| @@ -1,6939 +1,6939 | |||
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1 | 1 | |
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2 | 2 | import os |
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3 | 3 | import sys |
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4 | 4 | import numpy, math |
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5 | 5 | from scipy import interpolate |
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6 | 6 | from scipy.optimize import nnls |
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7 | 7 | from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation, MPDecorator |
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8 | 8 | from schainpy.model.data.jrodata import Voltage, hildebrand_sekhon |
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9 | 9 | from schainpy.utils import log |
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10 | 10 | from time import time, mktime, strptime, gmtime, ctime |
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11 | 11 | from scipy.optimize import least_squares |
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12 | 12 | import datetime |
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13 | 13 | import csv |
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14 | 14 | |
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15 | 15 | try: |
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16 | 16 | from schainpy.model.proc import fitacf_guess |
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17 | 17 | from schainpy.model.proc import fitacf_fit_short |
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18 | 18 | from schainpy.model.proc import fitacf_acf2 |
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19 | 19 | from schainpy.model.proc import full_profile_profile |
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20 | 20 | except: |
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21 | 21 | log.warning('Missing Faraday fortran libs') |
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22 | 22 | |
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23 | 23 | class VoltageProc(ProcessingUnit): |
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24 | 24 | |
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25 | 25 | def __init__(self): |
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26 | 26 | |
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27 | 27 | ProcessingUnit.__init__(self) |
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28 | 28 | |
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29 | 29 | self.dataOut = Voltage() |
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30 | 30 | self.flip = 1 |
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31 | 31 | self.setupReq = False |
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32 | 32 | #self.dataOut.test=1 |
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33 | 33 | |
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34 | 34 | |
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35 | 35 | def run(self, runNextUnit = 0): |
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36 | 36 | #import time |
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37 | 37 | #time.sleep(3) |
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38 | 38 | |
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39 | 39 | if self.dataIn.type == 'AMISR': |
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40 | 40 | self.__updateObjFromAmisrInput() |
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41 | 41 | |
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42 | 42 | if self.dataIn.type == 'Voltage': |
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43 | 43 | self.dataOut.copy(self.dataIn) |
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44 | 44 | self.dataOut.runNextUnit = runNextUnit |
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45 | 45 | |
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46 | 46 | |
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47 | 47 | #self.dataOut.flagNoData=True |
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48 | 48 | #print(self.dataOut.data[-1,:]) |
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49 | 49 | #print(ctime(self.dataOut.utctime)) |
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50 | 50 | #print(self.dataOut.heightList) |
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51 | 51 | #print(self.dataOut.nHeights) |
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52 | 52 | #exit(1) |
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53 | 53 | #print(self.dataOut.data[6,:32]) |
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54 | 54 | #print(self.dataOut.data[0,320-5:320+5-5]) |
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55 | 55 | ##print(self.dataOut.heightList[-20:]) |
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56 | 56 | #print(numpy.shape(self.dataOut.data)) |
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57 | 57 | #print(self.dataOut.code) |
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58 | 58 | #print(numpy.shape(self.dataOut.code)) |
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59 | 59 | #exit(1) |
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60 | 60 | #print(self.dataOut.CurrentBlock) |
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61 | 61 | #print(self.dataOut.data[0,:,0]) |
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62 | 62 | |
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63 | 63 | #print(numpy.shape(self.dataOut.data)) |
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64 | 64 | #print(self.dataOut.data[0,:,1666:1666+320]) |
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65 | 65 | #exit(1) |
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66 | 66 | |
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67 | 67 | #print(self.dataOut.utctime) |
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68 | 68 | #self.dataOut.test+=1 |
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69 | 69 | |
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70 | 70 | |
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71 | 71 | def __updateObjFromAmisrInput(self): |
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72 | 72 | |
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73 | 73 | self.dataOut.timeZone = self.dataIn.timeZone |
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74 | 74 | self.dataOut.dstFlag = self.dataIn.dstFlag |
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75 | 75 | self.dataOut.errorCount = self.dataIn.errorCount |
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76 | 76 | self.dataOut.useLocalTime = self.dataIn.useLocalTime |
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77 | 77 | |
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78 | 78 | self.dataOut.flagNoData = self.dataIn.flagNoData |
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79 | 79 | self.dataOut.data = self.dataIn.data |
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80 | 80 | self.dataOut.utctime = self.dataIn.utctime |
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81 | 81 | self.dataOut.channelList = self.dataIn.channelList |
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82 | 82 | # self.dataOut.timeInterval = self.dataIn.timeInterval |
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83 | 83 | self.dataOut.heightList = self.dataIn.heightList |
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84 | 84 | self.dataOut.nProfiles = self.dataIn.nProfiles |
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85 | 85 | |
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86 | 86 | self.dataOut.nCohInt = self.dataIn.nCohInt |
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87 | 87 | self.dataOut.ippSeconds = self.dataIn.ippSeconds |
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88 | 88 | self.dataOut.frequency = self.dataIn.frequency |
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89 | 89 | |
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90 | 90 | self.dataOut.azimuth = self.dataIn.azimuth |
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91 | 91 | self.dataOut.zenith = self.dataIn.zenith |
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92 | 92 | |
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93 | 93 | self.dataOut.beam.codeList = self.dataIn.beam.codeList |
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94 | 94 | self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList |
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95 | 95 | self.dataOut.beam.zenithList = self.dataIn.beam.zenithList |
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96 | 96 | |
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97 | 97 | class selectChannels(Operation): |
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98 | 98 | |
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99 | 99 | def run(self, dataOut, channelList): |
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100 | 100 | |
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101 | 101 | |
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102 | 102 | |
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103 | 103 | |
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104 | 104 | channelIndexList = [] |
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105 | 105 | self.dataOut = dataOut |
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106 | 106 | for channel in channelList: |
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107 | 107 | if channel not in self.dataOut.channelList: |
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108 | 108 | raise ValueError("Channel %d is not in %s" % (channel, str(self.dataOut.channelList))) |
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109 | 109 | |
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110 | 110 | index = self.dataOut.channelList.index(channel) |
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111 | 111 | channelIndexList.append(index) |
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112 | 112 | self.selectChannelsByIndex(channelIndexList) |
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113 | 113 | |
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114 | 114 | return self.dataOut |
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115 | 115 | |
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116 | 116 | |
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117 | 117 | def selectChannelsByIndex(self, channelIndexList): |
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118 | 118 | """ |
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119 | 119 | Selecciona un bloque de datos en base a canales segun el channelIndexList |
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120 | 120 | |
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121 | 121 | Input: |
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122 | 122 | channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7] |
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123 | 123 | |
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124 | 124 | Affected: |
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125 | 125 | self.dataOut.data |
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126 | 126 | self.dataOut.channelIndexList |
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127 | 127 | self.dataOut.nChannels |
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128 | 128 | self.dataOut.m_ProcessingHeader.totalSpectra |
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129 | 129 | self.dataOut.systemHeaderObj.numChannels |
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130 | 130 | self.dataOut.m_ProcessingHeader.blockSize |
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131 | 131 | |
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132 | 132 | Return: |
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133 | 133 | None |
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134 | 134 | """ |
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135 | 135 | |
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136 | 136 | for channelIndex in channelIndexList: |
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137 | 137 | if channelIndex not in self.dataOut.channelIndexList: |
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138 | 138 | raise ValueError("The value %d in channelIndexList is not valid" % channelIndex) |
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139 | 139 | |
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140 | 140 | if self.dataOut.type == 'Voltage': |
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141 | 141 | if self.dataOut.flagDataAsBlock: |
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142 | 142 | """ |
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143 | 143 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] |
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144 | 144 | """ |
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145 | 145 | data = self.dataOut.data[channelIndexList, :, :] |
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146 | 146 | else: |
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147 | 147 | data = self.dataOut.data[channelIndexList, :] |
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148 | 148 | |
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149 | 149 | self.dataOut.data = data |
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150 | 150 | # self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList] |
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151 | 151 | self.dataOut.channelList = range(len(channelIndexList)) |
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152 | 152 | |
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153 | 153 | elif self.dataOut.type == 'Spectra': |
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154 | 154 | data_spc = self.dataOut.data_spc[channelIndexList, :] |
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155 | 155 | data_dc = self.dataOut.data_dc[channelIndexList, :] |
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156 | 156 | |
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157 | 157 | self.dataOut.data_spc = data_spc |
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158 | 158 | self.dataOut.data_dc = data_dc |
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159 | 159 | |
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160 | 160 | # self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList] |
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161 | 161 | self.dataOut.channelList = range(len(channelIndexList)) |
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162 | 162 | self.__selectPairsByChannel(channelIndexList) |
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163 | 163 | |
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164 | 164 | return 1 |
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165 | 165 | |
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166 | 166 | def __selectPairsByChannel(self, channelList=None): |
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167 | 167 | |
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168 | 168 | if channelList == None: |
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169 | 169 | return |
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170 | 170 | |
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171 | 171 | pairsIndexListSelected = [] |
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172 | 172 | for pairIndex in self.dataOut.pairsIndexList: |
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173 | 173 | # First pair |
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174 | 174 | if self.dataOut.pairsList[pairIndex][0] not in channelList: |
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175 | 175 | continue |
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176 | 176 | # Second pair |
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177 | 177 | if self.dataOut.pairsList[pairIndex][1] not in channelList: |
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178 | 178 | continue |
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179 | 179 | |
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180 | 180 | pairsIndexListSelected.append(pairIndex) |
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181 | 181 | |
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182 | 182 | if not pairsIndexListSelected: |
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183 | 183 | self.dataOut.data_cspc = None |
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184 | 184 | self.dataOut.pairsList = [] |
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185 | 185 | return |
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186 | 186 | |
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187 | 187 | self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected] |
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188 | 188 | self.dataOut.pairsList = [self.dataOut.pairsList[i] |
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189 | 189 | for i in pairsIndexListSelected] |
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190 | 190 | |
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191 | 191 | return |
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192 | 192 | |
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193 | 193 | class selectHeights(Operation): |
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194 | 194 | |
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195 | 195 | def run(self, dataOut, minHei=None, maxHei=None, minIndex=None, maxIndex=None): |
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196 | 196 | """ |
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197 | 197 | Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango |
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198 | 198 | minHei <= height <= maxHei |
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199 | 199 | |
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200 | 200 | Input: |
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201 | 201 | minHei : valor minimo de altura a considerar |
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202 | 202 | maxHei : valor maximo de altura a considerar |
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203 | 203 | |
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204 | 204 | Affected: |
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205 | 205 | Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex |
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206 | 206 | |
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207 | 207 | Return: |
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208 | 208 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 |
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209 | 209 | """ |
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210 | 210 | |
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211 | 211 | self.dataOut = dataOut |
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212 | ||
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212 | ||
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213 | 213 | #if minHei and maxHei: |
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214 | 214 | if 1: |
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215 | 215 | if minHei == None: |
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216 | 216 | minHei = self.dataOut.heightList[0] |
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217 | ||
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217 | ||
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218 | 218 | if maxHei == None: |
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219 | 219 | maxHei = self.dataOut.heightList[-1] |
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220 | 220 | |
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221 | 221 | if (minHei < self.dataOut.heightList[0]): |
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222 | 222 | minHei = self.dataOut.heightList[0] |
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223 | 223 | |
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224 | 224 | if (maxHei > self.dataOut.heightList[-1]): |
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225 | 225 | maxHei = self.dataOut.heightList[-1] |
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226 | 226 | |
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227 | 227 | minIndex = 0 |
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228 | 228 | maxIndex = 0 |
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229 | 229 | heights = self.dataOut.heightList |
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230 | 230 | |
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231 | 231 | inda = numpy.where(heights >= minHei) |
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232 | 232 | indb = numpy.where(heights <= maxHei) |
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233 | 233 | |
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234 | 234 | try: |
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235 | 235 | minIndex = inda[0][0] |
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236 | 236 | except: |
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237 | 237 | minIndex = 0 |
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238 | 238 | |
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239 | 239 | try: |
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240 | 240 | maxIndex = indb[0][-1] |
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241 | 241 | except: |
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242 | 242 | maxIndex = len(heights) |
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243 | ||
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243 | ||
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244 | 244 | self.selectHeightsByIndex(minIndex, maxIndex) |
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245 | 245 | #print(self.dataOut.nHeights) |
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246 | 246 | |
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247 | 247 | |
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248 | 248 | return self.dataOut |
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249 | 249 | |
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250 | 250 | def selectHeightsByIndex(self, minIndex, maxIndex): |
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251 | 251 | """ |
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252 | 252 | Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango |
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253 | 253 | minIndex <= index <= maxIndex |
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254 | 254 | |
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255 | 255 | Input: |
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256 | 256 | minIndex : valor de indice minimo de altura a considerar |
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257 | 257 | maxIndex : valor de indice maximo de altura a considerar |
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258 | 258 | |
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259 | 259 | Affected: |
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260 | 260 | self.dataOut.data |
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261 | 261 | self.dataOut.heightList |
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262 | 262 | |
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263 | 263 | Return: |
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264 | 264 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 |
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265 | 265 | """ |
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266 | 266 | |
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267 | 267 | if self.dataOut.type == 'Voltage': |
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268 | 268 | if (minIndex < 0) or (minIndex > maxIndex): |
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269 | 269 | raise ValueError("Height index range (%d,%d) is not valid" % (minIndex, maxIndex)) |
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270 | 270 | |
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271 | 271 | if (maxIndex >= self.dataOut.nHeights): |
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272 | 272 | maxIndex = self.dataOut.nHeights |
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273 | 273 | |
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274 | 274 | # voltage |
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275 | 275 | if self.dataOut.flagDataAsBlock: |
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276 | 276 | """ |
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277 | 277 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] |
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278 | 278 | """ |
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279 | 279 | data = self.dataOut.data[:, :, minIndex:maxIndex] |
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280 | 280 | else: |
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281 | 281 | data = self.dataOut.data[:, minIndex:maxIndex] |
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282 | 282 | |
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283 | 283 | # firstHeight = self.dataOut.heightList[minIndex] |
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284 | 284 | |
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285 | 285 | self.dataOut.data = data |
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286 | 286 | self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex] |
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287 | 287 | |
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288 | 288 | if self.dataOut.nHeights <= 1: |
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289 | 289 | raise ValueError("selectHeights: Too few heights. Current number of heights is %d" % (self.dataOut.nHeights)) |
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290 | 290 | elif self.dataOut.type == 'Spectra': |
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291 | 291 | if (minIndex < 0) or (minIndex > maxIndex): |
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292 | 292 | raise ValueError("Error selecting heights: Index range (%d,%d) is not valid" % ( |
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293 | 293 | minIndex, maxIndex)) |
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294 | 294 | |
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295 | 295 | if (maxIndex >= self.dataOut.nHeights): |
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296 | 296 | maxIndex = self.dataOut.nHeights - 1 |
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297 | 297 | |
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298 | 298 | # Spectra |
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299 | 299 | data_spc = self.dataOut.data_spc[:, :, minIndex:maxIndex + 1] |
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300 | 300 | |
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301 | 301 | data_cspc = None |
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302 | 302 | if self.dataOut.data_cspc is not None: |
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303 | 303 | data_cspc = self.dataOut.data_cspc[:, :, minIndex:maxIndex + 1] |
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304 | 304 | |
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305 | 305 | data_dc = None |
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306 | 306 | if self.dataOut.data_dc is not None: |
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307 | 307 | data_dc = self.dataOut.data_dc[:, minIndex:maxIndex + 1] |
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308 | 308 | |
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309 | 309 | self.dataOut.data_spc = data_spc |
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310 | 310 | self.dataOut.data_cspc = data_cspc |
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311 | 311 | self.dataOut.data_dc = data_dc |
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312 | ||
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312 | ||
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313 | 313 | self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex + 1] |
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314 | 314 | |
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315 | 315 | return 1 |
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316 | 316 | |
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317 | 317 | |
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318 | 318 | class filterByHeights(Operation): |
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319 | 319 | |
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320 | 320 | def run(self, dataOut, window): |
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321 | 321 | |
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322 | 322 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
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323 | 323 | |
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324 | 324 | if window == None: |
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325 | 325 | window = (dataOut.radarControllerHeaderObj.txA / dataOut.radarControllerHeaderObj.nBaud) / deltaHeight |
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326 | 326 | |
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327 | 327 | newdelta = deltaHeight * window |
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328 | 328 | r = dataOut.nHeights % window |
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329 | 329 | newheights = (dataOut.nHeights - r) / window |
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330 | 330 | |
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331 | 331 | if newheights <= 1: |
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332 | 332 | raise ValueError("filterByHeights: Too few heights. Current number of heights is %d and window is %d" % (dataOut.nHeights, window)) |
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333 | 333 | |
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334 | 334 | if dataOut.flagDataAsBlock: |
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335 | 335 | """ |
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336 | 336 | Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis] |
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337 | 337 | """ |
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338 | 338 | buffer = dataOut.data[:, :, 0:int(dataOut.nHeights - r)] |
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339 | 339 | buffer = buffer.reshape(dataOut.nChannels, dataOut.nProfiles, int(dataOut.nHeights / window), window) |
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340 | 340 | buffer = numpy.sum(buffer, 3) |
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341 | 341 | |
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342 | 342 | else: |
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343 | 343 | buffer = dataOut.data[:, 0:int(dataOut.nHeights - r)] |
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344 | 344 | buffer = buffer.reshape(dataOut.nChannels, int(dataOut.nHeights / window), int(window)) |
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345 | 345 | buffer = numpy.sum(buffer, 2) |
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346 | 346 | |
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347 | 347 | dataOut.data = buffer |
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348 | 348 | dataOut.heightList = dataOut.heightList[0] + numpy.arange(newheights) * newdelta |
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349 | 349 | dataOut.windowOfFilter = window |
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350 | 350 | |
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351 | 351 | return dataOut |
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352 | 352 | |
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353 | 353 | class setOffset(Operation): |
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354 | 354 | |
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355 | 355 | def run(self, dataOut, offset=None): |
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356 | 356 | |
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357 | 357 | if not offset: |
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358 | 358 | offset = 0.0 |
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359 | 359 | |
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360 | 360 | newHeiRange = dataOut.heightList - offset |
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361 | 361 | |
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362 | 362 | dataOut.heightList = newHeiRange |
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363 | 363 | |
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364 |
return dataOut |
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365 | ||
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364 | return dataOut | |
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365 | ||
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366 | 366 | class setH0(Operation): |
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367 | 367 | |
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368 | 368 | def run(self, dataOut, h0, deltaHeight=None): |
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369 | 369 | |
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370 | 370 | if not deltaHeight: |
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371 | 371 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
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372 | 372 | |
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373 | 373 | nHeights = dataOut.nHeights |
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374 | 374 | |
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375 | 375 | newHeiRange = h0 + numpy.arange(nHeights) * deltaHeight |
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376 | 376 | |
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377 | 377 | dataOut.heightList = newHeiRange |
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378 | 378 | |
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379 | 379 | return dataOut |
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380 | 380 | |
|
|
381 | 381 | |
|
|
382 | 382 | class deFlip(Operation): |
|
|
383 | 383 | def __init__(self): |
|
|
384 | 384 | |
|
|
385 | 385 | self.flip = 1 |
|
|
386 | 386 | |
|
|
387 | 387 | def run(self, dataOut, channelList=[]): |
|
|
388 | 388 | |
|
|
389 | 389 | data = dataOut.data.copy() |
|
|
390 | 390 | #print(dataOut.channelList) |
|
|
391 | 391 | #exit() |
|
|
392 | 392 | |
|
|
393 | 393 | if channelList==1: #PARCHE |
|
|
394 | 394 | channelList=[1] |
|
|
395 | 395 | |
|
|
396 | 396 | |
|
|
397 | 397 | dataOut.FlipChannels=channelList |
|
|
398 | 398 | if dataOut.flagDataAsBlock: |
|
|
399 | 399 | flip = self.flip |
|
|
400 | 400 | profileList = list(range(dataOut.nProfiles)) |
|
|
401 | 401 | |
|
|
402 | 402 | if not channelList: |
|
|
403 | 403 | for thisProfile in profileList: |
|
|
404 | 404 | data[:, thisProfile, :] = data[:, thisProfile, :] * flip |
|
|
405 | 405 | flip *= -1.0 |
|
|
406 | 406 | else: |
|
|
407 | 407 | for thisChannel in channelList: |
|
|
408 | 408 | if thisChannel not in dataOut.channelList: |
|
|
409 | 409 | continue |
|
|
410 | 410 | |
|
|
411 | 411 | for thisProfile in profileList: |
|
|
412 | 412 | data[thisChannel, thisProfile, :] = data[thisChannel, thisProfile, :] * flip |
|
|
413 | 413 | flip *= -1.0 |
|
|
414 | 414 | |
|
|
415 | 415 | self.flip = flip |
|
|
416 | 416 | |
|
|
417 | 417 | else: |
|
|
418 | 418 | if not channelList: |
|
|
419 | 419 | data[:, :] = data[:, :] * self.flip |
|
|
420 | 420 | else: |
|
|
421 | 421 | #channelList=[1] |
|
|
422 | 422 | #print(self.flip) |
|
|
423 | 423 | for thisChannel in channelList: |
|
|
424 | 424 | if thisChannel not in dataOut.channelList: |
|
|
425 | 425 | continue |
|
|
426 | 426 | |
|
|
427 | 427 | data[thisChannel, :] = data[thisChannel, :] * self.flip |
|
|
428 | 428 | |
|
|
429 | 429 | self.flip *= -1. |
|
|
430 | 430 | |
|
|
431 | 431 | dataOut.data = data |
|
|
432 | 432 | |
|
|
433 | 433 | return dataOut |
|
|
434 | 434 | |
|
|
435 | 435 | class deFlipHP(Operation): |
|
|
436 | 436 | ''' |
|
|
437 | 437 | Written by R. Flores |
|
|
438 | 438 | ''' |
|
|
439 | 439 | def __init__(self): |
|
|
440 | 440 | |
|
|
441 | 441 | self.flip = 1 |
|
|
442 | 442 | |
|
|
443 | 443 | def run(self, dataOut, byHeights = False, channelList = [], HeiRangeList = None): |
|
|
444 | 444 | |
|
|
445 | 445 | data = dataOut.data.copy() |
|
|
446 | 446 | |
|
|
447 | 447 | firstHeight = HeiRangeList[0] |
|
|
448 | 448 | lastHeight = HeiRangeList[1]+1 |
|
|
449 | 449 | |
|
|
450 | 450 | #if channelList==1: #PARCHE #Lista de un solo canal produce error |
|
|
451 | 451 | #channelList=[1] |
|
|
452 | 452 | |
|
|
453 | 453 | dataOut.FlipChannels=channelList |
|
|
454 | 454 | if dataOut.flagDataAsBlock: |
|
|
455 | 455 | flip = self.flip |
|
|
456 | 456 | profileList = list(range(dataOut.nProfiles)) |
|
|
457 | 457 | |
|
|
458 | 458 | if not channelList: |
|
|
459 | 459 | for thisProfile in profileList: |
|
|
460 | 460 | data[:,thisProfile,:] = data[:,thisProfile,:]*flip |
|
|
461 | 461 | flip *= -1.0 |
|
|
462 | 462 | else: |
|
|
463 | 463 | for thisChannel in channelList: |
|
|
464 | 464 | if thisChannel not in dataOut.channelList: |
|
|
465 | 465 | continue |
|
|
466 | 466 | if not byHeights: |
|
|
467 | 467 | for thisProfile in profileList: |
|
|
468 | 468 | data[thisChannel,thisProfile,:] = data[thisChannel,thisProfile,:]*flip |
|
|
469 | 469 | flip *= -1.0 |
|
|
470 | 470 | |
|
|
471 | 471 | else: |
|
|
472 | 472 | firstHeight = HeiRangeList[0] |
|
|
473 | 473 | lastHeight = HeiRangeList[1]+1 |
|
|
474 | 474 | flip = -1.0 |
|
|
475 | 475 | data[thisChannel,:,firstHeight:lastHeight] = data[thisChannel,:,firstHeight:lastHeight]*flip |
|
|
476 | 476 | |
|
|
477 | 477 | |
|
|
478 | 478 | self.flip = flip |
|
|
479 | 479 | |
|
|
480 | 480 | else: |
|
|
481 | 481 | if not channelList: |
|
|
482 | 482 | data[:,:] = data[:,:]*self.flip |
|
|
483 | 483 | else: |
|
|
484 | 484 | #channelList=[1] |
|
|
485 | 485 | |
|
|
486 | 486 | for thisChannel in channelList: |
|
|
487 | 487 | if thisChannel not in dataOut.channelList: |
|
|
488 | 488 | continue |
|
|
489 | 489 | |
|
|
490 | 490 | if not byHeights: |
|
|
491 | 491 | data[thisChannel,:] = data[thisChannel,:]*flip |
|
|
492 | 492 | |
|
|
493 | 493 | else: |
|
|
494 | 494 | firstHeight = HeiRangeList[0] |
|
|
495 | 495 | lastHeight = HeiRangeList[1]+1 |
|
|
496 | 496 | flip = -1.0 |
|
|
497 | 497 | data[thisChannel,firstHeight:lastHeight] = data[thisChannel,firstHeight:lastHeight]*flip |
|
|
498 | 498 | |
|
|
499 | 499 | #data[thisChannel,:] = data[thisChannel,:]*self.flip |
|
|
500 | 500 | |
|
|
501 | 501 | self.flip *= -1. |
|
|
502 | 502 | |
|
|
503 | 503 | #print(dataOut.data[0,:12,1066+2]) |
|
|
504 | 504 | #print(dataOut.data[1,:12,1066+2]) |
|
|
505 | 505 | dataOut.data =data |
|
|
506 | 506 | #print(dataOut.data[0,:12,1066+2]) |
|
|
507 | 507 | #print(dataOut.data[1,:12,1066+2]) |
|
|
508 | 508 | #exit(1) |
|
|
509 | 509 | |
|
|
510 | 510 | return dataOut |
|
|
511 | 511 | |
|
|
512 | 512 | class setAttribute(Operation): |
|
|
513 | 513 | ''' |
|
|
514 | 514 | Set an arbitrary attribute(s) to dataOut |
|
|
515 | 515 | ''' |
|
|
516 | 516 | |
|
|
517 | 517 | def __init__(self): |
|
|
518 | 518 | |
|
|
519 | 519 | Operation.__init__(self) |
|
|
520 | 520 | self._ready = False |
|
|
521 | 521 | |
|
|
522 | 522 | def run(self, dataOut, **kwargs): |
|
|
523 | 523 | |
|
|
524 | 524 | for key, value in kwargs.items(): |
|
|
525 | 525 | setattr(dataOut, key, value) |
|
|
526 | 526 | |
|
|
527 | 527 | return dataOut |
|
|
528 | 528 | |
|
|
529 | 529 | |
|
|
530 | 530 | @MPDecorator |
|
|
531 | 531 | class printAttribute(Operation): |
|
|
532 | 532 | ''' |
|
|
533 | 533 | Print an arbitrary attribute of dataOut |
|
|
534 | 534 | ''' |
|
|
535 | 535 | |
|
|
536 | 536 | def __init__(self): |
|
|
537 | 537 | |
|
|
538 | 538 | Operation.__init__(self) |
|
|
539 | 539 | |
|
|
540 | 540 | def run(self, dataOut, attributes): |
|
|
541 | 541 | |
|
|
542 | 542 | if isinstance(attributes, str): |
|
|
543 | 543 | attributes = [attributes] |
|
|
544 | 544 | for attr in attributes: |
|
|
545 | 545 | if hasattr(dataOut, attr): |
|
|
546 | 546 | log.log(getattr(dataOut, attr), attr) |
|
|
547 | 547 | |
|
|
548 | 548 | |
|
|
549 | 549 | class interpolateHeights(Operation): |
|
|
550 | 550 | |
|
|
551 | 551 | def run(self, dataOut, topLim, botLim): |
|
|
552 | 552 | # 69 al 72 para julia |
|
|
553 | 553 | # 82-84 para meteoros |
|
|
554 | 554 | if len(numpy.shape(dataOut.data)) == 2: |
|
|
555 | 555 | sampInterp = (dataOut.data[:, botLim - 1] + dataOut.data[:, topLim + 1]) / 2 |
|
|
556 | 556 | sampInterp = numpy.transpose(numpy.tile(sampInterp, (topLim - botLim + 1, 1))) |
|
|
557 | 557 | # dataOut.data[:,botLim:limSup+1] = sampInterp |
|
|
558 | 558 | dataOut.data[:, botLim:topLim + 1] = sampInterp |
|
|
559 | 559 | else: |
|
|
560 | 560 | nHeights = dataOut.data.shape[2] |
|
|
561 | 561 | x = numpy.hstack((numpy.arange(botLim), numpy.arange(topLim + 1, nHeights))) |
|
|
562 | 562 | y = dataOut.data[:, :, list(range(botLim)) + list(range(topLim + 1, nHeights))] |
|
|
563 | 563 | f = interpolate.interp1d(x, y, axis=2) |
|
|
564 | 564 | xnew = numpy.arange(botLim, topLim + 1) |
|
|
565 | 565 | ynew = f(xnew) |
|
|
566 | 566 | dataOut.data[:, :, botLim:topLim + 1] = ynew |
|
|
567 | 567 | |
|
|
568 | 568 | return dataOut |
|
|
569 | 569 | |
|
|
570 | 570 | |
|
|
571 | 571 | class LagsReshape(Operation): |
|
|
572 | 572 | ''' |
|
|
573 | 573 | Written by R. Flores |
|
|
574 | 574 | ''' |
|
|
575 | 575 | """Operation to reshape input data into (Channels,Profiles(with same lag),Heights,Lags) and heights reconstruction. |
|
|
576 | 576 | |
|
|
577 | 577 | Parameters: |
|
|
578 | 578 | ----------- |
|
|
579 | 579 | |
|
|
580 | 580 | |
|
|
581 | 581 | Example |
|
|
582 | 582 | -------- |
|
|
583 | 583 | |
|
|
584 | 584 | op = proc_unit.addOperation(name='LagsReshape') |
|
|
585 | 585 | |
|
|
586 | 586 | |
|
|
587 | 587 | """ |
|
|
588 | 588 | |
|
|
589 | 589 | def __init__(self, **kwargs): |
|
|
590 | 590 | |
|
|
591 | 591 | Operation.__init__(self, **kwargs) |
|
|
592 | 592 | |
|
|
593 | 593 | self.buffer=None |
|
|
594 | 594 | self.buffer_HR=None |
|
|
595 | 595 | self.buffer_HRonelag=None |
|
|
596 | 596 | |
|
|
597 | 597 | def LagDistribution(self,dataOut): |
|
|
598 | 598 | |
|
|
599 | 599 | dataOut.datapure=numpy.copy(dataOut.data[:,0:dataOut.NSCAN,:]) |
|
|
600 | 600 | self.buffer = numpy.zeros((dataOut.nChannels, |
|
|
601 | 601 | int(dataOut.NSCAN/dataOut.DPL), |
|
|
602 | 602 | dataOut.nHeights,dataOut.DPL), |
|
|
603 | 603 | dtype='complex') |
|
|
604 | 604 | |
|
|
605 | 605 | for j in range(int(self.buffer.shape[1]/2)): |
|
|
606 | 606 | for i in range(dataOut.DPL): |
|
|
607 | 607 | if j+1==int(self.buffer.shape[1]/2) and i+1==dataOut.DPL: |
|
|
608 | 608 | self.buffer[:,2*j:,:,i]=dataOut.datapure[:,2*i+int(2*j*dataOut.DPL):,:] |
|
|
609 | 609 | else: |
|
|
610 | 610 | self.buffer[:,2*j:2*(j+1),:,i]=dataOut.datapure[:,2*i+int(2*j*dataOut.DPL):2*(i+1)+int(2*j*dataOut.DPL),:] |
|
|
611 | 611 | |
|
|
612 | 612 | return self.buffer |
|
|
613 | 613 | |
|
|
614 | 614 | def HeightReconstruction(self,dataOut): |
|
|
615 | 615 | |
|
|
616 | 616 | self.buffer_HR = numpy.zeros((int(dataOut.NSCAN/dataOut.DPL), |
|
|
617 | 617 | dataOut.nHeights,dataOut.DPL), |
|
|
618 | 618 | dtype='complex') |
|
|
619 | 619 | |
|
|
620 | 620 | for i in range(int(dataOut.DPL)): #Only channel B |
|
|
621 | 621 | if i==0: |
|
|
622 | 622 | self.buffer_HR[:,:,i]=dataOut.datalags[1,:,:,i] |
|
|
623 | 623 | else: |
|
|
624 | 624 | self.buffer_HR[:,:,i]=self.HRonelag(dataOut,i) |
|
|
625 | 625 | |
|
|
626 | 626 | return self.buffer_HR |
|
|
627 | 627 | |
|
|
628 | 628 | |
|
|
629 | 629 | def HRonelag(self,dataOut,whichlag): |
|
|
630 | 630 | self.buffer_HRonelag = numpy.zeros((int(dataOut.NSCAN/dataOut.DPL), |
|
|
631 | 631 | dataOut.nHeights), |
|
|
632 | 632 | dtype='complex') |
|
|
633 | 633 | |
|
|
634 | 634 | for i in range(self.buffer_HRonelag.shape[0]): |
|
|
635 | 635 | for j in range(dataOut.nHeights): |
|
|
636 | 636 | if j+int(2*whichlag)<dataOut.nHeights: |
|
|
637 | 637 | self.buffer_HRonelag[i,j]=dataOut.datalags[1,i,j+2*whichlag,whichlag] |
|
|
638 | 638 | else: |
|
|
639 | 639 | if whichlag!=10: |
|
|
640 | 640 | self.buffer_HRonelag[i,j]=dataOut.datalags[1,i,(j+2*whichlag)%dataOut.nHeights,whichlag+1] |
|
|
641 | 641 | else: |
|
|
642 | 642 | if i+2<self.buffer_HRonelag.shape[0]: |
|
|
643 | 643 | self.buffer_HRonelag[i,j]=dataOut.datalags[1,i+2,(j+2*whichlag)%dataOut.nHeights,0] |
|
|
644 | 644 | else: #i+1==self.buffer_HRonelag.shape[0]: |
|
|
645 | 645 | self.buffer_HRonelag[i,j]=dataOut.datalags[1,i,(j+2*whichlag)%dataOut.nHeights,whichlag] |
|
|
646 | 646 | |
|
|
647 | 647 | return self.buffer_HRonelag |
|
|
648 | 648 | |
|
|
649 | 649 | |
|
|
650 | 650 | |
|
|
651 | 651 | def run(self,dataOut,DPL=11,NSCAN=132): |
|
|
652 | 652 | |
|
|
653 | 653 | dataOut.DPL=DPL |
|
|
654 | 654 | dataOut.NSCAN=NSCAN |
|
|
655 | 655 | dataOut.paramInterval=0#int(dataOut.nint*dataOut.header[7][0]*2 ) |
|
|
656 | 656 | dataOut.lat=-11.95 |
|
|
657 | 657 | dataOut.lon=-76.87 |
|
|
658 | 658 | dataOut.datalags=None |
|
|
659 | 659 | |
|
|
660 | 660 | dataOut.datalags=numpy.copy(self.LagDistribution(dataOut)) |
|
|
661 | 661 | dataOut.datalags[1,:,:,:]=self.HeightReconstruction(dataOut) |
|
|
662 | 662 | |
|
|
663 | 663 | return dataOut |
|
|
664 | 664 | |
|
|
665 | 665 | class LagsReshapeHP(Operation): |
|
|
666 | 666 | ''' |
|
|
667 | 667 | Written by R. Flores |
|
|
668 | 668 | ''' |
|
|
669 | 669 | """Operation to reshape input data into (Channels,Profiles(with same lag),Heights,Lags) and heights reconstruction. |
|
|
670 | 670 | |
|
|
671 | 671 | Parameters: |
|
|
672 | 672 | ----------- |
|
|
673 | 673 | |
|
|
674 | 674 | |
|
|
675 | 675 | Example |
|
|
676 | 676 | -------- |
|
|
677 | 677 | |
|
|
678 | 678 | op = proc_unit.addOperation(name='LagsReshape') |
|
|
679 | 679 | |
|
|
680 | 680 | |
|
|
681 | 681 | """ |
|
|
682 | 682 | |
|
|
683 | 683 | def __init__(self, **kwargs): |
|
|
684 | 684 | |
|
|
685 | 685 | Operation.__init__(self, **kwargs) |
|
|
686 | 686 | |
|
|
687 | 687 | self.buffer=None |
|
|
688 | 688 | self.buffer_HR=None |
|
|
689 | 689 | self.buffer_HRonelag=None |
|
|
690 | 690 | |
|
|
691 | 691 | def LagDistribution(self,dataOut): |
|
|
692 | 692 | |
|
|
693 | 693 | dataOut.datapure=numpy.copy(dataOut.data[:,0:dataOut.NSCAN,:]) |
|
|
694 | 694 | self.buffer = numpy.zeros((dataOut.nChannels, |
|
|
695 | 695 | int(dataOut.NSCAN/dataOut.DPL), |
|
|
696 | 696 | dataOut.nHeights,dataOut.DPL), |
|
|
697 | 697 | dtype='complex') |
|
|
698 | 698 | |
|
|
699 | 699 | for j in range(int(self.buffer.shape[1]/2)): |
|
|
700 | 700 | for i in range(dataOut.DPL): |
|
|
701 | 701 | if j+1==int(self.buffer.shape[1]/2) and i+1==dataOut.DPL: |
|
|
702 | 702 | self.buffer[:,2*j:,:,i]=dataOut.datapure[:,2*i+int(2*j*dataOut.DPL):,:] |
|
|
703 | 703 | else: |
|
|
704 | 704 | self.buffer[:,2*j:2*(j+1),:,i]=dataOut.datapure[:,2*i+int(2*j*dataOut.DPL):2*(i+1)+int(2*j*dataOut.DPL),:] |
|
|
705 | 705 | |
|
|
706 | 706 | return self.buffer |
|
|
707 | 707 | |
|
|
708 | 708 | def HeightReconstruction(self,dataOut): |
|
|
709 | 709 | |
|
|
710 | 710 | self.buffer_HR = numpy.zeros((int(dataOut.NSCAN/dataOut.DPL), |
|
|
711 | 711 | dataOut.nHeights,dataOut.DPL), |
|
|
712 | 712 | dtype='complex') |
|
|
713 | 713 | |
|
|
714 | 714 | for i in range(int(dataOut.DPL)): #Only channel B |
|
|
715 | 715 | if i==0: |
|
|
716 | 716 | self.buffer_HR[:,:,i]=dataOut.datalags[1,:,:,i] |
|
|
717 | 717 | else: |
|
|
718 | 718 | self.buffer_HR[:,:,i]=self.HRonelag(dataOut,i) |
|
|
719 | 719 | |
|
|
720 | 720 | return self.buffer_HR |
|
|
721 | 721 | |
|
|
722 | 722 | |
|
|
723 | 723 | def HRonelag(self,dataOut,whichlag): |
|
|
724 | 724 | self.buffer_HRonelag = numpy.zeros((int(dataOut.NSCAN/dataOut.DPL), |
|
|
725 | 725 | dataOut.nHeights), |
|
|
726 | 726 | dtype='complex') |
|
|
727 | 727 | |
|
|
728 | 728 | for i in range(self.buffer_HRonelag.shape[0]): |
|
|
729 | 729 | for j in range(dataOut.nHeights): |
|
|
730 | 730 | if j+int(2*whichlag)<dataOut.nHeights: |
|
|
731 | 731 | self.buffer_HRonelag[i,j]=dataOut.datalags[1,i,j+2*whichlag,whichlag] |
|
|
732 | 732 | else: |
|
|
733 | 733 | if whichlag!=10: |
|
|
734 | 734 | self.buffer_HRonelag[i,j]=dataOut.datalags[1,i,(j+2*whichlag)%dataOut.nHeights,whichlag+1] |
|
|
735 | 735 | else: |
|
|
736 | 736 | if i+2<self.buffer_HRonelag.shape[0]: |
|
|
737 | 737 | self.buffer_HRonelag[i,j]=dataOut.datalags[1,i+2,(j+2*whichlag)%dataOut.nHeights,0] |
|
|
738 | 738 | else: #i+1==self.buffer_HRonelag.shape[0]: |
|
|
739 | 739 | self.buffer_HRonelag[i,j]=dataOut.datalags[1,i,(j+2*whichlag)%dataOut.nHeights,whichlag] |
|
|
740 | 740 | |
|
|
741 | 741 | return self.buffer_HRonelag |
|
|
742 | 742 | |
|
|
743 | 743 | |
|
|
744 | 744 | |
|
|
745 | 745 | def run(self,dataOut,DPL=11,NSCAN=132): |
|
|
746 | 746 | |
|
|
747 | 747 | dataOut.DPL=DPL |
|
|
748 | 748 | dataOut.NSCAN=NSCAN |
|
|
749 | 749 | dataOut.paramInterval=0#int(dataOut.nint*dataOut.header[7][0]*2 ) |
|
|
750 | 750 | dataOut.lat=-11.95 |
|
|
751 | 751 | dataOut.lon=-76.87 |
|
|
752 | 752 | dataOut.datalags=None |
|
|
753 | 753 | |
|
|
754 | 754 | dataOut.datalags=numpy.copy(self.LagDistribution(dataOut)) |
|
|
755 | 755 | dataOut.datalags[1,:,:,:]=self.HeightReconstruction(dataOut) |
|
|
756 | 756 | |
|
|
757 | 757 | return dataOut |
|
|
758 | 758 | |
|
|
759 | 759 | class LagsReshapeDP_V2(Operation): |
|
|
760 | 760 | ''' |
|
|
761 | 761 | Written by R. Flores |
|
|
762 | 762 | ''' |
|
|
763 | 763 | """Operation to reshape input data into (Channels,Profiles(with same lag),Heights,Lags) and heights reconstruction. |
|
|
764 | 764 | |
|
|
765 | 765 | Parameters: |
|
|
766 | 766 | ----------- |
|
|
767 | 767 | |
|
|
768 | 768 | |
|
|
769 | 769 | Example |
|
|
770 | 770 | -------- |
|
|
771 | 771 | |
|
|
772 | 772 | op = proc_unit.addOperation(name='LagsReshape') |
|
|
773 | 773 | |
|
|
774 | 774 | |
|
|
775 | 775 | """ |
|
|
776 | 776 | |
|
|
777 | 777 | def __init__(self, **kwargs): |
|
|
778 | 778 | |
|
|
779 | 779 | Operation.__init__(self, **kwargs) |
|
|
780 | 780 | |
|
|
781 | 781 | self.buffer=None |
|
|
782 | 782 | self.data_buffer = [] |
|
|
783 | 783 | |
|
|
784 | 784 | def setup(self,dataOut,DPL,NSCAN,NLAG,NRANGE,lagind,lagfirst): |
|
|
785 | 785 | dataOut.DPL=DPL |
|
|
786 | 786 | dataOut.NSCAN=NSCAN |
|
|
787 | 787 | dataOut.NLAG = NLAG |
|
|
788 | 788 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
789 | 789 | dataOut.NRANGE = NRANGE |
|
|
790 | 790 | dataOut.read_samples=int(dataOut.nHeights) |
|
|
791 | 791 | #print(dataOut.read_samples) |
|
|
792 | 792 | #print(dataOut.nHeights) |
|
|
793 | 793 | #exit(1) |
|
|
794 | 794 | dataOut.NDP = dataOut.NDT = int((dataOut.nHeights-dataOut.NRANGE)/2) |
|
|
795 | 795 | dataOut.heightList = numpy.arange(dataOut.NDP) *deltaHeight# + dataOut.heightList[0] |
|
|
796 | 796 | #dataOut.NDP = dataOut.NDT = int(dataOut.nHeights/2)#int((dataOut.nHeights-dataOut.NRANGE)/2) |
|
|
797 | 797 | #print(dataOut.NDP) |
|
|
798 | 798 | #print(dataOut.heightList) |
|
|
799 | 799 | dataOut.paramInterval=0#int(dataOut.nint*dataOut.header[7][0]*2 ) |
|
|
800 | 800 | dataOut.lat=-11.95 |
|
|
801 | 801 | dataOut.lon=-76.87 |
|
|
802 | 802 | dataOut.datalags=None |
|
|
803 | 803 | dataOut.lagind=lagind |
|
|
804 | 804 | dataOut.lagfirst=lagfirst |
|
|
805 | 805 | |
|
|
806 | 806 | |
|
|
807 | 807 | def LagDistribution(self,dataOut): |
|
|
808 | 808 | |
|
|
809 | 809 | self.buffer = numpy.zeros((dataOut.nChannels, |
|
|
810 | 810 | int(2*2*dataOut.NSCAN/dataOut.NLAG), |
|
|
811 | 811 | dataOut.NDP,dataOut.DPL), |
|
|
812 | 812 | dtype='complex') |
|
|
813 | 813 | |
|
|
814 | 814 | indProfile = numpy.arange(0,dataOut.NSCAN,1)//8 |
|
|
815 | 815 | |
|
|
816 | 816 | #dataOut.nNoiseProfiles = dataOut.nProfiles-dataOut.NSCAN |
|
|
817 | 817 | |
|
|
818 | 818 | for i in range(2): |
|
|
819 | 819 | if i==0: |
|
|
820 | 820 | aux = 0 |
|
|
821 | 821 | else: |
|
|
822 | 822 | aux =16 |
|
|
823 | 823 | for j in range(dataOut.NDP): |
|
|
824 | 824 | for k in range(int(dataOut.NSCAN)): |
|
|
825 | 825 | |
|
|
826 | 826 | n=dataOut.lagind[k%dataOut.NLAG] |
|
|
827 | 827 | |
|
|
828 | 828 | data_ChA=dataOut.data[0,k,dataOut.NRANGE+j+i*dataOut.NDT]#-dataOut.dc[0] |
|
|
829 | 829 | |
|
|
830 | 830 | if dataOut.NRANGE+j+i*dataOut.NDT+2*n<dataOut.read_samples: |
|
|
831 | 831 | |
|
|
832 | 832 | data_ChB=dataOut.data[1,k,dataOut.NRANGE+j+i*dataOut.NDT+2*n]#-dataOut.dc[1] |
|
|
833 | 833 | #print(data_ChB) |
|
|
834 | 834 | #exit(1) |
|
|
835 | 835 | #print("*1*") |
|
|
836 | 836 | |
|
|
837 | 837 | else: |
|
|
838 | 838 | #print(i,j,n) |
|
|
839 | 839 | #exit(1) |
|
|
840 | 840 | |
|
|
841 | 841 | if k+1<int(dataOut.NSCAN): |
|
|
842 | 842 | data_ChB=dataOut.data[1,k+1,(dataOut.NRANGE+j+i*dataOut.NDT+2*n)%dataOut.NDP] |
|
|
843 | 843 | #print(data_ChB) |
|
|
844 | 844 | #print("*2*") |
|
|
845 | 845 | #exit(1) |
|
|
846 | 846 | if k+1==int(dataOut.NSCAN): |
|
|
847 | 847 | data_ChB=dataOut.data[1,k,(dataOut.NRANGE+j+i*dataOut.NDT+2*n)%dataOut.NDP] |
|
|
848 | 848 | #print("*3*") |
|
|
849 | 849 | #if n == 7 and j == 65: |
|
|
850 | 850 | #print(k) |
|
|
851 | 851 | #print(data_ChB) |
|
|
852 | 852 | #exit(1) |
|
|
853 | 853 | if n == 8 or n == 9 or n == 10: |
|
|
854 | 854 | self.buffer[0,int((aux+indProfile[k]-1)/2),j,n] = data_ChA |
|
|
855 | 855 | self.buffer[1,int((aux+indProfile[k]-1)/2),j,n] = data_ChB |
|
|
856 | 856 | elif n == 1 or n == 2 or n == 7: |
|
|
857 | 857 | self.buffer[0,int((aux+indProfile[k])/2),j,n] = data_ChA |
|
|
858 | 858 | self.buffer[1,int((aux+indProfile[k])/2),j,n] = data_ChB |
|
|
859 | 859 | else: |
|
|
860 | 860 | self.buffer[0,aux+indProfile[k],j,n] = data_ChA |
|
|
861 | 861 | self.buffer[1,aux+indProfile[k],j,n] = data_ChB |
|
|
862 | 862 | |
|
|
863 | 863 | #FindMe |
|
|
864 | 864 | pa1 = 20 |
|
|
865 | 865 | pa2 = 10 |
|
|
866 | 866 | |
|
|
867 | 867 | #print(self.buffer[0,:,pa1,pa2]) |
|
|
868 | 868 | #print(self.buffer[1,:,pa1,pa2]) |
|
|
869 | 869 | ''' |
|
|
870 | 870 | print(sum(self.buffer[0,:,pa1,pa2])) |
|
|
871 | 871 | print(sum(self.buffer[1,:,pa1,pa2])) |
|
|
872 | 872 | #exit(1) |
|
|
873 | 873 | ''' |
|
|
874 | 874 | |
|
|
875 | 875 | ''' |
|
|
876 | 876 | for pa1 in range(67): |
|
|
877 | 877 | print(sum(self.buffer[0,:,pa1,pa2])) |
|
|
878 | 878 | print(sum(self.buffer[1,:,pa1,pa2])) |
|
|
879 | 879 | ''' |
|
|
880 | 880 | |
|
|
881 | 881 | ''' |
|
|
882 | 882 | import matplotlib.pyplot as plt |
|
|
883 | 883 | fft = numpy.fft.fft(self.buffer[0,:,pa1,pa2]) |
|
|
884 | 884 | fft2 = fft*numpy.conjugate(fft) |
|
|
885 | 885 | fft2 = fft2.real |
|
|
886 | 886 | fft2 = numpy.fft.fftshift(fft2) |
|
|
887 | 887 | ''' |
|
|
888 | 888 | #print("before",fft2) |
|
|
889 | 889 | #plt.plot(fft2) |
|
|
890 | 890 | #plt.show() |
|
|
891 | 891 | #import time |
|
|
892 | 892 | #time.sleep(5) |
|
|
893 | 893 | #plt.close('all') |
|
|
894 | 894 | #exit(1) |
|
|
895 | 895 | return self.buffer |
|
|
896 | 896 | |
|
|
897 | 897 | |
|
|
898 | 898 | |
|
|
899 | 899 | def run(self,dataOut,DPL=11,NSCAN=128,lagind=(0,1,2,3,4,5,6,7,0,3,4,5,6,8,9,10),lagfirst=(1,1,1,1,1,1,1,1,0,0,0,0,0,1,1,1), NLAG = 16, NRANGE = 200): |
|
|
900 | 900 | |
|
|
901 | 901 | if not self.isConfig: |
|
|
902 | 902 | self.setup(dataOut,DPL,NSCAN,NLAG,NRANGE,lagind,lagfirst) |
|
|
903 | 903 | self.isConfig = True |
|
|
904 | 904 | |
|
|
905 | 905 | #print(dataOut.data[1,:12,:15]) |
|
|
906 | 906 | #exit(1) |
|
|
907 | 907 | #print(numpy.shape(dataOut.data)) |
|
|
908 | 908 | #print(dataOut.profileIndex) |
|
|
909 | 909 | |
|
|
910 | 910 | if not dataOut.flagDataAsBlock: |
|
|
911 | 911 | |
|
|
912 | 912 | dataOut.flagNoData = True |
|
|
913 | 913 | #print("nProfiles: ",dataOut.nProfiles) |
|
|
914 | 914 | #if dataOut.profileIndex == 140: |
|
|
915 | 915 | #print("id: ",dataOut.profileIndex) |
|
|
916 | 916 | if dataOut.profileIndex == dataOut.nProfiles-1: |
|
|
917 | 917 | #print("here") |
|
|
918 | 918 | #print(dataOut.data.shape) |
|
|
919 | 919 | self.data_buffer.append(dataOut.data) |
|
|
920 | 920 | dataOut.data = numpy.transpose(numpy.array(self.data_buffer),(1,0,2)) |
|
|
921 | 921 | #print(dataOut.data.shape) |
|
|
922 | 922 | #print(numpy.sum(dataOut.data)) |
|
|
923 | 923 | #print(dataOut.data[1,100,:]) |
|
|
924 | 924 | #exit(1) |
|
|
925 | 925 | dataOut.datalags = numpy.copy(self.LagDistribution(dataOut)) |
|
|
926 | 926 | #print(numpy.shape(dataOut.datalags)) |
|
|
927 | 927 | #exit(1) |
|
|
928 | 928 | #print("AFTER RESHAPE DP") |
|
|
929 | 929 | |
|
|
930 | 930 | dataOut.data = dataOut.data[:,:,200:] |
|
|
931 | 931 | self.data_buffer = [] |
|
|
932 | 932 | dataOut.flagDataAsBlock = True |
|
|
933 | 933 | dataOut.flagNoData = False |
|
|
934 | 934 | |
|
|
935 | 935 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
936 | 936 | dataOut.heightList = numpy.arange(dataOut.NDP) *deltaHeight# + dataOut.heightList[0] |
|
|
937 | 937 | #exit(1) |
|
|
938 | 938 | #print(numpy.sum(dataOut.datalags)) |
|
|
939 | 939 | #exit(1) |
|
|
940 | 940 | |
|
|
941 | 941 | else: |
|
|
942 | 942 | self.data_buffer.append(dataOut.data) |
|
|
943 | 943 | #print(numpy.shape(dataOut.data)) |
|
|
944 | 944 | #exit(1) |
|
|
945 | 945 | else: |
|
|
946 | 946 | #print(dataOut.data.shape) |
|
|
947 | 947 | #print(numpy.sum(dataOut.data)) |
|
|
948 | 948 | #print(dataOut.data[1,100,:]) |
|
|
949 | 949 | #exit(1) |
|
|
950 | 950 | dataOut.datalags = numpy.copy(self.LagDistribution(dataOut)) |
|
|
951 | 951 | #print(dataOut.datalags.shape) |
|
|
952 | 952 | dataOut.data = dataOut.data[:,:,200:] |
|
|
953 | 953 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
954 | 954 | dataOut.heightList = numpy.arange(dataOut.NDP) * deltaHeight# + dataOut.heightList[0] |
|
|
955 | 955 | #print(dataOut.nHeights) |
|
|
956 | 956 | #print(numpy.sum(dataOut.datalags)) |
|
|
957 | 957 | #exit(1) |
|
|
958 | 958 | |
|
|
959 | 959 | return dataOut |
|
|
960 | 960 | |
|
|
961 | 961 | class CrossProdDP(Operation): |
|
|
962 | 962 | ''' |
|
|
963 | 963 | Written by R. Flores |
|
|
964 | 964 | ''' |
|
|
965 | 965 | """Operation to calculate cross products of the Double Pulse Experiment. |
|
|
966 | 966 | |
|
|
967 | 967 | Parameters: |
|
|
968 | 968 | ----------- |
|
|
969 | 969 | NLAG : int |
|
|
970 | 970 | Number of lags Long Pulse. |
|
|
971 | 971 | NRANGE : int |
|
|
972 | 972 | Number of samples for Long Pulse. |
|
|
973 | 973 | NCAL : int |
|
|
974 | 974 | .* |
|
|
975 | 975 | DPL : int |
|
|
976 | 976 | Number of lags Double Pulse. |
|
|
977 | 977 | NDN : int |
|
|
978 | 978 | .* |
|
|
979 | 979 | NDT : int |
|
|
980 | 980 | Number of heights for Double Pulse.* |
|
|
981 | 981 | NDP : int |
|
|
982 | 982 | Number of heights for Double Pulse.* |
|
|
983 | 983 | NSCAN : int |
|
|
984 | 984 | Number of profiles when the transmitter is on. |
|
|
985 | 985 | flags_array : intlist |
|
|
986 | 986 | .* |
|
|
987 | 987 | NAVG : int |
|
|
988 | 988 | Number of blocks to be "averaged". |
|
|
989 | 989 | nkill : int |
|
|
990 | 990 | Number of blocks not to be considered when averaging. |
|
|
991 | 991 | |
|
|
992 | 992 | Example |
|
|
993 | 993 | -------- |
|
|
994 | 994 | |
|
|
995 | 995 | op = proc_unit.addOperation(name='CrossProdDP', optype='other') |
|
|
996 | 996 | op.addParameter(name='NLAG', value='16', format='int') |
|
|
997 | 997 | op.addParameter(name='NRANGE', value='0', format='int') |
|
|
998 | 998 | op.addParameter(name='NCAL', value='0', format='int') |
|
|
999 | 999 | op.addParameter(name='DPL', value='11', format='int') |
|
|
1000 | 1000 | op.addParameter(name='NDN', value='0', format='int') |
|
|
1001 | 1001 | op.addParameter(name='NDT', value='66', format='int') |
|
|
1002 | 1002 | op.addParameter(name='NDP', value='66', format='int') |
|
|
1003 | 1003 | op.addParameter(name='NSCAN', value='132', format='int') |
|
|
1004 | 1004 | op.addParameter(name='flags_array', value='(0, 30, 60, 90, 120, 150, 180, 210, 240, 270, 300)', format='intlist') |
|
|
1005 | 1005 | op.addParameter(name='NAVG', value='16', format='int') |
|
|
1006 | 1006 | op.addParameter(name='nkill', value='6', format='int') |
|
|
1007 | 1007 | |
|
|
1008 | 1008 | """ |
|
|
1009 | 1009 | |
|
|
1010 | 1010 | def __init__(self, **kwargs): |
|
|
1011 | 1011 | |
|
|
1012 | 1012 | Operation.__init__(self, **kwargs) |
|
|
1013 | 1013 | self.bcounter=0 |
|
|
1014 | 1014 | self.aux=1 |
|
|
1015 | 1015 | self.lag_products_LP_median_estimates_aux=0 |
|
|
1016 | 1016 | |
|
|
1017 | 1017 | def set_header_output(self,dataOut): |
|
|
1018 | 1018 | |
|
|
1019 | 1019 | dataOut.read_samples=len(dataOut.heightList)#int(dataOut.systemHeaderObj.nSamples/dataOut.windowOfFilter) |
|
|
1020 | 1020 | padding=numpy.zeros(1,'int32') |
|
|
1021 | 1021 | hsize=numpy.zeros(1,'int32') |
|
|
1022 | 1022 | bufsize=numpy.zeros(1,'int32') |
|
|
1023 | 1023 | nr=numpy.zeros(1,'int32') |
|
|
1024 | 1024 | ngates=numpy.zeros(1,'int32') ### ### ### 2 |
|
|
1025 | 1025 | time1=numpy.zeros(1,'uint64') # pos 3 |
|
|
1026 | 1026 | time2=numpy.zeros(1,'uint64') # pos 4 |
|
|
1027 | 1027 | lcounter=numpy.zeros(1,'int32') |
|
|
1028 | 1028 | groups=numpy.zeros(1,'int32') |
|
|
1029 | 1029 | system=numpy.zeros(4,'int8') # pos 7 |
|
|
1030 | 1030 | h0=numpy.zeros(1,'float32') |
|
|
1031 | 1031 | dh=numpy.zeros(1,'float32') |
|
|
1032 | 1032 | ipp=numpy.zeros(1,'float32') |
|
|
1033 | 1033 | process=numpy.zeros(1,'int32') |
|
|
1034 | 1034 | tx=numpy.zeros(1,'int32') |
|
|
1035 | 1035 | ngates1=numpy.zeros(1,'int32') ### ### ### 13 |
|
|
1036 | 1036 | time0=numpy.zeros(1,'uint64') # pos 14 |
|
|
1037 | 1037 | nlags=numpy.zeros(1,'int32') |
|
|
1038 | 1038 | nlags1=numpy.zeros(1,'int32') |
|
|
1039 | 1039 | txb=numpy.zeros(1,'float32') ### ### ### 17 |
|
|
1040 | 1040 | time3=numpy.zeros(1,'uint64') # pos 18 |
|
|
1041 | 1041 | time4=numpy.zeros(1,'uint64') # pos 19 |
|
|
1042 | 1042 | h0_=numpy.zeros(1,'float32') |
|
|
1043 | 1043 | dh_=numpy.zeros(1,'float32') |
|
|
1044 | 1044 | ipp_=numpy.zeros(1,'float32') |
|
|
1045 | 1045 | txa_=numpy.zeros(1,'float32') |
|
|
1046 | 1046 | pad=numpy.zeros(100,'int32') |
|
|
1047 | 1047 | nbytes=numpy.zeros(1,'int32') |
|
|
1048 | 1048 | limits=numpy.zeros(1,'int32') |
|
|
1049 | 1049 | ngroups=numpy.zeros(1,'int32') ### ### ### 27 |
|
|
1050 | 1050 | |
|
|
1051 | 1051 | dataOut.header=[hsize,bufsize,nr,ngates,time1,time2, |
|
|
1052 | 1052 | lcounter,groups,system,h0,dh,ipp, |
|
|
1053 | 1053 | process,tx,ngates1,padding,time0,nlags, |
|
|
1054 | 1054 | nlags1,padding,txb,time3,time4,h0_,dh_, |
|
|
1055 | 1055 | ipp_,txa_,pad,nbytes,limits,padding,ngroups] |
|
|
1056 | 1056 | |
|
|
1057 | 1057 | |
|
|
1058 | 1058 | #dataOut.header[1][0]=81864 |
|
|
1059 | 1059 | dataOut.FirstHeight=int(dataOut.heightList[0]) |
|
|
1060 | 1060 | dataOut.MAXNRANGENDT=max(dataOut.NRANGE,dataOut.NDT) |
|
|
1061 | 1061 | dataOut.header[3][0]=max(dataOut.NRANGE,dataOut.NDT) |
|
|
1062 | 1062 | dataOut.header[7][0]=dataOut.NAVG |
|
|
1063 | 1063 | dataOut.header[9][0]=int(dataOut.heightList[0]) |
|
|
1064 | 1064 | dataOut.header[10][0]=dataOut.DH |
|
|
1065 | 1065 | dataOut.header[17][0]=dataOut.DPL |
|
|
1066 | 1066 | dataOut.header[18][0]=dataOut.NLAG |
|
|
1067 | 1067 | #self.header[5][0]=0 |
|
|
1068 | 1068 | dataOut.header[15][0]=dataOut.NDP |
|
|
1069 | 1069 | dataOut.header[2][0]=dataOut.NR |
|
|
1070 | 1070 | |
|
|
1071 | 1071 | |
|
|
1072 | 1072 | def get_products_cabxys(self,dataOut): |
|
|
1073 | 1073 | |
|
|
1074 | 1074 | if self.aux==1: |
|
|
1075 | 1075 | self.set_header_output(dataOut) |
|
|
1076 | 1076 | self.aux=0 |
|
|
1077 | 1077 | |
|
|
1078 | 1078 | dataOut.lags_array=[x / dataOut.DH for x in dataOut.flags_array] |
|
|
1079 | 1079 | self.cax=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
|
1080 | 1080 | self.cay=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
|
1081 | 1081 | self.cbx=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
|
1082 | 1082 | self.cby=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
|
1083 | 1083 | self.cax2=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
|
1084 | 1084 | self.cay2=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
|
1085 | 1085 | self.cbx2=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
|
1086 | 1086 | self.cby2=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
|
1087 | 1087 | self.caxbx=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
|
1088 | 1088 | self.caxby=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
|
1089 | 1089 | self.caybx=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
|
1090 | 1090 | self.cayby=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
|
1091 | 1091 | self.caxay=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
|
1092 | 1092 | self.cbxby=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
|
1093 | 1093 | |
|
|
1094 | 1094 | for i in range(2): |
|
|
1095 | 1095 | for j in range(dataOut.NDP): |
|
|
1096 | 1096 | for k in range(int(dataOut.NSCAN/2)): |
|
|
1097 | 1097 | n=k%dataOut.DPL |
|
|
1098 | 1098 | ax=dataOut.data[0,2*k+i,j].real |
|
|
1099 | 1099 | ay=dataOut.data[0,2*k+i,j].imag |
|
|
1100 | 1100 | if j+dataOut.lags_array[n]<dataOut.NDP: |
|
|
1101 | 1101 | bx=dataOut.data[1,2*k+i,j+int(dataOut.lags_array[n])].real |
|
|
1102 | 1102 | by=dataOut.data[1,2*k+i,j+int(dataOut.lags_array[n])].imag |
|
|
1103 | 1103 | else: |
|
|
1104 | 1104 | if k+1<int(dataOut.NSCAN/2): |
|
|
1105 | 1105 | bx=dataOut.data[1,2*(k+1)+i,(dataOut.NRANGE+dataOut.NCAL+j+int(dataOut.lags_array[n]))%dataOut.NDP].real |
|
|
1106 | 1106 | by=dataOut.data[1,2*(k+1)+i,(dataOut.NRANGE+dataOut.NCAL+j+int(dataOut.lags_array[n]))%dataOut.NDP].imag |
|
|
1107 | 1107 | |
|
|
1108 | 1108 | if k+1==int(dataOut.NSCAN/2): |
|
|
1109 | 1109 | bx=dataOut.data[1,2*k+i,(dataOut.NRANGE+dataOut.NCAL+j+int(dataOut.lags_array[n]))%dataOut.NDP].real |
|
|
1110 | 1110 | by=dataOut.data[1,2*k+i,(dataOut.NRANGE+dataOut.NCAL+j+int(dataOut.lags_array[n]))%dataOut.NDP].imag |
|
|
1111 | 1111 | |
|
|
1112 | 1112 | if(k<dataOut.DPL): |
|
|
1113 | 1113 | self.cax[j][n][i]=ax |
|
|
1114 | 1114 | self.cay[j][n][i]=ay |
|
|
1115 | 1115 | self.cbx[j][n][i]=bx |
|
|
1116 | 1116 | self.cby[j][n][i]=by |
|
|
1117 | 1117 | self.cax2[j][n][i]=ax*ax |
|
|
1118 | 1118 | self.cay2[j][n][i]=ay*ay |
|
|
1119 | 1119 | self.cbx2[j][n][i]=bx*bx |
|
|
1120 | 1120 | self.cby2[j][n][i]=by*by |
|
|
1121 | 1121 | self.caxbx[j][n][i]=ax*bx |
|
|
1122 | 1122 | self.caxby[j][n][i]=ax*by |
|
|
1123 | 1123 | self.caybx[j][n][i]=ay*bx |
|
|
1124 | 1124 | self.cayby[j][n][i]=ay*by |
|
|
1125 | 1125 | self.caxay[j][n][i]=ax*ay |
|
|
1126 | 1126 | self.cbxby[j][n][i]=bx*by |
|
|
1127 | 1127 | else: |
|
|
1128 | 1128 | self.cax[j][n][i]+=ax |
|
|
1129 | 1129 | self.cay[j][n][i]+=ay |
|
|
1130 | 1130 | self.cbx[j][n][i]+=bx |
|
|
1131 | 1131 | self.cby[j][n][i]+=by |
|
|
1132 | 1132 | self.cax2[j][n][i]+=ax*ax |
|
|
1133 | 1133 | self.cay2[j][n][i]+=ay*ay |
|
|
1134 | 1134 | self.cbx2[j][n][i]+=bx*bx |
|
|
1135 | 1135 | self.cby2[j][n][i]+=by*by |
|
|
1136 | 1136 | self.caxbx[j][n][i]+=ax*bx |
|
|
1137 | 1137 | self.caxby[j][n][i]+=ax*by |
|
|
1138 | 1138 | self.caybx[j][n][i]+=ay*bx |
|
|
1139 | 1139 | self.cayby[j][n][i]+=ay*by |
|
|
1140 | 1140 | self.caxay[j][n][i]+=ax*ay |
|
|
1141 | 1141 | self.cbxby[j][n][i]+=bx*by |
|
|
1142 | 1142 | |
|
|
1143 | 1143 | |
|
|
1144 | 1144 | def medi(self,data_navg,NAVG,nkill): |
|
|
1145 | 1145 | sorts=sorted(data_navg) |
|
|
1146 | 1146 | rsorts=numpy.arange(NAVG) |
|
|
1147 | 1147 | result=0.0 |
|
|
1148 | 1148 | for k in range(NAVG): |
|
|
1149 | 1149 | if k>=nkill/2 and k<NAVG-nkill/2: |
|
|
1150 | 1150 | result+=sorts[k]*float(NAVG)/(float)(NAVG-nkill) |
|
|
1151 | 1151 | return result |
|
|
1152 | 1152 | |
|
|
1153 | 1153 | |
|
|
1154 | 1154 | def get_dc(self,dataOut): |
|
|
1155 | 1155 | if self.bcounter==0: |
|
|
1156 | 1156 | dataOut.dc=numpy.zeros(dataOut.NR,dtype='complex64') |
|
|
1157 | 1157 | def cabxys_navg(self,dataOut): |
|
|
1158 | 1158 | |
|
|
1159 | 1159 | |
|
|
1160 | 1160 | dataOut.header[5][0]=dataOut.TimeBlockSeconds |
|
|
1161 | 1161 | |
|
|
1162 | 1162 | dataOut.LastAVGDate=dataOut.TimeBlockSeconds |
|
|
1163 | 1163 | |
|
|
1164 | 1164 | if self.bcounter==0: |
|
|
1165 | 1165 | dataOut.FirstAVGDate=dataOut.TimeBlockSeconds |
|
|
1166 | 1166 | dataOut.header[4][0]=dataOut.header[5][0]#firsttimeofNAVG |
|
|
1167 | 1167 | if dataOut.CurrentBlock==1: |
|
|
1168 | 1168 | dataOut.FirstBlockDate=dataOut.TimeBlockSeconds |
|
|
1169 | 1169 | dataOut.header[16][0]=dataOut.header[5][0]#FirsTimeOfTotalBlocks |
|
|
1170 | 1170 | |
|
|
1171 | 1171 | self.cax_navg=[] |
|
|
1172 | 1172 | self.cay_navg=[] |
|
|
1173 | 1173 | self.cbx_navg=[] |
|
|
1174 | 1174 | self.cby_navg=[] |
|
|
1175 | 1175 | self.cax2_navg=[] |
|
|
1176 | 1176 | self.cay2_navg=[] |
|
|
1177 | 1177 | self.cbx2_navg=[] |
|
|
1178 | 1178 | self.cby2_navg=[] |
|
|
1179 | 1179 | self.caxbx_navg=[] |
|
|
1180 | 1180 | self.caxby_navg=[] |
|
|
1181 | 1181 | self.caybx_navg=[] |
|
|
1182 | 1182 | self.cayby_navg=[] |
|
|
1183 | 1183 | self.caxay_navg=[] |
|
|
1184 | 1184 | self.cbxby_navg=[] |
|
|
1185 | 1185 | |
|
|
1186 | 1186 | dataOut.noisevector=numpy.zeros((dataOut.MAXNRANGENDT,dataOut.NR,dataOut.NAVG),'float32') #30/03/2020 |
|
|
1187 | 1187 | |
|
|
1188 | 1188 | dataOut.noisevector_=numpy.zeros((dataOut.read_samples,dataOut.NR,dataOut.NAVG),'float32') |
|
|
1189 | 1189 | |
|
|
1190 | 1190 | self.noisevectorizer(dataOut.NSCAN,dataOut.nProfiles,dataOut.NR,dataOut.MAXNRANGENDT,dataOut.noisevector,dataOut.data,dataOut.dc) #30/03/2020 |
|
|
1191 | 1191 | |
|
|
1192 | 1192 | self.cax_navg.append(self.cax) |
|
|
1193 | 1193 | self.cay_navg.append(self.cay) |
|
|
1194 | 1194 | self.cbx_navg.append(self.cbx) |
|
|
1195 | 1195 | self.cby_navg.append(self.cby) |
|
|
1196 | 1196 | self.cax2_navg.append(self.cax2) |
|
|
1197 | 1197 | self.cay2_navg.append(self.cay2) |
|
|
1198 | 1198 | self.cbx2_navg.append(self.cbx2) |
|
|
1199 | 1199 | self.cby2_navg.append(self.cby2) |
|
|
1200 | 1200 | self.caxbx_navg.append(self.caxbx) |
|
|
1201 | 1201 | self.caxby_navg.append(self.caxby) |
|
|
1202 | 1202 | self.caybx_navg.append(self.caybx) |
|
|
1203 | 1203 | self.cayby_navg.append(self.cayby) |
|
|
1204 | 1204 | self.caxay_navg.append(self.caxay) |
|
|
1205 | 1205 | self.cbxby_navg.append(self.cbxby) |
|
|
1206 | 1206 | self.bcounter+=1 |
|
|
1207 | 1207 | |
|
|
1208 | 1208 | def noise_estimation4x_DP(self,dataOut): |
|
|
1209 | 1209 | if self.bcounter==dataOut.NAVG: |
|
|
1210 | 1210 | dataOut.noise_final=numpy.zeros(dataOut.NR,'float32') |
|
|
1211 | 1211 | snoise=numpy.zeros((dataOut.NR,dataOut.NAVG),'float32') |
|
|
1212 | 1212 | nvector1=numpy.zeros((dataOut.NR,dataOut.NAVG,dataOut.MAXNRANGENDT),'float32') |
|
|
1213 | 1213 | for i in range(dataOut.NR): |
|
|
1214 | 1214 | dataOut.noise_final[i]=0.0 |
|
|
1215 | 1215 | for k in range(dataOut.NAVG): |
|
|
1216 | 1216 | snoise[i][k]=0.0 |
|
|
1217 | 1217 | for j in range(dataOut.MAXNRANGENDT): |
|
|
1218 | 1218 | nvector1[i][k][j]= dataOut.noisevector[j][i][k]; |
|
|
1219 | 1219 | snoise[i][k]=self.noise_hs4x(dataOut.MAXNRANGENDT, nvector1[i][k]) |
|
|
1220 | 1220 | dataOut.noise_final[i]=self.noise_hs4x(dataOut.NAVG, snoise[i]) |
|
|
1221 | 1221 | |
|
|
1222 | 1222 | def kabxys(self,dataOut): |
|
|
1223 | 1223 | |
|
|
1224 | 1224 | if self.bcounter==dataOut.NAVG: |
|
|
1225 | 1225 | |
|
|
1226 | 1226 | dataOut.flagNoData = False |
|
|
1227 | 1227 | |
|
|
1228 | 1228 | self.kax=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') |
|
|
1229 | 1229 | self.kay=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') |
|
|
1230 | 1230 | self.kbx=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') |
|
|
1231 | 1231 | self.kby=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') |
|
|
1232 | 1232 | self.kax2=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') |
|
|
1233 | 1233 | self.kay2=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') |
|
|
1234 | 1234 | self.kbx2=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') |
|
|
1235 | 1235 | self.kby2=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') |
|
|
1236 | 1236 | self.kaxbx=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') |
|
|
1237 | 1237 | self.kaxby=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') |
|
|
1238 | 1238 | self.kaybx=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') |
|
|
1239 | 1239 | self.kayby=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') |
|
|
1240 | 1240 | self.kaxay=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') |
|
|
1241 | 1241 | self.kbxby=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') |
|
|
1242 | 1242 | |
|
|
1243 | 1243 | for i in range(self.cax_navg[0].shape[0]): |
|
|
1244 | 1244 | for j in range(self.cax_navg[0].shape[1]): |
|
|
1245 | 1245 | for k in range(self.cax_navg[0].shape[2]): |
|
|
1246 | 1246 | data_navg=[item[i,j,k] for item in self.cax_navg] |
|
|
1247 | 1247 | self.kax[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) |
|
|
1248 | 1248 | data_navg=[item[i,j,k] for item in self.cay_navg] |
|
|
1249 | 1249 | self.kay[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) |
|
|
1250 | 1250 | data_navg=[item[i,j,k] for item in self.cbx_navg] |
|
|
1251 | 1251 | self.kbx[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) |
|
|
1252 | 1252 | data_navg=[item[i,j,k] for item in self.cby_navg] |
|
|
1253 | 1253 | self.kby[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) |
|
|
1254 | 1254 | data_navg=[item[i,j,k] for item in self.cax2_navg] |
|
|
1255 | 1255 | self.kax2[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) |
|
|
1256 | 1256 | data_navg=[item[i,j,k] for item in self.cay2_navg] |
|
|
1257 | 1257 | self.kay2[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) |
|
|
1258 | 1258 | data_navg=[item[i,j,k] for item in self.cbx2_navg] |
|
|
1259 | 1259 | self.kbx2[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) |
|
|
1260 | 1260 | data_navg=[item[i,j,k] for item in self.cby2_navg] |
|
|
1261 | 1261 | self.kby2[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) |
|
|
1262 | 1262 | data_navg=[item[i,j,k] for item in self.caxbx_navg] |
|
|
1263 | 1263 | self.kaxbx[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) |
|
|
1264 | 1264 | data_navg=[item[i,j,k] for item in self.caxby_navg] |
|
|
1265 | 1265 | self.kaxby[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) |
|
|
1266 | 1266 | data_navg=[item[i,j,k] for item in self.caybx_navg] |
|
|
1267 | 1267 | self.kaybx[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) |
|
|
1268 | 1268 | data_navg=[item[i,j,k] for item in self.cayby_navg] |
|
|
1269 | 1269 | self.kayby[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) |
|
|
1270 | 1270 | data_navg=[item[i,j,k] for item in self.caxay_navg] |
|
|
1271 | 1271 | self.kaxay[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) |
|
|
1272 | 1272 | data_navg=[item[i,j,k] for item in self.cbxby_navg] |
|
|
1273 | 1273 | self.kbxby[i,j,k]=self.medi(data_navg,dataOut.NAVG,dataOut.nkill) |
|
|
1274 | 1274 | |
|
|
1275 | 1275 | |
|
|
1276 | 1276 | dataOut.kax=self.kax |
|
|
1277 | 1277 | dataOut.kay=self.kay |
|
|
1278 | 1278 | dataOut.kbx=self.kbx |
|
|
1279 | 1279 | dataOut.kby=self.kby |
|
|
1280 | 1280 | dataOut.kax2=self.kax2 |
|
|
1281 | 1281 | dataOut.kay2=self.kay2 |
|
|
1282 | 1282 | dataOut.kbx2=self.kbx2 |
|
|
1283 | 1283 | dataOut.kby2=self.kby2 |
|
|
1284 | 1284 | dataOut.kaxbx=self.kaxbx |
|
|
1285 | 1285 | dataOut.kaxby=self.kaxby |
|
|
1286 | 1286 | dataOut.kaybx=self.kaybx |
|
|
1287 | 1287 | dataOut.kayby=self.kayby |
|
|
1288 | 1288 | dataOut.kaxay=self.kaxay |
|
|
1289 | 1289 | dataOut.kbxby=self.kbxby |
|
|
1290 | 1290 | |
|
|
1291 | 1291 | self.bcounter=0 |
|
|
1292 | 1292 | |
|
|
1293 | 1293 | dataOut.crossprods=numpy.zeros((3,4,numpy.shape(dataOut.kax)[0],numpy.shape(dataOut.kax)[1],numpy.shape(dataOut.kax)[2])) |
|
|
1294 | 1294 | |
|
|
1295 | 1295 | dataOut.crossprods[0]=[dataOut.kax,dataOut.kay,dataOut.kbx,dataOut.kby] |
|
|
1296 | 1296 | dataOut.crossprods[1]=[dataOut.kax2,dataOut.kay2,dataOut.kbx2,dataOut.kby2] |
|
|
1297 | 1297 | dataOut.crossprods[2]=[dataOut.kaxay,dataOut.kbxby,dataOut.kaxbx,dataOut.kaxby] |
|
|
1298 | 1298 | #print("before: ",self.dataOut.noise_final) |
|
|
1299 | 1299 | dataOut.data_for_RTI_DP=numpy.zeros((3,dataOut.NDP)) |
|
|
1300 | 1300 | dataOut.data_for_RTI_DP[0],dataOut.data_for_RTI_DP[1],dataOut.data_for_RTI_DP[2]=self.RTI_COLUMN(dataOut.kax2,dataOut.kay2,dataOut.kbx2,dataOut.kby2,dataOut.kaxbx,dataOut.kayby,dataOut.kaybx,dataOut.kaxby, dataOut.NDP) |
|
|
1301 | 1301 | |
|
|
1302 | 1302 | |
|
|
1303 | 1303 | |
|
|
1304 | 1304 | def RTI_COLUMN(self,kax2,kay2,kbx2,kby2,kaxbx,kayby,kaybx,kaxby, NDP): |
|
|
1305 | 1305 | x00=numpy.zeros(NDP,dtype='float32') |
|
|
1306 | 1306 | x01=numpy.zeros(NDP,dtype='float32') |
|
|
1307 | 1307 | x02=numpy.zeros(NDP,dtype='float32') |
|
|
1308 | 1308 | for j in range(2):# first couple lags |
|
|
1309 | 1309 | for k in range(2): #flip |
|
|
1310 | 1310 | for i in range(NDP): # |
|
|
1311 | 1311 | fx=numpy.sqrt((kaxbx[i,j,k]+kayby[i,j,k])**2+(kaybx[i,j,k]-kaxby[i,j,k])**2) |
|
|
1312 | 1312 | x00[i]=x00[i]+(kax2[i,j,k]+kay2[i,j,k]) |
|
|
1313 | 1313 | x01[i]=x01[i]+(kbx2[i,j,k]+kby2[i,j,k]) |
|
|
1314 | 1314 | x02[i]=x02[i]+fx |
|
|
1315 | 1315 | |
|
|
1316 | 1316 | x00[i]=10.0*numpy.log10(x00[i]/512.) |
|
|
1317 | 1317 | x01[i]=10.0*numpy.log10(x01[i]/512.) |
|
|
1318 | 1318 | x02[i]=10.0*numpy.log10(x02[i]) |
|
|
1319 | 1319 | return x02,x00,x01 |
|
|
1320 | 1320 | |
|
|
1321 | 1321 | |
|
|
1322 | 1322 | |
|
|
1323 | 1323 | |
|
|
1324 | 1324 | |
|
|
1325 | 1325 | |
|
|
1326 | 1326 | #30/03/2020: |
|
|
1327 | 1327 | def noisevectorizer(self,NSCAN,nProfiles,NR,MAXNRANGENDT,noisevector,data,dc): |
|
|
1328 | 1328 | |
|
|
1329 | 1329 | rnormalizer= 1./(float(nProfiles - NSCAN)) |
|
|
1330 | 1330 | #rnormalizer= float(NSCAN)/((float(nProfiles - NSCAN))*float(MAXNRANGENDT)) |
|
|
1331 | 1331 | for i in range(NR): |
|
|
1332 | 1332 | for j in range(MAXNRANGENDT): |
|
|
1333 | 1333 | for k in range(NSCAN,nProfiles): |
|
|
1334 | 1334 | #TODO:integrate just 2nd quartile gates |
|
|
1335 | 1335 | if k==NSCAN: |
|
|
1336 | 1336 | noisevector[j][i][self.bcounter]=(abs(data[i][k][j]-dc[i])**2)*rnormalizer |
|
|
1337 | 1337 | else: |
|
|
1338 | 1338 | noisevector[j][i][self.bcounter]+=(abs(data[i][k][j]-dc[i])**2)*rnormalizer |
|
|
1339 | 1339 | |
|
|
1340 | 1340 | |
|
|
1341 | 1341 | |
|
|
1342 | 1342 | |
|
|
1343 | 1343 | def noise_hs4x(self, ndatax, datax): |
|
|
1344 | 1344 | divider=10#divider was originally 10 |
|
|
1345 | 1345 | noise=0.0 |
|
|
1346 | 1346 | data=numpy.zeros(ndatax,'float32') |
|
|
1347 | 1347 | ndata1=int(ndatax/4) |
|
|
1348 | 1348 | ndata2=int(2.5*(ndatax/4.)) |
|
|
1349 | 1349 | ndata=int(ndata2-ndata1) |
|
|
1350 | 1350 | sorts=sorted(datax) |
|
|
1351 | 1351 | |
|
|
1352 | 1352 | for k in range(ndata2): # select just second quartile |
|
|
1353 | 1353 | data[k]=sorts[k+ndata1] |
|
|
1354 | 1354 | nums_min= int(ndata/divider) |
|
|
1355 | 1355 | if(int(ndata/divider)> 2): |
|
|
1356 | 1356 | nums_min= int(ndata/divider) |
|
|
1357 | 1357 | else: |
|
|
1358 | 1358 | nums_min=2 |
|
|
1359 | 1359 | sump=0.0 |
|
|
1360 | 1360 | sumq=0.0 |
|
|
1361 | 1361 | j=0 |
|
|
1362 | 1362 | cont=1 |
|
|
1363 | 1363 | while ( (cont==1) and (j<ndata)): |
|
|
1364 | 1364 | sump+=data[j] |
|
|
1365 | 1365 | sumq+= data[j]*data[j] |
|
|
1366 | 1366 | j=j+1 |
|
|
1367 | 1367 | if (j> nums_min): |
|
|
1368 | 1368 | rtest= float(j/(j-1)) +1.0/ndata |
|
|
1369 | 1369 | if( (sumq*j) > (rtest*sump*sump ) ): |
|
|
1370 | 1370 | j=j-1 |
|
|
1371 | 1371 | sump-= data[j] |
|
|
1372 | 1372 | sumq-=data[j]*data[j] |
|
|
1373 | 1373 | cont= 0 |
|
|
1374 | 1374 | noise= (sump/j) |
|
|
1375 | 1375 | |
|
|
1376 | 1376 | return noise |
|
|
1377 | 1377 | |
|
|
1378 | 1378 | |
|
|
1379 | 1379 | |
|
|
1380 | 1380 | def run(self, dataOut, NLAG=16, NRANGE=0, NCAL=0, DPL=11, |
|
|
1381 | 1381 | NDN=0, NDT=66, NDP=66, NSCAN=132, |
|
|
1382 | 1382 | flags_array=(0, 30, 60, 90, 120, 150, 180, 210, 240, 270, 300), NAVG=16, nkill=6, **kwargs): |
|
|
1383 | 1383 | |
|
|
1384 | 1384 | dataOut.NLAG=NLAG |
|
|
1385 | 1385 | dataOut.NR=len(dataOut.channelList) |
|
|
1386 | 1386 | dataOut.NRANGE=NRANGE |
|
|
1387 | 1387 | dataOut.NCAL=NCAL |
|
|
1388 | 1388 | dataOut.DPL=DPL |
|
|
1389 | 1389 | dataOut.NDN=NDN |
|
|
1390 | 1390 | dataOut.NDT=NDT |
|
|
1391 | 1391 | dataOut.NDP=NDP |
|
|
1392 | 1392 | dataOut.NSCAN=NSCAN |
|
|
1393 | 1393 | dataOut.DH=dataOut.heightList[1]-dataOut.heightList[0] |
|
|
1394 | 1394 | dataOut.H0=int(dataOut.heightList[0]) |
|
|
1395 | 1395 | dataOut.flags_array=flags_array |
|
|
1396 | 1396 | dataOut.NAVG=NAVG |
|
|
1397 | 1397 | dataOut.nkill=nkill |
|
|
1398 | 1398 | dataOut.flagNoData = True |
|
|
1399 | 1399 | |
|
|
1400 | 1400 | self.get_dc(dataOut) |
|
|
1401 | 1401 | self.get_products_cabxys(dataOut) |
|
|
1402 | 1402 | self.cabxys_navg(dataOut) |
|
|
1403 | 1403 | self.noise_estimation4x_DP(dataOut) |
|
|
1404 | 1404 | self.kabxys(dataOut) |
|
|
1405 | 1405 | |
|
|
1406 | 1406 | return dataOut |
|
|
1407 | 1407 | |
|
|
1408 | 1408 | |
|
|
1409 | 1409 | |
|
|
1410 | 1410 | class IntegrationDP(Operation): |
|
|
1411 | 1411 | ''' |
|
|
1412 | 1412 | Written by R. Flores |
|
|
1413 | 1413 | ''' |
|
|
1414 | 1414 | """Operation to integrate the Double Pulse data. |
|
|
1415 | 1415 | |
|
|
1416 | 1416 | Parameters: |
|
|
1417 | 1417 | ----------- |
|
|
1418 | 1418 | nint : int |
|
|
1419 | 1419 | Number of integrations. |
|
|
1420 | 1420 | |
|
|
1421 | 1421 | Example |
|
|
1422 | 1422 | -------- |
|
|
1423 | 1423 | |
|
|
1424 | 1424 | op = proc_unit.addOperation(name='IntegrationDP', optype='other') |
|
|
1425 | 1425 | op.addParameter(name='nint', value='30', format='int') |
|
|
1426 | 1426 | |
|
|
1427 | 1427 | """ |
|
|
1428 | 1428 | |
|
|
1429 | 1429 | def __init__(self, **kwargs): |
|
|
1430 | 1430 | |
|
|
1431 | 1431 | Operation.__init__(self, **kwargs) |
|
|
1432 | 1432 | |
|
|
1433 | 1433 | self.counter=0 |
|
|
1434 | 1434 | self.aux=0 |
|
|
1435 | 1435 | self.init_time=None |
|
|
1436 | 1436 | |
|
|
1437 | 1437 | def integration_for_double_pulse(self,dataOut): |
|
|
1438 | 1438 | |
|
|
1439 | 1439 | if self.aux==1: |
|
|
1440 | 1440 | |
|
|
1441 | 1441 | dataOut.TimeBlockSeconds_for_dp_power=dataOut.utctime |
|
|
1442 | 1442 | dataOut.bd_time=gmtime(dataOut.TimeBlockSeconds_for_dp_power) |
|
|
1443 | 1443 | dataOut.year=dataOut.bd_time.tm_year+(dataOut.bd_time.tm_yday-1)/364.0 |
|
|
1444 | 1444 | dataOut.ut_Faraday=dataOut.bd_time.tm_hour+dataOut.bd_time.tm_min/60.0+dataOut.bd_time.tm_sec/3600.0 |
|
|
1445 | 1445 | self.aux=0 |
|
|
1446 | 1446 | |
|
|
1447 | 1447 | if self.counter==0: |
|
|
1448 | 1448 | |
|
|
1449 | 1449 | tmpx=numpy.zeros((dataOut.NDP,dataOut.DPL,2),'float32') |
|
|
1450 | 1450 | dataOut.kabxys_integrated=[tmpx,tmpx,tmpx,tmpx,tmpx,tmpx,tmpx,tmpx,tmpx,tmpx,tmpx,tmpx,tmpx,tmpx] |
|
|
1451 | 1451 | self.init_time=dataOut.utctime |
|
|
1452 | 1452 | |
|
|
1453 | 1453 | if self.counter < dataOut.nint: |
|
|
1454 | 1454 | |
|
|
1455 | 1455 | dataOut.final_cross_products=[dataOut.kax,dataOut.kay,dataOut.kbx,dataOut.kby,dataOut.kax2,dataOut.kay2,dataOut.kbx2,dataOut.kby2,dataOut.kaxbx,dataOut.kaxby,dataOut.kaybx,dataOut.kayby,dataOut.kaxay,dataOut.kbxby] |
|
|
1456 | 1456 | |
|
|
1457 | 1457 | for ind in range(len(dataOut.kabxys_integrated)): #final cross products |
|
|
1458 | 1458 | dataOut.kabxys_integrated[ind]=dataOut.kabxys_integrated[ind]+dataOut.final_cross_products[ind] |
|
|
1459 | 1459 | |
|
|
1460 | 1460 | self.counter+=1 |
|
|
1461 | 1461 | |
|
|
1462 | 1462 | if self.counter==dataOut.nint-1: |
|
|
1463 | 1463 | self.aux=1 |
|
|
1464 | 1464 | |
|
|
1465 | 1465 | if self.counter==dataOut.nint: |
|
|
1466 | 1466 | dataOut.flagNoData=False |
|
|
1467 | 1467 | dataOut.utctime=self.init_time |
|
|
1468 | 1468 | self.counter=0 |
|
|
1469 | 1469 | |
|
|
1470 | 1470 | |
|
|
1471 | 1471 | def run(self,dataOut,nint=20): |
|
|
1472 | 1472 | |
|
|
1473 | 1473 | dataOut.flagNoData=True |
|
|
1474 | 1474 | dataOut.nint=nint |
|
|
1475 | 1475 | dataOut.paramInterval=0#int(dataOut.nint*dataOut.header[7][0]*2 ) |
|
|
1476 | 1476 | dataOut.lat=-11.95 |
|
|
1477 | 1477 | dataOut.lon=-76.87 |
|
|
1478 | 1478 | |
|
|
1479 | 1479 | self.integration_for_double_pulse(dataOut) |
|
|
1480 | 1480 | |
|
|
1481 | 1481 | return dataOut |
|
|
1482 | 1482 | |
|
|
1483 | 1483 | |
|
|
1484 | 1484 | class SumFlips(Operation): |
|
|
1485 | 1485 | ''' |
|
|
1486 | 1486 | Written by R. Flores |
|
|
1487 | 1487 | ''' |
|
|
1488 | 1488 | """Operation to sum the flip and unflip part of certain cross products of the Double Pulse. |
|
|
1489 | 1489 | |
|
|
1490 | 1490 | Parameters: |
|
|
1491 | 1491 | ----------- |
|
|
1492 | 1492 | None |
|
|
1493 | 1493 | |
|
|
1494 | 1494 | Example |
|
|
1495 | 1495 | -------- |
|
|
1496 | 1496 | |
|
|
1497 | 1497 | op = proc_unit.addOperation(name='SumFlips', optype='other') |
|
|
1498 | 1498 | |
|
|
1499 | 1499 | """ |
|
|
1500 | 1500 | |
|
|
1501 | 1501 | def __init__(self, **kwargs): |
|
|
1502 | 1502 | |
|
|
1503 | 1503 | Operation.__init__(self, **kwargs) |
|
|
1504 | 1504 | |
|
|
1505 | 1505 | |
|
|
1506 | 1506 | def rint2DP(self,dataOut): |
|
|
1507 | 1507 | |
|
|
1508 | 1508 | dataOut.rnint2=numpy.zeros(dataOut.DPL,'float32') |
|
|
1509 | 1509 | |
|
|
1510 | 1510 | for l in range(dataOut.DPL): |
|
|
1511 | 1511 | |
|
|
1512 | 1512 | dataOut.rnint2[l]=1.0/(dataOut.nint*dataOut.NAVG*12.0) |
|
|
1513 | 1513 | |
|
|
1514 | 1514 | |
|
|
1515 | 1515 | def SumLags(self,dataOut): |
|
|
1516 | 1516 | |
|
|
1517 | 1517 | for l in range(dataOut.DPL): |
|
|
1518 | 1518 | dataOut.kabxys_integrated[4][:,l,0]=(dataOut.kabxys_integrated[4][:,l,0]+dataOut.kabxys_integrated[4][:,l,1])*dataOut.rnint2[l] |
|
|
1519 | 1519 | dataOut.kabxys_integrated[5][:,l,0]=(dataOut.kabxys_integrated[5][:,l,0]+dataOut.kabxys_integrated[5][:,l,1])*dataOut.rnint2[l] |
|
|
1520 | 1520 | dataOut.kabxys_integrated[6][:,l,0]=(dataOut.kabxys_integrated[6][:,l,0]+dataOut.kabxys_integrated[6][:,l,1])*dataOut.rnint2[l] |
|
|
1521 | 1521 | dataOut.kabxys_integrated[7][:,l,0]=(dataOut.kabxys_integrated[7][:,l,0]+dataOut.kabxys_integrated[7][:,l,1])*dataOut.rnint2[l] |
|
|
1522 | 1522 | |
|
|
1523 | 1523 | dataOut.kabxys_integrated[8][:,l,0]=(dataOut.kabxys_integrated[8][:,l,0]-dataOut.kabxys_integrated[8][:,l,1])*dataOut.rnint2[l] |
|
|
1524 | 1524 | dataOut.kabxys_integrated[9][:,l,0]=(dataOut.kabxys_integrated[9][:,l,0]-dataOut.kabxys_integrated[9][:,l,1])*dataOut.rnint2[l] |
|
|
1525 | 1525 | dataOut.kabxys_integrated[10][:,l,0]=(dataOut.kabxys_integrated[10][:,l,0]-dataOut.kabxys_integrated[10][:,l,1])*dataOut.rnint2[l] |
|
|
1526 | 1526 | dataOut.kabxys_integrated[11][:,l,0]=(dataOut.kabxys_integrated[11][:,l,0]-dataOut.kabxys_integrated[11][:,l,1])*dataOut.rnint2[l] |
|
|
1527 | 1527 | |
|
|
1528 | 1528 | def run(self,dataOut): |
|
|
1529 | 1529 | |
|
|
1530 | 1530 | self.rint2DP(dataOut) |
|
|
1531 | 1531 | self.SumLags(dataOut) |
|
|
1532 | 1532 | |
|
|
1533 | 1533 | return dataOut |
|
|
1534 | 1534 | |
|
|
1535 | 1535 | |
|
|
1536 | 1536 | class FlagBadHeights(Operation): |
|
|
1537 | 1537 | ''' |
|
|
1538 | 1538 | Written by R. Flores |
|
|
1539 | 1539 | ''' |
|
|
1540 | 1540 | """Operation to flag bad heights (bad data) of the Double Pulse. |
|
|
1541 | 1541 | |
|
|
1542 | 1542 | Parameters: |
|
|
1543 | 1543 | ----------- |
|
|
1544 | 1544 | None |
|
|
1545 | 1545 | |
|
|
1546 | 1546 | Example |
|
|
1547 | 1547 | -------- |
|
|
1548 | 1548 | |
|
|
1549 | 1549 | op = proc_unit.addOperation(name='FlagBadHeights', optype='other') |
|
|
1550 | 1550 | |
|
|
1551 | 1551 | """ |
|
|
1552 | 1552 | |
|
|
1553 | 1553 | def __init__(self, **kwargs): |
|
|
1554 | 1554 | |
|
|
1555 | 1555 | Operation.__init__(self, **kwargs) |
|
|
1556 | 1556 | |
|
|
1557 | 1557 | def run(self,dataOut): |
|
|
1558 | 1558 | |
|
|
1559 | 1559 | dataOut.ibad=numpy.zeros((dataOut.NDP,dataOut.DPL),'int32') |
|
|
1560 | 1560 | |
|
|
1561 | 1561 | for j in range(dataOut.NDP): |
|
|
1562 | 1562 | for l in range(dataOut.DPL): |
|
|
1563 | 1563 | ip1=j+dataOut.NDP*(0+2*l) |
|
|
1564 | 1564 | |
|
|
1565 | 1565 | if( (dataOut.kabxys_integrated[5][j,l,0] <= 0.) or (dataOut.kabxys_integrated[4][j,l,0] <= 0.) or (dataOut.kabxys_integrated[7][j,l,0] <= 0.) or (dataOut.kabxys_integrated[6][j,l,0] <= 0.)): |
|
|
1566 | 1566 | dataOut.ibad[j][l]=1 |
|
|
1567 | 1567 | else: |
|
|
1568 | 1568 | dataOut.ibad[j][l]=0 |
|
|
1569 | 1569 | |
|
|
1570 | 1570 | return dataOut |
|
|
1571 | 1571 | |
|
|
1572 | 1572 | class FlagBadHeightsSpectra(Operation): |
|
|
1573 | 1573 | ''' |
|
|
1574 | 1574 | Written by R. Flores |
|
|
1575 | 1575 | ''' |
|
|
1576 | 1576 | """Operation to flag bad heights (bad data) of the Double Pulse. |
|
|
1577 | 1577 | |
|
|
1578 | 1578 | Parameters: |
|
|
1579 | 1579 | ----------- |
|
|
1580 | 1580 | None |
|
|
1581 | 1581 | |
|
|
1582 | 1582 | Example |
|
|
1583 | 1583 | -------- |
|
|
1584 | 1584 | |
|
|
1585 | 1585 | op = proc_unit.addOperation(name='FlagBadHeightsSpectra', optype='other') |
|
|
1586 | 1586 | |
|
|
1587 | 1587 | """ |
|
|
1588 | 1588 | |
|
|
1589 | 1589 | def __init__(self, **kwargs): |
|
|
1590 | 1590 | |
|
|
1591 | 1591 | Operation.__init__(self, **kwargs) |
|
|
1592 | 1592 | |
|
|
1593 | 1593 | def run(self,dataOut): |
|
|
1594 | 1594 | |
|
|
1595 | 1595 | dataOut.ibad=numpy.zeros((dataOut.NDP,dataOut.DPL),'int32') |
|
|
1596 | 1596 | |
|
|
1597 | 1597 | for j in range(dataOut.NDP): |
|
|
1598 | 1598 | for l in range(dataOut.DPL): |
|
|
1599 | 1599 | ip1=j+dataOut.NDP*(0+2*l) |
|
|
1600 | 1600 | |
|
|
1601 | 1601 | if( (dataOut.kabxys_integrated[4][j,l,0] <= 0.) or (dataOut.kabxys_integrated[6][j,l,0] <= 0.)): |
|
|
1602 | 1602 | dataOut.ibad[j][l]=1 |
|
|
1603 | 1603 | else: |
|
|
1604 | 1604 | dataOut.ibad[j][l]=0 |
|
|
1605 | 1605 | |
|
|
1606 | 1606 | return dataOut |
|
|
1607 | 1607 | |
|
|
1608 | 1608 | class CleanCohEchoes(Operation): |
|
|
1609 | 1609 | ''' |
|
|
1610 | 1610 | Written by R. Flores |
|
|
1611 | 1611 | ''' |
|
|
1612 | 1612 | """Operation to clean coherent echoes. |
|
|
1613 | 1613 | |
|
|
1614 | 1614 | Parameters: |
|
|
1615 | 1615 | ----------- |
|
|
1616 | 1616 | None |
|
|
1617 | 1617 | |
|
|
1618 | 1618 | Example |
|
|
1619 | 1619 | -------- |
|
|
1620 | 1620 | |
|
|
1621 | 1621 | op = proc_unit.addOperation(name='CleanCohEchoes') |
|
|
1622 | 1622 | |
|
|
1623 | 1623 | """ |
|
|
1624 | 1624 | |
|
|
1625 | 1625 | def __init__(self, **kwargs): |
|
|
1626 | 1626 | |
|
|
1627 | 1627 | Operation.__init__(self, **kwargs) |
|
|
1628 | 1628 | |
|
|
1629 | 1629 | def remove_coh(self,pow): |
|
|
1630 | 1630 | q75,q25 = numpy.percentile(pow,[75,25],axis=0) |
|
|
1631 | 1631 | intr_qr = q75-q25 |
|
|
1632 | 1632 | |
|
|
1633 | 1633 | max = q75+(1.5*intr_qr) |
|
|
1634 | 1634 | min = q25-(1.5*intr_qr) |
|
|
1635 | 1635 | |
|
|
1636 | 1636 | pow[pow > max] = numpy.nan |
|
|
1637 | 1637 | |
|
|
1638 | 1638 | return pow |
|
|
1639 | 1639 | |
|
|
1640 | 1640 | def mad_based_outlier_V0(self, points, thresh=3.5): |
|
|
1641 | 1641 | |
|
|
1642 | 1642 | if len(points.shape) == 1: |
|
|
1643 | 1643 | points = points[:,None] |
|
|
1644 | 1644 | median = numpy.nanmedian(points, axis=0) |
|
|
1645 | 1645 | diff = numpy.nansum((points - median)**2, axis=-1) |
|
|
1646 | 1646 | diff = numpy.sqrt(diff) |
|
|
1647 | 1647 | med_abs_deviation = numpy.nanmedian(diff) |
|
|
1648 | 1648 | |
|
|
1649 | 1649 | modified_z_score = 0.6745 * diff / med_abs_deviation |
|
|
1650 | 1650 | |
|
|
1651 | 1651 | return modified_z_score > thresh |
|
|
1652 | 1652 | |
|
|
1653 | 1653 | def mad_based_outlier(self, points, thresh=3.5): |
|
|
1654 | 1654 | |
|
|
1655 | 1655 | median = numpy.nanmedian(points) |
|
|
1656 | 1656 | diff = (points - median)**2 |
|
|
1657 | 1657 | diff = numpy.sqrt(diff) |
|
|
1658 | 1658 | med_abs_deviation = numpy.nanmedian(diff) |
|
|
1659 | 1659 | |
|
|
1660 | 1660 | modified_z_score = 0.6745 * diff / med_abs_deviation |
|
|
1661 | 1661 | |
|
|
1662 | 1662 | return modified_z_score > thresh |
|
|
1663 | 1663 | |
|
|
1664 | ||
|
|
1664 | ||
|
|
1665 | 1665 | |
|
|
1666 | 1666 | def removeSpreadF(self,dataOut): |
|
|
1667 | 1667 | |
|
|
1668 | 1668 | #Removing outliers from the profile |
|
|
1669 | 1669 | nlag = 9 |
|
|
1670 | 1670 | minHei = 180 |
|
|
1671 | 1671 | #maxHei = 600 |
|
|
1672 | 1672 | maxHei = 525 |
|
|
1673 | 1673 | inda = numpy.where(dataOut.heightList >= minHei) |
|
|
1674 | 1674 | indb = numpy.where(dataOut.heightList <= maxHei) |
|
|
1675 | 1675 | minIndex = inda[0][0] |
|
|
1676 | 1676 | maxIndex = indb[0][-1] |
|
|
1677 | 1677 | outliers_IDs = [] |
|
|
1678 | 1678 | |
|
|
1679 | 1679 | for i in range(15): |
|
|
1680 | 1680 | minIndex = 12+i#12 |
|
|
1681 | 1681 | #maxIndex = 22+i#35 |
|
|
1682 | 1682 | if gmtime(dataOut.utctime).tm_hour >= 23. or gmtime(dataOut.utctime).tm_hour < 3.: |
|
|
1683 | 1683 | maxIndex = 31+i#35 |
|
|
1684 | 1684 | else: |
|
|
1685 | 1685 | maxIndex = 22+i#35 |
|
|
1686 | 1686 | for lag in range(11): |
|
|
1687 | 1687 | outliers = self.mad_based_outlier(dataOut.kabxys_integrated[6][minIndex:maxIndex,lag,0]) |
|
|
1688 | 1688 | aux = minIndex+numpy.array(outliers.nonzero()).ravel() |
|
|
1689 | 1689 | outliers_IDs=numpy.append(outliers_IDs,aux) |
|
|
1690 | 1690 | if outliers_IDs != []: |
|
|
1691 | 1691 | outliers_IDs=numpy.array(outliers_IDs) |
|
|
1692 | 1692 | outliers_IDs=outliers_IDs.astype(numpy.dtype('int64')) |
|
|
1693 | 1693 | (uniq, freq) = (numpy.unique(outliers_IDs, return_counts=True)) |
|
|
1694 | 1694 | aux_arr = numpy.column_stack((uniq,freq)) |
|
|
1695 | 1695 | final_index = [] |
|
|
1696 | 1696 | for i in range(aux_arr.shape[0]): |
|
|
1697 | 1697 | if aux_arr[i,1] >= 3*11: |
|
|
1698 | 1698 | final_index.append(aux_arr[i,0]) |
|
|
1699 | 1699 | |
|
|
1700 | 1700 | if final_index != []:# and len(final_index) > 1: |
|
|
1701 | 1701 | following_index = final_index[-1]+1 #Remove following index to ensure we remove remaining SpreadF |
|
|
1702 | 1702 | previous_index = final_index[0]-1 #Remove previous index to ensure we remove remaning SpreadF |
|
|
1703 | 1703 | final_index = numpy.concatenate(([previous_index],final_index,[following_index])) |
|
|
1704 | 1704 | final_index = numpy.unique(final_index) #If there was only one outlier |
|
|
1705 | 1705 | dataOut.kabxys_integrated[4][final_index,:,0] = numpy.nan |
|
|
1706 | 1706 | dataOut.kabxys_integrated[6][final_index,:,0] = numpy.nan |
|
|
1707 | 1707 | |
|
|
1708 | 1708 | dataOut.flagSpreadF = True |
|
|
1709 | 1709 | |
|
|
1710 | 1710 | #Removing echoes greater than 35 dB |
|
|
1711 | 1711 | if hasattr(dataOut.pbn, "__len__"): |
|
|
1712 | 1712 | maxdB = 10*numpy.log10(dataOut.pbn[0]) + 10 #Lag 0 Noise |
|
|
1713 | 1713 | else: |
|
|
1714 | 1714 | maxdB = 10*numpy.log10(dataOut.pbn) + 10 |
|
|
1715 | 1715 | |
|
|
1716 | 1716 | data = numpy.copy(10*numpy.log10(dataOut.kabxys_integrated[6][:,0,0])) #Lag0 ChB |
|
|
1717 | 1717 | |
|
|
1718 | 1718 | for i in range(12,data.shape[0]): |
|
|
1719 | 1719 | if data[i]>maxdB: |
|
|
1720 | 1720 | dataOut.kabxys_integrated[4][i-2:i+3,:,0] = numpy.nan #Debido a que estos ecos son intensos, se |
|
|
1721 | 1721 | dataOut.kabxys_integrated[6][i-2:i+3,:,0] = numpy.nan #remueven además dos muestras antes y después |
|
|
1722 | 1722 | dataOut.flagSpreadF = True |
|
|
1723 | 1723 | |
|
|
1724 | 1724 | def run(self,dataOut): |
|
|
1725 | 1725 | dataOut.flagSpreadF = False |
|
|
1726 | 1726 | if gmtime(dataOut.utctime).tm_hour >= 23. or gmtime(dataOut.utctime).tm_hour < 11.: #18-06 LT |
|
|
1727 | 1727 | self.removeSpreadF(dataOut) |
|
|
1728 | 1728 | |
|
|
1729 | 1729 | return dataOut |
|
|
1730 | 1730 | class NoisePower(Operation): |
|
|
1731 | 1731 | ''' |
|
|
1732 | 1732 | Written by R. Flores |
|
|
1733 | 1733 | ''' |
|
|
1734 | 1734 | """Operation to get noise power from the integrated data of the Double Pulse. |
|
|
1735 | 1735 | |
|
|
1736 | 1736 | Parameters: |
|
|
1737 | 1737 | ----------- |
|
|
1738 | 1738 | None |
|
|
1739 | 1739 | |
|
|
1740 | 1740 | Example |
|
|
1741 | 1741 | -------- |
|
|
1742 | 1742 | |
|
|
1743 | 1743 | op = proc_unit.addOperation(name='NoisePower', optype='other') |
|
|
1744 | 1744 | |
|
|
1745 | 1745 | """ |
|
|
1746 | 1746 | |
|
|
1747 | 1747 | def __init__(self, **kwargs): |
|
|
1748 | 1748 | |
|
|
1749 | 1749 | Operation.__init__(self, **kwargs) |
|
|
1750 | 1750 | |
|
|
1751 | 1751 | def hildebrand(self,dataOut,data): |
|
|
1752 | 1752 | |
|
|
1753 | 1753 | divider=10 # divider was originally 10 |
|
|
1754 | 1754 | noise=0.0 |
|
|
1755 | 1755 | n1=0 |
|
|
1756 | 1756 | n2=int(dataOut.NDP/2) |
|
|
1757 | 1757 | sorts= sorted(data) |
|
|
1758 | 1758 | nums_min= dataOut.NDP/divider |
|
|
1759 | 1759 | if((dataOut.NDP/divider)> 2): |
|
|
1760 | 1760 | nums_min= int(dataOut.NDP/divider) |
|
|
1761 | 1761 | |
|
|
1762 | 1762 | else: |
|
|
1763 | 1763 | nums_min=2 |
|
|
1764 | 1764 | sump=0.0 |
|
|
1765 | 1765 | sumq=0.0 |
|
|
1766 | 1766 | j=0 |
|
|
1767 | 1767 | cont=1 |
|
|
1768 | 1768 | while( (cont==1) and (j<n2)): |
|
|
1769 | 1769 | sump+=sorts[j+n1] |
|
|
1770 | 1770 | sumq+= sorts[j+n1]*sorts[j+n1] |
|
|
1771 | 1771 | t3= sump/(j+1) |
|
|
1772 | 1772 | j=j+1 |
|
|
1773 | 1773 | if(j> nums_min): |
|
|
1774 | 1774 | rtest= float(j/(j-1)) +1.0/dataOut.NAVG |
|
|
1775 | 1775 | t1= (sumq*j) |
|
|
1776 | 1776 | t2=(rtest*sump*sump) |
|
|
1777 | 1777 | if( (t1/t2) > 0.990): |
|
|
1778 | 1778 | j=j-1 |
|
|
1779 | 1779 | sump-= sorts[j+n1] |
|
|
1780 | 1780 | sumq-=sorts[j+n1]*sorts[j+n1] |
|
|
1781 | 1781 | cont= 0 |
|
|
1782 | 1782 | |
|
|
1783 | 1783 | noise= sump/j |
|
|
1784 | 1784 | stdv=numpy.sqrt((sumq- noise*noise)/(j-1)) |
|
|
1785 | 1785 | return noise |
|
|
1786 | 1786 | |
|
|
1787 | 1787 | def run(self,dataOut): |
|
|
1788 | 1788 | |
|
|
1789 | 1789 | p=numpy.zeros((dataOut.NR,dataOut.NDP,dataOut.DPL),'float32') |
|
|
1790 | 1790 | av=numpy.zeros(dataOut.NDP,'float32') |
|
|
1791 | 1791 | dataOut.pnoise=numpy.zeros(dataOut.NR,'float32') |
|
|
1792 | 1792 | |
|
|
1793 | 1793 | p[0,:,:]=dataOut.kabxys_integrated[4][:,:,0]+dataOut.kabxys_integrated[5][:,:,0] #total power for channel 0, just pulse with non-flip |
|
|
1794 | 1794 | p[1,:,:]=dataOut.kabxys_integrated[6][:,:,0]+dataOut.kabxys_integrated[7][:,:,0] #total power for channel 1 |
|
|
1795 | 1795 | |
|
|
1796 | 1796 | for i in range(dataOut.NR): |
|
|
1797 | 1797 | dataOut.pnoise[i]=0.0 |
|
|
1798 | 1798 | for k in range(dataOut.DPL): |
|
|
1799 | 1799 | dataOut.pnoise[i]+= self.hildebrand(dataOut,p[i,:,k]) |
|
|
1800 | 1800 | |
|
|
1801 | 1801 | dataOut.pnoise[i]=dataOut.pnoise[i]/dataOut.DPL |
|
|
1802 | 1802 | |
|
|
1803 | 1803 | |
|
|
1804 | 1804 | dataOut.pan=1.0*dataOut.pnoise[0] # weights could change |
|
|
1805 | 1805 | dataOut.pbn=1.0*dataOut.pnoise[1] # weights could change |
|
|
1806 | 1806 | |
|
|
1807 | 1807 | return dataOut |
|
|
1808 | 1808 | |
|
|
1809 | 1809 | |
|
|
1810 | 1810 | class DoublePulseACFs(Operation): |
|
|
1811 | 1811 | ''' |
|
|
1812 | 1812 | Written by R. Flores |
|
|
1813 | 1813 | ''' |
|
|
1814 | 1814 | """Operation to get the ACFs of the Double Pulse. |
|
|
1815 | 1815 | |
|
|
1816 | 1816 | Parameters: |
|
|
1817 | 1817 | ----------- |
|
|
1818 | 1818 | None |
|
|
1819 | 1819 | |
|
|
1820 | 1820 | Example |
|
|
1821 | 1821 | -------- |
|
|
1822 | 1822 | |
|
|
1823 | 1823 | op = proc_unit.addOperation(name='DoublePulseACFs', optype='other') |
|
|
1824 | 1824 | |
|
|
1825 | 1825 | """ |
|
|
1826 | 1826 | |
|
|
1827 | 1827 | def __init__(self, **kwargs): |
|
|
1828 | 1828 | |
|
|
1829 | 1829 | Operation.__init__(self, **kwargs) |
|
|
1830 | 1830 | self.aux=1 |
|
|
1831 | 1831 | |
|
|
1832 | 1832 | def run(self,dataOut): |
|
|
1833 | 1833 | |
|
|
1834 | 1834 | dataOut.igcej=numpy.zeros((dataOut.NDP,dataOut.DPL),'int32') |
|
|
1835 | 1835 | |
|
|
1836 | 1836 | if self.aux==1: |
|
|
1837 | 1837 | dataOut.rhor=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) |
|
|
1838 | 1838 | dataOut.rhoi=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) |
|
|
1839 | 1839 | dataOut.sdp=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) |
|
|
1840 | 1840 | dataOut.sd=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) |
|
|
1841 | 1841 | dataOut.p=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) |
|
|
1842 | 1842 | dataOut.alag=numpy.zeros(dataOut.NDP,'float32') |
|
|
1843 | 1843 | for l in range(dataOut.DPL): |
|
|
1844 | 1844 | dataOut.alag[l]=l*dataOut.DH*2.0/150.0 |
|
|
1845 | 1845 | self.aux=0 |
|
|
1846 | 1846 | sn4=dataOut.pan*dataOut.pbn |
|
|
1847 | 1847 | rhorn=0 |
|
|
1848 | 1848 | rhoin=0 |
|
|
1849 | 1849 | panrm=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) |
|
|
1850 | 1850 | |
|
|
1851 | 1851 | for i in range(dataOut.NDP): |
|
|
1852 | 1852 | for j in range(dataOut.DPL): |
|
|
1853 | 1853 | ################# Total power |
|
|
1854 | 1854 | pa=numpy.abs(dataOut.kabxys_integrated[4][i,j,0]+dataOut.kabxys_integrated[5][i,j,0]) |
|
|
1855 | 1855 | pb=numpy.abs(dataOut.kabxys_integrated[6][i,j,0]+dataOut.kabxys_integrated[7][i,j,0]) |
|
|
1856 | 1856 | st4=pa*pb |
|
|
1857 | 1857 | dataOut.p[i,j]=pa+pb-(dataOut.pan+dataOut.pbn) |
|
|
1858 | 1858 | dataOut.sdp[i,j]=2*dataOut.rnint2[j]*((pa+pb)*(pa+pb)) |
|
|
1859 | 1859 | ## ACF |
|
|
1860 | 1860 | rhorp=dataOut.kabxys_integrated[8][i,j,0]+dataOut.kabxys_integrated[11][i,j,0] |
|
|
1861 | 1861 | rhoip=dataOut.kabxys_integrated[10][i,j,0]-dataOut.kabxys_integrated[9][i,j,0] |
|
|
1862 | 1862 | if ((pa>dataOut.pan)&(pb>dataOut.pbn)): |
|
|
1863 | 1863 | |
|
|
1864 | 1864 | ss4=numpy.abs((pa-dataOut.pan)*(pb-dataOut.pbn)) |
|
|
1865 | 1865 | panrm[i,j]=math.sqrt(ss4) |
|
|
1866 | 1866 | rnorm=1/panrm[i,j] |
|
|
1867 | 1867 | ## ACF |
|
|
1868 | 1868 | dataOut.rhor[i,j]=rhorp*rnorm |
|
|
1869 | 1869 | dataOut.rhoi[i,j]=rhoip*rnorm |
|
|
1870 | 1870 | ############# Compute standard error for ACF |
|
|
1871 | 1871 | stoss4=st4/ss4 |
|
|
1872 | 1872 | snoss4=sn4/ss4 |
|
|
1873 | 1873 | rp2=((rhorp*rhorp)+(rhoip*rhoip))/st4 |
|
|
1874 | 1874 | rn2=((rhorn*rhorn)+(rhoin*rhoin))/sn4 |
|
|
1875 | 1875 | rs2=(dataOut.rhor[i,j]*dataOut.rhor[i,j])+(dataOut.rhoi[i,j]*dataOut.rhoi[i,j]) |
|
|
1876 | 1876 | st=1.0+rs2*(stoss4-(2*math.sqrt(stoss4*snoss4))) |
|
|
1877 | 1877 | stn=1.0+rs2*(snoss4-(2*math.sqrt(stoss4*snoss4))) |
|
|
1878 | 1878 | dataOut.sd[i,j]=((stoss4*((1.0+rp2)*st+(2.0*rp2*rs2*snoss4)-4.0*math.sqrt(rs2*rp2)))+(0.25*snoss4*((1.0+rn2)*stn+(2.0*rn2*rs2*stoss4)-4.0*math.sqrt(rs2*rn2))))*dataOut.rnint2[j] |
|
|
1879 | 1879 | dataOut.sd[i,j]=numpy.abs(dataOut.sd[i,j]) |
|
|
1880 | 1880 | |
|
|
1881 | 1881 | else: #default values for bad points |
|
|
1882 | 1882 | rnorm=1/math.sqrt(st4) |
|
|
1883 | 1883 | dataOut.sd[i,j]=1.e30 |
|
|
1884 | 1884 | dataOut.ibad[i,j]=4 |
|
|
1885 | 1885 | dataOut.rhor[i,j]=rhorp*rnorm |
|
|
1886 | 1886 | dataOut.rhoi[i,j]=rhoip*rnorm |
|
|
1887 | 1887 | if ((pb/dataOut.pbn-1.0)>2.25*(pa/dataOut.pan-1.0)): #To flag bad points from the pulse and EEJ for lags != 0 for Channel B |
|
|
1888 | 1888 | #print(dataOut.heightList[i],"EJJ") |
|
|
1889 | 1889 | dataOut.igcej[i,j]=1 |
|
|
1890 | 1890 | elif ((pa/dataOut.pan-1.0)>2.25*(pb/dataOut.pbn-1.0)): |
|
|
1891 | 1891 | dataOut.igcej[i,j]=1 |
|
|
1892 | 1892 | |
|
|
1893 | 1893 | return dataOut |
|
|
1894 | 1894 | |
|
|
1895 | 1895 | class DoublePulseACFs_PerLag(Operation): |
|
|
1896 | 1896 | ''' |
|
|
1897 | 1897 | Written by R. Flores |
|
|
1898 | 1898 | ''' |
|
|
1899 | 1899 | """Operation to get the ACFs of the Double Pulse. |
|
|
1900 | 1900 | |
|
|
1901 | 1901 | Parameters: |
|
|
1902 | 1902 | ----------- |
|
|
1903 | 1903 | None |
|
|
1904 | 1904 | |
|
|
1905 | 1905 | Example |
|
|
1906 | 1906 | -------- |
|
|
1907 | 1907 | |
|
|
1908 | 1908 | op = proc_unit.addOperation(name='DoublePulseACFs', optype='other') |
|
|
1909 | 1909 | |
|
|
1910 | 1910 | """ |
|
|
1911 | 1911 | |
|
|
1912 | 1912 | def __init__(self, **kwargs): |
|
|
1913 | 1913 | |
|
|
1914 | 1914 | Operation.__init__(self, **kwargs) |
|
|
1915 | 1915 | self.aux=1 |
|
|
1916 | 1916 | |
|
|
1917 | 1917 | def run(self,dataOut): |
|
|
1918 | 1918 | |
|
|
1919 | 1919 | dataOut.igcej=numpy.zeros((dataOut.NDP,dataOut.DPL),'int32') |
|
|
1920 | 1920 | |
|
|
1921 | 1921 | if self.aux==1: |
|
|
1922 | 1922 | dataOut.rhor=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) |
|
|
1923 | 1923 | dataOut.rhoi=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) |
|
|
1924 | 1924 | dataOut.sdp=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) |
|
|
1925 | 1925 | dataOut.sd=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) |
|
|
1926 | 1926 | dataOut.p=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) |
|
|
1927 | 1927 | dataOut.alag=numpy.zeros(dataOut.NDP,'float32') |
|
|
1928 | 1928 | for l in range(dataOut.DPL): |
|
|
1929 | 1929 | dataOut.alag[l]=l*dataOut.DH*2.0/150.0 |
|
|
1930 | 1930 | self.aux=0 |
|
|
1931 | 1931 | sn4=dataOut.pan*dataOut.pbn |
|
|
1932 | 1932 | rhorn=0 |
|
|
1933 | 1933 | rhoin=0 |
|
|
1934 | 1934 | panrm=numpy.zeros((dataOut.NDP,dataOut.DPL), dtype=float) |
|
|
1935 | 1935 | |
|
|
1936 | 1936 | id = numpy.where(dataOut.heightList>700)[0] |
|
|
1937 | 1937 | |
|
|
1938 | 1938 | for i in range(dataOut.NDP): |
|
|
1939 | 1939 | for j in range(dataOut.DPL): |
|
|
1940 | 1940 | ################# Total power |
|
|
1941 | 1941 | pa=numpy.abs(dataOut.kabxys_integrated[4][i,j,0]+dataOut.kabxys_integrated[5][i,j,0]) |
|
|
1942 | 1942 | pb=numpy.abs(dataOut.kabxys_integrated[6][i,j,0]+dataOut.kabxys_integrated[7][i,j,0]) |
|
|
1943 | 1943 | st4=pa*pb |
|
|
1944 | 1944 | dataOut.p[i,j]=pa+pb-(dataOut.pan[j]+dataOut.pbn[j]) |
|
|
1945 | 1945 | dataOut.sdp[i,j]=2*dataOut.rnint2[j]*((pa+pb)*(pa+pb)) |
|
|
1946 | 1946 | ## ACF |
|
|
1947 | 1947 | rhorp=dataOut.kabxys_integrated[8][i,j,0]+dataOut.kabxys_integrated[11][i,j,0] |
|
|
1948 | 1948 | rhoip=dataOut.kabxys_integrated[10][i,j,0]-dataOut.kabxys_integrated[9][i,j,0] |
|
|
1949 | 1949 | |
|
|
1950 | 1950 | if ((pa>dataOut.pan[j])&(pb>dataOut.pbn[j])): |
|
|
1951 | 1951 | ss4=numpy.abs((pa-dataOut.pan[j])*(pb-dataOut.pbn[j])) |
|
|
1952 | 1952 | panrm[i,j]=math.sqrt(ss4) |
|
|
1953 | 1953 | rnorm=1/panrm[i,j] |
|
|
1954 | 1954 | ## ACF |
|
|
1955 | 1955 | dataOut.rhor[i,j]=rhorp*rnorm |
|
|
1956 | 1956 | dataOut.rhoi[i,j]=rhoip*rnorm |
|
|
1957 | 1957 | ############# Compute standard error for ACF |
|
|
1958 | 1958 | stoss4=st4/ss4 |
|
|
1959 | 1959 | snoss4=sn4[j]/ss4 |
|
|
1960 | 1960 | rp2=((rhorp*rhorp)+(rhoip*rhoip))/st4 |
|
|
1961 | 1961 | rn2=((rhorn*rhorn)+(rhoin*rhoin))/sn4[j] |
|
|
1962 | 1962 | rs2=(dataOut.rhor[i,j]*dataOut.rhor[i,j])+(dataOut.rhoi[i,j]*dataOut.rhoi[i,j]) |
|
|
1963 | 1963 | st=1.0+rs2*(stoss4-(2*math.sqrt(stoss4*snoss4))) |
|
|
1964 | 1964 | stn=1.0+rs2*(snoss4-(2*math.sqrt(stoss4*snoss4))) |
|
|
1965 | 1965 | dataOut.sd[i,j]=((stoss4*((1.0+rp2)*st+(2.0*rp2*rs2*snoss4)-4.0*math.sqrt(rs2*rp2)))+(0.25*snoss4*((1.0+rn2)*stn+(2.0*rn2*rs2*stoss4)-4.0*math.sqrt(rs2*rn2))))*dataOut.rnint2[j] |
|
|
1966 | 1966 | dataOut.sd[i,j]=numpy.abs(dataOut.sd[i,j]) |
|
|
1967 | 1967 | else: #default values for bad points |
|
|
1968 | 1968 | rnorm=1/math.sqrt(st4) |
|
|
1969 | 1969 | dataOut.sd[i,j]=1.e30 |
|
|
1970 | 1970 | dataOut.ibad[i,j]=4 |
|
|
1971 | 1971 | dataOut.rhor[i,j]=rhorp*rnorm |
|
|
1972 | 1972 | dataOut.rhoi[i,j]=rhoip*rnorm |
|
|
1973 | 1973 | if ((pb/dataOut.pbn[j]-1.0)>2.25*(pa/dataOut.pan[j]-1.0)): #To flag bad points from the pulse and EEJ for lags != 0 for Channel B |
|
|
1974 | 1974 | dataOut.igcej[i,j]=1 |
|
|
1975 | 1975 | |
|
|
1976 | 1976 | elif ((pa/dataOut.pan[j]-1.0)>2.25*(pb/dataOut.pbn[j]-1.0)): |
|
|
1977 | 1977 | dataOut.igcej[i,j]=1 |
|
|
1978 | 1978 | |
|
|
1979 | 1979 | return dataOut |
|
|
1980 | 1980 | |
|
|
1981 | 1981 | class FaradayAngleAndDPPower(Operation): |
|
|
1982 | 1982 | ''' |
|
|
1983 | 1983 | Written by R. Flores |
|
|
1984 | 1984 | ''' |
|
|
1985 | 1985 | """Operation to calculate Faraday angle and Double Pulse power. |
|
|
1986 | 1986 | |
|
|
1987 | 1987 | Parameters: |
|
|
1988 | 1988 | ----------- |
|
|
1989 | 1989 | None |
|
|
1990 | 1990 | |
|
|
1991 | 1991 | Example |
|
|
1992 | 1992 | -------- |
|
|
1993 | 1993 | |
|
|
1994 | 1994 | op = proc_unit.addOperation(name='FaradayAngleAndDPPower', optype='other') |
|
|
1995 | 1995 | |
|
|
1996 | 1996 | """ |
|
|
1997 | 1997 | |
|
|
1998 | 1998 | def __init__(self, **kwargs): |
|
|
1999 | 1999 | |
|
|
2000 | 2000 | Operation.__init__(self, **kwargs) |
|
|
2001 | 2001 | self.aux=1 |
|
|
2002 | 2002 | |
|
|
2003 | 2003 | def run(self,dataOut): |
|
|
2004 | 2004 | |
|
|
2005 | 2005 | if self.aux==1: |
|
|
2006 | 2006 | dataOut.h2=numpy.zeros(dataOut.MAXNRANGENDT,'float32') |
|
|
2007 | 2007 | dataOut.range1=numpy.zeros(dataOut.MAXNRANGENDT,order='F',dtype='float32') |
|
|
2008 | 2008 | dataOut.sdn2=numpy.zeros(dataOut.NDP,'float32') |
|
|
2009 | 2009 | dataOut.ph2=numpy.zeros(dataOut.NDP,'float32') |
|
|
2010 | 2010 | dataOut.sdp2=numpy.zeros(dataOut.NDP,'float32') |
|
|
2011 | 2011 | dataOut.ibd=numpy.zeros(dataOut.NDP,'float32') |
|
|
2012 | 2012 | dataOut.phi=numpy.zeros(dataOut.NDP,'float32') |
|
|
2013 | 2013 | |
|
|
2014 | 2014 | self.aux=0 |
|
|
2015 | 2015 | |
|
|
2016 | 2016 | for i in range(dataOut.MAXNRANGENDT): |
|
|
2017 | 2017 | dataOut.range1[i]=dataOut.H0 + i*dataOut.DH |
|
|
2018 | 2018 | dataOut.h2[i]=dataOut.range1[i]**2 |
|
|
2019 | 2019 | |
|
|
2020 | 2020 | for j in range(dataOut.NDP): |
|
|
2021 | 2021 | dataOut.ph2[j]=0. |
|
|
2022 | 2022 | dataOut.sdp2[j]=0. |
|
|
2023 | 2023 | ri=dataOut.rhoi[j][0]/dataOut.sd[j][0] |
|
|
2024 | 2024 | rr=dataOut.rhor[j][0]/dataOut.sd[j][0] |
|
|
2025 | 2025 | dataOut.sdn2[j]=1./dataOut.sd[j][0] |
|
|
2026 | 2026 | |
|
|
2027 | 2027 | pt=0.# // total power |
|
|
2028 | 2028 | st=0.# // total signal |
|
|
2029 | 2029 | ibt=0# // bad lags |
|
|
2030 | 2030 | ns=0# // no. good lags |
|
|
2031 | 2031 | for l in range(dataOut.DPL): |
|
|
2032 | 2032 | #add in other lags if outside of e-jet contamination |
|
|
2033 | 2033 | if( (dataOut.igcej[j][l] == 0) and (dataOut.ibad[j][l] == 0) ): |
|
|
2034 | 2034 | |
|
|
2035 | 2035 | dataOut.ph2[j]+=dataOut.p[j][l]/dataOut.sdp[j][l] |
|
|
2036 | 2036 | dataOut.sdp2[j]=dataOut.sdp2[j]+1./dataOut.sdp[j][l] |
|
|
2037 | 2037 | ns+=1 |
|
|
2038 | 2038 | |
|
|
2039 | 2039 | |
|
|
2040 | 2040 | pt+=dataOut.p[j][l]/dataOut.sdp[j][l] |
|
|
2041 | 2041 | st+=1./dataOut.sdp[j][l] |
|
|
2042 | 2042 | ibt|=dataOut.ibad[j][l]; |
|
|
2043 | 2043 | if(ns!= 0): |
|
|
2044 | 2044 | dataOut.ibd[j]=0 |
|
|
2045 | 2045 | dataOut.ph2[j]=dataOut.ph2[j]/dataOut.sdp2[j] |
|
|
2046 | 2046 | dataOut.sdp2[j]=1./dataOut.sdp2[j] |
|
|
2047 | 2047 | else: |
|
|
2048 | 2048 | dataOut.ibd[j]=ibt |
|
|
2049 | 2049 | dataOut.ph2[j]=pt/st |
|
|
2050 | 2050 | dataOut.sdp2[j]=1./st |
|
|
2051 | 2051 | |
|
|
2052 | 2052 | dataOut.ph2[j]=dataOut.ph2[j]*dataOut.h2[j] |
|
|
2053 | 2053 | dataOut.sdp2[j]=numpy.sqrt(dataOut.sdp2[j])*dataOut.h2[j] |
|
|
2054 | 2054 | rr=rr/dataOut.sdn2[j] |
|
|
2055 | 2055 | ri=ri/dataOut.sdn2[j] |
|
|
2056 | 2056 | #rm[j]=np.sqrt(rr*rr + ri*ri) it is not used in c program |
|
|
2057 | 2057 | dataOut.sdn2[j]=1./(dataOut.sdn2[j]*(rr*rr + ri*ri)) |
|
|
2058 | 2058 | if( (ri == 0.) and (rr == 0.) ): |
|
|
2059 | 2059 | dataOut.phi[j]=0. |
|
|
2060 | 2060 | else: |
|
|
2061 | 2061 | dataOut.phi[j]=math.atan2( ri , rr ) |
|
|
2062 | 2062 | |
|
|
2063 | 2063 | dataOut.flagTeTiCorrection = False |
|
|
2064 | 2064 | return dataOut |
|
|
2065 | 2065 | |
|
|
2066 | 2066 | |
|
|
2067 | 2067 | class ElectronDensityFaraday(Operation): |
|
|
2068 | 2068 | ''' |
|
|
2069 | 2069 | Written by R. Flores |
|
|
2070 | 2070 | ''' |
|
|
2071 | 2071 | """Operation to calculate electron density from Faraday angle. |
|
|
2072 | 2072 | |
|
|
2073 | 2073 | Parameters: |
|
|
2074 | 2074 | ----------- |
|
|
2075 | 2075 | NSHTS : int |
|
|
2076 | 2076 | .* |
|
|
2077 | 2077 | RATE : float |
|
|
2078 | 2078 | .* |
|
|
2079 | 2079 | |
|
|
2080 | 2080 | Example |
|
|
2081 | 2081 | -------- |
|
|
2082 | 2082 | |
|
|
2083 | 2083 | op = proc_unit.addOperation(name='ElectronDensityFaraday', optype='other') |
|
|
2084 | 2084 | op.addParameter(name='NSHTS', value='50', format='int') |
|
|
2085 | 2085 | op.addParameter(name='RATE', value='1.8978873e-6', format='float') |
|
|
2086 | 2086 | |
|
|
2087 | 2087 | """ |
|
|
2088 | 2088 | |
|
|
2089 | 2089 | def __init__(self, **kwargs): |
|
|
2090 | 2090 | |
|
|
2091 | 2091 | Operation.__init__(self, **kwargs) |
|
|
2092 | 2092 | self.aux=1 |
|
|
2093 | 2093 | |
|
|
2094 | 2094 | def run(self,dataOut,NSHTS=50,RATE=1.8978873e-6): |
|
|
2095 | 2095 | |
|
|
2096 | 2096 | dataOut.NSHTS=NSHTS |
|
|
2097 | 2097 | dataOut.RATE=RATE |
|
|
2098 | 2098 | |
|
|
2099 | 2099 | if self.aux==1: |
|
|
2100 | 2100 | dataOut.dphi=numpy.zeros(dataOut.NDP,'float32') |
|
|
2101 | 2101 | dataOut.sdn1=numpy.zeros(dataOut.NDP,'float32') |
|
|
2102 | 2102 | self.aux=0 |
|
|
2103 | 2103 | theta=numpy.zeros(dataOut.NDP,dtype=numpy.complex_) |
|
|
2104 | 2104 | thetai=numpy.zeros(dataOut.NDP,dtype=numpy.complex_) |
|
|
2105 | 2105 | # use complex numbers for phase |
|
|
2106 | 2106 | ''' |
|
|
2107 | 2107 | for i in range(dataOut.NSHTS): |
|
|
2108 | 2108 | theta[i]=math.cos(dataOut.phi[i])+math.sin(dataOut.phi[i])*1j |
|
|
2109 | 2109 | thetai[i]=-math.sin(dataOut.phi[i])+math.cos(dataOut.phi[i])*1j |
|
|
2110 | 2110 | ''' #Old Method |
|
|
2111 | 2111 | |
|
|
2112 | 2112 | # differentiate and convert to number density |
|
|
2113 | 2113 | ndphi=dataOut.NSHTS-4 |
|
|
2114 | 2114 | if hasattr(dataOut, 'flagSpreadF') and dataOut.flagSpreadF: |
|
|
2115 | 2115 | nanindex = numpy.argwhere(numpy.isnan(dataOut.phi)) |
|
|
2116 | 2116 | i1 = nanindex[-1][0] |
|
|
2117 | 2117 | #Analizar cuando SpreadF es Pluma |
|
|
2118 | 2118 | |
|
|
2119 | 2119 | dataOut.phi[i1+1:]=numpy.unwrap(dataOut.phi[i1+1:]) #Better results |
|
|
2120 | 2120 | else: |
|
|
2121 | 2121 | dataOut.phi[:]=numpy.unwrap(dataOut.phi[:]) #Better results |
|
|
2122 | 2122 | for i in range(2,dataOut.NSHTS-2): |
|
|
2123 | 2123 | fact=(-0.5/(dataOut.RATE*dataOut.DH))*dataOut.bki[i] |
|
|
2124 | 2124 | #print("fact: ", fact,dataOut.RATE,dataOut.DH,dataOut.bki[i]) |
|
|
2125 | 2125 | #four-point derivative, no phase unwrapping necessary |
|
|
2126 | 2126 | #####dataOut.dphi[i]=((((theta[i+1]-theta[i-1])+(2.0*(theta[i+2]-theta[i-2])))/thetai[i])).real/10.0 #Original from C program |
|
|
2127 | 2127 | |
|
|
2128 | 2128 | ##dataOut.dphi[i]=((((theta[i-2]-theta[i+2])+(8.0*(theta[i+1]-theta[i-1])))/thetai[i])).real/12.0 |
|
|
2129 | 2129 | dataOut.dphi[i]=((dataOut.phi[i+1]-dataOut.phi[i-1])+(2.0*(dataOut.phi[i+2]-dataOut.phi[i-2])))/10.0 #Better results |
|
|
2130 | 2130 | |
|
|
2131 | 2131 | #dataOut.dphi_uc[i] = abs(dataOut.phi[i]*dataOut.bki[i]*(-0.5)/dataOut.DH) |
|
|
2132 | 2132 | #dataOut.dphi[i]=abs(dataOut.dphi[i]*fact) |
|
|
2133 | 2133 | dataOut.dphi[i]=dataOut.dphi[i]*abs(fact) |
|
|
2134 | 2134 | dataOut.sdn1[i]=(4.*(dataOut.sdn2[i-2]+dataOut.sdn2[i+2])+dataOut.sdn2[i-1]+dataOut.sdn2[i+1]) |
|
|
2135 | 2135 | dataOut.sdn1[i]=numpy.sqrt(dataOut.sdn1[i])*fact |
|
|
2136 | 2136 | |
|
|
2137 | 2137 | return dataOut |
|
|
2138 | 2138 | |
|
|
2139 | 2139 | |
|
|
2140 | 2140 | class NormalizeDPPower(Operation): |
|
|
2141 | 2141 | ''' |
|
|
2142 | 2142 | Written by R. Flores |
|
|
2143 | 2143 | ''' |
|
|
2144 | 2144 | """Operation to normalize relative electron density from power with total electron density from Faraday angle. |
|
|
2145 | 2145 | |
|
|
2146 | 2146 | Parameters: |
|
|
2147 | 2147 | ----------- |
|
|
2148 | 2148 | None |
|
|
2149 | 2149 | |
|
|
2150 | 2150 | Example |
|
|
2151 | 2151 | -------- |
|
|
2152 | 2152 | |
|
|
2153 | 2153 | op = proc_unit.addOperation(name='NormalizeDPPower', optype='other') |
|
|
2154 | 2154 | |
|
|
2155 | 2155 | """ |
|
|
2156 | 2156 | |
|
|
2157 | 2157 | def __init__(self, **kwargs): |
|
|
2158 | 2158 | |
|
|
2159 | 2159 | Operation.__init__(self, **kwargs) |
|
|
2160 | 2160 | self.aux=1 |
|
|
2161 | 2161 | |
|
|
2162 | 2162 | def normal(self,a,b,n,m): |
|
|
2163 | 2163 | chmin=1.0e30 |
|
|
2164 | 2164 | chisq=numpy.zeros(150,'float32') |
|
|
2165 | 2165 | temp=numpy.zeros(150,'float32') |
|
|
2166 | 2166 | |
|
|
2167 | 2167 | for i in range(2*m-1): |
|
|
2168 | 2168 | an=al=be=chisq[i]=0.0 |
|
|
2169 | 2169 | for j in range(int(n/m)): |
|
|
2170 | 2170 | k=int(j+i*n/(2*m)) |
|
|
2171 | 2171 | if(a[k]>0.0 and b[k]>0.0): |
|
|
2172 | 2172 | al+=a[k]*b[k] |
|
|
2173 | 2173 | be+=b[k]*b[k] |
|
|
2174 | 2174 | |
|
|
2175 | 2175 | if(be>0.0): |
|
|
2176 | 2176 | temp[i]=al/be |
|
|
2177 | 2177 | else: |
|
|
2178 | 2178 | temp[i]=1.0 |
|
|
2179 | 2179 | |
|
|
2180 | 2180 | for j in range(int(n/m)): |
|
|
2181 | 2181 | k=int(j+i*n/(2*m)) |
|
|
2182 | 2182 | if(a[k]>0.0 and b[k]>0.0): |
|
|
2183 | 2183 | chisq[i]+=(numpy.log10(b[k]*temp[i]/a[k]))**2 |
|
|
2184 | 2184 | an=an+1 |
|
|
2185 | 2185 | |
|
|
2186 | 2186 | if(chisq[i]>0.0): |
|
|
2187 | 2187 | chisq[i]/=an |
|
|
2188 | 2188 | |
|
|
2189 | 2189 | for i in range(int(2*m-1)): |
|
|
2190 | 2190 | if(chisq[i]<chmin and chisq[i]>1.0e-6): |
|
|
2191 | 2191 | chmin=chisq[i] |
|
|
2192 | 2192 | cf=temp[i] |
|
|
2193 | 2193 | return cf |
|
|
2194 | 2194 | |
|
|
2195 | 2195 | def normalize(self,dataOut): |
|
|
2196 | 2196 | |
|
|
2197 | 2197 | if self.aux==1: |
|
|
2198 | 2198 | dataOut.cf=numpy.zeros(1,'float32') |
|
|
2199 | 2199 | dataOut.cflast=numpy.zeros(1,'float32') |
|
|
2200 | 2200 | self.aux=0 |
|
|
2201 | 2201 | |
|
|
2202 | 2202 | night_first=300.0 |
|
|
2203 | 2203 | night_first1= 310.0 |
|
|
2204 | 2204 | night_end= 450.0 |
|
|
2205 | 2205 | day_first=250.0 |
|
|
2206 | 2206 | day_end=400.0 |
|
|
2207 | 2207 | day_first_sunrise=190.0 |
|
|
2208 | 2208 | day_end_sunrise=280.0 |
|
|
2209 | 2209 | |
|
|
2210 | 2210 | #print(dataOut.ut_Faraday) |
|
|
2211 | 2211 | if(dataOut.ut_Faraday>4.0 and dataOut.ut_Faraday<11.0): #early |
|
|
2212 | 2212 | #print("EARLY") |
|
|
2213 | 2213 | i2=(night_end-dataOut.range1[0])/dataOut.DH |
|
|
2214 | 2214 | i1=(night_first -dataOut.range1[0])/dataOut.DH |
|
|
2215 | 2215 | elif (dataOut.ut_Faraday>0.0 and dataOut.ut_Faraday<4.0): #night |
|
|
2216 | 2216 | #print("NIGHT") |
|
|
2217 | 2217 | i2=(night_end-dataOut.range1[0])/dataOut.DH |
|
|
2218 | 2218 | i1=(night_first1 -dataOut.range1[0])/dataOut.DH |
|
|
2219 | 2219 | elif (dataOut.ut_Faraday>=11.0 and dataOut.ut_Faraday<13.5): #sunrise |
|
|
2220 | 2220 | #print("SUNRISE") |
|
|
2221 | 2221 | i2=( day_end_sunrise-dataOut.range1[0])/dataOut.DH |
|
|
2222 | 2222 | i1=(day_first_sunrise - dataOut.range1[0])/dataOut.DH |
|
|
2223 | 2223 | else: |
|
|
2224 | 2224 | #print("ELSE") |
|
|
2225 | 2225 | i2=(day_end-dataOut.range1[0])/dataOut.DH |
|
|
2226 | 2226 | i1=(day_first -dataOut.range1[0])/dataOut.DH |
|
|
2227 | 2227 | #print(i1*dataOut.DH) |
|
|
2228 | 2228 | #print(i2*dataOut.DH) |
|
|
2229 | 2229 | |
|
|
2230 | 2230 | i1=int(i1) |
|
|
2231 | 2231 | i2=int(i2) |
|
|
2232 | 2232 | |
|
|
2233 | 2233 | try: |
|
|
2234 | 2234 | dataOut.cf=self.normal(dataOut.dphi[i1::], dataOut.ph2[i1::], i2-i1, 1) |
|
|
2235 | 2235 | except: |
|
|
2236 | 2236 | pass |
|
|
2237 | 2237 | |
|
|
2238 | 2238 | #print(dataOut.ph2) |
|
|
2239 | 2239 | #input() |
|
|
2240 | 2240 | # in case of spread F, normalize much higher |
|
|
2241 | 2241 | if(dataOut.cf<dataOut.cflast[0]/10.0): |
|
|
2242 | 2242 | i1=(night_first1+100.-dataOut.range1[0])/dataOut.DH |
|
|
2243 | 2243 | i2=(night_end+100.0-dataOut.range1[0])/dataOut.DH |
|
|
2244 | 2244 | i1=int(i1) |
|
|
2245 | 2245 | i2=int(i2) |
|
|
2246 | 2246 | try: |
|
|
2247 | 2247 | dataOut.cf=self.normal(dataOut.dphi[int(i1)::], dataOut.ph2[int(i1)::], int(i2-i1), 1) |
|
|
2248 | 2248 | except: |
|
|
2249 | 2249 | pass |
|
|
2250 | 2250 | |
|
|
2251 | 2251 | dataOut.cflast[0]=dataOut.cf |
|
|
2252 | 2252 | |
|
|
2253 | 2253 | ## normalize double pulse power and error bars to Faraday |
|
|
2254 | 2254 | for i in range(dataOut.NSHTS): |
|
|
2255 | 2255 | dataOut.ph2[i]*=dataOut.cf |
|
|
2256 | 2256 | dataOut.sdp2[i]*=dataOut.cf |
|
|
2257 | 2257 | #print(dataOut.ph2) |
|
|
2258 | 2258 | #input() |
|
|
2259 | 2259 | |
|
|
2260 | 2260 | for i in range(dataOut.NSHTS): |
|
|
2261 | 2261 | dataOut.ph2[i]=(max(1.0, dataOut.ph2[i])) |
|
|
2262 | 2262 | dataOut.dphi[i]=(max(1.0, dataOut.dphi[i])) |
|
|
2263 | 2263 | |
|
|
2264 | 2264 | |
|
|
2265 | 2265 | def run(self,dataOut): |
|
|
2266 | 2266 | |
|
|
2267 | 2267 | self.normalize(dataOut) |
|
|
2268 | 2268 | #print(dataOut.ph2) |
|
|
2269 | 2269 | #print(dataOut.sdp2) |
|
|
2270 | 2270 | #input() |
|
|
2271 | 2271 | |
|
|
2272 | 2272 | |
|
|
2273 | 2273 | return dataOut |
|
|
2274 | 2274 | |
|
|
2275 | 2275 | class NormalizeDPPowerRoberto(Operation): |
|
|
2276 | 2276 | ''' |
|
|
2277 | 2277 | Written by R. Flores |
|
|
2278 | 2278 | ''' |
|
|
2279 | 2279 | """Operation to normalize relative electron density from power with total electron density from Farday angle. |
|
|
2280 | 2280 | |
|
|
2281 | 2281 | Parameters: |
|
|
2282 | 2282 | ----------- |
|
|
2283 | 2283 | None |
|
|
2284 | 2284 | |
|
|
2285 | 2285 | Example |
|
|
2286 | 2286 | -------- |
|
|
2287 | 2287 | |
|
|
2288 | 2288 | op = proc_unit.addOperation(name='NormalizeDPPower', optype='other') |
|
|
2289 | 2289 | |
|
|
2290 | 2290 | """ |
|
|
2291 | 2291 | |
|
|
2292 | 2292 | def __init__(self, **kwargs): |
|
|
2293 | 2293 | |
|
|
2294 | 2294 | Operation.__init__(self, **kwargs) |
|
|
2295 | 2295 | self.aux=1 |
|
|
2296 | 2296 | |
|
|
2297 | 2297 | def normal(self,a,b,n,m): |
|
|
2298 | 2298 | chmin=1.0e30 |
|
|
2299 | 2299 | chisq=numpy.zeros(150,'float32') |
|
|
2300 | 2300 | temp=numpy.zeros(150,'float32') |
|
|
2301 | 2301 | |
|
|
2302 | 2302 | for i in range(2*m-1): |
|
|
2303 | 2303 | an=al=be=chisq[i]=0.0 |
|
|
2304 | 2304 | for j in range(int(n/m)): |
|
|
2305 | 2305 | k=int(j+i*n/(2*m)) |
|
|
2306 | 2306 | if(a[k]>0.0 and b[k]>0.0): |
|
|
2307 | 2307 | al+=a[k]*b[k] |
|
|
2308 | 2308 | be+=b[k]*b[k] |
|
|
2309 | 2309 | |
|
|
2310 | 2310 | if(be>0.0): |
|
|
2311 | 2311 | temp[i]=al/be |
|
|
2312 | 2312 | else: |
|
|
2313 | 2313 | temp[i]=1.0 |
|
|
2314 | 2314 | |
|
|
2315 | 2315 | for j in range(int(n/m)): |
|
|
2316 | 2316 | k=int(j+i*n/(2*m)) |
|
|
2317 | 2317 | if(a[k]>0.0 and b[k]>0.0): |
|
|
2318 | 2318 | chisq[i]+=(numpy.log10(b[k]*temp[i]/a[k]))**2 |
|
|
2319 | 2319 | an=an+1 |
|
|
2320 | 2320 | |
|
|
2321 | 2321 | if(chisq[i]>0.0): |
|
|
2322 | 2322 | chisq[i]/=an |
|
|
2323 | 2323 | |
|
|
2324 | 2324 | for i in range(int(2*m-1)): |
|
|
2325 | 2325 | if(chisq[i]<chmin and chisq[i]>1.0e-6): |
|
|
2326 | 2326 | chmin=chisq[i] |
|
|
2327 | 2327 | cf=temp[i] |
|
|
2328 | 2328 | return cf |
|
|
2329 | 2329 | |
|
|
2330 | 2330 | def normalize(self,dataOut): |
|
|
2331 | 2331 | |
|
|
2332 | 2332 | if self.aux==1: |
|
|
2333 | 2333 | dataOut.cf=numpy.zeros(1,'float32') |
|
|
2334 | 2334 | dataOut.cflast=numpy.zeros(1,'float32') |
|
|
2335 | 2335 | self.aux=0 |
|
|
2336 | 2336 | |
|
|
2337 | 2337 | night_first=300.0 |
|
|
2338 | 2338 | night_first1= 310.0 |
|
|
2339 | 2339 | night_end= 450.0 |
|
|
2340 | 2340 | day_first=250.0 |
|
|
2341 | 2341 | day_end=400.0 |
|
|
2342 | 2342 | day_first_sunrise=190.0 |
|
|
2343 | 2343 | day_end_sunrise=350.0 |
|
|
2344 | 2344 | |
|
|
2345 | 2345 | print(dataOut.ut_Faraday) |
|
|
2346 | 2346 | ''' |
|
|
2347 | 2347 | if(dataOut.ut_Faraday>4.0 and dataOut.ut_Faraday<11.0): #early |
|
|
2348 | 2348 | print("EARLY") |
|
|
2349 | 2349 | i2=(night_end-dataOut.range1[0])/dataOut.DH |
|
|
2350 | 2350 | i1=(night_first -dataOut.range1[0])/dataOut.DH |
|
|
2351 | 2351 | elif (dataOut.ut_Faraday>0.0 and dataOut.ut_Faraday<4.0): #night |
|
|
2352 | 2352 | print("NIGHT") |
|
|
2353 | 2353 | i2=(night_end-dataOut.range1[0])/dataOut.DH |
|
|
2354 | 2354 | i1=(night_first1 -dataOut.range1[0])/dataOut.DH |
|
|
2355 | 2355 | elif (dataOut.ut_Faraday>=11.0 and dataOut.ut_Faraday<13.5): #sunrise |
|
|
2356 | 2356 | print("SUNRISE") |
|
|
2357 | 2357 | i2=( day_end_sunrise-dataOut.range1[0])/dataOut.DH |
|
|
2358 | 2358 | i1=(day_first_sunrise - dataOut.range1[0])/dataOut.DH |
|
|
2359 | 2359 | else: |
|
|
2360 | 2360 | print("ELSE") |
|
|
2361 | 2361 | i2=(day_end-dataOut.range1[0])/dataOut.DH |
|
|
2362 | 2362 | i1=(day_first -dataOut.range1[0])/dataOut.DH |
|
|
2363 | 2363 | ''' |
|
|
2364 | 2364 | i2=(420-dataOut.range1[0])/dataOut.DH |
|
|
2365 | 2365 | i1=(200 -dataOut.range1[0])/dataOut.DH |
|
|
2366 | 2366 | print(i1*dataOut.DH) |
|
|
2367 | 2367 | print(i2*dataOut.DH) |
|
|
2368 | 2368 | |
|
|
2369 | 2369 | i1=int(i1) |
|
|
2370 | 2370 | i2=int(i2) |
|
|
2371 | 2371 | |
|
|
2372 | 2372 | try: |
|
|
2373 | 2373 | dataOut.cf=self.normal(dataOut.dphi[i1::], dataOut.ph2[i1::], i2-i1, 1) |
|
|
2374 | 2374 | except: |
|
|
2375 | 2375 | pass |
|
|
2376 | 2376 | |
|
|
2377 | 2377 | #print(dataOut.ph2) |
|
|
2378 | 2378 | #input() |
|
|
2379 | 2379 | # in case of spread F, normalize much higher |
|
|
2380 | 2380 | if(dataOut.cf<dataOut.cflast[0]/10.0): |
|
|
2381 | 2381 | i1=(night_first1+100.-dataOut.range1[0])/dataOut.DH |
|
|
2382 | 2382 | i2=(night_end+100.0-dataOut.range1[0])/dataOut.DH |
|
|
2383 | 2383 | i1=int(i1) |
|
|
2384 | 2384 | i2=int(i2) |
|
|
2385 | 2385 | try: |
|
|
2386 | 2386 | dataOut.cf=self.normal(dataOut.dphi[int(i1)::], dataOut.ph2[int(i1)::], int(i2-i1), 1) |
|
|
2387 | 2387 | except: |
|
|
2388 | 2388 | pass |
|
|
2389 | 2389 | |
|
|
2390 | 2390 | dataOut.cflast[0]=dataOut.cf |
|
|
2391 | 2391 | |
|
|
2392 | 2392 | ## normalize double pulse power and error bars to Faraday |
|
|
2393 | 2393 | for i in range(dataOut.NSHTS): |
|
|
2394 | 2394 | dataOut.ph2[i]*=dataOut.cf |
|
|
2395 | 2395 | dataOut.sdp2[i]*=dataOut.cf |
|
|
2396 | 2396 | #print(dataOut.ph2) |
|
|
2397 | 2397 | #input() |
|
|
2398 | 2398 | |
|
|
2399 | 2399 | for i in range(dataOut.NSHTS): |
|
|
2400 | 2400 | dataOut.ph2[i]=(max(1.0, dataOut.ph2[i])) |
|
|
2401 | 2401 | dataOut.dphi[i]=(max(1.0, dataOut.dphi[i])) |
|
|
2402 | 2402 | |
|
|
2403 | 2403 | |
|
|
2404 | 2404 | def run(self,dataOut): |
|
|
2405 | 2405 | |
|
|
2406 | 2406 | self.normalize(dataOut) |
|
|
2407 | 2407 | #print(dataOut.ph2) |
|
|
2408 | 2408 | #print(dataOut.sdp2) |
|
|
2409 | 2409 | #input() |
|
|
2410 | 2410 | |
|
|
2411 | 2411 | |
|
|
2412 | 2412 | return dataOut |
|
|
2413 | 2413 | |
|
|
2414 | 2414 | class NormalizeDPPowerRoberto_V2(Operation): |
|
|
2415 | 2415 | ''' |
|
|
2416 | 2416 | Written by R. Flores |
|
|
2417 | 2417 | ''' |
|
|
2418 | 2418 | """Operation to normalize relative electron density from power with total electron density from Farday angle. |
|
|
2419 | 2419 | |
|
|
2420 | 2420 | Parameters: |
|
|
2421 | 2421 | ----------- |
|
|
2422 | 2422 | None |
|
|
2423 | 2423 | |
|
|
2424 | 2424 | Example |
|
|
2425 | 2425 | -------- |
|
|
2426 | 2426 | |
|
|
2427 | 2427 | op = proc_unit.addOperation(name='NormalizeDPPower', optype='other') |
|
|
2428 | 2428 | |
|
|
2429 | 2429 | """ |
|
|
2430 | 2430 | |
|
|
2431 | 2431 | def __init__(self, **kwargs): |
|
|
2432 | 2432 | |
|
|
2433 | 2433 | Operation.__init__(self, **kwargs) |
|
|
2434 | 2434 | self.aux=1 |
|
|
2435 | 2435 | |
|
|
2436 | 2436 | def normal(self,a,b,n,m): |
|
|
2437 | 2437 | chmin=1.0e30 |
|
|
2438 | 2438 | chisq=numpy.zeros(150,'float32') |
|
|
2439 | 2439 | temp=numpy.zeros(150,'float32') |
|
|
2440 | 2440 | |
|
|
2441 | 2441 | for i in range(2*m-1): |
|
|
2442 | 2442 | an=al=be=chisq[i]=0.0 |
|
|
2443 | 2443 | for j in range(int(n/m)): |
|
|
2444 | 2444 | k=int(j+i*n/(2*m)) |
|
|
2445 | 2445 | if(a[k]>0.0 and b[k]>0.0): |
|
|
2446 | 2446 | al+=a[k]*b[k] |
|
|
2447 | 2447 | be+=b[k]*b[k] |
|
|
2448 | 2448 | |
|
|
2449 | 2449 | if(be>0.0): |
|
|
2450 | 2450 | temp[i]=al/be |
|
|
2451 | 2451 | else: |
|
|
2452 | 2452 | temp[i]=1.0 |
|
|
2453 | 2453 | |
|
|
2454 | 2454 | for j in range(int(n/m)): |
|
|
2455 | 2455 | k=int(j+i*n/(2*m)) |
|
|
2456 | 2456 | if(a[k]>0.0 and b[k]>0.0): |
|
|
2457 | 2457 | chisq[i]+=(numpy.log10(b[k]*temp[i]/a[k]))**2 |
|
|
2458 | 2458 | an=an+1 |
|
|
2459 | 2459 | |
|
|
2460 | 2460 | if(chisq[i]>0.0): |
|
|
2461 | 2461 | chisq[i]/=an |
|
|
2462 | 2462 | |
|
|
2463 | 2463 | for i in range(int(2*m-1)): |
|
|
2464 | 2464 | if(chisq[i]<chmin and chisq[i]>1.0e-6): |
|
|
2465 | 2465 | chmin=chisq[i] |
|
|
2466 | 2466 | cf=temp[i] |
|
|
2467 | 2467 | return cf |
|
|
2468 | 2468 | |
|
|
2469 | ||
|
|
2469 | ||
|
|
2470 | 2470 | def normalize(self,dataOut): |
|
|
2471 | 2471 | |
|
|
2472 | 2472 | if self.aux==1: |
|
|
2473 | 2473 | dataOut.cf=numpy.zeros(1,'float32') |
|
|
2474 | 2474 | dataOut.cflast=numpy.zeros(1,'float32') |
|
|
2475 | 2475 | self.aux=0 |
|
|
2476 | 2476 | |
|
|
2477 | 2477 | if (dataOut.ut_Faraday>=11.5 and dataOut.ut_Faraday<23): |
|
|
2478 | 2478 | i2=(500.-dataOut.range1[0])/dataOut.DH |
|
|
2479 | 2479 | i1=(200.-dataOut.range1[0])/dataOut.DH |
|
|
2480 | 2480 | |
|
|
2481 | 2481 | else: |
|
|
2482 | 2482 | inda = numpy.where(dataOut.heightList >= 200) #200 km |
|
|
2483 | 2483 | minIndex = inda[0][0] |
|
|
2484 | 2484 | indb = numpy.where(dataOut.heightList < 700) # 700 km |
|
|
2485 | 2485 | maxIndex = indb[0][-1] |
|
|
2486 | 2486 | |
|
|
2487 | 2487 | ph2max_idx = numpy.nanargmax(dataOut.ph2[minIndex:maxIndex]) |
|
|
2488 | 2488 | ph2max_idx += minIndex |
|
|
2489 | 2489 | |
|
|
2490 | 2490 | i2 = ph2max_idx + 6 |
|
|
2491 | 2491 | i1 = ph2max_idx - 6 |
|
|
2492 | 2492 | |
|
|
2493 | 2493 | try: |
|
|
2494 | 2494 | dataOut.heightList[i2] |
|
|
2495 | 2495 | except: |
|
|
2496 | 2496 | i2 -= 1 |
|
|
2497 | 2497 | |
|
|
2498 | 2498 | i1=int(i1) |
|
|
2499 | 2499 | i2=int(i2) |
|
|
2500 | 2500 | |
|
|
2501 | 2501 | if dataOut.flagTeTiCorrection: |
|
|
2502 | 2502 | for i in range(dataOut.NSHTS): |
|
|
2503 | 2503 | dataOut.ph2[i]/=dataOut.cf |
|
|
2504 | 2504 | dataOut.sdp2[i]/=dataOut.cf |
|
|
2505 | 2505 | |
|
|
2506 | 2506 | if hasattr(dataOut, 'flagSpreadF') and dataOut.flagSpreadF: |
|
|
2507 | 2507 | i2=int((700-dataOut.range1[0])/dataOut.DH) |
|
|
2508 | 2508 | nanindex = numpy.argwhere(numpy.isnan(dataOut.ph2)) |
|
|
2509 | 2509 | i1 = nanindex[-1][0] #VER CUANDO i1>i2 |
|
|
2510 | 2510 | if i1 != numpy.shape(dataOut.heightList)[0]: |
|
|
2511 | 2511 | i1 += 1+2 #Se suma uno para no tomar el nan, se suma 2 para no tomar datos nan de "phi" debido al calculo de la derivada |
|
|
2512 | 2512 | if i1 >= i2: |
|
|
2513 | 2513 | i1 = i2-4 |
|
|
2514 | 2514 | |
|
|
2515 | 2515 | try: |
|
|
2516 | 2516 | dataOut.cf=self.normal(dataOut.dphi[i1::], dataOut.ph2[i1::], i2-i1, 1) |
|
|
2517 | 2517 | |
|
|
2518 | 2518 | except: |
|
|
2519 | 2519 | print("except") |
|
|
2520 | 2520 | dataOut.cf = numpy.nan |
|
|
2521 | 2521 | |
|
|
2522 | 2522 | night_first1= 300.0#350.0 |
|
|
2523 | 2523 | night_end= 450.0 |
|
|
2524 | 2524 | night_first1= 220.0#350.0 |
|
|
2525 | 2525 | night_end= 400.0 |
|
|
2526 | 2526 | |
|
|
2527 | 2527 | if(dataOut.cf<dataOut.cflast[0]/10.0): |
|
|
2528 | 2528 | i1=(night_first1-dataOut.range1[0])/dataOut.DH |
|
|
2529 | 2529 | i2=(night_end-dataOut.range1[0])/dataOut.DH |
|
|
2530 | 2530 | i1=int(i1) |
|
|
2531 | 2531 | i2=int(i2) |
|
|
2532 | 2532 | try: |
|
|
2533 | 2533 | dataOut.cf=self.normal(dataOut.dphi[int(i1)::], dataOut.ph2[int(i1)::], int(i2-i1), 1) |
|
|
2534 | 2534 | except: |
|
|
2535 | 2535 | pass |
|
|
2536 | 2536 | |
|
|
2537 | 2537 | dataOut.cflast[0]=dataOut.cf |
|
|
2538 | 2538 | |
|
|
2539 | 2539 | ## normalize double pulse power and error bars to Faraday |
|
|
2540 | 2540 | for i in range(dataOut.NSHTS): |
|
|
2541 | 2541 | dataOut.ph2[i]*=dataOut.cf |
|
|
2542 | 2542 | dataOut.sdp2[i]*=dataOut.cf |
|
|
2543 | 2543 | |
|
|
2544 | 2544 | for i in range(dataOut.NSHTS): |
|
|
2545 | 2545 | dataOut.ph2[i]=(max(1.0, dataOut.ph2[i])) |
|
|
2546 | 2546 | dataOut.dphi[i]=(max(1.0, dataOut.dphi[i])) |
|
|
2547 | 2547 | |
|
|
2548 | 2548 | def run(self,dataOut): |
|
|
2549 | 2549 | |
|
|
2550 | 2550 | self.normalize(dataOut) |
|
|
2551 | 2551 | |
|
|
2552 | 2552 | return dataOut |
|
|
2553 | 2553 | |
|
|
2554 | 2554 | class suppress_stdout_stderr(object): |
|
|
2555 | 2555 | ''' |
|
|
2556 | 2556 | A context manager for doing a "deep suppression" of stdout and stderr in |
|
|
2557 | 2557 | Python, i.e. will suppress all print, even if the print originates in a |
|
|
2558 | 2558 | compiled C/Fortran sub-function. |
|
|
2559 | 2559 | This will not suppress raised exceptions, since exceptions are printed |
|
|
2560 | 2560 | to stderr just before a script exits, and after the context manager has |
|
|
2561 | 2561 | exited (at least, I think that is why it lets exceptions through). |
|
|
2562 | 2562 | |
|
|
2563 | 2563 | ''' |
|
|
2564 | 2564 | def __init__(self): |
|
|
2565 | 2565 | # Open a pair of null files |
|
|
2566 | 2566 | self.null_fds = [os.open(os.devnull,os.O_RDWR) for x in range(2)] |
|
|
2567 | 2567 | # Save the actual stdout (1) and stderr (2) file descriptors. |
|
|
2568 | 2568 | self.save_fds = [os.dup(1), os.dup(2)] |
|
|
2569 | 2569 | |
|
|
2570 | 2570 | def __enter__(self): |
|
|
2571 | 2571 | # Assign the null pointers to stdout and stderr. |
|
|
2572 | 2572 | os.dup2(self.null_fds[0],1) |
|
|
2573 | 2573 | os.dup2(self.null_fds[1],2) |
|
|
2574 | 2574 | |
|
|
2575 | 2575 | def __exit__(self, *_): |
|
|
2576 | 2576 | # Re-assign the real stdout/stderr back to (1) and (2) |
|
|
2577 | 2577 | os.dup2(self.save_fds[0],1) |
|
|
2578 | 2578 | os.dup2(self.save_fds[1],2) |
|
|
2579 | 2579 | # Close all file descriptors |
|
|
2580 | 2580 | for fd in self.null_fds + self.save_fds: |
|
|
2581 | 2581 | os.close(fd) |
|
|
2582 | 2582 | |
|
|
2583 | 2583 | |
|
|
2584 | 2584 | class DPTemperaturesEstimation(Operation): |
|
|
2585 | 2585 | ''' |
|
|
2586 | 2586 | Written by R. Flores |
|
|
2587 | 2587 | ''' |
|
|
2588 | 2588 | """Operation to estimate temperatures for Double Pulse data. |
|
|
2589 | 2589 | |
|
|
2590 | 2590 | Parameters: |
|
|
2591 | 2591 | ----------- |
|
|
2592 | 2592 | IBITS : int |
|
|
2593 | 2593 | .* |
|
|
2594 | 2594 | |
|
|
2595 | 2595 | Example |
|
|
2596 | 2596 | -------- |
|
|
2597 | 2597 | |
|
|
2598 | 2598 | op = proc_unit.addOperation(name='DPTemperaturesEstimation', optype='other') |
|
|
2599 | 2599 | op.addParameter(name='IBITS', value='16', format='int') |
|
|
2600 | 2600 | |
|
|
2601 | 2601 | """ |
|
|
2602 | 2602 | |
|
|
2603 | 2603 | def __init__(self, **kwargs): |
|
|
2604 | 2604 | |
|
|
2605 | 2605 | Operation.__init__(self, **kwargs) |
|
|
2606 | 2606 | |
|
|
2607 | 2607 | self.aux=1 |
|
|
2608 | 2608 | |
|
|
2609 | 2609 | def Estimation(self,dataOut): |
|
|
2610 | 2610 | #with suppress_stdout_stderr(): |
|
|
2611 | 2611 | |
|
|
2612 | 2612 | if self.aux==1: |
|
|
2613 | 2613 | dataOut.ifit=numpy.zeros(5,order='F',dtype='int32') |
|
|
2614 | 2614 | dataOut.m=numpy.zeros(1,order='F',dtype='int32') |
|
|
2615 | 2615 | dataOut.te2=numpy.zeros(dataOut.NSHTS,order='F',dtype='float32') |
|
|
2616 | 2616 | dataOut.ti2=numpy.zeros(dataOut.NSHTS,order='F',dtype='float32') |
|
|
2617 | 2617 | dataOut.ete2=numpy.zeros(dataOut.NSHTS,order='F',dtype='float32') |
|
|
2618 | 2618 | dataOut.eti2=numpy.zeros(dataOut.NSHTS,order='F',dtype='float32') |
|
|
2619 | 2619 | |
|
|
2620 | 2620 | self.aux=0 |
|
|
2621 | 2621 | |
|
|
2622 | 2622 | dataOut.phy2=numpy.zeros(dataOut.NSHTS,order='F',dtype='float32') |
|
|
2623 | 2623 | dataOut.ephy2=numpy.zeros(dataOut.NSHTS,order='F',dtype='float32') |
|
|
2624 | 2624 | dataOut.info2=numpy.zeros(dataOut.NDP,order='F',dtype='float32') |
|
|
2625 | 2625 | dataOut.params=numpy.zeros(10,order='F',dtype='float32') |
|
|
2626 | 2626 | dataOut.cov=numpy.zeros(dataOut.IBITS*dataOut.IBITS,order='F',dtype='float32') |
|
|
2627 | 2627 | dataOut.covinv=numpy.zeros(dataOut.IBITS*dataOut.IBITS,order='F',dtype='float32') |
|
|
2628 | 2628 | |
|
|
2629 | 2629 | #null_fd = os.open(os.devnull, os.O_RDWR) |
|
|
2630 | 2630 | #os.dup2(null_fd, 1) |
|
|
2631 | 2631 | |
|
|
2632 | 2632 | for i in range(10,dataOut.NSHTS): #no point below 150 km |
|
|
2633 | 2633 | |
|
|
2634 | 2634 | #some definitions |
|
|
2635 | 2635 | iflag=0 # inicializado a cero? |
|
|
2636 | 2636 | wl = 3.0 |
|
|
2637 | 2637 | x=numpy.zeros(dataOut.DPL+dataOut.IBITS,order='F',dtype='float32') |
|
|
2638 | 2638 | y=numpy.zeros(dataOut.DPL+dataOut.IBITS,order='F',dtype='float32') |
|
|
2639 | 2639 | e=numpy.zeros(dataOut.DPL+dataOut.IBITS,order='F',dtype='float32') |
|
|
2640 | 2640 | eb=numpy.zeros(5,order='F',dtype='float32') |
|
|
2641 | 2641 | zero=numpy.zeros(1,order='F',dtype='float32') |
|
|
2642 | 2642 | depth=numpy.zeros(1,order='F',dtype='float32') |
|
|
2643 | 2643 | t1=numpy.zeros(1,order='F',dtype='float32') |
|
|
2644 | 2644 | t2=numpy.zeros(1,order='F',dtype='float32') |
|
|
2645 | 2645 | |
|
|
2646 | 2646 | if i>10 and l1>=0: |
|
|
2647 | 2647 | if l1==0: |
|
|
2648 | 2648 | l1=1 |
|
|
2649 | 2649 | |
|
|
2650 | 2650 | dataOut.cov=numpy.reshape(dataOut.cov,l1*l1) |
|
|
2651 | 2651 | dataOut.cov=numpy.resize(dataOut.cov,dataOut.DPL*dataOut.DPL) |
|
|
2652 | 2652 | dataOut.covinv=numpy.reshape(dataOut.covinv,l1*l1) |
|
|
2653 | 2653 | dataOut.covinv=numpy.resize(dataOut.covinv,dataOut.DPL*dataOut.DPL) |
|
|
2654 | 2654 | |
|
|
2655 | 2655 | for l in range(dataOut.DPL*dataOut.DPL): |
|
|
2656 | 2656 | dataOut.cov[l]=0.0 |
|
|
2657 | 2657 | acfm= (dataOut.rhor[i][0])**2 + (dataOut.rhoi[i][0])**2 |
|
|
2658 | 2658 | if acfm> 0.0: |
|
|
2659 | 2659 | cc=dataOut.rhor[i][0]/acfm |
|
|
2660 | 2660 | ss=dataOut.rhoi[i][0]/acfm |
|
|
2661 | 2661 | else: |
|
|
2662 | 2662 | cc=1. |
|
|
2663 | 2663 | ss=0. |
|
|
2664 | 2664 | # keep only uncontaminated data, don't pass zero lag to fitter |
|
|
2665 | 2665 | l1=0 |
|
|
2666 | 2666 | for l in range(0+1,dataOut.DPL): |
|
|
2667 | 2667 | if dataOut.igcej[i][l]==0 and dataOut.ibad[i][l]==0: |
|
|
2668 | 2668 | y[l1]=dataOut.rhor[i][l]*cc + dataOut.rhoi[i][l]*ss |
|
|
2669 | 2669 | x[l1]=dataOut.alag[l]*1.0e-3 |
|
|
2670 | 2670 | dataOut.sd[i][l]=dataOut.sd[i][l]/((acfm)**2)# important |
|
|
2671 | 2671 | e[l1]=dataOut.sd[i][l] #this is the variance, not the st. dev. |
|
|
2672 | 2672 | l1=l1+1 |
|
|
2673 | 2673 | |
|
|
2674 | 2674 | for l in range(l1*(l1+1)): |
|
|
2675 | 2675 | dataOut.cov[l]=0.0 |
|
|
2676 | 2676 | for l in range(l1): |
|
|
2677 | 2677 | dataOut.cov[l*(1+l1)]=e[l] |
|
|
2678 | 2678 | angle=dataOut.thb[i]*0.01745 |
|
|
2679 | 2679 | bm=dataOut.bfm[i] |
|
|
2680 | 2680 | dataOut.params[0]=1.0 #norm |
|
|
2681 | 2681 | dataOut.params[1]=1000.0 #te |
|
|
2682 | 2682 | dataOut.params[2]=800.0 #ti |
|
|
2683 | 2683 | dataOut.params[3]=0.00 #ph |
|
|
2684 | 2684 | dataOut.params[4]=0.00 #phe |
|
|
2685 | 2685 | |
|
|
2686 | 2686 | if l1!=0: |
|
|
2687 | 2687 | x=numpy.resize(x,l1) |
|
|
2688 | 2688 | y=numpy.resize(y,l1) |
|
|
2689 | 2689 | else: |
|
|
2690 | 2690 | x=numpy.resize(x,1) |
|
|
2691 | 2691 | y=numpy.resize(y,1) |
|
|
2692 | 2692 | |
|
|
2693 | 2693 | if True: #len(y)!=0: |
|
|
2694 | 2694 | with suppress_stdout_stderr(): |
|
|
2695 | 2695 | fitacf_guess.guess(y,x,zero,depth,t1,t2,len(y)) |
|
|
2696 | 2696 | t2=t1/t2 |
|
|
2697 | 2697 | |
|
|
2698 | 2698 | if (t1<5000.0 and t1> 600.0): |
|
|
2699 | 2699 | dataOut.params[1]=t1 |
|
|
2700 | 2700 | dataOut.params[2]=min(t2,t1) |
|
|
2701 | 2701 | dataOut.ifit[1]=dataOut.ifit[2]=1 |
|
|
2702 | 2702 | dataOut.ifit[0]=dataOut.ifit[3]=dataOut.ifit[4]=0 |
|
|
2703 | 2703 | |
|
|
2704 | 2704 | if dataOut.ut_Faraday<10.0 and dataOut.ut_Faraday>=0.5: |
|
|
2705 | 2705 | dataOut.ifit[2]=0 |
|
|
2706 | 2706 | |
|
|
2707 | 2707 | den=dataOut.ph2[i] |
|
|
2708 | 2708 | |
|
|
2709 | 2709 | if l1!=0: |
|
|
2710 | 2710 | dataOut.covinv=dataOut.covinv[0:l1*l1].reshape((l1,l1)) |
|
|
2711 | 2711 | dataOut.cov=dataOut.cov[0:l1*l1].reshape((l1,l1)) |
|
|
2712 | 2712 | e=numpy.resize(e,l1) |
|
|
2713 | 2713 | else: |
|
|
2714 | 2714 | dataOut.covinv=numpy.resize(dataOut.covinv,1) |
|
|
2715 | 2715 | dataOut.cov=numpy.resize(dataOut.cov,1) |
|
|
2716 | 2716 | e=numpy.resize(e,1) |
|
|
2717 | 2717 | |
|
|
2718 | 2718 | eb=numpy.resize(eb,10) |
|
|
2719 | 2719 | dataOut.ifit=numpy.resize(dataOut.ifit,10) |
|
|
2720 | 2720 | dataOut.covinv,e,dataOut.params,eb,dataOut.m=fitacf_fit_short.fit(wl,x,y,dataOut.cov,dataOut.covinv,e,dataOut.params,bm,angle,den,dataOut.range1[i],dataOut.year,dataOut.ifit,dataOut.m,l1) # |
|
|
2721 | 2721 | if dataOut.params[2]>dataOut.params[1]*1.05: |
|
|
2722 | 2722 | dataOut.ifit[2]=0 |
|
|
2723 | 2723 | dataOut.params[1]=dataOut.params[2]=t1 |
|
|
2724 | 2724 | dataOut.covinv,e,dataOut.params,eb,dataOut.m=fitacf_fit_short.fit(wl,x,y,dataOut.cov,dataOut.covinv,e,dataOut.params,bm,angle,den,dataOut.range1[i],dataOut.year,dataOut.ifit,dataOut.m,l1) # |
|
|
2725 | 2725 | if (dataOut.ifit[2]==0): |
|
|
2726 | 2726 | dataOut.params[2]=dataOut.params[1] |
|
|
2727 | 2727 | if (dataOut.ifit[3]==0 and iflag==0): |
|
|
2728 | 2728 | dataOut.params[3]=0.0 |
|
|
2729 | 2729 | if (dataOut.ifit[4]==0): |
|
|
2730 | 2730 | dataOut.params[4]=0.0 |
|
|
2731 | 2731 | dataOut.te2[i]=dataOut.params[1] |
|
|
2732 | 2732 | dataOut.ti2[i]=dataOut.params[2] |
|
|
2733 | 2733 | dataOut.ete2[i]=eb[1] |
|
|
2734 | 2734 | dataOut.eti2[i]=eb[2] |
|
|
2735 | 2735 | |
|
|
2736 | 2736 | if dataOut.eti2[i]==0: |
|
|
2737 | 2737 | dataOut.eti2[i]=dataOut.ete2[i] |
|
|
2738 | 2738 | |
|
|
2739 | 2739 | dataOut.phy2[i]=dataOut.params[3] |
|
|
2740 | 2740 | dataOut.ephy2[i]=eb[3] |
|
|
2741 | 2741 | if(iflag==1): |
|
|
2742 | 2742 | dataOut.ephy2[i]=0.0 |
|
|
2743 | 2743 | |
|
|
2744 | 2744 | if (dataOut.m<=3 and dataOut.m!= 0 and dataOut.te2[i]>400.0): |
|
|
2745 | 2745 | dataOut.info2[i]=1 |
|
|
2746 | 2746 | else: |
|
|
2747 | 2747 | dataOut.info2[i]=0 |
|
|
2748 | 2748 | |
|
|
2749 | 2749 | def run(self,dataOut,IBITS=16): |
|
|
2750 | 2750 | |
|
|
2751 | 2751 | dataOut.IBITS = IBITS |
|
|
2752 | 2752 | self.Estimation(dataOut) |
|
|
2753 | 2753 | |
|
|
2754 | 2754 | return dataOut |
|
|
2755 | 2755 | |
|
|
2756 | 2756 | |
|
|
2757 | 2757 | class DenCorrection(NormalizeDPPowerRoberto_V2): |
|
|
2758 | 2758 | ''' |
|
|
2759 | 2759 | Written by R. Flores |
|
|
2760 | 2760 | ''' |
|
|
2761 | 2761 | def __init__(self, **kwargs): |
|
|
2762 | 2762 | |
|
|
2763 | 2763 | Operation.__init__(self, **kwargs) |
|
|
2764 | 2764 | self.aux = 0 |
|
|
2765 | 2765 | self.csv_flag = 1 |
|
|
2766 | 2766 | |
|
|
2767 | 2767 | def gaussian(self, x, a, b, c): |
|
|
2768 | 2768 | val = a * numpy.exp(-(x - b)**2 / (2*c**2)) |
|
|
2769 | 2769 | return val |
|
|
2770 | 2770 | |
|
|
2771 | 2771 | def TeTiEstimation(self,dataOut): |
|
|
2772 | 2772 | |
|
|
2773 | 2773 | #dataOut.DPL = 2 #for MST |
|
|
2774 | 2774 | y=numpy.zeros(dataOut.DPL,order='F',dtype='float32') |
|
|
2775 | 2775 | |
|
|
2776 | 2776 | for i in range(dataOut.NSHTS): |
|
|
2777 | 2777 | y[0]=y[1]=dataOut.range1[i] |
|
|
2778 | 2778 | |
|
|
2779 | 2779 | y = y.astype(dtype='float64',order='F') |
|
|
2780 | 2780 | three=int(3) |
|
|
2781 | 2781 | wl = 3.0 |
|
|
2782 | 2782 | tion=numpy.zeros(three,order='F',dtype='float32') |
|
|
2783 | 2783 | fion=numpy.zeros(three,order='F',dtype='float32') |
|
|
2784 | 2784 | nui=numpy.zeros(three,order='F',dtype='float32') |
|
|
2785 | 2785 | wion=numpy.zeros(three,order='F',dtype='int32') |
|
|
2786 | 2786 | bline=0.0 |
|
|
2787 | 2787 | #bline=numpy.zeros(1,order='F',dtype='float32') |
|
|
2788 | 2788 | my_aux = numpy.ones(dataOut.NSHTS,order='F',dtype='float32') |
|
|
2789 | 2789 | acf_Temps = numpy.ones(dataOut.NSHTS,order='F',dtype='float32')*numpy.nan |
|
|
2790 | 2790 | acf_no_Temps = numpy.ones(dataOut.NSHTS,order='F',dtype='float32')*numpy.nan |
|
|
2791 | 2791 | |
|
|
2792 | 2792 | from scipy import signal |
|
|
2793 | 2793 | |
|
|
2794 | 2794 | def func(params): |
|
|
2795 | 2795 | return (ratio2-self.gaussian(dataOut.heightList[:dataOut.NSHTS],params[0],params[1],params[2])) |
|
|
2796 | 2796 | |
|
|
2797 | 2797 | dataOut.info2[0] = 1 |
|
|
2798 | 2798 | for i in range(dataOut.NSHTS): |
|
|
2799 | 2799 | if dataOut.info2[i]==1: |
|
|
2800 | 2800 | angle=dataOut.thb[i]*0.01745 |
|
|
2801 | 2801 | nue=nui[0]=nui[1]=nui[2]=0.0#nui[3]=0.0 |
|
|
2802 | 2802 | wion[0]=16 #O |
|
|
2803 | 2803 | wion[1]=1 #H |
|
|
2804 | 2804 | wion[2]=4 #He |
|
|
2805 | 2805 | tion[0]=tion[1]=tion[2]=dataOut.ti2[i] |
|
|
2806 | 2806 | #tion[0]=tion[1]=tion[2]=ti2_smooth[i] |
|
|
2807 | 2807 | fion[0]=1.0-dataOut.phy2[i] #1 |
|
|
2808 | 2808 | fion[1]=dataOut.phy2[i] #0 |
|
|
2809 | 2809 | fion[2]=0.0 #0 |
|
|
2810 | 2810 | for j in range(dataOut.DPL): |
|
|
2811 | 2811 | tau=dataOut.alag[j]*1.0e-3 |
|
|
2812 | 2812 | with suppress_stdout_stderr():#The smoothness in range of "y" depends on the smoothness of the input parameters |
|
|
2813 | 2813 | y[j]=fitacf_acf2.acf2(wl,tau,dataOut.te2[i],tion,fion,nue,nui,wion,angle,dataOut.ph2[i],dataOut.bfm[i],y[j],three) |
|
|
2814 | 2814 | |
|
|
2815 | 2815 | if dataOut.ut_Faraday>11.0 and dataOut.range1[i]>150.0 and dataOut.range1[i]<300.0: |
|
|
2816 |
|
|
|
|
2816 | tau=0.0 | |
|
|
2817 | 2817 | with suppress_stdout_stderr(): |
|
|
2818 | 2818 | bline=fitacf_acf2.acf2(wl,tau,tion,tion,fion,nue,nui,wion,angle,dataOut.ph2[i],dataOut.bfm[i],bline,three) |
|
|
2819 | 2819 | |
|
|
2820 | 2820 | cf=min(1.2,max(1.0,bline/y[0])) #FACTOR DE EFICIENCIA |
|
|
2821 | 2821 | my_aux[i] = cf |
|
|
2822 | 2822 | acf_Temps[i] = y[0] |
|
|
2823 | 2823 | acf_no_Temps[i] = bline |
|
|
2824 | 2824 | for j in range(1,dataOut.DPL): |
|
|
2825 | 2825 | y[j]=min(max((y[j]/y[0]),-1.0),1.0)*dataOut.DH+dataOut.range1[i] |
|
|
2826 | 2826 | y[0]=dataOut.range1[i]+dataOut.DH |
|
|
2827 | 2827 | |
|
|
2828 | 2828 | |
|
|
2829 | 2829 | ratio = my_aux-1 |
|
|
2830 | 2830 | def lsq_func(params): |
|
|
2831 | 2831 | return (ratio-self.gaussian(dataOut.heightList[:dataOut.NSHTS],params[0],params[1],params[2])) |
|
|
2832 | 2832 | |
|
|
2833 | 2833 | x0_value = numpy.array([max(ratio),250,20]) |
|
|
2834 | 2834 | |
|
|
2835 | 2835 | popt = least_squares(lsq_func,x0=x0_value,verbose=0) |
|
|
2836 | 2836 | |
|
|
2837 | 2837 | A = popt.x[0]; B = popt.x[1]; C = popt.x[2] |
|
|
2838 | 2838 | |
|
|
2839 | 2839 | aux = self.gaussian(dataOut.heightList[:dataOut.NSHTS], A, B, C) + 1 #ratio + 1 |
|
|
2840 | 2840 | |
|
|
2841 | 2841 | dataOut.ph2[:dataOut.NSHTS]*=aux |
|
|
2842 | 2842 | dataOut.sdp2[:dataOut.NSHTS]*=aux |
|
|
2843 | ||
|
|
2843 | ||
|
|
2844 | 2844 | def run(self,dataOut,savecf=0): |
|
|
2845 | 2845 | if gmtime(dataOut.utctime).tm_hour < 24. and gmtime(dataOut.utctime).tm_hour >= 11.: |
|
|
2846 | 2846 | if hasattr(dataOut, 'flagSpreadF') and dataOut.flagSpreadF: |
|
|
2847 | 2847 | pass |
|
|
2848 | 2848 | else: |
|
|
2849 | 2849 | self.TeTiEstimation(dataOut) |
|
|
2850 | 2850 | dataOut.flagTeTiCorrection = True |
|
|
2851 | 2851 | self.normalize(dataOut) |
|
|
2852 | 2852 | |
|
|
2853 | 2853 | return dataOut |
|
|
2854 | 2854 | |
|
|
2855 | 2855 | |
|
|
2856 | 2856 | |
|
|
2857 | 2857 | class DataSaveCleaner(Operation): |
|
|
2858 | 2858 | ''' |
|
|
2859 | 2859 | Written by R. Flores |
|
|
2860 | 2860 | ''' |
|
|
2861 | 2861 | def __init__(self, **kwargs): |
|
|
2862 | 2862 | |
|
|
2863 | 2863 | Operation.__init__(self, **kwargs) |
|
|
2864 | 2864 | self.csv_flag = 1 |
|
|
2865 | 2865 | |
|
|
2866 | 2866 | def run(self,dataOut,savecfclean=0): |
|
|
2867 | 2867 | dataOut.DensityFinal=numpy.zeros((1,dataOut.NDP)) |
|
|
2868 | 2868 | dataOut.dphiFinal=numpy.zeros((1,dataOut.NDP)) |
|
|
2869 | 2869 | dataOut.EDensityFinal=numpy.zeros((1,dataOut.NDP)) |
|
|
2870 | 2870 | dataOut.ElecTempFinal=numpy.zeros((1,dataOut.NDP)) |
|
|
2871 | 2871 | dataOut.EElecTempFinal=numpy.zeros((1,dataOut.NDP)) |
|
|
2872 | 2872 | dataOut.IonTempFinal=numpy.zeros((1,dataOut.NDP)) |
|
|
2873 | 2873 | dataOut.EIonTempFinal=numpy.zeros((1,dataOut.NDP)) |
|
|
2874 | 2874 | dataOut.PhyFinal=numpy.zeros((1,dataOut.NDP)) |
|
|
2875 | 2875 | dataOut.EPhyFinal=numpy.zeros((1,dataOut.NDP)) |
|
|
2876 | 2876 | |
|
|
2877 | 2877 | dataOut.DensityFinal[0]=numpy.copy(dataOut.ph2) |
|
|
2878 | 2878 | dataOut.dphiFinal[0]=numpy.copy(dataOut.dphi) |
|
|
2879 | 2879 | dataOut.EDensityFinal[0]=numpy.copy(dataOut.sdp2) |
|
|
2880 | 2880 | dataOut.ElecTempFinal[0,:dataOut.NSHTS]=numpy.copy(dataOut.te2) |
|
|
2881 | 2881 | dataOut.EElecTempFinal[0,:dataOut.NSHTS]=numpy.copy(dataOut.ete2) |
|
|
2882 | 2882 | dataOut.IonTempFinal[0,:dataOut.NSHTS]=numpy.copy(dataOut.ti2) |
|
|
2883 | 2883 | dataOut.EIonTempFinal[0,:dataOut.NSHTS]=numpy.copy(dataOut.eti2) |
|
|
2884 | 2884 | dataOut.PhyFinal[0,:dataOut.NSHTS]=numpy.copy(dataOut.phy2) |
|
|
2885 | 2885 | dataOut.EPhyFinal[0,:dataOut.NSHTS]=numpy.copy(dataOut.ephy2) |
|
|
2886 | 2886 | |
|
|
2887 | 2887 | missing=numpy.nan |
|
|
2888 | 2888 | temp_min=100.0 |
|
|
2889 | 2889 | temp_max=3000.0#6000.0e |
|
|
2890 | 2890 | den_err_percent = 100*dataOut.EDensityFinal[0]/dataOut.DensityFinal[0] |
|
|
2891 | 2891 | max_den_err_per = 35#30 #Densidades con error mayor al 35% se setean en NaN |
|
|
2892 | 2892 | for i in range(dataOut.NSHTS): |
|
|
2893 | 2893 | |
|
|
2894 | 2894 | if den_err_percent[i] >= max_den_err_per: |
|
|
2895 | 2895 | dataOut.DensityFinal[0,i]=dataOut.EDensityFinal[0,i]=missing |
|
|
2896 | 2896 | if i > 40: #Alturas mayores que 600 |
|
|
2897 | 2897 | dataOut.DensityFinal[0,i:]=dataOut.EDensityFinal[0,i:]=missing |
|
|
2898 | 2898 | |
|
|
2899 | 2899 | if dataOut.info2[i]!=1: |
|
|
2900 | 2900 | dataOut.ElecTempFinal[0,i]=dataOut.EElecTempFinal[0,i]=dataOut.IonTempFinal[0,i]=dataOut.EIonTempFinal[0,i]=missing |
|
|
2901 | 2901 | |
|
|
2902 | 2902 | if dataOut.ElecTempFinal[0,i]<=temp_min or dataOut.ElecTempFinal[0,i]>temp_max or dataOut.EElecTempFinal[0,i]>temp_max: |
|
|
2903 | 2903 | |
|
|
2904 | 2904 | dataOut.ElecTempFinal[0,i]=dataOut.EElecTempFinal[0,i]=missing |
|
|
2905 | 2905 | |
|
|
2906 | 2906 | if dataOut.IonTempFinal[0,i]<=temp_min or dataOut.IonTempFinal[0,i]>temp_max or dataOut.EIonTempFinal[0,i]>temp_max: |
|
|
2907 | 2907 | dataOut.IonTempFinal[0,i]=dataOut.EIonTempFinal[0,i]=missing |
|
|
2908 | 2908 | |
|
|
2909 | 2909 | if dataOut.lags_to_plot[i,:][~numpy.isnan(dataOut.lags_to_plot[i,:])].shape[0]<6: |
|
|
2910 | 2910 | dataOut.ElecTempFinal[0,i]=dataOut.EElecTempFinal[0,i]=dataOut.IonTempFinal[0,i]=dataOut.EIonTempFinal[0,i]=missing |
|
|
2911 | 2911 | |
|
|
2912 | 2912 | if dataOut.ut_Faraday>4 and dataOut.ut_Faraday<11: |
|
|
2913 | 2913 | if numpy.nanmax(dataOut.acfs_error_to_plot[i,:])>=10: |
|
|
2914 | 2914 | dataOut.ElecTempFinal[0,i]=dataOut.EElecTempFinal[0,i]=dataOut.IonTempFinal[0,i]=dataOut.EIonTempFinal[0,i]=missing |
|
|
2915 | 2915 | |
|
|
2916 | 2916 | if dataOut.EPhyFinal[0,i]<0.0 or dataOut.EPhyFinal[0,i]>1.0: |
|
|
2917 | 2917 | dataOut.PhyFinal[0,i]=dataOut.EPhyFinal[0,i]=missing |
|
|
2918 | 2918 | |
|
|
2919 | 2919 | if dataOut.EDensityFinal[0,i]>0.0 and dataOut.DensityFinal[0,i]>0.0 and dataOut.DensityFinal[0,i]<9.9e6: |
|
|
2920 | 2920 | dataOut.EDensityFinal[0,i]=max(dataOut.EDensityFinal[0,i],1000.0) |
|
|
2921 | 2921 | else: |
|
|
2922 | 2922 | dataOut.DensityFinal[0,i]=dataOut.EDensityFinal[0,i]=missing |
|
|
2923 | 2923 | |
|
|
2924 | 2924 | if dataOut.PhyFinal[0,i]==0 or dataOut.PhyFinal[0,i]>0.4: |
|
|
2925 | 2925 | dataOut.PhyFinal[0,i]=dataOut.EPhyFinal[0,i]=missing |
|
|
2926 | 2926 | if dataOut.ElecTempFinal[0,i]==dataOut.IonTempFinal[0,i]: |
|
|
2927 | 2927 | dataOut.EElecTempFinal[0,i]=dataOut.EIonTempFinal[0,i] |
|
|
2928 | 2928 | if numpy.isnan(dataOut.ElecTempFinal[0,i]): |
|
|
2929 | 2929 | dataOut.EElecTempFinal[0,i]=missing |
|
|
2930 | 2930 | if numpy.isnan(dataOut.IonTempFinal[0,i]): |
|
|
2931 | 2931 | dataOut.EIonTempFinal[0,i]=missing |
|
|
2932 | 2932 | if numpy.isnan(dataOut.ElecTempFinal[0,i]) or numpy.isnan(dataOut.EElecTempFinal[0,i]): |
|
|
2933 | 2933 | dataOut.ElecTempFinal[0,i]=dataOut.EElecTempFinal[0,i]=dataOut.IonTempFinal[0,i]=dataOut.EIonTempFinal[0,i]=missing |
|
|
2934 | 2934 | |
|
|
2935 | 2935 | for i in range(12,dataOut.NSHTS-1): |
|
|
2936 | 2936 | |
|
|
2937 | 2937 | if numpy.isnan(dataOut.ElecTempFinal[0,i-1]) and numpy.isnan(dataOut.ElecTempFinal[0,i+1]): |
|
|
2938 | 2938 | dataOut.ElecTempFinal[0,i]=dataOut.EElecTempFinal[0,i]=missing |
|
|
2939 | 2939 | |
|
|
2940 | 2940 | if numpy.isnan(dataOut.IonTempFinal[0,i-1]) and numpy.isnan(dataOut.IonTempFinal[0,i+1]): |
|
|
2941 | 2941 | dataOut.IonTempFinal[0,i]=dataOut.EIonTempFinal[0,i]=missing |
|
|
2942 | 2942 | |
|
|
2943 | 2943 | if dataOut.ut_Faraday>4 and dataOut.ut_Faraday<11: |
|
|
2944 | 2944 | |
|
|
2945 | 2945 | if numpy.isnan(dataOut.ElecTempFinal[0,i-1]) and numpy.isnan(dataOut.ElecTempFinal[0,i-2]) and numpy.isnan(dataOut.ElecTempFinal[0,i+2]) and numpy.isnan(dataOut.ElecTempFinal[0,i+3]): #and numpy.isnan(dataOut.ElecTempFinal[0,i-5]): |
|
|
2946 | 2946 | |
|
|
2947 | 2947 | dataOut.ElecTempFinal[0,i]=dataOut.EElecTempFinal[0,i]=missing |
|
|
2948 | 2948 | if numpy.isnan(dataOut.IonTempFinal[0,i-1]) and numpy.isnan(dataOut.IonTempFinal[0,i-2]) and numpy.isnan(dataOut.IonTempFinal[0,i+2]) and numpy.isnan(dataOut.IonTempFinal[0,i+3]): #and numpy.isnan(dataOut.IonTempFinal[0,i-5]): |
|
|
2949 | 2949 | |
|
|
2950 | 2950 | dataOut.IonTempFinal[0,i]=dataOut.EIonTempFinal[0,i]=missing |
|
|
2951 | 2951 | |
|
|
2952 | 2952 | if i>25: |
|
|
2953 | 2953 | if numpy.isnan(dataOut.ElecTempFinal[0,i-1]) and numpy.isnan(dataOut.ElecTempFinal[0,i-2]) and numpy.isnan(dataOut.ElecTempFinal[0,i-3]) and numpy.isnan(dataOut.ElecTempFinal[0,i-4]): #and numpy.isnan(dataOut.ElecTempFinal[0,i-5]): |
|
|
2954 | 2954 | dataOut.ElecTempFinal[0,i]=dataOut.EElecTempFinal[0,i]=missing |
|
|
2955 | 2955 | if numpy.isnan(dataOut.IonTempFinal[0,i-1]) and numpy.isnan(dataOut.IonTempFinal[0,i-2]) and numpy.isnan(dataOut.IonTempFinal[0,i-3]) and numpy.isnan(dataOut.IonTempFinal[0,i-4]): #and numpy.isnan(dataOut.IonTempFinal[0,i-5]): |
|
|
2956 | 2956 | |
|
|
2957 | 2957 | dataOut.IonTempFinal[0,i]=dataOut.EIonTempFinal[0,i]=missing |
|
|
2958 | 2958 | |
|
|
2959 | 2959 | if numpy.isnan(dataOut.ElecTempFinal[0,i]) or numpy.isnan(dataOut.EElecTempFinal[0,i]): |
|
|
2960 | 2960 | |
|
|
2961 | 2961 | dataOut.ElecTempFinal[0,i]=dataOut.EElecTempFinal[0,i]=dataOut.IonTempFinal[0,i]=dataOut.EIonTempFinal[0,i]=missing |
|
|
2962 | 2962 | |
|
|
2963 | 2963 | for i in range(12,dataOut.NSHTS-1): |
|
|
2964 | 2964 | |
|
|
2965 | 2965 | if numpy.isnan(dataOut.ElecTempFinal[0,i-1]) and numpy.isnan(dataOut.ElecTempFinal[0,i+1]): |
|
|
2966 | 2966 | dataOut.ElecTempFinal[0,i]=dataOut.EElecTempFinal[0,i]=missing |
|
|
2967 | 2967 | |
|
|
2968 | 2968 | if numpy.isnan(dataOut.IonTempFinal[0,i-1]) and numpy.isnan(dataOut.IonTempFinal[0,i+1]): |
|
|
2969 | 2969 | dataOut.IonTempFinal[0,i]=dataOut.EIonTempFinal[0,i]=missing |
|
|
2970 | 2970 | |
|
|
2971 | 2971 | if numpy.isnan(dataOut.DensityFinal[0,i-1]) and numpy.isnan(dataOut.DensityFinal[0,i+1]): ##NEW |
|
|
2972 | 2972 | dataOut.DensityFinal[0,i]=dataOut.EDensityFinal[0,i]=missing ##NEW |
|
|
2973 | 2973 | |
|
|
2974 | 2974 | if numpy.isnan(dataOut.ElecTempFinal[0,i]) or numpy.isnan(dataOut.EElecTempFinal[0,i]): |
|
|
2975 | 2975 | |
|
|
2976 | 2976 | dataOut.ElecTempFinal[0,i]=dataOut.EElecTempFinal[0,i]=dataOut.IonTempFinal[0,i]=dataOut.EIonTempFinal[0,i]=missing |
|
|
2977 | 2977 | |
|
|
2978 | 2978 | if numpy.count_nonzero(~numpy.isnan(dataOut.ElecTempFinal[0,12:50]))<5: |
|
|
2979 | 2979 | dataOut.ElecTempFinal[0,:]=dataOut.EElecTempFinal[0,:]=missing |
|
|
2980 | 2980 | if numpy.count_nonzero(~numpy.isnan(dataOut.IonTempFinal[0,12:50]))<5: |
|
|
2981 | 2981 | dataOut.IonTempFinal[0,:]=dataOut.EIonTempFinal[0,:]=missing |
|
|
2982 | 2982 | |
|
|
2983 | 2983 | |
|
|
2984 | 2984 | if numpy.count_nonzero(~numpy.isnan(dataOut.DensityFinal[0,12:50]))<=5: |
|
|
2985 | 2985 | dataOut.DensityFinal[0,:]=dataOut.EDensityFinal[0,:]=missing |
|
|
2986 | 2986 | |
|
|
2987 | 2987 | dataOut.DensityFinal[0,dataOut.NSHTS:]=missing |
|
|
2988 | 2988 | dataOut.EDensityFinal[0,dataOut.NSHTS:]=missing |
|
|
2989 | 2989 | dataOut.ElecTempFinal[0,dataOut.NSHTS:]=missing |
|
|
2990 | 2990 | dataOut.EElecTempFinal[0,dataOut.NSHTS:]=missing |
|
|
2991 | 2991 | dataOut.IonTempFinal[0,dataOut.NSHTS:]=missing |
|
|
2992 | 2992 | dataOut.EIonTempFinal[0,dataOut.NSHTS:]=missing |
|
|
2993 | 2993 | dataOut.PhyFinal[0,dataOut.NSHTS:]=missing |
|
|
2994 | 2994 | dataOut.EPhyFinal[0,dataOut.NSHTS:]=missing |
|
|
2995 | 2995 | |
|
|
2996 | 2996 | if gmtime(dataOut.utctime).tm_hour >= 13. and gmtime(dataOut.utctime).tm_hour < 21.: #07-16 LT |
|
|
2997 | 2997 | dataOut.DensityFinal[0,:13]=missing |
|
|
2998 | 2998 | dataOut.EDensityFinal[0,:13]=missing |
|
|
2999 | 2999 | dataOut.ElecTempFinal[0,:13]=missing |
|
|
3000 | 3000 | dataOut.EElecTempFinal[0,:13]=missing |
|
|
3001 | 3001 | dataOut.IonTempFinal[0,:13]=missing |
|
|
3002 | 3002 | dataOut.EIonTempFinal[0,:13]=missing |
|
|
3003 | 3003 | dataOut.PhyFinal[0,:13]=missing |
|
|
3004 | 3004 | dataOut.EPhyFinal[0,:13]=missing |
|
|
3005 | 3005 | |
|
|
3006 | 3006 | else: |
|
|
3007 | 3007 | if gmtime(dataOut.utctime).tm_hour == 9 and gmtime(dataOut.utctime).tm_min == 20: |
|
|
3008 | 3008 | pass |
|
|
3009 | 3009 | else: |
|
|
3010 | 3010 | dataOut.DensityFinal[0,:dataOut.min_id_eej+1]=missing |
|
|
3011 | 3011 | dataOut.EDensityFinal[0,:dataOut.min_id_eej+1]=missing |
|
|
3012 | 3012 | dataOut.ElecTempFinal[0,:dataOut.min_id_eej+1]=missing |
|
|
3013 | 3013 | dataOut.EElecTempFinal[0,:dataOut.min_id_eej+1]=missing |
|
|
3014 | 3014 | dataOut.IonTempFinal[0,:dataOut.min_id_eej+1]=missing |
|
|
3015 | 3015 | dataOut.EIonTempFinal[0,:dataOut.min_id_eej+1]=missing |
|
|
3016 | 3016 | dataOut.PhyFinal[0,:dataOut.min_id_eej+1]=missing |
|
|
3017 | 3017 | dataOut.EPhyFinal[0,:dataOut.min_id_eej+1]=missing |
|
|
3018 | 3018 | |
|
|
3019 | 3019 | dataOut.flagNoData = numpy.all(numpy.isnan(dataOut.DensityFinal)) #Si todos los valores son NaN no se prosigue |
|
|
3020 | 3020 | |
|
|
3021 | 3021 | if not dataOut.flagNoData: |
|
|
3022 | 3022 | if savecfclean: |
|
|
3023 | 3023 | try: |
|
|
3024 | 3024 | import pandas as pd |
|
|
3025 | 3025 | if self.csv_flag: |
|
|
3026 | 3026 | if not os.path.exists("./cfclean"): |
|
|
3027 | 3027 | os.makedirs("./cfclean") |
|
|
3028 | 3028 | self.doy_csv = datetime.datetime.fromtimestamp(dataOut.utctime).strftime('%j') |
|
|
3029 | 3029 | self.year_csv = datetime.datetime.fromtimestamp(dataOut.utctime).strftime('%Y') |
|
|
3030 | 3030 | file = open("./cfclean/cfclean{0}{1}.csv".format(self.year_csv,self.doy_csv), "x") |
|
|
3031 | 3031 | f = csv.writer(file) |
|
|
3032 | 3032 | f.writerow(numpy.array(["timestamp",'cf'])) |
|
|
3033 | 3033 | self.csv_flag = 0 |
|
|
3034 | 3034 | print("Creating cf clean File") |
|
|
3035 | 3035 | print("Writing cf clean File") |
|
|
3036 | 3036 | except: |
|
|
3037 | 3037 | file = open("./cfclean/cfclean{0}{1}.csv".format(self.year_csv,self.doy_csv), "a") |
|
|
3038 | 3038 | f = csv.writer(file) |
|
|
3039 | 3039 | print("Writing cf clean File") |
|
|
3040 | 3040 | cf = numpy.array([dataOut.utctime,dataOut.cf]) |
|
|
3041 | 3041 | f.writerow(cf) |
|
|
3042 | 3042 | file.close() |
|
|
3043 | 3043 | |
|
|
3044 | 3044 | dataOut.flagNoData = False #Descomentar solo para ploteo #Comentar para MADWriter |
|
|
3045 | 3045 | |
|
|
3046 | 3046 | dataOut.DensityFinal *= 1.e6 #Convert units to m^⁻3 |
|
|
3047 | 3047 | dataOut.EDensityFinal *= 1.e6 #Convert units to m^⁻3 |
|
|
3048 | 3048 | |
|
|
3049 | 3049 | return dataOut |
|
|
3050 | 3050 | |
|
|
3051 | 3051 | |
|
|
3052 | 3052 | class DataSaveCleanerHP(Operation): |
|
|
3053 | 3053 | ''' |
|
|
3054 | 3054 | Written by R. Flores |
|
|
3055 | 3055 | ''' |
|
|
3056 | 3056 | def __init__(self, **kwargs): |
|
|
3057 | 3057 | |
|
|
3058 | 3058 | Operation.__init__(self, **kwargs) |
|
|
3059 | 3059 | |
|
|
3060 | 3060 | def run(self,dataOut): |
|
|
3061 | 3061 | |
|
|
3062 | 3062 | dataOut.Density_DP=numpy.zeros(dataOut.cut) |
|
|
3063 | 3063 | dataOut.EDensity_DP=numpy.zeros(dataOut.cut) |
|
|
3064 | 3064 | dataOut.ElecTemp_DP=numpy.zeros(dataOut.cut) |
|
|
3065 | 3065 | dataOut.EElecTemp_DP=numpy.zeros(dataOut.cut) |
|
|
3066 | 3066 | dataOut.IonTemp_DP=numpy.zeros(dataOut.cut) |
|
|
3067 | 3067 | dataOut.EIonTemp_DP=numpy.zeros(dataOut.cut) |
|
|
3068 | 3068 | dataOut.Phy_DP=numpy.zeros(dataOut.cut) |
|
|
3069 | 3069 | dataOut.EPhy_DP=numpy.zeros(dataOut.cut) |
|
|
3070 | 3070 | dataOut.Phe_DP=numpy.empty(dataOut.cut) |
|
|
3071 | 3071 | dataOut.EPhe_DP=numpy.empty(dataOut.cut) |
|
|
3072 | 3072 | |
|
|
3073 | 3073 | dataOut.Density_DP[:]=numpy.copy(dataOut.ph2[:dataOut.cut]) |
|
|
3074 | 3074 | dataOut.EDensity_DP[:]=numpy.copy(dataOut.sdp2[:dataOut.cut]) |
|
|
3075 | 3075 | dataOut.ElecTemp_DP[:]=numpy.copy(dataOut.te2[:dataOut.cut]) |
|
|
3076 | 3076 | dataOut.EElecTemp_DP[:]=numpy.copy(dataOut.ete2[:dataOut.cut]) |
|
|
3077 | 3077 | dataOut.IonTemp_DP[:]=numpy.copy(dataOut.ti2[:dataOut.cut]) |
|
|
3078 | 3078 | dataOut.EIonTemp_DP[:]=numpy.copy(dataOut.eti2[:dataOut.cut]) |
|
|
3079 | 3079 | dataOut.Phy_DP[:]=numpy.copy(dataOut.phy2[:dataOut.cut]) |
|
|
3080 | 3080 | dataOut.EPhy_DP[:]=numpy.copy(dataOut.ephy2[:dataOut.cut]) |
|
|
3081 | 3081 | dataOut.Phe_DP[:]=numpy.nan |
|
|
3082 | 3082 | dataOut.EPhe_DP[:]=numpy.nan |
|
|
3083 | 3083 | |
|
|
3084 | 3084 | missing=numpy.nan |
|
|
3085 | 3085 | temp_min=100.0 |
|
|
3086 | 3086 | temp_max_dp=3000.0 |
|
|
3087 | 3087 | |
|
|
3088 | 3088 | for i in range(dataOut.cut): |
|
|
3089 | 3089 | if dataOut.info2[i]!=1: |
|
|
3090 | 3090 | dataOut.ElecTemp_DP[i]=dataOut.EElecTemp_DP[i]=dataOut.IonTemp_DP[i]=dataOut.EIonTemp_DP[i]=missing |
|
|
3091 | 3091 | |
|
|
3092 | 3092 | if dataOut.ElecTemp_DP[i]<=temp_min or dataOut.ElecTemp_DP[i]>temp_max_dp or dataOut.EElecTemp_DP[i]>temp_max_dp: |
|
|
3093 | 3093 | |
|
|
3094 | 3094 | dataOut.ElecTemp_DP[i]=dataOut.EElecTemp_DP[i]=missing |
|
|
3095 | 3095 | |
|
|
3096 | 3096 | if dataOut.IonTemp_DP[i]<=temp_min or dataOut.IonTemp_DP[i]>temp_max_dp or dataOut.EIonTemp_DP[i]>temp_max_dp: |
|
|
3097 | 3097 | dataOut.IonTemp_DP[i]=dataOut.EIonTemp_DP[i]=missing |
|
|
3098 | 3098 | |
|
|
3099 | 3099 | ####################################################################################### CHECK THIS |
|
|
3100 | 3100 | if dataOut.lags_to_plot[i,:][~numpy.isnan(dataOut.lags_to_plot[i,:])].shape[0]<6: |
|
|
3101 | 3101 | dataOut.ElecTemp_DP[i]=dataOut.EElecTemp_DP[i]=dataOut.IonTemp_DP[i]=dataOut.EIonTemp_DP[i]=missing |
|
|
3102 | 3102 | |
|
|
3103 | 3103 | if dataOut.ut_Faraday>4 and dataOut.ut_Faraday<11: |
|
|
3104 | 3104 | if numpy.nanmax(dataOut.acfs_error_to_plot[i,:])>=10: |
|
|
3105 | 3105 | dataOut.ElecTemp_DP[i]=dataOut.EElecTemp_DP[i]=dataOut.IonTemp_DP[i]=dataOut.EIonTemp_DP[i]=missing |
|
|
3106 | 3106 | ####################################################################################### |
|
|
3107 | 3107 | |
|
|
3108 | 3108 | if dataOut.EPhy_DP[i]<0.0 or dataOut.EPhy_DP[i]>1.0: |
|
|
3109 | 3109 | dataOut.Phy_DP[i]=dataOut.EPhy_DP[i]=missing |
|
|
3110 | 3110 | if dataOut.EDensity_DP[i]>0.0 and dataOut.Density_DP[i]>0.0 and dataOut.Density_DP[i]<9.9e6: |
|
|
3111 | 3111 | dataOut.EDensity_DP[i]=max(dataOut.EDensity_DP[i],1000.0) |
|
|
3112 | 3112 | else: |
|
|
3113 | 3113 | dataOut.Density_DP[i]=dataOut.EDensity_DP[i]=missing |
|
|
3114 | 3114 | if dataOut.Phy_DP[i]==0 or dataOut.Phy_DP[i]>0.4: |
|
|
3115 | 3115 | dataOut.Phy_DP[i]=dataOut.EPhy_DP[i]=missing |
|
|
3116 | 3116 | if dataOut.ElecTemp_DP[i]==dataOut.IonTemp_DP[i]: |
|
|
3117 | 3117 | dataOut.EElecTemp_DP[i]=dataOut.EIonTemp_DP[i] |
|
|
3118 | 3118 | if numpy.isnan(dataOut.ElecTemp_DP[i]): |
|
|
3119 | 3119 | dataOut.EElecTemp_DP[i]=missing |
|
|
3120 | 3120 | if numpy.isnan(dataOut.IonTemp_DP[i]): |
|
|
3121 | 3121 | dataOut.EIonTemp_DP[i]=missing |
|
|
3122 | 3122 | if numpy.isnan(dataOut.ElecTemp_DP[i]) or numpy.isnan(dataOut.EElecTemp_DP[i]): |
|
|
3123 | 3123 | dataOut.ElecTemp_DP[i]=dataOut.EElecTemp_DP[i]=dataOut.IonTemp_DP[i]=dataOut.EIonTemp_DP[i]=missing |
|
|
3124 | 3124 | |
|
|
3125 | 3125 | |
|
|
3126 | 3126 | |
|
|
3127 | 3127 | dataOut.Density_LP=numpy.zeros(dataOut.NACF-dataOut.cut) |
|
|
3128 | 3128 | dataOut.EDensity_LP=numpy.zeros(dataOut.NACF-dataOut.cut) |
|
|
3129 | 3129 | dataOut.ElecTemp_LP=numpy.zeros(dataOut.NACF-dataOut.cut) |
|
|
3130 | 3130 | dataOut.EElecTemp_LP=numpy.zeros(dataOut.NACF-dataOut.cut) |
|
|
3131 | 3131 | dataOut.IonTemp_LP=numpy.zeros(dataOut.NACF-dataOut.cut) |
|
|
3132 | 3132 | dataOut.EIonTemp_LP=numpy.zeros(dataOut.NACF-dataOut.cut) |
|
|
3133 | 3133 | dataOut.Phy_LP=numpy.zeros(dataOut.NACF-dataOut.cut) |
|
|
3134 | 3134 | dataOut.EPhy_LP=numpy.zeros(dataOut.NACF-dataOut.cut) |
|
|
3135 | 3135 | dataOut.Phe_LP=numpy.zeros(dataOut.NACF-dataOut.cut) |
|
|
3136 | 3136 | dataOut.EPhe_LP=numpy.zeros(dataOut.NACF-dataOut.cut) |
|
|
3137 | 3137 | |
|
|
3138 | 3138 | dataOut.Density_LP[:]=numpy.copy(dataOut.ne[dataOut.cut:dataOut.NACF]) |
|
|
3139 | 3139 | dataOut.EDensity_LP[:]=numpy.copy(dataOut.ene[dataOut.cut:dataOut.NACF]) |
|
|
3140 | 3140 | dataOut.ElecTemp_LP[:]=numpy.copy(dataOut.te[dataOut.cut:dataOut.NACF]) |
|
|
3141 | 3141 | dataOut.EElecTemp_LP[:]=numpy.copy(dataOut.ete[dataOut.cut:dataOut.NACF]) |
|
|
3142 | 3142 | dataOut.IonTemp_LP[:]=numpy.copy(dataOut.ti[dataOut.cut:dataOut.NACF]) |
|
|
3143 | 3143 | dataOut.EIonTemp_LP[:]=numpy.copy(dataOut.eti[dataOut.cut:dataOut.NACF]) |
|
|
3144 | 3144 | dataOut.Phy_LP[:]=numpy.copy(dataOut.ph[dataOut.cut:dataOut.NACF]) |
|
|
3145 | 3145 | dataOut.EPhy_LP[:]=numpy.copy(dataOut.eph[dataOut.cut:dataOut.NACF]) |
|
|
3146 | 3146 | dataOut.Phe_LP[:]=numpy.copy(dataOut.phe[dataOut.cut:dataOut.NACF]) |
|
|
3147 | 3147 | dataOut.EPhe_LP[:]=numpy.copy(dataOut.ephe[dataOut.cut:dataOut.NACF]) |
|
|
3148 | 3148 | |
|
|
3149 | 3149 | temp_max_lp=6000.0 |
|
|
3150 | 3150 | |
|
|
3151 | 3151 | for i in range(dataOut.NACF-dataOut.cut): |
|
|
3152 | 3152 | |
|
|
3153 | 3153 | if dataOut.ElecTemp_LP[i]<=temp_min or dataOut.ElecTemp_LP[i]>temp_max_lp or dataOut.EElecTemp_LP[i]>temp_max_lp: |
|
|
3154 | 3154 | |
|
|
3155 | 3155 | dataOut.ElecTemp_LP[i]=dataOut.EElecTemp_LP[i]=missing |
|
|
3156 | 3156 | |
|
|
3157 | 3157 | if dataOut.IonTemp_LP[i]<=temp_min or dataOut.IonTemp_LP[i]>temp_max_lp or dataOut.EIonTemp_LP[i]>temp_max_lp: |
|
|
3158 | 3158 | dataOut.IonTemp_LP[i]=dataOut.EIonTemp_LP[i]=missing |
|
|
3159 | 3159 | if dataOut.EPhy_LP[i]<0.0 or dataOut.EPhy_LP[i]>1.0: |
|
|
3160 | 3160 | dataOut.Phy_LP[i]=dataOut.EPhy_LP[i]=missing |
|
|
3161 | 3161 | |
|
|
3162 | 3162 | if dataOut.EPhe_LP[i]<0.0 or dataOut.EPhe_LP[i]>1.0: |
|
|
3163 | 3163 | dataOut.Phe_LP[i]=dataOut.EPhe_LP[i]=missing |
|
|
3164 | 3164 | if dataOut.EDensity_LP[i]>0.0 and dataOut.Density_LP[i]>0.0 and dataOut.Density_LP[i]<9.9e6 and dataOut.EDensity_LP[i]*dataOut.Density_LP[i]<9.9e6: |
|
|
3165 | 3165 | dataOut.EDensity_LP[i]=max(dataOut.EDensity_LP[i],1000.0/dataOut.Density_LP[i]) |
|
|
3166 | 3166 | else: |
|
|
3167 | 3167 | dataOut.Density_LP[i]=missing |
|
|
3168 | 3168 | dataOut.EDensity_LP[i]=1.0 |
|
|
3169 | 3169 | |
|
|
3170 | 3170 | if numpy.isnan(dataOut.Phy_LP[i]): |
|
|
3171 | 3171 | dataOut.EPhy_LP[i]=missing |
|
|
3172 | 3172 | |
|
|
3173 | 3173 | if numpy.isnan(dataOut.Phe_LP[i]): |
|
|
3174 | 3174 | dataOut.EPhe_LP[i]=missing |
|
|
3175 | 3175 | |
|
|
3176 | 3176 | |
|
|
3177 | 3177 | if dataOut.ElecTemp_LP[i]==dataOut.IonTemp_LP[i]: |
|
|
3178 | 3178 | dataOut.EElecTemp_LP[i]=dataOut.EIonTemp_LP[i] |
|
|
3179 | 3179 | if numpy.isnan(dataOut.ElecTemp_LP[i]): |
|
|
3180 | 3180 | dataOut.EElecTemp_LP[i]=missing |
|
|
3181 | 3181 | if numpy.isnan(dataOut.IonTemp_LP[i]): |
|
|
3182 | 3182 | dataOut.EIonTemp_LP[i]=missing |
|
|
3183 | 3183 | if numpy.isnan(dataOut.ElecTemp_LP[i]) or numpy.isnan(dataOut.EElecTemp_LP[i]): |
|
|
3184 | 3184 | dataOut.ElecTemp_LP[i]=dataOut.EElecTemp_LP[i]=dataOut.IonTemp_LP[i]=dataOut.EIonTemp_LP[i]=missing |
|
|
3185 | 3185 | |
|
|
3186 | 3186 | |
|
|
3187 | 3187 | dataOut.DensityFinal=numpy.reshape(numpy.concatenate((dataOut.Density_DP,dataOut.Density_LP)),(1,-1)) |
|
|
3188 | 3188 | dataOut.EDensityFinal=numpy.reshape(numpy.concatenate((dataOut.EDensity_DP,dataOut.EDensity_LP)),(1,-1)) |
|
|
3189 | 3189 | dataOut.ElecTempFinal=numpy.reshape(numpy.concatenate((dataOut.ElecTemp_DP,dataOut.ElecTemp_LP)),(1,-1)) |
|
|
3190 | 3190 | dataOut.EElecTempFinal=numpy.reshape(numpy.concatenate((dataOut.EElecTemp_DP,dataOut.EElecTemp_LP)),(1,-1)) |
|
|
3191 | 3191 | dataOut.IonTempFinal=numpy.reshape(numpy.concatenate((dataOut.IonTemp_DP,dataOut.IonTemp_LP)),(1,-1)) |
|
|
3192 | 3192 | dataOut.EIonTempFinal=numpy.reshape(numpy.concatenate((dataOut.EIonTemp_DP,dataOut.EIonTemp_LP)),(1,-1)) |
|
|
3193 | 3193 | dataOut.PhyFinal=numpy.reshape(numpy.concatenate((dataOut.Phy_DP,dataOut.Phy_LP)),(1,-1)) |
|
|
3194 | 3194 | dataOut.EPhyFinal=numpy.reshape(numpy.concatenate((dataOut.EPhy_DP,dataOut.EPhy_LP)),(1,-1)) |
|
|
3195 | 3195 | dataOut.PheFinal=numpy.reshape(numpy.concatenate((dataOut.Phe_DP,dataOut.Phe_LP)),(1,-1)) |
|
|
3196 | 3196 | dataOut.EPheFinal=numpy.reshape(numpy.concatenate((dataOut.EPhe_DP,dataOut.EPhe_LP)),(1,-1)) |
|
|
3197 | 3197 | |
|
|
3198 | 3198 | nan_array_2=numpy.empty(dataOut.NACF-dataOut.NDP) |
|
|
3199 | 3199 | nan_array_2[:]=numpy.nan |
|
|
3200 | 3200 | |
|
|
3201 | 3201 | dataOut.acfs_DP=numpy.zeros((dataOut.NACF,dataOut.DPL),'float32') |
|
|
3202 | 3202 | dataOut.acfs_error_DP=numpy.zeros((dataOut.NACF,dataOut.DPL),'float32') |
|
|
3203 | 3203 | acfs_dp_aux=dataOut.acfs_to_save.transpose() |
|
|
3204 | 3204 | acfs_error_dp_aux=dataOut.acfs_error_to_save.transpose() |
|
|
3205 | 3205 | for i in range(dataOut.DPL): |
|
|
3206 | 3206 | dataOut.acfs_DP[:,i]=numpy.concatenate((acfs_dp_aux[:,i],nan_array_2)) |
|
|
3207 | 3207 | dataOut.acfs_error_DP[:,i]=numpy.concatenate((acfs_error_dp_aux[:,i],nan_array_2)) |
|
|
3208 | 3208 | dataOut.acfs_DP=dataOut.acfs_DP.transpose() |
|
|
3209 | 3209 | dataOut.acfs_error_DP=dataOut.acfs_error_DP.transpose() |
|
|
3210 | 3210 | |
|
|
3211 | 3211 | dataOut.acfs_LP=numpy.zeros((dataOut.NACF,dataOut.IBITS),'float32') |
|
|
3212 | 3212 | dataOut.acfs_error_LP=numpy.zeros((dataOut.NACF,dataOut.IBITS),'float32') |
|
|
3213 | 3213 | |
|
|
3214 | 3214 | for i in range(dataOut.NACF): |
|
|
3215 | 3215 | for j in range(dataOut.IBITS): |
|
|
3216 | 3216 | if numpy.abs(dataOut.errors[j,i]/dataOut.output_LP_integrated.real[0,i,0])<1.0: |
|
|
3217 | 3217 | dataOut.acfs_LP[i,j]=dataOut.output_LP_integrated.real[j,i,0]/dataOut.output_LP_integrated.real[0,i,0] |
|
|
3218 | 3218 | dataOut.acfs_LP[i,j]=max(min(dataOut.acfs_LP[i,j],1.0),-1.0) |
|
|
3219 | 3219 | |
|
|
3220 | 3220 | dataOut.acfs_error_LP[i,j]=dataOut.errors[j,i]/dataOut.output_LP_integrated.real[0,i,0] |
|
|
3221 | 3221 | else: |
|
|
3222 | 3222 | dataOut.acfs_LP[i,j]=numpy.nan |
|
|
3223 | 3223 | |
|
|
3224 | 3224 | dataOut.acfs_error_LP[i,j]=numpy.nan |
|
|
3225 | 3225 | |
|
|
3226 | 3226 | dataOut.acfs_LP=dataOut.acfs_LP.transpose() |
|
|
3227 | 3227 | dataOut.acfs_error_LP=dataOut.acfs_error_LP.transpose() |
|
|
3228 | 3228 | |
|
|
3229 | 3229 | dataOut.DensityFinal *= 1.e6 #Convert units to m^⁻3 |
|
|
3230 | 3230 | dataOut.EDensityFinal *= 1.e6 #Convert units to m^⁻3 |
|
|
3231 | 3231 | |
|
|
3232 | 3232 | return dataOut |
|
|
3233 | 3233 | |
|
|
3234 | 3234 | |
|
|
3235 | 3235 | class ACFs(Operation): |
|
|
3236 | 3236 | ''' |
|
|
3237 | 3237 | Written by R. Flores |
|
|
3238 | 3238 | ''' |
|
|
3239 | 3239 | def __init__(self, **kwargs): |
|
|
3240 | 3240 | |
|
|
3241 | 3241 | Operation.__init__(self, **kwargs) |
|
|
3242 | 3242 | |
|
|
3243 | 3243 | self.aux=1 |
|
|
3244 | 3244 | |
|
|
3245 | 3245 | def run(self,dataOut): |
|
|
3246 | 3246 | |
|
|
3247 | 3247 | if self.aux: |
|
|
3248 | 3248 | self.taup=numpy.zeros(dataOut.DPL,'float32') |
|
|
3249 | 3249 | self.pacf=numpy.zeros(dataOut.DPL,'float32') |
|
|
3250 | 3250 | self.sacf=numpy.zeros(dataOut.DPL,'float32') |
|
|
3251 | 3251 | |
|
|
3252 | 3252 | self.taup_full=numpy.zeros(dataOut.DPL,'float32') |
|
|
3253 | 3253 | self.pacf_full=numpy.zeros(dataOut.DPL,'float32') |
|
|
3254 | 3254 | self.sacf_full=numpy.zeros(dataOut.DPL,'float32') |
|
|
3255 | 3255 | self.x_igcej=numpy.zeros(dataOut.DPL,'float32') |
|
|
3256 | 3256 | self.y_igcej=numpy.zeros(dataOut.DPL,'float32') |
|
|
3257 | 3257 | self.x_ibad=numpy.zeros(dataOut.DPL,'float32') |
|
|
3258 | 3258 | self.y_ibad=numpy.zeros(dataOut.DPL,'float32') |
|
|
3259 | 3259 | self.aux=0 |
|
|
3260 | 3260 | |
|
|
3261 | 3261 | dataOut.acfs_to_plot=numpy.zeros((dataOut.NDP,dataOut.DPL),'float32') |
|
|
3262 | 3262 | dataOut.acfs_to_save=numpy.zeros((dataOut.NDP,dataOut.DPL),'float32') |
|
|
3263 | 3263 | dataOut.acfs_error_to_plot=numpy.zeros((dataOut.NDP,dataOut.DPL),'float32') |
|
|
3264 | 3264 | dataOut.acfs_error_to_save=numpy.zeros((dataOut.NDP,dataOut.DPL),'float32') |
|
|
3265 | 3265 | dataOut.lags_to_plot=numpy.zeros((dataOut.NDP,dataOut.DPL),'float32') |
|
|
3266 | 3266 | dataOut.x_igcej_to_plot=numpy.zeros((dataOut.NDP,dataOut.DPL),'float32') |
|
|
3267 | 3267 | dataOut.x_ibad_to_plot=numpy.zeros((dataOut.NDP,dataOut.DPL),'float32') |
|
|
3268 | 3268 | dataOut.y_igcej_to_plot=numpy.zeros((dataOut.NDP,dataOut.DPL),'float32') |
|
|
3269 | 3269 | dataOut.y_ibad_to_plot=numpy.zeros((dataOut.NDP,dataOut.DPL),'float32') |
|
|
3270 | 3270 | |
|
|
3271 | 3271 | for i in range(dataOut.NSHTS): |
|
|
3272 | 3272 | |
|
|
3273 | 3273 | acfm=dataOut.rhor[i][0]**2+dataOut.rhoi[i][0]**2 |
|
|
3274 | 3274 | |
|
|
3275 | 3275 | if acfm>0: |
|
|
3276 | 3276 | cc=dataOut.rhor[i][0]/acfm |
|
|
3277 | 3277 | ss=dataOut.rhoi[i][0]/acfm |
|
|
3278 | 3278 | else: |
|
|
3279 | 3279 | cc=1. |
|
|
3280 | 3280 | ss=0. |
|
|
3281 | 3281 | |
|
|
3282 | 3282 | # keep only uncontaminated data |
|
|
3283 | 3283 | for l in range(dataOut.DPL): |
|
|
3284 | 3284 | fact=dataOut.DH |
|
|
3285 | 3285 | if (dataOut.igcej[i][l]==0 and dataOut.ibad[i][l]==0): |
|
|
3286 | 3286 | |
|
|
3287 | 3287 | self.pacf_full[l]=min(1.0,max(-1.0,(dataOut.rhor[i][l]*cc + dataOut.rhoi[i][l]*ss)))*fact+dataOut.range1[i] |
|
|
3288 | 3288 | self.sacf_full[l]=min(1.0,numpy.sqrt(dataOut.sd[i][l]))*fact |
|
|
3289 | 3289 | self.taup_full[l]=dataOut.alag[l] |
|
|
3290 | 3290 | self.x_igcej[l]=numpy.nan |
|
|
3291 | 3291 | self.y_igcej[l]=numpy.nan |
|
|
3292 | 3292 | self.x_ibad[l]=numpy.nan |
|
|
3293 | 3293 | self.y_ibad[l]=numpy.nan |
|
|
3294 | 3294 | |
|
|
3295 | 3295 | else: |
|
|
3296 | 3296 | self.pacf_full[l]=numpy.nan |
|
|
3297 | 3297 | self.sacf_full[l]=numpy.nan |
|
|
3298 | 3298 | self.taup_full[l]=numpy.nan |
|
|
3299 | 3299 | |
|
|
3300 | 3300 | if dataOut.igcej[i][l]: |
|
|
3301 | 3301 | self.x_igcej[l]=dataOut.alag[l] |
|
|
3302 | 3302 | self.y_igcej[l]=dataOut.range1[i] |
|
|
3303 | 3303 | self.x_ibad[l]=numpy.nan |
|
|
3304 | 3304 | self.y_ibad[l]=numpy.nan |
|
|
3305 | 3305 | |
|
|
3306 | 3306 | if dataOut.ibad[i][l]: |
|
|
3307 | 3307 | self.x_igcej[l]=numpy.nan |
|
|
3308 | 3308 | self.y_igcej[l]=numpy.nan |
|
|
3309 | 3309 | self.x_ibad[l]=dataOut.alag[l] |
|
|
3310 | 3310 | self.y_ibad[l]=dataOut.range1[i] |
|
|
3311 | 3311 | |
|
|
3312 | 3312 | pacf_new=numpy.copy((self.pacf_full-dataOut.range1[i])/dataOut.DH) |
|
|
3313 | 3313 | sacf_new=numpy.copy(self.sacf_full/dataOut.DH) |
|
|
3314 | 3314 | dataOut.acfs_to_save[i,:]=numpy.copy(pacf_new) |
|
|
3315 | 3315 | dataOut.acfs_error_to_save[i,:]=numpy.copy(sacf_new) |
|
|
3316 | 3316 | dataOut.acfs_to_plot[i,:]=numpy.copy(self.pacf_full) |
|
|
3317 | 3317 | dataOut.acfs_error_to_plot[i,:]=numpy.copy(self.sacf_full) |
|
|
3318 | 3318 | dataOut.lags_to_plot[i,:]=numpy.copy(self.taup_full) |
|
|
3319 | 3319 | dataOut.x_igcej_to_plot[i,:]=numpy.copy(self.x_igcej) |
|
|
3320 | 3320 | dataOut.x_ibad_to_plot[i,:]=numpy.copy(self.x_ibad) |
|
|
3321 | 3321 | dataOut.y_igcej_to_plot[i,:]=numpy.copy(self.y_igcej) |
|
|
3322 | 3322 | dataOut.y_ibad_to_plot[i,:]=numpy.copy(self.y_ibad) |
|
|
3323 | 3323 | |
|
|
3324 | 3324 | missing=numpy.nan#-32767 |
|
|
3325 | 3325 | |
|
|
3326 | 3326 | for i in range(dataOut.NSHTS,dataOut.NDP): |
|
|
3327 | 3327 | for j in range(dataOut.DPL): |
|
|
3328 | 3328 | dataOut.acfs_to_save[i,j]=missing |
|
|
3329 | 3329 | dataOut.acfs_error_to_save[i,j]=missing |
|
|
3330 | 3330 | dataOut.acfs_to_plot[i,j]=missing |
|
|
3331 | 3331 | dataOut.acfs_error_to_plot[i,j]=missing |
|
|
3332 | 3332 | dataOut.lags_to_plot[i,j]=missing |
|
|
3333 | 3333 | dataOut.x_igcej_to_plot[i,j]=missing |
|
|
3334 | 3334 | dataOut.x_ibad_to_plot[i,j]=missing |
|
|
3335 | 3335 | dataOut.y_igcej_to_plot[i,j]=missing |
|
|
3336 | 3336 | dataOut.y_ibad_to_plot[i,j]=missing |
|
|
3337 | 3337 | |
|
|
3338 | 3338 | dataOut.acfs_to_save=dataOut.acfs_to_save.transpose() |
|
|
3339 | 3339 | dataOut.acfs_error_to_save=dataOut.acfs_error_to_save.transpose() |
|
|
3340 | 3340 | |
|
|
3341 | 3341 | return dataOut |
|
|
3342 | 3342 | |
|
|
3343 | 3343 | |
|
|
3344 | 3344 | class CohInt(Operation): |
|
|
3345 | 3345 | |
|
|
3346 | 3346 | isConfig = False |
|
|
3347 | 3347 | __profIndex = 0 |
|
|
3348 | 3348 | __byTime = False |
|
|
3349 | 3349 | __initime = None |
|
|
3350 | 3350 | __lastdatatime = None |
|
|
3351 | 3351 | __integrationtime = None |
|
|
3352 | 3352 | __buffer = None |
|
|
3353 | 3353 | __bufferStride = [] |
|
|
3354 | 3354 | __dataReady = False |
|
|
3355 | 3355 | __profIndexStride = 0 |
|
|
3356 | 3356 | __dataToPutStride = False |
|
|
3357 | 3357 | n = None |
|
|
3358 | 3358 | |
|
|
3359 | 3359 | def __init__(self, **kwargs): |
|
|
3360 | 3360 | |
|
|
3361 | 3361 | Operation.__init__(self, **kwargs) |
|
|
3362 | 3362 | |
|
|
3363 | 3363 | # self.isConfig = False |
|
|
3364 | 3364 | |
|
|
3365 | 3365 | def setup(self, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False): |
|
|
3366 | 3366 | """ |
|
|
3367 | 3367 | Set the parameters of the integration class. |
|
|
3368 | 3368 | |
|
|
3369 | 3369 | Inputs: |
|
|
3370 | 3370 | |
|
|
3371 | 3371 | n : Number of coherent integrations |
|
|
3372 | 3372 | timeInterval : Time of integration. If the parameter "n" is selected this one does not work |
|
|
3373 | 3373 | overlapping : |
|
|
3374 | 3374 | """ |
|
|
3375 | 3375 | |
|
|
3376 | 3376 | self.__initime = None |
|
|
3377 | 3377 | self.__lastdatatime = 0 |
|
|
3378 | 3378 | self.__buffer = None |
|
|
3379 | 3379 | self.__dataReady = False |
|
|
3380 | 3380 | self.byblock = byblock |
|
|
3381 | 3381 | self.stride = stride |
|
|
3382 | 3382 | |
|
|
3383 | 3383 | if n == None and timeInterval == None: |
|
|
3384 | 3384 | raise ValueError("n or timeInterval should be specified ...") |
|
|
3385 | 3385 | |
|
|
3386 | 3386 | if n != None: |
|
|
3387 | 3387 | self.n = n |
|
|
3388 | 3388 | self.__byTime = False |
|
|
3389 | 3389 | else: |
|
|
3390 | 3390 | self.__integrationtime = timeInterval #* 60. #if (type(timeInterval)!=integer) -> change this line |
|
|
3391 | 3391 | self.n = 9999 |
|
|
3392 | 3392 | self.__byTime = True |
|
|
3393 | 3393 | |
|
|
3394 | 3394 | if overlapping: |
|
|
3395 | 3395 | self.__withOverlapping = True |
|
|
3396 | 3396 | self.__buffer = None |
|
|
3397 | 3397 | else: |
|
|
3398 | 3398 | self.__withOverlapping = False |
|
|
3399 | 3399 | self.__buffer = 0 |
|
|
3400 | 3400 | |
|
|
3401 | 3401 | self.__profIndex = 0 |
|
|
3402 | 3402 | |
|
|
3403 | 3403 | def putData(self, data): |
|
|
3404 | 3404 | |
|
|
3405 | 3405 | """ |
|
|
3406 | 3406 | Add a profile to the __buffer and increase in one the __profileIndex |
|
|
3407 | 3407 | |
|
|
3408 | 3408 | """ |
|
|
3409 | 3409 | |
|
|
3410 | 3410 | if not self.__withOverlapping: |
|
|
3411 | 3411 | self.__buffer += data.copy() |
|
|
3412 | 3412 | self.__profIndex += 1 |
|
|
3413 | 3413 | return |
|
|
3414 | 3414 | |
|
|
3415 | 3415 | #Overlapping data |
|
|
3416 | 3416 | nChannels, nHeis = data.shape |
|
|
3417 | 3417 | data = numpy.reshape(data, (1, nChannels, nHeis)) |
|
|
3418 | 3418 | |
|
|
3419 | 3419 | #If the buffer is empty then it takes the data value |
|
|
3420 | 3420 | if self.__buffer is None: |
|
|
3421 | 3421 | self.__buffer = data |
|
|
3422 | 3422 | self.__profIndex += 1 |
|
|
3423 | 3423 | return |
|
|
3424 | 3424 | |
|
|
3425 | 3425 | #If the buffer length is lower than n then stakcing the data value |
|
|
3426 | 3426 | if self.__profIndex < self.n: |
|
|
3427 | 3427 | self.__buffer = numpy.vstack((self.__buffer, data)) |
|
|
3428 | 3428 | self.__profIndex += 1 |
|
|
3429 | 3429 | return |
|
|
3430 | 3430 | |
|
|
3431 | 3431 | #If the buffer length is equal to n then replacing the last buffer value with the data value |
|
|
3432 | 3432 | self.__buffer = numpy.roll(self.__buffer, -1, axis=0) |
|
|
3433 | 3433 | self.__buffer[self.n-1] = data |
|
|
3434 | 3434 | self.__profIndex = self.n |
|
|
3435 | 3435 | return |
|
|
3436 | 3436 | |
|
|
3437 | 3437 | |
|
|
3438 | 3438 | def pushData(self): |
|
|
3439 | 3439 | """ |
|
|
3440 | 3440 | Return the sum of the last profiles and the profiles used in the sum. |
|
|
3441 | 3441 | |
|
|
3442 | 3442 | Affected: |
|
|
3443 | 3443 | |
|
|
3444 | 3444 | self.__profileIndex |
|
|
3445 | 3445 | |
|
|
3446 | 3446 | """ |
|
|
3447 | 3447 | |
|
|
3448 | 3448 | if not self.__withOverlapping: |
|
|
3449 | 3449 | data = self.__buffer |
|
|
3450 | 3450 | n = self.__profIndex |
|
|
3451 | 3451 | |
|
|
3452 | 3452 | self.__buffer = 0 |
|
|
3453 | 3453 | self.__profIndex = 0 |
|
|
3454 | 3454 | |
|
|
3455 | 3455 | return data, n |
|
|
3456 | 3456 | |
|
|
3457 | 3457 | #Integration with Overlapping |
|
|
3458 | 3458 | data = numpy.sum(self.__buffer, axis=0) |
|
|
3459 | 3459 | # print data |
|
|
3460 | 3460 | # raise |
|
|
3461 | 3461 | n = self.__profIndex |
|
|
3462 | 3462 | |
|
|
3463 | 3463 | return data, n |
|
|
3464 | 3464 | |
|
|
3465 | 3465 | def byProfiles(self, data): |
|
|
3466 | 3466 | |
|
|
3467 | 3467 | self.__dataReady = False |
|
|
3468 | 3468 | avgdata = None |
|
|
3469 | 3469 | # n = None |
|
|
3470 | 3470 | # print data |
|
|
3471 | 3471 | # raise |
|
|
3472 | 3472 | self.putData(data) |
|
|
3473 | 3473 | |
|
|
3474 | 3474 | if self.__profIndex == self.n: |
|
|
3475 | 3475 | avgdata, n = self.pushData() |
|
|
3476 | 3476 | self.__dataReady = True |
|
|
3477 | 3477 | |
|
|
3478 | 3478 | return avgdata |
|
|
3479 | 3479 | |
|
|
3480 | 3480 | def byTime(self, data, datatime): |
|
|
3481 | 3481 | |
|
|
3482 | 3482 | self.__dataReady = False |
|
|
3483 | 3483 | avgdata = None |
|
|
3484 | 3484 | n = None |
|
|
3485 | 3485 | |
|
|
3486 | 3486 | self.putData(data) |
|
|
3487 | 3487 | |
|
|
3488 | 3488 | if (datatime - self.__initime) >= self.__integrationtime: |
|
|
3489 | 3489 | avgdata, n = self.pushData() |
|
|
3490 | 3490 | self.n = n |
|
|
3491 | 3491 | self.__dataReady = True |
|
|
3492 | 3492 | |
|
|
3493 | 3493 | return avgdata |
|
|
3494 | 3494 | |
|
|
3495 | 3495 | def integrateByStride(self, data, datatime): |
|
|
3496 | 3496 | # print data |
|
|
3497 | 3497 | if self.__profIndex == 0: |
|
|
3498 | 3498 | self.__buffer = [[data.copy(), datatime]] |
|
|
3499 | 3499 | else: |
|
|
3500 | 3500 | self.__buffer.append([data.copy(),datatime]) |
|
|
3501 | 3501 | self.__profIndex += 1 |
|
|
3502 | 3502 | self.__dataReady = False |
|
|
3503 | 3503 | |
|
|
3504 | 3504 | if self.__profIndex == self.n * self.stride : |
|
|
3505 | 3505 | self.__dataToPutStride = True |
|
|
3506 | 3506 | self.__profIndexStride = 0 |
|
|
3507 | 3507 | self.__profIndex = 0 |
|
|
3508 | 3508 | self.__bufferStride = [] |
|
|
3509 | 3509 | for i in range(self.stride): |
|
|
3510 | 3510 | current = self.__buffer[i::self.stride] |
|
|
3511 | 3511 | data = numpy.sum([t[0] for t in current], axis=0) |
|
|
3512 | 3512 | avgdatatime = numpy.average([t[1] for t in current]) |
|
|
3513 | 3513 | # print data |
|
|
3514 | 3514 | self.__bufferStride.append((data, avgdatatime)) |
|
|
3515 | 3515 | |
|
|
3516 | 3516 | if self.__dataToPutStride: |
|
|
3517 | 3517 | self.__dataReady = True |
|
|
3518 | 3518 | self.__profIndexStride += 1 |
|
|
3519 | 3519 | if self.__profIndexStride == self.stride: |
|
|
3520 | 3520 | self.__dataToPutStride = False |
|
|
3521 | 3521 | # print self.__bufferStride[self.__profIndexStride - 1] |
|
|
3522 | 3522 | # raise |
|
|
3523 | 3523 | return self.__bufferStride[self.__profIndexStride - 1] |
|
|
3524 | 3524 | |
|
|
3525 | 3525 | |
|
|
3526 | 3526 | return None, None |
|
|
3527 | 3527 | |
|
|
3528 | 3528 | def integrate(self, data, datatime=None): |
|
|
3529 | 3529 | |
|
|
3530 | 3530 | if self.__initime == None: |
|
|
3531 | 3531 | self.__initime = datatime |
|
|
3532 | 3532 | |
|
|
3533 | 3533 | if self.__byTime: |
|
|
3534 | 3534 | avgdata = self.byTime(data, datatime) |
|
|
3535 | 3535 | else: |
|
|
3536 | 3536 | avgdata = self.byProfiles(data) |
|
|
3537 | 3537 | |
|
|
3538 | 3538 | |
|
|
3539 | 3539 | self.__lastdatatime = datatime |
|
|
3540 | 3540 | |
|
|
3541 | 3541 | if avgdata is None: |
|
|
3542 | 3542 | return None, None |
|
|
3543 | 3543 | |
|
|
3544 | 3544 | avgdatatime = self.__initime |
|
|
3545 | 3545 | |
|
|
3546 | 3546 | deltatime = datatime - self.__lastdatatime |
|
|
3547 | 3547 | |
|
|
3548 | 3548 | if not self.__withOverlapping: |
|
|
3549 | 3549 | self.__initime = datatime |
|
|
3550 | 3550 | else: |
|
|
3551 | 3551 | self.__initime += deltatime |
|
|
3552 | 3552 | |
|
|
3553 | 3553 | return avgdata, avgdatatime |
|
|
3554 | 3554 | |
|
|
3555 | 3555 | def integrateByBlock(self, dataOut): |
|
|
3556 | 3556 | |
|
|
3557 | 3557 | times = int(dataOut.data.shape[1]/self.n) |
|
|
3558 | 3558 | avgdata = numpy.zeros((dataOut.nChannels, times, dataOut.nHeights), dtype=complex) |
|
|
3559 | 3559 | |
|
|
3560 | 3560 | id_min = 0 |
|
|
3561 | 3561 | id_max = self.n |
|
|
3562 | 3562 | |
|
|
3563 | 3563 | for i in range(times): |
|
|
3564 | 3564 | junk = dataOut.data[:,id_min:id_max,:] |
|
|
3565 | 3565 | avgdata[:,i,:] = junk.sum(axis=1) |
|
|
3566 | 3566 | id_min += self.n |
|
|
3567 | 3567 | id_max += self.n |
|
|
3568 | 3568 | |
|
|
3569 | 3569 | timeInterval = dataOut.ippSeconds*self.n |
|
|
3570 | 3570 | avgdatatime = (times - 1) * timeInterval + dataOut.utctime |
|
|
3571 | 3571 | self.__dataReady = True |
|
|
3572 | 3572 | return avgdata, avgdatatime |
|
|
3573 | 3573 | |
|
|
3574 | 3574 | def run(self, dataOut, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False, **kwargs): |
|
|
3575 | 3575 | |
|
|
3576 | 3576 | if not self.isConfig: |
|
|
3577 | 3577 | self.setup(n=n, stride=stride, timeInterval=timeInterval, overlapping=overlapping, byblock=byblock, **kwargs) |
|
|
3578 | 3578 | self.isConfig = True |
|
|
3579 | 3579 | |
|
|
3580 | 3580 | if dataOut.flagDataAsBlock: |
|
|
3581 | 3581 | """ |
|
|
3582 | 3582 | Si la data es leida por bloques, dimension = [nChannels, nProfiles, nHeis] |
|
|
3583 | 3583 | """ |
|
|
3584 | 3584 | |
|
|
3585 | 3585 | avgdata, avgdatatime = self.integrateByBlock(dataOut) |
|
|
3586 | 3586 | dataOut.nProfiles /= self.n |
|
|
3587 | 3587 | else: |
|
|
3588 | 3588 | if stride is None: |
|
|
3589 | 3589 | avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime) |
|
|
3590 | 3590 | else: |
|
|
3591 | 3591 | avgdata, avgdatatime = self.integrateByStride(dataOut.data, dataOut.utctime) |
|
|
3592 | 3592 | |
|
|
3593 | 3593 | |
|
|
3594 | 3594 | # dataOut.timeInterval *= n |
|
|
3595 | 3595 | dataOut.flagNoData = True |
|
|
3596 | 3596 | |
|
|
3597 | 3597 | if self.__dataReady: |
|
|
3598 | 3598 | dataOut.data = avgdata |
|
|
3599 | 3599 | if not dataOut.flagCohInt: |
|
|
3600 | 3600 | dataOut.nCohInt *= self.n |
|
|
3601 | 3601 | dataOut.flagCohInt = True |
|
|
3602 | 3602 | dataOut.utctime = avgdatatime |
|
|
3603 | 3603 | # print avgdata, avgdatatime |
|
|
3604 | 3604 | # raise |
|
|
3605 | 3605 | # dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt |
|
|
3606 | 3606 | dataOut.flagNoData = False |
|
|
3607 | 3607 | return dataOut |
|
|
3608 | 3608 | |
|
|
3609 | 3609 | class TimesCode(Operation): |
|
|
3610 | 3610 | ''' |
|
|
3611 | 3611 | Written by R. Flores |
|
|
3612 | 3612 | ''' |
|
|
3613 | 3613 | """ |
|
|
3614 | 3614 | |
|
|
3615 | 3615 | """ |
|
|
3616 | 3616 | |
|
|
3617 | 3617 | def __init__(self, **kwargs): |
|
|
3618 | 3618 | |
|
|
3619 | 3619 | Operation.__init__(self, **kwargs) |
|
|
3620 | 3620 | |
|
|
3621 | 3621 | def run(self,dataOut,code): |
|
|
3622 | 3622 | |
|
|
3623 | 3623 | #code = numpy.repeat(code, repeats=osamp, axis=1) |
|
|
3624 | 3624 | nCodes = numpy.shape(code)[1] |
|
|
3625 | 3625 | #nprofcode = dataOut.nProfiles//nCodes |
|
|
3626 | 3626 | code = numpy.array(code) |
|
|
3627 | 3627 | #print("nHeights",dataOut.nHeights) |
|
|
3628 | 3628 | #print("nheicode",nheicode) |
|
|
3629 | 3629 | #print("Code.Shape",numpy.shape(code)) |
|
|
3630 | 3630 | #print("Code",code[0,:]) |
|
|
3631 | 3631 | nheicode = dataOut.nHeights//nCodes |
|
|
3632 | 3632 | res = dataOut.nHeights%nCodes |
|
|
3633 | 3633 | ''' |
|
|
3634 | 3634 | buffer = numpy.zeros((dataOut.nChannels, |
|
|
3635 | 3635 | nprofcode, |
|
|
3636 | 3636 | nCodes, |
|
|
3637 | 3637 | ndataOut.nHeights), |
|
|
3638 | 3638 | dtype='complex') |
|
|
3639 | 3639 | ''' |
|
|
3640 | 3640 | #exit(1) |
|
|
3641 | 3641 | #for ipr in range(dataOut.nProfiles): |
|
|
3642 | 3642 | #print(dataOut.nHeights) |
|
|
3643 | 3643 | #print(dataOut.data[0,384-2:]) |
|
|
3644 | 3644 | #print(dataOut.profileIndex) |
|
|
3645 | 3645 | #print(dataOut.data[0,:2]) |
|
|
3646 | 3646 | #print(dataOut.data[0,0:64]) |
|
|
3647 | 3647 | #print(dataOut.data[0,64:64+64]) |
|
|
3648 | 3648 | #exit(1) |
|
|
3649 | 3649 | for ich in range(dataOut.nChannels): |
|
|
3650 | 3650 | for ihe in range(nheicode): |
|
|
3651 | 3651 | #print(ihe*nCodes) |
|
|
3652 | 3652 | #print((ihe+1)*nCodes) |
|
|
3653 | 3653 | #dataOut.data[ich,ipr,ihe*nCodes:nCodes*(ihe+1)] |
|
|
3654 | 3654 | #code[ipr,:] |
|
|
3655 | 3655 | #print("before",dataOut.data[ich,ipr,ihe*nCodes:nCodes*(ihe+1)]) |
|
|
3656 | 3656 | #dataOut.data[ich,ipr,ihe*nCodes:nCodes*(ihe+1)] = numpy.prod([dataOut.data[ich,ipr,ihe*nCodes:nCodes*(ihe+1)],code[ipr,:]],axis=0) |
|
|
3657 | 3657 | dataOut.data[ich,ihe*nCodes:nCodes*(ihe+1)] = numpy.prod([dataOut.data[ich,ihe*nCodes:nCodes*(ihe+1)],code[dataOut.profileIndex,:]],axis=0) |
|
|
3658 | 3658 | |
|
|
3659 | 3659 | #print("after",dataOut.data[ich,ipr,ihe*nCodes:nCodes*(ihe+1)]) |
|
|
3660 | 3660 | #exit(1) |
|
|
3661 | 3661 | #print(dataOut.data[0,:2]) |
|
|
3662 | 3662 | #exit(1) |
|
|
3663 | 3663 | #print(nheicode) |
|
|
3664 | 3664 | #print((nheicode)*nCodes) |
|
|
3665 | 3665 | #print(((nheicode)*nCodes)+res) |
|
|
3666 | 3666 | if res != 0: |
|
|
3667 | 3667 | for ich in range(dataOut.nChannels): |
|
|
3668 | 3668 | dataOut.data[ich,nheicode*nCodes:] = numpy.prod([dataOut.data[ich,nheicode*nCodes:],code[dataOut.profileIndex,:res]],axis=0) |
|
|
3669 | 3669 | |
|
|
3670 | 3670 | #pass |
|
|
3671 | 3671 | #print(dataOut.data[0,384-2:]) |
|
|
3672 | 3672 | #exit(1) |
|
|
3673 | 3673 | #dataOut.data = numpy.mean(buffer,axis=1) |
|
|
3674 | 3674 | #print(numpy.shape(dataOut.data)) |
|
|
3675 | 3675 | #print(dataOut.nHeights) |
|
|
3676 | 3676 | #dataOut.heightList = dataOut.heightList[0:nheicode] |
|
|
3677 | 3677 | #print(dataOut.nHeights) |
|
|
3678 | 3678 | #dataOut.nHeights = numpy.shape(dataOut.data)[2] |
|
|
3679 | 3679 | #print(numpy.shape(dataOut.data)) |
|
|
3680 | 3680 | #exit(1) |
|
|
3681 | 3681 | |
|
|
3682 | 3682 | return dataOut |
|
|
3683 | 3683 | |
|
|
3684 | 3684 | ''' |
|
|
3685 | 3685 | class Spectrogram(Operation): |
|
|
3686 | 3686 | """ |
|
|
3687 | 3687 | |
|
|
3688 | 3688 | """ |
|
|
3689 | 3689 | |
|
|
3690 | 3690 | def __init__(self, **kwargs): |
|
|
3691 | 3691 | |
|
|
3692 | 3692 | Operation.__init__(self, **kwargs) |
|
|
3693 | 3693 | |
|
|
3694 | 3694 | |
|
|
3695 | 3695 | |
|
|
3696 | 3696 | def run(self,dataOut): |
|
|
3697 | 3697 | |
|
|
3698 | 3698 | import scipy |
|
|
3699 | 3699 | |
|
|
3700 | 3700 | |
|
|
3701 | 3701 | |
|
|
3702 | 3702 | fs = 3200*1e-6 |
|
|
3703 | 3703 | fs = fs/64 |
|
|
3704 | 3704 | fs = 1/fs |
|
|
3705 | 3705 | |
|
|
3706 | 3706 | nperseg=64 |
|
|
3707 | 3707 | noverlap=48 |
|
|
3708 | 3708 | |
|
|
3709 | 3709 | f, t, Sxx = signal.spectrogram(x, fs, return_onesided=False, nperseg=nperseg, noverlap=noverlap, mode='complex') |
|
|
3710 | 3710 | |
|
|
3711 | 3711 | |
|
|
3712 | 3712 | for ich in range(dataOut.nChannels): |
|
|
3713 | 3713 | for ihe in range(nheicode): |
|
|
3714 | 3714 | |
|
|
3715 | 3715 | |
|
|
3716 | 3716 | return dataOut |
|
|
3717 | 3717 | ''' |
|
|
3718 | 3718 | |
|
|
3719 | 3719 | |
|
|
3720 | 3720 | class RemoveDcHae(Operation): |
|
|
3721 | 3721 | ''' |
|
|
3722 | 3722 | Written by R. Flores |
|
|
3723 | 3723 | ''' |
|
|
3724 | 3724 | def __init__(self, **kwargs): |
|
|
3725 | 3725 | |
|
|
3726 | 3726 | Operation.__init__(self, **kwargs) |
|
|
3727 | 3727 | self.DcCounter = 0 |
|
|
3728 | 3728 | |
|
|
3729 | 3729 | def run(self, dataOut): |
|
|
3730 | 3730 | |
|
|
3731 | 3731 | if self.DcCounter == 0: |
|
|
3732 | 3732 | dataOut.DcHae = numpy.zeros((dataOut.data.shape[0],320),dtype='complex') |
|
|
3733 | 3733 | #dataOut.DcHae = [] |
|
|
3734 | 3734 | self.DcCounter = 1 |
|
|
3735 | 3735 | |
|
|
3736 | 3736 | dataOut.dataaux = numpy.copy(dataOut.data) |
|
|
3737 | 3737 | |
|
|
3738 | 3738 | #dataOut.DcHae += dataOut.dataaux[:,1666:1666+320] |
|
|
3739 | 3739 | dataOut.DcHae += dataOut.dataaux[:,0:0+320] |
|
|
3740 | 3740 | hei = 1666 |
|
|
3741 | 3741 | hei = 2000 |
|
|
3742 | 3742 | hei = 1000 |
|
|
3743 | 3743 | hei = 0 |
|
|
3744 | 3744 | #dataOut.DcHae = numpy.concatenate([dataOut.DcHae,dataOut.dataaux[0,hei]],axis = None) |
|
|
3745 | 3745 | |
|
|
3746 | 3746 | |
|
|
3747 | 3747 | |
|
|
3748 | 3748 | return dataOut |
|
|
3749 | 3749 | |
|
|
3750 | 3750 | |
|
|
3751 | 3751 | class SSheightProfiles(Operation): |
|
|
3752 | 3752 | |
|
|
3753 | 3753 | step = None |
|
|
3754 | 3754 | nsamples = None |
|
|
3755 | 3755 | bufferShape = None |
|
|
3756 | 3756 | profileShape = None |
|
|
3757 | 3757 | sshProfiles = None |
|
|
3758 | 3758 | profileIndex = None |
|
|
3759 | 3759 | |
|
|
3760 | 3760 | def __init__(self, **kwargs): |
|
|
3761 | 3761 | |
|
|
3762 | 3762 | Operation.__init__(self, **kwargs) |
|
|
3763 | 3763 | self.isConfig = False |
|
|
3764 | 3764 | |
|
|
3765 | 3765 | def setup(self,dataOut ,step = None , nsamples = None): |
|
|
3766 | 3766 | |
|
|
3767 | 3767 | if step == None and nsamples == None: |
|
|
3768 | 3768 | #pass |
|
|
3769 | 3769 | raise ValueError("step or nheights should be specified ...") |
|
|
3770 | 3770 | |
|
|
3771 | 3771 | self.step = step |
|
|
3772 | 3772 | self.nsamples = nsamples |
|
|
3773 | 3773 | self.__nChannels = dataOut.nChannels |
|
|
3774 | 3774 | self.__nProfiles = dataOut.nProfiles |
|
|
3775 | 3775 | self.__nHeis = dataOut.nHeights |
|
|
3776 | 3776 | shape = dataOut.data.shape #nchannels, nprofiles, nsamples |
|
|
3777 | 3777 | |
|
|
3778 | 3778 | residue = (shape[1] - self.nsamples) % self.step |
|
|
3779 | 3779 | if residue != 0: |
|
|
3780 | 3780 | print("The residue is %d, step=%d should be multiple of %d to avoid loss of %d samples"%(residue,step,shape[1] - self.nsamples,residue)) |
|
|
3781 | 3781 | |
|
|
3782 | 3782 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
3783 | 3783 | numberProfile = self.nsamples |
|
|
3784 | 3784 | numberSamples = (shape[1] - self.nsamples)/self.step |
|
|
3785 | 3785 | |
|
|
3786 | 3786 | self.bufferShape = int(shape[0]), int(numberSamples), int(numberProfile) # nchannels, nsamples , nprofiles |
|
|
3787 | 3787 | self.profileShape = int(shape[0]), int(numberProfile), int(numberSamples) # nchannels, nprofiles, nsamples |
|
|
3788 | 3788 | |
|
|
3789 | 3789 | self.buffer = numpy.zeros(self.bufferShape , dtype=complex) |
|
|
3790 | 3790 | self.sshProfiles = numpy.zeros(self.profileShape, dtype=complex) |
|
|
3791 | 3791 | |
|
|
3792 | 3792 | def run(self, dataOut, step, nsamples, code = None, repeat = None): |
|
|
3793 |
dataOut.flagNoData = True |
|
|
|
3793 | dataOut.flagNoData = True | |
|
|
3794 | 3794 | profileIndex = None |
|
|
3795 |
dataOut.flagDataAsBlock = False |
|
|
|
3795 | dataOut.flagDataAsBlock = False | |
|
|
3796 | 3796 | |
|
|
3797 | 3797 | if not self.isConfig: |
|
|
3798 | 3798 | self.setup(dataOut, step=step , nsamples=nsamples) |
|
|
3799 | 3799 | self.isConfig = True |
|
|
3800 | 3800 | |
|
|
3801 | 3801 | if code is not None: |
|
|
3802 | 3802 | code = numpy.array(code) |
|
|
3803 | 3803 | code_block = code |
|
|
3804 | 3804 | |
|
|
3805 | 3805 | if repeat is not None: |
|
|
3806 | 3806 | code_block = numpy.repeat(code_block, repeats=repeat, axis=1) |
|
|
3807 | 3807 | |
|
|
3808 | 3808 | for i in range(self.buffer.shape[1]): |
|
|
3809 | 3809 | if code is not None: |
|
|
3810 | 3810 | #self.buffer[:,i] = dataOut.data[:,i*self.step:i*self.step + self.nsamples]*code_block[dataOut.profileIndex,:] |
|
|
3811 | 3811 | self.buffer[:,i] = dataOut.data[:,i*self.step:i*self.step + self.nsamples]*code_block |
|
|
3812 | 3812 | else: |
|
|
3813 | 3813 | self.buffer[:,i] = dataOut.data[:,i*self.step:i*self.step + self.nsamples]#*code[dataOut.profileIndex,:] |
|
|
3814 | 3814 | |
|
|
3815 | 3815 | for j in range(self.buffer.shape[0]): |
|
|
3816 | 3816 | self.sshProfiles[j] = numpy.transpose(self.buffer[j]) |
|
|
3817 | 3817 | |
|
|
3818 | 3818 | profileIndex = self.nsamples |
|
|
3819 | 3819 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
3820 | 3820 | ippSeconds = (deltaHeight*1.0e-6)/(0.15) |
|
|
3821 | 3821 | |
|
|
3822 | 3822 | try: |
|
|
3823 | 3823 | if dataOut.concat_m is not None: |
|
|
3824 | 3824 | ippSeconds= ippSeconds/float(dataOut.concat_m) |
|
|
3825 | 3825 | except: |
|
|
3826 | 3826 | pass |
|
|
3827 | 3827 | |
|
|
3828 | 3828 | dataOut.data = self.sshProfiles |
|
|
3829 | 3829 | dataOut.flagNoData = False |
|
|
3830 | 3830 | dataOut.heightList = numpy.arange(self.buffer.shape[1]) *self.step*deltaHeight + dataOut.heightList[0] |
|
|
3831 | 3831 | dataOut.nProfiles = int(dataOut.nProfiles*self.nsamples) |
|
|
3832 | 3832 | |
|
|
3833 | 3833 | dataOut.profileIndex = profileIndex |
|
|
3834 | 3834 | dataOut.flagDataAsBlock = True |
|
|
3835 | 3835 | dataOut.ippSeconds = ippSeconds |
|
|
3836 | 3836 | dataOut.step = self.step |
|
|
3837 | 3837 | |
|
|
3838 | 3838 | return dataOut |
|
|
3839 | 3839 | |
|
|
3840 | 3840 | class removeDCHAE(Operation): |
|
|
3841 | 3841 | |
|
|
3842 | 3842 | def run(self, dataOut, minHei, maxHei): |
|
|
3843 | 3843 | |
|
|
3844 | 3844 | heights = dataOut.heightList |
|
|
3845 | 3845 | |
|
|
3846 | 3846 | inda = numpy.where(heights >= minHei) |
|
|
3847 | 3847 | indb = numpy.where(heights <= maxHei) |
|
|
3848 | 3848 | |
|
|
3849 | 3849 | minIndex = inda[0][0] |
|
|
3850 | 3850 | maxIndex = indb[0][-1] |
|
|
3851 | 3851 | |
|
|
3852 | 3852 | dc = numpy.average(dataOut.data[:,minIndex:maxIndex],axis=1) |
|
|
3853 | 3853 | #print(dc.shape) |
|
|
3854 | 3854 | dataOut.data = dataOut.data - dc[:,None] |
|
|
3855 | 3855 | #print(aux.shape) |
|
|
3856 | 3856 | #exit(1) |
|
|
3857 | 3857 | |
|
|
3858 | 3858 | return dataOut |
|
|
3859 | 3859 | |
|
|
3860 | 3860 | class Decoder(Operation): |
|
|
3861 | 3861 | |
|
|
3862 | 3862 | isConfig = False |
|
|
3863 | 3863 | __profIndex = 0 |
|
|
3864 | 3864 | |
|
|
3865 | 3865 | code = None |
|
|
3866 | 3866 | |
|
|
3867 | 3867 | nCode = None |
|
|
3868 | 3868 | nBaud = None |
|
|
3869 | 3869 | |
|
|
3870 | 3870 | def __init__(self, **kwargs): |
|
|
3871 | 3871 | |
|
|
3872 | 3872 | Operation.__init__(self, **kwargs) |
|
|
3873 | 3873 | |
|
|
3874 | 3874 | self.times = None |
|
|
3875 | 3875 | self.osamp = None |
|
|
3876 | 3876 | # self.__setValues = False |
|
|
3877 | 3877 | self.isConfig = False |
|
|
3878 | 3878 | self.setupReq = False |
|
|
3879 | 3879 | def setup(self, code, osamp, dataOut): |
|
|
3880 | 3880 | |
|
|
3881 | 3881 | self.__profIndex = 0 |
|
|
3882 | 3882 | |
|
|
3883 | 3883 | self.code = code |
|
|
3884 | 3884 | |
|
|
3885 | 3885 | self.nCode = len(code) |
|
|
3886 | 3886 | self.nBaud = len(code[0]) |
|
|
3887 | 3887 | |
|
|
3888 | 3888 | if (osamp != None) and (osamp >1): |
|
|
3889 | 3889 | self.osamp = osamp |
|
|
3890 | 3890 | self.code = numpy.repeat(code, repeats=self.osamp, axis=1) |
|
|
3891 | 3891 | self.nBaud = self.nBaud*self.osamp |
|
|
3892 | 3892 | |
|
|
3893 | 3893 | self.__nChannels = dataOut.nChannels |
|
|
3894 | 3894 | self.__nProfiles = dataOut.nProfiles |
|
|
3895 | 3895 | self.__nHeis = dataOut.nHeights |
|
|
3896 | 3896 | |
|
|
3897 | 3897 | if self.__nHeis < self.nBaud: |
|
|
3898 | 3898 | raise ValueError('Number of heights (%d) should be greater than number of bauds (%d)' %(self.__nHeis, self.nBaud)) |
|
|
3899 | 3899 | |
|
|
3900 | 3900 | #Frequency |
|
|
3901 | 3901 | __codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=complex) |
|
|
3902 | 3902 | |
|
|
3903 | 3903 | __codeBuffer[:,0:self.nBaud] = self.code |
|
|
3904 | 3904 | |
|
|
3905 | 3905 | self.fft_code = numpy.conj(numpy.fft.fft(__codeBuffer, axis=1)) |
|
|
3906 | 3906 | |
|
|
3907 | 3907 | if dataOut.flagDataAsBlock: |
|
|
3908 | 3908 | |
|
|
3909 | 3909 | self.ndatadec = self.__nHeis #- self.nBaud + 1 |
|
|
3910 | 3910 | |
|
|
3911 | 3911 | self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=complex) |
|
|
3912 | 3912 | |
|
|
3913 | 3913 | else: |
|
|
3914 | 3914 | |
|
|
3915 | 3915 | #Time |
|
|
3916 | 3916 | self.ndatadec = self.__nHeis #- self.nBaud + 1 |
|
|
3917 | 3917 | |
|
|
3918 | 3918 | |
|
|
3919 | 3919 | self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=complex) |
|
|
3920 | 3920 | |
|
|
3921 | 3921 | def __convolutionInFreq(self, data): |
|
|
3922 | 3922 | |
|
|
3923 | 3923 | fft_code = self.fft_code[self.__profIndex].reshape(1,-1) |
|
|
3924 | 3924 | |
|
|
3925 | 3925 | fft_data = numpy.fft.fft(data, axis=1) |
|
|
3926 | 3926 | |
|
|
3927 | 3927 | conv = fft_data*fft_code |
|
|
3928 | 3928 | |
|
|
3929 | 3929 | data = numpy.fft.ifft(conv,axis=1) |
|
|
3930 | 3930 | |
|
|
3931 | 3931 | return data |
|
|
3932 | 3932 | |
|
|
3933 | 3933 | def __convolutionInFreqOpt(self, data): |
|
|
3934 | 3934 | |
|
|
3935 | 3935 | raise NotImplementedError |
|
|
3936 | 3936 | |
|
|
3937 | 3937 | def __convolutionInTime(self, data): |
|
|
3938 | 3938 | |
|
|
3939 | 3939 | code = self.code[self.__profIndex] |
|
|
3940 | 3940 | for i in range(self.__nChannels): |
|
|
3941 | 3941 | #aux=numpy.correlate(data[i,:], code, mode='full') |
|
|
3942 | 3942 | #print(numpy.shape(aux)) |
|
|
3943 | 3943 | #print(numpy.shape(data[i,:])) |
|
|
3944 | 3944 | #print(numpy.shape(code)) |
|
|
3945 | 3945 | #exit(1) |
|
|
3946 | 3946 | self.datadecTime[i,:] = numpy.correlate(data[i,:], code, mode='full')[self.nBaud-1:] |
|
|
3947 | 3947 | |
|
|
3948 | 3948 | return self.datadecTime |
|
|
3949 | 3949 | |
|
|
3950 | 3950 | def __convolutionByBlockInTime(self, data): |
|
|
3951 | 3951 | |
|
|
3952 | 3952 | repetitions = int(self.__nProfiles / self.nCode) |
|
|
3953 | 3953 | junk = numpy.lib.stride_tricks.as_strided(self.code, (repetitions, self.code.size), (0, self.code.itemsize)) |
|
|
3954 | 3954 | junk = junk.flatten() |
|
|
3955 | 3955 | code_block = numpy.reshape(junk, (self.nCode*repetitions, self.nBaud)) |
|
|
3956 | 3956 | profilesList = range(self.__nProfiles) |
|
|
3957 | 3957 | #print(numpy.shape(self.datadecTime)) |
|
|
3958 | 3958 | #print(numpy.shape(data)) |
|
|
3959 | 3959 | for i in range(self.__nChannels): |
|
|
3960 | 3960 | for j in profilesList: |
|
|
3961 | 3961 | self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:] |
|
|
3962 | 3962 | return self.datadecTime |
|
|
3963 | 3963 | |
|
|
3964 | 3964 | def __convolutionByBlockInFreq(self, data): |
|
|
3965 | 3965 | |
|
|
3966 | 3966 | raise NotImplementedError("Decoder by frequency fro Blocks not implemented") |
|
|
3967 | 3967 | |
|
|
3968 | 3968 | |
|
|
3969 | 3969 | fft_code = self.fft_code[self.__profIndex].reshape(1,-1) |
|
|
3970 | 3970 | |
|
|
3971 | 3971 | fft_data = numpy.fft.fft(data, axis=2) |
|
|
3972 | 3972 | |
|
|
3973 | 3973 | conv = fft_data*fft_code |
|
|
3974 | 3974 | |
|
|
3975 | 3975 | data = numpy.fft.ifft(conv,axis=2) |
|
|
3976 | 3976 | |
|
|
3977 | 3977 | return data |
|
|
3978 | 3978 | |
|
|
3979 | 3979 | |
|
|
3980 | 3980 | def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0, osamp=None, times=None): |
|
|
3981 | 3981 | |
|
|
3982 | 3982 | if dataOut.flagDecodeData: |
|
|
3983 | 3983 | print("This data is already decoded, recoding again ...") |
|
|
3984 | 3984 | |
|
|
3985 | 3985 | if not self.isConfig: |
|
|
3986 | 3986 | |
|
|
3987 | 3987 | if code is None: |
|
|
3988 | 3988 | if dataOut.code is None: |
|
|
3989 | 3989 | raise ValueError("Code could not be read from %s instance. Enter a value in Code parameter" %dataOut.type) |
|
|
3990 | 3990 | |
|
|
3991 | 3991 | code = dataOut.code |
|
|
3992 | 3992 | else: |
|
|
3993 | 3993 | code = numpy.array(code).reshape(nCode,nBaud) |
|
|
3994 | 3994 | self.setup(code, osamp, dataOut) |
|
|
3995 | 3995 | |
|
|
3996 | 3996 | self.isConfig = True |
|
|
3997 | 3997 | |
|
|
3998 | 3998 | if mode == 3: |
|
|
3999 | 3999 | sys.stderr.write("Decoder Warning: mode=%d is not valid, using mode=0\n" %mode) |
|
|
4000 | 4000 | |
|
|
4001 | 4001 | if times != None: |
|
|
4002 | 4002 | sys.stderr.write("Decoder Warning: Argument 'times' in not used anymore\n") |
|
|
4003 | 4003 | |
|
|
4004 | 4004 | if self.code is None: |
|
|
4005 | 4005 | print("Fail decoding: Code is not defined.") |
|
|
4006 | 4006 | return |
|
|
4007 | 4007 | |
|
|
4008 | 4008 | self.__nProfiles = dataOut.nProfiles |
|
|
4009 | 4009 | datadec = None |
|
|
4010 | 4010 | |
|
|
4011 | 4011 | if mode == 3: |
|
|
4012 | 4012 | mode = 0 |
|
|
4013 | 4013 | |
|
|
4014 | 4014 | if dataOut.flagDataAsBlock: |
|
|
4015 | 4015 | """ |
|
|
4016 | 4016 | Decoding when data have been read as block, |
|
|
4017 | 4017 | """ |
|
|
4018 | 4018 | |
|
|
4019 | 4019 | if mode == 0: |
|
|
4020 | 4020 | datadec = self.__convolutionByBlockInTime(dataOut.data) |
|
|
4021 | 4021 | if mode == 1: |
|
|
4022 | 4022 | datadec = self.__convolutionByBlockInFreq(dataOut.data) |
|
|
4023 | 4023 | else: |
|
|
4024 | 4024 | """ |
|
|
4025 | 4025 | Decoding when data have been read profile by profile |
|
|
4026 | 4026 | """ |
|
|
4027 | 4027 | if mode == 0: |
|
|
4028 | 4028 | datadec = self.__convolutionInTime(dataOut.data) |
|
|
4029 | 4029 | |
|
|
4030 | 4030 | if mode == 1: |
|
|
4031 | 4031 | datadec = self.__convolutionInFreq(dataOut.data) |
|
|
4032 | 4032 | |
|
|
4033 | 4033 | if mode == 2: |
|
|
4034 | 4034 | datadec = self.__convolutionInFreqOpt(dataOut.data) |
|
|
4035 | 4035 | |
|
|
4036 | 4036 | if datadec is None: |
|
|
4037 | 4037 | raise ValueError("Codification mode selected is not valid: mode=%d. Try selecting 0 or 1" %mode) |
|
|
4038 | 4038 | |
|
|
4039 | 4039 | dataOut.code = self.code |
|
|
4040 | 4040 | dataOut.nCode = self.nCode |
|
|
4041 | 4041 | dataOut.nBaud = self.nBaud |
|
|
4042 | 4042 | |
|
|
4043 | 4043 | dataOut.data = datadec |
|
|
4044 | 4044 | #print("before",dataOut.heightList) |
|
|
4045 | 4045 | dataOut.heightList = dataOut.heightList[0:datadec.shape[-1]] |
|
|
4046 | 4046 | #print("after",dataOut.heightList) |
|
|
4047 | 4047 | |
|
|
4048 | 4048 | dataOut.flagDecodeData = True #asumo q la data esta decodificada |
|
|
4049 | 4049 | |
|
|
4050 | 4050 | if self.__profIndex == self.nCode-1: |
|
|
4051 | 4051 | self.__profIndex = 0 |
|
|
4052 | 4052 | return dataOut |
|
|
4053 | 4053 | |
|
|
4054 | 4054 | self.__profIndex += 1 |
|
|
4055 | 4055 | |
|
|
4056 | 4056 | #print("SHAPE",numpy.shape(dataOut.data)) |
|
|
4057 | 4057 | |
|
|
4058 | 4058 | return dataOut |
|
|
4059 | 4059 | # dataOut.flagDeflipData = True #asumo q la data no esta sin flip |
|
|
4060 | 4060 | |
|
|
4061 | 4061 | class DecoderRoll(Operation): |
|
|
4062 | 4062 | |
|
|
4063 | 4063 | isConfig = False |
|
|
4064 | 4064 | __profIndex = 0 |
|
|
4065 | 4065 | |
|
|
4066 | 4066 | code = None |
|
|
4067 | 4067 | |
|
|
4068 | 4068 | nCode = None |
|
|
4069 | 4069 | nBaud = None |
|
|
4070 | 4070 | |
|
|
4071 | 4071 | def __init__(self, **kwargs): |
|
|
4072 | 4072 | |
|
|
4073 | 4073 | Operation.__init__(self, **kwargs) |
|
|
4074 | 4074 | |
|
|
4075 | 4075 | self.times = None |
|
|
4076 | 4076 | self.osamp = None |
|
|
4077 | 4077 | # self.__setValues = False |
|
|
4078 | 4078 | self.isConfig = False |
|
|
4079 | 4079 | self.setupReq = False |
|
|
4080 | 4080 | def setup(self, code, osamp, dataOut): |
|
|
4081 | 4081 | |
|
|
4082 | 4082 | self.__profIndex = 0 |
|
|
4083 | 4083 | |
|
|
4084 | 4084 | |
|
|
4085 | 4085 | self.code = code |
|
|
4086 | 4086 | |
|
|
4087 | 4087 | self.nCode = len(code) |
|
|
4088 | 4088 | self.nBaud = len(code[0]) |
|
|
4089 | 4089 | |
|
|
4090 | 4090 | if (osamp != None) and (osamp >1): |
|
|
4091 | 4091 | self.osamp = osamp |
|
|
4092 | 4092 | self.code = numpy.repeat(code, repeats=self.osamp, axis=1) |
|
|
4093 | 4093 | self.nBaud = self.nBaud*self.osamp |
|
|
4094 | 4094 | |
|
|
4095 | 4095 | self.__nChannels = dataOut.nChannels |
|
|
4096 | 4096 | self.__nProfiles = dataOut.nProfiles |
|
|
4097 | 4097 | self.__nHeis = dataOut.nHeights |
|
|
4098 | 4098 | |
|
|
4099 | 4099 | if self.__nHeis < self.nBaud: |
|
|
4100 | 4100 | raise ValueError('Number of heights (%d) should be greater than number of bauds (%d)' %(self.__nHeis, self.nBaud)) |
|
|
4101 | 4101 | |
|
|
4102 | 4102 | #Frequency |
|
|
4103 | 4103 | __codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=complex) |
|
|
4104 | 4104 | |
|
|
4105 | 4105 | __codeBuffer[:,0:self.nBaud] = self.code |
|
|
4106 | 4106 | |
|
|
4107 | 4107 | self.fft_code = numpy.conj(numpy.fft.fft(__codeBuffer, axis=1)) |
|
|
4108 | 4108 | |
|
|
4109 | 4109 | if dataOut.flagDataAsBlock: |
|
|
4110 | 4110 | |
|
|
4111 | 4111 | self.ndatadec = self.__nHeis #- self.nBaud + 1 |
|
|
4112 | 4112 | |
|
|
4113 | 4113 | self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=complex) |
|
|
4114 | 4114 | |
|
|
4115 | 4115 | else: |
|
|
4116 | 4116 | |
|
|
4117 | 4117 | #Time |
|
|
4118 | 4118 | self.ndatadec = self.__nHeis #- self.nBaud + 1 |
|
|
4119 | 4119 | |
|
|
4120 | 4120 | |
|
|
4121 | 4121 | self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=complex) |
|
|
4122 | 4122 | |
|
|
4123 | 4123 | def __convolutionInFreq(self, data): |
|
|
4124 | 4124 | |
|
|
4125 | 4125 | fft_code = self.fft_code[self.__profIndex].reshape(1,-1) |
|
|
4126 | 4126 | |
|
|
4127 | 4127 | fft_data = numpy.fft.fft(data, axis=1) |
|
|
4128 | 4128 | |
|
|
4129 | 4129 | conv = fft_data*fft_code |
|
|
4130 | 4130 | |
|
|
4131 | 4131 | data = numpy.fft.ifft(conv,axis=1) |
|
|
4132 | 4132 | |
|
|
4133 | 4133 | return data |
|
|
4134 | 4134 | |
|
|
4135 | 4135 | def __convolutionInFreqOpt(self, data): |
|
|
4136 | 4136 | |
|
|
4137 | 4137 | raise NotImplementedError |
|
|
4138 | 4138 | |
|
|
4139 | 4139 | def __convolutionInTime(self, data): |
|
|
4140 | 4140 | |
|
|
4141 | 4141 | code = self.code[self.__profIndex] |
|
|
4142 | 4142 | #print("code",code[0,0]) |
|
|
4143 | 4143 | for i in range(self.__nChannels): |
|
|
4144 | 4144 | #aux=numpy.correlate(data[i,:], code, mode='full') |
|
|
4145 | 4145 | #print(numpy.shape(aux)) |
|
|
4146 | 4146 | #print(numpy.shape(data[i,:])) |
|
|
4147 | 4147 | #print(numpy.shape(code)) |
|
|
4148 | 4148 | #exit(1) |
|
|
4149 | 4149 | self.datadecTime[i,:] = numpy.correlate(data[i,:], code, mode='full')[self.nBaud-1:] |
|
|
4150 | 4150 | |
|
|
4151 | 4151 | return self.datadecTime |
|
|
4152 | 4152 | |
|
|
4153 | 4153 | def __convolutionByBlockInTime(self, data): |
|
|
4154 | 4154 | |
|
|
4155 | 4155 | repetitions = int(self.__nProfiles / self.nCode) |
|
|
4156 | 4156 | junk = numpy.lib.stride_tricks.as_strided(self.code, (repetitions, self.code.size), (0, self.code.itemsize)) |
|
|
4157 | 4157 | junk = junk.flatten() |
|
|
4158 | 4158 | code_block = numpy.reshape(junk, (self.nCode*repetitions, self.nBaud)) |
|
|
4159 | 4159 | profilesList = range(self.__nProfiles) |
|
|
4160 | 4160 | #print(numpy.shape(self.datadecTime)) |
|
|
4161 | 4161 | #print(numpy.shape(data)) |
|
|
4162 | 4162 | for i in range(self.__nChannels): |
|
|
4163 | 4163 | for j in profilesList: |
|
|
4164 | 4164 | self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:] |
|
|
4165 | 4165 | return self.datadecTime |
|
|
4166 | 4166 | |
|
|
4167 | 4167 | def __convolutionByBlockInFreq(self, data): |
|
|
4168 | 4168 | |
|
|
4169 | 4169 | raise NotImplementedError("Decoder by frequency fro Blocks not implemented") |
|
|
4170 | 4170 | |
|
|
4171 | 4171 | |
|
|
4172 | 4172 | fft_code = self.fft_code[self.__profIndex].reshape(1,-1) |
|
|
4173 | 4173 | |
|
|
4174 | 4174 | fft_data = numpy.fft.fft(data, axis=2) |
|
|
4175 | 4175 | |
|
|
4176 | 4176 | conv = fft_data*fft_code |
|
|
4177 | 4177 | |
|
|
4178 | 4178 | data = numpy.fft.ifft(conv,axis=2) |
|
|
4179 | 4179 | |
|
|
4180 | 4180 | return data |
|
|
4181 | 4181 | |
|
|
4182 | 4182 | |
|
|
4183 | 4183 | def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0, osamp=None, times=None): |
|
|
4184 | 4184 | |
|
|
4185 | 4185 | if dataOut.flagDecodeData: |
|
|
4186 | 4186 | print("This data is already decoded, recoding again ...") |
|
|
4187 | 4187 | |
|
|
4188 | 4188 | |
|
|
4189 | 4189 | roll = 0 |
|
|
4190 | 4190 | |
|
|
4191 | 4191 | if self.isConfig: |
|
|
4192 | 4192 | code = numpy.array(code) |
|
|
4193 | 4193 | |
|
|
4194 | 4194 | code = numpy.roll(code,roll,axis=0) |
|
|
4195 | 4195 | code = numpy.reshape(code,(5,100,64)) |
|
|
4196 | 4196 | block = dataOut.CurrentBlock%5 |
|
|
4197 | 4197 | #code = code[block-1,:,:] #NormalizeDPPower |
|
|
4198 | 4198 | code = code[block-1-1,:,:] #Next Day |
|
|
4199 | 4199 | self.code = numpy.repeat(code, repeats=self.osamp, axis=1) |
|
|
4200 | 4200 | |
|
|
4201 | 4201 | |
|
|
4202 | 4202 | if not self.isConfig: |
|
|
4203 | 4203 | |
|
|
4204 | 4204 | if code is None: |
|
|
4205 | 4205 | if dataOut.code is None: |
|
|
4206 | 4206 | raise ValueError("Code could not be read from %s instance. Enter a value in Code parameter" %dataOut.type) |
|
|
4207 | 4207 | |
|
|
4208 | 4208 | code = dataOut.code |
|
|
4209 | 4209 | else: |
|
|
4210 | 4210 | code = numpy.array(code) |
|
|
4211 | 4211 | |
|
|
4212 | 4212 | #roll = 29 |
|
|
4213 | 4213 | code = numpy.roll(code,roll,axis=0) |
|
|
4214 | 4214 | code = numpy.reshape(code,(5,100,64)) |
|
|
4215 | 4215 | block = dataOut.CurrentBlock%5 |
|
|
4216 | 4216 | code = code[block-1-1,:,:] |
|
|
4217 | 4217 | #print(code.shape()) |
|
|
4218 | 4218 | #exit(1) |
|
|
4219 | 4219 | |
|
|
4220 | 4220 | code = numpy.array(code).reshape(nCode,nBaud) |
|
|
4221 | 4221 | self.setup(code, osamp, dataOut) |
|
|
4222 | 4222 | |
|
|
4223 | 4223 | self.isConfig = True |
|
|
4224 | 4224 | |
|
|
4225 | 4225 | if mode == 3: |
|
|
4226 | 4226 | sys.stderr.write("Decoder Warning: mode=%d is not valid, using mode=0\n" %mode) |
|
|
4227 | 4227 | |
|
|
4228 | 4228 | if times != None: |
|
|
4229 | 4229 | sys.stderr.write("Decoder Warning: Argument 'times' in not used anymore\n") |
|
|
4230 | 4230 | |
|
|
4231 | 4231 | if self.code is None: |
|
|
4232 | 4232 | print("Fail decoding: Code is not defined.") |
|
|
4233 | 4233 | return |
|
|
4234 | 4234 | |
|
|
4235 | 4235 | self.__nProfiles = dataOut.nProfiles |
|
|
4236 | 4236 | datadec = None |
|
|
4237 | 4237 | |
|
|
4238 | 4238 | if mode == 3: |
|
|
4239 | 4239 | mode = 0 |
|
|
4240 | 4240 | |
|
|
4241 | 4241 | if dataOut.flagDataAsBlock: |
|
|
4242 | 4242 | """ |
|
|
4243 | 4243 | Decoding when data have been read as block, |
|
|
4244 | 4244 | """ |
|
|
4245 | 4245 | |
|
|
4246 | 4246 | if mode == 0: |
|
|
4247 | 4247 | datadec = self.__convolutionByBlockInTime(dataOut.data) |
|
|
4248 | 4248 | if mode == 1: |
|
|
4249 | 4249 | datadec = self.__convolutionByBlockInFreq(dataOut.data) |
|
|
4250 | 4250 | else: |
|
|
4251 | 4251 | """ |
|
|
4252 | 4252 | Decoding when data have been read profile by profile |
|
|
4253 | 4253 | """ |
|
|
4254 | 4254 | if mode == 0: |
|
|
4255 | 4255 | datadec = self.__convolutionInTime(dataOut.data) |
|
|
4256 | 4256 | |
|
|
4257 | 4257 | if mode == 1: |
|
|
4258 | 4258 | datadec = self.__convolutionInFreq(dataOut.data) |
|
|
4259 | 4259 | |
|
|
4260 | 4260 | if mode == 2: |
|
|
4261 | 4261 | datadec = self.__convolutionInFreqOpt(dataOut.data) |
|
|
4262 | 4262 | |
|
|
4263 | 4263 | if datadec is None: |
|
|
4264 | 4264 | raise ValueError("Codification mode selected is not valid: mode=%d. Try selecting 0 or 1" %mode) |
|
|
4265 | 4265 | |
|
|
4266 | 4266 | dataOut.code = self.code |
|
|
4267 | 4267 | dataOut.nCode = self.nCode |
|
|
4268 | 4268 | dataOut.nBaud = self.nBaud |
|
|
4269 | 4269 | |
|
|
4270 | 4270 | dataOut.data = datadec |
|
|
4271 | 4271 | #print("before",dataOut.heightList) |
|
|
4272 | 4272 | dataOut.heightList = dataOut.heightList[0:datadec.shape[-1]] |
|
|
4273 | 4273 | #print("after",dataOut.heightList) |
|
|
4274 | 4274 | |
|
|
4275 | 4275 | dataOut.flagDecodeData = True #asumo q la data esta decodificada |
|
|
4276 | 4276 | |
|
|
4277 | 4277 | if self.__profIndex == self.nCode-1: |
|
|
4278 | 4278 | self.__profIndex = 0 |
|
|
4279 | 4279 | return dataOut |
|
|
4280 | 4280 | |
|
|
4281 | 4281 | self.__profIndex += 1 |
|
|
4282 | 4282 | |
|
|
4283 | 4283 | #print("SHAPE",numpy.shape(dataOut.data)) |
|
|
4284 | 4284 | |
|
|
4285 | 4285 | return dataOut |
|
|
4286 | 4286 | |
|
|
4287 | 4287 | |
|
|
4288 | 4288 | class ProfileConcat(Operation): |
|
|
4289 | 4289 | |
|
|
4290 | 4290 | isConfig = False |
|
|
4291 | 4291 | buffer = None |
|
|
4292 | 4292 | |
|
|
4293 | 4293 | def __init__(self, **kwargs): |
|
|
4294 | 4294 | |
|
|
4295 | 4295 | Operation.__init__(self, **kwargs) |
|
|
4296 | 4296 | self.profileIndex = 0 |
|
|
4297 | 4297 | |
|
|
4298 | 4298 | def reset(self): |
|
|
4299 | 4299 | self.buffer = numpy.zeros_like(self.buffer) |
|
|
4300 | 4300 | self.start_index = 0 |
|
|
4301 | 4301 | self.times = 1 |
|
|
4302 | 4302 | |
|
|
4303 | 4303 | def setup(self, data, m, n=1): |
|
|
4304 | 4304 | self.buffer = numpy.zeros((data.shape[0],data.shape[1]*m),dtype=type(data[0,0])) |
|
|
4305 | 4305 | self.nHeights = data.shape[1]#.nHeights |
|
|
4306 | 4306 | self.start_index = 0 |
|
|
4307 | 4307 | self.times = 1 |
|
|
4308 | 4308 | |
|
|
4309 | 4309 | def concat(self, data): |
|
|
4310 | 4310 | |
|
|
4311 | 4311 | self.buffer[:,self.start_index:self.nHeights*self.times] = data.copy() |
|
|
4312 | 4312 | self.start_index = self.start_index + self.nHeights |
|
|
4313 | 4313 | |
|
|
4314 | 4314 | def run(self, dataOut, m): |
|
|
4315 | 4315 | dataOut.flagNoData = True |
|
|
4316 | 4316 | |
|
|
4317 | 4317 | if not self.isConfig: |
|
|
4318 | 4318 | self.setup(dataOut.data, m, 1) |
|
|
4319 | 4319 | self.isConfig = True |
|
|
4320 | 4320 | |
|
|
4321 | 4321 | if dataOut.flagDataAsBlock: |
|
|
4322 | 4322 | raise ValueError("ProfileConcat can only be used when voltage have been read profile by profile, getBlock = False") |
|
|
4323 | 4323 | |
|
|
4324 | 4324 | else: |
|
|
4325 | 4325 | self.concat(dataOut.data) |
|
|
4326 | 4326 | self.times += 1 |
|
|
4327 | 4327 | if self.times > m: |
|
|
4328 | 4328 | dataOut.data = self.buffer |
|
|
4329 | 4329 | self.reset() |
|
|
4330 | 4330 | dataOut.flagNoData = False |
|
|
4331 | 4331 | # se deben actualizar mas propiedades del header y del objeto dataOut, por ejemplo, las alturas |
|
|
4332 | 4332 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
4333 | 4333 | xf = dataOut.heightList[0] + dataOut.nHeights * deltaHeight * m |
|
|
4334 | 4334 | dataOut.heightList = numpy.arange(dataOut.heightList[0], xf, deltaHeight) |
|
|
4335 | 4335 | dataOut.ippSeconds *= m |
|
|
4336 | 4336 | return dataOut |
|
|
4337 | 4337 | |
|
|
4338 | 4338 | class ProfileSelector(Operation): |
|
|
4339 | 4339 | |
|
|
4340 | 4340 | profileIndex = None |
|
|
4341 | 4341 | # Tamanho total de los perfiles |
|
|
4342 | 4342 | nProfiles = None |
|
|
4343 | 4343 | |
|
|
4344 | 4344 | def __init__(self, **kwargs): |
|
|
4345 | 4345 | |
|
|
4346 | 4346 | Operation.__init__(self, **kwargs) |
|
|
4347 | 4347 | self.profileIndex = 0 |
|
|
4348 | 4348 | |
|
|
4349 | 4349 | def incProfileIndex(self): |
|
|
4350 | 4350 | |
|
|
4351 | 4351 | self.profileIndex += 1 |
|
|
4352 | 4352 | |
|
|
4353 | 4353 | if self.profileIndex >= self.nProfiles: |
|
|
4354 | 4354 | self.profileIndex = 0 |
|
|
4355 | 4355 | |
|
|
4356 | 4356 | def isThisProfileInRange(self, profileIndex, minIndex, maxIndex): |
|
|
4357 | 4357 | |
|
|
4358 | 4358 | if profileIndex < minIndex: |
|
|
4359 | 4359 | return False |
|
|
4360 | 4360 | |
|
|
4361 | 4361 | if profileIndex > maxIndex: |
|
|
4362 | 4362 | return False |
|
|
4363 | 4363 | |
|
|
4364 | 4364 | return True |
|
|
4365 | 4365 | |
|
|
4366 | 4366 | def isThisProfileInList(self, profileIndex, profileList): |
|
|
4367 | 4367 | |
|
|
4368 | 4368 | if profileIndex not in profileList: |
|
|
4369 | 4369 | return False |
|
|
4370 | 4370 | |
|
|
4371 | 4371 | return True |
|
|
4372 | 4372 | |
|
|
4373 | 4373 | def run(self, dataOut, profileList=None, profileRangeList=None, beam=None, byblock=False, rangeList = None, nProfiles=None): |
|
|
4374 | 4374 | |
|
|
4375 | 4375 | """ |
|
|
4376 | 4376 | ProfileSelector: |
|
|
4377 | 4377 | |
|
|
4378 | 4378 | Inputs: |
|
|
4379 | 4379 | profileList : Index of profiles selected. Example: profileList = (0,1,2,7,8) |
|
|
4380 | 4380 | |
|
|
4381 | 4381 | profileRangeList : Minimum and maximum profile indexes. Example: profileRangeList = (4, 30) |
|
|
4382 | 4382 | |
|
|
4383 | 4383 | rangeList : List of profile ranges. Example: rangeList = ((4, 30), (32, 64), (128, 256)) |
|
|
4384 | 4384 | |
|
|
4385 | 4385 | """ |
|
|
4386 | 4386 | |
|
|
4387 | 4387 | if rangeList is not None: |
|
|
4388 | 4388 | if type(rangeList[0]) not in (tuple, list): |
|
|
4389 | 4389 | rangeList = [rangeList] |
|
|
4390 | 4390 | |
|
|
4391 | 4391 | dataOut.flagNoData = True |
|
|
4392 | 4392 | |
|
|
4393 | 4393 | if dataOut.flagDataAsBlock: |
|
|
4394 | 4394 | """ |
|
|
4395 | 4395 | data dimension = [nChannels, nProfiles, nHeis] |
|
|
4396 | 4396 | """ |
|
|
4397 | 4397 | if profileList != None: |
|
|
4398 | 4398 | dataOut.data = dataOut.data[:,profileList,:] |
|
|
4399 | 4399 | |
|
|
4400 | 4400 | if profileRangeList != None: |
|
|
4401 | 4401 | minIndex = profileRangeList[0] |
|
|
4402 | 4402 | maxIndex = profileRangeList[1] |
|
|
4403 | 4403 | profileList = list(range(minIndex, maxIndex+1)) |
|
|
4404 | 4404 | |
|
|
4405 | 4405 | dataOut.data = dataOut.data[:,minIndex:maxIndex+1,:] |
|
|
4406 | 4406 | |
|
|
4407 | 4407 | if rangeList != None: |
|
|
4408 | 4408 | |
|
|
4409 | 4409 | profileList = [] |
|
|
4410 | 4410 | |
|
|
4411 | 4411 | for thisRange in rangeList: |
|
|
4412 | 4412 | minIndex = thisRange[0] |
|
|
4413 | 4413 | maxIndex = thisRange[1] |
|
|
4414 | 4414 | |
|
|
4415 | 4415 | profileList.extend(list(range(minIndex, maxIndex+1))) |
|
|
4416 | 4416 | |
|
|
4417 | 4417 | dataOut.data = dataOut.data[:,profileList,:] |
|
|
4418 | 4418 | |
|
|
4419 | 4419 | dataOut.nProfiles = len(profileList) |
|
|
4420 | 4420 | dataOut.profileIndex = dataOut.nProfiles - 1 |
|
|
4421 | 4421 | dataOut.flagNoData = False |
|
|
4422 | 4422 | |
|
|
4423 | 4423 | return dataOut |
|
|
4424 | 4424 | |
|
|
4425 | 4425 | """ |
|
|
4426 | 4426 | data dimension = [nChannels, nHeis] |
|
|
4427 | 4427 | """ |
|
|
4428 | 4428 | |
|
|
4429 | 4429 | if profileList != None: |
|
|
4430 | 4430 | |
|
|
4431 | 4431 | if self.isThisProfileInList(dataOut.profileIndex, profileList): |
|
|
4432 | 4432 | |
|
|
4433 | 4433 | self.nProfiles = len(profileList) |
|
|
4434 | 4434 | dataOut.nProfiles = self.nProfiles |
|
|
4435 | 4435 | dataOut.profileIndex = self.profileIndex |
|
|
4436 | 4436 | dataOut.flagNoData = False |
|
|
4437 | 4437 | |
|
|
4438 | 4438 | self.incProfileIndex() |
|
|
4439 | 4439 | return dataOut |
|
|
4440 | 4440 | |
|
|
4441 | 4441 | if profileRangeList != None: |
|
|
4442 | 4442 | |
|
|
4443 | 4443 | minIndex = profileRangeList[0] |
|
|
4444 | 4444 | maxIndex = profileRangeList[1] |
|
|
4445 | 4445 | |
|
|
4446 | 4446 | if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex): |
|
|
4447 | 4447 | |
|
|
4448 | 4448 | self.nProfiles = maxIndex - minIndex + 1 |
|
|
4449 | 4449 | dataOut.nProfiles = self.nProfiles |
|
|
4450 | 4450 | dataOut.profileIndex = self.profileIndex |
|
|
4451 | 4451 | dataOut.flagNoData = False |
|
|
4452 | 4452 | |
|
|
4453 | 4453 | self.incProfileIndex() |
|
|
4454 | 4454 | return dataOut |
|
|
4455 | 4455 | |
|
|
4456 | 4456 | if rangeList != None: |
|
|
4457 | 4457 | |
|
|
4458 | 4458 | nProfiles = 0 |
|
|
4459 | 4459 | |
|
|
4460 | 4460 | for thisRange in rangeList: |
|
|
4461 | 4461 | minIndex = thisRange[0] |
|
|
4462 | 4462 | maxIndex = thisRange[1] |
|
|
4463 | 4463 | |
|
|
4464 | 4464 | nProfiles += maxIndex - minIndex + 1 |
|
|
4465 | 4465 | |
|
|
4466 | 4466 | for thisRange in rangeList: |
|
|
4467 | 4467 | |
|
|
4468 | 4468 | minIndex = thisRange[0] |
|
|
4469 | 4469 | maxIndex = thisRange[1] |
|
|
4470 | 4470 | |
|
|
4471 | 4471 | if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex): |
|
|
4472 | 4472 | |
|
|
4473 | 4473 | self.nProfiles = nProfiles |
|
|
4474 | 4474 | dataOut.nProfiles = self.nProfiles |
|
|
4475 | 4475 | dataOut.profileIndex = self.profileIndex |
|
|
4476 | 4476 | dataOut.flagNoData = False |
|
|
4477 | 4477 | |
|
|
4478 | 4478 | self.incProfileIndex() |
|
|
4479 | 4479 | |
|
|
4480 | 4480 | break |
|
|
4481 | 4481 | |
|
|
4482 | 4482 | return dataOut |
|
|
4483 | 4483 | |
|
|
4484 | 4484 | |
|
|
4485 | 4485 | if beam != None: #beam is only for AMISR data |
|
|
4486 | 4486 | if self.isThisProfileInList(dataOut.profileIndex, dataOut.beamRangeDict[beam]): |
|
|
4487 | 4487 | dataOut.flagNoData = False |
|
|
4488 | 4488 | dataOut.profileIndex = self.profileIndex |
|
|
4489 | 4489 | |
|
|
4490 | 4490 | self.incProfileIndex() |
|
|
4491 | 4491 | |
|
|
4492 | 4492 | return dataOut |
|
|
4493 | 4493 | |
|
|
4494 | 4494 | raise ValueError("ProfileSelector needs profileList, profileRangeList or rangeList parameter") |
|
|
4495 | 4495 | |
|
|
4496 | 4496 | #return False |
|
|
4497 | 4497 | return dataOut |
|
|
4498 | 4498 | |
|
|
4499 | 4499 | class Reshaper(Operation): |
|
|
4500 | 4500 | |
|
|
4501 | 4501 | def __init__(self, **kwargs): |
|
|
4502 | 4502 | |
|
|
4503 | 4503 | Operation.__init__(self, **kwargs) |
|
|
4504 | 4504 | |
|
|
4505 | 4505 | self.__buffer = None |
|
|
4506 | 4506 | self.__nitems = 0 |
|
|
4507 | 4507 | |
|
|
4508 | 4508 | def __appendProfile(self, dataOut, nTxs): |
|
|
4509 | 4509 | |
|
|
4510 | 4510 | if self.__buffer is None: |
|
|
4511 | 4511 | shape = (dataOut.nChannels, int(dataOut.nHeights/nTxs) ) |
|
|
4512 | 4512 | self.__buffer = numpy.empty(shape, dtype = dataOut.data.dtype) |
|
|
4513 | 4513 | |
|
|
4514 | 4514 | ini = dataOut.nHeights * self.__nitems |
|
|
4515 | 4515 | end = ini + dataOut.nHeights |
|
|
4516 | 4516 | |
|
|
4517 | 4517 | self.__buffer[:, ini:end] = dataOut.data |
|
|
4518 | 4518 | |
|
|
4519 | 4519 | self.__nitems += 1 |
|
|
4520 | 4520 | |
|
|
4521 | 4521 | return int(self.__nitems*nTxs) |
|
|
4522 | 4522 | |
|
|
4523 | 4523 | def __getBuffer(self): |
|
|
4524 | 4524 | |
|
|
4525 | 4525 | if self.__nitems == int(1./self.__nTxs): |
|
|
4526 | 4526 | |
|
|
4527 | 4527 | self.__nitems = 0 |
|
|
4528 | 4528 | |
|
|
4529 | 4529 | return self.__buffer.copy() |
|
|
4530 | 4530 | |
|
|
4531 | 4531 | return None |
|
|
4532 | 4532 | |
|
|
4533 | 4533 | def __checkInputs(self, dataOut, shape, nTxs): |
|
|
4534 | 4534 | |
|
|
4535 | 4535 | if shape is None and nTxs is None: |
|
|
4536 | 4536 | raise ValueError("Reshaper: shape of factor should be defined") |
|
|
4537 | 4537 | |
|
|
4538 | 4538 | if nTxs: |
|
|
4539 | 4539 | if nTxs < 0: |
|
|
4540 | 4540 | raise ValueError("nTxs should be greater than 0") |
|
|
4541 | 4541 | |
|
|
4542 | 4542 | if nTxs < 1 and dataOut.nProfiles % (1./nTxs) != 0: |
|
|
4543 | 4543 | raise ValueError("nProfiles= %d is not divisibled by (1./nTxs) = %f" %(dataOut.nProfiles, (1./nTxs))) |
|
|
4544 | 4544 | |
|
|
4545 | 4545 | shape = [dataOut.nChannels, dataOut.nProfiles*nTxs, dataOut.nHeights/nTxs] |
|
|
4546 | 4546 | |
|
|
4547 | 4547 | return shape, nTxs |
|
|
4548 | 4548 | |
|
|
4549 | 4549 | if len(shape) != 2 and len(shape) != 3: |
|
|
4550 | 4550 | raise ValueError("shape dimension should be equal to 2 or 3. shape = (nProfiles, nHeis) or (nChannels, nProfiles, nHeis). Actually shape = (%d, %d, %d)" %(dataOut.nChannels, dataOut.nProfiles, dataOut.nHeights)) |
|
|
4551 | 4551 | |
|
|
4552 | 4552 | if len(shape) == 2: |
|
|
4553 | 4553 | shape_tuple = [dataOut.nChannels] |
|
|
4554 | 4554 | shape_tuple.extend(shape) |
|
|
4555 | 4555 | else: |
|
|
4556 | 4556 | shape_tuple = list(shape) |
|
|
4557 | 4557 | |
|
|
4558 | 4558 | nTxs = 1.0*shape_tuple[1]/dataOut.nProfiles |
|
|
4559 | 4559 | |
|
|
4560 | 4560 | return shape_tuple, nTxs |
|
|
4561 | 4561 | |
|
|
4562 | 4562 | def run(self, dataOut, shape=None, nTxs=None): |
|
|
4563 | 4563 | |
|
|
4564 | 4564 | shape_tuple, self.__nTxs = self.__checkInputs(dataOut, shape, nTxs) |
|
|
4565 | 4565 | |
|
|
4566 | 4566 | dataOut.flagNoData = True |
|
|
4567 | 4567 | profileIndex = None |
|
|
4568 | 4568 | |
|
|
4569 | 4569 | if dataOut.flagDataAsBlock: |
|
|
4570 | 4570 | |
|
|
4571 | 4571 | dataOut.data = numpy.reshape(dataOut.data, shape_tuple) |
|
|
4572 | 4572 | dataOut.flagNoData = False |
|
|
4573 | 4573 | |
|
|
4574 | 4574 | profileIndex = int(dataOut.nProfiles*self.__nTxs) - 1 |
|
|
4575 | 4575 | |
|
|
4576 | 4576 | else: |
|
|
4577 | 4577 | |
|
|
4578 | 4578 | |
|
|
4579 | 4579 | if self.__nTxs < 1: |
|
|
4580 | 4580 | |
|
|
4581 | 4581 | self.__appendProfile(dataOut, self.__nTxs) |
|
|
4582 | 4582 | new_data = self.__getBuffer() |
|
|
4583 | 4583 | |
|
|
4584 | 4584 | if new_data is not None: |
|
|
4585 | 4585 | dataOut.data = new_data |
|
|
4586 | 4586 | dataOut.flagNoData = False |
|
|
4587 | 4587 | |
|
|
4588 | 4588 | profileIndex = dataOut.profileIndex*nTxs |
|
|
4589 | 4589 | |
|
|
4590 | 4590 | else: |
|
|
4591 | 4591 | raise ValueError("nTxs should be greater than 0 and lower than 1, or use VoltageReader(..., getblock=True)") |
|
|
4592 | 4592 | |
|
|
4593 | 4593 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
4594 | 4594 | |
|
|
4595 | 4595 | dataOut.heightList = numpy.arange(dataOut.nHeights/self.__nTxs) * deltaHeight + dataOut.heightList[0] |
|
|
4596 | 4596 | |
|
|
4597 | 4597 | dataOut.nProfiles = int(dataOut.nProfiles*self.__nTxs) |
|
|
4598 | 4598 | |
|
|
4599 | 4599 | dataOut.profileIndex = profileIndex |
|
|
4600 | 4600 | |
|
|
4601 | 4601 | dataOut.ippSeconds /= self.__nTxs |
|
|
4602 | 4602 | |
|
|
4603 | 4603 | return dataOut |
|
|
4604 | 4604 | |
|
|
4605 | 4605 | class SplitProfiles(Operation): |
|
|
4606 | 4606 | |
|
|
4607 | 4607 | def __init__(self, **kwargs): |
|
|
4608 | 4608 | |
|
|
4609 | 4609 | Operation.__init__(self, **kwargs) |
|
|
4610 | 4610 | |
|
|
4611 | 4611 | def run(self, dataOut, n): |
|
|
4612 | 4612 | |
|
|
4613 | 4613 | dataOut.flagNoData = True |
|
|
4614 | 4614 | profileIndex = None |
|
|
4615 | 4615 | |
|
|
4616 | 4616 | if dataOut.flagDataAsBlock: |
|
|
4617 | 4617 | |
|
|
4618 | 4618 | #nchannels, nprofiles, nsamples |
|
|
4619 | 4619 | shape = dataOut.data.shape |
|
|
4620 | 4620 | |
|
|
4621 | 4621 | if shape[2] % n != 0: |
|
|
4622 | 4622 | raise ValueError("Could not split the data, n=%d has to be multiple of %d" %(n, shape[2])) |
|
|
4623 | 4623 | |
|
|
4624 | 4624 | new_shape = shape[0], shape[1]*n, int(shape[2]/n) |
|
|
4625 | 4625 | |
|
|
4626 | 4626 | dataOut.data = numpy.reshape(dataOut.data, new_shape) |
|
|
4627 | 4627 | dataOut.flagNoData = False |
|
|
4628 | 4628 | |
|
|
4629 | 4629 | profileIndex = int(dataOut.nProfiles/n) - 1 |
|
|
4630 | 4630 | |
|
|
4631 | 4631 | else: |
|
|
4632 | 4632 | |
|
|
4633 | 4633 | raise ValueError("Could not split the data when is read Profile by Profile. Use VoltageReader(..., getblock=True)") |
|
|
4634 | 4634 | |
|
|
4635 | 4635 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
4636 | 4636 | |
|
|
4637 | 4637 | dataOut.heightList = numpy.arange(dataOut.nHeights/n) * deltaHeight + dataOut.heightList[0] |
|
|
4638 | 4638 | |
|
|
4639 | 4639 | dataOut.nProfiles = int(dataOut.nProfiles*n) |
|
|
4640 | 4640 | |
|
|
4641 | 4641 | dataOut.profileIndex = profileIndex |
|
|
4642 | 4642 | |
|
|
4643 | 4643 | dataOut.ippSeconds /= n |
|
|
4644 | 4644 | |
|
|
4645 | 4645 | return dataOut |
|
|
4646 | 4646 | |
|
|
4647 | 4647 | class CombineProfiles(Operation): |
|
|
4648 | 4648 | def __init__(self, **kwargs): |
|
|
4649 | 4649 | |
|
|
4650 | 4650 | Operation.__init__(self, **kwargs) |
|
|
4651 | 4651 | |
|
|
4652 | 4652 | self.__remData = None |
|
|
4653 | 4653 | self.__profileIndex = 0 |
|
|
4654 | 4654 | |
|
|
4655 | 4655 | def run(self, dataOut, n): |
|
|
4656 | 4656 | |
|
|
4657 | 4657 | dataOut.flagNoData = True |
|
|
4658 | 4658 | profileIndex = None |
|
|
4659 | 4659 | |
|
|
4660 | 4660 | if dataOut.flagDataAsBlock: |
|
|
4661 | 4661 | |
|
|
4662 | 4662 | #nchannels, nprofiles, nsamples |
|
|
4663 | 4663 | shape = dataOut.data.shape |
|
|
4664 | 4664 | new_shape = shape[0], shape[1]/n, shape[2]*n |
|
|
4665 | 4665 | |
|
|
4666 | 4666 | if shape[1] % n != 0: |
|
|
4667 | 4667 | raise ValueError("Could not split the data, n=%d has to be multiple of %d" %(n, shape[1])) |
|
|
4668 | 4668 | |
|
|
4669 | 4669 | dataOut.data = numpy.reshape(dataOut.data, new_shape) |
|
|
4670 | 4670 | dataOut.flagNoData = False |
|
|
4671 | 4671 | |
|
|
4672 | 4672 | profileIndex = int(dataOut.nProfiles*n) - 1 |
|
|
4673 | 4673 | |
|
|
4674 | 4674 | else: |
|
|
4675 | 4675 | |
|
|
4676 | 4676 | #nchannels, nsamples |
|
|
4677 | 4677 | if self.__remData is None: |
|
|
4678 | 4678 | newData = dataOut.data |
|
|
4679 | 4679 | else: |
|
|
4680 | 4680 | newData = numpy.concatenate((self.__remData, dataOut.data), axis=1) |
|
|
4681 | 4681 | |
|
|
4682 | 4682 | self.__profileIndex += 1 |
|
|
4683 | 4683 | |
|
|
4684 | 4684 | if self.__profileIndex < n: |
|
|
4685 | 4685 | self.__remData = newData |
|
|
4686 | 4686 | #continue |
|
|
4687 | 4687 | return |
|
|
4688 | 4688 | |
|
|
4689 | 4689 | self.__profileIndex = 0 |
|
|
4690 | 4690 | self.__remData = None |
|
|
4691 | 4691 | |
|
|
4692 | 4692 | dataOut.data = newData |
|
|
4693 | 4693 | dataOut.flagNoData = False |
|
|
4694 | 4694 | |
|
|
4695 | 4695 | profileIndex = dataOut.profileIndex/n |
|
|
4696 | 4696 | |
|
|
4697 | 4697 | |
|
|
4698 | 4698 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
4699 | 4699 | |
|
|
4700 | 4700 | dataOut.heightList = numpy.arange(dataOut.nHeights*n) * deltaHeight + dataOut.heightList[0] |
|
|
4701 | 4701 | |
|
|
4702 | 4702 | dataOut.nProfiles = int(dataOut.nProfiles/n) |
|
|
4703 | 4703 | |
|
|
4704 | 4704 | dataOut.profileIndex = profileIndex |
|
|
4705 | 4705 | |
|
|
4706 | 4706 | dataOut.ippSeconds *= n |
|
|
4707 | 4707 | |
|
|
4708 | 4708 | return dataOut |
|
|
4709 | 4709 | # import collections |
|
|
4710 | 4710 | # from scipy.stats import mode |
|
|
4711 | 4711 | # |
|
|
4712 | 4712 | # class Synchronize(Operation): |
|
|
4713 | 4713 | # |
|
|
4714 | 4714 | # isConfig = False |
|
|
4715 | 4715 | # __profIndex = 0 |
|
|
4716 | 4716 | # |
|
|
4717 | 4717 | # def __init__(self, **kwargs): |
|
|
4718 | 4718 | # |
|
|
4719 | 4719 | # Operation.__init__(self, **kwargs) |
|
|
4720 | 4720 | # # self.isConfig = False |
|
|
4721 | 4721 | # self.__powBuffer = None |
|
|
4722 | 4722 | # self.__startIndex = 0 |
|
|
4723 | 4723 | # self.__pulseFound = False |
|
|
4724 | 4724 | # |
|
|
4725 | 4725 | # def __findTxPulse(self, dataOut, channel=0, pulse_with = None): |
|
|
4726 | 4726 | # |
|
|
4727 | 4727 | # #Read data |
|
|
4728 | 4728 | # |
|
|
4729 | 4729 | # powerdB = dataOut.getPower(channel = channel) |
|
|
4730 | 4730 | # noisedB = dataOut.getNoise(channel = channel)[0] |
|
|
4731 | 4731 | # |
|
|
4732 | 4732 | # self.__powBuffer.extend(powerdB.flatten()) |
|
|
4733 | 4733 | # |
|
|
4734 | 4734 | # dataArray = numpy.array(self.__powBuffer) |
|
|
4735 | 4735 | # |
|
|
4736 | 4736 | # filteredPower = numpy.correlate(dataArray, dataArray[0:self.__nSamples], "same") |
|
|
4737 | 4737 | # |
|
|
4738 | 4738 | # maxValue = numpy.nanmax(filteredPower) |
|
|
4739 | 4739 | # |
|
|
4740 | 4740 | # if maxValue < noisedB + 10: |
|
|
4741 | 4741 | # #No se encuentra ningun pulso de transmision |
|
|
4742 | 4742 | # return None |
|
|
4743 | 4743 | # |
|
|
4744 | 4744 | # maxValuesIndex = numpy.where(filteredPower > maxValue - 0.1*abs(maxValue))[0] |
|
|
4745 | 4745 | # |
|
|
4746 | 4746 | # if len(maxValuesIndex) < 2: |
|
|
4747 | 4747 | # #Solo se encontro un solo pulso de transmision de un baudio, esperando por el siguiente TX |
|
|
4748 | 4748 | # return None |
|
|
4749 | 4749 | # |
|
|
4750 | 4750 | # phasedMaxValuesIndex = maxValuesIndex - self.__nSamples |
|
|
4751 | 4751 | # |
|
|
4752 | 4752 | # #Seleccionar solo valores con un espaciamiento de nSamples |
|
|
4753 | 4753 | # pulseIndex = numpy.intersect1d(maxValuesIndex, phasedMaxValuesIndex) |
|
|
4754 | 4754 | # |
|
|
4755 | 4755 | # if len(pulseIndex) < 2: |
|
|
4756 | 4756 | # #Solo se encontro un pulso de transmision con ancho mayor a 1 |
|
|
4757 | 4757 | # return None |
|
|
4758 | 4758 | # |
|
|
4759 | 4759 | # spacing = pulseIndex[1:] - pulseIndex[:-1] |
|
|
4760 | 4760 | # |
|
|
4761 | 4761 | # #remover senales que se distancien menos de 10 unidades o muestras |
|
|
4762 | 4762 | # #(No deberian existir IPP menor a 10 unidades) |
|
|
4763 | 4763 | # |
|
|
4764 | 4764 | # realIndex = numpy.where(spacing > 10 )[0] |
|
|
4765 | 4765 | # |
|
|
4766 | 4766 | # if len(realIndex) < 2: |
|
|
4767 | 4767 | # #Solo se encontro un pulso de transmision con ancho mayor a 1 |
|
|
4768 | 4768 | # return None |
|
|
4769 | 4769 | # |
|
|
4770 | 4770 | # #Eliminar pulsos anchos (deja solo la diferencia entre IPPs) |
|
|
4771 | 4771 | # realPulseIndex = pulseIndex[realIndex] |
|
|
4772 | 4772 | # |
|
|
4773 | 4773 | # period = mode(realPulseIndex[1:] - realPulseIndex[:-1])[0][0] |
|
|
4774 | 4774 | # |
|
|
4775 | 4775 | # print "IPP = %d samples" %period |
|
|
4776 | 4776 | # |
|
|
4777 | 4777 | # self.__newNSamples = dataOut.nHeights #int(period) |
|
|
4778 | 4778 | # self.__startIndex = int(realPulseIndex[0]) |
|
|
4779 | 4779 | # |
|
|
4780 | 4780 | # return 1 |
|
|
4781 | 4781 | # |
|
|
4782 | 4782 | # |
|
|
4783 | 4783 | # def setup(self, nSamples, nChannels, buffer_size = 4): |
|
|
4784 | 4784 | # |
|
|
4785 | 4785 | # self.__powBuffer = collections.deque(numpy.zeros( buffer_size*nSamples,dtype=numpy.float), |
|
|
4786 | 4786 | # maxlen = buffer_size*nSamples) |
|
|
4787 | 4787 | # |
|
|
4788 | 4788 | # bufferList = [] |
|
|
4789 | 4789 | # |
|
|
4790 | 4790 | # for i in range(nChannels): |
|
|
4791 | 4791 | # bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=complex) + numpy.NAN, |
|
|
4792 | 4792 | # maxlen = buffer_size*nSamples) |
|
|
4793 | 4793 | # |
|
|
4794 | 4794 | # bufferList.append(bufferByChannel) |
|
|
4795 | 4795 | # |
|
|
4796 | 4796 | # self.__nSamples = nSamples |
|
|
4797 | 4797 | # self.__nChannels = nChannels |
|
|
4798 | 4798 | # self.__bufferList = bufferList |
|
|
4799 | 4799 | # |
|
|
4800 | 4800 | # def run(self, dataOut, channel = 0): |
|
|
4801 | 4801 | # |
|
|
4802 | 4802 | # if not self.isConfig: |
|
|
4803 | 4803 | # nSamples = dataOut.nHeights |
|
|
4804 | 4804 | # nChannels = dataOut.nChannels |
|
|
4805 | 4805 | # self.setup(nSamples, nChannels) |
|
|
4806 | 4806 | # self.isConfig = True |
|
|
4807 | 4807 | # |
|
|
4808 | 4808 | # #Append new data to internal buffer |
|
|
4809 | 4809 | # for thisChannel in range(self.__nChannels): |
|
|
4810 | 4810 | # bufferByChannel = self.__bufferList[thisChannel] |
|
|
4811 | 4811 | # bufferByChannel.extend(dataOut.data[thisChannel]) |
|
|
4812 | 4812 | # |
|
|
4813 | 4813 | # if self.__pulseFound: |
|
|
4814 | 4814 | # self.__startIndex -= self.__nSamples |
|
|
4815 | 4815 | # |
|
|
4816 | 4816 | # #Finding Tx Pulse |
|
|
4817 | 4817 | # if not self.__pulseFound: |
|
|
4818 | 4818 | # indexFound = self.__findTxPulse(dataOut, channel) |
|
|
4819 | 4819 | # |
|
|
4820 | 4820 | # if indexFound == None: |
|
|
4821 | 4821 | # dataOut.flagNoData = True |
|
|
4822 | 4822 | # return |
|
|
4823 | 4823 | # |
|
|
4824 | 4824 | # self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = complex) |
|
|
4825 | 4825 | # self.__pulseFound = True |
|
|
4826 | 4826 | # self.__startIndex = indexFound |
|
|
4827 | 4827 | # |
|
|
4828 | 4828 | # #If pulse was found ... |
|
|
4829 | 4829 | # for thisChannel in range(self.__nChannels): |
|
|
4830 | 4830 | # bufferByChannel = self.__bufferList[thisChannel] |
|
|
4831 | 4831 | # #print self.__startIndex |
|
|
4832 | 4832 | # x = numpy.array(bufferByChannel) |
|
|
4833 | 4833 | # self.__arrayBuffer[thisChannel] = x[self.__startIndex:self.__startIndex+self.__newNSamples] |
|
|
4834 | 4834 | # |
|
|
4835 | 4835 | # deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
4836 | 4836 | # dataOut.heightList = numpy.arange(self.__newNSamples)*deltaHeight |
|
|
4837 | 4837 | # # dataOut.ippSeconds = (self.__newNSamples / deltaHeight)/1e6 |
|
|
4838 | 4838 | # |
|
|
4839 | 4839 | # dataOut.data = self.__arrayBuffer |
|
|
4840 | 4840 | # |
|
|
4841 | 4841 | # self.__startIndex += self.__newNSamples |
|
|
4842 | 4842 | # |
|
|
4843 | 4843 | # return |
|
|
4844 | 4844 | |
|
|
4845 | 4845 | |
|
|
4846 | 4846 | |
|
|
4847 | 4847 | |
|
|
4848 | 4848 | |
|
|
4849 | 4849 | |
|
|
4850 | 4850 | |
|
|
4851 | 4851 | ##############################LONG PULSE############################## |
|
|
4852 | 4852 | |
|
|
4853 | 4853 | |
|
|
4854 | 4854 | |
|
|
4855 | 4855 | class CrossProdHybrid(CrossProdDP): |
|
|
4856 | 4856 | """Operation to calculate cross products of the Hybrid Experiment. |
|
|
4857 | 4857 | |
|
|
4858 | 4858 | Parameters: |
|
|
4859 | 4859 | ----------- |
|
|
4860 | 4860 | NLAG : int |
|
|
4861 | 4861 | Number of lags for Long Pulse. |
|
|
4862 | 4862 | NRANGE : int |
|
|
4863 | 4863 | Number of samples (heights) for Long Pulse. |
|
|
4864 | 4864 | NCAL : int |
|
|
4865 | 4865 | .* |
|
|
4866 | 4866 | DPL : int |
|
|
4867 | 4867 | Number of lags for Double Pulse. |
|
|
4868 | 4868 | NDN : int |
|
|
4869 | 4869 | .* |
|
|
4870 | 4870 | NDT : int |
|
|
4871 | 4871 | Number of heights for Double Pulse.* |
|
|
4872 | 4872 | NDP : int |
|
|
4873 | 4873 | Number of heights for Double Pulse.* |
|
|
4874 | 4874 | NSCAN : int |
|
|
4875 | 4875 | Number of profiles when the transmitter is on. |
|
|
4876 | 4876 | lagind : intlist |
|
|
4877 | 4877 | .* |
|
|
4878 | 4878 | lagfirst : intlist |
|
|
4879 | 4879 | .* |
|
|
4880 | 4880 | NAVG : int |
|
|
4881 | 4881 | Number of blocks to be "averaged". |
|
|
4882 | 4882 | nkill : int |
|
|
4883 | 4883 | Number of blocks not to be considered when averaging. |
|
|
4884 | 4884 | |
|
|
4885 | 4885 | Example |
|
|
4886 | 4886 | -------- |
|
|
4887 | 4887 | |
|
|
4888 | 4888 | op = proc_unit.addOperation(name='CrossProdHybrid', optype='other') |
|
|
4889 | 4889 | op.addParameter(name='NLAG', value='16', format='int') |
|
|
4890 | 4890 | op.addParameter(name='NRANGE', value='200', format='int') |
|
|
4891 | 4891 | op.addParameter(name='NCAL', value='0', format='int') |
|
|
4892 | 4892 | op.addParameter(name='DPL', value='11', format='int') |
|
|
4893 | 4893 | op.addParameter(name='NDN', value='0', format='int') |
|
|
4894 | 4894 | op.addParameter(name='NDT', value='67', format='int') |
|
|
4895 | 4895 | op.addParameter(name='NDP', value='67', format='int') |
|
|
4896 | 4896 | op.addParameter(name='NSCAN', value='128', format='int') |
|
|
4897 | 4897 | op.addParameter(name='lagind', value='(0,1,2,3,4,5,6,7,0,3,4,5,6,8,9,10)', format='intlist') |
|
|
4898 | 4898 | op.addParameter(name='lagfirst', value='(1,1,1,1,1,1,1,1,0,0,0,0,0,1,1,1)', format='intlist') |
|
|
4899 | 4899 | op.addParameter(name='NAVG', value='16', format='int') |
|
|
4900 | 4900 | op.addParameter(name='nkill', value='6', format='int') |
|
|
4901 | 4901 | |
|
|
4902 | 4902 | """ |
|
|
4903 | 4903 | |
|
|
4904 | 4904 | def __init__(self, **kwargs): |
|
|
4905 | 4905 | |
|
|
4906 | 4906 | Operation.__init__(self, **kwargs) |
|
|
4907 | 4907 | self.bcounter=0 |
|
|
4908 | 4908 | self.aux=1 |
|
|
4909 | 4909 | self.aux_cross_lp=1 |
|
|
4910 | 4910 | self.lag_products_LP_median_estimates_aux=1 |
|
|
4911 | 4911 | |
|
|
4912 | 4912 | def get_products_cabxys_HP(self,dataOut): |
|
|
4913 | 4913 | |
|
|
4914 | 4914 | if self.aux==1: |
|
|
4915 | 4915 | self.set_header_output(dataOut) |
|
|
4916 | 4916 | self.aux=0 |
|
|
4917 | 4917 | |
|
|
4918 | 4918 | self.cax=numpy.zeros((dataOut.NDP,dataOut.DPL,2))# hp:67x11x2 dp: 66x11x2 |
|
|
4919 | 4919 | self.cay=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
|
4920 | 4920 | self.cbx=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
|
4921 | 4921 | self.cby=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
|
4922 | 4922 | self.cax2=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
|
4923 | 4923 | self.cay2=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
|
4924 | 4924 | self.cbx2=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
|
4925 | 4925 | self.cby2=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
|
4926 | 4926 | self.caxbx=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
|
4927 | 4927 | self.caxby=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
|
4928 | 4928 | self.caybx=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
|
4929 | 4929 | self.cayby=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
|
4930 | 4930 | self.caxay=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
|
4931 | 4931 | self.cbxby=numpy.zeros((dataOut.NDP,dataOut.DPL,2)) |
|
|
4932 | 4932 | for i in range(2): # flipped and unflipped |
|
|
4933 | 4933 | for j in range(dataOut.NDP): # loop over true ranges # 67 |
|
|
4934 | 4934 | for k in range(int(dataOut.NSCAN)): # 128 |
|
|
4935 | 4935 | |
|
|
4936 | 4936 | n=dataOut.lagind[k%dataOut.NLAG] # 128=16x8 |
|
|
4937 | 4937 | |
|
|
4938 | 4938 | ax=dataOut.data[0,k,dataOut.NRANGE+dataOut.NCAL+j+i*dataOut.NDT].real-dataOut.dc.real[0] |
|
|
4939 | 4939 | ay=dataOut.data[0,k,dataOut.NRANGE+dataOut.NCAL+j+i*dataOut.NDT].imag-dataOut.dc.imag[0] |
|
|
4940 | 4940 | |
|
|
4941 | 4941 | if dataOut.NRANGE+dataOut.NCAL+j+i*dataOut.NDT+2*n<dataOut.read_samples: |
|
|
4942 | 4942 | |
|
|
4943 | 4943 | bx=dataOut.data[1,k,dataOut.NRANGE+dataOut.NCAL+j+i*dataOut.NDT+2*n].real-dataOut.dc.real[1] |
|
|
4944 | 4944 | by=dataOut.data[1,k,dataOut.NRANGE+dataOut.NCAL+j+i*dataOut.NDT+2*n].imag-dataOut.dc.imag[1] |
|
|
4945 | 4945 | |
|
|
4946 | 4946 | else: |
|
|
4947 | 4947 | |
|
|
4948 | 4948 | if k+1<int(dataOut.NSCAN): |
|
|
4949 | 4949 | bx=dataOut.data[1,k+1,(dataOut.NRANGE+dataOut.NCAL+j+i*dataOut.NDT+2*n)%dataOut.NDP].real |
|
|
4950 | 4950 | by=dataOut.data[1,k+1,(dataOut.NRANGE+dataOut.NCAL+j+i*dataOut.NDT+2*n)%dataOut.NDP].imag |
|
|
4951 | 4951 | |
|
|
4952 | 4952 | if k+1==int(dataOut.NSCAN):## ESTO ES UN PARCHE PUES NO SE TIENE EL SIGUIENTE BLOQUE |
|
|
4953 | 4953 | bx=dataOut.data[1,k,(dataOut.NRANGE+dataOut.NCAL+j+i*dataOut.NDT+2*n)%dataOut.NDP].real |
|
|
4954 | 4954 | by=dataOut.data[1,k,(dataOut.NRANGE+dataOut.NCAL+j+i*dataOut.NDT+2*n)%dataOut.NDP].imag |
|
|
4955 | 4955 | |
|
|
4956 | 4956 | if(k<dataOut.NLAG and dataOut.lagfirst[k%dataOut.NLAG]==1):# if(k<16 && lagfirst[k%16]==1) |
|
|
4957 | 4957 | self.cax[j][n][i]=ax |
|
|
4958 | 4958 | self.cay[j][n][i]=ay |
|
|
4959 | 4959 | self.cbx[j][n][i]=bx |
|
|
4960 | 4960 | self.cby[j][n][i]=by |
|
|
4961 | 4961 | self.cax2[j][n][i]=ax*ax |
|
|
4962 | 4962 | self.cay2[j][n][i]=ay*ay |
|
|
4963 | 4963 | self.cbx2[j][n][i]=bx*bx |
|
|
4964 | 4964 | self.cby2[j][n][i]=by*by |
|
|
4965 | 4965 | self.caxbx[j][n][i]=ax*bx |
|
|
4966 | 4966 | self.caxby[j][n][i]=ax*by |
|
|
4967 | 4967 | self.caybx[j][n][i]=ay*bx |
|
|
4968 | 4968 | self.cayby[j][n][i]=ay*by |
|
|
4969 | 4969 | self.caxay[j][n][i]=ax*ay |
|
|
4970 | 4970 | self.cbxby[j][n][i]=bx*by |
|
|
4971 | 4971 | else: |
|
|
4972 | 4972 | self.cax[j][n][i]+=ax |
|
|
4973 | 4973 | self.cay[j][n][i]+=ay |
|
|
4974 | 4974 | self.cbx[j][n][i]+=bx |
|
|
4975 | 4975 | self.cby[j][n][i]+=by |
|
|
4976 | 4976 | self.cax2[j][n][i]+=ax*ax |
|
|
4977 | 4977 | self.cay2[j][n][i]+=ay*ay |
|
|
4978 | 4978 | self.cbx2[j][n][i]+=bx*bx |
|
|
4979 | 4979 | self.cby2[j][n][i]+=by*by |
|
|
4980 | 4980 | self.caxbx[j][n][i]+=ax*bx |
|
|
4981 | 4981 | self.caxby[j][n][i]+=ax*by |
|
|
4982 | 4982 | self.caybx[j][n][i]+=ay*bx |
|
|
4983 | 4983 | self.cayby[j][n][i]+=ay*by |
|
|
4984 | 4984 | self.caxay[j][n][i]+=ax*ay |
|
|
4985 | 4985 | self.cbxby[j][n][i]+=bx*by |
|
|
4986 | 4986 | |
|
|
4987 | 4987 | |
|
|
4988 | 4988 | #print(self.cax2[2,0,1]) |
|
|
4989 | 4989 | #input() |
|
|
4990 | 4990 | |
|
|
4991 | 4991 | |
|
|
4992 | 4992 | def lag_products_LP(self,dataOut): |
|
|
4993 | 4993 | |
|
|
4994 | 4994 | |
|
|
4995 | 4995 | buffer=dataOut.data |
|
|
4996 | 4996 | if self.aux_cross_lp==1: |
|
|
4997 | 4997 | |
|
|
4998 | 4998 | #self.dataOut.nptsfft2=150 |
|
|
4999 | 4999 | self.cnorm=float((dataOut.nProfiles-dataOut.NSCAN)/dataOut.NSCAN) |
|
|
5000 | 5000 | self.lagp0=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') |
|
|
5001 | 5001 | self.lagp1=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') |
|
|
5002 | 5002 | self.lagp2=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') |
|
|
5003 | 5003 | self.lagp3=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') |
|
|
5004 | 5004 | |
|
|
5005 | 5005 | #self.lagp4=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') |
|
|
5006 | 5006 | self.aux_cross_lp=0 |
|
|
5007 | 5007 | |
|
|
5008 | 5008 | #print(self.dataOut.data[0,0,0]) |
|
|
5009 | 5009 | |
|
|
5010 | 5010 | for i in range(dataOut.NR): |
|
|
5011 | 5011 | #print("inside i",i) |
|
|
5012 | 5012 | buffer_dc=dataOut.dc[i] |
|
|
5013 | 5013 | for j in range(dataOut.NRANGE): |
|
|
5014 | 5014 | |
|
|
5015 | 5015 | range_for_n=numpy.min((dataOut.NRANGE-j,dataOut.NLAG)) |
|
|
5016 | 5016 | |
|
|
5017 | 5017 | buffer_aux=numpy.conj(buffer[i,:dataOut.nProfiles,j]-buffer_dc) |
|
|
5018 | 5018 | for n in range(range_for_n): |
|
|
5019 | 5019 | |
|
|
5020 | 5020 | c=(buffer_aux)*(buffer[i,:dataOut.nProfiles,j+n]-buffer_dc) |
|
|
5021 | 5021 | |
|
|
5022 | 5022 | if i==0: |
|
|
5023 | 5023 | self.lagp0[n][j][self.bcounter-1]=numpy.sum(c[:dataOut.NSCAN]) |
|
|
5024 | 5024 | self.lagp3[n][j][self.bcounter-1]=numpy.sum(c[dataOut.NSCAN:]/self.cnorm) |
|
|
5025 | 5025 | elif i==1: |
|
|
5026 | 5026 | self.lagp1[n][j][self.bcounter-1]=numpy.sum(c[:dataOut.NSCAN]) |
|
|
5027 | 5027 | elif i==2: |
|
|
5028 | 5028 | self.lagp2[n][j][self.bcounter-1]=numpy.sum(c[:dataOut.NSCAN]) |
|
|
5029 | 5029 | |
|
|
5030 | 5030 | |
|
|
5031 | 5031 | self.lagp0[:,:,self.bcounter-1]=numpy.conj(self.lagp0[:,:,self.bcounter-1]) |
|
|
5032 | 5032 | self.lagp1[:,:,self.bcounter-1]=numpy.conj(self.lagp1[:,:,self.bcounter-1]) |
|
|
5033 | 5033 | self.lagp2[:,:,self.bcounter-1]=numpy.conj(self.lagp2[:,:,self.bcounter-1]) |
|
|
5034 | 5034 | self.lagp3[:,:,self.bcounter-1]=numpy.conj(self.lagp3[:,:,self.bcounter-1]) |
|
|
5035 | 5035 | |
|
|
5036 | 5036 | |
|
|
5037 | 5037 | def LP_median_estimates(self,dataOut): |
|
|
5038 | 5038 | |
|
|
5039 | 5039 | if self.bcounter==dataOut.NAVG: |
|
|
5040 | 5040 | |
|
|
5041 | 5041 | if self.lag_products_LP_median_estimates_aux==1: |
|
|
5042 | 5042 | self.output=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NR),'complex64') |
|
|
5043 | 5043 | self.lag_products_LP_median_estimates_aux=0 |
|
|
5044 | 5044 | |
|
|
5045 | 5045 | |
|
|
5046 | 5046 | for i in range(dataOut.NLAG): |
|
|
5047 | 5047 | for j in range(dataOut.NRANGE): |
|
|
5048 | 5048 | for l in range(4): #four outputs |
|
|
5049 | 5049 | |
|
|
5050 | 5050 | for k in range(dataOut.NAVG): |
|
|
5051 | 5051 | |
|
|
5052 | 5052 | |
|
|
5053 | 5053 | if k==0: |
|
|
5054 | 5054 | self.output[i,j,l]=0.0+0.j |
|
|
5055 | 5055 | |
|
|
5056 | 5056 | if l==0: |
|
|
5057 | 5057 | self.lagp0[i,j,:]=sorted(self.lagp0[i,j,:], key=lambda x: x.real) #sorted(self.lagp0[i,j,:].real) |
|
|
5058 | 5058 | |
|
|
5059 | 5059 | if l==1: |
|
|
5060 | 5060 | self.lagp1[i,j,:]=sorted(self.lagp1[i,j,:], key=lambda x: x.real) #sorted(self.lagp1[i,j,:].real) |
|
|
5061 | 5061 | |
|
|
5062 | 5062 | if l==2: |
|
|
5063 | 5063 | self.lagp2[i,j,:]=sorted(self.lagp2[i,j,:], key=lambda x: x.real) #sorted(self.lagp2[i,j,:].real) |
|
|
5064 | 5064 | |
|
|
5065 | 5065 | if l==3: |
|
|
5066 | 5066 | self.lagp3[i,j,:]=sorted(self.lagp3[i,j,:], key=lambda x: x.real) #sorted(self.lagp3[i,j,:].real) |
|
|
5067 | 5067 | |
|
|
5068 | 5068 | |
|
|
5069 | 5069 | |
|
|
5070 | 5070 | if k>=dataOut.nkill/2 and k<dataOut.NAVG-dataOut.nkill/2: |
|
|
5071 | 5071 | if l==0: |
|
|
5072 | 5072 | |
|
|
5073 | 5073 | self.output[i,j,l]=self.output[i,j,l]+((float(dataOut.NAVG)/(float)(dataOut.NAVG-dataOut.nkill))*self.lagp0[i,j,k]) |
|
|
5074 | 5074 | if l==1: |
|
|
5075 | 5075 | #print("lagp1: ",self.lagp1[0,0,:]) |
|
|
5076 | 5076 | #input() |
|
|
5077 | 5077 | self.output[i,j,l]=self.output[i,j,l]+((float(dataOut.NAVG)/(float)(dataOut.NAVG-dataOut.nkill))*self.lagp1[i,j,k]) |
|
|
5078 | 5078 | #print("self.lagp1[i,j,k]: ",self.lagp1[i,j,k]) |
|
|
5079 | 5079 | #input() |
|
|
5080 | 5080 | if l==2: |
|
|
5081 | 5081 | self.output[i,j,l]=self.output[i,j,l]+((float(dataOut.NAVG)/(float)(dataOut.NAVG-dataOut.nkill))*self.lagp2[i,j,k]) |
|
|
5082 | 5082 | if l==3: |
|
|
5083 | 5083 | |
|
|
5084 | 5084 | self.output[i,j,l]=self.output[i,j,l]+((float(dataOut.NAVG)/(float)(dataOut.NAVG-dataOut.nkill))*self.lagp3[i,j,k]) |
|
|
5085 | 5085 | |
|
|
5086 | 5086 | |
|
|
5087 | 5087 | dataOut.output_LP=self.output |
|
|
5088 | 5088 | dataOut.data_for_RTI_LP=numpy.zeros((4,dataOut.NRANGE)) |
|
|
5089 | 5089 | dataOut.data_for_RTI_LP[0],dataOut.data_for_RTI_LP[1],dataOut.data_for_RTI_LP[2],dataOut.data_for_RTI_LP[3]=self.RTI_LP(dataOut.output_LP,dataOut.NRANGE) |
|
|
5090 | 5090 | |
|
|
5091 | 5091 | |
|
|
5092 | 5092 | def get_dc(self,dataOut): |
|
|
5093 | 5093 | |
|
|
5094 | 5094 | if self.bcounter==0: |
|
|
5095 | 5095 | dataOut.dc=numpy.zeros(dataOut.NR,dtype='complex64') |
|
|
5096 | 5096 | |
|
|
5097 | 5097 | #print(numpy.shape(dataOut.data)) |
|
|
5098 | 5098 | #input() |
|
|
5099 | 5099 | |
|
|
5100 | 5100 | dataOut.dc+=numpy.sum(dataOut.data[:,:,2*dataOut.NLAG:dataOut.NRANGE],axis=(1,2)) |
|
|
5101 | 5101 | |
|
|
5102 | 5102 | dataOut.dc=dataOut.dc/float(dataOut.nProfiles*(dataOut.NRANGE-2*dataOut.NLAG)) |
|
|
5103 | 5103 | |
|
|
5104 | 5104 | |
|
|
5105 | 5105 | #print("dc:",dataOut.dc[0]) |
|
|
5106 | 5106 | |
|
|
5107 | 5107 | def get_dc_new(self,dataOut): |
|
|
5108 | 5108 | |
|
|
5109 | 5109 | if self.bcounter==0: |
|
|
5110 | 5110 | dataOut.dc_dp=numpy.zeros(dataOut.NR,dtype='complex64') |
|
|
5111 | 5111 | dataOut.dc_lp=numpy.zeros(dataOut.NR,dtype='complex64') |
|
|
5112 | 5112 | |
|
|
5113 | 5113 | #print(numpy.shape(dataOut.data)) |
|
|
5114 | 5114 | #input() |
|
|
5115 | 5115 | |
|
|
5116 | 5116 | dataOut.dc+=numpy.sum(dataOut.data[:,:,2*dataOut.NLAG:dataOut.NRANGE],axis=(1,2)) |
|
|
5117 | 5117 | |
|
|
5118 | 5118 | dataOut.dc=dataOut.dc/float(dataOut.nProfiles*(dataOut.NRANGE-2*dataOut.NLAG)) |
|
|
5119 | 5119 | |
|
|
5120 | 5120 | |
|
|
5121 | 5121 | #print("dc:",dataOut.dc[0]) |
|
|
5122 | 5122 | |
|
|
5123 | 5123 | |
|
|
5124 | 5124 | def noise_estimation4x_HP(self,dataOut): |
|
|
5125 | 5125 | if self.bcounter==dataOut.NAVG: |
|
|
5126 | 5126 | dataOut.noise_final=numpy.zeros(dataOut.NR,'float32') |
|
|
5127 | 5127 | #snoise=numpy.zeros((NR,NAVG),'float32') |
|
|
5128 | 5128 | #nvector1=numpy.zeros((NR,NAVG,MAXNRANGENDT),'float32') |
|
|
5129 | 5129 | sorted_data=numpy.zeros((dataOut.MAXNRANGENDT,dataOut.NR,dataOut.NAVG),'float32') |
|
|
5130 | 5130 | for i in range(dataOut.NR): |
|
|
5131 | 5131 | dataOut.noise_final[i]=0.0 |
|
|
5132 | 5132 | for j in range(dataOut.MAXNRANGENDT): |
|
|
5133 | 5133 | sorted_data[j,i,:]=numpy.copy(sorted(dataOut.noisevector[j,i,:])) |
|
|
5134 | 5134 | #print(sorted(noisevector[j,i,:])) |
|
|
5135 | 5135 | #input() |
|
|
5136 | 5136 | l=dataOut.MAXNRANGENDT-2 |
|
|
5137 | 5137 | for k in range(dataOut.NAVG): |
|
|
5138 | 5138 | if k>=dataOut.nkill/2 and k<dataOut.NAVG-dataOut.nkill/2: |
|
|
5139 | 5139 | #print(k) |
|
|
5140 | 5140 | #print(sorted_data[min(j,l),i,k]) |
|
|
5141 | 5141 | dataOut.noise_final[i]+=sorted_data[min(j,l),i,k]*float(dataOut.NAVG)/float(dataOut.NAVG-dataOut.nkill) |
|
|
5142 | 5142 | #print(dataOut.noise_final[i]) |
|
|
5143 | 5143 | #input() |
|
|
5144 | 5144 | #print(dataOut.noise_final) |
|
|
5145 | 5145 | #input() |
|
|
5146 | 5146 | |
|
|
5147 | 5147 | def noisevectorizer(self,NSCAN,nProfiles,NR,MAXNRANGENDT,noisevector,data,dc): |
|
|
5148 | 5148 | |
|
|
5149 | 5149 | #rnormalizer= 1./(float(nProfiles - NSCAN)) |
|
|
5150 | 5150 | rnormalizer= float(NSCAN)/((float(nProfiles - NSCAN))*float(MAXNRANGENDT)) |
|
|
5151 | 5151 | for i in range(NR): |
|
|
5152 | 5152 | for j in range(MAXNRANGENDT): |
|
|
5153 | 5153 | for k in range(NSCAN,nProfiles): |
|
|
5154 | 5154 | #TODO:integrate just 2nd quartile gates |
|
|
5155 | 5155 | if k==NSCAN: |
|
|
5156 | 5156 | noisevector[j][i][self.bcounter]=(abs(data[i][k][j]-dc[i])**2)*rnormalizer |
|
|
5157 | 5157 | ##noisevector[j][i][iavg]=(abs(cdata[k][j][i])**2)*rnormalizer |
|
|
5158 | 5158 | else: |
|
|
5159 | 5159 | noisevector[j][i][self.bcounter]+=(abs(data[i][k][j]-dc[i])**2)*rnormalizer |
|
|
5160 | 5160 | |
|
|
5161 | 5161 | |
|
|
5162 | 5162 | def RTI_LP(self,output,NRANGE): |
|
|
5163 | 5163 | x00=numpy.zeros(NRANGE,dtype='float32') |
|
|
5164 | 5164 | x01=numpy.zeros(NRANGE,dtype='float32') |
|
|
5165 | 5165 | x02=numpy.zeros(NRANGE,dtype='float32') |
|
|
5166 | 5166 | x03=numpy.zeros(NRANGE,dtype='float32') |
|
|
5167 | 5167 | |
|
|
5168 | 5168 | for i in range(2): #first couple of lags |
|
|
5169 | 5169 | for j in range(NRANGE): # |
|
|
5170 | 5170 | #fx=numpy.sqrt((kaxbx[i,j,k]+kayby[i,j,k])**2+(kaybx[i,j,k]-kaxby[i,j,k])**2) |
|
|
5171 | 5171 | x00[j]+=numpy.abs(output[i,j,0]) #Ch0 |
|
|
5172 | 5172 | x01[j]+=numpy.abs(output[i,j,1]) #Ch1 |
|
|
5173 | 5173 | x02[j]+=numpy.abs(output[i,j,2]) #Ch2 |
|
|
5174 | 5174 | x03[j]+=numpy.abs(output[i,j,3]) #Ch3 |
|
|
5175 | 5175 | #x02[i]=x02[i]+fx |
|
|
5176 | 5176 | |
|
|
5177 | 5177 | x00[j]=10.0*numpy.log10(x00[j]/4.) |
|
|
5178 | 5178 | x01[j]=10.0*numpy.log10(x01[j]/4.) |
|
|
5179 | 5179 | x02[j]=10.0*numpy.log10(x02[j]/4.) |
|
|
5180 | 5180 | x03[j]=10.0*numpy.log10(x03[j]/4.) |
|
|
5181 | 5181 | #x02[i]=10.0*numpy.log10(x02[i]) |
|
|
5182 | 5182 | return x00,x01,x02,x03 |
|
|
5183 | 5183 | |
|
|
5184 | 5184 | def run(self, dataOut, NLAG=None, NRANGE=None, NCAL=None, DPL=None, |
|
|
5185 | 5185 | NDN=None, NDT=None, NDP=None, NSCAN=None, |
|
|
5186 | 5186 | lagind=None, lagfirst=None, |
|
|
5187 | 5187 | NAVG=None, nkill=None): |
|
|
5188 | 5188 | |
|
|
5189 | 5189 | dataOut.NLAG=NLAG |
|
|
5190 | 5190 | dataOut.NR=len(dataOut.channelList) |
|
|
5191 | 5191 | dataOut.NRANGE=NRANGE |
|
|
5192 | 5192 | dataOut.NCAL=NCAL |
|
|
5193 | 5193 | dataOut.DPL=DPL |
|
|
5194 | 5194 | dataOut.NDN=NDN |
|
|
5195 | 5195 | dataOut.NDT=NDT |
|
|
5196 | 5196 | dataOut.NDP=NDP |
|
|
5197 | 5197 | dataOut.NSCAN=NSCAN |
|
|
5198 | 5198 | dataOut.DH=dataOut.heightList[1]-dataOut.heightList[0] |
|
|
5199 | 5199 | dataOut.H0=int(dataOut.heightList[0]) |
|
|
5200 | 5200 | dataOut.lagind=lagind |
|
|
5201 | 5201 | dataOut.lagfirst=lagfirst |
|
|
5202 | 5202 | dataOut.NAVG=NAVG |
|
|
5203 | 5203 | dataOut.nkill=nkill |
|
|
5204 | 5204 | |
|
|
5205 | 5205 | dataOut.flagNoData = True |
|
|
5206 | 5206 | |
|
|
5207 | 5207 | self.get_dc(dataOut) |
|
|
5208 | 5208 | self.get_products_cabxys_HP(dataOut) |
|
|
5209 | 5209 | self.cabxys_navg(dataOut) |
|
|
5210 | 5210 | self.lag_products_LP(dataOut) |
|
|
5211 | 5211 | self.LP_median_estimates(dataOut) |
|
|
5212 | 5212 | self.noise_estimation4x_HP(dataOut) |
|
|
5213 | 5213 | self.kabxys(dataOut) |
|
|
5214 | 5214 | |
|
|
5215 | 5215 | return dataOut |
|
|
5216 | 5216 | |
|
|
5217 | 5217 | |
|
|
5218 | 5218 | class CrossProdLP(CrossProdDP): |
|
|
5219 | 5219 | """Operation to calculate cross products of the Hybrid Experiment. |
|
|
5220 | 5220 | |
|
|
5221 | 5221 | Parameters: |
|
|
5222 | 5222 | ----------- |
|
|
5223 | 5223 | NLAG : int |
|
|
5224 | 5224 | Number of lags for Long Pulse. |
|
|
5225 | 5225 | NRANGE : int |
|
|
5226 | 5226 | Number of samples (heights) for Long Pulse. |
|
|
5227 | 5227 | NCAL : int |
|
|
5228 | 5228 | .* |
|
|
5229 | 5229 | DPL : int |
|
|
5230 | 5230 | Number of lags for Double Pulse. |
|
|
5231 | 5231 | NDN : int |
|
|
5232 | 5232 | .* |
|
|
5233 | 5233 | NDT : int |
|
|
5234 | 5234 | Number of heights for Double Pulse.* |
|
|
5235 | 5235 | NDP : int |
|
|
5236 | 5236 | Number of heights for Double Pulse.* |
|
|
5237 | 5237 | NSCAN : int |
|
|
5238 | 5238 | Number of profiles when the transmitter is on. |
|
|
5239 | 5239 | lagind : intlist |
|
|
5240 | 5240 | .* |
|
|
5241 | 5241 | lagfirst : intlist |
|
|
5242 | 5242 | .* |
|
|
5243 | 5243 | NAVG : int |
|
|
5244 | 5244 | Number of blocks to be "averaged". |
|
|
5245 | 5245 | nkill : int |
|
|
5246 | 5246 | Number of blocks not to be considered when averaging. |
|
|
5247 | 5247 | |
|
|
5248 | 5248 | Example |
|
|
5249 | 5249 | -------- |
|
|
5250 | 5250 | |
|
|
5251 | 5251 | op = proc_unit.addOperation(name='CrossProdHybrid', optype='other') |
|
|
5252 | 5252 | op.addParameter(name='NLAG', value='16', format='int') |
|
|
5253 | 5253 | op.addParameter(name='NRANGE', value='200', format='int') |
|
|
5254 | 5254 | op.addParameter(name='NCAL', value='0', format='int') |
|
|
5255 | 5255 | op.addParameter(name='DPL', value='11', format='int') |
|
|
5256 | 5256 | op.addParameter(name='NDN', value='0', format='int') |
|
|
5257 | 5257 | op.addParameter(name='NDT', value='67', format='int') |
|
|
5258 | 5258 | op.addParameter(name='NDP', value='67', format='int') |
|
|
5259 | 5259 | op.addParameter(name='NSCAN', value='128', format='int') |
|
|
5260 | 5260 | op.addParameter(name='lagind', value='(0,1,2,3,4,5,6,7,0,3,4,5,6,8,9,10)', format='intlist') |
|
|
5261 | 5261 | op.addParameter(name='lagfirst', value='(1,1,1,1,1,1,1,1,0,0,0,0,0,1,1,1)', format='intlist') |
|
|
5262 | 5262 | op.addParameter(name='NAVG', value='16', format='int') |
|
|
5263 | 5263 | op.addParameter(name='nkill', value='6', format='int') |
|
|
5264 | 5264 | |
|
|
5265 | 5265 | """ |
|
|
5266 | 5266 | |
|
|
5267 | 5267 | def __init__(self, **kwargs): |
|
|
5268 | 5268 | |
|
|
5269 | 5269 | Operation.__init__(self, **kwargs) |
|
|
5270 | 5270 | self.bcounter=0 |
|
|
5271 | 5271 | self.aux=1 |
|
|
5272 | 5272 | self.aux_cross_lp=1 |
|
|
5273 | 5273 | self.lag_products_LP_median_estimates_aux=1 |
|
|
5274 | 5274 | |
|
|
5275 | 5275 | |
|
|
5276 | 5276 | |
|
|
5277 | 5277 | #print(self.cax2[2,0,1]) |
|
|
5278 | 5278 | #input() |
|
|
5279 | 5279 | |
|
|
5280 | 5280 | |
|
|
5281 | 5281 | def lag_products_LP(self,dataOut): |
|
|
5282 | 5282 | |
|
|
5283 | 5283 | |
|
|
5284 | 5284 | buffer=dataOut.data |
|
|
5285 | 5285 | if self.aux_cross_lp==1: |
|
|
5286 | 5286 | |
|
|
5287 | 5287 | #self.dataOut.nptsfft2=150 |
|
|
5288 | 5288 | self.cnorm=float((dataOut.nProfiles-dataOut.NSCAN)/dataOut.NSCAN) |
|
|
5289 | 5289 | self.lagp0=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') |
|
|
5290 | 5290 | self.lagp1=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') |
|
|
5291 | 5291 | self.lagp2=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') |
|
|
5292 | 5292 | self.lagp3=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') |
|
|
5293 | 5293 | self.lagp4=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') |
|
|
5294 | 5294 | self.lagp5=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') |
|
|
5295 | 5295 | |
|
|
5296 | 5296 | #self.lagp4=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NAVG),'complex64') |
|
|
5297 | 5297 | self.aux_cross_lp=0 |
|
|
5298 | 5298 | |
|
|
5299 | 5299 | dataOut.noisevector=numpy.zeros((dataOut.MAXNRANGENDT,dataOut.NR,dataOut.NAVG),'float32') |
|
|
5300 | 5300 | |
|
|
5301 | 5301 | #print(self.dataOut.data[0,0,0]) |
|
|
5302 | 5302 | self.noisevectorizer(dataOut.NSCAN,dataOut.nProfiles,dataOut.NR,dataOut.MAXNRANGENDT,dataOut.noisevector,dataOut.data,dataOut.dc) #30/03/2020 |
|
|
5303 | 5303 | |
|
|
5304 | 5304 | |
|
|
5305 | 5305 | for i in range(dataOut.NR): |
|
|
5306 | 5306 | #print("inside i",i) |
|
|
5307 | 5307 | buffer_dc=dataOut.dc[i] |
|
|
5308 | 5308 | for j in range(dataOut.NRANGE): |
|
|
5309 | 5309 | |
|
|
5310 | 5310 | range_for_n=numpy.min((dataOut.NRANGE-j,dataOut.NLAG)) |
|
|
5311 | 5311 | |
|
|
5312 | 5312 | buffer_aux=numpy.conj(buffer[i,:dataOut.nProfiles,j]-buffer_dc) |
|
|
5313 | 5313 | for n in range(range_for_n): |
|
|
5314 | 5314 | |
|
|
5315 | 5315 | c=(buffer_aux)*(buffer[i,:dataOut.nProfiles,j+n]-buffer_dc) |
|
|
5316 | 5316 | |
|
|
5317 | 5317 | if i==0: |
|
|
5318 | 5318 | self.lagp0[n][j][self.bcounter]=numpy.sum(c[:dataOut.NSCAN]) |
|
|
5319 | 5319 | #self.lagp3[n][j][self.bcounter-1]=numpy.sum(c[dataOut.NSCAN:]/self.cnorm) |
|
|
5320 | 5320 | elif i==1: |
|
|
5321 | 5321 | self.lagp1[n][j][self.bcounter]=numpy.sum(c[:dataOut.NSCAN]) |
|
|
5322 | 5322 | elif i==2: |
|
|
5323 | 5323 | self.lagp2[n][j][self.bcounter]=numpy.sum(c[:dataOut.NSCAN]) |
|
|
5324 | 5324 | elif i==3: |
|
|
5325 | 5325 | self.lagp3[n][j][self.bcounter]=numpy.sum(c[:dataOut.NSCAN]) |
|
|
5326 | 5326 | elif i==4: |
|
|
5327 | 5327 | self.lagp4[n][j][self.bcounter]=numpy.sum(c[:dataOut.NSCAN]) |
|
|
5328 | 5328 | elif i==5: |
|
|
5329 | 5329 | self.lagp5[n][j][self.bcounter]=numpy.sum(c[:dataOut.NSCAN]) |
|
|
5330 | 5330 | |
|
|
5331 | 5331 | |
|
|
5332 | 5332 | self.lagp0[:,:,self.bcounter]=numpy.conj(self.lagp0[:,:,self.bcounter]) |
|
|
5333 | 5333 | self.lagp1[:,:,self.bcounter]=numpy.conj(self.lagp1[:,:,self.bcounter]) |
|
|
5334 | 5334 | self.lagp2[:,:,self.bcounter]=numpy.conj(self.lagp2[:,:,self.bcounter]) |
|
|
5335 | 5335 | self.lagp3[:,:,self.bcounter]=numpy.conj(self.lagp3[:,:,self.bcounter]) |
|
|
5336 | 5336 | |
|
|
5337 | 5337 | self.bcounter += 1 |
|
|
5338 | 5338 | |
|
|
5339 | 5339 | |
|
|
5340 | 5340 | def LP_median_estimates(self,dataOut): |
|
|
5341 | 5341 | |
|
|
5342 | 5342 | if self.bcounter==dataOut.NAVG: |
|
|
5343 | 5343 | dataOut.flagNoData = False |
|
|
5344 | 5344 | |
|
|
5345 | 5345 | if self.lag_products_LP_median_estimates_aux==1: |
|
|
5346 | 5346 | self.output=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NR),'complex64') |
|
|
5347 | 5347 | self.lag_products_LP_median_estimates_aux=0 |
|
|
5348 | 5348 | |
|
|
5349 | 5349 | |
|
|
5350 | 5350 | for i in range(dataOut.NLAG): |
|
|
5351 | 5351 | for j in range(dataOut.NRANGE): |
|
|
5352 | 5352 | for l in range(4): #four outputs |
|
|
5353 | 5353 | |
|
|
5354 | 5354 | for k in range(dataOut.NAVG): |
|
|
5355 | 5355 | |
|
|
5356 | 5356 | |
|
|
5357 | 5357 | if k==0: |
|
|
5358 | 5358 | self.output[i,j,l]=0.0+0.j |
|
|
5359 | 5359 | |
|
|
5360 | 5360 | if l==0: |
|
|
5361 | 5361 | self.lagp0[i,j,:]=sorted(self.lagp0[i,j,:], key=lambda x: x.real) #sorted(self.lagp0[i,j,:].real) |
|
|
5362 | 5362 | |
|
|
5363 | 5363 | if l==1: |
|
|
5364 | 5364 | self.lagp1[i,j,:]=sorted(self.lagp1[i,j,:], key=lambda x: x.real) #sorted(self.lagp1[i,j,:].real) |
|
|
5365 | 5365 | |
|
|
5366 | 5366 | if l==2: |
|
|
5367 | 5367 | self.lagp2[i,j,:]=sorted(self.lagp2[i,j,:], key=lambda x: x.real) #sorted(self.lagp2[i,j,:].real) |
|
|
5368 | 5368 | |
|
|
5369 | 5369 | if l==3: |
|
|
5370 | 5370 | self.lagp3[i,j,:]=sorted(self.lagp3[i,j,:], key=lambda x: x.real) #sorted(self.lagp3[i,j,:].real) |
|
|
5371 | 5371 | |
|
|
5372 | 5372 | |
|
|
5373 | 5373 | |
|
|
5374 | 5374 | if k>=dataOut.nkill/2 and k<dataOut.NAVG-dataOut.nkill/2: |
|
|
5375 | 5375 | if l==0: |
|
|
5376 | 5376 | |
|
|
5377 | 5377 | self.output[i,j,l]=self.output[i,j,l]+((float(dataOut.NAVG)/(float)(dataOut.NAVG-dataOut.nkill))*self.lagp0[i,j,k]) |
|
|
5378 | 5378 | if l==1: |
|
|
5379 | 5379 | #print("lagp1: ",self.lagp1[0,0,:]) |
|
|
5380 | 5380 | #input() |
|
|
5381 | 5381 | self.output[i,j,l]=self.output[i,j,l]+((float(dataOut.NAVG)/(float)(dataOut.NAVG-dataOut.nkill))*self.lagp1[i,j,k]) |
|
|
5382 | 5382 | #print("self.lagp1[i,j,k]: ",self.lagp1[i,j,k]) |
|
|
5383 | 5383 | #input() |
|
|
5384 | 5384 | if l==2: |
|
|
5385 | 5385 | self.output[i,j,l]=self.output[i,j,l]+((float(dataOut.NAVG)/(float)(dataOut.NAVG-dataOut.nkill))*self.lagp2[i,j,k]) |
|
|
5386 | 5386 | if l==3: |
|
|
5387 | 5387 | |
|
|
5388 | 5388 | self.output[i,j,l]=self.output[i,j,l]+((float(dataOut.NAVG)/(float)(dataOut.NAVG-dataOut.nkill))*self.lagp3[i,j,k]) |
|
|
5389 | 5389 | |
|
|
5390 | 5390 | |
|
|
5391 | 5391 | dataOut.output_LP=self.output |
|
|
5392 | 5392 | dataOut.data_for_RTI_LP=numpy.zeros((4,dataOut.NRANGE)) |
|
|
5393 | 5393 | dataOut.data_for_RTI_LP[0],dataOut.data_for_RTI_LP[1],dataOut.data_for_RTI_LP[2],dataOut.data_for_RTI_LP[3]=self.RTI_LP(dataOut.output_LP,dataOut.NRANGE) |
|
|
5394 | 5394 | |
|
|
5395 | 5395 | self.bcounter = 0 |
|
|
5396 | 5396 | |
|
|
5397 | 5397 | def get_dc(self,dataOut): |
|
|
5398 | 5398 | |
|
|
5399 | 5399 | if self.bcounter==0: |
|
|
5400 | 5400 | dataOut.dc=numpy.zeros(dataOut.NR,dtype='complex64') |
|
|
5401 | 5401 | |
|
|
5402 | 5402 | #print(numpy.shape(dataOut.data)) |
|
|
5403 | 5403 | #input() |
|
|
5404 | 5404 | |
|
|
5405 | 5405 | dataOut.dc+=numpy.sum(dataOut.data[:,:,2*dataOut.NLAG:dataOut.NRANGE],axis=(1,2)) |
|
|
5406 | 5406 | |
|
|
5407 | 5407 | dataOut.dc=dataOut.dc/float(dataOut.nProfiles*(dataOut.NRANGE-2*dataOut.NLAG)) |
|
|
5408 | 5408 | |
|
|
5409 | 5409 | |
|
|
5410 | 5410 | #print("dc:",dataOut.dc[0]) |
|
|
5411 | 5411 | |
|
|
5412 | 5412 | |
|
|
5413 | 5413 | |
|
|
5414 | 5414 | |
|
|
5415 | 5415 | def noise_estimation4x_HP(self,dataOut): |
|
|
5416 | 5416 | if self.bcounter==dataOut.NAVG: |
|
|
5417 | 5417 | dataOut.noise_final=numpy.zeros(dataOut.NR,'float32') |
|
|
5418 | 5418 | #snoise=numpy.zeros((NR,NAVG),'float32') |
|
|
5419 | 5419 | #nvector1=numpy.zeros((NR,NAVG,MAXNRANGENDT),'float32') |
|
|
5420 | 5420 | sorted_data=numpy.zeros((dataOut.MAXNRANGENDT,dataOut.NR,dataOut.NAVG),'float32') |
|
|
5421 | 5421 | for i in range(dataOut.NR): |
|
|
5422 | 5422 | dataOut.noise_final[i]=0.0 |
|
|
5423 | 5423 | for j in range(dataOut.MAXNRANGENDT): |
|
|
5424 | 5424 | sorted_data[j,i,:]=numpy.copy(sorted(dataOut.noisevector[j,i,:])) |
|
|
5425 | 5425 | #print(sorted(noisevector[j,i,:])) |
|
|
5426 | 5426 | #input() |
|
|
5427 | 5427 | l=dataOut.MAXNRANGENDT-2 |
|
|
5428 | 5428 | for k in range(dataOut.NAVG): |
|
|
5429 | 5429 | if k>=dataOut.nkill/2 and k<dataOut.NAVG-dataOut.nkill/2: |
|
|
5430 | 5430 | #print(k) |
|
|
5431 | 5431 | #print(sorted_data[min(j,l),i,k]) |
|
|
5432 | 5432 | dataOut.noise_final[i]+=sorted_data[min(j,l),i,k]*float(dataOut.NAVG)/float(dataOut.NAVG-dataOut.nkill) |
|
|
5433 | 5433 | #print(dataOut.noise_final[i]) |
|
|
5434 | 5434 | #input() |
|
|
5435 | 5435 | #print(dataOut.noise_final) |
|
|
5436 | 5436 | #input() |
|
|
5437 | 5437 | |
|
|
5438 | 5438 | def noisevectorizer(self,NSCAN,nProfiles,NR,MAXNRANGENDT,noisevector,data,dc): |
|
|
5439 | 5439 | |
|
|
5440 | 5440 | #rnormalizer= 1./(float(nProfiles - NSCAN)) |
|
|
5441 | 5441 | #rnormalizer= float(NSCAN)/((float(nProfiles - NSCAN))*float(MAXNRANGENDT)) |
|
|
5442 | 5442 | rnormalizer= float(NSCAN)/((float(1))*float(MAXNRANGENDT)) |
|
|
5443 | 5443 | for i in range(NR): |
|
|
5444 | 5444 | for j in range(MAXNRANGENDT): |
|
|
5445 | 5445 | for k in range(NSCAN,nProfiles): |
|
|
5446 | 5446 | #TODO:integrate just 2nd quartile gates |
|
|
5447 | 5447 | if k==NSCAN: |
|
|
5448 | 5448 | noisevector[j][i][self.bcounter]=(abs(data[i][k][j]-dc[i])**2)*rnormalizer |
|
|
5449 | 5449 | ##noisevector[j][i][iavg]=(abs(cdata[k][j][i])**2)*rnormalizer |
|
|
5450 | 5450 | else: |
|
|
5451 | 5451 | noisevector[j][i][self.bcounter]+=(abs(data[i][k][j]-dc[i])**2)*rnormalizer |
|
|
5452 | 5452 | |
|
|
5453 | 5453 | |
|
|
5454 | 5454 | def RTI_LP(self,output,NRANGE): |
|
|
5455 | 5455 | x00=numpy.zeros(NRANGE,dtype='float32') |
|
|
5456 | 5456 | x01=numpy.zeros(NRANGE,dtype='float32') |
|
|
5457 | 5457 | x02=numpy.zeros(NRANGE,dtype='float32') |
|
|
5458 | 5458 | x03=numpy.zeros(NRANGE,dtype='float32') |
|
|
5459 | 5459 | |
|
|
5460 | 5460 | for i in range(1): #first couple of lags |
|
|
5461 | 5461 | for j in range(NRANGE): # |
|
|
5462 | 5462 | #fx=numpy.sqrt((kaxbx[i,j,k]+kayby[i,j,k])**2+(kaybx[i,j,k]-kaxby[i,j,k])**2) |
|
|
5463 | 5463 | x00[j]+=numpy.abs(output[i,j,0]) #Ch0 |
|
|
5464 | 5464 | x01[j]+=numpy.abs(output[i,j,1]) #Ch1 |
|
|
5465 | 5465 | x02[j]+=numpy.abs(output[i,j,2]) #Ch2 |
|
|
5466 | 5466 | x03[j]+=numpy.abs(output[i,j,3]) #Ch3 |
|
|
5467 | 5467 | #x02[i]=x02[i]+fx |
|
|
5468 | 5468 | |
|
|
5469 | 5469 | x00[j]=10.0*numpy.log10(x00[j]/4.) |
|
|
5470 | 5470 | x01[j]=10.0*numpy.log10(x01[j]/4.) |
|
|
5471 | 5471 | x02[j]=10.0*numpy.log10(x02[j]/4.) |
|
|
5472 | 5472 | x03[j]=10.0*numpy.log10(x03[j]/4.) |
|
|
5473 | 5473 | #x02[i]=10.0*numpy.log10(x02[i]) |
|
|
5474 | 5474 | return x00,x01,x02,x03 |
|
|
5475 | 5475 | |
|
|
5476 | 5476 | def run(self, dataOut, NLAG=None, NRANGE=None, NCAL=None, DPL=None, |
|
|
5477 | 5477 | NDN=None, NDT=None, NDP=None, NSCAN=None, |
|
|
5478 | 5478 | lagind=None, lagfirst=None, |
|
|
5479 | 5479 | NAVG=None, nkill=None): |
|
|
5480 | 5480 | |
|
|
5481 | 5481 | dataOut.NLAG=NLAG |
|
|
5482 | 5482 | dataOut.NR=len(dataOut.channelList) |
|
|
5483 | 5483 | #dataOut.NRANGE=NRANGE |
|
|
5484 | 5484 | dataOut.NRANGE=dataOut.nHeights |
|
|
5485 | 5485 | dataOut.NCAL=NCAL |
|
|
5486 | 5486 | dataOut.DPL=DPL |
|
|
5487 | 5487 | dataOut.NDN=NDN |
|
|
5488 | 5488 | dataOut.NDT=NDT |
|
|
5489 | 5489 | dataOut.NDP=NDP |
|
|
5490 | 5490 | dataOut.NSCAN=NSCAN |
|
|
5491 | 5491 | dataOut.DH=dataOut.heightList[1]-dataOut.heightList[0] |
|
|
5492 | 5492 | dataOut.H0=int(dataOut.heightList[0]) |
|
|
5493 | 5493 | dataOut.lagind=lagind |
|
|
5494 | 5494 | dataOut.lagfirst=lagfirst |
|
|
5495 | 5495 | dataOut.NAVG=NAVG |
|
|
5496 | 5496 | dataOut.nkill=nkill |
|
|
5497 | 5497 | |
|
|
5498 | 5498 | dataOut.MAXNRANGENDT = dataOut.NRANGE |
|
|
5499 | 5499 | |
|
|
5500 | 5500 | dataOut.flagNoData = True |
|
|
5501 | 5501 | |
|
|
5502 | 5502 | print(self.bcounter) |
|
|
5503 | 5503 | |
|
|
5504 | 5504 | self.get_dc(dataOut) |
|
|
5505 | 5505 | self.lag_products_LP(dataOut) |
|
|
5506 | 5506 | self.noise_estimation4x_HP(dataOut) |
|
|
5507 | 5507 | self.LP_median_estimates(dataOut) |
|
|
5508 | 5508 | |
|
|
5509 | 5509 | print("******************DONE******************") |
|
|
5510 | 5510 | |
|
|
5511 | 5511 | |
|
|
5512 | 5512 | |
|
|
5513 | 5513 | return dataOut |
|
|
5514 | 5514 | |
|
|
5515 | 5515 | |
|
|
5516 | 5516 | class RemoveDebris(Operation): |
|
|
5517 | 5517 | """Operation to remove blocks where an outlier is found for Double (Long) Pulse. |
|
|
5518 | 5518 | |
|
|
5519 | 5519 | Parameters: |
|
|
5520 | 5520 | ----------- |
|
|
5521 | 5521 | None |
|
|
5522 | 5522 | |
|
|
5523 | 5523 | Example |
|
|
5524 | 5524 | -------- |
|
|
5525 | 5525 | |
|
|
5526 | 5526 | op = proc_unit.addOperation(name='RemoveDebris', optype='other') |
|
|
5527 | 5527 | |
|
|
5528 | 5528 | """ |
|
|
5529 | 5529 | |
|
|
5530 | 5530 | def __init__(self, **kwargs): |
|
|
5531 | 5531 | |
|
|
5532 | 5532 | Operation.__init__(self, **kwargs) |
|
|
5533 | 5533 | |
|
|
5534 | 5534 | def run(self,dataOut): |
|
|
5535 | 5535 | debris=numpy.zeros(dataOut.NRANGE,'float32') |
|
|
5536 | 5536 | |
|
|
5537 | 5537 | for j in range(0,3): |
|
|
5538 | 5538 | for i in range(dataOut.NRANGE): |
|
|
5539 | 5539 | if j==0: |
|
|
5540 | 5540 | debris[i]=10*numpy.log10(numpy.abs(dataOut.output_LP[j,i,0])) |
|
|
5541 | 5541 | else: |
|
|
5542 | 5542 | debris[i]+=10*numpy.log10(numpy.abs(dataOut.output_LP[j,i,0])) |
|
|
5543 | 5543 | |
|
|
5544 | 5544 | thresh=8.0+4+4+4 |
|
|
5545 | 5545 | for i in range(47,100): |
|
|
5546 | 5546 | if ((debris[i-2]+debris[i-1]+debris[i]+debris[i+1])> |
|
|
5547 | 5547 | ((debris[i-12]+debris[i-11]+debris[i-10]+debris[i-9]+ |
|
|
5548 | 5548 | debris[i+12]+debris[i+11]+debris[i+10]+debris[i+9])/2.0+ |
|
|
5549 | 5549 | thresh)): |
|
|
5550 | 5550 | |
|
|
5551 | 5551 | dataOut.flagNoData=True |
|
|
5552 | 5552 | print("LP Debris detected at",i*15,"km") |
|
|
5553 | 5553 | |
|
|
5554 | 5554 | debris=numpy.zeros(dataOut.NDP,dtype='float32') |
|
|
5555 | 5555 | Range=numpy.arange(0,3000,15) |
|
|
5556 | 5556 | for k in range(2): #flip |
|
|
5557 | 5557 | for i in range(dataOut.NDP): # |
|
|
5558 | 5558 | debris[i]+=numpy.sqrt((dataOut.kaxbx[i,0,k]+dataOut.kayby[i,0,k])**2+(dataOut.kaybx[i,0,k]-dataOut.kaxby[i,0,k])**2) |
|
|
5559 | 5559 | |
|
|
5560 | 5560 | if gmtime(dataOut.utctime).tm_hour > 11: |
|
|
5561 | 5561 | for i in range(2,dataOut.NDP-2): |
|
|
5562 | 5562 | if (debris[i]>3.0*debris[i-2] and |
|
|
5563 | 5563 | debris[i]>3.0*debris[i+2] and |
|
|
5564 | 5564 | Range[i]>200.0 and Range[i]<=540.0): |
|
|
5565 | 5565 | dataOut.flagNoData=True |
|
|
5566 | 5566 | print("DP Debris detected at",i*15,"km") |
|
|
5567 | 5567 | |
|
|
5568 | 5568 | return dataOut |
|
|
5569 | 5569 | |
|
|
5570 | 5570 | |
|
|
5571 | 5571 | class IntegrationHP(IntegrationDP): |
|
|
5572 | 5572 | """Operation to integrate Double Pulse and Long Pulse data. |
|
|
5573 | 5573 | |
|
|
5574 | 5574 | Parameters: |
|
|
5575 | 5575 | ----------- |
|
|
5576 | 5576 | nint : int |
|
|
5577 | 5577 | Number of integrations. |
|
|
5578 | 5578 | |
|
|
5579 | 5579 | Example |
|
|
5580 | 5580 | -------- |
|
|
5581 | 5581 | |
|
|
5582 | 5582 | op = proc_unit.addOperation(name='IntegrationHP', optype='other') |
|
|
5583 | 5583 | op.addParameter(name='nint', value='30', format='int') |
|
|
5584 | 5584 | |
|
|
5585 | 5585 | """ |
|
|
5586 | 5586 | |
|
|
5587 | 5587 | def __init__(self, **kwargs): |
|
|
5588 | 5588 | |
|
|
5589 | 5589 | Operation.__init__(self, **kwargs) |
|
|
5590 | 5590 | |
|
|
5591 | 5591 | self.counter = 0 |
|
|
5592 | 5592 | self.aux = 0 |
|
|
5593 | 5593 | |
|
|
5594 | 5594 | def integration_noise(self,dataOut): |
|
|
5595 | 5595 | |
|
|
5596 | 5596 | if self.counter == 0: |
|
|
5597 | 5597 | dataOut.tnoise=numpy.zeros((dataOut.NR),dtype='float32') |
|
|
5598 | 5598 | |
|
|
5599 | 5599 | dataOut.tnoise+=dataOut.noise_final |
|
|
5600 | 5600 | |
|
|
5601 | 5601 | def integration_for_long_pulse(self,dataOut): |
|
|
5602 | 5602 | |
|
|
5603 | 5603 | if self.counter == 0: |
|
|
5604 | 5604 | dataOut.output_LP_integrated=numpy.zeros((dataOut.NLAG,dataOut.NRANGE,dataOut.NR),order='F',dtype='complex64') |
|
|
5605 | 5605 | |
|
|
5606 | 5606 | dataOut.output_LP_integrated+=dataOut.output_LP |
|
|
5607 | 5607 | |
|
|
5608 | 5608 | def run(self,dataOut,nint=None): |
|
|
5609 | 5609 | |
|
|
5610 | 5610 | dataOut.flagNoData=True |
|
|
5611 | 5611 | |
|
|
5612 | 5612 | dataOut.nint=nint |
|
|
5613 | 5613 | dataOut.paramInterval=0#int(dataOut.nint*dataOut.header[7][0]*2 ) |
|
|
5614 | 5614 | dataOut.lat=-11.95 |
|
|
5615 | 5615 | dataOut.lon=-76.87 |
|
|
5616 | 5616 | |
|
|
5617 | 5617 | self.integration_for_long_pulse(dataOut) |
|
|
5618 | 5618 | |
|
|
5619 | 5619 | self.integration_noise(dataOut) |
|
|
5620 | 5620 | |
|
|
5621 | 5621 | if self.counter==dataOut.nint-1: |
|
|
5622 | 5622 | dataOut.nis=dataOut.NSCAN*dataOut.NAVG*dataOut.nint*10 |
|
|
5623 | 5623 | dataOut.tnoise[0]*=0.995 |
|
|
5624 | 5624 | dataOut.tnoise[1]*=0.995 |
|
|
5625 | 5625 | dataOut.pan=dataOut.tnoise[0]/float(dataOut.NSCAN*dataOut.nint*dataOut.NAVG) |
|
|
5626 | 5626 | dataOut.pbn=dataOut.tnoise[1]/float(dataOut.NSCAN*dataOut.nint*dataOut.NAVG) |
|
|
5627 | 5627 | |
|
|
5628 | 5628 | self.integration_for_double_pulse(dataOut) |
|
|
5629 | 5629 | |
|
|
5630 | 5630 | |
|
|
5631 | 5631 | |
|
|
5632 | 5632 | return dataOut |
|
|
5633 | 5633 | |
|
|
5634 | 5634 | class SumFlipsHP(SumFlips): |
|
|
5635 | 5635 | """Operation to sum the flip and unflip part of certain cross products of the Double Pulse. |
|
|
5636 | 5636 | |
|
|
5637 | 5637 | Parameters: |
|
|
5638 | 5638 | ----------- |
|
|
5639 | 5639 | None |
|
|
5640 | 5640 | |
|
|
5641 | 5641 | Example |
|
|
5642 | 5642 | -------- |
|
|
5643 | 5643 | |
|
|
5644 | 5644 | op = proc_unit.addOperation(name='SumFlipsHP', optype='other') |
|
|
5645 | 5645 | |
|
|
5646 | 5646 | """ |
|
|
5647 | 5647 | |
|
|
5648 | 5648 | def __init__(self, **kwargs): |
|
|
5649 | 5649 | |
|
|
5650 | 5650 | Operation.__init__(self, **kwargs) |
|
|
5651 | 5651 | |
|
|
5652 | 5652 | def rint2HP(self,dataOut): |
|
|
5653 | 5653 | |
|
|
5654 | 5654 | dataOut.rnint2=numpy.zeros(dataOut.DPL,'float32') |
|
|
5655 | 5655 | #print(dataOut.nint,dataOut.NAVG) |
|
|
5656 | 5656 | for l in range(dataOut.DPL): |
|
|
5657 | 5657 | if(l==0 or (l>=3 and l <=6)): |
|
|
5658 | 5658 | dataOut.rnint2[l]=0.5/float(dataOut.nint*dataOut.NAVG*16.0) |
|
|
5659 | 5659 | else: |
|
|
5660 | 5660 | dataOut.rnint2[l]=0.5/float(dataOut.nint*dataOut.NAVG*8.0) |
|
|
5661 | 5661 | |
|
|
5662 | 5662 | def run(self,dataOut): |
|
|
5663 | 5663 | |
|
|
5664 | 5664 | self.rint2HP(dataOut) |
|
|
5665 | 5665 | self.SumLags(dataOut) |
|
|
5666 | 5666 | |
|
|
5667 | 5667 | hei = 2 |
|
|
5668 | 5668 | lag = 0 |
|
|
5669 | 5669 | ''' |
|
|
5670 | 5670 | for hei in range(67): |
|
|
5671 | 5671 | print("hei",hei) |
|
|
5672 | 5672 | print(dataOut.kabxys_integrated[8][hei,:,0]+dataOut.kabxys_integrated[11][hei,:,0]) |
|
|
5673 | 5673 | print(dataOut.kabxys_integrated[10][hei,:,0]-dataOut.kabxys_integrated[9][hei,:,0]) |
|
|
5674 | 5674 | exit(1) |
|
|
5675 | 5675 | ''' |
|
|
5676 | 5676 | ''' |
|
|
5677 | 5677 | print("b",(dataOut.kabxys_integrated[4][hei,lag,0]+dataOut.kabxys_integrated[5][hei,lag,0])) |
|
|
5678 | 5678 | print((dataOut.kabxys_integrated[6][hei,lag,0]+dataOut.kabxys_integrated[7][hei,lag,0])) |
|
|
5679 | 5679 | print("c",(dataOut.kabxys_integrated[8][hei,lag,0]+dataOut.kabxys_integrated[11][hei,lag,0])) |
|
|
5680 | 5680 | print((dataOut.kabxys_integrated[10][hei,lag,0]-dataOut.kabxys_integrated[9][hei,lag,0])) |
|
|
5681 | 5681 | exit(1) |
|
|
5682 | 5682 | ''' |
|
|
5683 | 5683 | #print(dataOut.rnint2) |
|
|
5684 | 5684 | #print(numpy.sum(dataOut.kabxys_integrated[4][:,1,0]+dataOut.kabxys_integrated[5][:,1,0])) |
|
|
5685 | 5685 | #print(dataOut.nis) |
|
|
5686 | 5686 | #exit(1) |
|
|
5687 | 5687 | return dataOut |
|
|
5688 | 5688 | |
|
|
5689 | 5689 | |
|
|
5690 | 5690 | class LongPulseAnalysis(Operation): |
|
|
5691 | 5691 | """Operation to estimate ACFs, temperatures, total electron density and Hydrogen/Helium fractions from the Long Pulse data. |
|
|
5692 | 5692 | |
|
|
5693 | 5693 | Parameters: |
|
|
5694 | 5694 | ----------- |
|
|
5695 | 5695 | NACF : int |
|
|
5696 | 5696 | .* |
|
|
5697 | 5697 | |
|
|
5698 | 5698 | Example |
|
|
5699 | 5699 | -------- |
|
|
5700 | 5700 | |
|
|
5701 | 5701 | op = proc_unit.addOperation(name='LongPulseAnalysis', optype='other') |
|
|
5702 | 5702 | op.addParameter(name='NACF', value='16', format='int') |
|
|
5703 | 5703 | |
|
|
5704 | 5704 | """ |
|
|
5705 | 5705 | |
|
|
5706 | 5706 | def __init__(self, **kwargs): |
|
|
5707 | 5707 | |
|
|
5708 | 5708 | Operation.__init__(self, **kwargs) |
|
|
5709 | 5709 | self.aux=1 |
|
|
5710 | 5710 | |
|
|
5711 | 5711 | def run(self,dataOut,NACF): |
|
|
5712 | 5712 | |
|
|
5713 | 5713 | dataOut.NACF=NACF |
|
|
5714 | 5714 | dataOut.heightList=dataOut.DH*(numpy.arange(dataOut.NACF)) |
|
|
5715 | 5715 | anoise0=dataOut.tnoise[0] |
|
|
5716 | 5716 | anoise1=anoise0*0.0 #seems to be noise in 1st lag 0.015 before '14 |
|
|
5717 | 5717 | #print(anoise0) |
|
|
5718 | 5718 | #exit(1) |
|
|
5719 | 5719 | if self.aux: |
|
|
5720 | 5720 | #dataOut.cut=31#26#height=31*15=465 |
|
|
5721 | 5721 | self.cal=numpy.zeros((dataOut.NLAG),'float32') |
|
|
5722 | 5722 | self.drift=numpy.zeros((200),'float32') |
|
|
5723 | 5723 | self.rdrift=numpy.zeros((200),'float32') |
|
|
5724 | 5724 | self.ddrift=numpy.zeros((200),'float32') |
|
|
5725 | 5725 | self.sigma=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') |
|
|
5726 | 5726 | self.powera=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') |
|
|
5727 | 5727 | self.powerb=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') |
|
|
5728 | 5728 | self.perror=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') |
|
|
5729 | 5729 | dataOut.ene=numpy.zeros((dataOut.NRANGE),'float32') |
|
|
5730 | 5730 | self.dpulse=numpy.zeros((dataOut.NACF),'float32') |
|
|
5731 | 5731 | self.lpulse=numpy.zeros((dataOut.NACF),'float32') |
|
|
5732 | 5732 | dataOut.lags_LP=numpy.zeros((dataOut.IBITS),order='F',dtype='float32') |
|
|
5733 | 5733 | self.lagp=numpy.zeros((dataOut.NACF),'float32') |
|
|
5734 | 5734 | self.u=numpy.zeros((2*dataOut.NACF,2*dataOut.NACF),'float32') |
|
|
5735 | 5735 | dataOut.ne=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') |
|
|
5736 | 5736 | dataOut.te=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
|
5737 | 5737 | dataOut.ete=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
|
5738 | 5738 | dataOut.ti=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
|
5739 | 5739 | dataOut.eti=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
|
5740 | 5740 | dataOut.ph=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
|
5741 | 5741 | dataOut.eph=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
|
5742 | 5742 | dataOut.phe=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
|
5743 | 5743 | dataOut.ephe=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
|
5744 | 5744 | dataOut.errors=numpy.zeros((dataOut.IBITS,max(dataOut.NRANGE,dataOut.NSHTS)),order='F',dtype='float32') |
|
|
5745 | 5745 | dataOut.fit_array_real=numpy.zeros((max(dataOut.NRANGE,dataOut.NSHTS),dataOut.NLAG),order='F',dtype='float32') |
|
|
5746 | 5746 | dataOut.status=numpy.zeros(1,'float32') |
|
|
5747 | 5747 | dataOut.tx=240.0 #debería provenir del header #hybrid |
|
|
5748 | 5748 | |
|
|
5749 | 5749 | for i in range(dataOut.IBITS): |
|
|
5750 | 5750 | dataOut.lags_LP[i]=float(i)*(dataOut.tx/150.0)/float(dataOut.IBITS) # (float)i*(header.tx/150.0)/(float)IBITS; |
|
|
5751 | 5751 | |
|
|
5752 | 5752 | self.aux=0 |
|
|
5753 | 5753 | |
|
|
5754 | 5754 | dataOut.cut=30 |
|
|
5755 | 5755 | for i in range(30,15,-1): #Aquí se calcula en donde se unirá DP y LP en la parte final |
|
|
5756 | 5756 | if numpy.nanmax(dataOut.acfs_error_to_plot[i,:])>=10 or dataOut.info2[i]==0: |
|
|
5757 | 5757 | dataOut.cut=i-1 |
|
|
5758 | 5758 | |
|
|
5759 | 5759 | for i in range(dataOut.NLAG): |
|
|
5760 | 5760 | self.cal[i]=sum(dataOut.output_LP_integrated[i,:,3].real) #Lag x Height x Channel |
|
|
5761 | 5761 | |
|
|
5762 | 5762 | #print(numpy.sum(self.cal)) #Coinciden |
|
|
5763 | 5763 | #exit(1) |
|
|
5764 | 5764 | self.cal/=float(dataOut.NRANGE) |
|
|
5765 | 5765 | #print(anoise0) |
|
|
5766 | 5766 | #print(anoise1) |
|
|
5767 | 5767 | #exit(1) |
|
|
5768 | 5768 | #print("nis: ", dataOut.nis) |
|
|
5769 | 5769 | #print("pan: ", dataOut.pan) |
|
|
5770 | 5770 | #print("pbn: ", dataOut.pbn) |
|
|
5771 | 5771 | #print(numpy.sum(dataOut.output_LP_integrated[0,:,0])) |
|
|
5772 | 5772 | ''' |
|
|
5773 | 5773 | import matplotlib.pyplot as plt |
|
|
5774 | 5774 | plt.plot(dataOut.output_LP_integrated[:,40,0]) |
|
|
5775 | 5775 | plt.show() |
|
|
5776 | 5776 | ''' |
|
|
5777 | 5777 | #print(dataOut.output_LP_integrated[0,40,0]) |
|
|
5778 | 5778 | #print(numpy.sum(dataOut.output_LP_integrated[:,0,0])) |
|
|
5779 | 5779 | #exit(1) |
|
|
5780 | 5780 | |
|
|
5781 | 5781 | #################### PROBAR MÁS INTEGRACIÓN, SINO MODIFICAR VALOR DE "NIS" #################### |
|
|
5782 | 5782 | # VER dataOut.nProfiles_LP # |
|
|
5783 | 5783 | |
|
|
5784 | 5784 | ''' |
|
|
5785 | 5785 | #PLOTEAR POTENCIA VS RUIDO, QUIZA SE ESTA REMOVIENDO MUCHA SEÑAL |
|
|
5786 | 5786 | #print(dataOut.heightList) |
|
|
5787 | 5787 | import matplotlib.pyplot as plt |
|
|
5788 | 5788 | plt.plot(10*numpy.log10(dataOut.output_LP_integrated.real[0,:,0]),dataOut.range1) |
|
|
5789 | 5789 | #plt.plot(10*numpy.log10(dataOut.output_LP_integrated.real[0,:,0]/dataOut.nProfiles_LP),dataOut.range1) |
|
|
5790 | 5790 | plt.axvline(10*numpy.log10(anoise0),color='k',linestyle='dashed') |
|
|
5791 | 5791 | plt.grid() |
|
|
5792 | 5792 | plt.xlim(20,100) |
|
|
5793 | 5793 | plt.show() |
|
|
5794 | 5794 | ''' |
|
|
5795 | 5795 | |
|
|
5796 | 5796 | |
|
|
5797 | 5797 | for j in range(dataOut.NACF+2*dataOut.IBITS+2): |
|
|
5798 | 5798 | |
|
|
5799 | 5799 | dataOut.output_LP_integrated.real[0,j,0]-=anoise0 #lag0 ch0 |
|
|
5800 | 5800 | dataOut.output_LP_integrated.real[1,j,0]-=anoise1 #lag1 ch0 |
|
|
5801 | 5801 | |
|
|
5802 | 5802 | for i in range(1,dataOut.NLAG): #remove cal data from certain lags |
|
|
5803 | 5803 | dataOut.output_LP_integrated.real[i,j,0]-=self.cal[i] |
|
|
5804 | 5804 | k=max(j,26) #constant power below range 26 |
|
|
5805 | 5805 | self.powera[j]=dataOut.output_LP_integrated.real[0,k,0] #Lag0 and Channel 0 |
|
|
5806 | 5806 | |
|
|
5807 | 5807 | ## examine drifts here - based on 60 'indep.' estimates |
|
|
5808 | 5808 | #print(numpy.sum(self.powera)) |
|
|
5809 | 5809 | #exit(1) |
|
|
5810 | 5810 | #nis=dataOut.NSCAN*dataOut.NAVG*dataOut.nint*10 |
|
|
5811 | 5811 | nis = dataOut.nis |
|
|
5812 | 5812 | #print("nis",nis) |
|
|
5813 | 5813 | alpha=beta=delta=0.0 |
|
|
5814 | 5814 | nest=0 |
|
|
5815 | 5815 | gamma=3.0/(2.0*numpy.pi*dataOut.lags_LP[1]*1.0e-3) |
|
|
5816 | 5816 | beta=gamma*(math.atan2(dataOut.output_LP_integrated.imag[14,0,2],dataOut.output_LP_integrated.real[14,0,2])-math.atan2(dataOut.output_LP_integrated.imag[1,0,2],dataOut.output_LP_integrated.real[1,0,2]))/13.0 |
|
|
5817 | 5817 | #print(gamma,beta) |
|
|
5818 | 5818 | #exit(1) |
|
|
5819 | 5819 | for i in range(1,3): |
|
|
5820 | 5820 | gamma=3.0/(2.0*numpy.pi*dataOut.lags_LP[i]*1.0e-3) |
|
|
5821 | 5821 | #print("gamma",gamma) |
|
|
5822 | 5822 | for j in range(34,44): |
|
|
5823 | 5823 | rho2=numpy.abs(dataOut.output_LP_integrated[i,j,0])/numpy.abs(dataOut.output_LP_integrated[0,j,0]) |
|
|
5824 | 5824 | dataOut.dphi2=(1.0/rho2-1.0)/(float(2*nis)) |
|
|
5825 | 5825 | dataOut.dphi2*=gamma**2 |
|
|
5826 | 5826 | pest=gamma*math.atan(dataOut.output_LP_integrated.imag[i,j,0]/dataOut.output_LP_integrated.real[i,j,0]) |
|
|
5827 | 5827 | #print("1",dataOut.output_LP_integrated.imag[i,j,0]) |
|
|
5828 | 5828 | #print("2",dataOut.output_LP_integrated.real[i,j,0]) |
|
|
5829 | 5829 | self.drift[nest]=pest |
|
|
5830 | 5830 | self.ddrift[nest]=dataOut.dphi2 |
|
|
5831 | 5831 | self.rdrift[nest]=float(nest) |
|
|
5832 | 5832 | nest+=1 |
|
|
5833 | 5833 | |
|
|
5834 | 5834 | sorted(self.drift[:nest]) |
|
|
5835 | 5835 | |
|
|
5836 | 5836 | #print(dataOut.dphi2) |
|
|
5837 | 5837 | #exit(1) |
|
|
5838 | 5838 | |
|
|
5839 | 5839 | for j in range(int(nest/4),int(3*nest/4)): |
|
|
5840 | 5840 | #i=int(self.rdrift[j]) |
|
|
5841 | 5841 | alpha+=self.drift[j]/self.ddrift[j] |
|
|
5842 | 5842 | delta+=1.0/self.ddrift[j] |
|
|
5843 | 5843 | |
|
|
5844 | 5844 | alpha/=delta |
|
|
5845 | 5845 | delta=1./numpy.sqrt(delta) |
|
|
5846 | 5846 | vdrift=alpha-beta |
|
|
5847 | 5847 | dvdrift=delta |
|
|
5848 | 5848 | |
|
|
5849 | 5849 | #need to develop estimate of complete density profile using all |
|
|
5850 | 5850 | #available data |
|
|
5851 | 5851 | |
|
|
5852 | 5852 | #estimate sample variances for long-pulse power profile |
|
|
5853 | 5853 | |
|
|
5854 | 5854 | #nis=dataOut.NSCAN*dataOut.NAVG*dataOut.nint |
|
|
5855 | 5855 | nis = dataOut.nis/10 |
|
|
5856 | 5856 | #print("nis",nis) |
|
|
5857 | 5857 | |
|
|
5858 | 5858 | self.sigma[:dataOut.NACF+2*dataOut.IBITS+2]=((anoise0+self.powera[:dataOut.NACF+2*dataOut.IBITS+2])**2)/float(nis) |
|
|
5859 | 5859 | #print(self.sigma) |
|
|
5860 | 5860 | #exit(1) |
|
|
5861 | 5861 | ioff=1 |
|
|
5862 | 5862 | |
|
|
5863 | 5863 | #deconvolve rectangular pulse shape from profile ==> powerb, perror |
|
|
5864 | 5864 | |
|
|
5865 | 5865 | |
|
|
5866 | 5866 | ############# START nnlswrap############# |
|
|
5867 | 5867 | |
|
|
5868 | 5868 | if dataOut.ut_Faraday>14.0: |
|
|
5869 | 5869 | alpha_nnlswrap=20.0 |
|
|
5870 | 5870 | else: |
|
|
5871 | 5871 | alpha_nnlswrap=30.0 |
|
|
5872 | 5872 | |
|
|
5873 | 5873 | range1_nnls=dataOut.NACF |
|
|
5874 | 5874 | range2_nnls=dataOut.NACF+dataOut.IBITS-1 |
|
|
5875 | 5875 | |
|
|
5876 | 5876 | g_nnlswrap=numpy.zeros((range1_nnls,range2_nnls),'float32') |
|
|
5877 | 5877 | a_nnlswrap=numpy.zeros((range2_nnls,range2_nnls),'float64') |
|
|
5878 | 5878 | |
|
|
5879 | 5879 | for i in range(range1_nnls): |
|
|
5880 | 5880 | for j in range(range2_nnls): |
|
|
5881 | 5881 | if j>=i and j<i+dataOut.IBITS: |
|
|
5882 | 5882 | g_nnlswrap[i,j]=1.0 |
|
|
5883 | 5883 | else: |
|
|
5884 | 5884 | g_nnlswrap[i,j]=0.0 |
|
|
5885 | 5885 | |
|
|
5886 | 5886 | a_nnlswrap[:]=numpy.matmul(numpy.transpose(g_nnlswrap),g_nnlswrap) |
|
|
5887 | 5887 | |
|
|
5888 | 5888 | numpy.fill_diagonal(a_nnlswrap,a_nnlswrap.diagonal()+alpha_nnlswrap**2) |
|
|
5889 | 5889 | |
|
|
5890 | 5890 | #ERROR ANALYSIS# |
|
|
5891 | 5891 | |
|
|
5892 | 5892 | self.perror[:range2_nnls]=0.0 |
|
|
5893 | 5893 | self.perror[:range2_nnls]=numpy.matmul(1./(self.sigma[dataOut.IBITS+ioff:range1_nnls+dataOut.IBITS+ioff]),g_nnlswrap**2) |
|
|
5894 | 5894 | self.perror[:range1_nnls]+=(alpha_nnlswrap**2)/(self.sigma[dataOut.IBITS+ioff:range1_nnls+dataOut.IBITS+ioff]) |
|
|
5895 | 5895 | self.perror[:range2_nnls]=1.00/self.perror[:range2_nnls] |
|
|
5896 | 5896 | |
|
|
5897 | 5897 | b_nnlswrap=numpy.zeros(range2_nnls,'float64') |
|
|
5898 | 5898 | b_nnlswrap[:]=numpy.matmul(self.powera[dataOut.IBITS+ioff:range1_nnls+dataOut.IBITS+ioff],g_nnlswrap) #match filter alturas |
|
|
5899 | 5899 | |
|
|
5900 | 5900 | x_nnlswrap=numpy.zeros(range2_nnls,'float64') |
|
|
5901 | 5901 | x_nnlswrap[:]=nnls(a_nnlswrap,b_nnlswrap)[0] |
|
|
5902 | 5902 | |
|
|
5903 | 5903 | self.powerb[:range2_nnls]=x_nnlswrap |
|
|
5904 | 5904 | #print(self.powerb[40]) |
|
|
5905 | 5905 | #print(self.powerb[66]) |
|
|
5906 | 5906 | #exit(1) |
|
|
5907 | 5907 | #############END nnlswrap############# |
|
|
5908 | 5908 | #print(numpy.sum(numpy.sqrt(self.perror[0:dataOut.NACF]))) |
|
|
5909 | 5909 | #print(self.powerb[0:dataOut.NACF]) |
|
|
5910 | 5910 | #exit(1) |
|
|
5911 | 5911 | #estimate relative error for deconvolved profile (scaling irrelevant) |
|
|
5912 | 5912 | #print(dataOut.NACF) |
|
|
5913 | 5913 | dataOut.ene[0:dataOut.NACF]=numpy.sqrt(self.perror[0:dataOut.NACF])/self.powerb[0:dataOut.NACF] |
|
|
5914 | 5914 | #print(numpy.sum(dataOut.ene)) |
|
|
5915 | 5915 | #exit(1) |
|
|
5916 | 5916 | aux=0 |
|
|
5917 | 5917 | |
|
|
5918 | 5918 | for i in range(dataOut.IBITS,dataOut.NACF): |
|
|
5919 | 5919 | self.dpulse[i]=self.lpulse[i]=0.0 |
|
|
5920 | 5920 | for j in range(dataOut.IBITS): |
|
|
5921 | 5921 | k=int(i-j) |
|
|
5922 | 5922 | if k<36-aux and k>16: |
|
|
5923 | 5923 | self.dpulse[i]+=dataOut.ph2[k]/dataOut.h2[k] |
|
|
5924 | 5924 | elif k>=36-aux: |
|
|
5925 | 5925 | self.lpulse[i]+=self.powerb[k] |
|
|
5926 | 5926 | self.lagp[i]=self.powera[i] |
|
|
5927 | 5927 | |
|
|
5928 | 5928 | #find scale factor that best merges profiles |
|
|
5929 | 5929 | |
|
|
5930 | 5930 | qi=sum(self.dpulse[32:dataOut.NACF]**2/(self.lagp[32:dataOut.NACF]+anoise0)**2) |
|
|
5931 | 5931 | ri=sum((self.dpulse[32:dataOut.NACF]*self.lpulse[32:dataOut.NACF])/(self.lagp[32:dataOut.NACF]+anoise0)**2) |
|
|
5932 | 5932 | si=sum((self.dpulse[32:dataOut.NACF]*self.lagp[32:dataOut.NACF])/(self.lagp[32:dataOut.NACF]+anoise0)**2) |
|
|
5933 | 5933 | ui=sum(self.lpulse[32:dataOut.NACF]**2/(self.lagp[32:dataOut.NACF]+anoise0)**2) |
|
|
5934 | 5934 | vi=sum((self.lpulse[32:dataOut.NACF]*self.lagp[32:dataOut.NACF])/(self.lagp[32:dataOut.NACF]+anoise0)**2) |
|
|
5935 | 5935 | |
|
|
5936 | 5936 | alpha=(si*ui-vi*ri)/(qi*ui-ri*ri) |
|
|
5937 | 5937 | beta=(qi*vi-ri*si)/(qi*ui-ri*ri) |
|
|
5938 | 5938 | |
|
|
5939 | 5939 | #form density profile estimate, merging rescaled power profiles |
|
|
5940 | 5940 | #print(dataOut.h2) |
|
|
5941 | 5941 | #print(numpy.sum(alpha)) |
|
|
5942 | 5942 | #print(numpy.sum(dataOut.ph2)) |
|
|
5943 | 5943 | self.powerb[16:36-aux]=alpha*dataOut.ph2[16:36-aux]/dataOut.h2[16:36-aux] |
|
|
5944 | 5944 | self.powerb[36-aux:dataOut.NACF]*=beta |
|
|
5945 | 5945 | |
|
|
5946 | 5946 | #form Ne estimate, fill in error estimate at low altitudes |
|
|
5947 | 5947 | |
|
|
5948 | 5948 | dataOut.ene[0:36-aux]=dataOut.sdp2[0:36-aux]/dataOut.ph2[0:36-aux] |
|
|
5949 | 5949 | dataOut.ne[:dataOut.NACF]=self.powerb[:dataOut.NACF]*dataOut.h2[:dataOut.NACF]/alpha |
|
|
5950 | 5950 | #print(numpy.sum(self.powerb)) |
|
|
5951 | 5951 | #print(numpy.sum(dataOut.ene)) |
|
|
5952 | 5952 | #print(numpy.sum(dataOut.ne)) |
|
|
5953 | 5953 | #exit(1) |
|
|
5954 | 5954 | #now do error propagation: store zero lag error covariance in u |
|
|
5955 | 5955 | |
|
|
5956 | 5956 | nis=dataOut.NSCAN*dataOut.NAVG*dataOut.nint/1 # DLH serious debris removal |
|
|
5957 | 5957 | |
|
|
5958 | 5958 | for i in range(dataOut.NACF): |
|
|
5959 | 5959 | for j in range(i,dataOut.NACF): |
|
|
5960 | 5960 | if j-i>=dataOut.IBITS: |
|
|
5961 | 5961 | self.u[i,j]=0.0 |
|
|
5962 | 5962 | else: |
|
|
5963 | 5963 | self.u[i,j]=dataOut.output_LP_integrated.real[j-i,i,0]**2/float(nis) |
|
|
5964 | 5964 | self.u[i,j]*=(anoise0+dataOut.output_LP_integrated.real[0,i,0])/dataOut.output_LP_integrated.real[0,i,0] |
|
|
5965 | 5965 | self.u[i,j]*=(anoise0+dataOut.output_LP_integrated.real[0,j,0])/dataOut.output_LP_integrated.real[0,j,0] |
|
|
5966 | 5966 | |
|
|
5967 | 5967 | self.u[j,i]=self.u[i,j] |
|
|
5968 | 5968 | |
|
|
5969 | 5969 | #now error analyis for lag product matrix (diag), place in acf_err |
|
|
5970 | 5970 | |
|
|
5971 | 5971 | for i in range(dataOut.NACF): |
|
|
5972 | 5972 | for j in range(dataOut.IBITS): |
|
|
5973 | 5973 | if j==0: |
|
|
5974 | 5974 | dataOut.errors[0,i]=numpy.sqrt(self.u[i,i]) |
|
|
5975 | 5975 | else: |
|
|
5976 | 5976 | dataOut.errors[j,i]=numpy.sqrt(((dataOut.output_LP_integrated.real[0,i,0]+anoise0)*(dataOut.output_LP_integrated.real[0,i+j,0]+anoise0)+dataOut.output_LP_integrated.real[j,i,0]**2)/float(2*nis)) |
|
|
5977 | 5977 | ''' |
|
|
5978 | 5978 | print(numpy.sum(dataOut.output_LP_integrated)) |
|
|
5979 | 5979 | print(numpy.sum(dataOut.errors)) |
|
|
5980 | 5980 | print(numpy.sum(self.powerb)) |
|
|
5981 | 5981 | print(numpy.sum(dataOut.ne)) |
|
|
5982 | 5982 | print(numpy.sum(dataOut.lags_LP)) |
|
|
5983 | 5983 | print(numpy.sum(dataOut.thb)) |
|
|
5984 | 5984 | print(numpy.sum(dataOut.bfm)) |
|
|
5985 | 5985 | print(numpy.sum(dataOut.te)) |
|
|
5986 | 5986 | print(numpy.sum(dataOut.ete)) |
|
|
5987 | 5987 | print(numpy.sum(dataOut.ti)) |
|
|
5988 | 5988 | print(numpy.sum(dataOut.eti)) |
|
|
5989 | 5989 | print(numpy.sum(dataOut.ph)) |
|
|
5990 | 5990 | print(numpy.sum(dataOut.eph)) |
|
|
5991 | 5991 | print(numpy.sum(dataOut.phe)) |
|
|
5992 | 5992 | print(numpy.sum(dataOut.ephe)) |
|
|
5993 | 5993 | print(numpy.sum(dataOut.range1)) |
|
|
5994 | 5994 | print(numpy.sum(dataOut.ut)) |
|
|
5995 | 5995 | print(numpy.sum(dataOut.NACF)) |
|
|
5996 | 5996 | print(numpy.sum(dataOut.fit_array_real)) |
|
|
5997 | 5997 | print(numpy.sum(dataOut.status)) |
|
|
5998 | 5998 | print(numpy.sum(dataOut.NRANGE)) |
|
|
5999 | 5999 | print(numpy.sum(dataOut.IBITS)) |
|
|
6000 | 6000 | exit(1) |
|
|
6001 | 6001 | ''' |
|
|
6002 | 6002 | ''' |
|
|
6003 | 6003 | print(dataOut.te2[13:16]) |
|
|
6004 | 6004 | print(numpy.sum(dataOut.te2)) |
|
|
6005 | 6005 | exit(1) |
|
|
6006 | 6006 | ''' |
|
|
6007 | 6007 | #print("Success 1") |
|
|
6008 | 6008 | ###################Correlation pulse and itself |
|
|
6009 | 6009 | |
|
|
6010 | 6010 | #print(dataOut.NRANGE) |
|
|
6011 | 6011 | print("LP Estimation") |
|
|
6012 | 6012 | with suppress_stdout_stderr(): |
|
|
6013 | 6013 | #pass |
|
|
6014 | 6014 | full_profile_profile.profile(numpy.transpose(dataOut.output_LP_integrated,(2,1,0)),numpy.transpose(dataOut.errors),self.powerb,dataOut.ne,dataOut.lags_LP,dataOut.thb,dataOut.bfm,dataOut.te,dataOut.ete,dataOut.ti,dataOut.eti,dataOut.ph,dataOut.eph,dataOut.phe,dataOut.ephe,dataOut.range1,dataOut.ut,dataOut.NACF,dataOut.fit_array_real,dataOut.status,dataOut.NRANGE,dataOut.IBITS) |
|
|
6015 | 6015 | |
|
|
6016 | 6016 | print("status: ",dataOut.status) |
|
|
6017 | 6017 | |
|
|
6018 | 6018 | if dataOut.status>=3.5: |
|
|
6019 | 6019 | dataOut.te[:]=numpy.nan |
|
|
6020 | 6020 | dataOut.ete[:]=numpy.nan |
|
|
6021 | 6021 | dataOut.ti[:]=numpy.nan |
|
|
6022 | 6022 | dataOut.eti[:]=numpy.nan |
|
|
6023 | 6023 | dataOut.ph[:]=numpy.nan |
|
|
6024 | 6024 | dataOut.eph[:]=numpy.nan |
|
|
6025 | 6025 | dataOut.phe[:]=numpy.nan |
|
|
6026 | 6026 | dataOut.ephe[:]=numpy.nan |
|
|
6027 | 6027 | |
|
|
6028 | 6028 | return dataOut |
|
|
6029 | 6029 | |
|
|
6030 | 6030 | class LongPulseAnalysisSpectra(Operation): |
|
|
6031 | 6031 | """Operation to estimate ACFs, temperatures, total electron density and Hydrogen/Helium fractions from the Long Pulse data. |
|
|
6032 | 6032 | |
|
|
6033 | 6033 | Parameters: |
|
|
6034 | 6034 | ----------- |
|
|
6035 | 6035 | NACF : int |
|
|
6036 | 6036 | .* |
|
|
6037 | 6037 | |
|
|
6038 | 6038 | Example |
|
|
6039 | 6039 | -------- |
|
|
6040 | 6040 | |
|
|
6041 | 6041 | op = proc_unit.addOperation(name='LongPulseAnalysis', optype='other') |
|
|
6042 | 6042 | op.addParameter(name='NACF', value='16', format='int') |
|
|
6043 | 6043 | |
|
|
6044 | 6044 | """ |
|
|
6045 | 6045 | |
|
|
6046 | 6046 | def __init__(self, **kwargs): |
|
|
6047 | 6047 | |
|
|
6048 | 6048 | Operation.__init__(self, **kwargs) |
|
|
6049 | 6049 | self.aux=1 |
|
|
6050 | 6050 | |
|
|
6051 | 6051 | def run(self,dataOut,NACF): |
|
|
6052 | 6052 | |
|
|
6053 | 6053 | dataOut.NACF=NACF |
|
|
6054 | 6054 | dataOut.heightList=dataOut.DH*(numpy.arange(dataOut.NACF)) |
|
|
6055 | 6055 | anoise0=dataOut.tnoise[0] |
|
|
6056 | 6056 | anoise1=anoise0*0.0 #seems to be noise in 1st lag 0.015 before '14 |
|
|
6057 | 6057 | #print(anoise0) |
|
|
6058 | 6058 | #exit(1) |
|
|
6059 | 6059 | if self.aux: |
|
|
6060 | 6060 | #dataOut.cut=31#26#height=31*15=465 |
|
|
6061 | 6061 | self.cal=numpy.zeros((dataOut.NLAG),'float32') |
|
|
6062 | 6062 | self.drift=numpy.zeros((200),'float32') |
|
|
6063 | 6063 | self.rdrift=numpy.zeros((200),'float32') |
|
|
6064 | 6064 | self.ddrift=numpy.zeros((200),'float32') |
|
|
6065 | 6065 | self.sigma=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') |
|
|
6066 | 6066 | self.powera=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') |
|
|
6067 | 6067 | self.powerb=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') |
|
|
6068 | 6068 | self.perror=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') |
|
|
6069 | 6069 | dataOut.ene=numpy.zeros((dataOut.NRANGE),'float32') |
|
|
6070 | 6070 | self.dpulse=numpy.zeros((dataOut.NACF),'float32') |
|
|
6071 | 6071 | self.lpulse=numpy.zeros((dataOut.NACF),'float32') |
|
|
6072 | 6072 | dataOut.lags_LP=numpy.zeros((dataOut.IBITS),order='F',dtype='float32') |
|
|
6073 | 6073 | self.lagp=numpy.zeros((dataOut.NACF),'float32') |
|
|
6074 | 6074 | self.u=numpy.zeros((2*dataOut.NACF,2*dataOut.NACF),'float32') |
|
|
6075 | 6075 | dataOut.ne=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') |
|
|
6076 | 6076 | dataOut.te=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
|
6077 | 6077 | dataOut.ete=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
|
6078 | 6078 | dataOut.ti=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
|
6079 | 6079 | dataOut.eti=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
|
6080 | 6080 | dataOut.ph=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
|
6081 | 6081 | dataOut.eph=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
|
6082 | 6082 | dataOut.phe=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
|
6083 | 6083 | dataOut.ephe=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
|
6084 | 6084 | dataOut.errors=numpy.zeros((dataOut.IBITS,max(dataOut.NRANGE,dataOut.NSHTS)),order='F',dtype='float32') |
|
|
6085 | 6085 | dataOut.fit_array_real=numpy.zeros((max(dataOut.NRANGE,dataOut.NSHTS),dataOut.NLAG),order='F',dtype='float32') |
|
|
6086 | 6086 | dataOut.status=numpy.zeros(1,'float32') |
|
|
6087 | 6087 | dataOut.tx=240.0 #debería provenir del header #hybrid |
|
|
6088 | 6088 | |
|
|
6089 | 6089 | for i in range(dataOut.IBITS): |
|
|
6090 | 6090 | dataOut.lags_LP[i]=float(i)*(dataOut.tx/150.0)/float(dataOut.IBITS) # (float)i*(header.tx/150.0)/(float)IBITS; |
|
|
6091 | 6091 | |
|
|
6092 | 6092 | self.aux=0 |
|
|
6093 | 6093 | |
|
|
6094 | 6094 | dataOut.cut=30 |
|
|
6095 | 6095 | for i in range(30,15,-1): #Aquí se calcula en donde se unirá DP y LP en la parte final |
|
|
6096 | 6096 | if numpy.nanmax(dataOut.acfs_error_to_plot[i,:])>=10 or dataOut.info2[i]==0: |
|
|
6097 | 6097 | dataOut.cut=i-1 |
|
|
6098 | 6098 | |
|
|
6099 | 6099 | for i in range(dataOut.NLAG): |
|
|
6100 | 6100 | self.cal[i]=sum(dataOut.output_LP_integrated[i,:,3].real) #Lag x Height x Channel |
|
|
6101 | 6101 | |
|
|
6102 | 6102 | #print(numpy.sum(self.cal)) #Coinciden |
|
|
6103 | 6103 | #exit(1) |
|
|
6104 | 6104 | self.cal/=float(dataOut.NRANGE) |
|
|
6105 | 6105 | |
|
|
6106 | 6106 | |
|
|
6107 | 6107 | #################### PROBAR MÁS INTEGRACIÓN, SINO MODIFICAR VALOR DE "NIS" #################### |
|
|
6108 | 6108 | # VER dataOut.nProfiles_LP # |
|
|
6109 | 6109 | |
|
|
6110 | 6110 | ''' |
|
|
6111 | 6111 | #PLOTEAR POTENCIA VS RUIDO, QUIZA SE ESTA REMOVIENDO MUCHA SEÑAL |
|
|
6112 | 6112 | #print(dataOut.heightList) |
|
|
6113 | 6113 | import matplotlib.pyplot as plt |
|
|
6114 | 6114 | plt.plot(10*numpy.log10(dataOut.output_LP_integrated.real[0,:,0]),dataOut.range1) |
|
|
6115 | 6115 | #plt.plot(10*numpy.log10(dataOut.output_LP_integrated.real[0,:,0]/dataOut.nProfiles_LP),dataOut.range1) |
|
|
6116 | 6116 | plt.axvline(10*numpy.log10(anoise0),color='k',linestyle='dashed') |
|
|
6117 | 6117 | plt.grid() |
|
|
6118 | 6118 | plt.xlim(20,100) |
|
|
6119 | 6119 | plt.show() |
|
|
6120 | 6120 | ''' |
|
|
6121 | 6121 | |
|
|
6122 | 6122 | |
|
|
6123 | 6123 | for j in range(dataOut.NACF+2*dataOut.IBITS+2): |
|
|
6124 | 6124 | |
|
|
6125 | 6125 | dataOut.output_LP_integrated.real[0,j,0]-=anoise0 #lag0 ch0 |
|
|
6126 | 6126 | dataOut.output_LP_integrated.real[1,j,0]-=anoise1 #lag1 ch0 |
|
|
6127 | 6127 | |
|
|
6128 | 6128 | for i in range(1,dataOut.NLAG): #remove cal data from certain lags |
|
|
6129 | 6129 | dataOut.output_LP_integrated.real[i,j,0]-=self.cal[i] |
|
|
6130 | 6130 | k=max(j,26) #constant power below range 26 |
|
|
6131 | 6131 | self.powera[j]=dataOut.output_LP_integrated.real[0,k,0] #Lag0 and Channel 0 |
|
|
6132 | 6132 | |
|
|
6133 | 6133 | ## examine drifts here - based on 60 'indep.' estimates |
|
|
6134 | 6134 | #print(numpy.sum(self.powera)) |
|
|
6135 | 6135 | #exit(1) |
|
|
6136 | 6136 | #nis=dataOut.NSCAN*dataOut.NAVG*dataOut.nint*10 |
|
|
6137 | 6137 | nis = dataOut.nis |
|
|
6138 | 6138 | #print("nis",nis) |
|
|
6139 | 6139 | alpha=beta=delta=0.0 |
|
|
6140 | 6140 | nest=0 |
|
|
6141 | 6141 | gamma=3.0/(2.0*numpy.pi*dataOut.lags_LP[1]*1.0e-3) |
|
|
6142 | 6142 | beta=gamma*(math.atan2(dataOut.output_LP_integrated.imag[14,0,2],dataOut.output_LP_integrated.real[14,0,2])-math.atan2(dataOut.output_LP_integrated.imag[1,0,2],dataOut.output_LP_integrated.real[1,0,2]))/13.0 |
|
|
6143 | 6143 | #print(gamma,beta) |
|
|
6144 | 6144 | #exit(1) |
|
|
6145 | 6145 | for i in range(1,3): |
|
|
6146 | 6146 | gamma=3.0/(2.0*numpy.pi*dataOut.lags_LP[i]*1.0e-3) |
|
|
6147 | 6147 | #print("gamma",gamma) |
|
|
6148 | 6148 | for j in range(34,44): |
|
|
6149 | 6149 | rho2=numpy.abs(dataOut.output_LP_integrated[i,j,0])/numpy.abs(dataOut.output_LP_integrated[0,j,0]) |
|
|
6150 | 6150 | dataOut.dphi2=(1.0/rho2-1.0)/(float(2*nis)) |
|
|
6151 | 6151 | dataOut.dphi2*=gamma**2 |
|
|
6152 | 6152 | pest=gamma*math.atan(dataOut.output_LP_integrated.imag[i,j,0]/dataOut.output_LP_integrated.real[i,j,0]) |
|
|
6153 | 6153 | #print("1",dataOut.output_LP_integrated.imag[i,j,0]) |
|
|
6154 | 6154 | #print("2",dataOut.output_LP_integrated.real[i,j,0]) |
|
|
6155 | 6155 | self.drift[nest]=pest |
|
|
6156 | 6156 | self.ddrift[nest]=dataOut.dphi2 |
|
|
6157 | 6157 | self.rdrift[nest]=float(nest) |
|
|
6158 | 6158 | nest+=1 |
|
|
6159 | 6159 | |
|
|
6160 | 6160 | sorted(self.drift[:nest]) |
|
|
6161 | 6161 | |
|
|
6162 | 6162 | #print(dataOut.dphi2) |
|
|
6163 | 6163 | #exit(1) |
|
|
6164 | 6164 | |
|
|
6165 | 6165 | for j in range(int(nest/4),int(3*nest/4)): |
|
|
6166 | 6166 | #i=int(self.rdrift[j]) |
|
|
6167 | 6167 | alpha+=self.drift[j]/self.ddrift[j] |
|
|
6168 | 6168 | delta+=1.0/self.ddrift[j] |
|
|
6169 | 6169 | |
|
|
6170 | 6170 | alpha/=delta |
|
|
6171 | 6171 | delta=1./numpy.sqrt(delta) |
|
|
6172 | 6172 | vdrift=alpha-beta |
|
|
6173 | 6173 | dvdrift=delta |
|
|
6174 | 6174 | |
|
|
6175 | 6175 | #need to develop estimate of complete density profile using all |
|
|
6176 | 6176 | #available data |
|
|
6177 | 6177 | |
|
|
6178 | 6178 | #estimate sample variances for long-pulse power profile |
|
|
6179 | 6179 | |
|
|
6180 | 6180 | #nis=dataOut.NSCAN*dataOut.NAVG*dataOut.nint |
|
|
6181 | 6181 | nis = dataOut.nis/10 |
|
|
6182 | 6182 | #print("nis",nis) |
|
|
6183 | 6183 | |
|
|
6184 | 6184 | self.sigma[:dataOut.NACF+2*dataOut.IBITS+2]=((anoise0+self.powera[:dataOut.NACF+2*dataOut.IBITS+2])**2)/float(nis) |
|
|
6185 | 6185 | #print(self.sigma) |
|
|
6186 | 6186 | #exit(1) |
|
|
6187 | 6187 | ioff=1 |
|
|
6188 | 6188 | |
|
|
6189 | 6189 | #deconvolve rectangular pulse shape from profile ==> powerb, perror |
|
|
6190 | 6190 | |
|
|
6191 | 6191 | ''' |
|
|
6192 | 6192 | ############# START nnlswrap############# |
|
|
6193 | 6193 | |
|
|
6194 | 6194 | if dataOut.ut_Faraday>14.0: |
|
|
6195 | 6195 | alpha_nnlswrap=20.0 |
|
|
6196 | 6196 | else: |
|
|
6197 | 6197 | alpha_nnlswrap=30.0 |
|
|
6198 | 6198 | |
|
|
6199 | 6199 | range1_nnls=dataOut.NACF |
|
|
6200 | 6200 | range2_nnls=dataOut.NACF+dataOut.IBITS-1 |
|
|
6201 | 6201 | |
|
|
6202 | 6202 | g_nnlswrap=numpy.zeros((range1_nnls,range2_nnls),'float32') |
|
|
6203 | 6203 | a_nnlswrap=numpy.zeros((range2_nnls,range2_nnls),'float64') |
|
|
6204 | 6204 | |
|
|
6205 | 6205 | for i in range(range1_nnls): |
|
|
6206 | 6206 | for j in range(range2_nnls): |
|
|
6207 | 6207 | if j>=i and j<i+dataOut.IBITS: |
|
|
6208 | 6208 | g_nnlswrap[i,j]=1.0 |
|
|
6209 | 6209 | else: |
|
|
6210 | 6210 | g_nnlswrap[i,j]=0.0 |
|
|
6211 | 6211 | |
|
|
6212 | 6212 | a_nnlswrap[:]=numpy.matmul(numpy.transpose(g_nnlswrap),g_nnlswrap) |
|
|
6213 | 6213 | |
|
|
6214 | 6214 | numpy.fill_diagonal(a_nnlswrap,a_nnlswrap.diagonal()+alpha_nnlswrap**2) |
|
|
6215 | 6215 | |
|
|
6216 | 6216 | #ERROR ANALYSIS# |
|
|
6217 | 6217 | |
|
|
6218 | 6218 | self.perror[:range2_nnls]=0.0 |
|
|
6219 | 6219 | self.perror[:range2_nnls]=numpy.matmul(1./(self.sigma[dataOut.IBITS+ioff:range1_nnls+dataOut.IBITS+ioff]),g_nnlswrap**2) |
|
|
6220 | 6220 | self.perror[:range1_nnls]+=(alpha_nnlswrap**2)/(self.sigma[dataOut.IBITS+ioff:range1_nnls+dataOut.IBITS+ioff]) |
|
|
6221 | 6221 | self.perror[:range2_nnls]=1.00/self.perror[:range2_nnls] |
|
|
6222 | 6222 | |
|
|
6223 | 6223 | b_nnlswrap=numpy.zeros(range2_nnls,'float64') |
|
|
6224 | 6224 | b_nnlswrap[:]=numpy.matmul(self.powera[dataOut.IBITS+ioff:range1_nnls+dataOut.IBITS+ioff],g_nnlswrap) |
|
|
6225 | 6225 | |
|
|
6226 | 6226 | x_nnlswrap=numpy.zeros(range2_nnls,'float64') |
|
|
6227 | 6227 | x_nnlswrap[:]=nnls(a_nnlswrap,b_nnlswrap)[0] |
|
|
6228 | 6228 | |
|
|
6229 | 6229 | self.powerb[:range2_nnls]=x_nnlswrap |
|
|
6230 | 6230 | #print(self.powerb[40]) |
|
|
6231 | 6231 | #print(self.powerb[66]) |
|
|
6232 | 6232 | #exit(1) |
|
|
6233 | 6233 | #############END nnlswrap############# |
|
|
6234 | 6234 | ''' |
|
|
6235 | 6235 | self.powerb[:] = self.powera |
|
|
6236 | 6236 | self.perror[:] = 0. |
|
|
6237 | 6237 | #print(numpy.sum(numpy.sqrt(self.perror[0:dataOut.NACF]))) |
|
|
6238 | 6238 | #print(self.powerb[0:dataOut.NACF]) |
|
|
6239 | 6239 | #exit(1) |
|
|
6240 | 6240 | #estimate relative error for deconvolved profile (scaling irrelevant) |
|
|
6241 | 6241 | #print(dataOut.NACF) |
|
|
6242 | 6242 | dataOut.ene[0:dataOut.NACF]=numpy.sqrt(self.perror[0:dataOut.NACF])/self.powerb[0:dataOut.NACF] |
|
|
6243 | 6243 | #print(numpy.sum(dataOut.ene)) |
|
|
6244 | 6244 | #exit(1) |
|
|
6245 | 6245 | aux=0 |
|
|
6246 | 6246 | |
|
|
6247 | 6247 | for i in range(dataOut.IBITS,dataOut.NACF): |
|
|
6248 | 6248 | self.dpulse[i]=self.lpulse[i]=0.0 |
|
|
6249 | 6249 | for j in range(dataOut.IBITS): |
|
|
6250 | 6250 | k=int(i-j) |
|
|
6251 | 6251 | if k<36-aux and k>16: |
|
|
6252 | 6252 | self.dpulse[i]+=dataOut.ph2[k]/dataOut.h2[k] |
|
|
6253 | 6253 | elif k>=36-aux: |
|
|
6254 | 6254 | self.lpulse[i]+=self.powerb[k] |
|
|
6255 | 6255 | self.lagp[i]=self.powera[i] |
|
|
6256 | 6256 | |
|
|
6257 | 6257 | #find scale factor that best merges profiles |
|
|
6258 | 6258 | |
|
|
6259 | 6259 | qi=sum(self.dpulse[32:dataOut.NACF]**2/(self.lagp[32:dataOut.NACF]+anoise0)**2) |
|
|
6260 | 6260 | ri=sum((self.dpulse[32:dataOut.NACF]*self.lpulse[32:dataOut.NACF])/(self.lagp[32:dataOut.NACF]+anoise0)**2) |
|
|
6261 | 6261 | si=sum((self.dpulse[32:dataOut.NACF]*self.lagp[32:dataOut.NACF])/(self.lagp[32:dataOut.NACF]+anoise0)**2) |
|
|
6262 | 6262 | ui=sum(self.lpulse[32:dataOut.NACF]**2/(self.lagp[32:dataOut.NACF]+anoise0)**2) |
|
|
6263 | 6263 | vi=sum((self.lpulse[32:dataOut.NACF]*self.lagp[32:dataOut.NACF])/(self.lagp[32:dataOut.NACF]+anoise0)**2) |
|
|
6264 | 6264 | |
|
|
6265 | 6265 | alpha=(si*ui-vi*ri)/(qi*ui-ri*ri) |
|
|
6266 | 6266 | beta=(qi*vi-ri*si)/(qi*ui-ri*ri) |
|
|
6267 | 6267 | |
|
|
6268 | 6268 | #form density profile estimate, merging rescaled power profiles |
|
|
6269 | 6269 | #print(dataOut.h2) |
|
|
6270 | 6270 | #print(numpy.sum(alpha)) |
|
|
6271 | 6271 | #print(numpy.sum(dataOut.ph2)) |
|
|
6272 | 6272 | self.powerb[16:36-aux]=alpha*dataOut.ph2[16:36-aux]/dataOut.h2[16:36-aux] |
|
|
6273 | 6273 | self.powerb[36-aux:dataOut.NACF]*=beta |
|
|
6274 | 6274 | |
|
|
6275 | 6275 | #form Ne estimate, fill in error estimate at low altitudes |
|
|
6276 | 6276 | |
|
|
6277 | 6277 | dataOut.ene[0:36-aux]=dataOut.sdp2[0:36-aux]/dataOut.ph2[0:36-aux] |
|
|
6278 | 6278 | dataOut.ne[:dataOut.NACF]=self.powerb[:dataOut.NACF]*dataOut.h2[:dataOut.NACF]/alpha |
|
|
6279 | 6279 | #print(numpy.sum(self.powerb)) |
|
|
6280 | 6280 | #print(numpy.sum(dataOut.ene)) |
|
|
6281 | 6281 | #print(numpy.sum(dataOut.ne)) |
|
|
6282 | 6282 | #exit(1) |
|
|
6283 | 6283 | #now do error propagation: store zero lag error covariance in u |
|
|
6284 | 6284 | |
|
|
6285 | 6285 | nis=dataOut.NSCAN*dataOut.NAVG*dataOut.nint/1 # DLH serious debris removal |
|
|
6286 | 6286 | |
|
|
6287 | 6287 | for i in range(dataOut.NACF): |
|
|
6288 | 6288 | for j in range(i,dataOut.NACF): |
|
|
6289 | 6289 | if j-i>=dataOut.IBITS: |
|
|
6290 | 6290 | self.u[i,j]=0.0 |
|
|
6291 | 6291 | else: |
|
|
6292 | 6292 | self.u[i,j]=dataOut.output_LP_integrated.real[j-i,i,0]**2/float(nis) |
|
|
6293 | 6293 | self.u[i,j]*=(anoise0+dataOut.output_LP_integrated.real[0,i,0])/dataOut.output_LP_integrated.real[0,i,0] |
|
|
6294 | 6294 | self.u[i,j]*=(anoise0+dataOut.output_LP_integrated.real[0,j,0])/dataOut.output_LP_integrated.real[0,j,0] |
|
|
6295 | 6295 | |
|
|
6296 | 6296 | self.u[j,i]=self.u[i,j] |
|
|
6297 | 6297 | |
|
|
6298 | 6298 | #now error analyis for lag product matrix (diag), place in acf_err |
|
|
6299 | 6299 | |
|
|
6300 | 6300 | for i in range(dataOut.NACF): |
|
|
6301 | 6301 | for j in range(dataOut.IBITS): |
|
|
6302 | 6302 | if j==0: |
|
|
6303 | 6303 | dataOut.errors[0,i]=numpy.sqrt(self.u[i,i]) |
|
|
6304 | 6304 | else: |
|
|
6305 | 6305 | dataOut.errors[j,i]=numpy.sqrt(((dataOut.output_LP_integrated.real[0,i,0]+anoise0)*(dataOut.output_LP_integrated.real[0,i+j,0]+anoise0)+dataOut.output_LP_integrated.real[j,i,0]**2)/float(2*nis)) |
|
|
6306 | 6306 | |
|
|
6307 | 6307 | print("Success") |
|
|
6308 | 6308 | #print(dataOut.NRANGE) |
|
|
6309 | 6309 | with suppress_stdout_stderr(): |
|
|
6310 | 6310 | pass |
|
|
6311 | 6311 | #full_profile_profile.profile(numpy.transpose(dataOut.output_LP_integrated,(2,1,0)),numpy.transpose(dataOut.errors),self.powerb,dataOut.ne,dataOut.lags_LP,dataOut.thb,dataOut.bfm,dataOut.te,dataOut.ete,dataOut.ti,dataOut.eti,dataOut.ph,dataOut.eph,dataOut.phe,dataOut.ephe,dataOut.range1,dataOut.ut,dataOut.NACF,dataOut.fit_array_real,dataOut.status,dataOut.NRANGE,dataOut.IBITS) |
|
|
6312 | 6312 | |
|
|
6313 | 6313 | print("status: ",dataOut.status) |
|
|
6314 | 6314 | |
|
|
6315 | 6315 | if dataOut.status>=3.5: |
|
|
6316 | 6316 | dataOut.te[:]=numpy.nan |
|
|
6317 | 6317 | dataOut.ete[:]=numpy.nan |
|
|
6318 | 6318 | dataOut.ti[:]=numpy.nan |
|
|
6319 | 6319 | dataOut.eti[:]=numpy.nan |
|
|
6320 | 6320 | dataOut.ph[:]=numpy.nan |
|
|
6321 | 6321 | dataOut.eph[:]=numpy.nan |
|
|
6322 | 6322 | dataOut.phe[:]=numpy.nan |
|
|
6323 | 6323 | dataOut.ephe[:]=numpy.nan |
|
|
6324 | 6324 | |
|
|
6325 | 6325 | return dataOut |
|
|
6326 | 6326 | |
|
|
6327 | 6327 | class LongPulseAnalysis_V2(Operation): |
|
|
6328 | 6328 | """Operation to estimate ACFs, temperatures, total electron density and Hydrogen/Helium fractions from the Long Pulse data. |
|
|
6329 | 6329 | |
|
|
6330 | 6330 | Parameters: |
|
|
6331 | 6331 | ----------- |
|
|
6332 | 6332 | NACF : int |
|
|
6333 | 6333 | .* |
|
|
6334 | 6334 | |
|
|
6335 | 6335 | Example |
|
|
6336 | 6336 | -------- |
|
|
6337 | 6337 | |
|
|
6338 | 6338 | op = proc_unit.addOperation(name='LongPulseAnalysis', optype='other') |
|
|
6339 | 6339 | op.addParameter(name='NACF', value='16', format='int') |
|
|
6340 | 6340 | |
|
|
6341 | 6341 | """ |
|
|
6342 | 6342 | |
|
|
6343 | 6343 | def __init__(self, **kwargs): |
|
|
6344 | 6344 | |
|
|
6345 | 6345 | Operation.__init__(self, **kwargs) |
|
|
6346 | 6346 | self.aux=1 |
|
|
6347 | 6347 | |
|
|
6348 | 6348 | def run(self,dataOut,NACF): |
|
|
6349 | 6349 | |
|
|
6350 | 6350 | dataOut.NACF=NACF |
|
|
6351 | 6351 | dataOut.heightList=dataOut.DH*(numpy.arange(dataOut.NACF)) |
|
|
6352 | 6352 | anoise0=dataOut.tnoise[0] |
|
|
6353 | 6353 | anoise1=anoise0*0.0 #seems to be noise in 1st lag 0.015 before '14 |
|
|
6354 | 6354 | #print(anoise0) |
|
|
6355 | 6355 | #exit(1) |
|
|
6356 | 6356 | if self.aux: |
|
|
6357 | 6357 | #dataOut.cut=31#26#height=31*15=465 |
|
|
6358 | 6358 | self.cal=numpy.zeros((dataOut.NLAG),'float32') |
|
|
6359 | 6359 | self.drift=numpy.zeros((200),'float32') |
|
|
6360 | 6360 | self.rdrift=numpy.zeros((200),'float32') |
|
|
6361 | 6361 | self.ddrift=numpy.zeros((200),'float32') |
|
|
6362 | 6362 | self.sigma=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') |
|
|
6363 | 6363 | self.powera=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') |
|
|
6364 | 6364 | self.powerb=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') |
|
|
6365 | 6365 | self.perror=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') |
|
|
6366 | 6366 | dataOut.ene=numpy.zeros((dataOut.NRANGE),'float32') |
|
|
6367 | 6367 | self.dpulse=numpy.zeros((dataOut.NACF),'float32') |
|
|
6368 | 6368 | self.lpulse=numpy.zeros((dataOut.NACF),'float32') |
|
|
6369 | 6369 | dataOut.lags_LP=numpy.zeros((dataOut.IBITS),order='F',dtype='float32') |
|
|
6370 | 6370 | self.lagp=numpy.zeros((dataOut.NACF),'float32') |
|
|
6371 | 6371 | self.u=numpy.zeros((2*dataOut.NACF,2*dataOut.NACF),'float32') |
|
|
6372 | 6372 | dataOut.ne=numpy.zeros((dataOut.NRANGE),order='F',dtype='float32') |
|
|
6373 | 6373 | dataOut.te=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
|
6374 | 6374 | dataOut.ete=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
|
6375 | 6375 | dataOut.ti=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
|
6376 | 6376 | dataOut.eti=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
|
6377 | 6377 | dataOut.ph=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
|
6378 | 6378 | dataOut.eph=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
|
6379 | 6379 | dataOut.phe=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
|
6380 | 6380 | dataOut.ephe=numpy.zeros((dataOut.NACF),order='F',dtype='float32') |
|
|
6381 | 6381 | dataOut.errors=numpy.zeros((dataOut.IBITS,max(dataOut.NRANGE,dataOut.NSHTS)),order='F',dtype='float32') |
|
|
6382 | 6382 | dataOut.fit_array_real=numpy.zeros((max(dataOut.NRANGE,dataOut.NSHTS),dataOut.NLAG),order='F',dtype='float32') |
|
|
6383 | 6383 | dataOut.status=numpy.zeros(1,'float32') |
|
|
6384 | 6384 | dataOut.tx=240.0 #debería provenir del header #hybrid |
|
|
6385 | 6385 | |
|
|
6386 | 6386 | for i in range(dataOut.IBITS): |
|
|
6387 | 6387 | dataOut.lags_LP[i]=float(i)*(dataOut.tx/150.0)/float(dataOut.IBITS) # (float)i*(header.tx/150.0)/(float)IBITS; |
|
|
6388 | 6388 | |
|
|
6389 | 6389 | self.aux=0 |
|
|
6390 | 6390 | |
|
|
6391 | 6391 | dataOut.cut=30 |
|
|
6392 | 6392 | for i in range(30,15,-1): |
|
|
6393 | 6393 | if numpy.nanmax(dataOut.acfs_error_to_plot[i,:])>=10 or dataOut.info2[i]==0: |
|
|
6394 | 6394 | dataOut.cut=i-1 |
|
|
6395 | 6395 | |
|
|
6396 | 6396 | for i in range(dataOut.NLAG): |
|
|
6397 | 6397 | self.cal[i]=sum(dataOut.output_LP_integrated[i,:,3].real) |
|
|
6398 | 6398 | |
|
|
6399 | 6399 | #print(numpy.sum(self.cal)) #Coinciden |
|
|
6400 | 6400 | #exit(1) |
|
|
6401 | 6401 | self.cal/=float(dataOut.NRANGE) |
|
|
6402 | 6402 | #print(anoise0) |
|
|
6403 | 6403 | #print(anoise1) |
|
|
6404 | 6404 | #exit(1) |
|
|
6405 | 6405 | |
|
|
6406 | 6406 | for j in range(dataOut.NACF+2*dataOut.IBITS+2): |
|
|
6407 | 6407 | |
|
|
6408 | 6408 | dataOut.output_LP_integrated.real[0,j,0]-=anoise0 #lag0 ch0 |
|
|
6409 | 6409 | dataOut.output_LP_integrated.real[1,j,0]-=anoise1 #lag1 ch0 |
|
|
6410 | 6410 | |
|
|
6411 | 6411 | for i in range(1,dataOut.NLAG): #remove cal data from certain lags |
|
|
6412 | 6412 | dataOut.output_LP_integrated.real[i,j,0]-=self.cal[i] |
|
|
6413 | 6413 | k=max(j,26) #constant power below range 26 |
|
|
6414 | 6414 | self.powera[j]=dataOut.output_LP_integrated.real[0,k,0] |
|
|
6415 | 6415 | |
|
|
6416 | 6416 | ## examine drifts here - based on 60 'indep.' estimates |
|
|
6417 | 6417 | #print(numpy.sum(self.powera)) |
|
|
6418 | 6418 | #exit(1) |
|
|
6419 | 6419 | #nis=dataOut.NSCAN*dataOut.NAVG*dataOut.nint*10 |
|
|
6420 | 6420 | nis = dataOut.nis |
|
|
6421 | 6421 | #print("nis",nis) |
|
|
6422 | 6422 | alpha=beta=delta=0.0 |
|
|
6423 | 6423 | nest=0 |
|
|
6424 | 6424 | gamma=3.0/(2.0*numpy.pi*dataOut.lags_LP[1]*1.0e-3) |
|
|
6425 | 6425 | beta=gamma*(math.atan2(dataOut.output_LP_integrated.imag[14,0,2],dataOut.output_LP_integrated.real[14,0,2])-math.atan2(dataOut.output_LP_integrated.imag[1,0,2],dataOut.output_LP_integrated.real[1,0,2]))/13.0 |
|
|
6426 | 6426 | #print(gamma,beta) |
|
|
6427 | 6427 | #exit(1) |
|
|
6428 | 6428 | for i in range(1,3): |
|
|
6429 | 6429 | gamma=3.0/(2.0*numpy.pi*dataOut.lags_LP[i]*1.0e-3) |
|
|
6430 | 6430 | #print("gamma",gamma) |
|
|
6431 | 6431 | for j in range(34,44): |
|
|
6432 | 6432 | rho2=numpy.abs(dataOut.output_LP_integrated[i,j,0])/numpy.abs(dataOut.output_LP_integrated[0,j,0]) |
|
|
6433 | 6433 | dataOut.dphi2=(1.0/rho2-1.0)/(float(2*nis)) |
|
|
6434 | 6434 | dataOut.dphi2*=gamma**2 |
|
|
6435 | 6435 | pest=gamma*math.atan(dataOut.output_LP_integrated.imag[i,j,0]/dataOut.output_LP_integrated.real[i,j,0]) |
|
|
6436 | 6436 | #print("1",dataOut.output_LP_integrated.imag[i,j,0]) |
|
|
6437 | 6437 | #print("2",dataOut.output_LP_integrated.real[i,j,0]) |
|
|
6438 | 6438 | self.drift[nest]=pest |
|
|
6439 | 6439 | self.ddrift[nest]=dataOut.dphi2 |
|
|
6440 | 6440 | self.rdrift[nest]=float(nest) |
|
|
6441 | 6441 | nest+=1 |
|
|
6442 | 6442 | |
|
|
6443 | 6443 | sorted(self.drift[:nest]) |
|
|
6444 | 6444 | |
|
|
6445 | 6445 | #print(dataOut.dphi2) |
|
|
6446 | 6446 | #exit(1) |
|
|
6447 | 6447 | |
|
|
6448 | 6448 | for j in range(int(nest/4),int(3*nest/4)): |
|
|
6449 | 6449 | #i=int(self.rdrift[j]) |
|
|
6450 | 6450 | alpha+=self.drift[j]/self.ddrift[j] |
|
|
6451 | 6451 | delta+=1.0/self.ddrift[j] |
|
|
6452 | 6452 | |
|
|
6453 | 6453 | alpha/=delta |
|
|
6454 | 6454 | delta=1./numpy.sqrt(delta) |
|
|
6455 | 6455 | vdrift=alpha-beta |
|
|
6456 | 6456 | dvdrift=delta |
|
|
6457 | 6457 | |
|
|
6458 | 6458 | #need to develop estimate of complete density profile using all |
|
|
6459 | 6459 | #available data |
|
|
6460 | 6460 | |
|
|
6461 | 6461 | #estimate sample variances for long-pulse power profile |
|
|
6462 | 6462 | |
|
|
6463 | 6463 | #nis=dataOut.NSCAN*dataOut.NAVG*dataOut.nint |
|
|
6464 | 6464 | nis = dataOut.nis/10 |
|
|
6465 | 6465 | #print("nis",nis) |
|
|
6466 | 6466 | |
|
|
6467 | 6467 | self.sigma[:dataOut.NACF+2*dataOut.IBITS+2]=((anoise0+self.powera[:dataOut.NACF+2*dataOut.IBITS+2])**2)/float(nis) |
|
|
6468 | 6468 | #print(self.sigma) |
|
|
6469 | 6469 | #exit(1) |
|
|
6470 | 6470 | ioff=1 |
|
|
6471 | 6471 | |
|
|
6472 | 6472 | #deconvolve rectangular pulse shape from profile ==> powerb, perror |
|
|
6473 | 6473 | |
|
|
6474 | 6474 | |
|
|
6475 | 6475 | ############# START nnlswrap############# |
|
|
6476 | 6476 | |
|
|
6477 | 6477 | if dataOut.ut_Faraday>14.0: |
|
|
6478 | 6478 | alpha_nnlswrap=20.0 |
|
|
6479 | 6479 | else: |
|
|
6480 | 6480 | alpha_nnlswrap=30.0 |
|
|
6481 | 6481 | |
|
|
6482 | 6482 | range1_nnls=dataOut.NACF |
|
|
6483 | 6483 | range2_nnls=dataOut.NACF+dataOut.IBITS-1 |
|
|
6484 | 6484 | |
|
|
6485 | 6485 | g_nnlswrap=numpy.zeros((range1_nnls,range2_nnls),'float32') |
|
|
6486 | 6486 | a_nnlswrap=numpy.zeros((range2_nnls,range2_nnls),'float64') |
|
|
6487 | 6487 | |
|
|
6488 | 6488 | for i in range(range1_nnls): |
|
|
6489 | 6489 | for j in range(range2_nnls): |
|
|
6490 | 6490 | if j>=i and j<i+dataOut.IBITS: |
|
|
6491 | 6491 | g_nnlswrap[i,j]=1.0 |
|
|
6492 | 6492 | else: |
|
|
6493 | 6493 | g_nnlswrap[i,j]=0.0 |
|
|
6494 | 6494 | |
|
|
6495 | 6495 | a_nnlswrap[:]=numpy.matmul(numpy.transpose(g_nnlswrap),g_nnlswrap) |
|
|
6496 | 6496 | |
|
|
6497 | 6497 | numpy.fill_diagonal(a_nnlswrap,a_nnlswrap.diagonal()+alpha_nnlswrap**2) |
|
|
6498 | 6498 | |
|
|
6499 | 6499 | #ERROR ANALYSIS# |
|
|
6500 | 6500 | |
|
|
6501 | 6501 | self.perror[:range2_nnls]=0.0 |
|
|
6502 | 6502 | self.perror[:range2_nnls]=numpy.matmul(1./(self.sigma[dataOut.IBITS+ioff:range1_nnls+dataOut.IBITS+ioff]),g_nnlswrap**2) |
|
|
6503 | 6503 | self.perror[:range1_nnls]+=(alpha_nnlswrap**2)/(self.sigma[dataOut.IBITS+ioff:range1_nnls+dataOut.IBITS+ioff]) |
|
|
6504 | 6504 | self.perror[:range2_nnls]=1.00/self.perror[:range2_nnls] |
|
|
6505 | 6505 | |
|
|
6506 | 6506 | b_nnlswrap=numpy.zeros(range2_nnls,'float64') |
|
|
6507 | 6507 | b_nnlswrap[:]=numpy.matmul(self.powera[dataOut.IBITS+ioff:range1_nnls+dataOut.IBITS+ioff],g_nnlswrap) |
|
|
6508 | 6508 | |
|
|
6509 | 6509 | x_nnlswrap=numpy.zeros(range2_nnls,'float64') |
|
|
6510 | 6510 | x_nnlswrap[:]=nnls(a_nnlswrap,b_nnlswrap)[0] |
|
|
6511 | 6511 | |
|
|
6512 | 6512 | self.powerb[:range2_nnls]=x_nnlswrap |
|
|
6513 | 6513 | #print(self.powerb[40]) |
|
|
6514 | 6514 | #print(self.powerb[66]) |
|
|
6515 | 6515 | #exit(1) |
|
|
6516 | 6516 | #############END nnlswrap############# |
|
|
6517 | 6517 | #print(numpy.sum(numpy.sqrt(self.perror[0:dataOut.NACF]))) |
|
|
6518 | 6518 | #print(self.powerb[0:dataOut.NACF]) |
|
|
6519 | 6519 | #exit(1) |
|
|
6520 | 6520 | #estimate relative error for deconvolved profile (scaling irrelevant) |
|
|
6521 | 6521 | #print(dataOut.NACF) |
|
|
6522 | 6522 | dataOut.ene[0:dataOut.NACF]=numpy.sqrt(self.perror[0:dataOut.NACF])/self.powerb[0:dataOut.NACF] |
|
|
6523 | 6523 | #print(numpy.sum(dataOut.ene)) |
|
|
6524 | 6524 | #exit(1) |
|
|
6525 | 6525 | aux=0 |
|
|
6526 | 6526 | |
|
|
6527 | 6527 | for i in range(dataOut.IBITS,dataOut.NACF): |
|
|
6528 | 6528 | self.dpulse[i]=self.lpulse[i]=0.0 |
|
|
6529 | 6529 | for j in range(dataOut.IBITS): |
|
|
6530 | 6530 | k=int(i-j) |
|
|
6531 | 6531 | if k<36-aux and k>16: |
|
|
6532 | 6532 | self.dpulse[i]+=dataOut.ph2[k]/dataOut.h2[k] |
|
|
6533 | 6533 | elif k>=36-aux: |
|
|
6534 | 6534 | self.lpulse[i]+=self.powerb[k] |
|
|
6535 | 6535 | self.lagp[i]=self.powera[i] |
|
|
6536 | 6536 | |
|
|
6537 | 6537 | #find scale factor that best merges profiles |
|
|
6538 | 6538 | |
|
|
6539 | 6539 | qi=sum(self.dpulse[32:dataOut.NACF]**2/(self.lagp[32:dataOut.NACF]+anoise0)**2) |
|
|
6540 | 6540 | ri=sum((self.dpulse[32:dataOut.NACF]*self.lpulse[32:dataOut.NACF])/(self.lagp[32:dataOut.NACF]+anoise0)**2) |
|
|
6541 | 6541 | si=sum((self.dpulse[32:dataOut.NACF]*self.lagp[32:dataOut.NACF])/(self.lagp[32:dataOut.NACF]+anoise0)**2) |
|
|
6542 | 6542 | ui=sum(self.lpulse[32:dataOut.NACF]**2/(self.lagp[32:dataOut.NACF]+anoise0)**2) |
|
|
6543 | 6543 | vi=sum((self.lpulse[32:dataOut.NACF]*self.lagp[32:dataOut.NACF])/(self.lagp[32:dataOut.NACF]+anoise0)**2) |
|
|
6544 | 6544 | |
|
|
6545 | 6545 | alpha=(si*ui-vi*ri)/(qi*ui-ri*ri) |
|
|
6546 | 6546 | beta=(qi*vi-ri*si)/(qi*ui-ri*ri) |
|
|
6547 | 6547 | |
|
|
6548 | 6548 | #form density profile estimate, merging rescaled power profiles |
|
|
6549 | 6549 | #print(dataOut.h2) |
|
|
6550 | 6550 | #print(numpy.sum(alpha)) |
|
|
6551 | 6551 | #print(numpy.sum(dataOut.ph2)) |
|
|
6552 | 6552 | self.powerb[16:36-aux]=alpha*dataOut.ph2[16:36-aux]/dataOut.h2[16:36-aux] |
|
|
6553 | 6553 | self.powerb[36-aux:dataOut.NACF]*=beta |
|
|
6554 | 6554 | |
|
|
6555 | 6555 | #form Ne estimate, fill in error estimate at low altitudes |
|
|
6556 | 6556 | |
|
|
6557 | 6557 | dataOut.ene[0:36-aux]=dataOut.sdp2[0:36-aux]/dataOut.ph2[0:36-aux] |
|
|
6558 | 6558 | dataOut.ne[:dataOut.NACF]=self.powerb[:dataOut.NACF]*dataOut.h2[:dataOut.NACF]/alpha |
|
|
6559 | 6559 | #print(numpy.sum(self.powerb)) |
|
|
6560 | 6560 | #print(numpy.sum(dataOut.ene)) |
|
|
6561 | 6561 | #print(numpy.sum(dataOut.ne)) |
|
|
6562 | 6562 | #exit(1) |
|
|
6563 | 6563 | #now do error propagation: store zero lag error covariance in u |
|
|
6564 | 6564 | |
|
|
6565 | 6565 | nis=dataOut.NSCAN*dataOut.NAVG*dataOut.nint/1 # DLH serious debris removal |
|
|
6566 | 6566 | |
|
|
6567 | 6567 | for i in range(dataOut.NACF): |
|
|
6568 | 6568 | for j in range(i,dataOut.NACF): |
|
|
6569 | 6569 | if j-i>=dataOut.IBITS: |
|
|
6570 | 6570 | self.u[i,j]=0.0 |
|
|
6571 | 6571 | else: |
|
|
6572 | 6572 | self.u[i,j]=dataOut.output_LP_integrated.real[j-i,i,0]**2/float(nis) |
|
|
6573 | 6573 | self.u[i,j]*=(anoise0+dataOut.output_LP_integrated.real[0,i,0])/dataOut.output_LP_integrated.real[0,i,0] |
|
|
6574 | 6574 | self.u[i,j]*=(anoise0+dataOut.output_LP_integrated.real[0,j,0])/dataOut.output_LP_integrated.real[0,j,0] |
|
|
6575 | 6575 | |
|
|
6576 | 6576 | self.u[j,i]=self.u[i,j] |
|
|
6577 | 6577 | |
|
|
6578 | 6578 | #now error analyis for lag product matrix (diag), place in acf_err |
|
|
6579 | 6579 | |
|
|
6580 | 6580 | for i in range(dataOut.NACF): |
|
|
6581 | 6581 | for j in range(dataOut.IBITS): |
|
|
6582 | 6582 | if j==0: |
|
|
6583 | 6583 | dataOut.errors[0,i]=numpy.sqrt(self.u[i,i]) |
|
|
6584 | 6584 | else: |
|
|
6585 | 6585 | dataOut.errors[j,i]=numpy.sqrt(((dataOut.output_LP_integrated.real[0,i,0]+anoise0)*(dataOut.output_LP_integrated.real[0,i+j,0]+anoise0)+dataOut.output_LP_integrated.real[j,i,0]**2)/float(2*nis)) |
|
|
6586 | 6586 | |
|
|
6587 | 6587 | print("Success") |
|
|
6588 | 6588 | with suppress_stdout_stderr(): |
|
|
6589 | 6589 | #pass |
|
|
6590 | 6590 | full_profile_profile.profile(numpy.transpose(dataOut.output_LP_integrated,(2,1,0)),numpy.transpose(dataOut.errors),self.powerb,dataOut.ne,dataOut.lags_LP,dataOut.thb,dataOut.bfm,dataOut.te,dataOut.ete,dataOut.ti,dataOut.eti,dataOut.ph,dataOut.eph,dataOut.phe,dataOut.ephe,dataOut.range1,dataOut.ut,dataOut.NACF,dataOut.fit_array_real,dataOut.status,dataOut.NRANGE,dataOut.IBITS) |
|
|
6591 | 6591 | |
|
|
6592 | 6592 | if dataOut.status>=3.5: |
|
|
6593 | 6593 | dataOut.te[:]=numpy.nan |
|
|
6594 | 6594 | dataOut.ete[:]=numpy.nan |
|
|
6595 | 6595 | dataOut.ti[:]=numpy.nan |
|
|
6596 | 6596 | dataOut.eti[:]=numpy.nan |
|
|
6597 | 6597 | dataOut.ph[:]=numpy.nan |
|
|
6598 | 6598 | dataOut.eph[:]=numpy.nan |
|
|
6599 | 6599 | dataOut.phe[:]=numpy.nan |
|
|
6600 | 6600 | dataOut.ephe[:]=numpy.nan |
|
|
6601 | 6601 | |
|
|
6602 | 6602 | return dataOut |
|
|
6603 | 6603 | |
|
|
6604 | 6604 | class PulsePairVoltage(Operation): |
|
|
6605 | 6605 | ''' |
|
|
6606 | 6606 | Function PulsePair(Signal Power, Velocity) |
|
|
6607 | 6607 | The real component of Lag[0] provides Intensity Information |
|
|
6608 | 6608 | The imag component of Lag[1] Phase provides Velocity Information |
|
|
6609 | 6609 | |
|
|
6610 | 6610 | Configuration Parameters: |
|
|
6611 | 6611 | nPRF = Number of Several PRF |
|
|
6612 | 6612 | theta = Degree Azimuth angel Boundaries |
|
|
6613 | 6613 | |
|
|
6614 | 6614 | Input: |
|
|
6615 | 6615 | self.dataOut |
|
|
6616 | 6616 | lag[N] |
|
|
6617 | 6617 | Affected: |
|
|
6618 | 6618 | self.dataOut.spc |
|
|
6619 | 6619 | ''' |
|
|
6620 | 6620 | isConfig = False |
|
|
6621 | 6621 | __profIndex = 0 |
|
|
6622 | 6622 | __initime = None |
|
|
6623 | 6623 | __lastdatatime = None |
|
|
6624 | 6624 | __buffer = None |
|
|
6625 | 6625 | noise = None |
|
|
6626 | 6626 | __dataReady = False |
|
|
6627 | 6627 | n = None |
|
|
6628 | 6628 | __nch = 0 |
|
|
6629 | 6629 | __nHeis = 0 |
|
|
6630 | 6630 | removeDC = False |
|
|
6631 | 6631 | ipp = None |
|
|
6632 | 6632 | lambda_ = 0 |
|
|
6633 | 6633 | |
|
|
6634 | 6634 | def __init__(self, **kwargs): |
|
|
6635 | 6635 | Operation.__init__(self, **kwargs) |
|
|
6636 | 6636 | |
|
|
6637 | 6637 | def setup(self, dataOut, n=None, removeDC=False): |
|
|
6638 | 6638 | ''' |
|
|
6639 | 6639 | n= Numero de PRF's de entrada |
|
|
6640 | 6640 | ''' |
|
|
6641 | 6641 | self.__initime = None |
|
|
6642 | 6642 | self.__lastdatatime = 0 |
|
|
6643 | 6643 | self.__dataReady = False |
|
|
6644 | 6644 | self.__buffer = 0 |
|
|
6645 | 6645 | self.__profIndex = 0 |
|
|
6646 | 6646 | self.noise = None |
|
|
6647 | 6647 | self.__nch = dataOut.nChannels |
|
|
6648 | 6648 | self.__nHeis = dataOut.nHeights |
|
|
6649 | 6649 | self.removeDC = removeDC |
|
|
6650 | 6650 | self.lambda_ = 3.0e8 / (9345.0e6) |
|
|
6651 | 6651 | self.ippSec = dataOut.ippSeconds |
|
|
6652 | 6652 | self.nCohInt = dataOut.nCohInt |
|
|
6653 | 6653 | print("IPPseconds", dataOut.ippSeconds) |
|
|
6654 | 6654 | |
|
|
6655 | 6655 | print("ELVALOR DE n es:", n) |
|
|
6656 | 6656 | if n == None: |
|
|
6657 | 6657 | raise ValueError("n should be specified.") |
|
|
6658 | 6658 | |
|
|
6659 | 6659 | if n != None: |
|
|
6660 | 6660 | if n < 2: |
|
|
6661 | 6661 | raise ValueError("n should be greater than 2") |
|
|
6662 | 6662 | |
|
|
6663 | 6663 | self.n = n |
|
|
6664 | 6664 | self.__nProf = n |
|
|
6665 | 6665 | |
|
|
6666 | 6666 | self.__buffer = numpy.zeros((dataOut.nChannels, |
|
|
6667 | 6667 | n, |
|
|
6668 | 6668 | dataOut.nHeights), |
|
|
6669 | 6669 | dtype='complex') |
|
|
6670 | 6670 | |
|
|
6671 | 6671 | def putData(self, data): |
|
|
6672 | 6672 | ''' |
|
|
6673 | 6673 | Add a profile to he __buffer and increase in one the __profiel Index |
|
|
6674 | 6674 | ''' |
|
|
6675 | 6675 | self.__buffer[:, self.__profIndex, :] = data |
|
|
6676 | 6676 | self.__profIndex += 1 |
|
|
6677 | 6677 | return |
|
|
6678 | 6678 | |
|
|
6679 | 6679 | def pushData(self, dataOut): |
|
|
6680 | 6680 | ''' |
|
|
6681 | 6681 | Return the PULSEPAIR and the profiles used in the operation |
|
|
6682 | 6682 | Affected : self.__profileIndex |
|
|
6683 | 6683 | ''' |
|
|
6684 | 6684 | #----------------- Remove DC----------------------------------- |
|
|
6685 | 6685 | if self.removeDC == True: |
|
|
6686 | 6686 | mean = numpy.mean(self.__buffer, 1) |
|
|
6687 | 6687 | tmp = mean.reshape(self.__nch, 1, self.__nHeis) |
|
|
6688 | 6688 | dc = numpy.tile(tmp, [1, self.__nProf, 1]) |
|
|
6689 | 6689 | self.__buffer = self.__buffer - dc |
|
|
6690 | 6690 | #------------------Calculo de Potencia ------------------------ |
|
|
6691 | 6691 | pair0 = self.__buffer * numpy.conj(self.__buffer) |
|
|
6692 | 6692 | pair0 = pair0.real |
|
|
6693 | 6693 | lag_0 = numpy.sum(pair0, 1) |
|
|
6694 | 6694 | #------------------Calculo de Ruido x canal-------------------- |
|
|
6695 | 6695 | self.noise = numpy.zeros(self.__nch) |
|
|
6696 | 6696 | for i in range(self.__nch): |
|
|
6697 | 6697 | daux = numpy.sort(pair0[i, :, :], axis=None) |
|
|
6698 | 6698 | self.noise[i] = hildebrand_sekhon(daux , self.nCohInt) |
|
|
6699 | 6699 | |
|
|
6700 | 6700 | self.noise = self.noise.reshape(self.__nch, 1) |
|
|
6701 | 6701 | self.noise = numpy.tile(self.noise, [1, self.__nHeis]) |
|
|
6702 | 6702 | noise_buffer = self.noise.reshape(self.__nch, 1, self.__nHeis) |
|
|
6703 | 6703 | noise_buffer = numpy.tile(noise_buffer, [1, self.__nProf, 1]) |
|
|
6704 | 6704 | #------------------ Potencia recibida= P , Potencia senal = S , Ruido= N-- |
|
|
6705 | 6705 | #------------------ P= S+N ,P=lag_0/N --------------------------------- |
|
|
6706 | 6706 | #-------------------- Power -------------------------------------------------- |
|
|
6707 | 6707 | data_power = lag_0 / (self.n * self.nCohInt) |
|
|
6708 | 6708 | #------------------ Senal --------------------------------------------------- |
|
|
6709 | 6709 | data_intensity = pair0 - noise_buffer |
|
|
6710 | 6710 | data_intensity = numpy.sum(data_intensity, axis=1) * (self.n * self.nCohInt) # *self.nCohInt) |
|
|
6711 | 6711 | # data_intensity = (lag_0-self.noise*self.n)*(self.n*self.nCohInt) |
|
|
6712 | 6712 | for i in range(self.__nch): |
|
|
6713 | 6713 | for j in range(self.__nHeis): |
|
|
6714 | 6714 | if data_intensity[i][j] < 0: |
|
|
6715 | 6715 | data_intensity[i][j] = numpy.min(numpy.absolute(data_intensity[i][j])) |
|
|
6716 | 6716 | |
|
|
6717 | 6717 | #----------------- Calculo de Frecuencia y Velocidad doppler-------- |
|
|
6718 | 6718 | pair1 = self.__buffer[:, :-1, :] * numpy.conjugate(self.__buffer[:, 1:, :]) |
|
|
6719 | 6719 | lag_1 = numpy.sum(pair1, 1) |
|
|
6720 | 6720 | data_freq = (-1 / (2.0 * math.pi * self.ippSec * self.nCohInt)) * numpy.angle(lag_1) |
|
|
6721 | 6721 | data_velocity = (self.lambda_ / 2.0) * data_freq |
|
|
6722 | 6722 | |
|
|
6723 | 6723 | #---------------- Potencia promedio estimada de la Senal----------- |
|
|
6724 | 6724 | lag_0 = lag_0 / self.n |
|
|
6725 | 6725 | S = lag_0 - self.noise |
|
|
6726 | 6726 | |
|
|
6727 | 6727 | #---------------- Frecuencia Doppler promedio --------------------- |
|
|
6728 | 6728 | lag_1 = lag_1 / (self.n - 1) |
|
|
6729 | 6729 | R1 = numpy.abs(lag_1) |
|
|
6730 | 6730 | |
|
|
6731 | 6731 | #---------------- Calculo del SNR---------------------------------- |
|
|
6732 | 6732 | data_snrPP = S / self.noise |
|
|
6733 | 6733 | for i in range(self.__nch): |
|
|
6734 | 6734 | for j in range(self.__nHeis): |
|
|
6735 | 6735 | if data_snrPP[i][j] < 1.e-20: |
|
|
6736 | 6736 | data_snrPP[i][j] = 1.e-20 |
|
|
6737 | 6737 | |
|
|
6738 | 6738 | #----------------- Calculo del ancho espectral ---------------------- |
|
|
6739 | 6739 | L = S / R1 |
|
|
6740 | 6740 | L = numpy.where(L < 0, 1, L) |
|
|
6741 | 6741 | L = numpy.log(L) |
|
|
6742 | 6742 | tmp = numpy.sqrt(numpy.absolute(L)) |
|
|
6743 | 6743 | data_specwidth = (self.lambda_ / (2 * math.sqrt(2) * math.pi * self.ippSec * self.nCohInt)) * tmp * numpy.sign(L) |
|
|
6744 | 6744 | n = self.__profIndex |
|
|
6745 | 6745 | |
|
|
6746 | 6746 | self.__buffer = numpy.zeros((self.__nch, self.__nProf, self.__nHeis), dtype='complex') |
|
|
6747 | 6747 | self.__profIndex = 0 |
|
|
6748 | 6748 | return data_power, data_intensity, data_velocity, data_snrPP, data_specwidth, n |
|
|
6749 | 6749 | |
|
|
6750 | 6750 | |
|
|
6751 | 6751 | def pulsePairbyProfiles(self, dataOut): |
|
|
6752 | 6752 | |
|
|
6753 | 6753 | self.__dataReady = False |
|
|
6754 | 6754 | data_power = None |
|
|
6755 | 6755 | data_intensity = None |
|
|
6756 | 6756 | data_velocity = None |
|
|
6757 | 6757 | data_specwidth = None |
|
|
6758 | 6758 | data_snrPP = None |
|
|
6759 | 6759 | self.putData(data=dataOut.data) |
|
|
6760 | 6760 | if self.__profIndex == self.n: |
|
|
6761 | 6761 | data_power, data_intensity, data_velocity, data_snrPP, data_specwidth, n = self.pushData(dataOut=dataOut) |
|
|
6762 | 6762 | self.__dataReady = True |
|
|
6763 | 6763 | |
|
|
6764 | 6764 | return data_power, data_intensity, data_velocity, data_snrPP, data_specwidth |
|
|
6765 | 6765 | |
|
|
6766 | 6766 | |
|
|
6767 | 6767 | def pulsePairOp(self, dataOut, datatime=None): |
|
|
6768 | 6768 | |
|
|
6769 | 6769 | if self.__initime == None: |
|
|
6770 | 6770 | self.__initime = datatime |
|
|
6771 | 6771 | data_power, data_intensity, data_velocity, data_snrPP, data_specwidth = self.pulsePairbyProfiles(dataOut) |
|
|
6772 | 6772 | self.__lastdatatime = datatime |
|
|
6773 | 6773 | |
|
|
6774 | 6774 | if data_power is None: |
|
|
6775 | 6775 | return None, None, None, None, None, None |
|
|
6776 | 6776 | |
|
|
6777 | 6777 | avgdatatime = self.__initime |
|
|
6778 | 6778 | deltatime = datatime - self.__lastdatatime |
|
|
6779 | 6779 | self.__initime = datatime |
|
|
6780 | 6780 | |
|
|
6781 | 6781 | return data_power, data_intensity, data_velocity, data_snrPP, data_specwidth, avgdatatime |
|
|
6782 | 6782 | |
|
|
6783 | 6783 | def run(self, dataOut, n=None, removeDC=False, overlapping=False, **kwargs): |
|
|
6784 | 6784 | |
|
|
6785 | 6785 | if not self.isConfig: |
|
|
6786 | 6786 | self.setup(dataOut=dataOut, n=n , removeDC=removeDC , **kwargs) |
|
|
6787 | 6787 | self.isConfig = True |
|
|
6788 | 6788 | data_power, data_intensity, data_velocity, data_snrPP, data_specwidth, avgdatatime = self.pulsePairOp(dataOut, dataOut.utctime) |
|
|
6789 | 6789 | dataOut.flagNoData = True |
|
|
6790 | 6790 | |
|
|
6791 | 6791 | if self.__dataReady: |
|
|
6792 | 6792 | dataOut.nCohInt *= self.n |
|
|
6793 | 6793 | dataOut.dataPP_POW = data_intensity # S |
|
|
6794 | 6794 | dataOut.dataPP_POWER = data_power # P |
|
|
6795 | 6795 | dataOut.dataPP_DOP = data_velocity |
|
|
6796 | 6796 | dataOut.dataPP_SNR = data_snrPP |
|
|
6797 | 6797 | dataOut.dataPP_WIDTH = data_specwidth |
|
|
6798 | 6798 | dataOut.PRFbyAngle = self.n # numero de PRF*cada angulo rotado que equivale a un tiempo. |
|
|
6799 | 6799 | dataOut.utctime = avgdatatime |
|
|
6800 | 6800 | dataOut.flagNoData = False |
|
|
6801 | 6801 | return dataOut |
|
|
6802 | 6802 | |
|
|
6803 | 6803 | |
|
|
6804 | 6804 | |
|
|
6805 | 6805 | # import collections |
|
|
6806 | 6806 | # from scipy.stats import mode |
|
|
6807 | 6807 | # |
|
|
6808 | 6808 | # class Synchronize(Operation): |
|
|
6809 | 6809 | # |
|
|
6810 | 6810 | # isConfig = False |
|
|
6811 | 6811 | # __profIndex = 0 |
|
|
6812 | 6812 | # |
|
|
6813 | 6813 | # def __init__(self, **kwargs): |
|
|
6814 | 6814 | # |
|
|
6815 | 6815 | # Operation.__init__(self, **kwargs) |
|
|
6816 | 6816 | # # self.isConfig = False |
|
|
6817 | 6817 | # self.__powBuffer = None |
|
|
6818 | 6818 | # self.__startIndex = 0 |
|
|
6819 | 6819 | # self.__pulseFound = False |
|
|
6820 | 6820 | # |
|
|
6821 | 6821 | # def __findTxPulse(self, dataOut, channel=0, pulse_with = None): |
|
|
6822 | 6822 | # |
|
|
6823 | 6823 | # #Read data |
|
|
6824 | 6824 | # |
|
|
6825 | 6825 | # powerdB = dataOut.getPower(channel = channel) |
|
|
6826 | 6826 | # noisedB = dataOut.getNoise(channel = channel)[0] |
|
|
6827 | 6827 | # |
|
|
6828 | 6828 | # self.__powBuffer.extend(powerdB.flatten()) |
|
|
6829 | 6829 | # |
|
|
6830 | 6830 | # dataArray = numpy.array(self.__powBuffer) |
|
|
6831 | 6831 | # |
|
|
6832 | 6832 | # filteredPower = numpy.correlate(dataArray, dataArray[0:self.__nSamples], "same") |
|
|
6833 | 6833 | # |
|
|
6834 | 6834 | # maxValue = numpy.nanmax(filteredPower) |
|
|
6835 | 6835 | # |
|
|
6836 | 6836 | # if maxValue < noisedB + 10: |
|
|
6837 | 6837 | # #No se encuentra ningun pulso de transmision |
|
|
6838 | 6838 | # return None |
|
|
6839 | 6839 | # |
|
|
6840 | 6840 | # maxValuesIndex = numpy.where(filteredPower > maxValue - 0.1*abs(maxValue))[0] |
|
|
6841 | 6841 | # |
|
|
6842 | 6842 | # if len(maxValuesIndex) < 2: |
|
|
6843 | 6843 | # #Solo se encontro un solo pulso de transmision de un baudio, esperando por el siguiente TX |
|
|
6844 | 6844 | # return None |
|
|
6845 | 6845 | # |
|
|
6846 | 6846 | # phasedMaxValuesIndex = maxValuesIndex - self.__nSamples |
|
|
6847 | 6847 | # |
|
|
6848 | 6848 | # #Seleccionar solo valores con un espaciamiento de nSamples |
|
|
6849 | 6849 | # pulseIndex = numpy.intersect1d(maxValuesIndex, phasedMaxValuesIndex) |
|
|
6850 | 6850 | # |
|
|
6851 | 6851 | # if len(pulseIndex) < 2: |
|
|
6852 | 6852 | # #Solo se encontro un pulso de transmision con ancho mayor a 1 |
|
|
6853 | 6853 | # return None |
|
|
6854 | 6854 | # |
|
|
6855 | 6855 | # spacing = pulseIndex[1:] - pulseIndex[:-1] |
|
|
6856 | 6856 | # |
|
|
6857 | 6857 | # #remover senales que se distancien menos de 10 unidades o muestras |
|
|
6858 | 6858 | # #(No deberian existir IPP menor a 10 unidades) |
|
|
6859 | 6859 | # |
|
|
6860 | 6860 | # realIndex = numpy.where(spacing > 10 )[0] |
|
|
6861 | 6861 | # |
|
|
6862 | 6862 | # if len(realIndex) < 2: |
|
|
6863 | 6863 | # #Solo se encontro un pulso de transmision con ancho mayor a 1 |
|
|
6864 | 6864 | # return None |
|
|
6865 | 6865 | # |
|
|
6866 | 6866 | # #Eliminar pulsos anchos (deja solo la diferencia entre IPPs) |
|
|
6867 | 6867 | # realPulseIndex = pulseIndex[realIndex] |
|
|
6868 | 6868 | # |
|
|
6869 | 6869 | # period = mode(realPulseIndex[1:] - realPulseIndex[:-1])[0][0] |
|
|
6870 | 6870 | # |
|
|
6871 | 6871 | # print "IPP = %d samples" %period |
|
|
6872 | 6872 | # |
|
|
6873 | 6873 | # self.__newNSamples = dataOut.nHeights #int(period) |
|
|
6874 | 6874 | # self.__startIndex = int(realPulseIndex[0]) |
|
|
6875 | 6875 | # |
|
|
6876 | 6876 | # return 1 |
|
|
6877 | 6877 | # |
|
|
6878 | 6878 | # |
|
|
6879 | 6879 | # def setup(self, nSamples, nChannels, buffer_size = 4): |
|
|
6880 | 6880 | # |
|
|
6881 | 6881 | # self.__powBuffer = collections.deque(numpy.zeros( buffer_size*nSamples,dtype=numpy.float), |
|
|
6882 | 6882 | # maxlen = buffer_size*nSamples) |
|
|
6883 | 6883 | # |
|
|
6884 | 6884 | # bufferList = [] |
|
|
6885 | 6885 | # |
|
|
6886 | 6886 | # for i in range(nChannels): |
|
|
6887 | 6887 | # bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=complex) + numpy.NAN, |
|
|
6888 | 6888 | # maxlen = buffer_size*nSamples) |
|
|
6889 | 6889 | # |
|
|
6890 | 6890 | # bufferList.append(bufferByChannel) |
|
|
6891 | 6891 | # |
|
|
6892 | 6892 | # self.__nSamples = nSamples |
|
|
6893 | 6893 | # self.__nChannels = nChannels |
|
|
6894 | 6894 | # self.__bufferList = bufferList |
|
|
6895 | 6895 | # |
|
|
6896 | 6896 | # def run(self, dataOut, channel = 0): |
|
|
6897 | 6897 | # |
|
|
6898 | 6898 | # if not self.isConfig: |
|
|
6899 | 6899 | # nSamples = dataOut.nHeights |
|
|
6900 | 6900 | # nChannels = dataOut.nChannels |
|
|
6901 | 6901 | # self.setup(nSamples, nChannels) |
|
|
6902 | 6902 | # self.isConfig = True |
|
|
6903 | 6903 | # |
|
|
6904 | 6904 | # #Append new data to internal buffer |
|
|
6905 | 6905 | # for thisChannel in range(self.__nChannels): |
|
|
6906 | 6906 | # bufferByChannel = self.__bufferList[thisChannel] |
|
|
6907 | 6907 | # bufferByChannel.extend(dataOut.data[thisChannel]) |
|
|
6908 | 6908 | # |
|
|
6909 | 6909 | # if self.__pulseFound: |
|
|
6910 | 6910 | # self.__startIndex -= self.__nSamples |
|
|
6911 | 6911 | # |
|
|
6912 | 6912 | # #Finding Tx Pulse |
|
|
6913 | 6913 | # if not self.__pulseFound: |
|
|
6914 | 6914 | # indexFound = self.__findTxPulse(dataOut, channel) |
|
|
6915 | 6915 | # |
|
|
6916 | 6916 | # if indexFound == None: |
|
|
6917 | 6917 | # dataOut.flagNoData = True |
|
|
6918 | 6918 | # return |
|
|
6919 | 6919 | # |
|
|
6920 | 6920 | # self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = complex) |
|
|
6921 | 6921 | # self.__pulseFound = True |
|
|
6922 | 6922 | # self.__startIndex = indexFound |
|
|
6923 | 6923 | # |
|
|
6924 | 6924 | # #If pulse was found ... |
|
|
6925 | 6925 | # for thisChannel in range(self.__nChannels): |
|
|
6926 | 6926 | # bufferByChannel = self.__bufferList[thisChannel] |
|
|
6927 | 6927 | # #print self.__startIndex |
|
|
6928 | 6928 | # x = numpy.array(bufferByChannel) |
|
|
6929 | 6929 | # self.__arrayBuffer[thisChannel] = x[self.__startIndex:self.__startIndex+self.__newNSamples] |
|
|
6930 | 6930 | # |
|
|
6931 | 6931 | # deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
|
|
6932 | 6932 | # dataOut.heightList = numpy.arange(self.__newNSamples)*deltaHeight |
|
|
6933 | 6933 | # # dataOut.ippSeconds = (self.__newNSamples / deltaHeight)/1e6 |
|
|
6934 | 6934 | # |
|
|
6935 | 6935 | # dataOut.data = self.__arrayBuffer |
|
|
6936 | 6936 | # |
|
|
6937 | 6937 | # self.__startIndex += self.__newNSamples |
|
|
6938 | 6938 | # |
|
|
6939 | 6939 | # return |
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