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1 | 1 | import numpy |
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2 | 2 | import math |
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3 | 3 | from scipy import optimize, interpolate, signal, stats, ndimage |
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4 | 4 | import scipy |
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5 | 5 | import re |
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6 | 6 | import datetime |
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7 | 7 | import copy |
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8 | 8 | import sys |
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9 | 9 | import importlib |
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10 | 10 | import itertools |
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11 | 11 | from multiprocessing import Pool, TimeoutError |
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12 | 12 | from multiprocessing.pool import ThreadPool |
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13 | 13 | import time |
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14 | 14 | |
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15 | 15 | from scipy.optimize import fmin_l_bfgs_b #optimize with bounds on state papameters |
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16 | 16 | from .jroproc_base import ProcessingUnit, Operation, MPDecorator |
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17 | 17 | from schainpy.model.data.jrodata import Parameters, hildebrand_sekhon |
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18 | 18 | from scipy import asarray as ar,exp |
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19 | 19 | from scipy.optimize import curve_fit |
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20 | 20 | from schainpy.utils import log |
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21 | 21 | import warnings |
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22 | 22 | from numpy import NaN |
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23 | 23 | from scipy.optimize.optimize import OptimizeWarning |
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24 | 24 | warnings.filterwarnings('ignore') |
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25 | 25 | |
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26 | 26 | import matplotlib.pyplot as plt |
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27 | 27 | |
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28 | 28 | SPEED_OF_LIGHT = 299792458 |
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29 | 29 | |
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30 | ||
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31 | 30 | '''solving pickling issue''' |
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32 | 31 | |
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33 | 32 | def _pickle_method(method): |
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34 | 33 | func_name = method.__func__.__name__ |
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35 | 34 | obj = method.__self__ |
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36 | 35 | cls = method.__self__.__class__ |
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37 | 36 | return _unpickle_method, (func_name, obj, cls) |
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38 | 37 | |
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39 | 38 | def _unpickle_method(func_name, obj, cls): |
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40 | 39 | for cls in cls.mro(): |
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41 | 40 | try: |
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42 | 41 | func = cls.__dict__[func_name] |
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43 | 42 | except KeyError: |
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44 | 43 | pass |
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45 | 44 | else: |
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46 | 45 | break |
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47 | 46 | return func.__get__(obj, cls) |
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48 | 47 | |
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49 | 48 | |
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50 | 49 | class ParametersProc(ProcessingUnit): |
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51 | 50 | |
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52 | 51 | METHODS = {} |
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53 | 52 | nSeconds = None |
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54 | 53 | |
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55 | 54 | def __init__(self): |
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56 | 55 | ProcessingUnit.__init__(self) |
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57 | 56 | |
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58 | 57 | # self.objectDict = {} |
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59 | 58 | self.buffer = None |
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60 | 59 | self.firstdatatime = None |
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61 | 60 | self.profIndex = 0 |
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62 | 61 | self.dataOut = Parameters() |
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63 | 62 | self.setupReq = False #Agregar a todas las unidades de proc |
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64 | 63 | |
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65 | 64 | def __updateObjFromInput(self): |
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66 | 65 | |
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67 | 66 | self.dataOut.inputUnit = self.dataIn.type |
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68 | 67 | |
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69 | 68 | self.dataOut.timeZone = self.dataIn.timeZone |
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70 | 69 | self.dataOut.dstFlag = self.dataIn.dstFlag |
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71 | 70 | self.dataOut.errorCount = self.dataIn.errorCount |
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72 | 71 | self.dataOut.useLocalTime = self.dataIn.useLocalTime |
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73 | 72 | |
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74 | 73 | self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() |
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75 | 74 | self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy() |
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76 | 75 | self.dataOut.channelList = self.dataIn.channelList |
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77 | 76 | self.dataOut.heightList = self.dataIn.heightList |
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78 | 77 | self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')]) |
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79 | 78 | # self.dataOut.nHeights = self.dataIn.nHeights |
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80 | 79 | # self.dataOut.nChannels = self.dataIn.nChannels |
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81 | 80 | # self.dataOut.nBaud = self.dataIn.nBaud |
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82 | 81 | # self.dataOut.nCode = self.dataIn.nCode |
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83 | 82 | # self.dataOut.code = self.dataIn.code |
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84 | 83 | # self.dataOut.nProfiles = self.dataOut.nFFTPoints |
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85 | 84 | self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock |
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86 | 85 | # self.dataOut.utctime = self.firstdatatime |
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87 | 86 | self.dataOut.utctime = self.dataIn.utctime |
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88 | 87 | self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada |
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89 | 88 | self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip |
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90 | 89 | self.dataOut.nCohInt = self.dataIn.nCohInt |
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91 | 90 | # self.dataOut.nIncohInt = 1 |
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92 | 91 | # self.dataOut.ippSeconds = self.dataIn.ippSeconds |
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93 | 92 | # self.dataOut.windowOfFilter = self.dataIn.windowOfFilter |
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94 | 93 | self.dataOut.timeInterval1 = self.dataIn.timeInterval |
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95 | 94 | self.dataOut.heightList = self.dataIn.heightList |
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96 | 95 | self.dataOut.frequency = self.dataIn.frequency |
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97 | 96 | # self.dataOut.noise = self.dataIn.noise |
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98 | 97 | |
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99 | 98 | def run(self): |
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100 | 99 | |
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101 | 100 | |
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102 | 101 | |
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103 | 102 | #---------------------- Voltage Data --------------------------- |
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104 | 103 | |
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105 | 104 | if self.dataIn.type == "Voltage": |
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106 | 105 | |
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107 | 106 | self.__updateObjFromInput() |
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108 | 107 | self.dataOut.data_pre = self.dataIn.data.copy() |
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109 | 108 | self.dataOut.flagNoData = False |
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110 | 109 | self.dataOut.utctimeInit = self.dataIn.utctime |
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111 | 110 | self.dataOut.paramInterval = self.dataIn.nProfiles*self.dataIn.nCohInt*self.dataIn.ippSeconds |
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112 | 111 | if hasattr(self.dataIn, 'dataPP_POW'): |
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113 | 112 | self.dataOut.dataPP_POW = self.dataIn.dataPP_POW |
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114 | 113 | |
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115 | 114 | if hasattr(self.dataIn, 'dataPP_POWER'): |
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116 | 115 | self.dataOut.dataPP_POWER = self.dataIn.dataPP_POWER |
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117 | 116 | |
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118 | 117 | if hasattr(self.dataIn, 'dataPP_DOP'): |
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119 | 118 | self.dataOut.dataPP_DOP = self.dataIn.dataPP_DOP |
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120 | 119 | |
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121 | 120 | if hasattr(self.dataIn, 'dataPP_SNR'): |
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122 | 121 | self.dataOut.dataPP_SNR = self.dataIn.dataPP_SNR |
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123 | 122 | |
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124 | 123 | if hasattr(self.dataIn, 'dataPP_WIDTH'): |
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125 | 124 | self.dataOut.dataPP_WIDTH = self.dataIn.dataPP_WIDTH |
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126 | 125 | return |
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127 | 126 | |
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128 | 127 | #---------------------- Spectra Data --------------------------- |
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129 | 128 | |
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130 | 129 | if self.dataIn.type == "Spectra": |
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131 | 130 | |
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132 | 131 | self.dataOut.data_pre = [self.dataIn.data_spc, self.dataIn.data_cspc] |
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133 | 132 | self.dataOut.data_spc = self.dataIn.data_spc |
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134 | 133 | self.dataOut.data_cspc = self.dataIn.data_cspc |
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135 | 134 | self.dataOut.nProfiles = self.dataIn.nProfiles |
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136 | 135 | self.dataOut.nIncohInt = self.dataIn.nIncohInt |
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137 | 136 | self.dataOut.nFFTPoints = self.dataIn.nFFTPoints |
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138 | 137 | self.dataOut.ippFactor = self.dataIn.ippFactor |
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139 | 138 | self.dataOut.abscissaList = self.dataIn.getVelRange(1) |
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140 | 139 | self.dataOut.spc_noise = self.dataIn.getNoise() |
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141 | 140 | self.dataOut.spc_range = (self.dataIn.getFreqRange(1) , self.dataIn.getAcfRange(1) , self.dataIn.getVelRange(1)) |
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142 | 141 | # self.dataOut.normFactor = self.dataIn.normFactor |
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143 | 142 | self.dataOut.pairsList = self.dataIn.pairsList |
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144 | 143 | self.dataOut.groupList = self.dataIn.pairsList |
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145 | 144 | self.dataOut.flagNoData = False |
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146 | 145 | |
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147 | 146 | if hasattr(self.dataIn, 'ChanDist'): #Distances of receiver channels |
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148 | 147 | self.dataOut.ChanDist = self.dataIn.ChanDist |
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149 | 148 | else: self.dataOut.ChanDist = None |
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150 | 149 | |
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151 | 150 | #if hasattr(self.dataIn, 'VelRange'): #Velocities range |
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152 | 151 | # self.dataOut.VelRange = self.dataIn.VelRange |
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153 | 152 | #else: self.dataOut.VelRange = None |
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154 | 153 | |
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155 | 154 | if hasattr(self.dataIn, 'RadarConst'): #Radar Constant |
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156 | 155 | self.dataOut.RadarConst = self.dataIn.RadarConst |
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157 | 156 | |
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158 | 157 | if hasattr(self.dataIn, 'NPW'): #NPW |
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159 | 158 | self.dataOut.NPW = self.dataIn.NPW |
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160 | 159 | |
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161 | 160 | if hasattr(self.dataIn, 'COFA'): #COFA |
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162 | 161 | self.dataOut.COFA = self.dataIn.COFA |
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163 | 162 | |
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164 | 163 | |
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165 | 164 | |
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166 | 165 | #---------------------- Correlation Data --------------------------- |
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167 | 166 | |
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168 | 167 | if self.dataIn.type == "Correlation": |
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169 | 168 | acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf = self.dataIn.splitFunctions() |
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170 | 169 | |
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171 | 170 | self.dataOut.data_pre = (self.dataIn.data_cf[acf_ind,:], self.dataIn.data_cf[ccf_ind,:,:]) |
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172 | 171 | self.dataOut.normFactor = (self.dataIn.normFactor[acf_ind,:], self.dataIn.normFactor[ccf_ind,:]) |
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173 | 172 | self.dataOut.groupList = (acf_pairs, ccf_pairs) |
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174 | 173 | |
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175 | 174 | self.dataOut.abscissaList = self.dataIn.lagRange |
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176 | 175 | self.dataOut.noise = self.dataIn.noise |
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177 | 176 | self.dataOut.data_snr = self.dataIn.SNR |
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178 | 177 | self.dataOut.flagNoData = False |
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179 | 178 | self.dataOut.nAvg = self.dataIn.nAvg |
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180 | 179 | |
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181 | 180 | #---------------------- Parameters Data --------------------------- |
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182 | 181 | |
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183 | 182 | if self.dataIn.type == "Parameters": |
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184 | 183 | self.dataOut.copy(self.dataIn) |
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185 | 184 | self.dataOut.flagNoData = False |
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186 | 185 | |
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187 | 186 | return True |
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188 | 187 | |
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189 | 188 | self.__updateObjFromInput() |
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190 | 189 | self.dataOut.utctimeInit = self.dataIn.utctime |
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191 | 190 | self.dataOut.paramInterval = self.dataIn.timeInterval |
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192 | 191 | |
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193 | 192 | return |
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194 | 193 | |
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195 | 194 | |
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196 | 195 | def target(tups): |
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197 | 196 | |
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198 | 197 | obj, args = tups |
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199 | 198 | |
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200 | 199 | return obj.FitGau(args) |
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201 | 200 | |
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202 | 201 | class RemoveWideGC(Operation): |
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203 | 202 | ''' This class remove the wide clutter and replace it with a simple interpolation points |
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204 | 203 | This mainly applies to CLAIRE radar |
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205 | 204 | |
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206 | 205 | ClutterWidth : Width to look for the clutter peak |
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207 | 206 | |
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208 | 207 | Input: |
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209 | 208 | |
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210 | 209 | self.dataOut.data_pre : SPC and CSPC |
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211 | 210 | self.dataOut.spc_range : To select wind and rainfall velocities |
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212 | 211 | |
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213 | 212 | Affected: |
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214 | 213 | |
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215 | 214 | self.dataOut.data_pre : It is used for the new SPC and CSPC ranges of wind |
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216 | 215 | |
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217 | 216 | Written by D. Scipión 25.02.2021 |
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218 | 217 | ''' |
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219 | 218 | def __init__(self): |
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220 | 219 | Operation.__init__(self) |
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221 | 220 | self.i = 0 |
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222 | 221 | self.ich = 0 |
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223 | 222 | self.ir = 0 |
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224 | 223 | |
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225 | 224 | def run(self, dataOut, ClutterWidth=2.5): |
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225 | # print ('Entering RemoveWideGC ... ') | |
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226 | 226 | |
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227 | 227 | self.spc = dataOut.data_pre[0].copy() |
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228 | 228 | self.spc_out = dataOut.data_pre[0].copy() |
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229 | 229 | self.Num_Chn = self.spc.shape[0] |
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230 |
self.Num_Hei = self.spc.shape[ |
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230 | self.Num_Hei = self.spc.shape[2] | |
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231 | 231 | VelRange = dataOut.spc_range[2][:-1] |
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232 | 232 | dv = VelRange[1]-VelRange[0] |
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233 | 233 | |
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234 | 234 | # Find the velocities that corresponds to zero |
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235 | gc_values = numpy.where(numpy.abs(VelRange) <= ClutterWidth) | |
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235 | gc_values = numpy.squeeze(numpy.where(numpy.abs(VelRange) <= ClutterWidth)) | |
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236 | 236 | |
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237 | 237 | # Removing novalid data from the spectra |
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238 | for ich in range(self.Num_Chn): | |
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239 | # Estimate the noise at aech range | |
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240 | ||
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241 | for ir in range(self.Num_Hei): | |
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242 | # Estimate the noise at aech range | |
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238 | for ich in range(self.Num_Chn) : | |
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239 | for ir in range(self.Num_Hei) : | |
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240 | # Estimate the noise at each range | |
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243 | 241 | HSn = hildebrand_sekhon(self.spc[ich,:,ir],dataOut.nIncohInt) |
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242 | ||
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244 | 243 | # Removing the noise floor at each range |
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245 | 244 | novalid = numpy.where(self.spc[ich,:,ir] < HSn) |
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246 | self.spc[novalid,ir] = HSn | |
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247 | ||
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248 |
junk = |
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249 |
j1index = numpy.where(numpy.diff |
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250 |
j2index = numpy.where(numpy.diff |
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251 | junk3 = numpy.diff(j1index) | |
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252 | junk4 = numpy.diff(j2index) | |
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253 | ||
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254 | valleyindex = j2index[junk4>1] | |
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255 | peakindex = j1index[junk3>1] | |
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256 | ||
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257 | # Identify the clutter (close to zero) | |
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258 | isvalid = numpy.where(where.abs(VelRange[gc_values[peakindex]]) <= 2.5*dv) | |
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259 | # if isempty(isvalid) | |
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260 | # continue | |
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261 |
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262 | # [~,vindex]= numpy.max(spc[gc_values[peakindex[isvalid]],ir]) | |
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263 | # isvalid = isvalid[vindex] | |
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245 | self.spc[ich,novalid,ir] = HSn | |
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246 | ||
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247 | junk = numpy.append(numpy.insert(numpy.squeeze(self.spc[ich,gc_values,ir]),0,HSn),HSn) | |
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248 | j1index = numpy.squeeze(numpy.where(numpy.diff(junk)>0)) | |
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249 | j2index = numpy.squeeze(numpy.where(numpy.diff(junk)<0)) | |
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250 | if ((numpy.size(j1index)<=1) | (numpy.size(j2index)<=1)) : | |
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251 | continue | |
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252 | junk3 = numpy.squeeze(numpy.diff(j1index)) | |
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253 | junk4 = numpy.squeeze(numpy.diff(j2index)) | |
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254 | ||
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255 | valleyindex = j2index[numpy.where(junk4>1)] | |
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256 | peakindex = j1index[numpy.where(junk3>1)] | |
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257 | ||
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258 | isvalid = numpy.squeeze(numpy.where(numpy.abs(VelRange[gc_values[peakindex]]) <= 2.5*dv)) | |
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259 | if numpy.size(isvalid) == 0 : | |
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260 | continue | |
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261 | if numpy.size(isvalid) >1 : | |
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262 | vindex = numpy.argmax(self.spc[ich,gc_values[peakindex[isvalid]],ir]) | |
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263 | isvalid = isvalid[vindex] | |
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264 | ||
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264 | 265 | # clutter peak |
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265 | 266 | gcpeak = peakindex[isvalid] |
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266 | # left and right index of the clutter | |
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267 | gcvl = valleyindex[numpy.where(valleyindex < gcpeak, 1, 'last' )] | |
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268 | gcvr = valleyindex[numpy.where(valleyindex > gcpeak, 1)] | |
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267 | vl = numpy.where(valleyindex < gcpeak) | |
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268 | if numpy.size(vl) == 0: | |
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269 | continue | |
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270 | gcvl = valleyindex[vl[0][-1]] | |
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271 | vr = numpy.where(valleyindex > gcpeak) | |
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272 | if numpy.size(vr) == 0: | |
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273 | continue | |
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274 | gcvr = valleyindex[vr[0][0]] | |
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269 | 275 | |
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270 | 276 | # Removing the clutter |
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271 |
interpindex = [gc_values |
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277 | interpindex = numpy.array([gc_values[gcvl], gc_values[gcvr]]) | |
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272 | 278 | gcindex = gc_values[gcvl+1:gcvr-1] |
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273 | 279 | self.spc_out[ich,gcindex,ir] = numpy.interp(VelRange[gcindex],VelRange[interpindex],self.spc[ich,interpindex,ir]) |
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274 | 280 | |
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275 | 281 | dataOut.data_pre[0] = self.spc_out |
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282 | #print ('Leaving RemoveWideGC ... ') | |
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276 | 283 | return dataOut |
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277 | 284 | |
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278 | 285 | class SpectralFilters(Operation): |
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279 | 286 | ''' This class allows to replace the novalid values with noise for each channel |
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280 | 287 | This applies to CLAIRE RADAR |
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281 | 288 | |
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282 | 289 | PositiveLimit : RightLimit of novalid data |
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283 | 290 | NegativeLimit : LeftLimit of novalid data |
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284 | 291 | |
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285 | 292 | Input: |
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286 | 293 | |
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287 | 294 | self.dataOut.data_pre : SPC and CSPC |
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288 | 295 | self.dataOut.spc_range : To select wind and rainfall velocities |
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289 | 296 | |
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290 | 297 | Affected: |
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291 | 298 | |
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292 | 299 | self.dataOut.data_pre : It is used for the new SPC and CSPC ranges of wind |
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293 | 300 | |
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294 | 301 | Written by D. Scipión 29.01.2021 |
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295 | 302 | ''' |
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296 | 303 | def __init__(self): |
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297 | 304 | Operation.__init__(self) |
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298 | 305 | self.i = 0 |
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299 | 306 | |
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300 |
def run(self, dataOut, |
|
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307 | def run(self, dataOut, ): | |
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301 | 308 | |
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302 | 309 | self.spc = dataOut.data_pre[0].copy() |
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303 | 310 | self.Num_Chn = self.spc.shape[0] |
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304 | 311 | VelRange = dataOut.spc_range[2] |
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305 | 312 | |
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306 | 313 | # novalid corresponds to data within the Negative and PositiveLimit |
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307 | novalid = numpy.where((VelRange[:-1] >= NegativeLimit) & (VelRange[:-1] <= PositiveLimit)) | |
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314 | ||
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308 | 315 | |
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309 | 316 | # Removing novalid data from the spectra |
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310 | 317 | for i in range(self.Num_Chn): |
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311 | 318 | self.spc[i,novalid,:] = dataOut.noise[i] |
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312 | 319 | dataOut.data_pre[0] = self.spc |
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313 | 320 | return dataOut |
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314 | 321 | |
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315 | 322 | class GaussianFit(Operation): |
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316 | 323 | |
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317 | 324 | ''' |
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318 | 325 | Function that fit of one and two generalized gaussians (gg) based |
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319 | 326 | on the PSD shape across an "power band" identified from a cumsum of |
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320 | 327 | the measured spectrum - noise. |
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321 | 328 | |
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322 | 329 | Input: |
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323 | 330 | self.dataOut.data_pre : SelfSpectra |
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324 | 331 | |
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325 | 332 | Output: |
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326 | 333 | self.dataOut.SPCparam : SPC_ch1, SPC_ch2 |
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327 | 334 | |
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328 | 335 | ''' |
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329 | 336 | def __init__(self): |
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330 | 337 | Operation.__init__(self) |
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331 | 338 | self.i=0 |
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332 | 339 | |
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333 | 340 | |
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334 | def run(self, dataOut, num_intg=7, pnoise=1., SNRlimit=-9): #num_intg: Incoherent integrations, pnoise: Noise, vel_arr: range of velocities, similar to the ftt points | |
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341 | # def run(self, dataOut, num_intg=7, pnoise=1., SNRlimit=-9): #num_intg: Incoherent integrations, pnoise: Noise, vel_arr: range of velocities, similar to the ftt points | |
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342 | def run(self, dataOut, SNRdBlimit=-9, method='generalized'): | |
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335 | 343 | """This routine will find a couple of generalized Gaussians to a power spectrum |
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344 | methods: generalized, squared | |
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336 | 345 | input: spc |
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337 | 346 | output: |
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338 |
|
|
|
347 | noise, amplitude0,shift0,width0,p0,Amplitude1,shift1,width1,p1 | |
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339 | 348 | """ |
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340 | ||
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349 | print ('Entering ',method,' double Gaussian fit') | |
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341 | 350 | self.spc = dataOut.data_pre[0].copy() |
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342 | 351 | self.Num_Hei = self.spc.shape[2] |
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343 | 352 | self.Num_Bin = self.spc.shape[1] |
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344 | 353 | self.Num_Chn = self.spc.shape[0] |
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345 | Vrange = dataOut.abscissaList | |
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346 | ||
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347 | GauSPC = numpy.empty([self.Num_Chn,self.Num_Bin,self.Num_Hei]) | |
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348 | SPC_ch1 = numpy.empty([self.Num_Bin,self.Num_Hei]) | |
|
349 | SPC_ch2 = numpy.empty([self.Num_Bin,self.Num_Hei]) | |
|
350 | SPC_ch1[:] = numpy.NaN | |
|
351 | SPC_ch2[:] = numpy.NaN | |
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352 | ||
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353 | 354 | |
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354 | 355 | start_time = time.time() |
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355 | 356 | |
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356 | noise_ = dataOut.spc_noise[0].copy() | |
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357 | ||
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358 | ||
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359 | 357 | pool = Pool(processes=self.Num_Chn) |
|
360 |
args = [( |
|
|
358 | args = [(dataOut.spc_range[2], ich, dataOut.spc_noise[ich], dataOut.nIncohInt, SNRdBlimit) for ich in range(self.Num_Chn)] | |
|
361 | 359 | objs = [self for __ in range(self.Num_Chn)] |
|
362 | 360 | attrs = list(zip(objs, args)) |
|
363 |
|
|
|
364 | dataOut.SPCparam = numpy.asarray(SPCparam) | |
|
365 | ||
|
366 | ''' Parameters: | |
|
367 | 1. Amplitude | |
|
368 | 2. Shift | |
|
369 | 3. Width | |
|
370 | 4. Power | |
|
371 | ''' | |
|
361 | DGauFitParam = pool.map(target, attrs) | |
|
362 | # Parameters: | |
|
363 | # 0. Noise, 1. Amplitude, 2. Shift, 3. Width 4. Power | |
|
364 | dataOut.DGauFitParams = numpy.asarray(DGauFitParam) | |
|
365 | ||
|
366 | # Double Gaussian Curves | |
|
367 | gau0 = numpy.zeros([self.Num_Chn,self.Num_Bin,self.Num_Hei]) | |
|
368 | gau0[:] = numpy.NaN | |
|
369 | gau1 = numpy.zeros([self.Num_Chn,self.Num_Bin,self.Num_Hei]) | |
|
370 | gau1[:] = numpy.NaN | |
|
371 | x_mtr = numpy.transpose(numpy.tile(dataOut.getVelRange(1)[:-1], (self.Num_Hei,1))) | |
|
372 | for iCh in range(self.Num_Chn): | |
|
373 | N0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][0,:,0]] * self.Num_Bin)) | |
|
374 | N1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][0,:,1]] * self.Num_Bin)) | |
|
375 | A0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][1,:,0]] * self.Num_Bin)) | |
|
376 | A1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][1,:,1]] * self.Num_Bin)) | |
|
377 | v0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][2,:,0]] * self.Num_Bin)) | |
|
378 | v1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][2,:,1]] * self.Num_Bin)) | |
|
379 | s0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][3,:,0]] * self.Num_Bin)) | |
|
380 | s1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][3,:,1]] * self.Num_Bin)) | |
|
381 | if method == 'genealized': | |
|
382 | p0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][4,:,0]] * self.Num_Bin)) | |
|
383 | p1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][4,:,1]] * self.Num_Bin)) | |
|
384 | elif method == 'squared': | |
|
385 | p0 = 2. | |
|
386 | p1 = 2. | |
|
387 | gau0[iCh] = A0*numpy.exp(-0.5*numpy.abs((x_mtr-v0)/s0)**p0)+N0 | |
|
388 | gau1[iCh] = A1*numpy.exp(-0.5*numpy.abs((x_mtr-v1)/s1)**p1)+N1 | |
|
389 | dataOut.GaussFit0 = gau0 | |
|
390 | dataOut.GaussFit1 = gau1 | |
|
391 | ||
|
392 | print('Leaving ',method ,' double Gaussian fit') | |
|
393 | return dataOut | |
|
372 | 394 | |
|
373 | 395 | def FitGau(self, X): |
|
374 | ||
|
375 | Vrange, ch, pnoise, noise_, num_intg, SNRlimit = X | |
|
376 | ||
|
377 | SPCparam = [] | |
|
378 | SPC_ch1 = numpy.empty([self.Num_Bin,self.Num_Hei]) | |
|
379 | SPC_ch2 = numpy.empty([self.Num_Bin,self.Num_Hei]) | |
|
380 | SPC_ch1[:] = 0 #numpy.NaN | |
|
381 | SPC_ch2[:] = 0 #numpy.NaN | |
|
382 | ||
|
383 | ||
|
384 | ||
|
396 | # print('Entering FitGau') | |
|
397 | # Assigning the variables | |
|
398 | Vrange, ch, wnoise, num_intg, SNRlimit = X | |
|
399 | # Noise Limits | |
|
400 | noisebl = wnoise * 0.9 | |
|
401 | noisebh = wnoise * 1.1 | |
|
402 | # Radar Velocity | |
|
403 | Va = max(Vrange) | |
|
404 | deltav = Vrange[1] - Vrange[0] | |
|
405 | x = numpy.arange(self.Num_Bin) | |
|
406 | ||
|
407 | # print ('stop 0') | |
|
408 | ||
|
409 | # 5 parameters, 2 Gaussians | |
|
410 | DGauFitParam = numpy.zeros([5, self.Num_Hei,2]) | |
|
411 | DGauFitParam[:] = numpy.NaN | |
|
412 | ||
|
413 | # SPCparam = [] | |
|
414 | # SPC_ch1 = numpy.zeros([self.Num_Bin,self.Num_Hei]) | |
|
415 | # SPC_ch2 = numpy.zeros([self.Num_Bin,self.Num_Hei]) | |
|
416 | # SPC_ch1[:] = 0 #numpy.NaN | |
|
417 | # SPC_ch2[:] = 0 #numpy.NaN | |
|
418 | # print ('stop 1') | |
|
385 | 419 | for ht in range(self.Num_Hei): |
|
386 | ||
|
387 | ||
|
420 | # print (ht) | |
|
421 | # print ('stop 2') | |
|
422 | # Spectra at each range | |
|
388 | 423 | spc = numpy.asarray(self.spc)[ch,:,ht] |
|
424 | snr = ( spc.mean() - wnoise ) / wnoise | |
|
425 | snrdB = 10.*numpy.log10(snr) | |
|
389 | 426 | |
|
427 | #print ('stop 3') | |
|
428 | if snrdB < SNRlimit : | |
|
429 | # snr = numpy.NaN | |
|
430 | # SPC_ch1[:,ht] = 0#numpy.NaN | |
|
431 | # SPC_ch1[:,ht] = 0#numpy.NaN | |
|
432 | # SPCparam = (SPC_ch1,SPC_ch2) | |
|
433 | # print ('SNR less than SNRth') | |
|
434 | continue | |
|
435 | # wnoise = hildebrand_sekhon(spc,num_intg) | |
|
436 | # print ('stop 2.01') | |
|
390 | 437 | ############################################# |
|
391 | 438 | # normalizing spc and noise |
|
392 | 439 | # This part differs from gg1 |
|
393 | spc_norm_max = max(spc) | |
|
440 | # spc_norm_max = max(spc) #commented by D. Scipión 19.03.2021 | |
|
394 | 441 | #spc = spc / spc_norm_max |
|
395 | pnoise = pnoise #/ spc_norm_max | |
|
442 | # pnoise = pnoise #/ spc_norm_max #commented by D. Scipión 19.03.2021 | |
|
396 | 443 | ############################################# |
|
397 | 444 | |
|
445 | # print ('stop 2.1') | |
|
398 | 446 | fatspectra=1.0 |
|
399 | ||
|
400 | wnoise = noise_ #/ spc_norm_max | |
|
447 | # noise per channel.... we might want to use the noise at each range | |
|
448 | ||
|
449 | # wnoise = noise_ #/ spc_norm_max #commented by D. Scipión 19.03.2021 | |
|
401 | 450 | #wnoise,stdv,i_max,index =enoise(spc,num_intg) #noise estimate using Hildebrand Sekhon, only wnoise is used |
|
402 | 451 | #if wnoise>1.1*pnoise: # to be tested later |
|
403 | 452 | # wnoise=pnoise |
|
404 |
noisebl=wnoise*0.9 |
|
|
405 | noisebh=wnoise*1.1 | |
|
406 | spc=spc-wnoise | |
|
453 | # noisebl = wnoise*0.9 | |
|
454 | # noisebh = wnoise*1.1 | |
|
455 | spc = spc - wnoise # signal | |
|
407 | 456 | |
|
408 | minx=numpy.argmin(spc) | |
|
457 | # print ('stop 2.2') | |
|
458 | minx = numpy.argmin(spc) | |
|
409 | 459 | #spcs=spc.copy() |
|
410 | spcs=numpy.roll(spc,-minx) | |
|
411 | cum=numpy.cumsum(spcs) | |
|
412 | tot_noise=wnoise * self.Num_Bin #64; | |
|
413 | ||
|
414 | snr = sum(spcs)/tot_noise | |
|
415 | snrdB=10.*numpy.log10(snr) | |
|
416 | ||
|
417 | if snrdB < SNRlimit : | |
|
418 |
|
|
|
419 |
|
|
|
420 | SPC_ch1[:,ht] = 0#numpy.NaN | |
|
421 | SPCparam = (SPC_ch1,SPC_ch2) | |
|
422 | continue | |
|
460 | spcs = numpy.roll(spc,-minx) | |
|
461 | cum = numpy.cumsum(spcs) | |
|
462 | # tot_noise = wnoise * self.Num_Bin #64; | |
|
463 | ||
|
464 | # print ('stop 2.3') | |
|
465 | # snr = sum(spcs) / tot_noise | |
|
466 | # snrdB = 10.*numpy.log10(snr) | |
|
467 | #print ('stop 3') | |
|
468 | # if snrdB < SNRlimit : | |
|
469 | # snr = numpy.NaN | |
|
470 | # SPC_ch1[:,ht] = 0#numpy.NaN | |
|
471 | # SPC_ch1[:,ht] = 0#numpy.NaN | |
|
472 | # SPCparam = (SPC_ch1,SPC_ch2) | |
|
473 | # print ('SNR less than SNRth') | |
|
474 | # continue | |
|
423 | 475 | |
|
424 | 476 | |
|
425 | 477 | #if snrdB<-18 or numpy.isnan(snrdB) or num_intg<4: |
|
426 | 478 | # return [None,]*4,[None,]*4,None,snrdB,None,None,[None,]*5,[None,]*9,None |
|
427 | ||
|
428 |
cummax=max(cum) |
|
|
429 | epsi=0.08*fatspectra # cumsum to narrow down the energy region | |
|
430 |
cumlo=cummax*epsi |
|
|
431 | cumhi=cummax*(1-epsi) | |
|
432 | powerindex=numpy.array(numpy.where(numpy.logical_and(cum>cumlo, cum<cumhi))[0]) | |
|
433 | ||
|
434 | ||
|
479 | # print ('stop 4') | |
|
480 | cummax = max(cum) | |
|
481 | epsi = 0.08 * fatspectra # cumsum to narrow down the energy region | |
|
482 | cumlo = cummax * epsi | |
|
483 | cumhi = cummax * (1-epsi) | |
|
484 | powerindex = numpy.array(numpy.where(numpy.logical_and(cum>cumlo, cum<cumhi))[0]) | |
|
485 | ||
|
486 | # print ('stop 5') | |
|
435 | 487 | if len(powerindex) < 1:# case for powerindex 0 |
|
488 | # print ('powerindex < 1') | |
|
436 | 489 | continue |
|
437 | powerlo=powerindex[0] | |
|
438 | powerhi=powerindex[-1] | |
|
439 | powerwidth=powerhi-powerlo | |
|
440 | ||
|
441 | firstpeak=powerlo+powerwidth/10.# first gaussian energy location | |
|
442 | secondpeak=powerhi-powerwidth/10.#second gaussian energy location | |
|
443 | midpeak=(firstpeak+secondpeak)/2. | |
|
444 |
|
|
|
445 | secondamp=spcs[int(secondpeak)] | |
|
446 | midamp=spcs[int(midpeak)] | |
|
490 | powerlo = powerindex[0] | |
|
491 | powerhi = powerindex[-1] | |
|
492 | powerwidth = powerhi-powerlo | |
|
493 | if powerwidth <= 1: | |
|
494 | # print('powerwidth <= 1') | |
|
495 | continue | |
|
496 | ||
|
497 | # print ('stop 6') | |
|
498 | firstpeak = powerlo + powerwidth/10.# first gaussian energy location | |
|
499 | secondpeak = powerhi - powerwidth/10. #second gaussian energy location | |
|
500 | midpeak = (firstpeak + secondpeak)/2. | |
|
501 | firstamp = spcs[int(firstpeak)] | |
|
502 | secondamp = spcs[int(secondpeak)] | |
|
503 | midamp = spcs[int(midpeak)] | |
|
447 | 504 | |
|
448 | x=numpy.arange( self.Num_Bin ) | |
|
449 | y_data=spc+wnoise | |
|
505 | y_data = spc + wnoise | |
|
450 | 506 | |
|
451 | 507 | ''' single Gaussian ''' |
|
452 | shift0=numpy.mod(midpeak+minx, self.Num_Bin ) | |
|
453 | width0=powerwidth/4.#Initialization entire power of spectrum divided by 4 | |
|
454 | power0=2. | |
|
455 | amplitude0=midamp | |
|
456 | state0=[shift0,width0,amplitude0,power0,wnoise] | |
|
457 |
bnds=(( |
|
|
458 | lsq1=fmin_l_bfgs_b(self.misfit1,state0,args=(y_data,x,num_intg),bounds=bnds,approx_grad=True) | |
|
459 | ||
|
460 | chiSq1=lsq1[1]; | |
|
461 | ||
|
462 | ||
|
508 | shift0 = numpy.mod(midpeak+minx, self.Num_Bin ) | |
|
509 | width0 = powerwidth/4.#Initialization entire power of spectrum divided by 4 | |
|
510 | power0 = 2. | |
|
511 | amplitude0 = midamp | |
|
512 | state0 = [shift0,width0,amplitude0,power0,wnoise] | |
|
513 | bnds = ((0,self.Num_Bin-1),(1,powerwidth),(0,None),(0.5,3.),(noisebl,noisebh)) | |
|
514 | lsq1 = fmin_l_bfgs_b(self.misfit1, state0, args=(y_data,x,num_intg), bounds=bnds, approx_grad=True) | |
|
515 | # print ('stop 7.1') | |
|
516 | # print (bnds) | |
|
517 | ||
|
518 | chiSq1=lsq1[1] | |
|
519 | ||
|
520 | # print ('stop 8') | |
|
463 | 521 | if fatspectra<1.0 and powerwidth<4: |
|
464 | 522 | choice=0 |
|
465 | 523 | Amplitude0=lsq1[0][2] |
|
466 | 524 | shift0=lsq1[0][0] |
|
467 | 525 | width0=lsq1[0][1] |
|
468 | 526 | p0=lsq1[0][3] |
|
469 | 527 | Amplitude1=0. |
|
470 | 528 | shift1=0. |
|
471 | 529 | width1=0. |
|
472 | 530 | p1=0. |
|
473 | 531 | noise=lsq1[0][4] |
|
474 | 532 | #return (numpy.array([shift0,width0,Amplitude0,p0]), |
|
475 | 533 | # numpy.array([shift1,width1,Amplitude1,p1]),noise,snrdB,chiSq1,6.,sigmas1,[None,]*9,choice) |
|
476 | ||
|
477 | ''' two gaussians ''' | |
|
534 | ||
|
535 | # print ('stop 9') | |
|
536 | ''' two Gaussians ''' | |
|
478 | 537 | #shift0=numpy.mod(firstpeak+minx,64); shift1=numpy.mod(secondpeak+minx,64) |
|
479 |
shift0=numpy.mod(firstpeak+minx, self.Num_Bin ) |
|
|
480 | shift1=numpy.mod(secondpeak+minx, self.Num_Bin ) | |
|
481 |
width0=powerwidth/6. |
|
|
482 | width1=width0 | |
|
483 |
power0=2. |
|
|
484 | power1=power0 | |
|
485 |
amplitude0=firstamp |
|
|
486 | amplitude1=secondamp | |
|
487 | state0=[shift0,width0,amplitude0,power0,shift1,width1,amplitude1,power1,wnoise] | |
|
538 | shift0 = numpy.mod(firstpeak+minx, self.Num_Bin ) | |
|
539 | shift1 = numpy.mod(secondpeak+minx, self.Num_Bin ) | |
|
540 | width0 = powerwidth/6. | |
|
541 | width1 = width0 | |
|
542 | power0 = 2. | |
|
543 | power1 = power0 | |
|
544 | amplitude0 = firstamp | |
|
545 | amplitude1 = secondamp | |
|
546 | state0 = [shift0,width0,amplitude0,power0,shift1,width1,amplitude1,power1,wnoise] | |
|
488 | 547 | #bnds=((0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(noisebl,noisebh)) |
|
489 |
bnds=(( |
|
|
548 | 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)) | |
|
490 | 549 | #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)) |
|
491 | 550 | |
|
551 | # print ('stop 10') | |
|
492 | 552 | lsq2 = fmin_l_bfgs_b( self.misfit2 , state0 , args=(y_data,x,num_intg) , bounds=bnds , approx_grad=True ) |
|
493 | 553 | |
|
554 | # print ('stop 11') | |
|
555 | chiSq2 = lsq2[1] | |
|
494 | 556 | |
|
495 | chiSq2=lsq2[1]; | |
|
496 | ||
|
497 | ||
|
557 | # print ('stop 12') | |
|
498 | 558 | |
|
499 | 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) | |
|
559 | 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) | |
|
500 | 560 | |
|
561 | # print ('stop 13') | |
|
501 | 562 | if snrdB>-12: # when SNR is strong pick the peak with least shift (LOS velocity) error |
|
502 | 563 | if oneG: |
|
503 | choice=0 | |
|
564 | choice = 0 | |
|
504 | 565 | else: |
|
505 | w1=lsq2[0][1]; w2=lsq2[0][5] | |
|
506 | a1=lsq2[0][2]; a2=lsq2[0][6] | |
|
507 | p1=lsq2[0][3]; p2=lsq2[0][7] | |
|
508 |
s1=(2**(1+1./p1))*scipy.special.gamma(1./p1)/p1 |
|
|
509 |
s2=(2**(1+1./p2))*scipy.special.gamma(1./p2)/p2 |
|
|
510 | gp1=a1*w1*s1; gp2=a2*w2*s2 # power content of each ggaussian with proper p scaling | |
|
566 | w1 = lsq2[0][1]; w2 = lsq2[0][5] | |
|
567 | a1 = lsq2[0][2]; a2 = lsq2[0][6] | |
|
568 | p1 = lsq2[0][3]; p2 = lsq2[0][7] | |
|
569 | s1 = (2**(1+1./p1))*scipy.special.gamma(1./p1)/p1 | |
|
570 | s2 = (2**(1+1./p2))*scipy.special.gamma(1./p2)/p2 | |
|
571 | gp1 = a1*w1*s1; gp2 = a2*w2*s2 # power content of each ggaussian with proper p scaling | |
|
511 | 572 | |
|
512 | 573 | if gp1>gp2: |
|
513 | 574 | if a1>0.7*a2: |
|
514 | choice=1 | |
|
575 | choice = 1 | |
|
515 | 576 | else: |
|
516 | choice=2 | |
|
577 | choice = 2 | |
|
517 | 578 | elif gp2>gp1: |
|
518 | 579 | if a2>0.7*a1: |
|
519 | choice=2 | |
|
580 | choice = 2 | |
|
520 | 581 | else: |
|
521 | choice=1 | |
|
582 | choice = 1 | |
|
522 | 583 | else: |
|
523 | choice=numpy.argmax([a1,a2])+1 | |
|
584 | choice = numpy.argmax([a1,a2])+1 | |
|
524 | 585 | #else: |
|
525 | 586 | #choice=argmin([std2a,std2b])+1 |
|
526 | 587 | |
|
527 | 588 | else: # with low SNR go to the most energetic peak |
|
528 | choice=numpy.argmax([lsq1[0][2]*lsq1[0][1],lsq2[0][2]*lsq2[0][1],lsq2[0][6]*lsq2[0][5]]) | |
|
529 | ||
|
530 | ||
|
531 |
shift0=lsq2[0][0] |
|
|
532 |
vel0=Vrange[0] + shift0* |
|
|
533 |
shift1=lsq2[0][4] |
|
|
534 |
vel1=Vrange[0] + shift1 |
|
|
535 | ||
|
536 | max_vel = 1.0 | |
|
537 | ||
|
589 | choice = numpy.argmax([lsq1[0][2]*lsq1[0][1],lsq2[0][2]*lsq2[0][1],lsq2[0][6]*lsq2[0][5]]) | |
|
590 | ||
|
591 | # print ('stop 14') | |
|
592 | shift0 = lsq2[0][0] | |
|
593 | vel0 = Vrange[0] + shift0 * deltav | |
|
594 | shift1 = lsq2[0][4] | |
|
595 | # vel1=Vrange[0] + shift1 * deltav | |
|
596 | ||
|
597 | # max_vel = 1.0 | |
|
598 | # Va = max(Vrange) | |
|
599 | # deltav = Vrange[1]-Vrange[0] | |
|
600 | # print ('stop 15') | |
|
538 | 601 | #first peak will be 0, second peak will be 1 |
|
539 | if vel0 > -1.0 and vel0 < max_vel : #first peak is in the correct range | |
|
540 | shift0=lsq2[0][0] | |
|
541 |
|
|
|
542 |
|
|
|
543 |
p0=lsq2[0][ |
|
|
544 | ||
|
545 | shift1=lsq2[0][4] | |
|
546 |
|
|
|
547 |
|
|
|
548 |
p1=lsq2[0][ |
|
|
549 |
|
|
|
602 | # if vel0 > -1.0 and vel0 < max_vel : #first peak is in the correct range # Commented by D.Scipión 19.03.2021 | |
|
603 | if vel0 > -Va and vel0 < Va : #first peak is in the correct range | |
|
604 | shift0 = lsq2[0][0] | |
|
605 | width0 = lsq2[0][1] | |
|
606 | Amplitude0 = lsq2[0][2] | |
|
607 | p0 = lsq2[0][3] | |
|
608 | ||
|
609 | shift1 = lsq2[0][4] | |
|
610 | width1 = lsq2[0][5] | |
|
611 | Amplitude1 = lsq2[0][6] | |
|
612 | p1 = lsq2[0][7] | |
|
613 | noise = lsq2[0][8] | |
|
550 | 614 | else: |
|
551 | shift1=lsq2[0][0] | |
|
552 | width1=lsq2[0][1] | |
|
553 | Amplitude1=lsq2[0][2] | |
|
554 | p1=lsq2[0][3] | |
|
615 | shift1 = lsq2[0][0] | |
|
616 | width1 = lsq2[0][1] | |
|
617 | Amplitude1 = lsq2[0][2] | |
|
618 | p1 = lsq2[0][3] | |
|
555 | 619 | |
|
556 | shift0=lsq2[0][4] | |
|
557 | width0=lsq2[0][5] | |
|
558 | Amplitude0=lsq2[0][6] | |
|
559 | p0=lsq2[0][7] | |
|
560 | noise=lsq2[0][8] | |
|
620 | shift0 = lsq2[0][4] | |
|
621 | width0 = lsq2[0][5] | |
|
622 | Amplitude0 = lsq2[0][6] | |
|
623 | p0 = lsq2[0][7] | |
|
624 | noise = lsq2[0][8] | |
|
561 | 625 | |
|
562 | 626 | if Amplitude0<0.05: # in case the peak is noise |
|
563 |
shift0,width0,Amplitude0,p0 = [ |
|
|
627 | shift0,width0,Amplitude0,p0 = 4*[numpy.NaN] | |
|
564 | 628 | if Amplitude1<0.05: |
|
565 |
shift1,width1,Amplitude1,p1 = [ |
|
|
566 | ||
|
567 | ||
|
568 |
SPC_ch1[:,ht] = noise + Amplitude0*numpy.exp(-0.5*(abs(x-shift0) |
|
|
569 |
SPC_ch2[:,ht] = noise + Amplitude1*numpy.exp(-0.5*(abs(x-shift1) |
|
|
570 | SPCparam = (SPC_ch1,SPC_ch2) | |
|
571 | ||
|
572 | ||
|
573 | return GauSPC | |
|
629 | shift1,width1,Amplitude1,p1 = 4*[numpy.NaN] | |
|
630 | ||
|
631 | # print ('stop 16 ') | |
|
632 | # SPC_ch1[:,ht] = noise + Amplitude0*numpy.exp(-0.5*(abs(x-shift0)/width0)**p0) | |
|
633 | # SPC_ch2[:,ht] = noise + Amplitude1*numpy.exp(-0.5*(abs(x-shift1)/width1)**p1) | |
|
634 | # SPCparam = (SPC_ch1,SPC_ch2) | |
|
635 | ||
|
636 | DGauFitParam[0,ht,0] = noise | |
|
637 | DGauFitParam[0,ht,1] = noise | |
|
638 | DGauFitParam[1,ht,0] = Amplitude0 | |
|
639 | DGauFitParam[1,ht,1] = Amplitude1 | |
|
640 | DGauFitParam[2,ht,0] = Vrange[0] + shift0 * deltav | |
|
641 | DGauFitParam[2,ht,1] = Vrange[0] + shift1 * deltav | |
|
642 | DGauFitParam[3,ht,0] = width0 * deltav | |
|
643 | DGauFitParam[3,ht,1] = width1 * deltav | |
|
644 | DGauFitParam[4,ht,0] = p0 | |
|
645 | DGauFitParam[4,ht,1] = p1 | |
|
646 | ||
|
647 | # print (DGauFitParam.shape) | |
|
648 | # print ('Leaving FitGau') | |
|
649 | return DGauFitParam | |
|
650 | # return SPCparam | |
|
651 | # return GauSPC | |
|
574 | 652 | |
|
575 | 653 | def y_model1(self,x,state): |
|
576 | shift0,width0,amplitude0,power0,noise=state | |
|
577 | model0=amplitude0*numpy.exp(-0.5*abs((x-shift0)/width0)**power0) | |
|
578 | ||
|
579 |
model0 |
|
|
580 | ||
|
581 | model0d=amplitude0*numpy.exp(-0.5*abs((x-shift0+ self.Num_Bin )/width0)**power0) | |
|
582 | return model0+model0u+model0d+noise | |
|
654 | shift0, width0, amplitude0, power0, noise = state | |
|
655 | model0 = amplitude0*numpy.exp(-0.5*abs((x - shift0)/width0)**power0) | |
|
656 | model0u = amplitude0*numpy.exp(-0.5*abs((x - shift0 - self.Num_Bin)/width0)**power0) | |
|
657 | model0d = amplitude0*numpy.exp(-0.5*abs((x - shift0 + self.Num_Bin)/width0)**power0) | |
|
658 | return model0 + model0u + model0d + noise | |
|
583 | 659 | |
|
584 | 660 | def y_model2(self,x,state): #Equation for two generalized Gaussians with Nyquist |
|
585 | shift0,width0,amplitude0,power0,shift1,width1,amplitude1,power1,noise=state | |
|
586 | model0=amplitude0*numpy.exp(-0.5*abs((x-shift0)/width0)**power0) | |
|
587 | ||
|
588 |
model0 |
|
|
589 | ||
|
590 |
model |
|
|
591 | model1=amplitude1*numpy.exp(-0.5*abs((x-shift1)/width1)**power1) | |
|
592 | ||
|
593 | model1u=amplitude1*numpy.exp(-0.5*abs((x-shift1- self.Num_Bin )/width1)**power1) | |
|
594 | ||
|
595 | model1d=amplitude1*numpy.exp(-0.5*abs((x-shift1+ self.Num_Bin )/width1)**power1) | |
|
596 | return model0+model0u+model0d+model1+model1u+model1d+noise | |
|
661 | shift0, width0, amplitude0, power0, shift1, width1, amplitude1, power1, noise = state | |
|
662 | model0 = amplitude0*numpy.exp(-0.5*abs((x-shift0)/width0)**power0) | |
|
663 | model0u = amplitude0*numpy.exp(-0.5*abs((x - shift0 - self.Num_Bin)/width0)**power0) | |
|
664 | model0d = amplitude0*numpy.exp(-0.5*abs((x - shift0 + self.Num_Bin)/width0)**power0) | |
|
665 | ||
|
666 | model1 = amplitude1*numpy.exp(-0.5*abs((x - shift1)/width1)**power1) | |
|
667 | model1u = amplitude1*numpy.exp(-0.5*abs((x - shift1 - self.Num_Bin)/width1)**power1) | |
|
668 | model1d = amplitude1*numpy.exp(-0.5*abs((x - shift1 + self.Num_Bin)/width1)**power1) | |
|
669 | return model0 + model0u + model0d + model1 + model1u + model1d + noise | |
|
597 | 670 | |
|
598 | 671 | 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. |
|
599 | 672 | |
|
600 | 673 | return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model1(x,state)))**2)#/(64-5.) # /(64-5.) can be commented |
|
601 | 674 | |
|
602 | 675 | def misfit2(self,state,y_data,x,num_intg): |
|
603 | 676 | return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model2(x,state)))**2)#/(64-9.) |
|
604 | 677 | |
|
605 | 678 | |
|
606 | 679 | |
|
607 | 680 | class PrecipitationProc(Operation): |
|
608 | 681 | |
|
609 | 682 | ''' |
|
610 | 683 | Operator that estimates Reflectivity factor (Z), and estimates rainfall Rate (R) |
|
611 | 684 | |
|
612 | 685 | Input: |
|
613 | 686 | self.dataOut.data_pre : SelfSpectra |
|
614 | 687 | |
|
615 | 688 | Output: |
|
616 | 689 | |
|
617 | 690 | self.dataOut.data_output : Reflectivity factor, rainfall Rate |
|
618 | 691 | |
|
619 | 692 | |
|
620 | 693 | Parameters affected: |
|
621 | 694 | ''' |
|
622 | 695 | |
|
623 | 696 | def __init__(self): |
|
624 | 697 | Operation.__init__(self) |
|
625 | 698 | self.i=0 |
|
626 | 699 | |
|
627 | 700 | def run(self, dataOut, radar=None, Pt=5000, Gt=295.1209, Gr=70.7945, Lambda=0.6741, aL=2.5118, |
|
628 | tauW=4e-06, ThetaT=0.1656317, ThetaR=0.36774087, Km2 = 0.93, Altitude=3350): | |
|
701 | tauW=4e-06, ThetaT=0.1656317, ThetaR=0.36774087, Km2 = 0.93, Altitude=3350,SNRdBlimit=-30): | |
|
702 | ||
|
703 | # print ('Entering PrecepitationProc ... ') | |
|
629 | 704 | |
|
630 | 705 | if radar == "MIRA35C" : |
|
631 | 706 | |
|
632 | 707 | self.spc = dataOut.data_pre[0].copy() |
|
633 | 708 | self.Num_Hei = self.spc.shape[2] |
|
634 | 709 | self.Num_Bin = self.spc.shape[1] |
|
635 | 710 | self.Num_Chn = self.spc.shape[0] |
|
636 | 711 | Ze = self.dBZeMODE2(dataOut) |
|
637 | 712 | |
|
638 | 713 | else: |
|
639 | 714 | |
|
640 | 715 | self.spc = dataOut.data_pre[0].copy() |
|
641 | 716 | |
|
642 | 717 | #NOTA SE DEBE REMOVER EL RANGO DEL PULSO TX |
|
643 | 718 | self.spc[:,:,0:7]= numpy.NaN |
|
644 | 719 | |
|
645 | 720 | self.Num_Hei = self.spc.shape[2] |
|
646 | 721 | self.Num_Bin = self.spc.shape[1] |
|
647 | 722 | self.Num_Chn = self.spc.shape[0] |
|
648 | 723 | |
|
649 | 724 | VelRange = dataOut.spc_range[2] |
|
650 | 725 | |
|
651 | 726 | ''' Se obtiene la constante del RADAR ''' |
|
652 | 727 | |
|
653 | 728 | self.Pt = Pt |
|
654 | 729 | self.Gt = Gt |
|
655 | 730 | self.Gr = Gr |
|
656 | 731 | self.Lambda = Lambda |
|
657 | 732 | self.aL = aL |
|
658 | 733 | self.tauW = tauW |
|
659 | 734 | self.ThetaT = ThetaT |
|
660 | 735 | self.ThetaR = ThetaR |
|
661 | 736 | self.GSys = 10**(36.63/10) # Ganancia de los LNA 36.63 dB |
|
662 | 737 | self.lt = 10**(1.67/10) # Perdida en cables Tx 1.67 dB |
|
663 | 738 | self.lr = 10**(5.73/10) # Perdida en cables Rx 5.73 dB |
|
664 | 739 | |
|
665 | 740 | Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) ) |
|
666 | 741 | Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * tauW * numpy.pi * ThetaT * ThetaR) |
|
667 | 742 | RadarConstant = 10e-26 * Numerator / Denominator # |
|
668 | 743 | ExpConstant = 10**(40/10) #Constante Experimental |
|
669 | 744 | |
|
670 | 745 | SignalPower = numpy.zeros([self.Num_Chn,self.Num_Bin,self.Num_Hei]) |
|
671 | 746 | for i in range(self.Num_Chn): |
|
672 | 747 | SignalPower[i,:,:] = self.spc[i,:,:] - dataOut.noise[i] |
|
673 | 748 | SignalPower[numpy.where(SignalPower < 0)] = 1e-20 |
|
674 | 749 | |
|
675 | 750 | SPCmean = numpy.mean(SignalPower, 0) |
|
676 | ||
|
677 | 751 | Pr = SPCmean[:,:]/dataOut.normFactor |
|
678 | 752 | |
|
679 | 753 | # Declaring auxiliary variables |
|
680 | 754 | Range = dataOut.heightList*1000. #Range in m |
|
681 | 755 | # replicate the heightlist to obtain a matrix [Num_Bin,Num_Hei] |
|
682 | 756 | rMtrx = numpy.transpose(numpy.transpose([dataOut.heightList*1000.] * self.Num_Bin)) |
|
683 | 757 | zMtrx = rMtrx+Altitude |
|
684 | 758 | # replicate the VelRange to obtain a matrix [Num_Bin,Num_Hei] |
|
685 | 759 | VelMtrx = numpy.transpose(numpy.tile(VelRange[:-1], (self.Num_Hei,1))) |
|
686 | 760 | |
|
687 | 761 | # height dependence to air density Foote and Du Toit (1969) |
|
688 | 762 | delv_z = 1 + 3.68e-5 * zMtrx + 1.71e-9 * zMtrx**2 |
|
689 | 763 | VMtrx = VelMtrx / delv_z #Normalized velocity |
|
690 | 764 | VMtrx[numpy.where(VMtrx> 9.6)] = numpy.NaN |
|
691 | 765 | # Diameter is related to the fall speed of falling drops |
|
692 | 766 | D_Vz = -1.667 * numpy.log( 0.9369 - 0.097087 * VMtrx ) # D in [mm] |
|
693 | 767 | # Only valid for D>= 0.16 mm |
|
694 | 768 | D_Vz[numpy.where(D_Vz < 0.16)] = numpy.NaN |
|
695 | 769 | |
|
696 | 770 | #Calculate Radar Reflectivity ETAn |
|
697 | 771 | ETAn = (RadarConstant *ExpConstant) * Pr * rMtrx**2 #Reflectivity (ETA) |
|
698 | 772 | ETAd = ETAn * 6.18 * exp( -0.6 * D_Vz ) * delv_z |
|
699 | 773 | # Radar Cross Section |
|
700 | 774 | sigmaD = Km2 * (D_Vz * 1e-3 )**6 * numpy.pi**5 / Lambda**4 |
|
701 | 775 | # Drop Size Distribution |
|
702 | 776 | DSD = ETAn / sigmaD |
|
703 | 777 | # Equivalente Reflectivy |
|
704 | 778 | Ze_eqn = numpy.nansum( DSD * D_Vz**6 ,axis=0) |
|
705 | 779 | Ze_org = numpy.nansum(ETAn * Lambda**4, axis=0) / (1e-18*numpy.pi**5 * Km2) # [mm^6 /m^3] |
|
706 | 780 | # RainFall Rate |
|
707 | 781 | RR = 0.0006*numpy.pi * numpy.nansum( D_Vz**3 * DSD * VelMtrx ,0) #mm/hr |
|
708 | 782 | |
|
709 | 783 | # Censoring the data |
|
710 | 784 | # Removing data with SNRth < 0dB se debe considerar el SNR por canal |
|
711 |
SNRth = 10**( |
|
|
712 |
novalid = numpy.where((dataOut.data_ |
|
|
713 |
W = numpy.nanmean(dataOut.data_ |
|
|
785 | SNRth = 10**(SNRdBlimit/10) #-30dB | |
|
786 | novalid = numpy.where((dataOut.data_snr[0,:] <SNRth) | (dataOut.data_snr[1,:] <SNRth) | (dataOut.data_snr[2,:] <SNRth)) # AND condition. Maybe OR condition better | |
|
787 | W = numpy.nanmean(dataOut.data_dop,0) | |
|
714 | 788 | W[novalid] = numpy.NaN |
|
715 | 789 | Ze_org[novalid] = numpy.NaN |
|
716 | 790 | RR[novalid] = numpy.NaN |
|
717 | 791 | |
|
718 | 792 | dataOut.data_output = RR[8] |
|
719 | 793 | dataOut.data_param = numpy.ones([3,self.Num_Hei]) |
|
720 | 794 | dataOut.channelList = [0,1,2] |
|
721 | 795 | |
|
722 | 796 | dataOut.data_param[0]=10*numpy.log10(Ze_org) |
|
723 | dataOut.data_param[1]=W | |
|
797 | dataOut.data_param[1]=-W | |
|
724 | 798 | dataOut.data_param[2]=RR |
|
799 | ||
|
800 | # print ('Leaving PrecepitationProc ... ') | |
|
725 | 801 | return dataOut |
|
726 | 802 | |
|
727 | 803 | def dBZeMODE2(self, dataOut): # Processing for MIRA35C |
|
728 | 804 | |
|
729 | 805 | NPW = dataOut.NPW |
|
730 | 806 | COFA = dataOut.COFA |
|
731 | 807 | |
|
732 | 808 | SNR = numpy.array([self.spc[0,:,:] / NPW[0]]) #, self.spc[1,:,:] / NPW[1]]) |
|
733 | 809 | RadarConst = dataOut.RadarConst |
|
734 | 810 | #frequency = 34.85*10**9 |
|
735 | 811 | |
|
736 | 812 | ETA = numpy.zeros(([self.Num_Chn ,self.Num_Hei])) |
|
737 | 813 | data_output = numpy.ones([self.Num_Chn , self.Num_Hei])*numpy.NaN |
|
738 | 814 | |
|
739 | 815 | ETA = numpy.sum(SNR,1) |
|
740 | 816 | |
|
741 | 817 | ETA = numpy.where(ETA != 0. , ETA, numpy.NaN) |
|
742 | 818 | |
|
743 | 819 | Ze = numpy.ones([self.Num_Chn, self.Num_Hei] ) |
|
744 | 820 | |
|
745 | 821 | for r in range(self.Num_Hei): |
|
746 | 822 | |
|
747 | 823 | Ze[0,r] = ( ETA[0,r] ) * COFA[0,r][0] * RadarConst * ((r/5000.)**2) |
|
748 | 824 | #Ze[1,r] = ( ETA[1,r] ) * COFA[1,r][0] * RadarConst * ((r/5000.)**2) |
|
749 | 825 | |
|
750 | 826 | return Ze |
|
751 | 827 | |
|
752 | 828 | # def GetRadarConstant(self): |
|
753 | 829 | # |
|
754 | 830 | # """ |
|
755 | 831 | # Constants: |
|
756 | 832 | # |
|
757 | 833 | # Pt: Transmission Power dB 5kW 5000 |
|
758 | 834 | # Gt: Transmission Gain dB 24.7 dB 295.1209 |
|
759 | 835 | # Gr: Reception Gain dB 18.5 dB 70.7945 |
|
760 | 836 | # Lambda: Wavelenght m 0.6741 m 0.6741 |
|
761 | 837 | # aL: Attenuation loses dB 4dB 2.5118 |
|
762 | 838 | # tauW: Width of transmission pulse s 4us 4e-6 |
|
763 | 839 | # ThetaT: Transmission antenna bean angle rad 0.1656317 rad 0.1656317 |
|
764 | 840 | # ThetaR: Reception antenna beam angle rad 0.36774087 rad 0.36774087 |
|
765 | 841 | # |
|
766 | 842 | # """ |
|
767 | 843 | # |
|
768 | 844 | # Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) ) |
|
769 | 845 | # Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * TauW * numpy.pi * ThetaT * TheraR) |
|
770 | 846 | # RadarConstant = Numerator / Denominator |
|
771 | 847 | # |
|
772 | 848 | # return RadarConstant |
|
773 | 849 | |
|
774 | 850 | |
|
775 | 851 | |
|
776 | 852 | class FullSpectralAnalysis(Operation): |
|
777 | 853 | |
|
778 | 854 | """ |
|
779 | 855 | Function that implements Full Spectral Analysis technique. |
|
780 | 856 | |
|
781 | 857 | Input: |
|
782 | 858 | self.dataOut.data_pre : SelfSpectra and CrossSpectra data |
|
783 | 859 | self.dataOut.groupList : Pairlist of channels |
|
784 | 860 | self.dataOut.ChanDist : Physical distance between receivers |
|
785 | 861 | |
|
786 | 862 | |
|
787 | 863 | Output: |
|
788 | 864 | |
|
789 | 865 | self.dataOut.data_output : Zonal wind, Meridional wind, and Vertical wind |
|
790 | 866 | |
|
791 | 867 | |
|
792 | 868 | Parameters affected: Winds, height range, SNR |
|
793 | 869 | |
|
794 | 870 | """ |
|
795 |
def run(self, dataOut, Xi01=None, Xi02=None, Xi12=None, Eta01=None, Eta02=None, Eta12=None, SNRlimit= |
|
|
796 | ||
|
797 | self.indice=int(numpy.random.rand()*1000) | |
|
871 | def run(self, dataOut, Xi01=None, Xi02=None, Xi12=None, Eta01=None, Eta02=None, Eta12=None, SNRdBlimit=-30, | |
|
872 | minheight=None, maxheight=None, NegativeLimit=None, PositiveLimit=None): | |
|
798 | 873 | |
|
799 | 874 | spc = dataOut.data_pre[0].copy() |
|
800 | 875 | cspc = dataOut.data_pre[1] |
|
801 | ||
|
802 | """Erick: NOTE THE RANGE OF THE PULSE TX MUST BE REMOVED""" | |
|
803 | ||
|
804 | SNRspc = spc.copy() | |
|
805 | SNRspc[:,:,0:7]= numpy.NaN # D. Scipión... the cleaning should not be hardwired in the code... it needs to be flexible... NEEDS TO BE REMOVED | |
|
806 | ||
|
807 | """##########################################""" | |
|
808 | ||
|
809 | ||
|
810 | nChannel = spc.shape[0] | |
|
811 | nProfiles = spc.shape[1] | |
|
812 | 876 | nHeights = spc.shape[2] |
|
813 | 877 | |
|
814 | 878 | # first_height = 0.75 #km (ref: data header 20170822) |
|
815 | 879 | # resolution_height = 0.075 #km |
|
816 | 880 | ''' |
|
817 | 881 | finding height range. check this when radar parameters are changed! |
|
818 | 882 | ''' |
|
819 | 883 | if maxheight is not None: |
|
820 | 884 | # range_max = math.ceil((maxheight - first_height) / resolution_height) # theoretical |
|
821 | 885 | range_max = math.ceil(13.26 * maxheight - 3) # empirical, works better |
|
822 | 886 | else: |
|
823 | 887 | range_max = nHeights |
|
824 | 888 | if minheight is not None: |
|
825 | 889 | # range_min = int((minheight - first_height) / resolution_height) # theoretical |
|
826 | 890 | range_min = int(13.26 * minheight - 5) # empirical, works better |
|
827 | 891 | if range_min < 0: |
|
828 | 892 | range_min = 0 |
|
829 | 893 | else: |
|
830 | 894 | range_min = 0 |
|
831 | 895 | |
|
832 | 896 | pairsList = dataOut.groupList |
|
833 | 897 | if dataOut.ChanDist is not None : |
|
834 | 898 | ChanDist = dataOut.ChanDist |
|
835 | 899 | else: |
|
836 | 900 | ChanDist = numpy.array([[Xi01, Eta01],[Xi02,Eta02],[Xi12,Eta12]]) |
|
837 | 901 | |
|
838 | FrecRange = dataOut.spc_range[0] | |
|
839 | ||
|
840 | data_SNR=numpy.zeros([nProfiles]) | |
|
841 | noise = dataOut.noise | |
|
842 | ||
|
843 | dataOut.data_snr = (numpy.mean(SNRspc,axis=1)- noise[0]) / noise[0] | |
|
844 | ||
|
845 | dataOut.data_snr[numpy.where( dataOut.data_snr <0 )] = 1e-20 | |
|
846 | ||
|
847 | ||
|
848 | data_output=numpy.ones([spc.shape[0],spc.shape[2]])*numpy.NaN | |
|
849 | ||
|
850 | velocityX=[] | |
|
851 | velocityY=[] | |
|
852 | velocityV=[] | |
|
902 | # 4 variables: zonal, meridional, vertical, and average SNR | |
|
903 | data_param = numpy.zeros([4,nHeights]) * numpy.NaN | |
|
904 | velocityX = numpy.zeros([nHeights]) * numpy.NaN | |
|
905 | velocityY = numpy.zeros([nHeights]) * numpy.NaN | |
|
906 | velocityZ = numpy.zeros([nHeights]) * numpy.NaN | |
|
853 | 907 | |
|
854 | dbSNR = 10*numpy.log10(dataOut.data_snr) | |
|
855 | dbSNR = numpy.average(dbSNR,0) | |
|
908 | dbSNR = 10*numpy.log10(numpy.average(dataOut.data_snr,0)) | |
|
856 | 909 | |
|
857 | 910 | '''***********************************************WIND ESTIMATION**************************************''' |
|
858 | ||
|
859 | 911 | for Height in range(nHeights): |
|
860 | 912 | |
|
861 | 913 | if Height >= range_min and Height < range_max: |
|
862 |
# error_code |
|
|
863 |
[Vzon,Vmer,Vver, error_code] = self.WindEstimation(spc[:,:,Height], cspc[:,:,Height], pairsList, |
|
|
864 | else: | |
|
865 | Vzon,Vmer,Vver = 0., 0., numpy.NaN | |
|
866 | ||
|
867 | ||
|
868 | if abs(Vzon) < 100. and abs(Vzon) > 0. and abs(Vmer) < 100. and abs(Vmer) > 0.: | |
|
869 | velocityX=numpy.append(velocityX, Vzon) | |
|
870 | velocityY=numpy.append(velocityY, -Vmer) | |
|
871 | ||
|
872 | else: | |
|
873 | velocityX=numpy.append(velocityX, numpy.NaN) | |
|
874 | velocityY=numpy.append(velocityY, numpy.NaN) | |
|
875 | ||
|
876 | if dbSNR[Height] > SNRlimit: | |
|
877 | velocityV=numpy.append(velocityV, -Vver) # reason for this minus sign -> convention? (taken from Ericks version) D.S. yes! | |
|
878 | else: | |
|
879 | velocityV=numpy.append(velocityV, numpy.NaN) | |
|
880 | ||
|
881 | ||
|
882 | '''Change the numpy.array (velocityX) sign when trying to process BLTR data (Erick)''' | |
|
883 | data_output[0] = numpy.array(velocityX) | |
|
884 | data_output[1] = numpy.array(velocityY) | |
|
885 | data_output[2] = velocityV | |
|
886 | ||
|
887 | ||
|
888 | dataOut.data_output = data_output | |
|
889 | ||
|
914 | # error_code will be useful in future analysis | |
|
915 | [Vzon,Vmer,Vver, error_code] = self.WindEstimation(spc[:,:,Height], cspc[:,:,Height], pairsList, | |
|
916 | ChanDist, Height, dataOut.noise, dataOut.spc_range, dbSNR[Height], SNRdBlimit, NegativeLimit, PositiveLimit,dataOut.frequency) | |
|
917 | ||
|
918 | if abs(Vzon) < 100. and abs(Vmer) < 100.: | |
|
919 | velocityX[Height] = Vzon | |
|
920 | velocityY[Height] = -Vmer | |
|
921 | velocityZ[Height] = Vver | |
|
922 | ||
|
923 | # Censoring data with SNR threshold | |
|
924 | dbSNR [dbSNR < SNRdBlimit] = numpy.NaN | |
|
925 | ||
|
926 | data_param[0] = velocityX | |
|
927 | data_param[1] = velocityY | |
|
928 | data_param[2] = velocityZ | |
|
929 | data_param[3] = dbSNR | |
|
930 | dataOut.data_param = data_param | |
|
890 | 931 | return dataOut |
|
891 | 932 | |
|
892 | ||
|
893 | 933 | def moving_average(self,x, N=2): |
|
894 | 934 | """ convolution for smoothenig data. note that last N-1 values are convolution with zeroes """ |
|
895 | 935 | return numpy.convolve(x, numpy.ones((N,))/N)[(N-1):] |
|
896 | 936 | |
|
897 | 937 | def gaus(self,xSamples,Amp,Mu,Sigma): |
|
898 |
return |
|
|
938 | return Amp * numpy.exp(-0.5*((xSamples - Mu)/Sigma)**2) | |
|
899 | 939 | |
|
900 | 940 | def Moments(self, ySamples, xSamples): |
|
901 | 941 | Power = numpy.nanmean(ySamples) # Power, 0th Moment |
|
902 | 942 | yNorm = ySamples / numpy.nansum(ySamples) |
|
903 | 943 | RadVel = numpy.nansum(xSamples * yNorm) # Radial Velocity, 1st Moment |
|
904 |
Sigma2 = |
|
|
905 |
StdDev = Sigma2 |
|
|
944 | Sigma2 = numpy.nansum(yNorm * (xSamples - RadVel)**2) # Spectral Width, 2nd Moment | |
|
945 | StdDev = numpy.sqrt(numpy.abs(Sigma2)) # Desv. Estandar, Ancho espectral | |
|
906 | 946 | return numpy.array([Power,RadVel,StdDev]) |
|
907 | 947 | |
|
908 | 948 | def StopWindEstimation(self, error_code): |
|
909 | ''' | |
|
910 | the wind calculation and returns zeros | |
|
911 | ''' | |
|
912 | Vzon = 0 | |
|
913 | Vmer = 0 | |
|
914 | Vver = numpy.nan | |
|
949 | Vzon = numpy.NaN | |
|
950 | Vmer = numpy.NaN | |
|
951 | Vver = numpy.NaN | |
|
915 | 952 | return Vzon, Vmer, Vver, error_code |
|
916 | 953 | |
|
917 | 954 | def AntiAliasing(self, interval, maxstep): |
|
918 | 955 | """ |
|
919 | 956 | function to prevent errors from aliased values when computing phaseslope |
|
920 | 957 | """ |
|
921 |
antialiased = numpy.zeros(len(interval)) |
|
|
958 | antialiased = numpy.zeros(len(interval)) | |
|
922 | 959 | copyinterval = interval.copy() |
|
923 | 960 | |
|
924 | 961 | antialiased[0] = copyinterval[0] |
|
925 | 962 | |
|
926 | 963 | for i in range(1,len(antialiased)): |
|
927 | ||
|
928 | 964 | step = interval[i] - interval[i-1] |
|
929 | ||
|
930 | 965 | if step > maxstep: |
|
931 | 966 | copyinterval -= 2*numpy.pi |
|
932 | 967 | antialiased[i] = copyinterval[i] |
|
933 | ||
|
934 | 968 | elif step < maxstep*(-1): |
|
935 | 969 | copyinterval += 2*numpy.pi |
|
936 | 970 | antialiased[i] = copyinterval[i] |
|
937 | ||
|
938 | 971 | else: |
|
939 | 972 | antialiased[i] = copyinterval[i].copy() |
|
940 | 973 | |
|
941 | 974 | return antialiased |
|
942 | 975 | |
|
943 | def WindEstimation(self, spc, cspc, pairsList, ChanDist, Height, noise, AbbsisaRange, dbSNR, SNRlimit): | |
|
976 | def WindEstimation(self, spc, cspc, pairsList, ChanDist, Height, noise, AbbsisaRange, dbSNR, SNRlimit, NegativeLimit, PositiveLimit, radfreq): | |
|
944 | 977 | """ |
|
945 | 978 | Function that Calculates Zonal, Meridional and Vertical wind velocities. |
|
946 | 979 | Initial Version by E. Bocanegra updated by J. Zibell until Nov. 2019. |
|
947 | 980 | |
|
948 | 981 | Input: |
|
949 | 982 | spc, cspc : self spectra and cross spectra data. In Briggs notation something like S_i*(S_i)_conj, (S_j)_conj respectively. |
|
950 | 983 | pairsList : Pairlist of channels |
|
951 | 984 | ChanDist : array of xi_ij and eta_ij |
|
952 | 985 | Height : height at which data is processed |
|
953 | 986 | noise : noise in [channels] format for specific height |
|
954 | 987 | Abbsisarange : range of the frequencies or velocities |
|
955 | 988 | dbSNR, SNRlimit : signal to noise ratio in db, lower limit |
|
956 | 989 | |
|
957 | 990 | Output: |
|
958 | 991 | Vzon, Vmer, Vver : wind velocities |
|
959 | 992 | error_code : int that states where code is terminated |
|
960 | 993 | |
|
961 | 994 | 0 : no error detected |
|
962 | 995 | 1 : Gaussian of mean spc exceeds widthlimit |
|
963 | 996 | 2 : no Gaussian of mean spc found |
|
964 | 997 | 3 : SNR to low or velocity to high -> prec. e.g. |
|
965 | 998 | 4 : at least one Gaussian of cspc exceeds widthlimit |
|
966 | 999 | 5 : zero out of three cspc Gaussian fits converged |
|
967 | 1000 | 6 : phase slope fit could not be found |
|
968 | 1001 | 7 : arrays used to fit phase have different length |
|
969 | 1002 | 8 : frequency range is either too short (len <= 5) or very long (> 30% of cspc) |
|
970 | 1003 | |
|
971 | 1004 | """ |
|
972 | 1005 | |
|
973 | 1006 | error_code = 0 |
|
974 | 1007 | |
|
975 | ||
|
976 | SPC_Samples = numpy.ones([spc.shape[0],spc.shape[1]]) # for normalized spc values for one height | |
|
977 | phase = numpy.ones([spc.shape[0],spc.shape[1]]) # phase between channels | |
|
978 | CSPC_Samples = numpy.ones([spc.shape[0],spc.shape[1]],dtype=numpy.complex_) # for normalized cspc values | |
|
979 | PhaseSlope = numpy.zeros(spc.shape[0]) # slope of the phases, channelwise | |
|
980 | PhaseInter = numpy.ones(spc.shape[0]) # intercept to the slope of the phases, channelwise | |
|
981 | xFrec = AbbsisaRange[0][0:spc.shape[1]] # frequency range | |
|
982 | xVel = AbbsisaRange[2][0:spc.shape[1]] # velocity range | |
|
983 | SPCav = numpy.average(spc, axis=0)-numpy.average(noise) # spc[0]-noise[0] %D.S. why??? I suggest only spc.... | |
|
984 | ||
|
985 | SPCmoments_vel = self.Moments(SPCav, xVel ) # SPCmoments_vel[1] corresponds to vertical velocity and is used to determine if signal corresponds to wind (if .. <3) | |
|
986 | # D.S. I suggest to each moment to be calculated independently, because the signal level y/o interferences are not the same in all channels and | |
|
987 | # signal or SNR seems to be contaminated | |
|
988 | CSPCmoments = [] | |
|
1008 | nChan = spc.shape[0] | |
|
1009 | nProf = spc.shape[1] | |
|
1010 | nPair = cspc.shape[0] | |
|
1011 | ||
|
1012 | SPC_Samples = numpy.zeros([nChan, nProf]) # for normalized spc values for one height | |
|
1013 | CSPC_Samples = numpy.zeros([nPair, nProf], dtype=numpy.complex_) # for normalized cspc values | |
|
1014 | phase = numpy.zeros([nPair, nProf]) # phase between channels | |
|
1015 | PhaseSlope = numpy.zeros(nPair) # slope of the phases, channelwise | |
|
1016 | PhaseInter = numpy.zeros(nPair) # intercept to the slope of the phases, channelwise | |
|
1017 | xFrec = AbbsisaRange[0][:-1] # frequency range | |
|
1018 | xVel = AbbsisaRange[2][:-1] # velocity range | |
|
1019 | xSamples = xFrec # the frequency range is taken | |
|
1020 | delta_x = xSamples[1] - xSamples[0] # delta_f or delta_x | |
|
1021 | ||
|
1022 | # only consider velocities with in NegativeLimit and PositiveLimit | |
|
1023 | if (NegativeLimit is None): | |
|
1024 | NegativeLimit = numpy.min(xVel) | |
|
1025 | if (PositiveLimit is None): | |
|
1026 | PositiveLimit = numpy.max(xVel) | |
|
1027 | xvalid = numpy.where((xVel > NegativeLimit) & (xVel < PositiveLimit)) | |
|
1028 | xSamples_zoom = xSamples[xvalid] | |
|
989 | 1029 | |
|
990 | 1030 | '''Getting Eij and Nij''' |
|
991 | ||
|
992 | 1031 | Xi01, Xi02, Xi12 = ChanDist[:,0] |
|
993 | 1032 | Eta01, Eta02, Eta12 = ChanDist[:,1] |
|
994 | 1033 | |
|
995 | # update nov 19 | |
|
996 | widthlimit = 7 # maximum width in Hz of the gaussian, empirically determined. Anything above 10 is unrealistic, often values between 1 and 5 correspond to proper fits. | |
|
997 | ||
|
1034 | # spwd limit - updated by D. Scipión 30.03.2021 | |
|
1035 | widthlimit = 10 | |
|
998 | 1036 | '''************************* SPC is normalized ********************************''' |
|
999 | ||
|
1000 | spc_norm = spc.copy() # need copy() because untouched spc is needed for normalization of cspc below | |
|
1001 | spc_norm = numpy.where(numpy.isfinite(spc_norm), spc_norm, numpy.NAN) | |
|
1002 | ||
|
1003 | # D. Scipión: It is necessary to define DeltaF or DeltaV... it is wrong to use Factor_Norm. It's constant... not a variable | |
|
1004 | ||
|
1037 | spc_norm = spc.copy() | |
|
1005 | 1038 | # For each channel |
|
1006 |
for i in range( |
|
|
1007 | ||
|
1008 | spc_sub = spc_norm[i,:] - noise[i] # spc not smoothed here or in previous version. | |
|
1009 | # D. Scipión: Factor_Norm has to be replaced by DeltaF or DeltaV - It's a constant | |
|
1010 | Factor_Norm = 2*numpy.max(xFrec) / numpy.count_nonzero(~numpy.isnan(spc_sub)) # usually = Freq range / nfft | |
|
1011 | normalized_spc = spc_sub / (numpy.nansum(numpy.abs(spc_sub)) * Factor_Norm) | |
|
1012 | ||
|
1013 | xSamples = xFrec # the frequency range is taken | |
|
1014 | SPC_Samples[i] = normalized_spc # Normalized SPC values are taken | |
|
1039 | for i in range(nChan): | |
|
1040 | spc_sub = spc_norm[i,:] - noise[i] # only the signal power | |
|
1041 | SPC_Samples[i] = spc_sub / (numpy.nansum(spc_sub) * delta_x) | |
|
1015 | 1042 | |
|
1016 | 1043 | '''********************** FITTING MEAN SPC GAUSSIAN **********************''' |
|
1017 | 1044 | |
|
1018 | 1045 | """ the gaussian of the mean: first subtract noise, then normalize. this is legal because |
|
1019 | 1046 | you only fit the curve and don't need the absolute value of height for calculation, |
|
1020 | 1047 | only for estimation of width. for normalization of cross spectra, you need initial, |
|
1021 | 1048 | unnormalized self-spectra With noise. |
|
1022 | 1049 | |
|
1023 | 1050 | Technically, you don't even need to normalize the self-spectra, as you only need the |
|
1024 | 1051 | width of the peak. However, it was left this way. Note that the normalization has a flaw: |
|
1025 | 1052 | due to subtraction of the noise, some values are below zero. Raw "spc" values should be |
|
1026 | 1053 | >= 0, as it is the modulus squared of the signals (complex * it's conjugate) |
|
1027 | 1054 | """ |
|
1028 | ||
|
1029 | SPCMean = numpy.average(SPC_Samples, axis=0) | |
|
1030 | ||
|
1031 | popt = [1e-10,0,1e-10] | |
|
1032 | SPCMoments = self.Moments(SPCMean, xSamples) | |
|
1033 | ||
|
1034 | if dbSNR > SNRlimit and numpy.abs(SPCmoments_vel[1]) < 3: | |
|
1055 | # initial conditions | |
|
1056 | popt = [1e-10,0,1e-10] | |
|
1057 | # Spectra average | |
|
1058 | SPCMean = numpy.average(SPC_Samples,0) | |
|
1059 | # Moments in frequency | |
|
1060 | SPCMoments = self.Moments(SPCMean[xvalid], xSamples_zoom) | |
|
1061 | ||
|
1062 | # Gauss Fit SPC in frequency domain | |
|
1063 | if dbSNR > SNRlimit: # only if SNR > SNRth | |
|
1035 | 1064 | try: |
|
1036 | popt,pcov = curve_fit(self.gaus,xSamples,SPCMean,p0=SPCMoments)#, bounds=(-numpy.inf, [numpy.inf, numpy.inf, 10])). Setting bounds does not make the code faster but only keeps the fit from finding the minimum. | |
|
1037 | if popt[2] > widthlimit: # CONDITION | |
|
1065 | popt,pcov = curve_fit(self.gaus,xSamples_zoom,SPCMean[xvalid],p0=SPCMoments) | |
|
1066 | if popt[2] <= 0 or popt[2] > widthlimit: # CONDITION | |
|
1038 | 1067 | return self.StopWindEstimation(error_code = 1) |
|
1039 | ||
|
1040 | FitGauss = self.gaus(xSamples,*popt) | |
|
1041 | ||
|
1068 | FitGauss = self.gaus(xSamples_zoom,*popt) | |
|
1042 | 1069 | except :#RuntimeError: |
|
1043 | 1070 | return self.StopWindEstimation(error_code = 2) |
|
1044 | ||
|
1045 | 1071 | else: |
|
1046 | 1072 | return self.StopWindEstimation(error_code = 3) |
|
1047 | 1073 | |
|
1048 | ||
|
1049 | ||
|
1050 | 1074 | '''***************************** CSPC Normalization ************************* |
|
1051 | new section: | |
|
1052 | 1075 | The Spc spectra are used to normalize the crossspectra. Peaks from precipitation |
|
1053 | 1076 | influence the norm which is not desired. First, a range is identified where the |
|
1054 | 1077 | wind peak is estimated -> sum_wind is sum of those frequencies. Next, the area |
|
1055 | 1078 | around it gets cut off and values replaced by mean determined by the boundary |
|
1056 | 1079 | data -> sum_noise (spc is not normalized here, thats why the noise is important) |
|
1057 | 1080 | |
|
1058 | 1081 | The sums are then added and multiplied by range/datapoints, because you need |
|
1059 | 1082 | an integral and not a sum for normalization. |
|
1060 | 1083 | |
|
1061 | 1084 | A norm is found according to Briggs 92. |
|
1062 | 1085 | ''' |
|
1063 | ||
|
1064 | radarWavelength = 0.6741 # meters | |
|
1065 | # D.S. This does not need to hardwired... It has to be in function of the radar frequency | |
|
1066 | ||
|
1067 | count_limit_freq = numpy.abs(popt[1]) + widthlimit # Hz, m/s can be also used if velocity is desired abscissa. | |
|
1068 | # count_limit_freq = numpy.max(xFrec) | |
|
1069 | ||
|
1070 | channel_integrals = numpy.zeros(3) | |
|
1071 | ||
|
1072 | for i in range(spc.shape[0]): | |
|
1073 | ''' | |
|
1074 | find the point in array corresponding to count_limit frequency. | |
|
1075 | sum over all frequencies in the range around zero Hz @ math.ceil(N_freq/2) | |
|
1076 | ''' | |
|
1077 | N_freq = numpy.count_nonzero(~numpy.isnan(spc[i,:])) | |
|
1078 | count_limit_int = int(math.ceil( count_limit_freq / numpy.max(xFrec) * (N_freq / 2) )) # gives integer point | |
|
1079 | sum_wind = numpy.nansum( spc[i, (math.ceil(N_freq/2) - count_limit_int) : (math.ceil(N_freq / 2) + count_limit_int)] ) #N_freq/2 is where frequency (velocity) is zero, i.e. middle of spectrum. | |
|
1080 | sum_noise = (numpy.mean(spc[i, :4]) + numpy.mean(spc[i, -6:-2]))/2.0 * (N_freq - 2*count_limit_int) | |
|
1081 | channel_integrals[i] = (sum_noise + sum_wind) * (2*numpy.max(xFrec) / N_freq) | |
|
1082 | ||
|
1083 | ||
|
1084 | cross_integrals_peak = numpy.zeros(3) | |
|
1085 | # cross_integrals_totalrange = numpy.zeros(3) | |
|
1086 | ||
|
1087 | for i in range(spc.shape[0]): | |
|
1088 | ||
|
1089 | cspc_norm = cspc[i,:].copy() # cspc not smoothed here or in previous version | |
|
1090 | ||
|
1086 | # for each pair | |
|
1087 | for i in range(nPair): | |
|
1088 | cspc_norm = cspc[i,:].copy() | |
|
1091 | 1089 | chan_index0 = pairsList[i][0] |
|
1092 | 1090 | chan_index1 = pairsList[i][1] |
|
1093 | ||
|
1094 | cross_integrals_peak[i] = channel_integrals[chan_index0]*channel_integrals[chan_index1] | |
|
1095 | normalized_cspc = cspc_norm / numpy.sqrt(cross_integrals_peak[i]) | |
|
1096 | CSPC_Samples[i] = normalized_cspc | |
|
1097 | ||
|
1098 | ''' Finding cross integrals without subtracting any peaks:''' | |
|
1099 | # FactorNorm0 = 2*numpy.max(xFrec) / numpy.count_nonzero(~numpy.isnan(spc[chan_index0,:])) | |
|
1100 | # FactorNorm1 = 2*numpy.max(xFrec) / numpy.count_nonzero(~numpy.isnan(spc[chan_index1,:])) | |
|
1101 | # cross_integrals_totalrange[i] = (numpy.nansum(spc[chan_index0,:])) * FactorNorm0 * (numpy.nansum(spc[chan_index1,:])) * FactorNorm1 | |
|
1102 | # normalized_cspc = cspc_norm / numpy.sqrt(cross_integrals_totalrange[i]) | |
|
1103 | # CSPC_Samples[i] = normalized_cspc | |
|
1104 | ||
|
1105 | ||
|
1091 | CSPC_Samples[i] = cspc_norm / (numpy.sqrt(numpy.nansum(spc_norm[chan_index0])*numpy.nansum(spc_norm[chan_index1])) * delta_x) | |
|
1106 | 1092 | phase[i] = numpy.arctan2(CSPC_Samples[i].imag, CSPC_Samples[i].real) |
|
1107 | 1093 | |
|
1094 | CSPCmoments = numpy.vstack([self.Moments(numpy.abs(CSPC_Samples[0,xvalid]), xSamples_zoom), | |
|
1095 | self.Moments(numpy.abs(CSPC_Samples[1,xvalid]), xSamples_zoom), | |
|
1096 | self.Moments(numpy.abs(CSPC_Samples[2,xvalid]), xSamples_zoom)]) | |
|
1108 | 1097 | |
|
1109 | CSPCmoments = numpy.vstack([self.Moments(numpy.abs(CSPC_Samples[0]), xSamples), | |
|
1110 | self.Moments(numpy.abs(CSPC_Samples[1]), xSamples), | |
|
1111 | self.Moments(numpy.abs(CSPC_Samples[2]), xSamples)]) | |
|
1112 | ||
|
1113 | ||
|
1114 | '''***Sorting out NaN entries***''' | |
|
1115 | CSPCMask01 = numpy.abs(CSPC_Samples[0]) | |
|
1116 | CSPCMask02 = numpy.abs(CSPC_Samples[1]) | |
|
1117 | CSPCMask12 = numpy.abs(CSPC_Samples[2]) | |
|
1118 | ||
|
1119 | mask01 = ~numpy.isnan(CSPCMask01) | |
|
1120 | mask02 = ~numpy.isnan(CSPCMask02) | |
|
1121 | mask12 = ~numpy.isnan(CSPCMask12) | |
|
1122 | ||
|
1123 | CSPCMask01 = CSPCMask01[mask01] | |
|
1124 | CSPCMask02 = CSPCMask02[mask02] | |
|
1125 | CSPCMask12 = CSPCMask12[mask12] | |
|
1126 | ||
|
1127 | ||
|
1128 | popt01, popt02, popt12 = [1e-10,1e-10,1e-10], [1e-10,1e-10,1e-10] ,[1e-10,1e-10,1e-10] | |
|
1129 | FitGauss01, FitGauss02, FitGauss12 = numpy.empty(len(xSamples))*0, numpy.empty(len(xSamples))*0, numpy.empty(len(xSamples))*0 | |
|
1098 | popt01, popt02, popt12 = [1e-10,0,1e-10], [1e-10,0,1e-10] ,[1e-10,0,1e-10] | |
|
1099 | FitGauss01, FitGauss02, FitGauss12 = numpy.zeros(len(xSamples)), numpy.zeros(len(xSamples)), numpy.zeros(len(xSamples)) | |
|
1130 | 1100 | |
|
1131 | 1101 | '''*******************************FIT GAUSS CSPC************************************''' |
|
1132 | ||
|
1133 | 1102 | try: |
|
1134 |
popt01,pcov = curve_fit(self.gaus,xSamples |
|
|
1103 | popt01,pcov = curve_fit(self.gaus,xSamples_zoom,numpy.abs(CSPC_Samples[0][xvalid]),p0=CSPCmoments[0]) | |
|
1135 | 1104 | if popt01[2] > widthlimit: # CONDITION |
|
1136 | 1105 | return self.StopWindEstimation(error_code = 4) |
|
1137 | ||
|
1138 | popt02,pcov = curve_fit(self.gaus,xSamples[mask02],numpy.abs(CSPCMask02),p0=CSPCmoments[1]) | |
|
1106 | popt02,pcov = curve_fit(self.gaus,xSamples_zoom,numpy.abs(CSPC_Samples[1][xvalid]),p0=CSPCmoments[1]) | |
|
1139 | 1107 | if popt02[2] > widthlimit: # CONDITION |
|
1140 | 1108 | return self.StopWindEstimation(error_code = 4) |
|
1141 | ||
|
1142 | popt12,pcov = curve_fit(self.gaus,xSamples[mask12],numpy.abs(CSPCMask12),p0=CSPCmoments[2]) | |
|
1109 | popt12,pcov = curve_fit(self.gaus,xSamples_zoom,numpy.abs(CSPC_Samples[2][xvalid]),p0=CSPCmoments[2]) | |
|
1143 | 1110 | if popt12[2] > widthlimit: # CONDITION |
|
1144 | 1111 | return self.StopWindEstimation(error_code = 4) |
|
1145 | 1112 | |
|
1146 | FitGauss01 = self.gaus(xSamples, *popt01) | |
|
1147 | FitGauss02 = self.gaus(xSamples, *popt02) | |
|
1148 | FitGauss12 = self.gaus(xSamples, *popt12) | |
|
1149 | ||
|
1113 | FitGauss01 = self.gaus(xSamples_zoom, *popt01) | |
|
1114 | FitGauss02 = self.gaus(xSamples_zoom, *popt02) | |
|
1115 | FitGauss12 = self.gaus(xSamples_zoom, *popt12) | |
|
1150 | 1116 | except: |
|
1151 | 1117 | return self.StopWindEstimation(error_code = 5) |
|
1152 | 1118 | |
|
1153 | 1119 | |
|
1154 | 1120 | '''************* Getting Fij ***************''' |
|
1155 | ||
|
1156 | ||
|
1157 | #Punto en Eje X de la Gaussiana donde se encuentra el centro -- x-axis point of the gaussian where the center is located | |
|
1158 | # -> PointGauCenter | |
|
1121 | # x-axis point of the gaussian where the center is located from GaussFit of spectra | |
|
1159 | 1122 | GaussCenter = popt[1] |
|
1160 | ClosestCenter = xSamples[numpy.abs(xSamples-GaussCenter).argmin()] | |
|
1161 | PointGauCenter = numpy.where(xSamples==ClosestCenter)[0][0] | |
|
1123 | ClosestCenter = xSamples_zoom[numpy.abs(xSamples_zoom-GaussCenter).argmin()] | |
|
1124 | PointGauCenter = numpy.where(xSamples_zoom==ClosestCenter)[0][0] | |
|
1162 | 1125 | |
|
1163 |
# |
|
|
1126 | # Point where e^-1 is located in the gaussian | |
|
1164 | 1127 | PeMinus1 = numpy.max(FitGauss) * numpy.exp(-1) |
|
1165 |
FijClosest = FitGauss[numpy.abs(FitGauss-PeMinus1).argmin()] # |
|
|
1128 | FijClosest = FitGauss[numpy.abs(FitGauss-PeMinus1).argmin()] # The closest point to"Peminus1" in "FitGauss" | |
|
1166 | 1129 | PointFij = numpy.where(FitGauss==FijClosest)[0][0] |
|
1167 | ||
|
1168 | Fij = numpy.abs(xSamples[PointFij] - xSamples[PointGauCenter]) | |
|
1130 | Fij = numpy.abs(xSamples_zoom[PointFij] - xSamples_zoom[PointGauCenter]) | |
|
1169 | 1131 | |
|
1170 | 1132 | '''********** Taking frequency ranges from mean SPCs **********''' |
|
1171 | ||
|
1172 | #GaussCenter = popt[1] #Primer momento 01 | |
|
1173 | GauWidth = popt[2] * 3/2 #Ancho de banda de Gau01 -- Bandwidth of Gau01 TODO why *3/2? | |
|
1133 | GauWidth = popt[2] * 3/2 # Bandwidth of Gau01 | |
|
1174 | 1134 | Range = numpy.empty(2) |
|
1175 | 1135 | Range[0] = GaussCenter - GauWidth |
|
1176 | 1136 | Range[1] = GaussCenter + GauWidth |
|
1177 |
# |
|
|
1178 | ClosRangeMin = xSamples[numpy.abs(xSamples-Range[0]).argmin()] | |
|
1179 | ClosRangeMax = xSamples[numpy.abs(xSamples-Range[1]).argmin()] | |
|
1180 | ||
|
1181 |
PointRangeM |
|
|
1182 | PointRangeMax = numpy.where(xSamples==ClosRangeMax)[0][0] | |
|
1183 | ||
|
1137 | # Point in x-axis where the bandwidth is located (min:max) | |
|
1138 | ClosRangeMin = xSamples_zoom[numpy.abs(xSamples_zoom-Range[0]).argmin()] | |
|
1139 | ClosRangeMax = xSamples_zoom[numpy.abs(xSamples_zoom-Range[1]).argmin()] | |
|
1140 | PointRangeMin = numpy.where(xSamples_zoom==ClosRangeMin)[0][0] | |
|
1141 | PointRangeMax = numpy.where(xSamples_zoom==ClosRangeMax)[0][0] | |
|
1184 | 1142 | Range = numpy.array([ PointRangeMin, PointRangeMax ]) |
|
1185 | ||
|
1186 | FrecRange = xFrec[ Range[0] : Range[1] ] | |
|
1187 | ||
|
1143 | FrecRange = xSamples_zoom[ Range[0] : Range[1] ] | |
|
1188 | 1144 | |
|
1189 | 1145 | '''************************** Getting Phase Slope ***************************''' |
|
1190 | ||
|
1191 | for i in range(1,3): # Changed to only compute two | |
|
1192 | ||
|
1193 | if len(FrecRange) > 5 and len(FrecRange) < spc.shape[1] * 0.3: | |
|
1194 | # PhaseRange=self.moving_average(phase[i,Range[0]:Range[1]],N=1) #used before to smooth phase with N=3 | |
|
1195 | PhaseRange = phase[i,Range[0]:Range[1]].copy() | |
|
1196 | ||
|
1146 | for i in range(nPair): | |
|
1147 | if len(FrecRange) > 5: | |
|
1148 | PhaseRange = phase[i, xvalid[0][Range[0]:Range[1]]].copy() | |
|
1197 | 1149 | mask = ~numpy.isnan(FrecRange) & ~numpy.isnan(PhaseRange) |
|
1198 | ||
|
1199 | ||
|
1200 | 1150 | if len(FrecRange) == len(PhaseRange): |
|
1201 | ||
|
1202 | 1151 | try: |
|
1203 | 1152 | slope, intercept, _, _, _ = stats.linregress(FrecRange[mask], self.AntiAliasing(PhaseRange[mask], 4.5)) |
|
1204 | 1153 | PhaseSlope[i] = slope |
|
1205 | 1154 | PhaseInter[i] = intercept |
|
1206 | ||
|
1207 | 1155 | except: |
|
1208 | 1156 | return self.StopWindEstimation(error_code = 6) |
|
1209 | ||
|
1210 | 1157 | else: |
|
1211 | 1158 | return self.StopWindEstimation(error_code = 7) |
|
1212 | ||
|
1213 | 1159 | else: |
|
1214 | 1160 | return self.StopWindEstimation(error_code = 8) |
|
1215 | 1161 | |
|
1216 | ||
|
1217 | ||
|
1218 | 1162 | '''*** Constants A-H correspond to the convention as in Briggs and Vincent 1992 ***''' |
|
1219 | 1163 | |
|
1220 | 1164 | '''Getting constant C''' |
|
1221 | 1165 | cC=(Fij*numpy.pi)**2 |
|
1222 | 1166 | |
|
1223 | 1167 | '''****** Getting constants F and G ******''' |
|
1224 | 1168 | MijEijNij = numpy.array([[Xi02,Eta02], [Xi12,Eta12]]) |
|
1225 | MijResult0 = (-PhaseSlope[1] * cC) / (2*numpy.pi) | |
|
1226 |
MijResult |
|
|
1227 | MijResults = numpy.array([MijResult0,MijResult1]) | |
|
1169 | # MijEijNij = numpy.array([[Xi01,Eta01], [Xi02,Eta02], [Xi12,Eta12]]) | |
|
1170 | # MijResult0 = (-PhaseSlope[0] * cC) / (2*numpy.pi) | |
|
1171 | MijResult1 = (-PhaseSlope[1] * cC) / (2*numpy.pi) | |
|
1172 | MijResult2 = (-PhaseSlope[2] * cC) / (2*numpy.pi) | |
|
1173 | # MijResults = numpy.array([MijResult0, MijResult1, MijResult2]) | |
|
1174 | MijResults = numpy.array([MijResult1, MijResult2]) | |
|
1228 | 1175 | (cF,cG) = numpy.linalg.solve(MijEijNij, MijResults) |
|
1229 | 1176 | |
|
1230 | 1177 | '''****** Getting constants A, B and H ******''' |
|
1231 | 1178 | W01 = numpy.nanmax( FitGauss01 ) |
|
1232 | 1179 | W02 = numpy.nanmax( FitGauss02 ) |
|
1233 | 1180 | W12 = numpy.nanmax( FitGauss12 ) |
|
1234 | 1181 | |
|
1235 | WijResult0 = ((cF * Xi01 + cG * Eta01)**2)/cC - numpy.log(W01 / numpy.sqrt(numpy.pi / cC)) | |
|
1236 |
WijResult |
|
|
1237 | WijResult2 = ((cF * Xi12 + cG * Eta12)**2)/cC - numpy.log(W12 / numpy.sqrt(numpy.pi / cC)) | |
|
1238 | ||
|
1239 | WijResults = numpy.array([WijResult0, WijResult1, WijResult2]) | |
|
1182 | WijResult01 = ((cF * Xi01 + cG * Eta01)**2)/cC - numpy.log(W01 / numpy.sqrt(numpy.pi / cC)) | |
|
1183 | WijResult02 = ((cF * Xi02 + cG * Eta02)**2)/cC - numpy.log(W02 / numpy.sqrt(numpy.pi / cC)) | |
|
1184 | WijResult12 = ((cF * Xi12 + cG * Eta12)**2)/cC - numpy.log(W12 / numpy.sqrt(numpy.pi / cC)) | |
|
1185 | WijResults = numpy.array([WijResult01, WijResult02, WijResult12]) | |
|
1240 | 1186 | |
|
1241 | 1187 | WijEijNij = numpy.array([ [Xi01**2, Eta01**2, 2*Xi01*Eta01] , [Xi02**2, Eta02**2, 2*Xi02*Eta02] , [Xi12**2, Eta12**2, 2*Xi12*Eta12] ]) |
|
1242 | 1188 | (cA,cB,cH) = numpy.linalg.solve(WijEijNij, WijResults) |
|
1243 | 1189 | |
|
1244 | 1190 | VxVy = numpy.array([[cA,cH],[cH,cB]]) |
|
1245 | 1191 | VxVyResults = numpy.array([-cF,-cG]) |
|
1246 |
(V |
|
|
1247 | ||
|
1248 | Vzon = Vy | |
|
1249 | Vmer = Vx | |
|
1250 | ||
|
1251 | # Vmag=numpy.sqrt(Vzon**2+Vmer**2) # unused | |
|
1252 | # Vang=numpy.arctan2(Vmer,Vzon) # unused | |
|
1253 | ||
|
1254 | ||
|
1255 | ''' using frequency as abscissa. Due to three channels, the offzenith angle is zero | |
|
1256 | and Vrad equal to Vver. formula taken from Briggs 92, figure 4. | |
|
1257 | ''' | |
|
1258 | if numpy.abs( popt[1] ) < 3.5 and len(FrecRange) > 4: | |
|
1259 | Vver = 0.5 * radarWavelength * popt[1] * 100 # *100 to get cm (/s) | |
|
1260 | else: | |
|
1261 | Vver = numpy.NaN | |
|
1262 | ||
|
1192 | (Vmer,Vzon) = numpy.linalg.solve(VxVy, VxVyResults) | |
|
1193 | Vver = -SPCMoments[1]*SPEED_OF_LIGHT/(2*radfreq) | |
|
1263 | 1194 | error_code = 0 |
|
1264 | 1195 | |
|
1265 | 1196 | return Vzon, Vmer, Vver, error_code |
|
1266 | 1197 | |
|
1267 | ||
|
1268 | 1198 | class SpectralMoments(Operation): |
|
1269 | 1199 | |
|
1270 | 1200 | ''' |
|
1271 | 1201 | Function SpectralMoments() |
|
1272 | 1202 | |
|
1273 | 1203 | Calculates moments (power, mean, standard deviation) and SNR of the signal |
|
1274 | 1204 | |
|
1275 | 1205 | Type of dataIn: Spectra |
|
1276 | 1206 | |
|
1277 | 1207 | Configuration Parameters: |
|
1278 | 1208 | |
|
1279 | 1209 | dirCosx : Cosine director in X axis |
|
1280 | 1210 | dirCosy : Cosine director in Y axis |
|
1281 | 1211 | |
|
1282 | 1212 | elevation : |
|
1283 | 1213 | azimuth : |
|
1284 | 1214 | |
|
1285 | 1215 | Input: |
|
1286 | 1216 | channelList : simple channel list to select e.g. [2,3,7] |
|
1287 | 1217 | self.dataOut.data_pre : Spectral data |
|
1288 | 1218 | self.dataOut.abscissaList : List of frequencies |
|
1289 | 1219 | self.dataOut.noise : Noise level per channel |
|
1290 | 1220 | |
|
1291 | 1221 | Affected: |
|
1292 | 1222 | self.dataOut.moments : Parameters per channel |
|
1293 | 1223 | self.dataOut.data_snr : SNR per channel |
|
1294 | 1224 | |
|
1295 | 1225 | ''' |
|
1296 | 1226 | |
|
1297 | 1227 | def run(self, dataOut): |
|
1298 | 1228 | |
|
1299 | 1229 | data = dataOut.data_pre[0] |
|
1300 | 1230 | absc = dataOut.abscissaList[:-1] |
|
1301 | 1231 | noise = dataOut.noise |
|
1302 | 1232 | nChannel = data.shape[0] |
|
1303 | 1233 | data_param = numpy.zeros((nChannel, 4, data.shape[2])) |
|
1304 | 1234 | |
|
1305 | 1235 | for ind in range(nChannel): |
|
1306 | 1236 | data_param[ind,:,:] = self.__calculateMoments( data[ind,:,:] , absc , noise[ind] ) |
|
1307 | 1237 | |
|
1308 | 1238 | dataOut.moments = data_param[:,1:,:] |
|
1309 | 1239 | dataOut.data_snr = data_param[:,0] |
|
1310 | 1240 | dataOut.data_pow = data_param[:,1] |
|
1311 | 1241 | dataOut.data_dop = data_param[:,2] |
|
1312 | 1242 | dataOut.data_width = data_param[:,3] |
|
1313 | 1243 | |
|
1314 | 1244 | return dataOut |
|
1315 | 1245 | |
|
1316 | 1246 | def __calculateMoments(self, oldspec, oldfreq, n0, |
|
1317 | 1247 | nicoh = None, graph = None, smooth = None, type1 = None, fwindow = None, snrth = None, dc = None, aliasing = None, oldfd = None, wwauto = None): |
|
1318 | 1248 | |
|
1319 | 1249 | if (nicoh is None): nicoh = 1 |
|
1320 | 1250 | if (graph is None): graph = 0 |
|
1321 | 1251 | if (smooth is None): smooth = 0 |
|
1322 | 1252 | elif (self.smooth < 3): smooth = 0 |
|
1323 | 1253 | |
|
1324 | 1254 | if (type1 is None): type1 = 0 |
|
1325 | 1255 | if (fwindow is None): fwindow = numpy.zeros(oldfreq.size) + 1 |
|
1326 | 1256 | if (snrth is None): snrth = -3 |
|
1327 | 1257 | if (dc is None): dc = 0 |
|
1328 | 1258 | if (aliasing is None): aliasing = 0 |
|
1329 | 1259 | if (oldfd is None): oldfd = 0 |
|
1330 | 1260 | if (wwauto is None): wwauto = 0 |
|
1331 | 1261 | |
|
1332 | 1262 | if (n0 < 1.e-20): n0 = 1.e-20 |
|
1333 | 1263 | |
|
1334 | 1264 | freq = oldfreq |
|
1335 | 1265 | vec_power = numpy.zeros(oldspec.shape[1]) |
|
1336 | 1266 | vec_fd = numpy.zeros(oldspec.shape[1]) |
|
1337 | 1267 | vec_w = numpy.zeros(oldspec.shape[1]) |
|
1338 | 1268 | vec_snr = numpy.zeros(oldspec.shape[1]) |
|
1339 | 1269 | |
|
1340 | 1270 | # oldspec = numpy.ma.masked_invalid(oldspec) |
|
1341 | 1271 | |
|
1342 | 1272 | for ind in range(oldspec.shape[1]): |
|
1343 | 1273 | |
|
1344 | 1274 | spec = oldspec[:,ind] |
|
1345 | 1275 | aux = spec*fwindow |
|
1346 | 1276 | max_spec = aux.max() |
|
1347 | 1277 | m = aux.tolist().index(max_spec) |
|
1348 | 1278 | |
|
1349 | 1279 | # Smooth |
|
1350 | 1280 | if (smooth == 0): |
|
1351 | 1281 | spec2 = spec |
|
1352 | 1282 | else: |
|
1353 | 1283 | spec2 = scipy.ndimage.filters.uniform_filter1d(spec,size=smooth) |
|
1354 | 1284 | |
|
1355 | 1285 | # Moments Estimation |
|
1356 | 1286 | bb = spec2[numpy.arange(m,spec2.size)] |
|
1357 | 1287 | bb = (bb<n0).nonzero() |
|
1358 | 1288 | bb = bb[0] |
|
1359 | 1289 | |
|
1360 | 1290 | ss = spec2[numpy.arange(0,m + 1)] |
|
1361 | 1291 | ss = (ss<n0).nonzero() |
|
1362 | 1292 | ss = ss[0] |
|
1363 | 1293 | |
|
1364 | 1294 | if (bb.size == 0): |
|
1365 | 1295 | bb0 = spec.size - 1 - m |
|
1366 | 1296 | else: |
|
1367 | 1297 | bb0 = bb[0] - 1 |
|
1368 | 1298 | if (bb0 < 0): |
|
1369 | 1299 | bb0 = 0 |
|
1370 | 1300 | |
|
1371 | 1301 | if (ss.size == 0): |
|
1372 | 1302 | ss1 = 1 |
|
1373 | 1303 | else: |
|
1374 | 1304 | ss1 = max(ss) + 1 |
|
1375 | 1305 | |
|
1376 | 1306 | if (ss1 > m): |
|
1377 | 1307 | ss1 = m |
|
1378 | 1308 | |
|
1379 | 1309 | valid = numpy.arange(int(m + bb0 - ss1 + 1)) + ss1 |
|
1380 | 1310 | |
|
1381 | 1311 | signal_power = ((spec2[valid] - n0) * fwindow[valid]).mean() # D. Scipión added with correct definition |
|
1382 | 1312 | total_power = (spec2[valid] * fwindow[valid]).mean() # D. Scipión added with correct definition |
|
1383 | 1313 | power = ((spec2[valid] - n0) * fwindow[valid]).sum() |
|
1384 | 1314 | fd = ((spec2[valid]- n0)*freq[valid] * fwindow[valid]).sum() / power |
|
1385 | 1315 | w = numpy.sqrt(((spec2[valid] - n0)*fwindow[valid]*(freq[valid]- fd)**2).sum() / power) |
|
1386 | 1316 | snr = (spec2.mean()-n0)/n0 |
|
1387 | 1317 | if (snr < 1.e-20) : |
|
1388 | 1318 | snr = 1.e-20 |
|
1389 | 1319 | |
|
1390 | 1320 | # vec_power[ind] = power #D. Scipión replaced with the line below |
|
1391 | 1321 | vec_power[ind] = total_power |
|
1392 | 1322 | vec_fd[ind] = fd |
|
1393 | 1323 | vec_w[ind] = w |
|
1394 | 1324 | vec_snr[ind] = snr |
|
1395 | 1325 | |
|
1396 | 1326 | return numpy.vstack((vec_snr, vec_power, vec_fd, vec_w)) |
|
1397 | 1327 | |
|
1398 | 1328 | #------------------ Get SA Parameters -------------------------- |
|
1399 | 1329 | |
|
1400 | 1330 | def GetSAParameters(self): |
|
1401 | 1331 | #SA en frecuencia |
|
1402 | 1332 | pairslist = self.dataOut.groupList |
|
1403 | 1333 | num_pairs = len(pairslist) |
|
1404 | 1334 | |
|
1405 | 1335 | vel = self.dataOut.abscissaList |
|
1406 | 1336 | spectra = self.dataOut.data_pre |
|
1407 | 1337 | cspectra = self.dataIn.data_cspc |
|
1408 | 1338 | delta_v = vel[1] - vel[0] |
|
1409 | 1339 | |
|
1410 | 1340 | #Calculating the power spectrum |
|
1411 | 1341 | spc_pow = numpy.sum(spectra, 3)*delta_v |
|
1412 | 1342 | #Normalizing Spectra |
|
1413 | 1343 | norm_spectra = spectra/spc_pow |
|
1414 | 1344 | #Calculating the norm_spectra at peak |
|
1415 | 1345 | max_spectra = numpy.max(norm_spectra, 3) |
|
1416 | 1346 | |
|
1417 | 1347 | #Normalizing Cross Spectra |
|
1418 | 1348 | norm_cspectra = numpy.zeros(cspectra.shape) |
|
1419 | 1349 | |
|
1420 | 1350 | for i in range(num_chan): |
|
1421 | 1351 | norm_cspectra[i,:,:] = cspectra[i,:,:]/numpy.sqrt(spc_pow[pairslist[i][0],:]*spc_pow[pairslist[i][1],:]) |
|
1422 | 1352 | |
|
1423 | 1353 | max_cspectra = numpy.max(norm_cspectra,2) |
|
1424 | 1354 | max_cspectra_index = numpy.argmax(norm_cspectra, 2) |
|
1425 | 1355 | |
|
1426 | 1356 | for i in range(num_pairs): |
|
1427 | 1357 | cspc_par[i,:,:] = __calculateMoments(norm_cspectra) |
|
1428 | 1358 | #------------------- Get Lags ---------------------------------- |
|
1429 | 1359 | |
|
1430 | 1360 | class SALags(Operation): |
|
1431 | 1361 | ''' |
|
1432 | 1362 | Function GetMoments() |
|
1433 | 1363 | |
|
1434 | 1364 | Input: |
|
1435 | 1365 | self.dataOut.data_pre |
|
1436 | 1366 | self.dataOut.abscissaList |
|
1437 | 1367 | self.dataOut.noise |
|
1438 | 1368 | self.dataOut.normFactor |
|
1439 | 1369 | self.dataOut.data_snr |
|
1440 | 1370 | self.dataOut.groupList |
|
1441 | 1371 | self.dataOut.nChannels |
|
1442 | 1372 | |
|
1443 | 1373 | Affected: |
|
1444 | 1374 | self.dataOut.data_param |
|
1445 | 1375 | |
|
1446 | 1376 | ''' |
|
1447 | 1377 | def run(self, dataOut): |
|
1448 | 1378 | data_acf = dataOut.data_pre[0] |
|
1449 | 1379 | data_ccf = dataOut.data_pre[1] |
|
1450 | 1380 | normFactor_acf = dataOut.normFactor[0] |
|
1451 | 1381 | normFactor_ccf = dataOut.normFactor[1] |
|
1452 | 1382 | pairs_acf = dataOut.groupList[0] |
|
1453 | 1383 | pairs_ccf = dataOut.groupList[1] |
|
1454 | 1384 | |
|
1455 | 1385 | nHeights = dataOut.nHeights |
|
1456 | 1386 | absc = dataOut.abscissaList |
|
1457 | 1387 | noise = dataOut.noise |
|
1458 | 1388 | SNR = dataOut.data_snr |
|
1459 | 1389 | nChannels = dataOut.nChannels |
|
1460 | 1390 | # pairsList = dataOut.groupList |
|
1461 | 1391 | # pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairsList, nChannels) |
|
1462 | 1392 | |
|
1463 | 1393 | for l in range(len(pairs_acf)): |
|
1464 | 1394 | data_acf[l,:,:] = data_acf[l,:,:]/normFactor_acf[l,:] |
|
1465 | 1395 | |
|
1466 | 1396 | for l in range(len(pairs_ccf)): |
|
1467 | 1397 | data_ccf[l,:,:] = data_ccf[l,:,:]/normFactor_ccf[l,:] |
|
1468 | 1398 | |
|
1469 | 1399 | dataOut.data_param = numpy.zeros((len(pairs_ccf)*2 + 1, nHeights)) |
|
1470 | 1400 | dataOut.data_param[:-1,:] = self.__calculateTaus(data_acf, data_ccf, absc) |
|
1471 | 1401 | dataOut.data_param[-1,:] = self.__calculateLag1Phase(data_acf, absc) |
|
1472 | 1402 | return |
|
1473 | 1403 | |
|
1474 | 1404 | # def __getPairsAutoCorr(self, pairsList, nChannels): |
|
1475 | 1405 | # |
|
1476 | 1406 | # pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan |
|
1477 | 1407 | # |
|
1478 | 1408 | # for l in range(len(pairsList)): |
|
1479 | 1409 | # firstChannel = pairsList[l][0] |
|
1480 | 1410 | # secondChannel = pairsList[l][1] |
|
1481 | 1411 | # |
|
1482 | 1412 | # #Obteniendo pares de Autocorrelacion |
|
1483 | 1413 | # if firstChannel == secondChannel: |
|
1484 | 1414 | # pairsAutoCorr[firstChannel] = int(l) |
|
1485 | 1415 | # |
|
1486 | 1416 | # pairsAutoCorr = pairsAutoCorr.astype(int) |
|
1487 | 1417 | # |
|
1488 | 1418 | # pairsCrossCorr = range(len(pairsList)) |
|
1489 | 1419 | # pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr) |
|
1490 | 1420 | # |
|
1491 | 1421 | # return pairsAutoCorr, pairsCrossCorr |
|
1492 | 1422 | |
|
1493 | 1423 | def __calculateTaus(self, data_acf, data_ccf, lagRange): |
|
1494 | 1424 | |
|
1495 | 1425 | lag0 = data_acf.shape[1]/2 |
|
1496 | 1426 | #Funcion de Autocorrelacion |
|
1497 | 1427 | mean_acf = stats.nanmean(data_acf, axis = 0) |
|
1498 | 1428 | |
|
1499 | 1429 | #Obtencion Indice de TauCross |
|
1500 | 1430 | ind_ccf = data_ccf.argmax(axis = 1) |
|
1501 | 1431 | #Obtencion Indice de TauAuto |
|
1502 | 1432 | ind_acf = numpy.zeros(ind_ccf.shape,dtype = 'int') |
|
1503 | 1433 | ccf_lag0 = data_ccf[:,lag0,:] |
|
1504 | 1434 | |
|
1505 | 1435 | for i in range(ccf_lag0.shape[0]): |
|
1506 | 1436 | ind_acf[i,:] = numpy.abs(mean_acf - ccf_lag0[i,:]).argmin(axis = 0) |
|
1507 | 1437 | |
|
1508 | 1438 | #Obtencion de TauCross y TauAuto |
|
1509 | 1439 | tau_ccf = lagRange[ind_ccf] |
|
1510 | 1440 | tau_acf = lagRange[ind_acf] |
|
1511 | 1441 | |
|
1512 | 1442 | Nan1, Nan2 = numpy.where(tau_ccf == lagRange[0]) |
|
1513 | 1443 | |
|
1514 | 1444 | tau_ccf[Nan1,Nan2] = numpy.nan |
|
1515 | 1445 | tau_acf[Nan1,Nan2] = numpy.nan |
|
1516 | 1446 | tau = numpy.vstack((tau_ccf,tau_acf)) |
|
1517 | 1447 | |
|
1518 | 1448 | return tau |
|
1519 | 1449 | |
|
1520 | 1450 | def __calculateLag1Phase(self, data, lagTRange): |
|
1521 | 1451 | data1 = stats.nanmean(data, axis = 0) |
|
1522 | 1452 | lag1 = numpy.where(lagTRange == 0)[0][0] + 1 |
|
1523 | 1453 | |
|
1524 | 1454 | phase = numpy.angle(data1[lag1,:]) |
|
1525 | 1455 | |
|
1526 | 1456 | return phase |
|
1527 | 1457 | |
|
1528 | 1458 | class SpectralFitting(Operation): |
|
1529 | 1459 | ''' |
|
1530 | 1460 | Function GetMoments() |
|
1531 | 1461 | |
|
1532 | 1462 | Input: |
|
1533 | 1463 | Output: |
|
1534 | 1464 | Variables modified: |
|
1535 | 1465 | ''' |
|
1536 | 1466 | |
|
1537 | 1467 | def run(self, dataOut, getSNR = True, path=None, file=None, groupList=None): |
|
1538 | 1468 | |
|
1539 | 1469 | |
|
1540 | 1470 | if path != None: |
|
1541 | 1471 | sys.path.append(path) |
|
1542 | 1472 | self.dataOut.library = importlib.import_module(file) |
|
1543 | 1473 | |
|
1544 | 1474 | #To be inserted as a parameter |
|
1545 | 1475 | groupArray = numpy.array(groupList) |
|
1546 | 1476 | # groupArray = numpy.array([[0,1],[2,3]]) |
|
1547 | 1477 | self.dataOut.groupList = groupArray |
|
1548 | 1478 | |
|
1549 | 1479 | nGroups = groupArray.shape[0] |
|
1550 | 1480 | nChannels = self.dataIn.nChannels |
|
1551 | 1481 | nHeights=self.dataIn.heightList.size |
|
1552 | 1482 | |
|
1553 | 1483 | #Parameters Array |
|
1554 | 1484 | self.dataOut.data_param = None |
|
1555 | 1485 | |
|
1556 | 1486 | #Set constants |
|
1557 | 1487 | constants = self.dataOut.library.setConstants(self.dataIn) |
|
1558 | 1488 | self.dataOut.constants = constants |
|
1559 | 1489 | M = self.dataIn.normFactor |
|
1560 | 1490 | N = self.dataIn.nFFTPoints |
|
1561 | 1491 | ippSeconds = self.dataIn.ippSeconds |
|
1562 | 1492 | K = self.dataIn.nIncohInt |
|
1563 | 1493 | pairsArray = numpy.array(self.dataIn.pairsList) |
|
1564 | 1494 | |
|
1565 | 1495 | #List of possible combinations |
|
1566 | 1496 | listComb = itertools.combinations(numpy.arange(groupArray.shape[1]),2) |
|
1567 | 1497 | indCross = numpy.zeros(len(list(listComb)), dtype = 'int') |
|
1568 | 1498 | |
|
1569 | 1499 | if getSNR: |
|
1570 | 1500 | listChannels = groupArray.reshape((groupArray.size)) |
|
1571 | 1501 | listChannels.sort() |
|
1572 | 1502 | noise = self.dataIn.getNoise() |
|
1573 | 1503 | self.dataOut.data_snr = self.__getSNR(self.dataIn.data_spc[listChannels,:,:], noise[listChannels]) |
|
1574 | 1504 | |
|
1575 | 1505 | for i in range(nGroups): |
|
1576 | 1506 | coord = groupArray[i,:] |
|
1577 | 1507 | |
|
1578 | 1508 | #Input data array |
|
1579 | 1509 | data = self.dataIn.data_spc[coord,:,:]/(M*N) |
|
1580 | 1510 | data = data.reshape((data.shape[0]*data.shape[1],data.shape[2])) |
|
1581 | 1511 | |
|
1582 | 1512 | #Cross Spectra data array for Covariance Matrixes |
|
1583 | 1513 | ind = 0 |
|
1584 | 1514 | for pairs in listComb: |
|
1585 | 1515 | pairsSel = numpy.array([coord[x],coord[y]]) |
|
1586 | 1516 | indCross[ind] = int(numpy.where(numpy.all(pairsArray == pairsSel, axis = 1))[0][0]) |
|
1587 | 1517 | ind += 1 |
|
1588 | 1518 | dataCross = self.dataIn.data_cspc[indCross,:,:]/(M*N) |
|
1589 | 1519 | dataCross = dataCross**2/K |
|
1590 | 1520 | |
|
1591 | 1521 | for h in range(nHeights): |
|
1592 | 1522 | |
|
1593 | 1523 | #Input |
|
1594 | 1524 | d = data[:,h] |
|
1595 | 1525 | |
|
1596 | 1526 | #Covariance Matrix |
|
1597 | 1527 | D = numpy.diag(d**2/K) |
|
1598 | 1528 | ind = 0 |
|
1599 | 1529 | for pairs in listComb: |
|
1600 | 1530 | #Coordinates in Covariance Matrix |
|
1601 | 1531 | x = pairs[0] |
|
1602 | 1532 | y = pairs[1] |
|
1603 | 1533 | #Channel Index |
|
1604 | 1534 | S12 = dataCross[ind,:,h] |
|
1605 | 1535 | D12 = numpy.diag(S12) |
|
1606 | 1536 | #Completing Covariance Matrix with Cross Spectras |
|
1607 | 1537 | D[x*N:(x+1)*N,y*N:(y+1)*N] = D12 |
|
1608 | 1538 | D[y*N:(y+1)*N,x*N:(x+1)*N] = D12 |
|
1609 | 1539 | ind += 1 |
|
1610 | 1540 | Dinv=numpy.linalg.inv(D) |
|
1611 | 1541 | L=numpy.linalg.cholesky(Dinv) |
|
1612 | 1542 | LT=L.T |
|
1613 | 1543 | |
|
1614 | 1544 | dp = numpy.dot(LT,d) |
|
1615 | 1545 | |
|
1616 | 1546 | #Initial values |
|
1617 | 1547 | data_spc = self.dataIn.data_spc[coord,:,h] |
|
1618 | 1548 | |
|
1619 | 1549 | if (h>0)and(error1[3]<5): |
|
1620 | 1550 | p0 = self.dataOut.data_param[i,:,h-1] |
|
1621 | 1551 | else: |
|
1622 | 1552 | p0 = numpy.array(self.dataOut.library.initialValuesFunction(data_spc, constants, i)) |
|
1623 | 1553 | |
|
1624 | 1554 | try: |
|
1625 | 1555 | #Least Squares |
|
1626 | 1556 | minp,covp,infodict,mesg,ier = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants),full_output=True) |
|
1627 | 1557 | # minp,covp = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants)) |
|
1628 | 1558 | #Chi square error |
|
1629 | 1559 | error0 = numpy.sum(infodict['fvec']**2)/(2*N) |
|
1630 | 1560 | #Error with Jacobian |
|
1631 | 1561 | error1 = self.dataOut.library.errorFunction(minp,constants,LT) |
|
1632 | 1562 | except: |
|
1633 | 1563 | minp = p0*numpy.nan |
|
1634 | 1564 | error0 = numpy.nan |
|
1635 | 1565 | error1 = p0*numpy.nan |
|
1636 | 1566 | |
|
1637 | 1567 | #Save |
|
1638 | 1568 | if self.dataOut.data_param is None: |
|
1639 | 1569 | self.dataOut.data_param = numpy.zeros((nGroups, p0.size, nHeights))*numpy.nan |
|
1640 | 1570 | self.dataOut.data_error = numpy.zeros((nGroups, p0.size + 1, nHeights))*numpy.nan |
|
1641 | 1571 | |
|
1642 | 1572 | self.dataOut.data_error[i,:,h] = numpy.hstack((error0,error1)) |
|
1643 | 1573 | self.dataOut.data_param[i,:,h] = minp |
|
1644 | 1574 | return |
|
1645 | 1575 | |
|
1646 | 1576 | def __residFunction(self, p, dp, LT, constants): |
|
1647 | 1577 | |
|
1648 | 1578 | fm = self.dataOut.library.modelFunction(p, constants) |
|
1649 | 1579 | fmp=numpy.dot(LT,fm) |
|
1650 | 1580 | |
|
1651 | 1581 | return dp-fmp |
|
1652 | 1582 | |
|
1653 | 1583 | def __getSNR(self, z, noise): |
|
1654 | 1584 | |
|
1655 | 1585 | avg = numpy.average(z, axis=1) |
|
1656 | 1586 | SNR = (avg.T-noise)/noise |
|
1657 | 1587 | SNR = SNR.T |
|
1658 | 1588 | return SNR |
|
1659 | 1589 | |
|
1660 | 1590 | def __chisq(p,chindex,hindex): |
|
1661 | 1591 | #similar to Resid but calculates CHI**2 |
|
1662 | 1592 | [LT,d,fm]=setupLTdfm(p,chindex,hindex) |
|
1663 | 1593 | dp=numpy.dot(LT,d) |
|
1664 | 1594 | fmp=numpy.dot(LT,fm) |
|
1665 | 1595 | chisq=numpy.dot((dp-fmp).T,(dp-fmp)) |
|
1666 | 1596 | return chisq |
|
1667 | 1597 | |
|
1668 | 1598 | class WindProfiler(Operation): |
|
1669 | 1599 | |
|
1670 | 1600 | __isConfig = False |
|
1671 | 1601 | |
|
1672 | 1602 | __initime = None |
|
1673 | 1603 | __lastdatatime = None |
|
1674 | 1604 | __integrationtime = None |
|
1675 | 1605 | |
|
1676 | 1606 | __buffer = None |
|
1677 | 1607 | |
|
1678 | 1608 | __dataReady = False |
|
1679 | 1609 | |
|
1680 | 1610 | __firstdata = None |
|
1681 | 1611 | |
|
1682 | 1612 | n = None |
|
1683 | 1613 | |
|
1684 | 1614 | def __init__(self): |
|
1685 | 1615 | Operation.__init__(self) |
|
1686 | 1616 | |
|
1687 | 1617 | def __calculateCosDir(self, elev, azim): |
|
1688 | 1618 | zen = (90 - elev)*numpy.pi/180 |
|
1689 | 1619 | azim = azim*numpy.pi/180 |
|
1690 | 1620 | cosDirX = numpy.sqrt((1-numpy.cos(zen)**2)/((1+numpy.tan(azim)**2))) |
|
1691 | 1621 | cosDirY = numpy.sqrt(1-numpy.cos(zen)**2-cosDirX**2) |
|
1692 | 1622 | |
|
1693 | 1623 | signX = numpy.sign(numpy.cos(azim)) |
|
1694 | 1624 | signY = numpy.sign(numpy.sin(azim)) |
|
1695 | 1625 | |
|
1696 | 1626 | cosDirX = numpy.copysign(cosDirX, signX) |
|
1697 | 1627 | cosDirY = numpy.copysign(cosDirY, signY) |
|
1698 | 1628 | return cosDirX, cosDirY |
|
1699 | 1629 | |
|
1700 | 1630 | def __calculateAngles(self, theta_x, theta_y, azimuth): |
|
1701 | 1631 | |
|
1702 | 1632 | dir_cosw = numpy.sqrt(1-theta_x**2-theta_y**2) |
|
1703 | 1633 | zenith_arr = numpy.arccos(dir_cosw) |
|
1704 | 1634 | azimuth_arr = numpy.arctan2(theta_x,theta_y) + azimuth*math.pi/180 |
|
1705 | 1635 | |
|
1706 | 1636 | dir_cosu = numpy.sin(azimuth_arr)*numpy.sin(zenith_arr) |
|
1707 | 1637 | dir_cosv = numpy.cos(azimuth_arr)*numpy.sin(zenith_arr) |
|
1708 | 1638 | |
|
1709 | 1639 | return azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw |
|
1710 | 1640 | |
|
1711 | 1641 | def __calculateMatA(self, dir_cosu, dir_cosv, dir_cosw, horOnly): |
|
1712 | 1642 | |
|
1713 | 1643 | # |
|
1714 | 1644 | if horOnly: |
|
1715 | 1645 | A = numpy.c_[dir_cosu,dir_cosv] |
|
1716 | 1646 | else: |
|
1717 | 1647 | A = numpy.c_[dir_cosu,dir_cosv,dir_cosw] |
|
1718 | 1648 | A = numpy.asmatrix(A) |
|
1719 | 1649 | A1 = numpy.linalg.inv(A.transpose()*A)*A.transpose() |
|
1720 | 1650 | |
|
1721 | 1651 | return A1 |
|
1722 | 1652 | |
|
1723 | 1653 | def __correctValues(self, heiRang, phi, velRadial, SNR): |
|
1724 | 1654 | listPhi = phi.tolist() |
|
1725 | 1655 | maxid = listPhi.index(max(listPhi)) |
|
1726 | 1656 | minid = listPhi.index(min(listPhi)) |
|
1727 | 1657 | |
|
1728 | 1658 | rango = list(range(len(phi))) |
|
1729 | 1659 | # rango = numpy.delete(rango,maxid) |
|
1730 | 1660 | |
|
1731 | 1661 | heiRang1 = heiRang*math.cos(phi[maxid]) |
|
1732 | 1662 | heiRangAux = heiRang*math.cos(phi[minid]) |
|
1733 | 1663 | indOut = (heiRang1 < heiRangAux[0]).nonzero() |
|
1734 | 1664 | heiRang1 = numpy.delete(heiRang1,indOut) |
|
1735 | 1665 | |
|
1736 | 1666 | velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
1737 | 1667 | SNR1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
1738 | 1668 | |
|
1739 | 1669 | for i in rango: |
|
1740 | 1670 | x = heiRang*math.cos(phi[i]) |
|
1741 | 1671 | y1 = velRadial[i,:] |
|
1742 | 1672 | f1 = interpolate.interp1d(x,y1,kind = 'cubic') |
|
1743 | 1673 | |
|
1744 | 1674 | x1 = heiRang1 |
|
1745 | 1675 | y11 = f1(x1) |
|
1746 | 1676 | |
|
1747 | 1677 | y2 = SNR[i,:] |
|
1748 | 1678 | f2 = interpolate.interp1d(x,y2,kind = 'cubic') |
|
1749 | 1679 | y21 = f2(x1) |
|
1750 | 1680 | |
|
1751 | 1681 | velRadial1[i,:] = y11 |
|
1752 | 1682 | SNR1[i,:] = y21 |
|
1753 | 1683 | |
|
1754 | 1684 | return heiRang1, velRadial1, SNR1 |
|
1755 | 1685 | |
|
1756 | 1686 | def __calculateVelUVW(self, A, velRadial): |
|
1757 | 1687 | |
|
1758 | 1688 | #Operacion Matricial |
|
1759 | 1689 | # velUVW = numpy.zeros((velRadial.shape[1],3)) |
|
1760 | 1690 | # for ind in range(velRadial.shape[1]): |
|
1761 | 1691 | # velUVW[ind,:] = numpy.dot(A,velRadial[:,ind]) |
|
1762 | 1692 | # velUVW = velUVW.transpose() |
|
1763 | 1693 | velUVW = numpy.zeros((A.shape[0],velRadial.shape[1])) |
|
1764 | 1694 | velUVW[:,:] = numpy.dot(A,velRadial) |
|
1765 | 1695 | |
|
1766 | 1696 | |
|
1767 | 1697 | return velUVW |
|
1768 | 1698 | |
|
1769 | 1699 | # def techniqueDBS(self, velRadial0, dirCosx, disrCosy, azimuth, correct, horizontalOnly, heiRang, SNR0): |
|
1770 | 1700 | |
|
1771 | 1701 | def techniqueDBS(self, kwargs): |
|
1772 | 1702 | """ |
|
1773 | 1703 | Function that implements Doppler Beam Swinging (DBS) technique. |
|
1774 | 1704 | |
|
1775 | 1705 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, |
|
1776 | 1706 | Direction correction (if necessary), Ranges and SNR |
|
1777 | 1707 | |
|
1778 | 1708 | Output: Winds estimation (Zonal, Meridional and Vertical) |
|
1779 | 1709 | |
|
1780 | 1710 | Parameters affected: Winds, height range, SNR |
|
1781 | 1711 | """ |
|
1782 | 1712 | velRadial0 = kwargs['velRadial'] |
|
1783 | 1713 | heiRang = kwargs['heightList'] |
|
1784 | 1714 | SNR0 = kwargs['SNR'] |
|
1785 | 1715 | |
|
1786 | 1716 | if 'dirCosx' in kwargs and 'dirCosy' in kwargs: |
|
1787 | 1717 | theta_x = numpy.array(kwargs['dirCosx']) |
|
1788 | 1718 | theta_y = numpy.array(kwargs['dirCosy']) |
|
1789 | 1719 | else: |
|
1790 | 1720 | elev = numpy.array(kwargs['elevation']) |
|
1791 | 1721 | azim = numpy.array(kwargs['azimuth']) |
|
1792 | 1722 | theta_x, theta_y = self.__calculateCosDir(elev, azim) |
|
1793 | 1723 | azimuth = kwargs['correctAzimuth'] |
|
1794 | 1724 | if 'horizontalOnly' in kwargs: |
|
1795 | 1725 | horizontalOnly = kwargs['horizontalOnly'] |
|
1796 | 1726 | else: horizontalOnly = False |
|
1797 | 1727 | if 'correctFactor' in kwargs: |
|
1798 | 1728 | correctFactor = kwargs['correctFactor'] |
|
1799 | 1729 | else: correctFactor = 1 |
|
1800 | 1730 | if 'channelList' in kwargs: |
|
1801 | 1731 | channelList = kwargs['channelList'] |
|
1802 | 1732 | if len(channelList) == 2: |
|
1803 | 1733 | horizontalOnly = True |
|
1804 | 1734 | arrayChannel = numpy.array(channelList) |
|
1805 | 1735 | param = param[arrayChannel,:,:] |
|
1806 | 1736 | theta_x = theta_x[arrayChannel] |
|
1807 | 1737 | theta_y = theta_y[arrayChannel] |
|
1808 | 1738 | |
|
1809 | 1739 | azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth) |
|
1810 | 1740 | heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, zenith_arr, correctFactor*velRadial0, SNR0) |
|
1811 | 1741 | A = self.__calculateMatA(dir_cosu, dir_cosv, dir_cosw, horizontalOnly) |
|
1812 | 1742 | |
|
1813 | 1743 | #Calculo de Componentes de la velocidad con DBS |
|
1814 | 1744 | winds = self.__calculateVelUVW(A,velRadial1) |
|
1815 | 1745 | |
|
1816 | 1746 | return winds, heiRang1, SNR1 |
|
1817 | 1747 | |
|
1818 | 1748 | def __calculateDistance(self, posx, posy, pairs_ccf, azimuth = None): |
|
1819 | 1749 | |
|
1820 | 1750 | nPairs = len(pairs_ccf) |
|
1821 | 1751 | posx = numpy.asarray(posx) |
|
1822 | 1752 | posy = numpy.asarray(posy) |
|
1823 | 1753 | |
|
1824 | 1754 | #Rotacion Inversa para alinear con el azimuth |
|
1825 | 1755 | if azimuth!= None: |
|
1826 | 1756 | azimuth = azimuth*math.pi/180 |
|
1827 | 1757 | posx1 = posx*math.cos(azimuth) + posy*math.sin(azimuth) |
|
1828 | 1758 | posy1 = -posx*math.sin(azimuth) + posy*math.cos(azimuth) |
|
1829 | 1759 | else: |
|
1830 | 1760 | posx1 = posx |
|
1831 | 1761 | posy1 = posy |
|
1832 | 1762 | |
|
1833 | 1763 | #Calculo de Distancias |
|
1834 | 1764 | distx = numpy.zeros(nPairs) |
|
1835 | 1765 | disty = numpy.zeros(nPairs) |
|
1836 | 1766 | dist = numpy.zeros(nPairs) |
|
1837 | 1767 | ang = numpy.zeros(nPairs) |
|
1838 | 1768 | |
|
1839 | 1769 | for i in range(nPairs): |
|
1840 | 1770 | distx[i] = posx1[pairs_ccf[i][1]] - posx1[pairs_ccf[i][0]] |
|
1841 | 1771 | disty[i] = posy1[pairs_ccf[i][1]] - posy1[pairs_ccf[i][0]] |
|
1842 | 1772 | dist[i] = numpy.sqrt(distx[i]**2 + disty[i]**2) |
|
1843 | 1773 | ang[i] = numpy.arctan2(disty[i],distx[i]) |
|
1844 | 1774 | |
|
1845 | 1775 | return distx, disty, dist, ang |
|
1846 | 1776 | #Calculo de Matrices |
|
1847 | 1777 | # nPairs = len(pairs) |
|
1848 | 1778 | # ang1 = numpy.zeros((nPairs, 2, 1)) |
|
1849 | 1779 | # dist1 = numpy.zeros((nPairs, 2, 1)) |
|
1850 | 1780 | # |
|
1851 | 1781 | # for j in range(nPairs): |
|
1852 | 1782 | # dist1[j,0,0] = dist[pairs[j][0]] |
|
1853 | 1783 | # dist1[j,1,0] = dist[pairs[j][1]] |
|
1854 | 1784 | # ang1[j,0,0] = ang[pairs[j][0]] |
|
1855 | 1785 | # ang1[j,1,0] = ang[pairs[j][1]] |
|
1856 | 1786 | # |
|
1857 | 1787 | # return distx,disty, dist1,ang1 |
|
1858 | 1788 | |
|
1859 | 1789 | |
|
1860 | 1790 | def __calculateVelVer(self, phase, lagTRange, _lambda): |
|
1861 | 1791 | |
|
1862 | 1792 | Ts = lagTRange[1] - lagTRange[0] |
|
1863 | 1793 | velW = -_lambda*phase/(4*math.pi*Ts) |
|
1864 | 1794 | |
|
1865 | 1795 | return velW |
|
1866 | 1796 | |
|
1867 | 1797 | def __calculateVelHorDir(self, dist, tau1, tau2, ang): |
|
1868 | 1798 | nPairs = tau1.shape[0] |
|
1869 | 1799 | nHeights = tau1.shape[1] |
|
1870 | 1800 | vel = numpy.zeros((nPairs,3,nHeights)) |
|
1871 | 1801 | dist1 = numpy.reshape(dist, (dist.size,1)) |
|
1872 | 1802 | |
|
1873 | 1803 | angCos = numpy.cos(ang) |
|
1874 | 1804 | angSin = numpy.sin(ang) |
|
1875 | 1805 | |
|
1876 | 1806 | vel0 = dist1*tau1/(2*tau2**2) |
|
1877 | 1807 | vel[:,0,:] = (vel0*angCos).sum(axis = 1) |
|
1878 | 1808 | vel[:,1,:] = (vel0*angSin).sum(axis = 1) |
|
1879 | 1809 | |
|
1880 | 1810 | ind = numpy.where(numpy.isinf(vel)) |
|
1881 | 1811 | vel[ind] = numpy.nan |
|
1882 | 1812 | |
|
1883 | 1813 | return vel |
|
1884 | 1814 | |
|
1885 | 1815 | # def __getPairsAutoCorr(self, pairsList, nChannels): |
|
1886 | 1816 | # |
|
1887 | 1817 | # pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan |
|
1888 | 1818 | # |
|
1889 | 1819 | # for l in range(len(pairsList)): |
|
1890 | 1820 | # firstChannel = pairsList[l][0] |
|
1891 | 1821 | # secondChannel = pairsList[l][1] |
|
1892 | 1822 | # |
|
1893 | 1823 | # #Obteniendo pares de Autocorrelacion |
|
1894 | 1824 | # if firstChannel == secondChannel: |
|
1895 | 1825 | # pairsAutoCorr[firstChannel] = int(l) |
|
1896 | 1826 | # |
|
1897 | 1827 | # pairsAutoCorr = pairsAutoCorr.astype(int) |
|
1898 | 1828 | # |
|
1899 | 1829 | # pairsCrossCorr = range(len(pairsList)) |
|
1900 | 1830 | # pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr) |
|
1901 | 1831 | # |
|
1902 | 1832 | # return pairsAutoCorr, pairsCrossCorr |
|
1903 | 1833 | |
|
1904 | 1834 | # def techniqueSA(self, pairsSelected, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, lagTRange, correctFactor): |
|
1905 | 1835 | def techniqueSA(self, kwargs): |
|
1906 | 1836 | |
|
1907 | 1837 | """ |
|
1908 | 1838 | Function that implements Spaced Antenna (SA) technique. |
|
1909 | 1839 | |
|
1910 | 1840 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, |
|
1911 | 1841 | Direction correction (if necessary), Ranges and SNR |
|
1912 | 1842 | |
|
1913 | 1843 | Output: Winds estimation (Zonal, Meridional and Vertical) |
|
1914 | 1844 | |
|
1915 | 1845 | Parameters affected: Winds |
|
1916 | 1846 | """ |
|
1917 | 1847 | position_x = kwargs['positionX'] |
|
1918 | 1848 | position_y = kwargs['positionY'] |
|
1919 | 1849 | azimuth = kwargs['azimuth'] |
|
1920 | 1850 | |
|
1921 | 1851 | if 'correctFactor' in kwargs: |
|
1922 | 1852 | correctFactor = kwargs['correctFactor'] |
|
1923 | 1853 | else: |
|
1924 | 1854 | correctFactor = 1 |
|
1925 | 1855 | |
|
1926 | 1856 | groupList = kwargs['groupList'] |
|
1927 | 1857 | pairs_ccf = groupList[1] |
|
1928 | 1858 | tau = kwargs['tau'] |
|
1929 | 1859 | _lambda = kwargs['_lambda'] |
|
1930 | 1860 | |
|
1931 | 1861 | #Cross Correlation pairs obtained |
|
1932 | 1862 | # pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairssList, nChannels) |
|
1933 | 1863 | # pairsArray = numpy.array(pairsList)[pairsCrossCorr] |
|
1934 | 1864 | # pairsSelArray = numpy.array(pairsSelected) |
|
1935 | 1865 | # pairs = [] |
|
1936 | 1866 | # |
|
1937 | 1867 | # #Wind estimation pairs obtained |
|
1938 | 1868 | # for i in range(pairsSelArray.shape[0]/2): |
|
1939 | 1869 | # ind1 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i], axis = 1))[0][0] |
|
1940 | 1870 | # ind2 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i + 1], axis = 1))[0][0] |
|
1941 | 1871 | # pairs.append((ind1,ind2)) |
|
1942 | 1872 | |
|
1943 | 1873 | indtau = tau.shape[0]/2 |
|
1944 | 1874 | tau1 = tau[:indtau,:] |
|
1945 | 1875 | tau2 = tau[indtau:-1,:] |
|
1946 | 1876 | # tau1 = tau1[pairs,:] |
|
1947 | 1877 | # tau2 = tau2[pairs,:] |
|
1948 | 1878 | phase1 = tau[-1,:] |
|
1949 | 1879 | |
|
1950 | 1880 | #--------------------------------------------------------------------- |
|
1951 | 1881 | #Metodo Directo |
|
1952 | 1882 | distx, disty, dist, ang = self.__calculateDistance(position_x, position_y, pairs_ccf,azimuth) |
|
1953 | 1883 | winds = self.__calculateVelHorDir(dist, tau1, tau2, ang) |
|
1954 | 1884 | winds = stats.nanmean(winds, axis=0) |
|
1955 | 1885 | #--------------------------------------------------------------------- |
|
1956 | 1886 | #Metodo General |
|
1957 | 1887 | # distx, disty, dist = self.calculateDistance(position_x,position_y,pairsCrossCorr, pairsList, azimuth) |
|
1958 | 1888 | # #Calculo Coeficientes de Funcion de Correlacion |
|
1959 | 1889 | # F,G,A,B,H = self.calculateCoef(tau1,tau2,distx,disty,n) |
|
1960 | 1890 | # #Calculo de Velocidades |
|
1961 | 1891 | # winds = self.calculateVelUV(F,G,A,B,H) |
|
1962 | 1892 | |
|
1963 | 1893 | #--------------------------------------------------------------------- |
|
1964 | 1894 | winds[2,:] = self.__calculateVelVer(phase1, lagTRange, _lambda) |
|
1965 | 1895 | winds = correctFactor*winds |
|
1966 | 1896 | return winds |
|
1967 | 1897 | |
|
1968 | 1898 | def __checkTime(self, currentTime, paramInterval, outputInterval): |
|
1969 | 1899 | |
|
1970 | 1900 | dataTime = currentTime + paramInterval |
|
1971 | 1901 | deltaTime = dataTime - self.__initime |
|
1972 | 1902 | |
|
1973 | 1903 | if deltaTime >= outputInterval or deltaTime < 0: |
|
1974 | 1904 | self.__dataReady = True |
|
1975 | 1905 | return |
|
1976 | 1906 | |
|
1977 | 1907 | def techniqueMeteors(self, arrayMeteor, meteorThresh, heightMin, heightMax): |
|
1978 | 1908 | ''' |
|
1979 | 1909 | Function that implements winds estimation technique with detected meteors. |
|
1980 | 1910 | |
|
1981 | 1911 | Input: Detected meteors, Minimum meteor quantity to wind estimation |
|
1982 | 1912 | |
|
1983 | 1913 | Output: Winds estimation (Zonal and Meridional) |
|
1984 | 1914 | |
|
1985 | 1915 | Parameters affected: Winds |
|
1986 | 1916 | ''' |
|
1987 | 1917 | #Settings |
|
1988 | 1918 | nInt = (heightMax - heightMin)/2 |
|
1989 | 1919 | nInt = int(nInt) |
|
1990 | 1920 | winds = numpy.zeros((2,nInt))*numpy.nan |
|
1991 | 1921 | |
|
1992 | 1922 | #Filter errors |
|
1993 | 1923 | error = numpy.where(arrayMeteor[:,-1] == 0)[0] |
|
1994 | 1924 | finalMeteor = arrayMeteor[error,:] |
|
1995 | 1925 | |
|
1996 | 1926 | #Meteor Histogram |
|
1997 | 1927 | finalHeights = finalMeteor[:,2] |
|
1998 | 1928 | hist = numpy.histogram(finalHeights, bins = nInt, range = (heightMin,heightMax)) |
|
1999 | 1929 | nMeteorsPerI = hist[0] |
|
2000 | 1930 | heightPerI = hist[1] |
|
2001 | 1931 | |
|
2002 | 1932 | #Sort of meteors |
|
2003 | 1933 | indSort = finalHeights.argsort() |
|
2004 | 1934 | finalMeteor2 = finalMeteor[indSort,:] |
|
2005 | 1935 | |
|
2006 | 1936 | # Calculating winds |
|
2007 | 1937 | ind1 = 0 |
|
2008 | 1938 | ind2 = 0 |
|
2009 | 1939 | |
|
2010 | 1940 | for i in range(nInt): |
|
2011 | 1941 | nMet = nMeteorsPerI[i] |
|
2012 | 1942 | ind1 = ind2 |
|
2013 | 1943 | ind2 = ind1 + nMet |
|
2014 | 1944 | |
|
2015 | 1945 | meteorAux = finalMeteor2[ind1:ind2,:] |
|
2016 | 1946 | |
|
2017 | 1947 | if meteorAux.shape[0] >= meteorThresh: |
|
2018 | 1948 | vel = meteorAux[:, 6] |
|
2019 | 1949 | zen = meteorAux[:, 4]*numpy.pi/180 |
|
2020 | 1950 | azim = meteorAux[:, 3]*numpy.pi/180 |
|
2021 | 1951 | |
|
2022 | 1952 | n = numpy.cos(zen) |
|
2023 | 1953 | # m = (1 - n**2)/(1 - numpy.tan(azim)**2) |
|
2024 | 1954 | # l = m*numpy.tan(azim) |
|
2025 | 1955 | l = numpy.sin(zen)*numpy.sin(azim) |
|
2026 | 1956 | m = numpy.sin(zen)*numpy.cos(azim) |
|
2027 | 1957 | |
|
2028 | 1958 | A = numpy.vstack((l, m)).transpose() |
|
2029 | 1959 | A1 = numpy.dot(numpy.linalg.inv( numpy.dot(A.transpose(),A) ),A.transpose()) |
|
2030 | 1960 | windsAux = numpy.dot(A1, vel) |
|
2031 | 1961 | |
|
2032 | 1962 | winds[0,i] = windsAux[0] |
|
2033 | 1963 | winds[1,i] = windsAux[1] |
|
2034 | 1964 | |
|
2035 | 1965 | return winds, heightPerI[:-1] |
|
2036 | 1966 | |
|
2037 | 1967 | def techniqueNSM_SA(self, **kwargs): |
|
2038 | 1968 | metArray = kwargs['metArray'] |
|
2039 | 1969 | heightList = kwargs['heightList'] |
|
2040 | 1970 | timeList = kwargs['timeList'] |
|
2041 | 1971 | |
|
2042 | 1972 | rx_location = kwargs['rx_location'] |
|
2043 | 1973 | groupList = kwargs['groupList'] |
|
2044 | 1974 | azimuth = kwargs['azimuth'] |
|
2045 | 1975 | dfactor = kwargs['dfactor'] |
|
2046 | 1976 | k = kwargs['k'] |
|
2047 | 1977 | |
|
2048 | 1978 | azimuth1, dist = self.__calculateAzimuth1(rx_location, groupList, azimuth) |
|
2049 | 1979 | d = dist*dfactor |
|
2050 | 1980 | #Phase calculation |
|
2051 | 1981 | metArray1 = self.__getPhaseSlope(metArray, heightList, timeList) |
|
2052 | 1982 | |
|
2053 | 1983 | metArray1[:,-2] = metArray1[:,-2]*metArray1[:,2]*1000/(k*d[metArray1[:,1].astype(int)]) #angles into velocities |
|
2054 | 1984 | |
|
2055 | 1985 | velEst = numpy.zeros((heightList.size,2))*numpy.nan |
|
2056 | 1986 | azimuth1 = azimuth1*numpy.pi/180 |
|
2057 | 1987 | |
|
2058 | 1988 | for i in range(heightList.size): |
|
2059 | 1989 | h = heightList[i] |
|
2060 | 1990 | indH = numpy.where((metArray1[:,2] == h)&(numpy.abs(metArray1[:,-2]) < 100))[0] |
|
2061 | 1991 | metHeight = metArray1[indH,:] |
|
2062 | 1992 | if metHeight.shape[0] >= 2: |
|
2063 | 1993 | velAux = numpy.asmatrix(metHeight[:,-2]).T #Radial Velocities |
|
2064 | 1994 | iazim = metHeight[:,1].astype(int) |
|
2065 | 1995 | azimAux = numpy.asmatrix(azimuth1[iazim]).T #Azimuths |
|
2066 | 1996 | A = numpy.hstack((numpy.cos(azimAux),numpy.sin(azimAux))) |
|
2067 | 1997 | A = numpy.asmatrix(A) |
|
2068 | 1998 | A1 = numpy.linalg.pinv(A.transpose()*A)*A.transpose() |
|
2069 | 1999 | velHor = numpy.dot(A1,velAux) |
|
2070 | 2000 | |
|
2071 | 2001 | velEst[i,:] = numpy.squeeze(velHor) |
|
2072 | 2002 | return velEst |
|
2073 | 2003 | |
|
2074 | 2004 | def __getPhaseSlope(self, metArray, heightList, timeList): |
|
2075 | 2005 | meteorList = [] |
|
2076 | 2006 | #utctime sec1 height SNR velRad ph0 ph1 ph2 coh0 coh1 coh2 |
|
2077 | 2007 | #Putting back together the meteor matrix |
|
2078 | 2008 | utctime = metArray[:,0] |
|
2079 | 2009 | uniqueTime = numpy.unique(utctime) |
|
2080 | 2010 | |
|
2081 | 2011 | phaseDerThresh = 0.5 |
|
2082 | 2012 | ippSeconds = timeList[1] - timeList[0] |
|
2083 | 2013 | sec = numpy.where(timeList>1)[0][0] |
|
2084 | 2014 | nPairs = metArray.shape[1] - 6 |
|
2085 | 2015 | nHeights = len(heightList) |
|
2086 | 2016 | |
|
2087 | 2017 | for t in uniqueTime: |
|
2088 | 2018 | metArray1 = metArray[utctime==t,:] |
|
2089 | 2019 | # phaseDerThresh = numpy.pi/4 #reducir Phase thresh |
|
2090 | 2020 | tmet = metArray1[:,1].astype(int) |
|
2091 | 2021 | hmet = metArray1[:,2].astype(int) |
|
2092 | 2022 | |
|
2093 | 2023 | metPhase = numpy.zeros((nPairs, heightList.size, timeList.size - 1)) |
|
2094 | 2024 | metPhase[:,:] = numpy.nan |
|
2095 | 2025 | metPhase[:,hmet,tmet] = metArray1[:,6:].T |
|
2096 | 2026 | |
|
2097 | 2027 | #Delete short trails |
|
2098 | 2028 | metBool = ~numpy.isnan(metPhase[0,:,:]) |
|
2099 | 2029 | heightVect = numpy.sum(metBool, axis = 1) |
|
2100 | 2030 | metBool[heightVect<sec,:] = False |
|
2101 | 2031 | metPhase[:,heightVect<sec,:] = numpy.nan |
|
2102 | 2032 | |
|
2103 | 2033 | #Derivative |
|
2104 | 2034 | metDer = numpy.abs(metPhase[:,:,1:] - metPhase[:,:,:-1]) |
|
2105 | 2035 | phDerAux = numpy.dstack((numpy.full((nPairs,nHeights,1), False, dtype=bool),metDer > phaseDerThresh)) |
|
2106 | 2036 | metPhase[phDerAux] = numpy.nan |
|
2107 | 2037 | |
|
2108 | 2038 | #--------------------------METEOR DETECTION ----------------------------------------- |
|
2109 | 2039 | indMet = numpy.where(numpy.any(metBool,axis=1))[0] |
|
2110 | 2040 | |
|
2111 | 2041 | for p in numpy.arange(nPairs): |
|
2112 | 2042 | phase = metPhase[p,:,:] |
|
2113 | 2043 | phDer = metDer[p,:,:] |
|
2114 | 2044 | |
|
2115 | 2045 | for h in indMet: |
|
2116 | 2046 | height = heightList[h] |
|
2117 | 2047 | phase1 = phase[h,:] #82 |
|
2118 | 2048 | phDer1 = phDer[h,:] |
|
2119 | 2049 | |
|
2120 | 2050 | phase1[~numpy.isnan(phase1)] = numpy.unwrap(phase1[~numpy.isnan(phase1)]) #Unwrap |
|
2121 | 2051 | |
|
2122 | 2052 | indValid = numpy.where(~numpy.isnan(phase1))[0] |
|
2123 | 2053 | initMet = indValid[0] |
|
2124 | 2054 | endMet = 0 |
|
2125 | 2055 | |
|
2126 | 2056 | for i in range(len(indValid)-1): |
|
2127 | 2057 | |
|
2128 | 2058 | #Time difference |
|
2129 | 2059 | inow = indValid[i] |
|
2130 | 2060 | inext = indValid[i+1] |
|
2131 | 2061 | idiff = inext - inow |
|
2132 | 2062 | #Phase difference |
|
2133 | 2063 | phDiff = numpy.abs(phase1[inext] - phase1[inow]) |
|
2134 | 2064 | |
|
2135 | 2065 | if idiff>sec or phDiff>numpy.pi/4 or inext==indValid[-1]: #End of Meteor |
|
2136 | 2066 | sizeTrail = inow - initMet + 1 |
|
2137 | 2067 | if sizeTrail>3*sec: #Too short meteors |
|
2138 | 2068 | x = numpy.arange(initMet,inow+1)*ippSeconds |
|
2139 | 2069 | y = phase1[initMet:inow+1] |
|
2140 | 2070 | ynnan = ~numpy.isnan(y) |
|
2141 | 2071 | x = x[ynnan] |
|
2142 | 2072 | y = y[ynnan] |
|
2143 | 2073 | slope, intercept, r_value, p_value, std_err = stats.linregress(x,y) |
|
2144 | 2074 | ylin = x*slope + intercept |
|
2145 | 2075 | rsq = r_value**2 |
|
2146 | 2076 | if rsq > 0.5: |
|
2147 | 2077 | vel = slope#*height*1000/(k*d) |
|
2148 | 2078 | estAux = numpy.array([utctime,p,height, vel, rsq]) |
|
2149 | 2079 | meteorList.append(estAux) |
|
2150 | 2080 | initMet = inext |
|
2151 | 2081 | metArray2 = numpy.array(meteorList) |
|
2152 | 2082 | |
|
2153 | 2083 | return metArray2 |
|
2154 | 2084 | |
|
2155 | 2085 | def __calculateAzimuth1(self, rx_location, pairslist, azimuth0): |
|
2156 | 2086 | |
|
2157 | 2087 | azimuth1 = numpy.zeros(len(pairslist)) |
|
2158 | 2088 | dist = numpy.zeros(len(pairslist)) |
|
2159 | 2089 | |
|
2160 | 2090 | for i in range(len(rx_location)): |
|
2161 | 2091 | ch0 = pairslist[i][0] |
|
2162 | 2092 | ch1 = pairslist[i][1] |
|
2163 | 2093 | |
|
2164 | 2094 | diffX = rx_location[ch0][0] - rx_location[ch1][0] |
|
2165 | 2095 | diffY = rx_location[ch0][1] - rx_location[ch1][1] |
|
2166 | 2096 | azimuth1[i] = numpy.arctan2(diffY,diffX)*180/numpy.pi |
|
2167 | 2097 | dist[i] = numpy.sqrt(diffX**2 + diffY**2) |
|
2168 | 2098 | |
|
2169 | 2099 | azimuth1 -= azimuth0 |
|
2170 | 2100 | return azimuth1, dist |
|
2171 | 2101 | |
|
2172 | 2102 | def techniqueNSM_DBS(self, **kwargs): |
|
2173 | 2103 | metArray = kwargs['metArray'] |
|
2174 | 2104 | heightList = kwargs['heightList'] |
|
2175 | 2105 | timeList = kwargs['timeList'] |
|
2176 | 2106 | azimuth = kwargs['azimuth'] |
|
2177 | 2107 | theta_x = numpy.array(kwargs['theta_x']) |
|
2178 | 2108 | theta_y = numpy.array(kwargs['theta_y']) |
|
2179 | 2109 | |
|
2180 | 2110 | utctime = metArray[:,0] |
|
2181 | 2111 | cmet = metArray[:,1].astype(int) |
|
2182 | 2112 | hmet = metArray[:,3].astype(int) |
|
2183 | 2113 | SNRmet = metArray[:,4] |
|
2184 | 2114 | vmet = metArray[:,5] |
|
2185 | 2115 | spcmet = metArray[:,6] |
|
2186 | 2116 | |
|
2187 | 2117 | nChan = numpy.max(cmet) + 1 |
|
2188 | 2118 | nHeights = len(heightList) |
|
2189 | 2119 | |
|
2190 | 2120 | azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth) |
|
2191 | 2121 | hmet = heightList[hmet] |
|
2192 | 2122 | h1met = hmet*numpy.cos(zenith_arr[cmet]) #Corrected heights |
|
2193 | 2123 | |
|
2194 | 2124 | velEst = numpy.zeros((heightList.size,2))*numpy.nan |
|
2195 | 2125 | |
|
2196 | 2126 | for i in range(nHeights - 1): |
|
2197 | 2127 | hmin = heightList[i] |
|
2198 | 2128 | hmax = heightList[i + 1] |
|
2199 | 2129 | |
|
2200 | 2130 | thisH = (h1met>=hmin) & (h1met<hmax) & (cmet!=2) & (SNRmet>8) & (vmet<50) & (spcmet<10) |
|
2201 | 2131 | indthisH = numpy.where(thisH) |
|
2202 | 2132 | |
|
2203 | 2133 | if numpy.size(indthisH) > 3: |
|
2204 | 2134 | |
|
2205 | 2135 | vel_aux = vmet[thisH] |
|
2206 | 2136 | chan_aux = cmet[thisH] |
|
2207 | 2137 | cosu_aux = dir_cosu[chan_aux] |
|
2208 | 2138 | cosv_aux = dir_cosv[chan_aux] |
|
2209 | 2139 | cosw_aux = dir_cosw[chan_aux] |
|
2210 | 2140 | |
|
2211 | 2141 | nch = numpy.size(numpy.unique(chan_aux)) |
|
2212 | 2142 | if nch > 1: |
|
2213 | 2143 | A = self.__calculateMatA(cosu_aux, cosv_aux, cosw_aux, True) |
|
2214 | 2144 | velEst[i,:] = numpy.dot(A,vel_aux) |
|
2215 | 2145 | |
|
2216 | 2146 | return velEst |
|
2217 | 2147 | |
|
2218 | 2148 | def run(self, dataOut, technique, nHours=1, hmin=70, hmax=110, **kwargs): |
|
2219 | 2149 | |
|
2220 | 2150 | param = dataOut.data_param |
|
2221 | 2151 | if dataOut.abscissaList != None: |
|
2222 | 2152 | absc = dataOut.abscissaList[:-1] |
|
2223 | 2153 | # noise = dataOut.noise |
|
2224 | 2154 | heightList = dataOut.heightList |
|
2225 | 2155 | SNR = dataOut.data_snr |
|
2226 | 2156 | |
|
2227 | 2157 | if technique == 'DBS': |
|
2228 | 2158 | |
|
2229 | 2159 | kwargs['velRadial'] = param[:,1,:] #Radial velocity |
|
2230 | 2160 | kwargs['heightList'] = heightList |
|
2231 | 2161 | kwargs['SNR'] = SNR |
|
2232 | 2162 | |
|
2233 | 2163 | dataOut.data_output, dataOut.heightList, dataOut.data_snr = self.techniqueDBS(kwargs) #DBS Function |
|
2234 | 2164 | dataOut.utctimeInit = dataOut.utctime |
|
2235 | 2165 | dataOut.outputInterval = dataOut.paramInterval |
|
2236 | 2166 | |
|
2237 | 2167 | elif technique == 'SA': |
|
2238 | 2168 | |
|
2239 | 2169 | #Parameters |
|
2240 | 2170 | # position_x = kwargs['positionX'] |
|
2241 | 2171 | # position_y = kwargs['positionY'] |
|
2242 | 2172 | # azimuth = kwargs['azimuth'] |
|
2243 | 2173 | # |
|
2244 | 2174 | # if kwargs.has_key('crosspairsList'): |
|
2245 | 2175 | # pairs = kwargs['crosspairsList'] |
|
2246 | 2176 | # else: |
|
2247 | 2177 | # pairs = None |
|
2248 | 2178 | # |
|
2249 | 2179 | # if kwargs.has_key('correctFactor'): |
|
2250 | 2180 | # correctFactor = kwargs['correctFactor'] |
|
2251 | 2181 | # else: |
|
2252 | 2182 | # correctFactor = 1 |
|
2253 | 2183 | |
|
2254 | 2184 | # tau = dataOut.data_param |
|
2255 | 2185 | # _lambda = dataOut.C/dataOut.frequency |
|
2256 | 2186 | # pairsList = dataOut.groupList |
|
2257 | 2187 | # nChannels = dataOut.nChannels |
|
2258 | 2188 | |
|
2259 | 2189 | kwargs['groupList'] = dataOut.groupList |
|
2260 | 2190 | kwargs['tau'] = dataOut.data_param |
|
2261 | 2191 | kwargs['_lambda'] = dataOut.C/dataOut.frequency |
|
2262 | 2192 | # dataOut.data_output = self.techniqueSA(pairs, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, absc, correctFactor) |
|
2263 | 2193 | dataOut.data_output = self.techniqueSA(kwargs) |
|
2264 | 2194 | dataOut.utctimeInit = dataOut.utctime |
|
2265 | 2195 | dataOut.outputInterval = dataOut.timeInterval |
|
2266 | 2196 | |
|
2267 | 2197 | elif technique == 'Meteors': |
|
2268 | 2198 | dataOut.flagNoData = True |
|
2269 | 2199 | self.__dataReady = False |
|
2270 | 2200 | |
|
2271 | 2201 | if 'nHours' in kwargs: |
|
2272 | 2202 | nHours = kwargs['nHours'] |
|
2273 | 2203 | else: |
|
2274 | 2204 | nHours = 1 |
|
2275 | 2205 | |
|
2276 | 2206 | if 'meteorsPerBin' in kwargs: |
|
2277 | 2207 | meteorThresh = kwargs['meteorsPerBin'] |
|
2278 | 2208 | else: |
|
2279 | 2209 | meteorThresh = 6 |
|
2280 | 2210 | |
|
2281 | 2211 | if 'hmin' in kwargs: |
|
2282 | 2212 | hmin = kwargs['hmin'] |
|
2283 | 2213 | else: hmin = 70 |
|
2284 | 2214 | if 'hmax' in kwargs: |
|
2285 | 2215 | hmax = kwargs['hmax'] |
|
2286 | 2216 | else: hmax = 110 |
|
2287 | 2217 | |
|
2288 | 2218 | dataOut.outputInterval = nHours*3600 |
|
2289 | 2219 | |
|
2290 | 2220 | if self.__isConfig == False: |
|
2291 | 2221 | # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) |
|
2292 | 2222 | #Get Initial LTC time |
|
2293 | 2223 | self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
2294 | 2224 | self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
2295 | 2225 | |
|
2296 | 2226 | self.__isConfig = True |
|
2297 | 2227 | |
|
2298 | 2228 | if self.__buffer is None: |
|
2299 | 2229 | self.__buffer = dataOut.data_param |
|
2300 | 2230 | self.__firstdata = copy.copy(dataOut) |
|
2301 | 2231 | |
|
2302 | 2232 | else: |
|
2303 | 2233 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
2304 | 2234 | |
|
2305 | 2235 | self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
2306 | 2236 | |
|
2307 | 2237 | if self.__dataReady: |
|
2308 | 2238 | dataOut.utctimeInit = self.__initime |
|
2309 | 2239 | |
|
2310 | 2240 | self.__initime += dataOut.outputInterval #to erase time offset |
|
2311 | 2241 | |
|
2312 | 2242 | dataOut.data_output, dataOut.heightList = self.techniqueMeteors(self.__buffer, meteorThresh, hmin, hmax) |
|
2313 | 2243 | dataOut.flagNoData = False |
|
2314 | 2244 | self.__buffer = None |
|
2315 | 2245 | |
|
2316 | 2246 | elif technique == 'Meteors1': |
|
2317 | 2247 | dataOut.flagNoData = True |
|
2318 | 2248 | self.__dataReady = False |
|
2319 | 2249 | |
|
2320 | 2250 | if 'nMins' in kwargs: |
|
2321 | 2251 | nMins = kwargs['nMins'] |
|
2322 | 2252 | else: nMins = 20 |
|
2323 | 2253 | if 'rx_location' in kwargs: |
|
2324 | 2254 | rx_location = kwargs['rx_location'] |
|
2325 | 2255 | else: rx_location = [(0,1),(1,1),(1,0)] |
|
2326 | 2256 | if 'azimuth' in kwargs: |
|
2327 | 2257 | azimuth = kwargs['azimuth'] |
|
2328 | 2258 | else: azimuth = 51.06 |
|
2329 | 2259 | if 'dfactor' in kwargs: |
|
2330 | 2260 | dfactor = kwargs['dfactor'] |
|
2331 | 2261 | if 'mode' in kwargs: |
|
2332 | 2262 | mode = kwargs['mode'] |
|
2333 | 2263 | if 'theta_x' in kwargs: |
|
2334 | 2264 | theta_x = kwargs['theta_x'] |
|
2335 | 2265 | if 'theta_y' in kwargs: |
|
2336 | 2266 | theta_y = kwargs['theta_y'] |
|
2337 | 2267 | else: mode = 'SA' |
|
2338 | 2268 | |
|
2339 | 2269 | #Borrar luego esto |
|
2340 | 2270 | if dataOut.groupList is None: |
|
2341 | 2271 | dataOut.groupList = [(0,1),(0,2),(1,2)] |
|
2342 | 2272 | groupList = dataOut.groupList |
|
2343 | 2273 | C = 3e8 |
|
2344 | 2274 | freq = 50e6 |
|
2345 | 2275 | lamb = C/freq |
|
2346 | 2276 | k = 2*numpy.pi/lamb |
|
2347 | 2277 | |
|
2348 | 2278 | timeList = dataOut.abscissaList |
|
2349 | 2279 | heightList = dataOut.heightList |
|
2350 | 2280 | |
|
2351 | 2281 | if self.__isConfig == False: |
|
2352 | 2282 | dataOut.outputInterval = nMins*60 |
|
2353 | 2283 | # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) |
|
2354 | 2284 | #Get Initial LTC time |
|
2355 | 2285 | initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
2356 | 2286 | minuteAux = initime.minute |
|
2357 | 2287 | minuteNew = int(numpy.floor(minuteAux/nMins)*nMins) |
|
2358 | 2288 | self.__initime = (initime.replace(minute = minuteNew, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
2359 | 2289 | |
|
2360 | 2290 | self.__isConfig = True |
|
2361 | 2291 | |
|
2362 | 2292 | if self.__buffer is None: |
|
2363 | 2293 | self.__buffer = dataOut.data_param |
|
2364 | 2294 | self.__firstdata = copy.copy(dataOut) |
|
2365 | 2295 | |
|
2366 | 2296 | else: |
|
2367 | 2297 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
2368 | 2298 | |
|
2369 | 2299 | self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
2370 | 2300 | |
|
2371 | 2301 | if self.__dataReady: |
|
2372 | 2302 | dataOut.utctimeInit = self.__initime |
|
2373 | 2303 | self.__initime += dataOut.outputInterval #to erase time offset |
|
2374 | 2304 | |
|
2375 | 2305 | metArray = self.__buffer |
|
2376 | 2306 | if mode == 'SA': |
|
2377 | 2307 | dataOut.data_output = self.techniqueNSM_SA(rx_location=rx_location, groupList=groupList, azimuth=azimuth, dfactor=dfactor, k=k,metArray=metArray, heightList=heightList,timeList=timeList) |
|
2378 | 2308 | elif mode == 'DBS': |
|
2379 | 2309 | dataOut.data_output = self.techniqueNSM_DBS(metArray=metArray,heightList=heightList,timeList=timeList, azimuth=azimuth, theta_x=theta_x, theta_y=theta_y) |
|
2380 | 2310 | dataOut.data_output = dataOut.data_output.T |
|
2381 | 2311 | dataOut.flagNoData = False |
|
2382 | 2312 | self.__buffer = None |
|
2383 | 2313 | |
|
2384 | 2314 | return |
|
2385 | 2315 | |
|
2386 | 2316 | class EWDriftsEstimation(Operation): |
|
2387 | 2317 | |
|
2388 | 2318 | def __init__(self): |
|
2389 | 2319 | Operation.__init__(self) |
|
2390 | 2320 | |
|
2391 | 2321 | def __correctValues(self, heiRang, phi, velRadial, SNR): |
|
2392 | 2322 | listPhi = phi.tolist() |
|
2393 | 2323 | maxid = listPhi.index(max(listPhi)) |
|
2394 | 2324 | minid = listPhi.index(min(listPhi)) |
|
2395 | 2325 | |
|
2396 | 2326 | rango = list(range(len(phi))) |
|
2397 | 2327 | # rango = numpy.delete(rango,maxid) |
|
2398 | 2328 | |
|
2399 | 2329 | heiRang1 = heiRang*math.cos(phi[maxid]) |
|
2400 | 2330 | heiRangAux = heiRang*math.cos(phi[minid]) |
|
2401 | 2331 | indOut = (heiRang1 < heiRangAux[0]).nonzero() |
|
2402 | 2332 | heiRang1 = numpy.delete(heiRang1,indOut) |
|
2403 | 2333 | |
|
2404 | 2334 | velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
2405 | 2335 | SNR1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
2406 | 2336 | |
|
2407 | 2337 | for i in rango: |
|
2408 | 2338 | x = heiRang*math.cos(phi[i]) |
|
2409 | 2339 | y1 = velRadial[i,:] |
|
2410 | 2340 | f1 = interpolate.interp1d(x,y1,kind = 'cubic') |
|
2411 | 2341 | |
|
2412 | 2342 | x1 = heiRang1 |
|
2413 | 2343 | y11 = f1(x1) |
|
2414 | 2344 | |
|
2415 | 2345 | y2 = SNR[i,:] |
|
2416 | 2346 | f2 = interpolate.interp1d(x,y2,kind = 'cubic') |
|
2417 | 2347 | y21 = f2(x1) |
|
2418 | 2348 | |
|
2419 | 2349 | velRadial1[i,:] = y11 |
|
2420 | 2350 | SNR1[i,:] = y21 |
|
2421 | 2351 | |
|
2422 | 2352 | return heiRang1, velRadial1, SNR1 |
|
2423 | 2353 | |
|
2424 | 2354 | def run(self, dataOut, zenith, zenithCorrection): |
|
2425 | 2355 | heiRang = dataOut.heightList |
|
2426 | 2356 | velRadial = dataOut.data_param[:,3,:] |
|
2427 | 2357 | SNR = dataOut.data_snr |
|
2428 | 2358 | |
|
2429 | 2359 | zenith = numpy.array(zenith) |
|
2430 | 2360 | zenith -= zenithCorrection |
|
2431 | 2361 | zenith *= numpy.pi/180 |
|
2432 | 2362 | |
|
2433 | 2363 | heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, numpy.abs(zenith), velRadial, SNR) |
|
2434 | 2364 | |
|
2435 | 2365 | alp = zenith[0] |
|
2436 | 2366 | bet = zenith[1] |
|
2437 | 2367 | |
|
2438 | 2368 | w_w = velRadial1[0,:] |
|
2439 | 2369 | w_e = velRadial1[1,:] |
|
2440 | 2370 | |
|
2441 | 2371 | w = (w_w*numpy.sin(bet) - w_e*numpy.sin(alp))/(numpy.cos(alp)*numpy.sin(bet) - numpy.cos(bet)*numpy.sin(alp)) |
|
2442 | 2372 | u = (w_w*numpy.cos(bet) - w_e*numpy.cos(alp))/(numpy.sin(alp)*numpy.cos(bet) - numpy.sin(bet)*numpy.cos(alp)) |
|
2443 | 2373 | |
|
2444 | 2374 | winds = numpy.vstack((u,w)) |
|
2445 | 2375 | |
|
2446 | 2376 | dataOut.heightList = heiRang1 |
|
2447 | 2377 | dataOut.data_output = winds |
|
2448 | 2378 | dataOut.data_snr = SNR1 |
|
2449 | 2379 | |
|
2450 | 2380 | dataOut.utctimeInit = dataOut.utctime |
|
2451 | 2381 | dataOut.outputInterval = dataOut.timeInterval |
|
2452 | 2382 | return |
|
2453 | 2383 | |
|
2454 | 2384 | #--------------- Non Specular Meteor ---------------- |
|
2455 | 2385 | |
|
2456 | 2386 | class NonSpecularMeteorDetection(Operation): |
|
2457 | 2387 | |
|
2458 | 2388 | def run(self, dataOut, mode, SNRthresh=8, phaseDerThresh=0.5, cohThresh=0.8, allData = False): |
|
2459 | 2389 | data_acf = dataOut.data_pre[0] |
|
2460 | 2390 | data_ccf = dataOut.data_pre[1] |
|
2461 | 2391 | pairsList = dataOut.groupList[1] |
|
2462 | 2392 | |
|
2463 | 2393 | lamb = dataOut.C/dataOut.frequency |
|
2464 | 2394 | tSamp = dataOut.ippSeconds*dataOut.nCohInt |
|
2465 | 2395 | paramInterval = dataOut.paramInterval |
|
2466 | 2396 | |
|
2467 | 2397 | nChannels = data_acf.shape[0] |
|
2468 | 2398 | nLags = data_acf.shape[1] |
|
2469 | 2399 | nProfiles = data_acf.shape[2] |
|
2470 | 2400 | nHeights = dataOut.nHeights |
|
2471 | 2401 | nCohInt = dataOut.nCohInt |
|
2472 | 2402 | sec = numpy.round(nProfiles/dataOut.paramInterval) |
|
2473 | 2403 | heightList = dataOut.heightList |
|
2474 | 2404 | ippSeconds = dataOut.ippSeconds*dataOut.nCohInt*dataOut.nAvg |
|
2475 | 2405 | utctime = dataOut.utctime |
|
2476 | 2406 | |
|
2477 | 2407 | dataOut.abscissaList = numpy.arange(0,paramInterval+ippSeconds,ippSeconds) |
|
2478 | 2408 | |
|
2479 | 2409 | #------------------------ SNR -------------------------------------- |
|
2480 | 2410 | power = data_acf[:,0,:,:].real |
|
2481 | 2411 | noise = numpy.zeros(nChannels) |
|
2482 | 2412 | SNR = numpy.zeros(power.shape) |
|
2483 | 2413 | for i in range(nChannels): |
|
2484 | 2414 | noise[i] = hildebrand_sekhon(power[i,:], nCohInt) |
|
2485 | 2415 | SNR[i] = (power[i]-noise[i])/noise[i] |
|
2486 | 2416 | SNRm = numpy.nanmean(SNR, axis = 0) |
|
2487 | 2417 | SNRdB = 10*numpy.log10(SNR) |
|
2488 | 2418 | |
|
2489 | 2419 | if mode == 'SA': |
|
2490 | 2420 | dataOut.groupList = dataOut.groupList[1] |
|
2491 | 2421 | nPairs = data_ccf.shape[0] |
|
2492 | 2422 | #---------------------- Coherence and Phase -------------------------- |
|
2493 | 2423 | phase = numpy.zeros(data_ccf[:,0,:,:].shape) |
|
2494 | 2424 | # phase1 = numpy.copy(phase) |
|
2495 | 2425 | coh1 = numpy.zeros(data_ccf[:,0,:,:].shape) |
|
2496 | 2426 | |
|
2497 | 2427 | for p in range(nPairs): |
|
2498 | 2428 | ch0 = pairsList[p][0] |
|
2499 | 2429 | ch1 = pairsList[p][1] |
|
2500 | 2430 | ccf = data_ccf[p,0,:,:]/numpy.sqrt(data_acf[ch0,0,:,:]*data_acf[ch1,0,:,:]) |
|
2501 | 2431 | phase[p,:,:] = ndimage.median_filter(numpy.angle(ccf), size = (5,1)) #median filter |
|
2502 | 2432 | # phase1[p,:,:] = numpy.angle(ccf) #median filter |
|
2503 | 2433 | coh1[p,:,:] = ndimage.median_filter(numpy.abs(ccf), 5) #median filter |
|
2504 | 2434 | # coh1[p,:,:] = numpy.abs(ccf) #median filter |
|
2505 | 2435 | coh = numpy.nanmax(coh1, axis = 0) |
|
2506 | 2436 | # struc = numpy.ones((5,1)) |
|
2507 | 2437 | # coh = ndimage.morphology.grey_dilation(coh, size=(10,1)) |
|
2508 | 2438 | #---------------------- Radial Velocity ---------------------------- |
|
2509 | 2439 | phaseAux = numpy.mean(numpy.angle(data_acf[:,1,:,:]), axis = 0) |
|
2510 | 2440 | velRad = phaseAux*lamb/(4*numpy.pi*tSamp) |
|
2511 | 2441 | |
|
2512 | 2442 | if allData: |
|
2513 | 2443 | boolMetFin = ~numpy.isnan(SNRm) |
|
2514 | 2444 | # coh[:-1,:] = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0) |
|
2515 | 2445 | else: |
|
2516 | 2446 | #------------------------ Meteor mask --------------------------------- |
|
2517 | 2447 | # #SNR mask |
|
2518 | 2448 | # boolMet = (SNRdB>SNRthresh)#|(~numpy.isnan(SNRdB)) |
|
2519 | 2449 | # |
|
2520 | 2450 | # #Erase small objects |
|
2521 | 2451 | # boolMet1 = self.__erase_small(boolMet, 2*sec, 5) |
|
2522 | 2452 | # |
|
2523 | 2453 | # auxEEJ = numpy.sum(boolMet1,axis=0) |
|
2524 | 2454 | # indOver = auxEEJ>nProfiles*0.8 #Use this later |
|
2525 | 2455 | # indEEJ = numpy.where(indOver)[0] |
|
2526 | 2456 | # indNEEJ = numpy.where(~indOver)[0] |
|
2527 | 2457 | # |
|
2528 | 2458 | # boolMetFin = boolMet1 |
|
2529 | 2459 | # |
|
2530 | 2460 | # if indEEJ.size > 0: |
|
2531 | 2461 | # boolMet1[:,indEEJ] = False #Erase heights with EEJ |
|
2532 | 2462 | # |
|
2533 | 2463 | # boolMet2 = coh > cohThresh |
|
2534 | 2464 | # boolMet2 = self.__erase_small(boolMet2, 2*sec,5) |
|
2535 | 2465 | # |
|
2536 | 2466 | # #Final Meteor mask |
|
2537 | 2467 | # boolMetFin = boolMet1|boolMet2 |
|
2538 | 2468 | |
|
2539 | 2469 | #Coherence mask |
|
2540 | 2470 | boolMet1 = coh > 0.75 |
|
2541 | 2471 | struc = numpy.ones((30,1)) |
|
2542 | 2472 | boolMet1 = ndimage.morphology.binary_dilation(boolMet1, structure=struc) |
|
2543 | 2473 | |
|
2544 | 2474 | #Derivative mask |
|
2545 | 2475 | derPhase = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0) |
|
2546 | 2476 | boolMet2 = derPhase < 0.2 |
|
2547 | 2477 | # boolMet2 = ndimage.morphology.binary_opening(boolMet2) |
|
2548 | 2478 | # boolMet2 = ndimage.morphology.binary_closing(boolMet2, structure = numpy.ones((10,1))) |
|
2549 | 2479 | boolMet2 = ndimage.median_filter(boolMet2,size=5) |
|
2550 | 2480 | boolMet2 = numpy.vstack((boolMet2,numpy.full((1,nHeights), True, dtype=bool))) |
|
2551 | 2481 | # #Final mask |
|
2552 | 2482 | # boolMetFin = boolMet2 |
|
2553 | 2483 | boolMetFin = boolMet1&boolMet2 |
|
2554 | 2484 | # boolMetFin = ndimage.morphology.binary_dilation(boolMetFin) |
|
2555 | 2485 | #Creating data_param |
|
2556 | 2486 | coordMet = numpy.where(boolMetFin) |
|
2557 | 2487 | |
|
2558 | 2488 | tmet = coordMet[0] |
|
2559 | 2489 | hmet = coordMet[1] |
|
2560 | 2490 | |
|
2561 | 2491 | data_param = numpy.zeros((tmet.size, 6 + nPairs)) |
|
2562 | 2492 | data_param[:,0] = utctime |
|
2563 | 2493 | data_param[:,1] = tmet |
|
2564 | 2494 | data_param[:,2] = hmet |
|
2565 | 2495 | data_param[:,3] = SNRm[tmet,hmet] |
|
2566 | 2496 | data_param[:,4] = velRad[tmet,hmet] |
|
2567 | 2497 | data_param[:,5] = coh[tmet,hmet] |
|
2568 | 2498 | data_param[:,6:] = phase[:,tmet,hmet].T |
|
2569 | 2499 | |
|
2570 | 2500 | elif mode == 'DBS': |
|
2571 | 2501 | dataOut.groupList = numpy.arange(nChannels) |
|
2572 | 2502 | |
|
2573 | 2503 | #Radial Velocities |
|
2574 | 2504 | phase = numpy.angle(data_acf[:,1,:,:]) |
|
2575 | 2505 | # phase = ndimage.median_filter(numpy.angle(data_acf[:,1,:,:]), size = (1,5,1)) |
|
2576 | 2506 | velRad = phase*lamb/(4*numpy.pi*tSamp) |
|
2577 | 2507 | |
|
2578 | 2508 | #Spectral width |
|
2579 | 2509 | # acf1 = ndimage.median_filter(numpy.abs(data_acf[:,1,:,:]), size = (1,5,1)) |
|
2580 | 2510 | # acf2 = ndimage.median_filter(numpy.abs(data_acf[:,2,:,:]), size = (1,5,1)) |
|
2581 | 2511 | acf1 = data_acf[:,1,:,:] |
|
2582 | 2512 | acf2 = data_acf[:,2,:,:] |
|
2583 | 2513 | |
|
2584 | 2514 | spcWidth = (lamb/(2*numpy.sqrt(6)*numpy.pi*tSamp))*numpy.sqrt(numpy.log(acf1/acf2)) |
|
2585 | 2515 | # velRad = ndimage.median_filter(velRad, size = (1,5,1)) |
|
2586 | 2516 | if allData: |
|
2587 | 2517 | boolMetFin = ~numpy.isnan(SNRdB) |
|
2588 | 2518 | else: |
|
2589 | 2519 | #SNR |
|
2590 | 2520 | boolMet1 = (SNRdB>SNRthresh) #SNR mask |
|
2591 | 2521 | boolMet1 = ndimage.median_filter(boolMet1, size=(1,5,5)) |
|
2592 | 2522 | |
|
2593 | 2523 | #Radial velocity |
|
2594 | 2524 | boolMet2 = numpy.abs(velRad) < 20 |
|
2595 | 2525 | boolMet2 = ndimage.median_filter(boolMet2, (1,5,5)) |
|
2596 | 2526 | |
|
2597 | 2527 | #Spectral Width |
|
2598 | 2528 | boolMet3 = spcWidth < 30 |
|
2599 | 2529 | boolMet3 = ndimage.median_filter(boolMet3, (1,5,5)) |
|
2600 | 2530 | # boolMetFin = self.__erase_small(boolMet1, 10,5) |
|
2601 | 2531 | boolMetFin = boolMet1&boolMet2&boolMet3 |
|
2602 | 2532 | |
|
2603 | 2533 | #Creating data_param |
|
2604 | 2534 | coordMet = numpy.where(boolMetFin) |
|
2605 | 2535 | |
|
2606 | 2536 | cmet = coordMet[0] |
|
2607 | 2537 | tmet = coordMet[1] |
|
2608 | 2538 | hmet = coordMet[2] |
|
2609 | 2539 | |
|
2610 | 2540 | data_param = numpy.zeros((tmet.size, 7)) |
|
2611 | 2541 | data_param[:,0] = utctime |
|
2612 | 2542 | data_param[:,1] = cmet |
|
2613 | 2543 | data_param[:,2] = tmet |
|
2614 | 2544 | data_param[:,3] = hmet |
|
2615 | 2545 | data_param[:,4] = SNR[cmet,tmet,hmet].T |
|
2616 | 2546 | data_param[:,5] = velRad[cmet,tmet,hmet].T |
|
2617 | 2547 | data_param[:,6] = spcWidth[cmet,tmet,hmet].T |
|
2618 | 2548 | |
|
2619 | 2549 | # self.dataOut.data_param = data_int |
|
2620 | 2550 | if len(data_param) == 0: |
|
2621 | 2551 | dataOut.flagNoData = True |
|
2622 | 2552 | else: |
|
2623 | 2553 | dataOut.data_param = data_param |
|
2624 | 2554 | |
|
2625 | 2555 | def __erase_small(self, binArray, threshX, threshY): |
|
2626 | 2556 | labarray, numfeat = ndimage.measurements.label(binArray) |
|
2627 | 2557 | binArray1 = numpy.copy(binArray) |
|
2628 | 2558 | |
|
2629 | 2559 | for i in range(1,numfeat + 1): |
|
2630 | 2560 | auxBin = (labarray==i) |
|
2631 | 2561 | auxSize = auxBin.sum() |
|
2632 | 2562 | |
|
2633 | 2563 | x,y = numpy.where(auxBin) |
|
2634 | 2564 | widthX = x.max() - x.min() |
|
2635 | 2565 | widthY = y.max() - y.min() |
|
2636 | 2566 | |
|
2637 | 2567 | #width X: 3 seg -> 12.5*3 |
|
2638 | 2568 | #width Y: |
|
2639 | 2569 | |
|
2640 | 2570 | if (auxSize < 50) or (widthX < threshX) or (widthY < threshY): |
|
2641 | 2571 | binArray1[auxBin] = False |
|
2642 | 2572 | |
|
2643 | 2573 | return binArray1 |
|
2644 | 2574 | |
|
2645 | 2575 | #--------------- Specular Meteor ---------------- |
|
2646 | 2576 | |
|
2647 | 2577 | class SMDetection(Operation): |
|
2648 | 2578 | ''' |
|
2649 | 2579 | Function DetectMeteors() |
|
2650 | 2580 | Project developed with paper: |
|
2651 | 2581 | HOLDSWORTH ET AL. 2004 |
|
2652 | 2582 | |
|
2653 | 2583 | Input: |
|
2654 | 2584 | self.dataOut.data_pre |
|
2655 | 2585 | |
|
2656 | 2586 | centerReceiverIndex: From the channels, which is the center receiver |
|
2657 | 2587 | |
|
2658 | 2588 | hei_ref: Height reference for the Beacon signal extraction |
|
2659 | 2589 | tauindex: |
|
2660 | 2590 | predefinedPhaseShifts: Predefined phase offset for the voltge signals |
|
2661 | 2591 | |
|
2662 | 2592 | cohDetection: Whether to user Coherent detection or not |
|
2663 | 2593 | cohDet_timeStep: Coherent Detection calculation time step |
|
2664 | 2594 | cohDet_thresh: Coherent Detection phase threshold to correct phases |
|
2665 | 2595 | |
|
2666 | 2596 | noise_timeStep: Noise calculation time step |
|
2667 | 2597 | noise_multiple: Noise multiple to define signal threshold |
|
2668 | 2598 | |
|
2669 | 2599 | multDet_timeLimit: Multiple Detection Removal time limit in seconds |
|
2670 | 2600 | multDet_rangeLimit: Multiple Detection Removal range limit in km |
|
2671 | 2601 | |
|
2672 | 2602 | phaseThresh: Maximum phase difference between receiver to be consider a meteor |
|
2673 | 2603 | SNRThresh: Minimum SNR threshold of the meteor signal to be consider a meteor |
|
2674 | 2604 | |
|
2675 | 2605 | hmin: Minimum Height of the meteor to use it in the further wind estimations |
|
2676 | 2606 | hmax: Maximum Height of the meteor to use it in the further wind estimations |
|
2677 | 2607 | azimuth: Azimuth angle correction |
|
2678 | 2608 | |
|
2679 | 2609 | Affected: |
|
2680 | 2610 | self.dataOut.data_param |
|
2681 | 2611 | |
|
2682 | 2612 | Rejection Criteria (Errors): |
|
2683 | 2613 | 0: No error; analysis OK |
|
2684 | 2614 | 1: SNR < SNR threshold |
|
2685 | 2615 | 2: angle of arrival (AOA) ambiguously determined |
|
2686 | 2616 | 3: AOA estimate not feasible |
|
2687 | 2617 | 4: Large difference in AOAs obtained from different antenna baselines |
|
2688 | 2618 | 5: echo at start or end of time series |
|
2689 | 2619 | 6: echo less than 5 examples long; too short for analysis |
|
2690 | 2620 | 7: echo rise exceeds 0.3s |
|
2691 | 2621 | 8: echo decay time less than twice rise time |
|
2692 | 2622 | 9: large power level before echo |
|
2693 | 2623 | 10: large power level after echo |
|
2694 | 2624 | 11: poor fit to amplitude for estimation of decay time |
|
2695 | 2625 | 12: poor fit to CCF phase variation for estimation of radial drift velocity |
|
2696 | 2626 | 13: height unresolvable echo: not valid height within 70 to 110 km |
|
2697 | 2627 | 14: height ambiguous echo: more then one possible height within 70 to 110 km |
|
2698 | 2628 | 15: radial drift velocity or projected horizontal velocity exceeds 200 m/s |
|
2699 | 2629 | 16: oscilatory echo, indicating event most likely not an underdense echo |
|
2700 | 2630 | |
|
2701 | 2631 | 17: phase difference in meteor Reestimation |
|
2702 | 2632 | |
|
2703 | 2633 | Data Storage: |
|
2704 | 2634 | Meteors for Wind Estimation (8): |
|
2705 | 2635 | Utc Time | Range Height |
|
2706 | 2636 | Azimuth Zenith errorCosDir |
|
2707 | 2637 | VelRad errorVelRad |
|
2708 | 2638 | Phase0 Phase1 Phase2 Phase3 |
|
2709 | 2639 | TypeError |
|
2710 | 2640 | |
|
2711 | 2641 | ''' |
|
2712 | 2642 | |
|
2713 | 2643 | def run(self, dataOut, hei_ref = None, tauindex = 0, |
|
2714 | 2644 | phaseOffsets = None, |
|
2715 | 2645 | cohDetection = False, cohDet_timeStep = 1, cohDet_thresh = 25, |
|
2716 | 2646 | noise_timeStep = 4, noise_multiple = 4, |
|
2717 | 2647 | multDet_timeLimit = 1, multDet_rangeLimit = 3, |
|
2718 | 2648 | phaseThresh = 20, SNRThresh = 5, |
|
2719 | 2649 | hmin = 50, hmax=150, azimuth = 0, |
|
2720 | 2650 | channelPositions = None) : |
|
2721 | 2651 | |
|
2722 | 2652 | |
|
2723 | 2653 | #Getting Pairslist |
|
2724 | 2654 | if channelPositions is None: |
|
2725 | 2655 | # channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T |
|
2726 | 2656 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella |
|
2727 | 2657 | meteorOps = SMOperations() |
|
2728 | 2658 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) |
|
2729 | 2659 | heiRang = dataOut.heightList |
|
2730 | 2660 | #Get Beacon signal - No Beacon signal anymore |
|
2731 | 2661 | # newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex]) |
|
2732 | 2662 | # |
|
2733 | 2663 | # if hei_ref != None: |
|
2734 | 2664 | # newheis = numpy.where(self.dataOut.heightList>hei_ref) |
|
2735 | 2665 | # |
|
2736 | 2666 | |
|
2737 | 2667 | |
|
2738 | 2668 | #****************REMOVING HARDWARE PHASE DIFFERENCES*************** |
|
2739 | 2669 | # see if the user put in pre defined phase shifts |
|
2740 | 2670 | voltsPShift = dataOut.data_pre.copy() |
|
2741 | 2671 | |
|
2742 | 2672 | # if predefinedPhaseShifts != None: |
|
2743 | 2673 | # hardwarePhaseShifts = numpy.array(predefinedPhaseShifts)*numpy.pi/180 |
|
2744 | 2674 | # |
|
2745 | 2675 | # # elif beaconPhaseShifts: |
|
2746 | 2676 | # # #get hardware phase shifts using beacon signal |
|
2747 | 2677 | # # hardwarePhaseShifts = self.__getHardwarePhaseDiff(self.dataOut.data_pre, pairslist, newheis, 10) |
|
2748 | 2678 | # # hardwarePhaseShifts = numpy.insert(hardwarePhaseShifts,centerReceiverIndex,0) |
|
2749 | 2679 | # |
|
2750 | 2680 | # else: |
|
2751 | 2681 | # hardwarePhaseShifts = numpy.zeros(5) |
|
2752 | 2682 | # |
|
2753 | 2683 | # voltsPShift = numpy.zeros((self.dataOut.data_pre.shape[0],self.dataOut.data_pre.shape[1],self.dataOut.data_pre.shape[2]), dtype = 'complex') |
|
2754 | 2684 | # for i in range(self.dataOut.data_pre.shape[0]): |
|
2755 | 2685 | # voltsPShift[i,:,:] = self.__shiftPhase(self.dataOut.data_pre[i,:,:], hardwarePhaseShifts[i]) |
|
2756 | 2686 | |
|
2757 | 2687 | #******************END OF REMOVING HARDWARE PHASE DIFFERENCES********* |
|
2758 | 2688 | |
|
2759 | 2689 | #Remove DC |
|
2760 | 2690 | voltsDC = numpy.mean(voltsPShift,1) |
|
2761 | 2691 | voltsDC = numpy.mean(voltsDC,1) |
|
2762 | 2692 | for i in range(voltsDC.shape[0]): |
|
2763 | 2693 | voltsPShift[i] = voltsPShift[i] - voltsDC[i] |
|
2764 | 2694 | |
|
2765 | 2695 | #Don't considerate last heights, theyre used to calculate Hardware Phase Shift |
|
2766 | 2696 | # voltsPShift = voltsPShift[:,:,:newheis[0][0]] |
|
2767 | 2697 | |
|
2768 | 2698 | #************ FIND POWER OF DATA W/COH OR NON COH DETECTION (3.4) ********** |
|
2769 | 2699 | #Coherent Detection |
|
2770 | 2700 | if cohDetection: |
|
2771 | 2701 | #use coherent detection to get the net power |
|
2772 | 2702 | cohDet_thresh = cohDet_thresh*numpy.pi/180 |
|
2773 | 2703 | voltsPShift = self.__coherentDetection(voltsPShift, cohDet_timeStep, dataOut.timeInterval, pairslist0, cohDet_thresh) |
|
2774 | 2704 | |
|
2775 | 2705 | #Non-coherent detection! |
|
2776 | 2706 | powerNet = numpy.nansum(numpy.abs(voltsPShift[:,:,:])**2,0) |
|
2777 | 2707 | #********** END OF COH/NON-COH POWER CALCULATION********************** |
|
2778 | 2708 | |
|
2779 | 2709 | #********** FIND THE NOISE LEVEL AND POSSIBLE METEORS **************** |
|
2780 | 2710 | #Get noise |
|
2781 | 2711 | noise, noise1 = self.__getNoise(powerNet, noise_timeStep, dataOut.timeInterval) |
|
2782 | 2712 | # noise = self.getNoise1(powerNet, noise_timeStep, self.dataOut.timeInterval) |
|
2783 | 2713 | #Get signal threshold |
|
2784 | 2714 | signalThresh = noise_multiple*noise |
|
2785 | 2715 | #Meteor echoes detection |
|
2786 | 2716 | listMeteors = self.__findMeteors(powerNet, signalThresh) |
|
2787 | 2717 | #******* END OF NOISE LEVEL AND POSSIBLE METEORS CACULATION ********** |
|
2788 | 2718 | |
|
2789 | 2719 | #************** REMOVE MULTIPLE DETECTIONS (3.5) *************************** |
|
2790 | 2720 | #Parameters |
|
2791 | 2721 | heiRange = dataOut.heightList |
|
2792 | 2722 | rangeInterval = heiRange[1] - heiRange[0] |
|
2793 | 2723 | rangeLimit = multDet_rangeLimit/rangeInterval |
|
2794 | 2724 | timeLimit = multDet_timeLimit/dataOut.timeInterval |
|
2795 | 2725 | #Multiple detection removals |
|
2796 | 2726 | listMeteors1 = self.__removeMultipleDetections(listMeteors, rangeLimit, timeLimit) |
|
2797 | 2727 | #************ END OF REMOVE MULTIPLE DETECTIONS ********************** |
|
2798 | 2728 | |
|
2799 | 2729 | #********************* METEOR REESTIMATION (3.7, 3.8, 3.9, 3.10) ******************** |
|
2800 | 2730 | #Parameters |
|
2801 | 2731 | phaseThresh = phaseThresh*numpy.pi/180 |
|
2802 | 2732 | thresh = [phaseThresh, noise_multiple, SNRThresh] |
|
2803 | 2733 | #Meteor reestimation (Errors N 1, 6, 12, 17) |
|
2804 | 2734 | listMeteors2, listMeteorsPower, listMeteorsVolts = self.__meteorReestimation(listMeteors1, voltsPShift, pairslist0, thresh, noise, dataOut.timeInterval, dataOut.frequency) |
|
2805 | 2735 | # listMeteors2, listMeteorsPower, listMeteorsVolts = self.meteorReestimation3(listMeteors2, listMeteorsPower, listMeteorsVolts, voltsPShift, pairslist, thresh, noise) |
|
2806 | 2736 | #Estimation of decay times (Errors N 7, 8, 11) |
|
2807 | 2737 | listMeteors3 = self.__estimateDecayTime(listMeteors2, listMeteorsPower, dataOut.timeInterval, dataOut.frequency) |
|
2808 | 2738 | #******************* END OF METEOR REESTIMATION ******************* |
|
2809 | 2739 | |
|
2810 | 2740 | #********************* METEOR PARAMETERS CALCULATION (3.11, 3.12, 3.13) ************************** |
|
2811 | 2741 | #Calculating Radial Velocity (Error N 15) |
|
2812 | 2742 | radialStdThresh = 10 |
|
2813 | 2743 | listMeteors4 = self.__getRadialVelocity(listMeteors3, listMeteorsVolts, radialStdThresh, pairslist0, dataOut.timeInterval) |
|
2814 | 2744 | |
|
2815 | 2745 | if len(listMeteors4) > 0: |
|
2816 | 2746 | #Setting New Array |
|
2817 | 2747 | date = dataOut.utctime |
|
2818 | 2748 | arrayParameters = self.__setNewArrays(listMeteors4, date, heiRang) |
|
2819 | 2749 | |
|
2820 | 2750 | #Correcting phase offset |
|
2821 | 2751 | if phaseOffsets != None: |
|
2822 | 2752 | phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180 |
|
2823 | 2753 | arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets) |
|
2824 | 2754 | |
|
2825 | 2755 | #Second Pairslist |
|
2826 | 2756 | pairsList = [] |
|
2827 | 2757 | pairx = (0,1) |
|
2828 | 2758 | pairy = (2,3) |
|
2829 | 2759 | pairsList.append(pairx) |
|
2830 | 2760 | pairsList.append(pairy) |
|
2831 | 2761 | |
|
2832 | 2762 | jph = numpy.array([0,0,0,0]) |
|
2833 | 2763 | h = (hmin,hmax) |
|
2834 | 2764 | arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph) |
|
2835 | 2765 | |
|
2836 | 2766 | # #Calculate AOA (Error N 3, 4) |
|
2837 | 2767 | # #JONES ET AL. 1998 |
|
2838 | 2768 | # error = arrayParameters[:,-1] |
|
2839 | 2769 | # AOAthresh = numpy.pi/8 |
|
2840 | 2770 | # phases = -arrayParameters[:,9:13] |
|
2841 | 2771 | # arrayParameters[:,4:7], arrayParameters[:,-1] = meteorOps.getAOA(phases, pairsList, error, AOAthresh, azimuth) |
|
2842 | 2772 | # |
|
2843 | 2773 | # #Calculate Heights (Error N 13 and 14) |
|
2844 | 2774 | # error = arrayParameters[:,-1] |
|
2845 | 2775 | # Ranges = arrayParameters[:,2] |
|
2846 | 2776 | # zenith = arrayParameters[:,5] |
|
2847 | 2777 | # arrayParameters[:,3], arrayParameters[:,-1] = meteorOps.getHeights(Ranges, zenith, error, hmin, hmax) |
|
2848 | 2778 | # error = arrayParameters[:,-1] |
|
2849 | 2779 | #********************* END OF PARAMETERS CALCULATION ************************** |
|
2850 | 2780 | |
|
2851 | 2781 | #***************************+ PASS DATA TO NEXT STEP ********************** |
|
2852 | 2782 | # arrayFinal = arrayParameters.reshape((1,arrayParameters.shape[0],arrayParameters.shape[1])) |
|
2853 | 2783 | dataOut.data_param = arrayParameters |
|
2854 | 2784 | |
|
2855 | 2785 | if arrayParameters is None: |
|
2856 | 2786 | dataOut.flagNoData = True |
|
2857 | 2787 | else: |
|
2858 | 2788 | dataOut.flagNoData = True |
|
2859 | 2789 | |
|
2860 | 2790 | return |
|
2861 | 2791 | |
|
2862 | 2792 | def __getHardwarePhaseDiff(self, voltage0, pairslist, newheis, n): |
|
2863 | 2793 | |
|
2864 | 2794 | minIndex = min(newheis[0]) |
|
2865 | 2795 | maxIndex = max(newheis[0]) |
|
2866 | 2796 | |
|
2867 | 2797 | voltage = voltage0[:,:,minIndex:maxIndex+1] |
|
2868 | 2798 | nLength = voltage.shape[1]/n |
|
2869 | 2799 | nMin = 0 |
|
2870 | 2800 | nMax = 0 |
|
2871 | 2801 | phaseOffset = numpy.zeros((len(pairslist),n)) |
|
2872 | 2802 | |
|
2873 | 2803 | for i in range(n): |
|
2874 | 2804 | nMax += nLength |
|
2875 | 2805 | phaseCCF = -numpy.angle(self.__calculateCCF(voltage[:,nMin:nMax,:], pairslist, [0])) |
|
2876 | 2806 | phaseCCF = numpy.mean(phaseCCF, axis = 2) |
|
2877 | 2807 | phaseOffset[:,i] = phaseCCF.transpose() |
|
2878 | 2808 | nMin = nMax |
|
2879 | 2809 | # phaseDiff, phaseArrival = self.estimatePhaseDifference(voltage, pairslist) |
|
2880 | 2810 | |
|
2881 | 2811 | #Remove Outliers |
|
2882 | 2812 | factor = 2 |
|
2883 | 2813 | wt = phaseOffset - signal.medfilt(phaseOffset,(1,5)) |
|
2884 | 2814 | dw = numpy.std(wt,axis = 1) |
|
2885 | 2815 | dw = dw.reshape((dw.size,1)) |
|
2886 | 2816 | ind = numpy.where(numpy.logical_or(wt>dw*factor,wt<-dw*factor)) |
|
2887 | 2817 | phaseOffset[ind] = numpy.nan |
|
2888 | 2818 | phaseOffset = stats.nanmean(phaseOffset, axis=1) |
|
2889 | 2819 | |
|
2890 | 2820 | return phaseOffset |
|
2891 | 2821 | |
|
2892 | 2822 | def __shiftPhase(self, data, phaseShift): |
|
2893 | 2823 | #this will shift the phase of a complex number |
|
2894 | 2824 | dataShifted = numpy.abs(data) * numpy.exp((numpy.angle(data)+phaseShift)*1j) |
|
2895 | 2825 | return dataShifted |
|
2896 | 2826 | |
|
2897 | 2827 | def __estimatePhaseDifference(self, array, pairslist): |
|
2898 | 2828 | nChannel = array.shape[0] |
|
2899 | 2829 | nHeights = array.shape[2] |
|
2900 | 2830 | numPairs = len(pairslist) |
|
2901 | 2831 | # phaseCCF = numpy.zeros((nChannel, 5, nHeights)) |
|
2902 | 2832 | phaseCCF = numpy.angle(self.__calculateCCF(array, pairslist, [-2,-1,0,1,2])) |
|
2903 | 2833 | |
|
2904 | 2834 | #Correct phases |
|
2905 | 2835 | derPhaseCCF = phaseCCF[:,1:,:] - phaseCCF[:,0:-1,:] |
|
2906 | 2836 | indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) |
|
2907 | 2837 | |
|
2908 | 2838 | if indDer[0].shape[0] > 0: |
|
2909 | 2839 | for i in range(indDer[0].shape[0]): |
|
2910 | 2840 | signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i],indDer[2][i]]) |
|
2911 | 2841 | phaseCCF[indDer[0][i],indDer[1][i]+1:,:] += signo*2*numpy.pi |
|
2912 | 2842 | |
|
2913 | 2843 | # for j in range(numSides): |
|
2914 | 2844 | # phaseCCFAux = self.calculateCCF(arrayCenter, arraySides[j,:,:], [-2,1,0,1,2]) |
|
2915 | 2845 | # phaseCCF[j,:,:] = numpy.angle(phaseCCFAux) |
|
2916 | 2846 | # |
|
2917 | 2847 | #Linear |
|
2918 | 2848 | phaseInt = numpy.zeros((numPairs,1)) |
|
2919 | 2849 | angAllCCF = phaseCCF[:,[0,1,3,4],0] |
|
2920 | 2850 | for j in range(numPairs): |
|
2921 | 2851 | fit = stats.linregress([-2,-1,1,2],angAllCCF[j,:]) |
|
2922 | 2852 | phaseInt[j] = fit[1] |
|
2923 | 2853 | #Phase Differences |
|
2924 | 2854 | phaseDiff = phaseInt - phaseCCF[:,2,:] |
|
2925 | 2855 | phaseArrival = phaseInt.reshape(phaseInt.size) |
|
2926 | 2856 | |
|
2927 | 2857 | #Dealias |
|
2928 | 2858 | phaseArrival = numpy.angle(numpy.exp(1j*phaseArrival)) |
|
2929 | 2859 | # indAlias = numpy.where(phaseArrival > numpy.pi) |
|
2930 | 2860 | # phaseArrival[indAlias] -= 2*numpy.pi |
|
2931 | 2861 | # indAlias = numpy.where(phaseArrival < -numpy.pi) |
|
2932 | 2862 | # phaseArrival[indAlias] += 2*numpy.pi |
|
2933 | 2863 | |
|
2934 | 2864 | return phaseDiff, phaseArrival |
|
2935 | 2865 | |
|
2936 | 2866 | def __coherentDetection(self, volts, timeSegment, timeInterval, pairslist, thresh): |
|
2937 | 2867 | #this function will run the coherent detection used in Holdworth et al. 2004 and return the net power |
|
2938 | 2868 | #find the phase shifts of each channel over 1 second intervals |
|
2939 | 2869 | #only look at ranges below the beacon signal |
|
2940 | 2870 | numProfPerBlock = numpy.ceil(timeSegment/timeInterval) |
|
2941 | 2871 | numBlocks = int(volts.shape[1]/numProfPerBlock) |
|
2942 | 2872 | numHeights = volts.shape[2] |
|
2943 | 2873 | nChannel = volts.shape[0] |
|
2944 | 2874 | voltsCohDet = volts.copy() |
|
2945 | 2875 | |
|
2946 | 2876 | pairsarray = numpy.array(pairslist) |
|
2947 | 2877 | indSides = pairsarray[:,1] |
|
2948 | 2878 | # indSides = numpy.array(range(nChannel)) |
|
2949 | 2879 | # indSides = numpy.delete(indSides, indCenter) |
|
2950 | 2880 | # |
|
2951 | 2881 | # listCenter = numpy.array_split(volts[indCenter,:,:], numBlocks, 0) |
|
2952 | 2882 | listBlocks = numpy.array_split(volts, numBlocks, 1) |
|
2953 | 2883 | |
|
2954 | 2884 | startInd = 0 |
|
2955 | 2885 | endInd = 0 |
|
2956 | 2886 | |
|
2957 | 2887 | for i in range(numBlocks): |
|
2958 | 2888 | startInd = endInd |
|
2959 | 2889 | endInd = endInd + listBlocks[i].shape[1] |
|
2960 | 2890 | |
|
2961 | 2891 | arrayBlock = listBlocks[i] |
|
2962 | 2892 | # arrayBlockCenter = listCenter[i] |
|
2963 | 2893 | |
|
2964 | 2894 | #Estimate the Phase Difference |
|
2965 | 2895 | phaseDiff, aux = self.__estimatePhaseDifference(arrayBlock, pairslist) |
|
2966 | 2896 | #Phase Difference RMS |
|
2967 | 2897 | arrayPhaseRMS = numpy.abs(phaseDiff) |
|
2968 | 2898 | phaseRMSaux = numpy.sum(arrayPhaseRMS < thresh,0) |
|
2969 | 2899 | indPhase = numpy.where(phaseRMSaux==4) |
|
2970 | 2900 | #Shifting |
|
2971 | 2901 | if indPhase[0].shape[0] > 0: |
|
2972 | 2902 | for j in range(indSides.size): |
|
2973 | 2903 | arrayBlock[indSides[j],:,indPhase] = self.__shiftPhase(arrayBlock[indSides[j],:,indPhase], phaseDiff[j,indPhase].transpose()) |
|
2974 | 2904 | voltsCohDet[:,startInd:endInd,:] = arrayBlock |
|
2975 | 2905 | |
|
2976 | 2906 | return voltsCohDet |
|
2977 | 2907 | |
|
2978 | 2908 | def __calculateCCF(self, volts, pairslist ,laglist): |
|
2979 | 2909 | |
|
2980 | 2910 | nHeights = volts.shape[2] |
|
2981 | 2911 | nPoints = volts.shape[1] |
|
2982 | 2912 | voltsCCF = numpy.zeros((len(pairslist), len(laglist), nHeights),dtype = 'complex') |
|
2983 | 2913 | |
|
2984 | 2914 | for i in range(len(pairslist)): |
|
2985 | 2915 | volts1 = volts[pairslist[i][0]] |
|
2986 | 2916 | volts2 = volts[pairslist[i][1]] |
|
2987 | 2917 | |
|
2988 | 2918 | for t in range(len(laglist)): |
|
2989 | 2919 | idxT = laglist[t] |
|
2990 | 2920 | if idxT >= 0: |
|
2991 | 2921 | vStacked = numpy.vstack((volts2[idxT:,:], |
|
2992 | 2922 | numpy.zeros((idxT, nHeights),dtype='complex'))) |
|
2993 | 2923 | else: |
|
2994 | 2924 | vStacked = numpy.vstack((numpy.zeros((-idxT, nHeights),dtype='complex'), |
|
2995 | 2925 | volts2[:(nPoints + idxT),:])) |
|
2996 | 2926 | voltsCCF[i,t,:] = numpy.sum((numpy.conjugate(volts1)*vStacked),axis=0) |
|
2997 | 2927 | |
|
2998 | 2928 | vStacked = None |
|
2999 | 2929 | return voltsCCF |
|
3000 | 2930 | |
|
3001 | 2931 | def __getNoise(self, power, timeSegment, timeInterval): |
|
3002 | 2932 | numProfPerBlock = numpy.ceil(timeSegment/timeInterval) |
|
3003 | 2933 | numBlocks = int(power.shape[0]/numProfPerBlock) |
|
3004 | 2934 | numHeights = power.shape[1] |
|
3005 | 2935 | |
|
3006 | 2936 | listPower = numpy.array_split(power, numBlocks, 0) |
|
3007 | 2937 | noise = numpy.zeros((power.shape[0], power.shape[1])) |
|
3008 | 2938 | noise1 = numpy.zeros((power.shape[0], power.shape[1])) |
|
3009 | 2939 | |
|
3010 | 2940 | startInd = 0 |
|
3011 | 2941 | endInd = 0 |
|
3012 | 2942 | |
|
3013 | 2943 | for i in range(numBlocks): #split por canal |
|
3014 | 2944 | startInd = endInd |
|
3015 | 2945 | endInd = endInd + listPower[i].shape[0] |
|
3016 | 2946 | |
|
3017 | 2947 | arrayBlock = listPower[i] |
|
3018 | 2948 | noiseAux = numpy.mean(arrayBlock, 0) |
|
3019 | 2949 | # noiseAux = numpy.median(noiseAux) |
|
3020 | 2950 | # noiseAux = numpy.mean(arrayBlock) |
|
3021 | 2951 | noise[startInd:endInd,:] = noise[startInd:endInd,:] + noiseAux |
|
3022 | 2952 | |
|
3023 | 2953 | noiseAux1 = numpy.mean(arrayBlock) |
|
3024 | 2954 | noise1[startInd:endInd,:] = noise1[startInd:endInd,:] + noiseAux1 |
|
3025 | 2955 | |
|
3026 | 2956 | return noise, noise1 |
|
3027 | 2957 | |
|
3028 | 2958 | def __findMeteors(self, power, thresh): |
|
3029 | 2959 | nProf = power.shape[0] |
|
3030 | 2960 | nHeights = power.shape[1] |
|
3031 | 2961 | listMeteors = [] |
|
3032 | 2962 | |
|
3033 | 2963 | for i in range(nHeights): |
|
3034 | 2964 | powerAux = power[:,i] |
|
3035 | 2965 | threshAux = thresh[:,i] |
|
3036 | 2966 | |
|
3037 | 2967 | indUPthresh = numpy.where(powerAux > threshAux)[0] |
|
3038 | 2968 | indDNthresh = numpy.where(powerAux <= threshAux)[0] |
|
3039 | 2969 | |
|
3040 | 2970 | j = 0 |
|
3041 | 2971 | |
|
3042 | 2972 | while (j < indUPthresh.size - 2): |
|
3043 | 2973 | if (indUPthresh[j + 2] == indUPthresh[j] + 2): |
|
3044 | 2974 | indDNAux = numpy.where(indDNthresh > indUPthresh[j]) |
|
3045 | 2975 | indDNthresh = indDNthresh[indDNAux] |
|
3046 | 2976 | |
|
3047 | 2977 | if (indDNthresh.size > 0): |
|
3048 | 2978 | indEnd = indDNthresh[0] - 1 |
|
3049 | 2979 | indInit = indUPthresh[j] |
|
3050 | 2980 | |
|
3051 | 2981 | meteor = powerAux[indInit:indEnd + 1] |
|
3052 | 2982 | indPeak = meteor.argmax() + indInit |
|
3053 | 2983 | FLA = sum(numpy.conj(meteor)*numpy.hstack((meteor[1:],0))) |
|
3054 | 2984 | |
|
3055 | 2985 | listMeteors.append(numpy.array([i,indInit,indPeak,indEnd,FLA])) #CHEQUEAR!!!!! |
|
3056 | 2986 | j = numpy.where(indUPthresh == indEnd)[0] + 1 |
|
3057 | 2987 | else: j+=1 |
|
3058 | 2988 | else: j+=1 |
|
3059 | 2989 | |
|
3060 | 2990 | return listMeteors |
|
3061 | 2991 | |
|
3062 | 2992 | def __removeMultipleDetections(self,listMeteors, rangeLimit, timeLimit): |
|
3063 | 2993 | |
|
3064 | 2994 | arrayMeteors = numpy.asarray(listMeteors) |
|
3065 | 2995 | listMeteors1 = [] |
|
3066 | 2996 | |
|
3067 | 2997 | while arrayMeteors.shape[0] > 0: |
|
3068 | 2998 | FLAs = arrayMeteors[:,4] |
|
3069 | 2999 | maxFLA = FLAs.argmax() |
|
3070 | 3000 | listMeteors1.append(arrayMeteors[maxFLA,:]) |
|
3071 | 3001 | |
|
3072 | 3002 | MeteorInitTime = arrayMeteors[maxFLA,1] |
|
3073 | 3003 | MeteorEndTime = arrayMeteors[maxFLA,3] |
|
3074 | 3004 | MeteorHeight = arrayMeteors[maxFLA,0] |
|
3075 | 3005 | |
|
3076 | 3006 | #Check neighborhood |
|
3077 | 3007 | maxHeightIndex = MeteorHeight + rangeLimit |
|
3078 | 3008 | minHeightIndex = MeteorHeight - rangeLimit |
|
3079 | 3009 | minTimeIndex = MeteorInitTime - timeLimit |
|
3080 | 3010 | maxTimeIndex = MeteorEndTime + timeLimit |
|
3081 | 3011 | |
|
3082 | 3012 | #Check Heights |
|
3083 | 3013 | indHeight = numpy.logical_and(arrayMeteors[:,0] >= minHeightIndex, arrayMeteors[:,0] <= maxHeightIndex) |
|
3084 | 3014 | indTime = numpy.logical_and(arrayMeteors[:,3] >= minTimeIndex, arrayMeteors[:,1] <= maxTimeIndex) |
|
3085 | 3015 | indBoth = numpy.where(numpy.logical_and(indTime,indHeight)) |
|
3086 | 3016 | |
|
3087 | 3017 | arrayMeteors = numpy.delete(arrayMeteors, indBoth, axis = 0) |
|
3088 | 3018 | |
|
3089 | 3019 | return listMeteors1 |
|
3090 | 3020 | |
|
3091 | 3021 | def __meteorReestimation(self, listMeteors, volts, pairslist, thresh, noise, timeInterval,frequency): |
|
3092 | 3022 | numHeights = volts.shape[2] |
|
3093 | 3023 | nChannel = volts.shape[0] |
|
3094 | 3024 | |
|
3095 | 3025 | thresholdPhase = thresh[0] |
|
3096 | 3026 | thresholdNoise = thresh[1] |
|
3097 | 3027 | thresholdDB = float(thresh[2]) |
|
3098 | 3028 | |
|
3099 | 3029 | thresholdDB1 = 10**(thresholdDB/10) |
|
3100 | 3030 | pairsarray = numpy.array(pairslist) |
|
3101 | 3031 | indSides = pairsarray[:,1] |
|
3102 | 3032 | |
|
3103 | 3033 | pairslist1 = list(pairslist) |
|
3104 | 3034 | pairslist1.append((0,1)) |
|
3105 | 3035 | pairslist1.append((3,4)) |
|
3106 | 3036 | |
|
3107 | 3037 | listMeteors1 = [] |
|
3108 | 3038 | listPowerSeries = [] |
|
3109 | 3039 | listVoltageSeries = [] |
|
3110 | 3040 | #volts has the war data |
|
3111 | 3041 | |
|
3112 | 3042 | if frequency == 30e6: |
|
3113 | 3043 | timeLag = 45*10**-3 |
|
3114 | 3044 | else: |
|
3115 | 3045 | timeLag = 15*10**-3 |
|
3116 | 3046 | lag = numpy.ceil(timeLag/timeInterval) |
|
3117 | 3047 | |
|
3118 | 3048 | for i in range(len(listMeteors)): |
|
3119 | 3049 | |
|
3120 | 3050 | ###################### 3.6 - 3.7 PARAMETERS REESTIMATION ######################### |
|
3121 | 3051 | meteorAux = numpy.zeros(16) |
|
3122 | 3052 | |
|
3123 | 3053 | #Loading meteor Data (mHeight, mStart, mPeak, mEnd) |
|
3124 | 3054 | mHeight = listMeteors[i][0] |
|
3125 | 3055 | mStart = listMeteors[i][1] |
|
3126 | 3056 | mPeak = listMeteors[i][2] |
|
3127 | 3057 | mEnd = listMeteors[i][3] |
|
3128 | 3058 | |
|
3129 | 3059 | #get the volt data between the start and end times of the meteor |
|
3130 | 3060 | meteorVolts = volts[:,mStart:mEnd+1,mHeight] |
|
3131 | 3061 | meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) |
|
3132 | 3062 | |
|
3133 | 3063 | #3.6. Phase Difference estimation |
|
3134 | 3064 | phaseDiff, aux = self.__estimatePhaseDifference(meteorVolts, pairslist) |
|
3135 | 3065 | |
|
3136 | 3066 | #3.7. Phase difference removal & meteor start, peak and end times reestimated |
|
3137 | 3067 | #meteorVolts0.- all Channels, all Profiles |
|
3138 | 3068 | meteorVolts0 = volts[:,:,mHeight] |
|
3139 | 3069 | meteorThresh = noise[:,mHeight]*thresholdNoise |
|
3140 | 3070 | meteorNoise = noise[:,mHeight] |
|
3141 | 3071 | meteorVolts0[indSides,:] = self.__shiftPhase(meteorVolts0[indSides,:], phaseDiff) #Phase Shifting |
|
3142 | 3072 | powerNet0 = numpy.nansum(numpy.abs(meteorVolts0)**2, axis = 0) #Power |
|
3143 | 3073 | |
|
3144 | 3074 | #Times reestimation |
|
3145 | 3075 | mStart1 = numpy.where(powerNet0[:mPeak] < meteorThresh[:mPeak])[0] |
|
3146 | 3076 | if mStart1.size > 0: |
|
3147 | 3077 | mStart1 = mStart1[-1] + 1 |
|
3148 | 3078 | |
|
3149 | 3079 | else: |
|
3150 | 3080 | mStart1 = mPeak |
|
3151 | 3081 | |
|
3152 | 3082 | mEnd1 = numpy.where(powerNet0[mPeak:] < meteorThresh[mPeak:])[0][0] + mPeak - 1 |
|
3153 | 3083 | mEndDecayTime1 = numpy.where(powerNet0[mPeak:] < meteorNoise[mPeak:])[0] |
|
3154 | 3084 | if mEndDecayTime1.size == 0: |
|
3155 | 3085 | mEndDecayTime1 = powerNet0.size |
|
3156 | 3086 | else: |
|
3157 | 3087 | mEndDecayTime1 = mEndDecayTime1[0] + mPeak - 1 |
|
3158 | 3088 | # mPeak1 = meteorVolts0[mStart1:mEnd1 + 1].argmax() |
|
3159 | 3089 | |
|
3160 | 3090 | #meteorVolts1.- all Channels, from start to end |
|
3161 | 3091 | meteorVolts1 = meteorVolts0[:,mStart1:mEnd1 + 1] |
|
3162 | 3092 | meteorVolts2 = meteorVolts0[:,mPeak + lag:mEnd1 + 1] |
|
3163 | 3093 | if meteorVolts2.shape[1] == 0: |
|
3164 | 3094 | meteorVolts2 = meteorVolts0[:,mPeak:mEnd1 + 1] |
|
3165 | 3095 | meteorVolts1 = meteorVolts1.reshape(meteorVolts1.shape[0], meteorVolts1.shape[1], 1) |
|
3166 | 3096 | meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1], 1) |
|
3167 | 3097 | ##################### END PARAMETERS REESTIMATION ######################### |
|
3168 | 3098 | |
|
3169 | 3099 | ##################### 3.8 PHASE DIFFERENCE REESTIMATION ######################## |
|
3170 | 3100 | # if mEnd1 - mStart1 > 4: #Error Number 6: echo less than 5 samples long; too short for analysis |
|
3171 | 3101 | if meteorVolts2.shape[1] > 0: |
|
3172 | 3102 | #Phase Difference re-estimation |
|
3173 | 3103 | phaseDiff1, phaseDiffint = self.__estimatePhaseDifference(meteorVolts2, pairslist1) #Phase Difference Estimation |
|
3174 | 3104 | # phaseDiff1, phaseDiffint = self.estimatePhaseDifference(meteorVolts2, pairslist) |
|
3175 | 3105 | meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1]) |
|
3176 | 3106 | phaseDiff11 = numpy.reshape(phaseDiff1, (phaseDiff1.shape[0],1)) |
|
3177 | 3107 | meteorVolts2[indSides,:] = self.__shiftPhase(meteorVolts2[indSides,:], phaseDiff11[0:4]) #Phase Shifting |
|
3178 | 3108 | |
|
3179 | 3109 | #Phase Difference RMS |
|
3180 | 3110 | phaseRMS1 = numpy.sqrt(numpy.mean(numpy.square(phaseDiff1))) |
|
3181 | 3111 | powerNet1 = numpy.nansum(numpy.abs(meteorVolts1[:,:])**2,0) |
|
3182 | 3112 | #Data from Meteor |
|
3183 | 3113 | mPeak1 = powerNet1.argmax() + mStart1 |
|
3184 | 3114 | mPeakPower1 = powerNet1.max() |
|
3185 | 3115 | noiseAux = sum(noise[mStart1:mEnd1 + 1,mHeight]) |
|
3186 | 3116 | mSNR1 = (sum(powerNet1)-noiseAux)/noiseAux |
|
3187 | 3117 | Meteor1 = numpy.array([mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1]) |
|
3188 | 3118 | Meteor1 = numpy.hstack((Meteor1,phaseDiffint)) |
|
3189 | 3119 | PowerSeries = powerNet0[mStart1:mEndDecayTime1 + 1] |
|
3190 | 3120 | #Vectorize |
|
3191 | 3121 | meteorAux[0:7] = [mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1] |
|
3192 | 3122 | meteorAux[7:11] = phaseDiffint[0:4] |
|
3193 | 3123 | |
|
3194 | 3124 | #Rejection Criterions |
|
3195 | 3125 | if phaseRMS1 > thresholdPhase: #Error Number 17: Phase variation |
|
3196 | 3126 | meteorAux[-1] = 17 |
|
3197 | 3127 | elif mSNR1 < thresholdDB1: #Error Number 1: SNR < threshold dB |
|
3198 | 3128 | meteorAux[-1] = 1 |
|
3199 | 3129 | |
|
3200 | 3130 | |
|
3201 | 3131 | else: |
|
3202 | 3132 | meteorAux[0:4] = [mHeight, mStart, mPeak, mEnd] |
|
3203 | 3133 | meteorAux[-1] = 6 #Error Number 6: echo less than 5 samples long; too short for analysis |
|
3204 | 3134 | PowerSeries = 0 |
|
3205 | 3135 | |
|
3206 | 3136 | listMeteors1.append(meteorAux) |
|
3207 | 3137 | listPowerSeries.append(PowerSeries) |
|
3208 | 3138 | listVoltageSeries.append(meteorVolts1) |
|
3209 | 3139 | |
|
3210 | 3140 | return listMeteors1, listPowerSeries, listVoltageSeries |
|
3211 | 3141 | |
|
3212 | 3142 | def __estimateDecayTime(self, listMeteors, listPower, timeInterval, frequency): |
|
3213 | 3143 | |
|
3214 | 3144 | threshError = 10 |
|
3215 | 3145 | #Depending if it is 30 or 50 MHz |
|
3216 | 3146 | if frequency == 30e6: |
|
3217 | 3147 | timeLag = 45*10**-3 |
|
3218 | 3148 | else: |
|
3219 | 3149 | timeLag = 15*10**-3 |
|
3220 | 3150 | lag = numpy.ceil(timeLag/timeInterval) |
|
3221 | 3151 | |
|
3222 | 3152 | listMeteors1 = [] |
|
3223 | 3153 | |
|
3224 | 3154 | for i in range(len(listMeteors)): |
|
3225 | 3155 | meteorPower = listPower[i] |
|
3226 | 3156 | meteorAux = listMeteors[i] |
|
3227 | 3157 | |
|
3228 | 3158 | if meteorAux[-1] == 0: |
|
3229 | 3159 | |
|
3230 | 3160 | try: |
|
3231 | 3161 | indmax = meteorPower.argmax() |
|
3232 | 3162 | indlag = indmax + lag |
|
3233 | 3163 | |
|
3234 | 3164 | y = meteorPower[indlag:] |
|
3235 | 3165 | x = numpy.arange(0, y.size)*timeLag |
|
3236 | 3166 | |
|
3237 | 3167 | #first guess |
|
3238 | 3168 | a = y[0] |
|
3239 | 3169 | tau = timeLag |
|
3240 | 3170 | #exponential fit |
|
3241 | 3171 | popt, pcov = optimize.curve_fit(self.__exponential_function, x, y, p0 = [a, tau]) |
|
3242 | 3172 | y1 = self.__exponential_function(x, *popt) |
|
3243 | 3173 | #error estimation |
|
3244 | 3174 | error = sum((y - y1)**2)/(numpy.var(y)*(y.size - popt.size)) |
|
3245 | 3175 | |
|
3246 | 3176 | decayTime = popt[1] |
|
3247 | 3177 | riseTime = indmax*timeInterval |
|
3248 | 3178 | meteorAux[11:13] = [decayTime, error] |
|
3249 | 3179 | |
|
3250 | 3180 | #Table items 7, 8 and 11 |
|
3251 | 3181 | if (riseTime > 0.3): #Number 7: Echo rise exceeds 0.3s |
|
3252 | 3182 | meteorAux[-1] = 7 |
|
3253 | 3183 | elif (decayTime < 2*riseTime) : #Number 8: Echo decay time less than than twice rise time |
|
3254 | 3184 | meteorAux[-1] = 8 |
|
3255 | 3185 | if (error > threshError): #Number 11: Poor fit to amplitude for estimation of decay time |
|
3256 | 3186 | meteorAux[-1] = 11 |
|
3257 | 3187 | |
|
3258 | 3188 | |
|
3259 | 3189 | except: |
|
3260 | 3190 | meteorAux[-1] = 11 |
|
3261 | 3191 | |
|
3262 | 3192 | |
|
3263 | 3193 | listMeteors1.append(meteorAux) |
|
3264 | 3194 | |
|
3265 | 3195 | return listMeteors1 |
|
3266 | 3196 | |
|
3267 | 3197 | #Exponential Function |
|
3268 | 3198 | |
|
3269 | 3199 | def __exponential_function(self, x, a, tau): |
|
3270 | 3200 | y = a*numpy.exp(-x/tau) |
|
3271 | 3201 | return y |
|
3272 | 3202 | |
|
3273 | 3203 | def __getRadialVelocity(self, listMeteors, listVolts, radialStdThresh, pairslist, timeInterval): |
|
3274 | 3204 | |
|
3275 | 3205 | pairslist1 = list(pairslist) |
|
3276 | 3206 | pairslist1.append((0,1)) |
|
3277 | 3207 | pairslist1.append((3,4)) |
|
3278 | 3208 | numPairs = len(pairslist1) |
|
3279 | 3209 | #Time Lag |
|
3280 | 3210 | timeLag = 45*10**-3 |
|
3281 | 3211 | c = 3e8 |
|
3282 | 3212 | lag = numpy.ceil(timeLag/timeInterval) |
|
3283 | 3213 | freq = 30e6 |
|
3284 | 3214 | |
|
3285 | 3215 | listMeteors1 = [] |
|
3286 | 3216 | |
|
3287 | 3217 | for i in range(len(listMeteors)): |
|
3288 | 3218 | meteorAux = listMeteors[i] |
|
3289 | 3219 | if meteorAux[-1] == 0: |
|
3290 | 3220 | mStart = listMeteors[i][1] |
|
3291 | 3221 | mPeak = listMeteors[i][2] |
|
3292 | 3222 | mLag = mPeak - mStart + lag |
|
3293 | 3223 | |
|
3294 | 3224 | #get the volt data between the start and end times of the meteor |
|
3295 | 3225 | meteorVolts = listVolts[i] |
|
3296 | 3226 | meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) |
|
3297 | 3227 | |
|
3298 | 3228 | #Get CCF |
|
3299 | 3229 | allCCFs = self.__calculateCCF(meteorVolts, pairslist1, [-2,-1,0,1,2]) |
|
3300 | 3230 | |
|
3301 | 3231 | #Method 2 |
|
3302 | 3232 | slopes = numpy.zeros(numPairs) |
|
3303 | 3233 | time = numpy.array([-2,-1,1,2])*timeInterval |
|
3304 | 3234 | angAllCCF = numpy.angle(allCCFs[:,[0,1,3,4],0]) |
|
3305 | 3235 | |
|
3306 | 3236 | #Correct phases |
|
3307 | 3237 | derPhaseCCF = angAllCCF[:,1:] - angAllCCF[:,0:-1] |
|
3308 | 3238 | indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) |
|
3309 | 3239 | |
|
3310 | 3240 | if indDer[0].shape[0] > 0: |
|
3311 | 3241 | for i in range(indDer[0].shape[0]): |
|
3312 | 3242 | signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i]]) |
|
3313 | 3243 | angAllCCF[indDer[0][i],indDer[1][i]+1:] += signo*2*numpy.pi |
|
3314 | 3244 | |
|
3315 | 3245 | # fit = scipy.stats.linregress(numpy.array([-2,-1,1,2])*timeInterval, numpy.array([phaseLagN2s[i],phaseLagN1s[i],phaseLag1s[i],phaseLag2s[i]])) |
|
3316 | 3246 | for j in range(numPairs): |
|
3317 | 3247 | fit = stats.linregress(time, angAllCCF[j,:]) |
|
3318 | 3248 | slopes[j] = fit[0] |
|
3319 | 3249 | |
|
3320 | 3250 | #Remove Outlier |
|
3321 | 3251 | # indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes))) |
|
3322 | 3252 | # slopes = numpy.delete(slopes,indOut) |
|
3323 | 3253 | # indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes))) |
|
3324 | 3254 | # slopes = numpy.delete(slopes,indOut) |
|
3325 | 3255 | |
|
3326 | 3256 | radialVelocity = -numpy.mean(slopes)*(0.25/numpy.pi)*(c/freq) |
|
3327 | 3257 | radialError = numpy.std(slopes)*(0.25/numpy.pi)*(c/freq) |
|
3328 | 3258 | meteorAux[-2] = radialError |
|
3329 | 3259 | meteorAux[-3] = radialVelocity |
|
3330 | 3260 | |
|
3331 | 3261 | #Setting Error |
|
3332 | 3262 | #Number 15: Radial Drift velocity or projected horizontal velocity exceeds 200 m/s |
|
3333 | 3263 | if numpy.abs(radialVelocity) > 200: |
|
3334 | 3264 | meteorAux[-1] = 15 |
|
3335 | 3265 | #Number 12: Poor fit to CCF variation for estimation of radial drift velocity |
|
3336 | 3266 | elif radialError > radialStdThresh: |
|
3337 | 3267 | meteorAux[-1] = 12 |
|
3338 | 3268 | |
|
3339 | 3269 | listMeteors1.append(meteorAux) |
|
3340 | 3270 | return listMeteors1 |
|
3341 | 3271 | |
|
3342 | 3272 | def __setNewArrays(self, listMeteors, date, heiRang): |
|
3343 | 3273 | |
|
3344 | 3274 | #New arrays |
|
3345 | 3275 | arrayMeteors = numpy.array(listMeteors) |
|
3346 | 3276 | arrayParameters = numpy.zeros((len(listMeteors), 13)) |
|
3347 | 3277 | |
|
3348 | 3278 | #Date inclusion |
|
3349 | 3279 | # date = re.findall(r'\((.*?)\)', date) |
|
3350 | 3280 | # date = date[0].split(',') |
|
3351 | 3281 | # date = map(int, date) |
|
3352 | 3282 | # |
|
3353 | 3283 | # if len(date)<6: |
|
3354 | 3284 | # date.append(0) |
|
3355 | 3285 | # |
|
3356 | 3286 | # date = [date[0]*10000 + date[1]*100 + date[2], date[3]*10000 + date[4]*100 + date[5]] |
|
3357 | 3287 | # arrayDate = numpy.tile(date, (len(listMeteors), 1)) |
|
3358 | 3288 | arrayDate = numpy.tile(date, (len(listMeteors))) |
|
3359 | 3289 | |
|
3360 | 3290 | #Meteor array |
|
3361 | 3291 | # arrayMeteors[:,0] = heiRang[arrayMeteors[:,0].astype(int)] |
|
3362 | 3292 | # arrayMeteors = numpy.hstack((arrayDate, arrayMeteors)) |
|
3363 | 3293 | |
|
3364 | 3294 | #Parameters Array |
|
3365 | 3295 | arrayParameters[:,0] = arrayDate #Date |
|
3366 | 3296 | arrayParameters[:,1] = heiRang[arrayMeteors[:,0].astype(int)] #Range |
|
3367 | 3297 | arrayParameters[:,6:8] = arrayMeteors[:,-3:-1] #Radial velocity and its error |
|
3368 | 3298 | arrayParameters[:,8:12] = arrayMeteors[:,7:11] #Phases |
|
3369 | 3299 | arrayParameters[:,-1] = arrayMeteors[:,-1] #Error |
|
3370 | 3300 | |
|
3371 | 3301 | |
|
3372 | 3302 | return arrayParameters |
|
3373 | 3303 | |
|
3374 | 3304 | class CorrectSMPhases(Operation): |
|
3375 | 3305 | |
|
3376 | 3306 | def run(self, dataOut, phaseOffsets, hmin = 50, hmax = 150, azimuth = 45, channelPositions = None): |
|
3377 | 3307 | |
|
3378 | 3308 | arrayParameters = dataOut.data_param |
|
3379 | 3309 | pairsList = [] |
|
3380 | 3310 | pairx = (0,1) |
|
3381 | 3311 | pairy = (2,3) |
|
3382 | 3312 | pairsList.append(pairx) |
|
3383 | 3313 | pairsList.append(pairy) |
|
3384 | 3314 | jph = numpy.zeros(4) |
|
3385 | 3315 | |
|
3386 | 3316 | phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180 |
|
3387 | 3317 | # arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets) |
|
3388 | 3318 | arrayParameters[:,8:12] = numpy.angle(numpy.exp(1j*(arrayParameters[:,8:12] + phaseOffsets))) |
|
3389 | 3319 | |
|
3390 | 3320 | meteorOps = SMOperations() |
|
3391 | 3321 | if channelPositions is None: |
|
3392 | 3322 | # channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T |
|
3393 | 3323 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella |
|
3394 | 3324 | |
|
3395 | 3325 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) |
|
3396 | 3326 | h = (hmin,hmax) |
|
3397 | 3327 | |
|
3398 | 3328 | arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph) |
|
3399 | 3329 | |
|
3400 | 3330 | dataOut.data_param = arrayParameters |
|
3401 | 3331 | return |
|
3402 | 3332 | |
|
3403 | 3333 | class SMPhaseCalibration(Operation): |
|
3404 | 3334 | |
|
3405 | 3335 | __buffer = None |
|
3406 | 3336 | |
|
3407 | 3337 | __initime = None |
|
3408 | 3338 | |
|
3409 | 3339 | __dataReady = False |
|
3410 | 3340 | |
|
3411 | 3341 | __isConfig = False |
|
3412 | 3342 | |
|
3413 | 3343 | def __checkTime(self, currentTime, initTime, paramInterval, outputInterval): |
|
3414 | 3344 | |
|
3415 | 3345 | dataTime = currentTime + paramInterval |
|
3416 | 3346 | deltaTime = dataTime - initTime |
|
3417 | 3347 | |
|
3418 | 3348 | if deltaTime >= outputInterval or deltaTime < 0: |
|
3419 | 3349 | return True |
|
3420 | 3350 | |
|
3421 | 3351 | return False |
|
3422 | 3352 | |
|
3423 | 3353 | def __getGammas(self, pairs, d, phases): |
|
3424 | 3354 | gammas = numpy.zeros(2) |
|
3425 | 3355 | |
|
3426 | 3356 | for i in range(len(pairs)): |
|
3427 | 3357 | |
|
3428 | 3358 | pairi = pairs[i] |
|
3429 | 3359 | |
|
3430 | 3360 | phip3 = phases[:,pairi[0]] |
|
3431 | 3361 | d3 = d[pairi[0]] |
|
3432 | 3362 | phip2 = phases[:,pairi[1]] |
|
3433 | 3363 | d2 = d[pairi[1]] |
|
3434 | 3364 | #Calculating gamma |
|
3435 | 3365 | # jdcos = alp1/(k*d1) |
|
3436 | 3366 | # jgamma = numpy.angle(numpy.exp(1j*(d0*alp1/d1 - alp0))) |
|
3437 | 3367 | jgamma = -phip2*d3/d2 - phip3 |
|
3438 | 3368 | jgamma = numpy.angle(numpy.exp(1j*jgamma)) |
|
3439 | 3369 | # jgamma[jgamma>numpy.pi] -= 2*numpy.pi |
|
3440 | 3370 | # jgamma[jgamma<-numpy.pi] += 2*numpy.pi |
|
3441 | 3371 | |
|
3442 | 3372 | #Revised distribution |
|
3443 | 3373 | jgammaArray = numpy.hstack((jgamma,jgamma+0.5*numpy.pi,jgamma-0.5*numpy.pi)) |
|
3444 | 3374 | |
|
3445 | 3375 | #Histogram |
|
3446 | 3376 | nBins = 64 |
|
3447 | 3377 | rmin = -0.5*numpy.pi |
|
3448 | 3378 | rmax = 0.5*numpy.pi |
|
3449 | 3379 | phaseHisto = numpy.histogram(jgammaArray, bins=nBins, range=(rmin,rmax)) |
|
3450 | 3380 | |
|
3451 | 3381 | meteorsY = phaseHisto[0] |
|
3452 | 3382 | phasesX = phaseHisto[1][:-1] |
|
3453 | 3383 | width = phasesX[1] - phasesX[0] |
|
3454 | 3384 | phasesX += width/2 |
|
3455 | 3385 | |
|
3456 | 3386 | #Gaussian aproximation |
|
3457 | 3387 | bpeak = meteorsY.argmax() |
|
3458 | 3388 | peak = meteorsY.max() |
|
3459 | 3389 | jmin = bpeak - 5 |
|
3460 | 3390 | jmax = bpeak + 5 + 1 |
|
3461 | 3391 | |
|
3462 | 3392 | if jmin<0: |
|
3463 | 3393 | jmin = 0 |
|
3464 | 3394 | jmax = 6 |
|
3465 | 3395 | elif jmax > meteorsY.size: |
|
3466 | 3396 | jmin = meteorsY.size - 6 |
|
3467 | 3397 | jmax = meteorsY.size |
|
3468 | 3398 | |
|
3469 | 3399 | x0 = numpy.array([peak,bpeak,50]) |
|
3470 | 3400 | coeff = optimize.leastsq(self.__residualFunction, x0, args=(meteorsY[jmin:jmax], phasesX[jmin:jmax])) |
|
3471 | 3401 | |
|
3472 | 3402 | #Gammas |
|
3473 | 3403 | gammas[i] = coeff[0][1] |
|
3474 | 3404 | |
|
3475 | 3405 | return gammas |
|
3476 | 3406 | |
|
3477 | 3407 | def __residualFunction(self, coeffs, y, t): |
|
3478 | 3408 | |
|
3479 | 3409 | return y - self.__gauss_function(t, coeffs) |
|
3480 | 3410 | |
|
3481 | 3411 | def __gauss_function(self, t, coeffs): |
|
3482 | 3412 | |
|
3483 | 3413 | return coeffs[0]*numpy.exp(-0.5*((t - coeffs[1]) / coeffs[2])**2) |
|
3484 | 3414 | |
|
3485 | 3415 | def __getPhases(self, azimuth, h, pairsList, d, gammas, meteorsArray): |
|
3486 | 3416 | meteorOps = SMOperations() |
|
3487 | 3417 | nchan = 4 |
|
3488 | 3418 | pairx = pairsList[0] #x es 0 |
|
3489 | 3419 | pairy = pairsList[1] #y es 1 |
|
3490 | 3420 | center_xangle = 0 |
|
3491 | 3421 | center_yangle = 0 |
|
3492 | 3422 | range_angle = numpy.array([10*numpy.pi,numpy.pi,numpy.pi/2,numpy.pi/4]) |
|
3493 | 3423 | ntimes = len(range_angle) |
|
3494 | 3424 | |
|
3495 | 3425 | nstepsx = 20 |
|
3496 | 3426 | nstepsy = 20 |
|
3497 | 3427 | |
|
3498 | 3428 | for iz in range(ntimes): |
|
3499 | 3429 | min_xangle = -range_angle[iz]/2 + center_xangle |
|
3500 | 3430 | max_xangle = range_angle[iz]/2 + center_xangle |
|
3501 | 3431 | min_yangle = -range_angle[iz]/2 + center_yangle |
|
3502 | 3432 | max_yangle = range_angle[iz]/2 + center_yangle |
|
3503 | 3433 | |
|
3504 | 3434 | inc_x = (max_xangle-min_xangle)/nstepsx |
|
3505 | 3435 | inc_y = (max_yangle-min_yangle)/nstepsy |
|
3506 | 3436 | |
|
3507 | 3437 | alpha_y = numpy.arange(nstepsy)*inc_y + min_yangle |
|
3508 | 3438 | alpha_x = numpy.arange(nstepsx)*inc_x + min_xangle |
|
3509 | 3439 | penalty = numpy.zeros((nstepsx,nstepsy)) |
|
3510 | 3440 | jph_array = numpy.zeros((nchan,nstepsx,nstepsy)) |
|
3511 | 3441 | jph = numpy.zeros(nchan) |
|
3512 | 3442 | |
|
3513 | 3443 | # Iterations looking for the offset |
|
3514 | 3444 | for iy in range(int(nstepsy)): |
|
3515 | 3445 | for ix in range(int(nstepsx)): |
|
3516 | 3446 | d3 = d[pairsList[1][0]] |
|
3517 | 3447 | d2 = d[pairsList[1][1]] |
|
3518 | 3448 | d5 = d[pairsList[0][0]] |
|
3519 | 3449 | d4 = d[pairsList[0][1]] |
|
3520 | 3450 | |
|
3521 | 3451 | alp2 = alpha_y[iy] #gamma 1 |
|
3522 | 3452 | alp4 = alpha_x[ix] #gamma 0 |
|
3523 | 3453 | |
|
3524 | 3454 | alp3 = -alp2*d3/d2 - gammas[1] |
|
3525 | 3455 | alp5 = -alp4*d5/d4 - gammas[0] |
|
3526 | 3456 | # jph[pairy[1]] = alpha_y[iy] |
|
3527 | 3457 | # jph[pairy[0]] = -gammas[1] - alpha_y[iy]*d[pairy[1]]/d[pairy[0]] |
|
3528 | 3458 | |
|
3529 | 3459 | # jph[pairx[1]] = alpha_x[ix] |
|
3530 | 3460 | # jph[pairx[0]] = -gammas[0] - alpha_x[ix]*d[pairx[1]]/d[pairx[0]] |
|
3531 | 3461 | jph[pairsList[0][1]] = alp4 |
|
3532 | 3462 | jph[pairsList[0][0]] = alp5 |
|
3533 | 3463 | jph[pairsList[1][0]] = alp3 |
|
3534 | 3464 | jph[pairsList[1][1]] = alp2 |
|
3535 | 3465 | jph_array[:,ix,iy] = jph |
|
3536 | 3466 | # d = [2.0,2.5,2.5,2.0] |
|
3537 | 3467 | #falta chequear si va a leer bien los meteoros |
|
3538 | 3468 | meteorsArray1 = meteorOps.getMeteorParams(meteorsArray, azimuth, h, pairsList, d, jph) |
|
3539 | 3469 | error = meteorsArray1[:,-1] |
|
3540 | 3470 | ind1 = numpy.where(error==0)[0] |
|
3541 | 3471 | penalty[ix,iy] = ind1.size |
|
3542 | 3472 | |
|
3543 | 3473 | i,j = numpy.unravel_index(penalty.argmax(), penalty.shape) |
|
3544 | 3474 | phOffset = jph_array[:,i,j] |
|
3545 | 3475 | |
|
3546 | 3476 | center_xangle = phOffset[pairx[1]] |
|
3547 | 3477 | center_yangle = phOffset[pairy[1]] |
|
3548 | 3478 | |
|
3549 | 3479 | phOffset = numpy.angle(numpy.exp(1j*jph_array[:,i,j])) |
|
3550 | 3480 | phOffset = phOffset*180/numpy.pi |
|
3551 | 3481 | return phOffset |
|
3552 | 3482 | |
|
3553 | 3483 | |
|
3554 | 3484 | def run(self, dataOut, hmin, hmax, channelPositions=None, nHours = 1): |
|
3555 | 3485 | |
|
3556 | 3486 | dataOut.flagNoData = True |
|
3557 | 3487 | self.__dataReady = False |
|
3558 | 3488 | dataOut.outputInterval = nHours*3600 |
|
3559 | 3489 | |
|
3560 | 3490 | if self.__isConfig == False: |
|
3561 | 3491 | # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) |
|
3562 | 3492 | #Get Initial LTC time |
|
3563 | 3493 | self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
3564 | 3494 | self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
3565 | 3495 | |
|
3566 | 3496 | self.__isConfig = True |
|
3567 | 3497 | |
|
3568 | 3498 | if self.__buffer is None: |
|
3569 | 3499 | self.__buffer = dataOut.data_param.copy() |
|
3570 | 3500 | |
|
3571 | 3501 | else: |
|
3572 | 3502 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
3573 | 3503 | |
|
3574 | 3504 | self.__dataReady = self.__checkTime(dataOut.utctime, self.__initime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
3575 | 3505 | |
|
3576 | 3506 | if self.__dataReady: |
|
3577 | 3507 | dataOut.utctimeInit = self.__initime |
|
3578 | 3508 | self.__initime += dataOut.outputInterval #to erase time offset |
|
3579 | 3509 | |
|
3580 | 3510 | freq = dataOut.frequency |
|
3581 | 3511 | c = dataOut.C #m/s |
|
3582 | 3512 | lamb = c/freq |
|
3583 | 3513 | k = 2*numpy.pi/lamb |
|
3584 | 3514 | azimuth = 0 |
|
3585 | 3515 | h = (hmin, hmax) |
|
3586 | 3516 | # pairs = ((0,1),(2,3)) #Estrella |
|
3587 | 3517 | # pairs = ((1,0),(2,3)) #T |
|
3588 | 3518 | |
|
3589 | 3519 | if channelPositions is None: |
|
3590 | 3520 | # channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T |
|
3591 | 3521 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella |
|
3592 | 3522 | meteorOps = SMOperations() |
|
3593 | 3523 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) |
|
3594 | 3524 | |
|
3595 | 3525 | #Checking correct order of pairs |
|
3596 | 3526 | pairs = [] |
|
3597 | 3527 | if distances[1] > distances[0]: |
|
3598 | 3528 | pairs.append((1,0)) |
|
3599 | 3529 | else: |
|
3600 | 3530 | pairs.append((0,1)) |
|
3601 | 3531 | |
|
3602 | 3532 | if distances[3] > distances[2]: |
|
3603 | 3533 | pairs.append((3,2)) |
|
3604 | 3534 | else: |
|
3605 | 3535 | pairs.append((2,3)) |
|
3606 | 3536 | # distances1 = [-distances[0]*lamb, distances[1]*lamb, -distances[2]*lamb, distances[3]*lamb] |
|
3607 | 3537 | |
|
3608 | 3538 | meteorsArray = self.__buffer |
|
3609 | 3539 | error = meteorsArray[:,-1] |
|
3610 | 3540 | boolError = (error==0)|(error==3)|(error==4)|(error==13)|(error==14) |
|
3611 | 3541 | ind1 = numpy.where(boolError)[0] |
|
3612 | 3542 | meteorsArray = meteorsArray[ind1,:] |
|
3613 | 3543 | meteorsArray[:,-1] = 0 |
|
3614 | 3544 | phases = meteorsArray[:,8:12] |
|
3615 | 3545 | |
|
3616 | 3546 | #Calculate Gammas |
|
3617 | 3547 | gammas = self.__getGammas(pairs, distances, phases) |
|
3618 | 3548 | # gammas = numpy.array([-21.70409463,45.76935864])*numpy.pi/180 |
|
3619 | 3549 | #Calculate Phases |
|
3620 | 3550 | phasesOff = self.__getPhases(azimuth, h, pairs, distances, gammas, meteorsArray) |
|
3621 | 3551 | phasesOff = phasesOff.reshape((1,phasesOff.size)) |
|
3622 | 3552 | dataOut.data_output = -phasesOff |
|
3623 | 3553 | dataOut.flagNoData = False |
|
3624 | 3554 | self.__buffer = None |
|
3625 | 3555 | |
|
3626 | 3556 | |
|
3627 | 3557 | return |
|
3628 | 3558 | |
|
3629 | 3559 | class SMOperations(): |
|
3630 | 3560 | |
|
3631 | 3561 | def __init__(self): |
|
3632 | 3562 | |
|
3633 | 3563 | return |
|
3634 | 3564 | |
|
3635 | 3565 | def getMeteorParams(self, arrayParameters0, azimuth, h, pairsList, distances, jph): |
|
3636 | 3566 | |
|
3637 | 3567 | arrayParameters = arrayParameters0.copy() |
|
3638 | 3568 | hmin = h[0] |
|
3639 | 3569 | hmax = h[1] |
|
3640 | 3570 | |
|
3641 | 3571 | #Calculate AOA (Error N 3, 4) |
|
3642 | 3572 | #JONES ET AL. 1998 |
|
3643 | 3573 | AOAthresh = numpy.pi/8 |
|
3644 | 3574 | error = arrayParameters[:,-1] |
|
3645 | 3575 | phases = -arrayParameters[:,8:12] + jph |
|
3646 | 3576 | # phases = numpy.unwrap(phases) |
|
3647 | 3577 | arrayParameters[:,3:6], arrayParameters[:,-1] = self.__getAOA(phases, pairsList, distances, error, AOAthresh, azimuth) |
|
3648 | 3578 | |
|
3649 | 3579 | #Calculate Heights (Error N 13 and 14) |
|
3650 | 3580 | error = arrayParameters[:,-1] |
|
3651 | 3581 | Ranges = arrayParameters[:,1] |
|
3652 | 3582 | zenith = arrayParameters[:,4] |
|
3653 | 3583 | arrayParameters[:,2], arrayParameters[:,-1] = self.__getHeights(Ranges, zenith, error, hmin, hmax) |
|
3654 | 3584 | |
|
3655 | 3585 | #----------------------- Get Final data ------------------------------------ |
|
3656 | 3586 | # error = arrayParameters[:,-1] |
|
3657 | 3587 | # ind1 = numpy.where(error==0)[0] |
|
3658 | 3588 | # arrayParameters = arrayParameters[ind1,:] |
|
3659 | 3589 | |
|
3660 | 3590 | return arrayParameters |
|
3661 | 3591 | |
|
3662 | 3592 | def __getAOA(self, phases, pairsList, directions, error, AOAthresh, azimuth): |
|
3663 | 3593 | |
|
3664 | 3594 | arrayAOA = numpy.zeros((phases.shape[0],3)) |
|
3665 | 3595 | cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList,directions) |
|
3666 | 3596 | |
|
3667 | 3597 | arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth) |
|
3668 | 3598 | cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1) |
|
3669 | 3599 | arrayAOA[:,2] = cosDirError |
|
3670 | 3600 | |
|
3671 | 3601 | azimuthAngle = arrayAOA[:,0] |
|
3672 | 3602 | zenithAngle = arrayAOA[:,1] |
|
3673 | 3603 | |
|
3674 | 3604 | #Setting Error |
|
3675 | 3605 | indError = numpy.where(numpy.logical_or(error == 3, error == 4))[0] |
|
3676 | 3606 | error[indError] = 0 |
|
3677 | 3607 | #Number 3: AOA not fesible |
|
3678 | 3608 | indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0] |
|
3679 | 3609 | error[indInvalid] = 3 |
|
3680 | 3610 | #Number 4: Large difference in AOAs obtained from different antenna baselines |
|
3681 | 3611 | indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0] |
|
3682 | 3612 | error[indInvalid] = 4 |
|
3683 | 3613 | return arrayAOA, error |
|
3684 | 3614 | |
|
3685 | 3615 | def __getDirectionCosines(self, arrayPhase, pairsList, distances): |
|
3686 | 3616 | |
|
3687 | 3617 | #Initializing some variables |
|
3688 | 3618 | ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi |
|
3689 | 3619 | ang_aux = ang_aux.reshape(1,ang_aux.size) |
|
3690 | 3620 | |
|
3691 | 3621 | cosdir = numpy.zeros((arrayPhase.shape[0],2)) |
|
3692 | 3622 | cosdir0 = numpy.zeros((arrayPhase.shape[0],2)) |
|
3693 | 3623 | |
|
3694 | 3624 | |
|
3695 | 3625 | for i in range(2): |
|
3696 | 3626 | ph0 = arrayPhase[:,pairsList[i][0]] |
|
3697 | 3627 | ph1 = arrayPhase[:,pairsList[i][1]] |
|
3698 | 3628 | d0 = distances[pairsList[i][0]] |
|
3699 | 3629 | d1 = distances[pairsList[i][1]] |
|
3700 | 3630 | |
|
3701 | 3631 | ph0_aux = ph0 + ph1 |
|
3702 | 3632 | ph0_aux = numpy.angle(numpy.exp(1j*ph0_aux)) |
|
3703 | 3633 | # ph0_aux[ph0_aux > numpy.pi] -= 2*numpy.pi |
|
3704 | 3634 | # ph0_aux[ph0_aux < -numpy.pi] += 2*numpy.pi |
|
3705 | 3635 | #First Estimation |
|
3706 | 3636 | cosdir0[:,i] = (ph0_aux)/(2*numpy.pi*(d0 - d1)) |
|
3707 | 3637 | |
|
3708 | 3638 | #Most-Accurate Second Estimation |
|
3709 | 3639 | phi1_aux = ph0 - ph1 |
|
3710 | 3640 | phi1_aux = phi1_aux.reshape(phi1_aux.size,1) |
|
3711 | 3641 | #Direction Cosine 1 |
|
3712 | 3642 | cosdir1 = (phi1_aux + ang_aux)/(2*numpy.pi*(d0 + d1)) |
|
3713 | 3643 | |
|
3714 | 3644 | #Searching the correct Direction Cosine |
|
3715 | 3645 | cosdir0_aux = cosdir0[:,i] |
|
3716 | 3646 | cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1) |
|
3717 | 3647 | #Minimum Distance |
|
3718 | 3648 | cosDiff = (cosdir1 - cosdir0_aux)**2 |
|
3719 | 3649 | indcos = cosDiff.argmin(axis = 1) |
|
3720 | 3650 | #Saving Value obtained |
|
3721 | 3651 | cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos] |
|
3722 | 3652 | |
|
3723 | 3653 | return cosdir0, cosdir |
|
3724 | 3654 | |
|
3725 | 3655 | def __calculateAOA(self, cosdir, azimuth): |
|
3726 | 3656 | cosdirX = cosdir[:,0] |
|
3727 | 3657 | cosdirY = cosdir[:,1] |
|
3728 | 3658 | |
|
3729 | 3659 | zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi |
|
3730 | 3660 | azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth#0 deg north, 90 deg east |
|
3731 | 3661 | angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose() |
|
3732 | 3662 | |
|
3733 | 3663 | return angles |
|
3734 | 3664 | |
|
3735 | 3665 | def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight): |
|
3736 | 3666 | |
|
3737 | 3667 | Ramb = 375 #Ramb = c/(2*PRF) |
|
3738 | 3668 | Re = 6371 #Earth Radius |
|
3739 | 3669 | heights = numpy.zeros(Ranges.shape) |
|
3740 | 3670 | |
|
3741 | 3671 | R_aux = numpy.array([0,1,2])*Ramb |
|
3742 | 3672 | R_aux = R_aux.reshape(1,R_aux.size) |
|
3743 | 3673 | |
|
3744 | 3674 | Ranges = Ranges.reshape(Ranges.size,1) |
|
3745 | 3675 | |
|
3746 | 3676 | Ri = Ranges + R_aux |
|
3747 | 3677 | hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re |
|
3748 | 3678 | |
|
3749 | 3679 | #Check if there is a height between 70 and 110 km |
|
3750 | 3680 | h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1) |
|
3751 | 3681 | ind_h = numpy.where(h_bool == 1)[0] |
|
3752 | 3682 | |
|
3753 | 3683 | hCorr = hi[ind_h, :] |
|
3754 | 3684 | ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight)) |
|
3755 | 3685 | |
|
3756 | 3686 | hCorr = hi[ind_hCorr][:len(ind_h)] |
|
3757 | 3687 | heights[ind_h] = hCorr |
|
3758 | 3688 | |
|
3759 | 3689 | #Setting Error |
|
3760 | 3690 | #Number 13: Height unresolvable echo: not valid height within 70 to 110 km |
|
3761 | 3691 | #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km |
|
3762 | 3692 | indError = numpy.where(numpy.logical_or(error == 13, error == 14))[0] |
|
3763 | 3693 | error[indError] = 0 |
|
3764 | 3694 | indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0] |
|
3765 | 3695 | error[indInvalid2] = 14 |
|
3766 | 3696 | indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0] |
|
3767 | 3697 | error[indInvalid1] = 13 |
|
3768 | 3698 | |
|
3769 | 3699 | return heights, error |
|
3770 | 3700 | |
|
3771 | 3701 | def getPhasePairs(self, channelPositions): |
|
3772 | 3702 | chanPos = numpy.array(channelPositions) |
|
3773 | 3703 | listOper = list(itertools.combinations(list(range(5)),2)) |
|
3774 | 3704 | |
|
3775 | 3705 | distances = numpy.zeros(4) |
|
3776 | 3706 | axisX = [] |
|
3777 | 3707 | axisY = [] |
|
3778 | 3708 | distX = numpy.zeros(3) |
|
3779 | 3709 | distY = numpy.zeros(3) |
|
3780 | 3710 | ix = 0 |
|
3781 | 3711 | iy = 0 |
|
3782 | 3712 | |
|
3783 | 3713 | pairX = numpy.zeros((2,2)) |
|
3784 | 3714 | pairY = numpy.zeros((2,2)) |
|
3785 | 3715 | |
|
3786 | 3716 | for i in range(len(listOper)): |
|
3787 | 3717 | pairi = listOper[i] |
|
3788 | 3718 | |
|
3789 | 3719 | posDif = numpy.abs(chanPos[pairi[0],:] - chanPos[pairi[1],:]) |
|
3790 | 3720 | |
|
3791 | 3721 | if posDif[0] == 0: |
|
3792 | 3722 | axisY.append(pairi) |
|
3793 | 3723 | distY[iy] = posDif[1] |
|
3794 | 3724 | iy += 1 |
|
3795 | 3725 | elif posDif[1] == 0: |
|
3796 | 3726 | axisX.append(pairi) |
|
3797 | 3727 | distX[ix] = posDif[0] |
|
3798 | 3728 | ix += 1 |
|
3799 | 3729 | |
|
3800 | 3730 | for i in range(2): |
|
3801 | 3731 | if i==0: |
|
3802 | 3732 | dist0 = distX |
|
3803 | 3733 | axis0 = axisX |
|
3804 | 3734 | else: |
|
3805 | 3735 | dist0 = distY |
|
3806 | 3736 | axis0 = axisY |
|
3807 | 3737 | |
|
3808 | 3738 | side = numpy.argsort(dist0)[:-1] |
|
3809 | 3739 | axis0 = numpy.array(axis0)[side,:] |
|
3810 | 3740 | chanC = int(numpy.intersect1d(axis0[0,:], axis0[1,:])[0]) |
|
3811 | 3741 | axis1 = numpy.unique(numpy.reshape(axis0,4)) |
|
3812 | 3742 | side = axis1[axis1 != chanC] |
|
3813 | 3743 | diff1 = chanPos[chanC,i] - chanPos[side[0],i] |
|
3814 | 3744 | diff2 = chanPos[chanC,i] - chanPos[side[1],i] |
|
3815 | 3745 | if diff1<0: |
|
3816 | 3746 | chan2 = side[0] |
|
3817 | 3747 | d2 = numpy.abs(diff1) |
|
3818 | 3748 | chan1 = side[1] |
|
3819 | 3749 | d1 = numpy.abs(diff2) |
|
3820 | 3750 | else: |
|
3821 | 3751 | chan2 = side[1] |
|
3822 | 3752 | d2 = numpy.abs(diff2) |
|
3823 | 3753 | chan1 = side[0] |
|
3824 | 3754 | d1 = numpy.abs(diff1) |
|
3825 | 3755 | |
|
3826 | 3756 | if i==0: |
|
3827 | 3757 | chanCX = chanC |
|
3828 | 3758 | chan1X = chan1 |
|
3829 | 3759 | chan2X = chan2 |
|
3830 | 3760 | distances[0:2] = numpy.array([d1,d2]) |
|
3831 | 3761 | else: |
|
3832 | 3762 | chanCY = chanC |
|
3833 | 3763 | chan1Y = chan1 |
|
3834 | 3764 | chan2Y = chan2 |
|
3835 | 3765 | distances[2:4] = numpy.array([d1,d2]) |
|
3836 | 3766 | # axisXsides = numpy.reshape(axisX[ix,:],4) |
|
3837 | 3767 | # |
|
3838 | 3768 | # channelCentX = int(numpy.intersect1d(pairX[0,:], pairX[1,:])[0]) |
|
3839 | 3769 | # channelCentY = int(numpy.intersect1d(pairY[0,:], pairY[1,:])[0]) |
|
3840 | 3770 | # |
|
3841 | 3771 | # ind25X = numpy.where(pairX[0,:] != channelCentX)[0][0] |
|
3842 | 3772 | # ind20X = numpy.where(pairX[1,:] != channelCentX)[0][0] |
|
3843 | 3773 | # channel25X = int(pairX[0,ind25X]) |
|
3844 | 3774 | # channel20X = int(pairX[1,ind20X]) |
|
3845 | 3775 | # ind25Y = numpy.where(pairY[0,:] != channelCentY)[0][0] |
|
3846 | 3776 | # ind20Y = numpy.where(pairY[1,:] != channelCentY)[0][0] |
|
3847 | 3777 | # channel25Y = int(pairY[0,ind25Y]) |
|
3848 | 3778 | # channel20Y = int(pairY[1,ind20Y]) |
|
3849 | 3779 | |
|
3850 | 3780 | # pairslist = [(channelCentX, channel25X),(channelCentX, channel20X),(channelCentY,channel25Y),(channelCentY, channel20Y)] |
|
3851 | 3781 | pairslist = [(chanCX, chan1X),(chanCX, chan2X),(chanCY,chan1Y),(chanCY, chan2Y)] |
|
3852 | 3782 | |
|
3853 | 3783 | return pairslist, distances |
|
3854 | 3784 | # def __getAOA(self, phases, pairsList, error, AOAthresh, azimuth): |
|
3855 | 3785 | # |
|
3856 | 3786 | # arrayAOA = numpy.zeros((phases.shape[0],3)) |
|
3857 | 3787 | # cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList) |
|
3858 | 3788 | # |
|
3859 | 3789 | # arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth) |
|
3860 | 3790 | # cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1) |
|
3861 | 3791 | # arrayAOA[:,2] = cosDirError |
|
3862 | 3792 | # |
|
3863 | 3793 | # azimuthAngle = arrayAOA[:,0] |
|
3864 | 3794 | # zenithAngle = arrayAOA[:,1] |
|
3865 | 3795 | # |
|
3866 | 3796 | # #Setting Error |
|
3867 | 3797 | # #Number 3: AOA not fesible |
|
3868 | 3798 | # indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0] |
|
3869 | 3799 | # error[indInvalid] = 3 |
|
3870 | 3800 | # #Number 4: Large difference in AOAs obtained from different antenna baselines |
|
3871 | 3801 | # indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0] |
|
3872 | 3802 | # error[indInvalid] = 4 |
|
3873 | 3803 | # return arrayAOA, error |
|
3874 | 3804 | # |
|
3875 | 3805 | # def __getDirectionCosines(self, arrayPhase, pairsList): |
|
3876 | 3806 | # |
|
3877 | 3807 | # #Initializing some variables |
|
3878 | 3808 | # ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi |
|
3879 | 3809 | # ang_aux = ang_aux.reshape(1,ang_aux.size) |
|
3880 | 3810 | # |
|
3881 | 3811 | # cosdir = numpy.zeros((arrayPhase.shape[0],2)) |
|
3882 | 3812 | # cosdir0 = numpy.zeros((arrayPhase.shape[0],2)) |
|
3883 | 3813 | # |
|
3884 | 3814 | # |
|
3885 | 3815 | # for i in range(2): |
|
3886 | 3816 | # #First Estimation |
|
3887 | 3817 | # phi0_aux = arrayPhase[:,pairsList[i][0]] + arrayPhase[:,pairsList[i][1]] |
|
3888 | 3818 | # #Dealias |
|
3889 | 3819 | # indcsi = numpy.where(phi0_aux > numpy.pi) |
|
3890 | 3820 | # phi0_aux[indcsi] -= 2*numpy.pi |
|
3891 | 3821 | # indcsi = numpy.where(phi0_aux < -numpy.pi) |
|
3892 | 3822 | # phi0_aux[indcsi] += 2*numpy.pi |
|
3893 | 3823 | # #Direction Cosine 0 |
|
3894 | 3824 | # cosdir0[:,i] = -(phi0_aux)/(2*numpy.pi*0.5) |
|
3895 | 3825 | # |
|
3896 | 3826 | # #Most-Accurate Second Estimation |
|
3897 | 3827 | # phi1_aux = arrayPhase[:,pairsList[i][0]] - arrayPhase[:,pairsList[i][1]] |
|
3898 | 3828 | # phi1_aux = phi1_aux.reshape(phi1_aux.size,1) |
|
3899 | 3829 | # #Direction Cosine 1 |
|
3900 | 3830 | # cosdir1 = -(phi1_aux + ang_aux)/(2*numpy.pi*4.5) |
|
3901 | 3831 | # |
|
3902 | 3832 | # #Searching the correct Direction Cosine |
|
3903 | 3833 | # cosdir0_aux = cosdir0[:,i] |
|
3904 | 3834 | # cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1) |
|
3905 | 3835 | # #Minimum Distance |
|
3906 | 3836 | # cosDiff = (cosdir1 - cosdir0_aux)**2 |
|
3907 | 3837 | # indcos = cosDiff.argmin(axis = 1) |
|
3908 | 3838 | # #Saving Value obtained |
|
3909 | 3839 | # cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos] |
|
3910 | 3840 | # |
|
3911 | 3841 | # return cosdir0, cosdir |
|
3912 | 3842 | # |
|
3913 | 3843 | # def __calculateAOA(self, cosdir, azimuth): |
|
3914 | 3844 | # cosdirX = cosdir[:,0] |
|
3915 | 3845 | # cosdirY = cosdir[:,1] |
|
3916 | 3846 | # |
|
3917 | 3847 | # zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi |
|
3918 | 3848 | # azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth #0 deg north, 90 deg east |
|
3919 | 3849 | # angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose() |
|
3920 | 3850 | # |
|
3921 | 3851 | # return angles |
|
3922 | 3852 | # |
|
3923 | 3853 | # def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight): |
|
3924 | 3854 | # |
|
3925 | 3855 | # Ramb = 375 #Ramb = c/(2*PRF) |
|
3926 | 3856 | # Re = 6371 #Earth Radius |
|
3927 | 3857 | # heights = numpy.zeros(Ranges.shape) |
|
3928 | 3858 | # |
|
3929 | 3859 | # R_aux = numpy.array([0,1,2])*Ramb |
|
3930 | 3860 | # R_aux = R_aux.reshape(1,R_aux.size) |
|
3931 | 3861 | # |
|
3932 | 3862 | # Ranges = Ranges.reshape(Ranges.size,1) |
|
3933 | 3863 | # |
|
3934 | 3864 | # Ri = Ranges + R_aux |
|
3935 | 3865 | # hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re |
|
3936 | 3866 | # |
|
3937 | 3867 | # #Check if there is a height between 70 and 110 km |
|
3938 | 3868 | # h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1) |
|
3939 | 3869 | # ind_h = numpy.where(h_bool == 1)[0] |
|
3940 | 3870 | # |
|
3941 | 3871 | # hCorr = hi[ind_h, :] |
|
3942 | 3872 | # ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight)) |
|
3943 | 3873 | # |
|
3944 | 3874 | # hCorr = hi[ind_hCorr] |
|
3945 | 3875 | # heights[ind_h] = hCorr |
|
3946 | 3876 | # |
|
3947 | 3877 | # #Setting Error |
|
3948 | 3878 | # #Number 13: Height unresolvable echo: not valid height within 70 to 110 km |
|
3949 | 3879 | # #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km |
|
3950 | 3880 | # |
|
3951 | 3881 | # indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0] |
|
3952 | 3882 | # error[indInvalid2] = 14 |
|
3953 | 3883 | # indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0] |
|
3954 | 3884 | # error[indInvalid1] = 13 |
|
3955 | 3885 | # |
|
3956 | 3886 | # return heights, error |
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