| @@ -1,7573 +1,7613 | |||||
| 1 | # MASTER |
|
1 | # MASTER | |
| 2 | import numpy |
|
2 | import numpy | |
| 3 | import math |
|
3 | import math | |
| 4 | from scipy import optimize, interpolate, signal, stats, ndimage |
|
4 | from scipy import optimize, interpolate, signal, stats, ndimage | |
| 5 | from scipy.fftpack import fft |
|
5 | from scipy.fftpack import fft | |
| 6 | import scipy |
|
6 | import scipy | |
| 7 | import re |
|
7 | import re | |
| 8 | import datetime |
|
8 | import datetime | |
| 9 | import copy |
|
9 | import copy | |
| 10 | import sys |
|
10 | import sys | |
| 11 | import importlib |
|
11 | import importlib | |
| 12 | import itertools |
|
12 | import itertools | |
| 13 | from multiprocessing import Pool, TimeoutError |
|
13 | from multiprocessing import Pool, TimeoutError | |
| 14 | from multiprocessing.pool import ThreadPool |
|
14 | from multiprocessing.pool import ThreadPool | |
| 15 | import time |
|
15 | import time | |
| 16 |
|
16 | |||
| 17 | from scipy.optimize import fmin_l_bfgs_b #optimize with bounds on state papameters |
|
17 | from scipy.optimize import fmin_l_bfgs_b #optimize with bounds on state papameters | |
| 18 | from .jroproc_base import ProcessingUnit, Operation, MPDecorator |
|
18 | from .jroproc_base import ProcessingUnit, Operation, MPDecorator | |
| 19 | from schainpy.model.data.jrodata import Parameters, hildebrand_sekhon |
|
19 | from schainpy.model.data.jrodata import Parameters, hildebrand_sekhon | |
| 20 | from schainpy.model.data.jrodata import Spectra |
|
20 | from schainpy.model.data.jrodata import Spectra | |
| 21 | #from scipy import asarray as ar,exp |
|
21 | #from scipy import asarray as ar,exp | |
| 22 | from scipy.optimize import fmin, curve_fit |
|
22 | from scipy.optimize import fmin, curve_fit | |
| 23 | from schainpy.utils import log |
|
23 | from schainpy.utils import log | |
| 24 | import warnings |
|
24 | import warnings | |
| 25 | from numpy import NaN |
|
25 | from numpy import NaN | |
| 26 | from scipy.optimize.optimize import OptimizeWarning |
|
26 | from scipy.optimize.optimize import OptimizeWarning | |
| 27 | warnings.filterwarnings('ignore') |
|
27 | warnings.filterwarnings('ignore') | |
| 28 |
|
28 | |||
|
|
29 | import os | |||
|
|
30 | import csv | |||
|
|
31 | from scipy import signal | |||
|
|
32 | import matplotlib.pyplot as plt | |||
| 29 |
|
33 | |||
| 30 | SPEED_OF_LIGHT = 299792458 |
|
34 | SPEED_OF_LIGHT = 299792458 | |
| 31 |
|
35 | |||
| 32 | '''solving pickling issue''' |
|
36 | '''solving pickling issue''' | |
| 33 |
|
37 | |||
| 34 | def _pickle_method(method): |
|
38 | def _pickle_method(method): | |
| 35 | func_name = method.__func__.__name__ |
|
39 | func_name = method.__func__.__name__ | |
| 36 | obj = method.__self__ |
|
40 | obj = method.__self__ | |
| 37 | cls = method.__self__.__class__ |
|
41 | cls = method.__self__.__class__ | |
| 38 | return _unpickle_method, (func_name, obj, cls) |
|
42 | return _unpickle_method, (func_name, obj, cls) | |
| 39 |
|
43 | |||
| 40 | def _unpickle_method(func_name, obj, cls): |
|
44 | def _unpickle_method(func_name, obj, cls): | |
| 41 | for cls in cls.mro(): |
|
45 | for cls in cls.mro(): | |
| 42 | try: |
|
46 | try: | |
| 43 | func = cls.__dict__[func_name] |
|
47 | func = cls.__dict__[func_name] | |
| 44 | except KeyError: |
|
48 | except KeyError: | |
| 45 | pass |
|
49 | pass | |
| 46 | else: |
|
50 | else: | |
| 47 | break |
|
51 | break | |
| 48 | return func.__get__(obj, cls) |
|
52 | return func.__get__(obj, cls) | |
| 49 |
|
53 | |||
| 50 |
|
54 | |||
| 51 | class ParametersProc(ProcessingUnit): |
|
55 | class ParametersProc(ProcessingUnit): | |
| 52 |
|
56 | |||
| 53 | METHODS = {} |
|
57 | METHODS = {} | |
| 54 | nSeconds = None |
|
58 | nSeconds = None | |
| 55 |
|
59 | |||
| 56 | def __init__(self): |
|
60 | def __init__(self): | |
| 57 | ProcessingUnit.__init__(self) |
|
61 | ProcessingUnit.__init__(self) | |
| 58 |
|
62 | |||
| 59 | self.buffer = None |
|
63 | self.buffer = None | |
| 60 | self.firstdatatime = None |
|
64 | self.firstdatatime = None | |
| 61 | self.profIndex = 0 |
|
65 | self.profIndex = 0 | |
| 62 | self.dataOut = Parameters() |
|
66 | self.dataOut = Parameters() | |
| 63 | self.setupReq = False #Agregar a todas las unidades de proc |
|
67 | self.setupReq = False #Agregar a todas las unidades de proc | |
| 64 |
|
68 | |||
| 65 | def __updateObjFromInput(self): |
|
69 | def __updateObjFromInput(self): | |
| 66 |
|
70 | |||
| 67 | self.dataOut.inputUnit = self.dataIn.type |
|
71 | self.dataOut.inputUnit = self.dataIn.type | |
| 68 |
|
72 | |||
| 69 | self.dataOut.timeZone = self.dataIn.timeZone |
|
73 | self.dataOut.timeZone = self.dataIn.timeZone | |
| 70 | self.dataOut.dstFlag = self.dataIn.dstFlag |
|
74 | self.dataOut.dstFlag = self.dataIn.dstFlag | |
| 71 | self.dataOut.errorCount = self.dataIn.errorCount |
|
75 | self.dataOut.errorCount = self.dataIn.errorCount | |
| 72 | self.dataOut.useLocalTime = self.dataIn.useLocalTime |
|
76 | self.dataOut.useLocalTime = self.dataIn.useLocalTime | |
| 73 |
|
77 | |||
| 74 | self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() |
|
78 | self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() | |
|
|
79 | self.dataOut.processingHeaderObj = self.dataIn.processingHeaderObj.copy() | |||
| 75 | self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy() |
|
80 | self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy() | |
|
|
81 | ||||
| 76 | self.dataOut.channelList = self.dataIn.channelList |
|
82 | self.dataOut.channelList = self.dataIn.channelList | |
| 77 | self.dataOut.heightList = self.dataIn.heightList |
|
83 | self.dataOut.heightList = self.dataIn.heightList | |
|
|
84 | self.dataOut.ipp = self.dataIn.ipp | |||
|
|
85 | self.dataOut.ippSeconds = self.dataIn.ippSeconds | |||
|
|
86 | self.dataOut.deltaHeight = self.dataIn.deltaHeight | |||
| 78 | self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')]) |
|
87 | self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')]) | |
| 79 | # self.dataOut.nBaud = self.dataIn.nBaud |
|
88 | ||
| 80 |
|
|
89 | self.dataOut.nBaud = self.dataIn.nBaud | |
| 81 |
|
|
90 | self.dataOut.nCode = self.dataIn.nCode | |
|
|
91 | self.dataOut.code = self.dataIn.code | |||
|
|
92 | self.dataOut.nProfiles = self.dataIn.nProfiles | |||
|
|
93 | ||||
| 82 | self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock |
|
94 | self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock | |
| 83 | self.dataOut.utctime = self.dataIn.utctime |
|
95 | self.dataOut.utctime = self.dataIn.utctime | |
| 84 | self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada |
|
96 | self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada | |
| 85 | self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip |
|
97 | self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip | |
| 86 | self.dataOut.nCohInt = self.dataIn.nCohInt |
|
98 | self.dataOut.nCohInt = self.dataIn.nCohInt | |
|
|
99 | self.dataOut.nIncohInt = self.dataIn.nIncohInt | |||
|
|
100 | self.dataOut.ippSeconds = self.dataIn.ippSeconds | |||
|
|
101 | self.dataOut.windowOfFilter = self.dataIn.windowOfFilter | |||
|
|
102 | ||||
| 87 | self.dataOut.timeInterval1 = self.dataIn.timeInterval |
|
103 | self.dataOut.timeInterval1 = self.dataIn.timeInterval | |
| 88 | self.dataOut.heightList = self.dataIn.heightList |
|
104 | self.dataOut.heightList = self.dataIn.heightList | |
| 89 | self.dataOut.frequency = self.dataIn.frequency |
|
105 | self.dataOut.frequency = self.dataIn.frequency | |
|
|
106 | self.dataOut.codeList = self.dataIn.codeList | |||
|
|
107 | self.dataOut.azimuthList = self.dataIn.azimuthList | |||
|
|
108 | self.dataOut.elevationList = self.dataIn.elevationList | |||
| 90 | self.dataOut.runNextUnit = self.dataIn.runNextUnit |
|
109 | self.dataOut.runNextUnit = self.dataIn.runNextUnit | |
| 91 |
|
110 | |||
| 92 | def run(self, runNextUnit=0): |
|
111 | def run(self, runNextUnit=0): | |
| 93 |
|
112 | |||
| 94 | self.dataIn.runNextUnit = runNextUnit |
|
113 | self.dataIn.runNextUnit = runNextUnit | |
| 95 | #---------------------- Voltage Data --------------------------- |
|
114 | #---------------------- Voltage Data --------------------------- | |
|
|
115 | try: | |||
|
|
116 | intype = self.dataIn.type.decode("utf-8") | |||
|
|
117 | self.dataIn.type = intype | |||
|
|
118 | except: | |||
|
|
119 | pass | |||
| 96 |
|
120 | |||
| 97 | if self.dataIn.type == "Voltage": |
|
121 | if self.dataIn.type == "Voltage": | |
| 98 |
|
122 | |||
| 99 | self.__updateObjFromInput() |
|
123 | self.__updateObjFromInput() | |
| 100 | self.dataOut.data_pre = self.dataIn.data.copy() |
|
124 | self.dataOut.data_pre = self.dataIn.data.copy() | |
| 101 | self.dataOut.flagNoData = False |
|
125 | self.dataOut.flagNoData = False | |
| 102 | self.dataOut.utctimeInit = self.dataIn.utctime |
|
126 | self.dataOut.utctimeInit = self.dataIn.utctime | |
| 103 | self.dataOut.paramInterval = self.dataIn.nProfiles*self.dataIn.nCohInt*self.dataIn.ippSeconds |
|
127 | self.dataOut.paramInterval = self.dataIn.nProfiles*self.dataIn.nCohInt*self.dataIn.ippSeconds | |
| 104 | if hasattr(self.dataIn, 'dataPP_POW'): |
|
128 | if hasattr(self.dataIn, 'dataPP_POW'): | |
| 105 | self.dataOut.dataPP_POW = self.dataIn.dataPP_POW |
|
129 | self.dataOut.dataPP_POW = self.dataIn.dataPP_POW | |
| 106 |
|
130 | |||
| 107 | if hasattr(self.dataIn, 'dataPP_POWER'): |
|
131 | if hasattr(self.dataIn, 'dataPP_POWER'): | |
| 108 | self.dataOut.dataPP_POWER = self.dataIn.dataPP_POWER |
|
132 | self.dataOut.dataPP_POWER = self.dataIn.dataPP_POWER | |
| 109 |
|
133 | |||
| 110 | if hasattr(self.dataIn, 'dataPP_DOP'): |
|
134 | if hasattr(self.dataIn, 'dataPP_DOP'): | |
| 111 | self.dataOut.dataPP_DOP = self.dataIn.dataPP_DOP |
|
135 | self.dataOut.dataPP_DOP = self.dataIn.dataPP_DOP | |
| 112 |
|
136 | |||
| 113 | if hasattr(self.dataIn, 'dataPP_SNR'): |
|
137 | if hasattr(self.dataIn, 'dataPP_SNR'): | |
| 114 | self.dataOut.dataPP_SNR = self.dataIn.dataPP_SNR |
|
138 | self.dataOut.dataPP_SNR = self.dataIn.dataPP_SNR | |
| 115 |
|
139 | |||
| 116 | if hasattr(self.dataIn, 'dataPP_WIDTH'): |
|
140 | if hasattr(self.dataIn, 'dataPP_WIDTH'): | |
| 117 | self.dataOut.dataPP_WIDTH = self.dataIn.dataPP_WIDTH |
|
141 | self.dataOut.dataPP_WIDTH = self.dataIn.dataPP_WIDTH | |
| 118 | return |
|
142 | return | |
| 119 |
|
143 | |||
| 120 | #---------------------- Spectra Data --------------------------- |
|
144 | #---------------------- Spectra Data --------------------------- | |
| 121 |
|
145 | |||
| 122 | if self.dataIn.type == "Spectra": |
|
146 | if self.dataIn.type == "Spectra": | |
| 123 |
|
147 | |||
| 124 | self.dataOut.data_pre = [self.dataIn.data_spc, self.dataIn.data_cspc] |
|
148 | self.dataOut.data_pre = [self.dataIn.data_spc, self.dataIn.data_cspc] | |
| 125 | self.dataOut.data_spc = self.dataIn.data_spc |
|
149 | self.dataOut.data_spc = self.dataIn.data_spc | |
| 126 | self.dataOut.data_cspc = self.dataIn.data_cspc |
|
150 | self.dataOut.data_cspc = self.dataIn.data_cspc | |
|
|
151 | if hasattr(self.dataIn, 'data_outlier'): | |||
|
|
152 | self.dataOut.data_outlier = self.dataIn.data_outlier | |||
|
|
153 | if hasattr(self.dataIn,'flagPRofilesByRange'): | |||
|
|
154 | self.dataOut.flagProfilesByRange = self.dataIn.flagProfilesByRange | |||
|
|
155 | if hasattr(self.dataIn,'nProfilesByRange'): | |||
|
|
156 | self.dataOut.nProfilesByRange = self.dataIn.nProfilesByRange | |||
|
|
157 | if hasattr(self.dataIn,'deltaHeight'): | |||
|
|
158 | self.dataOut.deltaHeight = self.dataIn.deltaHeight | |||
|
|
159 | if hasattr(self.dataIn,'noise_estimation'): | |||
|
|
160 | self.dataOut.noise_estimation = self.dataIn.noise_estimation | |||
|
|
161 | if hasattr(self.dataIn, 'channelList'): | |||
|
|
162 | self.dataOut.channelList = self.dataIn.channelList | |||
|
|
163 | if hasattr(self.dataIn, 'pairsList'): | |||
|
|
164 | self.dataOut.pairsList = self.dataIn.pairsList | |||
|
|
165 | self.dataOut.groupList = self.dataIn.pairsList | |||
| 127 | self.dataOut.nProfiles = self.dataIn.nProfiles |
|
166 | self.dataOut.nProfiles = self.dataIn.nProfiles | |
| 128 | self.dataOut.nIncohInt = self.dataIn.nIncohInt |
|
167 | self.dataOut.nIncohInt = self.dataIn.nIncohInt | |
| 129 | self.dataOut.nFFTPoints = self.dataIn.nFFTPoints |
|
168 | self.dataOut.nFFTPoints = self.dataIn.nFFTPoints | |
| 130 | self.dataOut.ippFactor = self.dataIn.ippFactor |
|
169 | self.dataOut.ippFactor = self.dataIn.ippFactor | |
| 131 | self.dataOut.abscissaList = self.dataIn.getVelRange(1) |
|
170 | self.dataOut.abscissaList = self.dataIn.getVelRange(1) | |
| 132 | self.dataOut.spc_noise = self.dataIn.getNoise() |
|
171 | self.dataOut.spc_noise = self.dataIn.getNoise() | |
| 133 | self.dataOut.spc_range = (self.dataIn.getFreqRange(1) , self.dataIn.getAcfRange(1) , self.dataIn.getVelRange(1)) |
|
172 | self.dataOut.spc_range = (self.dataIn.getFreqRange(1) , self.dataIn.getAcfRange(1) , self.dataIn.getVelRange(1)) | |
| 134 | # self.dataOut.normFactor = self.dataIn.normFactor |
|
173 | # self.dataOut.normFactor = self.dataIn.normFactor | |
| 135 | self.dataOut.pairsList = self.dataIn.pairsList |
|
|||
| 136 | self.dataOut.groupList = self.dataIn.pairsList |
|
|||
| 137 | self.dataOut.flagNoData = False |
|
174 | self.dataOut.flagNoData = False | |
| 138 |
|
175 | |||
| 139 | if hasattr(self.dataIn, 'ChanDist'): #Distances of receiver channels |
|
176 | if hasattr(self.dataIn, 'ChanDist'): #Distances of receiver channels | |
| 140 | self.dataOut.ChanDist = self.dataIn.ChanDist |
|
177 | self.dataOut.ChanDist = self.dataIn.ChanDist | |
| 141 | else: self.dataOut.ChanDist = None |
|
178 | else: self.dataOut.ChanDist = None | |
| 142 |
|
179 | |||
| 143 | #if hasattr(self.dataIn, 'VelRange'): #Velocities range |
|
180 | #if hasattr(self.dataIn, 'VelRange'): #Velocities range | |
| 144 | # self.dataOut.VelRange = self.dataIn.VelRange |
|
181 | # self.dataOut.VelRange = self.dataIn.VelRange | |
| 145 | #else: self.dataOut.VelRange = None |
|
182 | #else: self.dataOut.VelRange = None | |
| 146 |
|
183 | |||
| 147 | if hasattr(self.dataIn, 'RadarConst'): #Radar Constant |
|
184 | if hasattr(self.dataIn, 'RadarConst'): #Radar Constant | |
| 148 | self.dataOut.RadarConst = self.dataIn.RadarConst |
|
185 | self.dataOut.RadarConst = self.dataIn.RadarConst | |
| 149 |
|
186 | |||
| 150 | if hasattr(self.dataIn, 'NPW'): #NPW |
|
187 | if hasattr(self.dataIn, 'NPW'): #NPW | |
| 151 | self.dataOut.NPW = self.dataIn.NPW |
|
188 | self.dataOut.NPW = self.dataIn.NPW | |
| 152 |
|
189 | |||
| 153 | if hasattr(self.dataIn, 'COFA'): #COFA |
|
190 | if hasattr(self.dataIn, 'COFA'): #COFA | |
| 154 | self.dataOut.COFA = self.dataIn.COFA |
|
191 | self.dataOut.COFA = self.dataIn.COFA | |
| 155 |
|
192 | |||
| 156 |
|
193 | |||
| 157 |
|
194 | |||
| 158 | #---------------------- Correlation Data --------------------------- |
|
195 | #---------------------- Correlation Data --------------------------- | |
| 159 |
|
196 | |||
| 160 | if self.dataIn.type == "Correlation": |
|
197 | if self.dataIn.type == "Correlation": | |
| 161 | acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf = self.dataIn.splitFunctions() |
|
198 | acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf = self.dataIn.splitFunctions() | |
| 162 |
|
199 | |||
| 163 | self.dataOut.data_pre = (self.dataIn.data_cf[acf_ind,:], self.dataIn.data_cf[ccf_ind,:,:]) |
|
200 | self.dataOut.data_pre = (self.dataIn.data_cf[acf_ind,:], self.dataIn.data_cf[ccf_ind,:,:]) | |
| 164 | self.dataOut.normFactor = (self.dataIn.normFactor[acf_ind,:], self.dataIn.normFactor[ccf_ind,:]) |
|
201 | self.dataOut.normFactor = (self.dataIn.normFactor[acf_ind,:], self.dataIn.normFactor[ccf_ind,:]) | |
| 165 | self.dataOut.groupList = (acf_pairs, ccf_pairs) |
|
202 | self.dataOut.groupList = (acf_pairs, ccf_pairs) | |
| 166 |
|
203 | |||
| 167 | self.dataOut.abscissaList = self.dataIn.lagRange |
|
204 | self.dataOut.abscissaList = self.dataIn.lagRange | |
| 168 | self.dataOut.noise = self.dataIn.noise |
|
205 | self.dataOut.noise = self.dataIn.noise | |
| 169 | self.dataOut.data_snr = self.dataIn.SNR |
|
206 | self.dataOut.data_snr = self.dataIn.SNR | |
| 170 | self.dataOut.flagNoData = False |
|
207 | self.dataOut.flagNoData = False | |
| 171 | self.dataOut.nAvg = self.dataIn.nAvg |
|
208 | self.dataOut.nAvg = self.dataIn.nAvg | |
| 172 |
|
209 | |||
| 173 | #---------------------- Parameters Data --------------------------- |
|
210 | #---------------------- Parameters Data --------------------------- | |
| 174 |
|
211 | |||
| 175 | if self.dataIn.type == "Parameters": |
|
212 | if self.dataIn.type == "Parameters": | |
| 176 | self.dataOut.copy(self.dataIn) |
|
213 | self.dataOut.copy(self.dataIn) | |
|
|
214 | self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() | |||
|
|
215 | self.dataOut.processingHeaderObj = self.dataIn.processingHeaderObj.copy() | |||
| 177 | self.dataOut.flagNoData = False |
|
216 | self.dataOut.flagNoData = False | |
| 178 |
|
217 | if isinstance(self.dataIn.nIncohInt,numpy.ndarray): | ||
|
|
218 | nch, nheis = self.dataIn.nIncohInt.shape | |||
|
|
219 | if nch != self.dataIn.nChannels: | |||
|
|
220 | aux = numpy.repeat(self.dataIn.nIncohInt, self.dataIn.nChannels, axis=0) | |||
|
|
221 | self.dataOut.nIncohInt = aux | |||
| 179 | return True |
|
222 | return True | |
| 180 |
|
223 | |||
| 181 | self.__updateObjFromInput() |
|
224 | self.__updateObjFromInput() | |
| 182 | self.dataOut.utctimeInit = self.dataIn.utctime |
|
225 | self.dataOut.utctimeInit = self.dataIn.utctime | |
| 183 | self.dataOut.paramInterval = self.dataIn.timeInterval |
|
226 | self.dataOut.paramInterval = self.dataIn.timeInterval | |
| 184 | return |
|
227 | return | |
| 185 |
|
228 | |||
| 186 |
|
229 | |||
| 187 | def target(tups): |
|
230 | def target(tups): | |
| 188 |
|
231 | |||
| 189 | obj, args = tups |
|
232 | obj, args = tups | |
| 190 |
|
233 | |||
| 191 | return obj.FitGau(args) |
|
234 | return obj.FitGau(args) | |
| 192 |
|
235 | |||
| 193 | class RemoveWideGC(Operation): |
|
236 | class RemoveWideGC(Operation): | |
| 194 | ''' This class remove the wide clutter and replace it with a simple interpolation points |
|
237 | ''' This class remove the wide clutter and replace it with a simple interpolation points | |
| 195 | This mainly applies to CLAIRE radar |
|
238 | This mainly applies to CLAIRE radar | |
| 196 |
|
239 | |||
| 197 | ClutterWidth : Width to look for the clutter peak |
|
240 | ClutterWidth : Width to look for the clutter peak | |
| 198 |
|
241 | |||
| 199 | Input: |
|
242 | Input: | |
| 200 |
|
243 | |||
| 201 | self.dataOut.data_pre : SPC and CSPC |
|
244 | self.dataOut.data_pre : SPC and CSPC | |
| 202 | self.dataOut.spc_range : To select wind and rainfall velocities |
|
245 | self.dataOut.spc_range : To select wind and rainfall velocities | |
| 203 |
|
246 | |||
| 204 | Affected: |
|
247 | Affected: | |
| 205 |
|
248 | |||
| 206 | self.dataOut.data_pre : It is used for the new SPC and CSPC ranges of wind |
|
249 | self.dataOut.data_pre : It is used for the new SPC and CSPC ranges of wind | |
| 207 |
|
250 | |||
| 208 | Written by D. ScipiΓ³n 25.02.2021 |
|
251 | Written by D. ScipiΓ³n 25.02.2021 | |
| 209 | ''' |
|
252 | ''' | |
| 210 | def __init__(self): |
|
253 | def __init__(self): | |
| 211 | Operation.__init__(self) |
|
254 | Operation.__init__(self) | |
| 212 | self.i = 0 |
|
255 | self.i = 0 | |
| 213 | self.ich = 0 |
|
256 | self.ich = 0 | |
| 214 | self.ir = 0 |
|
257 | self.ir = 0 | |
| 215 |
|
258 | |||
| 216 | def run(self, dataOut, ClutterWidth=2.5): |
|
259 | def run(self, dataOut, ClutterWidth=2.5): | |
| 217 |
|
260 | |||
| 218 | self.spc = dataOut.data_pre[0].copy() |
|
261 | self.spc = dataOut.data_pre[0].copy() | |
| 219 | self.spc_out = dataOut.data_pre[0].copy() |
|
262 | self.spc_out = dataOut.data_pre[0].copy() | |
| 220 | self.Num_Chn = self.spc.shape[0] |
|
263 | self.Num_Chn = self.spc.shape[0] | |
| 221 | self.Num_Hei = self.spc.shape[2] |
|
264 | self.Num_Hei = self.spc.shape[2] | |
| 222 | VelRange = dataOut.spc_range[2][:-1] |
|
265 | VelRange = dataOut.spc_range[2][:-1] | |
| 223 | dv = VelRange[1]-VelRange[0] |
|
266 | dv = VelRange[1]-VelRange[0] | |
| 224 |
|
267 | |||
| 225 | # Find the velocities that corresponds to zero |
|
268 | # Find the velocities that corresponds to zero | |
| 226 | gc_values = numpy.squeeze(numpy.where(numpy.abs(VelRange) <= ClutterWidth)) |
|
269 | gc_values = numpy.squeeze(numpy.where(numpy.abs(VelRange) <= ClutterWidth)) | |
| 227 |
|
270 | |||
| 228 | # Removing novalid data from the spectra |
|
271 | # Removing novalid data from the spectra | |
| 229 | for ich in range(self.Num_Chn) : |
|
272 | for ich in range(self.Num_Chn) : | |
| 230 | for ir in range(self.Num_Hei) : |
|
273 | for ir in range(self.Num_Hei) : | |
| 231 | # Estimate the noise at each range |
|
274 | # Estimate the noise at each range | |
| 232 | HSn = hildebrand_sekhon(self.spc[ich,:,ir],dataOut.nIncohInt) |
|
275 | HSn = hildebrand_sekhon(self.spc[ich,:,ir],dataOut.nIncohInt) | |
| 233 |
|
276 | |||
| 234 | # Removing the noise floor at each range |
|
277 | # Removing the noise floor at each range | |
| 235 | novalid = numpy.where(self.spc[ich,:,ir] < HSn) |
|
278 | novalid = numpy.where(self.spc[ich,:,ir] < HSn) | |
| 236 | self.spc[ich,novalid,ir] = HSn |
|
279 | self.spc[ich,novalid,ir] = HSn | |
| 237 |
|
280 | |||
| 238 | junk = numpy.append(numpy.insert(numpy.squeeze(self.spc[ich,gc_values,ir]),0,HSn),HSn) |
|
281 | junk = numpy.append(numpy.insert(numpy.squeeze(self.spc[ich,gc_values,ir]),0,HSn),HSn) | |
| 239 | j1index = numpy.squeeze(numpy.where(numpy.diff(junk)>0)) |
|
282 | j1index = numpy.squeeze(numpy.where(numpy.diff(junk)>0)) | |
| 240 | j2index = numpy.squeeze(numpy.where(numpy.diff(junk)<0)) |
|
283 | j2index = numpy.squeeze(numpy.where(numpy.diff(junk)<0)) | |
| 241 | if ((numpy.size(j1index)<=1) | (numpy.size(j2index)<=1)) : |
|
284 | if ((numpy.size(j1index)<=1) | (numpy.size(j2index)<=1)) : | |
| 242 | continue |
|
285 | continue | |
| 243 | junk3 = numpy.squeeze(numpy.diff(j1index)) |
|
286 | junk3 = numpy.squeeze(numpy.diff(j1index)) | |
| 244 | junk4 = numpy.squeeze(numpy.diff(j2index)) |
|
287 | junk4 = numpy.squeeze(numpy.diff(j2index)) | |
| 245 | valleyindex = j2index[numpy.where(junk4>1)] |
|
288 | valleyindex = j2index[numpy.where(junk4>1)] | |
| 246 | peakindex = j1index[numpy.where(junk3>1)] |
|
289 | peakindex = j1index[numpy.where(junk3>1)] | |
| 247 |
|
290 | |||
| 248 | isvalid = numpy.squeeze(numpy.where(numpy.abs(VelRange[gc_values[peakindex]]) <= 2.5*dv)) |
|
291 | isvalid = numpy.squeeze(numpy.where(numpy.abs(VelRange[gc_values[peakindex]]) <= 2.5*dv)) | |
| 249 | if numpy.size(isvalid) == 0 : |
|
292 | if numpy.size(isvalid) == 0 : | |
| 250 | continue |
|
293 | continue | |
| 251 | if numpy.size(isvalid) >1 : |
|
294 | if numpy.size(isvalid) >1 : | |
| 252 | vindex = numpy.argmax(self.spc[ich,gc_values[peakindex[isvalid]],ir]) |
|
295 | vindex = numpy.argmax(self.spc[ich,gc_values[peakindex[isvalid]],ir]) | |
| 253 | isvalid = isvalid[vindex] |
|
296 | isvalid = isvalid[vindex] | |
| 254 | # clutter peak |
|
297 | # clutter peak | |
| 255 | gcpeak = peakindex[isvalid] |
|
298 | gcpeak = peakindex[isvalid] | |
| 256 | vl = numpy.where(valleyindex < gcpeak) |
|
299 | vl = numpy.where(valleyindex < gcpeak) | |
| 257 | if numpy.size(vl) == 0: |
|
300 | if numpy.size(vl) == 0: | |
| 258 | continue |
|
301 | continue | |
| 259 | gcvl = valleyindex[vl[0][-1]] |
|
302 | gcvl = valleyindex[vl[0][-1]] | |
| 260 | vr = numpy.where(valleyindex > gcpeak) |
|
303 | vr = numpy.where(valleyindex > gcpeak) | |
| 261 | if numpy.size(vr) == 0: |
|
304 | if numpy.size(vr) == 0: | |
| 262 | continue |
|
305 | continue | |
| 263 | gcvr = valleyindex[vr[0][0]] |
|
306 | gcvr = valleyindex[vr[0][0]] | |
| 264 |
|
307 | |||
| 265 | # Removing the clutter |
|
308 | # Removing the clutter | |
| 266 | interpindex = numpy.array([gc_values[gcvl], gc_values[gcvr]]) |
|
309 | interpindex = numpy.array([gc_values[gcvl], gc_values[gcvr]]) | |
| 267 | gcindex = gc_values[gcvl+1:gcvr-1] |
|
310 | gcindex = gc_values[gcvl+1:gcvr-1] | |
| 268 | self.spc_out[ich,gcindex,ir] = numpy.interp(VelRange[gcindex],VelRange[interpindex],self.spc[ich,interpindex,ir]) |
|
311 | self.spc_out[ich,gcindex,ir] = numpy.interp(VelRange[gcindex],VelRange[interpindex],self.spc[ich,interpindex,ir]) | |
| 269 |
|
312 | |||
| 270 | dataOut.data_pre[0] = self.spc_out |
|
313 | dataOut.data_pre[0] = self.spc_out | |
| 271 |
|
314 | |||
| 272 | return dataOut |
|
315 | return dataOut | |
| 273 |
|
316 | |||
| 274 | class SpectralFilters(Operation): |
|
317 | class SpectralFilters(Operation): | |
| 275 | ''' This class allows to replace the novalid values with noise for each channel |
|
318 | ''' This class allows to replace the novalid values with noise for each channel | |
| 276 | This applies to CLAIRE RADAR |
|
319 | This applies to CLAIRE RADAR | |
| 277 |
|
320 | |||
| 278 | PositiveLimit : RightLimit of novalid data |
|
321 | PositiveLimit : RightLimit of novalid data | |
| 279 | NegativeLimit : LeftLimit of novalid data |
|
322 | NegativeLimit : LeftLimit of novalid data | |
| 280 |
|
323 | |||
| 281 | Input: |
|
324 | Input: | |
| 282 |
|
325 | |||
| 283 | self.dataOut.data_pre : SPC and CSPC |
|
326 | self.dataOut.data_pre : SPC and CSPC | |
| 284 | self.dataOut.spc_range : To select wind and rainfall velocities |
|
327 | self.dataOut.spc_range : To select wind and rainfall velocities | |
| 285 |
|
328 | |||
| 286 | Affected: |
|
329 | Affected: | |
| 287 |
|
330 | |||
| 288 | self.dataOut.data_pre : It is used for the new SPC and CSPC ranges of wind |
|
331 | self.dataOut.data_pre : It is used for the new SPC and CSPC ranges of wind | |
| 289 |
|
332 | |||
| 290 | Written by D. ScipiΓ³n 29.01.2021 |
|
333 | Written by D. ScipiΓ³n 29.01.2021 | |
| 291 | ''' |
|
334 | ''' | |
| 292 | def __init__(self): |
|
335 | def __init__(self): | |
| 293 | Operation.__init__(self) |
|
336 | Operation.__init__(self) | |
| 294 | self.i = 0 |
|
337 | self.i = 0 | |
| 295 |
|
338 | |||
| 296 | def run(self, dataOut, ): |
|
339 | def run(self, dataOut, ): | |
| 297 |
|
340 | |||
| 298 | self.spc = dataOut.data_pre[0].copy() |
|
341 | self.spc = dataOut.data_pre[0].copy() | |
| 299 | self.Num_Chn = self.spc.shape[0] |
|
342 | self.Num_Chn = self.spc.shape[0] | |
| 300 | VelRange = dataOut.spc_range[2] |
|
343 | VelRange = dataOut.spc_range[2] | |
| 301 |
|
344 | |||
| 302 | # novalid corresponds to data within the Negative and PositiveLimit |
|
345 | # novalid corresponds to data within the Negative and PositiveLimit | |
| 303 | # Removing novalid data from the spectra |
|
346 | # Removing novalid data from the spectra | |
| 304 | for i in range(self.Num_Chn): |
|
347 | for i in range(self.Num_Chn): | |
| 305 | self.spc[i,novalid,:] = dataOut.noise[i] |
|
348 | self.spc[i,novalid,:] = dataOut.noise[i] | |
| 306 | dataOut.data_pre[0] = self.spc |
|
349 | dataOut.data_pre[0] = self.spc | |
| 307 | return dataOut |
|
350 | return dataOut | |
| 308 |
|
351 | |||
| 309 |
|
352 | |||
| 310 | class GaussianFit(Operation): |
|
353 | class GaussianFit(Operation): | |
| 311 |
|
354 | |||
| 312 | ''' |
|
355 | ''' | |
| 313 | Function that fit of one and two generalized gaussians (gg) based |
|
356 | Function that fit of one and two generalized gaussians (gg) based | |
| 314 | on the PSD shape across an "power band" identified from a cumsum of |
|
357 | on the PSD shape across an "power band" identified from a cumsum of | |
| 315 | the measured spectrum - noise. |
|
358 | the measured spectrum - noise. | |
| 316 |
|
359 | |||
| 317 | Input: |
|
360 | Input: | |
| 318 | self.dataOut.data_pre : SelfSpectra |
|
361 | self.dataOut.data_pre : SelfSpectra | |
| 319 |
|
362 | |||
| 320 | Output: |
|
363 | Output: | |
| 321 | self.dataOut.SPCparam : SPC_ch1, SPC_ch2 |
|
364 | self.dataOut.SPCparam : SPC_ch1, SPC_ch2 | |
| 322 |
|
365 | |||
| 323 | ''' |
|
366 | ''' | |
| 324 | def __init__(self): |
|
367 | def __init__(self): | |
| 325 | Operation.__init__(self) |
|
368 | Operation.__init__(self) | |
| 326 | self.i=0 |
|
369 | self.i=0 | |
| 327 |
|
370 | |||
| 328 | def run(self, dataOut, SNRdBlimit=-9, method='generalized'): |
|
371 | def run(self, dataOut, SNRdBlimit=-9, method='generalized'): | |
| 329 | """This routine will find a couple of generalized Gaussians to a power spectrum |
|
372 | """This routine will find a couple of generalized Gaussians to a power spectrum | |
| 330 | methods: generalized, squared |
|
373 | methods: generalized, squared | |
| 331 | input: spc |
|
374 | input: spc | |
| 332 | output: |
|
375 | output: | |
| 333 | noise, amplitude0,shift0,width0,p0,Amplitude1,shift1,width1,p1 |
|
376 | noise, amplitude0,shift0,width0,p0,Amplitude1,shift1,width1,p1 | |
| 334 | """ |
|
377 | """ | |
| 335 | print ('Entering ',method,' double Gaussian fit') |
|
378 | print ('Entering ',method,' double Gaussian fit') | |
| 336 | self.spc = dataOut.data_pre[0].copy() |
|
379 | self.spc = dataOut.data_pre[0].copy() | |
| 337 | self.Num_Hei = self.spc.shape[2] |
|
380 | self.Num_Hei = self.spc.shape[2] | |
| 338 | self.Num_Bin = self.spc.shape[1] |
|
381 | self.Num_Bin = self.spc.shape[1] | |
| 339 | self.Num_Chn = self.spc.shape[0] |
|
382 | self.Num_Chn = self.spc.shape[0] | |
| 340 |
|
383 | |||
| 341 | start_time = time.time() |
|
384 | start_time = time.time() | |
| 342 |
|
385 | |||
| 343 | pool = Pool(processes=self.Num_Chn) |
|
386 | pool = Pool(processes=self.Num_Chn) | |
| 344 | args = [(dataOut.spc_range[2], ich, dataOut.spc_noise[ich], dataOut.nIncohInt, SNRdBlimit) for ich in range(self.Num_Chn)] |
|
387 | args = [(dataOut.spc_range[2], ich, dataOut.spc_noise[ich], dataOut.nIncohInt, SNRdBlimit) for ich in range(self.Num_Chn)] | |
| 345 | objs = [self for __ in range(self.Num_Chn)] |
|
388 | objs = [self for __ in range(self.Num_Chn)] | |
| 346 | attrs = list(zip(objs, args)) |
|
389 | attrs = list(zip(objs, args)) | |
| 347 | DGauFitParam = pool.map(target, attrs) |
|
390 | DGauFitParam = pool.map(target, attrs) | |
| 348 | # Parameters: |
|
391 | # Parameters: | |
| 349 | # 0. Noise, 1. Amplitude, 2. Shift, 3. Width 4. Power |
|
392 | # 0. Noise, 1. Amplitude, 2. Shift, 3. Width 4. Power | |
| 350 | dataOut.DGauFitParams = numpy.asarray(DGauFitParam) |
|
393 | dataOut.DGauFitParams = numpy.asarray(DGauFitParam) | |
| 351 |
|
394 | |||
| 352 | # Double Gaussian Curves |
|
395 | # Double Gaussian Curves | |
| 353 | gau0 = numpy.zeros([self.Num_Chn,self.Num_Bin,self.Num_Hei]) |
|
396 | gau0 = numpy.zeros([self.Num_Chn,self.Num_Bin,self.Num_Hei]) | |
| 354 | gau0[:] = numpy.NaN |
|
397 | gau0[:] = numpy.NaN | |
| 355 | gau1 = numpy.zeros([self.Num_Chn,self.Num_Bin,self.Num_Hei]) |
|
398 | gau1 = numpy.zeros([self.Num_Chn,self.Num_Bin,self.Num_Hei]) | |
| 356 | gau1[:] = numpy.NaN |
|
399 | gau1[:] = numpy.NaN | |
| 357 | x_mtr = numpy.transpose(numpy.tile(dataOut.getVelRange(1)[:-1], (self.Num_Hei,1))) |
|
400 | x_mtr = numpy.transpose(numpy.tile(dataOut.getVelRange(1)[:-1], (self.Num_Hei,1))) | |
| 358 | for iCh in range(self.Num_Chn): |
|
401 | for iCh in range(self.Num_Chn): | |
| 359 | N0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][0,:,0]] * self.Num_Bin)) |
|
402 | N0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][0,:,0]] * self.Num_Bin)) | |
| 360 | N1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][0,:,1]] * self.Num_Bin)) |
|
403 | N1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][0,:,1]] * self.Num_Bin)) | |
| 361 | A0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][1,:,0]] * self.Num_Bin)) |
|
404 | A0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][1,:,0]] * self.Num_Bin)) | |
| 362 | A1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][1,:,1]] * self.Num_Bin)) |
|
405 | A1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][1,:,1]] * self.Num_Bin)) | |
| 363 | v0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][2,:,0]] * self.Num_Bin)) |
|
406 | v0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][2,:,0]] * self.Num_Bin)) | |
| 364 | v1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][2,:,1]] * self.Num_Bin)) |
|
407 | v1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][2,:,1]] * self.Num_Bin)) | |
| 365 | s0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][3,:,0]] * self.Num_Bin)) |
|
408 | s0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][3,:,0]] * self.Num_Bin)) | |
| 366 | s1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][3,:,1]] * self.Num_Bin)) |
|
409 | s1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][3,:,1]] * self.Num_Bin)) | |
| 367 | if method == 'generalized': |
|
410 | if method == 'generalized': | |
| 368 | p0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][4,:,0]] * self.Num_Bin)) |
|
411 | p0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][4,:,0]] * self.Num_Bin)) | |
| 369 | p1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][4,:,1]] * self.Num_Bin)) |
|
412 | p1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][4,:,1]] * self.Num_Bin)) | |
| 370 | elif method == 'squared': |
|
413 | elif method == 'squared': | |
| 371 | p0 = 2. |
|
414 | p0 = 2. | |
| 372 | p1 = 2. |
|
415 | p1 = 2. | |
| 373 | gau0[iCh] = A0*numpy.exp(-0.5*numpy.abs((x_mtr-v0)/s0)**p0)+N0 |
|
416 | gau0[iCh] = A0*numpy.exp(-0.5*numpy.abs((x_mtr-v0)/s0)**p0)+N0 | |
| 374 | gau1[iCh] = A1*numpy.exp(-0.5*numpy.abs((x_mtr-v1)/s1)**p1)+N1 |
|
417 | gau1[iCh] = A1*numpy.exp(-0.5*numpy.abs((x_mtr-v1)/s1)**p1)+N1 | |
| 375 | dataOut.GaussFit0 = gau0 |
|
418 | dataOut.GaussFit0 = gau0 | |
| 376 | dataOut.GaussFit1 = gau1 |
|
419 | dataOut.GaussFit1 = gau1 | |
| 377 |
|
420 | |||
| 378 | print('Leaving ',method ,' double Gaussian fit') |
|
421 | print('Leaving ',method ,' double Gaussian fit') | |
| 379 | return dataOut |
|
422 | return dataOut | |
| 380 |
|
423 | |||
| 381 | def FitGau(self, X): |
|
424 | def FitGau(self, X): | |
| 382 | # print('Entering FitGau') |
|
425 | # print('Entering FitGau') | |
| 383 | # Assigning the variables |
|
426 | # Assigning the variables | |
| 384 | Vrange, ch, wnoise, num_intg, SNRlimit = X |
|
427 | Vrange, ch, wnoise, num_intg, SNRlimit = X | |
| 385 | # Noise Limits |
|
428 | # Noise Limits | |
| 386 | noisebl = wnoise * 0.9 |
|
429 | noisebl = wnoise * 0.9 | |
| 387 | noisebh = wnoise * 1.1 |
|
430 | noisebh = wnoise * 1.1 | |
| 388 | # Radar Velocity |
|
431 | # Radar Velocity | |
| 389 | Va = max(Vrange) |
|
432 | Va = max(Vrange) | |
| 390 | deltav = Vrange[1] - Vrange[0] |
|
433 | deltav = Vrange[1] - Vrange[0] | |
| 391 | x = numpy.arange(self.Num_Bin) |
|
434 | x = numpy.arange(self.Num_Bin) | |
| 392 |
|
435 | |||
| 393 | # print ('stop 0') |
|
436 | # print ('stop 0') | |
| 394 |
|
437 | |||
| 395 | # 5 parameters, 2 Gaussians |
|
438 | # 5 parameters, 2 Gaussians | |
| 396 | DGauFitParam = numpy.zeros([5, self.Num_Hei,2]) |
|
439 | DGauFitParam = numpy.zeros([5, self.Num_Hei,2]) | |
| 397 | DGauFitParam[:] = numpy.NaN |
|
440 | DGauFitParam[:] = numpy.NaN | |
| 398 |
|
441 | |||
| 399 | # SPCparam = [] |
|
442 | # SPCparam = [] | |
| 400 | # SPC_ch1 = numpy.zeros([self.Num_Bin,self.Num_Hei]) |
|
443 | # SPC_ch1 = numpy.zeros([self.Num_Bin,self.Num_Hei]) | |
| 401 | # SPC_ch2 = numpy.zeros([self.Num_Bin,self.Num_Hei]) |
|
444 | # SPC_ch2 = numpy.zeros([self.Num_Bin,self.Num_Hei]) | |
| 402 | # SPC_ch1[:] = 0 #numpy.NaN |
|
445 | # SPC_ch1[:] = 0 #numpy.NaN | |
| 403 | # SPC_ch2[:] = 0 #numpy.NaN |
|
446 | # SPC_ch2[:] = 0 #numpy.NaN | |
| 404 | # print ('stop 1') |
|
447 | # print ('stop 1') | |
| 405 | for ht in range(self.Num_Hei): |
|
448 | for ht in range(self.Num_Hei): | |
| 406 | # print (ht) |
|
449 | # print (ht) | |
| 407 | # print ('stop 2') |
|
450 | # print ('stop 2') | |
| 408 | # Spectra at each range |
|
451 | # Spectra at each range | |
| 409 | spc = numpy.asarray(self.spc)[ch,:,ht] |
|
452 | spc = numpy.asarray(self.spc)[ch,:,ht] | |
| 410 | snr = ( spc.mean() - wnoise ) / wnoise |
|
453 | snr = ( spc.mean() - wnoise ) / wnoise | |
| 411 | snrdB = 10.*numpy.log10(snr) |
|
454 | snrdB = 10.*numpy.log10(snr) | |
| 412 |
|
455 | |||
| 413 | #print ('stop 3') |
|
456 | #print ('stop 3') | |
| 414 | if snrdB < SNRlimit : |
|
457 | if snrdB < SNRlimit : | |
| 415 | # snr = numpy.NaN |
|
458 | # snr = numpy.NaN | |
| 416 | # SPC_ch1[:,ht] = 0#numpy.NaN |
|
459 | # SPC_ch1[:,ht] = 0#numpy.NaN | |
| 417 | # SPC_ch1[:,ht] = 0#numpy.NaN |
|
460 | # SPC_ch1[:,ht] = 0#numpy.NaN | |
| 418 | # SPCparam = (SPC_ch1,SPC_ch2) |
|
461 | # SPCparam = (SPC_ch1,SPC_ch2) | |
| 419 | # print ('SNR less than SNRth') |
|
462 | # print ('SNR less than SNRth') | |
| 420 | continue |
|
463 | continue | |
| 421 | # wnoise = hildebrand_sekhon(spc,num_intg) |
|
464 | # wnoise = hildebrand_sekhon(spc,num_intg) | |
| 422 | # print ('stop 2.01') |
|
465 | # print ('stop 2.01') | |
| 423 | ############################################# |
|
466 | ############################################# | |
| 424 | # normalizing spc and noise |
|
467 | # normalizing spc and noise | |
| 425 | # This part differs from gg1 |
|
468 | # This part differs from gg1 | |
| 426 | # spc_norm_max = max(spc) #commented by D. ScipiΓ³n 19.03.2021 |
|
469 | # spc_norm_max = max(spc) #commented by D. ScipiΓ³n 19.03.2021 | |
| 427 | #spc = spc / spc_norm_max |
|
470 | #spc = spc / spc_norm_max | |
| 428 | # pnoise = pnoise #/ spc_norm_max #commented by D. ScipiΓ³n 19.03.2021 |
|
471 | # pnoise = pnoise #/ spc_norm_max #commented by D. ScipiΓ³n 19.03.2021 | |
| 429 | ############################################# |
|
472 | ############################################# | |
| 430 |
|
473 | |||
| 431 | # print ('stop 2.1') |
|
474 | # print ('stop 2.1') | |
| 432 | fatspectra=1.0 |
|
475 | fatspectra=1.0 | |
| 433 | # noise per channel.... we might want to use the noise at each range |
|
476 | # noise per channel.... we might want to use the noise at each range | |
| 434 | # wnoise = noise_ #/ spc_norm_max #commented by D. ScipiΓ³n 19.03.2021 |
|
477 | # wnoise = noise_ #/ spc_norm_max #commented by D. ScipiΓ³n 19.03.2021 | |
| 435 | #wnoise,stdv,i_max,index =enoise(spc,num_intg) #noise estimate using Hildebrand Sekhon, only wnoise is used |
|
478 | #wnoise,stdv,i_max,index =enoise(spc,num_intg) #noise estimate using Hildebrand Sekhon, only wnoise is used | |
| 436 | #if wnoise>1.1*pnoise: # to be tested later |
|
479 | #if wnoise>1.1*pnoise: # to be tested later | |
| 437 | # wnoise=pnoise |
|
480 | # wnoise=pnoise | |
| 438 | # noisebl = wnoise*0.9 |
|
481 | # noisebl = wnoise*0.9 | |
| 439 | # noisebh = wnoise*1.1 |
|
482 | # noisebh = wnoise*1.1 | |
| 440 | spc = spc - wnoise # signal |
|
483 | spc = spc - wnoise # signal | |
| 441 |
|
484 | |||
| 442 | # print ('stop 2.2') |
|
485 | # print ('stop 2.2') | |
| 443 | minx = numpy.argmin(spc) |
|
486 | minx = numpy.argmin(spc) | |
| 444 | #spcs=spc.copy() |
|
487 | #spcs=spc.copy() | |
| 445 | spcs = numpy.roll(spc,-minx) |
|
488 | spcs = numpy.roll(spc,-minx) | |
| 446 | cum = numpy.cumsum(spcs) |
|
489 | cum = numpy.cumsum(spcs) | |
| 447 | # tot_noise = wnoise * self.Num_Bin #64; |
|
490 | # tot_noise = wnoise * self.Num_Bin #64; | |
| 448 |
|
491 | |||
| 449 | # print ('stop 2.3') |
|
492 | # print ('stop 2.3') | |
| 450 | # snr = sum(spcs) / tot_noise |
|
493 | # snr = sum(spcs) / tot_noise | |
| 451 | # snrdB = 10.*numpy.log10(snr) |
|
494 | # snrdB = 10.*numpy.log10(snr) | |
| 452 | #print ('stop 3') |
|
495 | #print ('stop 3') | |
| 453 | # if snrdB < SNRlimit : |
|
496 | # if snrdB < SNRlimit : | |
| 454 | # snr = numpy.NaN |
|
497 | # snr = numpy.NaN | |
| 455 | # SPC_ch1[:,ht] = 0#numpy.NaN |
|
498 | # SPC_ch1[:,ht] = 0#numpy.NaN | |
| 456 | # SPC_ch1[:,ht] = 0#numpy.NaN |
|
499 | # SPC_ch1[:,ht] = 0#numpy.NaN | |
| 457 | # SPCparam = (SPC_ch1,SPC_ch2) |
|
500 | # SPCparam = (SPC_ch1,SPC_ch2) | |
| 458 | # print ('SNR less than SNRth') |
|
501 | # print ('SNR less than SNRth') | |
| 459 | # continue |
|
502 | # continue | |
| 460 |
|
503 | |||
| 461 |
|
504 | |||
| 462 | #if snrdB<-18 or numpy.isnan(snrdB) or num_intg<4: |
|
505 | #if snrdB<-18 or numpy.isnan(snrdB) or num_intg<4: | |
| 463 | # return [None,]*4,[None,]*4,None,snrdB,None,None,[None,]*5,[None,]*9,None |
|
506 | # return [None,]*4,[None,]*4,None,snrdB,None,None,[None,]*5,[None,]*9,None | |
| 464 | # print ('stop 4') |
|
507 | # print ('stop 4') | |
| 465 | cummax = max(cum) |
|
508 | cummax = max(cum) | |
| 466 | epsi = 0.08 * fatspectra # cumsum to narrow down the energy region |
|
509 | epsi = 0.08 * fatspectra # cumsum to narrow down the energy region | |
| 467 | cumlo = cummax * epsi |
|
510 | cumlo = cummax * epsi | |
| 468 | cumhi = cummax * (1-epsi) |
|
511 | cumhi = cummax * (1-epsi) | |
| 469 | powerindex = numpy.array(numpy.where(numpy.logical_and(cum>cumlo, cum<cumhi))[0]) |
|
512 | powerindex = numpy.array(numpy.where(numpy.logical_and(cum>cumlo, cum<cumhi))[0]) | |
| 470 |
|
513 | |||
| 471 | # print ('stop 5') |
|
514 | # print ('stop 5') | |
| 472 | if len(powerindex) < 1:# case for powerindex 0 |
|
515 | if len(powerindex) < 1:# case for powerindex 0 | |
| 473 | # print ('powerindex < 1') |
|
516 | # print ('powerindex < 1') | |
| 474 | continue |
|
517 | continue | |
| 475 | powerlo = powerindex[0] |
|
518 | powerlo = powerindex[0] | |
| 476 | powerhi = powerindex[-1] |
|
519 | powerhi = powerindex[-1] | |
| 477 | powerwidth = powerhi-powerlo |
|
520 | powerwidth = powerhi-powerlo | |
| 478 | if powerwidth <= 1: |
|
521 | if powerwidth <= 1: | |
| 479 | # print('powerwidth <= 1') |
|
522 | # print('powerwidth <= 1') | |
| 480 | continue |
|
523 | continue | |
| 481 |
|
524 | |||
| 482 | # print ('stop 6') |
|
525 | # print ('stop 6') | |
| 483 | firstpeak = powerlo + powerwidth/10.# first gaussian energy location |
|
526 | firstpeak = powerlo + powerwidth/10.# first gaussian energy location | |
| 484 | secondpeak = powerhi - powerwidth/10. #second gaussian energy location |
|
527 | secondpeak = powerhi - powerwidth/10. #second gaussian energy location | |
| 485 | midpeak = (firstpeak + secondpeak)/2. |
|
528 | midpeak = (firstpeak + secondpeak)/2. | |
| 486 | firstamp = spcs[int(firstpeak)] |
|
529 | firstamp = spcs[int(firstpeak)] | |
| 487 | secondamp = spcs[int(secondpeak)] |
|
530 | secondamp = spcs[int(secondpeak)] | |
| 488 | midamp = spcs[int(midpeak)] |
|
531 | midamp = spcs[int(midpeak)] | |
| 489 |
|
532 | |||
| 490 | y_data = spc + wnoise |
|
533 | y_data = spc + wnoise | |
| 491 |
|
534 | |||
| 492 | ''' single Gaussian ''' |
|
535 | ''' single Gaussian ''' | |
| 493 | shift0 = numpy.mod(midpeak+minx, self.Num_Bin ) |
|
536 | shift0 = numpy.mod(midpeak+minx, self.Num_Bin ) | |
| 494 | width0 = powerwidth/4.#Initialization entire power of spectrum divided by 4 |
|
537 | width0 = powerwidth/4.#Initialization entire power of spectrum divided by 4 | |
| 495 | power0 = 2. |
|
538 | power0 = 2. | |
| 496 | amplitude0 = midamp |
|
539 | amplitude0 = midamp | |
| 497 | state0 = [shift0,width0,amplitude0,power0,wnoise] |
|
540 | state0 = [shift0,width0,amplitude0,power0,wnoise] | |
| 498 | bnds = ((0,self.Num_Bin-1),(1,powerwidth),(0,None),(0.5,3.),(noisebl,noisebh)) |
|
541 | bnds = ((0,self.Num_Bin-1),(1,powerwidth),(0,None),(0.5,3.),(noisebl,noisebh)) | |
| 499 | lsq1 = fmin_l_bfgs_b(self.misfit1, state0, args=(y_data,x,num_intg), bounds=bnds, approx_grad=True) |
|
542 | lsq1 = fmin_l_bfgs_b(self.misfit1, state0, args=(y_data,x,num_intg), bounds=bnds, approx_grad=True) | |
| 500 | # print ('stop 7.1') |
|
543 | # print ('stop 7.1') | |
| 501 | # print (bnds) |
|
544 | # print (bnds) | |
| 502 |
|
545 | |||
| 503 | chiSq1=lsq1[1] |
|
546 | chiSq1=lsq1[1] | |
| 504 |
|
547 | |||
| 505 | # print ('stop 8') |
|
548 | # print ('stop 8') | |
| 506 | if fatspectra<1.0 and powerwidth<4: |
|
549 | if fatspectra<1.0 and powerwidth<4: | |
| 507 | choice=0 |
|
550 | choice=0 | |
| 508 | Amplitude0=lsq1[0][2] |
|
551 | Amplitude0=lsq1[0][2] | |
| 509 | shift0=lsq1[0][0] |
|
552 | shift0=lsq1[0][0] | |
| 510 | width0=lsq1[0][1] |
|
553 | width0=lsq1[0][1] | |
| 511 | p0=lsq1[0][3] |
|
554 | p0=lsq1[0][3] | |
| 512 | Amplitude1=0. |
|
555 | Amplitude1=0. | |
| 513 | shift1=0. |
|
556 | shift1=0. | |
| 514 | width1=0. |
|
557 | width1=0. | |
| 515 | p1=0. |
|
558 | p1=0. | |
| 516 | noise=lsq1[0][4] |
|
559 | noise=lsq1[0][4] | |
| 517 | #return (numpy.array([shift0,width0,Amplitude0,p0]), |
|
560 | #return (numpy.array([shift0,width0,Amplitude0,p0]), | |
| 518 | # numpy.array([shift1,width1,Amplitude1,p1]),noise,snrdB,chiSq1,6.,sigmas1,[None,]*9,choice) |
|
561 | # numpy.array([shift1,width1,Amplitude1,p1]),noise,snrdB,chiSq1,6.,sigmas1,[None,]*9,choice) | |
| 519 | # print ('stop 9') |
|
562 | # print ('stop 9') | |
| 520 | ''' two Gaussians ''' |
|
563 | ''' two Gaussians ''' | |
| 521 | #shift0=numpy.mod(firstpeak+minx,64); shift1=numpy.mod(secondpeak+minx,64) |
|
564 | #shift0=numpy.mod(firstpeak+minx,64); shift1=numpy.mod(secondpeak+minx,64) | |
| 522 | shift0 = numpy.mod(firstpeak+minx, self.Num_Bin ) |
|
565 | shift0 = numpy.mod(firstpeak+minx, self.Num_Bin ) | |
| 523 | shift1 = numpy.mod(secondpeak+minx, self.Num_Bin ) |
|
566 | shift1 = numpy.mod(secondpeak+minx, self.Num_Bin ) | |
| 524 | width0 = powerwidth/6. |
|
567 | width0 = powerwidth/6. | |
| 525 | width1 = width0 |
|
568 | width1 = width0 | |
| 526 | power0 = 2. |
|
569 | power0 = 2. | |
| 527 | power1 = power0 |
|
570 | power1 = power0 | |
| 528 | amplitude0 = firstamp |
|
571 | amplitude0 = firstamp | |
| 529 | amplitude1 = secondamp |
|
572 | amplitude1 = secondamp | |
| 530 | state0 = [shift0,width0,amplitude0,power0,shift1,width1,amplitude1,power1,wnoise] |
|
573 | state0 = [shift0,width0,amplitude0,power0,shift1,width1,amplitude1,power1,wnoise] | |
| 531 | #bnds=((0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(noisebl,noisebh)) |
|
574 | #bnds=((0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(noisebl,noisebh)) | |
| 532 | 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)) |
|
575 | 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)) | |
| 533 | #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)) |
|
576 | #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)) | |
| 534 |
|
577 | |||
| 535 | # print ('stop 10') |
|
578 | # print ('stop 10') | |
| 536 | lsq2 = fmin_l_bfgs_b( self.misfit2 , state0 , args=(y_data,x,num_intg) , bounds=bnds , approx_grad=True ) |
|
579 | lsq2 = fmin_l_bfgs_b( self.misfit2 , state0 , args=(y_data,x,num_intg) , bounds=bnds , approx_grad=True ) | |
| 537 |
|
580 | |||
| 538 | # print ('stop 11') |
|
581 | # print ('stop 11') | |
| 539 | chiSq2 = lsq2[1] |
|
582 | chiSq2 = lsq2[1] | |
| 540 |
|
583 | |||
| 541 | # print ('stop 12') |
|
584 | # print ('stop 12') | |
| 542 |
|
585 | |||
| 543 | 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) |
|
586 | 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) | |
| 544 |
|
587 | |||
| 545 | # print ('stop 13') |
|
588 | # print ('stop 13') | |
| 546 | if snrdB>-12: # when SNR is strong pick the peak with least shift (LOS velocity) error |
|
589 | if snrdB>-12: # when SNR is strong pick the peak with least shift (LOS velocity) error | |
| 547 | if oneG: |
|
590 | if oneG: | |
| 548 | choice = 0 |
|
591 | choice = 0 | |
| 549 | else: |
|
592 | else: | |
| 550 | w1 = lsq2[0][1]; w2 = lsq2[0][5] |
|
593 | w1 = lsq2[0][1]; w2 = lsq2[0][5] | |
| 551 | a1 = lsq2[0][2]; a2 = lsq2[0][6] |
|
594 | a1 = lsq2[0][2]; a2 = lsq2[0][6] | |
| 552 | p1 = lsq2[0][3]; p2 = lsq2[0][7] |
|
595 | p1 = lsq2[0][3]; p2 = lsq2[0][7] | |
| 553 | s1 = (2**(1+1./p1))*scipy.special.gamma(1./p1)/p1 |
|
596 | s1 = (2**(1+1./p1))*scipy.special.gamma(1./p1)/p1 | |
| 554 | s2 = (2**(1+1./p2))*scipy.special.gamma(1./p2)/p2 |
|
597 | s2 = (2**(1+1./p2))*scipy.special.gamma(1./p2)/p2 | |
| 555 | gp1 = a1*w1*s1; gp2 = a2*w2*s2 # power content of each ggaussian with proper p scaling |
|
598 | gp1 = a1*w1*s1; gp2 = a2*w2*s2 # power content of each ggaussian with proper p scaling | |
| 556 |
|
599 | |||
| 557 | if gp1>gp2: |
|
600 | if gp1>gp2: | |
| 558 | if a1>0.7*a2: |
|
601 | if a1>0.7*a2: | |
| 559 | choice = 1 |
|
602 | choice = 1 | |
| 560 | else: |
|
603 | else: | |
| 561 | choice = 2 |
|
604 | choice = 2 | |
| 562 | elif gp2>gp1: |
|
605 | elif gp2>gp1: | |
| 563 | if a2>0.7*a1: |
|
606 | if a2>0.7*a1: | |
| 564 | choice = 2 |
|
607 | choice = 2 | |
| 565 | else: |
|
608 | else: | |
| 566 | choice = 1 |
|
609 | choice = 1 | |
| 567 | else: |
|
610 | else: | |
| 568 | choice = numpy.argmax([a1,a2])+1 |
|
611 | choice = numpy.argmax([a1,a2])+1 | |
| 569 | #else: |
|
612 | #else: | |
| 570 | #choice=argmin([std2a,std2b])+1 |
|
613 | #choice=argmin([std2a,std2b])+1 | |
| 571 |
|
614 | |||
| 572 | else: # with low SNR go to the most energetic peak |
|
615 | else: # with low SNR go to the most energetic peak | |
| 573 | choice = numpy.argmax([lsq1[0][2]*lsq1[0][1],lsq2[0][2]*lsq2[0][1],lsq2[0][6]*lsq2[0][5]]) |
|
616 | choice = numpy.argmax([lsq1[0][2]*lsq1[0][1],lsq2[0][2]*lsq2[0][1],lsq2[0][6]*lsq2[0][5]]) | |
| 574 |
|
617 | |||
| 575 | # print ('stop 14') |
|
618 | # print ('stop 14') | |
| 576 | shift0 = lsq2[0][0] |
|
619 | shift0 = lsq2[0][0] | |
| 577 | vel0 = Vrange[0] + shift0 * deltav |
|
620 | vel0 = Vrange[0] + shift0 * deltav | |
| 578 | shift1 = lsq2[0][4] |
|
621 | shift1 = lsq2[0][4] | |
| 579 | # vel1=Vrange[0] + shift1 * deltav |
|
622 | # vel1=Vrange[0] + shift1 * deltav | |
| 580 |
|
623 | |||
| 581 | # max_vel = 1.0 |
|
624 | # max_vel = 1.0 | |
| 582 | # Va = max(Vrange) |
|
625 | # Va = max(Vrange) | |
| 583 | # deltav = Vrange[1]-Vrange[0] |
|
626 | # deltav = Vrange[1]-Vrange[0] | |
| 584 | # print ('stop 15') |
|
627 | # print ('stop 15') | |
| 585 | #first peak will be 0, second peak will be 1 |
|
628 | #first peak will be 0, second peak will be 1 | |
| 586 | # if vel0 > -1.0 and vel0 < max_vel : #first peak is in the correct range # Commented by D.ScipiΓ³n 19.03.2021 |
|
629 | # if vel0 > -1.0 and vel0 < max_vel : #first peak is in the correct range # Commented by D.ScipiΓ³n 19.03.2021 | |
| 587 | if vel0 > -Va and vel0 < Va : #first peak is in the correct range |
|
630 | if vel0 > -Va and vel0 < Va : #first peak is in the correct range | |
| 588 | shift0 = lsq2[0][0] |
|
631 | shift0 = lsq2[0][0] | |
| 589 | width0 = lsq2[0][1] |
|
632 | width0 = lsq2[0][1] | |
| 590 | Amplitude0 = lsq2[0][2] |
|
633 | Amplitude0 = lsq2[0][2] | |
| 591 | p0 = lsq2[0][3] |
|
634 | p0 = lsq2[0][3] | |
| 592 |
|
635 | |||
| 593 | shift1 = lsq2[0][4] |
|
636 | shift1 = lsq2[0][4] | |
| 594 | width1 = lsq2[0][5] |
|
637 | width1 = lsq2[0][5] | |
| 595 | Amplitude1 = lsq2[0][6] |
|
638 | Amplitude1 = lsq2[0][6] | |
| 596 | p1 = lsq2[0][7] |
|
639 | p1 = lsq2[0][7] | |
| 597 | noise = lsq2[0][8] |
|
640 | noise = lsq2[0][8] | |
| 598 | else: |
|
641 | else: | |
| 599 | shift1 = lsq2[0][0] |
|
642 | shift1 = lsq2[0][0] | |
| 600 | width1 = lsq2[0][1] |
|
643 | width1 = lsq2[0][1] | |
| 601 | Amplitude1 = lsq2[0][2] |
|
644 | Amplitude1 = lsq2[0][2] | |
| 602 | p1 = lsq2[0][3] |
|
645 | p1 = lsq2[0][3] | |
| 603 |
|
646 | |||
| 604 | shift0 = lsq2[0][4] |
|
647 | shift0 = lsq2[0][4] | |
| 605 | width0 = lsq2[0][5] |
|
648 | width0 = lsq2[0][5] | |
| 606 | Amplitude0 = lsq2[0][6] |
|
649 | Amplitude0 = lsq2[0][6] | |
| 607 | p0 = lsq2[0][7] |
|
650 | p0 = lsq2[0][7] | |
| 608 | noise = lsq2[0][8] |
|
651 | noise = lsq2[0][8] | |
| 609 |
|
652 | |||
| 610 | if Amplitude0<0.05: # in case the peak is noise |
|
653 | if Amplitude0<0.05: # in case the peak is noise | |
| 611 | shift0,width0,Amplitude0,p0 = 4*[numpy.NaN] |
|
654 | shift0,width0,Amplitude0,p0 = 4*[numpy.NaN] | |
| 612 | if Amplitude1<0.05: |
|
655 | if Amplitude1<0.05: | |
| 613 | shift1,width1,Amplitude1,p1 = 4*[numpy.NaN] |
|
656 | shift1,width1,Amplitude1,p1 = 4*[numpy.NaN] | |
| 614 |
|
657 | |||
| 615 | # print ('stop 16 ') |
|
658 | # print ('stop 16 ') | |
| 616 | # SPC_ch1[:,ht] = noise + Amplitude0*numpy.exp(-0.5*(abs(x-shift0)/width0)**p0) |
|
659 | # SPC_ch1[:,ht] = noise + Amplitude0*numpy.exp(-0.5*(abs(x-shift0)/width0)**p0) | |
| 617 | # SPC_ch2[:,ht] = noise + Amplitude1*numpy.exp(-0.5*(abs(x-shift1)/width1)**p1) |
|
660 | # SPC_ch2[:,ht] = noise + Amplitude1*numpy.exp(-0.5*(abs(x-shift1)/width1)**p1) | |
| 618 | # SPCparam = (SPC_ch1,SPC_ch2) |
|
661 | # SPCparam = (SPC_ch1,SPC_ch2) | |
| 619 |
|
662 | |||
| 620 | DGauFitParam[0,ht,0] = noise |
|
663 | DGauFitParam[0,ht,0] = noise | |
| 621 | DGauFitParam[0,ht,1] = noise |
|
664 | DGauFitParam[0,ht,1] = noise | |
| 622 | DGauFitParam[1,ht,0] = Amplitude0 |
|
665 | DGauFitParam[1,ht,0] = Amplitude0 | |
| 623 | DGauFitParam[1,ht,1] = Amplitude1 |
|
666 | DGauFitParam[1,ht,1] = Amplitude1 | |
| 624 | DGauFitParam[2,ht,0] = Vrange[0] + shift0 * deltav |
|
667 | DGauFitParam[2,ht,0] = Vrange[0] + shift0 * deltav | |
| 625 | DGauFitParam[2,ht,1] = Vrange[0] + shift1 * deltav |
|
668 | DGauFitParam[2,ht,1] = Vrange[0] + shift1 * deltav | |
| 626 | DGauFitParam[3,ht,0] = width0 * deltav |
|
669 | DGauFitParam[3,ht,0] = width0 * deltav | |
| 627 | DGauFitParam[3,ht,1] = width1 * deltav |
|
670 | DGauFitParam[3,ht,1] = width1 * deltav | |
| 628 | DGauFitParam[4,ht,0] = p0 |
|
671 | DGauFitParam[4,ht,0] = p0 | |
| 629 | DGauFitParam[4,ht,1] = p1 |
|
672 | DGauFitParam[4,ht,1] = p1 | |
| 630 |
|
673 | |||
| 631 | return DGauFitParam |
|
674 | return DGauFitParam | |
| 632 |
|
675 | |||
| 633 | def y_model1(self,x,state): |
|
676 | def y_model1(self,x,state): | |
| 634 | shift0, width0, amplitude0, power0, noise = state |
|
677 | shift0, width0, amplitude0, power0, noise = state | |
| 635 | model0 = amplitude0*numpy.exp(-0.5*abs((x - shift0)/width0)**power0) |
|
678 | model0 = amplitude0*numpy.exp(-0.5*abs((x - shift0)/width0)**power0) | |
| 636 | model0u = amplitude0*numpy.exp(-0.5*abs((x - shift0 - self.Num_Bin)/width0)**power0) |
|
679 | model0u = amplitude0*numpy.exp(-0.5*abs((x - shift0 - self.Num_Bin)/width0)**power0) | |
| 637 | model0d = amplitude0*numpy.exp(-0.5*abs((x - shift0 + self.Num_Bin)/width0)**power0) |
|
680 | model0d = amplitude0*numpy.exp(-0.5*abs((x - shift0 + self.Num_Bin)/width0)**power0) | |
| 638 | return model0 + model0u + model0d + noise |
|
681 | return model0 + model0u + model0d + noise | |
| 639 |
|
682 | |||
| 640 | def y_model2(self,x,state): #Equation for two generalized Gaussians with Nyquist |
|
683 | def y_model2(self,x,state): #Equation for two generalized Gaussians with Nyquist | |
| 641 | shift0, width0, amplitude0, power0, shift1, width1, amplitude1, power1, noise = state |
|
684 | shift0, width0, amplitude0, power0, shift1, width1, amplitude1, power1, noise = state | |
| 642 | model0 = amplitude0*numpy.exp(-0.5*abs((x-shift0)/width0)**power0) |
|
685 | model0 = amplitude0*numpy.exp(-0.5*abs((x-shift0)/width0)**power0) | |
| 643 | model0u = amplitude0*numpy.exp(-0.5*abs((x - shift0 - self.Num_Bin)/width0)**power0) |
|
686 | model0u = amplitude0*numpy.exp(-0.5*abs((x - shift0 - self.Num_Bin)/width0)**power0) | |
| 644 | model0d = amplitude0*numpy.exp(-0.5*abs((x - shift0 + self.Num_Bin)/width0)**power0) |
|
687 | model0d = amplitude0*numpy.exp(-0.5*abs((x - shift0 + self.Num_Bin)/width0)**power0) | |
| 645 |
|
688 | |||
| 646 | model1 = amplitude1*numpy.exp(-0.5*abs((x - shift1)/width1)**power1) |
|
689 | model1 = amplitude1*numpy.exp(-0.5*abs((x - shift1)/width1)**power1) | |
| 647 | model1u = amplitude1*numpy.exp(-0.5*abs((x - shift1 - self.Num_Bin)/width1)**power1) |
|
690 | model1u = amplitude1*numpy.exp(-0.5*abs((x - shift1 - self.Num_Bin)/width1)**power1) | |
| 648 | model1d = amplitude1*numpy.exp(-0.5*abs((x - shift1 + self.Num_Bin)/width1)**power1) |
|
691 | model1d = amplitude1*numpy.exp(-0.5*abs((x - shift1 + self.Num_Bin)/width1)**power1) | |
| 649 | return model0 + model0u + model0d + model1 + model1u + model1d + noise |
|
692 | return model0 + model0u + model0d + model1 + model1u + model1d + noise | |
| 650 |
|
693 | |||
| 651 | 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. |
|
694 | 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. | |
| 652 |
|
695 | |||
| 653 | return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model1(x,state)))**2)#/(64-5.) # /(64-5.) can be commented |
|
696 | return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model1(x,state)))**2)#/(64-5.) # /(64-5.) can be commented | |
| 654 |
|
697 | |||
| 655 | def misfit2(self,state,y_data,x,num_intg): |
|
698 | def misfit2(self,state,y_data,x,num_intg): | |
| 656 | return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model2(x,state)))**2)#/(64-9.) |
|
699 | return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model2(x,state)))**2)#/(64-9.) | |
| 657 |
|
700 | |||
| 658 | class Oblique_Gauss_Fit(Operation): |
|
701 | class Oblique_Gauss_Fit(Operation): | |
| 659 | ''' |
|
702 | ''' | |
| 660 | Written by R. Flores |
|
703 | Written by R. Flores | |
| 661 | ''' |
|
704 | ''' | |
| 662 | def __init__(self): |
|
705 | def __init__(self): | |
| 663 | Operation.__init__(self) |
|
706 | Operation.__init__(self) | |
| 664 |
|
707 | |||
| 665 | def Gauss_fit(self,spc,x,nGauss): |
|
708 | def Gauss_fit(self,spc,x,nGauss): | |
| 666 |
|
709 | |||
| 667 |
|
710 | |||
| 668 | def gaussian(x, a, b, c, d): |
|
711 | def gaussian(x, a, b, c, d): | |
| 669 | val = a * numpy.exp(-(x - b)**2 / (2*c**2)) + d |
|
712 | val = a * numpy.exp(-(x - b)**2 / (2*c**2)) + d | |
| 670 | return val |
|
713 | return val | |
| 671 |
|
714 | |||
| 672 | if nGauss == 'first': |
|
715 | if nGauss == 'first': | |
| 673 | spc_1_aux = numpy.copy(spc[:numpy.argmax(spc)+1]) |
|
716 | spc_1_aux = numpy.copy(spc[:numpy.argmax(spc)+1]) | |
| 674 | spc_2_aux = numpy.flip(spc_1_aux) |
|
717 | spc_2_aux = numpy.flip(spc_1_aux) | |
| 675 | spc_3_aux = numpy.concatenate((spc_1_aux,spc_2_aux[1:])) |
|
718 | spc_3_aux = numpy.concatenate((spc_1_aux,spc_2_aux[1:])) | |
| 676 |
|
719 | |||
| 677 | len_dif = len(x)-len(spc_3_aux) |
|
720 | len_dif = len(x)-len(spc_3_aux) | |
| 678 |
|
721 | |||
| 679 | spc_zeros = numpy.ones(len_dif)*spc_1_aux[0] |
|
722 | spc_zeros = numpy.ones(len_dif)*spc_1_aux[0] | |
| 680 |
|
723 | |||
| 681 | spc_new = numpy.concatenate((spc_3_aux,spc_zeros)) |
|
724 | spc_new = numpy.concatenate((spc_3_aux,spc_zeros)) | |
| 682 |
|
725 | |||
| 683 | y = spc_new |
|
726 | y = spc_new | |
| 684 |
|
727 | |||
| 685 | elif nGauss == 'second': |
|
728 | elif nGauss == 'second': | |
| 686 | y = spc |
|
729 | y = spc | |
| 687 |
|
730 | |||
| 688 |
|
731 | |||
| 689 | # estimate starting values from the data |
|
732 | # estimate starting values from the data | |
| 690 | a = y.max() |
|
733 | a = y.max() | |
| 691 | b = x[numpy.argmax(y)] |
|
734 | b = x[numpy.argmax(y)] | |
| 692 | if nGauss == 'first': |
|
735 | if nGauss == 'first': | |
| 693 | c = 1.#b#b#numpy.std(spc) |
|
736 | c = 1.#b#b#numpy.std(spc) | |
| 694 | elif nGauss == 'second': |
|
737 | elif nGauss == 'second': | |
| 695 | c = b |
|
738 | c = b | |
| 696 | else: |
|
739 | else: | |
| 697 | print("ERROR") |
|
740 | print("ERROR") | |
| 698 |
|
741 | |||
| 699 | d = numpy.mean(y[-100:]) |
|
742 | d = numpy.mean(y[-100:]) | |
| 700 |
|
743 | |||
| 701 | # define a least squares function to optimize |
|
744 | # define a least squares function to optimize | |
| 702 | def minfunc(params): |
|
745 | def minfunc(params): | |
| 703 | return sum((y-gaussian(x,params[0],params[1],params[2],params[3]))**2) |
|
746 | return sum((y-gaussian(x,params[0],params[1],params[2],params[3]))**2) | |
| 704 |
|
747 | |||
| 705 | # fit |
|
748 | # fit | |
| 706 | popt = fmin(minfunc,[a,b,c,d],disp=False) |
|
749 | popt = fmin(minfunc,[a,b,c,d],disp=False) | |
| 707 | #popt,fopt,niter,funcalls = fmin(minfunc,[a,b,c,d]) |
|
750 | #popt,fopt,niter,funcalls = fmin(minfunc,[a,b,c,d]) | |
| 708 |
|
751 | |||
| 709 |
|
752 | |||
| 710 | return gaussian(x, popt[0], popt[1], popt[2], popt[3]), popt[0], popt[1], popt[2], popt[3] |
|
753 | return gaussian(x, popt[0], popt[1], popt[2], popt[3]), popt[0], popt[1], popt[2], popt[3] | |
| 711 |
|
754 | |||
| 712 | def Gauss_fit_2(self,spc,x,nGauss): |
|
755 | def Gauss_fit_2(self,spc,x,nGauss): | |
| 713 |
|
756 | |||
| 714 |
|
757 | |||
| 715 | def gaussian(x, a, b, c, d): |
|
758 | def gaussian(x, a, b, c, d): | |
| 716 | val = a * numpy.exp(-(x - b)**2 / (2*c**2)) + d |
|
759 | val = a * numpy.exp(-(x - b)**2 / (2*c**2)) + d | |
| 717 | return val |
|
760 | return val | |
| 718 |
|
761 | |||
| 719 | if nGauss == 'first': |
|
762 | if nGauss == 'first': | |
| 720 | spc_1_aux = numpy.copy(spc[:numpy.argmax(spc)+1]) |
|
763 | spc_1_aux = numpy.copy(spc[:numpy.argmax(spc)+1]) | |
| 721 | spc_2_aux = numpy.flip(spc_1_aux) |
|
764 | spc_2_aux = numpy.flip(spc_1_aux) | |
| 722 | spc_3_aux = numpy.concatenate((spc_1_aux,spc_2_aux[1:])) |
|
765 | spc_3_aux = numpy.concatenate((spc_1_aux,spc_2_aux[1:])) | |
| 723 |
|
766 | |||
| 724 | len_dif = len(x)-len(spc_3_aux) |
|
767 | len_dif = len(x)-len(spc_3_aux) | |
| 725 |
|
768 | |||
| 726 | spc_zeros = numpy.ones(len_dif)*spc_1_aux[0] |
|
769 | spc_zeros = numpy.ones(len_dif)*spc_1_aux[0] | |
| 727 |
|
770 | |||
| 728 | spc_new = numpy.concatenate((spc_3_aux,spc_zeros)) |
|
771 | spc_new = numpy.concatenate((spc_3_aux,spc_zeros)) | |
| 729 |
|
772 | |||
| 730 | y = spc_new |
|
773 | y = spc_new | |
| 731 |
|
774 | |||
| 732 | elif nGauss == 'second': |
|
775 | elif nGauss == 'second': | |
| 733 | y = spc |
|
776 | y = spc | |
| 734 |
|
777 | |||
| 735 |
|
778 | |||
| 736 | # estimate starting values from the data |
|
779 | # estimate starting values from the data | |
| 737 | a = y.max() |
|
780 | a = y.max() | |
| 738 | b = x[numpy.argmax(y)] |
|
781 | b = x[numpy.argmax(y)] | |
| 739 | if nGauss == 'first': |
|
782 | if nGauss == 'first': | |
| 740 | c = 1.#b#b#numpy.std(spc) |
|
783 | c = 1.#b#b#numpy.std(spc) | |
| 741 | elif nGauss == 'second': |
|
784 | elif nGauss == 'second': | |
| 742 | c = b |
|
785 | c = b | |
| 743 | else: |
|
786 | else: | |
| 744 | print("ERROR") |
|
787 | print("ERROR") | |
| 745 |
|
788 | |||
| 746 | d = numpy.mean(y[-100:]) |
|
789 | d = numpy.mean(y[-100:]) | |
| 747 | popt,pcov = curve_fit(gaussian,x,y,p0=[a,b,c,d]) |
|
790 | popt,pcov = curve_fit(gaussian,x,y,p0=[a,b,c,d]) | |
| 748 | return gaussian(x, popt[0], popt[1], popt[2], popt[3]),popt[0], popt[1], popt[2], popt[3] |
|
791 | return gaussian(x, popt[0], popt[1], popt[2], popt[3]),popt[0], popt[1], popt[2], popt[3] | |
| 749 |
|
792 | |||
| 750 | def Double_Gauss_fit(self,spc,x,A1,B1,C1,A2,B2,C2,D): |
|
793 | def Double_Gauss_fit(self,spc,x,A1,B1,C1,A2,B2,C2,D): | |
| 751 |
|
794 | |||
| 752 | def double_gaussian(x, a1, b1, c1, a2, b2, c2, d): |
|
795 | def double_gaussian(x, a1, b1, c1, a2, b2, c2, d): | |
| 753 | val = a1 * numpy.exp(-(x - b1)**2 / (2*c1**2)) + a2 * numpy.exp(-(x - b2)**2 / (2*c2**2)) + d |
|
796 | val = a1 * numpy.exp(-(x - b1)**2 / (2*c1**2)) + a2 * numpy.exp(-(x - b2)**2 / (2*c2**2)) + d | |
| 754 | return val |
|
797 | return val | |
| 755 |
|
798 | |||
| 756 |
|
799 | |||
| 757 | y = spc |
|
800 | y = spc | |
| 758 |
|
801 | |||
| 759 | # estimate starting values from the data |
|
802 | # estimate starting values from the data | |
| 760 | a1 = A1 |
|
803 | a1 = A1 | |
| 761 | b1 = B1 |
|
804 | b1 = B1 | |
| 762 | c1 = C1#numpy.std(spc) |
|
805 | c1 = C1#numpy.std(spc) | |
| 763 |
|
806 | |||
| 764 | a2 = A2#y.max() |
|
807 | a2 = A2#y.max() | |
| 765 | b2 = B2#x[numpy.argmax(y)] |
|
808 | b2 = B2#x[numpy.argmax(y)] | |
| 766 | c2 = C2#numpy.std(spc) |
|
809 | c2 = C2#numpy.std(spc) | |
| 767 | d = D |
|
810 | d = D | |
| 768 |
|
811 | |||
| 769 | # define a least squares function to optimize |
|
812 | # define a least squares function to optimize | |
| 770 | def minfunc(params): |
|
813 | def minfunc(params): | |
| 771 | return sum((y-double_gaussian(x,params[0],params[1],params[2],params[3],params[4],params[5],params[6]))**2) |
|
814 | return sum((y-double_gaussian(x,params[0],params[1],params[2],params[3],params[4],params[5],params[6]))**2) | |
| 772 |
|
815 | |||
| 773 | # fit |
|
816 | # fit | |
| 774 | popt = fmin(minfunc,[a1,b1,c1,a2,b2,c2,d],disp=False) |
|
817 | popt = fmin(minfunc,[a1,b1,c1,a2,b2,c2,d],disp=False) | |
| 775 |
|
818 | |||
| 776 | return double_gaussian(x, popt[0], popt[1], popt[2], popt[3], popt[4], popt[5], popt[6]), popt[0], popt[1], popt[2], popt[3], popt[4], popt[5], popt[6] |
|
819 | return double_gaussian(x, popt[0], popt[1], popt[2], popt[3], popt[4], popt[5], popt[6]), popt[0], popt[1], popt[2], popt[3], popt[4], popt[5], popt[6] | |
| 777 |
|
820 | |||
| 778 | def Double_Gauss_fit_2(self,spc,x,A1,B1,C1,A2,B2,C2,D): |
|
821 | def Double_Gauss_fit_2(self,spc,x,A1,B1,C1,A2,B2,C2,D): | |
| 779 |
|
822 | |||
| 780 | def double_gaussian(x, a1, b1, c1, a2, b2, c2, d): |
|
823 | def double_gaussian(x, a1, b1, c1, a2, b2, c2, d): | |
| 781 | val = a1 * numpy.exp(-(x - b1)**2 / (2*c1**2)) + a2 * numpy.exp(-(x - b2)**2 / (2*c2**2)) + d |
|
824 | val = a1 * numpy.exp(-(x - b1)**2 / (2*c1**2)) + a2 * numpy.exp(-(x - b2)**2 / (2*c2**2)) + d | |
| 782 | return val |
|
825 | return val | |
| 783 |
|
826 | |||
| 784 |
|
827 | |||
| 785 | y = spc |
|
828 | y = spc | |
| 786 |
|
829 | |||
| 787 | # estimate starting values from the data |
|
830 | # estimate starting values from the data | |
| 788 | a1 = A1 |
|
831 | a1 = A1 | |
| 789 | b1 = B1 |
|
832 | b1 = B1 | |
| 790 | c1 = C1#numpy.std(spc) |
|
833 | c1 = C1#numpy.std(spc) | |
| 791 |
|
834 | |||
| 792 | a2 = A2#y.max() |
|
835 | a2 = A2#y.max() | |
| 793 | b2 = B2#x[numpy.argmax(y)] |
|
836 | b2 = B2#x[numpy.argmax(y)] | |
| 794 | c2 = C2#numpy.std(spc) |
|
837 | c2 = C2#numpy.std(spc) | |
| 795 | d = D |
|
838 | d = D | |
| 796 |
|
839 | |||
| 797 | # fit |
|
840 | # fit | |
| 798 | popt,pcov = curve_fit(double_gaussian,x,y,p0=[a1,b1,c1,a2,b2,c2,d]) |
|
841 | popt,pcov = curve_fit(double_gaussian,x,y,p0=[a1,b1,c1,a2,b2,c2,d]) | |
| 799 | error = numpy.sqrt(numpy.diag(pcov)) |
|
842 | error = numpy.sqrt(numpy.diag(pcov)) | |
| 800 |
|
843 | |||
| 801 | return popt[0], popt[1], popt[2], popt[3], popt[4], popt[5], popt[6], error[0], error[1], error[2], error[3], error[4], error[5], error[6] |
|
844 | return popt[0], popt[1], popt[2], popt[3], popt[4], popt[5], popt[6], error[0], error[1], error[2], error[3], error[4], error[5], error[6] | |
| 802 |
|
845 | |||
| 803 | def windowing_double(self,spc,x,A1,B1,C1,A2,B2,C2,D): |
|
846 | def windowing_double(self,spc,x,A1,B1,C1,A2,B2,C2,D): | |
| 804 | from scipy.optimize import curve_fit,fmin |
|
847 | from scipy.optimize import curve_fit,fmin | |
| 805 |
|
848 | |||
| 806 | def R_gaussian(x, a, b, c): |
|
849 | def R_gaussian(x, a, b, c): | |
| 807 | N = int(numpy.shape(x)[0]) |
|
850 | N = int(numpy.shape(x)[0]) | |
| 808 | val = a * numpy.exp(-((x)*c*2*2*numpy.pi)**2 / (2))* numpy.exp(1.j*b*x*4*numpy.pi) |
|
851 | val = a * numpy.exp(-((x)*c*2*2*numpy.pi)**2 / (2))* numpy.exp(1.j*b*x*4*numpy.pi) | |
| 809 | return val |
|
852 | return val | |
| 810 |
|
853 | |||
| 811 | def T(x,N): |
|
854 | def T(x,N): | |
| 812 | T = 1-abs(x)/N |
|
855 | T = 1-abs(x)/N | |
| 813 | return T |
|
856 | return T | |
| 814 |
|
857 | |||
| 815 | def R_T_spc_fun(x, a1, b1, c1, a2, b2, c2, d): |
|
858 | def R_T_spc_fun(x, a1, b1, c1, a2, b2, c2, d): | |
| 816 |
|
859 | |||
| 817 | N = int(numpy.shape(x)[0]) |
|
860 | N = int(numpy.shape(x)[0]) | |
| 818 |
|
861 | |||
| 819 | x_max = x[-1] |
|
862 | x_max = x[-1] | |
| 820 |
|
863 | |||
| 821 | x_pos = x[1600:] |
|
864 | x_pos = x[1600:] | |
| 822 | x_neg = x[:1600] |
|
865 | x_neg = x[:1600] | |
| 823 |
|
866 | |||
| 824 | R_T_neg_1 = R_gaussian(x, a1, b1, c1)[:1600]*T(x_neg,-x[0]) |
|
867 | R_T_neg_1 = R_gaussian(x, a1, b1, c1)[:1600]*T(x_neg,-x[0]) | |
| 825 | R_T_pos_1 = R_gaussian(x, a1, b1, c1)[1600:]*T(x_pos,x[-1]) |
|
868 | R_T_pos_1 = R_gaussian(x, a1, b1, c1)[1600:]*T(x_pos,x[-1]) | |
| 826 | R_T_sum_1 = R_T_pos_1 + R_T_neg_1 |
|
869 | R_T_sum_1 = R_T_pos_1 + R_T_neg_1 | |
| 827 | R_T_spc_1 = numpy.fft.fft(R_T_sum_1).real |
|
870 | R_T_spc_1 = numpy.fft.fft(R_T_sum_1).real | |
| 828 | R_T_spc_1 = numpy.fft.fftshift(R_T_spc_1) |
|
871 | R_T_spc_1 = numpy.fft.fftshift(R_T_spc_1) | |
| 829 | max_val_1 = numpy.max(R_T_spc_1) |
|
872 | max_val_1 = numpy.max(R_T_spc_1) | |
| 830 | R_T_spc_1 = R_T_spc_1*a1/max_val_1 |
|
873 | R_T_spc_1 = R_T_spc_1*a1/max_val_1 | |
| 831 |
|
874 | |||
| 832 | R_T_neg_2 = R_gaussian(x, a2, b2, c2)[:1600]*T(x_neg,-x[0]) |
|
875 | R_T_neg_2 = R_gaussian(x, a2, b2, c2)[:1600]*T(x_neg,-x[0]) | |
| 833 | R_T_pos_2 = R_gaussian(x, a2, b2, c2)[1600:]*T(x_pos,x[-1]) |
|
876 | R_T_pos_2 = R_gaussian(x, a2, b2, c2)[1600:]*T(x_pos,x[-1]) | |
| 834 | R_T_sum_2 = R_T_pos_2 + R_T_neg_2 |
|
877 | R_T_sum_2 = R_T_pos_2 + R_T_neg_2 | |
| 835 | R_T_spc_2 = numpy.fft.fft(R_T_sum_2).real |
|
878 | R_T_spc_2 = numpy.fft.fft(R_T_sum_2).real | |
| 836 | R_T_spc_2 = numpy.fft.fftshift(R_T_spc_2) |
|
879 | R_T_spc_2 = numpy.fft.fftshift(R_T_spc_2) | |
| 837 | max_val_2 = numpy.max(R_T_spc_2) |
|
880 | max_val_2 = numpy.max(R_T_spc_2) | |
| 838 | R_T_spc_2 = R_T_spc_2*a2/max_val_2 |
|
881 | R_T_spc_2 = R_T_spc_2*a2/max_val_2 | |
| 839 |
|
882 | |||
| 840 | R_T_d = d*numpy.fft.fftshift(signal.unit_impulse(N)) |
|
883 | R_T_d = d*numpy.fft.fftshift(signal.unit_impulse(N)) | |
| 841 | R_T_d_neg = R_T_d[:1600]*T(x_neg,-x[0]) |
|
884 | R_T_d_neg = R_T_d[:1600]*T(x_neg,-x[0]) | |
| 842 | R_T_d_pos = R_T_d[1600:]*T(x_pos,x[-1]) |
|
885 | R_T_d_pos = R_T_d[1600:]*T(x_pos,x[-1]) | |
| 843 | R_T_d_sum = R_T_d_pos + R_T_d_neg |
|
886 | R_T_d_sum = R_T_d_pos + R_T_d_neg | |
| 844 | R_T_spc_3 = numpy.fft.fft(R_T_d_sum).real |
|
887 | R_T_spc_3 = numpy.fft.fft(R_T_d_sum).real | |
| 845 | R_T_spc_3 = numpy.fft.fftshift(R_T_spc_3) |
|
888 | R_T_spc_3 = numpy.fft.fftshift(R_T_spc_3) | |
| 846 |
|
889 | |||
| 847 | R_T_final = R_T_spc_1 + R_T_spc_2 + R_T_spc_3 |
|
890 | R_T_final = R_T_spc_1 + R_T_spc_2 + R_T_spc_3 | |
| 848 |
|
891 | |||
| 849 | return R_T_final |
|
892 | return R_T_final | |
| 850 |
|
893 | |||
| 851 | y = spc#gaussian(x, a, meanY, sigmaY) + a*0.1*numpy.random.normal(0, 1, size=len(x)) |
|
894 | y = spc#gaussian(x, a, meanY, sigmaY) + a*0.1*numpy.random.normal(0, 1, size=len(x)) | |
| 852 |
|
895 | |||
| 853 | from scipy.stats import norm |
|
896 | from scipy.stats import norm | |
| 854 | mean,std=norm.fit(spc) |
|
897 | mean,std=norm.fit(spc) | |
| 855 |
|
898 | |||
| 856 | # estimate starting values from the data |
|
899 | # estimate starting values from the data | |
| 857 | a1 = A1 |
|
900 | a1 = A1 | |
| 858 | b1 = B1 |
|
901 | b1 = B1 | |
| 859 | c1 = C1#numpy.std(spc) |
|
902 | c1 = C1#numpy.std(spc) | |
| 860 |
|
903 | |||
| 861 | a2 = A2#y.max() |
|
904 | a2 = A2#y.max() | |
| 862 | b2 = B2#x[numpy.argmax(y)] |
|
905 | b2 = B2#x[numpy.argmax(y)] | |
| 863 | c2 = C2#numpy.std(spc) |
|
906 | c2 = C2#numpy.std(spc) | |
| 864 | d = D |
|
907 | d = D | |
| 865 |
|
908 | |||
| 866 | ippSeconds = 250*20*1.e-6/3 |
|
909 | ippSeconds = 250*20*1.e-6/3 | |
| 867 |
|
910 | |||
| 868 | x_t = ippSeconds * (numpy.arange(1600) -1600 / 2.) |
|
911 | x_t = ippSeconds * (numpy.arange(1600) -1600 / 2.) | |
| 869 |
|
912 | |||
| 870 | x_t = numpy.linspace(x_t[0],x_t[-1],3200) |
|
913 | x_t = numpy.linspace(x_t[0],x_t[-1],3200) | |
| 871 |
|
914 | |||
| 872 | x_freq = numpy.fft.fftfreq(1600,d=ippSeconds) |
|
915 | x_freq = numpy.fft.fftfreq(1600,d=ippSeconds) | |
| 873 | x_freq = numpy.fft.fftshift(x_freq) |
|
916 | x_freq = numpy.fft.fftshift(x_freq) | |
| 874 |
|
917 | |||
| 875 | # define a least squares function to optimize |
|
918 | # define a least squares function to optimize | |
| 876 | def minfunc(params): |
|
919 | def minfunc(params): | |
| 877 | return sum((y-R_T_spc_fun(x_t,params[0],params[1],params[2],params[3],params[4],params[5],params[6]))**2/1)#y**2) |
|
920 | return sum((y-R_T_spc_fun(x_t,params[0],params[1],params[2],params[3],params[4],params[5],params[6]))**2/1)#y**2) | |
| 878 |
|
921 | |||
| 879 | # fit |
|
922 | # fit | |
| 880 | popt_full = fmin(minfunc,[a1,b1,c1,a2,b2,c2,d],full_output=True) |
|
923 | popt_full = fmin(minfunc,[a1,b1,c1,a2,b2,c2,d],full_output=True) | |
| 881 | popt = popt_full[0] |
|
924 | popt = popt_full[0] | |
| 882 |
|
925 | |||
| 883 | return popt[0], popt[1], popt[2], popt[3], popt[4], popt[5], popt[6] |
|
926 | return popt[0], popt[1], popt[2], popt[3], popt[4], popt[5], popt[6] | |
| 884 |
|
927 | |||
| 885 | def Double_Gauss_fit_weight(self,spc,x,A1,B1,C1,A2,B2,C2,D): |
|
928 | def Double_Gauss_fit_weight(self,spc,x,A1,B1,C1,A2,B2,C2,D): | |
| 886 | from scipy.optimize import curve_fit,fmin |
|
929 | from scipy.optimize import curve_fit,fmin | |
| 887 |
|
930 | |||
| 888 | def double_gaussian(x, a1, b1, c1, a2, b2, c2, d): |
|
931 | def double_gaussian(x, a1, b1, c1, a2, b2, c2, d): | |
| 889 | val = a1 * numpy.exp(-(x - b1)**2 / (2*c1**2)) + a2 * numpy.exp(-(x - b2)**2 / (2*c2**2)) + d |
|
932 | val = a1 * numpy.exp(-(x - b1)**2 / (2*c1**2)) + a2 * numpy.exp(-(x - b2)**2 / (2*c2**2)) + d | |
| 890 | return val |
|
933 | return val | |
| 891 |
|
934 | |||
| 892 | y = spc |
|
935 | y = spc | |
| 893 |
|
936 | |||
| 894 | from scipy.stats import norm |
|
937 | from scipy.stats import norm | |
| 895 | mean,std=norm.fit(spc) |
|
938 | mean,std=norm.fit(spc) | |
| 896 |
|
939 | |||
| 897 | # estimate starting values from the data |
|
940 | # estimate starting values from the data | |
| 898 | a1 = A1 |
|
941 | a1 = A1 | |
| 899 | b1 = B1 |
|
942 | b1 = B1 | |
| 900 | c1 = C1#numpy.std(spc) |
|
943 | c1 = C1#numpy.std(spc) | |
| 901 |
|
944 | |||
| 902 | a2 = A2#y.max() |
|
945 | a2 = A2#y.max() | |
| 903 | b2 = B2#x[numpy.argmax(y)] |
|
946 | b2 = B2#x[numpy.argmax(y)] | |
| 904 | c2 = C2#numpy.std(spc) |
|
947 | c2 = C2#numpy.std(spc) | |
| 905 | d = D |
|
948 | d = D | |
| 906 |
|
949 | |||
| 907 | y_clean = signal.medfilt(y) |
|
950 | y_clean = signal.medfilt(y) | |
| 908 | # define a least squares function to optimize |
|
951 | # define a least squares function to optimize | |
| 909 | def minfunc(params): |
|
952 | def minfunc(params): | |
| 910 | return sum((y-double_gaussian(x,params[0],params[1],params[2],params[3],params[4],params[5],params[6]))**2/(y_clean**2/1)) |
|
953 | return sum((y-double_gaussian(x,params[0],params[1],params[2],params[3],params[4],params[5],params[6]))**2/(y_clean**2/1)) | |
| 911 |
|
954 | |||
| 912 | # fit |
|
955 | # fit | |
| 913 | popt_full = fmin(minfunc,[a1,b1,c1,a2,b2,c2,d], disp =False, full_output=True) |
|
956 | popt_full = fmin(minfunc,[a1,b1,c1,a2,b2,c2,d], disp =False, full_output=True) | |
| 914 | #print("nIter", popt_full[2]) |
|
957 | #print("nIter", popt_full[2]) | |
| 915 | popt = popt_full[0] |
|
958 | popt = popt_full[0] | |
| 916 | #popt,pcov = curve_fit(double_gaussian,x,y,p0=[a1,b1,c1,a2,b2,c2,d]) |
|
959 | #popt,pcov = curve_fit(double_gaussian,x,y,p0=[a1,b1,c1,a2,b2,c2,d]) | |
| 917 |
|
960 | |||
| 918 | #return double_gaussian(x, popt[0], popt[1], popt[2], popt[3], popt[4], popt[5], popt[6]), popt[0], popt[1], popt[2], popt[3], popt[4], popt[5], popt[6] |
|
961 | #return double_gaussian(x, popt[0], popt[1], popt[2], popt[3], popt[4], popt[5], popt[6]), popt[0], popt[1], popt[2], popt[3], popt[4], popt[5], popt[6] | |
| 919 | return popt[0], popt[1], popt[2], popt[3], popt[4], popt[5], popt[6] |
|
962 | return popt[0], popt[1], popt[2], popt[3], popt[4], popt[5], popt[6] | |
| 920 |
|
963 | |||
| 921 | def DH_mode(self,spectra,VelRange): |
|
964 | def DH_mode(self,spectra,VelRange): | |
| 922 |
|
965 | |||
| 923 | from scipy.optimize import curve_fit |
|
966 | from scipy.optimize import curve_fit | |
| 924 |
|
967 | |||
| 925 | def double_gauss(x, a1,b1,c1, a2,b2,c2, d): |
|
968 | def double_gauss(x, a1,b1,c1, a2,b2,c2, d): | |
| 926 | val = a1 * numpy.exp(-(x - b1)**2 / (2*c1**2)) + a2 * numpy.exp(-(x - b2)**2 / (2*c2**2)) + d |
|
969 | val = a1 * numpy.exp(-(x - b1)**2 / (2*c1**2)) + a2 * numpy.exp(-(x - b2)**2 / (2*c2**2)) + d | |
| 927 | return val |
|
970 | return val | |
| 928 |
|
971 | |||
| 929 | spec = (spectra.copy()).flatten() |
|
972 | spec = (spectra.copy()).flatten() | |
| 930 | amp=spec.max() |
|
973 | amp=spec.max() | |
| 931 | params=numpy.array([amp,-400,30,amp/4,-200,150,1.0e7]) |
|
974 | params=numpy.array([amp,-400,30,amp/4,-200,150,1.0e7]) | |
| 932 | #try: |
|
975 | #try: | |
| 933 | popt,pcov=curve_fit(double_gauss, VelRange, spec, p0=params,bounds=([0,-460,0,0,-400,120,0],[numpy.inf,-340,50,numpy.inf,0,250,numpy.inf])) |
|
976 | popt,pcov=curve_fit(double_gauss, VelRange, spec, p0=params,bounds=([0,-460,0,0,-400,120,0],[numpy.inf,-340,50,numpy.inf,0,250,numpy.inf])) | |
| 934 |
|
977 | |||
| 935 | error = numpy.sqrt(numpy.diag(pcov)) |
|
978 | error = numpy.sqrt(numpy.diag(pcov)) | |
| 936 | #doppler_2=popt[4] |
|
979 | #doppler_2=popt[4] | |
| 937 | #err_2 = numpy.sqrt(pcov[4][4]) |
|
980 | #err_2 = numpy.sqrt(pcov[4][4]) | |
| 938 |
|
981 | |||
| 939 | #except: |
|
982 | #except: | |
| 940 | #pass |
|
983 | #pass | |
| 941 | #doppler_2=numpy.NAN |
|
984 | #doppler_2=numpy.NAN | |
| 942 | #err_2 = numpy.NAN |
|
985 | #err_2 = numpy.NAN | |
| 943 |
|
986 | |||
| 944 | #return doppler_2, err_2 |
|
987 | #return doppler_2, err_2 | |
| 945 |
|
988 | |||
| 946 | return popt[0], popt[1], popt[2], popt[3], popt[4], popt[5], popt[6], error[0], error[1], error[2], error[3], error[4], error[5], error[6] |
|
989 | return popt[0], popt[1], popt[2], popt[3], popt[4], popt[5], popt[6], error[0], error[1], error[2], error[3], error[4], error[5], error[6] | |
| 947 |
|
990 | |||
| 948 | def Tri_Marco(self,spc,freq,a1,b1,c1,a2,b2,c2,d): |
|
991 | def Tri_Marco(self,spc,freq,a1,b1,c1,a2,b2,c2,d): | |
| 949 |
|
992 | |||
| 950 | from scipy.optimize import least_squares |
|
993 | from scipy.optimize import least_squares | |
| 951 |
|
994 | |||
| 952 | freq_max = numpy.max(numpy.abs(freq)) |
|
995 | freq_max = numpy.max(numpy.abs(freq)) | |
| 953 | spc_max = numpy.max(spc) |
|
996 | spc_max = numpy.max(spc) | |
| 954 |
|
997 | |||
| 955 | def tri_gaussian(x, a1, b1, c1, a2, b2, c2, a3, b3, c3, d): |
|
998 | def tri_gaussian(x, a1, b1, c1, a2, b2, c2, a3, b3, c3, d): | |
| 956 | z1 = (x-b1)/c1 |
|
999 | z1 = (x-b1)/c1 | |
| 957 | z2 = (x-b2)/c2 |
|
1000 | z2 = (x-b2)/c2 | |
| 958 | z3 = (x-b3)/c3 |
|
1001 | z3 = (x-b3)/c3 | |
| 959 | val = a1 * numpy.exp(-z1**2/2) + a2 * numpy.exp(-z2**2/2) + a3 * numpy.exp(-z3**2/2) + d |
|
1002 | val = a1 * numpy.exp(-z1**2/2) + a2 * numpy.exp(-z2**2/2) + a3 * numpy.exp(-z3**2/2) + d | |
| 960 | return val |
|
1003 | return val | |
| 961 |
|
1004 | |||
| 962 | from scipy.signal import medfilt |
|
1005 | from scipy.signal import medfilt | |
| 963 | Nincoh = 20 |
|
1006 | Nincoh = 20 | |
| 964 | spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) |
|
1007 | spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) | |
| 965 | c1 = abs(c1) |
|
1008 | c1 = abs(c1) | |
| 966 | c2 = abs(c2) |
|
1009 | c2 = abs(c2) | |
| 967 |
|
1010 | |||
| 968 | # define a least squares function to optimize |
|
1011 | # define a least squares function to optimize | |
| 969 | def lsq_func(params): |
|
1012 | def lsq_func(params): | |
| 970 | return (spc-tri_gaussian(freq,params[0],params[1],params[2],params[3],params[4],params[5],params[6],params[7],params[8],params[9]))/spcm |
|
1013 | return (spc-tri_gaussian(freq,params[0],params[1],params[2],params[3],params[4],params[5],params[6],params[7],params[8],params[9]))/spcm | |
| 971 |
|
1014 | |||
| 972 | # fit |
|
1015 | # fit | |
| 973 | bounds=([0,-numpy.inf,0,0,-numpy.inf,0,0,0,0,0],[numpy.inf,-100,numpy.inf,numpy.inf,0,numpy.inf,numpy.inf,600,numpy.inf,numpy.inf]) |
|
1016 | bounds=([0,-numpy.inf,0,0,-numpy.inf,0,0,0,0,0],[numpy.inf,-100,numpy.inf,numpy.inf,0,numpy.inf,numpy.inf,600,numpy.inf,numpy.inf]) | |
| 974 |
|
1017 | |||
| 975 | params_scale = [spc_max,freq_max,freq_max,spc_max,freq_max,freq_max,spc_max,freq_max,freq_max,spc_max] |
|
1018 | params_scale = [spc_max,freq_max,freq_max,spc_max,freq_max,freq_max,spc_max,freq_max,freq_max,spc_max] | |
| 976 | #print(a1,b1,c1,a2,b2,c2,d) |
|
1019 | #print(a1,b1,c1,a2,b2,c2,d) | |
| 977 | popt = least_squares(lsq_func,[a1,b1,c1,a2,b2,c2,a2/4,-b1,c1,d],x_scale=params_scale,bounds=bounds) |
|
1020 | popt = least_squares(lsq_func,[a1,b1,c1,a2,b2,c2,a2/4,-b1,c1,d],x_scale=params_scale,bounds=bounds) | |
| 978 |
|
1021 | |||
| 979 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2] |
|
1022 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2] | |
| 980 | A2f = popt.x[3]; B2f = popt.x[4]; C2f = popt.x[5] |
|
1023 | A2f = popt.x[3]; B2f = popt.x[4]; C2f = popt.x[5] | |
| 981 | A3f = popt.x[6]; B3f = popt.x[7]; C3f = popt.x[8] |
|
1024 | A3f = popt.x[6]; B3f = popt.x[7]; C3f = popt.x[8] | |
| 982 | Df = popt.x[9] |
|
1025 | Df = popt.x[9] | |
| 983 |
|
1026 | |||
| 984 | return A1f, B1f, C1f, A2f, B2f, C2f, Df |
|
1027 | return A1f, B1f, C1f, A2f, B2f, C2f, Df | |
| 985 |
|
1028 | |||
| 986 | def Tri_Marco(self,spc,freq,a1,b1,c1,a2,b2,c2,d): |
|
1029 | def Tri_Marco(self,spc,freq,a1,b1,c1,a2,b2,c2,d): | |
| 987 |
|
1030 | |||
| 988 | from scipy.optimize import least_squares |
|
1031 | from scipy.optimize import least_squares | |
| 989 |
|
1032 | |||
| 990 | freq_max = numpy.max(numpy.abs(freq)) |
|
1033 | freq_max = numpy.max(numpy.abs(freq)) | |
| 991 | spc_max = numpy.max(spc) |
|
1034 | spc_max = numpy.max(spc) | |
| 992 |
|
1035 | |||
| 993 | def duo_gaussian(x, a1, b1, c1, a2, b2, c2, d): |
|
1036 | def duo_gaussian(x, a1, b1, c1, a2, b2, c2, d): | |
| 994 | z1 = (x-b1)/c1 |
|
1037 | z1 = (x-b1)/c1 | |
| 995 | z2 = (x-b2)/c2 |
|
1038 | z2 = (x-b2)/c2 | |
| 996 | #z3 = (x-b3)/c3 |
|
1039 | #z3 = (x-b3)/c3 | |
| 997 | val = a1 * numpy.exp(-z1**2/2) + a2 * numpy.exp(-z2**2/2) + d |
|
1040 | val = a1 * numpy.exp(-z1**2/2) + a2 * numpy.exp(-z2**2/2) + d | |
| 998 | return val |
|
1041 | return val | |
| 999 |
|
1042 | |||
| 1000 | from scipy.signal import medfilt |
|
1043 | from scipy.signal import medfilt | |
| 1001 | Nincoh = 20 |
|
1044 | Nincoh = 20 | |
| 1002 | spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) |
|
1045 | spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) | |
| 1003 | c1 = abs(c1) |
|
1046 | c1 = abs(c1) | |
| 1004 | c2 = abs(c2) |
|
1047 | c2 = abs(c2) | |
| 1005 |
|
1048 | |||
| 1006 | # define a least squares function to optimize |
|
1049 | # define a least squares function to optimize | |
| 1007 | def lsq_func(params): |
|
1050 | def lsq_func(params): | |
| 1008 | return (spc-tri_gaussian(freq,params[0],params[1],params[2],params[3],params[4],params[5],params[6]))/spcm |
|
1051 | return (spc-tri_gaussian(freq,params[0],params[1],params[2],params[3],params[4],params[5],params[6]))/spcm | |
| 1009 |
|
1052 | |||
| 1010 | # fit |
|
1053 | # fit | |
| 1011 | bounds=([0,-numpy.inf,0,0,-numpy.inf,0,0],[numpy.inf,-100,numpy.inf,numpy.inf,0,numpy.inf,numpy.inf]) |
|
1054 | bounds=([0,-numpy.inf,0,0,-numpy.inf,0,0],[numpy.inf,-100,numpy.inf,numpy.inf,0,numpy.inf,numpy.inf]) | |
| 1012 |
|
1055 | |||
| 1013 | params_scale = [spc_max,freq_max,freq_max,spc_max,freq_max,freq_max,spc_max] |
|
1056 | params_scale = [spc_max,freq_max,freq_max,spc_max,freq_max,freq_max,spc_max] | |
| 1014 | popt = least_squares(lsq_func,[a1,b1,c1,a2,b2,c2,d],x_scale=params_scale,bounds=bounds) |
|
1057 | popt = least_squares(lsq_func,[a1,b1,c1,a2,b2,c2,d],x_scale=params_scale,bounds=bounds) | |
| 1015 |
|
1058 | |||
| 1016 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2] |
|
1059 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2] | |
| 1017 | A2f = popt.x[3]; B2f = popt.x[4]; C2f = popt.x[5] |
|
1060 | A2f = popt.x[3]; B2f = popt.x[4]; C2f = popt.x[5] | |
| 1018 | #A3f = popt.x[6]; B3f = popt.x[7]; C3f = popt.x[8] |
|
1061 | #A3f = popt.x[6]; B3f = popt.x[7]; C3f = popt.x[8] | |
| 1019 | Df = popt.x[9] |
|
1062 | Df = popt.x[9] | |
| 1020 |
|
1063 | |||
| 1021 | return A1f, B1f, C1f, A2f, B2f, C2f, Df |
|
1064 | return A1f, B1f, C1f, A2f, B2f, C2f, Df | |
| 1022 |
|
1065 | |||
| 1023 | def double_gaussian_skew(self,x, a1, b1, c1, a2, b2, c2, k2, d): |
|
1066 | def double_gaussian_skew(self,x, a1, b1, c1, a2, b2, c2, k2, d): | |
| 1024 | z1 = (x-b1)/c1 |
|
1067 | z1 = (x-b1)/c1 | |
| 1025 | z2 = (x-b2)/c2 |
|
1068 | z2 = (x-b2)/c2 | |
| 1026 | h2 = 1-k2*z2 |
|
1069 | h2 = 1-k2*z2 | |
| 1027 | h2[h2<0] = 0 |
|
1070 | h2[h2<0] = 0 | |
| 1028 | y2 = -1/k2*numpy.log(h2) |
|
1071 | y2 = -1/k2*numpy.log(h2) | |
| 1029 | val = a1 * numpy.exp(-z1**2/2) + a2 * numpy.exp(-y2**2/2)/(1-k2*z2) + d |
|
1072 | val = a1 * numpy.exp(-z1**2/2) + a2 * numpy.exp(-y2**2/2)/(1-k2*z2) + d | |
| 1030 | return val |
|
1073 | return val | |
| 1031 |
|
1074 | |||
| 1032 | def gaussian(self, x, a, b, c, d): |
|
1075 | def gaussian(self, x, a, b, c, d): | |
| 1033 | z = (x-b)/c |
|
1076 | z = (x-b)/c | |
| 1034 | val = a * numpy.exp(-z**2/2) + d |
|
1077 | val = a * numpy.exp(-z**2/2) + d | |
| 1035 | return val |
|
1078 | return val | |
| 1036 |
|
1079 | |||
| 1037 | def double_gaussian(self, x, a1, b1, c1, a2, b2, c2, d): |
|
1080 | def double_gaussian(self, x, a1, b1, c1, a2, b2, c2, d): | |
| 1038 | z1 = (x-b1)/c1 |
|
1081 | z1 = (x-b1)/c1 | |
| 1039 | z2 = (x-b2)/c2 |
|
1082 | z2 = (x-b2)/c2 | |
| 1040 | val = a1 * numpy.exp(-z1**2/2) + a2 * numpy.exp(-z2**2/2) + d |
|
1083 | val = a1 * numpy.exp(-z1**2/2) + a2 * numpy.exp(-z2**2/2) + d | |
| 1041 | return val |
|
1084 | return val | |
| 1042 |
|
1085 | |||
| 1043 | def double_gaussian_double_skew(self,x, a1, b1, c1, k1, a2, b2, c2, k2, d): |
|
1086 | def double_gaussian_double_skew(self,x, a1, b1, c1, k1, a2, b2, c2, k2, d): | |
| 1044 |
|
1087 | |||
| 1045 | z1 = (x-b1)/c1 |
|
1088 | z1 = (x-b1)/c1 | |
| 1046 | h1 = 1-k1*z1 |
|
1089 | h1 = 1-k1*z1 | |
| 1047 | h1[h1<0] = 0 |
|
1090 | h1[h1<0] = 0 | |
| 1048 | y1 = -1/k1*numpy.log(h1) |
|
1091 | y1 = -1/k1*numpy.log(h1) | |
| 1049 |
|
1092 | |||
| 1050 | z2 = (x-b2)/c2 |
|
1093 | z2 = (x-b2)/c2 | |
| 1051 | h2 = 1-k2*z2 |
|
1094 | h2 = 1-k2*z2 | |
| 1052 | h2[h2<0] = 0 |
|
1095 | h2[h2<0] = 0 | |
| 1053 | y2 = -1/k2*numpy.log(h2) |
|
1096 | y2 = -1/k2*numpy.log(h2) | |
| 1054 |
|
1097 | |||
| 1055 | val = a1 * numpy.exp(-y1**2/2)/(1-k1*z1) + a2 * numpy.exp(-y2**2/2)/(1-k2*z2) + d |
|
1098 | val = a1 * numpy.exp(-y1**2/2)/(1-k1*z1) + a2 * numpy.exp(-y2**2/2)/(1-k2*z2) + d | |
| 1056 | return val |
|
1099 | return val | |
| 1057 |
|
1100 | |||
| 1058 | def gaussian_skew(self,x, a2, b2, c2, k2, d): |
|
1101 | def gaussian_skew(self,x, a2, b2, c2, k2, d): | |
| 1059 | z2 = (x-b2)/c2 |
|
1102 | z2 = (x-b2)/c2 | |
| 1060 | h2 = 1-k2*z2 |
|
1103 | h2 = 1-k2*z2 | |
| 1061 | h2[h2<0] = 0 |
|
1104 | h2[h2<0] = 0 | |
| 1062 | y2 = -1/k2*numpy.log(h2) |
|
1105 | y2 = -1/k2*numpy.log(h2) | |
| 1063 | val = a2 * numpy.exp(-y2**2/2)/(1-k2*z2) + d |
|
1106 | val = a2 * numpy.exp(-y2**2/2)/(1-k2*z2) + d | |
| 1064 | return val |
|
1107 | return val | |
| 1065 |
|
1108 | |||
| 1066 | def triple_gaussian_skew(self,x, a1, b1, c1, a2, b2, c2, k2, a3, b3, c3, k3, d): |
|
1109 | def triple_gaussian_skew(self,x, a1, b1, c1, a2, b2, c2, k2, a3, b3, c3, k3, d): | |
| 1067 | z1 = (x-b1)/c1 |
|
1110 | z1 = (x-b1)/c1 | |
| 1068 | z2 = (x-b2)/c2 |
|
1111 | z2 = (x-b2)/c2 | |
| 1069 | z3 = (x-b3)/c3 |
|
1112 | z3 = (x-b3)/c3 | |
| 1070 | h2 = 1-k2*z2 |
|
1113 | h2 = 1-k2*z2 | |
| 1071 | h2[h2<0] = 0 |
|
1114 | h2[h2<0] = 0 | |
| 1072 | y2 = -1/k2*numpy.log(h2) |
|
1115 | y2 = -1/k2*numpy.log(h2) | |
| 1073 | h3 = 1-k3*z3 |
|
1116 | h3 = 1-k3*z3 | |
| 1074 | h3[h3<0] = 0 |
|
1117 | h3[h3<0] = 0 | |
| 1075 | y3 = -1/k3*numpy.log(h3) |
|
1118 | y3 = -1/k3*numpy.log(h3) | |
| 1076 | val = a1 * numpy.exp(-z1**2/2) + a2 * numpy.exp(-y2**2/2)/(1-k2*z2) + a3 * numpy.exp(-y3**2/2)/(1-k3*z3) + d |
|
1119 | val = a1 * numpy.exp(-z1**2/2) + a2 * numpy.exp(-y2**2/2)/(1-k2*z2) + a3 * numpy.exp(-y3**2/2)/(1-k3*z3) + d | |
| 1077 | return val |
|
1120 | return val | |
| 1078 |
|
1121 | |||
| 1079 | def Double_Gauss_Skew_fit_weight_bound_no_inputs(self,spc,freq): |
|
1122 | def Double_Gauss_Skew_fit_weight_bound_no_inputs(self,spc,freq): | |
| 1080 |
|
1123 | |||
| 1081 | from scipy.optimize import least_squares |
|
1124 | from scipy.optimize import least_squares | |
| 1082 |
|
1125 | |||
| 1083 | freq_max = numpy.max(numpy.abs(freq)) |
|
1126 | freq_max = numpy.max(numpy.abs(freq)) | |
| 1084 | spc_max = numpy.max(spc) |
|
1127 | spc_max = numpy.max(spc) | |
| 1085 |
|
1128 | |||
| 1086 | from scipy.signal import medfilt |
|
1129 | from scipy.signal import medfilt | |
| 1087 | Nincoh = 20 |
|
1130 | Nincoh = 20 | |
| 1088 | spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) |
|
1131 | spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) | |
| 1089 |
|
1132 | |||
| 1090 | # define a least squares function to optimize |
|
1133 | # define a least squares function to optimize | |
| 1091 | def lsq_func(params): |
|
1134 | def lsq_func(params): | |
| 1092 | return (spc-self.double_gaussian_skew(freq,params[0],params[1],params[2],params[3],params[4],params[5],params[6],params[7]))/spcm |
|
1135 | return (spc-self.double_gaussian_skew(freq,params[0],params[1],params[2],params[3],params[4],params[5],params[6],params[7]))/spcm | |
| 1093 |
|
1136 | |||
| 1094 | # fit |
|
1137 | # fit | |
| 1095 | bounds=([0,-numpy.inf,0,0,-400,0,0,0],[numpy.inf,-340,numpy.inf,numpy.inf,0,numpy.inf,numpy.inf,numpy.inf]) |
|
1138 | bounds=([0,-numpy.inf,0,0,-400,0,0,0],[numpy.inf,-340,numpy.inf,numpy.inf,0,numpy.inf,numpy.inf,numpy.inf]) | |
| 1096 | params_scale = [spc_max,freq_max,freq_max,spc_max,freq_max,freq_max,1,spc_max] |
|
1139 | params_scale = [spc_max,freq_max,freq_max,spc_max,freq_max,freq_max,1,spc_max] | |
| 1097 | x0_value = numpy.array([spc_max,-400,30,spc_max/4,-200,150,1,1.0e7]) |
|
1140 | x0_value = numpy.array([spc_max,-400,30,spc_max/4,-200,150,1,1.0e7]) | |
| 1098 | popt = least_squares(lsq_func,x0=x0_value,x_scale=params_scale,bounds=bounds,verbose=0) |
|
1141 | popt = least_squares(lsq_func,x0=x0_value,x_scale=params_scale,bounds=bounds,verbose=0) | |
| 1099 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2] |
|
1142 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2] | |
| 1100 | A2f = popt.x[3]; B2f = popt.x[4]; C2f = popt.x[5]; K2f = popt.x[6] |
|
1143 | A2f = popt.x[3]; B2f = popt.x[4]; C2f = popt.x[5]; K2f = popt.x[6] | |
| 1101 | Df = popt.x[7] |
|
1144 | Df = popt.x[7] | |
| 1102 |
|
1145 | |||
| 1103 | aux = self.gaussian_skew(freq, A2f, B2f, C2f, K2f, Df) |
|
1146 | aux = self.gaussian_skew(freq, A2f, B2f, C2f, K2f, Df) | |
| 1104 | doppler = freq[numpy.argmax(aux)] |
|
1147 | doppler = freq[numpy.argmax(aux)] | |
| 1105 |
|
1148 | |||
| 1106 | return A1f, B1f, C1f, A2f, B2f, C2f, K2f, Df, doppler |
|
1149 | return A1f, B1f, C1f, A2f, B2f, C2f, K2f, Df, doppler | |
| 1107 |
|
1150 | |||
| 1108 | def Double_Gauss_Double_Skew_fit_weight_bound_no_inputs(self,spc,freq,Nincoh,hei): |
|
1151 | def Double_Gauss_Double_Skew_fit_weight_bound_no_inputs(self,spc,freq,Nincoh,hei): | |
| 1109 |
|
1152 | |||
| 1110 | from scipy.optimize import least_squares |
|
1153 | from scipy.optimize import least_squares | |
| 1111 |
|
1154 | |||
| 1112 | freq_max = numpy.max(numpy.abs(freq)) |
|
1155 | freq_max = numpy.max(numpy.abs(freq)) | |
| 1113 | spc_max = numpy.max(spc) |
|
1156 | spc_max = numpy.max(spc) | |
| 1114 |
|
1157 | |||
| 1115 | #from scipy.signal import medfilt |
|
1158 | #from scipy.signal import medfilt | |
| 1116 | #Nincoh = 20 |
|
1159 | #Nincoh = 20 | |
| 1117 | #Nincoh = 80 |
|
1160 | #Nincoh = 80 | |
| 1118 | Nincoh = Nincoh |
|
1161 | Nincoh = Nincoh | |
| 1119 | #spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) |
|
1162 | #spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) | |
| 1120 | spcm = spc/numpy.sqrt(Nincoh) |
|
1163 | spcm = spc/numpy.sqrt(Nincoh) | |
| 1121 |
|
1164 | |||
| 1122 | # define a least squares function to optimize |
|
1165 | # define a least squares function to optimize | |
| 1123 | def lsq_func(params): |
|
1166 | def lsq_func(params): | |
| 1124 | return (spc-self.double_gaussian_double_skew(freq,params[0],params[1],params[2],params[3],params[4],params[5],params[6],params[7],params[8]))/spcm |
|
1167 | return (spc-self.double_gaussian_double_skew(freq,params[0],params[1],params[2],params[3],params[4],params[5],params[6],params[7],params[8]))/spcm | |
| 1125 |
|
1168 | |||
| 1126 | # fit |
|
1169 | # fit | |
| 1127 | bounds=([0,-numpy.inf,0,-5,0,-400,0,0,0],[numpy.inf,-200,numpy.inf,5,numpy.inf,0,numpy.inf,numpy.inf,numpy.inf]) |
|
1170 | bounds=([0,-numpy.inf,0,-5,0,-400,0,0,0],[numpy.inf,-200,numpy.inf,5,numpy.inf,0,numpy.inf,numpy.inf,numpy.inf]) | |
| 1128 |
|
1171 | |||
| 1129 | params_scale = [spc_max,freq_max,freq_max,1,spc_max,freq_max,freq_max,1,spc_max] |
|
1172 | params_scale = [spc_max,freq_max,freq_max,1,spc_max,freq_max,freq_max,1,spc_max] | |
| 1130 |
|
1173 | |||
| 1131 | dop1_x0 = freq[numpy.argmax(spc)] |
|
1174 | dop1_x0 = freq[numpy.argmax(spc)] | |
| 1132 | if dop1_x0 < 0: |
|
1175 | if dop1_x0 < 0: | |
| 1133 | dop2_x0 = dop1_x0 + 100 |
|
1176 | dop2_x0 = dop1_x0 + 100 | |
| 1134 | if dop1_x0 > 0: |
|
1177 | if dop1_x0 > 0: | |
| 1135 | dop2_x0 = dop1_x0 - 100 |
|
1178 | dop2_x0 = dop1_x0 - 100 | |
| 1136 |
|
1179 | |||
| 1137 | x0_value = numpy.array([spc_max,dop1_x0,30,-.1,spc_max/4, dop2_x0,150,1,1.0e7]) |
|
1180 | x0_value = numpy.array([spc_max,dop1_x0,30,-.1,spc_max/4, dop2_x0,150,1,1.0e7]) | |
| 1138 | popt = least_squares(lsq_func,x0=x0_value,x_scale=params_scale,bounds=bounds,verbose=0) |
|
1181 | popt = least_squares(lsq_func,x0=x0_value,x_scale=params_scale,bounds=bounds,verbose=0) | |
| 1139 | J = popt.jac |
|
1182 | J = popt.jac | |
| 1140 |
|
1183 | |||
| 1141 | try: |
|
1184 | try: | |
| 1142 | cov = numpy.linalg.inv(J.T.dot(J)) |
|
1185 | cov = numpy.linalg.inv(J.T.dot(J)) | |
| 1143 | error = numpy.sqrt(numpy.diagonal(cov)) |
|
1186 | error = numpy.sqrt(numpy.diagonal(cov)) | |
| 1144 | except: |
|
1187 | except: | |
| 1145 | error = numpy.ones((9))*numpy.NAN |
|
1188 | error = numpy.ones((9))*numpy.NAN | |
| 1146 |
|
1189 | |||
| 1147 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2]; K1f = popt.x[3] |
|
1190 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2]; K1f = popt.x[3] | |
| 1148 | A2f = popt.x[4]; B2f = popt.x[5]; C2f = popt.x[6]; K2f = popt.x[7] |
|
1191 | A2f = popt.x[4]; B2f = popt.x[5]; C2f = popt.x[6]; K2f = popt.x[7] | |
| 1149 | Df = popt.x[8] |
|
1192 | Df = popt.x[8] | |
| 1150 | aux1 = self.gaussian_skew(freq, A1f, B1f, C1f, K1f, Df) |
|
1193 | aux1 = self.gaussian_skew(freq, A1f, B1f, C1f, K1f, Df) | |
| 1151 | doppler1 = freq[numpy.argmax(aux1)] |
|
1194 | doppler1 = freq[numpy.argmax(aux1)] | |
| 1152 |
|
1195 | |||
| 1153 | aux2 = self.gaussian_skew(freq, A2f, B2f, C2f, K2f, Df) |
|
1196 | aux2 = self.gaussian_skew(freq, A2f, B2f, C2f, K2f, Df) | |
| 1154 | doppler2 = freq[numpy.argmax(aux2)] |
|
1197 | doppler2 = freq[numpy.argmax(aux2)] | |
| 1155 | #print("error",error) |
|
1198 | #print("error",error) | |
| 1156 | #exit(1) |
|
1199 | #exit(1) | |
| 1157 |
|
1200 | |||
| 1158 |
|
1201 | |||
| 1159 | return A1f, B1f, C1f, K1f, A2f, B2f, C2f, K2f, Df, doppler1, doppler2, error |
|
1202 | return A1f, B1f, C1f, K1f, A2f, B2f, C2f, K2f, Df, doppler1, doppler2, error | |
| 1160 |
|
1203 | |||
| 1161 | def Double_Gauss_fit_weight_bound_no_inputs(self,spc,freq,Nincoh): |
|
1204 | def Double_Gauss_fit_weight_bound_no_inputs(self,spc,freq,Nincoh): | |
| 1162 |
|
1205 | |||
| 1163 | from scipy.optimize import least_squares |
|
1206 | from scipy.optimize import least_squares | |
| 1164 |
|
1207 | |||
| 1165 | freq_max = numpy.max(numpy.abs(freq)) |
|
1208 | freq_max = numpy.max(numpy.abs(freq)) | |
| 1166 | spc_max = numpy.max(spc) |
|
1209 | spc_max = numpy.max(spc) | |
| 1167 |
|
1210 | |||
| 1168 | from scipy.signal import medfilt |
|
1211 | from scipy.signal import medfilt | |
| 1169 | Nincoh = 20 |
|
1212 | Nincoh = 20 | |
| 1170 | Nincoh = 80 |
|
1213 | Nincoh = 80 | |
| 1171 | Nincoh = Nincoh |
|
1214 | Nincoh = Nincoh | |
| 1172 | spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) |
|
1215 | spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) | |
| 1173 |
|
1216 | |||
| 1174 | # define a least squares function to optimize |
|
1217 | # define a least squares function to optimize | |
| 1175 | def lsq_func(params): |
|
1218 | def lsq_func(params): | |
| 1176 | return (spc-self.double_gaussian(freq,params[0],params[1],params[2],params[3],params[4],params[5],params[6]))/spcm |
|
1219 | return (spc-self.double_gaussian(freq,params[0],params[1],params[2],params[3],params[4],params[5],params[6]))/spcm | |
| 1177 |
|
1220 | |||
| 1178 | # fit |
|
1221 | # fit | |
| 1179 | # bounds=([0,-460,0,0,-400,120,0],[numpy.inf,-340,50,numpy.inf,0,250,numpy.inf]) |
|
1222 | # bounds=([0,-460,0,0,-400,120,0],[numpy.inf,-340,50,numpy.inf,0,250,numpy.inf]) | |
| 1180 | # bounds=([0,-numpy.inf,0,0,-numpy.inf,0,-numpy.inf,0],[numpy.inf,-200,numpy.inf,numpy.inf,0,numpy.inf,0,numpy.inf]) |
|
1223 | # bounds=([0,-numpy.inf,0,0,-numpy.inf,0,-numpy.inf,0],[numpy.inf,-200,numpy.inf,numpy.inf,0,numpy.inf,0,numpy.inf]) | |
| 1181 | #print(a1,b1,c1,a2,b2,c2,k2,d) |
|
1224 | #print(a1,b1,c1,a2,b2,c2,k2,d) | |
| 1182 |
|
1225 | |||
| 1183 | dop1_x0 = freq[numpy.argmax(spcm)] |
|
1226 | dop1_x0 = freq[numpy.argmax(spcm)] | |
| 1184 |
|
1227 | |||
| 1185 | bounds=([0,-numpy.inf,0,0,dop1_x0-50,0,0],[numpy.inf,-300,numpy.inf,numpy.inf,0,numpy.inf,numpy.inf]) |
|
1228 | bounds=([0,-numpy.inf,0,0,dop1_x0-50,0,0],[numpy.inf,-300,numpy.inf,numpy.inf,0,numpy.inf,numpy.inf]) | |
| 1186 | params_scale = [spc_max,freq_max,freq_max,spc_max,freq_max,freq_max,spc_max] |
|
1229 | params_scale = [spc_max,freq_max,freq_max,spc_max,freq_max,freq_max,spc_max] | |
| 1187 | x0_value = numpy.array([spc_max,-400.5,30,spc_max/4,dop1_x0,150,1.0e7]) |
|
1230 | x0_value = numpy.array([spc_max,-400.5,30,spc_max/4,dop1_x0,150,1.0e7]) | |
| 1188 | popt = least_squares(lsq_func,x0=x0_value,x_scale=params_scale,bounds=bounds,verbose=0) |
|
1231 | popt = least_squares(lsq_func,x0=x0_value,x_scale=params_scale,bounds=bounds,verbose=0) | |
| 1189 | J = popt.jac |
|
1232 | J = popt.jac | |
| 1190 |
|
1233 | |||
| 1191 | try: |
|
1234 | try: | |
| 1192 | cov = numpy.linalg.inv(J.T.dot(J)) |
|
1235 | cov = numpy.linalg.inv(J.T.dot(J)) | |
| 1193 | error = numpy.sqrt(numpy.diagonal(cov)) |
|
1236 | error = numpy.sqrt(numpy.diagonal(cov)) | |
| 1194 | except: |
|
1237 | except: | |
| 1195 | error = numpy.ones((7))*numpy.NAN |
|
1238 | error = numpy.ones((7))*numpy.NAN | |
| 1196 |
|
1239 | |||
| 1197 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2] |
|
1240 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2] | |
| 1198 | A2f = popt.x[3]; B2f = popt.x[4]; C2f = popt.x[5] |
|
1241 | A2f = popt.x[3]; B2f = popt.x[4]; C2f = popt.x[5] | |
| 1199 | Df = popt.x[6] |
|
1242 | Df = popt.x[6] | |
| 1200 | return A1f, B1f, C1f, A2f, B2f, C2f, Df, error |
|
1243 | return A1f, B1f, C1f, A2f, B2f, C2f, Df, error | |
| 1201 |
|
1244 | |||
| 1202 | def Double_Gauss_Double_Skew_fit_weight_bound_with_inputs(self, spc, freq, a1, b1, c1, a2, b2, c2, k2, d): |
|
1245 | def Double_Gauss_Double_Skew_fit_weight_bound_with_inputs(self, spc, freq, a1, b1, c1, a2, b2, c2, k2, d): | |
| 1203 |
|
1246 | |||
| 1204 | from scipy.optimize import least_squares |
|
1247 | from scipy.optimize import least_squares | |
| 1205 |
|
1248 | |||
| 1206 | freq_max = numpy.max(numpy.abs(freq)) |
|
1249 | freq_max = numpy.max(numpy.abs(freq)) | |
| 1207 | spc_max = numpy.max(spc) |
|
1250 | spc_max = numpy.max(spc) | |
| 1208 |
|
1251 | |||
| 1209 | from scipy.signal import medfilt |
|
1252 | from scipy.signal import medfilt | |
| 1210 | Nincoh = dataOut.nIncohInt |
|
1253 | Nincoh = dataOut.nIncohInt | |
| 1211 | spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) |
|
1254 | spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) | |
| 1212 |
|
1255 | |||
| 1213 | # define a least squares function to optimize |
|
1256 | # define a least squares function to optimize | |
| 1214 | def lsq_func(params): |
|
1257 | def lsq_func(params): | |
| 1215 | return (spc-self.double_gaussian_double_skew(freq,params[0],params[1],params[2],params[3],params[4],params[5],params[6],params[7],params[8]))/spcm |
|
1258 | return (spc-self.double_gaussian_double_skew(freq,params[0],params[1],params[2],params[3],params[4],params[5],params[6],params[7],params[8]))/spcm | |
| 1216 |
|
1259 | |||
| 1217 |
|
1260 | |||
| 1218 | bounds=([0,-numpy.inf,0,-numpy.inf,0,-400,0,0,0],[numpy.inf,-340,numpy.inf,0,numpy.inf,0,numpy.inf,numpy.inf,numpy.inf]) |
|
1261 | bounds=([0,-numpy.inf,0,-numpy.inf,0,-400,0,0,0],[numpy.inf,-340,numpy.inf,0,numpy.inf,0,numpy.inf,numpy.inf,numpy.inf]) | |
| 1219 |
|
1262 | |||
| 1220 | params_scale = [spc_max,freq_max,freq_max,1,spc_max,freq_max,freq_max,1,spc_max] |
|
1263 | params_scale = [spc_max,freq_max,freq_max,1,spc_max,freq_max,freq_max,1,spc_max] | |
| 1221 |
|
1264 | |||
| 1222 | x0_value = numpy.array([a1,b1,c1,-.1,a2,b2,c2,k2,d]) |
|
1265 | x0_value = numpy.array([a1,b1,c1,-.1,a2,b2,c2,k2,d]) | |
| 1223 |
|
1266 | |||
| 1224 | popt = least_squares(lsq_func,x0=x0_value,x_scale=params_scale,bounds=bounds,verbose=0) |
|
1267 | popt = least_squares(lsq_func,x0=x0_value,x_scale=params_scale,bounds=bounds,verbose=0) | |
| 1225 |
|
1268 | |||
| 1226 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2]; K1f = popt.x[3] |
|
1269 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2]; K1f = popt.x[3] | |
| 1227 | A2f = popt.x[4]; B2f = popt.x[5]; C2f = popt.x[6]; K2f = popt.x[7] |
|
1270 | A2f = popt.x[4]; B2f = popt.x[5]; C2f = popt.x[6]; K2f = popt.x[7] | |
| 1228 | Df = popt.x[8] |
|
1271 | Df = popt.x[8] | |
| 1229 |
|
1272 | |||
| 1230 | aux = self.gaussian_skew(freq, A2f, B2f, C2f, K2f, Df) |
|
1273 | aux = self.gaussian_skew(freq, A2f, B2f, C2f, K2f, Df) | |
| 1231 | doppler = x[numpy.argmax(aux)] |
|
1274 | doppler = x[numpy.argmax(aux)] | |
| 1232 |
|
1275 | |||
| 1233 | return A1f, B1f, C1f, K1f, A2f, B2f, C2f, K2f, Df, doppler |
|
1276 | return A1f, B1f, C1f, K1f, A2f, B2f, C2f, K2f, Df, doppler | |
| 1234 |
|
1277 | |||
| 1235 | def Triple_Gauss_Skew_fit_weight_bound_no_inputs(self,spc,freq): |
|
1278 | def Triple_Gauss_Skew_fit_weight_bound_no_inputs(self,spc,freq): | |
| 1236 |
|
1279 | |||
| 1237 | from scipy.optimize import least_squares |
|
1280 | from scipy.optimize import least_squares | |
| 1238 |
|
1281 | |||
| 1239 | freq_max = numpy.max(numpy.abs(freq)) |
|
1282 | freq_max = numpy.max(numpy.abs(freq)) | |
| 1240 | spc_max = numpy.max(spc) |
|
1283 | spc_max = numpy.max(spc) | |
| 1241 |
|
1284 | |||
| 1242 | from scipy.signal import medfilt |
|
1285 | from scipy.signal import medfilt | |
| 1243 | Nincoh = 20 |
|
1286 | Nincoh = 20 | |
| 1244 | spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) |
|
1287 | spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) | |
| 1245 |
|
1288 | |||
| 1246 | # define a least squares function to optimize |
|
1289 | # define a least squares function to optimize | |
| 1247 | def lsq_func(params): |
|
1290 | def lsq_func(params): | |
| 1248 | return (spc-self.triple_gaussian_skew(freq,params[0],params[1],params[2],params[3],params[4],params[5],params[6],params[7],params[8],params[9],params[10],params[11]))/spcm |
|
1291 | return (spc-self.triple_gaussian_skew(freq,params[0],params[1],params[2],params[3],params[4],params[5],params[6],params[7],params[8],params[9],params[10],params[11]))/spcm | |
| 1249 |
|
1292 | |||
| 1250 | # fit |
|
1293 | # fit | |
| 1251 | bounds=([0,-numpy.inf,0,0,-400,0,0,0,0,0,0,0],[numpy.inf,-340,numpy.inf,numpy.inf,0,numpy.inf,numpy.inf,numpy.inf,numpy.inf,numpy.inf,numpy.inf,numpy.inf]) |
|
1294 | bounds=([0,-numpy.inf,0,0,-400,0,0,0,0,0,0,0],[numpy.inf,-340,numpy.inf,numpy.inf,0,numpy.inf,numpy.inf,numpy.inf,numpy.inf,numpy.inf,numpy.inf,numpy.inf]) | |
| 1252 |
|
1295 | |||
| 1253 | params_scale = [spc_max,freq_max,freq_max,spc_max,freq_max,freq_max,1,spc_max,freq_max,freq_max,1,spc_max] |
|
1296 | params_scale = [spc_max,freq_max,freq_max,spc_max,freq_max,freq_max,1,spc_max,freq_max,freq_max,1,spc_max] | |
| 1254 | x0_value = numpy.array([spc_max,-400,30,spc_max/4,-200,150,1,spc_max/4,400,150,1,1.0e7]) |
|
1297 | x0_value = numpy.array([spc_max,-400,30,spc_max/4,-200,150,1,spc_max/4,400,150,1,1.0e7]) | |
| 1255 | popt = least_squares(lsq_func,x0=x0_value,x_scale=params_scale,bounds=bounds,verbose=0) |
|
1298 | popt = least_squares(lsq_func,x0=x0_value,x_scale=params_scale,bounds=bounds,verbose=0) | |
| 1256 |
|
1299 | |||
| 1257 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2] |
|
1300 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2] | |
| 1258 | A2f = popt.x[3]; B2f = popt.x[4]; C2f = popt.x[5]; K2f = popt.x[6] |
|
1301 | A2f = popt.x[3]; B2f = popt.x[4]; C2f = popt.x[5]; K2f = popt.x[6] | |
| 1259 | A3f = popt.x[7]; B3f = popt.x[8]; C3f = popt.x[9]; K3f = popt.x[10] |
|
1302 | A3f = popt.x[7]; B3f = popt.x[8]; C3f = popt.x[9]; K3f = popt.x[10] | |
| 1260 | Df = popt.x[11] |
|
1303 | Df = popt.x[11] | |
| 1261 |
|
1304 | |||
| 1262 | aux = self.gaussian_skew(freq, A2f, B2f, C2f, K2f, Df) |
|
1305 | aux = self.gaussian_skew(freq, A2f, B2f, C2f, K2f, Df) | |
| 1263 | doppler = freq[numpy.argmax(aux)] |
|
1306 | doppler = freq[numpy.argmax(aux)] | |
| 1264 |
|
1307 | |||
| 1265 | return A1f, B1f, C1f, A2f, B2f, C2f, K2f, A3f, B3f, C3f, K3f, Df, doppler |
|
1308 | return A1f, B1f, C1f, A2f, B2f, C2f, K2f, A3f, B3f, C3f, K3f, Df, doppler | |
| 1266 |
|
1309 | |||
| 1267 | def CEEJ_Skew_fit_weight_bound_no_inputs(self,spc,freq,Nincoh): |
|
1310 | def CEEJ_Skew_fit_weight_bound_no_inputs(self,spc,freq,Nincoh): | |
| 1268 |
|
1311 | |||
| 1269 | from scipy.optimize import least_squares |
|
1312 | from scipy.optimize import least_squares | |
| 1270 |
|
1313 | |||
| 1271 | freq_max = numpy.max(numpy.abs(freq)) |
|
1314 | freq_max = numpy.max(numpy.abs(freq)) | |
| 1272 | spc_max = numpy.max(spc) |
|
1315 | spc_max = numpy.max(spc) | |
| 1273 |
|
1316 | |||
| 1274 | from scipy.signal import medfilt |
|
1317 | from scipy.signal import medfilt | |
| 1275 | Nincoh = 20 |
|
1318 | Nincoh = 20 | |
| 1276 | Nincoh = 80 |
|
1319 | Nincoh = 80 | |
| 1277 | Nincoh = Nincoh |
|
1320 | Nincoh = Nincoh | |
| 1278 | spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) |
|
1321 | spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) | |
| 1279 |
|
1322 | |||
| 1280 | # define a least squares function to optimize |
|
1323 | # define a least squares function to optimize | |
| 1281 | def lsq_func(params): |
|
1324 | def lsq_func(params): | |
| 1282 | return (spc-self.gaussian_skew(freq,params[0],params[1],params[2],params[3],params[4]))#/spcm |
|
1325 | return (spc-self.gaussian_skew(freq,params[0],params[1],params[2],params[3],params[4]))#/spcm | |
| 1283 |
|
1326 | |||
| 1284 |
|
1327 | |||
| 1285 | bounds=([0,0,0,-numpy.inf,0],[numpy.inf,numpy.inf,numpy.inf,0,numpy.inf]) |
|
1328 | bounds=([0,0,0,-numpy.inf,0],[numpy.inf,numpy.inf,numpy.inf,0,numpy.inf]) | |
| 1286 |
|
1329 | |||
| 1287 | params_scale = [spc_max,freq_max,freq_max,1,spc_max] |
|
1330 | params_scale = [spc_max,freq_max,freq_max,1,spc_max] | |
| 1288 |
|
1331 | |||
| 1289 | x0_value = numpy.array([spc_max,freq[numpy.argmax(spc)],30,-.1,numpy.mean(spc[:50])]) |
|
1332 | x0_value = numpy.array([spc_max,freq[numpy.argmax(spc)],30,-.1,numpy.mean(spc[:50])]) | |
| 1290 |
|
1333 | |||
| 1291 | popt = least_squares(lsq_func,x0=x0_value,x_scale=params_scale,bounds=bounds,verbose=0) |
|
1334 | popt = least_squares(lsq_func,x0=x0_value,x_scale=params_scale,bounds=bounds,verbose=0) | |
| 1292 |
|
1335 | |||
| 1293 | J = popt.jac |
|
1336 | J = popt.jac | |
| 1294 |
|
1337 | |||
| 1295 | try: |
|
1338 | try: | |
| 1296 | error = numpy.ones((9))*numpy.NAN |
|
1339 | error = numpy.ones((9))*numpy.NAN | |
| 1297 | cov = numpy.linalg.inv(J.T.dot(J)) |
|
1340 | cov = numpy.linalg.inv(J.T.dot(J)) | |
| 1298 | error[:4] = numpy.sqrt(numpy.diagonal(cov))[:4] |
|
1341 | error[:4] = numpy.sqrt(numpy.diagonal(cov))[:4] | |
| 1299 | error[-1] = numpy.sqrt(numpy.diagonal(cov))[-1] |
|
1342 | error[-1] = numpy.sqrt(numpy.diagonal(cov))[-1] | |
| 1300 | except: |
|
1343 | except: | |
| 1301 | error = numpy.ones((9))*numpy.NAN |
|
1344 | error = numpy.ones((9))*numpy.NAN | |
| 1302 |
|
1345 | |||
| 1303 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2]; K1f = popt.x[3] |
|
1346 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2]; K1f = popt.x[3] | |
| 1304 | Df = popt.x[4] |
|
1347 | Df = popt.x[4] | |
| 1305 |
|
1348 | |||
| 1306 | aux1 = self.gaussian_skew(freq, A1f, B1f, C1f, K1f, Df) |
|
1349 | aux1 = self.gaussian_skew(freq, A1f, B1f, C1f, K1f, Df) | |
| 1307 | doppler1 = freq[numpy.argmax(aux1)] |
|
1350 | doppler1 = freq[numpy.argmax(aux1)] | |
| 1308 | #print("CEEJ ERROR:",error) |
|
1351 | #print("CEEJ ERROR:",error) | |
| 1309 |
|
1352 | |||
| 1310 | return A1f, B1f, C1f, K1f, numpy.NAN, numpy.NAN, numpy.NAN, numpy.NAN, Df, doppler1, numpy.NAN, error |
|
1353 | return A1f, B1f, C1f, K1f, numpy.NAN, numpy.NAN, numpy.NAN, numpy.NAN, Df, doppler1, numpy.NAN, error | |
| 1311 |
|
1354 | |||
| 1312 | def CEEJ_fit_weight_bound_no_inputs(self,spc,freq,Nincoh): |
|
1355 | def CEEJ_fit_weight_bound_no_inputs(self,spc,freq,Nincoh): | |
| 1313 |
|
1356 | |||
| 1314 | from scipy.optimize import least_squares |
|
1357 | from scipy.optimize import least_squares | |
| 1315 |
|
1358 | |||
| 1316 | freq_max = numpy.max(numpy.abs(freq)) |
|
1359 | freq_max = numpy.max(numpy.abs(freq)) | |
| 1317 | spc_max = numpy.max(spc) |
|
1360 | spc_max = numpy.max(spc) | |
| 1318 |
|
1361 | |||
| 1319 | from scipy.signal import medfilt |
|
1362 | from scipy.signal import medfilt | |
| 1320 | Nincoh = 20 |
|
1363 | Nincoh = 20 | |
| 1321 | Nincoh = 80 |
|
1364 | Nincoh = 80 | |
| 1322 | Nincoh = Nincoh |
|
1365 | Nincoh = Nincoh | |
| 1323 | spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) |
|
1366 | spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) | |
| 1324 |
|
1367 | |||
| 1325 | # define a least squares function to optimize |
|
1368 | # define a least squares function to optimize | |
| 1326 | def lsq_func(params): |
|
1369 | def lsq_func(params): | |
| 1327 | return (spc-self.gaussian(freq,params[0],params[1],params[2],params[3]))#/spcm |
|
1370 | return (spc-self.gaussian(freq,params[0],params[1],params[2],params[3]))#/spcm | |
| 1328 |
|
1371 | |||
| 1329 |
|
1372 | |||
| 1330 | bounds=([0,0,0,0],[numpy.inf,numpy.inf,numpy.inf,numpy.inf]) |
|
1373 | bounds=([0,0,0,0],[numpy.inf,numpy.inf,numpy.inf,numpy.inf]) | |
| 1331 |
|
1374 | |||
| 1332 | params_scale = [spc_max,freq_max,freq_max,spc_max] |
|
1375 | params_scale = [spc_max,freq_max,freq_max,spc_max] | |
| 1333 |
|
1376 | |||
| 1334 | x0_value = numpy.array([spc_max,freq[numpy.argmax(spcm)],30,numpy.mean(spc[:50])]) |
|
1377 | x0_value = numpy.array([spc_max,freq[numpy.argmax(spcm)],30,numpy.mean(spc[:50])]) | |
| 1335 |
|
1378 | |||
| 1336 | popt = least_squares(lsq_func,x0=x0_value,x_scale=params_scale,bounds=bounds,verbose=0) |
|
1379 | popt = least_squares(lsq_func,x0=x0_value,x_scale=params_scale,bounds=bounds,verbose=0) | |
| 1337 |
|
1380 | |||
| 1338 | J = popt.jac |
|
1381 | J = popt.jac | |
| 1339 |
|
1382 | |||
| 1340 | try: |
|
1383 | try: | |
| 1341 | error = numpy.ones((4))*numpy.NAN |
|
1384 | error = numpy.ones((4))*numpy.NAN | |
| 1342 | cov = numpy.linalg.inv(J.T.dot(J)) |
|
1385 | cov = numpy.linalg.inv(J.T.dot(J)) | |
| 1343 | error = numpy.sqrt(numpy.diagonal(cov)) |
|
1386 | error = numpy.sqrt(numpy.diagonal(cov)) | |
| 1344 | except: |
|
1387 | except: | |
| 1345 | error = numpy.ones((4))*numpy.NAN |
|
1388 | error = numpy.ones((4))*numpy.NAN | |
| 1346 |
|
1389 | |||
| 1347 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2] |
|
1390 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2] | |
| 1348 | Df = popt.x[3] |
|
1391 | Df = popt.x[3] | |
| 1349 |
|
1392 | |||
| 1350 | return A1f, B1f, C1f, Df, error |
|
1393 | return A1f, B1f, C1f, Df, error | |
| 1351 |
|
1394 | |||
| 1352 | def Simple_fit_bound(self,spc,freq,Nincoh): |
|
1395 | def Simple_fit_bound(self,spc,freq,Nincoh): | |
| 1353 |
|
1396 | |||
| 1354 | freq_max = numpy.max(numpy.abs(freq)) |
|
1397 | freq_max = numpy.max(numpy.abs(freq)) | |
| 1355 | spc_max = numpy.max(spc) |
|
1398 | spc_max = numpy.max(spc) | |
| 1356 |
|
1399 | |||
| 1357 | Nincoh = Nincoh |
|
1400 | Nincoh = Nincoh | |
| 1358 |
|
1401 | |||
| 1359 | def lsq_func(params): |
|
1402 | def lsq_func(params): | |
| 1360 | return (spc-self.gaussian(freq,params[0],params[1],params[2],params[3])) |
|
1403 | return (spc-self.gaussian(freq,params[0],params[1],params[2],params[3])) | |
| 1361 |
|
1404 | |||
| 1362 | bounds=([0,-50,0,0],[numpy.inf,+50,numpy.inf,numpy.inf]) |
|
1405 | bounds=([0,-50,0,0],[numpy.inf,+50,numpy.inf,numpy.inf]) | |
| 1363 |
|
1406 | |||
| 1364 | params_scale = [spc_max,freq_max,freq_max,spc_max] |
|
1407 | params_scale = [spc_max,freq_max,freq_max,spc_max] | |
| 1365 |
|
1408 | |||
| 1366 | x0_value = numpy.array([spc_max,-20.5,5,1.0e7]) |
|
1409 | x0_value = numpy.array([spc_max,-20.5,5,1.0e7]) | |
| 1367 |
|
1410 | |||
| 1368 | popt = least_squares(lsq_func,x0=x0_value,x_scale=params_scale,bounds=bounds,verbose=0) |
|
1411 | popt = least_squares(lsq_func,x0=x0_value,x_scale=params_scale,bounds=bounds,verbose=0) | |
| 1369 |
|
1412 | |||
| 1370 | J = popt.jac |
|
1413 | J = popt.jac | |
| 1371 |
|
1414 | |||
| 1372 | try: |
|
1415 | try: | |
| 1373 | cov = numpy.linalg.inv(J.T.dot(J)) |
|
1416 | cov = numpy.linalg.inv(J.T.dot(J)) | |
| 1374 | error = numpy.sqrt(numpy.diagonal(cov)) |
|
1417 | error = numpy.sqrt(numpy.diagonal(cov)) | |
| 1375 | except: |
|
1418 | except: | |
| 1376 | error = numpy.ones((4))*numpy.NAN |
|
1419 | error = numpy.ones((4))*numpy.NAN | |
| 1377 |
|
1420 | |||
| 1378 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2] |
|
1421 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2] | |
| 1379 | Df = popt.x[3] |
|
1422 | Df = popt.x[3] | |
| 1380 |
|
1423 | |||
| 1381 | return A1f, B1f, C1f, Df, error |
|
1424 | return A1f, B1f, C1f, Df, error | |
| 1382 |
|
1425 | |||
| 1383 | def clean_outliers(self,param): |
|
1426 | def clean_outliers(self,param): | |
| 1384 |
|
1427 | |||
| 1385 | threshold = 700 |
|
1428 | threshold = 700 | |
| 1386 |
|
1429 | |||
| 1387 | param = numpy.where(param < -threshold, numpy.nan, param) |
|
1430 | param = numpy.where(param < -threshold, numpy.nan, param) | |
| 1388 | param = numpy.where(param > +threshold, numpy.nan, param) |
|
1431 | param = numpy.where(param > +threshold, numpy.nan, param) | |
| 1389 |
|
1432 | |||
| 1390 | return param |
|
1433 | return param | |
| 1391 |
|
1434 | |||
| 1392 | def windowing_single(self,spc,x,A,B,C,D,nFFTPoints): |
|
1435 | def windowing_single(self,spc,x,A,B,C,D,nFFTPoints): | |
| 1393 | from scipy.optimize import curve_fit,fmin |
|
1436 | from scipy.optimize import curve_fit,fmin | |
| 1394 |
|
1437 | |||
| 1395 | def R_gaussian(x, a, b, c): |
|
1438 | def R_gaussian(x, a, b, c): | |
| 1396 | N = int(numpy.shape(x)[0]) |
|
1439 | N = int(numpy.shape(x)[0]) | |
| 1397 | val = a * numpy.exp(-((x)*c*2*2*numpy.pi)**2 / (2))* numpy.exp(1.j*b*x*4*numpy.pi) |
|
1440 | val = a * numpy.exp(-((x)*c*2*2*numpy.pi)**2 / (2))* numpy.exp(1.j*b*x*4*numpy.pi) | |
| 1398 | return val |
|
1441 | return val | |
| 1399 |
|
1442 | |||
| 1400 | def T(x,N): |
|
1443 | def T(x,N): | |
| 1401 | T = 1-abs(x)/N |
|
1444 | T = 1-abs(x)/N | |
| 1402 | return T |
|
1445 | return T | |
| 1403 |
|
1446 | |||
| 1404 | def R_T_spc_fun(x, a, b, c, d, nFFTPoints): |
|
1447 | def R_T_spc_fun(x, a, b, c, d, nFFTPoints): | |
| 1405 |
|
1448 | |||
| 1406 | N = int(numpy.shape(x)[0]) |
|
1449 | N = int(numpy.shape(x)[0]) | |
| 1407 |
|
1450 | |||
| 1408 | x_max = x[-1] |
|
1451 | x_max = x[-1] | |
| 1409 |
|
1452 | |||
| 1410 | x_pos = x[int(nFFTPoints/2):] |
|
1453 | x_pos = x[int(nFFTPoints/2):] | |
| 1411 | x_neg = x[:int(nFFTPoints/2)] |
|
1454 | x_neg = x[:int(nFFTPoints/2)] | |
| 1412 |
|
1455 | |||
| 1413 | R_T_neg_1 = R_gaussian(x, a, b, c)[:int(nFFTPoints/2)]*T(x_neg,-x[0]) |
|
1456 | R_T_neg_1 = R_gaussian(x, a, b, c)[:int(nFFTPoints/2)]*T(x_neg,-x[0]) | |
| 1414 | R_T_pos_1 = R_gaussian(x, a, b, c)[int(nFFTPoints/2):]*T(x_pos,x[-1]) |
|
1457 | R_T_pos_1 = R_gaussian(x, a, b, c)[int(nFFTPoints/2):]*T(x_pos,x[-1]) | |
| 1415 | R_T_sum_1 = R_T_pos_1 + R_T_neg_1 |
|
1458 | R_T_sum_1 = R_T_pos_1 + R_T_neg_1 | |
| 1416 | R_T_spc_1 = numpy.fft.fft(R_T_sum_1).real |
|
1459 | R_T_spc_1 = numpy.fft.fft(R_T_sum_1).real | |
| 1417 | R_T_spc_1 = numpy.fft.fftshift(R_T_spc_1) |
|
1460 | R_T_spc_1 = numpy.fft.fftshift(R_T_spc_1) | |
| 1418 | max_val_1 = numpy.max(R_T_spc_1) |
|
1461 | max_val_1 = numpy.max(R_T_spc_1) | |
| 1419 | R_T_spc_1 = R_T_spc_1*a/max_val_1 |
|
1462 | R_T_spc_1 = R_T_spc_1*a/max_val_1 | |
| 1420 |
|
1463 | |||
| 1421 | R_T_d = d*numpy.fft.fftshift(signal.unit_impulse(N)) |
|
1464 | R_T_d = d*numpy.fft.fftshift(signal.unit_impulse(N)) | |
| 1422 | R_T_d_neg = R_T_d[:int(nFFTPoints/2)]*T(x_neg,-x[0]) |
|
1465 | R_T_d_neg = R_T_d[:int(nFFTPoints/2)]*T(x_neg,-x[0]) | |
| 1423 | R_T_d_pos = R_T_d[int(nFFTPoints/2):]*T(x_pos,x[-1]) |
|
1466 | R_T_d_pos = R_T_d[int(nFFTPoints/2):]*T(x_pos,x[-1]) | |
| 1424 | R_T_d_sum = R_T_d_pos + R_T_d_neg |
|
1467 | R_T_d_sum = R_T_d_pos + R_T_d_neg | |
| 1425 | R_T_spc_3 = numpy.fft.fft(R_T_d_sum).real |
|
1468 | R_T_spc_3 = numpy.fft.fft(R_T_d_sum).real | |
| 1426 | R_T_spc_3 = numpy.fft.fftshift(R_T_spc_3) |
|
1469 | R_T_spc_3 = numpy.fft.fftshift(R_T_spc_3) | |
| 1427 |
|
1470 | |||
| 1428 | R_T_final = R_T_spc_1 + R_T_spc_3 |
|
1471 | R_T_final = R_T_spc_1 + R_T_spc_3 | |
| 1429 |
|
1472 | |||
| 1430 | return R_T_final |
|
1473 | return R_T_final | |
| 1431 |
|
1474 | |||
| 1432 | y = spc#gaussian(x, a, meanY, sigmaY) + a*0.1*numpy.random.normal(0, 1, size=len(x)) |
|
1475 | y = spc#gaussian(x, a, meanY, sigmaY) + a*0.1*numpy.random.normal(0, 1, size=len(x)) | |
| 1433 |
|
1476 | |||
| 1434 | from scipy.stats import norm |
|
1477 | from scipy.stats import norm | |
| 1435 | mean,std=norm.fit(spc) |
|
1478 | mean,std=norm.fit(spc) | |
| 1436 |
|
1479 | |||
| 1437 | # estimate starting values from the data |
|
1480 | # estimate starting values from the data | |
| 1438 | a = A |
|
1481 | a = A | |
| 1439 | b = B |
|
1482 | b = B | |
| 1440 | c = C#numpy.std(spc) |
|
1483 | c = C#numpy.std(spc) | |
| 1441 | d = D |
|
1484 | d = D | |
| 1442 | ''' |
|
1485 | ''' | |
| 1443 | ippSeconds = 250*20*1.e-6/3 |
|
1486 | ippSeconds = 250*20*1.e-6/3 | |
| 1444 |
|
1487 | |||
| 1445 | x_t = ippSeconds * (numpy.arange(1600) -1600 / 2.) |
|
1488 | x_t = ippSeconds * (numpy.arange(1600) -1600 / 2.) | |
| 1446 |
|
1489 | |||
| 1447 | x_t = numpy.linspace(x_t[0],x_t[-1],3200) |
|
1490 | x_t = numpy.linspace(x_t[0],x_t[-1],3200) | |
| 1448 |
|
1491 | |||
| 1449 | x_freq = numpy.fft.fftfreq(1600,d=ippSeconds) |
|
1492 | x_freq = numpy.fft.fftfreq(1600,d=ippSeconds) | |
| 1450 | x_freq = numpy.fft.fftshift(x_freq) |
|
1493 | x_freq = numpy.fft.fftshift(x_freq) | |
| 1451 | ''' |
|
1494 | ''' | |
| 1452 | # define a least squares function to optimize |
|
1495 | # define a least squares function to optimize | |
| 1453 | def minfunc(params): |
|
1496 | def minfunc(params): | |
| 1454 | return sum((y-R_T_spc_fun(x,params[0],params[1],params[2],params[3],params[4],params[5],params[6]))**2/1)#y**2) |
|
1497 | return sum((y-R_T_spc_fun(x,params[0],params[1],params[2],params[3],params[4],params[5],params[6]))**2/1)#y**2) | |
| 1455 |
|
1498 | |||
| 1456 | # fit |
|
1499 | # fit | |
| 1457 |
|
1500 | |||
| 1458 | popt_full = fmin(minfunc,[a,b,c,d],full_output=True) |
|
1501 | popt_full = fmin(minfunc,[a,b,c,d],full_output=True) | |
| 1459 | #print("nIter", popt_full[2]) |
|
1502 | #print("nIter", popt_full[2]) | |
| 1460 | popt = popt_full[0] |
|
1503 | popt = popt_full[0] | |
| 1461 |
|
1504 | |||
| 1462 | #return R_T_spc_fun(x_t,popt[0], popt[1], popt[2], popt[3], popt[4], popt[5], popt[6]), popt[0], popt[1], popt[2], popt[3], popt[4], popt[5], popt[6] |
|
1505 | #return R_T_spc_fun(x_t,popt[0], popt[1], popt[2], popt[3], popt[4], popt[5], popt[6]), popt[0], popt[1], popt[2], popt[3], popt[4], popt[5], popt[6] | |
| 1463 | return popt[0], popt[1], popt[2], popt[3] |
|
1506 | return popt[0], popt[1], popt[2], popt[3] | |
| 1464 |
|
1507 | |||
| 1465 | def run(self, dataOut, mode = 0, Hmin1 = None, Hmax1 = None, Hmin2 = None, Hmax2 = None, Dop = 'Shift'): |
|
1508 | def run(self, dataOut, mode = 0, Hmin1 = None, Hmax1 = None, Hmin2 = None, Hmax2 = None, Dop = 'Shift'): | |
| 1466 |
|
1509 | |||
| 1467 | pwcode = 1 |
|
1510 | pwcode = 1 | |
| 1468 |
|
1511 | |||
| 1469 | if dataOut.flagDecodeData: |
|
1512 | if dataOut.flagDecodeData: | |
| 1470 | pwcode = numpy.sum(dataOut.code[0]**2) |
|
1513 | pwcode = numpy.sum(dataOut.code[0]**2) | |
| 1471 | #normFactor = min(self.nFFTPoints,self.nProfiles)*self.nIncohInt*self.nCohInt*pwcode*self.windowOfFilter |
|
1514 | #normFactor = min(self.nFFTPoints,self.nProfiles)*self.nIncohInt*self.nCohInt*pwcode*self.windowOfFilter | |
| 1472 | normFactor = dataOut.nProfiles * dataOut.nIncohInt * dataOut.nCohInt * pwcode * dataOut.windowOfFilter |
|
1515 | normFactor = dataOut.nProfiles * dataOut.nIncohInt * dataOut.nCohInt * pwcode * dataOut.windowOfFilter | |
| 1473 | factor = normFactor |
|
1516 | factor = normFactor | |
| 1474 | z = dataOut.data_spc / factor |
|
1517 | z = dataOut.data_spc / factor | |
| 1475 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
1518 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) | |
| 1476 | dataOut.power = numpy.average(z, axis=1) |
|
1519 | dataOut.power = numpy.average(z, axis=1) | |
| 1477 | dataOut.powerdB = 10 * numpy.log10(dataOut.power) |
|
1520 | dataOut.powerdB = 10 * numpy.log10(dataOut.power) | |
| 1478 |
|
1521 | |||
| 1479 | x = dataOut.getVelRange(0) |
|
1522 | x = dataOut.getVelRange(0) | |
| 1480 |
|
1523 | |||
| 1481 | dataOut.Oblique_params = numpy.ones((1,7,dataOut.nHeights))*numpy.NAN |
|
1524 | dataOut.Oblique_params = numpy.ones((1,7,dataOut.nHeights))*numpy.NAN | |
| 1482 | dataOut.Oblique_param_errors = numpy.ones((1,7,dataOut.nHeights))*numpy.NAN |
|
1525 | dataOut.Oblique_param_errors = numpy.ones((1,7,dataOut.nHeights))*numpy.NAN | |
| 1483 | dataOut.dplr_2_u = numpy.ones((1,1,dataOut.nHeights))*numpy.NAN |
|
1526 | dataOut.dplr_2_u = numpy.ones((1,1,dataOut.nHeights))*numpy.NAN | |
| 1484 |
|
1527 | |||
| 1485 | if mode == 6: |
|
1528 | if mode == 6: | |
| 1486 | dataOut.Oblique_params = numpy.ones((1,9,dataOut.nHeights))*numpy.NAN |
|
1529 | dataOut.Oblique_params = numpy.ones((1,9,dataOut.nHeights))*numpy.NAN | |
| 1487 | elif mode == 7: |
|
1530 | elif mode == 7: | |
| 1488 | dataOut.Oblique_params = numpy.ones((1,13,dataOut.nHeights))*numpy.NAN |
|
1531 | dataOut.Oblique_params = numpy.ones((1,13,dataOut.nHeights))*numpy.NAN | |
| 1489 | elif mode == 8: |
|
1532 | elif mode == 8: | |
| 1490 | dataOut.Oblique_params = numpy.ones((1,10,dataOut.nHeights))*numpy.NAN |
|
1533 | dataOut.Oblique_params = numpy.ones((1,10,dataOut.nHeights))*numpy.NAN | |
| 1491 | elif mode == 9: |
|
1534 | elif mode == 9: | |
| 1492 | dataOut.Oblique_params = numpy.ones((1,11,dataOut.nHeights))*numpy.NAN |
|
1535 | dataOut.Oblique_params = numpy.ones((1,11,dataOut.nHeights))*numpy.NAN | |
| 1493 | dataOut.Oblique_param_errors = numpy.ones((1,9,dataOut.nHeights))*numpy.NAN |
|
1536 | dataOut.Oblique_param_errors = numpy.ones((1,9,dataOut.nHeights))*numpy.NAN | |
| 1494 | elif mode == 11: |
|
1537 | elif mode == 11: | |
| 1495 | dataOut.Oblique_params = numpy.ones((1,7,dataOut.nHeights))*numpy.NAN |
|
1538 | dataOut.Oblique_params = numpy.ones((1,7,dataOut.nHeights))*numpy.NAN | |
| 1496 | dataOut.Oblique_param_errors = numpy.ones((1,7,dataOut.nHeights))*numpy.NAN |
|
1539 | dataOut.Oblique_param_errors = numpy.ones((1,7,dataOut.nHeights))*numpy.NAN | |
| 1497 | elif mode == 10: #150 km |
|
1540 | elif mode == 10: #150 km | |
| 1498 | dataOut.Oblique_params = numpy.ones((1,4,dataOut.nHeights))*numpy.NAN |
|
1541 | dataOut.Oblique_params = numpy.ones((1,4,dataOut.nHeights))*numpy.NAN | |
| 1499 | dataOut.Oblique_param_errors = numpy.ones((1,4,dataOut.nHeights))*numpy.NAN |
|
1542 | dataOut.Oblique_param_errors = numpy.ones((1,4,dataOut.nHeights))*numpy.NAN | |
| 1500 | dataOut.snr_log10 = numpy.ones((1,dataOut.nHeights))*numpy.NAN |
|
1543 | dataOut.snr_log10 = numpy.ones((1,dataOut.nHeights))*numpy.NAN | |
| 1501 |
|
1544 | |||
| 1502 | dataOut.VelRange = x |
|
1545 | dataOut.VelRange = x | |
| 1503 |
|
1546 | |||
| 1504 |
|
1547 | |||
| 1505 |
|
1548 | |||
| 1506 | #l1=range(22,36) #+62 |
|
1549 | #l1=range(22,36) #+62 | |
| 1507 | #l1=range(32,36) |
|
1550 | #l1=range(32,36) | |
| 1508 | #l2=range(58,99) #+62 |
|
1551 | #l2=range(58,99) #+62 | |
| 1509 |
|
1552 | |||
| 1510 | #if Hmin1 == None or Hmax1 == None or Hmin2 == None or Hmax2 == None: |
|
1553 | #if Hmin1 == None or Hmax1 == None or Hmin2 == None or Hmax2 == None: | |
| 1511 |
|
1554 | |||
| 1512 | minHei1 = 105. |
|
1555 | minHei1 = 105. | |
| 1513 | maxHei1 = 122.5 |
|
1556 | maxHei1 = 122.5 | |
| 1514 | maxHei1 = 130.5 |
|
1557 | maxHei1 = 130.5 | |
| 1515 |
|
1558 | |||
| 1516 | if mode == 10: #150 km |
|
1559 | if mode == 10: #150 km | |
| 1517 | minHei1 = 100 |
|
1560 | minHei1 = 100 | |
| 1518 | maxHei1 = 100 |
|
1561 | maxHei1 = 100 | |
| 1519 |
|
1562 | |||
| 1520 | inda1 = numpy.where(dataOut.heightList >= minHei1) |
|
1563 | inda1 = numpy.where(dataOut.heightList >= minHei1) | |
| 1521 | indb1 = numpy.where(dataOut.heightList <= maxHei1) |
|
1564 | indb1 = numpy.where(dataOut.heightList <= maxHei1) | |
| 1522 |
|
1565 | |||
| 1523 | minIndex1 = inda1[0][0] |
|
1566 | minIndex1 = inda1[0][0] | |
| 1524 | maxIndex1 = indb1[0][-1] |
|
1567 | maxIndex1 = indb1[0][-1] | |
| 1525 |
|
1568 | |||
| 1526 | minHei2 = 150. |
|
1569 | minHei2 = 150. | |
| 1527 | maxHei2 = 201.25 |
|
1570 | maxHei2 = 201.25 | |
| 1528 | maxHei2 = 225.3 |
|
1571 | maxHei2 = 225.3 | |
| 1529 |
|
1572 | |||
| 1530 | if mode == 10: #150 km |
|
1573 | if mode == 10: #150 km | |
| 1531 | minHei2 = 110 |
|
1574 | minHei2 = 110 | |
| 1532 | maxHei2 = 165 |
|
1575 | maxHei2 = 165 | |
| 1533 |
|
1576 | |||
| 1534 | inda2 = numpy.where(dataOut.heightList >= minHei2) |
|
1577 | inda2 = numpy.where(dataOut.heightList >= minHei2) | |
| 1535 | indb2 = numpy.where(dataOut.heightList <= maxHei2) |
|
1578 | indb2 = numpy.where(dataOut.heightList <= maxHei2) | |
| 1536 |
|
1579 | |||
| 1537 | minIndex2 = inda2[0][0] |
|
1580 | minIndex2 = inda2[0][0] | |
| 1538 | maxIndex2 = indb2[0][-1] |
|
1581 | maxIndex2 = indb2[0][-1] | |
| 1539 |
|
1582 | |||
| 1540 | l1=range(minIndex1,maxIndex1) |
|
1583 | l1=range(minIndex1,maxIndex1) | |
| 1541 | l2=range(minIndex2,maxIndex2) |
|
1584 | l2=range(minIndex2,maxIndex2) | |
| 1542 |
|
1585 | |||
| 1543 | if mode == 4: |
|
1586 | if mode == 4: | |
| 1544 | ''' |
|
1587 | ''' | |
| 1545 | for ind in range(dataOut.nHeights): |
|
1588 | for ind in range(dataOut.nHeights): | |
| 1546 | if(dataOut.heightList[ind]>=168 and dataOut.heightList[ind]<188): |
|
1589 | if(dataOut.heightList[ind]>=168 and dataOut.heightList[ind]<188): | |
| 1547 | try: |
|
1590 | try: | |
| 1548 | dataOut.Oblique_params[0,0,ind],dataOut.Oblique_params[0,1,ind],dataOut.Oblique_params[0,2,ind],dataOut.Oblique_params[0,3,ind],dataOut.Oblique_params[0,4,ind],dataOut.Oblique_params[0,5,ind],dataOut.Oblique_params[0,6,ind],dataOut.Oblique_param_errors[0,0,ind],dataOut.Oblique_param_errors[0,1,ind],dataOut.Oblique_param_errors[0,2,ind],dataOut.Oblique_param_errors[0,3,ind],dataOut.Oblique_param_errors[0,4,ind],dataOut.Oblique_param_errors[0,5,ind],dataOut.Oblique_param_errors[0,6,ind] = self.DH_mode(dataOut.data_spc[0,:,ind],dataOut.VelRange) |
|
1591 | dataOut.Oblique_params[0,0,ind],dataOut.Oblique_params[0,1,ind],dataOut.Oblique_params[0,2,ind],dataOut.Oblique_params[0,3,ind],dataOut.Oblique_params[0,4,ind],dataOut.Oblique_params[0,5,ind],dataOut.Oblique_params[0,6,ind],dataOut.Oblique_param_errors[0,0,ind],dataOut.Oblique_param_errors[0,1,ind],dataOut.Oblique_param_errors[0,2,ind],dataOut.Oblique_param_errors[0,3,ind],dataOut.Oblique_param_errors[0,4,ind],dataOut.Oblique_param_errors[0,5,ind],dataOut.Oblique_param_errors[0,6,ind] = self.DH_mode(dataOut.data_spc[0,:,ind],dataOut.VelRange) | |
| 1549 | except: |
|
1592 | except: | |
| 1550 | pass |
|
1593 | pass | |
| 1551 | ''' |
|
1594 | ''' | |
| 1552 | for ind in itertools.chain(l1, l2): |
|
1595 | for ind in itertools.chain(l1, l2): | |
| 1553 |
|
1596 | |||
| 1554 | try: |
|
1597 | try: | |
| 1555 | dataOut.Oblique_params[0,0,ind],dataOut.Oblique_params[0,1,ind],dataOut.Oblique_params[0,2,ind],dataOut.Oblique_params[0,3,ind],dataOut.Oblique_params[0,4,ind],dataOut.Oblique_params[0,5,ind],dataOut.Oblique_params[0,6,ind],dataOut.Oblique_param_errors[0,0,ind],dataOut.Oblique_param_errors[0,1,ind],dataOut.Oblique_param_errors[0,2,ind],dataOut.Oblique_param_errors[0,3,ind],dataOut.Oblique_param_errors[0,4,ind],dataOut.Oblique_param_errors[0,5,ind],dataOut.Oblique_param_errors[0,6,ind] = self.DH_mode(dataOut.data_spc[0,:,ind],dataOut.VelRange) |
|
1598 | dataOut.Oblique_params[0,0,ind],dataOut.Oblique_params[0,1,ind],dataOut.Oblique_params[0,2,ind],dataOut.Oblique_params[0,3,ind],dataOut.Oblique_params[0,4,ind],dataOut.Oblique_params[0,5,ind],dataOut.Oblique_params[0,6,ind],dataOut.Oblique_param_errors[0,0,ind],dataOut.Oblique_param_errors[0,1,ind],dataOut.Oblique_param_errors[0,2,ind],dataOut.Oblique_param_errors[0,3,ind],dataOut.Oblique_param_errors[0,4,ind],dataOut.Oblique_param_errors[0,5,ind],dataOut.Oblique_param_errors[0,6,ind] = self.DH_mode(dataOut.data_spc[0,:,ind],dataOut.VelRange) | |
| 1556 | dataOut.dplr_2_u[0,0,ind] = dataOut.Oblique_params[0,4,ind]/numpy.sin(numpy.arccos(102/dataOut.heightList[ind])) |
|
1599 | dataOut.dplr_2_u[0,0,ind] = dataOut.Oblique_params[0,4,ind]/numpy.sin(numpy.arccos(102/dataOut.heightList[ind])) | |
| 1557 | except: |
|
1600 | except: | |
| 1558 | pass |
|
1601 | pass | |
| 1559 |
|
1602 | |||
| 1560 | else: |
|
1603 | else: | |
| 1561 | for hei in itertools.chain(l1, l2): |
|
1604 | for hei in itertools.chain(l1, l2): | |
| 1562 | if numpy.isnan(dataOut.snl[0,hei]) or dataOut.snl[0,hei]<.0: |
|
1605 | if numpy.isnan(dataOut.snl[0,hei]) or dataOut.snl[0,hei]<.0: | |
| 1563 |
|
1606 | |||
| 1564 | continue #Avoids the analysis when there is only noise |
|
1607 | continue #Avoids the analysis when there is only noise | |
| 1565 |
|
1608 | |||
| 1566 | try: |
|
1609 | try: | |
| 1567 | spc = dataOut.data_spc[0,:,hei] |
|
1610 | spc = dataOut.data_spc[0,:,hei] | |
| 1568 |
|
1611 | |||
| 1569 | if mode == 6: #Skew Weighted Bounded |
|
1612 | if mode == 6: #Skew Weighted Bounded | |
| 1570 | dataOut.Oblique_params[0,0,hei],dataOut.Oblique_params[0,1,hei],dataOut.Oblique_params[0,2,hei],dataOut.Oblique_params[0,3,hei],dataOut.Oblique_params[0,4,hei],dataOut.Oblique_params[0,5,hei],dataOut.Oblique_params[0,6,hei],dataOut.Oblique_params[0,7,hei],dataOut.Oblique_params[0,8,hei] = self.Double_Gauss_Skew_fit_weight_bound_no_inputs(spc,x) |
|
1613 | dataOut.Oblique_params[0,0,hei],dataOut.Oblique_params[0,1,hei],dataOut.Oblique_params[0,2,hei],dataOut.Oblique_params[0,3,hei],dataOut.Oblique_params[0,4,hei],dataOut.Oblique_params[0,5,hei],dataOut.Oblique_params[0,6,hei],dataOut.Oblique_params[0,7,hei],dataOut.Oblique_params[0,8,hei] = self.Double_Gauss_Skew_fit_weight_bound_no_inputs(spc,x) | |
| 1571 | dataOut.dplr_2_u[0,0,hei] = dataOut.Oblique_params[0,8,hei]/numpy.sin(numpy.arccos(100./dataOut.heightList[hei])) |
|
1614 | dataOut.dplr_2_u[0,0,hei] = dataOut.Oblique_params[0,8,hei]/numpy.sin(numpy.arccos(100./dataOut.heightList[hei])) | |
| 1572 |
|
1615 | |||
| 1573 | elif mode == 7: #Triple Skew Weighted Bounded |
|
1616 | elif mode == 7: #Triple Skew Weighted Bounded | |
| 1574 | dataOut.Oblique_params[0,0,hei],dataOut.Oblique_params[0,1,hei],dataOut.Oblique_params[0,2,hei],dataOut.Oblique_params[0,3,hei],dataOut.Oblique_params[0,4,hei],dataOut.Oblique_params[0,5,hei],dataOut.Oblique_params[0,6,hei],dataOut.Oblique_params[0,7,hei],dataOut.Oblique_params[0,8,hei],dataOut.Oblique_params[0,9,hei],dataOut.Oblique_params[0,10,hei],dataOut.Oblique_params[0,11,hei],dataOut.Oblique_params[0,12,hei] = self.Triple_Gauss_Skew_fit_weight_bound_no_inputs(spc,x) |
|
1617 | dataOut.Oblique_params[0,0,hei],dataOut.Oblique_params[0,1,hei],dataOut.Oblique_params[0,2,hei],dataOut.Oblique_params[0,3,hei],dataOut.Oblique_params[0,4,hei],dataOut.Oblique_params[0,5,hei],dataOut.Oblique_params[0,6,hei],dataOut.Oblique_params[0,7,hei],dataOut.Oblique_params[0,8,hei],dataOut.Oblique_params[0,9,hei],dataOut.Oblique_params[0,10,hei],dataOut.Oblique_params[0,11,hei],dataOut.Oblique_params[0,12,hei] = self.Triple_Gauss_Skew_fit_weight_bound_no_inputs(spc,x) | |
| 1575 | dataOut.dplr_2_u[0,0,hei] = dataOut.Oblique_params[0,12,hei]/numpy.sin(numpy.arccos(100./dataOut.heightList[hei])) |
|
1618 | dataOut.dplr_2_u[0,0,hei] = dataOut.Oblique_params[0,12,hei]/numpy.sin(numpy.arccos(100./dataOut.heightList[hei])) | |
| 1576 |
|
1619 | |||
| 1577 | elif mode == 8: #Double Skewed Weighted Bounded with inputs |
|
1620 | elif mode == 8: #Double Skewed Weighted Bounded with inputs | |
| 1578 | a1, b1, c1, a2, b2, c2, k2, d, dopp = self.Double_Gauss_Skew_fit_weight_bound_no_inputs(spc,x) |
|
1621 | a1, b1, c1, a2, b2, c2, k2, d, dopp = self.Double_Gauss_Skew_fit_weight_bound_no_inputs(spc,x) | |
| 1579 | dataOut.Oblique_params[0,0,hei],dataOut.Oblique_params[0,1,hei],dataOut.Oblique_params[0,2,hei],dataOut.Oblique_params[0,3,hei],dataOut.Oblique_params[0,4,hei],dataOut.Oblique_params[0,5,hei],dataOut.Oblique_params[0,6,hei],dataOut.Oblique_params[0,7,hei],dataOut.Oblique_params[0,8,hei],dataOut.Oblique_params[0,9,hei] = self.Double_Gauss_Skew_fit_weight_bound_no_inputs(spc,x, a1, b1, c1, a2, b2, c2, k2, d) |
|
1622 | dataOut.Oblique_params[0,0,hei],dataOut.Oblique_params[0,1,hei],dataOut.Oblique_params[0,2,hei],dataOut.Oblique_params[0,3,hei],dataOut.Oblique_params[0,4,hei],dataOut.Oblique_params[0,5,hei],dataOut.Oblique_params[0,6,hei],dataOut.Oblique_params[0,7,hei],dataOut.Oblique_params[0,8,hei],dataOut.Oblique_params[0,9,hei] = self.Double_Gauss_Skew_fit_weight_bound_no_inputs(spc,x, a1, b1, c1, a2, b2, c2, k2, d) | |
| 1580 | dataOut.dplr_2_u[0,0,hei] = dataOut.Oblique_params[0,9,hei]/numpy.sin(numpy.arccos(100./dataOut.heightList[hei])) |
|
1623 | dataOut.dplr_2_u[0,0,hei] = dataOut.Oblique_params[0,9,hei]/numpy.sin(numpy.arccos(100./dataOut.heightList[hei])) | |
| 1581 |
|
1624 | |||
| 1582 | elif mode == 9: #Double Skewed Weighted Bounded no inputs |
|
1625 | elif mode == 9: #Double Skewed Weighted Bounded no inputs | |
| 1583 | #if numpy.max(spc) <= 0: |
|
1626 | #if numpy.max(spc) <= 0: | |
| 1584 | from scipy.signal import medfilt |
|
1627 | from scipy.signal import medfilt | |
| 1585 | spcm = medfilt(spc,11) |
|
1628 | spcm = medfilt(spc,11) | |
| 1586 | if x[numpy.argmax(spcm)] <= 0: |
|
1629 | if x[numpy.argmax(spcm)] <= 0: | |
| 1587 | #print("EEJ", dataOut.heightList[hei], hei) |
|
1630 | #print("EEJ", dataOut.heightList[hei], hei) | |
| 1588 | #if hei != 70: |
|
1631 | #if hei != 70: | |
| 1589 | #continue |
|
1632 | #continue | |
| 1590 | #else: |
|
1633 | #else: | |
| 1591 | dataOut.Oblique_params[0,0,hei],dataOut.Oblique_params[0,1,hei],dataOut.Oblique_params[0,2,hei],dataOut.Oblique_params[0,3,hei],dataOut.Oblique_params[0,4,hei],dataOut.Oblique_params[0,5,hei],dataOut.Oblique_params[0,6,hei],dataOut.Oblique_params[0,7,hei],dataOut.Oblique_params[0,8,hei],dataOut.Oblique_params[0,9,hei],dataOut.Oblique_params[0,10,hei],dataOut.Oblique_param_errors[0,:,hei] = self.Double_Gauss_Double_Skew_fit_weight_bound_no_inputs(spcm,x,dataOut.nIncohInt,dataOut.heightList[hei]) |
|
1634 | dataOut.Oblique_params[0,0,hei],dataOut.Oblique_params[0,1,hei],dataOut.Oblique_params[0,2,hei],dataOut.Oblique_params[0,3,hei],dataOut.Oblique_params[0,4,hei],dataOut.Oblique_params[0,5,hei],dataOut.Oblique_params[0,6,hei],dataOut.Oblique_params[0,7,hei],dataOut.Oblique_params[0,8,hei],dataOut.Oblique_params[0,9,hei],dataOut.Oblique_params[0,10,hei],dataOut.Oblique_param_errors[0,:,hei] = self.Double_Gauss_Double_Skew_fit_weight_bound_no_inputs(spcm,x,dataOut.nIncohInt,dataOut.heightList[hei]) | |
| 1592 | #if dataOut.Oblique_params[0,-2,hei] < -500 or dataOut.Oblique_params[0,-2,hei] > 500 or dataOut.Oblique_params[0,-1,hei] < -500 or dataOut.Oblique_params[0,-1,hei] > 500: |
|
1635 | #if dataOut.Oblique_params[0,-2,hei] < -500 or dataOut.Oblique_params[0,-2,hei] > 500 or dataOut.Oblique_params[0,-1,hei] < -500 or dataOut.Oblique_params[0,-1,hei] > 500: | |
| 1593 | # dataOut.Oblique_params[0,:,hei] *= numpy.NAN |
|
1636 | # dataOut.Oblique_params[0,:,hei] *= numpy.NAN | |
| 1594 | dataOut.dplr_2_u[0,0,hei] = dataOut.Oblique_params[0,10,hei]/numpy.sin(numpy.arccos(100./dataOut.heightList[hei])) |
|
1637 | dataOut.dplr_2_u[0,0,hei] = dataOut.Oblique_params[0,10,hei]/numpy.sin(numpy.arccos(100./dataOut.heightList[hei])) | |
| 1595 |
|
1638 | |||
| 1596 | else: |
|
1639 | else: | |
| 1597 | #print("CEEJ") |
|
1640 | #print("CEEJ") | |
| 1598 | dataOut.Oblique_params[0,0,hei],dataOut.Oblique_params[0,1,hei],dataOut.Oblique_params[0,2,hei],dataOut.Oblique_params[0,3,hei],dataOut.Oblique_params[0,4,hei],dataOut.Oblique_params[0,5,hei],dataOut.Oblique_params[0,6,hei],dataOut.Oblique_params[0,7,hei],dataOut.Oblique_params[0,8,hei],dataOut.Oblique_params[0,9,hei],dataOut.Oblique_params[0,10,hei],dataOut.Oblique_param_errors[0,:,hei] = self.CEEJ_Skew_fit_weight_bound_no_inputs(spcm,x,dataOut.nIncohInt) |
|
1641 | dataOut.Oblique_params[0,0,hei],dataOut.Oblique_params[0,1,hei],dataOut.Oblique_params[0,2,hei],dataOut.Oblique_params[0,3,hei],dataOut.Oblique_params[0,4,hei],dataOut.Oblique_params[0,5,hei],dataOut.Oblique_params[0,6,hei],dataOut.Oblique_params[0,7,hei],dataOut.Oblique_params[0,8,hei],dataOut.Oblique_params[0,9,hei],dataOut.Oblique_params[0,10,hei],dataOut.Oblique_param_errors[0,:,hei] = self.CEEJ_Skew_fit_weight_bound_no_inputs(spcm,x,dataOut.nIncohInt) | |
| 1599 | #if dataOut.Oblique_params[0,-2,hei] < -500 or dataOut.Oblique_params[0,-2,hei] > 500 or dataOut.Oblique_params[0,-1,hei] < -500 or dataOut.Oblique_params[0,-1,hei] > 500: |
|
1642 | #if dataOut.Oblique_params[0,-2,hei] < -500 or dataOut.Oblique_params[0,-2,hei] > 500 or dataOut.Oblique_params[0,-1,hei] < -500 or dataOut.Oblique_params[0,-1,hei] > 500: | |
| 1600 | # dataOut.Oblique_params[0,:,hei] *= numpy.NAN |
|
1643 | # dataOut.Oblique_params[0,:,hei] *= numpy.NAN | |
| 1601 | dataOut.dplr_2_u[0,0,hei] = dataOut.Oblique_params[0,10,hei]/numpy.sin(numpy.arccos(100./dataOut.heightList[hei])) |
|
1644 | dataOut.dplr_2_u[0,0,hei] = dataOut.Oblique_params[0,10,hei]/numpy.sin(numpy.arccos(100./dataOut.heightList[hei])) | |
| 1602 | elif mode == 11: #Double Weighted Bounded no inputs |
|
1645 | elif mode == 11: #Double Weighted Bounded no inputs | |
| 1603 | #if numpy.max(spc) <= 0: |
|
1646 | #if numpy.max(spc) <= 0: | |
| 1604 | from scipy.signal import medfilt |
|
1647 | from scipy.signal import medfilt | |
| 1605 | spcm = medfilt(spc,11) |
|
1648 | spcm = medfilt(spc,11) | |
| 1606 |
|
1649 | |||
| 1607 | if x[numpy.argmax(spcm)] <= 0: |
|
1650 | if x[numpy.argmax(spcm)] <= 0: | |
| 1608 | #print("EEJ") |
|
1651 | #print("EEJ") | |
| 1609 | #print("EEJ",dataOut.heightList[hei]) |
|
1652 | #print("EEJ",dataOut.heightList[hei]) | |
| 1610 | dataOut.Oblique_params[0,0,hei],dataOut.Oblique_params[0,1,hei],dataOut.Oblique_params[0,2,hei],dataOut.Oblique_params[0,3,hei],dataOut.Oblique_params[0,4,hei],dataOut.Oblique_params[0,5,hei],dataOut.Oblique_params[0,6,hei],dataOut.Oblique_param_errors[0,:,hei] = self.Double_Gauss_fit_weight_bound_no_inputs(spc,x,dataOut.nIncohInt) |
|
1653 | dataOut.Oblique_params[0,0,hei],dataOut.Oblique_params[0,1,hei],dataOut.Oblique_params[0,2,hei],dataOut.Oblique_params[0,3,hei],dataOut.Oblique_params[0,4,hei],dataOut.Oblique_params[0,5,hei],dataOut.Oblique_params[0,6,hei],dataOut.Oblique_param_errors[0,:,hei] = self.Double_Gauss_fit_weight_bound_no_inputs(spc,x,dataOut.nIncohInt) | |
| 1611 | #if dataOut.Oblique_params[0,-2,hei] < -500 or dataOut.Oblique_params[0,-2,hei] > 500 or dataOut.Oblique_params[0,-1,hei] < -500 or dataOut.Oblique_params[0,-1,hei] > 500: |
|
1654 | #if dataOut.Oblique_params[0,-2,hei] < -500 or dataOut.Oblique_params[0,-2,hei] > 500 or dataOut.Oblique_params[0,-1,hei] < -500 or dataOut.Oblique_params[0,-1,hei] > 500: | |
| 1612 | # dataOut.Oblique_params[0,:,hei] *= numpy.NAN |
|
1655 | # dataOut.Oblique_params[0,:,hei] *= numpy.NAN | |
| 1613 | else: |
|
1656 | else: | |
| 1614 | #print("CEEJ",dataOut.heightList[hei]) |
|
1657 | #print("CEEJ",dataOut.heightList[hei]) | |
| 1615 | dataOut.Oblique_params[0,0,hei],dataOut.Oblique_params[0,1,hei],dataOut.Oblique_params[0,2,hei],dataOut.Oblique_params[0,3,hei],dataOut.Oblique_param_errors[0,:,hei] = self.CEEJ_fit_weight_bound_no_inputs(spc,x,dataOut.nIncohInt) |
|
1658 | dataOut.Oblique_params[0,0,hei],dataOut.Oblique_params[0,1,hei],dataOut.Oblique_params[0,2,hei],dataOut.Oblique_params[0,3,hei],dataOut.Oblique_param_errors[0,:,hei] = self.CEEJ_fit_weight_bound_no_inputs(spc,x,dataOut.nIncohInt) | |
| 1616 |
|
1659 | |||
| 1617 | elif mode == 10: #150km |
|
1660 | elif mode == 10: #150km | |
| 1618 | dataOut.Oblique_params[0,0,hei],dataOut.Oblique_params[0,1,hei],dataOut.Oblique_params[0,2,hei],dataOut.Oblique_params[0,3,hei],dataOut.Oblique_param_errors[0,:,hei] = self.Simple_fit_bound(spc,x,dataOut.nIncohInt) |
|
1661 | dataOut.Oblique_params[0,0,hei],dataOut.Oblique_params[0,1,hei],dataOut.Oblique_params[0,2,hei],dataOut.Oblique_params[0,3,hei],dataOut.Oblique_param_errors[0,:,hei] = self.Simple_fit_bound(spc,x,dataOut.nIncohInt) | |
| 1619 | snr = (dataOut.power[0,hei]*factor - dataOut.Oblique_params[0,3,hei])/dataOut.Oblique_params[0,3,hei] |
|
1662 | snr = (dataOut.power[0,hei]*factor - dataOut.Oblique_params[0,3,hei])/dataOut.Oblique_params[0,3,hei] | |
| 1620 | dataOut.snr_log10[0,hei] = numpy.log10(snr) |
|
1663 | dataOut.snr_log10[0,hei] = numpy.log10(snr) | |
| 1621 |
|
1664 | |||
| 1622 | else: |
|
1665 | else: | |
| 1623 | spc_fit, A1, B1, C1, D1 = self.Gauss_fit_2(spc,x,'first') |
|
1666 | spc_fit, A1, B1, C1, D1 = self.Gauss_fit_2(spc,x,'first') | |
| 1624 |
|
1667 | |||
| 1625 | spc_diff = spc - spc_fit |
|
1668 | spc_diff = spc - spc_fit | |
| 1626 | spc_diff[spc_diff < 0] = 0 |
|
1669 | spc_diff[spc_diff < 0] = 0 | |
| 1627 |
|
1670 | |||
| 1628 | spc_fit_diff, A2, B2, C2, D2 = self.Gauss_fit_2(spc_diff,x,'second') |
|
1671 | spc_fit_diff, A2, B2, C2, D2 = self.Gauss_fit_2(spc_diff,x,'second') | |
| 1629 |
|
1672 | |||
| 1630 | D = (D1+D2) |
|
1673 | D = (D1+D2) | |
| 1631 |
|
1674 | |||
| 1632 | if mode == 0: #Double Fit |
|
1675 | if mode == 0: #Double Fit | |
| 1633 | dataOut.Oblique_params[0,0,hei],dataOut.Oblique_params[0,1,hei],dataOut.Oblique_params[0,2,hei],dataOut.Oblique_params[0,3,hei],dataOut.Oblique_params[0,4,hei],dataOut.Oblique_params[0,5,hei],dataOut.Oblique_params[0,6,hei],dataOut.Oblique_param_errors[0,0,hei],dataOut.Oblique_param_errors[0,1,hei],dataOut.Oblique_param_errors[0,2,hei],dataOut.Oblique_param_errors[0,3,hei],dataOut.Oblique_param_errors[0,4,hei],dataOut.Oblique_param_errors[0,5,hei],dataOut.Oblique_param_errors[0,6,hei] = self.Double_Gauss_fit_2(spc,x,A1,B1,C1,A2,B2,C2,D) |
|
1676 | dataOut.Oblique_params[0,0,hei],dataOut.Oblique_params[0,1,hei],dataOut.Oblique_params[0,2,hei],dataOut.Oblique_params[0,3,hei],dataOut.Oblique_params[0,4,hei],dataOut.Oblique_params[0,5,hei],dataOut.Oblique_params[0,6,hei],dataOut.Oblique_param_errors[0,0,hei],dataOut.Oblique_param_errors[0,1,hei],dataOut.Oblique_param_errors[0,2,hei],dataOut.Oblique_param_errors[0,3,hei],dataOut.Oblique_param_errors[0,4,hei],dataOut.Oblique_param_errors[0,5,hei],dataOut.Oblique_param_errors[0,6,hei] = self.Double_Gauss_fit_2(spc,x,A1,B1,C1,A2,B2,C2,D) | |
| 1634 | #spc_double_fit,dataOut.Oblique_params = self.Double_Gauss_fit(spc,x,A1,B1,C1,A2,B2,C2,D) |
|
1677 | #spc_double_fit,dataOut.Oblique_params = self.Double_Gauss_fit(spc,x,A1,B1,C1,A2,B2,C2,D) | |
| 1635 |
|
1678 | |||
| 1636 | elif mode == 1: #Double Fit Windowed |
|
1679 | elif mode == 1: #Double Fit Windowed | |
| 1637 | dataOut.Oblique_params[0,0,hei],dataOut.Oblique_params[0,1,hei],dataOut.Oblique_params[0,2,hei],dataOut.Oblique_params[0,3,hei],dataOut.Oblique_params[0,4,hei],dataOut.Oblique_params[0,5,hei],dataOut.Oblique_params[0,6,hei] = self.windowing_double(spc,dataOut.getFreqRange(0),A1,B1,C1,A2,B2,C2,D) |
|
1680 | dataOut.Oblique_params[0,0,hei],dataOut.Oblique_params[0,1,hei],dataOut.Oblique_params[0,2,hei],dataOut.Oblique_params[0,3,hei],dataOut.Oblique_params[0,4,hei],dataOut.Oblique_params[0,5,hei],dataOut.Oblique_params[0,6,hei] = self.windowing_double(spc,dataOut.getFreqRange(0),A1,B1,C1,A2,B2,C2,D) | |
| 1638 |
|
1681 | |||
| 1639 | elif mode == 2: #Double Fit Weight |
|
1682 | elif mode == 2: #Double Fit Weight | |
| 1640 | dataOut.Oblique_params[0,0,hei],dataOut.Oblique_params[0,1,hei],dataOut.Oblique_params[0,2,hei],dataOut.Oblique_params[0,3,hei],dataOut.Oblique_params[0,4,hei],dataOut.Oblique_params[0,5,hei],dataOut.Oblique_params[0,6,hei] = self.Double_Gauss_fit_weight(spc,x,A1,B1,C1,A2,B2,C2,D) |
|
1683 | dataOut.Oblique_params[0,0,hei],dataOut.Oblique_params[0,1,hei],dataOut.Oblique_params[0,2,hei],dataOut.Oblique_params[0,3,hei],dataOut.Oblique_params[0,4,hei],dataOut.Oblique_params[0,5,hei],dataOut.Oblique_params[0,6,hei] = self.Double_Gauss_fit_weight(spc,x,A1,B1,C1,A2,B2,C2,D) | |
| 1641 |
|
1684 | |||
| 1642 | elif mode == 3: #Simple Fit |
|
1685 | elif mode == 3: #Simple Fit | |
| 1643 | dataOut.Oblique_params[0,0,hei] = A1 |
|
1686 | dataOut.Oblique_params[0,0,hei] = A1 | |
| 1644 | dataOut.Oblique_params[0,1,hei] = B1 |
|
1687 | dataOut.Oblique_params[0,1,hei] = B1 | |
| 1645 | dataOut.Oblique_params[0,2,hei] = C1 |
|
1688 | dataOut.Oblique_params[0,2,hei] = C1 | |
| 1646 | dataOut.Oblique_params[0,3,hei] = A2 |
|
1689 | dataOut.Oblique_params[0,3,hei] = A2 | |
| 1647 | dataOut.Oblique_params[0,4,hei] = B2 |
|
1690 | dataOut.Oblique_params[0,4,hei] = B2 | |
| 1648 | dataOut.Oblique_params[0,5,hei] = C2 |
|
1691 | dataOut.Oblique_params[0,5,hei] = C2 | |
| 1649 | dataOut.Oblique_params[0,6,hei] = D |
|
1692 | dataOut.Oblique_params[0,6,hei] = D | |
| 1650 |
|
1693 | |||
| 1651 | elif mode == 5: #Triple Fit Weight |
|
1694 | elif mode == 5: #Triple Fit Weight | |
| 1652 | if hei in l1: |
|
1695 | if hei in l1: | |
| 1653 | dataOut.Oblique_params[0,0,hei],dataOut.Oblique_params[0,1,hei],dataOut.Oblique_params[0,2,hei],dataOut.Oblique_params[0,3,hei],dataOut.Oblique_params[0,4,hei],dataOut.Oblique_params[0,5,hei],dataOut.Oblique_params[0,6,hei] = self.duo_Marco(spc,x,A1,B1,C1,A2,B2,C2,D) |
|
1696 | dataOut.Oblique_params[0,0,hei],dataOut.Oblique_params[0,1,hei],dataOut.Oblique_params[0,2,hei],dataOut.Oblique_params[0,3,hei],dataOut.Oblique_params[0,4,hei],dataOut.Oblique_params[0,5,hei],dataOut.Oblique_params[0,6,hei] = self.duo_Marco(spc,x,A1,B1,C1,A2,B2,C2,D) | |
| 1654 | dataOut.dplr_2_u[0,0,hei] = dataOut.Oblique_params[0,4,hei]/numpy.sin(numpy.arccos(102/dataOut.heightList[hei])) |
|
1697 | dataOut.dplr_2_u[0,0,hei] = dataOut.Oblique_params[0,4,hei]/numpy.sin(numpy.arccos(102/dataOut.heightList[hei])) | |
| 1655 | #print(dataOut.Oblique_params[0,0,hei]) |
|
1698 | #print(dataOut.Oblique_params[0,0,hei]) | |
| 1656 | #print(dataOut.dplr_2_u[0,0,hei]) |
|
1699 | #print(dataOut.dplr_2_u[0,0,hei]) | |
| 1657 | else: |
|
1700 | else: | |
| 1658 | dataOut.Oblique_params[0,0,hei],dataOut.Oblique_params[0,1,hei],dataOut.Oblique_params[0,2,hei],dataOut.Oblique_params[0,3,hei],dataOut.Oblique_params[0,4,hei],dataOut.Oblique_params[0,5,hei],dataOut.Oblique_params[0,6,hei] = self.Double_Gauss_fit_weight(spc,x,A1,B1,C1,A2,B2,C2,D) |
|
1701 | dataOut.Oblique_params[0,0,hei],dataOut.Oblique_params[0,1,hei],dataOut.Oblique_params[0,2,hei],dataOut.Oblique_params[0,3,hei],dataOut.Oblique_params[0,4,hei],dataOut.Oblique_params[0,5,hei],dataOut.Oblique_params[0,6,hei] = self.Double_Gauss_fit_weight(spc,x,A1,B1,C1,A2,B2,C2,D) | |
| 1659 | dataOut.dplr_2_u[0,0,hei] = dataOut.Oblique_params[0,4,hei]/numpy.sin(numpy.arccos(102/dataOut.heightList[hei])) |
|
1702 | dataOut.dplr_2_u[0,0,hei] = dataOut.Oblique_params[0,4,hei]/numpy.sin(numpy.arccos(102/dataOut.heightList[hei])) | |
| 1660 |
|
1703 | |||
| 1661 |
|
1704 | |||
| 1662 | except: |
|
1705 | except: | |
| 1663 | ###dataOut.Oblique_params[0,:,hei] = dataOut.Oblique_params[0,:,hei]*numpy.NAN |
|
1706 | ###dataOut.Oblique_params[0,:,hei] = dataOut.Oblique_params[0,:,hei]*numpy.NAN | |
| 1664 | pass |
|
1707 | pass | |
| 1665 |
|
1708 | |||
| 1666 | #exit(1) |
|
1709 | #exit(1) | |
| 1667 | dataOut.paramInterval = dataOut.nProfiles*dataOut.nCohInt*dataOut.ippSeconds |
|
1710 | dataOut.paramInterval = dataOut.nProfiles*dataOut.nCohInt*dataOut.ippSeconds | |
| 1668 | dataOut.lat=-11.95 |
|
1711 | dataOut.lat=-11.95 | |
| 1669 | dataOut.lon=-76.87 |
|
1712 | dataOut.lon=-76.87 | |
| 1670 | ''' |
|
1713 | ''' | |
| 1671 | dataOut.Oblique_params = numpy.where(dataOut.Oblique_params<-700, numpy.nan, dop_t1) |
|
1714 | dataOut.Oblique_params = numpy.where(dataOut.Oblique_params<-700, numpy.nan, dop_t1) | |
| 1672 | dataOut.Oblique_params = numpy.where(dataOut.Oblique_params<+700, numpy.nan, dop_t1) |
|
1715 | dataOut.Oblique_params = numpy.where(dataOut.Oblique_params<+700, numpy.nan, dop_t1) | |
| 1673 | AquΓ debo exceptuar las amplitudes |
|
1716 | AquΓ debo exceptuar las amplitudes | |
| 1674 | ''' |
|
1717 | ''' | |
| 1675 | if mode == 9: #Double Skew Gaussian |
|
1718 | if mode == 9: #Double Skew Gaussian | |
| 1676 | #dataOut.Dop_EEJ_T1 = dataOut.Oblique_params[:,-2,:] #Pos[Max_value] |
|
1719 | #dataOut.Dop_EEJ_T1 = dataOut.Oblique_params[:,-2,:] #Pos[Max_value] | |
| 1677 | #dataOut.Dop_EEJ_T1 = dataOut.Oblique_params[:,1,:] #Shift |
|
1720 | #dataOut.Dop_EEJ_T1 = dataOut.Oblique_params[:,1,:] #Shift | |
| 1678 | dataOut.Spec_W_T1 = dataOut.Oblique_params[:,2,:] |
|
1721 | dataOut.Spec_W_T1 = dataOut.Oblique_params[:,2,:] | |
| 1679 | #dataOut.Dop_EEJ_T2 = dataOut.Oblique_params[:,-1,:] #Pos[Max_value] |
|
1722 | #dataOut.Dop_EEJ_T2 = dataOut.Oblique_params[:,-1,:] #Pos[Max_value] | |
| 1680 | #dataOut.Dop_EEJ_T2 = dataOut.Oblique_params[:,5,:] #Shift |
|
1723 | #dataOut.Dop_EEJ_T2 = dataOut.Oblique_params[:,5,:] #Shift | |
| 1681 | dataOut.Spec_W_T2 = dataOut.Oblique_params[:,6,:] |
|
1724 | dataOut.Spec_W_T2 = dataOut.Oblique_params[:,6,:] | |
| 1682 | if Dop == 'Shift': |
|
1725 | if Dop == 'Shift': | |
| 1683 | dataOut.Dop_EEJ_T1 = dataOut.Oblique_params[:,1,:] #Shift |
|
1726 | dataOut.Dop_EEJ_T1 = dataOut.Oblique_params[:,1,:] #Shift | |
| 1684 | dataOut.Dop_EEJ_T2 = dataOut.Oblique_params[:,5,:] #Shift |
|
1727 | dataOut.Dop_EEJ_T2 = dataOut.Oblique_params[:,5,:] #Shift | |
| 1685 | elif Dop == 'Max': |
|
1728 | elif Dop == 'Max': | |
| 1686 | dataOut.Dop_EEJ_T1 = dataOut.Oblique_params[:,-2,:] #Pos[Max_value] |
|
1729 | dataOut.Dop_EEJ_T1 = dataOut.Oblique_params[:,-2,:] #Pos[Max_value] | |
| 1687 | dataOut.Dop_EEJ_T2 = dataOut.Oblique_params[:,-1,:] #Pos[Max_value] |
|
1730 | dataOut.Dop_EEJ_T2 = dataOut.Oblique_params[:,-1,:] #Pos[Max_value] | |
| 1688 |
|
1731 | |||
| 1689 | dataOut.Err_Dop_EEJ_T1 = dataOut.Oblique_param_errors[:,1,:] #En realidad este es el error? |
|
1732 | dataOut.Err_Dop_EEJ_T1 = dataOut.Oblique_param_errors[:,1,:] #En realidad este es el error? | |
| 1690 | dataOut.Err_Spec_W_T1 = dataOut.Oblique_param_errors[:,2,:] |
|
1733 | dataOut.Err_Spec_W_T1 = dataOut.Oblique_param_errors[:,2,:] | |
| 1691 | dataOut.Err_Dop_EEJ_T2 = dataOut.Oblique_param_errors[:,5,:] #En realidad este es el error? |
|
1734 | dataOut.Err_Dop_EEJ_T2 = dataOut.Oblique_param_errors[:,5,:] #En realidad este es el error? | |
| 1692 | dataOut.Err_Spec_W_T2 = dataOut.Oblique_param_errors[:,6,:] |
|
1735 | dataOut.Err_Spec_W_T2 = dataOut.Oblique_param_errors[:,6,:] | |
| 1693 |
|
1736 | |||
| 1694 | elif mode == 11: #Double Gaussian |
|
1737 | elif mode == 11: #Double Gaussian | |
| 1695 | dataOut.Dop_EEJ_T1 = dataOut.Oblique_params[:,1,:] |
|
1738 | dataOut.Dop_EEJ_T1 = dataOut.Oblique_params[:,1,:] | |
| 1696 | dataOut.Spec_W_T1 = dataOut.Oblique_params[:,2,:] |
|
1739 | dataOut.Spec_W_T1 = dataOut.Oblique_params[:,2,:] | |
| 1697 | dataOut.Dop_EEJ_T2 = dataOut.Oblique_params[:,4,:] |
|
1740 | dataOut.Dop_EEJ_T2 = dataOut.Oblique_params[:,4,:] | |
| 1698 | dataOut.Spec_W_T2 = dataOut.Oblique_params[:,5,:] |
|
1741 | dataOut.Spec_W_T2 = dataOut.Oblique_params[:,5,:] | |
| 1699 |
|
1742 | |||
| 1700 | dataOut.Err_Dop_EEJ_T1 = dataOut.Oblique_param_errors[:,1,:] |
|
1743 | dataOut.Err_Dop_EEJ_T1 = dataOut.Oblique_param_errors[:,1,:] | |
| 1701 | dataOut.Err_Spec_W_T1 = dataOut.Oblique_param_errors[:,2,:] |
|
1744 | dataOut.Err_Spec_W_T1 = dataOut.Oblique_param_errors[:,2,:] | |
| 1702 | dataOut.Err_Dop_EEJ_T2 = dataOut.Oblique_param_errors[:,4,:] |
|
1745 | dataOut.Err_Dop_EEJ_T2 = dataOut.Oblique_param_errors[:,4,:] | |
| 1703 | dataOut.Err_Spec_W_T2 = dataOut.Oblique_param_errors[:,5,:] |
|
1746 | dataOut.Err_Spec_W_T2 = dataOut.Oblique_param_errors[:,5,:] | |
| 1704 |
|
1747 | |||
| 1705 | #print("Before: ", dataOut.Dop_EEJ_T2) |
|
1748 | #print("Before: ", dataOut.Dop_EEJ_T2) | |
| 1706 | dataOut.Spec_W_T1 = self.clean_outliers(dataOut.Spec_W_T1) |
|
1749 | dataOut.Spec_W_T1 = self.clean_outliers(dataOut.Spec_W_T1) | |
| 1707 | dataOut.Spec_W_T2 = self.clean_outliers(dataOut.Spec_W_T2) |
|
1750 | dataOut.Spec_W_T2 = self.clean_outliers(dataOut.Spec_W_T2) | |
| 1708 | dataOut.Dop_EEJ_T1 = self.clean_outliers(dataOut.Dop_EEJ_T1) |
|
1751 | dataOut.Dop_EEJ_T1 = self.clean_outliers(dataOut.Dop_EEJ_T1) | |
| 1709 | dataOut.Dop_EEJ_T2 = self.clean_outliers(dataOut.Dop_EEJ_T2) |
|
1752 | dataOut.Dop_EEJ_T2 = self.clean_outliers(dataOut.Dop_EEJ_T2) | |
| 1710 | #print("After: ", dataOut.Dop_EEJ_T2) |
|
1753 | #print("After: ", dataOut.Dop_EEJ_T2) | |
| 1711 | dataOut.Err_Spec_W_T1 = self.clean_outliers(dataOut.Err_Spec_W_T1) |
|
1754 | dataOut.Err_Spec_W_T1 = self.clean_outliers(dataOut.Err_Spec_W_T1) | |
| 1712 | dataOut.Err_Spec_W_T2 = self.clean_outliers(dataOut.Err_Spec_W_T2) |
|
1755 | dataOut.Err_Spec_W_T2 = self.clean_outliers(dataOut.Err_Spec_W_T2) | |
| 1713 | dataOut.Err_Dop_EEJ_T1 = self.clean_outliers(dataOut.Err_Dop_EEJ_T1) |
|
1756 | dataOut.Err_Dop_EEJ_T1 = self.clean_outliers(dataOut.Err_Dop_EEJ_T1) | |
| 1714 | dataOut.Err_Dop_EEJ_T2 = self.clean_outliers(dataOut.Err_Dop_EEJ_T2) |
|
1757 | dataOut.Err_Dop_EEJ_T2 = self.clean_outliers(dataOut.Err_Dop_EEJ_T2) | |
| 1715 | #print("Before data_snr: ", dataOut.data_snr) |
|
1758 | #print("Before data_snr: ", dataOut.data_snr) | |
| 1716 | #dataOut.data_snr = numpy.where(numpy.isnan(dataOut.Dop_EEJ_T1), numpy.nan, dataOut.data_snr) |
|
1759 | #dataOut.data_snr = numpy.where(numpy.isnan(dataOut.Dop_EEJ_T1), numpy.nan, dataOut.data_snr) | |
| 1717 | dataOut.snl = numpy.where(numpy.isnan(dataOut.Dop_EEJ_T1), numpy.nan, dataOut.snl) |
|
1760 | dataOut.snl = numpy.where(numpy.isnan(dataOut.Dop_EEJ_T1), numpy.nan, dataOut.snl) | |
| 1718 |
|
1761 | |||
| 1719 | #print("After data_snr: ", dataOut.data_snr) |
|
1762 | #print("After data_snr: ", dataOut.data_snr) | |
| 1720 | dataOut.mode = mode |
|
1763 | dataOut.mode = mode | |
| 1721 | dataOut.flagNoData = numpy.all(numpy.isnan(dataOut.Dop_EEJ_T1)) #Si todos los valores son NaN no se prosigue |
|
1764 | dataOut.flagNoData = numpy.all(numpy.isnan(dataOut.Dop_EEJ_T1)) #Si todos los valores son NaN no se prosigue | |
| 1722 | ###dataOut.flagNoData = False #Descomentar solo para ploteo sino mantener comentado (para guardado) |
|
1765 | ###dataOut.flagNoData = False #Descomentar solo para ploteo sino mantener comentado (para guardado) | |
| 1723 |
|
1766 | |||
| 1724 | return dataOut |
|
1767 | return dataOut | |
| 1725 |
|
1768 | |||
| 1726 | class Gaussian_Windowed(Operation): |
|
1769 | class Gaussian_Windowed(Operation): | |
| 1727 | ''' |
|
1770 | ''' | |
| 1728 | Written by R. Flores |
|
1771 | Written by R. Flores | |
| 1729 | ''' |
|
1772 | ''' | |
| 1730 | def __init__(self): |
|
1773 | def __init__(self): | |
| 1731 | Operation.__init__(self) |
|
1774 | Operation.__init__(self) | |
| 1732 |
|
1775 | |||
| 1733 | def windowing_single(self,spc,x,A,B,C,D,nFFTPoints): |
|
1776 | def windowing_single(self,spc,x,A,B,C,D,nFFTPoints): | |
| 1734 | from scipy.optimize import curve_fit,fmin |
|
1777 | from scipy.optimize import curve_fit,fmin | |
| 1735 |
|
1778 | |||
| 1736 | def gaussian(x, a, b, c, d): |
|
1779 | def gaussian(x, a, b, c, d): | |
| 1737 | val = a * numpy.exp(-(x - b)**2 / (2*c**2)) + d |
|
1780 | val = a * numpy.exp(-(x - b)**2 / (2*c**2)) + d | |
| 1738 | return val |
|
1781 | return val | |
| 1739 |
|
1782 | |||
| 1740 | def R_gaussian(x, a, b, c): |
|
1783 | def R_gaussian(x, a, b, c): | |
| 1741 | N = int(numpy.shape(x)[0]) |
|
1784 | N = int(numpy.shape(x)[0]) | |
| 1742 | val = a * numpy.exp(-((x)*c*2*2*numpy.pi)**2 / (2))* numpy.exp(1.j*b*x*4*numpy.pi) |
|
1785 | val = a * numpy.exp(-((x)*c*2*2*numpy.pi)**2 / (2))* numpy.exp(1.j*b*x*4*numpy.pi) | |
| 1743 | return val |
|
1786 | return val | |
| 1744 |
|
1787 | |||
| 1745 | def T(x,N): |
|
1788 | def T(x,N): | |
| 1746 | T = 1-abs(x)/N |
|
1789 | T = 1-abs(x)/N | |
| 1747 | return T |
|
1790 | return T | |
| 1748 |
|
1791 | |||
| 1749 | def R_T_spc_fun(x, a, b, c, d, nFFTPoints): |
|
1792 | def R_T_spc_fun(x, a, b, c, d, nFFTPoints): | |
| 1750 |
|
1793 | |||
| 1751 | N = int(numpy.shape(x)[0]) |
|
1794 | N = int(numpy.shape(x)[0]) | |
| 1752 |
|
1795 | |||
| 1753 | x_max = x[-1] |
|
1796 | x_max = x[-1] | |
| 1754 |
|
1797 | |||
| 1755 | x_pos = x[nFFTPoints:] |
|
1798 | x_pos = x[nFFTPoints:] | |
| 1756 | x_neg = x[:nFFTPoints] |
|
1799 | x_neg = x[:nFFTPoints] | |
| 1757 | #print([int(nFFTPoints/2)) |
|
1800 | #print([int(nFFTPoints/2)) | |
| 1758 | #print("x: ", x) |
|
1801 | #print("x: ", x) | |
| 1759 | #print("x_neg: ", x_neg) |
|
1802 | #print("x_neg: ", x_neg) | |
| 1760 | #print("x_pos: ", x_pos) |
|
1803 | #print("x_pos: ", x_pos) | |
| 1761 |
|
1804 | |||
| 1762 |
|
1805 | |||
| 1763 | R_T_neg_1 = R_gaussian(x, a, b, c)[:nFFTPoints]*T(x_neg,-x[0]) |
|
1806 | R_T_neg_1 = R_gaussian(x, a, b, c)[:nFFTPoints]*T(x_neg,-x[0]) | |
| 1764 | R_T_pos_1 = R_gaussian(x, a, b, c)[nFFTPoints:]*T(x_pos,x[-1]) |
|
1807 | R_T_pos_1 = R_gaussian(x, a, b, c)[nFFTPoints:]*T(x_pos,x[-1]) | |
| 1765 | #print(T(x_pos,x[-1]),x_pos,x[-1]) |
|
1808 | #print(T(x_pos,x[-1]),x_pos,x[-1]) | |
| 1766 | #print(R_T_neg_1.shape,R_T_pos_1.shape) |
|
1809 | #print(R_T_neg_1.shape,R_T_pos_1.shape) | |
| 1767 | R_T_sum_1 = R_T_pos_1 + R_T_neg_1 |
|
1810 | R_T_sum_1 = R_T_pos_1 + R_T_neg_1 | |
| 1768 | R_T_spc_1 = numpy.fft.fft(R_T_sum_1).real |
|
1811 | R_T_spc_1 = numpy.fft.fft(R_T_sum_1).real | |
| 1769 | R_T_spc_1 = numpy.fft.fftshift(R_T_spc_1) |
|
1812 | R_T_spc_1 = numpy.fft.fftshift(R_T_spc_1) | |
| 1770 | max_val_1 = numpy.max(R_T_spc_1) |
|
1813 | max_val_1 = numpy.max(R_T_spc_1) | |
| 1771 | R_T_spc_1 = R_T_spc_1*a/max_val_1 |
|
1814 | R_T_spc_1 = R_T_spc_1*a/max_val_1 | |
| 1772 |
|
1815 | |||
| 1773 | R_T_d = d*numpy.fft.fftshift(signal.unit_impulse(N)) |
|
1816 | R_T_d = d*numpy.fft.fftshift(signal.unit_impulse(N)) | |
| 1774 | R_T_d_neg = R_T_d[:nFFTPoints]*T(x_neg,-x[0]) |
|
1817 | R_T_d_neg = R_T_d[:nFFTPoints]*T(x_neg,-x[0]) | |
| 1775 | R_T_d_pos = R_T_d[nFFTPoints:]*T(x_pos,x[-1]) |
|
1818 | R_T_d_pos = R_T_d[nFFTPoints:]*T(x_pos,x[-1]) | |
| 1776 | R_T_d_sum = R_T_d_pos + R_T_d_neg |
|
1819 | R_T_d_sum = R_T_d_pos + R_T_d_neg | |
| 1777 | R_T_spc_3 = numpy.fft.fft(R_T_d_sum).real |
|
1820 | R_T_spc_3 = numpy.fft.fft(R_T_d_sum).real | |
| 1778 | R_T_spc_3 = numpy.fft.fftshift(R_T_spc_3) |
|
1821 | R_T_spc_3 = numpy.fft.fftshift(R_T_spc_3) | |
| 1779 |
|
1822 | |||
| 1780 | R_T_final = R_T_spc_1 + R_T_spc_3 |
|
1823 | R_T_final = R_T_spc_1 + R_T_spc_3 | |
| 1781 |
|
1824 | |||
| 1782 | return R_T_final |
|
1825 | return R_T_final | |
| 1783 |
|
1826 | |||
| 1784 | y = spc#gaussian(x, a, meanY, sigmaY) + a*0.1*numpy.random.normal(0, 1, size=len(x)) |
|
1827 | y = spc#gaussian(x, a, meanY, sigmaY) + a*0.1*numpy.random.normal(0, 1, size=len(x)) | |
| 1785 |
|
1828 | |||
| 1786 | from scipy.stats import norm |
|
1829 | from scipy.stats import norm | |
| 1787 | mean,std=norm.fit(spc) |
|
1830 | mean,std=norm.fit(spc) | |
| 1788 |
|
1831 | |||
| 1789 | # estimate starting values from the data |
|
1832 | # estimate starting values from the data | |
| 1790 | a = A |
|
1833 | a = A | |
| 1791 | b = B |
|
1834 | b = B | |
| 1792 | c = C#numpy.std(spc) |
|
1835 | c = C#numpy.std(spc) | |
| 1793 | d = D |
|
1836 | d = D | |
| 1794 | #''' |
|
1837 | #''' | |
| 1795 | #ippSeconds = 250*20*1.e-6/3 |
|
1838 | #ippSeconds = 250*20*1.e-6/3 | |
| 1796 |
|
1839 | |||
| 1797 | #x_t = ippSeconds * (numpy.arange(nFFTPoints) - nFFTPoints / 2.) |
|
1840 | #x_t = ippSeconds * (numpy.arange(nFFTPoints) - nFFTPoints / 2.) | |
| 1798 |
|
1841 | |||
| 1799 | #x_t = numpy.linspace(x_t[0],x_t[-1],3200) |
|
1842 | #x_t = numpy.linspace(x_t[0],x_t[-1],3200) | |
| 1800 | #print("x_t: ", x_t) |
|
1843 | #print("x_t: ", x_t) | |
| 1801 | #print("nFFTPoints: ", nFFTPoints) |
|
1844 | #print("nFFTPoints: ", nFFTPoints) | |
| 1802 | x_vel = numpy.linspace(x[0],x[-1],int(2*nFFTPoints)) |
|
1845 | x_vel = numpy.linspace(x[0],x[-1],int(2*nFFTPoints)) | |
| 1803 | #print("x_vel: ", x_vel) |
|
1846 | #print("x_vel: ", x_vel) | |
| 1804 | #x_freq = numpy.fft.fftfreq(1600,d=ippSeconds) |
|
1847 | #x_freq = numpy.fft.fftfreq(1600,d=ippSeconds) | |
| 1805 | #x_freq = numpy.fft.fftshift(x_freq) |
|
1848 | #x_freq = numpy.fft.fftshift(x_freq) | |
| 1806 | #''' |
|
1849 | #''' | |
| 1807 | # define a least squares function to optimize |
|
1850 | # define a least squares function to optimize | |
| 1808 | def minfunc(params): |
|
1851 | def minfunc(params): | |
| 1809 | #print("y.shape: ", numpy.shape(y)) |
|
1852 | #print("y.shape: ", numpy.shape(y)) | |
| 1810 | return sum((y-R_T_spc_fun(x_vel,params[0],params[1],params[2],params[3],nFFTPoints))**2/1)#y**2) |
|
1853 | return sum((y-R_T_spc_fun(x_vel,params[0],params[1],params[2],params[3],nFFTPoints))**2/1)#y**2) | |
| 1811 |
|
1854 | |||
| 1812 | # fit |
|
1855 | # fit | |
| 1813 |
|
1856 | |||
| 1814 | popt_full = fmin(minfunc,[a,b,c,d], disp=False) |
|
1857 | popt_full = fmin(minfunc,[a,b,c,d], disp=False) | |
| 1815 | #print("nIter", popt_full[2]) |
|
1858 | #print("nIter", popt_full[2]) | |
| 1816 | popt = popt_full#[0] |
|
1859 | popt = popt_full#[0] | |
| 1817 |
|
1860 | |||
| 1818 | fun = gaussian(x, popt[0], popt[1], popt[2], popt[3]) |
|
1861 | fun = gaussian(x, popt[0], popt[1], popt[2], popt[3]) | |
| 1819 |
|
1862 | |||
| 1820 | #return R_T_spc_fun(x_t,popt[0], popt[1], popt[2], popt[3], popt[4], popt[5], popt[6]), popt[0], popt[1], popt[2], popt[3], popt[4], popt[5], popt[6] |
|
1863 | #return R_T_spc_fun(x_t,popt[0], popt[1], popt[2], popt[3], popt[4], popt[5], popt[6]), popt[0], popt[1], popt[2], popt[3], popt[4], popt[5], popt[6] | |
| 1821 | return fun, popt[0], popt[1], popt[2], popt[3] |
|
1864 | return fun, popt[0], popt[1], popt[2], popt[3] | |
| 1822 |
|
1865 | |||
| 1823 | def run(self, dataOut): |
|
1866 | def run(self, dataOut): | |
| 1824 |
|
1867 | |||
| 1825 | from scipy.signal import medfilt |
|
1868 | from scipy.signal import medfilt | |
| 1826 | import matplotlib.pyplot as plt |
|
1869 | import matplotlib.pyplot as plt | |
| 1827 | dataOut.moments = numpy.ones((dataOut.nChannels,4,dataOut.nHeights))*numpy.NAN |
|
1870 | dataOut.moments = numpy.ones((dataOut.nChannels,4,dataOut.nHeights))*numpy.NAN | |
| 1828 | dataOut.VelRange = dataOut.getVelRange(0) |
|
1871 | dataOut.VelRange = dataOut.getVelRange(0) | |
| 1829 | for nChannel in range(dataOut.nChannels): |
|
1872 | for nChannel in range(dataOut.nChannels): | |
| 1830 | for hei in range(dataOut.heightList.shape[0]): |
|
1873 | for hei in range(dataOut.heightList.shape[0]): | |
| 1831 | #print("ipp: ", dataOut.ippSeconds) |
|
1874 | #print("ipp: ", dataOut.ippSeconds) | |
| 1832 | spc = numpy.copy(dataOut.data_spc[nChannel,:,hei]) |
|
1875 | spc = numpy.copy(dataOut.data_spc[nChannel,:,hei]) | |
| 1833 |
|
1876 | |||
| 1834 | #print(VelRange) |
|
1877 | #print(VelRange) | |
| 1835 | #print(dataOut.getFreqRange(64)) |
|
1878 | #print(dataOut.getFreqRange(64)) | |
| 1836 | spcm = medfilt(spc,11) |
|
1879 | spcm = medfilt(spc,11) | |
| 1837 | spc_max = numpy.max(spcm) |
|
1880 | spc_max = numpy.max(spcm) | |
| 1838 | dop1_x0 = dataOut.VelRange[numpy.argmax(spcm)] |
|
1881 | dop1_x0 = dataOut.VelRange[numpy.argmax(spcm)] | |
| 1839 | D = numpy.min(spcm) |
|
1882 | D = numpy.min(spcm) | |
| 1840 |
|
1883 | |||
| 1841 | fun, A, B, C, D = self.windowing_single(spc,dataOut.VelRange,spc_max,dop1_x0,abs(dop1_x0),D,dataOut.nFFTPoints) |
|
1884 | fun, A, B, C, D = self.windowing_single(spc,dataOut.VelRange,spc_max,dop1_x0,abs(dop1_x0),D,dataOut.nFFTPoints) | |
| 1842 | dataOut.moments[nChannel,0,hei] = A |
|
1885 | dataOut.moments[nChannel,0,hei] = A | |
| 1843 | dataOut.moments[nChannel,1,hei] = B |
|
1886 | dataOut.moments[nChannel,1,hei] = B | |
| 1844 | dataOut.moments[nChannel,2,hei] = C |
|
1887 | dataOut.moments[nChannel,2,hei] = C | |
| 1845 | dataOut.moments[nChannel,3,hei] = D |
|
1888 | dataOut.moments[nChannel,3,hei] = D | |
| 1846 | ''' |
|
1889 | ''' | |
| 1847 | plt.figure() |
|
1890 | plt.figure() | |
| 1848 | plt.plot(VelRange,spc,marker='*',linestyle='') |
|
1891 | plt.plot(VelRange,spc,marker='*',linestyle='') | |
| 1849 | plt.plot(VelRange,fun) |
|
1892 | plt.plot(VelRange,fun) | |
| 1850 | plt.title(dataOut.heightList[hei]) |
|
1893 | plt.title(dataOut.heightList[hei]) | |
| 1851 | plt.show() |
|
1894 | plt.show() | |
| 1852 | ''' |
|
1895 | ''' | |
| 1853 |
|
1896 | |||
| 1854 | return dataOut |
|
1897 | return dataOut | |
| 1855 |
|
1898 | |||
| 1856 | class PrecipitationProc(Operation): |
|
1899 | class PrecipitationProc(Operation): | |
| 1857 |
|
1900 | |||
| 1858 | ''' |
|
1901 | ''' | |
| 1859 | Operator that estimates Reflectivity factor (Z), and estimates rainfall Rate (R) |
|
1902 | Operator that estimates Reflectivity factor (Z), and estimates rainfall Rate (R) | |
| 1860 |
|
1903 | |||
| 1861 | Input: |
|
1904 | Input: | |
| 1862 | self.dataOut.data_pre : SelfSpectra |
|
1905 | self.dataOut.data_pre : SelfSpectra | |
| 1863 |
|
1906 | |||
| 1864 | Output: |
|
1907 | Output: | |
| 1865 |
|
1908 | |||
| 1866 | self.dataOut.data_output : Reflectivity factor, rainfall Rate |
|
1909 | self.dataOut.data_output : Reflectivity factor, rainfall Rate | |
| 1867 |
|
1910 | |||
| 1868 |
|
1911 | |||
| 1869 | Parameters affected: |
|
1912 | Parameters affected: | |
| 1870 | ''' |
|
1913 | ''' | |
| 1871 |
|
1914 | |||
| 1872 | def __init__(self): |
|
1915 | def __init__(self): | |
| 1873 | Operation.__init__(self) |
|
1916 | Operation.__init__(self) | |
| 1874 | self.i=0 |
|
1917 | self.i=0 | |
| 1875 |
|
1918 | |||
| 1876 | def run(self, dataOut, radar=None, Pt=5000, Gt=295.1209, Gr=70.7945, Lambda=0.6741, aL=2.5118, |
|
1919 | def run(self, dataOut, radar=None, Pt=5000, Gt=295.1209, Gr=70.7945, Lambda=0.6741, aL=2.5118, | |
| 1877 | tauW=4e-06, ThetaT=0.1656317, ThetaR=0.36774087, Km2 = 0.93, Altitude=3350,SNRdBlimit=-30,channel=None): |
|
1920 | tauW=4e-06, ThetaT=0.1656317, ThetaR=0.36774087, Km2 = 0.93, Altitude=3350,SNRdBlimit=-30,channel=None): | |
| 1878 |
|
1921 | |||
| 1879 | # print ('Entering PrecepitationProc ... ') |
|
1922 | # print ('Entering PrecepitationProc ... ') | |
| 1880 |
|
1923 | |||
| 1881 | if radar == "MIRA35C" : |
|
1924 | if radar == "MIRA35C" : | |
| 1882 |
|
1925 | |||
| 1883 | self.spc = dataOut.data_pre[0].copy() |
|
1926 | self.spc = dataOut.data_pre[0].copy() | |
| 1884 | self.Num_Hei = self.spc.shape[2] |
|
1927 | self.Num_Hei = self.spc.shape[2] | |
| 1885 | self.Num_Bin = self.spc.shape[1] |
|
1928 | self.Num_Bin = self.spc.shape[1] | |
| 1886 | self.Num_Chn = self.spc.shape[0] |
|
1929 | self.Num_Chn = self.spc.shape[0] | |
| 1887 | Ze = self.dBZeMODE2(dataOut) |
|
1930 | Ze = self.dBZeMODE2(dataOut) | |
| 1888 |
|
1931 | |||
| 1889 | else: |
|
1932 | else: | |
| 1890 |
|
1933 | |||
| 1891 | self.spc = dataOut.data_pre[0].copy() |
|
1934 | self.spc = dataOut.data_pre[0].copy() | |
| 1892 |
|
1935 | |||
| 1893 | #NOTA SE DEBE REMOVER EL RANGO DEL PULSO TX |
|
1936 | #NOTA SE DEBE REMOVER EL RANGO DEL PULSO TX | |
| 1894 | self.spc[:,:,0:7]= numpy.NaN |
|
1937 | self.spc[:,:,0:7]= numpy.NaN | |
| 1895 |
|
1938 | |||
| 1896 | self.Num_Hei = self.spc.shape[2] |
|
1939 | self.Num_Hei = self.spc.shape[2] | |
| 1897 | self.Num_Bin = self.spc.shape[1] |
|
1940 | self.Num_Bin = self.spc.shape[1] | |
| 1898 | self.Num_Chn = self.spc.shape[0] |
|
1941 | self.Num_Chn = self.spc.shape[0] | |
| 1899 |
|
1942 | |||
| 1900 | VelRange = dataOut.spc_range[2] |
|
1943 | VelRange = dataOut.spc_range[2] | |
| 1901 |
|
1944 | |||
| 1902 | ''' Se obtiene la constante del RADAR ''' |
|
1945 | ''' Se obtiene la constante del RADAR ''' | |
| 1903 |
|
1946 | |||
| 1904 | self.Pt = Pt |
|
1947 | self.Pt = Pt | |
| 1905 | self.Gt = Gt |
|
1948 | self.Gt = Gt | |
| 1906 | self.Gr = Gr |
|
1949 | self.Gr = Gr | |
| 1907 | self.Lambda = Lambda |
|
1950 | self.Lambda = Lambda | |
| 1908 | self.aL = aL |
|
1951 | self.aL = aL | |
| 1909 | self.tauW = tauW |
|
1952 | self.tauW = tauW | |
| 1910 | self.ThetaT = ThetaT |
|
1953 | self.ThetaT = ThetaT | |
| 1911 | self.ThetaR = ThetaR |
|
1954 | self.ThetaR = ThetaR | |
| 1912 | self.GSys = 10**(36.63/10) # Ganancia de los LNA 36.63 dB |
|
1955 | self.GSys = 10**(36.63/10) # Ganancia de los LNA 36.63 dB | |
| 1913 | self.lt = 10**(1.67/10) # Perdida en cables Tx 1.67 dB |
|
1956 | self.lt = 10**(1.67/10) # Perdida en cables Tx 1.67 dB | |
| 1914 | self.lr = 10**(5.73/10) # Perdida en cables Rx 5.73 dB |
|
1957 | self.lr = 10**(5.73/10) # Perdida en cables Rx 5.73 dB | |
| 1915 |
|
1958 | |||
| 1916 | Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) ) |
|
1959 | Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) ) | |
| 1917 | Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * tauW * numpy.pi * ThetaT * ThetaR) |
|
1960 | Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * tauW * numpy.pi * ThetaT * ThetaR) | |
| 1918 | RadarConstant = 10e-26 * Numerator / Denominator # |
|
1961 | RadarConstant = 10e-26 * Numerator / Denominator # | |
| 1919 | ExpConstant = 10**(40/10) #Constante Experimental |
|
1962 | ExpConstant = 10**(40/10) #Constante Experimental | |
| 1920 |
|
1963 | |||
| 1921 | SignalPower = numpy.zeros([self.Num_Chn,self.Num_Bin,self.Num_Hei]) |
|
1964 | SignalPower = numpy.zeros([self.Num_Chn,self.Num_Bin,self.Num_Hei]) | |
| 1922 | for i in range(self.Num_Chn): |
|
1965 | for i in range(self.Num_Chn): | |
| 1923 | SignalPower[i,:,:] = self.spc[i,:,:] - dataOut.noise[i] |
|
1966 | SignalPower[i,:,:] = self.spc[i,:,:] - dataOut.noise[i] | |
| 1924 | SignalPower[numpy.where(SignalPower < 0)] = 1e-20 |
|
1967 | SignalPower[numpy.where(SignalPower < 0)] = 1e-20 | |
| 1925 |
|
1968 | |||
| 1926 | if channel is None: |
|
1969 | if channel is None: | |
| 1927 | SPCmean = numpy.mean(SignalPower, 0) |
|
1970 | SPCmean = numpy.mean(SignalPower, 0) | |
| 1928 | else: |
|
1971 | else: | |
| 1929 | SPCmean = SignalPower[channel] |
|
1972 | SPCmean = SignalPower[channel] | |
| 1930 | Pr = SPCmean[:,:]/dataOut.normFactor |
|
1973 | Pr = SPCmean[:,:]/dataOut.normFactor | |
| 1931 |
|
1974 | |||
| 1932 | # Declaring auxiliary variables |
|
1975 | # Declaring auxiliary variables | |
| 1933 | Range = dataOut.heightList*1000. #Range in m |
|
1976 | Range = dataOut.heightList*1000. #Range in m | |
| 1934 | # replicate the heightlist to obtain a matrix [Num_Bin,Num_Hei] |
|
1977 | # replicate the heightlist to obtain a matrix [Num_Bin,Num_Hei] | |
| 1935 | rMtrx = numpy.transpose(numpy.transpose([dataOut.heightList*1000.] * self.Num_Bin)) |
|
1978 | rMtrx = numpy.transpose(numpy.transpose([dataOut.heightList*1000.] * self.Num_Bin)) | |
| 1936 | zMtrx = rMtrx+Altitude |
|
1979 | zMtrx = rMtrx+Altitude | |
| 1937 | # replicate the VelRange to obtain a matrix [Num_Bin,Num_Hei] |
|
1980 | # replicate the VelRange to obtain a matrix [Num_Bin,Num_Hei] | |
| 1938 | VelMtrx = numpy.transpose(numpy.tile(VelRange[:-1], (self.Num_Hei,1))) |
|
1981 | VelMtrx = numpy.transpose(numpy.tile(VelRange[:-1], (self.Num_Hei,1))) | |
| 1939 |
|
1982 | |||
| 1940 | # height dependence to air density Foote and Du Toit (1969) |
|
1983 | # height dependence to air density Foote and Du Toit (1969) | |
| 1941 | delv_z = 1 + 3.68e-5 * zMtrx + 1.71e-9 * zMtrx**2 |
|
1984 | delv_z = 1 + 3.68e-5 * zMtrx + 1.71e-9 * zMtrx**2 | |
| 1942 | VMtrx = VelMtrx / delv_z #Normalized velocity |
|
1985 | VMtrx = VelMtrx / delv_z #Normalized velocity | |
| 1943 | VMtrx[numpy.where(VMtrx> 9.6)] = numpy.NaN |
|
1986 | VMtrx[numpy.where(VMtrx> 9.6)] = numpy.NaN | |
| 1944 | # Diameter is related to the fall speed of falling drops |
|
1987 | # Diameter is related to the fall speed of falling drops | |
| 1945 | D_Vz = -1.667 * numpy.log( 0.9369 - 0.097087 * VMtrx ) # D in [mm] |
|
1988 | D_Vz = -1.667 * numpy.log( 0.9369 - 0.097087 * VMtrx ) # D in [mm] | |
| 1946 | # Only valid for D>= 0.16 mm |
|
1989 | # Only valid for D>= 0.16 mm | |
| 1947 | D_Vz[numpy.where(D_Vz < 0.16)] = numpy.NaN |
|
1990 | D_Vz[numpy.where(D_Vz < 0.16)] = numpy.NaN | |
| 1948 |
|
1991 | |||
| 1949 | #Calculate Radar Reflectivity ETAn |
|
1992 | #Calculate Radar Reflectivity ETAn | |
| 1950 | ETAn = (RadarConstant *ExpConstant) * Pr * rMtrx**2 #Reflectivity (ETA) |
|
1993 | ETAn = (RadarConstant *ExpConstant) * Pr * rMtrx**2 #Reflectivity (ETA) | |
| 1951 | ETAd = ETAn * 6.18 * exp( -0.6 * D_Vz ) * delv_z |
|
1994 | ETAd = ETAn * 6.18 * exp( -0.6 * D_Vz ) * delv_z | |
| 1952 | # Radar Cross Section |
|
1995 | # Radar Cross Section | |
| 1953 | sigmaD = Km2 * (D_Vz * 1e-3 )**6 * numpy.pi**5 / Lambda**4 |
|
1996 | sigmaD = Km2 * (D_Vz * 1e-3 )**6 * numpy.pi**5 / Lambda**4 | |
| 1954 | # Drop Size Distribution |
|
1997 | # Drop Size Distribution | |
| 1955 | DSD = ETAn / sigmaD |
|
1998 | DSD = ETAn / sigmaD | |
| 1956 | # Equivalente Reflectivy |
|
1999 | # Equivalente Reflectivy | |
| 1957 | Ze_eqn = numpy.nansum( DSD * D_Vz**6 ,axis=0) |
|
2000 | Ze_eqn = numpy.nansum( DSD * D_Vz**6 ,axis=0) | |
| 1958 | Ze_org = numpy.nansum(ETAn * Lambda**4, axis=0) / (1e-18*numpy.pi**5 * Km2) # [mm^6 /m^3] |
|
2001 | Ze_org = numpy.nansum(ETAn * Lambda**4, axis=0) / (1e-18*numpy.pi**5 * Km2) # [mm^6 /m^3] | |
| 1959 | # RainFall Rate |
|
2002 | # RainFall Rate | |
| 1960 | RR = 0.0006*numpy.pi * numpy.nansum( D_Vz**3 * DSD * VelMtrx ,0) #mm/hr |
|
2003 | RR = 0.0006*numpy.pi * numpy.nansum( D_Vz**3 * DSD * VelMtrx ,0) #mm/hr | |
| 1961 |
|
2004 | |||
| 1962 | # Censoring the data |
|
2005 | # Censoring the data | |
| 1963 | # Removing data with SNRth < 0dB se debe considerar el SNR por canal |
|
2006 | # Removing data with SNRth < 0dB se debe considerar el SNR por canal | |
| 1964 | SNRth = 10**(SNRdBlimit/10) #-30dB |
|
2007 | SNRth = 10**(SNRdBlimit/10) #-30dB | |
| 1965 | novalid = numpy.where((dataOut.data_snr[0,:] <SNRth) | (dataOut.data_snr[1,:] <SNRth) | (dataOut.data_snr[2,:] <SNRth)) # AND condition. Maybe OR condition better |
|
2008 | novalid = numpy.where((dataOut.data_snr[0,:] <SNRth) | (dataOut.data_snr[1,:] <SNRth) | (dataOut.data_snr[2,:] <SNRth)) # AND condition. Maybe OR condition better | |
| 1966 | W = numpy.nanmean(dataOut.data_dop,0) |
|
2009 | W = numpy.nanmean(dataOut.data_dop,0) | |
| 1967 | W[novalid] = numpy.NaN |
|
2010 | W[novalid] = numpy.NaN | |
| 1968 | Ze_org[novalid] = numpy.NaN |
|
2011 | Ze_org[novalid] = numpy.NaN | |
| 1969 | RR[novalid] = numpy.NaN |
|
2012 | RR[novalid] = numpy.NaN | |
| 1970 |
|
2013 | |||
| 1971 | dataOut.data_output = RR[8] |
|
2014 | dataOut.data_output = RR[8] | |
| 1972 | dataOut.data_param = numpy.ones([3,self.Num_Hei]) |
|
2015 | dataOut.data_param = numpy.ones([3,self.Num_Hei]) | |
| 1973 | dataOut.channelList = [0,1,2] |
|
2016 | dataOut.channelList = [0,1,2] | |
| 1974 |
|
2017 | |||
| 1975 | dataOut.data_param[0]=10*numpy.log10(Ze_org) |
|
2018 | dataOut.data_param[0]=10*numpy.log10(Ze_org) | |
| 1976 | dataOut.data_param[1]=-W |
|
2019 | dataOut.data_param[1]=-W | |
| 1977 | dataOut.data_param[2]=RR |
|
2020 | dataOut.data_param[2]=RR | |
| 1978 |
|
2021 | |||
| 1979 | # print ('Leaving PrecepitationProc ... ') |
|
2022 | # print ('Leaving PrecepitationProc ... ') | |
| 1980 | return dataOut |
|
2023 | return dataOut | |
| 1981 |
|
2024 | |||
| 1982 | def dBZeMODE2(self, dataOut): # Processing for MIRA35C |
|
2025 | def dBZeMODE2(self, dataOut): # Processing for MIRA35C | |
| 1983 |
|
2026 | |||
| 1984 | NPW = dataOut.NPW |
|
2027 | NPW = dataOut.NPW | |
| 1985 | COFA = dataOut.COFA |
|
2028 | COFA = dataOut.COFA | |
| 1986 |
|
2029 | |||
| 1987 | SNR = numpy.array([self.spc[0,:,:] / NPW[0]]) #, self.spc[1,:,:] / NPW[1]]) |
|
2030 | SNR = numpy.array([self.spc[0,:,:] / NPW[0]]) #, self.spc[1,:,:] / NPW[1]]) | |
| 1988 | RadarConst = dataOut.RadarConst |
|
2031 | RadarConst = dataOut.RadarConst | |
| 1989 | #frequency = 34.85*10**9 |
|
2032 | #frequency = 34.85*10**9 | |
| 1990 |
|
2033 | |||
| 1991 | ETA = numpy.zeros(([self.Num_Chn ,self.Num_Hei])) |
|
2034 | ETA = numpy.zeros(([self.Num_Chn ,self.Num_Hei])) | |
| 1992 | data_output = numpy.ones([self.Num_Chn , self.Num_Hei])*numpy.NaN |
|
2035 | data_output = numpy.ones([self.Num_Chn , self.Num_Hei])*numpy.NaN | |
| 1993 |
|
2036 | |||
| 1994 | ETA = numpy.sum(SNR,1) |
|
2037 | ETA = numpy.sum(SNR,1) | |
| 1995 |
|
2038 | |||
| 1996 | ETA = numpy.where(ETA != 0. , ETA, numpy.NaN) |
|
2039 | ETA = numpy.where(ETA != 0. , ETA, numpy.NaN) | |
| 1997 |
|
2040 | |||
| 1998 | Ze = numpy.ones([self.Num_Chn, self.Num_Hei] ) |
|
2041 | Ze = numpy.ones([self.Num_Chn, self.Num_Hei] ) | |
| 1999 |
|
2042 | |||
| 2000 | for r in range(self.Num_Hei): |
|
2043 | for r in range(self.Num_Hei): | |
| 2001 |
|
2044 | |||
| 2002 | Ze[0,r] = ( ETA[0,r] ) * COFA[0,r][0] * RadarConst * ((r/5000.)**2) |
|
2045 | Ze[0,r] = ( ETA[0,r] ) * COFA[0,r][0] * RadarConst * ((r/5000.)**2) | |
| 2003 | #Ze[1,r] = ( ETA[1,r] ) * COFA[1,r][0] * RadarConst * ((r/5000.)**2) |
|
2046 | #Ze[1,r] = ( ETA[1,r] ) * COFA[1,r][0] * RadarConst * ((r/5000.)**2) | |
| 2004 |
|
2047 | |||
| 2005 | return Ze |
|
2048 | return Ze | |
| 2006 |
|
2049 | |||
| 2007 | # def GetRadarConstant(self): |
|
2050 | # def GetRadarConstant(self): | |
| 2008 | # |
|
2051 | # | |
| 2009 | # """ |
|
2052 | # """ | |
| 2010 | # Constants: |
|
2053 | # Constants: | |
| 2011 | # |
|
2054 | # | |
| 2012 | # Pt: Transmission Power dB 5kW 5000 |
|
2055 | # Pt: Transmission Power dB 5kW 5000 | |
| 2013 | # Gt: Transmission Gain dB 24.7 dB 295.1209 |
|
2056 | # Gt: Transmission Gain dB 24.7 dB 295.1209 | |
| 2014 | # Gr: Reception Gain dB 18.5 dB 70.7945 |
|
2057 | # Gr: Reception Gain dB 18.5 dB 70.7945 | |
| 2015 | # Lambda: Wavelenght m 0.6741 m 0.6741 |
|
2058 | # Lambda: Wavelenght m 0.6741 m 0.6741 | |
| 2016 | # aL: Attenuation loses dB 4dB 2.5118 |
|
2059 | # aL: Attenuation loses dB 4dB 2.5118 | |
| 2017 | # tauW: Width of transmission pulse s 4us 4e-6 |
|
2060 | # tauW: Width of transmission pulse s 4us 4e-6 | |
| 2018 | # ThetaT: Transmission antenna bean angle rad 0.1656317 rad 0.1656317 |
|
2061 | # ThetaT: Transmission antenna bean angle rad 0.1656317 rad 0.1656317 | |
| 2019 | # ThetaR: Reception antenna beam angle rad 0.36774087 rad 0.36774087 |
|
2062 | # ThetaR: Reception antenna beam angle rad 0.36774087 rad 0.36774087 | |
| 2020 | # |
|
2063 | # | |
| 2021 | # """ |
|
2064 | # """ | |
| 2022 | # |
|
2065 | # | |
| 2023 | # Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) ) |
|
2066 | # Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) ) | |
| 2024 | # Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * TauW * numpy.pi * ThetaT * TheraR) |
|
2067 | # Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * TauW * numpy.pi * ThetaT * TheraR) | |
| 2025 | # RadarConstant = Numerator / Denominator |
|
2068 | # RadarConstant = Numerator / Denominator | |
| 2026 | # |
|
2069 | # | |
| 2027 | # return RadarConstant |
|
2070 | # return RadarConstant | |
| 2028 |
|
2071 | |||
| 2029 |
|
2072 | |||
| 2030 | class FullSpectralAnalysis(Operation): |
|
2073 | class FullSpectralAnalysis(Operation): | |
| 2031 |
|
2074 | |||
| 2032 | """ |
|
2075 | """ | |
| 2033 | Function that implements Full Spectral Analysis technique. |
|
2076 | Function that implements Full Spectral Analysis technique. | |
| 2034 |
|
2077 | |||
| 2035 | Input: |
|
2078 | Input: | |
| 2036 | self.dataOut.data_pre : SelfSpectra and CrossSpectra data |
|
2079 | self.dataOut.data_pre : SelfSpectra and CrossSpectra data | |
| 2037 | self.dataOut.groupList : Pairlist of channels |
|
2080 | self.dataOut.groupList : Pairlist of channels | |
| 2038 | self.dataOut.ChanDist : Physical distance between receivers |
|
2081 | self.dataOut.ChanDist : Physical distance between receivers | |
| 2039 |
|
2082 | |||
| 2040 |
|
2083 | |||
| 2041 | Output: |
|
2084 | Output: | |
| 2042 |
|
2085 | |||
| 2043 | self.dataOut.data_output : Zonal wind, Meridional wind, and Vertical wind |
|
2086 | self.dataOut.data_output : Zonal wind, Meridional wind, and Vertical wind | |
| 2044 |
|
2087 | |||
| 2045 |
|
2088 | |||
| 2046 | Parameters affected: Winds, height range, SNR |
|
2089 | Parameters affected: Winds, height range, SNR | |
| 2047 |
|
2090 | |||
| 2048 | """ |
|
2091 | """ | |
| 2049 | def run(self, dataOut, Xi01=None, Xi02=None, Xi12=None, Eta01=None, Eta02=None, Eta12=None, SNRdBlimit=-30,minheight=None, maxheight=None, NegativeLimit=None, PositiveLimit=None): |
|
2092 | def run(self, dataOut, Xi01=None, Xi02=None, Xi12=None, Eta01=None, Eta02=None, Eta12=None, SNRdBlimit=-30,minheight=None, maxheight=None, NegativeLimit=None, PositiveLimit=None): | |
| 2050 |
|
2093 | |||
| 2051 | spc = dataOut.data_pre[0].copy() |
|
2094 | spc = dataOut.data_pre[0].copy() | |
| 2052 | cspc = dataOut.data_pre[1] |
|
2095 | cspc = dataOut.data_pre[1] | |
| 2053 | nHeights = spc.shape[2] |
|
2096 | nHeights = spc.shape[2] | |
| 2054 |
|
2097 | |||
| 2055 | # first_height = 0.75 #km (ref: data header 20170822) |
|
2098 | # first_height = 0.75 #km (ref: data header 20170822) | |
| 2056 | # resolution_height = 0.075 #km |
|
2099 | # resolution_height = 0.075 #km | |
| 2057 | ''' |
|
2100 | ''' | |
| 2058 | finding height range. check this when radar parameters are changed! |
|
2101 | finding height range. check this when radar parameters are changed! | |
| 2059 | ''' |
|
2102 | ''' | |
| 2060 | if maxheight is not None: |
|
2103 | if maxheight is not None: | |
| 2061 | # range_max = math.ceil((maxheight - first_height) / resolution_height) # theoretical |
|
2104 | # range_max = math.ceil((maxheight - first_height) / resolution_height) # theoretical | |
| 2062 | range_max = math.ceil(13.26 * maxheight - 3) # empirical, works better |
|
2105 | range_max = math.ceil(13.26 * maxheight - 3) # empirical, works better | |
| 2063 | else: |
|
2106 | else: | |
| 2064 | range_max = nHeights |
|
2107 | range_max = nHeights | |
| 2065 | if minheight is not None: |
|
2108 | if minheight is not None: | |
| 2066 | # range_min = int((minheight - first_height) / resolution_height) # theoretical |
|
2109 | # range_min = int((minheight - first_height) / resolution_height) # theoretical | |
| 2067 | range_min = int(13.26 * minheight - 5) # empirical, works better |
|
2110 | range_min = int(13.26 * minheight - 5) # empirical, works better | |
| 2068 | if range_min < 0: |
|
2111 | if range_min < 0: | |
| 2069 | range_min = 0 |
|
2112 | range_min = 0 | |
| 2070 | else: |
|
2113 | else: | |
| 2071 | range_min = 0 |
|
2114 | range_min = 0 | |
| 2072 |
|
2115 | |||
| 2073 | pairsList = dataOut.groupList |
|
2116 | pairsList = dataOut.groupList | |
| 2074 | if dataOut.ChanDist is not None : |
|
2117 | if dataOut.ChanDist is not None : | |
| 2075 | ChanDist = dataOut.ChanDist |
|
2118 | ChanDist = dataOut.ChanDist | |
| 2076 | else: |
|
2119 | else: | |
| 2077 | ChanDist = numpy.array([[Xi01, Eta01],[Xi02,Eta02],[Xi12,Eta12]]) |
|
2120 | ChanDist = numpy.array([[Xi01, Eta01],[Xi02,Eta02],[Xi12,Eta12]]) | |
| 2078 |
|
2121 | |||
| 2079 | # 4 variables: zonal, meridional, vertical, and average SNR |
|
2122 | # 4 variables: zonal, meridional, vertical, and average SNR | |
| 2080 | data_param = numpy.zeros([4,nHeights]) * numpy.NaN |
|
2123 | data_param = numpy.zeros([4,nHeights]) * numpy.NaN | |
| 2081 | velocityX = numpy.zeros([nHeights]) * numpy.NaN |
|
2124 | velocityX = numpy.zeros([nHeights]) * numpy.NaN | |
| 2082 | velocityY = numpy.zeros([nHeights]) * numpy.NaN |
|
2125 | velocityY = numpy.zeros([nHeights]) * numpy.NaN | |
| 2083 | velocityZ = numpy.zeros([nHeights]) * numpy.NaN |
|
2126 | velocityZ = numpy.zeros([nHeights]) * numpy.NaN | |
| 2084 |
|
2127 | |||
| 2085 | dbSNR = 10*numpy.log10(numpy.average(dataOut.data_snr,0)) |
|
2128 | dbSNR = 10*numpy.log10(numpy.average(dataOut.data_snr,0)) | |
| 2086 |
|
2129 | |||
| 2087 | '''***********************************************WIND ESTIMATION**************************************''' |
|
2130 | '''***********************************************WIND ESTIMATION**************************************''' | |
| 2088 | for Height in range(nHeights): |
|
2131 | for Height in range(nHeights): | |
| 2089 |
|
2132 | |||
| 2090 | if Height >= range_min and Height < range_max: |
|
2133 | if Height >= range_min and Height < range_max: | |
| 2091 | # error_code will be useful in future analysis |
|
2134 | # error_code will be useful in future analysis | |
| 2092 | [Vzon,Vmer,Vver, error_code] = self.WindEstimation(spc[:,:,Height], cspc[:,:,Height], pairsList, |
|
2135 | [Vzon,Vmer,Vver, error_code] = self.WindEstimation(spc[:,:,Height], cspc[:,:,Height], pairsList, | |
| 2093 | ChanDist, Height, dataOut.noise, dataOut.spc_range, dbSNR[Height], SNRdBlimit, NegativeLimit, PositiveLimit,dataOut.frequency) |
|
2136 | ChanDist, Height, dataOut.noise, dataOut.spc_range, dbSNR[Height], SNRdBlimit, NegativeLimit, PositiveLimit,dataOut.frequency) | |
| 2094 |
|
2137 | |||
| 2095 | if abs(Vzon) < 100. and abs(Vmer) < 100.: |
|
2138 | if abs(Vzon) < 100. and abs(Vmer) < 100.: | |
| 2096 | velocityX[Height] = Vzon |
|
2139 | velocityX[Height] = Vzon | |
| 2097 | velocityY[Height] = -Vmer |
|
2140 | velocityY[Height] = -Vmer | |
| 2098 | velocityZ[Height] = Vver |
|
2141 | velocityZ[Height] = Vver | |
| 2099 |
|
2142 | |||
| 2100 | # Censoring data with SNR threshold |
|
2143 | # Censoring data with SNR threshold | |
| 2101 | dbSNR [dbSNR < SNRdBlimit] = numpy.NaN |
|
2144 | dbSNR [dbSNR < SNRdBlimit] = numpy.NaN | |
| 2102 |
|
2145 | |||
| 2103 | data_param[0] = velocityX |
|
2146 | data_param[0] = velocityX | |
| 2104 | data_param[1] = velocityY |
|
2147 | data_param[1] = velocityY | |
| 2105 | data_param[2] = velocityZ |
|
2148 | data_param[2] = velocityZ | |
| 2106 | data_param[3] = dbSNR |
|
2149 | data_param[3] = dbSNR | |
| 2107 | dataOut.data_param = data_param |
|
2150 | dataOut.data_param = data_param | |
| 2108 | return dataOut |
|
2151 | return dataOut | |
| 2109 |
|
2152 | |||
| 2110 | def moving_average(self,x, N=2): |
|
2153 | def moving_average(self,x, N=2): | |
| 2111 | """ convolution for smoothenig data. note that last N-1 values are convolution with zeroes """ |
|
2154 | """ convolution for smoothenig data. note that last N-1 values are convolution with zeroes """ | |
| 2112 | return numpy.convolve(x, numpy.ones((N,))/N)[(N-1):] |
|
2155 | return numpy.convolve(x, numpy.ones((N,))/N)[(N-1):] | |
| 2113 |
|
2156 | |||
| 2114 | def gaus(self,xSamples,Amp,Mu,Sigma): |
|
2157 | def gaus(self,xSamples,Amp,Mu,Sigma): | |
| 2115 | return Amp * numpy.exp(-0.5*((xSamples - Mu)/Sigma)**2) |
|
2158 | return Amp * numpy.exp(-0.5*((xSamples - Mu)/Sigma)**2) | |
| 2116 |
|
2159 | |||
| 2117 | def Moments(self, ySamples, xSamples): |
|
2160 | def Moments(self, ySamples, xSamples): | |
| 2118 | Power = numpy.nanmean(ySamples) # Power, 0th Moment |
|
2161 | Power = numpy.nanmean(ySamples) # Power, 0th Moment | |
| 2119 | yNorm = ySamples / numpy.nansum(ySamples) |
|
2162 | yNorm = ySamples / numpy.nansum(ySamples) | |
| 2120 | RadVel = numpy.nansum(xSamples * yNorm) # Radial Velocity, 1st Moment |
|
2163 | RadVel = numpy.nansum(xSamples * yNorm) # Radial Velocity, 1st Moment | |
| 2121 | Sigma2 = numpy.nansum(yNorm * (xSamples - RadVel)**2) # Spectral Width, 2nd Moment |
|
2164 | Sigma2 = numpy.nansum(yNorm * (xSamples - RadVel)**2) # Spectral Width, 2nd Moment | |
| 2122 | StdDev = numpy.sqrt(numpy.abs(Sigma2)) # Desv. Estandar, Ancho espectral |
|
2165 | StdDev = numpy.sqrt(numpy.abs(Sigma2)) # Desv. Estandar, Ancho espectral | |
| 2123 | return numpy.array([Power,RadVel,StdDev]) |
|
2166 | return numpy.array([Power,RadVel,StdDev]) | |
| 2124 |
|
2167 | |||
| 2125 | def StopWindEstimation(self, error_code): |
|
2168 | def StopWindEstimation(self, error_code): | |
| 2126 | Vzon = numpy.NaN |
|
2169 | Vzon = numpy.NaN | |
| 2127 | Vmer = numpy.NaN |
|
2170 | Vmer = numpy.NaN | |
| 2128 | Vver = numpy.NaN |
|
2171 | Vver = numpy.NaN | |
| 2129 | return Vzon, Vmer, Vver, error_code |
|
2172 | return Vzon, Vmer, Vver, error_code | |
| 2130 |
|
2173 | |||
| 2131 | def AntiAliasing(self, interval, maxstep): |
|
2174 | def AntiAliasing(self, interval, maxstep): | |
| 2132 | """ |
|
2175 | """ | |
| 2133 | function to prevent errors from aliased values when computing phaseslope |
|
2176 | function to prevent errors from aliased values when computing phaseslope | |
| 2134 | """ |
|
2177 | """ | |
| 2135 | antialiased = numpy.zeros(len(interval)) |
|
2178 | antialiased = numpy.zeros(len(interval)) | |
| 2136 | copyinterval = interval.copy() |
|
2179 | copyinterval = interval.copy() | |
| 2137 |
|
2180 | |||
| 2138 | antialiased[0] = copyinterval[0] |
|
2181 | antialiased[0] = copyinterval[0] | |
| 2139 |
|
2182 | |||
| 2140 | for i in range(1,len(antialiased)): |
|
2183 | for i in range(1,len(antialiased)): | |
| 2141 | step = interval[i] - interval[i-1] |
|
2184 | step = interval[i] - interval[i-1] | |
| 2142 | if step > maxstep: |
|
2185 | if step > maxstep: | |
| 2143 | copyinterval -= 2*numpy.pi |
|
2186 | copyinterval -= 2*numpy.pi | |
| 2144 | antialiased[i] = copyinterval[i] |
|
2187 | antialiased[i] = copyinterval[i] | |
| 2145 | elif step < maxstep*(-1): |
|
2188 | elif step < maxstep*(-1): | |
| 2146 | copyinterval += 2*numpy.pi |
|
2189 | copyinterval += 2*numpy.pi | |
| 2147 | antialiased[i] = copyinterval[i] |
|
2190 | antialiased[i] = copyinterval[i] | |
| 2148 | else: |
|
2191 | else: | |
| 2149 | antialiased[i] = copyinterval[i].copy() |
|
2192 | antialiased[i] = copyinterval[i].copy() | |
| 2150 |
|
2193 | |||
| 2151 | return antialiased |
|
2194 | return antialiased | |
| 2152 |
|
2195 | |||
| 2153 | def WindEstimation(self, spc, cspc, pairsList, ChanDist, Height, noise, AbbsisaRange, dbSNR, SNRlimit, NegativeLimit, PositiveLimit, radfreq): |
|
2196 | def WindEstimation(self, spc, cspc, pairsList, ChanDist, Height, noise, AbbsisaRange, dbSNR, SNRlimit, NegativeLimit, PositiveLimit, radfreq): | |
| 2154 | """ |
|
2197 | """ | |
| 2155 | Function that Calculates Zonal, Meridional and Vertical wind velocities. |
|
2198 | Function that Calculates Zonal, Meridional and Vertical wind velocities. | |
| 2156 | Initial Version by E. Bocanegra updated by J. Zibell until Nov. 2019. |
|
2199 | Initial Version by E. Bocanegra updated by J. Zibell until Nov. 2019. | |
| 2157 |
|
2200 | |||
| 2158 | Input: |
|
2201 | Input: | |
| 2159 | spc, cspc : self spectra and cross spectra data. In Briggs notation something like S_i*(S_i)_conj, (S_j)_conj respectively. |
|
2202 | spc, cspc : self spectra and cross spectra data. In Briggs notation something like S_i*(S_i)_conj, (S_j)_conj respectively. | |
| 2160 | pairsList : Pairlist of channels |
|
2203 | pairsList : Pairlist of channels | |
| 2161 | ChanDist : array of xi_ij and eta_ij |
|
2204 | ChanDist : array of xi_ij and eta_ij | |
| 2162 | Height : height at which data is processed |
|
2205 | Height : height at which data is processed | |
| 2163 | noise : noise in [channels] format for specific height |
|
2206 | noise : noise in [channels] format for specific height | |
| 2164 | Abbsisarange : range of the frequencies or velocities |
|
2207 | Abbsisarange : range of the frequencies or velocities | |
| 2165 | dbSNR, SNRlimit : signal to noise ratio in db, lower limit |
|
2208 | dbSNR, SNRlimit : signal to noise ratio in db, lower limit | |
| 2166 |
|
2209 | |||
| 2167 | Output: |
|
2210 | Output: | |
| 2168 | Vzon, Vmer, Vver : wind velocities |
|
2211 | Vzon, Vmer, Vver : wind velocities | |
| 2169 | error_code : int that states where code is terminated |
|
2212 | error_code : int that states where code is terminated | |
| 2170 |
|
2213 | |||
| 2171 | 0 : no error detected |
|
2214 | 0 : no error detected | |
| 2172 | 1 : Gaussian of mean spc exceeds widthlimit |
|
2215 | 1 : Gaussian of mean spc exceeds widthlimit | |
| 2173 | 2 : no Gaussian of mean spc found |
|
2216 | 2 : no Gaussian of mean spc found | |
| 2174 | 3 : SNR to low or velocity to high -> prec. e.g. |
|
2217 | 3 : SNR to low or velocity to high -> prec. e.g. | |
| 2175 | 4 : at least one Gaussian of cspc exceeds widthlimit |
|
2218 | 4 : at least one Gaussian of cspc exceeds widthlimit | |
| 2176 | 5 : zero out of three cspc Gaussian fits converged |
|
2219 | 5 : zero out of three cspc Gaussian fits converged | |
| 2177 | 6 : phase slope fit could not be found |
|
2220 | 6 : phase slope fit could not be found | |
| 2178 | 7 : arrays used to fit phase have different length |
|
2221 | 7 : arrays used to fit phase have different length | |
| 2179 | 8 : frequency range is either too short (len <= 5) or very long (> 30% of cspc) |
|
2222 | 8 : frequency range is either too short (len <= 5) or very long (> 30% of cspc) | |
| 2180 |
|
2223 | |||
| 2181 | """ |
|
2224 | """ | |
| 2182 |
|
2225 | |||
| 2183 | error_code = 0 |
|
2226 | error_code = 0 | |
| 2184 |
|
2227 | |||
| 2185 | nChan = spc.shape[0] |
|
2228 | nChan = spc.shape[0] | |
| 2186 | nProf = spc.shape[1] |
|
2229 | nProf = spc.shape[1] | |
| 2187 | nPair = cspc.shape[0] |
|
2230 | nPair = cspc.shape[0] | |
| 2188 |
|
2231 | |||
| 2189 | SPC_Samples = numpy.zeros([nChan, nProf]) # for normalized spc values for one height |
|
2232 | SPC_Samples = numpy.zeros([nChan, nProf]) # for normalized spc values for one height | |
| 2190 | CSPC_Samples = numpy.zeros([nPair, nProf], dtype=numpy.complex_) # for normalized cspc values |
|
2233 | CSPC_Samples = numpy.zeros([nPair, nProf], dtype=numpy.complex_) # for normalized cspc values | |
| 2191 | phase = numpy.zeros([nPair, nProf]) # phase between channels |
|
2234 | phase = numpy.zeros([nPair, nProf]) # phase between channels | |
| 2192 | PhaseSlope = numpy.zeros(nPair) # slope of the phases, channelwise |
|
2235 | PhaseSlope = numpy.zeros(nPair) # slope of the phases, channelwise | |
| 2193 | PhaseInter = numpy.zeros(nPair) # intercept to the slope of the phases, channelwise |
|
2236 | PhaseInter = numpy.zeros(nPair) # intercept to the slope of the phases, channelwise | |
| 2194 | xFrec = AbbsisaRange[0][:-1] # frequency range |
|
2237 | xFrec = AbbsisaRange[0][:-1] # frequency range | |
| 2195 | xVel = AbbsisaRange[2][:-1] # velocity range |
|
2238 | xVel = AbbsisaRange[2][:-1] # velocity range | |
| 2196 | xSamples = xFrec # the frequency range is taken |
|
2239 | xSamples = xFrec # the frequency range is taken | |
| 2197 | delta_x = xSamples[1] - xSamples[0] # delta_f or delta_x |
|
2240 | delta_x = xSamples[1] - xSamples[0] # delta_f or delta_x | |
| 2198 |
|
2241 | |||
| 2199 | # only consider velocities with in NegativeLimit and PositiveLimit |
|
2242 | # only consider velocities with in NegativeLimit and PositiveLimit | |
| 2200 | if (NegativeLimit is None): |
|
2243 | if (NegativeLimit is None): | |
| 2201 | NegativeLimit = numpy.min(xVel) |
|
2244 | NegativeLimit = numpy.min(xVel) | |
| 2202 | if (PositiveLimit is None): |
|
2245 | if (PositiveLimit is None): | |
| 2203 | PositiveLimit = numpy.max(xVel) |
|
2246 | PositiveLimit = numpy.max(xVel) | |
| 2204 | xvalid = numpy.where((xVel > NegativeLimit) & (xVel < PositiveLimit)) |
|
2247 | xvalid = numpy.where((xVel > NegativeLimit) & (xVel < PositiveLimit)) | |
| 2205 | xSamples_zoom = xSamples[xvalid] |
|
2248 | xSamples_zoom = xSamples[xvalid] | |
| 2206 |
|
2249 | |||
| 2207 | '''Getting Eij and Nij''' |
|
2250 | '''Getting Eij and Nij''' | |
| 2208 | Xi01, Xi02, Xi12 = ChanDist[:,0] |
|
2251 | Xi01, Xi02, Xi12 = ChanDist[:,0] | |
| 2209 | Eta01, Eta02, Eta12 = ChanDist[:,1] |
|
2252 | Eta01, Eta02, Eta12 = ChanDist[:,1] | |
| 2210 |
|
2253 | |||
| 2211 | # spwd limit - updated by D. ScipiΓ³n 30.03.2021 |
|
2254 | # spwd limit - updated by D. ScipiΓ³n 30.03.2021 | |
| 2212 | widthlimit = 10 |
|
2255 | widthlimit = 10 | |
| 2213 | '''************************* SPC is normalized ********************************''' |
|
2256 | '''************************* SPC is normalized ********************************''' | |
| 2214 | spc_norm = spc.copy() |
|
2257 | spc_norm = spc.copy() | |
| 2215 | # For each channel |
|
2258 | # For each channel | |
| 2216 | for i in range(nChan): |
|
2259 | for i in range(nChan): | |
| 2217 | spc_sub = spc_norm[i,:] - noise[i] # only the signal power |
|
2260 | spc_sub = spc_norm[i,:] - noise[i] # only the signal power | |
| 2218 | SPC_Samples[i] = spc_sub / (numpy.nansum(spc_sub) * delta_x) |
|
2261 | SPC_Samples[i] = spc_sub / (numpy.nansum(spc_sub) * delta_x) | |
| 2219 |
|
2262 | |||
| 2220 | '''********************** FITTING MEAN SPC GAUSSIAN **********************''' |
|
2263 | '''********************** FITTING MEAN SPC GAUSSIAN **********************''' | |
| 2221 |
|
2264 | |||
| 2222 | """ the gaussian of the mean: first subtract noise, then normalize. this is legal because |
|
2265 | """ the gaussian of the mean: first subtract noise, then normalize. this is legal because | |
| 2223 | you only fit the curve and don't need the absolute value of height for calculation, |
|
2266 | you only fit the curve and don't need the absolute value of height for calculation, | |
| 2224 | only for estimation of width. for normalization of cross spectra, you need initial, |
|
2267 | only for estimation of width. for normalization of cross spectra, you need initial, | |
| 2225 | unnormalized self-spectra With noise. |
|
2268 | unnormalized self-spectra With noise. | |
| 2226 |
|
2269 | |||
| 2227 | Technically, you don't even need to normalize the self-spectra, as you only need the |
|
2270 | Technically, you don't even need to normalize the self-spectra, as you only need the | |
| 2228 | width of the peak. However, it was left this way. Note that the normalization has a flaw: |
|
2271 | width of the peak. However, it was left this way. Note that the normalization has a flaw: | |
| 2229 | due to subtraction of the noise, some values are below zero. Raw "spc" values should be |
|
2272 | due to subtraction of the noise, some values are below zero. Raw "spc" values should be | |
| 2230 | >= 0, as it is the modulus squared of the signals (complex * it's conjugate) |
|
2273 | >= 0, as it is the modulus squared of the signals (complex * it's conjugate) | |
| 2231 | """ |
|
2274 | """ | |
| 2232 | # initial conditions |
|
2275 | # initial conditions | |
| 2233 | popt = [1e-10,0,1e-10] |
|
2276 | popt = [1e-10,0,1e-10] | |
| 2234 | # Spectra average |
|
2277 | # Spectra average | |
| 2235 | SPCMean = numpy.average(SPC_Samples,0) |
|
2278 | SPCMean = numpy.average(SPC_Samples,0) | |
| 2236 | # Moments in frequency |
|
2279 | # Moments in frequency | |
| 2237 | SPCMoments = self.Moments(SPCMean[xvalid], xSamples_zoom) |
|
2280 | SPCMoments = self.Moments(SPCMean[xvalid], xSamples_zoom) | |
| 2238 |
|
2281 | |||
| 2239 | # Gauss Fit SPC in frequency domain |
|
2282 | # Gauss Fit SPC in frequency domain | |
| 2240 | if dbSNR > SNRlimit: # only if SNR > SNRth |
|
2283 | if dbSNR > SNRlimit: # only if SNR > SNRth | |
| 2241 | try: |
|
2284 | try: | |
| 2242 | popt,pcov = curve_fit(self.gaus,xSamples_zoom,SPCMean[xvalid],p0=SPCMoments) |
|
2285 | popt,pcov = curve_fit(self.gaus,xSamples_zoom,SPCMean[xvalid],p0=SPCMoments) | |
| 2243 | if popt[2] <= 0 or popt[2] > widthlimit: # CONDITION |
|
2286 | if popt[2] <= 0 or popt[2] > widthlimit: # CONDITION | |
| 2244 | return self.StopWindEstimation(error_code = 1) |
|
2287 | return self.StopWindEstimation(error_code = 1) | |
| 2245 | FitGauss = self.gaus(xSamples_zoom,*popt) |
|
2288 | FitGauss = self.gaus(xSamples_zoom,*popt) | |
| 2246 | except :#RuntimeError: |
|
2289 | except :#RuntimeError: | |
| 2247 | return self.StopWindEstimation(error_code = 2) |
|
2290 | return self.StopWindEstimation(error_code = 2) | |
| 2248 | else: |
|
2291 | else: | |
| 2249 | return self.StopWindEstimation(error_code = 3) |
|
2292 | return self.StopWindEstimation(error_code = 3) | |
| 2250 |
|
2293 | |||
| 2251 | '''***************************** CSPC Normalization ************************* |
|
2294 | '''***************************** CSPC Normalization ************************* | |
| 2252 | The Spc spectra are used to normalize the crossspectra. Peaks from precipitation |
|
2295 | The Spc spectra are used to normalize the crossspectra. Peaks from precipitation | |
| 2253 | influence the norm which is not desired. First, a range is identified where the |
|
2296 | influence the norm which is not desired. First, a range is identified where the | |
| 2254 | wind peak is estimated -> sum_wind is sum of those frequencies. Next, the area |
|
2297 | wind peak is estimated -> sum_wind is sum of those frequencies. Next, the area | |
| 2255 | around it gets cut off and values replaced by mean determined by the boundary |
|
2298 | around it gets cut off and values replaced by mean determined by the boundary | |
| 2256 | data -> sum_noise (spc is not normalized here, thats why the noise is important) |
|
2299 | data -> sum_noise (spc is not normalized here, thats why the noise is important) | |
| 2257 |
|
2300 | |||
| 2258 | The sums are then added and multiplied by range/datapoints, because you need |
|
2301 | The sums are then added and multiplied by range/datapoints, because you need | |
| 2259 | an integral and not a sum for normalization. |
|
2302 | an integral and not a sum for normalization. | |
| 2260 |
|
2303 | |||
| 2261 | A norm is found according to Briggs 92. |
|
2304 | A norm is found according to Briggs 92. | |
| 2262 | ''' |
|
2305 | ''' | |
| 2263 | # for each pair |
|
2306 | # for each pair | |
| 2264 | for i in range(nPair): |
|
2307 | for i in range(nPair): | |
| 2265 | cspc_norm = cspc[i,:].copy() |
|
2308 | cspc_norm = cspc[i,:].copy() | |
| 2266 | chan_index0 = pairsList[i][0] |
|
2309 | chan_index0 = pairsList[i][0] | |
| 2267 | chan_index1 = pairsList[i][1] |
|
2310 | chan_index1 = pairsList[i][1] | |
| 2268 | CSPC_Samples[i] = cspc_norm / (numpy.sqrt(numpy.nansum(spc_norm[chan_index0])*numpy.nansum(spc_norm[chan_index1])) * delta_x) |
|
2311 | CSPC_Samples[i] = cspc_norm / (numpy.sqrt(numpy.nansum(spc_norm[chan_index0])*numpy.nansum(spc_norm[chan_index1])) * delta_x) | |
| 2269 | phase[i] = numpy.arctan2(CSPC_Samples[i].imag, CSPC_Samples[i].real) |
|
2312 | phase[i] = numpy.arctan2(CSPC_Samples[i].imag, CSPC_Samples[i].real) | |
| 2270 |
|
2313 | |||
| 2271 | CSPCmoments = numpy.vstack([self.Moments(numpy.abs(CSPC_Samples[0,xvalid]), xSamples_zoom), |
|
2314 | CSPCmoments = numpy.vstack([self.Moments(numpy.abs(CSPC_Samples[0,xvalid]), xSamples_zoom), | |
| 2272 | self.Moments(numpy.abs(CSPC_Samples[1,xvalid]), xSamples_zoom), |
|
2315 | self.Moments(numpy.abs(CSPC_Samples[1,xvalid]), xSamples_zoom), | |
| 2273 | self.Moments(numpy.abs(CSPC_Samples[2,xvalid]), xSamples_zoom)]) |
|
2316 | self.Moments(numpy.abs(CSPC_Samples[2,xvalid]), xSamples_zoom)]) | |
| 2274 |
|
2317 | |||
| 2275 | popt01, popt02, popt12 = [1e-10,0,1e-10], [1e-10,0,1e-10] ,[1e-10,0,1e-10] |
|
2318 | popt01, popt02, popt12 = [1e-10,0,1e-10], [1e-10,0,1e-10] ,[1e-10,0,1e-10] | |
| 2276 | FitGauss01, FitGauss02, FitGauss12 = numpy.zeros(len(xSamples)), numpy.zeros(len(xSamples)), numpy.zeros(len(xSamples)) |
|
2319 | FitGauss01, FitGauss02, FitGauss12 = numpy.zeros(len(xSamples)), numpy.zeros(len(xSamples)), numpy.zeros(len(xSamples)) | |
| 2277 |
|
2320 | |||
| 2278 | '''*******************************FIT GAUSS CSPC************************************''' |
|
2321 | '''*******************************FIT GAUSS CSPC************************************''' | |
| 2279 | try: |
|
2322 | try: | |
| 2280 | popt01,pcov = curve_fit(self.gaus,xSamples_zoom,numpy.abs(CSPC_Samples[0][xvalid]),p0=CSPCmoments[0]) |
|
2323 | popt01,pcov = curve_fit(self.gaus,xSamples_zoom,numpy.abs(CSPC_Samples[0][xvalid]),p0=CSPCmoments[0]) | |
| 2281 | if popt01[2] > widthlimit: # CONDITION |
|
2324 | if popt01[2] > widthlimit: # CONDITION | |
| 2282 | return self.StopWindEstimation(error_code = 4) |
|
2325 | return self.StopWindEstimation(error_code = 4) | |
| 2283 | popt02,pcov = curve_fit(self.gaus,xSamples_zoom,numpy.abs(CSPC_Samples[1][xvalid]),p0=CSPCmoments[1]) |
|
2326 | popt02,pcov = curve_fit(self.gaus,xSamples_zoom,numpy.abs(CSPC_Samples[1][xvalid]),p0=CSPCmoments[1]) | |
| 2284 | if popt02[2] > widthlimit: # CONDITION |
|
2327 | if popt02[2] > widthlimit: # CONDITION | |
| 2285 | return self.StopWindEstimation(error_code = 4) |
|
2328 | return self.StopWindEstimation(error_code = 4) | |
| 2286 | popt12,pcov = curve_fit(self.gaus,xSamples_zoom,numpy.abs(CSPC_Samples[2][xvalid]),p0=CSPCmoments[2]) |
|
2329 | popt12,pcov = curve_fit(self.gaus,xSamples_zoom,numpy.abs(CSPC_Samples[2][xvalid]),p0=CSPCmoments[2]) | |
| 2287 | if popt12[2] > widthlimit: # CONDITION |
|
2330 | if popt12[2] > widthlimit: # CONDITION | |
| 2288 | return self.StopWindEstimation(error_code = 4) |
|
2331 | return self.StopWindEstimation(error_code = 4) | |
| 2289 |
|
2332 | |||
| 2290 | FitGauss01 = self.gaus(xSamples_zoom, *popt01) |
|
2333 | FitGauss01 = self.gaus(xSamples_zoom, *popt01) | |
| 2291 | FitGauss02 = self.gaus(xSamples_zoom, *popt02) |
|
2334 | FitGauss02 = self.gaus(xSamples_zoom, *popt02) | |
| 2292 | FitGauss12 = self.gaus(xSamples_zoom, *popt12) |
|
2335 | FitGauss12 = self.gaus(xSamples_zoom, *popt12) | |
| 2293 | except: |
|
2336 | except: | |
| 2294 | return self.StopWindEstimation(error_code = 5) |
|
2337 | return self.StopWindEstimation(error_code = 5) | |
| 2295 |
|
2338 | |||
| 2296 |
|
2339 | |||
| 2297 | '''************* Getting Fij ***************''' |
|
2340 | '''************* Getting Fij ***************''' | |
| 2298 | # x-axis point of the gaussian where the center is located from GaussFit of spectra |
|
2341 | # x-axis point of the gaussian where the center is located from GaussFit of spectra | |
| 2299 | GaussCenter = popt[1] |
|
2342 | GaussCenter = popt[1] | |
| 2300 | ClosestCenter = xSamples_zoom[numpy.abs(xSamples_zoom-GaussCenter).argmin()] |
|
2343 | ClosestCenter = xSamples_zoom[numpy.abs(xSamples_zoom-GaussCenter).argmin()] | |
| 2301 | PointGauCenter = numpy.where(xSamples_zoom==ClosestCenter)[0][0] |
|
2344 | PointGauCenter = numpy.where(xSamples_zoom==ClosestCenter)[0][0] | |
| 2302 |
|
2345 | |||
| 2303 | # Point where e^-1 is located in the gaussian |
|
2346 | # Point where e^-1 is located in the gaussian | |
| 2304 | PeMinus1 = numpy.max(FitGauss) * numpy.exp(-1) |
|
2347 | PeMinus1 = numpy.max(FitGauss) * numpy.exp(-1) | |
| 2305 | FijClosest = FitGauss[numpy.abs(FitGauss-PeMinus1).argmin()] # The closest point to"Peminus1" in "FitGauss" |
|
2348 | FijClosest = FitGauss[numpy.abs(FitGauss-PeMinus1).argmin()] # The closest point to"Peminus1" in "FitGauss" | |
| 2306 | PointFij = numpy.where(FitGauss==FijClosest)[0][0] |
|
2349 | PointFij = numpy.where(FitGauss==FijClosest)[0][0] | |
| 2307 | Fij = numpy.abs(xSamples_zoom[PointFij] - xSamples_zoom[PointGauCenter]) |
|
2350 | Fij = numpy.abs(xSamples_zoom[PointFij] - xSamples_zoom[PointGauCenter]) | |
| 2308 |
|
2351 | |||
| 2309 | '''********** Taking frequency ranges from mean SPCs **********''' |
|
2352 | '''********** Taking frequency ranges from mean SPCs **********''' | |
| 2310 | GauWidth = popt[2] * 3/2 # Bandwidth of Gau01 |
|
2353 | GauWidth = popt[2] * 3/2 # Bandwidth of Gau01 | |
| 2311 | Range = numpy.empty(2) |
|
2354 | Range = numpy.empty(2) | |
| 2312 | Range[0] = GaussCenter - GauWidth |
|
2355 | Range[0] = GaussCenter - GauWidth | |
| 2313 | Range[1] = GaussCenter + GauWidth |
|
2356 | Range[1] = GaussCenter + GauWidth | |
| 2314 | # Point in x-axis where the bandwidth is located (min:max) |
|
2357 | # Point in x-axis where the bandwidth is located (min:max) | |
| 2315 | ClosRangeMin = xSamples_zoom[numpy.abs(xSamples_zoom-Range[0]).argmin()] |
|
2358 | ClosRangeMin = xSamples_zoom[numpy.abs(xSamples_zoom-Range[0]).argmin()] | |
| 2316 | ClosRangeMax = xSamples_zoom[numpy.abs(xSamples_zoom-Range[1]).argmin()] |
|
2359 | ClosRangeMax = xSamples_zoom[numpy.abs(xSamples_zoom-Range[1]).argmin()] | |
| 2317 | PointRangeMin = numpy.where(xSamples_zoom==ClosRangeMin)[0][0] |
|
2360 | PointRangeMin = numpy.where(xSamples_zoom==ClosRangeMin)[0][0] | |
| 2318 | PointRangeMax = numpy.where(xSamples_zoom==ClosRangeMax)[0][0] |
|
2361 | PointRangeMax = numpy.where(xSamples_zoom==ClosRangeMax)[0][0] | |
| 2319 | Range = numpy.array([ PointRangeMin, PointRangeMax ]) |
|
2362 | Range = numpy.array([ PointRangeMin, PointRangeMax ]) | |
| 2320 | FrecRange = xSamples_zoom[ Range[0] : Range[1] ] |
|
2363 | FrecRange = xSamples_zoom[ Range[0] : Range[1] ] | |
| 2321 |
|
2364 | |||
| 2322 | '''************************** Getting Phase Slope ***************************''' |
|
2365 | '''************************** Getting Phase Slope ***************************''' | |
| 2323 | for i in range(nPair): |
|
2366 | for i in range(nPair): | |
| 2324 | if len(FrecRange) > 5: |
|
2367 | if len(FrecRange) > 5: | |
| 2325 | PhaseRange = phase[i, xvalid[0][Range[0]:Range[1]]].copy() |
|
2368 | PhaseRange = phase[i, xvalid[0][Range[0]:Range[1]]].copy() | |
| 2326 | mask = ~numpy.isnan(FrecRange) & ~numpy.isnan(PhaseRange) |
|
2369 | mask = ~numpy.isnan(FrecRange) & ~numpy.isnan(PhaseRange) | |
| 2327 | if len(FrecRange) == len(PhaseRange): |
|
2370 | if len(FrecRange) == len(PhaseRange): | |
| 2328 | try: |
|
2371 | try: | |
| 2329 | slope, intercept, _, _, _ = stats.linregress(FrecRange[mask], self.AntiAliasing(PhaseRange[mask], 4.5)) |
|
2372 | slope, intercept, _, _, _ = stats.linregress(FrecRange[mask], self.AntiAliasing(PhaseRange[mask], 4.5)) | |
| 2330 | PhaseSlope[i] = slope |
|
2373 | PhaseSlope[i] = slope | |
| 2331 | PhaseInter[i] = intercept |
|
2374 | PhaseInter[i] = intercept | |
| 2332 | except: |
|
2375 | except: | |
| 2333 | return self.StopWindEstimation(error_code = 6) |
|
2376 | return self.StopWindEstimation(error_code = 6) | |
| 2334 | else: |
|
2377 | else: | |
| 2335 | return self.StopWindEstimation(error_code = 7) |
|
2378 | return self.StopWindEstimation(error_code = 7) | |
| 2336 | else: |
|
2379 | else: | |
| 2337 | return self.StopWindEstimation(error_code = 8) |
|
2380 | return self.StopWindEstimation(error_code = 8) | |
| 2338 |
|
2381 | |||
| 2339 | '''*** Constants A-H correspond to the convention as in Briggs and Vincent 1992 ***''' |
|
2382 | '''*** Constants A-H correspond to the convention as in Briggs and Vincent 1992 ***''' | |
| 2340 |
|
2383 | |||
| 2341 | '''Getting constant C''' |
|
2384 | '''Getting constant C''' | |
| 2342 | cC=(Fij*numpy.pi)**2 |
|
2385 | cC=(Fij*numpy.pi)**2 | |
| 2343 |
|
2386 | |||
| 2344 | '''****** Getting constants F and G ******''' |
|
2387 | '''****** Getting constants F and G ******''' | |
| 2345 | MijEijNij = numpy.array([[Xi02,Eta02], [Xi12,Eta12]]) |
|
2388 | MijEijNij = numpy.array([[Xi02,Eta02], [Xi12,Eta12]]) | |
| 2346 | # MijEijNij = numpy.array([[Xi01,Eta01], [Xi02,Eta02], [Xi12,Eta12]]) |
|
2389 | # MijEijNij = numpy.array([[Xi01,Eta01], [Xi02,Eta02], [Xi12,Eta12]]) | |
| 2347 | # MijResult0 = (-PhaseSlope[0] * cC) / (2*numpy.pi) |
|
2390 | # MijResult0 = (-PhaseSlope[0] * cC) / (2*numpy.pi) | |
| 2348 | MijResult1 = (-PhaseSlope[1] * cC) / (2*numpy.pi) |
|
2391 | MijResult1 = (-PhaseSlope[1] * cC) / (2*numpy.pi) | |
| 2349 | MijResult2 = (-PhaseSlope[2] * cC) / (2*numpy.pi) |
|
2392 | MijResult2 = (-PhaseSlope[2] * cC) / (2*numpy.pi) | |
| 2350 | # MijResults = numpy.array([MijResult0, MijResult1, MijResult2]) |
|
2393 | # MijResults = numpy.array([MijResult0, MijResult1, MijResult2]) | |
| 2351 | MijResults = numpy.array([MijResult1, MijResult2]) |
|
2394 | MijResults = numpy.array([MijResult1, MijResult2]) | |
| 2352 | (cF,cG) = numpy.linalg.solve(MijEijNij, MijResults) |
|
2395 | (cF,cG) = numpy.linalg.solve(MijEijNij, MijResults) | |
| 2353 |
|
2396 | |||
| 2354 | '''****** Getting constants A, B and H ******''' |
|
2397 | '''****** Getting constants A, B and H ******''' | |
| 2355 | W01 = numpy.nanmax( FitGauss01 ) |
|
2398 | W01 = numpy.nanmax( FitGauss01 ) | |
| 2356 | W02 = numpy.nanmax( FitGauss02 ) |
|
2399 | W02 = numpy.nanmax( FitGauss02 ) | |
| 2357 | W12 = numpy.nanmax( FitGauss12 ) |
|
2400 | W12 = numpy.nanmax( FitGauss12 ) | |
| 2358 |
|
2401 | |||
| 2359 | WijResult01 = ((cF * Xi01 + cG * Eta01)**2)/cC - numpy.log(W01 / numpy.sqrt(numpy.pi / cC)) |
|
2402 | WijResult01 = ((cF * Xi01 + cG * Eta01)**2)/cC - numpy.log(W01 / numpy.sqrt(numpy.pi / cC)) | |
| 2360 | WijResult02 = ((cF * Xi02 + cG * Eta02)**2)/cC - numpy.log(W02 / numpy.sqrt(numpy.pi / cC)) |
|
2403 | WijResult02 = ((cF * Xi02 + cG * Eta02)**2)/cC - numpy.log(W02 / numpy.sqrt(numpy.pi / cC)) | |
| 2361 | WijResult12 = ((cF * Xi12 + cG * Eta12)**2)/cC - numpy.log(W12 / numpy.sqrt(numpy.pi / cC)) |
|
2404 | WijResult12 = ((cF * Xi12 + cG * Eta12)**2)/cC - numpy.log(W12 / numpy.sqrt(numpy.pi / cC)) | |
| 2362 | WijResults = numpy.array([WijResult01, WijResult02, WijResult12]) |
|
2405 | WijResults = numpy.array([WijResult01, WijResult02, WijResult12]) | |
| 2363 |
|
2406 | |||
| 2364 | WijEijNij = numpy.array([ [Xi01**2, Eta01**2, 2*Xi01*Eta01] , [Xi02**2, Eta02**2, 2*Xi02*Eta02] , [Xi12**2, Eta12**2, 2*Xi12*Eta12] ]) |
|
2407 | WijEijNij = numpy.array([ [Xi01**2, Eta01**2, 2*Xi01*Eta01] , [Xi02**2, Eta02**2, 2*Xi02*Eta02] , [Xi12**2, Eta12**2, 2*Xi12*Eta12] ]) | |
| 2365 | (cA,cB,cH) = numpy.linalg.solve(WijEijNij, WijResults) |
|
2408 | (cA,cB,cH) = numpy.linalg.solve(WijEijNij, WijResults) | |
| 2366 |
|
2409 | |||
| 2367 | VxVy = numpy.array([[cA,cH],[cH,cB]]) |
|
2410 | VxVy = numpy.array([[cA,cH],[cH,cB]]) | |
| 2368 | VxVyResults = numpy.array([-cF,-cG]) |
|
2411 | VxVyResults = numpy.array([-cF,-cG]) | |
| 2369 | (Vmer,Vzon) = numpy.linalg.solve(VxVy, VxVyResults) |
|
2412 | (Vmer,Vzon) = numpy.linalg.solve(VxVy, VxVyResults) | |
| 2370 | Vver = -SPCMoments[1]*SPEED_OF_LIGHT/(2*radfreq) |
|
2413 | Vver = -SPCMoments[1]*SPEED_OF_LIGHT/(2*radfreq) | |
| 2371 | error_code = 0 |
|
2414 | error_code = 0 | |
| 2372 |
|
2415 | |||
| 2373 | return Vzon, Vmer, Vver, error_code |
|
2416 | return Vzon, Vmer, Vver, error_code | |
| 2374 |
|
2417 | |||
| 2375 | class SpectralMoments(Operation): |
|
2418 | class SpectralMoments(Operation): | |
| 2376 |
|
2419 | |||
| 2377 | ''' |
|
2420 | ''' | |
| 2378 | Function SpectralMoments() |
|
2421 | Function SpectralMoments() | |
| 2379 |
|
2422 | |||
| 2380 | Calculates moments (power, mean, standard deviation) and SNR of the signal |
|
2423 | Calculates moments (power, mean, standard deviation) and SNR of the signal | |
| 2381 |
|
2424 | |||
| 2382 | Type of dataIn: Spectra |
|
2425 | Type of dataIn: Spectra | |
| 2383 |
|
2426 | |||
| 2384 | Configuration Parameters: |
|
2427 | Configuration Parameters: | |
| 2385 |
|
2428 | |||
| 2386 | dirCosx : Cosine director in X axis |
|
2429 | dirCosx : Cosine director in X axis | |
| 2387 | dirCosy : Cosine director in Y axis |
|
2430 | dirCosy : Cosine director in Y axis | |
| 2388 |
|
2431 | |||
| 2389 | elevation : |
|
2432 | elevation : | |
| 2390 | azimuth : |
|
2433 | azimuth : | |
| 2391 |
|
2434 | |||
| 2392 | Input: |
|
2435 | Input: | |
| 2393 | channelList : simple channel list to select e.g. [2,3,7] |
|
2436 | channelList : simple channel list to select e.g. [2,3,7] | |
| 2394 | self.dataOut.data_pre : Spectral data |
|
2437 | self.dataOut.data_pre : Spectral data | |
| 2395 | self.dataOut.abscissaList : List of frequencies |
|
2438 | self.dataOut.abscissaList : List of frequencies | |
| 2396 | self.dataOut.noise : Noise level per channel |
|
2439 | self.dataOut.noise : Noise level per channel | |
| 2397 |
|
2440 | |||
| 2398 | Affected: |
|
2441 | Affected: | |
| 2399 | self.dataOut.moments : Parameters per channel |
|
2442 | self.dataOut.moments : Parameters per channel | |
| 2400 | self.dataOut.data_snr : SNR per channel |
|
2443 | self.dataOut.data_snr : SNR per channel | |
| 2401 |
|
2444 | |||
| 2402 | ''' |
|
2445 | ''' | |
| 2403 |
|
2446 | |||
| 2404 | def run(self, dataOut, proc_type=0): |
|
2447 | def run(self, dataOut, proc_type=0): | |
| 2405 |
|
2448 | |||
| 2406 | absc = dataOut.abscissaList[:-1] |
|
2449 | absc = dataOut.abscissaList[:-1] | |
| 2407 | nChannel = dataOut.data_pre[0].shape[0] |
|
2450 | nChannel = dataOut.data_pre[0].shape[0] | |
| 2408 | nHei = dataOut.data_pre[0].shape[2] |
|
2451 | nHei = dataOut.data_pre[0].shape[2] | |
| 2409 | data_param = numpy.zeros((nChannel, 4 + proc_type*3, nHei)) |
|
2452 | data_param = numpy.zeros((nChannel, 4 + proc_type*3, nHei)) | |
| 2410 |
|
2453 | |||
| 2411 | if proc_type == 1: |
|
2454 | if proc_type == 1: | |
| 2412 | fwindow = numpy.zeros(absc.size) + 1 |
|
2455 | fwindow = numpy.zeros(absc.size) + 1 | |
| 2413 | b=64 |
|
2456 | b=64 | |
| 2414 | #b=16 |
|
2457 | #b=16 | |
| 2415 | fwindow[0:absc.size//2 - b] = 0 |
|
2458 | fwindow[0:absc.size//2 - b] = 0 | |
| 2416 | fwindow[absc.size//2 + b:] = 0 |
|
2459 | fwindow[absc.size//2 + b:] = 0 | |
| 2417 | type1 = 1 # moments calculation & gaussean fitting |
|
2460 | type1 = 1 # moments calculation & gaussean fitting | |
| 2418 | nProfiles = dataOut.nProfiles |
|
2461 | nProfiles = dataOut.nProfiles | |
| 2419 | nCohInt = dataOut.nCohInt |
|
2462 | nCohInt = dataOut.nCohInt | |
| 2420 | nIncohInt = dataOut.nIncohInt |
|
2463 | nIncohInt = dataOut.nIncohInt | |
| 2421 | M = numpy.power(numpy.array(1/(nProfiles * nCohInt) ,dtype='float32'),2) |
|
2464 | M = numpy.power(numpy.array(1/(nProfiles * nCohInt) ,dtype='float32'),2) | |
| 2422 | N = numpy.array(M / nIncohInt,dtype='float32') |
|
2465 | N = numpy.array(M / nIncohInt,dtype='float32') | |
| 2423 | data = dataOut.data_pre[0] * N |
|
2466 | data = dataOut.data_pre[0] * N | |
| 2424 | #noise = dataOut.noise * N |
|
2467 | #noise = dataOut.noise * N | |
| 2425 | noise = numpy.zeros(nChannel) |
|
2468 | noise = numpy.zeros(nChannel) | |
| 2426 | for ind in range(nChannel): |
|
2469 | for ind in range(nChannel): | |
| 2427 | noise[ind] = self.__NoiseByChannel(nProfiles, nIncohInt, data[ind,:,:]) |
|
2470 | noise[ind] = self.__NoiseByChannel(nProfiles, nIncohInt, data[ind,:,:]) | |
| 2428 | smooth=3 |
|
2471 | smooth=3 | |
| 2429 | else: |
|
2472 | else: | |
| 2430 | data = dataOut.data_pre[0] |
|
2473 | data = dataOut.data_pre[0] | |
| 2431 | noise = dataOut.noise |
|
2474 | noise = dataOut.noise | |
| 2432 | fwindow = None |
|
2475 | fwindow = None | |
| 2433 | type1 = 0 |
|
2476 | type1 = 0 | |
| 2434 | nIncohInt = None |
|
2477 | nIncohInt = None | |
| 2435 | smooth=None |
|
2478 | smooth=None | |
| 2436 |
|
2479 | |||
| 2437 | for ind in range(nChannel): |
|
2480 | for ind in range(nChannel): | |
| 2438 | data_param[ind,:,:] = self.__calculateMoments( data[ind,:,:] , absc , noise[ind], nicoh=nIncohInt, smooth=smooth, type1=type1, fwindow=fwindow, id_ch=ind) |
|
2481 | data_param[ind,:,:] = self.__calculateMoments( data[ind,:,:] , absc , noise[ind], nicoh=nIncohInt, smooth=smooth, type1=type1, fwindow=fwindow, id_ch=ind) | |
| 2439 |
|
2482 | |||
| 2440 | if proc_type == 1: |
|
2483 | if proc_type == 1: | |
| 2441 | dataOut.moments = data_param[:,1:,:] |
|
2484 | dataOut.moments = data_param[:,1:,:] | |
| 2442 | dataOut.data_dop = data_param[:,2] |
|
2485 | dataOut.data_dop = data_param[:,2] | |
| 2443 | dataOut.data_width = data_param[:,1] |
|
2486 | dataOut.data_width = data_param[:,1] | |
| 2444 | dataOut.data_snr = data_param[:,0] |
|
2487 | dataOut.data_snr = data_param[:,0] | |
| 2445 | dataOut.data_pow = data_param[:,6] # to compare with type0 proccessing |
|
2488 | dataOut.data_pow = data_param[:,6] # to compare with type0 proccessing | |
| 2446 | dataOut.spcpar=numpy.stack((dataOut.data_dop,dataOut.data_width,dataOut.data_snr, data_param[:,3], data_param[:,4],data_param[:,5]),axis=2) |
|
2489 | dataOut.spcpar=numpy.stack((dataOut.data_dop,dataOut.data_width,dataOut.data_snr, data_param[:,3], data_param[:,4],data_param[:,5]),axis=2) | |
| 2447 | else: |
|
2490 | else: | |
| 2448 | dataOut.moments = data_param[:,1:,:] |
|
2491 | dataOut.moments = data_param[:,1:,:] | |
| 2449 | dataOut.data_snr = data_param[:,0] |
|
2492 | dataOut.data_snr = data_param[:,0] | |
| 2450 | dataOut.data_pow = data_param[:,1] |
|
2493 | dataOut.data_pow = data_param[:,1] | |
| 2451 | dataOut.data_dop = data_param[:,2] |
|
2494 | dataOut.data_dop = data_param[:,2] | |
| 2452 | dataOut.data_width = data_param[:,3] |
|
2495 | dataOut.data_width = data_param[:,3] | |
| 2453 | dataOut.spcpar=numpy.stack((dataOut.data_dop,dataOut.data_width,dataOut.data_snr, dataOut.data_pow),axis=2) |
|
2496 | dataOut.spcpar=numpy.stack((dataOut.data_dop,dataOut.data_width,dataOut.data_snr, dataOut.data_pow),axis=2) | |
| 2454 |
|
2497 | |||
| 2455 | return dataOut |
|
2498 | return dataOut | |
| 2456 |
|
2499 | |||
| 2457 | def __calculateMoments(self, oldspec, oldfreq, n0, |
|
2500 | def __calculateMoments(self, oldspec, oldfreq, n0, normFactor = 1,nicoh = None, graph = None, smooth = None, type1 = None, fwindow = None, snrth = None, dc = None, aliasing = None, oldfd = None, wwauto = None,id_ch=0): | |
| 2458 | nicoh = None, graph = None, smooth = None, type1 = None, fwindow = None, snrth = None, dc = None, aliasing = None, oldfd = None, wwauto = None,id_ch=0): |
|
|||
| 2459 |
|
2501 | |||
| 2460 | def __GAUSSWINFIT1(A, flagPDER=0): |
|
2502 | def __GAUSSWINFIT1(A, flagPDER=0): | |
| 2461 | nonlocal truex, xvalid |
|
2503 | nonlocal truex, xvalid | |
| 2462 | nparams = 4 |
|
2504 | nparams = 4 | |
| 2463 | M=truex.size |
|
2505 | M=truex.size | |
| 2464 | mm=numpy.arange(M,dtype='f4') |
|
2506 | mm=numpy.arange(M,dtype='f4') | |
| 2465 | delta = numpy.zeros(M,dtype='f4') |
|
2507 | delta = numpy.zeros(M,dtype='f4') | |
| 2466 | delta[0] = 1.0 |
|
2508 | delta[0] = 1.0 | |
| 2467 | Ts = numpy.array([1.0/(2*truex[0])],dtype='f4')[0] |
|
2509 | Ts = numpy.array([1.0/(2*truex[0])],dtype='f4')[0] | |
| 2468 | jj = -1j |
|
2510 | jj = -1j | |
| 2469 | #if self.winauto is None: self.winauto = (1.0 - mm/M) |
|
2511 | #if self.winauto is None: self.winauto = (1.0 - mm/M) | |
| 2470 | winauto = (1.0 - mm/M) |
|
2512 | winauto = (1.0 - mm/M) | |
| 2471 | winauto = winauto/winauto.max() # Normalized to 1 |
|
2513 | winauto = winauto/winauto.max() # Normalized to 1 | |
| 2472 | #ON_ERROR,2 # IDL sentence: Return to caller if an error occurs |
|
2514 | #ON_ERROR,2 # IDL sentence: Return to caller if an error occurs | |
| 2473 | A[0] = numpy.abs(A[0]) |
|
2515 | A[0] = numpy.abs(A[0]) | |
| 2474 | A[2] = numpy.abs(A[2]) |
|
2516 | A[2] = numpy.abs(A[2]) | |
| 2475 | A[3] = numpy.abs(A[3]) |
|
2517 | A[3] = numpy.abs(A[3]) | |
| 2476 | pi=numpy.array([numpy.pi],dtype='f4')[0] |
|
2518 | pi=numpy.array([numpy.pi],dtype='f4')[0] | |
| 2477 | if A[2] != 0: |
|
2519 | if A[2] != 0: | |
| 2478 | Z = numpy.exp(-2*numpy.power((pi*A[2]*mm*Ts),2,dtype='f4')+jj*2*pi*A[1]*mm*Ts, dtype='c8') # Get Z |
|
2520 | Z = numpy.exp(-2*numpy.power((pi*A[2]*mm*Ts),2,dtype='f4')+jj*2*pi*A[1]*mm*Ts, dtype='c8') # Get Z | |
| 2479 | else: |
|
2521 | else: | |
| 2480 | Z = mm*0.0 |
|
2522 | Z = mm*0.0 | |
| 2481 | A[0] = 0.0 |
|
2523 | A[0] = 0.0 | |
| 2482 | junkF = numpy.roll(2*fft(winauto*(A[0]*Z+A[3]*delta)).real - \ |
|
2524 | junkF = numpy.roll(2*fft(winauto*(A[0]*Z+A[3]*delta)).real - \ | |
| 2483 | winauto[0]*(A[0]+A[3]), M//2) # *M scale for fft not needed in python |
|
2525 | winauto[0]*(A[0]+A[3]), M//2) # *M scale for fft not needed in python | |
| 2484 | F = junkF[xvalid] |
|
2526 | F = junkF[xvalid] | |
| 2485 | if flagPDER == 0: #NEED PARTIAL? |
|
2527 | if flagPDER == 0: #NEED PARTIAL? | |
| 2486 | return F |
|
2528 | return F | |
| 2487 | PDER = numpy.zeros((M,nparams)) #YES, MAKE ARRAY. |
|
2529 | PDER = numpy.zeros((M,nparams)) #YES, MAKE ARRAY. | |
| 2488 | PDER[:,0] = numpy.shift(2*(fft(winauto*Z)*M) - winauto[0], M/2) |
|
2530 | PDER[:,0] = numpy.shift(2*(fft(winauto*Z)*M) - winauto[0], M/2) | |
| 2489 | PDER[:,1] = numpy.shift(2*(fft(winauto*jj*2*numpy.pi*mm*Ts*A[0]*Z)*M), M/2) |
|
2531 | PDER[:,1] = numpy.shift(2*(fft(winauto*jj*2*numpy.pi*mm*Ts*A[0]*Z)*M), M/2) | |
| 2490 | PDER[:,2] = numpy.shift(2*(fft(winauto*(-4*numpy.power(numpy.pi*mm*Ts,2)*A[2]*A[0]*Z))*M), M/2) |
|
2532 | PDER[:,2] = numpy.shift(2*(fft(winauto*(-4*numpy.power(numpy.pi*mm*Ts,2)*A[2]*A[0]*Z))*M), M/2) | |
| 2491 | PDER[:,3] = numpy.shift(2*(fft(winauto*delta)*M) - winauto[0], M/2) |
|
2533 | PDER[:,3] = numpy.shift(2*(fft(winauto*delta)*M) - winauto[0], M/2) | |
| 2492 | PDER = PDER[xvalid,:] |
|
2534 | PDER = PDER[xvalid,:] | |
| 2493 | return F, PDER |
|
2535 | return F, PDER | |
| 2494 |
|
2536 | |||
| 2495 | def __curvefit_koki(y, a, Weights, FlagNoDerivative=1, |
|
2537 | def __curvefit_koki(y, a, Weights, FlagNoDerivative=1, | |
| 2496 | itmax=20, tol=None): |
|
2538 | itmax=20, tol=None): | |
| 2497 | #ON_ERROR,2 IDL SENTENCE: RETURN TO THE CALLER IF ERROR |
|
2539 | #ON_ERROR,2 IDL SENTENCE: RETURN TO THE CALLER IF ERROR | |
| 2498 | if tol == None: |
|
2540 | if tol == None: | |
| 2499 | tol = numpy.array([1.e-3],dtype='f4')[0] |
|
2541 | tol = numpy.array([1.e-3],dtype='f4')[0] | |
| 2500 | typ=a.dtype |
|
2542 | typ=a.dtype | |
| 2501 | double = 1 if typ == numpy.float64 else 0 |
|
2543 | double = 1 if typ == numpy.float64 else 0 | |
| 2502 | if typ != numpy.float32: |
|
2544 | if typ != numpy.float32: | |
| 2503 | a=a.astype(numpy.float32) #Make params floating |
|
2545 | a=a.astype(numpy.float32) #Make params floating | |
| 2504 | # if we will be estimating partial derivates then compute machine precision |
|
2546 | # if we will be estimating partial derivates then compute machine precision | |
| 2505 | if FlagNoDerivative == 1: |
|
2547 | if FlagNoDerivative == 1: | |
| 2506 | res=numpy.MachAr(float_conv=numpy.float32) |
|
2548 | res=numpy.MachAr(float_conv=numpy.float32) | |
| 2507 | eps=numpy.sqrt(res.eps) |
|
2549 | eps=numpy.sqrt(res.eps) | |
| 2508 |
|
2550 | |||
| 2509 | nterms = a.size # Number of parameters |
|
2551 | nterms = a.size # Number of parameters | |
| 2510 | nfree=numpy.array([numpy.size(y) - nterms],dtype='f4')[0] # Degrees of freedom |
|
2552 | nfree=numpy.array([numpy.size(y) - nterms],dtype='f4')[0] # Degrees of freedom | |
| 2511 | if nfree <= 0: print('Curvefit - not enough data points.') |
|
2553 | if nfree <= 0: print('Curvefit - not enough data points.') | |
| 2512 | flambda= numpy.array([0.001],dtype='f4')[0] # Initial lambda |
|
2554 | flambda= numpy.array([0.001],dtype='f4')[0] # Initial lambda | |
| 2513 | #diag=numpy.arange(nterms)*(nterms+1) # Subscripta of diagonal elements |
|
2555 | #diag=numpy.arange(nterms)*(nterms+1) # Subscripta of diagonal elements | |
| 2514 | # Use diag method in python |
|
2556 | # Use diag method in python | |
| 2515 | converge=1 |
|
2557 | converge=1 | |
| 2516 |
|
2558 | |||
| 2517 | #Define the partial derivative array |
|
2559 | #Define the partial derivative array | |
| 2518 | PDER = numpy.zeros((nterms,numpy.size(y)),dtype='f8') if double == 1 else numpy.zeros((nterms,numpy.size(y)),dtype='f4') |
|
2560 | PDER = numpy.zeros((nterms,numpy.size(y)),dtype='f8') if double == 1 else numpy.zeros((nterms,numpy.size(y)),dtype='f4') | |
| 2519 |
|
2561 | |||
| 2520 | for Niter in range(itmax): #Iteration loop |
|
2562 | for Niter in range(itmax): #Iteration loop | |
| 2521 |
|
2563 | |||
| 2522 | if FlagNoDerivative == 1: |
|
2564 | if FlagNoDerivative == 1: | |
| 2523 | #Evaluate function and estimate partial derivatives |
|
2565 | #Evaluate function and estimate partial derivatives | |
| 2524 | yfit = __GAUSSWINFIT1(a) |
|
2566 | yfit = __GAUSSWINFIT1(a) | |
| 2525 | for term in range(nterms): |
|
2567 | for term in range(nterms): | |
| 2526 | p=a.copy() # Copy current parameters |
|
2568 | p=a.copy() # Copy current parameters | |
| 2527 | #Increment size for forward difference derivative |
|
2569 | #Increment size for forward difference derivative | |
| 2528 | inc = eps * abs(p[term]) |
|
2570 | inc = eps * abs(p[term]) | |
| 2529 | if inc == 0: inc = eps |
|
2571 | if inc == 0: inc = eps | |
| 2530 | p[term] = p[term] + inc |
|
2572 | p[term] = p[term] + inc | |
| 2531 | yfit1 = __GAUSSWINFIT1(p) |
|
2573 | yfit1 = __GAUSSWINFIT1(p) | |
| 2532 | PDER[term,:] = (yfit1-yfit)/inc |
|
2574 | PDER[term,:] = (yfit1-yfit)/inc | |
| 2533 | else: |
|
2575 | else: | |
| 2534 | #The user's procedure will return partial derivatives |
|
2576 | #The user's procedure will return partial derivatives | |
| 2535 | yfit,PDER=__GAUSSWINFIT1(a, flagPDER=1) |
|
2577 | yfit,PDER=__GAUSSWINFIT1(a, flagPDER=1) | |
| 2536 |
|
2578 | |||
| 2537 | beta = numpy.dot(PDER,(y-yfit)*Weights) |
|
2579 | beta = numpy.dot(PDER,(y-yfit)*Weights) | |
| 2538 | alpha = numpy.dot(PDER * numpy.tile(Weights,(nterms,1)), numpy.transpose(PDER)) |
|
2580 | alpha = numpy.dot(PDER * numpy.tile(Weights,(nterms,1)), numpy.transpose(PDER)) | |
| 2539 | # save current values of return parameters |
|
2581 | # save current values of return parameters | |
| 2540 | sigma1 = numpy.sqrt( 1.0 / numpy.diag(alpha) ) # Current sigma. |
|
2582 | sigma1 = numpy.sqrt( 1.0 / numpy.diag(alpha) ) # Current sigma. | |
| 2541 | sigma = sigma1 |
|
2583 | sigma = sigma1 | |
| 2542 |
|
2584 | |||
| 2543 | chisq1 = numpy.sum(Weights*numpy.power(y-yfit,2,dtype='f4'),dtype='f4')/nfree # Current chi squared. |
|
2585 | chisq1 = numpy.sum(Weights*numpy.power(y-yfit,2,dtype='f4'),dtype='f4')/nfree # Current chi squared. | |
| 2544 | chisq = chisq1 |
|
2586 | chisq = chisq1 | |
| 2545 | yfit1 = yfit |
|
2587 | yfit1 = yfit | |
| 2546 | elev7=numpy.array([1.0e7],dtype='f4')[0] |
|
2588 | elev7=numpy.array([1.0e7],dtype='f4')[0] | |
| 2547 | compara =numpy.sum(abs(y))/elev7/nfree |
|
2589 | compara =numpy.sum(abs(y))/elev7/nfree | |
| 2548 | done_early = chisq1 < compara |
|
2590 | done_early = chisq1 < compara | |
| 2549 |
|
2591 | |||
| 2550 | if done_early: |
|
2592 | if done_early: | |
| 2551 | chi2 = chisq # Return chi-squared (chi2 obsolete-still works) |
|
2593 | chi2 = chisq # Return chi-squared (chi2 obsolete-still works) | |
| 2552 | if done_early: Niter -= 1 |
|
2594 | if done_early: Niter -= 1 | |
| 2553 | #save_tp(chisq,Niter,yfit) |
|
2595 | #save_tp(chisq,Niter,yfit) | |
| 2554 | return yfit, a, converge, sigma, chisq # return result |
|
2596 | return yfit, a, converge, sigma, chisq # return result | |
| 2555 | #c = numpy.dot(c, c) # this operator implemented at the next lines |
|
2597 | #c = numpy.dot(c, c) # this operator implemented at the next lines | |
| 2556 | c_tmp = numpy.sqrt(numpy.diag(alpha)) |
|
2598 | c_tmp = numpy.sqrt(numpy.diag(alpha)) | |
| 2557 | siz=len(c_tmp) |
|
2599 | siz=len(c_tmp) | |
| 2558 | c=numpy.dot(c_tmp.reshape(siz,1),c_tmp.reshape(1,siz)) |
|
2600 | c=numpy.dot(c_tmp.reshape(siz,1),c_tmp.reshape(1,siz)) | |
| 2559 | lambdaCount = 0 |
|
2601 | lambdaCount = 0 | |
| 2560 | while True: |
|
2602 | while True: | |
| 2561 | lambdaCount += 1 |
|
2603 | lambdaCount += 1 | |
| 2562 | # Normalize alpha to have unit diagonal. |
|
2604 | # Normalize alpha to have unit diagonal. | |
| 2563 | array = alpha / c |
|
2605 | array = alpha / c | |
| 2564 | # Augment the diagonal. |
|
2606 | # Augment the diagonal. | |
| 2565 | one=numpy.array([1.],dtype='f4')[0] |
|
2607 | one=numpy.array([1.],dtype='f4')[0] | |
| 2566 | numpy.fill_diagonal(array,numpy.diag(array)*(one+flambda)) |
|
2608 | numpy.fill_diagonal(array,numpy.diag(array)*(one+flambda)) | |
| 2567 | # Invert modified curvature matrix to find new parameters. |
|
2609 | # Invert modified curvature matrix to find new parameters. | |
| 2568 | try: |
|
2610 | try: | |
| 2569 | array = (1.0/array) if array.size == 1 else numpy.linalg.inv(array) |
|
2611 | array = (1.0/array) if array.size == 1 else numpy.linalg.inv(array) | |
| 2570 | except Exception as e: |
|
2612 | except Exception as e: | |
| 2571 | print(e) |
|
2613 | print(e) | |
| 2572 | array[:]=numpy.NaN |
|
2614 | array[:]=numpy.NaN | |
| 2573 |
|
2615 | |||
| 2574 | b = a + numpy.dot(numpy.transpose(beta),array/c) # New params |
|
2616 | b = a + numpy.dot(numpy.transpose(beta),array/c) # New params | |
| 2575 | yfit = __GAUSSWINFIT1(b) # Evaluate function |
|
2617 | yfit = __GAUSSWINFIT1(b) # Evaluate function | |
| 2576 | chisq = numpy.sum(Weights*numpy.power(y-yfit,2,dtype='f4'),dtype='f4')/nfree # New chisq |
|
2618 | chisq = numpy.sum(Weights*numpy.power(y-yfit,2,dtype='f4'),dtype='f4')/nfree # New chisq | |
| 2577 | sigma = numpy.sqrt(numpy.diag(array)/numpy.diag(alpha)) # New sigma |
|
2619 | sigma = numpy.sqrt(numpy.diag(array)/numpy.diag(alpha)) # New sigma | |
| 2578 | if (numpy.isfinite(chisq) == 0) or \ |
|
2620 | if (numpy.isfinite(chisq) == 0) or \ | |
| 2579 | (lambdaCount > 30 and chisq >= chisq1): |
|
2621 | (lambdaCount > 30 and chisq >= chisq1): | |
| 2580 | # Reject changes made this iteration, use old values. |
|
2622 | # Reject changes made this iteration, use old values. | |
| 2581 | yfit = yfit1 |
|
2623 | yfit = yfit1 | |
| 2582 | sigma = sigma1 |
|
2624 | sigma = sigma1 | |
| 2583 | chisq = chisq1 |
|
2625 | chisq = chisq1 | |
| 2584 | converge = 0 |
|
2626 | converge = 0 | |
| 2585 | #print('Failed to converge.') |
|
2627 | #print('Failed to converge.') | |
| 2586 | chi2 = chisq # Return chi-squared (chi2 obsolete-still works) |
|
2628 | chi2 = chisq # Return chi-squared (chi2 obsolete-still works) | |
| 2587 | if done_early: Niter -= 1 |
|
2629 | if done_early: Niter -= 1 | |
| 2588 | #save_tp(chisq,Niter,yfit) |
|
2630 | #save_tp(chisq,Niter,yfit) | |
| 2589 | return yfit, a, converge, sigma, chisq, chi2 # return result |
|
2631 | return yfit, a, converge, sigma, chisq, chi2 # return result | |
| 2590 | ten=numpy.array([10.0],dtype='f4')[0] |
|
2632 | ten=numpy.array([10.0],dtype='f4')[0] | |
| 2591 | flambda *= ten # Assume fit got worse |
|
2633 | flambda *= ten # Assume fit got worse | |
| 2592 | if chisq <= chisq1: |
|
2634 | if chisq <= chisq1: | |
| 2593 | break |
|
2635 | break | |
| 2594 | hundred=numpy.array([100.0],dtype='f4')[0] |
|
2636 | hundred=numpy.array([100.0],dtype='f4')[0] | |
| 2595 | flambda /= hundred |
|
2637 | flambda /= hundred | |
| 2596 |
|
2638 | |||
| 2597 | a=b # Save new parameter estimate. |
|
2639 | a=b # Save new parameter estimate. | |
| 2598 | if ((chisq1-chisq)/chisq1) <= tol: # Finished? |
|
2640 | if ((chisq1-chisq)/chisq1) <= tol: # Finished? | |
| 2599 | chi2 = chisq # Return chi-squared (chi2 obsolete-still works) |
|
2641 | chi2 = chisq # Return chi-squared (chi2 obsolete-still works) | |
| 2600 | if done_early: Niter -= 1 |
|
2642 | if done_early: Niter -= 1 | |
| 2601 | #save_tp(chisq,Niter,yfit) |
|
2643 | #save_tp(chisq,Niter,yfit) | |
| 2602 | return yfit, a, converge, sigma, chisq, chi2 # return result |
|
2644 | return yfit, a, converge, sigma, chisq, chi2 # return result | |
| 2603 | converge = 0 |
|
2645 | converge = 0 | |
| 2604 | chi2 = chisq |
|
2646 | chi2 = chisq | |
| 2605 | #print('Failed to converge.') |
|
2647 | #print('Failed to converge.') | |
| 2606 | #save_tp(chisq,Niter,yfit) |
|
2648 | #save_tp(chisq,Niter,yfit) | |
| 2607 | return yfit, a, converge, sigma, chisq, chi2 |
|
2649 | return yfit, a, converge, sigma, chisq, chi2 | |
| 2608 |
|
2650 | |||
| 2609 | if (nicoh is None): nicoh = 1 |
|
2651 | if (nicoh is None): nicoh = 1 | |
| 2610 | if (graph is None): graph = 0 |
|
2652 | if (graph is None): graph = 0 | |
| 2611 | if (smooth is None): smooth = 0 |
|
2653 | if (smooth is None): smooth = 0 | |
| 2612 | elif (self.smooth < 3): smooth = 0 |
|
2654 | elif (self.smooth < 3): smooth = 0 | |
| 2613 |
|
2655 | |||
| 2614 | if (type1 is None): type1 = 0 |
|
2656 | if (type1 is None): type1 = 0 | |
| 2615 | if (fwindow is None): fwindow = numpy.zeros(oldfreq.size) + 1 |
|
2657 | if (fwindow is None): fwindow = numpy.zeros(oldfreq.size) + 1 | |
| 2616 | if (snrth is None): snrth = -3 #-20.0 |
|
2658 | if (snrth is None): snrth = -3 #-20.0 | |
| 2617 | if (dc is None): dc = 0 |
|
2659 | if (dc is None): dc = 0 | |
| 2618 | if (aliasing is None): aliasing = 0 |
|
2660 | if (aliasing is None): aliasing = 0 | |
| 2619 | if (oldfd is None): oldfd = 0 |
|
2661 | if (oldfd is None): oldfd = 0 | |
| 2620 | if (wwauto is None): wwauto = 0 |
|
2662 | if (wwauto is None): wwauto = 0 | |
| 2621 |
|
2663 | |||
| 2622 | if (n0 < 1.e-20): n0 = 1.e-20 |
|
2664 | if (n0 < 1.e-20): n0 = 1.e-20 | |
| 2623 |
|
2665 | |||
| 2624 | xvalid = numpy.where(fwindow == 1)[0] |
|
2666 | xvalid = numpy.where(fwindow == 1)[0] | |
| 2625 | freq = oldfreq |
|
2667 | freq = oldfreq | |
| 2626 | truex = oldfreq |
|
2668 | truex = oldfreq | |
| 2627 | vec_power = numpy.zeros(oldspec.shape[1]) |
|
2669 | vec_power = numpy.zeros(oldspec.shape[1]) | |
| 2628 | vec_fd = numpy.zeros(oldspec.shape[1]) |
|
2670 | vec_fd = numpy.zeros(oldspec.shape[1]) | |
| 2629 | vec_w = numpy.zeros(oldspec.shape[1]) |
|
2671 | vec_w = numpy.zeros(oldspec.shape[1]) | |
| 2630 | vec_snr = numpy.zeros(oldspec.shape[1]) |
|
2672 | vec_snr = numpy.zeros(oldspec.shape[1]) | |
| 2631 | vec_n1 = numpy.empty(oldspec.shape[1]) |
|
2673 | vec_n1 = numpy.empty(oldspec.shape[1]) | |
| 2632 | vec_fp = numpy.empty(oldspec.shape[1]) |
|
2674 | vec_fp = numpy.empty(oldspec.shape[1]) | |
| 2633 | vec_sigma_fd = numpy.empty(oldspec.shape[1]) |
|
2675 | vec_sigma_fd = numpy.empty(oldspec.shape[1]) | |
|
|
2676 | norm = 1 | |||
| 2634 |
|
2677 | |||
| 2635 | for ind in range(oldspec.shape[1]): |
|
2678 | for ind in range(oldspec.shape[1]): | |
| 2636 |
|
2679 | |||
| 2637 | spec = oldspec[:,ind] |
|
2680 | spec = oldspec[:,ind] | |
| 2638 | if (smooth == 0): |
|
2681 | if (smooth == 0): | |
| 2639 | spec2 = spec |
|
2682 | spec2 = spec | |
| 2640 | else: |
|
2683 | else: | |
| 2641 | spec2 = scipy.ndimage.filters.uniform_filter1d(spec,size=smooth) |
|
2684 | spec2 = scipy.ndimage.filters.uniform_filter1d(spec,size=smooth) | |
| 2642 |
|
2685 | |||
| 2643 | aux = spec2*fwindow |
|
2686 | aux = spec2*fwindow | |
| 2644 | max_spec = aux.max() |
|
2687 | max_spec = aux.max() | |
| 2645 | m = aux.tolist().index(max_spec) |
|
2688 | m = aux.tolist().index(max_spec) | |
| 2646 |
|
2689 | |||
|
|
2690 | if hasattr(normFactor, "ndim"): | |||
|
|
2691 | if normFactor.ndim >= 1: | |||
|
|
2692 | norm = normFactor[ind] | |||
|
|
2693 | ||||
| 2647 | if m > 2 and m < oldfreq.size - 3: |
|
2694 | if m > 2 and m < oldfreq.size - 3: | |
| 2648 | newindex = m + numpy.array([-2,-1,0,1,2]) |
|
2695 | newindex = m + numpy.array([-2,-1,0,1,2]) | |
| 2649 | newfreq = numpy.arange(20)/20.0*(numpy.max(freq[newindex])-numpy.min(freq[newindex]))+numpy.min(freq[newindex]) |
|
2696 | newfreq = numpy.arange(20)/20.0*(numpy.max(freq[newindex])-numpy.min(freq[newindex]))+numpy.min(freq[newindex]) | |
| 2650 | #peakspec = SPLINE(,) |
|
2697 | #peakspec = SPLINE(,) | |
| 2651 | tck = interpolate.splrep(freq[newindex], spec2[newindex]) |
|
2698 | tck = interpolate.splrep(freq[newindex], spec2[newindex]) | |
| 2652 | peakspec = interpolate.splev(newfreq, tck) |
|
2699 | peakspec = interpolate.splev(newfreq, tck) | |
| 2653 | # max_spec = MAX(peakspec,) |
|
2700 | # max_spec = MAX(peakspec,) | |
| 2654 | max_spec = numpy.max(peakspec) |
|
2701 | max_spec = numpy.max(peakspec) | |
| 2655 | mnew = numpy.argmax(peakspec) |
|
2702 | mnew = numpy.argmax(peakspec) | |
| 2656 | #fp = newfreq(mnew) |
|
2703 | #fp = newfreq(mnew) | |
| 2657 | fp = newfreq[mnew] |
|
2704 | fp = newfreq[mnew] | |
| 2658 | else: |
|
2705 | else: | |
| 2659 | fp = freq[m] |
|
2706 | fp = freq[m] | |
| 2660 |
|
2707 | |||
| 2661 | if type1==0: |
|
2708 | if type1==0: | |
| 2662 |
|
2709 | |||
| 2663 | # Moments Estimation |
|
2710 | # Moments Estimation | |
| 2664 | bb = spec2[numpy.arange(m,spec2.size)] |
|
2711 | bb = spec2[numpy.arange(m,spec2.size)] | |
| 2665 | bb = (bb<n0).nonzero() |
|
2712 | bb = (bb<n0).nonzero() | |
| 2666 | bb = bb[0] |
|
2713 | bb = bb[0] | |
| 2667 |
|
2714 | |||
| 2668 | ss = spec2[numpy.arange(0,m + 1)] |
|
2715 | ss = spec2[numpy.arange(0,m + 1)] | |
| 2669 | ss = (ss<n0).nonzero() |
|
2716 | ss = (ss<n0).nonzero() | |
| 2670 | ss = ss[0] |
|
2717 | ss = ss[0] | |
| 2671 |
|
2718 | |||
| 2672 | if (bb.size == 0): |
|
2719 | if (bb.size == 0): | |
| 2673 | bb0 = spec.size - 1 - m |
|
2720 | bb0 = spec.size - 1 - m | |
| 2674 | else: |
|
2721 | else: | |
| 2675 | bb0 = bb[0] - 1 |
|
2722 | bb0 = bb[0] - 1 | |
| 2676 | if (bb0 < 0): |
|
2723 | if (bb0 < 0): | |
| 2677 | bb0 = 0 |
|
2724 | bb0 = 0 | |
| 2678 |
|
2725 | |||
| 2679 | if (ss.size == 0): |
|
2726 | if (ss.size == 0): | |
| 2680 | ss1 = 1 |
|
2727 | ss1 = 1 | |
| 2681 | else: |
|
2728 | else: | |
| 2682 | ss1 = max(ss) + 1 |
|
2729 | ss1 = max(ss) + 1 | |
| 2683 |
|
2730 | |||
| 2684 | if (ss1 > m): |
|
2731 | if (ss1 > m): | |
| 2685 | ss1 = m |
|
2732 | ss1 = m | |
| 2686 |
|
2733 | |||
| 2687 | valid = numpy.arange(int(m + bb0 - ss1 + 1)) + ss1 |
|
2734 | valid = numpy.arange(int(m + bb0 - ss1 + 1)) + ss1 | |
| 2688 |
|
2735 | |||
| 2689 | signal_power = ((spec2[valid] - n0) * fwindow[valid]).mean() # D. ScipiΓ³n added with correct definition |
|
2736 | signal_power = ((spec2[valid] - n0) * fwindow[valid]).mean() # D. ScipiΓ³n added with correct definition | |
| 2690 | total_power = (spec2[valid] * fwindow[valid]).mean() # D. ScipiΓ³n added with correct definition |
|
2737 | total_power = (spec2[valid] * fwindow[valid]).mean() # D. ScipiΓ³n added with correct definition | |
| 2691 | power = ((spec2[valid] - n0) * fwindow[valid]).sum() |
|
2738 | power = ((spec2[valid] - n0) * fwindow[valid]).sum() | |
| 2692 | fd = ((spec2[valid]- n0)*freq[valid] * fwindow[valid]).sum() / power |
|
2739 | fd = ((spec2[valid]- n0)*freq[valid] * fwindow[valid]).sum() / power | |
| 2693 | w = numpy.sqrt(((spec2[valid] - n0)*fwindow[valid]*(freq[valid]- fd)**2).sum() / power) |
|
2740 | w = numpy.sqrt(((spec2[valid] - n0)*fwindow[valid]*(freq[valid]- fd)**2).sum() / power) | |
|
|
2741 | spec2 /=(norm) #compensation for sats remove | |||
| 2694 | snr = (spec2.mean()-n0)/n0 |
|
2742 | snr = (spec2.mean()-n0)/n0 | |
| 2695 | if (snr < 1.e-20): snr = 1.e-20 |
|
2743 | if (snr < 1.e-20): snr = 1.e-20 | |
| 2696 |
|
2744 | |||
| 2697 | vec_power[ind] = total_power |
|
2745 | vec_power[ind] = total_power | |
| 2698 | vec_fd[ind] = fd |
|
2746 | vec_fd[ind] = fd | |
| 2699 | vec_w[ind] = w |
|
2747 | vec_w[ind] = w | |
| 2700 | vec_snr[ind] = snr |
|
2748 | vec_snr[ind] = snr | |
| 2701 | else: |
|
2749 | else: | |
| 2702 | # Noise by heights |
|
2750 | # Noise by heights | |
| 2703 | n1, stdv = self.__get_noise2(spec, nicoh) |
|
2751 | n1, stdv = self.__get_noise2(spec, nicoh) | |
| 2704 | # Moments Estimation |
|
2752 | # Moments Estimation | |
| 2705 | bb = spec2[numpy.arange(m,spec2.size)] |
|
2753 | bb = spec2[numpy.arange(m,spec2.size)] | |
| 2706 | bb = (bb<n1).nonzero() |
|
2754 | bb = (bb<n1).nonzero() | |
| 2707 | bb = bb[0] |
|
2755 | bb = bb[0] | |
| 2708 |
|
2756 | |||
| 2709 | ss = spec2[numpy.arange(0,m + 1)] |
|
2757 | ss = spec2[numpy.arange(0,m + 1)] | |
| 2710 | ss = (ss<n1).nonzero() |
|
2758 | ss = (ss<n1).nonzero() | |
| 2711 | ss = ss[0] |
|
2759 | ss = ss[0] | |
| 2712 |
|
2760 | |||
| 2713 | if (bb.size == 0): |
|
2761 | if (bb.size == 0): | |
| 2714 | bb0 = spec.size - 1 - m |
|
2762 | bb0 = spec.size - 1 - m | |
| 2715 | else: |
|
2763 | else: | |
| 2716 | bb0 = bb[0] - 1 |
|
2764 | bb0 = bb[0] - 1 | |
| 2717 | if (bb0 < 0): |
|
2765 | if (bb0 < 0): | |
| 2718 | bb0 = 0 |
|
2766 | bb0 = 0 | |
| 2719 |
|
2767 | |||
| 2720 | if (ss.size == 0): |
|
2768 | if (ss.size == 0): | |
| 2721 | ss1 = 1 |
|
2769 | ss1 = 1 | |
| 2722 | else: |
|
2770 | else: | |
| 2723 | ss1 = max(ss) + 1 |
|
2771 | ss1 = max(ss) + 1 | |
| 2724 |
|
2772 | |||
| 2725 | if (ss1 > m): |
|
2773 | if (ss1 > m): | |
| 2726 | ss1 = m |
|
2774 | ss1 = m | |
| 2727 |
|
2775 | |||
| 2728 | valid = numpy.arange(int(m + bb0 - ss1 + 1)) + ss1 |
|
2776 | valid = numpy.arange(int(m + bb0 - ss1 + 1)) + ss1 | |
| 2729 | power = ((spec[valid] - n1)*fwindow[valid]).sum() |
|
2777 | power = ((spec[valid] - n1)*fwindow[valid]).sum() | |
| 2730 | fd = ((spec[valid]- n1)*freq[valid]*fwindow[valid]).sum()/power |
|
2778 | fd = ((spec[valid]- n1)*freq[valid]*fwindow[valid]).sum()/power | |
| 2731 | try: |
|
2779 | try: | |
| 2732 | w = numpy.sqrt(((spec[valid] - n1)*fwindow[valid]*(freq[valid]- fd)**2).sum()/power) |
|
2780 | w = numpy.sqrt(((spec[valid] - n1)*fwindow[valid]*(freq[valid]- fd)**2).sum()/power) | |
| 2733 | except: |
|
2781 | except: | |
| 2734 | w = float("NaN") |
|
2782 | w = float("NaN") | |
| 2735 | snr = power/(n0*fwindow.sum()) |
|
2783 | snr = power/(n0*fwindow.sum()) | |
| 2736 | if snr < 1.e-20: snr = 1.e-20 |
|
2784 | if snr < 1.e-20: snr = 1.e-20 | |
| 2737 |
|
2785 | |||
| 2738 | # Here start gaussean adjustment |
|
2786 | # Here start gaussean adjustment | |
| 2739 |
|
2787 | |||
| 2740 | if snr > numpy.power(10,0.1*snrth): |
|
2788 | if snr > numpy.power(10,0.1*snrth): | |
| 2741 |
|
2789 | |||
| 2742 | a = numpy.zeros(4,dtype='f4') |
|
2790 | a = numpy.zeros(4,dtype='f4') | |
| 2743 | a[0] = snr * n0 |
|
2791 | a[0] = snr * n0 | |
| 2744 | a[1] = fd |
|
2792 | a[1] = fd | |
| 2745 | a[2] = w |
|
2793 | a[2] = w | |
| 2746 | a[3] = n0 |
|
2794 | a[3] = n0 | |
| 2747 |
|
2795 | |||
| 2748 | np = spec.size |
|
2796 | np = spec.size | |
| 2749 | aold = a.copy() |
|
2797 | aold = a.copy() | |
| 2750 | spec2 = spec.copy() |
|
2798 | spec2 = spec.copy() | |
| 2751 | oldxvalid = xvalid.copy() |
|
2799 | oldxvalid = xvalid.copy() | |
| 2752 |
|
2800 | |||
| 2753 | for i in range(2): |
|
2801 | for i in range(2): | |
| 2754 |
|
2802 | |||
| 2755 | ww = 1.0/(numpy.power(spec2,2)/nicoh) |
|
2803 | ww = 1.0/(numpy.power(spec2,2)/nicoh) | |
| 2756 | ww[np//2] = 0.0 |
|
2804 | ww[np//2] = 0.0 | |
| 2757 |
|
2805 | |||
| 2758 | a = aold.copy() |
|
2806 | a = aold.copy() | |
| 2759 | xvalid = oldxvalid.copy() |
|
2807 | xvalid = oldxvalid.copy() | |
| 2760 | #self.show_var(xvalid) |
|
2808 | #self.show_var(xvalid) | |
| 2761 |
|
2809 | |||
| 2762 | gaussfn = __curvefit_koki(spec[xvalid], a, ww[xvalid]) |
|
2810 | gaussfn = __curvefit_koki(spec[xvalid], a, ww[xvalid]) | |
| 2763 | a = gaussfn[1] |
|
2811 | a = gaussfn[1] | |
| 2764 | converge = gaussfn[2] |
|
2812 | converge = gaussfn[2] | |
| 2765 |
|
2813 | |||
| 2766 | xvalid = numpy.arange(np) |
|
2814 | xvalid = numpy.arange(np) | |
| 2767 | spec2 = __GAUSSWINFIT1(a) |
|
2815 | spec2 = __GAUSSWINFIT1(a) | |
| 2768 |
|
2816 | |||
| 2769 | xvalid = oldxvalid.copy() |
|
2817 | xvalid = oldxvalid.copy() | |
| 2770 | power = a[0] * np |
|
2818 | power = a[0] * np | |
| 2771 | fd = a[1] |
|
2819 | fd = a[1] | |
| 2772 | sigma_fd = gaussfn[3][1] |
|
2820 | sigma_fd = gaussfn[3][1] | |
| 2773 | snr = max(power/ (max(a[3],n0) * len(oldxvalid)) * converge, 1e-20) |
|
2821 | snr = max(power/ (max(a[3],n0) * len(oldxvalid)) * converge, 1e-20) | |
| 2774 | w = numpy.abs(a[2]) |
|
2822 | w = numpy.abs(a[2]) | |
| 2775 | n1 = max(a[3], n0) |
|
2823 | n1 = max(a[3], n0) | |
| 2776 |
|
2824 | |||
| 2777 | #gauss_adj=[fd,w,snr,n1,fp,sigma_fd] |
|
2825 | #gauss_adj=[fd,w,snr,n1,fp,sigma_fd] | |
| 2778 | else: |
|
2826 | else: | |
| 2779 | sigma_fd=numpy.nan # to avoid UnboundLocalError: local variable 'sigma_fd' referenced before assignment |
|
2827 | sigma_fd=numpy.nan # to avoid UnboundLocalError: local variable 'sigma_fd' referenced before assignment | |
| 2780 |
|
2828 | |||
| 2781 | vec_fd[ind] = fd |
|
2829 | vec_fd[ind] = fd | |
| 2782 | vec_w[ind] = w |
|
2830 | vec_w[ind] = w | |
| 2783 | vec_snr[ind] = snr |
|
2831 | vec_snr[ind] = snr | |
| 2784 | vec_n1[ind] = n1 |
|
2832 | vec_n1[ind] = n1 | |
| 2785 | vec_fp[ind] = fp |
|
2833 | vec_fp[ind] = fp | |
| 2786 | vec_sigma_fd[ind] = sigma_fd |
|
2834 | vec_sigma_fd[ind] = sigma_fd | |
| 2787 | vec_power[ind] = power # to compare with type 0 proccessing |
|
2835 | vec_power[ind] = power # to compare with type 0 proccessing | |
| 2788 |
|
2836 | |||
| 2789 | if type1==1: |
|
2837 | if type1==1: | |
| 2790 | return numpy.vstack((vec_snr, vec_w, vec_fd, vec_n1, vec_fp, vec_sigma_fd, vec_power)) # snr and fd exchanged to compare doppler of both types |
|
2838 | return numpy.vstack((vec_snr, vec_w, vec_fd, vec_n1, vec_fp, vec_sigma_fd, vec_power)) # snr and fd exchanged to compare doppler of both types | |
| 2791 | else: |
|
2839 | else: | |
| 2792 | return numpy.vstack((vec_snr, vec_power, vec_fd, vec_w)) |
|
2840 | return numpy.vstack((vec_snr, vec_power, vec_fd, vec_w)) | |
| 2793 |
|
2841 | |||
| 2794 | def __get_noise2(self,POWER, fft_avg, TALK=0): |
|
2842 | def __get_noise2(self,POWER, fft_avg, TALK=0): | |
| 2795 | ''' |
|
2843 | ''' | |
| 2796 | Rutina para cΓ‘lculo de ruido por alturas(n1). Similar a IDL |
|
2844 | Rutina para cΓ‘lculo de ruido por alturas(n1). Similar a IDL | |
| 2797 | ''' |
|
2845 | ''' | |
| 2798 | SPECT_PTS = len(POWER) |
|
2846 | SPECT_PTS = len(POWER) | |
| 2799 | fft_avg = fft_avg*1.0 |
|
2847 | fft_avg = fft_avg*1.0 | |
| 2800 | NOMIT = 0 |
|
2848 | NOMIT = 0 | |
| 2801 | NN = SPECT_PTS - NOMIT |
|
2849 | NN = SPECT_PTS - NOMIT | |
| 2802 | N = NN//2 |
|
2850 | N = NN//2 | |
| 2803 | ARR = numpy.concatenate((POWER[0:N+1],POWER[N+NOMIT+1:SPECT_PTS])) |
|
2851 | ARR = numpy.concatenate((POWER[0:N+1],POWER[N+NOMIT+1:SPECT_PTS])) | |
| 2804 | ARR = numpy.sort(ARR) |
|
2852 | ARR = numpy.sort(ARR) | |
| 2805 | NUMS_MIN = (SPECT_PTS+7)//8 |
|
2853 | NUMS_MIN = (SPECT_PTS+7)//8 | |
| 2806 | RTEST = (1.0+1.0/fft_avg) |
|
2854 | RTEST = (1.0+1.0/fft_avg) | |
| 2807 | SUM = 0.0 |
|
2855 | SUM = 0.0 | |
| 2808 | SUMSQ = 0.0 |
|
2856 | SUMSQ = 0.0 | |
| 2809 | J = 0 |
|
2857 | J = 0 | |
| 2810 | for I in range(NN): |
|
2858 | for I in range(NN): | |
| 2811 | J = J + 1 |
|
2859 | J = J + 1 | |
| 2812 | SUM = SUM + ARR[I] |
|
2860 | SUM = SUM + ARR[I] | |
| 2813 | SUMSQ = SUMSQ + ARR[I]*ARR[I] |
|
2861 | SUMSQ = SUMSQ + ARR[I]*ARR[I] | |
| 2814 | AVE = SUM*1.0/J |
|
2862 | AVE = SUM*1.0/J | |
| 2815 | if J > NUMS_MIN: |
|
2863 | if J > NUMS_MIN: | |
| 2816 | if (SUMSQ*J <= RTEST*SUM*SUM): RNOISE = AVE |
|
2864 | if (SUMSQ*J <= RTEST*SUM*SUM): RNOISE = AVE | |
| 2817 | else: |
|
2865 | else: | |
| 2818 | if J == NUMS_MIN: RNOISE = AVE |
|
2866 | if J == NUMS_MIN: RNOISE = AVE | |
| 2819 | if TALK == 1: print('Noise Power (2):%4.4f' %RNOISE) |
|
2867 | if TALK == 1: print('Noise Power (2):%4.4f' %RNOISE) | |
| 2820 | stdv = numpy.sqrt(SUMSQ/J - numpy.power(SUM/J,2)) |
|
2868 | stdv = numpy.sqrt(SUMSQ/J - numpy.power(SUM/J,2)) | |
| 2821 | return RNOISE, stdv |
|
2869 | return RNOISE, stdv | |
| 2822 |
|
2870 | |||
| 2823 | def __get_noise1(self, power, fft_avg, TALK=0): |
|
2871 | def __get_noise1(self, power, fft_avg, TALK=0): | |
| 2824 | ''' |
|
2872 | ''' | |
| 2825 | Rutina para cΓ‘lculo de ruido por alturas(n0). Similar a IDL |
|
2873 | Rutina para cΓ‘lculo de ruido por alturas(n0). Similar a IDL | |
| 2826 | ''' |
|
2874 | ''' | |
| 2827 | num_pts = numpy.size(power) |
|
2875 | num_pts = numpy.size(power) | |
| 2828 | #print('num_pts',num_pts) |
|
2876 | #print('num_pts',num_pts) | |
| 2829 | #print('power',power.shape) |
|
2877 | #print('power',power.shape) | |
| 2830 | #print(power[256:267,0:2]) |
|
2878 | #print(power[256:267,0:2]) | |
| 2831 | fft_avg = fft_avg*1.0 |
|
2879 | fft_avg = fft_avg*1.0 | |
| 2832 |
|
2880 | |||
| 2833 | ind = numpy.argsort(power, axis=None, kind='stable') |
|
2881 | ind = numpy.argsort(power, axis=None, kind='stable') | |
| 2834 | #ind = numpy.argsort(numpy.reshape(power,-1)) |
|
2882 | #ind = numpy.argsort(numpy.reshape(power,-1)) | |
| 2835 | #print(ind.shape) |
|
2883 | #print(ind.shape) | |
| 2836 | #print(ind[0:11]) |
|
2884 | #print(ind[0:11]) | |
| 2837 | #print(numpy.reshape(power,-1)[ind[0:11]]) |
|
2885 | #print(numpy.reshape(power,-1)[ind[0:11]]) | |
| 2838 | ARR = numpy.reshape(power,-1)[ind] |
|
2886 | ARR = numpy.reshape(power,-1)[ind] | |
| 2839 | #print('ARR',len(ARR)) |
|
2887 | #print('ARR',len(ARR)) | |
| 2840 | #print('ARR',ARR.shape) |
|
2888 | #print('ARR',ARR.shape) | |
| 2841 | NUMS_MIN = num_pts//10 |
|
2889 | NUMS_MIN = num_pts//10 | |
| 2842 | RTEST = (1.0+1.0/fft_avg) |
|
2890 | RTEST = (1.0+1.0/fft_avg) | |
| 2843 | SUM = 0.0 |
|
2891 | SUM = 0.0 | |
| 2844 | SUMSQ = 0.0 |
|
2892 | SUMSQ = 0.0 | |
| 2845 | J = 0 |
|
2893 | J = 0 | |
| 2846 | cont = 1 |
|
2894 | cont = 1 | |
| 2847 | while cont == 1 and J < num_pts: |
|
2895 | while cont == 1 and J < num_pts: | |
| 2848 |
|
2896 | |||
| 2849 | SUM = SUM + ARR[J] |
|
2897 | SUM = SUM + ARR[J] | |
| 2850 | SUMSQ = SUMSQ + ARR[J]*ARR[J] |
|
2898 | SUMSQ = SUMSQ + ARR[J]*ARR[J] | |
| 2851 | J = J + 1 |
|
2899 | J = J + 1 | |
| 2852 |
|
2900 | |||
| 2853 | if J > NUMS_MIN: |
|
2901 | if J > NUMS_MIN: | |
| 2854 | if (SUMSQ*J <= RTEST*SUM*SUM): |
|
2902 | if (SUMSQ*J <= RTEST*SUM*SUM): | |
| 2855 | LNOISE = SUM*1.0/J |
|
2903 | LNOISE = SUM*1.0/J | |
| 2856 | else: |
|
2904 | else: | |
| 2857 | J = J - 1 |
|
2905 | J = J - 1 | |
| 2858 | SUM = SUM - ARR[J] |
|
2906 | SUM = SUM - ARR[J] | |
| 2859 | SUMSQ = SUMSQ - ARR[J]*ARR[J] |
|
2907 | SUMSQ = SUMSQ - ARR[J]*ARR[J] | |
| 2860 | cont = 0 |
|
2908 | cont = 0 | |
| 2861 | else: |
|
2909 | else: | |
| 2862 | if J == NUMS_MIN: LNOISE = SUM*1.0/J |
|
2910 | if J == NUMS_MIN: LNOISE = SUM*1.0/J | |
| 2863 | if TALK == 1: print('Noise Power (1):%8.8f' %LNOISE) |
|
2911 | if TALK == 1: print('Noise Power (1):%8.8f' %LNOISE) | |
| 2864 | stdv = numpy.sqrt(SUMSQ/J - numpy.power(SUM/J,2)) |
|
2912 | stdv = numpy.sqrt(SUMSQ/J - numpy.power(SUM/J,2)) | |
| 2865 | return LNOISE, stdv |
|
2913 | return LNOISE, stdv | |
| 2866 |
|
2914 | |||
| 2867 | def __NoiseByChannel(self, num_prof, num_incoh, spectra,talk=0): |
|
2915 | def __NoiseByChannel(self, num_prof, num_incoh, spectra,talk=0): | |
| 2868 |
|
2916 | |||
| 2869 | val_frq = numpy.arange(num_prof-2)+1 |
|
2917 | val_frq = numpy.arange(num_prof-2)+1 | |
| 2870 | val_frq[(num_prof-2)//2:] = val_frq[(num_prof-2)//2:] + 1 |
|
2918 | val_frq[(num_prof-2)//2:] = val_frq[(num_prof-2)//2:] + 1 | |
| 2871 | junkspc = numpy.sum(spectra[val_frq,:], axis=1) |
|
2919 | junkspc = numpy.sum(spectra[val_frq,:], axis=1) | |
| 2872 | junkid = numpy.argsort(junkspc) |
|
2920 | junkid = numpy.argsort(junkspc) | |
| 2873 | noisezone = val_frq[junkid[0:num_prof//2]] |
|
2921 | noisezone = val_frq[junkid[0:num_prof//2]] | |
| 2874 | specnoise = spectra[noisezone,:] |
|
2922 | specnoise = spectra[noisezone,:] | |
| 2875 | noise, stdvnoise = self.__get_noise1(specnoise,num_incoh) |
|
2923 | noise, stdvnoise = self.__get_noise1(specnoise,num_incoh) | |
| 2876 |
|
2924 | |||
| 2877 | if talk: |
|
2925 | if talk: | |
| 2878 | print('noise =', noise) |
|
2926 | print('noise =', noise) | |
| 2879 | return noise |
|
2927 | return noise | |
| 2880 | #------------------ Get SA Parameters -------------------------- |
|
2928 | #------------------ Get SA Parameters -------------------------- | |
| 2881 |
|
2929 | |||
| 2882 | def GetSAParameters(self): |
|
2930 | def GetSAParameters(self): | |
| 2883 | #SA en frecuencia |
|
2931 | #SA en frecuencia | |
| 2884 | pairslist = self.dataOut.groupList |
|
2932 | pairslist = self.dataOut.groupList | |
| 2885 | num_pairs = len(pairslist) |
|
2933 | num_pairs = len(pairslist) | |
| 2886 |
|
2934 | |||
| 2887 | vel = self.dataOut.abscissaList |
|
2935 | vel = self.dataOut.abscissaList | |
| 2888 | spectra = self.dataOut.data_pre |
|
2936 | spectra = self.dataOut.data_pre | |
| 2889 | cspectra = self.dataIn.data_cspc |
|
2937 | cspectra = self.dataIn.data_cspc | |
| 2890 | delta_v = vel[1] - vel[0] |
|
2938 | delta_v = vel[1] - vel[0] | |
| 2891 |
|
2939 | |||
| 2892 | #Calculating the power spectrum |
|
2940 | #Calculating the power spectrum | |
| 2893 | spc_pow = numpy.sum(spectra, 3)*delta_v |
|
2941 | spc_pow = numpy.sum(spectra, 3)*delta_v | |
| 2894 | #Normalizing Spectra |
|
2942 | #Normalizing Spectra | |
| 2895 | norm_spectra = spectra/spc_pow |
|
2943 | norm_spectra = spectra/spc_pow | |
| 2896 | #Calculating the norm_spectra at peak |
|
2944 | #Calculating the norm_spectra at peak | |
| 2897 | max_spectra = numpy.max(norm_spectra, 3) |
|
2945 | max_spectra = numpy.max(norm_spectra, 3) | |
| 2898 |
|
2946 | |||
| 2899 | #Normalizing Cross Spectra |
|
2947 | #Normalizing Cross Spectra | |
| 2900 | norm_cspectra = numpy.zeros(cspectra.shape) |
|
2948 | norm_cspectra = numpy.zeros(cspectra.shape) | |
| 2901 |
|
2949 | |||
| 2902 | for i in range(num_chan): |
|
2950 | for i in range(num_chan): | |
| 2903 | norm_cspectra[i,:,:] = cspectra[i,:,:]/numpy.sqrt(spc_pow[pairslist[i][0],:]*spc_pow[pairslist[i][1],:]) |
|
2951 | norm_cspectra[i,:,:] = cspectra[i,:,:]/numpy.sqrt(spc_pow[pairslist[i][0],:]*spc_pow[pairslist[i][1],:]) | |
| 2904 |
|
2952 | |||
| 2905 | max_cspectra = numpy.max(norm_cspectra,2) |
|
2953 | max_cspectra = numpy.max(norm_cspectra,2) | |
| 2906 | max_cspectra_index = numpy.argmax(norm_cspectra, 2) |
|
2954 | max_cspectra_index = numpy.argmax(norm_cspectra, 2) | |
| 2907 |
|
2955 | |||
| 2908 | for i in range(num_pairs): |
|
2956 | for i in range(num_pairs): | |
| 2909 | cspc_par[i,:,:] = __calculateMoments(norm_cspectra) |
|
2957 | cspc_par[i,:,:] = __calculateMoments(norm_cspectra) | |
| 2910 | #------------------- Get Lags ---------------------------------- |
|
2958 | #------------------- Get Lags ---------------------------------- | |
| 2911 |
|
2959 | |||
| 2912 | class JULIADriftsEstimation(Operation): |
|
2960 | class JULIADriftsEstimation(Operation): | |
| 2913 |
|
2961 | |||
| 2914 | def __init__(self): |
|
2962 | def __init__(self): | |
| 2915 | Operation.__init__(self) |
|
2963 | Operation.__init__(self) | |
| 2916 |
|
2964 | |||
| 2917 | def newtotal(self, data): |
|
2965 | def newtotal(self, data): | |
| 2918 | return numpy.nansum(data) |
|
2966 | return numpy.nansum(data) | |
| 2919 |
|
2967 | |||
| 2920 | def data_filter(self, parm, snrth=-20, swth=20, wErrth=500): |
|
2968 | def data_filter(self, parm, snrth=-20, swth=20, wErrth=500): | |
| 2921 |
|
2969 | |||
| 2922 | Sz0 = parm.shape # Sz0: h,p |
|
2970 | Sz0 = parm.shape # Sz0: h,p | |
| 2923 | drift = parm[:,0] |
|
2971 | drift = parm[:,0] | |
| 2924 | sw = 2*parm[:,1] |
|
2972 | sw = 2*parm[:,1] | |
| 2925 | snr = 10*numpy.log10(parm[:,2]) |
|
2973 | snr = 10*numpy.log10(parm[:,2]) | |
| 2926 | Sz = drift.shape # Sz: h |
|
2974 | Sz = drift.shape # Sz: h | |
| 2927 | mask = numpy.ones((Sz[0])) |
|
2975 | mask = numpy.ones((Sz[0])) | |
| 2928 | th=0 |
|
2976 | th=0 | |
| 2929 | valid=numpy.where(numpy.isfinite(snr)) |
|
2977 | valid=numpy.where(numpy.isfinite(snr)) | |
| 2930 | cvalid = len(valid[0]) |
|
2978 | cvalid = len(valid[0]) | |
| 2931 | if cvalid >= 1: |
|
2979 | if cvalid >= 1: | |
| 2932 | # CΓ‘lculo del ruido promedio de snr para el i-Γ©simo grupo de alturas |
|
2980 | # CΓ‘lculo del ruido promedio de snr para el i-Γ©simo grupo de alturas | |
| 2933 | nbins = int(numpy.max(snr)-numpy.min(snr))+1 # bin size = 1, similar to IDL |
|
2981 | nbins = int(numpy.max(snr)-numpy.min(snr))+1 # bin size = 1, similar to IDL | |
| 2934 | h = numpy.histogram(snr,bins=nbins) |
|
2982 | h = numpy.histogram(snr,bins=nbins) | |
| 2935 | hist = h[0] |
|
2983 | hist = h[0] | |
| 2936 | values = numpy.round_(h[1]) |
|
2984 | values = numpy.round_(h[1]) | |
| 2937 | moda = values[numpy.where(hist == numpy.max(hist))] |
|
2985 | moda = values[numpy.where(hist == numpy.max(hist))] | |
| 2938 | indNoise = numpy.where(numpy.abs(snr - numpy.min(moda)) < 3)[0] |
|
2986 | indNoise = numpy.where(numpy.abs(snr - numpy.min(moda)) < 3)[0] | |
| 2939 |
|
2987 | |||
| 2940 | noise = snr[indNoise] |
|
2988 | noise = snr[indNoise] | |
| 2941 | noise_mean = numpy.sum(noise)/len(noise) |
|
2989 | noise_mean = numpy.sum(noise)/len(noise) | |
| 2942 | # CΓ‘lculo de media de snr |
|
2990 | # CΓ‘lculo de media de snr | |
| 2943 | med = numpy.median(snr) |
|
2991 | med = numpy.median(snr) | |
| 2944 | # Establece el umbral de snr |
|
2992 | # Establece el umbral de snr | |
| 2945 | if noise_mean > med + 3: |
|
2993 | if noise_mean > med + 3: | |
| 2946 | th = med |
|
2994 | th = med | |
| 2947 | else: |
|
2995 | else: | |
| 2948 | th = noise_mean + 3 |
|
2996 | th = noise_mean + 3 | |
| 2949 | # Establece mΓ‘scara |
|
2997 | # Establece mΓ‘scara | |
| 2950 | novalid = numpy.where(snr <= th)[0] |
|
2998 | novalid = numpy.where(snr <= th)[0] | |
| 2951 | mask[novalid] = numpy.nan |
|
2999 | mask[novalid] = numpy.nan | |
| 2952 | # Elimina datos que no sobrepasen el umbral: PARAMETRO |
|
3000 | # Elimina datos que no sobrepasen el umbral: PARAMETRO | |
| 2953 | novalid = numpy.where(snr <= snrth) |
|
3001 | novalid = numpy.where(snr <= snrth) | |
| 2954 | cnovalid = len(novalid[0]) |
|
3002 | cnovalid = len(novalid[0]) | |
| 2955 | if cnovalid > 0: |
|
3003 | if cnovalid > 0: | |
| 2956 | mask[novalid] = numpy.nan |
|
3004 | mask[novalid] = numpy.nan | |
| 2957 | novalid = numpy.where(numpy.isnan(snr)) |
|
3005 | novalid = numpy.where(numpy.isnan(snr)) | |
| 2958 | cnovalid = len(novalid[0]) |
|
3006 | cnovalid = len(novalid[0]) | |
| 2959 | if cnovalid > 0: |
|
3007 | if cnovalid > 0: | |
| 2960 | mask[novalid] = numpy.nan |
|
3008 | mask[novalid] = numpy.nan | |
| 2961 | new_parm = numpy.zeros((Sz0[0],Sz0[1])) |
|
3009 | new_parm = numpy.zeros((Sz0[0],Sz0[1])) | |
| 2962 | for h in range(Sz0[0]): |
|
3010 | for h in range(Sz0[0]): | |
| 2963 | for p in range(Sz0[1]): |
|
3011 | for p in range(Sz0[1]): | |
| 2964 | if numpy.isnan(mask[h]): |
|
3012 | if numpy.isnan(mask[h]): | |
| 2965 | new_parm[h,p]=numpy.nan |
|
3013 | new_parm[h,p]=numpy.nan | |
| 2966 | else: |
|
3014 | else: | |
| 2967 | new_parm[h,p]=parm[h,p] |
|
3015 | new_parm[h,p]=parm[h,p] | |
| 2968 |
|
3016 | |||
| 2969 | return new_parm, th |
|
3017 | return new_parm, th | |
| 2970 |
|
3018 | |||
| 2971 | def run(self, dataOut, zenith, zenithCorrection,heights=None, statistics=0, otype=0): |
|
3019 | def run(self, dataOut, zenith, zenithCorrection,heights=None, statistics=0, otype=0): | |
| 2972 |
|
3020 | |||
| 2973 | dataOut.lat=-11.95 |
|
3021 | dataOut.lat=-11.95 | |
| 2974 | dataOut.lon=-76.87 |
|
3022 | dataOut.lon=-76.87 | |
| 2975 | nCh=dataOut.spcpar.shape[0] |
|
3023 | nCh=dataOut.spcpar.shape[0] | |
| 2976 | nHei=dataOut.spcpar.shape[1] |
|
3024 | nHei=dataOut.spcpar.shape[1] | |
| 2977 | nParam=dataOut.spcpar.shape[2] |
|
3025 | nParam=dataOut.spcpar.shape[2] | |
| 2978 | # SelecciΓ³n de alturas |
|
3026 | # SelecciΓ³n de alturas | |
| 2979 |
|
3027 | |||
| 2980 | if not heights: |
|
3028 | if not heights: | |
| 2981 | parm = numpy.zeros((nCh,nHei,nParam)) |
|
3029 | parm = numpy.zeros((nCh,nHei,nParam)) | |
| 2982 | parm[:] = dataOut.spcpar[:] |
|
3030 | parm[:] = dataOut.spcpar[:] | |
| 2983 | else: |
|
3031 | else: | |
| 2984 | hei=dataOut.heightList |
|
3032 | hei=dataOut.heightList | |
| 2985 | hvalid=numpy.where([hei >= heights[0]][0] & [hei <= heights[1]][0])[0] |
|
3033 | hvalid=numpy.where([hei >= heights[0]][0] & [hei <= heights[1]][0])[0] | |
| 2986 | nhvalid=len(hvalid) |
|
3034 | nhvalid=len(hvalid) | |
| 2987 | dataOut.heightList = hei[hvalid] |
|
3035 | dataOut.heightList = hei[hvalid] | |
| 2988 | parm = numpy.zeros((nCh,nhvalid,nParam)) |
|
3036 | parm = numpy.zeros((nCh,nhvalid,nParam)) | |
| 2989 | parm[:] = dataOut.spcpar[:,hvalid,:] |
|
3037 | parm[:] = dataOut.spcpar[:,hvalid,:] | |
| 2990 |
|
3038 | |||
| 2991 |
|
3039 | |||
| 2992 | # Primer filtrado: Umbral de SNR |
|
3040 | # Primer filtrado: Umbral de SNR | |
| 2993 | for i in range(nCh): |
|
3041 | for i in range(nCh): | |
| 2994 | parm[i,:,:] = self.data_filter(parm[i,:,:])[0] |
|
3042 | parm[i,:,:] = self.data_filter(parm[i,:,:])[0] | |
| 2995 |
|
3043 | |||
| 2996 | zenith = numpy.array(zenith) |
|
3044 | zenith = numpy.array(zenith) | |
| 2997 | zenith -= zenithCorrection |
|
3045 | zenith -= zenithCorrection | |
| 2998 | zenith *= numpy.pi/180 |
|
3046 | zenith *= numpy.pi/180 | |
| 2999 | alpha = zenith[0] |
|
3047 | alpha = zenith[0] | |
| 3000 | beta = zenith[1] |
|
3048 | beta = zenith[1] | |
| 3001 | dopplerCH0 = parm[0,:,0] |
|
3049 | dopplerCH0 = parm[0,:,0] | |
| 3002 | dopplerCH1 = parm[1,:,0] |
|
3050 | dopplerCH1 = parm[1,:,0] | |
| 3003 | swCH0 = parm[0,:,1] |
|
3051 | swCH0 = parm[0,:,1] | |
| 3004 | swCH1 = parm[1,:,1] |
|
3052 | swCH1 = parm[1,:,1] | |
| 3005 | snrCH0 = 10*numpy.log10(parm[0,:,2]) |
|
3053 | snrCH0 = 10*numpy.log10(parm[0,:,2]) | |
| 3006 | snrCH1 = 10*numpy.log10(parm[1,:,2]) |
|
3054 | snrCH1 = 10*numpy.log10(parm[1,:,2]) | |
| 3007 | noiseCH0 = parm[0,:,3] |
|
3055 | noiseCH0 = parm[0,:,3] | |
| 3008 | noiseCH1 = parm[1,:,3] |
|
3056 | noiseCH1 = parm[1,:,3] | |
| 3009 | wErrCH0 = parm[0,:,5] |
|
3057 | wErrCH0 = parm[0,:,5] | |
| 3010 | wErrCH1 = parm[1,:,5] |
|
3058 | wErrCH1 = parm[1,:,5] | |
| 3011 |
|
3059 | |||
| 3012 | # Vertical and zonal calculation according to geometry |
|
3060 | # Vertical and zonal calculation according to geometry | |
| 3013 | sinB_A = numpy.sin(beta)*numpy.cos(alpha) - numpy.sin(alpha)* numpy.cos(beta) |
|
3061 | sinB_A = numpy.sin(beta)*numpy.cos(alpha) - numpy.sin(alpha)* numpy.cos(beta) | |
| 3014 | drift = -(dopplerCH0 * numpy.sin(beta) - dopplerCH1 * numpy.sin(alpha))/ sinB_A |
|
3062 | drift = -(dopplerCH0 * numpy.sin(beta) - dopplerCH1 * numpy.sin(alpha))/ sinB_A | |
| 3015 | zonal = (dopplerCH0 * numpy.cos(beta) - dopplerCH1 * numpy.cos(alpha))/ sinB_A |
|
3063 | zonal = (dopplerCH0 * numpy.cos(beta) - dopplerCH1 * numpy.cos(alpha))/ sinB_A | |
| 3016 | snr = (snrCH0 + snrCH1)/2 |
|
3064 | snr = (snrCH0 + snrCH1)/2 | |
| 3017 | noise = (noiseCH0 + noiseCH1)/2 |
|
3065 | noise = (noiseCH0 + noiseCH1)/2 | |
| 3018 | sw = (swCH0 + swCH1)/2 |
|
3066 | sw = (swCH0 + swCH1)/2 | |
| 3019 | w_w_err= numpy.sqrt(numpy.power(wErrCH0 * numpy.sin(beta)/numpy.abs(sinB_A),2) + numpy.power(wErrCH1 * numpy.sin(alpha)/numpy.abs(sinB_A),2)) |
|
3067 | w_w_err= numpy.sqrt(numpy.power(wErrCH0 * numpy.sin(beta)/numpy.abs(sinB_A),2) + numpy.power(wErrCH1 * numpy.sin(alpha)/numpy.abs(sinB_A),2)) | |
| 3020 | w_e_err= numpy.sqrt(numpy.power(wErrCH0 * numpy.cos(beta)/numpy.abs(-1*sinB_A),2) + numpy.power(wErrCH1 * numpy.cos(alpha)/numpy.abs(-1*sinB_A),2)) |
|
3068 | w_e_err= numpy.sqrt(numpy.power(wErrCH0 * numpy.cos(beta)/numpy.abs(-1*sinB_A),2) + numpy.power(wErrCH1 * numpy.cos(alpha)/numpy.abs(-1*sinB_A),2)) | |
| 3021 |
|
3069 | |||
| 3022 | # for statistics150km |
|
3070 | # for statistics150km | |
| 3023 | if statistics: |
|
3071 | if statistics: | |
| 3024 | print('Implemented offline.') |
|
3072 | print('Implemented offline.') | |
| 3025 |
|
3073 | |||
| 3026 | if otype == 0: |
|
3074 | if otype == 0: | |
| 3027 | winds = numpy.vstack((snr, drift, zonal, noise, sw, w_w_err, w_e_err)) # to process statistics drifts |
|
3075 | winds = numpy.vstack((snr, drift, zonal, noise, sw, w_w_err, w_e_err)) # to process statistics drifts | |
| 3028 | elif otype == 3: |
|
3076 | elif otype == 3: | |
| 3029 | winds = numpy.vstack((snr, drift, zonal)) # to generic plot: 3 RTI's |
|
3077 | winds = numpy.vstack((snr, drift, zonal)) # to generic plot: 3 RTI's | |
| 3030 | elif otype == 4: |
|
3078 | elif otype == 4: | |
| 3031 | winds = numpy.vstack((snrCH0, drift, snrCH1, zonal)) # to generic plot: 4 RTI's |
|
3079 | winds = numpy.vstack((snrCH0, drift, snrCH1, zonal)) # to generic plot: 4 RTI's | |
| 3032 |
|
3080 | |||
| 3033 | snr1 = numpy.vstack((snrCH0, snrCH1)) |
|
3081 | snr1 = numpy.vstack((snrCH0, snrCH1)) | |
| 3034 | dataOut.data_output = winds |
|
3082 | dataOut.data_output = winds | |
| 3035 | dataOut.data_snr = snr1 |
|
3083 | dataOut.data_snr = snr1 | |
| 3036 |
|
3084 | |||
| 3037 | dataOut.utctimeInit = dataOut.utctime |
|
3085 | dataOut.utctimeInit = dataOut.utctime | |
| 3038 | dataOut.outputInterval = dataOut.timeInterval |
|
3086 | dataOut.outputInterval = dataOut.timeInterval | |
| 3039 | try: |
|
3087 | try: | |
| 3040 | dataOut.flagNoData = numpy.all(numpy.isnan(dataOut.data_output[0])) # NAN vectors are not written MADRIGAL CASE |
|
3088 | dataOut.flagNoData = numpy.all(numpy.isnan(dataOut.data_output[0])) # NAN vectors are not written MADRIGAL CASE | |
| 3041 | except: |
|
3089 | except: | |
| 3042 | print("Check there is no Data") |
|
3090 | print("Check there is no Data") | |
| 3043 |
|
3091 | |||
| 3044 | return dataOut |
|
3092 | return dataOut | |
| 3045 |
|
3093 | |||
| 3046 | class SALags(Operation): |
|
3094 | class SALags(Operation): | |
| 3047 | ''' |
|
3095 | ''' | |
| 3048 | Function GetMoments() |
|
3096 | Function GetMoments() | |
| 3049 |
|
3097 | |||
| 3050 | Input: |
|
3098 | Input: | |
| 3051 | self.dataOut.data_pre |
|
3099 | self.dataOut.data_pre | |
| 3052 | self.dataOut.abscissaList |
|
3100 | self.dataOut.abscissaList | |
| 3053 | self.dataOut.noise |
|
3101 | self.dataOut.noise | |
| 3054 | self.dataOut.normFactor |
|
3102 | self.dataOut.normFactor | |
| 3055 | self.dataOut.data_snr |
|
3103 | self.dataOut.data_snr | |
| 3056 | self.dataOut.groupList |
|
3104 | self.dataOut.groupList | |
| 3057 | self.dataOut.nChannels |
|
3105 | self.dataOut.nChannels | |
| 3058 |
|
3106 | |||
| 3059 | Affected: |
|
3107 | Affected: | |
| 3060 | self.dataOut.data_param |
|
3108 | self.dataOut.data_param | |
| 3061 |
|
3109 | |||
| 3062 | ''' |
|
3110 | ''' | |
| 3063 | def run(self, dataOut): |
|
3111 | def run(self, dataOut): | |
| 3064 | data_acf = dataOut.data_pre[0] |
|
3112 | data_acf = dataOut.data_pre[0] | |
| 3065 | data_ccf = dataOut.data_pre[1] |
|
3113 | data_ccf = dataOut.data_pre[1] | |
| 3066 | normFactor_acf = dataOut.normFactor[0] |
|
3114 | normFactor_acf = dataOut.normFactor[0] | |
| 3067 | normFactor_ccf = dataOut.normFactor[1] |
|
3115 | normFactor_ccf = dataOut.normFactor[1] | |
| 3068 | pairs_acf = dataOut.groupList[0] |
|
3116 | pairs_acf = dataOut.groupList[0] | |
| 3069 | pairs_ccf = dataOut.groupList[1] |
|
3117 | pairs_ccf = dataOut.groupList[1] | |
| 3070 |
|
3118 | |||
| 3071 | nHeights = dataOut.nHeights |
|
3119 | nHeights = dataOut.nHeights | |
| 3072 | absc = dataOut.abscissaList |
|
3120 | absc = dataOut.abscissaList | |
| 3073 | noise = dataOut.noise |
|
3121 | noise = dataOut.noise | |
| 3074 | SNR = dataOut.data_snr |
|
3122 | SNR = dataOut.data_snr | |
| 3075 | nChannels = dataOut.nChannels |
|
3123 | nChannels = dataOut.nChannels | |
| 3076 | for l in range(len(pairs_acf)): |
|
3124 | for l in range(len(pairs_acf)): | |
| 3077 | data_acf[l,:,:] = data_acf[l,:,:]/normFactor_acf[l,:] |
|
3125 | data_acf[l,:,:] = data_acf[l,:,:]/normFactor_acf[l,:] | |
| 3078 |
|
3126 | |||
| 3079 | for l in range(len(pairs_ccf)): |
|
3127 | for l in range(len(pairs_ccf)): | |
| 3080 | data_ccf[l,:,:] = data_ccf[l,:,:]/normFactor_ccf[l,:] |
|
3128 | data_ccf[l,:,:] = data_ccf[l,:,:]/normFactor_ccf[l,:] | |
| 3081 |
|
3129 | |||
| 3082 | dataOut.data_param = numpy.zeros((len(pairs_ccf)*2 + 1, nHeights)) |
|
3130 | dataOut.data_param = numpy.zeros((len(pairs_ccf)*2 + 1, nHeights)) | |
| 3083 | dataOut.data_param[:-1,:] = self.__calculateTaus(data_acf, data_ccf, absc) |
|
3131 | dataOut.data_param[:-1,:] = self.__calculateTaus(data_acf, data_ccf, absc) | |
| 3084 | dataOut.data_param[-1,:] = self.__calculateLag1Phase(data_acf, absc) |
|
3132 | dataOut.data_param[-1,:] = self.__calculateLag1Phase(data_acf, absc) | |
| 3085 | return |
|
3133 | return | |
| 3086 |
|
3134 | |||
| 3087 | def __calculateTaus(self, data_acf, data_ccf, lagRange): |
|
3135 | def __calculateTaus(self, data_acf, data_ccf, lagRange): | |
| 3088 |
|
3136 | |||
| 3089 | lag0 = data_acf.shape[1]/2 |
|
3137 | lag0 = data_acf.shape[1]/2 | |
| 3090 | #Funcion de Autocorrelacion |
|
3138 | #Funcion de Autocorrelacion | |
| 3091 | mean_acf = stats.nanmean(data_acf, axis = 0) |
|
3139 | mean_acf = stats.nanmean(data_acf, axis = 0) | |
| 3092 |
|
3140 | |||
| 3093 | #Obtencion Indice de TauCross |
|
3141 | #Obtencion Indice de TauCross | |
| 3094 | ind_ccf = data_ccf.argmax(axis = 1) |
|
3142 | ind_ccf = data_ccf.argmax(axis = 1) | |
| 3095 | #Obtencion Indice de TauAuto |
|
3143 | #Obtencion Indice de TauAuto | |
| 3096 | ind_acf = numpy.zeros(ind_ccf.shape,dtype = 'int') |
|
3144 | ind_acf = numpy.zeros(ind_ccf.shape,dtype = 'int') | |
| 3097 | ccf_lag0 = data_ccf[:,lag0,:] |
|
3145 | ccf_lag0 = data_ccf[:,lag0,:] | |
| 3098 |
|
3146 | |||
| 3099 | for i in range(ccf_lag0.shape[0]): |
|
3147 | for i in range(ccf_lag0.shape[0]): | |
| 3100 | ind_acf[i,:] = numpy.abs(mean_acf - ccf_lag0[i,:]).argmin(axis = 0) |
|
3148 | ind_acf[i,:] = numpy.abs(mean_acf - ccf_lag0[i,:]).argmin(axis = 0) | |
| 3101 |
|
3149 | |||
| 3102 | #Obtencion de TauCross y TauAuto |
|
3150 | #Obtencion de TauCross y TauAuto | |
| 3103 | tau_ccf = lagRange[ind_ccf] |
|
3151 | tau_ccf = lagRange[ind_ccf] | |
| 3104 | tau_acf = lagRange[ind_acf] |
|
3152 | tau_acf = lagRange[ind_acf] | |
| 3105 |
|
3153 | |||
| 3106 | Nan1, Nan2 = numpy.where(tau_ccf == lagRange[0]) |
|
3154 | Nan1, Nan2 = numpy.where(tau_ccf == lagRange[0]) | |
| 3107 |
|
3155 | |||
| 3108 | tau_ccf[Nan1,Nan2] = numpy.nan |
|
3156 | tau_ccf[Nan1,Nan2] = numpy.nan | |
| 3109 | tau_acf[Nan1,Nan2] = numpy.nan |
|
3157 | tau_acf[Nan1,Nan2] = numpy.nan | |
| 3110 | tau = numpy.vstack((tau_ccf,tau_acf)) |
|
3158 | tau = numpy.vstack((tau_ccf,tau_acf)) | |
| 3111 |
|
3159 | |||
| 3112 | return tau |
|
3160 | return tau | |
| 3113 |
|
3161 | |||
| 3114 | def __calculateLag1Phase(self, data, lagTRange): |
|
3162 | def __calculateLag1Phase(self, data, lagTRange): | |
| 3115 | data1 = stats.nanmean(data, axis = 0) |
|
3163 | data1 = stats.nanmean(data, axis = 0) | |
| 3116 | lag1 = numpy.where(lagTRange == 0)[0][0] + 1 |
|
3164 | lag1 = numpy.where(lagTRange == 0)[0][0] + 1 | |
| 3117 |
|
3165 | |||
| 3118 | phase = numpy.angle(data1[lag1,:]) |
|
3166 | phase = numpy.angle(data1[lag1,:]) | |
| 3119 |
|
3167 | |||
| 3120 | return phase |
|
3168 | return phase | |
| 3121 |
|
3169 | |||
| 3122 | def fit_func( x, a0, a1, a2): #, a3, a4, a5): |
|
3170 | def fit_func( x, a0, a1, a2): #, a3, a4, a5): | |
| 3123 | z = (x - a1) / a2 |
|
3171 | z = (x - a1) / a2 | |
| 3124 | y = a0 * numpy.exp(-z**2 / a2) #+ a3 + a4 * x + a5 * x**2 |
|
3172 | y = a0 * numpy.exp(-z**2 / a2) #+ a3 + a4 * x + a5 * x**2 | |
| 3125 | return y |
|
3173 | return y | |
| 3126 |
|
3174 | |||
| 3127 |
|
3175 | |||
| 3128 | class SpectralFitting(Operation): |
|
3176 | class SpectralFitting(Operation): | |
| 3129 | ''' |
|
3177 | ''' | |
| 3130 | Function GetMoments() |
|
3178 | Function GetMoments() | |
| 3131 |
|
3179 | |||
| 3132 | Input: |
|
3180 | Input: | |
| 3133 | Output: |
|
3181 | Output: | |
| 3134 | Variables modified: |
|
3182 | Variables modified: | |
| 3135 | ''' |
|
3183 | ''' | |
| 3136 | isConfig = False |
|
3184 | isConfig = False | |
| 3137 | __dataReady = False |
|
3185 | __dataReady = False | |
| 3138 | bloques = None |
|
3186 | bloques = None | |
| 3139 | bloque0 = None |
|
3187 | bloque0 = None | |
| 3140 |
|
3188 | |||
| 3141 | def __init__(self): |
|
3189 | def __init__(self): | |
| 3142 | Operation.__init__(self) |
|
3190 | Operation.__init__(self) | |
| 3143 | self.i=0 |
|
3191 | self.i=0 | |
| 3144 | self.isConfig = False |
|
3192 | self.isConfig = False | |
| 3145 |
|
3193 | |||
| 3146 | def setup(self,nChan,nProf,nHei,nBlocks): |
|
3194 | def setup(self,nChan,nProf,nHei,nBlocks): | |
| 3147 | self.__dataReady = False |
|
3195 | self.__dataReady = False | |
| 3148 | self.bloques = numpy.zeros([2, nProf, nHei,nBlocks], dtype= complex) |
|
3196 | self.bloques = numpy.zeros([2, nProf, nHei,nBlocks], dtype= complex) | |
| 3149 | self.bloque0 = numpy.zeros([nChan, nProf, nHei, nBlocks]) |
|
3197 | self.bloque0 = numpy.zeros([nChan, nProf, nHei, nBlocks]) | |
| 3150 |
|
3198 | |||
| 3151 | def __calculateMoments(self,oldspec, oldfreq, n0, nicoh = None, graph = None, smooth = None, type1 = None, fwindow = None, snrth = None, dc = None, aliasing = None, oldfd = None, wwauto = None): |
|
3199 | def __calculateMoments(self,oldspec, oldfreq, n0, nicoh = None, graph = None, smooth = None, type1 = None, fwindow = None, snrth = None, dc = None, aliasing = None, oldfd = None, wwauto = None): | |
|
|
3200 | ||||
| 3152 | if (nicoh is None): nicoh = 1 |
|
3201 | if (nicoh is None): nicoh = 1 | |
| 3153 | if (graph is None): graph = 0 |
|
3202 | if (graph is None): graph = 0 | |
| 3154 | if (smooth is None): smooth = 0 |
|
3203 | if (smooth is None): smooth = 0 | |
| 3155 | elif (self.smooth < 3): smooth = 0 |
|
3204 | elif (self.smooth < 3): smooth = 0 | |
| 3156 |
|
3205 | |||
| 3157 | if (type1 is None): type1 = 0 |
|
3206 | if (type1 is None): type1 = 0 | |
| 3158 | if (fwindow is None): fwindow = numpy.zeros(oldfreq.size) + 1 |
|
3207 | if (fwindow is None): fwindow = numpy.zeros(oldfreq.size) + 1 | |
| 3159 | if (snrth is None): snrth = -3 |
|
3208 | if (snrth is None): snrth = -3 | |
| 3160 | if (dc is None): dc = 0 |
|
3209 | if (dc is None): dc = 0 | |
| 3161 | if (aliasing is None): aliasing = 0 |
|
3210 | if (aliasing is None): aliasing = 0 | |
| 3162 | if (oldfd is None): oldfd = 0 |
|
3211 | if (oldfd is None): oldfd = 0 | |
| 3163 | if (wwauto is None): wwauto = 0 |
|
3212 | if (wwauto is None): wwauto = 0 | |
| 3164 |
|
3213 | |||
| 3165 | if (n0 < 1.e-20): n0 = 1.e-20 |
|
3214 | if (n0 < 1.e-20): n0 = 1.e-20 | |
| 3166 | freq = oldfreq |
|
3215 | freq = oldfreq | |
| 3167 | vec_power = numpy.zeros(oldspec.shape[1]) |
|
3216 | vec_power = numpy.zeros(oldspec.shape[1]) | |
| 3168 | vec_fd = numpy.zeros(oldspec.shape[1]) |
|
3217 | vec_fd = numpy.zeros(oldspec.shape[1]) | |
| 3169 | vec_w = numpy.zeros(oldspec.shape[1]) |
|
3218 | vec_w = numpy.zeros(oldspec.shape[1]) | |
| 3170 | vec_snr = numpy.zeros(oldspec.shape[1]) |
|
3219 | vec_snr = numpy.zeros(oldspec.shape[1]) | |
| 3171 | oldspec = numpy.ma.masked_invalid(oldspec) |
|
3220 | oldspec = numpy.ma.masked_invalid(oldspec) | |
| 3172 |
|
3221 | |||
| 3173 | for ind in range(oldspec.shape[1]): |
|
3222 | for ind in range(oldspec.shape[1]): | |
| 3174 | spec = oldspec[:,ind] |
|
3223 | spec = oldspec[:,ind] | |
| 3175 | aux = spec*fwindow |
|
3224 | aux = spec*fwindow | |
| 3176 | max_spec = aux.max() |
|
3225 | max_spec = aux.max() | |
| 3177 | m = list(aux).index(max_spec) |
|
3226 | m = list(aux).index(max_spec) | |
| 3178 | #Smooth |
|
3227 | #Smooth | |
| 3179 | if (smooth == 0): spec2 = spec |
|
3228 | if (smooth == 0): spec2 = spec | |
| 3180 | else: spec2 = scipy.ndimage.filters.uniform_filter1d(spec,size=smooth) |
|
3229 | else: spec2 = scipy.ndimage.filters.uniform_filter1d(spec,size=smooth) | |
| 3181 |
|
3230 | |||
| 3182 | # Calculo de Momentos |
|
3231 | # Calculo de Momentos | |
| 3183 | bb = spec2[list(range(m,spec2.size))] |
|
3232 | bb = spec2[list(range(m,spec2.size))] | |
| 3184 | bb = (bb<n0).nonzero() |
|
3233 | bb = (bb<n0).nonzero() | |
| 3185 | bb = bb[0] |
|
3234 | bb = bb[0] | |
| 3186 |
|
3235 | |||
| 3187 | ss = spec2[list(range(0,m + 1))] |
|
3236 | ss = spec2[list(range(0,m + 1))] | |
| 3188 | ss = (ss<n0).nonzero() |
|
3237 | ss = (ss<n0).nonzero() | |
| 3189 | ss = ss[0] |
|
3238 | ss = ss[0] | |
| 3190 |
|
3239 | |||
| 3191 | if (bb.size == 0): |
|
3240 | if (bb.size == 0): | |
| 3192 | bb0 = spec.size - 1 - m |
|
3241 | bb0 = spec.size - 1 - m | |
| 3193 | else: |
|
3242 | else: | |
| 3194 | bb0 = bb[0] - 1 |
|
3243 | bb0 = bb[0] - 1 | |
| 3195 | if (bb0 < 0): |
|
3244 | if (bb0 < 0): | |
| 3196 | bb0 = 0 |
|
3245 | bb0 = 0 | |
| 3197 |
|
3246 | |||
| 3198 | if (ss.size == 0): ss1 = 1 |
|
3247 | if (ss.size == 0): ss1 = 1 | |
| 3199 | else: ss1 = max(ss) + 1 |
|
3248 | else: ss1 = max(ss) + 1 | |
| 3200 |
|
3249 | |||
| 3201 | if (ss1 > m): ss1 = m |
|
3250 | if (ss1 > m): ss1 = m | |
| 3202 |
|
3251 | |||
| 3203 | valid = numpy.asarray(list(range(int(m + bb0 - ss1 + 1)))) + ss1 |
|
3252 | valid = numpy.asarray(list(range(int(m + bb0 - ss1 + 1)))) + ss1 | |
| 3204 | power = ((spec2[valid] - n0)*fwindow[valid]).sum() |
|
3253 | power = ((spec2[valid] - n0)*fwindow[valid]).sum() | |
| 3205 | fd = ((spec2[valid]- n0)*freq[valid]*fwindow[valid]).sum()/power |
|
3254 | fd = ((spec2[valid]- n0)*freq[valid]*fwindow[valid]).sum()/power | |
| 3206 | w = math.sqrt(((spec2[valid] - n0)*fwindow[valid]*(freq[valid]- fd)**2).sum()/power) |
|
3255 | w = math.sqrt(((spec2[valid] - n0)*fwindow[valid]*(freq[valid]- fd)**2).sum()/power) | |
| 3207 | snr = (spec2.mean()-n0)/n0 |
|
3256 | snr = (spec2.mean()-n0)/n0 | |
| 3208 |
|
3257 | |||
| 3209 | if (snr < 1.e-20) : |
|
3258 | if (snr < 1.e-20) : | |
| 3210 | snr = 1.e-20 |
|
3259 | snr = 1.e-20 | |
| 3211 |
|
3260 | |||
| 3212 | vec_power[ind] = power |
|
3261 | vec_power[ind] = power | |
| 3213 | vec_fd[ind] = fd |
|
3262 | vec_fd[ind] = fd | |
| 3214 | vec_w[ind] = w |
|
3263 | vec_w[ind] = w | |
| 3215 | vec_snr[ind] = snr |
|
3264 | vec_snr[ind] = snr | |
| 3216 |
|
3265 | |||
| 3217 | moments = numpy.vstack((vec_snr, vec_power, vec_fd, vec_w)) |
|
3266 | moments = numpy.vstack((vec_snr, vec_power, vec_fd, vec_w)) | |
| 3218 | return moments |
|
3267 | return moments | |
| 3219 |
|
3268 | |||
| 3220 | def __DiffCoherent(self, spectra, cspectra, dataOut, noise, snrth, coh_th, hei_th): |
|
3269 | def __DiffCoherent(self, spectra, cspectra, dataOut, noise, snrth, coh_th, hei_th): | |
| 3221 |
|
3270 | |||
| 3222 | nProf = dataOut.nProfiles |
|
3271 | nProf = dataOut.nProfiles | |
| 3223 | heights = dataOut.heightList |
|
3272 | heights = dataOut.heightList | |
| 3224 | nHei = len(heights) |
|
3273 | nHei = len(heights) | |
| 3225 | channels = dataOut.channelList |
|
3274 | channels = dataOut.channelList | |
| 3226 | nChan = len(channels) |
|
3275 | nChan = len(channels) | |
| 3227 | crosspairs = dataOut.groupList |
|
3276 | crosspairs = dataOut.groupList | |
| 3228 | nPairs = len(crosspairs) |
|
3277 | nPairs = len(crosspairs) | |
| 3229 | #Separar espectros incoherentes de coherentes snr > 20 dB' |
|
3278 | #Separar espectros incoherentes de coherentes snr > 20 dB' | |
| 3230 | snr_th = 10**(snrth/10.0) |
|
3279 | snr_th = 10**(snrth/10.0) | |
| 3231 | my_incoh_spectra = numpy.zeros([nChan, nProf,nHei], dtype='float') |
|
3280 | my_incoh_spectra = numpy.zeros([nChan, nProf,nHei], dtype='float') | |
| 3232 | my_incoh_cspectra = numpy.zeros([nPairs,nProf, nHei], dtype='complex') |
|
3281 | my_incoh_cspectra = numpy.zeros([nPairs,nProf, nHei], dtype='complex') | |
| 3233 | my_incoh_aver = numpy.zeros([nChan, nHei]) |
|
3282 | my_incoh_aver = numpy.zeros([nChan, nHei]) | |
| 3234 | my_coh_aver = numpy.zeros([nChan, nHei]) |
|
3283 | my_coh_aver = numpy.zeros([nChan, nHei]) | |
| 3235 |
|
3284 | |||
| 3236 | coh_spectra = numpy.zeros([nChan, nProf, nHei], dtype='float') |
|
3285 | coh_spectra = numpy.zeros([nChan, nProf, nHei], dtype='float') | |
| 3237 | coh_cspectra = numpy.zeros([nPairs, nProf, nHei], dtype='complex') |
|
3286 | coh_cspectra = numpy.zeros([nPairs, nProf, nHei], dtype='complex') | |
| 3238 | coh_aver = numpy.zeros([nChan, nHei]) |
|
3287 | coh_aver = numpy.zeros([nChan, nHei]) | |
| 3239 |
|
3288 | |||
| 3240 | incoh_spectra = numpy.zeros([nChan, nProf, nHei], dtype='float') |
|
3289 | incoh_spectra = numpy.zeros([nChan, nProf, nHei], dtype='float') | |
| 3241 | incoh_cspectra = numpy.zeros([nPairs, nProf, nHei], dtype='complex') |
|
3290 | incoh_cspectra = numpy.zeros([nPairs, nProf, nHei], dtype='complex') | |
| 3242 | incoh_aver = numpy.zeros([nChan, nHei]) |
|
3291 | incoh_aver = numpy.zeros([nChan, nHei]) | |
| 3243 | power = numpy.sum(spectra, axis=1) |
|
3292 | power = numpy.sum(spectra, axis=1) | |
| 3244 |
|
3293 | |||
| 3245 | if coh_th == None : coh_th = numpy.array([0.75,0.65,0.15]) # 0.65 |
|
3294 | if coh_th == None : coh_th = numpy.array([0.75,0.65,0.15]) # 0.65 | |
| 3246 | if hei_th == None : hei_th = numpy.array([60,300,650]) |
|
3295 | if hei_th == None : hei_th = numpy.array([60,300,650]) | |
| 3247 | for ic in range(nPairs): |
|
3296 | for ic in range(nPairs): | |
| 3248 | pair = crosspairs[ic] |
|
3297 | pair = crosspairs[ic] | |
| 3249 | #si el SNR es mayor que el SNR threshold los datos se toman coherentes |
|
3298 | #si el SNR es mayor que el SNR threshold los datos se toman coherentes | |
| 3250 | s_n0 = power[pair[0],:]/noise[pair[0]] |
|
3299 | s_n0 = power[pair[0],:]/noise[pair[0]] | |
| 3251 | s_n1 = power[pair[1],:]/noise[pair[1]] |
|
3300 | s_n1 = power[pair[1],:]/noise[pair[1]] | |
| 3252 | valid1 =(s_n0>=snr_th).nonzero() |
|
3301 | valid1 =(s_n0>=snr_th).nonzero() | |
| 3253 | valid2 = (s_n1>=snr_th).nonzero() |
|
3302 | valid2 = (s_n1>=snr_th).nonzero() | |
| 3254 | valid1 = numpy.array(valid1[0]) |
|
3303 | valid1 = numpy.array(valid1[0]) | |
| 3255 | valid2 = numpy.array(valid2[0]) |
|
3304 | valid2 = numpy.array(valid2[0]) | |
| 3256 | valid = valid1 |
|
3305 | valid = valid1 | |
| 3257 | for iv in range(len(valid2)): |
|
3306 | for iv in range(len(valid2)): | |
| 3258 | indv = numpy.array((valid1 == valid2[iv]).nonzero()) |
|
3307 | indv = numpy.array((valid1 == valid2[iv]).nonzero()) | |
| 3259 | if len(indv[0]) == 0 : |
|
3308 | if len(indv[0]) == 0 : | |
| 3260 | valid = numpy.concatenate((valid,valid2[iv]), axis=None) |
|
3309 | valid = numpy.concatenate((valid,valid2[iv]), axis=None) | |
| 3261 | if len(valid)>0: |
|
3310 | if len(valid)>0: | |
| 3262 | my_coh_aver[pair[0],valid]=1 |
|
3311 | my_coh_aver[pair[0],valid]=1 | |
| 3263 | my_coh_aver[pair[1],valid]=1 |
|
3312 | my_coh_aver[pair[1],valid]=1 | |
| 3264 | # si la coherencia es mayor a la coherencia threshold los datos se toman |
|
3313 | # si la coherencia es mayor a la coherencia threshold los datos se toman | |
| 3265 | coh = numpy.squeeze(numpy.nansum(cspectra[ic,:,:], axis=0)/numpy.sqrt(numpy.nansum(spectra[pair[0],:,:], axis=0)*numpy.nansum(spectra[pair[1],:,:], axis=0))) |
|
3314 | coh = numpy.squeeze(numpy.nansum(cspectra[ic,:,:], axis=0)/numpy.sqrt(numpy.nansum(spectra[pair[0],:,:], axis=0)*numpy.nansum(spectra[pair[1],:,:], axis=0))) | |
| 3266 | for ih in range(len(hei_th)): |
|
3315 | for ih in range(len(hei_th)): | |
| 3267 | hvalid = (heights>hei_th[ih]).nonzero() |
|
3316 | hvalid = (heights>hei_th[ih]).nonzero() | |
| 3268 | hvalid = hvalid[0] |
|
3317 | hvalid = hvalid[0] | |
| 3269 | if len(hvalid)>0: |
|
3318 | if len(hvalid)>0: | |
| 3270 | valid = (numpy.absolute(coh[hvalid])>coh_th[ih]).nonzero() |
|
3319 | valid = (numpy.absolute(coh[hvalid])>coh_th[ih]).nonzero() | |
| 3271 | valid = valid[0] |
|
3320 | valid = valid[0] | |
| 3272 | if len(valid)>0: |
|
3321 | if len(valid)>0: | |
| 3273 | my_coh_aver[pair[0],hvalid[valid]] =1 |
|
3322 | my_coh_aver[pair[0],hvalid[valid]] =1 | |
| 3274 | my_coh_aver[pair[1],hvalid[valid]] =1 |
|
3323 | my_coh_aver[pair[1],hvalid[valid]] =1 | |
| 3275 |
|
3324 | |||
| 3276 | coh_echoes = (my_coh_aver[pair[0],:] == 1).nonzero() |
|
3325 | coh_echoes = (my_coh_aver[pair[0],:] == 1).nonzero() | |
| 3277 | incoh_echoes = (my_coh_aver[pair[0],:] != 1).nonzero() |
|
3326 | incoh_echoes = (my_coh_aver[pair[0],:] != 1).nonzero() | |
| 3278 | incoh_echoes = incoh_echoes[0] |
|
3327 | incoh_echoes = incoh_echoes[0] | |
| 3279 | if len(incoh_echoes) > 0: |
|
3328 | if len(incoh_echoes) > 0: | |
| 3280 | my_incoh_spectra[pair[0],:,incoh_echoes] = spectra[pair[0],:,incoh_echoes] |
|
3329 | my_incoh_spectra[pair[0],:,incoh_echoes] = spectra[pair[0],:,incoh_echoes] | |
| 3281 | my_incoh_spectra[pair[1],:,incoh_echoes] = spectra[pair[1],:,incoh_echoes] |
|
3330 | my_incoh_spectra[pair[1],:,incoh_echoes] = spectra[pair[1],:,incoh_echoes] | |
| 3282 | my_incoh_cspectra[ic,:,incoh_echoes] = cspectra[ic,:,incoh_echoes] |
|
3331 | my_incoh_cspectra[ic,:,incoh_echoes] = cspectra[ic,:,incoh_echoes] | |
| 3283 | my_incoh_aver[pair[0],incoh_echoes] = 1 |
|
3332 | my_incoh_aver[pair[0],incoh_echoes] = 1 | |
| 3284 | my_incoh_aver[pair[1],incoh_echoes] = 1 |
|
3333 | my_incoh_aver[pair[1],incoh_echoes] = 1 | |
| 3285 |
|
3334 | |||
| 3286 |
|
3335 | |||
| 3287 | for ic in range(nPairs): |
|
3336 | for ic in range(nPairs): | |
| 3288 | pair = crosspairs[ic] |
|
3337 | pair = crosspairs[ic] | |
| 3289 |
|
3338 | |||
| 3290 | valid1 =(my_coh_aver[pair[0],:]==1 ).nonzero() |
|
3339 | valid1 =(my_coh_aver[pair[0],:]==1 ).nonzero() | |
| 3291 | valid2 = (my_coh_aver[pair[1],:]==1).nonzero() |
|
3340 | valid2 = (my_coh_aver[pair[1],:]==1).nonzero() | |
| 3292 | valid1 = numpy.array(valid1[0]) |
|
3341 | valid1 = numpy.array(valid1[0]) | |
| 3293 | valid2 = numpy.array(valid2[0]) |
|
3342 | valid2 = numpy.array(valid2[0]) | |
| 3294 | valid = valid1 |
|
3343 | valid = valid1 | |
| 3295 |
|
3344 | |||
| 3296 | for iv in range(len(valid2)): |
|
3345 | for iv in range(len(valid2)): | |
| 3297 |
|
3346 | |||
| 3298 | indv = numpy.array((valid1 == valid2[iv]).nonzero()) |
|
3347 | indv = numpy.array((valid1 == valid2[iv]).nonzero()) | |
| 3299 | if len(indv[0]) == 0 : |
|
3348 | if len(indv[0]) == 0 : | |
| 3300 | valid = numpy.concatenate((valid,valid2[iv]), axis=None) |
|
3349 | valid = numpy.concatenate((valid,valid2[iv]), axis=None) | |
| 3301 | valid1 =(my_coh_aver[pair[0],:] !=1 ).nonzero() |
|
3350 | valid1 =(my_coh_aver[pair[0],:] !=1 ).nonzero() | |
| 3302 | valid2 = (my_coh_aver[pair[1],:] !=1).nonzero() |
|
3351 | valid2 = (my_coh_aver[pair[1],:] !=1).nonzero() | |
| 3303 | valid1 = numpy.array(valid1[0]) |
|
3352 | valid1 = numpy.array(valid1[0]) | |
| 3304 | valid2 = numpy.array(valid2[0]) |
|
3353 | valid2 = numpy.array(valid2[0]) | |
| 3305 | incoh_echoes = valid1 |
|
3354 | incoh_echoes = valid1 | |
| 3306 | for iv in range(len(valid2)): |
|
3355 | for iv in range(len(valid2)): | |
| 3307 |
|
3356 | |||
| 3308 | indv = numpy.array((valid1 == valid2[iv]).nonzero()) |
|
3357 | indv = numpy.array((valid1 == valid2[iv]).nonzero()) | |
| 3309 | if len(indv[0]) == 0 : |
|
3358 | if len(indv[0]) == 0 : | |
| 3310 | incoh_echoes = numpy.concatenate(( incoh_echoes,valid2[iv]), axis=None) |
|
3359 | incoh_echoes = numpy.concatenate(( incoh_echoes,valid2[iv]), axis=None) | |
| 3311 |
|
3360 | |||
| 3312 | if len(valid)>0: |
|
3361 | if len(valid)>0: | |
| 3313 | coh_spectra[pair[0],:,valid] = spectra[pair[0],:,valid] |
|
3362 | coh_spectra[pair[0],:,valid] = spectra[pair[0],:,valid] | |
| 3314 | coh_spectra[pair[1],:,valid] = spectra[pair[1],:,valid] |
|
3363 | coh_spectra[pair[1],:,valid] = spectra[pair[1],:,valid] | |
| 3315 | coh_cspectra[ic,:,valid] = cspectra[ic,:,valid] |
|
3364 | coh_cspectra[ic,:,valid] = cspectra[ic,:,valid] | |
| 3316 | coh_aver[pair[0],valid]=1 |
|
3365 | coh_aver[pair[0],valid]=1 | |
| 3317 | coh_aver[pair[1],valid]=1 |
|
3366 | coh_aver[pair[1],valid]=1 | |
| 3318 | if len(incoh_echoes)>0: |
|
3367 | if len(incoh_echoes)>0: | |
| 3319 | incoh_spectra[pair[0],:,incoh_echoes] = spectra[pair[0],:,incoh_echoes] |
|
3368 | incoh_spectra[pair[0],:,incoh_echoes] = spectra[pair[0],:,incoh_echoes] | |
| 3320 | incoh_spectra[pair[1],:,incoh_echoes] = spectra[pair[1],:,incoh_echoes] |
|
3369 | incoh_spectra[pair[1],:,incoh_echoes] = spectra[pair[1],:,incoh_echoes] | |
| 3321 | incoh_cspectra[ic,:,incoh_echoes] = cspectra[ic,:,incoh_echoes] |
|
3370 | incoh_cspectra[ic,:,incoh_echoes] = cspectra[ic,:,incoh_echoes] | |
| 3322 | incoh_aver[pair[0],incoh_echoes]=1 |
|
3371 | incoh_aver[pair[0],incoh_echoes]=1 | |
| 3323 | incoh_aver[pair[1],incoh_echoes]=1 |
|
3372 | incoh_aver[pair[1],incoh_echoes]=1 | |
| 3324 |
|
3373 | |||
| 3325 | return my_incoh_spectra ,my_incoh_cspectra,my_incoh_aver,my_coh_aver, incoh_spectra, coh_spectra, incoh_cspectra, coh_cspectra, incoh_aver, coh_aver |
|
3374 | return my_incoh_spectra ,my_incoh_cspectra,my_incoh_aver,my_coh_aver, incoh_spectra, coh_spectra, incoh_cspectra, coh_cspectra, incoh_aver, coh_aver | |
| 3326 |
|
3375 | |||
| 3327 | def __CleanCoherent(self,snrth, spectra, cspectra, coh_aver,dataOut, noise,clean_coh_echoes,index): |
|
3376 | def __CleanCoherent(self,snrth, spectra, cspectra, coh_aver,dataOut, noise,clean_coh_echoes,index): | |
| 3328 |
|
3377 | |||
| 3329 | nProf = dataOut.nProfiles |
|
3378 | nProf = dataOut.nProfiles | |
| 3330 | heights = dataOut.heightList |
|
3379 | heights = dataOut.heightList | |
| 3331 | nHei = len(heights) |
|
3380 | nHei = len(heights) | |
| 3332 | channels = dataOut.channelList |
|
3381 | channels = dataOut.channelList | |
| 3333 | nChan = len(channels) |
|
3382 | nChan = len(channels) | |
| 3334 | crosspairs = dataOut.groupList |
|
3383 | crosspairs = dataOut.groupList | |
| 3335 | nPairs = len(crosspairs) |
|
3384 | nPairs = len(crosspairs) | |
| 3336 |
|
3385 | |||
| 3337 | absc = dataOut.abscissaList[:-1] |
|
3386 | absc = dataOut.abscissaList[:-1] | |
| 3338 | data_param = numpy.zeros((nChan, 4, spectra.shape[2])) |
|
3387 | data_param = numpy.zeros((nChan, 4, spectra.shape[2])) | |
| 3339 |
|
3388 | |||
| 3340 | clean_coh_spectra = spectra.copy() |
|
3389 | clean_coh_spectra = spectra.copy() | |
| 3341 | clean_coh_cspectra = cspectra.copy() |
|
3390 | clean_coh_cspectra = cspectra.copy() | |
| 3342 | clean_coh_aver = coh_aver.copy() |
|
3391 | clean_coh_aver = coh_aver.copy() | |
| 3343 |
|
3392 | |||
| 3344 | spwd_th=[10,6] #spwd_th[0] --> For satellites ; spwd_th[1] --> For special events like SUN. |
|
3393 | spwd_th=[10,6] #spwd_th[0] --> For satellites ; spwd_th[1] --> For special events like SUN. | |
| 3345 | coh_th = 0.75 |
|
3394 | coh_th = 0.75 | |
| 3346 |
|
3395 | |||
| 3347 | rtime0 = [6,18] # periodo sin ESF |
|
3396 | rtime0 = [6,18] # periodo sin ESF | |
| 3348 | rtime1 = [10.5,13.5] # periodo con alta coherencia y alto ancho espectral (esperado): SOL. |
|
3397 | rtime1 = [10.5,13.5] # periodo con alta coherencia y alto ancho espectral (esperado): SOL. | |
| 3349 |
|
3398 | |||
| 3350 | time = index*5./60 # en base a 5 min de proceso |
|
3399 | time = index*5./60 # en base a 5 min de proceso | |
| 3351 | if clean_coh_echoes == 1 : |
|
3400 | if clean_coh_echoes == 1 : | |
| 3352 | for ind in range(nChan): |
|
3401 | for ind in range(nChan): | |
| 3353 | data_param[ind,:,:] = self.__calculateMoments( spectra[ind,:,:] , absc , noise[ind] ) |
|
3402 | data_param[ind,:,:] = self.__calculateMoments( spectra[ind,:,:] , absc , noise[ind] ) | |
| 3354 | spwd = data_param[:,3] |
|
3403 | spwd = data_param[:,3] | |
| 3355 | # SPECB_JULIA,header=anal_header,jspectra=spectra,vel=velocities,hei=heights, num_aver=1, mode_fit=0,smoothing=smoothing,jvelr=velr,jspwd=spwd,jsnr=snr,jnoise=noise,jstdvnoise=stdvnoise |
|
3404 | # SPECB_JULIA,header=anal_header,jspectra=spectra,vel=velocities,hei=heights, num_aver=1, mode_fit=0,smoothing=smoothing,jvelr=velr,jspwd=spwd,jsnr=snr,jnoise=noise,jstdvnoise=stdvnoise | |
| 3356 | # para obtener spwd |
|
3405 | # para obtener spwd | |
| 3357 | for ic in range(nPairs): |
|
3406 | for ic in range(nPairs): | |
| 3358 | pair = crosspairs[ic] |
|
3407 | pair = crosspairs[ic] | |
| 3359 | coh = numpy.squeeze(numpy.sum(cspectra[ic,:,:], axis=1)/numpy.sqrt(numpy.sum(spectra[pair[0],:,:], axis=1)*numpy.sum(spectra[pair[1],:,:], axis=1))) |
|
3408 | coh = numpy.squeeze(numpy.sum(cspectra[ic,:,:], axis=1)/numpy.sqrt(numpy.sum(spectra[pair[0],:,:], axis=1)*numpy.sum(spectra[pair[1],:,:], axis=1))) | |
| 3360 | for ih in range(nHei) : |
|
3409 | for ih in range(nHei) : | |
| 3361 | # Considering heights higher than 200km in order to avoid removing phenomena like EEJ. |
|
3410 | # Considering heights higher than 200km in order to avoid removing phenomena like EEJ. | |
| 3362 | if heights[ih] >= 200 and coh_aver[pair[0],ih] == 1 and coh_aver[pair[1],ih] == 1 : |
|
3411 | if heights[ih] >= 200 and coh_aver[pair[0],ih] == 1 and coh_aver[pair[1],ih] == 1 : | |
| 3363 | # Checking coherence |
|
3412 | # Checking coherence | |
| 3364 | if (numpy.abs(coh[ih]) <= coh_th) or (time >= rtime0[0] and time <= rtime0[1]) : |
|
3413 | if (numpy.abs(coh[ih]) <= coh_th) or (time >= rtime0[0] and time <= rtime0[1]) : | |
| 3365 | # Checking spectral widths |
|
3414 | # Checking spectral widths | |
| 3366 | if (spwd[pair[0],ih] > spwd_th[0]) or (spwd[pair[1],ih] > spwd_th[0]) : |
|
3415 | if (spwd[pair[0],ih] > spwd_th[0]) or (spwd[pair[1],ih] > spwd_th[0]) : | |
| 3367 | # satelite |
|
3416 | # satelite | |
| 3368 | clean_coh_spectra[pair,:,ih] = 0.0 |
|
3417 | clean_coh_spectra[pair,:,ih] = 0.0 | |
| 3369 | clean_coh_cspectra[ic,:,ih] = 0.0 |
|
3418 | clean_coh_cspectra[ic,:,ih] = 0.0 | |
| 3370 | clean_coh_aver[pair,ih] = 0 |
|
3419 | clean_coh_aver[pair,ih] = 0 | |
| 3371 | else : |
|
3420 | else : | |
| 3372 | if ((spwd[pair[0],ih] < spwd_th[1]) or (spwd[pair[1],ih] < spwd_th[1])) : |
|
3421 | if ((spwd[pair[0],ih] < spwd_th[1]) or (spwd[pair[1],ih] < spwd_th[1])) : | |
| 3373 | # Especial event like sun. |
|
3422 | # Especial event like sun. | |
| 3374 | clean_coh_spectra[pair,:,ih] = 0.0 |
|
3423 | clean_coh_spectra[pair,:,ih] = 0.0 | |
| 3375 | clean_coh_cspectra[ic,:,ih] = 0.0 |
|
3424 | clean_coh_cspectra[ic,:,ih] = 0.0 | |
| 3376 | clean_coh_aver[pair,ih] = 0 |
|
3425 | clean_coh_aver[pair,ih] = 0 | |
| 3377 |
|
3426 | |||
| 3378 | return clean_coh_spectra, clean_coh_cspectra, clean_coh_aver |
|
3427 | return clean_coh_spectra, clean_coh_cspectra, clean_coh_aver | |
| 3379 |
|
3428 | |||
| 3380 | def CleanRayleigh(self,dataOut,spectra,cspectra,save_drifts): |
|
3429 | def CleanRayleigh(self,dataOut,spectra,cspectra,save_drifts): | |
| 3381 |
|
3430 | |||
| 3382 | rfunc = cspectra.copy() |
|
3431 | rfunc = cspectra.copy() | |
| 3383 | n_funct = len(rfunc[0,:,0,0]) |
|
3432 | n_funct = len(rfunc[0,:,0,0]) | |
| 3384 | val_spc = spectra*0.0 |
|
3433 | val_spc = spectra*0.0 | |
| 3385 | val_cspc = cspectra*0.0 |
|
3434 | val_cspc = cspectra*0.0 | |
| 3386 | in_sat_spectra = spectra.copy() |
|
3435 | in_sat_spectra = spectra.copy() | |
| 3387 | in_sat_cspectra = cspectra.copy() |
|
3436 | in_sat_cspectra = cspectra.copy() | |
| 3388 |
|
3437 | |||
| 3389 | min_hei = 200 |
|
3438 | min_hei = 200 | |
| 3390 | nProf = dataOut.nProfiles |
|
3439 | nProf = dataOut.nProfiles | |
| 3391 | heights = dataOut.heightList |
|
3440 | heights = dataOut.heightList | |
| 3392 | nHei = len(heights) |
|
3441 | nHei = len(heights) | |
| 3393 | channels = dataOut.channelList |
|
3442 | channels = dataOut.channelList | |
| 3394 | nChan = len(channels) |
|
3443 | nChan = len(channels) | |
| 3395 | crosspairs = dataOut.groupList |
|
3444 | crosspairs = dataOut.groupList | |
| 3396 | nPairs = len(crosspairs) |
|
3445 | nPairs = len(crosspairs) | |
| 3397 | hval=(heights >= min_hei).nonzero() |
|
3446 | hval=(heights >= min_hei).nonzero() | |
| 3398 | ih=hval[0] |
|
3447 | ih=hval[0] | |
| 3399 |
|
3448 | |||
| 3400 | for ih in range(hval[0][0],nHei): |
|
3449 | for ih in range(hval[0][0],nHei): | |
| 3401 | for ifreq in range(nProf): |
|
3450 | for ifreq in range(nProf): | |
| 3402 | for ii in range(n_funct): |
|
3451 | for ii in range(n_funct): | |
| 3403 |
|
3452 | |||
| 3404 | func2clean = 10*numpy.log10(numpy.absolute(rfunc[:,ii,ifreq,ih])) |
|
3453 | func2clean = 10*numpy.log10(numpy.absolute(rfunc[:,ii,ifreq,ih])) | |
| 3405 | val = (numpy.isfinite(func2clean)==True).nonzero() |
|
3454 | val = (numpy.isfinite(func2clean)==True).nonzero() | |
| 3406 | if len(val)>0: |
|
3455 | if len(val)>0: | |
| 3407 | min_val = numpy.around(numpy.amin(func2clean)-2) #> (-40) |
|
3456 | min_val = numpy.around(numpy.amin(func2clean)-2) #> (-40) | |
| 3408 | if min_val <= -40 : min_val = -40 |
|
3457 | if min_val <= -40 : min_val = -40 | |
| 3409 | max_val = numpy.around(numpy.amax(func2clean)+2) #< 200 |
|
3458 | max_val = numpy.around(numpy.amax(func2clean)+2) #< 200 | |
| 3410 | if max_val >= 200 : max_val = 200 |
|
3459 | if max_val >= 200 : max_val = 200 | |
| 3411 | step = 1 |
|
3460 | step = 1 | |
| 3412 | #Getting bins and the histogram |
|
3461 | #Getting bins and the histogram | |
| 3413 | x_dist = min_val + numpy.arange(1 + ((max_val-(min_val))/step))*step |
|
3462 | x_dist = min_val + numpy.arange(1 + ((max_val-(min_val))/step))*step | |
| 3414 | y_dist,binstep = numpy.histogram(func2clean,bins=range(int(min_val),int(max_val+2),step)) |
|
3463 | y_dist,binstep = numpy.histogram(func2clean,bins=range(int(min_val),int(max_val+2),step)) | |
| 3415 | mean = numpy.sum(x_dist * y_dist) / numpy.sum(y_dist) |
|
3464 | mean = numpy.sum(x_dist * y_dist) / numpy.sum(y_dist) | |
| 3416 | sigma = numpy.sqrt(numpy.sum(y_dist * (x_dist - mean)**2) / numpy.sum(y_dist)) |
|
3465 | sigma = numpy.sqrt(numpy.sum(y_dist * (x_dist - mean)**2) / numpy.sum(y_dist)) | |
| 3417 | parg = [numpy.amax(y_dist),mean,sigma] |
|
3466 | parg = [numpy.amax(y_dist),mean,sigma] | |
| 3418 | try : |
|
3467 | try : | |
| 3419 | gauss_fit, covariance = curve_fit(fit_func, x_dist, y_dist,p0=parg) |
|
3468 | gauss_fit, covariance = curve_fit(fit_func, x_dist, y_dist,p0=parg) | |
| 3420 | mode = gauss_fit[1] |
|
3469 | mode = gauss_fit[1] | |
| 3421 | stdv = gauss_fit[2] |
|
3470 | stdv = gauss_fit[2] | |
| 3422 | except: |
|
3471 | except: | |
| 3423 | mode = mean |
|
3472 | mode = mean | |
| 3424 | stdv = sigma |
|
3473 | stdv = sigma | |
| 3425 |
|
3474 | |||
| 3426 | #Removing echoes greater than mode + 3*stdv |
|
3475 | #Removing echoes greater than mode + 3*stdv | |
| 3427 | factor_stdv = 2.5 |
|
3476 | factor_stdv = 2.5 | |
| 3428 | noval = (abs(func2clean - mode)>=(factor_stdv*stdv)).nonzero() |
|
3477 | noval = (abs(func2clean - mode)>=(factor_stdv*stdv)).nonzero() | |
| 3429 |
|
3478 | |||
| 3430 | if len(noval[0]) > 0: |
|
3479 | if len(noval[0]) > 0: | |
| 3431 | novall = ((func2clean - mode) >= (factor_stdv*stdv)).nonzero() |
|
3480 | novall = ((func2clean - mode) >= (factor_stdv*stdv)).nonzero() | |
| 3432 | cross_pairs = crosspairs[ii] |
|
3481 | cross_pairs = crosspairs[ii] | |
| 3433 | #Getting coherent echoes which are removed. |
|
3482 | #Getting coherent echoes which are removed. | |
| 3434 | if len(novall[0]) > 0: |
|
3483 | if len(novall[0]) > 0: | |
| 3435 | val_spc[novall[0],cross_pairs[0],ifreq,ih] = 1 |
|
3484 | val_spc[novall[0],cross_pairs[0],ifreq,ih] = 1 | |
| 3436 | val_spc[novall[0],cross_pairs[1],ifreq,ih] = 1 |
|
3485 | val_spc[novall[0],cross_pairs[1],ifreq,ih] = 1 | |
| 3437 | val_cspc[novall[0],ii,ifreq,ih] = 1 |
|
3486 | val_cspc[novall[0],ii,ifreq,ih] = 1 | |
| 3438 | #Removing coherent from ISR data |
|
3487 | #Removing coherent from ISR data | |
| 3439 | spectra[noval,cross_pairs[0],ifreq,ih] = numpy.nan |
|
3488 | spectra[noval,cross_pairs[0],ifreq,ih] = numpy.nan | |
| 3440 | spectra[noval,cross_pairs[1],ifreq,ih] = numpy.nan |
|
3489 | spectra[noval,cross_pairs[1],ifreq,ih] = numpy.nan | |
| 3441 | cspectra[noval,ii,ifreq,ih] = numpy.nan |
|
3490 | cspectra[noval,ii,ifreq,ih] = numpy.nan | |
| 3442 |
|
3491 | |||
| 3443 | #Getting average of the spectra and cross-spectra from incoherent echoes. |
|
3492 | #Getting average of the spectra and cross-spectra from incoherent echoes. | |
| 3444 | out_spectra = numpy.zeros([nChan,nProf,nHei], dtype=float) #+numpy.nan |
|
3493 | out_spectra = numpy.zeros([nChan,nProf,nHei], dtype=float) #+numpy.nan | |
| 3445 | out_cspectra = numpy.zeros([nPairs,nProf,nHei], dtype=complex) #+numpy.nan |
|
3494 | out_cspectra = numpy.zeros([nPairs,nProf,nHei], dtype=complex) #+numpy.nan | |
| 3446 | for ih in range(nHei): |
|
3495 | for ih in range(nHei): | |
| 3447 | for ifreq in range(nProf): |
|
3496 | for ifreq in range(nProf): | |
| 3448 | for ich in range(nChan): |
|
3497 | for ich in range(nChan): | |
| 3449 | tmp = spectra[:,ich,ifreq,ih] |
|
3498 | tmp = spectra[:,ich,ifreq,ih] | |
| 3450 | valid = (numpy.isfinite(tmp[:])==True).nonzero() |
|
3499 | valid = (numpy.isfinite(tmp[:])==True).nonzero() | |
| 3451 | if len(valid[0]) >0 : |
|
3500 | if len(valid[0]) >0 : | |
| 3452 | out_spectra[ich,ifreq,ih] = numpy.nansum(tmp)/len(valid[0]) |
|
3501 | out_spectra[ich,ifreq,ih] = numpy.nansum(tmp)/len(valid[0]) | |
| 3453 |
|
3502 | |||
| 3454 | for icr in range(nPairs): |
|
3503 | for icr in range(nPairs): | |
| 3455 | tmp = numpy.squeeze(cspectra[:,icr,ifreq,ih]) |
|
3504 | tmp = numpy.squeeze(cspectra[:,icr,ifreq,ih]) | |
| 3456 | valid = (numpy.isfinite(tmp)==True).nonzero() |
|
3505 | valid = (numpy.isfinite(tmp)==True).nonzero() | |
| 3457 | if len(valid[0]) > 0: |
|
3506 | if len(valid[0]) > 0: | |
| 3458 | out_cspectra[icr,ifreq,ih] = numpy.nansum(tmp)/len(valid[0]) |
|
3507 | out_cspectra[icr,ifreq,ih] = numpy.nansum(tmp)/len(valid[0]) | |
| 3459 | #Removing fake coherent echoes (at least 4 points around the point) |
|
3508 | #Removing fake coherent echoes (at least 4 points around the point) | |
| 3460 | val_spectra = numpy.sum(val_spc,0) |
|
3509 | val_spectra = numpy.sum(val_spc,0) | |
| 3461 | val_cspectra = numpy.sum(val_cspc,0) |
|
3510 | val_cspectra = numpy.sum(val_cspc,0) | |
| 3462 |
|
3511 | |||
| 3463 | val_spectra = self.REM_ISOLATED_POINTS(val_spectra,4) |
|
3512 | val_spectra = self.REM_ISOLATED_POINTS(val_spectra,4) | |
| 3464 | val_cspectra = self.REM_ISOLATED_POINTS(val_cspectra,4) |
|
3513 | val_cspectra = self.REM_ISOLATED_POINTS(val_cspectra,4) | |
| 3465 |
|
3514 | |||
| 3466 | for i in range(nChan): |
|
3515 | for i in range(nChan): | |
| 3467 | for j in range(nProf): |
|
3516 | for j in range(nProf): | |
| 3468 | for k in range(nHei): |
|
3517 | for k in range(nHei): | |
| 3469 | if numpy.isfinite(val_spectra[i,j,k]) and val_spectra[i,j,k] < 1 : |
|
3518 | if numpy.isfinite(val_spectra[i,j,k]) and val_spectra[i,j,k] < 1 : | |
| 3470 | val_spc[:,i,j,k] = 0.0 |
|
3519 | val_spc[:,i,j,k] = 0.0 | |
| 3471 | for i in range(nPairs): |
|
3520 | for i in range(nPairs): | |
| 3472 | for j in range(nProf): |
|
3521 | for j in range(nProf): | |
| 3473 | for k in range(nHei): |
|
3522 | for k in range(nHei): | |
| 3474 | if numpy.isfinite(val_cspectra[i,j,k]) and val_cspectra[i,j,k] < 1 : |
|
3523 | if numpy.isfinite(val_cspectra[i,j,k]) and val_cspectra[i,j,k] < 1 : | |
| 3475 | val_cspc[:,i,j,k] = 0.0 |
|
3524 | val_cspc[:,i,j,k] = 0.0 | |
| 3476 |
|
3525 | |||
| 3477 | tmp_sat_spectra = spectra.copy() |
|
3526 | tmp_sat_spectra = spectra.copy() | |
| 3478 | tmp_sat_spectra = tmp_sat_spectra*numpy.nan |
|
3527 | tmp_sat_spectra = tmp_sat_spectra*numpy.nan | |
| 3479 | tmp_sat_cspectra = cspectra.copy() |
|
3528 | tmp_sat_cspectra = cspectra.copy() | |
| 3480 | tmp_sat_cspectra = tmp_sat_cspectra*numpy.nan |
|
3529 | tmp_sat_cspectra = tmp_sat_cspectra*numpy.nan | |
| 3481 |
|
3530 | |||
| 3482 | val = (val_spc > 0).nonzero() |
|
3531 | val = (val_spc > 0).nonzero() | |
| 3483 | if len(val[0]) > 0: |
|
3532 | if len(val[0]) > 0: | |
| 3484 | tmp_sat_spectra[val] = in_sat_spectra[val] |
|
3533 | tmp_sat_spectra[val] = in_sat_spectra[val] | |
| 3485 |
|
3534 | |||
| 3486 | val = (val_cspc > 0).nonzero() |
|
3535 | val = (val_cspc > 0).nonzero() | |
| 3487 | if len(val[0]) > 0: |
|
3536 | if len(val[0]) > 0: | |
| 3488 | tmp_sat_cspectra[val] = in_sat_cspectra[val] |
|
3537 | tmp_sat_cspectra[val] = in_sat_cspectra[val] | |
| 3489 |
|
3538 | |||
| 3490 | #Getting average of the spectra and cross-spectra from incoherent echoes. |
|
3539 | #Getting average of the spectra and cross-spectra from incoherent echoes. | |
| 3491 | sat_spectra = numpy.zeros((nChan,nProf,nHei), dtype=float) |
|
3540 | sat_spectra = numpy.zeros((nChan,nProf,nHei), dtype=float) | |
| 3492 | sat_cspectra = numpy.zeros((nPairs,nProf,nHei), dtype=complex) |
|
3541 | sat_cspectra = numpy.zeros((nPairs,nProf,nHei), dtype=complex) | |
| 3493 | for ih in range(nHei): |
|
3542 | for ih in range(nHei): | |
| 3494 | for ifreq in range(nProf): |
|
3543 | for ifreq in range(nProf): | |
| 3495 | for ich in range(nChan): |
|
3544 | for ich in range(nChan): | |
| 3496 | tmp = numpy.squeeze(tmp_sat_spectra[:,ich,ifreq,ih]) |
|
3545 | tmp = numpy.squeeze(tmp_sat_spectra[:,ich,ifreq,ih]) | |
| 3497 | valid = (numpy.isfinite(tmp)).nonzero() |
|
3546 | valid = (numpy.isfinite(tmp)).nonzero() | |
| 3498 | if len(valid[0]) > 0: |
|
3547 | if len(valid[0]) > 0: | |
| 3499 | sat_spectra[ich,ifreq,ih] = numpy.nansum(tmp)/len(valid[0]) |
|
3548 | sat_spectra[ich,ifreq,ih] = numpy.nansum(tmp)/len(valid[0]) | |
| 3500 |
|
3549 | |||
| 3501 | for icr in range(nPairs): |
|
3550 | for icr in range(nPairs): | |
| 3502 | tmp = numpy.squeeze(tmp_sat_cspectra[:,icr,ifreq,ih]) |
|
3551 | tmp = numpy.squeeze(tmp_sat_cspectra[:,icr,ifreq,ih]) | |
| 3503 | valid = (numpy.isfinite(tmp)).nonzero() |
|
3552 | valid = (numpy.isfinite(tmp)).nonzero() | |
| 3504 | if len(valid[0]) > 0: |
|
3553 | if len(valid[0]) > 0: | |
| 3505 | sat_cspectra[icr,ifreq,ih] = numpy.nansum(tmp)/len(valid[0]) |
|
3554 | sat_cspectra[icr,ifreq,ih] = numpy.nansum(tmp)/len(valid[0]) | |
| 3506 |
|
3555 | |||
| 3507 | return out_spectra, out_cspectra,sat_spectra,sat_cspectra |
|
3556 | return out_spectra, out_cspectra,sat_spectra,sat_cspectra | |
| 3508 |
|
3557 | |||
| 3509 | def REM_ISOLATED_POINTS(self,array,rth): |
|
3558 | def REM_ISOLATED_POINTS(self,array,rth): | |
| 3510 | if rth == None : rth = 4 |
|
3559 | if rth == None : rth = 4 | |
| 3511 | num_prof = len(array[0,:,0]) |
|
3560 | num_prof = len(array[0,:,0]) | |
| 3512 | num_hei = len(array[0,0,:]) |
|
3561 | num_hei = len(array[0,0,:]) | |
| 3513 | n2d = len(array[:,0,0]) |
|
3562 | n2d = len(array[:,0,0]) | |
| 3514 |
|
3563 | |||
| 3515 | for ii in range(n2d) : |
|
3564 | for ii in range(n2d) : | |
| 3516 | tmp = array[ii,:,:] |
|
3565 | tmp = array[ii,:,:] | |
| 3517 | tmp = numpy.reshape(tmp,num_prof*num_hei) |
|
3566 | tmp = numpy.reshape(tmp,num_prof*num_hei) | |
| 3518 | indxs1 = (numpy.isfinite(tmp)==True).nonzero() |
|
3567 | indxs1 = (numpy.isfinite(tmp)==True).nonzero() | |
| 3519 | indxs2 = (tmp > 0).nonzero() |
|
3568 | indxs2 = (tmp > 0).nonzero() | |
| 3520 | indxs1 = (indxs1[0]) |
|
3569 | indxs1 = (indxs1[0]) | |
| 3521 | indxs2 = indxs2[0] |
|
3570 | indxs2 = indxs2[0] | |
| 3522 | indxs = None |
|
3571 | indxs = None | |
| 3523 |
|
3572 | |||
| 3524 | for iv in range(len(indxs2)): |
|
3573 | for iv in range(len(indxs2)): | |
| 3525 | indv = numpy.array((indxs1 == indxs2[iv]).nonzero()) |
|
3574 | indv = numpy.array((indxs1 == indxs2[iv]).nonzero()) | |
| 3526 | if len(indv[0]) > 0 : |
|
3575 | if len(indv[0]) > 0 : | |
| 3527 | indxs = numpy.concatenate((indxs,indxs2[iv]), axis=None) |
|
3576 | indxs = numpy.concatenate((indxs,indxs2[iv]), axis=None) | |
| 3528 |
|
3577 | |||
| 3529 | indxs = indxs[1:] |
|
3578 | indxs = indxs[1:] | |
| 3530 | if len(indxs) < 4 : |
|
3579 | if len(indxs) < 4 : | |
| 3531 | array[ii,:,:] = 0. |
|
3580 | array[ii,:,:] = 0. | |
| 3532 | return |
|
3581 | return | |
| 3533 |
|
3582 | |||
| 3534 | xpos = numpy.mod(indxs ,num_prof) |
|
3583 | xpos = numpy.mod(indxs ,num_prof) | |
| 3535 | ypos = (indxs / num_prof) |
|
3584 | ypos = (indxs / num_prof) | |
| 3536 | sx = numpy.argsort(xpos) # Ordering respect to "x" (time) |
|
3585 | sx = numpy.argsort(xpos) # Ordering respect to "x" (time) | |
| 3537 | xpos = xpos[sx] |
|
3586 | xpos = xpos[sx] | |
| 3538 | ypos = ypos[sx] |
|
3587 | ypos = ypos[sx] | |
| 3539 |
|
3588 | |||
| 3540 | # *********************************** Cleaning isolated points ********************************** |
|
3589 | # *********************************** Cleaning isolated points ********************************** | |
| 3541 | ic = 0 |
|
3590 | ic = 0 | |
| 3542 | while True : |
|
3591 | while True : | |
| 3543 | r = numpy.sqrt(list(numpy.power((xpos[ic]-xpos),2)+ numpy.power((ypos[ic]-ypos),2))) |
|
3592 | r = numpy.sqrt(list(numpy.power((xpos[ic]-xpos),2)+ numpy.power((ypos[ic]-ypos),2))) | |
| 3544 |
|
3593 | |||
| 3545 | no_coh1 = (numpy.isfinite(r)==True).nonzero() |
|
3594 | no_coh1 = (numpy.isfinite(r)==True).nonzero() | |
| 3546 | no_coh2 = (r <= rth).nonzero() |
|
3595 | no_coh2 = (r <= rth).nonzero() | |
| 3547 | no_coh1 = numpy.array(no_coh1[0]) |
|
3596 | no_coh1 = numpy.array(no_coh1[0]) | |
| 3548 | no_coh2 = numpy.array(no_coh2[0]) |
|
3597 | no_coh2 = numpy.array(no_coh2[0]) | |
| 3549 | no_coh = None |
|
3598 | no_coh = None | |
| 3550 | for iv in range(len(no_coh2)): |
|
3599 | for iv in range(len(no_coh2)): | |
| 3551 | indv = numpy.array((no_coh1 == no_coh2[iv]).nonzero()) |
|
3600 | indv = numpy.array((no_coh1 == no_coh2[iv]).nonzero()) | |
| 3552 | if len(indv[0]) > 0 : |
|
3601 | if len(indv[0]) > 0 : | |
| 3553 | no_coh = numpy.concatenate((no_coh,no_coh2[iv]), axis=None) |
|
3602 | no_coh = numpy.concatenate((no_coh,no_coh2[iv]), axis=None) | |
| 3554 | no_coh = no_coh[1:] |
|
3603 | no_coh = no_coh[1:] | |
| 3555 | if len(no_coh) < 4 : |
|
3604 | if len(no_coh) < 4 : | |
| 3556 | xpos[ic] = numpy.nan |
|
3605 | xpos[ic] = numpy.nan | |
| 3557 | ypos[ic] = numpy.nan |
|
3606 | ypos[ic] = numpy.nan | |
| 3558 |
|
3607 | |||
| 3559 | ic = ic + 1 |
|
3608 | ic = ic + 1 | |
| 3560 | if (ic == len(indxs)) : |
|
3609 | if (ic == len(indxs)) : | |
| 3561 | break |
|
3610 | break | |
| 3562 | indxs = (numpy.isfinite(list(xpos))==True).nonzero() |
|
3611 | indxs = (numpy.isfinite(list(xpos))==True).nonzero() | |
| 3563 | if len(indxs[0]) < 4 : |
|
3612 | if len(indxs[0]) < 4 : | |
| 3564 | array[ii,:,:] = 0. |
|
3613 | array[ii,:,:] = 0. | |
| 3565 | return |
|
3614 | return | |
| 3566 |
|
3615 | |||
| 3567 | xpos = xpos[indxs[0]] |
|
3616 | xpos = xpos[indxs[0]] | |
| 3568 | ypos = ypos[indxs[0]] |
|
3617 | ypos = ypos[indxs[0]] | |
| 3569 | for i in range(0,len(ypos)): |
|
3618 | for i in range(0,len(ypos)): | |
| 3570 | ypos[i]=int(ypos[i]) |
|
3619 | ypos[i]=int(ypos[i]) | |
| 3571 | junk = tmp |
|
3620 | junk = tmp | |
| 3572 | tmp = junk*0.0 |
|
3621 | tmp = junk*0.0 | |
| 3573 |
|
3622 | |||
| 3574 | tmp[list(xpos + (ypos*num_hei))] = junk[list(xpos + (ypos*num_hei))] |
|
3623 | tmp[list(xpos + (ypos*num_hei))] = junk[list(xpos + (ypos*num_hei))] | |
| 3575 | array[ii,:,:] = numpy.reshape(tmp,(num_prof,num_hei)) |
|
3624 | array[ii,:,:] = numpy.reshape(tmp,(num_prof,num_hei)) | |
| 3576 |
|
3625 | |||
| 3577 | return array |
|
3626 | return array | |
| 3578 |
|
3627 | |||
| 3579 | def moments(self,doppler,yarray,npoints): |
|
3628 | def moments(self,doppler,yarray,npoints): | |
| 3580 | ytemp = yarray |
|
3629 | ytemp = yarray | |
| 3581 | val = (ytemp > 0).nonzero() |
|
3630 | val = (ytemp > 0).nonzero() | |
| 3582 | val = val[0] |
|
3631 | val = val[0] | |
| 3583 | if len(val) == 0 : val = range(npoints-1) |
|
3632 | if len(val) == 0 : val = range(npoints-1) | |
| 3584 |
|
3633 | |||
| 3585 | ynew = 0.5*(ytemp[val[0]]+ytemp[val[len(val)-1]]) |
|
3634 | ynew = 0.5*(ytemp[val[0]]+ytemp[val[len(val)-1]]) | |
| 3586 | ytemp[len(ytemp):] = [ynew] |
|
3635 | ytemp[len(ytemp):] = [ynew] | |
| 3587 |
|
3636 | |||
| 3588 | index = 0 |
|
3637 | index = 0 | |
| 3589 | index = numpy.argmax(ytemp) |
|
3638 | index = numpy.argmax(ytemp) | |
| 3590 | ytemp = numpy.roll(ytemp,int(npoints/2)-1-index) |
|
3639 | ytemp = numpy.roll(ytemp,int(npoints/2)-1-index) | |
| 3591 | ytemp = ytemp[0:npoints-1] |
|
3640 | ytemp = ytemp[0:npoints-1] | |
| 3592 |
|
3641 | |||
| 3593 | fmom = numpy.sum(doppler*ytemp)/numpy.sum(ytemp)+(index-(npoints/2-1))*numpy.abs(doppler[1]-doppler[0]) |
|
3642 | fmom = numpy.sum(doppler*ytemp)/numpy.sum(ytemp)+(index-(npoints/2-1))*numpy.abs(doppler[1]-doppler[0]) | |
| 3594 | smom = numpy.sum(doppler*doppler*ytemp)/numpy.sum(ytemp) |
|
3643 | smom = numpy.sum(doppler*doppler*ytemp)/numpy.sum(ytemp) | |
| 3595 | return [fmom,numpy.sqrt(smom)] |
|
3644 | return [fmom,numpy.sqrt(smom)] | |
| 3596 |
|
3645 | |||
| 3597 | def windowing_single_old(self,spc,x,A,B,C,D,nFFTPoints): |
|
3646 | def windowing_single_old(self,spc,x,A,B,C,D,nFFTPoints): | |
| 3598 | ''' |
|
3647 | ''' | |
| 3599 | Written by R. Flores |
|
3648 | Written by R. Flores | |
| 3600 | ''' |
|
3649 | ''' | |
| 3601 | from scipy.optimize import curve_fit,fmin |
|
3650 | from scipy.optimize import curve_fit,fmin | |
| 3602 |
|
3651 | |||
| 3603 | def gaussian(x, a, b, c, d): |
|
3652 | def gaussian(x, a, b, c, d): | |
| 3604 | val = a * numpy.exp(-(x - b)**2 / (2*c**2)) + d |
|
3653 | val = a * numpy.exp(-(x - b)**2 / (2*c**2)) + d | |
| 3605 | return val |
|
3654 | return val | |
| 3606 |
|
3655 | |||
| 3607 | def R_gaussian(x, a, b, c): |
|
3656 | def R_gaussian(x, a, b, c): | |
| 3608 | N = int(numpy.shape(x)[0]) |
|
3657 | N = int(numpy.shape(x)[0]) | |
| 3609 | val = a * numpy.exp(-((x)*c*2*2*numpy.pi)**2 / (2))* numpy.exp(1.j*b*x*4*numpy.pi) |
|
3658 | val = a * numpy.exp(-((x)*c*2*2*numpy.pi)**2 / (2))* numpy.exp(1.j*b*x*4*numpy.pi) | |
| 3610 | return val |
|
3659 | return val | |
| 3611 |
|
3660 | |||
| 3612 | def T(x,N): |
|
3661 | def T(x,N): | |
| 3613 | T = 1-abs(x)/N |
|
3662 | T = 1-abs(x)/N | |
| 3614 | return T |
|
3663 | return T | |
| 3615 |
|
3664 | |||
| 3616 | def R_T_spc_fun(x, a, b, c, d, nFFTPoints): |
|
3665 | def R_T_spc_fun(x, a, b, c, d, nFFTPoints): | |
| 3617 |
|
3666 | |||
| 3618 | N = int(numpy.shape(x)[0]) |
|
3667 | N = int(numpy.shape(x)[0]) | |
| 3619 |
|
3668 | |||
| 3620 | x_max = x[-1] |
|
3669 | x_max = x[-1] | |
| 3621 |
|
3670 | |||
| 3622 | x_pos = x[nFFTPoints:] |
|
3671 | x_pos = x[nFFTPoints:] | |
| 3623 | x_neg = x[:nFFTPoints] |
|
3672 | x_neg = x[:nFFTPoints] | |
| 3624 |
|
3673 | |||
| 3625 | R_T_neg_1 = R_gaussian(x, a, b, c)[:nFFTPoints]*T(x_neg,-x[0]) |
|
3674 | R_T_neg_1 = R_gaussian(x, a, b, c)[:nFFTPoints]*T(x_neg,-x[0]) | |
| 3626 | R_T_pos_1 = R_gaussian(x, a, b, c)[nFFTPoints:]*T(x_pos,x[-1]) |
|
3675 | R_T_pos_1 = R_gaussian(x, a, b, c)[nFFTPoints:]*T(x_pos,x[-1]) | |
| 3627 | #print(T(x_pos,x[-1]),x_pos,x[-1]) |
|
3676 | #print(T(x_pos,x[-1]),x_pos,x[-1]) | |
| 3628 | #print(R_T_neg_1.shape,R_T_pos_1.shape) |
|
3677 | #print(R_T_neg_1.shape,R_T_pos_1.shape) | |
| 3629 | R_T_sum_1 = R_T_pos_1 + R_T_neg_1 |
|
3678 | R_T_sum_1 = R_T_pos_1 + R_T_neg_1 | |
| 3630 | R_T_spc_1 = numpy.fft.fft(R_T_sum_1).real |
|
3679 | R_T_spc_1 = numpy.fft.fft(R_T_sum_1).real | |
| 3631 | R_T_spc_1 = numpy.fft.fftshift(R_T_spc_1) |
|
3680 | R_T_spc_1 = numpy.fft.fftshift(R_T_spc_1) | |
| 3632 | max_val_1 = numpy.max(R_T_spc_1) |
|
3681 | max_val_1 = numpy.max(R_T_spc_1) | |
| 3633 | R_T_spc_1 = R_T_spc_1*a/max_val_1 |
|
3682 | R_T_spc_1 = R_T_spc_1*a/max_val_1 | |
| 3634 | print("R_T_spc_1: ", R_T_spc_1) |
|
3683 | print("R_T_spc_1: ", R_T_spc_1) | |
| 3635 |
|
3684 | |||
| 3636 | R_T_d = d*numpy.fft.fftshift(signal.unit_impulse(N)) |
|
3685 | R_T_d = d*numpy.fft.fftshift(signal.unit_impulse(N)) | |
| 3637 | R_T_d_neg = R_T_d[:nFFTPoints]*T(x_neg,-x[0]) |
|
3686 | R_T_d_neg = R_T_d[:nFFTPoints]*T(x_neg,-x[0]) | |
| 3638 | R_T_d_pos = R_T_d[nFFTPoints:]*T(x_pos,x[-1]) |
|
3687 | R_T_d_pos = R_T_d[nFFTPoints:]*T(x_pos,x[-1]) | |
| 3639 | R_T_d_sum = R_T_d_pos + R_T_d_neg |
|
3688 | R_T_d_sum = R_T_d_pos + R_T_d_neg | |
| 3640 | R_T_spc_3 = numpy.fft.fft(R_T_d_sum).real |
|
3689 | R_T_spc_3 = numpy.fft.fft(R_T_d_sum).real | |
| 3641 | R_T_spc_3 = numpy.fft.fftshift(R_T_spc_3) |
|
3690 | R_T_spc_3 = numpy.fft.fftshift(R_T_spc_3) | |
| 3642 |
|
3691 | |||
| 3643 | R_T_final = R_T_spc_1# + R_T_spc_3 |
|
3692 | R_T_final = R_T_spc_1# + R_T_spc_3 | |
| 3644 |
|
3693 | |||
| 3645 | return R_T_final |
|
3694 | return R_T_final | |
| 3646 |
|
3695 | |||
| 3647 | y = spc#gaussian(x, a, meanY, sigmaY) + a*0.1*numpy.random.normal(0, 1, size=len(x)) |
|
3696 | y = spc#gaussian(x, a, meanY, sigmaY) + a*0.1*numpy.random.normal(0, 1, size=len(x)) | |
| 3648 |
|
3697 | |||
| 3649 | from scipy.stats import norm |
|
3698 | from scipy.stats import norm | |
| 3650 | mean,std=norm.fit(spc) |
|
3699 | mean,std=norm.fit(spc) | |
| 3651 |
|
3700 | |||
| 3652 | # estimate starting values from the data |
|
3701 | # estimate starting values from the data | |
| 3653 | print("A: ", A) |
|
3702 | print("A: ", A) | |
| 3654 | a = A-D |
|
3703 | a = A-D | |
| 3655 | b = B |
|
3704 | b = B | |
| 3656 | c = C#numpy.std(spc) #C |
|
3705 | c = C#numpy.std(spc) #C | |
| 3657 | d = D |
|
3706 | d = D | |
| 3658 | #''' |
|
3707 | #''' | |
| 3659 | #ippSeconds = 250*20*1.e-6/3 |
|
3708 | #ippSeconds = 250*20*1.e-6/3 | |
| 3660 |
|
3709 | |||
| 3661 | #x_t = ippSeconds * (numpy.arange(nFFTPoints) - nFFTPoints / 2.) |
|
3710 | #x_t = ippSeconds * (numpy.arange(nFFTPoints) - nFFTPoints / 2.) | |
| 3662 |
|
3711 | |||
| 3663 | #x_t = numpy.linspace(x_t[0],x_t[-1],3200) |
|
3712 | #x_t = numpy.linspace(x_t[0],x_t[-1],3200) | |
| 3664 | #print("x_t: ", x_t) |
|
3713 | #print("x_t: ", x_t) | |
| 3665 | #print("nFFTPoints: ", nFFTPoints) |
|
3714 | #print("nFFTPoints: ", nFFTPoints) | |
| 3666 | x_vel = numpy.linspace(x[0],x[-1],int(2*nFFTPoints)) |
|
3715 | x_vel = numpy.linspace(x[0],x[-1],int(2*nFFTPoints)) | |
| 3667 | #print("x_vel: ", x_vel) |
|
3716 | #print("x_vel: ", x_vel) | |
| 3668 | #x_freq = numpy.fft.fftfreq(1600,d=ippSeconds) |
|
3717 | #x_freq = numpy.fft.fftfreq(1600,d=ippSeconds) | |
| 3669 | #x_freq = numpy.fft.fftshift(x_freq) |
|
3718 | #x_freq = numpy.fft.fftshift(x_freq) | |
| 3670 | #''' |
|
3719 | #''' | |
| 3671 | # define a least squares function to optimize |
|
3720 | # define a least squares function to optimize | |
| 3672 | import matplotlib.pyplot as plt |
|
3721 | import matplotlib.pyplot as plt | |
| 3673 | aui = R_T_spc_fun(x_vel,a,b,c,d,nFFTPoints) |
|
3722 | aui = R_T_spc_fun(x_vel,a,b,c,d,nFFTPoints) | |
| 3674 | print("aux_max: ", numpy.nanmax(aui)) |
|
3723 | print("aux_max: ", numpy.nanmax(aui)) | |
| 3675 | #print(dataOut.heightList[hei]) |
|
3724 | #print(dataOut.heightList[hei]) | |
| 3676 | plt.figure() |
|
3725 | plt.figure() | |
| 3677 | plt.plot(x,spc,marker='*',linestyle='--') |
|
3726 | plt.plot(x,spc,marker='*',linestyle='--') | |
| 3678 | plt.plot(x,gaussian(x, a, b, c, d),color='b',marker='^',linestyle='') |
|
3727 | plt.plot(x,gaussian(x, a, b, c, d),color='b',marker='^',linestyle='') | |
| 3679 | plt.plot(x,aui,color='k') |
|
3728 | plt.plot(x,aui,color='k') | |
| 3680 | #plt.title(dataOut.heightList[hei]) |
|
3729 | #plt.title(dataOut.heightList[hei]) | |
| 3681 | plt.show() |
|
3730 | plt.show() | |
| 3682 |
|
3731 | |||
| 3683 | def minfunc(params): |
|
3732 | def minfunc(params): | |
| 3684 | #print("y.shape: ", numpy.shape(y)) |
|
3733 | #print("y.shape: ", numpy.shape(y)) | |
| 3685 | return sum((y-R_T_spc_fun(x_vel,params[0],params[1],params[2],params[3],nFFTPoints))**2/1)#y**2) |
|
3734 | return sum((y-R_T_spc_fun(x_vel,params[0],params[1],params[2],params[3],nFFTPoints))**2/1)#y**2) | |
| 3686 |
|
3735 | |||
| 3687 | # fit |
|
3736 | # fit | |
| 3688 |
|
3737 | |||
| 3689 | popt_full = fmin(minfunc,[a,b,c,d], disp=False) |
|
3738 | popt_full = fmin(minfunc,[a,b,c,d], disp=False) | |
| 3690 | #print("nIter", popt_full[2]) |
|
3739 | #print("nIter", popt_full[2]) | |
| 3691 | popt = popt_full#[0] |
|
3740 | popt = popt_full#[0] | |
| 3692 |
|
3741 | |||
| 3693 | fun = gaussian(x, popt[0], popt[1], popt[2], popt[3]) |
|
3742 | fun = gaussian(x, popt[0], popt[1], popt[2], popt[3]) | |
| 3694 | print("pop1[0]: ", popt[0]) |
|
3743 | print("pop1[0]: ", popt[0]) | |
| 3695 | #return R_T_spc_fun(x_t,popt[0], popt[1], popt[2], popt[3], popt[4], popt[5], popt[6]), popt[0], popt[1], popt[2], popt[3], popt[4], popt[5], popt[6] |
|
3744 | #return R_T_spc_fun(x_t,popt[0], popt[1], popt[2], popt[3], popt[4], popt[5], popt[6]), popt[0], popt[1], popt[2], popt[3], popt[4], popt[5], popt[6] | |
| 3696 | return fun, popt[0], popt[1], popt[2], popt[3] |
|
3745 | return fun, popt[0], popt[1], popt[2], popt[3] | |
| 3697 |
|
3746 | |||
| 3698 | def windowing_single(self,spc,x,A,B,C,D,nFFTPoints): |
|
3747 | def windowing_single(self,spc,x,A,B,C,D,nFFTPoints): | |
| 3699 | ''' |
|
3748 | ''' | |
| 3700 | Written by R. Flores |
|
3749 | Written by R. Flores | |
| 3701 | ''' |
|
3750 | ''' | |
| 3702 | from scipy.optimize import curve_fit,fmin |
|
3751 | from scipy.optimize import curve_fit,fmin | |
| 3703 |
|
3752 | |||
| 3704 | def gaussian(x, a, b, c, d): |
|
3753 | def gaussian(x, a, b, c, d): | |
| 3705 | val = a * numpy.exp(-(x - b)**2 / (2*c**2)) + d |
|
3754 | val = a * numpy.exp(-(x - b)**2 / (2*c**2)) + d | |
| 3706 | return val |
|
3755 | return val | |
| 3707 |
|
3756 | |||
| 3708 | def R_gaussian(x, a, b, c): |
|
3757 | def R_gaussian(x, a, b, c): | |
| 3709 | N = int(numpy.shape(x)[0]) |
|
3758 | N = int(numpy.shape(x)[0]) | |
| 3710 |
|
3759 | |||
| 3711 | val = (a*numpy.exp((-(1/2)*x*(x*c**2 + 2*1.j*b)))/numpy.sqrt(1/c**2)) |
|
3760 | val = (a*numpy.exp((-(1/2)*x*(x*c**2 + 2*1.j*b)))/numpy.sqrt(1/c**2)) | |
| 3712 |
|
3761 | |||
| 3713 | return val |
|
3762 | return val | |
| 3714 |
|
3763 | |||
| 3715 | def T(x,N): |
|
3764 | def T(x,N): | |
| 3716 | T = 1-abs(x)/N |
|
3765 | T = 1-abs(x)/N | |
| 3717 | return T |
|
3766 | return T | |
| 3718 |
|
3767 | |||
| 3719 | def R_T_spc_fun(x, a, id_dop, c, d, nFFTPoints): |
|
3768 | def R_T_spc_fun(x, a, id_dop, c, d, nFFTPoints): | |
| 3720 |
|
3769 | |||
| 3721 | N = int(numpy.shape(x)[0]) |
|
3770 | N = int(numpy.shape(x)[0]) | |
| 3722 | b = 0 |
|
3771 | b = 0 | |
| 3723 | x_max = x[-1] |
|
3772 | x_max = x[-1] | |
| 3724 |
|
3773 | |||
| 3725 | x_pos = x[nFFTPoints:] |
|
3774 | x_pos = x[nFFTPoints:] | |
| 3726 | x_neg = x[:nFFTPoints] |
|
3775 | x_neg = x[:nFFTPoints] | |
| 3727 | R_T_neg_1 = R_gaussian(x, a, b, c)[:nFFTPoints]*T(x_neg,-x[0]) |
|
3776 | R_T_neg_1 = R_gaussian(x, a, b, c)[:nFFTPoints]*T(x_neg,-x[0]) | |
| 3728 | R_T_pos_1 = R_gaussian(x, a, b, c)[nFFTPoints:]*T(x_pos,x[-1]) |
|
3777 | R_T_pos_1 = R_gaussian(x, a, b, c)[nFFTPoints:]*T(x_pos,x[-1]) | |
| 3729 |
|
3778 | |||
| 3730 | R_T_sum_1 = R_T_pos_1 + R_T_neg_1 |
|
3779 | R_T_sum_1 = R_T_pos_1 + R_T_neg_1 | |
| 3731 | R_T_spc_1 = numpy.fft.fft(R_T_sum_1).real |
|
3780 | R_T_spc_1 = numpy.fft.fft(R_T_sum_1).real | |
| 3732 | R_T_spc_1 = numpy.fft.fftshift(R_T_spc_1) |
|
3781 | R_T_spc_1 = numpy.fft.fftshift(R_T_spc_1) | |
| 3733 | max_val_1 = numpy.max(R_T_spc_1) |
|
3782 | max_val_1 = numpy.max(R_T_spc_1) | |
| 3734 | R_T_spc_1 = R_T_spc_1*a/max_val_1 |
|
3783 | R_T_spc_1 = R_T_spc_1*a/max_val_1 | |
| 3735 | #raise NotImplementedError |
|
3784 | #raise NotImplementedError | |
| 3736 | R_T_d = d*numpy.fft.fftshift(signal.unit_impulse(N)) |
|
3785 | R_T_d = d*numpy.fft.fftshift(signal.unit_impulse(N)) | |
| 3737 | R_T_d_neg = R_T_d[:nFFTPoints]*T(x_neg,-x[0]) |
|
3786 | R_T_d_neg = R_T_d[:nFFTPoints]*T(x_neg,-x[0]) | |
| 3738 | R_T_d_pos = R_T_d[nFFTPoints:]*T(x_pos,x[-1]) |
|
3787 | R_T_d_pos = R_T_d[nFFTPoints:]*T(x_pos,x[-1]) | |
| 3739 | R_T_d_sum = R_T_d_pos + R_T_d_neg |
|
3788 | R_T_d_sum = R_T_d_pos + R_T_d_neg | |
| 3740 | R_T_spc_3 = numpy.fft.fft(R_T_d_sum).real |
|
3789 | R_T_spc_3 = numpy.fft.fft(R_T_d_sum).real | |
| 3741 | R_T_spc_3 = numpy.fft.fftshift(R_T_spc_3) |
|
3790 | R_T_spc_3 = numpy.fft.fftshift(R_T_spc_3) | |
| 3742 |
|
3791 | |||
| 3743 | R_T_final = R_T_spc_1 + R_T_spc_3 |
|
3792 | R_T_final = R_T_spc_1 + R_T_spc_3 | |
| 3744 |
|
3793 | |||
| 3745 | id_dop = int(id_dop) |
|
3794 | id_dop = int(id_dop) | |
| 3746 |
|
3795 | |||
| 3747 | R_T_final = numpy.roll(R_T_final,-id_dop) |
|
3796 | R_T_final = numpy.roll(R_T_final,-id_dop) | |
| 3748 |
|
3797 | |||
| 3749 | return R_T_final |
|
3798 | return R_T_final | |
| 3750 |
|
3799 | |||
| 3751 | y = spc#gaussian(x, a, meanY, sigmaY) + a*0.1*numpy.random.normal(0, 1, size=len(x)) |
|
3800 | y = spc#gaussian(x, a, meanY, sigmaY) + a*0.1*numpy.random.normal(0, 1, size=len(x)) | |
| 3752 |
|
3801 | |||
| 3753 | from scipy.stats import norm |
|
3802 | from scipy.stats import norm | |
| 3754 | mean,std=norm.fit(spc) |
|
3803 | mean,std=norm.fit(spc) | |
| 3755 |
|
3804 | |||
| 3756 | # estimate starting values from the data |
|
3805 | # estimate starting values from the data | |
| 3757 | a = A-D |
|
3806 | a = A-D | |
| 3758 | b = B |
|
3807 | b = B | |
| 3759 | c = C#numpy.std(spc) #C |
|
3808 | c = C#numpy.std(spc) #C | |
| 3760 | d = D |
|
3809 | d = D | |
| 3761 |
|
3810 | |||
| 3762 | id_dop = numpy.argmax(spc) |
|
3811 | id_dop = numpy.argmax(spc) | |
| 3763 | id_dop = int(spc.shape[0]/2 - id_dop) |
|
3812 | id_dop = int(spc.shape[0]/2 - id_dop) | |
| 3764 |
|
3813 | |||
| 3765 | x_vel = numpy.linspace(x[0],x[-1],int(2*nFFTPoints)) |
|
3814 | x_vel = numpy.linspace(x[0],x[-1],int(2*nFFTPoints)) | |
| 3766 |
|
3815 | |||
| 3767 | # define a least squares function to optimize |
|
3816 | # define a least squares function to optimize | |
| 3768 |
|
3817 | |||
| 3769 | def minfunc(params): |
|
3818 | def minfunc(params): | |
| 3770 | #print("y.shape: ", numpy.shape(y)) |
|
3819 | #print("y.shape: ", numpy.shape(y)) | |
| 3771 | return sum((y-R_T_spc_fun(x_vel,params[0],params[1],params[2],params[3],nFFTPoints))**2/1)#y**2) |
|
3820 | return sum((y-R_T_spc_fun(x_vel,params[0],params[1],params[2],params[3],nFFTPoints))**2/1)#y**2) | |
| 3772 |
|
3821 | |||
| 3773 | # fit |
|
3822 | # fit | |
| 3774 | popt_full = fmin(minfunc,[a,id_dop,c,d], disp=False) |
|
3823 | popt_full = fmin(minfunc,[a,id_dop,c,d], disp=False) | |
| 3775 | popt = popt_full#[0] |
|
3824 | popt = popt_full#[0] | |
| 3776 |
|
3825 | |||
| 3777 | fun = gaussian(x, a, 0, popt[2], popt[3]) |
|
3826 | fun = gaussian(x, a, 0, popt[2], popt[3]) | |
| 3778 | fun = numpy.roll(fun,-int(popt[1])) |
|
3827 | fun = numpy.roll(fun,-int(popt[1])) | |
| 3779 |
|
3828 | |||
| 3780 | return fun, popt[0], popt[1], popt[2], popt[3] |
|
3829 | return fun, popt[0], popt[1], popt[2], popt[3] | |
| 3781 |
|
3830 | |||
| 3782 | def windowing_single_direct(self,spc_mod,x,A,B,C,D,nFFTPoints,timeInterval): |
|
3831 | def windowing_single_direct(self,spc_mod,x,A,B,C,D,nFFTPoints,timeInterval): | |
| 3783 | ''' |
|
3832 | ''' | |
| 3784 | Written by R. Flores |
|
3833 | Written by R. Flores | |
| 3785 | ''' |
|
3834 | ''' | |
| 3786 | from scipy.optimize import curve_fit,fmin |
|
3835 | from scipy.optimize import curve_fit,fmin | |
| 3787 |
|
3836 | |||
| 3788 | def gaussian(x, a, b, c, d): |
|
3837 | def gaussian(x, a, b, c, d): | |
| 3789 | val = a * numpy.exp(-(x - b)**2 / (2*c**2)) + d |
|
3838 | val = a * numpy.exp(-(x - b)**2 / (2*c**2)) + d | |
| 3790 | return val |
|
3839 | return val | |
| 3791 |
|
3840 | |||
| 3792 | def R_gaussian(x, a, b, c, d): |
|
3841 | def R_gaussian(x, a, b, c, d): | |
| 3793 | N = int(numpy.shape(x)[0]) |
|
3842 | N = int(numpy.shape(x)[0]) | |
| 3794 | val = (a*numpy.exp(-2*c**2*x**2 + 2*x*1.j*b))*(numpy.sqrt(2*numpy.pi)*c)/((numpy.pi)) + d*signal.unit_impulse(N)*numpy.shape(x)[0]/2 |
|
3843 | val = (a*numpy.exp(-2*c**2*x**2 + 2*x*1.j*b))*(numpy.sqrt(2*numpy.pi)*c)/((numpy.pi)) + d*signal.unit_impulse(N)*numpy.shape(x)[0]/2 | |
| 3795 |
|
3844 | |||
| 3796 | return 2*val/numpy.shape(val)[0] |
|
3845 | return 2*val/numpy.shape(val)[0] | |
| 3797 |
|
3846 | |||
| 3798 | def T(x,N): |
|
3847 | def T(x,N): | |
| 3799 | T = 1-abs(x)/N |
|
3848 | T = 1-abs(x)/N | |
| 3800 | return T |
|
3849 | return T | |
| 3801 |
|
3850 | |||
| 3802 | def R_T_spc_fun(x, a, b, c, d, nFFTPoints, timeInterval): #"x" should be time |
|
3851 | def R_T_spc_fun(x, a, b, c, d, nFFTPoints, timeInterval): #"x" should be time | |
| 3803 |
|
3852 | |||
| 3804 | #timeInterval = 2 |
|
3853 | #timeInterval = 2 | |
| 3805 | x_double = numpy.linspace(0,timeInterval,nFFTPoints) |
|
3854 | x_double = numpy.linspace(0,timeInterval,nFFTPoints) | |
| 3806 | x_double_m = numpy.flip(x_double) |
|
3855 | x_double_m = numpy.flip(x_double) | |
| 3807 | x_double_aux = numpy.linspace(0,x_double[-2],nFFTPoints) |
|
3856 | x_double_aux = numpy.linspace(0,x_double[-2],nFFTPoints) | |
| 3808 | x_double_t = numpy.concatenate((x_double_m,x_double_aux)) |
|
3857 | x_double_t = numpy.concatenate((x_double_m,x_double_aux)) | |
| 3809 | x_double_t /= max(x_double_t) |
|
3858 | x_double_t /= max(x_double_t) | |
| 3810 |
|
3859 | |||
| 3811 |
|
3860 | |||
| 3812 | R_T_sum_1 = R_gaussian(x, a, b, c, d) |
|
3861 | R_T_sum_1 = R_gaussian(x, a, b, c, d) | |
| 3813 |
|
3862 | |||
| 3814 | R_T_sum_1_flip = numpy.copy(numpy.flip(R_T_sum_1)) |
|
3863 | R_T_sum_1_flip = numpy.copy(numpy.flip(R_T_sum_1)) | |
| 3815 | R_T_sum_1_flip[-1] = R_T_sum_1_flip[0] |
|
3864 | R_T_sum_1_flip[-1] = R_T_sum_1_flip[0] | |
| 3816 | R_T_sum_1_flip = numpy.roll(R_T_sum_1_flip,1) |
|
3865 | R_T_sum_1_flip = numpy.roll(R_T_sum_1_flip,1) | |
| 3817 |
|
3866 | |||
| 3818 | R_T_sum_1_flip.imag *= -1 |
|
3867 | R_T_sum_1_flip.imag *= -1 | |
| 3819 |
|
3868 | |||
| 3820 | R_T_sum_1_total = numpy.concatenate((R_T_sum_1,R_T_sum_1_flip)) |
|
3869 | R_T_sum_1_total = numpy.concatenate((R_T_sum_1,R_T_sum_1_flip)) | |
| 3821 | R_T_sum_1_total *= x_double_t #times trian_fun |
|
3870 | R_T_sum_1_total *= x_double_t #times trian_fun | |
| 3822 |
|
3871 | |||
| 3823 | R_T_sum_1_total = R_T_sum_1_total[:nFFTPoints] + R_T_sum_1_total[nFFTPoints:] |
|
3872 | R_T_sum_1_total = R_T_sum_1_total[:nFFTPoints] + R_T_sum_1_total[nFFTPoints:] | |
| 3824 |
|
3873 | |||
| 3825 | R_T_spc_1 = numpy.fft.fft(R_T_sum_1_total).real |
|
3874 | R_T_spc_1 = numpy.fft.fft(R_T_sum_1_total).real | |
| 3826 | R_T_spc_1 = numpy.fft.fftshift(R_T_spc_1) |
|
3875 | R_T_spc_1 = numpy.fft.fftshift(R_T_spc_1) | |
| 3827 |
|
3876 | |||
| 3828 | freq = numpy.fft.fftfreq(nFFTPoints, d=timeInterval/nFFTPoints) |
|
3877 | freq = numpy.fft.fftfreq(nFFTPoints, d=timeInterval/nFFTPoints) | |
| 3829 |
|
3878 | |||
| 3830 | freq = numpy.fft.fftshift(freq) |
|
3879 | freq = numpy.fft.fftshift(freq) | |
| 3831 |
|
3880 | |||
| 3832 | freq *= 6/2 #lambda/2 |
|
3881 | freq *= 6/2 #lambda/2 | |
| 3833 |
|
3882 | |||
| 3834 | return R_T_spc_1 |
|
3883 | return R_T_spc_1 | |
| 3835 |
|
3884 | |||
| 3836 | y = spc_mod |
|
3885 | y = spc_mod | |
| 3837 |
|
3886 | |||
| 3838 | #from scipy.stats import norm |
|
3887 | #from scipy.stats import norm | |
| 3839 |
|
3888 | |||
| 3840 | # estimate starting values from the data |
|
3889 | # estimate starting values from the data | |
| 3841 |
|
3890 | |||
| 3842 | a = A-D |
|
3891 | a = A-D | |
| 3843 | b = B |
|
3892 | b = B | |
| 3844 | c = C |
|
3893 | c = C | |
| 3845 | d = D |
|
3894 | d = D | |
| 3846 |
|
3895 | |||
| 3847 | # define a least squares function to optimize |
|
3896 | # define a least squares function to optimize | |
| 3848 | import matplotlib.pyplot as plt |
|
3897 | import matplotlib.pyplot as plt | |
| 3849 | #ippSeconds = 2 |
|
3898 | #ippSeconds = 2 | |
| 3850 | t_range = numpy.linspace(0,timeInterval,nFFTPoints) |
|
3899 | t_range = numpy.linspace(0,timeInterval,nFFTPoints) | |
| 3851 | #aui = R_T_spc_fun(t_range,a,b,c,d,nFFTPoints,timeInterval) |
|
3900 | #aui = R_T_spc_fun(t_range,a,b,c,d,nFFTPoints,timeInterval) | |
| 3852 |
|
3901 | |||
| 3853 | def minfunc(params): |
|
3902 | def minfunc(params): | |
| 3854 | return sum((y-R_T_spc_fun(t_range,params[0],params[1],params[2],params[3],nFFTPoints,timeInterval))**2/1)#y**2) |
|
3903 | return sum((y-R_T_spc_fun(t_range,params[0],params[1],params[2],params[3],nFFTPoints,timeInterval))**2/1)#y**2) | |
| 3855 |
|
3904 | |||
| 3856 | # fit |
|
3905 | # fit | |
| 3857 | popt_full = fmin(minfunc,[a,b,c,d], disp=False) |
|
3906 | popt_full = fmin(minfunc,[a,b,c,d], disp=False) | |
| 3858 | popt = popt_full |
|
3907 | popt = popt_full | |
| 3859 |
|
3908 | |||
| 3860 | fun = R_T_spc_fun(t_range,popt[0],popt[1],popt[2],popt[3],nFFTPoints,timeInterval) |
|
3909 | fun = R_T_spc_fun(t_range,popt[0],popt[1],popt[2],popt[3],nFFTPoints,timeInterval) | |
| 3861 |
|
3910 | |||
| 3862 | return fun, popt[0], popt[1], popt[2], popt[3] |
|
3911 | return fun, popt[0], popt[1], popt[2], popt[3] | |
| 3863 | # ********************************************************************************************** |
|
3912 | # ********************************************************************************************** | |
| 3864 | index = 0 |
|
3913 | index = 0 | |
| 3865 | fint = 0 |
|
3914 | fint = 0 | |
| 3866 | buffer = 0 |
|
3915 | buffer = 0 | |
| 3867 | buffer2 = 0 |
|
3916 | buffer2 = 0 | |
| 3868 | buffer3 = 0 |
|
3917 | buffer3 = 0 | |
| 3869 |
|
3918 | |||
| 3870 | def run(self, dataOut, getSNR = True, path=None, file=None, groupList=None, filec=None,coh_th=None, hei_th=None,taver=None,proc=None,nhei=None,nprofs=None,ipp=None,channelList=None,Gaussian_Windowed=0): |
|
3919 | def run(self, dataOut, getSNR = True, path=None, file=None, groupList=None, filec=None,coh_th=None, hei_th=None,taver=None,proc=None,nhei=None,nprofs=None,ipp=None,channelList=None,Gaussian_Windowed=0): | |
| 3871 |
|
3920 | |||
| 3872 | if not numpy.any(proc): |
|
3921 | if not numpy.any(proc): | |
| 3873 | nChannels = dataOut.nChannels |
|
3922 | nChannels = dataOut.nChannels | |
| 3874 | nHeights= dataOut.heightList.size |
|
3923 | nHeights= dataOut.heightList.size | |
| 3875 | nProf = dataOut.nProfiles |
|
3924 | nProf = dataOut.nProfiles | |
| 3876 | if numpy.any(taver): taver=int(taver) |
|
3925 | if numpy.any(taver): taver=int(taver) | |
| 3877 | else : taver = 5 |
|
3926 | else : taver = 5 | |
| 3878 | tini=time.localtime(dataOut.utctime) |
|
3927 | tini=time.localtime(dataOut.utctime) | |
| 3879 | if (tini.tm_min % taver) == 0 and (tini.tm_sec < 5 and self.fint==0): |
|
3928 | if (tini.tm_min % taver) == 0 and (tini.tm_sec < 5 and self.fint==0): | |
| 3880 | self.index = 0 |
|
3929 | self.index = 0 | |
| 3881 | jspc = self.buffer |
|
3930 | jspc = self.buffer | |
| 3882 | jcspc = self.buffer2 |
|
3931 | jcspc = self.buffer2 | |
| 3883 | jnoise = self.buffer3 |
|
3932 | jnoise = self.buffer3 | |
| 3884 | self.buffer = dataOut.data_spc |
|
3933 | self.buffer = dataOut.data_spc | |
| 3885 | self.buffer2 = dataOut.data_cspc |
|
3934 | self.buffer2 = dataOut.data_cspc | |
| 3886 | self.buffer3 = dataOut.noise |
|
3935 | self.buffer3 = dataOut.noise | |
| 3887 | self.fint = 1 |
|
3936 | self.fint = 1 | |
| 3888 | if numpy.any(jspc) : |
|
3937 | if numpy.any(jspc) : | |
| 3889 | jspc= numpy.reshape(jspc,(int(len(jspc)/nChannels),nChannels,nProf,nHeights)) |
|
3938 | jspc= numpy.reshape(jspc,(int(len(jspc)/nChannels),nChannels,nProf,nHeights)) | |
| 3890 | jcspc= numpy.reshape(jcspc,(int(len(jcspc)/int(nChannels/2)),int(nChannels/2),nProf,nHeights)) |
|
3939 | jcspc= numpy.reshape(jcspc,(int(len(jcspc)/int(nChannels/2)),int(nChannels/2),nProf,nHeights)) | |
| 3891 | jnoise= numpy.reshape(jnoise,(int(len(jnoise)/nChannels),nChannels)) |
|
3940 | jnoise= numpy.reshape(jnoise,(int(len(jnoise)/nChannels),nChannels)) | |
| 3892 | else: |
|
3941 | else: | |
| 3893 | dataOut.flagNoData = True |
|
3942 | dataOut.flagNoData = True | |
| 3894 | return dataOut |
|
3943 | return dataOut | |
| 3895 | else: |
|
3944 | else: | |
| 3896 | if (tini.tm_min % taver) == 0 : self.fint = 1 |
|
3945 | if (tini.tm_min % taver) == 0 : self.fint = 1 | |
| 3897 | else : self.fint = 0 |
|
3946 | else : self.fint = 0 | |
| 3898 | self.index += 1 |
|
3947 | self.index += 1 | |
| 3899 | if numpy.any(self.buffer): |
|
3948 | if numpy.any(self.buffer): | |
| 3900 | self.buffer = numpy.concatenate((self.buffer,dataOut.data_spc), axis=0) |
|
3949 | self.buffer = numpy.concatenate((self.buffer,dataOut.data_spc), axis=0) | |
| 3901 | self.buffer2 = numpy.concatenate((self.buffer2,dataOut.data_cspc), axis=0) |
|
3950 | self.buffer2 = numpy.concatenate((self.buffer2,dataOut.data_cspc), axis=0) | |
| 3902 | self.buffer3 = numpy.concatenate((self.buffer3,dataOut.noise), axis=0) |
|
3951 | self.buffer3 = numpy.concatenate((self.buffer3,dataOut.noise), axis=0) | |
| 3903 | else: |
|
3952 | else: | |
| 3904 | self.buffer = dataOut.data_spc |
|
3953 | self.buffer = dataOut.data_spc | |
| 3905 | self.buffer2 = dataOut.data_cspc |
|
3954 | self.buffer2 = dataOut.data_cspc | |
| 3906 | self.buffer3 = dataOut.noise |
|
3955 | self.buffer3 = dataOut.noise | |
| 3907 | dataOut.flagNoData = True |
|
3956 | dataOut.flagNoData = True | |
| 3908 | return dataOut |
|
3957 | return dataOut | |
| 3909 | if path != None: |
|
3958 | if path != None: | |
| 3910 | sys.path.append(path) |
|
3959 | sys.path.append(path) | |
| 3911 | self.library = importlib.import_module(file) |
|
3960 | self.library = importlib.import_module(file) | |
| 3912 | if filec != None: |
|
3961 | if filec != None: | |
| 3913 | self.weightf = importlib.import_module(filec) |
|
3962 | self.weightf = importlib.import_module(filec) | |
| 3914 | #self.weightf = importlib.import_module('weightfit') |
|
3963 | #self.weightf = importlib.import_module('weightfit') | |
| 3915 |
|
3964 | |||
| 3916 | #To be inserted as a parameter |
|
3965 | #To be inserted as a parameter | |
| 3917 | groupArray = numpy.array(groupList) |
|
3966 | groupArray = numpy.array(groupList) | |
| 3918 | #groupArray = numpy.array([[0,1],[2,3]]) |
|
3967 | #groupArray = numpy.array([[0,1],[2,3]]) | |
| 3919 | dataOut.groupList = groupArray |
|
3968 | dataOut.groupList = groupArray | |
| 3920 |
|
3969 | |||
| 3921 | nGroups = groupArray.shape[0] |
|
3970 | nGroups = groupArray.shape[0] | |
| 3922 | nChannels = dataOut.nChannels |
|
3971 | nChannels = dataOut.nChannels | |
| 3923 | nHeights = dataOut.heightList.size |
|
3972 | nHeights = dataOut.heightList.size | |
| 3924 |
|
3973 | |||
| 3925 | #Parameters Array |
|
3974 | #Parameters Array | |
| 3926 | dataOut.data_param = None |
|
3975 | dataOut.data_param = None | |
| 3927 | dataOut.data_paramC = None |
|
3976 | dataOut.data_paramC = None | |
| 3928 | dataOut.clean_num_aver = None |
|
3977 | dataOut.clean_num_aver = None | |
| 3929 | dataOut.coh_num_aver = None |
|
3978 | dataOut.coh_num_aver = None | |
| 3930 | dataOut.tmp_spectra_i = None |
|
3979 | dataOut.tmp_spectra_i = None | |
| 3931 | dataOut.tmp_cspectra_i = None |
|
3980 | dataOut.tmp_cspectra_i = None | |
| 3932 | dataOut.tmp_spectra_c = None |
|
3981 | dataOut.tmp_spectra_c = None | |
| 3933 | dataOut.tmp_cspectra_c = None |
|
3982 | dataOut.tmp_cspectra_c = None | |
| 3934 | dataOut.sat_spectra = None |
|
3983 | dataOut.sat_spectra = None | |
| 3935 | dataOut.sat_cspectra = None |
|
3984 | dataOut.sat_cspectra = None | |
| 3936 | dataOut.index = None |
|
3985 | dataOut.index = None | |
| 3937 |
|
3986 | |||
| 3938 | #Set constants |
|
3987 | #Set constants | |
| 3939 | constants = self.library.setConstants(dataOut) |
|
3988 | constants = self.library.setConstants(dataOut) | |
| 3940 | dataOut.constants = constants |
|
3989 | dataOut.constants = constants | |
| 3941 | M = dataOut.normFactor |
|
3990 | M = dataOut.normFactor | |
| 3942 | N = dataOut.nFFTPoints |
|
3991 | N = dataOut.nFFTPoints | |
| 3943 |
|
3992 | |||
| 3944 | ippSeconds = dataOut.ippSeconds |
|
3993 | ippSeconds = dataOut.ippSeconds | |
| 3945 | K = dataOut.nIncohInt |
|
3994 | K = dataOut.nIncohInt | |
| 3946 | pairsArray = numpy.array(dataOut.pairsList) |
|
3995 | pairsArray = numpy.array(dataOut.pairsList) | |
| 3947 |
|
3996 | |||
| 3948 | snrth= 15 |
|
3997 | snrth= 15 | |
| 3949 | spectra = dataOut.data_spc |
|
3998 | spectra = dataOut.data_spc | |
| 3950 | cspectra = dataOut.data_cspc |
|
3999 | cspectra = dataOut.data_cspc | |
| 3951 | nProf = dataOut.nProfiles |
|
4000 | nProf = dataOut.nProfiles | |
| 3952 | heights = dataOut.heightList |
|
4001 | heights = dataOut.heightList | |
| 3953 | nHei = len(heights) |
|
4002 | nHei = len(heights) | |
| 3954 | channels = dataOut.channelList |
|
4003 | channels = dataOut.channelList | |
| 3955 | nChan = len(channels) |
|
4004 | nChan = len(channels) | |
| 3956 | nIncohInt = dataOut.nIncohInt |
|
4005 | nIncohInt = dataOut.nIncohInt | |
| 3957 | crosspairs = dataOut.groupList |
|
4006 | crosspairs = dataOut.groupList | |
| 3958 | noise = dataOut.noise |
|
4007 | noise = dataOut.noise | |
| 3959 | jnoise = jnoise/N |
|
4008 | jnoise = jnoise/N | |
| 3960 | noise = numpy.nansum(jnoise,axis=0)#/len(jnoise) |
|
4009 | noise = numpy.nansum(jnoise,axis=0)#/len(jnoise) | |
| 3961 | power = numpy.sum(spectra, axis=1) |
|
4010 | power = numpy.sum(spectra, axis=1) | |
| 3962 | nPairs = len(crosspairs) |
|
4011 | nPairs = len(crosspairs) | |
| 3963 | absc = dataOut.abscissaList[:-1] |
|
4012 | absc = dataOut.abscissaList[:-1] | |
| 3964 | print('para escribir h5 ',dataOut.paramInterval) |
|
4013 | print('para escribir h5 ',dataOut.paramInterval) | |
| 3965 | if not self.isConfig: |
|
4014 | if not self.isConfig: | |
| 3966 | self.isConfig = True |
|
4015 | self.isConfig = True | |
| 3967 |
|
4016 | |||
| 3968 | index = tini.tm_hour*12+tini.tm_min/taver |
|
4017 | index = tini.tm_hour*12+tini.tm_min/taver | |
| 3969 | dataOut.index= index |
|
4018 | dataOut.index= index | |
| 3970 | jspc = jspc/N/N |
|
4019 | jspc = jspc/N/N | |
| 3971 | jcspc = jcspc/N/N |
|
4020 | jcspc = jcspc/N/N | |
| 3972 | tmp_spectra,tmp_cspectra,sat_spectra,sat_cspectra = self.CleanRayleigh(dataOut,jspc,jcspc,2) |
|
4021 | tmp_spectra,tmp_cspectra,sat_spectra,sat_cspectra = self.CleanRayleigh(dataOut,jspc,jcspc,2) | |
| 3973 | jspectra = tmp_spectra*len(jspc[:,0,0,0]) |
|
4022 | jspectra = tmp_spectra*len(jspc[:,0,0,0]) | |
| 3974 | jcspectra = tmp_cspectra*len(jspc[:,0,0,0]) |
|
4023 | jcspectra = tmp_cspectra*len(jspc[:,0,0,0]) | |
| 3975 | my_incoh_spectra ,my_incoh_cspectra,my_incoh_aver,my_coh_aver, incoh_spectra, coh_spectra, incoh_cspectra, coh_cspectra, incoh_aver, coh_aver = self.__DiffCoherent(jspectra, jcspectra, dataOut, noise, snrth,coh_th, hei_th) |
|
4024 | my_incoh_spectra ,my_incoh_cspectra,my_incoh_aver,my_coh_aver, incoh_spectra, coh_spectra, incoh_cspectra, coh_cspectra, incoh_aver, coh_aver = self.__DiffCoherent(jspectra, jcspectra, dataOut, noise, snrth,coh_th, hei_th) | |
| 3976 |
|
4025 | |||
| 3977 | clean_coh_spectra, clean_coh_cspectra, clean_coh_aver = self.__CleanCoherent(snrth, coh_spectra, coh_cspectra, coh_aver, dataOut, noise,1,index) |
|
4026 | clean_coh_spectra, clean_coh_cspectra, clean_coh_aver = self.__CleanCoherent(snrth, coh_spectra, coh_cspectra, coh_aver, dataOut, noise,1,index) | |
| 3978 | dataOut.data_spc = incoh_spectra |
|
4027 | dataOut.data_spc = incoh_spectra | |
| 3979 | dataOut.data_cspc = incoh_cspectra |
|
4028 | dataOut.data_cspc = incoh_cspectra | |
| 3980 | dataOut.sat_spectra = sat_spectra |
|
4029 | dataOut.sat_spectra = sat_spectra | |
| 3981 | dataOut.sat_cspectra = sat_cspectra |
|
4030 | dataOut.sat_cspectra = sat_cspectra | |
| 3982 | # dataOut.data_spc = tmp_spectra |
|
4031 | # dataOut.data_spc = tmp_spectra | |
| 3983 | # dataOut.data_cspc = tmp_cspectra |
|
4032 | # dataOut.data_cspc = tmp_cspectra | |
| 3984 |
|
4033 | |||
| 3985 | clean_num_aver = incoh_aver*len(jspc[:,0,0,0]) |
|
4034 | clean_num_aver = incoh_aver*len(jspc[:,0,0,0]) | |
| 3986 | coh_num_aver = clean_coh_aver*len(jspc[:,0,0,0]) |
|
4035 | coh_num_aver = clean_coh_aver*len(jspc[:,0,0,0]) | |
| 3987 | # clean_num_aver = (numpy.zeros([nChan, nHei])+1)*len(jspc[:,0,0,0]) |
|
4036 | # clean_num_aver = (numpy.zeros([nChan, nHei])+1)*len(jspc[:,0,0,0]) | |
| 3988 | # coh_num_aver = numpy.zeros([nChan, nHei])*0*len(jspc[:,0,0,0]) |
|
4037 | # coh_num_aver = numpy.zeros([nChan, nHei])*0*len(jspc[:,0,0,0]) | |
| 3989 | dataOut.clean_num_aver = clean_num_aver |
|
4038 | dataOut.clean_num_aver = clean_num_aver | |
| 3990 | dataOut.coh_num_aver = coh_num_aver |
|
4039 | dataOut.coh_num_aver = coh_num_aver | |
| 3991 | dataOut.tmp_spectra_i = incoh_spectra |
|
4040 | dataOut.tmp_spectra_i = incoh_spectra | |
| 3992 | dataOut.tmp_cspectra_i = incoh_cspectra |
|
4041 | dataOut.tmp_cspectra_i = incoh_cspectra | |
| 3993 | dataOut.tmp_spectra_c = clean_coh_spectra |
|
4042 | dataOut.tmp_spectra_c = clean_coh_spectra | |
| 3994 | dataOut.tmp_cspectra_c = clean_coh_cspectra |
|
4043 | dataOut.tmp_cspectra_c = clean_coh_cspectra | |
| 3995 | #List of possible combinations |
|
4044 | #List of possible combinations | |
| 3996 | listComb = itertools.combinations(numpy.arange(groupArray.shape[1]),2) |
|
4045 | listComb = itertools.combinations(numpy.arange(groupArray.shape[1]),2) | |
| 3997 | indCross = numpy.zeros(len(list(listComb)), dtype = 'int') |
|
4046 | indCross = numpy.zeros(len(list(listComb)), dtype = 'int') | |
| 3998 | if Gaussian_Windowed == 1: |
|
4047 | if Gaussian_Windowed == 1: | |
| 3999 | #dataOut.data_spc = jspectra |
|
4048 | #dataOut.data_spc = jspectra | |
| 4000 | ''' |
|
4049 | ''' | |
| 4001 | Written by R. Flores |
|
4050 | Written by R. Flores | |
| 4002 | ''' |
|
4051 | ''' | |
| 4003 | print("normFactor: ", dataOut.normFactor) |
|
4052 | print("normFactor: ", dataOut.normFactor) | |
| 4004 | data_spc_aux = numpy.copy(dataOut.data_spc)#[:,0,:] |
|
4053 | data_spc_aux = numpy.copy(dataOut.data_spc)#[:,0,:] | |
| 4005 | data_spc_aux[:,0,:] = (data_spc_aux[:,1,:]+data_spc_aux[:,-1,:])/2 |
|
4054 | data_spc_aux[:,0,:] = (data_spc_aux[:,1,:]+data_spc_aux[:,-1,:])/2 | |
| 4006 | #''' |
|
4055 | #''' | |
| 4007 | from scipy.signal import medfilt |
|
4056 | from scipy.signal import medfilt | |
| 4008 | import matplotlib.pyplot as plt |
|
4057 | import matplotlib.pyplot as plt | |
| 4009 | dataOut.moments = numpy.ones((dataOut.nChannels,4,dataOut.nHeights))*numpy.NAN |
|
4058 | dataOut.moments = numpy.ones((dataOut.nChannels,4,dataOut.nHeights))*numpy.NAN | |
| 4010 | dataOut.VelRange = dataOut.getVelRange(0) |
|
4059 | dataOut.VelRange = dataOut.getVelRange(0) | |
| 4011 | for nChannel in range(dataOut.nChannels): |
|
4060 | for nChannel in range(dataOut.nChannels): | |
| 4012 | for hei in range(dataOut.heightList.shape[0]): |
|
4061 | for hei in range(dataOut.heightList.shape[0]): | |
| 4013 | #print("ipp: ", dataOut.ippSeconds) |
|
4062 | #print("ipp: ", dataOut.ippSeconds) | |
| 4014 | #spc = numpy.copy(dataOut.data_spc[nChannel,:,hei]) |
|
4063 | #spc = numpy.copy(dataOut.data_spc[nChannel,:,hei]) | |
| 4015 | spc = data_spc_aux[nChannel,:,hei] |
|
4064 | spc = data_spc_aux[nChannel,:,hei] | |
| 4016 | if spc.all() == 0.: |
|
4065 | if spc.all() == 0.: | |
| 4017 | print("CONTINUE") |
|
4066 | print("CONTINUE") | |
| 4018 | continue |
|
4067 | continue | |
| 4019 | #print(VelRange) |
|
4068 | #print(VelRange) | |
| 4020 | #print(dataOut.getFreqRange(64)) |
|
4069 | #print(dataOut.getFreqRange(64)) | |
| 4021 | #print("Hei: ", dataOut.heightList[hei]) |
|
4070 | #print("Hei: ", dataOut.heightList[hei]) | |
| 4022 |
|
4071 | |||
| 4023 | spc_mod = numpy.copy(spc) |
|
4072 | spc_mod = numpy.copy(spc) | |
| 4024 | spcm = medfilt(spc_mod,11) |
|
4073 | spcm = medfilt(spc_mod,11) | |
| 4025 | spc_max = numpy.max(spcm) |
|
4074 | spc_max = numpy.max(spcm) | |
| 4026 | dop1_x0 = dataOut.VelRange[numpy.argmax(spcm)] |
|
4075 | dop1_x0 = dataOut.VelRange[numpy.argmax(spcm)] | |
| 4027 | #D = numpy.min(spcm) |
|
4076 | #D = numpy.min(spcm) | |
| 4028 | D_in = (numpy.mean(spcm[:15])+numpy.mean(spcm[-15:]))/2. |
|
4077 | D_in = (numpy.mean(spcm[:15])+numpy.mean(spcm[-15:]))/2. | |
| 4029 | #print("spc_max: ", spc_max) |
|
4078 | #print("spc_max: ", spc_max) | |
| 4030 | #print("dataOut.ippSeconds: ", dataOut.ippSeconds, dataOut.timeInterval) |
|
4079 | #print("dataOut.ippSeconds: ", dataOut.ippSeconds, dataOut.timeInterval) | |
| 4031 | ##fun, A, B, C, D = self.windowing_single(spc,dataOut.VelRange,spc_max,dop1_x0,abs(dop1_x0),D,dataOut.nFFTPoints) |
|
4080 | ##fun, A, B, C, D = self.windowing_single(spc,dataOut.VelRange,spc_max,dop1_x0,abs(dop1_x0),D,dataOut.nFFTPoints) | |
| 4032 | #fun, A, B, C, D = self.windowing_single(spc,dataOut.VelRange,spc_max,dop1_x0,abs(dop1_x0),D,dataOut.nFFTPoints) |
|
4081 | #fun, A, B, C, D = self.windowing_single(spc,dataOut.VelRange,spc_max,dop1_x0,abs(dop1_x0),D,dataOut.nFFTPoints) | |
| 4033 | fun, A, B, C, D = self.windowing_single_direct(spc_mod,dataOut.VelRange,spc_max,dop1_x0,abs(dop1_x0/5),D_in,dataOut.nFFTPoints,dataOut.timeInterval) |
|
4082 | fun, A, B, C, D = self.windowing_single_direct(spc_mod,dataOut.VelRange,spc_max,dop1_x0,abs(dop1_x0/5),D_in,dataOut.nFFTPoints,dataOut.timeInterval) | |
| 4034 |
|
4083 | |||
| 4035 | dataOut.moments[nChannel,0,hei] = A |
|
4084 | dataOut.moments[nChannel,0,hei] = A | |
| 4036 | dataOut.moments[nChannel,1,hei] = B |
|
4085 | dataOut.moments[nChannel,1,hei] = B | |
| 4037 | dataOut.moments[nChannel,2,hei] = C |
|
4086 | dataOut.moments[nChannel,2,hei] = C | |
| 4038 | dataOut.moments[nChannel,3,hei] = D |
|
4087 | dataOut.moments[nChannel,3,hei] = D | |
| 4039 | ''' |
|
4088 | ''' | |
| 4040 | if nChannel == 0: |
|
4089 | if nChannel == 0: | |
| 4041 | print(dataOut.heightList[hei]) |
|
4090 | print(dataOut.heightList[hei]) | |
| 4042 | plt.figure() |
|
4091 | plt.figure() | |
| 4043 | plt.plot(dataOut.VelRange,spc,marker='*',linestyle='--') |
|
4092 | plt.plot(dataOut.VelRange,spc,marker='*',linestyle='--') | |
| 4044 | plt.plot(dataOut.VelRange,fun) |
|
4093 | plt.plot(dataOut.VelRange,fun) | |
| 4045 | plt.title(dataOut.heightList[hei]) |
|
4094 | plt.title(dataOut.heightList[hei]) | |
| 4046 | plt.show() |
|
4095 | plt.show() | |
| 4047 | ''' |
|
4096 | ''' | |
| 4048 | #plt.show() |
|
4097 | #plt.show() | |
| 4049 | #''' |
|
4098 | #''' | |
| 4050 | dataOut.data_spc = jspectra |
|
4099 | dataOut.data_spc = jspectra | |
| 4051 | print("SUCCESS") |
|
4100 | print("SUCCESS") | |
| 4052 | return dataOut |
|
4101 | return dataOut | |
| 4053 |
|
4102 | |||
| 4054 | elif Gaussian_Windowed == 2: #Only to clean spc |
|
4103 | elif Gaussian_Windowed == 2: #Only to clean spc | |
| 4055 | dataOut.VelRange = dataOut.getVelRange(0) |
|
4104 | dataOut.VelRange = dataOut.getVelRange(0) | |
| 4056 | return dataOut |
|
4105 | return dataOut | |
| 4057 |
|
4106 | |||
| 4058 | if getSNR: |
|
4107 | if getSNR: | |
| 4059 | listChannels = groupArray.reshape((groupArray.size)) |
|
4108 | listChannels = groupArray.reshape((groupArray.size)) | |
| 4060 | listChannels.sort() |
|
4109 | listChannels.sort() | |
|
|
4110 | # norm Este factor debe estar implementado para ploteo o grabado como metadata | |||
|
|
4111 | # norm = dataOut.nProfiles * dataOut.nIncohInt * dataOut.nCohInt * dataOut.windowOfFilter | |||
| 4061 | dataOut.data_snr = self.__getSNR(dataOut.data_spc[listChannels,:,:], noise[listChannels]) |
|
4112 | dataOut.data_snr = self.__getSNR(dataOut.data_spc[listChannels,:,:], noise[listChannels]) | |
| 4062 | else: |
|
4113 | else: | |
| 4063 | if numpy.any(taver): taver=int(taver) |
|
4114 | if numpy.any(taver): taver=int(taver) | |
| 4064 | else : taver = 5 |
|
4115 | else : taver = 5 | |
| 4065 | tini=time.localtime(dataOut.utctime) |
|
4116 | tini=time.localtime(dataOut.utctime) | |
| 4066 | index = tini.tm_hour*12+tini.tm_min/taver |
|
4117 | index = tini.tm_hour*12+tini.tm_min/taver | |
| 4067 | clean_num_aver = dataOut.clean_num_aver |
|
4118 | clean_num_aver = dataOut.clean_num_aver | |
| 4068 | coh_num_aver = dataOut.coh_num_aver |
|
4119 | coh_num_aver = dataOut.coh_num_aver | |
| 4069 | dataOut.data_spc = dataOut.tmp_spectra_i |
|
4120 | dataOut.data_spc = dataOut.tmp_spectra_i | |
| 4070 | dataOut.data_cspc = dataOut.tmp_cspectra_i |
|
4121 | dataOut.data_cspc = dataOut.tmp_cspectra_i | |
| 4071 | clean_coh_spectra = dataOut.tmp_spectra_c |
|
4122 | clean_coh_spectra = dataOut.tmp_spectra_c | |
| 4072 | clean_coh_cspectra = dataOut.tmp_cspectra_c |
|
4123 | clean_coh_cspectra = dataOut.tmp_cspectra_c | |
| 4073 | jspectra = dataOut.data_spc+clean_coh_spectra |
|
4124 | jspectra = dataOut.data_spc+clean_coh_spectra | |
| 4074 | nHeights = len(dataOut.heightList) # nhei |
|
4125 | nHeights = len(dataOut.heightList) # nhei | |
| 4075 | nProf = int(dataOut.nProfiles) |
|
4126 | nProf = int(dataOut.nProfiles) | |
| 4076 | dataOut.nProfiles = nProf |
|
4127 | dataOut.nProfiles = nProf | |
| 4077 | dataOut.data_param = None |
|
4128 | dataOut.data_param = None | |
| 4078 | dataOut.data_paramC = None |
|
4129 | dataOut.data_paramC = None | |
| 4079 | dataOut.code = numpy.array([[-1.,-1.,1.],[1.,1.,-1.]]) |
|
4130 | dataOut.code = numpy.array([[-1.,-1.,1.],[1.,1.,-1.]]) | |
| 4080 | #dataOut.paramInterval = 2.0 |
|
4131 | #dataOut.paramInterval = 2.0 | |
| 4081 | #M=600 |
|
4132 | #M=600 | |
| 4082 | #N=200 |
|
4133 | #N=200 | |
| 4083 | dataOut.flagDecodeData=True |
|
4134 | dataOut.flagDecodeData=True | |
| 4084 | M = int(dataOut.normFactor) |
|
4135 | M = int(dataOut.normFactor) | |
| 4085 | N = int(dataOut.nFFTPoints) |
|
4136 | N = int(dataOut.nFFTPoints) | |
| 4086 | dataOut.nFFTPoints = N |
|
4137 | dataOut.nFFTPoints = N | |
| 4087 | dataOut.nIncohInt= int(dataOut.nIncohInt) |
|
4138 | dataOut.nIncohInt= int(dataOut.nIncohInt) | |
| 4088 | dataOut.nProfiles = int(dataOut.nProfiles) |
|
4139 | dataOut.nProfiles = int(dataOut.nProfiles) | |
| 4089 | dataOut.nCohInt = int(dataOut.nCohInt) |
|
4140 | dataOut.nCohInt = int(dataOut.nCohInt) | |
| 4090 | print('sale',dataOut.nProfiles,dataOut.nHeights) |
|
4141 | print('sale',dataOut.nProfiles,dataOut.nHeights) | |
| 4091 | #dataOut.nFFTPoints=nprofs |
|
4142 | #dataOut.nFFTPoints=nprofs | |
| 4092 | #dataOut.normFactor = nprofs |
|
4143 | #dataOut.normFactor = nprofs | |
| 4093 | dataOut.channelList = channelList |
|
4144 | dataOut.channelList = channelList | |
| 4094 | nChan = len(channelList) |
|
4145 | nChan = len(channelList) | |
| 4095 | #dataOut.ippFactor=1 |
|
4146 | #dataOut.ippFactor=1 | |
| 4096 | #ipp = ipp/150*1.e-3 |
|
4147 | #ipp = ipp/150*1.e-3 | |
| 4097 | vmax = (300000000/49920000.0/2) / (dataOut.ippSeconds) |
|
4148 | vmax = (300000000/49920000.0/2) / (dataOut.ippSeconds) | |
| 4098 | #dataOut.ippSeconds=ipp |
|
4149 | #dataOut.ippSeconds=ipp | |
| 4099 | absc = vmax*( numpy.arange(nProf,dtype='float')-nProf/2.)/nProf |
|
4150 | absc = vmax*( numpy.arange(nProf,dtype='float')-nProf/2.)/nProf | |
| 4100 | print('sale 2',dataOut.ippSeconds,M,N) |
|
4151 | print('sale 2',dataOut.ippSeconds,M,N) | |
| 4101 | print('Empieza procesamiento offline') |
|
4152 | print('Empieza procesamiento offline') | |
| 4102 | if path != None: |
|
4153 | if path != None: | |
| 4103 | sys.path.append(path) |
|
4154 | sys.path.append(path) | |
| 4104 | self.library = importlib.import_module(file) |
|
4155 | self.library = importlib.import_module(file) | |
| 4105 | constants = self.library.setConstants(dataOut) |
|
4156 | constants = self.library.setConstants(dataOut) | |
| 4106 | constants['M'] = M |
|
4157 | constants['M'] = M | |
| 4107 | dataOut.constants = constants |
|
4158 | dataOut.constants = constants | |
| 4108 | if filec != None: |
|
4159 | if filec != None: | |
| 4109 | self.weightf = importlib.import_module(filec) |
|
4160 | self.weightf = importlib.import_module(filec) | |
| 4110 |
|
4161 | |||
| 4111 | groupArray = numpy.array(groupList) |
|
4162 | groupArray = numpy.array(groupList) | |
| 4112 | dataOut.groupList = groupArray |
|
4163 | dataOut.groupList = groupArray | |
| 4113 | nGroups = groupArray.shape[0] |
|
4164 | nGroups = groupArray.shape[0] | |
| 4114 | #List of possible combinations |
|
4165 | #List of possible combinations | |
| 4115 | listComb = itertools.combinations(numpy.arange(groupArray.shape[1]),2) |
|
4166 | listComb = itertools.combinations(numpy.arange(groupArray.shape[1]),2) | |
| 4116 | indCross = numpy.zeros(len(list(listComb)), dtype = 'int') |
|
4167 | indCross = numpy.zeros(len(list(listComb)), dtype = 'int') | |
| 4117 | if dataOut.data_paramC is None: |
|
4168 | if dataOut.data_paramC is None: | |
| 4118 | dataOut.data_paramC = numpy.zeros((nGroups*4, nHeights,2))*numpy.nan |
|
4169 | dataOut.data_paramC = numpy.zeros((nGroups*4, nHeights,2))*numpy.nan | |
| 4119 | dataOut.data_snr1_i = numpy.zeros((nGroups*2, nHeights))*numpy.nan |
|
4170 | dataOut.data_snr1_i = numpy.zeros((nGroups*2, nHeights))*numpy.nan | |
| 4120 | # dataOut.smooth_i = numpy.zeros((nGroups*2, nHeights))*numpy.nan |
|
4171 | # dataOut.smooth_i = numpy.zeros((nGroups*2, nHeights))*numpy.nan | |
| 4121 |
|
4172 | |||
| 4122 | for i in range(nGroups): |
|
4173 | for i in range(nGroups): | |
| 4123 | coord = groupArray[i,:] |
|
4174 | coord = groupArray[i,:] | |
| 4124 | #Input data array |
|
4175 | #Input data array | |
| 4125 | data = dataOut.data_spc[coord,:,:]/(M*N) |
|
4176 | data = dataOut.data_spc[coord,:,:]/(M*N) | |
| 4126 | data = data.reshape((data.shape[0]*data.shape[1],data.shape[2])) |
|
4177 | data = data.reshape((data.shape[0]*data.shape[1],data.shape[2])) | |
| 4127 |
|
4178 | |||
| 4128 | #Cross Spectra data array for Covariance Matrixes |
|
4179 | #Cross Spectra data array for Covariance Matrixes | |
| 4129 | ind = 0 |
|
4180 | ind = 0 | |
| 4130 | for pairs in listComb: |
|
4181 | for pairs in listComb: | |
| 4131 | pairsSel = numpy.array([coord[x],coord[y]]) |
|
4182 | pairsSel = numpy.array([coord[x],coord[y]]) | |
| 4132 | indCross[ind] = int(numpy.where(numpy.all(pairsArray == pairsSel, axis = 1))[0][0]) |
|
4183 | indCross[ind] = int(numpy.where(numpy.all(pairsArray == pairsSel, axis = 1))[0][0]) | |
| 4133 | ind += 1 |
|
4184 | ind += 1 | |
| 4134 | dataCross = dataOut.data_cspc[indCross,:,:]/(M*N) |
|
4185 | dataCross = dataOut.data_cspc[indCross,:,:]/(M*N) | |
| 4135 | dataCross = dataCross**2 |
|
4186 | dataCross = dataCross**2 | |
| 4136 | nhei = nHeights |
|
4187 | nhei = nHeights | |
| 4137 | poweri = numpy.sum(dataOut.data_spc[:,1:nProf-0,:],axis=1)/clean_num_aver[:,:] |
|
4188 | poweri = numpy.sum(dataOut.data_spc[:,1:nProf-0,:],axis=1)/clean_num_aver[:,:] | |
| 4138 | if i == 0 : my_noises = numpy.zeros(4,dtype=float) |
|
4189 | if i == 0 : my_noises = numpy.zeros(4,dtype=float) | |
| 4139 | n0i = numpy.nanmin(poweri[0+i*2,0:nhei-0])/(nProf-1) |
|
4190 | n0i = numpy.nanmin(poweri[0+i*2,0:nhei-0])/(nProf-1) | |
| 4140 | n1i = numpy.nanmin(poweri[1+i*2,0:nhei-0])/(nProf-1) |
|
4191 | n1i = numpy.nanmin(poweri[1+i*2,0:nhei-0])/(nProf-1) | |
| 4141 | n0 = n0i |
|
4192 | n0 = n0i | |
| 4142 | n1= n1i |
|
4193 | n1= n1i | |
| 4143 | my_noises[2*i+0] = n0 |
|
4194 | my_noises[2*i+0] = n0 | |
| 4144 | my_noises[2*i+1] = n1 |
|
4195 | my_noises[2*i+1] = n1 | |
| 4145 | snrth = -13 #-13.0 # -4 -16 -25 |
|
4196 | snrth = -13 #-13.0 # -4 -16 -25 | |
| 4146 | snrth = 10**(snrth/10.0) |
|
4197 | snrth = 10**(snrth/10.0) | |
| 4147 | jvelr = numpy.zeros(nHeights, dtype = 'float') |
|
4198 | jvelr = numpy.zeros(nHeights, dtype = 'float') | |
| 4148 | #snr0 = numpy.zeros(nHeights, dtype = 'float') |
|
4199 | #snr0 = numpy.zeros(nHeights, dtype = 'float') | |
| 4149 | #snr1 = numpy.zeros(nHeights, dtype = 'float') |
|
4200 | #snr1 = numpy.zeros(nHeights, dtype = 'float') | |
| 4150 | hvalid = [0] |
|
4201 | hvalid = [0] | |
| 4151 |
|
4202 | |||
| 4152 | coh2 = abs(dataOut.data_cspc[i,1:nProf,:])**2/(dataOut.data_spc[0+i*2,1:nProf-0,:]*dataOut.data_spc[1+i*2,1:nProf-0,:]) |
|
4203 | coh2 = abs(dataOut.data_cspc[i,1:nProf,:])**2/(dataOut.data_spc[0+i*2,1:nProf-0,:]*dataOut.data_spc[1+i*2,1:nProf-0,:]) | |
| 4153 |
|
4204 | |||
| 4154 | for h in range(nHeights): |
|
4205 | for h in range(nHeights): | |
| 4155 | smooth = clean_num_aver[i+1,h] |
|
4206 | smooth = clean_num_aver[i+1,h] | |
| 4156 | signalpn0 = (dataOut.data_spc[i*2,1:(nProf-0),h])/smooth |
|
4207 | signalpn0 = (dataOut.data_spc[i*2,1:(nProf-0),h])/smooth | |
| 4157 | signalpn1 = (dataOut.data_spc[i*2+1,1:(nProf-0),h])/smooth |
|
4208 | signalpn1 = (dataOut.data_spc[i*2+1,1:(nProf-0),h])/smooth | |
| 4158 | signal0 = signalpn0-n0 |
|
4209 | signal0 = signalpn0-n0 | |
| 4159 | signal1 = signalpn1-n1 |
|
4210 | signal1 = signalpn1-n1 | |
| 4160 | snr0 = numpy.sum(signal0/n0)/(nProf-1) |
|
4211 | snr0 = numpy.sum(signal0/n0)/(nProf-1) | |
| 4161 | snr1 = numpy.sum(signal1/n1)/(nProf-1) |
|
4212 | snr1 = numpy.sum(signal1/n1)/(nProf-1) | |
| 4162 | #jmax0 = MAX(signal0,maxp0) |
|
4213 | #jmax0 = MAX(signal0,maxp0) | |
| 4163 | #jmax1 = MAX(signal1,maxp1) |
|
4214 | #jmax1 = MAX(signal1,maxp1) | |
| 4164 | gamma = coh2[:,h] |
|
4215 | gamma = coh2[:,h] | |
| 4165 |
|
4216 | |||
| 4166 | indxs = (numpy.isfinite(list(gamma))==True).nonzero() |
|
4217 | indxs = (numpy.isfinite(list(gamma))==True).nonzero() | |
| 4167 |
|
4218 | |||
| 4168 | if len(indxs) >0: |
|
4219 | if len(indxs) >0: | |
| 4169 | if numpy.nanmean(gamma) > 0.07: |
|
4220 | if numpy.nanmean(gamma) > 0.07: | |
| 4170 | maxp0 = numpy.argmax(signal0*gamma) |
|
4221 | maxp0 = numpy.argmax(signal0*gamma) | |
| 4171 | maxp1 = numpy.argmax(signal1*gamma) |
|
4222 | maxp1 = numpy.argmax(signal1*gamma) | |
| 4172 | #print('usa gamma',numpy.nanmean(gamma)) |
|
4223 | #print('usa gamma',numpy.nanmean(gamma)) | |
| 4173 | else: |
|
4224 | else: | |
| 4174 | maxp0 = numpy.argmax(signal0) |
|
4225 | maxp0 = numpy.argmax(signal0) | |
| 4175 | maxp1 = numpy.argmax(signal1) |
|
4226 | maxp1 = numpy.argmax(signal1) | |
| 4176 | jvelr[h] = (absc[maxp0]+absc[maxp1])/2. |
|
4227 | jvelr[h] = (absc[maxp0]+absc[maxp1])/2. | |
| 4177 | else: jvelr[h] = absc[0] |
|
4228 | else: jvelr[h] = absc[0] | |
| 4178 | if snr0 > 0.1 and snr1 > 0.1: hvalid = numpy.concatenate((hvalid,h), axis=None) |
|
4229 | if snr0 > 0.1 and snr1 > 0.1: hvalid = numpy.concatenate((hvalid,h), axis=None) | |
| 4179 | #print(maxp0,absc[maxp0],snr0,jvelr[h]) |
|
4230 | #print(maxp0,absc[maxp0],snr0,jvelr[h]) | |
| 4180 |
|
4231 | |||
| 4181 | if len(hvalid)> 1: fd0 = numpy.median(jvelr[hvalid[1:]])*-1 |
|
4232 | if len(hvalid)> 1: fd0 = numpy.median(jvelr[hvalid[1:]])*-1 | |
| 4182 | else: fd0 = numpy.nan |
|
4233 | else: fd0 = numpy.nan | |
| 4183 | print(fd0) |
|
4234 | print(fd0) | |
| 4184 | for h in range(nHeights): |
|
4235 | for h in range(nHeights): | |
| 4185 | d = data[:,h] |
|
4236 | d = data[:,h] | |
| 4186 | smooth = clean_num_aver[i+1,h] #dataOut.data_spc[:,1:nProf-0,:] |
|
4237 | smooth = clean_num_aver[i+1,h] #dataOut.data_spc[:,1:nProf-0,:] | |
| 4187 | signalpn0 = (dataOut.data_spc[i*2,1:(nProf-0),h])/smooth |
|
4238 | signalpn0 = (dataOut.data_spc[i*2,1:(nProf-0),h])/smooth | |
| 4188 | signalpn1 = (dataOut.data_spc[i*2+1,1:(nProf-0),h])/smooth |
|
4239 | signalpn1 = (dataOut.data_spc[i*2+1,1:(nProf-0),h])/smooth | |
| 4189 | signal0 = signalpn0-n0 |
|
4240 | signal0 = signalpn0-n0 | |
| 4190 | signal1 = signalpn1-n1 |
|
4241 | signal1 = signalpn1-n1 | |
| 4191 | snr0 = numpy.sum(signal0/n0)/(nProf-1) |
|
4242 | snr0 = numpy.sum(signal0/n0)/(nProf-1) | |
| 4192 | snr1 = numpy.sum(signal1/n1)/(nProf-1) |
|
4243 | snr1 = numpy.sum(signal1/n1)/(nProf-1) | |
| 4193 |
|
4244 | |||
| 4194 | if snr0 > snrth and snr1 > snrth and clean_num_aver[i+1,h] > 0 : |
|
4245 | if snr0 > snrth and snr1 > snrth and clean_num_aver[i+1,h] > 0 : | |
| 4195 | #Covariance Matrix |
|
4246 | #Covariance Matrix | |
| 4196 | D = numpy.diag(d**2) |
|
4247 | D = numpy.diag(d**2) | |
| 4197 | ind = 0 |
|
4248 | ind = 0 | |
| 4198 | for pairs in listComb: |
|
4249 | for pairs in listComb: | |
| 4199 | #Coordinates in Covariance Matrix |
|
4250 | #Coordinates in Covariance Matrix | |
| 4200 | x = pairs[0] |
|
4251 | x = pairs[0] | |
| 4201 | y = pairs[1] |
|
4252 | y = pairs[1] | |
| 4202 | #Channel Index |
|
4253 | #Channel Index | |
| 4203 | S12 = dataCross[ind,:,h] |
|
4254 | S12 = dataCross[ind,:,h] | |
| 4204 | D12 = numpy.diag(S12) |
|
4255 | D12 = numpy.diag(S12) | |
| 4205 | #Completing Covariance Matrix with Cross Spectras |
|
4256 | #Completing Covariance Matrix with Cross Spectras | |
| 4206 | D[x*N:(x+1)*N,y*N:(y+1)*N] = D12 |
|
4257 | D[x*N:(x+1)*N,y*N:(y+1)*N] = D12 | |
| 4207 | D[y*N:(y+1)*N,x*N:(x+1)*N] = D12 |
|
4258 | D[y*N:(y+1)*N,x*N:(x+1)*N] = D12 | |
| 4208 | ind += 1 |
|
4259 | ind += 1 | |
| 4209 | diagD = numpy.zeros(256) |
|
4260 | diagD = numpy.zeros(256) | |
| 4210 |
|
4261 | |||
| 4211 | try: |
|
4262 | try: | |
| 4212 | Dinv=numpy.linalg.inv(D) |
|
4263 | Dinv=numpy.linalg.inv(D) | |
| 4213 | L=numpy.linalg.cholesky(Dinv) |
|
4264 | L=numpy.linalg.cholesky(Dinv) | |
| 4214 | except: |
|
4265 | except: | |
| 4215 | Dinv = D*numpy.nan |
|
4266 | Dinv = D*numpy.nan | |
| 4216 | L= D*numpy.nan |
|
4267 | L= D*numpy.nan | |
| 4217 | LT=L.T |
|
4268 | LT=L.T | |
| 4218 |
|
4269 | |||
| 4219 | dp = numpy.dot(LT,d) |
|
4270 | dp = numpy.dot(LT,d) | |
| 4220 | #Initial values |
|
4271 | #Initial values | |
| 4221 | data_spc = dataOut.data_spc[coord,:,h] |
|
4272 | data_spc = dataOut.data_spc[coord,:,h] | |
| 4222 | w = data_spc/data_spc |
|
4273 | w = data_spc/data_spc | |
| 4223 | if filec != None: |
|
4274 | if filec != None: | |
| 4224 | w = self.weightf.weightfit(w,tini.tm_year,tini.tm_yday,index,h,i) |
|
4275 | w = self.weightf.weightfit(w,tini.tm_year,tini.tm_yday,index,h,i) | |
| 4225 | if (h>6) and (error1[3]<25): |
|
4276 | if (h>6) and (error1[3]<25): | |
| 4226 | p0 = dataOut.data_param[i,:,h-1].copy() |
|
4277 | p0 = dataOut.data_param[i,:,h-1].copy() | |
| 4227 | else: |
|
4278 | else: | |
| 4228 | p0 = numpy.array(self.library.initialValuesFunction(data_spc*w, constants))# sin el i(data_spc, constants, i) |
|
4279 | p0 = numpy.array(self.library.initialValuesFunction(data_spc*w, constants))# sin el i(data_spc, constants, i) | |
| 4229 | p0[3] = fd0 |
|
4280 | p0[3] = fd0 | |
| 4230 | if filec != None: |
|
4281 | if filec != None: | |
| 4231 | p0 = self.weightf.Vrfit(p0,tini.tm_year,tini.tm_yday,index,h,i) |
|
4282 | p0 = self.weightf.Vrfit(p0,tini.tm_year,tini.tm_yday,index,h,i) | |
| 4232 |
|
4283 | |||
| 4233 | try: |
|
4284 | try: | |
| 4234 | #Least Squares |
|
4285 | #Least Squares | |
| 4235 | minp,covp,infodict,mesg,ier = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants),full_output=True) |
|
4286 | minp,covp,infodict,mesg,ier = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants),full_output=True) | |
| 4236 | #minp,covp = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants)) |
|
4287 | #minp,covp = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants)) | |
| 4237 | #Chi square error |
|
4288 | #Chi square error | |
| 4238 | error0 = numpy.sum(infodict['fvec']**2)/(2*N) |
|
4289 | error0 = numpy.sum(infodict['fvec']**2)/(2*N) | |
| 4239 | #Error with Jacobian |
|
4290 | #Error with Jacobian | |
| 4240 | error1 = self.library.errorFunction(minp,constants,LT) |
|
4291 | error1 = self.library.errorFunction(minp,constants,LT) | |
| 4241 |
|
4292 | |||
| 4242 | except: |
|
4293 | except: | |
| 4243 | minp = p0*numpy.nan |
|
4294 | minp = p0*numpy.nan | |
| 4244 | error0 = numpy.nan |
|
4295 | error0 = numpy.nan | |
| 4245 | error1 = p0*numpy.nan |
|
4296 | error1 = p0*numpy.nan | |
| 4246 | else : |
|
4297 | else : | |
| 4247 | data_spc = dataOut.data_spc[coord,:,h] |
|
4298 | data_spc = dataOut.data_spc[coord,:,h] | |
| 4248 | p0 = numpy.array(self.library.initialValuesFunction(data_spc, constants)) |
|
4299 | p0 = numpy.array(self.library.initialValuesFunction(data_spc, constants)) | |
| 4249 | minp = p0*numpy.nan |
|
4300 | minp = p0*numpy.nan | |
| 4250 | error0 = numpy.nan |
|
4301 | error0 = numpy.nan | |
| 4251 | error1 = p0*numpy.nan |
|
4302 | error1 = p0*numpy.nan | |
| 4252 | if dataOut.data_param is None: |
|
4303 | if dataOut.data_param is None: | |
| 4253 | dataOut.data_param = numpy.zeros((nGroups, p0.size, nHeights))*numpy.nan |
|
4304 | dataOut.data_param = numpy.zeros((nGroups, p0.size, nHeights))*numpy.nan | |
| 4254 | dataOut.data_error = numpy.zeros((nGroups, p0.size + 1, nHeights))*numpy.nan |
|
4305 | dataOut.data_error = numpy.zeros((nGroups, p0.size + 1, nHeights))*numpy.nan | |
| 4255 |
|
4306 | |||
| 4256 | dataOut.data_error[i,:,h] = numpy.hstack((error0,error1)) |
|
4307 | dataOut.data_error[i,:,h] = numpy.hstack((error0,error1)) | |
| 4257 | dataOut.data_param[i,:,h] = minp |
|
4308 | dataOut.data_param[i,:,h] = minp | |
| 4258 | dataOut.data_snr1_i[i*2,h] = numpy.sum(signalpn0/(nProf-1))/n0 |
|
4309 | dataOut.data_snr1_i[i*2,h] = numpy.sum(signalpn0/(nProf-1))/n0 | |
| 4259 | dataOut.data_snr1_i[i*2+1,h] = numpy.sum(signalpn1/(nProf-1))/n1 |
|
4310 | dataOut.data_snr1_i[i*2+1,h] = numpy.sum(signalpn1/(nProf-1))/n1 | |
| 4260 |
|
4311 | |||
| 4261 | for ht in range(nHeights-1) : |
|
4312 | for ht in range(nHeights-1) : | |
| 4262 | smooth = coh_num_aver[i+1,ht] #datc[0,ht,0,beam] |
|
4313 | smooth = coh_num_aver[i+1,ht] #datc[0,ht,0,beam] | |
| 4263 | dataOut.data_paramC[4*i,ht,1] = smooth |
|
4314 | dataOut.data_paramC[4*i,ht,1] = smooth | |
| 4264 | signalpn0 = (clean_coh_spectra[i*2 ,1:(nProf-0),ht])/smooth #coh_spectra |
|
4315 | signalpn0 = (clean_coh_spectra[i*2 ,1:(nProf-0),ht])/smooth #coh_spectra | |
| 4265 | signalpn1 = (clean_coh_spectra[i*2+1,1:(nProf-0),ht])/smooth |
|
4316 | signalpn1 = (clean_coh_spectra[i*2+1,1:(nProf-0),ht])/smooth | |
| 4266 |
|
4317 | |||
| 4267 | val0 = (signalpn0 > 0).nonzero() |
|
4318 | val0 = (signalpn0 > 0).nonzero() | |
| 4268 | val0 = val0[0] |
|
4319 | val0 = val0[0] | |
| 4269 |
|
4320 | |||
| 4270 | if len(val0) == 0 : val0_npoints = nProf |
|
4321 | if len(val0) == 0 : val0_npoints = nProf | |
| 4271 | else : val0_npoints = len(val0) |
|
4322 | else : val0_npoints = len(val0) | |
| 4272 |
|
4323 | |||
| 4273 | val1 = (signalpn1 > 0).nonzero() |
|
4324 | val1 = (signalpn1 > 0).nonzero() | |
| 4274 | val1 = val1[0] |
|
4325 | val1 = val1[0] | |
| 4275 | if len(val1) == 0 : val1_npoints = nProf |
|
4326 | if len(val1) == 0 : val1_npoints = nProf | |
| 4276 | else : val1_npoints = len(val1) |
|
4327 | else : val1_npoints = len(val1) | |
| 4277 |
|
4328 | |||
| 4278 | dataOut.data_paramC[0+4*i,ht,0] = numpy.sum((signalpn0/val0_npoints))/n0 |
|
4329 | dataOut.data_paramC[0+4*i,ht,0] = numpy.sum((signalpn0/val0_npoints))/n0 | |
| 4279 | dataOut.data_paramC[1+4*i,ht,0] = numpy.sum((signalpn1/val1_npoints))/n1 |
|
4330 | dataOut.data_paramC[1+4*i,ht,0] = numpy.sum((signalpn1/val1_npoints))/n1 | |
| 4280 |
|
4331 | |||
| 4281 | signal0 = (signalpn0-n0) |
|
4332 | signal0 = (signalpn0-n0) | |
| 4282 | vali = (signal0 < 0).nonzero() |
|
4333 | vali = (signal0 < 0).nonzero() | |
| 4283 | vali = vali[0] |
|
4334 | vali = vali[0] | |
| 4284 | if len(vali) > 0 : signal0[vali] = 0 |
|
4335 | if len(vali) > 0 : signal0[vali] = 0 | |
| 4285 | signal1 = (signalpn1-n1) |
|
4336 | signal1 = (signalpn1-n1) | |
| 4286 | vali = (signal1 < 0).nonzero() |
|
4337 | vali = (signal1 < 0).nonzero() | |
| 4287 | vali = vali[0] |
|
4338 | vali = vali[0] | |
| 4288 | if len(vali) > 0 : signal1[vali] = 0 |
|
4339 | if len(vali) > 0 : signal1[vali] = 0 | |
| 4289 | snr0 = numpy.sum(signal0/n0)/(nProf-1) |
|
4340 | snr0 = numpy.sum(signal0/n0)/(nProf-1) | |
| 4290 | snr1 = numpy.sum(signal1/n1)/(nProf-1) |
|
4341 | snr1 = numpy.sum(signal1/n1)/(nProf-1) | |
| 4291 | doppler = absc[1:] |
|
4342 | doppler = absc[1:] | |
| 4292 | if snr0 >= snrth and snr1 >= snrth and smooth : |
|
4343 | if snr0 >= snrth and snr1 >= snrth and smooth : | |
| 4293 | signalpn0_n0 = signalpn0 |
|
4344 | signalpn0_n0 = signalpn0 | |
| 4294 | signalpn0_n0[val0] = signalpn0[val0] - n0 |
|
4345 | signalpn0_n0[val0] = signalpn0[val0] - n0 | |
| 4295 | mom0 = self.moments(doppler,signalpn0-n0,nProf) |
|
4346 | mom0 = self.moments(doppler,signalpn0-n0,nProf) | |
| 4296 |
|
4347 | |||
| 4297 | signalpn1_n1 = signalpn1 |
|
4348 | signalpn1_n1 = signalpn1 | |
| 4298 | signalpn1_n1[val1] = signalpn1[val1] - n1 |
|
4349 | signalpn1_n1[val1] = signalpn1[val1] - n1 | |
| 4299 | mom1 = self.moments(doppler,signalpn1_n1,nProf) |
|
4350 | mom1 = self.moments(doppler,signalpn1_n1,nProf) | |
| 4300 | dataOut.data_paramC[2+4*i,ht,0] = (mom0[0]+mom1[0])/2. |
|
4351 | dataOut.data_paramC[2+4*i,ht,0] = (mom0[0]+mom1[0])/2. | |
| 4301 | dataOut.data_paramC[3+4*i,ht,0] = (mom0[1]+mom1[1])/2. |
|
4352 | dataOut.data_paramC[3+4*i,ht,0] = (mom0[1]+mom1[1])/2. | |
| 4302 |
|
4353 | |||
| 4303 | dataOut.data_spc = jspectra |
|
4354 | dataOut.data_spc = jspectra | |
| 4304 | dataOut.spc_noise = my_noises*nProf*M |
|
4355 | dataOut.spc_noise = my_noises*nProf*M | |
| 4305 |
|
4356 | |||
| 4306 | if numpy.any(proc): dataOut.spc_noise = my_noises*nProf*M |
|
4357 | if numpy.any(proc): dataOut.spc_noise = my_noises*nProf*M | |
| 4307 | if 0: |
|
4358 | if 0: | |
| 4308 | listChannels = groupArray.reshape((groupArray.size)) |
|
4359 | listChannels = groupArray.reshape((groupArray.size)) | |
| 4309 | listChannels.sort() |
|
4360 | listChannels.sort() | |
|
|
4361 | # norm Este factor debe estar implementado para ploteo o grabado como metadata | |||
|
|
4362 | # norm = dataOut.nProfiles * dataOut.nIncohInt * dataOut.nCohInt * dataOut.windowOfFilter | |||
| 4310 | dataOut.data_snr = self.__getSNR(dataOut.data_spc[listChannels,:,:], my_noises[listChannels]) |
|
4363 | dataOut.data_snr = self.__getSNR(dataOut.data_spc[listChannels,:,:], my_noises[listChannels]) | |
| 4311 | #print(dataOut.data_snr1_i) |
|
4364 | #print(dataOut.data_snr1_i) | |
| 4312 | # Adding coherent echoes from possible satellites. |
|
4365 | # Adding coherent echoes from possible satellites. | |
| 4313 | #sat_spectra = numpy.zeros((nChan,nProf,nHei), dtype=float) |
|
4366 | #sat_spectra = numpy.zeros((nChan,nProf,nHei), dtype=float) | |
| 4314 | #sat_spectra = sat_spectra[*,*,anal_header.channels] |
|
4367 | #sat_spectra = sat_spectra[*,*,anal_header.channels] | |
| 4315 | isat_spectra = numpy.zeros([2,int(nChan/2),nProf,nhei], dtype=float) |
|
4368 | isat_spectra = numpy.zeros([2,int(nChan/2),nProf,nhei], dtype=float) | |
| 4316 |
|
4369 | |||
| 4317 | sat_fits = numpy.zeros([4,nhei], dtype=float) |
|
4370 | sat_fits = numpy.zeros([4,nhei], dtype=float) | |
| 4318 | noises = my_noises/nProf |
|
4371 | noises = my_noises/nProf | |
| 4319 | #nchan2 = int(nChan/2) |
|
4372 | #nchan2 = int(nChan/2) | |
| 4320 | for beam in range(int(nChan/2)-0) : |
|
4373 | for beam in range(int(nChan/2)-0) : | |
| 4321 | n0 = noises[2*beam] |
|
4374 | n0 = noises[2*beam] | |
| 4322 | n1 = noises[2*beam+1] |
|
4375 | n1 = noises[2*beam+1] | |
| 4323 | isat_spectra[0:2,beam,:,:] = dataOut.sat_spectra[2*beam +0:2*beam+2 ,:,:] |
|
4376 | isat_spectra[0:2,beam,:,:] = dataOut.sat_spectra[2*beam +0:2*beam+2 ,:,:] | |
| 4324 |
|
4377 | |||
| 4325 | for ht in range(nhei-1) : |
|
4378 | for ht in range(nhei-1) : | |
| 4326 | signalpn0 = isat_spectra[0,beam,:,ht] |
|
4379 | signalpn0 = isat_spectra[0,beam,:,ht] | |
| 4327 | signalpn0 = numpy.reshape(signalpn0,nProf) |
|
4380 | signalpn0 = numpy.reshape(signalpn0,nProf) | |
| 4328 | signalpn1 = isat_spectra[1,beam,:,ht] |
|
4381 | signalpn1 = isat_spectra[1,beam,:,ht] | |
| 4329 | signalpn1 = numpy.reshape(signalpn1,nProf) |
|
4382 | signalpn1 = numpy.reshape(signalpn1,nProf) | |
| 4330 |
|
4383 | |||
| 4331 | cval0 = len((signalpn0 > 0).nonzero()[0]) |
|
4384 | cval0 = len((signalpn0 > 0).nonzero()[0]) | |
| 4332 | if cval0 == 0 : val0_npoints = nProf |
|
4385 | if cval0 == 0 : val0_npoints = nProf | |
| 4333 | else: val0_npoints = cval0 |
|
4386 | else: val0_npoints = cval0 | |
| 4334 |
|
4387 | |||
| 4335 | cval1 = len((signalpn1 > 0).nonzero()[0]) |
|
4388 | cval1 = len((signalpn1 > 0).nonzero()[0]) | |
| 4336 | if cval1 == 0 : val1_npoints = nProf |
|
4389 | if cval1 == 0 : val1_npoints = nProf | |
| 4337 | else: val1_npoints = cval1 |
|
4390 | else: val1_npoints = cval1 | |
| 4338 |
|
4391 | |||
| 4339 | sat_fits[0+2*beam,ht] = numpy.sum(signalpn0/(val0_npoints*nProf))/n0 |
|
4392 | sat_fits[0+2*beam,ht] = numpy.sum(signalpn0/(val0_npoints*nProf))/n0 | |
| 4340 | sat_fits[1+2*beam,ht] = numpy.sum(signalpn1/(val1_npoints*nProf))/n1 |
|
4393 | sat_fits[1+2*beam,ht] = numpy.sum(signalpn1/(val1_npoints*nProf))/n1 | |
| 4341 |
|
4394 | |||
| 4342 | dataOut.sat_fits = sat_fits |
|
4395 | dataOut.sat_fits = sat_fits | |
| 4343 | return dataOut |
|
4396 | return dataOut | |
| 4344 |
|
4397 | |||
| 4345 | def __residFunction(self, p, dp, LT, constants): |
|
4398 | def __residFunction(self, p, dp, LT, constants): | |
| 4346 |
|
4399 | |||
| 4347 | fm = self.library.modelFunction(p, constants) |
|
4400 | fm = self.library.modelFunction(p, constants) | |
| 4348 | fmp=numpy.dot(LT,fm) |
|
4401 | fmp=numpy.dot(LT,fm) | |
| 4349 | return dp-fmp |
|
4402 | return dp-fmp | |
| 4350 |
|
4403 | |||
| 4351 | def __getSNR(self, z, noise): |
|
4404 | def __getSNR(self, z, noise, norm=1): | |
| 4352 |
|
4405 | |||
| 4353 | avg = numpy.average(z, axis=1) |
|
4406 | avg = numpy.average(z, axis=1) | |
| 4354 | SNR = (avg.T-noise)/noise |
|
4407 | SNR = (avg.T-noise)/noise | |
| 4355 | SNR = SNR.T |
|
4408 | SNR = SNR.T | |
| 4356 | return SNR |
|
4409 | return SNR | |
| 4357 |
|
4410 | |||
| 4358 | def __chisq(self, p, chindex, hindex): |
|
4411 | def __chisq(self, p, chindex, hindex): | |
| 4359 | #similar to Resid but calculates CHI**2 |
|
4412 | #similar to Resid but calculates CHI**2 | |
| 4360 | [LT,d,fm]=setupLTdfm(p,chindex,hindex) |
|
4413 | [LT,d,fm]=setupLTdfm(p,chindex,hindex) | |
| 4361 | dp=numpy.dot(LT,d) |
|
4414 | dp=numpy.dot(LT,d) | |
| 4362 | fmp=numpy.dot(LT,fm) |
|
4415 | fmp=numpy.dot(LT,fm) | |
| 4363 | chisq=numpy.dot((dp-fmp).T,(dp-fmp)) |
|
4416 | chisq=numpy.dot((dp-fmp).T,(dp-fmp)) | |
| 4364 | return chisq |
|
4417 | return chisq | |
| 4365 |
|
4418 | |||
| 4366 |
class WindProfiler |
|
4419 | class WindProfiler(Operation): | |
| 4367 |
|
4420 | |||
| 4368 | __isConfig = False |
|
4421 | __isConfig = False | |
| 4369 |
|
4422 | |||
| 4370 | __initime = None |
|
4423 | __initime = None | |
| 4371 | __lastdatatime = None |
|
4424 | __lastdatatime = None | |
| 4372 | __integrationtime = None |
|
4425 | __integrationtime = None | |
| 4373 |
|
4426 | |||
| 4374 | __buffer = None |
|
4427 | __buffer = None | |
| 4375 |
|
4428 | |||
| 4376 | __dataReady = False |
|
4429 | __dataReady = False | |
| 4377 |
|
4430 | |||
| 4378 | __firstdata = None |
|
4431 | __firstdata = None | |
| 4379 |
|
4432 | |||
| 4380 | n = None |
|
4433 | n = None | |
| 4381 |
|
4434 | |||
| 4382 | def __init__(self): |
|
4435 | def __init__(self): | |
| 4383 | Operation.__init__(self) |
|
4436 | Operation.__init__(self) | |
| 4384 |
|
4437 | |||
| 4385 | def __calculateCosDir(self, elev, azim): |
|
4438 | def __calculateCosDir(self, elev, azim): | |
| 4386 | zen = (90 - elev)*numpy.pi/180 |
|
4439 | zen = (90 - elev)*numpy.pi/180 | |
| 4387 | azim = azim*numpy.pi/180 |
|
4440 | azim = azim*numpy.pi/180 | |
| 4388 | cosDirX = numpy.sqrt((1-numpy.cos(zen)**2)/((1+numpy.tan(azim)**2))) |
|
4441 | cosDirX = numpy.sqrt((1-numpy.cos(zen)**2)/((1+numpy.tan(azim)**2))) | |
| 4389 | cosDirY = numpy.sqrt(1-numpy.cos(zen)**2-cosDirX**2) |
|
4442 | cosDirY = numpy.sqrt(1-numpy.cos(zen)**2-cosDirX**2) | |
| 4390 |
|
4443 | |||
| 4391 | signX = numpy.sign(numpy.cos(azim)) |
|
4444 | signX = numpy.sign(numpy.cos(azim)) | |
| 4392 | signY = numpy.sign(numpy.sin(azim)) |
|
4445 | signY = numpy.sign(numpy.sin(azim)) | |
| 4393 |
|
4446 | |||
| 4394 | cosDirX = numpy.copysign(cosDirX, signX) |
|
4447 | cosDirX = numpy.copysign(cosDirX, signX) | |
| 4395 | cosDirY = numpy.copysign(cosDirY, signY) |
|
4448 | cosDirY = numpy.copysign(cosDirY, signY) | |
| 4396 | return cosDirX, cosDirY |
|
4449 | return cosDirX, cosDirY | |
| 4397 |
|
4450 | |||
| 4398 | def __calculateAngles(self, theta_x, theta_y, azimuth): |
|
4451 | def __calculateAngles(self, theta_x, theta_y, azimuth): | |
| 4399 |
|
4452 | |||
| 4400 | dir_cosw = numpy.sqrt(1-theta_x**2-theta_y**2) |
|
4453 | dir_cosw = numpy.sqrt(1-theta_x**2-theta_y**2) | |
| 4401 | zenith_arr = numpy.arccos(dir_cosw) |
|
4454 | zenith_arr = numpy.arccos(dir_cosw) | |
| 4402 | azimuth_arr = numpy.arctan2(theta_x,theta_y) + azimuth*math.pi/180 |
|
4455 | azimuth_arr = numpy.arctan2(theta_x,theta_y) + azimuth*math.pi/180 | |
| 4403 |
|
4456 | |||
| 4404 | dir_cosu = numpy.sin(azimuth_arr)*numpy.sin(zenith_arr) |
|
4457 | dir_cosu = numpy.sin(azimuth_arr)*numpy.sin(zenith_arr) | |
| 4405 | dir_cosv = numpy.cos(azimuth_arr)*numpy.sin(zenith_arr) |
|
4458 | dir_cosv = numpy.cos(azimuth_arr)*numpy.sin(zenith_arr) | |
| 4406 |
|
4459 | |||
| 4407 | return azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw |
|
4460 | return azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw | |
| 4408 |
|
4461 | |||
| 4409 | def __calculateMatA(self, dir_cosu, dir_cosv, dir_cosw, horOnly): |
|
4462 | def __calculateMatA(self, dir_cosu, dir_cosv, dir_cosw, horOnly): | |
| 4410 |
|
4463 | |||
| 4411 | if horOnly: |
|
4464 | if horOnly: | |
| 4412 | A = numpy.c_[dir_cosu,dir_cosv] |
|
4465 | A = numpy.c_[dir_cosu,dir_cosv] | |
| 4413 | else: |
|
4466 | else: | |
| 4414 | A = numpy.c_[dir_cosu,dir_cosv,dir_cosw] |
|
4467 | A = numpy.c_[dir_cosu,dir_cosv,dir_cosw] | |
| 4415 | A = numpy.asmatrix(A) |
|
4468 | A = numpy.asmatrix(A) | |
| 4416 | A1 = numpy.linalg.inv(A.transpose()*A)*A.transpose() |
|
4469 | A1 = numpy.linalg.inv(A.transpose()*A)*A.transpose() | |
| 4417 |
|
4470 | |||
| 4418 | return A1 |
|
4471 | return A1 | |
| 4419 |
|
4472 | |||
| 4420 | def __correctValues(self, heiRang, phi, velRadial, SNR): |
|
4473 | def __correctValues(self, heiRang, phi, velRadial, SNR): | |
| 4421 | listPhi = phi.tolist() |
|
4474 | listPhi = phi.tolist() | |
| 4422 | maxid = listPhi.index(max(listPhi)) |
|
4475 | maxid = listPhi.index(max(listPhi)) | |
| 4423 | minid = listPhi.index(min(listPhi)) |
|
4476 | minid = listPhi.index(min(listPhi)) | |
| 4424 |
|
4477 | |||
| 4425 | rango = list(range(len(phi))) |
|
4478 | rango = list(range(len(phi))) | |
| 4426 | # rango = numpy.delete(rango,maxid) |
|
|||
| 4427 |
|
4479 | |||
| 4428 | heiRang1 = heiRang*math.cos(phi[maxid]) |
|
4480 | heiRang1 = heiRang*math.cos(phi[maxid]) | |
| 4429 | heiRangAux = heiRang*math.cos(phi[minid]) |
|
4481 | heiRangAux = heiRang*math.cos(phi[minid]) | |
| 4430 | indOut = (heiRang1 < heiRangAux[0]).nonzero() |
|
4482 | indOut = (heiRang1 < heiRangAux[0]).nonzero() | |
| 4431 | heiRang1 = numpy.delete(heiRang1,indOut) |
|
4483 | heiRang1 = numpy.delete(heiRang1,indOut) | |
| 4432 |
|
4484 | |||
| 4433 | velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
4485 | velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) | |
| 4434 | SNR1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
4486 | SNR1 = numpy.zeros([len(phi),len(heiRang1)]) | |
| 4435 |
|
4487 | |||
| 4436 | for i in rango: |
|
4488 | for i in rango: | |
| 4437 | x = heiRang*math.cos(phi[i]) |
|
4489 | x = heiRang*math.cos(phi[i]) | |
| 4438 | y1 = velRadial[i,:] |
|
4490 | y1 = velRadial[i,:] | |
| 4439 | f1 = interpolate.interp1d(x,y1,kind = 'cubic') |
|
4491 | f1 = interpolate.interp1d(x,y1,kind = 'cubic') | |
| 4440 |
|
4492 | |||
| 4441 | x1 = heiRang1 |
|
4493 | x1 = heiRang1 | |
| 4442 | y11 = f1(x1) |
|
4494 | y11 = f1(x1) | |
| 4443 |
|
4495 | |||
| 4444 | y2 = SNR[i,:] |
|
4496 | y2 = SNR[i,:] | |
| 4445 | f2 = interpolate.interp1d(x,y2,kind = 'cubic') |
|
4497 | f2 = interpolate.interp1d(x,y2,kind = 'cubic') | |
| 4446 | y21 = f2(x1) |
|
4498 | y21 = f2(x1) | |
| 4447 |
|
4499 | |||
| 4448 | velRadial1[i,:] = y11 |
|
4500 | velRadial1[i,:] = y11 | |
| 4449 | SNR1[i,:] = y21 |
|
4501 | SNR1[i,:] = y21 | |
| 4450 |
|
4502 | |||
| 4451 | return heiRang1, velRadial1, SNR1 |
|
4503 | return heiRang1, velRadial1, SNR1 | |
| 4452 |
|
4504 | |||
| 4453 | def __calculateVelUVW(self, A, velRadial): |
|
4505 | def __calculateVelUVW(self, A, velRadial): | |
| 4454 |
|
4506 | |||
| 4455 | #Operacion Matricial |
|
4507 | #Operacion Matricial | |
| 4456 | # velUVW = numpy.zeros((velRadial.shape[1],3)) |
|
|||
| 4457 | # for ind in range(velRadial.shape[1]): |
|
|||
| 4458 | # velUVW[ind,:] = numpy.dot(A,velRadial[:,ind]) |
|
|||
| 4459 | # velUVW = velUVW.transpose() |
|
|||
| 4460 | velUVW = numpy.zeros((A.shape[0],velRadial.shape[1])) |
|
4508 | velUVW = numpy.zeros((A.shape[0],velRadial.shape[1])) | |
| 4461 | velUVW[:,:] = numpy.dot(A,velRadial) |
|
4509 | velUVW[:,:] = numpy.dot(A,velRadial) | |
| 4462 |
|
4510 | |||
| 4463 |
|
4511 | |||
| 4464 | return velUVW |
|
4512 | return velUVW | |
| 4465 |
|
4513 | |||
| 4466 | # def techniqueDBS(self, velRadial0, dirCosx, disrCosy, azimuth, correct, horizontalOnly, heiRang, SNR0): |
|
|||
| 4467 |
|
||||
| 4468 | def techniqueDBS(self, kwargs): |
|
4514 | def techniqueDBS(self, kwargs): | |
| 4469 | """ |
|
4515 | """ | |
| 4470 | Function that implements Doppler Beam Swinging (DBS) technique. |
|
4516 | Function that implements Doppler Beam Swinging (DBS) technique. | |
| 4471 |
|
4517 | |||
| 4472 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, |
|
4518 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, | |
| 4473 | Direction correction (if necessary), Ranges and SNR |
|
4519 | Direction correction (if necessary), Ranges and SNR | |
| 4474 |
|
4520 | |||
| 4475 | Output: Winds estimation (Zonal, Meridional and Vertical) |
|
4521 | Output: Winds estimation (Zonal, Meridional and Vertical) | |
| 4476 |
|
4522 | |||
| 4477 | Parameters affected: Winds, height range, SNR |
|
4523 | Parameters affected: Winds, height range, SNR | |
| 4478 | """ |
|
4524 | """ | |
| 4479 | velRadial0 = kwargs['velRadial'] |
|
4525 | velRadial0 = kwargs['velRadial'] | |
| 4480 | heiRang = kwargs['heightList'] |
|
4526 | heiRang = kwargs['heightList'] | |
| 4481 | SNR0 = kwargs['SNR'] |
|
4527 | SNR0 = kwargs['SNR'] | |
| 4482 |
|
4528 | |||
| 4483 | if 'dirCosx' in kwargs and 'dirCosy' in kwargs: |
|
4529 | if 'dirCosx' in kwargs and 'dirCosy' in kwargs: | |
| 4484 | theta_x = numpy.array(kwargs['dirCosx']) |
|
4530 | theta_x = numpy.array(kwargs['dirCosx']) | |
| 4485 | theta_y = numpy.array(kwargs['dirCosy']) |
|
4531 | theta_y = numpy.array(kwargs['dirCosy']) | |
| 4486 | else: |
|
4532 | else: | |
| 4487 | elev = numpy.array(kwargs['elevation']) |
|
4533 | elev = numpy.array(kwargs['elevation']) | |
| 4488 | azim = numpy.array(kwargs['azimuth']) |
|
4534 | azim = numpy.array(kwargs['azimuth']) | |
| 4489 | theta_x, theta_y = self.__calculateCosDir(elev, azim) |
|
4535 | theta_x, theta_y = self.__calculateCosDir(elev, azim) | |
| 4490 | azimuth = kwargs['correctAzimuth'] |
|
4536 | azimuth = kwargs['correctAzimuth'] | |
| 4491 | if 'horizontalOnly' in kwargs: |
|
4537 | if 'horizontalOnly' in kwargs: | |
| 4492 | horizontalOnly = kwargs['horizontalOnly'] |
|
4538 | horizontalOnly = kwargs['horizontalOnly'] | |
| 4493 | else: horizontalOnly = False |
|
4539 | else: horizontalOnly = False | |
| 4494 | if 'correctFactor' in kwargs: |
|
4540 | if 'correctFactor' in kwargs: | |
| 4495 | correctFactor = kwargs['correctFactor'] |
|
4541 | correctFactor = kwargs['correctFactor'] | |
| 4496 | else: correctFactor = 1 |
|
4542 | else: correctFactor = 1 | |
| 4497 | if 'channelList' in kwargs: |
|
4543 | if 'channelList' in kwargs: | |
| 4498 | channelList = kwargs['channelList'] |
|
4544 | channelList = kwargs['channelList'] | |
| 4499 | if len(channelList) == 2: |
|
4545 | if len(channelList) == 2: | |
| 4500 | horizontalOnly = True |
|
4546 | horizontalOnly = True | |
| 4501 | arrayChannel = numpy.array(channelList) |
|
4547 | arrayChannel = numpy.array(channelList) | |
| 4502 | param = param[arrayChannel,:,:] |
|
4548 | param = param[arrayChannel,:,:] | |
| 4503 | theta_x = theta_x[arrayChannel] |
|
4549 | theta_x = theta_x[arrayChannel] | |
| 4504 | theta_y = theta_y[arrayChannel] |
|
4550 | theta_y = theta_y[arrayChannel] | |
| 4505 |
|
4551 | |||
| 4506 | azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth) |
|
4552 | azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth) | |
| 4507 | heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, zenith_arr, correctFactor*velRadial0, SNR0) |
|
4553 | heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, zenith_arr, correctFactor*velRadial0, SNR0) | |
| 4508 | A = self.__calculateMatA(dir_cosu, dir_cosv, dir_cosw, horizontalOnly) |
|
4554 | A = self.__calculateMatA(dir_cosu, dir_cosv, dir_cosw, horizontalOnly) | |
| 4509 |
|
4555 | |||
| 4510 | #Calculo de Componentes de la velocidad con DBS |
|
4556 | #Calculo de Componentes de la velocidad con DBS | |
| 4511 | winds = self.__calculateVelUVW(A,velRadial1) |
|
4557 | winds = self.__calculateVelUVW(A,velRadial1) | |
| 4512 |
|
4558 | |||
| 4513 | return winds, heiRang1, SNR1 |
|
4559 | return winds, heiRang1, SNR1 | |
| 4514 |
|
4560 | |||
| 4515 | def __calculateDistance(self, posx, posy, pairs_ccf, azimuth = None): |
|
4561 | def __calculateDistance(self, posx, posy, pairs_ccf, azimuth = None): | |
| 4516 |
|
4562 | |||
| 4517 | nPairs = len(pairs_ccf) |
|
4563 | nPairs = len(pairs_ccf) | |
| 4518 | posx = numpy.asarray(posx) |
|
4564 | posx = numpy.asarray(posx) | |
| 4519 | posy = numpy.asarray(posy) |
|
4565 | posy = numpy.asarray(posy) | |
| 4520 |
|
4566 | |||
| 4521 | #Rotacion Inversa para alinear con el azimuth |
|
4567 | #Rotacion Inversa para alinear con el azimuth | |
| 4522 | if azimuth!= None: |
|
4568 | if azimuth!= None: | |
| 4523 | azimuth = azimuth*math.pi/180 |
|
4569 | azimuth = azimuth*math.pi/180 | |
| 4524 | posx1 = posx*math.cos(azimuth) + posy*math.sin(azimuth) |
|
4570 | posx1 = posx*math.cos(azimuth) + posy*math.sin(azimuth) | |
| 4525 | posy1 = -posx*math.sin(azimuth) + posy*math.cos(azimuth) |
|
4571 | posy1 = -posx*math.sin(azimuth) + posy*math.cos(azimuth) | |
| 4526 | else: |
|
4572 | else: | |
| 4527 | posx1 = posx |
|
4573 | posx1 = posx | |
| 4528 | posy1 = posy |
|
4574 | posy1 = posy | |
| 4529 |
|
4575 | |||
| 4530 | #Calculo de Distancias |
|
4576 | #Calculo de Distancias | |
| 4531 | distx = numpy.zeros(nPairs) |
|
4577 | distx = numpy.zeros(nPairs) | |
| 4532 | disty = numpy.zeros(nPairs) |
|
4578 | disty = numpy.zeros(nPairs) | |
| 4533 | dist = numpy.zeros(nPairs) |
|
4579 | dist = numpy.zeros(nPairs) | |
| 4534 | ang = numpy.zeros(nPairs) |
|
4580 | ang = numpy.zeros(nPairs) | |
| 4535 |
|
4581 | |||
| 4536 | for i in range(nPairs): |
|
4582 | for i in range(nPairs): | |
| 4537 | distx[i] = posx1[pairs_ccf[i][1]] - posx1[pairs_ccf[i][0]] |
|
4583 | distx[i] = posx1[pairs_ccf[i][1]] - posx1[pairs_ccf[i][0]] | |
| 4538 | disty[i] = posy1[pairs_ccf[i][1]] - posy1[pairs_ccf[i][0]] |
|
4584 | disty[i] = posy1[pairs_ccf[i][1]] - posy1[pairs_ccf[i][0]] | |
| 4539 | dist[i] = numpy.sqrt(distx[i]**2 + disty[i]**2) |
|
4585 | dist[i] = numpy.sqrt(distx[i]**2 + disty[i]**2) | |
| 4540 | ang[i] = numpy.arctan2(disty[i],distx[i]) |
|
4586 | ang[i] = numpy.arctan2(disty[i],distx[i]) | |
| 4541 |
|
4587 | |||
| 4542 | return distx, disty, dist, ang |
|
4588 | return distx, disty, dist, ang | |
| 4543 | #Calculo de Matrices |
|
4589 | #Calculo de Matrices | |
| 4544 | # nPairs = len(pairs) |
|
|||
| 4545 | # ang1 = numpy.zeros((nPairs, 2, 1)) |
|
|||
| 4546 | # dist1 = numpy.zeros((nPairs, 2, 1)) |
|
|||
| 4547 | # |
|
|||
| 4548 | # for j in range(nPairs): |
|
|||
| 4549 | # dist1[j,0,0] = dist[pairs[j][0]] |
|
|||
| 4550 | # dist1[j,1,0] = dist[pairs[j][1]] |
|
|||
| 4551 | # ang1[j,0,0] = ang[pairs[j][0]] |
|
|||
| 4552 | # ang1[j,1,0] = ang[pairs[j][1]] |
|
|||
| 4553 | # |
|
|||
| 4554 | # return distx,disty, dist1,ang1 |
|
|||
| 4555 |
|
4590 | |||
| 4556 |
|
4591 | |||
| 4557 | def __calculateVelVer(self, phase, lagTRange, _lambda): |
|
4592 | def __calculateVelVer(self, phase, lagTRange, _lambda): | |
| 4558 |
|
4593 | |||
| 4559 | Ts = lagTRange[1] - lagTRange[0] |
|
4594 | Ts = lagTRange[1] - lagTRange[0] | |
| 4560 | velW = -_lambda*phase/(4*math.pi*Ts) |
|
4595 | velW = -_lambda*phase/(4*math.pi*Ts) | |
| 4561 |
|
4596 | |||
| 4562 | return velW |
|
4597 | return velW | |
| 4563 |
|
4598 | |||
| 4564 | def __calculateVelHorDir(self, dist, tau1, tau2, ang): |
|
4599 | def __calculateVelHorDir(self, dist, tau1, tau2, ang): | |
| 4565 | nPairs = tau1.shape[0] |
|
4600 | nPairs = tau1.shape[0] | |
| 4566 | nHeights = tau1.shape[1] |
|
4601 | nHeights = tau1.shape[1] | |
| 4567 | vel = numpy.zeros((nPairs,3,nHeights)) |
|
4602 | vel = numpy.zeros((nPairs,3,nHeights)) | |
| 4568 | dist1 = numpy.reshape(dist, (dist.size,1)) |
|
4603 | dist1 = numpy.reshape(dist, (dist.size,1)) | |
| 4569 |
|
4604 | |||
| 4570 | angCos = numpy.cos(ang) |
|
4605 | angCos = numpy.cos(ang) | |
| 4571 | angSin = numpy.sin(ang) |
|
4606 | angSin = numpy.sin(ang) | |
| 4572 |
|
4607 | |||
| 4573 | vel0 = dist1*tau1/(2*tau2**2) |
|
4608 | vel0 = dist1*tau1/(2*tau2**2) | |
| 4574 | vel[:,0,:] = (vel0*angCos).sum(axis = 1) |
|
4609 | vel[:,0,:] = (vel0*angCos).sum(axis = 1) | |
| 4575 | vel[:,1,:] = (vel0*angSin).sum(axis = 1) |
|
4610 | vel[:,1,:] = (vel0*angSin).sum(axis = 1) | |
| 4576 |
|
4611 | |||
| 4577 | ind = numpy.where(numpy.isinf(vel)) |
|
4612 | ind = numpy.where(numpy.isinf(vel)) | |
| 4578 | vel[ind] = numpy.nan |
|
4613 | vel[ind] = numpy.nan | |
| 4579 |
|
4614 | |||
| 4580 | return vel |
|
4615 | return vel | |
| 4581 |
|
4616 | |||
| 4582 | # def __getPairsAutoCorr(self, pairsList, nChannels): |
|
|||
| 4583 | # |
|
|||
| 4584 | # pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan |
|
|||
| 4585 | # |
|
|||
| 4586 | # for l in range(len(pairsList)): |
|
|||
| 4587 | # firstChannel = pairsList[l][0] |
|
|||
| 4588 | # secondChannel = pairsList[l][1] |
|
|||
| 4589 | # |
|
|||
| 4590 | # #Obteniendo pares de Autocorrelacion |
|
|||
| 4591 | # if firstChannel == secondChannel: |
|
|||
| 4592 | # pairsAutoCorr[firstChannel] = int(l) |
|
|||
| 4593 | # |
|
|||
| 4594 | # pairsAutoCorr = pairsAutoCorr.astype(int) |
|
|||
| 4595 | # |
|
|||
| 4596 | # pairsCrossCorr = range(len(pairsList)) |
|
|||
| 4597 | # pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr) |
|
|||
| 4598 | # |
|
|||
| 4599 | # return pairsAutoCorr, pairsCrossCorr |
|
|||
| 4600 |
|
||||
| 4601 | # def techniqueSA(self, pairsSelected, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, lagTRange, correctFactor): |
|
|||
| 4602 | def techniqueSA(self, kwargs): |
|
4617 | def techniqueSA(self, kwargs): | |
| 4603 |
|
4618 | |||
| 4604 | """ |
|
4619 | """ | |
| 4605 | Function that implements Spaced Antenna (SA) technique. |
|
4620 | Function that implements Spaced Antenna (SA) technique. | |
| 4606 |
|
4621 | |||
| 4607 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, |
|
4622 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, | |
| 4608 | Direction correction (if necessary), Ranges and SNR |
|
4623 | Direction correction (if necessary), Ranges and SNR | |
| 4609 |
|
4624 | |||
| 4610 | Output: Winds estimation (Zonal, Meridional and Vertical) |
|
4625 | Output: Winds estimation (Zonal, Meridional and Vertical) | |
| 4611 |
|
4626 | |||
| 4612 | Parameters affected: Winds |
|
4627 | Parameters affected: Winds | |
| 4613 | """ |
|
4628 | """ | |
| 4614 | position_x = kwargs['positionX'] |
|
4629 | position_x = kwargs['positionX'] | |
| 4615 | position_y = kwargs['positionY'] |
|
4630 | position_y = kwargs['positionY'] | |
| 4616 | azimuth = kwargs['azimuth'] |
|
4631 | azimuth = kwargs['azimuth'] | |
| 4617 |
|
4632 | |||
| 4618 | if 'correctFactor' in kwargs: |
|
4633 | if 'correctFactor' in kwargs: | |
| 4619 | correctFactor = kwargs['correctFactor'] |
|
4634 | correctFactor = kwargs['correctFactor'] | |
| 4620 | else: |
|
4635 | else: | |
| 4621 | correctFactor = 1 |
|
4636 | correctFactor = 1 | |
| 4622 |
|
4637 | |||
| 4623 | groupList = kwargs['groupList'] |
|
4638 | groupList = kwargs['groupList'] | |
| 4624 | pairs_ccf = groupList[1] |
|
4639 | pairs_ccf = groupList[1] | |
| 4625 | tau = kwargs['tau'] |
|
4640 | tau = kwargs['tau'] | |
| 4626 | _lambda = kwargs['_lambda'] |
|
4641 | _lambda = kwargs['_lambda'] | |
| 4627 |
|
4642 | |||
| 4628 | #Cross Correlation pairs obtained |
|
4643 | #Cross Correlation pairs obtained | |
| 4629 | # pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairssList, nChannels) |
|
|||
| 4630 | # pairsArray = numpy.array(pairsList)[pairsCrossCorr] |
|
|||
| 4631 | # pairsSelArray = numpy.array(pairsSelected) |
|
|||
| 4632 | # pairs = [] |
|
|||
| 4633 | # |
|
|||
| 4634 | # #Wind estimation pairs obtained |
|
|||
| 4635 | # for i in range(pairsSelArray.shape[0]/2): |
|
|||
| 4636 | # ind1 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i], axis = 1))[0][0] |
|
|||
| 4637 | # ind2 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i + 1], axis = 1))[0][0] |
|
|||
| 4638 | # pairs.append((ind1,ind2)) |
|
|||
| 4639 |
|
4644 | |||
| 4640 | indtau = tau.shape[0]/2 |
|
4645 | indtau = tau.shape[0]/2 | |
| 4641 | tau1 = tau[:indtau,:] |
|
4646 | tau1 = tau[:indtau,:] | |
| 4642 | tau2 = tau[indtau:-1,:] |
|
4647 | tau2 = tau[indtau:-1,:] | |
| 4643 | # tau1 = tau1[pairs,:] |
|
|||
| 4644 | # tau2 = tau2[pairs,:] |
|
|||
| 4645 | phase1 = tau[-1,:] |
|
4648 | phase1 = tau[-1,:] | |
| 4646 |
|
4649 | |||
| 4647 | #--------------------------------------------------------------------- |
|
4650 | #--------------------------------------------------------------------- | |
| 4648 | #Metodo Directo |
|
4651 | #Metodo Directo | |
| 4649 | distx, disty, dist, ang = self.__calculateDistance(position_x, position_y, pairs_ccf,azimuth) |
|
4652 | distx, disty, dist, ang = self.__calculateDistance(position_x, position_y, pairs_ccf,azimuth) | |
| 4650 | winds = self.__calculateVelHorDir(dist, tau1, tau2, ang) |
|
4653 | winds = self.__calculateVelHorDir(dist, tau1, tau2, ang) | |
| 4651 | winds = stats.nanmean(winds, axis=0) |
|
4654 | winds = stats.nanmean(winds, axis=0) | |
| 4652 | #--------------------------------------------------------------------- |
|
4655 | #--------------------------------------------------------------------- | |
| 4653 | #Metodo General |
|
4656 | #Metodo General | |
| 4654 | # distx, disty, dist = self.calculateDistance(position_x,position_y,pairsCrossCorr, pairsList, azimuth) |
|
|||
| 4655 | # #Calculo Coeficientes de Funcion de Correlacion |
|
|||
| 4656 | # F,G,A,B,H = self.calculateCoef(tau1,tau2,distx,disty,n) |
|
|||
| 4657 | # #Calculo de Velocidades |
|
|||
| 4658 | # winds = self.calculateVelUV(F,G,A,B,H) |
|
|||
| 4659 |
|
4657 | |||
| 4660 | #--------------------------------------------------------------------- |
|
4658 | #--------------------------------------------------------------------- | |
| 4661 | winds[2,:] = self.__calculateVelVer(phase1, lagTRange, _lambda) |
|
4659 | winds[2,:] = self.__calculateVelVer(phase1, lagTRange, _lambda) | |
| 4662 | winds = correctFactor*winds |
|
4660 | winds = correctFactor*winds | |
| 4663 | return winds |
|
4661 | return winds | |
| 4664 |
|
4662 | |||
| 4665 | def __checkTime(self, currentTime, paramInterval, outputInterval): |
|
4663 | def __checkTime(self, currentTime, paramInterval, outputInterval): | |
| 4666 |
|
4664 | |||
| 4667 | dataTime = currentTime + paramInterval |
|
4665 | dataTime = currentTime + paramInterval | |
| 4668 | deltaTime = dataTime - self.__initime |
|
4666 | deltaTime = dataTime - self.__initime | |
| 4669 |
|
4667 | |||
| 4670 | if deltaTime >= outputInterval or deltaTime < 0: |
|
4668 | if deltaTime >= outputInterval or deltaTime < 0: | |
| 4671 | self.__dataReady = True |
|
4669 | self.__dataReady = True | |
| 4672 | return |
|
4670 | return | |
| 4673 |
|
4671 | |||
| 4674 | def techniqueMeteors(self, arrayMeteor, meteorThresh, heightMin, heightMax): |
|
4672 | def techniqueMeteors(self, arrayMeteor, meteorThresh, heightMin, heightMax): | |
| 4675 | ''' |
|
4673 | ''' | |
| 4676 | Function that implements winds estimation technique with detected meteors. |
|
4674 | Function that implements winds estimation technique with detected meteors. | |
| 4677 |
|
4675 | |||
| 4678 | Input: Detected meteors, Minimum meteor quantity to wind estimation |
|
4676 | Input: Detected meteors, Minimum meteor quantity to wind estimation | |
| 4679 |
|
4677 | |||
| 4680 | Output: Winds estimation (Zonal and Meridional) |
|
4678 | Output: Winds estimation (Zonal and Meridional) | |
| 4681 |
|
4679 | |||
| 4682 | Parameters affected: Winds |
|
4680 | Parameters affected: Winds | |
| 4683 | ''' |
|
4681 | ''' | |
| 4684 | #Settings |
|
4682 | #Settings | |
| 4685 | nInt = (heightMax - heightMin)/2 |
|
4683 | nInt = (heightMax - heightMin)/2 | |
| 4686 | nInt = int(nInt) |
|
4684 | nInt = int(nInt) | |
| 4687 | winds = numpy.zeros((2,nInt))*numpy.nan |
|
4685 | winds = numpy.zeros((2,nInt))*numpy.nan | |
| 4688 |
|
4686 | |||
| 4689 | #Filter errors |
|
4687 | #Filter errors | |
| 4690 | error = numpy.where(arrayMeteor[:,-1] == 0)[0] |
|
4688 | error = numpy.where(arrayMeteor[:,-1] == 0)[0] | |
| 4691 | finalMeteor = arrayMeteor[error,:] |
|
4689 | finalMeteor = arrayMeteor[error,:] | |
| 4692 |
|
4690 | |||
| 4693 | #Meteor Histogram |
|
4691 | #Meteor Histogram | |
| 4694 | finalHeights = finalMeteor[:,2] |
|
4692 | finalHeights = finalMeteor[:,2] | |
| 4695 | hist = numpy.histogram(finalHeights, bins = nInt, range = (heightMin,heightMax)) |
|
4693 | hist = numpy.histogram(finalHeights, bins = nInt, range = (heightMin,heightMax)) | |
| 4696 | nMeteorsPerI = hist[0] |
|
4694 | nMeteorsPerI = hist[0] | |
| 4697 | heightPerI = hist[1] |
|
4695 | heightPerI = hist[1] | |
| 4698 |
|
4696 | |||
| 4699 | #Sort of meteors |
|
4697 | #Sort of meteors | |
| 4700 | indSort = finalHeights.argsort() |
|
4698 | indSort = finalHeights.argsort() | |
| 4701 | finalMeteor2 = finalMeteor[indSort,:] |
|
4699 | finalMeteor2 = finalMeteor[indSort,:] | |
| 4702 |
|
4700 | |||
| 4703 | # Calculating winds |
|
4701 | # Calculating winds | |
| 4704 | ind1 = 0 |
|
4702 | ind1 = 0 | |
| 4705 | ind2 = 0 |
|
4703 | ind2 = 0 | |
| 4706 |
|
4704 | |||
| 4707 | for i in range(nInt): |
|
4705 | for i in range(nInt): | |
| 4708 | nMet = nMeteorsPerI[i] |
|
4706 | nMet = nMeteorsPerI[i] | |
| 4709 | ind1 = ind2 |
|
4707 | ind1 = ind2 | |
| 4710 | ind2 = ind1 + nMet |
|
4708 | ind2 = ind1 + nMet | |
| 4711 |
|
4709 | |||
| 4712 | meteorAux = finalMeteor2[ind1:ind2,:] |
|
4710 | meteorAux = finalMeteor2[ind1:ind2,:] | |
| 4713 |
|
4711 | |||
| 4714 | if meteorAux.shape[0] >= meteorThresh: |
|
4712 | if meteorAux.shape[0] >= meteorThresh: | |
| 4715 | vel = meteorAux[:, 6] |
|
4713 | vel = meteorAux[:, 6] | |
| 4716 | zen = meteorAux[:, 4]*numpy.pi/180 |
|
4714 | zen = meteorAux[:, 4]*numpy.pi/180 | |
| 4717 | azim = meteorAux[:, 3]*numpy.pi/180 |
|
4715 | azim = meteorAux[:, 3]*numpy.pi/180 | |
| 4718 |
|
4716 | |||
| 4719 | n = numpy.cos(zen) |
|
4717 | n = numpy.cos(zen) | |
| 4720 | # m = (1 - n**2)/(1 - numpy.tan(azim)**2) |
|
4718 | # m = (1 - n**2)/(1 - numpy.tan(azim)**2) | |
| 4721 | # l = m*numpy.tan(azim) |
|
4719 | # l = m*numpy.tan(azim) | |
| 4722 | l = numpy.sin(zen)*numpy.sin(azim) |
|
4720 | l = numpy.sin(zen)*numpy.sin(azim) | |
| 4723 | m = numpy.sin(zen)*numpy.cos(azim) |
|
4721 | m = numpy.sin(zen)*numpy.cos(azim) | |
| 4724 |
|
4722 | |||
| 4725 | A = numpy.vstack((l, m)).transpose() |
|
4723 | A = numpy.vstack((l, m)).transpose() | |
| 4726 | A1 = numpy.dot(numpy.linalg.inv( numpy.dot(A.transpose(),A) ),A.transpose()) |
|
4724 | A1 = numpy.dot(numpy.linalg.inv( numpy.dot(A.transpose(),A) ),A.transpose()) | |
| 4727 | windsAux = numpy.dot(A1, vel) |
|
4725 | windsAux = numpy.dot(A1, vel) | |
| 4728 |
|
4726 | |||
| 4729 | winds[0,i] = windsAux[0] |
|
4727 | winds[0,i] = windsAux[0] | |
| 4730 | winds[1,i] = windsAux[1] |
|
4728 | winds[1,i] = windsAux[1] | |
| 4731 |
|
4729 | |||
| 4732 | return winds, heightPerI[:-1] |
|
4730 | return winds, heightPerI[:-1] | |
| 4733 |
|
4731 | |||
| 4734 | def techniqueNSM_SA(self, **kwargs): |
|
4732 | def techniqueNSM_SA(self, **kwargs): | |
| 4735 | metArray = kwargs['metArray'] |
|
4733 | metArray = kwargs['metArray'] | |
| 4736 | heightList = kwargs['heightList'] |
|
4734 | heightList = kwargs['heightList'] | |
| 4737 | timeList = kwargs['timeList'] |
|
4735 | timeList = kwargs['timeList'] | |
| 4738 |
|
4736 | |||
| 4739 | rx_location = kwargs['rx_location'] |
|
4737 | rx_location = kwargs['rx_location'] | |
| 4740 | groupList = kwargs['groupList'] |
|
4738 | groupList = kwargs['groupList'] | |
| 4741 | azimuth = kwargs['azimuth'] |
|
4739 | azimuth = kwargs['azimuth'] | |
| 4742 | dfactor = kwargs['dfactor'] |
|
4740 | dfactor = kwargs['dfactor'] | |
| 4743 | k = kwargs['k'] |
|
4741 | k = kwargs['k'] | |
| 4744 |
|
4742 | |||
| 4745 | azimuth1, dist = self.__calculateAzimuth1(rx_location, groupList, azimuth) |
|
4743 | azimuth1, dist = self.__calculateAzimuth1(rx_location, groupList, azimuth) | |
| 4746 | d = dist*dfactor |
|
4744 | d = dist*dfactor | |
| 4747 | #Phase calculation |
|
4745 | #Phase calculation | |
| 4748 | metArray1 = self.__getPhaseSlope(metArray, heightList, timeList) |
|
4746 | metArray1 = self.__getPhaseSlope(metArray, heightList, timeList) | |
| 4749 |
|
4747 | |||
| 4750 | metArray1[:,-2] = metArray1[:,-2]*metArray1[:,2]*1000/(k*d[metArray1[:,1].astype(int)]) #angles into velocities |
|
4748 | metArray1[:,-2] = metArray1[:,-2]*metArray1[:,2]*1000/(k*d[metArray1[:,1].astype(int)]) #angles into velocities | |
| 4751 |
|
4749 | |||
| 4752 | velEst = numpy.zeros((heightList.size,2))*numpy.nan |
|
4750 | velEst = numpy.zeros((heightList.size,2))*numpy.nan | |
| 4753 | azimuth1 = azimuth1*numpy.pi/180 |
|
4751 | azimuth1 = azimuth1*numpy.pi/180 | |
| 4754 |
|
4752 | |||
| 4755 | for i in range(heightList.size): |
|
4753 | for i in range(heightList.size): | |
| 4756 | h = heightList[i] |
|
4754 | h = heightList[i] | |
| 4757 | indH = numpy.where((metArray1[:,2] == h)&(numpy.abs(metArray1[:,-2]) < 100))[0] |
|
4755 | indH = numpy.where((metArray1[:,2] == h)&(numpy.abs(metArray1[:,-2]) < 100))[0] | |
| 4758 | metHeight = metArray1[indH,:] |
|
4756 | metHeight = metArray1[indH,:] | |
| 4759 | if metHeight.shape[0] >= 2: |
|
4757 | if metHeight.shape[0] >= 2: | |
| 4760 | velAux = numpy.asmatrix(metHeight[:,-2]).T #Radial Velocities |
|
4758 | velAux = numpy.asmatrix(metHeight[:,-2]).T #Radial Velocities | |
| 4761 | iazim = metHeight[:,1].astype(int) |
|
4759 | iazim = metHeight[:,1].astype(int) | |
| 4762 | azimAux = numpy.asmatrix(azimuth1[iazim]).T #Azimuths |
|
4760 | azimAux = numpy.asmatrix(azimuth1[iazim]).T #Azimuths | |
| 4763 | A = numpy.hstack((numpy.cos(azimAux),numpy.sin(azimAux))) |
|
4761 | A = numpy.hstack((numpy.cos(azimAux),numpy.sin(azimAux))) | |
| 4764 | A = numpy.asmatrix(A) |
|
4762 | A = numpy.asmatrix(A) | |
| 4765 | A1 = numpy.linalg.pinv(A.transpose()*A)*A.transpose() |
|
4763 | A1 = numpy.linalg.pinv(A.transpose()*A)*A.transpose() | |
| 4766 | velHor = numpy.dot(A1,velAux) |
|
4764 | velHor = numpy.dot(A1,velAux) | |
| 4767 |
|
4765 | |||
| 4768 | velEst[i,:] = numpy.squeeze(velHor) |
|
4766 | velEst[i,:] = numpy.squeeze(velHor) | |
| 4769 | return velEst |
|
4767 | return velEst | |
| 4770 |
|
4768 | |||
| 4771 | def __getPhaseSlope(self, metArray, heightList, timeList): |
|
4769 | def __getPhaseSlope(self, metArray, heightList, timeList): | |
| 4772 | meteorList = [] |
|
4770 | meteorList = [] | |
| 4773 | #utctime sec1 height SNR velRad ph0 ph1 ph2 coh0 coh1 coh2 |
|
4771 | #utctime sec1 height SNR velRad ph0 ph1 ph2 coh0 coh1 coh2 | |
| 4774 | #Putting back together the meteor matrix |
|
4772 | #Putting back together the meteor matrix | |
| 4775 | utctime = metArray[:,0] |
|
4773 | utctime = metArray[:,0] | |
| 4776 | uniqueTime = numpy.unique(utctime) |
|
4774 | uniqueTime = numpy.unique(utctime) | |
| 4777 |
|
4775 | |||
| 4778 | phaseDerThresh = 0.5 |
|
4776 | phaseDerThresh = 0.5 | |
| 4779 | ippSeconds = timeList[1] - timeList[0] |
|
4777 | ippSeconds = timeList[1] - timeList[0] | |
| 4780 | sec = numpy.where(timeList>1)[0][0] |
|
4778 | sec = numpy.where(timeList>1)[0][0] | |
| 4781 | nPairs = metArray.shape[1] - 6 |
|
4779 | nPairs = metArray.shape[1] - 6 | |
| 4782 | nHeights = len(heightList) |
|
4780 | nHeights = len(heightList) | |
| 4783 |
|
4781 | |||
| 4784 | for t in uniqueTime: |
|
4782 | for t in uniqueTime: | |
| 4785 | metArray1 = metArray[utctime==t,:] |
|
4783 | metArray1 = metArray[utctime==t,:] | |
| 4786 | # phaseDerThresh = numpy.pi/4 #reducir Phase thresh |
|
4784 | # phaseDerThresh = numpy.pi/4 #reducir Phase thresh | |
| 4787 | tmet = metArray1[:,1].astype(int) |
|
4785 | tmet = metArray1[:,1].astype(int) | |
| 4788 | hmet = metArray1[:,2].astype(int) |
|
4786 | hmet = metArray1[:,2].astype(int) | |
| 4789 |
|
4787 | |||
| 4790 | metPhase = numpy.zeros((nPairs, heightList.size, timeList.size - 1)) |
|
4788 | metPhase = numpy.zeros((nPairs, heightList.size, timeList.size - 1)) | |
| 4791 | metPhase[:,:] = numpy.nan |
|
4789 | metPhase[:,:] = numpy.nan | |
| 4792 | metPhase[:,hmet,tmet] = metArray1[:,6:].T |
|
4790 | metPhase[:,hmet,tmet] = metArray1[:,6:].T | |
| 4793 |
|
4791 | |||
| 4794 | #Delete short trails |
|
4792 | #Delete short trails | |
| 4795 | metBool = ~numpy.isnan(metPhase[0,:,:]) |
|
4793 | metBool = ~numpy.isnan(metPhase[0,:,:]) | |
| 4796 | heightVect = numpy.sum(metBool, axis = 1) |
|
4794 | heightVect = numpy.sum(metBool, axis = 1) | |
| 4797 | metBool[heightVect<sec,:] = False |
|
4795 | metBool[heightVect<sec,:] = False | |
| 4798 | metPhase[:,heightVect<sec,:] = numpy.nan |
|
4796 | metPhase[:,heightVect<sec,:] = numpy.nan | |
| 4799 |
|
4797 | |||
| 4800 | #Derivative |
|
4798 | #Derivative | |
| 4801 | metDer = numpy.abs(metPhase[:,:,1:] - metPhase[:,:,:-1]) |
|
4799 | metDer = numpy.abs(metPhase[:,:,1:] - metPhase[:,:,:-1]) | |
| 4802 | phDerAux = numpy.dstack((numpy.full((nPairs,nHeights,1), False, dtype=bool),metDer > phaseDerThresh)) |
|
4800 | phDerAux = numpy.dstack((numpy.full((nPairs,nHeights,1), False, dtype=bool),metDer > phaseDerThresh)) | |
| 4803 | metPhase[phDerAux] = numpy.nan |
|
4801 | metPhase[phDerAux] = numpy.nan | |
| 4804 |
|
4802 | |||
| 4805 | #--------------------------METEOR DETECTION ----------------------------------------- |
|
4803 | #--------------------------METEOR DETECTION ----------------------------------------- | |
| 4806 | indMet = numpy.where(numpy.any(metBool,axis=1))[0] |
|
4804 | indMet = numpy.where(numpy.any(metBool,axis=1))[0] | |
| 4807 |
|
4805 | |||
| 4808 | for p in numpy.arange(nPairs): |
|
4806 | for p in numpy.arange(nPairs): | |
| 4809 | phase = metPhase[p,:,:] |
|
4807 | phase = metPhase[p,:,:] | |
| 4810 | phDer = metDer[p,:,:] |
|
4808 | phDer = metDer[p,:,:] | |
| 4811 |
|
4809 | |||
| 4812 | for h in indMet: |
|
4810 | for h in indMet: | |
| 4813 | height = heightList[h] |
|
4811 | height = heightList[h] | |
| 4814 | phase1 = phase[h,:] #82 |
|
4812 | phase1 = phase[h,:] #82 | |
| 4815 | phDer1 = phDer[h,:] |
|
4813 | phDer1 = phDer[h,:] | |
| 4816 |
|
4814 | |||
| 4817 | phase1[~numpy.isnan(phase1)] = numpy.unwrap(phase1[~numpy.isnan(phase1)]) #Unwrap |
|
4815 | phase1[~numpy.isnan(phase1)] = numpy.unwrap(phase1[~numpy.isnan(phase1)]) #Unwrap | |
| 4818 |
|
4816 | |||
| 4819 | indValid = numpy.where(~numpy.isnan(phase1))[0] |
|
4817 | indValid = numpy.where(~numpy.isnan(phase1))[0] | |
| 4820 | initMet = indValid[0] |
|
4818 | initMet = indValid[0] | |
| 4821 | endMet = 0 |
|
4819 | endMet = 0 | |
| 4822 |
|
4820 | |||
| 4823 | for i in range(len(indValid)-1): |
|
4821 | for i in range(len(indValid)-1): | |
| 4824 |
|
4822 | |||
| 4825 | #Time difference |
|
4823 | #Time difference | |
| 4826 | inow = indValid[i] |
|
4824 | inow = indValid[i] | |
| 4827 | inext = indValid[i+1] |
|
4825 | inext = indValid[i+1] | |
| 4828 | idiff = inext - inow |
|
4826 | idiff = inext - inow | |
| 4829 | #Phase difference |
|
4827 | #Phase difference | |
| 4830 | phDiff = numpy.abs(phase1[inext] - phase1[inow]) |
|
4828 | phDiff = numpy.abs(phase1[inext] - phase1[inow]) | |
| 4831 |
|
4829 | |||
| 4832 | if idiff>sec or phDiff>numpy.pi/4 or inext==indValid[-1]: #End of Meteor |
|
4830 | if idiff>sec or phDiff>numpy.pi/4 or inext==indValid[-1]: #End of Meteor | |
| 4833 | sizeTrail = inow - initMet + 1 |
|
4831 | sizeTrail = inow - initMet + 1 | |
| 4834 | if sizeTrail>3*sec: #Too short meteors |
|
4832 | if sizeTrail>3*sec: #Too short meteors | |
| 4835 | x = numpy.arange(initMet,inow+1)*ippSeconds |
|
4833 | x = numpy.arange(initMet,inow+1)*ippSeconds | |
| 4836 | y = phase1[initMet:inow+1] |
|
4834 | y = phase1[initMet:inow+1] | |
| 4837 | ynnan = ~numpy.isnan(y) |
|
4835 | ynnan = ~numpy.isnan(y) | |
| 4838 | x = x[ynnan] |
|
4836 | x = x[ynnan] | |
| 4839 | y = y[ynnan] |
|
4837 | y = y[ynnan] | |
| 4840 | slope, intercept, r_value, p_value, std_err = stats.linregress(x,y) |
|
4838 | slope, intercept, r_value, p_value, std_err = stats.linregress(x,y) | |
| 4841 | ylin = x*slope + intercept |
|
4839 | ylin = x*slope + intercept | |
| 4842 | rsq = r_value**2 |
|
4840 | rsq = r_value**2 | |
| 4843 | if rsq > 0.5: |
|
4841 | if rsq > 0.5: | |
| 4844 | vel = slope#*height*1000/(k*d) |
|
4842 | vel = slope#*height*1000/(k*d) | |
| 4845 | estAux = numpy.array([utctime,p,height, vel, rsq]) |
|
4843 | estAux = numpy.array([utctime,p,height, vel, rsq]) | |
| 4846 | meteorList.append(estAux) |
|
4844 | meteorList.append(estAux) | |
| 4847 | initMet = inext |
|
4845 | initMet = inext | |
| 4848 | metArray2 = numpy.array(meteorList) |
|
4846 | metArray2 = numpy.array(meteorList) | |
| 4849 |
|
4847 | |||
| 4850 | return metArray2 |
|
4848 | return metArray2 | |
| 4851 |
|
4849 | |||
| 4852 | def __calculateAzimuth1(self, rx_location, pairslist, azimuth0): |
|
4850 | def __calculateAzimuth1(self, rx_location, pairslist, azimuth0): | |
| 4853 |
|
4851 | |||
| 4854 | azimuth1 = numpy.zeros(len(pairslist)) |
|
4852 | azimuth1 = numpy.zeros(len(pairslist)) | |
| 4855 | dist = numpy.zeros(len(pairslist)) |
|
4853 | dist = numpy.zeros(len(pairslist)) | |
| 4856 |
|
4854 | |||
| 4857 | for i in range(len(rx_location)): |
|
4855 | for i in range(len(rx_location)): | |
| 4858 | ch0 = pairslist[i][0] |
|
4856 | ch0 = pairslist[i][0] | |
| 4859 | ch1 = pairslist[i][1] |
|
4857 | ch1 = pairslist[i][1] | |
| 4860 |
|
4858 | |||
| 4861 | diffX = rx_location[ch0][0] - rx_location[ch1][0] |
|
4859 | diffX = rx_location[ch0][0] - rx_location[ch1][0] | |
| 4862 | diffY = rx_location[ch0][1] - rx_location[ch1][1] |
|
4860 | diffY = rx_location[ch0][1] - rx_location[ch1][1] | |
| 4863 | azimuth1[i] = numpy.arctan2(diffY,diffX)*180/numpy.pi |
|
4861 | azimuth1[i] = numpy.arctan2(diffY,diffX)*180/numpy.pi | |
| 4864 | dist[i] = numpy.sqrt(diffX**2 + diffY**2) |
|
4862 | dist[i] = numpy.sqrt(diffX**2 + diffY**2) | |
| 4865 |
|
4863 | |||
| 4866 | azimuth1 -= azimuth0 |
|
4864 | azimuth1 -= azimuth0 | |
| 4867 | return azimuth1, dist |
|
4865 | return azimuth1, dist | |
| 4868 |
|
4866 | |||
| 4869 | def techniqueNSM_DBS(self, **kwargs): |
|
4867 | def techniqueNSM_DBS(self, **kwargs): | |
| 4870 | metArray = kwargs['metArray'] |
|
4868 | metArray = kwargs['metArray'] | |
| 4871 | heightList = kwargs['heightList'] |
|
4869 | heightList = kwargs['heightList'] | |
| 4872 | timeList = kwargs['timeList'] |
|
4870 | timeList = kwargs['timeList'] | |
| 4873 | azimuth = kwargs['azimuth'] |
|
4871 | azimuth = kwargs['azimuth'] | |
| 4874 | theta_x = numpy.array(kwargs['theta_x']) |
|
4872 | theta_x = numpy.array(kwargs['theta_x']) | |
| 4875 | theta_y = numpy.array(kwargs['theta_y']) |
|
4873 | theta_y = numpy.array(kwargs['theta_y']) | |
| 4876 |
|
4874 | |||
| 4877 | utctime = metArray[:,0] |
|
4875 | utctime = metArray[:,0] | |
| 4878 | cmet = metArray[:,1].astype(int) |
|
4876 | cmet = metArray[:,1].astype(int) | |
| 4879 | hmet = metArray[:,3].astype(int) |
|
4877 | hmet = metArray[:,3].astype(int) | |
| 4880 | SNRmet = metArray[:,4] |
|
4878 | SNRmet = metArray[:,4] | |
| 4881 | vmet = metArray[:,5] |
|
4879 | vmet = metArray[:,5] | |
| 4882 | spcmet = metArray[:,6] |
|
4880 | spcmet = metArray[:,6] | |
| 4883 |
|
4881 | |||
| 4884 | nChan = numpy.max(cmet) + 1 |
|
4882 | nChan = numpy.max(cmet) + 1 | |
| 4885 | nHeights = len(heightList) |
|
4883 | nHeights = len(heightList) | |
| 4886 |
|
4884 | |||
| 4887 | azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth) |
|
4885 | azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth) | |
| 4888 | hmet = heightList[hmet] |
|
4886 | hmet = heightList[hmet] | |
| 4889 | h1met = hmet*numpy.cos(zenith_arr[cmet]) #Corrected heights |
|
4887 | h1met = hmet*numpy.cos(zenith_arr[cmet]) #Corrected heights | |
| 4890 |
|
4888 | |||
| 4891 | velEst = numpy.zeros((heightList.size,2))*numpy.nan |
|
4889 | velEst = numpy.zeros((heightList.size,2))*numpy.nan | |
| 4892 |
|
4890 | |||
| 4893 | for i in range(nHeights - 1): |
|
4891 | for i in range(nHeights - 1): | |
| 4894 | hmin = heightList[i] |
|
4892 | hmin = heightList[i] | |
| 4895 | hmax = heightList[i + 1] |
|
4893 | hmax = heightList[i + 1] | |
| 4896 |
|
4894 | |||
| 4897 | thisH = (h1met>=hmin) & (h1met<hmax) & (cmet!=2) & (SNRmet>8) & (vmet<50) & (spcmet<10) |
|
4895 | thisH = (h1met>=hmin) & (h1met<hmax) & (cmet!=2) & (SNRmet>8) & (vmet<50) & (spcmet<10) | |
| 4898 | indthisH = numpy.where(thisH) |
|
4896 | indthisH = numpy.where(thisH) | |
| 4899 |
|
4897 | |||
| 4900 | if numpy.size(indthisH) > 3: |
|
4898 | if numpy.size(indthisH) > 3: | |
| 4901 |
|
4899 | |||
| 4902 | vel_aux = vmet[thisH] |
|
4900 | vel_aux = vmet[thisH] | |
| 4903 | chan_aux = cmet[thisH] |
|
4901 | chan_aux = cmet[thisH] | |
| 4904 | cosu_aux = dir_cosu[chan_aux] |
|
4902 | cosu_aux = dir_cosu[chan_aux] | |
| 4905 | cosv_aux = dir_cosv[chan_aux] |
|
4903 | cosv_aux = dir_cosv[chan_aux] | |
| 4906 | cosw_aux = dir_cosw[chan_aux] |
|
4904 | cosw_aux = dir_cosw[chan_aux] | |
| 4907 |
|
4905 | |||
| 4908 | nch = numpy.size(numpy.unique(chan_aux)) |
|
4906 | nch = numpy.size(numpy.unique(chan_aux)) | |
| 4909 | if nch > 1: |
|
4907 | if nch > 1: | |
| 4910 | A = self.__calculateMatA(cosu_aux, cosv_aux, cosw_aux, True) |
|
4908 | A = self.__calculateMatA(cosu_aux, cosv_aux, cosw_aux, True) | |
| 4911 | velEst[i,:] = numpy.dot(A,vel_aux) |
|
4909 | velEst[i,:] = numpy.dot(A,vel_aux) | |
| 4912 |
|
4910 | |||
| 4913 | return velEst |
|
4911 | return velEst | |
| 4914 |
|
4912 | |||
| 4915 | def run(self, dataOut, technique, nHours=1, hmin=70, hmax=110, **kwargs): |
|
4913 | def run(self, dataOut, technique, nHours=1, hmin=70, hmax=110, **kwargs): | |
| 4916 |
|
4914 | |||
| 4917 | param = dataOut.data_param |
|
4915 | param = dataOut.data_param | |
| 4918 | #if dataOut.abscissaList != None: |
|
4916 | #if dataOut.abscissaList != None: | |
| 4919 | if numpy.any(dataOut.abscissaList): |
|
4917 | if numpy.any(dataOut.abscissaList): | |
| 4920 | absc = dataOut.abscissaList[:-1] |
|
4918 | absc = dataOut.abscissaList[:-1] | |
| 4921 | # noise = dataOut.noise |
|
4919 | # noise = dataOut.noise | |
| 4922 | heightList = dataOut.heightList |
|
4920 | heightList = dataOut.heightList | |
| 4923 | SNR = dataOut.data_snr |
|
4921 | SNR = dataOut.data_snr | |
| 4924 |
|
4922 | |||
| 4925 | if technique == 'DBS': |
|
4923 | if technique == 'DBS': | |
| 4926 |
|
4924 | |||
| 4927 | kwargs['velRadial'] = param[:,1,:] #Radial velocity |
|
4925 | kwargs['velRadial'] = param[:,1,:] #Radial velocity | |
| 4928 | kwargs['heightList'] = heightList |
|
4926 | kwargs['heightList'] = heightList | |
| 4929 | kwargs['SNR'] = SNR |
|
4927 | kwargs['SNR'] = SNR | |
| 4930 |
|
4928 | |||
| 4931 | dataOut.data_output, dataOut.heightList, dataOut.data_snr = self.techniqueDBS(kwargs) #DBS Function |
|
4929 | dataOut.data_output, dataOut.heightList, dataOut.data_snr = self.techniqueDBS(kwargs) #DBS Function | |
| 4932 | dataOut.utctimeInit = dataOut.utctime |
|
4930 | dataOut.utctimeInit = dataOut.utctime | |
| 4933 | dataOut.outputInterval = dataOut.paramInterval |
|
4931 | dataOut.outputInterval = dataOut.paramInterval | |
| 4934 |
|
4932 | |||
| 4935 | elif technique == 'SA': |
|
4933 | elif technique == 'SA': | |
| 4936 |
|
4934 | |||
| 4937 | #Parameters |
|
4935 | #Parameters | |
| 4938 | # position_x = kwargs['positionX'] |
|
4936 | # position_x = kwargs['positionX'] | |
| 4939 | # position_y = kwargs['positionY'] |
|
4937 | # position_y = kwargs['positionY'] | |
| 4940 | # azimuth = kwargs['azimuth'] |
|
4938 | # azimuth = kwargs['azimuth'] | |
| 4941 | # |
|
4939 | # | |
| 4942 | # if kwargs.has_key('crosspairsList'): |
|
4940 | # if kwargs.has_key('crosspairsList'): | |
| 4943 | # pairs = kwargs['crosspairsList'] |
|
4941 | # pairs = kwargs['crosspairsList'] | |
| 4944 | # else: |
|
4942 | # else: | |
| 4945 | # pairs = None |
|
4943 | # pairs = None | |
| 4946 | # |
|
4944 | # | |
| 4947 | # if kwargs.has_key('correctFactor'): |
|
4945 | # if kwargs.has_key('correctFactor'): | |
| 4948 | # correctFactor = kwargs['correctFactor'] |
|
4946 | # correctFactor = kwargs['correctFactor'] | |
| 4949 | # else: |
|
4947 | # else: | |
| 4950 | # correctFactor = 1 |
|
4948 | # correctFactor = 1 | |
| 4951 |
|
4949 | |||
| 4952 | # tau = dataOut.data_param |
|
4950 | # tau = dataOut.data_param | |
| 4953 | # _lambda = dataOut.C/dataOut.frequency |
|
4951 | # _lambda = dataOut.C/dataOut.frequency | |
| 4954 | # pairsList = dataOut.groupList |
|
4952 | # pairsList = dataOut.groupList | |
| 4955 | # nChannels = dataOut.nChannels |
|
4953 | # nChannels = dataOut.nChannels | |
| 4956 |
|
4954 | |||
| 4957 | kwargs['groupList'] = dataOut.groupList |
|
4955 | kwargs['groupList'] = dataOut.groupList | |
| 4958 | kwargs['tau'] = dataOut.data_param |
|
4956 | kwargs['tau'] = dataOut.data_param | |
| 4959 | kwargs['_lambda'] = dataOut.C/dataOut.frequency |
|
4957 | kwargs['_lambda'] = dataOut.C/dataOut.frequency | |
| 4960 | # dataOut.data_output = self.techniqueSA(pairs, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, absc, correctFactor) |
|
4958 | # dataOut.data_output = self.techniqueSA(pairs, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, absc, correctFactor) | |
| 4961 | dataOut.data_output = self.techniqueSA(kwargs) |
|
4959 | dataOut.data_output = self.techniqueSA(kwargs) | |
| 4962 | dataOut.utctimeInit = dataOut.utctime |
|
4960 | dataOut.utctimeInit = dataOut.utctime | |
| 4963 | dataOut.outputInterval = dataOut.timeInterval |
|
4961 | dataOut.outputInterval = dataOut.timeInterval | |
| 4964 |
|
4962 | |||
| 4965 | elif technique == 'Meteors': |
|
4963 | elif technique == 'Meteors': | |
| 4966 | dataOut.flagNoData = True |
|
4964 | dataOut.flagNoData = True | |
| 4967 | self.__dataReady = False |
|
4965 | self.__dataReady = False | |
| 4968 |
|
4966 | |||
| 4969 | if 'nHours' in kwargs: |
|
4967 | if 'nHours' in kwargs: | |
| 4970 | nHours = kwargs['nHours'] |
|
4968 | nHours = kwargs['nHours'] | |
| 4971 | else: |
|
4969 | else: | |
| 4972 | nHours = 1 |
|
4970 | nHours = 1 | |
| 4973 |
|
4971 | |||
| 4974 | if 'meteorsPerBin' in kwargs: |
|
4972 | if 'meteorsPerBin' in kwargs: | |
| 4975 | meteorThresh = kwargs['meteorsPerBin'] |
|
4973 | meteorThresh = kwargs['meteorsPerBin'] | |
| 4976 | else: |
|
4974 | else: | |
| 4977 | meteorThresh = 6 |
|
4975 | meteorThresh = 6 | |
| 4978 |
|
4976 | |||
| 4979 | if 'hmin' in kwargs: |
|
4977 | if 'hmin' in kwargs: | |
| 4980 | hmin = kwargs['hmin'] |
|
4978 | hmin = kwargs['hmin'] | |
| 4981 | else: hmin = 70 |
|
4979 | else: hmin = 70 | |
| 4982 | if 'hmax' in kwargs: |
|
4980 | if 'hmax' in kwargs: | |
| 4983 | hmax = kwargs['hmax'] |
|
4981 | hmax = kwargs['hmax'] | |
| 4984 | else: hmax = 110 |
|
4982 | else: hmax = 110 | |
| 4985 |
|
4983 | |||
| 4986 | dataOut.outputInterval = nHours*3600 |
|
4984 | dataOut.outputInterval = nHours*3600 | |
| 4987 |
|
4985 | |||
| 4988 | if self.__isConfig == False: |
|
4986 | if self.__isConfig == False: | |
| 4989 | # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) |
|
4987 | # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) | |
| 4990 | #Get Initial LTC time |
|
4988 | #Get Initial LTC time | |
| 4991 | self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
4989 | self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) | |
| 4992 | self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
4990 | self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() | |
| 4993 |
|
4991 | |||
| 4994 | self.__isConfig = True |
|
4992 | self.__isConfig = True | |
| 4995 |
|
4993 | |||
| 4996 | if self.__buffer is None: |
|
4994 | if self.__buffer is None: | |
| 4997 | self.__buffer = dataOut.data_param |
|
4995 | self.__buffer = dataOut.data_param | |
| 4998 | self.__firstdata = copy.copy(dataOut) |
|
4996 | self.__firstdata = copy.copy(dataOut) | |
| 4999 |
|
4997 | |||
| 5000 | else: |
|
4998 | else: | |
| 5001 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
4999 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) | |
| 5002 |
|
5000 | |||
| 5003 | self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
5001 | self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready | |
| 5004 |
|
5002 | |||
| 5005 | if self.__dataReady: |
|
5003 | if self.__dataReady: | |
| 5006 | dataOut.utctimeInit = self.__initime |
|
5004 | dataOut.utctimeInit = self.__initime | |
| 5007 |
|
5005 | |||
| 5008 | self.__initime += dataOut.outputInterval #to erase time offset |
|
5006 | self.__initime += dataOut.outputInterval #to erase time offset | |
| 5009 |
|
5007 | |||
| 5010 | dataOut.data_output, dataOut.heightList = self.techniqueMeteors(self.__buffer, meteorThresh, hmin, hmax) |
|
5008 | dataOut.data_output, dataOut.heightList = self.techniqueMeteors(self.__buffer, meteorThresh, hmin, hmax) | |
| 5011 | dataOut.flagNoData = False |
|
5009 | dataOut.flagNoData = False | |
| 5012 | self.__buffer = None |
|
5010 | self.__buffer = None | |
| 5013 |
|
5011 | |||
| 5014 | elif technique == 'Meteors1': |
|
5012 | elif technique == 'Meteors1': | |
| 5015 | dataOut.flagNoData = True |
|
5013 | dataOut.flagNoData = True | |
| 5016 | self.__dataReady = False |
|
5014 | self.__dataReady = False | |
| 5017 |
|
5015 | |||
| 5018 | if 'nMins' in kwargs: |
|
5016 | if 'nMins' in kwargs: | |
| 5019 | nMins = kwargs['nMins'] |
|
5017 | nMins = kwargs['nMins'] | |
| 5020 | else: nMins = 20 |
|
5018 | else: nMins = 20 | |
| 5021 | if 'rx_location' in kwargs: |
|
5019 | if 'rx_location' in kwargs: | |
| 5022 | rx_location = kwargs['rx_location'] |
|
5020 | rx_location = kwargs['rx_location'] | |
| 5023 | else: rx_location = [(0,1),(1,1),(1,0)] |
|
5021 | else: rx_location = [(0,1),(1,1),(1,0)] | |
| 5024 | if 'azimuth' in kwargs: |
|
5022 | if 'azimuth' in kwargs: | |
| 5025 | azimuth = kwargs['azimuth'] |
|
5023 | azimuth = kwargs['azimuth'] | |
| 5026 | else: azimuth = 51.06 |
|
5024 | else: azimuth = 51.06 | |
| 5027 | if 'dfactor' in kwargs: |
|
5025 | if 'dfactor' in kwargs: | |
| 5028 | dfactor = kwargs['dfactor'] |
|
5026 | dfactor = kwargs['dfactor'] | |
| 5029 | if 'mode' in kwargs: |
|
5027 | if 'mode' in kwargs: | |
| 5030 | mode = kwargs['mode'] |
|
5028 | mode = kwargs['mode'] | |
| 5031 | if 'theta_x' in kwargs: |
|
5029 | if 'theta_x' in kwargs: | |
| 5032 | theta_x = kwargs['theta_x'] |
|
5030 | theta_x = kwargs['theta_x'] | |
| 5033 | if 'theta_y' in kwargs: |
|
5031 | if 'theta_y' in kwargs: | |
| 5034 | theta_y = kwargs['theta_y'] |
|
5032 | theta_y = kwargs['theta_y'] | |
| 5035 | else: mode = 'SA' |
|
5033 | else: mode = 'SA' | |
| 5036 |
|
5034 | |||
| 5037 | #Borrar luego esto |
|
5035 | #Borrar luego esto | |
| 5038 | if dataOut.groupList is None: |
|
5036 | if dataOut.groupList is None: | |
| 5039 | dataOut.groupList = [(0,1),(0,2),(1,2)] |
|
5037 | dataOut.groupList = [(0,1),(0,2),(1,2)] | |
| 5040 | groupList = dataOut.groupList |
|
5038 | groupList = dataOut.groupList | |
| 5041 | C = 3e8 |
|
5039 | C = 3e8 | |
| 5042 | freq = 50e6 |
|
5040 | freq = 50e6 | |
| 5043 | lamb = C/freq |
|
5041 | lamb = C/freq | |
| 5044 | k = 2*numpy.pi/lamb |
|
5042 | k = 2*numpy.pi/lamb | |
| 5045 |
|
5043 | |||
| 5046 | timeList = dataOut.abscissaList |
|
5044 | timeList = dataOut.abscissaList | |
| 5047 | heightList = dataOut.heightList |
|
5045 | heightList = dataOut.heightList | |
| 5048 |
|
5046 | |||
| 5049 | if self.__isConfig == False: |
|
5047 | if self.__isConfig == False: | |
| 5050 | dataOut.outputInterval = nMins*60 |
|
5048 | dataOut.outputInterval = nMins*60 | |
| 5051 | # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) |
|
5049 | # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) | |
| 5052 | #Get Initial LTC time |
|
5050 | #Get Initial LTC time | |
| 5053 | initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
5051 | initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) | |
| 5054 | minuteAux = initime.minute |
|
5052 | minuteAux = initime.minute | |
| 5055 | minuteNew = int(numpy.floor(minuteAux/nMins)*nMins) |
|
5053 | minuteNew = int(numpy.floor(minuteAux/nMins)*nMins) | |
| 5056 | self.__initime = (initime.replace(minute = minuteNew, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
5054 | self.__initime = (initime.replace(minute = minuteNew, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() | |
| 5057 |
|
5055 | |||
| 5058 | self.__isConfig = True |
|
5056 | self.__isConfig = True | |
| 5059 |
|
5057 | |||
| 5060 | if self.__buffer is None: |
|
5058 | if self.__buffer is None: | |
| 5061 | self.__buffer = dataOut.data_param |
|
5059 | self.__buffer = dataOut.data_param | |
| 5062 | self.__firstdata = copy.copy(dataOut) |
|
5060 | self.__firstdata = copy.copy(dataOut) | |
| 5063 |
|
5061 | |||
| 5064 | else: |
|
5062 | else: | |
| 5065 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
5063 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) | |
| 5066 |
|
5064 | |||
| 5067 | self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
5065 | self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready | |
| 5068 |
|
5066 | |||
| 5069 | if self.__dataReady: |
|
5067 | if self.__dataReady: | |
| 5070 | dataOut.utctimeInit = self.__initime |
|
5068 | dataOut.utctimeInit = self.__initime | |
| 5071 | self.__initime += dataOut.outputInterval #to erase time offset |
|
5069 | self.__initime += dataOut.outputInterval #to erase time offset | |
| 5072 |
|
5070 | |||
| 5073 | metArray = self.__buffer |
|
5071 | metArray = self.__buffer | |
| 5074 | if mode == 'SA': |
|
5072 | if mode == 'SA': | |
| 5075 | dataOut.data_output = self.techniqueNSM_SA(rx_location=rx_location, groupList=groupList, azimuth=azimuth, dfactor=dfactor, k=k,metArray=metArray, heightList=heightList,timeList=timeList) |
|
5073 | dataOut.data_output = self.techniqueNSM_SA(rx_location=rx_location, groupList=groupList, azimuth=azimuth, dfactor=dfactor, k=k,metArray=metArray, heightList=heightList,timeList=timeList) | |
| 5076 | elif mode == 'DBS': |
|
5074 | elif mode == 'DBS': | |
| 5077 | dataOut.data_output = self.techniqueNSM_DBS(metArray=metArray,heightList=heightList,timeList=timeList, azimuth=azimuth, theta_x=theta_x, theta_y=theta_y) |
|
5075 | dataOut.data_output = self.techniqueNSM_DBS(metArray=metArray,heightList=heightList,timeList=timeList, azimuth=azimuth, theta_x=theta_x, theta_y=theta_y) | |
| 5078 | dataOut.data_output = dataOut.data_output.T |
|
5076 | dataOut.data_output = dataOut.data_output.T | |
| 5079 | dataOut.flagNoData = False |
|
5077 | dataOut.flagNoData = False | |
| 5080 | self.__buffer = None |
|
5078 | self.__buffer = None | |
| 5081 |
|
5079 | |||
| 5082 | return |
|
5080 | return | |
| 5083 |
|
5081 | |||
| 5084 | class WindProfiler(Operation): |
|
5082 | class WindProfiler(Operation): | |
| 5085 |
|
5083 | |||
| 5086 | __isConfig = False |
|
5084 | __isConfig = False | |
| 5087 |
|
5085 | |||
| 5088 | __initime = None |
|
5086 | __initime = None | |
| 5089 | __lastdatatime = None |
|
5087 | __lastdatatime = None | |
| 5090 | __integrationtime = None |
|
5088 | __integrationtime = None | |
| 5091 |
|
5089 | |||
| 5092 | __buffer = None |
|
5090 | __buffer = None | |
| 5093 |
|
5091 | |||
| 5094 | __dataReady = False |
|
5092 | __dataReady = False | |
| 5095 |
|
5093 | |||
| 5096 | __firstdata = None |
|
5094 | __firstdata = None | |
| 5097 |
|
5095 | |||
| 5098 | n = None |
|
5096 | n = None | |
| 5099 |
|
5097 | |||
| 5100 | def __init__(self): |
|
5098 | def __init__(self): | |
| 5101 | Operation.__init__(self) |
|
5099 | Operation.__init__(self) | |
| 5102 |
|
5100 | |||
| 5103 | def __calculateCosDir(self, elev, azim): |
|
5101 | def __calculateCosDir(self, elev, azim): | |
| 5104 | zen = (90 - elev)*numpy.pi/180 |
|
5102 | zen = (90 - elev)*numpy.pi/180 | |
| 5105 | azim = azim*numpy.pi/180 |
|
5103 | azim = azim*numpy.pi/180 | |
| 5106 | cosDirX = numpy.sqrt((1-numpy.cos(zen)**2)/((1+numpy.tan(azim)**2))) |
|
5104 | cosDirX = numpy.sqrt((1-numpy.cos(zen)**2)/((1+numpy.tan(azim)**2))) | |
| 5107 | cosDirY = numpy.sqrt(1-numpy.cos(zen)**2-cosDirX**2) |
|
5105 | cosDirY = numpy.sqrt(1-numpy.cos(zen)**2-cosDirX**2) | |
| 5108 |
|
5106 | |||
| 5109 | signX = numpy.sign(numpy.cos(azim)) |
|
5107 | signX = numpy.sign(numpy.cos(azim)) | |
| 5110 | signY = numpy.sign(numpy.sin(azim)) |
|
5108 | signY = numpy.sign(numpy.sin(azim)) | |
| 5111 |
|
5109 | |||
| 5112 | cosDirX = numpy.copysign(cosDirX, signX) |
|
5110 | cosDirX = numpy.copysign(cosDirX, signX) | |
| 5113 | cosDirY = numpy.copysign(cosDirY, signY) |
|
5111 | cosDirY = numpy.copysign(cosDirY, signY) | |
| 5114 | return cosDirX, cosDirY |
|
5112 | return cosDirX, cosDirY | |
| 5115 |
|
5113 | |||
| 5116 | def __calculateAngles(self, theta_x, theta_y, azimuth): |
|
5114 | def __calculateAngles(self, theta_x, theta_y, azimuth): | |
| 5117 |
|
5115 | |||
| 5118 | dir_cosw = numpy.sqrt(1-theta_x**2-theta_y**2) |
|
5116 | dir_cosw = numpy.sqrt(1-theta_x**2-theta_y**2) | |
| 5119 | zenith_arr = numpy.arccos(dir_cosw) |
|
5117 | zenith_arr = numpy.arccos(dir_cosw) | |
| 5120 | azimuth_arr = numpy.arctan2(theta_x,theta_y) + azimuth*math.pi/180 |
|
5118 | azimuth_arr = numpy.arctan2(theta_x,theta_y) + azimuth*math.pi/180 | |
| 5121 |
|
5119 | |||
| 5122 | dir_cosu = numpy.sin(azimuth_arr)*numpy.sin(zenith_arr) |
|
5120 | dir_cosu = numpy.sin(azimuth_arr)*numpy.sin(zenith_arr) | |
| 5123 | dir_cosv = numpy.cos(azimuth_arr)*numpy.sin(zenith_arr) |
|
5121 | dir_cosv = numpy.cos(azimuth_arr)*numpy.sin(zenith_arr) | |
| 5124 |
|
5122 | |||
| 5125 | return azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw |
|
5123 | return azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw | |
| 5126 |
|
5124 | |||
| 5127 | def __calculateMatA(self, dir_cosu, dir_cosv, dir_cosw, horOnly): |
|
5125 | def __calculateMatA(self, dir_cosu, dir_cosv, dir_cosw, horOnly): | |
| 5128 |
|
5126 | |||
| 5129 | if horOnly: |
|
5127 | if horOnly: | |
| 5130 | A = numpy.c_[dir_cosu,dir_cosv] |
|
5128 | A = numpy.c_[dir_cosu,dir_cosv] | |
| 5131 | else: |
|
5129 | else: | |
| 5132 | A = numpy.c_[dir_cosu,dir_cosv,dir_cosw] |
|
5130 | A = numpy.c_[dir_cosu,dir_cosv,dir_cosw] | |
| 5133 | A = numpy.asmatrix(A) |
|
5131 | A = numpy.asmatrix(A) | |
| 5134 | A1 = numpy.linalg.inv(A.transpose()*A)*A.transpose() |
|
5132 | A1 = numpy.linalg.inv(A.transpose()*A)*A.transpose() | |
| 5135 |
|
5133 | |||
| 5136 | return A1 |
|
5134 | return A1 | |
| 5137 |
|
5135 | |||
| 5138 | def __correctValues(self, heiRang, phi, velRadial, SNR): |
|
5136 | def __correctValues(self, heiRang, phi, velRadial, SNR): | |
| 5139 | listPhi = phi.tolist() |
|
5137 | listPhi = phi.tolist() | |
| 5140 | maxid = listPhi.index(max(listPhi)) |
|
5138 | maxid = listPhi.index(max(listPhi)) | |
| 5141 | minid = listPhi.index(min(listPhi)) |
|
5139 | minid = listPhi.index(min(listPhi)) | |
| 5142 |
|
5140 | |||
| 5143 | rango = list(range(len(phi))) |
|
5141 | rango = list(range(len(phi))) | |
| 5144 |
|
5142 | |||
| 5145 | heiRang1 = heiRang*math.cos(phi[maxid]) |
|
5143 | heiRang1 = heiRang*math.cos(phi[maxid]) | |
| 5146 | heiRangAux = heiRang*math.cos(phi[minid]) |
|
5144 | heiRangAux = heiRang*math.cos(phi[minid]) | |
| 5147 | indOut = (heiRang1 < heiRangAux[0]).nonzero() |
|
5145 | indOut = (heiRang1 < heiRangAux[0]).nonzero() | |
| 5148 | heiRang1 = numpy.delete(heiRang1,indOut) |
|
5146 | heiRang1 = numpy.delete(heiRang1,indOut) | |
| 5149 |
|
5147 | |||
| 5150 | velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
5148 | velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) | |
| 5151 | SNR1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
5149 | SNR1 = numpy.zeros([len(phi),len(heiRang1)]) | |
| 5152 |
|
5150 | |||
| 5153 | for i in rango: |
|
5151 | for i in rango: | |
| 5154 | x = heiRang*math.cos(phi[i]) |
|
5152 | x = heiRang*math.cos(phi[i]) | |
| 5155 | y1 = velRadial[i,:] |
|
5153 | y1 = velRadial[i,:] | |
| 5156 | f1 = interpolate.interp1d(x,y1,kind = 'cubic') |
|
5154 | f1 = interpolate.interp1d(x,y1,kind = 'cubic') | |
| 5157 |
|
5155 | |||
| 5158 | x1 = heiRang1 |
|
5156 | x1 = heiRang1 | |
| 5159 | y11 = f1(x1) |
|
5157 | y11 = f1(x1) | |
| 5160 |
|
5158 | |||
| 5161 | y2 = SNR[i,:] |
|
5159 | y2 = SNR[i,:] | |
| 5162 | f2 = interpolate.interp1d(x,y2,kind = 'cubic') |
|
5160 | f2 = interpolate.interp1d(x,y2,kind = 'cubic') | |
| 5163 | y21 = f2(x1) |
|
5161 | y21 = f2(x1) | |
| 5164 |
|
5162 | |||
| 5165 | velRadial1[i,:] = y11 |
|
5163 | velRadial1[i,:] = y11 | |
| 5166 | SNR1[i,:] = y21 |
|
5164 | SNR1[i,:] = y21 | |
| 5167 |
|
5165 | |||
| 5168 | return heiRang1, velRadial1, SNR1 |
|
5166 | return heiRang1, velRadial1, SNR1 | |
| 5169 |
|
5167 | |||
| 5170 | def __calculateVelUVW(self, A, velRadial): |
|
5168 | def __calculateVelUVW(self, A, velRadial): | |
| 5171 |
|
5169 | |||
| 5172 | #Operacion Matricial |
|
5170 | #Operacion Matricial | |
| 5173 | velUVW = numpy.zeros((A.shape[0],velRadial.shape[1])) |
|
5171 | velUVW = numpy.zeros((A.shape[0],velRadial.shape[1])) | |
| 5174 | velUVW[:,:] = numpy.dot(A,velRadial) |
|
5172 | velUVW[:,:] = numpy.dot(A,velRadial) | |
| 5175 |
|
5173 | |||
| 5176 |
|
5174 | |||
| 5177 | return velUVW |
|
5175 | return velUVW | |
| 5178 |
|
5176 | |||
| 5179 | def techniqueDBS(self, kwargs): |
|
5177 | def techniqueDBS(self, kwargs): | |
| 5180 | """ |
|
5178 | """ | |
| 5181 | Function that implements Doppler Beam Swinging (DBS) technique. |
|
5179 | Function that implements Doppler Beam Swinging (DBS) technique. | |
| 5182 |
|
5180 | |||
| 5183 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, |
|
5181 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, | |
| 5184 | Direction correction (if necessary), Ranges and SNR |
|
5182 | Direction correction (if necessary), Ranges and SNR | |
| 5185 |
|
5183 | |||
| 5186 | Output: Winds estimation (Zonal, Meridional and Vertical) |
|
5184 | Output: Winds estimation (Zonal, Meridional and Vertical) | |
| 5187 |
|
5185 | |||
| 5188 | Parameters affected: Winds, height range, SNR |
|
5186 | Parameters affected: Winds, height range, SNR | |
| 5189 | """ |
|
5187 | """ | |
| 5190 | velRadial0 = kwargs['velRadial'] |
|
5188 | velRadial0 = kwargs['velRadial'] | |
| 5191 | heiRang = kwargs['heightList'] |
|
5189 | heiRang = kwargs['heightList'] | |
| 5192 | SNR0 = kwargs['SNR'] |
|
5190 | SNR0 = kwargs['SNR'] | |
| 5193 |
|
5191 | |||
| 5194 | if 'dirCosx' in kwargs and 'dirCosy' in kwargs: |
|
5192 | if 'dirCosx' in kwargs and 'dirCosy' in kwargs: | |
| 5195 | theta_x = numpy.array(kwargs['dirCosx']) |
|
5193 | theta_x = numpy.array(kwargs['dirCosx']) | |
| 5196 | theta_y = numpy.array(kwargs['dirCosy']) |
|
5194 | theta_y = numpy.array(kwargs['dirCosy']) | |
| 5197 | else: |
|
5195 | else: | |
| 5198 | elev = numpy.array(kwargs['elevation']) |
|
5196 | elev = numpy.array(kwargs['elevation']) | |
| 5199 | azim = numpy.array(kwargs['azimuth']) |
|
5197 | azim = numpy.array(kwargs['azimuth']) | |
| 5200 | theta_x, theta_y = self.__calculateCosDir(elev, azim) |
|
5198 | theta_x, theta_y = self.__calculateCosDir(elev, azim) | |
| 5201 | azimuth = kwargs['correctAzimuth'] |
|
5199 | azimuth = kwargs['correctAzimuth'] | |
| 5202 | if 'horizontalOnly' in kwargs: |
|
5200 | if 'horizontalOnly' in kwargs: | |
| 5203 | horizontalOnly = kwargs['horizontalOnly'] |
|
5201 | horizontalOnly = kwargs['horizontalOnly'] | |
| 5204 | else: horizontalOnly = False |
|
5202 | else: horizontalOnly = False | |
| 5205 | if 'correctFactor' in kwargs: |
|
5203 | if 'correctFactor' in kwargs: | |
| 5206 | correctFactor = kwargs['correctFactor'] |
|
5204 | correctFactor = kwargs['correctFactor'] | |
| 5207 | else: correctFactor = 1 |
|
5205 | else: correctFactor = 1 | |
| 5208 | if 'channelList' in kwargs: |
|
5206 | if 'channelList' in kwargs: | |
| 5209 | channelList = kwargs['channelList'] |
|
5207 | channelList = kwargs['channelList'] | |
| 5210 | if len(channelList) == 2: |
|
5208 | if len(channelList) == 2: | |
| 5211 | horizontalOnly = True |
|
5209 | horizontalOnly = True | |
| 5212 | arrayChannel = numpy.array(channelList) |
|
5210 | arrayChannel = numpy.array(channelList) | |
| 5213 | param = param[arrayChannel,:,:] |
|
5211 | param = param[arrayChannel,:,:] | |
| 5214 | theta_x = theta_x[arrayChannel] |
|
5212 | theta_x = theta_x[arrayChannel] | |
| 5215 | theta_y = theta_y[arrayChannel] |
|
5213 | theta_y = theta_y[arrayChannel] | |
| 5216 |
|
5214 | |||
| 5217 | azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth) |
|
5215 | azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth) | |
| 5218 | heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, zenith_arr, correctFactor*velRadial0, SNR0) |
|
5216 | heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, zenith_arr, correctFactor*velRadial0, SNR0) | |
| 5219 | A = self.__calculateMatA(dir_cosu, dir_cosv, dir_cosw, horizontalOnly) |
|
5217 | A = self.__calculateMatA(dir_cosu, dir_cosv, dir_cosw, horizontalOnly) | |
| 5220 |
|
5218 | |||
| 5221 | #Calculo de Componentes de la velocidad con DBS |
|
5219 | #Calculo de Componentes de la velocidad con DBS | |
| 5222 | winds = self.__calculateVelUVW(A,velRadial1) |
|
5220 | winds = self.__calculateVelUVW(A,velRadial1) | |
| 5223 |
|
5221 | |||
| 5224 | return winds, heiRang1, SNR1 |
|
5222 | return winds, heiRang1, SNR1 | |
| 5225 |
|
5223 | |||
| 5226 | def __calculateDistance(self, posx, posy, pairs_ccf, azimuth = None): |
|
5224 | def __calculateDistance(self, posx, posy, pairs_ccf, azimuth = None): | |
| 5227 |
|
5225 | |||
| 5228 | nPairs = len(pairs_ccf) |
|
5226 | nPairs = len(pairs_ccf) | |
| 5229 | posx = numpy.asarray(posx) |
|
5227 | posx = numpy.asarray(posx) | |
| 5230 | posy = numpy.asarray(posy) |
|
5228 | posy = numpy.asarray(posy) | |
| 5231 |
|
5229 | |||
| 5232 | #Rotacion Inversa para alinear con el azimuth |
|
5230 | #Rotacion Inversa para alinear con el azimuth | |
| 5233 | if azimuth!= None: |
|
5231 | if azimuth!= None: | |
| 5234 | azimuth = azimuth*math.pi/180 |
|
5232 | azimuth = azimuth*math.pi/180 | |
| 5235 | posx1 = posx*math.cos(azimuth) + posy*math.sin(azimuth) |
|
5233 | posx1 = posx*math.cos(azimuth) + posy*math.sin(azimuth) | |
| 5236 | posy1 = -posx*math.sin(azimuth) + posy*math.cos(azimuth) |
|
5234 | posy1 = -posx*math.sin(azimuth) + posy*math.cos(azimuth) | |
| 5237 | else: |
|
5235 | else: | |
| 5238 | posx1 = posx |
|
5236 | posx1 = posx | |
| 5239 | posy1 = posy |
|
5237 | posy1 = posy | |
| 5240 |
|
5238 | |||
| 5241 | #Calculo de Distancias |
|
5239 | #Calculo de Distancias | |
| 5242 | distx = numpy.zeros(nPairs) |
|
5240 | distx = numpy.zeros(nPairs) | |
| 5243 | disty = numpy.zeros(nPairs) |
|
5241 | disty = numpy.zeros(nPairs) | |
| 5244 | dist = numpy.zeros(nPairs) |
|
5242 | dist = numpy.zeros(nPairs) | |
| 5245 | ang = numpy.zeros(nPairs) |
|
5243 | ang = numpy.zeros(nPairs) | |
| 5246 |
|
5244 | |||
| 5247 | for i in range(nPairs): |
|
5245 | for i in range(nPairs): | |
| 5248 | distx[i] = posx1[pairs_ccf[i][1]] - posx1[pairs_ccf[i][0]] |
|
5246 | distx[i] = posx1[pairs_ccf[i][1]] - posx1[pairs_ccf[i][0]] | |
| 5249 | disty[i] = posy1[pairs_ccf[i][1]] - posy1[pairs_ccf[i][0]] |
|
5247 | disty[i] = posy1[pairs_ccf[i][1]] - posy1[pairs_ccf[i][0]] | |
| 5250 | dist[i] = numpy.sqrt(distx[i]**2 + disty[i]**2) |
|
5248 | dist[i] = numpy.sqrt(distx[i]**2 + disty[i]**2) | |
| 5251 | ang[i] = numpy.arctan2(disty[i],distx[i]) |
|
5249 | ang[i] = numpy.arctan2(disty[i],distx[i]) | |
| 5252 |
|
5250 | |||
| 5253 | return distx, disty, dist, ang |
|
5251 | return distx, disty, dist, ang | |
| 5254 | #Calculo de Matrices |
|
5252 | #Calculo de Matrices | |
| 5255 |
|
5253 | |||
| 5256 | def __calculateVelVer(self, phase, lagTRange, _lambda): |
|
5254 | def __calculateVelVer(self, phase, lagTRange, _lambda): | |
| 5257 |
|
5255 | |||
| 5258 | Ts = lagTRange[1] - lagTRange[0] |
|
5256 | Ts = lagTRange[1] - lagTRange[0] | |
| 5259 | velW = -_lambda*phase/(4*math.pi*Ts) |
|
5257 | velW = -_lambda*phase/(4*math.pi*Ts) | |
| 5260 |
|
5258 | |||
| 5261 | return velW |
|
5259 | return velW | |
| 5262 |
|
5260 | |||
| 5263 | def __calculateVelHorDir(self, dist, tau1, tau2, ang): |
|
5261 | def __calculateVelHorDir(self, dist, tau1, tau2, ang): | |
| 5264 | nPairs = tau1.shape[0] |
|
5262 | nPairs = tau1.shape[0] | |
| 5265 | nHeights = tau1.shape[1] |
|
5263 | nHeights = tau1.shape[1] | |
| 5266 | vel = numpy.zeros((nPairs,3,nHeights)) |
|
5264 | vel = numpy.zeros((nPairs,3,nHeights)) | |
| 5267 | dist1 = numpy.reshape(dist, (dist.size,1)) |
|
5265 | dist1 = numpy.reshape(dist, (dist.size,1)) | |
| 5268 |
|
5266 | |||
| 5269 | angCos = numpy.cos(ang) |
|
5267 | angCos = numpy.cos(ang) | |
| 5270 | angSin = numpy.sin(ang) |
|
5268 | angSin = numpy.sin(ang) | |
| 5271 |
|
5269 | |||
| 5272 | vel0 = dist1*tau1/(2*tau2**2) |
|
5270 | vel0 = dist1*tau1/(2*tau2**2) | |
| 5273 | vel[:,0,:] = (vel0*angCos).sum(axis = 1) |
|
5271 | vel[:,0,:] = (vel0*angCos).sum(axis = 1) | |
| 5274 | vel[:,1,:] = (vel0*angSin).sum(axis = 1) |
|
5272 | vel[:,1,:] = (vel0*angSin).sum(axis = 1) | |
| 5275 |
|
5273 | |||
| 5276 | ind = numpy.where(numpy.isinf(vel)) |
|
5274 | ind = numpy.where(numpy.isinf(vel)) | |
| 5277 | vel[ind] = numpy.nan |
|
5275 | vel[ind] = numpy.nan | |
| 5278 |
|
5276 | |||
| 5279 | return vel |
|
5277 | return vel | |
| 5280 |
|
5278 | |||
| 5281 | def techniqueSA(self, kwargs): |
|
5279 | def techniqueSA(self, kwargs): | |
| 5282 |
|
5280 | |||
| 5283 | """ |
|
5281 | """ | |
| 5284 | Function that implements Spaced Antenna (SA) technique. |
|
5282 | Function that implements Spaced Antenna (SA) technique. | |
| 5285 |
|
5283 | |||
| 5286 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, |
|
5284 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, | |
| 5287 | Direction correction (if necessary), Ranges and SNR |
|
5285 | Direction correction (if necessary), Ranges and SNR | |
| 5288 |
|
5286 | |||
| 5289 | Output: Winds estimation (Zonal, Meridional and Vertical) |
|
5287 | Output: Winds estimation (Zonal, Meridional and Vertical) | |
| 5290 |
|
5288 | |||
| 5291 | Parameters affected: Winds |
|
5289 | Parameters affected: Winds | |
| 5292 | """ |
|
5290 | """ | |
| 5293 | position_x = kwargs['positionX'] |
|
5291 | position_x = kwargs['positionX'] | |
| 5294 | position_y = kwargs['positionY'] |
|
5292 | position_y = kwargs['positionY'] | |
| 5295 | azimuth = kwargs['azimuth'] |
|
5293 | azimuth = kwargs['azimuth'] | |
| 5296 |
|
5294 | |||
| 5297 | if 'correctFactor' in kwargs: |
|
5295 | if 'correctFactor' in kwargs: | |
| 5298 | correctFactor = kwargs['correctFactor'] |
|
5296 | correctFactor = kwargs['correctFactor'] | |
| 5299 | else: |
|
5297 | else: | |
| 5300 | correctFactor = 1 |
|
5298 | correctFactor = 1 | |
| 5301 |
|
5299 | |||
| 5302 | groupList = kwargs['groupList'] |
|
5300 | groupList = kwargs['groupList'] | |
| 5303 | pairs_ccf = groupList[1] |
|
5301 | pairs_ccf = groupList[1] | |
| 5304 | tau = kwargs['tau'] |
|
5302 | tau = kwargs['tau'] | |
| 5305 | _lambda = kwargs['_lambda'] |
|
5303 | _lambda = kwargs['_lambda'] | |
| 5306 |
|
5304 | |||
| 5307 | #Cross Correlation pairs obtained |
|
5305 | #Cross Correlation pairs obtained | |
| 5308 |
|
5306 | |||
| 5309 | indtau = tau.shape[0]/2 |
|
5307 | indtau = tau.shape[0]/2 | |
| 5310 | tau1 = tau[:indtau,:] |
|
5308 | tau1 = tau[:indtau,:] | |
| 5311 | tau2 = tau[indtau:-1,:] |
|
5309 | tau2 = tau[indtau:-1,:] | |
| 5312 | phase1 = tau[-1,:] |
|
5310 | phase1 = tau[-1,:] | |
| 5313 |
|
5311 | |||
| 5314 | #--------------------------------------------------------------------- |
|
5312 | #--------------------------------------------------------------------- | |
| 5315 | #Metodo Directo |
|
5313 | #Metodo Directo | |
| 5316 | distx, disty, dist, ang = self.__calculateDistance(position_x, position_y, pairs_ccf,azimuth) |
|
5314 | distx, disty, dist, ang = self.__calculateDistance(position_x, position_y, pairs_ccf,azimuth) | |
| 5317 | winds = self.__calculateVelHorDir(dist, tau1, tau2, ang) |
|
5315 | winds = self.__calculateVelHorDir(dist, tau1, tau2, ang) | |
| 5318 | winds = stats.nanmean(winds, axis=0) |
|
5316 | winds = stats.nanmean(winds, axis=0) | |
| 5319 | #--------------------------------------------------------------------- |
|
5317 | #--------------------------------------------------------------------- | |
| 5320 | #Metodo General |
|
5318 | #Metodo General | |
| 5321 |
|
5319 | |||
| 5322 | #--------------------------------------------------------------------- |
|
5320 | #--------------------------------------------------------------------- | |
| 5323 | winds[2,:] = self.__calculateVelVer(phase1, lagTRange, _lambda) |
|
5321 | winds[2,:] = self.__calculateVelVer(phase1, lagTRange, _lambda) | |
| 5324 | winds = correctFactor*winds |
|
5322 | winds = correctFactor*winds | |
| 5325 | return winds |
|
5323 | return winds | |
| 5326 |
|
5324 | |||
| 5327 | def __checkTime(self, currentTime, paramInterval, outputInterval): |
|
5325 | def __checkTime(self, currentTime, paramInterval, outputInterval): | |
| 5328 |
|
5326 | |||
| 5329 | dataTime = currentTime + paramInterval |
|
5327 | dataTime = currentTime + paramInterval | |
| 5330 | deltaTime = dataTime - self.__initime |
|
5328 | deltaTime = dataTime - self.__initime | |
| 5331 |
|
5329 | |||
| 5332 | if deltaTime >= outputInterval or deltaTime < 0: |
|
5330 | if deltaTime >= outputInterval or deltaTime < 0: | |
| 5333 | self.__dataReady = True |
|
5331 | self.__dataReady = True | |
| 5334 | return |
|
5332 | return | |
| 5335 |
|
5333 | |||
| 5336 | def techniqueMeteors(self, arrayMeteor, meteorThresh, heightMin, heightMax): |
|
5334 | def techniqueMeteors(self, arrayMeteor, meteorThresh, heightMin, heightMax): | |
| 5337 | ''' |
|
5335 | ''' | |
| 5338 | Function that implements winds estimation technique with detected meteors. |
|
5336 | Function that implements winds estimation technique with detected meteors. | |
| 5339 |
|
5337 | |||
| 5340 | Input: Detected meteors, Minimum meteor quantity to wind estimation |
|
5338 | Input: Detected meteors, Minimum meteor quantity to wind estimation | |
| 5341 |
|
5339 | |||
| 5342 | Output: Winds estimation (Zonal and Meridional) |
|
5340 | Output: Winds estimation (Zonal and Meridional) | |
| 5343 |
|
5341 | |||
| 5344 | Parameters affected: Winds |
|
5342 | Parameters affected: Winds | |
| 5345 | ''' |
|
5343 | ''' | |
| 5346 | #Settings |
|
5344 | #Settings | |
| 5347 | nInt = (heightMax - heightMin)/2 |
|
5345 | nInt = (heightMax - heightMin)/2 | |
| 5348 | nInt = int(nInt) |
|
5346 | nInt = int(nInt) | |
| 5349 | winds = numpy.zeros((2,nInt))*numpy.nan |
|
5347 | winds = numpy.zeros((2,nInt))*numpy.nan | |
| 5350 |
|
5348 | |||
| 5351 | #Filter errors |
|
5349 | #Filter errors | |
| 5352 | error = numpy.where(arrayMeteor[:,-1] == 0)[0] |
|
5350 | error = numpy.where(arrayMeteor[:,-1] == 0)[0] | |
| 5353 | finalMeteor = arrayMeteor[error,:] |
|
5351 | finalMeteor = arrayMeteor[error,:] | |
| 5354 |
|
5352 | |||
| 5355 | #Meteor Histogram |
|
5353 | #Meteor Histogram | |
| 5356 | finalHeights = finalMeteor[:,2] |
|
5354 | finalHeights = finalMeteor[:,2] | |
| 5357 | hist = numpy.histogram(finalHeights, bins = nInt, range = (heightMin,heightMax)) |
|
5355 | hist = numpy.histogram(finalHeights, bins = nInt, range = (heightMin,heightMax)) | |
| 5358 | nMeteorsPerI = hist[0] |
|
5356 | nMeteorsPerI = hist[0] | |
| 5359 | heightPerI = hist[1] |
|
5357 | heightPerI = hist[1] | |
| 5360 |
|
5358 | |||
| 5361 | #Sort of meteors |
|
5359 | #Sort of meteors | |
| 5362 | indSort = finalHeights.argsort() |
|
5360 | indSort = finalHeights.argsort() | |
| 5363 | finalMeteor2 = finalMeteor[indSort,:] |
|
5361 | finalMeteor2 = finalMeteor[indSort,:] | |
| 5364 |
|
5362 | |||
| 5365 | # Calculating winds |
|
5363 | # Calculating winds | |
| 5366 | ind1 = 0 |
|
5364 | ind1 = 0 | |
| 5367 | ind2 = 0 |
|
5365 | ind2 = 0 | |
| 5368 |
|
5366 | |||
| 5369 | for i in range(nInt): |
|
5367 | for i in range(nInt): | |
| 5370 | nMet = nMeteorsPerI[i] |
|
5368 | nMet = nMeteorsPerI[i] | |
| 5371 | ind1 = ind2 |
|
5369 | ind1 = ind2 | |
| 5372 | ind2 = ind1 + nMet |
|
5370 | ind2 = ind1 + nMet | |
| 5373 |
|
5371 | |||
| 5374 | meteorAux = finalMeteor2[ind1:ind2,:] |
|
5372 | meteorAux = finalMeteor2[ind1:ind2,:] | |
| 5375 |
|
5373 | |||
| 5376 | if meteorAux.shape[0] >= meteorThresh: |
|
5374 | if meteorAux.shape[0] >= meteorThresh: | |
| 5377 | vel = meteorAux[:, 6] |
|
5375 | vel = meteorAux[:, 6] | |
| 5378 | zen = meteorAux[:, 4]*numpy.pi/180 |
|
5376 | zen = meteorAux[:, 4]*numpy.pi/180 | |
| 5379 | azim = meteorAux[:, 3]*numpy.pi/180 |
|
5377 | azim = meteorAux[:, 3]*numpy.pi/180 | |
| 5380 |
|
5378 | |||
| 5381 | n = numpy.cos(zen) |
|
5379 | n = numpy.cos(zen) | |
| 5382 | l = numpy.sin(zen)*numpy.sin(azim) |
|
5380 | l = numpy.sin(zen)*numpy.sin(azim) | |
| 5383 | m = numpy.sin(zen)*numpy.cos(azim) |
|
5381 | m = numpy.sin(zen)*numpy.cos(azim) | |
| 5384 |
|
5382 | |||
| 5385 | A = numpy.vstack((l, m)).transpose() |
|
5383 | A = numpy.vstack((l, m)).transpose() | |
| 5386 | A1 = numpy.dot(numpy.linalg.inv( numpy.dot(A.transpose(),A) ),A.transpose()) |
|
5384 | A1 = numpy.dot(numpy.linalg.inv( numpy.dot(A.transpose(),A) ),A.transpose()) | |
| 5387 | windsAux = numpy.dot(A1, vel) |
|
5385 | windsAux = numpy.dot(A1, vel) | |
| 5388 |
|
5386 | |||
| 5389 | winds[0,i] = windsAux[0] |
|
5387 | winds[0,i] = windsAux[0] | |
| 5390 | winds[1,i] = windsAux[1] |
|
5388 | winds[1,i] = windsAux[1] | |
| 5391 |
|
5389 | |||
| 5392 | return winds, heightPerI[:-1] |
|
5390 | return winds, heightPerI[:-1] | |
| 5393 |
|
5391 | |||
| 5394 | def techniqueNSM_SA(self, **kwargs): |
|
5392 | def techniqueNSM_SA(self, **kwargs): | |
| 5395 | metArray = kwargs['metArray'] |
|
5393 | metArray = kwargs['metArray'] | |
| 5396 | heightList = kwargs['heightList'] |
|
5394 | heightList = kwargs['heightList'] | |
| 5397 | timeList = kwargs['timeList'] |
|
5395 | timeList = kwargs['timeList'] | |
| 5398 |
|
5396 | |||
| 5399 | rx_location = kwargs['rx_location'] |
|
5397 | rx_location = kwargs['rx_location'] | |
| 5400 | groupList = kwargs['groupList'] |
|
5398 | groupList = kwargs['groupList'] | |
| 5401 | azimuth = kwargs['azimuth'] |
|
5399 | azimuth = kwargs['azimuth'] | |
| 5402 | dfactor = kwargs['dfactor'] |
|
5400 | dfactor = kwargs['dfactor'] | |
| 5403 | k = kwargs['k'] |
|
5401 | k = kwargs['k'] | |
| 5404 |
|
5402 | |||
| 5405 | azimuth1, dist = self.__calculateAzimuth1(rx_location, groupList, azimuth) |
|
5403 | azimuth1, dist = self.__calculateAzimuth1(rx_location, groupList, azimuth) | |
| 5406 | d = dist*dfactor |
|
5404 | d = dist*dfactor | |
| 5407 | #Phase calculation |
|
5405 | #Phase calculation | |
| 5408 | metArray1 = self.__getPhaseSlope(metArray, heightList, timeList) |
|
5406 | metArray1 = self.__getPhaseSlope(metArray, heightList, timeList) | |
| 5409 |
|
5407 | |||
| 5410 | metArray1[:,-2] = metArray1[:,-2]*metArray1[:,2]*1000/(k*d[metArray1[:,1].astype(int)]) #angles into velocities |
|
5408 | metArray1[:,-2] = metArray1[:,-2]*metArray1[:,2]*1000/(k*d[metArray1[:,1].astype(int)]) #angles into velocities | |
| 5411 |
|
5409 | |||
| 5412 | velEst = numpy.zeros((heightList.size,2))*numpy.nan |
|
5410 | velEst = numpy.zeros((heightList.size,2))*numpy.nan | |
| 5413 | azimuth1 = azimuth1*numpy.pi/180 |
|
5411 | azimuth1 = azimuth1*numpy.pi/180 | |
| 5414 |
|
5412 | |||
| 5415 | for i in range(heightList.size): |
|
5413 | for i in range(heightList.size): | |
| 5416 | h = heightList[i] |
|
5414 | h = heightList[i] | |
| 5417 | indH = numpy.where((metArray1[:,2] == h)&(numpy.abs(metArray1[:,-2]) < 100))[0] |
|
5415 | indH = numpy.where((metArray1[:,2] == h)&(numpy.abs(metArray1[:,-2]) < 100))[0] | |
| 5418 | metHeight = metArray1[indH,:] |
|
5416 | metHeight = metArray1[indH,:] | |
| 5419 | if metHeight.shape[0] >= 2: |
|
5417 | if metHeight.shape[0] >= 2: | |
| 5420 | velAux = numpy.asmatrix(metHeight[:,-2]).T #Radial Velocities |
|
5418 | velAux = numpy.asmatrix(metHeight[:,-2]).T #Radial Velocities | |
| 5421 | iazim = metHeight[:,1].astype(int) |
|
5419 | iazim = metHeight[:,1].astype(int) | |
| 5422 | azimAux = numpy.asmatrix(azimuth1[iazim]).T #Azimuths |
|
5420 | azimAux = numpy.asmatrix(azimuth1[iazim]).T #Azimuths | |
| 5423 | A = numpy.hstack((numpy.cos(azimAux),numpy.sin(azimAux))) |
|
5421 | A = numpy.hstack((numpy.cos(azimAux),numpy.sin(azimAux))) | |
| 5424 | A = numpy.asmatrix(A) |
|
5422 | A = numpy.asmatrix(A) | |
| 5425 | A1 = numpy.linalg.pinv(A.transpose()*A)*A.transpose() |
|
5423 | A1 = numpy.linalg.pinv(A.transpose()*A)*A.transpose() | |
| 5426 | velHor = numpy.dot(A1,velAux) |
|
5424 | velHor = numpy.dot(A1,velAux) | |
| 5427 |
|
5425 | |||
| 5428 | velEst[i,:] = numpy.squeeze(velHor) |
|
5426 | velEst[i,:] = numpy.squeeze(velHor) | |
| 5429 | return velEst |
|
5427 | return velEst | |
| 5430 |
|
5428 | |||
| 5431 | def __getPhaseSlope(self, metArray, heightList, timeList): |
|
5429 | def __getPhaseSlope(self, metArray, heightList, timeList): | |
| 5432 | meteorList = [] |
|
5430 | meteorList = [] | |
| 5433 | #utctime sec1 height SNR velRad ph0 ph1 ph2 coh0 coh1 coh2 |
|
5431 | #utctime sec1 height SNR velRad ph0 ph1 ph2 coh0 coh1 coh2 | |
| 5434 | #Putting back together the meteor matrix |
|
5432 | #Putting back together the meteor matrix | |
| 5435 | utctime = metArray[:,0] |
|
5433 | utctime = metArray[:,0] | |
| 5436 | uniqueTime = numpy.unique(utctime) |
|
5434 | uniqueTime = numpy.unique(utctime) | |
| 5437 |
|
5435 | |||
| 5438 | phaseDerThresh = 0.5 |
|
5436 | phaseDerThresh = 0.5 | |
| 5439 | ippSeconds = timeList[1] - timeList[0] |
|
5437 | ippSeconds = timeList[1] - timeList[0] | |
| 5440 | sec = numpy.where(timeList>1)[0][0] |
|
5438 | sec = numpy.where(timeList>1)[0][0] | |
| 5441 | nPairs = metArray.shape[1] - 6 |
|
5439 | nPairs = metArray.shape[1] - 6 | |
| 5442 | nHeights = len(heightList) |
|
5440 | nHeights = len(heightList) | |
| 5443 |
|
5441 | |||
| 5444 | for t in uniqueTime: |
|
5442 | for t in uniqueTime: | |
| 5445 | metArray1 = metArray[utctime==t,:] |
|
5443 | metArray1 = metArray[utctime==t,:] | |
| 5446 | tmet = metArray1[:,1].astype(int) |
|
5444 | tmet = metArray1[:,1].astype(int) | |
| 5447 | hmet = metArray1[:,2].astype(int) |
|
5445 | hmet = metArray1[:,2].astype(int) | |
| 5448 |
|
5446 | |||
| 5449 | metPhase = numpy.zeros((nPairs, heightList.size, timeList.size - 1)) |
|
5447 | metPhase = numpy.zeros((nPairs, heightList.size, timeList.size - 1)) | |
| 5450 | metPhase[:,:] = numpy.nan |
|
5448 | metPhase[:,:] = numpy.nan | |
| 5451 | metPhase[:,hmet,tmet] = metArray1[:,6:].T |
|
5449 | metPhase[:,hmet,tmet] = metArray1[:,6:].T | |
| 5452 |
|
5450 | |||
| 5453 | #Delete short trails |
|
5451 | #Delete short trails | |
| 5454 | metBool = ~numpy.isnan(metPhase[0,:,:]) |
|
5452 | metBool = ~numpy.isnan(metPhase[0,:,:]) | |
| 5455 | heightVect = numpy.sum(metBool, axis = 1) |
|
5453 | heightVect = numpy.sum(metBool, axis = 1) | |
| 5456 | metBool[heightVect<sec,:] = False |
|
5454 | metBool[heightVect<sec,:] = False | |
| 5457 | metPhase[:,heightVect<sec,:] = numpy.nan |
|
5455 | metPhase[:,heightVect<sec,:] = numpy.nan | |
| 5458 |
|
5456 | |||
| 5459 | #Derivative |
|
5457 | #Derivative | |
| 5460 | metDer = numpy.abs(metPhase[:,:,1:] - metPhase[:,:,:-1]) |
|
5458 | metDer = numpy.abs(metPhase[:,:,1:] - metPhase[:,:,:-1]) | |
| 5461 | phDerAux = numpy.dstack((numpy.full((nPairs,nHeights,1), False, dtype=bool),metDer > phaseDerThresh)) |
|
5459 | phDerAux = numpy.dstack((numpy.full((nPairs,nHeights,1), False, dtype=bool),metDer > phaseDerThresh)) | |
| 5462 | metPhase[phDerAux] = numpy.nan |
|
5460 | metPhase[phDerAux] = numpy.nan | |
| 5463 |
|
5461 | |||
| 5464 | #--------------------------METEOR DETECTION ----------------------------------------- |
|
5462 | #--------------------------METEOR DETECTION ----------------------------------------- | |
| 5465 | indMet = numpy.where(numpy.any(metBool,axis=1))[0] |
|
5463 | indMet = numpy.where(numpy.any(metBool,axis=1))[0] | |
| 5466 |
|
5464 | |||
| 5467 | for p in numpy.arange(nPairs): |
|
5465 | for p in numpy.arange(nPairs): | |
| 5468 | phase = metPhase[p,:,:] |
|
5466 | phase = metPhase[p,:,:] | |
| 5469 | phDer = metDer[p,:,:] |
|
5467 | phDer = metDer[p,:,:] | |
| 5470 |
|
5468 | |||
| 5471 | for h in indMet: |
|
5469 | for h in indMet: | |
| 5472 | height = heightList[h] |
|
5470 | height = heightList[h] | |
| 5473 | phase1 = phase[h,:] #82 |
|
5471 | phase1 = phase[h,:] #82 | |
| 5474 | phDer1 = phDer[h,:] |
|
5472 | phDer1 = phDer[h,:] | |
| 5475 |
|
5473 | |||
| 5476 | phase1[~numpy.isnan(phase1)] = numpy.unwrap(phase1[~numpy.isnan(phase1)]) #Unwrap |
|
5474 | phase1[~numpy.isnan(phase1)] = numpy.unwrap(phase1[~numpy.isnan(phase1)]) #Unwrap | |
| 5477 |
|
5475 | |||
| 5478 | indValid = numpy.where(~numpy.isnan(phase1))[0] |
|
5476 | indValid = numpy.where(~numpy.isnan(phase1))[0] | |
| 5479 | initMet = indValid[0] |
|
5477 | initMet = indValid[0] | |
| 5480 | endMet = 0 |
|
5478 | endMet = 0 | |
| 5481 |
|
5479 | |||
| 5482 | for i in range(len(indValid)-1): |
|
5480 | for i in range(len(indValid)-1): | |
| 5483 |
|
5481 | |||
| 5484 | #Time difference |
|
5482 | #Time difference | |
| 5485 | inow = indValid[i] |
|
5483 | inow = indValid[i] | |
| 5486 | inext = indValid[i+1] |
|
5484 | inext = indValid[i+1] | |
| 5487 | idiff = inext - inow |
|
5485 | idiff = inext - inow | |
| 5488 | #Phase difference |
|
5486 | #Phase difference | |
| 5489 | phDiff = numpy.abs(phase1[inext] - phase1[inow]) |
|
5487 | phDiff = numpy.abs(phase1[inext] - phase1[inow]) | |
| 5490 |
|
5488 | |||
| 5491 | if idiff>sec or phDiff>numpy.pi/4 or inext==indValid[-1]: #End of Meteor |
|
5489 | if idiff>sec or phDiff>numpy.pi/4 or inext==indValid[-1]: #End of Meteor | |
| 5492 | sizeTrail = inow - initMet + 1 |
|
5490 | sizeTrail = inow - initMet + 1 | |
| 5493 | if sizeTrail>3*sec: #Too short meteors |
|
5491 | if sizeTrail>3*sec: #Too short meteors | |
| 5494 | x = numpy.arange(initMet,inow+1)*ippSeconds |
|
5492 | x = numpy.arange(initMet,inow+1)*ippSeconds | |
| 5495 | y = phase1[initMet:inow+1] |
|
5493 | y = phase1[initMet:inow+1] | |
| 5496 | ynnan = ~numpy.isnan(y) |
|
5494 | ynnan = ~numpy.isnan(y) | |
| 5497 | x = x[ynnan] |
|
5495 | x = x[ynnan] | |
| 5498 | y = y[ynnan] |
|
5496 | y = y[ynnan] | |
| 5499 | slope, intercept, r_value, p_value, std_err = stats.linregress(x,y) |
|
5497 | slope, intercept, r_value, p_value, std_err = stats.linregress(x,y) | |
| 5500 | ylin = x*slope + intercept |
|
5498 | ylin = x*slope + intercept | |
| 5501 | rsq = r_value**2 |
|
5499 | rsq = r_value**2 | |
| 5502 | if rsq > 0.5: |
|
5500 | if rsq > 0.5: | |
| 5503 | vel = slope#*height*1000/(k*d) |
|
5501 | vel = slope#*height*1000/(k*d) | |
| 5504 | estAux = numpy.array([utctime,p,height, vel, rsq]) |
|
5502 | estAux = numpy.array([utctime,p,height, vel, rsq]) | |
| 5505 | meteorList.append(estAux) |
|
5503 | meteorList.append(estAux) | |
| 5506 | initMet = inext |
|
5504 | initMet = inext | |
| 5507 | metArray2 = numpy.array(meteorList) |
|
5505 | metArray2 = numpy.array(meteorList) | |
| 5508 |
|
5506 | |||
| 5509 | return metArray2 |
|
5507 | return metArray2 | |
| 5510 |
|
5508 | |||
| 5511 | def __calculateAzimuth1(self, rx_location, pairslist, azimuth0): |
|
5509 | def __calculateAzimuth1(self, rx_location, pairslist, azimuth0): | |
| 5512 |
|
5510 | |||
| 5513 | azimuth1 = numpy.zeros(len(pairslist)) |
|
5511 | azimuth1 = numpy.zeros(len(pairslist)) | |
| 5514 | dist = numpy.zeros(len(pairslist)) |
|
5512 | dist = numpy.zeros(len(pairslist)) | |
| 5515 |
|
5513 | |||
| 5516 | for i in range(len(rx_location)): |
|
5514 | for i in range(len(rx_location)): | |
| 5517 | ch0 = pairslist[i][0] |
|
5515 | ch0 = pairslist[i][0] | |
| 5518 | ch1 = pairslist[i][1] |
|
5516 | ch1 = pairslist[i][1] | |
| 5519 |
|
5517 | |||
| 5520 | diffX = rx_location[ch0][0] - rx_location[ch1][0] |
|
5518 | diffX = rx_location[ch0][0] - rx_location[ch1][0] | |
| 5521 | diffY = rx_location[ch0][1] - rx_location[ch1][1] |
|
5519 | diffY = rx_location[ch0][1] - rx_location[ch1][1] | |
| 5522 | azimuth1[i] = numpy.arctan2(diffY,diffX)*180/numpy.pi |
|
5520 | azimuth1[i] = numpy.arctan2(diffY,diffX)*180/numpy.pi | |
| 5523 | dist[i] = numpy.sqrt(diffX**2 + diffY**2) |
|
5521 | dist[i] = numpy.sqrt(diffX**2 + diffY**2) | |
| 5524 |
|
5522 | |||
| 5525 | azimuth1 -= azimuth0 |
|
5523 | azimuth1 -= azimuth0 | |
| 5526 | return azimuth1, dist |
|
5524 | return azimuth1, dist | |
| 5527 |
|
5525 | |||
| 5528 | def techniqueNSM_DBS(self, **kwargs): |
|
5526 | def techniqueNSM_DBS(self, **kwargs): | |
| 5529 | metArray = kwargs['metArray'] |
|
5527 | metArray = kwargs['metArray'] | |
| 5530 | heightList = kwargs['heightList'] |
|
5528 | heightList = kwargs['heightList'] | |
| 5531 | timeList = kwargs['timeList'] |
|
5529 | timeList = kwargs['timeList'] | |
| 5532 | azimuth = kwargs['azimuth'] |
|
5530 | azimuth = kwargs['azimuth'] | |
| 5533 | theta_x = numpy.array(kwargs['theta_x']) |
|
5531 | theta_x = numpy.array(kwargs['theta_x']) | |
| 5534 | theta_y = numpy.array(kwargs['theta_y']) |
|
5532 | theta_y = numpy.array(kwargs['theta_y']) | |
| 5535 |
|
5533 | |||
| 5536 | utctime = metArray[:,0] |
|
5534 | utctime = metArray[:,0] | |
| 5537 | cmet = metArray[:,1].astype(int) |
|
5535 | cmet = metArray[:,1].astype(int) | |
| 5538 | hmet = metArray[:,3].astype(int) |
|
5536 | hmet = metArray[:,3].astype(int) | |
| 5539 | SNRmet = metArray[:,4] |
|
5537 | SNRmet = metArray[:,4] | |
| 5540 | vmet = metArray[:,5] |
|
5538 | vmet = metArray[:,5] | |
| 5541 | spcmet = metArray[:,6] |
|
5539 | spcmet = metArray[:,6] | |
| 5542 |
|
5540 | |||
| 5543 | nChan = numpy.max(cmet) + 1 |
|
5541 | nChan = numpy.max(cmet) + 1 | |
| 5544 | nHeights = len(heightList) |
|
5542 | nHeights = len(heightList) | |
| 5545 |
|
5543 | |||
| 5546 | azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth) |
|
5544 | azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth) | |
| 5547 | hmet = heightList[hmet] |
|
5545 | hmet = heightList[hmet] | |
| 5548 | h1met = hmet*numpy.cos(zenith_arr[cmet]) #Corrected heights |
|
5546 | h1met = hmet*numpy.cos(zenith_arr[cmet]) #Corrected heights | |
| 5549 |
|
5547 | |||
| 5550 | velEst = numpy.zeros((heightList.size,2))*numpy.nan |
|
5548 | velEst = numpy.zeros((heightList.size,2))*numpy.nan | |
| 5551 |
|
5549 | |||
| 5552 | for i in range(nHeights - 1): |
|
5550 | for i in range(nHeights - 1): | |
| 5553 | hmin = heightList[i] |
|
5551 | hmin = heightList[i] | |
| 5554 | hmax = heightList[i + 1] |
|
5552 | hmax = heightList[i + 1] | |
| 5555 |
|
5553 | |||
| 5556 | thisH = (h1met>=hmin) & (h1met<hmax) & (cmet!=2) & (SNRmet>8) & (vmet<50) & (spcmet<10) |
|
5554 | thisH = (h1met>=hmin) & (h1met<hmax) & (cmet!=2) & (SNRmet>8) & (vmet<50) & (spcmet<10) | |
| 5557 | indthisH = numpy.where(thisH) |
|
5555 | indthisH = numpy.where(thisH) | |
| 5558 |
|
5556 | |||
| 5559 | if numpy.size(indthisH) > 3: |
|
5557 | if numpy.size(indthisH) > 3: | |
| 5560 |
|
5558 | |||
| 5561 | vel_aux = vmet[thisH] |
|
5559 | vel_aux = vmet[thisH] | |
| 5562 | chan_aux = cmet[thisH] |
|
5560 | chan_aux = cmet[thisH] | |
| 5563 | cosu_aux = dir_cosu[chan_aux] |
|
5561 | cosu_aux = dir_cosu[chan_aux] | |
| 5564 | cosv_aux = dir_cosv[chan_aux] |
|
5562 | cosv_aux = dir_cosv[chan_aux] | |
| 5565 | cosw_aux = dir_cosw[chan_aux] |
|
5563 | cosw_aux = dir_cosw[chan_aux] | |
| 5566 |
|
5564 | |||
| 5567 | nch = numpy.size(numpy.unique(chan_aux)) |
|
5565 | nch = numpy.size(numpy.unique(chan_aux)) | |
| 5568 | if nch > 1: |
|
5566 | if nch > 1: | |
| 5569 | A = self.__calculateMatA(cosu_aux, cosv_aux, cosw_aux, True) |
|
5567 | A = self.__calculateMatA(cosu_aux, cosv_aux, cosw_aux, True) | |
| 5570 | velEst[i,:] = numpy.dot(A,vel_aux) |
|
5568 | velEst[i,:] = numpy.dot(A,vel_aux) | |
| 5571 |
|
5569 | |||
| 5572 | return velEst |
|
5570 | return velEst | |
| 5573 |
|
5571 | |||
| 5574 | def run(self, dataOut, technique, nHours=1, hmin=70, hmax=110, **kwargs): |
|
5572 | def run(self, dataOut, technique, nHours=1, hmin=70, hmax=110, **kwargs): | |
| 5575 |
|
5573 | |||
| 5576 | param = dataOut.moments |
|
5574 | param = dataOut.moments | |
| 5577 | if numpy.any(dataOut.abscissaList): |
|
5575 | if numpy.any(dataOut.abscissaList): | |
| 5578 | absc = dataOut.abscissaList[:-1] |
|
5576 | absc = dataOut.abscissaList[:-1] | |
| 5579 | # noise = dataOut.noise |
|
5577 | # noise = dataOut.noise | |
| 5580 | heightList = dataOut.heightList |
|
5578 | heightList = dataOut.heightList | |
| 5581 | SNR = dataOut.data_snr |
|
5579 | SNR = dataOut.data_snr | |
| 5582 |
|
5580 | |||
| 5583 | if technique == 'DBS': |
|
5581 | if technique == 'DBS': | |
| 5584 |
|
5582 | |||
| 5585 | kwargs['velRadial'] = param[:,1,:] #Radial velocity |
|
5583 | kwargs['velRadial'] = param[:,1,:] #Radial velocity | |
| 5586 | kwargs['heightList'] = heightList |
|
5584 | kwargs['heightList'] = heightList | |
| 5587 | kwargs['SNR'] = SNR |
|
5585 | kwargs['SNR'] = SNR | |
| 5588 |
|
5586 | |||
| 5589 | dataOut.data_output, dataOut.heightList, dataOut.data_snr = self.techniqueDBS(kwargs) #DBS Function |
|
5587 | dataOut.data_output, dataOut.heightList, dataOut.data_snr = self.techniqueDBS(kwargs) #DBS Function | |
| 5590 | dataOut.utctimeInit = dataOut.utctime |
|
5588 | dataOut.utctimeInit = dataOut.utctime | |
| 5591 | dataOut.outputInterval = dataOut.paramInterval |
|
5589 | dataOut.outputInterval = dataOut.paramInterval | |
| 5592 |
|
5590 | |||
| 5593 | elif technique == 'SA': |
|
5591 | elif technique == 'SA': | |
| 5594 |
|
5592 | |||
| 5595 | #Parameters |
|
5593 | #Parameters | |
| 5596 | kwargs['groupList'] = dataOut.groupList |
|
5594 | kwargs['groupList'] = dataOut.groupList | |
| 5597 | kwargs['tau'] = dataOut.data_param |
|
5595 | kwargs['tau'] = dataOut.data_param | |
| 5598 | kwargs['_lambda'] = dataOut.C/dataOut.frequency |
|
5596 | kwargs['_lambda'] = dataOut.C/dataOut.frequency | |
| 5599 | dataOut.data_output = self.techniqueSA(kwargs) |
|
5597 | dataOut.data_output = self.techniqueSA(kwargs) | |
| 5600 | dataOut.utctimeInit = dataOut.utctime |
|
5598 | dataOut.utctimeInit = dataOut.utctime | |
| 5601 | dataOut.outputInterval = dataOut.timeInterval |
|
5599 | dataOut.outputInterval = dataOut.timeInterval | |
| 5602 |
|
5600 | |||
| 5603 | elif technique == 'Meteors': |
|
5601 | elif technique == 'Meteors': | |
| 5604 | dataOut.flagNoData = True |
|
5602 | dataOut.flagNoData = True | |
| 5605 | self.__dataReady = False |
|
5603 | self.__dataReady = False | |
| 5606 |
|
5604 | |||
| 5607 | if 'nHours' in kwargs: |
|
5605 | if 'nHours' in kwargs: | |
| 5608 | nHours = kwargs['nHours'] |
|
5606 | nHours = kwargs['nHours'] | |
| 5609 | else: |
|
5607 | else: | |
| 5610 | nHours = 1 |
|
5608 | nHours = 1 | |
| 5611 |
|
5609 | |||
| 5612 | if 'meteorsPerBin' in kwargs: |
|
5610 | if 'meteorsPerBin' in kwargs: | |
| 5613 | meteorThresh = kwargs['meteorsPerBin'] |
|
5611 | meteorThresh = kwargs['meteorsPerBin'] | |
| 5614 | else: |
|
5612 | else: | |
| 5615 | meteorThresh = 6 |
|
5613 | meteorThresh = 6 | |
| 5616 |
|
5614 | |||
| 5617 | if 'hmin' in kwargs: |
|
5615 | if 'hmin' in kwargs: | |
| 5618 | hmin = kwargs['hmin'] |
|
5616 | hmin = kwargs['hmin'] | |
| 5619 | else: hmin = 70 |
|
5617 | else: hmin = 70 | |
| 5620 | if 'hmax' in kwargs: |
|
5618 | if 'hmax' in kwargs: | |
| 5621 | hmax = kwargs['hmax'] |
|
5619 | hmax = kwargs['hmax'] | |
| 5622 | else: hmax = 110 |
|
5620 | else: hmax = 110 | |
| 5623 |
|
5621 | |||
| 5624 | dataOut.outputInterval = nHours*3600 |
|
5622 | dataOut.outputInterval = nHours*3600 | |
| 5625 |
|
5623 | |||
| 5626 | if self.__isConfig == False: |
|
5624 | if self.__isConfig == False: | |
| 5627 | #Get Initial LTC time |
|
5625 | #Get Initial LTC time | |
| 5628 | self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
5626 | self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) | |
| 5629 | self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
5627 | self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() | |
| 5630 |
|
5628 | |||
| 5631 | self.__isConfig = True |
|
5629 | self.__isConfig = True | |
| 5632 |
|
5630 | |||
| 5633 | if self.__buffer is None: |
|
5631 | if self.__buffer is None: | |
| 5634 | self.__buffer = dataOut.data_param |
|
5632 | self.__buffer = dataOut.data_param | |
| 5635 | self.__firstdata = copy.copy(dataOut) |
|
5633 | self.__firstdata = copy.copy(dataOut) | |
| 5636 |
|
5634 | |||
| 5637 | else: |
|
5635 | else: | |
| 5638 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
5636 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) | |
| 5639 |
|
5637 | |||
| 5640 | self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
5638 | self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready | |
| 5641 |
|
5639 | |||
| 5642 | if self.__dataReady: |
|
5640 | if self.__dataReady: | |
| 5643 | dataOut.utctimeInit = self.__initime |
|
5641 | dataOut.utctimeInit = self.__initime | |
| 5644 |
|
5642 | |||
| 5645 | self.__initime += dataOut.outputInterval #to erase time offset |
|
5643 | self.__initime += dataOut.outputInterval #to erase time offset | |
| 5646 |
|
5644 | |||
| 5647 | dataOut.data_output, dataOut.heightList = self.techniqueMeteors(self.__buffer, meteorThresh, hmin, hmax) |
|
5645 | dataOut.data_output, dataOut.heightList = self.techniqueMeteors(self.__buffer, meteorThresh, hmin, hmax) | |
| 5648 | dataOut.flagNoData = False |
|
5646 | dataOut.flagNoData = False | |
| 5649 | self.__buffer = None |
|
5647 | self.__buffer = None | |
| 5650 |
|
5648 | |||
| 5651 | elif technique == 'Meteors1': |
|
5649 | elif technique == 'Meteors1': | |
| 5652 | dataOut.flagNoData = True |
|
5650 | dataOut.flagNoData = True | |
| 5653 | self.__dataReady = False |
|
5651 | self.__dataReady = False | |
| 5654 |
|
5652 | |||
| 5655 | if 'nMins' in kwargs: |
|
5653 | if 'nMins' in kwargs: | |
| 5656 | nMins = kwargs['nMins'] |
|
5654 | nMins = kwargs['nMins'] | |
| 5657 | else: nMins = 20 |
|
5655 | else: nMins = 20 | |
| 5658 | if 'rx_location' in kwargs: |
|
5656 | if 'rx_location' in kwargs: | |
| 5659 | rx_location = kwargs['rx_location'] |
|
5657 | rx_location = kwargs['rx_location'] | |
| 5660 | else: rx_location = [(0,1),(1,1),(1,0)] |
|
5658 | else: rx_location = [(0,1),(1,1),(1,0)] | |
| 5661 | if 'azimuth' in kwargs: |
|
5659 | if 'azimuth' in kwargs: | |
| 5662 | azimuth = kwargs['azimuth'] |
|
5660 | azimuth = kwargs['azimuth'] | |
| 5663 | else: azimuth = 51.06 |
|
5661 | else: azimuth = 51.06 | |
| 5664 | if 'dfactor' in kwargs: |
|
5662 | if 'dfactor' in kwargs: | |
| 5665 | dfactor = kwargs['dfactor'] |
|
5663 | dfactor = kwargs['dfactor'] | |
| 5666 | if 'mode' in kwargs: |
|
5664 | if 'mode' in kwargs: | |
| 5667 | mode = kwargs['mode'] |
|
5665 | mode = kwargs['mode'] | |
| 5668 | if 'theta_x' in kwargs: |
|
5666 | if 'theta_x' in kwargs: | |
| 5669 | theta_x = kwargs['theta_x'] |
|
5667 | theta_x = kwargs['theta_x'] | |
| 5670 | if 'theta_y' in kwargs: |
|
5668 | if 'theta_y' in kwargs: | |
| 5671 | theta_y = kwargs['theta_y'] |
|
5669 | theta_y = kwargs['theta_y'] | |
| 5672 | else: mode = 'SA' |
|
5670 | else: mode = 'SA' | |
| 5673 |
|
5671 | |||
| 5674 | #Borrar luego esto |
|
5672 | #Borrar luego esto | |
| 5675 | if dataOut.groupList is None: |
|
5673 | if dataOut.groupList is None: | |
| 5676 | dataOut.groupList = [(0,1),(0,2),(1,2)] |
|
5674 | dataOut.groupList = [(0,1),(0,2),(1,2)] | |
| 5677 | groupList = dataOut.groupList |
|
5675 | groupList = dataOut.groupList | |
| 5678 | C = 3e8 |
|
5676 | C = 3e8 | |
| 5679 | freq = 50e6 |
|
5677 | freq = 50e6 | |
| 5680 | lamb = C/freq |
|
5678 | lamb = C/freq | |
| 5681 | k = 2*numpy.pi/lamb |
|
5679 | k = 2*numpy.pi/lamb | |
| 5682 |
|
5680 | |||
| 5683 | timeList = dataOut.abscissaList |
|
5681 | timeList = dataOut.abscissaList | |
| 5684 | heightList = dataOut.heightList |
|
5682 | heightList = dataOut.heightList | |
| 5685 |
|
5683 | |||
| 5686 | if self.__isConfig == False: |
|
5684 | if self.__isConfig == False: | |
| 5687 | dataOut.outputInterval = nMins*60 |
|
5685 | dataOut.outputInterval = nMins*60 | |
| 5688 | #Get Initial LTC time |
|
5686 | #Get Initial LTC time | |
| 5689 | initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
5687 | initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) | |
| 5690 | minuteAux = initime.minute |
|
5688 | minuteAux = initime.minute | |
| 5691 | minuteNew = int(numpy.floor(minuteAux/nMins)*nMins) |
|
5689 | minuteNew = int(numpy.floor(minuteAux/nMins)*nMins) | |
| 5692 | self.__initime = (initime.replace(minute = minuteNew, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
5690 | self.__initime = (initime.replace(minute = minuteNew, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() | |
| 5693 |
|
5691 | |||
| 5694 | self.__isConfig = True |
|
5692 | self.__isConfig = True | |
| 5695 |
|
5693 | |||
| 5696 | if self.__buffer is None: |
|
5694 | if self.__buffer is None: | |
| 5697 | self.__buffer = dataOut.data_param |
|
5695 | self.__buffer = dataOut.data_param | |
| 5698 | self.__firstdata = copy.copy(dataOut) |
|
5696 | self.__firstdata = copy.copy(dataOut) | |
| 5699 |
|
5697 | |||
| 5700 | else: |
|
5698 | else: | |
| 5701 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
5699 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) | |
| 5702 |
|
5700 | |||
| 5703 | self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
5701 | self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready | |
| 5704 |
|
5702 | |||
| 5705 | if self.__dataReady: |
|
5703 | if self.__dataReady: | |
| 5706 | dataOut.utctimeInit = self.__initime |
|
5704 | dataOut.utctimeInit = self.__initime | |
| 5707 | self.__initime += dataOut.outputInterval #to erase time offset |
|
5705 | self.__initime += dataOut.outputInterval #to erase time offset | |
| 5708 |
|
5706 | |||
| 5709 | metArray = self.__buffer |
|
5707 | metArray = self.__buffer | |
| 5710 | if mode == 'SA': |
|
5708 | if mode == 'SA': | |
| 5711 | dataOut.data_output = self.techniqueNSM_SA(rx_location=rx_location, groupList=groupList, azimuth=azimuth, dfactor=dfactor, k=k,metArray=metArray, heightList=heightList,timeList=timeList) |
|
5709 | dataOut.data_output = self.techniqueNSM_SA(rx_location=rx_location, groupList=groupList, azimuth=azimuth, dfactor=dfactor, k=k,metArray=metArray, heightList=heightList,timeList=timeList) | |
| 5712 | elif mode == 'DBS': |
|
5710 | elif mode == 'DBS': | |
| 5713 | dataOut.data_output = self.techniqueNSM_DBS(metArray=metArray,heightList=heightList,timeList=timeList, azimuth=azimuth, theta_x=theta_x, theta_y=theta_y) |
|
5711 | dataOut.data_output = self.techniqueNSM_DBS(metArray=metArray,heightList=heightList,timeList=timeList, azimuth=azimuth, theta_x=theta_x, theta_y=theta_y) | |
| 5714 | dataOut.data_output = dataOut.data_output.T |
|
5712 | dataOut.data_output = dataOut.data_output.T | |
| 5715 | dataOut.flagNoData = False |
|
5713 | dataOut.flagNoData = False | |
| 5716 | self.__buffer = None |
|
5714 | self.__buffer = None | |
| 5717 |
|
5715 | |||
| 5718 | return dataOut |
|
5716 | return dataOut | |
| 5719 |
|
5717 | |||
| 5720 | class EWDriftsEstimation(Operation): |
|
5718 | class EWDriftsEstimation(Operation): | |
| 5721 |
|
5719 | |||
| 5722 | def __init__(self): |
|
5720 | def __init__(self): | |
| 5723 | Operation.__init__(self) |
|
5721 | Operation.__init__(self) | |
| 5724 |
|
5722 | |||
| 5725 | def __correctValues(self, heiRang, phi, velRadial, SNR): |
|
5723 | def __correctValues(self, heiRang, phi, velRadial, SNR): | |
| 5726 | listPhi = phi.tolist() |
|
5724 | listPhi = phi.tolist() | |
| 5727 | maxid = listPhi.index(max(listPhi)) |
|
5725 | maxid = listPhi.index(max(listPhi)) | |
| 5728 | minid = listPhi.index(min(listPhi)) |
|
5726 | minid = listPhi.index(min(listPhi)) | |
| 5729 |
|
5727 | |||
| 5730 | rango = list(range(len(phi))) |
|
5728 | rango = list(range(len(phi))) | |
| 5731 | heiRang1 = heiRang*math.cos(phi[maxid]) |
|
5729 | heiRang1 = heiRang*math.cos(phi[maxid]) | |
| 5732 | heiRangAux = heiRang*math.cos(phi[minid]) |
|
5730 | heiRangAux = heiRang*math.cos(phi[minid]) | |
| 5733 | indOut = (heiRang1 < heiRangAux[0]).nonzero() |
|
5731 | indOut = (heiRang1 < heiRangAux[0]).nonzero() | |
| 5734 | heiRang1 = numpy.delete(heiRang1,indOut) |
|
5732 | heiRang1 = numpy.delete(heiRang1,indOut) | |
| 5735 |
|
5733 | |||
| 5736 | velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
5734 | velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) | |
| 5737 | SNR1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
5735 | SNR1 = numpy.zeros([len(phi),len(heiRang1)]) | |
| 5738 |
|
5736 | |||
| 5739 | for i in rango: |
|
5737 | for i in rango: | |
| 5740 | x = heiRang*math.cos(phi[i]) |
|
5738 | x = heiRang*math.cos(phi[i]) | |
| 5741 | y1 = velRadial[i,:] |
|
5739 | y1 = velRadial[i,:] | |
| 5742 | vali= (numpy.isfinite(y1)==True).nonzero() |
|
5740 | vali= (numpy.isfinite(y1)==True).nonzero() | |
| 5743 | y1=y1[vali] |
|
5741 | y1=y1[vali] | |
| 5744 | x = x[vali] |
|
5742 | x = x[vali] | |
| 5745 | f1 = interpolate.interp1d(x,y1,kind = 'cubic',bounds_error=False) |
|
5743 | f1 = interpolate.interp1d(x,y1,kind = 'cubic',bounds_error=False) | |
| 5746 | x1 = heiRang1 |
|
5744 | x1 = heiRang1 | |
| 5747 | y11 = f1(x1) |
|
5745 | y11 = f1(x1) | |
| 5748 | y2 = SNR[i,:] |
|
5746 | y2 = SNR[i,:] | |
| 5749 | x = heiRang*math.cos(phi[i]) |
|
5747 | x = heiRang*math.cos(phi[i]) | |
| 5750 | vali= (y2 != -1).nonzero() |
|
5748 | vali= (y2 != -1).nonzero() | |
| 5751 | y2 = y2[vali] |
|
5749 | y2 = y2[vali] | |
| 5752 | x = x[vali] |
|
5750 | x = x[vali] | |
| 5753 | f2 = interpolate.interp1d(x,y2,kind = 'cubic',bounds_error=False) |
|
5751 | f2 = interpolate.interp1d(x,y2,kind = 'cubic',bounds_error=False) | |
| 5754 | y21 = f2(x1) |
|
5752 | y21 = f2(x1) | |
| 5755 |
|
5753 | |||
| 5756 | velRadial1[i,:] = y11 |
|
5754 | velRadial1[i,:] = y11 | |
| 5757 | SNR1[i,:] = y21 |
|
5755 | SNR1[i,:] = y21 | |
| 5758 |
|
5756 | |||
| 5759 | return heiRang1, velRadial1, SNR1 |
|
5757 | return heiRang1, velRadial1, SNR1 | |
| 5760 |
|
5758 | |||
| 5761 | def run(self, dataOut, zenith, zenithCorrection,fileDrifts): |
|
5759 | def run(self, dataOut, zenith, zenithCorrection,fileDrifts): | |
| 5762 |
|
5760 | |||
| 5763 | dataOut.lat = -11.95 |
|
5761 | dataOut.lat = -11.95 | |
| 5764 | dataOut.lon = -76.87 |
|
5762 | dataOut.lon = -76.87 | |
| 5765 | dataOut.spcst = 0.00666 |
|
5763 | dataOut.spcst = 0.00666 | |
| 5766 | dataOut.pl = 0.0003 |
|
5764 | dataOut.pl = 0.0003 | |
| 5767 | dataOut.cbadn = 3 |
|
5765 | dataOut.cbadn = 3 | |
| 5768 | dataOut.inttms = 300 |
|
5766 | dataOut.inttms = 300 | |
| 5769 | dataOut.azw = -115.687 |
|
5767 | dataOut.azw = -115.687 | |
| 5770 | dataOut.elw = 86.1095 |
|
5768 | dataOut.elw = 86.1095 | |
| 5771 | dataOut.aze = 130.052 |
|
5769 | dataOut.aze = 130.052 | |
| 5772 | dataOut.ele = 87.6558 |
|
5770 | dataOut.ele = 87.6558 | |
| 5773 | dataOut.jro14 = numpy.log10(dataOut.spc_noise[0]/dataOut.normFactor) |
|
5771 | dataOut.jro14 = numpy.log10(dataOut.spc_noise[0]/dataOut.normFactor) | |
| 5774 | dataOut.jro15 = numpy.log10(dataOut.spc_noise[1]/dataOut.normFactor) |
|
5772 | dataOut.jro15 = numpy.log10(dataOut.spc_noise[1]/dataOut.normFactor) | |
| 5775 | dataOut.jro16 = numpy.log10(dataOut.spc_noise[2]/dataOut.normFactor) |
|
5773 | dataOut.jro16 = numpy.log10(dataOut.spc_noise[2]/dataOut.normFactor) | |
| 5776 | dataOut.nwlos = numpy.log10(dataOut.spc_noise[3]/dataOut.normFactor) |
|
5774 | dataOut.nwlos = numpy.log10(dataOut.spc_noise[3]/dataOut.normFactor) | |
| 5777 |
|
5775 | |||
| 5778 | heiRang = dataOut.heightList |
|
5776 | heiRang = dataOut.heightList | |
| 5779 | velRadial = dataOut.data_param[:,3,:] |
|
5777 | velRadial = dataOut.data_param[:,3,:] | |
| 5780 | velRadialm = dataOut.data_param[:,2:4,:]*-1 |
|
5778 | velRadialm = dataOut.data_param[:,2:4,:]*-1 | |
| 5781 |
|
5779 | |||
| 5782 | rbufc=dataOut.data_paramC[:,:,0] |
|
5780 | rbufc=dataOut.data_paramC[:,:,0] | |
| 5783 | ebufc=dataOut.data_paramC[:,:,1] |
|
5781 | ebufc=dataOut.data_paramC[:,:,1] | |
| 5784 | #SNR = dataOut.data_snr |
|
|||
| 5785 | SNR = dataOut.data_snr1_i |
|
5782 | SNR = dataOut.data_snr1_i | |
| 5786 | rbufi = dataOut.data_snr1_i |
|
5783 | rbufi = dataOut.data_snr1_i | |
| 5787 | velRerr = dataOut.data_error[:,4,:] |
|
5784 | velRerr = dataOut.data_error[:,4,:] | |
| 5788 | range1 = dataOut.heightList |
|
5785 | range1 = dataOut.heightList | |
| 5789 | nhei = len(range1) |
|
5786 | nhei = len(range1) | |
| 5790 |
|
5787 | |||
| 5791 | sat_fits = dataOut.sat_fits |
|
5788 | sat_fits = dataOut.sat_fits | |
| 5792 |
|
5789 | |||
| 5793 | channels = dataOut.channelList |
|
5790 | channels = dataOut.channelList | |
| 5794 | nChan = len(channels) |
|
5791 | nChan = len(channels) | |
| 5795 | my_nbeams = nChan/2 |
|
5792 | my_nbeams = nChan/2 | |
| 5796 | if my_nbeams == 2: |
|
5793 | if my_nbeams == 2: | |
| 5797 | moments=numpy.vstack(([velRadialm[0,:]],[velRadialm[0,:]],[velRadialm[1,:]],[velRadialm[1,:]])) |
|
5794 | moments=numpy.vstack(([velRadialm[0,:]],[velRadialm[0,:]],[velRadialm[1,:]],[velRadialm[1,:]])) | |
| 5798 | else : |
|
5795 | else : | |
| 5799 | moments=numpy.vstack(([velRadialm[0,:]],[velRadialm[0,:]])) |
|
5796 | moments=numpy.vstack(([velRadialm[0,:]],[velRadialm[0,:]])) | |
| 5800 | dataOut.moments=moments |
|
5797 | dataOut.moments=moments | |
| 5801 | #Incoherent |
|
5798 | #Incoherent | |
| 5802 | smooth_w = dataOut.clean_num_aver[0,:] |
|
5799 | smooth_w = dataOut.clean_num_aver[0,:] | |
| 5803 | chisq_w = dataOut.data_error[0,0,:] |
|
5800 | chisq_w = dataOut.data_error[0,0,:] | |
| 5804 | p_w0 = rbufi[0,:] |
|
5801 | p_w0 = rbufi[0,:] | |
| 5805 | p_w1 = rbufi[1,:] |
|
5802 | p_w1 = rbufi[1,:] | |
| 5806 |
|
5803 | |||
| 5807 | # Coherent |
|
5804 | # Coherent | |
| 5808 | smooth_wC = ebufc[0,:] |
|
5805 | smooth_wC = ebufc[0,:] | |
| 5809 | p_w0C = rbufc[0,:] |
|
5806 | p_w0C = rbufc[0,:] | |
| 5810 | p_w1C = rbufc[1,:] |
|
5807 | p_w1C = rbufc[1,:] | |
| 5811 | w_wC = rbufc[2,:]*-1 #*radial_sign(radial EQ 1) |
|
5808 | w_wC = rbufc[2,:]*-1 #*radial_sign(radial EQ 1) | |
| 5812 | t_wC = rbufc[3,:] |
|
5809 | t_wC = rbufc[3,:] | |
| 5813 | val = (numpy.isfinite(p_w0)==False).nonzero() |
|
5810 | val = (numpy.isfinite(p_w0)==False).nonzero() | |
| 5814 | p_w0[val]=0 |
|
5811 | p_w0[val]=0 | |
| 5815 | val = (numpy.isfinite(p_w1)==False).nonzero() |
|
5812 | val = (numpy.isfinite(p_w1)==False).nonzero() | |
| 5816 | p_w1[val]=0 |
|
5813 | p_w1[val]=0 | |
| 5817 | val = (numpy.isfinite(p_w0C)==False).nonzero() |
|
5814 | val = (numpy.isfinite(p_w0C)==False).nonzero() | |
| 5818 | p_w0C[val]=0 |
|
5815 | p_w0C[val]=0 | |
| 5819 | val = (numpy.isfinite(p_w1C)==False).nonzero() |
|
5816 | val = (numpy.isfinite(p_w1C)==False).nonzero() | |
| 5820 | p_w1C[val]=0 |
|
5817 | p_w1C[val]=0 | |
| 5821 | val = (numpy.isfinite(smooth_w)==False).nonzero() |
|
5818 | val = (numpy.isfinite(smooth_w)==False).nonzero() | |
| 5822 | smooth_w[val]=0 |
|
5819 | smooth_w[val]=0 | |
| 5823 | val = (numpy.isfinite(smooth_wC)==False).nonzero() |
|
5820 | val = (numpy.isfinite(smooth_wC)==False).nonzero() | |
| 5824 | smooth_wC[val]=0 |
|
5821 | smooth_wC[val]=0 | |
| 5825 |
|
5822 | |||
| 5826 | #p_w0 = (p_w0*smooth_w+p_w0C*smooth_wC)/(smooth_w+smooth_wC) |
|
5823 | #p_w0 = (p_w0*smooth_w+p_w0C*smooth_wC)/(smooth_w+smooth_wC) | |
| 5827 | #p_w1 = (p_w1*smooth_w+p_w1C*smooth_wC)/(smooth_w+smooth_wC) |
|
5824 | #p_w1 = (p_w1*smooth_w+p_w1C*smooth_wC)/(smooth_w+smooth_wC) | |
| 5828 |
|
5825 | |||
| 5829 | if len(sat_fits) >0 : |
|
5826 | if len(sat_fits) >0 : | |
| 5830 | p_w0C = p_w0C + sat_fits[0,:] |
|
5827 | p_w0C = p_w0C + sat_fits[0,:] | |
| 5831 | p_w1C = p_w1C + sat_fits[1,:] |
|
5828 | p_w1C = p_w1C + sat_fits[1,:] | |
| 5832 |
|
5829 | |||
| 5833 | if my_nbeams == 1: |
|
5830 | if my_nbeams == 1: | |
| 5834 | w = velRadial[0,:] |
|
5831 | w = velRadial[0,:] | |
| 5835 | winds = velRadial.copy() |
|
5832 | winds = velRadial.copy() | |
| 5836 | w_err = velRerr[0,:] |
|
5833 | w_err = velRerr[0,:] | |
| 5837 | u = w*numpy.nan |
|
5834 | u = w*numpy.nan | |
| 5838 | u_err = w_err*numpy.nan |
|
5835 | u_err = w_err*numpy.nan | |
| 5839 | p_e0 = p_w0*numpy.nan |
|
5836 | p_e0 = p_w0*numpy.nan | |
| 5840 | p_e1 = p_w1*numpy.nan |
|
5837 | p_e1 = p_w1*numpy.nan | |
| 5841 | #snr1 = 10*numpy.log10(SNR[0]) |
|
5838 | #snr1 = 10*numpy.log10(SNR[0]) | |
| 5842 | if my_nbeams == 2: |
|
5839 | if my_nbeams == 2: | |
| 5843 |
|
5840 | |||
| 5844 | zenith = numpy.array(zenith) |
|
5841 | zenith = numpy.array(zenith) | |
| 5845 | zenith -= zenithCorrection |
|
5842 | zenith -= zenithCorrection | |
| 5846 | zenith *= numpy.pi/180 |
|
5843 | zenith *= numpy.pi/180 | |
| 5847 | if zenithCorrection != 0 : |
|
5844 | if zenithCorrection != 0 : | |
| 5848 | heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, numpy.abs(zenith), velRadial, SNR) |
|
5845 | heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, numpy.abs(zenith), velRadial, SNR) | |
| 5849 | else : |
|
5846 | else : | |
| 5850 | heiRang1 = heiRang |
|
5847 | heiRang1 = heiRang | |
| 5851 | velRadial1 = velRadial |
|
5848 | velRadial1 = velRadial | |
| 5852 | SNR1 = SNR |
|
5849 | SNR1 = SNR | |
| 5853 |
|
5850 | |||
| 5854 | alp = zenith[0] |
|
5851 | alp = zenith[0] | |
| 5855 | bet = zenith[1] |
|
5852 | bet = zenith[1] | |
| 5856 |
|
5853 | |||
| 5857 | w_w = velRadial1[0,:] |
|
5854 | w_w = velRadial1[0,:] | |
| 5858 | w_e = velRadial1[1,:] |
|
5855 | w_e = velRadial1[1,:] | |
| 5859 | w_w_err = velRerr[0,:] |
|
5856 | w_w_err = velRerr[0,:] | |
| 5860 | w_e_err = velRerr[1,:] |
|
5857 | w_e_err = velRerr[1,:] | |
| 5861 | smooth_e = dataOut.clean_num_aver[2,:] |
|
5858 | smooth_e = dataOut.clean_num_aver[2,:] | |
| 5862 | chisq_e = dataOut.data_error[1,0,:] |
|
5859 | chisq_e = dataOut.data_error[1,0,:] | |
| 5863 | p_e0 = rbufi[2,:] |
|
5860 | p_e0 = rbufi[2,:] | |
| 5864 | p_e1 = rbufi[3,:] |
|
5861 | p_e1 = rbufi[3,:] | |
| 5865 |
|
5862 | |||
| 5866 | tini=time.localtime(dataOut.utctime) |
|
5863 | tini=time.localtime(dataOut.utctime) | |
| 5867 |
|
5864 | |||
| 5868 | if tini[3] >= 6 and tini[3] < 18 : |
|
5865 | if tini[3] >= 6 and tini[3] < 18 : | |
| 5869 | w_wtmp = numpy.where(numpy.isfinite(w_wC)==True,w_wC,w_w) |
|
5866 | w_wtmp = numpy.where(numpy.isfinite(w_wC)==True,w_wC,w_w) | |
| 5870 | w_w_errtmp = numpy.where(numpy.isfinite(w_wC)==True,numpy.nan,w_w_err) |
|
5867 | w_w_errtmp = numpy.where(numpy.isfinite(w_wC)==True,numpy.nan,w_w_err) | |
| 5871 | else: |
|
5868 | else: | |
| 5872 | w_wtmp = numpy.where(numpy.isfinite(w_wC)==True,w_wC,w_w) |
|
5869 | w_wtmp = numpy.where(numpy.isfinite(w_wC)==True,w_wC,w_w) | |
| 5873 | w_wtmp = numpy.where(range1 > 200,w_w,w_wtmp) |
|
5870 | w_wtmp = numpy.where(range1 > 200,w_w,w_wtmp) | |
| 5874 | w_w_errtmp = numpy.where(numpy.isfinite(w_wC)==True,numpy.nan,w_w_err) |
|
5871 | w_w_errtmp = numpy.where(numpy.isfinite(w_wC)==True,numpy.nan,w_w_err) | |
| 5875 | w_w_errtmp = numpy.where(range1 > 200,w_w_err,w_w_errtmp) |
|
5872 | w_w_errtmp = numpy.where(range1 > 200,w_w_err,w_w_errtmp) | |
| 5876 | w_w = w_wtmp |
|
5873 | w_w = w_wtmp | |
| 5877 | w_w_err = w_w_errtmp |
|
5874 | w_w_err = w_w_errtmp | |
| 5878 |
|
5875 | |||
| 5879 | #if my_nbeams == 2: |
|
5876 | #if my_nbeams == 2: | |
| 5880 | smooth_eC=ebufc[4,:] |
|
5877 | smooth_eC=ebufc[4,:] | |
| 5881 | p_e0C = rbufc[4,:] |
|
5878 | p_e0C = rbufc[4,:] | |
| 5882 | p_e1C = rbufc[5,:] |
|
5879 | p_e1C = rbufc[5,:] | |
| 5883 | w_eC = rbufc[6,:]*-1 |
|
5880 | w_eC = rbufc[6,:]*-1 | |
| 5884 | t_eC = rbufc[7,:] |
|
5881 | t_eC = rbufc[7,:] | |
| 5885 | val = (numpy.isfinite(p_e0)==False).nonzero() |
|
5882 | val = (numpy.isfinite(p_e0)==False).nonzero() | |
| 5886 | p_e0[val]=0 |
|
5883 | p_e0[val]=0 | |
| 5887 | val = (numpy.isfinite(p_e1)==False).nonzero() |
|
5884 | val = (numpy.isfinite(p_e1)==False).nonzero() | |
| 5888 | p_e1[val]=0 |
|
5885 | p_e1[val]=0 | |
| 5889 | val = (numpy.isfinite(p_e0C)==False).nonzero() |
|
5886 | val = (numpy.isfinite(p_e0C)==False).nonzero() | |
| 5890 | p_e0C[val]=0 |
|
5887 | p_e0C[val]=0 | |
| 5891 | val = (numpy.isfinite(p_e1C)==False).nonzero() |
|
5888 | val = (numpy.isfinite(p_e1C)==False).nonzero() | |
| 5892 | p_e1C[val]=0 |
|
5889 | p_e1C[val]=0 | |
| 5893 | val = (numpy.isfinite(smooth_e)==False).nonzero() |
|
5890 | val = (numpy.isfinite(smooth_e)==False).nonzero() | |
| 5894 | smooth_e[val]=0 |
|
5891 | smooth_e[val]=0 | |
| 5895 | val = (numpy.isfinite(smooth_eC)==False).nonzero() |
|
5892 | val = (numpy.isfinite(smooth_eC)==False).nonzero() | |
| 5896 | smooth_eC[val]=0 |
|
5893 | smooth_eC[val]=0 | |
| 5897 | #p_e0 = (p_e0*smooth_e+p_e0C*smooth_eC)/(smooth_e+smooth_eC) |
|
5894 | #p_e0 = (p_e0*smooth_e+p_e0C*smooth_eC)/(smooth_e+smooth_eC) | |
| 5898 | #p_e1 = (p_e1*smooth_e+p_e1C*smooth_eC)/(smooth_e+smooth_eC) |
|
5895 | #p_e1 = (p_e1*smooth_e+p_e1C*smooth_eC)/(smooth_e+smooth_eC) | |
| 5899 |
|
5896 | |||
| 5900 | if len(sat_fits) >0 : |
|
5897 | if len(sat_fits) >0 : | |
| 5901 | p_e0C = p_e0C + sat_fits[2,:] |
|
5898 | p_e0C = p_e0C + sat_fits[2,:] | |
| 5902 | p_e1C = p_e1C + sat_fits[3,:] |
|
5899 | p_e1C = p_e1C + sat_fits[3,:] | |
| 5903 |
|
5900 | |||
| 5904 | if tini[3] >= 6 and tini[3] < 18 : |
|
5901 | if tini[3] >= 6 and tini[3] < 18 : | |
| 5905 | w_etmp = numpy.where(numpy.isfinite(w_eC)==True,w_eC,w_e) |
|
5902 | w_etmp = numpy.where(numpy.isfinite(w_eC)==True,w_eC,w_e) | |
| 5906 | w_e_errtmp = numpy.where(numpy.isfinite(w_eC)==True,numpy.nan,w_e_err) |
|
5903 | w_e_errtmp = numpy.where(numpy.isfinite(w_eC)==True,numpy.nan,w_e_err) | |
| 5907 | else: |
|
5904 | else: | |
| 5908 | w_etmp = numpy.where(numpy.isfinite(w_eC)==True,w_eC,w_e) |
|
5905 | w_etmp = numpy.where(numpy.isfinite(w_eC)==True,w_eC,w_e) | |
| 5909 | w_etmp = numpy.where(range1 > 200,w_e,w_etmp) |
|
5906 | w_etmp = numpy.where(range1 > 200,w_e,w_etmp) | |
| 5910 | w_e_errtmp = numpy.where(numpy.isfinite(w_eC)==True,numpy.nan,w_e_err) |
|
5907 | w_e_errtmp = numpy.where(numpy.isfinite(w_eC)==True,numpy.nan,w_e_err) | |
| 5911 | w_e_errtmp = numpy.where(range1 > 200,w_e_err,w_e_errtmp) |
|
5908 | w_e_errtmp = numpy.where(range1 > 200,w_e_err,w_e_errtmp) | |
| 5912 | w_e = w_etmp |
|
5909 | w_e = w_etmp | |
| 5913 | w_e_err = w_e_errtmp |
|
5910 | w_e_err = w_e_errtmp | |
| 5914 |
|
5911 | |||
| 5915 | w = (w_w*numpy.sin(bet) - w_e*numpy.sin(alp))/(numpy.cos(alp)*numpy.sin(bet) - numpy.cos(bet)*numpy.sin(alp)) |
|
5912 | w = (w_w*numpy.sin(bet) - w_e*numpy.sin(alp))/(numpy.cos(alp)*numpy.sin(bet) - numpy.cos(bet)*numpy.sin(alp)) | |
| 5916 | u = (w_w*numpy.cos(bet) - w_e*numpy.cos(alp))/(numpy.sin(alp)*numpy.cos(bet) - numpy.sin(bet)*numpy.cos(alp)) |
|
5913 | u = (w_w*numpy.cos(bet) - w_e*numpy.cos(alp))/(numpy.sin(alp)*numpy.cos(bet) - numpy.sin(bet)*numpy.cos(alp)) | |
| 5917 |
|
5914 | |||
| 5918 | w_err = numpy.sqrt((w_w_err*numpy.sin(bet))**2.+(w_e_err*numpy.sin(alp))**2.)/ numpy.absolute(numpy.cos(alp)*numpy.sin(bet)-numpy.cos(bet)*numpy.sin(alp)) |
|
5915 | w_err = numpy.sqrt((w_w_err*numpy.sin(bet))**2.+(w_e_err*numpy.sin(alp))**2.)/ numpy.absolute(numpy.cos(alp)*numpy.sin(bet)-numpy.cos(bet)*numpy.sin(alp)) | |
| 5919 | u_err = numpy.sqrt((w_w_err*numpy.cos(bet))**2.+(w_e_err*numpy.cos(alp))**2.)/ numpy.absolute(numpy.cos(alp)*numpy.sin(bet)-numpy.cos(bet)*numpy.sin(alp)) |
|
5916 | u_err = numpy.sqrt((w_w_err*numpy.cos(bet))**2.+(w_e_err*numpy.cos(alp))**2.)/ numpy.absolute(numpy.cos(alp)*numpy.sin(bet)-numpy.cos(bet)*numpy.sin(alp)) | |
| 5920 |
|
5917 | |||
| 5921 | winds = numpy.vstack((w,u)) |
|
5918 | winds = numpy.vstack((w,u)) | |
| 5922 | dataOut.heightList = heiRang1 |
|
5919 | dataOut.heightList = heiRang1 | |
| 5923 | #snr1 = 10*numpy.log10(SNR1[0]) |
|
5920 | #snr1 = 10*numpy.log10(SNR1[0]) | |
| 5924 | dataOut.data_output = winds |
|
5921 | dataOut.data_output = winds | |
| 5925 | range1 = dataOut.heightList |
|
5922 | range1 = dataOut.heightList | |
| 5926 | nhei = len(range1) |
|
5923 | nhei = len(range1) | |
| 5927 | #print('alt ',range1*numpy.sin(86.1*numpy.pi/180)) |
|
5924 | #print('alt ',range1*numpy.sin(86.1*numpy.pi/180)) | |
| 5928 | #print(numpy.min([dataOut.eldir7,dataOut.eldir8])) |
|
5925 | #print(numpy.min([dataOut.eldir7,dataOut.eldir8])) | |
| 5929 | galt = range1*numpy.sin(numpy.min([dataOut.elw,dataOut.ele])*numpy.pi/180.) |
|
5926 | galt = range1*numpy.sin(numpy.min([dataOut.elw,dataOut.ele])*numpy.pi/180.) | |
| 5930 | dataOut.params = numpy.vstack((range1,galt,w,w_err,u,u_err,w_w,w_w_err,w_e,w_e_err,numpy.log10(p_w0),numpy.log10(p_w0C),numpy.log10(p_w1),numpy.log10(p_w1C),numpy.log10(p_e0),numpy.log10(p_e0C),numpy.log10(p_e1),numpy.log10(p_e1C),chisq_w,chisq_e)) |
|
5927 | dataOut.params = numpy.vstack((range1,galt,w,w_err,u,u_err,w_w,w_w_err,w_e,w_e_err,numpy.log10(p_w0),numpy.log10(p_w0C),numpy.log10(p_w1),numpy.log10(p_w1C),numpy.log10(p_e0),numpy.log10(p_e0C),numpy.log10(p_e1),numpy.log10(p_e1C),chisq_w,chisq_e)) | |
| 5931 | #snr1 = 10*numpy.log10(SNR1[0]) |
|
5928 | #snr1 = 10*numpy.log10(SNR1[0]) | |
| 5932 | #print(min(snr1), max(snr1)) |
|
5929 | #print(min(snr1), max(snr1)) | |
| 5933 | snr1 = numpy.vstack((p_w0,p_w1,p_e0,p_e1)) |
|
5930 | snr1 = numpy.vstack((p_w0,p_w1,p_e0,p_e1)) | |
| 5934 | snr1db = 10*numpy.log10(snr1[0]) |
|
5931 | snr1db = 10*numpy.log10(snr1[0]) | |
| 5935 |
|
5932 | |||
| 5936 | #dataOut.data_snr1 = numpy.reshape(snr1,(1,snr1.shape[0])) |
|
5933 | #dataOut.data_snr1 = numpy.reshape(snr1,(1,snr1.shape[0])) | |
| 5937 | dataOut.data_snr1 = numpy.reshape(snr1db,(1,snr1db.shape[0])) |
|
5934 | dataOut.data_snr1 = numpy.reshape(snr1db,(1,snr1db.shape[0])) | |
| 5938 | dataOut.utctimeInit = dataOut.utctime |
|
5935 | dataOut.utctimeInit = dataOut.utctime | |
| 5939 | dataOut.outputInterval = dataOut.timeInterval |
|
5936 | dataOut.outputInterval = dataOut.timeInterval | |
| 5940 |
|
5937 | |||
| 5941 | hei_aver0 = 218 |
|
5938 | hei_aver0 = 218 | |
| 5942 | jrange = 450 #900 para HA drifts |
|
5939 | jrange = 450 #900 para HA drifts | |
| 5943 | deltah = 15.0 #dataOut.spacing(0) 25 HAD |
|
5940 | deltah = 15.0 #dataOut.spacing(0) 25 HAD | |
| 5944 | h0 = 0.0 #dataOut.first_height(0) |
|
5941 | h0 = 0.0 #dataOut.first_height(0) | |
| 5945 |
|
5942 | |||
| 5946 | range1 = numpy.arange(nhei) * deltah + h0 |
|
5943 | range1 = numpy.arange(nhei) * deltah + h0 | |
| 5947 | jhei = (range1 >= hei_aver0).nonzero() |
|
5944 | jhei = (range1 >= hei_aver0).nonzero() | |
| 5948 | if len(jhei[0]) > 0 : |
|
5945 | if len(jhei[0]) > 0 : | |
| 5949 | h0_index = jhei[0][0] # Initial height for getting averages 218km |
|
5946 | h0_index = jhei[0][0] # Initial height for getting averages 218km | |
| 5950 |
|
5947 | |||
| 5951 | mynhei = 7 |
|
5948 | mynhei = 7 | |
| 5952 | nhei_avg = int(jrange/deltah) |
|
5949 | nhei_avg = int(jrange/deltah) | |
| 5953 | h_avgs = int(nhei_avg/mynhei) |
|
5950 | h_avgs = int(nhei_avg/mynhei) | |
| 5954 | nhei_avg = h_avgs*(mynhei-1)+mynhei |
|
5951 | nhei_avg = h_avgs*(mynhei-1)+mynhei | |
| 5955 |
|
5952 | |||
| 5956 | navgs = numpy.zeros(mynhei,dtype='float') |
|
5953 | navgs = numpy.zeros(mynhei,dtype='float') | |
| 5957 | delta_h = numpy.zeros(mynhei,dtype='float') |
|
5954 | delta_h = numpy.zeros(mynhei,dtype='float') | |
| 5958 | range_aver = numpy.zeros(mynhei,dtype='float') |
|
5955 | range_aver = numpy.zeros(mynhei,dtype='float') | |
| 5959 | for ih in range( mynhei-1 ): |
|
5956 | for ih in range( mynhei-1 ): | |
| 5960 | range_aver[ih] = numpy.sum(range1[h0_index+h_avgs*ih:h0_index+h_avgs*(ih+1)-0])/h_avgs |
|
5957 | range_aver[ih] = numpy.sum(range1[h0_index+h_avgs*ih:h0_index+h_avgs*(ih+1)-0])/h_avgs | |
| 5961 | navgs[ih] = h_avgs |
|
5958 | navgs[ih] = h_avgs | |
| 5962 | delta_h[ih] = deltah*h_avgs |
|
5959 | delta_h[ih] = deltah*h_avgs | |
| 5963 |
|
5960 | |||
| 5964 | range_aver[mynhei-1] = numpy.sum(range1[h0_index:h0_index+6*h_avgs-0])/(6*h_avgs) |
|
5961 | range_aver[mynhei-1] = numpy.sum(range1[h0_index:h0_index+6*h_avgs-0])/(6*h_avgs) | |
| 5965 | navgs[mynhei-1] = 6*h_avgs |
|
5962 | navgs[mynhei-1] = 6*h_avgs | |
| 5966 | delta_h[mynhei-1] = deltah*6*h_avgs |
|
5963 | delta_h[mynhei-1] = deltah*6*h_avgs | |
| 5967 |
|
5964 | |||
| 5968 | wA = w[h0_index:h0_index+nhei_avg-0] |
|
5965 | wA = w[h0_index:h0_index+nhei_avg-0] | |
| 5969 | wA_err = w_err[h0_index:h0_index+nhei_avg-0] |
|
5966 | wA_err = w_err[h0_index:h0_index+nhei_avg-0] | |
| 5970 | for i in range(5) : |
|
5967 | for i in range(5) : | |
| 5971 | vals = wA[i*h_avgs:(i+1)*h_avgs-0] |
|
5968 | vals = wA[i*h_avgs:(i+1)*h_avgs-0] | |
| 5972 | errs = wA_err[i*h_avgs:(i+1)*h_avgs-0] |
|
5969 | errs = wA_err[i*h_avgs:(i+1)*h_avgs-0] | |
| 5973 | avg = numpy.nansum(vals/errs**2.)/numpy.nansum(1./errs**2.) |
|
5970 | avg = numpy.nansum(vals/errs**2.)/numpy.nansum(1./errs**2.) | |
| 5974 | sigma = numpy.sqrt(1./numpy.nansum(1./errs**2.)) |
|
5971 | sigma = numpy.sqrt(1./numpy.nansum(1./errs**2.)) | |
| 5975 | wA[6*h_avgs+i] = avg |
|
5972 | wA[6*h_avgs+i] = avg | |
| 5976 | wA_err[6*h_avgs+i] = sigma |
|
5973 | wA_err[6*h_avgs+i] = sigma | |
| 5977 |
|
5974 | |||
| 5978 | vals = wA[0:6*h_avgs-0] |
|
5975 | vals = wA[0:6*h_avgs-0] | |
| 5979 | errs=wA_err[0:6*h_avgs-0] |
|
5976 | errs=wA_err[0:6*h_avgs-0] | |
| 5980 | avg = numpy.nansum(vals/errs**2.)/numpy.nansum(1./errs**2) |
|
5977 | avg = numpy.nansum(vals/errs**2.)/numpy.nansum(1./errs**2) | |
| 5981 | sigma = numpy.sqrt(1./numpy.nansum(1./errs**2.)) |
|
5978 | sigma = numpy.sqrt(1./numpy.nansum(1./errs**2.)) | |
| 5982 | wA[nhei_avg-1] = avg |
|
5979 | wA[nhei_avg-1] = avg | |
| 5983 | wA_err[nhei_avg-1] = sigma |
|
5980 | wA_err[nhei_avg-1] = sigma | |
| 5984 |
|
5981 | |||
| 5985 | wA = wA[6*h_avgs:nhei_avg-0] |
|
5982 | wA = wA[6*h_avgs:nhei_avg-0] | |
| 5986 | wA_err=wA_err[6*h_avgs:nhei_avg-0] |
|
5983 | wA_err=wA_err[6*h_avgs:nhei_avg-0] | |
| 5987 | if my_nbeams == 2 : |
|
5984 | if my_nbeams == 2 : | |
| 5988 | uA = u[h0_index:h0_index+nhei_avg] |
|
5985 | uA = u[h0_index:h0_index+nhei_avg] | |
| 5989 | uA_err=u_err[h0_index:h0_index+nhei_avg] |
|
5986 | uA_err=u_err[h0_index:h0_index+nhei_avg] | |
| 5990 |
|
5987 | |||
| 5991 | for i in range(5) : |
|
5988 | for i in range(5) : | |
| 5992 | vals = uA[i*h_avgs:(i+1)*h_avgs-0] |
|
5989 | vals = uA[i*h_avgs:(i+1)*h_avgs-0] | |
| 5993 | errs=uA_err[i*h_avgs:(i+1)*h_avgs-0] |
|
5990 | errs=uA_err[i*h_avgs:(i+1)*h_avgs-0] | |
| 5994 | avg = numpy.nansum(vals/errs**2.)/numpy.nansum(1./errs**2.) |
|
5991 | avg = numpy.nansum(vals/errs**2.)/numpy.nansum(1./errs**2.) | |
| 5995 | sigma = numpy.sqrt(1./numpy.nansum(1./errs**2.)) |
|
5992 | sigma = numpy.sqrt(1./numpy.nansum(1./errs**2.)) | |
| 5996 | uA[6*h_avgs+i] = avg |
|
5993 | uA[6*h_avgs+i] = avg | |
| 5997 | uA_err[6*h_avgs+i]=sigma |
|
5994 | uA_err[6*h_avgs+i]=sigma | |
| 5998 |
|
5995 | |||
| 5999 | vals = uA[0:6*h_avgs-0] |
|
5996 | vals = uA[0:6*h_avgs-0] | |
| 6000 | errs = uA_err[0:6*h_avgs-0] |
|
5997 | errs = uA_err[0:6*h_avgs-0] | |
| 6001 | avg = numpy.nansum(vals/errs**2.)/numpy.nansum(1./errs**2.) |
|
5998 | avg = numpy.nansum(vals/errs**2.)/numpy.nansum(1./errs**2.) | |
| 6002 | sigma = numpy.sqrt(1./numpy.nansum(1./errs**2.)) |
|
5999 | sigma = numpy.sqrt(1./numpy.nansum(1./errs**2.)) | |
| 6003 | uA[nhei_avg-1] = avg |
|
6000 | uA[nhei_avg-1] = avg | |
| 6004 | uA_err[nhei_avg-1] = sigma |
|
6001 | uA_err[nhei_avg-1] = sigma | |
| 6005 | uA = uA[6*h_avgs:nhei_avg-0] |
|
6002 | uA = uA[6*h_avgs:nhei_avg-0] | |
| 6006 | uA_err = uA_err[6*h_avgs:nhei_avg-0] |
|
6003 | uA_err = uA_err[6*h_avgs:nhei_avg-0] | |
| 6007 | dataOut.drifts_avg = numpy.vstack((wA,uA)) |
|
6004 | dataOut.drifts_avg = numpy.vstack((wA,uA)) | |
| 6008 |
|
6005 | |||
| 6009 | if my_nbeams == 1: dataOut.drifts_avg = wA |
|
6006 | if my_nbeams == 1: dataOut.drifts_avg = wA | |
| 6010 | #deltahavg= wA*0.0+deltah |
|
6007 | #deltahavg= wA*0.0+deltah | |
| 6011 | dataOut.range = range1 |
|
6008 | dataOut.range = range1 | |
| 6012 | galtavg = range_aver*numpy.sin(numpy.min([dataOut.elw,dataOut.ele])*numpy.pi/180.) |
|
6009 | galtavg = range_aver*numpy.sin(numpy.min([dataOut.elw,dataOut.ele])*numpy.pi/180.) | |
| 6013 | dataOut.params_avg = numpy.vstack((wA,wA_err,uA,uA_err,range_aver,galtavg,delta_h)) |
|
6010 | dataOut.params_avg = numpy.vstack((wA,wA_err,uA,uA_err,range_aver,galtavg,delta_h)) | |
| 6014 |
|
6011 | |||
| 6015 | #print('comparando dim de avg ',wA.shape,deltahavg.shape,range_aver.shape) |
|
6012 | #print('comparando dim de avg ',wA.shape,deltahavg.shape,range_aver.shape) | |
| 6016 | tini=time.localtime(dataOut.utctime) |
|
6013 | tini=time.localtime(dataOut.utctime) | |
| 6017 | datefile= str(tini[0]).zfill(4)+str(tini[1]).zfill(2)+str(tini[2]).zfill(2) |
|
6014 | datefile= str(tini[0]).zfill(4)+str(tini[1]).zfill(2)+str(tini[2]).zfill(2) | |
| 6018 | nfile = fileDrifts+'/jro'+datefile+'drifts_sch3.txt' |
|
6015 | nfile = fileDrifts+'/jro'+datefile+'drifts_sch3.txt' | |
| 6019 |
|
6016 | |||
| 6020 | f1 = open(nfile,'a') |
|
6017 | f1 = open(nfile,'a') | |
| 6021 | datedriftavg=str(tini[0])+' '+str(tini[1])+' '+str(tini[2])+' '+str(tini[3])+' '+str(tini[4]) |
|
6018 | datedriftavg=str(tini[0])+' '+str(tini[1])+' '+str(tini[2])+' '+str(tini[3])+' '+str(tini[4]) | |
| 6022 | driftavgstr=str(dataOut.drifts_avg) |
|
6019 | driftavgstr=str(dataOut.drifts_avg) | |
| 6023 | numpy.savetxt(f1,numpy.column_stack([tini[0],tini[1],tini[2],tini[3],tini[4]]),fmt='%4i') |
|
6020 | numpy.savetxt(f1,numpy.column_stack([tini[0],tini[1],tini[2],tini[3],tini[4]]),fmt='%4i') | |
| 6024 | numpy.savetxt(f1,numpy.reshape(range_aver,(1,len(range_aver))) ,fmt='%10.2f') |
|
6021 | numpy.savetxt(f1,numpy.reshape(range_aver,(1,len(range_aver))) ,fmt='%10.2f') | |
| 6025 | numpy.savetxt(f1,dataOut.drifts_avg[:,:],fmt='%10.2f') |
|
6022 | numpy.savetxt(f1,dataOut.drifts_avg[:,:],fmt='%10.2f') | |
| 6026 | f1.close() |
|
6023 | f1.close() | |
| 6027 |
|
6024 | |||
| 6028 | swfile = fileDrifts+'/jro'+datefile+'drifts_sw.txt' |
|
6025 | swfile = fileDrifts+'/jro'+datefile+'drifts_sw.txt' | |
| 6029 | f1 = open(swfile,'a') |
|
6026 | f1 = open(swfile,'a') | |
| 6030 | numpy.savetxt(f1,numpy.column_stack([tini[0],tini[1],tini[2],tini[3],tini[4]]),fmt='%4i') |
|
6027 | numpy.savetxt(f1,numpy.column_stack([tini[0],tini[1],tini[2],tini[3],tini[4]]),fmt='%4i') | |
| 6031 | numpy.savetxt(f1,numpy.reshape(heiRang,(1,len(heiRang))),fmt='%10.2f') |
|
6028 | numpy.savetxt(f1,numpy.reshape(heiRang,(1,len(heiRang))),fmt='%10.2f') | |
| 6032 | numpy.savetxt(f1,dataOut.data_param[:,0,:],fmt='%10.2f') |
|
6029 | numpy.savetxt(f1,dataOut.data_param[:,0,:],fmt='%10.2f') | |
| 6033 | f1.close() |
|
6030 | f1.close() | |
| 6034 | dataOut.heightListtmp = dataOut.heightList |
|
6031 | dataOut.heightListtmp = dataOut.heightList | |
| 6035 | ''' |
|
6032 | ''' | |
| 6036 | #Envio data de drifts a mysql |
|
6033 | #Envio data de drifts a mysql | |
| 6037 | fechad = str(tini[0]).zfill(4)+'-'+str(tini[1]).zfill(2)+'-'+str(tini[2]).zfill(2)+' '+str(tini[3]).zfill(2)+':'+str(tini[4]).zfill(2)+':'+str(0).zfill(2) |
|
6034 | fechad = str(tini[0]).zfill(4)+'-'+str(tini[1]).zfill(2)+'-'+str(tini[2]).zfill(2)+' '+str(tini[3]).zfill(2)+':'+str(tini[4]).zfill(2)+':'+str(0).zfill(2) | |
| 6038 | mydb = mysql.connector.connect( |
|
6035 | mydb = mysql.connector.connect( | |
| 6039 | host="10.10.110.213", |
|
6036 | host="10.10.110.213", | |
| 6040 | user="user_clima", |
|
6037 | user="user_clima", | |
| 6041 | password="5D.bh(B2)Y_wRNz9", |
|
6038 | password="5D.bh(B2)Y_wRNz9", | |
| 6042 | database="clima_espacial" |
|
6039 | database="clima_espacial" | |
| 6043 | ) |
|
6040 | ) | |
| 6044 |
|
6041 | |||
| 6045 | mycursor = mydb.cursor() |
|
6042 | mycursor = mydb.cursor() | |
| 6046 | #mycursor.execute("CREATE TABLE drifts_vertical (id INT AUTO_INCREMENT PRIMARY KEY, fecha DATETIME(6), Vertical FLOAT(10,2))") |
|
6043 | #mycursor.execute("CREATE TABLE drifts_vertical (id INT AUTO_INCREMENT PRIMARY KEY, fecha DATETIME(6), Vertical FLOAT(10,2))") | |
| 6047 |
|
6044 | |||
| 6048 | sql = "INSERT INTO drifts_vertical (datetime, value) VALUES (%s, %s)" |
|
6045 | sql = "INSERT INTO drifts_vertical (datetime, value) VALUES (%s, %s)" | |
| 6049 | if numpy.isfinite(dataOut.drifts_avg[0,6]): vdql = dataOut.drifts_avg[0,6] |
|
6046 | if numpy.isfinite(dataOut.drifts_avg[0,6]): vdql = dataOut.drifts_avg[0,6] | |
| 6050 | else : vdql = 999 |
|
6047 | else : vdql = 999 | |
| 6051 | val = (fechad, vdql) |
|
6048 | val = (fechad, vdql) | |
| 6052 | mycursor.execute(sql, val) |
|
6049 | mycursor.execute(sql, val) | |
| 6053 | mydb.commit() |
|
6050 | mydb.commit() | |
| 6054 | sql = "INSERT INTO drifts_zonal (datetime, value) VALUES (%s, %s)" |
|
6051 | sql = "INSERT INTO drifts_zonal (datetime, value) VALUES (%s, %s)" | |
| 6055 | if numpy.isfinite(dataOut.drifts_avg[1,6]): zdql = dataOut.drifts_avg[1,6] |
|
6052 | if numpy.isfinite(dataOut.drifts_avg[1,6]): zdql = dataOut.drifts_avg[1,6] | |
| 6056 | else : zdql = 999 |
|
6053 | else : zdql = 999 | |
| 6057 | val = (fechad, zdql) |
|
6054 | val = (fechad, zdql) | |
| 6058 | mycursor.execute(sql, val) |
|
6055 | mycursor.execute(sql, val) | |
| 6059 | mydb.commit() |
|
6056 | mydb.commit() | |
| 6060 |
|
6057 | |||
| 6061 | print(mycursor.rowcount, "record inserted.") |
|
6058 | print(mycursor.rowcount, "record inserted.") | |
| 6062 | ''' |
|
6059 | ''' | |
| 6063 | return dataOut |
|
6060 | return dataOut | |
| 6064 |
|
6061 | |||
| 6065 | class setHeightDrifts(Operation): |
|
6062 | class setHeightDrifts(Operation): | |
| 6066 |
|
6063 | |||
| 6067 | def __init__(self): |
|
6064 | def __init__(self): | |
| 6068 | Operation.__init__(self) |
|
6065 | Operation.__init__(self) | |
| 6069 | def run(self, dataOut): |
|
6066 | def run(self, dataOut): | |
| 6070 | #print('h inicial ',dataOut.heightList,dataOut.heightListtmp) |
|
6067 | #print('h inicial ',dataOut.heightList,dataOut.heightListtmp) | |
| 6071 | dataOut.heightList = dataOut.heightListtmp |
|
6068 | dataOut.heightList = dataOut.heightListtmp | |
| 6072 | #print('regresa H ',dataOut.heightList) |
|
6069 | #print('regresa H ',dataOut.heightList) | |
| 6073 | return dataOut |
|
6070 | return dataOut | |
| 6074 | class setHeightDriftsavg(Operation): |
|
6071 | class setHeightDriftsavg(Operation): | |
| 6075 |
|
6072 | |||
| 6076 | def __init__(self): |
|
6073 | def __init__(self): | |
| 6077 | Operation.__init__(self) |
|
6074 | Operation.__init__(self) | |
| 6078 | def run(self, dataOut): |
|
6075 | def run(self, dataOut): | |
| 6079 | #print('h inicial ',dataOut.heightList) |
|
6076 | #print('h inicial ',dataOut.heightList) | |
| 6080 | dataOut.heightList = dataOut.params_avg[4] |
|
6077 | dataOut.heightList = dataOut.params_avg[4] | |
| 6081 | #print('cambia H ',dataOut.params_avg[4],dataOut.heightList) |
|
6078 | #print('cambia H ',dataOut.params_avg[4],dataOut.heightList) | |
| 6082 | return dataOut |
|
6079 | return dataOut | |
| 6083 |
|
6080 | |||
| 6084 | #--------------- Non Specular Meteor ---------------- |
|
6081 | #--------------- Non Specular Meteor ---------------- | |
| 6085 |
|
6082 | |||
| 6086 | class NonSpecularMeteorDetection(Operation): |
|
6083 | class NonSpecularMeteorDetection(Operation): | |
| 6087 |
|
6084 | |||
| 6088 | def run(self, dataOut, mode, SNRthresh=8, phaseDerThresh=0.5, cohThresh=0.8, allData = False): |
|
6085 | def run(self, dataOut, mode, SNRthresh=8, phaseDerThresh=0.5, cohThresh=0.8, allData = False): | |
| 6089 | data_acf = dataOut.data_pre[0] |
|
6086 | data_acf = dataOut.data_pre[0] | |
| 6090 | data_ccf = dataOut.data_pre[1] |
|
6087 | data_ccf = dataOut.data_pre[1] | |
| 6091 | pairsList = dataOut.groupList[1] |
|
6088 | pairsList = dataOut.groupList[1] | |
| 6092 |
|
6089 | |||
| 6093 | lamb = dataOut.C/dataOut.frequency |
|
6090 | lamb = dataOut.C/dataOut.frequency | |
| 6094 | tSamp = dataOut.ippSeconds*dataOut.nCohInt |
|
6091 | tSamp = dataOut.ippSeconds*dataOut.nCohInt | |
| 6095 | paramInterval = dataOut.paramInterval |
|
6092 | paramInterval = dataOut.paramInterval | |
| 6096 |
|
6093 | |||
| 6097 | nChannels = data_acf.shape[0] |
|
6094 | nChannels = data_acf.shape[0] | |
| 6098 | nLags = data_acf.shape[1] |
|
6095 | nLags = data_acf.shape[1] | |
| 6099 | nProfiles = data_acf.shape[2] |
|
6096 | nProfiles = data_acf.shape[2] | |
| 6100 | nHeights = dataOut.nHeights |
|
6097 | nHeights = dataOut.nHeights | |
| 6101 | nCohInt = dataOut.nCohInt |
|
6098 | nCohInt = dataOut.nCohInt | |
| 6102 | sec = numpy.round(nProfiles/dataOut.paramInterval) |
|
6099 | sec = numpy.round(nProfiles/dataOut.paramInterval) | |
| 6103 | heightList = dataOut.heightList |
|
6100 | heightList = dataOut.heightList | |
| 6104 | ippSeconds = dataOut.ippSeconds*dataOut.nCohInt*dataOut.nAvg |
|
6101 | ippSeconds = dataOut.ippSeconds*dataOut.nCohInt*dataOut.nAvg | |
| 6105 | utctime = dataOut.utctime |
|
6102 | utctime = dataOut.utctime | |
| 6106 |
|
6103 | |||
| 6107 | dataOut.abscissaList = numpy.arange(0,paramInterval+ippSeconds,ippSeconds) |
|
6104 | dataOut.abscissaList = numpy.arange(0,paramInterval+ippSeconds,ippSeconds) | |
| 6108 |
|
6105 | |||
| 6109 | #------------------------ SNR -------------------------------------- |
|
6106 | #------------------------ SNR -------------------------------------- | |
| 6110 | power = data_acf[:,0,:,:].real |
|
6107 | power = data_acf[:,0,:,:].real | |
| 6111 | noise = numpy.zeros(nChannels) |
|
6108 | noise = numpy.zeros(nChannels) | |
| 6112 | SNR = numpy.zeros(power.shape) |
|
6109 | SNR = numpy.zeros(power.shape) | |
| 6113 | for i in range(nChannels): |
|
6110 | for i in range(nChannels): | |
| 6114 | noise[i] = hildebrand_sekhon(power[i,:], nCohInt) |
|
6111 | noise[i] = hildebrand_sekhon(power[i,:], nCohInt) | |
| 6115 | SNR[i] = (power[i]-noise[i])/noise[i] |
|
6112 | SNR[i] = (power[i]-noise[i])/noise[i] | |
| 6116 | SNRm = numpy.nanmean(SNR, axis = 0) |
|
6113 | SNRm = numpy.nanmean(SNR, axis = 0) | |
| 6117 | SNRdB = 10*numpy.log10(SNR) |
|
6114 | SNRdB = 10*numpy.log10(SNR) | |
| 6118 |
|
6115 | |||
| 6119 | if mode == 'SA': |
|
6116 | if mode == 'SA': | |
| 6120 | dataOut.groupList = dataOut.groupList[1] |
|
6117 | dataOut.groupList = dataOut.groupList[1] | |
| 6121 | nPairs = data_ccf.shape[0] |
|
6118 | nPairs = data_ccf.shape[0] | |
| 6122 | #---------------------- Coherence and Phase -------------------------- |
|
6119 | #---------------------- Coherence and Phase -------------------------- | |
| 6123 | phase = numpy.zeros(data_ccf[:,0,:,:].shape) |
|
6120 | phase = numpy.zeros(data_ccf[:,0,:,:].shape) | |
| 6124 | coh1 = numpy.zeros(data_ccf[:,0,:,:].shape) |
|
6121 | coh1 = numpy.zeros(data_ccf[:,0,:,:].shape) | |
| 6125 |
|
6122 | |||
| 6126 | for p in range(nPairs): |
|
6123 | for p in range(nPairs): | |
| 6127 | ch0 = pairsList[p][0] |
|
6124 | ch0 = pairsList[p][0] | |
| 6128 | ch1 = pairsList[p][1] |
|
6125 | ch1 = pairsList[p][1] | |
| 6129 | ccf = data_ccf[p,0,:,:]/numpy.sqrt(data_acf[ch0,0,:,:]*data_acf[ch1,0,:,:]) |
|
6126 | ccf = data_ccf[p,0,:,:]/numpy.sqrt(data_acf[ch0,0,:,:]*data_acf[ch1,0,:,:]) | |
| 6130 | phase[p,:,:] = ndimage.median_filter(numpy.angle(ccf), size = (5,1)) #median filter |
|
6127 | phase[p,:,:] = ndimage.median_filter(numpy.angle(ccf), size = (5,1)) #median filter | |
| 6131 | coh1[p,:,:] = ndimage.median_filter(numpy.abs(ccf), 5) #median filter |
|
6128 | coh1[p,:,:] = ndimage.median_filter(numpy.abs(ccf), 5) #median filter | |
| 6132 | coh = numpy.nanmax(coh1, axis = 0) |
|
6129 | coh = numpy.nanmax(coh1, axis = 0) | |
| 6133 | #---------------------- Radial Velocity ---------------------------- |
|
6130 | #---------------------- Radial Velocity ---------------------------- | |
| 6134 | phaseAux = numpy.mean(numpy.angle(data_acf[:,1,:,:]), axis = 0) |
|
6131 | phaseAux = numpy.mean(numpy.angle(data_acf[:,1,:,:]), axis = 0) | |
| 6135 | velRad = phaseAux*lamb/(4*numpy.pi*tSamp) |
|
6132 | velRad = phaseAux*lamb/(4*numpy.pi*tSamp) | |
| 6136 |
|
6133 | |||
| 6137 | if allData: |
|
6134 | if allData: | |
| 6138 | boolMetFin = ~numpy.isnan(SNRm) |
|
6135 | boolMetFin = ~numpy.isnan(SNRm) | |
| 6139 | else: |
|
6136 | else: | |
| 6140 | #------------------------ Meteor mask --------------------------------- |
|
6137 | #------------------------ Meteor mask --------------------------------- | |
| 6141 |
|
6138 | |||
| 6142 | #Coherence mask |
|
6139 | #Coherence mask | |
| 6143 | boolMet1 = coh > 0.75 |
|
6140 | boolMet1 = coh > 0.75 | |
| 6144 | struc = numpy.ones((30,1)) |
|
6141 | struc = numpy.ones((30,1)) | |
| 6145 | boolMet1 = ndimage.morphology.binary_dilation(boolMet1, structure=struc) |
|
6142 | boolMet1 = ndimage.morphology.binary_dilation(boolMet1, structure=struc) | |
| 6146 |
|
6143 | |||
| 6147 | #Derivative mask |
|
6144 | #Derivative mask | |
| 6148 | derPhase = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0) |
|
6145 | derPhase = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0) | |
| 6149 | boolMet2 = derPhase < 0.2 |
|
6146 | boolMet2 = derPhase < 0.2 | |
| 6150 | boolMet2 = ndimage.median_filter(boolMet2,size=5) |
|
6147 | boolMet2 = ndimage.median_filter(boolMet2,size=5) | |
| 6151 | boolMet2 = numpy.vstack((boolMet2,numpy.full((1,nHeights), True, dtype=bool))) |
|
6148 | boolMet2 = numpy.vstack((boolMet2,numpy.full((1,nHeights), True, dtype=bool))) | |
| 6152 | boolMetFin = boolMet1&boolMet2 |
|
6149 | boolMetFin = boolMet1&boolMet2 | |
| 6153 | #Creating data_param |
|
6150 | #Creating data_param | |
| 6154 | coordMet = numpy.where(boolMetFin) |
|
6151 | coordMet = numpy.where(boolMetFin) | |
| 6155 |
|
6152 | |||
| 6156 | tmet = coordMet[0] |
|
6153 | tmet = coordMet[0] | |
| 6157 | hmet = coordMet[1] |
|
6154 | hmet = coordMet[1] | |
| 6158 |
|
6155 | |||
| 6159 | data_param = numpy.zeros((tmet.size, 6 + nPairs)) |
|
6156 | data_param = numpy.zeros((tmet.size, 6 + nPairs)) | |
| 6160 | data_param[:,0] = utctime |
|
6157 | data_param[:,0] = utctime | |
| 6161 | data_param[:,1] = tmet |
|
6158 | data_param[:,1] = tmet | |
| 6162 | data_param[:,2] = hmet |
|
6159 | data_param[:,2] = hmet | |
| 6163 | data_param[:,3] = SNRm[tmet,hmet] |
|
6160 | data_param[:,3] = SNRm[tmet,hmet] | |
| 6164 | data_param[:,4] = velRad[tmet,hmet] |
|
6161 | data_param[:,4] = velRad[tmet,hmet] | |
| 6165 | data_param[:,5] = coh[tmet,hmet] |
|
6162 | data_param[:,5] = coh[tmet,hmet] | |
| 6166 | data_param[:,6:] = phase[:,tmet,hmet].T |
|
6163 | data_param[:,6:] = phase[:,tmet,hmet].T | |
| 6167 |
|
6164 | |||
| 6168 | elif mode == 'DBS': |
|
6165 | elif mode == 'DBS': | |
| 6169 | dataOut.groupList = numpy.arange(nChannels) |
|
6166 | dataOut.groupList = numpy.arange(nChannels) | |
| 6170 |
|
6167 | |||
| 6171 | #Radial Velocities |
|
6168 | #Radial Velocities | |
| 6172 | phase = numpy.angle(data_acf[:,1,:,:]) |
|
6169 | phase = numpy.angle(data_acf[:,1,:,:]) | |
| 6173 | velRad = phase*lamb/(4*numpy.pi*tSamp) |
|
6170 | velRad = phase*lamb/(4*numpy.pi*tSamp) | |
| 6174 |
|
6171 | |||
| 6175 | #Spectral width |
|
6172 | #Spectral width | |
| 6176 | acf1 = data_acf[:,1,:,:] |
|
6173 | acf1 = data_acf[:,1,:,:] | |
| 6177 | acf2 = data_acf[:,2,:,:] |
|
6174 | acf2 = data_acf[:,2,:,:] | |
| 6178 |
|
6175 | |||
| 6179 | spcWidth = (lamb/(2*numpy.sqrt(6)*numpy.pi*tSamp))*numpy.sqrt(numpy.log(acf1/acf2)) |
|
6176 | spcWidth = (lamb/(2*numpy.sqrt(6)*numpy.pi*tSamp))*numpy.sqrt(numpy.log(acf1/acf2)) | |
| 6180 | if allData: |
|
6177 | if allData: | |
| 6181 | boolMetFin = ~numpy.isnan(SNRdB) |
|
6178 | boolMetFin = ~numpy.isnan(SNRdB) | |
| 6182 | else: |
|
6179 | else: | |
| 6183 | #SNR |
|
6180 | #SNR | |
| 6184 | boolMet1 = (SNRdB>SNRthresh) #SNR mask |
|
6181 | boolMet1 = (SNRdB>SNRthresh) #SNR mask | |
| 6185 | boolMet1 = ndimage.median_filter(boolMet1, size=(1,5,5)) |
|
6182 | boolMet1 = ndimage.median_filter(boolMet1, size=(1,5,5)) | |
| 6186 |
|
6183 | |||
| 6187 | #Radial velocity |
|
6184 | #Radial velocity | |
| 6188 | boolMet2 = numpy.abs(velRad) < 20 |
|
6185 | boolMet2 = numpy.abs(velRad) < 20 | |
| 6189 | boolMet2 = ndimage.median_filter(boolMet2, (1,5,5)) |
|
6186 | boolMet2 = ndimage.median_filter(boolMet2, (1,5,5)) | |
| 6190 |
|
6187 | |||
| 6191 | #Spectral Width |
|
6188 | #Spectral Width | |
| 6192 | boolMet3 = spcWidth < 30 |
|
6189 | boolMet3 = spcWidth < 30 | |
| 6193 | boolMet3 = ndimage.median_filter(boolMet3, (1,5,5)) |
|
6190 | boolMet3 = ndimage.median_filter(boolMet3, (1,5,5)) | |
| 6194 | boolMetFin = boolMet1&boolMet2&boolMet3 |
|
6191 | boolMetFin = boolMet1&boolMet2&boolMet3 | |
| 6195 |
|
6192 | |||
| 6196 | #Creating data_param |
|
6193 | #Creating data_param | |
| 6197 | coordMet = numpy.where(boolMetFin) |
|
6194 | coordMet = numpy.where(boolMetFin) | |
| 6198 |
|
6195 | |||
| 6199 | cmet = coordMet[0] |
|
6196 | cmet = coordMet[0] | |
| 6200 | tmet = coordMet[1] |
|
6197 | tmet = coordMet[1] | |
| 6201 | hmet = coordMet[2] |
|
6198 | hmet = coordMet[2] | |
| 6202 |
|
6199 | |||
| 6203 | data_param = numpy.zeros((tmet.size, 7)) |
|
6200 | data_param = numpy.zeros((tmet.size, 7)) | |
| 6204 | data_param[:,0] = utctime |
|
6201 | data_param[:,0] = utctime | |
| 6205 | data_param[:,1] = cmet |
|
6202 | data_param[:,1] = cmet | |
| 6206 | data_param[:,2] = tmet |
|
6203 | data_param[:,2] = tmet | |
| 6207 | data_param[:,3] = hmet |
|
6204 | data_param[:,3] = hmet | |
| 6208 | data_param[:,4] = SNR[cmet,tmet,hmet].T |
|
6205 | data_param[:,4] = SNR[cmet,tmet,hmet].T | |
| 6209 | data_param[:,5] = velRad[cmet,tmet,hmet].T |
|
6206 | data_param[:,5] = velRad[cmet,tmet,hmet].T | |
| 6210 | data_param[:,6] = spcWidth[cmet,tmet,hmet].T |
|
6207 | data_param[:,6] = spcWidth[cmet,tmet,hmet].T | |
| 6211 |
|
6208 | |||
| 6212 | if len(data_param) == 0: |
|
6209 | if len(data_param) == 0: | |
| 6213 | dataOut.flagNoData = True |
|
6210 | dataOut.flagNoData = True | |
| 6214 | else: |
|
6211 | else: | |
| 6215 | dataOut.data_param = data_param |
|
6212 | dataOut.data_param = data_param | |
| 6216 |
|
6213 | |||
| 6217 | def __erase_small(self, binArray, threshX, threshY): |
|
6214 | def __erase_small(self, binArray, threshX, threshY): | |
| 6218 | labarray, numfeat = ndimage.measurements.label(binArray) |
|
6215 | labarray, numfeat = ndimage.measurements.label(binArray) | |
| 6219 | binArray1 = numpy.copy(binArray) |
|
6216 | binArray1 = numpy.copy(binArray) | |
| 6220 |
|
6217 | |||
| 6221 | for i in range(1,numfeat + 1): |
|
6218 | for i in range(1,numfeat + 1): | |
| 6222 | auxBin = (labarray==i) |
|
6219 | auxBin = (labarray==i) | |
| 6223 | auxSize = auxBin.sum() |
|
6220 | auxSize = auxBin.sum() | |
| 6224 |
|
6221 | |||
| 6225 | x,y = numpy.where(auxBin) |
|
6222 | x,y = numpy.where(auxBin) | |
| 6226 | widthX = x.max() - x.min() |
|
6223 | widthX = x.max() - x.min() | |
| 6227 | widthY = y.max() - y.min() |
|
6224 | widthY = y.max() - y.min() | |
| 6228 |
|
6225 | |||
| 6229 | #width X: 3 seg -> 12.5*3 |
|
6226 | #width X: 3 seg -> 12.5*3 | |
| 6230 | #width Y: |
|
6227 | #width Y: | |
| 6231 |
|
6228 | |||
| 6232 | if (auxSize < 50) or (widthX < threshX) or (widthY < threshY): |
|
6229 | if (auxSize < 50) or (widthX < threshX) or (widthY < threshY): | |
| 6233 | binArray1[auxBin] = False |
|
6230 | binArray1[auxBin] = False | |
| 6234 |
|
6231 | |||
| 6235 | return binArray1 |
|
6232 | return binArray1 | |
| 6236 |
|
6233 | |||
| 6237 | #--------------- Specular Meteor ---------------- |
|
6234 | #--------------- Specular Meteor ---------------- | |
| 6238 |
|
6235 | |||
| 6239 | class SMDetection(Operation): |
|
6236 | class SMDetection(Operation): | |
| 6240 | ''' |
|
6237 | ''' | |
| 6241 | Function DetectMeteors() |
|
6238 | Function DetectMeteors() | |
| 6242 | Project developed with paper: |
|
6239 | Project developed with paper: | |
| 6243 | HOLDSWORTH ET AL. 2004 |
|
6240 | HOLDSWORTH ET AL. 2004 | |
| 6244 |
|
6241 | |||
| 6245 | Input: |
|
6242 | Input: | |
| 6246 | self.dataOut.data_pre |
|
6243 | self.dataOut.data_pre | |
| 6247 |
|
6244 | |||
| 6248 | centerReceiverIndex: From the channels, which is the center receiver |
|
6245 | centerReceiverIndex: From the channels, which is the center receiver | |
| 6249 |
|
6246 | |||
| 6250 | hei_ref: Height reference for the Beacon signal extraction |
|
6247 | hei_ref: Height reference for the Beacon signal extraction | |
| 6251 | tauindex: |
|
6248 | tauindex: | |
| 6252 | predefinedPhaseShifts: Predefined phase offset for the voltge signals |
|
6249 | predefinedPhaseShifts: Predefined phase offset for the voltge signals | |
| 6253 |
|
6250 | |||
| 6254 | cohDetection: Whether to user Coherent detection or not |
|
6251 | cohDetection: Whether to user Coherent detection or not | |
| 6255 | cohDet_timeStep: Coherent Detection calculation time step |
|
6252 | cohDet_timeStep: Coherent Detection calculation time step | |
| 6256 | cohDet_thresh: Coherent Detection phase threshold to correct phases |
|
6253 | cohDet_thresh: Coherent Detection phase threshold to correct phases | |
| 6257 |
|
6254 | |||
| 6258 | noise_timeStep: Noise calculation time step |
|
6255 | noise_timeStep: Noise calculation time step | |
| 6259 | noise_multiple: Noise multiple to define signal threshold |
|
6256 | noise_multiple: Noise multiple to define signal threshold | |
| 6260 |
|
6257 | |||
| 6261 | multDet_timeLimit: Multiple Detection Removal time limit in seconds |
|
6258 | multDet_timeLimit: Multiple Detection Removal time limit in seconds | |
| 6262 | multDet_rangeLimit: Multiple Detection Removal range limit in km |
|
6259 | multDet_rangeLimit: Multiple Detection Removal range limit in km | |
| 6263 |
|
6260 | |||
| 6264 | phaseThresh: Maximum phase difference between receiver to be consider a meteor |
|
6261 | phaseThresh: Maximum phase difference between receiver to be consider a meteor | |
| 6265 | SNRThresh: Minimum SNR threshold of the meteor signal to be consider a meteor |
|
6262 | SNRThresh: Minimum SNR threshold of the meteor signal to be consider a meteor | |
| 6266 |
|
6263 | |||
| 6267 | hmin: Minimum Height of the meteor to use it in the further wind estimations |
|
6264 | hmin: Minimum Height of the meteor to use it in the further wind estimations | |
| 6268 | hmax: Maximum Height of the meteor to use it in the further wind estimations |
|
6265 | hmax: Maximum Height of the meteor to use it in the further wind estimations | |
| 6269 | azimuth: Azimuth angle correction |
|
6266 | azimuth: Azimuth angle correction | |
| 6270 |
|
6267 | |||
| 6271 | Affected: |
|
6268 | Affected: | |
| 6272 | self.dataOut.data_param |
|
6269 | self.dataOut.data_param | |
| 6273 |
|
6270 | |||
| 6274 | Rejection Criteria (Errors): |
|
6271 | Rejection Criteria (Errors): | |
| 6275 | 0: No error; analysis OK |
|
6272 | 0: No error; analysis OK | |
| 6276 | 1: SNR < SNR threshold |
|
6273 | 1: SNR < SNR threshold | |
| 6277 | 2: angle of arrival (AOA) ambiguously determined |
|
6274 | 2: angle of arrival (AOA) ambiguously determined | |
| 6278 | 3: AOA estimate not feasible |
|
6275 | 3: AOA estimate not feasible | |
| 6279 | 4: Large difference in AOAs obtained from different antenna baselines |
|
6276 | 4: Large difference in AOAs obtained from different antenna baselines | |
| 6280 | 5: echo at start or end of time series |
|
6277 | 5: echo at start or end of time series | |
| 6281 | 6: echo less than 5 examples long; too short for analysis |
|
6278 | 6: echo less than 5 examples long; too short for analysis | |
| 6282 | 7: echo rise exceeds 0.3s |
|
6279 | 7: echo rise exceeds 0.3s | |
| 6283 | 8: echo decay time less than twice rise time |
|
6280 | 8: echo decay time less than twice rise time | |
| 6284 | 9: large power level before echo |
|
6281 | 9: large power level before echo | |
| 6285 | 10: large power level after echo |
|
6282 | 10: large power level after echo | |
| 6286 | 11: poor fit to amplitude for estimation of decay time |
|
6283 | 11: poor fit to amplitude for estimation of decay time | |
| 6287 | 12: poor fit to CCF phase variation for estimation of radial drift velocity |
|
6284 | 12: poor fit to CCF phase variation for estimation of radial drift velocity | |
| 6288 | 13: height unresolvable echo: not valid height within 70 to 110 km |
|
6285 | 13: height unresolvable echo: not valid height within 70 to 110 km | |
| 6289 | 14: height ambiguous echo: more then one possible height within 70 to 110 km |
|
6286 | 14: height ambiguous echo: more then one possible height within 70 to 110 km | |
| 6290 | 15: radial drift velocity or projected horizontal velocity exceeds 200 m/s |
|
6287 | 15: radial drift velocity or projected horizontal velocity exceeds 200 m/s | |
| 6291 | 16: oscilatory echo, indicating event most likely not an underdense echo |
|
6288 | 16: oscilatory echo, indicating event most likely not an underdense echo | |
| 6292 |
|
6289 | |||
| 6293 | 17: phase difference in meteor Reestimation |
|
6290 | 17: phase difference in meteor Reestimation | |
| 6294 |
|
6291 | |||
| 6295 | Data Storage: |
|
6292 | Data Storage: | |
| 6296 | Meteors for Wind Estimation (8): |
|
6293 | Meteors for Wind Estimation (8): | |
| 6297 | Utc Time | Range Height |
|
6294 | Utc Time | Range Height | |
| 6298 | Azimuth Zenith errorCosDir |
|
6295 | Azimuth Zenith errorCosDir | |
| 6299 | VelRad errorVelRad |
|
6296 | VelRad errorVelRad | |
| 6300 | Phase0 Phase1 Phase2 Phase3 |
|
6297 | Phase0 Phase1 Phase2 Phase3 | |
| 6301 | TypeError |
|
6298 | TypeError | |
| 6302 |
|
6299 | |||
| 6303 | ''' |
|
6300 | ''' | |
| 6304 |
|
6301 | |||
| 6305 | def run(self, dataOut, hei_ref = None, tauindex = 0, |
|
6302 | def run(self, dataOut, hei_ref = None, tauindex = 0, | |
| 6306 | phaseOffsets = None, |
|
6303 | phaseOffsets = None, | |
| 6307 | cohDetection = False, cohDet_timeStep = 1, cohDet_thresh = 25, |
|
6304 | cohDetection = False, cohDet_timeStep = 1, cohDet_thresh = 25, | |
| 6308 | noise_timeStep = 4, noise_multiple = 4, |
|
6305 | noise_timeStep = 4, noise_multiple = 4, | |
| 6309 | multDet_timeLimit = 1, multDet_rangeLimit = 3, |
|
6306 | multDet_timeLimit = 1, multDet_rangeLimit = 3, | |
| 6310 | phaseThresh = 20, SNRThresh = 5, |
|
6307 | phaseThresh = 20, SNRThresh = 5, | |
| 6311 | hmin = 50, hmax=150, azimuth = 0, |
|
6308 | hmin = 50, hmax=150, azimuth = 0, | |
| 6312 | channelPositions = None) : |
|
6309 | channelPositions = None) : | |
| 6313 |
|
6310 | |||
| 6314 |
|
6311 | |||
| 6315 | #Getting Pairslist |
|
6312 | #Getting Pairslist | |
| 6316 | if channelPositions is None: |
|
6313 | if channelPositions is None: | |
| 6317 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella |
|
6314 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella | |
| 6318 | meteorOps = SMOperations() |
|
6315 | meteorOps = SMOperations() | |
| 6319 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) |
|
6316 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) | |
| 6320 | heiRang = dataOut.heightList |
|
6317 | heiRang = dataOut.heightList | |
| 6321 | #Get Beacon signal - No Beacon signal anymore |
|
6318 | #Get Beacon signal - No Beacon signal anymore | |
| 6322 | #****************REMOVING HARDWARE PHASE DIFFERENCES*************** |
|
6319 | #****************REMOVING HARDWARE PHASE DIFFERENCES*************** | |
| 6323 | # see if the user put in pre defined phase shifts |
|
6320 | # see if the user put in pre defined phase shifts | |
| 6324 | voltsPShift = dataOut.data_pre.copy() |
|
6321 | voltsPShift = dataOut.data_pre.copy() | |
| 6325 |
|
6322 | |||
| 6326 | #******************END OF REMOVING HARDWARE PHASE DIFFERENCES********* |
|
6323 | #******************END OF REMOVING HARDWARE PHASE DIFFERENCES********* | |
| 6327 |
|
6324 | |||
| 6328 | #Remove DC |
|
6325 | #Remove DC | |
| 6329 | voltsDC = numpy.mean(voltsPShift,1) |
|
6326 | voltsDC = numpy.mean(voltsPShift,1) | |
| 6330 | voltsDC = numpy.mean(voltsDC,1) |
|
6327 | voltsDC = numpy.mean(voltsDC,1) | |
| 6331 | for i in range(voltsDC.shape[0]): |
|
6328 | for i in range(voltsDC.shape[0]): | |
| 6332 | voltsPShift[i] = voltsPShift[i] - voltsDC[i] |
|
6329 | voltsPShift[i] = voltsPShift[i] - voltsDC[i] | |
| 6333 |
|
6330 | |||
| 6334 | #Don't considerate last heights, theyre used to calculate Hardware Phase Shift |
|
6331 | #Don't considerate last heights, theyre used to calculate Hardware Phase Shift | |
| 6335 |
|
6332 | |||
| 6336 | #************ FIND POWER OF DATA W/COH OR NON COH DETECTION (3.4) ********** |
|
6333 | #************ FIND POWER OF DATA W/COH OR NON COH DETECTION (3.4) ********** | |
| 6337 | #Coherent Detection |
|
6334 | #Coherent Detection | |
| 6338 | if cohDetection: |
|
6335 | if cohDetection: | |
| 6339 | #use coherent detection to get the net power |
|
6336 | #use coherent detection to get the net power | |
| 6340 | cohDet_thresh = cohDet_thresh*numpy.pi/180 |
|
6337 | cohDet_thresh = cohDet_thresh*numpy.pi/180 | |
| 6341 | voltsPShift = self.__coherentDetection(voltsPShift, cohDet_timeStep, dataOut.timeInterval, pairslist0, cohDet_thresh) |
|
6338 | voltsPShift = self.__coherentDetection(voltsPShift, cohDet_timeStep, dataOut.timeInterval, pairslist0, cohDet_thresh) | |
| 6342 |
|
6339 | |||
| 6343 | #Non-coherent detection! |
|
6340 | #Non-coherent detection! | |
| 6344 | powerNet = numpy.nansum(numpy.abs(voltsPShift[:,:,:])**2,0) |
|
6341 | powerNet = numpy.nansum(numpy.abs(voltsPShift[:,:,:])**2,0) | |
| 6345 | #********** END OF COH/NON-COH POWER CALCULATION********************** |
|
6342 | #********** END OF COH/NON-COH POWER CALCULATION********************** | |
| 6346 |
|
6343 | |||
| 6347 | #********** FIND THE NOISE LEVEL AND POSSIBLE METEORS **************** |
|
6344 | #********** FIND THE NOISE LEVEL AND POSSIBLE METEORS **************** | |
| 6348 | #Get noise |
|
6345 | #Get noise | |
| 6349 | noise, noise1 = self.__getNoise(powerNet, noise_timeStep, dataOut.timeInterval) |
|
6346 | noise, noise1 = self.__getNoise(powerNet, noise_timeStep, dataOut.timeInterval) | |
| 6350 | #Get signal threshold |
|
6347 | #Get signal threshold | |
| 6351 | signalThresh = noise_multiple*noise |
|
6348 | signalThresh = noise_multiple*noise | |
| 6352 | #Meteor echoes detection |
|
6349 | #Meteor echoes detection | |
| 6353 | listMeteors = self.__findMeteors(powerNet, signalThresh) |
|
6350 | listMeteors = self.__findMeteors(powerNet, signalThresh) | |
| 6354 | #******* END OF NOISE LEVEL AND POSSIBLE METEORS CACULATION ********** |
|
6351 | #******* END OF NOISE LEVEL AND POSSIBLE METEORS CACULATION ********** | |
| 6355 |
|
6352 | |||
| 6356 | #************** REMOVE MULTIPLE DETECTIONS (3.5) *************************** |
|
6353 | #************** REMOVE MULTIPLE DETECTIONS (3.5) *************************** | |
| 6357 | #Parameters |
|
6354 | #Parameters | |
| 6358 | heiRange = dataOut.heightList |
|
6355 | heiRange = dataOut.heightList | |
| 6359 | rangeInterval = heiRange[1] - heiRange[0] |
|
6356 | rangeInterval = heiRange[1] - heiRange[0] | |
| 6360 | rangeLimit = multDet_rangeLimit/rangeInterval |
|
6357 | rangeLimit = multDet_rangeLimit/rangeInterval | |
| 6361 | timeLimit = multDet_timeLimit/dataOut.timeInterval |
|
6358 | timeLimit = multDet_timeLimit/dataOut.timeInterval | |
| 6362 | #Multiple detection removals |
|
6359 | #Multiple detection removals | |
| 6363 | listMeteors1 = self.__removeMultipleDetections(listMeteors, rangeLimit, timeLimit) |
|
6360 | listMeteors1 = self.__removeMultipleDetections(listMeteors, rangeLimit, timeLimit) | |
| 6364 | #************ END OF REMOVE MULTIPLE DETECTIONS ********************** |
|
6361 | #************ END OF REMOVE MULTIPLE DETECTIONS ********************** | |
| 6365 |
|
6362 | |||
| 6366 | #********************* METEOR REESTIMATION (3.7, 3.8, 3.9, 3.10) ******************** |
|
6363 | #********************* METEOR REESTIMATION (3.7, 3.8, 3.9, 3.10) ******************** | |
| 6367 | #Parameters |
|
6364 | #Parameters | |
| 6368 | phaseThresh = phaseThresh*numpy.pi/180 |
|
6365 | phaseThresh = phaseThresh*numpy.pi/180 | |
| 6369 | thresh = [phaseThresh, noise_multiple, SNRThresh] |
|
6366 | thresh = [phaseThresh, noise_multiple, SNRThresh] | |
| 6370 | #Meteor reestimation (Errors N 1, 6, 12, 17) |
|
6367 | #Meteor reestimation (Errors N 1, 6, 12, 17) | |
| 6371 | listMeteors2, listMeteorsPower, listMeteorsVolts = self.__meteorReestimation(listMeteors1, voltsPShift, pairslist0, thresh, noise, dataOut.timeInterval, dataOut.frequency) |
|
6368 | listMeteors2, listMeteorsPower, listMeteorsVolts = self.__meteorReestimation(listMeteors1, voltsPShift, pairslist0, thresh, noise, dataOut.timeInterval, dataOut.frequency) | |
| 6372 | #Estimation of decay times (Errors N 7, 8, 11) |
|
6369 | #Estimation of decay times (Errors N 7, 8, 11) | |
| 6373 | listMeteors3 = self.__estimateDecayTime(listMeteors2, listMeteorsPower, dataOut.timeInterval, dataOut.frequency) |
|
6370 | listMeteors3 = self.__estimateDecayTime(listMeteors2, listMeteorsPower, dataOut.timeInterval, dataOut.frequency) | |
| 6374 | #******************* END OF METEOR REESTIMATION ******************* |
|
6371 | #******************* END OF METEOR REESTIMATION ******************* | |
| 6375 |
|
6372 | |||
| 6376 | #********************* METEOR PARAMETERS CALCULATION (3.11, 3.12, 3.13) ************************** |
|
6373 | #********************* METEOR PARAMETERS CALCULATION (3.11, 3.12, 3.13) ************************** | |
| 6377 | #Calculating Radial Velocity (Error N 15) |
|
6374 | #Calculating Radial Velocity (Error N 15) | |
| 6378 | radialStdThresh = 10 |
|
6375 | radialStdThresh = 10 | |
| 6379 | listMeteors4 = self.__getRadialVelocity(listMeteors3, listMeteorsVolts, radialStdThresh, pairslist0, dataOut.timeInterval) |
|
6376 | listMeteors4 = self.__getRadialVelocity(listMeteors3, listMeteorsVolts, radialStdThresh, pairslist0, dataOut.timeInterval) | |
| 6380 |
|
6377 | |||
| 6381 | if len(listMeteors4) > 0: |
|
6378 | if len(listMeteors4) > 0: | |
| 6382 | #Setting New Array |
|
6379 | #Setting New Array | |
| 6383 | date = dataOut.utctime |
|
6380 | date = dataOut.utctime | |
| 6384 | arrayParameters = self.__setNewArrays(listMeteors4, date, heiRang) |
|
6381 | arrayParameters = self.__setNewArrays(listMeteors4, date, heiRang) | |
| 6385 |
|
6382 | |||
| 6386 | #Correcting phase offset |
|
6383 | #Correcting phase offset | |
| 6387 | if phaseOffsets != None: |
|
6384 | if phaseOffsets != None: | |
| 6388 | phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180 |
|
6385 | phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180 | |
| 6389 | arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets) |
|
6386 | arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets) | |
| 6390 |
|
6387 | |||
| 6391 | #Second Pairslist |
|
6388 | #Second Pairslist | |
| 6392 | pairsList = [] |
|
6389 | pairsList = [] | |
| 6393 | pairx = (0,1) |
|
6390 | pairx = (0,1) | |
| 6394 | pairy = (2,3) |
|
6391 | pairy = (2,3) | |
| 6395 | pairsList.append(pairx) |
|
6392 | pairsList.append(pairx) | |
| 6396 | pairsList.append(pairy) |
|
6393 | pairsList.append(pairy) | |
| 6397 |
|
6394 | |||
| 6398 | jph = numpy.array([0,0,0,0]) |
|
6395 | jph = numpy.array([0,0,0,0]) | |
| 6399 | h = (hmin,hmax) |
|
6396 | h = (hmin,hmax) | |
| 6400 | arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph) |
|
6397 | arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph) | |
| 6401 | dataOut.data_param = arrayParameters |
|
6398 | dataOut.data_param = arrayParameters | |
| 6402 |
|
6399 | |||
| 6403 | if arrayParameters is None: |
|
6400 | if arrayParameters is None: | |
| 6404 | dataOut.flagNoData = True |
|
6401 | dataOut.flagNoData = True | |
| 6405 | else: |
|
6402 | else: | |
| 6406 | dataOut.flagNoData = True |
|
6403 | dataOut.flagNoData = True | |
| 6407 |
|
6404 | |||
| 6408 | return |
|
6405 | return | |
| 6409 |
|
6406 | |||
| 6410 | def __getHardwarePhaseDiff(self, voltage0, pairslist, newheis, n): |
|
6407 | def __getHardwarePhaseDiff(self, voltage0, pairslist, newheis, n): | |
| 6411 |
|
6408 | |||
| 6412 | minIndex = min(newheis[0]) |
|
6409 | minIndex = min(newheis[0]) | |
| 6413 | maxIndex = max(newheis[0]) |
|
6410 | maxIndex = max(newheis[0]) | |
| 6414 |
|
6411 | |||
| 6415 | voltage = voltage0[:,:,minIndex:maxIndex+1] |
|
6412 | voltage = voltage0[:,:,minIndex:maxIndex+1] | |
| 6416 | nLength = voltage.shape[1]/n |
|
6413 | nLength = voltage.shape[1]/n | |
| 6417 | nMin = 0 |
|
6414 | nMin = 0 | |
| 6418 | nMax = 0 |
|
6415 | nMax = 0 | |
| 6419 | phaseOffset = numpy.zeros((len(pairslist),n)) |
|
6416 | phaseOffset = numpy.zeros((len(pairslist),n)) | |
| 6420 |
|
6417 | |||
| 6421 | for i in range(n): |
|
6418 | for i in range(n): | |
| 6422 | nMax += nLength |
|
6419 | nMax += nLength | |
| 6423 | phaseCCF = -numpy.angle(self.__calculateCCF(voltage[:,nMin:nMax,:], pairslist, [0])) |
|
6420 | phaseCCF = -numpy.angle(self.__calculateCCF(voltage[:,nMin:nMax,:], pairslist, [0])) | |
| 6424 | phaseCCF = numpy.mean(phaseCCF, axis = 2) |
|
6421 | phaseCCF = numpy.mean(phaseCCF, axis = 2) | |
| 6425 | phaseOffset[:,i] = phaseCCF.transpose() |
|
6422 | phaseOffset[:,i] = phaseCCF.transpose() | |
| 6426 | nMin = nMax |
|
6423 | nMin = nMax | |
| 6427 |
|
6424 | |||
| 6428 | #Remove Outliers |
|
6425 | #Remove Outliers | |
| 6429 | factor = 2 |
|
6426 | factor = 2 | |
| 6430 | wt = phaseOffset - signal.medfilt(phaseOffset,(1,5)) |
|
6427 | wt = phaseOffset - signal.medfilt(phaseOffset,(1,5)) | |
| 6431 | dw = numpy.std(wt,axis = 1) |
|
6428 | dw = numpy.std(wt,axis = 1) | |
| 6432 | dw = dw.reshape((dw.size,1)) |
|
6429 | dw = dw.reshape((dw.size,1)) | |
| 6433 | ind = numpy.where(numpy.logical_or(wt>dw*factor,wt<-dw*factor)) |
|
6430 | ind = numpy.where(numpy.logical_or(wt>dw*factor,wt<-dw*factor)) | |
| 6434 | phaseOffset[ind] = numpy.nan |
|
6431 | phaseOffset[ind] = numpy.nan | |
| 6435 | phaseOffset = stats.nanmean(phaseOffset, axis=1) |
|
6432 | phaseOffset = stats.nanmean(phaseOffset, axis=1) | |
| 6436 |
|
6433 | |||
| 6437 | return phaseOffset |
|
6434 | return phaseOffset | |
| 6438 |
|
6435 | |||
| 6439 | def __shiftPhase(self, data, phaseShift): |
|
6436 | def __shiftPhase(self, data, phaseShift): | |
| 6440 | #this will shift the phase of a complex number |
|
6437 | #this will shift the phase of a complex number | |
| 6441 | dataShifted = numpy.abs(data) * numpy.exp((numpy.angle(data)+phaseShift)*1j) |
|
6438 | dataShifted = numpy.abs(data) * numpy.exp((numpy.angle(data)+phaseShift)*1j) | |
| 6442 | return dataShifted |
|
6439 | return dataShifted | |
| 6443 |
|
6440 | |||
| 6444 | def __estimatePhaseDifference(self, array, pairslist): |
|
6441 | def __estimatePhaseDifference(self, array, pairslist): | |
| 6445 | nChannel = array.shape[0] |
|
6442 | nChannel = array.shape[0] | |
| 6446 | nHeights = array.shape[2] |
|
6443 | nHeights = array.shape[2] | |
| 6447 | numPairs = len(pairslist) |
|
6444 | numPairs = len(pairslist) | |
| 6448 | phaseCCF = numpy.angle(self.__calculateCCF(array, pairslist, [-2,-1,0,1,2])) |
|
6445 | phaseCCF = numpy.angle(self.__calculateCCF(array, pairslist, [-2,-1,0,1,2])) | |
| 6449 |
|
6446 | |||
| 6450 | #Correct phases |
|
6447 | #Correct phases | |
| 6451 | derPhaseCCF = phaseCCF[:,1:,:] - phaseCCF[:,0:-1,:] |
|
6448 | derPhaseCCF = phaseCCF[:,1:,:] - phaseCCF[:,0:-1,:] | |
| 6452 | indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) |
|
6449 | indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) | |
| 6453 |
|
6450 | |||
| 6454 | if indDer[0].shape[0] > 0: |
|
6451 | if indDer[0].shape[0] > 0: | |
| 6455 | for i in range(indDer[0].shape[0]): |
|
6452 | for i in range(indDer[0].shape[0]): | |
| 6456 | signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i],indDer[2][i]]) |
|
6453 | signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i],indDer[2][i]]) | |
| 6457 | phaseCCF[indDer[0][i],indDer[1][i]+1:,:] += signo*2*numpy.pi |
|
6454 | phaseCCF[indDer[0][i],indDer[1][i]+1:,:] += signo*2*numpy.pi | |
| 6458 |
|
6455 | |||
| 6459 | #Linear |
|
6456 | #Linear | |
| 6460 | phaseInt = numpy.zeros((numPairs,1)) |
|
6457 | phaseInt = numpy.zeros((numPairs,1)) | |
| 6461 | angAllCCF = phaseCCF[:,[0,1,3,4],0] |
|
6458 | angAllCCF = phaseCCF[:,[0,1,3,4],0] | |
| 6462 | for j in range(numPairs): |
|
6459 | for j in range(numPairs): | |
| 6463 | fit = stats.linregress([-2,-1,1,2],angAllCCF[j,:]) |
|
6460 | fit = stats.linregress([-2,-1,1,2],angAllCCF[j,:]) | |
| 6464 | phaseInt[j] = fit[1] |
|
6461 | phaseInt[j] = fit[1] | |
| 6465 | #Phase Differences |
|
6462 | #Phase Differences | |
| 6466 | phaseDiff = phaseInt - phaseCCF[:,2,:] |
|
6463 | phaseDiff = phaseInt - phaseCCF[:,2,:] | |
| 6467 | phaseArrival = phaseInt.reshape(phaseInt.size) |
|
6464 | phaseArrival = phaseInt.reshape(phaseInt.size) | |
| 6468 |
|
6465 | |||
| 6469 | #Dealias |
|
6466 | #Dealias | |
| 6470 | phaseArrival = numpy.angle(numpy.exp(1j*phaseArrival)) |
|
6467 | phaseArrival = numpy.angle(numpy.exp(1j*phaseArrival)) | |
| 6471 |
|
6468 | |||
| 6472 | return phaseDiff, phaseArrival |
|
6469 | return phaseDiff, phaseArrival | |
| 6473 |
|
6470 | |||
| 6474 | def __coherentDetection(self, volts, timeSegment, timeInterval, pairslist, thresh): |
|
6471 | def __coherentDetection(self, volts, timeSegment, timeInterval, pairslist, thresh): | |
| 6475 | #this function will run the coherent detection used in Holdworth et al. 2004 and return the net power |
|
6472 | #this function will run the coherent detection used in Holdworth et al. 2004 and return the net power | |
| 6476 | #find the phase shifts of each channel over 1 second intervals |
|
6473 | #find the phase shifts of each channel over 1 second intervals | |
| 6477 | #only look at ranges below the beacon signal |
|
6474 | #only look at ranges below the beacon signal | |
| 6478 | numProfPerBlock = numpy.ceil(timeSegment/timeInterval) |
|
6475 | numProfPerBlock = numpy.ceil(timeSegment/timeInterval) | |
| 6479 | numBlocks = int(volts.shape[1]/numProfPerBlock) |
|
6476 | numBlocks = int(volts.shape[1]/numProfPerBlock) | |
| 6480 | numHeights = volts.shape[2] |
|
6477 | numHeights = volts.shape[2] | |
| 6481 | nChannel = volts.shape[0] |
|
6478 | nChannel = volts.shape[0] | |
| 6482 | voltsCohDet = volts.copy() |
|
6479 | voltsCohDet = volts.copy() | |
| 6483 |
|
6480 | |||
| 6484 | pairsarray = numpy.array(pairslist) |
|
6481 | pairsarray = numpy.array(pairslist) | |
| 6485 | indSides = pairsarray[:,1] |
|
6482 | indSides = pairsarray[:,1] | |
| 6486 | listBlocks = numpy.array_split(volts, numBlocks, 1) |
|
6483 | listBlocks = numpy.array_split(volts, numBlocks, 1) | |
| 6487 |
|
6484 | |||
| 6488 | startInd = 0 |
|
6485 | startInd = 0 | |
| 6489 | endInd = 0 |
|
6486 | endInd = 0 | |
| 6490 |
|
6487 | |||
| 6491 | for i in range(numBlocks): |
|
6488 | for i in range(numBlocks): | |
| 6492 | startInd = endInd |
|
6489 | startInd = endInd | |
| 6493 | endInd = endInd + listBlocks[i].shape[1] |
|
6490 | endInd = endInd + listBlocks[i].shape[1] | |
| 6494 |
|
6491 | |||
| 6495 | arrayBlock = listBlocks[i] |
|
6492 | arrayBlock = listBlocks[i] | |
| 6496 |
|
6493 | |||
| 6497 | #Estimate the Phase Difference |
|
6494 | #Estimate the Phase Difference | |
| 6498 | phaseDiff, aux = self.__estimatePhaseDifference(arrayBlock, pairslist) |
|
6495 | phaseDiff, aux = self.__estimatePhaseDifference(arrayBlock, pairslist) | |
| 6499 | #Phase Difference RMS |
|
6496 | #Phase Difference RMS | |
| 6500 | arrayPhaseRMS = numpy.abs(phaseDiff) |
|
6497 | arrayPhaseRMS = numpy.abs(phaseDiff) | |
| 6501 | phaseRMSaux = numpy.sum(arrayPhaseRMS < thresh,0) |
|
6498 | phaseRMSaux = numpy.sum(arrayPhaseRMS < thresh,0) | |
| 6502 | indPhase = numpy.where(phaseRMSaux==4) |
|
6499 | indPhase = numpy.where(phaseRMSaux==4) | |
| 6503 | #Shifting |
|
6500 | #Shifting | |
| 6504 | if indPhase[0].shape[0] > 0: |
|
6501 | if indPhase[0].shape[0] > 0: | |
| 6505 | for j in range(indSides.size): |
|
6502 | for j in range(indSides.size): | |
| 6506 | arrayBlock[indSides[j],:,indPhase] = self.__shiftPhase(arrayBlock[indSides[j],:,indPhase], phaseDiff[j,indPhase].transpose()) |
|
6503 | arrayBlock[indSides[j],:,indPhase] = self.__shiftPhase(arrayBlock[indSides[j],:,indPhase], phaseDiff[j,indPhase].transpose()) | |
| 6507 | voltsCohDet[:,startInd:endInd,:] = arrayBlock |
|
6504 | voltsCohDet[:,startInd:endInd,:] = arrayBlock | |
| 6508 |
|
6505 | |||
| 6509 | return voltsCohDet |
|
6506 | return voltsCohDet | |
| 6510 |
|
6507 | |||
| 6511 | def __calculateCCF(self, volts, pairslist ,laglist): |
|
6508 | def __calculateCCF(self, volts, pairslist ,laglist): | |
| 6512 |
|
6509 | |||
| 6513 | nHeights = volts.shape[2] |
|
6510 | nHeights = volts.shape[2] | |
| 6514 | nPoints = volts.shape[1] |
|
6511 | nPoints = volts.shape[1] | |
| 6515 | voltsCCF = numpy.zeros((len(pairslist), len(laglist), nHeights),dtype = 'complex') |
|
6512 | voltsCCF = numpy.zeros((len(pairslist), len(laglist), nHeights),dtype = 'complex') | |
| 6516 |
|
6513 | |||
| 6517 | for i in range(len(pairslist)): |
|
6514 | for i in range(len(pairslist)): | |
| 6518 | volts1 = volts[pairslist[i][0]] |
|
6515 | volts1 = volts[pairslist[i][0]] | |
| 6519 | volts2 = volts[pairslist[i][1]] |
|
6516 | volts2 = volts[pairslist[i][1]] | |
| 6520 |
|
6517 | |||
| 6521 | for t in range(len(laglist)): |
|
6518 | for t in range(len(laglist)): | |
| 6522 | idxT = laglist[t] |
|
6519 | idxT = laglist[t] | |
| 6523 | if idxT >= 0: |
|
6520 | if idxT >= 0: | |
| 6524 | vStacked = numpy.vstack((volts2[idxT:,:], |
|
6521 | vStacked = numpy.vstack((volts2[idxT:,:], | |
| 6525 | numpy.zeros((idxT, nHeights),dtype='complex'))) |
|
6522 | numpy.zeros((idxT, nHeights),dtype='complex'))) | |
| 6526 | else: |
|
6523 | else: | |
| 6527 | vStacked = numpy.vstack((numpy.zeros((-idxT, nHeights),dtype='complex'), |
|
6524 | vStacked = numpy.vstack((numpy.zeros((-idxT, nHeights),dtype='complex'), | |
| 6528 | volts2[:(nPoints + idxT),:])) |
|
6525 | volts2[:(nPoints + idxT),:])) | |
| 6529 | voltsCCF[i,t,:] = numpy.sum((numpy.conjugate(volts1)*vStacked),axis=0) |
|
6526 | voltsCCF[i,t,:] = numpy.sum((numpy.conjugate(volts1)*vStacked),axis=0) | |
| 6530 |
|
6527 | |||
| 6531 | vStacked = None |
|
6528 | vStacked = None | |
| 6532 | return voltsCCF |
|
6529 | return voltsCCF | |
| 6533 |
|
6530 | |||
| 6534 | def __getNoise(self, power, timeSegment, timeInterval): |
|
6531 | def __getNoise(self, power, timeSegment, timeInterval): | |
| 6535 | numProfPerBlock = numpy.ceil(timeSegment/timeInterval) |
|
6532 | numProfPerBlock = numpy.ceil(timeSegment/timeInterval) | |
| 6536 | numBlocks = int(power.shape[0]/numProfPerBlock) |
|
6533 | numBlocks = int(power.shape[0]/numProfPerBlock) | |
| 6537 | numHeights = power.shape[1] |
|
6534 | numHeights = power.shape[1] | |
| 6538 |
|
6535 | |||
| 6539 | listPower = numpy.array_split(power, numBlocks, 0) |
|
6536 | listPower = numpy.array_split(power, numBlocks, 0) | |
| 6540 | noise = numpy.zeros((power.shape[0], power.shape[1])) |
|
6537 | noise = numpy.zeros((power.shape[0], power.shape[1])) | |
| 6541 | noise1 = numpy.zeros((power.shape[0], power.shape[1])) |
|
6538 | noise1 = numpy.zeros((power.shape[0], power.shape[1])) | |
| 6542 |
|
6539 | |||
| 6543 | startInd = 0 |
|
6540 | startInd = 0 | |
| 6544 | endInd = 0 |
|
6541 | endInd = 0 | |
| 6545 |
|
6542 | |||
| 6546 | for i in range(numBlocks): #split por canal |
|
6543 | for i in range(numBlocks): #split por canal | |
| 6547 | startInd = endInd |
|
6544 | startInd = endInd | |
| 6548 | endInd = endInd + listPower[i].shape[0] |
|
6545 | endInd = endInd + listPower[i].shape[0] | |
| 6549 |
|
6546 | |||
| 6550 | arrayBlock = listPower[i] |
|
6547 | arrayBlock = listPower[i] | |
| 6551 | noiseAux = numpy.mean(arrayBlock, 0) |
|
6548 | noiseAux = numpy.mean(arrayBlock, 0) | |
| 6552 | noise[startInd:endInd,:] = noise[startInd:endInd,:] + noiseAux |
|
6549 | noise[startInd:endInd,:] = noise[startInd:endInd,:] + noiseAux | |
| 6553 |
|
6550 | |||
| 6554 | noiseAux1 = numpy.mean(arrayBlock) |
|
6551 | noiseAux1 = numpy.mean(arrayBlock) | |
| 6555 | noise1[startInd:endInd,:] = noise1[startInd:endInd,:] + noiseAux1 |
|
6552 | noise1[startInd:endInd,:] = noise1[startInd:endInd,:] + noiseAux1 | |
| 6556 |
|
6553 | |||
| 6557 | return noise, noise1 |
|
6554 | return noise, noise1 | |
| 6558 |
|
6555 | |||
| 6559 | def __findMeteors(self, power, thresh): |
|
6556 | def __findMeteors(self, power, thresh): | |
| 6560 | nProf = power.shape[0] |
|
6557 | nProf = power.shape[0] | |
| 6561 | nHeights = power.shape[1] |
|
6558 | nHeights = power.shape[1] | |
| 6562 | listMeteors = [] |
|
6559 | listMeteors = [] | |
| 6563 |
|
6560 | |||
| 6564 | for i in range(nHeights): |
|
6561 | for i in range(nHeights): | |
| 6565 | powerAux = power[:,i] |
|
6562 | powerAux = power[:,i] | |
| 6566 | threshAux = thresh[:,i] |
|
6563 | threshAux = thresh[:,i] | |
| 6567 |
|
6564 | |||
| 6568 | indUPthresh = numpy.where(powerAux > threshAux)[0] |
|
6565 | indUPthresh = numpy.where(powerAux > threshAux)[0] | |
| 6569 | indDNthresh = numpy.where(powerAux <= threshAux)[0] |
|
6566 | indDNthresh = numpy.where(powerAux <= threshAux)[0] | |
| 6570 |
|
6567 | |||
| 6571 | j = 0 |
|
6568 | j = 0 | |
| 6572 |
|
6569 | |||
| 6573 | while (j < indUPthresh.size - 2): |
|
6570 | while (j < indUPthresh.size - 2): | |
| 6574 | if (indUPthresh[j + 2] == indUPthresh[j] + 2): |
|
6571 | if (indUPthresh[j + 2] == indUPthresh[j] + 2): | |
| 6575 | indDNAux = numpy.where(indDNthresh > indUPthresh[j]) |
|
6572 | indDNAux = numpy.where(indDNthresh > indUPthresh[j]) | |
| 6576 | indDNthresh = indDNthresh[indDNAux] |
|
6573 | indDNthresh = indDNthresh[indDNAux] | |
| 6577 |
|
6574 | |||
| 6578 | if (indDNthresh.size > 0): |
|
6575 | if (indDNthresh.size > 0): | |
| 6579 | indEnd = indDNthresh[0] - 1 |
|
6576 | indEnd = indDNthresh[0] - 1 | |
| 6580 | indInit = indUPthresh[j] |
|
6577 | indInit = indUPthresh[j] | |
| 6581 |
|
6578 | |||
| 6582 | meteor = powerAux[indInit:indEnd + 1] |
|
6579 | meteor = powerAux[indInit:indEnd + 1] | |
| 6583 | indPeak = meteor.argmax() + indInit |
|
6580 | indPeak = meteor.argmax() + indInit | |
| 6584 | FLA = sum(numpy.conj(meteor)*numpy.hstack((meteor[1:],0))) |
|
6581 | FLA = sum(numpy.conj(meteor)*numpy.hstack((meteor[1:],0))) | |
| 6585 |
|
6582 | |||
| 6586 | listMeteors.append(numpy.array([i,indInit,indPeak,indEnd,FLA])) #CHEQUEAR!!!!! |
|
6583 | listMeteors.append(numpy.array([i,indInit,indPeak,indEnd,FLA])) #CHEQUEAR!!!!! | |
| 6587 | j = numpy.where(indUPthresh == indEnd)[0] + 1 |
|
6584 | j = numpy.where(indUPthresh == indEnd)[0] + 1 | |
| 6588 | else: j+=1 |
|
6585 | else: j+=1 | |
| 6589 | else: j+=1 |
|
6586 | else: j+=1 | |
| 6590 |
|
6587 | |||
| 6591 | return listMeteors |
|
6588 | return listMeteors | |
| 6592 |
|
6589 | |||
| 6593 | def __removeMultipleDetections(self,listMeteors, rangeLimit, timeLimit): |
|
6590 | def __removeMultipleDetections(self,listMeteors, rangeLimit, timeLimit): | |
| 6594 |
|
6591 | |||
| 6595 | arrayMeteors = numpy.asarray(listMeteors) |
|
6592 | arrayMeteors = numpy.asarray(listMeteors) | |
| 6596 | listMeteors1 = [] |
|
6593 | listMeteors1 = [] | |
| 6597 |
|
6594 | |||
| 6598 | while arrayMeteors.shape[0] > 0: |
|
6595 | while arrayMeteors.shape[0] > 0: | |
| 6599 | FLAs = arrayMeteors[:,4] |
|
6596 | FLAs = arrayMeteors[:,4] | |
| 6600 | maxFLA = FLAs.argmax() |
|
6597 | maxFLA = FLAs.argmax() | |
| 6601 | listMeteors1.append(arrayMeteors[maxFLA,:]) |
|
6598 | listMeteors1.append(arrayMeteors[maxFLA,:]) | |
| 6602 |
|
6599 | |||
| 6603 | MeteorInitTime = arrayMeteors[maxFLA,1] |
|
6600 | MeteorInitTime = arrayMeteors[maxFLA,1] | |
| 6604 | MeteorEndTime = arrayMeteors[maxFLA,3] |
|
6601 | MeteorEndTime = arrayMeteors[maxFLA,3] | |
| 6605 | MeteorHeight = arrayMeteors[maxFLA,0] |
|
6602 | MeteorHeight = arrayMeteors[maxFLA,0] | |
| 6606 |
|
6603 | |||
| 6607 | #Check neighborhood |
|
6604 | #Check neighborhood | |
| 6608 | maxHeightIndex = MeteorHeight + rangeLimit |
|
6605 | maxHeightIndex = MeteorHeight + rangeLimit | |
| 6609 | minHeightIndex = MeteorHeight - rangeLimit |
|
6606 | minHeightIndex = MeteorHeight - rangeLimit | |
| 6610 | minTimeIndex = MeteorInitTime - timeLimit |
|
6607 | minTimeIndex = MeteorInitTime - timeLimit | |
| 6611 | maxTimeIndex = MeteorEndTime + timeLimit |
|
6608 | maxTimeIndex = MeteorEndTime + timeLimit | |
| 6612 |
|
6609 | |||
| 6613 | #Check Heights |
|
6610 | #Check Heights | |
| 6614 | indHeight = numpy.logical_and(arrayMeteors[:,0] >= minHeightIndex, arrayMeteors[:,0] <= maxHeightIndex) |
|
6611 | indHeight = numpy.logical_and(arrayMeteors[:,0] >= minHeightIndex, arrayMeteors[:,0] <= maxHeightIndex) | |
| 6615 | indTime = numpy.logical_and(arrayMeteors[:,3] >= minTimeIndex, arrayMeteors[:,1] <= maxTimeIndex) |
|
6612 | indTime = numpy.logical_and(arrayMeteors[:,3] >= minTimeIndex, arrayMeteors[:,1] <= maxTimeIndex) | |
| 6616 | indBoth = numpy.where(numpy.logical_and(indTime,indHeight)) |
|
6613 | indBoth = numpy.where(numpy.logical_and(indTime,indHeight)) | |
| 6617 |
|
6614 | |||
| 6618 | arrayMeteors = numpy.delete(arrayMeteors, indBoth, axis = 0) |
|
6615 | arrayMeteors = numpy.delete(arrayMeteors, indBoth, axis = 0) | |
| 6619 |
|
6616 | |||
| 6620 | return listMeteors1 |
|
6617 | return listMeteors1 | |
| 6621 |
|
6618 | |||
| 6622 | def __meteorReestimation(self, listMeteors, volts, pairslist, thresh, noise, timeInterval,frequency): |
|
6619 | def __meteorReestimation(self, listMeteors, volts, pairslist, thresh, noise, timeInterval,frequency): | |
| 6623 | numHeights = volts.shape[2] |
|
6620 | numHeights = volts.shape[2] | |
| 6624 | nChannel = volts.shape[0] |
|
6621 | nChannel = volts.shape[0] | |
| 6625 |
|
6622 | |||
| 6626 | thresholdPhase = thresh[0] |
|
6623 | thresholdPhase = thresh[0] | |
| 6627 | thresholdNoise = thresh[1] |
|
6624 | thresholdNoise = thresh[1] | |
| 6628 | thresholdDB = float(thresh[2]) |
|
6625 | thresholdDB = float(thresh[2]) | |
| 6629 |
|
6626 | |||
| 6630 | thresholdDB1 = 10**(thresholdDB/10) |
|
6627 | thresholdDB1 = 10**(thresholdDB/10) | |
| 6631 | pairsarray = numpy.array(pairslist) |
|
6628 | pairsarray = numpy.array(pairslist) | |
| 6632 | indSides = pairsarray[:,1] |
|
6629 | indSides = pairsarray[:,1] | |
| 6633 |
|
6630 | |||
| 6634 | pairslist1 = list(pairslist) |
|
6631 | pairslist1 = list(pairslist) | |
| 6635 | pairslist1.append((0,1)) |
|
6632 | pairslist1.append((0,1)) | |
| 6636 | pairslist1.append((3,4)) |
|
6633 | pairslist1.append((3,4)) | |
| 6637 |
|
6634 | |||
| 6638 | listMeteors1 = [] |
|
6635 | listMeteors1 = [] | |
| 6639 | listPowerSeries = [] |
|
6636 | listPowerSeries = [] | |
| 6640 | listVoltageSeries = [] |
|
6637 | listVoltageSeries = [] | |
| 6641 | #volts has the war data |
|
6638 | #volts has the war data | |
| 6642 |
|
6639 | |||
| 6643 | if frequency == 30e6: |
|
6640 | if frequency == 30e6: | |
| 6644 | timeLag = 45*10**-3 |
|
6641 | timeLag = 45*10**-3 | |
| 6645 | else: |
|
6642 | else: | |
| 6646 | timeLag = 15*10**-3 |
|
6643 | timeLag = 15*10**-3 | |
| 6647 | lag = numpy.ceil(timeLag/timeInterval) |
|
6644 | lag = numpy.ceil(timeLag/timeInterval) | |
| 6648 |
|
6645 | |||
| 6649 | for i in range(len(listMeteors)): |
|
6646 | for i in range(len(listMeteors)): | |
| 6650 |
|
6647 | |||
| 6651 | ###################### 3.6 - 3.7 PARAMETERS REESTIMATION ######################### |
|
6648 | ###################### 3.6 - 3.7 PARAMETERS REESTIMATION ######################### | |
| 6652 | meteorAux = numpy.zeros(16) |
|
6649 | meteorAux = numpy.zeros(16) | |
| 6653 |
|
6650 | |||
| 6654 | #Loading meteor Data (mHeight, mStart, mPeak, mEnd) |
|
6651 | #Loading meteor Data (mHeight, mStart, mPeak, mEnd) | |
| 6655 | mHeight = listMeteors[i][0] |
|
6652 | mHeight = listMeteors[i][0] | |
| 6656 | mStart = listMeteors[i][1] |
|
6653 | mStart = listMeteors[i][1] | |
| 6657 | mPeak = listMeteors[i][2] |
|
6654 | mPeak = listMeteors[i][2] | |
| 6658 | mEnd = listMeteors[i][3] |
|
6655 | mEnd = listMeteors[i][3] | |
| 6659 |
|
6656 | |||
| 6660 | #get the volt data between the start and end times of the meteor |
|
6657 | #get the volt data between the start and end times of the meteor | |
| 6661 | meteorVolts = volts[:,mStart:mEnd+1,mHeight] |
|
6658 | meteorVolts = volts[:,mStart:mEnd+1,mHeight] | |
| 6662 | meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) |
|
6659 | meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) | |
| 6663 |
|
6660 | |||
| 6664 | #3.6. Phase Difference estimation |
|
6661 | #3.6. Phase Difference estimation | |
| 6665 | phaseDiff, aux = self.__estimatePhaseDifference(meteorVolts, pairslist) |
|
6662 | phaseDiff, aux = self.__estimatePhaseDifference(meteorVolts, pairslist) | |
| 6666 |
|
6663 | |||
| 6667 | #3.7. Phase difference removal & meteor start, peak and end times reestimated |
|
6664 | #3.7. Phase difference removal & meteor start, peak and end times reestimated | |
| 6668 | #meteorVolts0.- all Channels, all Profiles |
|
6665 | #meteorVolts0.- all Channels, all Profiles | |
| 6669 | meteorVolts0 = volts[:,:,mHeight] |
|
6666 | meteorVolts0 = volts[:,:,mHeight] | |
| 6670 | meteorThresh = noise[:,mHeight]*thresholdNoise |
|
6667 | meteorThresh = noise[:,mHeight]*thresholdNoise | |
| 6671 | meteorNoise = noise[:,mHeight] |
|
6668 | meteorNoise = noise[:,mHeight] | |
| 6672 | meteorVolts0[indSides,:] = self.__shiftPhase(meteorVolts0[indSides,:], phaseDiff) #Phase Shifting |
|
6669 | meteorVolts0[indSides,:] = self.__shiftPhase(meteorVolts0[indSides,:], phaseDiff) #Phase Shifting | |
| 6673 | powerNet0 = numpy.nansum(numpy.abs(meteorVolts0)**2, axis = 0) #Power |
|
6670 | powerNet0 = numpy.nansum(numpy.abs(meteorVolts0)**2, axis = 0) #Power | |
| 6674 |
|
6671 | |||
| 6675 | #Times reestimation |
|
6672 | #Times reestimation | |
| 6676 | mStart1 = numpy.where(powerNet0[:mPeak] < meteorThresh[:mPeak])[0] |
|
6673 | mStart1 = numpy.where(powerNet0[:mPeak] < meteorThresh[:mPeak])[0] | |
| 6677 | if mStart1.size > 0: |
|
6674 | if mStart1.size > 0: | |
| 6678 | mStart1 = mStart1[-1] + 1 |
|
6675 | mStart1 = mStart1[-1] + 1 | |
| 6679 |
|
6676 | |||
| 6680 | else: |
|
6677 | else: | |
| 6681 | mStart1 = mPeak |
|
6678 | mStart1 = mPeak | |
| 6682 |
|
6679 | |||
| 6683 | mEnd1 = numpy.where(powerNet0[mPeak:] < meteorThresh[mPeak:])[0][0] + mPeak - 1 |
|
6680 | mEnd1 = numpy.where(powerNet0[mPeak:] < meteorThresh[mPeak:])[0][0] + mPeak - 1 | |
| 6684 | mEndDecayTime1 = numpy.where(powerNet0[mPeak:] < meteorNoise[mPeak:])[0] |
|
6681 | mEndDecayTime1 = numpy.where(powerNet0[mPeak:] < meteorNoise[mPeak:])[0] | |
| 6685 | if mEndDecayTime1.size == 0: |
|
6682 | if mEndDecayTime1.size == 0: | |
| 6686 | mEndDecayTime1 = powerNet0.size |
|
6683 | mEndDecayTime1 = powerNet0.size | |
| 6687 | else: |
|
6684 | else: | |
| 6688 | mEndDecayTime1 = mEndDecayTime1[0] + mPeak - 1 |
|
6685 | mEndDecayTime1 = mEndDecayTime1[0] + mPeak - 1 | |
| 6689 |
|
6686 | |||
| 6690 | #meteorVolts1.- all Channels, from start to end |
|
6687 | #meteorVolts1.- all Channels, from start to end | |
| 6691 | meteorVolts1 = meteorVolts0[:,mStart1:mEnd1 + 1] |
|
6688 | meteorVolts1 = meteorVolts0[:,mStart1:mEnd1 + 1] | |
| 6692 | meteorVolts2 = meteorVolts0[:,mPeak + lag:mEnd1 + 1] |
|
6689 | meteorVolts2 = meteorVolts0[:,mPeak + lag:mEnd1 + 1] | |
| 6693 | if meteorVolts2.shape[1] == 0: |
|
6690 | if meteorVolts2.shape[1] == 0: | |
| 6694 | meteorVolts2 = meteorVolts0[:,mPeak:mEnd1 + 1] |
|
6691 | meteorVolts2 = meteorVolts0[:,mPeak:mEnd1 + 1] | |
| 6695 | meteorVolts1 = meteorVolts1.reshape(meteorVolts1.shape[0], meteorVolts1.shape[1], 1) |
|
6692 | meteorVolts1 = meteorVolts1.reshape(meteorVolts1.shape[0], meteorVolts1.shape[1], 1) | |
| 6696 | meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1], 1) |
|
6693 | meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1], 1) | |
| 6697 | ##################### END PARAMETERS REESTIMATION ######################### |
|
6694 | ##################### END PARAMETERS REESTIMATION ######################### | |
| 6698 |
|
6695 | |||
| 6699 | ##################### 3.8 PHASE DIFFERENCE REESTIMATION ######################## |
|
6696 | ##################### 3.8 PHASE DIFFERENCE REESTIMATION ######################## | |
| 6700 | if meteorVolts2.shape[1] > 0: |
|
6697 | if meteorVolts2.shape[1] > 0: | |
| 6701 | #Phase Difference re-estimation |
|
6698 | #Phase Difference re-estimation | |
| 6702 | phaseDiff1, phaseDiffint = self.__estimatePhaseDifference(meteorVolts2, pairslist1) #Phase Difference Estimation |
|
6699 | phaseDiff1, phaseDiffint = self.__estimatePhaseDifference(meteorVolts2, pairslist1) #Phase Difference Estimation | |
| 6703 | meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1]) |
|
6700 | meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1]) | |
| 6704 | phaseDiff11 = numpy.reshape(phaseDiff1, (phaseDiff1.shape[0],1)) |
|
6701 | phaseDiff11 = numpy.reshape(phaseDiff1, (phaseDiff1.shape[0],1)) | |
| 6705 | meteorVolts2[indSides,:] = self.__shiftPhase(meteorVolts2[indSides,:], phaseDiff11[0:4]) #Phase Shifting |
|
6702 | meteorVolts2[indSides,:] = self.__shiftPhase(meteorVolts2[indSides,:], phaseDiff11[0:4]) #Phase Shifting | |
| 6706 |
|
6703 | |||
| 6707 | #Phase Difference RMS |
|
6704 | #Phase Difference RMS | |
| 6708 | phaseRMS1 = numpy.sqrt(numpy.mean(numpy.square(phaseDiff1))) |
|
6705 | phaseRMS1 = numpy.sqrt(numpy.mean(numpy.square(phaseDiff1))) | |
| 6709 | powerNet1 = numpy.nansum(numpy.abs(meteorVolts1[:,:])**2,0) |
|
6706 | powerNet1 = numpy.nansum(numpy.abs(meteorVolts1[:,:])**2,0) | |
| 6710 | #Data from Meteor |
|
6707 | #Data from Meteor | |
| 6711 | mPeak1 = powerNet1.argmax() + mStart1 |
|
6708 | mPeak1 = powerNet1.argmax() + mStart1 | |
| 6712 | mPeakPower1 = powerNet1.max() |
|
6709 | mPeakPower1 = powerNet1.max() | |
| 6713 | noiseAux = sum(noise[mStart1:mEnd1 + 1,mHeight]) |
|
6710 | noiseAux = sum(noise[mStart1:mEnd1 + 1,mHeight]) | |
| 6714 | mSNR1 = (sum(powerNet1)-noiseAux)/noiseAux |
|
6711 | mSNR1 = (sum(powerNet1)-noiseAux)/noiseAux | |
| 6715 | Meteor1 = numpy.array([mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1]) |
|
6712 | Meteor1 = numpy.array([mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1]) | |
| 6716 | Meteor1 = numpy.hstack((Meteor1,phaseDiffint)) |
|
6713 | Meteor1 = numpy.hstack((Meteor1,phaseDiffint)) | |
| 6717 | PowerSeries = powerNet0[mStart1:mEndDecayTime1 + 1] |
|
6714 | PowerSeries = powerNet0[mStart1:mEndDecayTime1 + 1] | |
| 6718 | #Vectorize |
|
6715 | #Vectorize | |
| 6719 | meteorAux[0:7] = [mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1] |
|
6716 | meteorAux[0:7] = [mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1] | |
| 6720 | meteorAux[7:11] = phaseDiffint[0:4] |
|
6717 | meteorAux[7:11] = phaseDiffint[0:4] | |
| 6721 |
|
6718 | |||
| 6722 | #Rejection Criterions |
|
6719 | #Rejection Criterions | |
| 6723 | if phaseRMS1 > thresholdPhase: #Error Number 17: Phase variation |
|
6720 | if phaseRMS1 > thresholdPhase: #Error Number 17: Phase variation | |
| 6724 | meteorAux[-1] = 17 |
|
6721 | meteorAux[-1] = 17 | |
| 6725 | elif mSNR1 < thresholdDB1: #Error Number 1: SNR < threshold dB |
|
6722 | elif mSNR1 < thresholdDB1: #Error Number 1: SNR < threshold dB | |
| 6726 | meteorAux[-1] = 1 |
|
6723 | meteorAux[-1] = 1 | |
| 6727 |
|
6724 | |||
| 6728 |
|
6725 | |||
| 6729 | else: |
|
6726 | else: | |
| 6730 | meteorAux[0:4] = [mHeight, mStart, mPeak, mEnd] |
|
6727 | meteorAux[0:4] = [mHeight, mStart, mPeak, mEnd] | |
| 6731 | meteorAux[-1] = 6 #Error Number 6: echo less than 5 samples long; too short for analysis |
|
6728 | meteorAux[-1] = 6 #Error Number 6: echo less than 5 samples long; too short for analysis | |
| 6732 | PowerSeries = 0 |
|
6729 | PowerSeries = 0 | |
| 6733 |
|
6730 | |||
| 6734 | listMeteors1.append(meteorAux) |
|
6731 | listMeteors1.append(meteorAux) | |
| 6735 | listPowerSeries.append(PowerSeries) |
|
6732 | listPowerSeries.append(PowerSeries) | |
| 6736 | listVoltageSeries.append(meteorVolts1) |
|
6733 | listVoltageSeries.append(meteorVolts1) | |
| 6737 |
|
6734 | |||
| 6738 | return listMeteors1, listPowerSeries, listVoltageSeries |
|
6735 | return listMeteors1, listPowerSeries, listVoltageSeries | |
| 6739 |
|
6736 | |||
| 6740 | def __estimateDecayTime(self, listMeteors, listPower, timeInterval, frequency): |
|
6737 | def __estimateDecayTime(self, listMeteors, listPower, timeInterval, frequency): | |
| 6741 |
|
6738 | |||
| 6742 | threshError = 10 |
|
6739 | threshError = 10 | |
| 6743 | #Depending if it is 30 or 50 MHz |
|
6740 | #Depending if it is 30 or 50 MHz | |
| 6744 | if frequency == 30e6: |
|
6741 | if frequency == 30e6: | |
| 6745 | timeLag = 45*10**-3 |
|
6742 | timeLag = 45*10**-3 | |
| 6746 | else: |
|
6743 | else: | |
| 6747 | timeLag = 15*10**-3 |
|
6744 | timeLag = 15*10**-3 | |
| 6748 | lag = numpy.ceil(timeLag/timeInterval) |
|
6745 | lag = numpy.ceil(timeLag/timeInterval) | |
| 6749 |
|
6746 | |||
| 6750 | listMeteors1 = [] |
|
6747 | listMeteors1 = [] | |
| 6751 |
|
6748 | |||
| 6752 | for i in range(len(listMeteors)): |
|
6749 | for i in range(len(listMeteors)): | |
| 6753 | meteorPower = listPower[i] |
|
6750 | meteorPower = listPower[i] | |
| 6754 | meteorAux = listMeteors[i] |
|
6751 | meteorAux = listMeteors[i] | |
| 6755 |
|
6752 | |||
| 6756 | if meteorAux[-1] == 0: |
|
6753 | if meteorAux[-1] == 0: | |
| 6757 |
|
6754 | |||
| 6758 | try: |
|
6755 | try: | |
| 6759 | indmax = meteorPower.argmax() |
|
6756 | indmax = meteorPower.argmax() | |
| 6760 | indlag = indmax + lag |
|
6757 | indlag = indmax + lag | |
| 6761 |
|
6758 | |||
| 6762 | y = meteorPower[indlag:] |
|
6759 | y = meteorPower[indlag:] | |
| 6763 | x = numpy.arange(0, y.size)*timeLag |
|
6760 | x = numpy.arange(0, y.size)*timeLag | |
| 6764 |
|
6761 | |||
| 6765 | #first guess |
|
6762 | #first guess | |
| 6766 | a = y[0] |
|
6763 | a = y[0] | |
| 6767 | tau = timeLag |
|
6764 | tau = timeLag | |
| 6768 | #exponential fit |
|
6765 | #exponential fit | |
| 6769 | popt, pcov = optimize.curve_fit(self.__exponential_function, x, y, p0 = [a, tau]) |
|
6766 | popt, pcov = optimize.curve_fit(self.__exponential_function, x, y, p0 = [a, tau]) | |
| 6770 | y1 = self.__exponential_function(x, *popt) |
|
6767 | y1 = self.__exponential_function(x, *popt) | |
| 6771 | #error estimation |
|
6768 | #error estimation | |
| 6772 | error = sum((y - y1)**2)/(numpy.var(y)*(y.size - popt.size)) |
|
6769 | error = sum((y - y1)**2)/(numpy.var(y)*(y.size - popt.size)) | |
| 6773 |
|
6770 | |||
| 6774 | decayTime = popt[1] |
|
6771 | decayTime = popt[1] | |
| 6775 | riseTime = indmax*timeInterval |
|
6772 | riseTime = indmax*timeInterval | |
| 6776 | meteorAux[11:13] = [decayTime, error] |
|
6773 | meteorAux[11:13] = [decayTime, error] | |
| 6777 |
|
6774 | |||
| 6778 | #Table items 7, 8 and 11 |
|
6775 | #Table items 7, 8 and 11 | |
| 6779 | if (riseTime > 0.3): #Number 7: Echo rise exceeds 0.3s |
|
6776 | if (riseTime > 0.3): #Number 7: Echo rise exceeds 0.3s | |
| 6780 | meteorAux[-1] = 7 |
|
6777 | meteorAux[-1] = 7 | |
| 6781 | elif (decayTime < 2*riseTime) : #Number 8: Echo decay time less than than twice rise time |
|
6778 | elif (decayTime < 2*riseTime) : #Number 8: Echo decay time less than than twice rise time | |
| 6782 | meteorAux[-1] = 8 |
|
6779 | meteorAux[-1] = 8 | |
| 6783 | if (error > threshError): #Number 11: Poor fit to amplitude for estimation of decay time |
|
6780 | if (error > threshError): #Number 11: Poor fit to amplitude for estimation of decay time | |
| 6784 | meteorAux[-1] = 11 |
|
6781 | meteorAux[-1] = 11 | |
| 6785 |
|
6782 | |||
| 6786 |
|
6783 | |||
| 6787 | except: |
|
6784 | except: | |
| 6788 | meteorAux[-1] = 11 |
|
6785 | meteorAux[-1] = 11 | |
| 6789 |
|
6786 | |||
| 6790 |
|
6787 | |||
| 6791 | listMeteors1.append(meteorAux) |
|
6788 | listMeteors1.append(meteorAux) | |
| 6792 |
|
6789 | |||
| 6793 | return listMeteors1 |
|
6790 | return listMeteors1 | |
| 6794 |
|
6791 | |||
| 6795 | #Exponential Function |
|
6792 | #Exponential Function | |
| 6796 |
|
6793 | |||
| 6797 | def __exponential_function(self, x, a, tau): |
|
6794 | def __exponential_function(self, x, a, tau): | |
| 6798 | y = a*numpy.exp(-x/tau) |
|
6795 | y = a*numpy.exp(-x/tau) | |
| 6799 | return y |
|
6796 | return y | |
| 6800 |
|
6797 | |||
| 6801 | def __getRadialVelocity(self, listMeteors, listVolts, radialStdThresh, pairslist, timeInterval): |
|
6798 | def __getRadialVelocity(self, listMeteors, listVolts, radialStdThresh, pairslist, timeInterval): | |
| 6802 |
|
6799 | |||
| 6803 | pairslist1 = list(pairslist) |
|
6800 | pairslist1 = list(pairslist) | |
| 6804 | pairslist1.append((0,1)) |
|
6801 | pairslist1.append((0,1)) | |
| 6805 | pairslist1.append((3,4)) |
|
6802 | pairslist1.append((3,4)) | |
| 6806 | numPairs = len(pairslist1) |
|
6803 | numPairs = len(pairslist1) | |
| 6807 | #Time Lag |
|
6804 | #Time Lag | |
| 6808 | timeLag = 45*10**-3 |
|
6805 | timeLag = 45*10**-3 | |
| 6809 | c = 3e8 |
|
6806 | c = 3e8 | |
| 6810 | lag = numpy.ceil(timeLag/timeInterval) |
|
6807 | lag = numpy.ceil(timeLag/timeInterval) | |
| 6811 | freq = 30e6 |
|
6808 | freq = 30e6 | |
| 6812 |
|
6809 | |||
| 6813 | listMeteors1 = [] |
|
6810 | listMeteors1 = [] | |
| 6814 |
|
6811 | |||
| 6815 | for i in range(len(listMeteors)): |
|
6812 | for i in range(len(listMeteors)): | |
| 6816 | meteorAux = listMeteors[i] |
|
6813 | meteorAux = listMeteors[i] | |
| 6817 | if meteorAux[-1] == 0: |
|
6814 | if meteorAux[-1] == 0: | |
| 6818 | mStart = listMeteors[i][1] |
|
6815 | mStart = listMeteors[i][1] | |
| 6819 | mPeak = listMeteors[i][2] |
|
6816 | mPeak = listMeteors[i][2] | |
| 6820 | mLag = mPeak - mStart + lag |
|
6817 | mLag = mPeak - mStart + lag | |
| 6821 |
|
6818 | |||
| 6822 | #get the volt data between the start and end times of the meteor |
|
6819 | #get the volt data between the start and end times of the meteor | |
| 6823 | meteorVolts = listVolts[i] |
|
6820 | meteorVolts = listVolts[i] | |
| 6824 | meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) |
|
6821 | meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) | |
| 6825 |
|
6822 | |||
| 6826 | #Get CCF |
|
6823 | #Get CCF | |
| 6827 | allCCFs = self.__calculateCCF(meteorVolts, pairslist1, [-2,-1,0,1,2]) |
|
6824 | allCCFs = self.__calculateCCF(meteorVolts, pairslist1, [-2,-1,0,1,2]) | |
| 6828 |
|
6825 | |||
| 6829 | #Method 2 |
|
6826 | #Method 2 | |
| 6830 | slopes = numpy.zeros(numPairs) |
|
6827 | slopes = numpy.zeros(numPairs) | |
| 6831 | time = numpy.array([-2,-1,1,2])*timeInterval |
|
6828 | time = numpy.array([-2,-1,1,2])*timeInterval | |
| 6832 | angAllCCF = numpy.angle(allCCFs[:,[0,1,3,4],0]) |
|
6829 | angAllCCF = numpy.angle(allCCFs[:,[0,1,3,4],0]) | |
| 6833 |
|
6830 | |||
| 6834 | #Correct phases |
|
6831 | #Correct phases | |
| 6835 | derPhaseCCF = angAllCCF[:,1:] - angAllCCF[:,0:-1] |
|
6832 | derPhaseCCF = angAllCCF[:,1:] - angAllCCF[:,0:-1] | |
| 6836 | indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) |
|
6833 | indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) | |
| 6837 |
|
6834 | |||
| 6838 | if indDer[0].shape[0] > 0: |
|
6835 | if indDer[0].shape[0] > 0: | |
| 6839 | for i in range(indDer[0].shape[0]): |
|
6836 | for i in range(indDer[0].shape[0]): | |
| 6840 | signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i]]) |
|
6837 | signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i]]) | |
| 6841 | angAllCCF[indDer[0][i],indDer[1][i]+1:] += signo*2*numpy.pi |
|
6838 | angAllCCF[indDer[0][i],indDer[1][i]+1:] += signo*2*numpy.pi | |
| 6842 |
|
6839 | |||
| 6843 | for j in range(numPairs): |
|
6840 | for j in range(numPairs): | |
| 6844 | fit = stats.linregress(time, angAllCCF[j,:]) |
|
6841 | fit = stats.linregress(time, angAllCCF[j,:]) | |
| 6845 | slopes[j] = fit[0] |
|
6842 | slopes[j] = fit[0] | |
| 6846 |
|
6843 | |||
| 6847 | #Remove Outlier |
|
6844 | #Remove Outlier | |
| 6848 | radialVelocity = -numpy.mean(slopes)*(0.25/numpy.pi)*(c/freq) |
|
6845 | radialVelocity = -numpy.mean(slopes)*(0.25/numpy.pi)*(c/freq) | |
| 6849 | radialError = numpy.std(slopes)*(0.25/numpy.pi)*(c/freq) |
|
6846 | radialError = numpy.std(slopes)*(0.25/numpy.pi)*(c/freq) | |
| 6850 | meteorAux[-2] = radialError |
|
6847 | meteorAux[-2] = radialError | |
| 6851 | meteorAux[-3] = radialVelocity |
|
6848 | meteorAux[-3] = radialVelocity | |
| 6852 |
|
6849 | |||
| 6853 | #Setting Error |
|
6850 | #Setting Error | |
| 6854 | #Number 15: Radial Drift velocity or projected horizontal velocity exceeds 200 m/s |
|
6851 | #Number 15: Radial Drift velocity or projected horizontal velocity exceeds 200 m/s | |
| 6855 | if numpy.abs(radialVelocity) > 200: |
|
6852 | if numpy.abs(radialVelocity) > 200: | |
| 6856 | meteorAux[-1] = 15 |
|
6853 | meteorAux[-1] = 15 | |
| 6857 | #Number 12: Poor fit to CCF variation for estimation of radial drift velocity |
|
6854 | #Number 12: Poor fit to CCF variation for estimation of radial drift velocity | |
| 6858 | elif radialError > radialStdThresh: |
|
6855 | elif radialError > radialStdThresh: | |
| 6859 | meteorAux[-1] = 12 |
|
6856 | meteorAux[-1] = 12 | |
| 6860 |
|
6857 | |||
| 6861 | listMeteors1.append(meteorAux) |
|
6858 | listMeteors1.append(meteorAux) | |
| 6862 | return listMeteors1 |
|
6859 | return listMeteors1 | |
| 6863 |
|
6860 | |||
| 6864 | def __setNewArrays(self, listMeteors, date, heiRang): |
|
6861 | def __setNewArrays(self, listMeteors, date, heiRang): | |
| 6865 |
|
6862 | |||
| 6866 | #New arrays |
|
6863 | #New arrays | |
| 6867 | arrayMeteors = numpy.array(listMeteors) |
|
6864 | arrayMeteors = numpy.array(listMeteors) | |
| 6868 | arrayParameters = numpy.zeros((len(listMeteors), 13)) |
|
6865 | arrayParameters = numpy.zeros((len(listMeteors), 13)) | |
| 6869 |
|
6866 | |||
| 6870 | #Date inclusion |
|
6867 | #Date inclusion | |
| 6871 | arrayDate = numpy.tile(date, (len(listMeteors))) |
|
6868 | arrayDate = numpy.tile(date, (len(listMeteors))) | |
| 6872 |
|
6869 | |||
| 6873 | #Meteor array |
|
6870 | #Meteor array | |
| 6874 | #Parameters Array |
|
6871 | #Parameters Array | |
| 6875 | arrayParameters[:,0] = arrayDate #Date |
|
6872 | arrayParameters[:,0] = arrayDate #Date | |
| 6876 | arrayParameters[:,1] = heiRang[arrayMeteors[:,0].astype(int)] #Range |
|
6873 | arrayParameters[:,1] = heiRang[arrayMeteors[:,0].astype(int)] #Range | |
| 6877 | arrayParameters[:,6:8] = arrayMeteors[:,-3:-1] #Radial velocity and its error |
|
6874 | arrayParameters[:,6:8] = arrayMeteors[:,-3:-1] #Radial velocity and its error | |
| 6878 | arrayParameters[:,8:12] = arrayMeteors[:,7:11] #Phases |
|
6875 | arrayParameters[:,8:12] = arrayMeteors[:,7:11] #Phases | |
| 6879 | arrayParameters[:,-1] = arrayMeteors[:,-1] #Error |
|
6876 | arrayParameters[:,-1] = arrayMeteors[:,-1] #Error | |
| 6880 |
|
6877 | |||
| 6881 |
|
6878 | |||
| 6882 | return arrayParameters |
|
6879 | return arrayParameters | |
| 6883 |
|
6880 | |||
| 6884 | class CorrectSMPhases(Operation): |
|
6881 | class CorrectSMPhases(Operation): | |
| 6885 |
|
6882 | |||
| 6886 | def run(self, dataOut, phaseOffsets, hmin = 50, hmax = 150, azimuth = 45, channelPositions = None): |
|
6883 | def run(self, dataOut, phaseOffsets, hmin = 50, hmax = 150, azimuth = 45, channelPositions = None): | |
| 6887 |
|
6884 | |||
| 6888 | arrayParameters = dataOut.data_param |
|
6885 | arrayParameters = dataOut.data_param | |
| 6889 | pairsList = [] |
|
6886 | pairsList = [] | |
| 6890 | pairx = (0,1) |
|
6887 | pairx = (0,1) | |
| 6891 | pairy = (2,3) |
|
6888 | pairy = (2,3) | |
| 6892 | pairsList.append(pairx) |
|
6889 | pairsList.append(pairx) | |
| 6893 | pairsList.append(pairy) |
|
6890 | pairsList.append(pairy) | |
| 6894 | jph = numpy.zeros(4) |
|
6891 | jph = numpy.zeros(4) | |
| 6895 |
|
6892 | |||
| 6896 | phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180 |
|
6893 | phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180 | |
| 6897 | # arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets) |
|
6894 | # arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets) | |
| 6898 | arrayParameters[:,8:12] = numpy.angle(numpy.exp(1j*(arrayParameters[:,8:12] + phaseOffsets))) |
|
6895 | arrayParameters[:,8:12] = numpy.angle(numpy.exp(1j*(arrayParameters[:,8:12] + phaseOffsets))) | |
| 6899 |
|
6896 | |||
| 6900 | meteorOps = SMOperations() |
|
6897 | meteorOps = SMOperations() | |
| 6901 | if channelPositions is None: |
|
6898 | if channelPositions is None: | |
| 6902 | # channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T |
|
6899 | # channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T | |
| 6903 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella |
|
6900 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella | |
| 6904 |
|
6901 | |||
| 6905 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) |
|
6902 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) | |
| 6906 | h = (hmin,hmax) |
|
6903 | h = (hmin,hmax) | |
| 6907 |
|
6904 | |||
| 6908 | arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph) |
|
6905 | arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph) | |
| 6909 |
|
6906 | |||
| 6910 | dataOut.data_param = arrayParameters |
|
6907 | dataOut.data_param = arrayParameters | |
| 6911 | return |
|
6908 | return | |
| 6912 |
|
6909 | |||
| 6913 | class SMPhaseCalibration(Operation): |
|
6910 | class SMPhaseCalibration(Operation): | |
| 6914 |
|
6911 | |||
| 6915 | __buffer = None |
|
6912 | __buffer = None | |
| 6916 |
|
6913 | |||
| 6917 | __initime = None |
|
6914 | __initime = None | |
| 6918 |
|
6915 | |||
| 6919 | __dataReady = False |
|
6916 | __dataReady = False | |
| 6920 |
|
6917 | |||
| 6921 | __isConfig = False |
|
6918 | __isConfig = False | |
| 6922 |
|
6919 | |||
| 6923 | def __checkTime(self, currentTime, initTime, paramInterval, outputInterval): |
|
6920 | def __checkTime(self, currentTime, initTime, paramInterval, outputInterval): | |
| 6924 |
|
6921 | |||
| 6925 | dataTime = currentTime + paramInterval |
|
6922 | dataTime = currentTime + paramInterval | |
| 6926 | deltaTime = dataTime - initTime |
|
6923 | deltaTime = dataTime - initTime | |
| 6927 |
|
6924 | |||
| 6928 | if deltaTime >= outputInterval or deltaTime < 0: |
|
6925 | if deltaTime >= outputInterval or deltaTime < 0: | |
| 6929 | return True |
|
6926 | return True | |
| 6930 |
|
6927 | |||
| 6931 | return False |
|
6928 | return False | |
| 6932 |
|
6929 | |||
| 6933 | def __getGammas(self, pairs, d, phases): |
|
6930 | def __getGammas(self, pairs, d, phases): | |
| 6934 | gammas = numpy.zeros(2) |
|
6931 | gammas = numpy.zeros(2) | |
| 6935 |
|
6932 | |||
| 6936 | for i in range(len(pairs)): |
|
6933 | for i in range(len(pairs)): | |
| 6937 |
|
6934 | |||
| 6938 | pairi = pairs[i] |
|
6935 | pairi = pairs[i] | |
| 6939 |
|
6936 | |||
| 6940 | phip3 = phases[:,pairi[0]] |
|
6937 | phip3 = phases[:,pairi[0]] | |
| 6941 | d3 = d[pairi[0]] |
|
6938 | d3 = d[pairi[0]] | |
| 6942 | phip2 = phases[:,pairi[1]] |
|
6939 | phip2 = phases[:,pairi[1]] | |
| 6943 | d2 = d[pairi[1]] |
|
6940 | d2 = d[pairi[1]] | |
| 6944 | #Calculating gamma |
|
6941 | #Calculating gamma | |
| 6945 | jgamma = -phip2*d3/d2 - phip3 |
|
6942 | jgamma = -phip2*d3/d2 - phip3 | |
| 6946 | jgamma = numpy.angle(numpy.exp(1j*jgamma)) |
|
6943 | jgamma = numpy.angle(numpy.exp(1j*jgamma)) | |
| 6947 |
|
6944 | |||
| 6948 | #Revised distribution |
|
6945 | #Revised distribution | |
| 6949 | jgammaArray = numpy.hstack((jgamma,jgamma+0.5*numpy.pi,jgamma-0.5*numpy.pi)) |
|
6946 | jgammaArray = numpy.hstack((jgamma,jgamma+0.5*numpy.pi,jgamma-0.5*numpy.pi)) | |
| 6950 |
|
6947 | |||
| 6951 | #Histogram |
|
6948 | #Histogram | |
| 6952 | nBins = 64 |
|
6949 | nBins = 64 | |
| 6953 | rmin = -0.5*numpy.pi |
|
6950 | rmin = -0.5*numpy.pi | |
| 6954 | rmax = 0.5*numpy.pi |
|
6951 | rmax = 0.5*numpy.pi | |
| 6955 | phaseHisto = numpy.histogram(jgammaArray, bins=nBins, range=(rmin,rmax)) |
|
6952 | phaseHisto = numpy.histogram(jgammaArray, bins=nBins, range=(rmin,rmax)) | |
| 6956 |
|
6953 | |||
| 6957 | meteorsY = phaseHisto[0] |
|
6954 | meteorsY = phaseHisto[0] | |
| 6958 | phasesX = phaseHisto[1][:-1] |
|
6955 | phasesX = phaseHisto[1][:-1] | |
| 6959 | width = phasesX[1] - phasesX[0] |
|
6956 | width = phasesX[1] - phasesX[0] | |
| 6960 | phasesX += width/2 |
|
6957 | phasesX += width/2 | |
| 6961 |
|
6958 | |||
| 6962 | #Gaussian aproximation |
|
6959 | #Gaussian aproximation | |
| 6963 | bpeak = meteorsY.argmax() |
|
6960 | bpeak = meteorsY.argmax() | |
| 6964 | peak = meteorsY.max() |
|
6961 | peak = meteorsY.max() | |
| 6965 | jmin = bpeak - 5 |
|
6962 | jmin = bpeak - 5 | |
| 6966 | jmax = bpeak + 5 + 1 |
|
6963 | jmax = bpeak + 5 + 1 | |
| 6967 |
|
6964 | |||
| 6968 | if jmin<0: |
|
6965 | if jmin<0: | |
| 6969 | jmin = 0 |
|
6966 | jmin = 0 | |
| 6970 | jmax = 6 |
|
6967 | jmax = 6 | |
| 6971 | elif jmax > meteorsY.size: |
|
6968 | elif jmax > meteorsY.size: | |
| 6972 | jmin = meteorsY.size - 6 |
|
6969 | jmin = meteorsY.size - 6 | |
| 6973 | jmax = meteorsY.size |
|
6970 | jmax = meteorsY.size | |
| 6974 |
|
6971 | |||
| 6975 | x0 = numpy.array([peak,bpeak,50]) |
|
6972 | x0 = numpy.array([peak,bpeak,50]) | |
| 6976 | coeff = optimize.leastsq(self.__residualFunction, x0, args=(meteorsY[jmin:jmax], phasesX[jmin:jmax])) |
|
6973 | coeff = optimize.leastsq(self.__residualFunction, x0, args=(meteorsY[jmin:jmax], phasesX[jmin:jmax])) | |
| 6977 |
|
6974 | |||
| 6978 | #Gammas |
|
6975 | #Gammas | |
| 6979 | gammas[i] = coeff[0][1] |
|
6976 | gammas[i] = coeff[0][1] | |
| 6980 |
|
6977 | |||
| 6981 | return gammas |
|
6978 | return gammas | |
| 6982 |
|
6979 | |||
| 6983 | def __residualFunction(self, coeffs, y, t): |
|
6980 | def __residualFunction(self, coeffs, y, t): | |
| 6984 |
|
6981 | |||
| 6985 | return y - self.__gauss_function(t, coeffs) |
|
6982 | return y - self.__gauss_function(t, coeffs) | |
| 6986 |
|
6983 | |||
| 6987 | def __gauss_function(self, t, coeffs): |
|
6984 | def __gauss_function(self, t, coeffs): | |
| 6988 |
|
6985 | |||
| 6989 | return coeffs[0]*numpy.exp(-0.5*((t - coeffs[1]) / coeffs[2])**2) |
|
6986 | return coeffs[0]*numpy.exp(-0.5*((t - coeffs[1]) / coeffs[2])**2) | |
| 6990 |
|
6987 | |||
| 6991 | def __getPhases(self, azimuth, h, pairsList, d, gammas, meteorsArray): |
|
6988 | def __getPhases(self, azimuth, h, pairsList, d, gammas, meteorsArray): | |
| 6992 | meteorOps = SMOperations() |
|
6989 | meteorOps = SMOperations() | |
| 6993 | nchan = 4 |
|
6990 | nchan = 4 | |
| 6994 | pairx = pairsList[0] #x es 0 |
|
6991 | pairx = pairsList[0] #x es 0 | |
| 6995 | pairy = pairsList[1] #y es 1 |
|
6992 | pairy = pairsList[1] #y es 1 | |
| 6996 | center_xangle = 0 |
|
6993 | center_xangle = 0 | |
| 6997 | center_yangle = 0 |
|
6994 | center_yangle = 0 | |
| 6998 | range_angle = numpy.array([10*numpy.pi,numpy.pi,numpy.pi/2,numpy.pi/4]) |
|
6995 | range_angle = numpy.array([10*numpy.pi,numpy.pi,numpy.pi/2,numpy.pi/4]) | |
| 6999 | ntimes = len(range_angle) |
|
6996 | ntimes = len(range_angle) | |
| 7000 |
|
6997 | |||
| 7001 | nstepsx = 20 |
|
6998 | nstepsx = 20 | |
| 7002 | nstepsy = 20 |
|
6999 | nstepsy = 20 | |
| 7003 |
|
7000 | |||
| 7004 | for iz in range(ntimes): |
|
7001 | for iz in range(ntimes): | |
| 7005 | min_xangle = -range_angle[iz]/2 + center_xangle |
|
7002 | min_xangle = -range_angle[iz]/2 + center_xangle | |
| 7006 | max_xangle = range_angle[iz]/2 + center_xangle |
|
7003 | max_xangle = range_angle[iz]/2 + center_xangle | |
| 7007 | min_yangle = -range_angle[iz]/2 + center_yangle |
|
7004 | min_yangle = -range_angle[iz]/2 + center_yangle | |
| 7008 | max_yangle = range_angle[iz]/2 + center_yangle |
|
7005 | max_yangle = range_angle[iz]/2 + center_yangle | |
| 7009 |
|
7006 | |||
| 7010 | inc_x = (max_xangle-min_xangle)/nstepsx |
|
7007 | inc_x = (max_xangle-min_xangle)/nstepsx | |
| 7011 | inc_y = (max_yangle-min_yangle)/nstepsy |
|
7008 | inc_y = (max_yangle-min_yangle)/nstepsy | |
| 7012 |
|
7009 | |||
| 7013 | alpha_y = numpy.arange(nstepsy)*inc_y + min_yangle |
|
7010 | alpha_y = numpy.arange(nstepsy)*inc_y + min_yangle | |
| 7014 | alpha_x = numpy.arange(nstepsx)*inc_x + min_xangle |
|
7011 | alpha_x = numpy.arange(nstepsx)*inc_x + min_xangle | |
| 7015 | penalty = numpy.zeros((nstepsx,nstepsy)) |
|
7012 | penalty = numpy.zeros((nstepsx,nstepsy)) | |
| 7016 | jph_array = numpy.zeros((nchan,nstepsx,nstepsy)) |
|
7013 | jph_array = numpy.zeros((nchan,nstepsx,nstepsy)) | |
| 7017 | jph = numpy.zeros(nchan) |
|
7014 | jph = numpy.zeros(nchan) | |
| 7018 |
|
7015 | |||
| 7019 | # Iterations looking for the offset |
|
7016 | # Iterations looking for the offset | |
| 7020 | for iy in range(int(nstepsy)): |
|
7017 | for iy in range(int(nstepsy)): | |
| 7021 | for ix in range(int(nstepsx)): |
|
7018 | for ix in range(int(nstepsx)): | |
| 7022 | d3 = d[pairsList[1][0]] |
|
7019 | d3 = d[pairsList[1][0]] | |
| 7023 | d2 = d[pairsList[1][1]] |
|
7020 | d2 = d[pairsList[1][1]] | |
| 7024 | d5 = d[pairsList[0][0]] |
|
7021 | d5 = d[pairsList[0][0]] | |
| 7025 | d4 = d[pairsList[0][1]] |
|
7022 | d4 = d[pairsList[0][1]] | |
| 7026 |
|
7023 | |||
| 7027 | alp2 = alpha_y[iy] #gamma 1 |
|
7024 | alp2 = alpha_y[iy] #gamma 1 | |
| 7028 | alp4 = alpha_x[ix] #gamma 0 |
|
7025 | alp4 = alpha_x[ix] #gamma 0 | |
| 7029 |
|
7026 | |||
| 7030 | alp3 = -alp2*d3/d2 - gammas[1] |
|
7027 | alp3 = -alp2*d3/d2 - gammas[1] | |
| 7031 | alp5 = -alp4*d5/d4 - gammas[0] |
|
7028 | alp5 = -alp4*d5/d4 - gammas[0] | |
| 7032 | jph[pairsList[0][1]] = alp4 |
|
7029 | jph[pairsList[0][1]] = alp4 | |
| 7033 | jph[pairsList[0][0]] = alp5 |
|
7030 | jph[pairsList[0][0]] = alp5 | |
| 7034 | jph[pairsList[1][0]] = alp3 |
|
7031 | jph[pairsList[1][0]] = alp3 | |
| 7035 | jph[pairsList[1][1]] = alp2 |
|
7032 | jph[pairsList[1][1]] = alp2 | |
| 7036 | jph_array[:,ix,iy] = jph |
|
7033 | jph_array[:,ix,iy] = jph | |
| 7037 | meteorsArray1 = meteorOps.getMeteorParams(meteorsArray, azimuth, h, pairsList, d, jph) |
|
7034 | meteorsArray1 = meteorOps.getMeteorParams(meteorsArray, azimuth, h, pairsList, d, jph) | |
| 7038 | error = meteorsArray1[:,-1] |
|
7035 | error = meteorsArray1[:,-1] | |
| 7039 | ind1 = numpy.where(error==0)[0] |
|
7036 | ind1 = numpy.where(error==0)[0] | |
| 7040 | penalty[ix,iy] = ind1.size |
|
7037 | penalty[ix,iy] = ind1.size | |
| 7041 |
|
7038 | |||
| 7042 | i,j = numpy.unravel_index(penalty.argmax(), penalty.shape) |
|
7039 | i,j = numpy.unravel_index(penalty.argmax(), penalty.shape) | |
| 7043 | phOffset = jph_array[:,i,j] |
|
7040 | phOffset = jph_array[:,i,j] | |
| 7044 |
|
7041 | |||
| 7045 | center_xangle = phOffset[pairx[1]] |
|
7042 | center_xangle = phOffset[pairx[1]] | |
| 7046 | center_yangle = phOffset[pairy[1]] |
|
7043 | center_yangle = phOffset[pairy[1]] | |
| 7047 |
|
7044 | |||
| 7048 | phOffset = numpy.angle(numpy.exp(1j*jph_array[:,i,j])) |
|
7045 | phOffset = numpy.angle(numpy.exp(1j*jph_array[:,i,j])) | |
| 7049 | phOffset = phOffset*180/numpy.pi |
|
7046 | phOffset = phOffset*180/numpy.pi | |
| 7050 | return phOffset |
|
7047 | return phOffset | |
| 7051 |
|
7048 | |||
| 7052 |
|
7049 | |||
| 7053 | def run(self, dataOut, hmin, hmax, channelPositions=None, nHours = 1): |
|
7050 | def run(self, dataOut, hmin, hmax, channelPositions=None, nHours = 1): | |
| 7054 |
|
7051 | |||
| 7055 | dataOut.flagNoData = True |
|
7052 | dataOut.flagNoData = True | |
| 7056 | self.__dataReady = False |
|
7053 | self.__dataReady = False | |
| 7057 | dataOut.outputInterval = nHours*3600 |
|
7054 | dataOut.outputInterval = nHours*3600 | |
| 7058 |
|
7055 | |||
| 7059 | if self.__isConfig == False: |
|
7056 | if self.__isConfig == False: | |
| 7060 | #Get Initial LTC time |
|
7057 | #Get Initial LTC time | |
| 7061 | self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
7058 | self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) | |
| 7062 | self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
7059 | self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() | |
| 7063 |
|
7060 | |||
| 7064 | self.__isConfig = True |
|
7061 | self.__isConfig = True | |
| 7065 |
|
7062 | |||
| 7066 | if self.__buffer is None: |
|
7063 | if self.__buffer is None: | |
| 7067 | self.__buffer = dataOut.data_param.copy() |
|
7064 | self.__buffer = dataOut.data_param.copy() | |
| 7068 |
|
7065 | |||
| 7069 | else: |
|
7066 | else: | |
| 7070 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
7067 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) | |
| 7071 |
|
7068 | |||
| 7072 | self.__dataReady = self.__checkTime(dataOut.utctime, self.__initime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
7069 | self.__dataReady = self.__checkTime(dataOut.utctime, self.__initime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready | |
| 7073 |
|
7070 | |||
| 7074 | if self.__dataReady: |
|
7071 | if self.__dataReady: | |
| 7075 | dataOut.utctimeInit = self.__initime |
|
7072 | dataOut.utctimeInit = self.__initime | |
| 7076 | self.__initime += dataOut.outputInterval #to erase time offset |
|
7073 | self.__initime += dataOut.outputInterval #to erase time offset | |
| 7077 |
|
7074 | |||
| 7078 | freq = dataOut.frequency |
|
7075 | freq = dataOut.frequency | |
| 7079 | c = dataOut.C #m/s |
|
7076 | c = dataOut.C #m/s | |
| 7080 | lamb = c/freq |
|
7077 | lamb = c/freq | |
| 7081 | k = 2*numpy.pi/lamb |
|
7078 | k = 2*numpy.pi/lamb | |
| 7082 | azimuth = 0 |
|
7079 | azimuth = 0 | |
| 7083 | h = (hmin, hmax) |
|
7080 | h = (hmin, hmax) | |
| 7084 |
|
7081 | |||
| 7085 | if channelPositions is None: |
|
7082 | if channelPositions is None: | |
| 7086 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella |
|
7083 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella | |
| 7087 | meteorOps = SMOperations() |
|
7084 | meteorOps = SMOperations() | |
| 7088 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) |
|
7085 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) | |
| 7089 |
|
7086 | |||
| 7090 | #Checking correct order of pairs |
|
7087 | #Checking correct order of pairs | |
| 7091 | pairs = [] |
|
7088 | pairs = [] | |
| 7092 | if distances[1] > distances[0]: |
|
7089 | if distances[1] > distances[0]: | |
| 7093 | pairs.append((1,0)) |
|
7090 | pairs.append((1,0)) | |
| 7094 | else: |
|
7091 | else: | |
| 7095 | pairs.append((0,1)) |
|
7092 | pairs.append((0,1)) | |
| 7096 |
|
7093 | |||
| 7097 | if distances[3] > distances[2]: |
|
7094 | if distances[3] > distances[2]: | |
| 7098 | pairs.append((3,2)) |
|
7095 | pairs.append((3,2)) | |
| 7099 | else: |
|
7096 | else: | |
| 7100 | pairs.append((2,3)) |
|
7097 | pairs.append((2,3)) | |
| 7101 |
|
7098 | |||
| 7102 | meteorsArray = self.__buffer |
|
7099 | meteorsArray = self.__buffer | |
| 7103 | error = meteorsArray[:,-1] |
|
7100 | error = meteorsArray[:,-1] | |
| 7104 | boolError = (error==0)|(error==3)|(error==4)|(error==13)|(error==14) |
|
7101 | boolError = (error==0)|(error==3)|(error==4)|(error==13)|(error==14) | |
| 7105 | ind1 = numpy.where(boolError)[0] |
|
7102 | ind1 = numpy.where(boolError)[0] | |
| 7106 | meteorsArray = meteorsArray[ind1,:] |
|
7103 | meteorsArray = meteorsArray[ind1,:] | |
| 7107 | meteorsArray[:,-1] = 0 |
|
7104 | meteorsArray[:,-1] = 0 | |
| 7108 | phases = meteorsArray[:,8:12] |
|
7105 | phases = meteorsArray[:,8:12] | |
| 7109 |
|
7106 | |||
| 7110 | #Calculate Gammas |
|
7107 | #Calculate Gammas | |
| 7111 | gammas = self.__getGammas(pairs, distances, phases) |
|
7108 | gammas = self.__getGammas(pairs, distances, phases) | |
| 7112 | #Calculate Phases |
|
7109 | #Calculate Phases | |
| 7113 | phasesOff = self.__getPhases(azimuth, h, pairs, distances, gammas, meteorsArray) |
|
7110 | phasesOff = self.__getPhases(azimuth, h, pairs, distances, gammas, meteorsArray) | |
| 7114 | phasesOff = phasesOff.reshape((1,phasesOff.size)) |
|
7111 | phasesOff = phasesOff.reshape((1,phasesOff.size)) | |
| 7115 | dataOut.data_output = -phasesOff |
|
7112 | dataOut.data_output = -phasesOff | |
| 7116 | dataOut.flagNoData = False |
|
7113 | dataOut.flagNoData = False | |
| 7117 | self.__buffer = None |
|
7114 | self.__buffer = None | |
| 7118 |
|
7115 | |||
| 7119 |
|
7116 | |||
| 7120 | return |
|
7117 | return | |
| 7121 |
|
7118 | |||
| 7122 | class SMOperations(): |
|
7119 | class SMOperations(): | |
| 7123 |
|
7120 | |||
| 7124 | def __init__(self): |
|
7121 | def __init__(self): | |
| 7125 |
|
7122 | |||
| 7126 | return |
|
7123 | return | |
| 7127 |
|
7124 | |||
| 7128 | def getMeteorParams(self, arrayParameters0, azimuth, h, pairsList, distances, jph): |
|
7125 | def getMeteorParams(self, arrayParameters0, azimuth, h, pairsList, distances, jph): | |
| 7129 |
|
7126 | |||
| 7130 | arrayParameters = arrayParameters0.copy() |
|
7127 | arrayParameters = arrayParameters0.copy() | |
| 7131 | hmin = h[0] |
|
7128 | hmin = h[0] | |
| 7132 | hmax = h[1] |
|
7129 | hmax = h[1] | |
| 7133 |
|
7130 | |||
| 7134 | #Calculate AOA (Error N 3, 4) |
|
7131 | #Calculate AOA (Error N 3, 4) | |
| 7135 | #JONES ET AL. 1998 |
|
7132 | #JONES ET AL. 1998 | |
| 7136 | AOAthresh = numpy.pi/8 |
|
7133 | AOAthresh = numpy.pi/8 | |
| 7137 | error = arrayParameters[:,-1] |
|
7134 | error = arrayParameters[:,-1] | |
| 7138 | phases = -arrayParameters[:,8:12] + jph |
|
7135 | phases = -arrayParameters[:,8:12] + jph | |
| 7139 | arrayParameters[:,3:6], arrayParameters[:,-1] = self.__getAOA(phases, pairsList, distances, error, AOAthresh, azimuth) |
|
7136 | arrayParameters[:,3:6], arrayParameters[:,-1] = self.__getAOA(phases, pairsList, distances, error, AOAthresh, azimuth) | |
| 7140 |
|
7137 | |||
| 7141 | #Calculate Heights (Error N 13 and 14) |
|
7138 | #Calculate Heights (Error N 13 and 14) | |
| 7142 | error = arrayParameters[:,-1] |
|
7139 | error = arrayParameters[:,-1] | |
| 7143 | Ranges = arrayParameters[:,1] |
|
7140 | Ranges = arrayParameters[:,1] | |
| 7144 | zenith = arrayParameters[:,4] |
|
7141 | zenith = arrayParameters[:,4] | |
| 7145 | arrayParameters[:,2], arrayParameters[:,-1] = self.__getHeights(Ranges, zenith, error, hmin, hmax) |
|
7142 | arrayParameters[:,2], arrayParameters[:,-1] = self.__getHeights(Ranges, zenith, error, hmin, hmax) | |
| 7146 |
|
7143 | |||
| 7147 | #----------------------- Get Final data ------------------------------------ |
|
7144 | #----------------------- Get Final data ------------------------------------ | |
| 7148 |
|
7145 | |||
| 7149 | return arrayParameters |
|
7146 | return arrayParameters | |
| 7150 |
|
7147 | |||
| 7151 | def __getAOA(self, phases, pairsList, directions, error, AOAthresh, azimuth): |
|
7148 | def __getAOA(self, phases, pairsList, directions, error, AOAthresh, azimuth): | |
| 7152 |
|
7149 | |||
| 7153 | arrayAOA = numpy.zeros((phases.shape[0],3)) |
|
7150 | arrayAOA = numpy.zeros((phases.shape[0],3)) | |
| 7154 | cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList,directions) |
|
7151 | cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList,directions) | |
| 7155 |
|
7152 | |||
| 7156 | arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth) |
|
7153 | arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth) | |
| 7157 | cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1) |
|
7154 | cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1) | |
| 7158 | arrayAOA[:,2] = cosDirError |
|
7155 | arrayAOA[:,2] = cosDirError | |
| 7159 |
|
7156 | |||
| 7160 | azimuthAngle = arrayAOA[:,0] |
|
7157 | azimuthAngle = arrayAOA[:,0] | |
| 7161 | zenithAngle = arrayAOA[:,1] |
|
7158 | zenithAngle = arrayAOA[:,1] | |
| 7162 |
|
7159 | |||
| 7163 | #Setting Error |
|
7160 | #Setting Error | |
| 7164 | indError = numpy.where(numpy.logical_or(error == 3, error == 4))[0] |
|
7161 | indError = numpy.where(numpy.logical_or(error == 3, error == 4))[0] | |
| 7165 | error[indError] = 0 |
|
7162 | error[indError] = 0 | |
| 7166 | #Number 3: AOA not fesible |
|
7163 | #Number 3: AOA not fesible | |
| 7167 | indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0] |
|
7164 | indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0] | |
| 7168 | error[indInvalid] = 3 |
|
7165 | error[indInvalid] = 3 | |
| 7169 | #Number 4: Large difference in AOAs obtained from different antenna baselines |
|
7166 | #Number 4: Large difference in AOAs obtained from different antenna baselines | |
| 7170 | indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0] |
|
7167 | indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0] | |
| 7171 | error[indInvalid] = 4 |
|
7168 | error[indInvalid] = 4 | |
| 7172 | return arrayAOA, error |
|
7169 | return arrayAOA, error | |
| 7173 |
|
7170 | |||
| 7174 | def __getDirectionCosines(self, arrayPhase, pairsList, distances): |
|
7171 | def __getDirectionCosines(self, arrayPhase, pairsList, distances): | |
| 7175 |
|
7172 | |||
| 7176 | #Initializing some variables |
|
7173 | #Initializing some variables | |
| 7177 | ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi |
|
7174 | ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi | |
| 7178 | ang_aux = ang_aux.reshape(1,ang_aux.size) |
|
7175 | ang_aux = ang_aux.reshape(1,ang_aux.size) | |
| 7179 |
|
7176 | |||
| 7180 | cosdir = numpy.zeros((arrayPhase.shape[0],2)) |
|
7177 | cosdir = numpy.zeros((arrayPhase.shape[0],2)) | |
| 7181 | cosdir0 = numpy.zeros((arrayPhase.shape[0],2)) |
|
7178 | cosdir0 = numpy.zeros((arrayPhase.shape[0],2)) | |
| 7182 |
|
7179 | |||
| 7183 |
|
7180 | |||
| 7184 | for i in range(2): |
|
7181 | for i in range(2): | |
| 7185 | ph0 = arrayPhase[:,pairsList[i][0]] |
|
7182 | ph0 = arrayPhase[:,pairsList[i][0]] | |
| 7186 | ph1 = arrayPhase[:,pairsList[i][1]] |
|
7183 | ph1 = arrayPhase[:,pairsList[i][1]] | |
| 7187 | d0 = distances[pairsList[i][0]] |
|
7184 | d0 = distances[pairsList[i][0]] | |
| 7188 | d1 = distances[pairsList[i][1]] |
|
7185 | d1 = distances[pairsList[i][1]] | |
| 7189 |
|
7186 | |||
| 7190 | ph0_aux = ph0 + ph1 |
|
7187 | ph0_aux = ph0 + ph1 | |
| 7191 | ph0_aux = numpy.angle(numpy.exp(1j*ph0_aux)) |
|
7188 | ph0_aux = numpy.angle(numpy.exp(1j*ph0_aux)) | |
| 7192 |
|
||||
| 7193 | #First Estimation |
|
7189 | #First Estimation | |
| 7194 | cosdir0[:,i] = (ph0_aux)/(2*numpy.pi*(d0 - d1)) |
|
7190 | cosdir0[:,i] = (ph0_aux)/(2*numpy.pi*(d0 - d1)) | |
| 7195 |
|
7191 | |||
| 7196 | #Most-Accurate Second Estimation |
|
7192 | #Most-Accurate Second Estimation | |
| 7197 | phi1_aux = ph0 - ph1 |
|
7193 | phi1_aux = ph0 - ph1 | |
| 7198 | phi1_aux = phi1_aux.reshape(phi1_aux.size,1) |
|
7194 | phi1_aux = phi1_aux.reshape(phi1_aux.size,1) | |
| 7199 | #Direction Cosine 1 |
|
7195 | #Direction Cosine 1 | |
| 7200 | cosdir1 = (phi1_aux + ang_aux)/(2*numpy.pi*(d0 + d1)) |
|
7196 | cosdir1 = (phi1_aux + ang_aux)/(2*numpy.pi*(d0 + d1)) | |
| 7201 |
|
7197 | |||
| 7202 | #Searching the correct Direction Cosine |
|
7198 | #Searching the correct Direction Cosine | |
| 7203 | cosdir0_aux = cosdir0[:,i] |
|
7199 | cosdir0_aux = cosdir0[:,i] | |
| 7204 | cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1) |
|
7200 | cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1) | |
| 7205 | #Minimum Distance |
|
7201 | #Minimum Distance | |
| 7206 | cosDiff = (cosdir1 - cosdir0_aux)**2 |
|
7202 | cosDiff = (cosdir1 - cosdir0_aux)**2 | |
| 7207 | indcos = cosDiff.argmin(axis = 1) |
|
7203 | indcos = cosDiff.argmin(axis = 1) | |
| 7208 | #Saving Value obtained |
|
7204 | #Saving Value obtained | |
| 7209 | cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos] |
|
7205 | cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos] | |
| 7210 |
|
7206 | |||
| 7211 | return cosdir0, cosdir |
|
7207 | return cosdir0, cosdir | |
| 7212 |
|
7208 | |||
| 7213 | def __calculateAOA(self, cosdir, azimuth): |
|
7209 | def __calculateAOA(self, cosdir, azimuth): | |
| 7214 | cosdirX = cosdir[:,0] |
|
7210 | cosdirX = cosdir[:,0] | |
| 7215 | cosdirY = cosdir[:,1] |
|
7211 | cosdirY = cosdir[:,1] | |
| 7216 |
|
7212 | |||
| 7217 | zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi |
|
7213 | zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi | |
| 7218 | azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth#0 deg north, 90 deg east |
|
7214 | azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth#0 deg north, 90 deg east | |
| 7219 | angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose() |
|
7215 | angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose() | |
| 7220 |
|
7216 | |||
| 7221 | return angles |
|
7217 | return angles | |
| 7222 |
|
7218 | |||
| 7223 | def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight): |
|
7219 | def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight): | |
| 7224 |
|
7220 | |||
| 7225 | Ramb = 375 #Ramb = c/(2*PRF) |
|
7221 | Ramb = 375 #Ramb = c/(2*PRF) | |
| 7226 | Re = 6371 #Earth Radius |
|
7222 | Re = 6371 #Earth Radius | |
| 7227 | heights = numpy.zeros(Ranges.shape) |
|
7223 | heights = numpy.zeros(Ranges.shape) | |
| 7228 |
|
7224 | |||
| 7229 | R_aux = numpy.array([0,1,2])*Ramb |
|
7225 | R_aux = numpy.array([0,1,2])*Ramb | |
| 7230 | R_aux = R_aux.reshape(1,R_aux.size) |
|
7226 | R_aux = R_aux.reshape(1,R_aux.size) | |
| 7231 |
|
7227 | |||
| 7232 | Ranges = Ranges.reshape(Ranges.size,1) |
|
7228 | Ranges = Ranges.reshape(Ranges.size,1) | |
| 7233 |
|
7229 | |||
| 7234 | Ri = Ranges + R_aux |
|
7230 | Ri = Ranges + R_aux | |
| 7235 | hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re |
|
7231 | hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re | |
| 7236 |
|
7232 | |||
| 7237 | #Check if there is a height between 70 and 110 km |
|
7233 | #Check if there is a height between 70 and 110 km | |
| 7238 | h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1) |
|
7234 | h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1) | |
| 7239 | ind_h = numpy.where(h_bool == 1)[0] |
|
7235 | ind_h = numpy.where(h_bool == 1)[0] | |
| 7240 |
|
7236 | |||
| 7241 | hCorr = hi[ind_h, :] |
|
7237 | hCorr = hi[ind_h, :] | |
| 7242 | ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight)) |
|
7238 | ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight)) | |
| 7243 |
|
7239 | |||
| 7244 | hCorr = hi[ind_hCorr][:len(ind_h)] |
|
7240 | hCorr = hi[ind_hCorr][:len(ind_h)] | |
| 7245 | heights[ind_h] = hCorr |
|
7241 | heights[ind_h] = hCorr | |
| 7246 |
|
7242 | |||
| 7247 | #Setting Error |
|
7243 | #Setting Error | |
| 7248 | #Number 13: Height unresolvable echo: not valid height within 70 to 110 km |
|
7244 | #Number 13: Height unresolvable echo: not valid height within 70 to 110 km | |
| 7249 | #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km |
|
7245 | #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km | |
| 7250 | indError = numpy.where(numpy.logical_or(error == 13, error == 14))[0] |
|
7246 | indError = numpy.where(numpy.logical_or(error == 13, error == 14))[0] | |
| 7251 | error[indError] = 0 |
|
7247 | error[indError] = 0 | |
| 7252 | indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0] |
|
7248 | indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0] | |
| 7253 | error[indInvalid2] = 14 |
|
7249 | error[indInvalid2] = 14 | |
| 7254 | indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0] |
|
7250 | indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0] | |
| 7255 | error[indInvalid1] = 13 |
|
7251 | error[indInvalid1] = 13 | |
| 7256 |
|
7252 | |||
| 7257 | return heights, error |
|
7253 | return heights, error | |
| 7258 |
|
7254 | |||
| 7259 | def getPhasePairs(self, channelPositions): |
|
7255 | def getPhasePairs(self, channelPositions): | |
| 7260 | chanPos = numpy.array(channelPositions) |
|
7256 | chanPos = numpy.array(channelPositions) | |
| 7261 | listOper = list(itertools.combinations(list(range(5)),2)) |
|
7257 | listOper = list(itertools.combinations(list(range(5)),2)) | |
| 7262 |
|
7258 | |||
| 7263 | distances = numpy.zeros(4) |
|
7259 | distances = numpy.zeros(4) | |
| 7264 | axisX = [] |
|
7260 | axisX = [] | |
| 7265 | axisY = [] |
|
7261 | axisY = [] | |
| 7266 | distX = numpy.zeros(3) |
|
7262 | distX = numpy.zeros(3) | |
| 7267 | distY = numpy.zeros(3) |
|
7263 | distY = numpy.zeros(3) | |
| 7268 | ix = 0 |
|
7264 | ix = 0 | |
| 7269 | iy = 0 |
|
7265 | iy = 0 | |
| 7270 |
|
7266 | |||
| 7271 | pairX = numpy.zeros((2,2)) |
|
7267 | pairX = numpy.zeros((2,2)) | |
| 7272 | pairY = numpy.zeros((2,2)) |
|
7268 | pairY = numpy.zeros((2,2)) | |
| 7273 |
|
7269 | |||
| 7274 | for i in range(len(listOper)): |
|
7270 | for i in range(len(listOper)): | |
| 7275 | pairi = listOper[i] |
|
7271 | pairi = listOper[i] | |
| 7276 |
|
7272 | |||
| 7277 | posDif = numpy.abs(chanPos[pairi[0],:] - chanPos[pairi[1],:]) |
|
7273 | posDif = numpy.abs(chanPos[pairi[0],:] - chanPos[pairi[1],:]) | |
| 7278 |
|
7274 | |||
| 7279 | if posDif[0] == 0: |
|
7275 | if posDif[0] == 0: | |
| 7280 | axisY.append(pairi) |
|
7276 | axisY.append(pairi) | |
| 7281 | distY[iy] = posDif[1] |
|
7277 | distY[iy] = posDif[1] | |
| 7282 | iy += 1 |
|
7278 | iy += 1 | |
| 7283 | elif posDif[1] == 0: |
|
7279 | elif posDif[1] == 0: | |
| 7284 | axisX.append(pairi) |
|
7280 | axisX.append(pairi) | |
| 7285 | distX[ix] = posDif[0] |
|
7281 | distX[ix] = posDif[0] | |
| 7286 | ix += 1 |
|
7282 | ix += 1 | |
| 7287 |
|
7283 | |||
| 7288 | for i in range(2): |
|
7284 | for i in range(2): | |
| 7289 | if i==0: |
|
7285 | if i==0: | |
| 7290 | dist0 = distX |
|
7286 | dist0 = distX | |
| 7291 | axis0 = axisX |
|
7287 | axis0 = axisX | |
| 7292 | else: |
|
7288 | else: | |
| 7293 | dist0 = distY |
|
7289 | dist0 = distY | |
| 7294 | axis0 = axisY |
|
7290 | axis0 = axisY | |
| 7295 |
|
7291 | |||
| 7296 | side = numpy.argsort(dist0)[:-1] |
|
7292 | side = numpy.argsort(dist0)[:-1] | |
| 7297 | axis0 = numpy.array(axis0)[side,:] |
|
7293 | axis0 = numpy.array(axis0)[side,:] | |
| 7298 | chanC = int(numpy.intersect1d(axis0[0,:], axis0[1,:])[0]) |
|
7294 | chanC = int(numpy.intersect1d(axis0[0,:], axis0[1,:])[0]) | |
| 7299 | axis1 = numpy.unique(numpy.reshape(axis0,4)) |
|
7295 | axis1 = numpy.unique(numpy.reshape(axis0,4)) | |
| 7300 | side = axis1[axis1 != chanC] |
|
7296 | side = axis1[axis1 != chanC] | |
| 7301 | diff1 = chanPos[chanC,i] - chanPos[side[0],i] |
|
7297 | diff1 = chanPos[chanC,i] - chanPos[side[0],i] | |
| 7302 | diff2 = chanPos[chanC,i] - chanPos[side[1],i] |
|
7298 | diff2 = chanPos[chanC,i] - chanPos[side[1],i] | |
| 7303 | if diff1<0: |
|
7299 | if diff1<0: | |
| 7304 | chan2 = side[0] |
|
7300 | chan2 = side[0] | |
| 7305 | d2 = numpy.abs(diff1) |
|
7301 | d2 = numpy.abs(diff1) | |
| 7306 | chan1 = side[1] |
|
7302 | chan1 = side[1] | |
| 7307 | d1 = numpy.abs(diff2) |
|
7303 | d1 = numpy.abs(diff2) | |
| 7308 | else: |
|
7304 | else: | |
| 7309 | chan2 = side[1] |
|
7305 | chan2 = side[1] | |
| 7310 | d2 = numpy.abs(diff2) |
|
7306 | d2 = numpy.abs(diff2) | |
| 7311 | chan1 = side[0] |
|
7307 | chan1 = side[0] | |
| 7312 | d1 = numpy.abs(diff1) |
|
7308 | d1 = numpy.abs(diff1) | |
| 7313 |
|
7309 | |||
| 7314 | if i==0: |
|
7310 | if i==0: | |
| 7315 | chanCX = chanC |
|
7311 | chanCX = chanC | |
| 7316 | chan1X = chan1 |
|
7312 | chan1X = chan1 | |
| 7317 | chan2X = chan2 |
|
7313 | chan2X = chan2 | |
| 7318 | distances[0:2] = numpy.array([d1,d2]) |
|
7314 | distances[0:2] = numpy.array([d1,d2]) | |
| 7319 | else: |
|
7315 | else: | |
| 7320 | chanCY = chanC |
|
7316 | chanCY = chanC | |
| 7321 | chan1Y = chan1 |
|
7317 | chan1Y = chan1 | |
| 7322 | chan2Y = chan2 |
|
7318 | chan2Y = chan2 | |
| 7323 | distances[2:4] = numpy.array([d1,d2]) |
|
7319 | distances[2:4] = numpy.array([d1,d2]) | |
| 7324 |
|
7320 | |||
| 7325 | pairslist = [(chanCX, chan1X),(chanCX, chan2X),(chanCY,chan1Y),(chanCY, chan2Y)] |
|
7321 | pairslist = [(chanCX, chan1X),(chanCX, chan2X),(chanCY,chan1Y),(chanCY, chan2Y)] | |
| 7326 |
|
7322 | |||
| 7327 | return pairslist, distances |
|
7323 | return pairslist, distances | |
| 7328 |
|
7324 | |||
| 7329 | class IGRFModel(Operation): |
|
7325 | class IGRFModel(Operation): | |
| 7330 | ''' |
|
7326 | ''' | |
| 7331 | Written by R. Flores |
|
7327 | Written by R. Flores | |
| 7332 | ''' |
|
7328 | ''' | |
| 7333 | """Operation to calculate Geomagnetic parameters. |
|
7329 | """Operation to calculate Geomagnetic parameters. | |
| 7334 |
|
7330 | |||
| 7335 | Parameters: |
|
7331 | Parameters: | |
| 7336 | ----------- |
|
7332 | ----------- | |
| 7337 | None |
|
7333 | None | |
| 7338 |
|
7334 | |||
| 7339 | Example |
|
7335 | Example | |
| 7340 | -------- |
|
7336 | -------- | |
| 7341 |
|
7337 | |||
| 7342 | op = proc_unit.addOperation(name='IGRFModel', optype='other') |
|
7338 | op = proc_unit.addOperation(name='IGRFModel', optype='other') | |
| 7343 |
|
7339 | |||
| 7344 | """ |
|
7340 | """ | |
| 7345 |
|
7341 | |||
| 7346 | def __init__(self, **kwargs): |
|
7342 | def __init__(self, **kwargs): | |
| 7347 |
|
7343 | |||
| 7348 | Operation.__init__(self, **kwargs) |
|
7344 | Operation.__init__(self, **kwargs) | |
| 7349 |
|
7345 | |||
| 7350 | self.aux=1 |
|
7346 | self.aux=1 | |
| 7351 |
|
7347 | |||
| 7352 | def run(self,dataOut): |
|
7348 | def run(self,dataOut): | |
| 7353 |
|
7349 | |||
| 7354 | try: |
|
7350 | try: | |
| 7355 | from schainpy.model.proc import mkfact_short_2020_2 |
|
7351 | from schainpy.model.proc import mkfact_short_2020_2 | |
| 7356 | except: |
|
7352 | except: | |
| 7357 | log.warning('You should install "mkfact_short_2020" module to process IGRF Model') |
|
7353 | log.warning('You should install "mkfact_short_2020" module to process IGRF Model') | |
| 7358 |
|
7354 | |||
| 7359 | if self.aux==1: |
|
7355 | if self.aux==1: | |
| 7360 |
|
7356 | |||
| 7361 | #dataOut.TimeBlockSeconds_First_Time=time.mktime(time.strptime(dataOut.TimeBlockDate)) |
|
7357 | #dataOut.TimeBlockSeconds_First_Time=time.mktime(time.strptime(dataOut.TimeBlockDate)) | |
| 7362 | #### we do not use dataOut.datatime.ctime() because it's the time of the second (next) block |
|
7358 | #### we do not use dataOut.datatime.ctime() because it's the time of the second (next) block | |
| 7363 | dataOut.TimeBlockSeconds_First_Time=dataOut.TimeBlockSeconds |
|
7359 | dataOut.TimeBlockSeconds_First_Time=dataOut.TimeBlockSeconds | |
| 7364 | dataOut.bd_time=time.gmtime(dataOut.TimeBlockSeconds_First_Time) |
|
7360 | dataOut.bd_time=time.gmtime(dataOut.TimeBlockSeconds_First_Time) | |
| 7365 | dataOut.year=dataOut.bd_time.tm_year+(dataOut.bd_time.tm_yday-1)/364.0 |
|
7361 | dataOut.year=dataOut.bd_time.tm_year+(dataOut.bd_time.tm_yday-1)/364.0 | |
| 7366 | dataOut.ut=dataOut.bd_time.tm_hour+dataOut.bd_time.tm_min/60.0+dataOut.bd_time.tm_sec/3600.0 |
|
7362 | dataOut.ut=dataOut.bd_time.tm_hour+dataOut.bd_time.tm_min/60.0+dataOut.bd_time.tm_sec/3600.0 | |
| 7367 |
|
7363 | |||
| 7368 | self.aux=0 |
|
7364 | self.aux=0 | |
| 7369 | dh = dataOut.heightList[1]-dataOut.heightList[0] |
|
7365 | dh = dataOut.heightList[1]-dataOut.heightList[0] | |
| 7370 | #dataOut.h=numpy.arange(0.0,15.0*dataOut.MAXNRANGENDT,15.0,dtype='float32') |
|
7366 | #dataOut.h=numpy.arange(0.0,15.0*dataOut.MAXNRANGENDT,15.0,dtype='float32') | |
| 7371 | dataOut.h=numpy.arange(0.0,dh*dataOut.MAXNRANGENDT,dh,dtype='float32') |
|
7367 | dataOut.h=numpy.arange(0.0,dh*dataOut.MAXNRANGENDT,dh,dtype='float32') | |
| 7372 | dataOut.bfm=numpy.zeros(dataOut.MAXNRANGENDT,dtype='float32') |
|
7368 | dataOut.bfm=numpy.zeros(dataOut.MAXNRANGENDT,dtype='float32') | |
| 7373 | dataOut.bfm=numpy.array(dataOut.bfm,order='F') |
|
7369 | dataOut.bfm=numpy.array(dataOut.bfm,order='F') | |
| 7374 | dataOut.thb=numpy.zeros(dataOut.MAXNRANGENDT,dtype='float32') |
|
7370 | dataOut.thb=numpy.zeros(dataOut.MAXNRANGENDT,dtype='float32') | |
| 7375 | dataOut.thb=numpy.array(dataOut.thb,order='F') |
|
7371 | dataOut.thb=numpy.array(dataOut.thb,order='F') | |
| 7376 | dataOut.bki=numpy.zeros(dataOut.MAXNRANGENDT,dtype='float32') |
|
7372 | dataOut.bki=numpy.zeros(dataOut.MAXNRANGENDT,dtype='float32') | |
| 7377 | dataOut.bki=numpy.array(dataOut.bki,order='F') |
|
7373 | dataOut.bki=numpy.array(dataOut.bki,order='F') | |
| 7378 |
|
7374 | |||
| 7379 | mkfact_short_2020_2.mkfact(dataOut.year,dataOut.h,dataOut.bfm,dataOut.thb,dataOut.bki,dataOut.MAXNRANGENDT) |
|
7375 | mkfact_short_2020_2.mkfact(dataOut.year,dataOut.h,dataOut.bfm,dataOut.thb,dataOut.bki,dataOut.MAXNRANGENDT) | |
| 7380 |
|
7376 | |||
| 7381 | return dataOut |
|
7377 | return dataOut | |
| 7382 |
|
7378 | |||
| 7383 | class MergeProc(ProcessingUnit): |
|
7379 | class MergeProc(ProcessingUnit): | |
| 7384 |
|
7380 | |||
| 7385 | def __init__(self): |
|
7381 | def __init__(self): | |
| 7386 | ProcessingUnit.__init__(self) |
|
7382 | ProcessingUnit.__init__(self) | |
| 7387 |
|
7383 | |||
| 7388 | def run(self, attr_data, attr_data_2 = None, attr_data_3 = None, attr_data_4 = None, attr_data_5 = None, mode=0): |
|
7384 | def run(self, attr_data, attr_data_2 = None, attr_data_3 = None, attr_data_4 = None, attr_data_5 = None, mode=0): | |
| 7389 |
|
7385 | |||
| 7390 | self.dataOut = getattr(self, self.inputs[0]) |
|
7386 | self.dataOut = getattr(self, self.inputs[0]) | |
| 7391 | data_inputs = [getattr(self, attr) for attr in self.inputs] |
|
7387 | data_inputs = [getattr(self, attr) for attr in self.inputs] | |
| 7392 |
|
7388 | |||
| 7393 | if mode==0: |
|
7389 | if mode==0: | |
| 7394 | data = numpy.concatenate([getattr(data, attr_data) for data in data_inputs]) |
|
7390 | data = numpy.concatenate([getattr(data, attr_data) for data in data_inputs]) | |
| 7395 | setattr(self.dataOut, attr_data, data) |
|
7391 | setattr(self.dataOut, attr_data, data) | |
| 7396 |
|
7392 | |||
| 7397 | if mode==1: #Hybrid |
|
7393 | if mode==1: #Hybrid | |
| 7398 | #data = numpy.concatenate([getattr(data, attr_data) for data in data_inputs],axis=1) |
|
7394 | #data = numpy.concatenate([getattr(data, attr_data) for data in data_inputs],axis=1) | |
| 7399 | #setattr(self.dataOut, attr_data, data) |
|
7395 | #setattr(self.dataOut, attr_data, data) | |
| 7400 | setattr(self.dataOut, 'dataLag_spc', [getattr(data, attr_data) for data in data_inputs][0]) |
|
7396 | setattr(self.dataOut, 'dataLag_spc', [getattr(data, attr_data) for data in data_inputs][0]) | |
| 7401 | setattr(self.dataOut, 'dataLag_spc_LP', [getattr(data, attr_data) for data in data_inputs][1]) |
|
7397 | setattr(self.dataOut, 'dataLag_spc_LP', [getattr(data, attr_data) for data in data_inputs][1]) | |
| 7402 | setattr(self.dataOut, 'dataLag_cspc', [getattr(data, attr_data_2) for data in data_inputs][0]) |
|
7398 | setattr(self.dataOut, 'dataLag_cspc', [getattr(data, attr_data_2) for data in data_inputs][0]) | |
| 7403 | setattr(self.dataOut, 'dataLag_cspc_LP', [getattr(data, attr_data_2) for data in data_inputs][1]) |
|
7399 | setattr(self.dataOut, 'dataLag_cspc_LP', [getattr(data, attr_data_2) for data in data_inputs][1]) | |
| 7404 | #setattr(self.dataOut, 'nIncohInt', [getattr(data, attr_data_3) for data in data_inputs][0]) |
|
7400 | #setattr(self.dataOut, 'nIncohInt', [getattr(data, attr_data_3) for data in data_inputs][0]) | |
| 7405 | #setattr(self.dataOut, 'nIncohInt_LP', [getattr(data, attr_data_3) for data in data_inputs][1]) |
|
7401 | #setattr(self.dataOut, 'nIncohInt_LP', [getattr(data, attr_data_3) for data in data_inputs][1]) | |
| 7406 | ''' |
|
7402 | ''' | |
| 7407 | print(self.dataOut.dataLag_spc_LP.shape) |
|
7403 | print(self.dataOut.dataLag_spc_LP.shape) | |
| 7408 | print(self.dataOut.dataLag_cspc_LP.shape) |
|
7404 | print(self.dataOut.dataLag_cspc_LP.shape) | |
| 7409 | exit(1) |
|
7405 | exit(1) | |
| 7410 | ''' |
|
7406 | ''' | |
| 7411 |
|
7407 | |||
| 7412 | #self.dataOut.dataLag_spc_LP = numpy.transpose(self.dataOut.dataLag_spc_LP[0],(2,0,1)) |
|
7408 | #self.dataOut.dataLag_spc_LP = numpy.transpose(self.dataOut.dataLag_spc_LP[0],(2,0,1)) | |
| 7413 | #self.dataOut.dataLag_cspc_LP = numpy.transpose(self.dataOut.dataLag_cspc_LP,(3,1,2,0)) |
|
7409 | #self.dataOut.dataLag_cspc_LP = numpy.transpose(self.dataOut.dataLag_cspc_LP,(3,1,2,0)) | |
| 7414 | ''' |
|
7410 | ''' | |
| 7415 | print("Merge") |
|
7411 | print("Merge") | |
| 7416 | print(numpy.shape(self.dataOut.dataLag_spc)) |
|
7412 | print(numpy.shape(self.dataOut.dataLag_spc)) | |
| 7417 | print(numpy.shape(self.dataOut.dataLag_spc_LP)) |
|
7413 | print(numpy.shape(self.dataOut.dataLag_spc_LP)) | |
| 7418 | print(numpy.shape(self.dataOut.dataLag_cspc)) |
|
7414 | print(numpy.shape(self.dataOut.dataLag_cspc)) | |
| 7419 | print(numpy.shape(self.dataOut.dataLag_cspc_LP)) |
|
7415 | print(numpy.shape(self.dataOut.dataLag_cspc_LP)) | |
| 7420 | exit(1) |
|
7416 | exit(1) | |
| 7421 | ''' |
|
7417 | ''' | |
| 7422 | #print(numpy.sum(self.dataOut.dataLag_spc_LP[2,:,164])/128) |
|
7418 | #print(numpy.sum(self.dataOut.dataLag_spc_LP[2,:,164])/128) | |
| 7423 | #print(numpy.sum(self.dataOut.dataLag_cspc_LP[0,:,30,1])/128) |
|
7419 | #print(numpy.sum(self.dataOut.dataLag_cspc_LP[0,:,30,1])/128) | |
| 7424 | #exit(1) |
|
7420 | #exit(1) | |
| 7425 | #print(self.dataOut.NDP) |
|
7421 | #print(self.dataOut.NDP) | |
| 7426 | #print(self.dataOut.nNoiseProfiles) |
|
7422 | #print(self.dataOut.nNoiseProfiles) | |
| 7427 |
|
7423 | |||
| 7428 | #self.dataOut.nIncohInt_LP = 128 |
|
7424 | #self.dataOut.nIncohInt_LP = 128 | |
| 7429 | self.dataOut.nProfiles_LP = 128#self.dataOut.nIncohInt_LP |
|
7425 | self.dataOut.nProfiles_LP = 128#self.dataOut.nIncohInt_LP | |
| 7430 | self.dataOut.nIncohInt_LP = self.dataOut.nIncohInt |
|
7426 | self.dataOut.nIncohInt_LP = self.dataOut.nIncohInt | |
| 7431 | self.dataOut.NLAG = 16 |
|
7427 | self.dataOut.NLAG = 16 | |
| 7432 | self.dataOut.NRANGE = 200 |
|
7428 | self.dataOut.NRANGE = 200 | |
| 7433 | self.dataOut.NSCAN = 128 |
|
7429 | self.dataOut.NSCAN = 128 | |
| 7434 | #print(numpy.shape(self.dataOut.data_spc)) |
|
7430 | #print(numpy.shape(self.dataOut.data_spc)) | |
| 7435 |
|
7431 | |||
| 7436 | #exit(1) |
|
7432 | #exit(1) | |
| 7437 |
|
7433 | |||
| 7438 | if mode==2: #HAE 2022 |
|
7434 | if mode==2: #HAE 2022 | |
| 7439 | data = numpy.sum([getattr(data, attr_data) for data in data_inputs],axis=0) |
|
7435 | data = numpy.sum([getattr(data, attr_data) for data in data_inputs],axis=0) | |
| 7440 | setattr(self.dataOut, attr_data, data) |
|
7436 | setattr(self.dataOut, attr_data, data) | |
| 7441 |
|
7437 | |||
| 7442 | self.dataOut.nIncohInt *= 2 |
|
7438 | self.dataOut.nIncohInt *= 2 | |
| 7443 | #meta = self.dataOut.getFreqRange(1)/1000. |
|
7439 | #meta = self.dataOut.getFreqRange(1)/1000. | |
| 7444 | self.dataOut.freqRange = self.dataOut.getFreqRange(1)/1000. |
|
7440 | self.dataOut.freqRange = self.dataOut.getFreqRange(1)/1000. | |
| 7445 |
|
7441 | |||
| 7446 | #exit(1) |
|
7442 | #exit(1) | |
| 7447 |
|
7443 | |||
| 7448 | if mode==4: #Hybrid LP-SSheightProfiles |
|
7444 | if mode==4: #Hybrid LP-SSheightProfiles | |
| 7449 | #data = numpy.concatenate([getattr(data, attr_data) for data in data_inputs],axis=1) |
|
7445 | #data = numpy.concatenate([getattr(data, attr_data) for data in data_inputs],axis=1) | |
| 7450 | #setattr(self.dataOut, attr_data, data) |
|
7446 | #setattr(self.dataOut, attr_data, data) | |
| 7451 | setattr(self.dataOut, 'dataLag_spc', getattr(data_inputs[0], attr_data)) #DP |
|
7447 | setattr(self.dataOut, 'dataLag_spc', getattr(data_inputs[0], attr_data)) #DP | |
| 7452 | setattr(self.dataOut, 'dataLag_cspc', getattr(data_inputs[0], attr_data_2)) #DP |
|
7448 | setattr(self.dataOut, 'dataLag_cspc', getattr(data_inputs[0], attr_data_2)) #DP | |
| 7453 | setattr(self.dataOut, 'dataLag_spc_LP', getattr(data_inputs[1], attr_data_3)) #LP |
|
7449 | setattr(self.dataOut, 'dataLag_spc_LP', getattr(data_inputs[1], attr_data_3)) #LP | |
| 7454 | #setattr(self.dataOut, 'dataLag_cspc_LP', getattr(data_inputs[1], attr_data_4)) #LP |
|
7450 | #setattr(self.dataOut, 'dataLag_cspc_LP', getattr(data_inputs[1], attr_data_4)) #LP | |
| 7455 | #setattr(self.dataOut, 'data_acf', getattr(data_inputs[1], attr_data_5)) #LP |
|
7451 | #setattr(self.dataOut, 'data_acf', getattr(data_inputs[1], attr_data_5)) #LP | |
| 7456 | setattr(self.dataOut, 'data_acf', getattr(data_inputs[1], attr_data_5)) #LP |
|
7452 | setattr(self.dataOut, 'data_acf', getattr(data_inputs[1], attr_data_5)) #LP | |
| 7457 | #print("Merge data_acf: ",self.dataOut.data_acf.shape) |
|
7453 | #print("Merge data_acf: ",self.dataOut.data_acf.shape) | |
| 7458 |
|
7454 | |||
| 7459 | #self.dataOut.nIncohInt_LP = 128 |
|
7455 | #self.dataOut.nIncohInt_LP = 128 | |
| 7460 | #self.dataOut.nProfiles_LP = 128#self.dataOut.nIncohInt_LP |
|
7456 | #self.dataOut.nProfiles_LP = 128#self.dataOut.nIncohInt_LP | |
| 7461 | self.dataOut.nProfiles_LP = 16#28#self.dataOut.nIncohInt_LP |
|
7457 | self.dataOut.nProfiles_LP = 16#28#self.dataOut.nIncohInt_LP | |
| 7462 | self.dataOut.nProfiles_LP = self.dataOut.data_acf.shape[1]#28#self.dataOut.nIncohInt_LP |
|
7458 | self.dataOut.nProfiles_LP = self.dataOut.data_acf.shape[1]#28#self.dataOut.nIncohInt_LP | |
| 7463 | self.dataOut.NSCAN = 128 |
|
7459 | self.dataOut.NSCAN = 128 | |
| 7464 | self.dataOut.nIncohInt_LP = self.dataOut.nIncohInt*self.dataOut.NSCAN |
|
7460 | self.dataOut.nIncohInt_LP = self.dataOut.nIncohInt*self.dataOut.NSCAN | |
| 7465 | #print("sahpi",self.dataOut.nIncohInt_LP) |
|
7461 | #print("sahpi",self.dataOut.nIncohInt_LP) | |
| 7466 | #exit(1) |
|
7462 | #exit(1) | |
| 7467 | self.dataOut.NLAG = 16 |
|
7463 | self.dataOut.NLAG = 16 | |
| 7468 | self.dataOut.NLAG = self.dataOut.data_acf.shape[1] |
|
7464 | self.dataOut.NLAG = self.dataOut.data_acf.shape[1] | |
| 7469 |
|
||||
| 7470 | self.dataOut.NRANGE = self.dataOut.data_acf.shape[-1] |
|
7465 | self.dataOut.NRANGE = self.dataOut.data_acf.shape[-1] | |
| 7471 |
|
7466 | |||
| 7472 | #print(numpy.shape(self.dataOut.data_spc)) |
|
7467 | #print(numpy.shape(self.dataOut.data_spc)) | |
| 7473 |
|
7468 | |||
| 7474 | #exit(1) |
|
7469 | #exit(1) | |
| 7475 | if mode==5: |
|
7470 | if mode==5: | |
| 7476 | data = numpy.concatenate([getattr(data, attr_data) for data in data_inputs]) |
|
7471 | data = numpy.concatenate([getattr(data, attr_data) for data in data_inputs]) | |
| 7477 | setattr(self.dataOut, attr_data, data) |
|
7472 | setattr(self.dataOut, attr_data, data) | |
| 7478 | data = numpy.concatenate([getattr(data, attr_data_2) for data in data_inputs]) |
|
7473 | data = numpy.concatenate([getattr(data, attr_data_2) for data in data_inputs]) | |
| 7479 | setattr(self.dataOut, attr_data_2, data) |
|
7474 | setattr(self.dataOut, attr_data_2, data) | |
| 7480 |
|
7475 | |||
| 7481 | if mode==6: #Hybrid Spectra-Voltage |
|
7476 | if mode==6: #Hybrid Spectra-Voltage | |
| 7482 | #data = numpy.concatenate([getattr(data, attr_data) for data in data_inputs],axis=1) |
|
7477 | #data = numpy.concatenate([getattr(data, attr_data) for data in data_inputs],axis=1) | |
| 7483 | #setattr(self.dataOut, attr_data, data) |
|
7478 | #setattr(self.dataOut, attr_data, data) | |
| 7484 | setattr(self.dataOut, 'dataLag_spc', getattr(data_inputs[1], attr_data)) #DP |
|
7479 | setattr(self.dataOut, 'dataLag_spc', getattr(data_inputs[1], attr_data)) #DP | |
| 7485 | setattr(self.dataOut, 'dataLag_cspc', getattr(data_inputs[1], attr_data_2)) #DP |
|
7480 | setattr(self.dataOut, 'dataLag_cspc', getattr(data_inputs[1], attr_data_2)) #DP | |
| 7486 | setattr(self.dataOut, 'output_LP_integrated', getattr(data_inputs[0], attr_data_3)) #LP |
|
7481 | setattr(self.dataOut, 'output_LP_integrated', getattr(data_inputs[0], attr_data_3)) #LP | |
| 7487 | #setattr(self.dataOut, 'dataLag_cspc_LP', getattr(data_inputs[1], attr_data_4)) #LP |
|
7482 | #setattr(self.dataOut, 'dataLag_cspc_LP', getattr(data_inputs[1], attr_data_4)) #LP | |
| 7488 | #setattr(self.dataOut, 'data_acf', getattr(data_inputs[1], attr_data_5)) #LP |
|
7483 | #setattr(self.dataOut, 'data_acf', getattr(data_inputs[1], attr_data_5)) #LP | |
| 7489 | #setattr(self.dataOut, 'data_acf', getattr(data_inputs[1], attr_data_5)) #LP |
|
7484 | #setattr(self.dataOut, 'data_acf', getattr(data_inputs[1], attr_data_5)) #LP | |
| 7490 | #print("Merge data_acf: ",self.dataOut.data_acf.shape) |
|
7485 | #print("Merge data_acf: ",self.dataOut.data_acf.shape) | |
| 7491 | #print(self.dataOut.NSCAN) |
|
7486 | #print(self.dataOut.NSCAN) | |
| 7492 | self.dataOut.nIncohInt = int(self.dataOut.NAVG * self.dataOut.nint) |
|
7487 | self.dataOut.nIncohInt = int(self.dataOut.NAVG * self.dataOut.nint) | |
| 7493 | #print(self.dataOut.dataLag_spc.shape) |
|
7488 | #print(self.dataOut.dataLag_spc.shape) | |
| 7494 | self.dataOut.nProfiles = self.dataOut.nProfiles_DP = self.dataOut.dataLag_spc.shape[1] |
|
7489 | self.dataOut.nProfiles = self.dataOut.nProfiles_DP = self.dataOut.dataLag_spc.shape[1] | |
| 7495 | ''' |
|
7490 | ''' | |
| 7496 | #self.dataOut.nIncohInt_LP = 128 |
|
7491 | #self.dataOut.nIncohInt_LP = 128 | |
| 7497 | #self.dataOut.nProfiles_LP = 128#self.dataOut.nIncohInt_LP |
|
7492 | #self.dataOut.nProfiles_LP = 128#self.dataOut.nIncohInt_LP | |
| 7498 | self.dataOut.nProfiles_LP = 16#28#self.dataOut.nIncohInt_LP |
|
7493 | self.dataOut.nProfiles_LP = 16#28#self.dataOut.nIncohInt_LP | |
| 7499 | self.dataOut.nProfiles_LP = self.dataOut.data_acf.shape[1]#28#self.dataOut.nIncohInt_LP |
|
7494 | self.dataOut.nProfiles_LP = self.dataOut.data_acf.shape[1]#28#self.dataOut.nIncohInt_LP | |
| 7500 | self.dataOut.NSCAN = 128 |
|
7495 | self.dataOut.NSCAN = 128 | |
| 7501 | self.dataOut.nIncohInt_LP = self.dataOut.nIncohInt*self.dataOut.NSCAN |
|
7496 | self.dataOut.nIncohInt_LP = self.dataOut.nIncohInt*self.dataOut.NSCAN | |
| 7502 | #print("sahpi",self.dataOut.nIncohInt_LP) |
|
7497 | #print("sahpi",self.dataOut.nIncohInt_LP) | |
| 7503 | #exit(1) |
|
7498 | #exit(1) | |
| 7504 | self.dataOut.NLAG = 16 |
|
7499 | self.dataOut.NLAG = 16 | |
| 7505 | self.dataOut.NLAG = self.dataOut.data_acf.shape[1] |
|
7500 | self.dataOut.NLAG = self.dataOut.data_acf.shape[1] | |
| 7506 | self.dataOut.NRANGE = self.dataOut.data_acf.shape[-1] |
|
7501 | self.dataOut.NRANGE = self.dataOut.data_acf.shape[-1] | |
| 7507 | ''' |
|
7502 | ''' | |
| 7508 | #print(numpy.shape(self.dataOut.data_spc)) |
|
7503 | #print(numpy.shape(self.dataOut.data_spc)) | |
| 7509 | #print("*************************GOOD*************************") |
|
7504 | #print("*************************GOOD*************************") | |
| 7510 | #exit(1) |
|
7505 | #exit(1) | |
| 7511 |
|
7506 | |||
| 7512 | if mode==11: #MST ISR |
|
7507 | if mode==11: #MST ISR | |
| 7513 | #data = numpy.concatenate([getattr(data, attr_data) for data in data_inputs],axis=1) |
|
7508 | #data = numpy.concatenate([getattr(data, attr_data) for data in data_inputs],axis=1) | |
| 7514 | #setattr(self.dataOut, attr_data, data) |
|
7509 | #setattr(self.dataOut, attr_data, data) | |
| 7515 | #setattr(self.dataOut, 'ph2', [getattr(data, attr_data) for data in data_inputs][1]) |
|
7510 | #setattr(self.dataOut, 'ph2', [getattr(data, attr_data) for data in data_inputs][1]) | |
| 7516 | #setattr(self.dataOut, 'dphi', [getattr(data, attr_data_2) for data in data_inputs][1]) |
|
7511 | #setattr(self.dataOut, 'dphi', [getattr(data, attr_data_2) for data in data_inputs][1]) | |
| 7517 | #setattr(self.dataOut, 'sdp2', [getattr(data, attr_data_3) for data in data_inputs][1]) |
|
7512 | #setattr(self.dataOut, 'sdp2', [getattr(data, attr_data_3) for data in data_inputs][1]) | |
| 7518 |
|
7513 | |||
| 7519 | setattr(self.dataOut, 'ph2', getattr(data_inputs[1], attr_data)) #DP |
|
7514 | setattr(self.dataOut, 'ph2', getattr(data_inputs[1], attr_data)) #DP | |
| 7520 | setattr(self.dataOut, 'dphi', getattr(data_inputs[1], attr_data_2)) #DP |
|
7515 | setattr(self.dataOut, 'dphi', getattr(data_inputs[1], attr_data_2)) #DP | |
| 7521 | setattr(self.dataOut, 'sdp2', getattr(data_inputs[1], attr_data_3)) #DP |
|
7516 | setattr(self.dataOut, 'sdp2', getattr(data_inputs[1], attr_data_3)) #DP | |
| 7522 |
|
7517 | |||
| 7523 | print("MST Density", numpy.shape(self.dataOut.ph2)) |
|
7518 | print("MST Density", numpy.shape(self.dataOut.ph2)) | |
| 7524 | print("cf MST: ", self.dataOut.cf) |
|
7519 | print("cf MST: ", self.dataOut.cf) | |
| 7525 | #exit(1) |
|
7520 | #exit(1) | |
| 7526 | #print("MST Density", self.dataOut.ph2[116:283]) |
|
7521 | #print("MST Density", self.dataOut.ph2[116:283]) | |
| 7527 | print("MST Density", self.dataOut.ph2[80:120]) |
|
7522 | print("MST Density", self.dataOut.ph2[80:120]) | |
| 7528 | print("MST dPhi", self.dataOut.dphi[80:120]) |
|
7523 | print("MST dPhi", self.dataOut.dphi[80:120]) | |
| 7529 | self.dataOut.ph2 *= self.dataOut.cf#0.0008136899 |
|
7524 | self.dataOut.ph2 *= self.dataOut.cf#0.0008136899 | |
| 7530 | #print("MST Density", self.dataOut.ph2[116:283]) |
|
7525 | #print("MST Density", self.dataOut.ph2[116:283]) | |
| 7531 | self.dataOut.sdp2 *= 0#self.dataOut.cf#0.0008136899 |
|
7526 | self.dataOut.sdp2 *= 0#self.dataOut.cf#0.0008136899 | |
| 7532 | #print("MST Density", self.dataOut.ph2[116:283]) |
|
7527 | #print("MST Density", self.dataOut.ph2[116:283]) | |
| 7533 | print("MST Density", self.dataOut.ph2[80:120]) |
|
7528 | print("MST Density", self.dataOut.ph2[80:120]) | |
| 7534 | self.dataOut.NSHTS = int(numpy.shape(self.dataOut.ph2)[0]) |
|
7529 | self.dataOut.NSHTS = int(numpy.shape(self.dataOut.ph2)[0]) | |
| 7535 | dH = self.dataOut.heightList[1]-self.dataOut.heightList[0] |
|
7530 | dH = self.dataOut.heightList[1]-self.dataOut.heightList[0] | |
| 7536 | dH /= self.dataOut.windowOfFilter |
|
7531 | dH /= self.dataOut.windowOfFilter | |
| 7537 | self.dataOut.heightList = numpy.arange(0,self.dataOut.NSHTS)*dH + dH |
|
7532 | self.dataOut.heightList = numpy.arange(0,self.dataOut.NSHTS)*dH + dH | |
| 7538 | #print("heightList: ", self.dataOut.heightList) |
|
7533 | #print("heightList: ", self.dataOut.heightList) | |
| 7539 | self.dataOut.NDP = self.dataOut.NSHTS |
|
7534 | self.dataOut.NDP = self.dataOut.NSHTS | |
| 7540 | #exit(1) |
|
7535 | #exit(1) | |
| 7541 | #print(self.dataOut.heightList) |
|
7536 | #print(self.dataOut.heightList) | |
| 7542 |
|
7537 | |||
| 7543 | class MST_Den_Conv(Operation): |
|
7538 | class MST_Den_Conv(Operation): | |
| 7544 | ''' |
|
7539 | ''' | |
| 7545 | Written by R. Flores |
|
7540 | Written by R. Flores | |
| 7546 | ''' |
|
7541 | ''' | |
| 7547 | """Operation to calculate Geomagnetic parameters. |
|
7542 | """Operation to calculate Geomagnetic parameters. | |
| 7548 |
|
7543 | |||
| 7549 | Parameters: |
|
7544 | Parameters: | |
| 7550 | ----------- |
|
7545 | ----------- | |
| 7551 | None |
|
7546 | None | |
| 7552 |
|
7547 | |||
| 7553 | Example |
|
7548 | Example | |
| 7554 | -------- |
|
7549 | -------- | |
| 7555 |
|
7550 | |||
| 7556 | op = proc_unit.addOperation(name='MST_Den_Conv', optype='other') |
|
7551 | op = proc_unit.addOperation(name='MST_Den_Conv', optype='other') | |
| 7557 |
|
7552 | |||
| 7558 | """ |
|
7553 | """ | |
| 7559 |
|
7554 | |||
| 7560 | def __init__(self, **kwargs): |
|
7555 | def __init__(self, **kwargs): | |
| 7561 |
|
7556 | |||
| 7562 | Operation.__init__(self, **kwargs) |
|
7557 | Operation.__init__(self, **kwargs) | |
| 7563 |
|
7558 | |||
| 7564 | def run(self,dataOut): |
|
7559 | def run(self,dataOut): | |
| 7565 |
|
7560 | |||
| 7566 | dataOut.PowDen = numpy.zeros((1,dataOut.NDP)) |
|
7561 | dataOut.PowDen = numpy.zeros((1,dataOut.NDP)) | |
| 7567 | dataOut.PowDen[0] = numpy.copy(dataOut.ph2[:dataOut.NDP]) |
|
7562 | dataOut.PowDen[0] = numpy.copy(dataOut.ph2[:dataOut.NDP]) | |
| 7568 |
|
7563 | |||
| 7569 | dataOut.FarDen = numpy.zeros((1,dataOut.NDP)) |
|
7564 | dataOut.FarDen = numpy.zeros((1,dataOut.NDP)) | |
| 7570 | dataOut.FarDen[0] = numpy.copy(dataOut.dphi[:dataOut.NDP]) |
|
7565 | dataOut.FarDen[0] = numpy.copy(dataOut.dphi[:dataOut.NDP]) | |
| 7571 | print("pow den shape", numpy.shape(dataOut.PowDen)) |
|
7566 | print("pow den shape", numpy.shape(dataOut.PowDen)) | |
| 7572 | print("far den shape", numpy.shape(dataOut.FarDen)) |
|
7567 | print("far den shape", numpy.shape(dataOut.FarDen)) | |
| 7573 | return dataOut |
|
7568 | return dataOut | |
|
|
7569 | ||||
|
|
7570 | class addTxPower(Operation): | |||
|
|
7571 | ''' | |||
|
|
7572 | Transmited power level integrated in the dataOut ->AMISR | |||
|
|
7573 | resolution 1 min | |||
|
|
7574 | The power files have the pattern power_YYYYMMDD.csv | |||
|
|
7575 | ''' | |||
|
|
7576 | __slots__ =('isConfig','dataDatetimes','txPowers') | |||
|
|
7577 | def __init__(self): | |||
|
|
7578 | ||||
|
|
7579 | Operation.__init__(self) | |||
|
|
7580 | self.isConfig = False | |||
|
|
7581 | self.dataDatetimes = [] | |||
|
|
7582 | self.txPowers = [] | |||
|
|
7583 | ||||
|
|
7584 | def setup(self, powerFile, dutyCycle): | |||
|
|
7585 | if not os.path.isfile(powerFile): | |||
|
|
7586 | raise schainpy.admin.SchainError('There is no file named :{}'.format(powerFile)) | |||
|
|
7587 | return | |||
|
|
7588 | ||||
|
|
7589 | with open(powerFile, newline='') as pfile: | |||
|
|
7590 | reader = csv.reader(pfile, delimiter=',', quotechar='|') | |||
|
|
7591 | next(reader) | |||
|
|
7592 | for row in reader: | |||
|
|
7593 | #'2022-10-25 00:00:00' | |||
|
|
7594 | self.dataDatetimes.append(datetime.datetime.strptime(row[0], "%Y-%m-%d %H:%M:%S")) | |||
|
|
7595 | self.txPowers.append(float(row[1])/dutyCycle) | |||
|
|
7596 | self.isConfig = True | |||
|
|
7597 | ||||
|
|
7598 | def run(self, dataOut, path, DS=0.05): | |||
|
|
7599 | ||||
|
|
7600 | #dataOut.flagNoData = True | |||
|
|
7601 | ||||
|
|
7602 | if not(self.isConfig): | |||
|
|
7603 | self.setup(path, DS) | |||
|
|
7604 | ||||
|
|
7605 | dataDate = datetime.datetime.utcfromtimestamp(dataOut.utctime).replace(second=0, microsecond=0)#no seconds | |||
|
|
7606 | try: | |||
|
|
7607 | indx = self.dataDatetimes.index(dataDate) | |||
|
|
7608 | dataOut.txPower = self.txPowers[indx] | |||
|
|
7609 | except: | |||
|
|
7610 | log.warning("No power available for the datetime {}, setting power to 0 w", self.name) | |||
|
|
7611 | dataOut.txPower = 0 | |||
|
|
7612 | ||||
|
|
7613 | return dataOut No newline at end of file | |||
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