| @@ -1,3857 +1,4238 | |||
|
|
1 | 1 | import numpy |
|
|
2 | 2 | import math |
|
|
3 | 3 | from scipy import optimize, interpolate, signal, stats, ndimage |
|
|
4 | 4 | import scipy |
|
|
5 | 5 | import re |
|
|
6 | 6 | import datetime |
|
|
7 | 7 | import copy |
|
|
8 | 8 | import sys |
|
|
9 | 9 | import importlib |
|
|
10 | 10 | import itertools |
|
|
11 | 11 | from multiprocessing import Pool, TimeoutError |
|
|
12 | 12 | from multiprocessing.pool import ThreadPool |
|
|
13 | 13 | import time |
|
|
14 | 14 | |
|
|
15 | 15 | from scipy.optimize import fmin_l_bfgs_b #optimize with bounds on state papameters |
|
|
16 | 16 | from .jroproc_base import ProcessingUnit, Operation, MPDecorator |
|
|
17 | 17 | from schainpy.model.data.jrodata import Parameters, hildebrand_sekhon |
|
|
18 | 18 | from scipy import asarray as ar,exp |
|
|
19 | 19 | from scipy.optimize import curve_fit |
|
|
20 | 20 | from schainpy.utils import log |
|
|
21 | 21 | import warnings |
|
|
22 | 22 | from numpy import NaN |
|
|
23 | 23 | from scipy.optimize.optimize import OptimizeWarning |
|
|
24 | 24 | warnings.filterwarnings('ignore') |
|
|
25 | 25 | |
|
|
26 | import matplotlib.pyplot as plt | |
|
|
26 | 27 | |
|
|
27 | 28 | SPEED_OF_LIGHT = 299792458 |
|
|
28 | 29 | |
|
|
29 | 30 | |
|
|
30 | 31 | '''solving pickling issue''' |
|
|
31 | 32 | |
|
|
32 | 33 | def _pickle_method(method): |
|
|
33 | 34 | func_name = method.__func__.__name__ |
|
|
34 | 35 | obj = method.__self__ |
|
|
35 | 36 | cls = method.__self__.__class__ |
|
|
36 | 37 | return _unpickle_method, (func_name, obj, cls) |
|
|
37 | 38 | |
|
|
38 | 39 | def _unpickle_method(func_name, obj, cls): |
|
|
39 | 40 | for cls in cls.mro(): |
|
|
40 | 41 | try: |
|
|
41 | 42 | func = cls.__dict__[func_name] |
|
|
42 | 43 | except KeyError: |
|
|
43 | 44 | pass |
|
|
44 | 45 | else: |
|
|
45 | 46 | break |
|
|
46 | 47 | return func.__get__(obj, cls) |
|
|
47 | 48 | |
|
|
48 | 49 | @MPDecorator |
|
|
49 | 50 | class ParametersProc(ProcessingUnit): |
|
|
50 | 51 | |
|
|
51 | 52 | METHODS = {} |
|
|
52 | 53 | nSeconds = None |
|
|
53 | 54 | |
|
|
54 | 55 | def __init__(self): |
|
|
55 | 56 | ProcessingUnit.__init__(self) |
|
|
56 | 57 | |
|
|
57 | 58 | # self.objectDict = {} |
|
|
58 | 59 | self.buffer = None |
|
|
59 | 60 | self.firstdatatime = None |
|
|
60 | 61 | self.profIndex = 0 |
|
|
61 | 62 | self.dataOut = Parameters() |
|
|
62 | 63 | self.setupReq = False #Agregar a todas las unidades de proc |
|
|
63 | 64 | |
|
|
64 | 65 | def __updateObjFromInput(self): |
|
|
65 | 66 | |
|
|
66 | 67 | self.dataOut.inputUnit = self.dataIn.type |
|
|
67 | 68 | |
|
|
68 | 69 | self.dataOut.timeZone = self.dataIn.timeZone |
|
|
69 | 70 | self.dataOut.dstFlag = self.dataIn.dstFlag |
|
|
70 | 71 | self.dataOut.errorCount = self.dataIn.errorCount |
|
|
71 | 72 | self.dataOut.useLocalTime = self.dataIn.useLocalTime |
|
|
72 | 73 | |
|
|
73 | 74 | self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() |
|
|
74 | 75 | self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy() |
|
|
75 | 76 | self.dataOut.channelList = self.dataIn.channelList |
|
|
76 | 77 | self.dataOut.heightList = self.dataIn.heightList |
|
|
77 | 78 | self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')]) |
|
|
78 | 79 | # self.dataOut.nHeights = self.dataIn.nHeights |
|
|
79 | 80 | # self.dataOut.nChannels = self.dataIn.nChannels |
|
|
80 | 81 | self.dataOut.nBaud = self.dataIn.nBaud |
|
|
81 | 82 | self.dataOut.nCode = self.dataIn.nCode |
|
|
82 | 83 | self.dataOut.code = self.dataIn.code |
|
|
83 | 84 | # self.dataOut.nProfiles = self.dataOut.nFFTPoints |
|
|
84 | 85 | self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock |
|
|
85 | 86 | # self.dataOut.utctime = self.firstdatatime |
|
|
86 | 87 | self.dataOut.utctime = self.dataIn.utctime |
|
|
87 | 88 | self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada |
|
|
88 | 89 | self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip |
|
|
89 | 90 | self.dataOut.nCohInt = self.dataIn.nCohInt |
|
|
90 | 91 | # self.dataOut.nIncohInt = 1 |
|
|
91 | 92 | self.dataOut.ippSeconds = self.dataIn.ippSeconds |
|
|
92 | 93 | # self.dataOut.windowOfFilter = self.dataIn.windowOfFilter |
|
|
93 | 94 | self.dataOut.timeInterval1 = self.dataIn.timeInterval |
|
|
94 | 95 | self.dataOut.heightList = self.dataIn.getHeiRange() |
|
|
95 | 96 | self.dataOut.frequency = self.dataIn.frequency |
|
|
96 | 97 | # self.dataOut.noise = self.dataIn.noise |
|
|
97 | 98 | |
|
|
98 | 99 | def run(self): |
|
|
99 | 100 | |
|
|
100 | 101 | |
|
|
101 | 102 | |
|
|
102 | 103 | #---------------------- Voltage Data --------------------------- |
|
|
103 | 104 | |
|
|
104 | 105 | if self.dataIn.type == "Voltage": |
|
|
105 | 106 | |
|
|
106 | 107 | self.__updateObjFromInput() |
|
|
107 | 108 | self.dataOut.data_pre = self.dataIn.data.copy() |
|
|
108 | 109 | self.dataOut.flagNoData = False |
|
|
109 | 110 | self.dataOut.utctimeInit = self.dataIn.utctime |
|
|
110 | 111 | self.dataOut.paramInterval = self.dataIn.nProfiles*self.dataIn.nCohInt*self.dataIn.ippSeconds |
|
|
111 | 112 | return |
|
|
112 | 113 | |
|
|
113 | 114 | #---------------------- Spectra Data --------------------------- |
|
|
114 | 115 | |
|
|
115 | 116 | if self.dataIn.type == "Spectra": |
|
|
116 | 117 | |
|
|
117 | 118 | self.dataOut.data_pre = (self.dataIn.data_spc, self.dataIn.data_cspc) |
|
|
118 | 119 | self.dataOut.data_spc = self.dataIn.data_spc |
|
|
119 | 120 | self.dataOut.data_cspc = self.dataIn.data_cspc |
|
|
120 | 121 | self.dataOut.nProfiles = self.dataIn.nProfiles |
|
|
121 | 122 | self.dataOut.nIncohInt = self.dataIn.nIncohInt |
|
|
122 | 123 | self.dataOut.nFFTPoints = self.dataIn.nFFTPoints |
|
|
123 | 124 | self.dataOut.ippFactor = self.dataIn.ippFactor |
|
|
124 | 125 | self.dataOut.abscissaList = self.dataIn.getVelRange(1) |
|
|
125 | 126 | self.dataOut.spc_noise = self.dataIn.getNoise() |
|
|
126 | 127 | self.dataOut.spc_range = (self.dataIn.getFreqRange(1) , self.dataIn.getAcfRange(1) , self.dataIn.getVelRange(1)) |
|
|
127 | 128 | # self.dataOut.normFactor = self.dataIn.normFactor |
|
|
128 | 129 | self.dataOut.pairsList = self.dataIn.pairsList |
|
|
129 | 130 | self.dataOut.groupList = self.dataIn.pairsList |
|
|
130 | 131 | self.dataOut.flagNoData = False |
|
|
131 | 132 | |
|
|
132 | 133 | if hasattr(self.dataIn, 'ChanDist'): #Distances of receiver channels |
|
|
133 | 134 | self.dataOut.ChanDist = self.dataIn.ChanDist |
|
|
134 | 135 | else: self.dataOut.ChanDist = None |
|
|
135 | 136 | |
|
|
136 | 137 | #if hasattr(self.dataIn, 'VelRange'): #Velocities range |
|
|
137 | 138 | # self.dataOut.VelRange = self.dataIn.VelRange |
|
|
138 | 139 | #else: self.dataOut.VelRange = None |
|
|
139 | 140 | |
|
|
140 | 141 | if hasattr(self.dataIn, 'RadarConst'): #Radar Constant |
|
|
141 | 142 | self.dataOut.RadarConst = self.dataIn.RadarConst |
|
|
142 | 143 | |
|
|
143 | 144 | if hasattr(self.dataIn, 'NPW'): #NPW |
|
|
144 | 145 | self.dataOut.NPW = self.dataIn.NPW |
|
|
145 | 146 | |
|
|
146 | 147 | if hasattr(self.dataIn, 'COFA'): #COFA |
|
|
147 | 148 | self.dataOut.COFA = self.dataIn.COFA |
|
|
148 | 149 | |
|
|
149 | 150 | |
|
|
150 | 151 | |
|
|
151 | 152 | #---------------------- Correlation Data --------------------------- |
|
|
152 | 153 | |
|
|
153 | 154 | if self.dataIn.type == "Correlation": |
|
|
154 | 155 | acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf = self.dataIn.splitFunctions() |
|
|
155 | 156 | |
|
|
156 | 157 | self.dataOut.data_pre = (self.dataIn.data_cf[acf_ind,:], self.dataIn.data_cf[ccf_ind,:,:]) |
|
|
157 | 158 | self.dataOut.normFactor = (self.dataIn.normFactor[acf_ind,:], self.dataIn.normFactor[ccf_ind,:]) |
|
|
158 | 159 | self.dataOut.groupList = (acf_pairs, ccf_pairs) |
|
|
159 | 160 | |
|
|
160 | 161 | self.dataOut.abscissaList = self.dataIn.lagRange |
|
|
161 | 162 | self.dataOut.noise = self.dataIn.noise |
|
|
162 | 163 | self.dataOut.data_SNR = self.dataIn.SNR |
|
|
163 | 164 | self.dataOut.flagNoData = False |
|
|
164 | 165 | self.dataOut.nAvg = self.dataIn.nAvg |
|
|
165 | 166 | |
|
|
166 | 167 | #---------------------- Parameters Data --------------------------- |
|
|
167 | 168 | |
|
|
168 | 169 | if self.dataIn.type == "Parameters": |
|
|
169 | 170 | self.dataOut.copy(self.dataIn) |
|
|
170 | 171 | self.dataOut.flagNoData = False |
|
|
171 | 172 | |
|
|
172 | 173 | return True |
|
|
173 | 174 | |
|
|
174 | 175 | self.__updateObjFromInput() |
|
|
175 | 176 | self.dataOut.utctimeInit = self.dataIn.utctime |
|
|
176 | 177 | self.dataOut.paramInterval = self.dataIn.timeInterval |
|
|
177 | 178 | |
|
|
178 | 179 | return |
|
|
179 | 180 | |
|
|
180 | 181 | |
|
|
181 | 182 | def target(tups): |
|
|
182 | 183 | |
|
|
183 | 184 | obj, args = tups |
|
|
184 | 185 | |
|
|
185 | 186 | return obj.FitGau(args) |
|
|
186 | 187 | |
|
|
187 | 188 | |
|
|
188 | 189 | class SpectralFilters(Operation): |
|
|
189 | 190 | |
|
|
190 | 191 | '''This class allows the Rainfall / Wind Selection for CLAIRE RADAR |
|
|
191 | 192 | |
|
|
192 | 193 | LimitR : It is the limit in m/s of Rainfall |
|
|
193 | 194 | LimitW : It is the limit in m/s for Winds |
|
|
194 | 195 | |
|
|
195 | 196 | Input: |
|
|
196 | 197 | |
|
|
197 | 198 | self.dataOut.data_pre : SPC and CSPC |
|
|
198 | 199 | self.dataOut.spc_range : To select wind and rainfall velocities |
|
|
199 | 200 | |
|
|
200 | 201 | Affected: |
|
|
201 | 202 | |
|
|
202 | 203 | self.dataOut.data_pre : It is used for the new SPC and CSPC ranges of wind |
|
|
203 | 204 | self.dataOut.spcparam_range : Used in SpcParamPlot |
|
|
204 | 205 | self.dataOut.SPCparam : Used in PrecipitationProc |
|
|
205 | 206 | |
|
|
206 | 207 | |
|
|
207 | 208 | ''' |
|
|
208 | 209 | |
|
|
209 | 210 | def __init__(self): |
|
|
210 | 211 | Operation.__init__(self) |
|
|
211 | 212 | self.i=0 |
|
|
212 | 213 | |
|
|
213 | 214 | def run(self, dataOut, PositiveLimit=1.5, NegativeLimit=2.5): |
|
|
214 | 215 | |
|
|
215 | 216 | |
|
|
216 | 217 | #Limite de vientos |
|
|
217 | 218 | LimitR = PositiveLimit |
|
|
218 | 219 | LimitN = NegativeLimit |
|
|
219 | 220 | |
|
|
220 | 221 | self.spc = dataOut.data_pre[0].copy() |
|
|
221 | 222 | self.cspc = dataOut.data_pre[1].copy() |
|
|
222 | 223 | |
|
|
223 | 224 | self.Num_Hei = self.spc.shape[2] |
|
|
224 | 225 | self.Num_Bin = self.spc.shape[1] |
|
|
225 | 226 | self.Num_Chn = self.spc.shape[0] |
|
|
226 | 227 | |
|
|
227 | 228 | VelRange = dataOut.spc_range[2] |
|
|
228 | 229 | TimeRange = dataOut.spc_range[1] |
|
|
229 | 230 | FrecRange = dataOut.spc_range[0] |
|
|
230 | 231 | |
|
|
231 | 232 | Vmax= 2*numpy.max(dataOut.spc_range[2]) |
|
|
232 | 233 | Tmax= 2*numpy.max(dataOut.spc_range[1]) |
|
|
233 | 234 | Fmax= 2*numpy.max(dataOut.spc_range[0]) |
|
|
234 | 235 | |
|
|
235 | 236 | Breaker1R=VelRange[numpy.abs(VelRange-(-LimitN)).argmin()] |
|
|
236 | 237 | Breaker1R=numpy.where(VelRange == Breaker1R) |
|
|
237 | 238 | |
|
|
238 | 239 | Delta = self.Num_Bin/2 - Breaker1R[0] |
|
|
239 | 240 | |
|
|
240 | 241 | |
|
|
241 | 242 | '''Reacomodando SPCrange''' |
|
|
242 | 243 | |
|
|
243 | 244 | VelRange=numpy.roll(VelRange,-(int(self.Num_Bin/2)) ,axis=0) |
|
|
244 | 245 | |
|
|
245 | 246 | VelRange[-(int(self.Num_Bin/2)):]+= Vmax |
|
|
246 | 247 | |
|
|
247 | 248 | FrecRange=numpy.roll(FrecRange,-(int(self.Num_Bin/2)),axis=0) |
|
|
248 | 249 | |
|
|
249 | 250 | FrecRange[-(int(self.Num_Bin/2)):]+= Fmax |
|
|
250 | 251 | |
|
|
251 | 252 | TimeRange=numpy.roll(TimeRange,-(int(self.Num_Bin/2)),axis=0) |
|
|
252 | 253 | |
|
|
253 | 254 | TimeRange[-(int(self.Num_Bin/2)):]+= Tmax |
|
|
254 | 255 | |
|
|
255 | 256 | ''' ------------------ ''' |
|
|
256 | 257 | |
|
|
257 | 258 | Breaker2R=VelRange[numpy.abs(VelRange-(LimitR)).argmin()] |
|
|
258 | 259 | Breaker2R=numpy.where(VelRange == Breaker2R) |
|
|
259 | 260 | |
|
|
260 | 261 | |
|
|
261 | 262 | SPCroll = numpy.roll(self.spc,-(int(self.Num_Bin/2)) ,axis=1) |
|
|
262 | 263 | |
|
|
263 | 264 | SPCcut = SPCroll.copy() |
|
|
264 | 265 | for i in range(self.Num_Chn): |
|
|
265 | 266 | |
|
|
266 | 267 | SPCcut[i,0:int(Breaker2R[0]),:] = dataOut.noise[i] |
|
|
267 | 268 | SPCcut[i,-int(Delta):,:] = dataOut.noise[i] |
|
|
268 | 269 | |
|
|
269 | 270 | SPCcut[i]=SPCcut[i]- dataOut.noise[i] |
|
|
270 | 271 | SPCcut[ numpy.where( SPCcut<0 ) ] = 1e-20 |
|
|
271 | 272 | |
|
|
272 | 273 | SPCroll[i]=SPCroll[i]-dataOut.noise[i] |
|
|
273 | 274 | SPCroll[ numpy.where( SPCroll<0 ) ] = 1e-20 |
|
|
274 | 275 | |
|
|
275 | 276 | SPC_ch1 = SPCroll |
|
|
276 | 277 | |
|
|
277 | 278 | SPC_ch2 = SPCcut |
|
|
278 | 279 | |
|
|
279 | 280 | SPCparam = (SPC_ch1, SPC_ch2, self.spc) |
|
|
280 | 281 | dataOut.SPCparam = numpy.asarray(SPCparam) |
|
|
281 | 282 | |
|
|
282 | 283 | |
|
|
283 | 284 | dataOut.spcparam_range=numpy.zeros([self.Num_Chn,self.Num_Bin+1]) |
|
|
284 | 285 | |
|
|
285 | 286 | dataOut.spcparam_range[2]=VelRange |
|
|
286 | 287 | dataOut.spcparam_range[1]=TimeRange |
|
|
287 | 288 | dataOut.spcparam_range[0]=FrecRange |
|
|
288 | 289 | return dataOut |
|
|
289 | 290 | |
|
|
290 | 291 | class GaussianFit(Operation): |
|
|
291 | 292 | |
|
|
292 | 293 | ''' |
|
|
293 | 294 | Function that fit of one and two generalized gaussians (gg) based |
|
|
294 | 295 | on the PSD shape across an "power band" identified from a cumsum of |
|
|
295 | 296 | the measured spectrum - noise. |
|
|
296 | 297 | |
|
|
297 | 298 | Input: |
|
|
298 | 299 | self.dataOut.data_pre : SelfSpectra |
|
|
299 | 300 | |
|
|
300 | 301 | Output: |
|
|
301 | 302 | self.dataOut.SPCparam : SPC_ch1, SPC_ch2 |
|
|
302 | 303 | |
|
|
303 | 304 | ''' |
|
|
304 | 305 | def __init__(self): |
|
|
305 | 306 | Operation.__init__(self) |
|
|
306 | 307 | self.i=0 |
|
|
307 | 308 | |
|
|
308 | 309 | |
|
|
309 | 310 | def run(self, dataOut, num_intg=7, pnoise=1., SNRlimit=-9): #num_intg: Incoherent integrations, pnoise: Noise, vel_arr: range of velocities, similar to the ftt points |
|
|
310 | 311 | """This routine will find a couple of generalized Gaussians to a power spectrum |
|
|
311 | 312 | input: spc |
|
|
312 | 313 | output: |
|
|
313 | 314 | Amplitude0,shift0,width0,p0,Amplitude1,shift1,width1,p1,noise |
|
|
314 | 315 | """ |
|
|
315 | 316 | |
|
|
316 | 317 | self.spc = dataOut.data_pre[0].copy() |
|
|
317 | 318 | self.Num_Hei = self.spc.shape[2] |
|
|
318 | 319 | self.Num_Bin = self.spc.shape[1] |
|
|
319 | 320 | self.Num_Chn = self.spc.shape[0] |
|
|
320 | 321 | Vrange = dataOut.abscissaList |
|
|
321 | 322 | |
|
|
322 | 323 | GauSPC = numpy.empty([self.Num_Chn,self.Num_Bin,self.Num_Hei]) |
|
|
323 | 324 | SPC_ch1 = numpy.empty([self.Num_Bin,self.Num_Hei]) |
|
|
324 | 325 | SPC_ch2 = numpy.empty([self.Num_Bin,self.Num_Hei]) |
|
|
325 | 326 | SPC_ch1[:] = numpy.NaN |
|
|
326 | 327 | SPC_ch2[:] = numpy.NaN |
|
|
327 | 328 | |
|
|
328 | 329 | |
|
|
329 | 330 | start_time = time.time() |
|
|
330 | 331 | |
|
|
331 | 332 | noise_ = dataOut.spc_noise[0].copy() |
|
|
332 | 333 | |
|
|
333 | 334 | |
|
|
334 | 335 | pool = Pool(processes=self.Num_Chn) |
|
|
335 | 336 | args = [(Vrange, Ch, pnoise, noise_, num_intg, SNRlimit) for Ch in range(self.Num_Chn)] |
|
|
336 | 337 | objs = [self for __ in range(self.Num_Chn)] |
|
|
337 | 338 | attrs = list(zip(objs, args)) |
|
|
338 | 339 | gauSPC = pool.map(target, attrs) |
|
|
339 | 340 | dataOut.SPCparam = numpy.asarray(SPCparam) |
|
|
340 | 341 | |
|
|
341 | 342 | ''' Parameters: |
|
|
342 | 343 | 1. Amplitude |
|
|
343 | 344 | 2. Shift |
|
|
344 | 345 | 3. Width |
|
|
345 | 346 | 4. Power |
|
|
346 | 347 | ''' |
|
|
347 | 348 | |
|
|
348 | 349 | def FitGau(self, X): |
|
|
349 | 350 | |
|
|
350 | 351 | Vrange, ch, pnoise, noise_, num_intg, SNRlimit = X |
|
|
351 | 352 | |
|
|
352 | 353 | SPCparam = [] |
|
|
353 | 354 | SPC_ch1 = numpy.empty([self.Num_Bin,self.Num_Hei]) |
|
|
354 | 355 | SPC_ch2 = numpy.empty([self.Num_Bin,self.Num_Hei]) |
|
|
355 | 356 | SPC_ch1[:] = 0#numpy.NaN |
|
|
356 | 357 | SPC_ch2[:] = 0#numpy.NaN |
|
|
357 | 358 | |
|
|
358 | 359 | |
|
|
359 | 360 | |
|
|
360 | 361 | for ht in range(self.Num_Hei): |
|
|
361 | 362 | |
|
|
362 | 363 | |
|
|
363 | 364 | spc = numpy.asarray(self.spc)[ch,:,ht] |
|
|
364 | 365 | |
|
|
365 | 366 | ############################################# |
|
|
366 | 367 | # normalizing spc and noise |
|
|
367 | 368 | # This part differs from gg1 |
|
|
368 | 369 | spc_norm_max = max(spc) |
|
|
369 | 370 | #spc = spc / spc_norm_max |
|
|
370 | 371 | pnoise = pnoise #/ spc_norm_max |
|
|
371 | 372 | ############################################# |
|
|
372 | 373 | |
|
|
373 | 374 | fatspectra=1.0 |
|
|
374 | 375 | |
|
|
375 | 376 | wnoise = noise_ #/ spc_norm_max |
|
|
376 | 377 | #wnoise,stdv,i_max,index =enoise(spc,num_intg) #noise estimate using Hildebrand Sekhon, only wnoise is used |
|
|
377 | 378 | #if wnoise>1.1*pnoise: # to be tested later |
|
|
378 | 379 | # wnoise=pnoise |
|
|
379 | 380 | noisebl=wnoise*0.9; |
|
|
380 | 381 | noisebh=wnoise*1.1 |
|
|
381 | 382 | spc=spc-wnoise |
|
|
382 | 383 | |
|
|
383 | 384 | minx=numpy.argmin(spc) |
|
|
384 | 385 | #spcs=spc.copy() |
|
|
385 | 386 | spcs=numpy.roll(spc,-minx) |
|
|
386 | 387 | cum=numpy.cumsum(spcs) |
|
|
387 | 388 | tot_noise=wnoise * self.Num_Bin #64; |
|
|
388 | 389 | |
|
|
389 | 390 | snr = sum(spcs)/tot_noise |
|
|
390 | 391 | snrdB=10.*numpy.log10(snr) |
|
|
391 | 392 | |
|
|
392 | 393 | if snrdB < SNRlimit : |
|
|
393 | 394 | snr = numpy.NaN |
|
|
394 | 395 | SPC_ch1[:,ht] = 0#numpy.NaN |
|
|
395 | 396 | SPC_ch1[:,ht] = 0#numpy.NaN |
|
|
396 | 397 | SPCparam = (SPC_ch1,SPC_ch2) |
|
|
397 | 398 | continue |
|
|
398 | 399 | |
|
|
399 | 400 | |
|
|
400 | 401 | #if snrdB<-18 or numpy.isnan(snrdB) or num_intg<4: |
|
|
401 | 402 | # return [None,]*4,[None,]*4,None,snrdB,None,None,[None,]*5,[None,]*9,None |
|
|
402 | 403 | |
|
|
403 | 404 | cummax=max(cum); |
|
|
404 | 405 | epsi=0.08*fatspectra # cumsum to narrow down the energy region |
|
|
405 | 406 | cumlo=cummax*epsi; |
|
|
406 | 407 | cumhi=cummax*(1-epsi) |
|
|
407 | 408 | powerindex=numpy.array(numpy.where(numpy.logical_and(cum>cumlo, cum<cumhi))[0]) |
|
|
408 | 409 | |
|
|
409 | 410 | |
|
|
410 | 411 | if len(powerindex) < 1:# case for powerindex 0 |
|
|
411 | 412 | continue |
|
|
412 | 413 | powerlo=powerindex[0] |
|
|
413 | 414 | powerhi=powerindex[-1] |
|
|
414 | 415 | powerwidth=powerhi-powerlo |
|
|
415 | 416 | |
|
|
416 | 417 | firstpeak=powerlo+powerwidth/10.# first gaussian energy location |
|
|
417 | 418 | secondpeak=powerhi-powerwidth/10.#second gaussian energy location |
|
|
418 | 419 | midpeak=(firstpeak+secondpeak)/2. |
|
|
419 | 420 | firstamp=spcs[int(firstpeak)] |
|
|
420 | 421 | secondamp=spcs[int(secondpeak)] |
|
|
421 | 422 | midamp=spcs[int(midpeak)] |
|
|
422 | 423 | |
|
|
423 | 424 | x=numpy.arange( self.Num_Bin ) |
|
|
424 | 425 | y_data=spc+wnoise |
|
|
425 | 426 | |
|
|
426 | 427 | ''' single Gaussian ''' |
|
|
427 | 428 | shift0=numpy.mod(midpeak+minx, self.Num_Bin ) |
|
|
428 | 429 | width0=powerwidth/4.#Initialization entire power of spectrum divided by 4 |
|
|
429 | 430 | power0=2. |
|
|
430 | 431 | amplitude0=midamp |
|
|
431 | 432 | state0=[shift0,width0,amplitude0,power0,wnoise] |
|
|
432 | 433 | bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth),(0,None),(0.5,3.),(noisebl,noisebh)) |
|
|
433 | 434 | lsq1=fmin_l_bfgs_b(self.misfit1,state0,args=(y_data,x,num_intg),bounds=bnds,approx_grad=True) |
|
|
434 | 435 | |
|
|
435 | 436 | chiSq1=lsq1[1]; |
|
|
436 | 437 | |
|
|
437 | 438 | |
|
|
438 | 439 | if fatspectra<1.0 and powerwidth<4: |
|
|
439 | 440 | choice=0 |
|
|
440 | 441 | Amplitude0=lsq1[0][2] |
|
|
441 | 442 | shift0=lsq1[0][0] |
|
|
442 | 443 | width0=lsq1[0][1] |
|
|
443 | 444 | p0=lsq1[0][3] |
|
|
444 | 445 | Amplitude1=0. |
|
|
445 | 446 | shift1=0. |
|
|
446 | 447 | width1=0. |
|
|
447 | 448 | p1=0. |
|
|
448 | 449 | noise=lsq1[0][4] |
|
|
449 | 450 | #return (numpy.array([shift0,width0,Amplitude0,p0]), |
|
|
450 | 451 | # numpy.array([shift1,width1,Amplitude1,p1]),noise,snrdB,chiSq1,6.,sigmas1,[None,]*9,choice) |
|
|
451 | 452 | |
|
|
452 | 453 | ''' two gaussians ''' |
|
|
453 | 454 | #shift0=numpy.mod(firstpeak+minx,64); shift1=numpy.mod(secondpeak+minx,64) |
|
|
454 | 455 | shift0=numpy.mod(firstpeak+minx, self.Num_Bin ); |
|
|
455 | 456 | shift1=numpy.mod(secondpeak+minx, self.Num_Bin ) |
|
|
456 | 457 | width0=powerwidth/6.; |
|
|
457 | 458 | width1=width0 |
|
|
458 | 459 | power0=2.; |
|
|
459 | 460 | power1=power0 |
|
|
460 | 461 | amplitude0=firstamp; |
|
|
461 | 462 | amplitude1=secondamp |
|
|
462 | 463 | state0=[shift0,width0,amplitude0,power0,shift1,width1,amplitude1,power1,wnoise] |
|
|
463 | 464 | #bnds=((0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(noisebl,noisebh)) |
|
|
464 | 465 | bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth/2.),(0,None),(0.5,3.),( 0,(self.Num_Bin-1)),(1,powerwidth/2.),(0,None),(0.5,3.),(noisebl,noisebh)) |
|
|
465 | 466 | #bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth/2.),(0,None),(0.5,3.),( 0,(self.Num_Bin-1)),(1,powerwidth/2.),(0,None),(0.5,3.),(0.1,0.5)) |
|
|
466 | 467 | |
|
|
467 | 468 | lsq2 = fmin_l_bfgs_b( self.misfit2 , state0 , args=(y_data,x,num_intg) , bounds=bnds , approx_grad=True ) |
|
|
468 | 469 | |
|
|
469 | 470 | |
|
|
470 | 471 | chiSq2=lsq2[1]; |
|
|
471 | 472 | |
|
|
472 | 473 | |
|
|
473 | 474 | |
|
|
474 | 475 | oneG=(chiSq1<5 and chiSq1/chiSq2<2.0) and (abs(lsq2[0][0]-lsq2[0][4])<(lsq2[0][1]+lsq2[0][5])/3. or abs(lsq2[0][0]-lsq2[0][4])<10) |
|
|
475 | 476 | |
|
|
476 | 477 | if snrdB>-12: # when SNR is strong pick the peak with least shift (LOS velocity) error |
|
|
477 | 478 | if oneG: |
|
|
478 | 479 | choice=0 |
|
|
479 | 480 | else: |
|
|
480 | 481 | w1=lsq2[0][1]; w2=lsq2[0][5] |
|
|
481 | 482 | a1=lsq2[0][2]; a2=lsq2[0][6] |
|
|
482 | 483 | p1=lsq2[0][3]; p2=lsq2[0][7] |
|
|
483 | 484 | s1=(2**(1+1./p1))*scipy.special.gamma(1./p1)/p1; |
|
|
484 | 485 | s2=(2**(1+1./p2))*scipy.special.gamma(1./p2)/p2; |
|
|
485 | 486 | gp1=a1*w1*s1; gp2=a2*w2*s2 # power content of each ggaussian with proper p scaling |
|
|
486 | 487 | |
|
|
487 | 488 | if gp1>gp2: |
|
|
488 | 489 | if a1>0.7*a2: |
|
|
489 | 490 | choice=1 |
|
|
490 | 491 | else: |
|
|
491 | 492 | choice=2 |
|
|
492 | 493 | elif gp2>gp1: |
|
|
493 | 494 | if a2>0.7*a1: |
|
|
494 | 495 | choice=2 |
|
|
495 | 496 | else: |
|
|
496 | 497 | choice=1 |
|
|
497 | 498 | else: |
|
|
498 | 499 | choice=numpy.argmax([a1,a2])+1 |
|
|
499 | 500 | #else: |
|
|
500 | 501 | #choice=argmin([std2a,std2b])+1 |
|
|
501 | 502 | |
|
|
502 | 503 | else: # with low SNR go to the most energetic peak |
|
|
503 | 504 | choice=numpy.argmax([lsq1[0][2]*lsq1[0][1],lsq2[0][2]*lsq2[0][1],lsq2[0][6]*lsq2[0][5]]) |
|
|
504 | 505 | |
|
|
505 | 506 | |
|
|
506 | 507 | shift0=lsq2[0][0]; |
|
|
507 | 508 | vel0=Vrange[0] + shift0*(Vrange[1]-Vrange[0]) |
|
|
508 | 509 | shift1=lsq2[0][4]; |
|
|
509 | 510 | vel1=Vrange[0] + shift1*(Vrange[1]-Vrange[0]) |
|
|
510 | 511 | |
|
|
511 | 512 | max_vel = 1.0 |
|
|
512 | 513 | |
|
|
513 | 514 | #first peak will be 0, second peak will be 1 |
|
|
514 | 515 | if vel0 > -1.0 and vel0 < max_vel : #first peak is in the correct range |
|
|
515 | 516 | shift0=lsq2[0][0] |
|
|
516 | 517 | width0=lsq2[0][1] |
|
|
517 | 518 | Amplitude0=lsq2[0][2] |
|
|
518 | 519 | p0=lsq2[0][3] |
|
|
519 | 520 | |
|
|
520 | 521 | shift1=lsq2[0][4] |
|
|
521 | 522 | width1=lsq2[0][5] |
|
|
522 | 523 | Amplitude1=lsq2[0][6] |
|
|
523 | 524 | p1=lsq2[0][7] |
|
|
524 | 525 | noise=lsq2[0][8] |
|
|
525 | 526 | else: |
|
|
526 | 527 | shift1=lsq2[0][0] |
|
|
527 | 528 | width1=lsq2[0][1] |
|
|
528 | 529 | Amplitude1=lsq2[0][2] |
|
|
529 | 530 | p1=lsq2[0][3] |
|
|
530 | 531 | |
|
|
531 | 532 | shift0=lsq2[0][4] |
|
|
532 | 533 | width0=lsq2[0][5] |
|
|
533 | 534 | Amplitude0=lsq2[0][6] |
|
|
534 | 535 | p0=lsq2[0][7] |
|
|
535 | 536 | noise=lsq2[0][8] |
|
|
536 | 537 | |
|
|
537 | 538 | if Amplitude0<0.05: # in case the peak is noise |
|
|
538 | 539 | shift0,width0,Amplitude0,p0 = [0,0,0,0]#4*[numpy.NaN] |
|
|
539 | 540 | if Amplitude1<0.05: |
|
|
540 | 541 | shift1,width1,Amplitude1,p1 = [0,0,0,0]#4*[numpy.NaN] |
|
|
541 | 542 | |
|
|
542 | 543 | |
|
|
543 | 544 | SPC_ch1[:,ht] = noise + Amplitude0*numpy.exp(-0.5*(abs(x-shift0))/width0)**p0 |
|
|
544 | 545 | SPC_ch2[:,ht] = noise + Amplitude1*numpy.exp(-0.5*(abs(x-shift1))/width1)**p1 |
|
|
545 | 546 | SPCparam = (SPC_ch1,SPC_ch2) |
|
|
546 | 547 | |
|
|
547 | 548 | |
|
|
548 | 549 | return GauSPC |
|
|
549 | 550 | |
|
|
550 | 551 | def y_model1(self,x,state): |
|
|
551 | 552 | shift0,width0,amplitude0,power0,noise=state |
|
|
552 | 553 | model0=amplitude0*numpy.exp(-0.5*abs((x-shift0)/width0)**power0) |
|
|
553 | 554 | |
|
|
554 | 555 | model0u=amplitude0*numpy.exp(-0.5*abs((x-shift0- self.Num_Bin )/width0)**power0) |
|
|
555 | 556 | |
|
|
556 | 557 | model0d=amplitude0*numpy.exp(-0.5*abs((x-shift0+ self.Num_Bin )/width0)**power0) |
|
|
557 | 558 | return model0+model0u+model0d+noise |
|
|
558 | 559 | |
|
|
559 | 560 | def y_model2(self,x,state): #Equation for two generalized Gaussians with Nyquist |
|
|
560 | 561 | shift0,width0,amplitude0,power0,shift1,width1,amplitude1,power1,noise=state |
|
|
561 | 562 | model0=amplitude0*numpy.exp(-0.5*abs((x-shift0)/width0)**power0) |
|
|
562 | 563 | |
|
|
563 | 564 | model0u=amplitude0*numpy.exp(-0.5*abs((x-shift0- self.Num_Bin )/width0)**power0) |
|
|
564 | 565 | |
|
|
565 | 566 | model0d=amplitude0*numpy.exp(-0.5*abs((x-shift0+ self.Num_Bin )/width0)**power0) |
|
|
566 | 567 | model1=amplitude1*numpy.exp(-0.5*abs((x-shift1)/width1)**power1) |
|
|
567 | 568 | |
|
|
568 | 569 | model1u=amplitude1*numpy.exp(-0.5*abs((x-shift1- self.Num_Bin )/width1)**power1) |
|
|
569 | 570 | |
|
|
570 | 571 | model1d=amplitude1*numpy.exp(-0.5*abs((x-shift1+ self.Num_Bin )/width1)**power1) |
|
|
571 | 572 | return model0+model0u+model0d+model1+model1u+model1d+noise |
|
|
572 | 573 | |
|
|
573 | 574 | def misfit1(self,state,y_data,x,num_intg): # This function compares how close real data is with the model data, the close it is, the better it is. |
|
|
574 | 575 | |
|
|
575 | 576 | return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model1(x,state)))**2)#/(64-5.) # /(64-5.) can be commented |
|
|
576 | 577 | |
|
|
577 | 578 | def misfit2(self,state,y_data,x,num_intg): |
|
|
578 | 579 | return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model2(x,state)))**2)#/(64-9.) |
|
|
579 | 580 | |
|
|
580 | 581 | |
|
|
581 | 582 | |
|
|
582 | 583 | class PrecipitationProc(Operation): |
|
|
583 | 584 | |
|
|
584 | 585 | ''' |
|
|
585 | 586 | Operator that estimates Reflectivity factor (Z), and estimates rainfall Rate (R) |
|
|
586 | 587 | |
|
|
587 | 588 | Input: |
|
|
588 | 589 | self.dataOut.data_pre : SelfSpectra |
|
|
589 | 590 | |
|
|
590 | 591 | Output: |
|
|
591 | 592 | |
|
|
592 | 593 | self.dataOut.data_output : Reflectivity factor, rainfall Rate |
|
|
593 | 594 | |
|
|
594 | 595 | |
|
|
595 | 596 | Parameters affected: |
|
|
596 | 597 | ''' |
|
|
597 | 598 | |
|
|
598 | 599 | def __init__(self): |
|
|
599 | 600 | Operation.__init__(self) |
|
|
600 | 601 | self.i=0 |
|
|
601 | 602 | |
|
|
602 | 603 | |
|
|
603 | 604 | def gaus(self,xSamples,Amp,Mu,Sigma): |
|
|
604 | 605 | return ( Amp / ((2*numpy.pi)**0.5 * Sigma) ) * numpy.exp( -( xSamples - Mu )**2 / ( 2 * (Sigma**2) )) |
|
|
605 | 606 | |
|
|
606 | 607 | |
|
|
607 | 608 | |
|
|
608 | 609 | def Moments(self, ySamples, xSamples): |
|
|
609 | 610 | Pot = numpy.nansum( ySamples ) # Potencia, momento 0 |
|
|
610 | 611 | yNorm = ySamples / Pot |
|
|
611 | 612 | |
|
|
612 | 613 | Vr = numpy.nansum( yNorm * xSamples ) # Velocidad radial, mu, corrimiento doppler, primer momento |
|
|
613 | 614 | Sigma2 = abs(numpy.nansum( yNorm * ( xSamples - Vr )**2 )) # Segundo Momento |
|
|
614 | 615 | Desv = Sigma2**0.5 # Desv. Estandar, Ancho espectral |
|
|
615 | 616 | |
|
|
616 | 617 | return numpy.array([Pot, Vr, Desv]) |
|
|
617 | 618 | |
|
|
618 | 619 | def run(self, dataOut, radar=None, Pt=5000, Gt=295.1209, Gr=70.7945, Lambda=0.6741, aL=2.5118, |
|
|
619 | 620 | tauW=4e-06, ThetaT=0.1656317, ThetaR=0.36774087, Km = 0.93, Altitude=3350): |
|
|
620 | 621 | |
|
|
621 | 622 | |
|
|
622 | 623 | Velrange = dataOut.spcparam_range[2] |
|
|
623 | 624 | FrecRange = dataOut.spcparam_range[0] |
|
|
624 | 625 | |
|
|
625 | 626 | dV= Velrange[1]-Velrange[0] |
|
|
626 | 627 | dF= FrecRange[1]-FrecRange[0] |
|
|
627 | 628 | |
|
|
628 | 629 | if radar == "MIRA35C" : |
|
|
629 | 630 | |
|
|
630 | 631 | self.spc = dataOut.data_pre[0].copy() |
|
|
631 | 632 | self.Num_Hei = self.spc.shape[2] |
|
|
632 | 633 | self.Num_Bin = self.spc.shape[1] |
|
|
633 | 634 | self.Num_Chn = self.spc.shape[0] |
|
|
634 | 635 | Ze = self.dBZeMODE2(dataOut) |
|
|
635 | 636 | |
|
|
636 | 637 | else: |
|
|
637 | 638 | |
|
|
638 | 639 | self.spc = dataOut.SPCparam[1].copy() #dataOut.data_pre[0].copy() # |
|
|
639 | 640 | |
|
|
640 | 641 | """NOTA SE DEBE REMOVER EL RANGO DEL PULSO TX""" |
|
|
641 | 642 | |
|
|
642 | 643 | self.spc[:,:,0:7]= numpy.NaN |
|
|
643 | 644 | |
|
|
644 | 645 | """##########################################""" |
|
|
645 | 646 | |
|
|
646 | 647 | self.Num_Hei = self.spc.shape[2] |
|
|
647 | 648 | self.Num_Bin = self.spc.shape[1] |
|
|
648 | 649 | self.Num_Chn = self.spc.shape[0] |
|
|
649 | 650 | |
|
|
650 | 651 | ''' Se obtiene la constante del RADAR ''' |
|
|
651 | 652 | |
|
|
652 | 653 | self.Pt = Pt |
|
|
653 | 654 | self.Gt = Gt |
|
|
654 | 655 | self.Gr = Gr |
|
|
655 | 656 | self.Lambda = Lambda |
|
|
656 | 657 | self.aL = aL |
|
|
657 | 658 | self.tauW = tauW |
|
|
658 | 659 | self.ThetaT = ThetaT |
|
|
659 | 660 | self.ThetaR = ThetaR |
|
|
660 | 661 | |
|
|
661 | 662 | Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) ) |
|
|
662 | 663 | Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * tauW * numpy.pi * ThetaT * ThetaR) |
|
|
663 | 664 | RadarConstant = 10e-26 * Numerator / Denominator # |
|
|
664 | 665 | |
|
|
665 | 666 | ''' ============================= ''' |
|
|
666 | 667 | |
|
|
667 | 668 | self.spc[0] = (self.spc[0]-dataOut.noise[0]) |
|
|
668 | 669 | self.spc[1] = (self.spc[1]-dataOut.noise[1]) |
|
|
669 | 670 | self.spc[2] = (self.spc[2]-dataOut.noise[2]) |
|
|
670 | 671 | |
|
|
671 | 672 | self.spc[ numpy.where(self.spc < 0)] = 0 |
|
|
672 | 673 | |
|
|
673 | 674 | SPCmean = (numpy.mean(self.spc,0) - numpy.mean(dataOut.noise)) |
|
|
674 | 675 | SPCmean[ numpy.where(SPCmean < 0)] = 0 |
|
|
675 | 676 | |
|
|
676 | 677 | ETAn = numpy.zeros([self.Num_Bin,self.Num_Hei]) |
|
|
677 | 678 | ETAv = numpy.zeros([self.Num_Bin,self.Num_Hei]) |
|
|
678 | 679 | ETAd = numpy.zeros([self.Num_Bin,self.Num_Hei]) |
|
|
679 | 680 | |
|
|
680 | 681 | Pr = SPCmean[:,:] |
|
|
681 | 682 | |
|
|
682 | 683 | VelMeteoro = numpy.mean(SPCmean,axis=0) |
|
|
683 | 684 | |
|
|
684 | 685 | D_range = numpy.zeros([self.Num_Bin,self.Num_Hei]) |
|
|
685 | 686 | SIGMA = numpy.zeros([self.Num_Bin,self.Num_Hei]) |
|
|
686 | 687 | N_dist = numpy.zeros([self.Num_Bin,self.Num_Hei]) |
|
|
687 | 688 | V_mean = numpy.zeros(self.Num_Hei) |
|
|
688 | 689 | del_V = numpy.zeros(self.Num_Hei) |
|
|
689 | 690 | Z = numpy.zeros(self.Num_Hei) |
|
|
690 | 691 | Ze = numpy.zeros(self.Num_Hei) |
|
|
691 | 692 | RR = numpy.zeros(self.Num_Hei) |
|
|
692 | 693 | |
|
|
693 | 694 | Range = dataOut.heightList*1000. |
|
|
694 | 695 | |
|
|
695 | 696 | for R in range(self.Num_Hei): |
|
|
696 | 697 | |
|
|
697 | 698 | h = Range[R] + Altitude #Range from ground to radar pulse altitude |
|
|
698 | 699 | del_V[R] = 1 + 3.68 * 10**-5 * h + 1.71 * 10**-9 * h**2 #Density change correction for velocity |
|
|
699 | 700 | |
|
|
700 | 701 | D_range[:,R] = numpy.log( (9.65 - (Velrange[0:self.Num_Bin] / del_V[R])) / 10.3 ) / -0.6 #Diameter range [m]x10**-3 |
|
|
701 | 702 | |
|
|
702 | 703 | '''NOTA: ETA(n) dn = ETA(f) df |
|
|
703 | 704 | |
|
|
704 | 705 | dn = 1 Diferencial de muestreo |
|
|
705 | 706 | df = ETA(n) / ETA(f) |
|
|
706 | 707 | |
|
|
707 | 708 | ''' |
|
|
708 | 709 | |
|
|
709 | 710 | ETAn[:,R] = RadarConstant * Pr[:,R] * (Range[R] )**2 #Reflectivity (ETA) |
|
|
710 | 711 | |
|
|
711 | 712 | ETAv[:,R]=ETAn[:,R]/dV |
|
|
712 | 713 | |
|
|
713 | 714 | ETAd[:,R]=ETAv[:,R]*6.18*exp(-0.6*D_range[:,R]) |
|
|
714 | 715 | |
|
|
715 | 716 | SIGMA[:,R] = Km * (D_range[:,R] * 1e-3 )**6 * numpy.pi**5 / Lambda**4 #Equivalent Section of drops (sigma) |
|
|
716 | 717 | |
|
|
717 | 718 | N_dist[:,R] = ETAn[:,R] / SIGMA[:,R] |
|
|
718 | 719 | |
|
|
719 | 720 | DMoments = self.Moments(Pr[:,R], Velrange[0:self.Num_Bin]) |
|
|
720 | 721 | |
|
|
721 | 722 | try: |
|
|
722 | 723 | popt01,pcov = curve_fit(self.gaus, Velrange[0:self.Num_Bin] , Pr[:,R] , p0=DMoments) |
|
|
723 | 724 | except: |
|
|
724 | 725 | popt01=numpy.zeros(3) |
|
|
725 | 726 | popt01[1]= DMoments[1] |
|
|
726 | 727 | |
|
|
727 | 728 | if popt01[1]<0 or popt01[1]>20: |
|
|
728 | 729 | popt01[1]=numpy.NaN |
|
|
729 | 730 | |
|
|
730 | 731 | |
|
|
731 | 732 | V_mean[R]=popt01[1] |
|
|
732 | 733 | |
|
|
733 | 734 | Z[R] = numpy.nansum( N_dist[:,R] * (D_range[:,R])**6 )#*10**-18 |
|
|
734 | 735 | |
|
|
735 | 736 | RR[R] = 0.0006*numpy.pi * numpy.nansum( D_range[:,R]**3 * N_dist[:,R] * Velrange[0:self.Num_Bin] ) #Rainfall rate |
|
|
736 | 737 | |
|
|
737 | 738 | Ze[R] = (numpy.nansum( ETAn[:,R]) * Lambda**4) / ( 10**-18*numpy.pi**5 * Km) |
|
|
738 | 739 | |
|
|
739 | 740 | |
|
|
740 | 741 | |
|
|
741 | 742 | RR2 = (Z/200)**(1/1.6) |
|
|
742 | 743 | dBRR = 10*numpy.log10(RR) |
|
|
743 | 744 | dBRR2 = 10*numpy.log10(RR2) |
|
|
744 | 745 | |
|
|
745 | 746 | dBZe = 10*numpy.log10(Ze) |
|
|
746 | 747 | dBZ = 10*numpy.log10(Z) |
|
|
747 | 748 | |
|
|
748 | 749 | dataOut.data_output = RR[8] |
|
|
749 | 750 | dataOut.data_param = numpy.ones([3,self.Num_Hei]) |
|
|
750 | 751 | dataOut.channelList = [0,1,2] |
|
|
751 | 752 | |
|
|
752 | 753 | dataOut.data_param[0]=dBZ |
|
|
753 | 754 | dataOut.data_param[1]=V_mean |
|
|
754 | 755 | dataOut.data_param[2]=RR |
|
|
755 | 756 | |
|
|
756 | 757 | return dataOut |
|
|
757 | 758 | |
|
|
758 | 759 | def dBZeMODE2(self, dataOut): # Processing for MIRA35C |
|
|
759 | 760 | |
|
|
760 | 761 | NPW = dataOut.NPW |
|
|
761 | 762 | COFA = dataOut.COFA |
|
|
762 | 763 | |
|
|
763 | 764 | SNR = numpy.array([self.spc[0,:,:] / NPW[0]]) #, self.spc[1,:,:] / NPW[1]]) |
|
|
764 | 765 | RadarConst = dataOut.RadarConst |
|
|
765 | 766 | #frequency = 34.85*10**9 |
|
|
766 | 767 | |
|
|
767 | 768 | ETA = numpy.zeros(([self.Num_Chn ,self.Num_Hei])) |
|
|
768 | 769 | data_output = numpy.ones([self.Num_Chn , self.Num_Hei])*numpy.NaN |
|
|
769 | 770 | |
|
|
770 | 771 | ETA = numpy.sum(SNR,1) |
|
|
771 | 772 | |
|
|
772 | 773 | ETA = numpy.where(ETA is not 0. , ETA, numpy.NaN) |
|
|
773 | 774 | |
|
|
774 | 775 | Ze = numpy.ones([self.Num_Chn, self.Num_Hei] ) |
|
|
775 | 776 | |
|
|
776 | 777 | for r in range(self.Num_Hei): |
|
|
777 | 778 | |
|
|
778 | 779 | Ze[0,r] = ( ETA[0,r] ) * COFA[0,r][0] * RadarConst * ((r/5000.)**2) |
|
|
779 | 780 | #Ze[1,r] = ( ETA[1,r] ) * COFA[1,r][0] * RadarConst * ((r/5000.)**2) |
|
|
780 | 781 | |
|
|
781 | 782 | return Ze |
|
|
782 | 783 | |
|
|
783 | 784 | # def GetRadarConstant(self): |
|
|
784 | 785 | # |
|
|
785 | 786 | # """ |
|
|
786 | 787 | # Constants: |
|
|
787 | 788 | # |
|
|
788 | 789 | # Pt: Transmission Power dB 5kW 5000 |
|
|
789 | 790 | # Gt: Transmission Gain dB 24.7 dB 295.1209 |
|
|
790 | 791 | # Gr: Reception Gain dB 18.5 dB 70.7945 |
|
|
791 | 792 | # Lambda: Wavelenght m 0.6741 m 0.6741 |
|
|
792 | 793 | # aL: Attenuation loses dB 4dB 2.5118 |
|
|
793 | 794 | # tauW: Width of transmission pulse s 4us 4e-6 |
|
|
794 | 795 | # ThetaT: Transmission antenna bean angle rad 0.1656317 rad 0.1656317 |
|
|
795 | 796 | # ThetaR: Reception antenna beam angle rad 0.36774087 rad 0.36774087 |
|
|
796 | 797 | # |
|
|
797 | 798 | # """ |
|
|
798 | 799 | # |
|
|
799 | 800 | # Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) ) |
|
|
800 | 801 | # Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * TauW * numpy.pi * ThetaT * TheraR) |
|
|
801 | 802 | # RadarConstant = Numerator / Denominator |
|
|
802 | 803 | # |
|
|
803 | 804 | # return RadarConstant |
|
|
804 | 805 | |
|
|
805 | 806 | |
|
|
806 | 807 | |
|
|
807 | 808 | class FullSpectralAnalysis(Operation): |
|
|
808 | 809 | |
|
|
809 | 810 | """ |
|
|
810 |
Function that implements Full Spectral Anal |
|
|
|
811 | Function that implements Full Spectral Analysis technique. | |
|
|
811 | 812 | |
|
|
812 | 813 | Input: |
|
|
813 |
self.dataOut.data_pre : SelfSpectra and CrossS |
|
|
|
814 | self.dataOut.data_pre : SelfSpectra and CrossSpectra data | |
|
|
814 | 815 | self.dataOut.groupList : Pairlist of channels |
|
|
815 | 816 | self.dataOut.ChanDist : Physical distance between receivers |
|
|
816 | 817 | |
|
|
817 | 818 | |
|
|
818 | 819 | Output: |
|
|
819 | 820 | |
|
|
820 | 821 | self.dataOut.data_output : Zonal wind, Meridional wind and Vertical wind |
|
|
821 | 822 | |
|
|
822 | 823 | |
|
|
823 | 824 | Parameters affected: Winds, height range, SNR |
|
|
824 | 825 | |
|
|
825 | 826 | """ |
|
|
826 | def run(self, dataOut, Xi01=None, Xi02=None, Xi12=None, Eta01=None, Eta02=None, Eta12=None, SNRlimit=7): | |
|
|
827 | def run(self, dataOut, Xi01=None, Xi02=None, Xi12=None, Eta01=None, Eta02=None, Eta12=None, SNRlimit=7, minheight=None, maxheight=None): | |
|
|
827 | 828 | |
|
|
828 | 829 | self.indice=int(numpy.random.rand()*1000) |
|
|
829 | 830 | |
|
|
830 | 831 | spc = dataOut.data_pre[0].copy() |
|
|
831 | 832 | cspc = dataOut.data_pre[1] |
|
|
832 | 833 | |
|
|
833 | """NOTA SE DEBE REMOVER EL RANGO DEL PULSO TX""" | |
|
|
834 | ||
|
|
834 | """Erick: NOTE THE RANGE OF THE PULSE TX MUST BE REMOVED""" | |
|
|
835 | ||
|
|
835 | 836 | SNRspc = spc.copy() |
|
|
836 | 837 | SNRspc[:,:,0:7]= numpy.NaN |
|
|
837 | 838 | |
|
|
838 | 839 | """##########################################""" |
|
|
839 | 840 | |
|
|
840 | 841 | |
|
|
841 | 842 | nChannel = spc.shape[0] |
|
|
842 | 843 | nProfiles = spc.shape[1] |
|
|
843 | 844 | nHeights = spc.shape[2] |
|
|
844 | 845 | |
|
|
846 | # first_height = 0.75 #km (ref: data header 20170822) | |
|
|
847 | # resolution_height = 0.075 #km | |
|
|
848 | ''' | |
|
|
849 | finding height range. check this when radar parameters are changed! | |
|
|
850 | ''' | |
|
|
851 | if maxheight is not None: | |
|
|
852 | # range_max = math.ceil((maxheight - first_height) / resolution_height) # theoretical | |
|
|
853 | range_max = math.ceil(13.26 * maxheight - 3) # empirical, works better | |
|
|
854 | else: | |
|
|
855 | range_max = nHeights | |
|
|
856 | if minheight is not None: | |
|
|
857 | # range_min = int((minheight - first_height) / resolution_height) # theoretical | |
|
|
858 | range_min = int(13.26 * minheight - 5) # empirical, works better | |
|
|
859 | if range_min < 0: | |
|
|
860 | range_min = 0 | |
|
|
861 | else: | |
|
|
862 | range_min = 0 | |
|
|
863 | ||
|
|
845 | 864 | pairsList = dataOut.groupList |
|
|
846 | 865 | if dataOut.ChanDist is not None : |
|
|
847 | 866 | ChanDist = dataOut.ChanDist |
|
|
848 | 867 | else: |
|
|
849 | 868 | ChanDist = numpy.array([[Xi01, Eta01],[Xi02,Eta02],[Xi12,Eta12]]) |
|
|
850 | 869 | |
|
|
851 | 870 | FrecRange = dataOut.spc_range[0] |
|
|
852 | 871 | |
|
|
853 | ySamples=numpy.ones([nChannel,nProfiles]) | |
|
|
854 | phase=numpy.ones([nChannel,nProfiles]) | |
|
|
855 | CSPCSamples=numpy.ones([nChannel,nProfiles],dtype=numpy.complex_) | |
|
|
856 | coherence=numpy.ones([nChannel,nProfiles]) | |
|
|
857 | PhaseSlope=numpy.ones(nChannel) | |
|
|
858 | PhaseInter=numpy.ones(nChannel) | |
|
|
859 | 872 | data_SNR=numpy.zeros([nProfiles]) |
|
|
860 | ||
|
|
861 | data = dataOut.data_pre | |
|
|
862 | 873 | noise = dataOut.noise |
|
|
863 | 874 | |
|
|
864 | 875 | dataOut.data_SNR = (numpy.mean(SNRspc,axis=1)- noise[0]) / noise[0] |
|
|
865 | 876 | |
|
|
866 | 877 | dataOut.data_SNR[numpy.where( dataOut.data_SNR <0 )] = 1e-20 |
|
|
867 | 878 | |
|
|
868 | 879 | |
|
|
869 | 880 | data_output=numpy.ones([spc.shape[0],spc.shape[2]])*numpy.NaN |
|
|
870 | 881 | |
|
|
871 | 882 | velocityX=[] |
|
|
872 | 883 | velocityY=[] |
|
|
873 | 884 | velocityV=[] |
|
|
874 | PhaseLine=[] | |
|
|
885 | PhaseLine=[] # unused afterwards | |
|
|
875 | 886 | |
|
|
876 | 887 | dbSNR = 10*numpy.log10(dataOut.data_SNR) |
|
|
877 | 888 | dbSNR = numpy.average(dbSNR,0) |
|
|
878 | 889 | |
|
|
890 | '''***********************************************WIND ESTIMATION**************************************''' | |
|
|
891 | ||
|
|
879 | 892 | for Height in range(nHeights): |
|
|
880 |
|
|
|
|
881 | [Vzon,Vmer,Vver, GaussCenter, PhaseSlope, FitGaussCSPC]= self.WindEstimation(spc, cspc, pairsList, ChanDist, Height, noise, dataOut.spc_range, dbSNR[Height], SNRlimit) | |
|
|
882 | PhaseLine = numpy.append(PhaseLine, PhaseSlope) | |
|
|
893 | ||
|
|
894 | if Height >= range_min and Height < range_max: | |
|
|
895 | # [Vzon,Vmer,Vver, GaussCenter, PhaseSlope, FitGaussCSPC] = self.WindEstimation(spc, cspc, pairsList, ChanDist, Height, noise, dataOut.spc_range, dbSNR[Height], SNRlimit) | |
|
|
896 | ||
|
|
897 | # error_code unused, yet maybe useful for future analysis. | |
|
|
898 | # Test | |
|
|
899 | [Vzon,Vmer,Vver, error_code] = self.TestWindEstimation(spc[:,:,Height], cspc[:,:,Height], pairsList, ChanDist, Height, noise, dataOut.spc_range, dbSNR[Height], SNRlimit) | |
|
|
900 | ||
|
|
901 | else: | |
|
|
902 | Vzon,Vmer,Vver = 0., 0., numpy.NaN | |
|
|
903 | ||
|
|
883 | 904 | |
|
|
884 | if abs(Vzon)<100. and abs(Vzon)> 0.: | |
|
|
885 |
velocityX=numpy.append(velocityX, Vzon) |
|
|
|
886 | ||
|
|
905 | if abs(Vzon) < 100. and abs(Vzon) > 0. and abs(Vmer) < 100. and abs(Vmer) > 0.: | |
|
|
906 | velocityX=numpy.append(velocityX, Vzon) | |
|
|
907 | velocityY=numpy.append(velocityY, -Vmer) | |
|
|
908 | ||
|
|
887 | 909 | else: |
|
|
888 | 910 | velocityX=numpy.append(velocityX, numpy.NaN) |
|
|
889 | ||
|
|
890 | if abs(Vmer)<100. and abs(Vmer) > 0.: | |
|
|
891 | velocityY=numpy.append(velocityY, -Vmer)#Vang | |
|
|
892 | ||
|
|
893 | else: | |
|
|
894 | 911 | velocityY=numpy.append(velocityY, numpy.NaN) |
|
|
895 | 912 | |
|
|
896 | 913 | if dbSNR[Height] > SNRlimit: |
|
|
897 |
velocityV=numpy.append(velocityV, -Vver) |
|
|
|
914 | velocityV=numpy.append(velocityV, -Vver) # reason for this minus sign -> convention? (taken from Ericks version) | |
|
|
898 | 915 | else: |
|
|
899 | 916 | velocityV=numpy.append(velocityV, numpy.NaN) |
|
|
900 | ||
|
|
901 | 917 | |
|
|
902 | 918 | |
|
|
903 |
''' |
|
|
|
904 |
data_output[0] = numpy.array(velocityX) |
|
|
|
905 |
data_output[1] = numpy.array(velocityY) |
|
|
|
906 |
data_output[2] = velocityV |
|
|
|
919 | '''Change the numpy.array (velocityX) sign when trying to process BLTR data (Erick)''' | |
|
|
920 | data_output[0] = numpy.array(velocityX) | |
|
|
921 | data_output[1] = numpy.array(velocityY) | |
|
|
922 | data_output[2] = velocityV | |
|
|
907 | 923 | |
|
|
908 | xFrec=FrecRange[0:spc.shape[1]] | |
|
|
909 | 924 | |
|
|
910 | dataOut.data_output=data_output | |
|
|
911 | ||
|
|
925 | dataOut.data_output = data_output | |
|
|
926 | ||
|
|
912 | 927 | return dataOut |
|
|
913 | 928 | |
|
|
914 | ||
|
|
929 | ||
|
|
915 | 930 | def moving_average(self,x, N=2): |
|
|
931 | """ convolution for smoothenig data. note that last N-1 values are convolution with zeroes """ | |
|
|
916 | 932 | return numpy.convolve(x, numpy.ones((N,))/N)[(N-1):] |
|
|
917 | 933 | |
|
|
918 | 934 | def gaus(self,xSamples,Amp,Mu,Sigma): |
|
|
919 | 935 | return ( Amp / ((2*numpy.pi)**0.5 * Sigma) ) * numpy.exp( -( xSamples - Mu )**2 / ( 2 * (Sigma**2) )) |
|
|
920 | 936 | |
|
|
921 | 937 | |
|
|
922 | 938 | |
|
|
923 | 939 | def Moments(self, ySamples, xSamples): |
|
|
924 | Pot = numpy.nansum( ySamples ) # Potencia, momento 0 | |
|
|
940 | '''*** | |
|
|
941 | Variables corresponding to moments of distribution. | |
|
|
942 | Also used as initial coefficients for curve_fit. | |
|
|
943 | Vr was corrected. Only a velocity when x is velocity, of course. | |
|
|
944 | ***''' | |
|
|
945 | Pot = numpy.nansum( ySamples ) # Potencia, momento 0 | |
|
|
925 | 946 | yNorm = ySamples / Pot |
|
|
926 | Vr = numpy.nansum( yNorm * xSamples ) # Velocidad radial, mu, corrimiento doppler, primer momento | |
|
|
927 | Sigma2 = abs(numpy.nansum( yNorm * ( xSamples - Vr )**2 )) # Segundo Momento | |
|
|
928 | Desv = Sigma2**0.5 # Desv. Estandar, Ancho espectral | |
|
|
947 | x_range = (numpy.max(xSamples)-numpy.min(xSamples)) | |
|
|
948 | Vr = numpy.nansum( yNorm * xSamples )*x_range/len(xSamples) # Velocidad radial, mu, corrimiento doppler, primer momento | |
|
|
949 | Sigma2 = abs(numpy.nansum( yNorm * ( xSamples - Vr )**2 )) # Segundo Momento | |
|
|
950 | Desv = Sigma2**0.5 # Desv. Estandar, Ancho espectral | |
|
|
929 | 951 | |
|
|
930 | 952 | return numpy.array([Pot, Vr, Desv]) |
|
|
931 | 953 | |
|
|
932 | 954 | def WindEstimation(self, spc, cspc, pairsList, ChanDist, Height, noise, AbbsisaRange, dbSNR, SNRlimit): |
|
|
933 | 955 | |
|
|
934 | 956 | |
|
|
935 | 957 | |
|
|
936 | 958 | ySamples=numpy.ones([spc.shape[0],spc.shape[1]]) |
|
|
937 | 959 | phase=numpy.ones([spc.shape[0],spc.shape[1]]) |
|
|
938 | 960 | CSPCSamples=numpy.ones([spc.shape[0],spc.shape[1]],dtype=numpy.complex_) |
|
|
939 | 961 | coherence=numpy.ones([spc.shape[0],spc.shape[1]]) |
|
|
940 | 962 | PhaseSlope=numpy.zeros(spc.shape[0]) |
|
|
941 | 963 | PhaseInter=numpy.ones(spc.shape[0]) |
|
|
942 | 964 | xFrec=AbbsisaRange[0][0:spc.shape[1]] |
|
|
943 | 965 | xVel =AbbsisaRange[2][0:spc.shape[1]] |
|
|
944 | 966 | Vv=numpy.empty(spc.shape[2])*0 |
|
|
945 | 967 | SPCav = numpy.average(spc, axis=0)-numpy.average(noise) #spc[0]-noise[0]# |
|
|
946 | 968 | |
|
|
947 | 969 | SPCmoments = self.Moments(SPCav[:,Height], xVel ) |
|
|
948 | 970 | CSPCmoments = [] |
|
|
949 | 971 | cspcNoise = numpy.empty(3) |
|
|
950 | 972 | |
|
|
951 | 973 | '''Getting Eij and Nij''' |
|
|
952 | 974 | |
|
|
953 | 975 | Xi01=ChanDist[0][0] |
|
|
954 | 976 | Eta01=ChanDist[0][1] |
|
|
955 | 977 | |
|
|
956 | 978 | Xi02=ChanDist[1][0] |
|
|
957 | 979 | Eta02=ChanDist[1][1] |
|
|
958 | 980 | |
|
|
959 | 981 | Xi12=ChanDist[2][0] |
|
|
960 | 982 | Eta12=ChanDist[2][1] |
|
|
961 | 983 | |
|
|
962 | 984 | z = spc.copy() |
|
|
963 | 985 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
|
964 | 986 | |
|
|
965 | 987 | for i in range(spc.shape[0]): |
|
|
966 | 988 | |
|
|
967 | 989 | '''****** Line of Data SPC ******''' |
|
|
968 | 990 | zline=z[i,:,Height].copy() - noise[i] # Se resta ruido |
|
|
969 | 991 | |
|
|
970 | 992 | '''****** SPC is normalized ******''' |
|
|
971 | 993 | SmoothSPC =self.moving_average(zline.copy(),N=1) # Se suaviza el ruido |
|
|
972 | 994 | FactNorm = SmoothSPC/numpy.nansum(SmoothSPC) # SPC Normalizado y suavizado |
|
|
973 | 995 | |
|
|
974 | 996 | xSamples = xFrec # Se toma el rango de frecuncias |
|
|
975 | 997 | ySamples[i] = FactNorm # Se toman los valores de SPC normalizado |
|
|
976 | 998 | |
|
|
977 | 999 | for i in range(spc.shape[0]): |
|
|
978 | 1000 | |
|
|
979 | 1001 | '''****** Line of Data CSPC ******''' |
|
|
980 | 1002 | cspcLine = ( cspc[i,:,Height].copy())# - noise[i] ) # no! Se resta el ruido |
|
|
981 | 1003 | SmoothCSPC =self.moving_average(cspcLine,N=1) # Se suaviza el ruido |
|
|
982 | 1004 | cspcNorm = SmoothCSPC/numpy.nansum(SmoothCSPC) # CSPC normalizado y suavizado |
|
|
983 | 1005 | |
|
|
984 | 1006 | '''****** CSPC is normalized with respect to Briggs and Vincent ******''' |
|
|
985 | 1007 | chan_index0 = pairsList[i][0] |
|
|
986 | 1008 | chan_index1 = pairsList[i][1] |
|
|
987 | 1009 | |
|
|
988 | 1010 | CSPCFactor= numpy.abs(numpy.nansum(ySamples[chan_index0]))**2 * numpy.abs(numpy.nansum(ySamples[chan_index1]))**2 |
|
|
989 | 1011 | CSPCNorm = cspcNorm / numpy.sqrt(CSPCFactor) |
|
|
990 | 1012 | |
|
|
991 | 1013 | CSPCSamples[i] = CSPCNorm |
|
|
992 | 1014 | |
|
|
993 | 1015 | coherence[i] = numpy.abs(CSPCSamples[i]) / numpy.sqrt(CSPCFactor) |
|
|
994 | 1016 | |
|
|
995 | 1017 | #coherence[i]= self.moving_average(coherence[i],N=1) |
|
|
996 | 1018 | |
|
|
997 | 1019 | phase[i] = self.moving_average( numpy.arctan2(CSPCSamples[i].imag, CSPCSamples[i].real),N=1)#*180/numpy.pi |
|
|
998 | 1020 | |
|
|
999 | 1021 | CSPCmoments = numpy.vstack([self.Moments(numpy.abs(CSPCSamples[0]), xSamples), |
|
|
1000 | 1022 | self.Moments(numpy.abs(CSPCSamples[1]), xSamples), |
|
|
1001 | 1023 | self.Moments(numpy.abs(CSPCSamples[2]), xSamples)]) |
|
|
1002 | 1024 | |
|
|
1003 | 1025 | |
|
|
1004 | 1026 | popt=[1e-10,0,1e-10] |
|
|
1005 | 1027 | popt01, popt02, popt12 = [1e-10,1e-10,1e-10], [1e-10,1e-10,1e-10] ,[1e-10,1e-10,1e-10] |
|
|
1006 | 1028 | FitGauss01, FitGauss02, FitGauss12 = numpy.empty(len(xSamples))*0, numpy.empty(len(xSamples))*0, numpy.empty(len(xSamples))*0 |
|
|
1007 | 1029 | |
|
|
1008 | 1030 | CSPCMask01 = numpy.abs(CSPCSamples[0]) |
|
|
1009 | 1031 | CSPCMask02 = numpy.abs(CSPCSamples[1]) |
|
|
1010 | 1032 | CSPCMask12 = numpy.abs(CSPCSamples[2]) |
|
|
1011 | 1033 | |
|
|
1012 | 1034 | mask01 = ~numpy.isnan(CSPCMask01) |
|
|
1013 | 1035 | mask02 = ~numpy.isnan(CSPCMask02) |
|
|
1014 | 1036 | mask12 = ~numpy.isnan(CSPCMask12) |
|
|
1015 | 1037 | |
|
|
1016 | 1038 | #mask = ~numpy.isnan(CSPCMask01) |
|
|
1017 | 1039 | CSPCMask01 = CSPCMask01[mask01] |
|
|
1018 | 1040 | CSPCMask02 = CSPCMask02[mask02] |
|
|
1019 | 1041 | CSPCMask12 = CSPCMask12[mask12] |
|
|
1020 | 1042 | #CSPCMask01 = numpy.ma.masked_invalid(CSPCMask01) |
|
|
1021 | 1043 | |
|
|
1022 | 1044 | |
|
|
1023 | 1045 | |
|
|
1024 | 1046 | '''***Fit Gauss CSPC01***''' |
|
|
1025 | 1047 | if dbSNR > SNRlimit and numpy.abs(SPCmoments[1])<3 : |
|
|
1026 | 1048 | try: |
|
|
1027 | 1049 | popt01,pcov = curve_fit(self.gaus,xSamples[mask01],numpy.abs(CSPCMask01),p0=CSPCmoments[0]) |
|
|
1028 | 1050 | popt02,pcov = curve_fit(self.gaus,xSamples[mask02],numpy.abs(CSPCMask02),p0=CSPCmoments[1]) |
|
|
1029 | 1051 | popt12,pcov = curve_fit(self.gaus,xSamples[mask12],numpy.abs(CSPCMask12),p0=CSPCmoments[2]) |
|
|
1030 | 1052 | FitGauss01 = self.gaus(xSamples,*popt01) |
|
|
1031 | 1053 | FitGauss02 = self.gaus(xSamples,*popt02) |
|
|
1032 | 1054 | FitGauss12 = self.gaus(xSamples,*popt12) |
|
|
1033 | 1055 | except: |
|
|
1034 | 1056 | FitGauss01=numpy.ones(len(xSamples))*numpy.mean(numpy.abs(CSPCSamples[0])) |
|
|
1035 | 1057 | FitGauss02=numpy.ones(len(xSamples))*numpy.mean(numpy.abs(CSPCSamples[1])) |
|
|
1036 | 1058 | FitGauss12=numpy.ones(len(xSamples))*numpy.mean(numpy.abs(CSPCSamples[2])) |
|
|
1037 | 1059 | |
|
|
1038 | 1060 | |
|
|
1039 | 1061 | CSPCopt = numpy.vstack([popt01,popt02,popt12]) |
|
|
1040 | 1062 | |
|
|
1041 | 1063 | '''****** Getting fij width ******''' |
|
|
1042 | 1064 | |
|
|
1043 | 1065 | yMean = numpy.average(ySamples, axis=0) # ySamples[0] |
|
|
1044 | 1066 | |
|
|
1045 | 1067 | '''******* Getting fitting Gaussian *******''' |
|
|
1046 | 1068 | meanGauss = sum(xSamples*yMean) / len(xSamples) # Mu, velocidad radial (frecuencia) |
|
|
1047 | 1069 | sigma2 = sum(yMean*(xSamples-meanGauss)**2) / len(xSamples) # Varianza, Ancho espectral (frecuencia) |
|
|
1048 | 1070 | |
|
|
1049 | 1071 | yMoments = self.Moments(yMean, xSamples) |
|
|
1050 | 1072 | |
|
|
1051 | 1073 | if dbSNR > SNRlimit and numpy.abs(SPCmoments[1])<3: # and abs(meanGauss/sigma2) > 0.00001: |
|
|
1052 | 1074 | try: |
|
|
1053 | 1075 | popt,pcov = curve_fit(self.gaus,xSamples,yMean,p0=yMoments) |
|
|
1054 | 1076 | FitGauss=self.gaus(xSamples,*popt) |
|
|
1055 | 1077 | |
|
|
1056 | 1078 | except :#RuntimeError: |
|
|
1057 | 1079 | FitGauss=numpy.ones(len(xSamples))*numpy.mean(yMean) |
|
|
1058 | 1080 | |
|
|
1059 | 1081 | |
|
|
1060 | 1082 | else: |
|
|
1061 | 1083 | FitGauss=numpy.ones(len(xSamples))*numpy.mean(yMean) |
|
|
1062 | 1084 | |
|
|
1063 | 1085 | |
|
|
1064 | 1086 | |
|
|
1065 | 1087 | '''****** Getting Fij ******''' |
|
|
1066 | 1088 | Fijcspc = CSPCopt[:,2]/2*3 |
|
|
1067 | 1089 | |
|
|
1068 | 1090 | |
|
|
1069 | 1091 | GaussCenter = popt[1] #xFrec[GCpos] |
|
|
1070 | 1092 | #Punto en Eje X de la Gaussiana donde se encuentra el centro |
|
|
1071 | 1093 | ClosestCenter = xSamples[numpy.abs(xSamples-GaussCenter).argmin()] |
|
|
1072 | 1094 | PointGauCenter = numpy.where(xSamples==ClosestCenter)[0][0] |
|
|
1073 | 1095 | |
|
|
1074 | 1096 | #Punto e^-1 hubicado en la Gaussiana |
|
|
1075 | 1097 | PeMinus1 = numpy.max(FitGauss)* numpy.exp(-1) |
|
|
1076 | 1098 | FijClosest = FitGauss[numpy.abs(FitGauss-PeMinus1).argmin()] # El punto mas cercano a "Peminus1" dentro de "FitGauss" |
|
|
1077 | 1099 | PointFij = numpy.where(FitGauss==FijClosest)[0][0] |
|
|
1078 | 1100 | |
|
|
1079 | 1101 | if xSamples[PointFij] > xSamples[PointGauCenter]: |
|
|
1080 | 1102 | Fij = xSamples[PointFij] - xSamples[PointGauCenter] |
|
|
1081 | 1103 | |
|
|
1082 | 1104 | else: |
|
|
1083 | 1105 | Fij = xSamples[PointGauCenter] - xSamples[PointFij] |
|
|
1084 | 1106 | |
|
|
1085 | 1107 | |
|
|
1086 | 1108 | '''****** Taking frequency ranges from SPCs ******''' |
|
|
1087 | 1109 | |
|
|
1088 | 1110 | |
|
|
1089 | 1111 | #GaussCenter = popt[1] #Primer momento 01 |
|
|
1090 | 1112 | GauWidth = popt[2] *3/2 #Ancho de banda de Gau01 |
|
|
1091 | 1113 | Range = numpy.empty(2) |
|
|
1092 | 1114 | Range[0] = GaussCenter - GauWidth |
|
|
1093 | 1115 | Range[1] = GaussCenter + GauWidth |
|
|
1094 | 1116 | #Punto en Eje X de la Gaussiana donde se encuentra ancho de banda (min:max) |
|
|
1095 | 1117 | ClosRangeMin = xSamples[numpy.abs(xSamples-Range[0]).argmin()] |
|
|
1096 | 1118 | ClosRangeMax = xSamples[numpy.abs(xSamples-Range[1]).argmin()] |
|
|
1097 | 1119 | |
|
|
1098 | 1120 | PointRangeMin = numpy.where(xSamples==ClosRangeMin)[0][0] |
|
|
1099 | 1121 | PointRangeMax = numpy.where(xSamples==ClosRangeMax)[0][0] |
|
|
1100 | 1122 | |
|
|
1101 | 1123 | Range=numpy.array([ PointRangeMin, PointRangeMax ]) |
|
|
1102 | 1124 | |
|
|
1103 | 1125 | FrecRange = xFrec[ Range[0] : Range[1] ] |
|
|
1104 | 1126 | VelRange = xVel[ Range[0] : Range[1] ] |
|
|
1105 | 1127 | |
|
|
1106 | 1128 | |
|
|
1107 | 1129 | '''****** Getting SCPC Slope ******''' |
|
|
1108 | 1130 | |
|
|
1109 | 1131 | for i in range(spc.shape[0]): |
|
|
1110 | 1132 | |
|
|
1111 | 1133 | if len(FrecRange)>5 and len(FrecRange)<spc.shape[1]*0.3: |
|
|
1112 | 1134 | PhaseRange=self.moving_average(phase[i,Range[0]:Range[1]],N=3) |
|
|
1113 | 1135 | |
|
|
1114 | 1136 | '''***********************VelRange******************''' |
|
|
1115 | 1137 | |
|
|
1116 | 1138 | mask = ~numpy.isnan(FrecRange) & ~numpy.isnan(PhaseRange) |
|
|
1117 | 1139 | |
|
|
1118 | 1140 | if len(FrecRange) == len(PhaseRange): |
|
|
1119 | 1141 | try: |
|
|
1120 | 1142 | slope, intercept, r_value, p_value, std_err = stats.linregress(FrecRange[mask], PhaseRange[mask]) |
|
|
1121 | 1143 | PhaseSlope[i]=slope |
|
|
1122 | 1144 | PhaseInter[i]=intercept |
|
|
1123 | 1145 | except: |
|
|
1124 | 1146 | PhaseSlope[i]=0 |
|
|
1125 | 1147 | PhaseInter[i]=0 |
|
|
1126 | 1148 | else: |
|
|
1127 | 1149 | PhaseSlope[i]=0 |
|
|
1128 | 1150 | PhaseInter[i]=0 |
|
|
1129 | 1151 | else: |
|
|
1130 | 1152 | PhaseSlope[i]=0 |
|
|
1131 | 1153 | PhaseInter[i]=0 |
|
|
1132 | 1154 | |
|
|
1133 | 1155 | |
|
|
1134 | 1156 | '''Getting constant C''' |
|
|
1135 | 1157 | cC=(Fij*numpy.pi)**2 |
|
|
1136 | 1158 | |
|
|
1137 | 1159 | '''****** Getting constants F and G ******''' |
|
|
1138 | 1160 | MijEijNij=numpy.array([[Xi02,Eta02], [Xi12,Eta12]]) |
|
|
1139 | 1161 | MijResult0=(-PhaseSlope[1]*cC) / (2*numpy.pi) |
|
|
1140 | 1162 | MijResult1=(-PhaseSlope[2]*cC) / (2*numpy.pi) |
|
|
1141 | 1163 | MijResults=numpy.array([MijResult0,MijResult1]) |
|
|
1142 | 1164 | (cF,cG) = numpy.linalg.solve(MijEijNij, MijResults) |
|
|
1143 | 1165 | |
|
|
1144 | 1166 | '''****** Getting constants A, B and H ******''' |
|
|
1145 | 1167 | W01=numpy.nanmax( FitGauss01 ) #numpy.abs(CSPCSamples[0])) |
|
|
1146 | 1168 | W02=numpy.nanmax( FitGauss02 ) #numpy.abs(CSPCSamples[1])) |
|
|
1147 | 1169 | W12=numpy.nanmax( FitGauss12 ) #numpy.abs(CSPCSamples[2])) |
|
|
1148 | 1170 | |
|
|
1149 | 1171 | WijResult0=((cF*Xi01+cG*Eta01)**2)/cC - numpy.log(W01 / numpy.sqrt(numpy.pi/cC)) |
|
|
1150 | 1172 | WijResult1=((cF*Xi02+cG*Eta02)**2)/cC - numpy.log(W02 / numpy.sqrt(numpy.pi/cC)) |
|
|
1151 | 1173 | WijResult2=((cF*Xi12+cG*Eta12)**2)/cC - numpy.log(W12 / numpy.sqrt(numpy.pi/cC)) |
|
|
1152 | 1174 | |
|
|
1153 | 1175 | WijResults=numpy.array([WijResult0, WijResult1, WijResult2]) |
|
|
1154 | 1176 | |
|
|
1155 | 1177 | WijEijNij=numpy.array([ [Xi01**2, Eta01**2, 2*Xi01*Eta01] , [Xi02**2, Eta02**2, 2*Xi02*Eta02] , [Xi12**2, Eta12**2, 2*Xi12*Eta12] ]) |
|
|
1156 | 1178 | (cA,cB,cH) = numpy.linalg.solve(WijEijNij, WijResults) |
|
|
1157 | 1179 | |
|
|
1158 | 1180 | VxVy=numpy.array([[cA,cH],[cH,cB]]) |
|
|
1159 | 1181 | VxVyResults=numpy.array([-cF,-cG]) |
|
|
1160 | 1182 | (Vx,Vy) = numpy.linalg.solve(VxVy, VxVyResults) |
|
|
1161 | 1183 | |
|
|
1162 | 1184 | Vzon = Vy |
|
|
1163 | 1185 | Vmer = Vx |
|
|
1164 | 1186 | Vmag=numpy.sqrt(Vzon**2+Vmer**2) |
|
|
1165 | 1187 | Vang=numpy.arctan2(Vmer,Vzon) |
|
|
1166 | 1188 | if numpy.abs( popt[1] ) < 3.5 and len(FrecRange)>4: |
|
|
1167 | 1189 | Vver=popt[1] |
|
|
1168 | 1190 | else: |
|
|
1169 | 1191 | Vver=numpy.NaN |
|
|
1170 | 1192 | FitGaussCSPC = numpy.array([FitGauss01,FitGauss02,FitGauss12]) |
|
|
1171 | 1193 | |
|
|
1172 | 1194 | |
|
|
1173 | 1195 | return Vzon, Vmer, Vver, GaussCenter, PhaseSlope, FitGaussCSPC |
|
|
1196 | ||
|
|
1197 | ||
|
|
1198 | ||
|
|
1199 | def StopWindEstimation(self, error_code): | |
|
|
1200 | ''' | |
|
|
1201 | the wind calculation and returns zeros | |
|
|
1202 | ''' | |
|
|
1203 | Vzon = 0 | |
|
|
1204 | Vmer = 0 | |
|
|
1205 | Vver = numpy.nan | |
|
|
1206 | return Vzon, Vmer, Vver, error_code | |
|
|
1207 | ||
|
|
1208 | def AntiAliasing(self, interval, maxstep): | |
|
|
1209 | """ | |
|
|
1210 | function to prevent errors from aliased values when computing phaseslope | |
|
|
1211 | """ | |
|
|
1212 | antialiased = numpy.zeros(len(interval))*0.0 | |
|
|
1213 | copyinterval = interval.copy() | |
|
|
1214 | ||
|
|
1215 | antialiased[0] = copyinterval[0] | |
|
|
1216 | ||
|
|
1217 | for i in range(1,len(antialiased)): | |
|
|
1218 | ||
|
|
1219 | step = interval[i] - interval[i-1] | |
|
|
1220 | ||
|
|
1221 | if step > maxstep: | |
|
|
1222 | copyinterval -= 2*numpy.pi | |
|
|
1223 | antialiased[i] = copyinterval[i] | |
|
|
1224 | ||
|
|
1225 | elif step < maxstep*(-1): | |
|
|
1226 | copyinterval += 2*numpy.pi | |
|
|
1227 | antialiased[i] = copyinterval[i] | |
|
|
1228 | ||
|
|
1229 | else: | |
|
|
1230 | antialiased[i] = copyinterval[i].copy() | |
|
|
1231 | ||
|
|
1232 | return antialiased | |
|
|
1233 | ||
|
|
1234 | ||
|
|
1235 | ||
|
|
1236 | def TestWindEstimation(self, spc, cspc, pairsList, ChanDist, Height, noise, AbbsisaRange, dbSNR, SNRlimit): | |
|
|
1237 | """ | |
|
|
1238 | Function that Calculates Zonal, Meridional and Vertical wind velocities. | |
|
|
1239 | Initial Version by E. Bocanegra updated by J. Zibell until Nov. 2019. | |
|
|
1240 | ||
|
|
1241 | Input: | |
|
|
1242 | spc, cspc : self spectra and cross spectra data. In Briggs notation something like S_i*(S_i)_conj, (S_j)_conj respectively. | |
|
|
1243 | pairsList : Pairlist of channels | |
|
|
1244 | ChanDist : array of xi_ij and eta_ij | |
|
|
1245 | Height : height at which data is processed | |
|
|
1246 | noise : noise in [channels] format for specific height | |
|
|
1247 | Abbsisarange : range of the frequencies or velocities | |
|
|
1248 | dbSNR, SNRlimit : signal to noise ratio in db, lower limit | |
|
|
1249 | ||
|
|
1250 | Output: | |
|
|
1251 | Vzon, Vmer, Vver : wind velocities | |
|
|
1252 | error_code : int that states where code is terminated | |
|
|
1253 | ||
|
|
1254 | 0 : no error detected | |
|
|
1255 | 1 : Gaussian of mean spc exceeds widthlimit | |
|
|
1256 | 2 : no Gaussian of mean spc found | |
|
|
1257 | 3 : SNR to low or velocity to high -> prec. e.g. | |
|
|
1258 | 4 : at least one Gaussian of cspc exceeds widthlimit | |
|
|
1259 | 5 : zero out of three cspc Gaussian fits converged | |
|
|
1260 | 6 : phase slope fit could not be found | |
|
|
1261 | 7 : arrays used to fit phase have different length | |
|
|
1262 | 8 : frequency range is either too short (len <= 5) or very long (> 30% of cspc) | |
|
|
1263 | ||
|
|
1264 | """ | |
|
|
1265 | ||
|
|
1266 | error_code = 0 | |
|
|
1267 | ||
|
|
1268 | ||
|
|
1269 | SPC_Samples = numpy.ones([spc.shape[0],spc.shape[1]]) # for normalized spc values for one height | |
|
|
1270 | phase = numpy.ones([spc.shape[0],spc.shape[1]]) # phase between channels | |
|
|
1271 | CSPC_Samples = numpy.ones([spc.shape[0],spc.shape[1]],dtype=numpy.complex_) # for normalized cspc values | |
|
|
1272 | PhaseSlope = numpy.zeros(spc.shape[0]) # slope of the phases, channelwise | |
|
|
1273 | PhaseInter = numpy.ones(spc.shape[0]) # intercept to the slope of the phases, channelwise | |
|
|
1274 | xFrec = AbbsisaRange[0][0:spc.shape[1]] # frequency range | |
|
|
1275 | xVel = AbbsisaRange[2][0:spc.shape[1]] # velocity range | |
|
|
1276 | SPCav = numpy.average(spc, axis=0)-numpy.average(noise) # spc[0]-noise[0] | |
|
|
1277 | ||
|
|
1278 | SPCmoments_vel = self.Moments(SPCav, xVel ) # SPCmoments_vel[1] corresponds to vertical velocity and is used to determine if signal corresponds to wind (if .. <3) | |
|
|
1279 | CSPCmoments = [] | |
|
|
1280 | ||
|
|
1281 | ||
|
|
1282 | '''Getting Eij and Nij''' | |
|
|
1283 | ||
|
|
1284 | Xi01, Xi02, Xi12 = ChanDist[:,0] | |
|
|
1285 | Eta01, Eta02, Eta12 = ChanDist[:,1] | |
|
|
1286 | ||
|
|
1287 | # update nov 19 | |
|
|
1288 | widthlimit = 7 # maximum width in Hz of the gaussian, empirically determined. Anything above 10 is unrealistic, often values between 1 and 5 correspond to proper fits. | |
|
|
1289 | ||
|
|
1290 | '''************************* SPC is normalized ********************************''' | |
|
|
1291 | ||
|
|
1292 | spc_norm = spc.copy() # need copy() because untouched spc is needed for normalization of cspc below | |
|
|
1293 | spc_norm = numpy.where(numpy.isfinite(spc_norm), spc_norm, numpy.NAN) | |
|
|
1294 | ||
|
|
1295 | for i in range(spc.shape[0]): | |
|
|
1296 | ||
|
|
1297 | spc_sub = spc_norm[i,:] - noise[i] # spc not smoothed here or in previous version. | |
|
|
1298 | ||
|
|
1299 | Factor_Norm = 2*numpy.max(xFrec) / numpy.count_nonzero(~numpy.isnan(spc_sub)) # usually = Freq range / nfft | |
|
|
1300 | normalized_spc = spc_sub / (numpy.nansum(numpy.abs(spc_sub)) * Factor_Norm) | |
|
|
1301 | ||
|
|
1302 | xSamples = xFrec # the frequency range is taken | |
|
|
1303 | SPC_Samples[i] = normalized_spc # Normalized SPC values are taken | |
|
|
1304 | ||
|
|
1305 | '''********************** FITTING MEAN SPC GAUSSIAN **********************''' | |
|
|
1306 | ||
|
|
1307 | """ the gaussian of the mean: first subtract noise, then normalize. this is legal because | |
|
|
1308 | you only fit the curve and don't need the absolute value of height for calculation, | |
|
|
1309 | only for estimation of width. for normalization of cross spectra, you need initial, | |
|
|
1310 | unnormalized self-spectra With noise. | |
|
|
1311 | ||
|
|
1312 | Technically, you don't even need to normalize the self-spectra, as you only need the | |
|
|
1313 | width of the peak. However, it was left this way. Note that the normalization has a flaw: | |
|
|
1314 | due to subtraction of the noise, some values are below zero. Raw "spc" values should be | |
|
|
1315 | >= 0, as it is the modulus squared of the signals (complex * it's conjugate) | |
|
|
1316 | """ | |
|
|
1317 | ||
|
|
1318 | SPCMean = numpy.average(SPC_Samples, axis=0) | |
|
|
1319 | ||
|
|
1320 | popt = [1e-10,0,1e-10] | |
|
|
1321 | SPCMoments = self.Moments(SPCMean, xSamples) | |
|
|
1322 | ||
|
|
1323 | if dbSNR > SNRlimit and numpy.abs(SPCmoments_vel[1]) < 3: | |
|
|
1324 | try: | |
|
|
1325 | popt,pcov = curve_fit(self.gaus,xSamples,SPCMean,p0=SPCMoments)#, bounds=(-numpy.inf, [numpy.inf, numpy.inf, 10])). Setting bounds does not make the code faster but only keeps the fit from finding the minimum. | |
|
|
1326 | if popt[2] > widthlimit: # CONDITION | |
|
|
1327 | return self.StopWindEstimation(error_code = 1) | |
|
|
1328 | ||
|
|
1329 | FitGauss = self.gaus(xSamples,*popt) | |
|
|
1330 | ||
|
|
1331 | except :#RuntimeError: | |
|
|
1332 | return self.StopWindEstimation(error_code = 2) | |
|
|
1333 | ||
|
|
1334 | else: | |
|
|
1335 | return self.StopWindEstimation(error_code = 3) | |
|
|
1336 | ||
|
|
1337 | ||
|
|
1338 | ||
|
|
1339 | '''***************************** CSPC Normalization ************************* | |
|
|
1340 | new section: | |
|
|
1341 | The Spc spectra are used to normalize the crossspectra. Peaks from precipitation | |
|
|
1342 | influence the norm which is not desired. First, a range is identified where the | |
|
|
1343 | wind peak is estimated -> sum_wind is sum of those frequencies. Next, the area | |
|
|
1344 | around it gets cut off and values replaced by mean determined by the boundary | |
|
|
1345 | data -> sum_noise (spc is not normalized here, thats why the noise is important) | |
|
|
1346 | ||
|
|
1347 | The sums are then added and multiplied by range/datapoints, because you need | |
|
|
1348 | an integral and not a sum for normalization. | |
|
|
1349 | ||
|
|
1350 | A norm is found according to Briggs 92. | |
|
|
1351 | ''' | |
|
|
1352 | ||
|
|
1353 | radarWavelength = 0.6741 # meters | |
|
|
1354 | count_limit_freq = numpy.abs(popt[1]) + widthlimit # Hz, m/s can be also used if velocity is desired abscissa. | |
|
|
1355 | # count_limit_freq = numpy.max(xFrec) | |
|
|
1356 | ||
|
|
1357 | channel_integrals = numpy.zeros(3) | |
|
|
1358 | ||
|
|
1359 | for i in range(spc.shape[0]): | |
|
|
1360 | ''' | |
|
|
1361 | find the point in array corresponding to count_limit frequency. | |
|
|
1362 | sum over all frequencies in the range around zero Hz @ math.ceil(N_freq/2) | |
|
|
1363 | ''' | |
|
|
1364 | N_freq = numpy.count_nonzero(~numpy.isnan(spc[i,:])) | |
|
|
1365 | count_limit_int = int(math.ceil( count_limit_freq / numpy.max(xFrec) * (N_freq / 2) )) # gives integer point | |
|
|
1366 | sum_wind = numpy.nansum( spc[i, (math.ceil(N_freq/2) - count_limit_int) : (math.ceil(N_freq / 2) + count_limit_int)] ) #N_freq/2 is where frequency (velocity) is zero, i.e. middle of spectrum. | |
|
|
1367 | sum_noise = (numpy.mean(spc[i, :4]) + numpy.mean(spc[i, -6:-2]))/2.0 * (N_freq - 2*count_limit_int) | |
|
|
1368 | channel_integrals[i] = (sum_noise + sum_wind) * (2*numpy.max(xFrec) / N_freq) | |
|
|
1369 | ||
|
|
1370 | ||
|
|
1371 | cross_integrals_peak = numpy.zeros(3) | |
|
|
1372 | # cross_integrals_totalrange = numpy.zeros(3) | |
|
|
1373 | ||
|
|
1374 | for i in range(spc.shape[0]): | |
|
|
1375 | ||
|
|
1376 | cspc_norm = cspc[i,:].copy() # cspc not smoothed here or in previous version | |
|
|
1377 | ||
|
|
1378 | chan_index0 = pairsList[i][0] | |
|
|
1379 | chan_index1 = pairsList[i][1] | |
|
|
1380 | ||
|
|
1381 | cross_integrals_peak[i] = channel_integrals[chan_index0]*channel_integrals[chan_index1] | |
|
|
1382 | normalized_cspc = cspc_norm / numpy.sqrt(cross_integrals_peak[i]) | |
|
|
1383 | CSPC_Samples[i] = normalized_cspc | |
|
|
1384 | ||
|
|
1385 | ''' Finding cross integrals without subtracting any peaks:''' | |
|
|
1386 | # FactorNorm0 = 2*numpy.max(xFrec) / numpy.count_nonzero(~numpy.isnan(spc[chan_index0,:])) | |
|
|
1387 | # FactorNorm1 = 2*numpy.max(xFrec) / numpy.count_nonzero(~numpy.isnan(spc[chan_index1,:])) | |
|
|
1388 | # cross_integrals_totalrange[i] = (numpy.nansum(spc[chan_index0,:])) * FactorNorm0 * (numpy.nansum(spc[chan_index1,:])) * FactorNorm1 | |
|
|
1389 | # normalized_cspc = cspc_norm / numpy.sqrt(cross_integrals_totalrange[i]) | |
|
|
1390 | # CSPC_Samples[i] = normalized_cspc | |
|
|
1391 | ||
|
|
1392 | ||
|
|
1393 | phase[i] = numpy.arctan2(CSPC_Samples[i].imag, CSPC_Samples[i].real) | |
|
|
1394 | ||
|
|
1395 | ||
|
|
1396 | CSPCmoments = numpy.vstack([self.Moments(numpy.abs(CSPC_Samples[0]), xSamples), | |
|
|
1397 | self.Moments(numpy.abs(CSPC_Samples[1]), xSamples), | |
|
|
1398 | self.Moments(numpy.abs(CSPC_Samples[2]), xSamples)]) | |
|
|
1399 | ||
|
|
1400 | ||
|
|
1401 | '''***Sorting out NaN entries***''' | |
|
|
1402 | CSPCMask01 = numpy.abs(CSPC_Samples[0]) | |
|
|
1403 | CSPCMask02 = numpy.abs(CSPC_Samples[1]) | |
|
|
1404 | CSPCMask12 = numpy.abs(CSPC_Samples[2]) | |
|
|
1405 | ||
|
|
1406 | mask01 = ~numpy.isnan(CSPCMask01) | |
|
|
1407 | mask02 = ~numpy.isnan(CSPCMask02) | |
|
|
1408 | mask12 = ~numpy.isnan(CSPCMask12) | |
|
|
1409 | ||
|
|
1410 | CSPCMask01 = CSPCMask01[mask01] | |
|
|
1411 | CSPCMask02 = CSPCMask02[mask02] | |
|
|
1412 | CSPCMask12 = CSPCMask12[mask12] | |
|
|
1413 | ||
|
|
1174 | 1414 | |
|
|
1415 | popt01, popt02, popt12 = [1e-10,1e-10,1e-10], [1e-10,1e-10,1e-10] ,[1e-10,1e-10,1e-10] | |
|
|
1416 | FitGauss01, FitGauss02, FitGauss12 = numpy.empty(len(xSamples))*0, numpy.empty(len(xSamples))*0, numpy.empty(len(xSamples))*0 | |
|
|
1417 | ||
|
|
1418 | '''*******************************FIT GAUSS CSPC************************************''' | |
|
|
1419 | ||
|
|
1420 | try: | |
|
|
1421 | popt01,pcov = curve_fit(self.gaus,xSamples[mask01],numpy.abs(CSPCMask01),p0=CSPCmoments[0]) | |
|
|
1422 | if popt01[2] > widthlimit: # CONDITION | |
|
|
1423 | return self.StopWindEstimation(error_code = 4) | |
|
|
1424 | ||
|
|
1425 | popt02,pcov = curve_fit(self.gaus,xSamples[mask02],numpy.abs(CSPCMask02),p0=CSPCmoments[1]) | |
|
|
1426 | if popt02[2] > widthlimit: # CONDITION | |
|
|
1427 | return self.StopWindEstimation(error_code = 4) | |
|
|
1428 | ||
|
|
1429 | popt12,pcov = curve_fit(self.gaus,xSamples[mask12],numpy.abs(CSPCMask12),p0=CSPCmoments[2]) | |
|
|
1430 | if popt12[2] > widthlimit: # CONDITION | |
|
|
1431 | return self.StopWindEstimation(error_code = 4) | |
|
|
1432 | ||
|
|
1433 | FitGauss01 = self.gaus(xSamples, *popt01) | |
|
|
1434 | FitGauss02 = self.gaus(xSamples, *popt02) | |
|
|
1435 | FitGauss12 = self.gaus(xSamples, *popt12) | |
|
|
1436 | ||
|
|
1437 | except: | |
|
|
1438 | return self.StopWindEstimation(error_code = 5) | |
|
|
1439 | ||
|
|
1440 | ||
|
|
1441 | '''************* Getting Fij ***************''' | |
|
|
1442 | ||
|
|
1443 | ||
|
|
1444 | #Punto en Eje X de la Gaussiana donde se encuentra el centro -- x-axis point of the gaussian where the center is located | |
|
|
1445 | # -> PointGauCenter | |
|
|
1446 | GaussCenter = popt[1] | |
|
|
1447 | ClosestCenter = xSamples[numpy.abs(xSamples-GaussCenter).argmin()] | |
|
|
1448 | PointGauCenter = numpy.where(xSamples==ClosestCenter)[0][0] | |
|
|
1449 | ||
|
|
1450 | #Punto e^-1 hubicado en la Gaussiana -- point where e^-1 is located in the gaussian | |
|
|
1451 | PeMinus1 = numpy.max(FitGauss) * numpy.exp(-1) | |
|
|
1452 | FijClosest = FitGauss[numpy.abs(FitGauss-PeMinus1).argmin()] # El punto mas cercano a "Peminus1" dentro de "FitGauss" | |
|
|
1453 | PointFij = numpy.where(FitGauss==FijClosest)[0][0] | |
|
|
1454 | ||
|
|
1455 | Fij = numpy.abs(xSamples[PointFij] - xSamples[PointGauCenter]) | |
|
|
1456 | ||
|
|
1457 | '''********** Taking frequency ranges from mean SPCs **********''' | |
|
|
1458 | ||
|
|
1459 | #GaussCenter = popt[1] #Primer momento 01 | |
|
|
1460 | GauWidth = popt[2] * 3/2 #Ancho de banda de Gau01 -- Bandwidth of Gau01 TODO why *3/2? | |
|
|
1461 | Range = numpy.empty(2) | |
|
|
1462 | Range[0] = GaussCenter - GauWidth | |
|
|
1463 | Range[1] = GaussCenter + GauWidth | |
|
|
1464 | #Punto en Eje X de la Gaussiana donde se encuentra ancho de banda (min:max) -- Point in x-axis where the bandwidth is located (min:max) | |
|
|
1465 | ClosRangeMin = xSamples[numpy.abs(xSamples-Range[0]).argmin()] | |
|
|
1466 | ClosRangeMax = xSamples[numpy.abs(xSamples-Range[1]).argmin()] | |
|
|
1467 | ||
|
|
1468 | PointRangeMin = numpy.where(xSamples==ClosRangeMin)[0][0] | |
|
|
1469 | PointRangeMax = numpy.where(xSamples==ClosRangeMax)[0][0] | |
|
|
1470 | ||
|
|
1471 | Range = numpy.array([ PointRangeMin, PointRangeMax ]) | |
|
|
1472 | ||
|
|
1473 | FrecRange = xFrec[ Range[0] : Range[1] ] | |
|
|
1474 | ||
|
|
1475 | ||
|
|
1476 | '''************************** Getting Phase Slope ***************************''' | |
|
|
1477 | ||
|
|
1478 | for i in range(1,3): # Changed to only compute two | |
|
|
1479 | ||
|
|
1480 | if len(FrecRange) > 5 and len(FrecRange) < spc.shape[1] * 0.3: | |
|
|
1481 | # PhaseRange=self.moving_average(phase[i,Range[0]:Range[1]],N=1) #used before to smooth phase with N=3 | |
|
|
1482 | PhaseRange = phase[i,Range[0]:Range[1]].copy() | |
|
|
1483 | ||
|
|
1484 | mask = ~numpy.isnan(FrecRange) & ~numpy.isnan(PhaseRange) | |
|
|
1485 | ||
|
|
1486 | ||
|
|
1487 | if len(FrecRange) == len(PhaseRange): | |
|
|
1488 | ||
|
|
1489 | try: | |
|
|
1490 | slope, intercept, _, _, _ = stats.linregress(FrecRange[mask], self.AntiAliasing(PhaseRange[mask], 4.5)) | |
|
|
1491 | PhaseSlope[i] = slope | |
|
|
1492 | PhaseInter[i] = intercept | |
|
|
1493 | ||
|
|
1494 | except: | |
|
|
1495 | return self.StopWindEstimation(error_code = 6) | |
|
|
1496 | ||
|
|
1497 | else: | |
|
|
1498 | return self.StopWindEstimation(error_code = 7) | |
|
|
1499 | ||
|
|
1500 | else: | |
|
|
1501 | return self.StopWindEstimation(error_code = 8) | |
|
|
1502 | ||
|
|
1503 | ||
|
|
1504 | ||
|
|
1505 | '''*** Constants A-H correspond to the convention as in Briggs and Vincent 1992 ***''' | |
|
|
1506 | ||
|
|
1507 | '''Getting constant C''' | |
|
|
1508 | cC=(Fij*numpy.pi)**2 | |
|
|
1509 | ||
|
|
1510 | '''****** Getting constants F and G ******''' | |
|
|
1511 | MijEijNij = numpy.array([[Xi02,Eta02], [Xi12,Eta12]]) | |
|
|
1512 | MijResult0 = (-PhaseSlope[1] * cC) / (2*numpy.pi) | |
|
|
1513 | MijResult1 = (-PhaseSlope[2] * cC) / (2*numpy.pi) | |
|
|
1514 | MijResults = numpy.array([MijResult0,MijResult1]) | |
|
|
1515 | (cF,cG) = numpy.linalg.solve(MijEijNij, MijResults) | |
|
|
1516 | ||
|
|
1517 | '''****** Getting constants A, B and H ******''' | |
|
|
1518 | W01 = numpy.nanmax( FitGauss01 ) | |
|
|
1519 | W02 = numpy.nanmax( FitGauss02 ) | |
|
|
1520 | W12 = numpy.nanmax( FitGauss12 ) | |
|
|
1521 | ||
|
|
1522 | WijResult0 = ((cF * Xi01 + cG * Eta01)**2)/cC - numpy.log(W01 / numpy.sqrt(numpy.pi / cC)) | |
|
|
1523 | WijResult1 = ((cF * Xi02 + cG * Eta02)**2)/cC - numpy.log(W02 / numpy.sqrt(numpy.pi / cC)) | |
|
|
1524 | WijResult2 = ((cF * Xi12 + cG * Eta12)**2)/cC - numpy.log(W12 / numpy.sqrt(numpy.pi / cC)) | |
|
|
1525 | ||
|
|
1526 | WijResults = numpy.array([WijResult0, WijResult1, WijResult2]) | |
|
|
1527 | ||
|
|
1528 | WijEijNij = numpy.array([ [Xi01**2, Eta01**2, 2*Xi01*Eta01] , [Xi02**2, Eta02**2, 2*Xi02*Eta02] , [Xi12**2, Eta12**2, 2*Xi12*Eta12] ]) | |
|
|
1529 | (cA,cB,cH) = numpy.linalg.solve(WijEijNij, WijResults) | |
|
|
1530 | ||
|
|
1531 | VxVy = numpy.array([[cA,cH],[cH,cB]]) | |
|
|
1532 | VxVyResults = numpy.array([-cF,-cG]) | |
|
|
1533 | (Vx,Vy) = numpy.linalg.solve(VxVy, VxVyResults) | |
|
|
1534 | ||
|
|
1535 | Vzon = Vy | |
|
|
1536 | Vmer = Vx | |
|
|
1537 | ||
|
|
1538 | # Vmag=numpy.sqrt(Vzon**2+Vmer**2) # unused | |
|
|
1539 | # Vang=numpy.arctan2(Vmer,Vzon) # unused | |
|
|
1540 | ||
|
|
1541 | ||
|
|
1542 | ''' using frequency as abscissa. Due to three channels, the offzenith angle is zero | |
|
|
1543 | and Vrad equal to Vver. formula taken from Briggs 92, figure 4. | |
|
|
1544 | ''' | |
|
|
1545 | if numpy.abs( popt[1] ) < 3.5 and len(FrecRange) > 4: | |
|
|
1546 | Vver = 0.5 * radarWavelength * popt[1] * 100 # *100 to get cm (/s) | |
|
|
1547 | else: | |
|
|
1548 | Vver = numpy.NaN | |
|
|
1549 | ||
|
|
1550 | error_code = 0 | |
|
|
1551 | ||
|
|
1552 | return Vzon, Vmer, Vver, error_code | |
|
|
1553 | ||
|
|
1554 | ||
|
|
1555 | ||
|
|
1175 | 1556 | class SpectralMoments(Operation): |
|
|
1176 | 1557 | |
|
|
1177 | 1558 | ''' |
|
|
1178 | 1559 | Function SpectralMoments() |
|
|
1179 | 1560 | |
|
|
1180 | 1561 | Calculates moments (power, mean, standard deviation) and SNR of the signal |
|
|
1181 | 1562 | |
|
|
1182 | 1563 | Type of dataIn: Spectra |
|
|
1183 | 1564 | |
|
|
1184 | 1565 | Configuration Parameters: |
|
|
1185 | 1566 | |
|
|
1186 | 1567 | dirCosx : Cosine director in X axis |
|
|
1187 | 1568 | dirCosy : Cosine director in Y axis |
|
|
1188 | 1569 | |
|
|
1189 | 1570 | elevation : |
|
|
1190 | 1571 | azimuth : |
|
|
1191 | 1572 | |
|
|
1192 | 1573 | Input: |
|
|
1193 | 1574 | channelList : simple channel list to select e.g. [2,3,7] |
|
|
1194 | 1575 | self.dataOut.data_pre : Spectral data |
|
|
1195 | 1576 | self.dataOut.abscissaList : List of frequencies |
|
|
1196 | 1577 | self.dataOut.noise : Noise level per channel |
|
|
1197 | 1578 | |
|
|
1198 | 1579 | Affected: |
|
|
1199 | 1580 | self.dataOut.moments : Parameters per channel |
|
|
1200 | 1581 | self.dataOut.data_SNR : SNR per channel |
|
|
1201 | 1582 | |
|
|
1202 | 1583 | ''' |
|
|
1203 | 1584 | |
|
|
1204 | 1585 | def run(self, dataOut): |
|
|
1205 | 1586 | |
|
|
1206 | 1587 | #dataOut.data_pre = dataOut.data_pre[0] |
|
|
1207 | 1588 | data = dataOut.data_pre[0] |
|
|
1208 | 1589 | absc = dataOut.abscissaList[:-1] |
|
|
1209 | 1590 | noise = dataOut.noise |
|
|
1210 | 1591 | nChannel = data.shape[0] |
|
|
1211 | 1592 | data_param = numpy.zeros((nChannel, 4, data.shape[2])) |
|
|
1212 | 1593 | |
|
|
1213 | 1594 | for ind in range(nChannel): |
|
|
1214 | 1595 | data_param[ind,:,:] = self.__calculateMoments( data[ind,:,:] , absc , noise[ind] ) |
|
|
1215 | 1596 | |
|
|
1216 | 1597 | dataOut.moments = data_param[:,1:,:] |
|
|
1217 | 1598 | dataOut.data_SNR = data_param[:,0] |
|
|
1218 | 1599 | dataOut.data_POW = data_param[:,1] |
|
|
1219 | 1600 | dataOut.data_DOP = data_param[:,2] |
|
|
1220 | 1601 | dataOut.data_WIDTH = data_param[:,3] |
|
|
1221 | 1602 | return dataOut |
|
|
1222 | 1603 | |
|
|
1223 | 1604 | def __calculateMoments(self, oldspec, oldfreq, n0, |
|
|
1224 | 1605 | nicoh = None, graph = None, smooth = None, type1 = None, fwindow = None, snrth = None, dc = None, aliasing = None, oldfd = None, wwauto = None): |
|
|
1225 | 1606 | |
|
|
1226 | 1607 | if (nicoh is None): nicoh = 1 |
|
|
1227 | 1608 | if (graph is None): graph = 0 |
|
|
1228 | 1609 | if (smooth is None): smooth = 0 |
|
|
1229 | 1610 | elif (self.smooth < 3): smooth = 0 |
|
|
1230 | 1611 | |
|
|
1231 | 1612 | if (type1 is None): type1 = 0 |
|
|
1232 | 1613 | if (fwindow is None): fwindow = numpy.zeros(oldfreq.size) + 1 |
|
|
1233 | 1614 | if (snrth is None): snrth = -3 |
|
|
1234 | 1615 | if (dc is None): dc = 0 |
|
|
1235 | 1616 | if (aliasing is None): aliasing = 0 |
|
|
1236 | 1617 | if (oldfd is None): oldfd = 0 |
|
|
1237 | 1618 | if (wwauto is None): wwauto = 0 |
|
|
1238 | 1619 | |
|
|
1239 | 1620 | if (n0 < 1.e-20): n0 = 1.e-20 |
|
|
1240 | 1621 | |
|
|
1241 | 1622 | freq = oldfreq |
|
|
1242 | 1623 | vec_power = numpy.zeros(oldspec.shape[1]) |
|
|
1243 | 1624 | vec_fd = numpy.zeros(oldspec.shape[1]) |
|
|
1244 | 1625 | vec_w = numpy.zeros(oldspec.shape[1]) |
|
|
1245 | 1626 | vec_snr = numpy.zeros(oldspec.shape[1]) |
|
|
1246 | 1627 | |
|
|
1247 | 1628 | oldspec = numpy.ma.masked_invalid(oldspec) |
|
|
1248 | 1629 | |
|
|
1249 | 1630 | for ind in range(oldspec.shape[1]): |
|
|
1250 | 1631 | |
|
|
1251 | 1632 | spec = oldspec[:,ind] |
|
|
1252 | 1633 | aux = spec*fwindow |
|
|
1253 | 1634 | max_spec = aux.max() |
|
|
1254 | 1635 | m = list(aux).index(max_spec) |
|
|
1255 | 1636 | |
|
|
1256 | 1637 | #Smooth |
|
|
1257 | 1638 | if (smooth == 0): spec2 = spec |
|
|
1258 | 1639 | else: spec2 = scipy.ndimage.filters.uniform_filter1d(spec,size=smooth) |
|
|
1259 | 1640 | |
|
|
1260 | 1641 | # Calculo de Momentos |
|
|
1261 | 1642 | bb = spec2[list(range(m,spec2.size))] |
|
|
1262 | 1643 | bb = (bb<n0).nonzero() |
|
|
1263 | 1644 | bb = bb[0] |
|
|
1264 | 1645 | |
|
|
1265 | 1646 | ss = spec2[list(range(0,m + 1))] |
|
|
1266 | 1647 | ss = (ss<n0).nonzero() |
|
|
1267 | 1648 | ss = ss[0] |
|
|
1268 | 1649 | |
|
|
1269 | 1650 | if (bb.size == 0): |
|
|
1270 | 1651 | bb0 = spec.size - 1 - m |
|
|
1271 | 1652 | else: |
|
|
1272 | 1653 | bb0 = bb[0] - 1 |
|
|
1273 | 1654 | if (bb0 < 0): |
|
|
1274 | 1655 | bb0 = 0 |
|
|
1275 | 1656 | |
|
|
1276 | 1657 | if (ss.size == 0): ss1 = 1 |
|
|
1277 | 1658 | else: ss1 = max(ss) + 1 |
|
|
1278 | 1659 | |
|
|
1279 | 1660 | if (ss1 > m): ss1 = m |
|
|
1280 | 1661 | |
|
|
1281 | 1662 | valid = numpy.asarray(list(range(int(m + bb0 - ss1 + 1)))) + ss1 |
|
|
1282 | 1663 | power = ((spec2[valid] - n0)*fwindow[valid]).sum() |
|
|
1283 | 1664 | fd = ((spec2[valid]- n0)*freq[valid]*fwindow[valid]).sum()/power |
|
|
1284 | 1665 | w = math.sqrt(((spec2[valid] - n0)*fwindow[valid]*(freq[valid]- fd)**2).sum()/power) |
|
|
1285 | 1666 | snr = (spec2.mean()-n0)/n0 |
|
|
1286 | 1667 | |
|
|
1287 | 1668 | if (snr < 1.e-20) : |
|
|
1288 | 1669 | snr = 1.e-20 |
|
|
1289 | 1670 | |
|
|
1290 | 1671 | vec_power[ind] = power |
|
|
1291 | 1672 | vec_fd[ind] = fd |
|
|
1292 | 1673 | vec_w[ind] = w |
|
|
1293 | 1674 | vec_snr[ind] = snr |
|
|
1294 | 1675 | |
|
|
1295 | 1676 | moments = numpy.vstack((vec_snr, vec_power, vec_fd, vec_w)) |
|
|
1296 | 1677 | return moments |
|
|
1297 | 1678 | |
|
|
1298 | 1679 | #------------------ Get SA Parameters -------------------------- |
|
|
1299 | 1680 | |
|
|
1300 | 1681 | def GetSAParameters(self): |
|
|
1301 | 1682 | #SA en frecuencia |
|
|
1302 | 1683 | pairslist = self.dataOut.groupList |
|
|
1303 | 1684 | num_pairs = len(pairslist) |
|
|
1304 | 1685 | |
|
|
1305 | 1686 | vel = self.dataOut.abscissaList |
|
|
1306 | 1687 | spectra = self.dataOut.data_pre |
|
|
1307 | 1688 | cspectra = self.dataIn.data_cspc |
|
|
1308 | 1689 | delta_v = vel[1] - vel[0] |
|
|
1309 | 1690 | |
|
|
1310 | 1691 | #Calculating the power spectrum |
|
|
1311 | 1692 | spc_pow = numpy.sum(spectra, 3)*delta_v |
|
|
1312 | 1693 | #Normalizing Spectra |
|
|
1313 | 1694 | norm_spectra = spectra/spc_pow |
|
|
1314 | 1695 | #Calculating the norm_spectra at peak |
|
|
1315 | 1696 | max_spectra = numpy.max(norm_spectra, 3) |
|
|
1316 | 1697 | |
|
|
1317 | 1698 | #Normalizing Cross Spectra |
|
|
1318 | 1699 | norm_cspectra = numpy.zeros(cspectra.shape) |
|
|
1319 | 1700 | |
|
|
1320 | 1701 | for i in range(num_chan): |
|
|
1321 | 1702 | norm_cspectra[i,:,:] = cspectra[i,:,:]/numpy.sqrt(spc_pow[pairslist[i][0],:]*spc_pow[pairslist[i][1],:]) |
|
|
1322 | 1703 | |
|
|
1323 | 1704 | max_cspectra = numpy.max(norm_cspectra,2) |
|
|
1324 | 1705 | max_cspectra_index = numpy.argmax(norm_cspectra, 2) |
|
|
1325 | 1706 | |
|
|
1326 | 1707 | for i in range(num_pairs): |
|
|
1327 | 1708 | cspc_par[i,:,:] = __calculateMoments(norm_cspectra) |
|
|
1328 | 1709 | #------------------- Get Lags ---------------------------------- |
|
|
1329 | 1710 | |
|
|
1330 | 1711 | class SALags(Operation): |
|
|
1331 | 1712 | ''' |
|
|
1332 | 1713 | Function GetMoments() |
|
|
1333 | 1714 | |
|
|
1334 | 1715 | Input: |
|
|
1335 | 1716 | self.dataOut.data_pre |
|
|
1336 | 1717 | self.dataOut.abscissaList |
|
|
1337 | 1718 | self.dataOut.noise |
|
|
1338 | 1719 | self.dataOut.normFactor |
|
|
1339 | 1720 | self.dataOut.data_SNR |
|
|
1340 | 1721 | self.dataOut.groupList |
|
|
1341 | 1722 | self.dataOut.nChannels |
|
|
1342 | 1723 | |
|
|
1343 | 1724 | Affected: |
|
|
1344 | 1725 | self.dataOut.data_param |
|
|
1345 | 1726 | |
|
|
1346 | 1727 | ''' |
|
|
1347 | 1728 | def run(self, dataOut): |
|
|
1348 | 1729 | data_acf = dataOut.data_pre[0] |
|
|
1349 | 1730 | data_ccf = dataOut.data_pre[1] |
|
|
1350 | 1731 | normFactor_acf = dataOut.normFactor[0] |
|
|
1351 | 1732 | normFactor_ccf = dataOut.normFactor[1] |
|
|
1352 | 1733 | pairs_acf = dataOut.groupList[0] |
|
|
1353 | 1734 | pairs_ccf = dataOut.groupList[1] |
|
|
1354 | 1735 | |
|
|
1355 | 1736 | nHeights = dataOut.nHeights |
|
|
1356 | 1737 | absc = dataOut.abscissaList |
|
|
1357 | 1738 | noise = dataOut.noise |
|
|
1358 | 1739 | SNR = dataOut.data_SNR |
|
|
1359 | 1740 | nChannels = dataOut.nChannels |
|
|
1360 | 1741 | # pairsList = dataOut.groupList |
|
|
1361 | 1742 | # pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairsList, nChannels) |
|
|
1362 | 1743 | |
|
|
1363 | 1744 | for l in range(len(pairs_acf)): |
|
|
1364 | 1745 | data_acf[l,:,:] = data_acf[l,:,:]/normFactor_acf[l,:] |
|
|
1365 | 1746 | |
|
|
1366 | 1747 | for l in range(len(pairs_ccf)): |
|
|
1367 | 1748 | data_ccf[l,:,:] = data_ccf[l,:,:]/normFactor_ccf[l,:] |
|
|
1368 | 1749 | |
|
|
1369 | 1750 | dataOut.data_param = numpy.zeros((len(pairs_ccf)*2 + 1, nHeights)) |
|
|
1370 | 1751 | dataOut.data_param[:-1,:] = self.__calculateTaus(data_acf, data_ccf, absc) |
|
|
1371 | 1752 | dataOut.data_param[-1,:] = self.__calculateLag1Phase(data_acf, absc) |
|
|
1372 | 1753 | return |
|
|
1373 | 1754 | |
|
|
1374 | 1755 | # def __getPairsAutoCorr(self, pairsList, nChannels): |
|
|
1375 | 1756 | # |
|
|
1376 | 1757 | # pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan |
|
|
1377 | 1758 | # |
|
|
1378 | 1759 | # for l in range(len(pairsList)): |
|
|
1379 | 1760 | # firstChannel = pairsList[l][0] |
|
|
1380 | 1761 | # secondChannel = pairsList[l][1] |
|
|
1381 | 1762 | # |
|
|
1382 | 1763 | # #Obteniendo pares de Autocorrelacion |
|
|
1383 | 1764 | # if firstChannel == secondChannel: |
|
|
1384 | 1765 | # pairsAutoCorr[firstChannel] = int(l) |
|
|
1385 | 1766 | # |
|
|
1386 | 1767 | # pairsAutoCorr = pairsAutoCorr.astype(int) |
|
|
1387 | 1768 | # |
|
|
1388 | 1769 | # pairsCrossCorr = range(len(pairsList)) |
|
|
1389 | 1770 | # pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr) |
|
|
1390 | 1771 | # |
|
|
1391 | 1772 | # return pairsAutoCorr, pairsCrossCorr |
|
|
1392 | 1773 | |
|
|
1393 | 1774 | def __calculateTaus(self, data_acf, data_ccf, lagRange): |
|
|
1394 | 1775 | |
|
|
1395 | 1776 | lag0 = data_acf.shape[1]/2 |
|
|
1396 | 1777 | #Funcion de Autocorrelacion |
|
|
1397 | 1778 | mean_acf = stats.nanmean(data_acf, axis = 0) |
|
|
1398 | 1779 | |
|
|
1399 | 1780 | #Obtencion Indice de TauCross |
|
|
1400 | 1781 | ind_ccf = data_ccf.argmax(axis = 1) |
|
|
1401 | 1782 | #Obtencion Indice de TauAuto |
|
|
1402 | 1783 | ind_acf = numpy.zeros(ind_ccf.shape,dtype = 'int') |
|
|
1403 | 1784 | ccf_lag0 = data_ccf[:,lag0,:] |
|
|
1404 | 1785 | |
|
|
1405 | 1786 | for i in range(ccf_lag0.shape[0]): |
|
|
1406 | 1787 | ind_acf[i,:] = numpy.abs(mean_acf - ccf_lag0[i,:]).argmin(axis = 0) |
|
|
1407 | 1788 | |
|
|
1408 | 1789 | #Obtencion de TauCross y TauAuto |
|
|
1409 | 1790 | tau_ccf = lagRange[ind_ccf] |
|
|
1410 | 1791 | tau_acf = lagRange[ind_acf] |
|
|
1411 | 1792 | |
|
|
1412 | 1793 | Nan1, Nan2 = numpy.where(tau_ccf == lagRange[0]) |
|
|
1413 | 1794 | |
|
|
1414 | 1795 | tau_ccf[Nan1,Nan2] = numpy.nan |
|
|
1415 | 1796 | tau_acf[Nan1,Nan2] = numpy.nan |
|
|
1416 | 1797 | tau = numpy.vstack((tau_ccf,tau_acf)) |
|
|
1417 | 1798 | |
|
|
1418 | 1799 | return tau |
|
|
1419 | 1800 | |
|
|
1420 | 1801 | def __calculateLag1Phase(self, data, lagTRange): |
|
|
1421 | 1802 | data1 = stats.nanmean(data, axis = 0) |
|
|
1422 | 1803 | lag1 = numpy.where(lagTRange == 0)[0][0] + 1 |
|
|
1423 | 1804 | |
|
|
1424 | 1805 | phase = numpy.angle(data1[lag1,:]) |
|
|
1425 | 1806 | |
|
|
1426 | 1807 | return phase |
|
|
1427 | 1808 | |
|
|
1428 | 1809 | class SpectralFitting(Operation): |
|
|
1429 | 1810 | ''' |
|
|
1430 | 1811 | Function GetMoments() |
|
|
1431 | 1812 | |
|
|
1432 | 1813 | Input: |
|
|
1433 | 1814 | Output: |
|
|
1434 | 1815 | Variables modified: |
|
|
1435 | 1816 | ''' |
|
|
1436 | 1817 | |
|
|
1437 | 1818 | def run(self, dataOut, getSNR = True, path=None, file=None, groupList=None): |
|
|
1438 | 1819 | |
|
|
1439 | 1820 | |
|
|
1440 | 1821 | if path != None: |
|
|
1441 | 1822 | sys.path.append(path) |
|
|
1442 | 1823 | self.dataOut.library = importlib.import_module(file) |
|
|
1443 | 1824 | |
|
|
1444 | 1825 | #To be inserted as a parameter |
|
|
1445 | 1826 | groupArray = numpy.array(groupList) |
|
|
1446 | 1827 | # groupArray = numpy.array([[0,1],[2,3]]) |
|
|
1447 | 1828 | self.dataOut.groupList = groupArray |
|
|
1448 | 1829 | |
|
|
1449 | 1830 | nGroups = groupArray.shape[0] |
|
|
1450 | 1831 | nChannels = self.dataIn.nChannels |
|
|
1451 | 1832 | nHeights=self.dataIn.heightList.size |
|
|
1452 | 1833 | |
|
|
1453 | 1834 | #Parameters Array |
|
|
1454 | 1835 | self.dataOut.data_param = None |
|
|
1455 | 1836 | |
|
|
1456 | 1837 | #Set constants |
|
|
1457 | 1838 | constants = self.dataOut.library.setConstants(self.dataIn) |
|
|
1458 | 1839 | self.dataOut.constants = constants |
|
|
1459 | 1840 | M = self.dataIn.normFactor |
|
|
1460 | 1841 | N = self.dataIn.nFFTPoints |
|
|
1461 | 1842 | ippSeconds = self.dataIn.ippSeconds |
|
|
1462 | 1843 | K = self.dataIn.nIncohInt |
|
|
1463 | 1844 | pairsArray = numpy.array(self.dataIn.pairsList) |
|
|
1464 | 1845 | |
|
|
1465 | 1846 | #List of possible combinations |
|
|
1466 | 1847 | listComb = itertools.combinations(numpy.arange(groupArray.shape[1]),2) |
|
|
1467 | 1848 | indCross = numpy.zeros(len(list(listComb)), dtype = 'int') |
|
|
1468 | 1849 | |
|
|
1469 | 1850 | if getSNR: |
|
|
1470 | 1851 | listChannels = groupArray.reshape((groupArray.size)) |
|
|
1471 | 1852 | listChannels.sort() |
|
|
1472 | 1853 | noise = self.dataIn.getNoise() |
|
|
1473 | 1854 | self.dataOut.data_SNR = self.__getSNR(self.dataIn.data_spc[listChannels,:,:], noise[listChannels]) |
|
|
1474 | 1855 | |
|
|
1475 | 1856 | for i in range(nGroups): |
|
|
1476 | 1857 | coord = groupArray[i,:] |
|
|
1477 | 1858 | |
|
|
1478 | 1859 | #Input data array |
|
|
1479 | 1860 | data = self.dataIn.data_spc[coord,:,:]/(M*N) |
|
|
1480 | 1861 | data = data.reshape((data.shape[0]*data.shape[1],data.shape[2])) |
|
|
1481 | 1862 | |
|
|
1482 | 1863 | #Cross Spectra data array for Covariance Matrixes |
|
|
1483 | 1864 | ind = 0 |
|
|
1484 | 1865 | for pairs in listComb: |
|
|
1485 | 1866 | pairsSel = numpy.array([coord[x],coord[y]]) |
|
|
1486 | 1867 | indCross[ind] = int(numpy.where(numpy.all(pairsArray == pairsSel, axis = 1))[0][0]) |
|
|
1487 | 1868 | ind += 1 |
|
|
1488 | 1869 | dataCross = self.dataIn.data_cspc[indCross,:,:]/(M*N) |
|
|
1489 | 1870 | dataCross = dataCross**2/K |
|
|
1490 | 1871 | |
|
|
1491 | 1872 | for h in range(nHeights): |
|
|
1492 | 1873 | |
|
|
1493 | 1874 | #Input |
|
|
1494 | 1875 | d = data[:,h] |
|
|
1495 | 1876 | |
|
|
1496 | 1877 | #Covariance Matrix |
|
|
1497 | 1878 | D = numpy.diag(d**2/K) |
|
|
1498 | 1879 | ind = 0 |
|
|
1499 | 1880 | for pairs in listComb: |
|
|
1500 | 1881 | #Coordinates in Covariance Matrix |
|
|
1501 | 1882 | x = pairs[0] |
|
|
1502 | 1883 | y = pairs[1] |
|
|
1503 | 1884 | #Channel Index |
|
|
1504 | 1885 | S12 = dataCross[ind,:,h] |
|
|
1505 | 1886 | D12 = numpy.diag(S12) |
|
|
1506 | 1887 | #Completing Covariance Matrix with Cross Spectras |
|
|
1507 | 1888 | D[x*N:(x+1)*N,y*N:(y+1)*N] = D12 |
|
|
1508 | 1889 | D[y*N:(y+1)*N,x*N:(x+1)*N] = D12 |
|
|
1509 | 1890 | ind += 1 |
|
|
1510 | 1891 | Dinv=numpy.linalg.inv(D) |
|
|
1511 | 1892 | L=numpy.linalg.cholesky(Dinv) |
|
|
1512 | 1893 | LT=L.T |
|
|
1513 | 1894 | |
|
|
1514 | 1895 | dp = numpy.dot(LT,d) |
|
|
1515 | 1896 | |
|
|
1516 | 1897 | #Initial values |
|
|
1517 | 1898 | data_spc = self.dataIn.data_spc[coord,:,h] |
|
|
1518 | 1899 | |
|
|
1519 | 1900 | if (h>0)and(error1[3]<5): |
|
|
1520 | 1901 | p0 = self.dataOut.data_param[i,:,h-1] |
|
|
1521 | 1902 | else: |
|
|
1522 | 1903 | p0 = numpy.array(self.dataOut.library.initialValuesFunction(data_spc, constants, i)) |
|
|
1523 | 1904 | |
|
|
1524 | 1905 | try: |
|
|
1525 | 1906 | #Least Squares |
|
|
1526 | 1907 | minp,covp,infodict,mesg,ier = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants),full_output=True) |
|
|
1527 | 1908 | # minp,covp = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants)) |
|
|
1528 | 1909 | #Chi square error |
|
|
1529 | 1910 | error0 = numpy.sum(infodict['fvec']**2)/(2*N) |
|
|
1530 | 1911 | #Error with Jacobian |
|
|
1531 | 1912 | error1 = self.dataOut.library.errorFunction(minp,constants,LT) |
|
|
1532 | 1913 | except: |
|
|
1533 | 1914 | minp = p0*numpy.nan |
|
|
1534 | 1915 | error0 = numpy.nan |
|
|
1535 | 1916 | error1 = p0*numpy.nan |
|
|
1536 | 1917 | |
|
|
1537 | 1918 | #Save |
|
|
1538 | 1919 | if self.dataOut.data_param is None: |
|
|
1539 | 1920 | self.dataOut.data_param = numpy.zeros((nGroups, p0.size, nHeights))*numpy.nan |
|
|
1540 | 1921 | self.dataOut.data_error = numpy.zeros((nGroups, p0.size + 1, nHeights))*numpy.nan |
|
|
1541 | 1922 | |
|
|
1542 | 1923 | self.dataOut.data_error[i,:,h] = numpy.hstack((error0,error1)) |
|
|
1543 | 1924 | self.dataOut.data_param[i,:,h] = minp |
|
|
1544 | 1925 | return |
|
|
1545 | 1926 | |
|
|
1546 | 1927 | def __residFunction(self, p, dp, LT, constants): |
|
|
1547 | 1928 | |
|
|
1548 | 1929 | fm = self.dataOut.library.modelFunction(p, constants) |
|
|
1549 | 1930 | fmp=numpy.dot(LT,fm) |
|
|
1550 | 1931 | |
|
|
1551 | 1932 | return dp-fmp |
|
|
1552 | 1933 | |
|
|
1553 | 1934 | def __getSNR(self, z, noise): |
|
|
1554 | 1935 | |
|
|
1555 | 1936 | avg = numpy.average(z, axis=1) |
|
|
1556 | 1937 | SNR = (avg.T-noise)/noise |
|
|
1557 | 1938 | SNR = SNR.T |
|
|
1558 | 1939 | return SNR |
|
|
1559 | 1940 | |
|
|
1560 | 1941 | def __chisq(p,chindex,hindex): |
|
|
1561 | 1942 | #similar to Resid but calculates CHI**2 |
|
|
1562 | 1943 | [LT,d,fm]=setupLTdfm(p,chindex,hindex) |
|
|
1563 | 1944 | dp=numpy.dot(LT,d) |
|
|
1564 | 1945 | fmp=numpy.dot(LT,fm) |
|
|
1565 | 1946 | chisq=numpy.dot((dp-fmp).T,(dp-fmp)) |
|
|
1566 | 1947 | return chisq |
|
|
1567 | 1948 | |
|
|
1568 | 1949 | class WindProfiler(Operation): |
|
|
1569 | 1950 | |
|
|
1570 | 1951 | __isConfig = False |
|
|
1571 | 1952 | |
|
|
1572 | 1953 | __initime = None |
|
|
1573 | 1954 | __lastdatatime = None |
|
|
1574 | 1955 | __integrationtime = None |
|
|
1575 | 1956 | |
|
|
1576 | 1957 | __buffer = None |
|
|
1577 | 1958 | |
|
|
1578 | 1959 | __dataReady = False |
|
|
1579 | 1960 | |
|
|
1580 | 1961 | __firstdata = None |
|
|
1581 | 1962 | |
|
|
1582 | 1963 | n = None |
|
|
1583 | 1964 | |
|
|
1584 | 1965 | def __init__(self): |
|
|
1585 | 1966 | Operation.__init__(self) |
|
|
1586 | 1967 | |
|
|
1587 | 1968 | def __calculateCosDir(self, elev, azim): |
|
|
1588 | 1969 | zen = (90 - elev)*numpy.pi/180 |
|
|
1589 | 1970 | azim = azim*numpy.pi/180 |
|
|
1590 | 1971 | cosDirX = numpy.sqrt((1-numpy.cos(zen)**2)/((1+numpy.tan(azim)**2))) |
|
|
1591 | 1972 | cosDirY = numpy.sqrt(1-numpy.cos(zen)**2-cosDirX**2) |
|
|
1592 | 1973 | |
|
|
1593 | 1974 | signX = numpy.sign(numpy.cos(azim)) |
|
|
1594 | 1975 | signY = numpy.sign(numpy.sin(azim)) |
|
|
1595 | 1976 | |
|
|
1596 | 1977 | cosDirX = numpy.copysign(cosDirX, signX) |
|
|
1597 | 1978 | cosDirY = numpy.copysign(cosDirY, signY) |
|
|
1598 | 1979 | return cosDirX, cosDirY |
|
|
1599 | 1980 | |
|
|
1600 | 1981 | def __calculateAngles(self, theta_x, theta_y, azimuth): |
|
|
1601 | 1982 | |
|
|
1602 | 1983 | dir_cosw = numpy.sqrt(1-theta_x**2-theta_y**2) |
|
|
1603 | 1984 | zenith_arr = numpy.arccos(dir_cosw) |
|
|
1604 | 1985 | azimuth_arr = numpy.arctan2(theta_x,theta_y) + azimuth*math.pi/180 |
|
|
1605 | 1986 | |
|
|
1606 | 1987 | dir_cosu = numpy.sin(azimuth_arr)*numpy.sin(zenith_arr) |
|
|
1607 | 1988 | dir_cosv = numpy.cos(azimuth_arr)*numpy.sin(zenith_arr) |
|
|
1608 | 1989 | |
|
|
1609 | 1990 | return azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw |
|
|
1610 | 1991 | |
|
|
1611 | 1992 | def __calculateMatA(self, dir_cosu, dir_cosv, dir_cosw, horOnly): |
|
|
1612 | 1993 | |
|
|
1613 | 1994 | # |
|
|
1614 | 1995 | if horOnly: |
|
|
1615 | 1996 | A = numpy.c_[dir_cosu,dir_cosv] |
|
|
1616 | 1997 | else: |
|
|
1617 | 1998 | A = numpy.c_[dir_cosu,dir_cosv,dir_cosw] |
|
|
1618 | 1999 | A = numpy.asmatrix(A) |
|
|
1619 | 2000 | A1 = numpy.linalg.inv(A.transpose()*A)*A.transpose() |
|
|
1620 | 2001 | |
|
|
1621 | 2002 | return A1 |
|
|
1622 | 2003 | |
|
|
1623 | 2004 | def __correctValues(self, heiRang, phi, velRadial, SNR): |
|
|
1624 | 2005 | listPhi = phi.tolist() |
|
|
1625 | 2006 | maxid = listPhi.index(max(listPhi)) |
|
|
1626 | 2007 | minid = listPhi.index(min(listPhi)) |
|
|
1627 | 2008 | |
|
|
1628 | 2009 | rango = list(range(len(phi))) |
|
|
1629 | 2010 | # rango = numpy.delete(rango,maxid) |
|
|
1630 | 2011 | |
|
|
1631 | 2012 | heiRang1 = heiRang*math.cos(phi[maxid]) |
|
|
1632 | 2013 | heiRangAux = heiRang*math.cos(phi[minid]) |
|
|
1633 | 2014 | indOut = (heiRang1 < heiRangAux[0]).nonzero() |
|
|
1634 | 2015 | heiRang1 = numpy.delete(heiRang1,indOut) |
|
|
1635 | 2016 | |
|
|
1636 | 2017 | velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
1637 | 2018 | SNR1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
1638 | 2019 | |
|
|
1639 | 2020 | for i in rango: |
|
|
1640 | 2021 | x = heiRang*math.cos(phi[i]) |
|
|
1641 | 2022 | y1 = velRadial[i,:] |
|
|
1642 | 2023 | f1 = interpolate.interp1d(x,y1,kind = 'cubic') |
|
|
1643 | 2024 | |
|
|
1644 | 2025 | x1 = heiRang1 |
|
|
1645 | 2026 | y11 = f1(x1) |
|
|
1646 | 2027 | |
|
|
1647 | 2028 | y2 = SNR[i,:] |
|
|
1648 | 2029 | f2 = interpolate.interp1d(x,y2,kind = 'cubic') |
|
|
1649 | 2030 | y21 = f2(x1) |
|
|
1650 | 2031 | |
|
|
1651 | 2032 | velRadial1[i,:] = y11 |
|
|
1652 | 2033 | SNR1[i,:] = y21 |
|
|
1653 | 2034 | |
|
|
1654 | 2035 | return heiRang1, velRadial1, SNR1 |
|
|
1655 | 2036 | |
|
|
1656 | 2037 | def __calculateVelUVW(self, A, velRadial): |
|
|
1657 | 2038 | |
|
|
1658 | 2039 | #Operacion Matricial |
|
|
1659 | 2040 | # velUVW = numpy.zeros((velRadial.shape[1],3)) |
|
|
1660 | 2041 | # for ind in range(velRadial.shape[1]): |
|
|
1661 | 2042 | # velUVW[ind,:] = numpy.dot(A,velRadial[:,ind]) |
|
|
1662 | 2043 | # velUVW = velUVW.transpose() |
|
|
1663 | 2044 | velUVW = numpy.zeros((A.shape[0],velRadial.shape[1])) |
|
|
1664 | 2045 | velUVW[:,:] = numpy.dot(A,velRadial) |
|
|
1665 | 2046 | |
|
|
1666 | 2047 | |
|
|
1667 | 2048 | return velUVW |
|
|
1668 | 2049 | |
|
|
1669 | 2050 | # def techniqueDBS(self, velRadial0, dirCosx, disrCosy, azimuth, correct, horizontalOnly, heiRang, SNR0): |
|
|
1670 | 2051 | |
|
|
1671 | 2052 | def techniqueDBS(self, kwargs): |
|
|
1672 | 2053 | """ |
|
|
1673 | 2054 | Function that implements Doppler Beam Swinging (DBS) technique. |
|
|
1674 | 2055 | |
|
|
1675 | 2056 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, |
|
|
1676 | 2057 | Direction correction (if necessary), Ranges and SNR |
|
|
1677 | 2058 | |
|
|
1678 | 2059 | Output: Winds estimation (Zonal, Meridional and Vertical) |
|
|
1679 | 2060 | |
|
|
1680 | 2061 | Parameters affected: Winds, height range, SNR |
|
|
1681 | 2062 | """ |
|
|
1682 | 2063 | velRadial0 = kwargs['velRadial'] |
|
|
1683 | 2064 | heiRang = kwargs['heightList'] |
|
|
1684 | 2065 | SNR0 = kwargs['SNR'] |
|
|
1685 | 2066 | |
|
|
1686 | 2067 | if 'dirCosx' in kwargs and 'dirCosy' in kwargs: |
|
|
1687 | 2068 | theta_x = numpy.array(kwargs['dirCosx']) |
|
|
1688 | 2069 | theta_y = numpy.array(kwargs['dirCosy']) |
|
|
1689 | 2070 | else: |
|
|
1690 | 2071 | elev = numpy.array(kwargs['elevation']) |
|
|
1691 | 2072 | azim = numpy.array(kwargs['azimuth']) |
|
|
1692 | 2073 | theta_x, theta_y = self.__calculateCosDir(elev, azim) |
|
|
1693 | 2074 | azimuth = kwargs['correctAzimuth'] |
|
|
1694 | 2075 | if 'horizontalOnly' in kwargs: |
|
|
1695 | 2076 | horizontalOnly = kwargs['horizontalOnly'] |
|
|
1696 | 2077 | else: horizontalOnly = False |
|
|
1697 | 2078 | if 'correctFactor' in kwargs: |
|
|
1698 | 2079 | correctFactor = kwargs['correctFactor'] |
|
|
1699 | 2080 | else: correctFactor = 1 |
|
|
1700 | 2081 | if 'channelList' in kwargs: |
|
|
1701 | 2082 | channelList = kwargs['channelList'] |
|
|
1702 | 2083 | if len(channelList) == 2: |
|
|
1703 | 2084 | horizontalOnly = True |
|
|
1704 | 2085 | arrayChannel = numpy.array(channelList) |
|
|
1705 | 2086 | param = param[arrayChannel,:,:] |
|
|
1706 | 2087 | theta_x = theta_x[arrayChannel] |
|
|
1707 | 2088 | theta_y = theta_y[arrayChannel] |
|
|
1708 | 2089 | |
|
|
1709 | 2090 | azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth) |
|
|
1710 | 2091 | heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, zenith_arr, correctFactor*velRadial0, SNR0) |
|
|
1711 | 2092 | A = self.__calculateMatA(dir_cosu, dir_cosv, dir_cosw, horizontalOnly) |
|
|
1712 | 2093 | |
|
|
1713 | 2094 | #Calculo de Componentes de la velocidad con DBS |
|
|
1714 | 2095 | winds = self.__calculateVelUVW(A,velRadial1) |
|
|
1715 | 2096 | |
|
|
1716 | 2097 | return winds, heiRang1, SNR1 |
|
|
1717 | 2098 | |
|
|
1718 | 2099 | def __calculateDistance(self, posx, posy, pairs_ccf, azimuth = None): |
|
|
1719 | 2100 | |
|
|
1720 | 2101 | nPairs = len(pairs_ccf) |
|
|
1721 | 2102 | posx = numpy.asarray(posx) |
|
|
1722 | 2103 | posy = numpy.asarray(posy) |
|
|
1723 | 2104 | |
|
|
1724 | 2105 | #Rotacion Inversa para alinear con el azimuth |
|
|
1725 | 2106 | if azimuth!= None: |
|
|
1726 | 2107 | azimuth = azimuth*math.pi/180 |
|
|
1727 | 2108 | posx1 = posx*math.cos(azimuth) + posy*math.sin(azimuth) |
|
|
1728 | 2109 | posy1 = -posx*math.sin(azimuth) + posy*math.cos(azimuth) |
|
|
1729 | 2110 | else: |
|
|
1730 | 2111 | posx1 = posx |
|
|
1731 | 2112 | posy1 = posy |
|
|
1732 | 2113 | |
|
|
1733 | 2114 | #Calculo de Distancias |
|
|
1734 | 2115 | distx = numpy.zeros(nPairs) |
|
|
1735 | 2116 | disty = numpy.zeros(nPairs) |
|
|
1736 | 2117 | dist = numpy.zeros(nPairs) |
|
|
1737 | 2118 | ang = numpy.zeros(nPairs) |
|
|
1738 | 2119 | |
|
|
1739 | 2120 | for i in range(nPairs): |
|
|
1740 | 2121 | distx[i] = posx1[pairs_ccf[i][1]] - posx1[pairs_ccf[i][0]] |
|
|
1741 | 2122 | disty[i] = posy1[pairs_ccf[i][1]] - posy1[pairs_ccf[i][0]] |
|
|
1742 | 2123 | dist[i] = numpy.sqrt(distx[i]**2 + disty[i]**2) |
|
|
1743 | 2124 | ang[i] = numpy.arctan2(disty[i],distx[i]) |
|
|
1744 | 2125 | |
|
|
1745 | 2126 | return distx, disty, dist, ang |
|
|
1746 | 2127 | #Calculo de Matrices |
|
|
1747 | 2128 | # nPairs = len(pairs) |
|
|
1748 | 2129 | # ang1 = numpy.zeros((nPairs, 2, 1)) |
|
|
1749 | 2130 | # dist1 = numpy.zeros((nPairs, 2, 1)) |
|
|
1750 | 2131 | # |
|
|
1751 | 2132 | # for j in range(nPairs): |
|
|
1752 | 2133 | # dist1[j,0,0] = dist[pairs[j][0]] |
|
|
1753 | 2134 | # dist1[j,1,0] = dist[pairs[j][1]] |
|
|
1754 | 2135 | # ang1[j,0,0] = ang[pairs[j][0]] |
|
|
1755 | 2136 | # ang1[j,1,0] = ang[pairs[j][1]] |
|
|
1756 | 2137 | # |
|
|
1757 | 2138 | # return distx,disty, dist1,ang1 |
|
|
1758 | 2139 | |
|
|
1759 | 2140 | |
|
|
1760 | 2141 | def __calculateVelVer(self, phase, lagTRange, _lambda): |
|
|
1761 | 2142 | |
|
|
1762 | 2143 | Ts = lagTRange[1] - lagTRange[0] |
|
|
1763 | 2144 | velW = -_lambda*phase/(4*math.pi*Ts) |
|
|
1764 | 2145 | |
|
|
1765 | 2146 | return velW |
|
|
1766 | 2147 | |
|
|
1767 | 2148 | def __calculateVelHorDir(self, dist, tau1, tau2, ang): |
|
|
1768 | 2149 | nPairs = tau1.shape[0] |
|
|
1769 | 2150 | nHeights = tau1.shape[1] |
|
|
1770 | 2151 | vel = numpy.zeros((nPairs,3,nHeights)) |
|
|
1771 | 2152 | dist1 = numpy.reshape(dist, (dist.size,1)) |
|
|
1772 | 2153 | |
|
|
1773 | 2154 | angCos = numpy.cos(ang) |
|
|
1774 | 2155 | angSin = numpy.sin(ang) |
|
|
1775 | 2156 | |
|
|
1776 | 2157 | vel0 = dist1*tau1/(2*tau2**2) |
|
|
1777 | 2158 | vel[:,0,:] = (vel0*angCos).sum(axis = 1) |
|
|
1778 | 2159 | vel[:,1,:] = (vel0*angSin).sum(axis = 1) |
|
|
1779 | 2160 | |
|
|
1780 | 2161 | ind = numpy.where(numpy.isinf(vel)) |
|
|
1781 | 2162 | vel[ind] = numpy.nan |
|
|
1782 | 2163 | |
|
|
1783 | 2164 | return vel |
|
|
1784 | 2165 | |
|
|
1785 | 2166 | # def __getPairsAutoCorr(self, pairsList, nChannels): |
|
|
1786 | 2167 | # |
|
|
1787 | 2168 | # pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan |
|
|
1788 | 2169 | # |
|
|
1789 | 2170 | # for l in range(len(pairsList)): |
|
|
1790 | 2171 | # firstChannel = pairsList[l][0] |
|
|
1791 | 2172 | # secondChannel = pairsList[l][1] |
|
|
1792 | 2173 | # |
|
|
1793 | 2174 | # #Obteniendo pares de Autocorrelacion |
|
|
1794 | 2175 | # if firstChannel == secondChannel: |
|
|
1795 | 2176 | # pairsAutoCorr[firstChannel] = int(l) |
|
|
1796 | 2177 | # |
|
|
1797 | 2178 | # pairsAutoCorr = pairsAutoCorr.astype(int) |
|
|
1798 | 2179 | # |
|
|
1799 | 2180 | # pairsCrossCorr = range(len(pairsList)) |
|
|
1800 | 2181 | # pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr) |
|
|
1801 | 2182 | # |
|
|
1802 | 2183 | # return pairsAutoCorr, pairsCrossCorr |
|
|
1803 | 2184 | |
|
|
1804 | 2185 | # def techniqueSA(self, pairsSelected, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, lagTRange, correctFactor): |
|
|
1805 | 2186 | def techniqueSA(self, kwargs): |
|
|
1806 | 2187 | |
|
|
1807 | 2188 | """ |
|
|
1808 | 2189 | Function that implements Spaced Antenna (SA) technique. |
|
|
1809 | 2190 | |
|
|
1810 | 2191 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, |
|
|
1811 | 2192 | Direction correction (if necessary), Ranges and SNR |
|
|
1812 | 2193 | |
|
|
1813 | 2194 | Output: Winds estimation (Zonal, Meridional and Vertical) |
|
|
1814 | 2195 | |
|
|
1815 | 2196 | Parameters affected: Winds |
|
|
1816 | 2197 | """ |
|
|
1817 | 2198 | position_x = kwargs['positionX'] |
|
|
1818 | 2199 | position_y = kwargs['positionY'] |
|
|
1819 | 2200 | azimuth = kwargs['azimuth'] |
|
|
1820 | 2201 | |
|
|
1821 | 2202 | if 'correctFactor' in kwargs: |
|
|
1822 | 2203 | correctFactor = kwargs['correctFactor'] |
|
|
1823 | 2204 | else: |
|
|
1824 | 2205 | correctFactor = 1 |
|
|
1825 | 2206 | |
|
|
1826 | 2207 | groupList = kwargs['groupList'] |
|
|
1827 | 2208 | pairs_ccf = groupList[1] |
|
|
1828 | 2209 | tau = kwargs['tau'] |
|
|
1829 | 2210 | _lambda = kwargs['_lambda'] |
|
|
1830 | 2211 | |
|
|
1831 | 2212 | #Cross Correlation pairs obtained |
|
|
1832 | 2213 | # pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairssList, nChannels) |
|
|
1833 | 2214 | # pairsArray = numpy.array(pairsList)[pairsCrossCorr] |
|
|
1834 | 2215 | # pairsSelArray = numpy.array(pairsSelected) |
|
|
1835 | 2216 | # pairs = [] |
|
|
1836 | 2217 | # |
|
|
1837 | 2218 | # #Wind estimation pairs obtained |
|
|
1838 | 2219 | # for i in range(pairsSelArray.shape[0]/2): |
|
|
1839 | 2220 | # ind1 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i], axis = 1))[0][0] |
|
|
1840 | 2221 | # ind2 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i + 1], axis = 1))[0][0] |
|
|
1841 | 2222 | # pairs.append((ind1,ind2)) |
|
|
1842 | 2223 | |
|
|
1843 | 2224 | indtau = tau.shape[0]/2 |
|
|
1844 | 2225 | tau1 = tau[:indtau,:] |
|
|
1845 | 2226 | tau2 = tau[indtau:-1,:] |
|
|
1846 | 2227 | # tau1 = tau1[pairs,:] |
|
|
1847 | 2228 | # tau2 = tau2[pairs,:] |
|
|
1848 | 2229 | phase1 = tau[-1,:] |
|
|
1849 | 2230 | |
|
|
1850 | 2231 | #--------------------------------------------------------------------- |
|
|
1851 | 2232 | #Metodo Directo |
|
|
1852 | 2233 | distx, disty, dist, ang = self.__calculateDistance(position_x, position_y, pairs_ccf,azimuth) |
|
|
1853 | 2234 | winds = self.__calculateVelHorDir(dist, tau1, tau2, ang) |
|
|
1854 | 2235 | winds = stats.nanmean(winds, axis=0) |
|
|
1855 | 2236 | #--------------------------------------------------------------------- |
|
|
1856 | 2237 | #Metodo General |
|
|
1857 | 2238 | # distx, disty, dist = self.calculateDistance(position_x,position_y,pairsCrossCorr, pairsList, azimuth) |
|
|
1858 | 2239 | # #Calculo Coeficientes de Funcion de Correlacion |
|
|
1859 | 2240 | # F,G,A,B,H = self.calculateCoef(tau1,tau2,distx,disty,n) |
|
|
1860 | 2241 | # #Calculo de Velocidades |
|
|
1861 | 2242 | # winds = self.calculateVelUV(F,G,A,B,H) |
|
|
1862 | 2243 | |
|
|
1863 | 2244 | #--------------------------------------------------------------------- |
|
|
1864 | 2245 | winds[2,:] = self.__calculateVelVer(phase1, lagTRange, _lambda) |
|
|
1865 | 2246 | winds = correctFactor*winds |
|
|
1866 | 2247 | return winds |
|
|
1867 | 2248 | |
|
|
1868 | 2249 | def __checkTime(self, currentTime, paramInterval, outputInterval): |
|
|
1869 | 2250 | |
|
|
1870 | 2251 | dataTime = currentTime + paramInterval |
|
|
1871 | 2252 | deltaTime = dataTime - self.__initime |
|
|
1872 | 2253 | |
|
|
1873 | 2254 | if deltaTime >= outputInterval or deltaTime < 0: |
|
|
1874 | 2255 | self.__dataReady = True |
|
|
1875 | 2256 | return |
|
|
1876 | 2257 | |
|
|
1877 | 2258 | def techniqueMeteors(self, arrayMeteor, meteorThresh, heightMin, heightMax): |
|
|
1878 | 2259 | ''' |
|
|
1879 | 2260 | Function that implements winds estimation technique with detected meteors. |
|
|
1880 | 2261 | |
|
|
1881 | 2262 | Input: Detected meteors, Minimum meteor quantity to wind estimation |
|
|
1882 | 2263 | |
|
|
1883 | 2264 | Output: Winds estimation (Zonal and Meridional) |
|
|
1884 | 2265 | |
|
|
1885 | 2266 | Parameters affected: Winds |
|
|
1886 | 2267 | ''' |
|
|
1887 | 2268 | #Settings |
|
|
1888 | 2269 | nInt = (heightMax - heightMin)/2 |
|
|
1889 | 2270 | nInt = int(nInt) |
|
|
1890 | 2271 | winds = numpy.zeros((2,nInt))*numpy.nan |
|
|
1891 | 2272 | |
|
|
1892 | 2273 | #Filter errors |
|
|
1893 | 2274 | error = numpy.where(arrayMeteor[:,-1] == 0)[0] |
|
|
1894 | 2275 | finalMeteor = arrayMeteor[error,:] |
|
|
1895 | 2276 | |
|
|
1896 | 2277 | #Meteor Histogram |
|
|
1897 | 2278 | finalHeights = finalMeteor[:,2] |
|
|
1898 | 2279 | hist = numpy.histogram(finalHeights, bins = nInt, range = (heightMin,heightMax)) |
|
|
1899 | 2280 | nMeteorsPerI = hist[0] |
|
|
1900 | 2281 | heightPerI = hist[1] |
|
|
1901 | 2282 | |
|
|
1902 | 2283 | #Sort of meteors |
|
|
1903 | 2284 | indSort = finalHeights.argsort() |
|
|
1904 | 2285 | finalMeteor2 = finalMeteor[indSort,:] |
|
|
1905 | 2286 | |
|
|
1906 | 2287 | # Calculating winds |
|
|
1907 | 2288 | ind1 = 0 |
|
|
1908 | 2289 | ind2 = 0 |
|
|
1909 | 2290 | |
|
|
1910 | 2291 | for i in range(nInt): |
|
|
1911 | 2292 | nMet = nMeteorsPerI[i] |
|
|
1912 | 2293 | ind1 = ind2 |
|
|
1913 | 2294 | ind2 = ind1 + nMet |
|
|
1914 | 2295 | |
|
|
1915 | 2296 | meteorAux = finalMeteor2[ind1:ind2,:] |
|
|
1916 | 2297 | |
|
|
1917 | 2298 | if meteorAux.shape[0] >= meteorThresh: |
|
|
1918 | 2299 | vel = meteorAux[:, 6] |
|
|
1919 | 2300 | zen = meteorAux[:, 4]*numpy.pi/180 |
|
|
1920 | 2301 | azim = meteorAux[:, 3]*numpy.pi/180 |
|
|
1921 | 2302 | |
|
|
1922 | 2303 | n = numpy.cos(zen) |
|
|
1923 | 2304 | # m = (1 - n**2)/(1 - numpy.tan(azim)**2) |
|
|
1924 | 2305 | # l = m*numpy.tan(azim) |
|
|
1925 | 2306 | l = numpy.sin(zen)*numpy.sin(azim) |
|
|
1926 | 2307 | m = numpy.sin(zen)*numpy.cos(azim) |
|
|
1927 | 2308 | |
|
|
1928 | 2309 | A = numpy.vstack((l, m)).transpose() |
|
|
1929 | 2310 | A1 = numpy.dot(numpy.linalg.inv( numpy.dot(A.transpose(),A) ),A.transpose()) |
|
|
1930 | 2311 | windsAux = numpy.dot(A1, vel) |
|
|
1931 | 2312 | |
|
|
1932 | 2313 | winds[0,i] = windsAux[0] |
|
|
1933 | 2314 | winds[1,i] = windsAux[1] |
|
|
1934 | 2315 | |
|
|
1935 | 2316 | return winds, heightPerI[:-1] |
|
|
1936 | 2317 | |
|
|
1937 | 2318 | def techniqueNSM_SA(self, **kwargs): |
|
|
1938 | 2319 | metArray = kwargs['metArray'] |
|
|
1939 | 2320 | heightList = kwargs['heightList'] |
|
|
1940 | 2321 | timeList = kwargs['timeList'] |
|
|
1941 | 2322 | |
|
|
1942 | 2323 | rx_location = kwargs['rx_location'] |
|
|
1943 | 2324 | groupList = kwargs['groupList'] |
|
|
1944 | 2325 | azimuth = kwargs['azimuth'] |
|
|
1945 | 2326 | dfactor = kwargs['dfactor'] |
|
|
1946 | 2327 | k = kwargs['k'] |
|
|
1947 | 2328 | |
|
|
1948 | 2329 | azimuth1, dist = self.__calculateAzimuth1(rx_location, groupList, azimuth) |
|
|
1949 | 2330 | d = dist*dfactor |
|
|
1950 | 2331 | #Phase calculation |
|
|
1951 | 2332 | metArray1 = self.__getPhaseSlope(metArray, heightList, timeList) |
|
|
1952 | 2333 | |
|
|
1953 | 2334 | metArray1[:,-2] = metArray1[:,-2]*metArray1[:,2]*1000/(k*d[metArray1[:,1].astype(int)]) #angles into velocities |
|
|
1954 | 2335 | |
|
|
1955 | 2336 | velEst = numpy.zeros((heightList.size,2))*numpy.nan |
|
|
1956 | 2337 | azimuth1 = azimuth1*numpy.pi/180 |
|
|
1957 | 2338 | |
|
|
1958 | 2339 | for i in range(heightList.size): |
|
|
1959 | 2340 | h = heightList[i] |
|
|
1960 | 2341 | indH = numpy.where((metArray1[:,2] == h)&(numpy.abs(metArray1[:,-2]) < 100))[0] |
|
|
1961 | 2342 | metHeight = metArray1[indH,:] |
|
|
1962 | 2343 | if metHeight.shape[0] >= 2: |
|
|
1963 | 2344 | velAux = numpy.asmatrix(metHeight[:,-2]).T #Radial Velocities |
|
|
1964 | 2345 | iazim = metHeight[:,1].astype(int) |
|
|
1965 | 2346 | azimAux = numpy.asmatrix(azimuth1[iazim]).T #Azimuths |
|
|
1966 | 2347 | A = numpy.hstack((numpy.cos(azimAux),numpy.sin(azimAux))) |
|
|
1967 | 2348 | A = numpy.asmatrix(A) |
|
|
1968 | 2349 | A1 = numpy.linalg.pinv(A.transpose()*A)*A.transpose() |
|
|
1969 | 2350 | velHor = numpy.dot(A1,velAux) |
|
|
1970 | 2351 | |
|
|
1971 | 2352 | velEst[i,:] = numpy.squeeze(velHor) |
|
|
1972 | 2353 | return velEst |
|
|
1973 | 2354 | |
|
|
1974 | 2355 | def __getPhaseSlope(self, metArray, heightList, timeList): |
|
|
1975 | 2356 | meteorList = [] |
|
|
1976 | 2357 | #utctime sec1 height SNR velRad ph0 ph1 ph2 coh0 coh1 coh2 |
|
|
1977 | 2358 | #Putting back together the meteor matrix |
|
|
1978 | 2359 | utctime = metArray[:,0] |
|
|
1979 | 2360 | uniqueTime = numpy.unique(utctime) |
|
|
1980 | 2361 | |
|
|
1981 | 2362 | phaseDerThresh = 0.5 |
|
|
1982 | 2363 | ippSeconds = timeList[1] - timeList[0] |
|
|
1983 | 2364 | sec = numpy.where(timeList>1)[0][0] |
|
|
1984 | 2365 | nPairs = metArray.shape[1] - 6 |
|
|
1985 | 2366 | nHeights = len(heightList) |
|
|
1986 | 2367 | |
|
|
1987 | 2368 | for t in uniqueTime: |
|
|
1988 | 2369 | metArray1 = metArray[utctime==t,:] |
|
|
1989 | 2370 | # phaseDerThresh = numpy.pi/4 #reducir Phase thresh |
|
|
1990 | 2371 | tmet = metArray1[:,1].astype(int) |
|
|
1991 | 2372 | hmet = metArray1[:,2].astype(int) |
|
|
1992 | 2373 | |
|
|
1993 | 2374 | metPhase = numpy.zeros((nPairs, heightList.size, timeList.size - 1)) |
|
|
1994 | 2375 | metPhase[:,:] = numpy.nan |
|
|
1995 | 2376 | metPhase[:,hmet,tmet] = metArray1[:,6:].T |
|
|
1996 | 2377 | |
|
|
1997 | 2378 | #Delete short trails |
|
|
1998 | 2379 | metBool = ~numpy.isnan(metPhase[0,:,:]) |
|
|
1999 | 2380 | heightVect = numpy.sum(metBool, axis = 1) |
|
|
2000 | 2381 | metBool[heightVect<sec,:] = False |
|
|
2001 | 2382 | metPhase[:,heightVect<sec,:] = numpy.nan |
|
|
2002 | 2383 | |
|
|
2003 | 2384 | #Derivative |
|
|
2004 | 2385 | metDer = numpy.abs(metPhase[:,:,1:] - metPhase[:,:,:-1]) |
|
|
2005 | 2386 | phDerAux = numpy.dstack((numpy.full((nPairs,nHeights,1), False, dtype=bool),metDer > phaseDerThresh)) |
|
|
2006 | 2387 | metPhase[phDerAux] = numpy.nan |
|
|
2007 | 2388 | |
|
|
2008 | 2389 | #--------------------------METEOR DETECTION ----------------------------------------- |
|
|
2009 | 2390 | indMet = numpy.where(numpy.any(metBool,axis=1))[0] |
|
|
2010 | 2391 | |
|
|
2011 | 2392 | for p in numpy.arange(nPairs): |
|
|
2012 | 2393 | phase = metPhase[p,:,:] |
|
|
2013 | 2394 | phDer = metDer[p,:,:] |
|
|
2014 | 2395 | |
|
|
2015 | 2396 | for h in indMet: |
|
|
2016 | 2397 | height = heightList[h] |
|
|
2017 | 2398 | phase1 = phase[h,:] #82 |
|
|
2018 | 2399 | phDer1 = phDer[h,:] |
|
|
2019 | 2400 | |
|
|
2020 | 2401 | phase1[~numpy.isnan(phase1)] = numpy.unwrap(phase1[~numpy.isnan(phase1)]) #Unwrap |
|
|
2021 | 2402 | |
|
|
2022 | 2403 | indValid = numpy.where(~numpy.isnan(phase1))[0] |
|
|
2023 | 2404 | initMet = indValid[0] |
|
|
2024 | 2405 | endMet = 0 |
|
|
2025 | 2406 | |
|
|
2026 | 2407 | for i in range(len(indValid)-1): |
|
|
2027 | 2408 | |
|
|
2028 | 2409 | #Time difference |
|
|
2029 | 2410 | inow = indValid[i] |
|
|
2030 | 2411 | inext = indValid[i+1] |
|
|
2031 | 2412 | idiff = inext - inow |
|
|
2032 | 2413 | #Phase difference |
|
|
2033 | 2414 | phDiff = numpy.abs(phase1[inext] - phase1[inow]) |
|
|
2034 | 2415 | |
|
|
2035 | 2416 | if idiff>sec or phDiff>numpy.pi/4 or inext==indValid[-1]: #End of Meteor |
|
|
2036 | 2417 | sizeTrail = inow - initMet + 1 |
|
|
2037 | 2418 | if sizeTrail>3*sec: #Too short meteors |
|
|
2038 | 2419 | x = numpy.arange(initMet,inow+1)*ippSeconds |
|
|
2039 | 2420 | y = phase1[initMet:inow+1] |
|
|
2040 | 2421 | ynnan = ~numpy.isnan(y) |
|
|
2041 | 2422 | x = x[ynnan] |
|
|
2042 | 2423 | y = y[ynnan] |
|
|
2043 | 2424 | slope, intercept, r_value, p_value, std_err = stats.linregress(x,y) |
|
|
2044 | 2425 | ylin = x*slope + intercept |
|
|
2045 | 2426 | rsq = r_value**2 |
|
|
2046 | 2427 | if rsq > 0.5: |
|
|
2047 | 2428 | vel = slope#*height*1000/(k*d) |
|
|
2048 | 2429 | estAux = numpy.array([utctime,p,height, vel, rsq]) |
|
|
2049 | 2430 | meteorList.append(estAux) |
|
|
2050 | 2431 | initMet = inext |
|
|
2051 | 2432 | metArray2 = numpy.array(meteorList) |
|
|
2052 | 2433 | |
|
|
2053 | 2434 | return metArray2 |
|
|
2054 | 2435 | |
|
|
2055 | 2436 | def __calculateAzimuth1(self, rx_location, pairslist, azimuth0): |
|
|
2056 | 2437 | |
|
|
2057 | 2438 | azimuth1 = numpy.zeros(len(pairslist)) |
|
|
2058 | 2439 | dist = numpy.zeros(len(pairslist)) |
|
|
2059 | 2440 | |
|
|
2060 | 2441 | for i in range(len(rx_location)): |
|
|
2061 | 2442 | ch0 = pairslist[i][0] |
|
|
2062 | 2443 | ch1 = pairslist[i][1] |
|
|
2063 | 2444 | |
|
|
2064 | 2445 | diffX = rx_location[ch0][0] - rx_location[ch1][0] |
|
|
2065 | 2446 | diffY = rx_location[ch0][1] - rx_location[ch1][1] |
|
|
2066 | 2447 | azimuth1[i] = numpy.arctan2(diffY,diffX)*180/numpy.pi |
|
|
2067 | 2448 | dist[i] = numpy.sqrt(diffX**2 + diffY**2) |
|
|
2068 | 2449 | |
|
|
2069 | 2450 | azimuth1 -= azimuth0 |
|
|
2070 | 2451 | return azimuth1, dist |
|
|
2071 | 2452 | |
|
|
2072 | 2453 | def techniqueNSM_DBS(self, **kwargs): |
|
|
2073 | 2454 | metArray = kwargs['metArray'] |
|
|
2074 | 2455 | heightList = kwargs['heightList'] |
|
|
2075 | 2456 | timeList = kwargs['timeList'] |
|
|
2076 | 2457 | azimuth = kwargs['azimuth'] |
|
|
2077 | 2458 | theta_x = numpy.array(kwargs['theta_x']) |
|
|
2078 | 2459 | theta_y = numpy.array(kwargs['theta_y']) |
|
|
2079 | 2460 | |
|
|
2080 | 2461 | utctime = metArray[:,0] |
|
|
2081 | 2462 | cmet = metArray[:,1].astype(int) |
|
|
2082 | 2463 | hmet = metArray[:,3].astype(int) |
|
|
2083 | 2464 | SNRmet = metArray[:,4] |
|
|
2084 | 2465 | vmet = metArray[:,5] |
|
|
2085 | 2466 | spcmet = metArray[:,6] |
|
|
2086 | 2467 | |
|
|
2087 | 2468 | nChan = numpy.max(cmet) + 1 |
|
|
2088 | 2469 | nHeights = len(heightList) |
|
|
2089 | 2470 | |
|
|
2090 | 2471 | azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth) |
|
|
2091 | 2472 | hmet = heightList[hmet] |
|
|
2092 | 2473 | h1met = hmet*numpy.cos(zenith_arr[cmet]) #Corrected heights |
|
|
2093 | 2474 | |
|
|
2094 | 2475 | velEst = numpy.zeros((heightList.size,2))*numpy.nan |
|
|
2095 | 2476 | |
|
|
2096 | 2477 | for i in range(nHeights - 1): |
|
|
2097 | 2478 | hmin = heightList[i] |
|
|
2098 | 2479 | hmax = heightList[i + 1] |
|
|
2099 | 2480 | |
|
|
2100 | 2481 | thisH = (h1met>=hmin) & (h1met<hmax) & (cmet!=2) & (SNRmet>8) & (vmet<50) & (spcmet<10) |
|
|
2101 | 2482 | indthisH = numpy.where(thisH) |
|
|
2102 | 2483 | |
|
|
2103 | 2484 | if numpy.size(indthisH) > 3: |
|
|
2104 | 2485 | |
|
|
2105 | 2486 | vel_aux = vmet[thisH] |
|
|
2106 | 2487 | chan_aux = cmet[thisH] |
|
|
2107 | 2488 | cosu_aux = dir_cosu[chan_aux] |
|
|
2108 | 2489 | cosv_aux = dir_cosv[chan_aux] |
|
|
2109 | 2490 | cosw_aux = dir_cosw[chan_aux] |
|
|
2110 | 2491 | |
|
|
2111 | 2492 | nch = numpy.size(numpy.unique(chan_aux)) |
|
|
2112 | 2493 | if nch > 1: |
|
|
2113 | 2494 | A = self.__calculateMatA(cosu_aux, cosv_aux, cosw_aux, True) |
|
|
2114 | 2495 | velEst[i,:] = numpy.dot(A,vel_aux) |
|
|
2115 | 2496 | |
|
|
2116 | 2497 | return velEst |
|
|
2117 | 2498 | |
|
|
2118 | 2499 | def run(self, dataOut, technique, nHours=1, hmin=70, hmax=110, **kwargs): |
|
|
2119 | 2500 | |
|
|
2120 | 2501 | param = dataOut.data_param |
|
|
2121 | 2502 | if dataOut.abscissaList != None: |
|
|
2122 | 2503 | absc = dataOut.abscissaList[:-1] |
|
|
2123 | 2504 | # noise = dataOut.noise |
|
|
2124 | 2505 | heightList = dataOut.heightList |
|
|
2125 | 2506 | SNR = dataOut.data_SNR |
|
|
2126 | 2507 | |
|
|
2127 | 2508 | if technique == 'DBS': |
|
|
2128 | 2509 | |
|
|
2129 | 2510 | kwargs['velRadial'] = param[:,1,:] #Radial velocity |
|
|
2130 | 2511 | kwargs['heightList'] = heightList |
|
|
2131 | 2512 | kwargs['SNR'] = SNR |
|
|
2132 | 2513 | |
|
|
2133 | 2514 | dataOut.data_output, dataOut.heightList, dataOut.data_SNR = self.techniqueDBS(kwargs) #DBS Function |
|
|
2134 | 2515 | dataOut.utctimeInit = dataOut.utctime |
|
|
2135 | 2516 | dataOut.outputInterval = dataOut.paramInterval |
|
|
2136 | 2517 | |
|
|
2137 | 2518 | elif technique == 'SA': |
|
|
2138 | 2519 | |
|
|
2139 | 2520 | #Parameters |
|
|
2140 | 2521 | # position_x = kwargs['positionX'] |
|
|
2141 | 2522 | # position_y = kwargs['positionY'] |
|
|
2142 | 2523 | # azimuth = kwargs['azimuth'] |
|
|
2143 | 2524 | # |
|
|
2144 | 2525 | # if kwargs.has_key('crosspairsList'): |
|
|
2145 | 2526 | # pairs = kwargs['crosspairsList'] |
|
|
2146 | 2527 | # else: |
|
|
2147 | 2528 | # pairs = None |
|
|
2148 | 2529 | # |
|
|
2149 | 2530 | # if kwargs.has_key('correctFactor'): |
|
|
2150 | 2531 | # correctFactor = kwargs['correctFactor'] |
|
|
2151 | 2532 | # else: |
|
|
2152 | 2533 | # correctFactor = 1 |
|
|
2153 | 2534 | |
|
|
2154 | 2535 | # tau = dataOut.data_param |
|
|
2155 | 2536 | # _lambda = dataOut.C/dataOut.frequency |
|
|
2156 | 2537 | # pairsList = dataOut.groupList |
|
|
2157 | 2538 | # nChannels = dataOut.nChannels |
|
|
2158 | 2539 | |
|
|
2159 | 2540 | kwargs['groupList'] = dataOut.groupList |
|
|
2160 | 2541 | kwargs['tau'] = dataOut.data_param |
|
|
2161 | 2542 | kwargs['_lambda'] = dataOut.C/dataOut.frequency |
|
|
2162 | 2543 | # dataOut.data_output = self.techniqueSA(pairs, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, absc, correctFactor) |
|
|
2163 | 2544 | dataOut.data_output = self.techniqueSA(kwargs) |
|
|
2164 | 2545 | dataOut.utctimeInit = dataOut.utctime |
|
|
2165 | 2546 | dataOut.outputInterval = dataOut.timeInterval |
|
|
2166 | 2547 | |
|
|
2167 | 2548 | elif technique == 'Meteors': |
|
|
2168 | 2549 | dataOut.flagNoData = True |
|
|
2169 | 2550 | self.__dataReady = False |
|
|
2170 | 2551 | |
|
|
2171 | 2552 | if 'nHours' in kwargs: |
|
|
2172 | 2553 | nHours = kwargs['nHours'] |
|
|
2173 | 2554 | else: |
|
|
2174 | 2555 | nHours = 1 |
|
|
2175 | 2556 | |
|
|
2176 | 2557 | if 'meteorsPerBin' in kwargs: |
|
|
2177 | 2558 | meteorThresh = kwargs['meteorsPerBin'] |
|
|
2178 | 2559 | else: |
|
|
2179 | 2560 | meteorThresh = 6 |
|
|
2180 | 2561 | |
|
|
2181 | 2562 | if 'hmin' in kwargs: |
|
|
2182 | 2563 | hmin = kwargs['hmin'] |
|
|
2183 | 2564 | else: hmin = 70 |
|
|
2184 | 2565 | if 'hmax' in kwargs: |
|
|
2185 | 2566 | hmax = kwargs['hmax'] |
|
|
2186 | 2567 | else: hmax = 110 |
|
|
2187 | 2568 | |
|
|
2188 | 2569 | dataOut.outputInterval = nHours*3600 |
|
|
2189 | 2570 | |
|
|
2190 | 2571 | if self.__isConfig == False: |
|
|
2191 | 2572 | # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) |
|
|
2192 | 2573 | #Get Initial LTC time |
|
|
2193 | 2574 | self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
|
2194 | 2575 | self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
2195 | 2576 | |
|
|
2196 | 2577 | self.__isConfig = True |
|
|
2197 | 2578 | |
|
|
2198 | 2579 | if self.__buffer is None: |
|
|
2199 | 2580 | self.__buffer = dataOut.data_param |
|
|
2200 | 2581 | self.__firstdata = copy.copy(dataOut) |
|
|
2201 | 2582 | |
|
|
2202 | 2583 | else: |
|
|
2203 | 2584 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
|
2204 | 2585 | |
|
|
2205 | 2586 | self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
|
2206 | 2587 | |
|
|
2207 | 2588 | if self.__dataReady: |
|
|
2208 | 2589 | dataOut.utctimeInit = self.__initime |
|
|
2209 | 2590 | |
|
|
2210 | 2591 | self.__initime += dataOut.outputInterval #to erase time offset |
|
|
2211 | 2592 | |
|
|
2212 | 2593 | dataOut.data_output, dataOut.heightList = self.techniqueMeteors(self.__buffer, meteorThresh, hmin, hmax) |
|
|
2213 | 2594 | dataOut.flagNoData = False |
|
|
2214 | 2595 | self.__buffer = None |
|
|
2215 | 2596 | |
|
|
2216 | 2597 | elif technique == 'Meteors1': |
|
|
2217 | 2598 | dataOut.flagNoData = True |
|
|
2218 | 2599 | self.__dataReady = False |
|
|
2219 | 2600 | |
|
|
2220 | 2601 | if 'nMins' in kwargs: |
|
|
2221 | 2602 | nMins = kwargs['nMins'] |
|
|
2222 | 2603 | else: nMins = 20 |
|
|
2223 | 2604 | if 'rx_location' in kwargs: |
|
|
2224 | 2605 | rx_location = kwargs['rx_location'] |
|
|
2225 | 2606 | else: rx_location = [(0,1),(1,1),(1,0)] |
|
|
2226 | 2607 | if 'azimuth' in kwargs: |
|
|
2227 | 2608 | azimuth = kwargs['azimuth'] |
|
|
2228 | 2609 | else: azimuth = 51.06 |
|
|
2229 | 2610 | if 'dfactor' in kwargs: |
|
|
2230 | 2611 | dfactor = kwargs['dfactor'] |
|
|
2231 | 2612 | if 'mode' in kwargs: |
|
|
2232 | 2613 | mode = kwargs['mode'] |
|
|
2233 | 2614 | if 'theta_x' in kwargs: |
|
|
2234 | 2615 | theta_x = kwargs['theta_x'] |
|
|
2235 | 2616 | if 'theta_y' in kwargs: |
|
|
2236 | 2617 | theta_y = kwargs['theta_y'] |
|
|
2237 | 2618 | else: mode = 'SA' |
|
|
2238 | 2619 | |
|
|
2239 | 2620 | #Borrar luego esto |
|
|
2240 | 2621 | if dataOut.groupList is None: |
|
|
2241 | 2622 | dataOut.groupList = [(0,1),(0,2),(1,2)] |
|
|
2242 | 2623 | groupList = dataOut.groupList |
|
|
2243 | 2624 | C = 3e8 |
|
|
2244 | 2625 | freq = 50e6 |
|
|
2245 | 2626 | lamb = C/freq |
|
|
2246 | 2627 | k = 2*numpy.pi/lamb |
|
|
2247 | 2628 | |
|
|
2248 | 2629 | timeList = dataOut.abscissaList |
|
|
2249 | 2630 | heightList = dataOut.heightList |
|
|
2250 | 2631 | |
|
|
2251 | 2632 | if self.__isConfig == False: |
|
|
2252 | 2633 | dataOut.outputInterval = nMins*60 |
|
|
2253 | 2634 | # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) |
|
|
2254 | 2635 | #Get Initial LTC time |
|
|
2255 | 2636 | initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
|
2256 | 2637 | minuteAux = initime.minute |
|
|
2257 | 2638 | minuteNew = int(numpy.floor(minuteAux/nMins)*nMins) |
|
|
2258 | 2639 | self.__initime = (initime.replace(minute = minuteNew, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
2259 | 2640 | |
|
|
2260 | 2641 | self.__isConfig = True |
|
|
2261 | 2642 | |
|
|
2262 | 2643 | if self.__buffer is None: |
|
|
2263 | 2644 | self.__buffer = dataOut.data_param |
|
|
2264 | 2645 | self.__firstdata = copy.copy(dataOut) |
|
|
2265 | 2646 | |
|
|
2266 | 2647 | else: |
|
|
2267 | 2648 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
|
2268 | 2649 | |
|
|
2269 | 2650 | self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
|
2270 | 2651 | |
|
|
2271 | 2652 | if self.__dataReady: |
|
|
2272 | 2653 | dataOut.utctimeInit = self.__initime |
|
|
2273 | 2654 | self.__initime += dataOut.outputInterval #to erase time offset |
|
|
2274 | 2655 | |
|
|
2275 | 2656 | metArray = self.__buffer |
|
|
2276 | 2657 | if mode == 'SA': |
|
|
2277 | 2658 | dataOut.data_output = self.techniqueNSM_SA(rx_location=rx_location, groupList=groupList, azimuth=azimuth, dfactor=dfactor, k=k,metArray=metArray, heightList=heightList,timeList=timeList) |
|
|
2278 | 2659 | elif mode == 'DBS': |
|
|
2279 | 2660 | dataOut.data_output = self.techniqueNSM_DBS(metArray=metArray,heightList=heightList,timeList=timeList, azimuth=azimuth, theta_x=theta_x, theta_y=theta_y) |
|
|
2280 | 2661 | dataOut.data_output = dataOut.data_output.T |
|
|
2281 | 2662 | dataOut.flagNoData = False |
|
|
2282 | 2663 | self.__buffer = None |
|
|
2283 | 2664 | |
|
|
2284 | 2665 | return |
|
|
2285 | 2666 | |
|
|
2286 | 2667 | class EWDriftsEstimation(Operation): |
|
|
2287 | 2668 | |
|
|
2288 | 2669 | def __init__(self): |
|
|
2289 | 2670 | Operation.__init__(self) |
|
|
2290 | 2671 | |
|
|
2291 | 2672 | def __correctValues(self, heiRang, phi, velRadial, SNR): |
|
|
2292 | 2673 | listPhi = phi.tolist() |
|
|
2293 | 2674 | maxid = listPhi.index(max(listPhi)) |
|
|
2294 | 2675 | minid = listPhi.index(min(listPhi)) |
|
|
2295 | 2676 | |
|
|
2296 | 2677 | rango = list(range(len(phi))) |
|
|
2297 | 2678 | # rango = numpy.delete(rango,maxid) |
|
|
2298 | 2679 | |
|
|
2299 | 2680 | heiRang1 = heiRang*math.cos(phi[maxid]) |
|
|
2300 | 2681 | heiRangAux = heiRang*math.cos(phi[minid]) |
|
|
2301 | 2682 | indOut = (heiRang1 < heiRangAux[0]).nonzero() |
|
|
2302 | 2683 | heiRang1 = numpy.delete(heiRang1,indOut) |
|
|
2303 | 2684 | |
|
|
2304 | 2685 | velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
2305 | 2686 | SNR1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
2306 | 2687 | |
|
|
2307 | 2688 | for i in rango: |
|
|
2308 | 2689 | x = heiRang*math.cos(phi[i]) |
|
|
2309 | 2690 | y1 = velRadial[i,:] |
|
|
2310 | 2691 | f1 = interpolate.interp1d(x,y1,kind = 'cubic') |
|
|
2311 | 2692 | |
|
|
2312 | 2693 | x1 = heiRang1 |
|
|
2313 | 2694 | y11 = f1(x1) |
|
|
2314 | 2695 | |
|
|
2315 | 2696 | y2 = SNR[i,:] |
|
|
2316 | 2697 | f2 = interpolate.interp1d(x,y2,kind = 'cubic') |
|
|
2317 | 2698 | y21 = f2(x1) |
|
|
2318 | 2699 | |
|
|
2319 | 2700 | velRadial1[i,:] = y11 |
|
|
2320 | 2701 | SNR1[i,:] = y21 |
|
|
2321 | 2702 | |
|
|
2322 | 2703 | return heiRang1, velRadial1, SNR1 |
|
|
2323 | 2704 | |
|
|
2324 | 2705 | def run(self, dataOut, zenith, zenithCorrection): |
|
|
2325 | 2706 | heiRang = dataOut.heightList |
|
|
2326 | 2707 | velRadial = dataOut.data_param[:,3,:] |
|
|
2327 | 2708 | SNR = dataOut.data_SNR |
|
|
2328 | 2709 | |
|
|
2329 | 2710 | zenith = numpy.array(zenith) |
|
|
2330 | 2711 | zenith -= zenithCorrection |
|
|
2331 | 2712 | zenith *= numpy.pi/180 |
|
|
2332 | 2713 | |
|
|
2333 | 2714 | heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, numpy.abs(zenith), velRadial, SNR) |
|
|
2334 | 2715 | |
|
|
2335 | 2716 | alp = zenith[0] |
|
|
2336 | 2717 | bet = zenith[1] |
|
|
2337 | 2718 | |
|
|
2338 | 2719 | w_w = velRadial1[0,:] |
|
|
2339 | 2720 | w_e = velRadial1[1,:] |
|
|
2340 | 2721 | |
|
|
2341 | 2722 | w = (w_w*numpy.sin(bet) - w_e*numpy.sin(alp))/(numpy.cos(alp)*numpy.sin(bet) - numpy.cos(bet)*numpy.sin(alp)) |
|
|
2342 | 2723 | u = (w_w*numpy.cos(bet) - w_e*numpy.cos(alp))/(numpy.sin(alp)*numpy.cos(bet) - numpy.sin(bet)*numpy.cos(alp)) |
|
|
2343 | 2724 | |
|
|
2344 | 2725 | winds = numpy.vstack((u,w)) |
|
|
2345 | 2726 | |
|
|
2346 | 2727 | dataOut.heightList = heiRang1 |
|
|
2347 | 2728 | dataOut.data_output = winds |
|
|
2348 | 2729 | dataOut.data_SNR = SNR1 |
|
|
2349 | 2730 | |
|
|
2350 | 2731 | dataOut.utctimeInit = dataOut.utctime |
|
|
2351 | 2732 | dataOut.outputInterval = dataOut.timeInterval |
|
|
2352 | 2733 | return |
|
|
2353 | 2734 | |
|
|
2354 | 2735 | #--------------- Non Specular Meteor ---------------- |
|
|
2355 | 2736 | |
|
|
2356 | 2737 | class NonSpecularMeteorDetection(Operation): |
|
|
2357 | 2738 | |
|
|
2358 | 2739 | def run(self, dataOut, mode, SNRthresh=8, phaseDerThresh=0.5, cohThresh=0.8, allData = False): |
|
|
2359 | 2740 | data_acf = dataOut.data_pre[0] |
|
|
2360 | 2741 | data_ccf = dataOut.data_pre[1] |
|
|
2361 | 2742 | pairsList = dataOut.groupList[1] |
|
|
2362 | 2743 | |
|
|
2363 | 2744 | lamb = dataOut.C/dataOut.frequency |
|
|
2364 | 2745 | tSamp = dataOut.ippSeconds*dataOut.nCohInt |
|
|
2365 | 2746 | paramInterval = dataOut.paramInterval |
|
|
2366 | 2747 | |
|
|
2367 | 2748 | nChannels = data_acf.shape[0] |
|
|
2368 | 2749 | nLags = data_acf.shape[1] |
|
|
2369 | 2750 | nProfiles = data_acf.shape[2] |
|
|
2370 | 2751 | nHeights = dataOut.nHeights |
|
|
2371 | 2752 | nCohInt = dataOut.nCohInt |
|
|
2372 | 2753 | sec = numpy.round(nProfiles/dataOut.paramInterval) |
|
|
2373 | 2754 | heightList = dataOut.heightList |
|
|
2374 | 2755 | ippSeconds = dataOut.ippSeconds*dataOut.nCohInt*dataOut.nAvg |
|
|
2375 | 2756 | utctime = dataOut.utctime |
|
|
2376 | 2757 | |
|
|
2377 | 2758 | dataOut.abscissaList = numpy.arange(0,paramInterval+ippSeconds,ippSeconds) |
|
|
2378 | 2759 | |
|
|
2379 | 2760 | #------------------------ SNR -------------------------------------- |
|
|
2380 | 2761 | power = data_acf[:,0,:,:].real |
|
|
2381 | 2762 | noise = numpy.zeros(nChannels) |
|
|
2382 | 2763 | SNR = numpy.zeros(power.shape) |
|
|
2383 | 2764 | for i in range(nChannels): |
|
|
2384 | 2765 | noise[i] = hildebrand_sekhon(power[i,:], nCohInt) |
|
|
2385 | 2766 | SNR[i] = (power[i]-noise[i])/noise[i] |
|
|
2386 | 2767 | SNRm = numpy.nanmean(SNR, axis = 0) |
|
|
2387 | 2768 | SNRdB = 10*numpy.log10(SNR) |
|
|
2388 | 2769 | |
|
|
2389 | 2770 | if mode == 'SA': |
|
|
2390 | 2771 | dataOut.groupList = dataOut.groupList[1] |
|
|
2391 | 2772 | nPairs = data_ccf.shape[0] |
|
|
2392 | 2773 | #---------------------- Coherence and Phase -------------------------- |
|
|
2393 | 2774 | phase = numpy.zeros(data_ccf[:,0,:,:].shape) |
|
|
2394 | 2775 | # phase1 = numpy.copy(phase) |
|
|
2395 | 2776 | coh1 = numpy.zeros(data_ccf[:,0,:,:].shape) |
|
|
2396 | 2777 | |
|
|
2397 | 2778 | for p in range(nPairs): |
|
|
2398 | 2779 | ch0 = pairsList[p][0] |
|
|
2399 | 2780 | ch1 = pairsList[p][1] |
|
|
2400 | 2781 | ccf = data_ccf[p,0,:,:]/numpy.sqrt(data_acf[ch0,0,:,:]*data_acf[ch1,0,:,:]) |
|
|
2401 | 2782 | phase[p,:,:] = ndimage.median_filter(numpy.angle(ccf), size = (5,1)) #median filter |
|
|
2402 | 2783 | # phase1[p,:,:] = numpy.angle(ccf) #median filter |
|
|
2403 | 2784 | coh1[p,:,:] = ndimage.median_filter(numpy.abs(ccf), 5) #median filter |
|
|
2404 | 2785 | # coh1[p,:,:] = numpy.abs(ccf) #median filter |
|
|
2405 | 2786 | coh = numpy.nanmax(coh1, axis = 0) |
|
|
2406 | 2787 | # struc = numpy.ones((5,1)) |
|
|
2407 | 2788 | # coh = ndimage.morphology.grey_dilation(coh, size=(10,1)) |
|
|
2408 | 2789 | #---------------------- Radial Velocity ---------------------------- |
|
|
2409 | 2790 | phaseAux = numpy.mean(numpy.angle(data_acf[:,1,:,:]), axis = 0) |
|
|
2410 | 2791 | velRad = phaseAux*lamb/(4*numpy.pi*tSamp) |
|
|
2411 | 2792 | |
|
|
2412 | 2793 | if allData: |
|
|
2413 | 2794 | boolMetFin = ~numpy.isnan(SNRm) |
|
|
2414 | 2795 | # coh[:-1,:] = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0) |
|
|
2415 | 2796 | else: |
|
|
2416 | 2797 | #------------------------ Meteor mask --------------------------------- |
|
|
2417 | 2798 | # #SNR mask |
|
|
2418 | 2799 | # boolMet = (SNRdB>SNRthresh)#|(~numpy.isnan(SNRdB)) |
|
|
2419 | 2800 | # |
|
|
2420 | 2801 | # #Erase small objects |
|
|
2421 | 2802 | # boolMet1 = self.__erase_small(boolMet, 2*sec, 5) |
|
|
2422 | 2803 | # |
|
|
2423 | 2804 | # auxEEJ = numpy.sum(boolMet1,axis=0) |
|
|
2424 | 2805 | # indOver = auxEEJ>nProfiles*0.8 #Use this later |
|
|
2425 | 2806 | # indEEJ = numpy.where(indOver)[0] |
|
|
2426 | 2807 | # indNEEJ = numpy.where(~indOver)[0] |
|
|
2427 | 2808 | # |
|
|
2428 | 2809 | # boolMetFin = boolMet1 |
|
|
2429 | 2810 | # |
|
|
2430 | 2811 | # if indEEJ.size > 0: |
|
|
2431 | 2812 | # boolMet1[:,indEEJ] = False #Erase heights with EEJ |
|
|
2432 | 2813 | # |
|
|
2433 | 2814 | # boolMet2 = coh > cohThresh |
|
|
2434 | 2815 | # boolMet2 = self.__erase_small(boolMet2, 2*sec,5) |
|
|
2435 | 2816 | # |
|
|
2436 | 2817 | # #Final Meteor mask |
|
|
2437 | 2818 | # boolMetFin = boolMet1|boolMet2 |
|
|
2438 | 2819 | |
|
|
2439 | 2820 | #Coherence mask |
|
|
2440 | 2821 | boolMet1 = coh > 0.75 |
|
|
2441 | 2822 | struc = numpy.ones((30,1)) |
|
|
2442 | 2823 | boolMet1 = ndimage.morphology.binary_dilation(boolMet1, structure=struc) |
|
|
2443 | 2824 | |
|
|
2444 | 2825 | #Derivative mask |
|
|
2445 | 2826 | derPhase = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0) |
|
|
2446 | 2827 | boolMet2 = derPhase < 0.2 |
|
|
2447 | 2828 | # boolMet2 = ndimage.morphology.binary_opening(boolMet2) |
|
|
2448 | 2829 | # boolMet2 = ndimage.morphology.binary_closing(boolMet2, structure = numpy.ones((10,1))) |
|
|
2449 | 2830 | boolMet2 = ndimage.median_filter(boolMet2,size=5) |
|
|
2450 | 2831 | boolMet2 = numpy.vstack((boolMet2,numpy.full((1,nHeights), True, dtype=bool))) |
|
|
2451 | 2832 | # #Final mask |
|
|
2452 | 2833 | # boolMetFin = boolMet2 |
|
|
2453 | 2834 | boolMetFin = boolMet1&boolMet2 |
|
|
2454 | 2835 | # boolMetFin = ndimage.morphology.binary_dilation(boolMetFin) |
|
|
2455 | 2836 | #Creating data_param |
|
|
2456 | 2837 | coordMet = numpy.where(boolMetFin) |
|
|
2457 | 2838 | |
|
|
2458 | 2839 | tmet = coordMet[0] |
|
|
2459 | 2840 | hmet = coordMet[1] |
|
|
2460 | 2841 | |
|
|
2461 | 2842 | data_param = numpy.zeros((tmet.size, 6 + nPairs)) |
|
|
2462 | 2843 | data_param[:,0] = utctime |
|
|
2463 | 2844 | data_param[:,1] = tmet |
|
|
2464 | 2845 | data_param[:,2] = hmet |
|
|
2465 | 2846 | data_param[:,3] = SNRm[tmet,hmet] |
|
|
2466 | 2847 | data_param[:,4] = velRad[tmet,hmet] |
|
|
2467 | 2848 | data_param[:,5] = coh[tmet,hmet] |
|
|
2468 | 2849 | data_param[:,6:] = phase[:,tmet,hmet].T |
|
|
2469 | 2850 | |
|
|
2470 | 2851 | elif mode == 'DBS': |
|
|
2471 | 2852 | dataOut.groupList = numpy.arange(nChannels) |
|
|
2472 | 2853 | |
|
|
2473 | 2854 | #Radial Velocities |
|
|
2474 | 2855 | phase = numpy.angle(data_acf[:,1,:,:]) |
|
|
2475 | 2856 | # phase = ndimage.median_filter(numpy.angle(data_acf[:,1,:,:]), size = (1,5,1)) |
|
|
2476 | 2857 | velRad = phase*lamb/(4*numpy.pi*tSamp) |
|
|
2477 | 2858 | |
|
|
2478 | 2859 | #Spectral width |
|
|
2479 | 2860 | # acf1 = ndimage.median_filter(numpy.abs(data_acf[:,1,:,:]), size = (1,5,1)) |
|
|
2480 | 2861 | # acf2 = ndimage.median_filter(numpy.abs(data_acf[:,2,:,:]), size = (1,5,1)) |
|
|
2481 | 2862 | acf1 = data_acf[:,1,:,:] |
|
|
2482 | 2863 | acf2 = data_acf[:,2,:,:] |
|
|
2483 | 2864 | |
|
|
2484 | 2865 | spcWidth = (lamb/(2*numpy.sqrt(6)*numpy.pi*tSamp))*numpy.sqrt(numpy.log(acf1/acf2)) |
|
|
2485 | 2866 | # velRad = ndimage.median_filter(velRad, size = (1,5,1)) |
|
|
2486 | 2867 | if allData: |
|
|
2487 | 2868 | boolMetFin = ~numpy.isnan(SNRdB) |
|
|
2488 | 2869 | else: |
|
|
2489 | 2870 | #SNR |
|
|
2490 | 2871 | boolMet1 = (SNRdB>SNRthresh) #SNR mask |
|
|
2491 | 2872 | boolMet1 = ndimage.median_filter(boolMet1, size=(1,5,5)) |
|
|
2492 | 2873 | |
|
|
2493 | 2874 | #Radial velocity |
|
|
2494 | 2875 | boolMet2 = numpy.abs(velRad) < 20 |
|
|
2495 | 2876 | boolMet2 = ndimage.median_filter(boolMet2, (1,5,5)) |
|
|
2496 | 2877 | |
|
|
2497 | 2878 | #Spectral Width |
|
|
2498 | 2879 | boolMet3 = spcWidth < 30 |
|
|
2499 | 2880 | boolMet3 = ndimage.median_filter(boolMet3, (1,5,5)) |
|
|
2500 | 2881 | # boolMetFin = self.__erase_small(boolMet1, 10,5) |
|
|
2501 | 2882 | boolMetFin = boolMet1&boolMet2&boolMet3 |
|
|
2502 | 2883 | |
|
|
2503 | 2884 | #Creating data_param |
|
|
2504 | 2885 | coordMet = numpy.where(boolMetFin) |
|
|
2505 | 2886 | |
|
|
2506 | 2887 | cmet = coordMet[0] |
|
|
2507 | 2888 | tmet = coordMet[1] |
|
|
2508 | 2889 | hmet = coordMet[2] |
|
|
2509 | 2890 | |
|
|
2510 | 2891 | data_param = numpy.zeros((tmet.size, 7)) |
|
|
2511 | 2892 | data_param[:,0] = utctime |
|
|
2512 | 2893 | data_param[:,1] = cmet |
|
|
2513 | 2894 | data_param[:,2] = tmet |
|
|
2514 | 2895 | data_param[:,3] = hmet |
|
|
2515 | 2896 | data_param[:,4] = SNR[cmet,tmet,hmet].T |
|
|
2516 | 2897 | data_param[:,5] = velRad[cmet,tmet,hmet].T |
|
|
2517 | 2898 | data_param[:,6] = spcWidth[cmet,tmet,hmet].T |
|
|
2518 | 2899 | |
|
|
2519 | 2900 | # self.dataOut.data_param = data_int |
|
|
2520 | 2901 | if len(data_param) == 0: |
|
|
2521 | 2902 | dataOut.flagNoData = True |
|
|
2522 | 2903 | else: |
|
|
2523 | 2904 | dataOut.data_param = data_param |
|
|
2524 | 2905 | |
|
|
2525 | 2906 | def __erase_small(self, binArray, threshX, threshY): |
|
|
2526 | 2907 | labarray, numfeat = ndimage.measurements.label(binArray) |
|
|
2527 | 2908 | binArray1 = numpy.copy(binArray) |
|
|
2528 | 2909 | |
|
|
2529 | 2910 | for i in range(1,numfeat + 1): |
|
|
2530 | 2911 | auxBin = (labarray==i) |
|
|
2531 | 2912 | auxSize = auxBin.sum() |
|
|
2532 | 2913 | |
|
|
2533 | 2914 | x,y = numpy.where(auxBin) |
|
|
2534 | 2915 | widthX = x.max() - x.min() |
|
|
2535 | 2916 | widthY = y.max() - y.min() |
|
|
2536 | 2917 | |
|
|
2537 | 2918 | #width X: 3 seg -> 12.5*3 |
|
|
2538 | 2919 | #width Y: |
|
|
2539 | 2920 | |
|
|
2540 | 2921 | if (auxSize < 50) or (widthX < threshX) or (widthY < threshY): |
|
|
2541 | 2922 | binArray1[auxBin] = False |
|
|
2542 | 2923 | |
|
|
2543 | 2924 | return binArray1 |
|
|
2544 | 2925 | |
|
|
2545 | 2926 | #--------------- Specular Meteor ---------------- |
|
|
2546 | 2927 | |
|
|
2547 | 2928 | class SMDetection(Operation): |
|
|
2548 | 2929 | ''' |
|
|
2549 | 2930 | Function DetectMeteors() |
|
|
2550 | 2931 | Project developed with paper: |
|
|
2551 | 2932 | HOLDSWORTH ET AL. 2004 |
|
|
2552 | 2933 | |
|
|
2553 | 2934 | Input: |
|
|
2554 | 2935 | self.dataOut.data_pre |
|
|
2555 | 2936 | |
|
|
2556 | 2937 | centerReceiverIndex: From the channels, which is the center receiver |
|
|
2557 | 2938 | |
|
|
2558 | 2939 | hei_ref: Height reference for the Beacon signal extraction |
|
|
2559 | 2940 | tauindex: |
|
|
2560 | 2941 | predefinedPhaseShifts: Predefined phase offset for the voltge signals |
|
|
2561 | 2942 | |
|
|
2562 | 2943 | cohDetection: Whether to user Coherent detection or not |
|
|
2563 | 2944 | cohDet_timeStep: Coherent Detection calculation time step |
|
|
2564 | 2945 | cohDet_thresh: Coherent Detection phase threshold to correct phases |
|
|
2565 | 2946 | |
|
|
2566 | 2947 | noise_timeStep: Noise calculation time step |
|
|
2567 | 2948 | noise_multiple: Noise multiple to define signal threshold |
|
|
2568 | 2949 | |
|
|
2569 | 2950 | multDet_timeLimit: Multiple Detection Removal time limit in seconds |
|
|
2570 | 2951 | multDet_rangeLimit: Multiple Detection Removal range limit in km |
|
|
2571 | 2952 | |
|
|
2572 | 2953 | phaseThresh: Maximum phase difference between receiver to be consider a meteor |
|
|
2573 | 2954 | SNRThresh: Minimum SNR threshold of the meteor signal to be consider a meteor |
|
|
2574 | 2955 | |
|
|
2575 | 2956 | hmin: Minimum Height of the meteor to use it in the further wind estimations |
|
|
2576 | 2957 | hmax: Maximum Height of the meteor to use it in the further wind estimations |
|
|
2577 | 2958 | azimuth: Azimuth angle correction |
|
|
2578 | 2959 | |
|
|
2579 | 2960 | Affected: |
|
|
2580 | 2961 | self.dataOut.data_param |
|
|
2581 | 2962 | |
|
|
2582 | 2963 | Rejection Criteria (Errors): |
|
|
2583 | 2964 | 0: No error; analysis OK |
|
|
2584 | 2965 | 1: SNR < SNR threshold |
|
|
2585 | 2966 | 2: angle of arrival (AOA) ambiguously determined |
|
|
2586 | 2967 | 3: AOA estimate not feasible |
|
|
2587 | 2968 | 4: Large difference in AOAs obtained from different antenna baselines |
|
|
2588 | 2969 | 5: echo at start or end of time series |
|
|
2589 | 2970 | 6: echo less than 5 examples long; too short for analysis |
|
|
2590 | 2971 | 7: echo rise exceeds 0.3s |
|
|
2591 | 2972 | 8: echo decay time less than twice rise time |
|
|
2592 | 2973 | 9: large power level before echo |
|
|
2593 | 2974 | 10: large power level after echo |
|
|
2594 | 2975 | 11: poor fit to amplitude for estimation of decay time |
|
|
2595 | 2976 | 12: poor fit to CCF phase variation for estimation of radial drift velocity |
|
|
2596 | 2977 | 13: height unresolvable echo: not valid height within 70 to 110 km |
|
|
2597 | 2978 | 14: height ambiguous echo: more then one possible height within 70 to 110 km |
|
|
2598 | 2979 | 15: radial drift velocity or projected horizontal velocity exceeds 200 m/s |
|
|
2599 | 2980 | 16: oscilatory echo, indicating event most likely not an underdense echo |
|
|
2600 | 2981 | |
|
|
2601 | 2982 | 17: phase difference in meteor Reestimation |
|
|
2602 | 2983 | |
|
|
2603 | 2984 | Data Storage: |
|
|
2604 | 2985 | Meteors for Wind Estimation (8): |
|
|
2605 | 2986 | Utc Time | Range Height |
|
|
2606 | 2987 | Azimuth Zenith errorCosDir |
|
|
2607 | 2988 | VelRad errorVelRad |
|
|
2608 | 2989 | Phase0 Phase1 Phase2 Phase3 |
|
|
2609 | 2990 | TypeError |
|
|
2610 | 2991 | |
|
|
2611 | 2992 | ''' |
|
|
2612 | 2993 | |
|
|
2613 | 2994 | def run(self, dataOut, hei_ref = None, tauindex = 0, |
|
|
2614 | 2995 | phaseOffsets = None, |
|
|
2615 | 2996 | cohDetection = False, cohDet_timeStep = 1, cohDet_thresh = 25, |
|
|
2616 | 2997 | noise_timeStep = 4, noise_multiple = 4, |
|
|
2617 | 2998 | multDet_timeLimit = 1, multDet_rangeLimit = 3, |
|
|
2618 | 2999 | phaseThresh = 20, SNRThresh = 5, |
|
|
2619 | 3000 | hmin = 50, hmax=150, azimuth = 0, |
|
|
2620 | 3001 | channelPositions = None) : |
|
|
2621 | 3002 | |
|
|
2622 | 3003 | |
|
|
2623 | 3004 | #Getting Pairslist |
|
|
2624 | 3005 | if channelPositions is None: |
|
|
2625 | 3006 | # channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T |
|
|
2626 | 3007 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella |
|
|
2627 | 3008 | meteorOps = SMOperations() |
|
|
2628 | 3009 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) |
|
|
2629 | 3010 | heiRang = dataOut.getHeiRange() |
|
|
2630 | 3011 | #Get Beacon signal - No Beacon signal anymore |
|
|
2631 | 3012 | # newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex]) |
|
|
2632 | 3013 | # |
|
|
2633 | 3014 | # if hei_ref != None: |
|
|
2634 | 3015 | # newheis = numpy.where(self.dataOut.heightList>hei_ref) |
|
|
2635 | 3016 | # |
|
|
2636 | 3017 | |
|
|
2637 | 3018 | |
|
|
2638 | 3019 | #****************REMOVING HARDWARE PHASE DIFFERENCES*************** |
|
|
2639 | 3020 | # see if the user put in pre defined phase shifts |
|
|
2640 | 3021 | voltsPShift = dataOut.data_pre.copy() |
|
|
2641 | 3022 | |
|
|
2642 | 3023 | # if predefinedPhaseShifts != None: |
|
|
2643 | 3024 | # hardwarePhaseShifts = numpy.array(predefinedPhaseShifts)*numpy.pi/180 |
|
|
2644 | 3025 | # |
|
|
2645 | 3026 | # # elif beaconPhaseShifts: |
|
|
2646 | 3027 | # # #get hardware phase shifts using beacon signal |
|
|
2647 | 3028 | # # hardwarePhaseShifts = self.__getHardwarePhaseDiff(self.dataOut.data_pre, pairslist, newheis, 10) |
|
|
2648 | 3029 | # # hardwarePhaseShifts = numpy.insert(hardwarePhaseShifts,centerReceiverIndex,0) |
|
|
2649 | 3030 | # |
|
|
2650 | 3031 | # else: |
|
|
2651 | 3032 | # hardwarePhaseShifts = numpy.zeros(5) |
|
|
2652 | 3033 | # |
|
|
2653 | 3034 | # voltsPShift = numpy.zeros((self.dataOut.data_pre.shape[0],self.dataOut.data_pre.shape[1],self.dataOut.data_pre.shape[2]), dtype = 'complex') |
|
|
2654 | 3035 | # for i in range(self.dataOut.data_pre.shape[0]): |
|
|
2655 | 3036 | # voltsPShift[i,:,:] = self.__shiftPhase(self.dataOut.data_pre[i,:,:], hardwarePhaseShifts[i]) |
|
|
2656 | 3037 | |
|
|
2657 | 3038 | #******************END OF REMOVING HARDWARE PHASE DIFFERENCES********* |
|
|
2658 | 3039 | |
|
|
2659 | 3040 | #Remove DC |
|
|
2660 | 3041 | voltsDC = numpy.mean(voltsPShift,1) |
|
|
2661 | 3042 | voltsDC = numpy.mean(voltsDC,1) |
|
|
2662 | 3043 | for i in range(voltsDC.shape[0]): |
|
|
2663 | 3044 | voltsPShift[i] = voltsPShift[i] - voltsDC[i] |
|
|
2664 | 3045 | |
|
|
2665 | 3046 | #Don't considerate last heights, theyre used to calculate Hardware Phase Shift |
|
|
2666 | 3047 | # voltsPShift = voltsPShift[:,:,:newheis[0][0]] |
|
|
2667 | 3048 | |
|
|
2668 | 3049 | #************ FIND POWER OF DATA W/COH OR NON COH DETECTION (3.4) ********** |
|
|
2669 | 3050 | #Coherent Detection |
|
|
2670 | 3051 | if cohDetection: |
|
|
2671 | 3052 | #use coherent detection to get the net power |
|
|
2672 | 3053 | cohDet_thresh = cohDet_thresh*numpy.pi/180 |
|
|
2673 | 3054 | voltsPShift = self.__coherentDetection(voltsPShift, cohDet_timeStep, dataOut.timeInterval, pairslist0, cohDet_thresh) |
|
|
2674 | 3055 | |
|
|
2675 | 3056 | #Non-coherent detection! |
|
|
2676 | 3057 | powerNet = numpy.nansum(numpy.abs(voltsPShift[:,:,:])**2,0) |
|
|
2677 | 3058 | #********** END OF COH/NON-COH POWER CALCULATION********************** |
|
|
2678 | 3059 | |
|
|
2679 | 3060 | #********** FIND THE NOISE LEVEL AND POSSIBLE METEORS **************** |
|
|
2680 | 3061 | #Get noise |
|
|
2681 | 3062 | noise, noise1 = self.__getNoise(powerNet, noise_timeStep, dataOut.timeInterval) |
|
|
2682 | 3063 | # noise = self.getNoise1(powerNet, noise_timeStep, self.dataOut.timeInterval) |
|
|
2683 | 3064 | #Get signal threshold |
|
|
2684 | 3065 | signalThresh = noise_multiple*noise |
|
|
2685 | 3066 | #Meteor echoes detection |
|
|
2686 | 3067 | listMeteors = self.__findMeteors(powerNet, signalThresh) |
|
|
2687 | 3068 | #******* END OF NOISE LEVEL AND POSSIBLE METEORS CACULATION ********** |
|
|
2688 | 3069 | |
|
|
2689 | 3070 | #************** REMOVE MULTIPLE DETECTIONS (3.5) *************************** |
|
|
2690 | 3071 | #Parameters |
|
|
2691 | 3072 | heiRange = dataOut.getHeiRange() |
|
|
2692 | 3073 | rangeInterval = heiRange[1] - heiRange[0] |
|
|
2693 | 3074 | rangeLimit = multDet_rangeLimit/rangeInterval |
|
|
2694 | 3075 | timeLimit = multDet_timeLimit/dataOut.timeInterval |
|
|
2695 | 3076 | #Multiple detection removals |
|
|
2696 | 3077 | listMeteors1 = self.__removeMultipleDetections(listMeteors, rangeLimit, timeLimit) |
|
|
2697 | 3078 | #************ END OF REMOVE MULTIPLE DETECTIONS ********************** |
|
|
2698 | 3079 | |
|
|
2699 | 3080 | #********************* METEOR REESTIMATION (3.7, 3.8, 3.9, 3.10) ******************** |
|
|
2700 | 3081 | #Parameters |
|
|
2701 | 3082 | phaseThresh = phaseThresh*numpy.pi/180 |
|
|
2702 | 3083 | thresh = [phaseThresh, noise_multiple, SNRThresh] |
|
|
2703 | 3084 | #Meteor reestimation (Errors N 1, 6, 12, 17) |
|
|
2704 | 3085 | listMeteors2, listMeteorsPower, listMeteorsVolts = self.__meteorReestimation(listMeteors1, voltsPShift, pairslist0, thresh, noise, dataOut.timeInterval, dataOut.frequency) |
|
|
2705 | 3086 | # listMeteors2, listMeteorsPower, listMeteorsVolts = self.meteorReestimation3(listMeteors2, listMeteorsPower, listMeteorsVolts, voltsPShift, pairslist, thresh, noise) |
|
|
2706 | 3087 | #Estimation of decay times (Errors N 7, 8, 11) |
|
|
2707 | 3088 | listMeteors3 = self.__estimateDecayTime(listMeteors2, listMeteorsPower, dataOut.timeInterval, dataOut.frequency) |
|
|
2708 | 3089 | #******************* END OF METEOR REESTIMATION ******************* |
|
|
2709 | 3090 | |
|
|
2710 | 3091 | #********************* METEOR PARAMETERS CALCULATION (3.11, 3.12, 3.13) ************************** |
|
|
2711 | 3092 | #Calculating Radial Velocity (Error N 15) |
|
|
2712 | 3093 | radialStdThresh = 10 |
|
|
2713 | 3094 | listMeteors4 = self.__getRadialVelocity(listMeteors3, listMeteorsVolts, radialStdThresh, pairslist0, dataOut.timeInterval) |
|
|
2714 | 3095 | |
|
|
2715 | 3096 | if len(listMeteors4) > 0: |
|
|
2716 | 3097 | #Setting New Array |
|
|
2717 | 3098 | date = dataOut.utctime |
|
|
2718 | 3099 | arrayParameters = self.__setNewArrays(listMeteors4, date, heiRang) |
|
|
2719 | 3100 | |
|
|
2720 | 3101 | #Correcting phase offset |
|
|
2721 | 3102 | if phaseOffsets != None: |
|
|
2722 | 3103 | phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180 |
|
|
2723 | 3104 | arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets) |
|
|
2724 | 3105 | |
|
|
2725 | 3106 | #Second Pairslist |
|
|
2726 | 3107 | pairsList = [] |
|
|
2727 | 3108 | pairx = (0,1) |
|
|
2728 | 3109 | pairy = (2,3) |
|
|
2729 | 3110 | pairsList.append(pairx) |
|
|
2730 | 3111 | pairsList.append(pairy) |
|
|
2731 | 3112 | |
|
|
2732 | 3113 | jph = numpy.array([0,0,0,0]) |
|
|
2733 | 3114 | h = (hmin,hmax) |
|
|
2734 | 3115 | arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph) |
|
|
2735 | 3116 | |
|
|
2736 | 3117 | # #Calculate AOA (Error N 3, 4) |
|
|
2737 | 3118 | # #JONES ET AL. 1998 |
|
|
2738 | 3119 | # error = arrayParameters[:,-1] |
|
|
2739 | 3120 | # AOAthresh = numpy.pi/8 |
|
|
2740 | 3121 | # phases = -arrayParameters[:,9:13] |
|
|
2741 | 3122 | # arrayParameters[:,4:7], arrayParameters[:,-1] = meteorOps.getAOA(phases, pairsList, error, AOAthresh, azimuth) |
|
|
2742 | 3123 | # |
|
|
2743 | 3124 | # #Calculate Heights (Error N 13 and 14) |
|
|
2744 | 3125 | # error = arrayParameters[:,-1] |
|
|
2745 | 3126 | # Ranges = arrayParameters[:,2] |
|
|
2746 | 3127 | # zenith = arrayParameters[:,5] |
|
|
2747 | 3128 | # arrayParameters[:,3], arrayParameters[:,-1] = meteorOps.getHeights(Ranges, zenith, error, hmin, hmax) |
|
|
2748 | 3129 | # error = arrayParameters[:,-1] |
|
|
2749 | 3130 | #********************* END OF PARAMETERS CALCULATION ************************** |
|
|
2750 | 3131 | |
|
|
2751 | 3132 | #***************************+ PASS DATA TO NEXT STEP ********************** |
|
|
2752 | 3133 | # arrayFinal = arrayParameters.reshape((1,arrayParameters.shape[0],arrayParameters.shape[1])) |
|
|
2753 | 3134 | dataOut.data_param = arrayParameters |
|
|
2754 | 3135 | |
|
|
2755 | 3136 | if arrayParameters is None: |
|
|
2756 | 3137 | dataOut.flagNoData = True |
|
|
2757 | 3138 | else: |
|
|
2758 | 3139 | dataOut.flagNoData = True |
|
|
2759 | 3140 | |
|
|
2760 | 3141 | return |
|
|
2761 | 3142 | |
|
|
2762 | 3143 | def __getHardwarePhaseDiff(self, voltage0, pairslist, newheis, n): |
|
|
2763 | 3144 | |
|
|
2764 | 3145 | minIndex = min(newheis[0]) |
|
|
2765 | 3146 | maxIndex = max(newheis[0]) |
|
|
2766 | 3147 | |
|
|
2767 | 3148 | voltage = voltage0[:,:,minIndex:maxIndex+1] |
|
|
2768 | 3149 | nLength = voltage.shape[1]/n |
|
|
2769 | 3150 | nMin = 0 |
|
|
2770 | 3151 | nMax = 0 |
|
|
2771 | 3152 | phaseOffset = numpy.zeros((len(pairslist),n)) |
|
|
2772 | 3153 | |
|
|
2773 | 3154 | for i in range(n): |
|
|
2774 | 3155 | nMax += nLength |
|
|
2775 | 3156 | phaseCCF = -numpy.angle(self.__calculateCCF(voltage[:,nMin:nMax,:], pairslist, [0])) |
|
|
2776 | 3157 | phaseCCF = numpy.mean(phaseCCF, axis = 2) |
|
|
2777 | 3158 | phaseOffset[:,i] = phaseCCF.transpose() |
|
|
2778 | 3159 | nMin = nMax |
|
|
2779 | 3160 | # phaseDiff, phaseArrival = self.estimatePhaseDifference(voltage, pairslist) |
|
|
2780 | 3161 | |
|
|
2781 | 3162 | #Remove Outliers |
|
|
2782 | 3163 | factor = 2 |
|
|
2783 | 3164 | wt = phaseOffset - signal.medfilt(phaseOffset,(1,5)) |
|
|
2784 | 3165 | dw = numpy.std(wt,axis = 1) |
|
|
2785 | 3166 | dw = dw.reshape((dw.size,1)) |
|
|
2786 | 3167 | ind = numpy.where(numpy.logical_or(wt>dw*factor,wt<-dw*factor)) |
|
|
2787 | 3168 | phaseOffset[ind] = numpy.nan |
|
|
2788 | 3169 | phaseOffset = stats.nanmean(phaseOffset, axis=1) |
|
|
2789 | 3170 | |
|
|
2790 | 3171 | return phaseOffset |
|
|
2791 | 3172 | |
|
|
2792 | 3173 | def __shiftPhase(self, data, phaseShift): |
|
|
2793 | 3174 | #this will shift the phase of a complex number |
|
|
2794 | 3175 | dataShifted = numpy.abs(data) * numpy.exp((numpy.angle(data)+phaseShift)*1j) |
|
|
2795 | 3176 | return dataShifted |
|
|
2796 | 3177 | |
|
|
2797 | 3178 | def __estimatePhaseDifference(self, array, pairslist): |
|
|
2798 | 3179 | nChannel = array.shape[0] |
|
|
2799 | 3180 | nHeights = array.shape[2] |
|
|
2800 | 3181 | numPairs = len(pairslist) |
|
|
2801 | 3182 | # phaseCCF = numpy.zeros((nChannel, 5, nHeights)) |
|
|
2802 | 3183 | phaseCCF = numpy.angle(self.__calculateCCF(array, pairslist, [-2,-1,0,1,2])) |
|
|
2803 | 3184 | |
|
|
2804 | 3185 | #Correct phases |
|
|
2805 | 3186 | derPhaseCCF = phaseCCF[:,1:,:] - phaseCCF[:,0:-1,:] |
|
|
2806 | 3187 | indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) |
|
|
2807 | 3188 | |
|
|
2808 | 3189 | if indDer[0].shape[0] > 0: |
|
|
2809 | 3190 | for i in range(indDer[0].shape[0]): |
|
|
2810 | 3191 | signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i],indDer[2][i]]) |
|
|
2811 | 3192 | phaseCCF[indDer[0][i],indDer[1][i]+1:,:] += signo*2*numpy.pi |
|
|
2812 | 3193 | |
|
|
2813 | 3194 | # for j in range(numSides): |
|
|
2814 | 3195 | # phaseCCFAux = self.calculateCCF(arrayCenter, arraySides[j,:,:], [-2,1,0,1,2]) |
|
|
2815 | 3196 | # phaseCCF[j,:,:] = numpy.angle(phaseCCFAux) |
|
|
2816 | 3197 | # |
|
|
2817 | 3198 | #Linear |
|
|
2818 | 3199 | phaseInt = numpy.zeros((numPairs,1)) |
|
|
2819 | 3200 | angAllCCF = phaseCCF[:,[0,1,3,4],0] |
|
|
2820 | 3201 | for j in range(numPairs): |
|
|
2821 | 3202 | fit = stats.linregress([-2,-1,1,2],angAllCCF[j,:]) |
|
|
2822 | 3203 | phaseInt[j] = fit[1] |
|
|
2823 | 3204 | #Phase Differences |
|
|
2824 | 3205 | phaseDiff = phaseInt - phaseCCF[:,2,:] |
|
|
2825 | 3206 | phaseArrival = phaseInt.reshape(phaseInt.size) |
|
|
2826 | 3207 | |
|
|
2827 | 3208 | #Dealias |
|
|
2828 | 3209 | phaseArrival = numpy.angle(numpy.exp(1j*phaseArrival)) |
|
|
2829 | 3210 | # indAlias = numpy.where(phaseArrival > numpy.pi) |
|
|
2830 | 3211 | # phaseArrival[indAlias] -= 2*numpy.pi |
|
|
2831 | 3212 | # indAlias = numpy.where(phaseArrival < -numpy.pi) |
|
|
2832 | 3213 | # phaseArrival[indAlias] += 2*numpy.pi |
|
|
2833 | 3214 | |
|
|
2834 | 3215 | return phaseDiff, phaseArrival |
|
|
2835 | 3216 | |
|
|
2836 | 3217 | def __coherentDetection(self, volts, timeSegment, timeInterval, pairslist, thresh): |
|
|
2837 | 3218 | #this function will run the coherent detection used in Holdworth et al. 2004 and return the net power |
|
|
2838 | 3219 | #find the phase shifts of each channel over 1 second intervals |
|
|
2839 | 3220 | #only look at ranges below the beacon signal |
|
|
2840 | 3221 | numProfPerBlock = numpy.ceil(timeSegment/timeInterval) |
|
|
2841 | 3222 | numBlocks = int(volts.shape[1]/numProfPerBlock) |
|
|
2842 | 3223 | numHeights = volts.shape[2] |
|
|
2843 | 3224 | nChannel = volts.shape[0] |
|
|
2844 | 3225 | voltsCohDet = volts.copy() |
|
|
2845 | 3226 | |
|
|
2846 | 3227 | pairsarray = numpy.array(pairslist) |
|
|
2847 | 3228 | indSides = pairsarray[:,1] |
|
|
2848 | 3229 | # indSides = numpy.array(range(nChannel)) |
|
|
2849 | 3230 | # indSides = numpy.delete(indSides, indCenter) |
|
|
2850 | 3231 | # |
|
|
2851 | 3232 | # listCenter = numpy.array_split(volts[indCenter,:,:], numBlocks, 0) |
|
|
2852 | 3233 | listBlocks = numpy.array_split(volts, numBlocks, 1) |
|
|
2853 | 3234 | |
|
|
2854 | 3235 | startInd = 0 |
|
|
2855 | 3236 | endInd = 0 |
|
|
2856 | 3237 | |
|
|
2857 | 3238 | for i in range(numBlocks): |
|
|
2858 | 3239 | startInd = endInd |
|
|
2859 | 3240 | endInd = endInd + listBlocks[i].shape[1] |
|
|
2860 | 3241 | |
|
|
2861 | 3242 | arrayBlock = listBlocks[i] |
|
|
2862 | 3243 | # arrayBlockCenter = listCenter[i] |
|
|
2863 | 3244 | |
|
|
2864 | 3245 | #Estimate the Phase Difference |
|
|
2865 | 3246 | phaseDiff, aux = self.__estimatePhaseDifference(arrayBlock, pairslist) |
|
|
2866 | 3247 | #Phase Difference RMS |
|
|
2867 | 3248 | arrayPhaseRMS = numpy.abs(phaseDiff) |
|
|
2868 | 3249 | phaseRMSaux = numpy.sum(arrayPhaseRMS < thresh,0) |
|
|
2869 | 3250 | indPhase = numpy.where(phaseRMSaux==4) |
|
|
2870 | 3251 | #Shifting |
|
|
2871 | 3252 | if indPhase[0].shape[0] > 0: |
|
|
2872 | 3253 | for j in range(indSides.size): |
|
|
2873 | 3254 | arrayBlock[indSides[j],:,indPhase] = self.__shiftPhase(arrayBlock[indSides[j],:,indPhase], phaseDiff[j,indPhase].transpose()) |
|
|
2874 | 3255 | voltsCohDet[:,startInd:endInd,:] = arrayBlock |
|
|
2875 | 3256 | |
|
|
2876 | 3257 | return voltsCohDet |
|
|
2877 | 3258 | |
|
|
2878 | 3259 | def __calculateCCF(self, volts, pairslist ,laglist): |
|
|
2879 | 3260 | |
|
|
2880 | 3261 | nHeights = volts.shape[2] |
|
|
2881 | 3262 | nPoints = volts.shape[1] |
|
|
2882 | 3263 | voltsCCF = numpy.zeros((len(pairslist), len(laglist), nHeights),dtype = 'complex') |
|
|
2883 | 3264 | |
|
|
2884 | 3265 | for i in range(len(pairslist)): |
|
|
2885 | 3266 | volts1 = volts[pairslist[i][0]] |
|
|
2886 | 3267 | volts2 = volts[pairslist[i][1]] |
|
|
2887 | 3268 | |
|
|
2888 | 3269 | for t in range(len(laglist)): |
|
|
2889 | 3270 | idxT = laglist[t] |
|
|
2890 | 3271 | if idxT >= 0: |
|
|
2891 | 3272 | vStacked = numpy.vstack((volts2[idxT:,:], |
|
|
2892 | 3273 | numpy.zeros((idxT, nHeights),dtype='complex'))) |
|
|
2893 | 3274 | else: |
|
|
2894 | 3275 | vStacked = numpy.vstack((numpy.zeros((-idxT, nHeights),dtype='complex'), |
|
|
2895 | 3276 | volts2[:(nPoints + idxT),:])) |
|
|
2896 | 3277 | voltsCCF[i,t,:] = numpy.sum((numpy.conjugate(volts1)*vStacked),axis=0) |
|
|
2897 | 3278 | |
|
|
2898 | 3279 | vStacked = None |
|
|
2899 | 3280 | return voltsCCF |
|
|
2900 | 3281 | |
|
|
2901 | 3282 | def __getNoise(self, power, timeSegment, timeInterval): |
|
|
2902 | 3283 | numProfPerBlock = numpy.ceil(timeSegment/timeInterval) |
|
|
2903 | 3284 | numBlocks = int(power.shape[0]/numProfPerBlock) |
|
|
2904 | 3285 | numHeights = power.shape[1] |
|
|
2905 | 3286 | |
|
|
2906 | 3287 | listPower = numpy.array_split(power, numBlocks, 0) |
|
|
2907 | 3288 | noise = numpy.zeros((power.shape[0], power.shape[1])) |
|
|
2908 | 3289 | noise1 = numpy.zeros((power.shape[0], power.shape[1])) |
|
|
2909 | 3290 | |
|
|
2910 | 3291 | startInd = 0 |
|
|
2911 | 3292 | endInd = 0 |
|
|
2912 | 3293 | |
|
|
2913 | 3294 | for i in range(numBlocks): #split por canal |
|
|
2914 | 3295 | startInd = endInd |
|
|
2915 | 3296 | endInd = endInd + listPower[i].shape[0] |
|
|
2916 | 3297 | |
|
|
2917 | 3298 | arrayBlock = listPower[i] |
|
|
2918 | 3299 | noiseAux = numpy.mean(arrayBlock, 0) |
|
|
2919 | 3300 | # noiseAux = numpy.median(noiseAux) |
|
|
2920 | 3301 | # noiseAux = numpy.mean(arrayBlock) |
|
|
2921 | 3302 | noise[startInd:endInd,:] = noise[startInd:endInd,:] + noiseAux |
|
|
2922 | 3303 | |
|
|
2923 | 3304 | noiseAux1 = numpy.mean(arrayBlock) |
|
|
2924 | 3305 | noise1[startInd:endInd,:] = noise1[startInd:endInd,:] + noiseAux1 |
|
|
2925 | 3306 | |
|
|
2926 | 3307 | return noise, noise1 |
|
|
2927 | 3308 | |
|
|
2928 | 3309 | def __findMeteors(self, power, thresh): |
|
|
2929 | 3310 | nProf = power.shape[0] |
|
|
2930 | 3311 | nHeights = power.shape[1] |
|
|
2931 | 3312 | listMeteors = [] |
|
|
2932 | 3313 | |
|
|
2933 | 3314 | for i in range(nHeights): |
|
|
2934 | 3315 | powerAux = power[:,i] |
|
|
2935 | 3316 | threshAux = thresh[:,i] |
|
|
2936 | 3317 | |
|
|
2937 | 3318 | indUPthresh = numpy.where(powerAux > threshAux)[0] |
|
|
2938 | 3319 | indDNthresh = numpy.where(powerAux <= threshAux)[0] |
|
|
2939 | 3320 | |
|
|
2940 | 3321 | j = 0 |
|
|
2941 | 3322 | |
|
|
2942 | 3323 | while (j < indUPthresh.size - 2): |
|
|
2943 | 3324 | if (indUPthresh[j + 2] == indUPthresh[j] + 2): |
|
|
2944 | 3325 | indDNAux = numpy.where(indDNthresh > indUPthresh[j]) |
|
|
2945 | 3326 | indDNthresh = indDNthresh[indDNAux] |
|
|
2946 | 3327 | |
|
|
2947 | 3328 | if (indDNthresh.size > 0): |
|
|
2948 | 3329 | indEnd = indDNthresh[0] - 1 |
|
|
2949 | 3330 | indInit = indUPthresh[j] |
|
|
2950 | 3331 | |
|
|
2951 | 3332 | meteor = powerAux[indInit:indEnd + 1] |
|
|
2952 | 3333 | indPeak = meteor.argmax() + indInit |
|
|
2953 | 3334 | FLA = sum(numpy.conj(meteor)*numpy.hstack((meteor[1:],0))) |
|
|
2954 | 3335 | |
|
|
2955 | 3336 | listMeteors.append(numpy.array([i,indInit,indPeak,indEnd,FLA])) #CHEQUEAR!!!!! |
|
|
2956 | 3337 | j = numpy.where(indUPthresh == indEnd)[0] + 1 |
|
|
2957 | 3338 | else: j+=1 |
|
|
2958 | 3339 | else: j+=1 |
|
|
2959 | 3340 | |
|
|
2960 | 3341 | return listMeteors |
|
|
2961 | 3342 | |
|
|
2962 | 3343 | def __removeMultipleDetections(self,listMeteors, rangeLimit, timeLimit): |
|
|
2963 | 3344 | |
|
|
2964 | 3345 | arrayMeteors = numpy.asarray(listMeteors) |
|
|
2965 | 3346 | listMeteors1 = [] |
|
|
2966 | 3347 | |
|
|
2967 | 3348 | while arrayMeteors.shape[0] > 0: |
|
|
2968 | 3349 | FLAs = arrayMeteors[:,4] |
|
|
2969 | 3350 | maxFLA = FLAs.argmax() |
|
|
2970 | 3351 | listMeteors1.append(arrayMeteors[maxFLA,:]) |
|
|
2971 | 3352 | |
|
|
2972 | 3353 | MeteorInitTime = arrayMeteors[maxFLA,1] |
|
|
2973 | 3354 | MeteorEndTime = arrayMeteors[maxFLA,3] |
|
|
2974 | 3355 | MeteorHeight = arrayMeteors[maxFLA,0] |
|
|
2975 | 3356 | |
|
|
2976 | 3357 | #Check neighborhood |
|
|
2977 | 3358 | maxHeightIndex = MeteorHeight + rangeLimit |
|
|
2978 | 3359 | minHeightIndex = MeteorHeight - rangeLimit |
|
|
2979 | 3360 | minTimeIndex = MeteorInitTime - timeLimit |
|
|
2980 | 3361 | maxTimeIndex = MeteorEndTime + timeLimit |
|
|
2981 | 3362 | |
|
|
2982 | 3363 | #Check Heights |
|
|
2983 | 3364 | indHeight = numpy.logical_and(arrayMeteors[:,0] >= minHeightIndex, arrayMeteors[:,0] <= maxHeightIndex) |
|
|
2984 | 3365 | indTime = numpy.logical_and(arrayMeteors[:,3] >= minTimeIndex, arrayMeteors[:,1] <= maxTimeIndex) |
|
|
2985 | 3366 | indBoth = numpy.where(numpy.logical_and(indTime,indHeight)) |
|
|
2986 | 3367 | |
|
|
2987 | 3368 | arrayMeteors = numpy.delete(arrayMeteors, indBoth, axis = 0) |
|
|
2988 | 3369 | |
|
|
2989 | 3370 | return listMeteors1 |
|
|
2990 | 3371 | |
|
|
2991 | 3372 | def __meteorReestimation(self, listMeteors, volts, pairslist, thresh, noise, timeInterval,frequency): |
|
|
2992 | 3373 | numHeights = volts.shape[2] |
|
|
2993 | 3374 | nChannel = volts.shape[0] |
|
|
2994 | 3375 | |
|
|
2995 | 3376 | thresholdPhase = thresh[0] |
|
|
2996 | 3377 | thresholdNoise = thresh[1] |
|
|
2997 | 3378 | thresholdDB = float(thresh[2]) |
|
|
2998 | 3379 | |
|
|
2999 | 3380 | thresholdDB1 = 10**(thresholdDB/10) |
|
|
3000 | 3381 | pairsarray = numpy.array(pairslist) |
|
|
3001 | 3382 | indSides = pairsarray[:,1] |
|
|
3002 | 3383 | |
|
|
3003 | 3384 | pairslist1 = list(pairslist) |
|
|
3004 | 3385 | pairslist1.append((0,1)) |
|
|
3005 | 3386 | pairslist1.append((3,4)) |
|
|
3006 | 3387 | |
|
|
3007 | 3388 | listMeteors1 = [] |
|
|
3008 | 3389 | listPowerSeries = [] |
|
|
3009 | 3390 | listVoltageSeries = [] |
|
|
3010 | 3391 | #volts has the war data |
|
|
3011 | 3392 | |
|
|
3012 | 3393 | if frequency == 30e6: |
|
|
3013 | 3394 | timeLag = 45*10**-3 |
|
|
3014 | 3395 | else: |
|
|
3015 | 3396 | timeLag = 15*10**-3 |
|
|
3016 | 3397 | lag = numpy.ceil(timeLag/timeInterval) |
|
|
3017 | 3398 | |
|
|
3018 | 3399 | for i in range(len(listMeteors)): |
|
|
3019 | 3400 | |
|
|
3020 | 3401 | ###################### 3.6 - 3.7 PARAMETERS REESTIMATION ######################### |
|
|
3021 | 3402 | meteorAux = numpy.zeros(16) |
|
|
3022 | 3403 | |
|
|
3023 | 3404 | #Loading meteor Data (mHeight, mStart, mPeak, mEnd) |
|
|
3024 | 3405 | mHeight = listMeteors[i][0] |
|
|
3025 | 3406 | mStart = listMeteors[i][1] |
|
|
3026 | 3407 | mPeak = listMeteors[i][2] |
|
|
3027 | 3408 | mEnd = listMeteors[i][3] |
|
|
3028 | 3409 | |
|
|
3029 | 3410 | #get the volt data between the start and end times of the meteor |
|
|
3030 | 3411 | meteorVolts = volts[:,mStart:mEnd+1,mHeight] |
|
|
3031 | 3412 | meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) |
|
|
3032 | 3413 | |
|
|
3033 | 3414 | #3.6. Phase Difference estimation |
|
|
3034 | 3415 | phaseDiff, aux = self.__estimatePhaseDifference(meteorVolts, pairslist) |
|
|
3035 | 3416 | |
|
|
3036 | 3417 | #3.7. Phase difference removal & meteor start, peak and end times reestimated |
|
|
3037 | 3418 | #meteorVolts0.- all Channels, all Profiles |
|
|
3038 | 3419 | meteorVolts0 = volts[:,:,mHeight] |
|
|
3039 | 3420 | meteorThresh = noise[:,mHeight]*thresholdNoise |
|
|
3040 | 3421 | meteorNoise = noise[:,mHeight] |
|
|
3041 | 3422 | meteorVolts0[indSides,:] = self.__shiftPhase(meteorVolts0[indSides,:], phaseDiff) #Phase Shifting |
|
|
3042 | 3423 | powerNet0 = numpy.nansum(numpy.abs(meteorVolts0)**2, axis = 0) #Power |
|
|
3043 | 3424 | |
|
|
3044 | 3425 | #Times reestimation |
|
|
3045 | 3426 | mStart1 = numpy.where(powerNet0[:mPeak] < meteorThresh[:mPeak])[0] |
|
|
3046 | 3427 | if mStart1.size > 0: |
|
|
3047 | 3428 | mStart1 = mStart1[-1] + 1 |
|
|
3048 | 3429 | |
|
|
3049 | 3430 | else: |
|
|
3050 | 3431 | mStart1 = mPeak |
|
|
3051 | 3432 | |
|
|
3052 | 3433 | mEnd1 = numpy.where(powerNet0[mPeak:] < meteorThresh[mPeak:])[0][0] + mPeak - 1 |
|
|
3053 | 3434 | mEndDecayTime1 = numpy.where(powerNet0[mPeak:] < meteorNoise[mPeak:])[0] |
|
|
3054 | 3435 | if mEndDecayTime1.size == 0: |
|
|
3055 | 3436 | mEndDecayTime1 = powerNet0.size |
|
|
3056 | 3437 | else: |
|
|
3057 | 3438 | mEndDecayTime1 = mEndDecayTime1[0] + mPeak - 1 |
|
|
3058 | 3439 | # mPeak1 = meteorVolts0[mStart1:mEnd1 + 1].argmax() |
|
|
3059 | 3440 | |
|
|
3060 | 3441 | #meteorVolts1.- all Channels, from start to end |
|
|
3061 | 3442 | meteorVolts1 = meteorVolts0[:,mStart1:mEnd1 + 1] |
|
|
3062 | 3443 | meteorVolts2 = meteorVolts0[:,mPeak + lag:mEnd1 + 1] |
|
|
3063 | 3444 | if meteorVolts2.shape[1] == 0: |
|
|
3064 | 3445 | meteorVolts2 = meteorVolts0[:,mPeak:mEnd1 + 1] |
|
|
3065 | 3446 | meteorVolts1 = meteorVolts1.reshape(meteorVolts1.shape[0], meteorVolts1.shape[1], 1) |
|
|
3066 | 3447 | meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1], 1) |
|
|
3067 | 3448 | ##################### END PARAMETERS REESTIMATION ######################### |
|
|
3068 | 3449 | |
|
|
3069 | 3450 | ##################### 3.8 PHASE DIFFERENCE REESTIMATION ######################## |
|
|
3070 | 3451 | # if mEnd1 - mStart1 > 4: #Error Number 6: echo less than 5 samples long; too short for analysis |
|
|
3071 | 3452 | if meteorVolts2.shape[1] > 0: |
|
|
3072 | 3453 | #Phase Difference re-estimation |
|
|
3073 | 3454 | phaseDiff1, phaseDiffint = self.__estimatePhaseDifference(meteorVolts2, pairslist1) #Phase Difference Estimation |
|
|
3074 | 3455 | # phaseDiff1, phaseDiffint = self.estimatePhaseDifference(meteorVolts2, pairslist) |
|
|
3075 | 3456 | meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1]) |
|
|
3076 | 3457 | phaseDiff11 = numpy.reshape(phaseDiff1, (phaseDiff1.shape[0],1)) |
|
|
3077 | 3458 | meteorVolts2[indSides,:] = self.__shiftPhase(meteorVolts2[indSides,:], phaseDiff11[0:4]) #Phase Shifting |
|
|
3078 | 3459 | |
|
|
3079 | 3460 | #Phase Difference RMS |
|
|
3080 | 3461 | phaseRMS1 = numpy.sqrt(numpy.mean(numpy.square(phaseDiff1))) |
|
|
3081 | 3462 | powerNet1 = numpy.nansum(numpy.abs(meteorVolts1[:,:])**2,0) |
|
|
3082 | 3463 | #Data from Meteor |
|
|
3083 | 3464 | mPeak1 = powerNet1.argmax() + mStart1 |
|
|
3084 | 3465 | mPeakPower1 = powerNet1.max() |
|
|
3085 | 3466 | noiseAux = sum(noise[mStart1:mEnd1 + 1,mHeight]) |
|
|
3086 | 3467 | mSNR1 = (sum(powerNet1)-noiseAux)/noiseAux |
|
|
3087 | 3468 | Meteor1 = numpy.array([mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1]) |
|
|
3088 | 3469 | Meteor1 = numpy.hstack((Meteor1,phaseDiffint)) |
|
|
3089 | 3470 | PowerSeries = powerNet0[mStart1:mEndDecayTime1 + 1] |
|
|
3090 | 3471 | #Vectorize |
|
|
3091 | 3472 | meteorAux[0:7] = [mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1] |
|
|
3092 | 3473 | meteorAux[7:11] = phaseDiffint[0:4] |
|
|
3093 | 3474 | |
|
|
3094 | 3475 | #Rejection Criterions |
|
|
3095 | 3476 | if phaseRMS1 > thresholdPhase: #Error Number 17: Phase variation |
|
|
3096 | 3477 | meteorAux[-1] = 17 |
|
|
3097 | 3478 | elif mSNR1 < thresholdDB1: #Error Number 1: SNR < threshold dB |
|
|
3098 | 3479 | meteorAux[-1] = 1 |
|
|
3099 | 3480 | |
|
|
3100 | 3481 | |
|
|
3101 | 3482 | else: |
|
|
3102 | 3483 | meteorAux[0:4] = [mHeight, mStart, mPeak, mEnd] |
|
|
3103 | 3484 | meteorAux[-1] = 6 #Error Number 6: echo less than 5 samples long; too short for analysis |
|
|
3104 | 3485 | PowerSeries = 0 |
|
|
3105 | 3486 | |
|
|
3106 | 3487 | listMeteors1.append(meteorAux) |
|
|
3107 | 3488 | listPowerSeries.append(PowerSeries) |
|
|
3108 | 3489 | listVoltageSeries.append(meteorVolts1) |
|
|
3109 | 3490 | |
|
|
3110 | 3491 | return listMeteors1, listPowerSeries, listVoltageSeries |
|
|
3111 | 3492 | |
|
|
3112 | 3493 | def __estimateDecayTime(self, listMeteors, listPower, timeInterval, frequency): |
|
|
3113 | 3494 | |
|
|
3114 | 3495 | threshError = 10 |
|
|
3115 | 3496 | #Depending if it is 30 or 50 MHz |
|
|
3116 | 3497 | if frequency == 30e6: |
|
|
3117 | 3498 | timeLag = 45*10**-3 |
|
|
3118 | 3499 | else: |
|
|
3119 | 3500 | timeLag = 15*10**-3 |
|
|
3120 | 3501 | lag = numpy.ceil(timeLag/timeInterval) |
|
|
3121 | 3502 | |
|
|
3122 | 3503 | listMeteors1 = [] |
|
|
3123 | 3504 | |
|
|
3124 | 3505 | for i in range(len(listMeteors)): |
|
|
3125 | 3506 | meteorPower = listPower[i] |
|
|
3126 | 3507 | meteorAux = listMeteors[i] |
|
|
3127 | 3508 | |
|
|
3128 | 3509 | if meteorAux[-1] == 0: |
|
|
3129 | 3510 | |
|
|
3130 | 3511 | try: |
|
|
3131 | 3512 | indmax = meteorPower.argmax() |
|
|
3132 | 3513 | indlag = indmax + lag |
|
|
3133 | 3514 | |
|
|
3134 | 3515 | y = meteorPower[indlag:] |
|
|
3135 | 3516 | x = numpy.arange(0, y.size)*timeLag |
|
|
3136 | 3517 | |
|
|
3137 | 3518 | #first guess |
|
|
3138 | 3519 | a = y[0] |
|
|
3139 | 3520 | tau = timeLag |
|
|
3140 | 3521 | #exponential fit |
|
|
3141 | 3522 | popt, pcov = optimize.curve_fit(self.__exponential_function, x, y, p0 = [a, tau]) |
|
|
3142 | 3523 | y1 = self.__exponential_function(x, *popt) |
|
|
3143 | 3524 | #error estimation |
|
|
3144 | 3525 | error = sum((y - y1)**2)/(numpy.var(y)*(y.size - popt.size)) |
|
|
3145 | 3526 | |
|
|
3146 | 3527 | decayTime = popt[1] |
|
|
3147 | 3528 | riseTime = indmax*timeInterval |
|
|
3148 | 3529 | meteorAux[11:13] = [decayTime, error] |
|
|
3149 | 3530 | |
|
|
3150 | 3531 | #Table items 7, 8 and 11 |
|
|
3151 | 3532 | if (riseTime > 0.3): #Number 7: Echo rise exceeds 0.3s |
|
|
3152 | 3533 | meteorAux[-1] = 7 |
|
|
3153 | 3534 | elif (decayTime < 2*riseTime) : #Number 8: Echo decay time less than than twice rise time |
|
|
3154 | 3535 | meteorAux[-1] = 8 |
|
|
3155 | 3536 | if (error > threshError): #Number 11: Poor fit to amplitude for estimation of decay time |
|
|
3156 | 3537 | meteorAux[-1] = 11 |
|
|
3157 | 3538 | |
|
|
3158 | 3539 | |
|
|
3159 | 3540 | except: |
|
|
3160 | 3541 | meteorAux[-1] = 11 |
|
|
3161 | 3542 | |
|
|
3162 | 3543 | |
|
|
3163 | 3544 | listMeteors1.append(meteorAux) |
|
|
3164 | 3545 | |
|
|
3165 | 3546 | return listMeteors1 |
|
|
3166 | 3547 | |
|
|
3167 | 3548 | #Exponential Function |
|
|
3168 | 3549 | |
|
|
3169 | 3550 | def __exponential_function(self, x, a, tau): |
|
|
3170 | 3551 | y = a*numpy.exp(-x/tau) |
|
|
3171 | 3552 | return y |
|
|
3172 | 3553 | |
|
|
3173 | 3554 | def __getRadialVelocity(self, listMeteors, listVolts, radialStdThresh, pairslist, timeInterval): |
|
|
3174 | 3555 | |
|
|
3175 | 3556 | pairslist1 = list(pairslist) |
|
|
3176 | 3557 | pairslist1.append((0,1)) |
|
|
3177 | 3558 | pairslist1.append((3,4)) |
|
|
3178 | 3559 | numPairs = len(pairslist1) |
|
|
3179 | 3560 | #Time Lag |
|
|
3180 | 3561 | timeLag = 45*10**-3 |
|
|
3181 | 3562 | c = 3e8 |
|
|
3182 | 3563 | lag = numpy.ceil(timeLag/timeInterval) |
|
|
3183 | 3564 | freq = 30e6 |
|
|
3184 | 3565 | |
|
|
3185 | 3566 | listMeteors1 = [] |
|
|
3186 | 3567 | |
|
|
3187 | 3568 | for i in range(len(listMeteors)): |
|
|
3188 | 3569 | meteorAux = listMeteors[i] |
|
|
3189 | 3570 | if meteorAux[-1] == 0: |
|
|
3190 | 3571 | mStart = listMeteors[i][1] |
|
|
3191 | 3572 | mPeak = listMeteors[i][2] |
|
|
3192 | 3573 | mLag = mPeak - mStart + lag |
|
|
3193 | 3574 | |
|
|
3194 | 3575 | #get the volt data between the start and end times of the meteor |
|
|
3195 | 3576 | meteorVolts = listVolts[i] |
|
|
3196 | 3577 | meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) |
|
|
3197 | 3578 | |
|
|
3198 | 3579 | #Get CCF |
|
|
3199 | 3580 | allCCFs = self.__calculateCCF(meteorVolts, pairslist1, [-2,-1,0,1,2]) |
|
|
3200 | 3581 | |
|
|
3201 | 3582 | #Method 2 |
|
|
3202 | 3583 | slopes = numpy.zeros(numPairs) |
|
|
3203 | 3584 | time = numpy.array([-2,-1,1,2])*timeInterval |
|
|
3204 | 3585 | angAllCCF = numpy.angle(allCCFs[:,[0,1,3,4],0]) |
|
|
3205 | 3586 | |
|
|
3206 | 3587 | #Correct phases |
|
|
3207 | 3588 | derPhaseCCF = angAllCCF[:,1:] - angAllCCF[:,0:-1] |
|
|
3208 | 3589 | indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) |
|
|
3209 | 3590 | |
|
|
3210 | 3591 | if indDer[0].shape[0] > 0: |
|
|
3211 | 3592 | for i in range(indDer[0].shape[0]): |
|
|
3212 | 3593 | signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i]]) |
|
|
3213 | 3594 | angAllCCF[indDer[0][i],indDer[1][i]+1:] += signo*2*numpy.pi |
|
|
3214 | 3595 | |
|
|
3215 | 3596 | # fit = scipy.stats.linregress(numpy.array([-2,-1,1,2])*timeInterval, numpy.array([phaseLagN2s[i],phaseLagN1s[i],phaseLag1s[i],phaseLag2s[i]])) |
|
|
3216 | 3597 | for j in range(numPairs): |
|
|
3217 | 3598 | fit = stats.linregress(time, angAllCCF[j,:]) |
|
|
3218 | 3599 | slopes[j] = fit[0] |
|
|
3219 | 3600 | |
|
|
3220 | 3601 | #Remove Outlier |
|
|
3221 | 3602 | # indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes))) |
|
|
3222 | 3603 | # slopes = numpy.delete(slopes,indOut) |
|
|
3223 | 3604 | # indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes))) |
|
|
3224 | 3605 | # slopes = numpy.delete(slopes,indOut) |
|
|
3225 | 3606 | |
|
|
3226 | 3607 | radialVelocity = -numpy.mean(slopes)*(0.25/numpy.pi)*(c/freq) |
|
|
3227 | 3608 | radialError = numpy.std(slopes)*(0.25/numpy.pi)*(c/freq) |
|
|
3228 | 3609 | meteorAux[-2] = radialError |
|
|
3229 | 3610 | meteorAux[-3] = radialVelocity |
|
|
3230 | 3611 | |
|
|
3231 | 3612 | #Setting Error |
|
|
3232 | 3613 | #Number 15: Radial Drift velocity or projected horizontal velocity exceeds 200 m/s |
|
|
3233 | 3614 | if numpy.abs(radialVelocity) > 200: |
|
|
3234 | 3615 | meteorAux[-1] = 15 |
|
|
3235 | 3616 | #Number 12: Poor fit to CCF variation for estimation of radial drift velocity |
|
|
3236 | 3617 | elif radialError > radialStdThresh: |
|
|
3237 | 3618 | meteorAux[-1] = 12 |
|
|
3238 | 3619 | |
|
|
3239 | 3620 | listMeteors1.append(meteorAux) |
|
|
3240 | 3621 | return listMeteors1 |
|
|
3241 | 3622 | |
|
|
3242 | 3623 | def __setNewArrays(self, listMeteors, date, heiRang): |
|
|
3243 | 3624 | |
|
|
3244 | 3625 | #New arrays |
|
|
3245 | 3626 | arrayMeteors = numpy.array(listMeteors) |
|
|
3246 | 3627 | arrayParameters = numpy.zeros((len(listMeteors), 13)) |
|
|
3247 | 3628 | |
|
|
3248 | 3629 | #Date inclusion |
|
|
3249 | 3630 | # date = re.findall(r'\((.*?)\)', date) |
|
|
3250 | 3631 | # date = date[0].split(',') |
|
|
3251 | 3632 | # date = map(int, date) |
|
|
3252 | 3633 | # |
|
|
3253 | 3634 | # if len(date)<6: |
|
|
3254 | 3635 | # date.append(0) |
|
|
3255 | 3636 | # |
|
|
3256 | 3637 | # date = [date[0]*10000 + date[1]*100 + date[2], date[3]*10000 + date[4]*100 + date[5]] |
|
|
3257 | 3638 | # arrayDate = numpy.tile(date, (len(listMeteors), 1)) |
|
|
3258 | 3639 | arrayDate = numpy.tile(date, (len(listMeteors))) |
|
|
3259 | 3640 | |
|
|
3260 | 3641 | #Meteor array |
|
|
3261 | 3642 | # arrayMeteors[:,0] = heiRang[arrayMeteors[:,0].astype(int)] |
|
|
3262 | 3643 | # arrayMeteors = numpy.hstack((arrayDate, arrayMeteors)) |
|
|
3263 | 3644 | |
|
|
3264 | 3645 | #Parameters Array |
|
|
3265 | 3646 | arrayParameters[:,0] = arrayDate #Date |
|
|
3266 | 3647 | arrayParameters[:,1] = heiRang[arrayMeteors[:,0].astype(int)] #Range |
|
|
3267 | 3648 | arrayParameters[:,6:8] = arrayMeteors[:,-3:-1] #Radial velocity and its error |
|
|
3268 | 3649 | arrayParameters[:,8:12] = arrayMeteors[:,7:11] #Phases |
|
|
3269 | 3650 | arrayParameters[:,-1] = arrayMeteors[:,-1] #Error |
|
|
3270 | 3651 | |
|
|
3271 | 3652 | |
|
|
3272 | 3653 | return arrayParameters |
|
|
3273 | 3654 | |
|
|
3274 | 3655 | class CorrectSMPhases(Operation): |
|
|
3275 | 3656 | |
|
|
3276 | 3657 | def run(self, dataOut, phaseOffsets, hmin = 50, hmax = 150, azimuth = 45, channelPositions = None): |
|
|
3277 | 3658 | |
|
|
3278 | 3659 | arrayParameters = dataOut.data_param |
|
|
3279 | 3660 | pairsList = [] |
|
|
3280 | 3661 | pairx = (0,1) |
|
|
3281 | 3662 | pairy = (2,3) |
|
|
3282 | 3663 | pairsList.append(pairx) |
|
|
3283 | 3664 | pairsList.append(pairy) |
|
|
3284 | 3665 | jph = numpy.zeros(4) |
|
|
3285 | 3666 | |
|
|
3286 | 3667 | phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180 |
|
|
3287 | 3668 | # arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets) |
|
|
3288 | 3669 | arrayParameters[:,8:12] = numpy.angle(numpy.exp(1j*(arrayParameters[:,8:12] + phaseOffsets))) |
|
|
3289 | 3670 | |
|
|
3290 | 3671 | meteorOps = SMOperations() |
|
|
3291 | 3672 | if channelPositions is None: |
|
|
3292 | 3673 | # channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T |
|
|
3293 | 3674 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella |
|
|
3294 | 3675 | |
|
|
3295 | 3676 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) |
|
|
3296 | 3677 | h = (hmin,hmax) |
|
|
3297 | 3678 | |
|
|
3298 | 3679 | arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph) |
|
|
3299 | 3680 | |
|
|
3300 | 3681 | dataOut.data_param = arrayParameters |
|
|
3301 | 3682 | return |
|
|
3302 | 3683 | |
|
|
3303 | 3684 | class SMPhaseCalibration(Operation): |
|
|
3304 | 3685 | |
|
|
3305 | 3686 | __buffer = None |
|
|
3306 | 3687 | |
|
|
3307 | 3688 | __initime = None |
|
|
3308 | 3689 | |
|
|
3309 | 3690 | __dataReady = False |
|
|
3310 | 3691 | |
|
|
3311 | 3692 | __isConfig = False |
|
|
3312 | 3693 | |
|
|
3313 | 3694 | def __checkTime(self, currentTime, initTime, paramInterval, outputInterval): |
|
|
3314 | 3695 | |
|
|
3315 | 3696 | dataTime = currentTime + paramInterval |
|
|
3316 | 3697 | deltaTime = dataTime - initTime |
|
|
3317 | 3698 | |
|
|
3318 | 3699 | if deltaTime >= outputInterval or deltaTime < 0: |
|
|
3319 | 3700 | return True |
|
|
3320 | 3701 | |
|
|
3321 | 3702 | return False |
|
|
3322 | 3703 | |
|
|
3323 | 3704 | def __getGammas(self, pairs, d, phases): |
|
|
3324 | 3705 | gammas = numpy.zeros(2) |
|
|
3325 | 3706 | |
|
|
3326 | 3707 | for i in range(len(pairs)): |
|
|
3327 | 3708 | |
|
|
3328 | 3709 | pairi = pairs[i] |
|
|
3329 | 3710 | |
|
|
3330 | 3711 | phip3 = phases[:,pairi[0]] |
|
|
3331 | 3712 | d3 = d[pairi[0]] |
|
|
3332 | 3713 | phip2 = phases[:,pairi[1]] |
|
|
3333 | 3714 | d2 = d[pairi[1]] |
|
|
3334 | 3715 | #Calculating gamma |
|
|
3335 | 3716 | # jdcos = alp1/(k*d1) |
|
|
3336 | 3717 | # jgamma = numpy.angle(numpy.exp(1j*(d0*alp1/d1 - alp0))) |
|
|
3337 | 3718 | jgamma = -phip2*d3/d2 - phip3 |
|
|
3338 | 3719 | jgamma = numpy.angle(numpy.exp(1j*jgamma)) |
|
|
3339 | 3720 | # jgamma[jgamma>numpy.pi] -= 2*numpy.pi |
|
|
3340 | 3721 | # jgamma[jgamma<-numpy.pi] += 2*numpy.pi |
|
|
3341 | 3722 | |
|
|
3342 | 3723 | #Revised distribution |
|
|
3343 | 3724 | jgammaArray = numpy.hstack((jgamma,jgamma+0.5*numpy.pi,jgamma-0.5*numpy.pi)) |
|
|
3344 | 3725 | |
|
|
3345 | 3726 | #Histogram |
|
|
3346 | 3727 | nBins = 64 |
|
|
3347 | 3728 | rmin = -0.5*numpy.pi |
|
|
3348 | 3729 | rmax = 0.5*numpy.pi |
|
|
3349 | 3730 | phaseHisto = numpy.histogram(jgammaArray, bins=nBins, range=(rmin,rmax)) |
|
|
3350 | 3731 | |
|
|
3351 | 3732 | meteorsY = phaseHisto[0] |
|
|
3352 | 3733 | phasesX = phaseHisto[1][:-1] |
|
|
3353 | 3734 | width = phasesX[1] - phasesX[0] |
|
|
3354 | 3735 | phasesX += width/2 |
|
|
3355 | 3736 | |
|
|
3356 | 3737 | #Gaussian aproximation |
|
|
3357 | 3738 | bpeak = meteorsY.argmax() |
|
|
3358 | 3739 | peak = meteorsY.max() |
|
|
3359 | 3740 | jmin = bpeak - 5 |
|
|
3360 | 3741 | jmax = bpeak + 5 + 1 |
|
|
3361 | 3742 | |
|
|
3362 | 3743 | if jmin<0: |
|
|
3363 | 3744 | jmin = 0 |
|
|
3364 | 3745 | jmax = 6 |
|
|
3365 | 3746 | elif jmax > meteorsY.size: |
|
|
3366 | 3747 | jmin = meteorsY.size - 6 |
|
|
3367 | 3748 | jmax = meteorsY.size |
|
|
3368 | 3749 | |
|
|
3369 | 3750 | x0 = numpy.array([peak,bpeak,50]) |
|
|
3370 | 3751 | coeff = optimize.leastsq(self.__residualFunction, x0, args=(meteorsY[jmin:jmax], phasesX[jmin:jmax])) |
|
|
3371 | 3752 | |
|
|
3372 | 3753 | #Gammas |
|
|
3373 | 3754 | gammas[i] = coeff[0][1] |
|
|
3374 | 3755 | |
|
|
3375 | 3756 | return gammas |
|
|
3376 | 3757 | |
|
|
3377 | 3758 | def __residualFunction(self, coeffs, y, t): |
|
|
3378 | 3759 | |
|
|
3379 | 3760 | return y - self.__gauss_function(t, coeffs) |
|
|
3380 | 3761 | |
|
|
3381 | 3762 | def __gauss_function(self, t, coeffs): |
|
|
3382 | 3763 | |
|
|
3383 | 3764 | return coeffs[0]*numpy.exp(-0.5*((t - coeffs[1]) / coeffs[2])**2) |
|
|
3384 | 3765 | |
|
|
3385 | 3766 | def __getPhases(self, azimuth, h, pairsList, d, gammas, meteorsArray): |
|
|
3386 | 3767 | meteorOps = SMOperations() |
|
|
3387 | 3768 | nchan = 4 |
|
|
3388 | 3769 | pairx = pairsList[0] #x es 0 |
|
|
3389 | 3770 | pairy = pairsList[1] #y es 1 |
|
|
3390 | 3771 | center_xangle = 0 |
|
|
3391 | 3772 | center_yangle = 0 |
|
|
3392 | 3773 | range_angle = numpy.array([10*numpy.pi,numpy.pi,numpy.pi/2,numpy.pi/4]) |
|
|
3393 | 3774 | ntimes = len(range_angle) |
|
|
3394 | 3775 | |
|
|
3395 | 3776 | nstepsx = 20 |
|
|
3396 | 3777 | nstepsy = 20 |
|
|
3397 | 3778 | |
|
|
3398 | 3779 | for iz in range(ntimes): |
|
|
3399 | 3780 | min_xangle = -range_angle[iz]/2 + center_xangle |
|
|
3400 | 3781 | max_xangle = range_angle[iz]/2 + center_xangle |
|
|
3401 | 3782 | min_yangle = -range_angle[iz]/2 + center_yangle |
|
|
3402 | 3783 | max_yangle = range_angle[iz]/2 + center_yangle |
|
|
3403 | 3784 | |
|
|
3404 | 3785 | inc_x = (max_xangle-min_xangle)/nstepsx |
|
|
3405 | 3786 | inc_y = (max_yangle-min_yangle)/nstepsy |
|
|
3406 | 3787 | |
|
|
3407 | 3788 | alpha_y = numpy.arange(nstepsy)*inc_y + min_yangle |
|
|
3408 | 3789 | alpha_x = numpy.arange(nstepsx)*inc_x + min_xangle |
|
|
3409 | 3790 | penalty = numpy.zeros((nstepsx,nstepsy)) |
|
|
3410 | 3791 | jph_array = numpy.zeros((nchan,nstepsx,nstepsy)) |
|
|
3411 | 3792 | jph = numpy.zeros(nchan) |
|
|
3412 | 3793 | |
|
|
3413 | 3794 | # Iterations looking for the offset |
|
|
3414 | 3795 | for iy in range(int(nstepsy)): |
|
|
3415 | 3796 | for ix in range(int(nstepsx)): |
|
|
3416 | 3797 | d3 = d[pairsList[1][0]] |
|
|
3417 | 3798 | d2 = d[pairsList[1][1]] |
|
|
3418 | 3799 | d5 = d[pairsList[0][0]] |
|
|
3419 | 3800 | d4 = d[pairsList[0][1]] |
|
|
3420 | 3801 | |
|
|
3421 | 3802 | alp2 = alpha_y[iy] #gamma 1 |
|
|
3422 | 3803 | alp4 = alpha_x[ix] #gamma 0 |
|
|
3423 | 3804 | |
|
|
3424 | 3805 | alp3 = -alp2*d3/d2 - gammas[1] |
|
|
3425 | 3806 | alp5 = -alp4*d5/d4 - gammas[0] |
|
|
3426 | 3807 | # jph[pairy[1]] = alpha_y[iy] |
|
|
3427 | 3808 | # jph[pairy[0]] = -gammas[1] - alpha_y[iy]*d[pairy[1]]/d[pairy[0]] |
|
|
3428 | 3809 | |
|
|
3429 | 3810 | # jph[pairx[1]] = alpha_x[ix] |
|
|
3430 | 3811 | # jph[pairx[0]] = -gammas[0] - alpha_x[ix]*d[pairx[1]]/d[pairx[0]] |
|
|
3431 | 3812 | jph[pairsList[0][1]] = alp4 |
|
|
3432 | 3813 | jph[pairsList[0][0]] = alp5 |
|
|
3433 | 3814 | jph[pairsList[1][0]] = alp3 |
|
|
3434 | 3815 | jph[pairsList[1][1]] = alp2 |
|
|
3435 | 3816 | jph_array[:,ix,iy] = jph |
|
|
3436 | 3817 | # d = [2.0,2.5,2.5,2.0] |
|
|
3437 | 3818 | #falta chequear si va a leer bien los meteoros |
|
|
3438 | 3819 | meteorsArray1 = meteorOps.getMeteorParams(meteorsArray, azimuth, h, pairsList, d, jph) |
|
|
3439 | 3820 | error = meteorsArray1[:,-1] |
|
|
3440 | 3821 | ind1 = numpy.where(error==0)[0] |
|
|
3441 | 3822 | penalty[ix,iy] = ind1.size |
|
|
3442 | 3823 | |
|
|
3443 | 3824 | i,j = numpy.unravel_index(penalty.argmax(), penalty.shape) |
|
|
3444 | 3825 | phOffset = jph_array[:,i,j] |
|
|
3445 | 3826 | |
|
|
3446 | 3827 | center_xangle = phOffset[pairx[1]] |
|
|
3447 | 3828 | center_yangle = phOffset[pairy[1]] |
|
|
3448 | 3829 | |
|
|
3449 | 3830 | phOffset = numpy.angle(numpy.exp(1j*jph_array[:,i,j])) |
|
|
3450 | 3831 | phOffset = phOffset*180/numpy.pi |
|
|
3451 | 3832 | return phOffset |
|
|
3452 | 3833 | |
|
|
3453 | 3834 | |
|
|
3454 | 3835 | def run(self, dataOut, hmin, hmax, channelPositions=None, nHours = 1): |
|
|
3455 | 3836 | |
|
|
3456 | 3837 | dataOut.flagNoData = True |
|
|
3457 | 3838 | self.__dataReady = False |
|
|
3458 | 3839 | dataOut.outputInterval = nHours*3600 |
|
|
3459 | 3840 | |
|
|
3460 | 3841 | if self.__isConfig == False: |
|
|
3461 | 3842 | # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) |
|
|
3462 | 3843 | #Get Initial LTC time |
|
|
3463 | 3844 | self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
|
3464 | 3845 | self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
3465 | 3846 | |
|
|
3466 | 3847 | self.__isConfig = True |
|
|
3467 | 3848 | |
|
|
3468 | 3849 | if self.__buffer is None: |
|
|
3469 | 3850 | self.__buffer = dataOut.data_param.copy() |
|
|
3470 | 3851 | |
|
|
3471 | 3852 | else: |
|
|
3472 | 3853 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
|
3473 | 3854 | |
|
|
3474 | 3855 | self.__dataReady = self.__checkTime(dataOut.utctime, self.__initime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
|
3475 | 3856 | |
|
|
3476 | 3857 | if self.__dataReady: |
|
|
3477 | 3858 | dataOut.utctimeInit = self.__initime |
|
|
3478 | 3859 | self.__initime += dataOut.outputInterval #to erase time offset |
|
|
3479 | 3860 | |
|
|
3480 | 3861 | freq = dataOut.frequency |
|
|
3481 | 3862 | c = dataOut.C #m/s |
|
|
3482 | 3863 | lamb = c/freq |
|
|
3483 | 3864 | k = 2*numpy.pi/lamb |
|
|
3484 | 3865 | azimuth = 0 |
|
|
3485 | 3866 | h = (hmin, hmax) |
|
|
3486 | 3867 | # pairs = ((0,1),(2,3)) #Estrella |
|
|
3487 | 3868 | # pairs = ((1,0),(2,3)) #T |
|
|
3488 | 3869 | |
|
|
3489 | 3870 | if channelPositions is None: |
|
|
3490 | 3871 | # channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T |
|
|
3491 | 3872 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella |
|
|
3492 | 3873 | meteorOps = SMOperations() |
|
|
3493 | 3874 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) |
|
|
3494 | 3875 | |
|
|
3495 | 3876 | #Checking correct order of pairs |
|
|
3496 | 3877 | pairs = [] |
|
|
3497 | 3878 | if distances[1] > distances[0]: |
|
|
3498 | 3879 | pairs.append((1,0)) |
|
|
3499 | 3880 | else: |
|
|
3500 | 3881 | pairs.append((0,1)) |
|
|
3501 | 3882 | |
|
|
3502 | 3883 | if distances[3] > distances[2]: |
|
|
3503 | 3884 | pairs.append((3,2)) |
|
|
3504 | 3885 | else: |
|
|
3505 | 3886 | pairs.append((2,3)) |
|
|
3506 | 3887 | # distances1 = [-distances[0]*lamb, distances[1]*lamb, -distances[2]*lamb, distances[3]*lamb] |
|
|
3507 | 3888 | |
|
|
3508 | 3889 | meteorsArray = self.__buffer |
|
|
3509 | 3890 | error = meteorsArray[:,-1] |
|
|
3510 | 3891 | boolError = (error==0)|(error==3)|(error==4)|(error==13)|(error==14) |
|
|
3511 | 3892 | ind1 = numpy.where(boolError)[0] |
|
|
3512 | 3893 | meteorsArray = meteorsArray[ind1,:] |
|
|
3513 | 3894 | meteorsArray[:,-1] = 0 |
|
|
3514 | 3895 | phases = meteorsArray[:,8:12] |
|
|
3515 | 3896 | |
|
|
3516 | 3897 | #Calculate Gammas |
|
|
3517 | 3898 | gammas = self.__getGammas(pairs, distances, phases) |
|
|
3518 | 3899 | # gammas = numpy.array([-21.70409463,45.76935864])*numpy.pi/180 |
|
|
3519 | 3900 | #Calculate Phases |
|
|
3520 | 3901 | phasesOff = self.__getPhases(azimuth, h, pairs, distances, gammas, meteorsArray) |
|
|
3521 | 3902 | phasesOff = phasesOff.reshape((1,phasesOff.size)) |
|
|
3522 | 3903 | dataOut.data_output = -phasesOff |
|
|
3523 | 3904 | dataOut.flagNoData = False |
|
|
3524 | 3905 | self.__buffer = None |
|
|
3525 | 3906 | |
|
|
3526 | 3907 | |
|
|
3527 | 3908 | return |
|
|
3528 | 3909 | |
|
|
3529 | 3910 | class SMOperations(): |
|
|
3530 | 3911 | |
|
|
3531 | 3912 | def __init__(self): |
|
|
3532 | 3913 | |
|
|
3533 | 3914 | return |
|
|
3534 | 3915 | |
|
|
3535 | 3916 | def getMeteorParams(self, arrayParameters0, azimuth, h, pairsList, distances, jph): |
|
|
3536 | 3917 | |
|
|
3537 | 3918 | arrayParameters = arrayParameters0.copy() |
|
|
3538 | 3919 | hmin = h[0] |
|
|
3539 | 3920 | hmax = h[1] |
|
|
3540 | 3921 | |
|
|
3541 | 3922 | #Calculate AOA (Error N 3, 4) |
|
|
3542 | 3923 | #JONES ET AL. 1998 |
|
|
3543 | 3924 | AOAthresh = numpy.pi/8 |
|
|
3544 | 3925 | error = arrayParameters[:,-1] |
|
|
3545 | 3926 | phases = -arrayParameters[:,8:12] + jph |
|
|
3546 | 3927 | # phases = numpy.unwrap(phases) |
|
|
3547 | 3928 | arrayParameters[:,3:6], arrayParameters[:,-1] = self.__getAOA(phases, pairsList, distances, error, AOAthresh, azimuth) |
|
|
3548 | 3929 | |
|
|
3549 | 3930 | #Calculate Heights (Error N 13 and 14) |
|
|
3550 | 3931 | error = arrayParameters[:,-1] |
|
|
3551 | 3932 | Ranges = arrayParameters[:,1] |
|
|
3552 | 3933 | zenith = arrayParameters[:,4] |
|
|
3553 | 3934 | arrayParameters[:,2], arrayParameters[:,-1] = self.__getHeights(Ranges, zenith, error, hmin, hmax) |
|
|
3554 | 3935 | |
|
|
3555 | 3936 | #----------------------- Get Final data ------------------------------------ |
|
|
3556 | 3937 | # error = arrayParameters[:,-1] |
|
|
3557 | 3938 | # ind1 = numpy.where(error==0)[0] |
|
|
3558 | 3939 | # arrayParameters = arrayParameters[ind1,:] |
|
|
3559 | 3940 | |
|
|
3560 | 3941 | return arrayParameters |
|
|
3561 | 3942 | |
|
|
3562 | 3943 | def __getAOA(self, phases, pairsList, directions, error, AOAthresh, azimuth): |
|
|
3563 | 3944 | |
|
|
3564 | 3945 | arrayAOA = numpy.zeros((phases.shape[0],3)) |
|
|
3565 | 3946 | cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList,directions) |
|
|
3566 | 3947 | |
|
|
3567 | 3948 | arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth) |
|
|
3568 | 3949 | cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1) |
|
|
3569 | 3950 | arrayAOA[:,2] = cosDirError |
|
|
3570 | 3951 | |
|
|
3571 | 3952 | azimuthAngle = arrayAOA[:,0] |
|
|
3572 | 3953 | zenithAngle = arrayAOA[:,1] |
|
|
3573 | 3954 | |
|
|
3574 | 3955 | #Setting Error |
|
|
3575 | 3956 | indError = numpy.where(numpy.logical_or(error == 3, error == 4))[0] |
|
|
3576 | 3957 | error[indError] = 0 |
|
|
3577 | 3958 | #Number 3: AOA not fesible |
|
|
3578 | 3959 | indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0] |
|
|
3579 | 3960 | error[indInvalid] = 3 |
|
|
3580 | 3961 | #Number 4: Large difference in AOAs obtained from different antenna baselines |
|
|
3581 | 3962 | indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0] |
|
|
3582 | 3963 | error[indInvalid] = 4 |
|
|
3583 | 3964 | return arrayAOA, error |
|
|
3584 | 3965 | |
|
|
3585 | 3966 | def __getDirectionCosines(self, arrayPhase, pairsList, distances): |
|
|
3586 | 3967 | |
|
|
3587 | 3968 | #Initializing some variables |
|
|
3588 | 3969 | ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi |
|
|
3589 | 3970 | ang_aux = ang_aux.reshape(1,ang_aux.size) |
|
|
3590 | 3971 | |
|
|
3591 | 3972 | cosdir = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
3592 | 3973 | cosdir0 = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
3593 | 3974 | |
|
|
3594 | 3975 | |
|
|
3595 | 3976 | for i in range(2): |
|
|
3596 | 3977 | ph0 = arrayPhase[:,pairsList[i][0]] |
|
|
3597 | 3978 | ph1 = arrayPhase[:,pairsList[i][1]] |
|
|
3598 | 3979 | d0 = distances[pairsList[i][0]] |
|
|
3599 | 3980 | d1 = distances[pairsList[i][1]] |
|
|
3600 | 3981 | |
|
|
3601 | 3982 | ph0_aux = ph0 + ph1 |
|
|
3602 | 3983 | ph0_aux = numpy.angle(numpy.exp(1j*ph0_aux)) |
|
|
3603 | 3984 | # ph0_aux[ph0_aux > numpy.pi] -= 2*numpy.pi |
|
|
3604 | 3985 | # ph0_aux[ph0_aux < -numpy.pi] += 2*numpy.pi |
|
|
3605 | 3986 | #First Estimation |
|
|
3606 | 3987 | cosdir0[:,i] = (ph0_aux)/(2*numpy.pi*(d0 - d1)) |
|
|
3607 | 3988 | |
|
|
3608 | 3989 | #Most-Accurate Second Estimation |
|
|
3609 | 3990 | phi1_aux = ph0 - ph1 |
|
|
3610 | 3991 | phi1_aux = phi1_aux.reshape(phi1_aux.size,1) |
|
|
3611 | 3992 | #Direction Cosine 1 |
|
|
3612 | 3993 | cosdir1 = (phi1_aux + ang_aux)/(2*numpy.pi*(d0 + d1)) |
|
|
3613 | 3994 | |
|
|
3614 | 3995 | #Searching the correct Direction Cosine |
|
|
3615 | 3996 | cosdir0_aux = cosdir0[:,i] |
|
|
3616 | 3997 | cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1) |
|
|
3617 | 3998 | #Minimum Distance |
|
|
3618 | 3999 | cosDiff = (cosdir1 - cosdir0_aux)**2 |
|
|
3619 | 4000 | indcos = cosDiff.argmin(axis = 1) |
|
|
3620 | 4001 | #Saving Value obtained |
|
|
3621 | 4002 | cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos] |
|
|
3622 | 4003 | |
|
|
3623 | 4004 | return cosdir0, cosdir |
|
|
3624 | 4005 | |
|
|
3625 | 4006 | def __calculateAOA(self, cosdir, azimuth): |
|
|
3626 | 4007 | cosdirX = cosdir[:,0] |
|
|
3627 | 4008 | cosdirY = cosdir[:,1] |
|
|
3628 | 4009 | |
|
|
3629 | 4010 | zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi |
|
|
3630 | 4011 | azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth#0 deg north, 90 deg east |
|
|
3631 | 4012 | angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose() |
|
|
3632 | 4013 | |
|
|
3633 | 4014 | return angles |
|
|
3634 | 4015 | |
|
|
3635 | 4016 | def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight): |
|
|
3636 | 4017 | |
|
|
3637 | 4018 | Ramb = 375 #Ramb = c/(2*PRF) |
|
|
3638 | 4019 | Re = 6371 #Earth Radius |
|
|
3639 | 4020 | heights = numpy.zeros(Ranges.shape) |
|
|
3640 | 4021 | |
|
|
3641 | 4022 | R_aux = numpy.array([0,1,2])*Ramb |
|
|
3642 | 4023 | R_aux = R_aux.reshape(1,R_aux.size) |
|
|
3643 | 4024 | |
|
|
3644 | 4025 | Ranges = Ranges.reshape(Ranges.size,1) |
|
|
3645 | 4026 | |
|
|
3646 | 4027 | Ri = Ranges + R_aux |
|
|
3647 | 4028 | hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re |
|
|
3648 | 4029 | |
|
|
3649 | 4030 | #Check if there is a height between 70 and 110 km |
|
|
3650 | 4031 | h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1) |
|
|
3651 | 4032 | ind_h = numpy.where(h_bool == 1)[0] |
|
|
3652 | 4033 | |
|
|
3653 | 4034 | hCorr = hi[ind_h, :] |
|
|
3654 | 4035 | ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight)) |
|
|
3655 | 4036 | |
|
|
3656 | 4037 | hCorr = hi[ind_hCorr][:len(ind_h)] |
|
|
3657 | 4038 | heights[ind_h] = hCorr |
|
|
3658 | 4039 | |
|
|
3659 | 4040 | #Setting Error |
|
|
3660 | 4041 | #Number 13: Height unresolvable echo: not valid height within 70 to 110 km |
|
|
3661 | 4042 | #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km |
|
|
3662 | 4043 | indError = numpy.where(numpy.logical_or(error == 13, error == 14))[0] |
|
|
3663 | 4044 | error[indError] = 0 |
|
|
3664 | 4045 | indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0] |
|
|
3665 | 4046 | error[indInvalid2] = 14 |
|
|
3666 | 4047 | indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0] |
|
|
3667 | 4048 | error[indInvalid1] = 13 |
|
|
3668 | 4049 | |
|
|
3669 | 4050 | return heights, error |
|
|
3670 | 4051 | |
|
|
3671 | 4052 | def getPhasePairs(self, channelPositions): |
|
|
3672 | 4053 | chanPos = numpy.array(channelPositions) |
|
|
3673 | 4054 | listOper = list(itertools.combinations(list(range(5)),2)) |
|
|
3674 | 4055 | |
|
|
3675 | 4056 | distances = numpy.zeros(4) |
|
|
3676 | 4057 | axisX = [] |
|
|
3677 | 4058 | axisY = [] |
|
|
3678 | 4059 | distX = numpy.zeros(3) |
|
|
3679 | 4060 | distY = numpy.zeros(3) |
|
|
3680 | 4061 | ix = 0 |
|
|
3681 | 4062 | iy = 0 |
|
|
3682 | 4063 | |
|
|
3683 | 4064 | pairX = numpy.zeros((2,2)) |
|
|
3684 | 4065 | pairY = numpy.zeros((2,2)) |
|
|
3685 | 4066 | |
|
|
3686 | 4067 | for i in range(len(listOper)): |
|
|
3687 | 4068 | pairi = listOper[i] |
|
|
3688 | 4069 | |
|
|
3689 | 4070 | posDif = numpy.abs(chanPos[pairi[0],:] - chanPos[pairi[1],:]) |
|
|
3690 | 4071 | |
|
|
3691 | 4072 | if posDif[0] == 0: |
|
|
3692 | 4073 | axisY.append(pairi) |
|
|
3693 | 4074 | distY[iy] = posDif[1] |
|
|
3694 | 4075 | iy += 1 |
|
|
3695 | 4076 | elif posDif[1] == 0: |
|
|
3696 | 4077 | axisX.append(pairi) |
|
|
3697 | 4078 | distX[ix] = posDif[0] |
|
|
3698 | 4079 | ix += 1 |
|
|
3699 | 4080 | |
|
|
3700 | 4081 | for i in range(2): |
|
|
3701 | 4082 | if i==0: |
|
|
3702 | 4083 | dist0 = distX |
|
|
3703 | 4084 | axis0 = axisX |
|
|
3704 | 4085 | else: |
|
|
3705 | 4086 | dist0 = distY |
|
|
3706 | 4087 | axis0 = axisY |
|
|
3707 | 4088 | |
|
|
3708 | 4089 | side = numpy.argsort(dist0)[:-1] |
|
|
3709 | 4090 | axis0 = numpy.array(axis0)[side,:] |
|
|
3710 | 4091 | chanC = int(numpy.intersect1d(axis0[0,:], axis0[1,:])[0]) |
|
|
3711 | 4092 | axis1 = numpy.unique(numpy.reshape(axis0,4)) |
|
|
3712 | 4093 | side = axis1[axis1 != chanC] |
|
|
3713 | 4094 | diff1 = chanPos[chanC,i] - chanPos[side[0],i] |
|
|
3714 | 4095 | diff2 = chanPos[chanC,i] - chanPos[side[1],i] |
|
|
3715 | 4096 | if diff1<0: |
|
|
3716 | 4097 | chan2 = side[0] |
|
|
3717 | 4098 | d2 = numpy.abs(diff1) |
|
|
3718 | 4099 | chan1 = side[1] |
|
|
3719 | 4100 | d1 = numpy.abs(diff2) |
|
|
3720 | 4101 | else: |
|
|
3721 | 4102 | chan2 = side[1] |
|
|
3722 | 4103 | d2 = numpy.abs(diff2) |
|
|
3723 | 4104 | chan1 = side[0] |
|
|
3724 | 4105 | d1 = numpy.abs(diff1) |
|
|
3725 | 4106 | |
|
|
3726 | 4107 | if i==0: |
|
|
3727 | 4108 | chanCX = chanC |
|
|
3728 | 4109 | chan1X = chan1 |
|
|
3729 | 4110 | chan2X = chan2 |
|
|
3730 | 4111 | distances[0:2] = numpy.array([d1,d2]) |
|
|
3731 | 4112 | else: |
|
|
3732 | 4113 | chanCY = chanC |
|
|
3733 | 4114 | chan1Y = chan1 |
|
|
3734 | 4115 | chan2Y = chan2 |
|
|
3735 | 4116 | distances[2:4] = numpy.array([d1,d2]) |
|
|
3736 | 4117 | # axisXsides = numpy.reshape(axisX[ix,:],4) |
|
|
3737 | 4118 | # |
|
|
3738 | 4119 | # channelCentX = int(numpy.intersect1d(pairX[0,:], pairX[1,:])[0]) |
|
|
3739 | 4120 | # channelCentY = int(numpy.intersect1d(pairY[0,:], pairY[1,:])[0]) |
|
|
3740 | 4121 | # |
|
|
3741 | 4122 | # ind25X = numpy.where(pairX[0,:] != channelCentX)[0][0] |
|
|
3742 | 4123 | # ind20X = numpy.where(pairX[1,:] != channelCentX)[0][0] |
|
|
3743 | 4124 | # channel25X = int(pairX[0,ind25X]) |
|
|
3744 | 4125 | # channel20X = int(pairX[1,ind20X]) |
|
|
3745 | 4126 | # ind25Y = numpy.where(pairY[0,:] != channelCentY)[0][0] |
|
|
3746 | 4127 | # ind20Y = numpy.where(pairY[1,:] != channelCentY)[0][0] |
|
|
3747 | 4128 | # channel25Y = int(pairY[0,ind25Y]) |
|
|
3748 | 4129 | # channel20Y = int(pairY[1,ind20Y]) |
|
|
3749 | 4130 | |
|
|
3750 | 4131 | # pairslist = [(channelCentX, channel25X),(channelCentX, channel20X),(channelCentY,channel25Y),(channelCentY, channel20Y)] |
|
|
3751 | 4132 | pairslist = [(chanCX, chan1X),(chanCX, chan2X),(chanCY,chan1Y),(chanCY, chan2Y)] |
|
|
3752 | 4133 | |
|
|
3753 | 4134 | return pairslist, distances |
|
|
3754 | 4135 | # def __getAOA(self, phases, pairsList, error, AOAthresh, azimuth): |
|
|
3755 | 4136 | # |
|
|
3756 | 4137 | # arrayAOA = numpy.zeros((phases.shape[0],3)) |
|
|
3757 | 4138 | # cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList) |
|
|
3758 | 4139 | # |
|
|
3759 | 4140 | # arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth) |
|
|
3760 | 4141 | # cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1) |
|
|
3761 | 4142 | # arrayAOA[:,2] = cosDirError |
|
|
3762 | 4143 | # |
|
|
3763 | 4144 | # azimuthAngle = arrayAOA[:,0] |
|
|
3764 | 4145 | # zenithAngle = arrayAOA[:,1] |
|
|
3765 | 4146 | # |
|
|
3766 | 4147 | # #Setting Error |
|
|
3767 | 4148 | # #Number 3: AOA not fesible |
|
|
3768 | 4149 | # indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0] |
|
|
3769 | 4150 | # error[indInvalid] = 3 |
|
|
3770 | 4151 | # #Number 4: Large difference in AOAs obtained from different antenna baselines |
|
|
3771 | 4152 | # indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0] |
|
|
3772 | 4153 | # error[indInvalid] = 4 |
|
|
3773 | 4154 | # return arrayAOA, error |
|
|
3774 | 4155 | # |
|
|
3775 | 4156 | # def __getDirectionCosines(self, arrayPhase, pairsList): |
|
|
3776 | 4157 | # |
|
|
3777 | 4158 | # #Initializing some variables |
|
|
3778 | 4159 | # ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi |
|
|
3779 | 4160 | # ang_aux = ang_aux.reshape(1,ang_aux.size) |
|
|
3780 | 4161 | # |
|
|
3781 | 4162 | # cosdir = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
3782 | 4163 | # cosdir0 = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
3783 | 4164 | # |
|
|
3784 | 4165 | # |
|
|
3785 | 4166 | # for i in range(2): |
|
|
3786 | 4167 | # #First Estimation |
|
|
3787 | 4168 | # phi0_aux = arrayPhase[:,pairsList[i][0]] + arrayPhase[:,pairsList[i][1]] |
|
|
3788 | 4169 | # #Dealias |
|
|
3789 | 4170 | # indcsi = numpy.where(phi0_aux > numpy.pi) |
|
|
3790 | 4171 | # phi0_aux[indcsi] -= 2*numpy.pi |
|
|
3791 | 4172 | # indcsi = numpy.where(phi0_aux < -numpy.pi) |
|
|
3792 | 4173 | # phi0_aux[indcsi] += 2*numpy.pi |
|
|
3793 | 4174 | # #Direction Cosine 0 |
|
|
3794 | 4175 | # cosdir0[:,i] = -(phi0_aux)/(2*numpy.pi*0.5) |
|
|
3795 | 4176 | # |
|
|
3796 | 4177 | # #Most-Accurate Second Estimation |
|
|
3797 | 4178 | # phi1_aux = arrayPhase[:,pairsList[i][0]] - arrayPhase[:,pairsList[i][1]] |
|
|
3798 | 4179 | # phi1_aux = phi1_aux.reshape(phi1_aux.size,1) |
|
|
3799 | 4180 | # #Direction Cosine 1 |
|
|
3800 | 4181 | # cosdir1 = -(phi1_aux + ang_aux)/(2*numpy.pi*4.5) |
|
|
3801 | 4182 | # |
|
|
3802 | 4183 | # #Searching the correct Direction Cosine |
|
|
3803 | 4184 | # cosdir0_aux = cosdir0[:,i] |
|
|
3804 | 4185 | # cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1) |
|
|
3805 | 4186 | # #Minimum Distance |
|
|
3806 | 4187 | # cosDiff = (cosdir1 - cosdir0_aux)**2 |
|
|
3807 | 4188 | # indcos = cosDiff.argmin(axis = 1) |
|
|
3808 | 4189 | # #Saving Value obtained |
|
|
3809 | 4190 | # cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos] |
|
|
3810 | 4191 | # |
|
|
3811 | 4192 | # return cosdir0, cosdir |
|
|
3812 | 4193 | # |
|
|
3813 | 4194 | # def __calculateAOA(self, cosdir, azimuth): |
|
|
3814 | 4195 | # cosdirX = cosdir[:,0] |
|
|
3815 | 4196 | # cosdirY = cosdir[:,1] |
|
|
3816 | 4197 | # |
|
|
3817 | 4198 | # zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi |
|
|
3818 | 4199 | # azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth #0 deg north, 90 deg east |
|
|
3819 | 4200 | # angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose() |
|
|
3820 | 4201 | # |
|
|
3821 | 4202 | # return angles |
|
|
3822 | 4203 | # |
|
|
3823 | 4204 | # def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight): |
|
|
3824 | 4205 | # |
|
|
3825 | 4206 | # Ramb = 375 #Ramb = c/(2*PRF) |
|
|
3826 | 4207 | # Re = 6371 #Earth Radius |
|
|
3827 | 4208 | # heights = numpy.zeros(Ranges.shape) |
|
|
3828 | 4209 | # |
|
|
3829 | 4210 | # R_aux = numpy.array([0,1,2])*Ramb |
|
|
3830 | 4211 | # R_aux = R_aux.reshape(1,R_aux.size) |
|
|
3831 | 4212 | # |
|
|
3832 | 4213 | # Ranges = Ranges.reshape(Ranges.size,1) |
|
|
3833 | 4214 | # |
|
|
3834 | 4215 | # Ri = Ranges + R_aux |
|
|
3835 | 4216 | # hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re |
|
|
3836 | 4217 | # |
|
|
3837 | 4218 | # #Check if there is a height between 70 and 110 km |
|
|
3838 | 4219 | # h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1) |
|
|
3839 | 4220 | # ind_h = numpy.where(h_bool == 1)[0] |
|
|
3840 | 4221 | # |
|
|
3841 | 4222 | # hCorr = hi[ind_h, :] |
|
|
3842 | 4223 | # ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight)) |
|
|
3843 | 4224 | # |
|
|
3844 | 4225 | # hCorr = hi[ind_hCorr] |
|
|
3845 | 4226 | # heights[ind_h] = hCorr |
|
|
3846 | 4227 | # |
|
|
3847 | 4228 | # #Setting Error |
|
|
3848 | 4229 | # #Number 13: Height unresolvable echo: not valid height within 70 to 110 km |
|
|
3849 | 4230 | # #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km |
|
|
3850 | 4231 | # |
|
|
3851 | 4232 | # indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0] |
|
|
3852 | 4233 | # error[indInvalid2] = 14 |
|
|
3853 | 4234 | # indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0] |
|
|
3854 | 4235 | # error[indInvalid1] = 13 |
|
|
3855 | 4236 | # |
|
|
3856 | 4237 | # return heights, error |
|
|
3857 | 4238 | No newline at end of file |
General Comments 0
You need to be logged in to leave comments.
Login now