| @@ -1,6252 +1,6239 | |||||
| 1 | import numpy |
|
1 | import numpy | |
| 2 | import math |
|
2 | import math | |
| 3 | from scipy import optimize, interpolate, signal, stats, ndimage |
|
3 | from scipy import optimize, interpolate, signal, stats, ndimage | |
| 4 | from scipy.fftpack import fft |
|
4 | from scipy.fftpack import fft | |
| 5 | import scipy |
|
5 | import scipy | |
| 6 | import re |
|
6 | import re | |
| 7 | import datetime |
|
7 | import datetime | |
| 8 | import copy |
|
8 | import copy | |
| 9 | import sys |
|
9 | import sys | |
| 10 | import importlib |
|
10 | import importlib | |
| 11 | import itertools |
|
11 | import itertools | |
| 12 | from multiprocessing import Pool, TimeoutError |
|
12 | from multiprocessing import Pool, TimeoutError | |
| 13 | from multiprocessing.pool import ThreadPool |
|
13 | from multiprocessing.pool import ThreadPool | |
| 14 | import time |
|
14 | import time | |
| 15 |
|
15 | |||
| 16 | from scipy.optimize import fmin_l_bfgs_b #optimize with bounds on state papameters |
|
16 | from scipy.optimize import fmin_l_bfgs_b #optimize with bounds on state papameters | |
| 17 | from .jroproc_base import ProcessingUnit, Operation, MPDecorator |
|
17 | from .jroproc_base import ProcessingUnit, Operation, MPDecorator | |
| 18 | from schainpy.model.data.jrodata import Parameters, hildebrand_sekhon |
|
18 | from schainpy.model.data.jrodata import Parameters, hildebrand_sekhon | |
| 19 | from schainpy.model.data.jrodata import Spectra |
|
19 | from schainpy.model.data.jrodata import Spectra | |
| 20 | from scipy import asarray as ar,exp |
|
20 | from scipy import asarray as ar,exp | |
| 21 | from scipy.optimize import fmin, curve_fit |
|
21 | from scipy.optimize import fmin, curve_fit | |
| 22 | from schainpy.utils import log |
|
22 | from schainpy.utils import log | |
| 23 | import warnings |
|
23 | import warnings | |
| 24 | from numpy import NaN |
|
24 | from numpy import NaN | |
| 25 | from scipy.optimize.optimize import OptimizeWarning |
|
25 | from scipy.optimize.optimize import OptimizeWarning | |
| 26 | warnings.filterwarnings('ignore') |
|
26 | warnings.filterwarnings('ignore') | |
| 27 |
|
27 | |||
| 28 |
|
28 | |||
| 29 | SPEED_OF_LIGHT = 299792458 |
|
29 | SPEED_OF_LIGHT = 299792458 | |
| 30 |
|
30 | |||
| 31 | '''solving pickling issue''' |
|
31 | '''solving pickling issue''' | |
| 32 |
|
32 | |||
| 33 | def _pickle_method(method): |
|
33 | def _pickle_method(method): | |
| 34 | func_name = method.__func__.__name__ |
|
34 | func_name = method.__func__.__name__ | |
| 35 | obj = method.__self__ |
|
35 | obj = method.__self__ | |
| 36 | cls = method.__self__.__class__ |
|
36 | cls = method.__self__.__class__ | |
| 37 | return _unpickle_method, (func_name, obj, cls) |
|
37 | return _unpickle_method, (func_name, obj, cls) | |
| 38 |
|
38 | |||
| 39 | def _unpickle_method(func_name, obj, cls): |
|
39 | def _unpickle_method(func_name, obj, cls): | |
| 40 | for cls in cls.mro(): |
|
40 | for cls in cls.mro(): | |
| 41 | try: |
|
41 | try: | |
| 42 | func = cls.__dict__[func_name] |
|
42 | func = cls.__dict__[func_name] | |
| 43 | except KeyError: |
|
43 | except KeyError: | |
| 44 | pass |
|
44 | pass | |
| 45 | else: |
|
45 | else: | |
| 46 | break |
|
46 | break | |
| 47 | return func.__get__(obj, cls) |
|
47 | return func.__get__(obj, cls) | |
| 48 |
|
48 | |||
| 49 |
|
49 | |||
| 50 | class ParametersProc(ProcessingUnit): |
|
50 | class ParametersProc(ProcessingUnit): | |
| 51 |
|
51 | |||
| 52 | METHODS = {} |
|
52 | METHODS = {} | |
| 53 | nSeconds = None |
|
53 | nSeconds = None | |
| 54 |
|
54 | |||
| 55 | def __init__(self): |
|
55 | def __init__(self): | |
| 56 | ProcessingUnit.__init__(self) |
|
56 | ProcessingUnit.__init__(self) | |
| 57 |
|
57 | |||
| 58 | self.buffer = None |
|
58 | self.buffer = None | |
| 59 | self.firstdatatime = None |
|
59 | self.firstdatatime = None | |
| 60 | self.profIndex = 0 |
|
60 | self.profIndex = 0 | |
| 61 | self.dataOut = Parameters() |
|
61 | self.dataOut = Parameters() | |
| 62 | self.setupReq = False #Agregar a todas las unidades de proc |
|
62 | self.setupReq = False #Agregar a todas las unidades de proc | |
| 63 |
|
63 | |||
| 64 | def __updateObjFromInput(self): |
|
64 | def __updateObjFromInput(self): | |
| 65 |
|
65 | |||
| 66 | self.dataOut.inputUnit = self.dataIn.type |
|
66 | self.dataOut.inputUnit = self.dataIn.type | |
| 67 |
|
67 | |||
| 68 | self.dataOut.timeZone = self.dataIn.timeZone |
|
68 | self.dataOut.timeZone = self.dataIn.timeZone | |
| 69 | self.dataOut.dstFlag = self.dataIn.dstFlag |
|
69 | self.dataOut.dstFlag = self.dataIn.dstFlag | |
| 70 | self.dataOut.errorCount = self.dataIn.errorCount |
|
70 | self.dataOut.errorCount = self.dataIn.errorCount | |
| 71 | self.dataOut.useLocalTime = self.dataIn.useLocalTime |
|
71 | self.dataOut.useLocalTime = self.dataIn.useLocalTime | |
| 72 |
|
72 | |||
| 73 | self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() |
|
73 | self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() | |
| 74 | self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy() |
|
74 | self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy() | |
| 75 | self.dataOut.channelList = self.dataIn.channelList |
|
75 | self.dataOut.channelList = self.dataIn.channelList | |
| 76 | self.dataOut.heightList = self.dataIn.heightList |
|
76 | self.dataOut.heightList = self.dataIn.heightList | |
| 77 | self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')]) |
|
77 | self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')]) | |
| 78 | # self.dataOut.nBaud = self.dataIn.nBaud |
|
78 | # self.dataOut.nBaud = self.dataIn.nBaud | |
| 79 | # self.dataOut.nCode = self.dataIn.nCode |
|
79 | # self.dataOut.nCode = self.dataIn.nCode | |
| 80 | # self.dataOut.code = self.dataIn.code |
|
80 | # self.dataOut.code = self.dataIn.code | |
| 81 | self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock |
|
81 | self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock | |
| 82 | self.dataOut.utctime = self.dataIn.utctime |
|
82 | self.dataOut.utctime = self.dataIn.utctime | |
| 83 | self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada |
|
83 | self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada | |
| 84 | self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip |
|
84 | self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip | |
| 85 | self.dataOut.nCohInt = self.dataIn.nCohInt |
|
85 | self.dataOut.nCohInt = self.dataIn.nCohInt | |
| 86 | self.dataOut.timeInterval1 = self.dataIn.timeInterval |
|
86 | self.dataOut.timeInterval1 = self.dataIn.timeInterval | |
| 87 | self.dataOut.heightList = self.dataIn.heightList |
|
87 | self.dataOut.heightList = self.dataIn.heightList | |
| 88 | self.dataOut.frequency = self.dataIn.frequency |
|
88 | self.dataOut.frequency = self.dataIn.frequency | |
| 89 | self.dataOut.runNextUnit = self.dataIn.runNextUnit |
|
89 | self.dataOut.runNextUnit = self.dataIn.runNextUnit | |
| 90 |
|
90 | |||
| 91 | def run(self, runNextUnit=0): |
|
91 | def run(self, runNextUnit=0): | |
| 92 |
|
92 | |||
| 93 | self.dataIn.runNextUnit = runNextUnit |
|
93 | self.dataIn.runNextUnit = runNextUnit | |
| 94 | #---------------------- Voltage Data --------------------------- |
|
94 | #---------------------- Voltage Data --------------------------- | |
| 95 |
|
95 | |||
| 96 | if self.dataIn.type == "Voltage": |
|
96 | if self.dataIn.type == "Voltage": | |
| 97 |
|
97 | |||
| 98 | self.__updateObjFromInput() |
|
98 | self.__updateObjFromInput() | |
| 99 | self.dataOut.data_pre = self.dataIn.data.copy() |
|
99 | self.dataOut.data_pre = self.dataIn.data.copy() | |
| 100 | self.dataOut.flagNoData = False |
|
100 | self.dataOut.flagNoData = False | |
| 101 | self.dataOut.utctimeInit = self.dataIn.utctime |
|
101 | self.dataOut.utctimeInit = self.dataIn.utctime | |
| 102 | self.dataOut.paramInterval = self.dataIn.nProfiles*self.dataIn.nCohInt*self.dataIn.ippSeconds |
|
102 | self.dataOut.paramInterval = self.dataIn.nProfiles*self.dataIn.nCohInt*self.dataIn.ippSeconds | |
| 103 | if hasattr(self.dataIn, 'dataPP_POW'): |
|
103 | if hasattr(self.dataIn, 'dataPP_POW'): | |
| 104 | self.dataOut.dataPP_POW = self.dataIn.dataPP_POW |
|
104 | self.dataOut.dataPP_POW = self.dataIn.dataPP_POW | |
| 105 |
|
105 | |||
| 106 | if hasattr(self.dataIn, 'dataPP_POWER'): |
|
106 | if hasattr(self.dataIn, 'dataPP_POWER'): | |
| 107 | self.dataOut.dataPP_POWER = self.dataIn.dataPP_POWER |
|
107 | self.dataOut.dataPP_POWER = self.dataIn.dataPP_POWER | |
| 108 |
|
108 | |||
| 109 | if hasattr(self.dataIn, 'dataPP_DOP'): |
|
109 | if hasattr(self.dataIn, 'dataPP_DOP'): | |
| 110 | self.dataOut.dataPP_DOP = self.dataIn.dataPP_DOP |
|
110 | self.dataOut.dataPP_DOP = self.dataIn.dataPP_DOP | |
| 111 |
|
111 | |||
| 112 | if hasattr(self.dataIn, 'dataPP_SNR'): |
|
112 | if hasattr(self.dataIn, 'dataPP_SNR'): | |
| 113 | self.dataOut.dataPP_SNR = self.dataIn.dataPP_SNR |
|
113 | self.dataOut.dataPP_SNR = self.dataIn.dataPP_SNR | |
| 114 |
|
114 | |||
| 115 | if hasattr(self.dataIn, 'dataPP_WIDTH'): |
|
115 | if hasattr(self.dataIn, 'dataPP_WIDTH'): | |
| 116 | self.dataOut.dataPP_WIDTH = self.dataIn.dataPP_WIDTH |
|
116 | self.dataOut.dataPP_WIDTH = self.dataIn.dataPP_WIDTH | |
| 117 | return |
|
117 | return | |
| 118 |
|
118 | |||
| 119 | #---------------------- Spectra Data --------------------------- |
|
119 | #---------------------- Spectra Data --------------------------- | |
| 120 |
|
120 | |||
| 121 | if self.dataIn.type == "Spectra": |
|
121 | if self.dataIn.type == "Spectra": | |
| 122 |
|
122 | |||
| 123 | self.dataOut.data_pre = [self.dataIn.data_spc, self.dataIn.data_cspc] |
|
123 | self.dataOut.data_pre = [self.dataIn.data_spc, self.dataIn.data_cspc] | |
| 124 | self.dataOut.data_spc = self.dataIn.data_spc |
|
124 | self.dataOut.data_spc = self.dataIn.data_spc | |
| 125 | self.dataOut.data_cspc = self.dataIn.data_cspc |
|
125 | self.dataOut.data_cspc = self.dataIn.data_cspc | |
| 126 | self.dataOut.nProfiles = self.dataIn.nProfiles |
|
126 | self.dataOut.nProfiles = self.dataIn.nProfiles | |
| 127 | self.dataOut.nIncohInt = self.dataIn.nIncohInt |
|
127 | self.dataOut.nIncohInt = self.dataIn.nIncohInt | |
| 128 | self.dataOut.nFFTPoints = self.dataIn.nFFTPoints |
|
128 | self.dataOut.nFFTPoints = self.dataIn.nFFTPoints | |
| 129 | self.dataOut.ippFactor = self.dataIn.ippFactor |
|
129 | self.dataOut.ippFactor = self.dataIn.ippFactor | |
| 130 | self.dataOut.abscissaList = self.dataIn.getVelRange(1) |
|
130 | self.dataOut.abscissaList = self.dataIn.getVelRange(1) | |
| 131 | self.dataOut.spc_noise = self.dataIn.getNoise() |
|
131 | self.dataOut.spc_noise = self.dataIn.getNoise() | |
| 132 | self.dataOut.spc_range = (self.dataIn.getFreqRange(1) , self.dataIn.getAcfRange(1) , self.dataIn.getVelRange(1)) |
|
132 | self.dataOut.spc_range = (self.dataIn.getFreqRange(1) , self.dataIn.getAcfRange(1) , self.dataIn.getVelRange(1)) | |
| 133 | # self.dataOut.normFactor = self.dataIn.normFactor |
|
133 | # self.dataOut.normFactor = self.dataIn.normFactor | |
| 134 | self.dataOut.pairsList = self.dataIn.pairsList |
|
134 | self.dataOut.pairsList = self.dataIn.pairsList | |
| 135 | self.dataOut.groupList = self.dataIn.pairsList |
|
135 | self.dataOut.groupList = self.dataIn.pairsList | |
| 136 | self.dataOut.flagNoData = False |
|
136 | self.dataOut.flagNoData = False | |
| 137 |
|
137 | |||
| 138 | if hasattr(self.dataIn, 'ChanDist'): #Distances of receiver channels |
|
138 | if hasattr(self.dataIn, 'ChanDist'): #Distances of receiver channels | |
| 139 | self.dataOut.ChanDist = self.dataIn.ChanDist |
|
139 | self.dataOut.ChanDist = self.dataIn.ChanDist | |
| 140 | else: self.dataOut.ChanDist = None |
|
140 | else: self.dataOut.ChanDist = None | |
| 141 |
|
141 | |||
| 142 | #if hasattr(self.dataIn, 'VelRange'): #Velocities range |
|
142 | #if hasattr(self.dataIn, 'VelRange'): #Velocities range | |
| 143 | # self.dataOut.VelRange = self.dataIn.VelRange |
|
143 | # self.dataOut.VelRange = self.dataIn.VelRange | |
| 144 | #else: self.dataOut.VelRange = None |
|
144 | #else: self.dataOut.VelRange = None | |
| 145 |
|
145 | |||
| 146 | if hasattr(self.dataIn, 'RadarConst'): #Radar Constant |
|
146 | if hasattr(self.dataIn, 'RadarConst'): #Radar Constant | |
| 147 | self.dataOut.RadarConst = self.dataIn.RadarConst |
|
147 | self.dataOut.RadarConst = self.dataIn.RadarConst | |
| 148 |
|
148 | |||
| 149 | if hasattr(self.dataIn, 'NPW'): #NPW |
|
149 | if hasattr(self.dataIn, 'NPW'): #NPW | |
| 150 | self.dataOut.NPW = self.dataIn.NPW |
|
150 | self.dataOut.NPW = self.dataIn.NPW | |
| 151 |
|
151 | |||
| 152 | if hasattr(self.dataIn, 'COFA'): #COFA |
|
152 | if hasattr(self.dataIn, 'COFA'): #COFA | |
| 153 | self.dataOut.COFA = self.dataIn.COFA |
|
153 | self.dataOut.COFA = self.dataIn.COFA | |
| 154 |
|
154 | |||
| 155 |
|
155 | |||
| 156 |
|
156 | |||
| 157 | #---------------------- Correlation Data --------------------------- |
|
157 | #---------------------- Correlation Data --------------------------- | |
| 158 |
|
158 | |||
| 159 | if self.dataIn.type == "Correlation": |
|
159 | if self.dataIn.type == "Correlation": | |
| 160 | acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf = self.dataIn.splitFunctions() |
|
160 | acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf = self.dataIn.splitFunctions() | |
| 161 |
|
161 | |||
| 162 | self.dataOut.data_pre = (self.dataIn.data_cf[acf_ind,:], self.dataIn.data_cf[ccf_ind,:,:]) |
|
162 | self.dataOut.data_pre = (self.dataIn.data_cf[acf_ind,:], self.dataIn.data_cf[ccf_ind,:,:]) | |
| 163 | self.dataOut.normFactor = (self.dataIn.normFactor[acf_ind,:], self.dataIn.normFactor[ccf_ind,:]) |
|
163 | self.dataOut.normFactor = (self.dataIn.normFactor[acf_ind,:], self.dataIn.normFactor[ccf_ind,:]) | |
| 164 | self.dataOut.groupList = (acf_pairs, ccf_pairs) |
|
164 | self.dataOut.groupList = (acf_pairs, ccf_pairs) | |
| 165 |
|
165 | |||
| 166 | self.dataOut.abscissaList = self.dataIn.lagRange |
|
166 | self.dataOut.abscissaList = self.dataIn.lagRange | |
| 167 | self.dataOut.noise = self.dataIn.noise |
|
167 | self.dataOut.noise = self.dataIn.noise | |
| 168 | self.dataOut.data_snr = self.dataIn.SNR |
|
168 | self.dataOut.data_snr = self.dataIn.SNR | |
| 169 | self.dataOut.flagNoData = False |
|
169 | self.dataOut.flagNoData = False | |
| 170 | self.dataOut.nAvg = self.dataIn.nAvg |
|
170 | self.dataOut.nAvg = self.dataIn.nAvg | |
| 171 |
|
171 | |||
| 172 | #---------------------- Parameters Data --------------------------- |
|
172 | #---------------------- Parameters Data --------------------------- | |
| 173 |
|
173 | |||
| 174 | if self.dataIn.type == "Parameters": |
|
174 | if self.dataIn.type == "Parameters": | |
| 175 | self.dataOut.copy(self.dataIn) |
|
175 | self.dataOut.copy(self.dataIn) | |
| 176 | self.dataOut.flagNoData = False |
|
176 | self.dataOut.flagNoData = False | |
| 177 |
|
177 | |||
| 178 | return True |
|
178 | return True | |
| 179 |
|
179 | |||
| 180 | self.__updateObjFromInput() |
|
180 | self.__updateObjFromInput() | |
| 181 | self.dataOut.utctimeInit = self.dataIn.utctime |
|
181 | self.dataOut.utctimeInit = self.dataIn.utctime | |
| 182 | self.dataOut.paramInterval = self.dataIn.timeInterval |
|
182 | self.dataOut.paramInterval = self.dataIn.timeInterval | |
| 183 |
|
183 | |||
| 184 | return |
|
184 | return | |
| 185 |
|
185 | |||
| 186 |
|
186 | |||
| 187 | def target(tups): |
|
187 | def target(tups): | |
| 188 |
|
188 | |||
| 189 | obj, args = tups |
|
189 | obj, args = tups | |
| 190 |
|
190 | |||
| 191 | return obj.FitGau(args) |
|
191 | return obj.FitGau(args) | |
| 192 |
|
192 | |||
| 193 | class RemoveWideGC(Operation): |
|
193 | class RemoveWideGC(Operation): | |
| 194 | ''' This class remove the wide clutter and replace it with a simple interpolation points |
|
194 | ''' This class remove the wide clutter and replace it with a simple interpolation points | |
| 195 | This mainly applies to CLAIRE radar |
|
195 | This mainly applies to CLAIRE radar | |
| 196 |
|
196 | |||
| 197 | ClutterWidth : Width to look for the clutter peak |
|
197 | ClutterWidth : Width to look for the clutter peak | |
| 198 |
|
198 | |||
| 199 | Input: |
|
199 | Input: | |
| 200 |
|
200 | |||
| 201 | self.dataOut.data_pre : SPC and CSPC |
|
201 | self.dataOut.data_pre : SPC and CSPC | |
| 202 | self.dataOut.spc_range : To select wind and rainfall velocities |
|
202 | self.dataOut.spc_range : To select wind and rainfall velocities | |
| 203 |
|
203 | |||
| 204 | Affected: |
|
204 | Affected: | |
| 205 |
|
205 | |||
| 206 | self.dataOut.data_pre : It is used for the new SPC and CSPC ranges of wind |
|
206 | self.dataOut.data_pre : It is used for the new SPC and CSPC ranges of wind | |
| 207 |
|
207 | |||
| 208 | Written by D. ScipiΓ³n 25.02.2021 |
|
208 | Written by D. ScipiΓ³n 25.02.2021 | |
| 209 | ''' |
|
209 | ''' | |
| 210 | def __init__(self): |
|
210 | def __init__(self): | |
| 211 | Operation.__init__(self) |
|
211 | Operation.__init__(self) | |
| 212 | self.i = 0 |
|
212 | self.i = 0 | |
| 213 | self.ich = 0 |
|
213 | self.ich = 0 | |
| 214 | self.ir = 0 |
|
214 | self.ir = 0 | |
| 215 |
|
215 | |||
| 216 | def run(self, dataOut, ClutterWidth=2.5): |
|
216 | def run(self, dataOut, ClutterWidth=2.5): | |
| 217 |
|
217 | |||
| 218 | self.spc = dataOut.data_pre[0].copy() |
|
218 | self.spc = dataOut.data_pre[0].copy() | |
| 219 | self.spc_out = dataOut.data_pre[0].copy() |
|
219 | self.spc_out = dataOut.data_pre[0].copy() | |
| 220 | self.Num_Chn = self.spc.shape[0] |
|
220 | self.Num_Chn = self.spc.shape[0] | |
| 221 | self.Num_Hei = self.spc.shape[2] |
|
221 | self.Num_Hei = self.spc.shape[2] | |
| 222 | VelRange = dataOut.spc_range[2][:-1] |
|
222 | VelRange = dataOut.spc_range[2][:-1] | |
| 223 | dv = VelRange[1]-VelRange[0] |
|
223 | dv = VelRange[1]-VelRange[0] | |
| 224 |
|
224 | |||
| 225 | # Find the velocities that corresponds to zero |
|
225 | # Find the velocities that corresponds to zero | |
| 226 | gc_values = numpy.squeeze(numpy.where(numpy.abs(VelRange) <= ClutterWidth)) |
|
226 | gc_values = numpy.squeeze(numpy.where(numpy.abs(VelRange) <= ClutterWidth)) | |
| 227 |
|
227 | |||
| 228 | # Removing novalid data from the spectra |
|
228 | # Removing novalid data from the spectra | |
| 229 | for ich in range(self.Num_Chn) : |
|
229 | for ich in range(self.Num_Chn) : | |
| 230 | for ir in range(self.Num_Hei) : |
|
230 | for ir in range(self.Num_Hei) : | |
| 231 | # Estimate the noise at each range |
|
231 | # Estimate the noise at each range | |
| 232 | HSn = hildebrand_sekhon(self.spc[ich,:,ir],dataOut.nIncohInt) |
|
232 | HSn = hildebrand_sekhon(self.spc[ich,:,ir],dataOut.nIncohInt) | |
| 233 |
|
233 | |||
| 234 | # Removing the noise floor at each range |
|
234 | # Removing the noise floor at each range | |
| 235 | novalid = numpy.where(self.spc[ich,:,ir] < HSn) |
|
235 | novalid = numpy.where(self.spc[ich,:,ir] < HSn) | |
| 236 | self.spc[ich,novalid,ir] = HSn |
|
236 | self.spc[ich,novalid,ir] = HSn | |
| 237 |
|
237 | |||
| 238 | junk = numpy.append(numpy.insert(numpy.squeeze(self.spc[ich,gc_values,ir]),0,HSn),HSn) |
|
238 | 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)) |
|
239 | j1index = numpy.squeeze(numpy.where(numpy.diff(junk)>0)) | |
| 240 | j2index = numpy.squeeze(numpy.where(numpy.diff(junk)<0)) |
|
240 | j2index = numpy.squeeze(numpy.where(numpy.diff(junk)<0)) | |
| 241 | if ((numpy.size(j1index)<=1) | (numpy.size(j2index)<=1)) : |
|
241 | if ((numpy.size(j1index)<=1) | (numpy.size(j2index)<=1)) : | |
| 242 | continue |
|
242 | continue | |
| 243 | junk3 = numpy.squeeze(numpy.diff(j1index)) |
|
243 | junk3 = numpy.squeeze(numpy.diff(j1index)) | |
| 244 | junk4 = numpy.squeeze(numpy.diff(j2index)) |
|
244 | junk4 = numpy.squeeze(numpy.diff(j2index)) | |
| 245 |
|
245 | |||
| 246 | valleyindex = j2index[numpy.where(junk4>1)] |
|
246 | valleyindex = j2index[numpy.where(junk4>1)] | |
| 247 | peakindex = j1index[numpy.where(junk3>1)] |
|
247 | peakindex = j1index[numpy.where(junk3>1)] | |
| 248 |
|
248 | |||
| 249 | isvalid = numpy.squeeze(numpy.where(numpy.abs(VelRange[gc_values[peakindex]]) <= 2.5*dv)) |
|
249 | isvalid = numpy.squeeze(numpy.where(numpy.abs(VelRange[gc_values[peakindex]]) <= 2.5*dv)) | |
| 250 | if numpy.size(isvalid) == 0 : |
|
250 | if numpy.size(isvalid) == 0 : | |
| 251 | continue |
|
251 | continue | |
| 252 | if numpy.size(isvalid) >1 : |
|
252 | if numpy.size(isvalid) >1 : | |
| 253 | vindex = numpy.argmax(self.spc[ich,gc_values[peakindex[isvalid]],ir]) |
|
253 | vindex = numpy.argmax(self.spc[ich,gc_values[peakindex[isvalid]],ir]) | |
| 254 | isvalid = isvalid[vindex] |
|
254 | isvalid = isvalid[vindex] | |
| 255 |
|
255 | |||
| 256 | # clutter peak |
|
256 | # clutter peak | |
| 257 | gcpeak = peakindex[isvalid] |
|
257 | gcpeak = peakindex[isvalid] | |
| 258 | vl = numpy.where(valleyindex < gcpeak) |
|
258 | vl = numpy.where(valleyindex < gcpeak) | |
| 259 | if numpy.size(vl) == 0: |
|
259 | if numpy.size(vl) == 0: | |
| 260 | continue |
|
260 | continue | |
| 261 | gcvl = valleyindex[vl[0][-1]] |
|
261 | gcvl = valleyindex[vl[0][-1]] | |
| 262 | vr = numpy.where(valleyindex > gcpeak) |
|
262 | vr = numpy.where(valleyindex > gcpeak) | |
| 263 | if numpy.size(vr) == 0: |
|
263 | if numpy.size(vr) == 0: | |
| 264 | continue |
|
264 | continue | |
| 265 | gcvr = valleyindex[vr[0][0]] |
|
265 | gcvr = valleyindex[vr[0][0]] | |
| 266 |
|
266 | |||
| 267 | # Removing the clutter |
|
267 | # Removing the clutter | |
| 268 | interpindex = numpy.array([gc_values[gcvl], gc_values[gcvr]]) |
|
268 | interpindex = numpy.array([gc_values[gcvl], gc_values[gcvr]]) | |
| 269 | gcindex = gc_values[gcvl+1:gcvr-1] |
|
269 | gcindex = gc_values[gcvl+1:gcvr-1] | |
| 270 | self.spc_out[ich,gcindex,ir] = numpy.interp(VelRange[gcindex],VelRange[interpindex],self.spc[ich,interpindex,ir]) |
|
270 | self.spc_out[ich,gcindex,ir] = numpy.interp(VelRange[gcindex],VelRange[interpindex],self.spc[ich,interpindex,ir]) | |
| 271 |
|
271 | |||
| 272 | dataOut.data_pre[0] = self.spc_out |
|
272 | dataOut.data_pre[0] = self.spc_out | |
| 273 |
|
273 | |||
| 274 | return dataOut |
|
274 | return dataOut | |
| 275 |
|
275 | |||
| 276 | class SpectralFilters(Operation): |
|
276 | class SpectralFilters(Operation): | |
| 277 | ''' This class allows to replace the novalid values with noise for each channel |
|
277 | ''' This class allows to replace the novalid values with noise for each channel | |
| 278 | This applies to CLAIRE RADAR |
|
278 | This applies to CLAIRE RADAR | |
| 279 |
|
279 | |||
| 280 | PositiveLimit : RightLimit of novalid data |
|
280 | PositiveLimit : RightLimit of novalid data | |
| 281 | NegativeLimit : LeftLimit of novalid data |
|
281 | NegativeLimit : LeftLimit of novalid data | |
| 282 |
|
282 | |||
| 283 | Input: |
|
283 | Input: | |
| 284 |
|
284 | |||
| 285 | self.dataOut.data_pre : SPC and CSPC |
|
285 | self.dataOut.data_pre : SPC and CSPC | |
| 286 | self.dataOut.spc_range : To select wind and rainfall velocities |
|
286 | self.dataOut.spc_range : To select wind and rainfall velocities | |
| 287 |
|
287 | |||
| 288 | Affected: |
|
288 | Affected: | |
| 289 |
|
289 | |||
| 290 | self.dataOut.data_pre : It is used for the new SPC and CSPC ranges of wind |
|
290 | self.dataOut.data_pre : It is used for the new SPC and CSPC ranges of wind | |
| 291 |
|
291 | |||
| 292 | Written by D. ScipiΓ³n 29.01.2021 |
|
292 | Written by D. ScipiΓ³n 29.01.2021 | |
| 293 | ''' |
|
293 | ''' | |
| 294 | def __init__(self): |
|
294 | def __init__(self): | |
| 295 | Operation.__init__(self) |
|
295 | Operation.__init__(self) | |
| 296 | self.i = 0 |
|
296 | self.i = 0 | |
| 297 |
|
297 | |||
| 298 | def run(self, dataOut, ): |
|
298 | def run(self, dataOut, ): | |
| 299 |
|
299 | |||
| 300 | self.spc = dataOut.data_pre[0].copy() |
|
300 | self.spc = dataOut.data_pre[0].copy() | |
| 301 | self.Num_Chn = self.spc.shape[0] |
|
301 | self.Num_Chn = self.spc.shape[0] | |
| 302 | VelRange = dataOut.spc_range[2] |
|
302 | VelRange = dataOut.spc_range[2] | |
| 303 |
|
303 | |||
| 304 | # novalid corresponds to data within the Negative and PositiveLimit |
|
304 | # novalid corresponds to data within the Negative and PositiveLimit | |
| 305 |
|
305 | |||
| 306 |
|
306 | |||
| 307 | # Removing novalid data from the spectra |
|
307 | # Removing novalid data from the spectra | |
| 308 | for i in range(self.Num_Chn): |
|
308 | for i in range(self.Num_Chn): | |
| 309 | self.spc[i,novalid,:] = dataOut.noise[i] |
|
309 | self.spc[i,novalid,:] = dataOut.noise[i] | |
| 310 | dataOut.data_pre[0] = self.spc |
|
310 | dataOut.data_pre[0] = self.spc | |
| 311 | return dataOut |
|
311 | return dataOut | |
| 312 |
|
312 | |||
| 313 |
|
313 | |||
| 314 | class GaussianFit(Operation): |
|
314 | class GaussianFit(Operation): | |
| 315 |
|
315 | |||
| 316 | ''' |
|
316 | ''' | |
| 317 | Function that fit of one and two generalized gaussians (gg) based |
|
317 | Function that fit of one and two generalized gaussians (gg) based | |
| 318 | on the PSD shape across an "power band" identified from a cumsum of |
|
318 | on the PSD shape across an "power band" identified from a cumsum of | |
| 319 | the measured spectrum - noise. |
|
319 | the measured spectrum - noise. | |
| 320 |
|
320 | |||
| 321 | Input: |
|
321 | Input: | |
| 322 | self.dataOut.data_pre : SelfSpectra |
|
322 | self.dataOut.data_pre : SelfSpectra | |
| 323 |
|
323 | |||
| 324 | Output: |
|
324 | Output: | |
| 325 | self.dataOut.SPCparam : SPC_ch1, SPC_ch2 |
|
325 | self.dataOut.SPCparam : SPC_ch1, SPC_ch2 | |
| 326 |
|
326 | |||
| 327 | ''' |
|
327 | ''' | |
| 328 | def __init__(self): |
|
328 | def __init__(self): | |
| 329 | Operation.__init__(self) |
|
329 | Operation.__init__(self) | |
| 330 | self.i=0 |
|
330 | self.i=0 | |
| 331 |
|
331 | |||
| 332 | def run(self, dataOut, SNRdBlimit=-9, method='generalized'): |
|
332 | def run(self, dataOut, SNRdBlimit=-9, method='generalized'): | |
| 333 | """This routine will find a couple of generalized Gaussians to a power spectrum |
|
333 | """This routine will find a couple of generalized Gaussians to a power spectrum | |
| 334 | methods: generalized, squared |
|
334 | methods: generalized, squared | |
| 335 | input: spc |
|
335 | input: spc | |
| 336 | output: |
|
336 | output: | |
| 337 | noise, amplitude0,shift0,width0,p0,Amplitude1,shift1,width1,p1 |
|
337 | noise, amplitude0,shift0,width0,p0,Amplitude1,shift1,width1,p1 | |
| 338 | """ |
|
338 | """ | |
| 339 | print ('Entering ',method,' double Gaussian fit') |
|
339 | print ('Entering ',method,' double Gaussian fit') | |
| 340 | self.spc = dataOut.data_pre[0].copy() |
|
340 | self.spc = dataOut.data_pre[0].copy() | |
| 341 | self.Num_Hei = self.spc.shape[2] |
|
341 | self.Num_Hei = self.spc.shape[2] | |
| 342 | self.Num_Bin = self.spc.shape[1] |
|
342 | self.Num_Bin = self.spc.shape[1] | |
| 343 | self.Num_Chn = self.spc.shape[0] |
|
343 | self.Num_Chn = self.spc.shape[0] | |
| 344 |
|
344 | |||
| 345 | start_time = time.time() |
|
345 | start_time = time.time() | |
| 346 |
|
346 | |||
| 347 | pool = Pool(processes=self.Num_Chn) |
|
347 | pool = Pool(processes=self.Num_Chn) | |
| 348 | args = [(dataOut.spc_range[2], ich, dataOut.spc_noise[ich], dataOut.nIncohInt, SNRdBlimit) for ich in range(self.Num_Chn)] |
|
348 | args = [(dataOut.spc_range[2], ich, dataOut.spc_noise[ich], dataOut.nIncohInt, SNRdBlimit) for ich in range(self.Num_Chn)] | |
| 349 | objs = [self for __ in range(self.Num_Chn)] |
|
349 | objs = [self for __ in range(self.Num_Chn)] | |
| 350 | attrs = list(zip(objs, args)) |
|
350 | attrs = list(zip(objs, args)) | |
| 351 | DGauFitParam = pool.map(target, attrs) |
|
351 | DGauFitParam = pool.map(target, attrs) | |
| 352 | # Parameters: |
|
352 | # Parameters: | |
| 353 | # 0. Noise, 1. Amplitude, 2. Shift, 3. Width 4. Power |
|
353 | # 0. Noise, 1. Amplitude, 2. Shift, 3. Width 4. Power | |
| 354 | dataOut.DGauFitParams = numpy.asarray(DGauFitParam) |
|
354 | dataOut.DGauFitParams = numpy.asarray(DGauFitParam) | |
| 355 |
|
355 | |||
| 356 | # Double Gaussian Curves |
|
356 | # Double Gaussian Curves | |
| 357 | gau0 = numpy.zeros([self.Num_Chn,self.Num_Bin,self.Num_Hei]) |
|
357 | gau0 = numpy.zeros([self.Num_Chn,self.Num_Bin,self.Num_Hei]) | |
| 358 | gau0[:] = numpy.NaN |
|
358 | gau0[:] = numpy.NaN | |
| 359 | gau1 = numpy.zeros([self.Num_Chn,self.Num_Bin,self.Num_Hei]) |
|
359 | gau1 = numpy.zeros([self.Num_Chn,self.Num_Bin,self.Num_Hei]) | |
| 360 | gau1[:] = numpy.NaN |
|
360 | gau1[:] = numpy.NaN | |
| 361 | x_mtr = numpy.transpose(numpy.tile(dataOut.getVelRange(1)[:-1], (self.Num_Hei,1))) |
|
361 | x_mtr = numpy.transpose(numpy.tile(dataOut.getVelRange(1)[:-1], (self.Num_Hei,1))) | |
| 362 | for iCh in range(self.Num_Chn): |
|
362 | for iCh in range(self.Num_Chn): | |
| 363 | N0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][0,:,0]] * self.Num_Bin)) |
|
363 | N0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][0,:,0]] * self.Num_Bin)) | |
| 364 | N1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][0,:,1]] * self.Num_Bin)) |
|
364 | N1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][0,:,1]] * self.Num_Bin)) | |
| 365 | A0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][1,:,0]] * self.Num_Bin)) |
|
365 | A0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][1,:,0]] * self.Num_Bin)) | |
| 366 | A1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][1,:,1]] * self.Num_Bin)) |
|
366 | A1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][1,:,1]] * self.Num_Bin)) | |
| 367 | v0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][2,:,0]] * self.Num_Bin)) |
|
367 | v0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][2,:,0]] * self.Num_Bin)) | |
| 368 | v1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][2,:,1]] * self.Num_Bin)) |
|
368 | v1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][2,:,1]] * self.Num_Bin)) | |
| 369 | s0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][3,:,0]] * self.Num_Bin)) |
|
369 | s0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][3,:,0]] * self.Num_Bin)) | |
| 370 | s1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][3,:,1]] * self.Num_Bin)) |
|
370 | s1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][3,:,1]] * self.Num_Bin)) | |
| 371 | if method == 'generalized': |
|
371 | if method == 'generalized': | |
| 372 | p0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][4,:,0]] * self.Num_Bin)) |
|
372 | p0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][4,:,0]] * self.Num_Bin)) | |
| 373 | p1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][4,:,1]] * self.Num_Bin)) |
|
373 | p1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][4,:,1]] * self.Num_Bin)) | |
| 374 | elif method == 'squared': |
|
374 | elif method == 'squared': | |
| 375 | p0 = 2. |
|
375 | p0 = 2. | |
| 376 | p1 = 2. |
|
376 | p1 = 2. | |
| 377 | gau0[iCh] = A0*numpy.exp(-0.5*numpy.abs((x_mtr-v0)/s0)**p0)+N0 |
|
377 | gau0[iCh] = A0*numpy.exp(-0.5*numpy.abs((x_mtr-v0)/s0)**p0)+N0 | |
| 378 | gau1[iCh] = A1*numpy.exp(-0.5*numpy.abs((x_mtr-v1)/s1)**p1)+N1 |
|
378 | gau1[iCh] = A1*numpy.exp(-0.5*numpy.abs((x_mtr-v1)/s1)**p1)+N1 | |
| 379 | dataOut.GaussFit0 = gau0 |
|
379 | dataOut.GaussFit0 = gau0 | |
| 380 | dataOut.GaussFit1 = gau1 |
|
380 | dataOut.GaussFit1 = gau1 | |
| 381 |
|
381 | |||
| 382 | print('Leaving ',method ,' double Gaussian fit') |
|
382 | print('Leaving ',method ,' double Gaussian fit') | |
| 383 | return dataOut |
|
383 | return dataOut | |
| 384 |
|
384 | |||
| 385 | def FitGau(self, X): |
|
385 | def FitGau(self, X): | |
| 386 | # print('Entering FitGau') |
|
386 | # print('Entering FitGau') | |
| 387 | # Assigning the variables |
|
387 | # Assigning the variables | |
| 388 | Vrange, ch, wnoise, num_intg, SNRlimit = X |
|
388 | Vrange, ch, wnoise, num_intg, SNRlimit = X | |
| 389 | # Noise Limits |
|
389 | # Noise Limits | |
| 390 | noisebl = wnoise * 0.9 |
|
390 | noisebl = wnoise * 0.9 | |
| 391 | noisebh = wnoise * 1.1 |
|
391 | noisebh = wnoise * 1.1 | |
| 392 | # Radar Velocity |
|
392 | # Radar Velocity | |
| 393 | Va = max(Vrange) |
|
393 | Va = max(Vrange) | |
| 394 | deltav = Vrange[1] - Vrange[0] |
|
394 | deltav = Vrange[1] - Vrange[0] | |
| 395 | x = numpy.arange(self.Num_Bin) |
|
395 | x = numpy.arange(self.Num_Bin) | |
| 396 |
|
396 | |||
| 397 | # print ('stop 0') |
|
397 | # print ('stop 0') | |
| 398 |
|
398 | |||
| 399 | # 5 parameters, 2 Gaussians |
|
399 | # 5 parameters, 2 Gaussians | |
| 400 | DGauFitParam = numpy.zeros([5, self.Num_Hei,2]) |
|
400 | DGauFitParam = numpy.zeros([5, self.Num_Hei,2]) | |
| 401 | DGauFitParam[:] = numpy.NaN |
|
401 | DGauFitParam[:] = numpy.NaN | |
| 402 |
|
402 | |||
| 403 | # SPCparam = [] |
|
403 | # SPCparam = [] | |
| 404 | # SPC_ch1 = numpy.zeros([self.Num_Bin,self.Num_Hei]) |
|
404 | # SPC_ch1 = numpy.zeros([self.Num_Bin,self.Num_Hei]) | |
| 405 | # SPC_ch2 = numpy.zeros([self.Num_Bin,self.Num_Hei]) |
|
405 | # SPC_ch2 = numpy.zeros([self.Num_Bin,self.Num_Hei]) | |
| 406 | # SPC_ch1[:] = 0 #numpy.NaN |
|
406 | # SPC_ch1[:] = 0 #numpy.NaN | |
| 407 | # SPC_ch2[:] = 0 #numpy.NaN |
|
407 | # SPC_ch2[:] = 0 #numpy.NaN | |
| 408 | # print ('stop 1') |
|
408 | # print ('stop 1') | |
| 409 | for ht in range(self.Num_Hei): |
|
409 | for ht in range(self.Num_Hei): | |
| 410 | # print (ht) |
|
410 | # print (ht) | |
| 411 | # print ('stop 2') |
|
411 | # print ('stop 2') | |
| 412 | # Spectra at each range |
|
412 | # Spectra at each range | |
| 413 | spc = numpy.asarray(self.spc)[ch,:,ht] |
|
413 | spc = numpy.asarray(self.spc)[ch,:,ht] | |
| 414 | snr = ( spc.mean() - wnoise ) / wnoise |
|
414 | snr = ( spc.mean() - wnoise ) / wnoise | |
| 415 | snrdB = 10.*numpy.log10(snr) |
|
415 | snrdB = 10.*numpy.log10(snr) | |
| 416 |
|
416 | |||
| 417 | #print ('stop 3') |
|
417 | #print ('stop 3') | |
| 418 | if snrdB < SNRlimit : |
|
418 | if snrdB < SNRlimit : | |
| 419 | # snr = numpy.NaN |
|
419 | # snr = numpy.NaN | |
| 420 | # SPC_ch1[:,ht] = 0#numpy.NaN |
|
420 | # SPC_ch1[:,ht] = 0#numpy.NaN | |
| 421 | # SPC_ch1[:,ht] = 0#numpy.NaN |
|
421 | # SPC_ch1[:,ht] = 0#numpy.NaN | |
| 422 | # SPCparam = (SPC_ch1,SPC_ch2) |
|
422 | # SPCparam = (SPC_ch1,SPC_ch2) | |
| 423 | # print ('SNR less than SNRth') |
|
423 | # print ('SNR less than SNRth') | |
| 424 | continue |
|
424 | continue | |
| 425 | # wnoise = hildebrand_sekhon(spc,num_intg) |
|
425 | # wnoise = hildebrand_sekhon(spc,num_intg) | |
| 426 | # print ('stop 2.01') |
|
426 | # print ('stop 2.01') | |
| 427 | ############################################# |
|
427 | ############################################# | |
| 428 | # normalizing spc and noise |
|
428 | # normalizing spc and noise | |
| 429 | # This part differs from gg1 |
|
429 | # This part differs from gg1 | |
| 430 | # spc_norm_max = max(spc) #commented by D. ScipiΓ³n 19.03.2021 |
|
430 | # spc_norm_max = max(spc) #commented by D. ScipiΓ³n 19.03.2021 | |
| 431 | #spc = spc / spc_norm_max |
|
431 | #spc = spc / spc_norm_max | |
| 432 | # pnoise = pnoise #/ spc_norm_max #commented by D. ScipiΓ³n 19.03.2021 |
|
432 | # pnoise = pnoise #/ spc_norm_max #commented by D. ScipiΓ³n 19.03.2021 | |
| 433 | ############################################# |
|
433 | ############################################# | |
| 434 |
|
434 | |||
| 435 | # print ('stop 2.1') |
|
435 | # print ('stop 2.1') | |
| 436 | fatspectra=1.0 |
|
436 | fatspectra=1.0 | |
| 437 | # noise per channel.... we might want to use the noise at each range |
|
437 | # noise per channel.... we might want to use the noise at each range | |
| 438 |
|
438 | |||
| 439 | # wnoise = noise_ #/ spc_norm_max #commented by D. ScipiΓ³n 19.03.2021 |
|
439 | # wnoise = noise_ #/ spc_norm_max #commented by D. ScipiΓ³n 19.03.2021 | |
| 440 | #wnoise,stdv,i_max,index =enoise(spc,num_intg) #noise estimate using Hildebrand Sekhon, only wnoise is used |
|
440 | #wnoise,stdv,i_max,index =enoise(spc,num_intg) #noise estimate using Hildebrand Sekhon, only wnoise is used | |
| 441 | #if wnoise>1.1*pnoise: # to be tested later |
|
441 | #if wnoise>1.1*pnoise: # to be tested later | |
| 442 | # wnoise=pnoise |
|
442 | # wnoise=pnoise | |
| 443 | # noisebl = wnoise*0.9 |
|
443 | # noisebl = wnoise*0.9 | |
| 444 | # noisebh = wnoise*1.1 |
|
444 | # noisebh = wnoise*1.1 | |
| 445 | spc = spc - wnoise # signal |
|
445 | spc = spc - wnoise # signal | |
| 446 |
|
446 | |||
| 447 | # print ('stop 2.2') |
|
447 | # print ('stop 2.2') | |
| 448 | minx = numpy.argmin(spc) |
|
448 | minx = numpy.argmin(spc) | |
| 449 | #spcs=spc.copy() |
|
449 | #spcs=spc.copy() | |
| 450 | spcs = numpy.roll(spc,-minx) |
|
450 | spcs = numpy.roll(spc,-minx) | |
| 451 | cum = numpy.cumsum(spcs) |
|
451 | cum = numpy.cumsum(spcs) | |
| 452 | # tot_noise = wnoise * self.Num_Bin #64; |
|
452 | # tot_noise = wnoise * self.Num_Bin #64; | |
| 453 |
|
453 | |||
| 454 | # print ('stop 2.3') |
|
454 | # print ('stop 2.3') | |
| 455 | # snr = sum(spcs) / tot_noise |
|
455 | # snr = sum(spcs) / tot_noise | |
| 456 | # snrdB = 10.*numpy.log10(snr) |
|
456 | # snrdB = 10.*numpy.log10(snr) | |
| 457 | #print ('stop 3') |
|
457 | #print ('stop 3') | |
| 458 | # if snrdB < SNRlimit : |
|
458 | # if snrdB < SNRlimit : | |
| 459 | # snr = numpy.NaN |
|
459 | # snr = numpy.NaN | |
| 460 | # SPC_ch1[:,ht] = 0#numpy.NaN |
|
460 | # SPC_ch1[:,ht] = 0#numpy.NaN | |
| 461 | # SPC_ch1[:,ht] = 0#numpy.NaN |
|
461 | # SPC_ch1[:,ht] = 0#numpy.NaN | |
| 462 | # SPCparam = (SPC_ch1,SPC_ch2) |
|
462 | # SPCparam = (SPC_ch1,SPC_ch2) | |
| 463 | # print ('SNR less than SNRth') |
|
463 | # print ('SNR less than SNRth') | |
| 464 | # continue |
|
464 | # continue | |
| 465 |
|
465 | |||
| 466 |
|
466 | |||
| 467 | #if snrdB<-18 or numpy.isnan(snrdB) or num_intg<4: |
|
467 | #if snrdB<-18 or numpy.isnan(snrdB) or num_intg<4: | |
| 468 | # return [None,]*4,[None,]*4,None,snrdB,None,None,[None,]*5,[None,]*9,None |
|
468 | # return [None,]*4,[None,]*4,None,snrdB,None,None,[None,]*5,[None,]*9,None | |
| 469 | # print ('stop 4') |
|
469 | # print ('stop 4') | |
| 470 | cummax = max(cum) |
|
470 | cummax = max(cum) | |
| 471 | epsi = 0.08 * fatspectra # cumsum to narrow down the energy region |
|
471 | epsi = 0.08 * fatspectra # cumsum to narrow down the energy region | |
| 472 | cumlo = cummax * epsi |
|
472 | cumlo = cummax * epsi | |
| 473 | cumhi = cummax * (1-epsi) |
|
473 | cumhi = cummax * (1-epsi) | |
| 474 | powerindex = numpy.array(numpy.where(numpy.logical_and(cum>cumlo, cum<cumhi))[0]) |
|
474 | powerindex = numpy.array(numpy.where(numpy.logical_and(cum>cumlo, cum<cumhi))[0]) | |
| 475 |
|
475 | |||
| 476 | # print ('stop 5') |
|
476 | # print ('stop 5') | |
| 477 | if len(powerindex) < 1:# case for powerindex 0 |
|
477 | if len(powerindex) < 1:# case for powerindex 0 | |
| 478 | # print ('powerindex < 1') |
|
478 | # print ('powerindex < 1') | |
| 479 | continue |
|
479 | continue | |
| 480 | powerlo = powerindex[0] |
|
480 | powerlo = powerindex[0] | |
| 481 | powerhi = powerindex[-1] |
|
481 | powerhi = powerindex[-1] | |
| 482 | powerwidth = powerhi-powerlo |
|
482 | powerwidth = powerhi-powerlo | |
| 483 | if powerwidth <= 1: |
|
483 | if powerwidth <= 1: | |
| 484 | # print('powerwidth <= 1') |
|
484 | # print('powerwidth <= 1') | |
| 485 | continue |
|
485 | continue | |
| 486 |
|
486 | |||
| 487 | # print ('stop 6') |
|
487 | # print ('stop 6') | |
| 488 | firstpeak = powerlo + powerwidth/10.# first gaussian energy location |
|
488 | firstpeak = powerlo + powerwidth/10.# first gaussian energy location | |
| 489 | secondpeak = powerhi - powerwidth/10. #second gaussian energy location |
|
489 | secondpeak = powerhi - powerwidth/10. #second gaussian energy location | |
| 490 | midpeak = (firstpeak + secondpeak)/2. |
|
490 | midpeak = (firstpeak + secondpeak)/2. | |
| 491 | firstamp = spcs[int(firstpeak)] |
|
491 | firstamp = spcs[int(firstpeak)] | |
| 492 | secondamp = spcs[int(secondpeak)] |
|
492 | secondamp = spcs[int(secondpeak)] | |
| 493 | midamp = spcs[int(midpeak)] |
|
493 | midamp = spcs[int(midpeak)] | |
| 494 |
|
494 | |||
| 495 | y_data = spc + wnoise |
|
495 | y_data = spc + wnoise | |
| 496 |
|
496 | |||
| 497 | ''' single Gaussian ''' |
|
497 | ''' single Gaussian ''' | |
| 498 | shift0 = numpy.mod(midpeak+minx, self.Num_Bin ) |
|
498 | shift0 = numpy.mod(midpeak+minx, self.Num_Bin ) | |
| 499 | width0 = powerwidth/4.#Initialization entire power of spectrum divided by 4 |
|
499 | width0 = powerwidth/4.#Initialization entire power of spectrum divided by 4 | |
| 500 | power0 = 2. |
|
500 | power0 = 2. | |
| 501 | amplitude0 = midamp |
|
501 | amplitude0 = midamp | |
| 502 | state0 = [shift0,width0,amplitude0,power0,wnoise] |
|
502 | state0 = [shift0,width0,amplitude0,power0,wnoise] | |
| 503 | bnds = ((0,self.Num_Bin-1),(1,powerwidth),(0,None),(0.5,3.),(noisebl,noisebh)) |
|
503 | bnds = ((0,self.Num_Bin-1),(1,powerwidth),(0,None),(0.5,3.),(noisebl,noisebh)) | |
| 504 | lsq1 = fmin_l_bfgs_b(self.misfit1, state0, args=(y_data,x,num_intg), bounds=bnds, approx_grad=True) |
|
504 | lsq1 = fmin_l_bfgs_b(self.misfit1, state0, args=(y_data,x,num_intg), bounds=bnds, approx_grad=True) | |
| 505 | # print ('stop 7.1') |
|
505 | # print ('stop 7.1') | |
| 506 | # print (bnds) |
|
506 | # print (bnds) | |
| 507 |
|
507 | |||
| 508 | chiSq1=lsq1[1] |
|
508 | chiSq1=lsq1[1] | |
| 509 |
|
509 | |||
| 510 | # print ('stop 8') |
|
510 | # print ('stop 8') | |
| 511 | if fatspectra<1.0 and powerwidth<4: |
|
511 | if fatspectra<1.0 and powerwidth<4: | |
| 512 | choice=0 |
|
512 | choice=0 | |
| 513 | Amplitude0=lsq1[0][2] |
|
513 | Amplitude0=lsq1[0][2] | |
| 514 | shift0=lsq1[0][0] |
|
514 | shift0=lsq1[0][0] | |
| 515 | width0=lsq1[0][1] |
|
515 | width0=lsq1[0][1] | |
| 516 | p0=lsq1[0][3] |
|
516 | p0=lsq1[0][3] | |
| 517 | Amplitude1=0. |
|
517 | Amplitude1=0. | |
| 518 | shift1=0. |
|
518 | shift1=0. | |
| 519 | width1=0. |
|
519 | width1=0. | |
| 520 | p1=0. |
|
520 | p1=0. | |
| 521 | noise=lsq1[0][4] |
|
521 | noise=lsq1[0][4] | |
| 522 | #return (numpy.array([shift0,width0,Amplitude0,p0]), |
|
522 | #return (numpy.array([shift0,width0,Amplitude0,p0]), | |
| 523 | # numpy.array([shift1,width1,Amplitude1,p1]),noise,snrdB,chiSq1,6.,sigmas1,[None,]*9,choice) |
|
523 | # numpy.array([shift1,width1,Amplitude1,p1]),noise,snrdB,chiSq1,6.,sigmas1,[None,]*9,choice) | |
| 524 | # print ('stop 9') |
|
524 | # print ('stop 9') | |
| 525 | ''' two Gaussians ''' |
|
525 | ''' two Gaussians ''' | |
| 526 | #shift0=numpy.mod(firstpeak+minx,64); shift1=numpy.mod(secondpeak+minx,64) |
|
526 | #shift0=numpy.mod(firstpeak+minx,64); shift1=numpy.mod(secondpeak+minx,64) | |
| 527 | shift0 = numpy.mod(firstpeak+minx, self.Num_Bin ) |
|
527 | shift0 = numpy.mod(firstpeak+minx, self.Num_Bin ) | |
| 528 | shift1 = numpy.mod(secondpeak+minx, self.Num_Bin ) |
|
528 | shift1 = numpy.mod(secondpeak+minx, self.Num_Bin ) | |
| 529 | width0 = powerwidth/6. |
|
529 | width0 = powerwidth/6. | |
| 530 | width1 = width0 |
|
530 | width1 = width0 | |
| 531 | power0 = 2. |
|
531 | power0 = 2. | |
| 532 | power1 = power0 |
|
532 | power1 = power0 | |
| 533 | amplitude0 = firstamp |
|
533 | amplitude0 = firstamp | |
| 534 | amplitude1 = secondamp |
|
534 | amplitude1 = secondamp | |
| 535 | state0 = [shift0,width0,amplitude0,power0,shift1,width1,amplitude1,power1,wnoise] |
|
535 | state0 = [shift0,width0,amplitude0,power0,shift1,width1,amplitude1,power1,wnoise] | |
| 536 | #bnds=((0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(noisebl,noisebh)) |
|
536 | #bnds=((0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(noisebl,noisebh)) | |
| 537 | 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)) |
|
537 | 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)) | |
| 538 | #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)) |
|
538 | #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)) | |
| 539 |
|
539 | |||
| 540 | # print ('stop 10') |
|
540 | # print ('stop 10') | |
| 541 | lsq2 = fmin_l_bfgs_b( self.misfit2 , state0 , args=(y_data,x,num_intg) , bounds=bnds , approx_grad=True ) |
|
541 | lsq2 = fmin_l_bfgs_b( self.misfit2 , state0 , args=(y_data,x,num_intg) , bounds=bnds , approx_grad=True ) | |
| 542 |
|
542 | |||
| 543 | # print ('stop 11') |
|
543 | # print ('stop 11') | |
| 544 | chiSq2 = lsq2[1] |
|
544 | chiSq2 = lsq2[1] | |
| 545 |
|
545 | |||
| 546 | # print ('stop 12') |
|
546 | # print ('stop 12') | |
| 547 |
|
547 | |||
| 548 | 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) |
|
548 | 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) | |
| 549 |
|
549 | |||
| 550 | # print ('stop 13') |
|
550 | # print ('stop 13') | |
| 551 | if snrdB>-12: # when SNR is strong pick the peak with least shift (LOS velocity) error |
|
551 | if snrdB>-12: # when SNR is strong pick the peak with least shift (LOS velocity) error | |
| 552 | if oneG: |
|
552 | if oneG: | |
| 553 | choice = 0 |
|
553 | choice = 0 | |
| 554 | else: |
|
554 | else: | |
| 555 | w1 = lsq2[0][1]; w2 = lsq2[0][5] |
|
555 | w1 = lsq2[0][1]; w2 = lsq2[0][5] | |
| 556 | a1 = lsq2[0][2]; a2 = lsq2[0][6] |
|
556 | a1 = lsq2[0][2]; a2 = lsq2[0][6] | |
| 557 | p1 = lsq2[0][3]; p2 = lsq2[0][7] |
|
557 | p1 = lsq2[0][3]; p2 = lsq2[0][7] | |
| 558 | s1 = (2**(1+1./p1))*scipy.special.gamma(1./p1)/p1 |
|
558 | s1 = (2**(1+1./p1))*scipy.special.gamma(1./p1)/p1 | |
| 559 | s2 = (2**(1+1./p2))*scipy.special.gamma(1./p2)/p2 |
|
559 | s2 = (2**(1+1./p2))*scipy.special.gamma(1./p2)/p2 | |
| 560 | gp1 = a1*w1*s1; gp2 = a2*w2*s2 # power content of each ggaussian with proper p scaling |
|
560 | gp1 = a1*w1*s1; gp2 = a2*w2*s2 # power content of each ggaussian with proper p scaling | |
| 561 |
|
561 | |||
| 562 | if gp1>gp2: |
|
562 | if gp1>gp2: | |
| 563 | if a1>0.7*a2: |
|
563 | if a1>0.7*a2: | |
| 564 | choice = 1 |
|
564 | choice = 1 | |
| 565 | else: |
|
565 | else: | |
| 566 | choice = 2 |
|
566 | choice = 2 | |
| 567 | elif gp2>gp1: |
|
567 | elif gp2>gp1: | |
| 568 | if a2>0.7*a1: |
|
568 | if a2>0.7*a1: | |
| 569 | choice = 2 |
|
569 | choice = 2 | |
| 570 | else: |
|
570 | else: | |
| 571 | choice = 1 |
|
571 | choice = 1 | |
| 572 | else: |
|
572 | else: | |
| 573 | choice = numpy.argmax([a1,a2])+1 |
|
573 | choice = numpy.argmax([a1,a2])+1 | |
| 574 | #else: |
|
574 | #else: | |
| 575 | #choice=argmin([std2a,std2b])+1 |
|
575 | #choice=argmin([std2a,std2b])+1 | |
| 576 |
|
576 | |||
| 577 | else: # with low SNR go to the most energetic peak |
|
577 | else: # with low SNR go to the most energetic peak | |
| 578 | choice = numpy.argmax([lsq1[0][2]*lsq1[0][1],lsq2[0][2]*lsq2[0][1],lsq2[0][6]*lsq2[0][5]]) |
|
578 | choice = numpy.argmax([lsq1[0][2]*lsq1[0][1],lsq2[0][2]*lsq2[0][1],lsq2[0][6]*lsq2[0][5]]) | |
| 579 |
|
579 | |||
| 580 | # print ('stop 14') |
|
580 | # print ('stop 14') | |
| 581 | shift0 = lsq2[0][0] |
|
581 | shift0 = lsq2[0][0] | |
| 582 | vel0 = Vrange[0] + shift0 * deltav |
|
582 | vel0 = Vrange[0] + shift0 * deltav | |
| 583 | shift1 = lsq2[0][4] |
|
583 | shift1 = lsq2[0][4] | |
| 584 | # vel1=Vrange[0] + shift1 * deltav |
|
584 | # vel1=Vrange[0] + shift1 * deltav | |
| 585 |
|
585 | |||
| 586 | # max_vel = 1.0 |
|
586 | # max_vel = 1.0 | |
| 587 | # Va = max(Vrange) |
|
587 | # Va = max(Vrange) | |
| 588 | # deltav = Vrange[1]-Vrange[0] |
|
588 | # deltav = Vrange[1]-Vrange[0] | |
| 589 | # print ('stop 15') |
|
589 | # print ('stop 15') | |
| 590 | #first peak will be 0, second peak will be 1 |
|
590 | #first peak will be 0, second peak will be 1 | |
| 591 | # if vel0 > -1.0 and vel0 < max_vel : #first peak is in the correct range # Commented by D.ScipiΓ³n 19.03.2021 |
|
591 | # if vel0 > -1.0 and vel0 < max_vel : #first peak is in the correct range # Commented by D.ScipiΓ³n 19.03.2021 | |
| 592 | if vel0 > -Va and vel0 < Va : #first peak is in the correct range |
|
592 | if vel0 > -Va and vel0 < Va : #first peak is in the correct range | |
| 593 | shift0 = lsq2[0][0] |
|
593 | shift0 = lsq2[0][0] | |
| 594 | width0 = lsq2[0][1] |
|
594 | width0 = lsq2[0][1] | |
| 595 | Amplitude0 = lsq2[0][2] |
|
595 | Amplitude0 = lsq2[0][2] | |
| 596 | p0 = lsq2[0][3] |
|
596 | p0 = lsq2[0][3] | |
| 597 |
|
597 | |||
| 598 | shift1 = lsq2[0][4] |
|
598 | shift1 = lsq2[0][4] | |
| 599 | width1 = lsq2[0][5] |
|
599 | width1 = lsq2[0][5] | |
| 600 | Amplitude1 = lsq2[0][6] |
|
600 | Amplitude1 = lsq2[0][6] | |
| 601 | p1 = lsq2[0][7] |
|
601 | p1 = lsq2[0][7] | |
| 602 | noise = lsq2[0][8] |
|
602 | noise = lsq2[0][8] | |
| 603 | else: |
|
603 | else: | |
| 604 | shift1 = lsq2[0][0] |
|
604 | shift1 = lsq2[0][0] | |
| 605 | width1 = lsq2[0][1] |
|
605 | width1 = lsq2[0][1] | |
| 606 | Amplitude1 = lsq2[0][2] |
|
606 | Amplitude1 = lsq2[0][2] | |
| 607 | p1 = lsq2[0][3] |
|
607 | p1 = lsq2[0][3] | |
| 608 |
|
608 | |||
| 609 | shift0 = lsq2[0][4] |
|
609 | shift0 = lsq2[0][4] | |
| 610 | width0 = lsq2[0][5] |
|
610 | width0 = lsq2[0][5] | |
| 611 | Amplitude0 = lsq2[0][6] |
|
611 | Amplitude0 = lsq2[0][6] | |
| 612 | p0 = lsq2[0][7] |
|
612 | p0 = lsq2[0][7] | |
| 613 | noise = lsq2[0][8] |
|
613 | noise = lsq2[0][8] | |
| 614 |
|
614 | |||
| 615 | if Amplitude0<0.05: # in case the peak is noise |
|
615 | if Amplitude0<0.05: # in case the peak is noise | |
| 616 | shift0,width0,Amplitude0,p0 = 4*[numpy.NaN] |
|
616 | shift0,width0,Amplitude0,p0 = 4*[numpy.NaN] | |
| 617 | if Amplitude1<0.05: |
|
617 | if Amplitude1<0.05: | |
| 618 | shift1,width1,Amplitude1,p1 = 4*[numpy.NaN] |
|
618 | shift1,width1,Amplitude1,p1 = 4*[numpy.NaN] | |
| 619 |
|
619 | |||
| 620 | # print ('stop 16 ') |
|
620 | # print ('stop 16 ') | |
| 621 | # SPC_ch1[:,ht] = noise + Amplitude0*numpy.exp(-0.5*(abs(x-shift0)/width0)**p0) |
|
621 | # SPC_ch1[:,ht] = noise + Amplitude0*numpy.exp(-0.5*(abs(x-shift0)/width0)**p0) | |
| 622 | # SPC_ch2[:,ht] = noise + Amplitude1*numpy.exp(-0.5*(abs(x-shift1)/width1)**p1) |
|
622 | # SPC_ch2[:,ht] = noise + Amplitude1*numpy.exp(-0.5*(abs(x-shift1)/width1)**p1) | |
| 623 | # SPCparam = (SPC_ch1,SPC_ch2) |
|
623 | # SPCparam = (SPC_ch1,SPC_ch2) | |
| 624 |
|
624 | |||
| 625 | DGauFitParam[0,ht,0] = noise |
|
625 | DGauFitParam[0,ht,0] = noise | |
| 626 | DGauFitParam[0,ht,1] = noise |
|
626 | DGauFitParam[0,ht,1] = noise | |
| 627 | DGauFitParam[1,ht,0] = Amplitude0 |
|
627 | DGauFitParam[1,ht,0] = Amplitude0 | |
| 628 | DGauFitParam[1,ht,1] = Amplitude1 |
|
628 | DGauFitParam[1,ht,1] = Amplitude1 | |
| 629 | DGauFitParam[2,ht,0] = Vrange[0] + shift0 * deltav |
|
629 | DGauFitParam[2,ht,0] = Vrange[0] + shift0 * deltav | |
| 630 | DGauFitParam[2,ht,1] = Vrange[0] + shift1 * deltav |
|
630 | DGauFitParam[2,ht,1] = Vrange[0] + shift1 * deltav | |
| 631 | DGauFitParam[3,ht,0] = width0 * deltav |
|
631 | DGauFitParam[3,ht,0] = width0 * deltav | |
| 632 | DGauFitParam[3,ht,1] = width1 * deltav |
|
632 | DGauFitParam[3,ht,1] = width1 * deltav | |
| 633 | DGauFitParam[4,ht,0] = p0 |
|
633 | DGauFitParam[4,ht,0] = p0 | |
| 634 | DGauFitParam[4,ht,1] = p1 |
|
634 | DGauFitParam[4,ht,1] = p1 | |
| 635 |
|
635 | |||
| 636 | return DGauFitParam |
|
636 | return DGauFitParam | |
| 637 |
|
637 | |||
| 638 | def y_model1(self,x,state): |
|
638 | def y_model1(self,x,state): | |
| 639 | shift0, width0, amplitude0, power0, noise = state |
|
639 | shift0, width0, amplitude0, power0, noise = state | |
| 640 | model0 = amplitude0*numpy.exp(-0.5*abs((x - shift0)/width0)**power0) |
|
640 | model0 = amplitude0*numpy.exp(-0.5*abs((x - shift0)/width0)**power0) | |
| 641 | model0u = amplitude0*numpy.exp(-0.5*abs((x - shift0 - self.Num_Bin)/width0)**power0) |
|
641 | model0u = amplitude0*numpy.exp(-0.5*abs((x - shift0 - self.Num_Bin)/width0)**power0) | |
| 642 | model0d = amplitude0*numpy.exp(-0.5*abs((x - shift0 + self.Num_Bin)/width0)**power0) |
|
642 | model0d = amplitude0*numpy.exp(-0.5*abs((x - shift0 + self.Num_Bin)/width0)**power0) | |
| 643 | return model0 + model0u + model0d + noise |
|
643 | return model0 + model0u + model0d + noise | |
| 644 |
|
644 | |||
| 645 | def y_model2(self,x,state): #Equation for two generalized Gaussians with Nyquist |
|
645 | def y_model2(self,x,state): #Equation for two generalized Gaussians with Nyquist | |
| 646 | shift0, width0, amplitude0, power0, shift1, width1, amplitude1, power1, noise = state |
|
646 | shift0, width0, amplitude0, power0, shift1, width1, amplitude1, power1, noise = state | |
| 647 | model0 = amplitude0*numpy.exp(-0.5*abs((x-shift0)/width0)**power0) |
|
647 | model0 = amplitude0*numpy.exp(-0.5*abs((x-shift0)/width0)**power0) | |
| 648 | model0u = amplitude0*numpy.exp(-0.5*abs((x - shift0 - self.Num_Bin)/width0)**power0) |
|
648 | model0u = amplitude0*numpy.exp(-0.5*abs((x - shift0 - self.Num_Bin)/width0)**power0) | |
| 649 | model0d = amplitude0*numpy.exp(-0.5*abs((x - shift0 + self.Num_Bin)/width0)**power0) |
|
649 | model0d = amplitude0*numpy.exp(-0.5*abs((x - shift0 + self.Num_Bin)/width0)**power0) | |
| 650 |
|
650 | |||
| 651 | model1 = amplitude1*numpy.exp(-0.5*abs((x - shift1)/width1)**power1) |
|
651 | model1 = amplitude1*numpy.exp(-0.5*abs((x - shift1)/width1)**power1) | |
| 652 | model1u = amplitude1*numpy.exp(-0.5*abs((x - shift1 - self.Num_Bin)/width1)**power1) |
|
652 | model1u = amplitude1*numpy.exp(-0.5*abs((x - shift1 - self.Num_Bin)/width1)**power1) | |
| 653 | model1d = amplitude1*numpy.exp(-0.5*abs((x - shift1 + self.Num_Bin)/width1)**power1) |
|
653 | model1d = amplitude1*numpy.exp(-0.5*abs((x - shift1 + self.Num_Bin)/width1)**power1) | |
| 654 | return model0 + model0u + model0d + model1 + model1u + model1d + noise |
|
654 | return model0 + model0u + model0d + model1 + model1u + model1d + noise | |
| 655 |
|
655 | |||
| 656 | 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. |
|
656 | 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. | |
| 657 |
|
657 | |||
| 658 | return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model1(x,state)))**2)#/(64-5.) # /(64-5.) can be commented |
|
658 | return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model1(x,state)))**2)#/(64-5.) # /(64-5.) can be commented | |
| 659 |
|
659 | |||
| 660 | def misfit2(self,state,y_data,x,num_intg): |
|
660 | def misfit2(self,state,y_data,x,num_intg): | |
| 661 | return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model2(x,state)))**2)#/(64-9.) |
|
661 | return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model2(x,state)))**2)#/(64-9.) | |
| 662 |
|
662 | |||
| 663 | class Oblique_Gauss_Fit(Operation): |
|
663 | class Oblique_Gauss_Fit(Operation): | |
| 664 | ''' |
|
664 | ''' | |
| 665 | Written by R. Flores |
|
665 | Written by R. Flores | |
| 666 | ''' |
|
666 | ''' | |
| 667 | def __init__(self): |
|
667 | def __init__(self): | |
| 668 | Operation.__init__(self) |
|
668 | Operation.__init__(self) | |
| 669 |
|
669 | |||
| 670 | def Gauss_fit(self,spc,x,nGauss): |
|
670 | def Gauss_fit(self,spc,x,nGauss): | |
| 671 |
|
671 | |||
| 672 |
|
672 | |||
| 673 | def gaussian(x, a, b, c, d): |
|
673 | def gaussian(x, a, b, c, d): | |
| 674 | val = a * numpy.exp(-(x - b)**2 / (2*c**2)) + d |
|
674 | val = a * numpy.exp(-(x - b)**2 / (2*c**2)) + d | |
| 675 | return val |
|
675 | return val | |
| 676 |
|
676 | |||
| 677 | if nGauss == 'first': |
|
677 | if nGauss == 'first': | |
| 678 | spc_1_aux = numpy.copy(spc[:numpy.argmax(spc)+1]) |
|
678 | spc_1_aux = numpy.copy(spc[:numpy.argmax(spc)+1]) | |
| 679 | spc_2_aux = numpy.flip(spc_1_aux) |
|
679 | spc_2_aux = numpy.flip(spc_1_aux) | |
| 680 | spc_3_aux = numpy.concatenate((spc_1_aux,spc_2_aux[1:])) |
|
680 | spc_3_aux = numpy.concatenate((spc_1_aux,spc_2_aux[1:])) | |
| 681 |
|
681 | |||
| 682 | len_dif = len(x)-len(spc_3_aux) |
|
682 | len_dif = len(x)-len(spc_3_aux) | |
| 683 |
|
683 | |||
| 684 | spc_zeros = numpy.ones(len_dif)*spc_1_aux[0] |
|
684 | spc_zeros = numpy.ones(len_dif)*spc_1_aux[0] | |
| 685 |
|
685 | |||
| 686 | spc_new = numpy.concatenate((spc_3_aux,spc_zeros)) |
|
686 | spc_new = numpy.concatenate((spc_3_aux,spc_zeros)) | |
| 687 |
|
687 | |||
| 688 | y = spc_new |
|
688 | y = spc_new | |
| 689 |
|
689 | |||
| 690 | elif nGauss == 'second': |
|
690 | elif nGauss == 'second': | |
| 691 | y = spc |
|
691 | y = spc | |
| 692 |
|
692 | |||
| 693 |
|
693 | |||
| 694 | # estimate starting values from the data |
|
694 | # estimate starting values from the data | |
| 695 | a = y.max() |
|
695 | a = y.max() | |
| 696 | b = x[numpy.argmax(y)] |
|
696 | b = x[numpy.argmax(y)] | |
| 697 | if nGauss == 'first': |
|
697 | if nGauss == 'first': | |
| 698 | c = 1.#b#b#numpy.std(spc) |
|
698 | c = 1.#b#b#numpy.std(spc) | |
| 699 | elif nGauss == 'second': |
|
699 | elif nGauss == 'second': | |
| 700 | c = b |
|
700 | c = b | |
| 701 | else: |
|
701 | else: | |
| 702 | print("ERROR") |
|
702 | print("ERROR") | |
| 703 |
|
703 | |||
| 704 | d = numpy.mean(y[-100:]) |
|
704 | d = numpy.mean(y[-100:]) | |
| 705 |
|
705 | |||
| 706 | # define a least squares function to optimize |
|
706 | # define a least squares function to optimize | |
| 707 | def minfunc(params): |
|
707 | def minfunc(params): | |
| 708 | return sum((y-gaussian(x,params[0],params[1],params[2],params[3]))**2) |
|
708 | return sum((y-gaussian(x,params[0],params[1],params[2],params[3]))**2) | |
| 709 |
|
709 | |||
| 710 | # fit |
|
710 | # fit | |
| 711 | popt = fmin(minfunc,[a,b,c,d],disp=False) |
|
711 | popt = fmin(minfunc,[a,b,c,d],disp=False) | |
| 712 | #popt,fopt,niter,funcalls = fmin(minfunc,[a,b,c,d]) |
|
712 | #popt,fopt,niter,funcalls = fmin(minfunc,[a,b,c,d]) | |
| 713 |
|
713 | |||
| 714 |
|
714 | |||
| 715 | return gaussian(x, popt[0], popt[1], popt[2], popt[3]), popt[0], popt[1], popt[2], popt[3] |
|
715 | return gaussian(x, popt[0], popt[1], popt[2], popt[3]), popt[0], popt[1], popt[2], popt[3] | |
| 716 |
|
716 | |||
| 717 |
|
717 | |||
| 718 | def Gauss_fit_2(self,spc,x,nGauss): |
|
718 | def Gauss_fit_2(self,spc,x,nGauss): | |
| 719 |
|
719 | |||
| 720 |
|
720 | |||
| 721 | def gaussian(x, a, b, c, d): |
|
721 | def gaussian(x, a, b, c, d): | |
| 722 | val = a * numpy.exp(-(x - b)**2 / (2*c**2)) + d |
|
722 | val = a * numpy.exp(-(x - b)**2 / (2*c**2)) + d | |
| 723 | return val |
|
723 | return val | |
| 724 |
|
724 | |||
| 725 | if nGauss == 'first': |
|
725 | if nGauss == 'first': | |
| 726 | spc_1_aux = numpy.copy(spc[:numpy.argmax(spc)+1]) |
|
726 | spc_1_aux = numpy.copy(spc[:numpy.argmax(spc)+1]) | |
| 727 | spc_2_aux = numpy.flip(spc_1_aux) |
|
727 | spc_2_aux = numpy.flip(spc_1_aux) | |
| 728 | spc_3_aux = numpy.concatenate((spc_1_aux,spc_2_aux[1:])) |
|
728 | spc_3_aux = numpy.concatenate((spc_1_aux,spc_2_aux[1:])) | |
| 729 |
|
729 | |||
| 730 | len_dif = len(x)-len(spc_3_aux) |
|
730 | len_dif = len(x)-len(spc_3_aux) | |
| 731 |
|
731 | |||
| 732 | spc_zeros = numpy.ones(len_dif)*spc_1_aux[0] |
|
732 | spc_zeros = numpy.ones(len_dif)*spc_1_aux[0] | |
| 733 |
|
733 | |||
| 734 | spc_new = numpy.concatenate((spc_3_aux,spc_zeros)) |
|
734 | spc_new = numpy.concatenate((spc_3_aux,spc_zeros)) | |
| 735 |
|
735 | |||
| 736 | y = spc_new |
|
736 | y = spc_new | |
| 737 |
|
737 | |||
| 738 | elif nGauss == 'second': |
|
738 | elif nGauss == 'second': | |
| 739 | y = spc |
|
739 | y = spc | |
| 740 |
|
740 | |||
| 741 |
|
741 | |||
| 742 | # estimate starting values from the data |
|
742 | # estimate starting values from the data | |
| 743 | a = y.max() |
|
743 | a = y.max() | |
| 744 | b = x[numpy.argmax(y)] |
|
744 | b = x[numpy.argmax(y)] | |
| 745 | if nGauss == 'first': |
|
745 | if nGauss == 'first': | |
| 746 | c = 1.#b#b#numpy.std(spc) |
|
746 | c = 1.#b#b#numpy.std(spc) | |
| 747 | elif nGauss == 'second': |
|
747 | elif nGauss == 'second': | |
| 748 | c = b |
|
748 | c = b | |
| 749 | else: |
|
749 | else: | |
| 750 | print("ERROR") |
|
750 | print("ERROR") | |
| 751 |
|
751 | |||
| 752 | d = numpy.mean(y[-100:]) |
|
752 | d = numpy.mean(y[-100:]) | |
| 753 | popt,pcov = curve_fit(gaussian,x,y,p0=[a,b,c,d]) |
|
753 | popt,pcov = curve_fit(gaussian,x,y,p0=[a,b,c,d]) | |
| 754 | return gaussian(x, popt[0], popt[1], popt[2], popt[3]),popt[0], popt[1], popt[2], popt[3] |
|
754 | return gaussian(x, popt[0], popt[1], popt[2], popt[3]),popt[0], popt[1], popt[2], popt[3] | |
| 755 |
|
755 | |||
| 756 | def Double_Gauss_fit(self,spc,x,A1,B1,C1,A2,B2,C2,D): |
|
756 | def Double_Gauss_fit(self,spc,x,A1,B1,C1,A2,B2,C2,D): | |
| 757 |
|
757 | |||
| 758 | def double_gaussian(x, a1, b1, c1, a2, b2, c2, d): |
|
758 | def double_gaussian(x, a1, b1, c1, a2, b2, c2, d): | |
| 759 | val = a1 * numpy.exp(-(x - b1)**2 / (2*c1**2)) + a2 * numpy.exp(-(x - b2)**2 / (2*c2**2)) + d |
|
759 | val = a1 * numpy.exp(-(x - b1)**2 / (2*c1**2)) + a2 * numpy.exp(-(x - b2)**2 / (2*c2**2)) + d | |
| 760 | return val |
|
760 | return val | |
| 761 |
|
761 | |||
| 762 |
|
762 | |||
| 763 | y = spc |
|
763 | y = spc | |
| 764 |
|
764 | |||
| 765 | # estimate starting values from the data |
|
765 | # estimate starting values from the data | |
| 766 | a1 = A1 |
|
766 | a1 = A1 | |
| 767 | b1 = B1 |
|
767 | b1 = B1 | |
| 768 | c1 = C1#numpy.std(spc) |
|
768 | c1 = C1#numpy.std(spc) | |
| 769 |
|
769 | |||
| 770 | a2 = A2#y.max() |
|
770 | a2 = A2#y.max() | |
| 771 | b2 = B2#x[numpy.argmax(y)] |
|
771 | b2 = B2#x[numpy.argmax(y)] | |
| 772 | c2 = C2#numpy.std(spc) |
|
772 | c2 = C2#numpy.std(spc) | |
| 773 | d = D |
|
773 | d = D | |
| 774 |
|
774 | |||
| 775 | # define a least squares function to optimize |
|
775 | # define a least squares function to optimize | |
| 776 | def minfunc(params): |
|
776 | def minfunc(params): | |
| 777 | return sum((y-double_gaussian(x,params[0],params[1],params[2],params[3],params[4],params[5],params[6]))**2) |
|
777 | return sum((y-double_gaussian(x,params[0],params[1],params[2],params[3],params[4],params[5],params[6]))**2) | |
| 778 |
|
778 | |||
| 779 | # fit |
|
779 | # fit | |
| 780 | popt = fmin(minfunc,[a1,b1,c1,a2,b2,c2,d],disp=False) |
|
780 | popt = fmin(minfunc,[a1,b1,c1,a2,b2,c2,d],disp=False) | |
| 781 |
|
781 | |||
| 782 | 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] |
|
782 | 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] | |
| 783 |
|
783 | |||
| 784 | def Double_Gauss_fit_2(self,spc,x,A1,B1,C1,A2,B2,C2,D): |
|
784 | def Double_Gauss_fit_2(self,spc,x,A1,B1,C1,A2,B2,C2,D): | |
| 785 |
|
785 | |||
| 786 | def double_gaussian(x, a1, b1, c1, a2, b2, c2, d): |
|
786 | def double_gaussian(x, a1, b1, c1, a2, b2, c2, d): | |
| 787 | val = a1 * numpy.exp(-(x - b1)**2 / (2*c1**2)) + a2 * numpy.exp(-(x - b2)**2 / (2*c2**2)) + d |
|
787 | val = a1 * numpy.exp(-(x - b1)**2 / (2*c1**2)) + a2 * numpy.exp(-(x - b2)**2 / (2*c2**2)) + d | |
| 788 | return val |
|
788 | return val | |
| 789 |
|
789 | |||
| 790 |
|
790 | |||
| 791 | y = spc |
|
791 | y = spc | |
| 792 |
|
792 | |||
| 793 | # estimate starting values from the data |
|
793 | # estimate starting values from the data | |
| 794 | a1 = A1 |
|
794 | a1 = A1 | |
| 795 | b1 = B1 |
|
795 | b1 = B1 | |
| 796 | c1 = C1#numpy.std(spc) |
|
796 | c1 = C1#numpy.std(spc) | |
| 797 |
|
797 | |||
| 798 | a2 = A2#y.max() |
|
798 | a2 = A2#y.max() | |
| 799 | b2 = B2#x[numpy.argmax(y)] |
|
799 | b2 = B2#x[numpy.argmax(y)] | |
| 800 | c2 = C2#numpy.std(spc) |
|
800 | c2 = C2#numpy.std(spc) | |
| 801 | d = D |
|
801 | d = D | |
| 802 |
|
802 | |||
| 803 | # fit |
|
803 | # fit | |
| 804 | popt,pcov = curve_fit(double_gaussian,x,y,p0=[a1,b1,c1,a2,b2,c2,d]) |
|
804 | popt,pcov = curve_fit(double_gaussian,x,y,p0=[a1,b1,c1,a2,b2,c2,d]) | |
| 805 | error = numpy.sqrt(numpy.diag(pcov)) |
|
805 | error = numpy.sqrt(numpy.diag(pcov)) | |
| 806 |
|
806 | |||
| 807 | 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] |
|
807 | 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] | |
| 808 |
|
808 | |||
| 809 | def windowing_double(self,spc,x,A1,B1,C1,A2,B2,C2,D): |
|
809 | def windowing_double(self,spc,x,A1,B1,C1,A2,B2,C2,D): | |
| 810 | from scipy.optimize import curve_fit,fmin |
|
810 | from scipy.optimize import curve_fit,fmin | |
| 811 |
|
811 | |||
| 812 | def R_gaussian(x, a, b, c): |
|
812 | def R_gaussian(x, a, b, c): | |
| 813 | N = int(numpy.shape(x)[0]) |
|
813 | N = int(numpy.shape(x)[0]) | |
| 814 | val = a * numpy.exp(-((x)*c*2*2*numpy.pi)**2 / (2))* numpy.exp(1.j*b*x*4*numpy.pi) |
|
814 | val = a * numpy.exp(-((x)*c*2*2*numpy.pi)**2 / (2))* numpy.exp(1.j*b*x*4*numpy.pi) | |
| 815 | return val |
|
815 | return val | |
| 816 |
|
816 | |||
| 817 | def T(x,N): |
|
817 | def T(x,N): | |
| 818 | T = 1-abs(x)/N |
|
818 | T = 1-abs(x)/N | |
| 819 | return T |
|
819 | return T | |
| 820 |
|
820 | |||
| 821 | def R_T_spc_fun(x, a1, b1, c1, a2, b2, c2, d): |
|
821 | def R_T_spc_fun(x, a1, b1, c1, a2, b2, c2, d): | |
| 822 |
|
822 | |||
| 823 | N = int(numpy.shape(x)[0]) |
|
823 | N = int(numpy.shape(x)[0]) | |
| 824 |
|
824 | |||
| 825 | x_max = x[-1] |
|
825 | x_max = x[-1] | |
| 826 |
|
826 | |||
| 827 | x_pos = x[1600:] |
|
827 | x_pos = x[1600:] | |
| 828 | x_neg = x[:1600] |
|
828 | x_neg = x[:1600] | |
| 829 |
|
829 | |||
| 830 | R_T_neg_1 = R_gaussian(x, a1, b1, c1)[:1600]*T(x_neg,-x[0]) |
|
830 | R_T_neg_1 = R_gaussian(x, a1, b1, c1)[:1600]*T(x_neg,-x[0]) | |
| 831 | R_T_pos_1 = R_gaussian(x, a1, b1, c1)[1600:]*T(x_pos,x[-1]) |
|
831 | R_T_pos_1 = R_gaussian(x, a1, b1, c1)[1600:]*T(x_pos,x[-1]) | |
| 832 | R_T_sum_1 = R_T_pos_1 + R_T_neg_1 |
|
832 | R_T_sum_1 = R_T_pos_1 + R_T_neg_1 | |
| 833 | R_T_spc_1 = numpy.fft.fft(R_T_sum_1).real |
|
833 | R_T_spc_1 = numpy.fft.fft(R_T_sum_1).real | |
| 834 | R_T_spc_1 = numpy.fft.fftshift(R_T_spc_1) |
|
834 | R_T_spc_1 = numpy.fft.fftshift(R_T_spc_1) | |
| 835 | max_val_1 = numpy.max(R_T_spc_1) |
|
835 | max_val_1 = numpy.max(R_T_spc_1) | |
| 836 | R_T_spc_1 = R_T_spc_1*a1/max_val_1 |
|
836 | R_T_spc_1 = R_T_spc_1*a1/max_val_1 | |
| 837 |
|
837 | |||
| 838 | R_T_neg_2 = R_gaussian(x, a2, b2, c2)[:1600]*T(x_neg,-x[0]) |
|
838 | R_T_neg_2 = R_gaussian(x, a2, b2, c2)[:1600]*T(x_neg,-x[0]) | |
| 839 | R_T_pos_2 = R_gaussian(x, a2, b2, c2)[1600:]*T(x_pos,x[-1]) |
|
839 | R_T_pos_2 = R_gaussian(x, a2, b2, c2)[1600:]*T(x_pos,x[-1]) | |
| 840 | R_T_sum_2 = R_T_pos_2 + R_T_neg_2 |
|
840 | R_T_sum_2 = R_T_pos_2 + R_T_neg_2 | |
| 841 | R_T_spc_2 = numpy.fft.fft(R_T_sum_2).real |
|
841 | R_T_spc_2 = numpy.fft.fft(R_T_sum_2).real | |
| 842 | R_T_spc_2 = numpy.fft.fftshift(R_T_spc_2) |
|
842 | R_T_spc_2 = numpy.fft.fftshift(R_T_spc_2) | |
| 843 | max_val_2 = numpy.max(R_T_spc_2) |
|
843 | max_val_2 = numpy.max(R_T_spc_2) | |
| 844 | R_T_spc_2 = R_T_spc_2*a2/max_val_2 |
|
844 | R_T_spc_2 = R_T_spc_2*a2/max_val_2 | |
| 845 |
|
845 | |||
| 846 | R_T_d = d*numpy.fft.fftshift(signal.unit_impulse(N)) |
|
846 | R_T_d = d*numpy.fft.fftshift(signal.unit_impulse(N)) | |
| 847 | R_T_d_neg = R_T_d[:1600]*T(x_neg,-x[0]) |
|
847 | R_T_d_neg = R_T_d[:1600]*T(x_neg,-x[0]) | |
| 848 | R_T_d_pos = R_T_d[1600:]*T(x_pos,x[-1]) |
|
848 | R_T_d_pos = R_T_d[1600:]*T(x_pos,x[-1]) | |
| 849 | R_T_d_sum = R_T_d_pos + R_T_d_neg |
|
849 | R_T_d_sum = R_T_d_pos + R_T_d_neg | |
| 850 | R_T_spc_3 = numpy.fft.fft(R_T_d_sum).real |
|
850 | R_T_spc_3 = numpy.fft.fft(R_T_d_sum).real | |
| 851 | R_T_spc_3 = numpy.fft.fftshift(R_T_spc_3) |
|
851 | R_T_spc_3 = numpy.fft.fftshift(R_T_spc_3) | |
| 852 |
|
852 | |||
| 853 | R_T_final = R_T_spc_1 + R_T_spc_2 + R_T_spc_3 |
|
853 | R_T_final = R_T_spc_1 + R_T_spc_2 + R_T_spc_3 | |
| 854 |
|
854 | |||
| 855 | return R_T_final |
|
855 | return R_T_final | |
| 856 |
|
856 | |||
| 857 | y = spc#gaussian(x, a, meanY, sigmaY) + a*0.1*numpy.random.normal(0, 1, size=len(x)) |
|
857 | y = spc#gaussian(x, a, meanY, sigmaY) + a*0.1*numpy.random.normal(0, 1, size=len(x)) | |
| 858 |
|
858 | |||
| 859 | from scipy.stats import norm |
|
859 | from scipy.stats import norm | |
| 860 | mean,std=norm.fit(spc) |
|
860 | mean,std=norm.fit(spc) | |
| 861 |
|
861 | |||
| 862 | # estimate starting values from the data |
|
862 | # estimate starting values from the data | |
| 863 | a1 = A1 |
|
863 | a1 = A1 | |
| 864 | b1 = B1 |
|
864 | b1 = B1 | |
| 865 | c1 = C1#numpy.std(spc) |
|
865 | c1 = C1#numpy.std(spc) | |
| 866 |
|
866 | |||
| 867 | a2 = A2#y.max() |
|
867 | a2 = A2#y.max() | |
| 868 | b2 = B2#x[numpy.argmax(y)] |
|
868 | b2 = B2#x[numpy.argmax(y)] | |
| 869 | c2 = C2#numpy.std(spc) |
|
869 | c2 = C2#numpy.std(spc) | |
| 870 | d = D |
|
870 | d = D | |
| 871 |
|
871 | |||
| 872 | ippSeconds = 250*20*1.e-6/3 |
|
872 | ippSeconds = 250*20*1.e-6/3 | |
| 873 |
|
873 | |||
| 874 | x_t = ippSeconds * (numpy.arange(1600) -1600 / 2.) |
|
874 | x_t = ippSeconds * (numpy.arange(1600) -1600 / 2.) | |
| 875 |
|
875 | |||
| 876 | x_t = numpy.linspace(x_t[0],x_t[-1],3200) |
|
876 | x_t = numpy.linspace(x_t[0],x_t[-1],3200) | |
| 877 |
|
877 | |||
| 878 | x_freq = numpy.fft.fftfreq(1600,d=ippSeconds) |
|
878 | x_freq = numpy.fft.fftfreq(1600,d=ippSeconds) | |
| 879 | x_freq = numpy.fft.fftshift(x_freq) |
|
879 | x_freq = numpy.fft.fftshift(x_freq) | |
| 880 |
|
880 | |||
| 881 | # define a least squares function to optimize |
|
881 | # define a least squares function to optimize | |
| 882 | def minfunc(params): |
|
882 | def minfunc(params): | |
| 883 | 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) |
|
883 | 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) | |
| 884 |
|
884 | |||
| 885 | # fit |
|
885 | # fit | |
| 886 | popt_full = fmin(minfunc,[a1,b1,c1,a2,b2,c2,d],full_output=True) |
|
886 | popt_full = fmin(minfunc,[a1,b1,c1,a2,b2,c2,d],full_output=True) | |
| 887 | popt = popt_full[0] |
|
887 | popt = popt_full[0] | |
| 888 |
|
888 | |||
| 889 | return popt[0], popt[1], popt[2], popt[3], popt[4], popt[5], popt[6] |
|
889 | return popt[0], popt[1], popt[2], popt[3], popt[4], popt[5], popt[6] | |
| 890 |
|
890 | |||
| 891 | def Double_Gauss_fit_weight(self,spc,x,A1,B1,C1,A2,B2,C2,D): |
|
891 | def Double_Gauss_fit_weight(self,spc,x,A1,B1,C1,A2,B2,C2,D): | |
| 892 | from scipy.optimize import curve_fit,fmin |
|
892 | from scipy.optimize import curve_fit,fmin | |
| 893 |
|
893 | |||
| 894 | def double_gaussian(x, a1, b1, c1, a2, b2, c2, d): |
|
894 | def double_gaussian(x, a1, b1, c1, a2, b2, c2, d): | |
| 895 | val = a1 * numpy.exp(-(x - b1)**2 / (2*c1**2)) + a2 * numpy.exp(-(x - b2)**2 / (2*c2**2)) + d |
|
895 | val = a1 * numpy.exp(-(x - b1)**2 / (2*c1**2)) + a2 * numpy.exp(-(x - b2)**2 / (2*c2**2)) + d | |
| 896 | return val |
|
896 | return val | |
| 897 |
|
897 | |||
| 898 | y = spc |
|
898 | y = spc | |
| 899 |
|
899 | |||
| 900 | from scipy.stats import norm |
|
900 | from scipy.stats import norm | |
| 901 | mean,std=norm.fit(spc) |
|
901 | mean,std=norm.fit(spc) | |
| 902 |
|
902 | |||
| 903 | # estimate starting values from the data |
|
903 | # estimate starting values from the data | |
| 904 | a1 = A1 |
|
904 | a1 = A1 | |
| 905 | b1 = B1 |
|
905 | b1 = B1 | |
| 906 | c1 = C1#numpy.std(spc) |
|
906 | c1 = C1#numpy.std(spc) | |
| 907 |
|
907 | |||
| 908 | a2 = A2#y.max() |
|
908 | a2 = A2#y.max() | |
| 909 | b2 = B2#x[numpy.argmax(y)] |
|
909 | b2 = B2#x[numpy.argmax(y)] | |
| 910 | c2 = C2#numpy.std(spc) |
|
910 | c2 = C2#numpy.std(spc) | |
| 911 | d = D |
|
911 | d = D | |
| 912 |
|
912 | |||
| 913 | y_clean = signal.medfilt(y) |
|
913 | y_clean = signal.medfilt(y) | |
| 914 | # define a least squares function to optimize |
|
914 | # define a least squares function to optimize | |
| 915 | def minfunc(params): |
|
915 | def minfunc(params): | |
| 916 | 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)) |
|
916 | 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)) | |
| 917 |
|
917 | |||
| 918 | # fit |
|
918 | # fit | |
| 919 | popt_full = fmin(minfunc,[a1,b1,c1,a2,b2,c2,d], disp =False, full_output=True) |
|
919 | popt_full = fmin(minfunc,[a1,b1,c1,a2,b2,c2,d], disp =False, full_output=True) | |
| 920 | #print("nIter", popt_full[2]) |
|
920 | #print("nIter", popt_full[2]) | |
| 921 | popt = popt_full[0] |
|
921 | popt = popt_full[0] | |
| 922 | #popt,pcov = curve_fit(double_gaussian,x,y,p0=[a1,b1,c1,a2,b2,c2,d]) |
|
922 | #popt,pcov = curve_fit(double_gaussian,x,y,p0=[a1,b1,c1,a2,b2,c2,d]) | |
| 923 |
|
923 | |||
| 924 | #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] |
|
924 | #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] | |
| 925 | return popt[0], popt[1], popt[2], popt[3], popt[4], popt[5], popt[6] |
|
925 | return popt[0], popt[1], popt[2], popt[3], popt[4], popt[5], popt[6] | |
| 926 |
|
926 | |||
| 927 | def DH_mode(self,spectra,VelRange): |
|
927 | def DH_mode(self,spectra,VelRange): | |
| 928 |
|
928 | |||
| 929 | from scipy.optimize import curve_fit |
|
929 | from scipy.optimize import curve_fit | |
| 930 |
|
930 | |||
| 931 | def double_gauss(x, a1,b1,c1, a2,b2,c2, d): |
|
931 | def double_gauss(x, a1,b1,c1, a2,b2,c2, d): | |
| 932 | 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 | |
| 933 | return val |
|
933 | return val | |
| 934 |
|
934 | |||
| 935 | spec = (spectra.copy()).flatten() |
|
935 | spec = (spectra.copy()).flatten() | |
| 936 | amp=spec.max() |
|
936 | amp=spec.max() | |
| 937 | params=numpy.array([amp,-400,30,amp/4,-200,150,1.0e7]) |
|
937 | params=numpy.array([amp,-400,30,amp/4,-200,150,1.0e7]) | |
| 938 | #try: |
|
938 | #try: | |
| 939 | 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])) |
|
939 | 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])) | |
| 940 |
|
940 | |||
| 941 | error = numpy.sqrt(numpy.diag(pcov)) |
|
941 | error = numpy.sqrt(numpy.diag(pcov)) | |
| 942 | #doppler_2=popt[4] |
|
942 | #doppler_2=popt[4] | |
| 943 | #err_2 = numpy.sqrt(pcov[4][4]) |
|
943 | #err_2 = numpy.sqrt(pcov[4][4]) | |
| 944 |
|
944 | |||
| 945 | #except: |
|
945 | #except: | |
| 946 | #pass |
|
946 | #pass | |
| 947 | #doppler_2=numpy.NAN |
|
947 | #doppler_2=numpy.NAN | |
| 948 | #err_2 = numpy.NAN |
|
948 | #err_2 = numpy.NAN | |
| 949 |
|
949 | |||
| 950 | #return doppler_2, err_2 |
|
950 | #return doppler_2, err_2 | |
| 951 |
|
951 | |||
| 952 | 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] |
|
952 | 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] | |
| 953 |
|
953 | |||
| 954 | def Tri_Marco(self,spc,freq,a1,b1,c1,a2,b2,c2,d): |
|
954 | def Tri_Marco(self,spc,freq,a1,b1,c1,a2,b2,c2,d): | |
| 955 |
|
955 | |||
| 956 | from scipy.optimize import least_squares |
|
956 | from scipy.optimize import least_squares | |
| 957 |
|
957 | |||
| 958 | freq_max = numpy.max(numpy.abs(freq)) |
|
958 | freq_max = numpy.max(numpy.abs(freq)) | |
| 959 | spc_max = numpy.max(spc) |
|
959 | spc_max = numpy.max(spc) | |
| 960 |
|
960 | |||
| 961 | def tri_gaussian(x, a1, b1, c1, a2, b2, c2, a3, b3, c3, d): |
|
961 | def tri_gaussian(x, a1, b1, c1, a2, b2, c2, a3, b3, c3, d): | |
| 962 | z1 = (x-b1)/c1 |
|
962 | z1 = (x-b1)/c1 | |
| 963 | z2 = (x-b2)/c2 |
|
963 | z2 = (x-b2)/c2 | |
| 964 | z3 = (x-b3)/c3 |
|
964 | z3 = (x-b3)/c3 | |
| 965 | val = a1 * numpy.exp(-z1**2/2) + a2 * numpy.exp(-z2**2/2) + a3 * numpy.exp(-z3**2/2) + d |
|
965 | val = a1 * numpy.exp(-z1**2/2) + a2 * numpy.exp(-z2**2/2) + a3 * numpy.exp(-z3**2/2) + d | |
| 966 | return val |
|
966 | return val | |
| 967 |
|
967 | |||
| 968 | from scipy.signal import medfilt |
|
968 | from scipy.signal import medfilt | |
| 969 | Nincoh = 20 |
|
969 | Nincoh = 20 | |
| 970 | spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) |
|
970 | spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) | |
| 971 | c1 = abs(c1) |
|
971 | c1 = abs(c1) | |
| 972 | c2 = abs(c2) |
|
972 | c2 = abs(c2) | |
| 973 |
|
973 | |||
| 974 | # define a least squares function to optimize |
|
974 | # define a least squares function to optimize | |
| 975 | def lsq_func(params): |
|
975 | def lsq_func(params): | |
| 976 | 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 |
|
976 | 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 | |
| 977 |
|
977 | |||
| 978 | # fit |
|
978 | # fit | |
| 979 | 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]) |
|
979 | 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]) | |
| 980 |
|
980 | |||
| 981 | params_scale = [spc_max,freq_max,freq_max,spc_max,freq_max,freq_max,spc_max,freq_max,freq_max,spc_max] |
|
981 | params_scale = [spc_max,freq_max,freq_max,spc_max,freq_max,freq_max,spc_max,freq_max,freq_max,spc_max] | |
| 982 | #print(a1,b1,c1,a2,b2,c2,d) |
|
982 | #print(a1,b1,c1,a2,b2,c2,d) | |
| 983 | popt = least_squares(lsq_func,[a1,b1,c1,a2,b2,c2,a2/4,-b1,c1,d],x_scale=params_scale,bounds=bounds) |
|
983 | popt = least_squares(lsq_func,[a1,b1,c1,a2,b2,c2,a2/4,-b1,c1,d],x_scale=params_scale,bounds=bounds) | |
| 984 |
|
984 | |||
| 985 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2] |
|
985 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2] | |
| 986 | A2f = popt.x[3]; B2f = popt.x[4]; C2f = popt.x[5] |
|
986 | A2f = popt.x[3]; B2f = popt.x[4]; C2f = popt.x[5] | |
| 987 | A3f = popt.x[6]; B3f = popt.x[7]; C3f = popt.x[8] |
|
987 | A3f = popt.x[6]; B3f = popt.x[7]; C3f = popt.x[8] | |
| 988 | Df = popt.x[9] |
|
988 | Df = popt.x[9] | |
| 989 |
|
989 | |||
| 990 | return A1f, B1f, C1f, A2f, B2f, C2f, Df |
|
990 | return A1f, B1f, C1f, A2f, B2f, C2f, Df | |
| 991 |
|
991 | |||
| 992 | def Tri_Marco(self,spc,freq,a1,b1,c1,a2,b2,c2,d): |
|
992 | def Tri_Marco(self,spc,freq,a1,b1,c1,a2,b2,c2,d): | |
| 993 |
|
993 | |||
| 994 | from scipy.optimize import least_squares |
|
994 | from scipy.optimize import least_squares | |
| 995 |
|
995 | |||
| 996 | freq_max = numpy.max(numpy.abs(freq)) |
|
996 | freq_max = numpy.max(numpy.abs(freq)) | |
| 997 | spc_max = numpy.max(spc) |
|
997 | spc_max = numpy.max(spc) | |
| 998 |
|
998 | |||
| 999 | def duo_gaussian(x, a1, b1, c1, a2, b2, c2, d): |
|
999 | def duo_gaussian(x, a1, b1, c1, a2, b2, c2, d): | |
| 1000 | z1 = (x-b1)/c1 |
|
1000 | z1 = (x-b1)/c1 | |
| 1001 | z2 = (x-b2)/c2 |
|
1001 | z2 = (x-b2)/c2 | |
| 1002 | #z3 = (x-b3)/c3 |
|
1002 | #z3 = (x-b3)/c3 | |
| 1003 | val = a1 * numpy.exp(-z1**2/2) + a2 * numpy.exp(-z2**2/2) + d |
|
1003 | val = a1 * numpy.exp(-z1**2/2) + a2 * numpy.exp(-z2**2/2) + d | |
| 1004 | return val |
|
1004 | return val | |
| 1005 |
|
1005 | |||
| 1006 | from scipy.signal import medfilt |
|
1006 | from scipy.signal import medfilt | |
| 1007 | Nincoh = 20 |
|
1007 | Nincoh = 20 | |
| 1008 | spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) |
|
1008 | spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) | |
| 1009 | c1 = abs(c1) |
|
1009 | c1 = abs(c1) | |
| 1010 | c2 = abs(c2) |
|
1010 | c2 = abs(c2) | |
| 1011 |
|
1011 | |||
| 1012 | # define a least squares function to optimize |
|
1012 | # define a least squares function to optimize | |
| 1013 | def lsq_func(params): |
|
1013 | def lsq_func(params): | |
| 1014 | return (spc-tri_gaussian(freq,params[0],params[1],params[2],params[3],params[4],params[5],params[6]))/spcm |
|
1014 | return (spc-tri_gaussian(freq,params[0],params[1],params[2],params[3],params[4],params[5],params[6]))/spcm | |
| 1015 |
|
1015 | |||
| 1016 | # fit |
|
1016 | # fit | |
| 1017 | bounds=([0,-numpy.inf,0,0,-numpy.inf,0,0],[numpy.inf,-100,numpy.inf,numpy.inf,0,numpy.inf,numpy.inf]) |
|
1017 | bounds=([0,-numpy.inf,0,0,-numpy.inf,0,0],[numpy.inf,-100,numpy.inf,numpy.inf,0,numpy.inf,numpy.inf]) | |
| 1018 |
|
1018 | |||
| 1019 | params_scale = [spc_max,freq_max,freq_max,spc_max,freq_max,freq_max,spc_max] |
|
1019 | params_scale = [spc_max,freq_max,freq_max,spc_max,freq_max,freq_max,spc_max] | |
| 1020 | popt = least_squares(lsq_func,[a1,b1,c1,a2,b2,c2,d],x_scale=params_scale,bounds=bounds) |
|
1020 | popt = least_squares(lsq_func,[a1,b1,c1,a2,b2,c2,d],x_scale=params_scale,bounds=bounds) | |
| 1021 |
|
1021 | |||
| 1022 | 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] | |
| 1023 | 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] | |
| 1024 | #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] | |
| 1025 | Df = popt.x[9] |
|
1025 | Df = popt.x[9] | |
| 1026 |
|
1026 | |||
| 1027 | return A1f, B1f, C1f, A2f, B2f, C2f, Df |
|
1027 | return A1f, B1f, C1f, A2f, B2f, C2f, Df | |
| 1028 |
|
1028 | |||
| 1029 | def double_gaussian_skew(self,x, a1, b1, c1, a2, b2, c2, k2, d): |
|
1029 | def double_gaussian_skew(self,x, a1, b1, c1, a2, b2, c2, k2, d): | |
| 1030 | z1 = (x-b1)/c1 |
|
1030 | z1 = (x-b1)/c1 | |
| 1031 | z2 = (x-b2)/c2 |
|
1031 | z2 = (x-b2)/c2 | |
| 1032 | h2 = 1-k2*z2 |
|
1032 | h2 = 1-k2*z2 | |
| 1033 | h2[h2<0] = 0 |
|
1033 | h2[h2<0] = 0 | |
| 1034 | y2 = -1/k2*numpy.log(h2) |
|
1034 | y2 = -1/k2*numpy.log(h2) | |
| 1035 | val = a1 * numpy.exp(-z1**2/2) + a2 * numpy.exp(-y2**2/2)/(1-k2*z2) + d |
|
1035 | val = a1 * numpy.exp(-z1**2/2) + a2 * numpy.exp(-y2**2/2)/(1-k2*z2) + d | |
| 1036 | return val |
|
1036 | return val | |
| 1037 |
|
1037 | |||
| 1038 | def gaussian(self, x, a, b, c, d): |
|
1038 | def gaussian(self, x, a, b, c, d): | |
| 1039 | z = (x-b)/c |
|
1039 | z = (x-b)/c | |
| 1040 | val = a * numpy.exp(-z**2/2) + d |
|
1040 | val = a * numpy.exp(-z**2/2) + d | |
| 1041 | return val |
|
1041 | return val | |
| 1042 |
|
1042 | |||
| 1043 | def double_gaussian(self, x, a1, b1, c1, a2, b2, c2, d): |
|
1043 | def double_gaussian(self, x, a1, b1, c1, a2, b2, c2, d): | |
| 1044 | z1 = (x-b1)/c1 |
|
1044 | z1 = (x-b1)/c1 | |
| 1045 | z2 = (x-b2)/c2 |
|
1045 | z2 = (x-b2)/c2 | |
| 1046 | val = a1 * numpy.exp(-z1**2/2) + a2 * numpy.exp(-z2**2/2) + d |
|
1046 | val = a1 * numpy.exp(-z1**2/2) + a2 * numpy.exp(-z2**2/2) + d | |
| 1047 | return val |
|
1047 | return val | |
| 1048 |
|
1048 | |||
| 1049 | def double_gaussian_double_skew(self,x, a1, b1, c1, k1, a2, b2, c2, k2, d): |
|
1049 | def double_gaussian_double_skew(self,x, a1, b1, c1, k1, a2, b2, c2, k2, d): | |
| 1050 |
|
1050 | |||
| 1051 | z1 = (x-b1)/c1 |
|
1051 | z1 = (x-b1)/c1 | |
| 1052 | h1 = 1-k1*z1 |
|
1052 | h1 = 1-k1*z1 | |
| 1053 | h1[h1<0] = 0 |
|
1053 | h1[h1<0] = 0 | |
| 1054 | y1 = -1/k1*numpy.log(h1) |
|
1054 | y1 = -1/k1*numpy.log(h1) | |
| 1055 |
|
1055 | |||
| 1056 | z2 = (x-b2)/c2 |
|
1056 | z2 = (x-b2)/c2 | |
| 1057 | h2 = 1-k2*z2 |
|
1057 | h2 = 1-k2*z2 | |
| 1058 | h2[h2<0] = 0 |
|
1058 | h2[h2<0] = 0 | |
| 1059 | y2 = -1/k2*numpy.log(h2) |
|
1059 | y2 = -1/k2*numpy.log(h2) | |
| 1060 |
|
1060 | |||
| 1061 | val = a1 * numpy.exp(-y1**2/2)/(1-k1*z1) + a2 * numpy.exp(-y2**2/2)/(1-k2*z2) + d |
|
1061 | val = a1 * numpy.exp(-y1**2/2)/(1-k1*z1) + a2 * numpy.exp(-y2**2/2)/(1-k2*z2) + d | |
| 1062 | return val |
|
1062 | return val | |
| 1063 |
|
1063 | |||
| 1064 | def gaussian_skew(self,x, a2, b2, c2, k2, d): |
|
1064 | def gaussian_skew(self,x, a2, b2, c2, k2, d): | |
| 1065 | z2 = (x-b2)/c2 |
|
1065 | z2 = (x-b2)/c2 | |
| 1066 | h2 = 1-k2*z2 |
|
1066 | h2 = 1-k2*z2 | |
| 1067 | h2[h2<0] = 0 |
|
1067 | h2[h2<0] = 0 | |
| 1068 | y2 = -1/k2*numpy.log(h2) |
|
1068 | y2 = -1/k2*numpy.log(h2) | |
| 1069 | val = a2 * numpy.exp(-y2**2/2)/(1-k2*z2) + d |
|
1069 | val = a2 * numpy.exp(-y2**2/2)/(1-k2*z2) + d | |
| 1070 | return val |
|
1070 | return val | |
| 1071 |
|
1071 | |||
| 1072 | def triple_gaussian_skew(self,x, a1, b1, c1, a2, b2, c2, k2, a3, b3, c3, k3, d): |
|
1072 | def triple_gaussian_skew(self,x, a1, b1, c1, a2, b2, c2, k2, a3, b3, c3, k3, d): | |
| 1073 | z1 = (x-b1)/c1 |
|
1073 | z1 = (x-b1)/c1 | |
| 1074 | z2 = (x-b2)/c2 |
|
1074 | z2 = (x-b2)/c2 | |
| 1075 | z3 = (x-b3)/c3 |
|
1075 | z3 = (x-b3)/c3 | |
| 1076 | h2 = 1-k2*z2 |
|
1076 | h2 = 1-k2*z2 | |
| 1077 | h2[h2<0] = 0 |
|
1077 | h2[h2<0] = 0 | |
| 1078 | y2 = -1/k2*numpy.log(h2) |
|
1078 | y2 = -1/k2*numpy.log(h2) | |
| 1079 | h3 = 1-k3*z3 |
|
1079 | h3 = 1-k3*z3 | |
| 1080 | h3[h3<0] = 0 |
|
1080 | h3[h3<0] = 0 | |
| 1081 | y3 = -1/k3*numpy.log(h3) |
|
1081 | y3 = -1/k3*numpy.log(h3) | |
| 1082 | 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 |
|
1082 | 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 | |
| 1083 | return val |
|
1083 | return val | |
| 1084 |
|
1084 | |||
| 1085 | def Double_Gauss_Skew_fit_weight_bound_no_inputs(self,spc,freq): |
|
1085 | def Double_Gauss_Skew_fit_weight_bound_no_inputs(self,spc,freq): | |
| 1086 |
|
1086 | |||
| 1087 | from scipy.optimize import least_squares |
|
1087 | from scipy.optimize import least_squares | |
| 1088 |
|
1088 | |||
| 1089 | freq_max = numpy.max(numpy.abs(freq)) |
|
1089 | freq_max = numpy.max(numpy.abs(freq)) | |
| 1090 | spc_max = numpy.max(spc) |
|
1090 | spc_max = numpy.max(spc) | |
| 1091 |
|
1091 | |||
| 1092 | from scipy.signal import medfilt |
|
1092 | from scipy.signal import medfilt | |
| 1093 | Nincoh = 20 |
|
1093 | Nincoh = 20 | |
| 1094 | spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) |
|
1094 | spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) | |
| 1095 |
|
1095 | |||
| 1096 | # define a least squares function to optimize |
|
1096 | # define a least squares function to optimize | |
| 1097 | def lsq_func(params): |
|
1097 | def lsq_func(params): | |
| 1098 | return (spc-self.double_gaussian_skew(freq,params[0],params[1],params[2],params[3],params[4],params[5],params[6],params[7]))/spcm |
|
1098 | return (spc-self.double_gaussian_skew(freq,params[0],params[1],params[2],params[3],params[4],params[5],params[6],params[7]))/spcm | |
| 1099 |
|
1099 | |||
| 1100 | # fit |
|
1100 | # fit | |
| 1101 | bounds=([0,-numpy.inf,0,0,-400,0,0,0],[numpy.inf,-340,numpy.inf,numpy.inf,0,numpy.inf,numpy.inf,numpy.inf]) |
|
1101 | bounds=([0,-numpy.inf,0,0,-400,0,0,0],[numpy.inf,-340,numpy.inf,numpy.inf,0,numpy.inf,numpy.inf,numpy.inf]) | |
| 1102 |
|
1102 | |||
| 1103 | params_scale = [spc_max,freq_max,freq_max,spc_max,freq_max,freq_max,1,spc_max] |
|
1103 | params_scale = [spc_max,freq_max,freq_max,spc_max,freq_max,freq_max,1,spc_max] | |
| 1104 | x0_value = numpy.array([spc_max,-400,30,spc_max/4,-200,150,1,1.0e7]) |
|
1104 | x0_value = numpy.array([spc_max,-400,30,spc_max/4,-200,150,1,1.0e7]) | |
| 1105 | popt = least_squares(lsq_func,x0=x0_value,x_scale=params_scale,bounds=bounds,verbose=0) |
|
1105 | popt = least_squares(lsq_func,x0=x0_value,x_scale=params_scale,bounds=bounds,verbose=0) | |
| 1106 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2] |
|
1106 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2] | |
| 1107 | A2f = popt.x[3]; B2f = popt.x[4]; C2f = popt.x[5]; K2f = popt.x[6] |
|
1107 | A2f = popt.x[3]; B2f = popt.x[4]; C2f = popt.x[5]; K2f = popt.x[6] | |
| 1108 | Df = popt.x[7] |
|
1108 | Df = popt.x[7] | |
| 1109 |
|
1109 | |||
| 1110 | aux = self.gaussian_skew(freq, A2f, B2f, C2f, K2f, Df) |
|
1110 | aux = self.gaussian_skew(freq, A2f, B2f, C2f, K2f, Df) | |
| 1111 | doppler = freq[numpy.argmax(aux)] |
|
1111 | doppler = freq[numpy.argmax(aux)] | |
| 1112 |
|
1112 | |||
| 1113 | return A1f, B1f, C1f, A2f, B2f, C2f, K2f, Df, doppler |
|
1113 | return A1f, B1f, C1f, A2f, B2f, C2f, K2f, Df, doppler | |
| 1114 |
|
1114 | |||
| 1115 | def Double_Gauss_Double_Skew_fit_weight_bound_no_inputs(self,spc,freq,Nincoh,hei): |
|
1115 | def Double_Gauss_Double_Skew_fit_weight_bound_no_inputs(self,spc,freq,Nincoh,hei): | |
| 1116 |
|
1116 | |||
| 1117 | from scipy.optimize import least_squares |
|
1117 | from scipy.optimize import least_squares | |
| 1118 |
|
1118 | |||
| 1119 | freq_max = numpy.max(numpy.abs(freq)) |
|
1119 | freq_max = numpy.max(numpy.abs(freq)) | |
| 1120 | spc_max = numpy.max(spc) |
|
1120 | spc_max = numpy.max(spc) | |
| 1121 |
|
1121 | |||
| 1122 | #from scipy.signal import medfilt |
|
1122 | #from scipy.signal import medfilt | |
| 1123 | #Nincoh = 20 |
|
1123 | #Nincoh = 20 | |
| 1124 | #Nincoh = 80 |
|
1124 | #Nincoh = 80 | |
| 1125 | Nincoh = Nincoh |
|
1125 | Nincoh = Nincoh | |
| 1126 | #spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) |
|
1126 | #spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) | |
| 1127 | spcm = spc/numpy.sqrt(Nincoh) |
|
1127 | spcm = spc/numpy.sqrt(Nincoh) | |
| 1128 |
|
1128 | |||
| 1129 | # define a least squares function to optimize |
|
1129 | # define a least squares function to optimize | |
| 1130 | def lsq_func(params): |
|
1130 | def lsq_func(params): | |
| 1131 | 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 |
|
1131 | 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 | |
| 1132 |
|
1132 | |||
| 1133 | # fit |
|
1133 | # fit | |
| 1134 | 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]) |
|
1134 | 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]) | |
| 1135 |
|
1135 | |||
| 1136 | params_scale = [spc_max,freq_max,freq_max,1,spc_max,freq_max,freq_max,1,spc_max] |
|
1136 | params_scale = [spc_max,freq_max,freq_max,1,spc_max,freq_max,freq_max,1,spc_max] | |
| 1137 |
|
1137 | |||
| 1138 | dop1_x0 = freq[numpy.argmax(spc)] |
|
1138 | dop1_x0 = freq[numpy.argmax(spc)] | |
| 1139 | if dop1_x0 < 0: |
|
1139 | if dop1_x0 < 0: | |
| 1140 | dop2_x0 = dop1_x0 + 100 |
|
1140 | dop2_x0 = dop1_x0 + 100 | |
| 1141 | if dop1_x0 > 0: |
|
1141 | if dop1_x0 > 0: | |
| 1142 | dop2_x0 = dop1_x0 - 100 |
|
1142 | dop2_x0 = dop1_x0 - 100 | |
| 1143 |
|
1143 | |||
| 1144 | x0_value = numpy.array([spc_max,dop1_x0,30,-.1,spc_max/4, dop2_x0,150,1,1.0e7]) |
|
1144 | x0_value = numpy.array([spc_max,dop1_x0,30,-.1,spc_max/4, dop2_x0,150,1,1.0e7]) | |
| 1145 | popt = least_squares(lsq_func,x0=x0_value,x_scale=params_scale,bounds=bounds,verbose=0) |
|
1145 | popt = least_squares(lsq_func,x0=x0_value,x_scale=params_scale,bounds=bounds,verbose=0) | |
| 1146 | J = popt.jac |
|
1146 | J = popt.jac | |
| 1147 |
|
1147 | |||
| 1148 | try: |
|
1148 | try: | |
| 1149 | cov = numpy.linalg.inv(J.T.dot(J)) |
|
1149 | cov = numpy.linalg.inv(J.T.dot(J)) | |
| 1150 | error = numpy.sqrt(numpy.diagonal(cov)) |
|
1150 | error = numpy.sqrt(numpy.diagonal(cov)) | |
| 1151 | except: |
|
1151 | except: | |
| 1152 | error = numpy.ones((9))*numpy.NAN |
|
1152 | error = numpy.ones((9))*numpy.NAN | |
| 1153 |
|
1153 | |||
| 1154 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2]; K1f = popt.x[3] |
|
1154 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2]; K1f = popt.x[3] | |
| 1155 | A2f = popt.x[4]; B2f = popt.x[5]; C2f = popt.x[6]; K2f = popt.x[7] |
|
1155 | A2f = popt.x[4]; B2f = popt.x[5]; C2f = popt.x[6]; K2f = popt.x[7] | |
| 1156 | Df = popt.x[8] |
|
1156 | Df = popt.x[8] | |
| 1157 | aux1 = self.gaussian_skew(freq, A1f, B1f, C1f, K1f, Df) |
|
1157 | aux1 = self.gaussian_skew(freq, A1f, B1f, C1f, K1f, Df) | |
| 1158 | doppler1 = freq[numpy.argmax(aux1)] |
|
1158 | doppler1 = freq[numpy.argmax(aux1)] | |
| 1159 |
|
1159 | |||
| 1160 | aux2 = self.gaussian_skew(freq, A2f, B2f, C2f, K2f, Df) |
|
1160 | aux2 = self.gaussian_skew(freq, A2f, B2f, C2f, K2f, Df) | |
| 1161 | doppler2 = freq[numpy.argmax(aux2)] |
|
1161 | doppler2 = freq[numpy.argmax(aux2)] | |
| 1162 | #print("error",error) |
|
1162 | #print("error",error) | |
| 1163 | #exit(1) |
|
1163 | #exit(1) | |
| 1164 |
|
1164 | |||
| 1165 |
|
1165 | |||
| 1166 | return A1f, B1f, C1f, K1f, A2f, B2f, C2f, K2f, Df, doppler1, doppler2, error |
|
1166 | return A1f, B1f, C1f, K1f, A2f, B2f, C2f, K2f, Df, doppler1, doppler2, error | |
| 1167 |
|
1167 | |||
| 1168 | def Double_Gauss_fit_weight_bound_no_inputs(self,spc,freq,Nincoh): |
|
1168 | def Double_Gauss_fit_weight_bound_no_inputs(self,spc,freq,Nincoh): | |
| 1169 |
|
1169 | |||
| 1170 | from scipy.optimize import least_squares |
|
1170 | from scipy.optimize import least_squares | |
| 1171 |
|
1171 | |||
| 1172 | freq_max = numpy.max(numpy.abs(freq)) |
|
1172 | freq_max = numpy.max(numpy.abs(freq)) | |
| 1173 | spc_max = numpy.max(spc) |
|
1173 | spc_max = numpy.max(spc) | |
| 1174 |
|
1174 | |||
| 1175 | from scipy.signal import medfilt |
|
1175 | from scipy.signal import medfilt | |
| 1176 | Nincoh = 20 |
|
1176 | Nincoh = 20 | |
| 1177 | Nincoh = 80 |
|
1177 | Nincoh = 80 | |
| 1178 | Nincoh = Nincoh |
|
1178 | Nincoh = Nincoh | |
| 1179 | spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) |
|
1179 | spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) | |
| 1180 |
|
1180 | |||
| 1181 | # define a least squares function to optimize |
|
1181 | # define a least squares function to optimize | |
| 1182 | def lsq_func(params): |
|
1182 | def lsq_func(params): | |
| 1183 | return (spc-self.double_gaussian(freq,params[0],params[1],params[2],params[3],params[4],params[5],params[6]))/spcm |
|
1183 | return (spc-self.double_gaussian(freq,params[0],params[1],params[2],params[3],params[4],params[5],params[6]))/spcm | |
| 1184 |
|
1184 | |||
| 1185 | # fit |
|
1185 | # fit | |
| 1186 | # bounds=([0,-460,0,0,-400,120,0],[numpy.inf,-340,50,numpy.inf,0,250,numpy.inf]) |
|
1186 | # bounds=([0,-460,0,0,-400,120,0],[numpy.inf,-340,50,numpy.inf,0,250,numpy.inf]) | |
| 1187 | # 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]) |
|
1187 | # 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]) | |
| 1188 | #print(a1,b1,c1,a2,b2,c2,k2,d) |
|
1188 | #print(a1,b1,c1,a2,b2,c2,k2,d) | |
| 1189 |
|
1189 | |||
| 1190 | dop1_x0 = freq[numpy.argmax(spcm)] |
|
1190 | dop1_x0 = freq[numpy.argmax(spcm)] | |
| 1191 |
|
1191 | |||
| 1192 | bounds=([0,-numpy.inf,0,0,dop1_x0-50,0,0],[numpy.inf,-300,numpy.inf,numpy.inf,0,numpy.inf,numpy.inf]) |
|
1192 | bounds=([0,-numpy.inf,0,0,dop1_x0-50,0,0],[numpy.inf,-300,numpy.inf,numpy.inf,0,numpy.inf,numpy.inf]) | |
| 1193 | params_scale = [spc_max,freq_max,freq_max,spc_max,freq_max,freq_max,spc_max] |
|
1193 | params_scale = [spc_max,freq_max,freq_max,spc_max,freq_max,freq_max,spc_max] | |
| 1194 | x0_value = numpy.array([spc_max,-400.5,30,spc_max/4,dop1_x0,150,1.0e7]) |
|
1194 | x0_value = numpy.array([spc_max,-400.5,30,spc_max/4,dop1_x0,150,1.0e7]) | |
| 1195 | popt = least_squares(lsq_func,x0=x0_value,x_scale=params_scale,bounds=bounds,verbose=0) |
|
1195 | popt = least_squares(lsq_func,x0=x0_value,x_scale=params_scale,bounds=bounds,verbose=0) | |
| 1196 | J = popt.jac |
|
1196 | J = popt.jac | |
| 1197 |
|
1197 | |||
| 1198 | try: |
|
1198 | try: | |
| 1199 | cov = numpy.linalg.inv(J.T.dot(J)) |
|
1199 | cov = numpy.linalg.inv(J.T.dot(J)) | |
| 1200 | error = numpy.sqrt(numpy.diagonal(cov)) |
|
1200 | error = numpy.sqrt(numpy.diagonal(cov)) | |
| 1201 | except: |
|
1201 | except: | |
| 1202 | error = numpy.ones((7))*numpy.NAN |
|
1202 | error = numpy.ones((7))*numpy.NAN | |
| 1203 |
|
1203 | |||
| 1204 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2] |
|
1204 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2] | |
| 1205 | A2f = popt.x[3]; B2f = popt.x[4]; C2f = popt.x[5] |
|
1205 | A2f = popt.x[3]; B2f = popt.x[4]; C2f = popt.x[5] | |
| 1206 | Df = popt.x[6] |
|
1206 | Df = popt.x[6] | |
| 1207 | return A1f, B1f, C1f, A2f, B2f, C2f, Df, error |
|
1207 | return A1f, B1f, C1f, A2f, B2f, C2f, Df, error | |
| 1208 |
|
1208 | |||
| 1209 | def Double_Gauss_Double_Skew_fit_weight_bound_with_inputs(self, spc, freq, a1, b1, c1, a2, b2, c2, k2, d): |
|
1209 | def Double_Gauss_Double_Skew_fit_weight_bound_with_inputs(self, spc, freq, a1, b1, c1, a2, b2, c2, k2, d): | |
| 1210 |
|
1210 | |||
| 1211 | from scipy.optimize import least_squares |
|
1211 | from scipy.optimize import least_squares | |
| 1212 |
|
1212 | |||
| 1213 | freq_max = numpy.max(numpy.abs(freq)) |
|
1213 | freq_max = numpy.max(numpy.abs(freq)) | |
| 1214 | spc_max = numpy.max(spc) |
|
1214 | spc_max = numpy.max(spc) | |
| 1215 |
|
1215 | |||
| 1216 | from scipy.signal import medfilt |
|
1216 | from scipy.signal import medfilt | |
| 1217 | Nincoh = dataOut.nIncohInt |
|
1217 | Nincoh = dataOut.nIncohInt | |
| 1218 | spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) |
|
1218 | spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) | |
| 1219 |
|
1219 | |||
| 1220 | # define a least squares function to optimize |
|
1220 | # define a least squares function to optimize | |
| 1221 | def lsq_func(params): |
|
1221 | def lsq_func(params): | |
| 1222 | 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 |
|
1222 | 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 | |
| 1223 |
|
1223 | |||
| 1224 |
|
1224 | |||
| 1225 | 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]) |
|
1225 | 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]) | |
| 1226 |
|
1226 | |||
| 1227 | params_scale = [spc_max,freq_max,freq_max,1,spc_max,freq_max,freq_max,1,spc_max] |
|
1227 | params_scale = [spc_max,freq_max,freq_max,1,spc_max,freq_max,freq_max,1,spc_max] | |
| 1228 |
|
1228 | |||
| 1229 | x0_value = numpy.array([a1,b1,c1,-.1,a2,b2,c2,k2,d]) |
|
1229 | x0_value = numpy.array([a1,b1,c1,-.1,a2,b2,c2,k2,d]) | |
| 1230 |
|
1230 | |||
| 1231 | 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) | |
| 1232 |
|
1232 | |||
| 1233 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2]; K1f = popt.x[3] |
|
1233 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2]; K1f = popt.x[3] | |
| 1234 | A2f = popt.x[4]; B2f = popt.x[5]; C2f = popt.x[6]; K2f = popt.x[7] |
|
1234 | A2f = popt.x[4]; B2f = popt.x[5]; C2f = popt.x[6]; K2f = popt.x[7] | |
| 1235 | Df = popt.x[8] |
|
1235 | Df = popt.x[8] | |
| 1236 |
|
1236 | |||
| 1237 | aux = self.gaussian_skew(freq, A2f, B2f, C2f, K2f, Df) |
|
1237 | aux = self.gaussian_skew(freq, A2f, B2f, C2f, K2f, Df) | |
| 1238 | doppler = x[numpy.argmax(aux)] |
|
1238 | doppler = x[numpy.argmax(aux)] | |
| 1239 |
|
1239 | |||
| 1240 | return A1f, B1f, C1f, K1f, A2f, B2f, C2f, K2f, Df, doppler |
|
1240 | return A1f, B1f, C1f, K1f, A2f, B2f, C2f, K2f, Df, doppler | |
| 1241 |
|
1241 | |||
| 1242 | def Triple_Gauss_Skew_fit_weight_bound_no_inputs(self,spc,freq): |
|
1242 | def Triple_Gauss_Skew_fit_weight_bound_no_inputs(self,spc,freq): | |
| 1243 |
|
1243 | |||
| 1244 | from scipy.optimize import least_squares |
|
1244 | from scipy.optimize import least_squares | |
| 1245 |
|
1245 | |||
| 1246 | freq_max = numpy.max(numpy.abs(freq)) |
|
1246 | freq_max = numpy.max(numpy.abs(freq)) | |
| 1247 | spc_max = numpy.max(spc) |
|
1247 | spc_max = numpy.max(spc) | |
| 1248 |
|
1248 | |||
| 1249 | from scipy.signal import medfilt |
|
1249 | from scipy.signal import medfilt | |
| 1250 | Nincoh = 20 |
|
1250 | Nincoh = 20 | |
| 1251 | spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) |
|
1251 | spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) | |
| 1252 |
|
1252 | |||
| 1253 | # define a least squares function to optimize |
|
1253 | # define a least squares function to optimize | |
| 1254 | def lsq_func(params): |
|
1254 | def lsq_func(params): | |
| 1255 | 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 |
|
1255 | 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 | |
| 1256 |
|
1256 | |||
| 1257 | # fit |
|
1257 | # fit | |
| 1258 | 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]) |
|
1258 | 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]) | |
| 1259 |
|
1259 | |||
| 1260 | params_scale = [spc_max,freq_max,freq_max,spc_max,freq_max,freq_max,1,spc_max,freq_max,freq_max,1,spc_max] |
|
1260 | params_scale = [spc_max,freq_max,freq_max,spc_max,freq_max,freq_max,1,spc_max,freq_max,freq_max,1,spc_max] | |
| 1261 | x0_value = numpy.array([spc_max,-400,30,spc_max/4,-200,150,1,spc_max/4,400,150,1,1.0e7]) |
|
1261 | x0_value = numpy.array([spc_max,-400,30,spc_max/4,-200,150,1,spc_max/4,400,150,1,1.0e7]) | |
| 1262 | popt = least_squares(lsq_func,x0=x0_value,x_scale=params_scale,bounds=bounds,verbose=0) |
|
1262 | popt = least_squares(lsq_func,x0=x0_value,x_scale=params_scale,bounds=bounds,verbose=0) | |
| 1263 |
|
1263 | |||
| 1264 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2] |
|
1264 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2] | |
| 1265 | A2f = popt.x[3]; B2f = popt.x[4]; C2f = popt.x[5]; K2f = popt.x[6] |
|
1265 | A2f = popt.x[3]; B2f = popt.x[4]; C2f = popt.x[5]; K2f = popt.x[6] | |
| 1266 | A3f = popt.x[7]; B3f = popt.x[8]; C3f = popt.x[9]; K3f = popt.x[10] |
|
1266 | A3f = popt.x[7]; B3f = popt.x[8]; C3f = popt.x[9]; K3f = popt.x[10] | |
| 1267 | Df = popt.x[11] |
|
1267 | Df = popt.x[11] | |
| 1268 |
|
1268 | |||
| 1269 | aux = self.gaussian_skew(freq, A2f, B2f, C2f, K2f, Df) |
|
1269 | aux = self.gaussian_skew(freq, A2f, B2f, C2f, K2f, Df) | |
| 1270 | doppler = freq[numpy.argmax(aux)] |
|
1270 | doppler = freq[numpy.argmax(aux)] | |
| 1271 |
|
1271 | |||
| 1272 | return A1f, B1f, C1f, A2f, B2f, C2f, K2f, A3f, B3f, C3f, K3f, Df, doppler |
|
1272 | return A1f, B1f, C1f, A2f, B2f, C2f, K2f, A3f, B3f, C3f, K3f, Df, doppler | |
| 1273 |
|
1273 | |||
| 1274 | def CEEJ_Skew_fit_weight_bound_no_inputs(self,spc,freq,Nincoh): |
|
1274 | def CEEJ_Skew_fit_weight_bound_no_inputs(self,spc,freq,Nincoh): | |
| 1275 |
|
1275 | |||
| 1276 | from scipy.optimize import least_squares |
|
1276 | from scipy.optimize import least_squares | |
| 1277 |
|
1277 | |||
| 1278 | freq_max = numpy.max(numpy.abs(freq)) |
|
1278 | freq_max = numpy.max(numpy.abs(freq)) | |
| 1279 | spc_max = numpy.max(spc) |
|
1279 | spc_max = numpy.max(spc) | |
| 1280 |
|
1280 | |||
| 1281 | from scipy.signal import medfilt |
|
1281 | from scipy.signal import medfilt | |
| 1282 | Nincoh = 20 |
|
1282 | Nincoh = 20 | |
| 1283 | Nincoh = 80 |
|
1283 | Nincoh = 80 | |
| 1284 | Nincoh = Nincoh |
|
1284 | Nincoh = Nincoh | |
| 1285 | spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) |
|
1285 | spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) | |
| 1286 |
|
1286 | |||
| 1287 | # define a least squares function to optimize |
|
1287 | # define a least squares function to optimize | |
| 1288 | def lsq_func(params): |
|
1288 | def lsq_func(params): | |
| 1289 | return (spc-self.gaussian_skew(freq,params[0],params[1],params[2],params[3],params[4]))#/spcm |
|
1289 | return (spc-self.gaussian_skew(freq,params[0],params[1],params[2],params[3],params[4]))#/spcm | |
| 1290 |
|
1290 | |||
| 1291 |
|
1291 | |||
| 1292 | bounds=([0,0,0,-numpy.inf,0],[numpy.inf,numpy.inf,numpy.inf,0,numpy.inf]) |
|
1292 | bounds=([0,0,0,-numpy.inf,0],[numpy.inf,numpy.inf,numpy.inf,0,numpy.inf]) | |
| 1293 |
|
1293 | |||
| 1294 | params_scale = [spc_max,freq_max,freq_max,1,spc_max] |
|
1294 | params_scale = [spc_max,freq_max,freq_max,1,spc_max] | |
| 1295 |
|
1295 | |||
| 1296 | x0_value = numpy.array([spc_max,freq[numpy.argmax(spc)],30,-.1,numpy.mean(spc[:50])]) |
|
1296 | x0_value = numpy.array([spc_max,freq[numpy.argmax(spc)],30,-.1,numpy.mean(spc[:50])]) | |
| 1297 |
|
1297 | |||
| 1298 | 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) | |
| 1299 |
|
1299 | |||
| 1300 | J = popt.jac |
|
1300 | J = popt.jac | |
| 1301 |
|
1301 | |||
| 1302 | try: |
|
1302 | try: | |
| 1303 | error = numpy.ones((9))*numpy.NAN |
|
1303 | error = numpy.ones((9))*numpy.NAN | |
| 1304 | cov = numpy.linalg.inv(J.T.dot(J)) |
|
1304 | cov = numpy.linalg.inv(J.T.dot(J)) | |
| 1305 | error[:4] = numpy.sqrt(numpy.diagonal(cov))[:4] |
|
1305 | error[:4] = numpy.sqrt(numpy.diagonal(cov))[:4] | |
| 1306 | error[-1] = numpy.sqrt(numpy.diagonal(cov))[-1] |
|
1306 | error[-1] = numpy.sqrt(numpy.diagonal(cov))[-1] | |
| 1307 | except: |
|
1307 | except: | |
| 1308 | error = numpy.ones((9))*numpy.NAN |
|
1308 | error = numpy.ones((9))*numpy.NAN | |
| 1309 |
|
1309 | |||
| 1310 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2]; K1f = popt.x[3] |
|
1310 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2]; K1f = popt.x[3] | |
| 1311 | Df = popt.x[4] |
|
1311 | Df = popt.x[4] | |
| 1312 |
|
1312 | |||
| 1313 | aux1 = self.gaussian_skew(freq, A1f, B1f, C1f, K1f, Df) |
|
1313 | aux1 = self.gaussian_skew(freq, A1f, B1f, C1f, K1f, Df) | |
| 1314 | doppler1 = freq[numpy.argmax(aux1)] |
|
1314 | doppler1 = freq[numpy.argmax(aux1)] | |
| 1315 | #print("CEEJ ERROR:",error) |
|
1315 | #print("CEEJ ERROR:",error) | |
| 1316 |
|
1316 | |||
| 1317 | return A1f, B1f, C1f, K1f, numpy.NAN, numpy.NAN, numpy.NAN, numpy.NAN, Df, doppler1, numpy.NAN, error |
|
1317 | return A1f, B1f, C1f, K1f, numpy.NAN, numpy.NAN, numpy.NAN, numpy.NAN, Df, doppler1, numpy.NAN, error | |
| 1318 |
|
1318 | |||
| 1319 | def CEEJ_fit_weight_bound_no_inputs(self,spc,freq,Nincoh): |
|
1319 | def CEEJ_fit_weight_bound_no_inputs(self,spc,freq,Nincoh): | |
| 1320 |
|
1320 | |||
| 1321 | from scipy.optimize import least_squares |
|
1321 | from scipy.optimize import least_squares | |
| 1322 |
|
1322 | |||
| 1323 | freq_max = numpy.max(numpy.abs(freq)) |
|
1323 | freq_max = numpy.max(numpy.abs(freq)) | |
| 1324 | spc_max = numpy.max(spc) |
|
1324 | spc_max = numpy.max(spc) | |
| 1325 |
|
1325 | |||
| 1326 | from scipy.signal import medfilt |
|
1326 | from scipy.signal import medfilt | |
| 1327 | Nincoh = 20 |
|
1327 | Nincoh = 20 | |
| 1328 | Nincoh = 80 |
|
1328 | Nincoh = 80 | |
| 1329 | Nincoh = Nincoh |
|
1329 | Nincoh = Nincoh | |
| 1330 | spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) |
|
1330 | spcm = medfilt(spc,11)/numpy.sqrt(Nincoh) | |
| 1331 |
|
1331 | |||
| 1332 | # define a least squares function to optimize |
|
1332 | # define a least squares function to optimize | |
| 1333 | def lsq_func(params): |
|
1333 | def lsq_func(params): | |
| 1334 | return (spc-self.gaussian(freq,params[0],params[1],params[2],params[3]))#/spcm |
|
1334 | return (spc-self.gaussian(freq,params[0],params[1],params[2],params[3]))#/spcm | |
| 1335 |
|
1335 | |||
| 1336 |
|
1336 | |||
| 1337 | bounds=([0,0,0,0],[numpy.inf,numpy.inf,numpy.inf,numpy.inf]) |
|
1337 | bounds=([0,0,0,0],[numpy.inf,numpy.inf,numpy.inf,numpy.inf]) | |
| 1338 |
|
1338 | |||
| 1339 | params_scale = [spc_max,freq_max,freq_max,spc_max] |
|
1339 | params_scale = [spc_max,freq_max,freq_max,spc_max] | |
| 1340 |
|
1340 | |||
| 1341 | x0_value = numpy.array([spc_max,freq[numpy.argmax(spcm)],30,numpy.mean(spc[:50])]) |
|
1341 | x0_value = numpy.array([spc_max,freq[numpy.argmax(spcm)],30,numpy.mean(spc[:50])]) | |
| 1342 |
|
1342 | |||
| 1343 | popt = least_squares(lsq_func,x0=x0_value,x_scale=params_scale,bounds=bounds,verbose=0) |
|
1343 | popt = least_squares(lsq_func,x0=x0_value,x_scale=params_scale,bounds=bounds,verbose=0) | |
| 1344 |
|
1344 | |||
| 1345 | J = popt.jac |
|
1345 | J = popt.jac | |
| 1346 |
|
1346 | |||
| 1347 | try: |
|
1347 | try: | |
| 1348 | error = numpy.ones((4))*numpy.NAN |
|
1348 | error = numpy.ones((4))*numpy.NAN | |
| 1349 | cov = numpy.linalg.inv(J.T.dot(J)) |
|
1349 | cov = numpy.linalg.inv(J.T.dot(J)) | |
| 1350 | error = numpy.sqrt(numpy.diagonal(cov)) |
|
1350 | error = numpy.sqrt(numpy.diagonal(cov)) | |
| 1351 | except: |
|
1351 | except: | |
| 1352 | error = numpy.ones((4))*numpy.NAN |
|
1352 | error = numpy.ones((4))*numpy.NAN | |
| 1353 |
|
1353 | |||
| 1354 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2] |
|
1354 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2] | |
| 1355 | Df = popt.x[3] |
|
1355 | Df = popt.x[3] | |
| 1356 |
|
1356 | |||
| 1357 | return A1f, B1f, C1f, Df, error |
|
1357 | return A1f, B1f, C1f, Df, error | |
| 1358 |
|
1358 | |||
| 1359 | def Simple_fit_bound(self,spc,freq,Nincoh): |
|
1359 | def Simple_fit_bound(self,spc,freq,Nincoh): | |
| 1360 |
|
1360 | |||
| 1361 | freq_max = numpy.max(numpy.abs(freq)) |
|
1361 | freq_max = numpy.max(numpy.abs(freq)) | |
| 1362 | spc_max = numpy.max(spc) |
|
1362 | spc_max = numpy.max(spc) | |
| 1363 |
|
1363 | |||
| 1364 | Nincoh = Nincoh |
|
1364 | Nincoh = Nincoh | |
| 1365 |
|
1365 | |||
| 1366 | def lsq_func(params): |
|
1366 | def lsq_func(params): | |
| 1367 | return (spc-self.gaussian(freq,params[0],params[1],params[2],params[3])) |
|
1367 | return (spc-self.gaussian(freq,params[0],params[1],params[2],params[3])) | |
| 1368 |
|
1368 | |||
| 1369 | bounds=([0,-50,0,0],[numpy.inf,+50,numpy.inf,numpy.inf]) |
|
1369 | bounds=([0,-50,0,0],[numpy.inf,+50,numpy.inf,numpy.inf]) | |
| 1370 |
|
1370 | |||
| 1371 | params_scale = [spc_max,freq_max,freq_max,spc_max] |
|
1371 | params_scale = [spc_max,freq_max,freq_max,spc_max] | |
| 1372 |
|
1372 | |||
| 1373 | x0_value = numpy.array([spc_max,-20.5,5,1.0e7]) |
|
1373 | x0_value = numpy.array([spc_max,-20.5,5,1.0e7]) | |
| 1374 |
|
1374 | |||
| 1375 | popt = least_squares(lsq_func,x0=x0_value,x_scale=params_scale,bounds=bounds,verbose=0) |
|
1375 | popt = least_squares(lsq_func,x0=x0_value,x_scale=params_scale,bounds=bounds,verbose=0) | |
| 1376 |
|
1376 | |||
| 1377 | J = popt.jac |
|
1377 | J = popt.jac | |
| 1378 |
|
1378 | |||
| 1379 | try: |
|
1379 | try: | |
| 1380 | cov = numpy.linalg.inv(J.T.dot(J)) |
|
1380 | cov = numpy.linalg.inv(J.T.dot(J)) | |
| 1381 | error = numpy.sqrt(numpy.diagonal(cov)) |
|
1381 | error = numpy.sqrt(numpy.diagonal(cov)) | |
| 1382 | except: |
|
1382 | except: | |
| 1383 | error = numpy.ones((4))*numpy.NAN |
|
1383 | error = numpy.ones((4))*numpy.NAN | |
| 1384 |
|
1384 | |||
| 1385 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2] |
|
1385 | A1f = popt.x[0]; B1f = popt.x[1]; C1f = popt.x[2] | |
| 1386 | Df = popt.x[3] |
|
1386 | Df = popt.x[3] | |
| 1387 |
|
1387 | |||
| 1388 | return A1f, B1f, C1f, Df, error |
|
1388 | return A1f, B1f, C1f, Df, error | |
| 1389 |
|
1389 | |||
| 1390 | def clean_outliers(self,param): |
|
1390 | def clean_outliers(self,param): | |
| 1391 |
|
1391 | |||
| 1392 | threshold = 700 |
|
1392 | threshold = 700 | |
| 1393 |
|
1393 | |||
| 1394 | param = numpy.where(param < -threshold, numpy.nan, param) |
|
1394 | param = numpy.where(param < -threshold, numpy.nan, param) | |
| 1395 | param = numpy.where(param > +threshold, numpy.nan, param) |
|
1395 | param = numpy.where(param > +threshold, numpy.nan, param) | |
| 1396 |
|
1396 | |||
| 1397 | return param |
|
1397 | return param | |
| 1398 |
|
1398 | |||
| 1399 | def windowing_single(self,spc,x,A,B,C,D,nFFTPoints): |
|
1399 | def windowing_single(self,spc,x,A,B,C,D,nFFTPoints): | |
| 1400 | from scipy.optimize import curve_fit,fmin |
|
1400 | from scipy.optimize import curve_fit,fmin | |
| 1401 |
|
1401 | |||
| 1402 | def R_gaussian(x, a, b, c): |
|
1402 | def R_gaussian(x, a, b, c): | |
| 1403 | N = int(numpy.shape(x)[0]) |
|
1403 | N = int(numpy.shape(x)[0]) | |
| 1404 | val = a * numpy.exp(-((x)*c*2*2*numpy.pi)**2 / (2))* numpy.exp(1.j*b*x*4*numpy.pi) |
|
1404 | val = a * numpy.exp(-((x)*c*2*2*numpy.pi)**2 / (2))* numpy.exp(1.j*b*x*4*numpy.pi) | |
| 1405 | return val |
|
1405 | return val | |
| 1406 |
|
1406 | |||
| 1407 | def T(x,N): |
|
1407 | def T(x,N): | |
| 1408 | T = 1-abs(x)/N |
|
1408 | T = 1-abs(x)/N | |
| 1409 | return T |
|
1409 | return T | |
| 1410 |
|
1410 | |||
| 1411 | def R_T_spc_fun(x, a, b, c, d, nFFTPoints): |
|
1411 | def R_T_spc_fun(x, a, b, c, d, nFFTPoints): | |
| 1412 |
|
1412 | |||
| 1413 | N = int(numpy.shape(x)[0]) |
|
1413 | N = int(numpy.shape(x)[0]) | |
| 1414 |
|
1414 | |||
| 1415 | x_max = x[-1] |
|
1415 | x_max = x[-1] | |
| 1416 |
|
1416 | |||
| 1417 | x_pos = x[int(nFFTPoints/2):] |
|
1417 | x_pos = x[int(nFFTPoints/2):] | |
| 1418 | x_neg = x[:int(nFFTPoints/2)] |
|
1418 | x_neg = x[:int(nFFTPoints/2)] | |
| 1419 |
|
1419 | |||
| 1420 | R_T_neg_1 = R_gaussian(x, a, b, c)[:int(nFFTPoints/2)]*T(x_neg,-x[0]) |
|
1420 | R_T_neg_1 = R_gaussian(x, a, b, c)[:int(nFFTPoints/2)]*T(x_neg,-x[0]) | |
| 1421 | R_T_pos_1 = R_gaussian(x, a, b, c)[int(nFFTPoints/2):]*T(x_pos,x[-1]) |
|
1421 | R_T_pos_1 = R_gaussian(x, a, b, c)[int(nFFTPoints/2):]*T(x_pos,x[-1]) | |
| 1422 | R_T_sum_1 = R_T_pos_1 + R_T_neg_1 |
|
1422 | R_T_sum_1 = R_T_pos_1 + R_T_neg_1 | |
| 1423 | R_T_spc_1 = numpy.fft.fft(R_T_sum_1).real |
|
1423 | R_T_spc_1 = numpy.fft.fft(R_T_sum_1).real | |
| 1424 | R_T_spc_1 = numpy.fft.fftshift(R_T_spc_1) |
|
1424 | R_T_spc_1 = numpy.fft.fftshift(R_T_spc_1) | |
| 1425 | max_val_1 = numpy.max(R_T_spc_1) |
|
1425 | max_val_1 = numpy.max(R_T_spc_1) | |
| 1426 | R_T_spc_1 = R_T_spc_1*a/max_val_1 |
|
1426 | R_T_spc_1 = R_T_spc_1*a/max_val_1 | |
| 1427 |
|
1427 | |||
| 1428 | R_T_d = d*numpy.fft.fftshift(signal.unit_impulse(N)) |
|
1428 | R_T_d = d*numpy.fft.fftshift(signal.unit_impulse(N)) | |
| 1429 | R_T_d_neg = R_T_d[:int(nFFTPoints/2)]*T(x_neg,-x[0]) |
|
1429 | R_T_d_neg = R_T_d[:int(nFFTPoints/2)]*T(x_neg,-x[0]) | |
| 1430 | R_T_d_pos = R_T_d[int(nFFTPoints/2):]*T(x_pos,x[-1]) |
|
1430 | R_T_d_pos = R_T_d[int(nFFTPoints/2):]*T(x_pos,x[-1]) | |
| 1431 | R_T_d_sum = R_T_d_pos + R_T_d_neg |
|
1431 | R_T_d_sum = R_T_d_pos + R_T_d_neg | |
| 1432 | R_T_spc_3 = numpy.fft.fft(R_T_d_sum).real |
|
1432 | R_T_spc_3 = numpy.fft.fft(R_T_d_sum).real | |
| 1433 | R_T_spc_3 = numpy.fft.fftshift(R_T_spc_3) |
|
1433 | R_T_spc_3 = numpy.fft.fftshift(R_T_spc_3) | |
| 1434 |
|
1434 | |||
| 1435 | R_T_final = R_T_spc_1 + R_T_spc_3 |
|
1435 | R_T_final = R_T_spc_1 + R_T_spc_3 | |
| 1436 |
|
1436 | |||
| 1437 | return R_T_final |
|
1437 | return R_T_final | |
| 1438 |
|
1438 | |||
| 1439 | y = spc#gaussian(x, a, meanY, sigmaY) + a*0.1*numpy.random.normal(0, 1, size=len(x)) |
|
1439 | y = spc#gaussian(x, a, meanY, sigmaY) + a*0.1*numpy.random.normal(0, 1, size=len(x)) | |
| 1440 |
|
1440 | |||
| 1441 | from scipy.stats import norm |
|
1441 | from scipy.stats import norm | |
| 1442 | mean,std=norm.fit(spc) |
|
1442 | mean,std=norm.fit(spc) | |
| 1443 |
|
1443 | |||
| 1444 | # estimate starting values from the data |
|
1444 | # estimate starting values from the data | |
| 1445 | a = A |
|
1445 | a = A | |
| 1446 | b = B |
|
1446 | b = B | |
| 1447 | c = C#numpy.std(spc) |
|
1447 | c = C#numpy.std(spc) | |
| 1448 | d = D |
|
1448 | d = D | |
| 1449 | ''' |
|
1449 | ''' | |
| 1450 | ippSeconds = 250*20*1.e-6/3 |
|
1450 | ippSeconds = 250*20*1.e-6/3 | |
| 1451 |
|
1451 | |||
| 1452 | x_t = ippSeconds * (numpy.arange(1600) -1600 / 2.) |
|
1452 | x_t = ippSeconds * (numpy.arange(1600) -1600 / 2.) | |
| 1453 |
|
1453 | |||
| 1454 | x_t = numpy.linspace(x_t[0],x_t[-1],3200) |
|
1454 | x_t = numpy.linspace(x_t[0],x_t[-1],3200) | |
| 1455 |
|
1455 | |||
| 1456 | x_freq = numpy.fft.fftfreq(1600,d=ippSeconds) |
|
1456 | x_freq = numpy.fft.fftfreq(1600,d=ippSeconds) | |
| 1457 | x_freq = numpy.fft.fftshift(x_freq) |
|
1457 | x_freq = numpy.fft.fftshift(x_freq) | |
| 1458 | ''' |
|
1458 | ''' | |
| 1459 | # define a least squares function to optimize |
|
1459 | # define a least squares function to optimize | |
| 1460 | def minfunc(params): |
|
1460 | def minfunc(params): | |
| 1461 | 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) |
|
1461 | 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) | |
| 1462 |
|
1462 | |||
| 1463 | # fit |
|
1463 | # fit | |
| 1464 |
|
1464 | |||
| 1465 | popt_full = fmin(minfunc,[a,b,c,d],full_output=True) |
|
1465 | popt_full = fmin(minfunc,[a,b,c,d],full_output=True) | |
| 1466 | #print("nIter", popt_full[2]) |
|
1466 | #print("nIter", popt_full[2]) | |
| 1467 | popt = popt_full[0] |
|
1467 | popt = popt_full[0] | |
| 1468 |
|
1468 | |||
| 1469 | #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] |
|
1469 | #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] | |
| 1470 | return popt[0], popt[1], popt[2], popt[3] |
|
1470 | return popt[0], popt[1], popt[2], popt[3] | |
| 1471 |
|
1471 | |||
| 1472 | def run(self, dataOut, mode = 0, Hmin1 = None, Hmax1 = None, Hmin2 = None, Hmax2 = None, Dop = 'Shift'): |
|
1472 | def run(self, dataOut, mode = 0, Hmin1 = None, Hmax1 = None, Hmin2 = None, Hmax2 = None, Dop = 'Shift'): | |
| 1473 |
|
1473 | |||
| 1474 | pwcode = 1 |
|
1474 | pwcode = 1 | |
| 1475 |
|
1475 | |||
| 1476 | if dataOut.flagDecodeData: |
|
1476 | if dataOut.flagDecodeData: | |
| 1477 | pwcode = numpy.sum(dataOut.code[0]**2) |
|
1477 | pwcode = numpy.sum(dataOut.code[0]**2) | |
| 1478 | #normFactor = min(self.nFFTPoints,self.nProfiles)*self.nIncohInt*self.nCohInt*pwcode*self.windowOfFilter |
|
1478 | #normFactor = min(self.nFFTPoints,self.nProfiles)*self.nIncohInt*self.nCohInt*pwcode*self.windowOfFilter | |
| 1479 | normFactor = dataOut.nProfiles * dataOut.nIncohInt * dataOut.nCohInt * pwcode * dataOut.windowOfFilter |
|
1479 | normFactor = dataOut.nProfiles * dataOut.nIncohInt * dataOut.nCohInt * pwcode * dataOut.windowOfFilter | |
| 1480 | factor = normFactor |
|
1480 | factor = normFactor | |
| 1481 | z = dataOut.data_spc / factor |
|
1481 | z = dataOut.data_spc / factor | |
| 1482 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
1482 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) | |
| 1483 | dataOut.power = numpy.average(z, axis=1) |
|
1483 | dataOut.power = numpy.average(z, axis=1) | |
| 1484 | dataOut.powerdB = 10 * numpy.log10(dataOut.power) |
|
1484 | dataOut.powerdB = 10 * numpy.log10(dataOut.power) | |
| 1485 |
|
1485 | |||
| 1486 | x = dataOut.getVelRange(0) |
|
1486 | x = dataOut.getVelRange(0) | |
| 1487 |
|
1487 | |||
| 1488 | dataOut.Oblique_params = numpy.ones((1,7,dataOut.nHeights))*numpy.NAN |
|
1488 | dataOut.Oblique_params = numpy.ones((1,7,dataOut.nHeights))*numpy.NAN | |
| 1489 | dataOut.Oblique_param_errors = numpy.ones((1,7,dataOut.nHeights))*numpy.NAN |
|
1489 | dataOut.Oblique_param_errors = numpy.ones((1,7,dataOut.nHeights))*numpy.NAN | |
| 1490 | dataOut.dplr_2_u = numpy.ones((1,1,dataOut.nHeights))*numpy.NAN |
|
1490 | dataOut.dplr_2_u = numpy.ones((1,1,dataOut.nHeights))*numpy.NAN | |
| 1491 |
|
1491 | |||
| 1492 | if mode == 6: |
|
1492 | if mode == 6: | |
| 1493 | dataOut.Oblique_params = numpy.ones((1,9,dataOut.nHeights))*numpy.NAN |
|
1493 | dataOut.Oblique_params = numpy.ones((1,9,dataOut.nHeights))*numpy.NAN | |
| 1494 | elif mode == 7: |
|
1494 | elif mode == 7: | |
| 1495 | dataOut.Oblique_params = numpy.ones((1,13,dataOut.nHeights))*numpy.NAN |
|
1495 | dataOut.Oblique_params = numpy.ones((1,13,dataOut.nHeights))*numpy.NAN | |
| 1496 | elif mode == 8: |
|
1496 | elif mode == 8: | |
| 1497 | dataOut.Oblique_params = numpy.ones((1,10,dataOut.nHeights))*numpy.NAN |
|
1497 | dataOut.Oblique_params = numpy.ones((1,10,dataOut.nHeights))*numpy.NAN | |
| 1498 | elif mode == 9: |
|
1498 | elif mode == 9: | |
| 1499 | dataOut.Oblique_params = numpy.ones((1,11,dataOut.nHeights))*numpy.NAN |
|
1499 | dataOut.Oblique_params = numpy.ones((1,11,dataOut.nHeights))*numpy.NAN | |
| 1500 | dataOut.Oblique_param_errors = numpy.ones((1,9,dataOut.nHeights))*numpy.NAN |
|
1500 | dataOut.Oblique_param_errors = numpy.ones((1,9,dataOut.nHeights))*numpy.NAN | |
| 1501 | elif mode == 11: |
|
1501 | elif mode == 11: | |
| 1502 | dataOut.Oblique_params = numpy.ones((1,7,dataOut.nHeights))*numpy.NAN |
|
1502 | dataOut.Oblique_params = numpy.ones((1,7,dataOut.nHeights))*numpy.NAN | |
| 1503 | dataOut.Oblique_param_errors = numpy.ones((1,7,dataOut.nHeights))*numpy.NAN |
|
1503 | dataOut.Oblique_param_errors = numpy.ones((1,7,dataOut.nHeights))*numpy.NAN | |
| 1504 | elif mode == 10: #150 km |
|
1504 | elif mode == 10: #150 km | |
| 1505 | dataOut.Oblique_params = numpy.ones((1,4,dataOut.nHeights))*numpy.NAN |
|
1505 | dataOut.Oblique_params = numpy.ones((1,4,dataOut.nHeights))*numpy.NAN | |
| 1506 | dataOut.Oblique_param_errors = numpy.ones((1,4,dataOut.nHeights))*numpy.NAN |
|
1506 | dataOut.Oblique_param_errors = numpy.ones((1,4,dataOut.nHeights))*numpy.NAN | |
| 1507 | dataOut.snr_log10 = numpy.ones((1,dataOut.nHeights))*numpy.NAN |
|
1507 | dataOut.snr_log10 = numpy.ones((1,dataOut.nHeights))*numpy.NAN | |
| 1508 |
|
1508 | |||
| 1509 | dataOut.VelRange = x |
|
1509 | dataOut.VelRange = x | |
| 1510 |
|
1510 | |||
| 1511 |
|
1511 | |||
| 1512 |
|
1512 | |||
| 1513 | #l1=range(22,36) #+62 |
|
1513 | #l1=range(22,36) #+62 | |
| 1514 | #l1=range(32,36) |
|
1514 | #l1=range(32,36) | |
| 1515 | #l2=range(58,99) #+62 |
|
1515 | #l2=range(58,99) #+62 | |
| 1516 |
|
1516 | |||
| 1517 | #if Hmin1 == None or Hmax1 == None or Hmin2 == None or Hmax2 == None: |
|
1517 | #if Hmin1 == None or Hmax1 == None or Hmin2 == None or Hmax2 == None: | |
| 1518 |
|
1518 | |||
| 1519 | minHei1 = 105. |
|
1519 | minHei1 = 105. | |
| 1520 | maxHei1 = 122.5 |
|
1520 | maxHei1 = 122.5 | |
| 1521 | maxHei1 = 130.5 |
|
1521 | maxHei1 = 130.5 | |
| 1522 |
|
1522 | |||
| 1523 | if mode == 10: #150 km |
|
1523 | if mode == 10: #150 km | |
| 1524 | minHei1 = 100 |
|
1524 | minHei1 = 100 | |
| 1525 | maxHei1 = 100 |
|
1525 | maxHei1 = 100 | |
| 1526 |
|
1526 | |||
| 1527 | inda1 = numpy.where(dataOut.heightList >= minHei1) |
|
1527 | inda1 = numpy.where(dataOut.heightList >= minHei1) | |
| 1528 | indb1 = numpy.where(dataOut.heightList <= maxHei1) |
|
1528 | indb1 = numpy.where(dataOut.heightList <= maxHei1) | |
| 1529 |
|
1529 | |||
| 1530 | minIndex1 = inda1[0][0] |
|
1530 | minIndex1 = inda1[0][0] | |
| 1531 | maxIndex1 = indb1[0][-1] |
|
1531 | maxIndex1 = indb1[0][-1] | |
| 1532 |
|
1532 | |||
| 1533 | minHei2 = 150. |
|
1533 | minHei2 = 150. | |
| 1534 | maxHei2 = 201.25 |
|
1534 | maxHei2 = 201.25 | |
| 1535 | maxHei2 = 225.3 |
|
1535 | maxHei2 = 225.3 | |
| 1536 |
|
1536 | |||
| 1537 | if mode == 10: #150 km |
|
1537 | if mode == 10: #150 km | |
| 1538 | minHei2 = 110 |
|
1538 | minHei2 = 110 | |
| 1539 | maxHei2 = 165 |
|
1539 | maxHei2 = 165 | |
| 1540 |
|
1540 | |||
| 1541 | inda2 = numpy.where(dataOut.heightList >= minHei2) |
|
1541 | inda2 = numpy.where(dataOut.heightList >= minHei2) | |
| 1542 | indb2 = numpy.where(dataOut.heightList <= maxHei2) |
|
1542 | indb2 = numpy.where(dataOut.heightList <= maxHei2) | |
| 1543 |
|
1543 | |||
| 1544 | minIndex2 = inda2[0][0] |
|
1544 | minIndex2 = inda2[0][0] | |
| 1545 | maxIndex2 = indb2[0][-1] |
|
1545 | maxIndex2 = indb2[0][-1] | |
| 1546 |
|
1546 | |||
| 1547 | l1=range(minIndex1,maxIndex1) |
|
1547 | l1=range(minIndex1,maxIndex1) | |
| 1548 | l2=range(minIndex2,maxIndex2) |
|
1548 | l2=range(minIndex2,maxIndex2) | |
| 1549 |
|
1549 | |||
| 1550 | if mode == 4: |
|
1550 | if mode == 4: | |
| 1551 | ''' |
|
1551 | ''' | |
| 1552 | for ind in range(dataOut.nHeights): |
|
1552 | for ind in range(dataOut.nHeights): | |
| 1553 | if(dataOut.heightList[ind]>=168 and dataOut.heightList[ind]<188): |
|
1553 | if(dataOut.heightList[ind]>=168 and dataOut.heightList[ind]<188): | |
| 1554 | try: |
|
1554 | 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) |
|
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) | |
| 1556 | except: |
|
1556 | except: | |
| 1557 | pass |
|
1557 | pass | |
| 1558 | ''' |
|
1558 | ''' | |
| 1559 | for ind in itertools.chain(l1, l2): |
|
1559 | for ind in itertools.chain(l1, l2): | |
| 1560 |
|
1560 | |||
| 1561 | try: |
|
1561 | try: | |
| 1562 | 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) |
|
1562 | 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) | |
| 1563 | dataOut.dplr_2_u[0,0,ind] = dataOut.Oblique_params[0,4,ind]/numpy.sin(numpy.arccos(102/dataOut.heightList[ind])) |
|
1563 | dataOut.dplr_2_u[0,0,ind] = dataOut.Oblique_params[0,4,ind]/numpy.sin(numpy.arccos(102/dataOut.heightList[ind])) | |
| 1564 | except: |
|
1564 | except: | |
| 1565 | pass |
|
1565 | pass | |
| 1566 |
|
1566 | |||
| 1567 | else: |
|
1567 | else: | |
| 1568 | for hei in itertools.chain(l1, l2): |
|
1568 | for hei in itertools.chain(l1, l2): | |
| 1569 | if numpy.isnan(dataOut.snl[0,hei]) or dataOut.snl[0,hei]<.0: |
|
1569 | if numpy.isnan(dataOut.snl[0,hei]) or dataOut.snl[0,hei]<.0: | |
| 1570 |
|
1570 | |||
| 1571 | continue #Avoids the analysis when there is only noise |
|
1571 | continue #Avoids the analysis when there is only noise | |
| 1572 |
|
1572 | |||
| 1573 | try: |
|
1573 | try: | |
| 1574 | spc = dataOut.data_spc[0,:,hei] |
|
1574 | spc = dataOut.data_spc[0,:,hei] | |
| 1575 |
|
1575 | |||
| 1576 | if mode == 6: #Skew Weighted Bounded |
|
1576 | if mode == 6: #Skew Weighted Bounded | |
| 1577 | 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) |
|
1577 | 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) | |
| 1578 | dataOut.dplr_2_u[0,0,hei] = dataOut.Oblique_params[0,8,hei]/numpy.sin(numpy.arccos(100./dataOut.heightList[hei])) |
|
1578 | dataOut.dplr_2_u[0,0,hei] = dataOut.Oblique_params[0,8,hei]/numpy.sin(numpy.arccos(100./dataOut.heightList[hei])) | |
| 1579 |
|
1579 | |||
| 1580 | elif mode == 7: #Triple Skew Weighted Bounded |
|
1580 | elif mode == 7: #Triple Skew Weighted Bounded | |
| 1581 | 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) |
|
1581 | 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) | |
| 1582 | dataOut.dplr_2_u[0,0,hei] = dataOut.Oblique_params[0,12,hei]/numpy.sin(numpy.arccos(100./dataOut.heightList[hei])) |
|
1582 | dataOut.dplr_2_u[0,0,hei] = dataOut.Oblique_params[0,12,hei]/numpy.sin(numpy.arccos(100./dataOut.heightList[hei])) | |
| 1583 |
|
1583 | |||
| 1584 | elif mode == 8: #Double Skewed Weighted Bounded with inputs |
|
1584 | elif mode == 8: #Double Skewed Weighted Bounded with inputs | |
| 1585 | a1, b1, c1, a2, b2, c2, k2, d, dopp = self.Double_Gauss_Skew_fit_weight_bound_no_inputs(spc,x) |
|
1585 | a1, b1, c1, a2, b2, c2, k2, d, dopp = self.Double_Gauss_Skew_fit_weight_bound_no_inputs(spc,x) | |
| 1586 | 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) |
|
1586 | 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) | |
| 1587 | dataOut.dplr_2_u[0,0,hei] = dataOut.Oblique_params[0,9,hei]/numpy.sin(numpy.arccos(100./dataOut.heightList[hei])) |
|
1587 | dataOut.dplr_2_u[0,0,hei] = dataOut.Oblique_params[0,9,hei]/numpy.sin(numpy.arccos(100./dataOut.heightList[hei])) | |
| 1588 |
|
1588 | |||
| 1589 | elif mode == 9: #Double Skewed Weighted Bounded no inputs |
|
1589 | elif mode == 9: #Double Skewed Weighted Bounded no inputs | |
| 1590 | #if numpy.max(spc) <= 0: |
|
1590 | #if numpy.max(spc) <= 0: | |
| 1591 | from scipy.signal import medfilt |
|
1591 | from scipy.signal import medfilt | |
| 1592 | spcm = medfilt(spc,11) |
|
1592 | spcm = medfilt(spc,11) | |
| 1593 | if x[numpy.argmax(spcm)] <= 0: |
|
1593 | if x[numpy.argmax(spcm)] <= 0: | |
| 1594 | #print("EEJ", dataOut.heightList[hei], hei) |
|
1594 | #print("EEJ", dataOut.heightList[hei], hei) | |
| 1595 | #if hei != 70: |
|
1595 | #if hei != 70: | |
| 1596 | #continue |
|
1596 | #continue | |
| 1597 | #else: |
|
1597 | #else: | |
| 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.Double_Gauss_Double_Skew_fit_weight_bound_no_inputs(spcm,x,dataOut.nIncohInt,dataOut.heightList[hei]) |
|
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.Double_Gauss_Double_Skew_fit_weight_bound_no_inputs(spcm,x,dataOut.nIncohInt,dataOut.heightList[hei]) | |
| 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: |
|
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: | |
| 1600 | # dataOut.Oblique_params[0,:,hei] *= numpy.NAN |
|
1600 | # 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])) |
|
1601 | dataOut.dplr_2_u[0,0,hei] = dataOut.Oblique_params[0,10,hei]/numpy.sin(numpy.arccos(100./dataOut.heightList[hei])) | |
| 1602 |
|
1602 | |||
| 1603 | else: |
|
1603 | else: | |
| 1604 | #print("CEEJ") |
|
1604 | #print("CEEJ") | |
| 1605 | 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) |
|
1605 | 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) | |
| 1606 | #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: |
|
1606 | #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: | |
| 1607 | # dataOut.Oblique_params[0,:,hei] *= numpy.NAN |
|
1607 | # dataOut.Oblique_params[0,:,hei] *= numpy.NAN | |
| 1608 | dataOut.dplr_2_u[0,0,hei] = dataOut.Oblique_params[0,10,hei]/numpy.sin(numpy.arccos(100./dataOut.heightList[hei])) |
|
1608 | dataOut.dplr_2_u[0,0,hei] = dataOut.Oblique_params[0,10,hei]/numpy.sin(numpy.arccos(100./dataOut.heightList[hei])) | |
| 1609 | elif mode == 11: #Double Weighted Bounded no inputs |
|
1609 | elif mode == 11: #Double Weighted Bounded no inputs | |
| 1610 | #if numpy.max(spc) <= 0: |
|
1610 | #if numpy.max(spc) <= 0: | |
| 1611 | from scipy.signal import medfilt |
|
1611 | from scipy.signal import medfilt | |
| 1612 | spcm = medfilt(spc,11) |
|
1612 | spcm = medfilt(spc,11) | |
| 1613 |
|
1613 | |||
| 1614 | if x[numpy.argmax(spcm)] <= 0: |
|
1614 | if x[numpy.argmax(spcm)] <= 0: | |
| 1615 | #print("EEJ") |
|
1615 | #print("EEJ") | |
| 1616 | #print("EEJ",dataOut.heightList[hei]) |
|
1616 | #print("EEJ",dataOut.heightList[hei]) | |
| 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_param_errors[0,:,hei] = self.Double_Gauss_fit_weight_bound_no_inputs(spc,x,dataOut.nIncohInt) |
|
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_param_errors[0,:,hei] = self.Double_Gauss_fit_weight_bound_no_inputs(spc,x,dataOut.nIncohInt) | |
| 1618 | #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: |
|
1618 | #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: | |
| 1619 | # dataOut.Oblique_params[0,:,hei] *= numpy.NAN |
|
1619 | # dataOut.Oblique_params[0,:,hei] *= numpy.NAN | |
| 1620 | else: |
|
1620 | else: | |
| 1621 | #print("CEEJ",dataOut.heightList[hei]) |
|
1621 | #print("CEEJ",dataOut.heightList[hei]) | |
| 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_param_errors[0,:,hei] = self.CEEJ_fit_weight_bound_no_inputs(spc,x,dataOut.nIncohInt) |
|
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_param_errors[0,:,hei] = self.CEEJ_fit_weight_bound_no_inputs(spc,x,dataOut.nIncohInt) | |
| 1623 |
|
1623 | |||
| 1624 | elif mode == 10: #150km |
|
1624 | elif mode == 10: #150km | |
| 1625 | 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) |
|
1625 | 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) | |
| 1626 | snr = (dataOut.power[0,hei]*factor - dataOut.Oblique_params[0,3,hei])/dataOut.Oblique_params[0,3,hei] |
|
1626 | snr = (dataOut.power[0,hei]*factor - dataOut.Oblique_params[0,3,hei])/dataOut.Oblique_params[0,3,hei] | |
| 1627 | dataOut.snr_log10[0,hei] = numpy.log10(snr) |
|
1627 | dataOut.snr_log10[0,hei] = numpy.log10(snr) | |
| 1628 |
|
1628 | |||
| 1629 | else: |
|
1629 | else: | |
| 1630 | spc_fit, A1, B1, C1, D1 = self.Gauss_fit_2(spc,x,'first') |
|
1630 | spc_fit, A1, B1, C1, D1 = self.Gauss_fit_2(spc,x,'first') | |
| 1631 |
|
1631 | |||
| 1632 | spc_diff = spc - spc_fit |
|
1632 | spc_diff = spc - spc_fit | |
| 1633 | spc_diff[spc_diff < 0] = 0 |
|
1633 | spc_diff[spc_diff < 0] = 0 | |
| 1634 |
|
1634 | |||
| 1635 | spc_fit_diff, A2, B2, C2, D2 = self.Gauss_fit_2(spc_diff,x,'second') |
|
1635 | spc_fit_diff, A2, B2, C2, D2 = self.Gauss_fit_2(spc_diff,x,'second') | |
| 1636 |
|
1636 | |||
| 1637 | D = (D1+D2) |
|
1637 | D = (D1+D2) | |
| 1638 |
|
1638 | |||
| 1639 | if mode == 0: #Double Fit |
|
1639 | if mode == 0: #Double Fit | |
| 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],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) |
|
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],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) | |
| 1641 | #spc_double_fit,dataOut.Oblique_params = self.Double_Gauss_fit(spc,x,A1,B1,C1,A2,B2,C2,D) |
|
1641 | #spc_double_fit,dataOut.Oblique_params = self.Double_Gauss_fit(spc,x,A1,B1,C1,A2,B2,C2,D) | |
| 1642 |
|
1642 | |||
| 1643 | elif mode == 1: #Double Fit Windowed |
|
1643 | elif mode == 1: #Double Fit Windowed | |
| 1644 | 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) |
|
1644 | 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) | |
| 1645 |
|
1645 | |||
| 1646 | elif mode == 2: #Double Fit Weight |
|
1646 | elif mode == 2: #Double Fit Weight | |
| 1647 | 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) |
|
1647 | 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) | |
| 1648 |
|
1648 | |||
| 1649 | elif mode == 3: #Simple Fit |
|
1649 | elif mode == 3: #Simple Fit | |
| 1650 | dataOut.Oblique_params[0,0,hei] = A1 |
|
1650 | dataOut.Oblique_params[0,0,hei] = A1 | |
| 1651 | dataOut.Oblique_params[0,1,hei] = B1 |
|
1651 | dataOut.Oblique_params[0,1,hei] = B1 | |
| 1652 | dataOut.Oblique_params[0,2,hei] = C1 |
|
1652 | dataOut.Oblique_params[0,2,hei] = C1 | |
| 1653 | dataOut.Oblique_params[0,3,hei] = A2 |
|
1653 | dataOut.Oblique_params[0,3,hei] = A2 | |
| 1654 | dataOut.Oblique_params[0,4,hei] = B2 |
|
1654 | dataOut.Oblique_params[0,4,hei] = B2 | |
| 1655 | dataOut.Oblique_params[0,5,hei] = C2 |
|
1655 | dataOut.Oblique_params[0,5,hei] = C2 | |
| 1656 | dataOut.Oblique_params[0,6,hei] = D |
|
1656 | dataOut.Oblique_params[0,6,hei] = D | |
| 1657 |
|
1657 | |||
| 1658 | elif mode == 5: #Triple Fit Weight |
|
1658 | elif mode == 5: #Triple Fit Weight | |
| 1659 | if hei in l1: |
|
1659 | if hei in l1: | |
| 1660 | 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) |
|
1660 | 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) | |
| 1661 | dataOut.dplr_2_u[0,0,hei] = dataOut.Oblique_params[0,4,hei]/numpy.sin(numpy.arccos(102/dataOut.heightList[hei])) |
|
1661 | dataOut.dplr_2_u[0,0,hei] = dataOut.Oblique_params[0,4,hei]/numpy.sin(numpy.arccos(102/dataOut.heightList[hei])) | |
| 1662 | #print(dataOut.Oblique_params[0,0,hei]) |
|
1662 | #print(dataOut.Oblique_params[0,0,hei]) | |
| 1663 | #print(dataOut.dplr_2_u[0,0,hei]) |
|
1663 | #print(dataOut.dplr_2_u[0,0,hei]) | |
| 1664 | else: |
|
1664 | else: | |
| 1665 | 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) |
|
1665 | 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) | |
| 1666 | dataOut.dplr_2_u[0,0,hei] = dataOut.Oblique_params[0,4,hei]/numpy.sin(numpy.arccos(102/dataOut.heightList[hei])) |
|
1666 | dataOut.dplr_2_u[0,0,hei] = dataOut.Oblique_params[0,4,hei]/numpy.sin(numpy.arccos(102/dataOut.heightList[hei])) | |
| 1667 |
|
1667 | |||
| 1668 |
|
1668 | |||
| 1669 | except: |
|
1669 | except: | |
| 1670 | ###dataOut.Oblique_params[0,:,hei] = dataOut.Oblique_params[0,:,hei]*numpy.NAN |
|
1670 | ###dataOut.Oblique_params[0,:,hei] = dataOut.Oblique_params[0,:,hei]*numpy.NAN | |
| 1671 | pass |
|
1671 | pass | |
| 1672 |
|
1672 | |||
| 1673 | #exit(1) |
|
1673 | #exit(1) | |
| 1674 | dataOut.paramInterval = dataOut.nProfiles*dataOut.nCohInt*dataOut.ippSeconds |
|
1674 | dataOut.paramInterval = dataOut.nProfiles*dataOut.nCohInt*dataOut.ippSeconds | |
| 1675 | dataOut.lat=-11.95 |
|
1675 | dataOut.lat=-11.95 | |
| 1676 | dataOut.lon=-76.87 |
|
1676 | dataOut.lon=-76.87 | |
| 1677 | ''' |
|
1677 | ''' | |
| 1678 | dataOut.Oblique_params = numpy.where(dataOut.Oblique_params<-700, numpy.nan, dop_t1) |
|
1678 | dataOut.Oblique_params = numpy.where(dataOut.Oblique_params<-700, numpy.nan, dop_t1) | |
| 1679 | dataOut.Oblique_params = numpy.where(dataOut.Oblique_params<+700, numpy.nan, dop_t1) |
|
1679 | dataOut.Oblique_params = numpy.where(dataOut.Oblique_params<+700, numpy.nan, dop_t1) | |
| 1680 | AquΓ debo exceptuar las amplitudes |
|
1680 | AquΓ debo exceptuar las amplitudes | |
| 1681 | ''' |
|
1681 | ''' | |
| 1682 | if mode == 9: #Double Skew Gaussian |
|
1682 | if mode == 9: #Double Skew Gaussian | |
| 1683 | #dataOut.Dop_EEJ_T1 = dataOut.Oblique_params[:,-2,:] #Pos[Max_value] |
|
1683 | #dataOut.Dop_EEJ_T1 = dataOut.Oblique_params[:,-2,:] #Pos[Max_value] | |
| 1684 | #dataOut.Dop_EEJ_T1 = dataOut.Oblique_params[:,1,:] #Shift |
|
1684 | #dataOut.Dop_EEJ_T1 = dataOut.Oblique_params[:,1,:] #Shift | |
| 1685 | dataOut.Spec_W_T1 = dataOut.Oblique_params[:,2,:] |
|
1685 | dataOut.Spec_W_T1 = dataOut.Oblique_params[:,2,:] | |
| 1686 | #dataOut.Dop_EEJ_T2 = dataOut.Oblique_params[:,-1,:] #Pos[Max_value] |
|
1686 | #dataOut.Dop_EEJ_T2 = dataOut.Oblique_params[:,-1,:] #Pos[Max_value] | |
| 1687 | #dataOut.Dop_EEJ_T2 = dataOut.Oblique_params[:,5,:] #Shift |
|
1687 | #dataOut.Dop_EEJ_T2 = dataOut.Oblique_params[:,5,:] #Shift | |
| 1688 | dataOut.Spec_W_T2 = dataOut.Oblique_params[:,6,:] |
|
1688 | dataOut.Spec_W_T2 = dataOut.Oblique_params[:,6,:] | |
| 1689 | if Dop == 'Shift': |
|
1689 | if Dop == 'Shift': | |
| 1690 | dataOut.Dop_EEJ_T1 = dataOut.Oblique_params[:,1,:] #Shift |
|
1690 | dataOut.Dop_EEJ_T1 = dataOut.Oblique_params[:,1,:] #Shift | |
| 1691 | dataOut.Dop_EEJ_T2 = dataOut.Oblique_params[:,5,:] #Shift |
|
1691 | dataOut.Dop_EEJ_T2 = dataOut.Oblique_params[:,5,:] #Shift | |
| 1692 | elif Dop == 'Max': |
|
1692 | elif Dop == 'Max': | |
| 1693 | dataOut.Dop_EEJ_T1 = dataOut.Oblique_params[:,-2,:] #Pos[Max_value] |
|
1693 | dataOut.Dop_EEJ_T1 = dataOut.Oblique_params[:,-2,:] #Pos[Max_value] | |
| 1694 | dataOut.Dop_EEJ_T2 = dataOut.Oblique_params[:,-1,:] #Pos[Max_value] |
|
1694 | dataOut.Dop_EEJ_T2 = dataOut.Oblique_params[:,-1,:] #Pos[Max_value] | |
| 1695 |
|
1695 | |||
| 1696 | dataOut.Err_Dop_EEJ_T1 = dataOut.Oblique_param_errors[:,1,:] #En realidad este es el error? |
|
1696 | dataOut.Err_Dop_EEJ_T1 = dataOut.Oblique_param_errors[:,1,:] #En realidad este es el error? | |
| 1697 | dataOut.Err_Spec_W_T1 = dataOut.Oblique_param_errors[:,2,:] |
|
1697 | dataOut.Err_Spec_W_T1 = dataOut.Oblique_param_errors[:,2,:] | |
| 1698 | dataOut.Err_Dop_EEJ_T2 = dataOut.Oblique_param_errors[:,5,:] #En realidad este es el error? |
|
1698 | dataOut.Err_Dop_EEJ_T2 = dataOut.Oblique_param_errors[:,5,:] #En realidad este es el error? | |
| 1699 | dataOut.Err_Spec_W_T2 = dataOut.Oblique_param_errors[:,6,:] |
|
1699 | dataOut.Err_Spec_W_T2 = dataOut.Oblique_param_errors[:,6,:] | |
| 1700 |
|
1700 | |||
| 1701 | elif mode == 11: #Double Gaussian |
|
1701 | elif mode == 11: #Double Gaussian | |
| 1702 | dataOut.Dop_EEJ_T1 = dataOut.Oblique_params[:,1,:] |
|
1702 | dataOut.Dop_EEJ_T1 = dataOut.Oblique_params[:,1,:] | |
| 1703 | dataOut.Spec_W_T1 = dataOut.Oblique_params[:,2,:] |
|
1703 | dataOut.Spec_W_T1 = dataOut.Oblique_params[:,2,:] | |
| 1704 | dataOut.Dop_EEJ_T2 = dataOut.Oblique_params[:,4,:] |
|
1704 | dataOut.Dop_EEJ_T2 = dataOut.Oblique_params[:,4,:] | |
| 1705 | dataOut.Spec_W_T2 = dataOut.Oblique_params[:,5,:] |
|
1705 | dataOut.Spec_W_T2 = dataOut.Oblique_params[:,5,:] | |
| 1706 |
|
1706 | |||
| 1707 | dataOut.Err_Dop_EEJ_T1 = dataOut.Oblique_param_errors[:,1,:] |
|
1707 | dataOut.Err_Dop_EEJ_T1 = dataOut.Oblique_param_errors[:,1,:] | |
| 1708 | dataOut.Err_Spec_W_T1 = dataOut.Oblique_param_errors[:,2,:] |
|
1708 | dataOut.Err_Spec_W_T1 = dataOut.Oblique_param_errors[:,2,:] | |
| 1709 | dataOut.Err_Dop_EEJ_T2 = dataOut.Oblique_param_errors[:,4,:] |
|
1709 | dataOut.Err_Dop_EEJ_T2 = dataOut.Oblique_param_errors[:,4,:] | |
| 1710 | dataOut.Err_Spec_W_T2 = dataOut.Oblique_param_errors[:,5,:] |
|
1710 | dataOut.Err_Spec_W_T2 = dataOut.Oblique_param_errors[:,5,:] | |
| 1711 |
|
1711 | |||
| 1712 | #print("Before: ", dataOut.Dop_EEJ_T2) |
|
1712 | #print("Before: ", dataOut.Dop_EEJ_T2) | |
| 1713 | dataOut.Spec_W_T1 = self.clean_outliers(dataOut.Spec_W_T1) |
|
1713 | dataOut.Spec_W_T1 = self.clean_outliers(dataOut.Spec_W_T1) | |
| 1714 | dataOut.Spec_W_T2 = self.clean_outliers(dataOut.Spec_W_T2) |
|
1714 | dataOut.Spec_W_T2 = self.clean_outliers(dataOut.Spec_W_T2) | |
| 1715 | dataOut.Dop_EEJ_T1 = self.clean_outliers(dataOut.Dop_EEJ_T1) |
|
1715 | dataOut.Dop_EEJ_T1 = self.clean_outliers(dataOut.Dop_EEJ_T1) | |
| 1716 | dataOut.Dop_EEJ_T2 = self.clean_outliers(dataOut.Dop_EEJ_T2) |
|
1716 | dataOut.Dop_EEJ_T2 = self.clean_outliers(dataOut.Dop_EEJ_T2) | |
| 1717 | #print("After: ", dataOut.Dop_EEJ_T2) |
|
1717 | #print("After: ", dataOut.Dop_EEJ_T2) | |
| 1718 | dataOut.Err_Spec_W_T1 = self.clean_outliers(dataOut.Err_Spec_W_T1) |
|
1718 | dataOut.Err_Spec_W_T1 = self.clean_outliers(dataOut.Err_Spec_W_T1) | |
| 1719 | dataOut.Err_Spec_W_T2 = self.clean_outliers(dataOut.Err_Spec_W_T2) |
|
1719 | dataOut.Err_Spec_W_T2 = self.clean_outliers(dataOut.Err_Spec_W_T2) | |
| 1720 | dataOut.Err_Dop_EEJ_T1 = self.clean_outliers(dataOut.Err_Dop_EEJ_T1) |
|
1720 | dataOut.Err_Dop_EEJ_T1 = self.clean_outliers(dataOut.Err_Dop_EEJ_T1) | |
| 1721 | dataOut.Err_Dop_EEJ_T2 = self.clean_outliers(dataOut.Err_Dop_EEJ_T2) |
|
1721 | dataOut.Err_Dop_EEJ_T2 = self.clean_outliers(dataOut.Err_Dop_EEJ_T2) | |
| 1722 | #print("Before data_snr: ", dataOut.data_snr) |
|
1722 | #print("Before data_snr: ", dataOut.data_snr) | |
| 1723 | #dataOut.data_snr = numpy.where(numpy.isnan(dataOut.Dop_EEJ_T1), numpy.nan, dataOut.data_snr) |
|
1723 | #dataOut.data_snr = numpy.where(numpy.isnan(dataOut.Dop_EEJ_T1), numpy.nan, dataOut.data_snr) | |
| 1724 | dataOut.snl = numpy.where(numpy.isnan(dataOut.Dop_EEJ_T1), numpy.nan, dataOut.snl) |
|
1724 | dataOut.snl = numpy.where(numpy.isnan(dataOut.Dop_EEJ_T1), numpy.nan, dataOut.snl) | |
| 1725 |
|
1725 | |||
| 1726 | #print("After data_snr: ", dataOut.data_snr) |
|
1726 | #print("After data_snr: ", dataOut.data_snr) | |
| 1727 | dataOut.mode = mode |
|
1727 | dataOut.mode = mode | |
| 1728 | dataOut.flagNoData = numpy.all(numpy.isnan(dataOut.Dop_EEJ_T1)) #Si todos los valores son NaN no se prosigue |
|
1728 | dataOut.flagNoData = numpy.all(numpy.isnan(dataOut.Dop_EEJ_T1)) #Si todos los valores son NaN no se prosigue | |
| 1729 | ###dataOut.flagNoData = False #Descomentar solo para ploteo sino mantener comentado (para guardado) |
|
1729 | ###dataOut.flagNoData = False #Descomentar solo para ploteo sino mantener comentado (para guardado) | |
| 1730 |
|
1730 | |||
| 1731 | return dataOut |
|
1731 | return dataOut | |
| 1732 |
|
1732 | |||
| 1733 | class Gaussian_Windowed(Operation): |
|
1733 | class Gaussian_Windowed(Operation): | |
| 1734 | ''' |
|
1734 | ''' | |
| 1735 | Written by R. Flores |
|
1735 | Written by R. Flores | |
| 1736 | ''' |
|
1736 | ''' | |
| 1737 | def __init__(self): |
|
1737 | def __init__(self): | |
| 1738 | Operation.__init__(self) |
|
1738 | Operation.__init__(self) | |
| 1739 |
|
1739 | |||
| 1740 | def windowing_single(self,spc,x,A,B,C,D,nFFTPoints): |
|
1740 | def windowing_single(self,spc,x,A,B,C,D,nFFTPoints): | |
| 1741 | from scipy.optimize import curve_fit,fmin |
|
1741 | from scipy.optimize import curve_fit,fmin | |
| 1742 |
|
1742 | |||
| 1743 | def gaussian(x, a, b, c, d): |
|
1743 | def gaussian(x, a, b, c, d): | |
| 1744 | val = a * numpy.exp(-(x - b)**2 / (2*c**2)) + d |
|
1744 | val = a * numpy.exp(-(x - b)**2 / (2*c**2)) + d | |
| 1745 | return val |
|
1745 | return val | |
| 1746 |
|
1746 | |||
| 1747 | def R_gaussian(x, a, b, c): |
|
1747 | def R_gaussian(x, a, b, c): | |
| 1748 | N = int(numpy.shape(x)[0]) |
|
1748 | N = int(numpy.shape(x)[0]) | |
| 1749 | val = a * numpy.exp(-((x)*c*2*2*numpy.pi)**2 / (2))* numpy.exp(1.j*b*x*4*numpy.pi) |
|
1749 | val = a * numpy.exp(-((x)*c*2*2*numpy.pi)**2 / (2))* numpy.exp(1.j*b*x*4*numpy.pi) | |
| 1750 | return val |
|
1750 | return val | |
| 1751 |
|
1751 | |||
| 1752 | def T(x,N): |
|
1752 | def T(x,N): | |
| 1753 | T = 1-abs(x)/N |
|
1753 | T = 1-abs(x)/N | |
| 1754 | return T |
|
1754 | return T | |
| 1755 |
|
1755 | |||
| 1756 | def R_T_spc_fun(x, a, b, c, d, nFFTPoints): |
|
1756 | def R_T_spc_fun(x, a, b, c, d, nFFTPoints): | |
| 1757 |
|
1757 | |||
| 1758 | N = int(numpy.shape(x)[0]) |
|
1758 | N = int(numpy.shape(x)[0]) | |
| 1759 |
|
1759 | |||
| 1760 | x_max = x[-1] |
|
1760 | x_max = x[-1] | |
| 1761 |
|
1761 | |||
| 1762 | x_pos = x[nFFTPoints:] |
|
1762 | x_pos = x[nFFTPoints:] | |
| 1763 | x_neg = x[:nFFTPoints] |
|
1763 | x_neg = x[:nFFTPoints] | |
| 1764 | #print([int(nFFTPoints/2)) |
|
1764 | #print([int(nFFTPoints/2)) | |
| 1765 | #print("x: ", x) |
|
1765 | #print("x: ", x) | |
| 1766 | #print("x_neg: ", x_neg) |
|
1766 | #print("x_neg: ", x_neg) | |
| 1767 | #print("x_pos: ", x_pos) |
|
1767 | #print("x_pos: ", x_pos) | |
| 1768 |
|
1768 | |||
| 1769 |
|
1769 | |||
| 1770 | R_T_neg_1 = R_gaussian(x, a, b, c)[:nFFTPoints]*T(x_neg,-x[0]) |
|
1770 | R_T_neg_1 = R_gaussian(x, a, b, c)[:nFFTPoints]*T(x_neg,-x[0]) | |
| 1771 | R_T_pos_1 = R_gaussian(x, a, b, c)[nFFTPoints:]*T(x_pos,x[-1]) |
|
1771 | R_T_pos_1 = R_gaussian(x, a, b, c)[nFFTPoints:]*T(x_pos,x[-1]) | |
| 1772 | #print(T(x_pos,x[-1]),x_pos,x[-1]) |
|
1772 | #print(T(x_pos,x[-1]),x_pos,x[-1]) | |
| 1773 | #print(R_T_neg_1.shape,R_T_pos_1.shape) |
|
1773 | #print(R_T_neg_1.shape,R_T_pos_1.shape) | |
| 1774 | R_T_sum_1 = R_T_pos_1 + R_T_neg_1 |
|
1774 | R_T_sum_1 = R_T_pos_1 + R_T_neg_1 | |
| 1775 | R_T_spc_1 = numpy.fft.fft(R_T_sum_1).real |
|
1775 | R_T_spc_1 = numpy.fft.fft(R_T_sum_1).real | |
| 1776 | R_T_spc_1 = numpy.fft.fftshift(R_T_spc_1) |
|
1776 | R_T_spc_1 = numpy.fft.fftshift(R_T_spc_1) | |
| 1777 | max_val_1 = numpy.max(R_T_spc_1) |
|
1777 | max_val_1 = numpy.max(R_T_spc_1) | |
| 1778 | R_T_spc_1 = R_T_spc_1*a/max_val_1 |
|
1778 | R_T_spc_1 = R_T_spc_1*a/max_val_1 | |
| 1779 |
|
1779 | |||
| 1780 | R_T_d = d*numpy.fft.fftshift(signal.unit_impulse(N)) |
|
1780 | R_T_d = d*numpy.fft.fftshift(signal.unit_impulse(N)) | |
| 1781 | R_T_d_neg = R_T_d[:nFFTPoints]*T(x_neg,-x[0]) |
|
1781 | R_T_d_neg = R_T_d[:nFFTPoints]*T(x_neg,-x[0]) | |
| 1782 | R_T_d_pos = R_T_d[nFFTPoints:]*T(x_pos,x[-1]) |
|
1782 | R_T_d_pos = R_T_d[nFFTPoints:]*T(x_pos,x[-1]) | |
| 1783 | R_T_d_sum = R_T_d_pos + R_T_d_neg |
|
1783 | R_T_d_sum = R_T_d_pos + R_T_d_neg | |
| 1784 | R_T_spc_3 = numpy.fft.fft(R_T_d_sum).real |
|
1784 | R_T_spc_3 = numpy.fft.fft(R_T_d_sum).real | |
| 1785 | R_T_spc_3 = numpy.fft.fftshift(R_T_spc_3) |
|
1785 | R_T_spc_3 = numpy.fft.fftshift(R_T_spc_3) | |
| 1786 |
|
1786 | |||
| 1787 | R_T_final = R_T_spc_1 + R_T_spc_3 |
|
1787 | R_T_final = R_T_spc_1 + R_T_spc_3 | |
| 1788 |
|
1788 | |||
| 1789 | return R_T_final |
|
1789 | return R_T_final | |
| 1790 |
|
1790 | |||
| 1791 | y = spc#gaussian(x, a, meanY, sigmaY) + a*0.1*numpy.random.normal(0, 1, size=len(x)) |
|
1791 | y = spc#gaussian(x, a, meanY, sigmaY) + a*0.1*numpy.random.normal(0, 1, size=len(x)) | |
| 1792 |
|
1792 | |||
| 1793 | from scipy.stats import norm |
|
1793 | from scipy.stats import norm | |
| 1794 | mean,std=norm.fit(spc) |
|
1794 | mean,std=norm.fit(spc) | |
| 1795 |
|
1795 | |||
| 1796 | # estimate starting values from the data |
|
1796 | # estimate starting values from the data | |
| 1797 | a = A |
|
1797 | a = A | |
| 1798 | b = B |
|
1798 | b = B | |
| 1799 | c = C#numpy.std(spc) |
|
1799 | c = C#numpy.std(spc) | |
| 1800 | d = D |
|
1800 | d = D | |
| 1801 | #''' |
|
1801 | #''' | |
| 1802 | #ippSeconds = 250*20*1.e-6/3 |
|
1802 | #ippSeconds = 250*20*1.e-6/3 | |
| 1803 |
|
1803 | |||
| 1804 | #x_t = ippSeconds * (numpy.arange(nFFTPoints) - nFFTPoints / 2.) |
|
1804 | #x_t = ippSeconds * (numpy.arange(nFFTPoints) - nFFTPoints / 2.) | |
| 1805 |
|
1805 | |||
| 1806 | #x_t = numpy.linspace(x_t[0],x_t[-1],3200) |
|
1806 | #x_t = numpy.linspace(x_t[0],x_t[-1],3200) | |
| 1807 | #print("x_t: ", x_t) |
|
1807 | #print("x_t: ", x_t) | |
| 1808 | #print("nFFTPoints: ", nFFTPoints) |
|
1808 | #print("nFFTPoints: ", nFFTPoints) | |
| 1809 | x_vel = numpy.linspace(x[0],x[-1],int(2*nFFTPoints)) |
|
1809 | x_vel = numpy.linspace(x[0],x[-1],int(2*nFFTPoints)) | |
| 1810 | #print("x_vel: ", x_vel) |
|
1810 | #print("x_vel: ", x_vel) | |
| 1811 | #x_freq = numpy.fft.fftfreq(1600,d=ippSeconds) |
|
1811 | #x_freq = numpy.fft.fftfreq(1600,d=ippSeconds) | |
| 1812 | #x_freq = numpy.fft.fftshift(x_freq) |
|
1812 | #x_freq = numpy.fft.fftshift(x_freq) | |
| 1813 | #''' |
|
1813 | #''' | |
| 1814 | # define a least squares function to optimize |
|
1814 | # define a least squares function to optimize | |
| 1815 | def minfunc(params): |
|
1815 | def minfunc(params): | |
| 1816 | #print("y.shape: ", numpy.shape(y)) |
|
1816 | #print("y.shape: ", numpy.shape(y)) | |
| 1817 | return sum((y-R_T_spc_fun(x_vel,params[0],params[1],params[2],params[3],nFFTPoints))**2/1)#y**2) |
|
1817 | return sum((y-R_T_spc_fun(x_vel,params[0],params[1],params[2],params[3],nFFTPoints))**2/1)#y**2) | |
| 1818 |
|
1818 | |||
| 1819 | # fit |
|
1819 | # fit | |
| 1820 |
|
1820 | |||
| 1821 | popt_full = fmin(minfunc,[a,b,c,d], disp=False) |
|
1821 | popt_full = fmin(minfunc,[a,b,c,d], disp=False) | |
| 1822 | #print("nIter", popt_full[2]) |
|
1822 | #print("nIter", popt_full[2]) | |
| 1823 | popt = popt_full#[0] |
|
1823 | popt = popt_full#[0] | |
| 1824 |
|
1824 | |||
| 1825 | fun = gaussian(x, popt[0], popt[1], popt[2], popt[3]) |
|
1825 | fun = gaussian(x, popt[0], popt[1], popt[2], popt[3]) | |
| 1826 |
|
1826 | |||
| 1827 | #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] |
|
1827 | #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] | |
| 1828 | return fun, popt[0], popt[1], popt[2], popt[3] |
|
1828 | return fun, popt[0], popt[1], popt[2], popt[3] | |
| 1829 |
|
1829 | |||
| 1830 | def run(self, dataOut): |
|
1830 | def run(self, dataOut): | |
| 1831 |
|
1831 | |||
| 1832 | from scipy.signal import medfilt |
|
1832 | from scipy.signal import medfilt | |
| 1833 | import matplotlib.pyplot as plt |
|
1833 | import matplotlib.pyplot as plt | |
| 1834 | dataOut.moments = numpy.ones((dataOut.nChannels,4,dataOut.nHeights))*numpy.NAN |
|
1834 | dataOut.moments = numpy.ones((dataOut.nChannels,4,dataOut.nHeights))*numpy.NAN | |
| 1835 | dataOut.VelRange = dataOut.getVelRange(0) |
|
1835 | dataOut.VelRange = dataOut.getVelRange(0) | |
| 1836 | for nChannel in range(dataOut.nChannels): |
|
1836 | for nChannel in range(dataOut.nChannels): | |
| 1837 | for hei in range(dataOut.heightList.shape[0]): |
|
1837 | for hei in range(dataOut.heightList.shape[0]): | |
| 1838 | #print("ipp: ", dataOut.ippSeconds) |
|
1838 | #print("ipp: ", dataOut.ippSeconds) | |
| 1839 | spc = numpy.copy(dataOut.data_spc[nChannel,:,hei]) |
|
1839 | spc = numpy.copy(dataOut.data_spc[nChannel,:,hei]) | |
| 1840 |
|
1840 | |||
| 1841 | #print(VelRange) |
|
1841 | #print(VelRange) | |
| 1842 | #print(dataOut.getFreqRange(64)) |
|
1842 | #print(dataOut.getFreqRange(64)) | |
| 1843 | spcm = medfilt(spc,11) |
|
1843 | spcm = medfilt(spc,11) | |
| 1844 | spc_max = numpy.max(spcm) |
|
1844 | spc_max = numpy.max(spcm) | |
| 1845 | dop1_x0 = dataOut.VelRange[numpy.argmax(spcm)] |
|
1845 | dop1_x0 = dataOut.VelRange[numpy.argmax(spcm)] | |
| 1846 | D = numpy.min(spcm) |
|
1846 | D = numpy.min(spcm) | |
| 1847 |
|
1847 | |||
| 1848 | fun, A, B, C, D = self.windowing_single(spc,dataOut.VelRange,spc_max,dop1_x0,abs(dop1_x0),D,dataOut.nFFTPoints) |
|
1848 | fun, A, B, C, D = self.windowing_single(spc,dataOut.VelRange,spc_max,dop1_x0,abs(dop1_x0),D,dataOut.nFFTPoints) | |
| 1849 | dataOut.moments[nChannel,0,hei] = A |
|
1849 | dataOut.moments[nChannel,0,hei] = A | |
| 1850 | dataOut.moments[nChannel,1,hei] = B |
|
1850 | dataOut.moments[nChannel,1,hei] = B | |
| 1851 | dataOut.moments[nChannel,2,hei] = C |
|
1851 | dataOut.moments[nChannel,2,hei] = C | |
| 1852 | dataOut.moments[nChannel,3,hei] = D |
|
1852 | dataOut.moments[nChannel,3,hei] = D | |
| 1853 | ''' |
|
1853 | ''' | |
| 1854 | plt.figure() |
|
1854 | plt.figure() | |
| 1855 | plt.plot(VelRange,spc,marker='*',linestyle='') |
|
1855 | plt.plot(VelRange,spc,marker='*',linestyle='') | |
| 1856 | plt.plot(VelRange,fun) |
|
1856 | plt.plot(VelRange,fun) | |
| 1857 | plt.title(dataOut.heightList[hei]) |
|
1857 | plt.title(dataOut.heightList[hei]) | |
| 1858 | plt.show() |
|
1858 | plt.show() | |
| 1859 | ''' |
|
1859 | ''' | |
| 1860 |
|
1860 | |||
| 1861 | return dataOut |
|
1861 | return dataOut | |
| 1862 |
|
1862 | |||
| 1863 | class PrecipitationProc(Operation): |
|
1863 | class PrecipitationProc(Operation): | |
| 1864 |
|
1864 | |||
| 1865 | ''' |
|
1865 | ''' | |
| 1866 | Operator that estimates Reflectivity factor (Z), and estimates rainfall Rate (R) |
|
1866 | Operator that estimates Reflectivity factor (Z), and estimates rainfall Rate (R) | |
| 1867 |
|
1867 | |||
| 1868 | Input: |
|
1868 | Input: | |
| 1869 | self.dataOut.data_pre : SelfSpectra |
|
1869 | self.dataOut.data_pre : SelfSpectra | |
| 1870 |
|
1870 | |||
| 1871 | Output: |
|
1871 | Output: | |
| 1872 |
|
1872 | |||
| 1873 | self.dataOut.data_output : Reflectivity factor, rainfall Rate |
|
1873 | self.dataOut.data_output : Reflectivity factor, rainfall Rate | |
| 1874 |
|
1874 | |||
| 1875 |
|
1875 | |||
| 1876 | Parameters affected: |
|
1876 | Parameters affected: | |
| 1877 | ''' |
|
1877 | ''' | |
| 1878 |
|
1878 | |||
| 1879 | def __init__(self): |
|
1879 | def __init__(self): | |
| 1880 | Operation.__init__(self) |
|
1880 | Operation.__init__(self) | |
| 1881 | self.i=0 |
|
1881 | self.i=0 | |
| 1882 |
|
1882 | |||
| 1883 | def run(self, dataOut, radar=None, Pt=5000, Gt=295.1209, Gr=70.7945, Lambda=0.6741, aL=2.5118, |
|
1883 | def run(self, dataOut, radar=None, Pt=5000, Gt=295.1209, Gr=70.7945, Lambda=0.6741, aL=2.5118, | |
| 1884 | tauW=4e-06, ThetaT=0.1656317, ThetaR=0.36774087, Km2 = 0.93, Altitude=3350,SNRdBlimit=-30, |
|
1884 | tauW=4e-06, ThetaT=0.1656317, ThetaR=0.36774087, Km2 = 0.93, Altitude=3350,SNRdBlimit=-30, | |
| 1885 | channel=None): |
|
1885 | channel=None): | |
| 1886 |
|
1886 | |||
| 1887 | # print ('Entering PrecepitationProc ... ') |
|
1887 | # print ('Entering PrecepitationProc ... ') | |
| 1888 |
|
1888 | |||
| 1889 | if radar == "MIRA35C" : |
|
1889 | if radar == "MIRA35C" : | |
| 1890 |
|
1890 | |||
| 1891 | self.spc = dataOut.data_pre[0].copy() |
|
1891 | self.spc = dataOut.data_pre[0].copy() | |
| 1892 | self.Num_Hei = self.spc.shape[2] |
|
1892 | self.Num_Hei = self.spc.shape[2] | |
| 1893 | self.Num_Bin = self.spc.shape[1] |
|
1893 | self.Num_Bin = self.spc.shape[1] | |
| 1894 | self.Num_Chn = self.spc.shape[0] |
|
1894 | self.Num_Chn = self.spc.shape[0] | |
| 1895 | Ze = self.dBZeMODE2(dataOut) |
|
1895 | Ze = self.dBZeMODE2(dataOut) | |
| 1896 |
|
1896 | |||
| 1897 | else: |
|
1897 | else: | |
| 1898 |
|
1898 | |||
| 1899 | self.spc = dataOut.data_pre[0].copy() |
|
1899 | self.spc = dataOut.data_pre[0].copy() | |
| 1900 |
|
1900 | |||
| 1901 | #NOTA SE DEBE REMOVER EL RANGO DEL PULSO TX |
|
1901 | #NOTA SE DEBE REMOVER EL RANGO DEL PULSO TX | |
| 1902 | self.spc[:,:,0:7]= numpy.NaN |
|
1902 | self.spc[:,:,0:7]= numpy.NaN | |
| 1903 |
|
1903 | |||
| 1904 | self.Num_Hei = self.spc.shape[2] |
|
1904 | self.Num_Hei = self.spc.shape[2] | |
| 1905 | self.Num_Bin = self.spc.shape[1] |
|
1905 | self.Num_Bin = self.spc.shape[1] | |
| 1906 | self.Num_Chn = self.spc.shape[0] |
|
1906 | self.Num_Chn = self.spc.shape[0] | |
| 1907 |
|
1907 | |||
| 1908 | VelRange = dataOut.spc_range[2] |
|
1908 | VelRange = dataOut.spc_range[2] | |
| 1909 |
|
1909 | |||
| 1910 | ''' Se obtiene la constante del RADAR ''' |
|
1910 | ''' Se obtiene la constante del RADAR ''' | |
| 1911 |
|
1911 | |||
| 1912 | self.Pt = Pt |
|
1912 | self.Pt = Pt | |
| 1913 | self.Gt = Gt |
|
1913 | self.Gt = Gt | |
| 1914 | self.Gr = Gr |
|
1914 | self.Gr = Gr | |
| 1915 | self.Lambda = Lambda |
|
1915 | self.Lambda = Lambda | |
| 1916 | self.aL = aL |
|
1916 | self.aL = aL | |
| 1917 | self.tauW = tauW |
|
1917 | self.tauW = tauW | |
| 1918 | self.ThetaT = ThetaT |
|
1918 | self.ThetaT = ThetaT | |
| 1919 | self.ThetaR = ThetaR |
|
1919 | self.ThetaR = ThetaR | |
| 1920 | self.GSys = 10**(36.63/10) # Ganancia de los LNA 36.63 dB |
|
1920 | self.GSys = 10**(36.63/10) # Ganancia de los LNA 36.63 dB | |
| 1921 | self.lt = 10**(1.67/10) # Perdida en cables Tx 1.67 dB |
|
1921 | self.lt = 10**(1.67/10) # Perdida en cables Tx 1.67 dB | |
| 1922 | self.lr = 10**(5.73/10) # Perdida en cables Rx 5.73 dB |
|
1922 | self.lr = 10**(5.73/10) # Perdida en cables Rx 5.73 dB | |
| 1923 |
|
1923 | |||
| 1924 | Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) ) |
|
1924 | Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) ) | |
| 1925 | Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * tauW * numpy.pi * ThetaT * ThetaR) |
|
1925 | Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * tauW * numpy.pi * ThetaT * ThetaR) | |
| 1926 | RadarConstant = 10e-26 * Numerator / Denominator # |
|
1926 | RadarConstant = 10e-26 * Numerator / Denominator # | |
| 1927 | ExpConstant = 10**(40/10) #Constante Experimental |
|
1927 | ExpConstant = 10**(40/10) #Constante Experimental | |
| 1928 |
|
1928 | |||
| 1929 | SignalPower = numpy.zeros([self.Num_Chn,self.Num_Bin,self.Num_Hei]) |
|
1929 | SignalPower = numpy.zeros([self.Num_Chn,self.Num_Bin,self.Num_Hei]) | |
| 1930 | for i in range(self.Num_Chn): |
|
1930 | for i in range(self.Num_Chn): | |
| 1931 | SignalPower[i,:,:] = self.spc[i,:,:] - dataOut.noise[i] |
|
1931 | SignalPower[i,:,:] = self.spc[i,:,:] - dataOut.noise[i] | |
| 1932 | SignalPower[numpy.where(SignalPower < 0)] = 1e-20 |
|
1932 | SignalPower[numpy.where(SignalPower < 0)] = 1e-20 | |
| 1933 |
|
1933 | |||
| 1934 | if channel is None: |
|
1934 | if channel is None: | |
| 1935 | SPCmean = numpy.mean(SignalPower, 0) |
|
1935 | SPCmean = numpy.mean(SignalPower, 0) | |
| 1936 | else: |
|
1936 | else: | |
| 1937 | SPCmean = SignalPower[channel] |
|
1937 | SPCmean = SignalPower[channel] | |
| 1938 | Pr = SPCmean[:,:]/dataOut.normFactor |
|
1938 | Pr = SPCmean[:,:]/dataOut.normFactor | |
| 1939 |
|
1939 | |||
| 1940 | # Declaring auxiliary variables |
|
1940 | # Declaring auxiliary variables | |
| 1941 | Range = dataOut.heightList*1000. #Range in m |
|
1941 | Range = dataOut.heightList*1000. #Range in m | |
| 1942 | # replicate the heightlist to obtain a matrix [Num_Bin,Num_Hei] |
|
1942 | # replicate the heightlist to obtain a matrix [Num_Bin,Num_Hei] | |
| 1943 | rMtrx = numpy.transpose(numpy.transpose([dataOut.heightList*1000.] * self.Num_Bin)) |
|
1943 | rMtrx = numpy.transpose(numpy.transpose([dataOut.heightList*1000.] * self.Num_Bin)) | |
| 1944 | zMtrx = rMtrx+Altitude |
|
1944 | zMtrx = rMtrx+Altitude | |
| 1945 | # replicate the VelRange to obtain a matrix [Num_Bin,Num_Hei] |
|
1945 | # replicate the VelRange to obtain a matrix [Num_Bin,Num_Hei] | |
| 1946 | VelMtrx = numpy.transpose(numpy.tile(VelRange[:-1], (self.Num_Hei,1))) |
|
1946 | VelMtrx = numpy.transpose(numpy.tile(VelRange[:-1], (self.Num_Hei,1))) | |
| 1947 |
|
1947 | |||
| 1948 | # height dependence to air density Foote and Du Toit (1969) |
|
1948 | # height dependence to air density Foote and Du Toit (1969) | |
| 1949 | delv_z = 1 + 3.68e-5 * zMtrx + 1.71e-9 * zMtrx**2 |
|
1949 | delv_z = 1 + 3.68e-5 * zMtrx + 1.71e-9 * zMtrx**2 | |
| 1950 | VMtrx = VelMtrx / delv_z #Normalized velocity |
|
1950 | VMtrx = VelMtrx / delv_z #Normalized velocity | |
| 1951 | VMtrx[numpy.where(VMtrx> 9.6)] = numpy.NaN |
|
1951 | VMtrx[numpy.where(VMtrx> 9.6)] = numpy.NaN | |
| 1952 | # Diameter is related to the fall speed of falling drops |
|
1952 | # Diameter is related to the fall speed of falling drops | |
| 1953 | D_Vz = -1.667 * numpy.log( 0.9369 - 0.097087 * VMtrx ) # D in [mm] |
|
1953 | D_Vz = -1.667 * numpy.log( 0.9369 - 0.097087 * VMtrx ) # D in [mm] | |
| 1954 | # Only valid for D>= 0.16 mm |
|
1954 | # Only valid for D>= 0.16 mm | |
| 1955 | D_Vz[numpy.where(D_Vz < 0.16)] = numpy.NaN |
|
1955 | D_Vz[numpy.where(D_Vz < 0.16)] = numpy.NaN | |
| 1956 |
|
1956 | |||
| 1957 | #Calculate Radar Reflectivity ETAn |
|
1957 | #Calculate Radar Reflectivity ETAn | |
| 1958 | ETAn = (RadarConstant *ExpConstant) * Pr * rMtrx**2 #Reflectivity (ETA) |
|
1958 | ETAn = (RadarConstant *ExpConstant) * Pr * rMtrx**2 #Reflectivity (ETA) | |
| 1959 | ETAd = ETAn * 6.18 * exp( -0.6 * D_Vz ) * delv_z |
|
1959 | ETAd = ETAn * 6.18 * exp( -0.6 * D_Vz ) * delv_z | |
| 1960 | # Radar Cross Section |
|
1960 | # Radar Cross Section | |
| 1961 | sigmaD = Km2 * (D_Vz * 1e-3 )**6 * numpy.pi**5 / Lambda**4 |
|
1961 | sigmaD = Km2 * (D_Vz * 1e-3 )**6 * numpy.pi**5 / Lambda**4 | |
| 1962 | # Drop Size Distribution |
|
1962 | # Drop Size Distribution | |
| 1963 | DSD = ETAn / sigmaD |
|
1963 | DSD = ETAn / sigmaD | |
| 1964 | # Equivalente Reflectivy |
|
1964 | # Equivalente Reflectivy | |
| 1965 | Ze_eqn = numpy.nansum( DSD * D_Vz**6 ,axis=0) |
|
1965 | Ze_eqn = numpy.nansum( DSD * D_Vz**6 ,axis=0) | |
| 1966 | Ze_org = numpy.nansum(ETAn * Lambda**4, axis=0) / (1e-18*numpy.pi**5 * Km2) # [mm^6 /m^3] |
|
1966 | Ze_org = numpy.nansum(ETAn * Lambda**4, axis=0) / (1e-18*numpy.pi**5 * Km2) # [mm^6 /m^3] | |
| 1967 | # RainFall Rate |
|
1967 | # RainFall Rate | |
| 1968 | RR = 0.0006*numpy.pi * numpy.nansum( D_Vz**3 * DSD * VelMtrx ,0) #mm/hr |
|
1968 | RR = 0.0006*numpy.pi * numpy.nansum( D_Vz**3 * DSD * VelMtrx ,0) #mm/hr | |
| 1969 |
|
1969 | |||
| 1970 | # Censoring the data |
|
1970 | # Censoring the data | |
| 1971 | # Removing data with SNRth < 0dB se debe considerar el SNR por canal |
|
1971 | # Removing data with SNRth < 0dB se debe considerar el SNR por canal | |
| 1972 | SNRth = 10**(SNRdBlimit/10) #-30dB |
|
1972 | SNRth = 10**(SNRdBlimit/10) #-30dB | |
| 1973 | novalid = numpy.where((dataOut.data_snr[0,:] <SNRth) | (dataOut.data_snr[1,:] <SNRth) | (dataOut.data_snr[2,:] <SNRth)) # AND condition. Maybe OR condition better |
|
1973 | novalid = numpy.where((dataOut.data_snr[0,:] <SNRth) | (dataOut.data_snr[1,:] <SNRth) | (dataOut.data_snr[2,:] <SNRth)) # AND condition. Maybe OR condition better | |
| 1974 | W = numpy.nanmean(dataOut.data_dop,0) |
|
1974 | W = numpy.nanmean(dataOut.data_dop,0) | |
| 1975 | W[novalid] = numpy.NaN |
|
1975 | W[novalid] = numpy.NaN | |
| 1976 | Ze_org[novalid] = numpy.NaN |
|
1976 | Ze_org[novalid] = numpy.NaN | |
| 1977 | RR[novalid] = numpy.NaN |
|
1977 | RR[novalid] = numpy.NaN | |
| 1978 |
|
1978 | |||
| 1979 | dataOut.data_output = RR[8] |
|
1979 | dataOut.data_output = RR[8] | |
| 1980 | dataOut.data_param = numpy.ones([3,self.Num_Hei]) |
|
1980 | dataOut.data_param = numpy.ones([3,self.Num_Hei]) | |
| 1981 | dataOut.channelList = [0,1,2] |
|
1981 | dataOut.channelList = [0,1,2] | |
| 1982 |
|
1982 | |||
| 1983 | dataOut.data_param[0]=10*numpy.log10(Ze_org) |
|
1983 | dataOut.data_param[0]=10*numpy.log10(Ze_org) | |
| 1984 | dataOut.data_param[1]=-W |
|
1984 | dataOut.data_param[1]=-W | |
| 1985 | dataOut.data_param[2]=RR |
|
1985 | dataOut.data_param[2]=RR | |
| 1986 |
|
1986 | |||
| 1987 | # print ('Leaving PrecepitationProc ... ') |
|
1987 | # print ('Leaving PrecepitationProc ... ') | |
| 1988 | return dataOut |
|
1988 | return dataOut | |
| 1989 |
|
1989 | |||
| 1990 | def dBZeMODE2(self, dataOut): # Processing for MIRA35C |
|
1990 | def dBZeMODE2(self, dataOut): # Processing for MIRA35C | |
| 1991 |
|
1991 | |||
| 1992 | NPW = dataOut.NPW |
|
1992 | NPW = dataOut.NPW | |
| 1993 | COFA = dataOut.COFA |
|
1993 | COFA = dataOut.COFA | |
| 1994 |
|
1994 | |||
| 1995 | SNR = numpy.array([self.spc[0,:,:] / NPW[0]]) #, self.spc[1,:,:] / NPW[1]]) |
|
1995 | SNR = numpy.array([self.spc[0,:,:] / NPW[0]]) #, self.spc[1,:,:] / NPW[1]]) | |
| 1996 | RadarConst = dataOut.RadarConst |
|
1996 | RadarConst = dataOut.RadarConst | |
| 1997 | #frequency = 34.85*10**9 |
|
1997 | #frequency = 34.85*10**9 | |
| 1998 |
|
1998 | |||
| 1999 | ETA = numpy.zeros(([self.Num_Chn ,self.Num_Hei])) |
|
1999 | ETA = numpy.zeros(([self.Num_Chn ,self.Num_Hei])) | |
| 2000 | data_output = numpy.ones([self.Num_Chn , self.Num_Hei])*numpy.NaN |
|
2000 | data_output = numpy.ones([self.Num_Chn , self.Num_Hei])*numpy.NaN | |
| 2001 |
|
2001 | |||
| 2002 | ETA = numpy.sum(SNR,1) |
|
2002 | ETA = numpy.sum(SNR,1) | |
| 2003 |
|
2003 | |||
| 2004 | ETA = numpy.where(ETA != 0. , ETA, numpy.NaN) |
|
2004 | ETA = numpy.where(ETA != 0. , ETA, numpy.NaN) | |
| 2005 |
|
2005 | |||
| 2006 | Ze = numpy.ones([self.Num_Chn, self.Num_Hei] ) |
|
2006 | Ze = numpy.ones([self.Num_Chn, self.Num_Hei] ) | |
| 2007 |
|
2007 | |||
| 2008 | for r in range(self.Num_Hei): |
|
2008 | for r in range(self.Num_Hei): | |
| 2009 |
|
2009 | |||
| 2010 | Ze[0,r] = ( ETA[0,r] ) * COFA[0,r][0] * RadarConst * ((r/5000.)**2) |
|
2010 | Ze[0,r] = ( ETA[0,r] ) * COFA[0,r][0] * RadarConst * ((r/5000.)**2) | |
| 2011 | #Ze[1,r] = ( ETA[1,r] ) * COFA[1,r][0] * RadarConst * ((r/5000.)**2) |
|
2011 | #Ze[1,r] = ( ETA[1,r] ) * COFA[1,r][0] * RadarConst * ((r/5000.)**2) | |
| 2012 |
|
2012 | |||
| 2013 | return Ze |
|
2013 | return Ze | |
| 2014 |
|
2014 | |||
| 2015 | # def GetRadarConstant(self): |
|
2015 | # def GetRadarConstant(self): | |
| 2016 | # |
|
2016 | # | |
| 2017 | # """ |
|
2017 | # """ | |
| 2018 | # Constants: |
|
2018 | # Constants: | |
| 2019 | # |
|
2019 | # | |
| 2020 | # Pt: Transmission Power dB 5kW 5000 |
|
2020 | # Pt: Transmission Power dB 5kW 5000 | |
| 2021 | # Gt: Transmission Gain dB 24.7 dB 295.1209 |
|
2021 | # Gt: Transmission Gain dB 24.7 dB 295.1209 | |
| 2022 | # Gr: Reception Gain dB 18.5 dB 70.7945 |
|
2022 | # Gr: Reception Gain dB 18.5 dB 70.7945 | |
| 2023 | # Lambda: Wavelenght m 0.6741 m 0.6741 |
|
2023 | # Lambda: Wavelenght m 0.6741 m 0.6741 | |
| 2024 | # aL: Attenuation loses dB 4dB 2.5118 |
|
2024 | # aL: Attenuation loses dB 4dB 2.5118 | |
| 2025 | # tauW: Width of transmission pulse s 4us 4e-6 |
|
2025 | # tauW: Width of transmission pulse s 4us 4e-6 | |
| 2026 | # ThetaT: Transmission antenna bean angle rad 0.1656317 rad 0.1656317 |
|
2026 | # ThetaT: Transmission antenna bean angle rad 0.1656317 rad 0.1656317 | |
| 2027 | # ThetaR: Reception antenna beam angle rad 0.36774087 rad 0.36774087 |
|
2027 | # ThetaR: Reception antenna beam angle rad 0.36774087 rad 0.36774087 | |
| 2028 | # |
|
2028 | # | |
| 2029 | # """ |
|
2029 | # """ | |
| 2030 | # |
|
2030 | # | |
| 2031 | # Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) ) |
|
2031 | # Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) ) | |
| 2032 | # Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * TauW * numpy.pi * ThetaT * TheraR) |
|
2032 | # Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * TauW * numpy.pi * ThetaT * TheraR) | |
| 2033 | # RadarConstant = Numerator / Denominator |
|
2033 | # RadarConstant = Numerator / Denominator | |
| 2034 | # |
|
2034 | # | |
| 2035 | # return RadarConstant |
|
2035 | # return RadarConstant | |
| 2036 |
|
2036 | |||
| 2037 |
|
2037 | |||
| 2038 | class FullSpectralAnalysis(Operation): |
|
2038 | class FullSpectralAnalysis(Operation): | |
| 2039 |
|
2039 | |||
| 2040 | """ |
|
2040 | """ | |
| 2041 | Function that implements Full Spectral Analysis technique. |
|
2041 | Function that implements Full Spectral Analysis technique. | |
| 2042 |
|
2042 | |||
| 2043 | Input: |
|
2043 | Input: | |
| 2044 | self.dataOut.data_pre : SelfSpectra and CrossSpectra data |
|
2044 | self.dataOut.data_pre : SelfSpectra and CrossSpectra data | |
| 2045 | self.dataOut.groupList : Pairlist of channels |
|
2045 | self.dataOut.groupList : Pairlist of channels | |
| 2046 | self.dataOut.ChanDist : Physical distance between receivers |
|
2046 | self.dataOut.ChanDist : Physical distance between receivers | |
| 2047 |
|
2047 | |||
| 2048 |
|
2048 | |||
| 2049 | Output: |
|
2049 | Output: | |
| 2050 |
|
2050 | |||
| 2051 | self.dataOut.data_output : Zonal wind, Meridional wind, and Vertical wind |
|
2051 | self.dataOut.data_output : Zonal wind, Meridional wind, and Vertical wind | |
| 2052 |
|
2052 | |||
| 2053 |
|
2053 | |||
| 2054 | Parameters affected: Winds, height range, SNR |
|
2054 | Parameters affected: Winds, height range, SNR | |
| 2055 |
|
2055 | |||
| 2056 | """ |
|
2056 | """ | |
| 2057 | def run(self, dataOut, Xi01=None, Xi02=None, Xi12=None, Eta01=None, Eta02=None, Eta12=None, SNRdBlimit=-30, |
|
2057 | def run(self, dataOut, Xi01=None, Xi02=None, Xi12=None, Eta01=None, Eta02=None, Eta12=None, SNRdBlimit=-30, | |
| 2058 | minheight=None, maxheight=None, NegativeLimit=None, PositiveLimit=None): |
|
2058 | minheight=None, maxheight=None, NegativeLimit=None, PositiveLimit=None): | |
| 2059 |
|
2059 | |||
| 2060 | spc = dataOut.data_pre[0].copy() |
|
2060 | spc = dataOut.data_pre[0].copy() | |
| 2061 | cspc = dataOut.data_pre[1] |
|
2061 | cspc = dataOut.data_pre[1] | |
| 2062 | nHeights = spc.shape[2] |
|
2062 | nHeights = spc.shape[2] | |
| 2063 |
|
2063 | |||
| 2064 | # first_height = 0.75 #km (ref: data header 20170822) |
|
2064 | # first_height = 0.75 #km (ref: data header 20170822) | |
| 2065 | # resolution_height = 0.075 #km |
|
2065 | # resolution_height = 0.075 #km | |
| 2066 | ''' |
|
2066 | ''' | |
| 2067 | finding height range. check this when radar parameters are changed! |
|
2067 | finding height range. check this when radar parameters are changed! | |
| 2068 | ''' |
|
2068 | ''' | |
| 2069 | if maxheight is not None: |
|
2069 | if maxheight is not None: | |
| 2070 | # range_max = math.ceil((maxheight - first_height) / resolution_height) # theoretical |
|
2070 | # range_max = math.ceil((maxheight - first_height) / resolution_height) # theoretical | |
| 2071 | range_max = math.ceil(13.26 * maxheight - 3) # empirical, works better |
|
2071 | range_max = math.ceil(13.26 * maxheight - 3) # empirical, works better | |
| 2072 | else: |
|
2072 | else: | |
| 2073 | range_max = nHeights |
|
2073 | range_max = nHeights | |
| 2074 | if minheight is not None: |
|
2074 | if minheight is not None: | |
| 2075 | # range_min = int((minheight - first_height) / resolution_height) # theoretical |
|
2075 | # range_min = int((minheight - first_height) / resolution_height) # theoretical | |
| 2076 | range_min = int(13.26 * minheight - 5) # empirical, works better |
|
2076 | range_min = int(13.26 * minheight - 5) # empirical, works better | |
| 2077 | if range_min < 0: |
|
2077 | if range_min < 0: | |
| 2078 | range_min = 0 |
|
2078 | range_min = 0 | |
| 2079 | else: |
|
2079 | else: | |
| 2080 | range_min = 0 |
|
2080 | range_min = 0 | |
| 2081 |
|
2081 | |||
| 2082 | pairsList = dataOut.groupList |
|
2082 | pairsList = dataOut.groupList | |
| 2083 | if dataOut.ChanDist is not None : |
|
2083 | if dataOut.ChanDist is not None : | |
| 2084 | ChanDist = dataOut.ChanDist |
|
2084 | ChanDist = dataOut.ChanDist | |
| 2085 | else: |
|
2085 | else: | |
| 2086 | ChanDist = numpy.array([[Xi01, Eta01],[Xi02,Eta02],[Xi12,Eta12]]) |
|
2086 | ChanDist = numpy.array([[Xi01, Eta01],[Xi02,Eta02],[Xi12,Eta12]]) | |
| 2087 |
|
2087 | |||
| 2088 | # 4 variables: zonal, meridional, vertical, and average SNR |
|
2088 | # 4 variables: zonal, meridional, vertical, and average SNR | |
| 2089 | data_param = numpy.zeros([4,nHeights]) * numpy.NaN |
|
2089 | data_param = numpy.zeros([4,nHeights]) * numpy.NaN | |
| 2090 | velocityX = numpy.zeros([nHeights]) * numpy.NaN |
|
2090 | velocityX = numpy.zeros([nHeights]) * numpy.NaN | |
| 2091 | velocityY = numpy.zeros([nHeights]) * numpy.NaN |
|
2091 | velocityY = numpy.zeros([nHeights]) * numpy.NaN | |
| 2092 | velocityZ = numpy.zeros([nHeights]) * numpy.NaN |
|
2092 | velocityZ = numpy.zeros([nHeights]) * numpy.NaN | |
| 2093 |
|
2093 | |||
| 2094 | dbSNR = 10*numpy.log10(numpy.average(dataOut.data_snr,0)) |
|
2094 | dbSNR = 10*numpy.log10(numpy.average(dataOut.data_snr,0)) | |
| 2095 |
|
2095 | |||
| 2096 | '''***********************************************WIND ESTIMATION**************************************''' |
|
2096 | '''***********************************************WIND ESTIMATION**************************************''' | |
| 2097 | for Height in range(nHeights): |
|
2097 | for Height in range(nHeights): | |
| 2098 |
|
2098 | |||
| 2099 | if Height >= range_min and Height < range_max: |
|
2099 | if Height >= range_min and Height < range_max: | |
| 2100 | # error_code will be useful in future analysis |
|
2100 | # error_code will be useful in future analysis | |
| 2101 | [Vzon,Vmer,Vver, error_code] = self.WindEstimation(spc[:,:,Height], cspc[:,:,Height], pairsList, |
|
2101 | [Vzon,Vmer,Vver, error_code] = self.WindEstimation(spc[:,:,Height], cspc[:,:,Height], pairsList, | |
| 2102 | ChanDist, Height, dataOut.noise, dataOut.spc_range, dbSNR[Height], SNRdBlimit, NegativeLimit, PositiveLimit,dataOut.frequency) |
|
2102 | ChanDist, Height, dataOut.noise, dataOut.spc_range, dbSNR[Height], SNRdBlimit, NegativeLimit, PositiveLimit,dataOut.frequency) | |
| 2103 |
|
2103 | |||
| 2104 | if abs(Vzon) < 100. and abs(Vmer) < 100.: |
|
2104 | if abs(Vzon) < 100. and abs(Vmer) < 100.: | |
| 2105 | velocityX[Height] = Vzon |
|
2105 | velocityX[Height] = Vzon | |
| 2106 | velocityY[Height] = -Vmer |
|
2106 | velocityY[Height] = -Vmer | |
| 2107 | velocityZ[Height] = Vver |
|
2107 | velocityZ[Height] = Vver | |
| 2108 |
|
2108 | |||
| 2109 | # Censoring data with SNR threshold |
|
2109 | # Censoring data with SNR threshold | |
| 2110 | dbSNR [dbSNR < SNRdBlimit] = numpy.NaN |
|
2110 | dbSNR [dbSNR < SNRdBlimit] = numpy.NaN | |
| 2111 |
|
2111 | |||
| 2112 | data_param[0] = velocityX |
|
2112 | data_param[0] = velocityX | |
| 2113 | data_param[1] = velocityY |
|
2113 | data_param[1] = velocityY | |
| 2114 | data_param[2] = velocityZ |
|
2114 | data_param[2] = velocityZ | |
| 2115 | data_param[3] = dbSNR |
|
2115 | data_param[3] = dbSNR | |
| 2116 | dataOut.data_param = data_param |
|
2116 | dataOut.data_param = data_param | |
| 2117 | return dataOut |
|
2117 | return dataOut | |
| 2118 |
|
2118 | |||
| 2119 | def moving_average(self,x, N=2): |
|
2119 | def moving_average(self,x, N=2): | |
| 2120 | """ convolution for smoothenig data. note that last N-1 values are convolution with zeroes """ |
|
2120 | """ convolution for smoothenig data. note that last N-1 values are convolution with zeroes """ | |
| 2121 | return numpy.convolve(x, numpy.ones((N,))/N)[(N-1):] |
|
2121 | return numpy.convolve(x, numpy.ones((N,))/N)[(N-1):] | |
| 2122 |
|
2122 | |||
| 2123 | def gaus(self,xSamples,Amp,Mu,Sigma): |
|
2123 | def gaus(self,xSamples,Amp,Mu,Sigma): | |
| 2124 | return Amp * numpy.exp(-0.5*((xSamples - Mu)/Sigma)**2) |
|
2124 | return Amp * numpy.exp(-0.5*((xSamples - Mu)/Sigma)**2) | |
| 2125 |
|
2125 | |||
| 2126 | def Moments(self, ySamples, xSamples): |
|
2126 | def Moments(self, ySamples, xSamples): | |
| 2127 | Power = numpy.nanmean(ySamples) # Power, 0th Moment |
|
2127 | Power = numpy.nanmean(ySamples) # Power, 0th Moment | |
| 2128 | yNorm = ySamples / numpy.nansum(ySamples) |
|
2128 | yNorm = ySamples / numpy.nansum(ySamples) | |
| 2129 | RadVel = numpy.nansum(xSamples * yNorm) # Radial Velocity, 1st Moment |
|
2129 | RadVel = numpy.nansum(xSamples * yNorm) # Radial Velocity, 1st Moment | |
| 2130 | Sigma2 = numpy.nansum(yNorm * (xSamples - RadVel)**2) # Spectral Width, 2nd Moment |
|
2130 | Sigma2 = numpy.nansum(yNorm * (xSamples - RadVel)**2) # Spectral Width, 2nd Moment | |
| 2131 | StdDev = numpy.sqrt(numpy.abs(Sigma2)) # Desv. Estandar, Ancho espectral |
|
2131 | StdDev = numpy.sqrt(numpy.abs(Sigma2)) # Desv. Estandar, Ancho espectral | |
| 2132 | return numpy.array([Power,RadVel,StdDev]) |
|
2132 | return numpy.array([Power,RadVel,StdDev]) | |
| 2133 |
|
2133 | |||
| 2134 | def StopWindEstimation(self, error_code): |
|
2134 | def StopWindEstimation(self, error_code): | |
| 2135 | Vzon = numpy.NaN |
|
2135 | Vzon = numpy.NaN | |
| 2136 | Vmer = numpy.NaN |
|
2136 | Vmer = numpy.NaN | |
| 2137 | Vver = numpy.NaN |
|
2137 | Vver = numpy.NaN | |
| 2138 | return Vzon, Vmer, Vver, error_code |
|
2138 | return Vzon, Vmer, Vver, error_code | |
| 2139 |
|
2139 | |||
| 2140 | def AntiAliasing(self, interval, maxstep): |
|
2140 | def AntiAliasing(self, interval, maxstep): | |
| 2141 | """ |
|
2141 | """ | |
| 2142 | function to prevent errors from aliased values when computing phaseslope |
|
2142 | function to prevent errors from aliased values when computing phaseslope | |
| 2143 | """ |
|
2143 | """ | |
| 2144 | antialiased = numpy.zeros(len(interval)) |
|
2144 | antialiased = numpy.zeros(len(interval)) | |
| 2145 | copyinterval = interval.copy() |
|
2145 | copyinterval = interval.copy() | |
| 2146 |
|
2146 | |||
| 2147 | antialiased[0] = copyinterval[0] |
|
2147 | antialiased[0] = copyinterval[0] | |
| 2148 |
|
2148 | |||
| 2149 | for i in range(1,len(antialiased)): |
|
2149 | for i in range(1,len(antialiased)): | |
| 2150 | step = interval[i] - interval[i-1] |
|
2150 | step = interval[i] - interval[i-1] | |
| 2151 | if step > maxstep: |
|
2151 | if step > maxstep: | |
| 2152 | copyinterval -= 2*numpy.pi |
|
2152 | copyinterval -= 2*numpy.pi | |
| 2153 | antialiased[i] = copyinterval[i] |
|
2153 | antialiased[i] = copyinterval[i] | |
| 2154 | elif step < maxstep*(-1): |
|
2154 | elif step < maxstep*(-1): | |
| 2155 | copyinterval += 2*numpy.pi |
|
2155 | copyinterval += 2*numpy.pi | |
| 2156 | antialiased[i] = copyinterval[i] |
|
2156 | antialiased[i] = copyinterval[i] | |
| 2157 | else: |
|
2157 | else: | |
| 2158 | antialiased[i] = copyinterval[i].copy() |
|
2158 | antialiased[i] = copyinterval[i].copy() | |
| 2159 |
|
2159 | |||
| 2160 | return antialiased |
|
2160 | return antialiased | |
| 2161 |
|
2161 | |||
| 2162 | def WindEstimation(self, spc, cspc, pairsList, ChanDist, Height, noise, AbbsisaRange, dbSNR, SNRlimit, NegativeLimit, PositiveLimit, radfreq): |
|
2162 | def WindEstimation(self, spc, cspc, pairsList, ChanDist, Height, noise, AbbsisaRange, dbSNR, SNRlimit, NegativeLimit, PositiveLimit, radfreq): | |
| 2163 | """ |
|
2163 | """ | |
| 2164 | Function that Calculates Zonal, Meridional and Vertical wind velocities. |
|
2164 | Function that Calculates Zonal, Meridional and Vertical wind velocities. | |
| 2165 | Initial Version by E. Bocanegra updated by J. Zibell until Nov. 2019. |
|
2165 | Initial Version by E. Bocanegra updated by J. Zibell until Nov. 2019. | |
| 2166 |
|
2166 | |||
| 2167 | Input: |
|
2167 | Input: | |
| 2168 | spc, cspc : self spectra and cross spectra data. In Briggs notation something like S_i*(S_i)_conj, (S_j)_conj respectively. |
|
2168 | spc, cspc : self spectra and cross spectra data. In Briggs notation something like S_i*(S_i)_conj, (S_j)_conj respectively. | |
| 2169 | pairsList : Pairlist of channels |
|
2169 | pairsList : Pairlist of channels | |
| 2170 | ChanDist : array of xi_ij and eta_ij |
|
2170 | ChanDist : array of xi_ij and eta_ij | |
| 2171 | Height : height at which data is processed |
|
2171 | Height : height at which data is processed | |
| 2172 | noise : noise in [channels] format for specific height |
|
2172 | noise : noise in [channels] format for specific height | |
| 2173 | Abbsisarange : range of the frequencies or velocities |
|
2173 | Abbsisarange : range of the frequencies or velocities | |
| 2174 | dbSNR, SNRlimit : signal to noise ratio in db, lower limit |
|
2174 | dbSNR, SNRlimit : signal to noise ratio in db, lower limit | |
| 2175 |
|
2175 | |||
| 2176 | Output: |
|
2176 | Output: | |
| 2177 | Vzon, Vmer, Vver : wind velocities |
|
2177 | Vzon, Vmer, Vver : wind velocities | |
| 2178 | error_code : int that states where code is terminated |
|
2178 | error_code : int that states where code is terminated | |
| 2179 |
|
2179 | |||
| 2180 | 0 : no error detected |
|
2180 | 0 : no error detected | |
| 2181 | 1 : Gaussian of mean spc exceeds widthlimit |
|
2181 | 1 : Gaussian of mean spc exceeds widthlimit | |
| 2182 | 2 : no Gaussian of mean spc found |
|
2182 | 2 : no Gaussian of mean spc found | |
| 2183 | 3 : SNR to low or velocity to high -> prec. e.g. |
|
2183 | 3 : SNR to low or velocity to high -> prec. e.g. | |
| 2184 | 4 : at least one Gaussian of cspc exceeds widthlimit |
|
2184 | 4 : at least one Gaussian of cspc exceeds widthlimit | |
| 2185 | 5 : zero out of three cspc Gaussian fits converged |
|
2185 | 5 : zero out of three cspc Gaussian fits converged | |
| 2186 | 6 : phase slope fit could not be found |
|
2186 | 6 : phase slope fit could not be found | |
| 2187 | 7 : arrays used to fit phase have different length |
|
2187 | 7 : arrays used to fit phase have different length | |
| 2188 | 8 : frequency range is either too short (len <= 5) or very long (> 30% of cspc) |
|
2188 | 8 : frequency range is either too short (len <= 5) or very long (> 30% of cspc) | |
| 2189 |
|
2189 | |||
| 2190 | """ |
|
2190 | """ | |
| 2191 |
|
2191 | |||
| 2192 | error_code = 0 |
|
2192 | error_code = 0 | |
| 2193 |
|
2193 | |||
| 2194 | nChan = spc.shape[0] |
|
2194 | nChan = spc.shape[0] | |
| 2195 | nProf = spc.shape[1] |
|
2195 | nProf = spc.shape[1] | |
| 2196 | nPair = cspc.shape[0] |
|
2196 | nPair = cspc.shape[0] | |
| 2197 |
|
2197 | |||
| 2198 | SPC_Samples = numpy.zeros([nChan, nProf]) # for normalized spc values for one height |
|
2198 | SPC_Samples = numpy.zeros([nChan, nProf]) # for normalized spc values for one height | |
| 2199 | CSPC_Samples = numpy.zeros([nPair, nProf], dtype=numpy.complex_) # for normalized cspc values |
|
2199 | CSPC_Samples = numpy.zeros([nPair, nProf], dtype=numpy.complex_) # for normalized cspc values | |
| 2200 | phase = numpy.zeros([nPair, nProf]) # phase between channels |
|
2200 | phase = numpy.zeros([nPair, nProf]) # phase between channels | |
| 2201 | PhaseSlope = numpy.zeros(nPair) # slope of the phases, channelwise |
|
2201 | PhaseSlope = numpy.zeros(nPair) # slope of the phases, channelwise | |
| 2202 | PhaseInter = numpy.zeros(nPair) # intercept to the slope of the phases, channelwise |
|
2202 | PhaseInter = numpy.zeros(nPair) # intercept to the slope of the phases, channelwise | |
| 2203 | xFrec = AbbsisaRange[0][:-1] # frequency range |
|
2203 | xFrec = AbbsisaRange[0][:-1] # frequency range | |
| 2204 | xVel = AbbsisaRange[2][:-1] # velocity range |
|
2204 | xVel = AbbsisaRange[2][:-1] # velocity range | |
| 2205 | xSamples = xFrec # the frequency range is taken |
|
2205 | xSamples = xFrec # the frequency range is taken | |
| 2206 | delta_x = xSamples[1] - xSamples[0] # delta_f or delta_x |
|
2206 | delta_x = xSamples[1] - xSamples[0] # delta_f or delta_x | |
| 2207 |
|
2207 | |||
| 2208 | # only consider velocities with in NegativeLimit and PositiveLimit |
|
2208 | # only consider velocities with in NegativeLimit and PositiveLimit | |
| 2209 | if (NegativeLimit is None): |
|
2209 | if (NegativeLimit is None): | |
| 2210 | NegativeLimit = numpy.min(xVel) |
|
2210 | NegativeLimit = numpy.min(xVel) | |
| 2211 | if (PositiveLimit is None): |
|
2211 | if (PositiveLimit is None): | |
| 2212 | PositiveLimit = numpy.max(xVel) |
|
2212 | PositiveLimit = numpy.max(xVel) | |
| 2213 | xvalid = numpy.where((xVel > NegativeLimit) & (xVel < PositiveLimit)) |
|
2213 | xvalid = numpy.where((xVel > NegativeLimit) & (xVel < PositiveLimit)) | |
| 2214 | xSamples_zoom = xSamples[xvalid] |
|
2214 | xSamples_zoom = xSamples[xvalid] | |
| 2215 |
|
2215 | |||
| 2216 | '''Getting Eij and Nij''' |
|
2216 | '''Getting Eij and Nij''' | |
| 2217 | Xi01, Xi02, Xi12 = ChanDist[:,0] |
|
2217 | Xi01, Xi02, Xi12 = ChanDist[:,0] | |
| 2218 | Eta01, Eta02, Eta12 = ChanDist[:,1] |
|
2218 | Eta01, Eta02, Eta12 = ChanDist[:,1] | |
| 2219 |
|
2219 | |||
| 2220 | # spwd limit - updated by D. ScipiΓ³n 30.03.2021 |
|
2220 | # spwd limit - updated by D. ScipiΓ³n 30.03.2021 | |
| 2221 | widthlimit = 10 |
|
2221 | widthlimit = 10 | |
| 2222 | '''************************* SPC is normalized ********************************''' |
|
2222 | '''************************* SPC is normalized ********************************''' | |
| 2223 | spc_norm = spc.copy() |
|
2223 | spc_norm = spc.copy() | |
| 2224 | # For each channel |
|
2224 | # For each channel | |
| 2225 | for i in range(nChan): |
|
2225 | for i in range(nChan): | |
| 2226 | spc_sub = spc_norm[i,:] - noise[i] # only the signal power |
|
2226 | spc_sub = spc_norm[i,:] - noise[i] # only the signal power | |
| 2227 | SPC_Samples[i] = spc_sub / (numpy.nansum(spc_sub) * delta_x) |
|
2227 | SPC_Samples[i] = spc_sub / (numpy.nansum(spc_sub) * delta_x) | |
| 2228 |
|
2228 | |||
| 2229 | '''********************** FITTING MEAN SPC GAUSSIAN **********************''' |
|
2229 | '''********************** FITTING MEAN SPC GAUSSIAN **********************''' | |
| 2230 |
|
2230 | |||
| 2231 | """ the gaussian of the mean: first subtract noise, then normalize. this is legal because |
|
2231 | """ the gaussian of the mean: first subtract noise, then normalize. this is legal because | |
| 2232 | you only fit the curve and don't need the absolute value of height for calculation, |
|
2232 | you only fit the curve and don't need the absolute value of height for calculation, | |
| 2233 | only for estimation of width. for normalization of cross spectra, you need initial, |
|
2233 | only for estimation of width. for normalization of cross spectra, you need initial, | |
| 2234 | unnormalized self-spectra With noise. |
|
2234 | unnormalized self-spectra With noise. | |
| 2235 |
|
2235 | |||
| 2236 | Technically, you don't even need to normalize the self-spectra, as you only need the |
|
2236 | Technically, you don't even need to normalize the self-spectra, as you only need the | |
| 2237 | width of the peak. However, it was left this way. Note that the normalization has a flaw: |
|
2237 | width of the peak. However, it was left this way. Note that the normalization has a flaw: | |
| 2238 | due to subtraction of the noise, some values are below zero. Raw "spc" values should be |
|
2238 | due to subtraction of the noise, some values are below zero. Raw "spc" values should be | |
| 2239 | >= 0, as it is the modulus squared of the signals (complex * it's conjugate) |
|
2239 | >= 0, as it is the modulus squared of the signals (complex * it's conjugate) | |
| 2240 | """ |
|
2240 | """ | |
| 2241 | # initial conditions |
|
2241 | # initial conditions | |
| 2242 | popt = [1e-10,0,1e-10] |
|
2242 | popt = [1e-10,0,1e-10] | |
| 2243 | # Spectra average |
|
2243 | # Spectra average | |
| 2244 | SPCMean = numpy.average(SPC_Samples,0) |
|
2244 | SPCMean = numpy.average(SPC_Samples,0) | |
| 2245 | # Moments in frequency |
|
2245 | # Moments in frequency | |
| 2246 | SPCMoments = self.Moments(SPCMean[xvalid], xSamples_zoom) |
|
2246 | SPCMoments = self.Moments(SPCMean[xvalid], xSamples_zoom) | |
| 2247 |
|
2247 | |||
| 2248 | # Gauss Fit SPC in frequency domain |
|
2248 | # Gauss Fit SPC in frequency domain | |
| 2249 | if dbSNR > SNRlimit: # only if SNR > SNRth |
|
2249 | if dbSNR > SNRlimit: # only if SNR > SNRth | |
| 2250 | try: |
|
2250 | try: | |
| 2251 | popt,pcov = curve_fit(self.gaus,xSamples_zoom,SPCMean[xvalid],p0=SPCMoments) |
|
2251 | popt,pcov = curve_fit(self.gaus,xSamples_zoom,SPCMean[xvalid],p0=SPCMoments) | |
| 2252 | if popt[2] <= 0 or popt[2] > widthlimit: # CONDITION |
|
2252 | if popt[2] <= 0 or popt[2] > widthlimit: # CONDITION | |
| 2253 | return self.StopWindEstimation(error_code = 1) |
|
2253 | return self.StopWindEstimation(error_code = 1) | |
| 2254 | FitGauss = self.gaus(xSamples_zoom,*popt) |
|
2254 | FitGauss = self.gaus(xSamples_zoom,*popt) | |
| 2255 | except :#RuntimeError: |
|
2255 | except :#RuntimeError: | |
| 2256 | return self.StopWindEstimation(error_code = 2) |
|
2256 | return self.StopWindEstimation(error_code = 2) | |
| 2257 | else: |
|
2257 | else: | |
| 2258 | return self.StopWindEstimation(error_code = 3) |
|
2258 | return self.StopWindEstimation(error_code = 3) | |
| 2259 |
|
2259 | |||
| 2260 | '''***************************** CSPC Normalization ************************* |
|
2260 | '''***************************** CSPC Normalization ************************* | |
| 2261 | The Spc spectra are used to normalize the crossspectra. Peaks from precipitation |
|
2261 | The Spc spectra are used to normalize the crossspectra. Peaks from precipitation | |
| 2262 | influence the norm which is not desired. First, a range is identified where the |
|
2262 | influence the norm which is not desired. First, a range is identified where the | |
| 2263 | wind peak is estimated -> sum_wind is sum of those frequencies. Next, the area |
|
2263 | wind peak is estimated -> sum_wind is sum of those frequencies. Next, the area | |
| 2264 | around it gets cut off and values replaced by mean determined by the boundary |
|
2264 | around it gets cut off and values replaced by mean determined by the boundary | |
| 2265 | data -> sum_noise (spc is not normalized here, thats why the noise is important) |
|
2265 | data -> sum_noise (spc is not normalized here, thats why the noise is important) | |
| 2266 |
|
2266 | |||
| 2267 | The sums are then added and multiplied by range/datapoints, because you need |
|
2267 | The sums are then added and multiplied by range/datapoints, because you need | |
| 2268 | an integral and not a sum for normalization. |
|
2268 | an integral and not a sum for normalization. | |
| 2269 |
|
2269 | |||
| 2270 | A norm is found according to Briggs 92. |
|
2270 | A norm is found according to Briggs 92. | |
| 2271 | ''' |
|
2271 | ''' | |
| 2272 | # for each pair |
|
2272 | # for each pair | |
| 2273 | for i in range(nPair): |
|
2273 | for i in range(nPair): | |
| 2274 | cspc_norm = cspc[i,:].copy() |
|
2274 | cspc_norm = cspc[i,:].copy() | |
| 2275 | chan_index0 = pairsList[i][0] |
|
2275 | chan_index0 = pairsList[i][0] | |
| 2276 | chan_index1 = pairsList[i][1] |
|
2276 | chan_index1 = pairsList[i][1] | |
| 2277 | CSPC_Samples[i] = cspc_norm / (numpy.sqrt(numpy.nansum(spc_norm[chan_index0])*numpy.nansum(spc_norm[chan_index1])) * delta_x) |
|
2277 | CSPC_Samples[i] = cspc_norm / (numpy.sqrt(numpy.nansum(spc_norm[chan_index0])*numpy.nansum(spc_norm[chan_index1])) * delta_x) | |
| 2278 | phase[i] = numpy.arctan2(CSPC_Samples[i].imag, CSPC_Samples[i].real) |
|
2278 | phase[i] = numpy.arctan2(CSPC_Samples[i].imag, CSPC_Samples[i].real) | |
| 2279 |
|
2279 | |||
| 2280 | CSPCmoments = numpy.vstack([self.Moments(numpy.abs(CSPC_Samples[0,xvalid]), xSamples_zoom), |
|
2280 | CSPCmoments = numpy.vstack([self.Moments(numpy.abs(CSPC_Samples[0,xvalid]), xSamples_zoom), | |
| 2281 | self.Moments(numpy.abs(CSPC_Samples[1,xvalid]), xSamples_zoom), |
|
2281 | self.Moments(numpy.abs(CSPC_Samples[1,xvalid]), xSamples_zoom), | |
| 2282 | self.Moments(numpy.abs(CSPC_Samples[2,xvalid]), xSamples_zoom)]) |
|
2282 | self.Moments(numpy.abs(CSPC_Samples[2,xvalid]), xSamples_zoom)]) | |
| 2283 |
|
2283 | |||
| 2284 | popt01, popt02, popt12 = [1e-10,0,1e-10], [1e-10,0,1e-10] ,[1e-10,0,1e-10] |
|
2284 | popt01, popt02, popt12 = [1e-10,0,1e-10], [1e-10,0,1e-10] ,[1e-10,0,1e-10] | |
| 2285 | FitGauss01, FitGauss02, FitGauss12 = numpy.zeros(len(xSamples)), numpy.zeros(len(xSamples)), numpy.zeros(len(xSamples)) |
|
2285 | FitGauss01, FitGauss02, FitGauss12 = numpy.zeros(len(xSamples)), numpy.zeros(len(xSamples)), numpy.zeros(len(xSamples)) | |
| 2286 |
|
2286 | |||
| 2287 | '''*******************************FIT GAUSS CSPC************************************''' |
|
2287 | '''*******************************FIT GAUSS CSPC************************************''' | |
| 2288 | try: |
|
2288 | try: | |
| 2289 | popt01,pcov = curve_fit(self.gaus,xSamples_zoom,numpy.abs(CSPC_Samples[0][xvalid]),p0=CSPCmoments[0]) |
|
2289 | popt01,pcov = curve_fit(self.gaus,xSamples_zoom,numpy.abs(CSPC_Samples[0][xvalid]),p0=CSPCmoments[0]) | |
| 2290 | if popt01[2] > widthlimit: # CONDITION |
|
2290 | if popt01[2] > widthlimit: # CONDITION | |
| 2291 | return self.StopWindEstimation(error_code = 4) |
|
2291 | return self.StopWindEstimation(error_code = 4) | |
| 2292 | popt02,pcov = curve_fit(self.gaus,xSamples_zoom,numpy.abs(CSPC_Samples[1][xvalid]),p0=CSPCmoments[1]) |
|
2292 | popt02,pcov = curve_fit(self.gaus,xSamples_zoom,numpy.abs(CSPC_Samples[1][xvalid]),p0=CSPCmoments[1]) | |
| 2293 | if popt02[2] > widthlimit: # CONDITION |
|
2293 | if popt02[2] > widthlimit: # CONDITION | |
| 2294 | return self.StopWindEstimation(error_code = 4) |
|
2294 | return self.StopWindEstimation(error_code = 4) | |
| 2295 | popt12,pcov = curve_fit(self.gaus,xSamples_zoom,numpy.abs(CSPC_Samples[2][xvalid]),p0=CSPCmoments[2]) |
|
2295 | popt12,pcov = curve_fit(self.gaus,xSamples_zoom,numpy.abs(CSPC_Samples[2][xvalid]),p0=CSPCmoments[2]) | |
| 2296 | if popt12[2] > widthlimit: # CONDITION |
|
2296 | if popt12[2] > widthlimit: # CONDITION | |
| 2297 | return self.StopWindEstimation(error_code = 4) |
|
2297 | return self.StopWindEstimation(error_code = 4) | |
| 2298 |
|
2298 | |||
| 2299 | FitGauss01 = self.gaus(xSamples_zoom, *popt01) |
|
2299 | FitGauss01 = self.gaus(xSamples_zoom, *popt01) | |
| 2300 | FitGauss02 = self.gaus(xSamples_zoom, *popt02) |
|
2300 | FitGauss02 = self.gaus(xSamples_zoom, *popt02) | |
| 2301 | FitGauss12 = self.gaus(xSamples_zoom, *popt12) |
|
2301 | FitGauss12 = self.gaus(xSamples_zoom, *popt12) | |
| 2302 | except: |
|
2302 | except: | |
| 2303 | return self.StopWindEstimation(error_code = 5) |
|
2303 | return self.StopWindEstimation(error_code = 5) | |
| 2304 |
|
2304 | |||
| 2305 |
|
2305 | |||
| 2306 | '''************* Getting Fij ***************''' |
|
2306 | '''************* Getting Fij ***************''' | |
| 2307 | # x-axis point of the gaussian where the center is located from GaussFit of spectra |
|
2307 | # x-axis point of the gaussian where the center is located from GaussFit of spectra | |
| 2308 | GaussCenter = popt[1] |
|
2308 | GaussCenter = popt[1] | |
| 2309 | ClosestCenter = xSamples_zoom[numpy.abs(xSamples_zoom-GaussCenter).argmin()] |
|
2309 | ClosestCenter = xSamples_zoom[numpy.abs(xSamples_zoom-GaussCenter).argmin()] | |
| 2310 | PointGauCenter = numpy.where(xSamples_zoom==ClosestCenter)[0][0] |
|
2310 | PointGauCenter = numpy.where(xSamples_zoom==ClosestCenter)[0][0] | |
| 2311 |
|
2311 | |||
| 2312 | # Point where e^-1 is located in the gaussian |
|
2312 | # Point where e^-1 is located in the gaussian | |
| 2313 | PeMinus1 = numpy.max(FitGauss) * numpy.exp(-1) |
|
2313 | PeMinus1 = numpy.max(FitGauss) * numpy.exp(-1) | |
| 2314 | FijClosest = FitGauss[numpy.abs(FitGauss-PeMinus1).argmin()] # The closest point to"Peminus1" in "FitGauss" |
|
2314 | FijClosest = FitGauss[numpy.abs(FitGauss-PeMinus1).argmin()] # The closest point to"Peminus1" in "FitGauss" | |
| 2315 | PointFij = numpy.where(FitGauss==FijClosest)[0][0] |
|
2315 | PointFij = numpy.where(FitGauss==FijClosest)[0][0] | |
| 2316 | Fij = numpy.abs(xSamples_zoom[PointFij] - xSamples_zoom[PointGauCenter]) |
|
2316 | Fij = numpy.abs(xSamples_zoom[PointFij] - xSamples_zoom[PointGauCenter]) | |
| 2317 |
|
2317 | |||
| 2318 | '''********** Taking frequency ranges from mean SPCs **********''' |
|
2318 | '''********** Taking frequency ranges from mean SPCs **********''' | |
| 2319 | GauWidth = popt[2] * 3/2 # Bandwidth of Gau01 |
|
2319 | GauWidth = popt[2] * 3/2 # Bandwidth of Gau01 | |
| 2320 | Range = numpy.empty(2) |
|
2320 | Range = numpy.empty(2) | |
| 2321 | Range[0] = GaussCenter - GauWidth |
|
2321 | Range[0] = GaussCenter - GauWidth | |
| 2322 | Range[1] = GaussCenter + GauWidth |
|
2322 | Range[1] = GaussCenter + GauWidth | |
| 2323 | # Point in x-axis where the bandwidth is located (min:max) |
|
2323 | # Point in x-axis where the bandwidth is located (min:max) | |
| 2324 | ClosRangeMin = xSamples_zoom[numpy.abs(xSamples_zoom-Range[0]).argmin()] |
|
2324 | ClosRangeMin = xSamples_zoom[numpy.abs(xSamples_zoom-Range[0]).argmin()] | |
| 2325 | ClosRangeMax = xSamples_zoom[numpy.abs(xSamples_zoom-Range[1]).argmin()] |
|
2325 | ClosRangeMax = xSamples_zoom[numpy.abs(xSamples_zoom-Range[1]).argmin()] | |
| 2326 | PointRangeMin = numpy.where(xSamples_zoom==ClosRangeMin)[0][0] |
|
2326 | PointRangeMin = numpy.where(xSamples_zoom==ClosRangeMin)[0][0] | |
| 2327 | PointRangeMax = numpy.where(xSamples_zoom==ClosRangeMax)[0][0] |
|
2327 | PointRangeMax = numpy.where(xSamples_zoom==ClosRangeMax)[0][0] | |
| 2328 | Range = numpy.array([ PointRangeMin, PointRangeMax ]) |
|
2328 | Range = numpy.array([ PointRangeMin, PointRangeMax ]) | |
| 2329 | FrecRange = xSamples_zoom[ Range[0] : Range[1] ] |
|
2329 | FrecRange = xSamples_zoom[ Range[0] : Range[1] ] | |
| 2330 |
|
2330 | |||
| 2331 | '''************************** Getting Phase Slope ***************************''' |
|
2331 | '''************************** Getting Phase Slope ***************************''' | |
| 2332 | for i in range(nPair): |
|
2332 | for i in range(nPair): | |
| 2333 | if len(FrecRange) > 5: |
|
2333 | if len(FrecRange) > 5: | |
| 2334 | PhaseRange = phase[i, xvalid[0][Range[0]:Range[1]]].copy() |
|
2334 | PhaseRange = phase[i, xvalid[0][Range[0]:Range[1]]].copy() | |
| 2335 | mask = ~numpy.isnan(FrecRange) & ~numpy.isnan(PhaseRange) |
|
2335 | mask = ~numpy.isnan(FrecRange) & ~numpy.isnan(PhaseRange) | |
| 2336 | if len(FrecRange) == len(PhaseRange): |
|
2336 | if len(FrecRange) == len(PhaseRange): | |
| 2337 | try: |
|
2337 | try: | |
| 2338 | slope, intercept, _, _, _ = stats.linregress(FrecRange[mask], self.AntiAliasing(PhaseRange[mask], 4.5)) |
|
2338 | slope, intercept, _, _, _ = stats.linregress(FrecRange[mask], self.AntiAliasing(PhaseRange[mask], 4.5)) | |
| 2339 | PhaseSlope[i] = slope |
|
2339 | PhaseSlope[i] = slope | |
| 2340 | PhaseInter[i] = intercept |
|
2340 | PhaseInter[i] = intercept | |
| 2341 | except: |
|
2341 | except: | |
| 2342 | return self.StopWindEstimation(error_code = 6) |
|
2342 | return self.StopWindEstimation(error_code = 6) | |
| 2343 | else: |
|
2343 | else: | |
| 2344 | return self.StopWindEstimation(error_code = 7) |
|
2344 | return self.StopWindEstimation(error_code = 7) | |
| 2345 | else: |
|
2345 | else: | |
| 2346 | return self.StopWindEstimation(error_code = 8) |
|
2346 | return self.StopWindEstimation(error_code = 8) | |
| 2347 |
|
2347 | |||
| 2348 | '''*** Constants A-H correspond to the convention as in Briggs and Vincent 1992 ***''' |
|
2348 | '''*** Constants A-H correspond to the convention as in Briggs and Vincent 1992 ***''' | |
| 2349 |
|
2349 | |||
| 2350 | '''Getting constant C''' |
|
2350 | '''Getting constant C''' | |
| 2351 | cC=(Fij*numpy.pi)**2 |
|
2351 | cC=(Fij*numpy.pi)**2 | |
| 2352 |
|
2352 | |||
| 2353 | '''****** Getting constants F and G ******''' |
|
2353 | '''****** Getting constants F and G ******''' | |
| 2354 | MijEijNij = numpy.array([[Xi02,Eta02], [Xi12,Eta12]]) |
|
2354 | MijEijNij = numpy.array([[Xi02,Eta02], [Xi12,Eta12]]) | |
| 2355 | # MijEijNij = numpy.array([[Xi01,Eta01], [Xi02,Eta02], [Xi12,Eta12]]) |
|
2355 | # MijEijNij = numpy.array([[Xi01,Eta01], [Xi02,Eta02], [Xi12,Eta12]]) | |
| 2356 | # MijResult0 = (-PhaseSlope[0] * cC) / (2*numpy.pi) |
|
2356 | # MijResult0 = (-PhaseSlope[0] * cC) / (2*numpy.pi) | |
| 2357 | MijResult1 = (-PhaseSlope[1] * cC) / (2*numpy.pi) |
|
2357 | MijResult1 = (-PhaseSlope[1] * cC) / (2*numpy.pi) | |
| 2358 | MijResult2 = (-PhaseSlope[2] * cC) / (2*numpy.pi) |
|
2358 | MijResult2 = (-PhaseSlope[2] * cC) / (2*numpy.pi) | |
| 2359 | # MijResults = numpy.array([MijResult0, MijResult1, MijResult2]) |
|
2359 | # MijResults = numpy.array([MijResult0, MijResult1, MijResult2]) | |
| 2360 | MijResults = numpy.array([MijResult1, MijResult2]) |
|
2360 | MijResults = numpy.array([MijResult1, MijResult2]) | |
| 2361 | (cF,cG) = numpy.linalg.solve(MijEijNij, MijResults) |
|
2361 | (cF,cG) = numpy.linalg.solve(MijEijNij, MijResults) | |
| 2362 |
|
2362 | |||
| 2363 | '''****** Getting constants A, B and H ******''' |
|
2363 | '''****** Getting constants A, B and H ******''' | |
| 2364 | W01 = numpy.nanmax( FitGauss01 ) |
|
2364 | W01 = numpy.nanmax( FitGauss01 ) | |
| 2365 | W02 = numpy.nanmax( FitGauss02 ) |
|
2365 | W02 = numpy.nanmax( FitGauss02 ) | |
| 2366 | W12 = numpy.nanmax( FitGauss12 ) |
|
2366 | W12 = numpy.nanmax( FitGauss12 ) | |
| 2367 |
|
2367 | |||
| 2368 | WijResult01 = ((cF * Xi01 + cG * Eta01)**2)/cC - numpy.log(W01 / numpy.sqrt(numpy.pi / cC)) |
|
2368 | WijResult01 = ((cF * Xi01 + cG * Eta01)**2)/cC - numpy.log(W01 / numpy.sqrt(numpy.pi / cC)) | |
| 2369 | WijResult02 = ((cF * Xi02 + cG * Eta02)**2)/cC - numpy.log(W02 / numpy.sqrt(numpy.pi / cC)) |
|
2369 | WijResult02 = ((cF * Xi02 + cG * Eta02)**2)/cC - numpy.log(W02 / numpy.sqrt(numpy.pi / cC)) | |
| 2370 | WijResult12 = ((cF * Xi12 + cG * Eta12)**2)/cC - numpy.log(W12 / numpy.sqrt(numpy.pi / cC)) |
|
2370 | WijResult12 = ((cF * Xi12 + cG * Eta12)**2)/cC - numpy.log(W12 / numpy.sqrt(numpy.pi / cC)) | |
| 2371 | WijResults = numpy.array([WijResult01, WijResult02, WijResult12]) |
|
2371 | WijResults = numpy.array([WijResult01, WijResult02, WijResult12]) | |
| 2372 |
|
2372 | |||
| 2373 | WijEijNij = numpy.array([ [Xi01**2, Eta01**2, 2*Xi01*Eta01] , [Xi02**2, Eta02**2, 2*Xi02*Eta02] , [Xi12**2, Eta12**2, 2*Xi12*Eta12] ]) |
|
2373 | WijEijNij = numpy.array([ [Xi01**2, Eta01**2, 2*Xi01*Eta01] , [Xi02**2, Eta02**2, 2*Xi02*Eta02] , [Xi12**2, Eta12**2, 2*Xi12*Eta12] ]) | |
| 2374 | (cA,cB,cH) = numpy.linalg.solve(WijEijNij, WijResults) |
|
2374 | (cA,cB,cH) = numpy.linalg.solve(WijEijNij, WijResults) | |
| 2375 |
|
2375 | |||
| 2376 | VxVy = numpy.array([[cA,cH],[cH,cB]]) |
|
2376 | VxVy = numpy.array([[cA,cH],[cH,cB]]) | |
| 2377 | VxVyResults = numpy.array([-cF,-cG]) |
|
2377 | VxVyResults = numpy.array([-cF,-cG]) | |
| 2378 | (Vmer,Vzon) = numpy.linalg.solve(VxVy, VxVyResults) |
|
2378 | (Vmer,Vzon) = numpy.linalg.solve(VxVy, VxVyResults) | |
| 2379 | Vver = -SPCMoments[1]*SPEED_OF_LIGHT/(2*radfreq) |
|
2379 | Vver = -SPCMoments[1]*SPEED_OF_LIGHT/(2*radfreq) | |
| 2380 | error_code = 0 |
|
2380 | error_code = 0 | |
| 2381 |
|
2381 | |||
| 2382 | return Vzon, Vmer, Vver, error_code |
|
2382 | return Vzon, Vmer, Vver, error_code | |
| 2383 |
|
2383 | |||
| 2384 | class SpectralMoments(Operation): |
|
2384 | class SpectralMoments(Operation): | |
| 2385 |
|
2385 | |||
| 2386 | ''' |
|
2386 | ''' | |
| 2387 | Function SpectralMoments() |
|
2387 | Function SpectralMoments() | |
| 2388 |
|
2388 | |||
| 2389 | Calculates moments (power, mean, standard deviation) and SNR of the signal |
|
2389 | Calculates moments (power, mean, standard deviation) and SNR of the signal | |
| 2390 |
|
2390 | |||
| 2391 | Type of dataIn: Spectra |
|
2391 | Type of dataIn: Spectra | |
| 2392 |
|
2392 | |||
| 2393 | Configuration Parameters: |
|
2393 | Configuration Parameters: | |
| 2394 |
|
2394 | |||
| 2395 | dirCosx : Cosine director in X axis |
|
2395 | dirCosx : Cosine director in X axis | |
| 2396 | dirCosy : Cosine director in Y axis |
|
2396 | dirCosy : Cosine director in Y axis | |
| 2397 |
|
2397 | |||
| 2398 | elevation : |
|
2398 | elevation : | |
| 2399 | azimuth : |
|
2399 | azimuth : | |
| 2400 |
|
2400 | |||
| 2401 | Input: |
|
2401 | Input: | |
| 2402 | channelList : simple channel list to select e.g. [2,3,7] |
|
2402 | channelList : simple channel list to select e.g. [2,3,7] | |
| 2403 | self.dataOut.data_pre : Spectral data |
|
2403 | self.dataOut.data_pre : Spectral data | |
| 2404 | self.dataOut.abscissaList : List of frequencies |
|
2404 | self.dataOut.abscissaList : List of frequencies | |
| 2405 | self.dataOut.noise : Noise level per channel |
|
2405 | self.dataOut.noise : Noise level per channel | |
| 2406 |
|
2406 | |||
| 2407 | Affected: |
|
2407 | Affected: | |
| 2408 | self.dataOut.moments : Parameters per channel |
|
2408 | self.dataOut.moments : Parameters per channel | |
| 2409 | self.dataOut.data_snr : SNR per channel |
|
2409 | self.dataOut.data_snr : SNR per channel | |
| 2410 |
|
2410 | |||
| 2411 | ''' |
|
2411 | ''' | |
| 2412 |
|
2412 | |||
| 2413 | def run(self, dataOut, proc_type=0): |
|
2413 | def run(self, dataOut, proc_type=0): | |
| 2414 |
|
2414 | |||
| 2415 | absc = dataOut.abscissaList[:-1] |
|
2415 | absc = dataOut.abscissaList[:-1] | |
| 2416 | nChannel = dataOut.data_pre[0].shape[0] |
|
2416 | nChannel = dataOut.data_pre[0].shape[0] | |
| 2417 | nHei = dataOut.data_pre[0].shape[2] |
|
2417 | nHei = dataOut.data_pre[0].shape[2] | |
| 2418 | data_param = numpy.zeros((nChannel, 4 + proc_type*3, nHei)) |
|
2418 | data_param = numpy.zeros((nChannel, 4 + proc_type*3, nHei)) | |
| 2419 |
|
2419 | |||
| 2420 | if proc_type == 1: |
|
2420 | if proc_type == 1: | |
| 2421 | fwindow = numpy.zeros(absc.size) + 1 |
|
2421 | fwindow = numpy.zeros(absc.size) + 1 | |
| 2422 | b=64 |
|
2422 | b=64 | |
| 2423 | #b=16 |
|
2423 | #b=16 | |
| 2424 | fwindow[0:absc.size//2 - b] = 0 |
|
2424 | fwindow[0:absc.size//2 - b] = 0 | |
| 2425 | fwindow[absc.size//2 + b:] = 0 |
|
2425 | fwindow[absc.size//2 + b:] = 0 | |
| 2426 | type1 = 1 # moments calculation & gaussean fitting |
|
2426 | type1 = 1 # moments calculation & gaussean fitting | |
| 2427 | nProfiles = dataOut.nProfiles |
|
2427 | nProfiles = dataOut.nProfiles | |
| 2428 | nCohInt = dataOut.nCohInt |
|
2428 | nCohInt = dataOut.nCohInt | |
| 2429 | nIncohInt = dataOut.nIncohInt |
|
2429 | nIncohInt = dataOut.nIncohInt | |
| 2430 | M = numpy.power(numpy.array(1/(nProfiles * nCohInt) ,dtype='float32'),2) |
|
2430 | M = numpy.power(numpy.array(1/(nProfiles * nCohInt) ,dtype='float32'),2) | |
| 2431 | N = numpy.array(M / nIncohInt,dtype='float32') |
|
2431 | N = numpy.array(M / nIncohInt,dtype='float32') | |
| 2432 | data = dataOut.data_pre[0] * N |
|
2432 | data = dataOut.data_pre[0] * N | |
| 2433 | #noise = dataOut.noise * N |
|
2433 | #noise = dataOut.noise * N | |
| 2434 | noise = numpy.zeros(nChannel) |
|
2434 | noise = numpy.zeros(nChannel) | |
| 2435 | for ind in range(nChannel): |
|
2435 | for ind in range(nChannel): | |
| 2436 | noise[ind] = self.__NoiseByChannel(nProfiles, nIncohInt, data[ind,:,:]) |
|
2436 | noise[ind] = self.__NoiseByChannel(nProfiles, nIncohInt, data[ind,:,:]) | |
| 2437 | smooth=3 |
|
2437 | smooth=3 | |
| 2438 | else: |
|
2438 | else: | |
| 2439 | data = dataOut.data_pre[0] |
|
2439 | data = dataOut.data_pre[0] | |
| 2440 | noise = dataOut.noise |
|
2440 | noise = dataOut.noise | |
| 2441 | fwindow = None |
|
2441 | fwindow = None | |
| 2442 | type1 = 0 |
|
2442 | type1 = 0 | |
| 2443 | nIncohInt = None |
|
2443 | nIncohInt = None | |
| 2444 | smooth=None |
|
2444 | smooth=None | |
| 2445 |
|
2445 | |||
| 2446 | for ind in range(nChannel): |
|
2446 | for ind in range(nChannel): | |
| 2447 | data_param[ind,:,:] = self.__calculateMoments( data[ind,:,:] , absc , noise[ind], nicoh=nIncohInt, smooth=smooth, type1=type1, fwindow=fwindow, id_ch=ind) |
|
2447 | data_param[ind,:,:] = self.__calculateMoments( data[ind,:,:] , absc , noise[ind], nicoh=nIncohInt, smooth=smooth, type1=type1, fwindow=fwindow, id_ch=ind) | |
| 2448 |
|
2448 | |||
| 2449 | if proc_type == 1: |
|
2449 | if proc_type == 1: | |
| 2450 | dataOut.moments = data_param[:,1:,:] |
|
2450 | dataOut.moments = data_param[:,1:,:] | |
| 2451 | dataOut.data_dop = data_param[:,2] |
|
2451 | dataOut.data_dop = data_param[:,2] | |
| 2452 | dataOut.data_width = data_param[:,1] |
|
2452 | dataOut.data_width = data_param[:,1] | |
| 2453 | dataOut.data_snr = data_param[:,0] |
|
2453 | dataOut.data_snr = data_param[:,0] | |
| 2454 | dataOut.data_pow = data_param[:,6] # to compare with type0 proccessing |
|
2454 | dataOut.data_pow = data_param[:,6] # to compare with type0 proccessing | |
| 2455 | dataOut.spcpar=numpy.stack((dataOut.data_dop,dataOut.data_width,dataOut.data_snr, data_param[:,3], data_param[:,4],data_param[:,5]),axis=2) |
|
2455 | dataOut.spcpar=numpy.stack((dataOut.data_dop,dataOut.data_width,dataOut.data_snr, data_param[:,3], data_param[:,4],data_param[:,5]),axis=2) | |
| 2456 | else: |
|
2456 | else: | |
| 2457 | dataOut.moments = data_param[:,1:,:] |
|
2457 | dataOut.moments = data_param[:,1:,:] | |
| 2458 | dataOut.data_snr = data_param[:,0] |
|
2458 | dataOut.data_snr = data_param[:,0] | |
| 2459 | dataOut.data_pow = data_param[:,1] |
|
2459 | dataOut.data_pow = data_param[:,1] | |
| 2460 | dataOut.data_dop = data_param[:,2] |
|
2460 | dataOut.data_dop = data_param[:,2] | |
| 2461 | dataOut.data_width = data_param[:,3] |
|
2461 | dataOut.data_width = data_param[:,3] | |
| 2462 | dataOut.spcpar=numpy.stack((dataOut.data_dop,dataOut.data_width,dataOut.data_snr, dataOut.data_pow),axis=2) |
|
2462 | dataOut.spcpar=numpy.stack((dataOut.data_dop,dataOut.data_width,dataOut.data_snr, dataOut.data_pow),axis=2) | |
| 2463 |
|
2463 | |||
| 2464 | return dataOut |
|
2464 | return dataOut | |
| 2465 |
|
2465 | |||
| 2466 | def __calculateMoments(self, oldspec, oldfreq, n0, |
|
2466 | def __calculateMoments(self, oldspec, oldfreq, n0, | |
| 2467 | nicoh = None, graph = None, smooth = None, type1 = None, fwindow = None, snrth = None, dc = None, aliasing = None, oldfd = None, wwauto = None,id_ch=0): |
|
2467 | nicoh = None, graph = None, smooth = None, type1 = None, fwindow = None, snrth = None, dc = None, aliasing = None, oldfd = None, wwauto = None,id_ch=0): | |
| 2468 |
|
2468 | |||
| 2469 | def __GAUSSWINFIT1(A, flagPDER=0): |
|
2469 | def __GAUSSWINFIT1(A, flagPDER=0): | |
| 2470 | nonlocal truex, xvalid |
|
2470 | nonlocal truex, xvalid | |
| 2471 | nparams = 4 |
|
2471 | nparams = 4 | |
| 2472 | M=truex.size |
|
2472 | M=truex.size | |
| 2473 | mm=numpy.arange(M,dtype='f4') |
|
2473 | mm=numpy.arange(M,dtype='f4') | |
| 2474 | delta = numpy.zeros(M,dtype='f4') |
|
2474 | delta = numpy.zeros(M,dtype='f4') | |
| 2475 | delta[0] = 1.0 |
|
2475 | delta[0] = 1.0 | |
| 2476 | Ts = numpy.array([1.0/(2*truex[0])],dtype='f4')[0] |
|
2476 | Ts = numpy.array([1.0/(2*truex[0])],dtype='f4')[0] | |
| 2477 | jj = -1j |
|
2477 | jj = -1j | |
| 2478 | #if self.winauto is None: self.winauto = (1.0 - mm/M) |
|
2478 | #if self.winauto is None: self.winauto = (1.0 - mm/M) | |
| 2479 | winauto = (1.0 - mm/M) |
|
2479 | winauto = (1.0 - mm/M) | |
| 2480 | winauto = winauto/winauto.max() # Normalized to 1 |
|
2480 | winauto = winauto/winauto.max() # Normalized to 1 | |
| 2481 | #ON_ERROR,2 # IDL sentence: Return to caller if an error occurs |
|
2481 | #ON_ERROR,2 # IDL sentence: Return to caller if an error occurs | |
| 2482 | A[0] = numpy.abs(A[0]) |
|
2482 | A[0] = numpy.abs(A[0]) | |
| 2483 | A[2] = numpy.abs(A[2]) |
|
2483 | A[2] = numpy.abs(A[2]) | |
| 2484 | A[3] = numpy.abs(A[3]) |
|
2484 | A[3] = numpy.abs(A[3]) | |
| 2485 | pi=numpy.array([numpy.pi],dtype='f4')[0] |
|
2485 | pi=numpy.array([numpy.pi],dtype='f4')[0] | |
| 2486 | if A[2] != 0: |
|
2486 | if A[2] != 0: | |
| 2487 | 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 |
|
2487 | 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 | |
| 2488 | else: |
|
2488 | else: | |
| 2489 | Z = mm*0.0 |
|
2489 | Z = mm*0.0 | |
| 2490 | A[0] = 0.0 |
|
2490 | A[0] = 0.0 | |
| 2491 | junkF = numpy.roll(2*fft(winauto*(A[0]*Z+A[3]*delta)).real - \ |
|
2491 | junkF = numpy.roll(2*fft(winauto*(A[0]*Z+A[3]*delta)).real - \ | |
| 2492 | winauto[0]*(A[0]+A[3]), M//2) # *M scale for fft not needed in python |
|
2492 | winauto[0]*(A[0]+A[3]), M//2) # *M scale for fft not needed in python | |
| 2493 | F = junkF[xvalid] |
|
2493 | F = junkF[xvalid] | |
| 2494 | if flagPDER == 0: #NEED PARTIAL? |
|
2494 | if flagPDER == 0: #NEED PARTIAL? | |
| 2495 | return F |
|
2495 | return F | |
| 2496 | PDER = numpy.zeros((M,nparams)) #YES, MAKE ARRAY. |
|
2496 | PDER = numpy.zeros((M,nparams)) #YES, MAKE ARRAY. | |
| 2497 | PDER[:,0] = numpy.shift(2*(fft(winauto*Z)*M) - winauto[0], M/2) |
|
2497 | PDER[:,0] = numpy.shift(2*(fft(winauto*Z)*M) - winauto[0], M/2) | |
| 2498 | PDER[:,1] = numpy.shift(2*(fft(winauto*jj*2*numpy.pi*mm*Ts*A[0]*Z)*M), M/2) |
|
2498 | PDER[:,1] = numpy.shift(2*(fft(winauto*jj*2*numpy.pi*mm*Ts*A[0]*Z)*M), M/2) | |
| 2499 | PDER[:,2] = numpy.shift(2*(fft(winauto*(-4*numpy.power(numpy.pi*mm*Ts,2)*A[2]*A[0]*Z))*M), M/2) |
|
2499 | PDER[:,2] = numpy.shift(2*(fft(winauto*(-4*numpy.power(numpy.pi*mm*Ts,2)*A[2]*A[0]*Z))*M), M/2) | |
| 2500 | PDER[:,3] = numpy.shift(2*(fft(winauto*delta)*M) - winauto[0], M/2) |
|
2500 | PDER[:,3] = numpy.shift(2*(fft(winauto*delta)*M) - winauto[0], M/2) | |
| 2501 | PDER = PDER[xvalid,:] |
|
2501 | PDER = PDER[xvalid,:] | |
| 2502 | return F, PDER |
|
2502 | return F, PDER | |
| 2503 |
|
2503 | |||
| 2504 | def __curvefit_koki(y, a, Weights, FlagNoDerivative=1, |
|
2504 | def __curvefit_koki(y, a, Weights, FlagNoDerivative=1, | |
| 2505 | itmax=20, tol=None): |
|
2505 | itmax=20, tol=None): | |
| 2506 | #ON_ERROR,2 IDL SENTENCE: RETURN TO THE CALLER IF ERROR |
|
2506 | #ON_ERROR,2 IDL SENTENCE: RETURN TO THE CALLER IF ERROR | |
| 2507 | if tol == None: |
|
2507 | if tol == None: | |
| 2508 | tol = numpy.array([1.e-3],dtype='f4')[0] |
|
2508 | tol = numpy.array([1.e-3],dtype='f4')[0] | |
| 2509 | typ=a.dtype |
|
2509 | typ=a.dtype | |
| 2510 | double = 1 if typ == numpy.float64 else 0 |
|
2510 | double = 1 if typ == numpy.float64 else 0 | |
| 2511 | if typ != numpy.float32: |
|
2511 | if typ != numpy.float32: | |
| 2512 | a=a.astype(numpy.float32) #Make params floating |
|
2512 | a=a.astype(numpy.float32) #Make params floating | |
| 2513 | # if we will be estimating partial derivates then compute machine precision |
|
2513 | # if we will be estimating partial derivates then compute machine precision | |
| 2514 | if FlagNoDerivative == 1: |
|
2514 | if FlagNoDerivative == 1: | |
| 2515 | res=numpy.MachAr(float_conv=numpy.float32) |
|
2515 | res=numpy.MachAr(float_conv=numpy.float32) | |
| 2516 | eps=numpy.sqrt(res.eps) |
|
2516 | eps=numpy.sqrt(res.eps) | |
| 2517 |
|
2517 | |||
| 2518 | nterms = a.size # Number of parameters |
|
2518 | nterms = a.size # Number of parameters | |
| 2519 | nfree=numpy.array([numpy.size(y) - nterms],dtype='f4')[0] # Degrees of freedom |
|
2519 | nfree=numpy.array([numpy.size(y) - nterms],dtype='f4')[0] # Degrees of freedom | |
| 2520 | if nfree <= 0: print('Curvefit - not enough data points.') |
|
2520 | if nfree <= 0: print('Curvefit - not enough data points.') | |
| 2521 | flambda= numpy.array([0.001],dtype='f4')[0] # Initial lambda |
|
2521 | flambda= numpy.array([0.001],dtype='f4')[0] # Initial lambda | |
| 2522 | #diag=numpy.arange(nterms)*(nterms+1) # Subscripta of diagonal elements |
|
2522 | #diag=numpy.arange(nterms)*(nterms+1) # Subscripta of diagonal elements | |
| 2523 | # Use diag method in python |
|
2523 | # Use diag method in python | |
| 2524 | converge=1 |
|
2524 | converge=1 | |
| 2525 |
|
2525 | |||
| 2526 | #Define the partial derivative array |
|
2526 | #Define the partial derivative array | |
| 2527 | PDER = numpy.zeros((nterms,numpy.size(y)),dtype='f8') if double == 1 else numpy.zeros((nterms,numpy.size(y)),dtype='f4') |
|
2527 | PDER = numpy.zeros((nterms,numpy.size(y)),dtype='f8') if double == 1 else numpy.zeros((nterms,numpy.size(y)),dtype='f4') | |
| 2528 |
|
2528 | |||
| 2529 | for Niter in range(itmax): #Iteration loop |
|
2529 | for Niter in range(itmax): #Iteration loop | |
| 2530 |
|
2530 | |||
| 2531 | if FlagNoDerivative == 1: |
|
2531 | if FlagNoDerivative == 1: | |
| 2532 | #Evaluate function and estimate partial derivatives |
|
2532 | #Evaluate function and estimate partial derivatives | |
| 2533 | yfit = __GAUSSWINFIT1(a) |
|
2533 | yfit = __GAUSSWINFIT1(a) | |
| 2534 | for term in range(nterms): |
|
2534 | for term in range(nterms): | |
| 2535 | p=a.copy() # Copy current parameters |
|
2535 | p=a.copy() # Copy current parameters | |
| 2536 | #Increment size for forward difference derivative |
|
2536 | #Increment size for forward difference derivative | |
| 2537 | inc = eps * abs(p[term]) |
|
2537 | inc = eps * abs(p[term]) | |
| 2538 | if inc == 0: inc = eps |
|
2538 | if inc == 0: inc = eps | |
| 2539 | p[term] = p[term] + inc |
|
2539 | p[term] = p[term] + inc | |
| 2540 | yfit1 = __GAUSSWINFIT1(p) |
|
2540 | yfit1 = __GAUSSWINFIT1(p) | |
| 2541 | PDER[term,:] = (yfit1-yfit)/inc |
|
2541 | PDER[term,:] = (yfit1-yfit)/inc | |
| 2542 | else: |
|
2542 | else: | |
| 2543 | #The user's procedure will return partial derivatives |
|
2543 | #The user's procedure will return partial derivatives | |
| 2544 | yfit,PDER=__GAUSSWINFIT1(a, flagPDER=1) |
|
2544 | yfit,PDER=__GAUSSWINFIT1(a, flagPDER=1) | |
| 2545 |
|
2545 | |||
| 2546 | beta = numpy.dot(PDER,(y-yfit)*Weights) |
|
2546 | beta = numpy.dot(PDER,(y-yfit)*Weights) | |
| 2547 | alpha = numpy.dot(PDER * numpy.tile(Weights,(nterms,1)), numpy.transpose(PDER)) |
|
2547 | alpha = numpy.dot(PDER * numpy.tile(Weights,(nterms,1)), numpy.transpose(PDER)) | |
| 2548 | # save current values of return parameters |
|
2548 | # save current values of return parameters | |
| 2549 | sigma1 = numpy.sqrt( 1.0 / numpy.diag(alpha) ) # Current sigma. |
|
2549 | sigma1 = numpy.sqrt( 1.0 / numpy.diag(alpha) ) # Current sigma. | |
| 2550 | sigma = sigma1 |
|
2550 | sigma = sigma1 | |
| 2551 |
|
2551 | |||
| 2552 | chisq1 = numpy.sum(Weights*numpy.power(y-yfit,2,dtype='f4'),dtype='f4')/nfree # Current chi squared. |
|
2552 | chisq1 = numpy.sum(Weights*numpy.power(y-yfit,2,dtype='f4'),dtype='f4')/nfree # Current chi squared. | |
| 2553 | chisq = chisq1 |
|
2553 | chisq = chisq1 | |
| 2554 | yfit1 = yfit |
|
2554 | yfit1 = yfit | |
| 2555 | elev7=numpy.array([1.0e7],dtype='f4')[0] |
|
2555 | elev7=numpy.array([1.0e7],dtype='f4')[0] | |
| 2556 | compara =numpy.sum(abs(y))/elev7/nfree |
|
2556 | compara =numpy.sum(abs(y))/elev7/nfree | |
| 2557 | done_early = chisq1 < compara |
|
2557 | done_early = chisq1 < compara | |
| 2558 |
|
2558 | |||
| 2559 | if done_early: |
|
2559 | if done_early: | |
| 2560 | chi2 = chisq # Return chi-squared (chi2 obsolete-still works) |
|
2560 | chi2 = chisq # Return chi-squared (chi2 obsolete-still works) | |
| 2561 | if done_early: Niter -= 1 |
|
2561 | if done_early: Niter -= 1 | |
| 2562 | #save_tp(chisq,Niter,yfit) |
|
2562 | #save_tp(chisq,Niter,yfit) | |
| 2563 | return yfit, a, converge, sigma, chisq # return result |
|
2563 | return yfit, a, converge, sigma, chisq # return result | |
| 2564 | #c = numpy.dot(c, c) # this operator implemented at the next lines |
|
2564 | #c = numpy.dot(c, c) # this operator implemented at the next lines | |
| 2565 | c_tmp = numpy.sqrt(numpy.diag(alpha)) |
|
2565 | c_tmp = numpy.sqrt(numpy.diag(alpha)) | |
| 2566 | siz=len(c_tmp) |
|
2566 | siz=len(c_tmp) | |
| 2567 | c=numpy.dot(c_tmp.reshape(siz,1),c_tmp.reshape(1,siz)) |
|
2567 | c=numpy.dot(c_tmp.reshape(siz,1),c_tmp.reshape(1,siz)) | |
| 2568 | lambdaCount = 0 |
|
2568 | lambdaCount = 0 | |
| 2569 | while True: |
|
2569 | while True: | |
| 2570 | lambdaCount += 1 |
|
2570 | lambdaCount += 1 | |
| 2571 | # Normalize alpha to have unit diagonal. |
|
2571 | # Normalize alpha to have unit diagonal. | |
| 2572 | array = alpha / c |
|
2572 | array = alpha / c | |
| 2573 | # Augment the diagonal. |
|
2573 | # Augment the diagonal. | |
| 2574 | one=numpy.array([1.],dtype='f4')[0] |
|
2574 | one=numpy.array([1.],dtype='f4')[0] | |
| 2575 | numpy.fill_diagonal(array,numpy.diag(array)*(one+flambda)) |
|
2575 | numpy.fill_diagonal(array,numpy.diag(array)*(one+flambda)) | |
| 2576 | # Invert modified curvature matrix to find new parameters. |
|
2576 | # Invert modified curvature matrix to find new parameters. | |
| 2577 | try: |
|
2577 | try: | |
| 2578 | array = (1.0/array) if array.size == 1 else numpy.linalg.inv(array) |
|
2578 | array = (1.0/array) if array.size == 1 else numpy.linalg.inv(array) | |
| 2579 | except Exception as e: |
|
2579 | except Exception as e: | |
| 2580 | print(e) |
|
2580 | print(e) | |
| 2581 | array[:]=numpy.NaN |
|
2581 | array[:]=numpy.NaN | |
| 2582 |
|
2582 | |||
| 2583 | b = a + numpy.dot(numpy.transpose(beta),array/c) # New params |
|
2583 | b = a + numpy.dot(numpy.transpose(beta),array/c) # New params | |
| 2584 | yfit = __GAUSSWINFIT1(b) # Evaluate function |
|
2584 | yfit = __GAUSSWINFIT1(b) # Evaluate function | |
| 2585 | chisq = numpy.sum(Weights*numpy.power(y-yfit,2,dtype='f4'),dtype='f4')/nfree # New chisq |
|
2585 | chisq = numpy.sum(Weights*numpy.power(y-yfit,2,dtype='f4'),dtype='f4')/nfree # New chisq | |
| 2586 | sigma = numpy.sqrt(numpy.diag(array)/numpy.diag(alpha)) # New sigma |
|
2586 | sigma = numpy.sqrt(numpy.diag(array)/numpy.diag(alpha)) # New sigma | |
| 2587 | if (numpy.isfinite(chisq) == 0) or \ |
|
2587 | if (numpy.isfinite(chisq) == 0) or \ | |
| 2588 | (lambdaCount > 30 and chisq >= chisq1): |
|
2588 | (lambdaCount > 30 and chisq >= chisq1): | |
| 2589 | # Reject changes made this iteration, use old values. |
|
2589 | # Reject changes made this iteration, use old values. | |
| 2590 | yfit = yfit1 |
|
2590 | yfit = yfit1 | |
| 2591 | sigma = sigma1 |
|
2591 | sigma = sigma1 | |
| 2592 | chisq = chisq1 |
|
2592 | chisq = chisq1 | |
| 2593 | converge = 0 |
|
2593 | converge = 0 | |
| 2594 | #print('Failed to converge.') |
|
2594 | #print('Failed to converge.') | |
| 2595 | chi2 = chisq # Return chi-squared (chi2 obsolete-still works) |
|
2595 | chi2 = chisq # Return chi-squared (chi2 obsolete-still works) | |
| 2596 | if done_early: Niter -= 1 |
|
2596 | if done_early: Niter -= 1 | |
| 2597 | #save_tp(chisq,Niter,yfit) |
|
2597 | #save_tp(chisq,Niter,yfit) | |
| 2598 | return yfit, a, converge, sigma, chisq, chi2 # return result |
|
2598 | return yfit, a, converge, sigma, chisq, chi2 # return result | |
| 2599 | ten=numpy.array([10.0],dtype='f4')[0] |
|
2599 | ten=numpy.array([10.0],dtype='f4')[0] | |
| 2600 | flambda *= ten # Assume fit got worse |
|
2600 | flambda *= ten # Assume fit got worse | |
| 2601 | if chisq <= chisq1: |
|
2601 | if chisq <= chisq1: | |
| 2602 | break |
|
2602 | break | |
| 2603 | hundred=numpy.array([100.0],dtype='f4')[0] |
|
2603 | hundred=numpy.array([100.0],dtype='f4')[0] | |
| 2604 | flambda /= hundred |
|
2604 | flambda /= hundred | |
| 2605 |
|
2605 | |||
| 2606 | a=b # Save new parameter estimate. |
|
2606 | a=b # Save new parameter estimate. | |
| 2607 | if ((chisq1-chisq)/chisq1) <= tol: # Finished? |
|
2607 | if ((chisq1-chisq)/chisq1) <= tol: # Finished? | |
| 2608 | chi2 = chisq # Return chi-squared (chi2 obsolete-still works) |
|
2608 | chi2 = chisq # Return chi-squared (chi2 obsolete-still works) | |
| 2609 | if done_early: Niter -= 1 |
|
2609 | if done_early: Niter -= 1 | |
| 2610 | #save_tp(chisq,Niter,yfit) |
|
2610 | #save_tp(chisq,Niter,yfit) | |
| 2611 | return yfit, a, converge, sigma, chisq, chi2 # return result |
|
2611 | return yfit, a, converge, sigma, chisq, chi2 # return result | |
| 2612 | converge = 0 |
|
2612 | converge = 0 | |
| 2613 | chi2 = chisq |
|
2613 | chi2 = chisq | |
| 2614 | #print('Failed to converge.') |
|
2614 | #print('Failed to converge.') | |
| 2615 | #save_tp(chisq,Niter,yfit) |
|
2615 | #save_tp(chisq,Niter,yfit) | |
| 2616 | return yfit, a, converge, sigma, chisq, chi2 |
|
2616 | return yfit, a, converge, sigma, chisq, chi2 | |
| 2617 |
|
2617 | |||
| 2618 | if (nicoh is None): nicoh = 1 |
|
2618 | if (nicoh is None): nicoh = 1 | |
| 2619 | if (graph is None): graph = 0 |
|
2619 | if (graph is None): graph = 0 | |
| 2620 | if (smooth is None): smooth = 0 |
|
2620 | if (smooth is None): smooth = 0 | |
| 2621 | elif (self.smooth < 3): smooth = 0 |
|
2621 | elif (self.smooth < 3): smooth = 0 | |
| 2622 |
|
2622 | |||
| 2623 | if (type1 is None): type1 = 0 |
|
2623 | if (type1 is None): type1 = 0 | |
| 2624 | if (fwindow is None): fwindow = numpy.zeros(oldfreq.size) + 1 |
|
2624 | if (fwindow is None): fwindow = numpy.zeros(oldfreq.size) + 1 | |
| 2625 | if (snrth is None): snrth = -20.0 |
|
2625 | if (snrth is None): snrth = -20.0 | |
| 2626 | if (dc is None): dc = 0 |
|
2626 | if (dc is None): dc = 0 | |
| 2627 | if (aliasing is None): aliasing = 0 |
|
2627 | if (aliasing is None): aliasing = 0 | |
| 2628 | if (oldfd is None): oldfd = 0 |
|
2628 | if (oldfd is None): oldfd = 0 | |
| 2629 | if (wwauto is None): wwauto = 0 |
|
2629 | if (wwauto is None): wwauto = 0 | |
| 2630 |
|
2630 | |||
| 2631 | if (n0 < 1.e-20): n0 = 1.e-20 |
|
2631 | if (n0 < 1.e-20): n0 = 1.e-20 | |
| 2632 |
|
2632 | |||
| 2633 | xvalid = numpy.where(fwindow == 1)[0] |
|
2633 | xvalid = numpy.where(fwindow == 1)[0] | |
| 2634 | freq = oldfreq |
|
2634 | freq = oldfreq | |
| 2635 | truex = oldfreq |
|
2635 | truex = oldfreq | |
| 2636 | vec_power = numpy.zeros(oldspec.shape[1]) |
|
2636 | vec_power = numpy.zeros(oldspec.shape[1]) | |
| 2637 | vec_fd = numpy.zeros(oldspec.shape[1]) |
|
2637 | vec_fd = numpy.zeros(oldspec.shape[1]) | |
| 2638 | vec_w = numpy.zeros(oldspec.shape[1]) |
|
2638 | vec_w = numpy.zeros(oldspec.shape[1]) | |
| 2639 | vec_snr = numpy.zeros(oldspec.shape[1]) |
|
2639 | vec_snr = numpy.zeros(oldspec.shape[1]) | |
| 2640 | vec_n1 = numpy.empty(oldspec.shape[1]) |
|
2640 | vec_n1 = numpy.empty(oldspec.shape[1]) | |
| 2641 | vec_fp = numpy.empty(oldspec.shape[1]) |
|
2641 | vec_fp = numpy.empty(oldspec.shape[1]) | |
| 2642 | vec_sigma_fd = numpy.empty(oldspec.shape[1]) |
|
2642 | vec_sigma_fd = numpy.empty(oldspec.shape[1]) | |
| 2643 |
|
2643 | |||
| 2644 | for ind in range(oldspec.shape[1]): |
|
2644 | for ind in range(oldspec.shape[1]): | |
| 2645 |
|
2645 | |||
| 2646 | spec = oldspec[:,ind] |
|
2646 | spec = oldspec[:,ind] | |
| 2647 | if (smooth == 0): |
|
2647 | if (smooth == 0): | |
| 2648 | spec2 = spec |
|
2648 | spec2 = spec | |
| 2649 | else: |
|
2649 | else: | |
| 2650 | spec2 = scipy.ndimage.filters.uniform_filter1d(spec,size=smooth) |
|
2650 | spec2 = scipy.ndimage.filters.uniform_filter1d(spec,size=smooth) | |
| 2651 |
|
2651 | |||
| 2652 | aux = spec2*fwindow |
|
2652 | aux = spec2*fwindow | |
| 2653 | max_spec = aux.max() |
|
2653 | max_spec = aux.max() | |
| 2654 | m = aux.tolist().index(max_spec) |
|
2654 | m = aux.tolist().index(max_spec) | |
| 2655 |
|
2655 | |||
| 2656 | if m > 2 and m < oldfreq.size - 3: |
|
2656 | if m > 2 and m < oldfreq.size - 3: | |
| 2657 | newindex = m + numpy.array([-2,-1,0,1,2]) |
|
2657 | newindex = m + numpy.array([-2,-1,0,1,2]) | |
| 2658 | newfreq = numpy.arange(20)/20.0*(numpy.max(freq[newindex])-numpy.min(freq[newindex]))+numpy.min(freq[newindex]) |
|
2658 | newfreq = numpy.arange(20)/20.0*(numpy.max(freq[newindex])-numpy.min(freq[newindex]))+numpy.min(freq[newindex]) | |
| 2659 | #peakspec = SPLINE(,) |
|
2659 | #peakspec = SPLINE(,) | |
| 2660 | tck = interpolate.splrep(freq[newindex], spec2[newindex]) |
|
2660 | tck = interpolate.splrep(freq[newindex], spec2[newindex]) | |
| 2661 | peakspec = interpolate.splev(newfreq, tck) |
|
2661 | peakspec = interpolate.splev(newfreq, tck) | |
| 2662 | # max_spec = MAX(peakspec,) |
|
2662 | # max_spec = MAX(peakspec,) | |
| 2663 | max_spec = numpy.max(peakspec) |
|
2663 | max_spec = numpy.max(peakspec) | |
| 2664 | mnew = numpy.argmax(peakspec) |
|
2664 | mnew = numpy.argmax(peakspec) | |
| 2665 | #fp = newfreq(mnew) |
|
2665 | #fp = newfreq(mnew) | |
| 2666 | fp = newfreq[mnew] |
|
2666 | fp = newfreq[mnew] | |
| 2667 | else: |
|
2667 | else: | |
| 2668 | fp = freq[m] |
|
2668 | fp = freq[m] | |
| 2669 |
|
2669 | |||
| 2670 | if type1==0: |
|
2670 | if type1==0: | |
| 2671 |
|
2671 | |||
| 2672 | # Moments Estimation |
|
2672 | # Moments Estimation | |
| 2673 | bb = spec2[numpy.arange(m,spec2.size)] |
|
2673 | bb = spec2[numpy.arange(m,spec2.size)] | |
| 2674 | bb = (bb<n0).nonzero() |
|
2674 | bb = (bb<n0).nonzero() | |
| 2675 | bb = bb[0] |
|
2675 | bb = bb[0] | |
| 2676 |
|
2676 | |||
| 2677 | ss = spec2[numpy.arange(0,m + 1)] |
|
2677 | ss = spec2[numpy.arange(0,m + 1)] | |
| 2678 | ss = (ss<n0).nonzero() |
|
2678 | ss = (ss<n0).nonzero() | |
| 2679 | ss = ss[0] |
|
2679 | ss = ss[0] | |
| 2680 |
|
2680 | |||
| 2681 | if (bb.size == 0): |
|
2681 | if (bb.size == 0): | |
| 2682 | bb0 = spec.size - 1 - m |
|
2682 | bb0 = spec.size - 1 - m | |
| 2683 | else: |
|
2683 | else: | |
| 2684 | bb0 = bb[0] - 1 |
|
2684 | bb0 = bb[0] - 1 | |
| 2685 | if (bb0 < 0): |
|
2685 | if (bb0 < 0): | |
| 2686 | bb0 = 0 |
|
2686 | bb0 = 0 | |
| 2687 |
|
2687 | |||
| 2688 | if (ss.size == 0): |
|
2688 | if (ss.size == 0): | |
| 2689 | ss1 = 1 |
|
2689 | ss1 = 1 | |
| 2690 | else: |
|
2690 | else: | |
| 2691 | ss1 = max(ss) + 1 |
|
2691 | ss1 = max(ss) + 1 | |
| 2692 |
|
2692 | |||
| 2693 | if (ss1 > m): |
|
2693 | if (ss1 > m): | |
| 2694 | ss1 = m |
|
2694 | ss1 = m | |
| 2695 |
|
2695 | |||
| 2696 | valid = numpy.arange(int(m + bb0 - ss1 + 1)) + ss1 |
|
2696 | valid = numpy.arange(int(m + bb0 - ss1 + 1)) + ss1 | |
| 2697 |
|
2697 | |||
| 2698 | signal_power = ((spec2[valid] - n0) * fwindow[valid]).mean() # D. ScipiΓ³n added with correct definition |
|
2698 | signal_power = ((spec2[valid] - n0) * fwindow[valid]).mean() # D. ScipiΓ³n added with correct definition | |
| 2699 | total_power = (spec2[valid] * fwindow[valid]).mean() # D. ScipiΓ³n added with correct definition |
|
2699 | total_power = (spec2[valid] * fwindow[valid]).mean() # D. ScipiΓ³n added with correct definition | |
| 2700 | power = ((spec2[valid] - n0) * fwindow[valid]).sum() |
|
2700 | power = ((spec2[valid] - n0) * fwindow[valid]).sum() | |
| 2701 | fd = ((spec2[valid]- n0)*freq[valid] * fwindow[valid]).sum() / power |
|
2701 | fd = ((spec2[valid]- n0)*freq[valid] * fwindow[valid]).sum() / power | |
| 2702 | w = numpy.sqrt(((spec2[valid] - n0)*fwindow[valid]*(freq[valid]- fd)**2).sum() / power) |
|
2702 | w = numpy.sqrt(((spec2[valid] - n0)*fwindow[valid]*(freq[valid]- fd)**2).sum() / power) | |
| 2703 | snr = (spec2.mean()-n0)/n0 |
|
2703 | snr = (spec2.mean()-n0)/n0 | |
| 2704 | if (snr < 1.e-20): snr = 1.e-20 |
|
2704 | if (snr < 1.e-20): snr = 1.e-20 | |
| 2705 |
|
2705 | |||
| 2706 | vec_power[ind] = total_power |
|
2706 | vec_power[ind] = total_power | |
| 2707 | vec_fd[ind] = fd |
|
2707 | vec_fd[ind] = fd | |
| 2708 | vec_w[ind] = w |
|
2708 | vec_w[ind] = w | |
| 2709 | vec_snr[ind] = snr |
|
2709 | vec_snr[ind] = snr | |
| 2710 | else: |
|
2710 | else: | |
| 2711 | # Noise by heights |
|
2711 | # Noise by heights | |
| 2712 | n1, stdv = self.__get_noise2(spec, nicoh) |
|
2712 | n1, stdv = self.__get_noise2(spec, nicoh) | |
| 2713 | # Moments Estimation |
|
2713 | # Moments Estimation | |
| 2714 | bb = spec2[numpy.arange(m,spec2.size)] |
|
2714 | bb = spec2[numpy.arange(m,spec2.size)] | |
| 2715 | bb = (bb<n1).nonzero() |
|
2715 | bb = (bb<n1).nonzero() | |
| 2716 | bb = bb[0] |
|
2716 | bb = bb[0] | |
| 2717 |
|
2717 | |||
| 2718 | ss = spec2[numpy.arange(0,m + 1)] |
|
2718 | ss = spec2[numpy.arange(0,m + 1)] | |
| 2719 | ss = (ss<n1).nonzero() |
|
2719 | ss = (ss<n1).nonzero() | |
| 2720 | ss = ss[0] |
|
2720 | ss = ss[0] | |
| 2721 |
|
2721 | |||
| 2722 | if (bb.size == 0): |
|
2722 | if (bb.size == 0): | |
| 2723 | bb0 = spec.size - 1 - m |
|
2723 | bb0 = spec.size - 1 - m | |
| 2724 | else: |
|
2724 | else: | |
| 2725 | bb0 = bb[0] - 1 |
|
2725 | bb0 = bb[0] - 1 | |
| 2726 | if (bb0 < 0): |
|
2726 | if (bb0 < 0): | |
| 2727 | bb0 = 0 |
|
2727 | bb0 = 0 | |
| 2728 |
|
2728 | |||
| 2729 | if (ss.size == 0): |
|
2729 | if (ss.size == 0): | |
| 2730 | ss1 = 1 |
|
2730 | ss1 = 1 | |
| 2731 | else: |
|
2731 | else: | |
| 2732 | ss1 = max(ss) + 1 |
|
2732 | ss1 = max(ss) + 1 | |
| 2733 |
|
2733 | |||
| 2734 | if (ss1 > m): |
|
2734 | if (ss1 > m): | |
| 2735 | ss1 = m |
|
2735 | ss1 = m | |
| 2736 |
|
2736 | |||
| 2737 | valid = numpy.arange(int(m + bb0 - ss1 + 1)) + ss1 |
|
2737 | valid = numpy.arange(int(m + bb0 - ss1 + 1)) + ss1 | |
| 2738 | power = ((spec[valid] - n1)*fwindow[valid]).sum() |
|
2738 | power = ((spec[valid] - n1)*fwindow[valid]).sum() | |
| 2739 | fd = ((spec[valid]- n1)*freq[valid]*fwindow[valid]).sum()/power |
|
2739 | fd = ((spec[valid]- n1)*freq[valid]*fwindow[valid]).sum()/power | |
| 2740 | try: |
|
2740 | try: | |
| 2741 | w = numpy.sqrt(((spec[valid] - n1)*fwindow[valid]*(freq[valid]- fd)**2).sum()/power) |
|
2741 | w = numpy.sqrt(((spec[valid] - n1)*fwindow[valid]*(freq[valid]- fd)**2).sum()/power) | |
| 2742 | except: |
|
2742 | except: | |
| 2743 | w = float("NaN") |
|
2743 | w = float("NaN") | |
| 2744 | snr = power/(n0*fwindow.sum()) |
|
2744 | snr = power/(n0*fwindow.sum()) | |
| 2745 | if snr < 1.e-20: snr = 1.e-20 |
|
2745 | if snr < 1.e-20: snr = 1.e-20 | |
| 2746 |
|
2746 | |||
| 2747 | # Here start gaussean adjustment |
|
2747 | # Here start gaussean adjustment | |
| 2748 |
|
2748 | |||
| 2749 | if snr > numpy.power(10,0.1*snrth): |
|
2749 | if snr > numpy.power(10,0.1*snrth): | |
| 2750 |
|
2750 | |||
| 2751 | a = numpy.zeros(4,dtype='f4') |
|
2751 | a = numpy.zeros(4,dtype='f4') | |
| 2752 | a[0] = snr * n0 |
|
2752 | a[0] = snr * n0 | |
| 2753 | a[1] = fd |
|
2753 | a[1] = fd | |
| 2754 | a[2] = w |
|
2754 | a[2] = w | |
| 2755 | a[3] = n0 |
|
2755 | a[3] = n0 | |
| 2756 |
|
2756 | |||
| 2757 | np = spec.size |
|
2757 | np = spec.size | |
| 2758 | aold = a.copy() |
|
2758 | aold = a.copy() | |
| 2759 | spec2 = spec.copy() |
|
2759 | spec2 = spec.copy() | |
| 2760 | oldxvalid = xvalid.copy() |
|
2760 | oldxvalid = xvalid.copy() | |
| 2761 |
|
2761 | |||
| 2762 | for i in range(2): |
|
2762 | for i in range(2): | |
| 2763 |
|
2763 | |||
| 2764 | ww = 1.0/(numpy.power(spec2,2)/nicoh) |
|
2764 | ww = 1.0/(numpy.power(spec2,2)/nicoh) | |
| 2765 | ww[np//2] = 0.0 |
|
2765 | ww[np//2] = 0.0 | |
| 2766 |
|
2766 | |||
| 2767 | a = aold.copy() |
|
2767 | a = aold.copy() | |
| 2768 | xvalid = oldxvalid.copy() |
|
2768 | xvalid = oldxvalid.copy() | |
| 2769 | #self.show_var(xvalid) |
|
2769 | #self.show_var(xvalid) | |
| 2770 |
|
2770 | |||
| 2771 | gaussfn = __curvefit_koki(spec[xvalid], a, ww[xvalid]) |
|
2771 | gaussfn = __curvefit_koki(spec[xvalid], a, ww[xvalid]) | |
| 2772 | a = gaussfn[1] |
|
2772 | a = gaussfn[1] | |
| 2773 | converge = gaussfn[2] |
|
2773 | converge = gaussfn[2] | |
| 2774 |
|
2774 | |||
| 2775 | xvalid = numpy.arange(np) |
|
2775 | xvalid = numpy.arange(np) | |
| 2776 | spec2 = __GAUSSWINFIT1(a) |
|
2776 | spec2 = __GAUSSWINFIT1(a) | |
| 2777 |
|
2777 | |||
| 2778 | xvalid = oldxvalid.copy() |
|
2778 | xvalid = oldxvalid.copy() | |
| 2779 | power = a[0] * np |
|
2779 | power = a[0] * np | |
| 2780 | fd = a[1] |
|
2780 | fd = a[1] | |
| 2781 | sigma_fd = gaussfn[3][1] |
|
2781 | sigma_fd = gaussfn[3][1] | |
| 2782 | snr = max(power/ (max(a[3],n0) * len(oldxvalid)) * converge, 1e-20) |
|
2782 | snr = max(power/ (max(a[3],n0) * len(oldxvalid)) * converge, 1e-20) | |
| 2783 | w = numpy.abs(a[2]) |
|
2783 | w = numpy.abs(a[2]) | |
| 2784 | n1 = max(a[3], n0) |
|
2784 | n1 = max(a[3], n0) | |
| 2785 |
|
2785 | |||
| 2786 | #gauss_adj=[fd,w,snr,n1,fp,sigma_fd] |
|
2786 | #gauss_adj=[fd,w,snr,n1,fp,sigma_fd] | |
| 2787 | else: |
|
2787 | else: | |
| 2788 | sigma_fd=numpy.nan # to avoid UnboundLocalError: local variable 'sigma_fd' referenced before assignment |
|
2788 | sigma_fd=numpy.nan # to avoid UnboundLocalError: local variable 'sigma_fd' referenced before assignment | |
| 2789 |
|
2789 | |||
| 2790 | vec_fd[ind] = fd |
|
2790 | vec_fd[ind] = fd | |
| 2791 | vec_w[ind] = w |
|
2791 | vec_w[ind] = w | |
| 2792 | vec_snr[ind] = snr |
|
2792 | vec_snr[ind] = snr | |
| 2793 | vec_n1[ind] = n1 |
|
2793 | vec_n1[ind] = n1 | |
| 2794 | vec_fp[ind] = fp |
|
2794 | vec_fp[ind] = fp | |
| 2795 | vec_sigma_fd[ind] = sigma_fd |
|
2795 | vec_sigma_fd[ind] = sigma_fd | |
| 2796 | vec_power[ind] = power # to compare with type 0 proccessing |
|
2796 | vec_power[ind] = power # to compare with type 0 proccessing | |
| 2797 |
|
2797 | |||
| 2798 | if type1==1: |
|
2798 | if type1==1: | |
| 2799 | 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 |
|
2799 | 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 | |
| 2800 | else: |
|
2800 | else: | |
| 2801 | return numpy.vstack((vec_snr, vec_power, vec_fd, vec_w)) |
|
2801 | return numpy.vstack((vec_snr, vec_power, vec_fd, vec_w)) | |
| 2802 |
|
2802 | |||
| 2803 | def __get_noise2(self,POWER, fft_avg, TALK=0): |
|
2803 | def __get_noise2(self,POWER, fft_avg, TALK=0): | |
| 2804 | ''' |
|
2804 | ''' | |
| 2805 | Rutina para cΓ‘lculo de ruido por alturas(n1). Similar a IDL |
|
2805 | Rutina para cΓ‘lculo de ruido por alturas(n1). Similar a IDL | |
| 2806 | ''' |
|
2806 | ''' | |
| 2807 | SPECT_PTS = len(POWER) |
|
2807 | SPECT_PTS = len(POWER) | |
| 2808 | fft_avg = fft_avg*1.0 |
|
2808 | fft_avg = fft_avg*1.0 | |
| 2809 | NOMIT = 0 |
|
2809 | NOMIT = 0 | |
| 2810 | NN = SPECT_PTS - NOMIT |
|
2810 | NN = SPECT_PTS - NOMIT | |
| 2811 | N = NN//2 |
|
2811 | N = NN//2 | |
| 2812 | ARR = numpy.concatenate((POWER[0:N+1],POWER[N+NOMIT+1:SPECT_PTS])) |
|
2812 | ARR = numpy.concatenate((POWER[0:N+1],POWER[N+NOMIT+1:SPECT_PTS])) | |
| 2813 | ARR = numpy.sort(ARR) |
|
2813 | ARR = numpy.sort(ARR) | |
| 2814 | NUMS_MIN = (SPECT_PTS+7)//8 |
|
2814 | NUMS_MIN = (SPECT_PTS+7)//8 | |
| 2815 | RTEST = (1.0+1.0/fft_avg) |
|
2815 | RTEST = (1.0+1.0/fft_avg) | |
| 2816 | SUM = 0.0 |
|
2816 | SUM = 0.0 | |
| 2817 | SUMSQ = 0.0 |
|
2817 | SUMSQ = 0.0 | |
| 2818 | J = 0 |
|
2818 | J = 0 | |
| 2819 | for I in range(NN): |
|
2819 | for I in range(NN): | |
| 2820 | J = J + 1 |
|
2820 | J = J + 1 | |
| 2821 | SUM = SUM + ARR[I] |
|
2821 | SUM = SUM + ARR[I] | |
| 2822 | SUMSQ = SUMSQ + ARR[I]*ARR[I] |
|
2822 | SUMSQ = SUMSQ + ARR[I]*ARR[I] | |
| 2823 | AVE = SUM*1.0/J |
|
2823 | AVE = SUM*1.0/J | |
| 2824 | if J > NUMS_MIN: |
|
2824 | if J > NUMS_MIN: | |
| 2825 | if (SUMSQ*J <= RTEST*SUM*SUM): RNOISE = AVE |
|
2825 | if (SUMSQ*J <= RTEST*SUM*SUM): RNOISE = AVE | |
| 2826 | else: |
|
2826 | else: | |
| 2827 | if J == NUMS_MIN: RNOISE = AVE |
|
2827 | if J == NUMS_MIN: RNOISE = AVE | |
| 2828 | if TALK == 1: print('Noise Power (2):%4.4f' %RNOISE) |
|
2828 | if TALK == 1: print('Noise Power (2):%4.4f' %RNOISE) | |
| 2829 | stdv = numpy.sqrt(SUMSQ/J - numpy.power(SUM/J,2)) |
|
2829 | stdv = numpy.sqrt(SUMSQ/J - numpy.power(SUM/J,2)) | |
| 2830 | return RNOISE, stdv |
|
2830 | return RNOISE, stdv | |
| 2831 |
|
2831 | |||
| 2832 | def __get_noise1(self, power, fft_avg, TALK=0): |
|
2832 | def __get_noise1(self, power, fft_avg, TALK=0): | |
| 2833 | ''' |
|
2833 | ''' | |
| 2834 | Rutina para cΓ‘lculo de ruido por alturas(n0). Similar a IDL |
|
2834 | Rutina para cΓ‘lculo de ruido por alturas(n0). Similar a IDL | |
| 2835 | ''' |
|
2835 | ''' | |
| 2836 | num_pts = numpy.size(power) |
|
2836 | num_pts = numpy.size(power) | |
| 2837 | #print('num_pts',num_pts) |
|
2837 | #print('num_pts',num_pts) | |
| 2838 | #print('power',power.shape) |
|
2838 | #print('power',power.shape) | |
| 2839 | #print(power[256:267,0:2]) |
|
2839 | #print(power[256:267,0:2]) | |
| 2840 | fft_avg = fft_avg*1.0 |
|
2840 | fft_avg = fft_avg*1.0 | |
| 2841 |
|
2841 | |||
| 2842 | ind = numpy.argsort(power, axis=None, kind='stable') |
|
2842 | ind = numpy.argsort(power, axis=None, kind='stable') | |
| 2843 | #ind = numpy.argsort(numpy.reshape(power,-1)) |
|
2843 | #ind = numpy.argsort(numpy.reshape(power,-1)) | |
| 2844 | #print(ind.shape) |
|
2844 | #print(ind.shape) | |
| 2845 | #print(ind[0:11]) |
|
2845 | #print(ind[0:11]) | |
| 2846 | #print(numpy.reshape(power,-1)[ind[0:11]]) |
|
2846 | #print(numpy.reshape(power,-1)[ind[0:11]]) | |
| 2847 | ARR = numpy.reshape(power,-1)[ind] |
|
2847 | ARR = numpy.reshape(power,-1)[ind] | |
| 2848 | #print('ARR',len(ARR)) |
|
2848 | #print('ARR',len(ARR)) | |
| 2849 | #print('ARR',ARR.shape) |
|
2849 | #print('ARR',ARR.shape) | |
| 2850 | NUMS_MIN = num_pts//10 |
|
2850 | NUMS_MIN = num_pts//10 | |
| 2851 | RTEST = (1.0+1.0/fft_avg) |
|
2851 | RTEST = (1.0+1.0/fft_avg) | |
| 2852 | SUM = 0.0 |
|
2852 | SUM = 0.0 | |
| 2853 | SUMSQ = 0.0 |
|
2853 | SUMSQ = 0.0 | |
| 2854 | J = 0 |
|
2854 | J = 0 | |
| 2855 | cont = 1 |
|
2855 | cont = 1 | |
| 2856 | while cont == 1 and J < num_pts: |
|
2856 | while cont == 1 and J < num_pts: | |
| 2857 |
|
2857 | |||
| 2858 | SUM = SUM + ARR[J] |
|
2858 | SUM = SUM + ARR[J] | |
| 2859 | SUMSQ = SUMSQ + ARR[J]*ARR[J] |
|
2859 | SUMSQ = SUMSQ + ARR[J]*ARR[J] | |
| 2860 | J = J + 1 |
|
2860 | J = J + 1 | |
| 2861 |
|
2861 | |||
| 2862 | if J > NUMS_MIN: |
|
2862 | if J > NUMS_MIN: | |
| 2863 | if (SUMSQ*J <= RTEST*SUM*SUM): |
|
2863 | if (SUMSQ*J <= RTEST*SUM*SUM): | |
| 2864 | LNOISE = SUM*1.0/J |
|
2864 | LNOISE = SUM*1.0/J | |
| 2865 | else: |
|
2865 | else: | |
| 2866 | J = J - 1 |
|
2866 | J = J - 1 | |
| 2867 | SUM = SUM - ARR[J] |
|
2867 | SUM = SUM - ARR[J] | |
| 2868 | SUMSQ = SUMSQ - ARR[J]*ARR[J] |
|
2868 | SUMSQ = SUMSQ - ARR[J]*ARR[J] | |
| 2869 | cont = 0 |
|
2869 | cont = 0 | |
| 2870 | else: |
|
2870 | else: | |
| 2871 | if J == NUMS_MIN: LNOISE = SUM*1.0/J |
|
2871 | if J == NUMS_MIN: LNOISE = SUM*1.0/J | |
| 2872 | if TALK == 1: print('Noise Power (1):%8.8f' %LNOISE) |
|
2872 | if TALK == 1: print('Noise Power (1):%8.8f' %LNOISE) | |
| 2873 | stdv = numpy.sqrt(SUMSQ/J - numpy.power(SUM/J,2)) |
|
2873 | stdv = numpy.sqrt(SUMSQ/J - numpy.power(SUM/J,2)) | |
| 2874 | return LNOISE, stdv |
|
2874 | return LNOISE, stdv | |
| 2875 |
|
2875 | |||
| 2876 | def __NoiseByChannel(self, num_prof, num_incoh, spectra,talk=0): |
|
2876 | def __NoiseByChannel(self, num_prof, num_incoh, spectra,talk=0): | |
| 2877 |
|
2877 | |||
| 2878 | val_frq = numpy.arange(num_prof-2)+1 |
|
2878 | val_frq = numpy.arange(num_prof-2)+1 | |
| 2879 | val_frq[(num_prof-2)//2:] = val_frq[(num_prof-2)//2:] + 1 |
|
2879 | val_frq[(num_prof-2)//2:] = val_frq[(num_prof-2)//2:] + 1 | |
| 2880 | junkspc = numpy.sum(spectra[val_frq,:], axis=1) |
|
2880 | junkspc = numpy.sum(spectra[val_frq,:], axis=1) | |
| 2881 | junkid = numpy.argsort(junkspc) |
|
2881 | junkid = numpy.argsort(junkspc) | |
| 2882 | noisezone = val_frq[junkid[0:num_prof//2]] |
|
2882 | noisezone = val_frq[junkid[0:num_prof//2]] | |
| 2883 | specnoise = spectra[noisezone,:] |
|
2883 | specnoise = spectra[noisezone,:] | |
| 2884 | noise, stdvnoise = self.__get_noise1(specnoise,num_incoh) |
|
2884 | noise, stdvnoise = self.__get_noise1(specnoise,num_incoh) | |
| 2885 |
|
2885 | |||
| 2886 | if talk: |
|
2886 | if talk: | |
| 2887 | print('noise =', noise) |
|
2887 | print('noise =', noise) | |
| 2888 | return noise |
|
2888 | return noise | |
| 2889 | #------------------ Get SA Parameters -------------------------- |
|
2889 | #------------------ Get SA Parameters -------------------------- | |
| 2890 |
|
2890 | |||
| 2891 | def GetSAParameters(self): |
|
2891 | def GetSAParameters(self): | |
| 2892 | #SA en frecuencia |
|
2892 | #SA en frecuencia | |
| 2893 | pairslist = self.dataOut.groupList |
|
2893 | pairslist = self.dataOut.groupList | |
| 2894 | num_pairs = len(pairslist) |
|
2894 | num_pairs = len(pairslist) | |
| 2895 |
|
2895 | |||
| 2896 | vel = self.dataOut.abscissaList |
|
2896 | vel = self.dataOut.abscissaList | |
| 2897 | spectra = self.dataOut.data_pre |
|
2897 | spectra = self.dataOut.data_pre | |
| 2898 | cspectra = self.dataIn.data_cspc |
|
2898 | cspectra = self.dataIn.data_cspc | |
| 2899 | delta_v = vel[1] - vel[0] |
|
2899 | delta_v = vel[1] - vel[0] | |
| 2900 |
|
2900 | |||
| 2901 | #Calculating the power spectrum |
|
2901 | #Calculating the power spectrum | |
| 2902 | spc_pow = numpy.sum(spectra, 3)*delta_v |
|
2902 | spc_pow = numpy.sum(spectra, 3)*delta_v | |
| 2903 | #Normalizing Spectra |
|
2903 | #Normalizing Spectra | |
| 2904 | norm_spectra = spectra/spc_pow |
|
2904 | norm_spectra = spectra/spc_pow | |
| 2905 | #Calculating the norm_spectra at peak |
|
2905 | #Calculating the norm_spectra at peak | |
| 2906 | max_spectra = numpy.max(norm_spectra, 3) |
|
2906 | max_spectra = numpy.max(norm_spectra, 3) | |
| 2907 |
|
2907 | |||
| 2908 | #Normalizing Cross Spectra |
|
2908 | #Normalizing Cross Spectra | |
| 2909 | norm_cspectra = numpy.zeros(cspectra.shape) |
|
2909 | norm_cspectra = numpy.zeros(cspectra.shape) | |
| 2910 |
|
2910 | |||
| 2911 | for i in range(num_chan): |
|
2911 | for i in range(num_chan): | |
| 2912 | norm_cspectra[i,:,:] = cspectra[i,:,:]/numpy.sqrt(spc_pow[pairslist[i][0],:]*spc_pow[pairslist[i][1],:]) |
|
2912 | norm_cspectra[i,:,:] = cspectra[i,:,:]/numpy.sqrt(spc_pow[pairslist[i][0],:]*spc_pow[pairslist[i][1],:]) | |
| 2913 |
|
2913 | |||
| 2914 | max_cspectra = numpy.max(norm_cspectra,2) |
|
2914 | max_cspectra = numpy.max(norm_cspectra,2) | |
| 2915 | max_cspectra_index = numpy.argmax(norm_cspectra, 2) |
|
2915 | max_cspectra_index = numpy.argmax(norm_cspectra, 2) | |
| 2916 |
|
2916 | |||
| 2917 | for i in range(num_pairs): |
|
2917 | for i in range(num_pairs): | |
| 2918 | cspc_par[i,:,:] = __calculateMoments(norm_cspectra) |
|
2918 | cspc_par[i,:,:] = __calculateMoments(norm_cspectra) | |
| 2919 | #------------------- Get Lags ---------------------------------- |
|
2919 | #------------------- Get Lags ---------------------------------- | |
| 2920 |
|
2920 | |||
| 2921 | class JULIADriftsEstimation(Operation): |
|
2921 | class JULIADriftsEstimation(Operation): | |
| 2922 |
|
2922 | |||
| 2923 | def __init__(self): |
|
2923 | def __init__(self): | |
| 2924 | Operation.__init__(self) |
|
2924 | Operation.__init__(self) | |
| 2925 |
|
2925 | |||
| 2926 | def newtotal(self, data): |
|
2926 | def newtotal(self, data): | |
| 2927 | return numpy.nansum(data) |
|
2927 | return numpy.nansum(data) | |
| 2928 |
|
2928 | |||
| 2929 | def data_filter(self, parm, snrth=-20, swth=20, wErrth=500): |
|
2929 | def data_filter(self, parm, snrth=-20, swth=20, wErrth=500): | |
| 2930 |
|
2930 | |||
| 2931 | Sz0 = parm.shape # Sz0: h,p |
|
2931 | Sz0 = parm.shape # Sz0: h,p | |
| 2932 | drift = parm[:,0] |
|
2932 | drift = parm[:,0] | |
| 2933 | sw = 2*parm[:,1] |
|
2933 | sw = 2*parm[:,1] | |
| 2934 | snr = 10*numpy.log10(parm[:,2]) |
|
2934 | snr = 10*numpy.log10(parm[:,2]) | |
| 2935 | Sz = drift.shape # Sz: h |
|
2935 | Sz = drift.shape # Sz: h | |
| 2936 | mask = numpy.ones((Sz[0])) |
|
2936 | mask = numpy.ones((Sz[0])) | |
| 2937 | th=0 |
|
2937 | th=0 | |
| 2938 | valid=numpy.where(numpy.isfinite(snr)) |
|
2938 | valid=numpy.where(numpy.isfinite(snr)) | |
| 2939 | cvalid = len(valid[0]) |
|
2939 | cvalid = len(valid[0]) | |
| 2940 | if cvalid >= 1: |
|
2940 | if cvalid >= 1: | |
| 2941 | # CΓ‘lculo del ruido promedio de snr para el i-Γ©simo grupo de alturas |
|
2941 | # CΓ‘lculo del ruido promedio de snr para el i-Γ©simo grupo de alturas | |
| 2942 | nbins = int(numpy.max(snr)-numpy.min(snr))+1 # bin size = 1, similar to IDL |
|
2942 | nbins = int(numpy.max(snr)-numpy.min(snr))+1 # bin size = 1, similar to IDL | |
| 2943 | h = numpy.histogram(snr,bins=nbins) |
|
2943 | h = numpy.histogram(snr,bins=nbins) | |
| 2944 | hist = h[0] |
|
2944 | hist = h[0] | |
| 2945 | values = numpy.round_(h[1]) |
|
2945 | values = numpy.round_(h[1]) | |
| 2946 | moda = values[numpy.where(hist == numpy.max(hist))] |
|
2946 | moda = values[numpy.where(hist == numpy.max(hist))] | |
| 2947 | indNoise = numpy.where(numpy.abs(snr - numpy.min(moda)) < 3)[0] |
|
2947 | indNoise = numpy.where(numpy.abs(snr - numpy.min(moda)) < 3)[0] | |
| 2948 |
|
2948 | |||
| 2949 | noise = snr[indNoise] |
|
2949 | noise = snr[indNoise] | |
| 2950 | noise_mean = numpy.sum(noise)/len(noise) |
|
2950 | noise_mean = numpy.sum(noise)/len(noise) | |
| 2951 | # CΓ‘lculo de media de snr |
|
2951 | # CΓ‘lculo de media de snr | |
| 2952 | med = numpy.median(snr) |
|
2952 | med = numpy.median(snr) | |
| 2953 | # Establece el umbral de snr |
|
2953 | # Establece el umbral de snr | |
| 2954 | if noise_mean > med + 3: |
|
2954 | if noise_mean > med + 3: | |
| 2955 | th = med |
|
2955 | th = med | |
| 2956 | else: |
|
2956 | else: | |
| 2957 | th = noise_mean + 3 |
|
2957 | th = noise_mean + 3 | |
| 2958 | # Establece mΓ‘scara |
|
2958 | # Establece mΓ‘scara | |
| 2959 | novalid = numpy.where(snr <= th)[0] |
|
2959 | novalid = numpy.where(snr <= th)[0] | |
| 2960 | mask[novalid] = numpy.nan |
|
2960 | mask[novalid] = numpy.nan | |
| 2961 | # Elimina datos que no sobrepasen el umbral: PARAMETRO |
|
2961 | # Elimina datos que no sobrepasen el umbral: PARAMETRO | |
| 2962 | novalid = numpy.where(snr <= snrth) |
|
2962 | novalid = numpy.where(snr <= snrth) | |
| 2963 | cnovalid = len(novalid[0]) |
|
2963 | cnovalid = len(novalid[0]) | |
| 2964 | if cnovalid > 0: |
|
2964 | if cnovalid > 0: | |
| 2965 | mask[novalid] = numpy.nan |
|
2965 | mask[novalid] = numpy.nan | |
| 2966 | novalid = numpy.where(numpy.isnan(snr)) |
|
2966 | novalid = numpy.where(numpy.isnan(snr)) | |
| 2967 | cnovalid = len(novalid[0]) |
|
2967 | cnovalid = len(novalid[0]) | |
| 2968 | if cnovalid > 0: |
|
2968 | if cnovalid > 0: | |
| 2969 | mask[novalid] = numpy.nan |
|
2969 | mask[novalid] = numpy.nan | |
| 2970 | new_parm = numpy.zeros((Sz0[0],Sz0[1])) |
|
2970 | new_parm = numpy.zeros((Sz0[0],Sz0[1])) | |
| 2971 | for h in range(Sz0[0]): |
|
2971 | for h in range(Sz0[0]): | |
| 2972 | for p in range(Sz0[1]): |
|
2972 | for p in range(Sz0[1]): | |
| 2973 | if numpy.isnan(mask[h]): |
|
2973 | if numpy.isnan(mask[h]): | |
| 2974 | new_parm[h,p]=numpy.nan |
|
2974 | new_parm[h,p]=numpy.nan | |
| 2975 | else: |
|
2975 | else: | |
| 2976 | new_parm[h,p]=parm[h,p] |
|
2976 | new_parm[h,p]=parm[h,p] | |
| 2977 |
|
2977 | |||
| 2978 | return new_parm, th |
|
2978 | return new_parm, th | |
| 2979 |
|
2979 | |||
| 2980 | def run(self, dataOut, zenith, zenithCorrection,heights=None, statistics=0, otype=0): |
|
2980 | def run(self, dataOut, zenith, zenithCorrection,heights=None, statistics=0, otype=0): | |
| 2981 |
|
2981 | |||
| 2982 | dataOut.lat=-11.95 |
|
2982 | dataOut.lat=-11.95 | |
| 2983 | dataOut.lon=-76.87 |
|
2983 | dataOut.lon=-76.87 | |
| 2984 | nCh=dataOut.spcpar.shape[0] |
|
2984 | nCh=dataOut.spcpar.shape[0] | |
| 2985 | nHei=dataOut.spcpar.shape[1] |
|
2985 | nHei=dataOut.spcpar.shape[1] | |
| 2986 | nParam=dataOut.spcpar.shape[2] |
|
2986 | nParam=dataOut.spcpar.shape[2] | |
| 2987 | # SelecciΓ³n de alturas |
|
2987 | # SelecciΓ³n de alturas | |
| 2988 |
|
2988 | |||
| 2989 | if not heights: |
|
2989 | if not heights: | |
| 2990 | parm = numpy.zeros((nCh,nHei,nParam)) |
|
2990 | parm = numpy.zeros((nCh,nHei,nParam)) | |
| 2991 | parm[:] = dataOut.spcpar[:] |
|
2991 | parm[:] = dataOut.spcpar[:] | |
| 2992 | else: |
|
2992 | else: | |
| 2993 | hei=dataOut.heightList |
|
2993 | hei=dataOut.heightList | |
| 2994 | hvalid=numpy.where([hei >= heights[0]][0] & [hei <= heights[1]][0])[0] |
|
2994 | hvalid=numpy.where([hei >= heights[0]][0] & [hei <= heights[1]][0])[0] | |
| 2995 | nhvalid=len(hvalid) |
|
2995 | nhvalid=len(hvalid) | |
| 2996 | dataOut.heightList = hei[hvalid] |
|
2996 | dataOut.heightList = hei[hvalid] | |
| 2997 | parm = numpy.zeros((nCh,nhvalid,nParam)) |
|
2997 | parm = numpy.zeros((nCh,nhvalid,nParam)) | |
| 2998 | parm[:] = dataOut.spcpar[:,hvalid,:] |
|
2998 | parm[:] = dataOut.spcpar[:,hvalid,:] | |
| 2999 |
|
2999 | |||
| 3000 |
|
3000 | |||
| 3001 | # Primer filtrado: Umbral de SNR |
|
3001 | # Primer filtrado: Umbral de SNR | |
| 3002 | for i in range(nCh): |
|
3002 | for i in range(nCh): | |
| 3003 | parm[i,:,:] = self.data_filter(parm[i,:,:])[0] |
|
3003 | parm[i,:,:] = self.data_filter(parm[i,:,:])[0] | |
| 3004 |
|
3004 | |||
| 3005 | zenith = numpy.array(zenith) |
|
3005 | zenith = numpy.array(zenith) | |
| 3006 | zenith -= zenithCorrection |
|
3006 | zenith -= zenithCorrection | |
| 3007 | zenith *= numpy.pi/180 |
|
3007 | zenith *= numpy.pi/180 | |
| 3008 | alpha = zenith[0] |
|
3008 | alpha = zenith[0] | |
| 3009 | beta = zenith[1] |
|
3009 | beta = zenith[1] | |
| 3010 | dopplerCH0 = parm[0,:,0] |
|
3010 | dopplerCH0 = parm[0,:,0] | |
| 3011 | dopplerCH1 = parm[1,:,0] |
|
3011 | dopplerCH1 = parm[1,:,0] | |
| 3012 | swCH0 = parm[0,:,1] |
|
3012 | swCH0 = parm[0,:,1] | |
| 3013 | swCH1 = parm[1,:,1] |
|
3013 | swCH1 = parm[1,:,1] | |
| 3014 | snrCH0 = 10*numpy.log10(parm[0,:,2]) |
|
3014 | snrCH0 = 10*numpy.log10(parm[0,:,2]) | |
| 3015 | snrCH1 = 10*numpy.log10(parm[1,:,2]) |
|
3015 | snrCH1 = 10*numpy.log10(parm[1,:,2]) | |
| 3016 | noiseCH0 = parm[0,:,3] |
|
3016 | noiseCH0 = parm[0,:,3] | |
| 3017 | noiseCH1 = parm[1,:,3] |
|
3017 | noiseCH1 = parm[1,:,3] | |
| 3018 | wErrCH0 = parm[0,:,5] |
|
3018 | wErrCH0 = parm[0,:,5] | |
| 3019 | wErrCH1 = parm[1,:,5] |
|
3019 | wErrCH1 = parm[1,:,5] | |
| 3020 |
|
3020 | |||
| 3021 | # Vertical and zonal calculation according to geometry |
|
3021 | # Vertical and zonal calculation according to geometry | |
| 3022 | sinB_A = numpy.sin(beta)*numpy.cos(alpha) - numpy.sin(alpha)* numpy.cos(beta) |
|
3022 | sinB_A = numpy.sin(beta)*numpy.cos(alpha) - numpy.sin(alpha)* numpy.cos(beta) | |
| 3023 | drift = -(dopplerCH0 * numpy.sin(beta) - dopplerCH1 * numpy.sin(alpha))/ sinB_A |
|
3023 | drift = -(dopplerCH0 * numpy.sin(beta) - dopplerCH1 * numpy.sin(alpha))/ sinB_A | |
| 3024 | zonal = (dopplerCH0 * numpy.cos(beta) - dopplerCH1 * numpy.cos(alpha))/ sinB_A |
|
3024 | zonal = (dopplerCH0 * numpy.cos(beta) - dopplerCH1 * numpy.cos(alpha))/ sinB_A | |
| 3025 | snr = (snrCH0 + snrCH1)/2 |
|
3025 | snr = (snrCH0 + snrCH1)/2 | |
| 3026 | noise = (noiseCH0 + noiseCH1)/2 |
|
3026 | noise = (noiseCH0 + noiseCH1)/2 | |
| 3027 | sw = (swCH0 + swCH1)/2 |
|
3027 | sw = (swCH0 + swCH1)/2 | |
| 3028 | 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)) |
|
3028 | 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)) | |
| 3029 | 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)) |
|
3029 | 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)) | |
| 3030 |
|
3030 | |||
| 3031 | # for statistics150km |
|
3031 | # for statistics150km | |
| 3032 | if statistics: |
|
3032 | if statistics: | |
| 3033 | print('Implemented offline.') |
|
3033 | print('Implemented offline.') | |
| 3034 |
|
3034 | |||
| 3035 | if otype == 0: |
|
3035 | if otype == 0: | |
| 3036 | winds = numpy.vstack((snr, drift, zonal, noise, sw, w_w_err, w_e_err)) # to process statistics drifts |
|
3036 | winds = numpy.vstack((snr, drift, zonal, noise, sw, w_w_err, w_e_err)) # to process statistics drifts | |
| 3037 | elif otype == 3: |
|
3037 | elif otype == 3: | |
| 3038 | winds = numpy.vstack((snr, drift, zonal)) # to generic plot: 3 RTI's |
|
3038 | winds = numpy.vstack((snr, drift, zonal)) # to generic plot: 3 RTI's | |
| 3039 | elif otype == 4: |
|
3039 | elif otype == 4: | |
| 3040 | winds = numpy.vstack((snrCH0, drift, snrCH1, zonal)) # to generic plot: 4 RTI's |
|
3040 | winds = numpy.vstack((snrCH0, drift, snrCH1, zonal)) # to generic plot: 4 RTI's | |
| 3041 |
|
3041 | |||
| 3042 | snr1 = numpy.vstack((snrCH0, snrCH1)) |
|
3042 | snr1 = numpy.vstack((snrCH0, snrCH1)) | |
| 3043 | dataOut.data_output = winds |
|
3043 | dataOut.data_output = winds | |
| 3044 | dataOut.data_snr = snr1 |
|
3044 | dataOut.data_snr = snr1 | |
| 3045 |
|
3045 | |||
| 3046 | dataOut.utctimeInit = dataOut.utctime |
|
3046 | dataOut.utctimeInit = dataOut.utctime | |
| 3047 | dataOut.outputInterval = dataOut.timeInterval |
|
3047 | dataOut.outputInterval = dataOut.timeInterval | |
| 3048 | try: |
|
3048 | try: | |
| 3049 | dataOut.flagNoData = numpy.all(numpy.isnan(dataOut.data_output[0])) # NAN vectors are not written MADRIGAL CASE |
|
3049 | dataOut.flagNoData = numpy.all(numpy.isnan(dataOut.data_output[0])) # NAN vectors are not written MADRIGAL CASE | |
| 3050 | except: |
|
3050 | except: | |
| 3051 | print("Check there is no Data") |
|
3051 | print("Check there is no Data") | |
| 3052 |
|
3052 | |||
| 3053 | return dataOut |
|
3053 | return dataOut | |
| 3054 |
|
3054 | |||
| 3055 | class SALags(Operation): |
|
3055 | class SALags(Operation): | |
| 3056 | ''' |
|
3056 | ''' | |
| 3057 | Function GetMoments() |
|
3057 | Function GetMoments() | |
| 3058 |
|
3058 | |||
| 3059 | Input: |
|
3059 | Input: | |
| 3060 | self.dataOut.data_pre |
|
3060 | self.dataOut.data_pre | |
| 3061 | self.dataOut.abscissaList |
|
3061 | self.dataOut.abscissaList | |
| 3062 | self.dataOut.noise |
|
3062 | self.dataOut.noise | |
| 3063 | self.dataOut.normFactor |
|
3063 | self.dataOut.normFactor | |
| 3064 | self.dataOut.data_snr |
|
3064 | self.dataOut.data_snr | |
| 3065 | self.dataOut.groupList |
|
3065 | self.dataOut.groupList | |
| 3066 | self.dataOut.nChannels |
|
3066 | self.dataOut.nChannels | |
| 3067 |
|
3067 | |||
| 3068 | Affected: |
|
3068 | Affected: | |
| 3069 | self.dataOut.data_param |
|
3069 | self.dataOut.data_param | |
| 3070 |
|
3070 | |||
| 3071 | ''' |
|
3071 | ''' | |
| 3072 | def run(self, dataOut): |
|
3072 | def run(self, dataOut): | |
| 3073 | data_acf = dataOut.data_pre[0] |
|
3073 | data_acf = dataOut.data_pre[0] | |
| 3074 | data_ccf = dataOut.data_pre[1] |
|
3074 | data_ccf = dataOut.data_pre[1] | |
| 3075 | normFactor_acf = dataOut.normFactor[0] |
|
3075 | normFactor_acf = dataOut.normFactor[0] | |
| 3076 | normFactor_ccf = dataOut.normFactor[1] |
|
3076 | normFactor_ccf = dataOut.normFactor[1] | |
| 3077 | pairs_acf = dataOut.groupList[0] |
|
3077 | pairs_acf = dataOut.groupList[0] | |
| 3078 | pairs_ccf = dataOut.groupList[1] |
|
3078 | pairs_ccf = dataOut.groupList[1] | |
| 3079 |
|
3079 | |||
| 3080 | nHeights = dataOut.nHeights |
|
3080 | nHeights = dataOut.nHeights | |
| 3081 | absc = dataOut.abscissaList |
|
3081 | absc = dataOut.abscissaList | |
| 3082 | noise = dataOut.noise |
|
3082 | noise = dataOut.noise | |
| 3083 | SNR = dataOut.data_snr |
|
3083 | SNR = dataOut.data_snr | |
| 3084 | nChannels = dataOut.nChannels |
|
3084 | nChannels = dataOut.nChannels | |
| 3085 | for l in range(len(pairs_acf)): |
|
3085 | for l in range(len(pairs_acf)): | |
| 3086 | data_acf[l,:,:] = data_acf[l,:,:]/normFactor_acf[l,:] |
|
3086 | data_acf[l,:,:] = data_acf[l,:,:]/normFactor_acf[l,:] | |
| 3087 |
|
3087 | |||
| 3088 | for l in range(len(pairs_ccf)): |
|
3088 | for l in range(len(pairs_ccf)): | |
| 3089 | data_ccf[l,:,:] = data_ccf[l,:,:]/normFactor_ccf[l,:] |
|
3089 | data_ccf[l,:,:] = data_ccf[l,:,:]/normFactor_ccf[l,:] | |
| 3090 |
|
3090 | |||
| 3091 | dataOut.data_param = numpy.zeros((len(pairs_ccf)*2 + 1, nHeights)) |
|
3091 | dataOut.data_param = numpy.zeros((len(pairs_ccf)*2 + 1, nHeights)) | |
| 3092 | dataOut.data_param[:-1,:] = self.__calculateTaus(data_acf, data_ccf, absc) |
|
3092 | dataOut.data_param[:-1,:] = self.__calculateTaus(data_acf, data_ccf, absc) | |
| 3093 | dataOut.data_param[-1,:] = self.__calculateLag1Phase(data_acf, absc) |
|
3093 | dataOut.data_param[-1,:] = self.__calculateLag1Phase(data_acf, absc) | |
| 3094 | return |
|
3094 | return | |
| 3095 |
|
3095 | |||
| 3096 | def __calculateTaus(self, data_acf, data_ccf, lagRange): |
|
3096 | def __calculateTaus(self, data_acf, data_ccf, lagRange): | |
| 3097 |
|
3097 | |||
| 3098 | lag0 = data_acf.shape[1]/2 |
|
3098 | lag0 = data_acf.shape[1]/2 | |
| 3099 | #Funcion de Autocorrelacion |
|
3099 | #Funcion de Autocorrelacion | |
| 3100 | mean_acf = stats.nanmean(data_acf, axis = 0) |
|
3100 | mean_acf = stats.nanmean(data_acf, axis = 0) | |
| 3101 |
|
3101 | |||
| 3102 | #Obtencion Indice de TauCross |
|
3102 | #Obtencion Indice de TauCross | |
| 3103 | ind_ccf = data_ccf.argmax(axis = 1) |
|
3103 | ind_ccf = data_ccf.argmax(axis = 1) | |
| 3104 | #Obtencion Indice de TauAuto |
|
3104 | #Obtencion Indice de TauAuto | |
| 3105 | ind_acf = numpy.zeros(ind_ccf.shape,dtype = 'int') |
|
3105 | ind_acf = numpy.zeros(ind_ccf.shape,dtype = 'int') | |
| 3106 | ccf_lag0 = data_ccf[:,lag0,:] |
|
3106 | ccf_lag0 = data_ccf[:,lag0,:] | |
| 3107 |
|
3107 | |||
| 3108 | for i in range(ccf_lag0.shape[0]): |
|
3108 | for i in range(ccf_lag0.shape[0]): | |
| 3109 | ind_acf[i,:] = numpy.abs(mean_acf - ccf_lag0[i,:]).argmin(axis = 0) |
|
3109 | ind_acf[i,:] = numpy.abs(mean_acf - ccf_lag0[i,:]).argmin(axis = 0) | |
| 3110 |
|
3110 | |||
| 3111 | #Obtencion de TauCross y TauAuto |
|
3111 | #Obtencion de TauCross y TauAuto | |
| 3112 | tau_ccf = lagRange[ind_ccf] |
|
3112 | tau_ccf = lagRange[ind_ccf] | |
| 3113 | tau_acf = lagRange[ind_acf] |
|
3113 | tau_acf = lagRange[ind_acf] | |
| 3114 |
|
3114 | |||
| 3115 | Nan1, Nan2 = numpy.where(tau_ccf == lagRange[0]) |
|
3115 | Nan1, Nan2 = numpy.where(tau_ccf == lagRange[0]) | |
| 3116 |
|
3116 | |||
| 3117 | tau_ccf[Nan1,Nan2] = numpy.nan |
|
3117 | tau_ccf[Nan1,Nan2] = numpy.nan | |
| 3118 | tau_acf[Nan1,Nan2] = numpy.nan |
|
3118 | tau_acf[Nan1,Nan2] = numpy.nan | |
| 3119 | tau = numpy.vstack((tau_ccf,tau_acf)) |
|
3119 | tau = numpy.vstack((tau_ccf,tau_acf)) | |
| 3120 |
|
3120 | |||
| 3121 | return tau |
|
3121 | return tau | |
| 3122 |
|
3122 | |||
| 3123 | def __calculateLag1Phase(self, data, lagTRange): |
|
3123 | def __calculateLag1Phase(self, data, lagTRange): | |
| 3124 | data1 = stats.nanmean(data, axis = 0) |
|
3124 | data1 = stats.nanmean(data, axis = 0) | |
| 3125 | lag1 = numpy.where(lagTRange == 0)[0][0] + 1 |
|
3125 | lag1 = numpy.where(lagTRange == 0)[0][0] + 1 | |
| 3126 |
|
3126 | |||
| 3127 | phase = numpy.angle(data1[lag1,:]) |
|
3127 | phase = numpy.angle(data1[lag1,:]) | |
| 3128 |
|
3128 | |||
| 3129 | return phase |
|
3129 | return phase | |
| 3130 |
|
3130 | |||
| 3131 | def fit_func( x, a0, a1, a2): #, a3, a4, a5): |
|
3131 | def fit_func( x, a0, a1, a2): #, a3, a4, a5): | |
| 3132 | z = (x - a1) / a2 |
|
3132 | z = (x - a1) / a2 | |
| 3133 | y = a0 * numpy.exp(-z**2 / a2) #+ a3 + a4 * x + a5 * x**2 |
|
3133 | y = a0 * numpy.exp(-z**2 / a2) #+ a3 + a4 * x + a5 * x**2 | |
| 3134 | return y |
|
3134 | return y | |
| 3135 |
|
3135 | |||
| 3136 |
|
3136 | |||
| 3137 | class SpectralFitting(Operation): |
|
3137 | class SpectralFitting(Operation): | |
| 3138 | ''' |
|
3138 | ''' | |
| 3139 | Function GetMoments() |
|
3139 | Function GetMoments() | |
| 3140 |
|
3140 | |||
| 3141 | Input: |
|
3141 | Input: | |
| 3142 | Output: |
|
3142 | Output: | |
| 3143 | Variables modified: |
|
3143 | Variables modified: | |
| 3144 | ''' |
|
3144 | ''' | |
| 3145 | isConfig = False |
|
3145 | isConfig = False | |
| 3146 | __dataReady = False |
|
3146 | __dataReady = False | |
| 3147 | bloques = None |
|
3147 | bloques = None | |
| 3148 | bloque0 = None |
|
3148 | bloque0 = None | |
| 3149 | index = 0 |
|
3149 | index = 0 | |
| 3150 | fint = 0 |
|
3150 | fint = 0 | |
| 3151 | buffer = 0 |
|
3151 | buffer = 0 | |
| 3152 | buffer2 = 0 |
|
3152 | buffer2 = 0 | |
| 3153 | buffer3 = 0 |
|
3153 | buffer3 = 0 | |
| 3154 |
|
3154 | |||
| 3155 | def __init__(self): |
|
3155 | def __init__(self): | |
| 3156 | Operation.__init__(self) |
|
3156 | Operation.__init__(self) | |
| 3157 | self.i=0 |
|
3157 | self.i=0 | |
| 3158 | self.isConfig = False |
|
3158 | self.isConfig = False | |
| 3159 |
|
3159 | |||
| 3160 |
|
3160 | |||
| 3161 | def setup(self,dataOut,groupList,path,file,filec): |
|
3161 | def setup(self,dataOut,groupList,path,file,filec): | |
| 3162 | self.__dataReady = False |
|
3162 | self.__dataReady = False | |
| 3163 | # new |
|
3163 | # new | |
| 3164 | self.nChannels = dataOut.nChannels |
|
3164 | self.nChannels = dataOut.nChannels | |
| 3165 | self.channels = dataOut.channelList |
|
3165 | self.channels = dataOut.channelList | |
| 3166 | self.nHeights = dataOut.heightList.size |
|
3166 | self.nHeights = dataOut.heightList.size | |
| 3167 | self.heights = dataOut.heightList |
|
3167 | self.heights = dataOut.heightList | |
| 3168 | self.nProf = dataOut.nProfiles |
|
3168 | self.nProf = dataOut.nProfiles | |
| 3169 | self.nIncohInt = dataOut.nIncohInt |
|
3169 | self.nIncohInt = dataOut.nIncohInt | |
| 3170 | self.absc = dataOut.abscissaList[:-1] |
|
3170 | self.absc = dataOut.abscissaList[:-1] | |
| 3171 |
|
3171 | |||
| 3172 |
|
3172 | |||
| 3173 | #To be inserted as a parameter |
|
3173 | #To be inserted as a parameter | |
| 3174 | try: |
|
3174 | try: | |
| 3175 | self.groupArray = numpy.array(groupList)#groupArray = numpy.array([[0,1],[2,3]]) |
|
3175 | self.groupArray = numpy.array(groupList)#groupArray = numpy.array([[0,1],[2,3]]) | |
| 3176 | except: |
|
3176 | except: | |
| 3177 | print("Please insert groupList. Example (0,1),(2,3) format multilist") |
|
3177 | print("Please insert groupList. Example (0,1),(2,3) format multilist") | |
| 3178 | dataOut.groupList = self.groupArray |
|
3178 | dataOut.groupList = self.groupArray | |
| 3179 | self.crosspairs = dataOut.groupList |
|
3179 | self.crosspairs = dataOut.groupList | |
| 3180 | self.nPairs = len(self.crosspairs) |
|
3180 | self.nPairs = len(self.crosspairs) | |
| 3181 | self.nGroups = self.groupArray.shape[0] |
|
3181 | self.nGroups = self.groupArray.shape[0] | |
| 3182 |
|
3182 | |||
| 3183 | #List of possible combinations |
|
3183 | #List of possible combinations | |
| 3184 |
|
3184 | |||
| 3185 | self.listComb = itertools.combinations(numpy.arange(self.groupArray.shape[1]),2) |
|
3185 | self.listComb = itertools.combinations(numpy.arange(self.groupArray.shape[1]),2) | |
| 3186 | self.indCross = numpy.zeros(len(list(self.listComb)), dtype = 'int') |
|
3186 | self.indCross = numpy.zeros(len(list(self.listComb)), dtype = 'int') | |
| 3187 |
|
3187 | |||
| 3188 | #Parameters Array |
|
3188 | #Parameters Array | |
| 3189 | dataOut.data_param = None |
|
3189 | dataOut.data_param = None | |
| 3190 | dataOut.data_paramC = None |
|
3190 | dataOut.data_paramC = None | |
| 3191 | dataOut.clean_num_aver = None |
|
3191 | dataOut.clean_num_aver = None | |
| 3192 | dataOut.coh_num_aver = None |
|
3192 | dataOut.coh_num_aver = None | |
| 3193 | dataOut.tmp_spectra_i = None |
|
3193 | dataOut.tmp_spectra_i = None | |
| 3194 | dataOut.tmp_cspectra_i = None |
|
3194 | dataOut.tmp_cspectra_i = None | |
| 3195 | dataOut.tmp_spectra_c = None |
|
3195 | dataOut.tmp_spectra_c = None | |
| 3196 | dataOut.tmp_cspectra_c = None |
|
3196 | dataOut.tmp_cspectra_c = None | |
| 3197 | dataOut.index = None |
|
3197 | dataOut.index = None | |
| 3198 |
|
3198 | |||
| 3199 | if path != None: |
|
3199 | if path != None: | |
| 3200 | sys.path.append(path) |
|
3200 | sys.path.append(path) | |
| 3201 | self.library = importlib.import_module(file) |
|
3201 | self.library = importlib.import_module(file) | |
| 3202 | if filec != None: |
|
3202 | if filec != None: | |
| 3203 | self.weightf = importlib.import_module(filec) |
|
3203 | self.weightf = importlib.import_module(filec) | |
| 3204 |
|
3204 | |||
| 3205 | #Set constants |
|
3205 | #Set constants | |
| 3206 | self.constants = self.library.setConstants(dataOut) |
|
3206 | self.constants = self.library.setConstants(dataOut) | |
| 3207 | dataOut.constants = self.constants |
|
3207 | dataOut.constants = self.constants | |
| 3208 | self.M = dataOut.normFactor |
|
3208 | self.M = dataOut.normFactor | |
| 3209 | self.N = dataOut.nFFTPoints |
|
3209 | self.N = dataOut.nFFTPoints | |
| 3210 | self.ippSeconds = dataOut.ippSeconds |
|
3210 | self.ippSeconds = dataOut.ippSeconds | |
| 3211 | self.K = dataOut.nIncohInt |
|
3211 | self.K = dataOut.nIncohInt | |
| 3212 | self.pairsArray = numpy.array(dataOut.pairsList) |
|
3212 | self.pairsArray = numpy.array(dataOut.pairsList) | |
| 3213 | self.snrth = 20 |
|
3213 | self.snrth = 20 | |
| 3214 |
|
3214 | |||
| 3215 | 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): |
|
3215 | 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): | |
| 3216 |
|
3216 | |||
| 3217 | if (nicoh is None): nicoh = 1 |
|
3217 | if (nicoh is None): nicoh = 1 | |
| 3218 | if (graph is None): graph = 0 |
|
3218 | if (graph is None): graph = 0 | |
| 3219 | if (smooth is None): smooth = 0 |
|
3219 | if (smooth is None): smooth = 0 | |
| 3220 | elif (self.smooth < 3): smooth = 0 |
|
3220 | elif (self.smooth < 3): smooth = 0 | |
| 3221 |
|
3221 | |||
| 3222 | if (type1 is None): type1 = 0 |
|
3222 | if (type1 is None): type1 = 0 | |
| 3223 | if (fwindow is None): fwindow = numpy.zeros(oldfreq.size) + 1 |
|
3223 | if (fwindow is None): fwindow = numpy.zeros(oldfreq.size) + 1 | |
| 3224 | if (snrth is None): snrth = -3 |
|
3224 | if (snrth is None): snrth = -3 | |
| 3225 | if (dc is None): dc = 0 |
|
3225 | if (dc is None): dc = 0 | |
| 3226 | if (aliasing is None): aliasing = 0 |
|
3226 | if (aliasing is None): aliasing = 0 | |
| 3227 | if (oldfd is None): oldfd = 0 |
|
3227 | if (oldfd is None): oldfd = 0 | |
| 3228 | if (wwauto is None): wwauto = 0 |
|
3228 | if (wwauto is None): wwauto = 0 | |
| 3229 |
|
3229 | |||
| 3230 | if (n0 < 1.e-20): n0 = 1.e-20 |
|
3230 | if (n0 < 1.e-20): n0 = 1.e-20 | |
| 3231 |
|
3231 | |||
| 3232 | freq = oldfreq |
|
3232 | freq = oldfreq | |
| 3233 | vec_power = numpy.zeros(oldspec.shape[1]) |
|
3233 | vec_power = numpy.zeros(oldspec.shape[1]) | |
| 3234 | vec_fd = numpy.zeros(oldspec.shape[1]) |
|
3234 | vec_fd = numpy.zeros(oldspec.shape[1]) | |
| 3235 | vec_w = numpy.zeros(oldspec.shape[1]) |
|
3235 | vec_w = numpy.zeros(oldspec.shape[1]) | |
| 3236 | vec_snr = numpy.zeros(oldspec.shape[1]) |
|
3236 | vec_snr = numpy.zeros(oldspec.shape[1]) | |
| 3237 |
|
3237 | |||
| 3238 | oldspec = numpy.ma.masked_invalid(oldspec) |
|
3238 | oldspec = numpy.ma.masked_invalid(oldspec) | |
| 3239 |
|
3239 | |||
| 3240 | for ind in range(oldspec.shape[1]): |
|
3240 | for ind in range(oldspec.shape[1]): | |
| 3241 |
|
3241 | |||
| 3242 | spec = oldspec[:,ind] |
|
3242 | spec = oldspec[:,ind] | |
| 3243 | aux = spec*fwindow |
|
3243 | aux = spec*fwindow | |
| 3244 | max_spec = aux.max() |
|
3244 | max_spec = aux.max() | |
| 3245 | m = list(aux).index(max_spec) |
|
3245 | m = list(aux).index(max_spec) | |
| 3246 |
|
3246 | |||
| 3247 | #Smooth |
|
3247 | #Smooth | |
| 3248 | if (smooth == 0): spec2 = spec |
|
3248 | if (smooth == 0): spec2 = spec | |
| 3249 | else: spec2 = scipy.ndimage.filters.uniform_filter1d(spec,size=smooth) |
|
3249 | else: spec2 = scipy.ndimage.filters.uniform_filter1d(spec,size=smooth) | |
| 3250 |
|
3250 | |||
| 3251 | # Calculo de Momentos |
|
3251 | # Calculo de Momentos | |
| 3252 | bb = spec2[list(range(m,spec2.size))] |
|
3252 | bb = spec2[list(range(m,spec2.size))] | |
| 3253 | bb = (bb<n0).nonzero() |
|
3253 | bb = (bb<n0).nonzero() | |
| 3254 | bb = bb[0] |
|
3254 | bb = bb[0] | |
| 3255 |
|
3255 | |||
| 3256 | ss = spec2[list(range(0,m + 1))] |
|
3256 | ss = spec2[list(range(0,m + 1))] | |
| 3257 | ss = (ss<n0).nonzero() |
|
3257 | ss = (ss<n0).nonzero() | |
| 3258 | ss = ss[0] |
|
3258 | ss = ss[0] | |
| 3259 |
|
3259 | |||
| 3260 | if (bb.size == 0): |
|
3260 | if (bb.size == 0): | |
| 3261 | bb0 = spec.size - 1 - m |
|
3261 | bb0 = spec.size - 1 - m | |
| 3262 | else: |
|
3262 | else: | |
| 3263 | bb0 = bb[0] - 1 |
|
3263 | bb0 = bb[0] - 1 | |
| 3264 | if (bb0 < 0): |
|
3264 | if (bb0 < 0): | |
| 3265 | bb0 = 0 |
|
3265 | bb0 = 0 | |
| 3266 |
|
3266 | |||
| 3267 | if (ss.size == 0): ss1 = 1 |
|
3267 | if (ss.size == 0): ss1 = 1 | |
| 3268 | else: ss1 = max(ss) + 1 |
|
3268 | else: ss1 = max(ss) + 1 | |
| 3269 |
|
3269 | |||
| 3270 | if (ss1 > m): ss1 = m |
|
3270 | if (ss1 > m): ss1 = m | |
| 3271 |
|
3271 | |||
| 3272 | valid = numpy.asarray(list(range(int(m + bb0 - ss1 + 1)))) + ss1 |
|
3272 | valid = numpy.asarray(list(range(int(m + bb0 - ss1 + 1)))) + ss1 | |
| 3273 | power = ((spec2[valid] - n0)*fwindow[valid]).sum() |
|
3273 | power = ((spec2[valid] - n0)*fwindow[valid]).sum() | |
| 3274 | fd = ((spec2[valid]- n0)*freq[valid]*fwindow[valid]).sum()/power |
|
3274 | fd = ((spec2[valid]- n0)*freq[valid]*fwindow[valid]).sum()/power | |
| 3275 | w = math.sqrt(((spec2[valid] - n0)*fwindow[valid]*(freq[valid]- fd)**2).sum()/power) |
|
3275 | w = math.sqrt(((spec2[valid] - n0)*fwindow[valid]*(freq[valid]- fd)**2).sum()/power) | |
| 3276 | snr = (spec2.mean()-n0)/n0 |
|
3276 | snr = (spec2.mean()-n0)/n0 | |
| 3277 |
|
3277 | |||
| 3278 | if (snr < 1.e-20) : |
|
3278 | if (snr < 1.e-20) : | |
| 3279 | snr = 1.e-20 |
|
3279 | snr = 1.e-20 | |
| 3280 |
|
3280 | |||
| 3281 | vec_power[ind] = power |
|
3281 | vec_power[ind] = power | |
| 3282 | vec_fd[ind] = fd |
|
3282 | vec_fd[ind] = fd | |
| 3283 | vec_w[ind] = w |
|
3283 | vec_w[ind] = w | |
| 3284 | vec_snr[ind] = snr |
|
3284 | vec_snr[ind] = snr | |
| 3285 |
|
3285 | |||
| 3286 | moments = numpy.vstack((vec_snr, vec_power, vec_fd, vec_w)) |
|
3286 | moments = numpy.vstack((vec_snr, vec_power, vec_fd, vec_w)) | |
| 3287 | return moments |
|
3287 | return moments | |
| 3288 |
|
3288 | |||
| 3289 | def __DiffCoherent(self, spectra, cspectra, dataOut, noise, snrth, coh_th, hei_th): |
|
3289 | def __DiffCoherent(self, spectra, cspectra, dataOut, noise, snrth, coh_th, hei_th): | |
| 3290 |
|
3290 | |||
| 3291 | nProf = dataOut.nProfiles |
|
3291 | nProf = dataOut.nProfiles | |
| 3292 | heights = dataOut.heightList |
|
3292 | heights = dataOut.heightList | |
| 3293 | nHei = len(heights) |
|
3293 | nHei = len(heights) | |
| 3294 | channels = dataOut.channelList |
|
3294 | channels = dataOut.channelList | |
| 3295 | nChan = len(channels) |
|
3295 | nChan = len(channels) | |
| 3296 | crosspairs = dataOut.groupList |
|
3296 | crosspairs = dataOut.groupList | |
| 3297 | nPairs = len(crosspairs) |
|
3297 | nPairs = len(crosspairs) | |
| 3298 | #Separar espectros incoherentes de coherentes snr > 20 dB' |
|
3298 | #Separar espectros incoherentes de coherentes snr > 20 dB' | |
| 3299 | snr_th = 10**(snrth/10.0) |
|
3299 | snr_th = 10**(snrth/10.0) | |
| 3300 | my_incoh_spectra = numpy.zeros([nChan, nProf,nHei], dtype='float') |
|
3300 | my_incoh_spectra = numpy.zeros([nChan, nProf,nHei], dtype='float') | |
| 3301 | my_incoh_cspectra = numpy.zeros([nPairs,nProf, nHei], dtype='complex') |
|
3301 | my_incoh_cspectra = numpy.zeros([nPairs,nProf, nHei], dtype='complex') | |
| 3302 | my_incoh_aver = numpy.zeros([nChan, nHei]) |
|
3302 | my_incoh_aver = numpy.zeros([nChan, nHei]) | |
| 3303 | my_coh_aver = numpy.zeros([nChan, nHei]) |
|
3303 | my_coh_aver = numpy.zeros([nChan, nHei]) | |
| 3304 |
|
3304 | |||
| 3305 | coh_spectra = numpy.zeros([nChan, nProf, nHei], dtype='float') |
|
3305 | coh_spectra = numpy.zeros([nChan, nProf, nHei], dtype='float') | |
| 3306 | coh_cspectra = numpy.zeros([nPairs, nProf, nHei], dtype='complex') |
|
3306 | coh_cspectra = numpy.zeros([nPairs, nProf, nHei], dtype='complex') | |
| 3307 | coh_aver = numpy.zeros([nChan, nHei]) |
|
3307 | coh_aver = numpy.zeros([nChan, nHei]) | |
| 3308 |
|
3308 | |||
| 3309 | incoh_spectra = numpy.zeros([nChan, nProf, nHei], dtype='float') |
|
3309 | incoh_spectra = numpy.zeros([nChan, nProf, nHei], dtype='float') | |
| 3310 | incoh_cspectra = numpy.zeros([nPairs, nProf, nHei], dtype='complex') |
|
3310 | incoh_cspectra = numpy.zeros([nPairs, nProf, nHei], dtype='complex') | |
| 3311 | incoh_aver = numpy.zeros([nChan, nHei]) |
|
3311 | incoh_aver = numpy.zeros([nChan, nHei]) | |
| 3312 | power = numpy.sum(spectra, axis=1) |
|
3312 | power = numpy.sum(spectra, axis=1) | |
| 3313 |
|
3313 | |||
| 3314 | if coh_th == None : coh_th = numpy.array([0.75,0.65,0.15]) # 0.65 |
|
3314 | if coh_th == None : coh_th = numpy.array([0.75,0.65,0.15]) # 0.65 | |
| 3315 | if hei_th == None : hei_th = numpy.array([60,300,650]) |
|
3315 | if hei_th == None : hei_th = numpy.array([60,300,650]) | |
| 3316 | for ic in range(nPairs): |
|
3316 | for ic in range(nPairs): | |
| 3317 | pair = crosspairs[ic] |
|
3317 | pair = crosspairs[ic] | |
| 3318 | #si el SNR es mayor que el SNR threshold los datos se toman coherentes |
|
3318 | #si el SNR es mayor que el SNR threshold los datos se toman coherentes | |
| 3319 | s_n0 = power[pair[0],:]/noise[pair[0]] |
|
3319 | s_n0 = power[pair[0],:]/noise[pair[0]] | |
| 3320 | s_n1 = power[pair[1],:]/noise[pair[1]] |
|
3320 | s_n1 = power[pair[1],:]/noise[pair[1]] | |
| 3321 | valid1 =(s_n0>=snr_th).nonzero() |
|
3321 | valid1 =(s_n0>=snr_th).nonzero() | |
| 3322 | valid2 = (s_n1>=snr_th).nonzero() |
|
3322 | valid2 = (s_n1>=snr_th).nonzero() | |
| 3323 | valid1 = numpy.array(valid1[0]) |
|
3323 | valid1 = numpy.array(valid1[0]) | |
| 3324 | valid2 = numpy.array(valid2[0]) |
|
3324 | valid2 = numpy.array(valid2[0]) | |
| 3325 | valid = valid1 |
|
3325 | valid = valid1 | |
| 3326 | for iv in range(len(valid2)): |
|
3326 | for iv in range(len(valid2)): | |
| 3327 | indv = numpy.array((valid1 == valid2[iv]).nonzero()) |
|
3327 | indv = numpy.array((valid1 == valid2[iv]).nonzero()) | |
| 3328 | if len(indv[0]) == 0 : |
|
3328 | if len(indv[0]) == 0 : | |
| 3329 | valid = numpy.concatenate((valid,valid2[iv]), axis=None) |
|
3329 | valid = numpy.concatenate((valid,valid2[iv]), axis=None) | |
| 3330 | if len(valid)>0: |
|
3330 | if len(valid)>0: | |
| 3331 | my_coh_aver[pair[0],valid]=1 |
|
3331 | my_coh_aver[pair[0],valid]=1 | |
| 3332 | my_coh_aver[pair[1],valid]=1 |
|
3332 | my_coh_aver[pair[1],valid]=1 | |
| 3333 | # si la coherencia es mayor a la coherencia threshold los datos se toman |
|
3333 | # si la coherencia es mayor a la coherencia threshold los datos se toman | |
| 3334 | 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))) |
|
3334 | 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))) | |
| 3335 | for ih in range(len(hei_th)): |
|
3335 | for ih in range(len(hei_th)): | |
| 3336 | hvalid = (heights>hei_th[ih]).nonzero() |
|
3336 | hvalid = (heights>hei_th[ih]).nonzero() | |
| 3337 | hvalid = hvalid[0] |
|
3337 | hvalid = hvalid[0] | |
| 3338 | if len(hvalid)>0: |
|
3338 | if len(hvalid)>0: | |
| 3339 | valid = (numpy.absolute(coh[hvalid])>coh_th[ih]).nonzero() |
|
3339 | valid = (numpy.absolute(coh[hvalid])>coh_th[ih]).nonzero() | |
| 3340 | valid = valid[0] |
|
3340 | valid = valid[0] | |
| 3341 | if len(valid)>0: |
|
3341 | if len(valid)>0: | |
| 3342 | my_coh_aver[pair[0],hvalid[valid]] =1 |
|
3342 | my_coh_aver[pair[0],hvalid[valid]] =1 | |
| 3343 | my_coh_aver[pair[1],hvalid[valid]] =1 |
|
3343 | my_coh_aver[pair[1],hvalid[valid]] =1 | |
| 3344 |
|
3344 | |||
| 3345 | coh_echoes = (my_coh_aver[pair[0],:] == 1).nonzero() |
|
3345 | coh_echoes = (my_coh_aver[pair[0],:] == 1).nonzero() | |
| 3346 | incoh_echoes = (my_coh_aver[pair[0],:] != 1).nonzero() |
|
3346 | incoh_echoes = (my_coh_aver[pair[0],:] != 1).nonzero() | |
| 3347 | incoh_echoes = incoh_echoes[0] |
|
3347 | incoh_echoes = incoh_echoes[0] | |
| 3348 | if len(incoh_echoes) > 0: |
|
3348 | if len(incoh_echoes) > 0: | |
| 3349 | my_incoh_spectra[pair[0],:,incoh_echoes] = spectra[pair[0],:,incoh_echoes] |
|
3349 | my_incoh_spectra[pair[0],:,incoh_echoes] = spectra[pair[0],:,incoh_echoes] | |
| 3350 | my_incoh_spectra[pair[1],:,incoh_echoes] = spectra[pair[1],:,incoh_echoes] |
|
3350 | my_incoh_spectra[pair[1],:,incoh_echoes] = spectra[pair[1],:,incoh_echoes] | |
| 3351 | my_incoh_cspectra[ic,:,incoh_echoes] = cspectra[ic,:,incoh_echoes] |
|
3351 | my_incoh_cspectra[ic,:,incoh_echoes] = cspectra[ic,:,incoh_echoes] | |
| 3352 | my_incoh_aver[pair[0],incoh_echoes] = 1 |
|
3352 | my_incoh_aver[pair[0],incoh_echoes] = 1 | |
| 3353 | my_incoh_aver[pair[1],incoh_echoes] = 1 |
|
3353 | my_incoh_aver[pair[1],incoh_echoes] = 1 | |
| 3354 |
|
3354 | |||
| 3355 |
|
3355 | |||
| 3356 | for ic in range(nPairs): |
|
3356 | for ic in range(nPairs): | |
| 3357 | pair = crosspairs[ic] |
|
3357 | pair = crosspairs[ic] | |
| 3358 |
|
3358 | |||
| 3359 | valid1 =(my_coh_aver[pair[0],:]==1 ).nonzero() |
|
3359 | valid1 =(my_coh_aver[pair[0],:]==1 ).nonzero() | |
| 3360 | valid2 = (my_coh_aver[pair[1],:]==1).nonzero() |
|
3360 | valid2 = (my_coh_aver[pair[1],:]==1).nonzero() | |
| 3361 | valid1 = numpy.array(valid1[0]) |
|
3361 | valid1 = numpy.array(valid1[0]) | |
| 3362 | valid2 = numpy.array(valid2[0]) |
|
3362 | valid2 = numpy.array(valid2[0]) | |
| 3363 | valid = valid1 |
|
3363 | valid = valid1 | |
| 3364 |
|
3364 | |||
| 3365 | for iv in range(len(valid2)): |
|
3365 | for iv in range(len(valid2)): | |
| 3366 |
|
3366 | |||
| 3367 | indv = numpy.array((valid1 == valid2[iv]).nonzero()) |
|
3367 | indv = numpy.array((valid1 == valid2[iv]).nonzero()) | |
| 3368 | if len(indv[0]) == 0 : |
|
3368 | if len(indv[0]) == 0 : | |
| 3369 | valid = numpy.concatenate((valid,valid2[iv]), axis=None) |
|
3369 | valid = numpy.concatenate((valid,valid2[iv]), axis=None) | |
| 3370 | valid1 =(my_coh_aver[pair[0],:] !=1 ).nonzero() |
|
3370 | valid1 =(my_coh_aver[pair[0],:] !=1 ).nonzero() | |
| 3371 | valid2 = (my_coh_aver[pair[1],:] !=1).nonzero() |
|
3371 | valid2 = (my_coh_aver[pair[1],:] !=1).nonzero() | |
| 3372 | valid1 = numpy.array(valid1[0]) |
|
3372 | valid1 = numpy.array(valid1[0]) | |
| 3373 | valid2 = numpy.array(valid2[0]) |
|
3373 | valid2 = numpy.array(valid2[0]) | |
| 3374 | incoh_echoes = valid1 |
|
3374 | incoh_echoes = valid1 | |
| 3375 | for iv in range(len(valid2)): |
|
3375 | for iv in range(len(valid2)): | |
| 3376 |
|
3376 | |||
| 3377 | indv = numpy.array((valid1 == valid2[iv]).nonzero()) |
|
3377 | indv = numpy.array((valid1 == valid2[iv]).nonzero()) | |
| 3378 | if len(indv[0]) == 0 : |
|
3378 | if len(indv[0]) == 0 : | |
| 3379 | incoh_echoes = numpy.concatenate(( incoh_echoes,valid2[iv]), axis=None) |
|
3379 | incoh_echoes = numpy.concatenate(( incoh_echoes,valid2[iv]), axis=None) | |
| 3380 |
|
3380 | |||
| 3381 | if len(valid)>0: |
|
3381 | if len(valid)>0: | |
| 3382 | coh_spectra[pair[0],:,valid] = spectra[pair[0],:,valid] |
|
3382 | coh_spectra[pair[0],:,valid] = spectra[pair[0],:,valid] | |
| 3383 | coh_spectra[pair[1],:,valid] = spectra[pair[1],:,valid] |
|
3383 | coh_spectra[pair[1],:,valid] = spectra[pair[1],:,valid] | |
| 3384 | coh_cspectra[ic,:,valid] = cspectra[ic,:,valid] |
|
3384 | coh_cspectra[ic,:,valid] = cspectra[ic,:,valid] | |
| 3385 | coh_aver[pair[0],valid]=1 |
|
3385 | coh_aver[pair[0],valid]=1 | |
| 3386 | coh_aver[pair[1],valid]=1 |
|
3386 | coh_aver[pair[1],valid]=1 | |
| 3387 | if len(incoh_echoes)>0: |
|
3387 | if len(incoh_echoes)>0: | |
| 3388 | incoh_spectra[pair[0],:,incoh_echoes] = spectra[pair[0],:,incoh_echoes] |
|
3388 | incoh_spectra[pair[0],:,incoh_echoes] = spectra[pair[0],:,incoh_echoes] | |
| 3389 | incoh_spectra[pair[1],:,incoh_echoes] = spectra[pair[1],:,incoh_echoes] |
|
3389 | incoh_spectra[pair[1],:,incoh_echoes] = spectra[pair[1],:,incoh_echoes] | |
| 3390 | incoh_cspectra[ic,:,incoh_echoes] = cspectra[ic,:,incoh_echoes] |
|
3390 | incoh_cspectra[ic,:,incoh_echoes] = cspectra[ic,:,incoh_echoes] | |
| 3391 | incoh_aver[pair[0],incoh_echoes]=1 |
|
3391 | incoh_aver[pair[0],incoh_echoes]=1 | |
| 3392 | incoh_aver[pair[1],incoh_echoes]=1 |
|
3392 | incoh_aver[pair[1],incoh_echoes]=1 | |
| 3393 | 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 |
|
3393 | 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 | |
| 3394 |
|
3394 | |||
|
|
3395 | ||||
| 3395 | def __CleanCoherent(self,snrth, spectra, cspectra, coh_aver,dataOut, noise,clean_coh_echoes,index): |
|
3396 | def __CleanCoherent(self,snrth, spectra, cspectra, coh_aver,dataOut, noise,clean_coh_echoes,index): | |
| 3396 |
|
3397 | |||
| 3397 | nProf = dataOut.nProfiles |
|
3398 | nProf = dataOut.nProfiles | |
| 3398 | heights = dataOut.heightList |
|
3399 | heights = dataOut.heightList | |
| 3399 | nHei = len(heights) |
|
3400 | nHei = len(heights) | |
| 3400 | channels = dataOut.channelList |
|
3401 | channels = dataOut.channelList | |
| 3401 | nChan = len(channels) |
|
3402 | nChan = len(channels) | |
| 3402 | crosspairs = dataOut.groupList |
|
3403 | crosspairs = dataOut.groupList | |
| 3403 | nPairs = len(crosspairs) |
|
3404 | nPairs = len(crosspairs) | |
| 3404 |
|
3405 | |||
| 3405 | absc = dataOut.abscissaList[:-1] |
|
3406 | absc = dataOut.abscissaList[:-1] | |
| 3406 | data_param = numpy.zeros((nChan, 4, spectra.shape[2])) |
|
3407 | data_param = numpy.zeros((nChan, 4, spectra.shape[2])) | |
| 3407 | clean_coh_spectra = spectra.copy() |
|
3408 | clean_coh_spectra = spectra.copy() | |
| 3408 | clean_coh_cspectra = cspectra.copy() |
|
3409 | clean_coh_cspectra = cspectra.copy() | |
| 3409 | clean_coh_aver = coh_aver.copy() |
|
3410 | clean_coh_aver = coh_aver.copy() | |
| 3410 |
|
3411 | |||
| 3411 | spwd_th=[10,6] #spwd_th[0] --> For satellites ; spwd_th[1] --> For special events like SUN. |
|
3412 | spwd_th=[10,6] #spwd_th[0] --> For satellites ; spwd_th[1] --> For special events like SUN. | |
| 3412 | coh_th = 0.75 |
|
3413 | coh_th = 0.75 | |
| 3413 |
|
3414 | |||
| 3414 | rtime0 = [6,18] # periodo sin ESF |
|
3415 | rtime0 = [6,18] # periodo sin ESF | |
| 3415 | rtime1 = [10.5,13.5] # periodo con alta coherencia y alto ancho espectral (esperado): SOL. |
|
3416 | rtime1 = [10.5,13.5] # periodo con alta coherencia y alto ancho espectral (esperado): SOL. | |
| 3416 |
|
3417 | |||
| 3417 | time = index*5./60 # en base a 5 min de proceso |
|
3418 | time = index*5./60 # en base a 5 min de proceso | |
| 3418 | if clean_coh_echoes == 1 : |
|
3419 | if clean_coh_echoes == 1 : | |
| 3419 | for ind in range(nChan): |
|
3420 | for ind in range(nChan): | |
| 3420 | data_param[ind,:,:] = self.__calculateMoments( spectra[ind,:,:] , absc , noise[ind] ) |
|
3421 | data_param[ind,:,:] = self.__calculateMoments( spectra[ind,:,:] , absc , noise[ind] ) | |
| 3421 | spwd = data_param[:,3] |
|
3422 | spwd = data_param[:,3] | |
| 3422 | # 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 |
|
3423 | # 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 | |
| 3423 | # para obtener spwd |
|
3424 | # para obtener spwd | |
| 3424 | for ic in range(nPairs): |
|
3425 | for ic in range(nPairs): | |
| 3425 | pair = crosspairs[ic] |
|
3426 | pair = crosspairs[ic] | |
| 3426 | 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))) |
|
3427 | 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))) | |
| 3427 | for ih in range(nHei) : |
|
3428 | for ih in range(nHei) : | |
| 3428 | # Considering heights higher than 200km in order to avoid removing phenomena like EEJ. |
|
3429 | # Considering heights higher than 200km in order to avoid removing phenomena like EEJ. | |
| 3429 | if heights[ih] >= 200 and coh_aver[pair[0],ih] == 1 and coh_aver[pair[1],ih] == 1 : |
|
3430 | if heights[ih] >= 200 and coh_aver[pair[0],ih] == 1 and coh_aver[pair[1],ih] == 1 : | |
| 3430 | # Checking coherence |
|
3431 | # Checking coherence | |
| 3431 | if (numpy.abs(coh[ih]) <= coh_th) or (time >= rtime0[0] and time <= rtime0[1]) : |
|
3432 | if (numpy.abs(coh[ih]) <= coh_th) or (time >= rtime0[0] and time <= rtime0[1]) : | |
| 3432 | # Checking spectral widths |
|
3433 | # Checking spectral widths | |
| 3433 | if (spwd[pair[0],ih] > spwd_th[0]) or (spwd[pair[1],ih] > spwd_th[0]) : |
|
3434 | if (spwd[pair[0],ih] > spwd_th[0]) or (spwd[pair[1],ih] > spwd_th[0]) : | |
| 3434 | # satelite |
|
3435 | # satelite | |
| 3435 | clean_coh_spectra[pair,ih,:] = 0.0 |
|
3436 | clean_coh_spectra[pair,ih,:] = 0.0 | |
| 3436 | clean_coh_cspectra[ic,ih,:] = 0.0 |
|
3437 | clean_coh_cspectra[ic,ih,:] = 0.0 | |
| 3437 | clean_coh_aver[pair,ih] = 0 |
|
3438 | clean_coh_aver[pair,ih] = 0 | |
| 3438 | else : |
|
3439 | else : | |
| 3439 | if ((spwd[pair[0],ih] < spwd_th[1]) or (spwd[pair[1],ih] < spwd_th[1])) : |
|
3440 | if ((spwd[pair[0],ih] < spwd_th[1]) or (spwd[pair[1],ih] < spwd_th[1])) : | |
| 3440 | # Especial event like sun. |
|
3441 | # Especial event like sun. | |
| 3441 | clean_coh_spectra[pair,ih,:] = 0.0 |
|
3442 | clean_coh_spectra[pair,ih,:] = 0.0 | |
| 3442 | clean_coh_cspectra[ic,ih,:] = 0.0 |
|
3443 | clean_coh_cspectra[ic,ih,:] = 0.0 | |
| 3443 | clean_coh_aver[pair,ih] = 0 |
|
3444 | clean_coh_aver[pair,ih] = 0 | |
| 3444 |
|
3445 | |||
| 3445 | return clean_coh_spectra, clean_coh_cspectra, clean_coh_aver |
|
3446 | return clean_coh_spectra, clean_coh_cspectra, clean_coh_aver | |
| 3446 |
|
3447 | |||
| 3447 | def CleanRayleigh(self,dataOut,spectra,cspectra,save_drifts): |
|
3448 | def CleanRayleigh(self,dataOut,spectra,cspectra,save_drifts): | |
| 3448 |
|
3449 | |||
| 3449 | rfunc = cspectra.copy() |
|
3450 | rfunc = cspectra.copy() | |
| 3450 | n_funct = len(rfunc[0,:,0,0]) |
|
3451 | n_funct = len(rfunc[0,:,0,0]) | |
| 3451 | val_spc = spectra*0.0 |
|
3452 | val_spc = spectra*0.0 | |
| 3452 | val_cspc = cspectra*0.0 |
|
3453 | val_cspc = cspectra*0.0 | |
| 3453 | in_sat_spectra = spectra.copy() |
|
3454 | in_sat_spectra = spectra.copy() | |
| 3454 | in_sat_cspectra = cspectra.copy() |
|
3455 | in_sat_cspectra = cspectra.copy() | |
| 3455 |
|
3456 | |||
| 3456 | min_hei = 200 |
|
3457 | min_hei = 200 | |
| 3457 | nProf = dataOut.nProfiles |
|
3458 | nProf = dataOut.nProfiles | |
| 3458 | heights = dataOut.heightList |
|
3459 | heights = dataOut.heightList | |
| 3459 | nHei = len(heights) |
|
3460 | nHei = len(heights) | |
| 3460 | channels = dataOut.channelList |
|
3461 | channels = dataOut.channelList | |
| 3461 | nChan = len(channels) |
|
3462 | nChan = len(channels) | |
| 3462 | crosspairs = dataOut.groupList |
|
3463 | crosspairs = dataOut.groupList | |
| 3463 | nPairs = len(crosspairs) |
|
3464 | nPairs = len(crosspairs) | |
| 3464 | hval=(heights >= min_hei).nonzero() |
|
3465 | hval=(heights >= min_hei).nonzero() | |
| 3465 | ih=hval[0] |
|
3466 | ih=hval[0] | |
| 3466 | for ih in range(hval[0][0],nHei): |
|
3467 | for ih in range(hval[0][0],nHei): | |
| 3467 | for ifreq in range(nProf): |
|
3468 | for ifreq in range(nProf): | |
| 3468 | for ii in range(n_funct): |
|
3469 | for ii in range(n_funct): | |
| 3469 |
|
3470 | |||
| 3470 | func2clean = 10*numpy.log10(numpy.absolute(rfunc[:,ii,ifreq,ih])) |
|
3471 | func2clean = 10*numpy.log10(numpy.absolute(rfunc[:,ii,ifreq,ih])) | |
| 3471 | val = (numpy.isfinite(func2clean)==True).nonzero() |
|
3472 | val = (numpy.isfinite(func2clean)==True).nonzero() | |
| 3472 | if len(val)>0: |
|
3473 | if len(val)>0: | |
| 3473 | min_val = numpy.around(numpy.amin(func2clean)-2) #> (-40) |
|
3474 | min_val = numpy.around(numpy.amin(func2clean)-2) #> (-40) | |
| 3474 | if min_val <= -40 : min_val = -40 |
|
3475 | if min_val <= -40 : min_val = -40 | |
| 3475 | max_val = numpy.around(numpy.amax(func2clean)+2) #< 200 |
|
3476 | max_val = numpy.around(numpy.amax(func2clean)+2) #< 200 | |
| 3476 | if max_val >= 200 : max_val = 200 |
|
3477 | if max_val >= 200 : max_val = 200 | |
| 3477 | step = 1 |
|
3478 | step = 1 | |
| 3478 | #Getting bins and the histogram |
|
3479 | #Getting bins and the histogram | |
| 3479 | x_dist = min_val + numpy.arange(1 + ((max_val-(min_val))/step))*step |
|
3480 | x_dist = min_val + numpy.arange(1 + ((max_val-(min_val))/step))*step | |
| 3480 | y_dist,binstep = numpy.histogram(func2clean,bins=range(int(min_val),int(max_val+2),step)) |
|
3481 | y_dist,binstep = numpy.histogram(func2clean,bins=range(int(min_val),int(max_val+2),step)) | |
| 3481 | mean = numpy.sum(x_dist * y_dist) / numpy.sum(y_dist) |
|
3482 | mean = numpy.sum(x_dist * y_dist) / numpy.sum(y_dist) | |
| 3482 | sigma = numpy.sqrt(numpy.sum(y_dist * (x_dist - mean)**2) / numpy.sum(y_dist)) |
|
3483 | sigma = numpy.sqrt(numpy.sum(y_dist * (x_dist - mean)**2) / numpy.sum(y_dist)) | |
| 3483 | parg = [numpy.amax(y_dist),mean,sigma] |
|
3484 | parg = [numpy.amax(y_dist),mean,sigma] | |
| 3484 | try : |
|
3485 | try : | |
| 3485 | gauss_fit, covariance = curve_fit(fit_func, x_dist, y_dist,p0=parg) |
|
3486 | gauss_fit, covariance = curve_fit(fit_func, x_dist, y_dist,p0=parg) | |
| 3486 | mode = gauss_fit[1] |
|
3487 | mode = gauss_fit[1] | |
| 3487 | stdv = gauss_fit[2] |
|
3488 | stdv = gauss_fit[2] | |
| 3488 | except: |
|
3489 | except: | |
| 3489 | mode = mean |
|
3490 | mode = mean | |
| 3490 | stdv = sigma |
|
3491 | stdv = sigma | |
| 3491 |
|
3492 | |||
| 3492 | #Removing echoes greater than mode + 3*stdv |
|
3493 | #Removing echoes greater than mode + 3*stdv | |
| 3493 | factor_stdv = 2.5 |
|
3494 | factor_stdv = 2.5 | |
| 3494 | noval = (abs(func2clean - mode)>=(factor_stdv*stdv)).nonzero() |
|
3495 | noval = (abs(func2clean - mode)>=(factor_stdv*stdv)).nonzero() | |
| 3495 |
|
3496 | |||
| 3496 | if len(noval[0]) > 0: |
|
3497 | if len(noval[0]) > 0: | |
| 3497 | novall = ((func2clean - mode) >= (factor_stdv*stdv)).nonzero() |
|
3498 | novall = ((func2clean - mode) >= (factor_stdv*stdv)).nonzero() | |
| 3498 | cross_pairs = crosspairs[ii] |
|
3499 | cross_pairs = crosspairs[ii] | |
| 3499 | #Getting coherent echoes which are removed. |
|
3500 | #Getting coherent echoes which are removed. | |
| 3500 | if len(novall[0]) > 0: |
|
3501 | if len(novall[0]) > 0: | |
| 3501 | val_spc[novall[0],cross_pairs[0],ifreq,ih] = 1 |
|
3502 | val_spc[novall[0],cross_pairs[0],ifreq,ih] = 1 | |
| 3502 | val_spc[novall[0],cross_pairs[1],ifreq,ih] = 1 |
|
3503 | val_spc[novall[0],cross_pairs[1],ifreq,ih] = 1 | |
| 3503 | val_cspc[novall[0],ii,ifreq,ih] = 1 |
|
3504 | val_cspc[novall[0],ii,ifreq,ih] = 1 | |
| 3504 | #Removing coherent from ISR data |
|
3505 | #Removing coherent from ISR data | |
| 3505 | spectra[noval,cross_pairs[0],ifreq,ih] = numpy.nan |
|
3506 | spectra[noval,cross_pairs[0],ifreq,ih] = numpy.nan | |
| 3506 | spectra[noval,cross_pairs[1],ifreq,ih] = numpy.nan |
|
3507 | spectra[noval,cross_pairs[1],ifreq,ih] = numpy.nan | |
| 3507 | cspectra[noval,ii,ifreq,ih] = numpy.nan |
|
3508 | cspectra[noval,ii,ifreq,ih] = numpy.nan | |
| 3508 |
|
3509 | |||
| 3509 | #Getting average of the spectra and cross-spectra from incoherent echoes. |
|
3510 | #Getting average of the spectra and cross-spectra from incoherent echoes. | |
| 3510 | out_spectra = numpy.zeros([nChan,nProf,nHei], dtype=float) #+numpy.nan |
|
3511 | out_spectra = numpy.zeros([nChan,nProf,nHei], dtype=float) #+numpy.nan | |
| 3511 | out_cspectra = numpy.zeros([nPairs,nProf,nHei], dtype=complex) #+numpy.nan |
|
3512 | out_cspectra = numpy.zeros([nPairs,nProf,nHei], dtype=complex) #+numpy.nan | |
| 3512 | for ih in range(nHei): |
|
3513 | for ih in range(nHei): | |
| 3513 | for ifreq in range(nProf): |
|
3514 | for ifreq in range(nProf): | |
| 3514 | for ich in range(nChan): |
|
3515 | for ich in range(nChan): | |
| 3515 | tmp = spectra[:,ich,ifreq,ih] |
|
3516 | tmp = spectra[:,ich,ifreq,ih] | |
| 3516 | valid = (numpy.isfinite(tmp[:])==True).nonzero() |
|
3517 | valid = (numpy.isfinite(tmp[:])==True).nonzero() | |
| 3517 | if len(valid[0]) >0 : |
|
3518 | if len(valid[0]) >0 : | |
| 3518 | out_spectra[ich,ifreq,ih] = numpy.nansum(tmp)/len(valid[0]) |
|
3519 | out_spectra[ich,ifreq,ih] = numpy.nansum(tmp)/len(valid[0]) | |
| 3519 | for icr in range(nPairs): |
|
3520 | for icr in range(nPairs): | |
| 3520 | tmp = numpy.squeeze(cspectra[:,icr,ifreq,ih]) |
|
3521 | tmp = numpy.squeeze(cspectra[:,icr,ifreq,ih]) | |
| 3521 | valid = (numpy.isfinite(tmp)==True).nonzero() |
|
3522 | valid = (numpy.isfinite(tmp)==True).nonzero() | |
| 3522 | if len(valid[0]) > 0: |
|
3523 | if len(valid[0]) > 0: | |
| 3523 | out_cspectra[icr,ifreq,ih] = numpy.nansum(tmp)/len(valid[0]) |
|
3524 | out_cspectra[icr,ifreq,ih] = numpy.nansum(tmp)/len(valid[0]) | |
| 3524 | #Removing fake coherent echoes (at least 4 points around the point) |
|
3525 | #Removing fake coherent echoes (at least 4 points around the point) | |
| 3525 | val_spectra = numpy.sum(val_spc,0) |
|
3526 | val_spectra = numpy.sum(val_spc,0) | |
| 3526 | val_cspectra = numpy.sum(val_cspc,0) |
|
3527 | val_cspectra = numpy.sum(val_cspc,0) | |
| 3527 |
|
3528 | |||
| 3528 | val_spectra = self.REM_ISOLATED_POINTS(val_spectra,4) |
|
3529 | val_spectra = self.REM_ISOLATED_POINTS(val_spectra,4) | |
| 3529 | val_cspectra = self.REM_ISOLATED_POINTS(val_cspectra,4) |
|
3530 | val_cspectra = self.REM_ISOLATED_POINTS(val_cspectra,4) | |
| 3530 |
|
3531 | |||
| 3531 | for i in range(nChan): |
|
3532 | for i in range(nChan): | |
| 3532 | for j in range(nProf): |
|
3533 | for j in range(nProf): | |
| 3533 | for k in range(nHei): |
|
3534 | for k in range(nHei): | |
| 3534 | if numpy.isfinite(val_spectra[i,j,k]) and val_spectra[i,j,k] < 1 : |
|
3535 | if numpy.isfinite(val_spectra[i,j,k]) and val_spectra[i,j,k] < 1 : | |
| 3535 | val_spc[:,i,j,k] = 0.0 |
|
3536 | val_spc[:,i,j,k] = 0.0 | |
| 3536 | for i in range(nPairs): |
|
3537 | for i in range(nPairs): | |
| 3537 | for j in range(nProf): |
|
3538 | for j in range(nProf): | |
| 3538 | for k in range(nHei): |
|
3539 | for k in range(nHei): | |
| 3539 | if numpy.isfinite(val_cspectra[i,j,k]) and val_cspectra[i,j,k] < 1 : |
|
3540 | if numpy.isfinite(val_cspectra[i,j,k]) and val_cspectra[i,j,k] < 1 : | |
| 3540 | val_cspc[:,i,j,k] = 0.0 |
|
3541 | val_cspc[:,i,j,k] = 0.0 | |
| 3541 |
|
3542 | |||
| 3542 | tmp_sat_spectra = spectra.copy() |
|
3543 | tmp_sat_spectra = spectra.copy() | |
| 3543 | tmp_sat_spectra = tmp_sat_spectra*numpy.nan |
|
3544 | tmp_sat_spectra = tmp_sat_spectra*numpy.nan | |
| 3544 | tmp_sat_cspectra = cspectra.copy() |
|
3545 | tmp_sat_cspectra = cspectra.copy() | |
| 3545 | tmp_sat_cspectra = tmp_sat_cspectra*numpy.nan |
|
3546 | tmp_sat_cspectra = tmp_sat_cspectra*numpy.nan | |
| 3546 | val = (val_spc > 0).nonzero() |
|
3547 | val = (val_spc > 0).nonzero() | |
| 3547 | if len(val[0]) > 0: |
|
3548 | if len(val[0]) > 0: | |
| 3548 | tmp_sat_spectra[val] = in_sat_spectra[val] |
|
3549 | tmp_sat_spectra[val] = in_sat_spectra[val] | |
| 3549 |
|
3550 | |||
| 3550 | val = (val_cspc > 0).nonzero() |
|
3551 | val = (val_cspc > 0).nonzero() | |
| 3551 | if len(val[0]) > 0: |
|
3552 | if len(val[0]) > 0: | |
| 3552 | tmp_sat_cspectra[val] = in_sat_cspectra[val] |
|
3553 | tmp_sat_cspectra[val] = in_sat_cspectra[val] | |
| 3553 |
|
3554 | |||
| 3554 | #Getting average of the spectra and cross-spectra from incoherent echoes. |
|
3555 | #Getting average of the spectra and cross-spectra from incoherent echoes. | |
| 3555 | sat_spectra = numpy.zeros((nChan,nProf,nHei), dtype=float) |
|
3556 | sat_spectra = numpy.zeros((nChan,nProf,nHei), dtype=float) | |
| 3556 | sat_cspectra = numpy.zeros((nPairs,nProf,nHei), dtype=complex) |
|
3557 | sat_cspectra = numpy.zeros((nPairs,nProf,nHei), dtype=complex) | |
| 3557 | for ih in range(nHei): |
|
3558 | for ih in range(nHei): | |
| 3558 | for ifreq in range(nProf): |
|
3559 | for ifreq in range(nProf): | |
| 3559 | for ich in range(nChan): |
|
3560 | for ich in range(nChan): | |
| 3560 | tmp = numpy.squeeze(tmp_sat_spectra[:,ich,ifreq,ih]) |
|
3561 | tmp = numpy.squeeze(tmp_sat_spectra[:,ich,ifreq,ih]) | |
| 3561 | valid = (numpy.isfinite(tmp)).nonzero() |
|
3562 | valid = (numpy.isfinite(tmp)).nonzero() | |
| 3562 | if len(valid[0]) > 0: |
|
3563 | if len(valid[0]) > 0: | |
| 3563 | sat_spectra[ich,ifreq,ih] = numpy.nansum(tmp)/len(valid[0]) |
|
3564 | sat_spectra[ich,ifreq,ih] = numpy.nansum(tmp)/len(valid[0]) | |
| 3564 |
|
3565 | |||
| 3565 | for icr in range(nPairs): |
|
3566 | for icr in range(nPairs): | |
| 3566 | tmp = numpy.squeeze(tmp_sat_cspectra[:,icr,ifreq,ih]) |
|
3567 | tmp = numpy.squeeze(tmp_sat_cspectra[:,icr,ifreq,ih]) | |
| 3567 | valid = (numpy.isfinite(tmp)).nonzero() |
|
3568 | valid = (numpy.isfinite(tmp)).nonzero() | |
| 3568 | if len(valid[0]) > 0: |
|
3569 | if len(valid[0]) > 0: | |
| 3569 | sat_cspectra[icr,ifreq,ih] = numpy.nansum(tmp)/len(valid[0]) |
|
3570 | sat_cspectra[icr,ifreq,ih] = numpy.nansum(tmp)/len(valid[0]) | |
| 3570 | return out_spectra, out_cspectra,sat_spectra,sat_cspectra |
|
3571 | return out_spectra, out_cspectra,sat_spectra,sat_cspectra | |
| 3571 |
|
3572 | |||
| 3572 | def REM_ISOLATED_POINTS(self,array,rth): |
|
3573 | def REM_ISOLATED_POINTS(self,array,rth): | |
| 3573 | if rth == None : rth = 4 |
|
3574 | if rth == None : rth = 4 | |
| 3574 | num_prof = len(array[0,:,0]) |
|
3575 | num_prof = len(array[0,:,0]) | |
| 3575 | num_hei = len(array[0,0,:]) |
|
3576 | num_hei = len(array[0,0,:]) | |
| 3576 | n2d = len(array[:,0,0]) |
|
3577 | n2d = len(array[:,0,0]) | |
| 3577 |
|
3578 | |||
| 3578 | for ii in range(n2d) : |
|
3579 | for ii in range(n2d) : | |
| 3579 | tmp = array[ii,:,:] |
|
3580 | tmp = array[ii,:,:] | |
| 3580 | tmp = numpy.reshape(tmp,num_prof*num_hei) |
|
3581 | tmp = numpy.reshape(tmp,num_prof*num_hei) | |
| 3581 | indxs1 = (numpy.isfinite(tmp)==True).nonzero() |
|
3582 | indxs1 = (numpy.isfinite(tmp)==True).nonzero() | |
| 3582 | indxs2 = (tmp > 0).nonzero() |
|
3583 | indxs2 = (tmp > 0).nonzero() | |
| 3583 | indxs1 = (indxs1[0]) |
|
3584 | indxs1 = (indxs1[0]) | |
| 3584 | indxs2 = indxs2[0] |
|
3585 | indxs2 = indxs2[0] | |
| 3585 | indxs = None |
|
3586 | indxs = None | |
| 3586 | for iv in range(len(indxs2)): |
|
3587 | for iv in range(len(indxs2)): | |
| 3587 | indv = numpy.array((indxs1 == indxs2[iv]).nonzero()) |
|
3588 | indv = numpy.array((indxs1 == indxs2[iv]).nonzero()) | |
| 3588 | if len(indv[0]) > 0 : |
|
3589 | if len(indv[0]) > 0 : | |
| 3589 | indxs = numpy.concatenate((indxs,indxs2[iv]), axis=None) |
|
3590 | indxs = numpy.concatenate((indxs,indxs2[iv]), axis=None) | |
| 3590 | indxs = indxs[1:] |
|
3591 | indxs = indxs[1:] | |
| 3591 | if len(indxs) < 4 : |
|
3592 | if len(indxs) < 4 : | |
| 3592 | array[ii,:,:] = 0. |
|
3593 | array[ii,:,:] = 0. | |
| 3593 | return |
|
3594 | return | |
| 3594 |
|
3595 | |||
| 3595 | xpos = numpy.mod(indxs ,num_hei) |
|
3596 | xpos = numpy.mod(indxs ,num_hei) | |
| 3596 | ypos = (indxs / num_hei) |
|
3597 | ypos = (indxs / num_hei) | |
| 3597 | sx = numpy.argsort(xpos) # Ordering respect to "x" (time) |
|
3598 | sx = numpy.argsort(xpos) # Ordering respect to "x" (time) | |
| 3598 | xpos = xpos[sx] |
|
3599 | xpos = xpos[sx] | |
| 3599 | ypos = ypos[sx] |
|
3600 | ypos = ypos[sx] | |
| 3600 | # *********************************** Cleaning isolated points ********************************** |
|
3601 | # *********************************** Cleaning isolated points ********************************** | |
| 3601 | ic = 0 |
|
3602 | ic = 0 | |
| 3602 | while True : |
|
3603 | while True : | |
| 3603 | r = numpy.sqrt(list(numpy.power((xpos[ic]-xpos),2)+ numpy.power((ypos[ic]-ypos),2))) |
|
3604 | r = numpy.sqrt(list(numpy.power((xpos[ic]-xpos),2)+ numpy.power((ypos[ic]-ypos),2))) | |
| 3604 | no_coh1 = (numpy.isfinite(r)==True).nonzero() |
|
3605 | no_coh1 = (numpy.isfinite(r)==True).nonzero() | |
| 3605 | no_coh2 = (r <= rth).nonzero() |
|
3606 | no_coh2 = (r <= rth).nonzero() | |
| 3606 | no_coh1 = numpy.array(no_coh1[0]) |
|
3607 | no_coh1 = numpy.array(no_coh1[0]) | |
| 3607 | no_coh2 = numpy.array(no_coh2[0]) |
|
3608 | no_coh2 = numpy.array(no_coh2[0]) | |
| 3608 | no_coh = None |
|
3609 | no_coh = None | |
| 3609 | for iv in range(len(no_coh2)): |
|
3610 | for iv in range(len(no_coh2)): | |
| 3610 | indv = numpy.array((no_coh1 == no_coh2[iv]).nonzero()) |
|
3611 | indv = numpy.array((no_coh1 == no_coh2[iv]).nonzero()) | |
| 3611 | if len(indv[0]) > 0 : |
|
3612 | if len(indv[0]) > 0 : | |
| 3612 | no_coh = numpy.concatenate((no_coh,no_coh2[iv]), axis=None) |
|
3613 | no_coh = numpy.concatenate((no_coh,no_coh2[iv]), axis=None) | |
| 3613 | no_coh = no_coh[1:] |
|
3614 | no_coh = no_coh[1:] | |
| 3614 | if len(no_coh) < 4 : |
|
3615 | if len(no_coh) < 4 : | |
| 3615 | xpos[ic] = numpy.nan |
|
3616 | xpos[ic] = numpy.nan | |
| 3616 | ypos[ic] = numpy.nan |
|
3617 | ypos[ic] = numpy.nan | |
| 3617 |
|
3618 | |||
| 3618 | ic = ic + 1 |
|
3619 | ic = ic + 1 | |
| 3619 | if (ic == len(indxs)) : |
|
3620 | if (ic == len(indxs)) : | |
| 3620 | break |
|
3621 | break | |
| 3621 | indxs = (numpy.isfinite(list(xpos))==True).nonzero() |
|
3622 | indxs = (numpy.isfinite(list(xpos))==True).nonzero() | |
| 3622 | if len(indxs[0]) < 4 : |
|
3623 | if len(indxs[0]) < 4 : | |
| 3623 | array[ii,:,:] = 0. |
|
3624 | array[ii,:,:] = 0. | |
| 3624 | return |
|
3625 | return | |
| 3625 |
|
3626 | |||
| 3626 | xpos = xpos[indxs[0]] |
|
3627 | xpos = xpos[indxs[0]] | |
| 3627 | ypos = ypos[indxs[0]] |
|
3628 | ypos = ypos[indxs[0]] | |
| 3628 | for i in range(0,len(ypos)): |
|
3629 | for i in range(0,len(ypos)): | |
| 3629 | ypos[i]=int(ypos[i]) |
|
3630 | ypos[i]=int(ypos[i]) | |
| 3630 | junk = tmp |
|
3631 | junk = tmp | |
| 3631 | tmp = junk*0.0 |
|
3632 | tmp = junk*0.0 | |
| 3632 |
|
3633 | |||
| 3633 | tmp[list(xpos + (ypos*num_hei))] = junk[list(xpos + (ypos*num_hei))] |
|
3634 | tmp[list(xpos + (ypos*num_hei))] = junk[list(xpos + (ypos*num_hei))] | |
| 3634 | array[ii,:,:] = numpy.reshape(tmp,(num_prof,num_hei)) |
|
3635 | array[ii,:,:] = numpy.reshape(tmp,(num_prof,num_hei)) | |
| 3635 | return array |
|
3636 | return array | |
| 3636 |
|
3637 | |||
| 3637 | def moments(self,doppler,yarray,npoints): |
|
3638 | def moments(self,doppler,yarray,npoints): | |
| 3638 | ytemp = yarray |
|
3639 | ytemp = yarray | |
| 3639 | val = (ytemp > 0).nonzero() |
|
3640 | val = (ytemp > 0).nonzero() | |
| 3640 | val = val[0] |
|
3641 | val = val[0] | |
| 3641 | if len(val) == 0 : val = range(npoints-1) |
|
3642 | if len(val) == 0 : val = range(npoints-1) | |
| 3642 |
|
3643 | |||
| 3643 | ynew = 0.5*(ytemp[val[0]]+ytemp[val[len(val)-1]]) |
|
3644 | ynew = 0.5*(ytemp[val[0]]+ytemp[val[len(val)-1]]) | |
| 3644 | ytemp[len(ytemp):] = [ynew] |
|
3645 | ytemp[len(ytemp):] = [ynew] | |
| 3645 |
|
3646 | |||
| 3646 | index = 0 |
|
3647 | index = 0 | |
| 3647 | index = numpy.argmax(ytemp) |
|
3648 | index = numpy.argmax(ytemp) | |
| 3648 | ytemp = numpy.roll(ytemp,int(npoints/2)-1-index) |
|
3649 | ytemp = numpy.roll(ytemp,int(npoints/2)-1-index) | |
| 3649 | ytemp = ytemp[0:npoints-1] |
|
3650 | ytemp = ytemp[0:npoints-1] | |
| 3650 |
|
3651 | |||
| 3651 | fmom = numpy.sum(doppler*ytemp)/numpy.sum(ytemp)+(index-(npoints/2-1))*numpy.abs(doppler[1]-doppler[0]) |
|
3652 | fmom = numpy.sum(doppler*ytemp)/numpy.sum(ytemp)+(index-(npoints/2-1))*numpy.abs(doppler[1]-doppler[0]) | |
| 3652 | smom = numpy.sum(doppler*doppler*ytemp)/numpy.sum(ytemp) |
|
3653 | smom = numpy.sum(doppler*doppler*ytemp)/numpy.sum(ytemp) | |
| 3653 | return [fmom,numpy.sqrt(smom)] |
|
3654 | return [fmom,numpy.sqrt(smom)] | |
| 3654 |
|
3655 | |||
| 3655 | 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): |
|
3656 | 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): | |
| 3656 | if not self.isConfig: |
|
3657 | if not self.isConfig: | |
| 3657 | self.setup(dataOut = dataOut,groupList=groupList,path=path,file=file,filec=filec) |
|
3658 | self.setup(dataOut = dataOut,groupList=groupList,path=path,file=file,filec=filec) | |
| 3658 | self.isConfig = True |
|
3659 | self.isConfig = True | |
| 3659 |
|
3660 | |||
| 3660 | if not numpy.any(proc): |
|
3661 | if not numpy.any(proc): | |
| 3661 | if numpy.any(taver): |
|
3662 | if numpy.any(taver): | |
| 3662 | taver = int(taver) |
|
3663 | taver = int(taver) | |
| 3663 | else : |
|
3664 | else : | |
| 3664 | taver = 5 |
|
3665 | taver = 5 | |
| 3665 | tini = time.localtime(dataOut.utctime) |
|
3666 | tini = time.localtime(dataOut.utctime) | |
| 3666 | if (tini.tm_min % taver) == 0 and (tini.tm_sec < 5 and self.fint==0): |
|
3667 | if (tini.tm_min % taver) == 0 and (tini.tm_sec < 5 and self.fint==0): | |
| 3667 | self.index = 0 |
|
3668 | self.index = 0 | |
| 3668 | jspc = self.buffer |
|
3669 | jspc = self.buffer | |
| 3669 | jcspc = self.buffer2 |
|
3670 | jcspc = self.buffer2 | |
| 3670 | jnoise = self.buffer3 |
|
3671 | jnoise = self.buffer3 | |
| 3671 | self.buffer = dataOut.data_spc |
|
3672 | self.buffer = dataOut.data_spc | |
| 3672 | self.buffer2 = dataOut.data_cspc |
|
3673 | self.buffer2 = dataOut.data_cspc | |
| 3673 | self.buffer3 = dataOut.noise |
|
3674 | self.buffer3 = dataOut.noise | |
| 3674 | self.fint = 1 |
|
3675 | self.fint = 1 | |
| 3675 | if numpy.any(jspc) : |
|
3676 | if numpy.any(jspc) : | |
| 3676 | jspc = numpy.reshape(jspc ,(int(len(jspc) / self.nChannels) , self.nChannels ,self.nProf,self.nHeights )) |
|
3677 | jspc = numpy.reshape(jspc ,(int(len(jspc) / self.nChannels) , self.nChannels ,self.nProf,self.nHeights )) | |
| 3677 | jcspc = numpy.reshape(jcspc ,(int(len(jcspc) /int(self.nChannels/2)),int(self.nChannels/2),self.nProf,self.nHeights )) |
|
3678 | jcspc = numpy.reshape(jcspc ,(int(len(jcspc) /int(self.nChannels/2)),int(self.nChannels/2),self.nProf,self.nHeights )) | |
| 3678 | jnoise = numpy.reshape(jnoise,(int(len(jnoise)/ self.nChannels) , self.nChannels)) |
|
3679 | jnoise = numpy.reshape(jnoise,(int(len(jnoise)/ self.nChannels) , self.nChannels)) | |
| 3679 | else: |
|
3680 | else: | |
| 3680 | dataOut.flagNoData = True |
|
3681 | dataOut.flagNoData = True | |
| 3681 | return dataOut |
|
3682 | return dataOut | |
| 3682 | else : |
|
3683 | else : | |
| 3683 | if (tini.tm_min % taver) == 0 : |
|
3684 | if (tini.tm_min % taver) == 0 : | |
| 3684 | self.fint = 1 |
|
3685 | self.fint = 1 | |
| 3685 | else : |
|
3686 | else : | |
| 3686 | self.fint = 0 |
|
3687 | self.fint = 0 | |
| 3687 |
|
3688 | |||
| 3688 | self.index += 1 |
|
3689 | self.index += 1 | |
| 3689 | if numpy.any(self.buffer): |
|
3690 | if numpy.any(self.buffer): | |
| 3690 | self.buffer = numpy.concatenate((self.buffer,dataOut.data_spc), axis=0) |
|
3691 | self.buffer = numpy.concatenate((self.buffer,dataOut.data_spc), axis=0) | |
| 3691 | self.buffer2 = numpy.concatenate((self.buffer2,dataOut.data_cspc), axis=0) |
|
3692 | self.buffer2 = numpy.concatenate((self.buffer2,dataOut.data_cspc), axis=0) | |
| 3692 | self.buffer3 = numpy.concatenate((self.buffer3,dataOut.noise), axis=0) |
|
3693 | self.buffer3 = numpy.concatenate((self.buffer3,dataOut.noise), axis=0) | |
| 3693 | else: |
|
3694 | else: | |
| 3694 | self.buffer = dataOut.data_spc |
|
3695 | self.buffer = dataOut.data_spc | |
| 3695 | self.buffer2 = dataOut.data_cspc |
|
3696 | self.buffer2 = dataOut.data_cspc | |
| 3696 | self.buffer3 = dataOut.noise |
|
3697 | self.buffer3 = dataOut.noise | |
| 3697 | dataOut.flagNoData = True |
|
3698 | dataOut.flagNoData = True | |
| 3698 | return dataOut |
|
3699 | return dataOut | |
| 3699 |
|
3700 | |||
| 3700 | jnoise = jnoise/self.N# creo que falta dividirlo entre N |
|
3701 | jnoise = jnoise/self.N# creo que falta dividirlo entre N | |
| 3701 | noise = numpy.nansum(jnoise,axis=0)#/len(jnoise) |
|
3702 | noise = numpy.nansum(jnoise,axis=0)#/len(jnoise) | |
| 3702 | index = tini.tm_hour*12+tini.tm_min/taver |
|
3703 | index = tini.tm_hour*12+tini.tm_min/taver | |
| 3703 | dataOut.index = index |
|
3704 | dataOut.index = index | |
| 3704 | jspc = jspc/self.N/self.N |
|
3705 | jspc = jspc/self.N/self.N | |
| 3705 | jcspc = jcspc/self.N/self.N |
|
3706 | jcspc = jcspc/self.N/self.N | |
| 3706 |
|
3707 | |||
| 3707 | tmp_spectra,tmp_cspectra,sat_spectra,sat_cspectra = self.CleanRayleigh(dataOut,jspc,jcspc,2) |
|
3708 | tmp_spectra,tmp_cspectra,sat_spectra,sat_cspectra = self.CleanRayleigh(dataOut,jspc,jcspc,2) | |
| 3708 | jspectra = tmp_spectra * len(jspc[:,0,0,0]) |
|
3709 | jspectra = tmp_spectra * len(jspc[:,0,0,0]) | |
| 3709 | jcspectra = tmp_cspectra * len(jspc[:,0,0,0]) |
|
3710 | jcspectra = tmp_cspectra * len(jspc[:,0,0,0]) | |
| 3710 |
|
3711 | |||
| 3711 | 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, self.snrth,coh_th, hei_th) |
|
3712 | 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, self.snrth,coh_th, hei_th) | |
| 3712 | clean_coh_spectra, clean_coh_cspectra, clean_coh_aver = self.__CleanCoherent(self.snrth, coh_spectra, coh_cspectra, coh_aver, dataOut, noise,1,index) |
|
3713 | clean_coh_spectra, clean_coh_cspectra, clean_coh_aver = self.__CleanCoherent(self.snrth, coh_spectra, coh_cspectra, coh_aver, dataOut, noise,1,index) | |
| 3713 |
|
3714 | |||
| 3714 | dataOut.data_spc = incoh_spectra |
|
3715 | dataOut.data_spc = incoh_spectra | |
| 3715 | dataOut.data_cspc = incoh_cspectra |
|
3716 | dataOut.data_cspc = incoh_cspectra | |
| 3716 | clean_num_aver = incoh_aver * len(jspc[:,0,0,0]) |
|
3717 | clean_num_aver = incoh_aver * len(jspc[:,0,0,0]) | |
| 3717 | coh_num_aver = clean_coh_aver* len(jspc[:,0,0,0]) |
|
3718 | coh_num_aver = clean_coh_aver* len(jspc[:,0,0,0]) | |
| 3718 | dataOut.clean_num_aver = clean_num_aver |
|
3719 | dataOut.clean_num_aver = clean_num_aver | |
| 3719 | dataOut.coh_num_aver = coh_num_aver |
|
3720 | dataOut.coh_num_aver = coh_num_aver | |
| 3720 |
|
3721 | |||
| 3721 | #List of possible combinations |
|
|||
| 3722 | #listComb = itertools.combinations(numpy.arange(groupArray.shape[1]),2) |
|
|||
| 3723 | #indCross = numpy.zeros(len(list(listComb)), dtype = 'int') |
|
|||
| 3724 | #if getSNR: |
|
|||
| 3725 | # listChannels = groupArray.reshape((groupArray.size)) |
|
|||
| 3726 | # listChannels.sort() |
|
|||
| 3727 | # print("AQUI ESTOY") |
|
|||
| 3728 | # dataOut.data_SNR = self.__getSNR(dataOut.data_spc[listChannels,:,:], noise[listChannels]) |
|
|||
| 3729 | else: |
|
3722 | else: | |
| 3730 | clean_num_aver = dataOut.clean_num_aver |
|
3723 | clean_num_aver = dataOut.clean_num_aver | |
| 3731 | coh_num_aver = dataOut.coh_num_aver |
|
3724 | coh_num_aver = dataOut.coh_num_aver | |
| 3732 | dataOut.data_spc = dataOut.tmp_spectra_i |
|
3725 | dataOut.data_spc = dataOut.tmp_spectra_i | |
| 3733 | dataOut.data_cspc = dataOut.tmp_cspectra_i |
|
3726 | dataOut.data_cspc = dataOut.tmp_cspectra_i | |
| 3734 | clean_coh_spectra = dataOut.tmp_spectra_c |
|
3727 | clean_coh_spectra = dataOut.tmp_spectra_c | |
| 3735 | clean_coh_cspectra = dataOut.tmp_cspectra_c |
|
3728 | clean_coh_cspectra = dataOut.tmp_cspectra_c | |
| 3736 | jspectra = dataOut.data_spc+clean_coh_spectra |
|
3729 | jspectra = dataOut.data_spc+clean_coh_spectra | |
| 3737 | nHeights = len(dataOut.heightList) # nhei |
|
3730 | nHeights = len(dataOut.heightList) # nhei | |
| 3738 | nProf = int(dataOut.nProfiles) |
|
3731 | nProf = int(dataOut.nProfiles) | |
| 3739 | dataOut.nProfiles = nProf |
|
3732 | dataOut.nProfiles = nProf | |
| 3740 | dataOut.data_param = None |
|
3733 | dataOut.data_param = None | |
| 3741 | dataOut.data_paramC = None |
|
3734 | dataOut.data_paramC = None | |
| 3742 | dataOut.code = numpy.array([[-1.,-1.,1.],[1.,1.,-1.]]) |
|
3735 | dataOut.code = numpy.array([[-1.,-1.,1.],[1.,1.,-1.]]) | |
| 3743 | #M=600 |
|
3736 | #M=600 | |
| 3744 | #N=200 |
|
3737 | #N=200 | |
| 3745 | dataOut.flagDecodeData=True |
|
3738 | dataOut.flagDecodeData=True | |
| 3746 | M = int(dataOut.normFactor) |
|
3739 | M = int(dataOut.normFactor) | |
| 3747 | N = int(dataOut.nFFTPoints) |
|
3740 | N = int(dataOut.nFFTPoints) | |
| 3748 | dataOut.nFFTPoints = N |
|
3741 | dataOut.nFFTPoints = N | |
| 3749 | dataOut.nIncohInt= int(dataOut.nIncohInt) |
|
3742 | dataOut.nIncohInt= int(dataOut.nIncohInt) | |
| 3750 | dataOut.nProfiles = int(dataOut.nProfiles) |
|
3743 | dataOut.nProfiles = int(dataOut.nProfiles) | |
| 3751 | dataOut.nCohInt = int(dataOut.nCohInt) |
|
3744 | dataOut.nCohInt = int(dataOut.nCohInt) | |
| 3752 | #dataOut.nFFTPoints=nprofs |
|
3745 | #dataOut.nFFTPoints=nprofs | |
| 3753 | #dataOut.normFactor = nprofs |
|
3746 | #dataOut.normFactor = nprofs | |
| 3754 | dataOut.channelList = channelList |
|
3747 | dataOut.channelList = channelList | |
| 3755 | #dataOut.ippFactor=1 |
|
3748 | #dataOut.ippFactor=1 | |
| 3756 | #ipp = ipp/150*1.e-3 |
|
3749 | #ipp = ipp/150*1.e-3 | |
| 3757 | vmax = (300000000/49920000.0/2) / (dataOut.ippSeconds) |
|
3750 | vmax = (300000000/49920000.0/2) / (dataOut.ippSeconds) | |
| 3758 | #dataOut.ippSeconds=ipp |
|
3751 | #dataOut.ippSeconds=ipp | |
| 3759 | absc = vmax*( numpy.arange(nProf,dtype='float')-nProf/2.)/nProf |
|
3752 | absc = vmax*( numpy.arange(nProf,dtype='float')-nProf/2.)/nProf | |
| 3760 | if path != None: |
|
3753 | if path != None: | |
| 3761 | sys.path.append(path) |
|
3754 | sys.path.append(path) | |
| 3762 | self.library = importlib.import_module(file) |
|
3755 | self.library = importlib.import_module(file) | |
| 3763 | constants = self.library.setConstants(dataOut) |
|
3756 | constants = self.library.setConstants(dataOut) | |
| 3764 | constants['M'] = M |
|
3757 | constants['M'] = M | |
| 3765 | dataOut.constants = constants |
|
3758 | dataOut.constants = constants | |
| 3766 |
|
3759 | |||
| 3767 | #List of possible combinations |
|
3760 | #List of possible combinations | |
| 3768 | listComb = itertools.combinations(numpy.arange(self.groupArray.shape[1]),2) |
|
3761 | listComb = itertools.combinations(numpy.arange(self.groupArray.shape[1]),2) | |
| 3769 | indCross = numpy.zeros(len(list(listComb)), dtype = 'int') |
|
3762 | indCross = numpy.zeros(len(list(listComb)), dtype = 'int') | |
| 3770 | if dataOut.data_paramC is None: |
|
3763 | if dataOut.data_paramC is None: | |
| 3771 | dataOut.data_paramC = numpy.zeros((self.nGroups*4, self.nHeights ,2))*numpy.nan |
|
3764 | dataOut.data_paramC = numpy.zeros((self.nGroups*4, self.nHeights ,2))*numpy.nan | |
| 3772 | for i in range(self.nGroups): |
|
3765 | for i in range(self.nGroups): | |
| 3773 | coord = self.groupArray[i,:] |
|
3766 | coord = self.groupArray[i,:] | |
| 3774 | #Input data array |
|
3767 | #Input data array | |
| 3775 | data = dataOut.data_spc[coord,:,:]/(self.M*self.N) |
|
3768 | data = dataOut.data_spc[coord,:,:]/(self.M*self.N) | |
| 3776 | data = data.reshape((data.shape[0]*data.shape[1],data.shape[2])) |
|
3769 | data = data.reshape((data.shape[0]*data.shape[1],data.shape[2])) | |
| 3777 |
|
3770 | |||
| 3778 | #Cross Spectra data array for Covariance Matrixes |
|
3771 | #Cross Spectra data array for Covariance Matrixes | |
| 3779 | ind = 0 |
|
3772 | ind = 0 | |
| 3780 | for pairs in listComb: |
|
3773 | for pairs in listComb: | |
| 3781 | pairsSel = numpy.array([coord[x],coord[y]]) |
|
3774 | pairsSel = numpy.array([coord[x],coord[y]]) | |
| 3782 | indCross[ind] = int(numpy.where(numpy.all(self.pairsArray == pairsSel, axis = 1))[0][0]) |
|
3775 | indCross[ind] = int(numpy.where(numpy.all(self.pairsArray == pairsSel, axis = 1))[0][0]) | |
| 3783 | ind += 1 |
|
3776 | ind += 1 | |
| 3784 | dataCross = dataOut.data_cspc[indCross,:,:]/(self.M*self.N) |
|
3777 | dataCross = dataOut.data_cspc[indCross,:,:]/(self.M*self.N) | |
| 3785 | dataCross = dataCross**2 |
|
3778 | dataCross = dataCross**2 | |
| 3786 | nhei = self.nHeights |
|
3779 | nhei = self.nHeights | |
| 3787 | poweri = numpy.sum(dataOut.data_spc[:,1:self.nProf-0,:],axis=1)/clean_num_aver[:,:] |
|
3780 | poweri = numpy.sum(dataOut.data_spc[:,1:self.nProf-0,:],axis=1)/clean_num_aver[:,:] | |
| 3788 |
|
3781 | |||
| 3789 | if i == 0 : my_noises = numpy.zeros(4,dtype=float) |
|
3782 | if i == 0 : my_noises = numpy.zeros(4,dtype=float) | |
| 3790 | n0i = numpy.nanmin(poweri[0+i*2,0:nhei-0])/(self.nProf-1) |
|
3783 | n0i = numpy.nanmin(poweri[0+i*2,0:nhei-0])/(self.nProf-1) | |
| 3791 | n1i = numpy.nanmin(poweri[1+i*2,0:nhei-0])/(self.nProf-1) |
|
3784 | n1i = numpy.nanmin(poweri[1+i*2,0:nhei-0])/(self.nProf-1) | |
| 3792 | n0 = n0i |
|
3785 | n0 = n0i | |
| 3793 | n1= n1i |
|
3786 | n1= n1i | |
| 3794 | my_noises[2*i+0] = n0 |
|
3787 | my_noises[2*i+0] = n0 | |
| 3795 | my_noises[2*i+1] = n1 |
|
3788 | my_noises[2*i+1] = n1 | |
| 3796 | snrth = -15.0 # -4 -16 -25 |
|
3789 | snrth = -15.0 # -4 -16 -25 | |
| 3797 | snrth = 10**(snrth/10.0) |
|
3790 | snrth = 10**(snrth/10.0) | |
| 3798 | jvelr = numpy.zeros(self.nHeights, dtype = 'float') |
|
3791 | jvelr = numpy.zeros(self.nHeights, dtype = 'float') | |
| 3799 | hvalid = [0] |
|
3792 | hvalid = [0] | |
| 3800 | coh2 = abs(dataOut.data_cspc[i,1:self.nProf,:])**2/(dataOut.data_spc[0+i*2,1:self.nProf-0,:]*dataOut.data_spc[1+i*2,1:self.nProf-0,:]) |
|
3793 | coh2 = abs(dataOut.data_cspc[i,1:self.nProf,:])**2/(dataOut.data_spc[0+i*2,1:self.nProf-0,:]*dataOut.data_spc[1+i*2,1:self.nProf-0,:]) | |
| 3801 |
|
3794 | |||
| 3802 | for h in range(self.nHeights): |
|
3795 | for h in range(self.nHeights): | |
| 3803 | smooth = clean_num_aver[i+1,h] |
|
3796 | smooth = clean_num_aver[i+1,h] | |
| 3804 | signalpn0 = (dataOut.data_spc[i*2,1:(self.nProf-0),h])/smooth |
|
3797 | signalpn0 = (dataOut.data_spc[i*2,1:(self.nProf-0),h])/smooth | |
| 3805 | signalpn1 = (dataOut.data_spc[i*2+1,1:(self.nProf-0),h])/smooth |
|
3798 | signalpn1 = (dataOut.data_spc[i*2+1,1:(self.nProf-0),h])/smooth | |
| 3806 | signal0 = signalpn0-n0 |
|
3799 | signal0 = signalpn0-n0 | |
| 3807 | signal1 = signalpn1-n1 |
|
3800 | signal1 = signalpn1-n1 | |
| 3808 | snr0 = numpy.sum(signal0/n0)/(self.nProf-1) |
|
3801 | snr0 = numpy.sum(signal0/n0)/(self.nProf-1) | |
| 3809 | snr1 = numpy.sum(signal1/n1)/(self.nProf-1) |
|
3802 | snr1 = numpy.sum(signal1/n1)/(self.nProf-1) | |
| 3810 | gamma = coh2[:,h] |
|
3803 | gamma = coh2[:,h] | |
| 3811 | indxs = (numpy.isfinite(list(gamma))==True).nonzero() |
|
3804 | indxs = (numpy.isfinite(list(gamma))==True).nonzero() | |
| 3812 | if len(indxs) >0: |
|
3805 | if len(indxs) >0: | |
| 3813 | if numpy.nanmean(gamma) > 0.07: |
|
3806 | if numpy.nanmean(gamma) > 0.07: | |
| 3814 | maxp0 = numpy.argmax(signal0*gamma) |
|
3807 | maxp0 = numpy.argmax(signal0*gamma) | |
| 3815 | maxp1 = numpy.argmax(signal1*gamma) |
|
3808 | maxp1 = numpy.argmax(signal1*gamma) | |
| 3816 | #print('usa gamma',numpy.nanmean(gamma)) |
|
3809 | #print('usa gamma',numpy.nanmean(gamma)) | |
| 3817 | else: |
|
3810 | else: | |
| 3818 | maxp0 = numpy.argmax(signal0) |
|
3811 | maxp0 = numpy.argmax(signal0) | |
| 3819 | maxp1 = numpy.argmax(signal1) |
|
3812 | maxp1 = numpy.argmax(signal1) | |
| 3820 | jvelr[h] = (self.absc[maxp0]+self.absc[maxp1])/2. |
|
3813 | jvelr[h] = (self.absc[maxp0]+self.absc[maxp1])/2. | |
| 3821 | else: jvelr[h] = self.absc[0] |
|
3814 | else: jvelr[h] = self.absc[0] | |
| 3822 | if snr0 > 0.1 and snr1 > 0.1: hvalid = numpy.concatenate((hvalid,h), axis=None) |
|
3815 | if snr0 > 0.1 and snr1 > 0.1: hvalid = numpy.concatenate((hvalid,h), axis=None) | |
| 3823 | #print(maxp0,absc[maxp0],snr0,jvelr[h]) |
|
3816 | #print(maxp0,absc[maxp0],snr0,jvelr[h]) | |
| 3824 |
|
3817 | |||
| 3825 | if len(hvalid)> 1: fd0 = numpy.median(jvelr[hvalid[1:]])*-1 |
|
3818 | if len(hvalid)> 1: fd0 = numpy.median(jvelr[hvalid[1:]])*-1 | |
| 3826 | else: fd0 = numpy.nan |
|
3819 | else: fd0 = numpy.nan | |
| 3827 | for h in range(self.nHeights): |
|
3820 | for h in range(self.nHeights): | |
| 3828 | d = data[:,h] |
|
3821 | d = data[:,h] | |
| 3829 | smooth = clean_num_aver[i+1,h] #dataOut.data_spc[:,1:nProf-0,:] |
|
3822 | smooth = clean_num_aver[i+1,h] #dataOut.data_spc[:,1:nProf-0,:] | |
| 3830 | signalpn0 = (dataOut.data_spc[i*2,1:(self.nProf-0),h])/smooth |
|
3823 | signalpn0 = (dataOut.data_spc[i*2,1:(self.nProf-0),h])/smooth | |
| 3831 | signalpn1 = (dataOut.data_spc[i*2+1,1:(self.nProf-0),h])/smooth |
|
3824 | signalpn1 = (dataOut.data_spc[i*2+1,1:(self.nProf-0),h])/smooth | |
| 3832 | signal0 = signalpn0-n0 |
|
3825 | signal0 = signalpn0-n0 | |
| 3833 | signal1 = signalpn1-n1 |
|
3826 | signal1 = signalpn1-n1 | |
| 3834 | snr0 = numpy.sum(signal0/n0)/(self.nProf-1) |
|
3827 | snr0 = numpy.sum(signal0/n0)/(self.nProf-1) | |
| 3835 | snr1 = numpy.sum(signal1/n1)/(self.nProf-1) |
|
3828 | snr1 = numpy.sum(signal1/n1)/(self.nProf-1) | |
| 3836 | if snr0 > snrth and snr1 > snrth and clean_num_aver[i+1,h] > 0 : |
|
3829 | if snr0 > snrth and snr1 > snrth and clean_num_aver[i+1,h] > 0 : | |
| 3837 | #Covariance Matrix |
|
3830 | #Covariance Matrix | |
| 3838 | D = numpy.diag(d**2) |
|
3831 | D = numpy.diag(d**2) | |
| 3839 | ind = 0 |
|
3832 | ind = 0 | |
| 3840 | for pairs in listComb: |
|
3833 | for pairs in listComb: | |
| 3841 | #Coordinates in Covariance Matrix |
|
3834 | #Coordinates in Covariance Matrix | |
| 3842 | x = pairs[0] |
|
3835 | x = pairs[0] | |
| 3843 | y = pairs[1] |
|
3836 | y = pairs[1] | |
| 3844 | #Channel Index |
|
3837 | #Channel Index | |
| 3845 | S12 = dataCross[ind,:,h] |
|
3838 | S12 = dataCross[ind,:,h] | |
| 3846 | D12 = numpy.diag(S12) |
|
3839 | D12 = numpy.diag(S12) | |
| 3847 | #Completing Covariance Matrix with Cross Spectras |
|
3840 | #Completing Covariance Matrix with Cross Spectras | |
| 3848 | D[x*N:(x+1)*N,y*N:(y+1)*N] = D12 |
|
3841 | D[x*N:(x+1)*N,y*N:(y+1)*N] = D12 | |
| 3849 | D[y*N:(y+1)*N,x*N:(x+1)*N] = D12 |
|
3842 | D[y*N:(y+1)*N,x*N:(x+1)*N] = D12 | |
| 3850 | ind += 1 |
|
3843 | ind += 1 | |
| 3851 | diagD = numpy.zeros(256) |
|
3844 | diagD = numpy.zeros(256) | |
| 3852 |
|
3845 | |||
| 3853 | try: |
|
3846 | try: | |
| 3854 | Dinv=numpy.linalg.inv(D) |
|
3847 | Dinv=numpy.linalg.inv(D) | |
| 3855 | L=numpy.linalg.cholesky(Dinv) |
|
3848 | L=numpy.linalg.cholesky(Dinv) | |
| 3856 | except: |
|
3849 | except: | |
| 3857 | Dinv = D*numpy.nan |
|
3850 | Dinv = D*numpy.nan | |
| 3858 | L= D*numpy.nan |
|
3851 | L= D*numpy.nan | |
| 3859 | LT=L.T |
|
3852 | LT=L.T | |
| 3860 |
|
3853 | |||
| 3861 | dp = numpy.dot(LT,d) |
|
3854 | dp = numpy.dot(LT,d) | |
| 3862 | #Initial values |
|
3855 | #Initial values | |
| 3863 | data_spc = dataOut.data_spc[coord,:,h] |
|
3856 | data_spc = dataOut.data_spc[coord,:,h] | |
| 3864 | w = data_spc/data_spc |
|
3857 | w = data_spc/data_spc | |
| 3865 | if filec != None: |
|
3858 | if filec != None: | |
| 3866 | w = self.weightf.weightfit(w,tini.tm_year,tini.tm_yday,index,h,i) |
|
3859 | w = self.weightf.weightfit(w,tini.tm_year,tini.tm_yday,index,h,i) | |
| 3867 | if (h>6)and(error1[3]<25): |
|
3860 | if (h>6)and(error1[3]<25): | |
| 3868 | p0 = dataOut.data_param[i,:,h-1].copy() |
|
3861 | p0 = dataOut.data_param[i,:,h-1].copy() | |
| 3869 | else: |
|
3862 | else: | |
| 3870 | p0 = numpy.array(self.library.initialValuesFunction(data_spc*w, self.constants))# sin el i(data_spc, constants, i) |
|
3863 | p0 = numpy.array(self.library.initialValuesFunction(data_spc*w, self.constants))# sin el i(data_spc, constants, i) | |
| 3871 | p0[3] = fd0 |
|
3864 | p0[3] = fd0 | |
| 3872 |
|
3865 | |||
| 3873 | if filec != None: |
|
3866 | if filec != None: | |
| 3874 | p0 = self.weightf.Vrfit(p0,tini.tm_year,tini.tm_yday,index,h,i) |
|
3867 | p0 = self.weightf.Vrfit(p0,tini.tm_year,tini.tm_yday,index,h,i) | |
| 3875 |
|
3868 | |||
| 3876 | try: |
|
3869 | try: | |
| 3877 | #Least Squares |
|
3870 | #Least Squares | |
| 3878 | minp,covp,infodict,mesg,ier = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,self.constants),full_output=True) |
|
3871 | minp,covp,infodict,mesg,ier = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,self.constants),full_output=True) | |
| 3879 | #minp,covp = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants)) |
|
3872 | #minp,covp = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants)) | |
| 3880 | #Chi square error |
|
3873 | #Chi square error | |
| 3881 | error0 = numpy.sum(infodict['fvec']**2)/(2*self.N) |
|
3874 | error0 = numpy.sum(infodict['fvec']**2)/(2*self.N) | |
| 3882 | #Error with Jacobian |
|
3875 | #Error with Jacobian | |
| 3883 | error1 = self.library.errorFunction(minp,self.constants,LT) |
|
3876 | error1 = self.library.errorFunction(minp,self.constants,LT) | |
| 3884 |
|
3877 | |||
| 3885 | except: |
|
3878 | except: | |
| 3886 | minp = p0*numpy.nan |
|
3879 | minp = p0*numpy.nan | |
| 3887 | error0 = numpy.nan |
|
3880 | error0 = numpy.nan | |
| 3888 | error1 = p0*numpy.nan |
|
3881 | error1 = p0*numpy.nan | |
| 3889 | else : |
|
3882 | else : | |
| 3890 | data_spc = dataOut.data_spc[coord,:,h] |
|
3883 | data_spc = dataOut.data_spc[coord,:,h] | |
| 3891 | p0 = numpy.array(self.library.initialValuesFunction(data_spc, self.constants)) |
|
3884 | p0 = numpy.array(self.library.initialValuesFunction(data_spc, self.constants)) | |
| 3892 | minp = p0*numpy.nan |
|
3885 | minp = p0*numpy.nan | |
| 3893 | error0 = numpy.nan |
|
3886 | error0 = numpy.nan | |
| 3894 | error1 = p0*numpy.nan |
|
3887 | error1 = p0*numpy.nan | |
| 3895 | if dataOut.data_param is None: |
|
3888 | if dataOut.data_param is None: | |
| 3896 | dataOut.data_param = numpy.zeros((self.nGroups, p0.size, self.nHeights ))*numpy.nan |
|
3889 | dataOut.data_param = numpy.zeros((self.nGroups, p0.size, self.nHeights ))*numpy.nan | |
| 3897 | dataOut.data_error = numpy.zeros((self.nGroups, p0.size + 1, self.nHeights ))*numpy.nan |
|
3890 | dataOut.data_error = numpy.zeros((self.nGroups, p0.size + 1, self.nHeights ))*numpy.nan | |
| 3898 | dataOut.data_error[i,:,h] = numpy.hstack((error0,error1)) |
|
3891 | dataOut.data_error[i,:,h] = numpy.hstack((error0,error1)) | |
| 3899 | dataOut.data_param[i,:,h] = minp |
|
3892 | dataOut.data_param[i,:,h] = minp | |
| 3900 |
|
3893 | |||
| 3901 | for ht in range(self.nHeights-1) : |
|
3894 | for ht in range(self.nHeights-1) : | |
| 3902 | smooth = coh_num_aver[i+1,ht] #datc[0,ht,0,beam] |
|
3895 | smooth = coh_num_aver[i+1,ht] #datc[0,ht,0,beam] | |
| 3903 | dataOut.data_paramC[4*i,ht,1] = smooth |
|
3896 | dataOut.data_paramC[4*i,ht,1] = smooth | |
| 3904 | signalpn0 = (clean_coh_spectra[i*2 ,1:(self.nProf-0),ht])/smooth #coh_spectra |
|
3897 | signalpn0 = (clean_coh_spectra[i*2 ,1:(self.nProf-0),ht])/smooth #coh_spectra | |
| 3905 | signalpn1 = (clean_coh_spectra[i*2+1,1:(self.nProf-0),ht])/smooth |
|
3898 | signalpn1 = (clean_coh_spectra[i*2+1,1:(self.nProf-0),ht])/smooth | |
| 3906 | val0 = (signalpn0 > 0).nonzero() |
|
3899 | val0 = (signalpn0 > 0).nonzero() | |
| 3907 | val0 = val0[0] |
|
3900 | val0 = val0[0] | |
| 3908 | if len(val0) == 0 : val0_npoints = self.nProf |
|
3901 | if len(val0) == 0 : val0_npoints = self.nProf | |
| 3909 | else : val0_npoints = len(val0) |
|
3902 | else : val0_npoints = len(val0) | |
| 3910 |
|
3903 | |||
| 3911 | val1 = (signalpn1 > 0).nonzero() |
|
3904 | val1 = (signalpn1 > 0).nonzero() | |
| 3912 | val1 = val1[0] |
|
3905 | val1 = val1[0] | |
| 3913 | if len(val1) == 0 : val1_npoints = self.nProf |
|
3906 | if len(val1) == 0 : val1_npoints = self.nProf | |
| 3914 | else : val1_npoints = len(val1) |
|
3907 | else : val1_npoints = len(val1) | |
| 3915 |
|
3908 | |||
| 3916 | dataOut.data_paramC[0+4*i,ht,0] = numpy.sum((signalpn0/val0_npoints))/n0 |
|
3909 | dataOut.data_paramC[0+4*i,ht,0] = numpy.sum((signalpn0/val0_npoints))/n0 | |
| 3917 | dataOut.data_paramC[1+4*i,ht,0] = numpy.sum((signalpn1/val1_npoints))/n1 |
|
3910 | dataOut.data_paramC[1+4*i,ht,0] = numpy.sum((signalpn1/val1_npoints))/n1 | |
| 3918 |
|
3911 | |||
| 3919 | signal0 = (signalpn0-n0) |
|
3912 | signal0 = (signalpn0-n0) | |
| 3920 | vali = (signal0 < 0).nonzero() |
|
3913 | vali = (signal0 < 0).nonzero() | |
| 3921 | vali = vali[0] |
|
3914 | vali = vali[0] | |
| 3922 | if len(vali) > 0 : signal0[vali] = 0 |
|
3915 | if len(vali) > 0 : signal0[vali] = 0 | |
| 3923 | signal1 = (signalpn1-n1) |
|
3916 | signal1 = (signalpn1-n1) | |
| 3924 | vali = (signal1 < 0).nonzero() |
|
3917 | vali = (signal1 < 0).nonzero() | |
| 3925 | vali = vali[0] |
|
3918 | vali = vali[0] | |
| 3926 | if len(vali) > 0 : signal1[vali] = 0 |
|
3919 | if len(vali) > 0 : signal1[vali] = 0 | |
| 3927 | snr0 = numpy.sum(signal0/n0)/(self.nProf-1) |
|
3920 | snr0 = numpy.sum(signal0/n0)/(self.nProf-1) | |
| 3928 | snr1 = numpy.sum(signal1/n1)/(self.nProf-1) |
|
3921 | snr1 = numpy.sum(signal1/n1)/(self.nProf-1) | |
| 3929 | doppler = self.absc[1:] |
|
3922 | doppler = self.absc[1:] | |
| 3930 | if snr0 >= snrth and snr1 >= snrth and smooth : |
|
3923 | if snr0 >= snrth and snr1 >= snrth and smooth : | |
| 3931 | signalpn0_n0 = signalpn0 |
|
3924 | signalpn0_n0 = signalpn0 | |
| 3932 | signalpn0_n0[val0] = signalpn0[val0] - n0 |
|
3925 | signalpn0_n0[val0] = signalpn0[val0] - n0 | |
| 3933 | mom0 = self.moments(doppler,signalpn0-n0,self.nProf) |
|
3926 | mom0 = self.moments(doppler,signalpn0-n0,self.nProf) | |
| 3934 | signalpn1_n1 = signalpn1 |
|
3927 | signalpn1_n1 = signalpn1 | |
| 3935 | signalpn1_n1[val1] = signalpn1[val1] - n1 |
|
3928 | signalpn1_n1[val1] = signalpn1[val1] - n1 | |
| 3936 | mom1 = self.moments(doppler,signalpn1_n1,self.nProf) |
|
3929 | mom1 = self.moments(doppler,signalpn1_n1,self.nProf) | |
| 3937 | dataOut.data_paramC[2+4*i,ht,0] = (mom0[0]+mom1[0])/2. |
|
3930 | dataOut.data_paramC[2+4*i,ht,0] = (mom0[0]+mom1[0])/2. | |
| 3938 | dataOut.data_paramC[3+4*i,ht,0] = (mom0[1]+mom1[1])/2. |
|
3931 | dataOut.data_paramC[3+4*i,ht,0] = (mom0[1]+mom1[1])/2. | |
| 3939 | dataOut.data_spc = jspectra |
|
3932 | dataOut.data_spc = jspectra | |
| 3940 | dataOut.spc_noise = my_noises*self.nProf*self.M |
|
3933 | dataOut.spc_noise = my_noises*self.nProf*self.M | |
| 3941 | if numpy.any(proc): dataOut.spc_noise = my_noises*self.nProf*self.M |
|
3934 | if numpy.any(proc): dataOut.spc_noise = my_noises*self.nProf*self.M | |
| 3942 | if getSNR: |
|
3935 | if getSNR: | |
| 3943 | print("self.groupArray",self.groupArray.size,self.groupArray) |
|
|||
| 3944 | listChannels = self.groupArray.reshape((self.groupArray.size)) |
|
3936 | listChannels = self.groupArray.reshape((self.groupArray.size)) | |
| 3945 | listChannels.sort() |
|
3937 | listChannels.sort() | |
| 3946 | # TEST |
|
3938 | # TEST | |
| 3947 | noise_C = numpy.zeros(self.nChannels) |
|
3939 | noise_C = numpy.zeros(self.nChannels) | |
| 3948 | noise_C = dataOut.getNoise() |
|
3940 | noise_C = dataOut.getNoise() | |
| 3949 | print("noise_C",noise_C) |
|
3941 | #print("noise_C",noise_C) | |
| 3950 | dataOut.data_snr = self.__getSNR(dataOut.data_spc[listChannels,:,:],noise_C/(600.0*1.15))# PRUEBA *nProf*M |
|
3942 | dataOut.data_snr = self.__getSNR(dataOut.data_spc[listChannels,:,:],noise_C/(600.0*1.15))# PRUEBA *nProf*M | |
| 3951 | #dataOut.data_snr = self.__getSNR(dataOut.data_spc[listChannels,:,:], noise_C[listChannels])# PRUEBA *nProf*M |
|
3943 | #dataOut.data_snr = self.__getSNR(dataOut.data_spc[listChannels,:,:], noise_C[listChannels])# PRUEBA *nProf*M | |
| 3952 | dataOut.flagNoData = False |
|
3944 | dataOut.flagNoData = False | |
| 3953 | return dataOut |
|
3945 | return dataOut | |
| 3954 |
|
3946 | |||
| 3955 | def __residFunction(self, p, dp, LT, constants): |
|
3947 | def __residFunction(self, p, dp, LT, constants): | |
| 3956 |
|
3948 | |||
| 3957 | fm = self.library.modelFunction(p, constants) |
|
3949 | fm = self.library.modelFunction(p, constants) | |
| 3958 | fmp=numpy.dot(LT,fm) |
|
3950 | fmp=numpy.dot(LT,fm) | |
| 3959 | return dp-fmp |
|
3951 | return dp-fmp | |
| 3960 |
|
3952 | |||
| 3961 | def __getSNR(self, z, noise): |
|
3953 | def __getSNR(self, z, noise): | |
| 3962 |
|
3954 | |||
| 3963 | avg = numpy.average(z, axis=1) |
|
3955 | avg = numpy.average(z, axis=1) | |
| 3964 | SNR = (avg.T-noise)/noise |
|
3956 | SNR = (avg.T-noise)/noise | |
| 3965 | print("------------------------------------------------------") |
|
|||
| 3966 | print("T - Noise", noise.shape) |
|
|||
| 3967 | print("T - Noise", 10*numpy.log10(noise[0])) |
|
|||
| 3968 | print("T - avg.T" , avg.T.shape) |
|
|||
| 3969 | print("T - avg.T" , 10*numpy.log10(avg.T[:,0])) |
|
|||
| 3970 | SNR = SNR.T |
|
3957 | SNR = SNR.T | |
| 3971 | return SNR |
|
3958 | return SNR | |
| 3972 |
|
3959 | |||
| 3973 | def __chisq(self, p, chindex, hindex): |
|
3960 | def __chisq(self, p, chindex, hindex): | |
| 3974 | #similar to Resid but calculates CHI**2 |
|
3961 | #similar to Resid but calculates CHI**2 | |
| 3975 | [LT,d,fm]=setupLTdfm(p,chindex,hindex) |
|
3962 | [LT,d,fm]=setupLTdfm(p,chindex,hindex) | |
| 3976 | dp=numpy.dot(LT,d) |
|
3963 | dp=numpy.dot(LT,d) | |
| 3977 | fmp=numpy.dot(LT,fm) |
|
3964 | fmp=numpy.dot(LT,fm) | |
| 3978 | chisq=numpy.dot((dp-fmp).T,(dp-fmp)) |
|
3965 | chisq=numpy.dot((dp-fmp).T,(dp-fmp)) | |
| 3979 | return chisq |
|
3966 | return chisq | |
| 3980 |
|
3967 | |||
| 3981 | class WindProfiler(Operation): |
|
3968 | class WindProfiler(Operation): | |
| 3982 |
|
3969 | |||
| 3983 | __isConfig = False |
|
3970 | __isConfig = False | |
| 3984 |
|
3971 | |||
| 3985 | __initime = None |
|
3972 | __initime = None | |
| 3986 | __lastdatatime = None |
|
3973 | __lastdatatime = None | |
| 3987 | __integrationtime = None |
|
3974 | __integrationtime = None | |
| 3988 |
|
3975 | |||
| 3989 | __buffer = None |
|
3976 | __buffer = None | |
| 3990 |
|
3977 | |||
| 3991 | __dataReady = False |
|
3978 | __dataReady = False | |
| 3992 |
|
3979 | |||
| 3993 | __firstdata = None |
|
3980 | __firstdata = None | |
| 3994 |
|
3981 | |||
| 3995 | n = None |
|
3982 | n = None | |
| 3996 |
|
3983 | |||
| 3997 | def __init__(self): |
|
3984 | def __init__(self): | |
| 3998 | Operation.__init__(self) |
|
3985 | Operation.__init__(self) | |
| 3999 |
|
3986 | |||
| 4000 | def __calculateCosDir(self, elev, azim): |
|
3987 | def __calculateCosDir(self, elev, azim): | |
| 4001 | zen = (90 - elev)*numpy.pi/180 |
|
3988 | zen = (90 - elev)*numpy.pi/180 | |
| 4002 | azim = azim*numpy.pi/180 |
|
3989 | azim = azim*numpy.pi/180 | |
| 4003 | cosDirX = numpy.sqrt((1-numpy.cos(zen)**2)/((1+numpy.tan(azim)**2))) |
|
3990 | cosDirX = numpy.sqrt((1-numpy.cos(zen)**2)/((1+numpy.tan(azim)**2))) | |
| 4004 | cosDirY = numpy.sqrt(1-numpy.cos(zen)**2-cosDirX**2) |
|
3991 | cosDirY = numpy.sqrt(1-numpy.cos(zen)**2-cosDirX**2) | |
| 4005 |
|
3992 | |||
| 4006 | signX = numpy.sign(numpy.cos(azim)) |
|
3993 | signX = numpy.sign(numpy.cos(azim)) | |
| 4007 | signY = numpy.sign(numpy.sin(azim)) |
|
3994 | signY = numpy.sign(numpy.sin(azim)) | |
| 4008 |
|
3995 | |||
| 4009 | cosDirX = numpy.copysign(cosDirX, signX) |
|
3996 | cosDirX = numpy.copysign(cosDirX, signX) | |
| 4010 | cosDirY = numpy.copysign(cosDirY, signY) |
|
3997 | cosDirY = numpy.copysign(cosDirY, signY) | |
| 4011 | return cosDirX, cosDirY |
|
3998 | return cosDirX, cosDirY | |
| 4012 |
|
3999 | |||
| 4013 | def __calculateAngles(self, theta_x, theta_y, azimuth): |
|
4000 | def __calculateAngles(self, theta_x, theta_y, azimuth): | |
| 4014 |
|
4001 | |||
| 4015 | dir_cosw = numpy.sqrt(1-theta_x**2-theta_y**2) |
|
4002 | dir_cosw = numpy.sqrt(1-theta_x**2-theta_y**2) | |
| 4016 | zenith_arr = numpy.arccos(dir_cosw) |
|
4003 | zenith_arr = numpy.arccos(dir_cosw) | |
| 4017 | azimuth_arr = numpy.arctan2(theta_x,theta_y) + azimuth*math.pi/180 |
|
4004 | azimuth_arr = numpy.arctan2(theta_x,theta_y) + azimuth*math.pi/180 | |
| 4018 |
|
4005 | |||
| 4019 | dir_cosu = numpy.sin(azimuth_arr)*numpy.sin(zenith_arr) |
|
4006 | dir_cosu = numpy.sin(azimuth_arr)*numpy.sin(zenith_arr) | |
| 4020 | dir_cosv = numpy.cos(azimuth_arr)*numpy.sin(zenith_arr) |
|
4007 | dir_cosv = numpy.cos(azimuth_arr)*numpy.sin(zenith_arr) | |
| 4021 |
|
4008 | |||
| 4022 | return azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw |
|
4009 | return azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw | |
| 4023 |
|
4010 | |||
| 4024 | def __calculateMatA(self, dir_cosu, dir_cosv, dir_cosw, horOnly): |
|
4011 | def __calculateMatA(self, dir_cosu, dir_cosv, dir_cosw, horOnly): | |
| 4025 |
|
4012 | |||
| 4026 | if horOnly: |
|
4013 | if horOnly: | |
| 4027 | A = numpy.c_[dir_cosu,dir_cosv] |
|
4014 | A = numpy.c_[dir_cosu,dir_cosv] | |
| 4028 | else: |
|
4015 | else: | |
| 4029 | A = numpy.c_[dir_cosu,dir_cosv,dir_cosw] |
|
4016 | A = numpy.c_[dir_cosu,dir_cosv,dir_cosw] | |
| 4030 | A = numpy.asmatrix(A) |
|
4017 | A = numpy.asmatrix(A) | |
| 4031 | A1 = numpy.linalg.inv(A.transpose()*A)*A.transpose() |
|
4018 | A1 = numpy.linalg.inv(A.transpose()*A)*A.transpose() | |
| 4032 |
|
4019 | |||
| 4033 | return A1 |
|
4020 | return A1 | |
| 4034 |
|
4021 | |||
| 4035 | def __correctValues(self, heiRang, phi, velRadial, SNR): |
|
4022 | def __correctValues(self, heiRang, phi, velRadial, SNR): | |
| 4036 | listPhi = phi.tolist() |
|
4023 | listPhi = phi.tolist() | |
| 4037 | maxid = listPhi.index(max(listPhi)) |
|
4024 | maxid = listPhi.index(max(listPhi)) | |
| 4038 | minid = listPhi.index(min(listPhi)) |
|
4025 | minid = listPhi.index(min(listPhi)) | |
| 4039 |
|
4026 | |||
| 4040 | rango = list(range(len(phi))) |
|
4027 | rango = list(range(len(phi))) | |
| 4041 |
|
4028 | |||
| 4042 | heiRang1 = heiRang*math.cos(phi[maxid]) |
|
4029 | heiRang1 = heiRang*math.cos(phi[maxid]) | |
| 4043 | heiRangAux = heiRang*math.cos(phi[minid]) |
|
4030 | heiRangAux = heiRang*math.cos(phi[minid]) | |
| 4044 | indOut = (heiRang1 < heiRangAux[0]).nonzero() |
|
4031 | indOut = (heiRang1 < heiRangAux[0]).nonzero() | |
| 4045 | heiRang1 = numpy.delete(heiRang1,indOut) |
|
4032 | heiRang1 = numpy.delete(heiRang1,indOut) | |
| 4046 |
|
4033 | |||
| 4047 | velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
4034 | velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) | |
| 4048 | SNR1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
4035 | SNR1 = numpy.zeros([len(phi),len(heiRang1)]) | |
| 4049 |
|
4036 | |||
| 4050 | for i in rango: |
|
4037 | for i in rango: | |
| 4051 | x = heiRang*math.cos(phi[i]) |
|
4038 | x = heiRang*math.cos(phi[i]) | |
| 4052 | y1 = velRadial[i,:] |
|
4039 | y1 = velRadial[i,:] | |
| 4053 | f1 = interpolate.interp1d(x,y1,kind = 'cubic') |
|
4040 | f1 = interpolate.interp1d(x,y1,kind = 'cubic') | |
| 4054 |
|
4041 | |||
| 4055 | x1 = heiRang1 |
|
4042 | x1 = heiRang1 | |
| 4056 | y11 = f1(x1) |
|
4043 | y11 = f1(x1) | |
| 4057 |
|
4044 | |||
| 4058 | y2 = SNR[i,:] |
|
4045 | y2 = SNR[i,:] | |
| 4059 | f2 = interpolate.interp1d(x,y2,kind = 'cubic') |
|
4046 | f2 = interpolate.interp1d(x,y2,kind = 'cubic') | |
| 4060 | y21 = f2(x1) |
|
4047 | y21 = f2(x1) | |
| 4061 |
|
4048 | |||
| 4062 | velRadial1[i,:] = y11 |
|
4049 | velRadial1[i,:] = y11 | |
| 4063 | SNR1[i,:] = y21 |
|
4050 | SNR1[i,:] = y21 | |
| 4064 |
|
4051 | |||
| 4065 | return heiRang1, velRadial1, SNR1 |
|
4052 | return heiRang1, velRadial1, SNR1 | |
| 4066 |
|
4053 | |||
| 4067 | def __calculateVelUVW(self, A, velRadial): |
|
4054 | def __calculateVelUVW(self, A, velRadial): | |
| 4068 |
|
4055 | |||
| 4069 | #Operacion Matricial |
|
4056 | #Operacion Matricial | |
| 4070 | velUVW = numpy.zeros((A.shape[0],velRadial.shape[1])) |
|
4057 | velUVW = numpy.zeros((A.shape[0],velRadial.shape[1])) | |
| 4071 | velUVW[:,:] = numpy.dot(A,velRadial) |
|
4058 | velUVW[:,:] = numpy.dot(A,velRadial) | |
| 4072 |
|
4059 | |||
| 4073 |
|
4060 | |||
| 4074 | return velUVW |
|
4061 | return velUVW | |
| 4075 |
|
4062 | |||
| 4076 | def techniqueDBS(self, kwargs): |
|
4063 | def techniqueDBS(self, kwargs): | |
| 4077 | """ |
|
4064 | """ | |
| 4078 | Function that implements Doppler Beam Swinging (DBS) technique. |
|
4065 | Function that implements Doppler Beam Swinging (DBS) technique. | |
| 4079 |
|
4066 | |||
| 4080 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, |
|
4067 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, | |
| 4081 | Direction correction (if necessary), Ranges and SNR |
|
4068 | Direction correction (if necessary), Ranges and SNR | |
| 4082 |
|
4069 | |||
| 4083 | Output: Winds estimation (Zonal, Meridional and Vertical) |
|
4070 | Output: Winds estimation (Zonal, Meridional and Vertical) | |
| 4084 |
|
4071 | |||
| 4085 | Parameters affected: Winds, height range, SNR |
|
4072 | Parameters affected: Winds, height range, SNR | |
| 4086 | """ |
|
4073 | """ | |
| 4087 | velRadial0 = kwargs['velRadial'] |
|
4074 | velRadial0 = kwargs['velRadial'] | |
| 4088 | heiRang = kwargs['heightList'] |
|
4075 | heiRang = kwargs['heightList'] | |
| 4089 | SNR0 = kwargs['SNR'] |
|
4076 | SNR0 = kwargs['SNR'] | |
| 4090 |
|
4077 | |||
| 4091 | if 'dirCosx' in kwargs and 'dirCosy' in kwargs: |
|
4078 | if 'dirCosx' in kwargs and 'dirCosy' in kwargs: | |
| 4092 | theta_x = numpy.array(kwargs['dirCosx']) |
|
4079 | theta_x = numpy.array(kwargs['dirCosx']) | |
| 4093 | theta_y = numpy.array(kwargs['dirCosy']) |
|
4080 | theta_y = numpy.array(kwargs['dirCosy']) | |
| 4094 | else: |
|
4081 | else: | |
| 4095 | elev = numpy.array(kwargs['elevation']) |
|
4082 | elev = numpy.array(kwargs['elevation']) | |
| 4096 | azim = numpy.array(kwargs['azimuth']) |
|
4083 | azim = numpy.array(kwargs['azimuth']) | |
| 4097 | theta_x, theta_y = self.__calculateCosDir(elev, azim) |
|
4084 | theta_x, theta_y = self.__calculateCosDir(elev, azim) | |
| 4098 | azimuth = kwargs['correctAzimuth'] |
|
4085 | azimuth = kwargs['correctAzimuth'] | |
| 4099 | if 'horizontalOnly' in kwargs: |
|
4086 | if 'horizontalOnly' in kwargs: | |
| 4100 | horizontalOnly = kwargs['horizontalOnly'] |
|
4087 | horizontalOnly = kwargs['horizontalOnly'] | |
| 4101 | else: horizontalOnly = False |
|
4088 | else: horizontalOnly = False | |
| 4102 | if 'correctFactor' in kwargs: |
|
4089 | if 'correctFactor' in kwargs: | |
| 4103 | correctFactor = kwargs['correctFactor'] |
|
4090 | correctFactor = kwargs['correctFactor'] | |
| 4104 | else: correctFactor = 1 |
|
4091 | else: correctFactor = 1 | |
| 4105 | if 'channelList' in kwargs: |
|
4092 | if 'channelList' in kwargs: | |
| 4106 | channelList = kwargs['channelList'] |
|
4093 | channelList = kwargs['channelList'] | |
| 4107 | if len(channelList) == 2: |
|
4094 | if len(channelList) == 2: | |
| 4108 | horizontalOnly = True |
|
4095 | horizontalOnly = True | |
| 4109 | arrayChannel = numpy.array(channelList) |
|
4096 | arrayChannel = numpy.array(channelList) | |
| 4110 | param = param[arrayChannel,:,:] |
|
4097 | param = param[arrayChannel,:,:] | |
| 4111 | theta_x = theta_x[arrayChannel] |
|
4098 | theta_x = theta_x[arrayChannel] | |
| 4112 | theta_y = theta_y[arrayChannel] |
|
4099 | theta_y = theta_y[arrayChannel] | |
| 4113 |
|
4100 | |||
| 4114 | azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth) |
|
4101 | azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth) | |
| 4115 | heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, zenith_arr, correctFactor*velRadial0, SNR0) |
|
4102 | heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, zenith_arr, correctFactor*velRadial0, SNR0) | |
| 4116 | A = self.__calculateMatA(dir_cosu, dir_cosv, dir_cosw, horizontalOnly) |
|
4103 | A = self.__calculateMatA(dir_cosu, dir_cosv, dir_cosw, horizontalOnly) | |
| 4117 |
|
4104 | |||
| 4118 | #Calculo de Componentes de la velocidad con DBS |
|
4105 | #Calculo de Componentes de la velocidad con DBS | |
| 4119 | winds = self.__calculateVelUVW(A,velRadial1) |
|
4106 | winds = self.__calculateVelUVW(A,velRadial1) | |
| 4120 |
|
4107 | |||
| 4121 | return winds, heiRang1, SNR1 |
|
4108 | return winds, heiRang1, SNR1 | |
| 4122 |
|
4109 | |||
| 4123 | def __calculateDistance(self, posx, posy, pairs_ccf, azimuth = None): |
|
4110 | def __calculateDistance(self, posx, posy, pairs_ccf, azimuth = None): | |
| 4124 |
|
4111 | |||
| 4125 | nPairs = len(pairs_ccf) |
|
4112 | nPairs = len(pairs_ccf) | |
| 4126 | posx = numpy.asarray(posx) |
|
4113 | posx = numpy.asarray(posx) | |
| 4127 | posy = numpy.asarray(posy) |
|
4114 | posy = numpy.asarray(posy) | |
| 4128 |
|
4115 | |||
| 4129 | #Rotacion Inversa para alinear con el azimuth |
|
4116 | #Rotacion Inversa para alinear con el azimuth | |
| 4130 | if azimuth!= None: |
|
4117 | if azimuth!= None: | |
| 4131 | azimuth = azimuth*math.pi/180 |
|
4118 | azimuth = azimuth*math.pi/180 | |
| 4132 | posx1 = posx*math.cos(azimuth) + posy*math.sin(azimuth) |
|
4119 | posx1 = posx*math.cos(azimuth) + posy*math.sin(azimuth) | |
| 4133 | posy1 = -posx*math.sin(azimuth) + posy*math.cos(azimuth) |
|
4120 | posy1 = -posx*math.sin(azimuth) + posy*math.cos(azimuth) | |
| 4134 | else: |
|
4121 | else: | |
| 4135 | posx1 = posx |
|
4122 | posx1 = posx | |
| 4136 | posy1 = posy |
|
4123 | posy1 = posy | |
| 4137 |
|
4124 | |||
| 4138 | #Calculo de Distancias |
|
4125 | #Calculo de Distancias | |
| 4139 | distx = numpy.zeros(nPairs) |
|
4126 | distx = numpy.zeros(nPairs) | |
| 4140 | disty = numpy.zeros(nPairs) |
|
4127 | disty = numpy.zeros(nPairs) | |
| 4141 | dist = numpy.zeros(nPairs) |
|
4128 | dist = numpy.zeros(nPairs) | |
| 4142 | ang = numpy.zeros(nPairs) |
|
4129 | ang = numpy.zeros(nPairs) | |
| 4143 |
|
4130 | |||
| 4144 | for i in range(nPairs): |
|
4131 | for i in range(nPairs): | |
| 4145 | distx[i] = posx1[pairs_ccf[i][1]] - posx1[pairs_ccf[i][0]] |
|
4132 | distx[i] = posx1[pairs_ccf[i][1]] - posx1[pairs_ccf[i][0]] | |
| 4146 | disty[i] = posy1[pairs_ccf[i][1]] - posy1[pairs_ccf[i][0]] |
|
4133 | disty[i] = posy1[pairs_ccf[i][1]] - posy1[pairs_ccf[i][0]] | |
| 4147 | dist[i] = numpy.sqrt(distx[i]**2 + disty[i]**2) |
|
4134 | dist[i] = numpy.sqrt(distx[i]**2 + disty[i]**2) | |
| 4148 | ang[i] = numpy.arctan2(disty[i],distx[i]) |
|
4135 | ang[i] = numpy.arctan2(disty[i],distx[i]) | |
| 4149 |
|
4136 | |||
| 4150 | return distx, disty, dist, ang |
|
4137 | return distx, disty, dist, ang | |
| 4151 | #Calculo de Matrices |
|
4138 | #Calculo de Matrices | |
| 4152 |
|
4139 | |||
| 4153 |
|
4140 | |||
| 4154 | def __calculateVelVer(self, phase, lagTRange, _lambda): |
|
4141 | def __calculateVelVer(self, phase, lagTRange, _lambda): | |
| 4155 |
|
4142 | |||
| 4156 | Ts = lagTRange[1] - lagTRange[0] |
|
4143 | Ts = lagTRange[1] - lagTRange[0] | |
| 4157 | velW = -_lambda*phase/(4*math.pi*Ts) |
|
4144 | velW = -_lambda*phase/(4*math.pi*Ts) | |
| 4158 |
|
4145 | |||
| 4159 | return velW |
|
4146 | return velW | |
| 4160 |
|
4147 | |||
| 4161 | def __calculateVelHorDir(self, dist, tau1, tau2, ang): |
|
4148 | def __calculateVelHorDir(self, dist, tau1, tau2, ang): | |
| 4162 | nPairs = tau1.shape[0] |
|
4149 | nPairs = tau1.shape[0] | |
| 4163 | nHeights = tau1.shape[1] |
|
4150 | nHeights = tau1.shape[1] | |
| 4164 | vel = numpy.zeros((nPairs,3,nHeights)) |
|
4151 | vel = numpy.zeros((nPairs,3,nHeights)) | |
| 4165 | dist1 = numpy.reshape(dist, (dist.size,1)) |
|
4152 | dist1 = numpy.reshape(dist, (dist.size,1)) | |
| 4166 |
|
4153 | |||
| 4167 | angCos = numpy.cos(ang) |
|
4154 | angCos = numpy.cos(ang) | |
| 4168 | angSin = numpy.sin(ang) |
|
4155 | angSin = numpy.sin(ang) | |
| 4169 |
|
4156 | |||
| 4170 | vel0 = dist1*tau1/(2*tau2**2) |
|
4157 | vel0 = dist1*tau1/(2*tau2**2) | |
| 4171 | vel[:,0,:] = (vel0*angCos).sum(axis = 1) |
|
4158 | vel[:,0,:] = (vel0*angCos).sum(axis = 1) | |
| 4172 | vel[:,1,:] = (vel0*angSin).sum(axis = 1) |
|
4159 | vel[:,1,:] = (vel0*angSin).sum(axis = 1) | |
| 4173 |
|
4160 | |||
| 4174 | ind = numpy.where(numpy.isinf(vel)) |
|
4161 | ind = numpy.where(numpy.isinf(vel)) | |
| 4175 | vel[ind] = numpy.nan |
|
4162 | vel[ind] = numpy.nan | |
| 4176 |
|
4163 | |||
| 4177 | return vel |
|
4164 | return vel | |
| 4178 |
|
4165 | |||
| 4179 | def techniqueSA(self, kwargs): |
|
4166 | def techniqueSA(self, kwargs): | |
| 4180 |
|
4167 | |||
| 4181 | """ |
|
4168 | """ | |
| 4182 | Function that implements Spaced Antenna (SA) technique. |
|
4169 | Function that implements Spaced Antenna (SA) technique. | |
| 4183 |
|
4170 | |||
| 4184 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, |
|
4171 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, | |
| 4185 | Direction correction (if necessary), Ranges and SNR |
|
4172 | Direction correction (if necessary), Ranges and SNR | |
| 4186 |
|
4173 | |||
| 4187 | Output: Winds estimation (Zonal, Meridional and Vertical) |
|
4174 | Output: Winds estimation (Zonal, Meridional and Vertical) | |
| 4188 |
|
4175 | |||
| 4189 | Parameters affected: Winds |
|
4176 | Parameters affected: Winds | |
| 4190 | """ |
|
4177 | """ | |
| 4191 | position_x = kwargs['positionX'] |
|
4178 | position_x = kwargs['positionX'] | |
| 4192 | position_y = kwargs['positionY'] |
|
4179 | position_y = kwargs['positionY'] | |
| 4193 | azimuth = kwargs['azimuth'] |
|
4180 | azimuth = kwargs['azimuth'] | |
| 4194 |
|
4181 | |||
| 4195 | if 'correctFactor' in kwargs: |
|
4182 | if 'correctFactor' in kwargs: | |
| 4196 | correctFactor = kwargs['correctFactor'] |
|
4183 | correctFactor = kwargs['correctFactor'] | |
| 4197 | else: |
|
4184 | else: | |
| 4198 | correctFactor = 1 |
|
4185 | correctFactor = 1 | |
| 4199 |
|
4186 | |||
| 4200 | groupList = kwargs['groupList'] |
|
4187 | groupList = kwargs['groupList'] | |
| 4201 | pairs_ccf = groupList[1] |
|
4188 | pairs_ccf = groupList[1] | |
| 4202 | tau = kwargs['tau'] |
|
4189 | tau = kwargs['tau'] | |
| 4203 | _lambda = kwargs['_lambda'] |
|
4190 | _lambda = kwargs['_lambda'] | |
| 4204 |
|
4191 | |||
| 4205 | #Cross Correlation pairs obtained |
|
4192 | #Cross Correlation pairs obtained | |
| 4206 |
|
4193 | |||
| 4207 | indtau = tau.shape[0]/2 |
|
4194 | indtau = tau.shape[0]/2 | |
| 4208 | tau1 = tau[:indtau,:] |
|
4195 | tau1 = tau[:indtau,:] | |
| 4209 | tau2 = tau[indtau:-1,:] |
|
4196 | tau2 = tau[indtau:-1,:] | |
| 4210 | phase1 = tau[-1,:] |
|
4197 | phase1 = tau[-1,:] | |
| 4211 |
|
4198 | |||
| 4212 | #--------------------------------------------------------------------- |
|
4199 | #--------------------------------------------------------------------- | |
| 4213 | #Metodo Directo |
|
4200 | #Metodo Directo | |
| 4214 | distx, disty, dist, ang = self.__calculateDistance(position_x, position_y, pairs_ccf,azimuth) |
|
4201 | distx, disty, dist, ang = self.__calculateDistance(position_x, position_y, pairs_ccf,azimuth) | |
| 4215 | winds = self.__calculateVelHorDir(dist, tau1, tau2, ang) |
|
4202 | winds = self.__calculateVelHorDir(dist, tau1, tau2, ang) | |
| 4216 | winds = stats.nanmean(winds, axis=0) |
|
4203 | winds = stats.nanmean(winds, axis=0) | |
| 4217 | #--------------------------------------------------------------------- |
|
4204 | #--------------------------------------------------------------------- | |
| 4218 | #Metodo General |
|
4205 | #Metodo General | |
| 4219 |
|
4206 | |||
| 4220 | #--------------------------------------------------------------------- |
|
4207 | #--------------------------------------------------------------------- | |
| 4221 | winds[2,:] = self.__calculateVelVer(phase1, lagTRange, _lambda) |
|
4208 | winds[2,:] = self.__calculateVelVer(phase1, lagTRange, _lambda) | |
| 4222 | winds = correctFactor*winds |
|
4209 | winds = correctFactor*winds | |
| 4223 | return winds |
|
4210 | return winds | |
| 4224 |
|
4211 | |||
| 4225 | def __checkTime(self, currentTime, paramInterval, outputInterval): |
|
4212 | def __checkTime(self, currentTime, paramInterval, outputInterval): | |
| 4226 |
|
4213 | |||
| 4227 | dataTime = currentTime + paramInterval |
|
4214 | dataTime = currentTime + paramInterval | |
| 4228 | deltaTime = dataTime - self.__initime |
|
4215 | deltaTime = dataTime - self.__initime | |
| 4229 |
|
4216 | |||
| 4230 | if deltaTime >= outputInterval or deltaTime < 0: |
|
4217 | if deltaTime >= outputInterval or deltaTime < 0: | |
| 4231 | self.__dataReady = True |
|
4218 | self.__dataReady = True | |
| 4232 | return |
|
4219 | return | |
| 4233 |
|
4220 | |||
| 4234 | def techniqueMeteors(self, arrayMeteor, meteorThresh, heightMin, heightMax): |
|
4221 | def techniqueMeteors(self, arrayMeteor, meteorThresh, heightMin, heightMax): | |
| 4235 | ''' |
|
4222 | ''' | |
| 4236 | Function that implements winds estimation technique with detected meteors. |
|
4223 | Function that implements winds estimation technique with detected meteors. | |
| 4237 |
|
4224 | |||
| 4238 | Input: Detected meteors, Minimum meteor quantity to wind estimation |
|
4225 | Input: Detected meteors, Minimum meteor quantity to wind estimation | |
| 4239 |
|
4226 | |||
| 4240 | Output: Winds estimation (Zonal and Meridional) |
|
4227 | Output: Winds estimation (Zonal and Meridional) | |
| 4241 |
|
4228 | |||
| 4242 | Parameters affected: Winds |
|
4229 | Parameters affected: Winds | |
| 4243 | ''' |
|
4230 | ''' | |
| 4244 | #Settings |
|
4231 | #Settings | |
| 4245 | nInt = (heightMax - heightMin)/2 |
|
4232 | nInt = (heightMax - heightMin)/2 | |
| 4246 | nInt = int(nInt) |
|
4233 | nInt = int(nInt) | |
| 4247 | winds = numpy.zeros((2,nInt))*numpy.nan |
|
4234 | winds = numpy.zeros((2,nInt))*numpy.nan | |
| 4248 |
|
4235 | |||
| 4249 | #Filter errors |
|
4236 | #Filter errors | |
| 4250 | error = numpy.where(arrayMeteor[:,-1] == 0)[0] |
|
4237 | error = numpy.where(arrayMeteor[:,-1] == 0)[0] | |
| 4251 | finalMeteor = arrayMeteor[error,:] |
|
4238 | finalMeteor = arrayMeteor[error,:] | |
| 4252 |
|
4239 | |||
| 4253 | #Meteor Histogram |
|
4240 | #Meteor Histogram | |
| 4254 | finalHeights = finalMeteor[:,2] |
|
4241 | finalHeights = finalMeteor[:,2] | |
| 4255 | hist = numpy.histogram(finalHeights, bins = nInt, range = (heightMin,heightMax)) |
|
4242 | hist = numpy.histogram(finalHeights, bins = nInt, range = (heightMin,heightMax)) | |
| 4256 | nMeteorsPerI = hist[0] |
|
4243 | nMeteorsPerI = hist[0] | |
| 4257 | heightPerI = hist[1] |
|
4244 | heightPerI = hist[1] | |
| 4258 |
|
4245 | |||
| 4259 | #Sort of meteors |
|
4246 | #Sort of meteors | |
| 4260 | indSort = finalHeights.argsort() |
|
4247 | indSort = finalHeights.argsort() | |
| 4261 | finalMeteor2 = finalMeteor[indSort,:] |
|
4248 | finalMeteor2 = finalMeteor[indSort,:] | |
| 4262 |
|
4249 | |||
| 4263 | # Calculating winds |
|
4250 | # Calculating winds | |
| 4264 | ind1 = 0 |
|
4251 | ind1 = 0 | |
| 4265 | ind2 = 0 |
|
4252 | ind2 = 0 | |
| 4266 |
|
4253 | |||
| 4267 | for i in range(nInt): |
|
4254 | for i in range(nInt): | |
| 4268 | nMet = nMeteorsPerI[i] |
|
4255 | nMet = nMeteorsPerI[i] | |
| 4269 | ind1 = ind2 |
|
4256 | ind1 = ind2 | |
| 4270 | ind2 = ind1 + nMet |
|
4257 | ind2 = ind1 + nMet | |
| 4271 |
|
4258 | |||
| 4272 | meteorAux = finalMeteor2[ind1:ind2,:] |
|
4259 | meteorAux = finalMeteor2[ind1:ind2,:] | |
| 4273 |
|
4260 | |||
| 4274 | if meteorAux.shape[0] >= meteorThresh: |
|
4261 | if meteorAux.shape[0] >= meteorThresh: | |
| 4275 | vel = meteorAux[:, 6] |
|
4262 | vel = meteorAux[:, 6] | |
| 4276 | zen = meteorAux[:, 4]*numpy.pi/180 |
|
4263 | zen = meteorAux[:, 4]*numpy.pi/180 | |
| 4277 | azim = meteorAux[:, 3]*numpy.pi/180 |
|
4264 | azim = meteorAux[:, 3]*numpy.pi/180 | |
| 4278 |
|
4265 | |||
| 4279 | n = numpy.cos(zen) |
|
4266 | n = numpy.cos(zen) | |
| 4280 | l = numpy.sin(zen)*numpy.sin(azim) |
|
4267 | l = numpy.sin(zen)*numpy.sin(azim) | |
| 4281 | m = numpy.sin(zen)*numpy.cos(azim) |
|
4268 | m = numpy.sin(zen)*numpy.cos(azim) | |
| 4282 |
|
4269 | |||
| 4283 | A = numpy.vstack((l, m)).transpose() |
|
4270 | A = numpy.vstack((l, m)).transpose() | |
| 4284 | A1 = numpy.dot(numpy.linalg.inv( numpy.dot(A.transpose(),A) ),A.transpose()) |
|
4271 | A1 = numpy.dot(numpy.linalg.inv( numpy.dot(A.transpose(),A) ),A.transpose()) | |
| 4285 | windsAux = numpy.dot(A1, vel) |
|
4272 | windsAux = numpy.dot(A1, vel) | |
| 4286 |
|
4273 | |||
| 4287 | winds[0,i] = windsAux[0] |
|
4274 | winds[0,i] = windsAux[0] | |
| 4288 | winds[1,i] = windsAux[1] |
|
4275 | winds[1,i] = windsAux[1] | |
| 4289 |
|
4276 | |||
| 4290 | return winds, heightPerI[:-1] |
|
4277 | return winds, heightPerI[:-1] | |
| 4291 |
|
4278 | |||
| 4292 | def techniqueNSM_SA(self, **kwargs): |
|
4279 | def techniqueNSM_SA(self, **kwargs): | |
| 4293 | metArray = kwargs['metArray'] |
|
4280 | metArray = kwargs['metArray'] | |
| 4294 | heightList = kwargs['heightList'] |
|
4281 | heightList = kwargs['heightList'] | |
| 4295 | timeList = kwargs['timeList'] |
|
4282 | timeList = kwargs['timeList'] | |
| 4296 |
|
4283 | |||
| 4297 | rx_location = kwargs['rx_location'] |
|
4284 | rx_location = kwargs['rx_location'] | |
| 4298 | groupList = kwargs['groupList'] |
|
4285 | groupList = kwargs['groupList'] | |
| 4299 | azimuth = kwargs['azimuth'] |
|
4286 | azimuth = kwargs['azimuth'] | |
| 4300 | dfactor = kwargs['dfactor'] |
|
4287 | dfactor = kwargs['dfactor'] | |
| 4301 | k = kwargs['k'] |
|
4288 | k = kwargs['k'] | |
| 4302 |
|
4289 | |||
| 4303 | azimuth1, dist = self.__calculateAzimuth1(rx_location, groupList, azimuth) |
|
4290 | azimuth1, dist = self.__calculateAzimuth1(rx_location, groupList, azimuth) | |
| 4304 | d = dist*dfactor |
|
4291 | d = dist*dfactor | |
| 4305 | #Phase calculation |
|
4292 | #Phase calculation | |
| 4306 | metArray1 = self.__getPhaseSlope(metArray, heightList, timeList) |
|
4293 | metArray1 = self.__getPhaseSlope(metArray, heightList, timeList) | |
| 4307 |
|
4294 | |||
| 4308 | metArray1[:,-2] = metArray1[:,-2]*metArray1[:,2]*1000/(k*d[metArray1[:,1].astype(int)]) #angles into velocities |
|
4295 | metArray1[:,-2] = metArray1[:,-2]*metArray1[:,2]*1000/(k*d[metArray1[:,1].astype(int)]) #angles into velocities | |
| 4309 |
|
4296 | |||
| 4310 | velEst = numpy.zeros((heightList.size,2))*numpy.nan |
|
4297 | velEst = numpy.zeros((heightList.size,2))*numpy.nan | |
| 4311 | azimuth1 = azimuth1*numpy.pi/180 |
|
4298 | azimuth1 = azimuth1*numpy.pi/180 | |
| 4312 |
|
4299 | |||
| 4313 | for i in range(heightList.size): |
|
4300 | for i in range(heightList.size): | |
| 4314 | h = heightList[i] |
|
4301 | h = heightList[i] | |
| 4315 | indH = numpy.where((metArray1[:,2] == h)&(numpy.abs(metArray1[:,-2]) < 100))[0] |
|
4302 | indH = numpy.where((metArray1[:,2] == h)&(numpy.abs(metArray1[:,-2]) < 100))[0] | |
| 4316 | metHeight = metArray1[indH,:] |
|
4303 | metHeight = metArray1[indH,:] | |
| 4317 | if metHeight.shape[0] >= 2: |
|
4304 | if metHeight.shape[0] >= 2: | |
| 4318 | velAux = numpy.asmatrix(metHeight[:,-2]).T #Radial Velocities |
|
4305 | velAux = numpy.asmatrix(metHeight[:,-2]).T #Radial Velocities | |
| 4319 | iazim = metHeight[:,1].astype(int) |
|
4306 | iazim = metHeight[:,1].astype(int) | |
| 4320 | azimAux = numpy.asmatrix(azimuth1[iazim]).T #Azimuths |
|
4307 | azimAux = numpy.asmatrix(azimuth1[iazim]).T #Azimuths | |
| 4321 | A = numpy.hstack((numpy.cos(azimAux),numpy.sin(azimAux))) |
|
4308 | A = numpy.hstack((numpy.cos(azimAux),numpy.sin(azimAux))) | |
| 4322 | A = numpy.asmatrix(A) |
|
4309 | A = numpy.asmatrix(A) | |
| 4323 | A1 = numpy.linalg.pinv(A.transpose()*A)*A.transpose() |
|
4310 | A1 = numpy.linalg.pinv(A.transpose()*A)*A.transpose() | |
| 4324 | velHor = numpy.dot(A1,velAux) |
|
4311 | velHor = numpy.dot(A1,velAux) | |
| 4325 |
|
4312 | |||
| 4326 | velEst[i,:] = numpy.squeeze(velHor) |
|
4313 | velEst[i,:] = numpy.squeeze(velHor) | |
| 4327 | return velEst |
|
4314 | return velEst | |
| 4328 |
|
4315 | |||
| 4329 | def __getPhaseSlope(self, metArray, heightList, timeList): |
|
4316 | def __getPhaseSlope(self, metArray, heightList, timeList): | |
| 4330 | meteorList = [] |
|
4317 | meteorList = [] | |
| 4331 | #utctime sec1 height SNR velRad ph0 ph1 ph2 coh0 coh1 coh2 |
|
4318 | #utctime sec1 height SNR velRad ph0 ph1 ph2 coh0 coh1 coh2 | |
| 4332 | #Putting back together the meteor matrix |
|
4319 | #Putting back together the meteor matrix | |
| 4333 | utctime = metArray[:,0] |
|
4320 | utctime = metArray[:,0] | |
| 4334 | uniqueTime = numpy.unique(utctime) |
|
4321 | uniqueTime = numpy.unique(utctime) | |
| 4335 |
|
4322 | |||
| 4336 | phaseDerThresh = 0.5 |
|
4323 | phaseDerThresh = 0.5 | |
| 4337 | ippSeconds = timeList[1] - timeList[0] |
|
4324 | ippSeconds = timeList[1] - timeList[0] | |
| 4338 | sec = numpy.where(timeList>1)[0][0] |
|
4325 | sec = numpy.where(timeList>1)[0][0] | |
| 4339 | nPairs = metArray.shape[1] - 6 |
|
4326 | nPairs = metArray.shape[1] - 6 | |
| 4340 | nHeights = len(heightList) |
|
4327 | nHeights = len(heightList) | |
| 4341 |
|
4328 | |||
| 4342 | for t in uniqueTime: |
|
4329 | for t in uniqueTime: | |
| 4343 | metArray1 = metArray[utctime==t,:] |
|
4330 | metArray1 = metArray[utctime==t,:] | |
| 4344 | tmet = metArray1[:,1].astype(int) |
|
4331 | tmet = metArray1[:,1].astype(int) | |
| 4345 | hmet = metArray1[:,2].astype(int) |
|
4332 | hmet = metArray1[:,2].astype(int) | |
| 4346 |
|
4333 | |||
| 4347 | metPhase = numpy.zeros((nPairs, heightList.size, timeList.size - 1)) |
|
4334 | metPhase = numpy.zeros((nPairs, heightList.size, timeList.size - 1)) | |
| 4348 | metPhase[:,:] = numpy.nan |
|
4335 | metPhase[:,:] = numpy.nan | |
| 4349 | metPhase[:,hmet,tmet] = metArray1[:,6:].T |
|
4336 | metPhase[:,hmet,tmet] = metArray1[:,6:].T | |
| 4350 |
|
4337 | |||
| 4351 | #Delete short trails |
|
4338 | #Delete short trails | |
| 4352 | metBool = ~numpy.isnan(metPhase[0,:,:]) |
|
4339 | metBool = ~numpy.isnan(metPhase[0,:,:]) | |
| 4353 | heightVect = numpy.sum(metBool, axis = 1) |
|
4340 | heightVect = numpy.sum(metBool, axis = 1) | |
| 4354 | metBool[heightVect<sec,:] = False |
|
4341 | metBool[heightVect<sec,:] = False | |
| 4355 | metPhase[:,heightVect<sec,:] = numpy.nan |
|
4342 | metPhase[:,heightVect<sec,:] = numpy.nan | |
| 4356 |
|
4343 | |||
| 4357 | #Derivative |
|
4344 | #Derivative | |
| 4358 | metDer = numpy.abs(metPhase[:,:,1:] - metPhase[:,:,:-1]) |
|
4345 | metDer = numpy.abs(metPhase[:,:,1:] - metPhase[:,:,:-1]) | |
| 4359 | phDerAux = numpy.dstack((numpy.full((nPairs,nHeights,1), False, dtype=bool),metDer > phaseDerThresh)) |
|
4346 | phDerAux = numpy.dstack((numpy.full((nPairs,nHeights,1), False, dtype=bool),metDer > phaseDerThresh)) | |
| 4360 | metPhase[phDerAux] = numpy.nan |
|
4347 | metPhase[phDerAux] = numpy.nan | |
| 4361 |
|
4348 | |||
| 4362 | #--------------------------METEOR DETECTION ----------------------------------------- |
|
4349 | #--------------------------METEOR DETECTION ----------------------------------------- | |
| 4363 | indMet = numpy.where(numpy.any(metBool,axis=1))[0] |
|
4350 | indMet = numpy.where(numpy.any(metBool,axis=1))[0] | |
| 4364 |
|
4351 | |||
| 4365 | for p in numpy.arange(nPairs): |
|
4352 | for p in numpy.arange(nPairs): | |
| 4366 | phase = metPhase[p,:,:] |
|
4353 | phase = metPhase[p,:,:] | |
| 4367 | phDer = metDer[p,:,:] |
|
4354 | phDer = metDer[p,:,:] | |
| 4368 |
|
4355 | |||
| 4369 | for h in indMet: |
|
4356 | for h in indMet: | |
| 4370 | height = heightList[h] |
|
4357 | height = heightList[h] | |
| 4371 | phase1 = phase[h,:] #82 |
|
4358 | phase1 = phase[h,:] #82 | |
| 4372 | phDer1 = phDer[h,:] |
|
4359 | phDer1 = phDer[h,:] | |
| 4373 |
|
4360 | |||
| 4374 | phase1[~numpy.isnan(phase1)] = numpy.unwrap(phase1[~numpy.isnan(phase1)]) #Unwrap |
|
4361 | phase1[~numpy.isnan(phase1)] = numpy.unwrap(phase1[~numpy.isnan(phase1)]) #Unwrap | |
| 4375 |
|
4362 | |||
| 4376 | indValid = numpy.where(~numpy.isnan(phase1))[0] |
|
4363 | indValid = numpy.where(~numpy.isnan(phase1))[0] | |
| 4377 | initMet = indValid[0] |
|
4364 | initMet = indValid[0] | |
| 4378 | endMet = 0 |
|
4365 | endMet = 0 | |
| 4379 |
|
4366 | |||
| 4380 | for i in range(len(indValid)-1): |
|
4367 | for i in range(len(indValid)-1): | |
| 4381 |
|
4368 | |||
| 4382 | #Time difference |
|
4369 | #Time difference | |
| 4383 | inow = indValid[i] |
|
4370 | inow = indValid[i] | |
| 4384 | inext = indValid[i+1] |
|
4371 | inext = indValid[i+1] | |
| 4385 | idiff = inext - inow |
|
4372 | idiff = inext - inow | |
| 4386 | #Phase difference |
|
4373 | #Phase difference | |
| 4387 | phDiff = numpy.abs(phase1[inext] - phase1[inow]) |
|
4374 | phDiff = numpy.abs(phase1[inext] - phase1[inow]) | |
| 4388 |
|
4375 | |||
| 4389 | if idiff>sec or phDiff>numpy.pi/4 or inext==indValid[-1]: #End of Meteor |
|
4376 | if idiff>sec or phDiff>numpy.pi/4 or inext==indValid[-1]: #End of Meteor | |
| 4390 | sizeTrail = inow - initMet + 1 |
|
4377 | sizeTrail = inow - initMet + 1 | |
| 4391 | if sizeTrail>3*sec: #Too short meteors |
|
4378 | if sizeTrail>3*sec: #Too short meteors | |
| 4392 | x = numpy.arange(initMet,inow+1)*ippSeconds |
|
4379 | x = numpy.arange(initMet,inow+1)*ippSeconds | |
| 4393 | y = phase1[initMet:inow+1] |
|
4380 | y = phase1[initMet:inow+1] | |
| 4394 | ynnan = ~numpy.isnan(y) |
|
4381 | ynnan = ~numpy.isnan(y) | |
| 4395 | x = x[ynnan] |
|
4382 | x = x[ynnan] | |
| 4396 | y = y[ynnan] |
|
4383 | y = y[ynnan] | |
| 4397 | slope, intercept, r_value, p_value, std_err = stats.linregress(x,y) |
|
4384 | slope, intercept, r_value, p_value, std_err = stats.linregress(x,y) | |
| 4398 | ylin = x*slope + intercept |
|
4385 | ylin = x*slope + intercept | |
| 4399 | rsq = r_value**2 |
|
4386 | rsq = r_value**2 | |
| 4400 | if rsq > 0.5: |
|
4387 | if rsq > 0.5: | |
| 4401 | vel = slope#*height*1000/(k*d) |
|
4388 | vel = slope#*height*1000/(k*d) | |
| 4402 | estAux = numpy.array([utctime,p,height, vel, rsq]) |
|
4389 | estAux = numpy.array([utctime,p,height, vel, rsq]) | |
| 4403 | meteorList.append(estAux) |
|
4390 | meteorList.append(estAux) | |
| 4404 | initMet = inext |
|
4391 | initMet = inext | |
| 4405 | metArray2 = numpy.array(meteorList) |
|
4392 | metArray2 = numpy.array(meteorList) | |
| 4406 |
|
4393 | |||
| 4407 | return metArray2 |
|
4394 | return metArray2 | |
| 4408 |
|
4395 | |||
| 4409 | def __calculateAzimuth1(self, rx_location, pairslist, azimuth0): |
|
4396 | def __calculateAzimuth1(self, rx_location, pairslist, azimuth0): | |
| 4410 |
|
4397 | |||
| 4411 | azimuth1 = numpy.zeros(len(pairslist)) |
|
4398 | azimuth1 = numpy.zeros(len(pairslist)) | |
| 4412 | dist = numpy.zeros(len(pairslist)) |
|
4399 | dist = numpy.zeros(len(pairslist)) | |
| 4413 |
|
4400 | |||
| 4414 | for i in range(len(rx_location)): |
|
4401 | for i in range(len(rx_location)): | |
| 4415 | ch0 = pairslist[i][0] |
|
4402 | ch0 = pairslist[i][0] | |
| 4416 | ch1 = pairslist[i][1] |
|
4403 | ch1 = pairslist[i][1] | |
| 4417 |
|
4404 | |||
| 4418 | diffX = rx_location[ch0][0] - rx_location[ch1][0] |
|
4405 | diffX = rx_location[ch0][0] - rx_location[ch1][0] | |
| 4419 | diffY = rx_location[ch0][1] - rx_location[ch1][1] |
|
4406 | diffY = rx_location[ch0][1] - rx_location[ch1][1] | |
| 4420 | azimuth1[i] = numpy.arctan2(diffY,diffX)*180/numpy.pi |
|
4407 | azimuth1[i] = numpy.arctan2(diffY,diffX)*180/numpy.pi | |
| 4421 | dist[i] = numpy.sqrt(diffX**2 + diffY**2) |
|
4408 | dist[i] = numpy.sqrt(diffX**2 + diffY**2) | |
| 4422 |
|
4409 | |||
| 4423 | azimuth1 -= azimuth0 |
|
4410 | azimuth1 -= azimuth0 | |
| 4424 | return azimuth1, dist |
|
4411 | return azimuth1, dist | |
| 4425 |
|
4412 | |||
| 4426 | def techniqueNSM_DBS(self, **kwargs): |
|
4413 | def techniqueNSM_DBS(self, **kwargs): | |
| 4427 | metArray = kwargs['metArray'] |
|
4414 | metArray = kwargs['metArray'] | |
| 4428 | heightList = kwargs['heightList'] |
|
4415 | heightList = kwargs['heightList'] | |
| 4429 | timeList = kwargs['timeList'] |
|
4416 | timeList = kwargs['timeList'] | |
| 4430 | azimuth = kwargs['azimuth'] |
|
4417 | azimuth = kwargs['azimuth'] | |
| 4431 | theta_x = numpy.array(kwargs['theta_x']) |
|
4418 | theta_x = numpy.array(kwargs['theta_x']) | |
| 4432 | theta_y = numpy.array(kwargs['theta_y']) |
|
4419 | theta_y = numpy.array(kwargs['theta_y']) | |
| 4433 |
|
4420 | |||
| 4434 | utctime = metArray[:,0] |
|
4421 | utctime = metArray[:,0] | |
| 4435 | cmet = metArray[:,1].astype(int) |
|
4422 | cmet = metArray[:,1].astype(int) | |
| 4436 | hmet = metArray[:,3].astype(int) |
|
4423 | hmet = metArray[:,3].astype(int) | |
| 4437 | SNRmet = metArray[:,4] |
|
4424 | SNRmet = metArray[:,4] | |
| 4438 | vmet = metArray[:,5] |
|
4425 | vmet = metArray[:,5] | |
| 4439 | spcmet = metArray[:,6] |
|
4426 | spcmet = metArray[:,6] | |
| 4440 |
|
4427 | |||
| 4441 | nChan = numpy.max(cmet) + 1 |
|
4428 | nChan = numpy.max(cmet) + 1 | |
| 4442 | nHeights = len(heightList) |
|
4429 | nHeights = len(heightList) | |
| 4443 |
|
4430 | |||
| 4444 | azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth) |
|
4431 | azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth) | |
| 4445 | hmet = heightList[hmet] |
|
4432 | hmet = heightList[hmet] | |
| 4446 | h1met = hmet*numpy.cos(zenith_arr[cmet]) #Corrected heights |
|
4433 | h1met = hmet*numpy.cos(zenith_arr[cmet]) #Corrected heights | |
| 4447 |
|
4434 | |||
| 4448 | velEst = numpy.zeros((heightList.size,2))*numpy.nan |
|
4435 | velEst = numpy.zeros((heightList.size,2))*numpy.nan | |
| 4449 |
|
4436 | |||
| 4450 | for i in range(nHeights - 1): |
|
4437 | for i in range(nHeights - 1): | |
| 4451 | hmin = heightList[i] |
|
4438 | hmin = heightList[i] | |
| 4452 | hmax = heightList[i + 1] |
|
4439 | hmax = heightList[i + 1] | |
| 4453 |
|
4440 | |||
| 4454 | thisH = (h1met>=hmin) & (h1met<hmax) & (cmet!=2) & (SNRmet>8) & (vmet<50) & (spcmet<10) |
|
4441 | thisH = (h1met>=hmin) & (h1met<hmax) & (cmet!=2) & (SNRmet>8) & (vmet<50) & (spcmet<10) | |
| 4455 | indthisH = numpy.where(thisH) |
|
4442 | indthisH = numpy.where(thisH) | |
| 4456 |
|
4443 | |||
| 4457 | if numpy.size(indthisH) > 3: |
|
4444 | if numpy.size(indthisH) > 3: | |
| 4458 |
|
4445 | |||
| 4459 | vel_aux = vmet[thisH] |
|
4446 | vel_aux = vmet[thisH] | |
| 4460 | chan_aux = cmet[thisH] |
|
4447 | chan_aux = cmet[thisH] | |
| 4461 | cosu_aux = dir_cosu[chan_aux] |
|
4448 | cosu_aux = dir_cosu[chan_aux] | |
| 4462 | cosv_aux = dir_cosv[chan_aux] |
|
4449 | cosv_aux = dir_cosv[chan_aux] | |
| 4463 | cosw_aux = dir_cosw[chan_aux] |
|
4450 | cosw_aux = dir_cosw[chan_aux] | |
| 4464 |
|
4451 | |||
| 4465 | nch = numpy.size(numpy.unique(chan_aux)) |
|
4452 | nch = numpy.size(numpy.unique(chan_aux)) | |
| 4466 | if nch > 1: |
|
4453 | if nch > 1: | |
| 4467 | A = self.__calculateMatA(cosu_aux, cosv_aux, cosw_aux, True) |
|
4454 | A = self.__calculateMatA(cosu_aux, cosv_aux, cosw_aux, True) | |
| 4468 | velEst[i,:] = numpy.dot(A,vel_aux) |
|
4455 | velEst[i,:] = numpy.dot(A,vel_aux) | |
| 4469 |
|
4456 | |||
| 4470 | return velEst |
|
4457 | return velEst | |
| 4471 |
|
4458 | |||
| 4472 | def run(self, dataOut, technique, nHours=1, hmin=70, hmax=110, **kwargs): |
|
4459 | def run(self, dataOut, technique, nHours=1, hmin=70, hmax=110, **kwargs): | |
| 4473 |
|
4460 | |||
| 4474 | param = dataOut.moments |
|
4461 | param = dataOut.moments | |
| 4475 | if numpy.any(dataOut.abscissaList): |
|
4462 | if numpy.any(dataOut.abscissaList): | |
| 4476 | absc = dataOut.abscissaList[:-1] |
|
4463 | absc = dataOut.abscissaList[:-1] | |
| 4477 | # noise = dataOut.noise |
|
4464 | # noise = dataOut.noise | |
| 4478 | heightList = dataOut.heightList |
|
4465 | heightList = dataOut.heightList | |
| 4479 | SNR = dataOut.data_snr |
|
4466 | SNR = dataOut.data_snr | |
| 4480 |
|
4467 | |||
| 4481 | if technique == 'DBS': |
|
4468 | if technique == 'DBS': | |
| 4482 |
|
4469 | |||
| 4483 | kwargs['velRadial'] = param[:,1,:] #Radial velocity |
|
4470 | kwargs['velRadial'] = param[:,1,:] #Radial velocity | |
| 4484 | kwargs['heightList'] = heightList |
|
4471 | kwargs['heightList'] = heightList | |
| 4485 | kwargs['SNR'] = SNR |
|
4472 | kwargs['SNR'] = SNR | |
| 4486 |
|
4473 | |||
| 4487 | dataOut.data_output, dataOut.heightList, dataOut.data_snr = self.techniqueDBS(kwargs) #DBS Function |
|
4474 | dataOut.data_output, dataOut.heightList, dataOut.data_snr = self.techniqueDBS(kwargs) #DBS Function | |
| 4488 | dataOut.utctimeInit = dataOut.utctime |
|
4475 | dataOut.utctimeInit = dataOut.utctime | |
| 4489 | dataOut.outputInterval = dataOut.paramInterval |
|
4476 | dataOut.outputInterval = dataOut.paramInterval | |
| 4490 |
|
4477 | |||
| 4491 | elif technique == 'SA': |
|
4478 | elif technique == 'SA': | |
| 4492 |
|
4479 | |||
| 4493 | #Parameters |
|
4480 | #Parameters | |
| 4494 | kwargs['groupList'] = dataOut.groupList |
|
4481 | kwargs['groupList'] = dataOut.groupList | |
| 4495 | kwargs['tau'] = dataOut.data_param |
|
4482 | kwargs['tau'] = dataOut.data_param | |
| 4496 | kwargs['_lambda'] = dataOut.C/dataOut.frequency |
|
4483 | kwargs['_lambda'] = dataOut.C/dataOut.frequency | |
| 4497 | dataOut.data_output = self.techniqueSA(kwargs) |
|
4484 | dataOut.data_output = self.techniqueSA(kwargs) | |
| 4498 | dataOut.utctimeInit = dataOut.utctime |
|
4485 | dataOut.utctimeInit = dataOut.utctime | |
| 4499 | dataOut.outputInterval = dataOut.timeInterval |
|
4486 | dataOut.outputInterval = dataOut.timeInterval | |
| 4500 |
|
4487 | |||
| 4501 | elif technique == 'Meteors': |
|
4488 | elif technique == 'Meteors': | |
| 4502 | dataOut.flagNoData = True |
|
4489 | dataOut.flagNoData = True | |
| 4503 | self.__dataReady = False |
|
4490 | self.__dataReady = False | |
| 4504 |
|
4491 | |||
| 4505 | if 'nHours' in kwargs: |
|
4492 | if 'nHours' in kwargs: | |
| 4506 | nHours = kwargs['nHours'] |
|
4493 | nHours = kwargs['nHours'] | |
| 4507 | else: |
|
4494 | else: | |
| 4508 | nHours = 1 |
|
4495 | nHours = 1 | |
| 4509 |
|
4496 | |||
| 4510 | if 'meteorsPerBin' in kwargs: |
|
4497 | if 'meteorsPerBin' in kwargs: | |
| 4511 | meteorThresh = kwargs['meteorsPerBin'] |
|
4498 | meteorThresh = kwargs['meteorsPerBin'] | |
| 4512 | else: |
|
4499 | else: | |
| 4513 | meteorThresh = 6 |
|
4500 | meteorThresh = 6 | |
| 4514 |
|
4501 | |||
| 4515 | if 'hmin' in kwargs: |
|
4502 | if 'hmin' in kwargs: | |
| 4516 | hmin = kwargs['hmin'] |
|
4503 | hmin = kwargs['hmin'] | |
| 4517 | else: hmin = 70 |
|
4504 | else: hmin = 70 | |
| 4518 | if 'hmax' in kwargs: |
|
4505 | if 'hmax' in kwargs: | |
| 4519 | hmax = kwargs['hmax'] |
|
4506 | hmax = kwargs['hmax'] | |
| 4520 | else: hmax = 110 |
|
4507 | else: hmax = 110 | |
| 4521 |
|
4508 | |||
| 4522 | dataOut.outputInterval = nHours*3600 |
|
4509 | dataOut.outputInterval = nHours*3600 | |
| 4523 |
|
4510 | |||
| 4524 | if self.__isConfig == False: |
|
4511 | if self.__isConfig == False: | |
| 4525 | #Get Initial LTC time |
|
4512 | #Get Initial LTC time | |
| 4526 | self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
4513 | self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) | |
| 4527 | self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
4514 | self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() | |
| 4528 |
|
4515 | |||
| 4529 | self.__isConfig = True |
|
4516 | self.__isConfig = True | |
| 4530 |
|
4517 | |||
| 4531 | if self.__buffer is None: |
|
4518 | if self.__buffer is None: | |
| 4532 | self.__buffer = dataOut.data_param |
|
4519 | self.__buffer = dataOut.data_param | |
| 4533 | self.__firstdata = copy.copy(dataOut) |
|
4520 | self.__firstdata = copy.copy(dataOut) | |
| 4534 |
|
4521 | |||
| 4535 | else: |
|
4522 | else: | |
| 4536 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
4523 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) | |
| 4537 |
|
4524 | |||
| 4538 | self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
4525 | self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready | |
| 4539 |
|
4526 | |||
| 4540 | if self.__dataReady: |
|
4527 | if self.__dataReady: | |
| 4541 | dataOut.utctimeInit = self.__initime |
|
4528 | dataOut.utctimeInit = self.__initime | |
| 4542 |
|
4529 | |||
| 4543 | self.__initime += dataOut.outputInterval #to erase time offset |
|
4530 | self.__initime += dataOut.outputInterval #to erase time offset | |
| 4544 |
|
4531 | |||
| 4545 | dataOut.data_output, dataOut.heightList = self.techniqueMeteors(self.__buffer, meteorThresh, hmin, hmax) |
|
4532 | dataOut.data_output, dataOut.heightList = self.techniqueMeteors(self.__buffer, meteorThresh, hmin, hmax) | |
| 4546 | dataOut.flagNoData = False |
|
4533 | dataOut.flagNoData = False | |
| 4547 | self.__buffer = None |
|
4534 | self.__buffer = None | |
| 4548 |
|
4535 | |||
| 4549 | elif technique == 'Meteors1': |
|
4536 | elif technique == 'Meteors1': | |
| 4550 | dataOut.flagNoData = True |
|
4537 | dataOut.flagNoData = True | |
| 4551 | self.__dataReady = False |
|
4538 | self.__dataReady = False | |
| 4552 |
|
4539 | |||
| 4553 | if 'nMins' in kwargs: |
|
4540 | if 'nMins' in kwargs: | |
| 4554 | nMins = kwargs['nMins'] |
|
4541 | nMins = kwargs['nMins'] | |
| 4555 | else: nMins = 20 |
|
4542 | else: nMins = 20 | |
| 4556 | if 'rx_location' in kwargs: |
|
4543 | if 'rx_location' in kwargs: | |
| 4557 | rx_location = kwargs['rx_location'] |
|
4544 | rx_location = kwargs['rx_location'] | |
| 4558 | else: rx_location = [(0,1),(1,1),(1,0)] |
|
4545 | else: rx_location = [(0,1),(1,1),(1,0)] | |
| 4559 | if 'azimuth' in kwargs: |
|
4546 | if 'azimuth' in kwargs: | |
| 4560 | azimuth = kwargs['azimuth'] |
|
4547 | azimuth = kwargs['azimuth'] | |
| 4561 | else: azimuth = 51.06 |
|
4548 | else: azimuth = 51.06 | |
| 4562 | if 'dfactor' in kwargs: |
|
4549 | if 'dfactor' in kwargs: | |
| 4563 | dfactor = kwargs['dfactor'] |
|
4550 | dfactor = kwargs['dfactor'] | |
| 4564 | if 'mode' in kwargs: |
|
4551 | if 'mode' in kwargs: | |
| 4565 | mode = kwargs['mode'] |
|
4552 | mode = kwargs['mode'] | |
| 4566 | if 'theta_x' in kwargs: |
|
4553 | if 'theta_x' in kwargs: | |
| 4567 | theta_x = kwargs['theta_x'] |
|
4554 | theta_x = kwargs['theta_x'] | |
| 4568 | if 'theta_y' in kwargs: |
|
4555 | if 'theta_y' in kwargs: | |
| 4569 | theta_y = kwargs['theta_y'] |
|
4556 | theta_y = kwargs['theta_y'] | |
| 4570 | else: mode = 'SA' |
|
4557 | else: mode = 'SA' | |
| 4571 |
|
4558 | |||
| 4572 | #Borrar luego esto |
|
4559 | #Borrar luego esto | |
| 4573 | if dataOut.groupList is None: |
|
4560 | if dataOut.groupList is None: | |
| 4574 | dataOut.groupList = [(0,1),(0,2),(1,2)] |
|
4561 | dataOut.groupList = [(0,1),(0,2),(1,2)] | |
| 4575 | groupList = dataOut.groupList |
|
4562 | groupList = dataOut.groupList | |
| 4576 | C = 3e8 |
|
4563 | C = 3e8 | |
| 4577 | freq = 50e6 |
|
4564 | freq = 50e6 | |
| 4578 | lamb = C/freq |
|
4565 | lamb = C/freq | |
| 4579 | k = 2*numpy.pi/lamb |
|
4566 | k = 2*numpy.pi/lamb | |
| 4580 |
|
4567 | |||
| 4581 | timeList = dataOut.abscissaList |
|
4568 | timeList = dataOut.abscissaList | |
| 4582 | heightList = dataOut.heightList |
|
4569 | heightList = dataOut.heightList | |
| 4583 |
|
4570 | |||
| 4584 | if self.__isConfig == False: |
|
4571 | if self.__isConfig == False: | |
| 4585 | dataOut.outputInterval = nMins*60 |
|
4572 | dataOut.outputInterval = nMins*60 | |
| 4586 | #Get Initial LTC time |
|
4573 | #Get Initial LTC time | |
| 4587 | initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
4574 | initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) | |
| 4588 | minuteAux = initime.minute |
|
4575 | minuteAux = initime.minute | |
| 4589 | minuteNew = int(numpy.floor(minuteAux/nMins)*nMins) |
|
4576 | minuteNew = int(numpy.floor(minuteAux/nMins)*nMins) | |
| 4590 | self.__initime = (initime.replace(minute = minuteNew, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
4577 | self.__initime = (initime.replace(minute = minuteNew, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() | |
| 4591 |
|
4578 | |||
| 4592 | self.__isConfig = True |
|
4579 | self.__isConfig = True | |
| 4593 |
|
4580 | |||
| 4594 | if self.__buffer is None: |
|
4581 | if self.__buffer is None: | |
| 4595 | self.__buffer = dataOut.data_param |
|
4582 | self.__buffer = dataOut.data_param | |
| 4596 | self.__firstdata = copy.copy(dataOut) |
|
4583 | self.__firstdata = copy.copy(dataOut) | |
| 4597 |
|
4584 | |||
| 4598 | else: |
|
4585 | else: | |
| 4599 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
4586 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) | |
| 4600 |
|
4587 | |||
| 4601 | self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
4588 | self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready | |
| 4602 |
|
4589 | |||
| 4603 | if self.__dataReady: |
|
4590 | if self.__dataReady: | |
| 4604 | dataOut.utctimeInit = self.__initime |
|
4591 | dataOut.utctimeInit = self.__initime | |
| 4605 | self.__initime += dataOut.outputInterval #to erase time offset |
|
4592 | self.__initime += dataOut.outputInterval #to erase time offset | |
| 4606 |
|
4593 | |||
| 4607 | metArray = self.__buffer |
|
4594 | metArray = self.__buffer | |
| 4608 | if mode == 'SA': |
|
4595 | if mode == 'SA': | |
| 4609 | dataOut.data_output = self.techniqueNSM_SA(rx_location=rx_location, groupList=groupList, azimuth=azimuth, dfactor=dfactor, k=k,metArray=metArray, heightList=heightList,timeList=timeList) |
|
4596 | dataOut.data_output = self.techniqueNSM_SA(rx_location=rx_location, groupList=groupList, azimuth=azimuth, dfactor=dfactor, k=k,metArray=metArray, heightList=heightList,timeList=timeList) | |
| 4610 | elif mode == 'DBS': |
|
4597 | elif mode == 'DBS': | |
| 4611 | dataOut.data_output = self.techniqueNSM_DBS(metArray=metArray,heightList=heightList,timeList=timeList, azimuth=azimuth, theta_x=theta_x, theta_y=theta_y) |
|
4598 | dataOut.data_output = self.techniqueNSM_DBS(metArray=metArray,heightList=heightList,timeList=timeList, azimuth=azimuth, theta_x=theta_x, theta_y=theta_y) | |
| 4612 | dataOut.data_output = dataOut.data_output.T |
|
4599 | dataOut.data_output = dataOut.data_output.T | |
| 4613 | dataOut.flagNoData = False |
|
4600 | dataOut.flagNoData = False | |
| 4614 | self.__buffer = None |
|
4601 | self.__buffer = None | |
| 4615 |
|
4602 | |||
| 4616 | return dataOut |
|
4603 | return dataOut | |
| 4617 |
|
4604 | |||
| 4618 | class EWDriftsEstimation(Operation): |
|
4605 | class EWDriftsEstimation(Operation): | |
| 4619 |
|
4606 | |||
| 4620 | def __init__(self): |
|
4607 | def __init__(self): | |
| 4621 | Operation.__init__(self) |
|
4608 | Operation.__init__(self) | |
| 4622 |
|
4609 | |||
| 4623 | def __correctValues(self, heiRang, phi, velRadial, SNR): |
|
4610 | def __correctValues(self, heiRang, phi, velRadial, SNR): | |
| 4624 | listPhi = phi.tolist() |
|
4611 | listPhi = phi.tolist() | |
| 4625 | maxid = listPhi.index(max(listPhi)) |
|
4612 | maxid = listPhi.index(max(listPhi)) | |
| 4626 | minid = listPhi.index(min(listPhi)) |
|
4613 | minid = listPhi.index(min(listPhi)) | |
| 4627 |
|
4614 | |||
| 4628 | rango = list(range(len(phi))) |
|
4615 | rango = list(range(len(phi))) | |
| 4629 | heiRang1 = heiRang*math.cos(phi[maxid]) |
|
4616 | heiRang1 = heiRang*math.cos(phi[maxid]) | |
| 4630 | heiRangAux = heiRang*math.cos(phi[minid]) |
|
4617 | heiRangAux = heiRang*math.cos(phi[minid]) | |
| 4631 | indOut = (heiRang1 < heiRangAux[0]).nonzero() |
|
4618 | indOut = (heiRang1 < heiRangAux[0]).nonzero() | |
| 4632 | heiRang1 = numpy.delete(heiRang1,indOut) |
|
4619 | heiRang1 = numpy.delete(heiRang1,indOut) | |
| 4633 |
|
4620 | |||
| 4634 | velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
4621 | velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) | |
| 4635 | SNR1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
4622 | SNR1 = numpy.zeros([len(phi),len(heiRang1)]) | |
| 4636 |
|
4623 | |||
| 4637 | for i in rango: |
|
4624 | for i in rango: | |
| 4638 | x = heiRang*math.cos(phi[i]) |
|
4625 | x = heiRang*math.cos(phi[i]) | |
| 4639 | y1 = velRadial[i,:] |
|
4626 | y1 = velRadial[i,:] | |
| 4640 | vali= (numpy.isfinite(y1)==True).nonzero() |
|
4627 | vali= (numpy.isfinite(y1)==True).nonzero() | |
| 4641 | y1=y1[vali] |
|
4628 | y1=y1[vali] | |
| 4642 | x = x[vali] |
|
4629 | x = x[vali] | |
| 4643 | f1 = interpolate.interp1d(x,y1,kind = 'cubic',bounds_error=False) |
|
4630 | f1 = interpolate.interp1d(x,y1,kind = 'cubic',bounds_error=False) | |
| 4644 | x1 = heiRang1 |
|
4631 | x1 = heiRang1 | |
| 4645 | y11 = f1(x1) |
|
4632 | y11 = f1(x1) | |
| 4646 | y2 = SNR[i,:] |
|
4633 | y2 = SNR[i,:] | |
| 4647 | x = heiRang*math.cos(phi[i]) |
|
4634 | x = heiRang*math.cos(phi[i]) | |
| 4648 | vali= (y2 != -1).nonzero() |
|
4635 | vali= (y2 != -1).nonzero() | |
| 4649 | y2 = y2[vali] |
|
4636 | y2 = y2[vali] | |
| 4650 | x = x[vali] |
|
4637 | x = x[vali] | |
| 4651 | f2 = interpolate.interp1d(x,y2,kind = 'cubic',bounds_error=False) |
|
4638 | f2 = interpolate.interp1d(x,y2,kind = 'cubic',bounds_error=False) | |
| 4652 | y21 = f2(x1) |
|
4639 | y21 = f2(x1) | |
| 4653 |
|
4640 | |||
| 4654 | velRadial1[i,:] = y11 |
|
4641 | velRadial1[i,:] = y11 | |
| 4655 | SNR1[i,:] = y21 |
|
4642 | SNR1[i,:] = y21 | |
| 4656 |
|
4643 | |||
| 4657 | return heiRang1, velRadial1, SNR1 |
|
4644 | return heiRang1, velRadial1, SNR1 | |
| 4658 |
|
4645 | |||
| 4659 | def run(self, dataOut, zenith, zenithCorrection,fileDrifts): |
|
4646 | def run(self, dataOut, zenith, zenithCorrection,fileDrifts): | |
| 4660 |
|
4647 | |||
| 4661 | dataOut.lat=-11.95 |
|
4648 | dataOut.lat=-11.95 | |
| 4662 | dataOut.lon=-76.87 |
|
4649 | dataOut.lon=-76.87 | |
| 4663 | heiRang = dataOut.heightList |
|
4650 | heiRang = dataOut.heightList | |
| 4664 | velRadial = dataOut.data_param[:,3,:] |
|
4651 | velRadial = dataOut.data_param[:,3,:] | |
| 4665 | velRadialm = dataOut.data_param[:,2:4,:]*-1 |
|
4652 | velRadialm = dataOut.data_param[:,2:4,:]*-1 | |
| 4666 | rbufc=dataOut.data_paramC[:,:,0] |
|
4653 | rbufc=dataOut.data_paramC[:,:,0] | |
| 4667 | ebufc=dataOut.data_paramC[:,:,1] |
|
4654 | ebufc=dataOut.data_paramC[:,:,1] | |
| 4668 | SNR = dataOut.data_snr |
|
4655 | SNR = dataOut.data_snr | |
| 4669 | velRerr = dataOut.data_error[:,4,:] |
|
4656 | velRerr = dataOut.data_error[:,4,:] | |
| 4670 | channels = dataOut.channelList |
|
4657 | channels = dataOut.channelList | |
| 4671 | nChan = len(channels) |
|
4658 | nChan = len(channels) | |
| 4672 | my_nbeams = nChan/2 |
|
4659 | my_nbeams = nChan/2 | |
| 4673 | if my_nbeams == 2: |
|
4660 | if my_nbeams == 2: | |
| 4674 | moments=numpy.vstack(([velRadialm[0,:]],[velRadialm[0,:]],[velRadialm[1,:]],[velRadialm[1,:]])) |
|
4661 | moments=numpy.vstack(([velRadialm[0,:]],[velRadialm[0,:]],[velRadialm[1,:]],[velRadialm[1,:]])) | |
| 4675 | else : |
|
4662 | else : | |
| 4676 | moments=numpy.vstack(([velRadialm[0,:]],[velRadialm[0,:]])) |
|
4663 | moments=numpy.vstack(([velRadialm[0,:]],[velRadialm[0,:]])) | |
| 4677 | dataOut.moments=moments |
|
4664 | dataOut.moments=moments | |
| 4678 | # Coherent |
|
4665 | # Coherent | |
| 4679 | smooth_wC = ebufc[0,:] |
|
4666 | smooth_wC = ebufc[0,:] | |
| 4680 | p_w0C = rbufc[0,:] |
|
4667 | p_w0C = rbufc[0,:] | |
| 4681 | p_w1C = rbufc[1,:] |
|
4668 | p_w1C = rbufc[1,:] | |
| 4682 | w_wC = rbufc[2,:]*-1 #*radial_sign(radial EQ 1) |
|
4669 | w_wC = rbufc[2,:]*-1 #*radial_sign(radial EQ 1) | |
| 4683 | t_wC = rbufc[3,:] |
|
4670 | t_wC = rbufc[3,:] | |
| 4684 | if my_nbeams == 1: |
|
4671 | if my_nbeams == 1: | |
| 4685 | w = velRadial[0,:] |
|
4672 | w = velRadial[0,:] | |
| 4686 | winds = velRadial.copy() |
|
4673 | winds = velRadial.copy() | |
| 4687 | w_err = velRerr[0,:] |
|
4674 | w_err = velRerr[0,:] | |
| 4688 | snr1 = 10*numpy.log10(SNR[0]) |
|
4675 | snr1 = 10*numpy.log10(SNR[0]) | |
| 4689 | if my_nbeams == 2: |
|
4676 | if my_nbeams == 2: | |
| 4690 | zenith = numpy.array(zenith) |
|
4677 | zenith = numpy.array(zenith) | |
| 4691 | zenith -= zenithCorrection |
|
4678 | zenith -= zenithCorrection | |
| 4692 | zenith *= numpy.pi/180 |
|
4679 | zenith *= numpy.pi/180 | |
| 4693 | if zenithCorrection != 0 : |
|
4680 | if zenithCorrection != 0 : | |
| 4694 | heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, numpy.abs(zenith), velRadial, SNR) |
|
4681 | heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, numpy.abs(zenith), velRadial, SNR) | |
| 4695 | else : |
|
4682 | else : | |
| 4696 | heiRang1 = heiRang |
|
4683 | heiRang1 = heiRang | |
| 4697 | velRadial1 = velRadial |
|
4684 | velRadial1 = velRadial | |
| 4698 | SNR1 = SNR |
|
4685 | SNR1 = SNR | |
| 4699 |
|
4686 | |||
| 4700 | alp = zenith[0] |
|
4687 | alp = zenith[0] | |
| 4701 | bet = zenith[1] |
|
4688 | bet = zenith[1] | |
| 4702 |
|
4689 | |||
| 4703 | w_w = velRadial1[0,:] |
|
4690 | w_w = velRadial1[0,:] | |
| 4704 | w_e = velRadial1[1,:] |
|
4691 | w_e = velRadial1[1,:] | |
| 4705 | w_w_err = velRerr[0,:] |
|
4692 | w_w_err = velRerr[0,:] | |
| 4706 | w_e_err = velRerr[1,:] |
|
4693 | w_e_err = velRerr[1,:] | |
| 4707 |
|
4694 | |||
| 4708 | val = (numpy.isfinite(w_w)==False).nonzero() |
|
4695 | val = (numpy.isfinite(w_w)==False).nonzero() | |
| 4709 | val = val[0] |
|
4696 | val = val[0] | |
| 4710 | bad = val |
|
4697 | bad = val | |
| 4711 | if len(bad) > 0 : |
|
4698 | if len(bad) > 0 : | |
| 4712 | w_w[bad] = w_wC[bad] |
|
4699 | w_w[bad] = w_wC[bad] | |
| 4713 | w_w_err[bad]= numpy.nan |
|
4700 | w_w_err[bad]= numpy.nan | |
| 4714 | smooth_eC=ebufc[4,:] |
|
4701 | smooth_eC=ebufc[4,:] | |
| 4715 | p_e0C = rbufc[4,:] |
|
4702 | p_e0C = rbufc[4,:] | |
| 4716 | p_e1C = rbufc[5,:] |
|
4703 | p_e1C = rbufc[5,:] | |
| 4717 | w_eC = rbufc[6,:]*-1 |
|
4704 | w_eC = rbufc[6,:]*-1 | |
| 4718 | t_eC = rbufc[7,:] |
|
4705 | t_eC = rbufc[7,:] | |
| 4719 | val = (numpy.isfinite(w_e)==False).nonzero() |
|
4706 | val = (numpy.isfinite(w_e)==False).nonzero() | |
| 4720 | val = val[0] |
|
4707 | val = val[0] | |
| 4721 | bad = val |
|
4708 | bad = val | |
| 4722 | if len(bad) > 0 : |
|
4709 | if len(bad) > 0 : | |
| 4723 | w_e[bad] = w_eC[bad] |
|
4710 | w_e[bad] = w_eC[bad] | |
| 4724 | w_e_err[bad]= numpy.nan |
|
4711 | w_e_err[bad]= numpy.nan | |
| 4725 |
|
4712 | |||
| 4726 | w = (w_w*numpy.sin(bet) - w_e*numpy.sin(alp))/(numpy.cos(alp)*numpy.sin(bet) - numpy.cos(bet)*numpy.sin(alp)) |
|
4713 | w = (w_w*numpy.sin(bet) - w_e*numpy.sin(alp))/(numpy.cos(alp)*numpy.sin(bet) - numpy.cos(bet)*numpy.sin(alp)) | |
| 4727 | u = (w_w*numpy.cos(bet) - w_e*numpy.cos(alp))/(numpy.sin(alp)*numpy.cos(bet) - numpy.sin(bet)*numpy.cos(alp)) |
|
4714 | u = (w_w*numpy.cos(bet) - w_e*numpy.cos(alp))/(numpy.sin(alp)*numpy.cos(bet) - numpy.sin(bet)*numpy.cos(alp)) | |
| 4728 |
|
4715 | |||
| 4729 | 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)) |
|
4716 | 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)) | |
| 4730 | 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)) |
|
4717 | 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)) | |
| 4731 |
|
4718 | |||
| 4732 | winds = numpy.vstack((w,u)) |
|
4719 | winds = numpy.vstack((w,u)) | |
| 4733 |
|
4720 | |||
| 4734 | dataOut.heightList = heiRang1 |
|
4721 | dataOut.heightList = heiRang1 | |
| 4735 | snr1 = 10*numpy.log10(SNR1[0]) |
|
4722 | snr1 = 10*numpy.log10(SNR1[0]) | |
| 4736 | dataOut.data_output = winds |
|
4723 | dataOut.data_output = winds | |
| 4737 | #snr1 = 10*numpy.log10(SNR1[0])# estaba comentado |
|
4724 | #snr1 = 10*numpy.log10(SNR1[0])# estaba comentado | |
| 4738 | dataOut.data_snr1 = numpy.reshape(snr1,(1,snr1.shape[0])) |
|
4725 | dataOut.data_snr1 = numpy.reshape(snr1,(1,snr1.shape[0])) | |
| 4739 | print("data_snr1",dataOut.data_snr1) |
|
4726 | #print("data_snr1",dataOut.data_snr1) | |
| 4740 | dataOut.utctimeInit = dataOut.utctime |
|
4727 | dataOut.utctimeInit = dataOut.utctime | |
| 4741 | dataOut.outputInterval = dataOut.timeInterval |
|
4728 | dataOut.outputInterval = dataOut.timeInterval | |
| 4742 |
|
4729 | |||
| 4743 | hei_aver0 = 218 |
|
4730 | hei_aver0 = 218 | |
| 4744 | jrange = 450 #900 para HA drifts |
|
4731 | jrange = 450 #900 para HA drifts | |
| 4745 | deltah = 15.0 #dataOut.spacing(0) 25 HAD |
|
4732 | deltah = 15.0 #dataOut.spacing(0) 25 HAD | |
| 4746 | h0 = 0.0 #dataOut.first_height(0) |
|
4733 | h0 = 0.0 #dataOut.first_height(0) | |
| 4747 | heights = dataOut.heightList |
|
4734 | heights = dataOut.heightList | |
| 4748 | nhei = len(heights) |
|
4735 | nhei = len(heights) | |
| 4749 |
|
4736 | |||
| 4750 | range1 = numpy.arange(nhei) * deltah + h0 |
|
4737 | range1 = numpy.arange(nhei) * deltah + h0 | |
| 4751 | jhei = (range1 >= hei_aver0).nonzero() |
|
4738 | jhei = (range1 >= hei_aver0).nonzero() | |
| 4752 | if len(jhei[0]) > 0 : |
|
4739 | if len(jhei[0]) > 0 : | |
| 4753 | h0_index = jhei[0][0] # Initial height for getting averages 218km |
|
4740 | h0_index = jhei[0][0] # Initial height for getting averages 218km | |
| 4754 |
|
4741 | |||
| 4755 | mynhei = 7 |
|
4742 | mynhei = 7 | |
| 4756 | nhei_avg = int(jrange/deltah) |
|
4743 | nhei_avg = int(jrange/deltah) | |
| 4757 | h_avgs = int(nhei_avg/mynhei) |
|
4744 | h_avgs = int(nhei_avg/mynhei) | |
| 4758 | nhei_avg = h_avgs*(mynhei-1)+mynhei |
|
4745 | nhei_avg = h_avgs*(mynhei-1)+mynhei | |
| 4759 |
|
4746 | |||
| 4760 | navgs = numpy.zeros(mynhei,dtype='float') |
|
4747 | navgs = numpy.zeros(mynhei,dtype='float') | |
| 4761 | delta_h = numpy.zeros(mynhei,dtype='float') |
|
4748 | delta_h = numpy.zeros(mynhei,dtype='float') | |
| 4762 | range_aver = numpy.zeros(mynhei,dtype='float') |
|
4749 | range_aver = numpy.zeros(mynhei,dtype='float') | |
| 4763 | for ih in range( mynhei-1 ): |
|
4750 | for ih in range( mynhei-1 ): | |
| 4764 | range_aver[ih] = numpy.sum(range1[h0_index+h_avgs*ih:h0_index+h_avgs*(ih+1)-0])/h_avgs |
|
4751 | range_aver[ih] = numpy.sum(range1[h0_index+h_avgs*ih:h0_index+h_avgs*(ih+1)-0])/h_avgs | |
| 4765 | navgs[ih] = h_avgs |
|
4752 | navgs[ih] = h_avgs | |
| 4766 | delta_h[ih] = deltah*h_avgs |
|
4753 | delta_h[ih] = deltah*h_avgs | |
| 4767 |
|
4754 | |||
| 4768 | range_aver[mynhei-1] = numpy.sum(range1[h0_index:h0_index+6*h_avgs-0])/(6*h_avgs) |
|
4755 | range_aver[mynhei-1] = numpy.sum(range1[h0_index:h0_index+6*h_avgs-0])/(6*h_avgs) | |
| 4769 | navgs[mynhei-1] = 6*h_avgs |
|
4756 | navgs[mynhei-1] = 6*h_avgs | |
| 4770 | delta_h[mynhei-1] = deltah*6*h_avgs |
|
4757 | delta_h[mynhei-1] = deltah*6*h_avgs | |
| 4771 |
|
4758 | |||
| 4772 | wA = w[h0_index:h0_index+nhei_avg-0] |
|
4759 | wA = w[h0_index:h0_index+nhei_avg-0] | |
| 4773 | wA_err = w_err[h0_index:h0_index+nhei_avg-0] |
|
4760 | wA_err = w_err[h0_index:h0_index+nhei_avg-0] | |
| 4774 | for i in range(5) : |
|
4761 | for i in range(5) : | |
| 4775 | vals = wA[i*h_avgs:(i+1)*h_avgs-0] |
|
4762 | vals = wA[i*h_avgs:(i+1)*h_avgs-0] | |
| 4776 | errs = wA_err[i*h_avgs:(i+1)*h_avgs-0] |
|
4763 | errs = wA_err[i*h_avgs:(i+1)*h_avgs-0] | |
| 4777 | avg = numpy.nansum(vals/errs**2.)/numpy.nansum(1./errs**2.) |
|
4764 | avg = numpy.nansum(vals/errs**2.)/numpy.nansum(1./errs**2.) | |
| 4778 | sigma = numpy.sqrt(1./numpy.nansum(1./errs**2.)) |
|
4765 | sigma = numpy.sqrt(1./numpy.nansum(1./errs**2.)) | |
| 4779 | wA[6*h_avgs+i] = avg |
|
4766 | wA[6*h_avgs+i] = avg | |
| 4780 | wA_err[6*h_avgs+i] = sigma |
|
4767 | wA_err[6*h_avgs+i] = sigma | |
| 4781 |
|
4768 | |||
| 4782 |
|
4769 | |||
| 4783 | vals = wA[0:6*h_avgs-0] |
|
4770 | vals = wA[0:6*h_avgs-0] | |
| 4784 | errs=wA_err[0:6*h_avgs-0] |
|
4771 | errs=wA_err[0:6*h_avgs-0] | |
| 4785 | avg = numpy.nansum(vals/errs**2.)/numpy.nansum(1./errs**2) |
|
4772 | avg = numpy.nansum(vals/errs**2.)/numpy.nansum(1./errs**2) | |
| 4786 | sigma = numpy.sqrt(1./numpy.nansum(1./errs**2.)) |
|
4773 | sigma = numpy.sqrt(1./numpy.nansum(1./errs**2.)) | |
| 4787 | wA[nhei_avg-1] = avg |
|
4774 | wA[nhei_avg-1] = avg | |
| 4788 | wA_err[nhei_avg-1] = sigma |
|
4775 | wA_err[nhei_avg-1] = sigma | |
| 4789 |
|
4776 | |||
| 4790 | wA = wA[6*h_avgs:nhei_avg-0] |
|
4777 | wA = wA[6*h_avgs:nhei_avg-0] | |
| 4791 | wA_err=wA_err[6*h_avgs:nhei_avg-0] |
|
4778 | wA_err=wA_err[6*h_avgs:nhei_avg-0] | |
| 4792 | if my_nbeams == 2 : |
|
4779 | if my_nbeams == 2 : | |
| 4793 | uA = u[h0_index:h0_index+nhei_avg] |
|
4780 | uA = u[h0_index:h0_index+nhei_avg] | |
| 4794 | uA_err=u_err[h0_index:h0_index+nhei_avg] |
|
4781 | uA_err=u_err[h0_index:h0_index+nhei_avg] | |
| 4795 |
|
4782 | |||
| 4796 | for i in range(5) : |
|
4783 | for i in range(5) : | |
| 4797 | vals = uA[i*h_avgs:(i+1)*h_avgs-0] |
|
4784 | vals = uA[i*h_avgs:(i+1)*h_avgs-0] | |
| 4798 | errs=uA_err[i*h_avgs:(i+1)*h_avgs-0] |
|
4785 | errs=uA_err[i*h_avgs:(i+1)*h_avgs-0] | |
| 4799 | avg = numpy.nansum(vals/errs**2.)/numpy.nansum(1./errs**2.) |
|
4786 | avg = numpy.nansum(vals/errs**2.)/numpy.nansum(1./errs**2.) | |
| 4800 | sigma = numpy.sqrt(1./numpy.nansum(1./errs**2.)) |
|
4787 | sigma = numpy.sqrt(1./numpy.nansum(1./errs**2.)) | |
| 4801 | uA[6*h_avgs+i] = avg |
|
4788 | uA[6*h_avgs+i] = avg | |
| 4802 | uA_err[6*h_avgs+i]=sigma |
|
4789 | uA_err[6*h_avgs+i]=sigma | |
| 4803 |
|
4790 | |||
| 4804 | vals = uA[0:6*h_avgs-0] |
|
4791 | vals = uA[0:6*h_avgs-0] | |
| 4805 | errs = uA_err[0:6*h_avgs-0] |
|
4792 | errs = uA_err[0:6*h_avgs-0] | |
| 4806 | avg = numpy.nansum(vals/errs**2.)/numpy.nansum(1./errs**2.) |
|
4793 | avg = numpy.nansum(vals/errs**2.)/numpy.nansum(1./errs**2.) | |
| 4807 | sigma = numpy.sqrt(1./numpy.nansum(1./errs**2.)) |
|
4794 | sigma = numpy.sqrt(1./numpy.nansum(1./errs**2.)) | |
| 4808 | uA[nhei_avg-1] = avg |
|
4795 | uA[nhei_avg-1] = avg | |
| 4809 | uA_err[nhei_avg-1] = sigma |
|
4796 | uA_err[nhei_avg-1] = sigma | |
| 4810 | uA = uA[6*h_avgs:nhei_avg-0] |
|
4797 | uA = uA[6*h_avgs:nhei_avg-0] | |
| 4811 | uA_err = uA_err[6*h_avgs:nhei_avg-0] |
|
4798 | uA_err = uA_err[6*h_avgs:nhei_avg-0] | |
| 4812 | dataOut.drifts_avg = numpy.vstack((wA,uA)) |
|
4799 | dataOut.drifts_avg = numpy.vstack((wA,uA)) | |
| 4813 | if my_nbeams == 1: dataOut.drifts_avg = wA |
|
4800 | if my_nbeams == 1: dataOut.drifts_avg = wA | |
| 4814 | tini=time.localtime(dataOut.utctime) |
|
4801 | tini=time.localtime(dataOut.utctime) | |
| 4815 | datefile= str(tini[0]).zfill(4)+str(tini[1]).zfill(2)+str(tini[2]).zfill(2) |
|
4802 | datefile= str(tini[0]).zfill(4)+str(tini[1]).zfill(2)+str(tini[2]).zfill(2) | |
| 4816 | nfile = fileDrifts+'/jro'+datefile+'drifts.txt' |
|
4803 | nfile = fileDrifts+'/jro'+datefile+'drifts.txt' | |
| 4817 | f1 = open(nfile,'a') |
|
4804 | f1 = open(nfile,'a') | |
| 4818 | datedriftavg=str(tini[0])+' '+str(tini[1])+' '+str(tini[2])+' '+str(tini[3])+' '+str(tini[4]) |
|
4805 | datedriftavg=str(tini[0])+' '+str(tini[1])+' '+str(tini[2])+' '+str(tini[3])+' '+str(tini[4]) | |
| 4819 | driftavgstr=str(dataOut.drifts_avg) |
|
4806 | driftavgstr=str(dataOut.drifts_avg) | |
| 4820 | numpy.savetxt(f1,numpy.column_stack([tini[0],tini[1],tini[2],tini[3],tini[4]]),fmt='%4i') |
|
4807 | numpy.savetxt(f1,numpy.column_stack([tini[0],tini[1],tini[2],tini[3],tini[4]]),fmt='%4i') | |
| 4821 | numpy.savetxt(f1,dataOut.drifts_avg,fmt='%10.2f') |
|
4808 | numpy.savetxt(f1,dataOut.drifts_avg,fmt='%10.2f') | |
| 4822 | f1.close() |
|
4809 | f1.close() | |
| 4823 |
|
4810 | |||
| 4824 | return dataOut |
|
4811 | return dataOut | |
| 4825 |
|
4812 | |||
| 4826 | #--------------- Non Specular Meteor ---------------- |
|
4813 | #--------------- Non Specular Meteor ---------------- | |
| 4827 |
|
4814 | |||
| 4828 | class NonSpecularMeteorDetection(Operation): |
|
4815 | class NonSpecularMeteorDetection(Operation): | |
| 4829 |
|
4816 | |||
| 4830 | def run(self, dataOut, mode, SNRthresh=8, phaseDerThresh=0.5, cohThresh=0.8, allData = False): |
|
4817 | def run(self, dataOut, mode, SNRthresh=8, phaseDerThresh=0.5, cohThresh=0.8, allData = False): | |
| 4831 | data_acf = dataOut.data_pre[0] |
|
4818 | data_acf = dataOut.data_pre[0] | |
| 4832 | data_ccf = dataOut.data_pre[1] |
|
4819 | data_ccf = dataOut.data_pre[1] | |
| 4833 | pairsList = dataOut.groupList[1] |
|
4820 | pairsList = dataOut.groupList[1] | |
| 4834 |
|
4821 | |||
| 4835 | lamb = dataOut.C/dataOut.frequency |
|
4822 | lamb = dataOut.C/dataOut.frequency | |
| 4836 | tSamp = dataOut.ippSeconds*dataOut.nCohInt |
|
4823 | tSamp = dataOut.ippSeconds*dataOut.nCohInt | |
| 4837 | paramInterval = dataOut.paramInterval |
|
4824 | paramInterval = dataOut.paramInterval | |
| 4838 |
|
4825 | |||
| 4839 | nChannels = data_acf.shape[0] |
|
4826 | nChannels = data_acf.shape[0] | |
| 4840 | nLags = data_acf.shape[1] |
|
4827 | nLags = data_acf.shape[1] | |
| 4841 | nProfiles = data_acf.shape[2] |
|
4828 | nProfiles = data_acf.shape[2] | |
| 4842 | nHeights = dataOut.nHeights |
|
4829 | nHeights = dataOut.nHeights | |
| 4843 | nCohInt = dataOut.nCohInt |
|
4830 | nCohInt = dataOut.nCohInt | |
| 4844 | sec = numpy.round(nProfiles/dataOut.paramInterval) |
|
4831 | sec = numpy.round(nProfiles/dataOut.paramInterval) | |
| 4845 | heightList = dataOut.heightList |
|
4832 | heightList = dataOut.heightList | |
| 4846 | ippSeconds = dataOut.ippSeconds*dataOut.nCohInt*dataOut.nAvg |
|
4833 | ippSeconds = dataOut.ippSeconds*dataOut.nCohInt*dataOut.nAvg | |
| 4847 | utctime = dataOut.utctime |
|
4834 | utctime = dataOut.utctime | |
| 4848 |
|
4835 | |||
| 4849 | dataOut.abscissaList = numpy.arange(0,paramInterval+ippSeconds,ippSeconds) |
|
4836 | dataOut.abscissaList = numpy.arange(0,paramInterval+ippSeconds,ippSeconds) | |
| 4850 |
|
4837 | |||
| 4851 | #------------------------ SNR -------------------------------------- |
|
4838 | #------------------------ SNR -------------------------------------- | |
| 4852 | power = data_acf[:,0,:,:].real |
|
4839 | power = data_acf[:,0,:,:].real | |
| 4853 | noise = numpy.zeros(nChannels) |
|
4840 | noise = numpy.zeros(nChannels) | |
| 4854 | SNR = numpy.zeros(power.shape) |
|
4841 | SNR = numpy.zeros(power.shape) | |
| 4855 | for i in range(nChannels): |
|
4842 | for i in range(nChannels): | |
| 4856 | noise[i] = hildebrand_sekhon(power[i,:], nCohInt) |
|
4843 | noise[i] = hildebrand_sekhon(power[i,:], nCohInt) | |
| 4857 | SNR[i] = (power[i]-noise[i])/noise[i] |
|
4844 | SNR[i] = (power[i]-noise[i])/noise[i] | |
| 4858 | SNRm = numpy.nanmean(SNR, axis = 0) |
|
4845 | SNRm = numpy.nanmean(SNR, axis = 0) | |
| 4859 | SNRdB = 10*numpy.log10(SNR) |
|
4846 | SNRdB = 10*numpy.log10(SNR) | |
| 4860 |
|
4847 | |||
| 4861 | if mode == 'SA': |
|
4848 | if mode == 'SA': | |
| 4862 | dataOut.groupList = dataOut.groupList[1] |
|
4849 | dataOut.groupList = dataOut.groupList[1] | |
| 4863 | nPairs = data_ccf.shape[0] |
|
4850 | nPairs = data_ccf.shape[0] | |
| 4864 | #---------------------- Coherence and Phase -------------------------- |
|
4851 | #---------------------- Coherence and Phase -------------------------- | |
| 4865 | phase = numpy.zeros(data_ccf[:,0,:,:].shape) |
|
4852 | phase = numpy.zeros(data_ccf[:,0,:,:].shape) | |
| 4866 | coh1 = numpy.zeros(data_ccf[:,0,:,:].shape) |
|
4853 | coh1 = numpy.zeros(data_ccf[:,0,:,:].shape) | |
| 4867 |
|
4854 | |||
| 4868 | for p in range(nPairs): |
|
4855 | for p in range(nPairs): | |
| 4869 | ch0 = pairsList[p][0] |
|
4856 | ch0 = pairsList[p][0] | |
| 4870 | ch1 = pairsList[p][1] |
|
4857 | ch1 = pairsList[p][1] | |
| 4871 | ccf = data_ccf[p,0,:,:]/numpy.sqrt(data_acf[ch0,0,:,:]*data_acf[ch1,0,:,:]) |
|
4858 | ccf = data_ccf[p,0,:,:]/numpy.sqrt(data_acf[ch0,0,:,:]*data_acf[ch1,0,:,:]) | |
| 4872 | phase[p,:,:] = ndimage.median_filter(numpy.angle(ccf), size = (5,1)) #median filter |
|
4859 | phase[p,:,:] = ndimage.median_filter(numpy.angle(ccf), size = (5,1)) #median filter | |
| 4873 | coh1[p,:,:] = ndimage.median_filter(numpy.abs(ccf), 5) #median filter |
|
4860 | coh1[p,:,:] = ndimage.median_filter(numpy.abs(ccf), 5) #median filter | |
| 4874 | coh = numpy.nanmax(coh1, axis = 0) |
|
4861 | coh = numpy.nanmax(coh1, axis = 0) | |
| 4875 | #---------------------- Radial Velocity ---------------------------- |
|
4862 | #---------------------- Radial Velocity ---------------------------- | |
| 4876 | phaseAux = numpy.mean(numpy.angle(data_acf[:,1,:,:]), axis = 0) |
|
4863 | phaseAux = numpy.mean(numpy.angle(data_acf[:,1,:,:]), axis = 0) | |
| 4877 | velRad = phaseAux*lamb/(4*numpy.pi*tSamp) |
|
4864 | velRad = phaseAux*lamb/(4*numpy.pi*tSamp) | |
| 4878 |
|
4865 | |||
| 4879 | if allData: |
|
4866 | if allData: | |
| 4880 | boolMetFin = ~numpy.isnan(SNRm) |
|
4867 | boolMetFin = ~numpy.isnan(SNRm) | |
| 4881 | else: |
|
4868 | else: | |
| 4882 | #------------------------ Meteor mask --------------------------------- |
|
4869 | #------------------------ Meteor mask --------------------------------- | |
| 4883 |
|
4870 | |||
| 4884 | #Coherence mask |
|
4871 | #Coherence mask | |
| 4885 | boolMet1 = coh > 0.75 |
|
4872 | boolMet1 = coh > 0.75 | |
| 4886 | struc = numpy.ones((30,1)) |
|
4873 | struc = numpy.ones((30,1)) | |
| 4887 | boolMet1 = ndimage.morphology.binary_dilation(boolMet1, structure=struc) |
|
4874 | boolMet1 = ndimage.morphology.binary_dilation(boolMet1, structure=struc) | |
| 4888 |
|
4875 | |||
| 4889 | #Derivative mask |
|
4876 | #Derivative mask | |
| 4890 | derPhase = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0) |
|
4877 | derPhase = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0) | |
| 4891 | boolMet2 = derPhase < 0.2 |
|
4878 | boolMet2 = derPhase < 0.2 | |
| 4892 | boolMet2 = ndimage.median_filter(boolMet2,size=5) |
|
4879 | boolMet2 = ndimage.median_filter(boolMet2,size=5) | |
| 4893 | boolMet2 = numpy.vstack((boolMet2,numpy.full((1,nHeights), True, dtype=bool))) |
|
4880 | boolMet2 = numpy.vstack((boolMet2,numpy.full((1,nHeights), True, dtype=bool))) | |
| 4894 | boolMetFin = boolMet1&boolMet2 |
|
4881 | boolMetFin = boolMet1&boolMet2 | |
| 4895 | #Creating data_param |
|
4882 | #Creating data_param | |
| 4896 | coordMet = numpy.where(boolMetFin) |
|
4883 | coordMet = numpy.where(boolMetFin) | |
| 4897 |
|
4884 | |||
| 4898 | tmet = coordMet[0] |
|
4885 | tmet = coordMet[0] | |
| 4899 | hmet = coordMet[1] |
|
4886 | hmet = coordMet[1] | |
| 4900 |
|
4887 | |||
| 4901 | data_param = numpy.zeros((tmet.size, 6 + nPairs)) |
|
4888 | data_param = numpy.zeros((tmet.size, 6 + nPairs)) | |
| 4902 | data_param[:,0] = utctime |
|
4889 | data_param[:,0] = utctime | |
| 4903 | data_param[:,1] = tmet |
|
4890 | data_param[:,1] = tmet | |
| 4904 | data_param[:,2] = hmet |
|
4891 | data_param[:,2] = hmet | |
| 4905 | data_param[:,3] = SNRm[tmet,hmet] |
|
4892 | data_param[:,3] = SNRm[tmet,hmet] | |
| 4906 | data_param[:,4] = velRad[tmet,hmet] |
|
4893 | data_param[:,4] = velRad[tmet,hmet] | |
| 4907 | data_param[:,5] = coh[tmet,hmet] |
|
4894 | data_param[:,5] = coh[tmet,hmet] | |
| 4908 | data_param[:,6:] = phase[:,tmet,hmet].T |
|
4895 | data_param[:,6:] = phase[:,tmet,hmet].T | |
| 4909 |
|
4896 | |||
| 4910 | elif mode == 'DBS': |
|
4897 | elif mode == 'DBS': | |
| 4911 | dataOut.groupList = numpy.arange(nChannels) |
|
4898 | dataOut.groupList = numpy.arange(nChannels) | |
| 4912 |
|
4899 | |||
| 4913 | #Radial Velocities |
|
4900 | #Radial Velocities | |
| 4914 | phase = numpy.angle(data_acf[:,1,:,:]) |
|
4901 | phase = numpy.angle(data_acf[:,1,:,:]) | |
| 4915 | velRad = phase*lamb/(4*numpy.pi*tSamp) |
|
4902 | velRad = phase*lamb/(4*numpy.pi*tSamp) | |
| 4916 |
|
4903 | |||
| 4917 | #Spectral width |
|
4904 | #Spectral width | |
| 4918 | acf1 = data_acf[:,1,:,:] |
|
4905 | acf1 = data_acf[:,1,:,:] | |
| 4919 | acf2 = data_acf[:,2,:,:] |
|
4906 | acf2 = data_acf[:,2,:,:] | |
| 4920 |
|
4907 | |||
| 4921 | spcWidth = (lamb/(2*numpy.sqrt(6)*numpy.pi*tSamp))*numpy.sqrt(numpy.log(acf1/acf2)) |
|
4908 | spcWidth = (lamb/(2*numpy.sqrt(6)*numpy.pi*tSamp))*numpy.sqrt(numpy.log(acf1/acf2)) | |
| 4922 | if allData: |
|
4909 | if allData: | |
| 4923 | boolMetFin = ~numpy.isnan(SNRdB) |
|
4910 | boolMetFin = ~numpy.isnan(SNRdB) | |
| 4924 | else: |
|
4911 | else: | |
| 4925 | #SNR |
|
4912 | #SNR | |
| 4926 | boolMet1 = (SNRdB>SNRthresh) #SNR mask |
|
4913 | boolMet1 = (SNRdB>SNRthresh) #SNR mask | |
| 4927 | boolMet1 = ndimage.median_filter(boolMet1, size=(1,5,5)) |
|
4914 | boolMet1 = ndimage.median_filter(boolMet1, size=(1,5,5)) | |
| 4928 |
|
4915 | |||
| 4929 | #Radial velocity |
|
4916 | #Radial velocity | |
| 4930 | boolMet2 = numpy.abs(velRad) < 20 |
|
4917 | boolMet2 = numpy.abs(velRad) < 20 | |
| 4931 | boolMet2 = ndimage.median_filter(boolMet2, (1,5,5)) |
|
4918 | boolMet2 = ndimage.median_filter(boolMet2, (1,5,5)) | |
| 4932 |
|
4919 | |||
| 4933 | #Spectral Width |
|
4920 | #Spectral Width | |
| 4934 | boolMet3 = spcWidth < 30 |
|
4921 | boolMet3 = spcWidth < 30 | |
| 4935 | boolMet3 = ndimage.median_filter(boolMet3, (1,5,5)) |
|
4922 | boolMet3 = ndimage.median_filter(boolMet3, (1,5,5)) | |
| 4936 | boolMetFin = boolMet1&boolMet2&boolMet3 |
|
4923 | boolMetFin = boolMet1&boolMet2&boolMet3 | |
| 4937 |
|
4924 | |||
| 4938 | #Creating data_param |
|
4925 | #Creating data_param | |
| 4939 | coordMet = numpy.where(boolMetFin) |
|
4926 | coordMet = numpy.where(boolMetFin) | |
| 4940 |
|
4927 | |||
| 4941 | cmet = coordMet[0] |
|
4928 | cmet = coordMet[0] | |
| 4942 | tmet = coordMet[1] |
|
4929 | tmet = coordMet[1] | |
| 4943 | hmet = coordMet[2] |
|
4930 | hmet = coordMet[2] | |
| 4944 |
|
4931 | |||
| 4945 | data_param = numpy.zeros((tmet.size, 7)) |
|
4932 | data_param = numpy.zeros((tmet.size, 7)) | |
| 4946 | data_param[:,0] = utctime |
|
4933 | data_param[:,0] = utctime | |
| 4947 | data_param[:,1] = cmet |
|
4934 | data_param[:,1] = cmet | |
| 4948 | data_param[:,2] = tmet |
|
4935 | data_param[:,2] = tmet | |
| 4949 | data_param[:,3] = hmet |
|
4936 | data_param[:,3] = hmet | |
| 4950 | data_param[:,4] = SNR[cmet,tmet,hmet].T |
|
4937 | data_param[:,4] = SNR[cmet,tmet,hmet].T | |
| 4951 | data_param[:,5] = velRad[cmet,tmet,hmet].T |
|
4938 | data_param[:,5] = velRad[cmet,tmet,hmet].T | |
| 4952 | data_param[:,6] = spcWidth[cmet,tmet,hmet].T |
|
4939 | data_param[:,6] = spcWidth[cmet,tmet,hmet].T | |
| 4953 |
|
4940 | |||
| 4954 | if len(data_param) == 0: |
|
4941 | if len(data_param) == 0: | |
| 4955 | dataOut.flagNoData = True |
|
4942 | dataOut.flagNoData = True | |
| 4956 | else: |
|
4943 | else: | |
| 4957 | dataOut.data_param = data_param |
|
4944 | dataOut.data_param = data_param | |
| 4958 |
|
4945 | |||
| 4959 | def __erase_small(self, binArray, threshX, threshY): |
|
4946 | def __erase_small(self, binArray, threshX, threshY): | |
| 4960 | labarray, numfeat = ndimage.measurements.label(binArray) |
|
4947 | labarray, numfeat = ndimage.measurements.label(binArray) | |
| 4961 | binArray1 = numpy.copy(binArray) |
|
4948 | binArray1 = numpy.copy(binArray) | |
| 4962 |
|
4949 | |||
| 4963 | for i in range(1,numfeat + 1): |
|
4950 | for i in range(1,numfeat + 1): | |
| 4964 | auxBin = (labarray==i) |
|
4951 | auxBin = (labarray==i) | |
| 4965 | auxSize = auxBin.sum() |
|
4952 | auxSize = auxBin.sum() | |
| 4966 |
|
4953 | |||
| 4967 | x,y = numpy.where(auxBin) |
|
4954 | x,y = numpy.where(auxBin) | |
| 4968 | widthX = x.max() - x.min() |
|
4955 | widthX = x.max() - x.min() | |
| 4969 | widthY = y.max() - y.min() |
|
4956 | widthY = y.max() - y.min() | |
| 4970 |
|
4957 | |||
| 4971 | #width X: 3 seg -> 12.5*3 |
|
4958 | #width X: 3 seg -> 12.5*3 | |
| 4972 | #width Y: |
|
4959 | #width Y: | |
| 4973 |
|
4960 | |||
| 4974 | if (auxSize < 50) or (widthX < threshX) or (widthY < threshY): |
|
4961 | if (auxSize < 50) or (widthX < threshX) or (widthY < threshY): | |
| 4975 | binArray1[auxBin] = False |
|
4962 | binArray1[auxBin] = False | |
| 4976 |
|
4963 | |||
| 4977 | return binArray1 |
|
4964 | return binArray1 | |
| 4978 |
|
4965 | |||
| 4979 | #--------------- Specular Meteor ---------------- |
|
4966 | #--------------- Specular Meteor ---------------- | |
| 4980 |
|
4967 | |||
| 4981 | class SMDetection(Operation): |
|
4968 | class SMDetection(Operation): | |
| 4982 | ''' |
|
4969 | ''' | |
| 4983 | Function DetectMeteors() |
|
4970 | Function DetectMeteors() | |
| 4984 | Project developed with paper: |
|
4971 | Project developed with paper: | |
| 4985 | HOLDSWORTH ET AL. 2004 |
|
4972 | HOLDSWORTH ET AL. 2004 | |
| 4986 |
|
4973 | |||
| 4987 | Input: |
|
4974 | Input: | |
| 4988 | self.dataOut.data_pre |
|
4975 | self.dataOut.data_pre | |
| 4989 |
|
4976 | |||
| 4990 | centerReceiverIndex: From the channels, which is the center receiver |
|
4977 | centerReceiverIndex: From the channels, which is the center receiver | |
| 4991 |
|
4978 | |||
| 4992 | hei_ref: Height reference for the Beacon signal extraction |
|
4979 | hei_ref: Height reference for the Beacon signal extraction | |
| 4993 | tauindex: |
|
4980 | tauindex: | |
| 4994 | predefinedPhaseShifts: Predefined phase offset for the voltge signals |
|
4981 | predefinedPhaseShifts: Predefined phase offset for the voltge signals | |
| 4995 |
|
4982 | |||
| 4996 | cohDetection: Whether to user Coherent detection or not |
|
4983 | cohDetection: Whether to user Coherent detection or not | |
| 4997 | cohDet_timeStep: Coherent Detection calculation time step |
|
4984 | cohDet_timeStep: Coherent Detection calculation time step | |
| 4998 | cohDet_thresh: Coherent Detection phase threshold to correct phases |
|
4985 | cohDet_thresh: Coherent Detection phase threshold to correct phases | |
| 4999 |
|
4986 | |||
| 5000 | noise_timeStep: Noise calculation time step |
|
4987 | noise_timeStep: Noise calculation time step | |
| 5001 | noise_multiple: Noise multiple to define signal threshold |
|
4988 | noise_multiple: Noise multiple to define signal threshold | |
| 5002 |
|
4989 | |||
| 5003 | multDet_timeLimit: Multiple Detection Removal time limit in seconds |
|
4990 | multDet_timeLimit: Multiple Detection Removal time limit in seconds | |
| 5004 | multDet_rangeLimit: Multiple Detection Removal range limit in km |
|
4991 | multDet_rangeLimit: Multiple Detection Removal range limit in km | |
| 5005 |
|
4992 | |||
| 5006 | phaseThresh: Maximum phase difference between receiver to be consider a meteor |
|
4993 | phaseThresh: Maximum phase difference between receiver to be consider a meteor | |
| 5007 | SNRThresh: Minimum SNR threshold of the meteor signal to be consider a meteor |
|
4994 | SNRThresh: Minimum SNR threshold of the meteor signal to be consider a meteor | |
| 5008 |
|
4995 | |||
| 5009 | hmin: Minimum Height of the meteor to use it in the further wind estimations |
|
4996 | hmin: Minimum Height of the meteor to use it in the further wind estimations | |
| 5010 | hmax: Maximum Height of the meteor to use it in the further wind estimations |
|
4997 | hmax: Maximum Height of the meteor to use it in the further wind estimations | |
| 5011 | azimuth: Azimuth angle correction |
|
4998 | azimuth: Azimuth angle correction | |
| 5012 |
|
4999 | |||
| 5013 | Affected: |
|
5000 | Affected: | |
| 5014 | self.dataOut.data_param |
|
5001 | self.dataOut.data_param | |
| 5015 |
|
5002 | |||
| 5016 | Rejection Criteria (Errors): |
|
5003 | Rejection Criteria (Errors): | |
| 5017 | 0: No error; analysis OK |
|
5004 | 0: No error; analysis OK | |
| 5018 | 1: SNR < SNR threshold |
|
5005 | 1: SNR < SNR threshold | |
| 5019 | 2: angle of arrival (AOA) ambiguously determined |
|
5006 | 2: angle of arrival (AOA) ambiguously determined | |
| 5020 | 3: AOA estimate not feasible |
|
5007 | 3: AOA estimate not feasible | |
| 5021 | 4: Large difference in AOAs obtained from different antenna baselines |
|
5008 | 4: Large difference in AOAs obtained from different antenna baselines | |
| 5022 | 5: echo at start or end of time series |
|
5009 | 5: echo at start or end of time series | |
| 5023 | 6: echo less than 5 examples long; too short for analysis |
|
5010 | 6: echo less than 5 examples long; too short for analysis | |
| 5024 | 7: echo rise exceeds 0.3s |
|
5011 | 7: echo rise exceeds 0.3s | |
| 5025 | 8: echo decay time less than twice rise time |
|
5012 | 8: echo decay time less than twice rise time | |
| 5026 | 9: large power level before echo |
|
5013 | 9: large power level before echo | |
| 5027 | 10: large power level after echo |
|
5014 | 10: large power level after echo | |
| 5028 | 11: poor fit to amplitude for estimation of decay time |
|
5015 | 11: poor fit to amplitude for estimation of decay time | |
| 5029 | 12: poor fit to CCF phase variation for estimation of radial drift velocity |
|
5016 | 12: poor fit to CCF phase variation for estimation of radial drift velocity | |
| 5030 | 13: height unresolvable echo: not valid height within 70 to 110 km |
|
5017 | 13: height unresolvable echo: not valid height within 70 to 110 km | |
| 5031 | 14: height ambiguous echo: more then one possible height within 70 to 110 km |
|
5018 | 14: height ambiguous echo: more then one possible height within 70 to 110 km | |
| 5032 | 15: radial drift velocity or projected horizontal velocity exceeds 200 m/s |
|
5019 | 15: radial drift velocity or projected horizontal velocity exceeds 200 m/s | |
| 5033 | 16: oscilatory echo, indicating event most likely not an underdense echo |
|
5020 | 16: oscilatory echo, indicating event most likely not an underdense echo | |
| 5034 |
|
5021 | |||
| 5035 | 17: phase difference in meteor Reestimation |
|
5022 | 17: phase difference in meteor Reestimation | |
| 5036 |
|
5023 | |||
| 5037 | Data Storage: |
|
5024 | Data Storage: | |
| 5038 | Meteors for Wind Estimation (8): |
|
5025 | Meteors for Wind Estimation (8): | |
| 5039 | Utc Time | Range Height |
|
5026 | Utc Time | Range Height | |
| 5040 | Azimuth Zenith errorCosDir |
|
5027 | Azimuth Zenith errorCosDir | |
| 5041 | VelRad errorVelRad |
|
5028 | VelRad errorVelRad | |
| 5042 | Phase0 Phase1 Phase2 Phase3 |
|
5029 | Phase0 Phase1 Phase2 Phase3 | |
| 5043 | TypeError |
|
5030 | TypeError | |
| 5044 |
|
5031 | |||
| 5045 | ''' |
|
5032 | ''' | |
| 5046 |
|
5033 | |||
| 5047 | def run(self, dataOut, hei_ref = None, tauindex = 0, |
|
5034 | def run(self, dataOut, hei_ref = None, tauindex = 0, | |
| 5048 | phaseOffsets = None, |
|
5035 | phaseOffsets = None, | |
| 5049 | cohDetection = False, cohDet_timeStep = 1, cohDet_thresh = 25, |
|
5036 | cohDetection = False, cohDet_timeStep = 1, cohDet_thresh = 25, | |
| 5050 | noise_timeStep = 4, noise_multiple = 4, |
|
5037 | noise_timeStep = 4, noise_multiple = 4, | |
| 5051 | multDet_timeLimit = 1, multDet_rangeLimit = 3, |
|
5038 | multDet_timeLimit = 1, multDet_rangeLimit = 3, | |
| 5052 | phaseThresh = 20, SNRThresh = 5, |
|
5039 | phaseThresh = 20, SNRThresh = 5, | |
| 5053 | hmin = 50, hmax=150, azimuth = 0, |
|
5040 | hmin = 50, hmax=150, azimuth = 0, | |
| 5054 | channelPositions = None) : |
|
5041 | channelPositions = None) : | |
| 5055 |
|
5042 | |||
| 5056 |
|
5043 | |||
| 5057 | #Getting Pairslist |
|
5044 | #Getting Pairslist | |
| 5058 | if channelPositions is None: |
|
5045 | if channelPositions is None: | |
| 5059 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella |
|
5046 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella | |
| 5060 | meteorOps = SMOperations() |
|
5047 | meteorOps = SMOperations() | |
| 5061 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) |
|
5048 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) | |
| 5062 | heiRang = dataOut.heightList |
|
5049 | heiRang = dataOut.heightList | |
| 5063 | #Get Beacon signal - No Beacon signal anymore |
|
5050 | #Get Beacon signal - No Beacon signal anymore | |
| 5064 | #****************REMOVING HARDWARE PHASE DIFFERENCES*************** |
|
5051 | #****************REMOVING HARDWARE PHASE DIFFERENCES*************** | |
| 5065 | # see if the user put in pre defined phase shifts |
|
5052 | # see if the user put in pre defined phase shifts | |
| 5066 | voltsPShift = dataOut.data_pre.copy() |
|
5053 | voltsPShift = dataOut.data_pre.copy() | |
| 5067 |
|
5054 | |||
| 5068 | #******************END OF REMOVING HARDWARE PHASE DIFFERENCES********* |
|
5055 | #******************END OF REMOVING HARDWARE PHASE DIFFERENCES********* | |
| 5069 |
|
5056 | |||
| 5070 | #Remove DC |
|
5057 | #Remove DC | |
| 5071 | voltsDC = numpy.mean(voltsPShift,1) |
|
5058 | voltsDC = numpy.mean(voltsPShift,1) | |
| 5072 | voltsDC = numpy.mean(voltsDC,1) |
|
5059 | voltsDC = numpy.mean(voltsDC,1) | |
| 5073 | for i in range(voltsDC.shape[0]): |
|
5060 | for i in range(voltsDC.shape[0]): | |
| 5074 | voltsPShift[i] = voltsPShift[i] - voltsDC[i] |
|
5061 | voltsPShift[i] = voltsPShift[i] - voltsDC[i] | |
| 5075 |
|
5062 | |||
| 5076 | #Don't considerate last heights, theyre used to calculate Hardware Phase Shift |
|
5063 | #Don't considerate last heights, theyre used to calculate Hardware Phase Shift | |
| 5077 |
|
5064 | |||
| 5078 | #************ FIND POWER OF DATA W/COH OR NON COH DETECTION (3.4) ********** |
|
5065 | #************ FIND POWER OF DATA W/COH OR NON COH DETECTION (3.4) ********** | |
| 5079 | #Coherent Detection |
|
5066 | #Coherent Detection | |
| 5080 | if cohDetection: |
|
5067 | if cohDetection: | |
| 5081 | #use coherent detection to get the net power |
|
5068 | #use coherent detection to get the net power | |
| 5082 | cohDet_thresh = cohDet_thresh*numpy.pi/180 |
|
5069 | cohDet_thresh = cohDet_thresh*numpy.pi/180 | |
| 5083 | voltsPShift = self.__coherentDetection(voltsPShift, cohDet_timeStep, dataOut.timeInterval, pairslist0, cohDet_thresh) |
|
5070 | voltsPShift = self.__coherentDetection(voltsPShift, cohDet_timeStep, dataOut.timeInterval, pairslist0, cohDet_thresh) | |
| 5084 |
|
5071 | |||
| 5085 | #Non-coherent detection! |
|
5072 | #Non-coherent detection! | |
| 5086 | powerNet = numpy.nansum(numpy.abs(voltsPShift[:,:,:])**2,0) |
|
5073 | powerNet = numpy.nansum(numpy.abs(voltsPShift[:,:,:])**2,0) | |
| 5087 | #********** END OF COH/NON-COH POWER CALCULATION********************** |
|
5074 | #********** END OF COH/NON-COH POWER CALCULATION********************** | |
| 5088 |
|
5075 | |||
| 5089 | #********** FIND THE NOISE LEVEL AND POSSIBLE METEORS **************** |
|
5076 | #********** FIND THE NOISE LEVEL AND POSSIBLE METEORS **************** | |
| 5090 | #Get noise |
|
5077 | #Get noise | |
| 5091 | noise, noise1 = self.__getNoise(powerNet, noise_timeStep, dataOut.timeInterval) |
|
5078 | noise, noise1 = self.__getNoise(powerNet, noise_timeStep, dataOut.timeInterval) | |
| 5092 | #Get signal threshold |
|
5079 | #Get signal threshold | |
| 5093 | signalThresh = noise_multiple*noise |
|
5080 | signalThresh = noise_multiple*noise | |
| 5094 | #Meteor echoes detection |
|
5081 | #Meteor echoes detection | |
| 5095 | listMeteors = self.__findMeteors(powerNet, signalThresh) |
|
5082 | listMeteors = self.__findMeteors(powerNet, signalThresh) | |
| 5096 | #******* END OF NOISE LEVEL AND POSSIBLE METEORS CACULATION ********** |
|
5083 | #******* END OF NOISE LEVEL AND POSSIBLE METEORS CACULATION ********** | |
| 5097 |
|
5084 | |||
| 5098 | #************** REMOVE MULTIPLE DETECTIONS (3.5) *************************** |
|
5085 | #************** REMOVE MULTIPLE DETECTIONS (3.5) *************************** | |
| 5099 | #Parameters |
|
5086 | #Parameters | |
| 5100 | heiRange = dataOut.heightList |
|
5087 | heiRange = dataOut.heightList | |
| 5101 | rangeInterval = heiRange[1] - heiRange[0] |
|
5088 | rangeInterval = heiRange[1] - heiRange[0] | |
| 5102 | rangeLimit = multDet_rangeLimit/rangeInterval |
|
5089 | rangeLimit = multDet_rangeLimit/rangeInterval | |
| 5103 | timeLimit = multDet_timeLimit/dataOut.timeInterval |
|
5090 | timeLimit = multDet_timeLimit/dataOut.timeInterval | |
| 5104 | #Multiple detection removals |
|
5091 | #Multiple detection removals | |
| 5105 | listMeteors1 = self.__removeMultipleDetections(listMeteors, rangeLimit, timeLimit) |
|
5092 | listMeteors1 = self.__removeMultipleDetections(listMeteors, rangeLimit, timeLimit) | |
| 5106 | #************ END OF REMOVE MULTIPLE DETECTIONS ********************** |
|
5093 | #************ END OF REMOVE MULTIPLE DETECTIONS ********************** | |
| 5107 |
|
5094 | |||
| 5108 | #********************* METEOR REESTIMATION (3.7, 3.8, 3.9, 3.10) ******************** |
|
5095 | #********************* METEOR REESTIMATION (3.7, 3.8, 3.9, 3.10) ******************** | |
| 5109 | #Parameters |
|
5096 | #Parameters | |
| 5110 | phaseThresh = phaseThresh*numpy.pi/180 |
|
5097 | phaseThresh = phaseThresh*numpy.pi/180 | |
| 5111 | thresh = [phaseThresh, noise_multiple, SNRThresh] |
|
5098 | thresh = [phaseThresh, noise_multiple, SNRThresh] | |
| 5112 | #Meteor reestimation (Errors N 1, 6, 12, 17) |
|
5099 | #Meteor reestimation (Errors N 1, 6, 12, 17) | |
| 5113 | listMeteors2, listMeteorsPower, listMeteorsVolts = self.__meteorReestimation(listMeteors1, voltsPShift, pairslist0, thresh, noise, dataOut.timeInterval, dataOut.frequency) |
|
5100 | listMeteors2, listMeteorsPower, listMeteorsVolts = self.__meteorReestimation(listMeteors1, voltsPShift, pairslist0, thresh, noise, dataOut.timeInterval, dataOut.frequency) | |
| 5114 | #Estimation of decay times (Errors N 7, 8, 11) |
|
5101 | #Estimation of decay times (Errors N 7, 8, 11) | |
| 5115 | listMeteors3 = self.__estimateDecayTime(listMeteors2, listMeteorsPower, dataOut.timeInterval, dataOut.frequency) |
|
5102 | listMeteors3 = self.__estimateDecayTime(listMeteors2, listMeteorsPower, dataOut.timeInterval, dataOut.frequency) | |
| 5116 | #******************* END OF METEOR REESTIMATION ******************* |
|
5103 | #******************* END OF METEOR REESTIMATION ******************* | |
| 5117 |
|
5104 | |||
| 5118 | #********************* METEOR PARAMETERS CALCULATION (3.11, 3.12, 3.13) ************************** |
|
5105 | #********************* METEOR PARAMETERS CALCULATION (3.11, 3.12, 3.13) ************************** | |
| 5119 | #Calculating Radial Velocity (Error N 15) |
|
5106 | #Calculating Radial Velocity (Error N 15) | |
| 5120 | radialStdThresh = 10 |
|
5107 | radialStdThresh = 10 | |
| 5121 | listMeteors4 = self.__getRadialVelocity(listMeteors3, listMeteorsVolts, radialStdThresh, pairslist0, dataOut.timeInterval) |
|
5108 | listMeteors4 = self.__getRadialVelocity(listMeteors3, listMeteorsVolts, radialStdThresh, pairslist0, dataOut.timeInterval) | |
| 5122 |
|
5109 | |||
| 5123 | if len(listMeteors4) > 0: |
|
5110 | if len(listMeteors4) > 0: | |
| 5124 | #Setting New Array |
|
5111 | #Setting New Array | |
| 5125 | date = dataOut.utctime |
|
5112 | date = dataOut.utctime | |
| 5126 | arrayParameters = self.__setNewArrays(listMeteors4, date, heiRang) |
|
5113 | arrayParameters = self.__setNewArrays(listMeteors4, date, heiRang) | |
| 5127 |
|
5114 | |||
| 5128 | #Correcting phase offset |
|
5115 | #Correcting phase offset | |
| 5129 | if phaseOffsets != None: |
|
5116 | if phaseOffsets != None: | |
| 5130 | phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180 |
|
5117 | phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180 | |
| 5131 | arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets) |
|
5118 | arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets) | |
| 5132 |
|
5119 | |||
| 5133 | #Second Pairslist |
|
5120 | #Second Pairslist | |
| 5134 | pairsList = [] |
|
5121 | pairsList = [] | |
| 5135 | pairx = (0,1) |
|
5122 | pairx = (0,1) | |
| 5136 | pairy = (2,3) |
|
5123 | pairy = (2,3) | |
| 5137 | pairsList.append(pairx) |
|
5124 | pairsList.append(pairx) | |
| 5138 | pairsList.append(pairy) |
|
5125 | pairsList.append(pairy) | |
| 5139 |
|
5126 | |||
| 5140 | jph = numpy.array([0,0,0,0]) |
|
5127 | jph = numpy.array([0,0,0,0]) | |
| 5141 | h = (hmin,hmax) |
|
5128 | h = (hmin,hmax) | |
| 5142 | arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph) |
|
5129 | arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph) | |
| 5143 | dataOut.data_param = arrayParameters |
|
5130 | dataOut.data_param = arrayParameters | |
| 5144 |
|
5131 | |||
| 5145 | if arrayParameters is None: |
|
5132 | if arrayParameters is None: | |
| 5146 | dataOut.flagNoData = True |
|
5133 | dataOut.flagNoData = True | |
| 5147 | else: |
|
5134 | else: | |
| 5148 | dataOut.flagNoData = True |
|
5135 | dataOut.flagNoData = True | |
| 5149 |
|
5136 | |||
| 5150 | return |
|
5137 | return | |
| 5151 |
|
5138 | |||
| 5152 | def __getHardwarePhaseDiff(self, voltage0, pairslist, newheis, n): |
|
5139 | def __getHardwarePhaseDiff(self, voltage0, pairslist, newheis, n): | |
| 5153 |
|
5140 | |||
| 5154 | minIndex = min(newheis[0]) |
|
5141 | minIndex = min(newheis[0]) | |
| 5155 | maxIndex = max(newheis[0]) |
|
5142 | maxIndex = max(newheis[0]) | |
| 5156 |
|
5143 | |||
| 5157 | voltage = voltage0[:,:,minIndex:maxIndex+1] |
|
5144 | voltage = voltage0[:,:,minIndex:maxIndex+1] | |
| 5158 | nLength = voltage.shape[1]/n |
|
5145 | nLength = voltage.shape[1]/n | |
| 5159 | nMin = 0 |
|
5146 | nMin = 0 | |
| 5160 | nMax = 0 |
|
5147 | nMax = 0 | |
| 5161 | phaseOffset = numpy.zeros((len(pairslist),n)) |
|
5148 | phaseOffset = numpy.zeros((len(pairslist),n)) | |
| 5162 |
|
5149 | |||
| 5163 | for i in range(n): |
|
5150 | for i in range(n): | |
| 5164 | nMax += nLength |
|
5151 | nMax += nLength | |
| 5165 | phaseCCF = -numpy.angle(self.__calculateCCF(voltage[:,nMin:nMax,:], pairslist, [0])) |
|
5152 | phaseCCF = -numpy.angle(self.__calculateCCF(voltage[:,nMin:nMax,:], pairslist, [0])) | |
| 5166 | phaseCCF = numpy.mean(phaseCCF, axis = 2) |
|
5153 | phaseCCF = numpy.mean(phaseCCF, axis = 2) | |
| 5167 | phaseOffset[:,i] = phaseCCF.transpose() |
|
5154 | phaseOffset[:,i] = phaseCCF.transpose() | |
| 5168 | nMin = nMax |
|
5155 | nMin = nMax | |
| 5169 |
|
5156 | |||
| 5170 | #Remove Outliers |
|
5157 | #Remove Outliers | |
| 5171 | factor = 2 |
|
5158 | factor = 2 | |
| 5172 | wt = phaseOffset - signal.medfilt(phaseOffset,(1,5)) |
|
5159 | wt = phaseOffset - signal.medfilt(phaseOffset,(1,5)) | |
| 5173 | dw = numpy.std(wt,axis = 1) |
|
5160 | dw = numpy.std(wt,axis = 1) | |
| 5174 | dw = dw.reshape((dw.size,1)) |
|
5161 | dw = dw.reshape((dw.size,1)) | |
| 5175 | ind = numpy.where(numpy.logical_or(wt>dw*factor,wt<-dw*factor)) |
|
5162 | ind = numpy.where(numpy.logical_or(wt>dw*factor,wt<-dw*factor)) | |
| 5176 | phaseOffset[ind] = numpy.nan |
|
5163 | phaseOffset[ind] = numpy.nan | |
| 5177 | phaseOffset = stats.nanmean(phaseOffset, axis=1) |
|
5164 | phaseOffset = stats.nanmean(phaseOffset, axis=1) | |
| 5178 |
|
5165 | |||
| 5179 | return phaseOffset |
|
5166 | return phaseOffset | |
| 5180 |
|
5167 | |||
| 5181 | def __shiftPhase(self, data, phaseShift): |
|
5168 | def __shiftPhase(self, data, phaseShift): | |
| 5182 | #this will shift the phase of a complex number |
|
5169 | #this will shift the phase of a complex number | |
| 5183 | dataShifted = numpy.abs(data) * numpy.exp((numpy.angle(data)+phaseShift)*1j) |
|
5170 | dataShifted = numpy.abs(data) * numpy.exp((numpy.angle(data)+phaseShift)*1j) | |
| 5184 | return dataShifted |
|
5171 | return dataShifted | |
| 5185 |
|
5172 | |||
| 5186 | def __estimatePhaseDifference(self, array, pairslist): |
|
5173 | def __estimatePhaseDifference(self, array, pairslist): | |
| 5187 | nChannel = array.shape[0] |
|
5174 | nChannel = array.shape[0] | |
| 5188 | nHeights = array.shape[2] |
|
5175 | nHeights = array.shape[2] | |
| 5189 | numPairs = len(pairslist) |
|
5176 | numPairs = len(pairslist) | |
| 5190 | phaseCCF = numpy.angle(self.__calculateCCF(array, pairslist, [-2,-1,0,1,2])) |
|
5177 | phaseCCF = numpy.angle(self.__calculateCCF(array, pairslist, [-2,-1,0,1,2])) | |
| 5191 |
|
5178 | |||
| 5192 | #Correct phases |
|
5179 | #Correct phases | |
| 5193 | derPhaseCCF = phaseCCF[:,1:,:] - phaseCCF[:,0:-1,:] |
|
5180 | derPhaseCCF = phaseCCF[:,1:,:] - phaseCCF[:,0:-1,:] | |
| 5194 | indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) |
|
5181 | indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) | |
| 5195 |
|
5182 | |||
| 5196 | if indDer[0].shape[0] > 0: |
|
5183 | if indDer[0].shape[0] > 0: | |
| 5197 | for i in range(indDer[0].shape[0]): |
|
5184 | for i in range(indDer[0].shape[0]): | |
| 5198 | signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i],indDer[2][i]]) |
|
5185 | signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i],indDer[2][i]]) | |
| 5199 | phaseCCF[indDer[0][i],indDer[1][i]+1:,:] += signo*2*numpy.pi |
|
5186 | phaseCCF[indDer[0][i],indDer[1][i]+1:,:] += signo*2*numpy.pi | |
| 5200 |
|
5187 | |||
| 5201 | #Linear |
|
5188 | #Linear | |
| 5202 | phaseInt = numpy.zeros((numPairs,1)) |
|
5189 | phaseInt = numpy.zeros((numPairs,1)) | |
| 5203 | angAllCCF = phaseCCF[:,[0,1,3,4],0] |
|
5190 | angAllCCF = phaseCCF[:,[0,1,3,4],0] | |
| 5204 | for j in range(numPairs): |
|
5191 | for j in range(numPairs): | |
| 5205 | fit = stats.linregress([-2,-1,1,2],angAllCCF[j,:]) |
|
5192 | fit = stats.linregress([-2,-1,1,2],angAllCCF[j,:]) | |
| 5206 | phaseInt[j] = fit[1] |
|
5193 | phaseInt[j] = fit[1] | |
| 5207 | #Phase Differences |
|
5194 | #Phase Differences | |
| 5208 | phaseDiff = phaseInt - phaseCCF[:,2,:] |
|
5195 | phaseDiff = phaseInt - phaseCCF[:,2,:] | |
| 5209 | phaseArrival = phaseInt.reshape(phaseInt.size) |
|
5196 | phaseArrival = phaseInt.reshape(phaseInt.size) | |
| 5210 |
|
5197 | |||
| 5211 | #Dealias |
|
5198 | #Dealias | |
| 5212 | phaseArrival = numpy.angle(numpy.exp(1j*phaseArrival)) |
|
5199 | phaseArrival = numpy.angle(numpy.exp(1j*phaseArrival)) | |
| 5213 |
|
5200 | |||
| 5214 | return phaseDiff, phaseArrival |
|
5201 | return phaseDiff, phaseArrival | |
| 5215 |
|
5202 | |||
| 5216 | def __coherentDetection(self, volts, timeSegment, timeInterval, pairslist, thresh): |
|
5203 | def __coherentDetection(self, volts, timeSegment, timeInterval, pairslist, thresh): | |
| 5217 | #this function will run the coherent detection used in Holdworth et al. 2004 and return the net power |
|
5204 | #this function will run the coherent detection used in Holdworth et al. 2004 and return the net power | |
| 5218 | #find the phase shifts of each channel over 1 second intervals |
|
5205 | #find the phase shifts of each channel over 1 second intervals | |
| 5219 | #only look at ranges below the beacon signal |
|
5206 | #only look at ranges below the beacon signal | |
| 5220 | numProfPerBlock = numpy.ceil(timeSegment/timeInterval) |
|
5207 | numProfPerBlock = numpy.ceil(timeSegment/timeInterval) | |
| 5221 | numBlocks = int(volts.shape[1]/numProfPerBlock) |
|
5208 | numBlocks = int(volts.shape[1]/numProfPerBlock) | |
| 5222 | numHeights = volts.shape[2] |
|
5209 | numHeights = volts.shape[2] | |
| 5223 | nChannel = volts.shape[0] |
|
5210 | nChannel = volts.shape[0] | |
| 5224 | voltsCohDet = volts.copy() |
|
5211 | voltsCohDet = volts.copy() | |
| 5225 |
|
5212 | |||
| 5226 | pairsarray = numpy.array(pairslist) |
|
5213 | pairsarray = numpy.array(pairslist) | |
| 5227 | indSides = pairsarray[:,1] |
|
5214 | indSides = pairsarray[:,1] | |
| 5228 | listBlocks = numpy.array_split(volts, numBlocks, 1) |
|
5215 | listBlocks = numpy.array_split(volts, numBlocks, 1) | |
| 5229 |
|
5216 | |||
| 5230 | startInd = 0 |
|
5217 | startInd = 0 | |
| 5231 | endInd = 0 |
|
5218 | endInd = 0 | |
| 5232 |
|
5219 | |||
| 5233 | for i in range(numBlocks): |
|
5220 | for i in range(numBlocks): | |
| 5234 | startInd = endInd |
|
5221 | startInd = endInd | |
| 5235 | endInd = endInd + listBlocks[i].shape[1] |
|
5222 | endInd = endInd + listBlocks[i].shape[1] | |
| 5236 |
|
5223 | |||
| 5237 | arrayBlock = listBlocks[i] |
|
5224 | arrayBlock = listBlocks[i] | |
| 5238 |
|
5225 | |||
| 5239 | #Estimate the Phase Difference |
|
5226 | #Estimate the Phase Difference | |
| 5240 | phaseDiff, aux = self.__estimatePhaseDifference(arrayBlock, pairslist) |
|
5227 | phaseDiff, aux = self.__estimatePhaseDifference(arrayBlock, pairslist) | |
| 5241 | #Phase Difference RMS |
|
5228 | #Phase Difference RMS | |
| 5242 | arrayPhaseRMS = numpy.abs(phaseDiff) |
|
5229 | arrayPhaseRMS = numpy.abs(phaseDiff) | |
| 5243 | phaseRMSaux = numpy.sum(arrayPhaseRMS < thresh,0) |
|
5230 | phaseRMSaux = numpy.sum(arrayPhaseRMS < thresh,0) | |
| 5244 | indPhase = numpy.where(phaseRMSaux==4) |
|
5231 | indPhase = numpy.where(phaseRMSaux==4) | |
| 5245 | #Shifting |
|
5232 | #Shifting | |
| 5246 | if indPhase[0].shape[0] > 0: |
|
5233 | if indPhase[0].shape[0] > 0: | |
| 5247 | for j in range(indSides.size): |
|
5234 | for j in range(indSides.size): | |
| 5248 | arrayBlock[indSides[j],:,indPhase] = self.__shiftPhase(arrayBlock[indSides[j],:,indPhase], phaseDiff[j,indPhase].transpose()) |
|
5235 | arrayBlock[indSides[j],:,indPhase] = self.__shiftPhase(arrayBlock[indSides[j],:,indPhase], phaseDiff[j,indPhase].transpose()) | |
| 5249 | voltsCohDet[:,startInd:endInd,:] = arrayBlock |
|
5236 | voltsCohDet[:,startInd:endInd,:] = arrayBlock | |
| 5250 |
|
5237 | |||
| 5251 | return voltsCohDet |
|
5238 | return voltsCohDet | |
| 5252 |
|
5239 | |||
| 5253 | def __calculateCCF(self, volts, pairslist ,laglist): |
|
5240 | def __calculateCCF(self, volts, pairslist ,laglist): | |
| 5254 |
|
5241 | |||
| 5255 | nHeights = volts.shape[2] |
|
5242 | nHeights = volts.shape[2] | |
| 5256 | nPoints = volts.shape[1] |
|
5243 | nPoints = volts.shape[1] | |
| 5257 | voltsCCF = numpy.zeros((len(pairslist), len(laglist), nHeights),dtype = 'complex') |
|
5244 | voltsCCF = numpy.zeros((len(pairslist), len(laglist), nHeights),dtype = 'complex') | |
| 5258 |
|
5245 | |||
| 5259 | for i in range(len(pairslist)): |
|
5246 | for i in range(len(pairslist)): | |
| 5260 | volts1 = volts[pairslist[i][0]] |
|
5247 | volts1 = volts[pairslist[i][0]] | |
| 5261 | volts2 = volts[pairslist[i][1]] |
|
5248 | volts2 = volts[pairslist[i][1]] | |
| 5262 |
|
5249 | |||
| 5263 | for t in range(len(laglist)): |
|
5250 | for t in range(len(laglist)): | |
| 5264 | idxT = laglist[t] |
|
5251 | idxT = laglist[t] | |
| 5265 | if idxT >= 0: |
|
5252 | if idxT >= 0: | |
| 5266 | vStacked = numpy.vstack((volts2[idxT:,:], |
|
5253 | vStacked = numpy.vstack((volts2[idxT:,:], | |
| 5267 | numpy.zeros((idxT, nHeights),dtype='complex'))) |
|
5254 | numpy.zeros((idxT, nHeights),dtype='complex'))) | |
| 5268 | else: |
|
5255 | else: | |
| 5269 | vStacked = numpy.vstack((numpy.zeros((-idxT, nHeights),dtype='complex'), |
|
5256 | vStacked = numpy.vstack((numpy.zeros((-idxT, nHeights),dtype='complex'), | |
| 5270 | volts2[:(nPoints + idxT),:])) |
|
5257 | volts2[:(nPoints + idxT),:])) | |
| 5271 | voltsCCF[i,t,:] = numpy.sum((numpy.conjugate(volts1)*vStacked),axis=0) |
|
5258 | voltsCCF[i,t,:] = numpy.sum((numpy.conjugate(volts1)*vStacked),axis=0) | |
| 5272 |
|
5259 | |||
| 5273 | vStacked = None |
|
5260 | vStacked = None | |
| 5274 | return voltsCCF |
|
5261 | return voltsCCF | |
| 5275 |
|
5262 | |||
| 5276 | def __getNoise(self, power, timeSegment, timeInterval): |
|
5263 | def __getNoise(self, power, timeSegment, timeInterval): | |
| 5277 | numProfPerBlock = numpy.ceil(timeSegment/timeInterval) |
|
5264 | numProfPerBlock = numpy.ceil(timeSegment/timeInterval) | |
| 5278 | numBlocks = int(power.shape[0]/numProfPerBlock) |
|
5265 | numBlocks = int(power.shape[0]/numProfPerBlock) | |
| 5279 | numHeights = power.shape[1] |
|
5266 | numHeights = power.shape[1] | |
| 5280 |
|
5267 | |||
| 5281 | listPower = numpy.array_split(power, numBlocks, 0) |
|
5268 | listPower = numpy.array_split(power, numBlocks, 0) | |
| 5282 | noise = numpy.zeros((power.shape[0], power.shape[1])) |
|
5269 | noise = numpy.zeros((power.shape[0], power.shape[1])) | |
| 5283 | noise1 = numpy.zeros((power.shape[0], power.shape[1])) |
|
5270 | noise1 = numpy.zeros((power.shape[0], power.shape[1])) | |
| 5284 |
|
5271 | |||
| 5285 | startInd = 0 |
|
5272 | startInd = 0 | |
| 5286 | endInd = 0 |
|
5273 | endInd = 0 | |
| 5287 |
|
5274 | |||
| 5288 | for i in range(numBlocks): #split por canal |
|
5275 | for i in range(numBlocks): #split por canal | |
| 5289 | startInd = endInd |
|
5276 | startInd = endInd | |
| 5290 | endInd = endInd + listPower[i].shape[0] |
|
5277 | endInd = endInd + listPower[i].shape[0] | |
| 5291 |
|
5278 | |||
| 5292 | arrayBlock = listPower[i] |
|
5279 | arrayBlock = listPower[i] | |
| 5293 | noiseAux = numpy.mean(arrayBlock, 0) |
|
5280 | noiseAux = numpy.mean(arrayBlock, 0) | |
| 5294 | noise[startInd:endInd,:] = noise[startInd:endInd,:] + noiseAux |
|
5281 | noise[startInd:endInd,:] = noise[startInd:endInd,:] + noiseAux | |
| 5295 |
|
5282 | |||
| 5296 | noiseAux1 = numpy.mean(arrayBlock) |
|
5283 | noiseAux1 = numpy.mean(arrayBlock) | |
| 5297 | noise1[startInd:endInd,:] = noise1[startInd:endInd,:] + noiseAux1 |
|
5284 | noise1[startInd:endInd,:] = noise1[startInd:endInd,:] + noiseAux1 | |
| 5298 |
|
5285 | |||
| 5299 | return noise, noise1 |
|
5286 | return noise, noise1 | |
| 5300 |
|
5287 | |||
| 5301 | def __findMeteors(self, power, thresh): |
|
5288 | def __findMeteors(self, power, thresh): | |
| 5302 | nProf = power.shape[0] |
|
5289 | nProf = power.shape[0] | |
| 5303 | nHeights = power.shape[1] |
|
5290 | nHeights = power.shape[1] | |
| 5304 | listMeteors = [] |
|
5291 | listMeteors = [] | |
| 5305 |
|
5292 | |||
| 5306 | for i in range(nHeights): |
|
5293 | for i in range(nHeights): | |
| 5307 | powerAux = power[:,i] |
|
5294 | powerAux = power[:,i] | |
| 5308 | threshAux = thresh[:,i] |
|
5295 | threshAux = thresh[:,i] | |
| 5309 |
|
5296 | |||
| 5310 | indUPthresh = numpy.where(powerAux > threshAux)[0] |
|
5297 | indUPthresh = numpy.where(powerAux > threshAux)[0] | |
| 5311 | indDNthresh = numpy.where(powerAux <= threshAux)[0] |
|
5298 | indDNthresh = numpy.where(powerAux <= threshAux)[0] | |
| 5312 |
|
5299 | |||
| 5313 | j = 0 |
|
5300 | j = 0 | |
| 5314 |
|
5301 | |||
| 5315 | while (j < indUPthresh.size - 2): |
|
5302 | while (j < indUPthresh.size - 2): | |
| 5316 | if (indUPthresh[j + 2] == indUPthresh[j] + 2): |
|
5303 | if (indUPthresh[j + 2] == indUPthresh[j] + 2): | |
| 5317 | indDNAux = numpy.where(indDNthresh > indUPthresh[j]) |
|
5304 | indDNAux = numpy.where(indDNthresh > indUPthresh[j]) | |
| 5318 | indDNthresh = indDNthresh[indDNAux] |
|
5305 | indDNthresh = indDNthresh[indDNAux] | |
| 5319 |
|
5306 | |||
| 5320 | if (indDNthresh.size > 0): |
|
5307 | if (indDNthresh.size > 0): | |
| 5321 | indEnd = indDNthresh[0] - 1 |
|
5308 | indEnd = indDNthresh[0] - 1 | |
| 5322 | indInit = indUPthresh[j] |
|
5309 | indInit = indUPthresh[j] | |
| 5323 |
|
5310 | |||
| 5324 | meteor = powerAux[indInit:indEnd + 1] |
|
5311 | meteor = powerAux[indInit:indEnd + 1] | |
| 5325 | indPeak = meteor.argmax() + indInit |
|
5312 | indPeak = meteor.argmax() + indInit | |
| 5326 | FLA = sum(numpy.conj(meteor)*numpy.hstack((meteor[1:],0))) |
|
5313 | FLA = sum(numpy.conj(meteor)*numpy.hstack((meteor[1:],0))) | |
| 5327 |
|
5314 | |||
| 5328 | listMeteors.append(numpy.array([i,indInit,indPeak,indEnd,FLA])) #CHEQUEAR!!!!! |
|
5315 | listMeteors.append(numpy.array([i,indInit,indPeak,indEnd,FLA])) #CHEQUEAR!!!!! | |
| 5329 | j = numpy.where(indUPthresh == indEnd)[0] + 1 |
|
5316 | j = numpy.where(indUPthresh == indEnd)[0] + 1 | |
| 5330 | else: j+=1 |
|
5317 | else: j+=1 | |
| 5331 | else: j+=1 |
|
5318 | else: j+=1 | |
| 5332 |
|
5319 | |||
| 5333 | return listMeteors |
|
5320 | return listMeteors | |
| 5334 |
|
5321 | |||
| 5335 | def __removeMultipleDetections(self,listMeteors, rangeLimit, timeLimit): |
|
5322 | def __removeMultipleDetections(self,listMeteors, rangeLimit, timeLimit): | |
| 5336 |
|
5323 | |||
| 5337 | arrayMeteors = numpy.asarray(listMeteors) |
|
5324 | arrayMeteors = numpy.asarray(listMeteors) | |
| 5338 | listMeteors1 = [] |
|
5325 | listMeteors1 = [] | |
| 5339 |
|
5326 | |||
| 5340 | while arrayMeteors.shape[0] > 0: |
|
5327 | while arrayMeteors.shape[0] > 0: | |
| 5341 | FLAs = arrayMeteors[:,4] |
|
5328 | FLAs = arrayMeteors[:,4] | |
| 5342 | maxFLA = FLAs.argmax() |
|
5329 | maxFLA = FLAs.argmax() | |
| 5343 | listMeteors1.append(arrayMeteors[maxFLA,:]) |
|
5330 | listMeteors1.append(arrayMeteors[maxFLA,:]) | |
| 5344 |
|
5331 | |||
| 5345 | MeteorInitTime = arrayMeteors[maxFLA,1] |
|
5332 | MeteorInitTime = arrayMeteors[maxFLA,1] | |
| 5346 | MeteorEndTime = arrayMeteors[maxFLA,3] |
|
5333 | MeteorEndTime = arrayMeteors[maxFLA,3] | |
| 5347 | MeteorHeight = arrayMeteors[maxFLA,0] |
|
5334 | MeteorHeight = arrayMeteors[maxFLA,0] | |
| 5348 |
|
5335 | |||
| 5349 | #Check neighborhood |
|
5336 | #Check neighborhood | |
| 5350 | maxHeightIndex = MeteorHeight + rangeLimit |
|
5337 | maxHeightIndex = MeteorHeight + rangeLimit | |
| 5351 | minHeightIndex = MeteorHeight - rangeLimit |
|
5338 | minHeightIndex = MeteorHeight - rangeLimit | |
| 5352 | minTimeIndex = MeteorInitTime - timeLimit |
|
5339 | minTimeIndex = MeteorInitTime - timeLimit | |
| 5353 | maxTimeIndex = MeteorEndTime + timeLimit |
|
5340 | maxTimeIndex = MeteorEndTime + timeLimit | |
| 5354 |
|
5341 | |||
| 5355 | #Check Heights |
|
5342 | #Check Heights | |
| 5356 | indHeight = numpy.logical_and(arrayMeteors[:,0] >= minHeightIndex, arrayMeteors[:,0] <= maxHeightIndex) |
|
5343 | indHeight = numpy.logical_and(arrayMeteors[:,0] >= minHeightIndex, arrayMeteors[:,0] <= maxHeightIndex) | |
| 5357 | indTime = numpy.logical_and(arrayMeteors[:,3] >= minTimeIndex, arrayMeteors[:,1] <= maxTimeIndex) |
|
5344 | indTime = numpy.logical_and(arrayMeteors[:,3] >= minTimeIndex, arrayMeteors[:,1] <= maxTimeIndex) | |
| 5358 | indBoth = numpy.where(numpy.logical_and(indTime,indHeight)) |
|
5345 | indBoth = numpy.where(numpy.logical_and(indTime,indHeight)) | |
| 5359 |
|
5346 | |||
| 5360 | arrayMeteors = numpy.delete(arrayMeteors, indBoth, axis = 0) |
|
5347 | arrayMeteors = numpy.delete(arrayMeteors, indBoth, axis = 0) | |
| 5361 |
|
5348 | |||
| 5362 | return listMeteors1 |
|
5349 | return listMeteors1 | |
| 5363 |
|
5350 | |||
| 5364 | def __meteorReestimation(self, listMeteors, volts, pairslist, thresh, noise, timeInterval,frequency): |
|
5351 | def __meteorReestimation(self, listMeteors, volts, pairslist, thresh, noise, timeInterval,frequency): | |
| 5365 | numHeights = volts.shape[2] |
|
5352 | numHeights = volts.shape[2] | |
| 5366 | nChannel = volts.shape[0] |
|
5353 | nChannel = volts.shape[0] | |
| 5367 |
|
5354 | |||
| 5368 | thresholdPhase = thresh[0] |
|
5355 | thresholdPhase = thresh[0] | |
| 5369 | thresholdNoise = thresh[1] |
|
5356 | thresholdNoise = thresh[1] | |
| 5370 | thresholdDB = float(thresh[2]) |
|
5357 | thresholdDB = float(thresh[2]) | |
| 5371 |
|
5358 | |||
| 5372 | thresholdDB1 = 10**(thresholdDB/10) |
|
5359 | thresholdDB1 = 10**(thresholdDB/10) | |
| 5373 | pairsarray = numpy.array(pairslist) |
|
5360 | pairsarray = numpy.array(pairslist) | |
| 5374 | indSides = pairsarray[:,1] |
|
5361 | indSides = pairsarray[:,1] | |
| 5375 |
|
5362 | |||
| 5376 | pairslist1 = list(pairslist) |
|
5363 | pairslist1 = list(pairslist) | |
| 5377 | pairslist1.append((0,1)) |
|
5364 | pairslist1.append((0,1)) | |
| 5378 | pairslist1.append((3,4)) |
|
5365 | pairslist1.append((3,4)) | |
| 5379 |
|
5366 | |||
| 5380 | listMeteors1 = [] |
|
5367 | listMeteors1 = [] | |
| 5381 | listPowerSeries = [] |
|
5368 | listPowerSeries = [] | |
| 5382 | listVoltageSeries = [] |
|
5369 | listVoltageSeries = [] | |
| 5383 | #volts has the war data |
|
5370 | #volts has the war data | |
| 5384 |
|
5371 | |||
| 5385 | if frequency == 30e6: |
|
5372 | if frequency == 30e6: | |
| 5386 | timeLag = 45*10**-3 |
|
5373 | timeLag = 45*10**-3 | |
| 5387 | else: |
|
5374 | else: | |
| 5388 | timeLag = 15*10**-3 |
|
5375 | timeLag = 15*10**-3 | |
| 5389 | lag = numpy.ceil(timeLag/timeInterval) |
|
5376 | lag = numpy.ceil(timeLag/timeInterval) | |
| 5390 |
|
5377 | |||
| 5391 | for i in range(len(listMeteors)): |
|
5378 | for i in range(len(listMeteors)): | |
| 5392 |
|
5379 | |||
| 5393 | ###################### 3.6 - 3.7 PARAMETERS REESTIMATION ######################### |
|
5380 | ###################### 3.6 - 3.7 PARAMETERS REESTIMATION ######################### | |
| 5394 | meteorAux = numpy.zeros(16) |
|
5381 | meteorAux = numpy.zeros(16) | |
| 5395 |
|
5382 | |||
| 5396 | #Loading meteor Data (mHeight, mStart, mPeak, mEnd) |
|
5383 | #Loading meteor Data (mHeight, mStart, mPeak, mEnd) | |
| 5397 | mHeight = listMeteors[i][0] |
|
5384 | mHeight = listMeteors[i][0] | |
| 5398 | mStart = listMeteors[i][1] |
|
5385 | mStart = listMeteors[i][1] | |
| 5399 | mPeak = listMeteors[i][2] |
|
5386 | mPeak = listMeteors[i][2] | |
| 5400 | mEnd = listMeteors[i][3] |
|
5387 | mEnd = listMeteors[i][3] | |
| 5401 |
|
5388 | |||
| 5402 | #get the volt data between the start and end times of the meteor |
|
5389 | #get the volt data between the start and end times of the meteor | |
| 5403 | meteorVolts = volts[:,mStart:mEnd+1,mHeight] |
|
5390 | meteorVolts = volts[:,mStart:mEnd+1,mHeight] | |
| 5404 | meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) |
|
5391 | meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) | |
| 5405 |
|
5392 | |||
| 5406 | #3.6. Phase Difference estimation |
|
5393 | #3.6. Phase Difference estimation | |
| 5407 | phaseDiff, aux = self.__estimatePhaseDifference(meteorVolts, pairslist) |
|
5394 | phaseDiff, aux = self.__estimatePhaseDifference(meteorVolts, pairslist) | |
| 5408 |
|
5395 | |||
| 5409 | #3.7. Phase difference removal & meteor start, peak and end times reestimated |
|
5396 | #3.7. Phase difference removal & meteor start, peak and end times reestimated | |
| 5410 | #meteorVolts0.- all Channels, all Profiles |
|
5397 | #meteorVolts0.- all Channels, all Profiles | |
| 5411 | meteorVolts0 = volts[:,:,mHeight] |
|
5398 | meteorVolts0 = volts[:,:,mHeight] | |
| 5412 | meteorThresh = noise[:,mHeight]*thresholdNoise |
|
5399 | meteorThresh = noise[:,mHeight]*thresholdNoise | |
| 5413 | meteorNoise = noise[:,mHeight] |
|
5400 | meteorNoise = noise[:,mHeight] | |
| 5414 | meteorVolts0[indSides,:] = self.__shiftPhase(meteorVolts0[indSides,:], phaseDiff) #Phase Shifting |
|
5401 | meteorVolts0[indSides,:] = self.__shiftPhase(meteorVolts0[indSides,:], phaseDiff) #Phase Shifting | |
| 5415 | powerNet0 = numpy.nansum(numpy.abs(meteorVolts0)**2, axis = 0) #Power |
|
5402 | powerNet0 = numpy.nansum(numpy.abs(meteorVolts0)**2, axis = 0) #Power | |
| 5416 |
|
5403 | |||
| 5417 | #Times reestimation |
|
5404 | #Times reestimation | |
| 5418 | mStart1 = numpy.where(powerNet0[:mPeak] < meteorThresh[:mPeak])[0] |
|
5405 | mStart1 = numpy.where(powerNet0[:mPeak] < meteorThresh[:mPeak])[0] | |
| 5419 | if mStart1.size > 0: |
|
5406 | if mStart1.size > 0: | |
| 5420 | mStart1 = mStart1[-1] + 1 |
|
5407 | mStart1 = mStart1[-1] + 1 | |
| 5421 |
|
5408 | |||
| 5422 | else: |
|
5409 | else: | |
| 5423 | mStart1 = mPeak |
|
5410 | mStart1 = mPeak | |
| 5424 |
|
5411 | |||
| 5425 | mEnd1 = numpy.where(powerNet0[mPeak:] < meteorThresh[mPeak:])[0][0] + mPeak - 1 |
|
5412 | mEnd1 = numpy.where(powerNet0[mPeak:] < meteorThresh[mPeak:])[0][0] + mPeak - 1 | |
| 5426 | mEndDecayTime1 = numpy.where(powerNet0[mPeak:] < meteorNoise[mPeak:])[0] |
|
5413 | mEndDecayTime1 = numpy.where(powerNet0[mPeak:] < meteorNoise[mPeak:])[0] | |
| 5427 | if mEndDecayTime1.size == 0: |
|
5414 | if mEndDecayTime1.size == 0: | |
| 5428 | mEndDecayTime1 = powerNet0.size |
|
5415 | mEndDecayTime1 = powerNet0.size | |
| 5429 | else: |
|
5416 | else: | |
| 5430 | mEndDecayTime1 = mEndDecayTime1[0] + mPeak - 1 |
|
5417 | mEndDecayTime1 = mEndDecayTime1[0] + mPeak - 1 | |
| 5431 |
|
5418 | |||
| 5432 | #meteorVolts1.- all Channels, from start to end |
|
5419 | #meteorVolts1.- all Channels, from start to end | |
| 5433 | meteorVolts1 = meteorVolts0[:,mStart1:mEnd1 + 1] |
|
5420 | meteorVolts1 = meteorVolts0[:,mStart1:mEnd1 + 1] | |
| 5434 | meteorVolts2 = meteorVolts0[:,mPeak + lag:mEnd1 + 1] |
|
5421 | meteorVolts2 = meteorVolts0[:,mPeak + lag:mEnd1 + 1] | |
| 5435 | if meteorVolts2.shape[1] == 0: |
|
5422 | if meteorVolts2.shape[1] == 0: | |
| 5436 | meteorVolts2 = meteorVolts0[:,mPeak:mEnd1 + 1] |
|
5423 | meteorVolts2 = meteorVolts0[:,mPeak:mEnd1 + 1] | |
| 5437 | meteorVolts1 = meteorVolts1.reshape(meteorVolts1.shape[0], meteorVolts1.shape[1], 1) |
|
5424 | meteorVolts1 = meteorVolts1.reshape(meteorVolts1.shape[0], meteorVolts1.shape[1], 1) | |
| 5438 | meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1], 1) |
|
5425 | meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1], 1) | |
| 5439 | ##################### END PARAMETERS REESTIMATION ######################### |
|
5426 | ##################### END PARAMETERS REESTIMATION ######################### | |
| 5440 |
|
5427 | |||
| 5441 | ##################### 3.8 PHASE DIFFERENCE REESTIMATION ######################## |
|
5428 | ##################### 3.8 PHASE DIFFERENCE REESTIMATION ######################## | |
| 5442 | if meteorVolts2.shape[1] > 0: |
|
5429 | if meteorVolts2.shape[1] > 0: | |
| 5443 | #Phase Difference re-estimation |
|
5430 | #Phase Difference re-estimation | |
| 5444 | phaseDiff1, phaseDiffint = self.__estimatePhaseDifference(meteorVolts2, pairslist1) #Phase Difference Estimation |
|
5431 | phaseDiff1, phaseDiffint = self.__estimatePhaseDifference(meteorVolts2, pairslist1) #Phase Difference Estimation | |
| 5445 | meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1]) |
|
5432 | meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1]) | |
| 5446 | phaseDiff11 = numpy.reshape(phaseDiff1, (phaseDiff1.shape[0],1)) |
|
5433 | phaseDiff11 = numpy.reshape(phaseDiff1, (phaseDiff1.shape[0],1)) | |
| 5447 | meteorVolts2[indSides,:] = self.__shiftPhase(meteorVolts2[indSides,:], phaseDiff11[0:4]) #Phase Shifting |
|
5434 | meteorVolts2[indSides,:] = self.__shiftPhase(meteorVolts2[indSides,:], phaseDiff11[0:4]) #Phase Shifting | |
| 5448 |
|
5435 | |||
| 5449 | #Phase Difference RMS |
|
5436 | #Phase Difference RMS | |
| 5450 | phaseRMS1 = numpy.sqrt(numpy.mean(numpy.square(phaseDiff1))) |
|
5437 | phaseRMS1 = numpy.sqrt(numpy.mean(numpy.square(phaseDiff1))) | |
| 5451 | powerNet1 = numpy.nansum(numpy.abs(meteorVolts1[:,:])**2,0) |
|
5438 | powerNet1 = numpy.nansum(numpy.abs(meteorVolts1[:,:])**2,0) | |
| 5452 | #Data from Meteor |
|
5439 | #Data from Meteor | |
| 5453 | mPeak1 = powerNet1.argmax() + mStart1 |
|
5440 | mPeak1 = powerNet1.argmax() + mStart1 | |
| 5454 | mPeakPower1 = powerNet1.max() |
|
5441 | mPeakPower1 = powerNet1.max() | |
| 5455 | noiseAux = sum(noise[mStart1:mEnd1 + 1,mHeight]) |
|
5442 | noiseAux = sum(noise[mStart1:mEnd1 + 1,mHeight]) | |
| 5456 | mSNR1 = (sum(powerNet1)-noiseAux)/noiseAux |
|
5443 | mSNR1 = (sum(powerNet1)-noiseAux)/noiseAux | |
| 5457 | Meteor1 = numpy.array([mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1]) |
|
5444 | Meteor1 = numpy.array([mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1]) | |
| 5458 | Meteor1 = numpy.hstack((Meteor1,phaseDiffint)) |
|
5445 | Meteor1 = numpy.hstack((Meteor1,phaseDiffint)) | |
| 5459 | PowerSeries = powerNet0[mStart1:mEndDecayTime1 + 1] |
|
5446 | PowerSeries = powerNet0[mStart1:mEndDecayTime1 + 1] | |
| 5460 | #Vectorize |
|
5447 | #Vectorize | |
| 5461 | meteorAux[0:7] = [mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1] |
|
5448 | meteorAux[0:7] = [mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1] | |
| 5462 | meteorAux[7:11] = phaseDiffint[0:4] |
|
5449 | meteorAux[7:11] = phaseDiffint[0:4] | |
| 5463 |
|
5450 | |||
| 5464 | #Rejection Criterions |
|
5451 | #Rejection Criterions | |
| 5465 | if phaseRMS1 > thresholdPhase: #Error Number 17: Phase variation |
|
5452 | if phaseRMS1 > thresholdPhase: #Error Number 17: Phase variation | |
| 5466 | meteorAux[-1] = 17 |
|
5453 | meteorAux[-1] = 17 | |
| 5467 | elif mSNR1 < thresholdDB1: #Error Number 1: SNR < threshold dB |
|
5454 | elif mSNR1 < thresholdDB1: #Error Number 1: SNR < threshold dB | |
| 5468 | meteorAux[-1] = 1 |
|
5455 | meteorAux[-1] = 1 | |
| 5469 |
|
5456 | |||
| 5470 |
|
5457 | |||
| 5471 | else: |
|
5458 | else: | |
| 5472 | meteorAux[0:4] = [mHeight, mStart, mPeak, mEnd] |
|
5459 | meteorAux[0:4] = [mHeight, mStart, mPeak, mEnd] | |
| 5473 | meteorAux[-1] = 6 #Error Number 6: echo less than 5 samples long; too short for analysis |
|
5460 | meteorAux[-1] = 6 #Error Number 6: echo less than 5 samples long; too short for analysis | |
| 5474 | PowerSeries = 0 |
|
5461 | PowerSeries = 0 | |
| 5475 |
|
5462 | |||
| 5476 | listMeteors1.append(meteorAux) |
|
5463 | listMeteors1.append(meteorAux) | |
| 5477 | listPowerSeries.append(PowerSeries) |
|
5464 | listPowerSeries.append(PowerSeries) | |
| 5478 | listVoltageSeries.append(meteorVolts1) |
|
5465 | listVoltageSeries.append(meteorVolts1) | |
| 5479 |
|
5466 | |||
| 5480 | return listMeteors1, listPowerSeries, listVoltageSeries |
|
5467 | return listMeteors1, listPowerSeries, listVoltageSeries | |
| 5481 |
|
5468 | |||
| 5482 | def __estimateDecayTime(self, listMeteors, listPower, timeInterval, frequency): |
|
5469 | def __estimateDecayTime(self, listMeteors, listPower, timeInterval, frequency): | |
| 5483 |
|
5470 | |||
| 5484 | threshError = 10 |
|
5471 | threshError = 10 | |
| 5485 | #Depending if it is 30 or 50 MHz |
|
5472 | #Depending if it is 30 or 50 MHz | |
| 5486 | if frequency == 30e6: |
|
5473 | if frequency == 30e6: | |
| 5487 | timeLag = 45*10**-3 |
|
5474 | timeLag = 45*10**-3 | |
| 5488 | else: |
|
5475 | else: | |
| 5489 | timeLag = 15*10**-3 |
|
5476 | timeLag = 15*10**-3 | |
| 5490 | lag = numpy.ceil(timeLag/timeInterval) |
|
5477 | lag = numpy.ceil(timeLag/timeInterval) | |
| 5491 |
|
5478 | |||
| 5492 | listMeteors1 = [] |
|
5479 | listMeteors1 = [] | |
| 5493 |
|
5480 | |||
| 5494 | for i in range(len(listMeteors)): |
|
5481 | for i in range(len(listMeteors)): | |
| 5495 | meteorPower = listPower[i] |
|
5482 | meteorPower = listPower[i] | |
| 5496 | meteorAux = listMeteors[i] |
|
5483 | meteorAux = listMeteors[i] | |
| 5497 |
|
5484 | |||
| 5498 | if meteorAux[-1] == 0: |
|
5485 | if meteorAux[-1] == 0: | |
| 5499 |
|
5486 | |||
| 5500 | try: |
|
5487 | try: | |
| 5501 | indmax = meteorPower.argmax() |
|
5488 | indmax = meteorPower.argmax() | |
| 5502 | indlag = indmax + lag |
|
5489 | indlag = indmax + lag | |
| 5503 |
|
5490 | |||
| 5504 | y = meteorPower[indlag:] |
|
5491 | y = meteorPower[indlag:] | |
| 5505 | x = numpy.arange(0, y.size)*timeLag |
|
5492 | x = numpy.arange(0, y.size)*timeLag | |
| 5506 |
|
5493 | |||
| 5507 | #first guess |
|
5494 | #first guess | |
| 5508 | a = y[0] |
|
5495 | a = y[0] | |
| 5509 | tau = timeLag |
|
5496 | tau = timeLag | |
| 5510 | #exponential fit |
|
5497 | #exponential fit | |
| 5511 | popt, pcov = optimize.curve_fit(self.__exponential_function, x, y, p0 = [a, tau]) |
|
5498 | popt, pcov = optimize.curve_fit(self.__exponential_function, x, y, p0 = [a, tau]) | |
| 5512 | y1 = self.__exponential_function(x, *popt) |
|
5499 | y1 = self.__exponential_function(x, *popt) | |
| 5513 | #error estimation |
|
5500 | #error estimation | |
| 5514 | error = sum((y - y1)**2)/(numpy.var(y)*(y.size - popt.size)) |
|
5501 | error = sum((y - y1)**2)/(numpy.var(y)*(y.size - popt.size)) | |
| 5515 |
|
5502 | |||
| 5516 | decayTime = popt[1] |
|
5503 | decayTime = popt[1] | |
| 5517 | riseTime = indmax*timeInterval |
|
5504 | riseTime = indmax*timeInterval | |
| 5518 | meteorAux[11:13] = [decayTime, error] |
|
5505 | meteorAux[11:13] = [decayTime, error] | |
| 5519 |
|
5506 | |||
| 5520 | #Table items 7, 8 and 11 |
|
5507 | #Table items 7, 8 and 11 | |
| 5521 | if (riseTime > 0.3): #Number 7: Echo rise exceeds 0.3s |
|
5508 | if (riseTime > 0.3): #Number 7: Echo rise exceeds 0.3s | |
| 5522 | meteorAux[-1] = 7 |
|
5509 | meteorAux[-1] = 7 | |
| 5523 | elif (decayTime < 2*riseTime) : #Number 8: Echo decay time less than than twice rise time |
|
5510 | elif (decayTime < 2*riseTime) : #Number 8: Echo decay time less than than twice rise time | |
| 5524 | meteorAux[-1] = 8 |
|
5511 | meteorAux[-1] = 8 | |
| 5525 | if (error > threshError): #Number 11: Poor fit to amplitude for estimation of decay time |
|
5512 | if (error > threshError): #Number 11: Poor fit to amplitude for estimation of decay time | |
| 5526 | meteorAux[-1] = 11 |
|
5513 | meteorAux[-1] = 11 | |
| 5527 |
|
5514 | |||
| 5528 |
|
5515 | |||
| 5529 | except: |
|
5516 | except: | |
| 5530 | meteorAux[-1] = 11 |
|
5517 | meteorAux[-1] = 11 | |
| 5531 |
|
5518 | |||
| 5532 |
|
5519 | |||
| 5533 | listMeteors1.append(meteorAux) |
|
5520 | listMeteors1.append(meteorAux) | |
| 5534 |
|
5521 | |||
| 5535 | return listMeteors1 |
|
5522 | return listMeteors1 | |
| 5536 |
|
5523 | |||
| 5537 | #Exponential Function |
|
5524 | #Exponential Function | |
| 5538 |
|
5525 | |||
| 5539 | def __exponential_function(self, x, a, tau): |
|
5526 | def __exponential_function(self, x, a, tau): | |
| 5540 | y = a*numpy.exp(-x/tau) |
|
5527 | y = a*numpy.exp(-x/tau) | |
| 5541 | return y |
|
5528 | return y | |
| 5542 |
|
5529 | |||
| 5543 | def __getRadialVelocity(self, listMeteors, listVolts, radialStdThresh, pairslist, timeInterval): |
|
5530 | def __getRadialVelocity(self, listMeteors, listVolts, radialStdThresh, pairslist, timeInterval): | |
| 5544 |
|
5531 | |||
| 5545 | pairslist1 = list(pairslist) |
|
5532 | pairslist1 = list(pairslist) | |
| 5546 | pairslist1.append((0,1)) |
|
5533 | pairslist1.append((0,1)) | |
| 5547 | pairslist1.append((3,4)) |
|
5534 | pairslist1.append((3,4)) | |
| 5548 | numPairs = len(pairslist1) |
|
5535 | numPairs = len(pairslist1) | |
| 5549 | #Time Lag |
|
5536 | #Time Lag | |
| 5550 | timeLag = 45*10**-3 |
|
5537 | timeLag = 45*10**-3 | |
| 5551 | c = 3e8 |
|
5538 | c = 3e8 | |
| 5552 | lag = numpy.ceil(timeLag/timeInterval) |
|
5539 | lag = numpy.ceil(timeLag/timeInterval) | |
| 5553 | freq = 30e6 |
|
5540 | freq = 30e6 | |
| 5554 |
|
5541 | |||
| 5555 | listMeteors1 = [] |
|
5542 | listMeteors1 = [] | |
| 5556 |
|
5543 | |||
| 5557 | for i in range(len(listMeteors)): |
|
5544 | for i in range(len(listMeteors)): | |
| 5558 | meteorAux = listMeteors[i] |
|
5545 | meteorAux = listMeteors[i] | |
| 5559 | if meteorAux[-1] == 0: |
|
5546 | if meteorAux[-1] == 0: | |
| 5560 | mStart = listMeteors[i][1] |
|
5547 | mStart = listMeteors[i][1] | |
| 5561 | mPeak = listMeteors[i][2] |
|
5548 | mPeak = listMeteors[i][2] | |
| 5562 | mLag = mPeak - mStart + lag |
|
5549 | mLag = mPeak - mStart + lag | |
| 5563 |
|
5550 | |||
| 5564 | #get the volt data between the start and end times of the meteor |
|
5551 | #get the volt data between the start and end times of the meteor | |
| 5565 | meteorVolts = listVolts[i] |
|
5552 | meteorVolts = listVolts[i] | |
| 5566 | meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) |
|
5553 | meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) | |
| 5567 |
|
5554 | |||
| 5568 | #Get CCF |
|
5555 | #Get CCF | |
| 5569 | allCCFs = self.__calculateCCF(meteorVolts, pairslist1, [-2,-1,0,1,2]) |
|
5556 | allCCFs = self.__calculateCCF(meteorVolts, pairslist1, [-2,-1,0,1,2]) | |
| 5570 |
|
5557 | |||
| 5571 | #Method 2 |
|
5558 | #Method 2 | |
| 5572 | slopes = numpy.zeros(numPairs) |
|
5559 | slopes = numpy.zeros(numPairs) | |
| 5573 | time = numpy.array([-2,-1,1,2])*timeInterval |
|
5560 | time = numpy.array([-2,-1,1,2])*timeInterval | |
| 5574 | angAllCCF = numpy.angle(allCCFs[:,[0,1,3,4],0]) |
|
5561 | angAllCCF = numpy.angle(allCCFs[:,[0,1,3,4],0]) | |
| 5575 |
|
5562 | |||
| 5576 | #Correct phases |
|
5563 | #Correct phases | |
| 5577 | derPhaseCCF = angAllCCF[:,1:] - angAllCCF[:,0:-1] |
|
5564 | derPhaseCCF = angAllCCF[:,1:] - angAllCCF[:,0:-1] | |
| 5578 | indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) |
|
5565 | indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) | |
| 5579 |
|
5566 | |||
| 5580 | if indDer[0].shape[0] > 0: |
|
5567 | if indDer[0].shape[0] > 0: | |
| 5581 | for i in range(indDer[0].shape[0]): |
|
5568 | for i in range(indDer[0].shape[0]): | |
| 5582 | signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i]]) |
|
5569 | signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i]]) | |
| 5583 | angAllCCF[indDer[0][i],indDer[1][i]+1:] += signo*2*numpy.pi |
|
5570 | angAllCCF[indDer[0][i],indDer[1][i]+1:] += signo*2*numpy.pi | |
| 5584 |
|
5571 | |||
| 5585 | for j in range(numPairs): |
|
5572 | for j in range(numPairs): | |
| 5586 | fit = stats.linregress(time, angAllCCF[j,:]) |
|
5573 | fit = stats.linregress(time, angAllCCF[j,:]) | |
| 5587 | slopes[j] = fit[0] |
|
5574 | slopes[j] = fit[0] | |
| 5588 |
|
5575 | |||
| 5589 | #Remove Outlier |
|
5576 | #Remove Outlier | |
| 5590 | radialVelocity = -numpy.mean(slopes)*(0.25/numpy.pi)*(c/freq) |
|
5577 | radialVelocity = -numpy.mean(slopes)*(0.25/numpy.pi)*(c/freq) | |
| 5591 | radialError = numpy.std(slopes)*(0.25/numpy.pi)*(c/freq) |
|
5578 | radialError = numpy.std(slopes)*(0.25/numpy.pi)*(c/freq) | |
| 5592 | meteorAux[-2] = radialError |
|
5579 | meteorAux[-2] = radialError | |
| 5593 | meteorAux[-3] = radialVelocity |
|
5580 | meteorAux[-3] = radialVelocity | |
| 5594 |
|
5581 | |||
| 5595 | #Setting Error |
|
5582 | #Setting Error | |
| 5596 | #Number 15: Radial Drift velocity or projected horizontal velocity exceeds 200 m/s |
|
5583 | #Number 15: Radial Drift velocity or projected horizontal velocity exceeds 200 m/s | |
| 5597 | if numpy.abs(radialVelocity) > 200: |
|
5584 | if numpy.abs(radialVelocity) > 200: | |
| 5598 | meteorAux[-1] = 15 |
|
5585 | meteorAux[-1] = 15 | |
| 5599 | #Number 12: Poor fit to CCF variation for estimation of radial drift velocity |
|
5586 | #Number 12: Poor fit to CCF variation for estimation of radial drift velocity | |
| 5600 | elif radialError > radialStdThresh: |
|
5587 | elif radialError > radialStdThresh: | |
| 5601 | meteorAux[-1] = 12 |
|
5588 | meteorAux[-1] = 12 | |
| 5602 |
|
5589 | |||
| 5603 | listMeteors1.append(meteorAux) |
|
5590 | listMeteors1.append(meteorAux) | |
| 5604 | return listMeteors1 |
|
5591 | return listMeteors1 | |
| 5605 |
|
5592 | |||
| 5606 | def __setNewArrays(self, listMeteors, date, heiRang): |
|
5593 | def __setNewArrays(self, listMeteors, date, heiRang): | |
| 5607 |
|
5594 | |||
| 5608 | #New arrays |
|
5595 | #New arrays | |
| 5609 | arrayMeteors = numpy.array(listMeteors) |
|
5596 | arrayMeteors = numpy.array(listMeteors) | |
| 5610 | arrayParameters = numpy.zeros((len(listMeteors), 13)) |
|
5597 | arrayParameters = numpy.zeros((len(listMeteors), 13)) | |
| 5611 |
|
5598 | |||
| 5612 | #Date inclusion |
|
5599 | #Date inclusion | |
| 5613 | arrayDate = numpy.tile(date, (len(listMeteors))) |
|
5600 | arrayDate = numpy.tile(date, (len(listMeteors))) | |
| 5614 |
|
5601 | |||
| 5615 | #Meteor array |
|
5602 | #Meteor array | |
| 5616 | #Parameters Array |
|
5603 | #Parameters Array | |
| 5617 | arrayParameters[:,0] = arrayDate #Date |
|
5604 | arrayParameters[:,0] = arrayDate #Date | |
| 5618 | arrayParameters[:,1] = heiRang[arrayMeteors[:,0].astype(int)] #Range |
|
5605 | arrayParameters[:,1] = heiRang[arrayMeteors[:,0].astype(int)] #Range | |
| 5619 | arrayParameters[:,6:8] = arrayMeteors[:,-3:-1] #Radial velocity and its error |
|
5606 | arrayParameters[:,6:8] = arrayMeteors[:,-3:-1] #Radial velocity and its error | |
| 5620 | arrayParameters[:,8:12] = arrayMeteors[:,7:11] #Phases |
|
5607 | arrayParameters[:,8:12] = arrayMeteors[:,7:11] #Phases | |
| 5621 | arrayParameters[:,-1] = arrayMeteors[:,-1] #Error |
|
5608 | arrayParameters[:,-1] = arrayMeteors[:,-1] #Error | |
| 5622 |
|
5609 | |||
| 5623 |
|
5610 | |||
| 5624 | return arrayParameters |
|
5611 | return arrayParameters | |
| 5625 |
|
5612 | |||
| 5626 | class CorrectSMPhases(Operation): |
|
5613 | class CorrectSMPhases(Operation): | |
| 5627 |
|
5614 | |||
| 5628 | def run(self, dataOut, phaseOffsets, hmin = 50, hmax = 150, azimuth = 45, channelPositions = None): |
|
5615 | def run(self, dataOut, phaseOffsets, hmin = 50, hmax = 150, azimuth = 45, channelPositions = None): | |
| 5629 |
|
5616 | |||
| 5630 | arrayParameters = dataOut.data_param |
|
5617 | arrayParameters = dataOut.data_param | |
| 5631 | pairsList = [] |
|
5618 | pairsList = [] | |
| 5632 | pairx = (0,1) |
|
5619 | pairx = (0,1) | |
| 5633 | pairy = (2,3) |
|
5620 | pairy = (2,3) | |
| 5634 | pairsList.append(pairx) |
|
5621 | pairsList.append(pairx) | |
| 5635 | pairsList.append(pairy) |
|
5622 | pairsList.append(pairy) | |
| 5636 | jph = numpy.zeros(4) |
|
5623 | jph = numpy.zeros(4) | |
| 5637 |
|
5624 | |||
| 5638 | phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180 |
|
5625 | phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180 | |
| 5639 | # arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets) |
|
5626 | # arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets) | |
| 5640 | arrayParameters[:,8:12] = numpy.angle(numpy.exp(1j*(arrayParameters[:,8:12] + phaseOffsets))) |
|
5627 | arrayParameters[:,8:12] = numpy.angle(numpy.exp(1j*(arrayParameters[:,8:12] + phaseOffsets))) | |
| 5641 |
|
5628 | |||
| 5642 | meteorOps = SMOperations() |
|
5629 | meteorOps = SMOperations() | |
| 5643 | if channelPositions is None: |
|
5630 | if channelPositions is None: | |
| 5644 | # channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T |
|
5631 | # channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T | |
| 5645 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella |
|
5632 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella | |
| 5646 |
|
5633 | |||
| 5647 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) |
|
5634 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) | |
| 5648 | h = (hmin,hmax) |
|
5635 | h = (hmin,hmax) | |
| 5649 |
|
5636 | |||
| 5650 | arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph) |
|
5637 | arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph) | |
| 5651 |
|
5638 | |||
| 5652 | dataOut.data_param = arrayParameters |
|
5639 | dataOut.data_param = arrayParameters | |
| 5653 | return |
|
5640 | return | |
| 5654 |
|
5641 | |||
| 5655 | class SMPhaseCalibration(Operation): |
|
5642 | class SMPhaseCalibration(Operation): | |
| 5656 |
|
5643 | |||
| 5657 | __buffer = None |
|
5644 | __buffer = None | |
| 5658 |
|
5645 | |||
| 5659 | __initime = None |
|
5646 | __initime = None | |
| 5660 |
|
5647 | |||
| 5661 | __dataReady = False |
|
5648 | __dataReady = False | |
| 5662 |
|
5649 | |||
| 5663 | __isConfig = False |
|
5650 | __isConfig = False | |
| 5664 |
|
5651 | |||
| 5665 | def __checkTime(self, currentTime, initTime, paramInterval, outputInterval): |
|
5652 | def __checkTime(self, currentTime, initTime, paramInterval, outputInterval): | |
| 5666 |
|
5653 | |||
| 5667 | dataTime = currentTime + paramInterval |
|
5654 | dataTime = currentTime + paramInterval | |
| 5668 | deltaTime = dataTime - initTime |
|
5655 | deltaTime = dataTime - initTime | |
| 5669 |
|
5656 | |||
| 5670 | if deltaTime >= outputInterval or deltaTime < 0: |
|
5657 | if deltaTime >= outputInterval or deltaTime < 0: | |
| 5671 | return True |
|
5658 | return True | |
| 5672 |
|
5659 | |||
| 5673 | return False |
|
5660 | return False | |
| 5674 |
|
5661 | |||
| 5675 | def __getGammas(self, pairs, d, phases): |
|
5662 | def __getGammas(self, pairs, d, phases): | |
| 5676 | gammas = numpy.zeros(2) |
|
5663 | gammas = numpy.zeros(2) | |
| 5677 |
|
5664 | |||
| 5678 | for i in range(len(pairs)): |
|
5665 | for i in range(len(pairs)): | |
| 5679 |
|
5666 | |||
| 5680 | pairi = pairs[i] |
|
5667 | pairi = pairs[i] | |
| 5681 |
|
5668 | |||
| 5682 | phip3 = phases[:,pairi[0]] |
|
5669 | phip3 = phases[:,pairi[0]] | |
| 5683 | d3 = d[pairi[0]] |
|
5670 | d3 = d[pairi[0]] | |
| 5684 | phip2 = phases[:,pairi[1]] |
|
5671 | phip2 = phases[:,pairi[1]] | |
| 5685 | d2 = d[pairi[1]] |
|
5672 | d2 = d[pairi[1]] | |
| 5686 | #Calculating gamma |
|
5673 | #Calculating gamma | |
| 5687 | jgamma = -phip2*d3/d2 - phip3 |
|
5674 | jgamma = -phip2*d3/d2 - phip3 | |
| 5688 | jgamma = numpy.angle(numpy.exp(1j*jgamma)) |
|
5675 | jgamma = numpy.angle(numpy.exp(1j*jgamma)) | |
| 5689 |
|
5676 | |||
| 5690 | #Revised distribution |
|
5677 | #Revised distribution | |
| 5691 | jgammaArray = numpy.hstack((jgamma,jgamma+0.5*numpy.pi,jgamma-0.5*numpy.pi)) |
|
5678 | jgammaArray = numpy.hstack((jgamma,jgamma+0.5*numpy.pi,jgamma-0.5*numpy.pi)) | |
| 5692 |
|
5679 | |||
| 5693 | #Histogram |
|
5680 | #Histogram | |
| 5694 | nBins = 64 |
|
5681 | nBins = 64 | |
| 5695 | rmin = -0.5*numpy.pi |
|
5682 | rmin = -0.5*numpy.pi | |
| 5696 | rmax = 0.5*numpy.pi |
|
5683 | rmax = 0.5*numpy.pi | |
| 5697 | phaseHisto = numpy.histogram(jgammaArray, bins=nBins, range=(rmin,rmax)) |
|
5684 | phaseHisto = numpy.histogram(jgammaArray, bins=nBins, range=(rmin,rmax)) | |
| 5698 |
|
5685 | |||
| 5699 | meteorsY = phaseHisto[0] |
|
5686 | meteorsY = phaseHisto[0] | |
| 5700 | phasesX = phaseHisto[1][:-1] |
|
5687 | phasesX = phaseHisto[1][:-1] | |
| 5701 | width = phasesX[1] - phasesX[0] |
|
5688 | width = phasesX[1] - phasesX[0] | |
| 5702 | phasesX += width/2 |
|
5689 | phasesX += width/2 | |
| 5703 |
|
5690 | |||
| 5704 | #Gaussian aproximation |
|
5691 | #Gaussian aproximation | |
| 5705 | bpeak = meteorsY.argmax() |
|
5692 | bpeak = meteorsY.argmax() | |
| 5706 | peak = meteorsY.max() |
|
5693 | peak = meteorsY.max() | |
| 5707 | jmin = bpeak - 5 |
|
5694 | jmin = bpeak - 5 | |
| 5708 | jmax = bpeak + 5 + 1 |
|
5695 | jmax = bpeak + 5 + 1 | |
| 5709 |
|
5696 | |||
| 5710 | if jmin<0: |
|
5697 | if jmin<0: | |
| 5711 | jmin = 0 |
|
5698 | jmin = 0 | |
| 5712 | jmax = 6 |
|
5699 | jmax = 6 | |
| 5713 | elif jmax > meteorsY.size: |
|
5700 | elif jmax > meteorsY.size: | |
| 5714 | jmin = meteorsY.size - 6 |
|
5701 | jmin = meteorsY.size - 6 | |
| 5715 | jmax = meteorsY.size |
|
5702 | jmax = meteorsY.size | |
| 5716 |
|
5703 | |||
| 5717 | x0 = numpy.array([peak,bpeak,50]) |
|
5704 | x0 = numpy.array([peak,bpeak,50]) | |
| 5718 | coeff = optimize.leastsq(self.__residualFunction, x0, args=(meteorsY[jmin:jmax], phasesX[jmin:jmax])) |
|
5705 | coeff = optimize.leastsq(self.__residualFunction, x0, args=(meteorsY[jmin:jmax], phasesX[jmin:jmax])) | |
| 5719 |
|
5706 | |||
| 5720 | #Gammas |
|
5707 | #Gammas | |
| 5721 | gammas[i] = coeff[0][1] |
|
5708 | gammas[i] = coeff[0][1] | |
| 5722 |
|
5709 | |||
| 5723 | return gammas |
|
5710 | return gammas | |
| 5724 |
|
5711 | |||
| 5725 | def __residualFunction(self, coeffs, y, t): |
|
5712 | def __residualFunction(self, coeffs, y, t): | |
| 5726 |
|
5713 | |||
| 5727 | return y - self.__gauss_function(t, coeffs) |
|
5714 | return y - self.__gauss_function(t, coeffs) | |
| 5728 |
|
5715 | |||
| 5729 | def __gauss_function(self, t, coeffs): |
|
5716 | def __gauss_function(self, t, coeffs): | |
| 5730 |
|
5717 | |||
| 5731 | return coeffs[0]*numpy.exp(-0.5*((t - coeffs[1]) / coeffs[2])**2) |
|
5718 | return coeffs[0]*numpy.exp(-0.5*((t - coeffs[1]) / coeffs[2])**2) | |
| 5732 |
|
5719 | |||
| 5733 | def __getPhases(self, azimuth, h, pairsList, d, gammas, meteorsArray): |
|
5720 | def __getPhases(self, azimuth, h, pairsList, d, gammas, meteorsArray): | |
| 5734 | meteorOps = SMOperations() |
|
5721 | meteorOps = SMOperations() | |
| 5735 | nchan = 4 |
|
5722 | nchan = 4 | |
| 5736 | pairx = pairsList[0] #x es 0 |
|
5723 | pairx = pairsList[0] #x es 0 | |
| 5737 | pairy = pairsList[1] #y es 1 |
|
5724 | pairy = pairsList[1] #y es 1 | |
| 5738 | center_xangle = 0 |
|
5725 | center_xangle = 0 | |
| 5739 | center_yangle = 0 |
|
5726 | center_yangle = 0 | |
| 5740 | range_angle = numpy.array([10*numpy.pi,numpy.pi,numpy.pi/2,numpy.pi/4]) |
|
5727 | range_angle = numpy.array([10*numpy.pi,numpy.pi,numpy.pi/2,numpy.pi/4]) | |
| 5741 | ntimes = len(range_angle) |
|
5728 | ntimes = len(range_angle) | |
| 5742 |
|
5729 | |||
| 5743 | nstepsx = 20 |
|
5730 | nstepsx = 20 | |
| 5744 | nstepsy = 20 |
|
5731 | nstepsy = 20 | |
| 5745 |
|
5732 | |||
| 5746 | for iz in range(ntimes): |
|
5733 | for iz in range(ntimes): | |
| 5747 | min_xangle = -range_angle[iz]/2 + center_xangle |
|
5734 | min_xangle = -range_angle[iz]/2 + center_xangle | |
| 5748 | max_xangle = range_angle[iz]/2 + center_xangle |
|
5735 | max_xangle = range_angle[iz]/2 + center_xangle | |
| 5749 | min_yangle = -range_angle[iz]/2 + center_yangle |
|
5736 | min_yangle = -range_angle[iz]/2 + center_yangle | |
| 5750 | max_yangle = range_angle[iz]/2 + center_yangle |
|
5737 | max_yangle = range_angle[iz]/2 + center_yangle | |
| 5751 |
|
5738 | |||
| 5752 | inc_x = (max_xangle-min_xangle)/nstepsx |
|
5739 | inc_x = (max_xangle-min_xangle)/nstepsx | |
| 5753 | inc_y = (max_yangle-min_yangle)/nstepsy |
|
5740 | inc_y = (max_yangle-min_yangle)/nstepsy | |
| 5754 |
|
5741 | |||
| 5755 | alpha_y = numpy.arange(nstepsy)*inc_y + min_yangle |
|
5742 | alpha_y = numpy.arange(nstepsy)*inc_y + min_yangle | |
| 5756 | alpha_x = numpy.arange(nstepsx)*inc_x + min_xangle |
|
5743 | alpha_x = numpy.arange(nstepsx)*inc_x + min_xangle | |
| 5757 | penalty = numpy.zeros((nstepsx,nstepsy)) |
|
5744 | penalty = numpy.zeros((nstepsx,nstepsy)) | |
| 5758 | jph_array = numpy.zeros((nchan,nstepsx,nstepsy)) |
|
5745 | jph_array = numpy.zeros((nchan,nstepsx,nstepsy)) | |
| 5759 | jph = numpy.zeros(nchan) |
|
5746 | jph = numpy.zeros(nchan) | |
| 5760 |
|
5747 | |||
| 5761 | # Iterations looking for the offset |
|
5748 | # Iterations looking for the offset | |
| 5762 | for iy in range(int(nstepsy)): |
|
5749 | for iy in range(int(nstepsy)): | |
| 5763 | for ix in range(int(nstepsx)): |
|
5750 | for ix in range(int(nstepsx)): | |
| 5764 | d3 = d[pairsList[1][0]] |
|
5751 | d3 = d[pairsList[1][0]] | |
| 5765 | d2 = d[pairsList[1][1]] |
|
5752 | d2 = d[pairsList[1][1]] | |
| 5766 | d5 = d[pairsList[0][0]] |
|
5753 | d5 = d[pairsList[0][0]] | |
| 5767 | d4 = d[pairsList[0][1]] |
|
5754 | d4 = d[pairsList[0][1]] | |
| 5768 |
|
5755 | |||
| 5769 | alp2 = alpha_y[iy] #gamma 1 |
|
5756 | alp2 = alpha_y[iy] #gamma 1 | |
| 5770 | alp4 = alpha_x[ix] #gamma 0 |
|
5757 | alp4 = alpha_x[ix] #gamma 0 | |
| 5771 |
|
5758 | |||
| 5772 | alp3 = -alp2*d3/d2 - gammas[1] |
|
5759 | alp3 = -alp2*d3/d2 - gammas[1] | |
| 5773 | alp5 = -alp4*d5/d4 - gammas[0] |
|
5760 | alp5 = -alp4*d5/d4 - gammas[0] | |
| 5774 | jph[pairsList[0][1]] = alp4 |
|
5761 | jph[pairsList[0][1]] = alp4 | |
| 5775 | jph[pairsList[0][0]] = alp5 |
|
5762 | jph[pairsList[0][0]] = alp5 | |
| 5776 | jph[pairsList[1][0]] = alp3 |
|
5763 | jph[pairsList[1][0]] = alp3 | |
| 5777 | jph[pairsList[1][1]] = alp2 |
|
5764 | jph[pairsList[1][1]] = alp2 | |
| 5778 | jph_array[:,ix,iy] = jph |
|
5765 | jph_array[:,ix,iy] = jph | |
| 5779 | meteorsArray1 = meteorOps.getMeteorParams(meteorsArray, azimuth, h, pairsList, d, jph) |
|
5766 | meteorsArray1 = meteorOps.getMeteorParams(meteorsArray, azimuth, h, pairsList, d, jph) | |
| 5780 | error = meteorsArray1[:,-1] |
|
5767 | error = meteorsArray1[:,-1] | |
| 5781 | ind1 = numpy.where(error==0)[0] |
|
5768 | ind1 = numpy.where(error==0)[0] | |
| 5782 | penalty[ix,iy] = ind1.size |
|
5769 | penalty[ix,iy] = ind1.size | |
| 5783 |
|
5770 | |||
| 5784 | i,j = numpy.unravel_index(penalty.argmax(), penalty.shape) |
|
5771 | i,j = numpy.unravel_index(penalty.argmax(), penalty.shape) | |
| 5785 | phOffset = jph_array[:,i,j] |
|
5772 | phOffset = jph_array[:,i,j] | |
| 5786 |
|
5773 | |||
| 5787 | center_xangle = phOffset[pairx[1]] |
|
5774 | center_xangle = phOffset[pairx[1]] | |
| 5788 | center_yangle = phOffset[pairy[1]] |
|
5775 | center_yangle = phOffset[pairy[1]] | |
| 5789 |
|
5776 | |||
| 5790 | phOffset = numpy.angle(numpy.exp(1j*jph_array[:,i,j])) |
|
5777 | phOffset = numpy.angle(numpy.exp(1j*jph_array[:,i,j])) | |
| 5791 | phOffset = phOffset*180/numpy.pi |
|
5778 | phOffset = phOffset*180/numpy.pi | |
| 5792 | return phOffset |
|
5779 | return phOffset | |
| 5793 |
|
5780 | |||
| 5794 |
|
5781 | |||
| 5795 | def run(self, dataOut, hmin, hmax, channelPositions=None, nHours = 1): |
|
5782 | def run(self, dataOut, hmin, hmax, channelPositions=None, nHours = 1): | |
| 5796 |
|
5783 | |||
| 5797 | dataOut.flagNoData = True |
|
5784 | dataOut.flagNoData = True | |
| 5798 | self.__dataReady = False |
|
5785 | self.__dataReady = False | |
| 5799 | dataOut.outputInterval = nHours*3600 |
|
5786 | dataOut.outputInterval = nHours*3600 | |
| 5800 |
|
5787 | |||
| 5801 | if self.__isConfig == False: |
|
5788 | if self.__isConfig == False: | |
| 5802 | #Get Initial LTC time |
|
5789 | #Get Initial LTC time | |
| 5803 | self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
5790 | self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) | |
| 5804 | self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
5791 | self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() | |
| 5805 |
|
5792 | |||
| 5806 | self.__isConfig = True |
|
5793 | self.__isConfig = True | |
| 5807 |
|
5794 | |||
| 5808 | if self.__buffer is None: |
|
5795 | if self.__buffer is None: | |
| 5809 | self.__buffer = dataOut.data_param.copy() |
|
5796 | self.__buffer = dataOut.data_param.copy() | |
| 5810 |
|
5797 | |||
| 5811 | else: |
|
5798 | else: | |
| 5812 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
5799 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) | |
| 5813 |
|
5800 | |||
| 5814 | self.__dataReady = self.__checkTime(dataOut.utctime, self.__initime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
5801 | self.__dataReady = self.__checkTime(dataOut.utctime, self.__initime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready | |
| 5815 |
|
5802 | |||
| 5816 | if self.__dataReady: |
|
5803 | if self.__dataReady: | |
| 5817 | dataOut.utctimeInit = self.__initime |
|
5804 | dataOut.utctimeInit = self.__initime | |
| 5818 | self.__initime += dataOut.outputInterval #to erase time offset |
|
5805 | self.__initime += dataOut.outputInterval #to erase time offset | |
| 5819 |
|
5806 | |||
| 5820 | freq = dataOut.frequency |
|
5807 | freq = dataOut.frequency | |
| 5821 | c = dataOut.C #m/s |
|
5808 | c = dataOut.C #m/s | |
| 5822 | lamb = c/freq |
|
5809 | lamb = c/freq | |
| 5823 | k = 2*numpy.pi/lamb |
|
5810 | k = 2*numpy.pi/lamb | |
| 5824 | azimuth = 0 |
|
5811 | azimuth = 0 | |
| 5825 | h = (hmin, hmax) |
|
5812 | h = (hmin, hmax) | |
| 5826 |
|
5813 | |||
| 5827 | if channelPositions is None: |
|
5814 | if channelPositions is None: | |
| 5828 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella |
|
5815 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella | |
| 5829 | meteorOps = SMOperations() |
|
5816 | meteorOps = SMOperations() | |
| 5830 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) |
|
5817 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) | |
| 5831 |
|
5818 | |||
| 5832 | #Checking correct order of pairs |
|
5819 | #Checking correct order of pairs | |
| 5833 | pairs = [] |
|
5820 | pairs = [] | |
| 5834 | if distances[1] > distances[0]: |
|
5821 | if distances[1] > distances[0]: | |
| 5835 | pairs.append((1,0)) |
|
5822 | pairs.append((1,0)) | |
| 5836 | else: |
|
5823 | else: | |
| 5837 | pairs.append((0,1)) |
|
5824 | pairs.append((0,1)) | |
| 5838 |
|
5825 | |||
| 5839 | if distances[3] > distances[2]: |
|
5826 | if distances[3] > distances[2]: | |
| 5840 | pairs.append((3,2)) |
|
5827 | pairs.append((3,2)) | |
| 5841 | else: |
|
5828 | else: | |
| 5842 | pairs.append((2,3)) |
|
5829 | pairs.append((2,3)) | |
| 5843 |
|
5830 | |||
| 5844 | meteorsArray = self.__buffer |
|
5831 | meteorsArray = self.__buffer | |
| 5845 | error = meteorsArray[:,-1] |
|
5832 | error = meteorsArray[:,-1] | |
| 5846 | boolError = (error==0)|(error==3)|(error==4)|(error==13)|(error==14) |
|
5833 | boolError = (error==0)|(error==3)|(error==4)|(error==13)|(error==14) | |
| 5847 | ind1 = numpy.where(boolError)[0] |
|
5834 | ind1 = numpy.where(boolError)[0] | |
| 5848 | meteorsArray = meteorsArray[ind1,:] |
|
5835 | meteorsArray = meteorsArray[ind1,:] | |
| 5849 | meteorsArray[:,-1] = 0 |
|
5836 | meteorsArray[:,-1] = 0 | |
| 5850 | phases = meteorsArray[:,8:12] |
|
5837 | phases = meteorsArray[:,8:12] | |
| 5851 |
|
5838 | |||
| 5852 | #Calculate Gammas |
|
5839 | #Calculate Gammas | |
| 5853 | gammas = self.__getGammas(pairs, distances, phases) |
|
5840 | gammas = self.__getGammas(pairs, distances, phases) | |
| 5854 | #Calculate Phases |
|
5841 | #Calculate Phases | |
| 5855 | phasesOff = self.__getPhases(azimuth, h, pairs, distances, gammas, meteorsArray) |
|
5842 | phasesOff = self.__getPhases(azimuth, h, pairs, distances, gammas, meteorsArray) | |
| 5856 | phasesOff = phasesOff.reshape((1,phasesOff.size)) |
|
5843 | phasesOff = phasesOff.reshape((1,phasesOff.size)) | |
| 5857 | dataOut.data_output = -phasesOff |
|
5844 | dataOut.data_output = -phasesOff | |
| 5858 | dataOut.flagNoData = False |
|
5845 | dataOut.flagNoData = False | |
| 5859 | self.__buffer = None |
|
5846 | self.__buffer = None | |
| 5860 |
|
5847 | |||
| 5861 |
|
5848 | |||
| 5862 | return |
|
5849 | return | |
| 5863 |
|
5850 | |||
| 5864 | class SMOperations(): |
|
5851 | class SMOperations(): | |
| 5865 |
|
5852 | |||
| 5866 | def __init__(self): |
|
5853 | def __init__(self): | |
| 5867 |
|
5854 | |||
| 5868 | return |
|
5855 | return | |
| 5869 |
|
5856 | |||
| 5870 | def getMeteorParams(self, arrayParameters0, azimuth, h, pairsList, distances, jph): |
|
5857 | def getMeteorParams(self, arrayParameters0, azimuth, h, pairsList, distances, jph): | |
| 5871 |
|
5858 | |||
| 5872 | arrayParameters = arrayParameters0.copy() |
|
5859 | arrayParameters = arrayParameters0.copy() | |
| 5873 | hmin = h[0] |
|
5860 | hmin = h[0] | |
| 5874 | hmax = h[1] |
|
5861 | hmax = h[1] | |
| 5875 |
|
5862 | |||
| 5876 | #Calculate AOA (Error N 3, 4) |
|
5863 | #Calculate AOA (Error N 3, 4) | |
| 5877 | #JONES ET AL. 1998 |
|
5864 | #JONES ET AL. 1998 | |
| 5878 | AOAthresh = numpy.pi/8 |
|
5865 | AOAthresh = numpy.pi/8 | |
| 5879 | error = arrayParameters[:,-1] |
|
5866 | error = arrayParameters[:,-1] | |
| 5880 | phases = -arrayParameters[:,8:12] + jph |
|
5867 | phases = -arrayParameters[:,8:12] + jph | |
| 5881 | arrayParameters[:,3:6], arrayParameters[:,-1] = self.__getAOA(phases, pairsList, distances, error, AOAthresh, azimuth) |
|
5868 | arrayParameters[:,3:6], arrayParameters[:,-1] = self.__getAOA(phases, pairsList, distances, error, AOAthresh, azimuth) | |
| 5882 |
|
5869 | |||
| 5883 | #Calculate Heights (Error N 13 and 14) |
|
5870 | #Calculate Heights (Error N 13 and 14) | |
| 5884 | error = arrayParameters[:,-1] |
|
5871 | error = arrayParameters[:,-1] | |
| 5885 | Ranges = arrayParameters[:,1] |
|
5872 | Ranges = arrayParameters[:,1] | |
| 5886 | zenith = arrayParameters[:,4] |
|
5873 | zenith = arrayParameters[:,4] | |
| 5887 | arrayParameters[:,2], arrayParameters[:,-1] = self.__getHeights(Ranges, zenith, error, hmin, hmax) |
|
5874 | arrayParameters[:,2], arrayParameters[:,-1] = self.__getHeights(Ranges, zenith, error, hmin, hmax) | |
| 5888 |
|
5875 | |||
| 5889 | #----------------------- Get Final data ------------------------------------ |
|
5876 | #----------------------- Get Final data ------------------------------------ | |
| 5890 |
|
5877 | |||
| 5891 | return arrayParameters |
|
5878 | return arrayParameters | |
| 5892 |
|
5879 | |||
| 5893 | def __getAOA(self, phases, pairsList, directions, error, AOAthresh, azimuth): |
|
5880 | def __getAOA(self, phases, pairsList, directions, error, AOAthresh, azimuth): | |
| 5894 |
|
5881 | |||
| 5895 | arrayAOA = numpy.zeros((phases.shape[0],3)) |
|
5882 | arrayAOA = numpy.zeros((phases.shape[0],3)) | |
| 5896 | cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList,directions) |
|
5883 | cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList,directions) | |
| 5897 |
|
5884 | |||
| 5898 | arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth) |
|
5885 | arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth) | |
| 5899 | cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1) |
|
5886 | cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1) | |
| 5900 | arrayAOA[:,2] = cosDirError |
|
5887 | arrayAOA[:,2] = cosDirError | |
| 5901 |
|
5888 | |||
| 5902 | azimuthAngle = arrayAOA[:,0] |
|
5889 | azimuthAngle = arrayAOA[:,0] | |
| 5903 | zenithAngle = arrayAOA[:,1] |
|
5890 | zenithAngle = arrayAOA[:,1] | |
| 5904 |
|
5891 | |||
| 5905 | #Setting Error |
|
5892 | #Setting Error | |
| 5906 | indError = numpy.where(numpy.logical_or(error == 3, error == 4))[0] |
|
5893 | indError = numpy.where(numpy.logical_or(error == 3, error == 4))[0] | |
| 5907 | error[indError] = 0 |
|
5894 | error[indError] = 0 | |
| 5908 | #Number 3: AOA not fesible |
|
5895 | #Number 3: AOA not fesible | |
| 5909 | indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0] |
|
5896 | indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0] | |
| 5910 | error[indInvalid] = 3 |
|
5897 | error[indInvalid] = 3 | |
| 5911 | #Number 4: Large difference in AOAs obtained from different antenna baselines |
|
5898 | #Number 4: Large difference in AOAs obtained from different antenna baselines | |
| 5912 | indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0] |
|
5899 | indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0] | |
| 5913 | error[indInvalid] = 4 |
|
5900 | error[indInvalid] = 4 | |
| 5914 | return arrayAOA, error |
|
5901 | return arrayAOA, error | |
| 5915 |
|
5902 | |||
| 5916 | def __getDirectionCosines(self, arrayPhase, pairsList, distances): |
|
5903 | def __getDirectionCosines(self, arrayPhase, pairsList, distances): | |
| 5917 |
|
5904 | |||
| 5918 | #Initializing some variables |
|
5905 | #Initializing some variables | |
| 5919 | ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi |
|
5906 | ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi | |
| 5920 | ang_aux = ang_aux.reshape(1,ang_aux.size) |
|
5907 | ang_aux = ang_aux.reshape(1,ang_aux.size) | |
| 5921 |
|
5908 | |||
| 5922 | cosdir = numpy.zeros((arrayPhase.shape[0],2)) |
|
5909 | cosdir = numpy.zeros((arrayPhase.shape[0],2)) | |
| 5923 | cosdir0 = numpy.zeros((arrayPhase.shape[0],2)) |
|
5910 | cosdir0 = numpy.zeros((arrayPhase.shape[0],2)) | |
| 5924 |
|
5911 | |||
| 5925 |
|
5912 | |||
| 5926 | for i in range(2): |
|
5913 | for i in range(2): | |
| 5927 | ph0 = arrayPhase[:,pairsList[i][0]] |
|
5914 | ph0 = arrayPhase[:,pairsList[i][0]] | |
| 5928 | ph1 = arrayPhase[:,pairsList[i][1]] |
|
5915 | ph1 = arrayPhase[:,pairsList[i][1]] | |
| 5929 | d0 = distances[pairsList[i][0]] |
|
5916 | d0 = distances[pairsList[i][0]] | |
| 5930 | d1 = distances[pairsList[i][1]] |
|
5917 | d1 = distances[pairsList[i][1]] | |
| 5931 |
|
5918 | |||
| 5932 | ph0_aux = ph0 + ph1 |
|
5919 | ph0_aux = ph0 + ph1 | |
| 5933 | ph0_aux = numpy.angle(numpy.exp(1j*ph0_aux)) |
|
5920 | ph0_aux = numpy.angle(numpy.exp(1j*ph0_aux)) | |
| 5934 | #First Estimation |
|
5921 | #First Estimation | |
| 5935 | cosdir0[:,i] = (ph0_aux)/(2*numpy.pi*(d0 - d1)) |
|
5922 | cosdir0[:,i] = (ph0_aux)/(2*numpy.pi*(d0 - d1)) | |
| 5936 |
|
5923 | |||
| 5937 | #Most-Accurate Second Estimation |
|
5924 | #Most-Accurate Second Estimation | |
| 5938 | phi1_aux = ph0 - ph1 |
|
5925 | phi1_aux = ph0 - ph1 | |
| 5939 | phi1_aux = phi1_aux.reshape(phi1_aux.size,1) |
|
5926 | phi1_aux = phi1_aux.reshape(phi1_aux.size,1) | |
| 5940 | #Direction Cosine 1 |
|
5927 | #Direction Cosine 1 | |
| 5941 | cosdir1 = (phi1_aux + ang_aux)/(2*numpy.pi*(d0 + d1)) |
|
5928 | cosdir1 = (phi1_aux + ang_aux)/(2*numpy.pi*(d0 + d1)) | |
| 5942 |
|
5929 | |||
| 5943 | #Searching the correct Direction Cosine |
|
5930 | #Searching the correct Direction Cosine | |
| 5944 | cosdir0_aux = cosdir0[:,i] |
|
5931 | cosdir0_aux = cosdir0[:,i] | |
| 5945 | cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1) |
|
5932 | cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1) | |
| 5946 | #Minimum Distance |
|
5933 | #Minimum Distance | |
| 5947 | cosDiff = (cosdir1 - cosdir0_aux)**2 |
|
5934 | cosDiff = (cosdir1 - cosdir0_aux)**2 | |
| 5948 | indcos = cosDiff.argmin(axis = 1) |
|
5935 | indcos = cosDiff.argmin(axis = 1) | |
| 5949 | #Saving Value obtained |
|
5936 | #Saving Value obtained | |
| 5950 | cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos] |
|
5937 | cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos] | |
| 5951 |
|
5938 | |||
| 5952 | return cosdir0, cosdir |
|
5939 | return cosdir0, cosdir | |
| 5953 |
|
5940 | |||
| 5954 | def __calculateAOA(self, cosdir, azimuth): |
|
5941 | def __calculateAOA(self, cosdir, azimuth): | |
| 5955 | cosdirX = cosdir[:,0] |
|
5942 | cosdirX = cosdir[:,0] | |
| 5956 | cosdirY = cosdir[:,1] |
|
5943 | cosdirY = cosdir[:,1] | |
| 5957 |
|
5944 | |||
| 5958 | zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi |
|
5945 | zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi | |
| 5959 | azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth#0 deg north, 90 deg east |
|
5946 | azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth#0 deg north, 90 deg east | |
| 5960 | angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose() |
|
5947 | angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose() | |
| 5961 |
|
5948 | |||
| 5962 | return angles |
|
5949 | return angles | |
| 5963 |
|
5950 | |||
| 5964 | def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight): |
|
5951 | def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight): | |
| 5965 |
|
5952 | |||
| 5966 | Ramb = 375 #Ramb = c/(2*PRF) |
|
5953 | Ramb = 375 #Ramb = c/(2*PRF) | |
| 5967 | Re = 6371 #Earth Radius |
|
5954 | Re = 6371 #Earth Radius | |
| 5968 | heights = numpy.zeros(Ranges.shape) |
|
5955 | heights = numpy.zeros(Ranges.shape) | |
| 5969 |
|
5956 | |||
| 5970 | R_aux = numpy.array([0,1,2])*Ramb |
|
5957 | R_aux = numpy.array([0,1,2])*Ramb | |
| 5971 | R_aux = R_aux.reshape(1,R_aux.size) |
|
5958 | R_aux = R_aux.reshape(1,R_aux.size) | |
| 5972 |
|
5959 | |||
| 5973 | Ranges = Ranges.reshape(Ranges.size,1) |
|
5960 | Ranges = Ranges.reshape(Ranges.size,1) | |
| 5974 |
|
5961 | |||
| 5975 | Ri = Ranges + R_aux |
|
5962 | Ri = Ranges + R_aux | |
| 5976 | hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re |
|
5963 | hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re | |
| 5977 |
|
5964 | |||
| 5978 | #Check if there is a height between 70 and 110 km |
|
5965 | #Check if there is a height between 70 and 110 km | |
| 5979 | h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1) |
|
5966 | h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1) | |
| 5980 | ind_h = numpy.where(h_bool == 1)[0] |
|
5967 | ind_h = numpy.where(h_bool == 1)[0] | |
| 5981 |
|
5968 | |||
| 5982 | hCorr = hi[ind_h, :] |
|
5969 | hCorr = hi[ind_h, :] | |
| 5983 | ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight)) |
|
5970 | ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight)) | |
| 5984 |
|
5971 | |||
| 5985 | hCorr = hi[ind_hCorr][:len(ind_h)] |
|
5972 | hCorr = hi[ind_hCorr][:len(ind_h)] | |
| 5986 | heights[ind_h] = hCorr |
|
5973 | heights[ind_h] = hCorr | |
| 5987 |
|
5974 | |||
| 5988 | #Setting Error |
|
5975 | #Setting Error | |
| 5989 | #Number 13: Height unresolvable echo: not valid height within 70 to 110 km |
|
5976 | #Number 13: Height unresolvable echo: not valid height within 70 to 110 km | |
| 5990 | #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km |
|
5977 | #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km | |
| 5991 | indError = numpy.where(numpy.logical_or(error == 13, error == 14))[0] |
|
5978 | indError = numpy.where(numpy.logical_or(error == 13, error == 14))[0] | |
| 5992 | error[indError] = 0 |
|
5979 | error[indError] = 0 | |
| 5993 | indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0] |
|
5980 | indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0] | |
| 5994 | error[indInvalid2] = 14 |
|
5981 | error[indInvalid2] = 14 | |
| 5995 | indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0] |
|
5982 | indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0] | |
| 5996 | error[indInvalid1] = 13 |
|
5983 | error[indInvalid1] = 13 | |
| 5997 |
|
5984 | |||
| 5998 | return heights, error |
|
5985 | return heights, error | |
| 5999 |
|
5986 | |||
| 6000 | def getPhasePairs(self, channelPositions): |
|
5987 | def getPhasePairs(self, channelPositions): | |
| 6001 | chanPos = numpy.array(channelPositions) |
|
5988 | chanPos = numpy.array(channelPositions) | |
| 6002 | listOper = list(itertools.combinations(list(range(5)),2)) |
|
5989 | listOper = list(itertools.combinations(list(range(5)),2)) | |
| 6003 |
|
5990 | |||
| 6004 | distances = numpy.zeros(4) |
|
5991 | distances = numpy.zeros(4) | |
| 6005 | axisX = [] |
|
5992 | axisX = [] | |
| 6006 | axisY = [] |
|
5993 | axisY = [] | |
| 6007 | distX = numpy.zeros(3) |
|
5994 | distX = numpy.zeros(3) | |
| 6008 | distY = numpy.zeros(3) |
|
5995 | distY = numpy.zeros(3) | |
| 6009 | ix = 0 |
|
5996 | ix = 0 | |
| 6010 | iy = 0 |
|
5997 | iy = 0 | |
| 6011 |
|
5998 | |||
| 6012 | pairX = numpy.zeros((2,2)) |
|
5999 | pairX = numpy.zeros((2,2)) | |
| 6013 | pairY = numpy.zeros((2,2)) |
|
6000 | pairY = numpy.zeros((2,2)) | |
| 6014 |
|
6001 | |||
| 6015 | for i in range(len(listOper)): |
|
6002 | for i in range(len(listOper)): | |
| 6016 | pairi = listOper[i] |
|
6003 | pairi = listOper[i] | |
| 6017 |
|
6004 | |||
| 6018 | posDif = numpy.abs(chanPos[pairi[0],:] - chanPos[pairi[1],:]) |
|
6005 | posDif = numpy.abs(chanPos[pairi[0],:] - chanPos[pairi[1],:]) | |
| 6019 |
|
6006 | |||
| 6020 | if posDif[0] == 0: |
|
6007 | if posDif[0] == 0: | |
| 6021 | axisY.append(pairi) |
|
6008 | axisY.append(pairi) | |
| 6022 | distY[iy] = posDif[1] |
|
6009 | distY[iy] = posDif[1] | |
| 6023 | iy += 1 |
|
6010 | iy += 1 | |
| 6024 | elif posDif[1] == 0: |
|
6011 | elif posDif[1] == 0: | |
| 6025 | axisX.append(pairi) |
|
6012 | axisX.append(pairi) | |
| 6026 | distX[ix] = posDif[0] |
|
6013 | distX[ix] = posDif[0] | |
| 6027 | ix += 1 |
|
6014 | ix += 1 | |
| 6028 |
|
6015 | |||
| 6029 | for i in range(2): |
|
6016 | for i in range(2): | |
| 6030 | if i==0: |
|
6017 | if i==0: | |
| 6031 | dist0 = distX |
|
6018 | dist0 = distX | |
| 6032 | axis0 = axisX |
|
6019 | axis0 = axisX | |
| 6033 | else: |
|
6020 | else: | |
| 6034 | dist0 = distY |
|
6021 | dist0 = distY | |
| 6035 | axis0 = axisY |
|
6022 | axis0 = axisY | |
| 6036 |
|
6023 | |||
| 6037 | side = numpy.argsort(dist0)[:-1] |
|
6024 | side = numpy.argsort(dist0)[:-1] | |
| 6038 | axis0 = numpy.array(axis0)[side,:] |
|
6025 | axis0 = numpy.array(axis0)[side,:] | |
| 6039 | chanC = int(numpy.intersect1d(axis0[0,:], axis0[1,:])[0]) |
|
6026 | chanC = int(numpy.intersect1d(axis0[0,:], axis0[1,:])[0]) | |
| 6040 | axis1 = numpy.unique(numpy.reshape(axis0,4)) |
|
6027 | axis1 = numpy.unique(numpy.reshape(axis0,4)) | |
| 6041 | side = axis1[axis1 != chanC] |
|
6028 | side = axis1[axis1 != chanC] | |
| 6042 | diff1 = chanPos[chanC,i] - chanPos[side[0],i] |
|
6029 | diff1 = chanPos[chanC,i] - chanPos[side[0],i] | |
| 6043 | diff2 = chanPos[chanC,i] - chanPos[side[1],i] |
|
6030 | diff2 = chanPos[chanC,i] - chanPos[side[1],i] | |
| 6044 | if diff1<0: |
|
6031 | if diff1<0: | |
| 6045 | chan2 = side[0] |
|
6032 | chan2 = side[0] | |
| 6046 | d2 = numpy.abs(diff1) |
|
6033 | d2 = numpy.abs(diff1) | |
| 6047 | chan1 = side[1] |
|
6034 | chan1 = side[1] | |
| 6048 | d1 = numpy.abs(diff2) |
|
6035 | d1 = numpy.abs(diff2) | |
| 6049 | else: |
|
6036 | else: | |
| 6050 | chan2 = side[1] |
|
6037 | chan2 = side[1] | |
| 6051 | d2 = numpy.abs(diff2) |
|
6038 | d2 = numpy.abs(diff2) | |
| 6052 | chan1 = side[0] |
|
6039 | chan1 = side[0] | |
| 6053 | d1 = numpy.abs(diff1) |
|
6040 | d1 = numpy.abs(diff1) | |
| 6054 |
|
6041 | |||
| 6055 | if i==0: |
|
6042 | if i==0: | |
| 6056 | chanCX = chanC |
|
6043 | chanCX = chanC | |
| 6057 | chan1X = chan1 |
|
6044 | chan1X = chan1 | |
| 6058 | chan2X = chan2 |
|
6045 | chan2X = chan2 | |
| 6059 | distances[0:2] = numpy.array([d1,d2]) |
|
6046 | distances[0:2] = numpy.array([d1,d2]) | |
| 6060 | else: |
|
6047 | else: | |
| 6061 | chanCY = chanC |
|
6048 | chanCY = chanC | |
| 6062 | chan1Y = chan1 |
|
6049 | chan1Y = chan1 | |
| 6063 | chan2Y = chan2 |
|
6050 | chan2Y = chan2 | |
| 6064 | distances[2:4] = numpy.array([d1,d2]) |
|
6051 | distances[2:4] = numpy.array([d1,d2]) | |
| 6065 |
|
6052 | |||
| 6066 | pairslist = [(chanCX, chan1X),(chanCX, chan2X),(chanCY,chan1Y),(chanCY, chan2Y)] |
|
6053 | pairslist = [(chanCX, chan1X),(chanCX, chan2X),(chanCY,chan1Y),(chanCY, chan2Y)] | |
| 6067 |
|
6054 | |||
| 6068 | return pairslist, distances |
|
6055 | return pairslist, distances | |
| 6069 |
|
6056 | |||
| 6070 | class IGRFModel(Operation): |
|
6057 | class IGRFModel(Operation): | |
| 6071 | """Operation to calculate Geomagnetic parameters. |
|
6058 | """Operation to calculate Geomagnetic parameters. | |
| 6072 |
|
6059 | |||
| 6073 | Parameters: |
|
6060 | Parameters: | |
| 6074 | ----------- |
|
6061 | ----------- | |
| 6075 | None |
|
6062 | None | |
| 6076 |
|
6063 | |||
| 6077 | Example |
|
6064 | Example | |
| 6078 | -------- |
|
6065 | -------- | |
| 6079 |
|
6066 | |||
| 6080 | op = proc_unit.addOperation(name='IGRFModel', optype='other') |
|
6067 | op = proc_unit.addOperation(name='IGRFModel', optype='other') | |
| 6081 |
|
6068 | |||
| 6082 | """ |
|
6069 | """ | |
| 6083 |
|
6070 | |||
| 6084 | def __init__(self, **kwargs): |
|
6071 | def __init__(self, **kwargs): | |
| 6085 |
|
6072 | |||
| 6086 | Operation.__init__(self, **kwargs) |
|
6073 | Operation.__init__(self, **kwargs) | |
| 6087 |
|
6074 | |||
| 6088 | self.aux=1 |
|
6075 | self.aux=1 | |
| 6089 |
|
6076 | |||
| 6090 | def run(self,dataOut): |
|
6077 | def run(self,dataOut): | |
| 6091 |
|
6078 | |||
| 6092 | try: |
|
6079 | try: | |
| 6093 | from schainpy.model.proc import mkfact_short_2020 |
|
6080 | from schainpy.model.proc import mkfact_short_2020 | |
| 6094 | except: |
|
6081 | except: | |
| 6095 | log.warning('You should install "mkfact_short_2020" module to process IGRF Model') |
|
6082 | log.warning('You should install "mkfact_short_2020" module to process IGRF Model') | |
| 6096 |
|
6083 | |||
| 6097 | if self.aux==1: |
|
6084 | if self.aux==1: | |
| 6098 |
|
6085 | |||
| 6099 | #dataOut.TimeBlockSeconds_First_Time=time.mktime(time.strptime(dataOut.TimeBlockDate)) |
|
6086 | #dataOut.TimeBlockSeconds_First_Time=time.mktime(time.strptime(dataOut.TimeBlockDate)) | |
| 6100 | #### we do not use dataOut.datatime.ctime() because it's the time of the second (next) block |
|
6087 | #### we do not use dataOut.datatime.ctime() because it's the time of the second (next) block | |
| 6101 | dataOut.TimeBlockSeconds_First_Time=dataOut.TimeBlockSeconds |
|
6088 | dataOut.TimeBlockSeconds_First_Time=dataOut.TimeBlockSeconds | |
| 6102 | dataOut.bd_time=time.gmtime(dataOut.TimeBlockSeconds_First_Time) |
|
6089 | dataOut.bd_time=time.gmtime(dataOut.TimeBlockSeconds_First_Time) | |
| 6103 | dataOut.year=dataOut.bd_time.tm_year+(dataOut.bd_time.tm_yday-1)/364.0 |
|
6090 | dataOut.year=dataOut.bd_time.tm_year+(dataOut.bd_time.tm_yday-1)/364.0 | |
| 6104 | dataOut.ut=dataOut.bd_time.tm_hour+dataOut.bd_time.tm_min/60.0+dataOut.bd_time.tm_sec/3600.0 |
|
6091 | dataOut.ut=dataOut.bd_time.tm_hour+dataOut.bd_time.tm_min/60.0+dataOut.bd_time.tm_sec/3600.0 | |
| 6105 |
|
6092 | |||
| 6106 | self.aux=0 |
|
6093 | self.aux=0 | |
| 6107 |
|
6094 | |||
| 6108 | dataOut.h=numpy.arange(0.0,15.0*dataOut.MAXNRANGENDT,15.0,dtype='float32') |
|
6095 | dataOut.h=numpy.arange(0.0,15.0*dataOut.MAXNRANGENDT,15.0,dtype='float32') | |
| 6109 | dataOut.bfm=numpy.zeros(dataOut.MAXNRANGENDT,dtype='float32') |
|
6096 | dataOut.bfm=numpy.zeros(dataOut.MAXNRANGENDT,dtype='float32') | |
| 6110 | dataOut.bfm=numpy.array(dataOut.bfm,order='F') |
|
6097 | dataOut.bfm=numpy.array(dataOut.bfm,order='F') | |
| 6111 | dataOut.thb=numpy.zeros(dataOut.MAXNRANGENDT,dtype='float32') |
|
6098 | dataOut.thb=numpy.zeros(dataOut.MAXNRANGENDT,dtype='float32') | |
| 6112 | dataOut.thb=numpy.array(dataOut.thb,order='F') |
|
6099 | dataOut.thb=numpy.array(dataOut.thb,order='F') | |
| 6113 | dataOut.bki=numpy.zeros(dataOut.MAXNRANGENDT,dtype='float32') |
|
6100 | dataOut.bki=numpy.zeros(dataOut.MAXNRANGENDT,dtype='float32') | |
| 6114 | dataOut.bki=numpy.array(dataOut.bki,order='F') |
|
6101 | dataOut.bki=numpy.array(dataOut.bki,order='F') | |
| 6115 |
|
6102 | |||
| 6116 | mkfact_short_2020.mkfact(dataOut.year,dataOut.h,dataOut.bfm,dataOut.thb,dataOut.bki,dataOut.MAXNRANGENDT) |
|
6103 | mkfact_short_2020.mkfact(dataOut.year,dataOut.h,dataOut.bfm,dataOut.thb,dataOut.bki,dataOut.MAXNRANGENDT) | |
| 6117 |
|
6104 | |||
| 6118 | return dataOut |
|
6105 | return dataOut | |
| 6119 |
|
6106 | |||
| 6120 | class MergeProc(ProcessingUnit): |
|
6107 | class MergeProc(ProcessingUnit): | |
| 6121 |
|
6108 | |||
| 6122 | def __init__(self): |
|
6109 | def __init__(self): | |
| 6123 | ProcessingUnit.__init__(self) |
|
6110 | ProcessingUnit.__init__(self) | |
| 6124 |
|
6111 | |||
| 6125 | def run(self, attr_data, attr_data_2 = None, attr_data_3 = None, attr_data_4 = None, attr_data_5 = None, mode=0): |
|
6112 | def run(self, attr_data, attr_data_2 = None, attr_data_3 = None, attr_data_4 = None, attr_data_5 = None, mode=0): | |
| 6126 |
|
6113 | |||
| 6127 | self.dataOut = getattr(self, self.inputs[0]) |
|
6114 | self.dataOut = getattr(self, self.inputs[0]) | |
| 6128 | data_inputs = [getattr(self, attr) for attr in self.inputs] |
|
6115 | data_inputs = [getattr(self, attr) for attr in self.inputs] | |
| 6129 | #print(self.inputs) |
|
6116 | #print(self.inputs) | |
| 6130 | #print(numpy.shape([getattr(data, attr_data) for data in data_inputs][1])) |
|
6117 | #print(numpy.shape([getattr(data, attr_data) for data in data_inputs][1])) | |
| 6131 | #exit(1) |
|
6118 | #exit(1) | |
| 6132 | if mode==0: |
|
6119 | if mode==0: | |
| 6133 | data = numpy.concatenate([getattr(data, attr_data) for data in data_inputs]) |
|
6120 | data = numpy.concatenate([getattr(data, attr_data) for data in data_inputs]) | |
| 6134 | setattr(self.dataOut, attr_data, data) |
|
6121 | setattr(self.dataOut, attr_data, data) | |
| 6135 |
|
6122 | |||
| 6136 | if mode==1: #Hybrid |
|
6123 | if mode==1: #Hybrid | |
| 6137 | #data = numpy.concatenate([getattr(data, attr_data) for data in data_inputs],axis=1) |
|
6124 | #data = numpy.concatenate([getattr(data, attr_data) for data in data_inputs],axis=1) | |
| 6138 | #setattr(self.dataOut, attr_data, data) |
|
6125 | #setattr(self.dataOut, attr_data, data) | |
| 6139 | setattr(self.dataOut, 'dataLag_spc', [getattr(data, attr_data) for data in data_inputs][0]) |
|
6126 | setattr(self.dataOut, 'dataLag_spc', [getattr(data, attr_data) for data in data_inputs][0]) | |
| 6140 | setattr(self.dataOut, 'dataLag_spc_LP', [getattr(data, attr_data) for data in data_inputs][1]) |
|
6127 | setattr(self.dataOut, 'dataLag_spc_LP', [getattr(data, attr_data) for data in data_inputs][1]) | |
| 6141 | setattr(self.dataOut, 'dataLag_cspc', [getattr(data, attr_data_2) for data in data_inputs][0]) |
|
6128 | setattr(self.dataOut, 'dataLag_cspc', [getattr(data, attr_data_2) for data in data_inputs][0]) | |
| 6142 | setattr(self.dataOut, 'dataLag_cspc_LP', [getattr(data, attr_data_2) for data in data_inputs][1]) |
|
6129 | setattr(self.dataOut, 'dataLag_cspc_LP', [getattr(data, attr_data_2) for data in data_inputs][1]) | |
| 6143 | #setattr(self.dataOut, 'nIncohInt', [getattr(data, attr_data_3) for data in data_inputs][0]) |
|
6130 | #setattr(self.dataOut, 'nIncohInt', [getattr(data, attr_data_3) for data in data_inputs][0]) | |
| 6144 | #setattr(self.dataOut, 'nIncohInt_LP', [getattr(data, attr_data_3) for data in data_inputs][1]) |
|
6131 | #setattr(self.dataOut, 'nIncohInt_LP', [getattr(data, attr_data_3) for data in data_inputs][1]) | |
| 6145 | ''' |
|
6132 | ''' | |
| 6146 | print(self.dataOut.dataLag_spc_LP.shape) |
|
6133 | print(self.dataOut.dataLag_spc_LP.shape) | |
| 6147 | print(self.dataOut.dataLag_cspc_LP.shape) |
|
6134 | print(self.dataOut.dataLag_cspc_LP.shape) | |
| 6148 | exit(1) |
|
6135 | exit(1) | |
| 6149 | ''' |
|
6136 | ''' | |
| 6150 |
|
6137 | |||
| 6151 | #self.dataOut.dataLag_spc_LP = numpy.transpose(self.dataOut.dataLag_spc_LP[0],(2,0,1)) |
|
6138 | #self.dataOut.dataLag_spc_LP = numpy.transpose(self.dataOut.dataLag_spc_LP[0],(2,0,1)) | |
| 6152 | #self.dataOut.dataLag_cspc_LP = numpy.transpose(self.dataOut.dataLag_cspc_LP,(3,1,2,0)) |
|
6139 | #self.dataOut.dataLag_cspc_LP = numpy.transpose(self.dataOut.dataLag_cspc_LP,(3,1,2,0)) | |
| 6153 | ''' |
|
6140 | ''' | |
| 6154 | print("Merge") |
|
6141 | print("Merge") | |
| 6155 | print(numpy.shape(self.dataOut.dataLag_spc)) |
|
6142 | print(numpy.shape(self.dataOut.dataLag_spc)) | |
| 6156 | print(numpy.shape(self.dataOut.dataLag_spc_LP)) |
|
6143 | print(numpy.shape(self.dataOut.dataLag_spc_LP)) | |
| 6157 | print(numpy.shape(self.dataOut.dataLag_cspc)) |
|
6144 | print(numpy.shape(self.dataOut.dataLag_cspc)) | |
| 6158 | print(numpy.shape(self.dataOut.dataLag_cspc_LP)) |
|
6145 | print(numpy.shape(self.dataOut.dataLag_cspc_LP)) | |
| 6159 | exit(1) |
|
6146 | exit(1) | |
| 6160 | ''' |
|
6147 | ''' | |
| 6161 | #print(numpy.sum(self.dataOut.dataLag_spc_LP[2,:,164])/128) |
|
6148 | #print(numpy.sum(self.dataOut.dataLag_spc_LP[2,:,164])/128) | |
| 6162 | #print(numpy.sum(self.dataOut.dataLag_cspc_LP[0,:,30,1])/128) |
|
6149 | #print(numpy.sum(self.dataOut.dataLag_cspc_LP[0,:,30,1])/128) | |
| 6163 | #exit(1) |
|
6150 | #exit(1) | |
| 6164 | #print(self.dataOut.NDP) |
|
6151 | #print(self.dataOut.NDP) | |
| 6165 | #print(self.dataOut.nNoiseProfiles) |
|
6152 | #print(self.dataOut.nNoiseProfiles) | |
| 6166 |
|
6153 | |||
| 6167 | #self.dataOut.nIncohInt_LP = 128 |
|
6154 | #self.dataOut.nIncohInt_LP = 128 | |
| 6168 | self.dataOut.nProfiles_LP = 128#self.dataOut.nIncohInt_LP |
|
6155 | self.dataOut.nProfiles_LP = 128#self.dataOut.nIncohInt_LP | |
| 6169 | self.dataOut.nIncohInt_LP = self.dataOut.nIncohInt |
|
6156 | self.dataOut.nIncohInt_LP = self.dataOut.nIncohInt | |
| 6170 | self.dataOut.NLAG = 16 |
|
6157 | self.dataOut.NLAG = 16 | |
| 6171 | self.dataOut.NRANGE = 200 |
|
6158 | self.dataOut.NRANGE = 200 | |
| 6172 | self.dataOut.NSCAN = 128 |
|
6159 | self.dataOut.NSCAN = 128 | |
| 6173 | #print(numpy.shape(self.dataOut.data_spc)) |
|
6160 | #print(numpy.shape(self.dataOut.data_spc)) | |
| 6174 |
|
6161 | |||
| 6175 | #exit(1) |
|
6162 | #exit(1) | |
| 6176 |
|
6163 | |||
| 6177 | if mode==2: #HAE 2022 |
|
6164 | if mode==2: #HAE 2022 | |
| 6178 | data = numpy.sum([getattr(data, attr_data) for data in data_inputs],axis=0) |
|
6165 | data = numpy.sum([getattr(data, attr_data) for data in data_inputs],axis=0) | |
| 6179 | setattr(self.dataOut, attr_data, data) |
|
6166 | setattr(self.dataOut, attr_data, data) | |
| 6180 |
|
6167 | |||
| 6181 | self.dataOut.nIncohInt *= 2 |
|
6168 | self.dataOut.nIncohInt *= 2 | |
| 6182 | #meta = self.dataOut.getFreqRange(1)/1000. |
|
6169 | #meta = self.dataOut.getFreqRange(1)/1000. | |
| 6183 | self.dataOut.freqRange = self.dataOut.getFreqRange(1)/1000. |
|
6170 | self.dataOut.freqRange = self.dataOut.getFreqRange(1)/1000. | |
| 6184 |
|
6171 | |||
| 6185 | #exit(1) |
|
6172 | #exit(1) | |
| 6186 |
|
6173 | |||
| 6187 | if mode==4: #Hybrid LP-SSheightProfiles |
|
6174 | if mode==4: #Hybrid LP-SSheightProfiles | |
| 6188 | #data = numpy.concatenate([getattr(data, attr_data) for data in data_inputs],axis=1) |
|
6175 | #data = numpy.concatenate([getattr(data, attr_data) for data in data_inputs],axis=1) | |
| 6189 | #setattr(self.dataOut, attr_data, data) |
|
6176 | #setattr(self.dataOut, attr_data, data) | |
| 6190 | setattr(self.dataOut, 'dataLag_spc', getattr(data_inputs[0], attr_data)) #DP |
|
6177 | setattr(self.dataOut, 'dataLag_spc', getattr(data_inputs[0], attr_data)) #DP | |
| 6191 | setattr(self.dataOut, 'dataLag_cspc', getattr(data_inputs[0], attr_data_2)) #DP |
|
6178 | setattr(self.dataOut, 'dataLag_cspc', getattr(data_inputs[0], attr_data_2)) #DP | |
| 6192 | setattr(self.dataOut, 'dataLag_spc_LP', getattr(data_inputs[1], attr_data_3)) #LP |
|
6179 | setattr(self.dataOut, 'dataLag_spc_LP', getattr(data_inputs[1], attr_data_3)) #LP | |
| 6193 | #setattr(self.dataOut, 'dataLag_cspc_LP', getattr(data_inputs[1], attr_data_4)) #LP |
|
6180 | #setattr(self.dataOut, 'dataLag_cspc_LP', getattr(data_inputs[1], attr_data_4)) #LP | |
| 6194 | #setattr(self.dataOut, 'data_acf', getattr(data_inputs[1], attr_data_5)) #LP |
|
6181 | #setattr(self.dataOut, 'data_acf', getattr(data_inputs[1], attr_data_5)) #LP | |
| 6195 | setattr(self.dataOut, 'data_acf', getattr(data_inputs[1], attr_data_5)) #LP |
|
6182 | setattr(self.dataOut, 'data_acf', getattr(data_inputs[1], attr_data_5)) #LP | |
| 6196 | #print("Merge data_acf: ",self.dataOut.data_acf.shape) |
|
6183 | #print("Merge data_acf: ",self.dataOut.data_acf.shape) | |
| 6197 | #exit(1) |
|
6184 | #exit(1) | |
| 6198 | #print(self.dataOut.data_spc_LP.shape) |
|
6185 | #print(self.dataOut.data_spc_LP.shape) | |
| 6199 | #print("Exit") |
|
6186 | #print("Exit") | |
| 6200 | #exit(1) |
|
6187 | #exit(1) | |
| 6201 | #setattr(self.dataOut, 'dataLag_cspc', [getattr(data, attr_data_2) for data in data_inputs][0]) |
|
6188 | #setattr(self.dataOut, 'dataLag_cspc', [getattr(data, attr_data_2) for data in data_inputs][0]) | |
| 6202 | #setattr(self.dataOut, 'dataLag_cspc_LP', [getattr(data, attr_data_2) for data in data_inputs][1]) |
|
6189 | #setattr(self.dataOut, 'dataLag_cspc_LP', [getattr(data, attr_data_2) for data in data_inputs][1]) | |
| 6203 | #setattr(self.dataOut, 'nIncohInt', [getattr(data, attr_data_3) for data in data_inputs][0]) |
|
6190 | #setattr(self.dataOut, 'nIncohInt', [getattr(data, attr_data_3) for data in data_inputs][0]) | |
| 6204 | #setattr(self.dataOut, 'nIncohInt_LP', [getattr(data, attr_data_3) for data in data_inputs][1]) |
|
6191 | #setattr(self.dataOut, 'nIncohInt_LP', [getattr(data, attr_data_3) for data in data_inputs][1]) | |
| 6205 | ''' |
|
6192 | ''' | |
| 6206 | print(self.dataOut.dataLag_spc_LP.shape) |
|
6193 | print(self.dataOut.dataLag_spc_LP.shape) | |
| 6207 | print(self.dataOut.dataLag_cspc_LP.shape) |
|
6194 | print(self.dataOut.dataLag_cspc_LP.shape) | |
| 6208 | exit(1) |
|
6195 | exit(1) | |
| 6209 | ''' |
|
6196 | ''' | |
| 6210 | ''' |
|
6197 | ''' | |
| 6211 | print(self.dataOut.dataLag_spc_LP[0,:,100]) |
|
6198 | print(self.dataOut.dataLag_spc_LP[0,:,100]) | |
| 6212 | print(self.dataOut.dataLag_spc_LP[1,:,100]) |
|
6199 | print(self.dataOut.dataLag_spc_LP[1,:,100]) | |
| 6213 | exit(1) |
|
6200 | exit(1) | |
| 6214 | ''' |
|
6201 | ''' | |
| 6215 | #self.dataOut.dataLag_spc_LP = numpy.transpose(self.dataOut.dataLag_spc_LP[0],(2,0,1)) |
|
6202 | #self.dataOut.dataLag_spc_LP = numpy.transpose(self.dataOut.dataLag_spc_LP[0],(2,0,1)) | |
| 6216 | #self.dataOut.dataLag_cspc_LP = numpy.transpose(self.dataOut.dataLag_cspc_LP,(3,1,2,0)) |
|
6203 | #self.dataOut.dataLag_cspc_LP = numpy.transpose(self.dataOut.dataLag_cspc_LP,(3,1,2,0)) | |
| 6217 | ''' |
|
6204 | ''' | |
| 6218 | print("Merge") |
|
6205 | print("Merge") | |
| 6219 | print(numpy.shape(self.dataOut.dataLag_spc)) |
|
6206 | print(numpy.shape(self.dataOut.dataLag_spc)) | |
| 6220 | print(numpy.shape(self.dataOut.dataLag_spc_LP)) |
|
6207 | print(numpy.shape(self.dataOut.dataLag_spc_LP)) | |
| 6221 | print(numpy.shape(self.dataOut.dataLag_cspc)) |
|
6208 | print(numpy.shape(self.dataOut.dataLag_cspc)) | |
| 6222 | print(numpy.shape(self.dataOut.dataLag_cspc_LP)) |
|
6209 | print(numpy.shape(self.dataOut.dataLag_cspc_LP)) | |
| 6223 | exit(1) |
|
6210 | exit(1) | |
| 6224 | ''' |
|
6211 | ''' | |
| 6225 | #print(numpy.sum(self.dataOut.dataLag_spc_LP[2,:,164])/128) |
|
6212 | #print(numpy.sum(self.dataOut.dataLag_spc_LP[2,:,164])/128) | |
| 6226 | #print(numpy.sum(self.dataOut.dataLag_cspc_LP[0,:,30,1])/128) |
|
6213 | #print(numpy.sum(self.dataOut.dataLag_cspc_LP[0,:,30,1])/128) | |
| 6227 | #exit(1) |
|
6214 | #exit(1) | |
| 6228 | #print(self.dataOut.NDP) |
|
6215 | #print(self.dataOut.NDP) | |
| 6229 | #print(self.dataOut.nNoiseProfiles) |
|
6216 | #print(self.dataOut.nNoiseProfiles) | |
| 6230 |
|
6217 | |||
| 6231 | #self.dataOut.nIncohInt_LP = 128 |
|
6218 | #self.dataOut.nIncohInt_LP = 128 | |
| 6232 | #self.dataOut.nProfiles_LP = 128#self.dataOut.nIncohInt_LP |
|
6219 | #self.dataOut.nProfiles_LP = 128#self.dataOut.nIncohInt_LP | |
| 6233 | self.dataOut.nProfiles_LP = 16#28#self.dataOut.nIncohInt_LP |
|
6220 | self.dataOut.nProfiles_LP = 16#28#self.dataOut.nIncohInt_LP | |
| 6234 | self.dataOut.nProfiles_LP = self.dataOut.data_acf.shape[1]#28#self.dataOut.nIncohInt_LP |
|
6221 | self.dataOut.nProfiles_LP = self.dataOut.data_acf.shape[1]#28#self.dataOut.nIncohInt_LP | |
| 6235 | self.dataOut.NSCAN = 128 |
|
6222 | self.dataOut.NSCAN = 128 | |
| 6236 | self.dataOut.nIncohInt_LP = self.dataOut.nIncohInt*self.dataOut.NSCAN |
|
6223 | self.dataOut.nIncohInt_LP = self.dataOut.nIncohInt*self.dataOut.NSCAN | |
| 6237 | #print("sahpi",self.dataOut.nIncohInt_LP) |
|
6224 | #print("sahpi",self.dataOut.nIncohInt_LP) | |
| 6238 | #exit(1) |
|
6225 | #exit(1) | |
| 6239 | self.dataOut.NLAG = 16 |
|
6226 | self.dataOut.NLAG = 16 | |
| 6240 | self.dataOut.NRANGE = self.dataOut.data_acf.shape[-1] |
|
6227 | self.dataOut.NRANGE = self.dataOut.data_acf.shape[-1] | |
| 6241 |
|
6228 | |||
| 6242 | #print(numpy.shape(self.dataOut.data_spc)) |
|
6229 | #print(numpy.shape(self.dataOut.data_spc)) | |
| 6243 |
|
6230 | |||
| 6244 | #exit(1) |
|
6231 | #exit(1) | |
| 6245 | if mode==5: |
|
6232 | if mode==5: | |
| 6246 | data = numpy.concatenate([getattr(data, attr_data) for data in data_inputs]) |
|
6233 | data = numpy.concatenate([getattr(data, attr_data) for data in data_inputs]) | |
| 6247 | setattr(self.dataOut, attr_data, data) |
|
6234 | setattr(self.dataOut, attr_data, data) | |
| 6248 | data = numpy.concatenate([getattr(data, attr_data_2) for data in data_inputs]) |
|
6235 | data = numpy.concatenate([getattr(data, attr_data_2) for data in data_inputs]) | |
| 6249 | setattr(self.dataOut, attr_data_2, data) |
|
6236 | setattr(self.dataOut, attr_data_2, data) | |
| 6250 | #data = numpy.concatenate([getattr(data, attr_data_3) for data in data_inputs]) |
|
6237 | #data = numpy.concatenate([getattr(data, attr_data_3) for data in data_inputs]) | |
| 6251 | #setattr(self.dataOut, attr_data_3, data) |
|
6238 | #setattr(self.dataOut, attr_data_3, data) | |
| 6252 | #print(self.dataOut.moments.shape,self.dataOut.data_snr.shape,self.dataOut.heightList.shape) No newline at end of file |
|
6239 | #print(self.dataOut.moments.shape,self.dataOut.data_snr.shape,self.dataOut.heightList.shape) | |
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