
| 631 | dataOut.nHeights), |
|
631 | dataOut.nHeights), | |
| 632 | dtype='complex') |
|
632 | dtype='complex') | |
| 633 |
|
633 | |||
| 634 | for i in range(self.buffer_HRonelag.shape[0]): |
|
634 | for i in range(self.buffer_HRonelag.shape[0]): | |
| 635 | for j in range(dataOut.nHeights): |
|
635 | for j in range(dataOut.nHeights): | |
| 636 | if j+int(2*whichlag)<dataOut.nHeights: |
|
636 | if j+int(2*whichlag)<dataOut.nHeights: | |
| 637 | self.buffer_HRonelag[i,j]=dataOut.datalags[1,i,j+2*whichlag,whichlag] |
|
637 | self.buffer_HRonelag[i,j]=dataOut.datalags[1,i,j+2*whichlag,whichlag] | |
| 638 | else: |
|
638 | else: | |
| 639 | if whichlag!=10: |
|
639 | if whichlag!=10: | |
| 640 | self.buffer_HRonelag[i,j]=dataOut.datalags[1,i,(j+2*whichlag)%dataOut.nHeights,whichlag+1] |
|
640 | self.buffer_HRonelag[i,j]=dataOut.datalags[1,i,(j+2*whichlag)%dataOut.nHeights,whichlag+1] | |
| 641 | else: |
|
641 | else: | |
| 642 | if i+2<self.buffer_HRonelag.shape[0]: |
|
642 | if i+2<self.buffer_HRonelag.shape[0]: | |
| 643 | self.buffer_HRonelag[i,j]=dataOut.datalags[1,i+2,(j+2*whichlag)%dataOut.nHeights,0] |
|
643 | self.buffer_HRonelag[i,j]=dataOut.datalags[1,i+2,(j+2*whichlag)%dataOut.nHeights,0] | |
| 644 | else: #i+1==self.buffer_HRonelag.shape[0]: |
|
644 | else: #i+1==self.buffer_HRonelag.shape[0]: | |
| 645 | self.buffer_HRonelag[i,j]=dataOut.datalags[1,i,(j+2*whichlag)%dataOut.nHeights,whichlag] |
|
645 | self.buffer_HRonelag[i,j]=dataOut.datalags[1,i,(j+2*whichlag)%dataOut.nHeights,whichlag] | |
| 646 |
|
646 | |||
| 647 | return self.buffer_HRonelag |
|
647 | return self.buffer_HRonelag | |
| 648 |
|
648 | |||
| 649 |
|
649 | |||
| 650 |
|
650 | |||
| 651 | def run(self,dataOut,DPL=11,NSCAN=132): |
|
651 | def run(self,dataOut,DPL=11,NSCAN=132): | |
| 652 |
|
652 | |||
| 653 | dataOut.DPL=DPL |
|
653 | dataOut.DPL=DPL | |
| 654 | dataOut.NSCAN=NSCAN |
|
654 | dataOut.NSCAN=NSCAN | |
| 655 | dataOut.paramInterval=0#int(dataOut.nint*dataOut.header[7][0]*2 ) |
|
655 | dataOut.paramInterval=0#int(dataOut.nint*dataOut.header[7][0]*2 ) | |
| 656 | dataOut.lat=-11.95 |
|
656 | dataOut.lat=-11.95 | |
| 657 | dataOut.lon=-76.87 |
|
657 | dataOut.lon=-76.87 | |
| 658 | dataOut.datalags=None |
|
658 | dataOut.datalags=None | |
| 659 |
|
659 | |||
| 660 | dataOut.datalags=numpy.copy(self.LagDistribution(dataOut)) |
|
660 | dataOut.datalags=numpy.copy(self.LagDistribution(dataOut)) | |
| 661 | dataOut.datalags[1,:,:,:]=self.HeightReconstruction(dataOut) |
|
661 | dataOut.datalags[1,:,:,:]=self.HeightReconstruction(dataOut) | |
| 662 |
|
662 | |||
| 663 | return dataOut |
|
663 | return dataOut | |
| 664 |
|
664 | |||
| 665 | class LagsReshapeHP(Operation): |
|
665 | class LagsReshapeHP(Operation): | |
| 666 | ''' |
|
666 | ''' | |
| 667 | Written by R. Flores |
|
667 | Written by R. Flores | |
| 668 | ''' |
|
668 | ''' | |
| 669 | """Operation to reshape input data into (Channels,Profiles(with same lag),Heights,Lags) and heights reconstruction. |
|
669 | """Operation to reshape input data into (Channels,Profiles(with same lag),Heights,Lags) and heights reconstruction. | |
| 670 |
|
670 | |||
| 671 | Parameters: |
|
671 | Parameters: | |
| 672 | ----------- |
|
672 | ----------- | |
| 673 |
|
673 | |||
| 674 |
|
674 | |||
| 675 | Example |
|
675 | Example | |
| 676 | -------- |
|
676 | -------- | |
| 677 |
|
677 | |||
| 678 | op = proc_unit.addOperation(name='LagsReshape') |
|
678 | op = proc_unit.addOperation(name='LagsReshape') | |
| 679 |
|
679 | |||
| 680 |
|
680 | |||
| 681 | """ |
|
681 | """ | |
| 682 |
|
682 | |||
| 683 | def __init__(self, **kwargs): |
|
683 | def __init__(self, **kwargs): | |
| 684 |
|
684 | |||
| 685 | Operation.__init__(self, **kwargs) |
|
685 | Operation.__init__(self, **kwargs) | |
| 686 |
|
686 | |||
| 687 | self.buffer=None |
|
687 | self.buffer=None | |
| 688 | self.buffer_HR=None |
|
688 | self.buffer_HR=None | |
| 689 | self.buffer_HRonelag=None |
|
689 | self.buffer_HRonelag=None | |
| 690 |
|
690 | |||
| 691 | def LagDistribution(self,dataOut): |
|
691 | def LagDistribution(self,dataOut): | |
| 692 |
|
692 | |||
| 693 | dataOut.datapure=numpy.copy(dataOut.data[:,0:dataOut.NSCAN,:]) |
|
693 | dataOut.datapure=numpy.copy(dataOut.data[:,0:dataOut.NSCAN,:]) | |
| 694 | self.buffer = numpy.zeros((dataOut.nChannels, |
|
694 | self.buffer = numpy.zeros((dataOut.nChannels, | |
| 695 | int(dataOut.NSCAN/dataOut.DPL), |
|
695 | int(dataOut.NSCAN/dataOut.DPL), | |
| 696 | dataOut.nHeights,dataOut.DPL), |
|
696 | dataOut.nHeights,dataOut.DPL), | |
| 697 | dtype='complex') |
|
697 | dtype='complex') | |
| 698 |
|
698 | |||
| 699 | for j in range(int(self.buffer.shape[1]/2)): |
|
699 | for j in range(int(self.buffer.shape[1]/2)): | |
| 700 | for i in range(dataOut.DPL): |
|
700 | for i in range(dataOut.DPL): | |
| 701 | if j+1==int(self.buffer.shape[1]/2) and i+1==dataOut.DPL: |
|
701 | if j+1==int(self.buffer.shape[1]/2) and i+1==dataOut.DPL: | |
| 702 | self.buffer[:,2*j:,:,i]=dataOut.datapure[:,2*i+int(2*j*dataOut.DPL):,:] |
|
702 | self.buffer[:,2*j:,:,i]=dataOut.datapure[:,2*i+int(2*j*dataOut.DPL):,:] | |
| 703 | else: |
|
703 | else: | |
| 704 | self.buffer[:,2*j:2*(j+1),:,i]=dataOut.datapure[:,2*i+int(2*j*dataOut.DPL):2*(i+1)+int(2*j*dataOut.DPL),:] |
|
704 | self.buffer[:,2*j:2*(j+1),:,i]=dataOut.datapure[:,2*i+int(2*j*dataOut.DPL):2*(i+1)+int(2*j*dataOut.DPL),:] | |
| 705 |
|
705 | |||
| 706 | return self.buffer |
|
706 | return self.buffer | |
| 707 |
|
707 | |||
| 708 | def HeightReconstruction(self,dataOut): |
|
708 | def HeightReconstruction(self,dataOut): | |
| 709 |
|
709 | |||
| 710 | self.buffer_HR = numpy.zeros((int(dataOut.NSCAN/dataOut.DPL), |
|
710 | self.buffer_HR = numpy.zeros((int(dataOut.NSCAN/dataOut.DPL), | |
| 711 | dataOut.nHeights,dataOut.DPL), |
|
711 | dataOut.nHeights,dataOut.DPL), | |
| 712 | dtype='complex') |
|
712 | dtype='complex') | |
| 713 |
|
713 | |||
| 714 | for i in range(int(dataOut.DPL)): #Only channel B |
|
714 | for i in range(int(dataOut.DPL)): #Only channel B | |
| 715 | if i==0: |
|
715 | if i==0: | |
| 716 | self.buffer_HR[:,:,i]=dataOut.datalags[1,:,:,i] |
|
716 | self.buffer_HR[:,:,i]=dataOut.datalags[1,:,:,i] | |
| 717 | else: |
|
717 | else: | |
| 718 | self.buffer_HR[:,:,i]=self.HRonelag(dataOut,i) |
|
718 | self.buffer_HR[:,:,i]=self.HRonelag(dataOut,i) | |
| 719 |
|
719 | |||
| 720 | return self.buffer_HR |
|
720 | return self.buffer_HR | |
| 721 |
|
721 | |||
| 722 |
|
722 | |||
| 723 | def HRonelag(self,dataOut,whichlag): |
|
723 | def HRonelag(self,dataOut,whichlag): | |
| 724 | self.buffer_HRonelag = numpy.zeros((int(dataOut.NSCAN/dataOut.DPL), |
|
724 | self.buffer_HRonelag = numpy.zeros((int(dataOut.NSCAN/dataOut.DPL), | |
| 725 | dataOut.nHeights), |
|
725 | dataOut.nHeights), | |
| 726 | dtype='complex') |
|
726 | dtype='complex') | |
| 727 |
|
727 | |||
| 728 | for i in range(self.buffer_HRonelag.shape[0]): |
|
728 | for i in range(self.buffer_HRonelag.shape[0]): | |
| 729 | for j in range(dataOut.nHeights): |
|
729 | for j in range(dataOut.nHeights): | |
| 730 | if j+int(2*whichlag)<dataOut.nHeights: |
|
730 | if j+int(2*whichlag)<dataOut.nHeights: | |
| 731 | self.buffer_HRonelag[i,j]=dataOut.datalags[1,i,j+2*whichlag,whichlag] |
|
731 | self.buffer_HRonelag[i,j]=dataOut.datalags[1,i,j+2*whichlag,whichlag] | |
| 732 | else: |
|
732 | else: | |
| 733 | if whichlag!=10: |
|
733 | if whichlag!=10: | |
| 734 | self.buffer_HRonelag[i,j]=dataOut.datalags[1,i,(j+2*whichlag)%dataOut.nHeights,whichlag+1] |
|
734 | self.buffer_HRonelag[i,j]=dataOut.datalags[1,i,(j+2*whichlag)%dataOut.nHeights,whichlag+1] | |
| 735 | else: |
|
735 | else: | |
| 736 | if i+2<self.buffer_HRonelag.shape[0]: |
|
736 | if i+2<self.buffer_HRonelag.shape[0]: | |
| 737 | self.buffer_HRonelag[i,j]=dataOut.datalags[1,i+2,(j+2*whichlag)%dataOut.nHeights,0] |
|
737 | self.buffer_HRonelag[i,j]=dataOut.datalags[1,i+2,(j+2*whichlag)%dataOut.nHeights,0] | |
| 738 | else: #i+1==self.buffer_HRonelag.shape[0]: |
|
738 | else: #i+1==self.buffer_HRonelag.shape[0]: | |
| 739 | self.buffer_HRonelag[i,j]=dataOut.datalags[1,i,(j+2*whichlag)%dataOut.nHeights,whichlag] |
|
739 | self.buffer_HRonelag[i,j]=dataOut.datalags[1,i,(j+2*whichlag)%dataOut.nHeights,whichlag] | |
| 740 |
|
740 | |||
| 741 | return self.buffer_HRonelag |
|
741 | return self.buffer_HRonelag | |
| 742 |
|
742 | |||
| 743 |
|
743 | |||
| 744 |
|
744 | |||
| 745 | def run(self,dataOut,DPL=11,NSCAN=132): |
|
745 | def run(self,dataOut,DPL=11,NSCAN=132): | |
| 746 |
|
746 | |||
| 747 | dataOut.DPL=DPL |
|
747 | dataOut.DPL=DPL | |
| 748 | dataOut.NSCAN=NSCAN |
|
748 | dataOut.NSCAN=NSCAN | |
| 749 | dataOut.paramInterval=0#int(dataOut.nint*dataOut.header[7][0]*2 ) |
|
749 | dataOut.paramInterval=0#int(dataOut.nint*dataOut.header[7][0]*2 ) | |
| 750 | dataOut.lat=-11.95 |
|
750 | dataOut.lat=-11.95 | |
| 751 | dataOut.lon=-76.87 |
|
751 | dataOut.lon=-76.87 | |
| 752 | dataOut.datalags=None |
|
752 | dataOut.datalags=None | |
| 753 |
|
753 | |||
| 754 | dataOut.datalags=numpy.copy(self.LagDistribution(dataOut)) |
|
754 | dataOut.datalags=numpy.copy(self.LagDistribution(dataOut)) | |
| 755 | dataOut.datalags[1,:,:,:]=self.HeightReconstruction(dataOut) |
|
755 | dataOut.datalags[1,:,:,:]=self.HeightReconstruction(dataOut) | |
| 756 |
|
756 | |||
| 757 | return dataOut |
|
757 | return dataOut | |
| 758 |
|
758 | |||
| 759 | class LagsReshapeDP_V2(Operation): |
|
759 | class LagsReshapeDP_V2(Operation): | |
| 760 | ''' |
|
760 | ''' | |
| 761 | Written by R. Flores |
|
761 | Written by R. Flores | |
| 762 | ''' |
|
762 | ''' | |
| 763 | """Operation to reshape input data into (Channels,Profiles(with same lag),Heights,Lags) and heights reconstruction. |
|
763 | """Operation to reshape input data into (Channels,Profiles(with same lag),Heights,Lags) and heights reconstruction. | |
| 764 |
|
764 | |||
| 765 | Parameters: |
|
765 | Parameters: | |
| 766 | ----------- |
|
766 | ----------- | |
| 767 |
|
767 | |||
| 768 |
|
768 | |||
| 769 | Example |
|
769 | Example | |
| 770 | -------- |
|
770 | -------- | |
| 771 |
|
771 | |||
| 772 | op = proc_unit.addOperation(name='LagsReshape') |
|
772 | op = proc_unit.addOperation(name='LagsReshape') | |
| 773 |
|
773 | |||
| 774 |
|
774 | |||
| 775 | """ |
|
775 | """ | |
| 776 |
|
776 | |||
| 777 | def __init__(self, **kwargs): |
|
777 | def __init__(self, **kwargs): | |
| 778 |
|
778 | |||
| 779 | Operation.__init__(self, **kwargs) |
|
779 | Operation.__init__(self, **kwargs) | |
| 780 |
|
780 | |||
| 781 | self.buffer=None |
|
781 | self.buffer=None | |
| 782 | self.data_buffer = [] |
|
782 | self.data_buffer = [] | |
| 783 |
|
783 | |||
| 784 | def setup(self,dataOut,DPL,NSCAN,NLAG,NRANGE,lagind,lagfirst): |
|
784 | def setup(self,dataOut,DPL,NSCAN,NLAG,NRANGE,lagind,lagfirst): | |
| 785 | dataOut.DPL=DPL |
|
785 | dataOut.DPL=DPL | |
| 786 | dataOut.NSCAN=NSCAN |
|
786 | dataOut.NSCAN=NSCAN | |
| 787 | dataOut.NLAG = NLAG |
|
787 | dataOut.NLAG = NLAG | |
| 788 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
788 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] | |
| 789 | dataOut.NRANGE = NRANGE |
|
789 | dataOut.NRANGE = NRANGE | |
| 790 | dataOut.read_samples=int(dataOut.nHeights) |
|
790 | dataOut.read_samples=int(dataOut.nHeights) | |
| 791 | #print(dataOut.read_samples) |
|
791 | #print(dataOut.read_samples) | |
| 792 | #print(dataOut.nHeights) |
|
792 | #print(dataOut.nHeights) | |
| 793 | #exit(1) |
|
793 | #exit(1) | |
| 794 | dataOut.NDP = dataOut.NDT = int((dataOut.nHeights-dataOut.NRANGE)/2) |
|
794 | dataOut.NDP = dataOut.NDT = int((dataOut.nHeights-dataOut.NRANGE)/2) | |
| 795 | dataOut.heightList = numpy.arange(dataOut.NDP) *deltaHeight# + dataOut.heightList[0] |
|
795 | dataOut.heightList = numpy.arange(dataOut.NDP) *deltaHeight# + dataOut.heightList[0] | |
| 796 | #dataOut.NDP = dataOut.NDT = int(dataOut.nHeights/2)#int((dataOut.nHeights-dataOut.NRANGE)/2) |
|
796 | #dataOut.NDP = dataOut.NDT = int(dataOut.nHeights/2)#int((dataOut.nHeights-dataOut.NRANGE)/2) | |
| 797 | #print(dataOut.NDP) |
|
797 | #print(dataOut.NDP) | |
| 798 | #print(dataOut.heightList) |
|
798 | #print(dataOut.heightList) | |
| 799 | dataOut.paramInterval=0#int(dataOut.nint*dataOut.header[7][0]*2 ) |
|
799 | dataOut.paramInterval=0#int(dataOut.nint*dataOut.header[7][0]*2 ) | |
| 800 | dataOut.lat=-11.95 |
|
800 | dataOut.lat=-11.95 | |
| 801 | dataOut.lon=-76.87 |
|
801 | dataOut.lon=-76.87 | |
| 802 | dataOut.datalags=None |
|
802 | dataOut.datalags=None | |
| 803 | dataOut.lagind=lagind |
|
803 | dataOut.lagind=lagind | |
| 804 | dataOut.lagfirst=lagfirst |
|
804 | dataOut.lagfirst=lagfirst | |
| 805 |
|
805 | |||
| 806 |
|
806 | |||
| 807 | def LagDistribution(self,dataOut): |
|
807 | def LagDistribution(self,dataOut): | |
| 808 |
|
808 | |||
| 809 | self.buffer = numpy.zeros((dataOut.nChannels, |
|
809 | self.buffer = numpy.zeros((dataOut.nChannels, | |
| 810 | int(2*2*dataOut.NSCAN/dataOut.NLAG), |
|
810 | int(2*2*dataOut.NSCAN/dataOut.NLAG), | |
| 811 | dataOut.NDP,dataOut.DPL), |
|
811 | dataOut.NDP,dataOut.DPL), | |
| 812 | dtype='complex') |
|
812 | dtype='complex') | |
| 813 |
|
813 | |||
| 814 | indProfile = numpy.arange(0,dataOut.NSCAN,1)//8 |
|
814 | indProfile = numpy.arange(0,dataOut.NSCAN,1)//8 | |
| 815 |
|
815 | |||
| 816 | #dataOut.nNoiseProfiles = dataOut.nProfiles-dataOut.NSCAN |
|
816 | #dataOut.nNoiseProfiles = dataOut.nProfiles-dataOut.NSCAN | |
| 817 |
|
817 | |||
| 818 | for i in range(2): |
|
818 | for i in range(2): | |
| 819 | if i==0: |
|
819 | if i==0: | |
| 820 | aux = 0 |
|
820 | aux = 0 | |
| 821 | else: |
|
821 | else: | |
| 822 | aux =16 |
|
822 | aux =16 | |
| 823 | for j in range(dataOut.NDP): |
|
823 | for j in range(dataOut.NDP): | |
| 824 | for k in range(int(dataOut.NSCAN)): |
|
824 | for k in range(int(dataOut.NSCAN)): | |
| 825 |
|
825 | |||
| 826 | n=dataOut.lagind[k%dataOut.NLAG] |
|
826 | n=dataOut.lagind[k%dataOut.NLAG] | |
| 827 |
|
827 | |||
| 828 | data_ChA=dataOut.data[0,k,dataOut.NRANGE+j+i*dataOut.NDT]#-dataOut.dc[0] |
|
828 | data_ChA=dataOut.data[0,k,dataOut.NRANGE+j+i*dataOut.NDT]#-dataOut.dc[0] | |
| 829 |
|
829 | |||
| 830 | if dataOut.NRANGE+j+i*dataOut.NDT+2*n<dataOut.read_samples: |
|
830 | if dataOut.NRANGE+j+i*dataOut.NDT+2*n<dataOut.read_samples: | |
| 831 |
|
831 | |||
| 832 | data_ChB=dataOut.data[1,k,dataOut.NRANGE+j+i*dataOut.NDT+2*n]#-dataOut.dc[1] |
|
832 | data_ChB=dataOut.data[1,k,dataOut.NRANGE+j+i*dataOut.NDT+2*n]#-dataOut.dc[1] | |
| 833 | #print(data_ChB) |
|
833 | #print(data_ChB) | |
| 834 | #exit(1) |
|
834 | #exit(1) | |
| 835 | #print("*1*") |
|
835 | #print("*1*") | |
| 836 |
|
836 | |||
| 837 | else: |
|
837 | else: | |
| 838 | #print(i,j,n) |
|
838 | #print(i,j,n) | |
| 839 | #exit(1) |
|
839 | #exit(1) | |
| 840 |
|
840 | |||
| 841 | if k+1<int(dataOut.NSCAN): |
|
841 | if k+1<int(dataOut.NSCAN): | |
| 842 | data_ChB=dataOut.data[1,k+1,(dataOut.NRANGE+j+i*dataOut.NDT+2*n)%dataOut.NDP] |
|
842 | data_ChB=dataOut.data[1,k+1,(dataOut.NRANGE+j+i*dataOut.NDT+2*n)%dataOut.NDP] | |
| 843 | #print(data_ChB) |
|
843 | #print(data_ChB) | |
| 844 | #print("*2*") |
|
844 | #print("*2*") | |
| 845 | #exit(1) |
|
845 | #exit(1) | |
| 846 | if k+1==int(dataOut.NSCAN): |
|
846 | if k+1==int(dataOut.NSCAN): | |
| 847 | data_ChB=dataOut.data[1,k,(dataOut.NRANGE+j+i*dataOut.NDT+2*n)%dataOut.NDP] |
|
847 | data_ChB=dataOut.data[1,k,(dataOut.NRANGE+j+i*dataOut.NDT+2*n)%dataOut.NDP] | |
| 848 | #print("*3*") |
|
848 | #print("*3*") | |
| 849 | #if n == 7 and j == 65: |
|
849 | #if n == 7 and j == 65: | |
| 850 | #print(k) |
|
850 | #print(k) | |
| 851 | #print(data_ChB) |
|
851 | #print(data_ChB) | |
| 852 | #exit(1) |
|
852 | #exit(1) | |
| 853 | if n == 8 or n == 9 or n == 10: |
|
853 | if n == 8 or n == 9 or n == 10: | |
| 854 | self.buffer[0,int((aux+indProfile[k]-1)/2),j,n] = data_ChA |
|
854 | self.buffer[0,int((aux+indProfile[k]-1)/2),j,n] = data_ChA | |
| 855 | self.buffer[1,int((aux+indProfile[k]-1)/2),j,n] = data_ChB |
|
855 | self.buffer[1,int((aux+indProfile[k]-1)/2),j,n] = data_ChB | |
| 856 | elif n == 1 or n == 2 or n == 7: |
|
856 | elif n == 1 or n == 2 or n == 7: | |
| 857 | self.buffer[0,int((aux+indProfile[k])/2),j,n] = data_ChA |
|
857 | self.buffer[0,int((aux+indProfile[k])/2),j,n] = data_ChA | |
| 858 | self.buffer[1,int((aux+indProfile[k])/2),j,n] = data_ChB |
|
858 | self.buffer[1,int((aux+indProfile[k])/2),j,n] = data_ChB | |
| 859 | else: |
|
859 | else: | |
| 860 | self.buffer[0,aux+indProfile[k],j,n] = data_ChA |
|
860 | self.buffer[0,aux+indProfile[k],j,n] = data_ChA | |
| 861 | self.buffer[1,aux+indProfile[k],j,n] = data_ChB |
|
861 | self.buffer[1,aux+indProfile[k],j,n] = data_ChB | |
| 862 |
|
862 | |||
| 863 | #FindMe |
|
863 | #FindMe | |
| 864 | pa1 = 20 |
|
864 | pa1 = 20 | |
| 865 | pa2 = 10 |
|
865 | pa2 = 10 | |
| 866 |
|
866 | |||
| 867 | #print(self.buffer[0,:,pa1,pa2]) |
|
867 | #print(self.buffer[0,:,pa1,pa2]) | |
| 868 | #print(self.buffer[1,:,pa1,pa2]) |
|
868 | #print(self.buffer[1,:,pa1,pa2]) | |
| 869 | ''' |
|
869 | ''' | |
| 870 | print(sum(self.buffer[0,:,pa1,pa2])) |
|
870 | print(sum(self.buffer[0,:,pa1,pa2])) | |
| 871 | print(sum(self.buffer[1,:,pa1,pa2])) |
|
871 | print(sum(self.buffer[1,:,pa1,pa2])) | |
| 872 | #exit(1) |
|
872 | #exit(1) | |
| 873 | ''' |
|
873 | ''' | |
| 874 |
|
874 | |||
| 875 | ''' |
|
875 | ''' | |
| 876 | for pa1 in range(67): |
|
876 | for pa1 in range(67): | |
| 877 | print(sum(self.buffer[0,:,pa1,pa2])) |
|
877 | print(sum(self.buffer[0,:,pa1,pa2])) | |
| 878 | print(sum(self.buffer[1,:,pa1,pa2])) |
|
878 | print(sum(self.buffer[1,:,pa1,pa2])) | |
| 879 | ''' |
|
879 | ''' | |
| 880 |
|
880 | |||
| 881 | ''' |
|
881 | ''' | |
| 882 | import matplotlib.pyplot as plt |
|
882 | import matplotlib.pyplot as plt | |
| 883 | fft = numpy.fft.fft(self.buffer[0,:,pa1,pa2]) |
|
883 | fft = numpy.fft.fft(self.buffer[0,:,pa1,pa2]) | |
| 884 | fft2 = fft*numpy.conjugate(fft) |
|
884 | fft2 = fft*numpy.conjugate(fft) | |
| 885 | fft2 = fft2.real |
|
885 | fft2 = fft2.real | |
| 886 | fft2 = numpy.fft.fftshift(fft2) |
|
886 | fft2 = numpy.fft.fftshift(fft2) | |
| 887 | ''' |
|
887 | ''' | |
| 888 | #print("before",fft2) |
|
888 | #print("before",fft2) | |
| 889 | #plt.plot(fft2) |
|
889 | #plt.plot(fft2) | |
| 890 | #plt.show() |
|
890 | #plt.show() | |
| 891 | #import time |
|
891 | #import time | |
| 892 | #time.sleep(5) |
|
892 | #time.sleep(5) | |
| 893 | #plt.close('all') |
|
893 | #plt.close('all') | |
| 894 | #exit(1) |
|
894 | #exit(1) | |
| 895 | return self.buffer |
|
895 | return self.buffer | |
| 896 |
|
896 | |||
| 897 |
|
897 | |||
| 898 |
|
898 | |||
| 899 | def run(self,dataOut,DPL=11,NSCAN=128,lagind=(0,1,2,3,4,5,6,7,0,3,4,5,6,8,9,10),lagfirst=(1,1,1,1,1,1,1,1,0,0,0,0,0,1,1,1), NLAG = 16, NRANGE = 200): |
|
899 | def run(self,dataOut,DPL=11,NSCAN=128,lagind=(0,1,2,3,4,5,6,7,0,3,4,5,6,8,9,10),lagfirst=(1,1,1,1,1,1,1,1,0,0,0,0,0,1,1,1), NLAG = 16, NRANGE = 200): | |
| 900 |
|
900 | |||
| 901 | if not self.isConfig: |
|
901 | if not self.isConfig: | |
| 902 | self.setup(dataOut,DPL,NSCAN,NLAG,NRANGE,lagind,lagfirst) |
|
902 | self.setup(dataOut,DPL,NSCAN,NLAG,NRANGE,lagind,lagfirst) | |
| 903 | self.isConfig = True |
|
903 | self.isConfig = True | |
| 904 |
|
904 | |||
| 905 | #print(dataOut.data[1,:12,:15]) |
|
905 | #print(dataOut.data[1,:12,:15]) | |
| 906 | #exit(1) |
|
906 | #exit(1) | |
| 907 | #print(numpy.shape(dataOut.data)) |
|
907 | #print(numpy.shape(dataOut.data)) | |
| 908 | #print(dataOut.profileIndex) |
|
908 | #print(dataOut.profileIndex) | |
| 909 |
|
909 | |||
| 910 | if not dataOut.flagDataAsBlock: |
|
910 | if not dataOut.flagDataAsBlock: | |
| 911 |
|
911 | |||
| 912 | dataOut.flagNoData = True |
|
912 | dataOut.flagNoData = True | |
| 913 | #print("nProfiles: ",dataOut.nProfiles) |
|
913 | #print("nProfiles: ",dataOut.nProfiles) | |
| 914 | #if dataOut.profileIndex == 140: |
|
914 | #if dataOut.profileIndex == 140: | |
| 915 | #print("id: ",dataOut.profileIndex) |
|
915 | #print("id: ",dataOut.profileIndex) | |
| 916 | if dataOut.profileIndex == dataOut.nProfiles-1: |
|
916 | if dataOut.profileIndex == dataOut.nProfiles-1: | |
| 917 | #print("here") |
|
917 | #print("here") | |
| 918 | #print(dataOut.data.shape) |
|
918 | #print(dataOut.data.shape) | |
| 919 | self.data_buffer.append(dataOut.data) |
|
919 | self.data_buffer.append(dataOut.data) | |
| 920 | dataOut.data = numpy.transpose(numpy.array(self.data_buffer),(1,0,2)) |
|
920 | dataOut.data = numpy.transpose(numpy.array(self.data_buffer),(1,0,2)) | |
| 921 | #print(dataOut.data.shape) |
|
921 | #print(dataOut.data.shape) | |
| 922 | #print(numpy.sum(dataOut.data)) |
|
922 | #print(numpy.sum(dataOut.data)) | |
| 923 | #print(dataOut.data[1,100,:]) |
|
923 | #print(dataOut.data[1,100,:]) | |
| 924 | #exit(1) |
|
924 | #exit(1) | |
| 925 | dataOut.datalags = numpy.copy(self.LagDistribution(dataOut)) |
|
925 | dataOut.datalags = numpy.copy(self.LagDistribution(dataOut)) | |
| 926 | #print(numpy.shape(dataOut.datalags)) |
|
926 | #print(numpy.shape(dataOut.datalags)) | |
| 927 | #exit(1) |
|
927 | #exit(1) | |
| 928 | #print("AFTER RESHAPE DP") |
|
928 | #print("AFTER RESHAPE DP") | |
| 929 |
|
929 | |||
| 930 | dataOut.data = dataOut.data[:,:,200:] |
|
930 | dataOut.data = dataOut.data[:,:,200:] | |
| 931 | self.data_buffer = [] |
|
931 | self.data_buffer = [] | |
| 932 | dataOut.flagDataAsBlock = True |
|
932 | dataOut.flagDataAsBlock = True | |
| 933 | dataOut.flagNoData = False |
|
933 | dataOut.flagNoData = False | |
| 934 |
|
934 | |||
| 935 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
935 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] | |
| 936 | dataOut.heightList = numpy.arange(dataOut.NDP) *deltaHeight# + dataOut.heightList[0] |
|
936 | dataOut.heightList = numpy.arange(dataOut.NDP) *deltaHeight# + dataOut.heightList[0] | |
| 937 | #exit(1) |
|
937 | #exit(1) | |
| 938 | #print(numpy.sum(dataOut.datalags)) |
|
938 | #print(numpy.sum(dataOut.datalags)) | |
| 939 | #exit(1) |
|
939 | #exit(1) | |
| 940 |
|
940 | |||
| 941 | else: |
|
941 | else: | |
| 942 | self.data_buffer.append(dataOut.data) |
|
942 | self.data_buffer.append(dataOut.data) | |
| 943 | #print(numpy.shape(dataOut.data)) |
|
943 | #print(numpy.shape(dataOut.data)) | |
| 944 | #exit(1) |
|
944 | #exit(1) | |
| 945 | else: |
|
945 | else: | |
| 946 | #print(dataOut.data.shape) |
|
946 | #print(dataOut.data.shape) | |
| 947 | #print(numpy.sum(dataOut.data)) |
|
947 | #print(numpy.sum(dataOut.data)) | |
| 948 | #print(dataOut.data[1,100,:]) |
|
948 | #print(dataOut.data[1,100,:]) | |
| 949 | #exit(1) |
|
949 | #exit(1) | |
| 950 | dataOut.datalags = numpy.copy(self.LagDistribution(dataOut)) |
|
950 | dataOut.datalags = numpy.copy(self.LagDistribution(dataOut)) | |
| 951 | #print(dataOut.datalags.shape) |
|
951 | #print(dataOut.datalags.shape) | |
| 952 | dataOut.data = dataOut.data[:,:,200:] |
|
952 | dataOut.data = dataOut.data[:,:,200:] | |
| 953 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
953 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] | |
| 954 | dataOut.heightList = numpy.arange(dataOut.NDP) * deltaHeight# + dataOut.heightList[0] |
|
954 | dataOut.heightList = numpy.arange(dataOut.NDP) * deltaHeight# + dataOut.heightList[0] | |
| 955 | #print(dataOut.nHeights) |
|
955 | #print(dataOut.nHeights) | |
| 956 | #print(numpy.sum(dataOut.datalags)) |
|
956 | #print(numpy.sum(dataOut.datalags)) | |
| 957 | #exit(1) |
|
957 | #exit(1) | |
| 958 |
|
958 | |||
| 959 | return dataOut |
|
959 | return dataOut | |
| 960 |
|
960 | |||
| 961 | class CrossProdDP(Operation): |
|
961 | class CrossProdDP(Operation): | |
| 962 | ''' |
|
962 | ''' | |
| 963 | Written by R. Flores |
|
963 | Written by R. Flores | |
| 964 | ''' |
|
964 | ''' | |
| 965 | """Operation to calculate cross products of the Double Pulse Experiment. |
|
965 | """Operation to calculate cross products of the Double Pulse Experiment. | |
| 966 |
|
966 | |||
| 967 | Parameters: |
|
967 | Parameters: | |
| 968 | ----------- |
|
968 | ----------- | |
| 969 | NLAG : int |
|
969 | NLAG : int | |
| 970 | Number of lags Long Pulse. |
|
970 | Number of lags Long Pulse. | |
| 971 | NRANGE : int |
|
971 | NRANGE : int | |
| 972 | Number of samples for Long Pulse. |
|
972 | Number of samples for Long Pulse. | |
| 973 | NCAL : int |
|
973 | NCAL : int | |
| 974 | .* |
|
974 | .* | |
| 975 | DPL : int |
|
975 | DPL : int | |
| 976 | Number of lags Double Pulse. |
|
976 | Number of lags Double Pulse. | |
| 977 | NDN : int |
|
977 | NDN : int | |
| 978 | .* |
|
978 | .* | |
| 979 | NDT : int |
|
979 | NDT : int | |
| 980 | Number of heights for Double Pulse.* |
|
980 | Number of heights for Double Pulse.* | |
| 981 | NDP : int |
|
981 | NDP : int | |
| 982 | Number of heights for Double Pulse.* |
|
982 | Number of heights for Double Pulse.* | |
| 983 | NSCAN : int |
|
983 | NSCAN : int | |
| 984 | Number of profiles when the transmitter is on. |
|
984 | Number of profiles when the transmitter is on. | |
| 985 | flags_array : intlist |
|
985 | flags_array : intlist | |
| 986 | .* |
|
986 | .* | |
| 987 | NAVG : int |
|
987 | NAVG : int | |
| 988 | Number of blocks to be "averaged". |
|
988 | Number of blocks to be "averaged". | |
| 989 | nkill : int |
|
989 | nkill : int | |
| 990 | Number of blocks not to be considered when averaging. |
|
990 | Number of blocks not to be considered when averaging. | |
| 991 |
|
991 | |||
| 992 | Example |
|
992 | Example | |
| 993 | -------- |
|
993 | -------- | |
| 994 |
|
994 | |||
| 995 | op = proc_unit.addOperation(name='CrossProdDP', optype='other') |
|
995 | op = proc_unit.addOperation(name='CrossProdDP', optype='other') | |
| 996 | op.addParameter(name='NLAG', value='16', format='int') |
|
996 | op.addParameter(name='NLAG', value='16', format='int') | |
| 997 | op.addParameter(name='NRANGE', value='0', format='int') |
|
997 | op.addParameter(name='NRANGE', value='0', format='int') | |
| 998 | op.addParameter(name='NCAL', value='0', format='int') |
|
998 | op.addParameter(name='NCAL', value='0', format='int') | |
| 999 | op.addParameter(name='DPL', value='11', format='int') |
|
999 | op.addParameter(name='DPL', value='11', format='int') | |
| 1000 | op.addParameter(name='NDN', value='0', format='int') |
|
1000 | op.addParameter(name='NDN', value='0', format='int') | |
| 1001 | op.addParameter(name='NDT', value='66', format='int') |
|
1001 | op.addParameter(name='NDT', value='66', format='int') | |
| 1002 | op.addParameter(name='NDP', value='66', format='int') |
|
1002 | op.addParameter(name='NDP', value='66', format='int') | |
| 1003 | op.addParameter(name='NSCAN', value='132', format='int') |
|
1003 | op.addParameter(name='NSCAN', value='132', format='int') | |
| 1004 | op.addParameter(name='flags_array', value='(0, 30, 60, 90, 120, 150, 180, 210, 240, 270, 300)', format='intlist') |
|
1004 | op.addParameter(name='flags_array', value='(0, 30, 60, 90, 120, 150, 180, 210, 240, 270, 300)', format='intlist') | |
| 1005 | op.addParameter(name='NAVG', value='16', format='int') |
|
1005 | op.addParameter(name='NAVG', value='16', format='int') | |
| 1006 | op.addParameter(name='nkill', value='6', format='int') |
|
1006 | op.addParameter(name='nkill', value='6', format='int') | |
| 1007 |
|
1007 | |||
| 1008 | """ |
|
1008 | """ | |
| 1009 |
|
1009 | |||
| 1010 | def __init__(self, **kwargs): |
|
1010 | def __init__(self, **kwargs): | |
| 1011 |
|
1011 | |||
| 1012 | Operation.__init__(self, **kwargs) |
|
1012 | Operation.__init__(self, **kwargs) | |
| 1013 | self.bcounter=0 |
|
1013 | self.bcounter=0 | |
| 1014 | self.aux=1 |
|
1014 | self.aux=1 | |
| 1015 | self.lag_products_LP_median_estimates_aux=0 |
|
1015 | self.lag_products_LP_median_estimates_aux=0 | |
| 1016 |
|
1016 | |||
| 1017 | def set_header_output(self,dataOut): |
|
1017 | def set_header_output(self,dataOut): | |
| 1018 |
|
1018 | |||
| 1019 | dataOut.read_samples=len(dataOut.heightList)#int(dataOut.systemHeaderObj.nSamples/dataOut.windowOfFilter) |
|
1019 | dataOut.read_samples=len(dataOut.heightList)#int(dataOut.systemHeaderObj.nSamples/dataOut.windowOfFilter) | |
| 1020 | padding=numpy.zeros(1,'int32') |
|
1020 | padding=numpy.zeros(1,'int32') | |
| 1021 | hsize=numpy.zeros(1,'int32') |
|
1021 | hsize=numpy.zeros(1,'int32') | |
| 1022 | bufsize=numpy.zeros(1,'int32') |
|
1022 | bufsize=numpy.zeros(1,'int32') | |
| 1023 | nr=numpy.zeros(1,'int32') |
|
1023 | nr=numpy.zeros(1,'int32') | |
| 1024 | ngates=numpy.zeros(1,'int32') ### ### ### 2 |
|
1024 | ngates=numpy.zeros(1,'int32') ### ### ### 2 | |
| 1025 | time1=numpy.zeros(1,'uint64') # pos 3 |
|
1025 | time1=numpy.zeros(1,'uint64') # pos 3 | |
| 1026 | time2=numpy.zeros(1,'uint64') # pos 4 |
|
1026 | time2=numpy.zeros(1,'uint64') # pos 4 | |
| 1027 | lcounter=numpy.zeros(1,'int32') |
|
1027 | lcounter=numpy.zeros(1,'int32') | |
| 1028 | groups=numpy.zeros(1,'int32') |
|
1028 | groups=numpy.zeros(1,'int32') | |
| 1029 | system=numpy.zeros(4,'int8') # pos 7 |
|
1029 | system=numpy.zeros(4,'int8') # pos 7 | |
| 1030 | h0=numpy.zeros(1,'float32') |
|
1030 | h0=numpy.zeros(1,'float32') | |
| 1031 | dh=numpy.zeros(1,'float32') |
|
1031 | dh=numpy.zeros(1,'float32') | |
| 1032 | ipp=numpy.zeros(1,'float32') |
|
1032 | ipp=numpy.zeros(1,'float32') | |
| 1033 | process=numpy.zeros(1,'int32') |
|
1033 | process=numpy.zeros(1,'int32') | |
| 1034 | tx=numpy.zeros(1,'int32') |
|
1034 | tx=numpy.zeros(1,'int32') | |
| 1035 | ngates1=numpy.zeros(1,'int32') ### ### ### 13 |
|
1035 | ngates1=numpy.zeros(1,'int32') ### ### ### 13 | |
| 1036 | time0=numpy.zeros(1,'uint64') # pos 14 |
|
1036 | time0=numpy.zeros(1,'uint64') # pos 14 | |
| 1037 | nlags=numpy.zeros(1,'int32') |
|
1037 | nlags=numpy.zeros(1,'int32') | |
| 1038 | nlags1=numpy.zeros(1,'int32') |
|
1038 | nlags1=numpy.zeros(1,'int32') | |
| 1039 | txb=numpy.zeros(1,'float32') ### ### ### 17 |
|
1039 | txb=numpy.zeros(1,'float32') ### ### ### 17 | |
| 1040 | time3=numpy.zeros(1,'uint64') # pos 18 |
|
1040 | time3=numpy.zeros(1,'uint64') # pos 18 | |
| 1041 | time4=numpy.zeros(1,'uint64') # pos 19 |
|
1041 | time4=numpy.zeros(1,'uint64') # pos 19 | |
| 1042 | h0_=numpy.zeros(1,'float32') |
|
1042 | h0_=numpy.zeros(1,'float32') | |
| 1043 | dh_=numpy.zeros(1,'float32') |
|
1043 | dh_=numpy.zeros(1,'float32') | |
| 1044 | ipp_=numpy.zeros(1,'float32') |
|
1044 | ipp_=numpy.zeros(1,'float32') | |
| 1045 | txa_=numpy.zeros(1,'float32') |
|
1045 | txa_=numpy.zeros(1,'float32') | |
| 1046 | pad=numpy.zeros(100,'int32') |
|
1046 | pad=numpy.zeros(100,'int32') | |
| 1047 | nbytes=numpy.zeros(1,'int32') |
|
1047 | nbytes=numpy.zeros(1,'int32') | |
| 1048 | limits=numpy.zeros(1,'int32') |
|
1048 | limits=numpy.zeros(1,'int32') | |
| 1049 | ngroups=numpy.zeros(1,'int32') ### ### ### 27 |
|
1049 | ngroups=numpy.zeros(1,'int32') ### ### ### 27 | |
| 1050 |
|
1050 | |||
| 1051 | dataOut.header=[hsize,bufsize,nr,ngates,time1,time2, |
|
1051 | dataOut.header=[hsize,bufsize,nr,ngates,time1,time2, | |
| 1052 | lcounter,groups,system,h0,dh,ipp, |
|
1052 | lcounter,groups,system,h0,dh,ipp, | |
| 1053 | process,tx,ngates1,padding,time0,nlags, |
|
1053 | process,tx,ngates1,padding,time0,nlags, | |
| 1054 | nlags1,padding,txb,time3,time4,h0_,dh_, |
|
1054 | nlags1,padding,txb,time3,time4,h0_,dh_, | |
| 1055 | ipp_,txa_,pad,nbytes,limits,padding,ngroups] |
|
1055 | ipp_,txa_,pad,nbytes,limits,padding,ngroups] | |
| 1056 |
|
1056 | |||
| 1057 |
|
1057 | |||
| 1058 | #dataOut.header[1][0]=81864 |
|
1058 | #dataOut.header[1][0]=81864 | |
| 1059 | dataOut.FirstHeight=int(dataOut.heightList[0]) |
|
1059 | dataOut.FirstHeight=int(dataOut.heightList[0]) | |
| 1060 | dataOut.MAXNRANGENDT=max(dataOut.NRANGE,dataOut.NDT) |
|
1060 | dataOut.MAXNRANGENDT=max(dataOut.NRANGE,dataOut.NDT) | |
| 1061 | dataOut.header[3][0]=max(dataOut.NRANGE,dataOut.NDT) |
|
1061 | dataOut.header[3][0]=max(dataOut.NRANGE,dataOut.NDT) | |
| 1062 | dataOut.header[7][0]=dataOut.NAVG |
|
1062 | dataOut.header[7][0]=dataOut.NAVG | |
| 1063 | dataOut.header[9][0]=int(dataOut.heightList[0]) |
|
1063 | dataOut.header[9][0]=int(dataOut.heightList[0]) | |
| 1064 | dataOut.header[10][0]=dataOut.DH |
|
1064 | dataOut.header[10][0]=dataOut.DH | |
| 1065 | dataOut.header[17][0]=dataOut.DPL |
|
1065 | dataOut.header[17][0]=dataOut.DPL | |
| 1066 | dataOut.header[18][0]=dataOut.NLAG |
|
1066 | dataOut.header[18][0]=dataOut.NLAG | |
| 1067 | #self.header[5][0]=0 |
|
1067 | #self.header[5][0]=0 | |
| 1068 | dataOut.header[15][0]=dataOut.NDP |
|
1068 | dataOut.header[15][0]=dataOut.NDP | |
| 1069 | dataOut.header[2][0]=dataOut.NR |
|
1069 | dataOut.header[2][0]=dataOut.NR | |
| 1070 |
|
1070 | |||
| 1071 |
|
1071 | |||
| 1072 | def get_products_cabxys(self,dataOut): |
|
1072 | def get_products_cabxys(self,dataOut): | |
| 1073 |
|
1073 | |||
| 1074 | if self.aux==1: |
|
1074 | if self.aux==1: | |
| 1075 | self.set_header_output(dataOut) |
|
1075 | self.set_header_output(dataOut) | |
| 1076 | self.aux=0 |
|
1076 | self.aux=0 | |
| 1077 |
|
1077 | |||
| 1078 | dataOut.lags_array=[x / dataOut.DH for x in dataOut.flags_array] |
|
1078 | dataOut.lags_array=[x / dataOut.DH for x in dataOut.flags_array] | |
| 1079 | self.cax=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
1079 | self.cax=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) | |
| 1080 | self.cay=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
1080 | self.cay=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) | |
| 1081 | self.cbx=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
1081 | self.cbx=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) | |
| 1082 | self.cby=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
1082 | self.cby=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) | |
| 1083 | self.cax2=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
1083 | self.cax2=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) | |
| 1084 | self.cay2=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
1084 | self.cay2=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) | |
| 1085 | self.cbx2=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
1085 | self.cbx2=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) | |
| 1086 | self.cby2=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
1086 | self.cby2=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) | |
| 1087 | self.caxbx=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
1087 | self.caxbx=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) | |
| 1088 | self.caxby=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
1088 | self.caxby=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) | |
| 1089 | self.caybx=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
1089 | self.caybx=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) | |
| 1090 | self.cayby=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
1090 | self.cayby=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) | |
| 1091 | self.caxay=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
1091 | self.caxay=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) | |
| 1092 | self.cbxby=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
1092 | self.cbxby=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) | |
| 1093 |
|
1093 | |||
| 1094 | for i in range(2): |
|
1094 | for i in range(2): | |
| 1095 | for j in range(dataOut.NDP): |
|
1095 | for j in range(dataOut.NDP): | |
| 1096 | for k in range(int(dataOut.NSCAN/2)): |
|
1096 | for k in range(int(dataOut.NSCAN/2)): | |
| 1097 | n=k%dataOut.DPL |
|
1097 | n=k%dataOut.DPL | |
| 1098 | ax=dataOut.data[0,2*k+i,j].real |
|
1098 | ax=dataOut.data[0,2*k+i,j].real | |
| 1099 | ay=dataOut.data[0,2*k+i,j].imag |
|
1099 | ay=dataOut.data[0,2*k+i,j].imag | |
| 1100 | if j+dataOut.lags_array[n]<dataOut.NDP: |
|
1100 | if j+dataOut.lags_array[n]<dataOut.NDP: | |
| 1101 | bx=dataOut.data[1,2*k+i,j+int(dataOut.lags_array[n])].real |
|
1101 | bx=dataOut.data[1,2*k+i,j+int(dataOut.lags_array[n])].real | |
| 1102 | by=dataOut.data[1,2*k+i,j+int(dataOut.lags_array[n])].imag |
|
1102 | by=dataOut.data[1,2*k+i,j+int(dataOut.lags_array[n])].imag | |
| 1103 | else: |
|
1103 | else: | |
| 1104 | if k+1<int(dataOut.NSCAN/2): |
|
1104 | if k+1<int(dataOut.NSCAN/2): | |
| 1105 | bx=dataOut.data[1,2*(k+1)+i,(dataOut.NRANGE+dataOut.NCAL+j+int(dataOut.lags_array[n]))%dataOut.NDP].real |
|
1105 | bx=dataOut.data[1,2*(k+1)+i,(dataOut.NRANGE+dataOut.NCAL+j+int(dataOut.lags_array[n]))%dataOut.NDP].real | |
| 1106 | by=dataOut.data[1,2*(k+1)+i,(dataOut.NRANGE+dataOut.NCAL+j+int(dataOut.lags_array[n]))%dataOut.NDP].imag |
|
1106 | by=dataOut.data[1,2*(k+1)+i,(dataOut.NRANGE+dataOut.NCAL+j+int(dataOut.lags_array[n]))%dataOut.NDP].imag | |
| 1107 |
|
1107 | |||
| 1108 | if k+1==int(dataOut.NSCAN/2): |
|
1108 | if k+1==int(dataOut.NSCAN/2): | |
| 1109 | bx=dataOut.data[1,2*k+i,(dataOut.NRANGE+dataOut.NCAL+j+int(dataOut.lags_array[n]))%dataOut.NDP].real |
|
1109 | bx=dataOut.data[1,2*k+i,(dataOut.NRANGE+dataOut.NCAL+j+int(dataOut.lags_array[n]))%dataOut.NDP].real | |
| 1110 | by=dataOut.data[1,2*k+i,(dataOut.NRANGE+dataOut.NCAL+j+int(dataOut.lags_array[n]))%dataOut.NDP].imag |
|
1110 | by=dataOut.data[1,2*k+i,(dataOut.NRANGE+dataOut.NCAL+j+int(dataOut.lags_array[n]))%dataOut.NDP].imag | |
| 1111 |
|
1111 | |||
| 1112 | if(k<dataOut.DPL): |
|
1112 | if(k<dataOut.DPL): | |
| 1113 | self.cax[j][n][i]=ax |
|
1113 | self.cax[j][n][i]=ax | |
| 1114 | self.cay[j][n][i]=ay |
|
1114 | self.cay[j][n][i]=ay | |
| 1115 | self.cbx[j][n][i]=bx |
|
1115 | self.cbx[j][n][i]=bx | |
| 1116 | self.cby[j][n][i]=by |
|
1116 | self.cby[j][n][i]=by | |
| 1117 | self.cax2[j][n][i]=ax*ax |
|
1117 | self.cax2[j][n][i]=ax*ax | |
| 1118 | self.cay2[j][n][i]=ay*ay |
|
1118 | self.cay2[j][n][i]=ay*ay | |
| 1119 | self.cbx2[j][n][i]=bx*bx |
|
1119 | self.cbx2[j][n][i]=bx*bx | |
| 1120 | self.cby2[j][n][i]=by*by |
|
1120 | self.cby2[j][n][i]=by*by | |
| 1121 | self.caxbx[j][n][i]=ax*bx |
|
1121 | self.caxbx[j][n][i]=ax*bx | |
| 1122 | self.caxby[j][n][i]=ax*by |
|
1122 | self.caxby[j][n][i]=ax*by | |
| 1123 | self.caybx[j][n][i]=ay*bx |
|
1123 | self.caybx[j][n][i]=ay*bx | |
| 1124 | self.cayby[j][n][i]=ay*by |
|
1124 | self.cayby[j][n][i]=ay*by | |
| 1125 | self.caxay[j][n][i]=ax*ay |
|
1125 | self.caxay[j][n][i]=ax*ay | |
| 1126 | self.cbxby[j][n][i]=bx*by |
|
1126 | self.cbxby[j][n][i]=bx*by | |
| 1127 | else: |
|
1127 | else: | |
| 1128 | self.cax[j][n][i]+=ax |
|
1128 | self.cax[j][n][i]+=ax | |
| 1129 | self.cay[j][n][i]+=ay |
|
1129 | self.cay[j][n][i]+=ay | |
| 1130 | self.cbx[j][n][i]+=bx |
|
1130 | self.cbx[j][n][i]+=bx | |
| 1131 | self.cby[j][n][i]+=by |
|
1131 | self.cby[j][n][i]+=by | |
| 1132 | self.cax2[j][n][i]+=ax*ax |
|
1132 | self.cax2[j][n][i]+=ax*ax | |
| 1133 | self.cay2[j][n][i]+=ay*ay |
|
1133 | self.cay2[j][n][i]+=ay*ay | |
| 1134 | self.cbx2[j][n][i]+=bx*bx |
|
1134 | self.cbx2[j][n][i]+=bx*bx | |
| 1135 | self.cby2[j][n][i]+=by*by |
|
1135 | self.cby2[j][n][i]+=by*by | |
| 1136 | self.caxbx[j][n][i]+=ax*bx |
|
1136 | self.caxbx[j][n][i]+=ax*bx | |
| 1137 | self.caxby[j][n][i]+=ax*by |
|
1137 | self.caxby[j][n][i]+=ax*by | |
| 1138 | self.caybx[j][n][i]+=ay*bx |
|
1138 | self.caybx[j][n][i]+=ay*bx | |
| 1139 | self.cayby[j][n][i]+=ay*by |
|
1139 | self.cayby[j][n][i]+=ay*by | |
| 1140 | self.caxay[j][n][i]+=ax*ay |
|
1140 | self.caxay[j][n][i]+=ax*ay | |
| 1141 | self.cbxby[j][n][i]+=bx*by |
|
1141 | self.cbxby[j][n][i]+=bx*by | |
| 1142 |
|
1142 | |||
| 1143 |
|
1143 | |||
| 1144 | def medi(self,data_navg,NAVG,nkill): |
|
1144 | def medi(self,data_navg,NAVG,nkill): | |
| 1145 | sorts=sorted(data_navg) |
|
1145 | sorts=sorted(data_navg) | |
| 1146 | rsorts=numpy.arange(NAVG) |
|
1146 | rsorts=numpy.arange(NAVG) | |
| 1147 | result=0.0 |
|
1147 | result=0.0 | |
| 1148 | for k in range(NAVG): |
|
1148 | for k in range(NAVG): | |
| 1149 | if k>=nkill/2 and k<NAVG-nkill/2: |
|
1149 | if k>=nkill/2 and k<NAVG-nkill/2: | |
| 1150 | result+=sorts[k]*float(NAVG)/(float)(NAVG-nkill) |
|
1150 | result+=sorts[k]*float(NAVG)/(float)(NAVG-nkill) | |
| 1151 | return result |
|
1151 | return result | |
| 1152 |
|
1152 | |||
| 1153 |
|
1153 | |||
| 1154 | def get_dc(self,dataOut): |
|
1154 | def get_dc(self,dataOut): | |
| 1155 | if self.bcounter==0: |
|
1155 | if self.bcounter==0: | |
| 1156 | dataOut.dc=numpy.zeros(dataOut.NR,dtype='complex64') |
|
1156 | dataOut.dc=numpy.zeros(dataOut.NR,dtype='complex64') | |
| 1157 | def cabxys_navg(self,dataOut): |
|
1157 | def cabxys_navg(self,dataOut): | |
| 1158 |
|
1158 | |||
| 1159 |
|
1159 | |||
| 1160 | dataOut.header[5][0]=dataOut.TimeBlockSeconds |
|
1160 | dataOut.header[5][0]=dataOut.TimeBlockSeconds | |
| 1161 |
|
1161 | |||
| 1162 | dataOut.LastAVGDate=dataOut.TimeBlockSeconds |
|
1162 | dataOut.LastAVGDate=dataOut.TimeBlockSeconds | |
| 1163 |
|
1163 | |||
| 1164 | if self.bcounter==0: |
|
1164 | if self.bcounter==0: | |
| 1165 | dataOut.FirstAVGDate=dataOut.TimeBlockSeconds |
|
1165 | dataOut.FirstAVGDate=dataOut.TimeBlockSeconds | |
| 1166 | dataOut.header[4][0]=dataOut.header[5][0]#firsttimeofNAVG |
|
1166 | dataOut.header[4][0]=dataOut.header[5][0]#firsttimeofNAVG | |
| 1167 | if dataOut.CurrentBlock==1: |
|
1167 | if dataOut.CurrentBlock==1: | |
| 1168 | dataOut.FirstBlockDate=dataOut.TimeBlockSeconds |
|
1168 | dataOut.FirstBlockDate=dataOut.TimeBlockSeconds | |
| 1169 | dataOut.header[16][0]=dataOut.header[5][0]#FirsTimeOfTotalBlocks |
|
1169 | dataOut.header[16][0]=dataOut.header[5][0]#FirsTimeOfTotalBlocks | |
| 1170 |
|
1170 | |||
| 1171 | self.cax_navg=[] |
|
1171 | self.cax_navg=[] | |
| 1172 | self.cay_navg=[] |
|
1172 | self.cay_navg=[] | |
| 1173 | self.cbx_navg=[] |
|
1173 | self.cbx_navg=[] | |
| 1174 | self.cby_navg=[] |
|
1174 | self.cby_navg=[] | |
| 1175 | self.cax2_navg=[] |
|
1175 | self.cax2_navg=[] | |
| 1176 | self.cay2_navg=[] |
|
1176 | self.cay2_navg=[] | |
| 1177 | self.cbx2_navg=[] |
|
1177 | self.cbx2_navg=[] | |
| 1178 | self.cby2_navg=[] |
|
1178 | self.cby2_navg=[] | |
| 1179 | self.caxbx_navg=[] |
|
1179 | self.caxbx_navg=[] | |
| 1180 | self.caxby_navg=[] |
|
1180 | self.caxby_navg=[] | |
| 1181 | self.caybx_navg=[] |
|
1181 | self.caybx_navg=[] | |
| 1182 | self.cayby_navg=[] |
|
1182 | self.cayby_navg=[] | |
| 1183 | self.caxay_navg=[] |
|
1183 | self.caxay_navg=[] | |
| 1184 | self.cbxby_navg=[] |
|
1184 | self.cbxby_navg=[] | |
| 1185 |
|
1185 | |||
| 1186 | dataOut.noisevector=numpy.zeros((dataOut.MAXNRANGENDT,dataOut.NR,dataOut.NAVG),'float32') #30/03/2020 |
|
1186 | dataOut.noisevector=numpy.zeros((dataOut.MAXNRANGENDT,dataOut.NR,dataOut.NAVG),'float32') #30/03/2020 | |
| 1187 |
|
1187 | |||
| 1188 | dataOut.noisevector_=numpy.zeros((dataOut.read_samples,dataOut.NR,dataOut.NAVG),'float32') |
|
1188 | dataOut.noisevector_=numpy.zeros((dataOut.read_samples,dataOut.NR,dataOut.NAVG),'float32') | |
| 1189 |
|
1189 | |||
| 1190 | self.noisevectorizer(dataOut.NSCAN,dataOut.nProfiles,dataOut.NR,dataOut.MAXNRANGENDT,dataOut.noisevector,dataOut.data,dataOut.dc) #30/03/2020 |
|
1190 | self.noisevectorizer(dataOut.NSCAN,dataOut.nProfiles,dataOut.NR,dataOut.MAXNRANGENDT,dataOut.noisevector,dataOut.data,dataOut.dc) #30/03/2020 | |
| 1191 |
|
1191 | |||
| 1192 | self.cax_navg.append(self.cax) |
|
1192 | self.cax_navg.append(self.cax) | |
| 1193 | self.cay_navg.append(self.cay) |
|
1193 | self.cay_navg.append(self.cay) | |
| 1194 | self.cbx_navg.append(self.cbx) |
|
1194 | self.cbx_navg.append(self.cbx) | |
| 1195 | self.cby_navg.append(self.cby) |
|
1195 | self.cby_navg.append(self.cby) | |
| 1196 | self.cax2_navg.append(self.cax2) |
|
1196 | self.cax2_navg.append(self.cax2) | |
| 1197 | self.cay2_navg.append(self.cay2) |
|
1197 | self.cay2_navg.append(self.cay2) | |
| 1198 | self.cbx2_navg.append(self.cbx2) |
|
1198 | self.cbx2_navg.append(self.cbx2) | |
| 1199 | self.cby2_navg.append(self.cby2) |
|
1199 | self.cby2_navg.append(self.cby2) | |
| 1200 | self.caxbx_navg.append(self.caxbx) |
|
1200 | self.caxbx_navg.append(self.caxbx) | |
| 1201 | self.caxby_navg.append(self.caxby) |
|
1201 | self.caxby_navg.append(self.caxby) | |
| 1202 | self.caybx_navg.append(self.caybx) |
|
1202 | self.caybx_navg.append(self.caybx) | |
| 1203 | self.cayby_navg.append(self.cayby) |
|
1203 | self.cayby_navg.append(self.cayby) | |
| 1204 | self.caxay_navg.append(self.caxay) |
|
1204 | self.caxay_navg.append(self.caxay) | |
| 1205 | self.cbxby_navg.append(self.cbxby) |
|
1205 | self.cbxby_navg.append(self.cbxby) | |
| 1206 | self.bcounter+=1 |
|
1206 | self.bcounter+=1 | |
| 1207 |
|
1207 | |||
| 1208 | def noise_estimation4x_DP(self,dataOut): |
|
1208 | def noise_estimation4x_DP(self,dataOut): | |
| 1209 | if self.bcounter==dataOut.NAVG: |
|
1209 | if self.bcounter==dataOut.NAVG: | |
| 1210 | dataOut.noise_final=numpy.zeros(dataOut.NR,'float32') |
|
1210 | dataOut.noise_final=numpy.zeros(dataOut.NR,'float32') | |
| 1211 | snoise=numpy.zeros((dataOut.NR,dataOut.NAVG),'float32') |
|
1211 | snoise=numpy.zeros((dataOut.NR,dataOut.NAVG),'float32') | |
| 1212 | nvector1=numpy.zeros((dataOut.NR,dataOut.NAVG,dataOut.MAXNRANGENDT),'float32') |
|
1212 | nvector1=numpy.zeros((dataOut.NR,dataOut.NAVG,dataOut.MAXNRANGENDT),'float32') | |
| 1213 | for i in range(dataOut.NR): |
|
1213 | for i in range(dataOut.NR): | |
| 1214 | dataOut.noise_final[i]=0.0 |
|
1214 | dataOut.noise_final[i]=0.0 | |
| 1215 | for k in range(dataOut.NAVG): |
|
1215 | for k in range(dataOut.NAVG): | |
| 1216 | snoise[i][k]=0.0 |
|
1216 | snoise[i][k]=0.0 | |
| 1217 | for j in range(dataOut.MAXNRANGENDT): |
|
1217 | for j in range(dataOut.MAXNRANGENDT): | |
| 1218 | nvector1[i][k][j]= dataOut.noisevector[j][i][k]; |
|
1218 | nvector1[i][k][j]= dataOut.noisevector[j][i][k]; | |
| 1219 | snoise[i][k]=self.noise_hs4x(dataOut.MAXNRANGENDT, nvector1[i][k]) |
|
1219 | snoise[i][k]=self.noise_hs4x(dataOut.MAXNRANGENDT, nvector1[i][k]) | |
| 1220 | dataOut.noise_final[i]=self.noise_hs4x(dataOut.NAVG, snoise[i]) |
|
1220 | dataOut.noise_final[i]=self.noise_hs4x(dataOut.NAVG, snoise[i]) | |
| 1221 |
|
1221 | |||
| 1222 | def kabxys(self,dataOut): |
|
1222 | def kabxys(self,dataOut): | |
| 1223 |
|
1223 | |||
| 1224 | if self.bcounter==dataOut.NAVG: |
|
1224 | if self.bcounter==dataOut.NAVG: | |
| 1225 |
|
1225 | |||
| 1226 | dataOut.flagNoData = False |
|
1226 | dataOut.flagNoData = False | |
| 1227 |
|
1227 | |||
| 1228 | self.kax=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') |
|
1228 | self.kax=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') | |
| 1229 | self.kay=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') |
|
1229 | self.kay=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') | |
| 1230 | self.kbx=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') |
|
1230 | self.kbx=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') | |
| 1231 | self.kby=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') |
|
1231 | self.kby=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') | |
| 1232 | self.kax2=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') |
|
1232 | self.kax2=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') | |
| 1233 | self.kay2=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') |
|
1233 | self.kay2=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') | |
| 1234 | self.kbx2=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') |
|
1234 | self.kbx2=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') | |
| 1235 | self.kby2=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') |
|
1235 | self.kby2=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') | |
| 1236 | self.kaxbx=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') |
|
1236 | self.kaxbx=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') | |
| 1237 | self.kaxby=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') |
|
1237 | self.kaxby=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') | |
| 1238 | self.kaybx=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') |
|
1238 | self.kaybx=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') | |
| 1239 | self.kayby=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') |
|
1239 | self.kayby=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') | |
| 1240 | self.kaxay=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') |
|
1240 | self.kaxay=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') | |
| 1241 | self.kbxby=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') |
|
1241 | self.kbxby=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') | |
| 1242 |
|
1242 | |||
| 1243 | for i in range(self.cax_navg[0].shape[0]): |
|
1243 | for i in range(self.cax_navg[0].shape[0]): | |
| 1244 | for j in range(self.cax_navg[0].shape[1]): |
|
1244 | for j in range(self.cax_navg[0].shape[1]): | |
| 1245 | for k in range(self.cax_navg[0].shape[2]): |
|
1245 | for k in range(self.cax_navg[0].shape[2]): | |
| 1246 | data_navg=[item[i,j,k] for item in self.cax_navg] |
|
1246 | data_navg=[item[i,j,k] for item in self.cax_navg] | |
| 1247 | self.kax[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) |
|
1247 | self.kax[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) | |
| 1248 | data_navg=[item[i,j,k] for item in self.cay_navg] |
|
1248 | data_navg=[item[i,j,k] for item in self.cay_navg] | |
| 1249 | self.kay[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) |
|
1249 | self.kay[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) | |
| 1250 | data_navg=[item[i,j,k] for item in self.cbx_navg] |
|
1250 | data_navg=[item[i,j,k] for item in self.cbx_navg] | |
| 1251 | self.kbx[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) |
|
1251 | self.kbx[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) | |
| 1252 | data_navg=[item[i,j,k] for item in self.cby_navg] |
|
1252 | data_navg=[item[i,j,k] for item in self.cby_navg] | |
| 1253 | self.kby[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) |
|
1253 | self.kby[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) | |
| 1254 | data_navg=[item[i,j,k] for item in self.cax2_navg] |
|
1254 | data_navg=[item[i,j,k] for item in self.cax2_navg] | |
| 1255 | self.kax2[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) |
|
1255 | self.kax2[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) | |
| 1256 | data_navg=[item[i,j,k] for item in self.cay2_navg] |
|
1256 | data_navg=[item[i,j,k] for item in self.cay2_navg] | |
| 1257 | self.kay2[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) |
|
1257 | self.kay2[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) | |
| 1258 | data_navg=[item[i,j,k] for item in self.cbx2_navg] |
|
1258 | data_navg=[item[i,j,k] for item in self.cbx2_navg] | |
| 1259 | self.kbx2[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) |
|
1259 | self.kbx2[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) | |
| 1260 | data_navg=[item[i,j,k] for item in self.cby2_navg] |
|
1260 | data_navg=[item[i,j,k] for item in self.cby2_navg] | |
| 1261 | self.kby2[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) |
|
1261 | self.kby2[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) | |
| 1262 | data_navg=[item[i,j,k] for item in self.caxbx_navg] |
|
1262 | data_navg=[item[i,j,k] for item in self.caxbx_navg] | |
| 1263 | self.kaxbx[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) |
|
1263 | self.kaxbx[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) | |
| 1264 | data_navg=[item[i,j,k] for item in self.caxby_navg] |
|
1264 | data_navg=[item[i,j,k] for item in self.caxby_navg] | |
| 1265 | self.kaxby[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) |
|
1265 | self.kaxby[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) | |
| 1266 | data_navg=[item[i,j,k] for item in self.caybx_navg] |
|
1266 | data_navg=[item[i,j,k] for item in self.caybx_navg] | |
| 1267 | self.kaybx[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) |
|
1267 | self.kaybx[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) | |
| 1268 | data_navg=[item[i,j,k] for item in self.cayby_navg] |
|
1268 | data_navg=[item[i,j,k] for item in self.cayby_navg] | |
| 1269 | self.kayby[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) |
|
1269 | self.kayby[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) | |
| 1270 | data_navg=[item[i,j,k] for item in self.caxay_navg] |
|
1270 | data_navg=[item[i,j,k] for item in self.caxay_navg] | |
| 1271 | self.kaxay[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) |
|
1271 | self.kaxay[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) | |
| 1272 | data_navg=[item[i,j,k] for item in self.cbxby_navg] |
|
1272 | data_navg=[item[i,j,k] for item in self.cbxby_navg] | |
| 1273 | self.kbxby[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) |
|
1273 | self.kbxby[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) | |
| 1274 |
|
1274 | |||
| 1275 |
|
1275 | |||
| 1276 | dataOut.kax=self.kax |
|
1276 | dataOut.kax=self.kax | |
| 1277 | dataOut.kay=self.kay |
|
1277 | dataOut.kay=self.kay | |
| 1278 | dataOut.kbx=self.kbx |
|
1278 | dataOut.kbx=self.kbx | |
| 1279 | dataOut.kby=self.kby |
|
1279 | dataOut.kby=self.kby | |
| 1280 | dataOut.kax2=self.kax2 |
|
1280 | dataOut.kax2=self.kax2 | |
| 1281 | dataOut.kay2=self.kay2 |
|
1281 | dataOut.kay2=self.kay2 | |
| 1282 | dataOut.kbx2=self.kbx2 |
|
1282 | dataOut.kbx2=self.kbx2 | |
| 1283 | dataOut.kby2=self.kby2 |
|
1283 | dataOut.kby2=self.kby2 | |
| 1284 | dataOut.kaxbx=self.kaxbx |
|
1284 | dataOut.kaxbx=self.kaxbx | |
| 1285 | dataOut.kaxby=self.kaxby |
|
1285 | dataOut.kaxby=self.kaxby | |
| 1286 | dataOut.kaybx=self.kaybx |
|
1286 | dataOut.kaybx=self.kaybx | |
| 1287 | dataOut.kayby=self.kayby |
|
1287 | dataOut.kayby=self.kayby | |
| 1288 | dataOut.kaxay=self.kaxay |
|
1288 | dataOut.kaxay=self.kaxay | |
| 1289 | dataOut.kbxby=self.kbxby |
|
1289 | dataOut.kbxby=self.kbxby | |
| 1290 |
|
1290 | |||
| 1291 | self.bcounter=0 |
|
1291 | self.bcounter=0 | |
| 1292 |
|
1292 | |||
| 1293 | dataOut.crossprods=numpy.zeros((3,4,numpy.shape(dataOut.kax)[0],numpy.shape(dataOut.kax)[1],numpy.shape(dataOut.kax)[2])) |
|
1293 | dataOut.crossprods=numpy.zeros((3,4,numpy.shape(dataOut.kax)[0],numpy.shape(dataOut.kax)[1],numpy.shape(dataOut.kax)[2])) | |
| 1294 |
|
1294 | |||
| 1295 | dataOut.crossprods[0]=[dataOut.kax,dataOut.kay,dataOut.kbx,dataOut.kby] |
|
1295 | dataOut.crossprods[0]=[dataOut.kax,dataOut.kay,dataOut.kbx,dataOut.kby] | |
| 1296 | dataOut.crossprods[1]=[dataOut.kax2,dataOut.kay2,dataOut.kbx2,dataOut.kby2] |
|
1296 | dataOut.crossprods[1]=[dataOut.kax2,dataOut.kay2,dataOut.kbx2,dataOut.kby2] | |
| 1297 | dataOut.crossprods[2]=[dataOut.kaxay,dataOut.kbxby,dataOut.kaxbx,dataOut.kaxby] |
|
1297 | dataOut.crossprods[2]=[dataOut.kaxay,dataOut.kbxby,dataOut.kaxbx,dataOut.kaxby] | |
| 1298 | #print("before: ",self.dataOut.noise_final) |
|
1298 | #print("before: ",self.dataOut.noise_final) | |
| 1299 | dataOut.data_for_RTI_DP=numpy.zeros((3,dataOut.NDP)) |
|
1299 | dataOut.data_for_RTI_DP=numpy.zeros((3,dataOut.NDP)) | |
| 1300 | dataOut.data_for_RTI_DP[0],dataOut.data_for_RTI_DP[1],dataOut.data_for_RTI_DP[2]=self.RTI_COLUMN(dataOut.kax2,dataOut.kay2,dataOut.kbx2,dataOut.kby2,dataOut.kaxbx,dataOut.kayby,dataOut.kaybx,dataOut.kaxby, dataOut.NDP) |
|
1300 | dataOut.data_for_RTI_DP[0],dataOut.data_for_RTI_DP[1],dataOut.data_for_RTI_DP[2]=self.RTI_COLUMN(dataOut.kax2,dataOut.kay2,dataOut.kbx2,dataOut.kby2,dataOut.kaxbx,dataOut.kayby,dataOut.kaybx,dataOut.kaxby, dataOut.NDP) | |
| 1301 |
|
1301 | |||
| 1302 |
|
1302 | |||
| 1303 |
|
1303 | |||
| 1304 | def RTI_COLUMN(self,kax2,kay2,kbx2,kby2,kaxbx,kayby,kaybx,kaxby, NDP): |
|
1304 | def RTI_COLUMN(self,kax2,kay2,kbx2,kby2,kaxbx,kayby,kaybx,kaxby, NDP): | |
| 1305 | x00=numpy.zeros(NDP,dtype='float32') |
|
1305 | x00=numpy.zeros(NDP,dtype='float32') | |
| 1306 | x01=numpy.zeros(NDP,dtype='float32') |
|
1306 | x01=numpy.zeros(NDP,dtype='float32') | |
| 1307 | x02=numpy.zeros(NDP,dtype='float32') |
|
1307 | x02=numpy.zeros(NDP,dtype='float32') | |
| 1308 | for j in range(2):# first couple lags |
|
1308 | for j in range(2):# first couple lags | |
| 1309 | for k in range(2): #flip |
|
1309 | for k in range(2): #flip | |
| 1310 | for i in range(NDP): # |
|
1310 | for i in range(NDP): # | |
| 1311 | fx=numpy.sqrt((kaxbx[i,j,k]+kayby[i,j,k])**2+(kaybx[i,j,k]-kaxby[i,j,k])**2) |
|
1311 | fx=numpy.sqrt((kaxbx[i,j,k]+kayby[i,j,k])**2+(kaybx[i,j,k]-kaxby[i,j,k])**2) | |
| 1312 | x00[i]=x00[i]+(kax2[i,j,k]+kay2[i,j,k]) |
|
1312 | x00[i]=x00[i]+(kax2[i,j,k]+kay2[i,j,k]) | |
| 1313 | x01[i]=x01[i]+(kbx2[i,j,k]+kby2[i,j,k]) |
|
1313 | x01[i]=x01[i]+(kbx2[i,j,k]+kby2[i,j,k]) | |
| 1314 | x02[i]=x02[i]+fx |
|
1314 | x02[i]=x02[i]+fx | |
| 1315 |
|
1315 | |||
| 1316 | x00[i]=10.0*numpy.log10(x00[i]/512.) |
|
1316 | x00[i]=10.0*numpy.log10(x00[i]/512.) | |
| 1317 | x01[i]=10.0*numpy.log10(x01[i]/512.) |
|
1317 | x01[i]=10.0*numpy.log10(x01[i]/512.) | |
| 1318 | x02[i]=10.0*numpy.log10(x02[i]) |
|
1318 | x02[i]=10.0*numpy.log10(x02[i]) | |
| 1319 | return x02,x00,x01 |
|
1319 | return x02,x00,x01 | |
| 1320 |
|
1320 | |||
| 1321 |
|
1321 | |||
| 1322 |
|
1322 | |||
| 1323 |
|
1323 | |||
| 1324 |
|
1324 | |||
| 1325 |
|
1325 | |||
| 1326 | #30/03/2020: |
|
1326 | #30/03/2020: | |
| 1327 | def noisevectorizer(self,NSCAN,nProfiles,NR,MAXNRANGENDT,noisevector,data,dc): |
|
1327 | def noisevectorizer(self,NSCAN,nProfiles,NR,MAXNRANGENDT,noisevector,data,dc): | |
| 1328 |
|
1328 | |||
| 1329 | rnormalizer= 1./(float(nProfiles - NSCAN)) |
|
1329 | rnormalizer= 1./(float(nProfiles - NSCAN)) | |
| 1330 | #rnormalizer= float(NSCAN)/((float(nProfiles - NSCAN))*float(MAXNRANGENDT)) |
|
1330 | #rnormalizer= float(NSCAN)/((float(nProfiles - NSCAN))*float(MAXNRANGENDT)) | |
| 1331 | for i in range(NR): |
|
1331 | for i in range(NR): | |
| 1332 | for j in range(MAXNRANGENDT): |
|
1332 | for j in range(MAXNRANGENDT): | |
| 1333 | for k in range(NSCAN,nProfiles): |
|
1333 | for k in range(NSCAN,nProfiles): | |
| 1334 | #TODO:integrate just 2nd quartile gates |
|
1334 | #TODO:integrate just 2nd quartile gates | |
| 1335 | if k==NSCAN: |
|
1335 | if k==NSCAN: | |
| 1336 | noisevector[j][i][self.bcounter]=(abs(data[i][k][j]-dc[i])**2)*rnormalizer |
|
1336 | noisevector[j][i][self.bcounter]=(abs(data[i][k][j]-dc[i])**2)*rnormalizer | |
| 1337 | else: |
|
1337 | else: | |
| 1338 | noisevector[j][i][self.bcounter]+=(abs(data[i][k][j]-dc[i])**2)*rnormalizer |
|
1338 | noisevector[j][i][self.bcounter]+=(abs(data[i][k][j]-dc[i])**2)*rnormalizer | |
| 1339 |
|
1339 | |||
| 1340 |
|
1340 | |||
| 1341 |
|
1341 | |||
| 1342 |
|
1342 | |||
| 1343 | def noise_hs4x(self, ndatax, datax): |
|
1343 | def noise_hs4x(self, ndatax, datax): | |
| 1344 | divider=10#divider was originally 10 |
|
1344 | divider=10#divider was originally 10 | |
| 1345 | noise=0.0 |
|
1345 | noise=0.0 | |
| 1346 | data=numpy.zeros(ndatax,'float32') |
|
1346 | data=numpy.zeros(ndatax,'float32') | |
| 1347 | ndata1=int(ndatax/4) |
|
1347 | ndata1=int(ndatax/4) | |
| 1348 | ndata2=int(2.5*(ndatax/4.)) |
|
1348 | ndata2=int(2.5*(ndatax/4.)) | |
| 1349 | ndata=int(ndata2-ndata1) |
|
1349 | ndata=int(ndata2-ndata1) | |
| 1350 | sorts=sorted(datax) |
|
1350 | sorts=sorted(datax) | |
| 1351 |
|
1351 | |||
| 1352 | for k in range(ndata2): # select just second quartile |
|
1352 | for k in range(ndata2): # select just second quartile | |
| 1353 | data[k]=sorts[k+ndata1] |
|
1353 | data[k]=sorts[k+ndata1] | |
| 1354 | nums_min= int(ndata/divider) |
|
1354 | nums_min= int(ndata/divider) | |
| 1355 | if(int(ndata/divider)> 2): |
|
1355 | if(int(ndata/divider)> 2): | |
| 1356 | nums_min= int(ndata/divider) |
|
1356 | nums_min= int(ndata/divider) | |
| 1357 | else: |
|
1357 | else: | |
| 1358 | nums_min=2 |
|
1358 | nums_min=2 | |
| 1359 | sump=0.0 |
|
1359 | sump=0.0 | |
| 1360 | sumq=0.0 |
|
1360 | sumq=0.0 | |
| 1361 | j=0 |
|
1361 | j=0 | |
| 1362 | cont=1 |
|
1362 | cont=1 | |
| 1363 | while ( (cont==1) and (j<ndata)): |
|
1363 | while ( (cont==1) and (j<ndata)): | |
| 1364 | sump+=data[j] |
|
1364 | sump+=data[j] | |
| 1365 | sumq+= data[j]*data[j] |
|
1365 | sumq+= data[j]*data[j] | |
| 1366 | j=j+1 |
|
1366 | j=j+1 | |
| 1367 | if (j> nums_min): |
|
1367 | if (j> nums_min): | |
| 1368 | rtest= float(j/(j-1)) +1.0/ndata |
|
1368 | rtest= float(j/(j-1)) +1.0/ndata | |
| 1369 | if( (sumq*j) > (rtest*sump*sump ) ): |
|
1369 | if( (sumq*j) > (rtest*sump*sump ) ): | |
| 1370 | j=j-1 |
|
1370 | j=j-1 | |
| 1371 | sump-= data[j] |
|
1371 | sump-= data[j] | |
| 1372 | sumq-=data[j]*data[j] |
|
1372 | sumq-=data[j]*data[j] | |
| 1373 | cont= 0 |
|
1373 | cont= 0 | |
| 1374 | noise= (sump/j) |
|
1374 | noise= (sump/j) | |
| 1375 |
|
1375 | |||
| 1376 | return noise |
|
1376 | return noise | |
| 1377 |
|
1377 | |||
| 1378 |
|
1378 | |||
| 1379 |
|
1379 | |||
| 1380 | def run(self, dataOut, NLAG=16, NRANGE=0, NCAL=0, DPL=11, |
|
1380 | def run(self, dataOut, NLAG=16, NRANGE=0, NCAL=0, DPL=11, | |
| 1381 | NDN=0, NDT=66, NDP=66, NSCAN=132, |
|
1381 | NDN=0, NDT=66, NDP=66, NSCAN=132, | |
| 1382 | flags_array=(0, 30, 60, 90, 120, 150, 180, 210, 240, 270, 300), NAVG=16, nkill=6, **kwargs): |
|
1382 | flags_array=(0, 30, 60, 90, 120, 150, 180, 210, 240, 270, 300), NAVG=16, nkill=6, **kwargs): | |
| 1383 |
|
1383 | |||
| 1384 | dataOut.NLAG=NLAG |
|
1384 | dataOut.NLAG=NLAG | |
| 1385 | dataOut.NR=len(dataOut.channelList) |
|
1385 | dataOut.NR=len(dataOut.channelList) | |
| 1386 | dataOut.NRANGE=NRANGE |
|
1386 | dataOut.NRANGE=NRANGE | |
| 1387 | dataOut.NCAL=NCAL |
|
1387 | dataOut.NCAL=NCAL | |
| 1388 | dataOut.DPL=DPL |
|
1388 | dataOut.DPL=DPL | |
| 1389 | dataOut.NDN=NDN |
|
1389 | dataOut.NDN=NDN | |
| 1390 | dataOut.NDT=NDT |
|
1390 | dataOut.NDT=NDT | |
| 1391 | dataOut.NDP=NDP |
|
1391 | dataOut.NDP=NDP | |
| 1392 | dataOut.NSCAN=NSCAN |
|
1392 | dataOut.NSCAN=NSCAN | |
| 1393 | dataOut.DH=dataOut.heightList[1]-dataOut.heightList[0] |
|
1393 | dataOut.DH=dataOut.heightList[1]-dataOut.heightList[0] | |
| 1394 | dataOut.H0=int(dataOut.heightList[0]) |
|
1394 | dataOut.H0=int(dataOut.heightList[0]) | |
| 1395 | dataOut.flags_array=flags_array |
|
1395 | dataOut.flags_array=flags_array | |
| 1396 | dataOut.NAVG=NAVG |
|
1396 | dataOut.NAVG=NAVG | |
| 1397 | dataOut.nkill=nkill |
|
1397 | dataOut.nkill=nkill | |
| 1398 | dataOut.flagNoData = True |
|
1398 | dataOut.flagNoData = True | |
| 1399 |
|
1399 | |||
| 1400 | self.get_dc(dataOut) |
|
1400 | self.get_dc(dataOut) | |
| 1401 | self.get_products_cabxys(dataOut) |
|
1401 | self.get_products_cabxys(dataOut) | |
| 1402 | self.cabxys_navg(dataOut) |
|
1402 | self.cabxys_navg(dataOut) | |
| 1403 | self.noise_estimation4x_DP(dataOut) |
|
1403 | self.noise_estimation4x_DP(dataOut) | |
| 1404 | self.kabxys(dataOut) |
|
1404 | self.kabxys(dataOut) | |
| 1405 |
|
1405 | |||
| 1406 | return dataOut |
|
1406 | return dataOut | |
| 1407 |
|
1407 | |||
| 1408 |
|
1408 | |||
| 1409 |
|
1409 | |||
| 1410 | class IntegrationDP(Operation): |
|
1410 | class IntegrationDP(Operation): | |
| 1411 | ''' |
|
1411 | ''' | |
| 1412 | Written by R. Flores |
|
1412 | Written by R. Flores | |
| 1413 | ''' |
|
1413 | ''' | |
| 1414 | """Operation to integrate the Double Pulse data. |
|
1414 | """Operation to integrate the Double Pulse data. | |
| 1415 |
|
1415 | |||
| 1416 | Parameters: |
|
1416 | Parameters: | |
| 1417 | ----------- |
|
1417 | ----------- | |
| 1418 | nint : int |
|
1418 | nint : int | |
| 1419 | Number of integrations. |
|
1419 | Number of integrations. | |
| 1420 |
|
1420 | |||
| 1421 | Example |
|
1421 | Example | |
| 1422 | -------- |
|
1422 | -------- | |
| 1423 |
|
1423 | |||
| 1424 | op = proc_unit.addOperation(name='IntegrationDP', optype='other') |
|
1424 | op = proc_unit.addOperation(name='IntegrationDP', optype='other') | |
| 1425 | op.addParameter(name='nint', value='30', format='int') |
|
1425 | op.addParameter(name='nint', value='30', format='int') | |
| 1426 |
|
1426 | |||
| 1427 | """ |
|
1427 | """ | |
| 1428 |
|
1428 | |||
| 1429 | def __init__(self, **kwargs): |
|
1429 | def __init__(self, **kwargs): | |
| 1430 |
|
1430 | |||
| 1431 | Operation.__init__(self, **kwargs) |
|
1431 | Operation.__init__(self, **kwargs) | |
| 1432 |
|
1432 | |||
| 1433 | self.counter=0 |
|
1433 | self.counter=0 | |
| 1434 | self.aux=0 |
|
1434 | self.aux=0 | |
| 1435 | self.init_time=None |
|
1435 | self.init_time=None | |
| 1436 |
|
1436 | |||
| 1437 | def integration_for_double_pulse(self,dataOut): |
|
1437 | def integration_for_double_pulse(self,dataOut): | |
| 1438 |
|
1438 | |||
| 1439 | if self.aux==1: |
|
1439 | if self.aux==1: | |
| 1440 |
|
1440 | |||
| 1441 | dataOut.TimeBlockSeconds_for_dp_power=dataOut.utctime |
|
1441 | dataOut.TimeBlockSeconds_for_dp_power=dataOut.utctime | |
| 1442 | dataOut.bd_time=gmtime(dataOut.TimeBlockSeconds_for_dp_power) |
|
1442 | dataOut.bd_time=gmtime(dataOut.TimeBlockSeconds_for_dp_power) | |
| 1443 | dataOut.year=dataOut.bd_time.tm_year+(dataOut.bd_time.tm_yday-1)/364.0 |
|
1443 | dataOut.year=dataOut.bd_time.tm_year+(dataOut.bd_time.tm_yday-1)/364.0 | |
| 1444 | dataOut.ut_Faraday=dataOut.bd_time.tm_hour+dataOut.bd_time.tm_min/60.0+dataOut.bd_time.tm_sec/3600.0 |
|
1444 | dataOut.ut_Faraday=dataOut.bd_time.tm_hour+dataOut.bd_time.tm_min/60.0+dataOut.bd_time.tm_sec/3600.0 | |
| 1445 | self.aux=0 |
|
1445 | self.aux=0 | |
| 1446 |
|
1446 | |||
| 1447 | if self.counter==0: |
|
1447 | if self.counter==0: | |
| 1448 |
|
1448 | |||
| 1449 | tmpx=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') |
|
1449 | tmpx=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') | |
| 1450 | dataOut.kabxys_integrated=[tmpx,tmpx,tmpx,tmpx,tmpx,tmpx,tmpx,tmpx,tmpx,tmpx,tmpx,tmpx,tmpx,tmpx] |
|
1450 | dataOut.kabxys_integrated=[tmpx,tmpx,tmpx,tmpx,tmpx,tmpx,tmpx,tmpx,tmpx,tmpx,tmpx,tmpx,tmpx,tmpx] | |
| 1451 | self.init_time=dataOut.utctime |
|
1451 | self.init_time=dataOut.utctime | |
| 1452 |
|
1452 | |||
| 1453 | if self.counter < dataOut.nint: |
|
1453 | if self.counter < dataOut.nint: | |
| 1454 |
|
1454 | |||
| 1455 | dataOut.final_cross_products=[dataOut.kax,dataOut.kay,dataOut.kbx,dataOut.kby,dataOut.kax2,dataOut.kay2,dataOut.kbx2,dataOut.kby2,dataOut.kaxbx,dataOut.kaxby,dataOut.kaybx,dataOut.kayby,dataOut.kaxay,dataOut.kbxby] |
|
1455 | dataOut.final_cross_products=[dataOut.kax,dataOut.kay,dataOut.kbx,dataOut.kby,dataOut.kax2,dataOut.kay2,dataOut.kbx2,dataOut.kby2,dataOut.kaxbx,dataOut.kaxby,dataOut.kaybx,dataOut.kayby,dataOut.kaxay,dataOut.kbxby] | |
| 1456 |
|
1456 | |||
| 1457 | for ind in range(len(dataOut.kabxys_integrated)): #final cross products |
|
1457 | for ind in range(len(dataOut.kabxys_integrated)): #final cross products | |
| 1458 | dataOut.kabxys_integrated[ind]=dataOut.kabxys_integrated[ind]+dataOut.final_cross_products[ind] |
|
1458 | dataOut.kabxys_integrated[ind]=dataOut.kabxys_integrated[ind]+dataOut.final_cross_products[ind] | |
| 1459 |
|
1459 | |||
| 1460 | self.counter+=1 |
|
1460 | self.counter+=1 | |
| 1461 |
|
1461 | |||
| 1462 | if self.counter==dataOut.nint-1: |
|
1462 | if self.counter==dataOut.nint-1: | |
| 1463 | self.aux=1 |
|
1463 | self.aux=1 | |
| 1464 |
|
1464 | |||
| 1465 | if self.counter==dataOut.nint: |
|
1465 | if self.counter==dataOut.nint: | |
| 1466 | dataOut.flagNoData=False |
|
1466 | dataOut.flagNoData=False | |
| 1467 | dataOut.utctime=self.init_time |
|
1467 | dataOut.utctime=self.init_time | |
| 1468 | self.counter=0 |
|
1468 | self.counter=0 | |
| 1469 |
|
1469 | |||
| 1470 |
|
1470 | |||
| 1471 | def run(self,dataOut,nint=20): |
|
1471 | def run(self,dataOut,nint=20): | |
| 1472 |
|
1472 | |||
| 1473 | dataOut.flagNoData=True |
|
1473 | dataOut.flagNoData=True | |
| 1474 | dataOut.nint=nint |
|
1474 | dataOut.nint=nint | |
| 1475 | dataOut.paramInterval=0#int(dataOut.nint*dataOut.header[7][0]*2 ) |
|
1475 | dataOut.paramInterval=0#int(dataOut.nint*dataOut.header[7][0]*2 ) | |
| 1476 | dataOut.lat=-11.95 |
|
1476 | dataOut.lat=-11.95 | |
| 1477 | dataOut.lon=-76.87 |
|
1477 | dataOut.lon=-76.87 | |
| 1478 |
|
1478 | |||
| 1479 | self.integration_for_double_pulse(dataOut) |
|
1479 | self.integration_for_double_pulse(dataOut) | |
| 1480 |
|
1480 | |||
| 1481 | return dataOut |
|
1481 | return dataOut | |
| 1482 |
|
1482 | |||
| 1483 |
|
1483 | |||
| 1484 | class SumFlips(Operation): |
|
1484 | class SumFlips(Operation): | |
| 1485 | ''' |
|
1485 | ''' | |
| 1486 | Written by R. Flores |
|
1486 | Written by R. Flores | |
| 1487 | ''' |
|
1487 | ''' | |
| 1488 | """Operation to sum the flip and unflip part of certain cross products of the Double Pulse. |
|
1488 | """Operation to sum the flip and unflip part of certain cross products of the Double Pulse. | |
| 1489 |
|
1489 | |||
| 1490 | Parameters: |
|
1490 | Parameters: | |
| 1491 | ----------- |
|
1491 | ----------- | |
| 1492 | None |
|
1492 | None | |
| 1493 |
|
1493 | |||
| 1494 | Example |
|
1494 | Example | |
| 1495 | -------- |
|
1495 | -------- | |
| 1496 |
|
1496 | |||
| 1497 | op = proc_unit.addOperation(name='SumFlips', optype='other') |
|
1497 | op = proc_unit.addOperation(name='SumFlips', optype='other') | |
| 1498 |
|
1498 | |||
| 1499 | """ |
|
1499 | """ | |
| 1500 |
|
1500 | |||
| 1501 | def __init__(self, **kwargs): |
|
1501 | def __init__(self, **kwargs): | |
| 1502 |
|
1502 | |||
| 1503 | Operation.__init__(self, **kwargs) |
|
1503 | Operation.__init__(self, **kwargs) | |
| 1504 |
|
1504 | |||
| 1505 |
|
1505 | |||
| 1506 | def rint2DP(self,dataOut): |
|
1506 | def rint2DP(self,dataOut): | |
| 1507 |
|
1507 | |||
| 1508 | dataOut.rnint2=numpy.zeros(dataOut.DPL,'float32') |
|
1508 | dataOut.rnint2=numpy.zeros(dataOut.DPL,'float32') | |
| 1509 |
|
1509 | |||
| 1510 | for l in range(dataOut.DPL): |
|
1510 | for l in range(dataOut.DPL): | |
| 1511 |
|
1511 | |||
| 1512 | dataOut.rnint2[l]=1.0/(dataOut.nint*dataOut.NAVG*12.0) |
|
1512 | dataOut.rnint2[l]=1.0/(dataOut.nint*dataOut.NAVG*12.0) | |
| 1513 |
|
1513 | |||
| 1514 |
|
1514 | |||
| 1515 | def SumLags(self,dataOut): |
|
1515 | def SumLags(self,dataOut): | |
| 1516 |
|
1516 | |||
| 1517 | for l in range(dataOut.DPL): |
|
1517 | for l in range(dataOut.DPL): | |
| 1518 | dataOut.kabxys_integrated[4][:,l,0]=(dataOut.kabxys_integrated[4][:,l,0]+dataOut.kabxys_integrated[4][:,l,1])*dataOut.rnint2[l] |
|
1518 | dataOut.kabxys_integrated[4][:,l,0]=(dataOut.kabxys_integrated[4][:,l,0]+dataOut.kabxys_integrated[4][:,l,1])*dataOut.rnint2[l] | |
| 1519 | dataOut.kabxys_integrated[5][:,l,0]=(dataOut.kabxys_integrated[5][:,l,0]+dataOut.kabxys_integrated[5][:,l,1])*dataOut.rnint2[l] |
|
1519 | dataOut.kabxys_integrated[5][:,l,0]=(dataOut.kabxys_integrated[5][:,l,0]+dataOut.kabxys_integrated[5][:,l,1])*dataOut.rnint2[l] | |
| 1520 | dataOut.kabxys_integrated[6][:,l,0]=(dataOut.kabxys_integrated[6][:,l,0]+dataOut.kabxys_integrated[6][:,l,1])*dataOut.rnint2[l] |
|
1520 | dataOut.kabxys_integrated[6][:,l,0]=(dataOut.kabxys_integrated[6][:,l,0]+dataOut.kabxys_integrated[6][:,l,1])*dataOut.rnint2[l] | |
| 1521 | dataOut.kabxys_integrated[7][:,l,0]=(dataOut.kabxys_integrated[7][:,l,0]+dataOut.kabxys_integrated[7][:,l,1])*dataOut.rnint2[l] |
|
1521 | dataOut.kabxys_integrated[7][:,l,0]=(dataOut.kabxys_integrated[7][:,l,0]+dataOut.kabxys_integrated[7][:,l,1])*dataOut.rnint2[l] | |
| 1522 |
|
1522 | |||
| 1523 | dataOut.kabxys_integrated[8][:,l,0]=(dataOut.kabxys_integrated[8][:,l,0]-dataOut.kabxys_integrated[8][:,l,1])*dataOut.rnint2[l] |
|
1523 | dataOut.kabxys_integrated[8][:,l,0]=(dataOut.kabxys_integrated[8][:,l,0]-dataOut.kabxys_integrated[8][:,l,1])*dataOut.rnint2[l] | |
| 1524 | dataOut.kabxys_integrated[9][:,l,0]=(dataOut.kabxys_integrated[9][:,l,0]-dataOut.kabxys_integrated[9][:,l,1])*dataOut.rnint2[l] |
|
1524 | dataOut.kabxys_integrated[9][:,l,0]=(dataOut.kabxys_integrated[9][:,l,0]-dataOut.kabxys_integrated[9][:,l,1])*dataOut.rnint2[l] | |
| 1525 | dataOut.kabxys_integrated[10][:,l,0]=(dataOut.kabxys_integrated[10][:,l,0]-dataOut.kabxys_integrated[10][:,l,1])*dataOut.rnint2[l] |
|
1525 | dataOut.kabxys_integrated[10][:,l,0]=(dataOut.kabxys_integrated[10][:,l,0]-dataOut.kabxys_integrated[10][:,l,1])*dataOut.rnint2[l] | |
| 1526 | dataOut.kabxys_integrated[11][:,l,0]=(dataOut.kabxys_integrated[11][:,l,0]-dataOut.kabxys_integrated[11][:,l,1])*dataOut.rnint2[l] |
|
1526 | dataOut.kabxys_integrated[11][:,l,0]=(dataOut.kabxys_integrated[11][:,l,0]-dataOut.kabxys_integrated[11][:,l,1])*dataOut.rnint2[l] | |
| 1527 |
|
1527 | |||
| 1528 | def run(self,dataOut): |
|
1528 | def run(self,dataOut): | |
| 1529 |
|
1529 | |||
| 1530 | self.rint2DP(dataOut) |
|
1530 | self.rint2DP(dataOut) | |
| 1531 | self.SumLags(dataOut) |
|
1531 | self.SumLags(dataOut) | |
| 1532 |
|
1532 | |||
| 1533 | return dataOut |
|
1533 | return dataOut | |
| 1534 |
|
1534 | |||
| 1535 |
|
1535 | |||
| 1536 | class FlagBadHeights(Operation): |
|
1536 | class FlagBadHeights(Operation): | |
| 1537 | ''' |
|
1537 | ''' | |
| 1538 | Written by R. Flores |
|
1538 | Written by R. Flores | |
| 1539 | ''' |
|
1539 | ''' | |
| 1540 | """Operation to flag bad heights (bad data) of the Double Pulse. |
|
1540 | """Operation to flag bad heights (bad data) of the Double Pulse. | |
| 1541 |
|
1541 | |||
| 1542 | Parameters: |
|
1542 | Parameters: | |
| 1543 | ----------- |
|
1543 | ----------- | |
| 1544 | None |
|
1544 | None | |
| 1545 |
|
1545 | |||
| 1546 | Example |
|
1546 | Example | |
| 1547 | -------- |
|
1547 | -------- | |
| 1548 |
|
1548 | |||
| 1549 | op = proc_unit.addOperation(name='FlagBadHeights', optype='other') |
|
1549 | op = proc_unit.addOperation(name='FlagBadHeights', optype='other') | |
| 1550 |
|
1550 | |||
| 1551 | """ |
|
1551 | """ | |
| 1552 |
|
1552 | |||
| 1553 | def __init__(self, **kwargs): |
|
1553 | def __init__(self, **kwargs): | |
| 1554 |
|
1554 | |||
| 1555 | Operation.__init__(self, **kwargs) |
|
1555 | Operation.__init__(self, **kwargs) | |
| 1556 |
|
1556 | |||
| 1557 | def run(self,dataOut): |
|
1557 | def run(self,dataOut): | |
| 1558 |
|
1558 | |||
| 1559 | dataOut.ibad=numpy.zeros((dataOut.NDP,dataOut.DPL),'int32') |
|
1559 | dataOut.ibad=numpy.zeros((dataOut.NDP,dataOut.DPL),'int32') | |
| 1560 |
|
1560 | |||
| 1561 | for j in range(dataOut.NDP): |
|
1561 | for j in range(dataOut.NDP): | |
| 1562 | for l in range(dataOut.DPL): |
|
1562 | for l in range(dataOut.DPL): | |
| 1563 | ip1=j+dataOut.NDP*(0+2*l) |
|
1563 | ip1=j+dataOut.NDP*(0+2*l) | |
| 1564 |
|
1564 | |||
| 1565 | if( (dataOut.kabxys_integrated[5][j,l,0] <= 0.) or (dataOut.kabxys_integrated[4][j,l,0] <= 0.) or (dataOut.kabxys_integrated[7][j,l,0] <= 0.) or (dataOut.kabxys_integrated[6][j,l,0] <= 0.)): |
|
1565 | if( (dataOut.kabxys_integrated[5][j,l,0] <= 0.) or (dataOut.kabxys_integrated[4][j,l,0] <= 0.) or (dataOut.kabxys_integrated[7][j,l,0] <= 0.) or (dataOut.kabxys_integrated[6][j,l,0] <= 0.)): | |
| 1566 | dataOut.ibad[j][l]=1 |
|
1566 | dataOut.ibad[j][l]=1 | |
| 1567 | else: |
|
1567 | else: | |
| 1568 | dataOut.ibad[j][l]=0 |
|
1568 | dataOut.ibad[j][l]=0 | |
| 1569 |
|
1569 | |||
| 1570 | return dataOut |
|
1570 | return dataOut | |
| 1571 |
|
1571 | |||
| 1572 | class FlagBadHeightsSpectra(Operation): |
|
1572 | class FlagBadHeightsSpectra(Operation): | |
| 1573 | ''' |
|
1573 | ''' | |
| 1574 | Written by R. Flores |
|
1574 | Written by R. Flores | |
| 1575 | ''' |
|
1575 | ''' | |
| 1576 | """Operation to flag bad heights (bad data) of the Double Pulse. |
|
1576 | """Operation to flag bad heights (bad data) of the Double Pulse. | |
| 1577 |
|
1577 | |||
| 1578 | Parameters: |
|
1578 | Parameters: | |
| 1579 | ----------- |
|
1579 | ----------- | |
| 1580 | None |
|
1580 | None | |
| 1581 |
|
1581 | |||
| 1582 | Example |
|
1582 | Example | |
| 1583 | -------- |
|
1583 | -------- | |
| 1584 |
|
1584 | |||
| 1585 | op = proc_unit.addOperation(name='FlagBadHeightsSpectra', optype='other') |
|
1585 | op = proc_unit.addOperation(name='FlagBadHeightsSpectra', optype='other') | |
| 1586 |
|
1586 | |||
| 1587 | """ |
|
1587 | """ | |
| 1588 |
|
1588 | |||
| 1589 | def __init__(self, **kwargs): |
|
1589 | def __init__(self, **kwargs): | |
| 1590 |
|
1590 | |||
| 1591 | Operation.__init__(self, **kwargs) |
|
1591 | Operation.__init__(self, **kwargs) | |
| 1592 |
|
1592 | |||
| 1593 | def run(self,dataOut): |
|
1593 | def run(self,dataOut): | |
| 1594 |
|
1594 | |||
| 1595 | dataOut.ibad=numpy.zeros((dataOut.NDP,dataOut.DPL),'int32') |
|
1595 | dataOut.ibad=numpy.zeros((dataOut.NDP,dataOut.DPL),'int32') | |
| 1596 |
|
1596 | |||
| 1597 | for j in range(dataOut.NDP): |
|
1597 | for j in range(dataOut.NDP): | |
| 1598 | for l in range(dataOut.DPL): |
|
1598 | for l in range(dataOut.DPL): | |
| 1599 | ip1=j+dataOut.NDP*(0+2*l) |
|
1599 | ip1=j+dataOut.NDP*(0+2*l) | |
| 1600 |
|
1600 | |||
| 1601 | if( (dataOut.kabxys_integrated[4][j,l,0] <= 0.) or (dataOut.kabxys_integrated[6][j,l,0] <= 0.)): |
|
1601 | if( (dataOut.kabxys_integrated[4][j,l,0] <= 0.) or (dataOut.kabxys_integrated[6][j,l,0] <= 0.)): | |
| 1602 | dataOut.ibad[j][l]=1 |
|
1602 | dataOut.ibad[j][l]=1 | |
| 1603 | else: |
|
1603 | else: | |
| 1604 | dataOut.ibad[j][l]=0 |
|
1604 | dataOut.ibad[j][l]=0 | |
| 1605 |
|
1605 | |||
| 1606 | return dataOut |
|
1606 | return dataOut | |
| 1607 |
|
1607 | |||
| 1608 | class CleanCohEchoes(Operation): |
|
1608 | class CleanCohEchoes(Operation): | |
| 1609 | ''' |
|
1609 | ''' | |
| 1610 | Written by R. Flores |
|
1610 | Written by R. Flores | |
| 1611 | ''' |
|
1611 | ''' | |
| 1612 | """Operation to clean coherent echoes. |
|
1612 | """Operation to clean coherent echoes. | |
| 1613 |
|
1613 | |||
| 1614 | Parameters: |
|
1614 | Parameters: | |
| 1615 | ----------- |
|
1615 | ----------- | |
| 1616 | None |
|
1616 | None | |
| 1617 |
|
1617 | |||
| 1618 | Example |
|
1618 | Example | |
| 1619 | -------- |
|
1619 | -------- | |
| 1620 |
|
1620 | |||
| 1621 | op = proc_unit.addOperation(name='CleanCohEchoes') |
|
1621 | op = proc_unit.addOperation(name='CleanCohEchoes') | |
| 1622 |
|
1622 | |||
| 1623 | """ |
|
1623 | """ | |
| 1624 |
|
1624 | |||
| 1625 | def __init__(self, **kwargs): |
|
1625 | def __init__(self, **kwargs): | |
| 1626 |
|
1626 | |||
| 1627 | Operation.__init__(self, **kwargs) |
|
1627 | Operation.__init__(self, **kwargs) | |
| 1628 |
|
1628 | |||
| 1629 | def remove_coh(self,pow): |
|
1629 | def remove_coh(self,pow): | |
| 1630 | q75,q25 = numpy.percentile(pow,[75,25],axis=0) |
|
1630 | q75,q25 = numpy.percentile(pow,[75,25],axis=0) | |
| 1631 | intr_qr = q75-q25 |
|
1631 | intr_qr = q75-q25 | |
| 1632 |
|
1632 | |||
| 1633 | max = q75+(1.5*intr_qr) |
|
1633 | max = q75+(1.5*intr_qr) | |
| 1634 | min = q25-(1.5*intr_qr) |
|
1634 | min = q25-(1.5*intr_qr) | |
| 1635 |
|
1635 | |||
| 1636 | pow[pow > max] = numpy.nan |
|
1636 | pow[pow > max] = numpy.nan | |
| 1637 |
|
1637 | |||
| 1638 | return pow |
|
1638 | return pow | |
| 1639 |
|
1639 | |||
| 1640 | def mad_based_outlier_V0(self, points, thresh=3.5): |
|
1640 | def mad_based_outlier_V0(self, points, thresh=3.5): | |
| 1641 |
|
1641 | |||
| 1642 | if len(points.shape) == 1: |
|
1642 | if len(points.shape) == 1: | |
| 1643 | points = points[:,None] |
|
1643 | points = points[:,None] | |
| 1644 | median = numpy.nanmedian(points, axis=0) |
|
1644 | median = numpy.nanmedian(points, axis=0) | |
| 1645 | diff = numpy.nansum((points - median)**2, axis=-1) |
|
1645 | diff = numpy.nansum((points - median)**2, axis=-1) | |
| 1646 | diff = numpy.sqrt(diff) |
|
1646 | diff = numpy.sqrt(diff) | |
| 1647 | med_abs_deviation = numpy.nanmedian(diff) |
|
1647 | med_abs_deviation = numpy.nanmedian(diff) | |
| 1648 |
|
1648 | |||
| 1649 | modified_z_score = 0.6745 * diff / med_abs_deviation |
|
1649 | modified_z_score = 0.6745 * diff / med_abs_deviation | |
| 1650 |
|
1650 | |||
| 1651 | return modified_z_score > thresh |
|
1651 | return modified_z_score > thresh | |
| 1652 |
|
1652 | |||
| 1653 | def mad_based_outlier(self, points, thresh=3.5): |
|
1653 | def mad_based_outlier(self, points, thresh=3.5): | |
| 1654 |
|
1654 | |||
| 1655 | median = numpy.nanmedian(points) |
|
1655 | median = numpy.nanmedian(points) | |
| 1656 | diff = (points - median)**2 |
|
1656 | diff = (points - median)**2 | |
| 1657 | diff = numpy.sqrt(diff) |
|
1657 | diff = numpy.sqrt(diff) | |
| 1658 | med_abs_deviation = numpy.nanmedian(diff) |
|
1658 | med_abs_deviation = numpy.nanmedian(diff) | |
| 1659 |
|
1659 | |||
| 1660 | modified_z_score = 0.6745 * diff / med_abs_deviation |
|
1660 | modified_z_score = 0.6745 * diff / med_abs_deviation | |
| 1661 |
|
1661 | |||
| 1662 | return modified_z_score > thresh |
|
1662 | return modified_z_score > thresh | |
| 1663 |
|
1663 | |||
| 1664 |
|
1664 | |||
| 1665 |
|
1665 | |||
| 1666 | def removeSpreadF(self,dataOut): |
|
1666 | def removeSpreadF(self,dataOut): | |
| 1667 |
|
1667 | |||
| 1668 | #Removing outliers from the profile |
|
1668 | #Removing outliers from the profile | |
| 1669 | nlag = 9 |
|
1669 | nlag = 9 | |
| 1670 | minHei = 180 |
|
1670 | minHei = 180 | |
| 1671 | #maxHei = 600 |
|
1671 | #maxHei = 600 | |
| 1672 | maxHei = 525 |
|
1672 | maxHei = 525 | |
| 1673 | inda = numpy.where(dataOut.heightList >= minHei) |
|
1673 | inda = numpy.where(dataOut.heightList >= minHei) | |
| 1674 | indb = numpy.where(dataOut.heightList <= maxHei) |
|
1674 | indb = numpy.where(dataOut.heightList <= maxHei) | |
| 1675 | minIndex = inda[0][0] |
|
1675 | minIndex = inda[0][0] | |
| 1676 | maxIndex = indb[0][-1] |
|
1676 | maxIndex = indb[0][-1] | |
| 1677 | outliers_IDs = [] |
|
1677 | outliers_IDs = [] | |
| 1678 |
|
1678 | |||
| 1679 | for i in range(15): |
|
1679 | for i in range(15): | |
| 1680 | minIndex = 12+i#12 |
|
1680 | minIndex = 12+i#12 | |
| 1681 | #maxIndex = 22+i#35 |
|
1681 | #maxIndex = 22+i#35 | |
| 1682 | if gmtime(dataOut.utctime).tm_hour >= 23. or gmtime(dataOut.utctime).tm_hour < 3.: |
|
1682 | if gmtime(dataOut.utctime).tm_hour >= 23. or gmtime(dataOut.utctime).tm_hour < 3.: | |
| 1683 | maxIndex = 31+i#35 |
|
1683 | maxIndex = 31+i#35 | |
| 1684 | else: |
|
1684 | else: | |
| 1685 | maxIndex = 22+i#35 |
|
1685 | maxIndex = 22+i#35 | |
| 1686 | for lag in range(11): |
|
1686 | for lag in range(11): | |
| 1687 | outliers = self.mad_based_outlier(dataOut.kabxys_integrated[6][minIndex:maxIndex,lag,0]) |
|
1687 | outliers = self.mad_based_outlier(dataOut.kabxys_integrated[6][minIndex:maxIndex,lag,0]) | |
| 1688 | aux = minIndex+numpy.array(outliers.nonzero()).ravel() |
|
1688 | aux = minIndex+numpy.array(outliers.nonzero()).ravel() | |
| 1689 | outliers_IDs=numpy.append(outliers_IDs,aux) |
|
1689 | outliers_IDs=numpy.append(outliers_IDs,aux) | |
| 1690 | if outliers_IDs != []: |
|
1690 | if outliers_IDs != []: | |
| 1691 | outliers_IDs=numpy.array(outliers_IDs) |
|
1691 | outliers_IDs=numpy.array(outliers_IDs) | |
| 1692 | outliers_IDs=outliers_IDs.astype(numpy.dtype('int64')) |
|
1692 | outliers_IDs=outliers_IDs.astype(numpy.dtype('int64')) | |
| 1693 | (uniq, freq) = (numpy.unique(outliers_IDs, return_counts=True)) |
|
1693 | (uniq, freq) = (numpy.unique(outliers_IDs, return_counts=True)) | |
| 1694 | aux_arr = numpy.column_stack((uniq,freq)) |
|
1694 | aux_arr = numpy.column_stack((uniq,freq)) | |
| 1695 | final_index = [] |
|
1695 | final_index = [] | |
| 1696 | for i in range(aux_arr.shape[0]): |
|
1696 | for i in range(aux_arr.shape[0]): | |
| 1697 | if aux_arr[i,1] >= 3*11: |
|
1697 | if aux_arr[i,1] >= 3*11: | |
| 1698 | final_index.append(aux_arr[i,0]) |
|
1698 | final_index.append(aux_arr[i,0]) | |
| 1699 |
|
1699 | |||
| 1700 | if final_index != []:# and len(final_index) > 1: |
|
1700 | if final_index != []:# and len(final_index) > 1: | |
| 1701 | following_index = final_index[-1]+1 #Remove following index to ensure we remove remaining SpreadF |
|
1701 | following_index = final_index[-1]+1 #Remove following index to ensure we remove remaining SpreadF | |
| 1702 | previous_index = final_index[0]-1 #Remove previous index to ensure we remove remaning SpreadF |
|
1702 | previous_index = final_index[0]-1 #Remove previous index to ensure we remove remaning SpreadF | |
| 1703 | final_index = numpy.concatenate(([previous_index],final_index,[following_index])) |
|
1703 | final_index = numpy.concatenate(([previous_index],final_index,[following_index])) | |
| 1704 | final_index = numpy.unique(final_index) #If there was only one outlier |
|
1704 | final_index = numpy.unique(final_index) #If there was only one outlier | |
| 1705 | dataOut.kabxys_integrated[4][final_index,:,0] = numpy.nan |
|
1705 | dataOut.kabxys_integrated[4][final_index,:,0] = numpy.nan | |
| 1706 | dataOut.kabxys_integrated[6][final_index,:,0] = numpy.nan |
|
1706 | dataOut.kabxys_integrated[6][final_index,:,0] = numpy.nan | |
| 1707 |
|
1707 | |||
| 1708 | dataOut.flagSpreadF = True |
|
1708 | dataOut.flagSpreadF = True | |
| 1709 |
|
1709 | |||
| 1710 | #Removing echoes greater than 35 dB |
|
1710 | #Removing echoes greater than 35 dB | |
| 1711 | if hasattr(dataOut.pbn, "__len__"): |
|
1711 | if hasattr(dataOut.pbn, "__len__"): | |
| 1712 | maxdB = 10*numpy.log10(dataOut.pbn[0]) + 10 #Lag 0 Noise |
|
1712 | maxdB = 10*numpy.log10(dataOut.pbn[0]) + 10 #Lag 0 Noise | |
| 1713 | else: |
|
1713 | else: | |
| 1714 | maxdB = 10*numpy.log10(dataOut.pbn) + 10 |
|
1714 | maxdB = 10*numpy.log10(dataOut.pbn) + 10 | |
| 1715 |
|
1715 | |||
| 1716 | data = numpy.copy(10*numpy.log10(dataOut.kabxys_integrated[6][:,0,0])) #Lag0 ChB |
|
1716 | data = numpy.copy(10*numpy.log10(dataOut.kabxys_integrated[6][:,0,0])) #Lag0 ChB | |
| 1717 |
|
1717 | |||
| 1718 | for i in range(12,data.shape[0]): |
|
1718 | for i in range(12,data.shape[0]): | |
| 1719 | if data[i]>maxdB: |
|
1719 | if data[i]>maxdB: | |
| 1720 | dataOut.kabxys_integrated[4][i-2:i+3,:,0] = numpy.nan #Debido a que estos ecos son intensos, se |
|
1720 | dataOut.kabxys_integrated[4][i-2:i+3,:,0] = numpy.nan #Debido a que estos ecos son intensos, se | |
| 1721 | dataOut.kabxys_integrated[6][i-2:i+3,:,0] = numpy.nan #remueven además dos muestras antes y después |
|
1721 | dataOut.kabxys_integrated[6][i-2:i+3,:,0] = numpy.nan #remueven además dos muestras antes y después | |
| 1722 | dataOut.flagSpreadF = True |
|
1722 | dataOut.flagSpreadF = True | |
| 1723 |
|
1723 | |||
| 1724 | def run(self,dataOut): |
|
1724 | def run(self,dataOut): | |
| 1725 | dataOut.flagSpreadF = False |
|
1725 | dataOut.flagSpreadF = False | |
| 1726 | if gmtime(dataOut.utctime).tm_hour >= 23. or gmtime(dataOut.utctime).tm_hour < 11.: #18-06 LT |
|
1726 | if gmtime(dataOut.utctime).tm_hour >= 23. or gmtime(dataOut.utctime).tm_hour < 11.: #18-06 LT | |
| 1727 | self.removeSpreadF(dataOut) |
|
1727 | self.removeSpreadF(dataOut) | |
| 1728 |
|
1728 | |||
| 1729 | return dataOut |
|
1729 | return dataOut | |
| 1730 | class NoisePower(Operation): |
|
1730 | class NoisePower(Operation): | |
| 1731 | ''' |
|
1731 | ''' | |
| 1732 | Written by R. Flores |
|
1732 | Written by R. Flores | |
| 1733 | ''' |
|
1733 | ''' | |
| 1734 | """Operation to get noise power from the integrated data of the Double Pulse. |
|
1734 | """Operation to get noise power from the integrated data of the Double Pulse. | |
| 1735 |
|
1735 | |||
| 1736 | Parameters: |
|
1736 | Parameters: | |
| 1737 | ----------- |
|
1737 | ----------- | |
| 1738 | None |
|
1738 | None | |
| 1739 |
|
1739 | |||
| 1740 | Example |
|
1740 | Example | |
| 1741 | -------- |
|
1741 | -------- | |
| 1742 |
|
1742 | |||
| 1743 | op = proc_unit.addOperation(name='NoisePower', optype='other') |
|
1743 | op = proc_unit.addOperation(name='NoisePower', optype='other') | |
| 1744 |
|
1744 | |||
| 1745 | """ |
|
1745 | """ | |
| 1746 |
|
1746 | |||
| 1747 | def __init__(self, **kwargs): |
|
1747 | def __init__(self, **kwargs): | |
| 1748 |
|
1748 | |||
| 1749 | Operation.__init__(self, **kwargs) |
|
1749 | Operation.__init__(self, **kwargs) | |
| 1750 |
|
1750 | |||
| 1751 | def hildebrand(self,dataOut,data): |
|
1751 | def hildebrand(self,dataOut,data): | |
| 1752 |
|
1752 | |||
| 1753 | divider=10 # divider was originally 10 |
|
1753 | divider=10 # divider was originally 10 | |
| 1754 | noise=0.0 |
|
1754 | noise=0.0 | |
| 1755 | n1=0 |
|
1755 | n1=0 | |
| 1756 | n2=int(dataOut.NDP/2) |
|
1756 | n2=int(dataOut.NDP/2) | |
| 1757 | sorts= sorted(data) |
|
1757 | sorts= sorted(data) | |
| 1758 | nums_min= dataOut.NDP/divider |
|
1758 | nums_min= dataOut.NDP/divider | |
| 1759 | if((dataOut.NDP/divider)> 2): |
|
1759 | if((dataOut.NDP/divider)> 2): | |
| 1760 | nums_min= int(dataOut.NDP/divider) |
|
1760 | nums_min= int(dataOut.NDP/divider) | |
| 1761 |
|
1761 | |||
| 1762 | else: |
|
1762 | else: | |
| 1763 | nums_min=2 |
|
1763 | nums_min=2 | |
| 1764 | sump=0.0 |
|
1764 | sump=0.0 | |
| 1765 | sumq=0.0 |
|
1765 | sumq=0.0 | |
| 1766 | j=0 |
|
1766 | j=0 | |
| 1767 | cont=1 |
|
1767 | cont=1 | |
| 1768 | while( (cont==1) and (j<n2)): |
|
1768 | while( (cont==1) and (j<n2)): | |
| 1769 | sump+=sorts[j+n1] |
|
1769 | sump+=sorts[j+n1] | |
| 1770 | sumq+= sorts[j+n1]*sorts[j+n1] |
|
1770 | sumq+= sorts[j+n1]*sorts[j+n1] | |
| 1771 | t3= sump/(j+1) |
|
1771 | t3= sump/(j+1) | |
| 1772 | j=j+1 |
|
1772 | j=j+1 | |
| 1773 | if(j> nums_min): |
|
1773 | if(j> nums_min): | |
| 1774 | rtest= float(j/(j-1)) +1.0/dataOut.NAVG |
|
1774 | rtest= float(j/(j-1)) +1.0/dataOut.NAVG | |
| 1775 | t1= (sumq*j) |
|
1775 | t1= (sumq*j) | |
| 1776 | t2=(rtest*sump*sump) |
|
1776 | t2=(rtest*sump*sump) | |
| 1777 | if( (t1/t2) > 0.990): |
|
1777 | if( (t1/t2) > 0.990): | |
| 1778 | j=j-1 |
|
1778 | j=j-1 | |
| 1779 | sump-= sorts[j+n1] |
|
1779 | sump-= sorts[j+n1] | |
| 1780 | sumq-=sorts[j+n1]*sorts[j+n1] |
|
1780 | sumq-=sorts[j+n1]*sorts[j+n1] | |
| 1781 | cont= 0 |
|
1781 | cont= 0 | |
| 1782 |
|
1782 | |||
| 1783 | noise= sump/j |
|
1783 | noise= sump/j | |
| 1784 | stdv=numpy.sqrt((sumq- noise*noise)/(j-1)) |
|
1784 | stdv=numpy.sqrt((sumq- noise*noise)/(j-1)) | |
| 1785 | return noise |
|
1785 | return noise | |
| 1786 |
|
1786 | |||
| 1787 | def run(self,dataOut): |
|
1787 | def run(self,dataOut): | |
| 1788 |
|
1788 | |||
| 1789 | p=numpy.zeros((dataOut.NR,dataOut.NDP,dataOut.DPL),'float32') |
|
1789 | p=numpy.zeros((dataOut.NR,dataOut.NDP,dataOut.DPL),'float32') | |
| 1790 | av=numpy.zeros(dataOut.NDP,'float32') |
|
1790 | av=numpy.zeros(dataOut.NDP,'float32') | |
| 1791 | dataOut.pnoise=numpy.zeros(dataOut.NR,'float32') |
|
1791 | dataOut.pnoise=numpy.zeros(dataOut.NR,'float32') | |
| 1792 |
|
1792 | |||
| 1793 | p[0,:,:]=dataOut.kabxys_integrated[4][:,:,0]+dataOut.kabxys_integrated[5][:,:,0] #total power for channel 0, just pulse with non-flip |
|
1793 | p[0,:,:]=dataOut.kabxys_integrated[4][:,:,0]+dataOut.kabxys_integrated[5][:,:,0] #total power for channel 0, just pulse with non-flip | |
| 1794 | p[1,:,:]=dataOut.kabxys_integrated[6][:,:,0]+dataOut.kabxys_integrated[7][:,:,0] #total power for channel 1 |
|
1794 | p[1,:,:]=dataOut.kabxys_integrated[6][:,:,0]+dataOut.kabxys_integrated[7][:,:,0] #total power for channel 1 | |
| 1795 |
|
1795 | |||
| 1796 | for i in range(dataOut.NR): |
|
1796 | for i in range(dataOut.NR): | |
| 1797 | dataOut.pnoise[i]=0.0 |
|
1797 | dataOut.pnoise[i]=0.0 | |
| 1798 | for k in range(dataOut.DPL): |
|
1798 | for k in range(dataOut.DPL): | |
| 1799 | dataOut.pnoise[i]+= self.hildebrand(dataOut,p[i,:,k]) |
|
1799 | dataOut.pnoise[i]+= self.hildebrand(dataOut,p[i,:,k]) | |
| 1800 |
|
1800 | |||
| 1801 | dataOut.pnoise[i]=dataOut.pnoise[i]/dataOut.DPL |
|
1801 | dataOut.pnoise[i]=dataOut.pnoise[i]/dataOut.DPL | |
| 1802 |
|
1802 | |||
| 1803 |
|
1803 | |||
| 1804 | dataOut.pan=1.0*dataOut.pnoise[0] # weights could change |
|
1804 | dataOut.pan=1.0*dataOut.pnoise[0] # weights could change | |
| 1805 | dataOut.pbn=1.0*dataOut.pnoise[1] # weights could change |
|
1805 | dataOut.pbn=1.0*dataOut.pnoise[1] # weights could change | |
| 1806 |
|
1806 | |||
| 1807 | return dataOut |
|
1807 | return dataOut | |
| 1808 |
|
1808 | |||
| 1809 |
|
1809 | |||
| 1810 | class DoublePulseACFs(Operation): |
|
1810 | class DoublePulseACFs(Operation): | |
| 1811 | ''' |
|
1811 | ''' | |
| 1812 | Written by R. Flores |
|
1812 | Written by R. Flores | |
| 1813 | ''' |
|
1813 | ''' | |
| 1814 | """Operation to get the ACFs of the Double Pulse. |
|
1814 | """Operation to get the ACFs of the Double Pulse. | |
| 1815 |
|
1815 | |||
| 1816 | Parameters: |
|
1816 | Parameters: | |
| 1817 | ----------- |
|
1817 | ----------- | |
| 1818 | None |
|
1818 | None | |
| 1819 |
|
1819 | |||
| 1820 | Example |
|
1820 | Example | |
| 1821 | -------- |
|
1821 | -------- | |
| 1822 |
|
1822 | |||
| 1823 | op = proc_unit.addOperation(name='DoublePulseACFs', optype='other') |
|
1823 | op = proc_unit.addOperation(name='DoublePulseACFs', optype='other') | |
| 1824 |
|
1824 | |||
| 1825 | """ |
|
1825 | """ | |
| 1826 |
|
1826 | |||
| 1827 | def __init__(self, **kwargs): |
|
1827 | def __init__(self, **kwargs): | |
| 1828 |
|
1828 | |||
| 1829 | Operation.__init__(self, **kwargs) |
|
1829 | Operation.__init__(self, **kwargs) | |
| 1830 | self.aux=1 |
|
1830 | self.aux=1 | |
| 1831 |
|
1831 | |||
| 1832 | def run(self,dataOut): |
|
1832 | def run(self,dataOut): | |
| 1833 |
|
1833 | |||
| 1834 | dataOut.igcej=numpy.zeros((dataOut.NDP,dataOut.DPL),'int32') |
|
1834 | dataOut.igcej=numpy.zeros((dataOut.NDP,dataOut.DPL),'int32') | |
| 1835 |
|
1835 | |||
| 1836 | if self.aux==1: |
|
1836 | if self.aux==1: | |
| 1837 | dataOut.rhor=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) |
|
1837 | dataOut.rhor=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) | |
| 1838 | dataOut.rhoi=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) |
|
1838 | dataOut.rhoi=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) | |
| 1839 | dataOut.sdp=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) |
|
1839 | dataOut.sdp=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) | |
| 1840 | dataOut.sd=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) |
|
1840 | dataOut.sd=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) | |
| 1841 | dataOut.p=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) |
|
1841 | dataOut.p=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) | |
| 1842 | dataOut.alag=numpy.zeros(dataOut.NDP,'float32') |
|
1842 | dataOut.alag=numpy.zeros(dataOut.NDP,'float32') | |
| 1843 | for l in range(dataOut.DPL): |
|
1843 | for l in range(dataOut.DPL): | |
| 1844 | dataOut.alag[l]=l*dataOut.DH*2.0/150.0 |
|
1844 | dataOut.alag[l]=l*dataOut.DH*2.0/150.0 | |
| 1845 | self.aux=0 |
|
1845 | self.aux=0 | |
| 1846 | sn4=dataOut.pan*dataOut.pbn |
|
1846 | sn4=dataOut.pan*dataOut.pbn | |
| 1847 | rhorn=0 |
|
1847 | rhorn=0 | |
| 1848 | rhoin=0 |
|
1848 | rhoin=0 | |
| 1849 | panrm=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) |
|
1849 | panrm=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) | |
| 1850 |
|
1850 | |||
| 1851 | for i in range(dataOut.NDP): |
|
1851 | for i in range(dataOut.NDP): | |
| 1852 | for j in range(dataOut.DPL): |
|
1852 | for j in range(dataOut.DPL): | |
| 1853 | ################# Total power |
|
1853 | ################# Total power | |
| 1854 | pa=numpy.abs(dataOut.kabxys_integrated[4][i,j,0]+dataOut.kabxys_integrated[5][i,j,0]) |
|
1854 | pa=numpy.abs(dataOut.kabxys_integrated[4][i,j,0]+dataOut.kabxys_integrated[5][i,j,0]) | |
| 1855 | pb=numpy.abs(dataOut.kabxys_integrated[6][i,j,0]+dataOut.kabxys_integrated[7][i,j,0]) |
|
1855 | pb=numpy.abs(dataOut.kabxys_integrated[6][i,j,0]+dataOut.kabxys_integrated[7][i,j,0]) | |
| 1856 | st4=pa*pb |
|
1856 | st4=pa*pb | |
| 1857 | dataOut.p[i,j]=pa+pb-(dataOut.pan+dataOut.pbn) |
|
1857 | dataOut.p[i,j]=pa+pb-(dataOut.pan+dataOut.pbn) | |
| 1858 | dataOut.sdp[i,j]=2*dataOut.rnint2[j]*((pa+pb)*(pa+pb)) |
|
1858 | dataOut.sdp[i,j]=2*dataOut.rnint2[j]*((pa+pb)*(pa+pb)) | |
| 1859 | ## ACF |
|
1859 | ## ACF | |
| 1860 | rhorp=dataOut.kabxys_integrated[8][i,j,0]+dataOut.kabxys_integrated[11][i,j,0] |
|
1860 | rhorp=dataOut.kabxys_integrated[8][i,j,0]+dataOut.kabxys_integrated[11][i,j,0] | |
| 1861 | rhoip=dataOut.kabxys_integrated[10][i,j,0]-dataOut.kabxys_integrated[9][i,j,0] |
|
1861 | rhoip=dataOut.kabxys_integrated[10][i,j,0]-dataOut.kabxys_integrated[9][i,j,0] | |
| 1862 | if ((pa>dataOut.pan)&(pb>dataOut.pbn)): |
|
1862 | if ((pa>dataOut.pan)&(pb>dataOut.pbn)): | |
| 1863 |
|
1863 | |||
| 1864 | ss4=numpy.abs((pa-dataOut.pan)*(pb-dataOut.pbn)) |
|
1864 | ss4=numpy.abs((pa-dataOut.pan)*(pb-dataOut.pbn)) | |
| 1865 | panrm[i,j]=math.sqrt(ss4) |
|
1865 | panrm[i,j]=math.sqrt(ss4) | |
| 1866 | rnorm=1/panrm[i,j] |
|
1866 | rnorm=1/panrm[i,j] | |
| 1867 | ## ACF |
|
1867 | ## ACF | |
| 1868 | dataOut.rhor[i,j]=rhorp*rnorm |
|
1868 | dataOut.rhor[i,j]=rhorp*rnorm | |
| 1869 | dataOut.rhoi[i,j]=rhoip*rnorm |
|
1869 | dataOut.rhoi[i,j]=rhoip*rnorm | |
| 1870 | ############# Compute standard error for ACF |
|
1870 | ############# Compute standard error for ACF | |
| 1871 | stoss4=st4/ss4 |
|
1871 | stoss4=st4/ss4 | |
| 1872 | snoss4=sn4/ss4 |
|
1872 | snoss4=sn4/ss4 | |
| 1873 | rp2=((rhorp*rhorp)+(rhoip*rhoip))/st4 |
|
1873 | rp2=((rhorp*rhorp)+(rhoip*rhoip))/st4 | |
| 1874 | rn2=((rhorn*rhorn)+(rhoin*rhoin))/sn4 |
|
1874 | rn2=((rhorn*rhorn)+(rhoin*rhoin))/sn4 | |
| 1875 | rs2=(dataOut.rhor[i,j]*dataOut.rhor[i,j])+(dataOut.rhoi[i,j]*dataOut.rhoi[i,j]) |
|
1875 | rs2=(dataOut.rhor[i,j]*dataOut.rhor[i,j])+(dataOut.rhoi[i,j]*dataOut.rhoi[i,j]) | |
| 1876 | st=1.0+rs2*(stoss4-(2*math.sqrt(stoss4*snoss4))) |
|
1876 | st=1.0+rs2*(stoss4-(2*math.sqrt(stoss4*snoss4))) | |
| 1877 | stn=1.0+rs2*(snoss4-(2*math.sqrt(stoss4*snoss4))) |
|
1877 | stn=1.0+rs2*(snoss4-(2*math.sqrt(stoss4*snoss4))) | |
| 1878 | dataOut.sd[i,j]=((stoss4*((1.0+rp2)*st+(2.0*rp2*rs2*snoss4)-4.0*math.sqrt(rs2*rp2)))+(0.25*snoss4*((1.0+rn2)*stn+(2.0*rn2*rs2*stoss4)-4.0*math.sqrt(rs2*rn2))))*dataOut.rnint2[j] |
|
1878 | dataOut.sd[i,j]=((stoss4*((1.0+rp2)*st+(2.0*rp2*rs2*snoss4)-4.0*math.sqrt(rs2*rp2)))+(0.25*snoss4*((1.0+rn2)*stn+(2.0*rn2*rs2*stoss4)-4.0*math.sqrt(rs2*rn2))))*dataOut.rnint2[j] | |
| 1879 | dataOut.sd[i,j]=numpy.abs(dataOut.sd[i,j]) |
|
1879 | dataOut.sd[i,j]=numpy.abs(dataOut.sd[i,j]) | |
| 1880 |
|
1880 | |||
| 1881 | else: #default values for bad points |
|
1881 | else: #default values for bad points | |
| 1882 | rnorm=1/math.sqrt(st4) |
|
1882 | rnorm=1/math.sqrt(st4) | |
| 1883 | dataOut.sd[i,j]=1.e30 |
|
1883 | dataOut.sd[i,j]=1.e30 | |
| 1884 | dataOut.ibad[i,j]=4 |
|
1884 | dataOut.ibad[i,j]=4 | |
| 1885 | dataOut.rhor[i,j]=rhorp*rnorm |
|
1885 | dataOut.rhor[i,j]=rhorp*rnorm | |
| 1886 | dataOut.rhoi[i,j]=rhoip*rnorm |
|
1886 | dataOut.rhoi[i,j]=rhoip*rnorm | |
| 1887 | if ((pb/dataOut.pbn-1.0)>2.25*(pa/dataOut.pan-1.0)): #To flag bad points from the pulse and EEJ for lags != 0 for Channel B |
|
1887 | if ((pb/dataOut.pbn-1.0)>2.25*(pa/dataOut.pan-1.0)): #To flag bad points from the pulse and EEJ for lags != 0 for Channel B | |
| 1888 | #print(dataOut.heightList[i],"EJJ") |
|
1888 | #print(dataOut.heightList[i],"EJJ") | |
| 1889 | dataOut.igcej[i,j]=1 |
|
1889 | dataOut.igcej[i,j]=1 | |
| 1890 | elif ((pa/dataOut.pan-1.0)>2.25*(pb/dataOut.pbn-1.0)): |
|
1890 | elif ((pa/dataOut.pan-1.0)>2.25*(pb/dataOut.pbn-1.0)): | |
| 1891 | dataOut.igcej[i,j]=1 |
|
1891 | dataOut.igcej[i,j]=1 | |
| 1892 |
|
1892 | |||
| 1893 | return dataOut |
|
1893 | return dataOut | |
| 1894 |
|
1894 | |||
| 1895 | class DoublePulseACFs_PerLag(Operation): |
|
1895 | class DoublePulseACFs_PerLag(Operation): | |
| 1896 | ''' |
|
1896 | ''' | |
| 1897 | Written by R. Flores |
|
1897 | Written by R. Flores | |
| 1898 | ''' |
|
1898 | ''' | |
| 1899 | """Operation to get the ACFs of the Double Pulse. |
|
1899 | """Operation to get the ACFs of the Double Pulse. | |
| 1900 |
|
1900 | |||
| 1901 | Parameters: |
|
1901 | Parameters: | |
| 1902 | ----------- |
|
1902 | ----------- | |
| 1903 | None |
|
1903 | None | |
| 1904 |
|
1904 | |||
| 1905 | Example |
|
1905 | Example | |
| 1906 | -------- |
|
1906 | -------- | |
| 1907 |
|
1907 | |||
| 1908 | op = proc_unit.addOperation(name='DoublePulseACFs', optype='other') |
|
1908 | op = proc_unit.addOperation(name='DoublePulseACFs', optype='other') | |
| 1909 |
|
1909 | |||
| 1910 | """ |
|
1910 | """ | |
| 1911 |
|
1911 | |||
| 1912 | def __init__(self, **kwargs): |
|
1912 | def __init__(self, **kwargs): | |
| 1913 |
|
1913 | |||
| 1914 | Operation.__init__(self, **kwargs) |
|
1914 | Operation.__init__(self, **kwargs) | |
| 1915 | self.aux=1 |
|
1915 | self.aux=1 | |
| 1916 |
|
1916 | |||
| 1917 | def run(self,dataOut): |
|
1917 | def run(self,dataOut): | |
| 1918 |
|
1918 | |||
| 1919 | dataOut.igcej=numpy.zeros((dataOut.NDP,dataOut.DPL),'int32') |
|
1919 | dataOut.igcej=numpy.zeros((dataOut.NDP,dataOut.DPL),'int32') | |
| 1920 |
|
1920 | |||
| 1921 | if self.aux==1: |
|
1921 | if self.aux==1: | |
| 1922 | dataOut.rhor=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) |
|
1922 | dataOut.rhor=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) | |
| 1923 | dataOut.rhoi=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) |
|
1923 | dataOut.rhoi=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) | |
| 1924 | dataOut.sdp=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) |
|
1924 | dataOut.sdp=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) | |
| 1925 | dataOut.sd=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) |
|
1925 | dataOut.sd=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) | |
| 1926 | dataOut.p=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) |
|
1926 | dataOut.p=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) | |
| 1927 | dataOut.alag=numpy.zeros(dataOut.NDP,'float32') |
|
1927 | dataOut.alag=numpy.zeros(dataOut.NDP,'float32') | |
| 1928 | for l in range(dataOut.DPL): |
|
1928 | for l in range(dataOut.DPL): | |
| 1929 | dataOut.alag[l]=l*dataOut.DH*2.0/150.0 |
|
1929 | dataOut.alag[l]=l*dataOut.DH*2.0/150.0 | |
| 1930 | self.aux=0 |
|
1930 | self.aux=0 | |
| 1931 | sn4=dataOut.pan*dataOut.pbn |
|
1931 | sn4=dataOut.pan*dataOut.pbn | |
| 1932 | rhorn=0 |
|
1932 | rhorn=0 | |
| 1933 | rhoin=0 |
|
1933 | rhoin=0 | |
| 1934 | panrm=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) |
|
1934 | panrm=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) | |
| 1935 |
|
1935 | |||
| 1936 | id = numpy.where(dataOut.heightList>700)[0] |
|
1936 | id = numpy.where(dataOut.heightList>700)[0] | |
| 1937 |
|
1937 | |||
| 1938 | for i in range(dataOut.NDP): |
|
1938 | for i in range(dataOut.NDP): | |
| 1939 | for j in range(dataOut.DPL): |
|
1939 | for j in range(dataOut.DPL): | |
| 1940 | ################# Total power |
|
1940 | ################# Total power | |
| 1941 | pa=numpy.abs(dataOut.kabxys_integrated[4][i,j,0]+dataOut.kabxys_integrated[5][i,j,0]) |
|
1941 | pa=numpy.abs(dataOut.kabxys_integrated[4][i,j,0]+dataOut.kabxys_integrated[5][i,j,0]) | |
| 1942 | pb=numpy.abs(dataOut.kabxys_integrated[6][i,j,0]+dataOut.kabxys_integrated[7][i,j,0]) |
|
1942 | pb=numpy.abs(dataOut.kabxys_integrated[6][i,j,0]+dataOut.kabxys_integrated[7][i,j,0]) | |
| 1943 | st4=pa*pb |
|
1943 | st4=pa*pb | |
| 1944 | dataOut.p[i,j]=pa+pb-(dataOut.pan[j]+dataOut.pbn[j]) |
|
1944 | dataOut.p[i,j]=pa+pb-(dataOut.pan[j]+dataOut.pbn[j]) | |
| 1945 | dataOut.sdp[i,j]=2*dataOut.rnint2[j]*((pa+pb)*(pa+pb)) |
|
1945 | dataOut.sdp[i,j]=2*dataOut.rnint2[j]*((pa+pb)*(pa+pb)) | |
| 1946 | ## ACF |
|
1946 | ## ACF | |
| 1947 | rhorp=dataOut.kabxys_integrated[8][i,j,0]+dataOut.kabxys_integrated[11][i,j,0] |
|
1947 | rhorp=dataOut.kabxys_integrated[8][i,j,0]+dataOut.kabxys_integrated[11][i,j,0] | |
| 1948 | rhoip=dataOut.kabxys_integrated[10][i,j,0]-dataOut.kabxys_integrated[9][i,j,0] |
|
1948 | rhoip=dataOut.kabxys_integrated[10][i,j,0]-dataOut.kabxys_integrated[9][i,j,0] | |
| 1949 |
|
1949 | |||
| 1950 | if ((pa>dataOut.pan[j])&(pb>dataOut.pbn[j])): |
|
1950 | if ((pa>dataOut.pan[j])&(pb>dataOut.pbn[j])): | |
| 1951 | ss4=numpy.abs((pa-dataOut.pan[j])*(pb-dataOut.pbn[j])) |
|
1951 | ss4=numpy.abs((pa-dataOut.pan[j])*(pb-dataOut.pbn[j])) | |
| 1952 | panrm[i,j]=math.sqrt(ss4) |
|
1952 | panrm[i,j]=math.sqrt(ss4) | |
| 1953 | rnorm=1/panrm[i,j] |
|
1953 | rnorm=1/panrm[i,j] | |
| 1954 | ## ACF |
|
1954 | ## ACF | |
| 1955 | dataOut.rhor[i,j]=rhorp*rnorm |
|
1955 | dataOut.rhor[i,j]=rhorp*rnorm | |
| 1956 | dataOut.rhoi[i,j]=rhoip*rnorm |
|
1956 | dataOut.rhoi[i,j]=rhoip*rnorm | |
| 1957 | ############# Compute standard error for ACF |
|
1957 | ############# Compute standard error for ACF | |
| 1958 | stoss4=st4/ss4 |
|
1958 | stoss4=st4/ss4 | |
| 1959 | snoss4=sn4[j]/ss4 |
|
1959 | snoss4=sn4[j]/ss4 | |
| 1960 | rp2=((rhorp*rhorp)+(rhoip*rhoip))/st4 |
|
1960 | rp2=((rhorp*rhorp)+(rhoip*rhoip))/st4 | |
| 1961 | rn2=((rhorn*rhorn)+(rhoin*rhoin))/sn4[j] |
|
1961 | rn2=((rhorn*rhorn)+(rhoin*rhoin))/sn4[j] | |
| 1962 | rs2=(dataOut.rhor[i,j]*dataOut.rhor[i,j])+(dataOut.rhoi[i,j]*dataOut.rhoi[i,j]) |
|
1962 | rs2=(dataOut.rhor[i,j]*dataOut.rhor[i,j])+(dataOut.rhoi[i,j]*dataOut.rhoi[i,j]) | |
| 1963 | st=1.0+rs2*(stoss4-(2*math.sqrt(stoss4*snoss4))) |
|
1963 | st=1.0+rs2*(stoss4-(2*math.sqrt(stoss4*snoss4))) | |
| 1964 | stn=1.0+rs2*(snoss4-(2*math.sqrt(stoss4*snoss4))) |
|
1964 | stn=1.0+rs2*(snoss4-(2*math.sqrt(stoss4*snoss4))) | |
| 1965 | dataOut.sd[i,j]=((stoss4*((1.0+rp2)*st+(2.0*rp2*rs2*snoss4)-4.0*math.sqrt(rs2*rp2)))+(0.25*snoss4*((1.0+rn2)*stn+(2.0*rn2*rs2*stoss4)-4.0*math.sqrt(rs2*rn2))))*dataOut.rnint2[j] |
|
1965 | dataOut.sd[i,j]=((stoss4*((1.0+rp2)*st+(2.0*rp2*rs2*snoss4)-4.0*math.sqrt(rs2*rp2)))+(0.25*snoss4*((1.0+rn2)*stn+(2.0*rn2*rs2*stoss4)-4.0*math.sqrt(rs2*rn2))))*dataOut.rnint2[j] | |
| 1966 | dataOut.sd[i,j]=numpy.abs(dataOut.sd[i,j]) |
|
1966 | dataOut.sd[i,j]=numpy.abs(dataOut.sd[i,j]) | |
| 1967 | else: #default values for bad points |
|
1967 | else: #default values for bad points | |
| 1968 | rnorm=1/math.sqrt(st4) |
|
1968 | rnorm=1/math.sqrt(st4) | |
| 1969 | dataOut.sd[i,j]=1.e30 |
|
1969 | dataOut.sd[i,j]=1.e30 | |
| 1970 | dataOut.ibad[i,j]=4 |
|
1970 | dataOut.ibad[i,j]=4 | |
| 1971 | dataOut.rhor[i,j]=rhorp*rnorm |
|
1971 | dataOut.rhor[i,j]=rhorp*rnorm | |
| 1972 | dataOut.rhoi[i,j]=rhoip*rnorm |
|
1972 | dataOut.rhoi[i,j]=rhoip*rnorm | |
| 1973 | if ((pb/dataOut.pbn[j]-1.0)>2.25*(pa/dataOut.pan[j]-1.0)): #To flag bad points from the pulse and EEJ for lags != 0 for Channel B |
|
1973 | if ((pb/dataOut.pbn[j]-1.0)>2.25*(pa/dataOut.pan[j]-1.0)): #To flag bad points from the pulse and EEJ for lags != 0 for Channel B | |
| 1974 | dataOut.igcej[i,j]=1 |
|
1974 | dataOut.igcej[i,j]=1 | |
| 1975 |
|
1975 | |||
| 1976 | elif ((pa/dataOut.pan[j]-1.0)>2.25*(pb/dataOut.pbn[j]-1.0)): |
|
1976 | elif ((pa/dataOut.pan[j]-1.0)>2.25*(pb/dataOut.pbn[j]-1.0)): | |
| 1977 | dataOut.igcej[i,j]=1 |
|
1977 | dataOut.igcej[i,j]=1 | |
| 1978 |
|
1978 | |||
| 1979 | return dataOut |
|
1979 | return dataOut | |
| 1980 |
|
1980 | |||
| 1981 | class FaradayAngleAndDPPower(Operation): |
|
1981 | class FaradayAngleAndDPPower(Operation): | |
| 1982 | ''' |
|
1982 | ''' | |
| 1983 | Written by R. Flores |
|
1983 | Written by R. Flores | |
| 1984 | ''' |
|
1984 | ''' | |
| 1985 | """Operation to calculate Faraday angle and Double Pulse power. |
|
1985 | """Operation to calculate Faraday angle and Double Pulse power. | |
| 1986 |
|
1986 | |||
| 1987 | Parameters: |
|
1987 | Parameters: | |
| 1988 | ----------- |
|
1988 | ----------- | |
| 1989 | None |
|
1989 | None | |
| 1990 |
|
1990 | |||
| 1991 | Example |
|
1991 | Example | |
| 1992 | -------- |
|
1992 | -------- | |
| 1993 |
|
1993 | |||
| 1994 | op = proc_unit.addOperation(name='FaradayAngleAndDPPower', optype='other') |
|
1994 | op = proc_unit.addOperation(name='FaradayAngleAndDPPower', optype='other') | |
| 1995 |
|
1995 | |||
| 1996 | """ |
|
1996 | """ | |
| 1997 |
|
1997 | |||
| 1998 | def __init__(self, **kwargs): |
|
1998 | def __init__(self, **kwargs): | |
| 1999 |
|
1999 | |||
| 2000 | Operation.__init__(self, **kwargs) |
|
2000 | Operation.__init__(self, **kwargs) | |
| 2001 | self.aux=1 |
|
2001 | self.aux=1 | |
| 2002 |
|
2002 | |||
| 2003 | def run(self,dataOut): |
|
2003 | def run(self,dataOut): | |
| 2004 |
|
2004 | |||
| 2005 | if self.aux==1: |
|
2005 | if self.aux==1: | |
| 2006 | dataOut.h2=numpy.zeros(dataOut.MAXNRANGENDT,'float32') |
|
2006 | dataOut.h2=numpy.zeros(dataOut.MAXNRANGENDT,'float32') | |
| 2007 | dataOut.range1=numpy.zeros(dataOut.MAXNRANGENDT,order='F',dtype='float32') |
|
2007 | dataOut.range1=numpy.zeros(dataOut.MAXNRANGENDT,order='F',dtype='float32') | |
| 2008 | dataOut.sdn2=numpy.zeros(dataOut.NDP,'float32') |
|
2008 | dataOut.sdn2=numpy.zeros(dataOut.NDP,'float32') | |
| 2009 | dataOut.ph2=numpy.zeros(dataOut.NDP,'float32') |
|
2009 | dataOut.ph2=numpy.zeros(dataOut.NDP,'float32') | |
| 2010 | dataOut.sdp2=numpy.zeros(dataOut.NDP,'float32') |
|
2010 | dataOut.sdp2=numpy.zeros(dataOut.NDP,'float32') | |
| 2011 | dataOut.ibd=numpy.zeros(dataOut.NDP,'float32') |
|
2011 | dataOut.ibd=numpy.zeros(dataOut.NDP,'float32') | |
| 2012 | dataOut.phi=numpy.zeros(dataOut.NDP,'float32') |
|
2012 | dataOut.phi=numpy.zeros(dataOut.NDP,'float32') | |
| 2013 |
|
2013 | |||
| 2014 | self.aux=0 |
|
2014 | self.aux=0 | |
| 2015 |
|
2015 | |||
| 2016 | for i in range(dataOut.MAXNRANGENDT): |
|
2016 | for i in range(dataOut.MAXNRANGENDT): | |
| 2017 | dataOut.range1[i]=dataOut.H0 + i*dataOut.DH |
|
2017 | dataOut.range1[i]=dataOut.H0 + i*dataOut.DH | |
| 2018 | dataOut.h2[i]=dataOut.range1[i]**2 |
|
2018 | dataOut.h2[i]=dataOut.range1[i]**2 | |
| 2019 |
|
2019 | |||
| 2020 | for j in range(dataOut.NDP): |
|
2020 | for j in range(dataOut.NDP): | |
| 2021 | dataOut.ph2[j]=0. |
|
2021 | dataOut.ph2[j]=0. | |
| 2022 | dataOut.sdp2[j]=0. |
|
2022 | dataOut.sdp2[j]=0. | |
| 2023 | ri=dataOut.rhoi[j][0]/dataOut.sd[j][0] |
|
2023 | ri=dataOut.rhoi[j][0]/dataOut.sd[j][0] | |
| 2024 | rr=dataOut.rhor[j][0]/dataOut.sd[j][0] |
|
2024 | rr=dataOut.rhor[j][0]/dataOut.sd[j][0] | |
| 2025 | dataOut.sdn2[j]=1./dataOut.sd[j][0] |
|
2025 | dataOut.sdn2[j]=1./dataOut.sd[j][0] | |
| 2026 |
|
2026 | |||
| 2027 | pt=0.# // total power |
|
2027 | pt=0.# // total power | |
| 2028 | st=0.# // total signal |
|
2028 | st=0.# // total signal | |
| 2029 | ibt=0# // bad lags |
|
2029 | ibt=0# // bad lags | |
| 2030 | ns=0# // no. good lags |
|
2030 | ns=0# // no. good lags | |
| 2031 | for l in range(dataOut.DPL): |
|
2031 | for l in range(dataOut.DPL): | |
| 2032 | #add in other lags if outside of e-jet contamination |
|
2032 | #add in other lags if outside of e-jet contamination | |
| 2033 | if( (dataOut.igcej[j][l] == 0) and (dataOut.ibad[j][l] == 0) ): |
|
2033 | if( (dataOut.igcej[j][l] == 0) and (dataOut.ibad[j][l] == 0) ): | |
| 2034 |
|
2034 | |||
| 2035 | dataOut.ph2[j]+=dataOut.p[j][l]/dataOut.sdp[j][l] |
|
2035 | dataOut.ph2[j]+=dataOut.p[j][l]/dataOut.sdp[j][l] | |
| 2036 | dataOut.sdp2[j]=dataOut.sdp2[j]+1./dataOut.sdp[j][l] |
|
2036 | dataOut.sdp2[j]=dataOut.sdp2[j]+1./dataOut.sdp[j][l] | |
| 2037 | ns+=1 |
|
2037 | ns+=1 | |
| 2038 |
|
2038 | |||
| 2039 |
|
2039 | |||
| 2040 | pt+=dataOut.p[j][l]/dataOut.sdp[j][l] |
|
2040 | pt+=dataOut.p[j][l]/dataOut.sdp[j][l] | |
| 2041 | st+=1./dataOut.sdp[j][l] |
|
2041 | st+=1./dataOut.sdp[j][l] | |
| 2042 | ibt|=dataOut.ibad[j][l]; |
|
2042 | ibt|=dataOut.ibad[j][l]; | |
| 2043 | if(ns!= 0): |
|
2043 | if(ns!= 0): | |
| 2044 | dataOut.ibd[j]=0 |
|
2044 | dataOut.ibd[j]=0 | |
| 2045 | dataOut.ph2[j]=dataOut.ph2[j]/dataOut.sdp2[j] |
|
2045 | dataOut.ph2[j]=dataOut.ph2[j]/dataOut.sdp2[j] | |
| 2046 | dataOut.sdp2[j]=1./dataOut.sdp2[j] |
|
2046 | dataOut.sdp2[j]=1./dataOut.sdp2[j] | |
| 2047 | else: |
|
2047 | else: | |
| 2048 | dataOut.ibd[j]=ibt |
|
2048 | dataOut.ibd[j]=ibt | |
| 2049 | dataOut.ph2[j]=pt/st |
|
2049 | dataOut.ph2[j]=pt/st | |
| 2050 | dataOut.sdp2[j]=1./st |
|
2050 | dataOut.sdp2[j]=1./st | |
| 2051 |
|
2051 | |||
| 2052 | dataOut.ph2[j]=dataOut.ph2[j]*dataOut.h2[j] |
|
2052 | dataOut.ph2[j]=dataOut.ph2[j]*dataOut.h2[j] | |
| 2053 | dataOut.sdp2[j]=numpy.sqrt(dataOut.sdp2[j])*dataOut.h2[j] |
|
2053 | dataOut.sdp2[j]=numpy.sqrt(dataOut.sdp2[j])*dataOut.h2[j] | |
| 2054 | rr=rr/dataOut.sdn2[j] |
|
2054 | rr=rr/dataOut.sdn2[j] | |
| 2055 | ri=ri/dataOut.sdn2[j] |
|
2055 | ri=ri/dataOut.sdn2[j] | |
| 2056 | #rm[j]=np.sqrt(rr*rr + ri*ri) it is not used in c program |
|
2056 | #rm[j]=np.sqrt(rr*rr + ri*ri) it is not used in c program | |
| 2057 | dataOut.sdn2[j]=1./(dataOut.sdn2[j]*(rr*rr + ri*ri)) |
|
2057 | dataOut.sdn2[j]=1./(dataOut.sdn2[j]*(rr*rr + ri*ri)) | |
| 2058 | if( (ri == 0.) and (rr == 0.) ): |
|
2058 | if( (ri == 0.) and (rr == 0.) ): | |
| 2059 | dataOut.phi[j]=0. |
|
2059 | dataOut.phi[j]=0. | |
| 2060 | else: |
|
2060 | else: | |
| 2061 | dataOut.phi[j]=math.atan2( ri , rr ) |
|
2061 | dataOut.phi[j]=math.atan2( ri , rr ) | |
| 2062 |
|
2062 | |||
| 2063 | dataOut.flagTeTiCorrection = False |
|
2063 | dataOut.flagTeTiCorrection = False | |
| 2064 | return dataOut |
|
2064 | return dataOut | |
| 2065 |
|
2065 | |||
| 2066 |
|
2066 | |||
| 2067 | class ElectronDensityFaraday(Operation): |
|
2067 | class ElectronDensityFaraday(Operation): | |
| 2068 | ''' |
|
2068 | ''' | |
| 2069 | Written by R. Flores |
|
2069 | Written by R. Flores | |
| 2070 | ''' |
|
2070 | ''' | |
| 2071 | """Operation to calculate electron density from Faraday angle. |
|
2071 | """Operation to calculate electron density from Faraday angle. | |
| 2072 |
|
2072 | |||
| 2073 | Parameters: |
|
2073 | Parameters: | |
| 2074 | ----------- |
|
2074 | ----------- | |
| 2075 | NSHTS : int |
|
2075 | NSHTS : int | |
| 2076 | .* |
|
2076 | .* | |
| 2077 | RATE : float |
|
2077 | RATE : float | |
| 2078 | .* |
|
2078 | .* | |
| 2079 |
|
2079 | |||
| 2080 | Example |
|
2080 | Example | |
| 2081 | -------- |
|
2081 | -------- | |
| 2082 |
|
2082 | |||
| 2083 | op = proc_unit.addOperation(name='ElectronDensityFaraday', optype='other') |
|
2083 | op = proc_unit.addOperation(name='ElectronDensityFaraday', optype='other') | |
| 2084 | op.addParameter(name='NSHTS', value='50', format='int') |
|
2084 | op.addParameter(name='NSHTS', value='50', format='int') | |
| 2085 | op.addParameter(name='RATE', value='1.8978873e-6', format='float') |
|
2085 | op.addParameter(name='RATE', value='1.8978873e-6', format='float') | |
| 2086 |
|
2086 | |||
| 2087 | """ |
|
2087 | """ | |
| 2088 |
|
2088 | |||
| 2089 | def __init__(self, **kwargs): |
|
2089 | def __init__(self, **kwargs): | |
| 2090 |
|
2090 | |||
| 2091 | Operation.__init__(self, **kwargs) |
|
2091 | Operation.__init__(self, **kwargs) | |
| 2092 | self.aux=1 |
|
2092 | self.aux=1 | |
| 2093 |
|
2093 | |||
| 2094 | def run(self,dataOut,NSHTS=50,RATE=1.8978873e-6): |
|
2094 | def run(self,dataOut,NSHTS=50,RATE=1.8978873e-6): | |
| 2095 |
|
2095 | |||
| 2096 | dataOut.NSHTS=NSHTS |
|
2096 | dataOut.NSHTS=NSHTS | |
| 2097 | dataOut.RATE=RATE |
|
2097 | dataOut.RATE=RATE | |
| 2098 |
|
2098 | |||
| 2099 | if self.aux==1: |
|
2099 | if self.aux==1: | |
| 2100 | dataOut.dphi=numpy.zeros(dataOut.NDP,'float32') |
|
2100 | dataOut.dphi=numpy.zeros(dataOut.NDP,'float32') | |
| 2101 | dataOut.sdn1=numpy.zeros(dataOut.NDP,'float32') |
|
2101 | dataOut.sdn1=numpy.zeros(dataOut.NDP,'float32') | |
| 2102 | self.aux=0 |
|
2102 | self.aux=0 | |
| 2103 | theta=numpy.zeros(dataOut.NDP,dtype=numpy.complex_) |
|
2103 | theta=numpy.zeros(dataOut.NDP,dtype=numpy.complex_) | |
| 2104 | thetai=numpy.zeros(dataOut.NDP,dtype=numpy.complex_) |
|
2104 | thetai=numpy.zeros(dataOut.NDP,dtype=numpy.complex_) | |
| 2105 | # use complex numbers for phase |
|
2105 | # use complex numbers for phase | |
| 2106 | ''' |
|
2106 | ''' | |
| 2107 | for i in range(dataOut.NSHTS): |
|
2107 | for i in range(dataOut.NSHTS): | |
| 2108 | theta[i]=math.cos(dataOut.phi[i])+math.sin(dataOut.phi[i])*1j |
|
2108 | theta[i]=math.cos(dataOut.phi[i])+math.sin(dataOut.phi[i])*1j | |
| 2109 | thetai[i]=-math.sin(dataOut.phi[i])+math.cos(dataOut.phi[i])*1j |
|
2109 | thetai[i]=-math.sin(dataOut.phi[i])+math.cos(dataOut.phi[i])*1j | |
| 2110 | ''' #Old Method |
|
2110 | ''' #Old Method | |
| 2111 |
|
2111 | |||
| 2112 | # differentiate and convert to number density |
|
2112 | # differentiate and convert to number density | |
| 2113 | ndphi=dataOut.NSHTS-4 |
|
2113 | ndphi=dataOut.NSHTS-4 | |
| 2114 | if hasattr(dataOut, 'flagSpreadF') and dataOut.flagSpreadF: |
|
2114 | if hasattr(dataOut, 'flagSpreadF') and dataOut.flagSpreadF: | |
| 2115 | nanindex = numpy.argwhere(numpy.isnan(dataOut.phi)) |
|
2115 | nanindex = numpy.argwhere(numpy.isnan(dataOut.phi)) | |
| 2116 | i1 = nanindex[-1][0] |
|
2116 | i1 = nanindex[-1][0] | |
| 2117 | #Analizar cuando SpreadF es Pluma |
|
2117 | #Analizar cuando SpreadF es Pluma | |
| 2118 |
|
2118 | |||
| 2119 | dataOut.phi[i1+1:]=numpy.unwrap(dataOut.phi[i1+1:]) #Better results |
|
2119 | dataOut.phi[i1+1:]=numpy.unwrap(dataOut.phi[i1+1:]) #Better results | |
| 2120 | else: |
|
2120 | else: | |
| 2121 | dataOut.phi[:]=numpy.unwrap(dataOut.phi[:]) #Better results |
|
2121 | dataOut.phi[:]=numpy.unwrap(dataOut.phi[:]) #Better results | |
| 2122 | for i in range(2,dataOut.NSHTS-2): |
|
2122 | for i in range(2,dataOut.NSHTS-2): | |
| 2123 | fact=(-0.5/(dataOut.RATE*dataOut.DH))*dataOut.bki[i] |
|
2123 | fact=(-0.5/(dataOut.RATE*dataOut.DH))*dataOut.bki[i] | |
| 2124 | #print("fact: ", fact,dataOut.RATE,dataOut.DH,dataOut.bki[i]) |
|
2124 | #print("fact: ", fact,dataOut.RATE,dataOut.DH,dataOut.bki[i]) | |
| 2125 | #four-point derivative, no phase unwrapping necessary |
|
2125 | #four-point derivative, no phase unwrapping necessary | |
| 2126 | #####dataOut.dphi[i]=((((theta[i+1]-theta[i-1])+(2.0*(theta[i+2]-theta[i-2])))/thetai[i])).real/10.0 #Original from C program |
|
2126 | #####dataOut.dphi[i]=((((theta[i+1]-theta[i-1])+(2.0*(theta[i+2]-theta[i-2])))/thetai[i])).real/10.0 #Original from C program | |
| 2127 |
|
2127 | |||
| 2128 | ##dataOut.dphi[i]=((((theta[i-2]-theta[i+2])+(8.0*(theta[i+1]-theta[i-1])))/thetai[i])).real/12.0 |
|
2128 | ##dataOut.dphi[i]=((((theta[i-2]-theta[i+2])+(8.0*(theta[i+1]-theta[i-1])))/thetai[i])).real/12.0 | |
| 2129 | dataOut.dphi[i]=((dataOut.phi[i+1]-dataOut.phi[i-1])+(2.0*(dataOut.phi[i+2]-dataOut.phi[i-2])))/10.0 #Better results |
|
2129 | dataOut.dphi[i]=((dataOut.phi[i+1]-dataOut.phi[i-1])+(2.0*(dataOut.phi[i+2]-dataOut.phi[i-2])))/10.0 #Better results | |
| 2130 |
|
2130 | |||
| 2131 | #dataOut.dphi_uc[i] = abs(dataOut.phi[i]*dataOut.bki[i]*(-0.5)/dataOut.DH) |
|
2131 | #dataOut.dphi_uc[i] = abs(dataOut.phi[i]*dataOut.bki[i]*(-0.5)/dataOut.DH) | |
| 2132 | #dataOut.dphi[i]=abs(dataOut.dphi[i]*fact) |
|
2132 | #dataOut.dphi[i]=abs(dataOut.dphi[i]*fact) | |
| 2133 | dataOut.dphi[i]=dataOut.dphi[i]*abs(fact) |
|
2133 | dataOut.dphi[i]=dataOut.dphi[i]*abs(fact) | |
| 2134 | dataOut.sdn1[i]=(4.*(dataOut.sdn2[i-2]+dataOut.sdn2[i+2])+dataOut.sdn2[i-1]+dataOut.sdn2[i+1]) |
|
2134 | dataOut.sdn1[i]=(4.*(dataOut.sdn2[i-2]+dataOut.sdn2[i+2])+dataOut.sdn2[i-1]+dataOut.sdn2[i+1]) | |
| 2135 | dataOut.sdn1[i]=numpy.sqrt(dataOut.sdn1[i])*fact |
|
2135 | dataOut.sdn1[i]=numpy.sqrt(dataOut.sdn1[i])*fact | |
| 2136 |
|
2136 | |||
| 2137 | return dataOut |
|
2137 | return dataOut | |
| 2138 |
|
2138 | |||
| 2139 |
|
2139 | |||
| 2140 | class NormalizeDPPower(Operation): |
|
2140 | class NormalizeDPPower(Operation): | |
| 2141 | ''' |
|
2141 | ''' | |
| 2142 | Written by R. Flores |
|
2142 | Written by R. Flores | |
| 2143 | ''' |
|
2143 | ''' | |
| 2144 | """Operation to normalize relative electron density from power with total electron density from Faraday angle. |
|
2144 | """Operation to normalize relative electron density from power with total electron density from Faraday angle. | |
| 2145 |
|
2145 | |||
| 2146 | Parameters: |
|
2146 | Parameters: | |
| 2147 | ----------- |
|
2147 | ----------- | |
| 2148 | None |
|
2148 | None | |
| 2149 |
|
2149 | |||
| 2150 | Example |
|
2150 | Example | |
| 2151 | -------- |
|
2151 | -------- | |
| 2152 |
|
2152 | |||
| 2153 | op = proc_unit.addOperation(name='NormalizeDPPower', optype='other') |
|
2153 | op = proc_unit.addOperation(name='NormalizeDPPower', optype='other') | |
| 2154 |
|
2154 | |||
| 2155 | """ |
|
2155 | """ | |
| 2156 |
|
2156 | |||
| 2157 | def __init__(self, **kwargs): |
|
2157 | def __init__(self, **kwargs): | |
| 2158 |
|
2158 | |||
| 2159 | Operation.__init__(self, **kwargs) |
|
2159 | Operation.__init__(self, **kwargs) | |
| 2160 | self.aux=1 |
|
2160 | self.aux=1 | |
| 2161 |
|
2161 | |||
| 2162 | def normal(self,a,b,n,m): |
|
2162 | def normal(self,a,b,n,m): | |
| 2163 | chmin=1.0e30 |
|
2163 | chmin=1.0e30 | |
| 2164 | chisq=numpy.zeros(150,'float32') |
|
2164 | chisq=numpy.zeros(150,'float32') | |
| 2165 | temp=numpy.zeros(150,'float32') |
|
2165 | temp=numpy.zeros(150,'float32') | |
| 2166 |
|
2166 | |||
| 2167 | for i in range(2*m-1): |
|
2167 | for i in range(2*m-1): | |
| 2168 | an=al=be=chisq[i]=0.0 |
|
2168 | an=al=be=chisq[i]=0.0 | |
| 2169 | for j in range(int(n/m)): |
|
2169 | for j in range(int(n/m)): | |
| 2170 | k=int(j+i*n/(2*m)) |
|
2170 | k=int(j+i*n/(2*m)) | |
| 2171 | if(a[k]>0.0 and b[k]>0.0): |
|
2171 | if(a[k]>0.0 and b[k]>0.0): | |
| 2172 | al+=a[k]*b[k] |
|
2172 | al+=a[k]*b[k] | |
| 2173 | be+=b[k]*b[k] |
|
2173 | be+=b[k]*b[k] | |
| 2174 |
|
2174 | |||
| 2175 | if(be>0.0): |
|
2175 | if(be>0.0): | |
| 2176 | temp[i]=al/be |
|
2176 | temp[i]=al/be | |
| 2177 | else: |
|
2177 | else: | |
| 2178 | temp[i]=1.0 |
|
2178 | temp[i]=1.0 | |
| 2179 |
|
2179 | |||
| 2180 | for j in range(int(n/m)): |
|
2180 | for j in range(int(n/m)): | |
| 2181 | k=int(j+i*n/(2*m)) |
|
2181 | k=int(j+i*n/(2*m)) | |
| 2182 | if(a[k]>0.0 and b[k]>0.0): |
|
2182 | if(a[k]>0.0 and b[k]>0.0): | |
| 2183 | chisq[i]+=(numpy.log10(b[k]*temp[i]/a[k]))**2 |
|
2183 | chisq[i]+=(numpy.log10(b[k]*temp[i]/a[k]))**2 | |
| 2184 | an=an+1 |
|
2184 | an=an+1 | |
| 2185 |
|
2185 | |||
| 2186 | if(chisq[i]>0.0): |
|
2186 | if(chisq[i]>0.0): | |
| 2187 | chisq[i]/=an |
|
2187 | chisq[i]/=an | |
| 2188 |
|
2188 | |||
| 2189 | for i in range(int(2*m-1)): |
|
2189 | for i in range(int(2*m-1)): | |
| 2190 | if(chisq[i]<chmin and chisq[i]>1.0e-6): |
|
2190 | if(chisq[i]<chmin and chisq[i]>1.0e-6): | |
| 2191 | chmin=chisq[i] |
|
2191 | chmin=chisq[i] | |
| 2192 | cf=temp[i] |
|
2192 | cf=temp[i] | |
| 2193 | return cf |
|
2193 | return cf | |
| 2194 |
|
2194 | |||
| 2195 | def normalize(self,dataOut): |
|
2195 | def normalize(self,dataOut): | |
| 2196 |
|
2196 | |||
| 2197 | if self.aux==1: |
|
2197 | if self.aux==1: | |
| 2198 | dataOut.cf=numpy.zeros(1,'float32') |
|
2198 | dataOut.cf=numpy.zeros(1,'float32') | |
| 2199 | dataOut.cflast=numpy.zeros(1,'float32') |
|
2199 | dataOut.cflast=numpy.zeros(1,'float32') | |
| 2200 | self.aux=0 |
|
2200 | self.aux=0 | |
| 2201 |
|
2201 | |||
| 2202 | night_first=300.0 |
|
2202 | night_first=300.0 | |
| 2203 | night_first1= 310.0 |
|
2203 | night_first1= 310.0 | |
| 2204 | night_end= 450.0 |
|
2204 | night_end= 450.0 | |
| 2205 | day_first=250.0 |
|
2205 | day_first=250.0 | |
| 2206 | day_end=400.0 |
|
2206 | day_end=400.0 | |
| 2207 | day_first_sunrise=190.0 |
|
2207 | day_first_sunrise=190.0 | |
| 2208 | day_end_sunrise=280.0 |
|
2208 | day_end_sunrise=280.0 | |
| 2209 |
|
2209 | |||
| 2210 | #print(dataOut.ut_Faraday) |
|
2210 | #print(dataOut.ut_Faraday) | |
| 2211 | if(dataOut.ut_Faraday>4.0 and dataOut.ut_Faraday<11.0): #early |
|
2211 | if(dataOut.ut_Faraday>4.0 and dataOut.ut_Faraday<11.0): #early | |
| 2212 | #print("EARLY") |
|
2212 | #print("EARLY") | |
| 2213 | i2=(night_end-dataOut.range1[0])/dataOut.DH |
|
2213 | i2=(night_end-dataOut.range1[0])/dataOut.DH | |
| 2214 | i1=(night_first -dataOut.range1[0])/dataOut.DH |
|
2214 | i1=(night_first -dataOut.range1[0])/dataOut.DH | |
| 2215 | elif (dataOut.ut_Faraday>0.0 and dataOut.ut_Faraday<4.0): #night |
|
2215 | elif (dataOut.ut_Faraday>0.0 and dataOut.ut_Faraday<4.0): #night | |
| 2216 | #print("NIGHT") |
|
2216 | #print("NIGHT") | |
| 2217 | i2=(night_end-dataOut.range1[0])/dataOut.DH |
|
2217 | i2=(night_end-dataOut.range1[0])/dataOut.DH | |
| 2218 | i1=(night_first1 -dataOut.range1[0])/dataOut.DH |
|
2218 | i1=(night_first1 -dataOut.range1[0])/dataOut.DH | |
| 2219 | elif (dataOut.ut_Faraday>=11.0 and dataOut.ut_Faraday<13.5): #sunrise |
|
2219 | elif (dataOut.ut_Faraday>=11.0 and dataOut.ut_Faraday<13.5): #sunrise | |
| 2220 | #print("SUNRISE") |
|
2220 | #print("SUNRISE") | |
| 2221 | i2=( day_end_sunrise-dataOut.range1[0])/dataOut.DH |
|
2221 | i2=( day_end_sunrise-dataOut.range1[0])/dataOut.DH | |
| 2222 | i1=(day_first_sunrise - dataOut.range1[0])/dataOut.DH |
|
2222 | i1=(day_first_sunrise - dataOut.range1[0])/dataOut.DH | |
| 2223 | else: |
|
2223 | else: | |
| 2224 | #print("ELSE") |
|
2224 | #print("ELSE") | |
| 2225 | i2=(day_end-dataOut.range1[0])/dataOut.DH |
|
2225 | i2=(day_end-dataOut.range1[0])/dataOut.DH | |
| 2226 | i1=(day_first -dataOut.range1[0])/dataOut.DH |
|
2226 | i1=(day_first -dataOut.range1[0])/dataOut.DH | |
| 2227 | #print(i1*dataOut.DH) |
|
2227 | #print(i1*dataOut.DH) | |
| 2228 | #print(i2*dataOut.DH) |
|
2228 | #print(i2*dataOut.DH) | |
| 2229 |
|
2229 | |||
| 2230 | i1=int(i1) |
|
2230 | i1=int(i1) | |
| 2231 | i2=int(i2) |
|
2231 | i2=int(i2) | |
| 2232 |
|
2232 | |||
| 2233 | try: |
|
2233 | try: | |
| 2234 | dataOut.cf=self.normal(dataOut.dphi[i1::], dataOut.ph2[i1::], i2-i1, 1) |
|
2234 | dataOut.cf=self.normal(dataOut.dphi[i1::], dataOut.ph2[i1::], i2-i1, 1) | |
| 2235 | except: |
|
2235 | except: | |
| 2236 | pass |
|
2236 | pass | |
| 2237 |
|
2237 | |||
| 2238 | #print(dataOut.ph2) |
|
2238 | #print(dataOut.ph2) | |
| 2239 | #input() |
|
2239 | #input() | |
| 2240 | # in case of spread F, normalize much higher |
|
2240 | # in case of spread F, normalize much higher | |
| 2241 | if(dataOut.cf<dataOut.cflast[0]/10.0): |
|
2241 | if(dataOut.cf<dataOut.cflast[0]/10.0): | |
| 2242 | i1=(night_first1+100.-dataOut.range1[0])/dataOut.DH |
|
2242 | i1=(night_first1+100.-dataOut.range1[0])/dataOut.DH | |
| 2243 | i2=(night_end+100.0-dataOut.range1[0])/dataOut.DH |
|
2243 | i2=(night_end+100.0-dataOut.range1[0])/dataOut.DH | |
| 2244 | i1=int(i1) |
|
2244 | i1=int(i1) | |
| 2245 | i2=int(i2) |
|
2245 | i2=int(i2) | |
| 2246 | try: |
|
2246 | try: | |
| 2247 | dataOut.cf=self.normal(dataOut.dphi[int(i1)::], dataOut.ph2[int(i1)::], int(i2-i1), 1) |
|
2247 | dataOut.cf=self.normal(dataOut.dphi[int(i1)::], dataOut.ph2[int(i1)::], int(i2-i1), 1) | |
| 2248 | except: |
|
2248 | except: | |
| 2249 | pass |
|
2249 | pass | |
| 2250 |
|
2250 | |||
| 2251 | dataOut.cflast[0]=dataOut.cf |
|
2251 | dataOut.cflast[0]=dataOut.cf | |
| 2252 |
|
2252 | |||
| 2253 | ## normalize double pulse power and error bars to Faraday |
|
2253 | ## normalize double pulse power and error bars to Faraday | |
| 2254 | for i in range(dataOut.NSHTS): |
|
2254 | for i in range(dataOut.NSHTS): | |
| 2255 | dataOut.ph2[i]*=dataOut.cf |
|
2255 | dataOut.ph2[i]*=dataOut.cf | |
| 2256 | dataOut.sdp2[i]*=dataOut.cf |
|
2256 | dataOut.sdp2[i]*=dataOut.cf | |
| 2257 | #print(dataOut.ph2) |
|
2257 | #print(dataOut.ph2) | |
| 2258 | #input() |
|
2258 | #input() | |
| 2259 |
|
2259 | |||
| 2260 | for i in range(dataOut.NSHTS): |
|
2260 | for i in range(dataOut.NSHTS): | |
| 2261 | dataOut.ph2[i]=(max(1.0, dataOut.ph2[i])) |
|
2261 | dataOut.ph2[i]=(max(1.0, dataOut.ph2[i])) | |
| 2262 | dataOut.dphi[i]=(max(1.0, dataOut.dphi[i])) |
|
2262 | dataOut.dphi[i]=(max(1.0, dataOut.dphi[i])) | |
| 2263 |
|
2263 | |||
| 2264 |
|
2264 | |||
| 2265 | def run(self,dataOut): |
|
2265 | def run(self,dataOut): | |
| 2266 |
|
2266 | |||
| 2267 | self.normalize(dataOut) |
|
2267 | self.normalize(dataOut) | |
| 2268 | #print(dataOut.ph2) |
|
2268 | #print(dataOut.ph2) | |
| 2269 | #print(dataOut.sdp2) |
|
2269 | #print(dataOut.sdp2) | |
| 2270 | #input() |
|
2270 | #input() | |
| 2271 |
|
2271 | |||
| 2272 |
|
2272 | |||
| 2273 | return dataOut |
|
2273 | return dataOut | |
| 2274 |
|
2274 | |||
| 2275 | class NormalizeDPPowerRoberto(Operation): |
|
2275 | class NormalizeDPPowerRoberto(Operation): | |
| 2276 | ''' |
|
2276 | ''' | |
| 2277 | Written by R. Flores |
|
2277 | Written by R. Flores | |
| 2278 | ''' |
|
2278 | ''' | |
| 2279 | """Operation to normalize relative electron density from power with total electron density from Farday angle. |
|
2279 | """Operation to normalize relative electron density from power with total electron density from Farday angle. | |
| 2280 |
|
2280 | |||
| 2281 | Parameters: |
|
2281 | Parameters: | |
| 2282 | ----------- |
|
2282 | ----------- | |
| 2283 | None |
|
2283 | None | |
| 2284 |
|
2284 | |||
| 2285 | Example |
|
2285 | Example | |
| 2286 | -------- |
|
2286 | -------- | |
| 2287 |
|
2287 | |||
| 2288 | op = proc_unit.addOperation(name='NormalizeDPPower', optype='other') |
|
2288 | op = proc_unit.addOperation(name='NormalizeDPPower', optype='other') | |
| 2289 |
|
2289 | |||
| 2290 | """ |
|
2290 | """ | |
| 2291 |
|
2291 | |||
| 2292 | def __init__(self, **kwargs): |
|
2292 | def __init__(self, **kwargs): | |
| 2293 |
|
2293 | |||
| 2294 | Operation.__init__(self, **kwargs) |
|
2294 | Operation.__init__(self, **kwargs) | |
| 2295 | self.aux=1 |
|
2295 | self.aux=1 | |
| 2296 |
|
2296 | |||
| 2297 | def normal(self,a,b,n,m): |
|
2297 | def normal(self,a,b,n,m): | |
| 2298 | chmin=1.0e30 |
|
2298 | chmin=1.0e30 | |
| 2299 | chisq=numpy.zeros(150,'float32') |
|
2299 | chisq=numpy.zeros(150,'float32') | |
| 2300 | temp=numpy.zeros(150,'float32') |
|
2300 | temp=numpy.zeros(150,'float32') | |
| 2301 |
|
2301 | |||
| 2302 | for i in range(2*m-1): |
|
2302 | for i in range(2*m-1): | |
| 2303 | an=al=be=chisq[i]=0.0 |
|
2303 | an=al=be=chisq[i]=0.0 | |
| 2304 | for j in range(int(n/m)): |
|
2304 | for j in range(int(n/m)): | |
| 2305 | k=int(j+i*n/(2*m)) |
|
2305 | k=int(j+i*n/(2*m)) | |
| 2306 | if(a[k]>0.0 and b[k]>0.0): |
|
2306 | if(a[k]>0.0 and b[k]>0.0): | |
| 2307 | al+=a[k]*b[k] |
|
2307 | al+=a[k]*b[k] | |
| 2308 | be+=b[k]*b[k] |
|
2308 | be+=b[k]*b[k] | |
| 2309 |
|
2309 | |||
| 2310 | if(be>0.0): |
|
2310 | if(be>0.0): | |
| 2311 | temp[i]=al/be |
|
2311 | temp[i]=al/be | |
| 2312 | else: |
|
2312 | else: | |
| 2313 | temp[i]=1.0 |
|
2313 | temp[i]=1.0 | |
| 2314 |
|
2314 | |||
| 2315 | for j in range(int(n/m)): |
|
2315 | for j in range(int(n/m)): | |
| 2316 | k=int(j+i*n/(2*m)) |
|
2316 | k=int(j+i*n/(2*m)) | |
| 2317 | if(a[k]>0.0 and b[k]>0.0): |
|
2317 | if(a[k]>0.0 and b[k]>0.0): | |
| 2318 | chisq[i]+=(numpy.log10(b[k]*temp[i]/a[k]))**2 |
|
2318 | chisq[i]+=(numpy.log10(b[k]*temp[i]/a[k]))**2 | |
| 2319 | an=an+1 |
|
2319 | an=an+1 | |
| 2320 |
|
2320 | |||
| 2321 | if(chisq[i]>0.0): |
|
2321 | if(chisq[i]>0.0): | |
| 2322 | chisq[i]/=an |
|
2322 | chisq[i]/=an | |
| 2323 |
|
2323 | |||
| 2324 | for i in range(int(2*m-1)): |
|
2324 | for i in range(int(2*m-1)): | |
| 2325 | if(chisq[i]<chmin and chisq[i]>1.0e-6): |
|
2325 | if(chisq[i]<chmin and chisq[i]>1.0e-6): | |
| 2326 | chmin=chisq[i] |
|
2326 | chmin=chisq[i] | |
| 2327 | cf=temp[i] |
|
2327 | cf=temp[i] | |
| 2328 | return cf |
|
2328 | return cf | |
| 2329 |
|
2329 | |||
| 2330 | def normalize(self,dataOut): |
|
2330 | def normalize(self,dataOut): | |
| 2331 |
|
2331 | |||
| 2332 | if self.aux==1: |
|
2332 | if self.aux==1: | |
| 2333 | dataOut.cf=numpy.zeros(1,'float32') |
|
2333 | dataOut.cf=numpy.zeros(1,'float32') | |
| 2334 | dataOut.cflast=numpy.zeros(1,'float32') |
|
2334 | dataOut.cflast=numpy.zeros(1,'float32') | |
| 2335 | self.aux=0 |
|
2335 | self.aux=0 | |
| 2336 |
|
2336 | |||
| 2337 | night_first=300.0 |
|
2337 | night_first=300.0 | |
| 2338 | night_first1= 310.0 |
|
2338 | night_first1= 310.0 | |
| 2339 | night_end= 450.0 |
|
2339 | night_end= 450.0 | |
| 2340 | day_first=250.0 |
|
2340 | day_first=250.0 | |
| 2341 | day_end=400.0 |
|
2341 | day_end=400.0 | |
| 2342 | day_first_sunrise=190.0 |
|
2342 | day_first_sunrise=190.0 | |
| 2343 | day_end_sunrise=350.0 |
|
2343 | day_end_sunrise=350.0 | |
| 2344 |
|
2344 | |||
| 2345 | print(dataOut.ut_Faraday) |
|
2345 | print(dataOut.ut_Faraday) | |
| 2346 | ''' |
|
2346 | ''' | |
| 2347 | if(dataOut.ut_Faraday>4.0 and dataOut.ut_Faraday<11.0): #early |
|
2347 | if(dataOut.ut_Faraday>4.0 and dataOut.ut_Faraday<11.0): #early | |
| 2348 | print("EARLY") |
|
2348 | print("EARLY") | |
| 2349 | i2=(night_end-dataOut.range1[0])/dataOut.DH |
|
2349 | i2=(night_end-dataOut.range1[0])/dataOut.DH | |
| 2350 | i1=(night_first -dataOut.range1[0])/dataOut.DH |
|
2350 | i1=(night_first -dataOut.range1[0])/dataOut.DH | |
| 2351 | elif (dataOut.ut_Faraday>0.0 and dataOut.ut_Faraday<4.0): #night |
|
2351 | elif (dataOut.ut_Faraday>0.0 and dataOut.ut_Faraday<4.0): #night | |
| 2352 | print("NIGHT") |
|
2352 | print("NIGHT") | |
| 2353 | i2=(night_end-dataOut.range1[0])/dataOut.DH |
|
2353 | i2=(night_end-dataOut.range1[0])/dataOut.DH | |
| 2354 | i1=(night_first1 -dataOut.range1[0])/dataOut.DH |
|
2354 | i1=(night_first1 -dataOut.range1[0])/dataOut.DH | |
| 2355 | elif (dataOut.ut_Faraday>=11.0 and dataOut.ut_Faraday<13.5): #sunrise |
|
2355 | elif (dataOut.ut_Faraday>=11.0 and dataOut.ut_Faraday<13.5): #sunrise | |
| 2356 | print("SUNRISE") |
|
2356 | print("SUNRISE") | |
| 2357 | i2=( day_end_sunrise-dataOut.range1[0])/dataOut.DH |
|
2357 | i2=( day_end_sunrise-dataOut.range1[0])/dataOut.DH | |
| 2358 | i1=(day_first_sunrise - dataOut.range1[0])/dataOut.DH |
|
2358 | i1=(day_first_sunrise - dataOut.range1[0])/dataOut.DH | |
| 2359 | else: |
|
2359 | else: | |
| 2360 | print("ELSE") |
|
2360 | print("ELSE") | |
| 2361 | i2=(day_end-dataOut.range1[0])/dataOut.DH |
|
2361 | i2=(day_end-dataOut.range1[0])/dataOut.DH | |
| 2362 | i1=(day_first -dataOut.range1[0])/dataOut.DH |
|
2362 | i1=(day_first -dataOut.range1[0])/dataOut.DH | |
| 2363 | ''' |
|
2363 | ''' | |
| 2364 | i2=(420-dataOut.range1[0])/dataOut.DH |
|
2364 | i2=(420-dataOut.range1[0])/dataOut.DH | |
| 2365 | i1=(200 -dataOut.range1[0])/dataOut.DH |
|
2365 | i1=(200 -dataOut.range1[0])/dataOut.DH | |
| 2366 | print(i1*dataOut.DH) |
|
2366 | print(i1*dataOut.DH) | |
| 2367 | print(i2*dataOut.DH) |
|
2367 | print(i2*dataOut.DH) | |
| 2368 |
|
2368 | |||
| 2369 | i1=int(i1) |
|
2369 | i1=int(i1) | |
| 2370 | i2=int(i2) |
|
2370 | i2=int(i2) | |
| 2371 |
|
2371 | |||
| 2372 | try: |
|
2372 | try: | |
| 2373 | dataOut.cf=self.normal(dataOut.dphi[i1::], dataOut.ph2[i1::], i2-i1, 1) |
|
2373 | dataOut.cf=self.normal(dataOut.dphi[i1::], dataOut.ph2[i1::], i2-i1, 1) | |
| 2374 | except: |
|
2374 | except: | |
| 2375 | pass |
|
2375 | pass | |
| 2376 |
|
2376 | |||
| 2377 | #print(dataOut.ph2) |
|
2377 | #print(dataOut.ph2) | |
| 2378 | #input() |
|
2378 | #input() | |
| 2379 | # in case of spread F, normalize much higher |
|
2379 | # in case of spread F, normalize much higher | |
| 2380 | if(dataOut.cf<dataOut.cflast[0]/10.0): |
|
2380 | if(dataOut.cf<dataOut.cflast[0]/10.0): | |
| 2381 | i1=(night_first1+100.-dataOut.range1[0])/dataOut.DH |
|
2381 | i1=(night_first1+100.-dataOut.range1[0])/dataOut.DH | |
| 2382 | i2=(night_end+100.0-dataOut.range1[0])/dataOut.DH |
|
2382 | i2=(night_end+100.0-dataOut.range1[0])/dataOut.DH | |
| 2383 | i1=int(i1) |
|
2383 | i1=int(i1) | |
| 2384 | i2=int(i2) |
|
2384 | i2=int(i2) | |
| 2385 | try: |
|
2385 | try: | |
| 2386 | dataOut.cf=self.normal(dataOut.dphi[int(i1)::], dataOut.ph2[int(i1)::], int(i2-i1), 1) |
|
2386 | dataOut.cf=self.normal(dataOut.dphi[int(i1)::], dataOut.ph2[int(i1)::], int(i2-i1), 1) | |
| 2387 | except: |
|
2387 | except: | |
| 2388 | pass |
|
2388 | pass | |
| 2389 |
|
2389 | |||
| 2390 | dataOut.cflast[0]=dataOut.cf |
|
2390 | dataOut.cflast[0]=dataOut.cf | |
| 2391 |
|
2391 | |||
| 2392 | ## normalize double pulse power and error bars to Faraday |
|
2392 | ## normalize double pulse power and error bars to Faraday | |
| 2393 | for i in range(dataOut.NSHTS): |
|
2393 | for i in range(dataOut.NSHTS): | |
| 2394 | dataOut.ph2[i]*=dataOut.cf |
|
2394 | dataOut.ph2[i]*=dataOut.cf | |
| 2395 | dataOut.sdp2[i]*=dataOut.cf |
|
2395 | dataOut.sdp2[i]*=dataOut.cf | |
| 2396 | #print(dataOut.ph2) |
|
2396 | #print(dataOut.ph2) | |
| 2397 | #input() |
|
2397 | #input() | |
| 2398 |
|
2398 | |||
| 2399 | for i in range(dataOut.NSHTS): |
|
2399 | for i in range(dataOut.NSHTS): | |
| 2400 | dataOut.ph2[i]=(max(1.0, dataOut.ph2[i])) |
|
2400 | dataOut.ph2[i]=(max(1.0, dataOut.ph2[i])) | |
| 2401 | dataOut.dphi[i]=(max(1.0, dataOut.dphi[i])) |
|
2401 | dataOut.dphi[i]=(max(1.0, dataOut.dphi[i])) | |
| 2402 |
|
2402 | |||
| 2403 |
|
2403 | |||
| 2404 | def run(self,dataOut): |
|
2404 | def run(self,dataOut): | |
| 2405 |
|
2405 | |||
| 2406 | self.normalize(dataOut) |
|
2406 | self.normalize(dataOut) | |
| 2407 | #print(dataOut.ph2) |
|
2407 | #print(dataOut.ph2) | |
| 2408 | #print(dataOut.sdp2) |
|
2408 | #print(dataOut.sdp2) | |
| 2409 | #input() |
|
2409 | #input() | |
| 2410 |
|
2410 | |||
| 2411 |
|
2411 | |||
| 2412 | return dataOut |
|
2412 | return dataOut | |
| 2413 |
|
2413 | |||
| 2414 | class NormalizeDPPowerRoberto_V2(Operation): |
|
2414 | class NormalizeDPPowerRoberto_V2(Operation): | |
| 2415 | ''' |
|
2415 | ''' | |
| 2416 | Written by R. Flores |
|
2416 | Written by R. Flores | |
| 2417 | ''' |
|
2417 | ''' | |
| 2418 | """Operation to normalize relative electron density from power with total electron density from Farday angle. |
|
2418 | """Operation to normalize relative electron density from power with total electron density from Farday angle. | |
| 2419 |
|
2419 | |||
| 2420 | Parameters: |
|
2420 | Parameters: | |
| 2421 | ----------- |
|
2421 | ----------- | |
| 2422 | None |
|
2422 | None | |
| 2423 |
|
2423 | |||
| 2424 | Example |
|
2424 | Example | |
| 2425 | -------- |
|
2425 | -------- | |
| 2426 |
|
2426 | |||
| 2427 | op = proc_unit.addOperation(name='NormalizeDPPower', optype='other') |
|
2427 | op = proc_unit.addOperation(name='NormalizeDPPower', optype='other') | |
| 2428 |
|
2428 | |||
| 2429 | """ |
|
2429 | """ | |
| 2430 |
|
2430 | |||
| 2431 | def __init__(self, **kwargs): |
|
2431 | def __init__(self, **kwargs): | |
| 2432 |
|
2432 | |||
| 2433 | Operation.__init__(self, **kwargs) |
|
2433 | Operation.__init__(self, **kwargs) | |
| 2434 | self.aux=1 |
|
2434 | self.aux=1 | |
| 2435 |
|
2435 | |||
| 2436 | def normal(self,a,b,n,m): |
|
2436 | def normal(self,a,b,n,m): | |
| 2437 | chmin=1.0e30 |
|
2437 | chmin=1.0e30 | |
| 2438 | chisq=numpy.zeros(150,'float32') |
|
2438 | chisq=numpy.zeros(150,'float32') | |
| 2439 | temp=numpy.zeros(150,'float32') |
|
2439 | temp=numpy.zeros(150,'float32') | |
| 2440 |
|
2440 | |||
| 2441 | for i in range(2*m-1): |
|
2441 | for i in range(2*m-1): | |
| 2442 | an=al=be=chisq[i]=0.0 |
|
2442 | an=al=be=chisq[i]=0.0 | |
| 2443 | for j in range(int(n/m)): |
|
2443 | for j in range(int(n/m)): | |
| 2444 | k=int(j+i*n/(2*m)) |
|
2444 | k=int(j+i*n/(2*m)) | |
| 2445 | if(a[k]>0.0 and b[k]>0.0): |
|
2445 | if(a[k]>0.0 and b[k]>0.0): | |
| 2446 | al+=a[k]*b[k] |
|
2446 | al+=a[k]*b[k] | |
| 2447 | be+=b[k]*b[k] |
|
2447 | be+=b[k]*b[k] | |
| 2448 |
|
2448 | |||
| 2449 | if(be>0.0): |
|
2449 | if(be>0.0): | |
| 2450 | temp[i]=al/be |
|
2450 | temp[i]=al/be | |
| 2451 | else: |
|
2451 | else: | |
| 2452 | temp[i]=1.0 |
|
2452 | temp[i]=1.0 | |
| 2453 |
|
2453 | |||
| 2454 | for j in range(int(n/m)): |
|
2454 | for j in range(int(n/m)): | |
| 2455 | k=int(j+i*n/(2*m)) |
|
2455 | k=int(j+i*n/(2*m)) | |
| 2456 | if(a[k]>0.0 and b[k]>0.0): |
|
2456 | if(a[k]>0.0 and b[k]>0.0): | |
| 2457 | chisq[i]+=(numpy.log10(b[k]*temp[i]/a[k]))**2 |
|
2457 | chisq[i]+=(numpy.log10(b[k]*temp[i]/a[k]))**2 | |
| 2458 | an=an+1 |
|
2458 | an=an+1 | |
| 2459 |
|
2459 | |||
| 2460 | if(chisq[i]>0.0): |
|
2460 | if(chisq[i]>0.0): | |
| 2461 | chisq[i]/=an |
|
2461 | chisq[i]/=an | |
| 2462 |
|
2462 | |||
| 2463 | for i in range(int(2*m-1)): |
|
2463 | for i in range(int(2*m-1)): | |
| 2464 | if(chisq[i]<chmin and chisq[i]>1.0e-6): |
|
2464 | if(chisq[i]<chmin and chisq[i]>1.0e-6): | |
| 2465 | chmin=chisq[i] |
|
2465 | chmin=chisq[i] | |
| 2466 | cf=temp[i] |
|
2466 | cf=temp[i] | |
| 2467 | return cf |
|
2467 | return cf | |
| 2468 |
|
2468 | |||
| 2469 |
|
2469 | |||
| 2470 | def normalize(self,dataOut): |
|
2470 | def normalize(self,dataOut): | |
| 2471 |
|
2471 | |||
| 2472 | if self.aux==1: |
|
2472 | if self.aux==1: | |
| 2473 | dataOut.cf=numpy.zeros(1,'float32') |
|
2473 | dataOut.cf=numpy.zeros(1,'float32') | |
| 2474 | dataOut.cflast=numpy.zeros(1,'float32') |
|
2474 | dataOut.cflast=numpy.zeros(1,'float32') | |
| 2475 | self.aux=0 |
|
2475 | self.aux=0 | |
| 2476 |
|
2476 | |||
| 2477 | if (dataOut.ut_Faraday>=11.5 and dataOut.ut_Faraday<23): |
|
2477 | if (dataOut.ut_Faraday>=11.5 and dataOut.ut_Faraday<23): | |
| 2478 | i2=(500.-dataOut.range1[0])/dataOut.DH |
|
2478 | i2=(500.-dataOut.range1[0])/dataOut.DH | |
| 2479 | i1=(200.-dataOut.range1[0])/dataOut.DH |
|
2479 | i1=(200.-dataOut.range1[0])/dataOut.DH | |
| 2480 |
|
2480 | |||
| 2481 | else: |
|
2481 | else: | |
| 2482 | inda = numpy.where(dataOut.heightList >= 200) #200 km |
|
2482 | inda = numpy.where(dataOut.heightList >= 200) #200 km | |
| 2483 | minIndex = inda[0][0] |
|
2483 | minIndex = inda[0][0] | |
| 2484 | indb = numpy.where(dataOut.heightList < 700) # 700 km |
|
2484 | indb = numpy.where(dataOut.heightList < 700) # 700 km | |
| 2485 | maxIndex = indb[0][-1] |
|
2485 | maxIndex = indb[0][-1] | |
| 2486 |
|
2486 | |||
| 2487 | ph2max_idx = numpy.nanargmax(dataOut.ph2[minIndex:maxIndex]) |
|
2487 | ph2max_idx = numpy.nanargmax(dataOut.ph2[minIndex:maxIndex]) | |
| 2488 | ph2max_idx += minIndex |
|
2488 | ph2max_idx += minIndex | |
| 2489 |
|
2489 | |||
| 2490 | i2 = ph2max_idx + 6 |
|
2490 | i2 = ph2max_idx + 6 | |
| 2491 | i1 = ph2max_idx - 6 |
|
2491 | i1 = ph2max_idx - 6 | |
| 2492 |
|
2492 | |||
| 2493 | try: |
|
2493 | try: | |
| 2494 | dataOut.heightList[i2] |
|
2494 | dataOut.heightList[i2] | |
| 2495 | except: |
|
2495 | except: | |
| 2496 | i2 -= 1 |
|
2496 | i2 -= 1 | |
| 2497 |
|
2497 | |||
| 2498 | i1=int(i1) |
|
2498 | i1=int(i1) | |
| 2499 | i2=int(i2) |
|
2499 | i2=int(i2) | |
| 2500 |
|
2500 | |||
| 2501 | if dataOut.flagTeTiCorrection: |
|
2501 | if dataOut.flagTeTiCorrection: | |
| 2502 | for i in range(dataOut.NSHTS): |
|
2502 | for i in range(dataOut.NSHTS): | |
| 2503 | dataOut.ph2[i]/=dataOut.cf |
|
2503 | dataOut.ph2[i]/=dataOut.cf | |
| 2504 | dataOut.sdp2[i]/=dataOut.cf |
|
2504 | dataOut.sdp2[i]/=dataOut.cf | |
| 2505 |
|
2505 | |||
| 2506 | if hasattr(dataOut, 'flagSpreadF') and dataOut.flagSpreadF: |
|
2506 | if hasattr(dataOut, 'flagSpreadF') and dataOut.flagSpreadF: | |
| 2507 | i2=int((700-dataOut.range1[0])/dataOut.DH) |
|
2507 | i2=int((700-dataOut.range1[0])/dataOut.DH) | |
| 2508 | nanindex = numpy.argwhere(numpy.isnan(dataOut.ph2)) |
|
2508 | nanindex = numpy.argwhere(numpy.isnan(dataOut.ph2)) | |
| 2509 | i1 = nanindex[-1][0] #VER CUANDO i1>i2 |
|
2509 | i1 = nanindex[-1][0] #VER CUANDO i1>i2 | |
| 2510 | if i1 != numpy.shape(dataOut.heightList)[0]: |
|
2510 | if i1 != numpy.shape(dataOut.heightList)[0]: | |
| 2511 | i1 += 1+2 #Se suma uno para no tomar el nan, se suma 2 para no tomar datos nan de "phi" debido al calculo de la derivada |
|
2511 | i1 += 1+2 #Se suma uno para no tomar el nan, se suma 2 para no tomar datos nan de "phi" debido al calculo de la derivada | |
| 2512 | if i1 >= i2: |
|
2512 | if i1 >= i2: | |
| 2513 | i1 = i2-4 |
|
2513 | i1 = i2-4 | |
| 2514 |
|
2514 | |||
| 2515 | try: |
|
2515 | try: | |
| 2516 | dataOut.cf=self.normal(dataOut.dphi[i1::], dataOut.ph2[i1::], i2-i1, 1) |
|
2516 | dataOut.cf=self.normal(dataOut.dphi[i1::], dataOut.ph2[i1::], i2-i1, 1) | |
| 2517 |
|
2517 | |||
| 2518 | except: |
|
2518 | except: | |
| 2519 | print("except") |
|
2519 | print("except") | |
| 2520 | dataOut.cf = numpy.nan |
|
2520 | dataOut.cf = numpy.nan | |
| 2521 |
|
2521 | |||
| 2522 | night_first1= 300.0#350.0 |
|
2522 | night_first1= 300.0#350.0 | |
| 2523 | night_end= 450.0 |
|
2523 | night_end= 450.0 | |
| 2524 | night_first1= 220.0#350.0 |
|
2524 | night_first1= 220.0#350.0 | |
| 2525 | night_end= 400.0 |
|
2525 | night_end= 400.0 | |
| 2526 |
|
2526 | |||
| 2527 | if(dataOut.cf<dataOut.cflast[0]/10.0): |
|
2527 | if(dataOut.cf<dataOut.cflast[0]/10.0): | |
| 2528 | i1=(night_first1-dataOut.range1[0])/dataOut.DH |
|
2528 | i1=(night_first1-dataOut.range1[0])/dataOut.DH | |
| 2529 | i2=(night_end-dataOut.range1[0])/dataOut.DH |
|
2529 | i2=(night_end-dataOut.range1[0])/dataOut.DH | |
| 2530 | i1=int(i1) |
|
2530 | i1=int(i1) | |
| 2531 | i2=int(i2) |
|
2531 | i2=int(i2) | |
| 2532 | try: |
|
2532 | try: | |
| 2533 | dataOut.cf=self.normal(dataOut.dphi[int(i1)::], dataOut.ph2[int(i1)::], int(i2-i1), 1) |
|
2533 | dataOut.cf=self.normal(dataOut.dphi[int(i1)::], dataOut.ph2[int(i1)::], int(i2-i1), 1) | |
| 2534 | except: |
|
2534 | except: | |
| 2535 | pass |
|
2535 | pass | |
| 2536 |
|
2536 | |||
| 2537 | dataOut.cflast[0]=dataOut.cf |
|
2537 | dataOut.cflast[0]=dataOut.cf | |
| 2538 |
|
2538 | |||
| 2539 | ## normalize double pulse power and error bars to Faraday |
|
2539 | ## normalize double pulse power and error bars to Faraday | |
| 2540 | for i in range(dataOut.NSHTS): |
|
2540 | for i in range(dataOut.NSHTS): | |
| 2541 | dataOut.ph2[i]*=dataOut.cf |
|
2541 | dataOut.ph2[i]*=dataOut.cf | |
| 2542 | dataOut.sdp2[i]*=dataOut.cf |
|
2542 | dataOut.sdp2[i]*=dataOut.cf | |
| 2543 |
|
2543 | |||
| 2544 | for i in range(dataOut.NSHTS): |
|
2544 | for i in range(dataOut.NSHTS): | |
| 2545 | dataOut.ph2[i]=(max(1.0, dataOut.ph2[i])) |
|
2545 | dataOut.ph2[i]=(max(1.0, dataOut.ph2[i])) | |
| 2546 | dataOut.dphi[i]=(max(1.0, dataOut.dphi[i])) |
|
2546 | dataOut.dphi[i]=(max(1.0, dataOut.dphi[i])) | |
| 2547 |
|
2547 | |||
| 2548 | def run(self,dataOut): |
|
2548 | def run(self,dataOut): | |
| 2549 |
|
2549 | |||
| 2550 | self.normalize(dataOut) |
|
2550 | self.normalize(dataOut) | |
| 2551 |
|
2551 | |||
| 2552 | return dataOut |
|
2552 | return dataOut | |
| 2553 |
|
2553 | |||
| 2554 | class suppress_stdout_stderr(object): |
|
2554 | class suppress_stdout_stderr(object): | |
| 2555 | ''' |
|
2555 | ''' | |
| 2556 | A context manager for doing a "deep suppression" of stdout and stderr in |
|
2556 | A context manager for doing a "deep suppression" of stdout and stderr in | |
| 2557 | Python, i.e. will suppress all print, even if the print originates in a |
|
2557 | Python, i.e. will suppress all print, even if the print originates in a | |
| 2558 | compiled C/Fortran sub-function. |
|
2558 | compiled C/Fortran sub-function. | |
| 2559 | This will not suppress raised exceptions, since exceptions are printed |
|
2559 | This will not suppress raised exceptions, since exceptions are printed | |
| 2560 | to stderr just before a script exits, and after the context manager has |
|
2560 | to stderr just before a script exits, and after the context manager has | |
| 2561 | exited (at least, I think that is why it lets exceptions through). |
|
2561 | exited (at least, I think that is why it lets exceptions through). | |
| 2562 |
|
2562 | |||
| 2563 | ''' |
|
2563 | ''' | |
| 2564 | def __init__(self): |
|
2564 | def __init__(self): | |
| 2565 | # Open a pair of null files |
|
2565 | # Open a pair of null files | |
| 2566 | self.null_fds = [os.open(os.devnull,os.O_RDWR) for x in range(2)] |
|
2566 | self.null_fds = [os.open(os.devnull,os.O_RDWR) for x in range(2)] | |
| 2567 | # Save the actual stdout (1) and stderr (2) file descriptors. |
|
2567 | # Save the actual stdout (1) and stderr (2) file descriptors. | |
| 2568 | self.save_fds = [os.dup(1), os.dup(2)] |
|
2568 | self.save_fds = [os.dup(1), os.dup(2)] | |
| 2569 |
|
2569 | |||
| 2570 | def __enter__(self): |
|
2570 | def __enter__(self): | |
| 2571 | # Assign the null pointers to stdout and stderr. |
|
2571 | # Assign the null pointers to stdout and stderr. | |
| 2572 | os.dup2(self.null_fds[0],1) |
|
2572 | os.dup2(self.null_fds[0],1) | |
| 2573 | os.dup2(self.null_fds[1],2) |
|
2573 | os.dup2(self.null_fds[1],2) | |
| 2574 |
|
2574 | |||
| 2575 | def __exit__(self, *_): |
|
2575 | def __exit__(self, *_): | |
| 2576 | # Re-assign the real stdout/stderr back to (1) and (2) |
|
2576 | # Re-assign the real stdout/stderr back to (1) and (2) | |
| 2577 | os.dup2(self.save_fds[0],1) |
|
2577 | os.dup2(self.save_fds[0],1) | |
| 2578 | os.dup2(self.save_fds[1],2) |
|
2578 | os.dup2(self.save_fds[1],2) | |
| 2579 | # Close all file descriptors |
|
2579 | # Close all file descriptors | |
| 2580 | for fd in self.null_fds + self.save_fds: |
|
2580 | for fd in self.null_fds + self.save_fds: | |
| 2581 | os.close(fd) |
|
2581 | os.close(fd) | |
| 2582 |
|
2582 | |||
| 2583 |
|
2583 | |||
| 2584 | class DPTemperaturesEstimation(Operation): |
|
2584 | class DPTemperaturesEstimation(Operation): | |
| 2585 | ''' |
|
2585 | ''' | |
| 2586 | Written by R. Flores |
|
2586 | Written by R. Flores | |
| 2587 | ''' |
|
2587 | ''' | |
| 2588 | """Operation to estimate temperatures for Double Pulse data. |
|
2588 | """Operation to estimate temperatures for Double Pulse data. | |
| 2589 |
|
2589 | |||
| 2590 | Parameters: |
|
2590 | Parameters: | |
| 2591 | ----------- |
|
2591 | ----------- | |
| 2592 | IBITS : int |
|
2592 | IBITS : int | |
| 2593 | .* |
|
2593 | .* | |
| 2594 |
|
2594 | |||
| 2595 | Example |
|
2595 | Example | |
| 2596 | -------- |
|
2596 | -------- | |
| 2597 |
|
2597 | |||
| 2598 | op = proc_unit.addOperation(name='DPTemperaturesEstimation', optype='other') |
|
2598 | op = proc_unit.addOperation(name='DPTemperaturesEstimation', optype='other') | |
| 2599 | op.addParameter(name='IBITS', value='16', format='int') |
|
2599 | op.addParameter(name='IBITS', value='16', format='int') | |
| 2600 |
|
2600 | |||
| 2601 | """ |
|
2601 | """ | |
| 2602 |
|
2602 | |||
| 2603 | def __init__(self, **kwargs): |
|
2603 | def __init__(self, **kwargs): | |
| 2604 |
|
2604 | |||
| 2605 | Operation.__init__(self, **kwargs) |
|
2605 | Operation.__init__(self, **kwargs) | |
| 2606 |
|
2606 | |||
| 2607 | self.aux=1 |
|
2607 | self.aux=1 | |
| 2608 |
|
2608 | |||
| 2609 | def Estimation(self,dataOut): |
|
2609 | def Estimation(self,dataOut): | |
| 2610 | #with suppress_stdout_stderr(): |
|
2610 | #with suppress_stdout_stderr(): | |
| 2611 |
|
2611 | |||
| 2612 | if self.aux==1: |
|
2612 | if self.aux==1: | |
| 2613 | dataOut.ifit=numpy.zeros(5,order='F',dtype='int32') |
|
2613 | dataOut.ifit=numpy.zeros(5,order='F',dtype='int32') | |
| 2614 | dataOut.m=numpy.zeros(1,order='F',dtype='int32') |
|
2614 | dataOut.m=numpy.zeros(1,order='F',dtype='int32') | |
| 2615 | dataOut.te2=numpy.zeros(dataOut.NSHTS,order='F',dtype='float32') |
|
2615 | dataOut.te2=numpy.zeros(dataOut.NSHTS,order='F',dtype='float32') | |
| 2616 | dataOut.ti2=numpy.zeros(dataOut.NSHTS,order='F',dtype='float32') |
|
2616 | dataOut.ti2=numpy.zeros(dataOut.NSHTS,order='F',dtype='float32') | |
| 2617 | dataOut.ete2=numpy.zeros(dataOut.NSHTS,order='F',dtype='float32') |
|
2617 | dataOut.ete2=numpy.zeros(dataOut.NSHTS,order='F',dtype='float32') | |
| 2618 | dataOut.eti2=numpy.zeros(dataOut.NSHTS,order='F',dtype='float32') |
|
2618 | dataOut.eti2=numpy.zeros(dataOut.NSHTS,order='F',dtype='float32') | |
| 2619 |
|
2619 | |||
| 2620 | self.aux=0 |
|
2620 | self.aux=0 | |
| 2621 |
|
2621 | |||
| 2622 | dataOut.phy2=numpy.zeros(dataOut.NSHTS,order='F',dtype='float32') |
|
2622 | dataOut.phy2=numpy.zeros(dataOut.NSHTS,order='F',dtype='float32') | |
| 2623 | dataOut.ephy2=numpy.zeros(dataOut.NSHTS,order='F',dtype='float32') |
|
2623 | dataOut.ephy2=numpy.zeros(dataOut.NSHTS,order='F',dtype='float32') | |
| 2624 | dataOut.info2=numpy.zeros(dataOut.NDP,order='F',dtype='float32') |
|
2624 | dataOut.info2=numpy.zeros(dataOut.NDP,order='F',dtype='float32') | |
| 2625 | dataOut.params=numpy.zeros(10,order='F',dtype='float32') |
|
2625 | dataOut.params=numpy.zeros(10,order='F',dtype='float32') | |
| 2626 | dataOut.cov=numpy.zeros(dataOut.IBITS*dataOut.IBITS,order='F',dtype='float32') |
|
2626 | dataOut.cov=numpy.zeros(dataOut.IBITS*dataOut.IBITS,order='F',dtype='float32') | |
| 2627 | dataOut.covinv=numpy.zeros(dataOut.IBITS*dataOut.IBITS,order='F',dtype='float32') |
|
2627 | dataOut.covinv=numpy.zeros(dataOut.IBITS*dataOut.IBITS,order='F',dtype='float32') | |
| 2628 |
|
2628 | |||
| 2629 | #null_fd = os.open(os.devnull, os.O_RDWR) |
|
2629 | #null_fd = os.open(os.devnull, os.O_RDWR) | |
| 2630 | #os.dup2(null_fd, 1) |
|
2630 | #os.dup2(null_fd, 1) | |
| 2631 |
|
2631 | |||
| 2632 | for i in range(10,dataOut.NSHTS): #no point below 150 km |
|
2632 | for i in range(10,dataOut.NSHTS): #no point below 150 km | |
| 2633 |
|
2633 | |||
| 2634 | #some definitions |
|
2634 | #some definitions | |
| 2635 | iflag=0 # inicializado a cero? |
|
2635 | iflag=0 # inicializado a cero? | |
| 2636 | wl = 3.0 |
|
2636 | wl = 3.0 | |
| 2637 | x=numpy.zeros(dataOut.DPL+dataOut.IBITS,order='F',dtype='float32') |
|
2637 | x=numpy.zeros(dataOut.DPL+dataOut.IBITS,order='F',dtype='float32') | |
| 2638 | y=numpy.zeros(dataOut.DPL+dataOut.IBITS,order='F',dtype='float32') |
|
2638 | y=numpy.zeros(dataOut.DPL+dataOut.IBITS,order='F',dtype='float32') | |
| 2639 | e=numpy.zeros(dataOut.DPL+dataOut.IBITS,order='F',dtype='float32') |
|
2639 | e=numpy.zeros(dataOut.DPL+dataOut.IBITS,order='F',dtype='float32') | |
| 2640 | eb=numpy.zeros(5,order='F',dtype='float32') |
|
2640 | eb=numpy.zeros(5,order='F',dtype='float32') | |
| 2641 | zero=numpy.zeros(1,order='F',dtype='float32') |
|
2641 | zero=numpy.zeros(1,order='F',dtype='float32') | |
| 2642 | depth=numpy.zeros(1,order='F',dtype='float32') |
|
2642 | depth=numpy.zeros(1,order='F',dtype='float32') | |
| 2643 | t1=numpy.zeros(1,order='F',dtype='float32') |
|
2643 | t1=numpy.zeros(1,order='F',dtype='float32') | |
| 2644 | t2=numpy.zeros(1,order='F',dtype='float32') |
|
2644 | t2=numpy.zeros(1,order='F',dtype='float32') | |
| 2645 |
|
2645 | |||
| 2646 | if i>10 and l1>=0: |
|
2646 | if i>10 and l1>=0: | |
| 2647 | if l1==0: |
|
2647 | if l1==0: | |
| 2648 | l1=1 |
|
2648 | l1=1 | |
| 2649 |
|
2649 | |||
| 2650 | dataOut.cov=numpy.reshape(dataOut.cov,l1*l1) |
|
2650 | dataOut.cov=numpy.reshape(dataOut.cov,l1*l1) | |
| 2651 | dataOut.cov=numpy.resize(dataOut.cov,dataOut.DPL*dataOut.DPL) |
|
2651 | dataOut.cov=numpy.resize(dataOut.cov,dataOut.DPL*dataOut.DPL) | |
| 2652 | dataOut.covinv=numpy.reshape(dataOut.covinv,l1*l1) |
|
2652 | dataOut.covinv=numpy.reshape(dataOut.covinv,l1*l1) | |
| 2653 | dataOut.covinv=numpy.resize(dataOut.covinv,dataOut.DPL*dataOut.DPL) |
|
2653 | dataOut.covinv=numpy.resize(dataOut.covinv,dataOut.DPL*dataOut.DPL) | |
| 2654 |
|
2654 | |||
| 2655 | for l in range(dataOut.DPL*dataOut.DPL): |
|
2655 | for l in range(dataOut.DPL*dataOut.DPL): | |
| 2656 | dataOut.cov[l]=0.0 |
|
2656 | dataOut.cov[l]=0.0 | |
| 2657 | acfm= (dataOut.rhor[i][0])**2 + (dataOut.rhoi[i][0])**2 |
|
2657 | acfm= (dataOut.rhor[i][0])**2 + (dataOut.rhoi[i][0])**2 | |
| 2658 | if acfm> 0.0: |
|
2658 | if acfm> 0.0: | |
| 2659 | cc=dataOut.rhor[i][0]/acfm |
|
2659 | cc=dataOut.rhor[i][0]/acfm | |
| 2660 | ss=dataOut.rhoi[i][0]/acfm |
|
2660 | ss=dataOut.rhoi[i][0]/acfm | |
| 2661 | else: |
|
2661 | else: | |
| 2662 | cc=1. |
|
2662 | cc=1. | |
| 2663 | ss=0. |
|
2663 | ss=0. | |
| 2664 | # keep only uncontaminated data, don't pass zero lag to fitter |
|
2664 | # keep only uncontaminated data, don't pass zero lag to fitter | |
| 2665 | l1=0 |
|
2665 | l1=0 | |
| 2666 | for l in range(0+1,dataOut.DPL): |
|
2666 | for l in range(0+1,dataOut.DPL): | |
| 2667 | if dataOut.igcej[i][l]==0 and dataOut.ibad[i][l]==0: |
|
2667 | if dataOut.igcej[i][l]==0 and dataOut.ibad[i][l]==0: | |
| 2668 | y[l1]=dataOut.rhor[i][l]*cc + dataOut.rhoi[i][l]*ss |
|
2668 | y[l1]=dataOut.rhor[i][l]*cc + dataOut.rhoi[i][l]*ss | |
| 2669 | x[l1]=dataOut.alag[l]*1.0e-3 |
|
2669 | x[l1]=dataOut.alag[l]*1.0e-3 | |
| 2670 | dataOut.sd[i][l]=dataOut.sd[i][l]/((acfm)**2)# important |
|
2670 | dataOut.sd[i][l]=dataOut.sd[i][l]/((acfm)**2)# important | |
| 2671 | e[l1]=dataOut.sd[i][l] #this is the variance, not the st. dev. |
|
2671 | e[l1]=dataOut.sd[i][l] #this is the variance, not the st. dev. | |
| 2672 | l1=l1+1 |
|
2672 | l1=l1+1 | |
| 2673 |
|
2673 | |||
| 2674 | for l in range(l1*(l1+1)): |
|
2674 | for l in range(l1*(l1+1)): | |
| 2675 | dataOut.cov[l]=0.0 |
|
2675 | dataOut.cov[l]=0.0 | |
| 2676 | for l in range(l1): |
|
2676 | for l in range(l1): | |
| 2677 | dataOut.cov[l*(1+l1)]=e[l] |
|
2677 | dataOut.cov[l*(1+l1)]=e[l] | |
| 2678 | angle=dataOut.thb[i]*0.01745 |
|
2678 | angle=dataOut.thb[i]*0.01745 | |
| 2679 | bm=dataOut.bfm[i] |
|
2679 | bm=dataOut.bfm[i] | |
| 2680 | dataOut.params[0]=1.0 #norm |
|
2680 | dataOut.params[0]=1.0 #norm | |
| 2681 | dataOut.params[1]=1000.0 #te |
|
2681 | dataOut.params[1]=1000.0 #te | |
| 2682 | dataOut.params[2]=800.0 #ti |
|
2682 | dataOut.params[2]=800.0 #ti | |
| 2683 | dataOut.params[3]=0.00 #ph |
|
2683 | dataOut.params[3]=0.00 #ph | |
| 2684 | dataOut.params[4]=0.00 #phe |
|
2684 | dataOut.params[4]=0.00 #phe | |
| 2685 |
|
2685 | |||
| 2686 | if l1!=0: |
|
2686 | if l1!=0: | |
| 2687 | x=numpy.resize(x,l1) |
|
2687 | x=numpy.resize(x,l1) | |
| 2688 | y=numpy.resize(y,l1) |
|
2688 | y=numpy.resize(y,l1) | |
| 2689 | else: |
|
2689 | else: | |
| 2690 | x=numpy.resize(x,1) |
|
2690 | x=numpy.resize(x,1) | |
| 2691 | y=numpy.resize(y,1) |
|
2691 | y=numpy.resize(y,1) | |
| 2692 |
|
2692 | |||
| 2693 | if True: #len(y)!=0: |
|
2693 | if True: #len(y)!=0: | |
| 2694 | with suppress_stdout_stderr(): |
|
2694 | with suppress_stdout_stderr(): | |
| 2695 | fitacf_guess.guess(y,x,zero,depth,t1,t2,len(y)) |
|
2695 | fitacf_guess.guess(y,x,zero,depth,t1,t2,len(y)) | |
| 2696 | t2=t1/t2 |
|
2696 | t2=t1/t2 | |
| 2697 |
|
2697 | |||
| 2698 | if (t1<5000.0 and t1> 600.0): |
|
2698 | if (t1<5000.0 and t1> 600.0): | |
| 2699 | dataOut.params[1]=t1 |
|
2699 | dataOut.params[1]=t1 | |
| 2700 | dataOut.params[2]=min(t2,t1) |
|
2700 | dataOut.params[2]=min(t2,t1) | |
| 2701 | dataOut.ifit[1]=dataOut.ifit[2]=1 |
|
2701 | dataOut.ifit[1]=dataOut.ifit[2]=1 | |
| 2702 | dataOut.ifit[0]=dataOut.ifit[3]=dataOut.ifit[4]=0 |
|
2702 | dataOut.ifit[0]=dataOut.ifit[3]=dataOut.ifit[4]=0 | |
| 2703 |
|
2703 | |||
| 2704 | if dataOut.ut_Faraday<10.0 and dataOut.ut_Faraday>=0.5: |
|
2704 | if dataOut.ut_Faraday<10.0 and dataOut.ut_Faraday>=0.5: | |
| 2705 | dataOut.ifit[2]=0 |
|
2705 | dataOut.ifit[2]=0 | |
| 2706 |
|
2706 | |||
| 2707 | den=dataOut.ph2[i] |
|
2707 | den=dataOut.ph2[i] | |
| 2708 |
|
2708 | |||
| 2709 | if l1!=0: |
|
2709 | if l1!=0: | |
| 2710 | dataOut.covinv=dataOut.covinv[0:l1*l1].reshape((l1,l1)) |
|
2710 | dataOut.covinv=dataOut.covinv[0:l1*l1].reshape((l1,l1)) | |
| 2711 | dataOut.cov=dataOut.cov[0:l1*l1].reshape((l1,l1)) |
|
2711 | dataOut.cov=dataOut.cov[0:l1*l1].reshape((l1,l1)) | |
| 2712 | e=numpy.resize(e,l1) |
|
2712 | e=numpy.resize(e,l1) | |
| 2713 | else: |
|
2713 | else: | |
| 2714 | dataOut.covinv=numpy.resize(dataOut.covinv,1) |
|
2714 | dataOut.covinv=numpy.resize(dataOut.covinv,1) | |
| 2715 | dataOut.cov=numpy.resize(dataOut.cov,1) |
|
2715 | dataOut.cov=numpy.resize(dataOut.cov,1) | |
| 2716 | e=numpy.resize(e,1) |
|
2716 | e=numpy.resize(e,1) | |
| 2717 |
|
2717 | |||
| 2718 | eb=numpy.resize(eb,10) |
|
2718 | eb=numpy.resize(eb,10) | |
| 2719 | dataOut.ifit=numpy.resize(dataOut.ifit,10) |
|
2719 | dataOut.ifit=numpy.resize(dataOut.ifit,10) | |
|
|
2720 | with suppress_stdout_stderr(): | |||
| 2720 | dataOut.covinv,e,dataOut.params,eb,dataOut.m=fitacf_fit_short.fit(wl,x,y,dataOut.cov,dataOut.covinv,e,dataOut.params,bm,angle,den,dataOut.range1[i],dataOut.year,dataOut.ifit,dataOut.m,l1) # |
|
2721 | dataOut.covinv,e,dataOut.params,eb,dataOut.m=fitacf_fit_short.fit(wl,x,y,dataOut.cov,dataOut.covinv,e,dataOut.params,bm,angle,den,dataOut.range1[i],dataOut.year,dataOut.ifit,dataOut.m,l1) # | |
| 2721 | if dataOut.params[2]>dataOut.params[1]*1.05: |
|
2722 | if dataOut.params[2]>dataOut.params[1]*1.05: | |
| 2722 | dataOut.ifit[2]=0 |
|
2723 | dataOut.ifit[2]=0 | |
| 2723 | dataOut.params[1]=dataOut.params[2]=t1 |
|
2724 | dataOut.params[1]=dataOut.params[2]=t1 | |
| 2724 | dataOut.covinv,e,dataOut.params,eb,dataOut.m=fitacf_fit_short.fit(wl,x,y,dataOut.cov,dataOut.covinv,e,dataOut.params,bm,angle,den,dataOut.range1[i],dataOut.year,dataOut.ifit,dataOut.m,l1) # |
|
2725 | dataOut.covinv,e,dataOut.params,eb,dataOut.m=fitacf_fit_short.fit(wl,x,y,dataOut.cov,dataOut.covinv,e,dataOut.params,bm,angle,den,dataOut.range1[i],dataOut.year,dataOut.ifit,dataOut.m,l1) # | |
| 2725 | if (dataOut.ifit[2]==0): |
|
2726 | if (dataOut.ifit[2]==0): | |
| 2726 | dataOut.params[2]=dataOut.params[1] |
|
2727 | dataOut.params[2]=dataOut.params[1] | |
|
|
2728 | with suppress_stdout_stderr(): | |||
|
|
2729 | dataOut.covinv,e,dataOut.params,eb,dataOut.m=fitacf_fit_short.fit(wl,x,y,dataOut.cov,dataOut.covinv,e,dataOut.params,bm,angle,den,dataOut.range1[i],dataOut.year,dataOut.ifit,dataOut.m,l1) # | |||
|
|
2730 | if (dataOut.ifit[2]==0): | |||
|
|
2731 | dataOut.params[2]=dataOut.params[1] | |||
| 2727 | if (dataOut.ifit[3]==0 and iflag==0): |
|
2732 | if (dataOut.ifit[3]==0 and iflag==0): | |
| 2728 | dataOut.params[3]=0.0 |
|
2733 | dataOut.params[3]=0.0 | |
| 2729 | if (dataOut.ifit[4]==0): |
|
2734 | if (dataOut.ifit[4]==0): | |
| 2730 | dataOut.params[4]=0.0 |
|
2735 | dataOut.params[4]=0.0 | |
| 2731 | dataOut.te2[i]=dataOut.params[1] |
|
2736 | dataOut.te2[i]=dataOut.params[1] | |
| 2732 | dataOut.ti2[i]=dataOut.params[2] |
|
2737 | dataOut.ti2[i]=dataOut.params[2] | |
| 2733 | dataOut.ete2[i]=eb[1] |
|
2738 | dataOut.ete2[i]=eb[1] | |
| 2734 | dataOut.eti2[i]=eb[2] |
|
2739 | dataOut.eti2[i]=eb[2] | |
| 2735 |
|
2740 | |||
| 2736 | if dataOut.eti2[i]==0: |
|
2741 | if dataOut.eti2[i]==0: | |
| 2737 | dataOut.eti2[i]=dataOut.ete2[i] |
|
2742 | dataOut.eti2[i]=dataOut.ete2[i] | |
| 2738 |
|
2743 | |||
| 2739 | dataOut.phy2[i]=dataOut.params[3] |
|
2744 | dataOut.phy2[i]=dataOut.params[3] | |
| 2740 | dataOut.ephy2[i]=eb[3] |
|
2745 | dataOut.ephy2[i]=eb[3] | |
| 2741 | if(iflag==1): |
|
2746 | if(iflag==1): | |
| 2742 | dataOut.ephy2[i]=0.0 |
|
2747 | dataOut.ephy2[i]=0.0 | |
| 2743 |
|
2748 | |||
| 2744 | if (dataOut.m<=3 and dataOut.m!= 0 and dataOut.te2[i]>400.0): |
|
2749 | if (dataOut.m<=3 and dataOut.m!= 0 and dataOut.te2[i]>400.0): | |
| 2745 | dataOut.info2[i]=1 |
|
2750 | dataOut.info2[i]=1 | |
| 2746 | else: |
|
2751 | else: | |
| 2747 | dataOut.info2[i]=0 |
|
2752 | dataOut.info2[i]=0 | |
| 2748 |
|
2753 | |||
| 2749 | def run(self,dataOut,IBITS=16): |
|
2754 | def run(self,dataOut,IBITS=16): | |
| 2750 |
|
2755 | |||
| 2751 | dataOut.IBITS = IBITS |
|
2756 | dataOut.IBITS = IBITS | |
| 2752 | self.Estimation(dataOut) |
|
2757 | self.Estimation(dataOut) | |
| 2753 |
|
2758 | |||
| 2754 | return dataOut |
|
2759 | return dataOut | |
| 2755 |
|
2760 | |||
| 2756 |
|
2761 | |||
| 2757 | class DenCorrection(NormalizeDPPowerRoberto_V2): |
|
2762 | class DenCorrection(NormalizeDPPowerRoberto_V2): | |
| 2758 | ''' |
|
2763 | ''' | |
| 2759 | Written by R. Flores |
|
2764 | Written by R. Flores | |
| 2760 | ''' |
|
2765 | ''' | |
| 2761 | def __init__(self, **kwargs): |
|
2766 | def __init__(self, **kwargs): | |
| 2762 |
|
2767 | |||
| 2763 | Operation.__init__(self, **kwargs) |
|
2768 | Operation.__init__(self, **kwargs) | |
| 2764 | self.aux = 0 |
|
2769 | self.aux = 0 | |
| 2765 | self.csv_flag = 1 |
|
2770 | self.csv_flag = 1 | |
| 2766 |
|
2771 | |||
| 2767 | def gaussian(self, x, a, b, c): |
|
2772 | def gaussian(self, x, a, b, c): | |
| 2768 | val = a * numpy.exp(-(x - b)**2 / (2*c**2)) |
|
2773 | val = a * numpy.exp(-(x - b)**2 / (2*c**2)) | |
| 2769 | return val |
|
2774 | return val | |
| 2770 |
|
2775 | |||
| 2771 | def TeTiEstimation(self,dataOut): |
|
2776 | def TeTiEstimation(self,dataOut): | |
| 2772 |
|
2777 | |||
| 2773 | #dataOut.DPL = 2 #for MST |
|
2778 | #dataOut.DPL = 2 #for MST | |
| 2774 | y=numpy.zeros(dataOut.DPL,order='F',dtype='float32') |
|
2779 | y=numpy.zeros(dataOut.DPL,order='F',dtype='float32') | |
| 2775 |
|
2780 | |||
| 2776 | for i in range(dataOut.NSHTS): |
|
2781 | for i in range(dataOut.NSHTS): | |
| 2777 | y[0]=y[1]=dataOut.range1[i] |
|
2782 | y[0]=y[1]=dataOut.range1[i] | |
| 2778 |
|
2783 | |||
| 2779 | y = y.astype(dtype='float64',order='F') |
|
2784 | y = y.astype(dtype='float64',order='F') | |
| 2780 | three=int(3) |
|
2785 | three=int(3) | |
| 2781 | wl = 3.0 |
|
2786 | wl = 3.0 | |
| 2782 | tion=numpy.zeros(three,order='F',dtype='float32') |
|
2787 | tion=numpy.zeros(three,order='F',dtype='float32') | |
| 2783 | fion=numpy.zeros(three,order='F',dtype='float32') |
|
2788 | fion=numpy.zeros(three,order='F',dtype='float32') | |
| 2784 | nui=numpy.zeros(three,order='F',dtype='float32') |
|
2789 | nui=numpy.zeros(three,order='F',dtype='float32') | |
| 2785 | wion=numpy.zeros(three,order='F',dtype='int32') |
|
2790 | wion=numpy.zeros(three,order='F',dtype='int32') | |
| 2786 | bline=0.0 |
|
2791 | bline=0.0 | |
| 2787 | #bline=numpy.zeros(1,order='F',dtype='float32') |
|
2792 | #bline=numpy.zeros(1,order='F',dtype='float32') | |
| 2788 | my_aux = numpy.ones(dataOut.NSHTS,order='F',dtype='float32') |
|
2793 | my_aux = numpy.ones(dataOut.NSHTS,order='F',dtype='float32') | |
| 2789 | acf_Temps = numpy.ones(dataOut.NSHTS,order='F',dtype='float32')*numpy.nan |
|
2794 | acf_Temps = numpy.ones(dataOut.NSHTS,order='F',dtype='float32')*numpy.nan | |
| 2790 | acf_no_Temps = numpy.ones(dataOut.NSHTS,order='F',dtype='float32')*numpy.nan |
|
2795 | acf_no_Temps = numpy.ones(dataOut.NSHTS,order='F',dtype='float32')*numpy.nan | |
| 2791 |
|
2796 | |||
| 2792 | from scipy import signal |
|
2797 | from scipy import signal | |
| 2793 |
|
2798 | |||
| 2794 | def func(params): |
|
2799 | def func(params): | |
| 2795 | return (ratio2-self.gaussian(dataOut.heightList[:dataOut.NSHTS],params[0],params[1],params[2])) |
|
2800 | return (ratio2-self.gaussian(dataOut.heightList[:dataOut.NSHTS],params[0],params[1],params[2])) | |
| 2796 |
|
2801 | |||
| 2797 | dataOut.info2[0] = 1 |
|
2802 | dataOut.info2[0] = 1 | |
| 2798 | for i in range(dataOut.NSHTS): |
|
2803 | for i in range(dataOut.NSHTS): | |
| 2799 | if dataOut.info2[i]==1: |
|
2804 | if dataOut.info2[i]==1: | |
| 2800 | angle=dataOut.thb[i]*0.01745 |
|
2805 | angle=dataOut.thb[i]*0.01745 | |
| 2801 | nue=nui[0]=nui[1]=nui[2]=0.0#nui[3]=0.0 |
|
2806 | nue=nui[0]=nui[1]=nui[2]=0.0#nui[3]=0.0 | |
| 2802 | wion[0]=16 #O |
|
2807 | wion[0]=16 #O | |
| 2803 | wion[1]=1 #H |
|
2808 | wion[1]=1 #H | |
| 2804 | wion[2]=4 #He |
|
2809 | wion[2]=4 #He | |
| 2805 | tion[0]=tion[1]=tion[2]=dataOut.ti2[i] |
|
2810 | tion[0]=tion[1]=tion[2]=dataOut.ti2[i] | |
| 2806 | #tion[0]=tion[1]=tion[2]=ti2_smooth[i] |
|
2811 | #tion[0]=tion[1]=tion[2]=ti2_smooth[i] | |
| 2807 | fion[0]=1.0-dataOut.phy2[i] #1 |
|
2812 | fion[0]=1.0-dataOut.phy2[i] #1 | |
| 2808 | fion[1]=dataOut.phy2[i] #0 |
|
2813 | fion[1]=dataOut.phy2[i] #0 | |
| 2809 | fion[2]=0.0 #0 |
|
2814 | fion[2]=0.0 #0 | |
| 2810 | for j in range(dataOut.DPL): |
|
2815 | for j in range(dataOut.DPL): | |
| 2811 | tau=dataOut.alag[j]*1.0e-3 |
|
2816 | tau=dataOut.alag[j]*1.0e-3 | |
| 2812 | with suppress_stdout_stderr():#The smoothness in range of "y" depends on the smoothness of the input parameters |
|
2817 | with suppress_stdout_stderr():#The smoothness in range of "y" depends on the smoothness of the input parameters | |
| 2813 | y[j]=fitacf_acf2.acf2(wl,tau,dataOut.te2[i],tion,fion,nue,nui,wion,angle,dataOut.ph2[i],dataOut.bfm[i],y[j],three) |
|
2818 | y[j]=fitacf_acf2.acf2(wl,tau,dataOut.te2[i],tion,fion,nue,nui,wion,angle,dataOut.ph2[i],dataOut.bfm[i],y[j],three) | |
| 2814 |
|
2819 | |||
| 2815 | if dataOut.ut_Faraday>11.0 and dataOut.range1[i]>150.0 and dataOut.range1[i]<300.0: |
|
2820 | if dataOut.ut_Faraday>11.0 and dataOut.range1[i]>150.0 and dataOut.range1[i]<300.0: | |
| 2816 | tau=0.0 |
|
2821 | tau=0.0 | |
| 2817 | with suppress_stdout_stderr(): |
|
2822 | with suppress_stdout_stderr(): | |
| 2818 | bline=fitacf_acf2.acf2(wl,tau,tion,tion,fion,nue,nui,wion,angle,dataOut.ph2[i],dataOut.bfm[i],bline,three) |
|
2823 | bline=fitacf_acf2.acf2(wl,tau,tion,tion,fion,nue,nui,wion,angle,dataOut.ph2[i],dataOut.bfm[i],bline,three) | |
| 2819 |
|
2824 | |||
| 2820 | cf=min(1.2,max(1.0,bline/y[0])) #FACTOR DE EFICIENCIA |
|
2825 | cf=min(1.2,max(1.0,bline/y[0])) #FACTOR DE EFICIENCIA | |
| 2821 | my_aux[i] = cf |
|
2826 | my_aux[i] = cf | |
| 2822 | acf_Temps[i] = y[0] |
|
2827 | acf_Temps[i] = y[0] | |
| 2823 | acf_no_Temps[i] = bline |
|
2828 | acf_no_Temps[i] = bline | |
| 2824 | for j in range(1,dataOut.DPL): |
|
2829 | for j in range(1,dataOut.DPL): | |
| 2825 | y[j]=min(max((y[j]/y[0]),-1.0),1.0)*dataOut.DH+dataOut.range1[i] |
|
2830 | y[j]=min(max((y[j]/y[0]),-1.0),1.0)*dataOut.DH+dataOut.range1[i] | |
| 2826 | y[0]=dataOut.range1[i]+dataOut.DH |
|
2831 | y[0]=dataOut.range1[i]+dataOut.DH | |
| 2827 |
|
2832 | |||
| 2828 |
|
2833 | |||
| 2829 | ratio = my_aux-1 |
|
2834 | ratio = my_aux-1 | |
| 2830 | def lsq_func(params): |
|
2835 | def lsq_func(params): | |
| 2831 | return (ratio-self.gaussian(dataOut.heightList[:dataOut.NSHTS],params[0],params[1],params[2])) |
|
2836 | return (ratio-self.gaussian(dataOut.heightList[:dataOut.NSHTS],params[0],params[1],params[2])) | |
| 2832 |
|
2837 | |||
| 2833 | x0_value = numpy.array([max(ratio),250,20]) |
|
2838 | x0_value = numpy.array([max(ratio),250,20]) | |
| 2834 |
|
2839 | |||
| 2835 | popt = least_squares(lsq_func,x0=x0_value,verbose=0) |
|
2840 | popt = least_squares(lsq_func,x0=x0_value,verbose=0) | |
| 2836 |
|
2841 | |||
| 2837 | A = popt.x[0]; B = popt.x[1]; C = popt.x[2] |
|
2842 | A = popt.x[0]; B = popt.x[1]; C = popt.x[2] | |
| 2838 |
|
2843 | |||
| 2839 | aux = self.gaussian(dataOut.heightList[:dataOut.NSHTS], A, B, C) + 1 #ratio + 1 |
|
2844 | aux = self.gaussian(dataOut.heightList[:dataOut.NSHTS], A, B, C) + 1 #ratio + 1 | |
| 2840 |
|
2845 | |||
| 2841 | dataOut.ph2[:dataOut.NSHTS]*=aux |
|
2846 | dataOut.ph2[:dataOut.NSHTS]*=aux | |
| 2842 | dataOut.sdp2[:dataOut.NSHTS]*=aux |
|
2847 | dataOut.sdp2[:dataOut.NSHTS]*=aux | |
| 2843 |
|
2848 | |||
| 2844 | def run(self,dataOut,savecf=0): |
|
2849 | def run(self,dataOut,savecf=0): | |
| 2845 | if gmtime(dataOut.utctime).tm_hour < 24. and gmtime(dataOut.utctime).tm_hour >= 11.: |
|
2850 | if gmtime(dataOut.utctime).tm_hour < 24. and gmtime(dataOut.utctime).tm_hour >= 11.: | |
| 2846 | if hasattr(dataOut, 'flagSpreadF') and dataOut.flagSpreadF: |
|
2851 | if hasattr(dataOut, 'flagSpreadF') and dataOut.flagSpreadF: | |
| 2847 | pass |
|
2852 | pass | |
| 2848 | else: |
|
2853 | else: | |
| 2849 | self.TeTiEstimation(dataOut) |
|
2854 | self.TeTiEstimation(dataOut) | |
| 2850 | dataOut.flagTeTiCorrection = True |
|
2855 | dataOut.flagTeTiCorrection = True | |
| 2851 | self.normalize(dataOut) |
|
2856 | self.normalize(dataOut) | |
| 2852 |
|
2857 | |||
| 2853 | return dataOut |
|
2858 | return dataOut | |
| 2854 |
|
2859 | |||
| 2855 |
|
2860 | |||
| 2856 |
|
2861 | |||
| 2857 | class DataSaveCleaner(Operation): |
|
2862 | class DataSaveCleaner(Operation): | |
| 2858 | ''' |
|
2863 | ''' | |
| 2859 | Written by R. Flores |
|
2864 | Written by R. Flores | |
| 2860 | ''' |
|
2865 | ''' | |
| 2861 | def __init__(self, **kwargs): |
|
2866 | def __init__(self, **kwargs): | |
| 2862 |
|
2867 | |||
| 2863 | Operation.__init__(self, **kwargs) |
|
2868 | Operation.__init__(self, **kwargs) | |
| 2864 | self.csv_flag = 1 |
|
2869 | self.csv_flag = 1 | |
| 2865 |
|
2870 | |||
| 2866 | def run(self,dataOut,savecfclean=0): |
|
2871 | def run(self,dataOut,savecfclean=0): | |
| 2867 | dataOut.DensityFinal=numpy.zeros((1,dataOut.NDP)) |
|
2872 | dataOut.DensityFinal=numpy.zeros((1,dataOut.NDP)) | |
| 2868 | dataOut.dphiFinal=numpy.zeros((1,dataOut.NDP)) |
|
2873 | dataOut.dphiFinal=numpy.zeros((1,dataOut.NDP)) | |
| 2869 | dataOut.EDensityFinal=numpy.zeros((1,dataOut.NDP)) |
|
2874 | dataOut.EDensityFinal=numpy.zeros((1,dataOut.NDP)) | |
| 2870 | dataOut.ElecTempFinal=numpy.zeros((1,dataOut.NDP)) |
|
2875 | dataOut.ElecTempFinal=numpy.zeros((1,dataOut.NDP)) | |
| 2871 | dataOut.EElecTempFinal=numpy.zeros((1,dataOut.NDP)) |
|
2876 | dataOut.EElecTempFinal=numpy.zeros((1,dataOut.NDP)) | |
| 2872 | dataOut.IonTempFinal=numpy.zeros((1,dataOut.NDP)) |
|
2877 | dataOut.IonTempFinal=numpy.zeros((1,dataOut.NDP)) | |
| 2873 | dataOut.EIonTempFinal=numpy.zeros((1,dataOut.NDP)) |
|
2878 | dataOut.EIonTempFinal=numpy.zeros((1,dataOut.NDP)) | |
| 2874 | dataOut.PhyFinal=numpy.zeros((1,dataOut.NDP)) |
|
2879 | dataOut.PhyFinal=numpy.zeros((1,dataOut.NDP)) | |
| 2875 | dataOut.EPhyFinal=numpy.zeros((1,dataOut.NDP)) |
|
2880 | dataOut.EPhyFinal=numpy.zeros((1,dataOut.NDP)) | |
| 2876 |
|
2881 | |||
| 2877 | dataOut.DensityFinal[0]=numpy.copy(dataOut.ph2) |
|
2882 | dataOut.DensityFinal[0]=numpy.copy(dataOut.ph2) | |
| 2878 | dataOut.dphiFinal[0]=numpy.copy(dataOut.dphi) |
|
2883 | dataOut.dphiFinal[0]=numpy.copy(dataOut.dphi) | |
| 2879 | dataOut.EDensityFinal[0]=numpy.copy(dataOut.sdp2) |
|
2884 | dataOut.EDensityFinal[0]=numpy.copy(dataOut.sdp2) | |
| 2880 | dataOut.ElecTempFinal[0,:dataOut.NSHTS]=numpy.copy(dataOut.te2) |
|
2885 | dataOut.ElecTempFinal[0,:dataOut.NSHTS]=numpy.copy(dataOut.te2) | |
| 2881 | dataOut.EElecTempFinal[0,:dataOut.NSHTS]=numpy.copy(dataOut.ete2) |
|
2886 | dataOut.EElecTempFinal[0,:dataOut.NSHTS]=numpy.copy(dataOut.ete2) | |
| 2882 | dataOut.IonTempFinal[0,:dataOut.NSHTS]=numpy.copy(dataOut.ti2) |
|
2887 | dataOut.IonTempFinal[0,:dataOut.NSHTS]=numpy.copy(dataOut.ti2) | |
| 2883 | dataOut.EIonTempFinal[0,:dataOut.NSHTS]=numpy.copy(dataOut.eti2) |
|
2888 | dataOut.EIonTempFinal[0,:dataOut.NSHTS]=numpy.copy(dataOut.eti2) | |
| 2884 | dataOut.PhyFinal[0,:dataOut.NSHTS]=numpy.copy(dataOut.phy2) |
|
2889 | dataOut.PhyFinal[0,:dataOut.NSHTS]=numpy.copy(dataOut.phy2) | |
| 2885 | dataOut.EPhyFinal[0,:dataOut.NSHTS]=numpy.copy(dataOut.ephy2) |
|
2890 | dataOut.EPhyFinal[0,:dataOut.NSHTS]=numpy.copy(dataOut.ephy2) | |
| 2886 |
|
2891 | |||
| 2887 | missing=numpy.nan |
|
2892 | missing=numpy.nan | |
| 2888 | temp_min=100.0 |
|
2893 | temp_min=100.0 | |
| 2889 | temp_max=3000.0#6000.0e |
|
2894 | temp_max=3000.0#6000.0e | |
| 2890 | den_err_percent = 100*dataOut.EDensityFinal[0]/dataOut.DensityFinal[0] |
|
2895 | den_err_percent = 100*dataOut.EDensityFinal[0]/dataOut.DensityFinal[0] | |
| 2891 | max_den_err_per = 35#30 #Densidades con error mayor al 35% se setean en NaN |
|
2896 | max_den_err_per = 35#30 #Densidades con error mayor al 35% se setean en NaN | |
| 2892 | for i in range(dataOut.NSHTS): |
|
2897 | for i in range(dataOut.NSHTS): | |
| 2893 |
|
2898 | |||
| 2894 | if den_err_percent[i] >= max_den_err_per: |
|
2899 | if den_err_percent[i] >= max_den_err_per: | |
| 2895 | dataOut.DensityFinal[0,i]=dataOut.EDensityFinal[0,i]=missing |
|
2900 | dataOut.DensityFinal[0,i]=dataOut.EDensityFinal[0,i]=missing | |
| 2896 | if i > 40: #Alturas mayores que 600 |
|
2901 | if i > 40: #Alturas mayores que 600 | |
| 2897 | dataOut.DensityFinal[0,i:]=dataOut.EDensityFinal[0,i:]=missing |
|
2902 | dataOut.DensityFinal[0,i:]=dataOut.EDensityFinal[0,i:]=missing | |
| 2898 |
|
2903 | |||
| 2899 | if dataOut.info2[i]!=1: |
|
2904 | if dataOut.info2[i]!=1: | |
| 2900 | dataOut.ElecTempFinal[0,i]=dataOut.EElecTempFinal[0,i]=dataOut.IonTempFinal[0,i]=dataOut.EIonTempFinal[0,i]=missing |
|
2905 | dataOut.ElecTempFinal[0,i]=dataOut.EElecTempFinal[0,i]=dataOut.IonTempFinal[0,i]=dataOut.EIonTempFinal[0,i]=missing | |
| 2901 |
|
2906 | |||
| 2902 | if dataOut.ElecTempFinal[0,i]<=temp_min or dataOut.ElecTempFinal[0,i]>temp_max or dataOut.EElecTempFinal[0,i]>temp_max: |
|
2907 | if dataOut.ElecTempFinal[0,i]<=temp_min or dataOut.ElecTempFinal[0,i]>temp_max or dataOut.EElecTempFinal[0,i]>temp_max: | |
| 2903 |
|
2908 | |||
| 2904 | dataOut.ElecTempFinal[0,i]=dataOut.EElecTempFinal[0,i]=missing |
|
2909 | dataOut.ElecTempFinal[0,i]=dataOut.EElecTempFinal[0,i]=missing | |
| 2905 |
|
2910 | |||
| 2906 | if dataOut.IonTempFinal[0,i]<=temp_min or dataOut.IonTempFinal[0,i]>temp_max or dataOut.EIonTempFinal[0,i]>temp_max: |
|
2911 | if dataOut.IonTempFinal[0,i]<=temp_min or dataOut.IonTempFinal[0,i]>temp_max or dataOut.EIonTempFinal[0,i]>temp_max: | |
| 2907 | dataOut.IonTempFinal[0,i]=dataOut.EIonTempFinal[0,i]=missing |
|
2912 | dataOut.IonTempFinal[0,i]=dataOut.EIonTempFinal[0,i]=missing | |
| 2908 |
|
2913 | |||
| 2909 | if dataOut.lags_to_plot[i,:][~numpy.isnan(dataOut.lags_to_plot[i,:])].shape[0]<6: |
|
2914 | if dataOut.lags_to_plot[i,:][~numpy.isnan(dataOut.lags_to_plot[i,:])].shape[0]<6: | |
| 2910 | dataOut.ElecTempFinal[0,i]=dataOut.EElecTempFinal[0,i]=dataOut.IonTempFinal[0,i]=dataOut.EIonTempFinal[0,i]=missing |
|
2915 | dataOut.ElecTempFinal[0,i]=dataOut.EElecTempFinal[0,i]=dataOut.IonTempFinal[0,i]=dataOut.EIonTempFinal[0,i]=missing | |
| 2911 |
|
2916 | |||
| 2912 | if dataOut.ut_Faraday>4 and dataOut.ut_Faraday<11: |
|
2917 | if dataOut.ut_Faraday>4 and dataOut.ut_Faraday<11: | |
| 2913 | if numpy.nanmax(dataOut.acfs_error_to_plot[i,:])>=10: |
|
2918 | if numpy.nanmax(dataOut.acfs_error_to_plot[i,:])>=10: | |
| 2914 | dataOut.ElecTempFinal[0,i]=dataOut.EElecTempFinal[0,i]=dataOut.IonTempFinal[0,i]=dataOut.EIonTempFinal[0,i]=missing |
|
2919 | dataOut.ElecTempFinal[0,i]=dataOut.EElecTempFinal[0,i]=dataOut.IonTempFinal[0,i]=dataOut.EIonTempFinal[0,i]=missing | |
| 2915 |
|
2920 | |||
| 2916 | if dataOut.EPhyFinal[0,i]<0.0 or dataOut.EPhyFinal[0,i]>1.0: |
|
2921 | if dataOut.EPhyFinal[0,i]<0.0 or dataOut.EPhyFinal[0,i]>1.0: | |
| 2917 | dataOut.PhyFinal[0,i]=dataOut.EPhyFinal[0,i]=missing |
|
2922 | dataOut.PhyFinal[0,i]=dataOut.EPhyFinal[0,i]=missing | |
| 2918 |
|
2923 | |||
| 2919 | if dataOut.EDensityFinal[0,i]>0.0 and dataOut.DensityFinal[0,i]>0.0 and dataOut.DensityFinal[0,i]<9.9e6: |
|
2924 | if dataOut.EDensityFinal[0,i]>0.0 and dataOut.DensityFinal[0,i]>0.0 and dataOut.DensityFinal[0,i]<9.9e6: | |
| 2920 | dataOut.EDensityFinal[0,i]=max(dataOut.EDensityFinal[0,i],1000.0) |
|
2925 | dataOut.EDensityFinal[0,i]=max(dataOut.EDensityFinal[0,i],1000.0) | |
| 2921 | else: |
|
2926 | else: | |
| 2922 | dataOut.DensityFinal[0,i]=dataOut.EDensityFinal[0,i]=missing |
|
2927 | dataOut.DensityFinal[0,i]=dataOut.EDensityFinal[0,i]=missing | |
| 2923 |
|
2928 | |||
| 2924 | if dataOut.PhyFinal[0,i]==0 or dataOut.PhyFinal[0,i]>0.4: |
|
2929 | if dataOut.PhyFinal[0,i]==0 or dataOut.PhyFinal[0,i]>0.4: | |
| 2925 | dataOut.PhyFinal[0,i]=dataOut.EPhyFinal[0,i]=missing |
|
2930 | dataOut.PhyFinal[0,i]=dataOut.EPhyFinal[0,i]=missing | |
| 2926 | if dataOut.ElecTempFinal[0,i]==dataOut.IonTempFinal[0,i]: |
|
2931 | if dataOut.ElecTempFinal[0,i]==dataOut.IonTempFinal[0,i]: | |
| 2927 | dataOut.EElecTempFinal[0,i]=dataOut.EIonTempFinal[0,i] |
|
2932 | dataOut.EElecTempFinal[0,i]=dataOut.EIonTempFinal[0,i] | |
| 2928 | if numpy.isnan(dataOut.ElecTempFinal[0,i]): |
|
2933 | if numpy.isnan(dataOut.ElecTempFinal[0,i]): | |
| 2929 | dataOut.EElecTempFinal[0,i]=missing |
|
2934 | dataOut.EElecTempFinal[0,i]=missing | |
| 2930 | if numpy.isnan(dataOut.IonTempFinal[0,i]): |
|
2935 | if numpy.isnan(dataOut.IonTempFinal[0,i]): | |
| 2931 | dataOut.EIonTempFinal[0,i]=missing |
|
2936 | dataOut.EIonTempFinal[0,i]=missing | |
| 2932 | if numpy.isnan(dataOut.ElecTempFinal[0,i]) or numpy.isnan(dataOut.EElecTempFinal[0,i]): |
|
2937 | if numpy.isnan(dataOut.ElecTempFinal[0,i]) or numpy.isnan(dataOut.EElecTempFinal[0,i]): | |
| 2933 | dataOut.ElecTempFinal[0,i]=dataOut.EElecTempFinal[0,i]=dataOut.IonTempFinal[0,i]=dataOut.EIonTempFinal[0,i]=missing |
|
2938 | dataOut.ElecTempFinal[0,i]=dataOut.EElecTempFinal[0,i]=dataOut.IonTempFinal[0,i]=dataOut.EIonTempFinal[0,i]=missing | |
| 2934 |
|
2939 | |||
| 2935 | for i in range(12,dataOut.NSHTS-1): |
|
2940 | for i in range(12,dataOut.NSHTS-1): | |
| 2936 |
|
2941 | |||
| 2937 | if numpy.isnan(dataOut.ElecTempFinal[0,i-1]) and numpy.isnan(dataOut.ElecTempFinal[0,i+1]): |
|
2942 | if numpy.isnan(dataOut.ElecTempFinal[0,i-1]) and numpy.isnan(dataOut.ElecTempFinal[0,i+1]): | |
| 2938 | dataOut.ElecTempFinal[0,i]=dataOut.EElecTempFinal[0,i]=missing |
|
2943 | dataOut.ElecTempFinal[0,i]=dataOut.EElecTempFinal[0,i]=missing | |
| 2939 |
|
2944 | |||
| 2940 | if numpy.isnan(dataOut.IonTempFinal[0,i-1]) and numpy.isnan(dataOut.IonTempFinal[0,i+1]): |
|
2945 | if numpy.isnan(dataOut.IonTempFinal[0,i-1]) and numpy.isnan(dataOut.IonTempFinal[0,i+1]): | |
| 2941 | dataOut.IonTempFinal[0,i]=dataOut.EIonTempFinal[0,i]=missing |
|
2946 | dataOut.IonTempFinal[0,i]=dataOut.EIonTempFinal[0,i]=missing | |
| 2942 |
|
2947 | |||
| 2943 | if dataOut.ut_Faraday>4 and dataOut.ut_Faraday<11: |
|
2948 | if dataOut.ut_Faraday>4 and dataOut.ut_Faraday<11: | |
| 2944 |
|
2949 | |||
| 2945 | if numpy.isnan(dataOut.ElecTempFinal[0,i-1]) and numpy.isnan(dataOut.ElecTempFinal[0,i-2]) and numpy.isnan(dataOut.ElecTempFinal[0,i+2]) and numpy.isnan(dataOut.ElecTempFinal[0,i+3]): #and numpy.isnan(dataOut.ElecTempFinal[0,i-5]): |
|
2950 | if numpy.isnan(dataOut.ElecTempFinal[0,i-1]) and numpy.isnan(dataOut.ElecTempFinal[0,i-2]) and numpy.isnan(dataOut.ElecTempFinal[0,i+2]) and numpy.isnan(dataOut.ElecTempFinal[0,i+3]): #and numpy.isnan(dataOut.ElecTempFinal[0,i-5]): | |
| 2946 |
|
2951 | |||
| 2947 | dataOut.ElecTempFinal[0,i]=dataOut.EElecTempFinal[0,i]=missing |
|
2952 | dataOut.ElecTempFinal[0,i]=dataOut.EElecTempFinal[0,i]=missing | |
| 2948 | if numpy.isnan(dataOut.IonTempFinal[0,i-1]) and numpy.isnan(dataOut.IonTempFinal[0,i-2]) and numpy.isnan(dataOut.IonTempFinal[0,i+2]) and numpy.isnan(dataOut.IonTempFinal[0,i+3]): #and numpy.isnan(dataOut.IonTempFinal[0,i-5]): |
|
2953 | if numpy.isnan(dataOut.IonTempFinal[0,i-1]) and numpy.isnan(dataOut.IonTempFinal[0,i-2]) and numpy.isnan(dataOut.IonTempFinal[0,i+2]) and numpy.isnan(dataOut.IonTempFinal[0,i+3]): #and numpy.isnan(dataOut.IonTempFinal[0,i-5]): | |
| 2949 |
|
2954 | |||
| 2950 | dataOut.IonTempFinal[0,i]=dataOut.EIonTempFinal[0,i]=missing |
|
2955 | dataOut.IonTempFinal[0,i]=dataOut.EIonTempFinal[0,i]=missing | |
| 2951 |
|
2956 | |||
| 2952 | if i>25: |
|
2957 | if i>25: | |
| 2953 | if numpy.isnan(dataOut.ElecTempFinal[0,i-1]) and numpy.isnan(dataOut.ElecTempFinal[0,i-2]) and numpy.isnan(dataOut.ElecTempFinal[0,i-3]) and numpy.isnan(dataOut.ElecTempFinal[0,i-4]): #and numpy.isnan(dataOut.ElecTempFinal[0,i-5]): |
|
2958 | if numpy.isnan(dataOut.ElecTempFinal[0,i-1]) and numpy.isnan(dataOut.ElecTempFinal[0,i-2]) and numpy.isnan(dataOut.ElecTempFinal[0,i-3]) and numpy.isnan(dataOut.ElecTempFinal[0,i-4]): #and numpy.isnan(dataOut.ElecTempFinal[0,i-5]): | |
| 2954 | dataOut.ElecTempFinal[0,i]=dataOut.EElecTempFinal[0,i]=missing |
|
2959 | dataOut.ElecTempFinal[0,i]=dataOut.EElecTempFinal[0,i]=missing | |
| 2955 | if numpy.isnan(dataOut.IonTempFinal[0,i-1]) and numpy.isnan(dataOut.IonTempFinal[0,i-2]) and numpy.isnan(dataOut.IonTempFinal[0,i-3]) and numpy.isnan(dataOut.IonTempFinal[0,i-4]): #and numpy.isnan(dataOut.IonTempFinal[0,i-5]): |
|
2960 | if numpy.isnan(dataOut.IonTempFinal[0,i-1]) and numpy.isnan(dataOut.IonTempFinal[0,i-2]) and numpy.isnan(dataOut.IonTempFinal[0,i-3]) and numpy.isnan(dataOut.IonTempFinal[0,i-4]): #and numpy.isnan(dataOut.IonTempFinal[0,i-5]): | |
| 2956 |
|
2961 | |||
| 2957 | dataOut.IonTempFinal[0,i]=dataOut.EIonTempFinal[0,i]=missing |
|
2962 | dataOut.IonTempFinal[0,i]=dataOut.EIonTempFinal[0,i]=missing | |
| 2958 |
|
2963 | |||
| 2959 | if numpy.isnan(dataOut.ElecTempFinal[0,i]) or numpy.isnan(dataOut.EElecTempFinal[0,i]): |
|
2964 | if numpy.isnan(dataOut.ElecTempFinal[0,i]) or numpy.isnan(dataOut.EElecTempFinal[0,i]): | |
| 2960 |
|
2965 | |||
| 2961 | dataOut.ElecTempFinal[0,i]=dataOut.EElecTempFinal[0,i]=dataOut.IonTempFinal[0,i]=dataOut.EIonTempFinal[0,i]=missing |
|
2966 | dataOut.ElecTempFinal[0,i]=dataOut.EElecTempFinal[0,i]=dataOut.IonTempFinal[0,i]=dataOut.EIonTempFinal[0,i]=missing | |
| 2962 |
|
2967 | |||
| 2963 | for i in range(12,dataOut.NSHTS-1): |
|
2968 | for i in range(12,dataOut.NSHTS-1): | |
| 2964 |
|
2969 | |||
| 2965 | if numpy.isnan(dataOut.ElecTempFinal[0,i-1]) and numpy.isnan(dataOut.ElecTempFinal[0,i+1]): |
|
2970 | if numpy.isnan(dataOut.ElecTempFinal[0,i-1]) and numpy.isnan(dataOut.ElecTempFinal[0,i+1]): | |
| 2966 | dataOut.ElecTempFinal[0,i]=dataOut.EElecTempFinal[0,i]=missing |
|
2971 | dataOut.ElecTempFinal[0,i]=dataOut.EElecTempFinal[0,i]=missing | |
| 2967 |
|
2972 | |||
| 2968 | if numpy.isnan(dataOut.IonTempFinal[0,i-1]) and numpy.isnan(dataOut.IonTempFinal[0,i+1]): |
|
2973 | if numpy.isnan(dataOut.IonTempFinal[0,i-1]) and numpy.isnan(dataOut.IonTempFinal[0,i+1]): | |
| 2969 | dataOut.IonTempFinal[0,i]=dataOut.EIonTempFinal[0,i]=missing |
|
2974 | dataOut.IonTempFinal[0,i]=dataOut.EIonTempFinal[0,i]=missing | |
| 2970 |
|
2975 | |||
| 2971 | if numpy.isnan(dataOut.DensityFinal[0,i-1]) and numpy.isnan(dataOut.DensityFinal[0,i+1]): ##NEW |
|
2976 | if numpy.isnan(dataOut.DensityFinal[0,i-1]) and numpy.isnan(dataOut.DensityFinal[0,i+1]): ##NEW | |
| 2972 | dataOut.DensityFinal[0,i]=dataOut.EDensityFinal[0,i]=missing ##NEW |
|
2977 | dataOut.DensityFinal[0,i]=dataOut.EDensityFinal[0,i]=missing ##NEW | |
| 2973 |
|
2978 | |||
| 2974 | if numpy.isnan(dataOut.ElecTempFinal[0,i]) or numpy.isnan(dataOut.EElecTempFinal[0,i]): |
|
2979 | if numpy.isnan(dataOut.ElecTempFinal[0,i]) or numpy.isnan(dataOut.EElecTempFinal[0,i]): | |
| 2975 |
|
2980 | |||
| 2976 | dataOut.ElecTempFinal[0,i]=dataOut.EElecTempFinal[0,i]=dataOut.IonTempFinal[0,i]=dataOut.EIonTempFinal[0,i]=missing |
|
2981 | dataOut.ElecTempFinal[0,i]=dataOut.EElecTempFinal[0,i]=dataOut.IonTempFinal[0,i]=dataOut.EIonTempFinal[0,i]=missing | |
| 2977 |
|
2982 | |||
| 2978 | if numpy.count_nonzero(~numpy.isnan(dataOut.ElecTempFinal[0,12:50]))<5: |
|
2983 | if numpy.count_nonzero(~numpy.isnan(dataOut.ElecTempFinal[0,12:50]))<5: | |
| 2979 | dataOut.ElecTempFinal[0,:]=dataOut.EElecTempFinal[0,:]=missing |
|
2984 | dataOut.ElecTempFinal[0,:]=dataOut.EElecTempFinal[0,:]=missing | |
| 2980 | if numpy.count_nonzero(~numpy.isnan(dataOut.IonTempFinal[0,12:50]))<5: |
|
2985 | if numpy.count_nonzero(~numpy.isnan(dataOut.IonTempFinal[0,12:50]))<5: | |
| 2981 | dataOut.IonTempFinal[0,:]=dataOut.EIonTempFinal[0,:]=missing |
|
2986 | dataOut.IonTempFinal[0,:]=dataOut.EIonTempFinal[0,:]=missing | |
| 2982 |
|
2987 | |||
| 2983 |
|
2988 | |||
| 2984 | if numpy.count_nonzero(~numpy.isnan(dataOut.DensityFinal[0,12:50]))<=5: |
|
2989 | if numpy.count_nonzero(~numpy.isnan(dataOut.DensityFinal[0,12:50]))<=5: | |
| 2985 | dataOut.DensityFinal[0,:]=dataOut.EDensityFinal[0,:]=missing |
|
2990 | dataOut.DensityFinal[0,:]=dataOut.EDensityFinal[0,:]=missing | |
| 2986 |
|
2991 | |||
| 2987 | dataOut.DensityFinal[0,dataOut.NSHTS:]=missing |
|
2992 | dataOut.DensityFinal[0,dataOut.NSHTS:]=missing | |
| 2988 | dataOut.EDensityFinal[0,dataOut.NSHTS:]=missing |
|
2993 | dataOut.EDensityFinal[0,dataOut.NSHTS:]=missing | |
| 2989 | dataOut.ElecTempFinal[0,dataOut.NSHTS:]=missing |
|
2994 | dataOut.ElecTempFinal[0,dataOut.NSHTS:]=missing | |
| 2990 | dataOut.EElecTempFinal[0,dataOut.NSHTS:]=missing |
|
2995 | dataOut.EElecTempFinal[0,dataOut.NSHTS:]=missing | |
| 2991 | dataOut.IonTempFinal[0,dataOut.NSHTS:]=missing |
|
2996 | dataOut.IonTempFinal[0,dataOut.NSHTS:]=missing | |
| 2992 | dataOut.EIonTempFinal[0,dataOut.NSHTS:]=missing |
|
2997 | dataOut.EIonTempFinal[0,dataOut.NSHTS:]=missing | |
| 2993 | dataOut.PhyFinal[0,dataOut.NSHTS:]=missing |
|
2998 | dataOut.PhyFinal[0,dataOut.NSHTS:]=missing | |
| 2994 | dataOut.EPhyFinal[0,dataOut.NSHTS:]=missing |
|
2999 | dataOut.EPhyFinal[0,dataOut.NSHTS:]=missing | |
| 2995 |
|
3000 | |||
| 2996 | if gmtime(dataOut.utctime).tm_hour >= 13. and gmtime(dataOut.utctime).tm_hour < 21.: #07-16 LT |
|
3001 | if gmtime(dataOut.utctime).tm_hour >= 13. and gmtime(dataOut.utctime).tm_hour < 21.: #07-16 LT | |
| 2997 | dataOut.DensityFinal[0,:13]=missing |
|
3002 | dataOut.DensityFinal[0,:13]=missing | |
| 2998 | dataOut.EDensityFinal[0,:13]=missing |
|
3003 | dataOut.EDensityFinal[0,:13]=missing | |
| 2999 | dataOut.ElecTempFinal[0,:13]=missing |
|
3004 | dataOut.ElecTempFinal[0,:13]=missing | |
| 3000 | dataOut.EElecTempFinal[0,:13]=missing |
|
3005 | dataOut.EElecTempFinal[0,:13]=missing | |
| 3001 | dataOut.IonTempFinal[0,:13]=missing |
|
3006 | dataOut.IonTempFinal[0,:13]=missing | |
| 3002 | dataOut.EIonTempFinal[0,:13]=missing |
|
3007 | dataOut.EIonTempFinal[0,:13]=missing | |
| 3003 | dataOut.PhyFinal[0,:13]=missing |
|
3008 | dataOut.PhyFinal[0,:13]=missing | |
| 3004 | dataOut.EPhyFinal[0,:13]=missing |
|
3009 | dataOut.EPhyFinal[0,:13]=missing | |
| 3005 |
|
3010 | |||
| 3006 | else: |
|
3011 | else: | |
| 3007 | if gmtime(dataOut.utctime).tm_hour == 9 and gmtime(dataOut.utctime).tm_min == 20: |
|
3012 | if gmtime(dataOut.utctime).tm_hour == 9 and gmtime(dataOut.utctime).tm_min == 20: | |
| 3008 | pass |
|
3013 | pass | |
| 3009 | else: |
|
3014 | else: | |
| 3010 | dataOut.DensityFinal[0,:dataOut.min_id_eej+1]=missing |
|
3015 | dataOut.DensityFinal[0,:dataOut.min_id_eej+1]=missing | |
| 3011 | dataOut.EDensityFinal[0,:dataOut.min_id_eej+1]=missing |
|
3016 | dataOut.EDensityFinal[0,:dataOut.min_id_eej+1]=missing | |
| 3012 | dataOut.ElecTempFinal[0,:dataOut.min_id_eej+1]=missing |
|
3017 | dataOut.ElecTempFinal[0,:dataOut.min_id_eej+1]=missing | |
| 3013 | dataOut.EElecTempFinal[0,:dataOut.min_id_eej+1]=missing |
|
3018 | dataOut.EElecTempFinal[0,:dataOut.min_id_eej+1]=missing | |
| 3014 | dataOut.IonTempFinal[0,:dataOut.min_id_eej+1]=missing |
|
3019 | dataOut.IonTempFinal[0,:dataOut.min_id_eej+1]=missing | |
| 3015 | dataOut.EIonTempFinal[0,:dataOut.min_id_eej+1]=missing |
|
3020 | dataOut.EIonTempFinal[0,:dataOut.min_id_eej+1]=missing | |
| 3016 | dataOut.PhyFinal[0,:dataOut.min_id_eej+1]=missing |
|
3021 | dataOut.PhyFinal[0,:dataOut.min_id_eej+1]=missing | |
| 3017 | dataOut.EPhyFinal[0,:dataOut.min_id_eej+1]=missing |
|
3022 | dataOut.EPhyFinal[0,:dataOut.min_id_eej+1]=missing | |
| 3018 |
|
3023 | |||
| 3019 | dataOut.flagNoData = numpy.all(numpy.isnan(dataOut.DensityFinal)) #Si todos los valores son NaN no se prosigue |
|
3024 | dataOut.flagNoData = numpy.all(numpy.isnan(dataOut.DensityFinal)) #Si todos los valores son NaN no se prosigue | |
| 3020 |
|
3025 | |||
| 3021 | if not dataOut.flagNoData: |
|
3026 | if not dataOut.flagNoData: | |
| 3022 | if savecfclean: |
|
3027 | if savecfclean: | |
| 3023 | try: |
|
3028 | try: | |
| 3024 | import pandas as pd |
|
3029 | import pandas as pd | |
| 3025 | if self.csv_flag: |
|
3030 | if self.csv_flag: | |
| 3026 | if not os.path.exists("./cfclean"): |
|
3031 | if not os.path.exists("./cfclean"): | |
| 3027 | os.makedirs("./cfclean") |
|
3032 | os.makedirs("./cfclean") | |
| 3028 | self.doy_csv = datetime.datetime.fromtimestamp(dataOut.utctime).strftime('%j') |
|
3033 | self.doy_csv = datetime.datetime.fromtimestamp(dataOut.utctime).strftime('%j') | |
| 3029 | self.year_csv = datetime.datetime.fromtimestamp(dataOut.utctime).strftime('%Y') |
|
3034 | self.year_csv = datetime.datetime.fromtimestamp(dataOut.utctime).strftime('%Y') | |
| 3030 | file = open("./cfclean/cfclean{0}{1}.csv".format(self.year_csv,self.doy_csv), "x") |
|
3035 | file = open("./cfclean/cfclean{0}{1}.csv".format(self.year_csv,self.doy_csv), "x") | |
| 3031 | f = csv.writer(file) |
|
3036 | f = csv.writer(file) | |
| 3032 | f.writerow(numpy.array(["timestamp",'cf'])) |
|
3037 | f.writerow(numpy.array(["timestamp",'cf'])) | |
| 3033 | self.csv_flag = 0 |
|
3038 | self.csv_flag = 0 | |
| 3034 | print("Creating cf clean File") |
|
3039 | print("Creating cf clean File") | |
| 3035 | print("Writing cf clean File") |
|
3040 | print("Writing cf clean File") | |
| 3036 | except: |
|
3041 | except: | |
| 3037 | file = open("./cfclean/cfclean{0}{1}.csv".format(self.year_csv,self.doy_csv), "a") |
|
3042 | file = open("./cfclean/cfclean{0}{1}.csv".format(self.year_csv,self.doy_csv), "a") | |
| 3038 | f = csv.writer(file) |
|
3043 | f = csv.writer(file) | |
| 3039 | print("Writing cf clean File") |
|
3044 | print("Writing cf clean File") | |
| 3040 | cf = numpy.array([dataOut.utctime,dataOut.cf]) |
|
3045 | cf = numpy.array([dataOut.utctime,dataOut.cf]) | |
| 3041 | f.writerow(cf) |
|
3046 | f.writerow(cf) | |
| 3042 | file.close() |
|
3047 | file.close() | |
| 3043 |
|
3048 | |||
| 3044 | dataOut.flagNoData = False #Descomentar solo para ploteo #Comentar para MADWriter |
|
3049 | dataOut.flagNoData = False #Descomentar solo para ploteo #Comentar para MADWriter | |
| 3045 |
|
3050 | |||
| 3046 | dataOut.DensityFinal *= 1.e6 #Convert units to m^⁻3 |
|
3051 | dataOut.DensityFinal *= 1.e6 #Convert units to m^⁻3 | |
| 3047 | dataOut.EDensityFinal *= 1.e6 #Convert units to m^⁻3 |
|
3052 | dataOut.EDensityFinal *= 1.e6 #Convert units to m^⁻3 | |
| 3048 |
|
3053 | |||
| 3049 | return dataOut |
|
3054 | return dataOut | |
| 3050 |
|
3055 | |||
| 3051 |
|
3056 | |||
| 3052 | class DataSaveCleanerHP(Operation): |
|
3057 | class DataSaveCleanerHP(Operation): | |
| 3053 | ''' |
|
3058 | ''' | |
| 3054 | Written by R. Flores |
|
3059 | Written by R. Flores | |
| 3055 | ''' |
|
3060 | ''' | |
| 3056 | def __init__(self, **kwargs): |
|
3061 | def __init__(self, **kwargs): | |
| 3057 |
|
3062 | |||
| 3058 | Operation.__init__(self, **kwargs) |
|
3063 | Operation.__init__(self, **kwargs) | |
| 3059 |
|
3064 | |||
| 3060 | def run(self,dataOut): |
|
3065 | def run(self,dataOut): | |
| 3061 |
|
3066 | |||
| 3062 | dataOut.Density_DP=numpy.zeros(dataOut.cut) |
|
3067 | dataOut.Density_DP=numpy.zeros(dataOut.cut) | |
| 3063 | dataOut.EDensity_DP=numpy.zeros(dataOut.cut) |
|
3068 | dataOut.EDensity_DP=numpy.zeros(dataOut.cut) | |
| 3064 | dataOut.ElecTemp_DP=numpy.zeros(dataOut.cut) |
|
3069 | dataOut.ElecTemp_DP=numpy.zeros(dataOut.cut) | |
| 3065 | dataOut.EElecTemp_DP=numpy.zeros(dataOut.cut) |
|
3070 | dataOut.EElecTemp_DP=numpy.zeros(dataOut.cut) | |
| 3066 | dataOut.IonTemp_DP=numpy.zeros(dataOut.cut) |
|
3071 | dataOut.IonTemp_DP=numpy.zeros(dataOut.cut) | |
| 3067 | dataOut.EIonTemp_DP=numpy.zeros(dataOut.cut) |
|
3072 | dataOut.EIonTemp_DP=numpy.zeros(dataOut.cut) | |
| 3068 | dataOut.Phy_DP=numpy.zeros(dataOut.cut) |
|
3073 | dataOut.Phy_DP=numpy.zeros(dataOut.cut) | |
| 3069 | dataOut.EPhy_DP=numpy.zeros(dataOut.cut) |
|
3074 | dataOut.EPhy_DP=numpy.zeros(dataOut.cut) | |
| 3070 | dataOut.Phe_DP=numpy.empty(dataOut.cut) |
|
3075 | dataOut.Phe_DP=numpy.empty(dataOut.cut) | |
| 3071 | dataOut.EPhe_DP=numpy.empty(dataOut.cut) |
|
3076 | dataOut.EPhe_DP=numpy.empty(dataOut.cut) | |
| 3072 |
|
3077 | |||
| 3073 | dataOut.Density_DP[:]=numpy.copy(dataOut.ph2[:dataOut.cut]) |
|
3078 | dataOut.Density_DP[:]=numpy.copy(dataOut.ph2[:dataOut.cut]) | |
| 3074 | dataOut.EDensity_DP[:]=numpy.copy(dataOut.sdp2[:dataOut.cut]) |
|
3079 | dataOut.EDensity_DP[:]=numpy.copy(dataOut.sdp2[:dataOut.cut]) | |
| 3075 | dataOut.ElecTemp_DP[:]=numpy.copy(dataOut.te2[:dataOut.cut]) |
|
3080 | dataOut.ElecTemp_DP[:]=numpy.copy(dataOut.te2[:dataOut.cut]) | |
| 3076 | dataOut.EElecTemp_DP[:]=numpy.copy(dataOut.ete2[:dataOut.cut]) |
|
3081 | dataOut.EElecTemp_DP[:]=numpy.copy(dataOut.ete2[:dataOut.cut]) | |
| 3077 | dataOut.IonTemp_DP[:]=numpy.copy(dataOut.ti2[:dataOut.cut]) |
|
3082 | dataOut.IonTemp_DP[:]=numpy.copy(dataOut.ti2[:dataOut.cut]) | |
| 3078 | dataOut.EIonTemp_DP[:]=numpy.copy(dataOut.eti2[:dataOut.cut]) |
|
3083 | dataOut.EIonTemp_DP[:]=numpy.copy(dataOut.eti2[:dataOut.cut]) | |
| 3079 | dataOut.Phy_DP[:]=numpy.copy(dataOut.phy2[:dataOut.cut]) |
|
3084 | dataOut.Phy_DP[:]=numpy.copy(dataOut.phy2[:dataOut.cut]) | |
| 3080 | dataOut.EPhy_DP[:]=numpy.copy(dataOut.ephy2[:dataOut.cut]) |
|
3085 | dataOut.EPhy_DP[:]=numpy.copy(dataOut.ephy2[:dataOut.cut]) | |
| 3081 | dataOut.Phe_DP[:]=numpy.nan |
|
3086 | dataOut.Phe_DP[:]=numpy.nan | |
| 3082 | dataOut.EPhe_DP[:]=numpy.nan |
|
3087 | dataOut.EPhe_DP[:]=numpy.nan | |
| 3083 |
|
3088 | |||
| 3084 | missing=numpy.nan |
|
3089 | missing=numpy.nan | |
| 3085 | temp_min=100.0 |
|
3090 | temp_min=100.0 | |
| 3086 | temp_max_dp=3000.0 |
|
3091 | temp_max_dp=3000.0 | |
| 3087 |
|
3092 | |||
| 3088 | for i in range(dataOut.cut): |
|
3093 | for i in range(dataOut.cut): | |
| 3089 | if dataOut.info2[i]!=1: |
|
3094 | if dataOut.info2[i]!=1: | |
| 3090 | dataOut.ElecTemp_DP[i]=dataOut.EElecTemp_DP[i]=dataOut.IonTemp_DP[i]=dataOut.EIonTemp_DP[i]=missing |
|
3095 | dataOut.ElecTemp_DP[i]=dataOut.EElecTemp_DP[i]=dataOut.IonTemp_DP[i]=dataOut.EIonTemp_DP[i]=missing | |
| 3091 |
|
3096 | |||
| 3092 | if dataOut.ElecTemp_DP[i]<=temp_min or dataOut.ElecTemp_DP[i]>temp_max_dp or dataOut.EElecTemp_DP[i]>temp_max_dp: |
|
3097 | if dataOut.ElecTemp_DP[i]<=temp_min or dataOut.ElecTemp_DP[i]>temp_max_dp or dataOut.EElecTemp_DP[i]>temp_max_dp: | |
| 3093 |
|
3098 | |||
| 3094 | dataOut.ElecTemp_DP[i]=dataOut.EElecTemp_DP[i]=missing |
|
3099 | dataOut.ElecTemp_DP[i]=dataOut.EElecTemp_DP[i]=missing | |
| 3095 |
|
3100 | |||
| 3096 | if dataOut.IonTemp_DP[i]<=temp_min or dataOut.IonTemp_DP[i]>temp_max_dp or dataOut.EIonTemp_DP[i]>temp_max_dp: |
|
3101 | if dataOut.IonTemp_DP[i]<=temp_min or dataOut.IonTemp_DP[i]>temp_max_dp or dataOut.EIonTemp_DP[i]>temp_max_dp: | |
| 3097 | dataOut.IonTemp_DP[i]=dataOut.EIonTemp_DP[i]=missing |
|
3102 | dataOut.IonTemp_DP[i]=dataOut.EIonTemp_DP[i]=missing | |
| 3098 |
|
3103 | |||
| 3099 | ####################################################################################### CHECK THIS |
|
3104 | ####################################################################################### CHECK THIS | |
| 3100 | if dataOut.lags_to_plot[i,:][~numpy.isnan(dataOut.lags_to_plot[i,:])].shape[0]<6: |
|
3105 | if dataOut.lags_to_plot[i,:][~numpy.isnan(dataOut.lags_to_plot[i,:])].shape[0]<6: | |
| 3101 | dataOut.ElecTemp_DP[i]=dataOut.EElecTemp_DP[i]=dataOut.IonTemp_DP[i]=dataOut.EIonTemp_DP[i]=missing |
|
3106 | dataOut.ElecTemp_DP[i]=dataOut.EElecTemp_DP[i]=dataOut.IonTemp_DP[i]=dataOut.EIonTemp_DP[i]=missing | |
| 3102 |
|
3107 | |||
| 3103 | if dataOut.ut_Faraday>4 and dataOut.ut_Faraday<11: |
|
3108 | if dataOut.ut_Faraday>4 and dataOut.ut_Faraday<11: | |
| 3104 | if numpy.nanmax(dataOut.acfs_error_to_plot[i,:])>=10: |
|
3109 | if numpy.nanmax(dataOut.acfs_error_to_plot[i,:])>=10: | |
| 3105 | dataOut.ElecTemp_DP[i]=dataOut.EElecTemp_DP[i]=dataOut.IonTemp_DP[i]=dataOut.EIonTemp_DP[i]=missing |
|
3110 | dataOut.ElecTemp_DP[i]=dataOut.EElecTemp_DP[i]=dataOut.IonTemp_DP[i]=dataOut.EIonTemp_DP[i]=missing | |
| 3106 | ####################################################################################### |
|
3111 | ####################################################################################### | |
| 3107 |
|
3112 | |||
| 3108 | if dataOut.EPhy_DP[i]<0.0 or dataOut.EPhy_DP[i]>1.0: |
|
3113 | if dataOut.EPhy_DP[i]<0.0 or dataOut.EPhy_DP[i]>1.0: | |
| 3109 | dataOut.Phy_DP[i]=dataOut.EPhy_DP[i]=missing |
|
3114 | dataOut.Phy_DP[i]=dataOut.EPhy_DP[i]=missing | |
| 3110 | if dataOut.EDensity_DP[i]>0.0 and dataOut.Density_DP[i]>0.0 and dataOut.Density_DP[i]<9.9e6: |
|
3115 | if dataOut.EDensity_DP[i]>0.0 and dataOut.Density_DP[i]>0.0 and dataOut.Density_DP[i]<9.9e6: | |
| 3111 | dataOut.EDensity_DP[i]=max(dataOut.EDensity_DP[i],1000.0) |
|
3116 | dataOut.EDensity_DP[i]=max(dataOut.EDensity_DP[i],1000.0) | |
| 3112 | else: |
|
3117 | else: | |
| 3113 | dataOut.Density_DP[i]=dataOut.EDensity_DP[i]=missing |
|
3118 | dataOut.Density_DP[i]=dataOut.EDensity_DP[i]=missing | |
| 3114 | if dataOut.Phy_DP[i]==0 or dataOut.Phy_DP[i]>0.4: |
|
3119 | if dataOut.Phy_DP[i]==0 or dataOut.Phy_DP[i]>0.4: | |
| 3115 | dataOut.Phy_DP[i]=dataOut.EPhy_DP[i]=missing |
|
3120 | dataOut.Phy_DP[i]=dataOut.EPhy_DP[i]=missing | |
| 3116 | if dataOut.ElecTemp_DP[i]==dataOut.IonTemp_DP[i]: |
|
3121 | if dataOut.ElecTemp_DP[i]==dataOut.IonTemp_DP[i]: | |
| 3117 | dataOut.EElecTemp_DP[i]=dataOut.EIonTemp_DP[i] |
|
3122 | dataOut.EElecTemp_DP[i]=dataOut.EIonTemp_DP[i] | |
| 3118 | if numpy.isnan(dataOut.ElecTemp_DP[i]): |
|
3123 | if numpy.isnan(dataOut.ElecTemp_DP[i]): | |
| 3119 | dataOut.EElecTemp_DP[i]=missing |
|
3124 | dataOut.EElecTemp_DP[i]=missing | |
| 3120 | if numpy.isnan(dataOut.IonTemp_DP[i]): |
|
3125 | if numpy.isnan(dataOut.IonTemp_DP[i]): | |
| 3121 | dataOut.EIonTemp_DP[i]=missing |
|
3126 | dataOut.EIonTemp_DP[i]=missing | |
| 3122 | if numpy.isnan(dataOut.ElecTemp_DP[i]) or numpy.isnan(dataOut.EElecTemp_DP[i]): |
|
3127 | if numpy.isnan(dataOut.ElecTemp_DP[i]) or numpy.isnan(dataOut.EElecTemp_DP[i]): | |
| 3123 | dataOut.ElecTemp_DP[i]=dataOut.EElecTemp_DP[i]=dataOut.IonTemp_DP[i]=dataOut.EIonTemp_DP[i]=missing |
|
3128 | dataOut.ElecTemp_DP[i]=dataOut.EElecTemp_DP[i]=dataOut.IonTemp_DP[i]=dataOut.EIonTemp_DP[i]=missing | |
| 3124 |
|
3129 | |||
| 3125 |
|
3130 | |||
| 3126 |
|
3131 | |||
| 3127 | dataOut.Density_LP=numpy.zeros(dataOut.NACF-dataOut.cut) |
|
3132 | dataOut.Density_LP=numpy.zeros(dataOut.NACF-dataOut.cut) | |
| 3128 | dataOut.EDensity_LP=numpy.zeros(dataOut.NACF-dataOut.cut) |
|
3133 | dataOut.EDensity_LP=numpy.zeros(dataOut.NACF-dataOut.cut) | |
| 3129 | dataOut.ElecTemp_LP=numpy.zeros(dataOut.NACF-dataOut.cut) |
|
3134 | dataOut.ElecTemp_LP=numpy.zeros(dataOut.NACF-dataOut.cut) | |
| 3130 | dataOut.EElecTemp_LP=numpy.zeros(dataOut.NACF-dataOut.cut) |
|
3135 | dataOut.EElecTemp_LP=numpy.zeros(dataOut.NACF-dataOut.cut) | |
| 3131 | dataOut.IonTemp_LP=numpy.zeros(dataOut.NACF-dataOut.cut) |
|
3136 | dataOut.IonTemp_LP=numpy.zeros(dataOut.NACF-dataOut.cut) | |
| 3132 | dataOut.EIonTemp_LP=numpy.zeros(dataOut.NACF-dataOut.cut) |
|
3137 | dataOut.EIonTemp_LP=numpy.zeros(dataOut.NACF-dataOut.cut) | |
| 3133 | dataOut.Phy_LP=numpy.zeros(dataOut.NACF-dataOut.cut) |
|
3138 | dataOut.Phy_LP=numpy.zeros(dataOut.NACF-dataOut.cut) | |
| 3134 | dataOut.EPhy_LP=numpy.zeros(dataOut.NACF-dataOut.cut) |
|
3139 | dataOut.EPhy_LP=numpy.zeros(dataOut.NACF-dataOut.cut) | |
| 3135 | dataOut.Phe_LP=numpy.zeros(dataOut.NACF-dataOut.cut) |
|
3140 | dataOut.Phe_LP=numpy.zeros(dataOut.NACF-dataOut.cut) | |
| 3136 | dataOut.EPhe_LP=numpy.zeros(dataOut.NACF-dataOut.cut) |
|
3141 | dataOut.EPhe_LP=numpy.zeros(dataOut.NACF-dataOut.cut) | |
| 3137 |
|
3142 | |||
| 3138 | dataOut.Density_LP[:]=numpy.copy(dataOut.ne[dataOut.cut:dataOut.NACF]) |
|
3143 | dataOut.Density_LP[:]=numpy.copy(dataOut.ne[dataOut.cut:dataOut.NACF]) | |
| 3139 | dataOut.EDensity_LP[:]=numpy.copy(dataOut.ene[dataOut.cut:dataOut.NACF]) |
|
3144 | dataOut.EDensity_LP[:]=numpy.copy(dataOut.ene[dataOut.cut:dataOut.NACF]) | |
| 3140 | dataOut.ElecTemp_LP[:]=numpy.copy(dataOut.te[dataOut.cut:dataOut.NACF]) |
|
3145 | dataOut.ElecTemp_LP[:]=numpy.copy(dataOut.te[dataOut.cut:dataOut.NACF]) | |
| 3141 | dataOut.EElecTemp_LP[:]=numpy.copy(dataOut.ete[dataOut.cut:dataOut.NACF]) |
|
3146 | dataOut.EElecTemp_LP[:]=numpy.copy(dataOut.ete[dataOut.cut:dataOut.NACF]) | |
| 3142 | dataOut.IonTemp_LP[:]=numpy.copy(dataOut.ti[dataOut.cut:dataOut.NACF]) |
|
3147 | dataOut.IonTemp_LP[:]=numpy.copy(dataOut.ti[dataOut.cut:dataOut.NACF]) | |
| 3143 | dataOut.EIonTemp_LP[:]=numpy.copy(dataOut.eti[dataOut.cut:dataOut.NACF]) |
|
3148 | dataOut.EIonTemp_LP[:]=numpy.copy(dataOut.eti[dataOut.cut:dataOut.NACF]) | |
| 3144 | dataOut.Phy_LP[:]=numpy.copy(dataOut.ph[dataOut.cut:dataOut.NACF]) |
|
3149 | dataOut.Phy_LP[:]=numpy.copy(dataOut.ph[dataOut.cut:dataOut.NACF]) | |
| 3145 | dataOut.EPhy_LP[:]=numpy.copy(dataOut.eph[dataOut.cut:dataOut.NACF]) |
|
3150 | dataOut.EPhy_LP[:]=numpy.copy(dataOut.eph[dataOut.cut:dataOut.NACF]) | |
| 3146 | dataOut.Phe_LP[:]=numpy.copy(dataOut.phe[dataOut.cut:dataOut.NACF]) |
|
3151 | dataOut.Phe_LP[:]=numpy.copy(dataOut.phe[dataOut.cut:dataOut.NACF]) | |
| 3147 | dataOut.EPhe_LP[:]=numpy.copy(dataOut.ephe[dataOut.cut:dataOut.NACF]) |
|
3152 | dataOut.EPhe_LP[:]=numpy.copy(dataOut.ephe[dataOut.cut:dataOut.NACF]) | |
| 3148 |
|
3153 | |||
| 3149 | temp_max_lp=6000.0 |
|
3154 | temp_max_lp=6000.0 | |
| 3150 |
|
3155 | |||
| 3151 | for i in range(dataOut.NACF-dataOut.cut): |
|
3156 | for i in range(dataOut.NACF-dataOut.cut): | |
| 3152 |
|
3157 | |||
| 3153 | if dataOut.ElecTemp_LP[i]<=temp_min or dataOut.ElecTemp_LP[i]>temp_max_lp or dataOut.EElecTemp_LP[i]>temp_max_lp: |
|
3158 | if dataOut.ElecTemp_LP[i]<=temp_min or dataOut.ElecTemp_LP[i]>temp_max_lp or dataOut.EElecTemp_LP[i]>temp_max_lp: | |
| 3154 |
|
3159 | |||
| 3155 | dataOut.ElecTemp_LP[i]=dataOut.EElecTemp_LP[i]=missing |
|
3160 | dataOut.ElecTemp_LP[i]=dataOut.EElecTemp_LP[i]=missing | |
| 3156 |
|
3161 | |||
| 3157 | if dataOut.IonTemp_LP[i]<=temp_min or dataOut.IonTemp_LP[i]>temp_max_lp or dataOut.EIonTemp_LP[i]>temp_max_lp: |
|
3162 | if dataOut.IonTemp_LP[i]<=temp_min or dataOut.IonTemp_LP[i]>temp_max_lp or dataOut.EIonTemp_LP[i]>temp_max_lp: | |
| 3158 | dataOut.IonTemp_LP[i]=dataOut.EIonTemp_LP[i]=missing |
|
3163 | dataOut.IonTemp_LP[i]=dataOut.EIonTemp_LP[i]=missing | |
| 3159 | if dataOut.EPhy_LP[i]<0.0 or dataOut.EPhy_LP[i]>1.0: |
|
3164 | if dataOut.EPhy_LP[i]<0.0 or dataOut.EPhy_LP[i]>1.0: | |
| 3160 | dataOut.Phy_LP[i]=dataOut.EPhy_LP[i]=missing |
|
3165 | dataOut.Phy_LP[i]=dataOut.EPhy_LP[i]=missing | |
| 3161 |
|
3166 | |||
| 3162 | if dataOut.EPhe_LP[i]<0.0 or dataOut.EPhe_LP[i]>1.0: |
|
3167 | if dataOut.EPhe_LP[i]<0.0 or dataOut.EPhe_LP[i]>1.0: | |
| 3163 | dataOut.Phe_LP[i]=dataOut.EPhe_LP[i]=missing |
|
3168 | dataOut.Phe_LP[i]=dataOut.EPhe_LP[i]=missing | |
| 3164 | if dataOut.EDensity_LP[i]>0.0 and dataOut.Density_LP[i]>0.0 and dataOut.Density_LP[i]<9.9e6 and dataOut.EDensity_LP[i]*dataOut.Density_LP[i]<9.9e6: |
|
3169 | if dataOut.EDensity_LP[i]>0.0 and dataOut.Density_LP[i]>0.0 and dataOut.Density_LP[i]<9.9e6 and dataOut.EDensity_LP[i]*dataOut.Density_LP[i]<9.9e6: | |
| 3165 | dataOut.EDensity_LP[i]=max(dataOut.EDensity_LP[i],1000.0/dataOut.Density_LP[i]) |
|
3170 | dataOut.EDensity_LP[i]=max(dataOut.EDensity_LP[i],1000.0/dataOut.Density_LP[i]) | |
| 3166 | else: |
|
3171 | else: | |
| 3167 | dataOut.Density_LP[i]=missing |
|
3172 | dataOut.Density_LP[i]=missing | |
| 3168 | dataOut.EDensity_LP[i]=1.0 |
|
3173 | dataOut.EDensity_LP[i]=1.0 | |
| 3169 |
|
3174 | |||
| 3170 | if numpy.isnan(dataOut.Phy_LP[i]): |
|
3175 | if numpy.isnan(dataOut.Phy_LP[i]): | |
| 3171 | dataOut.EPhy_LP[i]=missing |
|
3176 | dataOut.EPhy_LP[i]=missing | |
| 3172 |
|
3177 | |||
| 3173 | if numpy.isnan(dataOut.Phe_LP[i]): |
|
3178 | if numpy.isnan(dataOut.Phe_LP[i]): | |
| 3174 | dataOut.EPhe_LP[i]=missing |
|
3179 | dataOut.EPhe_LP[i]=missing | |
| 3175 |
|
3180 | |||
| 3176 |
|
3181 | |||
| 3177 | if dataOut.ElecTemp_LP[i]==dataOut.IonTemp_LP[i]: |
|
3182 | if dataOut.ElecTemp_LP[i]==dataOut.IonTemp_LP[i]: | |
| 3178 | dataOut.EElecTemp_LP[i]=dataOut.EIonTemp_LP[i] |
|
3183 | dataOut.EElecTemp_LP[i]=dataOut.EIonTemp_LP[i] | |
| 3179 | if numpy.isnan(dataOut.ElecTemp_LP[i]): |
|
3184 | if numpy.isnan(dataOut.ElecTemp_LP[i]): | |
| 3180 | dataOut.EElecTemp_LP[i]=missing |
|
3185 | dataOut.EElecTemp_LP[i]=missing | |
| 3181 | if numpy.isnan(dataOut.IonTemp_LP[i]): |
|
3186 | if numpy.isnan(dataOut.IonTemp_LP[i]): | |
| 3182 | dataOut.EIonTemp_LP[i]=missing |
|
3187 | dataOut.EIonTemp_LP[i]=missing | |
| 3183 | if numpy.isnan(dataOut.ElecTemp_LP[i]) or numpy.isnan(dataOut.EElecTemp_LP[i]): |
|
3188 | if numpy.isnan(dataOut.ElecTemp_LP[i]) or numpy.isnan(dataOut.EElecTemp_LP[i]): | |
| 3184 | dataOut.ElecTemp_LP[i]=dataOut.EElecTemp_LP[i]=dataOut.IonTemp_LP[i]=dataOut.EIonTemp_LP[i]=missing |
|
3189 | dataOut.ElecTemp_LP[i]=dataOut.EElecTemp_LP[i]=dataOut.IonTemp_LP[i]=dataOut.EIonTemp_LP[i]=missing | |
| 3185 |
|
3190 | |||
| 3186 |
|
3191 | |||
| 3187 | dataOut.DensityFinal=numpy.reshape(numpy.concatenate((dataOut.Density_DP,dataOut.Density_LP)),(1,-1)) |
|
3192 | dataOut.DensityFinal=numpy.reshape(numpy.concatenate((dataOut.Density_DP,dataOut.Density_LP)),(1,-1)) | |
| 3188 | dataOut.EDensityFinal=numpy.reshape(numpy.concatenate((dataOut.EDensity_DP,dataOut.EDensity_LP)),(1,-1)) |
|
3193 | dataOut.EDensityFinal=numpy.reshape(numpy.concatenate((dataOut.EDensity_DP,dataOut.EDensity_LP)),(1,-1)) | |
| 3189 | dataOut.ElecTempFinal=numpy.reshape(numpy.concatenate((dataOut.ElecTemp_DP,dataOut.ElecTemp_LP)),(1,-1)) |
|
3194 | dataOut.ElecTempFinal=numpy.reshape(numpy.concatenate((dataOut.ElecTemp_DP,dataOut.ElecTemp_LP)),(1,-1)) | |
| 3190 | dataOut.EElecTempFinal=numpy.reshape(numpy.concatenate((dataOut.EElecTemp_DP,dataOut.EElecTemp_LP)),(1,-1)) |
|
3195 | dataOut.EElecTempFinal=numpy.reshape(numpy.concatenate((dataOut.EElecTemp_DP,dataOut.EElecTemp_LP)),(1,-1)) | |
| 3191 | dataOut.IonTempFinal=numpy.reshape(numpy.concatenate((dataOut.IonTemp_DP,dataOut.IonTemp_LP)),(1,-1)) |
|
3196 | dataOut.IonTempFinal=numpy.reshape(numpy.concatenate((dataOut.IonTemp_DP,dataOut.IonTemp_LP)),(1,-1)) | |
| 3192 | dataOut.EIonTempFinal=numpy.reshape(numpy.concatenate((dataOut.EIonTemp_DP,dataOut.EIonTemp_LP)),(1,-1)) |
|
3197 | dataOut.EIonTempFinal=numpy.reshape(numpy.concatenate((dataOut.EIonTemp_DP,dataOut.EIonTemp_LP)),(1,-1)) | |
| 3193 | dataOut.PhyFinal=numpy.reshape(numpy.concatenate((dataOut.Phy_DP,dataOut.Phy_LP)),(1,-1)) |
|
3198 | dataOut.PhyFinal=numpy.reshape(numpy.concatenate((dataOut.Phy_DP,dataOut.Phy_LP)),(1,-1)) | |
| 3194 | dataOut.EPhyFinal=numpy.reshape(numpy.concatenate((dataOut.EPhy_DP,dataOut.EPhy_LP)),(1,-1)) |
|
3199 | dataOut.EPhyFinal=numpy.reshape(numpy.concatenate((dataOut.EPhy_DP,dataOut.EPhy_LP)),(1,-1)) | |
| 3195 | dataOut.PheFinal=numpy.reshape(numpy.concatenate((dataOut.Phe_DP,dataOut.Phe_LP)),(1,-1)) |
|
3200 | dataOut.PheFinal=numpy.reshape(numpy.concatenate((dataOut.Phe_DP,dataOut.Phe_LP)),(1,-1)) | |
| 3196 | dataOut.EPheFinal=numpy.reshape(numpy.concatenate((dataOut.EPhe_DP,dataOut.EPhe_LP)),(1,-1)) |
|
3201 | dataOut.EPheFinal=numpy.reshape(numpy.concatenate((dataOut.EPhe_DP,dataOut.EPhe_LP)),(1,-1)) | |
| 3197 |
|
3202 | |||
| 3198 | nan_array_2=numpy.empty(dataOut.NACF-dataOut.NDP) |
|
3203 | nan_array_2=numpy.empty(dataOut.NACF-dataOut.NDP) | |
| 3199 | nan_array_2[:]=numpy.nan |
|
3204 | nan_array_2[:]=numpy.nan | |
| 3200 |
|
3205 | |||
| 3201 | dataOut.acfs_DP=numpy.zeros((dataOut.NACF,dataOut.DPL),'float32') |
|
3206 | dataOut.acfs_DP=numpy.zeros((dataOut.NACF,dataOut.DPL),'float32') | |
| 3202 | dataOut.acfs_error_DP=numpy.zeros((dataOut.NACF,dataOut.DPL),'float32') |
|
3207 | dataOut.acfs_error_DP=numpy.zeros((dataOut.NACF,dataOut.DPL),'float32') | |
| 3203 | acfs_dp_aux=dataOut.acfs_to_save.transpose() |
|
3208 | acfs_dp_aux=dataOut.acfs_to_save.transpose() | |
| 3204 | acfs_error_dp_aux=dataOut.acfs_error_to_save.transpose() |
|
3209 | acfs_error_dp_aux=dataOut.acfs_error_to_save.transpose() | |
| 3205 | for i in range(dataOut.DPL): |
|
3210 | for i in range(dataOut.DPL): | |
| 3206 | dataOut.acfs_DP[:,i]=numpy.concatenate((acfs_dp_aux[:,i],nan_array_2)) |
|
3211 | dataOut.acfs_DP[:,i]=numpy.concatenate((acfs_dp_aux[:,i],nan_array_2)) | |
| 3207 | dataOut.acfs_error_DP[:,i]=numpy.concatenate((acfs_error_dp_aux[:,i],nan_array_2)) |
|
3212 | dataOut.acfs_error_DP[:,i]=numpy.concatenate((acfs_error_dp_aux[:,i],nan_array_2)) | |
| 3208 | dataOut.acfs_DP=dataOut.acfs_DP.transpose() |
|
3213 | dataOut.acfs_DP=dataOut.acfs_DP.transpose() | |
| 3209 | dataOut.acfs_error_DP=dataOut.acfs_error_DP.transpose() |
|
3214 | dataOut.acfs_error_DP=dataOut.acfs_error_DP.transpose() | |
| 3210 |
|
3215 | |||
| 3211 | dataOut.acfs_LP=numpy.zeros((dataOut.NACF,dataOut.IBITS),'float32') |
|
3216 | dataOut.acfs_LP=numpy.zeros((dataOut.NACF,dataOut.IBITS),'float32') | |
| 3212 | dataOut.acfs_error_LP=numpy.zeros((dataOut.NACF,dataOut.IBITS),'float32') |
|
3217 | dataOut.acfs_error_LP=numpy.zeros((dataOut.NACF,dataOut.IBITS),'float32') | |
| 3213 |
|
3218 | |||
| 3214 | for i in range(dataOut.NACF): |
|
3219 | for i in range(dataOut.NACF): | |
| 3215 | for j in range(dataOut.IBITS): |
|
3220 | for j in range(dataOut.IBITS): | |
| 3216 | if numpy.abs(dataOut.errors[j,i]/dataOut.output_LP_integrated.real[0,i,0])<1.0: |
|
3221 | if numpy.abs(dataOut.errors[j,i]/dataOut.output_LP_integrated.real[0,i,0])<1.0: | |
| 3217 | dataOut.acfs_LP[i,j]=dataOut.output_LP_integrated.real[j,i,0]/dataOut.output_LP_integrated.real[0,i,0] |
|
3222 | dataOut.acfs_LP[i,j]=dataOut.output_LP_integrated.real[j,i,0]/dataOut.output_LP_integrated.real[0,i,0] | |
| 3218 | dataOut.acfs_LP[i,j]=max(min(dataOut.acfs_LP[i,j],1.0),-1.0) |
|
3223 | dataOut.acfs_LP[i,j]=max(min(dataOut.acfs_LP[i,j],1.0),-1.0) | |
| 3219 |
|
3224 | |||
| 3220 | dataOut.acfs_error_LP[i,j]=dataOut.errors[j,i]/dataOut.output_LP_integrated.real[0,i,0] |
|
3225 | dataOut.acfs_error_LP[i,j]=dataOut.errors[j,i]/dataOut.output_LP_integrated.real[0,i,0] | |
| 3221 | else: |
|
3226 | else: | |
| 3222 | dataOut.acfs_LP[i,j]=numpy.nan |
|
3227 | dataOut.acfs_LP[i,j]=numpy.nan | |
| 3223 |
|
3228 | |||
| 3224 | dataOut.acfs_error_LP[i,j]=numpy.nan |
|
3229 | dataOut.acfs_error_LP[i,j]=numpy.nan | |
| 3225 |
|
3230 | |||
| 3226 | dataOut.acfs_LP=dataOut.acfs_LP.transpose() |
|
3231 | dataOut.acfs_LP=dataOut.acfs_LP.transpose() | |
| 3227 | dataOut.acfs_error_LP=dataOut.acfs_error_LP.transpose() |
|
3232 | dataOut.acfs_error_LP=dataOut.acfs_error_LP.transpose() | |
| 3228 |
|
3233 | |||
| 3229 | dataOut.DensityFinal *= 1.e6 #Convert units to m^⁻3 |
|
3234 | dataOut.DensityFinal *= 1.e6 #Convert units to m^⁻3 | |
| 3230 | dataOut.EDensityFinal *= 1.e6 #Convert units to m^⁻3 |
|
3235 | dataOut.EDensityFinal *= 1.e6 #Convert units to m^⁻3 | |
| 3231 |
|
3236 | |||
| 3232 | return dataOut |
|
3237 | return dataOut | |
| 3233 |
|
3238 | |||
| 3234 |
|
3239 | |||
| 3235 | class ACFs(Operation): |
|
3240 | class ACFs(Operation): | |
| 3236 | ''' |
|
3241 | ''' | |
| 3237 | Written by R. Flores |
|
3242 | Written by R. Flores | |
| 3238 | ''' |
|
3243 | ''' | |
| 3239 | def __init__(self, **kwargs): |
|
3244 | def __init__(self, **kwargs): | |
| 3240 |
|
3245 | |||
| 3241 | Operation.__init__(self, **kwargs) |
|
3246 | Operation.__init__(self, **kwargs) | |
| 3242 |
|
3247 | |||
| 3243 | self.aux=1 |
|
3248 | self.aux=1 | |
| 3244 |
|
3249 | |||
| 3245 | def run(self,dataOut): |
|
3250 | def run(self,dataOut): | |
| 3246 |
|
3251 | |||
| 3247 | if self.aux: |
|
3252 | if self.aux: | |
| 3248 | self.taup=numpy.zeros(dataOut.DPL,'float32') |
|
3253 | self.taup=numpy.zeros(dataOut.DPL,'float32') | |
| 3249 | self.pacf=numpy.zeros(dataOut.DPL,'float32') |
|
3254 | self.pacf=numpy.zeros(dataOut.DPL,'float32') | |
| 3250 | self.sacf=numpy.zeros(dataOut.DPL,'float32') |
|
3255 | self.sacf=numpy.zeros(dataOut.DPL,'float32') | |
| 3251 |
|
3256 | |||
| 3252 | self.taup_full=numpy.zeros(dataOut.DPL,'float32') |
|
3257 | self.taup_full=numpy.zeros(dataOut.DPL,'float32') | |
| 3253 | self.pacf_full=numpy.zeros(dataOut.DPL,'float32') |
|
3258 | self.pacf_full=numpy.zeros(dataOut.DPL,'float32') | |
| 3254 | self.sacf_full=numpy.zeros(dataOut.DPL,'float32') |
|
3259 | self.sacf_full=numpy.zeros(dataOut.DPL,'float32') | |
| 3255 | self.x_igcej=numpy.zeros(dataOut.DPL,'float32') |
|
3260 | self.x_igcej=numpy.zeros(dataOut.DPL,'float32') | |
| 3256 | self.y_igcej=numpy.zeros(dataOut.DPL,'float32') |
|
3261 | self.y_igcej=numpy.zeros(dataOut.DPL,'float32') | |
| 3257 | self.x_ibad=numpy.zeros(dataOut.DPL,'float32') |
|
3262 | self.x_ibad=numpy.zeros(dataOut.DPL,'float32') | |
| 3258 | self.y_ibad=numpy.zeros(dataOut.DPL,'float32') |
|
3263 | self.y_ibad=numpy.zeros(dataOut.DPL,'float32') | |
| 3259 | self.aux=0 |
|
3264 | self.aux=0 | |
| 3260 |
|
3265 | |||
| 3261 | dataOut.acfs_to_plot=numpy.zeros((dataOut.NDP,dataOut.DPL),'float32') |
|
3266 | dataOut.acfs_to_plot=numpy.zeros((dataOut.NDP,dataOut.DPL),'float32') | |
| 3262 | dataOut.acfs_to_save=numpy.zeros((dataOut.NDP,dataOut.DPL),'float32') |
|
3267 | dataOut.acfs_to_save=numpy.zeros((dataOut.NDP,dataOut.DPL),'float32') | |
| 3263 | dataOut.acfs_error_to_plot=numpy.zeros((dataOut.NDP,dataOut.DPL),'float32') |
|
3268 | dataOut.acfs_error_to_plot=numpy.zeros((dataOut.NDP,dataOut.DPL),'float32') | |
| 3264 | dataOut.acfs_error_to_save=numpy.zeros((dataOut.NDP,dataOut.DPL),'float32') |
|
3269 | dataOut.acfs_error_to_save=numpy.zeros((dataOut.NDP,dataOut.DPL),'float32') | |
| 3265 | dataOut.lags_to_plot=numpy.zeros((dataOut.NDP,dataOut.DPL),'float32') |
|
3270 | dataOut.lags_to_plot=numpy.zeros((dataOut.NDP,dataOut.DPL),'float32') | |
| 3266 | dataOut.x_igcej_to_plot=numpy.zeros((dataOut.NDP,dataOut.DPL),'float32') |
|
3271 | dataOut.x_igcej_to_plot=numpy.zeros((dataOut.NDP,dataOut.DPL),'float32') | |
| 3267 | dataOut.x_ibad_to_plot=numpy.zeros((dataOut.NDP,dataOut.DPL),'float32') |
|
3272 | dataOut.x_ibad_to_plot=numpy.zeros((dataOut.NDP,dataOut.DPL),'float32') | |
| 3268 | dataOut.y_igcej_to_plot=numpy.zeros((dataOut.NDP,dataOut.DPL),'float32') |
|
3273 | dataOut.y_igcej_to_plot=numpy.zeros((dataOut.NDP,dataOut.DPL),'float32') | |
| 3269 | dataOut.y_ibad_to_plot=numpy.zeros((dataOut.NDP,dataOut.DPL),'float32') |
|
3274 | dataOut.y_ibad_to_plot=numpy.zeros((dataOut.NDP,dataOut.DPL),'float32') | |
| 3270 |
|
3275 | |||
| 3271 | for i in range(dataOut.NSHTS): |
|
3276 | for i in range(dataOut.NSHTS): | |
| 3272 |
|
3277 | |||
| 3273 | acfm=dataOut.rhor[i][0]**2+dataOut.rhoi[i][0]**2 |
|
3278 | acfm=dataOut.rhor[i][0]**2+dataOut.rhoi[i][0]**2 | |
| 3274 |
|
3279 | |||
| 3275 | if acfm>0: |
|
3280 | if acfm>0: | |
| 3276 | cc=dataOut.rhor[i][0]/acfm |
|
3281 | cc=dataOut.rhor[i][0]/acfm | |
| 3277 | ss=dataOut.rhoi[i][0]/acfm |
|
3282 | ss=dataOut.rhoi[i][0]/acfm | |
| 3278 | else: |
|
3283 | else: | |
| 3279 | cc=1. |
|
3284 | cc=1. | |
| 3280 | ss=0. |
|
3285 | ss=0. | |
| 3281 |
|
3286 | |||
| 3282 | # keep only uncontaminated data |
|
3287 | # keep only uncontaminated data | |
| 3283 | for l in range(dataOut.DPL): |
|
3288 | for l in range(dataOut.DPL): | |
| 3284 | fact=dataOut.DH |
|
3289 | fact=dataOut.DH | |
| 3285 | if (dataOut.igcej[i][l]==0 and dataOut.ibad[i][l]==0): |
|
3290 | if (dataOut.igcej[i][l]==0 and dataOut.ibad[i][l]==0): | |
| 3286 |
|
3291 | |||
| 3287 | self.pacf_full[l]=min(1.0,max(-1.0,(dataOut.rhor[i][l]*cc + dataOut.rhoi[i][l]*ss)))*fact+dataOut.range1[i] |
|
3292 | self.pacf_full[l]=min(1.0,max(-1.0,(dataOut.rhor[i][l]*cc + dataOut.rhoi[i][l]*ss)))*fact+dataOut.range1[i] | |
| 3288 | self.sacf_full[l]=min(1.0,numpy.sqrt(dataOut.sd[i][l]))*fact |
|
3293 | self.sacf_full[l]=min(1.0,numpy.sqrt(dataOut.sd[i][l]))*fact | |
| 3289 | self.taup_full[l]=dataOut.alag[l] |
|
3294 | self.taup_full[l]=dataOut.alag[l] | |
| 3290 | self.x_igcej[l]=numpy.nan |
|
3295 | self.x_igcej[l]=numpy.nan | |
| 3291 | self.y_igcej[l]=numpy.nan |
|
3296 | self.y_igcej[l]=numpy.nan | |
| 3292 | self.x_ibad[l]=numpy.nan |
|
3297 | self.x_ibad[l]=numpy.nan | |
| 3293 | self.y_ibad[l]=numpy.nan |
|
3298 | self.y_ibad[l]=numpy.nan | |
| 3294 |
|
3299 | |||
| 3295 | else: |
|
3300 | else: | |
| 3296 | self.pacf_full[l]=numpy.nan |
|
3301 | self.pacf_full[l]=numpy.nan | |
| 3297 | self.sacf_full[l]=numpy.nan |
|
3302 | self.sacf_full[l]=numpy.nan | |
| 3298 | self.taup_full[l]=numpy.nan |
|
3303 | self.taup_full[l]=numpy.nan | |
| 3299 |
|
3304 | |||
| 3300 | if dataOut.igcej[i][l]: |
|
3305 | if dataOut.igcej[i][l]: | |
| 3301 | self.x_igcej[l]=dataOut.alag[l] |
|
3306 | self.x_igcej[l]=dataOut.alag[l] | |
| 3302 | self.y_igcej[l]=dataOut.range1[i] |
|
3307 | self.y_igcej[l]=dataOut.range1[i] | |
| 3303 | self.x_ibad[l]=numpy.nan |
|
3308 | self.x_ibad[l]=numpy.nan | |
| 3304 | self.y_ibad[l]=numpy.nan |
|
3309 | self.y_ibad[l]=numpy.nan | |
| 3305 |
|
3310 | |||
| 3306 | if dataOut.ibad[i][l]: |
|
3311 | if dataOut.ibad[i][l]: | |
| 3307 | self.x_igcej[l]=numpy.nan |
|
3312 | self.x_igcej[l]=numpy.nan | |
| 3308 | self.y_igcej[l]=numpy.nan |
|
3313 | self.y_igcej[l]=numpy.nan | |
| 3309 | self.x_ibad[l]=dataOut.alag[l] |
|
3314 | self.x_ibad[l]=dataOut.alag[l] | |
| 3310 | self.y_ibad[l]=dataOut.range1[i] |
|
3315 | self.y_ibad[l]=dataOut.range1[i] | |
| 3311 |
|
3316 | |||
| 3312 | pacf_new=numpy.copy((self.pacf_full-dataOut.range1[i])/dataOut.DH) |
|
3317 | pacf_new=numpy.copy((self.pacf_full-dataOut.range1[i])/dataOut.DH) | |
| 3313 | sacf_new=numpy.copy(self.sacf_full/dataOut.DH) |
|
3318 | sacf_new=numpy.copy(self.sacf_full/dataOut.DH) | |
| 3314 | dataOut.acfs_to_save[i,:]=numpy.copy(pacf_new) |
|
3319 | dataOut.acfs_to_save[i,:]=numpy.copy(pacf_new) | |
| 3315 | dataOut.acfs_error_to_save[i,:]=numpy.copy(sacf_new) |
|
3320 | dataOut.acfs_error_to_save[i,:]=numpy.copy(sacf_new) | |
| 3316 | dataOut.acfs_to_plot[i,:]=numpy.copy(self.pacf_full) |
|
3321 | dataOut.acfs_to_plot[i,:]=numpy.copy(self.pacf_full) | |
| 3317 | dataOut.acfs_error_to_plot[i,:]=numpy.copy(self.sacf_full) |
|
3322 | dataOut.acfs_error_to_plot[i,:]=numpy.copy(self.sacf_full) | |
| 3318 | dataOut.lags_to_plot[i,:]=numpy.copy(self.taup_full) |
|
3323 | dataOut.lags_to_plot[i,:]=numpy.copy(self.taup_full) | |
| 3319 | dataOut.x_igcej_to_plot[i,:]=numpy.copy(self.x_igcej) |
|
3324 | dataOut.x_igcej_to_plot[i,:]=numpy.copy(self.x_igcej) | |
| 3320 | dataOut.x_ibad_to_plot[i,:]=numpy.copy(self.x_ibad) |
|
3325 | dataOut.x_ibad_to_plot[i,:]=numpy.copy(self.x_ibad) | |
| 3321 | dataOut.y_igcej_to_plot[i,:]=numpy.copy(self.y_igcej) |
|
3326 | dataOut.y_igcej_to_plot[i,:]=numpy.copy(self.y_igcej) | |
| 3322 | dataOut.y_ibad_to_plot[i,:]=numpy.copy(self.y_ibad) |
|
3327 | dataOut.y_ibad_to_plot[i,:]=numpy.copy(self.y_ibad) | |
| 3323 |
|
3328 | |||
| 3324 | missing=numpy.nan#-32767 |
|
3329 | missing=numpy.nan#-32767 | |
| 3325 |
|
3330 | |||
| 3326 | for i in range(dataOut.NSHTS,dataOut.NDP): |
|
3331 | for i in range(dataOut.NSHTS,dataOut.NDP): | |
| 3327 | for j in range(dataOut.DPL): |
|
3332 | for j in range(dataOut.DPL): | |
| 3328 | dataOut.acfs_to_save[i,j]=missing |
|
3333 | dataOut.acfs_to_save[i,j]=missing | |
| 3329 | dataOut.acfs_error_to_save[i,j]=missing |
|
3334 | dataOut.acfs_error_to_save[i,j]=missing | |
| 3330 | dataOut.acfs_to_plot[i,j]=missing |
|
3335 | dataOut.acfs_to_plot[i,j]=missing | |
| 3331 | dataOut.acfs_error_to_plot[i,j]=missing |
|
3336 | dataOut.acfs_error_to_plot[i,j]=missing | |
| 3332 | dataOut.lags_to_plot[i,j]=missing |
|
3337 | dataOut.lags_to_plot[i,j]=missing | |
| 3333 | dataOut.x_igcej_to_plot[i,j]=missing |
|
3338 | dataOut.x_igcej_to_plot[i,j]=missing | |
| 3334 | dataOut.x_ibad_to_plot[i,j]=missing |
|
3339 | dataOut.x_ibad_to_plot[i,j]=missing | |
| 3335 | dataOut.y_igcej_to_plot[i,j]=missing |
|
3340 | dataOut.y_igcej_to_plot[i,j]=missing | |
| 3336 | dataOut.y_ibad_to_plot[i,j]=missing |
|
3341 | dataOut.y_ibad_to_plot[i,j]=missing | |
| 3337 |
|
3342 | |||
| 3338 | dataOut.acfs_to_save=dataOut.acfs_to_save.transpose() |
|
3343 | dataOut.acfs_to_save=dataOut.acfs_to_save.transpose() | |
| 3339 | dataOut.acfs_error_to_save=dataOut.acfs_error_to_save.transpose() |
|
3344 | dataOut.acfs_error_to_save=dataOut.acfs_error_to_save.transpose() | |
| 3340 |
|
3345 | |||
| 3341 | return dataOut |
|
3346 | return dataOut | |
| 3342 |
|
3347 | |||
| 3343 |
|
3348 | |||
| 3344 | class CohInt(Operation): |
|
3349 | class CohInt(Operation): | |
| 3345 |
|
3350 | |||
| 3346 | isConfig = False |
|
3351 | isConfig = False | |
| 3347 | __profIndex = 0 |
|
3352 | __profIndex = 0 | |
| 3348 | __byTime = False |
|
3353 | __byTime = False | |
| 3349 | __initime = None |
|
3354 | __initime = None | |
| 3350 | __lastdatatime = None |
|
3355 | __lastdatatime = None | |
| 3351 | __integrationtime = None |
|
3356 | __integrationtime = None | |
| 3352 | __buffer = None |
|
3357 | __buffer = None | |
| 3353 | __bufferStride = [] |
|
3358 | __bufferStride = [] | |
| 3354 | __dataReady = False |
|
3359 | __dataReady = False | |
| 3355 | __profIndexStride = 0 |
|
3360 | __profIndexStride = 0 | |
| 3356 | __dataToPutStride = False |
|
3361 | __dataToPutStride = False | |
| 3357 | n = None |
|
3362 | n = None | |
| 3358 |
|
3363 | |||
| 3359 | def __init__(self, **kwargs): |
|
3364 | def __init__(self, **kwargs): | |
| 3360 |
|
3365 | |||
| 3361 | Operation.__init__(self, **kwargs) |
|
3366 | Operation.__init__(self, **kwargs) | |
| 3362 |
|
3367 | |||
| 3363 | # self.isConfig = False |
|
3368 | # self.isConfig = False | |
| 3364 |
|
3369 | |||
| 3365 | def setup(self, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False): |
|
3370 | def setup(self, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False): | |
| 3366 | """ |
|
3371 | """ | |
| 3367 | Set the parameters of the integration class. |
|
3372 | Set the parameters of the integration class. | |
| 3368 |
|
3373 | |||
| 3369 | Inputs: |
|
3374 | Inputs: | |
| 3370 |
|
3375 | |||
| 3371 | n : Number of coherent integrations |
|
3376 | n : Number of coherent integrations | |
| 3372 | timeInterval : Time of integration. If the parameter "n" is selected this one does not work |
|
3377 | timeInterval : Time of integration. If the parameter "n" is selected this one does not work | |
| 3373 | overlapping : |
|
3378 | overlapping : | |
| 3374 | """ |
|
3379 | """ | |
| 3375 |
|
3380 | |||
| 3376 | self.__initime = None |
|
3381 | self.__initime = None | |
| 3377 | self.__lastdatatime = 0 |
|
3382 | self.__lastdatatime = 0 | |
| 3378 | self.__buffer = None |
|
3383 | self.__buffer = None | |
| 3379 | self.__dataReady = False |
|
3384 | self.__dataReady = False | |
| 3380 | self.byblock = byblock |
|
3385 | self.byblock = byblock | |
| 3381 | self.stride = stride |
|
3386 | self.stride = stride | |
| 3382 |
|
3387 | |||
| 3383 | if n == None and timeInterval == None: |
|
3388 | if n == None and timeInterval == None: | |
| 3384 | raise ValueError("n or timeInterval should be specified ...") |
|
3389 | raise ValueError("n or timeInterval should be specified ...") | |
| 3385 |
|
3390 | |||
| 3386 | if n != None: |
|
3391 | if n != None: | |
| 3387 | self.n = n |
|
3392 | self.n = n | |
| 3388 | self.__byTime = False |
|
3393 | self.__byTime = False | |
| 3389 | else: |
|
3394 | else: | |
| 3390 | self.__integrationtime = timeInterval #* 60. #if (type(timeInterval)!=integer) -> change this line |
|
3395 | self.__integrationtime = timeInterval #* 60. #if (type(timeInterval)!=integer) -> change this line | |
| 3391 | self.n = 9999 |
|
3396 | self.n = 9999 | |
| 3392 | self.__byTime = True |
|
3397 | self.__byTime = True | |
| 3393 |
|
3398 | |||
| 3394 | if overlapping: |
|
3399 | if overlapping: | |
| 3395 | self.__withOverlapping = True |
|
3400 | self.__withOverlapping = True | |
| 3396 | self.__buffer = None |
|
3401 | self.__buffer = None | |
| 3397 | else: |
|
3402 | else: | |
| 3398 | self.__withOverlapping = False |
|
3403 | self.__withOverlapping = False | |
| 3399 | self.__buffer = 0 |
|
3404 | self.__buffer = 0 | |
| 3400 |
|
3405 | |||
| 3401 | self.__profIndex = 0 |
|
3406 | self.__profIndex = 0 | |
| 3402 |
|
3407 | |||
| 3403 | def putData(self, data): |
|
3408 | def putData(self, data): | |
| 3404 |
|
3409 | |||
| 3405 | """ |
|
3410 | """ | |
| 3406 | Add a profile to the __buffer and increase in one the __profileIndex |
|
3411 | Add a profile to the __buffer and increase in one the __profileIndex | |
| 3407 |
|
3412 | |||
| 3408 | """ |
|
3413 | """ | |
| 3409 |
|
3414 | |||
| 3410 | if not self.__withOverlapping: |
|
3415 | if not self.__withOverlapping: | |
| 3411 | self.__buffer += data.copy() |
|
3416 | self.__buffer += data.copy() | |
| 3412 | self.__profIndex += 1 |
|
3417 | self.__profIndex += 1 | |
| 3413 | return |
|
3418 | return | |
| 3414 |
|
3419 | |||
| 3415 | #Overlapping data |
|
3420 | #Overlapping data | |
| 3416 | nChannels, nHeis = data.shape |
|
3421 | nChannels, nHeis = data.shape | |
| 3417 | data = numpy.reshape(data, (1, nChannels, nHeis)) |
|
3422 | data = numpy.reshape(data, (1, nChannels, nHeis)) | |
| 3418 |
|
3423 | |||
| 3419 | #If the buffer is empty then it takes the data value |
|
3424 | #If the buffer is empty then it takes the data value | |
| 3420 | if self.__buffer is None: |
|
3425 | if self.__buffer is None: | |
| 3421 | self.__buffer = data |
|
3426 | self.__buffer = data | |
| 3422 | self.__profIndex += 1 |
|
3427 | self.__profIndex += 1 | |
| 3423 | return |
|
3428 | return | |
| 3424 |
|
3429 | |||
| 3425 | #If the buffer length is lower than n then stakcing the data value |
|
3430 | #If the buffer length is lower than n then stakcing the data value | |
| 3426 | if self.__profIndex < self.n: |
|
3431 | if self.__profIndex < self.n: | |
| 3427 | self.__buffer = numpy.vstack((self.__buffer, data)) |
|
3432 | self.__buffer = numpy.vstack((self.__buffer, data)) | |
| 3428 | self.__profIndex += 1 |
|
3433 | self.__profIndex += 1 | |
| 3429 | return |
|
3434 | return | |
| 3430 |
|
3435 | |||
| 3431 | #If the buffer length is equal to n then replacing the last buffer value with the data value |
|
3436 | #If the buffer length is equal to n then replacing the last buffer value with the data value | |
| 3432 | self.__buffer = numpy.roll(self.__buffer, -1, axis=0) |
|
3437 | self.__buffer = numpy.roll(self.__buffer, -1, axis=0) | |
| 3433 | self.__buffer[self.n-1] = data |
|
3438 | self.__buffer[self.n-1] = data | |
| 3434 | self.__profIndex = self.n |
|
3439 | self.__profIndex = self.n | |
| 3435 | return |
|
3440 | return | |
| 3436 |
|
3441 | |||
| 3437 |
|
3442 | |||
| 3438 | def pushData(self): |
|
3443 | def pushData(self): | |
| 3439 | """ |
|
3444 | """ | |
| 3440 | Return the sum of the last profiles and the profiles used in the sum. |
|
3445 | Return the sum of the last profiles and the profiles used in the sum. | |
| 3441 |
|
3446 | |||
| 3442 | Affected: |
|
3447 | Affected: | |
| 3443 |
|
3448 | |||
| 3444 | self.__profileIndex |
|
3449 | self.__profileIndex | |
| 3445 |
|
3450 | |||
| 3446 | """ |
|
3451 | """ | |
| 3447 |
|
3452 | |||
| 3448 | if not self.__withOverlapping: |
|
3453 | if not self.__withOverlapping: | |
| 3449 | data = self.__buffer |
|
3454 | data = self.__buffer | |
| 3450 | n = self.__profIndex |
|
3455 | n = self.__profIndex | |
| 3451 |
|
3456 | |||
| 3452 | self.__buffer = 0 |
|
3457 | self.__buffer = 0 | |
| 3453 | self.__profIndex = 0 |
|
3458 | self.__profIndex = 0 | |
| 3454 |
|
3459 | |||
| 3455 | return data, n |
|
3460 | return data, n | |
| 3456 |
|
3461 | |||
| 3457 | #Integration with Overlapping |
|
3462 | #Integration with Overlapping | |
| 3458 | data = numpy.sum(self.__buffer, axis=0) |
|
3463 | data = numpy.sum(self.__buffer, axis=0) | |
| 3459 | # print data |
|
3464 | # print data | |
| 3460 | # raise |
|
3465 | # raise | |
| 3461 | n = self.__profIndex |
|
3466 | n = self.__profIndex | |
| 3462 |
|
3467 | |||
| 3463 | return data, n |
|
3468 | return data, n | |
| 3464 |
|
3469 | |||
| 3465 | def byProfiles(self, data): |
|
3470 | def byProfiles(self, data): | |
| 3466 |
|
3471 | |||
| 3467 | self.__dataReady = False |
|
3472 | self.__dataReady = False | |
| 3468 | avgdata = None |
|
3473 | avgdata = None | |
| 3469 | # n = None |
|
3474 | # n = None | |
| 3470 | # print data |
|
3475 | # print data | |
| 3471 | # raise |
|
3476 | # raise | |
| 3472 | self.putData(data) |
|
3477 | self.putData(data) | |
| 3473 |
|
3478 | |||
| 3474 | if self.__profIndex == self.n: |
|
3479 | if self.__profIndex == self.n: | |
| 3475 | avgdata, n = self.pushData() |
|
3480 | avgdata, n = self.pushData() | |
| 3476 | self.__dataReady = True |
|
3481 | self.__dataReady = True | |
| 3477 |
|
3482 | |||
| 3478 | return avgdata |
|
3483 | return avgdata | |
| 3479 |
|
3484 | |||
| 3480 | def byTime(self, data, datatime): |
|
3485 | def byTime(self, data, datatime): | |
| 3481 |
|
3486 | |||
| 3482 | self.__dataReady = False |
|
3487 | self.__dataReady = False | |
| 3483 | avgdata = None |
|
3488 | avgdata = None | |
| 3484 | n = None |
|
3489 | n = None | |
| 3485 |
|
3490 | |||
| 3486 | self.putData(data) |
|
3491 | self.putData(data) | |
| 3487 |
|
3492 | |||
| 3488 | if (datatime - self.__initime) >= self.__integrationtime: |
|
3493 | if (datatime - self.__initime) >= self.__integrationtime: | |
| 3489 | avgdata, n = self.pushData() |
|
3494 | avgdata, n = self.pushData() | |
| 3490 | self.n = n |
|
3495 | self.n = n | |
| 3491 | self.__dataReady = True |
|
3496 | self.__dataReady = True | |
| 3492 |
|
3497 | |||
| 3493 | return avgdata |
|
3498 | return avgdata | |
| 3494 |
|
3499 | |||
| 3495 | def integrateByStride(self, data, datatime): |
|
3500 | def integrateByStride(self, data, datatime): | |
| 3496 | # print data |
|
3501 | # print data | |
| 3497 | if self.__profIndex == 0: |
|
3502 | if self.__profIndex == 0: | |
| 3498 | self.__buffer = [[data.copy(), datatime]] |
|
3503 | self.__buffer = [[data.copy(), datatime]] | |
| 3499 | else: |
|
3504 | else: | |
| 3500 | self.__buffer.append([data.copy(),datatime]) |
|
3505 | self.__buffer.append([data.copy(),datatime]) | |
| 3501 | self.__profIndex += 1 |
|
3506 | self.__profIndex += 1 | |
| 3502 | self.__dataReady = False |
|
3507 | self.__dataReady = False | |
| 3503 |
|
3508 | |||
| 3504 | if self.__profIndex == self.n * self.stride : |
|
3509 | if self.__profIndex == self.n * self.stride : | |
| 3505 | self.__dataToPutStride = True |
|
3510 | self.__dataToPutStride = True | |
| 3506 | self.__profIndexStride = 0 |
|
3511 | self.__profIndexStride = 0 | |
| 3507 | self.__profIndex = 0 |
|
3512 | self.__profIndex = 0 | |
| 3508 | self.__bufferStride = [] |
|
3513 | self.__bufferStride = [] | |
| 3509 | for i in range(self.stride): |
|
3514 | for i in range(self.stride): | |
| 3510 | current = self.__buffer[i::self.stride] |
|
3515 | current = self.__buffer[i::self.stride] | |
| 3511 | data = numpy.sum([t[0] for t in current], axis=0) |
|
3516 | data = numpy.sum([t[0] for t in current], axis=0) | |
| 3512 | avgdatatime = numpy.average([t[1] for t in current]) |
|
3517 | avgdatatime = numpy.average([t[1] for t in current]) | |
| 3513 | # print data |
|
3518 | # print data | |
| 3514 | self.__bufferStride.append((data, avgdatatime)) |
|
3519 | self.__bufferStride.append((data, avgdatatime)) | |
| 3515 |
|
3520 | |||
| 3516 | if self.__dataToPutStride: |
|
3521 | if self.__dataToPutStride: | |
| 3517 | self.__dataReady = True |
|
3522 | self.__dataReady = True | |
| 3518 | self.__profIndexStride += 1 |
|
3523 | self.__profIndexStride += 1 | |
| 3519 | if self.__profIndexStride == self.stride: |
|
3524 | if self.__profIndexStride == self.stride: | |
| 3520 | self.__dataToPutStride = False |
|
3525 | self.__dataToPutStride = False | |
| 3521 | # print self.__bufferStride[self.__profIndexStride - 1] |
|
3526 | # print self.__bufferStride[self.__profIndexStride - 1] | |
| 3522 | # raise |
|
3527 | # raise | |
| 3523 | return self.__bufferStride[self.__profIndexStride - 1] |
|
3528 | return self.__bufferStride[self.__profIndexStride - 1] | |
| 3524 |
|
3529 | |||
| 3525 |
|
3530 | |||
| 3526 | return None, None |
|
3531 | return None, None | |
| 3527 |
|
3532 | |||
| 3528 | def integrate(self, data, datatime=None): |
|
3533 | def integrate(self, data, datatime=None): | |
| 3529 |
|
3534 | |||
| 3530 | if self.__initime == None: |
|
3535 | if self.__initime == None: | |
| 3531 | self.__initime = datatime |
|
3536 | self.__initime = datatime | |
| 3532 |
|
3537 | |||
| 3533 | if self.__byTime: |
|
3538 | if self.__byTime: | |
| 3534 | avgdata = self.byTime(data, datatime) |
|
3539 | avgdata = self.byTime(data, datatime) | |
| 3535 | else: |
|
3540 | else: | |
| 3536 | avgdata = self.byProfiles(data) |
|
3541 | avgdata = self.byProfiles(data) | |
| 3537 |
|
3542 | |||
| 3538 |
|
3543 | |||
| 3539 | self.__lastdatatime = datatime |
|
3544 | self.__lastdatatime = datatime | |
| 3540 |
|
3545 | |||
| 3541 | if avgdata is None: |
|
3546 | if avgdata is None: | |
| 3542 | return None, None |
|
3547 | return None, None | |
| 3543 |
|
3548 | |||
| 3544 | avgdatatime = self.__initime |
|
3549 | avgdatatime = self.__initime | |
| 3545 |
|
3550 | |||
| 3546 | deltatime = datatime - self.__lastdatatime |
|
3551 | deltatime = datatime - self.__lastdatatime | |
| 3547 |
|
3552 | |||
| 3548 | if not self.__withOverlapping: |
|
3553 | if not self.__withOverlapping: | |
| 3549 | self.__initime = datatime |
|
3554 | self.__initime = datatime | |
| 3550 | else: |
|
3555 | else: | |
| 3551 | self.__initime += deltatime |
|
3556 | self.__initime += deltatime | |
| 3552 |
|
3557 | |||
| 3553 | return avgdata, avgdatatime |
|
3558 | return avgdata, avgdatatime | |
| 3554 |
|
3559 | |||
| 3555 | def integrateByBlock(self, dataOut): |
|
3560 | def integrateByBlock(self, dataOut): | |
| 3556 |
|
3561 | |||
| 3557 | times = int(dataOut.data.shape[1]/self.n) |
|
3562 | times = int(dataOut.data.shape[1]/self.n) | |
| 3558 | avgdata = numpy.zeros((dataOut.nChannels, times, dataOut.nHeights), dtype=complex) |
|
3563 | avgdata = numpy.zeros((dataOut.nChannels, times, dataOut.nHeights), dtype=complex) | |
| 3559 |
|
3564 | |||
| 3560 | id_min = 0 |
|
3565 | id_min = 0 | |
| 3561 | id_max = self.n |
|
3566 | id_max = self.n | |
| 3562 |
|
3567 | |||
| 3563 | for i in range(times): |
|
3568 | for i in range(times): | |
| 3564 | junk = dataOut.data[:,id_min:id_max,:] |
|
3569 | junk = dataOut.data[:,id_min:id_max,:] | |
| 3565 | avgdata[:,i,:] = junk.sum(axis=1) |
|
3570 | avgdata[:,i,:] = junk.sum(axis=1) | |
| 3566 | id_min += self.n |
|
3571 | id_min += self.n | |
| 3567 | id_max += self.n |
|
3572 | id_max += self.n | |
| 3568 |
|
3573 | |||
| 3569 | timeInterval = dataOut.ippSeconds*self.n |
|
3574 | timeInterval = dataOut.ippSeconds*self.n | |
| 3570 | avgdatatime = (times - 1) * timeInterval + dataOut.utctime |
|
3575 | avgdatatime = (times - 1) * timeInterval + dataOut.utctime | |
| 3571 | self.__dataReady = True |
|
3576 | self.__dataReady = True | |
| 3572 | return avgdata, avgdatatime |
|
3577 | return avgdata, avgdatatime | |
| 3573 |
|
3578 | |||
| 3574 | def run(self, dataOut, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False, **kwargs): |
|
3579 | def run(self, dataOut, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False, **kwargs): | |
| 3575 |
|
3580 | |||
| 3576 | if not self.isConfig: |
|
3581 | if not self.isConfig: | |
| 3577 | self.setup(n=n, stride=stride, timeInterval=timeInterval, overlapping=overlapping, byblock=byblock, **kwargs) |
|
3582 | self.setup(n=n, stride=stride, timeInterval=timeInterval, overlapping=overlapping, byblock=byblock, **kwargs) | |
| 3578 | self.isConfig = True |
|
3583 | self.isConfig = True | |
| 3579 |
|
3584 | |||
| 3580 | if dataOut.flagDataAsBlock: |
|
3585 | if dataOut.flagDataAsBlock: | |
| 3581 | """ |
|
3586 | """ | |
| 3582 | Si la data es leida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
3587 | Si la data es leida por bloques, dimension = [nChannels, nProfiles, nHeis] | |
| 3583 | """ |
|
3588 | """ | |
| 3584 |
|
3589 | |||
| 3585 | avgdata, avgdatatime = self.integrateByBlock(dataOut) |
|
3590 | avgdata, avgdatatime = self.integrateByBlock(dataOut) | |
| 3586 | dataOut.nProfiles /= self.n |
|
3591 | dataOut.nProfiles /= self.n | |
| 3587 | else: |
|
3592 | else: | |
| 3588 | if stride is None: |
|
3593 | if stride is None: | |
| 3589 | avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime) |
|
3594 | avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime) | |
| 3590 | else: |
|
3595 | else: | |
| 3591 | avgdata, avgdatatime = self.integrateByStride(dataOut.data, dataOut.utctime) |
|
3596 | avgdata, avgdatatime = self.integrateByStride(dataOut.data, dataOut.utctime) | |
| 3592 |
|
3597 | |||
| 3593 |
|
3598 | |||
| 3594 | # dataOut.timeInterval *= n |
|
3599 | # dataOut.timeInterval *= n | |
| 3595 | dataOut.flagNoData = True |
|
3600 | dataOut.flagNoData = True | |
| 3596 |
|
3601 | |||
| 3597 | if self.__dataReady: |
|
3602 | if self.__dataReady: | |
| 3598 | dataOut.data = avgdata |
|
3603 | dataOut.data = avgdata | |
| 3599 | if not dataOut.flagCohInt: |
|
3604 | if not dataOut.flagCohInt: | |
| 3600 | dataOut.nCohInt *= self.n |
|
3605 | dataOut.nCohInt *= self.n | |
| 3601 | dataOut.flagCohInt = True |
|
3606 | dataOut.flagCohInt = True | |
| 3602 | dataOut.utctime = avgdatatime |
|
3607 | dataOut.utctime = avgdatatime | |
| 3603 | # print avgdata, avgdatatime |
|
3608 | # print avgdata, avgdatatime | |
| 3604 | # raise |
|
3609 | # raise | |
| 3605 | # dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt |
|
3610 | # dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt | |
| 3606 | dataOut.flagNoData = False |
|
3611 | dataOut.flagNoData = False | |
| 3607 | return dataOut |
|
3612 | return dataOut | |
| 3608 |
|
3613 | |||
| 3609 | class TimesCode(Operation): |
|
3614 | class TimesCode(Operation): | |
| 3610 | ''' |
|
3615 | ''' | |
| 3611 | Written by R. Flores |
|
3616 | Written by R. Flores | |
| 3612 | ''' |
|
3617 | ''' | |
| 3613 | """ |
|
3618 | """ | |
| 3614 |
|
3619 | |||
| 3615 | """ |
|
3620 | """ | |
| 3616 |
|
3621 | |||
| 3617 | def __init__(self, **kwargs): |
|
3622 | def __init__(self, **kwargs): | |
| 3618 |
|
3623 | |||
| 3619 | Operation.__init__(self, **kwargs) |
|
3624 | Operation.__init__(self, **kwargs) | |
| 3620 |
|
3625 | |||
| 3621 | def run(self,dataOut,code): |
|
3626 | def run(self,dataOut,code): | |
| 3622 |
|
3627 | |||
| 3623 | #code = numpy.repeat(code, repeats=osamp, axis=1) |
|
3628 | #code = numpy.repeat(code, repeats=osamp, axis=1) | |
| 3624 | nCodes = numpy.shape(code)[1] |
|
3629 | nCodes = numpy.shape(code)[1] | |
| 3625 | #nprofcode = dataOut.nProfiles//nCodes |
|
3630 | #nprofcode = dataOut.nProfiles//nCodes | |
| 3626 | code = numpy.array(code) |
|
3631 | code = numpy.array(code) | |
| 3627 | #print("nHeights",dataOut.nHeights) |
|
3632 | #print("nHeights",dataOut.nHeights) | |
| 3628 | #print("nheicode",nheicode) |
|
3633 | #print("nheicode",nheicode) | |
| 3629 | #print("Code.Shape",numpy.shape(code)) |
|
3634 | #print("Code.Shape",numpy.shape(code)) | |
| 3630 | #print("Code",code[0,:]) |
|
3635 | #print("Code",code[0,:]) | |
| 3631 | nheicode = dataOut.nHeights//nCodes |
|
3636 | nheicode = dataOut.nHeights//nCodes | |
| 3632 | res = dataOut.nHeights%nCodes |
|
3637 | res = dataOut.nHeights%nCodes | |
| 3633 | ''' |
|
3638 | ''' | |
| 3634 | buffer = numpy.zeros((dataOut.nChannels, |
|
3639 | buffer = numpy.zeros((dataOut.nChannels, | |
| 3635 | nprofcode, |
|
3640 | nprofcode, | |
| 3636 | nCodes, |
|
3641 | nCodes, | |
| 3637 | ndataOut.nHeights), |
|
3642 | ndataOut.nHeights), | |
| 3638 | dtype='complex') |
|
3643 | dtype='complex') | |
| 3639 | ''' |
|
3644 | ''' | |
| 3640 | #exit(1) |
|
3645 | #exit(1) | |
| 3641 | #for ipr in range(dataOut.nProfiles): |
|
3646 | #for ipr in range(dataOut.nProfiles): | |
| 3642 | #print(dataOut.nHeights) |
|
3647 | #print(dataOut.nHeights) | |
| 3643 | #print(dataOut.data[0,384-2:]) |
|
3648 | #print(dataOut.data[0,384-2:]) | |
| 3644 | #print(dataOut.profileIndex) |
|
3649 | #print(dataOut.profileIndex) | |
| 3645 | #print(dataOut.data[0,:2]) |
|
3650 | #print(dataOut.data[0,:2]) | |
| 3646 | #print(dataOut.data[0,0:64]) |
|
3651 | #print(dataOut.data[0,0:64]) | |
| 3647 | #print(dataOut.data[0,64:64+64]) |
|
3652 | #print(dataOut.data[0,64:64+64]) | |
| 3648 | #exit(1) |
|
3653 | #exit(1) | |
| 3649 | for ich in range(dataOut.nChannels): |
|
3654 | for ich in range(dataOut.nChannels): | |
| 3650 | for ihe in range(nheicode): |
|
3655 | for ihe in range(nheicode): | |
| 3651 | #print(ihe*nCodes) |
|
3656 | #print(ihe*nCodes) | |
| 3652 | #print((ihe+1)*nCodes) |
|
3657 | #print((ihe+1)*nCodes) | |
| 3653 | #dataOut.data[ich,ipr,ihe*nCodes:nCodes*(ihe+1)] |
|
3658 | #dataOut.data[ich,ipr,ihe*nCodes:nCodes*(ihe+1)] | |
| 3654 | #code[ipr,:] |
|
3659 | #code[ipr,:] | |
| 3655 | #print("before",dataOut.data[ich,ipr,ihe*nCodes:nCodes*(ihe+1)]) |
|
3660 | #print("before",dataOut.data[ich,ipr,ihe*nCodes:nCodes*(ihe+1)]) | |
| 3656 | #dataOut.data[ich,ipr,ihe*nCodes:nCodes*(ihe+1)] = numpy.prod([dataOut.data[ich,ipr,ihe*nCodes:nCodes*(ihe+1)],code[ipr,:]],axis=0) |
|
3661 | #dataOut.data[ich,ipr,ihe*nCodes:nCodes*(ihe+1)] = numpy.prod([dataOut.data[ich,ipr,ihe*nCodes:nCodes*(ihe+1)],code[ipr,:]],axis=0) | |
| 3657 | dataOut.data[ich,ihe*nCodes:nCodes*(ihe+1)] = numpy.prod([dataOut.data[ich,ihe*nCodes:nCodes*(ihe+1)],code[dataOut.profileIndex,:]],axis=0) |
|
3662 | dataOut.data[ich,ihe*nCodes:nCodes*(ihe+1)] = numpy.prod([dataOut.data[ich,ihe*nCodes:nCodes*(ihe+1)],code[dataOut.profileIndex,:]],axis=0) | |
| 3658 |
|
3663 | |||
| 3659 | #print("after",dataOut.data[ich,ipr,ihe*nCodes:nCodes*(ihe+1)]) |
|
3664 | #print("after",dataOut.data[ich,ipr,ihe*nCodes:nCodes*(ihe+1)]) | |
| 3660 | #exit(1) |
|
3665 | #exit(1) | |
| 3661 | #print(dataOut.data[0,:2]) |
|
3666 | #print(dataOut.data[0,:2]) | |
| 3662 | #exit(1) |
|
3667 | #exit(1) | |
| 3663 | #print(nheicode) |
|
3668 | #print(nheicode) | |
| 3664 | #print((nheicode)*nCodes) |
|
3669 | #print((nheicode)*nCodes) | |
| 3665 | #print(((nheicode)*nCodes)+res) |
|
3670 | #print(((nheicode)*nCodes)+res) | |
| 3666 | if res != 0: |
|
3671 | if res != 0: | |
| 3667 | for ich in range(dataOut.nChannels): |
|
3672 | for ich in range(dataOut.nChannels): | |
| 3668 | dataOut.data[ich,nheicode*nCodes:] = numpy.prod([dataOut.data[ich,nheicode*nCodes:],code[dataOut.profileIndex,:res]],axis=0) |
|
3673 | dataOut.data[ich,nheicode*nCodes:] = numpy.prod([dataOut.data[ich,nheicode*nCodes:],code[dataOut.profileIndex,:res]],axis=0) | |
| 3669 |
|
3674 | |||
| 3670 | #pass |
|
3675 | #pass | |
| 3671 | #print(dataOut.data[0,384-2:]) |
|
3676 | #print(dataOut.data[0,384-2:]) | |
| 3672 | #exit(1) |
|
3677 | #exit(1) | |
| 3673 | #dataOut.data = numpy.mean(buffer,axis=1) |
|
3678 | #dataOut.data = numpy.mean(buffer,axis=1) | |
| 3674 | #print(numpy.shape(dataOut.data)) |
|
3679 | #print(numpy.shape(dataOut.data)) | |
| 3675 | #print(dataOut.nHeights) |
|
3680 | #print(dataOut.nHeights) | |
| 3676 | #dataOut.heightList = dataOut.heightList[0:nheicode] |
|
3681 | #dataOut.heightList = dataOut.heightList[0:nheicode] | |
| 3677 | #print(dataOut.nHeights) |
|
3682 | #print(dataOut.nHeights) | |
| 3678 | #dataOut.nHeights = numpy.shape(dataOut.data)[2] |
|
3683 | #dataOut.nHeights = numpy.shape(dataOut.data)[2] | |
| 3679 | #print(numpy.shape(dataOut.data)) |
|
3684 | #print(numpy.shape(dataOut.data)) | |
| 3680 | #exit(1) |
|
3685 | #exit(1) | |
| 3681 |
|
3686 | |||
| 3682 | return dataOut |
|
3687 | return dataOut | |
| 3683 |
|
3688 | |||
| 3684 | ''' |
|
3689 | ''' | |
| 3685 | class Spectrogram(Operation): |
|
3690 | class Spectrogram(Operation): | |
| 3686 | """ |
|
3691 | """ | |
| 3687 |
|
3692 | |||
| 3688 | """ |
|
3693 | """ | |
| 3689 |
|
3694 | |||
| 3690 | def __init__(self, **kwargs): |
|
3695 | def __init__(self, **kwargs): | |
| 3691 |
|
3696 | |||
| 3692 | Operation.__init__(self, **kwargs) |
|
3697 | Operation.__init__(self, **kwargs) | |
| 3693 |
|
3698 | |||
| 3694 |
|
3699 | |||
| 3695 |
|
3700 | |||
| 3696 | def run(self,dataOut): |
|
3701 | def run(self,dataOut): | |
| 3697 |
|
3702 | |||
| 3698 | import scipy |
|
3703 | import scipy | |
| 3699 |
|
3704 | |||
| 3700 |
|
3705 | |||
| 3701 |
|
3706 | |||
| 3702 | fs = 3200*1e-6 |
|
3707 | fs = 3200*1e-6 | |
| 3703 | fs = fs/64 |
|
3708 | fs = fs/64 | |
| 3704 | fs = 1/fs |
|
3709 | fs = 1/fs | |
| 3705 |
|
3710 | |||
| 3706 | nperseg=64 |
|
3711 | nperseg=64 | |
| 3707 | noverlap=48 |
|
3712 | noverlap=48 | |
| 3708 |
|
3713 | |||
| 3709 | f, t, Sxx = signal.spectrogram(x, fs, return_onesided=False, nperseg=nperseg, noverlap=noverlap, mode='complex') |
|
3714 | f, t, Sxx = signal.spectrogram(x, fs, return_onesided=False, nperseg=nperseg, noverlap=noverlap, mode='complex') | |
| 3710 |
|
3715 | |||
| 3711 |
|
3716 | |||
| 3712 | for ich in range(dataOut.nChannels): |
|
3717 | for ich in range(dataOut.nChannels): | |
| 3713 | for ihe in range(nheicode): |
|
3718 | for ihe in range(nheicode): | |
| 3714 |
|
3719 | |||
| 3715 |
|
3720 | |||
| 3716 | return dataOut |
|
3721 | return dataOut | |
| 3717 | ''' |
|
3722 | ''' | |
| 3718 |
|
3723 | |||
| 3719 |
|
3724 | |||
| 3720 | class RemoveDcHae(Operation): |
|
3725 | class RemoveDcHae(Operation): | |
| 3721 | ''' |
|
3726 | ''' | |
| 3722 | Written by R. Flores |
|
3727 | Written by R. Flores | |
| 3723 | ''' |
|
3728 | ''' | |
| 3724 | def __init__(self, **kwargs): |
|
3729 | def __init__(self, **kwargs): | |
| 3725 |
|
3730 | |||
| 3726 | Operation.__init__(self, **kwargs) |
|
3731 | Operation.__init__(self, **kwargs) | |
| 3727 | self.DcCounter = 0 |
|
3732 | self.DcCounter = 0 | |
| 3728 |
|
3733 | |||
| 3729 | def run(self, dataOut): |
|
3734 | def run(self, dataOut): | |
| 3730 |
|
3735 | |||
| 3731 | if self.DcCounter == 0: |
|
3736 | if self.DcCounter == 0: | |
| 3732 | dataOut.DcHae = numpy.zeros((dataOut.data.shape[0],320),dtype='complex') |
|
3737 | dataOut.DcHae = numpy.zeros((dataOut.data.shape[0],320),dtype='complex') | |
| 3733 | #dataOut.DcHae = [] |
|
3738 | #dataOut.DcHae = [] | |
| 3734 | self.DcCounter = 1 |
|
3739 | self.DcCounter = 1 | |
| 3735 |
|
3740 | |||
| 3736 | dataOut.dataaux = numpy.copy(dataOut.data) |
|
3741 | dataOut.dataaux = numpy.copy(dataOut.data) | |
| 3737 |
|
3742 | |||
| 3738 | #dataOut.DcHae += dataOut.dataaux[:,1666:1666+320] |
|
3743 | #dataOut.DcHae += dataOut.dataaux[:,1666:1666+320] | |
| 3739 | dataOut.DcHae += dataOut.dataaux[:,0:0+320] |
|
3744 | dataOut.DcHae += dataOut.dataaux[:,0:0+320] | |
| 3740 | hei = 1666 |
|
3745 | hei = 1666 | |
| 3741 | hei = 2000 |
|
3746 | hei = 2000 | |
| 3742 | hei = 1000 |
|
3747 | hei = 1000 | |
| 3743 | hei = 0 |
|
3748 | hei = 0 | |
| 3744 | #dataOut.DcHae = numpy.concatenate([dataOut.DcHae,dataOut.dataaux[0,hei]],axis = None) |
|
3749 | #dataOut.DcHae = numpy.concatenate([dataOut.DcHae,dataOut.dataaux[0,hei]],axis = None) | |
| 3745 |
|
3750 | |||
| 3746 |
|
3751 | |||
| 3747 |
|
3752 | |||
| 3748 | return dataOut |
|
3753 | return dataOut | |
| 3749 |
|
3754 | |||
| 3750 |
|
3755 | |||
| 3751 | class SSheightProfiles(Operation): |
|
3756 | class SSheightProfiles(Operation): | |
| 3752 |
|
3757 | |||
| 3753 | step = None |
|
3758 | step = None | |
| 3754 | nsamples = None |
|
3759 | nsamples = None | |
| 3755 | bufferShape = None |
|
3760 | bufferShape = None | |
| 3756 | profileShape = None |
|
3761 | profileShape = None | |
| 3757 | sshProfiles = None |
|
3762 | sshProfiles = None | |
| 3758 | profileIndex = None |
|
3763 | profileIndex = None | |
| 3759 |
|
3764 | |||
| 3760 | def __init__(self, **kwargs): |
|
3765 | def __init__(self, **kwargs): | |
| 3761 |
|
3766 | |||
| 3762 | Operation.__init__(self, **kwargs) |
|
3767 | Operation.__init__(self, **kwargs) | |
| 3763 | self.isConfig = False |
|
3768 | self.isConfig = False | |
| 3764 |
|
3769 | |||
| 3765 | def setup(self,dataOut ,step = None , nsamples = None): |
|
3770 | def setup(self,dataOut ,step = None , nsamples = None): | |
| 3766 |
|
3771 | |||
| 3767 | if step == None and nsamples == None: |
|
3772 | if step == None and nsamples == None: | |
| 3768 | #pass |
|
3773 | #pass | |
| 3769 | raise ValueError("step or nheights should be specified ...") |
|
3774 | raise ValueError("step or nheights should be specified ...") | |
| 3770 |
|
3775 | |||
| 3771 | self.step = step |
|
3776 | self.step = step | |
| 3772 | self.nsamples = nsamples |
|
3777 | self.nsamples = nsamples | |
| 3773 | self.__nChannels = dataOut.nChannels |
|
3778 | self.__nChannels = dataOut.nChannels | |
| 3774 | self.__nProfiles = dataOut.nProfiles |
|
3779 | self.__nProfiles = dataOut.nProfiles | |
| 3775 | self.__nHeis = dataOut.nHeights |
|
3780 | self.__nHeis = dataOut.nHeights | |
| 3776 | shape = dataOut.data.shape #nchannels, nprofiles, nsamples |
|
3781 | shape = dataOut.data.shape #nchannels, nprofiles, nsamples | |
| 3777 |
|
3782 | |||
| 3778 | residue = (shape[1] - self.nsamples) % self.step |
|
3783 | residue = (shape[1] - self.nsamples) % self.step | |
| 3779 | if residue != 0: |
|
3784 | if residue != 0: | |
| 3780 | print("The residue is %d, step=%d should be multiple of %d to avoid loss of %d samples"%(residue,step,shape[1] - self.nsamples,residue)) |
|
3785 | print("The residue is %d, step=%d should be multiple of %d to avoid loss of %d samples"%(residue,step,shape[1] - self.nsamples,residue)) | |
| 3781 |
|
3786 | |||
| 3782 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
3787 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] | |
| 3783 | numberProfile = self.nsamples |
|
3788 | numberProfile = self.nsamples | |
| 3784 | numberSamples = (shape[1] - self.nsamples)/self.step |
|
3789 | numberSamples = (shape[1] - self.nsamples)/self.step | |
| 3785 |
|
3790 | |||
| 3786 | self.bufferShape = int(shape[0]), int(numberSamples), int(numberProfile) # nchannels, nsamples , nprofiles |
|
3791 | self.bufferShape = int(shape[0]), int(numberSamples), int(numberProfile) # nchannels, nsamples , nprofiles | |
| 3787 | self.profileShape = int(shape[0]), int(numberProfile), int(numberSamples) # nchannels, nprofiles, nsamples |
|
3792 | self.profileShape = int(shape[0]), int(numberProfile), int(numberSamples) # nchannels, nprofiles, nsamples | |
| 3788 |
|
3793 | |||
| 3789 | self.buffer = numpy.zeros(self.bufferShape , dtype=complex) |
|
3794 | self.buffer = numpy.zeros(self.bufferShape , dtype=complex) | |
| 3790 | self.sshProfiles = numpy.zeros(self.profileShape, dtype=complex) |
|
3795 | self.sshProfiles = numpy.zeros(self.profileShape, dtype=complex) | |
| 3791 |
|
3796 | |||
| 3792 | def run(self, dataOut, step, nsamples, code = None, repeat = None): |
|
3797 | def run(self, dataOut, step, nsamples, code = None, repeat = None): | |
| 3793 | dataOut.flagNoData = True |
|
3798 | dataOut.flagNoData = True | |
| 3794 | profileIndex = None |
|
3799 | profileIndex = None | |
| 3795 | dataOut.flagDataAsBlock = False |
|
3800 | dataOut.flagDataAsBlock = False | |
| 3796 |
|
3801 | |||
| 3797 | if not self.isConfig: |
|
3802 | if not self.isConfig: | |
| 3798 | self.setup(dataOut, step=step , nsamples=nsamples) |
|
3803 | self.setup(dataOut, step=step , nsamples=nsamples) | |
| 3799 | self.isConfig = True |
|
3804 | self.isConfig = True | |
| 3800 |
|
3805 | |||
| 3801 | if code is not None: |
|
3806 | if code is not None: | |
| 3802 | code = numpy.array(code) |
|
3807 | code = numpy.array(code) | |
| 3803 | code_block = code |
|
3808 | code_block = code | |
| 3804 |
|
3809 | |||
| 3805 | if repeat is not None: |
|
3810 | if repeat is not None: | |
| 3806 | code_block = numpy.repeat(code_block, repeats=repeat, axis=1) |
|
3811 | code_block = numpy.repeat(code_block, repeats=repeat, axis=1) | |
| 3807 |
|
3812 | |||
| 3808 | for i in range(self.buffer.shape[1]): |
|
3813 | for i in range(self.buffer.shape[1]): | |
| 3809 | if code is not None: |
|
3814 | if code is not None: | |
| 3810 | #self.buffer[:,i] = dataOut.data[:,i*self.step:i*self.step + self.nsamples]*code_block[dataOut.profileIndex,:] |
|
3815 | #self.buffer[:,i] = dataOut.data[:,i*self.step:i*self.step + self.nsamples]*code_block[dataOut.profileIndex,:] | |
| 3811 | self.buffer[:,i] = dataOut.data[:,i*self.step:i*self.step + self.nsamples]*code_block |
|
3816 | self.buffer[:,i] = dataOut.data[:,i*self.step:i*self.step + self.nsamples]*code_block | |
| 3812 | else: |
|
3817 | else: | |
| 3813 | self.buffer[:,i] = dataOut.data[:,i*self.step:i*self.step + self.nsamples]#*code[dataOut.profileIndex,:] |
|
3818 | self.buffer[:,i] = dataOut.data[:,i*self.step:i*self.step + self.nsamples]#*code[dataOut.profileIndex,:] | |
| 3814 |
|
3819 | |||
| 3815 | for j in range(self.buffer.shape[0]): |
|
3820 | for j in range(self.buffer.shape[0]): | |
| 3816 | self.sshProfiles[j] = numpy.transpose(self.buffer[j]) |
|
3821 | self.sshProfiles[j] = numpy.transpose(self.buffer[j]) | |
| 3817 |
|
3822 | |||
| 3818 | profileIndex = self.nsamples |
|
3823 | profileIndex = self.nsamples | |
| 3819 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
3824 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] | |
| 3820 | ippSeconds = (deltaHeight*1.0e-6)/(0.15) |
|
3825 | ippSeconds = (deltaHeight*1.0e-6)/(0.15) | |
| 3821 |
|
3826 | |||
| 3822 | try: |
|
3827 | try: | |
| 3823 | if dataOut.concat_m is not None: |
|
3828 | if dataOut.concat_m is not None: | |
| 3824 | ippSeconds= ippSeconds/float(dataOut.concat_m) |
|
3829 | ippSeconds= ippSeconds/float(dataOut.concat_m) | |
| 3825 | except: |
|
3830 | except: | |
| 3826 | pass |
|
3831 | pass | |
| 3827 |
|
3832 | |||
| 3828 | dataOut.data = self.sshProfiles |
|
3833 | dataOut.data = self.sshProfiles | |
| 3829 | dataOut.flagNoData = False |
|
3834 | dataOut.flagNoData = False | |
| 3830 | dataOut.heightList = numpy.arange(self.buffer.shape[1]) *self.step*deltaHeight + dataOut.heightList[0] |
|
3835 | dataOut.heightList = numpy.arange(self.buffer.shape[1]) *self.step*deltaHeight + dataOut.heightList[0] | |
| 3831 | dataOut.nProfiles = int(dataOut.nProfiles*self.nsamples) |
|
3836 | dataOut.nProfiles = int(dataOut.nProfiles*self.nsamples) | |
| 3832 |
|
3837 | |||
| 3833 | dataOut.profileIndex = profileIndex |
|
3838 | dataOut.profileIndex = profileIndex | |
| 3834 | dataOut.flagDataAsBlock = True |
|
3839 | dataOut.flagDataAsBlock = True | |
| 3835 | dataOut.ippSeconds = ippSeconds |
|
3840 | dataOut.ippSeconds = ippSeconds | |
| 3836 | dataOut.step = self.step |
|
3841 | dataOut.step = self.step | |
| 3837 |
|
3842 | |||
| 3838 | return dataOut |
|
3843 | return dataOut | |
| 3839 |
|
3844 | |||
| 3840 | class removeDCHAE(Operation): |
|
3845 | class removeDCHAE(Operation): | |
| 3841 |
|
3846 | |||
| 3842 | def run(self, dataOut, minHei, maxHei): |
|
3847 | def run(self, dataOut, minHei, maxHei): | |
| 3843 |
|
3848 | |||
| 3844 | heights = dataOut.heightList |
|
3849 | heights = dataOut.heightList | |
| 3845 |
|
3850 | |||
| 3846 | inda = numpy.where(heights >= minHei) |
|
3851 | inda = numpy.where(heights >= minHei) | |
| 3847 | indb = numpy.where(heights <= maxHei) |
|
3852 | indb = numpy.where(heights <= maxHei) | |
| 3848 |
|
3853 | |||
| 3849 | minIndex = inda[0][0] |
|
3854 | minIndex = inda[0][0] | |
| 3850 | maxIndex = indb[0][-1] |
|
3855 | maxIndex = indb[0][-1] | |
| 3851 |
|
3856 | |||
| 3852 | dc = numpy.average(dataOut.data[:,minIndex:maxIndex],axis=1) |
|
3857 | dc = numpy.average(dataOut.data[:,minIndex:maxIndex],axis=1) | |
| 3853 | #print(dc.shape) |
|
3858 | #print(dc.shape) | |
| 3854 | dataOut.data = dataOut.data - dc[:,None] |
|
3859 | dataOut.data = dataOut.data - dc[:,None] | |
| 3855 | #print(aux.shape) |
|
3860 | #print(aux.shape) | |
| 3856 | #exit(1) |
|
3861 | #exit(1) | |
| 3857 |
|
3862 | |||
| 3858 | return dataOut |
|
3863 | return dataOut | |
| 3859 |
|
3864 | |||
| 3860 | class Decoder(Operation): |
|
3865 | class Decoder(Operation): | |
| 3861 |
|
3866 | |||
| 3862 | isConfig = False |
|
3867 | isConfig = False | |
| 3863 | __profIndex = 0 |
|
3868 | __profIndex = 0 | |
| 3864 |
|
3869 | |||
| 3865 | code = None |
|
3870 | code = None | |
| 3866 |
|
3871 | |||
| 3867 | nCode = None |
|
3872 | nCode = None | |
| 3868 | nBaud = None |
|
3873 | nBaud = None | |
| 3869 |
|
3874 | |||
| 3870 | def __init__(self, **kwargs): |
|
3875 | def __init__(self, **kwargs): | |
| 3871 |
|
3876 | |||
| 3872 | Operation.__init__(self, **kwargs) |
|
3877 | Operation.__init__(self, **kwargs) | |
| 3873 |
|
3878 | |||
| 3874 | self.times = None |
|
3879 | self.times = None | |
| 3875 | self.osamp = None |
|
3880 | self.osamp = None | |
| 3876 | # self.__setValues = False |
|
3881 | # self.__setValues = False | |
| 3877 | self.isConfig = False |
|
3882 | self.isConfig = False | |
| 3878 | self.setupReq = False |
|
3883 | self.setupReq = False | |
| 3879 | def setup(self, code, osamp, dataOut): |
|
3884 | def setup(self, code, osamp, dataOut): | |
| 3880 |
|
3885 | |||
| 3881 | self.__profIndex = 0 |
|
3886 | self.__profIndex = 0 | |
| 3882 |
|
3887 | |||
| 3883 | self.code = code |
|
3888 | self.code = code | |
| 3884 |
|
3889 | |||
| 3885 | self.nCode = len(code) |
|
3890 | self.nCode = len(code) | |
| 3886 | self.nBaud = len(code[0]) |
|
3891 | self.nBaud = len(code[0]) | |
| 3887 |
|
3892 | |||
| 3888 | if (osamp != None) and (osamp >1): |
|
3893 | if (osamp != None) and (osamp >1): | |
| 3889 | self.osamp = osamp |
|
3894 | self.osamp = osamp | |
| 3890 | self.code = numpy.repeat(code, repeats=self.osamp, axis=1) |
|
3895 | self.code = numpy.repeat(code, repeats=self.osamp, axis=1) | |
| 3891 | self.nBaud = self.nBaud*self.osamp |
|
3896 | self.nBaud = self.nBaud*self.osamp | |
| 3892 |
|
3897 | |||
| 3893 | self.__nChannels = dataOut.nChannels |
|
3898 | self.__nChannels = dataOut.nChannels | |
| 3894 | self.__nProfiles = dataOut.nProfiles |
|
3899 | self.__nProfiles = dataOut.nProfiles | |
| 3895 | self.__nHeis = dataOut.nHeights |
|
3900 | self.__nHeis = dataOut.nHeights | |
| 3896 |
|
3901 | |||
| 3897 | if self.__nHeis < self.nBaud: |
|
3902 | if self.__nHeis < self.nBaud: | |
| 3898 | raise ValueError('Number of heights (%d) should be greater than number of bauds (%d)' %(self.__nHeis, self.nBaud)) |
|
3903 | raise ValueError('Number of heights (%d) should be greater than number of bauds (%d)' %(self.__nHeis, self.nBaud)) | |
| 3899 |
|
3904 | |||
| 3900 | #Frequency |
|
3905 | #Frequency | |
| 3901 | __codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=complex) |
|
3906 | __codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=complex) | |
| 3902 |
|
3907 | |||
| 3903 | __codeBuffer[:,0:self.nBaud] = self.code |
|
3908 | __codeBuffer[:,0:self.nBaud] = self.code | |
| 3904 |
|
3909 | |||
| 3905 | self.fft_code = numpy.conj(numpy.fft.fft(__codeBuffer, axis=1)) |
|
3910 | self.fft_code = numpy.conj(numpy.fft.fft(__codeBuffer, axis=1)) | |
| 3906 |
|
3911 | |||
| 3907 | if dataOut.flagDataAsBlock: |
|
3912 | if dataOut.flagDataAsBlock: | |
| 3908 |
|
3913 | |||
| 3909 | self.ndatadec = self.__nHeis #- self.nBaud + 1 |
|
3914 | self.ndatadec = self.__nHeis #- self.nBaud + 1 | |
| 3910 |
|
3915 | |||
| 3911 | self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=complex) |
|
3916 | self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=complex) | |
| 3912 |
|
3917 | |||
| 3913 | else: |
|
3918 | else: | |
| 3914 |
|
3919 | |||
| 3915 | #Time |
|
3920 | #Time | |
| 3916 | self.ndatadec = self.__nHeis #- self.nBaud + 1 |
|
3921 | self.ndatadec = self.__nHeis #- self.nBaud + 1 | |
| 3917 |
|
3922 | |||
| 3918 |
|
3923 | |||
| 3919 | self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=complex) |
|
3924 | self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=complex) | |
| 3920 |
|
3925 | |||
| 3921 | def __convolutionInFreq(self, data): |
|
3926 | def __convolutionInFreq(self, data): | |
| 3922 |
|
3927 | |||
| 3923 | fft_code = self.fft_code[self.__profIndex].reshape(1,-1) |
|
3928 | fft_code = self.fft_code[self.__profIndex].reshape(1,-1) | |
| 3924 |
|
3929 | |||
| 3925 | fft_data = numpy.fft.fft(data, axis=1) |
|
3930 | fft_data = numpy.fft.fft(data, axis=1) | |
| 3926 |
|
3931 | |||
| 3927 | conv = fft_data*fft_code |
|
3932 | conv = fft_data*fft_code | |
| 3928 |
|
3933 | |||
| 3929 | data = numpy.fft.ifft(conv,axis=1) |
|
3934 | data = numpy.fft.ifft(conv,axis=1) | |
| 3930 |
|
3935 | |||
| 3931 | return data |
|
3936 | return data | |
| 3932 |
|
3937 | |||
| 3933 | def __convolutionInFreqOpt(self, data): |
|
3938 | def __convolutionInFreqOpt(self, data): | |
| 3934 |
|
3939 | |||
| 3935 | raise NotImplementedError |
|
3940 | raise NotImplementedError | |
| 3936 |
|
3941 | |||
| 3937 | def __convolutionInTime(self, data): |
|
3942 | def __convolutionInTime(self, data): | |
| 3938 |
|
3943 | |||
| 3939 | code = self.code[self.__profIndex] |
|
3944 | code = self.code[self.__profIndex] | |
| 3940 | for i in range(self.__nChannels): |
|
3945 | for i in range(self.__nChannels): | |
| 3941 | #aux=numpy.correlate(data[i,:], code, mode='full') |
|
3946 | #aux=numpy.correlate(data[i,:], code, mode='full') | |
| 3942 | #print(numpy.shape(aux)) |
|
3947 | #print(numpy.shape(aux)) | |
| 3943 | #print(numpy.shape(data[i,:])) |
|
3948 | #print(numpy.shape(data[i,:])) | |
| 3944 | #print(numpy.shape(code)) |
|
3949 | #print(numpy.shape(code)) | |
| 3945 | #exit(1) |
|
3950 | #exit(1) | |
| 3946 | self.datadecTime[i,:] = numpy.correlate(data[i,:], code, mode='full')[self.nBaud-1:] |
|
3951 | self.datadecTime[i,:] = numpy.correlate(data[i,:], code, mode='full')[self.nBaud-1:] | |
| 3947 |
|
3952 | |||
| 3948 | return self.datadecTime |
|
3953 | return self.datadecTime | |
| 3949 |
|
3954 | |||
| 3950 | def __convolutionByBlockInTime(self, data): |
|
3955 | def __convolutionByBlockInTime(self, data): | |
| 3951 |
|
3956 | |||
| 3952 | repetitions = int(self.__nProfiles / self.nCode) |
|
3957 | repetitions = int(self.__nProfiles / self.nCode) | |
| 3953 | junk = numpy.lib.stride_tricks.as_strided(self.code, (repetitions, self.code.size), (0, self.code.itemsize)) |
|
3958 | junk = numpy.lib.stride_tricks.as_strided(self.code, (repetitions, self.code.size), (0, self.code.itemsize)) | |
| 3954 | junk = junk.flatten() |
|
3959 | junk = junk.flatten() | |
| 3955 | code_block = numpy.reshape(junk, (self.nCode*repetitions, self.nBaud)) |
|
3960 | code_block = numpy.reshape(junk, (self.nCode*repetitions, self.nBaud)) | |
| 3956 | profilesList = range(self.__nProfiles) |
|
3961 | profilesList = range(self.__nProfiles) | |
| 3957 | #print(numpy.shape(self.datadecTime)) |
|
3962 | #print(numpy.shape(self.datadecTime)) | |
| 3958 | #print(numpy.shape(data)) |
|
3963 | #print(numpy.shape(data)) | |
| 3959 | for i in range(self.__nChannels): |
|
3964 | for i in range(self.__nChannels): | |
| 3960 | for j in profilesList: |
|
3965 | for j in profilesList: | |
| 3961 | self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:] |
|
3966 | self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:] | |
| 3962 | return self.datadecTime |
|
3967 | return self.datadecTime | |
| 3963 |
|
3968 | |||
| 3964 | def __convolutionByBlockInFreq(self, data): |
|
3969 | def __convolutionByBlockInFreq(self, data): | |
| 3965 |
|
3970 | |||
| 3966 | raise NotImplementedError("Decoder by frequency fro Blocks not implemented") |
|
3971 | raise NotImplementedError("Decoder by frequency fro Blocks not implemented") | |
| 3967 |
|
3972 | |||
| 3968 |
|
3973 | |||
| 3969 | fft_code = self.fft_code[self.__profIndex].reshape(1,-1) |
|
3974 | fft_code = self.fft_code[self.__profIndex].reshape(1,-1) | |
| 3970 |
|
3975 | |||
| 3971 | fft_data = numpy.fft.fft(data, axis=2) |
|
3976 | fft_data = numpy.fft.fft(data, axis=2) | |
| 3972 |
|
3977 | |||
| 3973 | conv = fft_data*fft_code |
|
3978 | conv = fft_data*fft_code | |
| 3974 |
|
3979 | |||
| 3975 | data = numpy.fft.ifft(conv,axis=2) |
|
3980 | data = numpy.fft.ifft(conv,axis=2) | |
| 3976 |
|
3981 | |||
| 3977 | return data |
|
3982 | return data | |
| 3978 |
|
3983 | |||
| 3979 |
|
3984 | |||
| 3980 | def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0, osamp=None, times=None): |
|
3985 | def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0, osamp=None, times=None): | |
| 3981 |
|
3986 | |||
| 3982 | if dataOut.flagDecodeData: |
|
3987 | if dataOut.flagDecodeData: | |
| 3983 | print("This data is already decoded, recoding again ...") |
|
3988 | print("This data is already decoded, recoding again ...") | |
| 3984 |
|
3989 | |||
| 3985 | if not self.isConfig: |
|
3990 | if not self.isConfig: | |
| 3986 |
|
3991 | |||
| 3987 | if code is None: |
|
3992 | if code is None: | |
| 3988 | if dataOut.code is None: |
|
3993 | if dataOut.code is None: | |
| 3989 | raise ValueError("Code could not be read from %s instance. Enter a value in Code parameter" %dataOut.type) |
|
3994 | raise ValueError("Code could not be read from %s instance. Enter a value in Code parameter" %dataOut.type) | |
| 3990 |
|
3995 | |||
| 3991 | code = dataOut.code |
|
3996 | code = dataOut.code | |
| 3992 | else: |
|
3997 | else: | |
| 3993 | code = numpy.array(code).reshape(nCode,nBaud) |
|
3998 | code = numpy.array(code).reshape(nCode,nBaud) | |
| 3994 | self.setup(code, osamp, dataOut) |
|
3999 | self.setup(code, osamp, dataOut) | |
| 3995 |
|
4000 | |||
| 3996 | self.isConfig = True |
|
4001 | self.isConfig = True | |
| 3997 |
|
4002 | |||
| 3998 | if mode == 3: |
|
4003 | if mode == 3: | |
| 3999 | sys.stderr.write("Decoder Warning: mode=%d is not valid, using mode=0\n" %mode) |
|
4004 | sys.stderr.write("Decoder Warning: mode=%d is not valid, using mode=0\n" %mode) | |
| 4000 |
|
4005 | |||
| 4001 | if times != None: |
|
4006 | if times != None: | |
| 4002 | sys.stderr.write("Decoder Warning: Argument 'times' in not used anymore\n") |
|
4007 | sys.stderr.write("Decoder Warning: Argument 'times' in not used anymore\n") | |
| 4003 |
|
4008 | |||
| 4004 | if self.code is None: |
|
4009 | if self.code is None: | |
| 4005 | print("Fail decoding: Code is not defined.") |
|
4010 | print("Fail decoding: Code is not defined.") | |
| 4006 | return |
|
4011 | return | |
| 4007 |
|
4012 | |||
| 4008 | self.__nProfiles = dataOut.nProfiles |
|
4013 | self.__nProfiles = dataOut.nProfiles | |
| 4009 | datadec = None |
|
4014 | datadec = None | |
| 4010 |
|
4015 | |||
| 4011 | if mode == 3: |
|
4016 | if mode == 3: | |
| 4012 | mode = 0 |
|
4017 | mode = 0 | |
| 4013 |
|
4018 | |||
| 4014 | if dataOut.flagDataAsBlock: |
|
4019 | if dataOut.flagDataAsBlock: | |
| 4015 | """ |
|
4020 | """ | |
| 4016 | Decoding when data have been read as block, |
|
4021 | Decoding when data have been read as block, | |
| 4017 | """ |
|
4022 | """ | |
| 4018 |
|
4023 | |||
| 4019 | if mode == 0: |
|
4024 | if mode == 0: | |
| 4020 | datadec = self.__convolutionByBlockInTime(dataOut.data) |
|
4025 | datadec = self.__convolutionByBlockInTime(dataOut.data) | |
| 4021 | if mode == 1: |
|
4026 | if mode == 1: | |
| 4022 | datadec = self.__convolutionByBlockInFreq(dataOut.data) |
|
4027 | datadec = self.__convolutionByBlockInFreq(dataOut.data) | |
| 4023 | else: |
|
4028 | else: | |
| 4024 | """ |
|
4029 | """ | |
| 4025 | Decoding when data have been read profile by profile |
|
4030 | Decoding when data have been read profile by profile | |
| 4026 | """ |
|
4031 | """ | |
| 4027 | if mode == 0: |
|
4032 | if mode == 0: | |
| 4028 | datadec = self.__convolutionInTime(dataOut.data) |
|
4033 | datadec = self.__convolutionInTime(dataOut.data) | |
| 4029 |
|
4034 | |||
| 4030 | if mode == 1: |
|
4035 | if mode == 1: | |
| 4031 | datadec = self.__convolutionInFreq(dataOut.data) |
|
4036 | datadec = self.__convolutionInFreq(dataOut.data) | |
| 4032 |
|
4037 | |||
| 4033 | if mode == 2: |
|
4038 | if mode == 2: | |
| 4034 | datadec = self.__convolutionInFreqOpt(dataOut.data) |
|
4039 | datadec = self.__convolutionInFreqOpt(dataOut.data) | |
| 4035 |
|
4040 | |||
| 4036 | if datadec is None: |
|
4041 | if datadec is None: | |
| 4037 | raise ValueError("Codification mode selected is not valid: mode=%d. Try selecting 0 or 1" %mode) |
|
4042 | raise ValueError("Codification mode selected is not valid: mode=%d. Try selecting 0 or 1" %mode) | |
| 4038 |
|
4043 | |||
| 4039 | dataOut.code = self.code |
|
4044 | dataOut.code = self.code | |
| 4040 | dataOut.nCode = self.nCode |
|
4045 | dataOut.nCode = self.nCode | |
| 4041 | dataOut.nBaud = self.nBaud |
|
4046 | dataOut.nBaud = self.nBaud | |
| 4042 |
|
4047 | |||
| 4043 | dataOut.data = datadec |
|
4048 | dataOut.data = datadec | |
| 4044 | #print("before",dataOut.heightList) |
|
4049 | #print("before",dataOut.heightList) | |
| 4045 | dataOut.heightList = dataOut.heightList[0:datadec.shape[-1]] |
|
4050 | dataOut.heightList = dataOut.heightList[0:datadec.shape[-1]] | |
| 4046 | #print("after",dataOut.heightList) |
|
4051 | #print("after",dataOut.heightList) | |
| 4047 |
|
4052 | |||
| 4048 | dataOut.flagDecodeData = True #asumo q la data esta decodificada |
|
4053 | dataOut.flagDecodeData = True #asumo q la data esta decodificada | |
| 4049 |
|
4054 | |||
| 4050 | if self.__profIndex == self.nCode-1: |
|
4055 | if self.__profIndex == self.nCode-1: | |
| 4051 | self.__profIndex = 0 |
|
4056 | self.__profIndex = 0 | |
| 4052 | return dataOut |
|
4057 | return dataOut | |
| 4053 |
|
4058 | |||
| 4054 | self.__profIndex += 1 |
|
4059 | self.__profIndex += 1 | |
| 4055 |
|
4060 | |||
| 4056 | #print("SHAPE",numpy.shape(dataOut.data)) |
|
4061 | #print("SHAPE",numpy.shape(dataOut.data)) | |
| 4057 |
|
4062 | |||
| 4058 | return dataOut |
|
4063 | return dataOut | |
| 4059 | # dataOut.flagDeflipData = True #asumo q la data no esta sin flip |
|
4064 | # dataOut.flagDeflipData = True #asumo q la data no esta sin flip | |
| 4060 |
|
4065 | |||
| 4061 | class DecoderRoll(Operation): |
|
4066 | class DecoderRoll(Operation): | |
| 4062 |
|
4067 | |||
| 4063 | isConfig = False |
|
4068 | isConfig = False | |
| 4064 | __profIndex = 0 |
|
4069 | __profIndex = 0 | |
| 4065 |
|
4070 | |||
| 4066 | code = None |
|
4071 | code = None | |
| 4067 |
|
4072 | |||
| 4068 | nCode = None |
|
4073 | nCode = None | |
| 4069 | nBaud = None |
|
4074 | nBaud = None | |
| 4070 |
|
4075 | |||
| 4071 | def __init__(self, **kwargs): |
|
4076 | def __init__(self, **kwargs): | |
| 4072 |
|
4077 | |||
| 4073 | Operation.__init__(self, **kwargs) |
|
4078 | Operation.__init__(self, **kwargs) | |
| 4074 |
|
4079 | |||
| 4075 | self.times = None |
|
4080 | self.times = None | |
| 4076 | self.osamp = None |
|
4081 | self.osamp = None | |
| 4077 | # self.__setValues = False |
|
4082 | # self.__setValues = False | |
| 4078 | self.isConfig = False |
|
4083 | self.isConfig = False | |
| 4079 | self.setupReq = False |
|
4084 | self.setupReq = False | |
| 4080 | def setup(self, code, osamp, dataOut): |
|
4085 | def setup(self, code, osamp, dataOut): | |
| 4081 |
|
4086 | |||
| 4082 | self.__profIndex = 0 |
|
4087 | self.__profIndex = 0 | |
| 4083 |
|
4088 | |||
| 4084 |
|
4089 | |||
| 4085 | self.code = code |
|
4090 | self.code = code | |
| 4086 |
|
4091 | |||
| 4087 | self.nCode = len(code) |
|
4092 | self.nCode = len(code) | |
| 4088 | self.nBaud = len(code[0]) |
|
4093 | self.nBaud = len(code[0]) | |
| 4089 |
|
4094 | |||
| 4090 | if (osamp != None) and (osamp >1): |
|
4095 | if (osamp != None) and (osamp >1): | |
| 4091 | self.osamp = osamp |
|
4096 | self.osamp = osamp | |
| 4092 | self.code = numpy.repeat(code, repeats=self.osamp, axis=1) |
|
4097 | self.code = numpy.repeat(code, repeats=self.osamp, axis=1) | |
| 4093 | self.nBaud = self.nBaud*self.osamp |
|
4098 | self.nBaud = self.nBaud*self.osamp | |
| 4094 |
|
4099 | |||
| 4095 | self.__nChannels = dataOut.nChannels |
|
4100 | self.__nChannels = dataOut.nChannels | |
| 4096 | self.__nProfiles = dataOut.nProfiles |
|
4101 | self.__nProfiles = dataOut.nProfiles | |
| 4097 | self.__nHeis = dataOut.nHeights |
|
4102 | self.__nHeis = dataOut.nHeights | |
| 4098 |
|
4103 | |||
| 4099 | if self.__nHeis < self.nBaud: |
|
4104 | if self.__nHeis < self.nBaud: | |
| 4100 | raise ValueError('Number of heights (%d) should be greater than number of bauds (%d)' %(self.__nHeis, self.nBaud)) |
|
4105 | raise ValueError('Number of heights (%d) should be greater than number of bauds (%d)' %(self.__nHeis, self.nBaud)) | |
| 4101 |
|
4106 | |||
| 4102 | #Frequency |
|
4107 | #Frequency | |
| 4103 | __codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=complex) |
|
4108 | __codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=complex) | |
| 4104 |
|
4109 | |||
| 4105 | __codeBuffer[:,0:self.nBaud] = self.code |
|
4110 | __codeBuffer[:,0:self.nBaud] = self.code | |
| 4106 |
|
4111 | |||
| 4107 | self.fft_code = numpy.conj(numpy.fft.fft(__codeBuffer, axis=1)) |
|
4112 | self.fft_code = numpy.conj(numpy.fft.fft(__codeBuffer, axis=1)) | |
| 4108 |
|
4113 | |||
| 4109 | if dataOut.flagDataAsBlock: |
|
4114 | if dataOut.flagDataAsBlock: | |
| 4110 |
|
4115 | |||
| 4111 | self.ndatadec = self.__nHeis #- self.nBaud + 1 |
|
4116 | self.ndatadec = self.__nHeis #- self.nBaud + 1 | |
| 4112 |
|
4117 | |||
| 4113 | self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=complex) |
|
4118 | self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=complex) | |
| 4114 |
|
4119 | |||
| 4115 | else: |
|
4120 | else: | |
| 4116 |
|
4121 | |||
| 4117 | #Time |
|
4122 | #Time | |
| 4118 | self.ndatadec = self.__nHeis #- self.nBaud + 1 |
|
4123 | self.ndatadec = self.__nHeis #- self.nBaud + 1 | |
| 4119 |
|
4124 | |||
| 4120 |
|
4125 | |||
| 4121 | self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=complex) |
|
4126 | self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=complex) | |
| 4122 |
|
4127 | |||
| 4123 | def __convolutionInFreq(self, data): |
|
4128 | def __convolutionInFreq(self, data): | |
| 4124 |
|
4129 | |||
| 4125 | fft_code = self.fft_code[self.__profIndex].reshape(1,-1) |
|
4130 | fft_code = self.fft_code[self.__profIndex].reshape(1,-1) | |
| 4126 |
|
4131 | |||
| 4127 | fft_data = numpy.fft.fft(data, axis=1) |
|
4132 | fft_data = numpy.fft.fft(data, axis=1) | |
| 4128 |
|
4133 | |||
| 4129 | conv = fft_data*fft_code |
|
4134 | conv = fft_data*fft_code | |
| 4130 |
|
4135 | |||
| 4131 | data = numpy.fft.ifft(conv,axis=1) |
|
4136 | data = numpy.fft.ifft(conv,axis=1) | |
| 4132 |
|
4137 | |||
| 4133 | return data |
|
4138 | return data | |
| 4134 |
|
4139 | |||
| 4135 | def __convolutionInFreqOpt(self, data): |
|
4140 | def __convolutionInFreqOpt(self, data): | |
| 4136 |
|
4141 | |||
| 4137 | raise NotImplementedError |
|
4142 | raise NotImplementedError | |
| 4138 |
|
4143 | |||
| 4139 | def __convolutionInTime(self, data): |
|
4144 | def __convolutionInTime(self, data): | |
| 4140 |
|
4145 | |||
| 4141 | code = self.code[self.__profIndex] |
|
4146 | code = self.code[self.__profIndex] | |
| 4142 | #print("code",code[0,0]) |
|
4147 | #print("code",code[0,0]) | |
| 4143 | for i in range(self.__nChannels): |
|
4148 | for i in range(self.__nChannels): | |
| 4144 | #aux=numpy.correlate(data[i,:], code, mode='full') |
|
4149 | #aux=numpy.correlate(data[i,:], code, mode='full') | |
| 4145 | #print(numpy.shape(aux)) |
|
4150 | #print(numpy.shape(aux)) | |
| 4146 | #print(numpy.shape(data[i,:])) |
|
4151 | #print(numpy.shape(data[i,:])) | |
| 4147 | #print(numpy.shape(code)) |
|
4152 | #print(numpy.shape(code)) | |
| 4148 | #exit(1) |
|
4153 | #exit(1) | |
| 4149 | self.datadecTime[i,:] = numpy.correlate(data[i,:], code, mode='full')[self.nBaud-1:] |
|
4154 | self.datadecTime[i,:] = numpy.correlate(data[i,:], code, mode='full')[self.nBaud-1:] | |
| 4150 |
|
4155 | |||
| 4151 | return self.datadecTime |
|
4156 | return self.datadecTime | |
| 4152 |
|
4157 | |||
| 4153 | def __convolutionByBlockInTime(self, data): |
|
4158 | def __convolutionByBlockInTime(self, data): | |
| 4154 |
|
4159 | |||
| 4155 | repetitions = int(self.__nProfiles / self.nCode) |
|
4160 | repetitions = int(self.__nProfiles / self.nCode) | |
| 4156 | junk = numpy.lib.stride_tricks.as_strided(self.code, (repetitions, self.code.size), (0, self.code.itemsize)) |
|
4161 | junk = numpy.lib.stride_tricks.as_strided(self.code, (repetitions, self.code.size), (0, self.code.itemsize)) | |
| 4157 | junk = junk.flatten() |
|
4162 | junk = junk.flatten() | |
| 4158 | code_block = numpy.reshape(junk, (self.nCode*repetitions, self.nBaud)) |
|
4163 | code_block = numpy.reshape(junk, (self.nCode*repetitions, self.nBaud)) | |
| 4159 | profilesList = range(self.__nProfiles) |
|
4164 | profilesList = range(self.__nProfiles) | |
| 4160 | #print(numpy.shape(self.datadecTime)) |
|
4165 | #print(numpy.shape(self.datadecTime)) | |
| 4161 | #print(numpy.shape(data)) |
|
4166 | #print(numpy.shape(data)) | |
| 4162 | for i in range(self.__nChannels): |
|
4167 | for i in range(self.__nChannels): | |
| 4163 | for j in profilesList: |
|
4168 | for j in profilesList: | |
| 4164 | self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:] |
|
4169 | self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:] | |
| 4165 | return self.datadecTime |
|
4170 | return self.datadecTime | |
| 4166 |
|
4171 | |||
| 4167 | def __convolutionByBlockInFreq(self, data): |
|
4172 | def __convolutionByBlockInFreq(self, data): | |
| 4168 |
|
4173 | |||
| 4169 | raise NotImplementedError("Decoder by frequency fro Blocks not implemented") |
|
4174 | raise NotImplementedError("Decoder by frequency fro Blocks not implemented") | |
| 4170 |
|
4175 | |||
| 4171 |
|
4176 | |||
| 4172 | fft_code = self.fft_code[self.__profIndex].reshape(1,-1) |
|
4177 | fft_code = self.fft_code[self.__profIndex].reshape(1,-1) | |
| 4173 |
|
4178 | |||
| 4174 | fft_data = numpy.fft.fft(data, axis=2) |
|
4179 | fft_data = numpy.fft.fft(data, axis=2) | |
| 4175 |
|
4180 | |||
| 4176 | conv = fft_data*fft_code |
|
4181 | conv = fft_data*fft_code | |
| 4177 |
|
4182 | |||
| 4178 | data = numpy.fft.ifft(conv,axis=2) |
|
4183 | data = numpy.fft.ifft(conv,axis=2) | |
| 4179 |
|
4184 | |||
| 4180 | return data |
|
4185 | return data | |
| 4181 |
|
4186 | |||
| 4182 |
|
4187 | |||
| 4183 | def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0, osamp=None, times=None): |
|
4188 | def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0, osamp=None, times=None): | |
| 4184 |
|
4189 | |||
| 4185 | if dataOut.flagDecodeData: |
|
4190 | if dataOut.flagDecodeData: | |
| 4186 | print("This data is already decoded, recoding again ...") |
|
4191 | print("This data is already decoded, recoding again ...") | |
| 4187 |
|
4192 | |||
| 4188 |
|
4193 | |||
| 4189 | roll = 0 |
|
4194 | roll = 0 | |
| 4190 |
|
4195 | |||
| 4191 | if self.isConfig: |
|
4196 | if self.isConfig: | |
| 4192 | code = numpy.array(code) |
|
4197 | code = numpy.array(code) | |
| 4193 |
|
4198 | |||
| 4194 | code = numpy.roll(code,roll,axis=0) |
|
4199 | code = numpy.roll(code,roll,axis=0) | |
| 4195 | code = numpy.reshape(code,(5,100,64)) |
|
4200 | code = numpy.reshape(code,(5,100,64)) | |
| 4196 | block = dataOut.CurrentBlock%5 |
|
4201 | block = dataOut.CurrentBlock%5 | |
| 4197 | #code = code[block-1,:,:] #NormalizeDPPower |
|
4202 | #code = code[block-1,:,:] #NormalizeDPPower | |
| 4198 | code = code[block-1-1,:,:] #Next Day |
|
4203 | code = code[block-1-1,:,:] #Next Day | |
| 4199 | self.code = numpy.repeat(code, repeats=self.osamp, axis=1) |
|
4204 | self.code = numpy.repeat(code, repeats=self.osamp, axis=1) | |
| 4200 |
|
4205 | |||
| 4201 |
|
4206 | |||
| 4202 | if not self.isConfig: |
|
4207 | if not self.isConfig: | |
| 4203 |
|
4208 | |||
| 4204 | if code is None: |
|
4209 | if code is None: | |
| 4205 | if dataOut.code is None: |
|
4210 | if dataOut.code is None: | |
| 4206 | raise ValueError("Code could not be read from %s instance. Enter a value in Code parameter" %dataOut.type) |
|
4211 | raise ValueError("Code could not be read from %s instance. Enter a value in Code parameter" %dataOut.type) | |
| 4207 |
|
4212 | |||
| 4208 | code = dataOut.code |
|
4213 | code = dataOut.code | |
| 4209 | else: |
|
4214 | else: | |
| 4210 | code = numpy.array(code) |
|
4215 | code = numpy.array(code) | |
| 4211 |
|
4216 | |||
| 4212 | #roll = 29 |
|
4217 | #roll = 29 | |
| 4213 | code = numpy.roll(code,roll,axis=0) |
|
4218 | code = numpy.roll(code,roll,axis=0) | |
| 4214 | code = numpy.reshape(code,(5,100,64)) |
|
4219 | code = numpy.reshape(code,(5,100,64)) | |
| 4215 | block = dataOut.CurrentBlock%5 |
|
4220 | block = dataOut.CurrentBlock%5 | |
| 4216 | code = code[block-1-1,:,:] |
|
4221 | code = code[block-1-1,:,:] | |
| 4217 | #print(code.shape()) |
|
4222 | #print(code.shape()) | |
| 4218 | #exit(1) |
|
4223 | #exit(1) | |
| 4219 |
|
4224 | |||
| 4220 | code = numpy.array(code).reshape(nCode,nBaud) |
|
4225 | code = numpy.array(code).reshape(nCode,nBaud) | |
| 4221 | self.setup(code, osamp, dataOut) |
|
4226 | self.setup(code, osamp, dataOut) | |
| 4222 |
|
4227 | |||
| 4223 | self.isConfig = True |
|
4228 | self.isConfig = True | |
| 4224 |
|
4229 | |||
| 4225 | if mode == 3: |
|
4230 | if mode == 3: | |
| 4226 | sys.stderr.write("Decoder Warning: mode=%d is not valid, using mode=0\n" %mode) |
|
4231 | sys.stderr.write("Decoder Warning: mode=%d is not valid, using mode=0\n" %mode) | |
| 4227 |
|
4232 | |||
| 4228 | if times != None: |
|
4233 | if times != None: | |
| 4229 | sys.stderr.write("Decoder Warning: Argument 'times' in not used anymore\n") |
|
4234 | sys.stderr.write("Decoder Warning: Argument 'times' in not used anymore\n") | |
| 4230 |
|
4235 | |||
| 4231 | if self.code is None: |
|
4236 | if self.code is None: | |
| 4232 | print("Fail decoding: Code is not defined.") |
|
4237 | print("Fail decoding: Code is not defined.") | |
| 4233 | return |
|
4238 | return | |
| 4234 |
|
4239 | |||
| 4235 | self.__nProfiles = dataOut.nProfiles |
|
4240 | self.__nProfiles = dataOut.nProfiles | |
| 4236 | datadec = None |
|
4241 | datadec = None | |
| 4237 |
|
4242 | |||
| 4238 | if mode == 3: |
|
4243 | if mode == 3: | |
| 4239 | mode = 0 |
|
4244 | mode = 0 | |
| 4240 |
|
4245 | |||
| 4241 | if dataOut.flagDataAsBlock: |
|
4246 | if dataOut.flagDataAsBlock: | |
| 4242 | """ |
|
4247 | """ | |
| 4243 | Decoding when data have been read as block, |
|
4248 | Decoding when data have been read as block, | |
| 4244 | """ |
|
4249 | """ | |
| 4245 |
|
4250 | |||
| 4246 | if mode == 0: |
|
4251 | if mode == 0: | |
| 4247 | datadec = self.__convolutionByBlockInTime(dataOut.data) |
|
4252 | datadec = self.__convolutionByBlockInTime(dataOut.data) | |
| 4248 | if mode == 1: |
|
4253 | if mode == 1: | |
| 4249 | datadec = self.__convolutionByBlockInFreq(dataOut.data) |
|
4254 | datadec = self.__convolutionByBlockInFreq(dataOut.data) | |
| 4250 | else: |
|
4255 | else: | |
| 4251 | """ |
|
4256 | """ | |
| 4252 | Decoding when data have been read profile by profile |
|
4257 | Decoding when data have been read profile by profile | |
| 4253 | """ |
|
4258 | """ | |
| 4254 | if mode == 0: |
|
4259 | if mode == 0: | |
| 4255 | datadec = self.__convolutionInTime(dataOut.data) |
|
4260 | datadec = self.__convolutionInTime(dataOut.data) | |
| 4256 |
|
4261 | |||
| 4257 | if mode == 1: |
|
4262 | if mode == 1: | |
| 4258 | datadec = self.__convolutionInFreq(dataOut.data) |
|
4263 | datadec = self.__convolutionInFreq(dataOut.data) | |
| 4259 |
|
4264 | |||
| 4260 | if mode == 2: |
|
4265 | if mode == 2: | |
| 4261 | datadec = self.__convolutionInFreqOpt(dataOut.data) |
|
4266 | datadec = self.__convolutionInFreqOpt(dataOut.data) | |
| 4262 |
|
4267 | |||
| 4263 | if datadec is None: |
|
4268 | if datadec is None: | |
| 4264 | raise ValueError("Codification mode selected is not valid: mode=%d. Try selecting 0 or 1" %mode) |
|
4269 | raise ValueError("Codification mode selected is not valid: mode=%d. Try selecting 0 or 1" %mode) | |
| 4265 |
|
4270 | |||
| 4266 | dataOut.code = self.code |
|
4271 | dataOut.code = self.code | |
| 4267 | dataOut.nCode = self.nCode |
|
4272 | dataOut.nCode = self.nCode | |
| 4268 | dataOut.nBaud = self.nBaud |
|
4273 | dataOut.nBaud = self.nBaud | |
| 4269 |
|
4274 | |||
| 4270 | dataOut.data = datadec |
|
4275 | dataOut.data = datadec | |
| 4271 | #print("before",dataOut.heightList) |
|
4276 | #print("before",dataOut.heightList) | |
| 4272 | dataOut.heightList = dataOut.heightList[0:datadec.shape[-1]] |
|
4277 | dataOut.heightList = dataOut.heightList[0:datadec.shape[-1]] | |
| 4273 | #print("after",dataOut.heightList) |
|
4278 | #print("after",dataOut.heightList) | |
| 4274 |
|
4279 | |||
| 4275 | dataOut.flagDecodeData = True #asumo q la data esta decodificada |
|
4280 | dataOut.flagDecodeData = True #asumo q la data esta decodificada | |
| 4276 |
|
4281 | |||
| 4277 | if self.__profIndex == self.nCode-1: |
|
4282 | if self.__profIndex == self.nCode-1: | |
| 4278 | self.__profIndex = 0 |
|
4283 | self.__profIndex = 0 | |
| 4279 | return dataOut |
|
4284 | return dataOut | |
| 4280 |
|
4285 | |||
| 4281 | self.__profIndex += 1 |
|
4286 | self.__profIndex += 1 | |
| 4282 |
|
4287 | |||
| 4283 | #print("SHAPE",numpy.shape(dataOut.data)) |
|
4288 | #print("SHAPE",numpy.shape(dataOut.data)) | |
| 4284 |
|
4289 | |||
| 4285 | return dataOut |
|
4290 | return dataOut | |
| 4286 |
|
4291 | |||
| 4287 |
|
4292 | |||
| 4288 | class ProfileConcat(Operation): |
|
4293 | class ProfileConcat(Operation): | |
| 4289 |
|
4294 | |||
| 4290 | isConfig = False |
|
4295 | isConfig = False | |
| 4291 | buffer = None |
|
4296 | buffer = None | |
| 4292 |
|
4297 | |||
| 4293 | def __init__(self, **kwargs): |
|
4298 | def __init__(self, **kwargs): | |
| 4294 |
|
4299 | |||
| 4295 | Operation.__init__(self, **kwargs) |
|
4300 | Operation.__init__(self, **kwargs) | |
| 4296 | self.profileIndex = 0 |
|
4301 | self.profileIndex = 0 | |
| 4297 |
|
4302 | |||
| 4298 | def reset(self): |
|
4303 | def reset(self): | |
| 4299 | self.buffer = numpy.zeros_like(self.buffer) |
|
4304 | self.buffer = numpy.zeros_like(self.buffer) | |
| 4300 | self.start_index = 0 |
|
4305 | self.start_index = 0 | |
| 4301 | self.times = 1 |
|
4306 | self.times = 1 | |
| 4302 |
|
4307 | |||
| 4303 | def setup(self, data, m, n=1): |
|
4308 | def setup(self, data, m, n=1): | |
| 4304 | self.buffer = numpy.zeros((data.shape[0],data.shape[1]*m),dtype=type(data[0,0])) |
|
4309 | self.buffer = numpy.zeros((data.shape[0],data.shape[1]*m),dtype=type(data[0,0])) | |
| 4305 | self.nHeights = data.shape[1]#.nHeights |
|
4310 | self.nHeights = data.shape[1]#.nHeights | |
| 4306 | self.start_index = 0 |
|
4311 | self.start_index = 0 | |
| 4307 | self.times = 1 |
|
4312 | self.times = 1 | |
| 4308 |
|
4313 | |||
| 4309 | def concat(self, data): |
|
4314 | def concat(self, data): | |
| 4310 |
|
4315 | |||
| 4311 | self.buffer[:,self.start_index:self.nHeights*self.times] = data.copy() |
|
4316 | self.buffer[:,self.start_index:self.nHeights*self.times] = data.copy() | |
| 4312 | self.start_index = self.start_index + self.nHeights |
|
4317 | self.start_index = self.start_index + self.nHeights | |
| 4313 |
|
4318 | |||
| 4314 | def run(self, dataOut, m): |
|
4319 | def run(self, dataOut, m): | |
| 4315 | dataOut.flagNoData = True |
|
4320 | dataOut.flagNoData = True | |
| 4316 |
|
4321 | |||
| 4317 | if not self.isConfig: |
|
4322 | if not self.isConfig: | |
| 4318 | self.setup(dataOut.data, m, 1) |
|
4323 | self.setup(dataOut.data, m, 1) | |
| 4319 | self.isConfig = True |
|
4324 | self.isConfig = True | |
| 4320 |
|
4325 | |||
| 4321 | if dataOut.flagDataAsBlock: |
|
4326 | if dataOut.flagDataAsBlock: | |
| 4322 | raise ValueError("ProfileConcat can only be used when voltage have been read profile by profile, getBlock = False") |
|
4327 | raise ValueError("ProfileConcat can only be used when voltage have been read profile by profile, getBlock = False") | |
| 4323 |
|
4328 | |||
| 4324 | else: |
|
4329 | else: | |
| 4325 | self.concat(dataOut.data) |
|
4330 | self.concat(dataOut.data) | |
| 4326 | self.times += 1 |
|
4331 | self.times += 1 | |
| 4327 | if self.times > m: |
|
4332 | if self.times > m: | |
| 4328 | dataOut.data = self.buffer |
|
4333 | dataOut.data = self.buffer | |
| 4329 | self.reset() |
|
4334 | self.reset() | |
| 4330 | dataOut.flagNoData = False |
|
4335 | dataOut.flagNoData = False | |
| 4331 | # se deben actualizar mas propiedades del header y del objeto dataOut, por ejemplo, las alturas |
|
4336 | # se deben actualizar mas propiedades del header y del objeto dataOut, por ejemplo, las alturas | |
| 4332 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
4337 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] | |
| 4333 | xf = dataOut.heightList[0] + dataOut.nHeights * deltaHeight * m |
|
4338 | xf = dataOut.heightList[0] + dataOut.nHeights * deltaHeight * m | |
| 4334 | dataOut.heightList = numpy.arange(dataOut.heightList[0], xf, deltaHeight) |
|
4339 | dataOut.heightList = numpy.arange(dataOut.heightList[0], xf, deltaHeight) | |
| 4335 | dataOut.ippSeconds *= m |
|
4340 | dataOut.ippSeconds *= m | |
| 4336 | return dataOut |
|
4341 | return dataOut | |
| 4337 |
|
4342 | |||
| 4338 | class ProfileSelector(Operation): |
|
4343 | class ProfileSelector(Operation): | |
| 4339 |
|
4344 | |||
| 4340 | profileIndex = None |
|
4345 | profileIndex = None | |
| 4341 | # Tamanho total de los perfiles |
|
4346 | # Tamanho total de los perfiles | |
| 4342 | nProfiles = None |
|
4347 | nProfiles = None | |
| 4343 |
|
4348 | |||
| 4344 | def __init__(self, **kwargs): |
|
4349 | def __init__(self, **kwargs): | |
| 4345 |
|
4350 | |||
| 4346 | Operation.__init__(self, **kwargs) |
|
4351 | Operation.__init__(self, **kwargs) | |
| 4347 | self.profileIndex = 0 |
|
4352 | self.profileIndex = 0 | |
| 4348 |
|
4353 | |||
| 4349 | def incProfileIndex(self): |
|
4354 | def incProfileIndex(self): | |
| 4350 |
|
4355 | |||
| 4351 | self.profileIndex += 1 |
|
4356 | self.profileIndex += 1 | |
| 4352 |
|
4357 | |||
| 4353 | if self.profileIndex >= self.nProfiles: |
|
4358 | if self.profileIndex >= self.nProfiles: | |
| 4354 | self.profileIndex = 0 |
|
4359 | self.profileIndex = 0 | |
| 4355 |
|
4360 | |||
| 4356 | def isThisProfileInRange(self, profileIndex, minIndex, maxIndex): |
|
4361 | def isThisProfileInRange(self, profileIndex, minIndex, maxIndex): | |
| 4357 |
|
4362 | |||
| 4358 | if profileIndex < minIndex: |
|
4363 | if profileIndex < minIndex: | |
| 4359 | return False |
|
4364 | return False | |
| 4360 |
|
4365 | |||
| 4361 | if profileIndex > maxIndex: |
|
4366 | if profileIndex > maxIndex: | |
| 4362 | return False |
|
4367 | return False | |
| 4363 |
|
4368 | |||
| 4364 | return True |
|
4369 | return True | |
| 4365 |
|
4370 | |||
| 4366 | def isThisProfileInList(self, profileIndex, profileList): |
|
4371 | def isThisProfileInList(self, profileIndex, profileList): | |
| 4367 |
|
4372 | |||
| 4368 | if profileIndex not in profileList: |
|
4373 | if profileIndex not in profileList: | |
| 4369 | return False |
|
4374 | return False | |
| 4370 |
|
4375 | |||
| 4371 | return True |
|
4376 | return True | |
| 4372 |
|
4377 | |||
| 4373 | def run(self, dataOut, profileList=None, profileRangeList=None, beam=None, byblock=False, rangeList = None, nProfiles=None): |
|
4378 | def run(self, dataOut, profileList=None, profileRangeList=None, beam=None, byblock=False, rangeList = None, nProfiles=None): | |
| 4374 |
|
4379 | |||
| 4375 | """ |
|
4380 | """ | |
| 4376 | ProfileSelector: |
|
4381 | ProfileSelector: | |
| 4377 |
|
4382 | |||
| 4378 | Inputs: |
|
4383 | Inputs: | |
| 4379 | profileList : Index of profiles selected. Example: profileList = (0,1,2,7,8) |
|
4384 | profileList : Index of profiles selected. Example: profileList = (0,1,2,7,8) | |
| 4380 |
|
4385 | |||
| 4381 | profileRangeList : Minimum and maximum profile indexes. Example: profileRangeList = (4, 30) |
|
4386 | profileRangeList : Minimum and maximum profile indexes. Example: profileRangeList = (4, 30) | |
| 4382 |
|
4387 | |||
| 4383 | rangeList : List of profile ranges. Example: rangeList = ((4, 30), (32, 64), (128, 256)) |
|
4388 | rangeList : List of profile ranges. Example: rangeList = ((4, 30), (32, 64), (128, 256)) | |
| 4384 |
|
4389 | |||
| 4385 | """ |
|
4390 | """ | |
| 4386 |
|
4391 | |||
| 4387 | if rangeList is not None: |
|
4392 | if rangeList is not None: | |
| 4388 | if type(rangeList[0]) not in (tuple, list): |
|
4393 | if type(rangeList[0]) not in (tuple, list): | |
| 4389 | rangeList = [rangeList] |
|
4394 | rangeList = [rangeList] | |
| 4390 |
|
4395 | |||
| 4391 | dataOut.flagNoData = True |
|
4396 | dataOut.flagNoData = True | |
| 4392 |
|
4397 | |||
| 4393 | if dataOut.flagDataAsBlock: |
|
4398 | if dataOut.flagDataAsBlock: | |
| 4394 | """ |
|
4399 | """ | |
| 4395 | data dimension = [nChannels, nProfiles, nHeis] |
|
4400 | data dimension = [nChannels, nProfiles, nHeis] | |
| 4396 | """ |
|
4401 | """ | |
| 4397 | if profileList != None: |
|
4402 | if profileList != None: | |
| 4398 | dataOut.data = dataOut.data[:,profileList,:] |
|
4403 | dataOut.data = dataOut.data[:,profileList,:] | |
| 4399 |
|
4404 | |||
| 4400 | if profileRangeList != None: |
|
4405 | if profileRangeList != None: | |
| 4401 | minIndex = profileRangeList[0] |
|
4406 | minIndex = profileRangeList[0] | |
| 4402 | maxIndex = profileRangeList[1] |
|
4407 | maxIndex = profileRangeList[1] | |
| 4403 | profileList = list(range(minIndex, maxIndex+1)) |
|
4408 | profileList = list(range(minIndex, maxIndex+1)) | |
| 4404 |
|
4409 | |||
| 4405 | dataOut.data = dataOut.data[:,minIndex:maxIndex+1,:] |
|
4410 | dataOut.data = dataOut.data[:,minIndex:maxIndex+1,:] | |
| 4406 |
|
4411 | |||
| 4407 | if rangeList != None: |
|
4412 | if rangeList != None: | |
| 4408 |
|
4413 | |||
| 4409 | profileList = [] |
|
4414 | profileList = [] | |
| 4410 |
|
4415 | |||
| 4411 | for thisRange in rangeList: |
|
4416 | for thisRange in rangeList: | |
| 4412 | minIndex = thisRange[0] |
|
4417 | minIndex = thisRange[0] | |
| 4413 | maxIndex = thisRange[1] |
|
4418 | maxIndex = thisRange[1] | |
| 4414 |
|
4419 | |||
| 4415 | profileList.extend(list(range(minIndex, maxIndex+1))) |
|
4420 | profileList.extend(list(range(minIndex, maxIndex+1))) | |
| 4416 |
|
4421 | |||
| 4417 | dataOut.data = dataOut.data[:,profileList,:] |
|
4422 | dataOut.data = dataOut.data[:,profileList,:] | |
| 4418 |
|
4423 | |||
| 4419 | dataOut.nProfiles = len(profileList) |
|
4424 | dataOut.nProfiles = len(profileList) | |
| 4420 | dataOut.profileIndex = dataOut.nProfiles - 1 |
|
4425 | dataOut.profileIndex = dataOut.nProfiles - 1 | |
| 4421 | dataOut.flagNoData = False |
|
4426 | dataOut.flagNoData = False | |
| 4422 |
|
4427 | |||
| 4423 | return dataOut |
|
4428 | return dataOut | |
| 4424 |
|
4429 | |||
| 4425 | """ |
|
4430 | """ | |
| 4426 | data dimension = [nChannels, nHeis] |
|
4431 | data dimension = [nChannels, nHeis] | |
| 4427 | """ |
|
4432 | """ | |
| 4428 |
|
4433 | |||
| 4429 | if profileList != None: |
|
4434 | if profileList != None: | |
| 4430 |
|
4435 | |||
| 4431 | if self.isThisProfileInList(dataOut.profileIndex, profileList): |
|
4436 | if self.isThisProfileInList(dataOut.profileIndex, profileList): | |
| 4432 |
|
4437 | |||
| 4433 | self.nProfiles = len(profileList) |
|
4438 | self.nProfiles = len(profileList) | |
| 4434 | dataOut.nProfiles = self.nProfiles |
|
4439 | dataOut.nProfiles = self.nProfiles | |
| 4435 | dataOut.profileIndex = self.profileIndex |
|
4440 | dataOut.profileIndex = self.profileIndex | |
| 4436 | dataOut.flagNoData = False |
|
4441 | dataOut.flagNoData = False | |
| 4437 |
|
4442 | |||
| 4438 | self.incProfileIndex() |
|
4443 | self.incProfileIndex() | |
| 4439 | return dataOut |
|
4444 | return dataOut | |
| 4440 |
|
4445 | |||
| 4441 | if profileRangeList != None: |
|
4446 | if profileRangeList != None: | |
| 4442 |
|
4447 | |||
| 4443 | minIndex = profileRangeList[0] |
|
4448 | minIndex = profileRangeList[0] | |
| 4444 | maxIndex = profileRangeList[1] |
|
4449 | maxIndex = profileRangeList[1] | |
| 4445 |
|
4450 | |||
| 4446 | if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex): |
|
4451 | if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex): | |
| 4447 |
|
4452 | |||
| 4448 | self.nProfiles = maxIndex - minIndex + 1 |
|
4453 | self.nProfiles = maxIndex - minIndex + 1 | |
| 4449 | dataOut.nProfiles = self.nProfiles |
|
4454 | dataOut.nProfiles = self.nProfiles | |
| 4450 | dataOut.profileIndex = self.profileIndex |
|
4455 | dataOut.profileIndex = self.profileIndex | |
| 4451 | dataOut.flagNoData = False |
|
4456 | dataOut.flagNoData = False | |
| 4452 |
|
4457 | |||
| 4453 | self.incProfileIndex() |
|
4458 | self.incProfileIndex() | |
| 4454 | return dataOut |
|
4459 | return dataOut | |
| 4455 |
|
4460 | |||
| 4456 | if rangeList != None: |
|
4461 | if rangeList != None: | |
| 4457 |
|
4462 | |||
| 4458 | nProfiles = 0 |
|
4463 | nProfiles = 0 | |
| 4459 |
|
4464 | |||
| 4460 | for thisRange in rangeList: |
|
4465 | for thisRange in rangeList: | |
| 4461 | minIndex = thisRange[0] |
|
4466 | minIndex = thisRange[0] | |
| 4462 | maxIndex = thisRange[1] |
|
4467 | maxIndex = thisRange[1] | |
| 4463 |
|
4468 | |||
| 4464 | nProfiles += maxIndex - minIndex + 1 |
|
4469 | nProfiles += maxIndex - minIndex + 1 | |
| 4465 |
|
4470 | |||
| 4466 | for thisRange in rangeList: |
|
4471 | for thisRange in rangeList: | |
| 4467 |
|
4472 | |||
| 4468 | minIndex = thisRange[0] |
|
4473 | minIndex = thisRange[0] | |
| 4469 | maxIndex = thisRange[1] |
|
4474 | maxIndex = thisRange[1] | |
| 4470 |
|
4475 | |||
| 4471 | if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex): |
|
4476 | if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex): | |
| 4472 |
|
4477 | |||
| 4473 | self.nProfiles = nProfiles |
|
4478 | self.nProfiles = nProfiles | |
| 4474 | dataOut.nProfiles = self.nProfiles |
|
4479 | dataOut.nProfiles = self.nProfiles | |
| 4475 | dataOut.profileIndex = self.profileIndex |
|
4480 | dataOut.profileIndex = self.profileIndex | |
| 4476 | dataOut.flagNoData = False |
|
4481 | dataOut.flagNoData = False | |
| 4477 |
|
4482 | |||
| 4478 | self.incProfileIndex() |
|
4483 | self.incProfileIndex() | |
| 4479 |
|
4484 | |||
| 4480 | break |
|
4485 | break | |
| 4481 |
|
4486 | |||
| 4482 | return dataOut |
|
4487 | return dataOut | |
| 4483 |
|
4488 | |||
| 4484 |
|
4489 | |||
| 4485 | if beam != None: #beam is only for AMISR data |
|
4490 | if beam != None: #beam is only for AMISR data | |
| 4486 | if self.isThisProfileInList(dataOut.profileIndex, dataOut.beamRangeDict[beam]): |
|
4491 | if self.isThisProfileInList(dataOut.profileIndex, dataOut.beamRangeDict[beam]): | |
| 4487 | dataOut.flagNoData = False |
|
4492 | dataOut.flagNoData = False | |
| 4488 | dataOut.profileIndex = self.profileIndex |
|
4493 | dataOut.profileIndex = self.profileIndex | |
| 4489 |
|
4494 | |||
| 4490 | self.incProfileIndex() |
|
4495 | self.incProfileIndex() | |
| 4491 |
|
4496 | |||
| 4492 | return dataOut |
|
4497 | return dataOut | |
| 4493 |
|
4498 | |||
| 4494 | raise ValueError("ProfileSelector needs profileList, profileRangeList or rangeList parameter") |
|
4499 | raise ValueError("ProfileSelector needs profileList, profileRangeList or rangeList parameter") | |
| 4495 |
|
4500 | |||
| 4496 | #return False |
|
4501 | #return False | |
| 4497 | return dataOut |
|
4502 | return dataOut | |
| 4498 |
|
4503 | |||
| 4499 | class Reshaper(Operation): |
|
4504 | class Reshaper(Operation): | |
| 4500 |
|
4505 | |||
| 4501 | def __init__(self, **kwargs): |
|
4506 | def __init__(self, **kwargs): | |
| 4502 |
|
4507 | |||
| 4503 | Operation.__init__(self, **kwargs) |
|
4508 | Operation.__init__(self, **kwargs) | |
| 4504 |
|
4509 | |||
| 4505 | self.__buffer = None |
|
4510 | self.__buffer = None | |
| 4506 | self.__nitems = 0 |
|
4511 | self.__nitems = 0 | |
| 4507 |
|
4512 | |||
| 4508 | def __appendProfile(self, dataOut, nTxs): |
|
4513 | def __appendProfile(self, dataOut, nTxs): | |
| 4509 |
|
4514 | |||
| 4510 | if self.__buffer is None: |
|
4515 | if self.__buffer is None: | |
| 4511 | shape = (dataOut.nChannels, int(dataOut.nHeights/nTxs) ) |
|
4516 | shape = (dataOut.nChannels, int(dataOut.nHeights/nTxs) ) | |
| 4512 | self.__buffer = numpy.empty(shape, dtype = dataOut.data.dtype) |
|
4517 | self.__buffer = numpy.empty(shape, dtype = dataOut.data.dtype) | |
| 4513 |
|
4518 | |||
| 4514 | ini = dataOut.nHeights * self.__nitems |
|
4519 | ini = dataOut.nHeights * self.__nitems | |
| 4515 | end = ini + dataOut.nHeights |
|
4520 | end = ini + dataOut.nHeights | |
| 4516 |
|
4521 | |||
| 4517 | self.__buffer[:, ini:end] = dataOut.data |
|
4522 | self.__buffer[:, ini:end] = dataOut.data | |
| 4518 |
|
4523 | |||
| 4519 | self.__nitems += 1 |
|
4524 | self.__nitems += 1 | |
| 4520 |
|
4525 | |||
| 4521 | return int(self.__nitems*nTxs) |
|
4526 | return int(self.__nitems*nTxs) | |
| 4522 |
|
4527 | |||
| 4523 | def __getBuffer(self): |
|
4528 | def __getBuffer(self): | |
| 4524 |
|
4529 | |||
| 4525 | if self.__nitems == int(1./self.__nTxs): |
|
4530 | if self.__nitems == int(1./self.__nTxs): | |
| 4526 |
|
4531 | |||
| 4527 | self.__nitems = 0 |
|
4532 | self.__nitems = 0 | |
| 4528 |
|
4533 | |||
| 4529 | return self.__buffer.copy() |
|
4534 | return self.__buffer.copy() | |
| 4530 |
|
4535 | |||
| 4531 | return None |
|
4536 | return None | |
| 4532 |
|
4537 | |||
| 4533 | def __checkInputs(self, dataOut, shape, nTxs): |
|
4538 | def __checkInputs(self, dataOut, shape, nTxs): | |
| 4534 |
|
4539 | |||
| 4535 | if shape is None and nTxs is None: |
|
4540 | if shape is None and nTxs is None: | |
| 4536 | raise ValueError("Reshaper: shape of factor should be defined") |
|
4541 | raise ValueError("Reshaper: shape of factor should be defined") | |
| 4537 |
|
4542 | |||
| 4538 | if nTxs: |
|
4543 | if nTxs: | |
| 4539 | if nTxs < 0: |
|
4544 | if nTxs < 0: | |
| 4540 | raise ValueError("nTxs should be greater than 0") |
|
4545 | raise ValueError("nTxs should be greater than 0") | |
| 4541 |
|
4546 | |||
| 4542 | if nTxs < 1 and dataOut.nProfiles % (1./nTxs) != 0: |
|
4547 | if nTxs < 1 and dataOut.nProfiles % (1./nTxs) != 0: | |
| 4543 | raise ValueError("nProfiles= %d is not divisibled by (1./nTxs) = %f" %(dataOut.nProfiles, (1./nTxs))) |
|
4548 | raise ValueError("nProfiles= %d is not divisibled by (1./nTxs) = %f" %(dataOut.nProfiles, (1./nTxs))) | |
| 4544 |
|
4549 | |||
| 4545 | shape = [dataOut.nChannels, dataOut.nProfiles*nTxs, dataOut.nHeights/nTxs] |
|
4550 | shape = [dataOut.nChannels, dataOut.nProfiles*nTxs, dataOut.nHeights/nTxs] | |
| 4546 |
|
4551 | |||
| 4547 | return shape, nTxs |
|
4552 | return shape, nTxs | |
| 4548 |
|
4553 | |||
| 4549 | if len(shape) != 2 and len(shape) != 3: |
|
4554 | if len(shape) != 2 and len(shape) != 3: | |
| 4550 | 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)) |
|
4555 | 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)) | |
| 4551 |
|
4556 | |||
| 4552 | if len(shape) == 2: |
|
4557 | if len(shape) == 2: | |
| 4553 | shape_tuple = [dataOut.nChannels] |
|
4558 | shape_tuple = [dataOut.nChannels] | |
| 4554 | shape_tuple.extend(shape) |
|
4559 | shape_tuple.extend(shape) | |
| 4555 | else: |
|
4560 | else: | |
| 4556 | shape_tuple = list(shape) |
|
4561 | shape_tuple = list(shape) | |
| 4557 |
|
4562 | |||
| 4558 | nTxs = 1.0*shape_tuple[1]/dataOut.nProfiles |
|
4563 | nTxs = 1.0*shape_tuple[1]/dataOut.nProfiles | |
| 4559 |
|
4564 | |||
| 4560 | return shape_tuple, nTxs |
|
4565 | return shape_tuple, nTxs | |
| 4561 |
|
4566 | |||
| 4562 | def run(self, dataOut, shape=None, nTxs=None): |
|
4567 | def run(self, dataOut, shape=None, nTxs=None): | |
| 4563 |
|
4568 | |||
| 4564 | shape_tuple, self.__nTxs = self.__checkInputs(dataOut, shape, nTxs) |
|
4569 | shape_tuple, self.__nTxs = self.__checkInputs(dataOut, shape, nTxs) | |
| 4565 |
|
4570 | |||
| 4566 | dataOut.flagNoData = True |
|
4571 | dataOut.flagNoData = True | |
| 4567 | profileIndex = None |
|
4572 | profileIndex = None | |
| 4568 |
|
4573 | |||
| 4569 | if dataOut.flagDataAsBlock: |
|
4574 | if dataOut.flagDataAsBlock: | |
| 4570 |
|
4575 | |||
| 4571 | dataOut.data = numpy.reshape(dataOut.data, shape_tuple) |
|
4576 | dataOut.data = numpy.reshape(dataOut.data, shape_tuple) | |
| 4572 | dataOut.flagNoData = False |
|
4577 | dataOut.flagNoData = False | |
| 4573 |
|
4578 | |||
| 4574 | profileIndex = int(dataOut.nProfiles*self.__nTxs) - 1 |
|
4579 | profileIndex = int(dataOut.nProfiles*self.__nTxs) - 1 | |
| 4575 |
|
4580 | |||
| 4576 | else: |
|
4581 | else: | |
| 4577 |
|
4582 | |||
| 4578 |
|
4583 | |||
| 4579 | if self.__nTxs < 1: |
|
4584 | if self.__nTxs < 1: | |
| 4580 |
|
4585 | |||
| 4581 | self.__appendProfile(dataOut, self.__nTxs) |
|
4586 | self.__appendProfile(dataOut, self.__nTxs) | |
| 4582 | new_data = self.__getBuffer() |
|
4587 | new_data = self.__getBuffer() | |
| 4583 |
|
4588 | |||
| 4584 | if new_data is not None: |
|
4589 | if new_data is not None: | |
| 4585 | dataOut.data = new_data |
|
4590 | dataOut.data = new_data | |
| 4586 | dataOut.flagNoData = False |
|
4591 | dataOut.flagNoData = False | |
| 4587 |
|
4592 | |||
| 4588 | profileIndex = dataOut.profileIndex*nTxs |
|
4593 | profileIndex = dataOut.profileIndex*nTxs | |
| 4589 |
|
4594 | |||
| 4590 | else: |
|
4595 | else: | |
| 4591 | raise ValueError("nTxs should be greater than 0 and lower than 1, or use VoltageReader(..., getblock=True)") |
|
4596 | raise ValueError("nTxs should be greater than 0 and lower than 1, or use VoltageReader(..., getblock=True)") | |
| 4592 |
|
4597 | |||
| 4593 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
4598 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] | |
| 4594 |
|
4599 | |||
| 4595 | dataOut.heightList = numpy.arange(dataOut.nHeights/self.__nTxs) * deltaHeight + dataOut.heightList[0] |
|
4600 | dataOut.heightList = numpy.arange(dataOut.nHeights/self.__nTxs) * deltaHeight + dataOut.heightList[0] | |
| 4596 |
|
4601 | |||
| 4597 | dataOut.nProfiles = int(dataOut.nProfiles*self.__nTxs) |
|
4602 | dataOut.nProfiles = int(dataOut.nProfiles*self.__nTxs) | |
| 4598 |
|
4603 | |||
| 4599 | dataOut.profileIndex = profileIndex |
|
4604 | dataOut.profileIndex = profileIndex | |
| 4600 |
|
4605 | |||
| 4601 | dataOut.ippSeconds /= self.__nTxs |
|
4606 | dataOut.ippSeconds /= self.__nTxs | |
| 4602 |
|
4607 | |||
| 4603 | return dataOut |
|
4608 | return dataOut | |
| 4604 |
|
4609 | |||
| 4605 | class SplitProfiles(Operation): |
|
4610 | class SplitProfiles(Operation): | |
| 4606 |
|
4611 | |||
| 4607 | def __init__(self, **kwargs): |
|
4612 | def __init__(self, **kwargs): | |
| 4608 |
|
4613 | |||
| 4609 | Operation.__init__(self, **kwargs) |
|
4614 | Operation.__init__(self, **kwargs) | |
| 4610 |
|
4615 | |||
| 4611 | def run(self, dataOut, n): |
|
4616 | def run(self, dataOut, n): | |
| 4612 |
|
4617 | |||
| 4613 | dataOut.flagNoData = True |
|
4618 | dataOut.flagNoData = True | |
| 4614 | profileIndex = None |
|
4619 | profileIndex = None | |
| 4615 |
|
4620 | |||
| 4616 | if dataOut.flagDataAsBlock: |
|
4621 | if dataOut.flagDataAsBlock: | |
| 4617 |
|
4622 | |||
| 4618 | #nchannels, nprofiles, nsamples |
|
4623 | #nchannels, nprofiles, nsamples | |
| 4619 | shape = dataOut.data.shape |
|
4624 | shape = dataOut.data.shape | |
| 4620 |
|
4625 | |||
| 4621 | if shape[2] % n != 0: |
|
4626 | if shape[2] % n != 0: | |
| 4622 | raise ValueError("Could not split the data, n=%d has to be multiple of %d" %(n, shape[2])) |
|
4627 | raise ValueError("Could not split the data, n=%d has to be multiple of %d" %(n, shape[2])) | |
| 4623 |
|
4628 | |||
| 4624 | new_shape = shape[0], shape[1]*n, int(shape[2]/n) |
|
4629 | new_shape = shape[0], shape[1]*n, int(shape[2]/n) | |
| 4625 |
|
4630 | |||
| 4626 | dataOut.data = numpy.reshape(dataOut.data, new_shape) |
|
4631 | dataOut.data = numpy.reshape(dataOut.data, new_shape) | |
| 4627 | dataOut.flagNoData = False |
|
4632 | dataOut.flagNoData = False | |
| 4628 |
|
4633 | |||
| 4629 | profileIndex = int(dataOut.nProfiles/n) - 1 |
|
4634 | profileIndex = int(dataOut.nProfiles/n) - 1 | |
| 4630 |
|
4635 | |||
| 4631 | else: |
|
4636 | else: | |
| 4632 |
|
4637 | |||
| 4633 | raise ValueError("Could not split the data when is read Profile by Profile. Use VoltageReader(..., getblock=True)") |
|
4638 | raise ValueError("Could not split the data when is read Profile by Profile. Use VoltageReader(..., getblock=True)") | |
| 4634 |
|
4639 | |||
| 4635 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
4640 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] | |
| 4636 |
|
4641 | |||
| 4637 | dataOut.heightList = numpy.arange(dataOut.nHeights/n) * deltaHeight + dataOut.heightList[0] |
|
4642 | dataOut.heightList = numpy.arange(dataOut.nHeights/n) * deltaHeight + dataOut.heightList[0] | |
| 4638 |
|
4643 | |||
| 4639 | dataOut.nProfiles = int(dataOut.nProfiles*n) |
|
4644 | dataOut.nProfiles = int(dataOut.nProfiles*n) | |
| 4640 |
|
4645 | |||
| 4641 | dataOut.profileIndex = profileIndex |
|
4646 | dataOut.profileIndex = profileIndex | |
| 4642 |
|
4647 | |||
| 4643 | dataOut.ippSeconds /= n |
|
4648 | dataOut.ippSeconds /= n | |
| 4644 |
|
4649 | |||
| 4645 | return dataOut |
|
4650 | return dataOut | |
| 4646 |
|
4651 | |||
| 4647 | class CombineProfiles(Operation): |
|
4652 | class CombineProfiles(Operation): | |
| 4648 | def __init__(self, **kwargs): |
|
4653 | def __init__(self, **kwargs): | |
| 4649 |
|
4654 | |||
| 4650 | Operation.__init__(self, **kwargs) |
|
4655 | Operation.__init__(self, **kwargs) | |
| 4651 |
|
4656 | |||
| 4652 | self.__remData = None |
|
4657 | self.__remData = None | |
| 4653 | self.__profileIndex = 0 |
|
4658 | self.__profileIndex = 0 | |
| 4654 |
|
4659 | |||
| 4655 | def run(self, dataOut, n): |
|
4660 | def run(self, dataOut, n): | |
| 4656 |
|
4661 | |||
| 4657 | dataOut.flagNoData = True |
|
4662 | dataOut.flagNoData = True | |
| 4658 | profileIndex = None |
|
4663 | profileIndex = None | |
| 4659 |
|
4664 | |||
| 4660 | if dataOut.flagDataAsBlock: |
|
4665 | if dataOut.flagDataAsBlock: | |
| 4661 |
|
4666 | |||
| 4662 | #nchannels, nprofiles, nsamples |
|
4667 | #nchannels, nprofiles, nsamples | |
| 4663 | shape = dataOut.data.shape |
|
4668 | shape = dataOut.data.shape | |
| 4664 | new_shape = shape[0], shape[1]/n, shape[2]*n |
|
4669 | new_shape = shape[0], shape[1]/n, shape[2]*n | |
| 4665 |
|
4670 | |||
| 4666 | if shape[1] % n != 0: |
|
4671 | if shape[1] % n != 0: | |
| 4667 | raise ValueError("Could not split the data, n=%d has to be multiple of %d" %(n, shape[1])) |
|
4672 | raise ValueError("Could not split the data, n=%d has to be multiple of %d" %(n, shape[1])) | |
| 4668 |
|
4673 | |||
| 4669 | dataOut.data = numpy.reshape(dataOut.data, new_shape) |
|
4674 | dataOut.data = numpy.reshape(dataOut.data, new_shape) | |
| 4670 | dataOut.flagNoData = False |
|
4675 | dataOut.flagNoData = False | |
| 4671 |
|
4676 | |||
| 4672 | profileIndex = int(dataOut.nProfiles*n) - 1 |
|
4677 | profileIndex = int(dataOut.nProfiles*n) - 1 | |
| 4673 |
|
4678 | |||
| 4674 | else: |
|
4679 | else: | |
| 4675 |
|
4680 | |||
| 4676 | #nchannels, nsamples |
|
4681 | #nchannels, nsamples | |
| 4677 | if self.__remData is None: |
|
4682 | if self.__remData is None: | |
| 4678 | newData = dataOut.data |
|
4683 | newData = dataOut.data | |
| 4679 | else: |
|
4684 | else: | |
| 4680 | newData = numpy.concatenate((self.__remData, dataOut.data), axis=1) |
|
4685 | newData = numpy.concatenate((self.__remData, dataOut.data), axis=1) | |
| 4681 |
|
4686 | |||
| 4682 | self.__profileIndex += 1 |
|
4687 | self.__profileIndex += 1 | |
| 4683 |
|
4688 | |||
| 4684 | if self.__profileIndex < n: |
|
4689 | if self.__profileIndex < n: | |
| 4685 | self.__remData = newData |
|
4690 | self.__remData = newData | |
| 4686 | #continue |
|
4691 | #continue | |
| 4687 | return |
|
4692 | return | |
| 4688 |
|
4693 | |||
| 4689 | self.__profileIndex = 0 |
|
4694 | self.__profileIndex = 0 | |
| 4690 | self.__remData = None |
|
4695 | self.__remData = None | |
| 4691 |
|
4696 | |||
| 4692 | dataOut.data = newData |
|
4697 | dataOut.data = newData | |
| 4693 | dataOut.flagNoData = False |
|
4698 | dataOut.flagNoData = False | |
| 4694 |
|
4699 | |||
| 4695 | profileIndex = dataOut.profileIndex/n |
|
4700 | profileIndex = dataOut.profileIndex/n | |
| 4696 |
|
4701 | |||
| 4697 |
|
4702 | |||
| 4698 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
4703 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] | |
| 4699 |
|
4704 | |||
| 4700 | dataOut.heightList = numpy.arange(dataOut.nHeights*n) * deltaHeight + dataOut.heightList[0] |
|
4705 | dataOut.heightList = numpy.arange(dataOut.nHeights*n) * deltaHeight + dataOut.heightList[0] | |
| 4701 |
|
4706 | |||
| 4702 | dataOut.nProfiles = int(dataOut.nProfiles/n) |
|
4707 | dataOut.nProfiles = int(dataOut.nProfiles/n) | |
| 4703 |
|
4708 | |||
| 4704 | dataOut.profileIndex = profileIndex |
|
4709 | dataOut.profileIndex = profileIndex | |
| 4705 |
|
4710 | |||
| 4706 | dataOut.ippSeconds *= n |
|
4711 | dataOut.ippSeconds *= n | |
| 4707 |
|
4712 | |||
| 4708 | return dataOut |
|
4713 | return dataOut | |
| 4709 | # import collections |
|
4714 | # import collections | |
| 4710 | # from scipy.stats import mode |
|
4715 | # from scipy.stats import mode | |
| 4711 | # |
|
4716 | # | |
| 4712 | # class Synchronize(Operation): |
|
4717 | # class Synchronize(Operation): | |
| 4713 | # |
|
4718 | # | |
| 4714 | # isConfig = False |
|
4719 | # isConfig = False | |
| 4715 | # __profIndex = 0 |
|
4720 | # __profIndex = 0 | |
| 4716 | # |
|
4721 | # | |
| 4717 | # def __init__(self, **kwargs): |
|
4722 | # def __init__(self, **kwargs): | |
| 4718 | # |
|
4723 | # | |
| 4719 | # Operation.__init__(self, **kwargs) |
|
4724 | # Operation.__init__(self, **kwargs) | |
| 4720 | # # self.isConfig = False |
|
4725 | # # self.isConfig = False | |
| 4721 | # self.__powBuffer = None |
|
4726 | # self.__powBuffer = None | |
| 4722 | # self.__startIndex = 0 |
|
4727 | # self.__startIndex = 0 | |
| 4723 | # self.__pulseFound = False |
|
4728 | # self.__pulseFound = False | |
| 4724 | # |
|
4729 | # | |
| 4725 | # def __findTxPulse(self, dataOut, channel=0, pulse_with = None): |
|
4730 | # def __findTxPulse(self, dataOut, channel=0, pulse_with = None): | |
| 4726 | # |
|
4731 | # | |
| 4727 | # #Read data |
|
4732 | # #Read data | |
| 4728 | # |
|
4733 | # | |
| 4729 | # powerdB = dataOut.getPower(channel = channel) |
|
4734 | # powerdB = dataOut.getPower(channel = channel) | |
| 4730 | # noisedB = dataOut.getNoise(channel = channel)[0] |
|
4735 | # noisedB = dataOut.getNoise(channel = channel)[0] | |
| 4731 | # |
|
4736 | # | |
| 4732 | # self.__powBuffer.extend(powerdB.flatten()) |
|
4737 | # self.__powBuffer.extend(powerdB.flatten()) | |
| 4733 | # |
|
4738 | # | |
| 4734 | # dataArray = numpy.array(self.__powBuffer) |
|
4739 | # dataArray = numpy.array(self.__powBuffer) | |
| 4735 | # |
|
4740 | # | |
| 4736 | # filteredPower = numpy.correlate(dataArray, dataArray[0:self.__nSamples], "same") |
|
4741 | # filteredPower = numpy.correlate(dataArray, dataArray[0:self.__nSamples], "same") | |
| 4737 | # |
|
4742 | # | |
| 4738 | # maxValue = numpy.nanmax(filteredPower) |
|
4743 | # maxValue = numpy.nanmax(filteredPower) | |
| 4739 | # |
|
4744 | # | |
| 4740 | # if maxValue < noisedB + 10: |
|
4745 | # if maxValue < noisedB + 10: | |
| 4741 | # #No se encuentra ningun pulso de transmision |
|
4746 | # #No se encuentra ningun pulso de transmision | |
| 4742 | # return None |
|
4747 | # return None | |
| 4743 | # |
|
4748 | # | |
| 4744 | # maxValuesIndex = numpy.where(filteredPower > maxValue - 0.1*abs(maxValue))[0] |
|
4749 | # maxValuesIndex = numpy.where(filteredPower > maxValue - 0.1*abs(maxValue))[0] | |
| 4745 | # |
|
4750 | # | |
| 4746 | # if len(maxValuesIndex) < 2: |
|
4751 | # if len(maxValuesIndex) < 2: | |
| 4747 | # #Solo se encontro un solo pulso de transmision de un baudio, esperando por el siguiente TX |
|
4752 | # #Solo se encontro un solo pulso de transmision de un baudio, esperando por el siguiente TX | |
| 4748 | # return None |
|
4753 | # return None | |
| 4749 | # |
|
4754 | # | |
| 4750 | # phasedMaxValuesIndex = maxValuesIndex - self.__nSamples |
|
4755 | # phasedMaxValuesIndex = maxValuesIndex - self.__nSamples | |
| 4751 | # |
|
4756 | # | |
| 4752 | # #Seleccionar solo valores con un espaciamiento de nSamples |
|
4757 | # #Seleccionar solo valores con un espaciamiento de nSamples | |
| 4753 | # pulseIndex = numpy.intersect1d(maxValuesIndex, phasedMaxValuesIndex) |
|
4758 | # pulseIndex = numpy.intersect1d(maxValuesIndex, phasedMaxValuesIndex) | |
| 4754 | # |
|
4759 | # | |
| 4755 | # if len(pulseIndex) < 2: |
|
4760 | # if len(pulseIndex) < 2: | |
| 4756 | # #Solo se encontro un pulso de transmision con ancho mayor a 1 |
|
4761 | # #Solo se encontro un pulso de transmision con ancho mayor a 1 | |
| 4757 | # return None |
|
4762 | # return None | |
| 4758 | # |
|
4763 | # | |
| 4759 | # spacing = pulseIndex[1:] - pulseIndex[:-1] |
|
4764 | # spacing = pulseIndex[1:] - pulseIndex[:-1] | |
| 4760 | # |
|
4765 | # | |
| 4761 | # #remover senales que se distancien menos de 10 unidades o muestras |
|
4766 | # #remover senales que se distancien menos de 10 unidades o muestras | |
| 4762 | # #(No deberian existir IPP menor a 10 unidades) |
|
4767 | # #(No deberian existir IPP menor a 10 unidades) | |
| 4763 | # |
|
4768 | # | |
| 4764 | # realIndex = numpy.where(spacing > 10 )[0] |
|
4769 | # realIndex = numpy.where(spacing > 10 )[0] | |
| 4765 | # |
|
4770 | # | |
| 4766 | # if len(realIndex) < 2: |
|
4771 | # if len(realIndex) < 2: | |
| 4767 | # #Solo se encontro un pulso de transmision con ancho mayor a 1 |
|
4772 | # #Solo se encontro un pulso de transmision con ancho mayor a 1 | |
| 4768 | # return None |
|
4773 | # return None | |
| 4769 | # |
|
4774 | # | |
| 4770 | # #Eliminar pulsos anchos (deja solo la diferencia entre IPPs) |
|
4775 | # #Eliminar pulsos anchos (deja solo la diferencia entre IPPs) | |
| 4771 | # realPulseIndex = pulseIndex[realIndex] |
|
4776 | # realPulseIndex = pulseIndex[realIndex] | |
| 4772 | # |
|
4777 | # | |
| 4773 | # period = mode(realPulseIndex[1:] - realPulseIndex[:-1])[0][0] |
|
4778 | # period = mode(realPulseIndex[1:] - realPulseIndex[:-1])[0][0] | |
| 4774 | # |
|
4779 | # | |
| 4775 | # print "IPP = %d samples" %period |
|
4780 | # print "IPP = %d samples" %period | |
| 4776 | # |
|
4781 | # | |
| 4777 | # self.__newNSamples = dataOut.nHeights #int(period) |
|
4782 | # self.__newNSamples = dataOut.nHeights #int(period) | |
| 4778 | # self.__startIndex = int(realPulseIndex[0]) |
|
4783 | # self.__startIndex = int(realPulseIndex[0]) | |
| 4779 | # |
|
4784 | # | |
| 4780 | # return 1 |
|
4785 | # return 1 | |
| 4781 | # |
|
4786 | # | |
| 4782 | # |
|
4787 | # | |
| 4783 | # def setup(self, nSamples, nChannels, buffer_size = 4): |
|
4788 | # def setup(self, nSamples, nChannels, buffer_size = 4): | |
| 4784 | # |
|
4789 | # | |
| 4785 | # self.__powBuffer = collections.deque(numpy.zeros( buffer_size*nSamples,dtype=numpy.float), |
|
4790 | # self.__powBuffer = collections.deque(numpy.zeros( buffer_size*nSamples,dtype=numpy.float), | |
| 4786 | # maxlen = buffer_size*nSamples) |
|
4791 | # maxlen = buffer_size*nSamples) | |
| 4787 | # |
|
4792 | # | |
| 4788 | # bufferList = [] |
|
4793 | # bufferList = [] | |
| 4789 | # |
|
4794 | # | |
| 4790 | # for i in range(nChannels): |
|
4795 | # for i in range(nChannels): | |
| 4791 | # bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=complex) + numpy.NAN, |
|
4796 | # bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=complex) + numpy.NAN, | |
| 4792 | # maxlen = buffer_size*nSamples) |
|
4797 | # maxlen = buffer_size*nSamples) | |
| 4793 | # |
|
4798 | # | |
| 4794 | # bufferList.append(bufferByChannel) |
|
4799 | # bufferList.append(bufferByChannel) | |
| 4795 | # |
|
4800 | # | |
| 4796 | # self.__nSamples = nSamples |
|
4801 | # self.__nSamples = nSamples | |
| 4797 | # self.__nChannels = nChannels |
|
4802 | # self.__nChannels = nChannels | |
| 4798 | # self.__bufferList = bufferList |
|
4803 | # self.__bufferList = bufferList | |
| 4799 | # |
|
4804 | # | |
| 4800 | # def run(self, dataOut, channel = 0): |
|
4805 | # def run(self, dataOut, channel = 0): | |
| 4801 | # |
|
4806 | # | |
| 4802 | # if not self.isConfig: |
|
4807 | # if not self.isConfig: | |
| 4803 | # nSamples = dataOut.nHeights |
|
4808 | # nSamples = dataOut.nHeights | |
| 4804 | # nChannels = dataOut.nChannels |
|
4809 | # nChannels = dataOut.nChannels | |
| 4805 | # self.setup(nSamples, nChannels) |
|
4810 | # self.setup(nSamples, nChannels) | |
| 4806 | # self.isConfig = True |
|
4811 | # self.isConfig = True | |
| 4807 | # |
|
4812 | # | |
| 4808 | # #Append new data to internal buffer |
|
4813 | # #Append new data to internal buffer | |
| 4809 | # for thisChannel in range(self.__nChannels): |
|
4814 | # for thisChannel in range(self.__nChannels): | |
| 4810 | # bufferByChannel = self.__bufferList[thisChannel] |
|
4815 | # bufferByChannel = self.__bufferList[thisChannel] | |
| 4811 | # bufferByChannel.extend(dataOut.data[thisChannel]) |
|
4816 | # bufferByChannel.extend(dataOut.data[thisChannel]) | |
| 4812 | # |
|
4817 | # | |
| 4813 | # if self.__pulseFound: |
|
4818 | # if self.__pulseFound: | |
| 4814 | # self.__startIndex -= self.__nSamples |
|
4819 | # self.__startIndex -= self.__nSamples | |
| 4815 | # |
|
4820 | # | |
| 4816 | # #Finding Tx Pulse |
|
4821 | # #Finding Tx Pulse | |
| 4817 | # if not self.__pulseFound: |
|
4822 | # if not self.__pulseFound: | |
| 4818 | # indexFound = self.__findTxPulse(dataOut, channel) |
|
4823 | # indexFound = self.__findTxPulse(dataOut, channel) | |
| 4819 | # |
|
4824 | # | |
| 4820 | # if indexFound == None: |
|
4825 | # if indexFound == None: | |
| 4821 | # dataOut.flagNoData = True |
|
4826 | # dataOut.flagNoData = True | |
| 4822 | # return |
|
4827 | # return | |
| 4823 | # |
|
4828 | # | |
| 4824 | # self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = complex) |
|
4829 | # self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = complex) | |
| 4825 | # self.__pulseFound = True |
|
4830 | # self.__pulseFound = True | |
| 4826 | # self.__startIndex = indexFound |
|
4831 | # self.__startIndex = indexFound | |
| 4827 | # |
|
4832 | # | |
| 4828 | # #If pulse was found ... |
|
4833 | # #If pulse was found ... | |
| 4829 | # for thisChannel in range(self.__nChannels): |
|
4834 | # for thisChannel in range(self.__nChannels): | |
| 4830 | # bufferByChannel = self.__bufferList[thisChannel] |
|
4835 | # bufferByChannel = self.__bufferList[thisChannel] | |
| 4831 | # #print self.__startIndex |
|
4836 | # #print self.__startIndex | |
| 4832 | # x = numpy.array(bufferByChannel) |
|
4837 | # x = numpy.array(bufferByChannel) | |
| 4833 | # self.__arrayBuffer[thisChannel] = x[self.__startIndex:self.__startIndex+self.__newNSamples] |
|
4838 | # self.__arrayBuffer[thisChannel] = x[self.__startIndex:self.__startIndex+self.__newNSamples] | |
| 4834 | # |
|
4839 | # | |
| 4835 | # deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
4840 | # deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] | |
| 4836 | # dataOut.heightList = numpy.arange(self.__newNSamples)*deltaHeight |
|
4841 | # dataOut.heightList = numpy.arange(self.__newNSamples)*deltaHeight | |
| 4837 | # # dataOut.ippSeconds = (self.__newNSamples / deltaHeight)/1e6 |
|
4842 | # # dataOut.ippSeconds = (self.__newNSamples / deltaHeight)/1e6 | |
| 4838 | # |
|
4843 | # | |
| 4839 | # dataOut.data = self.__arrayBuffer |
|
4844 | # dataOut.data = self.__arrayBuffer | |
| 4840 | # |
|
4845 | # | |
| 4841 | # self.__startIndex += self.__newNSamples |
|
4846 | # self.__startIndex += self.__newNSamples | |
| 4842 | # |
|
4847 | # | |
| 4843 | # return |
|
4848 | # return | |
| 4844 |
|
4849 | |||
| 4845 |
|
4850 | |||
| 4846 |
|
4851 | |||
| 4847 |
|
4852 | |||
| 4848 |
|
4853 | |||
| 4849 |
|
4854 | |||
| 4850 |
|
4855 | |||
| 4851 | ##############################LONG PULSE############################## |
|
4856 | ##############################LONG PULSE############################## | |
| 4852 |
|
4857 | |||
| 4853 |
|
4858 | |||
| 4854 |
|
4859 | |||
| 4855 | class CrossProdHybrid(CrossProdDP): |
|
4860 | class CrossProdHybrid(CrossProdDP): | |
| 4856 | """Operation to calculate cross products of the Hybrid Experiment. |
|
4861 | """Operation to calculate cross products of the Hybrid Experiment. | |
| 4857 |
|
4862 | |||
| 4858 | Parameters: |
|
4863 | Parameters: | |
| 4859 | ----------- |
|
4864 | ----------- | |
| 4860 | NLAG : int |
|
4865 | NLAG : int | |
| 4861 | Number of lags for Long Pulse. |
|
4866 | Number of lags for Long Pulse. | |
| 4862 | NRANGE : int |
|
4867 | NRANGE : int | |
| 4863 | Number of samples (heights) for Long Pulse. |
|
4868 | Number of samples (heights) for Long Pulse. | |
| 4864 | NCAL : int |
|
4869 | NCAL : int | |
| 4865 | .* |
|
4870 | .* | |
| 4866 | DPL : int |
|
4871 | DPL : int | |
| 4867 | Number of lags for Double Pulse. |
|
4872 | Number of lags for Double Pulse. | |
| 4868 | NDN : int |
|
4873 | NDN : int | |
| 4869 | .* |
|
4874 | .* | |
| 4870 | NDT : int |
|
4875 | NDT : int | |
| 4871 | Number of heights for Double Pulse.* |
|
4876 | Number of heights for Double Pulse.* | |
| 4872 | NDP : int |
|
4877 | NDP : int | |
| 4873 | Number of heights for Double Pulse.* |
|
4878 | Number of heights for Double Pulse.* | |
| 4874 | NSCAN : int |
|
4879 | NSCAN : int | |
| 4875 | Number of profiles when the transmitter is on. |
|
4880 | Number of profiles when the transmitter is on. | |
| 4876 | lagind : intlist |
|
4881 | lagind : intlist | |
| 4877 | .* |
|
4882 | .* | |
| 4878 | lagfirst : intlist |
|
4883 | lagfirst : intlist | |
| 4879 | .* |
|
4884 | .* | |
| 4880 | NAVG : int |
|
4885 | NAVG : int | |
| 4881 | Number of blocks to be "averaged". |
|
4886 | Number of blocks to be "averaged". | |
| 4882 | nkill : int |
|
4887 | nkill : int | |
| 4883 | Number of blocks not to be considered when averaging. |
|
4888 | Number of blocks not to be considered when averaging. | |
| 4884 |
|
4889 | |||
| 4885 | Example |
|
4890 | Example | |
| 4886 | -------- |
|
4891 | -------- | |
| 4887 |
|
4892 | |||
| 4888 | op = proc_unit.addOperation(name='CrossProdHybrid', optype='other') |
|
4893 | op = proc_unit.addOperation(name='CrossProdHybrid', optype='other') | |
| 4889 | op.addParameter(name='NLAG', value='16', format='int') |
|
4894 | op.addParameter(name='NLAG', value='16', format='int') | |
| 4890 | op.addParameter(name='NRANGE', value='200', format='int') |
|
4895 | op.addParameter(name='NRANGE', value='200', format='int') | |
| 4891 | op.addParameter(name='NCAL', value='0', format='int') |
|
4896 | op.addParameter(name='NCAL', value='0', format='int') | |
| 4892 | op.addParameter(name='DPL', value='11', format='int') |
|
4897 | op.addParameter(name='DPL', value='11', format='int') | |
| 4893 | op.addParameter(name='NDN', value='0', format='int') |
|
4898 | op.addParameter(name='NDN', value='0', format='int') | |
| 4894 | op.addParameter(name='NDT', value='67', format='int') |
|
4899 | op.addParameter(name='NDT', value='67', format='int') | |
| 4895 | op.addParameter(name='NDP', value='67', format='int') |
|
4900 | op.addParameter(name='NDP', value='67', format='int') | |
| 4896 | op.addParameter(name='NSCAN', value='128', format='int') |
|
4901 | op.addParameter(name='NSCAN', value='128', format='int') | |
| 4897 | op.addParameter(name='lagind', value='(0,1,2,3,4,5,6,7,0,3,4,5,6,8,9,10)', format='intlist') |
|
4902 | op.addParameter(name='lagind', value='(0,1,2,3,4,5,6,7,0,3,4,5,6,8,9,10)', format='intlist') | |
| 4898 | op.addParameter(name='lagfirst', value='(1,1,1,1,1,1,1,1,0,0,0,0,0,1,1,1)', format='intlist') |
|
4903 | op.addParameter(name='lagfirst', value='(1,1,1,1,1,1,1,1,0,0,0,0,0,1,1,1)', format='intlist') | |
| 4899 | op.addParameter(name='NAVG', value='16', format='int') |
|
4904 | op.addParameter(name='NAVG', value='16', format='int') | |
| 4900 | op.addParameter(name='nkill', value='6', format='int') |
|
4905 | op.addParameter(name='nkill', value='6', format='int') | |
| 4901 |
|
4906 | |||
| 4902 | """ |
|
4907 | """ | |
| 4903 |
|
4908 | |||
| 4904 | def __init__(self, **kwargs): |
|
4909 | def __init__(self, **kwargs): | |
| 4905 |
|
4910 | |||
| 4906 | Operation.__init__(self, **kwargs) |
|
4911 | Operation.__init__(self, **kwargs) | |
| 4907 | self.bcounter=0 |
|
4912 | self.bcounter=0 | |
| 4908 | self.aux=1 |
|
4913 | self.aux=1 | |
| 4909 | self.aux_cross_lp=1 |
|
4914 | self.aux_cross_lp=1 | |
| 4910 | self.lag_products_LP_median_estimates_aux=1 |
|
4915 | self.lag_products_LP_median_estimates_aux=1 | |
| 4911 |
|
4916 | |||
| 4912 | def get_products_cabxys_HP(self,dataOut): |
|
4917 | def get_products_cabxys_HP(self,dataOut): | |
| 4913 |
|
4918 | |||
| 4914 | if self.aux==1: |
|
4919 | if self.aux==1: | |
| 4915 | self.set_header_output(dataOut) |
|
4920 | self.set_header_output(dataOut) | |
| 4916 | self.aux=0 |
|
4921 | self.aux=0 | |
| 4917 |
|
4922 | |||
| 4918 | self.cax=numpy.zeros((dataOut.NDP,dataOut.DPL,2))# hp:67x11x2 dp: 66x11x2 |
|
4923 | self.cax=numpy.zeros((dataOut.NDP,dataOut.DPL,2))# hp:67x11x2 dp: 66x11x2 | |
| 4919 | self.cay=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
4924 | self.cay=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) | |
| 4920 | self.cbx=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
4925 | self.cbx=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) | |
| 4921 | self.cby=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
4926 | self.cby=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) | |
| 4922 | self.cax2=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
4927 | self.cax2=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) | |
| 4923 | self.cay2=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
4928 | self.cay2=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) | |
| 4924 | self.cbx2=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
4929 | self.cbx2=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) | |
| 4925 | self.cby2=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
4930 | self.cby2=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) | |
| 4926 | self.caxbx=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
4931 | self.caxbx=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) | |
| 4927 | self.caxby=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
4932 | self.caxby=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) | |
| 4928 | self.caybx=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
4933 | self.caybx=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) | |
| 4929 | self.cayby=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
4934 | self.cayby=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) | |
| 4930 | self.caxay=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
4935 | self.caxay=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) | |
| 4931 | self.cbxby=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
4936 | self.cbxby=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) | |
| 4932 | for i in range(2): # flipped and unflipped |
|
4937 | for i in range(2): # flipped and unflipped | |
| 4933 | for j in range(dataOut.NDP): # loop over true ranges # 67 |
|
4938 | for j in range(dataOut.NDP): # loop over true ranges # 67 | |
| 4934 | for k in range(int(dataOut.NSCAN)): # 128 |
|
4939 | for k in range(int(dataOut.NSCAN)): # 128 | |
| 4935 |
|
4940 | |||
| 4936 | n=dataOut.lagind[k%dataOut.NLAG] # 128=16x8 |
|
4941 | n=dataOut.lagind[k%dataOut.NLAG] # 128=16x8 | |
| 4937 |
|
4942 | |||
| 4938 | ax=dataOut.data[0,k,dataOut.NRANGE+dataOut.NCAL+j+i*dataOut.NDT].real-dataOut.dc.real[0] |
|
4943 | ax=dataOut.data[0,k,dataOut.NRANGE+dataOut.NCAL+j+i*dataOut.NDT].real-dataOut.dc.real[0] | |
| 4939 | ay=dataOut.data[0,k,dataOut.NRANGE+dataOut.NCAL+j+i*dataOut.NDT].imag-dataOut.dc.imag[0] |
|
4944 | ay=dataOut.data[0,k,dataOut.NRANGE+dataOut.NCAL+j+i*dataOut.NDT].imag-dataOut.dc.imag[0] | |
| 4940 |
|
4945 | |||
| 4941 | if dataOut.NRANGE+dataOut.NCAL+j+i*dataOut.NDT+2*n<dataOut.read_samples: |
|
4946 | if dataOut.NRANGE+dataOut.NCAL+j+i*dataOut.NDT+2*n<dataOut.read_samples: | |
| 4942 |
|
4947 | |||
| 4943 | bx=dataOut.data[1,k,dataOut.NRANGE+dataOut.NCAL+j+i*dataOut.NDT+2*n].real-dataOut.dc.real[1] |
|
4948 | bx=dataOut.data[1,k,dataOut.NRANGE+dataOut.NCAL+j+i*dataOut.NDT+2*n].real-dataOut.dc.real[1] | |
| 4944 | by=dataOut.data[1,k,dataOut.NRANGE+dataOut.NCAL+j+i*dataOut.NDT+2*n].imag-dataOut.dc.imag[1] |
|
4949 | by=dataOut.data[1,k,dataOut.NRANGE+dataOut.NCAL+j+i*dataOut.NDT+2*n].imag-dataOut.dc.imag[1] | |
| 4945 |
|
4950 | |||
| 4946 | else: |
|
4951 | else: | |
| 4947 |
|
4952 | |||
| 4948 | if k+1<int(dataOut.NSCAN): |
|
4953 | if k+1<int(dataOut.NSCAN): | |
| 4949 | bx=dataOut.data[1,k+1,(dataOut.NRANGE+dataOut.NCAL+j+i*dataOut.NDT+2*n)%dataOut.NDP].real |
|
4954 | bx=dataOut.data[1,k+1,(dataOut.NRANGE+dataOut.NCAL+j+i*dataOut.NDT+2*n)%dataOut.NDP].real | |
| 4950 | by=dataOut.data[1,k+1,(dataOut.NRANGE+dataOut.NCAL+j+i*dataOut.NDT+2*n)%dataOut.NDP].imag |
|
4955 | by=dataOut.data[1,k+1,(dataOut.NRANGE+dataOut.NCAL+j+i*dataOut.NDT+2*n)%dataOut.NDP].imag | |
| 4951 |
|
4956 | |||
| 4952 | if k+1==int(dataOut.NSCAN):## ESTO ES UN PARCHE PUES NO SE TIENE EL SIGUIENTE BLOQUE |
|
4957 | if k+1==int(dataOut.NSCAN):## ESTO ES UN PARCHE PUES NO SE TIENE EL SIGUIENTE BLOQUE | |
| 4953 | bx=dataOut.data[1,k,(dataOut.NRANGE+dataOut.NCAL+j+i*dataOut.NDT+2*n)%dataOut.NDP].real |
|
4958 | bx=dataOut.data[1,k,(dataOut.NRANGE+dataOut.NCAL+j+i*dataOut.NDT+2*n)%dataOut.NDP].real | |
| 4954 | by=dataOut.data[1,k,(dataOut.NRANGE+dataOut.NCAL+j+i*dataOut.NDT+2*n)%dataOut.NDP].imag |
|
4959 | by=dataOut.data[1,k,(dataOut.NRANGE+dataOut.NCAL+j+i*dataOut.NDT+2*n)%dataOut.NDP].imag | |
| 4955 |
|
4960 | |||
| 4956 | if(k<dataOut.NLAG and dataOut.lagfirst[k%dataOut.NLAG]==1):# if(k<16 && lagfirst[k%16]==1) |
|
4961 | if(k<dataOut.NLAG and dataOut.lagfirst[k%dataOut.NLAG]==1):# if(k<16 && lagfirst[k%16]==1) | |
| 4957 | self.cax[j][n][i]=ax |
|
4962 | self.cax[j][n][i]=ax | |
| 4958 | self.cay[j][n][i]=ay |
|
4963 | self.cay[j][n][i]=ay | |
| 4959 | self.cbx[j][n][i]=bx |
|
4964 | self.cbx[j][n][i]=bx | |
| 4960 | self.cby[j][n][i]=by |
|
4965 | self.cby[j][n][i]=by | |
| 4961 | self.cax2[j][n][i]=ax*ax |
|
4966 | self.cax2[j][n][i]=ax*ax | |
| 4962 | self.cay2[j][n][i]=ay*ay |
|
4967 | self.cay2[j][n][i]=ay*ay | |
| 4963 | self.cbx2[j][n][i]=bx*bx |
|
4968 | self.cbx2[j][n][i]=bx*bx | |
| 4964 | self.cby2[j][n][i]=by*by |
|
4969 | self.cby2[j][n][i]=by*by | |
| 4965 | self.caxbx[j][n][i]=ax*bx |
|
4970 | self.caxbx[j][n][i]=ax*bx | |
| 4966 | self.caxby[j][n][i]=ax*by |
|
4971 | self.caxby[j][n][i]=ax*by | |
| 4967 | self.caybx[j][n][i]=ay*bx |
|
4972 | self.caybx[j][n][i]=ay*bx | |
| 4968 | self.cayby[j][n][i]=ay*by |
|
4973 | self.cayby[j][n][i]=ay*by | |
| 4969 | self.caxay[j][n][i]=ax*ay |
|
4974 | self.caxay[j][n][i]=ax*ay | |
| 4970 | self.cbxby[j][n][i]=bx*by |
|
4975 | self.cbxby[j][n][i]=bx*by | |
| 4971 | else: |
|
4976 | else: | |
| 4972 | self.cax[j][n][i]+=ax |
|
4977 | self.cax[j][n][i]+=ax | |
| 4973 | self.cay[j][n][i]+=ay |
|
4978 | self.cay[j][n][i]+=ay | |
| 4974 | self.cbx[j][n][i]+=bx |
|
4979 | self.cbx[j][n][i]+=bx | |
| 4975 | self.cby[j][n][i]+=by |
|
4980 | self.cby[j][n][i]+=by | |
| 4976 | self.cax2[j][n][i]+=ax*ax |
|
4981 | self.cax2[j][n][i]+=ax*ax | |
| 4977 | self.cay2[j][n][i]+=ay*ay |
|
4982 | self.cay2[j][n][i]+=ay*ay | |
| 4978 | self.cbx2[j][n][i]+=bx*bx |
|
4983 | self.cbx2[j][n][i]+=bx*bx | |
| 4979 | self.cby2[j][n][i]+=by*by |
|
4984 | self.cby2[j][n][i]+=by*by | |
| 4980 | self.caxbx[j][n][i]+=ax*bx |
|
4985 | self.caxbx[j][n][i]+=ax*bx | |
| 4981 | self.caxby[j][n][i]+=ax*by |
|
4986 | self.caxby[j][n][i]+=ax*by | |
| 4982 | self.caybx[j][n][i]+=ay*bx |
|
4987 | self.caybx[j][n][i]+=ay*bx | |
| 4983 | self.cayby[j][n][i]+=ay*by |
|
4988 | self.cayby[j][n][i]+=ay*by | |
| 4984 | self.caxay[j][n][i]+=ax*ay |
|
4989 | self.caxay[j][n][i]+=ax*ay | |
| 4985 | self.cbxby[j][n][i]+=bx*by |
|
4990 | self.cbxby[j][n][i]+=bx*by | |
| 4986 |
|
4991 | |||
| 4987 |
|
4992 | |||
| 4988 | #print(self.cax2[2,0,1]) |
|
4993 | #print(self.cax2[2,0,1]) | |
| 4989 | #input() |
|
4994 | #input() | |
| 4990 |
|
4995 | |||
| 4991 |
|
4996 | |||
| 4992 | def lag_products_LP(self,dataOut): |
|
4997 | def lag_products_LP(self,dataOut): | |
| 4993 |
|
4998 | |||
| 4994 |
|
4999 | |||
| 4995 | buffer=dataOut.data |
|
5000 | buffer=dataOut.data | |
| 4996 | if self.aux_cross_lp==1: |
|
5001 | if self.aux_cross_lp==1: | |
| 4997 |
|
5002 | |||
| 4998 | #self.dataOut.nptsfft2=150 |
|
5003 | #self.dataOut.nptsfft2=150 | |
| 4999 | self.cnorm=float((dataOut.nProfiles-dataOut.NSCAN)/dataOut.NSCAN) |
|
5004 | self.cnorm=float((dataOut.nProfiles-dataOut.NSCAN)/dataOut.NSCAN) | |
| 5000 | self.lagp0=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') |
|
5005 | self.lagp0=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') | |
| 5001 | self.lagp1=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') |
|
5006 | self.lagp1=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') | |
| 5002 | self.lagp2=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') |
|
5007 | self.lagp2=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') | |
| 5003 | self.lagp3=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') |
|
5008 | self.lagp3=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') | |
| 5004 |
|
5009 | |||
| 5005 | #self.lagp4=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') |
|
5010 | #self.lagp4=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') | |
| 5006 | self.aux_cross_lp=0 |
|
5011 | self.aux_cross_lp=0 | |
| 5007 |
|
5012 | |||
| 5008 | #print(self.dataOut.data[0,0,0]) |
|
5013 | #print(self.dataOut.data[0,0,0]) | |
| 5009 |
|
5014 | |||
| 5010 | for i in range(dataOut.NR): |
|
5015 | for i in range(dataOut.NR): | |
| 5011 | #print("inside i",i) |
|
5016 | #print("inside i",i) | |
| 5012 | buffer_dc=dataOut.dc[i] |
|
5017 | buffer_dc=dataOut.dc[i] | |
| 5013 | for j in range(dataOut.NRANGE): |
|
5018 | for j in range(dataOut.NRANGE): | |
| 5014 |
|
5019 | |||
| 5015 | range_for_n=numpy.min((dataOut.NRANGE-j,dataOut.NLAG)) |
|
5020 | range_for_n=numpy.min((dataOut.NRANGE-j,dataOut.NLAG)) | |
| 5016 |
|
5021 | |||
| 5017 | buffer_aux=numpy.conj(buffer[i,:dataOut.nProfiles,j]-buffer_dc) |
|
5022 | buffer_aux=numpy.conj(buffer[i,:dataOut.nProfiles,j]-buffer_dc) | |
| 5018 | for n in range(range_for_n): |
|
5023 | for n in range(range_for_n): | |
| 5019 |
|
5024 | |||
| 5020 | c=(buffer_aux)*(buffer[i,:dataOut.nProfiles,j+n]-buffer_dc) |
|
5025 | c=(buffer_aux)*(buffer[i,:dataOut.nProfiles,j+n]-buffer_dc) | |
| 5021 |
|
5026 | |||
| 5022 | if i==0: |
|
5027 | if i==0: | |
| 5023 | self.lagp0[n][j][self.bcounter-1]=numpy.sum(c[:dataOut.NSCAN]) |
|
5028 | self.lagp0[n][j][self.bcounter-1]=numpy.sum(c[:dataOut.NSCAN]) | |
| 5024 | self.lagp3[n][j][self.bcounter-1]=numpy.sum(c[dataOut.NSCAN:]/self.cnorm) |
|
5029 | self.lagp3[n][j][self.bcounter-1]=numpy.sum(c[dataOut.NSCAN:]/self.cnorm) | |
| 5025 | elif i==1: |
|
5030 | elif i==1: | |
| 5026 | self.lagp1[n][j][self.bcounter-1]=numpy.sum(c[:dataOut.NSCAN]) |
|
5031 | self.lagp1[n][j][self.bcounter-1]=numpy.sum(c[:dataOut.NSCAN]) | |
| 5027 | elif i==2: |
|
5032 | elif i==2: | |
| 5028 | self.lagp2[n][j][self.bcounter-1]=numpy.sum(c[:dataOut.NSCAN]) |
|
5033 | self.lagp2[n][j][self.bcounter-1]=numpy.sum(c[:dataOut.NSCAN]) | |
| 5029 |
|
5034 | |||
| 5030 |
|
5035 | |||
| 5031 | self.lagp0[:,:,self.bcounter-1]=numpy.conj(self.lagp0[:,:,self.bcounter-1]) |
|
5036 | self.lagp0[:,:,self.bcounter-1]=numpy.conj(self.lagp0[:,:,self.bcounter-1]) | |
| 5032 | self.lagp1[:,:,self.bcounter-1]=numpy.conj(self.lagp1[:,:,self.bcounter-1]) |
|
5037 | self.lagp1[:,:,self.bcounter-1]=numpy.conj(self.lagp1[:,:,self.bcounter-1]) | |
| 5033 | self.lagp2[:,:,self.bcounter-1]=numpy.conj(self.lagp2[:,:,self.bcounter-1]) |
|
5038 | self.lagp2[:,:,self.bcounter-1]=numpy.conj(self.lagp2[:,:,self.bcounter-1]) | |
| 5034 | self.lagp3[:,:,self.bcounter-1]=numpy.conj(self.lagp3[:,:,self.bcounter-1]) |
|
5039 | self.lagp3[:,:,self.bcounter-1]=numpy.conj(self.lagp3[:,:,self.bcounter-1]) | |
| 5035 |
|
5040 | |||
| 5036 |
|
5041 | |||
| 5037 | def LP_median_estimates(self,dataOut): |
|
5042 | def LP_median_estimates(self,dataOut): | |
| 5038 |
|
5043 | |||
| 5039 | if self.bcounter==dataOut.NAVG: |
|
5044 | if self.bcounter==dataOut.NAVG: | |
| 5040 |
|
5045 | |||
| 5041 | if self.lag_products_LP_median_estimates_aux==1: |
|
5046 | if self.lag_products_LP_median_estimates_aux==1: | |
| 5042 | self.output=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NR),'complex64') |
|
5047 | self.output=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NR),'complex64') | |
| 5043 | self.lag_products_LP_median_estimates_aux=0 |
|
5048 | self.lag_products_LP_median_estimates_aux=0 | |
| 5044 |
|
5049 | |||
| 5045 |
|
5050 | |||
| 5046 | for i in range(dataOut.NLAG): |
|
5051 | for i in range(dataOut.NLAG): | |
| 5047 | for j in range(dataOut.NRANGE): |
|
5052 | for j in range(dataOut.NRANGE): | |
| 5048 | for l in range(4): #four outputs |
|
5053 | for l in range(4): #four outputs | |
| 5049 |
|
5054 | |||
| 5050 | for k in range(dataOut.NAVG): |
|
5055 | for k in range(dataOut.NAVG): | |
| 5051 |
|
5056 | |||
| 5052 |
|
5057 | |||
| 5053 | if k==0: |
|
5058 | if k==0: | |
| 5054 | self.output[i,j,l]=0.0+0.j |
|
5059 | self.output[i,j,l]=0.0+0.j | |
| 5055 |
|
5060 | |||
| 5056 | if l==0: |
|
5061 | if l==0: | |
| 5057 | self.lagp0[i,j,:]=sorted(self.lagp0[i,j,:], key=lambda x: x.real) #sorted(self.lagp0[i,j,:].real) |
|
5062 | self.lagp0[i,j,:]=sorted(self.lagp0[i,j,:], key=lambda x: x.real) #sorted(self.lagp0[i,j,:].real) | |
| 5058 |
|
5063 | |||
| 5059 | if l==1: |
|
5064 | if l==1: | |
| 5060 | self.lagp1[i,j,:]=sorted(self.lagp1[i,j,:], key=lambda x: x.real) #sorted(self.lagp1[i,j,:].real) |
|
5065 | self.lagp1[i,j,:]=sorted(self.lagp1[i,j,:], key=lambda x: x.real) #sorted(self.lagp1[i,j,:].real) | |
| 5061 |
|
5066 | |||
| 5062 | if l==2: |
|
5067 | if l==2: | |
| 5063 | self.lagp2[i,j,:]=sorted(self.lagp2[i,j,:], key=lambda x: x.real) #sorted(self.lagp2[i,j,:].real) |
|
5068 | self.lagp2[i,j,:]=sorted(self.lagp2[i,j,:], key=lambda x: x.real) #sorted(self.lagp2[i,j,:].real) | |
| 5064 |
|
5069 | |||
| 5065 | if l==3: |
|
5070 | if l==3: | |
| 5066 | self.lagp3[i,j,:]=sorted(self.lagp3[i,j,:], key=lambda x: x.real) #sorted(self.lagp3[i,j,:].real) |
|
5071 | self.lagp3[i,j,:]=sorted(self.lagp3[i,j,:], key=lambda x: x.real) #sorted(self.lagp3[i,j,:].real) | |
| 5067 |
|
5072 | |||
| 5068 |
|
5073 | |||
| 5069 |
|
5074 | |||
| 5070 | if k>=dataOut.nkill/2 and k<dataOut.NAVG-dataOut.nkill/2: |
|
5075 | if k>=dataOut.nkill/2 and k<dataOut.NAVG-dataOut.nkill/2: | |
| 5071 | if l==0: |
|
5076 | if l==0: | |
| 5072 |
|
5077 | |||
| 5073 | self.output[i,j,l]=self.output[i,j,l]+((float(dataOut.NAVG)/(float)(dataOut.NAVG-dataOut.nkill))*self.lagp0[i,j,k]) |
|
5078 | self.output[i,j,l]=self.output[i,j,l]+((float(dataOut.NAVG)/(float)(dataOut.NAVG-dataOut.nkill))*self.lagp0[i,j,k]) | |
| 5074 | if l==1: |
|
5079 | if l==1: | |
| 5075 | #print("lagp1: ",self.lagp1[0,0,:]) |
|
5080 | #print("lagp1: ",self.lagp1[0,0,:]) | |
| 5076 | #input() |
|
5081 | #input() | |
| 5077 | self.output[i,j,l]=self.output[i,j,l]+((float(dataOut.NAVG)/(float)(dataOut.NAVG-dataOut.nkill))*self.lagp1[i,j,k]) |
|
5082 | self.output[i,j,l]=self.output[i,j,l]+((float(dataOut.NAVG)/(float)(dataOut.NAVG-dataOut.nkill))*self.lagp1[i,j,k]) | |
| 5078 | #print("self.lagp1[i,j,k]: ",self.lagp1[i,j,k]) |
|
5083 | #print("self.lagp1[i,j,k]: ",self.lagp1[i,j,k]) | |
| 5079 | #input() |
|
5084 | #input() | |
| 5080 | if l==2: |
|
5085 | if l==2: | |
| 5081 | self.output[i,j,l]=self.output[i,j,l]+((float(dataOut.NAVG)/(float)(dataOut.NAVG-dataOut.nkill))*self.lagp2[i,j,k]) |
|
5086 | self.output[i,j,l]=self.output[i,j,l]+((float(dataOut.NAVG)/(float)(dataOut.NAVG-dataOut.nkill))*self.lagp2[i,j,k]) | |
| 5082 | if l==3: |
|
5087 | if l==3: | |
| 5083 |
|
5088 | |||
| 5084 | self.output[i,j,l]=self.output[i,j,l]+((float(dataOut.NAVG)/(float)(dataOut.NAVG-dataOut.nkill))*self.lagp3[i,j,k]) |
|
5089 | self.output[i,j,l]=self.output[i,j,l]+((float(dataOut.NAVG)/(float)(dataOut.NAVG-dataOut.nkill))*self.lagp3[i,j,k]) | |
| 5085 |
|
5090 | |||
| 5086 |
|
5091 | |||
| 5087 | dataOut.output_LP=self.output |
|
5092 | dataOut.output_LP=self.output | |
| 5088 | dataOut.data_for_RTI_LP=numpy.zeros((4,dataOut.NRANGE)) |
|
5093 | dataOut.data_for_RTI_LP=numpy.zeros((4,dataOut.NRANGE)) | |
| 5089 | dataOut.data_for_RTI_LP[0],dataOut.data_for_RTI_LP[1],dataOut.data_for_RTI_LP[2],dataOut.data_for_RTI_LP[3]=self.RTI_LP(dataOut.output_LP,dataOut.NRANGE) |
|
5094 | dataOut.data_for_RTI_LP[0],dataOut.data_for_RTI_LP[1],dataOut.data_for_RTI_LP[2],dataOut.data_for_RTI_LP[3]=self.RTI_LP(dataOut.output_LP,dataOut.NRANGE) | |
| 5090 |
|
5095 | |||
| 5091 |
|
5096 | |||
| 5092 | def get_dc(self,dataOut): |
|
5097 | def get_dc(self,dataOut): | |
| 5093 |
|
5098 | |||
| 5094 | if self.bcounter==0: |
|
5099 | if self.bcounter==0: | |
| 5095 | dataOut.dc=numpy.zeros(dataOut.NR,dtype='complex64') |
|
5100 | dataOut.dc=numpy.zeros(dataOut.NR,dtype='complex64') | |
| 5096 |
|
5101 | |||
| 5097 | #print(numpy.shape(dataOut.data)) |
|
5102 | #print(numpy.shape(dataOut.data)) | |
| 5098 | #input() |
|
5103 | #input() | |
| 5099 |
|
5104 | |||
| 5100 | dataOut.dc+=numpy.sum(dataOut.data[:,:,2*dataOut.NLAG:dataOut.NRANGE],axis=(1,2)) |
|
5105 | dataOut.dc+=numpy.sum(dataOut.data[:,:,2*dataOut.NLAG:dataOut.NRANGE],axis=(1,2)) | |
| 5101 |
|
5106 | |||
| 5102 | dataOut.dc=dataOut.dc/float(dataOut.nProfiles*(dataOut.NRANGE-2*dataOut.NLAG)) |
|
5107 | dataOut.dc=dataOut.dc/float(dataOut.nProfiles*(dataOut.NRANGE-2*dataOut.NLAG)) | |
| 5103 |
|
5108 | |||
| 5104 |
|
5109 | |||
| 5105 | #print("dc:",dataOut.dc[0]) |
|
5110 | #print("dc:",dataOut.dc[0]) | |
| 5106 |
|
5111 | |||
| 5107 | def get_dc_new(self,dataOut): |
|
5112 | def get_dc_new(self,dataOut): | |
| 5108 |
|
5113 | |||
| 5109 | if self.bcounter==0: |
|
5114 | if self.bcounter==0: | |
| 5110 | dataOut.dc_dp=numpy.zeros(dataOut.NR,dtype='complex64') |
|
5115 | dataOut.dc_dp=numpy.zeros(dataOut.NR,dtype='complex64') | |
| 5111 | dataOut.dc_lp=numpy.zeros(dataOut.NR,dtype='complex64') |
|
5116 | dataOut.dc_lp=numpy.zeros(dataOut.NR,dtype='complex64') | |
| 5112 |
|
5117 | |||
| 5113 | #print(numpy.shape(dataOut.data)) |
|
5118 | #print(numpy.shape(dataOut.data)) | |
| 5114 | #input() |
|
5119 | #input() | |
| 5115 |
|
5120 | |||
| 5116 | dataOut.dc+=numpy.sum(dataOut.data[:,:,2*dataOut.NLAG:dataOut.NRANGE],axis=(1,2)) |
|
5121 | dataOut.dc+=numpy.sum(dataOut.data[:,:,2*dataOut.NLAG:dataOut.NRANGE],axis=(1,2)) | |
| 5117 |
|
5122 | |||
| 5118 | dataOut.dc=dataOut.dc/float(dataOut.nProfiles*(dataOut.NRANGE-2*dataOut.NLAG)) |
|
5123 | dataOut.dc=dataOut.dc/float(dataOut.nProfiles*(dataOut.NRANGE-2*dataOut.NLAG)) | |
| 5119 |
|
5124 | |||
| 5120 |
|
5125 | |||
| 5121 | #print("dc:",dataOut.dc[0]) |
|
5126 | #print("dc:",dataOut.dc[0]) | |
| 5122 |
|
5127 | |||
| 5123 |
|
5128 | |||
| 5124 | def noise_estimation4x_HP(self,dataOut): |
|
5129 | def noise_estimation4x_HP(self,dataOut): | |
| 5125 | if self.bcounter==dataOut.NAVG: |
|
5130 | if self.bcounter==dataOut.NAVG: | |
| 5126 | dataOut.noise_final=numpy.zeros(dataOut.NR,'float32') |
|
5131 | dataOut.noise_final=numpy.zeros(dataOut.NR,'float32') | |
| 5127 | #snoise=numpy.zeros((NR,NAVG),'float32') |
|
5132 | #snoise=numpy.zeros((NR,NAVG),'float32') | |
| 5128 | #nvector1=numpy.zeros((NR,NAVG,MAXNRANGENDT),'float32') |
|
5133 | #nvector1=numpy.zeros((NR,NAVG,MAXNRANGENDT),'float32') | |
| 5129 | sorted_data=numpy.zeros((dataOut.MAXNRANGENDT,dataOut.NR,dataOut.NAVG),'float32') |
|
5134 | sorted_data=numpy.zeros((dataOut.MAXNRANGENDT,dataOut.NR,dataOut.NAVG),'float32') | |
| 5130 | for i in range(dataOut.NR): |
|
5135 | for i in range(dataOut.NR): | |
| 5131 | dataOut.noise_final[i]=0.0 |
|
5136 | dataOut.noise_final[i]=0.0 | |
| 5132 | for j in range(dataOut.MAXNRANGENDT): |
|
5137 | for j in range(dataOut.MAXNRANGENDT): | |
| 5133 | sorted_data[j,i,:]=numpy.copy(sorted(dataOut.noisevector[j,i,:])) |
|
5138 | sorted_data[j,i,:]=numpy.copy(sorted(dataOut.noisevector[j,i,:])) | |
| 5134 | #print(sorted(noisevector[j,i,:])) |
|
5139 | #print(sorted(noisevector[j,i,:])) | |
| 5135 | #input() |
|
5140 | #input() | |
| 5136 | l=dataOut.MAXNRANGENDT-2 |
|
5141 | l=dataOut.MAXNRANGENDT-2 | |
| 5137 | for k in range(dataOut.NAVG): |
|
5142 | for k in range(dataOut.NAVG): | |
| 5138 | if k>=dataOut.nkill/2 and k<dataOut.NAVG-dataOut.nkill/2: |
|
5143 | if k>=dataOut.nkill/2 and k<dataOut.NAVG-dataOut.nkill/2: | |
| 5139 | #print(k) |
|
5144 | #print(k) | |
| 5140 | #print(sorted_data[min(j,l),i,k]) |
|
5145 | #print(sorted_data[min(j,l),i,k]) | |
| 5141 | dataOut.noise_final[i]+=sorted_data[min(j,l),i,k]*float(dataOut.NAVG)/float(dataOut.NAVG-dataOut.nkill) |
|
5146 | dataOut.noise_final[i]+=sorted_data[min(j,l),i,k]*float(dataOut.NAVG)/float(dataOut.NAVG-dataOut.nkill) | |
| 5142 | #print(dataOut.noise_final[i]) |
|
5147 | #print(dataOut.noise_final[i]) | |
| 5143 | #input() |
|
5148 | #input() | |
| 5144 | #print(dataOut.noise_final) |
|
5149 | #print(dataOut.noise_final) | |
| 5145 | #input() |
|
5150 | #input() | |
| 5146 |
|
5151 | |||
| 5147 | def noisevectorizer(self,NSCAN,nProfiles,NR,MAXNRANGENDT,noisevector,data,dc): |
|
5152 | def noisevectorizer(self,NSCAN,nProfiles,NR,MAXNRANGENDT,noisevector,data,dc): | |
| 5148 |
|
5153 | |||
| 5149 | #rnormalizer= 1./(float(nProfiles - NSCAN)) |
|
5154 | #rnormalizer= 1./(float(nProfiles - NSCAN)) | |
| 5150 | rnormalizer= float(NSCAN)/((float(nProfiles - NSCAN))*float(MAXNRANGENDT)) |
|
5155 | rnormalizer= float(NSCAN)/((float(nProfiles - NSCAN))*float(MAXNRANGENDT)) | |
| 5151 | for i in range(NR): |
|
5156 | for i in range(NR): | |
| 5152 | for j in range(MAXNRANGENDT): |
|
5157 | for j in range(MAXNRANGENDT): | |
| 5153 | for k in range(NSCAN,nProfiles): |
|
5158 | for k in range(NSCAN,nProfiles): | |
| 5154 | #TODO:integrate just 2nd quartile gates |
|
5159 | #TODO:integrate just 2nd quartile gates | |
| 5155 | if k==NSCAN: |
|
5160 | if k==NSCAN: | |
| 5156 | noisevector[j][i][self.bcounter]=(abs(data[i][k][j]-dc[i])**2)*rnormalizer |
|
5161 | noisevector[j][i][self.bcounter]=(abs(data[i][k][j]-dc[i])**2)*rnormalizer | |
| 5157 | ##noisevector[j][i][iavg]=(abs(cdata[k][j][i])**2)*rnormalizer |
|
5162 | ##noisevector[j][i][iavg]=(abs(cdata[k][j][i])**2)*rnormalizer | |
| 5158 | else: |
|
5163 | else: | |
| 5159 | noisevector[j][i][self.bcounter]+=(abs(data[i][k][j]-dc[i])**2)*rnormalizer |
|
5164 | noisevector[j][i][self.bcounter]+=(abs(data[i][k][j]-dc[i])**2)*rnormalizer | |
| 5160 |
|
5165 | |||
| 5161 |
|
5166 | |||
| 5162 | def RTI_LP(self,output,NRANGE): |
|
5167 | def RTI_LP(self,output,NRANGE): | |
| 5163 | x00=numpy.zeros(NRANGE,dtype='float32') |
|
5168 | x00=numpy.zeros(NRANGE,dtype='float32') | |
| 5164 | x01=numpy.zeros(NRANGE,dtype='float32') |
|
5169 | x01=numpy.zeros(NRANGE,dtype='float32') | |
| 5165 | x02=numpy.zeros(NRANGE,dtype='float32') |
|
5170 | x02=numpy.zeros(NRANGE,dtype='float32') | |
| 5166 | x03=numpy.zeros(NRANGE,dtype='float32') |
|
5171 | x03=numpy.zeros(NRANGE,dtype='float32') | |
| 5167 |
|
5172 | |||
| 5168 | for i in range(2): #first couple of lags |
|
5173 | for i in range(2): #first couple of lags | |
| 5169 | for j in range(NRANGE): # |
|
5174 | for j in range(NRANGE): # | |
| 5170 | #fx=numpy.sqrt((kaxbx[i,j,k]+kayby[i,j,k])**2+(kaybx[i,j,k]-kaxby[i,j,k])**2) |
|
5175 | #fx=numpy.sqrt((kaxbx[i,j,k]+kayby[i,j,k])**2+(kaybx[i,j,k]-kaxby[i,j,k])**2) | |
| 5171 | x00[j]+=numpy.abs(output[i,j,0]) #Ch0 |
|
5176 | x00[j]+=numpy.abs(output[i,j,0]) #Ch0 | |
| 5172 | x01[j]+=numpy.abs(output[i,j,1]) #Ch1 |
|
5177 | x01[j]+=numpy.abs(output[i,j,1]) #Ch1 | |
| 5173 | x02[j]+=numpy.abs(output[i,j,2]) #Ch2 |
|
5178 | x02[j]+=numpy.abs(output[i,j,2]) #Ch2 | |
| 5174 | x03[j]+=numpy.abs(output[i,j,3]) #Ch3 |
|
5179 | x03[j]+=numpy.abs(output[i,j,3]) #Ch3 | |
| 5175 | #x02[i]=x02[i]+fx |
|
5180 | #x02[i]=x02[i]+fx | |
| 5176 |
|
5181 | |||
| 5177 | x00[j]=10.0*numpy.log10(x00[j]/4.) |
|
5182 | x00[j]=10.0*numpy.log10(x00[j]/4.) | |
| 5178 | x01[j]=10.0*numpy.log10(x01[j]/4.) |
|
5183 | x01[j]=10.0*numpy.log10(x01[j]/4.) | |
| 5179 | x02[j]=10.0*numpy.log10(x02[j]/4.) |
|
5184 | x02[j]=10.0*numpy.log10(x02[j]/4.) | |
| 5180 | x03[j]=10.0*numpy.log10(x03[j]/4.) |
|
5185 | x03[j]=10.0*numpy.log10(x03[j]/4.) | |
| 5181 | #x02[i]=10.0*numpy.log10(x02[i]) |
|
5186 | #x02[i]=10.0*numpy.log10(x02[i]) | |
| 5182 | return x00,x01,x02,x03 |
|
5187 | return x00,x01,x02,x03 | |
| 5183 |
|
5188 | |||
| 5184 | def run(self, dataOut, NLAG=None, NRANGE=None, NCAL=None, DPL=None, |
|
5189 | def run(self, dataOut, NLAG=None, NRANGE=None, NCAL=None, DPL=None, | |
| 5185 | NDN=None, NDT=None, NDP=None, NSCAN=None, |
|
5190 | NDN=None, NDT=None, NDP=None, NSCAN=None, | |
| 5186 | lagind=None, lagfirst=None, |
|
5191 | lagind=None, lagfirst=None, | |
| 5187 | NAVG=None, nkill=None): |
|
5192 | NAVG=None, nkill=None): | |
| 5188 |
|
5193 | |||
| 5189 | dataOut.NLAG=NLAG |
|
5194 | dataOut.NLAG=NLAG | |
| 5190 | dataOut.NR=len(dataOut.channelList) |
|
5195 | dataOut.NR=len(dataOut.channelList) | |
| 5191 | dataOut.NRANGE=NRANGE |
|
5196 | dataOut.NRANGE=NRANGE | |
| 5192 | dataOut.NCAL=NCAL |
|
5197 | dataOut.NCAL=NCAL | |
| 5193 | dataOut.DPL=DPL |
|
5198 | dataOut.DPL=DPL | |
| 5194 | dataOut.NDN=NDN |
|
5199 | dataOut.NDN=NDN | |
| 5195 | dataOut.NDT=NDT |
|
5200 | dataOut.NDT=NDT | |
| 5196 | dataOut.NDP=NDP |
|
5201 | dataOut.NDP=NDP | |
| 5197 | dataOut.NSCAN=NSCAN |
|
5202 | dataOut.NSCAN=NSCAN | |
| 5198 | dataOut.DH=dataOut.heightList[1]-dataOut.heightList[0] |
|
5203 | dataOut.DH=dataOut.heightList[1]-dataOut.heightList[0] | |
| 5199 | dataOut.H0=int(dataOut.heightList[0]) |
|
5204 | dataOut.H0=int(dataOut.heightList[0]) | |
| 5200 | dataOut.lagind=lagind |
|
5205 | dataOut.lagind=lagind | |
| 5201 | dataOut.lagfirst=lagfirst |
|
5206 | dataOut.lagfirst=lagfirst | |
| 5202 | dataOut.NAVG=NAVG |
|
5207 | dataOut.NAVG=NAVG | |
| 5203 | dataOut.nkill=nkill |
|
5208 | dataOut.nkill=nkill | |
| 5204 |
|
5209 | |||
| 5205 | dataOut.flagNoData = True |
|
5210 | dataOut.flagNoData = True | |
| 5206 |
|
5211 | |||
| 5207 | self.get_dc(dataOut) |
|
5212 | self.get_dc(dataOut) | |
| 5208 | self.get_products_cabxys_HP(dataOut) |
|
5213 | self.get_products_cabxys_HP(dataOut) | |
| 5209 | self.cabxys_navg(dataOut) |
|
5214 | self.cabxys_navg(dataOut) | |
| 5210 | self.lag_products_LP(dataOut) |
|
5215 | self.lag_products_LP(dataOut) | |
| 5211 | self.LP_median_estimates(dataOut) |
|
5216 | self.LP_median_estimates(dataOut) | |
| 5212 | self.noise_estimation4x_HP(dataOut) |
|
5217 | self.noise_estimation4x_HP(dataOut) | |
| 5213 | self.kabxys(dataOut) |
|
5218 | self.kabxys(dataOut) | |
| 5214 |
|
5219 | |||
| 5215 | return dataOut |
|
5220 | return dataOut | |
| 5216 |
|
5221 | |||
| 5217 |
|
5222 | |||
| 5218 | class CrossProdLP(CrossProdDP): |
|
5223 | class CrossProdLP(CrossProdDP): | |
| 5219 | """Operation to calculate cross products of the Hybrid Experiment. |
|
5224 | """Operation to calculate cross products of the Hybrid Experiment. | |
| 5220 |
|
5225 | |||
| 5221 | Parameters: |
|
5226 | Parameters: | |
| 5222 | ----------- |
|
5227 | ----------- | |
| 5223 | NLAG : int |
|
5228 | NLAG : int | |
| 5224 | Number of lags for Long Pulse. |
|
5229 | Number of lags for Long Pulse. | |
| 5225 | NRANGE : int |
|
5230 | NRANGE : int | |
| 5226 | Number of samples (heights) for Long Pulse. |
|
5231 | Number of samples (heights) for Long Pulse. | |
| 5227 | NCAL : int |
|
5232 | NCAL : int | |
| 5228 | .* |
|
5233 | .* | |
| 5229 | DPL : int |
|
5234 | DPL : int | |
| 5230 | Number of lags for Double Pulse. |
|
5235 | Number of lags for Double Pulse. | |
| 5231 | NDN : int |
|
5236 | NDN : int | |
| 5232 | .* |
|
5237 | .* | |
| 5233 | NDT : int |
|
5238 | NDT : int | |
| 5234 | Number of heights for Double Pulse.* |
|
5239 | Number of heights for Double Pulse.* | |
| 5235 | NDP : int |
|
5240 | NDP : int | |
| 5236 | Number of heights for Double Pulse.* |
|
5241 | Number of heights for Double Pulse.* | |
| 5237 | NSCAN : int |
|
5242 | NSCAN : int | |
| 5238 | Number of profiles when the transmitter is on. |
|
5243 | Number of profiles when the transmitter is on. | |
| 5239 | lagind : intlist |
|
5244 | lagind : intlist | |
| 5240 | .* |
|
5245 | .* | |
| 5241 | lagfirst : intlist |
|
5246 | lagfirst : intlist | |
| 5242 | .* |
|
5247 | .* | |
| 5243 | NAVG : int |
|
5248 | NAVG : int | |
| 5244 | Number of blocks to be "averaged". |
|
5249 | Number of blocks to be "averaged". | |
| 5245 | nkill : int |
|
5250 | nkill : int | |
| 5246 | Number of blocks not to be considered when averaging. |
|
5251 | Number of blocks not to be considered when averaging. | |
| 5247 |
|
5252 | |||
| 5248 | Example |
|
5253 | Example | |
| 5249 | -------- |
|
5254 | -------- | |
| 5250 |
|
5255 | |||
| 5251 | op = proc_unit.addOperation(name='CrossProdHybrid', optype='other') |
|
5256 | op = proc_unit.addOperation(name='CrossProdHybrid', optype='other') | |
| 5252 | op.addParameter(name='NLAG', value='16', format='int') |
|
5257 | op.addParameter(name='NLAG', value='16', format='int') | |
| 5253 | op.addParameter(name='NRANGE', value='200', format='int') |
|
5258 | op.addParameter(name='NRANGE', value='200', format='int') | |
| 5254 | op.addParameter(name='NCAL', value='0', format='int') |
|
5259 | op.addParameter(name='NCAL', value='0', format='int') | |
| 5255 | op.addParameter(name='DPL', value='11', format='int') |
|
5260 | op.addParameter(name='DPL', value='11', format='int') | |
| 5256 | op.addParameter(name='NDN', value='0', format='int') |
|
5261 | op.addParameter(name='NDN', value='0', format='int') | |
| 5257 | op.addParameter(name='NDT', value='67', format='int') |
|
5262 | op.addParameter(name='NDT', value='67', format='int') | |
| 5258 | op.addParameter(name='NDP', value='67', format='int') |
|
5263 | op.addParameter(name='NDP', value='67', format='int') | |
| 5259 | op.addParameter(name='NSCAN', value='128', format='int') |
|
5264 | op.addParameter(name='NSCAN', value='128', format='int') | |
| 5260 | op.addParameter(name='lagind', value='(0,1,2,3,4,5,6,7,0,3,4,5,6,8,9,10)', format='intlist') |
|
5265 | op.addParameter(name='lagind', value='(0,1,2,3,4,5,6,7,0,3,4,5,6,8,9,10)', format='intlist') | |
| 5261 | op.addParameter(name='lagfirst', value='(1,1,1,1,1,1,1,1,0,0,0,0,0,1,1,1)', format='intlist') |
|
5266 | op.addParameter(name='lagfirst', value='(1,1,1,1,1,1,1,1,0,0,0,0,0,1,1,1)', format='intlist') | |
| 5262 | op.addParameter(name='NAVG', value='16', format='int') |
|
5267 | op.addParameter(name='NAVG', value='16', format='int') | |
| 5263 | op.addParameter(name='nkill', value='6', format='int') |
|
5268 | op.addParameter(name='nkill', value='6', format='int') | |
| 5264 |
|
5269 | |||
| 5265 | """ |
|
5270 | """ | |
| 5266 |
|
5271 | |||
| 5267 | def __init__(self, **kwargs): |
|
5272 | def __init__(self, **kwargs): | |
| 5268 |
|
5273 | |||
| 5269 | Operation.__init__(self, **kwargs) |
|
5274 | Operation.__init__(self, **kwargs) | |
| 5270 | self.bcounter=0 |
|
5275 | self.bcounter=0 | |
| 5271 | self.aux=1 |
|
5276 | self.aux=1 | |
| 5272 | self.aux_cross_lp=1 |
|
5277 | self.aux_cross_lp=1 | |
| 5273 | self.lag_products_LP_median_estimates_aux=1 |
|
5278 | self.lag_products_LP_median_estimates_aux=1 | |
| 5274 |
|
5279 | |||
| 5275 |
|
5280 | |||
| 5276 |
|
5281 | |||
| 5277 | #print(self.cax2[2,0,1]) |
|
5282 | #print(self.cax2[2,0,1]) | |
| 5278 | #input() |
|
5283 | #input() | |
| 5279 |
|
5284 | |||
| 5280 |
|
5285 | |||
| 5281 | def lag_products_LP(self,dataOut): |
|
5286 | def lag_products_LP(self,dataOut): | |
| 5282 |
|
5287 | |||
| 5283 |
|
5288 | |||
| 5284 | buffer=dataOut.data |
|
5289 | buffer=dataOut.data | |
| 5285 | if self.aux_cross_lp==1: |
|
5290 | if self.aux_cross_lp==1: | |
| 5286 |
|
5291 | |||
| 5287 | #self.dataOut.nptsfft2=150 |
|
5292 | #self.dataOut.nptsfft2=150 | |
| 5288 | self.cnorm=float((dataOut.nProfiles-dataOut.NSCAN)/dataOut.NSCAN) |
|
5293 | self.cnorm=float((dataOut.nProfiles-dataOut.NSCAN)/dataOut.NSCAN) | |
| 5289 | self.lagp0=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') |
|
5294 | self.lagp0=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') | |
| 5290 | self.lagp1=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') |
|
5295 | self.lagp1=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') | |
| 5291 | self.lagp2=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') |
|
5296 | self.lagp2=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') | |
| 5292 | self.lagp3=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') |
|
5297 | self.lagp3=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') | |
| 5293 | self.lagp4=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') |
|
5298 | self.lagp4=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') | |
| 5294 | self.lagp5=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') |
|
5299 | self.lagp5=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') | |
| 5295 |
|
5300 | |||
| 5296 | #self.lagp4=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') |
|
5301 | #self.lagp4=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') | |
| 5297 | self.aux_cross_lp=0 |
|
5302 | self.aux_cross_lp=0 | |
| 5298 |
|
5303 | |||
| 5299 | dataOut.noisevector=numpy.zeros((dataOut.MAXNRANGENDT,dataOut.NR,dataOut.NAVG),'float32') |
|
5304 | dataOut.noisevector=numpy.zeros((dataOut.MAXNRANGENDT,dataOut.NR,dataOut.NAVG),'float32') | |
| 5300 |
|
5305 | |||
| 5301 | #print(self.dataOut.data[0,0,0]) |
|
5306 | #print(self.dataOut.data[0,0,0]) | |
| 5302 | self.noisevectorizer(dataOut.NSCAN,dataOut.nProfiles,dataOut.NR,dataOut.MAXNRANGENDT,dataOut.noisevector,dataOut.data,dataOut.dc) #30/03/2020 |
|
5307 | self.noisevectorizer(dataOut.NSCAN,dataOut.nProfiles,dataOut.NR,dataOut.MAXNRANGENDT,dataOut.noisevector,dataOut.data,dataOut.dc) #30/03/2020 | |
| 5303 |
|
5308 | |||
| 5304 |
|
5309 | |||
| 5305 | for i in range(dataOut.NR): |
|
5310 | for i in range(dataOut.NR): | |
| 5306 | #print("inside i",i) |
|
5311 | #print("inside i",i) | |
| 5307 | buffer_dc=dataOut.dc[i] |
|
5312 | buffer_dc=dataOut.dc[i] | |
| 5308 | for j in range(dataOut.NRANGE): |
|
5313 | for j in range(dataOut.NRANGE): | |
| 5309 |
|
5314 | |||
| 5310 | range_for_n=numpy.min((dataOut.NRANGE-j,dataOut.NLAG)) |
|
5315 | range_for_n=numpy.min((dataOut.NRANGE-j,dataOut.NLAG)) | |
| 5311 |
|
5316 | |||
| 5312 | buffer_aux=numpy.conj(buffer[i,:dataOut.nProfiles,j]-buffer_dc) |
|
5317 | buffer_aux=numpy.conj(buffer[i,:dataOut.nProfiles,j]-buffer_dc) | |
| 5313 | for n in range(range_for_n): |
|
5318 | for n in range(range_for_n): | |
| 5314 |
|
5319 | |||
| 5315 | c=(buffer_aux)*(buffer[i,:dataOut.nProfiles,j+n]-buffer_dc) |
|
5320 | c=(buffer_aux)*(buffer[i,:dataOut.nProfiles,j+n]-buffer_dc) | |
| 5316 |
|
5321 | |||
| 5317 | if i==0: |
|
5322 | if i==0: | |
| 5318 | self.lagp0[n][j][self.bcounter]=numpy.sum(c[:dataOut.NSCAN]) |
|
5323 | self.lagp0[n][j][self.bcounter]=numpy.sum(c[:dataOut.NSCAN]) | |
| 5319 | #self.lagp3[n][j][self.bcounter-1]=numpy.sum(c[dataOut.NSCAN:]/self.cnorm) |
|
5324 | #self.lagp3[n][j][self.bcounter-1]=numpy.sum(c[dataOut.NSCAN:]/self.cnorm) | |
| 5320 | elif i==1: |
|
5325 | elif i==1: | |
| 5321 | self.lagp1[n][j][self.bcounter]=numpy.sum(c[:dataOut.NSCAN]) |
|
5326 | self.lagp1[n][j][self.bcounter]=numpy.sum(c[:dataOut.NSCAN]) | |
| 5322 | elif i==2: |
|
5327 | elif i==2: | |
| 5323 | self.lagp2[n][j][self.bcounter]=numpy.sum(c[:dataOut.NSCAN]) |
|
5328 | self.lagp2[n][j][self.bcounter]=numpy.sum(c[:dataOut.NSCAN]) | |
| 5324 | elif i==3: |
|
5329 | elif i==3: | |
| 5325 | self.lagp3[n][j][self.bcounter]=numpy.sum(c[:dataOut.NSCAN]) |
|
5330 | self.lagp3[n][j][self.bcounter]=numpy.sum(c[:dataOut.NSCAN]) | |
| 5326 | elif i==4: |
|
5331 | elif i==4: | |
| 5327 | self.lagp4[n][j][self.bcounter]=numpy.sum(c[:dataOut.NSCAN]) |
|
5332 | self.lagp4[n][j][self.bcounter]=numpy.sum(c[:dataOut.NSCAN]) | |
| 5328 | elif i==5: |
|
5333 | elif i==5: | |
| 5329 | self.lagp5[n][j][self.bcounter]=numpy.sum(c[:dataOut.NSCAN]) |
|
5334 | self.lagp5[n][j][self.bcounter]=numpy.sum(c[:dataOut.NSCAN]) | |
| 5330 |
|
5335 | |||
| 5331 |
|
5336 | |||
| 5332 | self.lagp0[:,:,self.bcounter]=numpy.conj(self.lagp0[:,:,self.bcounter]) |
|
5337 | self.lagp0[:,:,self.bcounter]=numpy.conj(self.lagp0[:,:,self.bcounter]) | |
| 5333 | self.lagp1[:,:,self.bcounter]=numpy.conj(self.lagp1[:,:,self.bcounter]) |
|
5338 | self.lagp1[:,:,self.bcounter]=numpy.conj(self.lagp1[:,:,self.bcounter]) | |
| 5334 | self.lagp2[:,:,self.bcounter]=numpy.conj(self.lagp2[:,:,self.bcounter]) |
|
5339 | self.lagp2[:,:,self.bcounter]=numpy.conj(self.lagp2[:,:,self.bcounter]) | |
| 5335 | self.lagp3[:,:,self.bcounter]=numpy.conj(self.lagp3[:,:,self.bcounter]) |
|
5340 | self.lagp3[:,:,self.bcounter]=numpy.conj(self.lagp3[:,:,self.bcounter]) | |
| 5336 |
|
5341 | |||
| 5337 | self.bcounter += 1 |
|
5342 | self.bcounter += 1 | |
| 5338 |
|
5343 | |||
| 5339 |
|
5344 | |||
| 5340 | def LP_median_estimates(self,dataOut): |
|
5345 | def LP_median_estimates(self,dataOut): | |
| 5341 |
|
5346 | |||
| 5342 | if self.bcounter==dataOut.NAVG: |
|
5347 | if self.bcounter==dataOut.NAVG: | |
| 5343 | dataOut.flagNoData = False |
|
5348 | dataOut.flagNoData = False | |
| 5344 |
|
5349 | |||
| 5345 | if self.lag_products_LP_median_estimates_aux==1: |
|
5350 | if self.lag_products_LP_median_estimates_aux==1: | |
| 5346 | self.output=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NR),'complex64') |
|
5351 | self.output=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NR),'complex64') | |
| 5347 | self.lag_products_LP_median_estimates_aux=0 |
|
5352 | self.lag_products_LP_median_estimates_aux=0 | |
| 5348 |
|
5353 | |||
| 5349 |
|
5354 | |||
| 5350 | for i in range(dataOut.NLAG): |
|
5355 | for i in range(dataOut.NLAG): | |
| 5351 | for j in range(dataOut.NRANGE): |
|
5356 | for j in range(dataOut.NRANGE): | |
| 5352 | for l in range(4): #four outputs |
|
5357 | for l in range(4): #four outputs | |
| 5353 |
|
5358 | |||
| 5354 | for k in range(dataOut.NAVG): |
|
5359 | for k in range(dataOut.NAVG): | |
| 5355 |
|
5360 | |||
| 5356 |
|
5361 | |||
| 5357 | if k==0: |
|
5362 | if k==0: | |
| 5358 | self.output[i,j,l]=0.0+0.j |
|
5363 | self.output[i,j,l]=0.0+0.j | |
| 5359 |
|
5364 | |||
| 5360 | if l==0: |
|
5365 | if l==0: | |
| 5361 | self.lagp0[i,j,:]=sorted(self.lagp0[i,j,:], key=lambda x: x.real) #sorted(self.lagp0[i,j,:].real) |
|
5366 | self.lagp0[i,j,:]=sorted(self.lagp0[i,j,:], key=lambda x: x.real) #sorted(self.lagp0[i,j,:].real) | |
| 5362 |
|
5367 | |||
| 5363 | if l==1: |
|
5368 | if l==1: | |
| 5364 | self.lagp1[i,j,:]=sorted(self.lagp1[i,j,:], key=lambda x: x.real) #sorted(self.lagp1[i,j,:].real) |
|
5369 | self.lagp1[i,j,:]=sorted(self.lagp1[i,j,:], key=lambda x: x.real) #sorted(self.lagp1[i,j,:].real) | |
| 5365 |
|
5370 | |||
| 5366 | if l==2: |
|
5371 | if l==2: | |
| 5367 | self.lagp2[i,j,:]=sorted(self.lagp2[i,j,:], key=lambda x: x.real) #sorted(self.lagp2[i,j,:].real) |
|
5372 | self.lagp2[i,j,:]=sorted(self.lagp2[i,j,:], key=lambda x: x.real) #sorted(self.lagp2[i,j,:].real) | |
| 5368 |
|
5373 | |||
| 5369 | if l==3: |
|
5374 | if l==3: | |
| 5370 | self.lagp3[i,j,:]=sorted(self.lagp3[i,j,:], key=lambda x: x.real) #sorted(self.lagp3[i,j,:].real) |
|
5375 | self.lagp3[i,j,:]=sorted(self.lagp3[i,j,:], key=lambda x: x.real) #sorted(self.lagp3[i,j,:].real) | |
| 5371 |
|
5376 | |||
| 5372 |
|
5377 | |||
| 5373 |
|
5378 | |||
| 5374 | if k>=dataOut.nkill/2 and k<dataOut.NAVG-dataOut.nkill/2: |
|
5379 | if k>=dataOut.nkill/2 and k<dataOut.NAVG-dataOut.nkill/2: | |
| 5375 | if l==0: |
|
5380 | if l==0: | |
| 5376 |
|
5381 | |||
| 5377 | self.output[i,j,l]=self.output[i,j,l]+((float(dataOut.NAVG)/(float)(dataOut.NAVG-dataOut.nkill))*self.lagp0[i,j,k]) |
|
5382 | self.output[i,j,l]=self.output[i,j,l]+((float(dataOut.NAVG)/(float)(dataOut.NAVG-dataOut.nkill))*self.lagp0[i,j,k]) | |
| 5378 | if l==1: |
|
5383 | if l==1: | |
| 5379 | #print("lagp1: ",self.lagp1[0,0,:]) |
|
5384 | #print("lagp1: ",self.lagp1[0,0,:]) | |
| 5380 | #input() |
|
5385 | #input() | |
| 5381 | self.output[i,j,l]=self.output[i,j,l]+((float(dataOut.NAVG)/(float)(dataOut.NAVG-dataOut.nkill))*self.lagp1[i,j,k]) |
|
5386 | self.output[i,j,l]=self.output[i,j,l]+((float(dataOut.NAVG)/(float)(dataOut.NAVG-dataOut.nkill))*self.lagp1[i,j,k]) | |
| 5382 | #print("self.lagp1[i,j,k]: ",self.lagp1[i,j,k]) |
|
5387 | #print("self.lagp1[i,j,k]: ",self.lagp1[i,j,k]) | |
| 5383 | #input() |
|
5388 | #input() | |
| 5384 | if l==2: |
|
5389 | if l==2: | |
| 5385 | self.output[i,j,l]=self.output[i,j,l]+((float(dataOut.NAVG)/(float)(dataOut.NAVG-dataOut.nkill))*self.lagp2[i,j,k]) |
|
5390 | self.output[i,j,l]=self.output[i,j,l]+((float(dataOut.NAVG)/(float)(dataOut.NAVG-dataOut.nkill))*self.lagp2[i,j,k]) | |
| 5386 | if l==3: |
|
5391 | if l==3: | |
| 5387 |
|
5392 | |||
| 5388 | self.output[i,j,l]=self.output[i,j,l]+((float(dataOut.NAVG)/(float)(dataOut.NAVG-dataOut.nkill))*self.lagp3[i,j,k]) |
|
5393 | self.output[i,j,l]=self.output[i,j,l]+((float(dataOut.NAVG)/(float)(dataOut.NAVG-dataOut.nkill))*self.lagp3[i,j,k]) | |
| 5389 |
|
5394 | |||
| 5390 |
|
5395 | |||
| 5391 | dataOut.output_LP=self.output |
|
5396 | dataOut.output_LP=self.output | |
| 5392 | dataOut.data_for_RTI_LP=numpy.zeros((4,dataOut.NRANGE)) |
|
5397 | dataOut.data_for_RTI_LP=numpy.zeros((4,dataOut.NRANGE)) | |
| 5393 | dataOut.data_for_RTI_LP[0],dataOut.data_for_RTI_LP[1],dataOut.data_for_RTI_LP[2],dataOut.data_for_RTI_LP[3]=self.RTI_LP(dataOut.output_LP,dataOut.NRANGE) |
|
5398 | dataOut.data_for_RTI_LP[0],dataOut.data_for_RTI_LP[1],dataOut.data_for_RTI_LP[2],dataOut.data_for_RTI_LP[3]=self.RTI_LP(dataOut.output_LP,dataOut.NRANGE) | |
| 5394 |
|
5399 | |||
| 5395 | self.bcounter = 0 |
|
5400 | self.bcounter = 0 | |
| 5396 |
|
5401 | |||
| 5397 | def get_dc(self,dataOut): |
|
5402 | def get_dc(self,dataOut): | |
| 5398 |
|
5403 | |||
| 5399 | if self.bcounter==0: |
|
5404 | if self.bcounter==0: | |
| 5400 | dataOut.dc=numpy.zeros(dataOut.NR,dtype='complex64') |
|
5405 | dataOut.dc=numpy.zeros(dataOut.NR,dtype='complex64') | |
| 5401 |
|
5406 | |||
| 5402 | #print(numpy.shape(dataOut.data)) |
|
5407 | #print(numpy.shape(dataOut.data)) | |
| 5403 | #input() |
|
5408 | #input() | |
| 5404 |
|
5409 | |||
| 5405 | dataOut.dc+=numpy.sum(dataOut.data[:,:,2*dataOut.NLAG:dataOut.NRANGE],axis=(1,2)) |
|
5410 | dataOut.dc+=numpy.sum(dataOut.data[:,:,2*dataOut.NLAG:dataOut.NRANGE],axis=(1,2)) | |
| 5406 |
|
5411 | |||
| 5407 | dataOut.dc=dataOut.dc/float(dataOut.nProfiles*(dataOut.NRANGE-2*dataOut.NLAG)) |
|
5412 | dataOut.dc=dataOut.dc/float(dataOut.nProfiles*(dataOut.NRANGE-2*dataOut.NLAG)) | |
| 5408 |
|
5413 | |||
| 5409 |
|
5414 | |||
| 5410 | #print("dc:",dataOut.dc[0]) |
|
5415 | #print("dc:",dataOut.dc[0]) | |
| 5411 |
|
5416 | |||
| 5412 |
|
5417 | |||
| 5413 |
|
5418 | |||
| 5414 |
|
5419 | |||
| 5415 | def noise_estimation4x_HP(self,dataOut): |
|
5420 | def noise_estimation4x_HP(self,dataOut): | |
| 5416 | if self.bcounter==dataOut.NAVG: |
|
5421 | if self.bcounter==dataOut.NAVG: | |
| 5417 | dataOut.noise_final=numpy.zeros(dataOut.NR,'float32') |
|
5422 | dataOut.noise_final=numpy.zeros(dataOut.NR,'float32') | |
| 5418 | #snoise=numpy.zeros((NR,NAVG),'float32') |
|
5423 | #snoise=numpy.zeros((NR,NAVG),'float32') | |
| 5419 | #nvector1=numpy.zeros((NR,NAVG,MAXNRANGENDT),'float32') |
|
5424 | #nvector1=numpy.zeros((NR,NAVG,MAXNRANGENDT),'float32') | |
| 5420 | sorted_data=numpy.zeros((dataOut.MAXNRANGENDT,dataOut.NR,dataOut.NAVG),'float32') |
|
5425 | sorted_data=numpy.zeros((dataOut.MAXNRANGENDT,dataOut.NR,dataOut.NAVG),'float32') | |
| 5421 | for i in range(dataOut.NR): |
|
5426 | for i in range(dataOut.NR): | |
| 5422 | dataOut.noise_final[i]=0.0 |
|
5427 | dataOut.noise_final[i]=0.0 | |
| 5423 | for j in range(dataOut.MAXNRANGENDT): |
|
5428 | for j in range(dataOut.MAXNRANGENDT): | |
| 5424 | sorted_data[j,i,:]=numpy.copy(sorted(dataOut.noisevector[j,i,:])) |
|
5429 | sorted_data[j,i,:]=numpy.copy(sorted(dataOut.noisevector[j,i,:])) | |
| 5425 | #print(sorted(noisevector[j,i,:])) |
|
5430 | #print(sorted(noisevector[j,i,:])) | |
| 5426 | #input() |
|
5431 | #input() | |
| 5427 | l=dataOut.MAXNRANGENDT-2 |
|
5432 | l=dataOut.MAXNRANGENDT-2 | |
| 5428 | for k in range(dataOut.NAVG): |
|
5433 | for k in range(dataOut.NAVG): | |
| 5429 | if k>=dataOut.nkill/2 and k<dataOut.NAVG-dataOut.nkill/2: |
|
5434 | if k>=dataOut.nkill/2 and k<dataOut.NAVG-dataOut.nkill/2: | |
| 5430 | #print(k) |
|
5435 | #print(k) | |
| 5431 | #print(sorted_data[min(j,l),i,k]) |
|
5436 | #print(sorted_data[min(j,l),i,k]) | |
| 5432 | dataOut.noise_final[i]+=sorted_data[min(j,l),i,k]*float(dataOut.NAVG)/float(dataOut.NAVG-dataOut.nkill) |
|
5437 | dataOut.noise_final[i]+=sorted_data[min(j,l),i,k]*float(dataOut.NAVG)/float(dataOut.NAVG-dataOut.nkill) | |
| 5433 | #print(dataOut.noise_final[i]) |
|
5438 | #print(dataOut.noise_final[i]) | |
| 5434 | #input() |
|
5439 | #input() | |
| 5435 | #print(dataOut.noise_final) |
|
5440 | #print(dataOut.noise_final) | |
| 5436 | #input() |
|
5441 | #input() | |
| 5437 |
|
5442 | |||
| 5438 | def noisevectorizer(self,NSCAN,nProfiles,NR,MAXNRANGENDT,noisevector,data,dc): |
|
5443 | def noisevectorizer(self,NSCAN,nProfiles,NR,MAXNRANGENDT,noisevector,data,dc): | |
| 5439 |
|
5444 | |||
| 5440 | #rnormalizer= 1./(float(nProfiles - NSCAN)) |
|
5445 | #rnormalizer= 1./(float(nProfiles - NSCAN)) | |
| 5441 | #rnormalizer= float(NSCAN)/((float(nProfiles - NSCAN))*float(MAXNRANGENDT)) |
|
5446 | #rnormalizer= float(NSCAN)/((float(nProfiles - NSCAN))*float(MAXNRANGENDT)) | |
| 5442 | rnormalizer= float(NSCAN)/((float(1))*float(MAXNRANGENDT)) |
|
5447 | rnormalizer= float(NSCAN)/((float(1))*float(MAXNRANGENDT)) | |
| 5443 | for i in range(NR): |
|
5448 | for i in range(NR): | |
| 5444 | for j in range(MAXNRANGENDT): |
|
5449 | for j in range(MAXNRANGENDT): | |
| 5445 | for k in range(NSCAN,nProfiles): |
|
5450 | for k in range(NSCAN,nProfiles): | |
| 5446 | #TODO:integrate just 2nd quartile gates |
|
5451 | #TODO:integrate just 2nd quartile gates | |
| 5447 | if k==NSCAN: |
|
5452 | if k==NSCAN: | |
| 5448 | noisevector[j][i][self.bcounter]=(abs(data[i][k][j]-dc[i])**2)*rnormalizer |
|
5453 | noisevector[j][i][self.bcounter]=(abs(data[i][k][j]-dc[i])**2)*rnormalizer | |
| 5449 | ##noisevector[j][i][iavg]=(abs(cdata[k][j][i])**2)*rnormalizer |
|
5454 | ##noisevector[j][i][iavg]=(abs(cdata[k][j][i])**2)*rnormalizer | |
| 5450 | else: |
|
5455 | else: | |
| 5451 | noisevector[j][i][self.bcounter]+=(abs(data[i][k][j]-dc[i])**2)*rnormalizer |
|
5456 | noisevector[j][i][self.bcounter]+=(abs(data[i][k][j]-dc[i])**2)*rnormalizer | |
| 5452 |
|
5457 | |||
| 5453 |
|
5458 | |||
| 5454 | def RTI_LP(self,output,NRANGE): |
|
5459 | def RTI_LP(self,output,NRANGE): | |
| 5455 | x00=numpy.zeros(NRANGE,dtype='float32') |
|
5460 | x00=numpy.zeros(NRANGE,dtype='float32') | |
| 5456 | x01=numpy.zeros(NRANGE,dtype='float32') |
|
5461 | x01=numpy.zeros(NRANGE,dtype='float32') | |
| 5457 | x02=numpy.zeros(NRANGE,dtype='float32') |
|
5462 | x02=numpy.zeros(NRANGE,dtype='float32') | |
| 5458 | x03=numpy.zeros(NRANGE,dtype='float32') |
|
5463 | x03=numpy.zeros(NRANGE,dtype='float32') | |
| 5459 |
|
5464 | |||
| 5460 | for i in range(1): #first couple of lags |
|
5465 | for i in range(1): #first couple of lags | |
| 5461 | for j in range(NRANGE): # |
|
5466 | for j in range(NRANGE): # | |
| 5462 | #fx=numpy.sqrt((kaxbx[i,j,k]+kayby[i,j,k])**2+(kaybx[i,j,k]-kaxby[i,j,k])**2) |
|
5467 | #fx=numpy.sqrt((kaxbx[i,j,k]+kayby[i,j,k])**2+(kaybx[i,j,k]-kaxby[i,j,k])**2) | |
| 5463 | x00[j]+=numpy.abs(output[i,j,0]) #Ch0 |
|
5468 | x00[j]+=numpy.abs(output[i,j,0]) #Ch0 | |
| 5464 | x01[j]+=numpy.abs(output[i,j,1]) #Ch1 |
|
5469 | x01[j]+=numpy.abs(output[i,j,1]) #Ch1 | |
| 5465 | x02[j]+=numpy.abs(output[i,j,2]) #Ch2 |
|
5470 | x02[j]+=numpy.abs(output[i,j,2]) #Ch2 | |
| 5466 | x03[j]+=numpy.abs(output[i,j,3]) #Ch3 |
|
5471 | x03[j]+=numpy.abs(output[i,j,3]) #Ch3 | |
| 5467 | #x02[i]=x02[i]+fx |
|
5472 | #x02[i]=x02[i]+fx | |
| 5468 |
|
5473 | |||
| 5469 | x00[j]=10.0*numpy.log10(x00[j]/4.) |
|
5474 | x00[j]=10.0*numpy.log10(x00[j]/4.) | |
| 5470 | x01[j]=10.0*numpy.log10(x01[j]/4.) |
|
5475 | x01[j]=10.0*numpy.log10(x01[j]/4.) | |
| 5471 | x02[j]=10.0*numpy.log10(x02[j]/4.) |
|
5476 | x02[j]=10.0*numpy.log10(x02[j]/4.) | |
| 5472 | x03[j]=10.0*numpy.log10(x03[j]/4.) |
|
5477 | x03[j]=10.0*numpy.log10(x03[j]/4.) | |
| 5473 | #x02[i]=10.0*numpy.log10(x02[i]) |
|
5478 | #x02[i]=10.0*numpy.log10(x02[i]) | |
| 5474 | return x00,x01,x02,x03 |
|
5479 | return x00,x01,x02,x03 | |
| 5475 |
|
5480 | |||
| 5476 | def run(self, dataOut, NLAG=None, NRANGE=None, NCAL=None, DPL=None, |
|
5481 | def run(self, dataOut, NLAG=None, NRANGE=None, NCAL=None, DPL=None, | |
| 5477 | NDN=None, NDT=None, NDP=None, NSCAN=None, |
|
5482 | NDN=None, NDT=None, NDP=None, NSCAN=None, | |
| 5478 | lagind=None, lagfirst=None, |
|
5483 | lagind=None, lagfirst=None, | |
| 5479 | NAVG=None, nkill=None): |
|
5484 | NAVG=None, nkill=None): | |
| 5480 |
|
5485 | |||
| 5481 | dataOut.NLAG=NLAG |
|
5486 | dataOut.NLAG=NLAG | |
| 5482 | dataOut.NR=len(dataOut.channelList) |
|
5487 | dataOut.NR=len(dataOut.channelList) | |
| 5483 | #dataOut.NRANGE=NRANGE |
|
5488 | #dataOut.NRANGE=NRANGE | |
| 5484 | dataOut.NRANGE=dataOut.nHeights |
|
5489 | dataOut.NRANGE=dataOut.nHeights | |
| 5485 | dataOut.NCAL=NCAL |
|
5490 | dataOut.NCAL=NCAL | |
| 5486 | dataOut.DPL=DPL |
|
5491 | dataOut.DPL=DPL | |
| 5487 | dataOut.NDN=NDN |
|
5492 | dataOut.NDN=NDN | |
| 5488 | dataOut.NDT=NDT |
|
5493 | dataOut.NDT=NDT | |
| 5489 | dataOut.NDP=NDP |
|
5494 | dataOut.NDP=NDP | |
| 5490 | dataOut.NSCAN=NSCAN |
|
5495 | dataOut.NSCAN=NSCAN | |
| 5491 | dataOut.DH=dataOut.heightList[1]-dataOut.heightList[0] |
|
5496 | dataOut.DH=dataOut.heightList[1]-dataOut.heightList[0] | |
| 5492 | dataOut.H0=int(dataOut.heightList[0]) |
|
5497 | dataOut.H0=int(dataOut.heightList[0]) | |
| 5493 | dataOut.lagind=lagind |
|
5498 | dataOut.lagind=lagind | |
| 5494 | dataOut.lagfirst=lagfirst |
|
5499 | dataOut.lagfirst=lagfirst | |
| 5495 | dataOut.NAVG=NAVG |
|
5500 | dataOut.NAVG=NAVG | |
| 5496 | dataOut.nkill=nkill |
|
5501 | dataOut.nkill=nkill | |
| 5497 |
|
5502 | |||
| 5498 | dataOut.MAXNRANGENDT = dataOut.NRANGE |
|
5503 | dataOut.MAXNRANGENDT = dataOut.NRANGE | |
| 5499 |
|
5504 | |||
| 5500 | dataOut.flagNoData = True |
|
5505 | dataOut.flagNoData = True | |
| 5501 |
|
5506 | |||
| 5502 | print(self.bcounter) |
|
5507 | print(self.bcounter) | |
| 5503 |
|
5508 | |||
| 5504 | self.get_dc(dataOut) |
|
5509 | self.get_dc(dataOut) | |
| 5505 | self.lag_products_LP(dataOut) |
|
5510 | self.lag_products_LP(dataOut) | |
| 5506 | self.noise_estimation4x_HP(dataOut) |
|
5511 | self.noise_estimation4x_HP(dataOut) | |
| 5507 | self.LP_median_estimates(dataOut) |
|
5512 | self.LP_median_estimates(dataOut) | |
| 5508 |
|
5513 | |||
| 5509 | print("******************DONE******************") |
|
5514 | print("******************DONE******************") | |
| 5510 |
|
5515 | |||
| 5511 |
|
5516 | |||
| 5512 |
|
5517 | |||
| 5513 | return dataOut |
|
5518 | return dataOut | |
| 5514 |
|
5519 | |||
| 5515 |
|
5520 | |||
| 5516 | class RemoveDebris(Operation): |
|
5521 | class RemoveDebris(Operation): | |
| 5517 | """Operation to remove blocks where an outlier is found for Double (Long) Pulse. |
|
5522 | """Operation to remove blocks where an outlier is found for Double (Long) Pulse. | |
| 5518 |
|
5523 | |||
| 5519 | Parameters: |
|
5524 | Parameters: | |
| 5520 | ----------- |
|
5525 | ----------- | |
| 5521 | None |
|
5526 | None | |
| 5522 |
|
5527 | |||
| 5523 | Example |
|
5528 | Example | |
| 5524 | -------- |
|
5529 | -------- | |
| 5525 |
|
5530 | |||
| 5526 | op = proc_unit.addOperation(name='RemoveDebris', optype='other') |
|
5531 | op = proc_unit.addOperation(name='RemoveDebris', optype='other') | |
| 5527 |
|
5532 | |||
| 5528 | """ |
|
5533 | """ | |
| 5529 |
|
5534 | |||
| 5530 | def __init__(self, **kwargs): |
|
5535 | def __init__(self, **kwargs): | |
| 5531 |
|
5536 | |||
| 5532 | Operation.__init__(self, **kwargs) |
|
5537 | Operation.__init__(self, **kwargs) | |
| 5533 |
|
5538 | |||
| 5534 | def run(self,dataOut): |
|
5539 | def run(self,dataOut): | |
| 5535 | debris=numpy.zeros(dataOut.NRANGE,'float32') |
|
5540 | debris=numpy.zeros(dataOut.NRANGE,'float32') | |
| 5536 |
|
5541 | |||
| 5537 | for j in range(0,3): |
|
5542 | for j in range(0,3): | |
| 5538 | for i in range(dataOut.NRANGE): |
|
5543 | for i in range(dataOut.NRANGE): | |
| 5539 | if j==0: |
|
5544 | if j==0: | |
| 5540 | debris[i]=10*numpy.log10(numpy.abs(dataOut.output_LP[j,i,0])) |
|
5545 | debris[i]=10*numpy.log10(numpy.abs(dataOut.output_LP[j,i,0])) | |
| 5541 | else: |
|
5546 | else: | |
| 5542 | debris[i]+=10*numpy.log10(numpy.abs(dataOut.output_LP[j,i,0])) |
|
5547 | debris[i]+=10*numpy.log10(numpy.abs(dataOut.output_LP[j,i,0])) | |
| 5543 |
|
5548 | |||
| 5544 | thresh=8.0+4+4+4 |
|
5549 | thresh=8.0+4+4+4 | |
| 5545 | for i in range(47,100): |
|
5550 | for i in range(47,100): | |
| 5546 | if ((debris[i-2]+debris[i-1]+debris[i]+debris[i+1])> |
|
5551 | if ((debris[i-2]+debris[i-1]+debris[i]+debris[i+1])> | |
| 5547 | ((debris[i-12]+debris[i-11]+debris[i-10]+debris[i-9]+ |
|
5552 | ((debris[i-12]+debris[i-11]+debris[i-10]+debris[i-9]+ | |
| 5548 | debris[i+12]+debris[i+11]+debris[i+10]+debris[i+9])/2.0+ |
|
5553 | debris[i+12]+debris[i+11]+debris[i+10]+debris[i+9])/2.0+ | |
| 5549 | thresh)): |
|
5554 | thresh)): | |
| 5550 |
|
5555 | |||
| 5551 | dataOut.flagNoData=True |
|
5556 | dataOut.flagNoData=True | |
| 5552 | print("LP Debris detected at",i*15,"km") |
|
5557 | print("LP Debris detected at",i*15,"km") | |
| 5553 |
|
5558 | |||
| 5554 | debris=numpy.zeros(dataOut.NDP,dtype='float32') |
|
5559 | debris=numpy.zeros(dataOut.NDP,dtype='float32') | |
| 5555 | Range=numpy.arange(0,3000,15) |
|
5560 | Range=numpy.arange(0,3000,15) | |
| 5556 | for k in range(2): #flip |
|
5561 | for k in range(2): #flip | |
| 5557 | for i in range(dataOut.NDP): # |
|
5562 | for i in range(dataOut.NDP): # | |
| 5558 | debris[i]+=numpy.sqrt((dataOut.kaxbx[i,0,k]+dataOut.kayby[i,0,k])**2+(dataOut.kaybx[i,0,k]-dataOut.kaxby[i,0,k])**2) |
|
5563 | debris[i]+=numpy.sqrt((dataOut.kaxbx[i,0,k]+dataOut.kayby[i,0,k])**2+(dataOut.kaybx[i,0,k]-dataOut.kaxby[i,0,k])**2) | |
| 5559 |
|
5564 | |||
| 5560 | if gmtime(dataOut.utctime).tm_hour > 11: |
|
5565 | if gmtime(dataOut.utctime).tm_hour > 11: | |
| 5561 | for i in range(2,dataOut.NDP-2): |
|
5566 | for i in range(2,dataOut.NDP-2): | |
| 5562 | if (debris[i]>3.0*debris[i-2] and |
|
5567 | if (debris[i]>3.0*debris[i-2] and | |
| 5563 | debris[i]>3.0*debris[i+2] and |
|
5568 | debris[i]>3.0*debris[i+2] and | |
| 5564 | Range[i]>200.0 and Range[i]<=540.0): |
|
5569 | Range[i]>200.0 and Range[i]<=540.0): | |
| 5565 | dataOut.flagNoData=True |
|
5570 | dataOut.flagNoData=True | |
| 5566 | print("DP Debris detected at",i*15,"km") |
|
5571 | print("DP Debris detected at",i*15,"km") | |
| 5567 |
|
5572 | |||
| 5568 | return dataOut |
|
5573 | return dataOut | |
| 5569 |
|
5574 | |||
| 5570 |
|
5575 | |||
| 5571 | class IntegrationHP(IntegrationDP): |
|
5576 | class IntegrationHP(IntegrationDP): | |
| 5572 | """Operation to integrate Double Pulse and Long Pulse data. |
|
5577 | """Operation to integrate Double Pulse and Long Pulse data. | |
| 5573 |
|
5578 | |||
| 5574 | Parameters: |
|
5579 | Parameters: | |
| 5575 | ----------- |
|
5580 | ----------- | |
| 5576 | nint : int |
|
5581 | nint : int | |
| 5577 | Number of integrations. |
|
5582 | Number of integrations. | |
| 5578 |
|
5583 | |||
| 5579 | Example |
|
5584 | Example | |
| 5580 | -------- |
|
5585 | -------- | |
| 5581 |
|
5586 | |||
| 5582 | op = proc_unit.addOperation(name='IntegrationHP', optype='other') |
|
5587 | op = proc_unit.addOperation(name='IntegrationHP', optype='other') | |
| 5583 | op.addParameter(name='nint', value='30', format='int') |
|
5588 | op.addParameter(name='nint', value='30', format='int') | |
| 5584 |
|
5589 | |||
| 5585 | """ |
|
5590 | """ | |
| 5586 |
|
5591 | |||
| 5587 | def __init__(self, **kwargs): |
|
5592 | def __init__(self, **kwargs): | |
| 5588 |
|
5593 | |||
| 5589 | Operation.__init__(self, **kwargs) |
|
5594 | Operation.__init__(self, **kwargs) | |
| 5590 |
|
5595 | |||
| 5591 | self.counter = 0 |
|
5596 | self.counter = 0 | |
| 5592 | self.aux = 0 |
|
5597 | self.aux = 0 | |
| 5593 |
|
5598 | |||
| 5594 | def integration_noise(self,dataOut): |
|
5599 | def integration_noise(self,dataOut): | |
| 5595 |
|
5600 | |||
| 5596 | if self.counter == 0: |
|
5601 | if self.counter == 0: | |
| 5597 | dataOut.tnoise=numpy.zeros((dataOut.NR),dtype='float32') |
|
5602 | dataOut.tnoise=numpy.zeros((dataOut.NR),dtype='float32') | |
| 5598 |
|
5603 | |||
| 5599 | dataOut.tnoise+=dataOut.noise_final |
|
5604 | dataOut.tnoise+=dataOut.noise_final | |
| 5600 |
|
5605 | |||
| 5601 | def integration_for_long_pulse(self,dataOut): |
|
5606 | def integration_for_long_pulse(self,dataOut): | |
| 5602 |
|
5607 | |||
| 5603 | if self.counter == 0: |
|
5608 | if self.counter == 0: | |
| 5604 | dataOut.output_LP_integrated=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NR),order='F',dtype='complex64') |
|
5609 | dataOut.output_LP_integrated=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NR),order='F',dtype='complex64') | |
| 5605 |
|
5610 | |||
| 5606 | dataOut.output_LP_integrated+=dataOut.output_LP |
|
5611 | dataOut.output_LP_integrated+=dataOut.output_LP | |
| 5607 |
|
5612 | |||
| 5608 | def run(self,dataOut,nint=None): |
|
5613 | def run(self,dataOut,nint=None): | |
| 5609 |
|
5614 | |||
| 5610 | dataOut.flagNoData=True |
|
5615 | dataOut.flagNoData=True | |
| 5611 |
|
5616 | |||
| 5612 | dataOut.nint=nint |
|
5617 | dataOut.nint=nint | |
| 5613 | dataOut.paramInterval=0#int(dataOut.nint*dataOut.header[7][0]*2 ) |
|
5618 | dataOut.paramInterval=0#int(dataOut.nint*dataOut.header[7][0]*2 ) | |
| 5614 | dataOut.lat=-11.95 |
|
5619 | dataOut.lat=-11.95 | |
| 5615 | dataOut.lon=-76.87 |
|
5620 | dataOut.lon=-76.87 | |
| 5616 |
|
5621 | |||
| 5617 | self.integration_for_long_pulse(dataOut) |
|
5622 | self.integration_for_long_pulse(dataOut) | |
| 5618 |
|
5623 | |||
| 5619 | self.integration_noise(dataOut) |
|
5624 | self.integration_noise(dataOut) | |
| 5620 |
|
5625 | |||
| 5621 | if self.counter==dataOut.nint-1: |
|
5626 | if self.counter==dataOut.nint-1: | |
| 5622 | dataOut.nis=dataOut.NSCAN*dataOut.NAVG*dataOut.nint*10 |
|
5627 | dataOut.nis=dataOut.NSCAN*dataOut.NAVG*dataOut.nint*10 | |
| 5623 | dataOut.tnoise[0]*=0.995 |
|
5628 | dataOut.tnoise[0]*=0.995 | |
| 5624 | dataOut.tnoise[1]*=0.995 |
|
5629 | dataOut.tnoise[1]*=0.995 | |
| 5625 | dataOut.pan=dataOut.tnoise[0]/float(dataOut.NSCAN*dataOut.nint*dataOut.NAVG) |
|
5630 | dataOut.pan=dataOut.tnoise[0]/float(dataOut.NSCAN*dataOut.nint*dataOut.NAVG) | |
| 5626 | dataOut.pbn=dataOut.tnoise[1]/float(dataOut.NSCAN*dataOut.nint*dataOut.NAVG) |
|
5631 | dataOut.pbn=dataOut.tnoise[1]/float(dataOut.NSCAN*dataOut.nint*dataOut.NAVG) | |
| 5627 |
|
5632 | |||
| 5628 | self.integration_for_double_pulse(dataOut) |
|
5633 | self.integration_for_double_pulse(dataOut) | |
| 5629 |
|
5634 | |||
| 5630 |
|
5635 | |||
| 5631 |
|
5636 | |||
| 5632 | return dataOut |
|
5637 | return dataOut | |
| 5633 |
|
5638 | |||
| 5634 | class SumFlipsHP(SumFlips): |
|
5639 | class SumFlipsHP(SumFlips): | |
| 5635 | """Operation to sum the flip and unflip part of certain cross products of the Double Pulse. |
|
5640 | """Operation to sum the flip and unflip part of certain cross products of the Double Pulse. | |
| 5636 |
|
5641 | |||
| 5637 | Parameters: |
|
5642 | Parameters: | |
| 5638 | ----------- |
|
5643 | ----------- | |
| 5639 | None |
|
5644 | None | |
| 5640 |
|
5645 | |||
| 5641 | Example |
|
5646 | Example | |
| 5642 | -------- |
|
5647 | -------- | |
| 5643 |
|
5648 | |||
| 5644 | op = proc_unit.addOperation(name='SumFlipsHP', optype='other') |
|
5649 | op = proc_unit.addOperation(name='SumFlipsHP', optype='other') | |
| 5645 |
|
5650 | |||
| 5646 | """ |
|
5651 | """ | |
| 5647 |
|
5652 | |||
| 5648 | def __init__(self, **kwargs): |
|
5653 | def __init__(self, **kwargs): | |
| 5649 |
|
5654 | |||
| 5650 | Operation.__init__(self, **kwargs) |
|
5655 | Operation.__init__(self, **kwargs) | |
| 5651 |
|
5656 | |||
| 5652 | def rint2HP(self,dataOut): |
|
5657 | def rint2HP(self,dataOut): | |
| 5653 |
|
5658 | |||
| 5654 | dataOut.rnint2=numpy.zeros(dataOut.DPL,'float32') |
|
5659 | dataOut.rnint2=numpy.zeros(dataOut.DPL,'float32') | |
| 5655 | #print(dataOut.nint,dataOut.NAVG) |
|
5660 | #print(dataOut.nint,dataOut.NAVG) | |
| 5656 | for l in range(dataOut.DPL): |
|
5661 | for l in range(dataOut.DPL): | |
| 5657 | if(l==0 or (l>=3 and l <=6)): |
|
5662 | if(l==0 or (l>=3 and l <=6)): | |
| 5658 | dataOut.rnint2[l]=0.5/float(dataOut.nint*dataOut.NAVG*16.0) |
|
5663 | dataOut.rnint2[l]=0.5/float(dataOut.nint*dataOut.NAVG*16.0) | |
| 5659 | else: |
|
5664 | else: | |
| 5660 | dataOut.rnint2[l]=0.5/float(dataOut.nint*dataOut.NAVG*8.0) |
|
5665 | dataOut.rnint2[l]=0.5/float(dataOut.nint*dataOut.NAVG*8.0) | |
| 5661 |
|
5666 | |||
| 5662 | def run(self,dataOut): |
|
5667 | def run(self,dataOut): | |
| 5663 |
|
5668 | |||
| 5664 | self.rint2HP(dataOut) |
|
5669 | self.rint2HP(dataOut) | |
| 5665 | self.SumLags(dataOut) |
|
5670 | self.SumLags(dataOut) | |
| 5666 |
|
5671 | |||
| 5667 | hei = 2 |
|
5672 | hei = 2 | |
| 5668 | lag = 0 |
|
5673 | lag = 0 | |
| 5669 | ''' |
|
5674 | ''' | |
| 5670 | for hei in range(67): |
|
5675 | for hei in range(67): | |
| 5671 | print("hei",hei) |
|
5676 | print("hei",hei) | |
| 5672 | print(dataOut.kabxys_integrated[8][hei,:,0]+dataOut.kabxys_integrated[11][hei,:,0]) |
|
5677 | print(dataOut.kabxys_integrated[8][hei,:,0]+dataOut.kabxys_integrated[11][hei,:,0]) | |
| 5673 | print(dataOut.kabxys_integrated[10][hei,:,0]-dataOut.kabxys_integrated[9][hei,:,0]) |
|
5678 | print(dataOut.kabxys_integrated[10][hei,:,0]-dataOut.kabxys_integrated[9][hei,:,0]) | |
| 5674 | exit(1) |
|
5679 | exit(1) | |
| 5675 | ''' |
|
5680 | ''' | |
| 5676 | ''' |
|
5681 | ''' | |
| 5677 | print("b",(dataOut.kabxys_integrated[4][hei,lag,0]+dataOut.kabxys_integrated[5][hei,lag,0])) |
|
5682 | print("b",(dataOut.kabxys_integrated[4][hei,lag,0]+dataOut.kabxys_integrated[5][hei,lag,0])) | |
| 5678 | print((dataOut.kabxys_integrated[6][hei,lag,0]+dataOut.kabxys_integrated[7][hei,lag,0])) |
|
5683 | print((dataOut.kabxys_integrated[6][hei,lag,0]+dataOut.kabxys_integrated[7][hei,lag,0])) | |
| 5679 | print("c",(dataOut.kabxys_integrated[8][hei,lag,0]+dataOut.kabxys_integrated[11][hei,lag,0])) |
|
5684 | print("c",(dataOut.kabxys_integrated[8][hei,lag,0]+dataOut.kabxys_integrated[11][hei,lag,0])) | |
| 5680 | print((dataOut.kabxys_integrated[10][hei,lag,0]-dataOut.kabxys_integrated[9][hei,lag,0])) |
|
5685 | print((dataOut.kabxys_integrated[10][hei,lag,0]-dataOut.kabxys_integrated[9][hei,lag,0])) | |
| 5681 | exit(1) |
|
5686 | exit(1) | |
| 5682 | ''' |
|
5687 | ''' | |
| 5683 | #print(dataOut.rnint2) |
|
5688 | #print(dataOut.rnint2) | |
| 5684 | #print(numpy.sum(dataOut.kabxys_integrated[4][:,1,0]+dataOut.kabxys_integrated[5][:,1,0])) |
|
5689 | #print(numpy.sum(dataOut.kabxys_integrated[4][:,1,0]+dataOut.kabxys_integrated[5][:,1,0])) | |
| 5685 | #print(dataOut.nis) |
|
5690 | #print(dataOut.nis) | |
| 5686 | #exit(1) |
|
5691 | #exit(1) | |
| 5687 | return dataOut |
|
5692 | return dataOut | |
| 5688 |
|
5693 | |||
| 5689 |
|
5694 | |||
| 5690 | class LongPulseAnalysis(Operation): |
|
5695 | class LongPulseAnalysis(Operation): | |
| 5691 | """Operation to estimate ACFs, temperatures, total electron density and Hydrogen/Helium fractions from the Long Pulse data. |
|
5696 | """Operation to estimate ACFs, temperatures, total electron density and Hydrogen/Helium fractions from the Long Pulse data. | |
| 5692 |
|
5697 | |||
| 5693 | Parameters: |
|
5698 | Parameters: | |
| 5694 | ----------- |
|
5699 | ----------- | |
| 5695 | NACF : int |
|
5700 | NACF : int | |
| 5696 | .* |
|
5701 | .* | |
| 5697 |
|
5702 | |||
| 5698 | Example |
|
5703 | Example | |
| 5699 | -------- |
|
5704 | -------- | |
| 5700 |
|
5705 | |||
| 5701 | op = proc_unit.addOperation(name='LongPulseAnalysis', optype='other') |
|
5706 | op = proc_unit.addOperation(name='LongPulseAnalysis', optype='other') | |
| 5702 | op.addParameter(name='NACF', value='16', format='int') |
|
5707 | op.addParameter(name='NACF', value='16', format='int') | |
| 5703 |
|
5708 | |||
| 5704 | """ |
|
5709 | """ | |
| 5705 |
|
5710 | |||
| 5706 | def __init__(self, **kwargs): |
|
5711 | def __init__(self, **kwargs): | |
| 5707 |
|
5712 | |||
| 5708 | Operation.__init__(self, **kwargs) |
|
5713 | Operation.__init__(self, **kwargs) | |
| 5709 | self.aux=1 |
|
5714 | self.aux=1 | |
| 5710 |
|
5715 | |||
| 5711 | def run(self,dataOut,NACF): |
|
5716 | def run(self,dataOut,NACF): | |
| 5712 |
|
5717 | |||
| 5713 | dataOut.NACF=NACF |
|
5718 | dataOut.NACF=NACF | |
| 5714 | dataOut.heightList=dataOut.DH*(numpy.arange(dataOut.NACF)) |
|
5719 | dataOut.heightList=dataOut.DH*(numpy.arange(dataOut.NACF)) | |
| 5715 | anoise0=dataOut.tnoise[0] |
|
5720 | anoise0=dataOut.tnoise[0] | |
| 5716 | anoise1=anoise0*0.0 #seems to be noise in 1st lag 0.015 before '14 |
|
5721 | anoise1=anoise0*0.0 #seems to be noise in 1st lag 0.015 before '14 | |
| 5717 | #print(anoise0) |
|
5722 | #print(anoise0) | |
| 5718 | #exit(1) |
|
5723 | #exit(1) | |
| 5719 | if self.aux: |
|
5724 | if self.aux: | |
| 5720 | #dataOut.cut=31#26#height=31*15=465 |
|
5725 | #dataOut.cut=31#26#height=31*15=465 | |
| 5721 | self.cal=numpy.zeros((dataOut.NLAG),'float32') |
|
5726 | self.cal=numpy.zeros((dataOut.NLAG),'float32') | |
| 5722 | self.drift=numpy.zeros((200),'float32') |
|
5727 | self.drift=numpy.zeros((200),'float32') | |
| 5723 | self.rdrift=numpy.zeros((200),'float32') |
|
5728 | self.rdrift=numpy.zeros((200),'float32') | |
| 5724 | self.ddrift=numpy.zeros((200),'float32') |
|
5729 | self.ddrift=numpy.zeros((200),'float32') | |
| 5725 | self.sigma=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') |
|
5730 | self.sigma=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') | |
| 5726 | self.powera=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') |
|
5731 | self.powera=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') | |
| 5727 | self.powerb=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') |
|
5732 | self.powerb=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') | |
| 5728 | self.perror=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') |
|
5733 | self.perror=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') | |
| 5729 | dataOut.ene=numpy.zeros((dataOut.NRANGE),'float32') |
|
5734 | dataOut.ene=numpy.zeros((dataOut.NRANGE),'float32') | |
| 5730 | self.dpulse=numpy.zeros((dataOut.NACF),'float32') |
|
5735 | self.dpulse=numpy.zeros((dataOut.NACF),'float32') | |
| 5731 | self.lpulse=numpy.zeros((dataOut.NACF),'float32') |
|
5736 | self.lpulse=numpy.zeros((dataOut.NACF),'float32') | |
| 5732 | dataOut.lags_LP=numpy.zeros((dataOut.IBITS),order='F',dtype='float32') |
|
5737 | dataOut.lags_LP=numpy.zeros((dataOut.IBITS),order='F',dtype='float32') | |
| 5733 | self.lagp=numpy.zeros((dataOut.NACF),'float32') |
|
5738 | self.lagp=numpy.zeros((dataOut.NACF),'float32') | |
| 5734 | self.u=numpy.zeros((2*dataOut.NACF,2*dataOut.NACF),'float32') |
|
5739 | self.u=numpy.zeros((2*dataOut.NACF,2*dataOut.NACF),'float32') | |
| 5735 | dataOut.ne=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') |
|
5740 | dataOut.ne=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') | |
| 5736 | dataOut.te=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
5741 | dataOut.te=numpy.zeros((dataOut.NACF),order='F',dtype='float32') | |
| 5737 | dataOut.ete=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
5742 | dataOut.ete=numpy.zeros((dataOut.NACF),order='F',dtype='float32') | |
| 5738 | dataOut.ti=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
5743 | dataOut.ti=numpy.zeros((dataOut.NACF),order='F',dtype='float32') | |
| 5739 | dataOut.eti=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
5744 | dataOut.eti=numpy.zeros((dataOut.NACF),order='F',dtype='float32') | |
| 5740 | dataOut.ph=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
5745 | dataOut.ph=numpy.zeros((dataOut.NACF),order='F',dtype='float32') | |
| 5741 | dataOut.eph=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
5746 | dataOut.eph=numpy.zeros((dataOut.NACF),order='F',dtype='float32') | |
| 5742 | dataOut.phe=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
5747 | dataOut.phe=numpy.zeros((dataOut.NACF),order='F',dtype='float32') | |
| 5743 | dataOut.ephe=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
5748 | dataOut.ephe=numpy.zeros((dataOut.NACF),order='F',dtype='float32') | |
| 5744 | dataOut.errors=numpy.zeros((dataOut.IBITS,max(dataOut.NRANGE,dataOut.NSHTS)),order='F',dtype='float32') |
|
5749 | dataOut.errors=numpy.zeros((dataOut.IBITS,max(dataOut.NRANGE,dataOut.NSHTS)),order='F',dtype='float32') | |
| 5745 | dataOut.fit_array_real=numpy.zeros((max(dataOut.NRANGE,dataOut.NSHTS),dataOut.NLAG),order='F',dtype='float32') |
|
5750 | dataOut.fit_array_real=numpy.zeros((max(dataOut.NRANGE,dataOut.NSHTS),dataOut.NLAG),order='F',dtype='float32') | |
| 5746 | dataOut.status=numpy.zeros(1,'float32') |
|
5751 | dataOut.status=numpy.zeros(1,'float32') | |
| 5747 | dataOut.tx=240.0 #debería provenir del header #hybrid |
|
5752 | dataOut.tx=240.0 #debería provenir del header #hybrid | |
| 5748 |
|
5753 | |||
| 5749 | for i in range(dataOut.IBITS): |
|
5754 | for i in range(dataOut.IBITS): | |
| 5750 | dataOut.lags_LP[i]=float(i)*(dataOut.tx/150.0)/float(dataOut.IBITS) # (float)i*(header.tx/150.0)/(float)IBITS; |
|
5755 | dataOut.lags_LP[i]=float(i)*(dataOut.tx/150.0)/float(dataOut.IBITS) # (float)i*(header.tx/150.0)/(float)IBITS; | |
| 5751 |
|
5756 | |||
| 5752 | self.aux=0 |
|
5757 | self.aux=0 | |
| 5753 |
|
5758 | |||
| 5754 | dataOut.cut=30 |
|
5759 | dataOut.cut=30 | |
| 5755 | for i in range(30,15,-1): #Aquí se calcula en donde se unirá DP y LP en la parte final |
|
5760 | for i in range(30,15,-1): #Aquí se calcula en donde se unirá DP y LP en la parte final | |
| 5756 | if numpy.nanmax(dataOut.acfs_error_to_plot[i,:])>=10 or dataOut.info2[i]==0: |
|
5761 | if numpy.nanmax(dataOut.acfs_error_to_plot[i,:])>=10 or dataOut.info2[i]==0: | |
| 5757 | dataOut.cut=i-1 |
|
5762 | dataOut.cut=i-1 | |
| 5758 |
|
5763 | |||
| 5759 | for i in range(dataOut.NLAG): |
|
5764 | for i in range(dataOut.NLAG): | |
| 5760 | self.cal[i]=sum(dataOut.output_LP_integrated[i,:,3].real) #Lag x Height x Channel |
|
5765 | self.cal[i]=sum(dataOut.output_LP_integrated[i,:,3].real) #Lag x Height x Channel | |
| 5761 |
|
5766 | |||
| 5762 | #print(numpy.sum(self.cal)) #Coinciden |
|
5767 | #print(numpy.sum(self.cal)) #Coinciden | |
| 5763 | #exit(1) |
|
5768 | #exit(1) | |
| 5764 | self.cal/=float(dataOut.NRANGE) |
|
5769 | self.cal/=float(dataOut.NRANGE) | |
| 5765 | #print(anoise0) |
|
5770 | #print(anoise0) | |
| 5766 | #print(anoise1) |
|
5771 | #print(anoise1) | |
| 5767 | #exit(1) |
|
5772 | #exit(1) | |
| 5768 | #print("nis: ", dataOut.nis) |
|
5773 | #print("nis: ", dataOut.nis) | |
| 5769 | #print("pan: ", dataOut.pan) |
|
5774 | #print("pan: ", dataOut.pan) | |
| 5770 | #print("pbn: ", dataOut.pbn) |
|
5775 | #print("pbn: ", dataOut.pbn) | |
| 5771 | #print(numpy.sum(dataOut.output_LP_integrated[0,:,0])) |
|
5776 | #print(numpy.sum(dataOut.output_LP_integrated[0,:,0])) | |
| 5772 | ''' |
|
5777 | ''' | |
| 5773 | import matplotlib.pyplot as plt |
|
5778 | import matplotlib.pyplot as plt | |
| 5774 | plt.plot(dataOut.output_LP_integrated[:,40,0]) |
|
5779 | plt.plot(dataOut.output_LP_integrated[:,40,0]) | |
| 5775 | plt.show() |
|
5780 | plt.show() | |
| 5776 | ''' |
|
5781 | ''' | |
| 5777 | #print(dataOut.output_LP_integrated[0,40,0]) |
|
5782 | #print(dataOut.output_LP_integrated[0,40,0]) | |
| 5778 | #print(numpy.sum(dataOut.output_LP_integrated[:,0,0])) |
|
5783 | #print(numpy.sum(dataOut.output_LP_integrated[:,0,0])) | |
| 5779 | #exit(1) |
|
5784 | #exit(1) | |
| 5780 |
|
5785 | |||
| 5781 | #################### PROBAR MÁS INTEGRACIÓN, SINO MODIFICAR VALOR DE "NIS" #################### |
|
5786 | #################### PROBAR MÁS INTEGRACIÓN, SINO MODIFICAR VALOR DE "NIS" #################### | |
| 5782 | # VER dataOut.nProfiles_LP # |
|
5787 | # VER dataOut.nProfiles_LP # | |
| 5783 |
|
5788 | |||
| 5784 | ''' |
|
5789 | ''' | |
| 5785 | #PLOTEAR POTENCIA VS RUIDO, QUIZA SE ESTA REMOVIENDO MUCHA SEÑAL |
|
5790 | #PLOTEAR POTENCIA VS RUIDO, QUIZA SE ESTA REMOVIENDO MUCHA SEÑAL | |
| 5786 | #print(dataOut.heightList) |
|
5791 | #print(dataOut.heightList) | |
| 5787 | import matplotlib.pyplot as plt |
|
5792 | import matplotlib.pyplot as plt | |
| 5788 | plt.plot(10*numpy.log10(dataOut.output_LP_integrated.real[0,:,0]),dataOut.range1) |
|
5793 | plt.plot(10*numpy.log10(dataOut.output_LP_integrated.real[0,:,0]),dataOut.range1) | |
| 5789 | #plt.plot(10*numpy.log10(dataOut.output_LP_integrated.real[0,:,0]/dataOut.nProfiles_LP),dataOut.range1) |
|
5794 | #plt.plot(10*numpy.log10(dataOut.output_LP_integrated.real[0,:,0]/dataOut.nProfiles_LP),dataOut.range1) | |
| 5790 | plt.axvline(10*numpy.log10(anoise0),color='k',linestyle='dashed') |
|
5795 | plt.axvline(10*numpy.log10(anoise0),color='k',linestyle='dashed') | |
| 5791 | plt.grid() |
|
5796 | plt.grid() | |
| 5792 | plt.xlim(20,100) |
|
5797 | plt.xlim(20,100) | |
| 5793 | plt.show() |
|
5798 | plt.show() | |
| 5794 | ''' |
|
5799 | ''' | |
| 5795 |
|
5800 | |||
| 5796 |
|
5801 | |||
| 5797 | for j in range(dataOut.NACF+2*dataOut.IBITS+2): |
|
5802 | for j in range(dataOut.NACF+2*dataOut.IBITS+2): | |
| 5798 |
|
5803 | |||
| 5799 | dataOut.output_LP_integrated.real[0,j,0]-=anoise0 #lag0 ch0 |
|
5804 | dataOut.output_LP_integrated.real[0,j,0]-=anoise0 #lag0 ch0 | |
| 5800 | dataOut.output_LP_integrated.real[1,j,0]-=anoise1 #lag1 ch0 |
|
5805 | dataOut.output_LP_integrated.real[1,j,0]-=anoise1 #lag1 ch0 | |
| 5801 |
|
5806 | |||
| 5802 | for i in range(1,dataOut.NLAG): #remove cal data from certain lags |
|
5807 | for i in range(1,dataOut.NLAG): #remove cal data from certain lags | |
| 5803 | dataOut.output_LP_integrated.real[i,j,0]-=self.cal[i] |
|
5808 | dataOut.output_LP_integrated.real[i,j,0]-=self.cal[i] | |
| 5804 | k=max(j,26) #constant power below range 26 |
|
5809 | k=max(j,26) #constant power below range 26 | |
| 5805 | self.powera[j]=dataOut.output_LP_integrated.real[0,k,0] #Lag0 and Channel 0 |
|
5810 | self.powera[j]=dataOut.output_LP_integrated.real[0,k,0] #Lag0 and Channel 0 | |
| 5806 |
|
5811 | |||
| 5807 | ## examine drifts here - based on 60 'indep.' estimates |
|
5812 | ## examine drifts here - based on 60 'indep.' estimates | |
| 5808 | #print(numpy.sum(self.powera)) |
|
5813 | #print(numpy.sum(self.powera)) | |
| 5809 | #exit(1) |
|
5814 | #exit(1) | |
| 5810 | #nis=dataOut.NSCAN*dataOut.NAVG*dataOut.nint*10 |
|
5815 | #nis=dataOut.NSCAN*dataOut.NAVG*dataOut.nint*10 | |
| 5811 | nis = dataOut.nis |
|
5816 | nis = dataOut.nis | |
| 5812 | #print("nis",nis) |
|
5817 | #print("nis",nis) | |
| 5813 | alpha=beta=delta=0.0 |
|
5818 | alpha=beta=delta=0.0 | |
| 5814 | nest=0 |
|
5819 | nest=0 | |
| 5815 | gamma=3.0/(2.0*numpy.pi*dataOut.lags_LP[1]*1.0e-3) |
|
5820 | gamma=3.0/(2.0*numpy.pi*dataOut.lags_LP[1]*1.0e-3) | |
| 5816 | beta=gamma*(math.atan2(dataOut.output_LP_integrated.imag[14,0,2],dataOut.output_LP_integrated.real[14,0,2])-math.atan2(dataOut.output_LP_integrated.imag[1,0,2],dataOut.output_LP_integrated.real[1,0,2]))/13.0 |
|
5821 | beta=gamma*(math.atan2(dataOut.output_LP_integrated.imag[14,0,2],dataOut.output_LP_integrated.real[14,0,2])-math.atan2(dataOut.output_LP_integrated.imag[1,0,2],dataOut.output_LP_integrated.real[1,0,2]))/13.0 | |
| 5817 | #print(gamma,beta) |
|
5822 | #print(gamma,beta) | |
| 5818 | #exit(1) |
|
5823 | #exit(1) | |
| 5819 | for i in range(1,3): |
|
5824 | for i in range(1,3): | |
| 5820 | gamma=3.0/(2.0*numpy.pi*dataOut.lags_LP[i]*1.0e-3) |
|
5825 | gamma=3.0/(2.0*numpy.pi*dataOut.lags_LP[i]*1.0e-3) | |
| 5821 | #print("gamma",gamma) |
|
5826 | #print("gamma",gamma) | |
| 5822 | for j in range(34,44): |
|
5827 | for j in range(34,44): | |
| 5823 | rho2=numpy.abs(dataOut.output_LP_integrated[i,j,0])/numpy.abs(dataOut.output_LP_integrated[0,j,0]) |
|
5828 | rho2=numpy.abs(dataOut.output_LP_integrated[i,j,0])/numpy.abs(dataOut.output_LP_integrated[0,j,0]) | |
| 5824 | dataOut.dphi2=(1.0/rho2-1.0)/(float(2*nis)) |
|
5829 | dataOut.dphi2=(1.0/rho2-1.0)/(float(2*nis)) | |
| 5825 | dataOut.dphi2*=gamma**2 |
|
5830 | dataOut.dphi2*=gamma**2 | |
| 5826 | pest=gamma*math.atan(dataOut.output_LP_integrated.imag[i,j,0]/dataOut.output_LP_integrated.real[i,j,0]) |
|
5831 | pest=gamma*math.atan(dataOut.output_LP_integrated.imag[i,j,0]/dataOut.output_LP_integrated.real[i,j,0]) | |
| 5827 | #print("1",dataOut.output_LP_integrated.imag[i,j,0]) |
|
5832 | #print("1",dataOut.output_LP_integrated.imag[i,j,0]) | |
| 5828 | #print("2",dataOut.output_LP_integrated.real[i,j,0]) |
|
5833 | #print("2",dataOut.output_LP_integrated.real[i,j,0]) | |
| 5829 | self.drift[nest]=pest |
|
5834 | self.drift[nest]=pest | |
| 5830 | self.ddrift[nest]=dataOut.dphi2 |
|
5835 | self.ddrift[nest]=dataOut.dphi2 | |
| 5831 | self.rdrift[nest]=float(nest) |
|
5836 | self.rdrift[nest]=float(nest) | |
| 5832 | nest+=1 |
|
5837 | nest+=1 | |
| 5833 |
|
5838 | |||
| 5834 | sorted(self.drift[:nest]) |
|
5839 | sorted(self.drift[:nest]) | |
| 5835 |
|
5840 | |||
| 5836 | #print(dataOut.dphi2) |
|
5841 | #print(dataOut.dphi2) | |
| 5837 | #exit(1) |
|
5842 | #exit(1) | |
| 5838 |
|
5843 | |||
| 5839 | for j in range(int(nest/4),int(3*nest/4)): |
|
5844 | for j in range(int(nest/4),int(3*nest/4)): | |
| 5840 | #i=int(self.rdrift[j]) |
|
5845 | #i=int(self.rdrift[j]) | |
| 5841 | alpha+=self.drift[j]/self.ddrift[j] |
|
5846 | alpha+=self.drift[j]/self.ddrift[j] | |
| 5842 | delta+=1.0/self.ddrift[j] |
|
5847 | delta+=1.0/self.ddrift[j] | |
| 5843 |
|
5848 | |||
| 5844 | alpha/=delta |
|
5849 | alpha/=delta | |
| 5845 | delta=1./numpy.sqrt(delta) |
|
5850 | delta=1./numpy.sqrt(delta) | |
| 5846 | vdrift=alpha-beta |
|
5851 | vdrift=alpha-beta | |
| 5847 | dvdrift=delta |
|
5852 | dvdrift=delta | |
| 5848 |
|
5853 | |||
| 5849 | #need to develop estimate of complete density profile using all |
|
5854 | #need to develop estimate of complete density profile using all | |
| 5850 | #available data |
|
5855 | #available data | |
| 5851 |
|
5856 | |||
| 5852 | #estimate sample variances for long-pulse power profile |
|
5857 | #estimate sample variances for long-pulse power profile | |
| 5853 |
|
5858 | |||
| 5854 | #nis=dataOut.NSCAN*dataOut.NAVG*dataOut.nint |
|
5859 | #nis=dataOut.NSCAN*dataOut.NAVG*dataOut.nint | |
| 5855 | nis = dataOut.nis/10 |
|
5860 | nis = dataOut.nis/10 | |
| 5856 | #print("nis",nis) |
|
5861 | #print("nis",nis) | |
| 5857 |
|
5862 | |||
| 5858 | self.sigma[:dataOut.NACF+2*dataOut.IBITS+2]=((anoise0+self.powera[:dataOut.NACF+2*dataOut.IBITS+2])**2)/float(nis) |
|
5863 | self.sigma[:dataOut.NACF+2*dataOut.IBITS+2]=((anoise0+self.powera[:dataOut.NACF+2*dataOut.IBITS+2])**2)/float(nis) | |
| 5859 | #print(self.sigma) |
|
5864 | #print(self.sigma) | |
| 5860 | #exit(1) |
|
5865 | #exit(1) | |
| 5861 | ioff=1 |
|
5866 | ioff=1 | |
| 5862 |
|
5867 | |||
| 5863 | #deconvolve rectangular pulse shape from profile ==> powerb, perror |
|
5868 | #deconvolve rectangular pulse shape from profile ==> powerb, perror | |
| 5864 |
|
5869 | |||
| 5865 |
|
5870 | |||
| 5866 | ############# START nnlswrap############# |
|
5871 | ############# START nnlswrap############# | |
| 5867 |
|
5872 | |||
| 5868 | if dataOut.ut_Faraday>14.0: |
|
5873 | if dataOut.ut_Faraday>14.0: | |
| 5869 | alpha_nnlswrap=20.0 |
|
5874 | alpha_nnlswrap=20.0 | |
| 5870 | else: |
|
5875 | else: | |
| 5871 | alpha_nnlswrap=30.0 |
|
5876 | alpha_nnlswrap=30.0 | |
| 5872 |
|
5877 | |||
| 5873 | range1_nnls=dataOut.NACF |
|
5878 | range1_nnls=dataOut.NACF | |
| 5874 | range2_nnls=dataOut.NACF+dataOut.IBITS-1 |
|
5879 | range2_nnls=dataOut.NACF+dataOut.IBITS-1 | |
| 5875 |
|
5880 | |||
| 5876 | g_nnlswrap=numpy.zeros((range1_nnls,range2_nnls),'float32') |
|
5881 | g_nnlswrap=numpy.zeros((range1_nnls,range2_nnls),'float32') | |
| 5877 | a_nnlswrap=numpy.zeros((range2_nnls,range2_nnls),'float64') |
|
5882 | a_nnlswrap=numpy.zeros((range2_nnls,range2_nnls),'float64') | |
| 5878 |
|
5883 | |||
| 5879 | for i in range(range1_nnls): |
|
5884 | for i in range(range1_nnls): | |
| 5880 | for j in range(range2_nnls): |
|
5885 | for j in range(range2_nnls): | |
| 5881 | if j>=i and j<i+dataOut.IBITS: |
|
5886 | if j>=i and j<i+dataOut.IBITS: | |
| 5882 | g_nnlswrap[i,j]=1.0 |
|
5887 | g_nnlswrap[i,j]=1.0 | |
| 5883 | else: |
|
5888 | else: | |
| 5884 | g_nnlswrap[i,j]=0.0 |
|
5889 | g_nnlswrap[i,j]=0.0 | |
| 5885 |
|
5890 | |||
| 5886 | a_nnlswrap[:]=numpy.matmul(numpy.transpose(g_nnlswrap),g_nnlswrap) |
|
5891 | a_nnlswrap[:]=numpy.matmul(numpy.transpose(g_nnlswrap),g_nnlswrap) | |
| 5887 |
|
5892 | |||
| 5888 | numpy.fill_diagonal(a_nnlswrap,a_nnlswrap.diagonal()+alpha_nnlswrap**2) |
|
5893 | numpy.fill_diagonal(a_nnlswrap,a_nnlswrap.diagonal()+alpha_nnlswrap**2) | |
| 5889 |
|
5894 | |||
| 5890 | #ERROR ANALYSIS# |
|
5895 | #ERROR ANALYSIS# | |
| 5891 |
|
5896 | |||
| 5892 | self.perror[:range2_nnls]=0.0 |
|
5897 | self.perror[:range2_nnls]=0.0 | |
| 5893 | self.perror[:range2_nnls]=numpy.matmul(1./(self.sigma[dataOut.IBITS+ioff:range1_nnls+dataOut.IBITS+ioff]),g_nnlswrap**2) |
|
5898 | self.perror[:range2_nnls]=numpy.matmul(1./(self.sigma[dataOut.IBITS+ioff:range1_nnls+dataOut.IBITS+ioff]),g_nnlswrap**2) | |
| 5894 | self.perror[:range1_nnls]+=(alpha_nnlswrap**2)/(self.sigma[dataOut.IBITS+ioff:range1_nnls+dataOut.IBITS+ioff]) |
|
5899 | self.perror[:range1_nnls]+=(alpha_nnlswrap**2)/(self.sigma[dataOut.IBITS+ioff:range1_nnls+dataOut.IBITS+ioff]) | |
| 5895 | self.perror[:range2_nnls]=1.00/self.perror[:range2_nnls] |
|
5900 | self.perror[:range2_nnls]=1.00/self.perror[:range2_nnls] | |
| 5896 |
|
5901 | |||
| 5897 | b_nnlswrap=numpy.zeros(range2_nnls,'float64') |
|
5902 | b_nnlswrap=numpy.zeros(range2_nnls,'float64') | |
| 5898 | b_nnlswrap[:]=numpy.matmul(self.powera[dataOut.IBITS+ioff:range1_nnls+dataOut.IBITS+ioff],g_nnlswrap) #match filter alturas |
|
5903 | b_nnlswrap[:]=numpy.matmul(self.powera[dataOut.IBITS+ioff:range1_nnls+dataOut.IBITS+ioff],g_nnlswrap) #match filter alturas | |
| 5899 |
|
5904 | |||
| 5900 | x_nnlswrap=numpy.zeros(range2_nnls,'float64') |
|
5905 | x_nnlswrap=numpy.zeros(range2_nnls,'float64') | |
| 5901 | x_nnlswrap[:]=nnls(a_nnlswrap,b_nnlswrap)[0] |
|
5906 | x_nnlswrap[:]=nnls(a_nnlswrap,b_nnlswrap)[0] | |
| 5902 |
|
5907 | |||
| 5903 | self.powerb[:range2_nnls]=x_nnlswrap |
|
5908 | self.powerb[:range2_nnls]=x_nnlswrap | |
| 5904 | #print(self.powerb[40]) |
|
5909 | #print(self.powerb[40]) | |
| 5905 | #print(self.powerb[66]) |
|
5910 | #print(self.powerb[66]) | |
| 5906 | #exit(1) |
|
5911 | #exit(1) | |
| 5907 | #############END nnlswrap############# |
|
5912 | #############END nnlswrap############# | |
| 5908 | #print(numpy.sum(numpy.sqrt(self.perror[0:dataOut.NACF]))) |
|
5913 | #print(numpy.sum(numpy.sqrt(self.perror[0:dataOut.NACF]))) | |
| 5909 | #print(self.powerb[0:dataOut.NACF]) |
|
5914 | #print(self.powerb[0:dataOut.NACF]) | |
| 5910 | #exit(1) |
|
5915 | #exit(1) | |
| 5911 | #estimate relative error for deconvolved profile (scaling irrelevant) |
|
5916 | #estimate relative error for deconvolved profile (scaling irrelevant) | |
| 5912 | #print(dataOut.NACF) |
|
5917 | #print(dataOut.NACF) | |
| 5913 | dataOut.ene[0:dataOut.NACF]=numpy.sqrt(self.perror[0:dataOut.NACF])/self.powerb[0:dataOut.NACF] |
|
5918 | dataOut.ene[0:dataOut.NACF]=numpy.sqrt(self.perror[0:dataOut.NACF])/self.powerb[0:dataOut.NACF] | |
| 5914 | #print(numpy.sum(dataOut.ene)) |
|
5919 | #print(numpy.sum(dataOut.ene)) | |
| 5915 | #exit(1) |
|
5920 | #exit(1) | |
| 5916 | aux=0 |
|
5921 | aux=0 | |
| 5917 |
|
5922 | |||
| 5918 | for i in range(dataOut.IBITS,dataOut.NACF): |
|
5923 | for i in range(dataOut.IBITS,dataOut.NACF): | |
| 5919 | self.dpulse[i]=self.lpulse[i]=0.0 |
|
5924 | self.dpulse[i]=self.lpulse[i]=0.0 | |
| 5920 | for j in range(dataOut.IBITS): |
|
5925 | for j in range(dataOut.IBITS): | |
| 5921 | k=int(i-j) |
|
5926 | k=int(i-j) | |
| 5922 | if k<36-aux and k>16: |
|
5927 | if k<36-aux and k>16: | |
| 5923 | self.dpulse[i]+=dataOut.ph2[k]/dataOut.h2[k] |
|
5928 | self.dpulse[i]+=dataOut.ph2[k]/dataOut.h2[k] | |
| 5924 | elif k>=36-aux: |
|
5929 | elif k>=36-aux: | |
| 5925 | self.lpulse[i]+=self.powerb[k] |
|
5930 | self.lpulse[i]+=self.powerb[k] | |
| 5926 | self.lagp[i]=self.powera[i] |
|
5931 | self.lagp[i]=self.powera[i] | |
| 5927 |
|
5932 | |||
| 5928 | #find scale factor that best merges profiles |
|
5933 | #find scale factor that best merges profiles | |
| 5929 |
|
5934 | |||
| 5930 | qi=sum(self.dpulse[32:dataOut.NACF]**2/(self.lagp[32:dataOut.NACF]+anoise0)**2) |
|
5935 | qi=sum(self.dpulse[32:dataOut.NACF]**2/(self.lagp[32:dataOut.NACF]+anoise0)**2) | |
| 5931 | ri=sum((self.dpulse[32:dataOut.NACF]*self.lpulse[32:dataOut.NACF])/(self.lagp[32:dataOut.NACF]+anoise0)**2) |
|
5936 | ri=sum((self.dpulse[32:dataOut.NACF]*self.lpulse[32:dataOut.NACF])/(self.lagp[32:dataOut.NACF]+anoise0)**2) | |
| 5932 | si=sum((self.dpulse[32:dataOut.NACF]*self.lagp[32:dataOut.NACF])/(self.lagp[32:dataOut.NACF]+anoise0)**2) |
|
5937 | si=sum((self.dpulse[32:dataOut.NACF]*self.lagp[32:dataOut.NACF])/(self.lagp[32:dataOut.NACF]+anoise0)**2) | |
| 5933 | ui=sum(self.lpulse[32:dataOut.NACF]**2/(self.lagp[32:dataOut.NACF]+anoise0)**2) |
|
5938 | ui=sum(self.lpulse[32:dataOut.NACF]**2/(self.lagp[32:dataOut.NACF]+anoise0)**2) | |
| 5934 | vi=sum((self.lpulse[32:dataOut.NACF]*self.lagp[32:dataOut.NACF])/(self.lagp[32:dataOut.NACF]+anoise0)**2) |
|
5939 | vi=sum((self.lpulse[32:dataOut.NACF]*self.lagp[32:dataOut.NACF])/(self.lagp[32:dataOut.NACF]+anoise0)**2) | |
| 5935 |
|
5940 | |||
| 5936 | alpha=(si*ui-vi*ri)/(qi*ui-ri*ri) |
|
5941 | alpha=(si*ui-vi*ri)/(qi*ui-ri*ri) | |
| 5937 | beta=(qi*vi-ri*si)/(qi*ui-ri*ri) |
|
5942 | beta=(qi*vi-ri*si)/(qi*ui-ri*ri) | |
| 5938 |
|
5943 | |||
| 5939 | #form density profile estimate, merging rescaled power profiles |
|
5944 | #form density profile estimate, merging rescaled power profiles | |
| 5940 | #print(dataOut.h2) |
|
5945 | #print(dataOut.h2) | |
| 5941 | #print(numpy.sum(alpha)) |
|
5946 | #print(numpy.sum(alpha)) | |
| 5942 | #print(numpy.sum(dataOut.ph2)) |
|
5947 | #print(numpy.sum(dataOut.ph2)) | |
| 5943 | self.powerb[16:36-aux]=alpha*dataOut.ph2[16:36-aux]/dataOut.h2[16:36-aux] |
|
5948 | self.powerb[16:36-aux]=alpha*dataOut.ph2[16:36-aux]/dataOut.h2[16:36-aux] | |
| 5944 | self.powerb[36-aux:dataOut.NACF]*=beta |
|
5949 | self.powerb[36-aux:dataOut.NACF]*=beta | |
| 5945 |
|
5950 | |||
| 5946 | #form Ne estimate, fill in error estimate at low altitudes |
|
5951 | #form Ne estimate, fill in error estimate at low altitudes | |
| 5947 |
|
5952 | |||
| 5948 | dataOut.ene[0:36-aux]=dataOut.sdp2[0:36-aux]/dataOut.ph2[0:36-aux] |
|
5953 | dataOut.ene[0:36-aux]=dataOut.sdp2[0:36-aux]/dataOut.ph2[0:36-aux] | |
| 5949 | dataOut.ne[:dataOut.NACF]=self.powerb[:dataOut.NACF]*dataOut.h2[:dataOut.NACF]/alpha |
|
5954 | dataOut.ne[:dataOut.NACF]=self.powerb[:dataOut.NACF]*dataOut.h2[:dataOut.NACF]/alpha | |
| 5950 | #print(numpy.sum(self.powerb)) |
|
5955 | #print(numpy.sum(self.powerb)) | |
| 5951 | #print(numpy.sum(dataOut.ene)) |
|
5956 | #print(numpy.sum(dataOut.ene)) | |
| 5952 | #print(numpy.sum(dataOut.ne)) |
|
5957 | #print(numpy.sum(dataOut.ne)) | |
| 5953 | #exit(1) |
|
5958 | #exit(1) | |
| 5954 | #now do error propagation: store zero lag error covariance in u |
|
5959 | #now do error propagation: store zero lag error covariance in u | |
| 5955 |
|
5960 | |||
| 5956 | nis=dataOut.NSCAN*dataOut.NAVG*dataOut.nint/1 # DLH serious debris removal |
|
5961 | nis=dataOut.NSCAN*dataOut.NAVG*dataOut.nint/1 # DLH serious debris removal | |
| 5957 |
|
5962 | |||
| 5958 | for i in range(dataOut.NACF): |
|
5963 | for i in range(dataOut.NACF): | |
| 5959 | for j in range(i,dataOut.NACF): |
|
5964 | for j in range(i,dataOut.NACF): | |
| 5960 | if j-i>=dataOut.IBITS: |
|
5965 | if j-i>=dataOut.IBITS: | |
| 5961 | self.u[i,j]=0.0 |
|
5966 | self.u[i,j]=0.0 | |
| 5962 | else: |
|
5967 | else: | |
| 5963 | self.u[i,j]=dataOut.output_LP_integrated.real[j-i,i,0]**2/float(nis) |
|
5968 | self.u[i,j]=dataOut.output_LP_integrated.real[j-i,i,0]**2/float(nis) | |
| 5964 | self.u[i,j]*=(anoise0+dataOut.output_LP_integrated.real[0,i,0])/dataOut.output_LP_integrated.real[0,i,0] |
|
5969 | self.u[i,j]*=(anoise0+dataOut.output_LP_integrated.real[0,i,0])/dataOut.output_LP_integrated.real[0,i,0] | |
| 5965 | self.u[i,j]*=(anoise0+dataOut.output_LP_integrated.real[0,j,0])/dataOut.output_LP_integrated.real[0,j,0] |
|
5970 | self.u[i,j]*=(anoise0+dataOut.output_LP_integrated.real[0,j,0])/dataOut.output_LP_integrated.real[0,j,0] | |
| 5966 |
|
5971 | |||
| 5967 | self.u[j,i]=self.u[i,j] |
|
5972 | self.u[j,i]=self.u[i,j] | |
| 5968 |
|
5973 | |||
| 5969 | #now error analyis for lag product matrix (diag), place in acf_err |
|
5974 | #now error analyis for lag product matrix (diag), place in acf_err | |
| 5970 |
|
5975 | |||
| 5971 | for i in range(dataOut.NACF): |
|
5976 | for i in range(dataOut.NACF): | |
| 5972 | for j in range(dataOut.IBITS): |
|
5977 | for j in range(dataOut.IBITS): | |
| 5973 | if j==0: |
|
5978 | if j==0: | |
| 5974 | dataOut.errors[0,i]=numpy.sqrt(self.u[i,i]) |
|
5979 | dataOut.errors[0,i]=numpy.sqrt(self.u[i,i]) | |
| 5975 | else: |
|
5980 | else: | |
| 5976 | dataOut.errors[j,i]=numpy.sqrt(((dataOut.output_LP_integrated.real[0,i,0]+anoise0)*(dataOut.output_LP_integrated.real[0,i+j,0]+anoise0)+dataOut.output_LP_integrated.real[j,i,0]**2)/float(2*nis)) |
|
5981 | dataOut.errors[j,i]=numpy.sqrt(((dataOut.output_LP_integrated.real[0,i,0]+anoise0)*(dataOut.output_LP_integrated.real[0,i+j,0]+anoise0)+dataOut.output_LP_integrated.real[j,i,0]**2)/float(2*nis)) | |
| 5977 | ''' |
|
5982 | ''' | |
| 5978 | print(numpy.sum(dataOut.output_LP_integrated)) |
|
5983 | print(numpy.sum(dataOut.output_LP_integrated)) | |
| 5979 | print(numpy.sum(dataOut.errors)) |
|
5984 | print(numpy.sum(dataOut.errors)) | |
| 5980 | print(numpy.sum(self.powerb)) |
|
5985 | print(numpy.sum(self.powerb)) | |
| 5981 | print(numpy.sum(dataOut.ne)) |
|
5986 | print(numpy.sum(dataOut.ne)) | |
| 5982 | print(numpy.sum(dataOut.lags_LP)) |
|
5987 | print(numpy.sum(dataOut.lags_LP)) | |
| 5983 | print(numpy.sum(dataOut.thb)) |
|
5988 | print(numpy.sum(dataOut.thb)) | |
| 5984 | print(numpy.sum(dataOut.bfm)) |
|
5989 | print(numpy.sum(dataOut.bfm)) | |
| 5985 | print(numpy.sum(dataOut.te)) |
|
5990 | print(numpy.sum(dataOut.te)) | |
| 5986 | print(numpy.sum(dataOut.ete)) |
|
5991 | print(numpy.sum(dataOut.ete)) | |
| 5987 | print(numpy.sum(dataOut.ti)) |
|
5992 | print(numpy.sum(dataOut.ti)) | |
| 5988 | print(numpy.sum(dataOut.eti)) |
|
5993 | print(numpy.sum(dataOut.eti)) | |
| 5989 | print(numpy.sum(dataOut.ph)) |
|
5994 | print(numpy.sum(dataOut.ph)) | |
| 5990 | print(numpy.sum(dataOut.eph)) |
|
5995 | print(numpy.sum(dataOut.eph)) | |
| 5991 | print(numpy.sum(dataOut.phe)) |
|
5996 | print(numpy.sum(dataOut.phe)) | |
| 5992 | print(numpy.sum(dataOut.ephe)) |
|
5997 | print(numpy.sum(dataOut.ephe)) | |
| 5993 | print(numpy.sum(dataOut.range1)) |
|
5998 | print(numpy.sum(dataOut.range1)) | |
| 5994 | print(numpy.sum(dataOut.ut)) |
|
5999 | print(numpy.sum(dataOut.ut)) | |
| 5995 | print(numpy.sum(dataOut.NACF)) |
|
6000 | print(numpy.sum(dataOut.NACF)) | |
| 5996 | print(numpy.sum(dataOut.fit_array_real)) |
|
6001 | print(numpy.sum(dataOut.fit_array_real)) | |
| 5997 | print(numpy.sum(dataOut.status)) |
|
6002 | print(numpy.sum(dataOut.status)) | |
| 5998 | print(numpy.sum(dataOut.NRANGE)) |
|
6003 | print(numpy.sum(dataOut.NRANGE)) | |
| 5999 | print(numpy.sum(dataOut.IBITS)) |
|
6004 | print(numpy.sum(dataOut.IBITS)) | |
| 6000 | exit(1) |
|
6005 | exit(1) | |
| 6001 | ''' |
|
6006 | ''' | |
| 6002 | ''' |
|
6007 | ''' | |
| 6003 | print(dataOut.te2[13:16]) |
|
6008 | print(dataOut.te2[13:16]) | |
| 6004 | print(numpy.sum(dataOut.te2)) |
|
6009 | print(numpy.sum(dataOut.te2)) | |
| 6005 | exit(1) |
|
6010 | exit(1) | |
| 6006 | ''' |
|
6011 | ''' | |
| 6007 | #print("Success 1") |
|
6012 | #print("Success 1") | |
| 6008 | ###################Correlation pulse and itself |
|
6013 | ###################Correlation pulse and itself | |
| 6009 |
|
6014 | |||
| 6010 | #print(dataOut.NRANGE) |
|
6015 | #print(dataOut.NRANGE) | |
| 6011 | print("LP Estimation") |
|
6016 | print("LP Estimation") | |
| 6012 | with suppress_stdout_stderr(): |
|
6017 | with suppress_stdout_stderr(): | |
| 6013 | #pass |
|
6018 | #pass | |
| 6014 | full_profile_profile.profile(numpy.transpose(dataOut.output_LP_integrated,(2,1,0)),numpy.transpose(dataOut.errors),self.powerb,dataOut.ne,dataOut.lags_LP,dataOut.thb,dataOut.bfm,dataOut.te,dataOut.ete,dataOut.ti,dataOut.eti,dataOut.ph,dataOut.eph,dataOut.phe,dataOut.ephe,dataOut.range1,dataOut.ut,dataOut.NACF,dataOut.fit_array_real,dataOut.status,dataOut.NRANGE,dataOut.IBITS) |
|
6019 | full_profile_profile.profile(numpy.transpose(dataOut.output_LP_integrated,(2,1,0)),numpy.transpose(dataOut.errors),self.powerb,dataOut.ne,dataOut.lags_LP,dataOut.thb,dataOut.bfm,dataOut.te,dataOut.ete,dataOut.ti,dataOut.eti,dataOut.ph,dataOut.eph,dataOut.phe,dataOut.ephe,dataOut.range1,dataOut.ut,dataOut.NACF,dataOut.fit_array_real,dataOut.status,dataOut.NRANGE,dataOut.IBITS) | |
| 6015 |
|
6020 | |||
| 6016 | print("status: ",dataOut.status) |
|
6021 | print("status: ",dataOut.status) | |
| 6017 |
|
6022 | |||
| 6018 | if dataOut.status>=3.5: |
|
6023 | if dataOut.status>=3.5: | |
| 6019 | dataOut.te[:]=numpy.nan |
|
6024 | dataOut.te[:]=numpy.nan | |
| 6020 | dataOut.ete[:]=numpy.nan |
|
6025 | dataOut.ete[:]=numpy.nan | |
| 6021 | dataOut.ti[:]=numpy.nan |
|
6026 | dataOut.ti[:]=numpy.nan | |
| 6022 | dataOut.eti[:]=numpy.nan |
|
6027 | dataOut.eti[:]=numpy.nan | |
| 6023 | dataOut.ph[:]=numpy.nan |
|
6028 | dataOut.ph[:]=numpy.nan | |
| 6024 | dataOut.eph[:]=numpy.nan |
|
6029 | dataOut.eph[:]=numpy.nan | |
| 6025 | dataOut.phe[:]=numpy.nan |
|
6030 | dataOut.phe[:]=numpy.nan | |
| 6026 | dataOut.ephe[:]=numpy.nan |
|
6031 | dataOut.ephe[:]=numpy.nan | |
| 6027 |
|
6032 | |||
| 6028 | return dataOut |
|
6033 | return dataOut | |
| 6029 |
|
6034 | |||
| 6030 | class LongPulseAnalysisSpectra(Operation): |
|
6035 | class LongPulseAnalysisSpectra(Operation): | |
| 6031 | """Operation to estimate ACFs, temperatures, total electron density and Hydrogen/Helium fractions from the Long Pulse data. |
|
6036 | """Operation to estimate ACFs, temperatures, total electron density and Hydrogen/Helium fractions from the Long Pulse data. | |
| 6032 |
|
6037 | |||
| 6033 | Parameters: |
|
6038 | Parameters: | |
| 6034 | ----------- |
|
6039 | ----------- | |
| 6035 | NACF : int |
|
6040 | NACF : int | |
| 6036 | .* |
|
6041 | .* | |
| 6037 |
|
6042 | |||
| 6038 | Example |
|
6043 | Example | |
| 6039 | -------- |
|
6044 | -------- | |
| 6040 |
|
6045 | |||
| 6041 | op = proc_unit.addOperation(name='LongPulseAnalysis', optype='other') |
|
6046 | op = proc_unit.addOperation(name='LongPulseAnalysis', optype='other') | |
| 6042 | op.addParameter(name='NACF', value='16', format='int') |
|
6047 | op.addParameter(name='NACF', value='16', format='int') | |
| 6043 |
|
6048 | |||
| 6044 | """ |
|
6049 | """ | |
| 6045 |
|
6050 | |||
| 6046 | def __init__(self, **kwargs): |
|
6051 | def __init__(self, **kwargs): | |
| 6047 |
|
6052 | |||
| 6048 | Operation.__init__(self, **kwargs) |
|
6053 | Operation.__init__(self, **kwargs) | |
| 6049 | self.aux=1 |
|
6054 | self.aux=1 | |
| 6050 |
|
6055 | |||
| 6051 | def run(self,dataOut,NACF): |
|
6056 | def run(self,dataOut,NACF): | |
| 6052 |
|
6057 | |||
| 6053 | dataOut.NACF=NACF |
|
6058 | dataOut.NACF=NACF | |
| 6054 | dataOut.heightList=dataOut.DH*(numpy.arange(dataOut.NACF)) |
|
6059 | dataOut.heightList=dataOut.DH*(numpy.arange(dataOut.NACF)) | |
| 6055 | anoise0=dataOut.tnoise[0] |
|
6060 | anoise0=dataOut.tnoise[0] | |
| 6056 | anoise1=anoise0*0.0 #seems to be noise in 1st lag 0.015 before '14 |
|
6061 | anoise1=anoise0*0.0 #seems to be noise in 1st lag 0.015 before '14 | |
| 6057 | #print(anoise0) |
|
6062 | #print(anoise0) | |
| 6058 | #exit(1) |
|
6063 | #exit(1) | |
| 6059 | if self.aux: |
|
6064 | if self.aux: | |
| 6060 | #dataOut.cut=31#26#height=31*15=465 |
|
6065 | #dataOut.cut=31#26#height=31*15=465 | |
| 6061 | self.cal=numpy.zeros((dataOut.NLAG),'float32') |
|
6066 | self.cal=numpy.zeros((dataOut.NLAG),'float32') | |
| 6062 | self.drift=numpy.zeros((200),'float32') |
|
6067 | self.drift=numpy.zeros((200),'float32') | |
| 6063 | self.rdrift=numpy.zeros((200),'float32') |
|
6068 | self.rdrift=numpy.zeros((200),'float32') | |
| 6064 | self.ddrift=numpy.zeros((200),'float32') |
|
6069 | self.ddrift=numpy.zeros((200),'float32') | |
| 6065 | self.sigma=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') |
|
6070 | self.sigma=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') | |
| 6066 | self.powera=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') |
|
6071 | self.powera=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') | |
| 6067 | self.powerb=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') |
|
6072 | self.powerb=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') | |
| 6068 | self.perror=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') |
|
6073 | self.perror=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') | |
| 6069 | dataOut.ene=numpy.zeros((dataOut.NRANGE),'float32') |
|
6074 | dataOut.ene=numpy.zeros((dataOut.NRANGE),'float32') | |
| 6070 | self.dpulse=numpy.zeros((dataOut.NACF),'float32') |
|
6075 | self.dpulse=numpy.zeros((dataOut.NACF),'float32') | |
| 6071 | self.lpulse=numpy.zeros((dataOut.NACF),'float32') |
|
6076 | self.lpulse=numpy.zeros((dataOut.NACF),'float32') | |
| 6072 | dataOut.lags_LP=numpy.zeros((dataOut.IBITS),order='F',dtype='float32') |
|
6077 | dataOut.lags_LP=numpy.zeros((dataOut.IBITS),order='F',dtype='float32') | |
| 6073 | self.lagp=numpy.zeros((dataOut.NACF),'float32') |
|
6078 | self.lagp=numpy.zeros((dataOut.NACF),'float32') | |
| 6074 | self.u=numpy.zeros((2*dataOut.NACF,2*dataOut.NACF),'float32') |
|
6079 | self.u=numpy.zeros((2*dataOut.NACF,2*dataOut.NACF),'float32') | |
| 6075 | dataOut.ne=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') |
|
6080 | dataOut.ne=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') | |
| 6076 | dataOut.te=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
6081 | dataOut.te=numpy.zeros((dataOut.NACF),order='F',dtype='float32') | |
| 6077 | dataOut.ete=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
6082 | dataOut.ete=numpy.zeros((dataOut.NACF),order='F',dtype='float32') | |
| 6078 | dataOut.ti=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
6083 | dataOut.ti=numpy.zeros((dataOut.NACF),order='F',dtype='float32') | |
| 6079 | dataOut.eti=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
6084 | dataOut.eti=numpy.zeros((dataOut.NACF),order='F',dtype='float32') | |
| 6080 | dataOut.ph=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
6085 | dataOut.ph=numpy.zeros((dataOut.NACF),order='F',dtype='float32') | |
| 6081 | dataOut.eph=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
6086 | dataOut.eph=numpy.zeros((dataOut.NACF),order='F',dtype='float32') | |
| 6082 | dataOut.phe=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
6087 | dataOut.phe=numpy.zeros((dataOut.NACF),order='F',dtype='float32') | |
| 6083 | dataOut.ephe=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
6088 | dataOut.ephe=numpy.zeros((dataOut.NACF),order='F',dtype='float32') | |
| 6084 | dataOut.errors=numpy.zeros((dataOut.IBITS,max(dataOut.NRANGE,dataOut.NSHTS)),order='F',dtype='float32') |
|
6089 | dataOut.errors=numpy.zeros((dataOut.IBITS,max(dataOut.NRANGE,dataOut.NSHTS)),order='F',dtype='float32') | |
| 6085 | dataOut.fit_array_real=numpy.zeros((max(dataOut.NRANGE,dataOut.NSHTS),dataOut.NLAG),order='F',dtype='float32') |
|
6090 | dataOut.fit_array_real=numpy.zeros((max(dataOut.NRANGE,dataOut.NSHTS),dataOut.NLAG),order='F',dtype='float32') | |
| 6086 | dataOut.status=numpy.zeros(1,'float32') |
|
6091 | dataOut.status=numpy.zeros(1,'float32') | |
| 6087 | dataOut.tx=240.0 #debería provenir del header #hybrid |
|
6092 | dataOut.tx=240.0 #debería provenir del header #hybrid | |
| 6088 |
|
6093 | |||
| 6089 | for i in range(dataOut.IBITS): |
|
6094 | for i in range(dataOut.IBITS): | |
| 6090 | dataOut.lags_LP[i]=float(i)*(dataOut.tx/150.0)/float(dataOut.IBITS) # (float)i*(header.tx/150.0)/(float)IBITS; |
|
6095 | dataOut.lags_LP[i]=float(i)*(dataOut.tx/150.0)/float(dataOut.IBITS) # (float)i*(header.tx/150.0)/(float)IBITS; | |
| 6091 |
|
6096 | |||
| 6092 | self.aux=0 |
|
6097 | self.aux=0 | |
| 6093 |
|
6098 | |||
| 6094 | dataOut.cut=30 |
|
6099 | dataOut.cut=30 | |
| 6095 | for i in range(30,15,-1): #Aquí se calcula en donde se unirá DP y LP en la parte final |
|
6100 | for i in range(30,15,-1): #Aquí se calcula en donde se unirá DP y LP en la parte final | |
| 6096 | if numpy.nanmax(dataOut.acfs_error_to_plot[i,:])>=10 or dataOut.info2[i]==0: |
|
6101 | if numpy.nanmax(dataOut.acfs_error_to_plot[i,:])>=10 or dataOut.info2[i]==0: | |
| 6097 | dataOut.cut=i-1 |
|
6102 | dataOut.cut=i-1 | |
| 6098 |
|
6103 | |||
| 6099 | for i in range(dataOut.NLAG): |
|
6104 | for i in range(dataOut.NLAG): | |
| 6100 | self.cal[i]=sum(dataOut.output_LP_integrated[i,:,3].real) #Lag x Height x Channel |
|
6105 | self.cal[i]=sum(dataOut.output_LP_integrated[i,:,3].real) #Lag x Height x Channel | |
| 6101 |
|
6106 | |||
| 6102 | #print(numpy.sum(self.cal)) #Coinciden |
|
6107 | #print(numpy.sum(self.cal)) #Coinciden | |
| 6103 | #exit(1) |
|
6108 | #exit(1) | |
| 6104 | self.cal/=float(dataOut.NRANGE) |
|
6109 | self.cal/=float(dataOut.NRANGE) | |
| 6105 |
|
6110 | |||
| 6106 |
|
6111 | |||
| 6107 | #################### PROBAR MÁS INTEGRACIÓN, SINO MODIFICAR VALOR DE "NIS" #################### |
|
6112 | #################### PROBAR MÁS INTEGRACIÓN, SINO MODIFICAR VALOR DE "NIS" #################### | |
| 6108 | # VER dataOut.nProfiles_LP # |
|
6113 | # VER dataOut.nProfiles_LP # | |
| 6109 |
|
6114 | |||
| 6110 | ''' |
|
6115 | ''' | |
| 6111 | #PLOTEAR POTENCIA VS RUIDO, QUIZA SE ESTA REMOVIENDO MUCHA SEÑAL |
|
6116 | #PLOTEAR POTENCIA VS RUIDO, QUIZA SE ESTA REMOVIENDO MUCHA SEÑAL | |
| 6112 | #print(dataOut.heightList) |
|
6117 | #print(dataOut.heightList) | |
| 6113 | import matplotlib.pyplot as plt |
|
6118 | import matplotlib.pyplot as plt | |
| 6114 | plt.plot(10*numpy.log10(dataOut.output_LP_integrated.real[0,:,0]),dataOut.range1) |
|
6119 | plt.plot(10*numpy.log10(dataOut.output_LP_integrated.real[0,:,0]),dataOut.range1) | |
| 6115 | #plt.plot(10*numpy.log10(dataOut.output_LP_integrated.real[0,:,0]/dataOut.nProfiles_LP),dataOut.range1) |
|
6120 | #plt.plot(10*numpy.log10(dataOut.output_LP_integrated.real[0,:,0]/dataOut.nProfiles_LP),dataOut.range1) | |
| 6116 | plt.axvline(10*numpy.log10(anoise0),color='k',linestyle='dashed') |
|
6121 | plt.axvline(10*numpy.log10(anoise0),color='k',linestyle='dashed') | |
| 6117 | plt.grid() |
|
6122 | plt.grid() | |
| 6118 | plt.xlim(20,100) |
|
6123 | plt.xlim(20,100) | |
| 6119 | plt.show() |
|
6124 | plt.show() | |
| 6120 | ''' |
|
6125 | ''' | |
| 6121 |
|
6126 | |||
| 6122 |
|
6127 | |||
| 6123 | for j in range(dataOut.NACF+2*dataOut.IBITS+2): |
|
6128 | for j in range(dataOut.NACF+2*dataOut.IBITS+2): | |
| 6124 |
|
6129 | |||
| 6125 | dataOut.output_LP_integrated.real[0,j,0]-=anoise0 #lag0 ch0 |
|
6130 | dataOut.output_LP_integrated.real[0,j,0]-=anoise0 #lag0 ch0 | |
| 6126 | dataOut.output_LP_integrated.real[1,j,0]-=anoise1 #lag1 ch0 |
|
6131 | dataOut.output_LP_integrated.real[1,j,0]-=anoise1 #lag1 ch0 | |
| 6127 |
|
6132 | |||
| 6128 | for i in range(1,dataOut.NLAG): #remove cal data from certain lags |
|
6133 | for i in range(1,dataOut.NLAG): #remove cal data from certain lags | |
| 6129 | dataOut.output_LP_integrated.real[i,j,0]-=self.cal[i] |
|
6134 | dataOut.output_LP_integrated.real[i,j,0]-=self.cal[i] | |
| 6130 | k=max(j,26) #constant power below range 26 |
|
6135 | k=max(j,26) #constant power below range 26 | |
| 6131 | self.powera[j]=dataOut.output_LP_integrated.real[0,k,0] #Lag0 and Channel 0 |
|
6136 | self.powera[j]=dataOut.output_LP_integrated.real[0,k,0] #Lag0 and Channel 0 | |
| 6132 |
|
6137 | |||
| 6133 | ## examine drifts here - based on 60 'indep.' estimates |
|
6138 | ## examine drifts here - based on 60 'indep.' estimates | |
| 6134 | #print(numpy.sum(self.powera)) |
|
6139 | #print(numpy.sum(self.powera)) | |
| 6135 | #exit(1) |
|
6140 | #exit(1) | |
| 6136 | #nis=dataOut.NSCAN*dataOut.NAVG*dataOut.nint*10 |
|
6141 | #nis=dataOut.NSCAN*dataOut.NAVG*dataOut.nint*10 | |
| 6137 | nis = dataOut.nis |
|
6142 | nis = dataOut.nis | |
| 6138 | #print("nis",nis) |
|
6143 | #print("nis",nis) | |
| 6139 | alpha=beta=delta=0.0 |
|
6144 | alpha=beta=delta=0.0 | |
| 6140 | nest=0 |
|
6145 | nest=0 | |
| 6141 | gamma=3.0/(2.0*numpy.pi*dataOut.lags_LP[1]*1.0e-3) |
|
6146 | gamma=3.0/(2.0*numpy.pi*dataOut.lags_LP[1]*1.0e-3) | |
| 6142 | beta=gamma*(math.atan2(dataOut.output_LP_integrated.imag[14,0,2],dataOut.output_LP_integrated.real[14,0,2])-math.atan2(dataOut.output_LP_integrated.imag[1,0,2],dataOut.output_LP_integrated.real[1,0,2]))/13.0 |
|
6147 | beta=gamma*(math.atan2(dataOut.output_LP_integrated.imag[14,0,2],dataOut.output_LP_integrated.real[14,0,2])-math.atan2(dataOut.output_LP_integrated.imag[1,0,2],dataOut.output_LP_integrated.real[1,0,2]))/13.0 | |
| 6143 | #print(gamma,beta) |
|
6148 | #print(gamma,beta) | |
| 6144 | #exit(1) |
|
6149 | #exit(1) | |
| 6145 | for i in range(1,3): |
|
6150 | for i in range(1,3): | |
| 6146 | gamma=3.0/(2.0*numpy.pi*dataOut.lags_LP[i]*1.0e-3) |
|
6151 | gamma=3.0/(2.0*numpy.pi*dataOut.lags_LP[i]*1.0e-3) | |
| 6147 | #print("gamma",gamma) |
|
6152 | #print("gamma",gamma) | |
| 6148 | for j in range(34,44): |
|
6153 | for j in range(34,44): | |
| 6149 | rho2=numpy.abs(dataOut.output_LP_integrated[i,j,0])/numpy.abs(dataOut.output_LP_integrated[0,j,0]) |
|
6154 | rho2=numpy.abs(dataOut.output_LP_integrated[i,j,0])/numpy.abs(dataOut.output_LP_integrated[0,j,0]) | |
| 6150 | dataOut.dphi2=(1.0/rho2-1.0)/(float(2*nis)) |
|
6155 | dataOut.dphi2=(1.0/rho2-1.0)/(float(2*nis)) | |
| 6151 | dataOut.dphi2*=gamma**2 |
|
6156 | dataOut.dphi2*=gamma**2 | |
| 6152 | pest=gamma*math.atan(dataOut.output_LP_integrated.imag[i,j,0]/dataOut.output_LP_integrated.real[i,j,0]) |
|
6157 | pest=gamma*math.atan(dataOut.output_LP_integrated.imag[i,j,0]/dataOut.output_LP_integrated.real[i,j,0]) | |
| 6153 | #print("1",dataOut.output_LP_integrated.imag[i,j,0]) |
|
6158 | #print("1",dataOut.output_LP_integrated.imag[i,j,0]) | |
| 6154 | #print("2",dataOut.output_LP_integrated.real[i,j,0]) |
|
6159 | #print("2",dataOut.output_LP_integrated.real[i,j,0]) | |
| 6155 | self.drift[nest]=pest |
|
6160 | self.drift[nest]=pest | |
| 6156 | self.ddrift[nest]=dataOut.dphi2 |
|
6161 | self.ddrift[nest]=dataOut.dphi2 | |
| 6157 | self.rdrift[nest]=float(nest) |
|
6162 | self.rdrift[nest]=float(nest) | |
| 6158 | nest+=1 |
|
6163 | nest+=1 | |
| 6159 |
|
6164 | |||
| 6160 | sorted(self.drift[:nest]) |
|
6165 | sorted(self.drift[:nest]) | |
| 6161 |
|
6166 | |||
| 6162 | #print(dataOut.dphi2) |
|
6167 | #print(dataOut.dphi2) | |
| 6163 | #exit(1) |
|
6168 | #exit(1) | |
| 6164 |
|
6169 | |||
| 6165 | for j in range(int(nest/4),int(3*nest/4)): |
|
6170 | for j in range(int(nest/4),int(3*nest/4)): | |
| 6166 | #i=int(self.rdrift[j]) |
|
6171 | #i=int(self.rdrift[j]) | |
| 6167 | alpha+=self.drift[j]/self.ddrift[j] |
|
6172 | alpha+=self.drift[j]/self.ddrift[j] | |
| 6168 | delta+=1.0/self.ddrift[j] |
|
6173 | delta+=1.0/self.ddrift[j] | |
| 6169 |
|
6174 | |||
| 6170 | alpha/=delta |
|
6175 | alpha/=delta | |
| 6171 | delta=1./numpy.sqrt(delta) |
|
6176 | delta=1./numpy.sqrt(delta) | |
| 6172 | vdrift=alpha-beta |
|
6177 | vdrift=alpha-beta | |
| 6173 | dvdrift=delta |
|
6178 | dvdrift=delta | |
| 6174 |
|
6179 | |||
| 6175 | #need to develop estimate of complete density profile using all |
|
6180 | #need to develop estimate of complete density profile using all | |
| 6176 | #available data |
|
6181 | #available data | |
| 6177 |
|
6182 | |||
| 6178 | #estimate sample variances for long-pulse power profile |
|
6183 | #estimate sample variances for long-pulse power profile | |
| 6179 |
|
6184 | |||
| 6180 | #nis=dataOut.NSCAN*dataOut.NAVG*dataOut.nint |
|
6185 | #nis=dataOut.NSCAN*dataOut.NAVG*dataOut.nint | |
| 6181 | nis = dataOut.nis/10 |
|
6186 | nis = dataOut.nis/10 | |
| 6182 | #print("nis",nis) |
|
6187 | #print("nis",nis) | |
| 6183 |
|
6188 | |||
| 6184 | self.sigma[:dataOut.NACF+2*dataOut.IBITS+2]=((anoise0+self.powera[:dataOut.NACF+2*dataOut.IBITS+2])**2)/float(nis) |
|
6189 | self.sigma[:dataOut.NACF+2*dataOut.IBITS+2]=((anoise0+self.powera[:dataOut.NACF+2*dataOut.IBITS+2])**2)/float(nis) | |
| 6185 | #print(self.sigma) |
|
6190 | #print(self.sigma) | |
| 6186 | #exit(1) |
|
6191 | #exit(1) | |
| 6187 | ioff=1 |
|
6192 | ioff=1 | |
| 6188 |
|
6193 | |||
| 6189 | #deconvolve rectangular pulse shape from profile ==> powerb, perror |
|
6194 | #deconvolve rectangular pulse shape from profile ==> powerb, perror | |
| 6190 |
|
6195 | |||
| 6191 | ''' |
|
6196 | ''' | |
| 6192 | ############# START nnlswrap############# |
|
6197 | ############# START nnlswrap############# | |
| 6193 |
|
6198 | |||
| 6194 | if dataOut.ut_Faraday>14.0: |
|
6199 | if dataOut.ut_Faraday>14.0: | |
| 6195 | alpha_nnlswrap=20.0 |
|
6200 | alpha_nnlswrap=20.0 | |
| 6196 | else: |
|
6201 | else: | |
| 6197 | alpha_nnlswrap=30.0 |
|
6202 | alpha_nnlswrap=30.0 | |
| 6198 |
|
6203 | |||
| 6199 | range1_nnls=dataOut.NACF |
|
6204 | range1_nnls=dataOut.NACF | |
| 6200 | range2_nnls=dataOut.NACF+dataOut.IBITS-1 |
|
6205 | range2_nnls=dataOut.NACF+dataOut.IBITS-1 | |
| 6201 |
|
6206 | |||
| 6202 | g_nnlswrap=numpy.zeros((range1_nnls,range2_nnls),'float32') |
|
6207 | g_nnlswrap=numpy.zeros((range1_nnls,range2_nnls),'float32') | |
| 6203 | a_nnlswrap=numpy.zeros((range2_nnls,range2_nnls),'float64') |
|
6208 | a_nnlswrap=numpy.zeros((range2_nnls,range2_nnls),'float64') | |
| 6204 |
|
6209 | |||
| 6205 | for i in range(range1_nnls): |
|
6210 | for i in range(range1_nnls): | |
| 6206 | for j in range(range2_nnls): |
|
6211 | for j in range(range2_nnls): | |
| 6207 | if j>=i and j<i+dataOut.IBITS: |
|
6212 | if j>=i and j<i+dataOut.IBITS: | |
| 6208 | g_nnlswrap[i,j]=1.0 |
|
6213 | g_nnlswrap[i,j]=1.0 | |
| 6209 | else: |
|
6214 | else: | |
| 6210 | g_nnlswrap[i,j]=0.0 |
|
6215 | g_nnlswrap[i,j]=0.0 | |
| 6211 |
|
6216 | |||
| 6212 | a_nnlswrap[:]=numpy.matmul(numpy.transpose(g_nnlswrap),g_nnlswrap) |
|
6217 | a_nnlswrap[:]=numpy.matmul(numpy.transpose(g_nnlswrap),g_nnlswrap) | |
| 6213 |
|
6218 | |||
| 6214 | numpy.fill_diagonal(a_nnlswrap,a_nnlswrap.diagonal()+alpha_nnlswrap**2) |
|
6219 | numpy.fill_diagonal(a_nnlswrap,a_nnlswrap.diagonal()+alpha_nnlswrap**2) | |
| 6215 |
|
6220 | |||
| 6216 | #ERROR ANALYSIS# |
|
6221 | #ERROR ANALYSIS# | |
| 6217 |
|
6222 | |||
| 6218 | self.perror[:range2_nnls]=0.0 |
|
6223 | self.perror[:range2_nnls]=0.0 | |
| 6219 | self.perror[:range2_nnls]=numpy.matmul(1./(self.sigma[dataOut.IBITS+ioff:range1_nnls+dataOut.IBITS+ioff]),g_nnlswrap**2) |
|
6224 | self.perror[:range2_nnls]=numpy.matmul(1./(self.sigma[dataOut.IBITS+ioff:range1_nnls+dataOut.IBITS+ioff]),g_nnlswrap**2) | |
| 6220 | self.perror[:range1_nnls]+=(alpha_nnlswrap**2)/(self.sigma[dataOut.IBITS+ioff:range1_nnls+dataOut.IBITS+ioff]) |
|
6225 | self.perror[:range1_nnls]+=(alpha_nnlswrap**2)/(self.sigma[dataOut.IBITS+ioff:range1_nnls+dataOut.IBITS+ioff]) | |
| 6221 | self.perror[:range2_nnls]=1.00/self.perror[:range2_nnls] |
|
6226 | self.perror[:range2_nnls]=1.00/self.perror[:range2_nnls] | |
| 6222 |
|
6227 | |||
| 6223 | b_nnlswrap=numpy.zeros(range2_nnls,'float64') |
|
6228 | b_nnlswrap=numpy.zeros(range2_nnls,'float64') | |
| 6224 | b_nnlswrap[:]=numpy.matmul(self.powera[dataOut.IBITS+ioff:range1_nnls+dataOut.IBITS+ioff],g_nnlswrap) |
|
6229 | b_nnlswrap[:]=numpy.matmul(self.powera[dataOut.IBITS+ioff:range1_nnls+dataOut.IBITS+ioff],g_nnlswrap) | |
| 6225 |
|
6230 | |||
| 6226 | x_nnlswrap=numpy.zeros(range2_nnls,'float64') |
|
6231 | x_nnlswrap=numpy.zeros(range2_nnls,'float64') | |
| 6227 | x_nnlswrap[:]=nnls(a_nnlswrap,b_nnlswrap)[0] |
|
6232 | x_nnlswrap[:]=nnls(a_nnlswrap,b_nnlswrap)[0] | |
| 6228 |
|
6233 | |||
| 6229 | self.powerb[:range2_nnls]=x_nnlswrap |
|
6234 | self.powerb[:range2_nnls]=x_nnlswrap | |
| 6230 | #print(self.powerb[40]) |
|
6235 | #print(self.powerb[40]) | |
| 6231 | #print(self.powerb[66]) |
|
6236 | #print(self.powerb[66]) | |
| 6232 | #exit(1) |
|
6237 | #exit(1) | |
| 6233 | #############END nnlswrap############# |
|
6238 | #############END nnlswrap############# | |
| 6234 | ''' |
|
6239 | ''' | |
| 6235 | self.powerb[:] = self.powera |
|
6240 | self.powerb[:] = self.powera | |
| 6236 | self.perror[:] = 0. |
|
6241 | self.perror[:] = 0. | |
| 6237 | #print(numpy.sum(numpy.sqrt(self.perror[0:dataOut.NACF]))) |
|
6242 | #print(numpy.sum(numpy.sqrt(self.perror[0:dataOut.NACF]))) | |
| 6238 | #print(self.powerb[0:dataOut.NACF]) |
|
6243 | #print(self.powerb[0:dataOut.NACF]) | |
| 6239 | #exit(1) |
|
6244 | #exit(1) | |
| 6240 | #estimate relative error for deconvolved profile (scaling irrelevant) |
|
6245 | #estimate relative error for deconvolved profile (scaling irrelevant) | |
| 6241 | #print(dataOut.NACF) |
|
6246 | #print(dataOut.NACF) | |
| 6242 | dataOut.ene[0:dataOut.NACF]=numpy.sqrt(self.perror[0:dataOut.NACF])/self.powerb[0:dataOut.NACF] |
|
6247 | dataOut.ene[0:dataOut.NACF]=numpy.sqrt(self.perror[0:dataOut.NACF])/self.powerb[0:dataOut.NACF] | |
| 6243 | #print(numpy.sum(dataOut.ene)) |
|
6248 | #print(numpy.sum(dataOut.ene)) | |
| 6244 | #exit(1) |
|
6249 | #exit(1) | |
| 6245 | aux=0 |
|
6250 | aux=0 | |
| 6246 |
|
6251 | |||
| 6247 | for i in range(dataOut.IBITS,dataOut.NACF): |
|
6252 | for i in range(dataOut.IBITS,dataOut.NACF): | |
| 6248 | self.dpulse[i]=self.lpulse[i]=0.0 |
|
6253 | self.dpulse[i]=self.lpulse[i]=0.0 | |
| 6249 | for j in range(dataOut.IBITS): |
|
6254 | for j in range(dataOut.IBITS): | |
| 6250 | k=int(i-j) |
|
6255 | k=int(i-j) | |
| 6251 | if k<36-aux and k>16: |
|
6256 | if k<36-aux and k>16: | |
| 6252 | self.dpulse[i]+=dataOut.ph2[k]/dataOut.h2[k] |
|
6257 | self.dpulse[i]+=dataOut.ph2[k]/dataOut.h2[k] | |
| 6253 | elif k>=36-aux: |
|
6258 | elif k>=36-aux: | |
| 6254 | self.lpulse[i]+=self.powerb[k] |
|
6259 | self.lpulse[i]+=self.powerb[k] | |
| 6255 | self.lagp[i]=self.powera[i] |
|
6260 | self.lagp[i]=self.powera[i] | |
| 6256 |
|
6261 | |||
| 6257 | #find scale factor that best merges profiles |
|
6262 | #find scale factor that best merges profiles | |
| 6258 |
|
6263 | |||
| 6259 | qi=sum(self.dpulse[32:dataOut.NACF]**2/(self.lagp[32:dataOut.NACF]+anoise0)**2) |
|
6264 | qi=sum(self.dpulse[32:dataOut.NACF]**2/(self.lagp[32:dataOut.NACF]+anoise0)**2) | |
| 6260 | ri=sum((self.dpulse[32:dataOut.NACF]*self.lpulse[32:dataOut.NACF])/(self.lagp[32:dataOut.NACF]+anoise0)**2) |
|
6265 | ri=sum((self.dpulse[32:dataOut.NACF]*self.lpulse[32:dataOut.NACF])/(self.lagp[32:dataOut.NACF]+anoise0)**2) | |
| 6261 | si=sum((self.dpulse[32:dataOut.NACF]*self.lagp[32:dataOut.NACF])/(self.lagp[32:dataOut.NACF]+anoise0)**2) |
|
6266 | si=sum((self.dpulse[32:dataOut.NACF]*self.lagp[32:dataOut.NACF])/(self.lagp[32:dataOut.NACF]+anoise0)**2) | |
| 6262 | ui=sum(self.lpulse[32:dataOut.NACF]**2/(self.lagp[32:dataOut.NACF]+anoise0)**2) |
|
6267 | ui=sum(self.lpulse[32:dataOut.NACF]**2/(self.lagp[32:dataOut.NACF]+anoise0)**2) | |
| 6263 | vi=sum((self.lpulse[32:dataOut.NACF]*self.lagp[32:dataOut.NACF])/(self.lagp[32:dataOut.NACF]+anoise0)**2) |
|
6268 | vi=sum((self.lpulse[32:dataOut.NACF]*self.lagp[32:dataOut.NACF])/(self.lagp[32:dataOut.NACF]+anoise0)**2) | |
| 6264 |
|
6269 | |||
| 6265 | alpha=(si*ui-vi*ri)/(qi*ui-ri*ri) |
|
6270 | alpha=(si*ui-vi*ri)/(qi*ui-ri*ri) | |
| 6266 | beta=(qi*vi-ri*si)/(qi*ui-ri*ri) |
|
6271 | beta=(qi*vi-ri*si)/(qi*ui-ri*ri) | |
| 6267 |
|
6272 | |||
| 6268 | #form density profile estimate, merging rescaled power profiles |
|
6273 | #form density profile estimate, merging rescaled power profiles | |
| 6269 | #print(dataOut.h2) |
|
6274 | #print(dataOut.h2) | |
| 6270 | #print(numpy.sum(alpha)) |
|
6275 | #print(numpy.sum(alpha)) | |
| 6271 | #print(numpy.sum(dataOut.ph2)) |
|
6276 | #print(numpy.sum(dataOut.ph2)) | |
| 6272 | self.powerb[16:36-aux]=alpha*dataOut.ph2[16:36-aux]/dataOut.h2[16:36-aux] |
|
6277 | self.powerb[16:36-aux]=alpha*dataOut.ph2[16:36-aux]/dataOut.h2[16:36-aux] | |
| 6273 | self.powerb[36-aux:dataOut.NACF]*=beta |
|
6278 | self.powerb[36-aux:dataOut.NACF]*=beta | |
| 6274 |
|
6279 | |||
| 6275 | #form Ne estimate, fill in error estimate at low altitudes |
|
6280 | #form Ne estimate, fill in error estimate at low altitudes | |
| 6276 |
|
6281 | |||
| 6277 | dataOut.ene[0:36-aux]=dataOut.sdp2[0:36-aux]/dataOut.ph2[0:36-aux] |
|
6282 | dataOut.ene[0:36-aux]=dataOut.sdp2[0:36-aux]/dataOut.ph2[0:36-aux] | |
| 6278 | dataOut.ne[:dataOut.NACF]=self.powerb[:dataOut.NACF]*dataOut.h2[:dataOut.NACF]/alpha |
|
6283 | dataOut.ne[:dataOut.NACF]=self.powerb[:dataOut.NACF]*dataOut.h2[:dataOut.NACF]/alpha | |
| 6279 | #print(numpy.sum(self.powerb)) |
|
6284 | #print(numpy.sum(self.powerb)) | |
| 6280 | #print(numpy.sum(dataOut.ene)) |
|
6285 | #print(numpy.sum(dataOut.ene)) | |
| 6281 | #print(numpy.sum(dataOut.ne)) |
|
6286 | #print(numpy.sum(dataOut.ne)) | |
| 6282 | #exit(1) |
|
6287 | #exit(1) | |
| 6283 | #now do error propagation: store zero lag error covariance in u |
|
6288 | #now do error propagation: store zero lag error covariance in u | |
| 6284 |
|
6289 | |||
| 6285 | nis=dataOut.NSCAN*dataOut.NAVG*dataOut.nint/1 # DLH serious debris removal |
|
6290 | nis=dataOut.NSCAN*dataOut.NAVG*dataOut.nint/1 # DLH serious debris removal | |
| 6286 |
|
6291 | |||
| 6287 | for i in range(dataOut.NACF): |
|
6292 | for i in range(dataOut.NACF): | |
| 6288 | for j in range(i,dataOut.NACF): |
|
6293 | for j in range(i,dataOut.NACF): | |
| 6289 | if j-i>=dataOut.IBITS: |
|
6294 | if j-i>=dataOut.IBITS: | |
| 6290 | self.u[i,j]=0.0 |
|
6295 | self.u[i,j]=0.0 | |
| 6291 | else: |
|
6296 | else: | |
| 6292 | self.u[i,j]=dataOut.output_LP_integrated.real[j-i,i,0]**2/float(nis) |
|
6297 | self.u[i,j]=dataOut.output_LP_integrated.real[j-i,i,0]**2/float(nis) | |
| 6293 | self.u[i,j]*=(anoise0+dataOut.output_LP_integrated.real[0,i,0])/dataOut.output_LP_integrated.real[0,i,0] |
|
6298 | self.u[i,j]*=(anoise0+dataOut.output_LP_integrated.real[0,i,0])/dataOut.output_LP_integrated.real[0,i,0] | |
| 6294 | self.u[i,j]*=(anoise0+dataOut.output_LP_integrated.real[0,j,0])/dataOut.output_LP_integrated.real[0,j,0] |
|
6299 | self.u[i,j]*=(anoise0+dataOut.output_LP_integrated.real[0,j,0])/dataOut.output_LP_integrated.real[0,j,0] | |
| 6295 |
|
6300 | |||
| 6296 | self.u[j,i]=self.u[i,j] |
|
6301 | self.u[j,i]=self.u[i,j] | |
| 6297 |
|
6302 | |||
| 6298 | #now error analyis for lag product matrix (diag), place in acf_err |
|
6303 | #now error analyis for lag product matrix (diag), place in acf_err | |
| 6299 |
|
6304 | |||
| 6300 | for i in range(dataOut.NACF): |
|
6305 | for i in range(dataOut.NACF): | |
| 6301 | for j in range(dataOut.IBITS): |
|
6306 | for j in range(dataOut.IBITS): | |
| 6302 | if j==0: |
|
6307 | if j==0: | |
| 6303 | dataOut.errors[0,i]=numpy.sqrt(self.u[i,i]) |
|
6308 | dataOut.errors[0,i]=numpy.sqrt(self.u[i,i]) | |
| 6304 | else: |
|
6309 | else: | |
| 6305 | dataOut.errors[j,i]=numpy.sqrt(((dataOut.output_LP_integrated.real[0,i,0]+anoise0)*(dataOut.output_LP_integrated.real[0,i+j,0]+anoise0)+dataOut.output_LP_integrated.real[j,i,0]**2)/float(2*nis)) |
|
6310 | dataOut.errors[j,i]=numpy.sqrt(((dataOut.output_LP_integrated.real[0,i,0]+anoise0)*(dataOut.output_LP_integrated.real[0,i+j,0]+anoise0)+dataOut.output_LP_integrated.real[j,i,0]**2)/float(2*nis)) | |
| 6306 |
|
6311 | |||
| 6307 | print("Success") |
|
6312 | print("Success") | |
| 6308 | #print(dataOut.NRANGE) |
|
6313 | #print(dataOut.NRANGE) | |
| 6309 | with suppress_stdout_stderr(): |
|
6314 | with suppress_stdout_stderr(): | |
| 6310 | pass |
|
6315 | pass | |
| 6311 | #full_profile_profile.profile(numpy.transpose(dataOut.output_LP_integrated,(2,1,0)),numpy.transpose(dataOut.errors),self.powerb,dataOut.ne,dataOut.lags_LP,dataOut.thb,dataOut.bfm,dataOut.te,dataOut.ete,dataOut.ti,dataOut.eti,dataOut.ph,dataOut.eph,dataOut.phe,dataOut.ephe,dataOut.range1,dataOut.ut,dataOut.NACF,dataOut.fit_array_real,dataOut.status,dataOut.NRANGE,dataOut.IBITS) |
|
6316 | #full_profile_profile.profile(numpy.transpose(dataOut.output_LP_integrated,(2,1,0)),numpy.transpose(dataOut.errors),self.powerb,dataOut.ne,dataOut.lags_LP,dataOut.thb,dataOut.bfm,dataOut.te,dataOut.ete,dataOut.ti,dataOut.eti,dataOut.ph,dataOut.eph,dataOut.phe,dataOut.ephe,dataOut.range1,dataOut.ut,dataOut.NACF,dataOut.fit_array_real,dataOut.status,dataOut.NRANGE,dataOut.IBITS) | |
| 6312 |
|
6317 | |||
| 6313 | print("status: ",dataOut.status) |
|
6318 | print("status: ",dataOut.status) | |
| 6314 |
|
6319 | |||
| 6315 | if dataOut.status>=3.5: |
|
6320 | if dataOut.status>=3.5: | |
| 6316 | dataOut.te[:]=numpy.nan |
|
6321 | dataOut.te[:]=numpy.nan | |
| 6317 | dataOut.ete[:]=numpy.nan |
|
6322 | dataOut.ete[:]=numpy.nan | |
| 6318 | dataOut.ti[:]=numpy.nan |
|
6323 | dataOut.ti[:]=numpy.nan | |
| 6319 | dataOut.eti[:]=numpy.nan |
|
6324 | dataOut.eti[:]=numpy.nan | |
| 6320 | dataOut.ph[:]=numpy.nan |
|
6325 | dataOut.ph[:]=numpy.nan | |
| 6321 | dataOut.eph[:]=numpy.nan |
|
6326 | dataOut.eph[:]=numpy.nan | |
| 6322 | dataOut.phe[:]=numpy.nan |
|
6327 | dataOut.phe[:]=numpy.nan | |
| 6323 | dataOut.ephe[:]=numpy.nan |
|
6328 | dataOut.ephe[:]=numpy.nan | |
| 6324 |
|
6329 | |||
| 6325 | return dataOut |
|
6330 | return dataOut | |
| 6326 |
|
6331 | |||
| 6327 | class LongPulseAnalysis_V2(Operation): |
|
6332 | class LongPulseAnalysis_V2(Operation): | |
| 6328 | """Operation to estimate ACFs, temperatures, total electron density and Hydrogen/Helium fractions from the Long Pulse data. |
|
6333 | """Operation to estimate ACFs, temperatures, total electron density and Hydrogen/Helium fractions from the Long Pulse data. | |
| 6329 |
|
6334 | |||
| 6330 | Parameters: |
|
6335 | Parameters: | |
| 6331 | ----------- |
|
6336 | ----------- | |
| 6332 | NACF : int |
|
6337 | NACF : int | |
| 6333 | .* |
|
6338 | .* | |
| 6334 |
|
6339 | |||
| 6335 | Example |
|
6340 | Example | |
| 6336 | -------- |
|
6341 | -------- | |
| 6337 |
|
6342 | |||
| 6338 | op = proc_unit.addOperation(name='LongPulseAnalysis', optype='other') |
|
6343 | op = proc_unit.addOperation(name='LongPulseAnalysis', optype='other') | |
| 6339 | op.addParameter(name='NACF', value='16', format='int') |
|
6344 | op.addParameter(name='NACF', value='16', format='int') | |
| 6340 |
|
6345 | |||
| 6341 | """ |
|
6346 | """ | |
| 6342 |
|
6347 | |||
| 6343 | def __init__(self, **kwargs): |
|
6348 | def __init__(self, **kwargs): | |
| 6344 |
|
6349 | |||
| 6345 | Operation.__init__(self, **kwargs) |
|
6350 | Operation.__init__(self, **kwargs) | |
| 6346 | self.aux=1 |
|
6351 | self.aux=1 | |
| 6347 |
|
6352 | |||
| 6348 | def run(self,dataOut,NACF): |
|
6353 | def run(self,dataOut,NACF): | |
| 6349 |
|
6354 | |||
| 6350 | dataOut.NACF=NACF |
|
6355 | dataOut.NACF=NACF | |
| 6351 | dataOut.heightList=dataOut.DH*(numpy.arange(dataOut.NACF)) |
|
6356 | dataOut.heightList=dataOut.DH*(numpy.arange(dataOut.NACF)) | |
| 6352 | anoise0=dataOut.tnoise[0] |
|
6357 | anoise0=dataOut.tnoise[0] | |
| 6353 | anoise1=anoise0*0.0 #seems to be noise in 1st lag 0.015 before '14 |
|
6358 | anoise1=anoise0*0.0 #seems to be noise in 1st lag 0.015 before '14 | |
| 6354 | #print(anoise0) |
|
6359 | #print(anoise0) | |
| 6355 | #exit(1) |
|
6360 | #exit(1) | |
| 6356 | if self.aux: |
|
6361 | if self.aux: | |
| 6357 | #dataOut.cut=31#26#height=31*15=465 |
|
6362 | #dataOut.cut=31#26#height=31*15=465 | |
| 6358 | self.cal=numpy.zeros((dataOut.NLAG),'float32') |
|
6363 | self.cal=numpy.zeros((dataOut.NLAG),'float32') | |
| 6359 | self.drift=numpy.zeros((200),'float32') |
|
6364 | self.drift=numpy.zeros((200),'float32') | |
| 6360 | self.rdrift=numpy.zeros((200),'float32') |
|
6365 | self.rdrift=numpy.zeros((200),'float32') | |
| 6361 | self.ddrift=numpy.zeros((200),'float32') |
|
6366 | self.ddrift=numpy.zeros((200),'float32') | |
| 6362 | self.sigma=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') |
|
6367 | self.sigma=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') | |
| 6363 | self.powera=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') |
|
6368 | self.powera=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') | |
| 6364 | self.powerb=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') |
|
6369 | self.powerb=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') | |
| 6365 | self.perror=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') |
|
6370 | self.perror=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') | |
| 6366 | dataOut.ene=numpy.zeros((dataOut.NRANGE),'float32') |
|
6371 | dataOut.ene=numpy.zeros((dataOut.NRANGE),'float32') | |
| 6367 | self.dpulse=numpy.zeros((dataOut.NACF),'float32') |
|
6372 | self.dpulse=numpy.zeros((dataOut.NACF),'float32') | |
| 6368 | self.lpulse=numpy.zeros((dataOut.NACF),'float32') |
|
6373 | self.lpulse=numpy.zeros((dataOut.NACF),'float32') | |
| 6369 | dataOut.lags_LP=numpy.zeros((dataOut.IBITS),order='F',dtype='float32') |
|
6374 | dataOut.lags_LP=numpy.zeros((dataOut.IBITS),order='F',dtype='float32') | |
| 6370 | self.lagp=numpy.zeros((dataOut.NACF),'float32') |
|
6375 | self.lagp=numpy.zeros((dataOut.NACF),'float32') | |
| 6371 | self.u=numpy.zeros((2*dataOut.NACF,2*dataOut.NACF),'float32') |
|
6376 | self.u=numpy.zeros((2*dataOut.NACF,2*dataOut.NACF),'float32') | |
| 6372 | dataOut.ne=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') |
|
6377 | dataOut.ne=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') | |
| 6373 | dataOut.te=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
6378 | dataOut.te=numpy.zeros((dataOut.NACF),order='F',dtype='float32') | |
| 6374 | dataOut.ete=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
6379 | dataOut.ete=numpy.zeros((dataOut.NACF),order='F',dtype='float32') | |
| 6375 | dataOut.ti=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
6380 | dataOut.ti=numpy.zeros((dataOut.NACF),order='F',dtype='float32') | |
| 6376 | dataOut.eti=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
6381 | dataOut.eti=numpy.zeros((dataOut.NACF),order='F',dtype='float32') | |
| 6377 | dataOut.ph=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
6382 | dataOut.ph=numpy.zeros((dataOut.NACF),order='F',dtype='float32') | |
| 6378 | dataOut.eph=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
6383 | dataOut.eph=numpy.zeros((dataOut.NACF),order='F',dtype='float32') | |
| 6379 | dataOut.phe=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
6384 | dataOut.phe=numpy.zeros((dataOut.NACF),order='F',dtype='float32') | |
| 6380 | dataOut.ephe=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
6385 | dataOut.ephe=numpy.zeros((dataOut.NACF),order='F',dtype='float32') | |
| 6381 | dataOut.errors=numpy.zeros((dataOut.IBITS,max(dataOut.NRANGE,dataOut.NSHTS)),order='F',dtype='float32') |
|
6386 | dataOut.errors=numpy.zeros((dataOut.IBITS,max(dataOut.NRANGE,dataOut.NSHTS)),order='F',dtype='float32') | |
| 6382 | dataOut.fit_array_real=numpy.zeros((max(dataOut.NRANGE,dataOut.NSHTS),dataOut.NLAG),order='F',dtype='float32') |
|
6387 | dataOut.fit_array_real=numpy.zeros((max(dataOut.NRANGE,dataOut.NSHTS),dataOut.NLAG),order='F',dtype='float32') | |
| 6383 | dataOut.status=numpy.zeros(1,'float32') |
|
6388 | dataOut.status=numpy.zeros(1,'float32') | |
| 6384 | dataOut.tx=240.0 #debería provenir del header #hybrid |
|
6389 | dataOut.tx=240.0 #debería provenir del header #hybrid | |
| 6385 |
|
6390 | |||
| 6386 | for i in range(dataOut.IBITS): |
|
6391 | for i in range(dataOut.IBITS): | |
| 6387 | dataOut.lags_LP[i]=float(i)*(dataOut.tx/150.0)/float(dataOut.IBITS) # (float)i*(header.tx/150.0)/(float)IBITS; |
|
6392 | dataOut.lags_LP[i]=float(i)*(dataOut.tx/150.0)/float(dataOut.IBITS) # (float)i*(header.tx/150.0)/(float)IBITS; | |
| 6388 |
|
6393 | |||
| 6389 | self.aux=0 |
|
6394 | self.aux=0 | |
| 6390 |
|
6395 | |||
| 6391 | dataOut.cut=30 |
|
6396 | dataOut.cut=30 | |
| 6392 | for i in range(30,15,-1): |
|
6397 | for i in range(30,15,-1): | |
| 6393 | if numpy.nanmax(dataOut.acfs_error_to_plot[i,:])>=10 or dataOut.info2[i]==0: |
|
6398 | if numpy.nanmax(dataOut.acfs_error_to_plot[i,:])>=10 or dataOut.info2[i]==0: | |
| 6394 | dataOut.cut=i-1 |
|
6399 | dataOut.cut=i-1 | |
| 6395 |
|
6400 | |||
| 6396 | for i in range(dataOut.NLAG): |
|
6401 | for i in range(dataOut.NLAG): | |
| 6397 | self.cal[i]=sum(dataOut.output_LP_integrated[i,:,3].real) |
|
6402 | self.cal[i]=sum(dataOut.output_LP_integrated[i,:,3].real) | |
| 6398 |
|
6403 | |||
| 6399 | #print(numpy.sum(self.cal)) #Coinciden |
|
6404 | #print(numpy.sum(self.cal)) #Coinciden | |
| 6400 | #exit(1) |
|
6405 | #exit(1) | |
| 6401 | self.cal/=float(dataOut.NRANGE) |
|
6406 | self.cal/=float(dataOut.NRANGE) | |
| 6402 | #print(anoise0) |
|
6407 | #print(anoise0) | |
| 6403 | #print(anoise1) |
|
6408 | #print(anoise1) | |
| 6404 | #exit(1) |
|
6409 | #exit(1) | |
| 6405 |
|
6410 | |||
| 6406 | for j in range(dataOut.NACF+2*dataOut.IBITS+2): |
|
6411 | for j in range(dataOut.NACF+2*dataOut.IBITS+2): | |
| 6407 |
|
6412 | |||
| 6408 | dataOut.output_LP_integrated.real[0,j,0]-=anoise0 #lag0 ch0 |
|
6413 | dataOut.output_LP_integrated.real[0,j,0]-=anoise0 #lag0 ch0 | |
| 6409 | dataOut.output_LP_integrated.real[1,j,0]-=anoise1 #lag1 ch0 |
|
6414 | dataOut.output_LP_integrated.real[1,j,0]-=anoise1 #lag1 ch0 | |
| 6410 |
|
6415 | |||
| 6411 | for i in range(1,dataOut.NLAG): #remove cal data from certain lags |
|
6416 | for i in range(1,dataOut.NLAG): #remove cal data from certain lags | |
| 6412 | dataOut.output_LP_integrated.real[i,j,0]-=self.cal[i] |
|
6417 | dataOut.output_LP_integrated.real[i,j,0]-=self.cal[i] | |
| 6413 | k=max(j,26) #constant power below range 26 |
|
6418 | k=max(j,26) #constant power below range 26 | |
| 6414 | self.powera[j]=dataOut.output_LP_integrated.real[0,k,0] |
|
6419 | self.powera[j]=dataOut.output_LP_integrated.real[0,k,0] | |
| 6415 |
|
6420 | |||
| 6416 | ## examine drifts here - based on 60 'indep.' estimates |
|
6421 | ## examine drifts here - based on 60 'indep.' estimates | |
| 6417 | #print(numpy.sum(self.powera)) |
|
6422 | #print(numpy.sum(self.powera)) | |
| 6418 | #exit(1) |
|
6423 | #exit(1) | |
| 6419 | #nis=dataOut.NSCAN*dataOut.NAVG*dataOut.nint*10 |
|
6424 | #nis=dataOut.NSCAN*dataOut.NAVG*dataOut.nint*10 | |
| 6420 | nis = dataOut.nis |
|
6425 | nis = dataOut.nis | |
| 6421 | #print("nis",nis) |
|
6426 | #print("nis",nis) | |
| 6422 | alpha=beta=delta=0.0 |
|
6427 | alpha=beta=delta=0.0 | |
| 6423 | nest=0 |
|
6428 | nest=0 | |
| 6424 | gamma=3.0/(2.0*numpy.pi*dataOut.lags_LP[1]*1.0e-3) |
|
6429 | gamma=3.0/(2.0*numpy.pi*dataOut.lags_LP[1]*1.0e-3) | |
| 6425 | beta=gamma*(math.atan2(dataOut.output_LP_integrated.imag[14,0,2],dataOut.output_LP_integrated.real[14,0,2])-math.atan2(dataOut.output_LP_integrated.imag[1,0,2],dataOut.output_LP_integrated.real[1,0,2]))/13.0 |
|
6430 | beta=gamma*(math.atan2(dataOut.output_LP_integrated.imag[14,0,2],dataOut.output_LP_integrated.real[14,0,2])-math.atan2(dataOut.output_LP_integrated.imag[1,0,2],dataOut.output_LP_integrated.real[1,0,2]))/13.0 | |
| 6426 | #print(gamma,beta) |
|
6431 | #print(gamma,beta) | |
| 6427 | #exit(1) |
|
6432 | #exit(1) | |
| 6428 | for i in range(1,3): |
|
6433 | for i in range(1,3): | |
| 6429 | gamma=3.0/(2.0*numpy.pi*dataOut.lags_LP[i]*1.0e-3) |
|
6434 | gamma=3.0/(2.0*numpy.pi*dataOut.lags_LP[i]*1.0e-3) | |
| 6430 | #print("gamma",gamma) |
|
6435 | #print("gamma",gamma) | |
| 6431 | for j in range(34,44): |
|
6436 | for j in range(34,44): | |
| 6432 | rho2=numpy.abs(dataOut.output_LP_integrated[i,j,0])/numpy.abs(dataOut.output_LP_integrated[0,j,0]) |
|
6437 | rho2=numpy.abs(dataOut.output_LP_integrated[i,j,0])/numpy.abs(dataOut.output_LP_integrated[0,j,0]) | |
| 6433 | dataOut.dphi2=(1.0/rho2-1.0)/(float(2*nis)) |
|
6438 | dataOut.dphi2=(1.0/rho2-1.0)/(float(2*nis)) | |
| 6434 | dataOut.dphi2*=gamma**2 |
|
6439 | dataOut.dphi2*=gamma**2 | |
| 6435 | pest=gamma*math.atan(dataOut.output_LP_integrated.imag[i,j,0]/dataOut.output_LP_integrated.real[i,j,0]) |
|
6440 | pest=gamma*math.atan(dataOut.output_LP_integrated.imag[i,j,0]/dataOut.output_LP_integrated.real[i,j,0]) | |
| 6436 | #print("1",dataOut.output_LP_integrated.imag[i,j,0]) |
|
6441 | #print("1",dataOut.output_LP_integrated.imag[i,j,0]) | |
| 6437 | #print("2",dataOut.output_LP_integrated.real[i,j,0]) |
|
6442 | #print("2",dataOut.output_LP_integrated.real[i,j,0]) | |
| 6438 | self.drift[nest]=pest |
|
6443 | self.drift[nest]=pest | |
| 6439 | self.ddrift[nest]=dataOut.dphi2 |
|
6444 | self.ddrift[nest]=dataOut.dphi2 | |
| 6440 | self.rdrift[nest]=float(nest) |
|
6445 | self.rdrift[nest]=float(nest) | |
| 6441 | nest+=1 |
|
6446 | nest+=1 | |
| 6442 |
|
6447 | |||
| 6443 | sorted(self.drift[:nest]) |
|
6448 | sorted(self.drift[:nest]) | |
| 6444 |
|
6449 | |||
| 6445 | #print(dataOut.dphi2) |
|
6450 | #print(dataOut.dphi2) | |
| 6446 | #exit(1) |
|
6451 | #exit(1) | |
| 6447 |
|
6452 | |||
| 6448 | for j in range(int(nest/4),int(3*nest/4)): |
|
6453 | for j in range(int(nest/4),int(3*nest/4)): | |
| 6449 | #i=int(self.rdrift[j]) |
|
6454 | #i=int(self.rdrift[j]) | |
| 6450 | alpha+=self.drift[j]/self.ddrift[j] |
|
6455 | alpha+=self.drift[j]/self.ddrift[j] | |
| 6451 | delta+=1.0/self.ddrift[j] |
|
6456 | delta+=1.0/self.ddrift[j] | |
| 6452 |
|
6457 | |||
| 6453 | alpha/=delta |
|
6458 | alpha/=delta | |
| 6454 | delta=1./numpy.sqrt(delta) |
|
6459 | delta=1./numpy.sqrt(delta) | |
| 6455 | vdrift=alpha-beta |
|
6460 | vdrift=alpha-beta | |
| 6456 | dvdrift=delta |
|
6461 | dvdrift=delta | |
| 6457 |
|
6462 | |||
| 6458 | #need to develop estimate of complete density profile using all |
|
6463 | #need to develop estimate of complete density profile using all | |
| 6459 | #available data |
|
6464 | #available data | |
| 6460 |
|
6465 | |||
| 6461 | #estimate sample variances for long-pulse power profile |
|
6466 | #estimate sample variances for long-pulse power profile | |
| 6462 |
|
6467 | |||
| 6463 | #nis=dataOut.NSCAN*dataOut.NAVG*dataOut.nint |
|
6468 | #nis=dataOut.NSCAN*dataOut.NAVG*dataOut.nint | |
| 6464 | nis = dataOut.nis/10 |
|
6469 | nis = dataOut.nis/10 | |
| 6465 | #print("nis",nis) |
|
6470 | #print("nis",nis) | |
| 6466 |
|
6471 | |||
| 6467 | self.sigma[:dataOut.NACF+2*dataOut.IBITS+2]=((anoise0+self.powera[:dataOut.NACF+2*dataOut.IBITS+2])**2)/float(nis) |
|
6472 | self.sigma[:dataOut.NACF+2*dataOut.IBITS+2]=((anoise0+self.powera[:dataOut.NACF+2*dataOut.IBITS+2])**2)/float(nis) | |
| 6468 | #print(self.sigma) |
|
6473 | #print(self.sigma) | |
| 6469 | #exit(1) |
|
6474 | #exit(1) | |
| 6470 | ioff=1 |
|
6475 | ioff=1 | |
| 6471 |
|
6476 | |||
| 6472 | #deconvolve rectangular pulse shape from profile ==> powerb, perror |
|
6477 | #deconvolve rectangular pulse shape from profile ==> powerb, perror | |
| 6473 |
|
6478 | |||
| 6474 |
|
6479 | |||
| 6475 | ############# START nnlswrap############# |
|
6480 | ############# START nnlswrap############# | |
| 6476 |
|
6481 | |||
| 6477 | if dataOut.ut_Faraday>14.0: |
|
6482 | if dataOut.ut_Faraday>14.0: | |
| 6478 | alpha_nnlswrap=20.0 |
|
6483 | alpha_nnlswrap=20.0 | |
| 6479 | else: |
|
6484 | else: | |
| 6480 | alpha_nnlswrap=30.0 |
|
6485 | alpha_nnlswrap=30.0 | |
| 6481 |
|
6486 | |||
| 6482 | range1_nnls=dataOut.NACF |
|
6487 | range1_nnls=dataOut.NACF | |
| 6483 | range2_nnls=dataOut.NACF+dataOut.IBITS-1 |
|
6488 | range2_nnls=dataOut.NACF+dataOut.IBITS-1 | |
| 6484 |
|
6489 | |||
| 6485 | g_nnlswrap=numpy.zeros((range1_nnls,range2_nnls),'float32') |
|
6490 | g_nnlswrap=numpy.zeros((range1_nnls,range2_nnls),'float32') | |
| 6486 | a_nnlswrap=numpy.zeros((range2_nnls,range2_nnls),'float64') |
|
6491 | a_nnlswrap=numpy.zeros((range2_nnls,range2_nnls),'float64') | |
| 6487 |
|
6492 | |||
| 6488 | for i in range(range1_nnls): |
|
6493 | for i in range(range1_nnls): | |
| 6489 | for j in range(range2_nnls): |
|
6494 | for j in range(range2_nnls): | |
| 6490 | if j>=i and j<i+dataOut.IBITS: |
|
6495 | if j>=i and j<i+dataOut.IBITS: | |
| 6491 | g_nnlswrap[i,j]=1.0 |
|
6496 | g_nnlswrap[i,j]=1.0 | |
| 6492 | else: |
|
6497 | else: | |
| 6493 | g_nnlswrap[i,j]=0.0 |
|
6498 | g_nnlswrap[i,j]=0.0 | |
| 6494 |
|
6499 | |||
| 6495 | a_nnlswrap[:]=numpy.matmul(numpy.transpose(g_nnlswrap),g_nnlswrap) |
|
6500 | a_nnlswrap[:]=numpy.matmul(numpy.transpose(g_nnlswrap),g_nnlswrap) | |
| 6496 |
|
6501 | |||
| 6497 | numpy.fill_diagonal(a_nnlswrap,a_nnlswrap.diagonal()+alpha_nnlswrap**2) |
|
6502 | numpy.fill_diagonal(a_nnlswrap,a_nnlswrap.diagonal()+alpha_nnlswrap**2) | |
| 6498 |
|
6503 | |||
| 6499 | #ERROR ANALYSIS# |
|
6504 | #ERROR ANALYSIS# | |
| 6500 |
|
6505 | |||
| 6501 | self.perror[:range2_nnls]=0.0 |
|
6506 | self.perror[:range2_nnls]=0.0 | |
| 6502 | self.perror[:range2_nnls]=numpy.matmul(1./(self.sigma[dataOut.IBITS+ioff:range1_nnls+dataOut.IBITS+ioff]),g_nnlswrap**2) |
|
6507 | self.perror[:range2_nnls]=numpy.matmul(1./(self.sigma[dataOut.IBITS+ioff:range1_nnls+dataOut.IBITS+ioff]),g_nnlswrap**2) | |
| 6503 | self.perror[:range1_nnls]+=(alpha_nnlswrap**2)/(self.sigma[dataOut.IBITS+ioff:range1_nnls+dataOut.IBITS+ioff]) |
|
6508 | self.perror[:range1_nnls]+=(alpha_nnlswrap**2)/(self.sigma[dataOut.IBITS+ioff:range1_nnls+dataOut.IBITS+ioff]) | |
| 6504 | self.perror[:range2_nnls]=1.00/self.perror[:range2_nnls] |
|
6509 | self.perror[:range2_nnls]=1.00/self.perror[:range2_nnls] | |
| 6505 |
|
6510 | |||
| 6506 | b_nnlswrap=numpy.zeros(range2_nnls,'float64') |
|
6511 | b_nnlswrap=numpy.zeros(range2_nnls,'float64') | |
| 6507 | b_nnlswrap[:]=numpy.matmul(self.powera[dataOut.IBITS+ioff:range1_nnls+dataOut.IBITS+ioff],g_nnlswrap) |
|
6512 | b_nnlswrap[:]=numpy.matmul(self.powera[dataOut.IBITS+ioff:range1_nnls+dataOut.IBITS+ioff],g_nnlswrap) | |
| 6508 |
|
6513 | |||
| 6509 | x_nnlswrap=numpy.zeros(range2_nnls,'float64') |
|
6514 | x_nnlswrap=numpy.zeros(range2_nnls,'float64') | |
| 6510 | x_nnlswrap[:]=nnls(a_nnlswrap,b_nnlswrap)[0] |
|
6515 | x_nnlswrap[:]=nnls(a_nnlswrap,b_nnlswrap)[0] | |
| 6511 |
|
6516 | |||
| 6512 | self.powerb[:range2_nnls]=x_nnlswrap |
|
6517 | self.powerb[:range2_nnls]=x_nnlswrap | |
| 6513 | #print(self.powerb[40]) |
|
6518 | #print(self.powerb[40]) | |
| 6514 | #print(self.powerb[66]) |
|
6519 | #print(self.powerb[66]) | |
| 6515 | #exit(1) |
|
6520 | #exit(1) | |
| 6516 | #############END nnlswrap############# |
|
6521 | #############END nnlswrap############# | |
| 6517 | #print(numpy.sum(numpy.sqrt(self.perror[0:dataOut.NACF]))) |
|
6522 | #print(numpy.sum(numpy.sqrt(self.perror[0:dataOut.NACF]))) | |
| 6518 | #print(self.powerb[0:dataOut.NACF]) |
|
6523 | #print(self.powerb[0:dataOut.NACF]) | |
| 6519 | #exit(1) |
|
6524 | #exit(1) | |
| 6520 | #estimate relative error for deconvolved profile (scaling irrelevant) |
|
6525 | #estimate relative error for deconvolved profile (scaling irrelevant) | |
| 6521 | #print(dataOut.NACF) |
|
6526 | #print(dataOut.NACF) | |
| 6522 | dataOut.ene[0:dataOut.NACF]=numpy.sqrt(self.perror[0:dataOut.NACF])/self.powerb[0:dataOut.NACF] |
|
6527 | dataOut.ene[0:dataOut.NACF]=numpy.sqrt(self.perror[0:dataOut.NACF])/self.powerb[0:dataOut.NACF] | |
| 6523 | #print(numpy.sum(dataOut.ene)) |
|
6528 | #print(numpy.sum(dataOut.ene)) | |
| 6524 | #exit(1) |
|
6529 | #exit(1) | |
| 6525 | aux=0 |
|
6530 | aux=0 | |
| 6526 |
|
6531 | |||
| 6527 | for i in range(dataOut.IBITS,dataOut.NACF): |
|
6532 | for i in range(dataOut.IBITS,dataOut.NACF): | |
| 6528 | self.dpulse[i]=self.lpulse[i]=0.0 |
|
6533 | self.dpulse[i]=self.lpulse[i]=0.0 | |
| 6529 | for j in range(dataOut.IBITS): |
|
6534 | for j in range(dataOut.IBITS): | |
| 6530 | k=int(i-j) |
|
6535 | k=int(i-j) | |
| 6531 | if k<36-aux and k>16: |
|
6536 | if k<36-aux and k>16: | |
| 6532 | self.dpulse[i]+=dataOut.ph2[k]/dataOut.h2[k] |
|
6537 | self.dpulse[i]+=dataOut.ph2[k]/dataOut.h2[k] | |
| 6533 | elif k>=36-aux: |
|
6538 | elif k>=36-aux: | |
| 6534 | self.lpulse[i]+=self.powerb[k] |
|
6539 | self.lpulse[i]+=self.powerb[k] | |
| 6535 | self.lagp[i]=self.powera[i] |
|
6540 | self.lagp[i]=self.powera[i] | |
| 6536 |
|
6541 | |||
| 6537 | #find scale factor that best merges profiles |
|
6542 | #find scale factor that best merges profiles | |
| 6538 |
|
6543 | |||
| 6539 | qi=sum(self.dpulse[32:dataOut.NACF]**2/(self.lagp[32:dataOut.NACF]+anoise0)**2) |
|
6544 | qi=sum(self.dpulse[32:dataOut.NACF]**2/(self.lagp[32:dataOut.NACF]+anoise0)**2) | |
| 6540 | ri=sum((self.dpulse[32:dataOut.NACF]*self.lpulse[32:dataOut.NACF])/(self.lagp[32:dataOut.NACF]+anoise0)**2) |
|
6545 | ri=sum((self.dpulse[32:dataOut.NACF]*self.lpulse[32:dataOut.NACF])/(self.lagp[32:dataOut.NACF]+anoise0)**2) | |
| 6541 | si=sum((self.dpulse[32:dataOut.NACF]*self.lagp[32:dataOut.NACF])/(self.lagp[32:dataOut.NACF]+anoise0)**2) |
|
6546 | si=sum((self.dpulse[32:dataOut.NACF]*self.lagp[32:dataOut.NACF])/(self.lagp[32:dataOut.NACF]+anoise0)**2) | |
| 6542 | ui=sum(self.lpulse[32:dataOut.NACF]**2/(self.lagp[32:dataOut.NACF]+anoise0)**2) |
|
6547 | ui=sum(self.lpulse[32:dataOut.NACF]**2/(self.lagp[32:dataOut.NACF]+anoise0)**2) | |
| 6543 | vi=sum((self.lpulse[32:dataOut.NACF]*self.lagp[32:dataOut.NACF])/(self.lagp[32:dataOut.NACF]+anoise0)**2) |
|
6548 | vi=sum((self.lpulse[32:dataOut.NACF]*self.lagp[32:dataOut.NACF])/(self.lagp[32:dataOut.NACF]+anoise0)**2) | |
| 6544 |
|
6549 | |||
| 6545 | alpha=(si*ui-vi*ri)/(qi*ui-ri*ri) |
|
6550 | alpha=(si*ui-vi*ri)/(qi*ui-ri*ri) | |
| 6546 | beta=(qi*vi-ri*si)/(qi*ui-ri*ri) |
|
6551 | beta=(qi*vi-ri*si)/(qi*ui-ri*ri) | |
| 6547 |
|
6552 | |||
| 6548 | #form density profile estimate, merging rescaled power profiles |
|
6553 | #form density profile estimate, merging rescaled power profiles | |
| 6549 | #print(dataOut.h2) |
|
6554 | #print(dataOut.h2) | |
| 6550 | #print(numpy.sum(alpha)) |
|
6555 | #print(numpy.sum(alpha)) | |
| 6551 | #print(numpy.sum(dataOut.ph2)) |
|
6556 | #print(numpy.sum(dataOut.ph2)) | |
| 6552 | self.powerb[16:36-aux]=alpha*dataOut.ph2[16:36-aux]/dataOut.h2[16:36-aux] |
|
6557 | self.powerb[16:36-aux]=alpha*dataOut.ph2[16:36-aux]/dataOut.h2[16:36-aux] | |
| 6553 | self.powerb[36-aux:dataOut.NACF]*=beta |
|
6558 | self.powerb[36-aux:dataOut.NACF]*=beta | |
| 6554 |
|
6559 | |||
| 6555 | #form Ne estimate, fill in error estimate at low altitudes |
|
6560 | #form Ne estimate, fill in error estimate at low altitudes | |
| 6556 |
|
6561 | |||
| 6557 | dataOut.ene[0:36-aux]=dataOut.sdp2[0:36-aux]/dataOut.ph2[0:36-aux] |
|
6562 | dataOut.ene[0:36-aux]=dataOut.sdp2[0:36-aux]/dataOut.ph2[0:36-aux] | |
| 6558 | dataOut.ne[:dataOut.NACF]=self.powerb[:dataOut.NACF]*dataOut.h2[:dataOut.NACF]/alpha |
|
6563 | dataOut.ne[:dataOut.NACF]=self.powerb[:dataOut.NACF]*dataOut.h2[:dataOut.NACF]/alpha | |
| 6559 | #print(numpy.sum(self.powerb)) |
|
6564 | #print(numpy.sum(self.powerb)) | |
| 6560 | #print(numpy.sum(dataOut.ene)) |
|
6565 | #print(numpy.sum(dataOut.ene)) | |
| 6561 | #print(numpy.sum(dataOut.ne)) |
|
6566 | #print(numpy.sum(dataOut.ne)) | |
| 6562 | #exit(1) |
|
6567 | #exit(1) | |
| 6563 | #now do error propagation: store zero lag error covariance in u |
|
6568 | #now do error propagation: store zero lag error covariance in u | |
| 6564 |
|
6569 | |||
| 6565 | nis=dataOut.NSCAN*dataOut.NAVG*dataOut.nint/1 # DLH serious debris removal |
|
6570 | nis=dataOut.NSCAN*dataOut.NAVG*dataOut.nint/1 # DLH serious debris removal | |
| 6566 |
|
6571 | |||
| 6567 | for i in range(dataOut.NACF): |
|
6572 | for i in range(dataOut.NACF): | |
| 6568 | for j in range(i,dataOut.NACF): |
|
6573 | for j in range(i,dataOut.NACF): | |
| 6569 | if j-i>=dataOut.IBITS: |
|
6574 | if j-i>=dataOut.IBITS: | |
| 6570 | self.u[i,j]=0.0 |
|
6575 | self.u[i,j]=0.0 | |
| 6571 | else: |
|
6576 | else: | |
| 6572 | self.u[i,j]=dataOut.output_LP_integrated.real[j-i,i,0]**2/float(nis) |
|
6577 | self.u[i,j]=dataOut.output_LP_integrated.real[j-i,i,0]**2/float(nis) | |
| 6573 | self.u[i,j]*=(anoise0+dataOut.output_LP_integrated.real[0,i,0])/dataOut.output_LP_integrated.real[0,i,0] |
|
6578 | self.u[i,j]*=(anoise0+dataOut.output_LP_integrated.real[0,i,0])/dataOut.output_LP_integrated.real[0,i,0] | |
| 6574 | self.u[i,j]*=(anoise0+dataOut.output_LP_integrated.real[0,j,0])/dataOut.output_LP_integrated.real[0,j,0] |
|
6579 | self.u[i,j]*=(anoise0+dataOut.output_LP_integrated.real[0,j,0])/dataOut.output_LP_integrated.real[0,j,0] | |
| 6575 |
|
6580 | |||
| 6576 | self.u[j,i]=self.u[i,j] |
|
6581 | self.u[j,i]=self.u[i,j] | |
| 6577 |
|
6582 | |||
| 6578 | #now error analyis for lag product matrix (diag), place in acf_err |
|
6583 | #now error analyis for lag product matrix (diag), place in acf_err | |
| 6579 |
|
6584 | |||
| 6580 | for i in range(dataOut.NACF): |
|
6585 | for i in range(dataOut.NACF): | |
| 6581 | for j in range(dataOut.IBITS): |
|
6586 | for j in range(dataOut.IBITS): | |
| 6582 | if j==0: |
|
6587 | if j==0: | |
| 6583 | dataOut.errors[0,i]=numpy.sqrt(self.u[i,i]) |
|
6588 | dataOut.errors[0,i]=numpy.sqrt(self.u[i,i]) | |
| 6584 | else: |
|
6589 | else: | |
| 6585 | dataOut.errors[j,i]=numpy.sqrt(((dataOut.output_LP_integrated.real[0,i,0]+anoise0)*(dataOut.output_LP_integrated.real[0,i+j,0]+anoise0)+dataOut.output_LP_integrated.real[j,i,0]**2)/float(2*nis)) |
|
6590 | dataOut.errors[j,i]=numpy.sqrt(((dataOut.output_LP_integrated.real[0,i,0]+anoise0)*(dataOut.output_LP_integrated.real[0,i+j,0]+anoise0)+dataOut.output_LP_integrated.real[j,i,0]**2)/float(2*nis)) | |
| 6586 |
|
6591 | |||
| 6587 | print("Success") |
|
6592 | print("Success") | |
| 6588 | with suppress_stdout_stderr(): |
|
6593 | with suppress_stdout_stderr(): | |
| 6589 | #pass |
|
6594 | #pass | |
| 6590 | full_profile_profile.profile(numpy.transpose(dataOut.output_LP_integrated,(2,1,0)),numpy.transpose(dataOut.errors),self.powerb,dataOut.ne,dataOut.lags_LP,dataOut.thb,dataOut.bfm,dataOut.te,dataOut.ete,dataOut.ti,dataOut.eti,dataOut.ph,dataOut.eph,dataOut.phe,dataOut.ephe,dataOut.range1,dataOut.ut,dataOut.NACF,dataOut.fit_array_real,dataOut.status,dataOut.NRANGE,dataOut.IBITS) |
|
6595 | full_profile_profile.profile(numpy.transpose(dataOut.output_LP_integrated,(2,1,0)),numpy.transpose(dataOut.errors),self.powerb,dataOut.ne,dataOut.lags_LP,dataOut.thb,dataOut.bfm,dataOut.te,dataOut.ete,dataOut.ti,dataOut.eti,dataOut.ph,dataOut.eph,dataOut.phe,dataOut.ephe,dataOut.range1,dataOut.ut,dataOut.NACF,dataOut.fit_array_real,dataOut.status,dataOut.NRANGE,dataOut.IBITS) | |
| 6591 |
|
6596 | |||
| 6592 | if dataOut.status>=3.5: |
|
6597 | if dataOut.status>=3.5: | |
| 6593 | dataOut.te[:]=numpy.nan |
|
6598 | dataOut.te[:]=numpy.nan | |
| 6594 | dataOut.ete[:]=numpy.nan |
|
6599 | dataOut.ete[:]=numpy.nan | |
| 6595 | dataOut.ti[:]=numpy.nan |
|
6600 | dataOut.ti[:]=numpy.nan | |
| 6596 | dataOut.eti[:]=numpy.nan |
|
6601 | dataOut.eti[:]=numpy.nan | |
| 6597 | dataOut.ph[:]=numpy.nan |
|
6602 | dataOut.ph[:]=numpy.nan | |
| 6598 | dataOut.eph[:]=numpy.nan |
|
6603 | dataOut.eph[:]=numpy.nan | |
| 6599 | dataOut.phe[:]=numpy.nan |
|
6604 | dataOut.phe[:]=numpy.nan | |
| 6600 | dataOut.ephe[:]=numpy.nan |
|
6605 | dataOut.ephe[:]=numpy.nan | |
| 6601 |
|
6606 | |||
| 6602 | return dataOut |
|
6607 | return dataOut | |
| 6603 |
|
6608 | |||
| 6604 | class PulsePairVoltage(Operation): |
|
6609 | class PulsePairVoltage(Operation): | |
| 6605 | ''' |
|
6610 | ''' | |
| 6606 | Function PulsePair(Signal Power, Velocity) |
|
6611 | Function PulsePair(Signal Power, Velocity) | |
| 6607 | The real component of Lag[0] provides Intensity Information |
|
6612 | The real component of Lag[0] provides Intensity Information | |
| 6608 | The imag component of Lag[1] Phase provides Velocity Information |
|
6613 | The imag component of Lag[1] Phase provides Velocity Information | |
| 6609 |
|
6614 | |||
| 6610 | Configuration Parameters: |
|
6615 | Configuration Parameters: | |
| 6611 | nPRF = Number of Several PRF |
|
6616 | nPRF = Number of Several PRF | |
| 6612 | theta = Degree Azimuth angel Boundaries |
|
6617 | theta = Degree Azimuth angel Boundaries | |
| 6613 |
|
6618 | |||
| 6614 | Input: |
|
6619 | Input: | |
| 6615 | self.dataOut |
|
6620 | self.dataOut | |
| 6616 | lag[N] |
|
6621 | lag[N] | |
| 6617 | Affected: |
|
6622 | Affected: | |
| 6618 | self.dataOut.spc |
|
6623 | self.dataOut.spc | |
| 6619 | ''' |
|
6624 | ''' | |
| 6620 | isConfig = False |
|
6625 | isConfig = False | |
| 6621 | __profIndex = 0 |
|
6626 | __profIndex = 0 | |
| 6622 | __initime = None |
|
6627 | __initime = None | |
| 6623 | __lastdatatime = None |
|
6628 | __lastdatatime = None | |
| 6624 | __buffer = None |
|
6629 | __buffer = None | |
| 6625 | noise = None |
|
6630 | noise = None | |
| 6626 | __dataReady = False |
|
6631 | __dataReady = False | |
| 6627 | n = None |
|
6632 | n = None | |
| 6628 | __nch = 0 |
|
6633 | __nch = 0 | |
| 6629 | __nHeis = 0 |
|
6634 | __nHeis = 0 | |
| 6630 | removeDC = False |
|
6635 | removeDC = False | |
| 6631 | ipp = None |
|
6636 | ipp = None | |
| 6632 | lambda_ = 0 |
|
6637 | lambda_ = 0 | |
| 6633 |
|
6638 | |||
| 6634 | def __init__(self, **kwargs): |
|
6639 | def __init__(self, **kwargs): | |
| 6635 | Operation.__init__(self, **kwargs) |
|
6640 | Operation.__init__(self, **kwargs) | |
| 6636 |
|
6641 | |||
| 6637 | def setup(self, dataOut, n=None, removeDC=False): |
|
6642 | def setup(self, dataOut, n=None, removeDC=False): | |
| 6638 | ''' |
|
6643 | ''' | |
| 6639 | n= Numero de PRF's de entrada |
|
6644 | n= Numero de PRF's de entrada | |
| 6640 | ''' |
|
6645 | ''' | |
| 6641 | self.__initime = None |
|
6646 | self.__initime = None | |
| 6642 | self.__lastdatatime = 0 |
|
6647 | self.__lastdatatime = 0 | |
| 6643 | self.__dataReady = False |
|
6648 | self.__dataReady = False | |
| 6644 | self.__buffer = 0 |
|
6649 | self.__buffer = 0 | |
| 6645 | self.__profIndex = 0 |
|
6650 | self.__profIndex = 0 | |
| 6646 | self.noise = None |
|
6651 | self.noise = None | |
| 6647 | self.__nch = dataOut.nChannels |
|
6652 | self.__nch = dataOut.nChannels | |
| 6648 | self.__nHeis = dataOut.nHeights |
|
6653 | self.__nHeis = dataOut.nHeights | |
| 6649 | self.removeDC = removeDC |
|
6654 | self.removeDC = removeDC | |
| 6650 | self.lambda_ = 3.0e8 / (9345.0e6) |
|
6655 | self.lambda_ = 3.0e8 / (9345.0e6) | |
| 6651 | self.ippSec = dataOut.ippSeconds |
|
6656 | self.ippSec = dataOut.ippSeconds | |
| 6652 | self.nCohInt = dataOut.nCohInt |
|
6657 | self.nCohInt = dataOut.nCohInt | |
| 6653 | print("IPPseconds", dataOut.ippSeconds) |
|
6658 | print("IPPseconds", dataOut.ippSeconds) | |
| 6654 |
|
6659 | |||
| 6655 | print("ELVALOR DE n es:", n) |
|
6660 | print("ELVALOR DE n es:", n) | |
| 6656 | if n == None: |
|
6661 | if n == None: | |
| 6657 | raise ValueError("n should be specified.") |
|
6662 | raise ValueError("n should be specified.") | |
| 6658 |
|
6663 | |||
| 6659 | if n != None: |
|
6664 | if n != None: | |
| 6660 | if n < 2: |
|
6665 | if n < 2: | |
| 6661 | raise ValueError("n should be greater than 2") |
|
6666 | raise ValueError("n should be greater than 2") | |
| 6662 |
|
6667 | |||
| 6663 | self.n = n |
|
6668 | self.n = n | |
| 6664 | self.__nProf = n |
|
6669 | self.__nProf = n | |
| 6665 |
|
6670 | |||
| 6666 | self.__buffer = numpy.zeros((dataOut.nChannels, |
|
6671 | self.__buffer = numpy.zeros((dataOut.nChannels, | |
| 6667 | n, |
|
6672 | n, | |
| 6668 | dataOut.nHeights), |
|
6673 | dataOut.nHeights), | |
| 6669 | dtype='complex') |
|
6674 | dtype='complex') | |
| 6670 |
|
6675 | |||
| 6671 | def putData(self, data): |
|
6676 | def putData(self, data): | |
| 6672 | ''' |
|
6677 | ''' | |
| 6673 | Add a profile to he __buffer and increase in one the __profiel Index |
|
6678 | Add a profile to he __buffer and increase in one the __profiel Index | |
| 6674 | ''' |
|
6679 | ''' | |
| 6675 | self.__buffer[:, self.__profIndex, :] = data |
|
6680 | self.__buffer[:, self.__profIndex, :] = data | |
| 6676 | self.__profIndex += 1 |
|
6681 | self.__profIndex += 1 | |
| 6677 | return |
|
6682 | return | |
| 6678 |
|
6683 | |||
| 6679 | def pushData(self, dataOut): |
|
6684 | def pushData(self, dataOut): | |
| 6680 | ''' |
|
6685 | ''' | |
| 6681 | Return the PULSEPAIR and the profiles used in the operation |
|
6686 | Return the PULSEPAIR and the profiles used in the operation | |
| 6682 | Affected : self.__profileIndex |
|
6687 | Affected : self.__profileIndex | |
| 6683 | ''' |
|
6688 | ''' | |
| 6684 | #----------------- Remove DC----------------------------------- |
|
6689 | #----------------- Remove DC----------------------------------- | |
| 6685 | if self.removeDC == True: |
|
6690 | if self.removeDC == True: | |
| 6686 | mean = numpy.mean(self.__buffer, 1) |
|
6691 | mean = numpy.mean(self.__buffer, 1) | |
| 6687 | tmp = mean.reshape(self.__nch, 1, self.__nHeis) |
|
6692 | tmp = mean.reshape(self.__nch, 1, self.__nHeis) | |
| 6688 | dc = numpy.tile(tmp, [1, self.__nProf, 1]) |
|
6693 | dc = numpy.tile(tmp, [1, self.__nProf, 1]) | |
| 6689 | self.__buffer = self.__buffer - dc |
|
6694 | self.__buffer = self.__buffer - dc | |
| 6690 | #------------------Calculo de Potencia ------------------------ |
|
6695 | #------------------Calculo de Potencia ------------------------ | |
| 6691 | pair0 = self.__buffer * numpy.conj(self.__buffer) |
|
6696 | pair0 = self.__buffer * numpy.conj(self.__buffer) | |
| 6692 | pair0 = pair0.real |
|
6697 | pair0 = pair0.real | |
| 6693 | lag_0 = numpy.sum(pair0, 1) |
|
6698 | lag_0 = numpy.sum(pair0, 1) | |
| 6694 | #------------------Calculo de Ruido x canal-------------------- |
|
6699 | #------------------Calculo de Ruido x canal-------------------- | |
| 6695 | self.noise = numpy.zeros(self.__nch) |
|
6700 | self.noise = numpy.zeros(self.__nch) | |
| 6696 | for i in range(self.__nch): |
|
6701 | for i in range(self.__nch): | |
| 6697 | daux = numpy.sort(pair0[i, :, :], axis=None) |
|
6702 | daux = numpy.sort(pair0[i, :, :], axis=None) | |
| 6698 | self.noise[i] = hildebrand_sekhon(daux , self.nCohInt) |
|
6703 | self.noise[i] = hildebrand_sekhon(daux , self.nCohInt) | |
| 6699 |
|
6704 | |||
| 6700 | self.noise = self.noise.reshape(self.__nch, 1) |
|
6705 | self.noise = self.noise.reshape(self.__nch, 1) | |
| 6701 | self.noise = numpy.tile(self.noise, [1, self.__nHeis]) |
|
6706 | self.noise = numpy.tile(self.noise, [1, self.__nHeis]) | |
| 6702 | noise_buffer = self.noise.reshape(self.__nch, 1, self.__nHeis) |
|
6707 | noise_buffer = self.noise.reshape(self.__nch, 1, self.__nHeis) | |
| 6703 | noise_buffer = numpy.tile(noise_buffer, [1, self.__nProf, 1]) |
|
6708 | noise_buffer = numpy.tile(noise_buffer, [1, self.__nProf, 1]) | |
| 6704 | #------------------ Potencia recibida= P , Potencia senal = S , Ruido= N-- |
|
6709 | #------------------ Potencia recibida= P , Potencia senal = S , Ruido= N-- | |
| 6705 | #------------------ P= S+N ,P=lag_0/N --------------------------------- |
|
6710 | #------------------ P= S+N ,P=lag_0/N --------------------------------- | |
| 6706 | #-------------------- Power -------------------------------------------------- |
|
6711 | #-------------------- Power -------------------------------------------------- | |
| 6707 | data_power = lag_0 / (self.n * self.nCohInt) |
|
6712 | data_power = lag_0 / (self.n * self.nCohInt) | |
| 6708 | #------------------ Senal --------------------------------------------------- |
|
6713 | #------------------ Senal --------------------------------------------------- | |
| 6709 | data_intensity = pair0 - noise_buffer |
|
6714 | data_intensity = pair0 - noise_buffer | |
| 6710 | data_intensity = numpy.sum(data_intensity, axis=1) * (self.n * self.nCohInt) # *self.nCohInt) |
|
6715 | data_intensity = numpy.sum(data_intensity, axis=1) * (self.n * self.nCohInt) # *self.nCohInt) | |
| 6711 | # data_intensity = (lag_0-self.noise*self.n)*(self.n*self.nCohInt) |
|
6716 | # data_intensity = (lag_0-self.noise*self.n)*(self.n*self.nCohInt) | |
| 6712 | for i in range(self.__nch): |
|
6717 | for i in range(self.__nch): | |
| 6713 | for j in range(self.__nHeis): |
|
6718 | for j in range(self.__nHeis): | |
| 6714 | if data_intensity[i][j] < 0: |
|
6719 | if data_intensity[i][j] < 0: | |
| 6715 | data_intensity[i][j] = numpy.min(numpy.absolute(data_intensity[i][j])) |
|
6720 | data_intensity[i][j] = numpy.min(numpy.absolute(data_intensity[i][j])) | |
| 6716 |
|
6721 | |||
| 6717 | #----------------- Calculo de Frecuencia y Velocidad doppler-------- |
|
6722 | #----------------- Calculo de Frecuencia y Velocidad doppler-------- | |
| 6718 | pair1 = self.__buffer[:, :-1, :] * numpy.conjugate(self.__buffer[:, 1:, :]) |
|
6723 | pair1 = self.__buffer[:, :-1, :] * numpy.conjugate(self.__buffer[:, 1:, :]) | |
| 6719 | lag_1 = numpy.sum(pair1, 1) |
|
6724 | lag_1 = numpy.sum(pair1, 1) | |
| 6720 | data_freq = (-1 / (2.0 * math.pi * self.ippSec * self.nCohInt)) * numpy.angle(lag_1) |
|
6725 | data_freq = (-1 / (2.0 * math.pi * self.ippSec * self.nCohInt)) * numpy.angle(lag_1) | |
| 6721 | data_velocity = (self.lambda_ / 2.0) * data_freq |
|
6726 | data_velocity = (self.lambda_ / 2.0) * data_freq | |
| 6722 |
|
6727 | |||
| 6723 | #---------------- Potencia promedio estimada de la Senal----------- |
|
6728 | #---------------- Potencia promedio estimada de la Senal----------- | |
| 6724 | lag_0 = lag_0 / self.n |
|
6729 | lag_0 = lag_0 / self.n | |
| 6725 | S = lag_0 - self.noise |
|
6730 | S = lag_0 - self.noise | |
| 6726 |
|
6731 | |||
| 6727 | #---------------- Frecuencia Doppler promedio --------------------- |
|
6732 | #---------------- Frecuencia Doppler promedio --------------------- | |
| 6728 | lag_1 = lag_1 / (self.n - 1) |
|
6733 | lag_1 = lag_1 / (self.n - 1) | |
| 6729 | R1 = numpy.abs(lag_1) |
|
6734 | R1 = numpy.abs(lag_1) | |
| 6730 |
|
6735 | |||
| 6731 | #---------------- Calculo del SNR---------------------------------- |
|
6736 | #---------------- Calculo del SNR---------------------------------- | |
| 6732 | data_snrPP = S / self.noise |
|
6737 | data_snrPP = S / self.noise | |
| 6733 | for i in range(self.__nch): |
|
6738 | for i in range(self.__nch): | |
| 6734 | for j in range(self.__nHeis): |
|
6739 | for j in range(self.__nHeis): | |
| 6735 | if data_snrPP[i][j] < 1.e-20: |
|
6740 | if data_snrPP[i][j] < 1.e-20: | |
| 6736 | data_snrPP[i][j] = 1.e-20 |
|
6741 | data_snrPP[i][j] = 1.e-20 | |
| 6737 |
|
6742 | |||
| 6738 | #----------------- Calculo del ancho espectral ---------------------- |
|
6743 | #----------------- Calculo del ancho espectral ---------------------- | |
| 6739 | L = S / R1 |
|
6744 | L = S / R1 | |
| 6740 | L = numpy.where(L < 0, 1, L) |
|
6745 | L = numpy.where(L < 0, 1, L) | |
| 6741 | L = numpy.log(L) |
|
6746 | L = numpy.log(L) | |
| 6742 | tmp = numpy.sqrt(numpy.absolute(L)) |
|
6747 | tmp = numpy.sqrt(numpy.absolute(L)) | |
| 6743 | data_specwidth = (self.lambda_ / (2 * math.sqrt(2) * math.pi * self.ippSec * self.nCohInt)) * tmp * numpy.sign(L) |
|
6748 | data_specwidth = (self.lambda_ / (2 * math.sqrt(2) * math.pi * self.ippSec * self.nCohInt)) * tmp * numpy.sign(L) | |
| 6744 | n = self.__profIndex |
|
6749 | n = self.__profIndex | |
| 6745 |
|
6750 | |||
| 6746 | self.__buffer = numpy.zeros((self.__nch, self.__nProf, self.__nHeis), dtype='complex') |
|
6751 | self.__buffer = numpy.zeros((self.__nch, self.__nProf, self.__nHeis), dtype='complex') | |
| 6747 | self.__profIndex = 0 |
|
6752 | self.__profIndex = 0 | |
| 6748 | return data_power, data_intensity, data_velocity, data_snrPP, data_specwidth, n |
|
6753 | return data_power, data_intensity, data_velocity, data_snrPP, data_specwidth, n | |
| 6749 |
|
6754 | |||
| 6750 |
|
6755 | |||
| 6751 | def pulsePairbyProfiles(self, dataOut): |
|
6756 | def pulsePairbyProfiles(self, dataOut): | |
| 6752 |
|
6757 | |||
| 6753 | self.__dataReady = False |
|
6758 | self.__dataReady = False | |
| 6754 | data_power = None |
|
6759 | data_power = None | |
| 6755 | data_intensity = None |
|
6760 | data_intensity = None | |
| 6756 | data_velocity = None |
|
6761 | data_velocity = None | |
| 6757 | data_specwidth = None |
|
6762 | data_specwidth = None | |
| 6758 | data_snrPP = None |
|
6763 | data_snrPP = None | |
| 6759 | self.putData(data=dataOut.data) |
|
6764 | self.putData(data=dataOut.data) | |
| 6760 | if self.__profIndex == self.n: |
|
6765 | if self.__profIndex == self.n: | |
| 6761 | data_power, data_intensity, data_velocity, data_snrPP, data_specwidth, n = self.pushData(dataOut=dataOut) |
|
6766 | data_power, data_intensity, data_velocity, data_snrPP, data_specwidth, n = self.pushData(dataOut=dataOut) | |
| 6762 | self.__dataReady = True |
|
6767 | self.__dataReady = True | |
| 6763 |
|
6768 | |||
| 6764 | return data_power, data_intensity, data_velocity, data_snrPP, data_specwidth |
|
6769 | return data_power, data_intensity, data_velocity, data_snrPP, data_specwidth | |
| 6765 |
|
6770 | |||
| 6766 |
|
6771 | |||
| 6767 | def pulsePairOp(self, dataOut, datatime=None): |
|
6772 | def pulsePairOp(self, dataOut, datatime=None): | |
| 6768 |
|
6773 | |||
| 6769 | if self.__initime == None: |
|
6774 | if self.__initime == None: | |
| 6770 | self.__initime = datatime |
|
6775 | self.__initime = datatime | |
| 6771 | data_power, data_intensity, data_velocity, data_snrPP, data_specwidth = self.pulsePairbyProfiles(dataOut) |
|
6776 | data_power, data_intensity, data_velocity, data_snrPP, data_specwidth = self.pulsePairbyProfiles(dataOut) | |
| 6772 | self.__lastdatatime = datatime |
|
6777 | self.__lastdatatime = datatime | |
| 6773 |
|
6778 | |||
| 6774 | if data_power is None: |
|
6779 | if data_power is None: | |
| 6775 | return None, None, None, None, None, None |
|
6780 | return None, None, None, None, None, None | |
| 6776 |
|
6781 | |||
| 6777 | avgdatatime = self.__initime |
|
6782 | avgdatatime = self.__initime | |
| 6778 | deltatime = datatime - self.__lastdatatime |
|
6783 | deltatime = datatime - self.__lastdatatime | |
| 6779 | self.__initime = datatime |
|
6784 | self.__initime = datatime | |
| 6780 |
|
6785 | |||
| 6781 | return data_power, data_intensity, data_velocity, data_snrPP, data_specwidth, avgdatatime |
|
6786 | return data_power, data_intensity, data_velocity, data_snrPP, data_specwidth, avgdatatime | |
| 6782 |
|
6787 | |||
| 6783 | def run(self, dataOut, n=None, removeDC=False, overlapping=False, **kwargs): |
|
6788 | def run(self, dataOut, n=None, removeDC=False, overlapping=False, **kwargs): | |
| 6784 |
|
6789 | |||
| 6785 | if not self.isConfig: |
|
6790 | if not self.isConfig: | |
| 6786 | self.setup(dataOut=dataOut, n=n , removeDC=removeDC , **kwargs) |
|
6791 | self.setup(dataOut=dataOut, n=n , removeDC=removeDC , **kwargs) | |
| 6787 | self.isConfig = True |
|
6792 | self.isConfig = True | |
| 6788 | data_power, data_intensity, data_velocity, data_snrPP, data_specwidth, avgdatatime = self.pulsePairOp(dataOut, dataOut.utctime) |
|
6793 | data_power, data_intensity, data_velocity, data_snrPP, data_specwidth, avgdatatime = self.pulsePairOp(dataOut, dataOut.utctime) | |
| 6789 | dataOut.flagNoData = True |
|
6794 | dataOut.flagNoData = True | |
| 6790 |
|
6795 | |||
| 6791 | if self.__dataReady: |
|
6796 | if self.__dataReady: | |
| 6792 | dataOut.nCohInt *= self.n |
|
6797 | dataOut.nCohInt *= self.n | |
| 6793 | dataOut.dataPP_POW = data_intensity # S |
|
6798 | dataOut.dataPP_POW = data_intensity # S | |
| 6794 | dataOut.dataPP_POWER = data_power # P |
|
6799 | dataOut.dataPP_POWER = data_power # P | |
| 6795 | dataOut.dataPP_DOP = data_velocity |
|
6800 | dataOut.dataPP_DOP = data_velocity | |
| 6796 | dataOut.dataPP_SNR = data_snrPP |
|
6801 | dataOut.dataPP_SNR = data_snrPP | |
| 6797 | dataOut.dataPP_WIDTH = data_specwidth |
|
6802 | dataOut.dataPP_WIDTH = data_specwidth | |
| 6798 | dataOut.PRFbyAngle = self.n # numero de PRF*cada angulo rotado que equivale a un tiempo. |
|
6803 | dataOut.PRFbyAngle = self.n # numero de PRF*cada angulo rotado que equivale a un tiempo. | |
| 6799 | dataOut.utctime = avgdatatime |
|
6804 | dataOut.utctime = avgdatatime | |
| 6800 | dataOut.flagNoData = False |
|
6805 | dataOut.flagNoData = False | |
| 6801 | return dataOut |
|
6806 | return dataOut | |
| 6802 |
|
6807 | |||
| 6803 |
|
6808 | |||
| 6804 |
|
6809 | |||
| 6805 | # import collections |
|
6810 | # import collections | |
| 6806 | # from scipy.stats import mode |
|
6811 | # from scipy.stats import mode | |
| 6807 | # |
|
6812 | # | |
| 6808 | # class Synchronize(Operation): |
|
6813 | # class Synchronize(Operation): | |
| 6809 | # |
|
6814 | # | |
| 6810 | # isConfig = False |
|
6815 | # isConfig = False | |
| 6811 | # __profIndex = 0 |
|
6816 | # __profIndex = 0 | |
| 6812 | # |
|
6817 | # | |
| 6813 | # def __init__(self, **kwargs): |
|
6818 | # def __init__(self, **kwargs): | |
| 6814 | # |
|
6819 | # | |
| 6815 | # Operation.__init__(self, **kwargs) |
|
6820 | # Operation.__init__(self, **kwargs) | |
| 6816 | # # self.isConfig = False |
|
6821 | # # self.isConfig = False | |
| 6817 | # self.__powBuffer = None |
|
6822 | # self.__powBuffer = None | |
| 6818 | # self.__startIndex = 0 |
|
6823 | # self.__startIndex = 0 | |
| 6819 | # self.__pulseFound = False |
|
6824 | # self.__pulseFound = False | |
| 6820 | # |
|
6825 | # | |
| 6821 | # def __findTxPulse(self, dataOut, channel=0, pulse_with = None): |
|
6826 | # def __findTxPulse(self, dataOut, channel=0, pulse_with = None): | |
| 6822 | # |
|
6827 | # | |
| 6823 | # #Read data |
|
6828 | # #Read data | |
| 6824 | # |
|
6829 | # | |
| 6825 | # powerdB = dataOut.getPower(channel = channel) |
|
6830 | # powerdB = dataOut.getPower(channel = channel) | |
| 6826 | # noisedB = dataOut.getNoise(channel = channel)[0] |
|
6831 | # noisedB = dataOut.getNoise(channel = channel)[0] | |
| 6827 | # |
|
6832 | # | |
| 6828 | # self.__powBuffer.extend(powerdB.flatten()) |
|
6833 | # self.__powBuffer.extend(powerdB.flatten()) | |
| 6829 | # |
|
6834 | # | |
| 6830 | # dataArray = numpy.array(self.__powBuffer) |
|
6835 | # dataArray = numpy.array(self.__powBuffer) | |
| 6831 | # |
|
6836 | # | |
| 6832 | # filteredPower = numpy.correlate(dataArray, dataArray[0:self.__nSamples], "same") |
|
6837 | # filteredPower = numpy.correlate(dataArray, dataArray[0:self.__nSamples], "same") | |
| 6833 | # |
|
6838 | # | |
| 6834 | # maxValue = numpy.nanmax(filteredPower) |
|
6839 | # maxValue = numpy.nanmax(filteredPower) | |
| 6835 | # |
|
6840 | # | |
| 6836 | # if maxValue < noisedB + 10: |
|
6841 | # if maxValue < noisedB + 10: | |
| 6837 | # #No se encuentra ningun pulso de transmision |
|
6842 | # #No se encuentra ningun pulso de transmision | |
| 6838 | # return None |
|
6843 | # return None | |
| 6839 | # |
|
6844 | # | |
| 6840 | # maxValuesIndex = numpy.where(filteredPower > maxValue - 0.1*abs(maxValue))[0] |
|
6845 | # maxValuesIndex = numpy.where(filteredPower > maxValue - 0.1*abs(maxValue))[0] | |
| 6841 | # |
|
6846 | # | |
| 6842 | # if len(maxValuesIndex) < 2: |
|
6847 | # if len(maxValuesIndex) < 2: | |
| 6843 | # #Solo se encontro un solo pulso de transmision de un baudio, esperando por el siguiente TX |
|
6848 | # #Solo se encontro un solo pulso de transmision de un baudio, esperando por el siguiente TX | |
| 6844 | # return None |
|
6849 | # return None | |
| 6845 | # |
|
6850 | # | |
| 6846 | # phasedMaxValuesIndex = maxValuesIndex - self.__nSamples |
|
6851 | # phasedMaxValuesIndex = maxValuesIndex - self.__nSamples | |
| 6847 | # |
|
6852 | # | |
| 6848 | # #Seleccionar solo valores con un espaciamiento de nSamples |
|
6853 | # #Seleccionar solo valores con un espaciamiento de nSamples | |
| 6849 | # pulseIndex = numpy.intersect1d(maxValuesIndex, phasedMaxValuesIndex) |
|
6854 | # pulseIndex = numpy.intersect1d(maxValuesIndex, phasedMaxValuesIndex) | |
| 6850 | # |
|
6855 | # | |
| 6851 | # if len(pulseIndex) < 2: |
|
6856 | # if len(pulseIndex) < 2: | |
| 6852 | # #Solo se encontro un pulso de transmision con ancho mayor a 1 |
|
6857 | # #Solo se encontro un pulso de transmision con ancho mayor a 1 | |
| 6853 | # return None |
|
6858 | # return None | |
| 6854 | # |
|
6859 | # | |
| 6855 | # spacing = pulseIndex[1:] - pulseIndex[:-1] |
|
6860 | # spacing = pulseIndex[1:] - pulseIndex[:-1] | |
| 6856 | # |
|
6861 | # | |
| 6857 | # #remover senales que se distancien menos de 10 unidades o muestras |
|
6862 | # #remover senales que se distancien menos de 10 unidades o muestras | |
| 6858 | # #(No deberian existir IPP menor a 10 unidades) |
|
6863 | # #(No deberian existir IPP menor a 10 unidades) | |
| 6859 | # |
|
6864 | # | |
| 6860 | # realIndex = numpy.where(spacing > 10 )[0] |
|
6865 | # realIndex = numpy.where(spacing > 10 )[0] | |
| 6861 | # |
|
6866 | # | |
| 6862 | # if len(realIndex) < 2: |
|
6867 | # if len(realIndex) < 2: | |
| 6863 | # #Solo se encontro un pulso de transmision con ancho mayor a 1 |
|
6868 | # #Solo se encontro un pulso de transmision con ancho mayor a 1 | |
| 6864 | # return None |
|
6869 | # return None | |
| 6865 | # |
|
6870 | # | |
| 6866 | # #Eliminar pulsos anchos (deja solo la diferencia entre IPPs) |
|
6871 | # #Eliminar pulsos anchos (deja solo la diferencia entre IPPs) | |
| 6867 | # realPulseIndex = pulseIndex[realIndex] |
|
6872 | # realPulseIndex = pulseIndex[realIndex] | |
| 6868 | # |
|
6873 | # | |
| 6869 | # period = mode(realPulseIndex[1:] - realPulseIndex[:-1])[0][0] |
|
6874 | # period = mode(realPulseIndex[1:] - realPulseIndex[:-1])[0][0] | |
| 6870 | # |
|
6875 | # | |
| 6871 | # print "IPP = %d samples" %period |
|
6876 | # print "IPP = %d samples" %period | |
| 6872 | # |
|
6877 | # | |
| 6873 | # self.__newNSamples = dataOut.nHeights #int(period) |
|
6878 | # self.__newNSamples = dataOut.nHeights #int(period) | |
| 6874 | # self.__startIndex = int(realPulseIndex[0]) |
|
6879 | # self.__startIndex = int(realPulseIndex[0]) | |
| 6875 | # |
|
6880 | # | |
| 6876 | # return 1 |
|
6881 | # return 1 | |
| 6877 | # |
|
6882 | # | |
| 6878 | # |
|
6883 | # | |
| 6879 | # def setup(self, nSamples, nChannels, buffer_size = 4): |
|
6884 | # def setup(self, nSamples, nChannels, buffer_size = 4): | |
| 6880 | # |
|
6885 | # | |
| 6881 | # self.__powBuffer = collections.deque(numpy.zeros( buffer_size*nSamples,dtype=numpy.float), |
|
6886 | # self.__powBuffer = collections.deque(numpy.zeros( buffer_size*nSamples,dtype=numpy.float), | |
| 6882 | # maxlen = buffer_size*nSamples) |
|
6887 | # maxlen = buffer_size*nSamples) | |
| 6883 | # |
|
6888 | # | |
| 6884 | # bufferList = [] |
|
6889 | # bufferList = [] | |
| 6885 | # |
|
6890 | # | |
| 6886 | # for i in range(nChannels): |
|
6891 | # for i in range(nChannels): | |
| 6887 | # bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=complex) + numpy.NAN, |
|
6892 | # bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=complex) + numpy.NAN, | |
| 6888 | # maxlen = buffer_size*nSamples) |
|
6893 | # maxlen = buffer_size*nSamples) | |
| 6889 | # |
|
6894 | # | |
| 6890 | # bufferList.append(bufferByChannel) |
|
6895 | # bufferList.append(bufferByChannel) | |
| 6891 | # |
|
6896 | # | |
| 6892 | # self.__nSamples = nSamples |
|
6897 | # self.__nSamples = nSamples | |
| 6893 | # self.__nChannels = nChannels |
|
6898 | # self.__nChannels = nChannels | |
| 6894 | # self.__bufferList = bufferList |
|
6899 | # self.__bufferList = bufferList | |
| 6895 | # |
|
6900 | # | |
| 6896 | # def run(self, dataOut, channel = 0): |
|
6901 | # def run(self, dataOut, channel = 0): | |
| 6897 | # |
|
6902 | # | |
| 6898 | # if not self.isConfig: |
|
6903 | # if not self.isConfig: | |
| 6899 | # nSamples = dataOut.nHeights |
|
6904 | # nSamples = dataOut.nHeights | |
| 6900 | # nChannels = dataOut.nChannels |
|
6905 | # nChannels = dataOut.nChannels | |
| 6901 | # self.setup(nSamples, nChannels) |
|
6906 | # self.setup(nSamples, nChannels) | |
| 6902 | # self.isConfig = True |
|
6907 | # self.isConfig = True | |
| 6903 | # |
|
6908 | # | |
| 6904 | # #Append new data to internal buffer |
|
6909 | # #Append new data to internal buffer | |
| 6905 | # for thisChannel in range(self.__nChannels): |
|
6910 | # for thisChannel in range(self.__nChannels): | |
| 6906 | # bufferByChannel = self.__bufferList[thisChannel] |
|
6911 | # bufferByChannel = self.__bufferList[thisChannel] | |
| 6907 | # bufferByChannel.extend(dataOut.data[thisChannel]) |
|
6912 | # bufferByChannel.extend(dataOut.data[thisChannel]) | |
| 6908 | # |
|
6913 | # | |
| 6909 | # if self.__pulseFound: |
|
6914 | # if self.__pulseFound: | |
| 6910 | # self.__startIndex -= self.__nSamples |
|
6915 | # self.__startIndex -= self.__nSamples | |
| 6911 | # |
|
6916 | # | |
| 6912 | # #Finding Tx Pulse |
|
6917 | # #Finding Tx Pulse | |
| 6913 | # if not self.__pulseFound: |
|
6918 | # if not self.__pulseFound: | |
| 6914 | # indexFound = self.__findTxPulse(dataOut, channel) |
|
6919 | # indexFound = self.__findTxPulse(dataOut, channel) | |
| 6915 | # |
|
6920 | # | |
| 6916 | # if indexFound == None: |
|
6921 | # if indexFound == None: | |
| 6917 | # dataOut.flagNoData = True |
|
6922 | # dataOut.flagNoData = True | |
| 6918 | # return |
|
6923 | # return | |
| 6919 | # |
|
6924 | # | |
| 6920 | # self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = complex) |
|
6925 | # self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = complex) | |
| 6921 | # self.__pulseFound = True |
|
6926 | # self.__pulseFound = True | |
| 6922 | # self.__startIndex = indexFound |
|
6927 | # self.__startIndex = indexFound | |
| 6923 | # |
|
6928 | # | |
| 6924 | # #If pulse was found ... |
|
6929 | # #If pulse was found ... | |
| 6925 | # for thisChannel in range(self.__nChannels): |
|
6930 | # for thisChannel in range(self.__nChannels): | |
| 6926 | # bufferByChannel = self.__bufferList[thisChannel] |
|
6931 | # bufferByChannel = self.__bufferList[thisChannel] | |
| 6927 | # #print self.__startIndex |
|
6932 | # #print self.__startIndex | |
| 6928 | # x = numpy.array(bufferByChannel) |
|
6933 | # x = numpy.array(bufferByChannel) | |
| 6929 | # self.__arrayBuffer[thisChannel] = x[self.__startIndex:self.__startIndex+self.__newNSamples] |
|
6934 | # self.__arrayBuffer[thisChannel] = x[self.__startIndex:self.__startIndex+self.__newNSamples] | |
| 6930 | # |
|
6935 | # | |
| 6931 | # deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
6936 | # deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] | |
| 6932 | # dataOut.heightList = numpy.arange(self.__newNSamples)*deltaHeight |
|
6937 | # dataOut.heightList = numpy.arange(self.__newNSamples)*deltaHeight | |
| 6933 | # # dataOut.ippSeconds = (self.__newNSamples / deltaHeight)/1e6 |
|
6938 | # # dataOut.ippSeconds = (self.__newNSamples / deltaHeight)/1e6 | |
| 6934 | # |
|
6939 | # | |
| 6935 | # dataOut.data = self.__arrayBuffer |
|
6940 | # dataOut.data = self.__arrayBuffer | |
| 6936 | # |
|
6941 | # | |
| 6937 | # self.__startIndex += self.__newNSamples |
|
6942 | # self.__startIndex += self.__newNSamples | |
| 6938 | # |
|
6943 | # | |
| 6939 | # return |
|
6944 | # return | |
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