| @@ -1,2181 +1,2185 | |||
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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 schainpy.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.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock |
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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 = 100, 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 | |
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75 | 75 | |
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76 | 76 | if self.buffer == None: |
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77 | 77 | self.nSeconds = nSeconds |
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78 | 78 | self.nProfiles= int(numpy.floor(nSeconds/(self.dataIn.ippSeconds*self.dataIn.nCohInt))) |
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79 | 79 | |
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80 | 80 | self.buffer = numpy.zeros((self.dataIn.nChannels, |
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81 | 81 | self.nProfiles, |
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82 | 82 | self.dataIn.nHeights), |
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83 | 83 | dtype='complex') |
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84 | 84 | |
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85 | 85 | if self.profIndex == 7990: |
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86 | 86 | a = 1 |
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87 | 87 | |
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88 | 88 | self.buffer[:,self.profIndex,:] = self.dataIn.data.copy() |
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89 | 89 | self.profIndex += 1 |
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90 | 90 | |
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91 | 91 | if self.profIndex == self.nProfiles: |
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92 | 92 | |
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93 | 93 | self.__updateObjFromInput() |
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94 | 94 | self.dataOut.data_pre = self.buffer.copy() |
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95 | 95 | self.dataOut.paramInterval = nSeconds |
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96 | 96 | self.dataOut.flagNoData = False |
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97 | 97 | |
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98 | 98 | self.buffer = None |
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99 | 99 | self.firstdatatime = None |
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100 | 100 | self.profIndex = 0 |
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101 | 101 | return |
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102 | 102 | |
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103 | 103 | #---------------------- Spectra Data --------------------------- |
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104 | 104 | |
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105 | 105 | if self.dataIn.type == "Spectra": |
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106 | 106 | self.dataOut.data_pre = self.dataIn.data_spc.copy() |
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107 | 107 | self.dataOut.abscissaList = self.dataIn.getVelRange(1) |
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108 | 108 | self.dataOut.noise = self.dataIn.getNoise() |
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109 | 109 | self.dataOut.normFactor = self.dataIn.normFactor |
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110 | 110 | self.dataOut.groupList = self.dataIn.pairsList |
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111 | 111 | self.dataOut.flagNoData = False |
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112 | 112 | |
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113 | 113 | #---------------------- Correlation Data --------------------------- |
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114 | 114 | |
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115 | 115 | if self.dataIn.type == "Correlation": |
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116 | 116 | lagRRange = self.dataIn.lagR |
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117 | 117 | indR = numpy.where(lagRRange == 0)[0][0] |
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118 | 118 | |
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119 | 119 | self.dataOut.data_pre = self.dataIn.data_corr.copy()[:,:,indR,:] |
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120 | 120 | self.dataOut.abscissaList = self.dataIn.getLagTRange(1) |
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121 | 121 | self.dataOut.noise = self.dataIn.noise |
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122 | 122 | self.dataOut.normFactor = self.dataIn.normFactor |
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123 | 123 | self.dataOut.data_SNR = self.dataIn.SNR |
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124 | 124 | self.dataOut.groupList = self.dataIn.pairsList |
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125 | 125 | self.dataOut.flagNoData = False |
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126 | 126 | |
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127 | 127 | #---------------------- Correlation Data --------------------------- |
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128 | 128 | |
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129 | 129 | if self.dataIn.type == "Parameters": |
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130 | 130 | self.dataOut.copy(self.dataIn) |
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131 | 131 | self.dataOut.flagNoData = False |
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132 | 132 | |
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133 | 133 | return True |
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134 | 134 | |
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135 | 135 | self.__updateObjFromInput() |
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136 | 136 | self.firstdatatime = None |
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137 | 137 | self.dataOut.utctimeInit = self.dataIn.utctime |
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138 | 138 | self.dataOut.paramInterval = self.dataIn.timeInterval |
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139 | 139 | |
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140 | 140 | #------------------- Get Moments ---------------------------------- |
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141 | 141 | def GetMoments(self, channelList = None): |
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142 | 142 | ''' |
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143 | 143 | Function GetMoments() |
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144 | 144 | |
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145 | 145 | Input: |
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146 | 146 | channelList : simple channel list to select e.g. [2,3,7] |
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147 | 147 | self.dataOut.data_pre |
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148 | 148 | self.dataOut.abscissaList |
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149 | 149 | self.dataOut.noise |
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150 | 150 | |
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151 | 151 | Affected: |
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152 | 152 | self.dataOut.data_param |
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153 | 153 | self.dataOut.data_SNR |
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154 | 154 | |
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155 | 155 | ''' |
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156 | 156 | data = self.dataOut.data_pre |
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157 | 157 | absc = self.dataOut.abscissaList[:-1] |
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158 | 158 | noise = self.dataOut.noise |
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159 | 159 | |
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160 | 160 | data_param = numpy.zeros((data.shape[0], 4, data.shape[2])) |
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161 | 161 | |
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162 | 162 | if channelList== None: |
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163 | 163 | channelList = self.dataIn.channelList |
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164 | 164 | self.dataOut.channelList = channelList |
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165 | 165 | |
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166 | 166 | for ind in channelList: |
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167 | 167 | data_param[ind,:,:] = self.__calculateMoments(data[ind,:,:], absc, noise[ind]) |
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168 | 168 | |
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169 | 169 | self.dataOut.data_param = data_param[:,1:,:] |
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170 | 170 | self.dataOut.data_SNR = data_param[:,0] |
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171 | 171 | return |
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172 | 172 | |
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173 | 173 | 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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174 | 174 | |
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175 | 175 | if (nicoh == None): nicoh = 1 |
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176 | 176 | if (graph == None): graph = 0 |
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177 | 177 | if (smooth == None): smooth = 0 |
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178 | 178 | elif (self.smooth < 3): smooth = 0 |
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179 | 179 | |
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180 | 180 | if (type1 == None): type1 = 0 |
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181 | 181 | if (fwindow == None): fwindow = numpy.zeros(oldfreq.size) + 1 |
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182 | 182 | if (snrth == None): snrth = -3 |
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183 | 183 | if (dc == None): dc = 0 |
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184 | 184 | if (aliasing == None): aliasing = 0 |
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185 | 185 | if (oldfd == None): oldfd = 0 |
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186 | 186 | if (wwauto == None): wwauto = 0 |
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187 | 187 | |
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188 | 188 | if (n0 < 1.e-20): n0 = 1.e-20 |
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189 | 189 | |
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190 | 190 | freq = oldfreq |
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191 | 191 | vec_power = numpy.zeros(oldspec.shape[1]) |
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192 | 192 | vec_fd = numpy.zeros(oldspec.shape[1]) |
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193 | 193 | vec_w = numpy.zeros(oldspec.shape[1]) |
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194 | 194 | vec_snr = numpy.zeros(oldspec.shape[1]) |
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195 | 195 | |
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196 | 196 | for ind in range(oldspec.shape[1]): |
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197 | 197 | |
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198 | 198 | spec = oldspec[:,ind] |
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199 | 199 | aux = spec*fwindow |
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200 | 200 | max_spec = aux.max() |
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201 | 201 | m = list(aux).index(max_spec) |
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202 | 202 | |
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203 | 203 | #Smooth |
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204 | 204 | if (smooth == 0): spec2 = spec |
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205 | 205 | else: spec2 = scipy.ndimage.filters.uniform_filter1d(spec,size=smooth) |
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206 | 206 | |
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207 | 207 | # Calculo de Momentos |
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208 | 208 | bb = spec2[range(m,spec2.size)] |
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209 | 209 | bb = (bb<n0).nonzero() |
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210 | 210 | bb = bb[0] |
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211 | 211 | |
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212 | 212 | ss = spec2[range(0,m + 1)] |
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213 | 213 | ss = (ss<n0).nonzero() |
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214 | 214 | ss = ss[0] |
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215 | 215 | |
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216 | 216 | if (bb.size == 0): |
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217 | 217 | bb0 = spec.size - 1 - m |
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218 | 218 | else: |
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219 | 219 | bb0 = bb[0] - 1 |
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220 | 220 | if (bb0 < 0): |
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221 | 221 | bb0 = 0 |
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222 | 222 | |
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223 | 223 | if (ss.size == 0): ss1 = 1 |
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224 | 224 | else: ss1 = max(ss) + 1 |
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225 | 225 | |
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226 | 226 | if (ss1 > m): ss1 = m |
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227 | 227 | |
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228 | 228 | valid = numpy.asarray(range(int(m + bb0 - ss1 + 1))) + ss1 |
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229 | 229 | power = ((spec2[valid] - n0)*fwindow[valid]).sum() |
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230 | 230 | fd = ((spec2[valid]- n0)*freq[valid]*fwindow[valid]).sum()/power |
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231 | 231 | w = math.sqrt(((spec2[valid] - n0)*fwindow[valid]*(freq[valid]- fd)**2).sum()/power) |
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232 | 232 | snr = (spec2.mean()-n0)/n0 |
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233 | 233 | |
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234 | 234 | if (snr < 1.e-20) : |
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235 | 235 | snr = 1.e-20 |
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236 | 236 | |
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237 | 237 | vec_power[ind] = power |
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238 | 238 | vec_fd[ind] = fd |
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239 | 239 | vec_w[ind] = w |
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240 | 240 | vec_snr[ind] = snr |
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241 | 241 | |
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242 | 242 | moments = numpy.vstack((vec_snr, vec_power, vec_fd, vec_w)) |
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243 | 243 | return moments |
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244 | 244 | |
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245 | 245 | #------------------ Get SA Parameters -------------------------- |
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246 | 246 | |
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247 | 247 | def GetSAParameters(self): |
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248 | 248 | pairslist = self.dataOut.groupList |
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249 | 249 | num_pairs = len(pairslist) |
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250 | 250 | |
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251 | 251 | vel = self.dataOut.abscissaList |
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252 | 252 | spectra = self.dataOut.data_pre |
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253 | 253 | cspectra = self.dataIn.data_cspc |
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254 | 254 | delta_v = vel[1] - vel[0] |
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255 | 255 | |
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256 | 256 | #Calculating the power spectrum |
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257 | 257 | spc_pow = numpy.sum(spectra, 3)*delta_v |
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258 | 258 | #Normalizing Spectra |
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259 | 259 | norm_spectra = spectra/spc_pow |
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260 | 260 | #Calculating the norm_spectra at peak |
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261 | 261 | max_spectra = numpy.max(norm_spectra, 3) |
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262 | 262 | |
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263 | 263 | #Normalizing Cross Spectra |
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264 | 264 | norm_cspectra = numpy.zeros(cspectra.shape) |
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265 | 265 | |
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266 | 266 | for i in range(num_chan): |
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267 | 267 | norm_cspectra[i,:,:] = cspectra[i,:,:]/numpy.sqrt(spc_pow[pairslist[i][0],:]*spc_pow[pairslist[i][1],:]) |
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268 | 268 | |
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269 | 269 | max_cspectra = numpy.max(norm_cspectra,2) |
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270 | 270 | max_cspectra_index = numpy.argmax(norm_cspectra, 2) |
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271 | 271 | |
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272 | 272 | for i in range(num_pairs): |
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273 | 273 | cspc_par[i,:,:] = __calculateMoments(norm_cspectra) |
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274 | 274 | #------------------- Get Lags ---------------------------------- |
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275 | 275 | |
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276 | 276 | def GetLags(self): |
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277 | 277 | ''' |
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278 | 278 | Function GetMoments() |
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279 | 279 | |
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280 | 280 | Input: |
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281 | 281 | self.dataOut.data_pre |
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282 | 282 | self.dataOut.abscissaList |
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283 | 283 | self.dataOut.noise |
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284 | 284 | self.dataOut.normFactor |
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285 | 285 | self.dataOut.data_SNR |
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286 | 286 | self.dataOut.groupList |
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287 | 287 | self.dataOut.nChannels |
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288 | 288 | |
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289 | 289 | Affected: |
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290 | 290 | self.dataOut.data_param |
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291 | 291 | |
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292 | 292 | ''' |
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293 | 293 | |
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294 | 294 | data = self.dataOut.data_pre |
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295 | 295 | normFactor = self.dataOut.normFactor |
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296 | 296 | nHeights = self.dataOut.nHeights |
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297 | 297 | absc = self.dataOut.abscissaList[:-1] |
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298 | 298 | noise = self.dataOut.noise |
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299 | 299 | SNR = self.dataOut.data_SNR |
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300 | 300 | pairsList = self.dataOut.groupList |
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301 | 301 | nChannels = self.dataOut.nChannels |
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302 | 302 | pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairsList, nChannels) |
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303 | 303 | self.dataOut.data_param = numpy.zeros((len(pairsCrossCorr)*2 + 1, nHeights)) |
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304 | 304 | |
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305 | 305 | dataNorm = numpy.abs(data) |
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306 | 306 | for l in range(len(pairsList)): |
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307 | 307 | dataNorm[l,:,:] = dataNorm[l,:,:]/normFactor[l,:] |
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308 | 308 | |
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309 | 309 | self.dataOut.data_param[:-1,:] = self.__calculateTaus(dataNorm, pairsCrossCorr, pairsAutoCorr, absc) |
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310 | 310 | self.dataOut.data_param[-1,:] = self.__calculateLag1Phase(data, pairsAutoCorr, absc) |
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311 | 311 | return |
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312 | 312 | |
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313 | 313 | def __getPairsAutoCorr(self, pairsList, nChannels): |
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314 | 314 | |
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315 | 315 | pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan |
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316 | 316 | |
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317 | 317 | for l in range(len(pairsList)): |
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318 | 318 | firstChannel = pairsList[l][0] |
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319 | 319 | secondChannel = pairsList[l][1] |
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320 | 320 | |
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321 | 321 | #Obteniendo pares de Autocorrelacion |
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322 | 322 | if firstChannel == secondChannel: |
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323 | 323 | pairsAutoCorr[firstChannel] = int(l) |
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324 | 324 | |
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325 | 325 | pairsAutoCorr = pairsAutoCorr.astype(int) |
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326 | 326 | |
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327 | 327 | pairsCrossCorr = range(len(pairsList)) |
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328 | 328 | pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr) |
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329 | 329 | |
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330 | 330 | return pairsAutoCorr, pairsCrossCorr |
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331 | 331 | |
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332 | 332 | def __calculateTaus(self, data, pairsCrossCorr, pairsAutoCorr, lagTRange): |
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333 | 333 | |
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334 | 334 | Pt0 = data.shape[1]/2 |
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335 | 335 | #Funcion de Autocorrelacion |
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336 | 336 | dataAutoCorr = stats.nanmean(data[pairsAutoCorr,:,:], axis = 0) |
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337 | 337 | |
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338 | 338 | #Obtencion Indice de TauCross |
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339 | 339 | indCross = data[pairsCrossCorr,:,:].argmax(axis = 1) |
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340 | 340 | #Obtencion Indice de TauAuto |
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341 | 341 | indAuto = numpy.zeros(indCross.shape,dtype = 'int') |
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342 | 342 | CCValue = data[pairsCrossCorr,Pt0,:] |
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343 | 343 | for i in range(pairsCrossCorr.size): |
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344 | 344 | indAuto[i,:] = numpy.abs(dataAutoCorr - CCValue[i,:]).argmin(axis = 0) |
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345 | 345 | |
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346 | 346 | #Obtencion de TauCross y TauAuto |
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347 | 347 | tauCross = lagTRange[indCross] |
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348 | 348 | tauAuto = lagTRange[indAuto] |
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349 | 349 | |
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350 | 350 | Nan1, Nan2 = numpy.where(tauCross == lagTRange[0]) |
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351 | 351 | |
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352 | 352 | tauCross[Nan1,Nan2] = numpy.nan |
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353 | 353 | tauAuto[Nan1,Nan2] = numpy.nan |
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354 | 354 | tau = numpy.vstack((tauCross,tauAuto)) |
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355 | 355 | |
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356 | 356 | return tau |
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357 | 357 | |
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358 | 358 | def __calculateLag1Phase(self, data, pairs, lagTRange): |
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359 | 359 | data1 = stats.nanmean(data[pairs,:,:], axis = 0) |
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360 | 360 | lag1 = numpy.where(lagTRange == 0)[0][0] + 1 |
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361 | 361 | |
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362 | 362 | phase = numpy.angle(data1[lag1,:]) |
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363 | 363 | |
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364 | 364 | return phase |
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365 | 365 | #------------------- Detect Meteors ------------------------------ |
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366 | 366 | |
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367 | 367 | def MeteorDetection(self, hei_ref = None, tauindex = 0, |
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368 |
p |
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368 | phaseOffsets = None, | |
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369 | 369 | cohDetection = False, cohDet_timeStep = 1, cohDet_thresh = 25, |
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370 | 370 | noise_timeStep = 4, noise_multiple = 4, |
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371 | 371 | multDet_timeLimit = 1, multDet_rangeLimit = 3, |
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372 | 372 | phaseThresh = 20, SNRThresh = 8, |
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373 |
hmin = 50, hmax=150, azimuth = 0 |
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373 | hmin = 50, hmax=150, azimuth = 0, | |
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374 | channel25X = 0, channel20X = 4, channelCentX = 2, | |
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375 | channel25Y = 1, channel20Y = 3, channelCentY = 2) : | |
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374 | 376 | |
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375 | 377 | ''' |
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376 | 378 | Function DetectMeteors() |
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377 | 379 | Project developed with paper: |
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378 | 380 | HOLDSWORTH ET AL. 2004 |
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379 | 381 | |
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380 | 382 | Input: |
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381 | 383 | self.dataOut.data_pre |
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382 | 384 | |
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383 | 385 | centerReceiverIndex: From the channels, which is the center receiver |
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384 | 386 | |
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385 | 387 | hei_ref: Height reference for the Beacon signal extraction |
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386 | 388 | tauindex: |
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387 | 389 | predefinedPhaseShifts: Predefined phase offset for the voltge signals |
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388 | 390 | |
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389 | 391 | cohDetection: Whether to user Coherent detection or not |
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390 | 392 | cohDet_timeStep: Coherent Detection calculation time step |
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391 | 393 | cohDet_thresh: Coherent Detection phase threshold to correct phases |
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392 | 394 | |
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393 | 395 | noise_timeStep: Noise calculation time step |
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394 | 396 | noise_multiple: Noise multiple to define signal threshold |
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395 | 397 | |
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396 | 398 | multDet_timeLimit: Multiple Detection Removal time limit in seconds |
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397 | 399 | multDet_rangeLimit: Multiple Detection Removal range limit in km |
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398 | 400 | |
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399 | 401 | phaseThresh: Maximum phase difference between receiver to be consider a meteor |
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400 | 402 | SNRThresh: Minimum SNR threshold of the meteor signal to be consider a meteor |
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401 | 403 | |
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402 | 404 | hmin: Minimum Height of the meteor to use it in the further wind estimations |
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403 | 405 | hmax: Maximum Height of the meteor to use it in the further wind estimations |
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404 | 406 | azimuth: Azimuth angle correction |
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405 | 407 | |
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406 | 408 | Affected: |
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407 | 409 | self.dataOut.data_param |
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408 | 410 | |
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409 | 411 | Rejection Criteria (Errors): |
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410 | 412 | 0: No error; analysis OK |
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411 | 413 | 1: SNR < SNR threshold |
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412 | 414 | 2: angle of arrival (AOA) ambiguously determined |
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413 | 415 | 3: AOA estimate not feasible |
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414 | 416 | 4: Large difference in AOAs obtained from different antenna baselines |
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415 | 417 | 5: echo at start or end of time series |
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416 | 418 | 6: echo less than 5 examples long; too short for analysis |
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417 | 419 | 7: echo rise exceeds 0.3s |
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418 | 420 | 8: echo decay time less than twice rise time |
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419 | 421 | 9: large power level before echo |
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420 | 422 | 10: large power level after echo |
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421 | 423 | 11: poor fit to amplitude for estimation of decay time |
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422 | 424 | 12: poor fit to CCF phase variation for estimation of radial drift velocity |
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423 | 425 | 13: height unresolvable echo: not valid height within 70 to 110 km |
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424 | 426 | 14: height ambiguous echo: more then one possible height within 70 to 110 km |
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425 | 427 | 15: radial drift velocity or projected horizontal velocity exceeds 200 m/s |
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426 | 428 | 16: oscilatory echo, indicating event most likely not an underdense echo |
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427 | 429 | |
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428 | 430 | 17: phase difference in meteor Reestimation |
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429 | 431 | |
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430 | 432 | Data Storage: |
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431 | 433 | Meteors for Wind Estimation (8): |
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432 | 434 | Utc Time | Range Height |
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433 | 435 | Azimuth Zenith errorCosDir |
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434 | 436 | VelRad errorVelRad |
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435 | 437 | Phase0 Phase1 Phase2 Phase3 |
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436 | 438 | TypeError |
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437 | 439 | |
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438 | 440 | ''' |
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439 | 441 | #Get Beacon signal |
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440 | 442 | newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex]) |
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441 | 443 | |
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442 | 444 | if hei_ref != None: |
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443 | 445 | newheis = numpy.where(self.dataOut.heightList>hei_ref) |
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444 | 446 | |
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445 | 447 | heiRang = self.dataOut.getHeiRange() |
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446 | 448 | #Pairs List |
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447 | 449 | ''' |
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448 | 450 | Cambiar este pairslist |
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449 | 451 | ''' |
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450 | pairslist = [] | |
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451 | nChannel = self.dataOut.nChannels | |
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452 | for i in range(nChannel): | |
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453 | if i != centerReceiverIndex: | |
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454 |
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452 | pairslist = [(channelCentX, channel25X),(channelCentX, channel20X),(channelCentY,channel25Y),(channelCentY, channel20Y)] | |
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453 | ||
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454 | # nChannel = self.dataOut.nChannels | |
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455 | # for i in range(nChannel): | |
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456 | # if i != centerReceiverIndex: | |
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457 | # pairslist.append((centerReceiverIndex,i)) | |
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455 | 458 | |
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456 | 459 | #****************REMOVING HARDWARE PHASE DIFFERENCES*************** |
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457 | 460 | # see if the user put in pre defined phase shifts |
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458 | 461 | voltsPShift = self.dataOut.data_pre.copy() |
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459 | 462 | |
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460 | if predefinedPhaseShifts != None: | |
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461 | hardwarePhaseShifts = numpy.array(predefinedPhaseShifts)*numpy.pi/180 | |
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462 | ||
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463 | # elif beaconPhaseShifts: | |
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464 | # #get hardware phase shifts using beacon signal | |
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465 | # hardwarePhaseShifts = self.__getHardwarePhaseDiff(self.dataOut.data_pre, pairslist, newheis, 10) | |
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466 | # hardwarePhaseShifts = numpy.insert(hardwarePhaseShifts,centerReceiverIndex,0) | |
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467 | ||
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468 | else: | |
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469 | hardwarePhaseShifts = numpy.zeros(5) | |
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470 | ||
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471 | ||
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472 | voltsPShift = numpy.zeros((self.dataOut.data_pre.shape[0],self.dataOut.data_pre.shape[1],self.dataOut.data_pre.shape[2]), dtype = 'complex') | |
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473 | for i in range(self.dataOut.data_pre.shape[0]): | |
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474 | voltsPShift[i,:,:] = self.__shiftPhase(self.dataOut.data_pre[i,:,:], hardwarePhaseShifts[i]) | |
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475 | ||
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463 | # if predefinedPhaseShifts != None: | |
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464 | # hardwarePhaseShifts = numpy.array(predefinedPhaseShifts)*numpy.pi/180 | |
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465 | # | |
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466 | # # elif beaconPhaseShifts: | |
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467 | # # #get hardware phase shifts using beacon signal | |
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468 | # # hardwarePhaseShifts = self.__getHardwarePhaseDiff(self.dataOut.data_pre, pairslist, newheis, 10) | |
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469 | # # hardwarePhaseShifts = numpy.insert(hardwarePhaseShifts,centerReceiverIndex,0) | |
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470 | # | |
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471 | # else: | |
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472 | # hardwarePhaseShifts = numpy.zeros(5) | |
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473 | # | |
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474 | # voltsPShift = numpy.zeros((self.dataOut.data_pre.shape[0],self.dataOut.data_pre.shape[1],self.dataOut.data_pre.shape[2]), dtype = 'complex') | |
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475 | # for i in range(self.dataOut.data_pre.shape[0]): | |
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476 | # voltsPShift[i,:,:] = self.__shiftPhase(self.dataOut.data_pre[i,:,:], hardwarePhaseShifts[i]) | |
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476 | 477 | |
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477 | 478 | #******************END OF REMOVING HARDWARE PHASE DIFFERENCES********* |
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478 | 479 | |
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479 | 480 | #Remove DC |
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480 | 481 | voltsDC = numpy.mean(voltsPShift,1) |
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481 | 482 | voltsDC = numpy.mean(voltsDC,1) |
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482 | 483 | for i in range(voltsDC.shape[0]): |
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483 | 484 | voltsPShift[i] = voltsPShift[i] - voltsDC[i] |
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484 | 485 | |
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485 | 486 | #Don't considerate last heights, theyre used to calculate Hardware Phase Shift |
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486 | 487 | voltsPShift = voltsPShift[:,:,:newheis[0][0]] |
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487 | 488 | |
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488 | 489 | #************ FIND POWER OF DATA W/COH OR NON COH DETECTION (3.4) ********** |
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489 | 490 | #Coherent Detection |
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490 | 491 | if cohDetection: |
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491 | 492 | #use coherent detection to get the net power |
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492 | 493 | cohDet_thresh = cohDet_thresh*numpy.pi/180 |
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493 | 494 | voltsPShift = self.__coherentDetection(voltsPShift, cohDet_timeStep, self.dataOut.timeInterval, pairslist, cohDet_thresh) |
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494 | 495 | |
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495 | 496 | #Non-coherent detection! |
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496 | 497 | powerNet = numpy.nansum(numpy.abs(voltsPShift[:,:,:])**2,0) |
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497 | 498 | #********** END OF COH/NON-COH POWER CALCULATION********************** |
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498 | 499 | |
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499 | 500 | #********** FIND THE NOISE LEVEL AND POSSIBLE METEORS **************** |
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500 | 501 | #Get noise |
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501 | 502 | noise, noise1 = self.__getNoise(powerNet, noise_timeStep, self.dataOut.timeInterval) |
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502 | 503 | # noise = self.getNoise1(powerNet, noise_timeStep, self.dataOut.timeInterval) |
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503 | 504 | #Get signal threshold |
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504 | 505 | signalThresh = noise_multiple*noise |
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505 | 506 | #Meteor echoes detection |
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506 | 507 | listMeteors = self.__findMeteors(powerNet, signalThresh) |
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507 | 508 | #******* END OF NOISE LEVEL AND POSSIBLE METEORS CACULATION ********** |
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508 | 509 | |
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509 | 510 | #************** REMOVE MULTIPLE DETECTIONS (3.5) *************************** |
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510 | 511 | #Parameters |
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511 | 512 | heiRange = self.dataOut.getHeiRange() |
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512 | 513 | rangeInterval = heiRange[1] - heiRange[0] |
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513 | 514 | rangeLimit = multDet_rangeLimit/rangeInterval |
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514 | 515 | timeLimit = multDet_timeLimit/self.dataOut.timeInterval |
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515 | 516 | #Multiple detection removals |
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516 | 517 | listMeteors1 = self.__removeMultipleDetections(listMeteors, rangeLimit, timeLimit) |
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517 | 518 | #************ END OF REMOVE MULTIPLE DETECTIONS ********************** |
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518 | 519 | |
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519 | 520 | #********************* METEOR REESTIMATION (3.7, 3.8, 3.9, 3.10) ******************** |
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520 | 521 | #Parameters |
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521 | 522 | phaseThresh = phaseThresh*numpy.pi/180 |
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522 | 523 | thresh = [phaseThresh, noise_multiple, SNRThresh] |
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523 | 524 | #Meteor reestimation (Errors N 1, 6, 12, 17) |
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524 | 525 | listMeteors2, listMeteorsPower, listMeteorsVolts = self.__meteorReestimation(listMeteors1, voltsPShift, pairslist, thresh, noise, self.dataOut.timeInterval, self.dataOut.frequency) |
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525 | 526 | # listMeteors2, listMeteorsPower, listMeteorsVolts = self.meteorReestimation3(listMeteors2, listMeteorsPower, listMeteorsVolts, voltsPShift, pairslist, thresh, noise) |
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526 | 527 | #Estimation of decay times (Errors N 7, 8, 11) |
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527 | 528 | listMeteors3 = self.__estimateDecayTime(listMeteors2, listMeteorsPower, self.dataOut.timeInterval, self.dataOut.frequency) |
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528 | 529 | #******************* END OF METEOR REESTIMATION ******************* |
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529 | 530 | |
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530 | 531 | #********************* METEOR PARAMETERS CALCULATION (3.11, 3.12, 3.13) ************************** |
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531 | 532 | #Calculating Radial Velocity (Error N 15) |
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532 | 533 | radialStdThresh = 10 |
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533 | 534 | listMeteors4 = self.__getRadialVelocity(listMeteors3, listMeteorsVolts, radialStdThresh, pairslist, self.dataOut.timeInterval) |
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534 | 535 | |
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535 | 536 | if len(listMeteors4) > 0: |
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536 | 537 | #Setting New Array |
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537 | 538 | date = self.dataOut.utctime |
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538 | 539 | arrayParameters = self.__setNewArrays(listMeteors4, date, heiRang) |
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539 | 540 | |
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541 | #Correcting phase offset | |
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542 | if phaseOffsets != None: | |
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543 | phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180 | |
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544 | arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets) | |
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545 | ||
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546 | #Second Pairslist | |
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540 | 547 | pairsList = [] |
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541 |
pairx = (0, |
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542 |
pairy = ( |
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548 | pairx = (0,1) | |
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549 | pairy = (2,3) | |
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543 | 550 | pairsList.append(pairx) |
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544 | 551 | pairsList.append(pairy) |
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545 | 552 | |
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546 | 553 | meteorOps = MeteorOperations() |
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547 | 554 | jph = numpy.array([0,0,0,0]) |
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548 | 555 | h = (hmin,hmax) |
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549 | 556 | arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, jph) |
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550 | 557 | |
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551 | 558 | # #Calculate AOA (Error N 3, 4) |
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552 | 559 | # #JONES ET AL. 1998 |
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553 | 560 | # error = arrayParameters[:,-1] |
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554 | 561 | # AOAthresh = numpy.pi/8 |
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555 | 562 | # phases = -arrayParameters[:,9:13] |
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556 | 563 | # arrayParameters[:,4:7], arrayParameters[:,-1] = meteorOps.getAOA(phases, pairsList, error, AOAthresh, azimuth) |
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557 | 564 | # |
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558 | 565 | # #Calculate Heights (Error N 13 and 14) |
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559 | 566 | # error = arrayParameters[:,-1] |
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560 | 567 | # Ranges = arrayParameters[:,2] |
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561 | 568 | # zenith = arrayParameters[:,5] |
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562 | 569 | # arrayParameters[:,3], arrayParameters[:,-1] = meteorOps.getHeights(Ranges, zenith, error, hmin, hmax) |
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563 | 570 | # error = arrayParameters[:,-1] |
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564 | 571 | #********************* END OF PARAMETERS CALCULATION ************************** |
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565 | 572 | |
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566 | 573 | #***************************+ PASS DATA TO NEXT STEP ********************** |
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567 | 574 | # arrayFinal = arrayParameters.reshape((1,arrayParameters.shape[0],arrayParameters.shape[1])) |
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568 | 575 | self.dataOut.data_param = arrayParameters |
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569 | 576 | |
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570 | 577 | if arrayParameters == None: |
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571 | 578 | self.dataOut.flagNoData = True |
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572 | 579 | |
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573 | 580 | return |
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574 | 581 | |
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575 | 582 | def CorrectMeteorPhases(self, phaseOffsets, hmin = 50, hmax = 150, azimuth = 45): |
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576 | 583 | |
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577 | 584 | arrayParameters = self.dataOut.data_param |
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578 | 585 | pairsList = [] |
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579 | 586 | pairx = (0,3) |
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580 | 587 | pairy = (1,2) |
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581 | 588 | pairsList.append(pairx) |
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582 | 589 | pairsList.append(pairy) |
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583 | 590 | jph = numpy.zeros(4) |
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584 | 591 | |
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585 | 592 | phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180 |
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586 | 593 | arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets) |
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587 | 594 | |
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588 | 595 | meteorOps = MeteorOperations() |
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589 | 596 | h = (hmin,hmax) |
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590 | 597 | |
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591 | 598 | arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, jph) |
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592 | 599 | self.dataOut.data_param = arrayParameters |
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593 | 600 | return |
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594 | 601 | |
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595 | 602 | def __getHardwarePhaseDiff(self, voltage0, pairslist, newheis, n): |
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596 | 603 | |
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597 | 604 | minIndex = min(newheis[0]) |
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598 | 605 | maxIndex = max(newheis[0]) |
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599 | 606 | |
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600 | 607 | voltage = voltage0[:,:,minIndex:maxIndex+1] |
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601 | 608 | nLength = voltage.shape[1]/n |
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602 | 609 | nMin = 0 |
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603 | 610 | nMax = 0 |
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604 | 611 | phaseOffset = numpy.zeros((len(pairslist),n)) |
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605 | 612 | |
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606 | 613 | for i in range(n): |
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607 | 614 | nMax += nLength |
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608 | 615 | phaseCCF = -numpy.angle(self.__calculateCCF(voltage[:,nMin:nMax,:], pairslist, [0])) |
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609 | 616 | phaseCCF = numpy.mean(phaseCCF, axis = 2) |
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610 | 617 | phaseOffset[:,i] = phaseCCF.transpose() |
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611 | 618 | nMin = nMax |
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612 | 619 | # phaseDiff, phaseArrival = self.estimatePhaseDifference(voltage, pairslist) |
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613 | 620 | |
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614 | 621 | #Remove Outliers |
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615 | 622 | factor = 2 |
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616 | 623 | wt = phaseOffset - signal.medfilt(phaseOffset,(1,5)) |
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617 | 624 | dw = numpy.std(wt,axis = 1) |
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618 | 625 | dw = dw.reshape((dw.size,1)) |
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619 | 626 | ind = numpy.where(numpy.logical_or(wt>dw*factor,wt<-dw*factor)) |
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620 | 627 | phaseOffset[ind] = numpy.nan |
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621 | 628 | phaseOffset = stats.nanmean(phaseOffset, axis=1) |
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622 | 629 | |
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623 | 630 | return phaseOffset |
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624 | 631 | |
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625 | 632 | def __shiftPhase(self, data, phaseShift): |
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626 | 633 | #this will shift the phase of a complex number |
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627 | 634 | dataShifted = numpy.abs(data) * numpy.exp((numpy.angle(data)+phaseShift)*1j) |
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628 | 635 | return dataShifted |
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629 | 636 | |
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630 | 637 | def __estimatePhaseDifference(self, array, pairslist): |
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631 | 638 | nChannel = array.shape[0] |
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632 | 639 | nHeights = array.shape[2] |
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633 | 640 | numPairs = len(pairslist) |
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634 | 641 | # phaseCCF = numpy.zeros((nChannel, 5, nHeights)) |
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635 | 642 | phaseCCF = numpy.angle(self.__calculateCCF(array, pairslist, [-2,-1,0,1,2])) |
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636 | 643 | |
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637 | 644 | #Correct phases |
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638 | 645 | derPhaseCCF = phaseCCF[:,1:,:] - phaseCCF[:,0:-1,:] |
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639 | 646 | indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) |
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640 | 647 | |
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641 | 648 | if indDer[0].shape[0] > 0: |
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642 | 649 | for i in range(indDer[0].shape[0]): |
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643 | 650 | signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i],indDer[2][i]]) |
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644 | 651 | phaseCCF[indDer[0][i],indDer[1][i]+1:,:] += signo*2*numpy.pi |
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645 | 652 | |
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646 | 653 | # for j in range(numSides): |
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647 | 654 | # phaseCCFAux = self.calculateCCF(arrayCenter, arraySides[j,:,:], [-2,1,0,1,2]) |
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648 | 655 | # phaseCCF[j,:,:] = numpy.angle(phaseCCFAux) |
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649 | 656 | # |
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650 | 657 | #Linear |
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651 | 658 | phaseInt = numpy.zeros((numPairs,1)) |
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652 | 659 | angAllCCF = phaseCCF[:,[0,1,3,4],0] |
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653 | 660 | for j in range(numPairs): |
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654 | 661 | fit = stats.linregress([-2,-1,1,2],angAllCCF[j,:]) |
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655 | 662 | phaseInt[j] = fit[1] |
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656 | 663 | #Phase Differences |
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657 | 664 | phaseDiff = phaseInt - phaseCCF[:,2,:] |
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658 | 665 | phaseArrival = phaseInt.reshape(phaseInt.size) |
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659 | 666 | |
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660 | 667 | #Dealias |
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661 | 668 | indAlias = numpy.where(phaseArrival > numpy.pi) |
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662 | 669 | phaseArrival[indAlias] -= 2*numpy.pi |
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663 | 670 | indAlias = numpy.where(phaseArrival < -numpy.pi) |
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664 | 671 | phaseArrival[indAlias] += 2*numpy.pi |
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665 | 672 | |
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666 | 673 | return phaseDiff, phaseArrival |
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667 | 674 | |
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668 | 675 | def __coherentDetection(self, volts, timeSegment, timeInterval, pairslist, thresh): |
|
|
669 | 676 | #this function will run the coherent detection used in Holdworth et al. 2004 and return the net power |
|
|
670 | 677 | #find the phase shifts of each channel over 1 second intervals |
|
|
671 | 678 | #only look at ranges below the beacon signal |
|
|
672 | 679 | numProfPerBlock = numpy.ceil(timeSegment/timeInterval) |
|
|
673 | 680 | numBlocks = int(volts.shape[1]/numProfPerBlock) |
|
|
674 | 681 | numHeights = volts.shape[2] |
|
|
675 | 682 | nChannel = volts.shape[0] |
|
|
676 | 683 | voltsCohDet = volts.copy() |
|
|
677 | 684 | |
|
|
678 | 685 | pairsarray = numpy.array(pairslist) |
|
|
679 | 686 | indSides = pairsarray[:,1] |
|
|
680 | 687 | # indSides = numpy.array(range(nChannel)) |
|
|
681 | 688 | # indSides = numpy.delete(indSides, indCenter) |
|
|
682 | 689 | # |
|
|
683 | 690 | # listCenter = numpy.array_split(volts[indCenter,:,:], numBlocks, 0) |
|
|
684 | 691 | listBlocks = numpy.array_split(volts, numBlocks, 1) |
|
|
685 | 692 | |
|
|
686 | 693 | startInd = 0 |
|
|
687 | 694 | endInd = 0 |
|
|
688 | 695 | |
|
|
689 | 696 | for i in range(numBlocks): |
|
|
690 | 697 | startInd = endInd |
|
|
691 | 698 | endInd = endInd + listBlocks[i].shape[1] |
|
|
692 | 699 | |
|
|
693 | 700 | arrayBlock = listBlocks[i] |
|
|
694 | 701 | # arrayBlockCenter = listCenter[i] |
|
|
695 | 702 | |
|
|
696 | 703 | #Estimate the Phase Difference |
|
|
697 | 704 | phaseDiff, aux = self.__estimatePhaseDifference(arrayBlock, pairslist) |
|
|
698 | 705 | #Phase Difference RMS |
|
|
699 | 706 | arrayPhaseRMS = numpy.abs(phaseDiff) |
|
|
700 | 707 | phaseRMSaux = numpy.sum(arrayPhaseRMS < thresh,0) |
|
|
701 | 708 | indPhase = numpy.where(phaseRMSaux==4) |
|
|
702 | 709 | #Shifting |
|
|
703 | 710 | if indPhase[0].shape[0] > 0: |
|
|
704 | 711 | for j in range(indSides.size): |
|
|
705 | 712 | arrayBlock[indSides[j],:,indPhase] = self.__shiftPhase(arrayBlock[indSides[j],:,indPhase], phaseDiff[j,indPhase].transpose()) |
|
|
706 | 713 | voltsCohDet[:,startInd:endInd,:] = arrayBlock |
|
|
707 | 714 | |
|
|
708 | 715 | return voltsCohDet |
|
|
709 | 716 | |
|
|
710 | 717 | def __calculateCCF(self, volts, pairslist ,laglist): |
|
|
711 | 718 | |
|
|
712 | 719 | nHeights = volts.shape[2] |
|
|
713 | 720 | nPoints = volts.shape[1] |
|
|
714 | 721 | voltsCCF = numpy.zeros((len(pairslist), len(laglist), nHeights),dtype = 'complex') |
|
|
715 | 722 | |
|
|
716 | 723 | for i in range(len(pairslist)): |
|
|
717 | 724 | volts1 = volts[pairslist[i][0]] |
|
|
718 | 725 | volts2 = volts[pairslist[i][1]] |
|
|
719 | 726 | |
|
|
720 | 727 | for t in range(len(laglist)): |
|
|
721 | 728 | idxT = laglist[t] |
|
|
722 | 729 | if idxT >= 0: |
|
|
723 | 730 | vStacked = numpy.vstack((volts2[idxT:,:], |
|
|
724 | 731 | numpy.zeros((idxT, nHeights),dtype='complex'))) |
|
|
725 | 732 | else: |
|
|
726 | 733 | vStacked = numpy.vstack((numpy.zeros((-idxT, nHeights),dtype='complex'), |
|
|
727 | 734 | volts2[:(nPoints + idxT),:])) |
|
|
728 | 735 | voltsCCF[i,t,:] = numpy.sum((numpy.conjugate(volts1)*vStacked),axis=0) |
|
|
729 | 736 | |
|
|
730 | 737 | vStacked = None |
|
|
731 | 738 | return voltsCCF |
|
|
732 | 739 | |
|
|
733 | 740 | def __getNoise(self, power, timeSegment, timeInterval): |
|
|
734 | 741 | numProfPerBlock = numpy.ceil(timeSegment/timeInterval) |
|
|
735 | 742 | numBlocks = int(power.shape[0]/numProfPerBlock) |
|
|
736 | 743 | numHeights = power.shape[1] |
|
|
737 | 744 | |
|
|
738 | 745 | listPower = numpy.array_split(power, numBlocks, 0) |
|
|
739 | 746 | noise = numpy.zeros((power.shape[0], power.shape[1])) |
|
|
740 | 747 | noise1 = numpy.zeros((power.shape[0], power.shape[1])) |
|
|
741 | 748 | |
|
|
742 | 749 | startInd = 0 |
|
|
743 | 750 | endInd = 0 |
|
|
744 | 751 | |
|
|
745 | 752 | for i in range(numBlocks): #split por canal |
|
|
746 | 753 | startInd = endInd |
|
|
747 | 754 | endInd = endInd + listPower[i].shape[0] |
|
|
748 | 755 | |
|
|
749 | 756 | arrayBlock = listPower[i] |
|
|
750 | 757 | noiseAux = numpy.mean(arrayBlock, 0) |
|
|
751 | 758 | # noiseAux = numpy.median(noiseAux) |
|
|
752 | 759 | # noiseAux = numpy.mean(arrayBlock) |
|
|
753 | 760 | noise[startInd:endInd,:] = noise[startInd:endInd,:] + noiseAux |
|
|
754 | 761 | |
|
|
755 | 762 | noiseAux1 = numpy.mean(arrayBlock) |
|
|
756 | 763 | noise1[startInd:endInd,:] = noise1[startInd:endInd,:] + noiseAux1 |
|
|
757 | 764 | |
|
|
758 | 765 | return noise, noise1 |
|
|
759 | 766 | |
|
|
760 | 767 | def __findMeteors(self, power, thresh): |
|
|
761 | 768 | nProf = power.shape[0] |
|
|
762 | 769 | nHeights = power.shape[1] |
|
|
763 | 770 | listMeteors = [] |
|
|
764 | 771 | |
|
|
765 | 772 | for i in range(nHeights): |
|
|
766 | 773 | powerAux = power[:,i] |
|
|
767 | 774 | threshAux = thresh[:,i] |
|
|
768 | 775 | |
|
|
769 | 776 | indUPthresh = numpy.where(powerAux > threshAux)[0] |
|
|
770 | 777 | indDNthresh = numpy.where(powerAux <= threshAux)[0] |
|
|
771 | 778 | |
|
|
772 | 779 | j = 0 |
|
|
773 | 780 | |
|
|
774 | 781 | while (j < indUPthresh.size - 2): |
|
|
775 | 782 | if (indUPthresh[j + 2] == indUPthresh[j] + 2): |
|
|
776 | 783 | indDNAux = numpy.where(indDNthresh > indUPthresh[j]) |
|
|
777 | 784 | indDNthresh = indDNthresh[indDNAux] |
|
|
778 | 785 | |
|
|
779 | 786 | if (indDNthresh.size > 0): |
|
|
780 | 787 | indEnd = indDNthresh[0] - 1 |
|
|
781 | 788 | indInit = indUPthresh[j] |
|
|
782 | 789 | |
|
|
783 | 790 | meteor = powerAux[indInit:indEnd + 1] |
|
|
784 | 791 | indPeak = meteor.argmax() + indInit |
|
|
785 | 792 | FLA = sum(numpy.conj(meteor)*numpy.hstack((meteor[1:],0))) |
|
|
786 | 793 | |
|
|
787 | 794 | listMeteors.append(numpy.array([i,indInit,indPeak,indEnd,FLA])) #CHEQUEAR!!!!! |
|
|
788 | 795 | j = numpy.where(indUPthresh == indEnd)[0] + 1 |
|
|
789 | 796 | else: j+=1 |
|
|
790 | 797 | else: j+=1 |
|
|
791 | 798 | |
|
|
792 | 799 | return listMeteors |
|
|
793 | 800 | |
|
|
794 | 801 | def __removeMultipleDetections(self,listMeteors, rangeLimit, timeLimit): |
|
|
795 | 802 | |
|
|
796 | 803 | arrayMeteors = numpy.asarray(listMeteors) |
|
|
797 | 804 | listMeteors1 = [] |
|
|
798 | 805 | |
|
|
799 | 806 | while arrayMeteors.shape[0] > 0: |
|
|
800 | 807 | FLAs = arrayMeteors[:,4] |
|
|
801 | 808 | maxFLA = FLAs.argmax() |
|
|
802 | 809 | listMeteors1.append(arrayMeteors[maxFLA,:]) |
|
|
803 | 810 | |
|
|
804 | 811 | MeteorInitTime = arrayMeteors[maxFLA,1] |
|
|
805 | 812 | MeteorEndTime = arrayMeteors[maxFLA,3] |
|
|
806 | 813 | MeteorHeight = arrayMeteors[maxFLA,0] |
|
|
807 | 814 | |
|
|
808 | 815 | #Check neighborhood |
|
|
809 | 816 | maxHeightIndex = MeteorHeight + rangeLimit |
|
|
810 | 817 | minHeightIndex = MeteorHeight - rangeLimit |
|
|
811 | 818 | minTimeIndex = MeteorInitTime - timeLimit |
|
|
812 | 819 | maxTimeIndex = MeteorEndTime + timeLimit |
|
|
813 | 820 | |
|
|
814 | 821 | #Check Heights |
|
|
815 | 822 | indHeight = numpy.logical_and(arrayMeteors[:,0] >= minHeightIndex, arrayMeteors[:,0] <= maxHeightIndex) |
|
|
816 | 823 | indTime = numpy.logical_and(arrayMeteors[:,3] >= minTimeIndex, arrayMeteors[:,1] <= maxTimeIndex) |
|
|
817 | 824 | indBoth = numpy.where(numpy.logical_and(indTime,indHeight)) |
|
|
818 | 825 | |
|
|
819 | 826 | arrayMeteors = numpy.delete(arrayMeteors, indBoth, axis = 0) |
|
|
820 | 827 | |
|
|
821 | 828 | return listMeteors1 |
|
|
822 | 829 | |
|
|
823 | 830 | def __meteorReestimation(self, listMeteors, volts, pairslist, thresh, noise, timeInterval,frequency): |
|
|
824 | 831 | numHeights = volts.shape[2] |
|
|
825 | 832 | nChannel = volts.shape[0] |
|
|
826 | 833 | |
|
|
827 | 834 | thresholdPhase = thresh[0] |
|
|
828 | 835 | thresholdNoise = thresh[1] |
|
|
829 | 836 | thresholdDB = float(thresh[2]) |
|
|
830 | 837 | |
|
|
831 | 838 | thresholdDB1 = 10**(thresholdDB/10) |
|
|
832 | 839 | pairsarray = numpy.array(pairslist) |
|
|
833 | 840 | indSides = pairsarray[:,1] |
|
|
834 | 841 | |
|
|
835 | 842 | pairslist1 = list(pairslist) |
|
|
836 | 843 | pairslist1.append((0,1)) |
|
|
837 | 844 | pairslist1.append((3,4)) |
|
|
838 | 845 | |
|
|
839 | 846 | listMeteors1 = [] |
|
|
840 | 847 | listPowerSeries = [] |
|
|
841 | 848 | listVoltageSeries = [] |
|
|
842 | 849 | #volts has the war data |
|
|
843 | 850 | |
|
|
844 | 851 | if frequency == 30e6: |
|
|
845 | 852 | timeLag = 45*10**-3 |
|
|
846 | 853 | else: |
|
|
847 | 854 | timeLag = 15*10**-3 |
|
|
848 | 855 | lag = numpy.ceil(timeLag/timeInterval) |
|
|
849 | 856 | |
|
|
850 | 857 | for i in range(len(listMeteors)): |
|
|
851 | 858 | |
|
|
852 | 859 | ###################### 3.6 - 3.7 PARAMETERS REESTIMATION ######################### |
|
|
853 | 860 | meteorAux = numpy.zeros(16) |
|
|
854 | 861 | |
|
|
855 | 862 | #Loading meteor Data (mHeight, mStart, mPeak, mEnd) |
|
|
856 | 863 | mHeight = listMeteors[i][0] |
|
|
857 | 864 | mStart = listMeteors[i][1] |
|
|
858 | 865 | mPeak = listMeteors[i][2] |
|
|
859 | 866 | mEnd = listMeteors[i][3] |
|
|
860 | 867 | |
|
|
861 | 868 | #get the volt data between the start and end times of the meteor |
|
|
862 | 869 | meteorVolts = volts[:,mStart:mEnd+1,mHeight] |
|
|
863 | 870 | meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) |
|
|
864 | 871 | |
|
|
865 | 872 | #3.6. Phase Difference estimation |
|
|
866 | 873 | phaseDiff, aux = self.__estimatePhaseDifference(meteorVolts, pairslist) |
|
|
867 | 874 | |
|
|
868 | 875 | #3.7. Phase difference removal & meteor start, peak and end times reestimated |
|
|
869 | 876 | #meteorVolts0.- all Channels, all Profiles |
|
|
870 | 877 | meteorVolts0 = volts[:,:,mHeight] |
|
|
871 | 878 | meteorThresh = noise[:,mHeight]*thresholdNoise |
|
|
872 | 879 | meteorNoise = noise[:,mHeight] |
|
|
873 | 880 | meteorVolts0[indSides,:] = self.__shiftPhase(meteorVolts0[indSides,:], phaseDiff) #Phase Shifting |
|
|
874 | 881 | powerNet0 = numpy.nansum(numpy.abs(meteorVolts0)**2, axis = 0) #Power |
|
|
875 | 882 | |
|
|
876 | 883 | #Times reestimation |
|
|
877 | 884 | mStart1 = numpy.where(powerNet0[:mPeak] < meteorThresh[:mPeak])[0] |
|
|
878 | 885 | if mStart1.size > 0: |
|
|
879 | 886 | mStart1 = mStart1[-1] + 1 |
|
|
880 | 887 | |
|
|
881 | 888 | else: |
|
|
882 | 889 | mStart1 = mPeak |
|
|
883 | 890 | |
|
|
884 | 891 | mEnd1 = numpy.where(powerNet0[mPeak:] < meteorThresh[mPeak:])[0][0] + mPeak - 1 |
|
|
885 | 892 | mEndDecayTime1 = numpy.where(powerNet0[mPeak:] < meteorNoise[mPeak:])[0] |
|
|
886 | 893 | if mEndDecayTime1.size == 0: |
|
|
887 | 894 | mEndDecayTime1 = powerNet0.size |
|
|
888 | 895 | else: |
|
|
889 | 896 | mEndDecayTime1 = mEndDecayTime1[0] + mPeak - 1 |
|
|
890 | 897 | # mPeak1 = meteorVolts0[mStart1:mEnd1 + 1].argmax() |
|
|
891 | 898 | |
|
|
892 | 899 | #meteorVolts1.- all Channels, from start to end |
|
|
893 | 900 | meteorVolts1 = meteorVolts0[:,mStart1:mEnd1 + 1] |
|
|
894 | 901 | meteorVolts2 = meteorVolts0[:,mPeak + lag:mEnd1 + 1] |
|
|
895 | 902 | if meteorVolts2.shape[1] == 0: |
|
|
896 | 903 | meteorVolts2 = meteorVolts0[:,mPeak:mEnd1 + 1] |
|
|
897 | 904 | meteorVolts1 = meteorVolts1.reshape(meteorVolts1.shape[0], meteorVolts1.shape[1], 1) |
|
|
898 | 905 | meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1], 1) |
|
|
899 | 906 | ##################### END PARAMETERS REESTIMATION ######################### |
|
|
900 | 907 | |
|
|
901 | 908 | ##################### 3.8 PHASE DIFFERENCE REESTIMATION ######################## |
|
|
902 | 909 | # if mEnd1 - mStart1 > 4: #Error Number 6: echo less than 5 samples long; too short for analysis |
|
|
903 | 910 | if meteorVolts2.shape[1] > 0: |
|
|
904 | 911 | #Phase Difference re-estimation |
|
|
905 | 912 | phaseDiff1, phaseDiffint = self.__estimatePhaseDifference(meteorVolts2, pairslist1) #Phase Difference Estimation |
|
|
906 | 913 | # phaseDiff1, phaseDiffint = self.estimatePhaseDifference(meteorVolts2, pairslist) |
|
|
907 | 914 | meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1]) |
|
|
908 | 915 | phaseDiff11 = numpy.reshape(phaseDiff1, (phaseDiff1.shape[0],1)) |
|
|
909 | 916 | meteorVolts2[indSides,:] = self.__shiftPhase(meteorVolts2[indSides,:], phaseDiff11[0:4]) #Phase Shifting |
|
|
910 | 917 | |
|
|
911 | 918 | #Phase Difference RMS |
|
|
912 | 919 | phaseRMS1 = numpy.sqrt(numpy.mean(numpy.square(phaseDiff1))) |
|
|
913 | 920 | powerNet1 = numpy.nansum(numpy.abs(meteorVolts1[:,:])**2,0) |
|
|
914 | 921 | #Data from Meteor |
|
|
915 | 922 | mPeak1 = powerNet1.argmax() + mStart1 |
|
|
916 | 923 | mPeakPower1 = powerNet1.max() |
|
|
917 | 924 | noiseAux = sum(noise[mStart1:mEnd1 + 1,mHeight]) |
|
|
918 | 925 | mSNR1 = (sum(powerNet1)-noiseAux)/noiseAux |
|
|
919 | 926 | Meteor1 = numpy.array([mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1]) |
|
|
920 | 927 | Meteor1 = numpy.hstack((Meteor1,phaseDiffint)) |
|
|
921 | 928 | PowerSeries = powerNet0[mStart1:mEndDecayTime1 + 1] |
|
|
922 | 929 | #Vectorize |
|
|
923 | 930 | meteorAux[0:7] = [mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1] |
|
|
924 | 931 | meteorAux[7:11] = phaseDiffint[0:4] |
|
|
925 | 932 | |
|
|
926 | 933 | #Rejection Criterions |
|
|
927 | 934 | if phaseRMS1 > thresholdPhase: #Error Number 17: Phase variation |
|
|
928 | 935 | meteorAux[-1] = 17 |
|
|
929 | 936 | elif mSNR1 < thresholdDB1: #Error Number 1: SNR < threshold dB |
|
|
930 | 937 | meteorAux[-1] = 1 |
|
|
931 | 938 | |
|
|
932 | 939 | |
|
|
933 | 940 | else: |
|
|
934 | 941 | meteorAux[0:4] = [mHeight, mStart, mPeak, mEnd] |
|
|
935 | 942 | meteorAux[-1] = 6 #Error Number 6: echo less than 5 samples long; too short for analysis |
|
|
936 | 943 | PowerSeries = 0 |
|
|
937 | 944 | |
|
|
938 | 945 | listMeteors1.append(meteorAux) |
|
|
939 | 946 | listPowerSeries.append(PowerSeries) |
|
|
940 | 947 | listVoltageSeries.append(meteorVolts1) |
|
|
941 | 948 | |
|
|
942 | 949 | return listMeteors1, listPowerSeries, listVoltageSeries |
|
|
943 | 950 | |
|
|
944 | 951 | def __estimateDecayTime(self, listMeteors, listPower, timeInterval, frequency): |
|
|
945 | 952 | |
|
|
946 | 953 | threshError = 10 |
|
|
947 | 954 | #Depending if it is 30 or 50 MHz |
|
|
948 | 955 | if frequency == 30e6: |
|
|
949 | 956 | timeLag = 45*10**-3 |
|
|
950 | 957 | else: |
|
|
951 | 958 | timeLag = 15*10**-3 |
|
|
952 | 959 | lag = numpy.ceil(timeLag/timeInterval) |
|
|
953 | 960 | |
|
|
954 | 961 | listMeteors1 = [] |
|
|
955 | 962 | |
|
|
956 | 963 | for i in range(len(listMeteors)): |
|
|
957 | 964 | meteorPower = listPower[i] |
|
|
958 | 965 | meteorAux = listMeteors[i] |
|
|
959 | 966 | |
|
|
960 | 967 | if meteorAux[-1] == 0: |
|
|
961 | 968 | |
|
|
962 | 969 | try: |
|
|
963 | 970 | indmax = meteorPower.argmax() |
|
|
964 | 971 | indlag = indmax + lag |
|
|
965 | 972 | |
|
|
966 | 973 | y = meteorPower[indlag:] |
|
|
967 | 974 | x = numpy.arange(0, y.size)*timeLag |
|
|
968 | 975 | |
|
|
969 | 976 | #first guess |
|
|
970 | 977 | a = y[0] |
|
|
971 | 978 | tau = timeLag |
|
|
972 | 979 | #exponential fit |
|
|
973 | 980 | popt, pcov = optimize.curve_fit(self.__exponential_function, x, y, p0 = [a, tau]) |
|
|
974 | 981 | y1 = self.__exponential_function(x, *popt) |
|
|
975 | 982 | #error estimation |
|
|
976 | 983 | error = sum((y - y1)**2)/(numpy.var(y)*(y.size - popt.size)) |
|
|
977 | 984 | |
|
|
978 | 985 | decayTime = popt[1] |
|
|
979 | 986 | riseTime = indmax*timeInterval |
|
|
980 | 987 | meteorAux[11:13] = [decayTime, error] |
|
|
981 | 988 | |
|
|
982 | 989 | #Table items 7, 8 and 11 |
|
|
983 | 990 | if (riseTime > 0.3): #Number 7: Echo rise exceeds 0.3s |
|
|
984 | 991 | meteorAux[-1] = 7 |
|
|
985 | 992 | elif (decayTime < 2*riseTime) : #Number 8: Echo decay time less than than twice rise time |
|
|
986 | 993 | meteorAux[-1] = 8 |
|
|
987 | 994 | if (error > threshError): #Number 11: Poor fit to amplitude for estimation of decay time |
|
|
988 | 995 | meteorAux[-1] = 11 |
|
|
989 | 996 | |
|
|
990 | 997 | |
|
|
991 | 998 | except: |
|
|
992 | 999 | meteorAux[-1] = 11 |
|
|
993 | 1000 | |
|
|
994 | 1001 | |
|
|
995 | 1002 | listMeteors1.append(meteorAux) |
|
|
996 | 1003 | |
|
|
997 | 1004 | return listMeteors1 |
|
|
998 | 1005 | |
|
|
999 | 1006 | #Exponential Function |
|
|
1000 | 1007 | |
|
|
1001 | 1008 | def __exponential_function(self, x, a, tau): |
|
|
1002 | 1009 | y = a*numpy.exp(-x/tau) |
|
|
1003 | 1010 | return y |
|
|
1004 | 1011 | |
|
|
1005 | 1012 | def __getRadialVelocity(self, listMeteors, listVolts, radialStdThresh, pairslist, timeInterval): |
|
|
1006 | 1013 | |
|
|
1007 | 1014 | pairslist1 = list(pairslist) |
|
|
1008 | 1015 | pairslist1.append((0,1)) |
|
|
1009 | 1016 | pairslist1.append((3,4)) |
|
|
1010 | 1017 | numPairs = len(pairslist1) |
|
|
1011 | 1018 | #Time Lag |
|
|
1012 | 1019 | timeLag = 45*10**-3 |
|
|
1013 | 1020 | c = 3e8 |
|
|
1014 | 1021 | lag = numpy.ceil(timeLag/timeInterval) |
|
|
1015 | 1022 | freq = 30e6 |
|
|
1016 | 1023 | |
|
|
1017 | 1024 | listMeteors1 = [] |
|
|
1018 | 1025 | |
|
|
1019 | 1026 | for i in range(len(listMeteors)): |
|
|
1020 | 1027 | meteorAux = listMeteors[i] |
|
|
1021 | 1028 | if meteorAux[-1] == 0: |
|
|
1022 | 1029 | mStart = listMeteors[i][1] |
|
|
1023 | 1030 | mPeak = listMeteors[i][2] |
|
|
1024 | 1031 | mLag = mPeak - mStart + lag |
|
|
1025 | 1032 | |
|
|
1026 | 1033 | #get the volt data between the start and end times of the meteor |
|
|
1027 | 1034 | meteorVolts = listVolts[i] |
|
|
1028 | 1035 | meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) |
|
|
1029 | 1036 | |
|
|
1030 | 1037 | #Get CCF |
|
|
1031 | 1038 | allCCFs = self.__calculateCCF(meteorVolts, pairslist1, [-2,-1,0,1,2]) |
|
|
1032 | 1039 | |
|
|
1033 | 1040 | #Method 2 |
|
|
1034 | 1041 | slopes = numpy.zeros(numPairs) |
|
|
1035 | 1042 | time = numpy.array([-2,-1,1,2])*timeInterval |
|
|
1036 | 1043 | angAllCCF = numpy.angle(allCCFs[:,[0,1,3,4],0]) |
|
|
1037 | 1044 | |
|
|
1038 | 1045 | #Correct phases |
|
|
1039 | 1046 | derPhaseCCF = angAllCCF[:,1:] - angAllCCF[:,0:-1] |
|
|
1040 | 1047 | indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) |
|
|
1041 | 1048 | |
|
|
1042 | 1049 | if indDer[0].shape[0] > 0: |
|
|
1043 | 1050 | for i in range(indDer[0].shape[0]): |
|
|
1044 | 1051 | signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i]]) |
|
|
1045 | 1052 | angAllCCF[indDer[0][i],indDer[1][i]+1:] += signo*2*numpy.pi |
|
|
1046 | 1053 | |
|
|
1047 | 1054 | # fit = scipy.stats.linregress(numpy.array([-2,-1,1,2])*timeInterval, numpy.array([phaseLagN2s[i],phaseLagN1s[i],phaseLag1s[i],phaseLag2s[i]])) |
|
|
1048 | 1055 | for j in range(numPairs): |
|
|
1049 | 1056 | fit = stats.linregress(time, angAllCCF[j,:]) |
|
|
1050 | 1057 | slopes[j] = fit[0] |
|
|
1051 | 1058 | |
|
|
1052 | 1059 | #Remove Outlier |
|
|
1053 | 1060 | # indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes))) |
|
|
1054 | 1061 | # slopes = numpy.delete(slopes,indOut) |
|
|
1055 | 1062 | # indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes))) |
|
|
1056 | 1063 | # slopes = numpy.delete(slopes,indOut) |
|
|
1057 | 1064 | |
|
|
1058 | 1065 | radialVelocity = -numpy.mean(slopes)*(0.25/numpy.pi)*(c/freq) |
|
|
1059 | 1066 | radialError = numpy.std(slopes)*(0.25/numpy.pi)*(c/freq) |
|
|
1060 | 1067 | meteorAux[-2] = radialError |
|
|
1061 | 1068 | meteorAux[-3] = radialVelocity |
|
|
1062 | 1069 | |
|
|
1063 | 1070 | #Setting Error |
|
|
1064 | 1071 | #Number 15: Radial Drift velocity or projected horizontal velocity exceeds 200 m/s |
|
|
1065 | 1072 | if numpy.abs(radialVelocity) > 200: |
|
|
1066 | 1073 | meteorAux[-1] = 15 |
|
|
1067 | 1074 | #Number 12: Poor fit to CCF variation for estimation of radial drift velocity |
|
|
1068 | 1075 | elif radialError > radialStdThresh: |
|
|
1069 | 1076 | meteorAux[-1] = 12 |
|
|
1070 | 1077 | |
|
|
1071 | 1078 | listMeteors1.append(meteorAux) |
|
|
1072 | 1079 | return listMeteors1 |
|
|
1073 | 1080 | |
|
|
1074 | 1081 | def __setNewArrays(self, listMeteors, date, heiRang): |
|
|
1075 | 1082 | |
|
|
1076 | 1083 | #New arrays |
|
|
1077 | 1084 | arrayMeteors = numpy.array(listMeteors) |
|
|
1078 | 1085 | arrayParameters = numpy.zeros((len(listMeteors), 13)) |
|
|
1079 | 1086 | |
|
|
1080 | 1087 | #Date inclusion |
|
|
1081 | 1088 | # date = re.findall(r'\((.*?)\)', date) |
|
|
1082 | 1089 | # date = date[0].split(',') |
|
|
1083 | 1090 | # date = map(int, date) |
|
|
1084 | 1091 | # |
|
|
1085 | 1092 | # if len(date)<6: |
|
|
1086 | 1093 | # date.append(0) |
|
|
1087 | 1094 | # |
|
|
1088 | 1095 | # date = [date[0]*10000 + date[1]*100 + date[2], date[3]*10000 + date[4]*100 + date[5]] |
|
|
1089 | 1096 | # arrayDate = numpy.tile(date, (len(listMeteors), 1)) |
|
|
1090 | 1097 | arrayDate = numpy.tile(date, (len(listMeteors))) |
|
|
1091 | 1098 | |
|
|
1092 | 1099 | #Meteor array |
|
|
1093 | 1100 | # arrayMeteors[:,0] = heiRang[arrayMeteors[:,0].astype(int)] |
|
|
1094 | 1101 | # arrayMeteors = numpy.hstack((arrayDate, arrayMeteors)) |
|
|
1095 | 1102 | |
|
|
1096 | 1103 | #Parameters Array |
|
|
1097 | 1104 | arrayParameters[:,0] = arrayDate #Date |
|
|
1098 | 1105 | arrayParameters[:,1] = heiRang[arrayMeteors[:,0].astype(int)] #Range |
|
|
1099 | 1106 | arrayParameters[:,6:8] = arrayMeteors[:,-3:-1] #Radial velocity and its error |
|
|
1100 | 1107 | arrayParameters[:,8:12] = arrayMeteors[:,7:11] #Phases |
|
|
1101 | 1108 | arrayParameters[:,-1] = arrayMeteors[:,-1] #Error |
|
|
1102 | 1109 | |
|
|
1103 | 1110 | |
|
|
1104 | 1111 | return arrayParameters |
|
|
1105 | 1112 | |
|
|
1106 | 1113 | # def __getAOA(self, phases, pairsList, error, AOAthresh, azimuth): |
|
|
1107 | 1114 | # |
|
|
1108 | 1115 | # arrayAOA = numpy.zeros((phases.shape[0],3)) |
|
|
1109 | 1116 | # cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList) |
|
|
1110 | 1117 | # |
|
|
1111 | 1118 | # arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth) |
|
|
1112 | 1119 | # cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1) |
|
|
1113 | 1120 | # arrayAOA[:,2] = cosDirError |
|
|
1114 | 1121 | # |
|
|
1115 | 1122 | # azimuthAngle = arrayAOA[:,0] |
|
|
1116 | 1123 | # zenithAngle = arrayAOA[:,1] |
|
|
1117 | 1124 | # |
|
|
1118 | 1125 | # #Setting Error |
|
|
1119 | 1126 | # #Number 3: AOA not fesible |
|
|
1120 | 1127 | # indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0] |
|
|
1121 | 1128 | # error[indInvalid] = 3 |
|
|
1122 | 1129 | # #Number 4: Large difference in AOAs obtained from different antenna baselines |
|
|
1123 | 1130 | # indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0] |
|
|
1124 | 1131 | # error[indInvalid] = 4 |
|
|
1125 | 1132 | # return arrayAOA, error |
|
|
1126 | 1133 | # |
|
|
1127 | 1134 | # def __getDirectionCosines(self, arrayPhase, pairsList): |
|
|
1128 | 1135 | # |
|
|
1129 | 1136 | # #Initializing some variables |
|
|
1130 | 1137 | # ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi |
|
|
1131 | 1138 | # ang_aux = ang_aux.reshape(1,ang_aux.size) |
|
|
1132 | 1139 | # |
|
|
1133 | 1140 | # cosdir = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
1134 | 1141 | # cosdir0 = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
1135 | 1142 | # |
|
|
1136 | 1143 | # |
|
|
1137 | 1144 | # for i in range(2): |
|
|
1138 | 1145 | # #First Estimation |
|
|
1139 | 1146 | # phi0_aux = arrayPhase[:,pairsList[i][0]] + arrayPhase[:,pairsList[i][1]] |
|
|
1140 | 1147 | # #Dealias |
|
|
1141 | 1148 | # indcsi = numpy.where(phi0_aux > numpy.pi) |
|
|
1142 | 1149 | # phi0_aux[indcsi] -= 2*numpy.pi |
|
|
1143 | 1150 | # indcsi = numpy.where(phi0_aux < -numpy.pi) |
|
|
1144 | 1151 | # phi0_aux[indcsi] += 2*numpy.pi |
|
|
1145 | 1152 | # #Direction Cosine 0 |
|
|
1146 | 1153 | # cosdir0[:,i] = -(phi0_aux)/(2*numpy.pi*0.5) |
|
|
1147 | 1154 | # |
|
|
1148 | 1155 | # #Most-Accurate Second Estimation |
|
|
1149 | 1156 | # phi1_aux = arrayPhase[:,pairsList[i][0]] - arrayPhase[:,pairsList[i][1]] |
|
|
1150 | 1157 | # phi1_aux = phi1_aux.reshape(phi1_aux.size,1) |
|
|
1151 | 1158 | # #Direction Cosine 1 |
|
|
1152 | 1159 | # cosdir1 = -(phi1_aux + ang_aux)/(2*numpy.pi*4.5) |
|
|
1153 | 1160 | # |
|
|
1154 | 1161 | # #Searching the correct Direction Cosine |
|
|
1155 | 1162 | # cosdir0_aux = cosdir0[:,i] |
|
|
1156 | 1163 | # cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1) |
|
|
1157 | 1164 | # #Minimum Distance |
|
|
1158 | 1165 | # cosDiff = (cosdir1 - cosdir0_aux)**2 |
|
|
1159 | 1166 | # indcos = cosDiff.argmin(axis = 1) |
|
|
1160 | 1167 | # #Saving Value obtained |
|
|
1161 | 1168 | # cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos] |
|
|
1162 | 1169 | # |
|
|
1163 | 1170 | # return cosdir0, cosdir |
|
|
1164 | 1171 | # |
|
|
1165 | 1172 | # def __calculateAOA(self, cosdir, azimuth): |
|
|
1166 | 1173 | # cosdirX = cosdir[:,0] |
|
|
1167 | 1174 | # cosdirY = cosdir[:,1] |
|
|
1168 | 1175 | # |
|
|
1169 | 1176 | # zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi |
|
|
1170 | 1177 | # azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth #0 deg north, 90 deg east |
|
|
1171 | 1178 | # angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose() |
|
|
1172 | 1179 | # |
|
|
1173 | 1180 | # return angles |
|
|
1174 | 1181 | # |
|
|
1175 | 1182 | # def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight): |
|
|
1176 | 1183 | # |
|
|
1177 | 1184 | # Ramb = 375 #Ramb = c/(2*PRF) |
|
|
1178 | 1185 | # Re = 6371 #Earth Radius |
|
|
1179 | 1186 | # heights = numpy.zeros(Ranges.shape) |
|
|
1180 | 1187 | # |
|
|
1181 | 1188 | # R_aux = numpy.array([0,1,2])*Ramb |
|
|
1182 | 1189 | # R_aux = R_aux.reshape(1,R_aux.size) |
|
|
1183 | 1190 | # |
|
|
1184 | 1191 | # Ranges = Ranges.reshape(Ranges.size,1) |
|
|
1185 | 1192 | # |
|
|
1186 | 1193 | # Ri = Ranges + R_aux |
|
|
1187 | 1194 | # hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re |
|
|
1188 | 1195 | # |
|
|
1189 | 1196 | # #Check if there is a height between 70 and 110 km |
|
|
1190 | 1197 | # h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1) |
|
|
1191 | 1198 | # ind_h = numpy.where(h_bool == 1)[0] |
|
|
1192 | 1199 | # |
|
|
1193 | 1200 | # hCorr = hi[ind_h, :] |
|
|
1194 | 1201 | # ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight)) |
|
|
1195 | 1202 | # |
|
|
1196 | 1203 | # hCorr = hi[ind_hCorr] |
|
|
1197 | 1204 | # heights[ind_h] = hCorr |
|
|
1198 | 1205 | # |
|
|
1199 | 1206 | # #Setting Error |
|
|
1200 | 1207 | # #Number 13: Height unresolvable echo: not valid height within 70 to 110 km |
|
|
1201 | 1208 | # #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km |
|
|
1202 | 1209 | # |
|
|
1203 | 1210 | # indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0] |
|
|
1204 | 1211 | # error[indInvalid2] = 14 |
|
|
1205 | 1212 | # indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0] |
|
|
1206 | 1213 | # error[indInvalid1] = 13 |
|
|
1207 | 1214 | # |
|
|
1208 | 1215 | # return heights, error |
|
|
1209 | 1216 | |
|
|
1210 | 1217 | def SpectralFitting(self, getSNR = True, path=None, file=None, groupList=None): |
|
|
1211 | 1218 | |
|
|
1212 | 1219 | ''' |
|
|
1213 | 1220 | Function GetMoments() |
|
|
1214 | 1221 | |
|
|
1215 | 1222 | Input: |
|
|
1216 | 1223 | Output: |
|
|
1217 | 1224 | Variables modified: |
|
|
1218 | 1225 | ''' |
|
|
1219 | 1226 | if path != None: |
|
|
1220 | 1227 | sys.path.append(path) |
|
|
1221 | 1228 | self.dataOut.library = importlib.import_module(file) |
|
|
1222 | 1229 | |
|
|
1223 | 1230 | #To be inserted as a parameter |
|
|
1224 | 1231 | groupArray = numpy.array(groupList) |
|
|
1225 | 1232 | # groupArray = numpy.array([[0,1],[2,3]]) |
|
|
1226 | 1233 | self.dataOut.groupList = groupArray |
|
|
1227 | 1234 | |
|
|
1228 | 1235 | nGroups = groupArray.shape[0] |
|
|
1229 | 1236 | nChannels = self.dataIn.nChannels |
|
|
1230 | 1237 | nHeights=self.dataIn.heightList.size |
|
|
1231 | 1238 | |
|
|
1232 | 1239 | #Parameters Array |
|
|
1233 | 1240 | self.dataOut.data_param = None |
|
|
1234 | 1241 | |
|
|
1235 | 1242 | #Set constants |
|
|
1236 | 1243 | constants = self.dataOut.library.setConstants(self.dataIn) |
|
|
1237 | 1244 | self.dataOut.constants = constants |
|
|
1238 | 1245 | M = self.dataIn.normFactor |
|
|
1239 | 1246 | N = self.dataIn.nFFTPoints |
|
|
1240 | 1247 | ippSeconds = self.dataIn.ippSeconds |
|
|
1241 | 1248 | K = self.dataIn.nIncohInt |
|
|
1242 | 1249 | pairsArray = numpy.array(self.dataIn.pairsList) |
|
|
1243 | 1250 | |
|
|
1244 | 1251 | #List of possible combinations |
|
|
1245 | 1252 | listComb = itertools.combinations(numpy.arange(groupArray.shape[1]),2) |
|
|
1246 | 1253 | indCross = numpy.zeros(len(list(listComb)), dtype = 'int') |
|
|
1247 | 1254 | |
|
|
1248 | 1255 | if getSNR: |
|
|
1249 | 1256 | listChannels = groupArray.reshape((groupArray.size)) |
|
|
1250 | 1257 | listChannels.sort() |
|
|
1251 | 1258 | noise = self.dataIn.getNoise() |
|
|
1252 | 1259 | self.dataOut.data_SNR = self.__getSNR(self.dataIn.data_spc[listChannels,:,:], noise[listChannels]) |
|
|
1253 | 1260 | |
|
|
1254 | 1261 | for i in range(nGroups): |
|
|
1255 | 1262 | coord = groupArray[i,:] |
|
|
1256 | 1263 | |
|
|
1257 | 1264 | #Input data array |
|
|
1258 | 1265 | data = self.dataIn.data_spc[coord,:,:]/(M*N) |
|
|
1259 | 1266 | data = data.reshape((data.shape[0]*data.shape[1],data.shape[2])) |
|
|
1260 | 1267 | |
|
|
1261 | 1268 | #Cross Spectra data array for Covariance Matrixes |
|
|
1262 | 1269 | ind = 0 |
|
|
1263 | 1270 | for pairs in listComb: |
|
|
1264 | 1271 | pairsSel = numpy.array([coord[x],coord[y]]) |
|
|
1265 | 1272 | indCross[ind] = int(numpy.where(numpy.all(pairsArray == pairsSel, axis = 1))[0][0]) |
|
|
1266 | 1273 | ind += 1 |
|
|
1267 | 1274 | dataCross = self.dataIn.data_cspc[indCross,:,:]/(M*N) |
|
|
1268 | 1275 | dataCross = dataCross**2/K |
|
|
1269 | 1276 | |
|
|
1270 | 1277 | for h in range(nHeights): |
|
|
1271 | 1278 | # print self.dataOut.heightList[h] |
|
|
1272 | 1279 | |
|
|
1273 | 1280 | #Input |
|
|
1274 | 1281 | d = data[:,h] |
|
|
1275 | 1282 | |
|
|
1276 | 1283 | #Covariance Matrix |
|
|
1277 | 1284 | D = numpy.diag(d**2/K) |
|
|
1278 | 1285 | ind = 0 |
|
|
1279 | 1286 | for pairs in listComb: |
|
|
1280 | 1287 | #Coordinates in Covariance Matrix |
|
|
1281 | 1288 | x = pairs[0] |
|
|
1282 | 1289 | y = pairs[1] |
|
|
1283 | 1290 | #Channel Index |
|
|
1284 | 1291 | S12 = dataCross[ind,:,h] |
|
|
1285 | 1292 | D12 = numpy.diag(S12) |
|
|
1286 | 1293 | #Completing Covariance Matrix with Cross Spectras |
|
|
1287 | 1294 | D[x*N:(x+1)*N,y*N:(y+1)*N] = D12 |
|
|
1288 | 1295 | D[y*N:(y+1)*N,x*N:(x+1)*N] = D12 |
|
|
1289 | 1296 | ind += 1 |
|
|
1290 | 1297 | Dinv=numpy.linalg.inv(D) |
|
|
1291 | 1298 | L=numpy.linalg.cholesky(Dinv) |
|
|
1292 | 1299 | LT=L.T |
|
|
1293 | 1300 | |
|
|
1294 | 1301 | dp = numpy.dot(LT,d) |
|
|
1295 | 1302 | |
|
|
1296 | 1303 | #Initial values |
|
|
1297 | 1304 | data_spc = self.dataIn.data_spc[coord,:,h] |
|
|
1298 | 1305 | |
|
|
1299 | 1306 | if (h>0)and(error1[3]<5): |
|
|
1300 | 1307 | p0 = self.dataOut.data_param[i,:,h-1] |
|
|
1301 | 1308 | else: |
|
|
1302 | 1309 | p0 = numpy.array(self.dataOut.library.initialValuesFunction(data_spc, constants, i)) |
|
|
1303 | 1310 | |
|
|
1304 | 1311 | try: |
|
|
1305 | 1312 | #Least Squares |
|
|
1306 | 1313 | minp,covp,infodict,mesg,ier = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants),full_output=True) |
|
|
1307 | 1314 | # minp,covp = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants)) |
|
|
1308 | 1315 | #Chi square error |
|
|
1309 | 1316 | error0 = numpy.sum(infodict['fvec']**2)/(2*N) |
|
|
1310 | 1317 | #Error with Jacobian |
|
|
1311 | 1318 | error1 = self.dataOut.library.errorFunction(minp,constants,LT) |
|
|
1312 | 1319 | except: |
|
|
1313 | 1320 | minp = p0*numpy.nan |
|
|
1314 | 1321 | error0 = numpy.nan |
|
|
1315 | 1322 | error1 = p0*numpy.nan |
|
|
1316 | 1323 | |
|
|
1317 | 1324 | #Save |
|
|
1318 | 1325 | if self.dataOut.data_param == None: |
|
|
1319 | 1326 | self.dataOut.data_param = numpy.zeros((nGroups, p0.size, nHeights))*numpy.nan |
|
|
1320 | 1327 | self.dataOut.data_error = numpy.zeros((nGroups, p0.size + 1, nHeights))*numpy.nan |
|
|
1321 | 1328 | |
|
|
1322 | 1329 | self.dataOut.data_error[i,:,h] = numpy.hstack((error0,error1)) |
|
|
1323 | 1330 | self.dataOut.data_param[i,:,h] = minp |
|
|
1324 | 1331 | return |
|
|
1325 | 1332 | |
|
|
1326 | 1333 | def __residFunction(self, p, dp, LT, constants): |
|
|
1327 | 1334 | |
|
|
1328 | 1335 | fm = self.dataOut.library.modelFunction(p, constants) |
|
|
1329 | 1336 | fmp=numpy.dot(LT,fm) |
|
|
1330 | 1337 | |
|
|
1331 | 1338 | return dp-fmp |
|
|
1332 | 1339 | |
|
|
1333 | 1340 | def __getSNR(self, z, noise): |
|
|
1334 | 1341 | |
|
|
1335 | 1342 | avg = numpy.average(z, axis=1) |
|
|
1336 | 1343 | SNR = (avg.T-noise)/noise |
|
|
1337 | 1344 | SNR = SNR.T |
|
|
1338 | 1345 | return SNR |
|
|
1339 | 1346 | |
|
|
1340 | 1347 | def __chisq(p,chindex,hindex): |
|
|
1341 | 1348 | #similar to Resid but calculates CHI**2 |
|
|
1342 | 1349 | [LT,d,fm]=setupLTdfm(p,chindex,hindex) |
|
|
1343 | 1350 | dp=numpy.dot(LT,d) |
|
|
1344 | 1351 | fmp=numpy.dot(LT,fm) |
|
|
1345 | 1352 | chisq=numpy.dot((dp-fmp).T,(dp-fmp)) |
|
|
1346 | 1353 | return chisq |
|
|
1347 | 1354 | |
|
|
1348 | 1355 | |
|
|
1349 | 1356 | class WindProfiler(Operation): |
|
|
1350 | 1357 | |
|
|
1351 | 1358 | __isConfig = False |
|
|
1352 | 1359 | |
|
|
1353 | 1360 | __initime = None |
|
|
1354 | 1361 | __lastdatatime = None |
|
|
1355 | 1362 | __integrationtime = None |
|
|
1356 | 1363 | |
|
|
1357 | 1364 | __buffer = None |
|
|
1358 | 1365 | |
|
|
1359 | 1366 | __dataReady = False |
|
|
1360 | 1367 | |
|
|
1361 | 1368 | __firstdata = None |
|
|
1362 | 1369 | |
|
|
1363 | 1370 | n = None |
|
|
1364 | 1371 | |
|
|
1365 | 1372 | def __init__(self): |
|
|
1366 | 1373 | Operation.__init__(self) |
|
|
1367 | 1374 | |
|
|
1368 | 1375 | def __calculateCosDir(self, elev, azim): |
|
|
1369 | 1376 | zen = (90 - elev)*numpy.pi/180 |
|
|
1370 | 1377 | azim = azim*numpy.pi/180 |
|
|
1371 | 1378 | cosDirX = numpy.sqrt((1-numpy.cos(zen)**2)/((1+numpy.tan(azim)**2))) |
|
|
1372 | 1379 | cosDirY = numpy.sqrt(1-numpy.cos(zen)**2-cosDirX**2) |
|
|
1373 | 1380 | |
|
|
1374 | 1381 | signX = numpy.sign(numpy.cos(azim)) |
|
|
1375 | 1382 | signY = numpy.sign(numpy.sin(azim)) |
|
|
1376 | 1383 | |
|
|
1377 | 1384 | cosDirX = numpy.copysign(cosDirX, signX) |
|
|
1378 | 1385 | cosDirY = numpy.copysign(cosDirY, signY) |
|
|
1379 | 1386 | return cosDirX, cosDirY |
|
|
1380 | 1387 | |
|
|
1381 | 1388 | def __calculateAngles(self, theta_x, theta_y, azimuth): |
|
|
1382 | 1389 | |
|
|
1383 | 1390 | dir_cosw = numpy.sqrt(1-theta_x**2-theta_y**2) |
|
|
1384 | 1391 | zenith_arr = numpy.arccos(dir_cosw) |
|
|
1385 | 1392 | azimuth_arr = numpy.arctan2(theta_x,theta_y) + azimuth*math.pi/180 |
|
|
1386 | 1393 | |
|
|
1387 | 1394 | dir_cosu = numpy.sin(azimuth_arr)*numpy.sin(zenith_arr) |
|
|
1388 | 1395 | dir_cosv = numpy.cos(azimuth_arr)*numpy.sin(zenith_arr) |
|
|
1389 | 1396 | |
|
|
1390 | 1397 | return azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw |
|
|
1391 | 1398 | |
|
|
1392 | 1399 | def __calculateMatA(self, dir_cosu, dir_cosv, dir_cosw, horOnly): |
|
|
1393 | 1400 | |
|
|
1394 | 1401 | # |
|
|
1395 | 1402 | if horOnly: |
|
|
1396 | 1403 | A = numpy.c_[dir_cosu,dir_cosv] |
|
|
1397 | 1404 | else: |
|
|
1398 | 1405 | A = numpy.c_[dir_cosu,dir_cosv,dir_cosw] |
|
|
1399 | 1406 | A = numpy.asmatrix(A) |
|
|
1400 | 1407 | A1 = numpy.linalg.inv(A.transpose()*A)*A.transpose() |
|
|
1401 | 1408 | |
|
|
1402 | 1409 | return A1 |
|
|
1403 | 1410 | |
|
|
1404 | 1411 | def __correctValues(self, heiRang, phi, velRadial, SNR): |
|
|
1405 | 1412 | listPhi = phi.tolist() |
|
|
1406 | 1413 | maxid = listPhi.index(max(listPhi)) |
|
|
1407 | 1414 | minid = listPhi.index(min(listPhi)) |
|
|
1408 | 1415 | |
|
|
1409 | 1416 | rango = range(len(phi)) |
|
|
1410 | 1417 | # rango = numpy.delete(rango,maxid) |
|
|
1411 | 1418 | |
|
|
1412 | 1419 | heiRang1 = heiRang*math.cos(phi[maxid]) |
|
|
1413 | 1420 | heiRangAux = heiRang*math.cos(phi[minid]) |
|
|
1414 | 1421 | indOut = (heiRang1 < heiRangAux[0]).nonzero() |
|
|
1415 | 1422 | heiRang1 = numpy.delete(heiRang1,indOut) |
|
|
1416 | 1423 | |
|
|
1417 | 1424 | velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
1418 | 1425 | SNR1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
1419 | 1426 | |
|
|
1420 | 1427 | for i in rango: |
|
|
1421 | 1428 | x = heiRang*math.cos(phi[i]) |
|
|
1422 | 1429 | y1 = velRadial[i,:] |
|
|
1423 | 1430 | f1 = interpolate.interp1d(x,y1,kind = 'cubic') |
|
|
1424 | 1431 | |
|
|
1425 | 1432 | x1 = heiRang1 |
|
|
1426 | 1433 | y11 = f1(x1) |
|
|
1427 | 1434 | |
|
|
1428 | 1435 | y2 = SNR[i,:] |
|
|
1429 | 1436 | f2 = interpolate.interp1d(x,y2,kind = 'cubic') |
|
|
1430 | 1437 | y21 = f2(x1) |
|
|
1431 | 1438 | |
|
|
1432 | 1439 | velRadial1[i,:] = y11 |
|
|
1433 | 1440 | SNR1[i,:] = y21 |
|
|
1434 | 1441 | |
|
|
1435 | 1442 | return heiRang1, velRadial1, SNR1 |
|
|
1436 | 1443 | |
|
|
1437 | 1444 | def __calculateVelUVW(self, A, velRadial): |
|
|
1438 | 1445 | |
|
|
1439 | 1446 | #Operacion Matricial |
|
|
1440 | 1447 | # velUVW = numpy.zeros((velRadial.shape[1],3)) |
|
|
1441 | 1448 | # for ind in range(velRadial.shape[1]): |
|
|
1442 | 1449 | # velUVW[ind,:] = numpy.dot(A,velRadial[:,ind]) |
|
|
1443 | 1450 | # velUVW = velUVW.transpose() |
|
|
1444 | 1451 | velUVW = numpy.zeros((A.shape[0],velRadial.shape[1])) |
|
|
1445 | 1452 | velUVW[:,:] = numpy.dot(A,velRadial) |
|
|
1446 | 1453 | |
|
|
1447 | 1454 | |
|
|
1448 | 1455 | return velUVW |
|
|
1449 | 1456 | |
|
|
1450 | 1457 | def techniqueDBS(self, velRadial0, dirCosx, disrCosy, azimuth, correct, horizontalOnly, heiRang, SNR0): |
|
|
1451 | 1458 | """ |
|
|
1452 | 1459 | Function that implements Doppler Beam Swinging (DBS) technique. |
|
|
1453 | 1460 | |
|
|
1454 | 1461 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, |
|
|
1455 | 1462 | Direction correction (if necessary), Ranges and SNR |
|
|
1456 | 1463 | |
|
|
1457 | 1464 | Output: Winds estimation (Zonal, Meridional and Vertical) |
|
|
1458 | 1465 | |
|
|
1459 | 1466 | Parameters affected: Winds, height range, SNR |
|
|
1460 | 1467 | """ |
|
|
1461 | 1468 | azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(dirCosx, disrCosy, azimuth) |
|
|
1462 | 1469 | heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, zenith_arr, correct*velRadial0, SNR0) |
|
|
1463 | 1470 | A = self.__calculateMatA(dir_cosu, dir_cosv, dir_cosw, horizontalOnly) |
|
|
1464 | 1471 | |
|
|
1465 | 1472 | #Calculo de Componentes de la velocidad con DBS |
|
|
1466 | 1473 | winds = self.__calculateVelUVW(A,velRadial1) |
|
|
1467 | 1474 | |
|
|
1468 | 1475 | return winds, heiRang1, SNR1 |
|
|
1469 | 1476 | |
|
|
1470 | 1477 | def __calculateDistance(self, posx, posy, pairsCrossCorr, pairsList, pairs, azimuth = None): |
|
|
1471 | 1478 | |
|
|
1472 | 1479 | posx = numpy.asarray(posx) |
|
|
1473 | 1480 | posy = numpy.asarray(posy) |
|
|
1474 | 1481 | |
|
|
1475 | 1482 | #Rotacion Inversa para alinear con el azimuth |
|
|
1476 | 1483 | if azimuth!= None: |
|
|
1477 | 1484 | azimuth = azimuth*math.pi/180 |
|
|
1478 | 1485 | posx1 = posx*math.cos(azimuth) + posy*math.sin(azimuth) |
|
|
1479 | 1486 | posy1 = -posx*math.sin(azimuth) + posy*math.cos(azimuth) |
|
|
1480 | 1487 | else: |
|
|
1481 | 1488 | posx1 = posx |
|
|
1482 | 1489 | posy1 = posy |
|
|
1483 | 1490 | |
|
|
1484 | 1491 | #Calculo de Distancias |
|
|
1485 | 1492 | distx = numpy.zeros(pairsCrossCorr.size) |
|
|
1486 | 1493 | disty = numpy.zeros(pairsCrossCorr.size) |
|
|
1487 | 1494 | dist = numpy.zeros(pairsCrossCorr.size) |
|
|
1488 | 1495 | ang = numpy.zeros(pairsCrossCorr.size) |
|
|
1489 | 1496 | |
|
|
1490 | 1497 | for i in range(pairsCrossCorr.size): |
|
|
1491 | 1498 | distx[i] = posx1[pairsList[pairsCrossCorr[i]][1]] - posx1[pairsList[pairsCrossCorr[i]][0]] |
|
|
1492 | 1499 | disty[i] = posy1[pairsList[pairsCrossCorr[i]][1]] - posy1[pairsList[pairsCrossCorr[i]][0]] |
|
|
1493 | 1500 | dist[i] = numpy.sqrt(distx[i]**2 + disty[i]**2) |
|
|
1494 | 1501 | ang[i] = numpy.arctan2(disty[i],distx[i]) |
|
|
1495 | 1502 | #Calculo de Matrices |
|
|
1496 | 1503 | nPairs = len(pairs) |
|
|
1497 | 1504 | ang1 = numpy.zeros((nPairs, 2, 1)) |
|
|
1498 | 1505 | dist1 = numpy.zeros((nPairs, 2, 1)) |
|
|
1499 | 1506 | |
|
|
1500 | 1507 | for j in range(nPairs): |
|
|
1501 | 1508 | dist1[j,0,0] = dist[pairs[j][0]] |
|
|
1502 | 1509 | dist1[j,1,0] = dist[pairs[j][1]] |
|
|
1503 | 1510 | ang1[j,0,0] = ang[pairs[j][0]] |
|
|
1504 | 1511 | ang1[j,1,0] = ang[pairs[j][1]] |
|
|
1505 | 1512 | |
|
|
1506 | 1513 | return distx,disty, dist1,ang1 |
|
|
1507 | 1514 | |
|
|
1508 | 1515 | def __calculateVelVer(self, phase, lagTRange, _lambda): |
|
|
1509 | 1516 | |
|
|
1510 | 1517 | Ts = lagTRange[1] - lagTRange[0] |
|
|
1511 | 1518 | velW = -_lambda*phase/(4*math.pi*Ts) |
|
|
1512 | 1519 | |
|
|
1513 | 1520 | return velW |
|
|
1514 | 1521 | |
|
|
1515 | 1522 | def __calculateVelHorDir(self, dist, tau1, tau2, ang): |
|
|
1516 | 1523 | nPairs = tau1.shape[0] |
|
|
1517 | 1524 | vel = numpy.zeros((nPairs,3,tau1.shape[2])) |
|
|
1518 | 1525 | |
|
|
1519 | 1526 | angCos = numpy.cos(ang) |
|
|
1520 | 1527 | angSin = numpy.sin(ang) |
|
|
1521 | 1528 | |
|
|
1522 | 1529 | vel0 = dist*tau1/(2*tau2**2) |
|
|
1523 | 1530 | vel[:,0,:] = (vel0*angCos).sum(axis = 1) |
|
|
1524 | 1531 | vel[:,1,:] = (vel0*angSin).sum(axis = 1) |
|
|
1525 | 1532 | |
|
|
1526 | 1533 | ind = numpy.where(numpy.isinf(vel)) |
|
|
1527 | 1534 | vel[ind] = numpy.nan |
|
|
1528 | 1535 | |
|
|
1529 | 1536 | return vel |
|
|
1530 | 1537 | |
|
|
1531 | 1538 | def __getPairsAutoCorr(self, pairsList, nChannels): |
|
|
1532 | 1539 | |
|
|
1533 | 1540 | pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan |
|
|
1534 | 1541 | |
|
|
1535 | 1542 | for l in range(len(pairsList)): |
|
|
1536 | 1543 | firstChannel = pairsList[l][0] |
|
|
1537 | 1544 | secondChannel = pairsList[l][1] |
|
|
1538 | 1545 | |
|
|
1539 | 1546 | #Obteniendo pares de Autocorrelacion |
|
|
1540 | 1547 | if firstChannel == secondChannel: |
|
|
1541 | 1548 | pairsAutoCorr[firstChannel] = int(l) |
|
|
1542 | 1549 | |
|
|
1543 | 1550 | pairsAutoCorr = pairsAutoCorr.astype(int) |
|
|
1544 | 1551 | |
|
|
1545 | 1552 | pairsCrossCorr = range(len(pairsList)) |
|
|
1546 | 1553 | pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr) |
|
|
1547 | 1554 | |
|
|
1548 | 1555 | return pairsAutoCorr, pairsCrossCorr |
|
|
1549 | 1556 | |
|
|
1550 | 1557 | def techniqueSA(self, pairsSelected, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, lagTRange, correctFactor): |
|
|
1551 | 1558 | """ |
|
|
1552 | 1559 | Function that implements Spaced Antenna (SA) technique. |
|
|
1553 | 1560 | |
|
|
1554 | 1561 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, |
|
|
1555 | 1562 | Direction correction (if necessary), Ranges and SNR |
|
|
1556 | 1563 | |
|
|
1557 | 1564 | Output: Winds estimation (Zonal, Meridional and Vertical) |
|
|
1558 | 1565 | |
|
|
1559 | 1566 | Parameters affected: Winds |
|
|
1560 | 1567 | """ |
|
|
1561 | 1568 | #Cross Correlation pairs obtained |
|
|
1562 | 1569 | pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairsList, nChannels) |
|
|
1563 | 1570 | pairsArray = numpy.array(pairsList)[pairsCrossCorr] |
|
|
1564 | 1571 | pairsSelArray = numpy.array(pairsSelected) |
|
|
1565 | 1572 | pairs = [] |
|
|
1566 | 1573 | |
|
|
1567 | 1574 | #Wind estimation pairs obtained |
|
|
1568 | 1575 | for i in range(pairsSelArray.shape[0]/2): |
|
|
1569 | 1576 | ind1 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i], axis = 1))[0][0] |
|
|
1570 | 1577 | ind2 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i + 1], axis = 1))[0][0] |
|
|
1571 | 1578 | pairs.append((ind1,ind2)) |
|
|
1572 | 1579 | |
|
|
1573 | 1580 | indtau = tau.shape[0]/2 |
|
|
1574 | 1581 | tau1 = tau[:indtau,:] |
|
|
1575 | 1582 | tau2 = tau[indtau:-1,:] |
|
|
1576 | 1583 | tau1 = tau1[pairs,:] |
|
|
1577 | 1584 | tau2 = tau2[pairs,:] |
|
|
1578 | 1585 | phase1 = tau[-1,:] |
|
|
1579 | 1586 | |
|
|
1580 | 1587 | #--------------------------------------------------------------------- |
|
|
1581 | 1588 | #Metodo Directo |
|
|
1582 | 1589 | distx, disty, dist, ang = self.__calculateDistance(position_x, position_y, pairsCrossCorr, pairsList, pairs,azimuth) |
|
|
1583 | 1590 | winds = self.__calculateVelHorDir(dist, tau1, tau2, ang) |
|
|
1584 | 1591 | winds = stats.nanmean(winds, axis=0) |
|
|
1585 | 1592 | #--------------------------------------------------------------------- |
|
|
1586 | 1593 | #Metodo General |
|
|
1587 | 1594 | # distx, disty, dist = self.calculateDistance(position_x,position_y,pairsCrossCorr, pairsList, azimuth) |
|
|
1588 | 1595 | # #Calculo Coeficientes de Funcion de Correlacion |
|
|
1589 | 1596 | # F,G,A,B,H = self.calculateCoef(tau1,tau2,distx,disty,n) |
|
|
1590 | 1597 | # #Calculo de Velocidades |
|
|
1591 | 1598 | # winds = self.calculateVelUV(F,G,A,B,H) |
|
|
1592 | 1599 | |
|
|
1593 | 1600 | #--------------------------------------------------------------------- |
|
|
1594 | 1601 | winds[2,:] = self.__calculateVelVer(phase1, lagTRange, _lambda) |
|
|
1595 | 1602 | winds = correctFactor*winds |
|
|
1596 | 1603 | return winds |
|
|
1597 | 1604 | |
|
|
1598 | 1605 | def __checkTime(self, currentTime, paramInterval, outputInterval): |
|
|
1599 | 1606 | |
|
|
1600 | 1607 | dataTime = currentTime + paramInterval |
|
|
1601 | 1608 | deltaTime = dataTime - self.__initime |
|
|
1602 | 1609 | |
|
|
1603 | 1610 | if deltaTime >= outputInterval or deltaTime < 0: |
|
|
1604 | 1611 | self.__dataReady = True |
|
|
1605 | 1612 | return |
|
|
1606 | 1613 | |
|
|
1607 | 1614 | def techniqueMeteors(self, arrayMeteor, meteorThresh, heightMin, heightMax): |
|
|
1608 | 1615 | ''' |
|
|
1609 | 1616 | Function that implements winds estimation technique with detected meteors. |
|
|
1610 | 1617 | |
|
|
1611 | 1618 | Input: Detected meteors, Minimum meteor quantity to wind estimation |
|
|
1612 | 1619 | |
|
|
1613 | 1620 | Output: Winds estimation (Zonal and Meridional) |
|
|
1614 | 1621 | |
|
|
1615 | 1622 | Parameters affected: Winds |
|
|
1616 | 1623 | ''' |
|
|
1617 | 1624 | # print arrayMeteor.shape |
|
|
1618 | 1625 | #Settings |
|
|
1619 | 1626 | nInt = (heightMax - heightMin)/2 |
|
|
1620 | 1627 | # print nInt |
|
|
1621 | 1628 | nInt = int(nInt) |
|
|
1622 | 1629 | # print nInt |
|
|
1623 | 1630 | winds = numpy.zeros((2,nInt))*numpy.nan |
|
|
1624 | 1631 | |
|
|
1625 | 1632 | #Filter errors |
|
|
1626 | 1633 | error = numpy.where(arrayMeteor[:,-1] == 0)[0] |
|
|
1627 | 1634 | finalMeteor = arrayMeteor[error,:] |
|
|
1628 | 1635 | |
|
|
1629 | 1636 | #Meteor Histogram |
|
|
1630 | 1637 | finalHeights = finalMeteor[:,2] |
|
|
1631 | 1638 | hist = numpy.histogram(finalHeights, bins = nInt, range = (heightMin,heightMax)) |
|
|
1632 | 1639 | nMeteorsPerI = hist[0] |
|
|
1633 | 1640 | heightPerI = hist[1] |
|
|
1634 | 1641 | |
|
|
1635 | 1642 | #Sort of meteors |
|
|
1636 | 1643 | indSort = finalHeights.argsort() |
|
|
1637 | 1644 | finalMeteor2 = finalMeteor[indSort,:] |
|
|
1638 | 1645 | |
|
|
1639 | 1646 | # Calculating winds |
|
|
1640 | 1647 | ind1 = 0 |
|
|
1641 | 1648 | ind2 = 0 |
|
|
1642 | 1649 | |
|
|
1643 | 1650 | for i in range(nInt): |
|
|
1644 | 1651 | nMet = nMeteorsPerI[i] |
|
|
1645 | 1652 | ind1 = ind2 |
|
|
1646 | 1653 | ind2 = ind1 + nMet |
|
|
1647 | 1654 | |
|
|
1648 | 1655 | meteorAux = finalMeteor2[ind1:ind2,:] |
|
|
1649 | 1656 | |
|
|
1650 | 1657 | if meteorAux.shape[0] >= meteorThresh: |
|
|
1651 | 1658 | vel = meteorAux[:, 6] |
|
|
1652 | 1659 | zen = meteorAux[:, 4]*numpy.pi/180 |
|
|
1653 | 1660 | azim = meteorAux[:, 3]*numpy.pi/180 |
|
|
1654 | 1661 | |
|
|
1655 | 1662 | n = numpy.cos(zen) |
|
|
1656 | 1663 | # m = (1 - n**2)/(1 - numpy.tan(azim)**2) |
|
|
1657 | 1664 | # l = m*numpy.tan(azim) |
|
|
1658 | 1665 | l = numpy.sin(zen)*numpy.sin(azim) |
|
|
1659 | 1666 | m = numpy.sin(zen)*numpy.cos(azim) |
|
|
1660 | 1667 | |
|
|
1661 | 1668 | A = numpy.vstack((l, m)).transpose() |
|
|
1662 | 1669 | A1 = numpy.dot(numpy.linalg.inv( numpy.dot(A.transpose(),A) ),A.transpose()) |
|
|
1663 | 1670 | windsAux = numpy.dot(A1, vel) |
|
|
1664 | 1671 | |
|
|
1665 | 1672 | winds[0,i] = windsAux[0] |
|
|
1666 | 1673 | winds[1,i] = windsAux[1] |
|
|
1667 | 1674 | |
|
|
1668 | 1675 | return winds, heightPerI[:-1] |
|
|
1669 | 1676 | |
|
|
1670 | 1677 | def run(self, dataOut, technique, **kwargs): |
|
|
1671 | 1678 | |
|
|
1672 | 1679 | param = dataOut.data_param |
|
|
1673 | 1680 | if dataOut.abscissaList != None: |
|
|
1674 | 1681 | absc = dataOut.abscissaList[:-1] |
|
|
1675 | 1682 | noise = dataOut.noise |
|
|
1676 | 1683 | heightList = dataOut.heightList |
|
|
1677 | 1684 | SNR = dataOut.data_SNR |
|
|
1678 | 1685 | |
|
|
1679 | 1686 | if technique == 'DBS': |
|
|
1680 | 1687 | |
|
|
1681 | 1688 | if kwargs.has_key('dirCosx') and kwargs.has_key('dirCosy'): |
|
|
1682 | 1689 | theta_x = numpy.array(kwargs['dirCosx']) |
|
|
1683 | 1690 | theta_y = numpy.array(kwargs['dirCosy']) |
|
|
1684 | 1691 | else: |
|
|
1685 | 1692 | elev = numpy.array(kwargs['elevation']) |
|
|
1686 | 1693 | azim = numpy.array(kwargs['azimuth']) |
|
|
1687 | 1694 | theta_x, theta_y = self.__calculateCosDir(elev, azim) |
|
|
1688 | 1695 | azimuth = kwargs['correctAzimuth'] |
|
|
1689 | 1696 | if kwargs.has_key('horizontalOnly'): |
|
|
1690 | 1697 | horizontalOnly = kwargs['horizontalOnly'] |
|
|
1691 | 1698 | else: horizontalOnly = False |
|
|
1692 | 1699 | if kwargs.has_key('correctFactor'): |
|
|
1693 | 1700 | correctFactor = kwargs['correctFactor'] |
|
|
1694 | 1701 | else: correctFactor = 1 |
|
|
1695 | 1702 | if kwargs.has_key('channelList'): |
|
|
1696 | 1703 | channelList = kwargs['channelList'] |
|
|
1697 | 1704 | if len(channelList) == 2: |
|
|
1698 | 1705 | horizontalOnly = True |
|
|
1699 | 1706 | arrayChannel = numpy.array(channelList) |
|
|
1700 | 1707 | param = param[arrayChannel,:,:] |
|
|
1701 | 1708 | theta_x = theta_x[arrayChannel] |
|
|
1702 | 1709 | theta_y = theta_y[arrayChannel] |
|
|
1703 | 1710 | |
|
|
1704 | 1711 | velRadial0 = param[:,1,:] #Radial velocity |
|
|
1705 | 1712 | dataOut.data_output, dataOut.heightList, dataOut.data_SNR = self.techniqueDBS(velRadial0, theta_x, theta_y, azimuth, correctFactor, horizontalOnly, heightList, SNR) #DBS Function |
|
|
1706 | 1713 | dataOut.utctimeInit = dataOut.utctime |
|
|
1707 | 1714 | dataOut.outputInterval = dataOut.timeInterval |
|
|
1708 | 1715 | |
|
|
1709 | 1716 | elif technique == 'SA': |
|
|
1710 | 1717 | |
|
|
1711 | 1718 | #Parameters |
|
|
1712 | 1719 | position_x = kwargs['positionX'] |
|
|
1713 | 1720 | position_y = kwargs['positionY'] |
|
|
1714 | 1721 | azimuth = kwargs['azimuth'] |
|
|
1715 | 1722 | |
|
|
1716 | 1723 | if kwargs.has_key('crosspairsList'): |
|
|
1717 | 1724 | pairs = kwargs['crosspairsList'] |
|
|
1718 | 1725 | else: |
|
|
1719 | 1726 | pairs = None |
|
|
1720 | 1727 | |
|
|
1721 | 1728 | if kwargs.has_key('correctFactor'): |
|
|
1722 | 1729 | correctFactor = kwargs['correctFactor'] |
|
|
1723 | 1730 | else: |
|
|
1724 | 1731 | correctFactor = 1 |
|
|
1725 | 1732 | |
|
|
1726 | 1733 | tau = dataOut.data_param |
|
|
1727 | 1734 | _lambda = dataOut.C/dataOut.frequency |
|
|
1728 | 1735 | pairsList = dataOut.groupList |
|
|
1729 | 1736 | nChannels = dataOut.nChannels |
|
|
1730 | 1737 | |
|
|
1731 | 1738 | dataOut.data_output = self.techniqueSA(pairs, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, absc, correctFactor) |
|
|
1732 | 1739 | dataOut.utctimeInit = dataOut.utctime |
|
|
1733 | 1740 | dataOut.outputInterval = dataOut.timeInterval |
|
|
1734 | 1741 | |
|
|
1735 | 1742 | elif technique == 'Meteors': |
|
|
1736 | 1743 | dataOut.flagNoData = True |
|
|
1737 | 1744 | self.__dataReady = False |
|
|
1738 | 1745 | |
|
|
1739 | 1746 | if kwargs.has_key('nHours'): |
|
|
1740 | 1747 | nHours = kwargs['nHours'] |
|
|
1741 | 1748 | else: |
|
|
1742 | 1749 | nHours = 1 |
|
|
1743 | 1750 | |
|
|
1744 | 1751 | if kwargs.has_key('meteorsPerBin'): |
|
|
1745 | 1752 | meteorThresh = kwargs['meteorsPerBin'] |
|
|
1746 | 1753 | else: |
|
|
1747 | 1754 | meteorThresh = 6 |
|
|
1748 | 1755 | |
|
|
1749 | 1756 | if kwargs.has_key('hmin'): |
|
|
1750 | 1757 | hmin = kwargs['hmin'] |
|
|
1751 | 1758 | else: hmin = 70 |
|
|
1752 | 1759 | if kwargs.has_key('hmax'): |
|
|
1753 | 1760 | hmax = kwargs['hmax'] |
|
|
1754 | 1761 | else: hmax = 110 |
|
|
1755 | 1762 | |
|
|
1756 | 1763 | dataOut.outputInterval = nHours*3600 |
|
|
1757 | 1764 | |
|
|
1758 | 1765 | if self.__isConfig == False: |
|
|
1759 | 1766 | # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) |
|
|
1760 | 1767 | #Get Initial LTC time |
|
|
1761 | 1768 | self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
|
1762 | 1769 | self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
1763 | 1770 | |
|
|
1764 | 1771 | self.__isConfig = True |
|
|
1765 | 1772 | |
|
|
1766 | 1773 | if self.__buffer == None: |
|
|
1767 | 1774 | self.__buffer = dataOut.data_param |
|
|
1768 | 1775 | self.__firstdata = copy.copy(dataOut) |
|
|
1769 | 1776 | |
|
|
1770 | 1777 | else: |
|
|
1771 | 1778 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
|
1772 | 1779 | |
|
|
1773 | 1780 | self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
|
1774 | 1781 | |
|
|
1775 | 1782 | if self.__dataReady: |
|
|
1776 | 1783 | dataOut.utctimeInit = self.__initime |
|
|
1777 | 1784 | |
|
|
1778 | 1785 | self.__initime += dataOut.outputInterval #to erase time offset |
|
|
1779 | 1786 | |
|
|
1780 | 1787 | dataOut.data_output, dataOut.heightList = self.techniqueMeteors(self.__buffer, meteorThresh, hmin, hmax) |
|
|
1781 | 1788 | dataOut.flagNoData = False |
|
|
1782 | 1789 | self.__buffer = None |
|
|
1783 | 1790 | |
|
|
1784 | 1791 | return |
|
|
1785 | 1792 | |
|
|
1786 | 1793 | class EWDriftsEstimation(Operation): |
|
|
1787 | 1794 | |
|
|
1788 | 1795 | |
|
|
1789 | 1796 | def __init__(self): |
|
|
1790 | 1797 | Operation.__init__(self) |
|
|
1791 | 1798 | |
|
|
1792 | 1799 | def __correctValues(self, heiRang, phi, velRadial, SNR): |
|
|
1793 | 1800 | listPhi = phi.tolist() |
|
|
1794 | 1801 | maxid = listPhi.index(max(listPhi)) |
|
|
1795 | 1802 | minid = listPhi.index(min(listPhi)) |
|
|
1796 | 1803 | |
|
|
1797 | 1804 | rango = range(len(phi)) |
|
|
1798 | 1805 | # rango = numpy.delete(rango,maxid) |
|
|
1799 | 1806 | |
|
|
1800 | 1807 | heiRang1 = heiRang*math.cos(phi[maxid]) |
|
|
1801 | 1808 | heiRangAux = heiRang*math.cos(phi[minid]) |
|
|
1802 | 1809 | indOut = (heiRang1 < heiRangAux[0]).nonzero() |
|
|
1803 | 1810 | heiRang1 = numpy.delete(heiRang1,indOut) |
|
|
1804 | 1811 | |
|
|
1805 | 1812 | velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
1806 | 1813 | SNR1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
1807 | 1814 | |
|
|
1808 | 1815 | for i in rango: |
|
|
1809 | 1816 | x = heiRang*math.cos(phi[i]) |
|
|
1810 | 1817 | y1 = velRadial[i,:] |
|
|
1811 | 1818 | f1 = interpolate.interp1d(x,y1,kind = 'cubic') |
|
|
1812 | 1819 | |
|
|
1813 | 1820 | x1 = heiRang1 |
|
|
1814 | 1821 | y11 = f1(x1) |
|
|
1815 | 1822 | |
|
|
1816 | 1823 | y2 = SNR[i,:] |
|
|
1817 | 1824 | f2 = interpolate.interp1d(x,y2,kind = 'cubic') |
|
|
1818 | 1825 | y21 = f2(x1) |
|
|
1819 | 1826 | |
|
|
1820 | 1827 | velRadial1[i,:] = y11 |
|
|
1821 | 1828 | SNR1[i,:] = y21 |
|
|
1822 | 1829 | |
|
|
1823 | 1830 | return heiRang1, velRadial1, SNR1 |
|
|
1824 | 1831 | |
|
|
1825 | 1832 | def run(self, dataOut, zenith, zenithCorrection): |
|
|
1826 | 1833 | heiRang = dataOut.heightList |
|
|
1827 | 1834 | velRadial = dataOut.data_param[:,3,:] |
|
|
1828 | 1835 | SNR = dataOut.data_SNR |
|
|
1829 | 1836 | |
|
|
1830 | 1837 | zenith = numpy.array(zenith) |
|
|
1831 | 1838 | zenith -= zenithCorrection |
|
|
1832 | 1839 | zenith *= numpy.pi/180 |
|
|
1833 | 1840 | |
|
|
1834 | 1841 | heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, numpy.abs(zenith), velRadial, SNR) |
|
|
1835 | 1842 | |
|
|
1836 | 1843 | alp = zenith[0] |
|
|
1837 | 1844 | bet = zenith[1] |
|
|
1838 | 1845 | |
|
|
1839 | 1846 | w_w = velRadial1[0,:] |
|
|
1840 | 1847 | w_e = velRadial1[1,:] |
|
|
1841 | 1848 | |
|
|
1842 | 1849 | w = (w_w*numpy.sin(bet) - w_e*numpy.sin(alp))/(numpy.cos(alp)*numpy.sin(bet) - numpy.cos(bet)*numpy.sin(alp)) |
|
|
1843 | 1850 | u = (w_w*numpy.cos(bet) - w_e*numpy.cos(alp))/(numpy.sin(alp)*numpy.cos(bet) - numpy.sin(bet)*numpy.cos(alp)) |
|
|
1844 | 1851 | |
|
|
1845 | 1852 | winds = numpy.vstack((u,w)) |
|
|
1846 | 1853 | |
|
|
1847 | 1854 | dataOut.heightList = heiRang1 |
|
|
1848 | 1855 | dataOut.data_output = winds |
|
|
1849 | 1856 | dataOut.data_SNR = SNR1 |
|
|
1850 | 1857 | |
|
|
1851 | 1858 | dataOut.utctimeInit = dataOut.utctime |
|
|
1852 | 1859 | dataOut.outputInterval = dataOut.timeInterval |
|
|
1853 | 1860 | return |
|
|
1854 | 1861 | |
|
|
1855 | 1862 | class PhaseCalibration(Operation): |
|
|
1856 | 1863 | |
|
|
1857 | 1864 | __buffer = None |
|
|
1858 | 1865 | |
|
|
1859 | 1866 | __initime = None |
|
|
1860 | 1867 | |
|
|
1861 | 1868 | __dataReady = False |
|
|
1862 | 1869 | |
|
|
1863 | 1870 | __isConfig = False |
|
|
1864 | 1871 | |
|
|
1865 | 1872 | def __checkTime(self, currentTime, initTime, paramInterval, outputInterval): |
|
|
1866 | 1873 | |
|
|
1867 | 1874 | dataTime = currentTime + paramInterval |
|
|
1868 | 1875 | deltaTime = dataTime - initTime |
|
|
1869 | 1876 | |
|
|
1870 | 1877 | if deltaTime >= outputInterval or deltaTime < 0: |
|
|
1871 | 1878 | return True |
|
|
1872 | 1879 | |
|
|
1873 | 1880 | return False |
|
|
1874 | 1881 | |
|
|
1875 | 1882 | def __getGammas(self, pairs, k, d, phases): |
|
|
1876 | 1883 | gammas = numpy.zeros(2) |
|
|
1877 | 1884 | |
|
|
1878 | 1885 | for i in range(len(pairs)): |
|
|
1879 | 1886 | |
|
|
1880 | 1887 | pairi = pairs[i] |
|
|
1881 | 1888 | |
|
|
1882 | 1889 | #Calculating gamma |
|
|
1883 | 1890 | jdcos = phases[:,pairi[1]]/(k*d[pairi[1]]) |
|
|
1884 | 1891 | jgamma = numpy.angle(numpy.exp(1j*(k*d[pairi[0]]*jdcos - phases[:,pairi[0]]))) |
|
|
1885 | 1892 | |
|
|
1886 | 1893 | #Revised distribution |
|
|
1887 | 1894 | jgammaArray = numpy.hstack((jgamma,jgamma+0.5*numpy.pi,jgamma-0.5*numpy.pi)) |
|
|
1888 | 1895 | |
|
|
1889 | 1896 | #Histogram |
|
|
1890 | 1897 | nBins = 64.0 |
|
|
1891 | 1898 | rmin = -0.5*numpy.pi |
|
|
1892 | 1899 | rmax = 0.5*numpy.pi |
|
|
1893 | 1900 | phaseHisto = numpy.histogram(jgammaArray, bins=nBins, range=(rmin,rmax)) |
|
|
1894 | 1901 | |
|
|
1895 | 1902 | meteorsY = phaseHisto[0] |
|
|
1896 | 1903 | phasesX = phaseHisto[1][:-1] |
|
|
1897 | 1904 | width = phasesX[1] - phasesX[0] |
|
|
1898 | 1905 | phasesX += width/2 |
|
|
1899 | 1906 | |
|
|
1900 | 1907 | #Gaussian aproximation |
|
|
1901 | 1908 | bpeak = meteorsY.argmax() |
|
|
1902 | 1909 | peak = meteorsY.max() |
|
|
1903 | 1910 | jmin = bpeak - 5 |
|
|
1904 | 1911 | jmax = bpeak + 5 + 1 |
|
|
1905 | 1912 | |
|
|
1906 | 1913 | if jmin<0: |
|
|
1907 | 1914 | jmin = 0 |
|
|
1908 | 1915 | jmax = 6 |
|
|
1909 | 1916 | elif jmax > meteorsY.size: |
|
|
1910 | 1917 | jmin = meteorsY.size - 6 |
|
|
1911 | 1918 | jmax = meteorsY.size |
|
|
1912 | 1919 | |
|
|
1913 | 1920 | x0 = numpy.array([peak,bpeak,50]) |
|
|
1914 | 1921 | coeff = optimize.leastsq(self.__residualFunction, x0, args=(meteorsY[jmin:jmax], phasesX[jmin:jmax])) |
|
|
1915 | 1922 | |
|
|
1916 | 1923 | #Gammas |
|
|
1917 | 1924 | gammas[i] = coeff[0][1] |
|
|
1918 | 1925 | |
|
|
1919 | 1926 | return gammas |
|
|
1920 | 1927 | |
|
|
1921 | 1928 | def __residualFunction(self, coeffs, y, t): |
|
|
1922 | 1929 | |
|
|
1923 | 1930 | return y - self.__gauss_function(t, coeffs) |
|
|
1924 | 1931 | |
|
|
1925 | 1932 | def __gauss_function(self, t, coeffs): |
|
|
1926 | 1933 | |
|
|
1927 | 1934 | return coeffs[0]*numpy.exp(-0.5*((t - coeffs[1]) / coeffs[2])**2) |
|
|
1928 | 1935 | |
|
|
1929 | 1936 | def __getPhases(self, azimuth, h, pairsList, d, gammas, meteorsArray): |
|
|
1930 | 1937 | meteorOps = MeteorOperations() |
|
|
1931 | 1938 | nchan = 4 |
|
|
1932 | 1939 | pairx = pairsList[0] |
|
|
1933 | 1940 | pairy = pairsList[1] |
|
|
1934 | 1941 | center_xangle = 0 |
|
|
1935 | 1942 | center_yangle = 0 |
|
|
1936 | 1943 | range_angle = numpy.array([10*numpy.pi,numpy.pi,numpy.pi/2,numpy.pi/4]) |
|
|
1937 | 1944 | ntimes = len(range_angle) |
|
|
1938 | 1945 | |
|
|
1939 | 1946 | nstepsx = 20.0 |
|
|
1940 | 1947 | nstepsy = 20.0 |
|
|
1941 | 1948 | |
|
|
1942 | 1949 | for iz in range(ntimes): |
|
|
1943 | 1950 | min_xangle = -range_angle[iz]/2 + center_xangle |
|
|
1944 | 1951 | max_xangle = range_angle[iz]/2 + center_xangle |
|
|
1945 | 1952 | min_yangle = -range_angle[iz]/2 + center_yangle |
|
|
1946 | 1953 | max_yangle = range_angle[iz]/2 + center_yangle |
|
|
1947 | 1954 | |
|
|
1948 | 1955 | inc_x = (max_xangle-min_xangle)/nstepsx |
|
|
1949 | 1956 | inc_y = (max_yangle-min_yangle)/nstepsy |
|
|
1950 | 1957 | |
|
|
1951 | 1958 | alpha_y = numpy.arange(nstepsy)*inc_y + min_yangle |
|
|
1952 | 1959 | alpha_x = numpy.arange(nstepsx)*inc_x + min_xangle |
|
|
1953 | 1960 | penalty = numpy.zeros((nstepsx,nstepsy)) |
|
|
1954 | 1961 | jph_array = numpy.zeros((nchan,nstepsx,nstepsy)) |
|
|
1955 | 1962 | jph = numpy.zeros(nchan) |
|
|
1956 | 1963 | |
|
|
1957 | 1964 | # Iterations looking for the offset |
|
|
1958 | 1965 | for iy in range(int(nstepsy)): |
|
|
1959 | 1966 | for ix in range(int(nstepsx)): |
|
|
1960 | 1967 | jph[pairy[1]] = alpha_y[iy] |
|
|
1961 | 1968 | jph[pairy[0]] = -gammas[1] + alpha_y[iy]*d[pairy[0]]/d[pairy[1]] |
|
|
1962 | 1969 | |
|
|
1963 | 1970 | jph[pairx[1]] = alpha_x[ix] |
|
|
1964 | 1971 | jph[pairx[0]] = -gammas[0] + alpha_x[ix]*d[pairx[0]]/d[pairx[1]] |
|
|
1965 | 1972 | |
|
|
1966 | 1973 | jph_array[:,ix,iy] = jph |
|
|
1967 | 1974 | |
|
|
1968 | 1975 | meteorsArray1 = meteorOps.getMeteorParams(meteorsArray, azimuth, h, pairsList, jph) |
|
|
1969 | 1976 | error = meteorsArray1[:,-1] |
|
|
1970 | 1977 | ind1 = numpy.where(error==0)[0] |
|
|
1971 | 1978 | penalty[ix,iy] = ind1.size |
|
|
1972 | 1979 | |
|
|
1973 | 1980 | i,j = numpy.unravel_index(penalty.argmax(), penalty.shape) |
|
|
1974 | 1981 | phOffset = jph_array[:,i,j] |
|
|
1975 | 1982 | |
|
|
1976 | 1983 | center_xangle = phOffset[pairx[1]] |
|
|
1977 | 1984 | center_yangle = phOffset[pairy[1]] |
|
|
1978 | 1985 | |
|
|
1979 | 1986 | phOffset = numpy.angle(numpy.exp(1j*jph_array[:,i,j])) |
|
|
1980 | 1987 | phOffset = phOffset*180/numpy.pi |
|
|
1981 | 1988 | return phOffset |
|
|
1982 | 1989 | |
|
|
1983 | 1990 | |
|
|
1984 | def run(self, dataOut, pairs, distances, hmin, hmax, nHours = None): | |
|
|
1991 | def run(self, dataOut, hmin, hmax, direction_25X=-1, direction_20X=1, direction_25Y=-1, direction_20Y=1, nHours = 1): | |
|
|
1985 | 1992 | |
|
|
1986 | 1993 | dataOut.flagNoData = True |
|
|
1987 | self.__dataReady = False | |
|
|
1988 | ||
|
|
1989 | if nHours == None: | |
|
|
1990 | nHours = 1 | |
|
|
1991 | ||
|
|
1994 | self.__dataReady = False | |
|
|
1992 | 1995 | dataOut.outputInterval = nHours*3600 |
|
|
1993 | 1996 | |
|
|
1994 | 1997 | if self.__isConfig == False: |
|
|
1995 | 1998 | # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) |
|
|
1996 | 1999 | #Get Initial LTC time |
|
|
1997 | 2000 | self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) |
|
|
1998 | 2001 | self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
1999 | 2002 | |
|
|
2000 | 2003 | self.__isConfig = True |
|
|
2001 | 2004 | |
|
|
2002 | 2005 | if self.__buffer == None: |
|
|
2003 | 2006 | self.__buffer = dataOut.data_param.copy() |
|
|
2004 | 2007 | |
|
|
2005 | 2008 | else: |
|
|
2006 |
self.__buffer = numpy. |
|
|
|
2009 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) | |
|
|
2007 | 2010 | |
|
|
2008 | 2011 | self.__dataReady = self.__checkTime(dataOut.utctime, self.__initime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
|
2009 | 2012 | |
|
|
2010 | 2013 | if self.__dataReady: |
|
|
2011 | 2014 | dataOut.utctimeInit = self.__initime |
|
|
2012 | 2015 | self.__initime += dataOut.outputInterval #to erase time offset |
|
|
2013 | 2016 | |
|
|
2014 | 2017 | freq = dataOut.frequency |
|
|
2015 | 2018 | c = dataOut.C #m/s |
|
|
2016 | 2019 | lamb = c/freq |
|
|
2017 | 2020 | k = 2*numpy.pi/lamb |
|
|
2018 | 2021 | azimuth = 0 |
|
|
2019 | 2022 | h = (hmin, hmax) |
|
|
2020 |
pairs |
|
|
|
2023 | pairs = ((0,1),(2,3)) | |
|
|
2024 | distances = [direction_25X*2.5*lamb, direction_20X*2*lamb, direction_25Y*2.5*lamb, direction_20Y*2*lamb] | |
|
|
2021 | 2025 | |
|
|
2022 | 2026 | meteorsArray = self.__buffer |
|
|
2023 | 2027 | error = meteorsArray[:,-1] |
|
|
2024 | 2028 | boolError = (error==0)|(error==3)|(error==4)|(error==13)|(error==14) |
|
|
2025 | 2029 | ind1 = numpy.where(boolError)[0] |
|
|
2026 | 2030 | meteorsArray = meteorsArray[ind1,:] |
|
|
2027 | 2031 | meteorsArray[:,-1] = 0 |
|
|
2028 | 2032 | phases = meteorsArray[:,8:12] |
|
|
2029 | 2033 | |
|
|
2030 | 2034 | #Calculate Gammas |
|
|
2031 | 2035 | gammas = self.__getGammas(pairs, k, distances, phases) |
|
|
2032 | 2036 | # gammas = numpy.array([-21.70409463,45.76935864])*numpy.pi/180 |
|
|
2033 | 2037 | #Calculate Phases |
|
|
2034 |
phasesOff = self.__getPhases(azimuth, h, pairs |
|
|
|
2038 | phasesOff = self.__getPhases(azimuth, h, pairs, distances, gammas, meteorsArray) | |
|
|
2035 | 2039 | phasesOff = phasesOff.reshape((1,phasesOff.size)) |
|
|
2036 | 2040 | dataOut.data_output = -phasesOff |
|
|
2037 | 2041 | dataOut.flagNoData = False |
|
|
2038 | 2042 | self.__buffer = None |
|
|
2039 | 2043 | |
|
|
2040 | 2044 | |
|
|
2041 | 2045 | return |
|
|
2042 | 2046 | |
|
|
2043 | 2047 | class MeteorOperations(): |
|
|
2044 | 2048 | |
|
|
2045 | 2049 | def __init__(self): |
|
|
2046 | 2050 | |
|
|
2047 | 2051 | return |
|
|
2048 | 2052 | |
|
|
2049 | 2053 | def getMeteorParams(self, arrayParameters0, azimuth, h, pairsList, jph): |
|
|
2050 | 2054 | |
|
|
2051 | 2055 | arrayParameters = arrayParameters0.copy() |
|
|
2052 | 2056 | hmin = h[0] |
|
|
2053 | 2057 | hmax = h[1] |
|
|
2054 | 2058 | |
|
|
2055 | 2059 | #Calculate AOA (Error N 3, 4) |
|
|
2056 | 2060 | #JONES ET AL. 1998 |
|
|
2057 | 2061 | AOAthresh = numpy.pi/8 |
|
|
2058 | 2062 | error = arrayParameters[:,-1] |
|
|
2059 | 2063 | phases = -arrayParameters[:,8:12] + jph |
|
|
2060 | 2064 | # phases = numpy.unwrap(phases) |
|
|
2061 | 2065 | arrayParameters[:,3:6], arrayParameters[:,-1] = self.__getAOA(phases, pairsList, error, AOAthresh, azimuth) |
|
|
2062 | 2066 | |
|
|
2063 | 2067 | #Calculate Heights (Error N 13 and 14) |
|
|
2064 | 2068 | error = arrayParameters[:,-1] |
|
|
2065 | 2069 | Ranges = arrayParameters[:,1] |
|
|
2066 | 2070 | zenith = arrayParameters[:,4] |
|
|
2067 | 2071 | arrayParameters[:,2], arrayParameters[:,-1] = self.__getHeights(Ranges, zenith, error, hmin, hmax) |
|
|
2068 | 2072 | |
|
|
2069 | 2073 | #----------------------- Get Final data ------------------------------------ |
|
|
2070 | 2074 | # error = arrayParameters[:,-1] |
|
|
2071 | 2075 | # ind1 = numpy.where(error==0)[0] |
|
|
2072 | 2076 | # arrayParameters = arrayParameters[ind1,:] |
|
|
2073 | 2077 | |
|
|
2074 | 2078 | return arrayParameters |
|
|
2075 | 2079 | |
|
|
2076 | 2080 | def __getAOA(self, phases, pairsList, error, AOAthresh, azimuth): |
|
|
2077 | 2081 | |
|
|
2078 | 2082 | arrayAOA = numpy.zeros((phases.shape[0],3)) |
|
|
2079 | 2083 | cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList) |
|
|
2080 | 2084 | |
|
|
2081 | 2085 | arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth) |
|
|
2082 | 2086 | cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1) |
|
|
2083 | 2087 | arrayAOA[:,2] = cosDirError |
|
|
2084 | 2088 | |
|
|
2085 | 2089 | azimuthAngle = arrayAOA[:,0] |
|
|
2086 | 2090 | zenithAngle = arrayAOA[:,1] |
|
|
2087 | 2091 | |
|
|
2088 | 2092 | #Setting Error |
|
|
2089 | 2093 | indError = numpy.where(numpy.logical_or(error == 3, error == 4))[0] |
|
|
2090 | 2094 | error[indError] = 0 |
|
|
2091 | 2095 | #Number 3: AOA not fesible |
|
|
2092 | 2096 | indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0] |
|
|
2093 | 2097 | error[indInvalid] = 3 |
|
|
2094 | 2098 | #Number 4: Large difference in AOAs obtained from different antenna baselines |
|
|
2095 | 2099 | indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0] |
|
|
2096 | 2100 | error[indInvalid] = 4 |
|
|
2097 | 2101 | return arrayAOA, error |
|
|
2098 | 2102 | |
|
|
2099 | 2103 | def __getDirectionCosines(self, arrayPhase, pairsList): |
|
|
2100 | 2104 | |
|
|
2101 | 2105 | #Initializing some variables |
|
|
2102 | 2106 | ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi |
|
|
2103 | 2107 | ang_aux = ang_aux.reshape(1,ang_aux.size) |
|
|
2104 | 2108 | |
|
|
2105 | 2109 | cosdir = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
2106 | 2110 | cosdir0 = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
2107 | 2111 | |
|
|
2108 | 2112 | |
|
|
2109 | 2113 | for i in range(2): |
|
|
2110 | 2114 | #First Estimation |
|
|
2111 | 2115 | phi0_aux = arrayPhase[:,pairsList[i][0]] + arrayPhase[:,pairsList[i][1]] |
|
|
2112 | 2116 | #Dealias |
|
|
2113 | 2117 | indcsi = numpy.where(phi0_aux > numpy.pi) |
|
|
2114 | 2118 | phi0_aux[indcsi] -= 2*numpy.pi |
|
|
2115 | 2119 | indcsi = numpy.where(phi0_aux < -numpy.pi) |
|
|
2116 | 2120 | phi0_aux[indcsi] += 2*numpy.pi |
|
|
2117 | 2121 | #Direction Cosine 0 |
|
|
2118 | 2122 | cosdir0[:,i] = -(phi0_aux)/(2*numpy.pi*0.5) |
|
|
2119 | 2123 | |
|
|
2120 | 2124 | #Most-Accurate Second Estimation |
|
|
2121 | 2125 | phi1_aux = arrayPhase[:,pairsList[i][0]] - arrayPhase[:,pairsList[i][1]] |
|
|
2122 | 2126 | phi1_aux = phi1_aux.reshape(phi1_aux.size,1) |
|
|
2123 | 2127 | #Direction Cosine 1 |
|
|
2124 | 2128 | cosdir1 = -(phi1_aux + ang_aux)/(2*numpy.pi*4.5) |
|
|
2125 | 2129 | |
|
|
2126 | 2130 | #Searching the correct Direction Cosine |
|
|
2127 | 2131 | cosdir0_aux = cosdir0[:,i] |
|
|
2128 | 2132 | cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1) |
|
|
2129 | 2133 | #Minimum Distance |
|
|
2130 | 2134 | cosDiff = (cosdir1 - cosdir0_aux)**2 |
|
|
2131 | 2135 | indcos = cosDiff.argmin(axis = 1) |
|
|
2132 | 2136 | #Saving Value obtained |
|
|
2133 | 2137 | cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos] |
|
|
2134 | 2138 | |
|
|
2135 | 2139 | return cosdir0, cosdir |
|
|
2136 | 2140 | |
|
|
2137 | 2141 | def __calculateAOA(self, cosdir, azimuth): |
|
|
2138 | 2142 | cosdirX = cosdir[:,0] |
|
|
2139 | 2143 | cosdirY = cosdir[:,1] |
|
|
2140 | 2144 | |
|
|
2141 | 2145 | zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi |
|
|
2142 | 2146 | azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth #0 deg north, 90 deg east |
|
|
2143 | 2147 | angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose() |
|
|
2144 | 2148 | |
|
|
2145 | 2149 | return angles |
|
|
2146 | 2150 | |
|
|
2147 | 2151 | def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight): |
|
|
2148 | 2152 | |
|
|
2149 | 2153 | Ramb = 375 #Ramb = c/(2*PRF) |
|
|
2150 | 2154 | Re = 6371 #Earth Radius |
|
|
2151 | 2155 | heights = numpy.zeros(Ranges.shape) |
|
|
2152 | 2156 | |
|
|
2153 | 2157 | R_aux = numpy.array([0,1,2])*Ramb |
|
|
2154 | 2158 | R_aux = R_aux.reshape(1,R_aux.size) |
|
|
2155 | 2159 | |
|
|
2156 | 2160 | Ranges = Ranges.reshape(Ranges.size,1) |
|
|
2157 | 2161 | |
|
|
2158 | 2162 | Ri = Ranges + R_aux |
|
|
2159 | 2163 | hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re |
|
|
2160 | 2164 | |
|
|
2161 | 2165 | #Check if there is a height between 70 and 110 km |
|
|
2162 | 2166 | h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1) |
|
|
2163 | 2167 | ind_h = numpy.where(h_bool == 1)[0] |
|
|
2164 | 2168 | |
|
|
2165 | 2169 | hCorr = hi[ind_h, :] |
|
|
2166 | 2170 | ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight)) |
|
|
2167 | 2171 | |
|
|
2168 | 2172 | hCorr = hi[ind_hCorr] |
|
|
2169 | 2173 | heights[ind_h] = hCorr |
|
|
2170 | 2174 | |
|
|
2171 | 2175 | #Setting Error |
|
|
2172 | 2176 | #Number 13: Height unresolvable echo: not valid height within 70 to 110 km |
|
|
2173 | 2177 | #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km |
|
|
2174 | 2178 | indError = numpy.where(numpy.logical_or(error == 13, error == 14))[0] |
|
|
2175 | 2179 | error[indError] = 0 |
|
|
2176 | 2180 | indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0] |
|
|
2177 | 2181 | error[indInvalid2] = 14 |
|
|
2178 | 2182 | indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0] |
|
|
2179 | 2183 | error[indInvalid1] = 13 |
|
|
2180 | 2184 | |
|
|
2181 | 2185 | return heights, error No newline at end of file |
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