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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 |
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4 | 4 | from scipy import interpolate |
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5 | 5 | from scipy import signal |
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6 | 6 | from scipy import stats |
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7 | 7 | import re |
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8 | 8 | import datetime |
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9 | 9 | import copy |
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10 | 10 | import sys |
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11 | 11 | import importlib |
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12 | 12 | import itertools |
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13 | 13 | |
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14 | 14 | from jroproc_base import ProcessingUnit, Operation |
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15 | 15 | from model.data.jrodata import Parameters |
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16 | 16 | |
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17 | 17 | |
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18 | 18 | class ParametersProc(ProcessingUnit): |
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19 | 19 | |
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20 | 20 | nSeconds = None |
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21 | 21 | |
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22 | 22 | def __init__(self): |
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23 | 23 | ProcessingUnit.__init__(self) |
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24 | 24 | |
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25 | 25 | # self.objectDict = {} |
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26 | 26 | self.buffer = None |
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27 | 27 | self.firstdatatime = None |
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28 | 28 | self.profIndex = 0 |
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29 | 29 | self.dataOut = Parameters() |
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30 | 30 | |
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31 | 31 | def __updateObjFromInput(self): |
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32 | 32 | |
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33 | 33 | self.dataOut.inputUnit = self.dataIn.type |
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34 | 34 | |
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35 | 35 | self.dataOut.timeZone = self.dataIn.timeZone |
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36 | 36 | self.dataOut.dstFlag = self.dataIn.dstFlag |
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37 | 37 | self.dataOut.errorCount = self.dataIn.errorCount |
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38 | 38 | self.dataOut.useLocalTime = self.dataIn.useLocalTime |
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39 | 39 | |
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40 | 40 | self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() |
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41 | 41 | self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy() |
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42 | 42 | self.dataOut.channelList = self.dataIn.channelList |
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43 | 43 | self.dataOut.heightList = self.dataIn.heightList |
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44 | 44 | self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')]) |
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45 | 45 | # self.dataOut.nHeights = self.dataIn.nHeights |
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46 | 46 | # self.dataOut.nChannels = self.dataIn.nChannels |
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47 | 47 | self.dataOut.nBaud = self.dataIn.nBaud |
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48 | 48 | self.dataOut.nCode = self.dataIn.nCode |
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49 | 49 | self.dataOut.code = self.dataIn.code |
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50 | 50 | # self.dataOut.nProfiles = self.dataOut.nFFTPoints |
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51 | 51 | self.dataOut.flagTimeBlock = self.dataIn.flagTimeBlock |
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52 | 52 | self.dataOut.utctime = self.firstdatatime |
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53 | 53 | self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada |
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54 | 54 | self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip |
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55 | 55 | # self.dataOut.nCohInt = self.dataIn.nCohInt |
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56 | 56 | # self.dataOut.nIncohInt = 1 |
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57 | 57 | self.dataOut.ippSeconds = self.dataIn.ippSeconds |
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58 | 58 | # self.dataOut.windowOfFilter = self.dataIn.windowOfFilter |
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59 | 59 | self.dataOut.timeInterval = self.dataIn.timeInterval |
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60 | 60 | self.dataOut.heightList = self.dataIn.getHeiRange() |
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61 | 61 | self.dataOut.frequency = self.dataIn.frequency |
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62 | 62 | |
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63 | 63 | def run(self, nSeconds = None, nProfiles = None): |
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64 | 64 | |
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65 | 65 | |
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66 | 66 | |
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67 | 67 | if self.firstdatatime == None: |
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68 | 68 | self.firstdatatime = self.dataIn.utctime |
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69 | 69 | |
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70 | 70 | #---------------------- Voltage Data --------------------------- |
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71 | 71 | |
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72 | 72 | if self.dataIn.type == "Voltage": |
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73 | 73 | self.dataOut.flagNoData = True |
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74 | 74 | if nSeconds != None: |
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75 | 75 | self.nSeconds = nSeconds |
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76 | 76 | self.nProfiles= int(numpy.floor(nSeconds/(self.dataIn.ippSeconds*self.dataIn.nCohInt))) |
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77 | 77 | |
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78 | 78 | if self.buffer == None: |
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79 | 79 | self.buffer = numpy.zeros((self.dataIn.nChannels, |
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80 | 80 | self.nProfiles, |
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81 | 81 | self.dataIn.nHeights), |
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82 | 82 | dtype='complex') |
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83 | 83 | |
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84 | 84 | self.buffer[:,self.profIndex,:] = self.dataIn.data.copy() |
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85 | 85 | self.profIndex += 1 |
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86 | 86 | |
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87 | 87 | if self.profIndex == self.nProfiles: |
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88 | 88 | |
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89 | 89 | self.__updateObjFromInput() |
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90 | 90 | self.dataOut.data_pre = self.buffer.copy() |
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91 | 91 | self.dataOut.paramInterval = nSeconds |
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92 | 92 | self.dataOut.flagNoData = False |
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93 | 93 | |
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94 | 94 | self.buffer = None |
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95 | 95 | self.firstdatatime = None |
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96 | 96 | self.profIndex = 0 |
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97 | 97 | return |
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98 | 98 | |
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99 | 99 | #---------------------- Spectra Data --------------------------- |
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100 | 100 | |
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101 | 101 | if self.dataIn.type == "Spectra": |
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102 | 102 | self.dataOut.data_pre = self.dataIn.data_spc.copy() |
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103 | 103 | self.dataOut.abscissaList = self.dataIn.getVelRange(1) |
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104 | 104 | self.dataOut.noise = self.dataIn.getNoise() |
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105 | 105 | self.dataOut.normFactor = self.dataIn.normFactor |
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106 | self.dataOut.groupList = self.dataIn.pairsList | |
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106 | 107 | self.dataOut.flagNoData = False |
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107 | 108 | |
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108 | 109 | #---------------------- Correlation Data --------------------------- |
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109 | 110 | |
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110 | 111 | if self.dataIn.type == "Correlation": |
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111 | 112 | lagRRange = self.dataIn.lagR |
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112 | 113 | indR = numpy.where(lagRRange == 0)[0][0] |
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113 | 114 | |
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114 | 115 | self.dataOut.data_pre = self.dataIn.data_corr.copy()[:,:,indR,:] |
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115 | 116 | self.dataOut.abscissaList = self.dataIn.getLagTRange(1) |
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116 | 117 | self.dataOut.noise = self.dataIn.noise |
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117 | 118 | self.dataOut.normFactor = self.dataIn.normFactor |
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118 | 119 | self.dataOut.data_SNR = self.dataIn.SNR |
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119 | 120 | self.dataOut.groupList = self.dataIn.pairsList |
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120 | 121 | self.dataOut.flagNoData = False |
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121 | 122 | |
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122 | 123 | #---------------------- Correlation Data --------------------------- |
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123 | 124 | |
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124 | 125 | if self.dataIn.type == "Parameters": |
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125 | 126 | self.dataOut.copy(self.dataIn) |
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126 | 127 | self.dataOut.flagNoData = False |
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127 | 128 | |
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128 | 129 | return True |
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129 | 130 | |
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130 | 131 | self.__updateObjFromInput() |
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131 | 132 | self.firstdatatime = None |
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132 | 133 | self.dataOut.utctimeInit = self.dataIn.utctime |
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133 | 134 | self.dataOut.outputInterval = self.dataIn.timeInterval |
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134 | 135 | |
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135 | 136 | #------------------- Get Moments ---------------------------------- |
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136 | 137 | def GetMoments(self, channelList = None): |
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137 | 138 | ''' |
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138 | 139 | Function GetMoments() |
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139 | 140 | |
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140 | 141 | Input: |
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141 | 142 | channelList : simple channel list to select e.g. [2,3,7] |
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142 | 143 | self.dataOut.data_pre |
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143 | 144 | self.dataOut.abscissaList |
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144 | 145 | self.dataOut.noise |
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145 | 146 | |
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146 | 147 | Affected: |
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147 | 148 | self.dataOut.data_param |
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148 | 149 | self.dataOut.data_SNR |
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149 | 150 | |
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150 | 151 | ''' |
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151 | 152 | data = self.dataOut.data_pre |
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152 | 153 | absc = self.dataOut.abscissaList[:-1] |
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153 | 154 | noise = self.dataOut.noise |
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154 | 155 | |
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155 | 156 | data_param = numpy.zeros((data.shape[0], 4, data.shape[2])) |
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156 | 157 | |
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157 | 158 | if channelList== None: |
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158 | 159 | channelList = self.dataIn.channelList |
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159 | 160 | self.dataOut.channelList = channelList |
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160 | 161 | |
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161 | 162 | for ind in channelList: |
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162 | 163 | data_param[ind,:,:] = self.__calculateMoments(data[ind,:,:], absc, noise[ind]) |
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163 | 164 | |
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164 | 165 | self.dataOut.data_param = data_param[:,1:,:] |
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165 | 166 | self.dataOut.data_SNR = data_param[:,0] |
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166 | 167 | return |
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167 | 168 | |
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168 | 169 | def __calculateMoments(self, oldspec, oldfreq, n0, nicoh = None, graph = None, smooth = None, type1 = None, fwindow = None, snrth = None, dc = None, aliasing = None, oldfd = None, wwauto = None): |
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169 | 170 | |
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170 | 171 | if (nicoh == None): nicoh = 1 |
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171 | 172 | if (graph == None): graph = 0 |
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172 | 173 | if (smooth == None): smooth = 0 |
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173 | 174 | elif (self.smooth < 3): smooth = 0 |
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174 | 175 | |
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175 | 176 | if (type1 == None): type1 = 0 |
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176 | 177 | if (fwindow == None): fwindow = numpy.zeros(oldfreq.size) + 1 |
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177 | 178 | if (snrth == None): snrth = -3 |
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178 | 179 | if (dc == None): dc = 0 |
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179 | 180 | if (aliasing == None): aliasing = 0 |
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180 | 181 | if (oldfd == None): oldfd = 0 |
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181 | 182 | if (wwauto == None): wwauto = 0 |
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182 | 183 | |
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183 | 184 | if (n0 < 1.e-20): n0 = 1.e-20 |
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184 | 185 | |
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185 | 186 | freq = oldfreq |
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186 | 187 | vec_power = numpy.zeros(oldspec.shape[1]) |
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187 | 188 | vec_fd = numpy.zeros(oldspec.shape[1]) |
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188 | 189 | vec_w = numpy.zeros(oldspec.shape[1]) |
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189 | 190 | vec_snr = numpy.zeros(oldspec.shape[1]) |
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190 | 191 | |
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191 | 192 | for ind in range(oldspec.shape[1]): |
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192 | 193 | |
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193 | 194 | spec = oldspec[:,ind] |
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194 | 195 | aux = spec*fwindow |
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195 | 196 | max_spec = aux.max() |
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196 | 197 | m = list(aux).index(max_spec) |
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197 | 198 | |
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198 | 199 | #Smooth |
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199 | 200 | if (smooth == 0): spec2 = spec |
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200 | 201 | else: spec2 = scipy.ndimage.filters.uniform_filter1d(spec,size=smooth) |
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201 | 202 | |
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202 | 203 | # Calculo de Momentos |
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203 | 204 | bb = spec2[range(m,spec2.size)] |
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204 | 205 | bb = (bb<n0).nonzero() |
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205 | 206 | bb = bb[0] |
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206 | 207 | |
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207 | 208 | ss = spec2[range(0,m + 1)] |
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208 | 209 | ss = (ss<n0).nonzero() |
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209 | 210 | ss = ss[0] |
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210 | 211 | |
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211 | 212 | if (bb.size == 0): |
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212 | 213 | bb0 = spec.size - 1 - m |
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213 | 214 | else: |
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214 | 215 | bb0 = bb[0] - 1 |
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215 | 216 | if (bb0 < 0): |
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216 | 217 | bb0 = 0 |
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217 | 218 | |
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218 | 219 | if (ss.size == 0): ss1 = 1 |
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219 | 220 | else: ss1 = max(ss) + 1 |
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220 | 221 | |
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221 | 222 | if (ss1 > m): ss1 = m |
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222 | 223 | |
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223 | 224 | valid = numpy.asarray(range(int(m + bb0 - ss1 + 1))) + ss1 |
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224 | 225 | power = ((spec2[valid] - n0)*fwindow[valid]).sum() |
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225 | 226 | fd = ((spec2[valid]- n0)*freq[valid]*fwindow[valid]).sum()/power |
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226 | 227 | w = math.sqrt(((spec2[valid] - n0)*fwindow[valid]*(freq[valid]- fd)**2).sum()/power) |
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227 | 228 | snr = (spec2.mean()-n0)/n0 |
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228 | 229 | |
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229 | 230 | if (snr < 1.e-20) : |
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230 | 231 | snr = 1.e-20 |
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231 | 232 | |
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232 | 233 | vec_power[ind] = power |
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233 | 234 | vec_fd[ind] = fd |
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234 | 235 | vec_w[ind] = w |
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235 | 236 | vec_snr[ind] = snr |
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236 | 237 | |
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237 | 238 | moments = numpy.vstack((vec_snr, vec_power, vec_fd, vec_w)) |
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238 | 239 | return moments |
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239 | 240 | |
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241 | #------------------ Get SA Parameters -------------------------- | |
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242 | def GetSAParameters(self): | |
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243 | data = self.dataOut.data_pre | |
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244 | crossdata = self.dataIn.data_cspc | |
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245 | a = 1 | |
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246 | ||
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247 | ||
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248 | ||
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240 | 249 | #------------------- Get Lags ---------------------------------- |
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241 | 250 | |
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242 | 251 | def GetLags(self): |
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243 | 252 | ''' |
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244 | 253 | Function GetMoments() |
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245 | 254 | |
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246 | 255 | Input: |
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247 | 256 | self.dataOut.data_pre |
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248 | 257 | self.dataOut.abscissaList |
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249 | 258 | self.dataOut.noise |
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250 | 259 | self.dataOut.normFactor |
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251 | 260 | self.dataOut.data_SNR |
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252 | 261 | self.dataOut.groupList |
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253 | 262 | self.dataOut.nChannels |
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254 | 263 | |
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255 | 264 | Affected: |
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256 | 265 | self.dataOut.data_param |
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257 | 266 | |
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258 | 267 | ''' |
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268 | ||
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259 | 269 | data = self.dataOut.data_pre |
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260 | 270 | normFactor = self.dataOut.normFactor |
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261 | 271 | nHeights = self.dataOut.nHeights |
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262 | 272 | absc = self.dataOut.abscissaList[:-1] |
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263 | 273 | noise = self.dataOut.noise |
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264 | 274 | SNR = self.dataOut.data_SNR |
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265 | 275 | pairsList = self.dataOut.groupList |
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266 | 276 | nChannels = self.dataOut.nChannels |
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267 | 277 | pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairsList, nChannels) |
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268 | 278 | self.dataOut.data_param = numpy.zeros((len(pairsCrossCorr)*2 + 1, nHeights)) |
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269 | 279 | |
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270 | 280 | dataNorm = numpy.abs(data) |
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271 | 281 | for l in range(len(pairsList)): |
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272 | 282 | dataNorm[l,:,:] = dataNorm[l,:,:]/normFactor[l,:] |
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273 | 283 | |
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274 | 284 | self.dataOut.data_param[:-1,:] = self.__calculateTaus(dataNorm, pairsCrossCorr, pairsAutoCorr, absc) |
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275 | 285 | self.dataOut.data_param[-1,:] = self.__calculateLag1Phase(data, pairsAutoCorr, absc) |
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276 | 286 | return |
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277 | 287 | |
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278 | 288 | def __getPairsAutoCorr(self, pairsList, nChannels): |
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279 | 289 | |
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280 | 290 | pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan |
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281 | 291 | |
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282 | 292 | for l in range(len(pairsList)): |
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283 | 293 | firstChannel = pairsList[l][0] |
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284 | 294 | secondChannel = pairsList[l][1] |
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285 | 295 | |
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286 | 296 | #Obteniendo pares de Autocorrelacion |
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287 | 297 | if firstChannel == secondChannel: |
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288 | 298 | pairsAutoCorr[firstChannel] = int(l) |
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289 | 299 | |
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290 | 300 | pairsAutoCorr = pairsAutoCorr.astype(int) |
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291 | 301 | |
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292 | 302 | pairsCrossCorr = range(len(pairsList)) |
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293 | 303 | pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr) |
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294 | 304 | |
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295 | 305 | return pairsAutoCorr, pairsCrossCorr |
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296 | 306 | |
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297 | 307 | def __calculateTaus(self, data, pairsCrossCorr, pairsAutoCorr, lagTRange): |
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298 | 308 | |
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299 | 309 | Pt0 = data.shape[1]/2 |
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300 | 310 | #Funcion de Autocorrelacion |
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301 | 311 | dataAutoCorr = stats.nanmean(data[pairsAutoCorr,:,:], axis = 0) |
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302 | 312 | |
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303 | 313 | #Obtencion Indice de TauCross |
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304 | 314 | indCross = data[pairsCrossCorr,:,:].argmax(axis = 1) |
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305 | 315 | #Obtencion Indice de TauAuto |
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306 | 316 | indAuto = numpy.zeros(indCross.shape,dtype = 'int') |
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307 | 317 | CCValue = data[pairsCrossCorr,Pt0,:] |
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308 | 318 | for i in range(pairsCrossCorr.size): |
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309 | 319 | indAuto[i,:] = numpy.abs(dataAutoCorr - CCValue[i,:]).argmin(axis = 0) |
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310 | 320 | |
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311 | 321 | #Obtencion de TauCross y TauAuto |
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312 | 322 | tauCross = lagTRange[indCross] |
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313 | 323 | tauAuto = lagTRange[indAuto] |
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314 | 324 | |
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315 | 325 | Nan1, Nan2 = numpy.where(tauCross == lagTRange[0]) |
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316 | 326 | |
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317 | 327 | tauCross[Nan1,Nan2] = numpy.nan |
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318 | 328 | tauAuto[Nan1,Nan2] = numpy.nan |
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319 | 329 | tau = numpy.vstack((tauCross,tauAuto)) |
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320 | 330 | |
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321 | 331 | return tau |
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322 | 332 | |
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323 | 333 | def __calculateLag1Phase(self, data, pairs, lagTRange): |
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324 | 334 | data1 = stats.nanmean(data[pairs,:,:], axis = 0) |
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325 | 335 | lag1 = numpy.where(lagTRange == 0)[0][0] + 1 |
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326 | 336 | |
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327 | 337 | phase = numpy.angle(data1[lag1,:]) |
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328 | 338 | |
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329 | 339 | return phase |
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330 | 340 | #------------------- Detect Meteors ------------------------------ |
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331 | 341 | |
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332 | 342 | def DetectMeteors(self, hei_ref = None, tauindex = 0, |
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333 | 343 | predefinedPhaseShifts = None, centerReceiverIndex = 2, |
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334 | 344 | cohDetection = False, cohDet_timeStep = 1, cohDet_thresh = 25, |
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335 | 345 | noise_timeStep = 4, noise_multiple = 4, |
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336 | 346 | multDet_timeLimit = 1, multDet_rangeLimit = 3, |
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337 | 347 | phaseThresh = 20, SNRThresh = 8, |
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338 | 348 | hmin = 70, hmax=110, azimuth = 0) : |
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339 | 349 | |
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340 | 350 | ''' |
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341 | 351 | Function DetectMeteors() |
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342 | 352 | Project developed with paper: |
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343 | 353 | HOLDSWORTH ET AL. 2004 |
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344 | 354 | |
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345 | 355 | Input: |
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346 | 356 | self.dataOut.data_pre |
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347 | 357 | |
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348 | 358 | centerReceiverIndex: From the channels, which is the center receiver |
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349 | 359 | |
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350 | 360 | hei_ref: Height reference for the Beacon signal extraction |
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351 | 361 | tauindex: |
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352 | 362 | predefinedPhaseShifts: Predefined phase offset for the voltge signals |
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353 | 363 | |
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354 | 364 | cohDetection: Whether to user Coherent detection or not |
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355 | 365 | cohDet_timeStep: Coherent Detection calculation time step |
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356 | 366 | cohDet_thresh: Coherent Detection phase threshold to correct phases |
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357 | 367 | |
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358 | 368 | noise_timeStep: Noise calculation time step |
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359 | 369 | noise_multiple: Noise multiple to define signal threshold |
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360 | 370 | |
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361 | 371 | multDet_timeLimit: Multiple Detection Removal time limit in seconds |
|
|
362 | 372 | multDet_rangeLimit: Multiple Detection Removal range limit in km |
|
|
363 | 373 | |
|
|
364 | 374 | phaseThresh: Maximum phase difference between receiver to be consider a meteor |
|
|
365 | 375 | SNRThresh: Minimum SNR threshold of the meteor signal to be consider a meteor |
|
|
366 | 376 | |
|
|
367 | 377 | hmin: Minimum Height of the meteor to use it in the further wind estimations |
|
|
368 | 378 | hmax: Maximum Height of the meteor to use it in the further wind estimations |
|
|
369 | 379 | azimuth: Azimuth angle correction |
|
|
370 | 380 | |
|
|
371 | 381 | Affected: |
|
|
372 | 382 | self.dataOut.data_param |
|
|
373 | 383 | |
|
|
374 | 384 | Rejection Criteria (Errors): |
|
|
375 | 385 | 0: No error; analysis OK |
|
|
376 | 386 | 1: SNR < SNR threshold |
|
|
377 | 387 | 2: angle of arrival (AOA) ambiguously determined |
|
|
378 | 388 | 3: AOA estimate not feasible |
|
|
379 | 389 | 4: Large difference in AOAs obtained from different antenna baselines |
|
|
380 | 390 | 5: echo at start or end of time series |
|
|
381 | 391 | 6: echo less than 5 examples long; too short for analysis |
|
|
382 | 392 | 7: echo rise exceeds 0.3s |
|
|
383 | 393 | 8: echo decay time less than twice rise time |
|
|
384 | 394 | 9: large power level before echo |
|
|
385 | 395 | 10: large power level after echo |
|
|
386 | 396 | 11: poor fit to amplitude for estimation of decay time |
|
|
387 | 397 | 12: poor fit to CCF phase variation for estimation of radial drift velocity |
|
|
388 | 398 | 13: height unresolvable echo: not valid height within 70 to 110 km |
|
|
389 | 399 | 14: height ambiguous echo: more then one possible height within 70 to 110 km |
|
|
390 | 400 | 15: radial drift velocity or projected horizontal velocity exceeds 200 m/s |
|
|
391 | 401 | 16: oscilatory echo, indicating event most likely not an underdense echo |
|
|
392 | 402 | |
|
|
393 | 403 | 17: phase difference in meteor Reestimation |
|
|
394 | 404 | |
|
|
395 | 405 | Data Storage: |
|
|
396 | 406 | Meteors for Wind Estimation (8): |
|
|
397 | 407 | Day Hour | Range Height |
|
|
398 | 408 | Azimuth Zenith errorCosDir |
|
|
399 | 409 | VelRad errorVelRad |
|
|
400 | 410 | TypeError |
|
|
401 | 411 | |
|
|
402 | 412 | ''' |
|
|
403 | 413 | #Get Beacon signal |
|
|
404 | 414 | newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex]) |
|
|
405 | 415 | |
|
|
406 | 416 | if hei_ref != None: |
|
|
407 | 417 | newheis = numpy.where(self.dataOut.heightList>hei_ref) |
|
|
408 | 418 | |
|
|
409 | 419 | heiRang = self.dataOut.getHeiRange() |
|
|
410 | 420 | #Pairs List |
|
|
411 | 421 | pairslist = [] |
|
|
412 | 422 | nChannel = self.dataOut.nChannels |
|
|
413 | 423 | for i in range(nChannel): |
|
|
414 | 424 | if i != centerReceiverIndex: |
|
|
415 | 425 | pairslist.append((centerReceiverIndex,i)) |
|
|
416 | 426 | |
|
|
417 | 427 | #****************REMOVING HARDWARE PHASE DIFFERENCES*************** |
|
|
418 | 428 | # see if the user put in pre defined phase shifts |
|
|
419 | 429 | voltsPShift = self.dataOut.data_pre.copy() |
|
|
420 | 430 | |
|
|
421 | 431 | if predefinedPhaseShifts != None: |
|
|
422 | 432 | hardwarePhaseShifts = numpy.array(predefinedPhaseShifts)*numpy.pi/180 |
|
|
423 | 433 | else: |
|
|
424 | 434 | #get hardware phase shifts using beacon signal |
|
|
425 | 435 | hardwarePhaseShifts = self.__getHardwarePhaseDiff(self.dataOut.data_pre, pairslist, newheis, 10) |
|
|
426 | 436 | hardwarePhaseShifts = numpy.insert(hardwarePhaseShifts,centerReceiverIndex,0) |
|
|
427 | 437 | |
|
|
428 | 438 | voltsPShift = numpy.zeros((self.dataOut.data_pre.shape[0],self.dataOut.data_pre.shape[1],self.dataOut.data_pre.shape[2]), dtype = 'complex') |
|
|
429 | 439 | for i in range(self.dataOut.data_pre.shape[0]): |
|
|
430 | 440 | voltsPShift[i,:,:] = self.__shiftPhase(self.dataOut.data_pre[i,:,:], hardwarePhaseShifts[i]) |
|
|
431 | 441 | #******************END OF REMOVING HARDWARE PHASE DIFFERENCES********* |
|
|
432 | 442 | |
|
|
433 | 443 | #Remove DC |
|
|
434 | 444 | voltsDC = numpy.mean(voltsPShift,1) |
|
|
435 | 445 | voltsDC = numpy.mean(voltsDC,1) |
|
|
436 | 446 | for i in range(voltsDC.shape[0]): |
|
|
437 | 447 | voltsPShift[i] = voltsPShift[i] - voltsDC[i] |
|
|
438 | 448 | |
|
|
439 | 449 | #Don't considerate last heights, theyre used to calculate Hardware Phase Shift |
|
|
440 | 450 | voltsPShift = voltsPShift[:,:,:newheis[0][0]] |
|
|
441 | 451 | |
|
|
442 | 452 | #************ FIND POWER OF DATA W/COH OR NON COH DETECTION (3.4) ********** |
|
|
443 | 453 | #Coherent Detection |
|
|
444 | 454 | if cohDetection: |
|
|
445 | 455 | #use coherent detection to get the net power |
|
|
446 | 456 | cohDet_thresh = cohDet_thresh*numpy.pi/180 |
|
|
447 | 457 | voltsPShift = self.__coherentDetection(voltsPShift, cohDet_timeStep, self.dataOut.timeInterval, pairslist, cohDet_thresh) |
|
|
448 | 458 | |
|
|
449 | 459 | #Non-coherent detection! |
|
|
450 | 460 | powerNet = numpy.nansum(numpy.abs(voltsPShift[:,:,:])**2,0) |
|
|
451 | 461 | #********** END OF COH/NON-COH POWER CALCULATION********************** |
|
|
452 | 462 | |
|
|
453 | 463 | #********** FIND THE NOISE LEVEL AND POSSIBLE METEORS **************** |
|
|
454 | 464 | #Get noise |
|
|
455 | 465 | noise, noise1 = self.__getNoise(powerNet, noise_timeStep, self.dataOut.timeInterval) |
|
|
456 | 466 | # noise = self.getNoise1(powerNet, noise_timeStep, self.dataOut.timeInterval) |
|
|
457 | 467 | #Get signal threshold |
|
|
458 | 468 | signalThresh = noise_multiple*noise |
|
|
459 | 469 | #Meteor echoes detection |
|
|
460 | 470 | listMeteors = self.__findMeteors(powerNet, signalThresh) |
|
|
461 | 471 | #******* END OF NOISE LEVEL AND POSSIBLE METEORS CACULATION ********** |
|
|
462 | 472 | |
|
|
463 | 473 | #************** REMOVE MULTIPLE DETECTIONS (3.5) *************************** |
|
|
464 | 474 | #Parameters |
|
|
465 | 475 | heiRange = self.dataOut.getHeiRange() |
|
|
466 | 476 | rangeInterval = heiRange[1] - heiRange[0] |
|
|
467 | 477 | rangeLimit = multDet_rangeLimit/rangeInterval |
|
|
468 | 478 | timeLimit = multDet_timeLimit/self.dataOut.timeInterval |
|
|
469 | 479 | #Multiple detection removals |
|
|
470 | 480 | listMeteors1 = self.__removeMultipleDetections(listMeteors, rangeLimit, timeLimit) |
|
|
471 | 481 | #************ END OF REMOVE MULTIPLE DETECTIONS ********************** |
|
|
472 | 482 | |
|
|
473 | 483 | #********************* METEOR REESTIMATION (3.7, 3.8, 3.9, 3.10) ******************** |
|
|
474 | 484 | #Parameters |
|
|
475 | 485 | phaseThresh = phaseThresh*numpy.pi/180 |
|
|
476 | 486 | thresh = [phaseThresh, noise_multiple, SNRThresh] |
|
|
477 | 487 | #Meteor reestimation (Errors N 1, 6, 12, 17) |
|
|
478 | 488 | listMeteors2, listMeteorsPower, listMeteorsVolts = self.__meteorReestimation(listMeteors1, voltsPShift, pairslist, thresh, noise, self.dataOut.timeInterval, self.dataOut.frequency) |
|
|
479 | 489 | # listMeteors2, listMeteorsPower, listMeteorsVolts = self.meteorReestimation3(listMeteors2, listMeteorsPower, listMeteorsVolts, voltsPShift, pairslist, thresh, noise) |
|
|
480 | 490 | #Estimation of decay times (Errors N 7, 8, 11) |
|
|
481 | 491 | listMeteors3 = self.__estimateDecayTime(listMeteors2, listMeteorsPower, self.dataOut.timeInterval, self.dataOut.frequency) |
|
|
482 | 492 | #******************* END OF METEOR REESTIMATION ******************* |
|
|
483 | 493 | |
|
|
484 | 494 | #********************* METEOR PARAMETERS CALCULATION (3.11, 3.12, 3.13) ************************** |
|
|
485 | 495 | #Calculating Radial Velocity (Error N 15) |
|
|
486 | 496 | radialStdThresh = 10 |
|
|
487 | 497 | listMeteors4 = self.__getRadialVelocity(listMeteors3, listMeteorsVolts, radialStdThresh, pairslist, self.dataOut.timeInterval) |
|
|
488 | 498 | |
|
|
489 | 499 | if len(listMeteors4) > 0: |
|
|
490 | 500 | #Setting New Array |
|
|
491 | 501 | date = repr(self.dataOut.datatime) |
|
|
492 | 502 | arrayMeteors4, arrayParameters = self.__setNewArrays(listMeteors4, date, heiRang) |
|
|
493 | 503 | |
|
|
494 | 504 | #Calculate AOA (Error N 3, 4) |
|
|
495 | 505 | #JONES ET AL. 1998 |
|
|
496 | 506 | AOAthresh = numpy.pi/8 |
|
|
497 | 507 | error = arrayParameters[:,-1] |
|
|
498 | 508 | phases = -arrayMeteors4[:,9:13] |
|
|
499 | 509 | pairsList = [] |
|
|
500 | 510 | pairsList.append((0,3)) |
|
|
501 | 511 | pairsList.append((1,2)) |
|
|
502 | 512 | arrayParameters[:,4:7], arrayParameters[:,-1] = self.__getAOA(phases, pairsList, error, AOAthresh, azimuth) |
|
|
503 | 513 | |
|
|
504 | 514 | #Calculate Heights (Error N 13 and 14) |
|
|
505 | 515 | error = arrayParameters[:,-1] |
|
|
506 | 516 | Ranges = arrayParameters[:,2] |
|
|
507 | 517 | zenith = arrayParameters[:,5] |
|
|
508 | 518 | arrayParameters[:,3], arrayParameters[:,-1] = self.__getHeights(Ranges, zenith, error, hmin, hmax) |
|
|
509 | 519 | #********************* END OF PARAMETERS CALCULATION ************************** |
|
|
510 | 520 | |
|
|
511 | 521 | #***************************+ SAVE DATA IN HDF5 FORMAT ********************** |
|
|
512 | 522 | self.dataOut.data_param = arrayParameters |
|
|
513 | 523 | |
|
|
514 | 524 | return |
|
|
515 | 525 | |
|
|
516 | 526 | def __getHardwarePhaseDiff(self, voltage0, pairslist, newheis, n): |
|
|
517 | 527 | |
|
|
518 | 528 | minIndex = min(newheis[0]) |
|
|
519 | 529 | maxIndex = max(newheis[0]) |
|
|
520 | 530 | |
|
|
521 | 531 | voltage = voltage0[:,:,minIndex:maxIndex+1] |
|
|
522 | 532 | nLength = voltage.shape[1]/n |
|
|
523 | 533 | nMin = 0 |
|
|
524 | 534 | nMax = 0 |
|
|
525 | 535 | phaseOffset = numpy.zeros((len(pairslist),n)) |
|
|
526 | 536 | |
|
|
527 | 537 | for i in range(n): |
|
|
528 | 538 | nMax += nLength |
|
|
529 | 539 | phaseCCF = -numpy.angle(self.__calculateCCF(voltage[:,nMin:nMax,:], pairslist, [0])) |
|
|
530 | 540 | phaseCCF = numpy.mean(phaseCCF, axis = 2) |
|
|
531 | 541 | phaseOffset[:,i] = phaseCCF.transpose() |
|
|
532 | 542 | nMin = nMax |
|
|
533 | 543 | # phaseDiff, phaseArrival = self.estimatePhaseDifference(voltage, pairslist) |
|
|
534 | 544 | |
|
|
535 | 545 | #Remove Outliers |
|
|
536 | 546 | factor = 2 |
|
|
537 | 547 | wt = phaseOffset - signal.medfilt(phaseOffset,(1,5)) |
|
|
538 | 548 | dw = numpy.std(wt,axis = 1) |
|
|
539 | 549 | dw = dw.reshape((dw.size,1)) |
|
|
540 | 550 | ind = numpy.where(numpy.logical_or(wt>dw*factor,wt<-dw*factor)) |
|
|
541 | 551 | phaseOffset[ind] = numpy.nan |
|
|
542 | 552 | phaseOffset = stats.nanmean(phaseOffset, axis=1) |
|
|
543 | 553 | |
|
|
544 | 554 | return phaseOffset |
|
|
545 | 555 | |
|
|
546 | 556 | def __shiftPhase(self, data, phaseShift): |
|
|
547 | 557 | #this will shift the phase of a complex number |
|
|
548 | 558 | dataShifted = numpy.abs(data) * numpy.exp((numpy.angle(data)+phaseShift)*1j) |
|
|
549 | 559 | return dataShifted |
|
|
550 | 560 | |
|
|
551 | 561 | def __estimatePhaseDifference(self, array, pairslist): |
|
|
552 | 562 | nChannel = array.shape[0] |
|
|
553 | 563 | nHeights = array.shape[2] |
|
|
554 | 564 | numPairs = len(pairslist) |
|
|
555 | 565 | # phaseCCF = numpy.zeros((nChannel, 5, nHeights)) |
|
|
556 | 566 | phaseCCF = numpy.angle(self.__calculateCCF(array, pairslist, [-2,-1,0,1,2])) |
|
|
557 | 567 | |
|
|
558 | 568 | #Correct phases |
|
|
559 | 569 | derPhaseCCF = phaseCCF[:,1:,:] - phaseCCF[:,0:-1,:] |
|
|
560 | 570 | indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) |
|
|
561 | 571 | |
|
|
562 | 572 | if indDer[0].shape[0] > 0: |
|
|
563 | 573 | for i in range(indDer[0].shape[0]): |
|
|
564 | 574 | signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i],indDer[2][i]]) |
|
|
565 | 575 | phaseCCF[indDer[0][i],indDer[1][i]+1:,:] += signo*2*numpy.pi |
|
|
566 | 576 | |
|
|
567 | 577 | # for j in range(numSides): |
|
|
568 | 578 | # phaseCCFAux = self.calculateCCF(arrayCenter, arraySides[j,:,:], [-2,1,0,1,2]) |
|
|
569 | 579 | # phaseCCF[j,:,:] = numpy.angle(phaseCCFAux) |
|
|
570 | 580 | # |
|
|
571 | 581 | #Linear |
|
|
572 | 582 | phaseInt = numpy.zeros((numPairs,1)) |
|
|
573 | 583 | angAllCCF = phaseCCF[:,[0,1,3,4],0] |
|
|
574 | 584 | for j in range(numPairs): |
|
|
575 | 585 | fit = stats.linregress([-2,-1,1,2],angAllCCF[j,:]) |
|
|
576 | 586 | phaseInt[j] = fit[1] |
|
|
577 | 587 | #Phase Differences |
|
|
578 | 588 | phaseDiff = phaseInt - phaseCCF[:,2,:] |
|
|
579 | 589 | phaseArrival = phaseInt.reshape(phaseInt.size) |
|
|
580 | 590 | |
|
|
581 | 591 | #Dealias |
|
|
582 | 592 | indAlias = numpy.where(phaseArrival > numpy.pi) |
|
|
583 | 593 | phaseArrival[indAlias] -= 2*numpy.pi |
|
|
584 | 594 | indAlias = numpy.where(phaseArrival < -numpy.pi) |
|
|
585 | 595 | phaseArrival[indAlias] += 2*numpy.pi |
|
|
586 | 596 | |
|
|
587 | 597 | return phaseDiff, phaseArrival |
|
|
588 | 598 | |
|
|
589 | 599 | def __coherentDetection(self, volts, timeSegment, timeInterval, pairslist, thresh): |
|
|
590 | 600 | #this function will run the coherent detection used in Holdworth et al. 2004 and return the net power |
|
|
591 | 601 | #find the phase shifts of each channel over 1 second intervals |
|
|
592 | 602 | #only look at ranges below the beacon signal |
|
|
593 | 603 | numProfPerBlock = numpy.ceil(timeSegment/timeInterval) |
|
|
594 | 604 | numBlocks = int(volts.shape[1]/numProfPerBlock) |
|
|
595 | 605 | numHeights = volts.shape[2] |
|
|
596 | 606 | nChannel = volts.shape[0] |
|
|
597 | 607 | voltsCohDet = volts.copy() |
|
|
598 | 608 | |
|
|
599 | 609 | pairsarray = numpy.array(pairslist) |
|
|
600 | 610 | indSides = pairsarray[:,1] |
|
|
601 | 611 | # indSides = numpy.array(range(nChannel)) |
|
|
602 | 612 | # indSides = numpy.delete(indSides, indCenter) |
|
|
603 | 613 | # |
|
|
604 | 614 | # listCenter = numpy.array_split(volts[indCenter,:,:], numBlocks, 0) |
|
|
605 | 615 | listBlocks = numpy.array_split(volts, numBlocks, 1) |
|
|
606 | 616 | |
|
|
607 | 617 | startInd = 0 |
|
|
608 | 618 | endInd = 0 |
|
|
609 | 619 | |
|
|
610 | 620 | for i in range(numBlocks): |
|
|
611 | 621 | startInd = endInd |
|
|
612 | 622 | endInd = endInd + listBlocks[i].shape[1] |
|
|
613 | 623 | |
|
|
614 | 624 | arrayBlock = listBlocks[i] |
|
|
615 | 625 | # arrayBlockCenter = listCenter[i] |
|
|
616 | 626 | |
|
|
617 | 627 | #Estimate the Phase Difference |
|
|
618 | 628 | phaseDiff, aux = self.__estimatePhaseDifference(arrayBlock, pairslist) |
|
|
619 | 629 | #Phase Difference RMS |
|
|
620 | 630 | arrayPhaseRMS = numpy.abs(phaseDiff) |
|
|
621 | 631 | phaseRMSaux = numpy.sum(arrayPhaseRMS < thresh,0) |
|
|
622 | 632 | indPhase = numpy.where(phaseRMSaux==4) |
|
|
623 | 633 | #Shifting |
|
|
624 | 634 | if indPhase[0].shape[0] > 0: |
|
|
625 | 635 | for j in range(indSides.size): |
|
|
626 | 636 | arrayBlock[indSides[j],:,indPhase] = self.__shiftPhase(arrayBlock[indSides[j],:,indPhase], phaseDiff[j,indPhase].transpose()) |
|
|
627 | 637 | voltsCohDet[:,startInd:endInd,:] = arrayBlock |
|
|
628 | 638 | |
|
|
629 | 639 | return voltsCohDet |
|
|
630 | 640 | |
|
|
631 | 641 | def __calculateCCF(self, volts, pairslist ,laglist): |
|
|
632 | 642 | |
|
|
633 | 643 | nHeights = volts.shape[2] |
|
|
634 | 644 | nPoints = volts.shape[1] |
|
|
635 | 645 | voltsCCF = numpy.zeros((len(pairslist), len(laglist), nHeights),dtype = 'complex') |
|
|
636 | 646 | |
|
|
637 | 647 | for i in range(len(pairslist)): |
|
|
638 | 648 | volts1 = volts[pairslist[i][0]] |
|
|
639 | 649 | volts2 = volts[pairslist[i][1]] |
|
|
640 | 650 | |
|
|
641 | 651 | for t in range(len(laglist)): |
|
|
642 | 652 | idxT = laglist[t] |
|
|
643 | 653 | if idxT >= 0: |
|
|
644 | 654 | vStacked = numpy.vstack((volts2[idxT:,:], |
|
|
645 | 655 | numpy.zeros((idxT, nHeights),dtype='complex'))) |
|
|
646 | 656 | else: |
|
|
647 | 657 | vStacked = numpy.vstack((numpy.zeros((-idxT, nHeights),dtype='complex'), |
|
|
648 | 658 | volts2[:(nPoints + idxT),:])) |
|
|
649 | 659 | voltsCCF[i,t,:] = numpy.sum((numpy.conjugate(volts1)*vStacked),axis=0) |
|
|
650 | 660 | |
|
|
651 | 661 | vStacked = None |
|
|
652 | 662 | return voltsCCF |
|
|
653 | 663 | |
|
|
654 | 664 | def __getNoise(self, power, timeSegment, timeInterval): |
|
|
655 | 665 | numProfPerBlock = numpy.ceil(timeSegment/timeInterval) |
|
|
656 | 666 | numBlocks = int(power.shape[0]/numProfPerBlock) |
|
|
657 | 667 | numHeights = power.shape[1] |
|
|
658 | 668 | |
|
|
659 | 669 | listPower = numpy.array_split(power, numBlocks, 0) |
|
|
660 | 670 | noise = numpy.zeros((power.shape[0], power.shape[1])) |
|
|
661 | 671 | noise1 = numpy.zeros((power.shape[0], power.shape[1])) |
|
|
662 | 672 | |
|
|
663 | 673 | startInd = 0 |
|
|
664 | 674 | endInd = 0 |
|
|
665 | 675 | |
|
|
666 | 676 | for i in range(numBlocks): #split por canal |
|
|
667 | 677 | startInd = endInd |
|
|
668 | 678 | endInd = endInd + listPower[i].shape[0] |
|
|
669 | 679 | |
|
|
670 | 680 | arrayBlock = listPower[i] |
|
|
671 | 681 | noiseAux = numpy.mean(arrayBlock, 0) |
|
|
672 | 682 | # noiseAux = numpy.median(noiseAux) |
|
|
673 | 683 | # noiseAux = numpy.mean(arrayBlock) |
|
|
674 | 684 | noise[startInd:endInd,:] = noise[startInd:endInd,:] + noiseAux |
|
|
675 | 685 | |
|
|
676 | 686 | noiseAux1 = numpy.mean(arrayBlock) |
|
|
677 | 687 | noise1[startInd:endInd,:] = noise1[startInd:endInd,:] + noiseAux1 |
|
|
678 | 688 | |
|
|
679 | 689 | return noise, noise1 |
|
|
680 | 690 | |
|
|
681 | 691 | def __findMeteors(self, power, thresh): |
|
|
682 | 692 | nProf = power.shape[0] |
|
|
683 | 693 | nHeights = power.shape[1] |
|
|
684 | 694 | listMeteors = [] |
|
|
685 | 695 | |
|
|
686 | 696 | for i in range(nHeights): |
|
|
687 | 697 | powerAux = power[:,i] |
|
|
688 | 698 | threshAux = thresh[:,i] |
|
|
689 | 699 | |
|
|
690 | 700 | indUPthresh = numpy.where(powerAux > threshAux)[0] |
|
|
691 | 701 | indDNthresh = numpy.where(powerAux <= threshAux)[0] |
|
|
692 | 702 | |
|
|
693 | 703 | j = 0 |
|
|
694 | 704 | |
|
|
695 | 705 | while (j < indUPthresh.size - 2): |
|
|
696 | 706 | if (indUPthresh[j + 2] == indUPthresh[j] + 2): |
|
|
697 | 707 | indDNAux = numpy.where(indDNthresh > indUPthresh[j]) |
|
|
698 | 708 | indDNthresh = indDNthresh[indDNAux] |
|
|
699 | 709 | |
|
|
700 | 710 | if (indDNthresh.size > 0): |
|
|
701 | 711 | indEnd = indDNthresh[0] - 1 |
|
|
702 | 712 | indInit = indUPthresh[j] |
|
|
703 | 713 | |
|
|
704 | 714 | meteor = powerAux[indInit:indEnd + 1] |
|
|
705 | 715 | indPeak = meteor.argmax() + indInit |
|
|
706 | 716 | FLA = sum(numpy.conj(meteor)*numpy.hstack((meteor[1:],0))) |
|
|
707 | 717 | |
|
|
708 | 718 | listMeteors.append(numpy.array([i,indInit,indPeak,indEnd,FLA])) #CHEQUEAR!!!!! |
|
|
709 | 719 | j = numpy.where(indUPthresh == indEnd)[0] + 1 |
|
|
710 | 720 | else: j+=1 |
|
|
711 | 721 | else: j+=1 |
|
|
712 | 722 | |
|
|
713 | 723 | return listMeteors |
|
|
714 | 724 | |
|
|
715 | 725 | def __removeMultipleDetections(self,listMeteors, rangeLimit, timeLimit): |
|
|
716 | 726 | |
|
|
717 | 727 | arrayMeteors = numpy.asarray(listMeteors) |
|
|
718 | 728 | listMeteors1 = [] |
|
|
719 | 729 | |
|
|
720 | 730 | while arrayMeteors.shape[0] > 0: |
|
|
721 | 731 | FLAs = arrayMeteors[:,4] |
|
|
722 | 732 | maxFLA = FLAs.argmax() |
|
|
723 | 733 | listMeteors1.append(arrayMeteors[maxFLA,:]) |
|
|
724 | 734 | |
|
|
725 | 735 | MeteorInitTime = arrayMeteors[maxFLA,1] |
|
|
726 | 736 | MeteorEndTime = arrayMeteors[maxFLA,3] |
|
|
727 | 737 | MeteorHeight = arrayMeteors[maxFLA,0] |
|
|
728 | 738 | |
|
|
729 | 739 | #Check neighborhood |
|
|
730 | 740 | maxHeightIndex = MeteorHeight + rangeLimit |
|
|
731 | 741 | minHeightIndex = MeteorHeight - rangeLimit |
|
|
732 | 742 | minTimeIndex = MeteorInitTime - timeLimit |
|
|
733 | 743 | maxTimeIndex = MeteorEndTime + timeLimit |
|
|
734 | 744 | |
|
|
735 | 745 | #Check Heights |
|
|
736 | 746 | indHeight = numpy.logical_and(arrayMeteors[:,0] >= minHeightIndex, arrayMeteors[:,0] <= maxHeightIndex) |
|
|
737 | 747 | indTime = numpy.logical_and(arrayMeteors[:,3] >= minTimeIndex, arrayMeteors[:,1] <= maxTimeIndex) |
|
|
738 | 748 | indBoth = numpy.where(numpy.logical_and(indTime,indHeight)) |
|
|
739 | 749 | |
|
|
740 | 750 | arrayMeteors = numpy.delete(arrayMeteors, indBoth, axis = 0) |
|
|
741 | 751 | |
|
|
742 | 752 | return listMeteors1 |
|
|
743 | 753 | |
|
|
744 | 754 | def __meteorReestimation(self, listMeteors, volts, pairslist, thresh, noise, timeInterval,frequency): |
|
|
745 | 755 | numHeights = volts.shape[2] |
|
|
746 | 756 | nChannel = volts.shape[0] |
|
|
747 | 757 | |
|
|
748 | 758 | thresholdPhase = thresh[0] |
|
|
749 | 759 | thresholdNoise = thresh[1] |
|
|
750 | 760 | thresholdDB = float(thresh[2]) |
|
|
751 | 761 | |
|
|
752 | 762 | thresholdDB1 = 10**(thresholdDB/10) |
|
|
753 | 763 | pairsarray = numpy.array(pairslist) |
|
|
754 | 764 | indSides = pairsarray[:,1] |
|
|
755 | 765 | |
|
|
756 | 766 | pairslist1 = list(pairslist) |
|
|
757 | 767 | pairslist1.append((0,1)) |
|
|
758 | 768 | pairslist1.append((3,4)) |
|
|
759 | 769 | |
|
|
760 | 770 | listMeteors1 = [] |
|
|
761 | 771 | listPowerSeries = [] |
|
|
762 | 772 | listVoltageSeries = [] |
|
|
763 | 773 | #volts has the war data |
|
|
764 | 774 | |
|
|
765 | 775 | if frequency == 30e6: |
|
|
766 | 776 | timeLag = 45*10**-3 |
|
|
767 | 777 | else: |
|
|
768 | 778 | timeLag = 15*10**-3 |
|
|
769 | 779 | lag = numpy.ceil(timeLag/timeInterval) |
|
|
770 | 780 | |
|
|
771 | 781 | for i in range(len(listMeteors)): |
|
|
772 | 782 | |
|
|
773 | 783 | ###################### 3.6 - 3.7 PARAMETERS REESTIMATION ######################### |
|
|
774 | 784 | meteorAux = numpy.zeros(16) |
|
|
775 | 785 | |
|
|
776 | 786 | #Loading meteor Data (mHeight, mStart, mPeak, mEnd) |
|
|
777 | 787 | mHeight = listMeteors[i][0] |
|
|
778 | 788 | mStart = listMeteors[i][1] |
|
|
779 | 789 | mPeak = listMeteors[i][2] |
|
|
780 | 790 | mEnd = listMeteors[i][3] |
|
|
781 | 791 | |
|
|
782 | 792 | #get the volt data between the start and end times of the meteor |
|
|
783 | 793 | meteorVolts = volts[:,mStart:mEnd+1,mHeight] |
|
|
784 | 794 | meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) |
|
|
785 | 795 | |
|
|
786 | 796 | #3.6. Phase Difference estimation |
|
|
787 | 797 | phaseDiff, aux = self.__estimatePhaseDifference(meteorVolts, pairslist) |
|
|
788 | 798 | |
|
|
789 | 799 | #3.7. Phase difference removal & meteor start, peak and end times reestimated |
|
|
790 | 800 | #meteorVolts0.- all Channels, all Profiles |
|
|
791 | 801 | meteorVolts0 = volts[:,:,mHeight] |
|
|
792 | 802 | meteorThresh = noise[:,mHeight]*thresholdNoise |
|
|
793 | 803 | meteorNoise = noise[:,mHeight] |
|
|
794 | 804 | meteorVolts0[indSides,:] = self.__shiftPhase(meteorVolts0[indSides,:], phaseDiff) #Phase Shifting |
|
|
795 | 805 | powerNet0 = numpy.nansum(numpy.abs(meteorVolts0)**2, axis = 0) #Power |
|
|
796 | 806 | |
|
|
797 | 807 | #Times reestimation |
|
|
798 | 808 | mStart1 = numpy.where(powerNet0[:mPeak] < meteorThresh[:mPeak])[0] |
|
|
799 | 809 | if mStart1.size > 0: |
|
|
800 | 810 | mStart1 = mStart1[-1] + 1 |
|
|
801 | 811 | |
|
|
802 | 812 | else: |
|
|
803 | 813 | mStart1 = mPeak |
|
|
804 | 814 | |
|
|
805 | 815 | mEnd1 = numpy.where(powerNet0[mPeak:] < meteorThresh[mPeak:])[0][0] + mPeak - 1 |
|
|
806 | 816 | mEndDecayTime1 = numpy.where(powerNet0[mPeak:] < meteorNoise[mPeak:])[0] |
|
|
807 | 817 | if mEndDecayTime1.size == 0: |
|
|
808 | 818 | mEndDecayTime1 = powerNet0.size |
|
|
809 | 819 | else: |
|
|
810 | 820 | mEndDecayTime1 = mEndDecayTime1[0] + mPeak - 1 |
|
|
811 | 821 | # mPeak1 = meteorVolts0[mStart1:mEnd1 + 1].argmax() |
|
|
812 | 822 | |
|
|
813 | 823 | #meteorVolts1.- all Channels, from start to end |
|
|
814 | 824 | meteorVolts1 = meteorVolts0[:,mStart1:mEnd1 + 1] |
|
|
815 | 825 | meteorVolts2 = meteorVolts0[:,mPeak + lag:mEnd1 + 1] |
|
|
816 | 826 | if meteorVolts2.shape[1] == 0: |
|
|
817 | 827 | meteorVolts2 = meteorVolts0[:,mPeak:mEnd1 + 1] |
|
|
818 | 828 | meteorVolts1 = meteorVolts1.reshape(meteorVolts1.shape[0], meteorVolts1.shape[1], 1) |
|
|
819 | 829 | meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1], 1) |
|
|
820 | 830 | ##################### END PARAMETERS REESTIMATION ######################### |
|
|
821 | 831 | |
|
|
822 | 832 | ##################### 3.8 PHASE DIFFERENCE REESTIMATION ######################## |
|
|
823 | 833 | # if mEnd1 - mStart1 > 4: #Error Number 6: echo less than 5 samples long; too short for analysis |
|
|
824 | 834 | if meteorVolts2.shape[1] > 0: |
|
|
825 | 835 | #Phase Difference re-estimation |
|
|
826 | 836 | phaseDiff1, phaseDiffint = self.__estimatePhaseDifference(meteorVolts2, pairslist1) #Phase Difference Estimation |
|
|
827 | 837 | # phaseDiff1, phaseDiffint = self.estimatePhaseDifference(meteorVolts2, pairslist) |
|
|
828 | 838 | meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1]) |
|
|
829 | 839 | phaseDiff11 = numpy.reshape(phaseDiff1, (phaseDiff1.shape[0],1)) |
|
|
830 | 840 | meteorVolts2[indSides,:] = self.__shiftPhase(meteorVolts2[indSides,:], phaseDiff11[0:4]) #Phase Shifting |
|
|
831 | 841 | |
|
|
832 | 842 | #Phase Difference RMS |
|
|
833 | 843 | phaseRMS1 = numpy.sqrt(numpy.mean(numpy.square(phaseDiff1))) |
|
|
834 | 844 | powerNet1 = numpy.nansum(numpy.abs(meteorVolts1[:,:])**2,0) |
|
|
835 | 845 | #Data from Meteor |
|
|
836 | 846 | mPeak1 = powerNet1.argmax() + mStart1 |
|
|
837 | 847 | mPeakPower1 = powerNet1.max() |
|
|
838 | 848 | noiseAux = sum(noise[mStart1:mEnd1 + 1,mHeight]) |
|
|
839 | 849 | mSNR1 = (sum(powerNet1)-noiseAux)/noiseAux |
|
|
840 | 850 | Meteor1 = numpy.array([mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1]) |
|
|
841 | 851 | Meteor1 = numpy.hstack((Meteor1,phaseDiffint)) |
|
|
842 | 852 | PowerSeries = powerNet0[mStart1:mEndDecayTime1 + 1] |
|
|
843 | 853 | #Vectorize |
|
|
844 | 854 | meteorAux[0:7] = [mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1] |
|
|
845 | 855 | meteorAux[7:11] = phaseDiffint[0:4] |
|
|
846 | 856 | |
|
|
847 | 857 | #Rejection Criterions |
|
|
848 | 858 | if phaseRMS1 > thresholdPhase: #Error Number 17: Phase variation |
|
|
849 | 859 | meteorAux[-1] = 17 |
|
|
850 | 860 | elif mSNR1 < thresholdDB1: #Error Number 1: SNR < threshold dB |
|
|
851 | 861 | meteorAux[-1] = 1 |
|
|
852 | 862 | |
|
|
853 | 863 | |
|
|
854 | 864 | else: |
|
|
855 | 865 | meteorAux[0:4] = [mHeight, mStart, mPeak, mEnd] |
|
|
856 | 866 | meteorAux[-1] = 6 #Error Number 6: echo less than 5 samples long; too short for analysis |
|
|
857 | 867 | PowerSeries = 0 |
|
|
858 | 868 | |
|
|
859 | 869 | listMeteors1.append(meteorAux) |
|
|
860 | 870 | listPowerSeries.append(PowerSeries) |
|
|
861 | 871 | listVoltageSeries.append(meteorVolts1) |
|
|
862 | 872 | |
|
|
863 | 873 | return listMeteors1, listPowerSeries, listVoltageSeries |
|
|
864 | 874 | |
|
|
865 | 875 | def __estimateDecayTime(self, listMeteors, listPower, timeInterval, frequency): |
|
|
866 | 876 | |
|
|
867 | 877 | threshError = 10 |
|
|
868 | 878 | #Depending if it is 30 or 50 MHz |
|
|
869 | 879 | if frequency == 30e6: |
|
|
870 | 880 | timeLag = 45*10**-3 |
|
|
871 | 881 | else: |
|
|
872 | 882 | timeLag = 15*10**-3 |
|
|
873 | 883 | lag = numpy.ceil(timeLag/timeInterval) |
|
|
874 | 884 | |
|
|
875 | 885 | listMeteors1 = [] |
|
|
876 | 886 | |
|
|
877 | 887 | for i in range(len(listMeteors)): |
|
|
878 | 888 | meteorPower = listPower[i] |
|
|
879 | 889 | meteorAux = listMeteors[i] |
|
|
880 | 890 | |
|
|
881 | 891 | if meteorAux[-1] == 0: |
|
|
882 | 892 | |
|
|
883 | 893 | try: |
|
|
884 | 894 | indmax = meteorPower.argmax() |
|
|
885 | 895 | indlag = indmax + lag |
|
|
886 | 896 | |
|
|
887 | 897 | y = meteorPower[indlag:] |
|
|
888 | 898 | x = numpy.arange(0, y.size)*timeLag |
|
|
889 | 899 | |
|
|
890 | 900 | #first guess |
|
|
891 | 901 | a = y[0] |
|
|
892 | 902 | tau = timeLag |
|
|
893 | 903 | #exponential fit |
|
|
894 | 904 | popt, pcov = optimize.curve_fit(self.__exponential_function, x, y, p0 = [a, tau]) |
|
|
895 | 905 | y1 = self.__exponential_function(x, *popt) |
|
|
896 | 906 | #error estimation |
|
|
897 | 907 | error = sum((y - y1)**2)/(numpy.var(y)*(y.size - popt.size)) |
|
|
898 | 908 | |
|
|
899 | 909 | decayTime = popt[1] |
|
|
900 | 910 | riseTime = indmax*timeInterval |
|
|
901 | 911 | meteorAux[11:13] = [decayTime, error] |
|
|
902 | 912 | |
|
|
903 | 913 | #Table items 7, 8 and 11 |
|
|
904 | 914 | if (riseTime > 0.3): #Number 7: Echo rise exceeds 0.3s |
|
|
905 | 915 | meteorAux[-1] = 7 |
|
|
906 | 916 | elif (decayTime < 2*riseTime) : #Number 8: Echo decay time less than than twice rise time |
|
|
907 | 917 | meteorAux[-1] = 8 |
|
|
908 | 918 | if (error > threshError): #Number 11: Poor fit to amplitude for estimation of decay time |
|
|
909 | 919 | meteorAux[-1] = 11 |
|
|
910 | 920 | |
|
|
911 | 921 | |
|
|
912 | 922 | except: |
|
|
913 | 923 | meteorAux[-1] = 11 |
|
|
914 | 924 | |
|
|
915 | 925 | |
|
|
916 | 926 | listMeteors1.append(meteorAux) |
|
|
917 | 927 | |
|
|
918 | 928 | return listMeteors1 |
|
|
919 | 929 | |
|
|
920 | 930 | #Exponential Function |
|
|
921 | 931 | |
|
|
922 | 932 | def __exponential_function(self, x, a, tau): |
|
|
923 | 933 | y = a*numpy.exp(-x/tau) |
|
|
924 | 934 | return y |
|
|
925 | 935 | |
|
|
926 | 936 | def __getRadialVelocity(self, listMeteors, listVolts, radialStdThresh, pairslist, timeInterval): |
|
|
927 | 937 | |
|
|
928 | 938 | pairslist1 = list(pairslist) |
|
|
929 | 939 | pairslist1.append((0,1)) |
|
|
930 | 940 | pairslist1.append((3,4)) |
|
|
931 | 941 | numPairs = len(pairslist1) |
|
|
932 | 942 | #Time Lag |
|
|
933 | 943 | timeLag = 45*10**-3 |
|
|
934 | 944 | c = 3e8 |
|
|
935 | 945 | lag = numpy.ceil(timeLag/timeInterval) |
|
|
936 | 946 | freq = 30e6 |
|
|
937 | 947 | |
|
|
938 | 948 | listMeteors1 = [] |
|
|
939 | 949 | |
|
|
940 | 950 | for i in range(len(listMeteors)): |
|
|
941 | 951 | meteor = listMeteors[i] |
|
|
942 | 952 | meteorAux = numpy.hstack((meteor[:-1], 0, 0, meteor[-1])) |
|
|
943 | 953 | if meteor[-1] == 0: |
|
|
944 | 954 | mStart = listMeteors[i][1] |
|
|
945 | 955 | mPeak = listMeteors[i][2] |
|
|
946 | 956 | mLag = mPeak - mStart + lag |
|
|
947 | 957 | |
|
|
948 | 958 | #get the volt data between the start and end times of the meteor |
|
|
949 | 959 | meteorVolts = listVolts[i] |
|
|
950 | 960 | meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) |
|
|
951 | 961 | |
|
|
952 | 962 | #Get CCF |
|
|
953 | 963 | allCCFs = self.__calculateCCF(meteorVolts, pairslist1, [-2,-1,0,1,2]) |
|
|
954 | 964 | |
|
|
955 | 965 | #Method 2 |
|
|
956 | 966 | slopes = numpy.zeros(numPairs) |
|
|
957 | 967 | time = numpy.array([-2,-1,1,2])*timeInterval |
|
|
958 | 968 | angAllCCF = numpy.angle(allCCFs[:,[0,1,3,4],0]) |
|
|
959 | 969 | |
|
|
960 | 970 | #Correct phases |
|
|
961 | 971 | derPhaseCCF = angAllCCF[:,1:] - angAllCCF[:,0:-1] |
|
|
962 | 972 | indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) |
|
|
963 | 973 | |
|
|
964 | 974 | if indDer[0].shape[0] > 0: |
|
|
965 | 975 | for i in range(indDer[0].shape[0]): |
|
|
966 | 976 | signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i]]) |
|
|
967 | 977 | angAllCCF[indDer[0][i],indDer[1][i]+1:] += signo*2*numpy.pi |
|
|
968 | 978 | |
|
|
969 | 979 | # fit = scipy.stats.linregress(numpy.array([-2,-1,1,2])*timeInterval, numpy.array([phaseLagN2s[i],phaseLagN1s[i],phaseLag1s[i],phaseLag2s[i]])) |
|
|
970 | 980 | for j in range(numPairs): |
|
|
971 | 981 | fit = stats.linregress(time, angAllCCF[j,:]) |
|
|
972 | 982 | slopes[j] = fit[0] |
|
|
973 | 983 | |
|
|
974 | 984 | #Remove Outlier |
|
|
975 | 985 | # indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes))) |
|
|
976 | 986 | # slopes = numpy.delete(slopes,indOut) |
|
|
977 | 987 | # indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes))) |
|
|
978 | 988 | # slopes = numpy.delete(slopes,indOut) |
|
|
979 | 989 | |
|
|
980 | 990 | radialVelocity = -numpy.mean(slopes)*(0.25/numpy.pi)*(c/freq) |
|
|
981 | 991 | radialError = numpy.std(slopes)*(0.25/numpy.pi)*(c/freq) |
|
|
982 | 992 | meteorAux[-2] = radialError |
|
|
983 | 993 | meteorAux[-3] = radialVelocity |
|
|
984 | 994 | |
|
|
985 | 995 | #Setting Error |
|
|
986 | 996 | #Number 15: Radial Drift velocity or projected horizontal velocity exceeds 200 m/s |
|
|
987 | 997 | if numpy.abs(radialVelocity) > 200: |
|
|
988 | 998 | meteorAux[-1] = 15 |
|
|
989 | 999 | #Number 12: Poor fit to CCF variation for estimation of radial drift velocity |
|
|
990 | 1000 | elif radialError > radialStdThresh: |
|
|
991 | 1001 | meteorAux[-1] = 12 |
|
|
992 | 1002 | |
|
|
993 | 1003 | listMeteors1.append(meteorAux) |
|
|
994 | 1004 | return listMeteors1 |
|
|
995 | 1005 | |
|
|
996 | 1006 | def __setNewArrays(self, listMeteors, date, heiRang): |
|
|
997 | 1007 | |
|
|
998 | 1008 | #New arrays |
|
|
999 | 1009 | arrayMeteors = numpy.array(listMeteors) |
|
|
1000 | 1010 | arrayParameters = numpy.zeros((len(listMeteors),10)) |
|
|
1001 | 1011 | |
|
|
1002 | 1012 | #Date inclusion |
|
|
1003 | 1013 | date = re.findall(r'\((.*?)\)', date) |
|
|
1004 | 1014 | date = date[0].split(',') |
|
|
1005 | 1015 | date = map(int, date) |
|
|
1006 | 1016 | date = [date[0]*10000 + date[1]*100 + date[2], date[3]*10000 + date[4]*100 + date[5]] |
|
|
1007 | 1017 | arrayDate = numpy.tile(date, (len(listMeteors), 1)) |
|
|
1008 | 1018 | |
|
|
1009 | 1019 | #Meteor array |
|
|
1010 | 1020 | arrayMeteors[:,0] = heiRang[arrayMeteors[:,0].astype(int)] |
|
|
1011 | 1021 | arrayMeteors = numpy.hstack((arrayDate, arrayMeteors)) |
|
|
1012 | 1022 | |
|
|
1013 | 1023 | #Parameters Array |
|
|
1014 | 1024 | arrayParameters[:,0:3] = arrayMeteors[:,0:3] |
|
|
1015 | 1025 | arrayParameters[:,-3:] = arrayMeteors[:,-3:] |
|
|
1016 | 1026 | |
|
|
1017 | 1027 | return arrayMeteors, arrayParameters |
|
|
1018 | 1028 | |
|
|
1019 | 1029 | def __getAOA(self, phases, pairsList, error, AOAthresh, azimuth): |
|
|
1020 | 1030 | |
|
|
1021 | 1031 | arrayAOA = numpy.zeros((phases.shape[0],3)) |
|
|
1022 | 1032 | cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList) |
|
|
1023 | 1033 | |
|
|
1024 | 1034 | arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth) |
|
|
1025 | 1035 | cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1) |
|
|
1026 | 1036 | arrayAOA[:,2] = cosDirError |
|
|
1027 | 1037 | |
|
|
1028 | 1038 | azimuthAngle = arrayAOA[:,0] |
|
|
1029 | 1039 | zenithAngle = arrayAOA[:,1] |
|
|
1030 | 1040 | |
|
|
1031 | 1041 | #Setting Error |
|
|
1032 | 1042 | #Number 3: AOA not fesible |
|
|
1033 | 1043 | indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0] |
|
|
1034 | 1044 | error[indInvalid] = 3 |
|
|
1035 | 1045 | #Number 4: Large difference in AOAs obtained from different antenna baselines |
|
|
1036 | 1046 | indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0] |
|
|
1037 | 1047 | error[indInvalid] = 4 |
|
|
1038 | 1048 | return arrayAOA, error |
|
|
1039 | 1049 | |
|
|
1040 | 1050 | def __getDirectionCosines(self, arrayPhase, pairsList): |
|
|
1041 | 1051 | |
|
|
1042 | 1052 | #Initializing some variables |
|
|
1043 | 1053 | ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi |
|
|
1044 | 1054 | ang_aux = ang_aux.reshape(1,ang_aux.size) |
|
|
1045 | 1055 | |
|
|
1046 | 1056 | cosdir = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
1047 | 1057 | cosdir0 = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
1048 | 1058 | |
|
|
1049 | 1059 | |
|
|
1050 | 1060 | for i in range(2): |
|
|
1051 | 1061 | #First Estimation |
|
|
1052 | 1062 | phi0_aux = arrayPhase[:,pairsList[i][0]] + arrayPhase[:,pairsList[i][1]] |
|
|
1053 | 1063 | #Dealias |
|
|
1054 | 1064 | indcsi = numpy.where(phi0_aux > numpy.pi) |
|
|
1055 | 1065 | phi0_aux[indcsi] -= 2*numpy.pi |
|
|
1056 | 1066 | indcsi = numpy.where(phi0_aux < -numpy.pi) |
|
|
1057 | 1067 | phi0_aux[indcsi] += 2*numpy.pi |
|
|
1058 | 1068 | #Direction Cosine 0 |
|
|
1059 | 1069 | cosdir0[:,i] = -(phi0_aux)/(2*numpy.pi*0.5) |
|
|
1060 | 1070 | |
|
|
1061 | 1071 | #Most-Accurate Second Estimation |
|
|
1062 | 1072 | phi1_aux = arrayPhase[:,pairsList[i][0]] - arrayPhase[:,pairsList[i][1]] |
|
|
1063 | 1073 | phi1_aux = phi1_aux.reshape(phi1_aux.size,1) |
|
|
1064 | 1074 | #Direction Cosine 1 |
|
|
1065 | 1075 | cosdir1 = -(phi1_aux + ang_aux)/(2*numpy.pi*4.5) |
|
|
1066 | 1076 | |
|
|
1067 | 1077 | #Searching the correct Direction Cosine |
|
|
1068 | 1078 | cosdir0_aux = cosdir0[:,i] |
|
|
1069 | 1079 | cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1) |
|
|
1070 | 1080 | #Minimum Distance |
|
|
1071 | 1081 | cosDiff = (cosdir1 - cosdir0_aux)**2 |
|
|
1072 | 1082 | indcos = cosDiff.argmin(axis = 1) |
|
|
1073 | 1083 | #Saving Value obtained |
|
|
1074 | 1084 | cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos] |
|
|
1075 | 1085 | |
|
|
1076 | 1086 | return cosdir0, cosdir |
|
|
1077 | 1087 | |
|
|
1078 | 1088 | def __calculateAOA(self, cosdir, azimuth): |
|
|
1079 | 1089 | cosdirX = cosdir[:,0] |
|
|
1080 | 1090 | cosdirY = cosdir[:,1] |
|
|
1081 | 1091 | |
|
|
1082 | 1092 | zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi |
|
|
1083 | 1093 | azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth #0 deg north, 90 deg east |
|
|
1084 | 1094 | angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose() |
|
|
1085 | 1095 | |
|
|
1086 | 1096 | return angles |
|
|
1087 | 1097 | |
|
|
1088 | 1098 | def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight): |
|
|
1089 | 1099 | |
|
|
1090 | 1100 | Ramb = 375 #Ramb = c/(2*PRF) |
|
|
1091 | 1101 | Re = 6371 #Earth Radius |
|
|
1092 | 1102 | heights = numpy.zeros(Ranges.shape) |
|
|
1093 | 1103 | |
|
|
1094 | 1104 | R_aux = numpy.array([0,1,2])*Ramb |
|
|
1095 | 1105 | R_aux = R_aux.reshape(1,R_aux.size) |
|
|
1096 | 1106 | |
|
|
1097 | 1107 | Ranges = Ranges.reshape(Ranges.size,1) |
|
|
1098 | 1108 | |
|
|
1099 | 1109 | Ri = Ranges + R_aux |
|
|
1100 | 1110 | hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re |
|
|
1101 | 1111 | |
|
|
1102 | 1112 | #Check if there is a height between 70 and 110 km |
|
|
1103 | 1113 | h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1) |
|
|
1104 | 1114 | ind_h = numpy.where(h_bool == 1)[0] |
|
|
1105 | 1115 | |
|
|
1106 | 1116 | hCorr = hi[ind_h, :] |
|
|
1107 | 1117 | ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight)) |
|
|
1108 | 1118 | |
|
|
1109 | 1119 | hCorr = hi[ind_hCorr] |
|
|
1110 | 1120 | heights[ind_h] = hCorr |
|
|
1111 | 1121 | |
|
|
1112 | 1122 | #Setting Error |
|
|
1113 | 1123 | #Number 13: Height unresolvable echo: not valid height within 70 to 110 km |
|
|
1114 | 1124 | #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km |
|
|
1115 | 1125 | |
|
|
1116 | 1126 | indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0] |
|
|
1117 | 1127 | error[indInvalid2] = 14 |
|
|
1118 | 1128 | indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0] |
|
|
1119 | 1129 | error[indInvalid1] = 13 |
|
|
1120 | 1130 | |
|
|
1121 | 1131 | return heights, error |
|
|
1122 | 1132 | |
|
|
1123 | 1133 | def SpectralFitting(self, getSNR = True, path=None, file=None, groupList=None): |
|
|
1124 | 1134 | |
|
|
1125 | 1135 | ''' |
|
|
1126 | 1136 | Function GetMoments() |
|
|
1127 | 1137 | |
|
|
1128 | 1138 | Input: |
|
|
1129 | 1139 | Output: |
|
|
1130 | 1140 | Variables modified: |
|
|
1131 | 1141 | ''' |
|
|
1132 | 1142 | if path != None: |
|
|
1133 | 1143 | sys.path.append(path) |
|
|
1134 | 1144 | self.dataOut.library = importlib.import_module(file) |
|
|
1135 | 1145 | |
|
|
1136 | 1146 | #To be inserted as a parameter |
|
|
1137 | 1147 | groupArray = numpy.array(groupList) |
|
|
1138 | 1148 | # groupArray = numpy.array([[0,1],[2,3]]) |
|
|
1139 | 1149 | self.dataOut.groupList = groupArray |
|
|
1140 | 1150 | |
|
|
1141 | 1151 | nGroups = groupArray.shape[0] |
|
|
1142 | 1152 | nChannels = self.dataIn.nChannels |
|
|
1143 | 1153 | nHeights=self.dataIn.heightList.size |
|
|
1144 | 1154 | |
|
|
1145 | 1155 | #Parameters Array |
|
|
1146 | 1156 | self.dataOut.data_param = None |
|
|
1147 | 1157 | |
|
|
1148 | 1158 | #Set constants |
|
|
1149 | 1159 | constants = self.dataOut.library.setConstants(self.dataIn) |
|
|
1150 | 1160 | self.dataOut.constants = constants |
|
|
1151 | 1161 | M = self.dataIn.normFactor |
|
|
1152 | 1162 | N = self.dataIn.nFFTPoints |
|
|
1153 | 1163 | ippSeconds = self.dataIn.ippSeconds |
|
|
1154 | 1164 | K = self.dataIn.nIncohInt |
|
|
1155 | 1165 | pairsArray = numpy.array(self.dataIn.pairsList) |
|
|
1156 | 1166 | |
|
|
1157 | 1167 | #List of possible combinations |
|
|
1158 | 1168 | listComb = itertools.combinations(numpy.arange(groupArray.shape[1]),2) |
|
|
1159 | 1169 | indCross = numpy.zeros(len(list(listComb)), dtype = 'int') |
|
|
1160 | 1170 | |
|
|
1161 | 1171 | if getSNR: |
|
|
1162 | 1172 | listChannels = groupArray.reshape((groupArray.size)) |
|
|
1163 | 1173 | listChannels.sort() |
|
|
1164 | 1174 | noise = self.dataIn.getNoise() |
|
|
1165 | 1175 | self.dataOut.data_SNR = self.__getSNR(self.dataIn.data_spc[listChannels,:,:], noise[listChannels]) |
|
|
1166 | 1176 | |
|
|
1167 | 1177 | for i in range(nGroups): |
|
|
1168 | 1178 | coord = groupArray[i,:] |
|
|
1169 | 1179 | |
|
|
1170 | 1180 | #Input data array |
|
|
1171 | 1181 | data = self.dataIn.data_spc[coord,:,:]/(M*N) |
|
|
1172 | 1182 | data = data.reshape((data.shape[0]*data.shape[1],data.shape[2])) |
|
|
1173 | 1183 | |
|
|
1174 | 1184 | #Cross Spectra data array for Covariance Matrixes |
|
|
1175 | 1185 | ind = 0 |
|
|
1176 | 1186 | for pairs in listComb: |
|
|
1177 | 1187 | pairsSel = numpy.array([coord[x],coord[y]]) |
|
|
1178 | 1188 | indCross[ind] = int(numpy.where(numpy.all(pairsArray == pairsSel, axis = 1))[0][0]) |
|
|
1179 | 1189 | ind += 1 |
|
|
1180 | 1190 | dataCross = self.dataIn.data_cspc[indCross,:,:]/(M*N) |
|
|
1181 | 1191 | dataCross = dataCross**2/K |
|
|
1182 | 1192 | |
|
|
1183 | 1193 | for h in range(nHeights): |
|
|
1184 | 1194 | # print self.dataOut.heightList[h] |
|
|
1185 | 1195 | |
|
|
1186 | 1196 | #Input |
|
|
1187 | 1197 | d = data[:,h] |
|
|
1188 | 1198 | |
|
|
1189 | 1199 | #Covariance Matrix |
|
|
1190 | 1200 | D = numpy.diag(d**2/K) |
|
|
1191 | 1201 | ind = 0 |
|
|
1192 | 1202 | for pairs in listComb: |
|
|
1193 | 1203 | #Coordinates in Covariance Matrix |
|
|
1194 | 1204 | x = pairs[0] |
|
|
1195 | 1205 | y = pairs[1] |
|
|
1196 | 1206 | #Channel Index |
|
|
1197 | 1207 | S12 = dataCross[ind,:,h] |
|
|
1198 | 1208 | D12 = numpy.diag(S12) |
|
|
1199 | 1209 | #Completing Covariance Matrix with Cross Spectras |
|
|
1200 | 1210 | D[x*N:(x+1)*N,y*N:(y+1)*N] = D12 |
|
|
1201 | 1211 | D[y*N:(y+1)*N,x*N:(x+1)*N] = D12 |
|
|
1202 | 1212 | ind += 1 |
|
|
1203 | 1213 | Dinv=numpy.linalg.inv(D) |
|
|
1204 | 1214 | L=numpy.linalg.cholesky(Dinv) |
|
|
1205 | 1215 | LT=L.T |
|
|
1206 | 1216 | |
|
|
1207 | 1217 | dp = numpy.dot(LT,d) |
|
|
1208 | 1218 | |
|
|
1209 | 1219 | #Initial values |
|
|
1210 | 1220 | data_spc = self.dataIn.data_spc[coord,:,h] |
|
|
1211 | 1221 | |
|
|
1212 | 1222 | if (h>0)and(error1[3]<5): |
|
|
1213 | 1223 | p0 = self.dataOut.data_param[i,:,h-1] |
|
|
1214 | 1224 | else: |
|
|
1215 | 1225 | p0 = numpy.array(self.dataOut.library.initialValuesFunction(data_spc, constants, i)) |
|
|
1216 | 1226 | |
|
|
1217 | 1227 | try: |
|
|
1218 | 1228 | #Least Squares |
|
|
1219 | 1229 | minp,covp,infodict,mesg,ier = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants),full_output=True) |
|
|
1220 | 1230 | # minp,covp = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants)) |
|
|
1221 | 1231 | #Chi square error |
|
|
1222 | 1232 | error0 = numpy.sum(infodict['fvec']**2)/(2*N) |
|
|
1223 | 1233 | #Error with Jacobian |
|
|
1224 | 1234 | error1 = self.dataOut.library.errorFunction(minp,constants,LT) |
|
|
1225 | 1235 | except: |
|
|
1226 | 1236 | minp = p0*numpy.nan |
|
|
1227 | 1237 | error0 = numpy.nan |
|
|
1228 | 1238 | error1 = p0*numpy.nan |
|
|
1229 | 1239 | |
|
|
1230 | 1240 | #Save |
|
|
1231 | 1241 | if self.dataOut.data_param == None: |
|
|
1232 | 1242 | self.dataOut.data_param = numpy.zeros((nGroups, p0.size, nHeights))*numpy.nan |
|
|
1233 | 1243 | self.dataOut.data_error = numpy.zeros((nGroups, p0.size + 1, nHeights))*numpy.nan |
|
|
1234 | 1244 | |
|
|
1235 | 1245 | self.dataOut.data_error[i,:,h] = numpy.hstack((error0,error1)) |
|
|
1236 | 1246 | self.dataOut.data_param[i,:,h] = minp |
|
|
1237 | 1247 | return |
|
|
1238 | 1248 | |
|
|
1239 | 1249 | |
|
|
1240 | 1250 | def __residFunction(self, p, dp, LT, constants): |
|
|
1241 | 1251 | |
|
|
1242 | 1252 | fm = self.dataOut.library.modelFunction(p, constants) |
|
|
1243 | 1253 | fmp=numpy.dot(LT,fm) |
|
|
1244 | 1254 | |
|
|
1245 | 1255 | return dp-fmp |
|
|
1246 | 1256 | |
|
|
1247 | 1257 | def __getSNR(self, z, noise): |
|
|
1248 | 1258 | |
|
|
1249 | 1259 | avg = numpy.average(z, axis=1) |
|
|
1250 | 1260 | SNR = (avg.T-noise)/noise |
|
|
1251 | 1261 | SNR = SNR.T |
|
|
1252 | 1262 | return SNR |
|
|
1253 | 1263 | |
|
|
1254 | 1264 | def __chisq(p,chindex,hindex): |
|
|
1255 | 1265 | #similar to Resid but calculates CHI**2 |
|
|
1256 | 1266 | [LT,d,fm]=setupLTdfm(p,chindex,hindex) |
|
|
1257 | 1267 | dp=numpy.dot(LT,d) |
|
|
1258 | 1268 | fmp=numpy.dot(LT,fm) |
|
|
1259 | 1269 | chisq=numpy.dot((dp-fmp).T,(dp-fmp)) |
|
|
1260 | 1270 | return chisq |
|
|
1261 | 1271 | |
|
|
1262 | 1272 | |
|
|
1263 | 1273 | |
|
|
1264 | 1274 | class WindProfiler(Operation): |
|
|
1265 | 1275 | |
|
|
1266 | 1276 | __isConfig = False |
|
|
1267 | 1277 | |
|
|
1268 | 1278 | __initime = None |
|
|
1269 | 1279 | __lastdatatime = None |
|
|
1270 | 1280 | __integrationtime = None |
|
|
1271 | 1281 | |
|
|
1272 | 1282 | __buffer = None |
|
|
1273 | 1283 | |
|
|
1274 | 1284 | __dataReady = False |
|
|
1275 | 1285 | |
|
|
1276 | 1286 | __firstdata = None |
|
|
1277 | 1287 | |
|
|
1278 | 1288 | n = None |
|
|
1279 | 1289 | |
|
|
1280 | 1290 | def __init__(self): |
|
|
1281 | 1291 | Operation.__init__(self) |
|
|
1282 | 1292 | |
|
|
1283 | 1293 | def __calculateCosDir(self, elev, azim): |
|
|
1284 | 1294 | zen = (90 - elev)*numpy.pi/180 |
|
|
1285 | 1295 | azim = azim*numpy.pi/180 |
|
|
1286 | 1296 | cosDirX = numpy.sqrt((1-numpy.cos(zen)**2)/((1+numpy.tan(azim)**2))) |
|
|
1287 | 1297 | cosDirY = numpy.sqrt(1-numpy.cos(zen)**2-cosDirX**2) |
|
|
1288 | 1298 | |
|
|
1289 | 1299 | signX = numpy.sign(numpy.cos(azim)) |
|
|
1290 | 1300 | signY = numpy.sign(numpy.sin(azim)) |
|
|
1291 | 1301 | |
|
|
1292 | 1302 | cosDirX = numpy.copysign(cosDirX, signX) |
|
|
1293 | 1303 | cosDirY = numpy.copysign(cosDirY, signY) |
|
|
1294 | 1304 | return cosDirX, cosDirY |
|
|
1295 | 1305 | |
|
|
1296 | 1306 | def __calculateAngles(self, theta_x, theta_y, azimuth): |
|
|
1297 | 1307 | |
|
|
1298 | 1308 | dir_cosw = numpy.sqrt(1-theta_x**2-theta_y**2) |
|
|
1299 | 1309 | zenith_arr = numpy.arccos(dir_cosw) |
|
|
1300 | 1310 | azimuth_arr = numpy.arctan2(theta_x,theta_y) + azimuth*math.pi/180 |
|
|
1301 | 1311 | |
|
|
1302 | 1312 | dir_cosu = numpy.sin(azimuth_arr)*numpy.sin(zenith_arr) |
|
|
1303 | 1313 | dir_cosv = numpy.cos(azimuth_arr)*numpy.sin(zenith_arr) |
|
|
1304 | 1314 | |
|
|
1305 | 1315 | return azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw |
|
|
1306 | 1316 | |
|
|
1307 | 1317 | def __calculateMatA(self, dir_cosu, dir_cosv, dir_cosw, horOnly): |
|
|
1308 | 1318 | |
|
|
1309 | 1319 | # |
|
|
1310 | 1320 | if horOnly: |
|
|
1311 | 1321 | A = numpy.c_[dir_cosu,dir_cosv] |
|
|
1312 | 1322 | else: |
|
|
1313 | 1323 | A = numpy.c_[dir_cosu,dir_cosv,dir_cosw] |
|
|
1314 | 1324 | A = numpy.asmatrix(A) |
|
|
1315 | 1325 | A1 = numpy.linalg.inv(A.transpose()*A)*A.transpose() |
|
|
1316 | 1326 | |
|
|
1317 | 1327 | return A1 |
|
|
1318 | 1328 | |
|
|
1319 | 1329 | def __correctValues(self, heiRang, phi, velRadial, SNR): |
|
|
1320 | 1330 | listPhi = phi.tolist() |
|
|
1321 | 1331 | maxid = listPhi.index(max(listPhi)) |
|
|
1322 | 1332 | minid = listPhi.index(min(listPhi)) |
|
|
1323 | 1333 | |
|
|
1324 | 1334 | rango = range(len(phi)) |
|
|
1325 | 1335 | # rango = numpy.delete(rango,maxid) |
|
|
1326 | 1336 | |
|
|
1327 | 1337 | heiRang1 = heiRang*math.cos(phi[maxid]) |
|
|
1328 | 1338 | heiRangAux = heiRang*math.cos(phi[minid]) |
|
|
1329 | 1339 | indOut = (heiRang1 < heiRangAux[0]).nonzero() |
|
|
1330 | 1340 | heiRang1 = numpy.delete(heiRang1,indOut) |
|
|
1331 | 1341 | |
|
|
1332 | 1342 | velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
1333 | 1343 | SNR1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
1334 | 1344 | |
|
|
1335 | 1345 | for i in rango: |
|
|
1336 | 1346 | x = heiRang*math.cos(phi[i]) |
|
|
1337 | 1347 | y1 = velRadial[i,:] |
|
|
1338 | 1348 | f1 = interpolate.interp1d(x,y1,kind = 'cubic') |
|
|
1339 | 1349 | |
|
|
1340 | 1350 | x1 = heiRang1 |
|
|
1341 | 1351 | y11 = f1(x1) |
|
|
1342 | 1352 | |
|
|
1343 | 1353 | y2 = SNR[i,:] |
|
|
1344 | 1354 | f2 = interpolate.interp1d(x,y2,kind = 'cubic') |
|
|
1345 | 1355 | y21 = f2(x1) |
|
|
1346 | 1356 | |
|
|
1347 | 1357 | velRadial1[i,:] = y11 |
|
|
1348 | 1358 | SNR1[i,:] = y21 |
|
|
1349 | 1359 | |
|
|
1350 | 1360 | return heiRang1, velRadial1, SNR1 |
|
|
1351 | 1361 | |
|
|
1352 | 1362 | def __calculateVelUVW(self, A, velRadial): |
|
|
1353 | 1363 | |
|
|
1354 | 1364 | #Operacion Matricial |
|
|
1355 | 1365 | # velUVW = numpy.zeros((velRadial.shape[1],3)) |
|
|
1356 | 1366 | # for ind in range(velRadial.shape[1]): |
|
|
1357 | 1367 | # velUVW[ind,:] = numpy.dot(A,velRadial[:,ind]) |
|
|
1358 | 1368 | # velUVW = velUVW.transpose() |
|
|
1359 | 1369 | velUVW = numpy.zeros((A.shape[0],velRadial.shape[1])) |
|
|
1360 | 1370 | velUVW[:,:] = numpy.dot(A,velRadial) |
|
|
1361 | 1371 | |
|
|
1362 | 1372 | |
|
|
1363 | 1373 | return velUVW |
|
|
1364 | 1374 | |
|
|
1365 | 1375 | def techniqueDBS(self, velRadial0, dirCosx, disrCosy, azimuth, correct, horizontalOnly, heiRang, SNR0): |
|
|
1366 | 1376 | """ |
|
|
1367 | 1377 | Function that implements Doppler Beam Swinging (DBS) technique. |
|
|
1368 | 1378 | |
|
|
1369 | 1379 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, |
|
|
1370 | 1380 | Direction correction (if necessary), Ranges and SNR |
|
|
1371 | 1381 | |
|
|
1372 | 1382 | Output: Winds estimation (Zonal, Meridional and Vertical) |
|
|
1373 | 1383 | |
|
|
1374 | 1384 | Parameters affected: Winds, height range, SNR |
|
|
1375 | 1385 | """ |
|
|
1376 | 1386 | azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(dirCosx, disrCosy, azimuth) |
|
|
1377 | 1387 | heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, zenith_arr, correct*velRadial0, SNR0) |
|
|
1378 | 1388 | A = self.__calculateMatA(dir_cosu, dir_cosv, dir_cosw, horizontalOnly) |
|
|
1379 | 1389 | |
|
|
1380 | 1390 | #Calculo de Componentes de la velocidad con DBS |
|
|
1381 | 1391 | winds = self.__calculateVelUVW(A,velRadial1) |
|
|
1382 | 1392 | |
|
|
1383 | 1393 | return winds, heiRang1, SNR1 |
|
|
1384 | 1394 | |
|
|
1385 | 1395 | def __calculateDistance(self, posx, posy, pairsCrossCorr, pairsList, pairs, azimuth = None): |
|
|
1386 | 1396 | |
|
|
1387 | 1397 | posx = numpy.asarray(posx) |
|
|
1388 | 1398 | posy = numpy.asarray(posy) |
|
|
1389 | 1399 | |
|
|
1390 | 1400 | #Rotacion Inversa para alinear con el azimuth |
|
|
1391 | 1401 | if azimuth!= None: |
|
|
1392 | 1402 | azimuth = azimuth*math.pi/180 |
|
|
1393 | 1403 | posx1 = posx*math.cos(azimuth) + posy*math.sin(azimuth) |
|
|
1394 | 1404 | posy1 = -posx*math.sin(azimuth) + posy*math.cos(azimuth) |
|
|
1395 | 1405 | else: |
|
|
1396 | 1406 | posx1 = posx |
|
|
1397 | 1407 | posy1 = posy |
|
|
1398 | 1408 | |
|
|
1399 | 1409 | #Calculo de Distancias |
|
|
1400 | 1410 | distx = numpy.zeros(pairsCrossCorr.size) |
|
|
1401 | 1411 | disty = numpy.zeros(pairsCrossCorr.size) |
|
|
1402 | 1412 | dist = numpy.zeros(pairsCrossCorr.size) |
|
|
1403 | 1413 | ang = numpy.zeros(pairsCrossCorr.size) |
|
|
1404 | 1414 | |
|
|
1405 | 1415 | for i in range(pairsCrossCorr.size): |
|
|
1406 | 1416 | distx[i] = posx1[pairsList[pairsCrossCorr[i]][1]] - posx1[pairsList[pairsCrossCorr[i]][0]] |
|
|
1407 | 1417 | disty[i] = posy1[pairsList[pairsCrossCorr[i]][1]] - posy1[pairsList[pairsCrossCorr[i]][0]] |
|
|
1408 | 1418 | dist[i] = numpy.sqrt(distx[i]**2 + disty[i]**2) |
|
|
1409 | 1419 | ang[i] = numpy.arctan2(disty[i],distx[i]) |
|
|
1410 | 1420 | #Calculo de Matrices |
|
|
1411 | 1421 | nPairs = len(pairs) |
|
|
1412 | 1422 | ang1 = numpy.zeros((nPairs, 2, 1)) |
|
|
1413 | 1423 | dist1 = numpy.zeros((nPairs, 2, 1)) |
|
|
1414 | 1424 | |
|
|
1415 | 1425 | for j in range(nPairs): |
|
|
1416 | 1426 | dist1[j,0,0] = dist[pairs[j][0]] |
|
|
1417 | 1427 | dist1[j,1,0] = dist[pairs[j][1]] |
|
|
1418 | 1428 | ang1[j,0,0] = ang[pairs[j][0]] |
|
|
1419 | 1429 | ang1[j,1,0] = ang[pairs[j][1]] |
|
|
1420 | 1430 | |
|
|
1421 | 1431 | return distx,disty, dist1,ang1 |
|
|
1422 | 1432 | |
|
|
1423 | 1433 | def __calculateVelVer(self, phase, lagTRange, _lambda): |
|
|
1424 | 1434 | |
|
|
1425 | 1435 | Ts = lagTRange[1] - lagTRange[0] |
|
|
1426 | 1436 | velW = -_lambda*phase/(4*math.pi*Ts) |
|
|
1427 | 1437 | |
|
|
1428 | 1438 | return velW |
|
|
1429 | 1439 | |
|
|
1430 | 1440 | def __calculateVelHorDir(self, dist, tau1, tau2, ang): |
|
|
1431 | 1441 | nPairs = tau1.shape[0] |
|
|
1432 | 1442 | vel = numpy.zeros((nPairs,3,tau1.shape[2])) |
|
|
1433 | 1443 | |
|
|
1434 | 1444 | angCos = numpy.cos(ang) |
|
|
1435 | 1445 | angSin = numpy.sin(ang) |
|
|
1436 | 1446 | |
|
|
1437 | 1447 | vel0 = dist*tau1/(2*tau2**2) |
|
|
1438 | 1448 | vel[:,0,:] = (vel0*angCos).sum(axis = 1) |
|
|
1439 | 1449 | vel[:,1,:] = (vel0*angSin).sum(axis = 1) |
|
|
1440 | 1450 | |
|
|
1441 | 1451 | ind = numpy.where(numpy.isinf(vel)) |
|
|
1442 | 1452 | vel[ind] = numpy.nan |
|
|
1443 | 1453 | |
|
|
1444 | 1454 | return vel |
|
|
1445 | 1455 | |
|
|
1446 | 1456 | def __getPairsAutoCorr(self, pairsList, nChannels): |
|
|
1447 | 1457 | |
|
|
1448 | 1458 | pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan |
|
|
1449 | 1459 | |
|
|
1450 | 1460 | for l in range(len(pairsList)): |
|
|
1451 | 1461 | firstChannel = pairsList[l][0] |
|
|
1452 | 1462 | secondChannel = pairsList[l][1] |
|
|
1453 | 1463 | |
|
|
1454 | 1464 | #Obteniendo pares de Autocorrelacion |
|
|
1455 | 1465 | if firstChannel == secondChannel: |
|
|
1456 | 1466 | pairsAutoCorr[firstChannel] = int(l) |
|
|
1457 | 1467 | |
|
|
1458 | 1468 | pairsAutoCorr = pairsAutoCorr.astype(int) |
|
|
1459 | 1469 | |
|
|
1460 | 1470 | pairsCrossCorr = range(len(pairsList)) |
|
|
1461 | 1471 | pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr) |
|
|
1462 | 1472 | |
|
|
1463 | 1473 | return pairsAutoCorr, pairsCrossCorr |
|
|
1464 | 1474 | |
|
|
1465 | 1475 | def techniqueSA(self, pairsSelected, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, lagTRange, correctFactor): |
|
|
1466 | 1476 | """ |
|
|
1467 | 1477 | Function that implements Spaced Antenna (SA) technique. |
|
|
1468 | 1478 | |
|
|
1469 | 1479 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, |
|
|
1470 | 1480 | Direction correction (if necessary), Ranges and SNR |
|
|
1471 | 1481 | |
|
|
1472 | 1482 | Output: Winds estimation (Zonal, Meridional and Vertical) |
|
|
1473 | 1483 | |
|
|
1474 | 1484 | Parameters affected: Winds |
|
|
1475 | 1485 | """ |
|
|
1476 | 1486 | #Cross Correlation pairs obtained |
|
|
1477 | 1487 | pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairsList, nChannels) |
|
|
1478 | 1488 | pairsArray = numpy.array(pairsList)[pairsCrossCorr] |
|
|
1479 | 1489 | pairsSelArray = numpy.array(pairsSelected) |
|
|
1480 | 1490 | pairs = [] |
|
|
1481 | 1491 | |
|
|
1482 | 1492 | #Wind estimation pairs obtained |
|
|
1483 | 1493 | for i in range(pairsSelArray.shape[0]/2): |
|
|
1484 | 1494 | ind1 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i], axis = 1))[0][0] |
|
|
1485 | 1495 | ind2 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i + 1], axis = 1))[0][0] |
|
|
1486 | 1496 | pairs.append((ind1,ind2)) |
|
|
1487 | 1497 | |
|
|
1488 | 1498 | indtau = tau.shape[0]/2 |
|
|
1489 | 1499 | tau1 = tau[:indtau,:] |
|
|
1490 | 1500 | tau2 = tau[indtau:-1,:] |
|
|
1491 | 1501 | tau1 = tau1[pairs,:] |
|
|
1492 | 1502 | tau2 = tau2[pairs,:] |
|
|
1493 | 1503 | phase1 = tau[-1,:] |
|
|
1494 | 1504 | |
|
|
1495 | 1505 | #--------------------------------------------------------------------- |
|
|
1496 | 1506 | #Metodo Directo |
|
|
1497 | 1507 | distx, disty, dist, ang = self.__calculateDistance(position_x, position_y, pairsCrossCorr, pairsList, pairs,azimuth) |
|
|
1498 | 1508 | winds = self.__calculateVelHorDir(dist, tau1, tau2, ang) |
|
|
1499 | 1509 | winds = stats.nanmean(winds, axis=0) |
|
|
1500 | 1510 | #--------------------------------------------------------------------- |
|
|
1501 | 1511 | #Metodo General |
|
|
1502 | 1512 | # distx, disty, dist = self.calculateDistance(position_x,position_y,pairsCrossCorr, pairsList, azimuth) |
|
|
1503 | 1513 | # #Calculo Coeficientes de Funcion de Correlacion |
|
|
1504 | 1514 | # F,G,A,B,H = self.calculateCoef(tau1,tau2,distx,disty,n) |
|
|
1505 | 1515 | # #Calculo de Velocidades |
|
|
1506 | 1516 | # winds = self.calculateVelUV(F,G,A,B,H) |
|
|
1507 | 1517 | |
|
|
1508 | 1518 | #--------------------------------------------------------------------- |
|
|
1509 | 1519 | winds[2,:] = self.__calculateVelVer(phase1, lagTRange, _lambda) |
|
|
1510 | 1520 | winds = correctFactor*winds |
|
|
1511 | 1521 | return winds |
|
|
1512 | 1522 | |
|
|
1513 | 1523 | def __checkTime(self, currentTime, paramInterval, outputInterval): |
|
|
1514 | 1524 | |
|
|
1515 | 1525 | dataTime = currentTime + paramInterval |
|
|
1516 | 1526 | deltaTime = dataTime - self.__initime |
|
|
1517 | 1527 | |
|
|
1518 | 1528 | if deltaTime >= outputInterval or deltaTime < 0: |
|
|
1519 | 1529 | self.__dataReady = True |
|
|
1520 | 1530 | return |
|
|
1521 | 1531 | |
|
|
1522 | 1532 | def techniqueMeteors(self, arrayMeteor, meteorThresh, heightMin, heightMax): |
|
|
1523 | 1533 | ''' |
|
|
1524 | 1534 | Function that implements winds estimation technique with detected meteors. |
|
|
1525 | 1535 | |
|
|
1526 | 1536 | Input: Detected meteors, Minimum meteor quantity to wind estimation |
|
|
1527 | 1537 | |
|
|
1528 | 1538 | Output: Winds estimation (Zonal and Meridional) |
|
|
1529 | 1539 | |
|
|
1530 | 1540 | Parameters affected: Winds |
|
|
1531 | 1541 | ''' |
|
|
1532 | 1542 | #Settings |
|
|
1533 | 1543 | nInt = (heightMax - heightMin)/2 |
|
|
1534 | 1544 | winds = numpy.zeros((2,nInt))*numpy.nan |
|
|
1535 | 1545 | |
|
|
1536 | 1546 | #Filter errors |
|
|
1537 | 1547 | error = numpy.where(arrayMeteor[:,-1] == 0)[0] |
|
|
1538 | 1548 | finalMeteor = arrayMeteor[error,:] |
|
|
1539 | 1549 | |
|
|
1540 | 1550 | #Meteor Histogram |
|
|
1541 | 1551 | finalHeights = finalMeteor[:,3] |
|
|
1542 | 1552 | hist = numpy.histogram(finalHeights, bins = nInt, range = (heightMin,heightMax)) |
|
|
1543 | 1553 | nMeteorsPerI = hist[0] |
|
|
1544 | 1554 | heightPerI = hist[1] |
|
|
1545 | 1555 | |
|
|
1546 | 1556 | #Sort of meteors |
|
|
1547 | 1557 | indSort = finalHeights.argsort() |
|
|
1548 | 1558 | finalMeteor2 = finalMeteor[indSort,:] |
|
|
1549 | 1559 | |
|
|
1550 | 1560 | # Calculating winds |
|
|
1551 | 1561 | ind1 = 0 |
|
|
1552 | 1562 | ind2 = 0 |
|
|
1553 | 1563 | |
|
|
1554 | 1564 | for i in range(nInt): |
|
|
1555 | 1565 | nMet = nMeteorsPerI[i] |
|
|
1556 | 1566 | ind1 = ind2 |
|
|
1557 | 1567 | ind2 = ind1 + nMet |
|
|
1558 | 1568 | |
|
|
1559 | 1569 | meteorAux = finalMeteor2[ind1:ind2,:] |
|
|
1560 | 1570 | |
|
|
1561 | 1571 | if meteorAux.shape[0] >= meteorThresh: |
|
|
1562 | 1572 | vel = meteorAux[:, 7] |
|
|
1563 | 1573 | zen = meteorAux[:, 5]*numpy.pi/180 |
|
|
1564 | 1574 | azim = meteorAux[:, 4]*numpy.pi/180 |
|
|
1565 | 1575 | |
|
|
1566 | 1576 | n = numpy.cos(zen) |
|
|
1567 | 1577 | # m = (1 - n**2)/(1 - numpy.tan(azim)**2) |
|
|
1568 | 1578 | # l = m*numpy.tan(azim) |
|
|
1569 | 1579 | l = numpy.sin(zen)*numpy.sin(azim) |
|
|
1570 | 1580 | m = numpy.sin(zen)*numpy.cos(azim) |
|
|
1571 | 1581 | |
|
|
1572 | 1582 | A = numpy.vstack((l, m)).transpose() |
|
|
1573 | 1583 | A1 = numpy.dot(numpy.linalg.inv( numpy.dot(A.transpose(),A) ),A.transpose()) |
|
|
1574 | 1584 | windsAux = numpy.dot(A1, vel) |
|
|
1575 | 1585 | |
|
|
1576 | 1586 | winds[0,i] = windsAux[0] |
|
|
1577 | 1587 | winds[1,i] = windsAux[1] |
|
|
1578 | 1588 | |
|
|
1579 | 1589 | return winds, heightPerI[:-1] |
|
|
1580 | 1590 | |
|
|
1581 | 1591 | def run(self, dataOut, technique, **kwargs): |
|
|
1582 | 1592 | |
|
|
1583 | 1593 | param = dataOut.data_param |
|
|
1584 | 1594 | if dataOut.abscissaList != None: |
|
|
1585 | 1595 | absc = dataOut.abscissaList[:-1] |
|
|
1586 | 1596 | noise = dataOut.noise |
|
|
1587 | 1597 | heightList = dataOut.getHeiRange() |
|
|
1588 | 1598 | SNR = dataOut.data_SNR |
|
|
1589 | 1599 | |
|
|
1590 | 1600 | if technique == 'DBS': |
|
|
1591 | 1601 | |
|
|
1592 | 1602 | if kwargs.has_key('dirCosx') and kwargs.has_key('dirCosy'): |
|
|
1593 | 1603 | theta_x = numpy.array(kwargs['dirCosx']) |
|
|
1594 | 1604 | theta_y = numpy.array(kwargs['dirCosy']) |
|
|
1595 | 1605 | else: |
|
|
1596 | 1606 | elev = numpy.array(kwargs['elevation']) |
|
|
1597 | 1607 | azim = numpy.array(kwargs['azimuth']) |
|
|
1598 | 1608 | theta_x, theta_y = self.__calculateCosDir(elev, azim) |
|
|
1599 | 1609 | azimuth = kwargs['correctAzimuth'] |
|
|
1600 | 1610 | if kwargs.has_key('horizontalOnly'): |
|
|
1601 | 1611 | horizontalOnly = kwargs['horizontalOnly'] |
|
|
1602 | 1612 | else: horizontalOnly = False |
|
|
1603 | 1613 | if kwargs.has_key('correctFactor'): |
|
|
1604 | 1614 | correctFactor = kwargs['correctFactor'] |
|
|
1605 | 1615 | else: correctFactor = 1 |
|
|
1606 | 1616 | if kwargs.has_key('channelList'): |
|
|
1607 | 1617 | channelList = kwargs['channelList'] |
|
|
1608 | 1618 | if len(channelList) == 2: |
|
|
1609 | 1619 | horizontalOnly = True |
|
|
1610 | 1620 | arrayChannel = numpy.array(channelList) |
|
|
1611 | 1621 | param = param[arrayChannel,:,:] |
|
|
1612 | 1622 | theta_x = theta_x[arrayChannel] |
|
|
1613 | 1623 | theta_y = theta_y[arrayChannel] |
|
|
1614 | 1624 | |
|
|
1615 | 1625 | velRadial0 = param[:,1,:] #Radial velocity |
|
|
1616 | 1626 | dataOut.data_output, dataOut.heightList, dataOut.data_SNR = self.techniqueDBS(velRadial0, theta_x, theta_y, azimuth, correctFactor, horizontalOnly, heightList, SNR) #DBS Function |
|
|
1617 | 1627 | dataOut.utctimeInit = dataOut.utctime |
|
|
1618 | 1628 | dataOut.outputInterval = dataOut.timeInterval |
|
|
1619 | 1629 | |
|
|
1620 | 1630 | elif technique == 'SA': |
|
|
1621 | 1631 | |
|
|
1622 | 1632 | #Parameters |
|
|
1623 | 1633 | position_x = kwargs['positionX'] |
|
|
1624 | 1634 | position_y = kwargs['positionY'] |
|
|
1625 | 1635 | azimuth = kwargs['azimuth'] |
|
|
1626 | 1636 | |
|
|
1627 | 1637 | if kwargs.has_key('crosspairsList'): |
|
|
1628 | 1638 | pairs = kwargs['crosspairsList'] |
|
|
1629 | 1639 | else: |
|
|
1630 | 1640 | pairs = None |
|
|
1631 | 1641 | |
|
|
1632 | 1642 | if kwargs.has_key('correctFactor'): |
|
|
1633 | 1643 | correctFactor = kwargs['correctFactor'] |
|
|
1634 | 1644 | else: |
|
|
1635 | 1645 | correctFactor = 1 |
|
|
1636 | 1646 | |
|
|
1637 | 1647 | tau = dataOut.data_param |
|
|
1638 | 1648 | _lambda = dataOut.C/dataOut.frequency |
|
|
1639 | 1649 | pairsList = dataOut.groupList |
|
|
1640 | 1650 | nChannels = dataOut.nChannels |
|
|
1641 | 1651 | |
|
|
1642 | 1652 | dataOut.data_output = self.techniqueSA(pairs, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, absc, correctFactor) |
|
|
1643 | 1653 | dataOut.utctimeInit = dataOut.utctime |
|
|
1644 | 1654 | dataOut.outputInterval = dataOut.timeInterval |
|
|
1645 | 1655 | |
|
|
1646 | 1656 | elif technique == 'Meteors': |
|
|
1647 | 1657 | dataOut.flagNoData = True |
|
|
1648 | 1658 | self.__dataReady = False |
|
|
1649 | 1659 | |
|
|
1650 | 1660 | if kwargs.has_key('nHours'): |
|
|
1651 | 1661 | nHours = kwargs['nHours'] |
|
|
1652 | 1662 | else: |
|
|
1653 | 1663 | nHours = 1 |
|
|
1654 | 1664 | |
|
|
1655 | 1665 | if kwargs.has_key('meteorsPerBin'): |
|
|
1656 | 1666 | meteorThresh = kwargs['meteorsPerBin'] |
|
|
1657 | 1667 | else: |
|
|
1658 | 1668 | meteorThresh = 6 |
|
|
1659 | 1669 | |
|
|
1660 | 1670 | if kwargs.has_key('hmin'): |
|
|
1661 | 1671 | hmin = kwargs['hmin'] |
|
|
1662 | 1672 | else: hmin = 70 |
|
|
1663 | 1673 | if kwargs.has_key('hmax'): |
|
|
1664 | 1674 | hmax = kwargs['hmax'] |
|
|
1665 | 1675 | else: hmax = 110 |
|
|
1666 | 1676 | |
|
|
1667 | 1677 | dataOut.outputInterval = nHours*3600 |
|
|
1668 | 1678 | |
|
|
1669 | 1679 | if self.__isConfig == False: |
|
|
1670 | 1680 | # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) |
|
|
1671 | 1681 | #Get Initial LTC time |
|
|
1672 | 1682 | self.__initime = datetime.datetime.utcfromtimestamp(self.dataOut.utctime) |
|
|
1673 | 1683 | self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
1674 | 1684 | |
|
|
1675 | 1685 | self.__isConfig = True |
|
|
1676 | 1686 | |
|
|
1677 | 1687 | if self.__buffer == None: |
|
|
1678 | 1688 | self.__buffer = dataOut.data_param |
|
|
1679 | 1689 | self.__firstdata = copy.copy(dataOut) |
|
|
1680 | 1690 | |
|
|
1681 | 1691 | else: |
|
|
1682 | 1692 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
|
1683 | 1693 | |
|
|
1684 | 1694 | self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
|
1685 | 1695 | |
|
|
1686 | 1696 | if self.__dataReady: |
|
|
1687 | 1697 | dataOut.utctimeInit = self.__initime |
|
|
1688 | 1698 | |
|
|
1689 | 1699 | self.__initime += dataOut.outputInterval #to erase time offset |
|
|
1690 | 1700 | |
|
|
1691 | 1701 | dataOut.data_output, dataOut.heightList = self.techniqueMeteors(self.__buffer, meteorThresh, hmin, hmax) |
|
|
1692 | 1702 | dataOut.flagNoData = False |
|
|
1693 | 1703 | self.__buffer = None |
|
|
1694 | 1704 | |
|
|
1695 | 1705 | return |
|
|
1696 | 1706 | |
|
|
1697 | 1707 | class EWDriftsEstimation(Operation): |
|
|
1698 | 1708 | |
|
|
1699 | 1709 | |
|
|
1700 | 1710 | def __init__(self): |
|
|
1701 | 1711 | Operation.__init__(self) |
|
|
1702 | 1712 | |
|
|
1703 | 1713 | def __correctValues(self, heiRang, phi, velRadial, SNR): |
|
|
1704 | 1714 | listPhi = phi.tolist() |
|
|
1705 | 1715 | maxid = listPhi.index(max(listPhi)) |
|
|
1706 | 1716 | minid = listPhi.index(min(listPhi)) |
|
|
1707 | 1717 | |
|
|
1708 | 1718 | rango = range(len(phi)) |
|
|
1709 | 1719 | # rango = numpy.delete(rango,maxid) |
|
|
1710 | 1720 | |
|
|
1711 | 1721 | heiRang1 = heiRang*math.cos(phi[maxid]) |
|
|
1712 | 1722 | heiRangAux = heiRang*math.cos(phi[minid]) |
|
|
1713 | 1723 | indOut = (heiRang1 < heiRangAux[0]).nonzero() |
|
|
1714 | 1724 | heiRang1 = numpy.delete(heiRang1,indOut) |
|
|
1715 | 1725 | |
|
|
1716 | 1726 | velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
1717 | 1727 | SNR1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
1718 | 1728 | |
|
|
1719 | 1729 | for i in rango: |
|
|
1720 | 1730 | x = heiRang*math.cos(phi[i]) |
|
|
1721 | 1731 | y1 = velRadial[i,:] |
|
|
1722 | 1732 | f1 = interpolate.interp1d(x,y1,kind = 'cubic') |
|
|
1723 | 1733 | |
|
|
1724 | 1734 | x1 = heiRang1 |
|
|
1725 | 1735 | y11 = f1(x1) |
|
|
1726 | 1736 | |
|
|
1727 | 1737 | y2 = SNR[i,:] |
|
|
1728 | 1738 | f2 = interpolate.interp1d(x,y2,kind = 'cubic') |
|
|
1729 | 1739 | y21 = f2(x1) |
|
|
1730 | 1740 | |
|
|
1731 | 1741 | velRadial1[i,:] = y11 |
|
|
1732 | 1742 | SNR1[i,:] = y21 |
|
|
1733 | 1743 | |
|
|
1734 | 1744 | return heiRang1, velRadial1, SNR1 |
|
|
1735 | 1745 | |
|
|
1736 | 1746 | def run(self, dataOut, zenith, zenithCorrection): |
|
|
1737 | 1747 | heiRang = dataOut.heightList |
|
|
1738 | 1748 | velRadial = dataOut.data_param[:,3,:] |
|
|
1739 | 1749 | SNR = dataOut.data_SNR |
|
|
1740 | 1750 | |
|
|
1741 | 1751 | zenith = numpy.array(zenith) |
|
|
1742 | 1752 | zenith -= zenithCorrection |
|
|
1743 | 1753 | zenith *= numpy.pi/180 |
|
|
1744 | 1754 | |
|
|
1745 | 1755 | heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, numpy.abs(zenith), velRadial, SNR) |
|
|
1746 | 1756 | |
|
|
1747 | 1757 | alp = zenith[0] |
|
|
1748 | 1758 | bet = zenith[1] |
|
|
1749 | 1759 | |
|
|
1750 | 1760 | w_w = velRadial1[0,:] |
|
|
1751 | 1761 | w_e = velRadial1[1,:] |
|
|
1752 | 1762 | |
|
|
1753 | 1763 | w = (w_w*numpy.sin(bet) - w_e*numpy.sin(alp))/(numpy.cos(alp)*numpy.sin(bet) - numpy.cos(bet)*numpy.sin(alp)) |
|
|
1754 | 1764 | u = (w_w*numpy.cos(bet) - w_e*numpy.cos(alp))/(numpy.sin(alp)*numpy.cos(bet) - numpy.sin(bet)*numpy.cos(alp)) |
|
|
1755 | 1765 | |
|
|
1756 | 1766 | winds = numpy.vstack((u,w)) |
|
|
1757 | 1767 | |
|
|
1758 | 1768 | dataOut.heightList = heiRang1 |
|
|
1759 | 1769 | dataOut.data_output = winds |
|
|
1760 | 1770 | dataOut.data_SNR = SNR1 |
|
|
1761 | 1771 | |
|
|
1762 | 1772 | dataOut.utctimeInit = dataOut.utctime |
|
|
1763 | 1773 | dataOut.outputInterval = dataOut.timeInterval |
|
|
1764 | 1774 | return |
|
|
1765 | 1775 | |
|
|
1766 | 1776 | |
|
|
1767 | 1777 | |
|
|
1768 | 1778 | |
|
|
1769 | 1779 | |
|
|
1770 | 1780 | No newline at end of file |
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