| @@ -1,4000 +1,3956 | |||||
| 1 | import numpy |
|
1 | import numpy | |
| 2 |
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2 | import math | |
| 3 |
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3 | from scipy import optimize, interpolate, signal, stats, ndimage | |
| 4 |
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4 | import scipy | |
| 5 |
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5 | import re | |
| 6 |
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6 | import datetime | |
| 7 |
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7 | import copy | |
| 8 |
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8 | import sys | |
| 9 |
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9 | import importlib | |
| 10 |
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10 | import itertools | |
| 11 |
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11 | from multiprocessing import Pool, TimeoutError | |
| 12 |
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12 | from multiprocessing.pool import ThreadPool | |
| 13 |
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13 | import time | |
| 14 |
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14 | |||
| 15 |
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15 | from scipy.optimize import fmin_l_bfgs_b #optimize with bounds on state papameters | |
| 16 |
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16 | from .jroproc_base import ProcessingUnit, Operation, MPDecorator | |
| 17 |
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17 | from schainpy.model.data.jrodata import Parameters, hildebrand_sekhon | |
| 18 |
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18 | from scipy import asarray as ar,exp | |
| 19 |
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19 | from scipy.optimize import curve_fit | |
| 20 |
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20 | from schainpy.utils import log | |
| 21 |
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21 | import warnings | |
| 22 |
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22 | from numpy import NaN | |
| 23 |
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23 | from scipy.optimize.optimize import OptimizeWarning | |
| 24 |
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24 | warnings.filterwarnings('ignore') | |
| 25 |
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25 | |||
| 26 |
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26 | import matplotlib.pyplot as plt | |
| 27 |
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27 | |||
| 28 |
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28 | SPEED_OF_LIGHT = 299792458 | |
| 29 |
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29 | |||
| 30 |
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30 | |||
| 31 |
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31 | '''solving pickling issue''' | |
| 32 |
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32 | |||
| 33 |
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33 | def _pickle_method(method): | |
| 34 |
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34 | func_name = method.__func__.__name__ | |
| 35 |
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35 | obj = method.__self__ | |
| 36 |
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36 | cls = method.__self__.__class__ | |
| 37 |
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37 | return _unpickle_method, (func_name, obj, cls) | |
| 38 |
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38 | |||
| 39 |
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39 | def _unpickle_method(func_name, obj, cls): | |
| 40 |
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40 | for cls in cls.mro(): | |
| 41 |
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41 | try: | |
| 42 |
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42 | func = cls.__dict__[func_name] | |
| 43 |
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43 | except KeyError: | |
| 44 |
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44 | pass | |
| 45 |
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45 | else: | |
| 46 |
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46 | break | |
| 47 |
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47 | return func.__get__(obj, cls) | |
| 48 |
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48 | |||
| 49 |
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49 | |||
| 50 |
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50 | class ParametersProc(ProcessingUnit): | |
| 51 |
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51 | |||
| 52 |
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52 | METHODS = {} | |
| 53 |
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53 | nSeconds = None | |
| 54 |
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54 | |||
| 55 |
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55 | def __init__(self): | |
| 56 |
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56 | ProcessingUnit.__init__(self) | |
| 57 |
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57 | |||
| 58 |
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58 | # self.objectDict = {} | |
| 59 |
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59 | self.buffer = None | |
| 60 |
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60 | self.firstdatatime = None | |
| 61 |
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61 | self.profIndex = 0 | |
| 62 |
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62 | self.dataOut = Parameters() | |
| 63 |
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63 | self.setupReq = False #Agregar a todas las unidades de proc | |
| 64 |
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64 | |||
| 65 |
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65 | def __updateObjFromInput(self): | |
| 66 |
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66 | |||
| 67 |
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67 | self.dataOut.inputUnit = self.dataIn.type | |
| 68 |
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68 | |||
| 69 |
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69 | self.dataOut.timeZone = self.dataIn.timeZone | |
| 70 |
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70 | self.dataOut.dstFlag = self.dataIn.dstFlag | |
| 71 |
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71 | self.dataOut.errorCount = self.dataIn.errorCount | |
| 72 |
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72 | self.dataOut.useLocalTime = self.dataIn.useLocalTime | |
| 73 |
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73 | |||
| 74 |
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74 | self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() | |
| 75 |
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75 | self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy() | |
| 76 |
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76 | self.dataOut.channelList = self.dataIn.channelList | |
| 77 |
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77 | self.dataOut.heightList = self.dataIn.heightList | |
| 78 |
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78 | self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')]) | |
| 79 |
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79 | # self.dataOut.nHeights = self.dataIn.nHeights | |
| 80 |
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80 | # self.dataOut.nChannels = self.dataIn.nChannels | |
| 81 |
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81 | # self.dataOut.nBaud = self.dataIn.nBaud | |
| 82 |
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82 | # self.dataOut.nCode = self.dataIn.nCode | |
| 83 |
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83 | # self.dataOut.code = self.dataIn.code | |
| 84 |
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84 | # self.dataOut.nProfiles = self.dataOut.nFFTPoints | |
| 85 |
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85 | self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock | |
| 86 |
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86 | # self.dataOut.utctime = self.firstdatatime | |
| 87 |
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87 | self.dataOut.utctime = self.dataIn.utctime | |
| 88 |
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88 | self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada | |
| 89 |
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89 | self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip | |
| 90 |
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90 | self.dataOut.nCohInt = self.dataIn.nCohInt | |
| 91 |
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91 | # self.dataOut.nIncohInt = 1 | |
| 92 |
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92 | # self.dataOut.ippSeconds = self.dataIn.ippSeconds | |
| 93 |
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93 | # self.dataOut.windowOfFilter = self.dataIn.windowOfFilter | |
| 94 |
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94 | self.dataOut.timeInterval1 = self.dataIn.timeInterval | |
| 95 |
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95 | self.dataOut.heightList = self.dataIn.heightList | |
| 96 |
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96 | self.dataOut.frequency = self.dataIn.frequency | |
| 97 |
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97 | # self.dataOut.noise = self.dataIn.noise | |
| 98 |
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98 | |||
| 99 |
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99 | def run(self): | |
| 100 |
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100 | |||
| 101 |
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101 | |||
| 102 |
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102 | |||
| 103 |
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103 | #---------------------- Voltage Data --------------------------- | |
| 104 |
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104 | |||
| 105 |
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105 | if self.dataIn.type == "Voltage": | |
| 106 |
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106 | |||
| 107 |
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107 | self.__updateObjFromInput() | |
| 108 |
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108 | self.dataOut.data_pre = self.dataIn.data.copy() | |
| 109 |
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109 | self.dataOut.flagNoData = False | |
| 110 |
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110 | self.dataOut.utctimeInit = self.dataIn.utctime | |
| 111 |
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111 | self.dataOut.paramInterval = self.dataIn.nProfiles*self.dataIn.nCohInt*self.dataIn.ippSeconds | |
| 112 |
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112 | if hasattr(self.dataIn, 'dataPP_POW'): | |
| 113 |
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113 | self.dataOut.dataPP_POW = self.dataIn.dataPP_POW | |
| 114 |
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114 | |||
| 115 |
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115 | if hasattr(self.dataIn, 'dataPP_POWER'): | |
| 116 |
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116 | self.dataOut.dataPP_POWER = self.dataIn.dataPP_POWER | |
| 117 |
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117 | |||
| 118 |
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118 | if hasattr(self.dataIn, 'dataPP_DOP'): | |
| 119 |
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119 | self.dataOut.dataPP_DOP = self.dataIn.dataPP_DOP | |
| 120 |
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120 | |||
| 121 |
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121 | if hasattr(self.dataIn, 'dataPP_SNR'): | |
| 122 |
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122 | self.dataOut.dataPP_SNR = self.dataIn.dataPP_SNR | |
| 123 |
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123 | |||
| 124 |
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124 | if hasattr(self.dataIn, 'dataPP_WIDTH'): | |
| 125 |
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125 | self.dataOut.dataPP_WIDTH = self.dataIn.dataPP_WIDTH | |
| 126 |
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126 | return | |
| 127 |
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127 | |||
| 128 |
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128 | #---------------------- Spectra Data --------------------------- | |
| 129 |
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129 | |||
| 130 |
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130 | if self.dataIn.type == "Spectra": | |
| 131 |
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131 | |||
| 132 |
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132 | self.dataOut.data_pre = [self.dataIn.data_spc, self.dataIn.data_cspc] | |
| 133 |
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133 | self.dataOut.data_spc = self.dataIn.data_spc | |
| 134 |
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134 | self.dataOut.data_cspc = self.dataIn.data_cspc | |
| 135 |
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135 | self.dataOut.nProfiles = self.dataIn.nProfiles | |
| 136 |
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136 | self.dataOut.nIncohInt = self.dataIn.nIncohInt | |
| 137 |
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137 | self.dataOut.nFFTPoints = self.dataIn.nFFTPoints | |
| 138 |
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138 | self.dataOut.ippFactor = self.dataIn.ippFactor | |
| 139 |
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139 | self.dataOut.abscissaList = self.dataIn.getVelRange(1) | |
| 140 |
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140 | self.dataOut.spc_noise = self.dataIn.getNoise() | |
| 141 |
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141 | self.dataOut.spc_range = (self.dataIn.getFreqRange(1) , self.dataIn.getAcfRange(1) , self.dataIn.getVelRange(1)) | |
| 142 |
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142 | # self.dataOut.normFactor = self.dataIn.normFactor | |
| 143 |
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143 | self.dataOut.pairsList = self.dataIn.pairsList | |
| 144 |
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144 | self.dataOut.groupList = self.dataIn.pairsList | |
| 145 |
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145 | self.dataOut.flagNoData = False | |
| 146 |
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146 | |||
| 147 |
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147 | if hasattr(self.dataIn, 'ChanDist'): #Distances of receiver channels | |
| 148 |
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148 | self.dataOut.ChanDist = self.dataIn.ChanDist | |
| 149 |
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149 | else: self.dataOut.ChanDist = None | |
| 150 |
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150 | |||
| 151 |
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151 | #if hasattr(self.dataIn, 'VelRange'): #Velocities range | |
| 152 |
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152 | # self.dataOut.VelRange = self.dataIn.VelRange | |
| 153 |
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153 | #else: self.dataOut.VelRange = None | |
| 154 |
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154 | |||
| 155 |
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155 | if hasattr(self.dataIn, 'RadarConst'): #Radar Constant | |
| 156 |
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156 | self.dataOut.RadarConst = self.dataIn.RadarConst | |
| 157 |
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157 | |||
| 158 |
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158 | if hasattr(self.dataIn, 'NPW'): #NPW | |
| 159 |
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159 | self.dataOut.NPW = self.dataIn.NPW | |
| 160 |
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160 | |||
| 161 |
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161 | if hasattr(self.dataIn, 'COFA'): #COFA | |
| 162 |
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162 | self.dataOut.COFA = self.dataIn.COFA | |
| 163 |
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163 | |||
| 164 |
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164 | |||
| 165 |
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165 | |||
| 166 |
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166 | #---------------------- Correlation Data --------------------------- | |
| 167 |
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167 | |||
| 168 |
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168 | if self.dataIn.type == "Correlation": | |
| 169 |
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169 | acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf = self.dataIn.splitFunctions() | |
| 170 |
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170 | |||
| 171 |
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171 | self.dataOut.data_pre = (self.dataIn.data_cf[acf_ind,:], self.dataIn.data_cf[ccf_ind,:,:]) | |
| 172 |
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172 | self.dataOut.normFactor = (self.dataIn.normFactor[acf_ind,:], self.dataIn.normFactor[ccf_ind,:]) | |
| 173 |
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173 | self.dataOut.groupList = (acf_pairs, ccf_pairs) | |
| 174 |
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174 | |||
| 175 |
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175 | self.dataOut.abscissaList = self.dataIn.lagRange | |
| 176 |
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176 | self.dataOut.noise = self.dataIn.noise | |
| 177 |
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177 | self.dataOut.data_SNR = self.dataIn.SNR | |
| 178 |
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178 | self.dataOut.flagNoData = False | |
| 179 |
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179 | self.dataOut.nAvg = self.dataIn.nAvg | |
| 180 |
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180 | |||
| 181 |
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181 | #---------------------- Parameters Data --------------------------- | |
| 182 |
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182 | |||
| 183 |
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183 | if self.dataIn.type == "Parameters": | |
| 184 |
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184 | self.dataOut.copy(self.dataIn) | |
| 185 |
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185 | self.dataOut.flagNoData = False | |
| 186 |
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186 | |||
| 187 |
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187 | return True | |
| 188 |
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188 | |||
| 189 |
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189 | self.__updateObjFromInput() | |
| 190 |
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190 | self.dataOut.utctimeInit = self.dataIn.utctime | |
| 191 |
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191 | self.dataOut.paramInterval = self.dataIn.timeInterval | |
| 192 |
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192 | |||
| 193 |
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193 | return | |
| 194 |
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194 | |||
| 195 |
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195 | |||
| 196 |
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196 | def target(tups): | |
| 197 |
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197 | |||
| 198 |
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198 | obj, args = tups | |
| 199 |
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199 | |||
| 200 |
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200 | return obj.FitGau(args) | |
| 201 |
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201 | |||
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202 | class RemoveWideGC(Operation): | |||
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203 | ''' This class remove the wide clutter and replace it with a simple interpolation points | |||
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204 | This mainly applies to CLAIRE radar | |||
| 202 |
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205 | |||
| 203 | class SpectralFilters(Operation): |
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206 | ClutterWidth : Width to look for the clutter peak | |
| 204 |
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| 205 | '''This class allows the Rainfall / Wind Selection for CLAIRE RADAR |
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| 206 |
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| 207 | LimitR : It is the limit in m/s of Rainfall |
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| 208 | LimitW : It is the limit in m/s for Winds |
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| 209 |
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207 | |||
| 210 | Input: |
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208 | Input: | |
| 211 |
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209 | |||
| 212 | self.dataOut.data_pre : SPC and CSPC |
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210 | self.dataOut.data_pre : SPC and CSPC | |
| 213 | self.dataOut.spc_range : To select wind and rainfall velocities |
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211 | self.dataOut.spc_range : To select wind and rainfall velocities | |
| 214 |
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212 | |||
| 215 | Affected: |
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213 | Affected: | |
| 216 |
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214 | |||
| 217 | self.dataOut.data_pre : It is used for the new SPC and CSPC ranges of wind |
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215 | self.dataOut.data_pre : It is used for the new SPC and CSPC ranges of wind | |
| 218 | self.dataOut.spcparam_range : Used in SpcParamPlot |
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| 219 | self.dataOut.SPCparam : Used in PrecipitationProc |
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| 220 |
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| 221 |
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216 | |||
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217 | Written by D. Scipión 25.02.2021 | |||
| 222 | ''' |
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218 | ''' | |
| 223 |
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| 224 |
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219 | def __init__(self): | |
| 225 |
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220 | Operation.__init__(self) | |
| 226 |
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221 | self.i = 0 | |
| 227 |
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222 | self.ich = 0 | ||
| 228 | def run(self, dataOut, PositiveLimit=1.5, NegativeLimit=2.5): |
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223 | self.ir = 0 | |
| 229 |
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224 | |||
| 230 |
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225 | def run(self, dataOut, ClutterWidth=2.5): | ||
| 231 | #Limite de vientos |
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| 232 | LimitR = PositiveLimit |
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| 233 | LimitN = NegativeLimit |
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| 234 |
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226 | |||
| 235 |
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227 | self.spc = dataOut.data_pre[0].copy() | |
| 236 |
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228 | self.spc_out = dataOut.data_pre[0].copy() | |
| 237 |
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| 238 | self.Num_Hei = self.spc.shape[2] |
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| 239 | self.Num_Bin = self.spc.shape[1] |
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| 240 |
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229 | self.Num_Chn = self.spc.shape[0] | |
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230 | self.Num_Hei = self.spc.shape[1] | |||
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231 | VelRange = dataOut.spc_range[2][:-1] | |||
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232 | dv = VelRange[1]-VelRange[0] | |||
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233 | ||||
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234 | # Find the velocities that corresponds to zero | |||
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235 | gc_values = numpy.where(numpy.abs(VelRange) <= ClutterWidth) | |||
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236 | ||||
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237 | # Removing novalid data from the spectra | |||
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238 | for ich in range(self.Num_Chn): | |||
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239 | # Estimate the noise at aech range | |||
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240 | ||||
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241 | for ir in range(self.Num_Hei): | |||
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242 | # Estimate the noise at aech range | |||
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243 | HSn = hildebrand_sekhon(self.spc[ich,:,ir],dataOut.nIncohInt) | |||
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244 | # Removing the noise floor at each range | |||
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245 | novalid = numpy.where(self.spc[ich,:,ir] < HSn) | |||
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246 | self.spc[novalid,ir] = HSn | |||
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247 | ||||
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248 | junk = [HSn, numpy.transpose(self.spc[ich,gc_values,ir]), HSn] | |||
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249 | j1index = numpy.where(numpy.diff[junk]>0) | |||
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250 | j2index = numpy.where(numpy.diff[junk]<0) | |||
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251 | junk3 = numpy.diff(j1index) | |||
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252 | junk4 = numpy.diff(j2index) | |||
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253 | ||||
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254 | valleyindex = j2index[junk4>1] | |||
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255 | peakindex = j1index[junk3>1] | |||
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256 | ||||
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257 | # Identify the clutter (close to zero) | |||
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258 | isvalid = numpy.where(where.abs(VelRange[gc_values[peakindex]]) <= 2.5*dv) | |||
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259 | # if isempty(isvalid) | |||
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260 | # continue | |||
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261 | # if numel(isvalid)>1 | |||
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262 | # [~,vindex]= numpy.max(spc[gc_values[peakindex[isvalid]],ir]) | |||
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263 | # isvalid = isvalid[vindex] | |||
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264 | # clutter peak | |||
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265 | gcpeak = peakindex[isvalid] | |||
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266 | # left and right index of the clutter | |||
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267 | gcvl = valleyindex[numpy.where(valleyindex < gcpeak, 1, 'last' )] | |||
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268 | gcvr = valleyindex[numpy.where(valleyindex > gcpeak, 1)] | |||
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269 | ||||
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270 | # Removing the clutter | |||
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271 | interpindex = [gc_values(gcvl), gc_values(gcvr)] | |||
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272 | gcindex = gc_values[gcvl+1:gcvr-1] | |||
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273 | self.spc_out[ich,gcindex,ir] = numpy.interp(VelRange[gcindex],VelRange[interpindex],self.spc[ich,interpindex,ir]) | |||
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274 | ||||
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275 | dataOut.data_pre[0] = self.spc_out | |||
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276 | return dataOut | |||
| 241 |
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277 | |||
| 242 | VelRange = dataOut.spc_range[2] |
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278 | class SpectralFilters(Operation): | |
| 243 | TimeRange = dataOut.spc_range[1] |
|
279 | ''' This class allows to replace the novalid values with noise for each channel | |
| 244 | FrecRange = dataOut.spc_range[0] |
|
280 | This applies to CLAIRE RADAR | |
| 245 |
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| 246 | Vmax= 2*numpy.max(dataOut.spc_range[2]) |
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| 247 | Tmax= 2*numpy.max(dataOut.spc_range[1]) |
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| 248 | Fmax= 2*numpy.max(dataOut.spc_range[0]) |
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| 249 |
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| 250 | Breaker1R=VelRange[numpy.abs(VelRange-(-LimitN)).argmin()] |
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| 251 | Breaker1R=numpy.where(VelRange == Breaker1R) |
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| 252 |
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| 253 | Delta = self.Num_Bin/2 - Breaker1R[0] |
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| 254 |
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| 255 |
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| 256 | '''Reacomodando SPCrange''' |
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| 257 |
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| 258 | VelRange=numpy.roll(VelRange,-(int(self.Num_Bin/2)) ,axis=0) |
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| 259 |
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| 260 | VelRange[-(int(self.Num_Bin/2)):]+= Vmax |
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| 261 |
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281 | |||
| 262 | FrecRange=numpy.roll(FrecRange,-(int(self.Num_Bin/2)),axis=0) |
|
282 | PositiveLimit : RightLimit of novalid data | |
|
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283 | NegativeLimit : LeftLimit of novalid data | |||
| 263 |
|
284 | |||
| 264 | FrecRange[-(int(self.Num_Bin/2)):]+= Fmax |
|
285 | Input: | |
| 265 |
|
286 | |||
| 266 | TimeRange=numpy.roll(TimeRange,-(int(self.Num_Bin/2)),axis=0) |
|
287 | self.dataOut.data_pre : SPC and CSPC | |
|
|
288 | self.dataOut.spc_range : To select wind and rainfall velocities | |||
| 267 |
|
289 | |||
| 268 | TimeRange[-(int(self.Num_Bin/2)):]+= Tmax |
|
290 | Affected: | |
| 269 |
|
291 | |||
| 270 | ''' ------------------ ''' |
|
292 | self.dataOut.data_pre : It is used for the new SPC and CSPC ranges of wind | |
| 271 |
|
293 | |||
| 272 | Breaker2R=VelRange[numpy.abs(VelRange-(LimitR)).argmin()] |
|
294 | Written by D. Scipión 29.01.2021 | |
| 273 | Breaker2R=numpy.where(VelRange == Breaker2R) |
|
295 | ''' | |
|
|
296 | def __init__(self): | |||
|
|
297 | Operation.__init__(self) | |||
|
|
298 | self.i = 0 | |||
|
|
299 | ||||
|
|
300 | def run(self, dataOut, PositiveLimit=1.5, NegativeLimit=-1.5): | |||
| 274 |
|
301 | |||
|
|
302 | self.spc = dataOut.data_pre[0].copy() | |||
|
|
303 | self.Num_Chn = self.spc.shape[0] | |||
|
|
304 | VelRange = dataOut.spc_range[2] | |||
| 275 |
|
305 | |||
| 276 | SPCroll = numpy.roll(self.spc,-(int(self.Num_Bin/2)) ,axis=1) |
|
306 | # novalid corresponds to data within the Negative and PositiveLimit | |
|
|
307 | novalid = numpy.where((VelRange[:-1] >= NegativeLimit) & (VelRange[:-1] <= PositiveLimit)) | |||
| 277 |
|
308 | |||
| 278 | SPCcut = SPCroll.copy() |
|
309 | # Removing novalid data from the spectra | |
| 279 |
|
|
310 | for i in range(self.Num_Chn): | |
| 280 |
|
311 | self.spc[i,novalid,:] = dataOut.noise[i] | ||
| 281 | SPCcut[i,0:int(Breaker2R[0]),:] = dataOut.noise[i] |
|
312 | dataOut.data_pre[0] = self.spc | |
| 282 | SPCcut[i,-int(Delta):,:] = dataOut.noise[i] |
|
|||
| 283 |
|
||||
| 284 | SPCcut[i]=SPCcut[i]- dataOut.noise[i] |
|
|||
| 285 | SPCcut[ numpy.where( SPCcut<0 ) ] = 1e-20 |
|
|||
| 286 |
|
||||
| 287 | SPCroll[i]=SPCroll[i]-dataOut.noise[i] |
|
|||
| 288 | SPCroll[ numpy.where( SPCroll<0 ) ] = 1e-20 |
|
|||
| 289 |
|
||||
| 290 | SPC_ch1 = SPCroll |
|
|||
| 291 |
|
||||
| 292 | SPC_ch2 = SPCcut |
|
|||
| 293 |
|
||||
| 294 | SPCparam = (SPC_ch1, SPC_ch2, self.spc) |
|
|||
| 295 | dataOut.SPCparam = numpy.asarray(SPCparam) |
|
|||
| 296 |
|
||||
| 297 |
|
||||
| 298 | dataOut.spcparam_range=numpy.zeros([self.Num_Chn,self.Num_Bin+1]) |
|
|||
| 299 |
|
||||
| 300 | dataOut.spcparam_range[2]=VelRange |
|
|||
| 301 | dataOut.spcparam_range[1]=TimeRange |
|
|||
| 302 | dataOut.spcparam_range[0]=FrecRange |
|
|||
| 303 |
|
|
313 | return dataOut | |
| 304 |
|
314 | |||
| 305 |
|
|
315 | class GaussianFit(Operation): | |
| 306 |
|
316 | |||
| 307 |
|
|
317 | ''' | |
| 308 | Function that fit of one and two generalized gaussians (gg) based |
|
318 | Function that fit of one and two generalized gaussians (gg) based | |
| 309 | on the PSD shape across an "power band" identified from a cumsum of |
|
319 | on the PSD shape across an "power band" identified from a cumsum of | |
| 310 | the measured spectrum - noise. |
|
320 | the measured spectrum - noise. | |
| 311 |
|
321 | |||
| 312 | Input: |
|
322 | Input: | |
| 313 | self.dataOut.data_pre : SelfSpectra |
|
323 | self.dataOut.data_pre : SelfSpectra | |
| 314 |
|
324 | |||
| 315 | Output: |
|
325 | Output: | |
| 316 | self.dataOut.SPCparam : SPC_ch1, SPC_ch2 |
|
326 | self.dataOut.SPCparam : SPC_ch1, SPC_ch2 | |
| 317 |
|
327 | |||
| 318 | ''' |
|
328 | ''' | |
| 319 |
|
|
329 | def __init__(self): | |
| 320 |
|
|
330 | Operation.__init__(self) | |
| 321 |
|
|
331 | self.i=0 | |
| 322 |
|
332 | |||
| 323 |
|
333 | |||
| 324 |
|
|
334 | def run(self, dataOut, num_intg=7, pnoise=1., SNRlimit=-9): #num_intg: Incoherent integrations, pnoise: Noise, vel_arr: range of velocities, similar to the ftt points | |
| 325 |
|
|
335 | """This routine will find a couple of generalized Gaussians to a power spectrum | |
| 326 | input: spc |
|
336 | input: spc | |
| 327 | output: |
|
337 | output: | |
| 328 | Amplitude0,shift0,width0,p0,Amplitude1,shift1,width1,p1,noise |
|
338 | Amplitude0,shift0,width0,p0,Amplitude1,shift1,width1,p1,noise | |
| 329 | """ |
|
339 | """ | |
| 330 |
|
340 | |||
| 331 |
|
|
341 | self.spc = dataOut.data_pre[0].copy() | |
| 332 |
|
|
342 | self.Num_Hei = self.spc.shape[2] | |
| 333 |
|
|
343 | self.Num_Bin = self.spc.shape[1] | |
| 334 |
|
|
344 | self.Num_Chn = self.spc.shape[0] | |
| 335 |
|
|
345 | Vrange = dataOut.abscissaList | |
| 336 |
|
346 | |||
| 337 |
|
|
347 | GauSPC = numpy.empty([self.Num_Chn,self.Num_Bin,self.Num_Hei]) | |
| 338 |
|
|
348 | SPC_ch1 = numpy.empty([self.Num_Bin,self.Num_Hei]) | |
| 339 |
|
|
349 | SPC_ch2 = numpy.empty([self.Num_Bin,self.Num_Hei]) | |
| 340 |
|
|
350 | SPC_ch1[:] = numpy.NaN | |
| 341 |
|
|
351 | SPC_ch2[:] = numpy.NaN | |
| 342 |
|
352 | |||
| 343 |
|
353 | |||
| 344 |
|
|
354 | start_time = time.time() | |
| 345 |
|
355 | |||
| 346 |
|
|
356 | noise_ = dataOut.spc_noise[0].copy() | |
| 347 |
|
357 | |||
| 348 |
|
358 | |||
| 349 |
|
|
359 | pool = Pool(processes=self.Num_Chn) | |
| 350 |
|
|
360 | args = [(Vrange, Ch, pnoise, noise_, num_intg, SNRlimit) for Ch in range(self.Num_Chn)] | |
| 351 |
|
|
361 | objs = [self for __ in range(self.Num_Chn)] | |
| 352 |
|
|
362 | attrs = list(zip(objs, args)) | |
| 353 |
|
|
363 | gauSPC = pool.map(target, attrs) | |
| 354 |
|
|
364 | dataOut.SPCparam = numpy.asarray(SPCparam) | |
| 355 |
|
365 | |||
| 356 |
|
|
366 | ''' Parameters: | |
| 357 | 1. Amplitude |
|
367 | 1. Amplitude | |
| 358 | 2. Shift |
|
368 | 2. Shift | |
| 359 | 3. Width |
|
369 | 3. Width | |
| 360 | 4. Power |
|
370 | 4. Power | |
| 361 | ''' |
|
371 | ''' | |
| 362 |
|
372 | |||
| 363 |
|
|
373 | def FitGau(self, X): | |
| 364 |
|
374 | |||
| 365 |
|
|
375 | Vrange, ch, pnoise, noise_, num_intg, SNRlimit = X | |
| 366 |
|
376 | |||
| 367 |
|
|
377 | SPCparam = [] | |
| 368 |
|
|
378 | SPC_ch1 = numpy.empty([self.Num_Bin,self.Num_Hei]) | |
| 369 |
|
|
379 | SPC_ch2 = numpy.empty([self.Num_Bin,self.Num_Hei]) | |
| 370 |
|
|
380 | SPC_ch1[:] = 0 #numpy.NaN | |
| 371 |
|
|
381 | SPC_ch2[:] = 0 #numpy.NaN | |
| 372 |
|
382 | |||
| 373 |
|
383 | |||
| 374 |
|
384 | |||
| 375 |
|
|
385 | for ht in range(self.Num_Hei): | |
| 376 |
|
386 | |||
| 377 |
|
387 | |||
| 378 |
|
|
388 | spc = numpy.asarray(self.spc)[ch,:,ht] | |
| 379 |
|
389 | |||
| 380 |
|
|
390 | ############################################# | |
| 381 |
|
|
391 | # normalizing spc and noise | |
| 382 |
|
|
392 | # This part differs from gg1 | |
| 383 |
|
|
393 | spc_norm_max = max(spc) | |
| 384 |
|
|
394 | #spc = spc / spc_norm_max | |
| 385 |
|
|
395 | pnoise = pnoise #/ spc_norm_max | |
| 386 |
|
|
396 | ############################################# | |
| 387 |
|
397 | |||
| 388 |
|
|
398 | fatspectra=1.0 | |
| 389 |
|
399 | |||
| 390 |
|
|
400 | wnoise = noise_ #/ spc_norm_max | |
| 391 |
|
|
401 | #wnoise,stdv,i_max,index =enoise(spc,num_intg) #noise estimate using Hildebrand Sekhon, only wnoise is used | |
| 392 |
|
|
402 | #if wnoise>1.1*pnoise: # to be tested later | |
| 393 |
|
|
403 | # wnoise=pnoise | |
| 394 |
|
|
404 | noisebl=wnoise*0.9; | |
| 395 |
|
|
405 | noisebh=wnoise*1.1 | |
| 396 |
|
|
406 | spc=spc-wnoise | |
| 397 |
|
407 | |||
| 398 |
|
|
408 | minx=numpy.argmin(spc) | |
| 399 |
|
|
409 | #spcs=spc.copy() | |
| 400 |
|
|
410 | spcs=numpy.roll(spc,-minx) | |
| 401 |
|
|
411 | cum=numpy.cumsum(spcs) | |
| 402 |
|
|
412 | tot_noise=wnoise * self.Num_Bin #64; | |
| 403 |
|
413 | |||
| 404 |
|
|
414 | snr = sum(spcs)/tot_noise | |
| 405 |
|
|
415 | snrdB=10.*numpy.log10(snr) | |
| 406 |
|
416 | |||
| 407 |
|
|
417 | if snrdB < SNRlimit : | |
| 408 |
|
|
418 | snr = numpy.NaN | |
| 409 |
|
|
419 | SPC_ch1[:,ht] = 0#numpy.NaN | |
| 410 |
|
|
420 | SPC_ch1[:,ht] = 0#numpy.NaN | |
| 411 |
|
|
421 | SPCparam = (SPC_ch1,SPC_ch2) | |
| 412 |
|
|
422 | continue | |
| 413 |
|
423 | |||
| 414 |
|
424 | |||
| 415 |
|
|
425 | #if snrdB<-18 or numpy.isnan(snrdB) or num_intg<4: | |
| 416 |
|
|
426 | # return [None,]*4,[None,]*4,None,snrdB,None,None,[None,]*5,[None,]*9,None | |
| 417 |
|
427 | |||
| 418 |
|
|
428 | cummax=max(cum); | |
| 419 |
|
|
429 | epsi=0.08*fatspectra # cumsum to narrow down the energy region | |
| 420 |
|
|
430 | cumlo=cummax*epsi; | |
| 421 |
|
|
431 | cumhi=cummax*(1-epsi) | |
| 422 |
|
|
432 | powerindex=numpy.array(numpy.where(numpy.logical_and(cum>cumlo, cum<cumhi))[0]) | |
| 423 |
|
433 | |||
| 424 |
|
434 | |||
| 425 |
|
|
435 | if len(powerindex) < 1:# case for powerindex 0 | |
| 426 |
|
|
436 | continue | |
| 427 |
|
|
437 | powerlo=powerindex[0] | |
| 428 |
|
|
438 | powerhi=powerindex[-1] | |
| 429 |
|
|
439 | powerwidth=powerhi-powerlo | |
| 430 |
|
440 | |||
| 431 |
|
|
441 | firstpeak=powerlo+powerwidth/10.# first gaussian energy location | |
| 432 |
|
|
442 | secondpeak=powerhi-powerwidth/10.#second gaussian energy location | |
| 433 |
|
|
443 | midpeak=(firstpeak+secondpeak)/2. | |
| 434 |
|
|
444 | firstamp=spcs[int(firstpeak)] | |
| 435 |
|
|
445 | secondamp=spcs[int(secondpeak)] | |
| 436 |
|
|
446 | midamp=spcs[int(midpeak)] | |
| 437 |
|
447 | |||
| 438 |
|
|
448 | x=numpy.arange( self.Num_Bin ) | |
| 439 |
|
|
449 | y_data=spc+wnoise | |
| 440 |
|
450 | |||
| 441 |
|
|
451 | ''' single Gaussian ''' | |
| 442 |
|
|
452 | shift0=numpy.mod(midpeak+minx, self.Num_Bin ) | |
| 443 |
|
|
453 | width0=powerwidth/4.#Initialization entire power of spectrum divided by 4 | |
| 444 |
|
|
454 | power0=2. | |
| 445 |
|
|
455 | amplitude0=midamp | |
| 446 |
|
|
456 | state0=[shift0,width0,amplitude0,power0,wnoise] | |
| 447 |
|
|
457 | bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth),(0,None),(0.5,3.),(noisebl,noisebh)) | |
| 448 |
|
|
458 | lsq1=fmin_l_bfgs_b(self.misfit1,state0,args=(y_data,x,num_intg),bounds=bnds,approx_grad=True) | |
| 449 |
|
459 | |||
| 450 |
|
|
460 | chiSq1=lsq1[1]; | |
| 451 |
|
461 | |||
| 452 |
|
462 | |||
| 453 |
|
|
463 | if fatspectra<1.0 and powerwidth<4: | |
| 454 |
|
|
464 | choice=0 | |
| 455 |
|
|
465 | Amplitude0=lsq1[0][2] | |
| 456 |
|
|
466 | shift0=lsq1[0][0] | |
| 457 |
|
|
467 | width0=lsq1[0][1] | |
| 458 |
|
|
468 | p0=lsq1[0][3] | |
| 459 |
|
|
469 | Amplitude1=0. | |
| 460 |
|
|
470 | shift1=0. | |
| 461 |
|
|
471 | width1=0. | |
| 462 |
|
|
472 | p1=0. | |
| 463 |
|
|
473 | noise=lsq1[0][4] | |
| 464 |
|
|
474 | #return (numpy.array([shift0,width0,Amplitude0,p0]), | |
| 465 |
|
|
475 | # numpy.array([shift1,width1,Amplitude1,p1]),noise,snrdB,chiSq1,6.,sigmas1,[None,]*9,choice) | |
| 466 |
|
476 | |||
| 467 |
|
|
477 | ''' two gaussians ''' | |
| 468 |
|
|
478 | #shift0=numpy.mod(firstpeak+minx,64); shift1=numpy.mod(secondpeak+minx,64) | |
| 469 |
|
|
479 | shift0=numpy.mod(firstpeak+minx, self.Num_Bin ); | |
| 470 |
|
|
480 | shift1=numpy.mod(secondpeak+minx, self.Num_Bin ) | |
| 471 |
|
|
481 | width0=powerwidth/6.; | |
| 472 |
|
|
482 | width1=width0 | |
| 473 |
|
|
483 | power0=2.; | |
| 474 |
|
|
484 | power1=power0 | |
| 475 |
|
|
485 | amplitude0=firstamp; | |
| 476 |
|
|
486 | amplitude1=secondamp | |
| 477 |
|
|
487 | state0=[shift0,width0,amplitude0,power0,shift1,width1,amplitude1,power1,wnoise] | |
| 478 |
|
|
488 | #bnds=((0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(noisebl,noisebh)) | |
| 479 |
|
|
489 | bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth/2.),(0,None),(0.5,3.),( 0,(self.Num_Bin-1)),(1,powerwidth/2.),(0,None),(0.5,3.),(noisebl,noisebh)) | |
| 480 |
|
|
490 | #bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth/2.),(0,None),(0.5,3.),( 0,(self.Num_Bin-1)),(1,powerwidth/2.),(0,None),(0.5,3.),(0.1,0.5)) | |
| 481 |
|
491 | |||
| 482 |
|
|
492 | lsq2 = fmin_l_bfgs_b( self.misfit2 , state0 , args=(y_data,x,num_intg) , bounds=bnds , approx_grad=True ) | |
| 483 |
|
493 | |||
| 484 |
|
494 | |||
| 485 |
|
|
495 | chiSq2=lsq2[1]; | |
| 486 |
|
496 | |||
| 487 |
|
497 | |||
| 488 |
|
498 | |||
| 489 |
|
|
499 | oneG=(chiSq1<5 and chiSq1/chiSq2<2.0) and (abs(lsq2[0][0]-lsq2[0][4])<(lsq2[0][1]+lsq2[0][5])/3. or abs(lsq2[0][0]-lsq2[0][4])<10) | |
| 490 |
|
500 | |||
| 491 |
|
|
501 | if snrdB>-12: # when SNR is strong pick the peak with least shift (LOS velocity) error | |
| 492 |
|
|
502 | if oneG: | |
| 493 |
|
|
503 | choice=0 | |
| 494 |
|
|
504 | else: | |
| 495 |
|
|
505 | w1=lsq2[0][1]; w2=lsq2[0][5] | |
| 496 |
|
|
506 | a1=lsq2[0][2]; a2=lsq2[0][6] | |
| 497 |
|
|
507 | p1=lsq2[0][3]; p2=lsq2[0][7] | |
| 498 |
|
|
508 | s1=(2**(1+1./p1))*scipy.special.gamma(1./p1)/p1; | |
| 499 |
|
|
509 | s2=(2**(1+1./p2))*scipy.special.gamma(1./p2)/p2; | |
| 500 |
|
|
510 | gp1=a1*w1*s1; gp2=a2*w2*s2 # power content of each ggaussian with proper p scaling | |
| 501 |
|
511 | |||
| 502 |
|
|
512 | if gp1>gp2: | |
| 503 |
|
|
513 | if a1>0.7*a2: | |
| 504 |
|
|
514 | choice=1 | |
| 505 |
|
|
515 | else: | |
| 506 |
|
|
516 | choice=2 | |
| 507 |
|
|
517 | elif gp2>gp1: | |
| 508 |
|
|
518 | if a2>0.7*a1: | |
| 509 |
|
|
519 | choice=2 | |
| 510 |
|
|
520 | else: | |
| 511 |
|
|
521 | choice=1 | |
| 512 |
|
|
522 | else: | |
| 513 |
|
|
523 | choice=numpy.argmax([a1,a2])+1 | |
| 514 |
|
|
524 | #else: | |
| 515 |
|
|
525 | #choice=argmin([std2a,std2b])+1 | |
| 516 |
|
526 | |||
| 517 |
|
|
527 | else: # with low SNR go to the most energetic peak | |
| 518 |
|
|
528 | choice=numpy.argmax([lsq1[0][2]*lsq1[0][1],lsq2[0][2]*lsq2[0][1],lsq2[0][6]*lsq2[0][5]]) | |
| 519 |
|
529 | |||
| 520 |
|
530 | |||
| 521 |
|
|
531 | shift0=lsq2[0][0]; | |
| 522 |
|
|
532 | vel0=Vrange[0] + shift0*(Vrange[1]-Vrange[0]) | |
| 523 |
|
|
533 | shift1=lsq2[0][4]; | |
| 524 |
|
|
534 | vel1=Vrange[0] + shift1*(Vrange[1]-Vrange[0]) | |
| 525 |
|
535 | |||
| 526 |
|
|
536 | max_vel = 1.0 | |
| 527 |
|
537 | |||
| 528 |
|
|
538 | #first peak will be 0, second peak will be 1 | |
| 529 |
|
|
539 | if vel0 > -1.0 and vel0 < max_vel : #first peak is in the correct range | |
| 530 |
|
|
540 | shift0=lsq2[0][0] | |
| 531 |
|
|
541 | width0=lsq2[0][1] | |
| 532 |
|
|
542 | Amplitude0=lsq2[0][2] | |
| 533 |
|
|
543 | p0=lsq2[0][3] | |
| 534 |
|
544 | |||
| 535 |
|
|
545 | shift1=lsq2[0][4] | |
| 536 |
|
|
546 | width1=lsq2[0][5] | |
| 537 |
|
|
547 | Amplitude1=lsq2[0][6] | |
| 538 |
|
|
548 | p1=lsq2[0][7] | |
| 539 |
|
|
549 | noise=lsq2[0][8] | |
| 540 |
|
|
550 | else: | |
| 541 |
|
|
551 | shift1=lsq2[0][0] | |
| 542 |
|
|
552 | width1=lsq2[0][1] | |
| 543 |
|
|
553 | Amplitude1=lsq2[0][2] | |
| 544 |
|
|
554 | p1=lsq2[0][3] | |
| 545 |
|
555 | |||
| 546 |
|
|
556 | shift0=lsq2[0][4] | |
| 547 |
|
|
557 | width0=lsq2[0][5] | |
| 548 |
|
|
558 | Amplitude0=lsq2[0][6] | |
| 549 |
|
|
559 | p0=lsq2[0][7] | |
| 550 |
|
|
560 | noise=lsq2[0][8] | |
| 551 |
|
561 | |||
| 552 |
|
|
562 | if Amplitude0<0.05: # in case the peak is noise | |
| 553 |
|
|
563 | shift0,width0,Amplitude0,p0 = [0,0,0,0]#4*[numpy.NaN] | |
| 554 |
|
|
564 | if Amplitude1<0.05: | |
| 555 |
|
|
565 | shift1,width1,Amplitude1,p1 = [0,0,0,0]#4*[numpy.NaN] | |
| 556 |
|
566 | |||
| 557 |
|
567 | |||
| 558 |
|
|
568 | SPC_ch1[:,ht] = noise + Amplitude0*numpy.exp(-0.5*(abs(x-shift0))/width0)**p0 | |
| 559 |
|
|
569 | SPC_ch2[:,ht] = noise + Amplitude1*numpy.exp(-0.5*(abs(x-shift1))/width1)**p1 | |
| 560 |
|
|
570 | SPCparam = (SPC_ch1,SPC_ch2) | |
| 561 |
|
571 | |||
| 562 |
|
572 | |||
| 563 |
|
|
573 | return GauSPC | |
| 564 |
|
574 | |||
| 565 |
|
|
575 | def y_model1(self,x,state): | |
| 566 |
|
|
576 | shift0,width0,amplitude0,power0,noise=state | |
| 567 |
|
|
577 | model0=amplitude0*numpy.exp(-0.5*abs((x-shift0)/width0)**power0) | |
| 568 |
|
578 | |||
| 569 |
|
|
579 | model0u=amplitude0*numpy.exp(-0.5*abs((x-shift0- self.Num_Bin )/width0)**power0) | |
| 570 |
|
580 | |||
| 571 |
|
|
581 | model0d=amplitude0*numpy.exp(-0.5*abs((x-shift0+ self.Num_Bin )/width0)**power0) | |
| 572 |
|
|
582 | return model0+model0u+model0d+noise | |
| 573 |
|
583 | |||
| 574 |
|
|
584 | def y_model2(self,x,state): #Equation for two generalized Gaussians with Nyquist | |
| 575 |
|
|
585 | shift0,width0,amplitude0,power0,shift1,width1,amplitude1,power1,noise=state | |
| 576 |
|
|
586 | model0=amplitude0*numpy.exp(-0.5*abs((x-shift0)/width0)**power0) | |
| 577 |
|
587 | |||
| 578 |
|
|
588 | model0u=amplitude0*numpy.exp(-0.5*abs((x-shift0- self.Num_Bin )/width0)**power0) | |
| 579 |
|
589 | |||
| 580 |
|
|
590 | model0d=amplitude0*numpy.exp(-0.5*abs((x-shift0+ self.Num_Bin )/width0)**power0) | |
| 581 |
|
|
591 | model1=amplitude1*numpy.exp(-0.5*abs((x-shift1)/width1)**power1) | |
| 582 |
|
592 | |||
| 583 |
|
|
593 | model1u=amplitude1*numpy.exp(-0.5*abs((x-shift1- self.Num_Bin )/width1)**power1) | |
| 584 |
|
594 | |||
| 585 |
|
|
595 | model1d=amplitude1*numpy.exp(-0.5*abs((x-shift1+ self.Num_Bin )/width1)**power1) | |
| 586 |
|
|
596 | return model0+model0u+model0d+model1+model1u+model1d+noise | |
| 587 |
|
597 | |||
| 588 |
|
|
598 | def misfit1(self,state,y_data,x,num_intg): # This function compares how close real data is with the model data, the close it is, the better it is. | |
| 589 |
|
599 | |||
| 590 |
|
|
600 | return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model1(x,state)))**2)#/(64-5.) # /(64-5.) can be commented | |
| 591 |
|
601 | |||
| 592 |
|
|
602 | def misfit2(self,state,y_data,x,num_intg): | |
| 593 |
|
|
603 | return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model2(x,state)))**2)#/(64-9.) | |
| 594 |
|
604 | |||
| 595 |
|
605 | |||
| 596 |
|
606 | |||
| 597 |
|
|
607 | class PrecipitationProc(Operation): | |
| 598 |
|
608 | |||
| 599 |
|
|
609 | ''' | |
| 600 | Operator that estimates Reflectivity factor (Z), and estimates rainfall Rate (R) |
|
610 | Operator that estimates Reflectivity factor (Z), and estimates rainfall Rate (R) | |
| 601 |
|
611 | |||
| 602 | Input: |
|
612 | Input: | |
| 603 | self.dataOut.data_pre : SelfSpectra |
|
613 | self.dataOut.data_pre : SelfSpectra | |
| 604 |
|
614 | |||
| 605 | Output: |
|
615 | Output: | |
| 606 |
|
616 | |||
| 607 | self.dataOut.data_output : Reflectivity factor, rainfall Rate |
|
617 | self.dataOut.data_output : Reflectivity factor, rainfall Rate | |
| 608 |
|
618 | |||
| 609 |
|
619 | |||
| 610 | Parameters affected: |
|
620 | Parameters affected: | |
| 611 | ''' |
|
621 | ''' | |
| 612 |
|
622 | |||
| 613 |
|
|
623 | def __init__(self): | |
| 614 |
|
|
624 | Operation.__init__(self) | |
| 615 |
|
|
625 | self.i=0 | |
| 616 |
|
626 | |||
| 617 |
|
||||
| 618 | def gaus(self,xSamples,Amp,Mu,Sigma): |
|
|||
| 619 | return ( Amp / ((2*numpy.pi)**0.5 * Sigma) ) * numpy.exp( -( xSamples - Mu )**2 / ( 2 * (Sigma**2) )) |
|
|||
| 620 |
|
||||
| 621 |
|
||||
| 622 |
|
||||
| 623 | def Moments(self, ySamples, xSamples): |
|
|||
| 624 | Pot = numpy.nansum( ySamples ) # Potencia, momento 0 |
|
|||
| 625 | yNorm = ySamples / Pot |
|
|||
| 626 |
|
||||
| 627 | Vr = numpy.nansum( yNorm * xSamples ) # Velocidad radial, mu, corrimiento doppler, primer momento |
|
|||
| 628 | Sigma2 = abs(numpy.nansum( yNorm * ( xSamples - Vr )**2 )) # Segundo Momento |
|
|||
| 629 | Desv = Sigma2**0.5 # Desv. Estandar, Ancho espectral |
|
|||
| 630 |
|
||||
| 631 | return numpy.array([Pot, Vr, Desv]) |
|
|||
| 632 |
|
||||
| 633 |
|
|
627 | def run(self, dataOut, radar=None, Pt=5000, Gt=295.1209, Gr=70.7945, Lambda=0.6741, aL=2.5118, | |
| 634 |
|
|
628 | tauW=4e-06, ThetaT=0.1656317, ThetaR=0.36774087, Km2 = 0.93, Altitude=3350): | |
| 635 |
|
||||
| 636 |
|
||||
| 637 | Velrange = dataOut.spcparam_range[2] |
|
|||
| 638 | FrecRange = dataOut.spcparam_range[0] |
|
|||
| 639 |
|
||||
| 640 | dV= Velrange[1]-Velrange[0] |
|
|||
| 641 | dF= FrecRange[1]-FrecRange[0] |
|
|||
| 642 |
|
629 | |||
| 643 |
|
|
630 | if radar == "MIRA35C" : | |
| 644 |
|
631 | |||
| 645 |
|
|
632 | self.spc = dataOut.data_pre[0].copy() | |
| 646 |
|
|
633 | self.Num_Hei = self.spc.shape[2] | |
| 647 |
|
|
634 | self.Num_Bin = self.spc.shape[1] | |
| 648 |
|
|
635 | self.Num_Chn = self.spc.shape[0] | |
| 649 |
|
|
636 | Ze = self.dBZeMODE2(dataOut) | |
| 650 |
|
637 | |||
| 651 |
|
|
638 | else: | |
| 652 |
|
639 | |||
| 653 |
|
|
640 | self.spc = dataOut.data_pre[0].copy() | |
| 654 |
|
||||
| 655 | """NOTA SE DEBE REMOVER EL RANGO DEL PULSO TX""" |
|
|||
| 656 |
|
641 | |||
|
|
642 | #NOTA SE DEBE REMOVER EL RANGO DEL PULSO TX | |||
| 657 |
|
|
643 | self.spc[:,:,0:7]= numpy.NaN | |
| 658 |
|
644 | |||
| 659 | """##########################################""" |
|
|||
| 660 |
|
||||
| 661 |
|
|
645 | self.Num_Hei = self.spc.shape[2] | |
| 662 |
|
|
646 | self.Num_Bin = self.spc.shape[1] | |
| 663 |
|
|
647 | self.Num_Chn = self.spc.shape[0] | |
| 664 |
|
648 | |||
|
|
649 | VelRange = dataOut.spc_range[2] | |||
|
|
650 | ||||
| 665 |
|
|
651 | ''' Se obtiene la constante del RADAR ''' | |
| 666 |
|
652 | |||
| 667 |
|
|
653 | self.Pt = Pt | |
| 668 |
|
|
654 | self.Gt = Gt | |
| 669 |
|
|
655 | self.Gr = Gr | |
| 670 |
|
|
656 | self.Lambda = Lambda | |
| 671 |
|
|
657 | self.aL = aL | |
| 672 |
|
|
658 | self.tauW = tauW | |
| 673 |
|
|
659 | self.ThetaT = ThetaT | |
| 674 |
|
|
660 | self.ThetaR = ThetaR | |
|
|
661 | self.GSys = 10**(36.63/10) # Ganancia de los LNA 36.63 dB | |||
|
|
662 | self.lt = 10**(1.67/10) # Perdida en cables Tx 1.67 dB | |||
|
|
663 | self.lr = 10**(5.73/10) # Perdida en cables Rx 5.73 dB | |||
| 675 |
|
664 | |||
| 676 |
|
|
665 | Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) ) | |
| 677 |
|
|
666 | Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * tauW * numpy.pi * ThetaT * ThetaR) | |
| 678 |
|
|
667 | RadarConstant = 10e-26 * Numerator / Denominator # | |
| 679 |
|
668 | ExpConstant = 10**(40/10) #Constante Experimental | ||
| 680 | ''' ============================= ''' |
|
669 | ||
| 681 |
|
670 | SignalPower = numpy.zeros([self.Num_Chn,self.Num_Bin,self.Num_Hei]) | ||
| 682 | self.spc[0] = (self.spc[0]-dataOut.noise[0]) |
|
671 | for i in range(self.Num_Chn): | |
| 683 |
self.spc[ |
|
672 | SignalPower[i,:,:] = self.spc[i,:,:] - dataOut.noise[i] | |
| 684 | self.spc[2] = (self.spc[2]-dataOut.noise[2]) |
|
673 | SignalPower[numpy.where(SignalPower < 0)] = 1e-20 | |
| 685 |
|
674 | |||
| 686 | self.spc[ numpy.where(self.spc < 0)] = 0 |
|
675 | SPCmean = numpy.mean(SignalPower, 0) | |
| 687 |
|
676 | |||
| 688 | SPCmean = (numpy.mean(self.spc,0) - numpy.mean(dataOut.noise)) |
|
677 | Pr = SPCmean[:,:]/dataOut.normFactor | |
| 689 | SPCmean[ numpy.where(SPCmean < 0)] = 0 |
|
678 | ||
| 690 |
|
679 | # Declaring auxiliary variables | ||
| 691 | ETAn = numpy.zeros([self.Num_Bin,self.Num_Hei]) |
|
680 | Range = dataOut.heightList*1000. #Range in m | |
| 692 | ETAv = numpy.zeros([self.Num_Bin,self.Num_Hei]) |
|
681 | # replicate the heightlist to obtain a matrix [Num_Bin,Num_Hei] | |
| 693 | ETAd = numpy.zeros([self.Num_Bin,self.Num_Hei]) |
|
682 | rMtrx = numpy.transpose(numpy.transpose([dataOut.heightList*1000.] * self.Num_Bin)) | |
| 694 |
|
683 | zMtrx = rMtrx+Altitude | ||
| 695 | Pr = SPCmean[:,:] |
|
684 | # replicate the VelRange to obtain a matrix [Num_Bin,Num_Hei] | |
| 696 |
|
685 | VelMtrx = numpy.transpose(numpy.tile(VelRange[:-1], (self.Num_Hei,1))) | ||
| 697 | VelMeteoro = numpy.mean(SPCmean,axis=0) |
|
686 | ||
| 698 |
|
687 | # height dependence to air density Foote and Du Toit (1969) | ||
| 699 | D_range = numpy.zeros([self.Num_Bin,self.Num_Hei]) |
|
688 | delv_z = 1 + 3.68e-5 * zMtrx + 1.71e-9 * zMtrx**2 | |
| 700 | SIGMA = numpy.zeros([self.Num_Bin,self.Num_Hei]) |
|
689 | VMtrx = VelMtrx / delv_z #Normalized velocity | |
| 701 | N_dist = numpy.zeros([self.Num_Bin,self.Num_Hei]) |
|
690 | VMtrx[numpy.where(VMtrx> 9.6)] = numpy.NaN | |
| 702 | V_mean = numpy.zeros(self.Num_Hei) |
|
691 | # Diameter is related to the fall speed of falling drops | |
| 703 | del_V = numpy.zeros(self.Num_Hei) |
|
692 | D_Vz = -1.667 * numpy.log( 0.9369 - 0.097087 * VMtrx ) # D in [mm] | |
| 704 | Z = numpy.zeros(self.Num_Hei) |
|
693 | # Only valid for D>= 0.16 mm | |
| 705 | Ze = numpy.zeros(self.Num_Hei) |
|
694 | D_Vz[numpy.where(D_Vz < 0.16)] = numpy.NaN | |
| 706 | RR = numpy.zeros(self.Num_Hei) |
|
695 | ||
| 707 |
|
696 | #Calculate Radar Reflectivity ETAn | ||
| 708 | Range = dataOut.heightList*1000. |
|
697 | ETAn = (RadarConstant *ExpConstant) * Pr * rMtrx**2 #Reflectivity (ETA) | |
| 709 |
|
698 | ETAd = ETAn * 6.18 * exp( -0.6 * D_Vz ) * delv_z | ||
| 710 | for R in range(self.Num_Hei): |
|
699 | # Radar Cross Section | |
| 711 |
|
700 | sigmaD = Km2 * (D_Vz * 1e-3 )**6 * numpy.pi**5 / Lambda**4 | ||
| 712 | h = Range[R] + Altitude #Range from ground to radar pulse altitude |
|
701 | # Drop Size Distribution | |
| 713 | del_V[R] = 1 + 3.68 * 10**-5 * h + 1.71 * 10**-9 * h**2 #Density change correction for velocity |
|
702 | DSD = ETAn / sigmaD | |
| 714 |
|
703 | # Equivalente Reflectivy | ||
| 715 | D_range[:,R] = numpy.log( (9.65 - (Velrange[0:self.Num_Bin] / del_V[R])) / 10.3 ) / -0.6 #Diameter range [m]x10**-3 |
|
704 | Ze_eqn = numpy.nansum( DSD * D_Vz**6 ,axis=0) | |
| 716 |
|
705 | Ze_org = numpy.nansum(ETAn * Lambda**4, axis=0) / (1e-18*numpy.pi**5 * Km2) # [mm^6 /m^3] | ||
| 717 | '''NOTA: ETA(n) dn = ETA(f) df |
|
706 | # RainFall Rate | |
| 718 |
|
707 | RR = 0.0006*numpy.pi * numpy.nansum( D_Vz**3 * DSD * VelMtrx ,0) #mm/hr | ||
| 719 | dn = 1 Diferencial de muestreo |
|
708 | ||
| 720 | df = ETA(n) / ETA(f) |
|
709 | # Censoring the data | |
| 721 |
|
710 | # Removing data with SNRth < 0dB se debe considerar el SNR por canal | ||
| 722 | ''' |
|
711 | SNRth = 10**(-30/10) #-20dB | |
| 723 |
|
712 | novalid = numpy.where((dataOut.data_SNR[0,:] <SNRth) | (dataOut.data_SNR[1,:] <SNRth) | (dataOut.data_SNR[2,:] <SNRth)) # AND condition. Maybe OR condition better | ||
| 724 | ETAn[:,R] = RadarConstant * Pr[:,R] * (Range[R] )**2 #Reflectivity (ETA) |
|
713 | W = numpy.nanmean(dataOut.data_DOP,0) | |
| 725 |
|
714 | W[novalid] = numpy.NaN | ||
| 726 | ETAv[:,R]=ETAn[:,R]/dV |
|
715 | Ze_org[novalid] = numpy.NaN | |
| 727 |
|
716 | RR[novalid] = numpy.NaN | ||
| 728 | ETAd[:,R]=ETAv[:,R]*6.18*exp(-0.6*D_range[:,R]) |
|
|||
| 729 |
|
||||
| 730 | SIGMA[:,R] = Km * (D_range[:,R] * 1e-3 )**6 * numpy.pi**5 / Lambda**4 #Equivalent Section of drops (sigma) |
|
|||
| 731 |
|
||||
| 732 | N_dist[:,R] = ETAn[:,R] / SIGMA[:,R] |
|
|||
| 733 |
|
||||
| 734 | DMoments = self.Moments(Pr[:,R], Velrange[0:self.Num_Bin]) |
|
|||
| 735 |
|
||||
| 736 | try: |
|
|||
| 737 | popt01,pcov = curve_fit(self.gaus, Velrange[0:self.Num_Bin] , Pr[:,R] , p0=DMoments) |
|
|||
| 738 | except: |
|
|||
| 739 | popt01=numpy.zeros(3) |
|
|||
| 740 | popt01[1]= DMoments[1] |
|
|||
| 741 |
|
||||
| 742 | if popt01[1]<0 or popt01[1]>20: |
|
|||
| 743 | popt01[1]=numpy.NaN |
|
|||
| 744 |
|
||||
| 745 |
|
||||
| 746 | V_mean[R]=popt01[1] |
|
|||
| 747 |
|
||||
| 748 | Z[R] = numpy.nansum( N_dist[:,R] * (D_range[:,R])**6 )#*10**-18 |
|
|||
| 749 |
|
||||
| 750 | RR[R] = 0.0006*numpy.pi * numpy.nansum( D_range[:,R]**3 * N_dist[:,R] * Velrange[0:self.Num_Bin] ) #Rainfall rate |
|
|||
| 751 |
|
||||
| 752 | Ze[R] = (numpy.nansum( ETAn[:,R]) * Lambda**4) / ( 10**-18*numpy.pi**5 * Km) |
|
|||
| 753 |
|
||||
| 754 |
|
||||
| 755 |
|
||||
| 756 | RR2 = (Z/200)**(1/1.6) |
|
|||
| 757 | dBRR = 10*numpy.log10(RR) |
|
|||
| 758 | dBRR2 = 10*numpy.log10(RR2) |
|
|||
| 759 |
|
||||
| 760 | dBZe = 10*numpy.log10(Ze) |
|
|||
| 761 | dBZ = 10*numpy.log10(Z) |
|
|||
| 762 |
|
717 | |||
| 763 |
|
|
718 | dataOut.data_output = RR[8] | |
| 764 |
|
|
719 | dataOut.data_param = numpy.ones([3,self.Num_Hei]) | |
| 765 |
|
|
720 | dataOut.channelList = [0,1,2] | |
| 766 |
|
721 | |||
| 767 |
|
|
722 | dataOut.data_param[0]=10*numpy.log10(Ze_org) | |
| 768 |
|
|
723 | dataOut.data_param[1]=W | |
| 769 |
|
|
724 | dataOut.data_param[2]=RR | |
| 770 |
|
||||
| 771 |
|
|
725 | return dataOut | |
| 772 |
|
726 | |||
| 773 |
|
|
727 | def dBZeMODE2(self, dataOut): # Processing for MIRA35C | |
| 774 |
|
728 | |||
| 775 |
|
|
729 | NPW = dataOut.NPW | |
| 776 |
|
|
730 | COFA = dataOut.COFA | |
| 777 |
|
731 | |||
| 778 |
|
|
732 | SNR = numpy.array([self.spc[0,:,:] / NPW[0]]) #, self.spc[1,:,:] / NPW[1]]) | |
| 779 |
|
|
733 | RadarConst = dataOut.RadarConst | |
| 780 |
|
|
734 | #frequency = 34.85*10**9 | |
| 781 |
|
735 | |||
| 782 |
|
|
736 | ETA = numpy.zeros(([self.Num_Chn ,self.Num_Hei])) | |
| 783 |
|
|
737 | data_output = numpy.ones([self.Num_Chn , self.Num_Hei])*numpy.NaN | |
| 784 |
|
738 | |||
| 785 |
|
|
739 | ETA = numpy.sum(SNR,1) | |
| 786 |
|
740 | |||
| 787 |
|
|
741 | ETA = numpy.where(ETA != 0. , ETA, numpy.NaN) | |
| 788 |
|
742 | |||
| 789 |
|
|
743 | Ze = numpy.ones([self.Num_Chn, self.Num_Hei] ) | |
| 790 |
|
744 | |||
| 791 |
|
|
745 | for r in range(self.Num_Hei): | |
| 792 |
|
746 | |||
| 793 |
|
|
747 | Ze[0,r] = ( ETA[0,r] ) * COFA[0,r][0] * RadarConst * ((r/5000.)**2) | |
| 794 |
|
|
748 | #Ze[1,r] = ( ETA[1,r] ) * COFA[1,r][0] * RadarConst * ((r/5000.)**2) | |
| 795 |
|
749 | |||
| 796 |
|
|
750 | return Ze | |
| 797 |
|
751 | |||
| 798 |
|
|
752 | # def GetRadarConstant(self): | |
| 799 |
|
|
753 | # | |
| 800 |
|
|
754 | # """ | |
| 801 |
|
|
755 | # Constants: | |
| 802 |
|
|
756 | # | |
| 803 |
|
|
757 | # Pt: Transmission Power dB 5kW 5000 | |
| 804 |
|
|
758 | # Gt: Transmission Gain dB 24.7 dB 295.1209 | |
| 805 |
|
|
759 | # Gr: Reception Gain dB 18.5 dB 70.7945 | |
| 806 |
|
|
760 | # Lambda: Wavelenght m 0.6741 m 0.6741 | |
| 807 |
|
|
761 | # aL: Attenuation loses dB 4dB 2.5118 | |
| 808 |
|
|
762 | # tauW: Width of transmission pulse s 4us 4e-6 | |
| 809 |
|
|
763 | # ThetaT: Transmission antenna bean angle rad 0.1656317 rad 0.1656317 | |
| 810 |
|
|
764 | # ThetaR: Reception antenna beam angle rad 0.36774087 rad 0.36774087 | |
| 811 |
|
|
765 | # | |
| 812 |
|
|
766 | # """ | |
| 813 |
|
|
767 | # | |
| 814 |
|
|
768 | # Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) ) | |
| 815 |
|
|
769 | # Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * TauW * numpy.pi * ThetaT * TheraR) | |
| 816 |
|
|
770 | # RadarConstant = Numerator / Denominator | |
| 817 |
|
|
771 | # | |
| 818 |
|
|
772 | # return RadarConstant | |
| 819 |
|
773 | |||
| 820 |
|
774 | |||
| 821 |
|
775 | |||
| 822 |
|
|
776 | class FullSpectralAnalysis(Operation): | |
| 823 |
|
777 | |||
| 824 |
|
|
778 | """ | |
| 825 | Function that implements Full Spectral Analysis technique. |
|
779 | Function that implements Full Spectral Analysis technique. | |
| 826 |
|
780 | |||
| 827 | Input: |
|
781 | Input: | |
| 828 | self.dataOut.data_pre : SelfSpectra and CrossSpectra data |
|
782 | self.dataOut.data_pre : SelfSpectra and CrossSpectra data | |
| 829 | self.dataOut.groupList : Pairlist of channels |
|
783 | self.dataOut.groupList : Pairlist of channels | |
| 830 | self.dataOut.ChanDist : Physical distance between receivers |
|
784 | self.dataOut.ChanDist : Physical distance between receivers | |
| 831 |
|
785 | |||
| 832 |
|
786 | |||
| 833 | Output: |
|
787 | Output: | |
| 834 |
|
788 | |||
| 835 | self.dataOut.data_output : Zonal wind, Meridional wind and Vertical wind |
|
789 | self.dataOut.data_output : Zonal wind, Meridional wind, and Vertical wind | |
| 836 |
|
790 | |||
| 837 |
|
791 | |||
| 838 | Parameters affected: Winds, height range, SNR |
|
792 | Parameters affected: Winds, height range, SNR | |
| 839 |
|
793 | |||
| 840 | """ |
|
794 | """ | |
| 841 |
|
|
795 | def run(self, dataOut, Xi01=None, Xi02=None, Xi12=None, Eta01=None, Eta02=None, Eta12=None, SNRlimit=7, minheight=None, maxheight=None): | |
| 842 |
|
796 | |||
| 843 |
|
|
797 | self.indice=int(numpy.random.rand()*1000) | |
| 844 |
|
798 | |||
| 845 |
|
|
799 | spc = dataOut.data_pre[0].copy() | |
| 846 |
|
|
800 | cspc = dataOut.data_pre[1] | |
| 847 |
|
801 | |||
| 848 |
|
|
802 | """Erick: NOTE THE RANGE OF THE PULSE TX MUST BE REMOVED""" | |
| 849 |
|
803 | |||
| 850 |
|
|
804 | SNRspc = spc.copy() | |
| 851 | SNRspc[:,:,0:7]= numpy.NaN |
|
805 | SNRspc[:,:,0:7]= numpy.NaN # D. Scipión... the cleaning should not be hardwired in the code... it needs to be flexible... NEEDS TO BE REMOVED | |
| 852 |
|
806 | |||
| 853 |
|
|
807 | """##########################################""" | |
| 854 |
|
808 | |||
| 855 |
|
809 | |||
| 856 |
|
|
810 | nChannel = spc.shape[0] | |
| 857 |
|
|
811 | nProfiles = spc.shape[1] | |
| 858 |
|
|
812 | nHeights = spc.shape[2] | |
| 859 |
|
813 | |||
| 860 |
|
|
814 | # first_height = 0.75 #km (ref: data header 20170822) | |
| 861 |
|
|
815 | # resolution_height = 0.075 #km | |
| 862 |
|
|
816 | ''' | |
| 863 | finding height range. check this when radar parameters are changed! |
|
817 | finding height range. check this when radar parameters are changed! | |
| 864 | ''' |
|
818 | ''' | |
| 865 |
|
|
819 | if maxheight is not None: | |
| 866 |
|
|
820 | # range_max = math.ceil((maxheight - first_height) / resolution_height) # theoretical | |
| 867 |
|
|
821 | range_max = math.ceil(13.26 * maxheight - 3) # empirical, works better | |
| 868 |
|
|
822 | else: | |
| 869 |
|
|
823 | range_max = nHeights | |
| 870 |
|
|
824 | if minheight is not None: | |
| 871 |
|
|
825 | # range_min = int((minheight - first_height) / resolution_height) # theoretical | |
| 872 |
|
|
826 | range_min = int(13.26 * minheight - 5) # empirical, works better | |
| 873 |
|
|
827 | if range_min < 0: | |
| 874 |
|
|
828 | range_min = 0 | |
| 875 |
|
|
829 | else: | |
| 876 |
|
|
830 | range_min = 0 | |
| 877 |
|
831 | |||
| 878 |
|
|
832 | pairsList = dataOut.groupList | |
| 879 |
|
|
833 | if dataOut.ChanDist is not None : | |
| 880 |
|
|
834 | ChanDist = dataOut.ChanDist | |
| 881 |
|
|
835 | else: | |
| 882 |
|
|
836 | ChanDist = numpy.array([[Xi01, Eta01],[Xi02,Eta02],[Xi12,Eta12]]) | |
| 883 |
|
837 | |||
| 884 |
|
|
838 | FrecRange = dataOut.spc_range[0] | |
| 885 |
|
839 | |||
| 886 |
|
|
840 | data_SNR=numpy.zeros([nProfiles]) | |
| 887 |
|
|
841 | noise = dataOut.noise | |
| 888 |
|
842 | |||
| 889 |
|
|
843 | dataOut.data_SNR = (numpy.mean(SNRspc,axis=1)- noise[0]) / noise[0] | |
| 890 |
|
844 | |||
| 891 |
|
|
845 | dataOut.data_SNR[numpy.where( dataOut.data_SNR <0 )] = 1e-20 | |
| 892 |
|
846 | |||
| 893 |
|
847 | |||
| 894 |
|
|
848 | data_output=numpy.ones([spc.shape[0],spc.shape[2]])*numpy.NaN | |
| 895 |
|
849 | |||
| 896 |
|
|
850 | velocityX=[] | |
| 897 |
|
|
851 | velocityY=[] | |
| 898 |
|
|
852 | velocityV=[] | |
| 899 |
|
853 | |||
| 900 |
|
|
854 | dbSNR = 10*numpy.log10(dataOut.data_SNR) | |
| 901 |
|
|
855 | dbSNR = numpy.average(dbSNR,0) | |
| 902 |
|
856 | |||
| 903 |
|
|
857 | '''***********************************************WIND ESTIMATION**************************************''' | |
| 904 |
|
858 | |||
| 905 |
|
|
859 | for Height in range(nHeights): | |
| 906 |
|
860 | |||
| 907 |
|
|
861 | if Height >= range_min and Height < range_max: | |
| 908 |
|
|
862 | # error_code unused, yet maybe useful for future analysis. | |
| 909 |
|
|
863 | [Vzon,Vmer,Vver, error_code] = self.WindEstimation(spc[:,:,Height], cspc[:,:,Height], pairsList, ChanDist, Height, noise, dataOut.spc_range, dbSNR[Height], SNRlimit) | |
| 910 |
|
|
864 | else: | |
| 911 |
|
|
865 | Vzon,Vmer,Vver = 0., 0., numpy.NaN | |
| 912 |
|
866 | |||
| 913 |
|
867 | |||
| 914 |
|
|
868 | if abs(Vzon) < 100. and abs(Vzon) > 0. and abs(Vmer) < 100. and abs(Vmer) > 0.: | |
| 915 |
|
|
869 | velocityX=numpy.append(velocityX, Vzon) | |
| 916 |
|
|
870 | velocityY=numpy.append(velocityY, -Vmer) | |
| 917 |
|
871 | |||
| 918 |
|
|
872 | else: | |
| 919 |
|
|
873 | velocityX=numpy.append(velocityX, numpy.NaN) | |
| 920 |
|
|
874 | velocityY=numpy.append(velocityY, numpy.NaN) | |
| 921 |
|
875 | |||
| 922 |
|
|
876 | if dbSNR[Height] > SNRlimit: | |
| 923 |
|
|
877 | velocityV=numpy.append(velocityV, -Vver) # reason for this minus sign -> convention? (taken from Ericks version) D.S. yes! | |
| 924 |
|
|
878 | else: | |
| 925 |
|
|
879 | velocityV=numpy.append(velocityV, numpy.NaN) | |
| 926 |
|
880 | |||
| 927 |
|
881 | |||
| 928 |
|
|
882 | '''Change the numpy.array (velocityX) sign when trying to process BLTR data (Erick)''' | |
| 929 |
|
|
883 | data_output[0] = numpy.array(velocityX) | |
| 930 |
|
|
884 | data_output[1] = numpy.array(velocityY) | |
| 931 |
|
|
885 | data_output[2] = velocityV | |
| 932 |
|
886 | |||
| 933 |
|
887 | |||
| 934 |
|
|
888 | dataOut.data_output = data_output | |
| 935 |
|
889 | |||
| 936 |
|
|
890 | return dataOut | |
| 937 |
|
891 | |||
| 938 |
|
892 | |||
| 939 |
|
|
893 | def moving_average(self,x, N=2): | |
| 940 |
|
|
894 | """ convolution for smoothenig data. note that last N-1 values are convolution with zeroes """ | |
| 941 |
|
|
895 | return numpy.convolve(x, numpy.ones((N,))/N)[(N-1):] | |
| 942 |
|
896 | |||
| 943 |
|
|
897 | def gaus(self,xSamples,Amp,Mu,Sigma): | |
| 944 |
|
|
898 | return ( Amp / ((2*numpy.pi)**0.5 * Sigma) ) * numpy.exp( -( xSamples - Mu )**2 / ( 2 * (Sigma**2) )) | |
| 945 |
|
899 | |||
| 946 |
|
|
900 | def Moments(self, ySamples, xSamples): | |
| 947 | '''*** |
|
901 | Power = numpy.nanmean(ySamples) # Power, 0th Moment | |
| 948 | Variables corresponding to moments of distribution. |
|
902 | yNorm = ySamples / numpy.nansum(ySamples) | |
| 949 | Also used as initial coefficients for curve_fit. |
|
903 | RadVel = numpy.nansum(xSamples * yNorm) # Radial Velocity, 1st Moment | |
| 950 | Vr was corrected. Only a velocity when x is velocity, of course. |
|
904 | Sigma2 = abs(numpy.nansum(yNorm * (xSamples - RadVel)**2)) # Spectral Width, 2nd Moment | |
| 951 | ***''' |
|
905 | StdDev = Sigma2**0.5 # Desv. Estandar, Ancho espectral | |
| 952 | Pot = numpy.nansum( ySamples ) # Potencia, momento 0 |
|
906 | return numpy.array([Power,RadVel,StdDev]) | |
| 953 | yNorm = ySamples / Pot |
|
|||
| 954 | x_range = (numpy.max(xSamples)-numpy.min(xSamples)) |
|
|||
| 955 | Vr = numpy.nansum( yNorm * xSamples )*x_range/len(xSamples) # Velocidad radial, mu, corrimiento doppler, primer momento |
|
|||
| 956 | Sigma2 = abs(numpy.nansum( yNorm * ( xSamples - Vr )**2 )) # Segundo Momento |
|
|||
| 957 | Desv = Sigma2**0.5 # Desv. Estandar, Ancho espectral |
|
|||
| 958 |
|
||||
| 959 | return numpy.array([Pot, Vr, Desv]) |
|
|||
| 960 |
|
907 | |||
| 961 |
|
|
908 | def StopWindEstimation(self, error_code): | |
| 962 |
|
|
909 | ''' | |
| 963 | the wind calculation and returns zeros |
|
910 | the wind calculation and returns zeros | |
| 964 | ''' |
|
911 | ''' | |
| 965 |
|
|
912 | Vzon = 0 | |
| 966 |
|
|
913 | Vmer = 0 | |
| 967 |
|
|
914 | Vver = numpy.nan | |
| 968 |
|
|
915 | return Vzon, Vmer, Vver, error_code | |
| 969 |
|
916 | |||
| 970 |
|
|
917 | def AntiAliasing(self, interval, maxstep): | |
| 971 |
|
|
918 | """ | |
| 972 | function to prevent errors from aliased values when computing phaseslope |
|
919 | function to prevent errors from aliased values when computing phaseslope | |
| 973 | """ |
|
920 | """ | |
| 974 |
|
|
921 | antialiased = numpy.zeros(len(interval))*0.0 | |
| 975 |
|
|
922 | copyinterval = interval.copy() | |
| 976 |
|
923 | |||
| 977 |
|
|
924 | antialiased[0] = copyinterval[0] | |
| 978 |
|
925 | |||
| 979 |
|
|
926 | for i in range(1,len(antialiased)): | |
| 980 |
|
927 | |||
| 981 |
|
|
928 | step = interval[i] - interval[i-1] | |
| 982 |
|
929 | |||
| 983 |
|
|
930 | if step > maxstep: | |
| 984 |
|
|
931 | copyinterval -= 2*numpy.pi | |
| 985 |
|
|
932 | antialiased[i] = copyinterval[i] | |
| 986 |
|
933 | |||
| 987 |
|
|
934 | elif step < maxstep*(-1): | |
| 988 |
|
|
935 | copyinterval += 2*numpy.pi | |
| 989 |
|
|
936 | antialiased[i] = copyinterval[i] | |
| 990 |
|
937 | |||
| 991 |
|
|
938 | else: | |
| 992 |
|
|
939 | antialiased[i] = copyinterval[i].copy() | |
| 993 |
|
940 | |||
| 994 |
|
|
941 | return antialiased | |
| 995 |
|
942 | |||
| 996 |
|
|
943 | def WindEstimation(self, spc, cspc, pairsList, ChanDist, Height, noise, AbbsisaRange, dbSNR, SNRlimit): | |
| 997 |
|
|
944 | """ | |
| 998 | Function that Calculates Zonal, Meridional and Vertical wind velocities. |
|
945 | Function that Calculates Zonal, Meridional and Vertical wind velocities. | |
| 999 | Initial Version by E. Bocanegra updated by J. Zibell until Nov. 2019. |
|
946 | Initial Version by E. Bocanegra updated by J. Zibell until Nov. 2019. | |
| 1000 |
|
947 | |||
| 1001 | Input: |
|
948 | Input: | |
| 1002 | spc, cspc : self spectra and cross spectra data. In Briggs notation something like S_i*(S_i)_conj, (S_j)_conj respectively. |
|
949 | spc, cspc : self spectra and cross spectra data. In Briggs notation something like S_i*(S_i)_conj, (S_j)_conj respectively. | |
| 1003 | pairsList : Pairlist of channels |
|
950 | pairsList : Pairlist of channels | |
| 1004 | ChanDist : array of xi_ij and eta_ij |
|
951 | ChanDist : array of xi_ij and eta_ij | |
| 1005 | Height : height at which data is processed |
|
952 | Height : height at which data is processed | |
| 1006 | noise : noise in [channels] format for specific height |
|
953 | noise : noise in [channels] format for specific height | |
| 1007 | Abbsisarange : range of the frequencies or velocities |
|
954 | Abbsisarange : range of the frequencies or velocities | |
| 1008 | dbSNR, SNRlimit : signal to noise ratio in db, lower limit |
|
955 | dbSNR, SNRlimit : signal to noise ratio in db, lower limit | |
| 1009 |
|
956 | |||
| 1010 | Output: |
|
957 | Output: | |
| 1011 | Vzon, Vmer, Vver : wind velocities |
|
958 | Vzon, Vmer, Vver : wind velocities | |
| 1012 | error_code : int that states where code is terminated |
|
959 | error_code : int that states where code is terminated | |
| 1013 |
|
960 | |||
| 1014 | 0 : no error detected |
|
961 | 0 : no error detected | |
| 1015 | 1 : Gaussian of mean spc exceeds widthlimit |
|
962 | 1 : Gaussian of mean spc exceeds widthlimit | |
| 1016 | 2 : no Gaussian of mean spc found |
|
963 | 2 : no Gaussian of mean spc found | |
| 1017 | 3 : SNR to low or velocity to high -> prec. e.g. |
|
964 | 3 : SNR to low or velocity to high -> prec. e.g. | |
| 1018 | 4 : at least one Gaussian of cspc exceeds widthlimit |
|
965 | 4 : at least one Gaussian of cspc exceeds widthlimit | |
| 1019 | 5 : zero out of three cspc Gaussian fits converged |
|
966 | 5 : zero out of three cspc Gaussian fits converged | |
| 1020 | 6 : phase slope fit could not be found |
|
967 | 6 : phase slope fit could not be found | |
| 1021 | 7 : arrays used to fit phase have different length |
|
968 | 7 : arrays used to fit phase have different length | |
| 1022 | 8 : frequency range is either too short (len <= 5) or very long (> 30% of cspc) |
|
969 | 8 : frequency range is either too short (len <= 5) or very long (> 30% of cspc) | |
| 1023 |
|
970 | |||
| 1024 | """ |
|
971 | """ | |
| 1025 |
|
972 | |||
| 1026 |
|
|
973 | error_code = 0 | |
| 1027 |
|
974 | |||
| 1028 |
|
975 | |||
| 1029 |
|
|
976 | SPC_Samples = numpy.ones([spc.shape[0],spc.shape[1]]) # for normalized spc values for one height | |
| 1030 |
|
|
977 | phase = numpy.ones([spc.shape[0],spc.shape[1]]) # phase between channels | |
| 1031 |
|
|
978 | CSPC_Samples = numpy.ones([spc.shape[0],spc.shape[1]],dtype=numpy.complex_) # for normalized cspc values | |
| 1032 |
|
|
979 | PhaseSlope = numpy.zeros(spc.shape[0]) # slope of the phases, channelwise | |
| 1033 |
|
|
980 | PhaseInter = numpy.ones(spc.shape[0]) # intercept to the slope of the phases, channelwise | |
| 1034 |
|
|
981 | xFrec = AbbsisaRange[0][0:spc.shape[1]] # frequency range | |
| 1035 |
|
|
982 | xVel = AbbsisaRange[2][0:spc.shape[1]] # velocity range | |
| 1036 |
|
|
983 | SPCav = numpy.average(spc, axis=0)-numpy.average(noise) # spc[0]-noise[0] %D.S. why??? I suggest only spc.... | |
| 1037 |
|
984 | |||
| 1038 |
|
|
985 | SPCmoments_vel = self.Moments(SPCav, xVel ) # SPCmoments_vel[1] corresponds to vertical velocity and is used to determine if signal corresponds to wind (if .. <3) | |
|
|
986 | # D.S. I suggest to each moment to be calculated independently, because the signal level y/o interferences are not the same in all channels and | |||
|
|
987 | # signal or SNR seems to be contaminated | |||
| 1039 |
|
|
988 | CSPCmoments = [] | |
| 1040 |
|
989 | |||
| 1041 |
|
||||
| 1042 |
|
|
990 | '''Getting Eij and Nij''' | |
| 1043 |
|
991 | |||
| 1044 |
|
|
992 | Xi01, Xi02, Xi12 = ChanDist[:,0] | |
| 1045 |
|
|
993 | Eta01, Eta02, Eta12 = ChanDist[:,1] | |
| 1046 |
|
994 | |||
| 1047 |
|
|
995 | # update nov 19 | |
| 1048 |
|
|
996 | widthlimit = 7 # maximum width in Hz of the gaussian, empirically determined. Anything above 10 is unrealistic, often values between 1 and 5 correspond to proper fits. | |
| 1049 |
|
997 | |||
| 1050 |
|
|
998 | '''************************* SPC is normalized ********************************''' | |
| 1051 |
|
999 | |||
| 1052 |
|
|
1000 | spc_norm = spc.copy() # need copy() because untouched spc is needed for normalization of cspc below | |
| 1053 |
|
|
1001 | spc_norm = numpy.where(numpy.isfinite(spc_norm), spc_norm, numpy.NAN) | |
| 1054 |
|
1002 | |||
|
|
1003 | # D. Scipión: It is necessary to define DeltaF or DeltaV... it is wrong to use Factor_Norm. It's constant... not a variable | |||
|
|
1004 | ||||
|
|
1005 | # For each channel | |||
| 1055 |
|
|
1006 | for i in range(spc.shape[0]): | |
| 1056 |
|
1007 | |||
| 1057 |
|
|
1008 | spc_sub = spc_norm[i,:] - noise[i] # spc not smoothed here or in previous version. | |
| 1058 |
|
1009 | # D. Scipión: Factor_Norm has to be replaced by DeltaF or DeltaV - It's a constant | ||
| 1059 |
|
|
1010 | Factor_Norm = 2*numpy.max(xFrec) / numpy.count_nonzero(~numpy.isnan(spc_sub)) # usually = Freq range / nfft | |
| 1060 |
|
|
1011 | normalized_spc = spc_sub / (numpy.nansum(numpy.abs(spc_sub)) * Factor_Norm) | |
| 1061 |
|
1012 | |||
| 1062 |
|
|
1013 | xSamples = xFrec # the frequency range is taken | |
| 1063 |
|
|
1014 | SPC_Samples[i] = normalized_spc # Normalized SPC values are taken | |
| 1064 |
|
1015 | |||
| 1065 |
|
|
1016 | '''********************** FITTING MEAN SPC GAUSSIAN **********************''' | |
| 1066 |
|
1017 | |||
| 1067 |
|
|
1018 | """ the gaussian of the mean: first subtract noise, then normalize. this is legal because | |
| 1068 | you only fit the curve and don't need the absolute value of height for calculation, |
|
1019 | you only fit the curve and don't need the absolute value of height for calculation, | |
| 1069 | only for estimation of width. for normalization of cross spectra, you need initial, |
|
1020 | only for estimation of width. for normalization of cross spectra, you need initial, | |
| 1070 | unnormalized self-spectra With noise. |
|
1021 | unnormalized self-spectra With noise. | |
| 1071 |
|
1022 | |||
| 1072 | Technically, you don't even need to normalize the self-spectra, as you only need the |
|
1023 | Technically, you don't even need to normalize the self-spectra, as you only need the | |
| 1073 | width of the peak. However, it was left this way. Note that the normalization has a flaw: |
|
1024 | width of the peak. However, it was left this way. Note that the normalization has a flaw: | |
| 1074 | due to subtraction of the noise, some values are below zero. Raw "spc" values should be |
|
1025 | due to subtraction of the noise, some values are below zero. Raw "spc" values should be | |
| 1075 | >= 0, as it is the modulus squared of the signals (complex * it's conjugate) |
|
1026 | >= 0, as it is the modulus squared of the signals (complex * it's conjugate) | |
| 1076 | """ |
|
1027 | """ | |
| 1077 |
|
1028 | |||
| 1078 |
|
|
1029 | SPCMean = numpy.average(SPC_Samples, axis=0) | |
| 1079 |
|
1030 | |||
| 1080 |
|
|
1031 | popt = [1e-10,0,1e-10] | |
| 1081 |
|
|
1032 | SPCMoments = self.Moments(SPCMean, xSamples) | |
| 1082 |
|
1033 | |||
| 1083 |
|
|
1034 | if dbSNR > SNRlimit and numpy.abs(SPCmoments_vel[1]) < 3: | |
| 1084 |
|
|
1035 | try: | |
| 1085 |
|
|
1036 | popt,pcov = curve_fit(self.gaus,xSamples,SPCMean,p0=SPCMoments)#, bounds=(-numpy.inf, [numpy.inf, numpy.inf, 10])). Setting bounds does not make the code faster but only keeps the fit from finding the minimum. | |
| 1086 |
|
|
1037 | if popt[2] > widthlimit: # CONDITION | |
| 1087 |
|
|
1038 | return self.StopWindEstimation(error_code = 1) | |
| 1088 |
|
1039 | |||
| 1089 |
|
|
1040 | FitGauss = self.gaus(xSamples,*popt) | |
| 1090 |
|
1041 | |||
| 1091 |
|
|
1042 | except :#RuntimeError: | |
| 1092 |
|
|
1043 | return self.StopWindEstimation(error_code = 2) | |
| 1093 |
|
1044 | |||
| 1094 |
|
|
1045 | else: | |
| 1095 |
|
|
1046 | return self.StopWindEstimation(error_code = 3) | |
| 1096 |
|
1047 | |||
| 1097 |
|
1048 | |||
| 1098 |
|
1049 | |||
| 1099 |
|
|
1050 | '''***************************** CSPC Normalization ************************* | |
| 1100 | new section: |
|
1051 | new section: | |
| 1101 | The Spc spectra are used to normalize the crossspectra. Peaks from precipitation |
|
1052 | The Spc spectra are used to normalize the crossspectra. Peaks from precipitation | |
| 1102 | influence the norm which is not desired. First, a range is identified where the |
|
1053 | influence the norm which is not desired. First, a range is identified where the | |
| 1103 | wind peak is estimated -> sum_wind is sum of those frequencies. Next, the area |
|
1054 | wind peak is estimated -> sum_wind is sum of those frequencies. Next, the area | |
| 1104 | around it gets cut off and values replaced by mean determined by the boundary |
|
1055 | around it gets cut off and values replaced by mean determined by the boundary | |
| 1105 | data -> sum_noise (spc is not normalized here, thats why the noise is important) |
|
1056 | data -> sum_noise (spc is not normalized here, thats why the noise is important) | |
| 1106 |
|
1057 | |||
| 1107 | The sums are then added and multiplied by range/datapoints, because you need |
|
1058 | The sums are then added and multiplied by range/datapoints, because you need | |
| 1108 | an integral and not a sum for normalization. |
|
1059 | an integral and not a sum for normalization. | |
| 1109 |
|
1060 | |||
| 1110 | A norm is found according to Briggs 92. |
|
1061 | A norm is found according to Briggs 92. | |
| 1111 | ''' |
|
1062 | ''' | |
| 1112 |
|
1063 | |||
| 1113 |
|
|
1064 | radarWavelength = 0.6741 # meters | |
|
|
1065 | # D.S. This does not need to hardwired... It has to be in function of the radar frequency | |||
|
|
1066 | ||||
| 1114 |
|
|
1067 | count_limit_freq = numpy.abs(popt[1]) + widthlimit # Hz, m/s can be also used if velocity is desired abscissa. | |
| 1115 |
|
|
1068 | # count_limit_freq = numpy.max(xFrec) | |
| 1116 |
|
1069 | |||
| 1117 |
|
|
1070 | channel_integrals = numpy.zeros(3) | |
| 1118 |
|
1071 | |||
| 1119 |
|
|
1072 | for i in range(spc.shape[0]): | |
| 1120 |
|
|
1073 | ''' | |
| 1121 | find the point in array corresponding to count_limit frequency. |
|
1074 | find the point in array corresponding to count_limit frequency. | |
| 1122 | sum over all frequencies in the range around zero Hz @ math.ceil(N_freq/2) |
|
1075 | sum over all frequencies in the range around zero Hz @ math.ceil(N_freq/2) | |
| 1123 | ''' |
|
1076 | ''' | |
| 1124 |
|
|
1077 | N_freq = numpy.count_nonzero(~numpy.isnan(spc[i,:])) | |
| 1125 |
|
|
1078 | count_limit_int = int(math.ceil( count_limit_freq / numpy.max(xFrec) * (N_freq / 2) )) # gives integer point | |
| 1126 |
|
|
1079 | sum_wind = numpy.nansum( spc[i, (math.ceil(N_freq/2) - count_limit_int) : (math.ceil(N_freq / 2) + count_limit_int)] ) #N_freq/2 is where frequency (velocity) is zero, i.e. middle of spectrum. | |
| 1127 |
|
|
1080 | sum_noise = (numpy.mean(spc[i, :4]) + numpy.mean(spc[i, -6:-2]))/2.0 * (N_freq - 2*count_limit_int) | |
| 1128 |
|
|
1081 | channel_integrals[i] = (sum_noise + sum_wind) * (2*numpy.max(xFrec) / N_freq) | |
| 1129 |
|
1082 | |||
| 1130 |
|
1083 | |||
| 1131 |
|
|
1084 | cross_integrals_peak = numpy.zeros(3) | |
| 1132 |
|
|
1085 | # cross_integrals_totalrange = numpy.zeros(3) | |
| 1133 |
|
1086 | |||
| 1134 |
|
|
1087 | for i in range(spc.shape[0]): | |
| 1135 |
|
1088 | |||
| 1136 |
|
|
1089 | cspc_norm = cspc[i,:].copy() # cspc not smoothed here or in previous version | |
| 1137 |
|
1090 | |||
| 1138 |
|
|
1091 | chan_index0 = pairsList[i][0] | |
| 1139 |
|
|
1092 | chan_index1 = pairsList[i][1] | |
| 1140 |
|
1093 | |||
| 1141 |
|
|
1094 | cross_integrals_peak[i] = channel_integrals[chan_index0]*channel_integrals[chan_index1] | |
| 1142 |
|
|
1095 | normalized_cspc = cspc_norm / numpy.sqrt(cross_integrals_peak[i]) | |
| 1143 |
|
|
1096 | CSPC_Samples[i] = normalized_cspc | |
| 1144 |
|
1097 | |||
| 1145 |
|
|
1098 | ''' Finding cross integrals without subtracting any peaks:''' | |
| 1146 |
|
|
1099 | # FactorNorm0 = 2*numpy.max(xFrec) / numpy.count_nonzero(~numpy.isnan(spc[chan_index0,:])) | |
| 1147 |
|
|
1100 | # FactorNorm1 = 2*numpy.max(xFrec) / numpy.count_nonzero(~numpy.isnan(spc[chan_index1,:])) | |
| 1148 |
|
|
1101 | # cross_integrals_totalrange[i] = (numpy.nansum(spc[chan_index0,:])) * FactorNorm0 * (numpy.nansum(spc[chan_index1,:])) * FactorNorm1 | |
| 1149 |
|
|
1102 | # normalized_cspc = cspc_norm / numpy.sqrt(cross_integrals_totalrange[i]) | |
| 1150 |
|
|
1103 | # CSPC_Samples[i] = normalized_cspc | |
| 1151 |
|
1104 | |||
| 1152 |
|
1105 | |||
| 1153 |
|
|
1106 | phase[i] = numpy.arctan2(CSPC_Samples[i].imag, CSPC_Samples[i].real) | |
| 1154 |
|
1107 | |||
| 1155 |
|
1108 | |||
| 1156 |
|
|
1109 | CSPCmoments = numpy.vstack([self.Moments(numpy.abs(CSPC_Samples[0]), xSamples), | |
| 1157 |
|
|
1110 | self.Moments(numpy.abs(CSPC_Samples[1]), xSamples), | |
| 1158 |
|
|
1111 | self.Moments(numpy.abs(CSPC_Samples[2]), xSamples)]) | |
| 1159 |
|
1112 | |||
| 1160 |
|
1113 | |||
| 1161 |
|
|
1114 | '''***Sorting out NaN entries***''' | |
| 1162 |
|
|
1115 | CSPCMask01 = numpy.abs(CSPC_Samples[0]) | |
| 1163 |
|
|
1116 | CSPCMask02 = numpy.abs(CSPC_Samples[1]) | |
| 1164 |
|
|
1117 | CSPCMask12 = numpy.abs(CSPC_Samples[2]) | |
| 1165 |
|
1118 | |||
| 1166 |
|
|
1119 | mask01 = ~numpy.isnan(CSPCMask01) | |
| 1167 |
|
|
1120 | mask02 = ~numpy.isnan(CSPCMask02) | |
| 1168 |
|
|
1121 | mask12 = ~numpy.isnan(CSPCMask12) | |
| 1169 |
|
1122 | |||
| 1170 |
|
|
1123 | CSPCMask01 = CSPCMask01[mask01] | |
| 1171 |
|
|
1124 | CSPCMask02 = CSPCMask02[mask02] | |
| 1172 |
|
|
1125 | CSPCMask12 = CSPCMask12[mask12] | |
| 1173 |
|
1126 | |||
| 1174 |
|
1127 | |||
| 1175 |
|
|
1128 | popt01, popt02, popt12 = [1e-10,1e-10,1e-10], [1e-10,1e-10,1e-10] ,[1e-10,1e-10,1e-10] | |
| 1176 |
|
|
1129 | FitGauss01, FitGauss02, FitGauss12 = numpy.empty(len(xSamples))*0, numpy.empty(len(xSamples))*0, numpy.empty(len(xSamples))*0 | |
| 1177 |
|
1130 | |||
| 1178 |
|
|
1131 | '''*******************************FIT GAUSS CSPC************************************''' | |
| 1179 |
|
1132 | |||
| 1180 |
|
|
1133 | try: | |
| 1181 |
|
|
1134 | popt01,pcov = curve_fit(self.gaus,xSamples[mask01],numpy.abs(CSPCMask01),p0=CSPCmoments[0]) | |
| 1182 |
|
|
1135 | if popt01[2] > widthlimit: # CONDITION | |
| 1183 |
|
|
1136 | return self.StopWindEstimation(error_code = 4) | |
| 1184 |
|
1137 | |||
| 1185 |
|
|
1138 | popt02,pcov = curve_fit(self.gaus,xSamples[mask02],numpy.abs(CSPCMask02),p0=CSPCmoments[1]) | |
| 1186 |
|
|
1139 | if popt02[2] > widthlimit: # CONDITION | |
| 1187 |
|
|
1140 | return self.StopWindEstimation(error_code = 4) | |
| 1188 |
|
1141 | |||
| 1189 |
|
|
1142 | popt12,pcov = curve_fit(self.gaus,xSamples[mask12],numpy.abs(CSPCMask12),p0=CSPCmoments[2]) | |
| 1190 |
|
|
1143 | if popt12[2] > widthlimit: # CONDITION | |
| 1191 |
|
|
1144 | return self.StopWindEstimation(error_code = 4) | |
| 1192 |
|
1145 | |||
| 1193 |
|
|
1146 | FitGauss01 = self.gaus(xSamples, *popt01) | |
| 1194 |
|
|
1147 | FitGauss02 = self.gaus(xSamples, *popt02) | |
| 1195 |
|
|
1148 | FitGauss12 = self.gaus(xSamples, *popt12) | |
| 1196 |
|
1149 | |||
| 1197 |
|
|
1150 | except: | |
| 1198 |
|
|
1151 | return self.StopWindEstimation(error_code = 5) | |
| 1199 |
|
1152 | |||
| 1200 |
|
1153 | |||
| 1201 |
|
|
1154 | '''************* Getting Fij ***************''' | |
| 1202 |
|
1155 | |||
| 1203 |
|
1156 | |||
| 1204 |
|
|
1157 | #Punto en Eje X de la Gaussiana donde se encuentra el centro -- x-axis point of the gaussian where the center is located | |
| 1205 |
|
|
1158 | # -> PointGauCenter | |
| 1206 |
|
|
1159 | GaussCenter = popt[1] | |
| 1207 |
|
|
1160 | ClosestCenter = xSamples[numpy.abs(xSamples-GaussCenter).argmin()] | |
| 1208 |
|
|
1161 | PointGauCenter = numpy.where(xSamples==ClosestCenter)[0][0] | |
| 1209 |
|
1162 | |||
| 1210 |
|
|
1163 | #Punto e^-1 hubicado en la Gaussiana -- point where e^-1 is located in the gaussian | |
| 1211 |
|
|
1164 | PeMinus1 = numpy.max(FitGauss) * numpy.exp(-1) | |
| 1212 |
|
|
1165 | FijClosest = FitGauss[numpy.abs(FitGauss-PeMinus1).argmin()] # El punto mas cercano a "Peminus1" dentro de "FitGauss" | |
| 1213 |
|
|
1166 | PointFij = numpy.where(FitGauss==FijClosest)[0][0] | |
| 1214 |
|
1167 | |||
| 1215 |
|
|
1168 | Fij = numpy.abs(xSamples[PointFij] - xSamples[PointGauCenter]) | |
| 1216 |
|
1169 | |||
| 1217 |
|
|
1170 | '''********** Taking frequency ranges from mean SPCs **********''' | |
| 1218 |
|
1171 | |||
| 1219 |
|
|
1172 | #GaussCenter = popt[1] #Primer momento 01 | |
| 1220 |
|
|
1173 | GauWidth = popt[2] * 3/2 #Ancho de banda de Gau01 -- Bandwidth of Gau01 TODO why *3/2? | |
| 1221 |
|
|
1174 | Range = numpy.empty(2) | |
| 1222 |
|
|
1175 | Range[0] = GaussCenter - GauWidth | |
| 1223 |
|
|
1176 | Range[1] = GaussCenter + GauWidth | |
| 1224 |
|
|
1177 | #Punto en Eje X de la Gaussiana donde se encuentra ancho de banda (min:max) -- Point in x-axis where the bandwidth is located (min:max) | |
| 1225 |
|
|
1178 | ClosRangeMin = xSamples[numpy.abs(xSamples-Range[0]).argmin()] | |
| 1226 |
|
|
1179 | ClosRangeMax = xSamples[numpy.abs(xSamples-Range[1]).argmin()] | |
| 1227 |
|
1180 | |||
| 1228 |
|
|
1181 | PointRangeMin = numpy.where(xSamples==ClosRangeMin)[0][0] | |
| 1229 |
|
|
1182 | PointRangeMax = numpy.where(xSamples==ClosRangeMax)[0][0] | |
| 1230 |
|
1183 | |||
| 1231 |
|
|
1184 | Range = numpy.array([ PointRangeMin, PointRangeMax ]) | |
| 1232 |
|
1185 | |||
| 1233 |
|
|
1186 | FrecRange = xFrec[ Range[0] : Range[1] ] | |
| 1234 |
|
1187 | |||
| 1235 |
|
1188 | |||
| 1236 |
|
|
1189 | '''************************** Getting Phase Slope ***************************''' | |
| 1237 |
|
1190 | |||
| 1238 |
|
|
1191 | for i in range(1,3): # Changed to only compute two | |
| 1239 |
|
1192 | |||
| 1240 |
|
|
1193 | if len(FrecRange) > 5 and len(FrecRange) < spc.shape[1] * 0.3: | |
| 1241 |
|
|
1194 | # PhaseRange=self.moving_average(phase[i,Range[0]:Range[1]],N=1) #used before to smooth phase with N=3 | |
| 1242 |
|
|
1195 | PhaseRange = phase[i,Range[0]:Range[1]].copy() | |
| 1243 |
|
1196 | |||
| 1244 |
|
|
1197 | mask = ~numpy.isnan(FrecRange) & ~numpy.isnan(PhaseRange) | |
| 1245 |
|
1198 | |||
| 1246 |
|
1199 | |||
| 1247 |
|
|
1200 | if len(FrecRange) == len(PhaseRange): | |
| 1248 |
|
1201 | |||
| 1249 |
|
|
1202 | try: | |
| 1250 |
|
|
1203 | slope, intercept, _, _, _ = stats.linregress(FrecRange[mask], self.AntiAliasing(PhaseRange[mask], 4.5)) | |
| 1251 |
|
|
1204 | PhaseSlope[i] = slope | |
| 1252 |
|
|
1205 | PhaseInter[i] = intercept | |
| 1253 |
|
1206 | |||
| 1254 |
|
|
1207 | except: | |
| 1255 |
|
|
1208 | return self.StopWindEstimation(error_code = 6) | |
| 1256 |
|
1209 | |||
| 1257 |
|
|
1210 | else: | |
| 1258 |
|
|
1211 | return self.StopWindEstimation(error_code = 7) | |
| 1259 |
|
1212 | |||
| 1260 |
|
|
1213 | else: | |
| 1261 |
|
|
1214 | return self.StopWindEstimation(error_code = 8) | |
| 1262 |
|
1215 | |||
| 1263 |
|
1216 | |||
| 1264 |
|
1217 | |||
| 1265 |
|
|
1218 | '''*** Constants A-H correspond to the convention as in Briggs and Vincent 1992 ***''' | |
| 1266 |
|
1219 | |||
| 1267 |
|
|
1220 | '''Getting constant C''' | |
| 1268 |
|
|
1221 | cC=(Fij*numpy.pi)**2 | |
| 1269 |
|
1222 | |||
| 1270 |
|
|
1223 | '''****** Getting constants F and G ******''' | |
| 1271 |
|
|
1224 | MijEijNij = numpy.array([[Xi02,Eta02], [Xi12,Eta12]]) | |
| 1272 |
|
|
1225 | MijResult0 = (-PhaseSlope[1] * cC) / (2*numpy.pi) | |
| 1273 |
|
|
1226 | MijResult1 = (-PhaseSlope[2] * cC) / (2*numpy.pi) | |
| 1274 |
|
|
1227 | MijResults = numpy.array([MijResult0,MijResult1]) | |
| 1275 |
|
|
1228 | (cF,cG) = numpy.linalg.solve(MijEijNij, MijResults) | |
| 1276 |
|
1229 | |||
| 1277 |
|
|
1230 | '''****** Getting constants A, B and H ******''' | |
| 1278 |
|
|
1231 | W01 = numpy.nanmax( FitGauss01 ) | |
| 1279 |
|
|
1232 | W02 = numpy.nanmax( FitGauss02 ) | |
| 1280 |
|
|
1233 | W12 = numpy.nanmax( FitGauss12 ) | |
| 1281 |
|
1234 | |||
| 1282 |
|
|
1235 | WijResult0 = ((cF * Xi01 + cG * Eta01)**2)/cC - numpy.log(W01 / numpy.sqrt(numpy.pi / cC)) | |
| 1283 |
|
|
1236 | WijResult1 = ((cF * Xi02 + cG * Eta02)**2)/cC - numpy.log(W02 / numpy.sqrt(numpy.pi / cC)) | |
| 1284 |
|
|
1237 | WijResult2 = ((cF * Xi12 + cG * Eta12)**2)/cC - numpy.log(W12 / numpy.sqrt(numpy.pi / cC)) | |
| 1285 |
|
1238 | |||
| 1286 |
|
|
1239 | WijResults = numpy.array([WijResult0, WijResult1, WijResult2]) | |
| 1287 |
|
1240 | |||
| 1288 |
|
|
1241 | WijEijNij = numpy.array([ [Xi01**2, Eta01**2, 2*Xi01*Eta01] , [Xi02**2, Eta02**2, 2*Xi02*Eta02] , [Xi12**2, Eta12**2, 2*Xi12*Eta12] ]) | |
| 1289 |
|
|
1242 | (cA,cB,cH) = numpy.linalg.solve(WijEijNij, WijResults) | |
| 1290 |
|
1243 | |||
| 1291 |
|
|
1244 | VxVy = numpy.array([[cA,cH],[cH,cB]]) | |
| 1292 |
|
|
1245 | VxVyResults = numpy.array([-cF,-cG]) | |
| 1293 |
|
|
1246 | (Vx,Vy) = numpy.linalg.solve(VxVy, VxVyResults) | |
| 1294 |
|
1247 | |||
| 1295 |
|
|
1248 | Vzon = Vy | |
| 1296 |
|
|
1249 | Vmer = Vx | |
| 1297 |
|
1250 | |||
| 1298 |
|
|
1251 | # Vmag=numpy.sqrt(Vzon**2+Vmer**2) # unused | |
| 1299 |
|
|
1252 | # Vang=numpy.arctan2(Vmer,Vzon) # unused | |
| 1300 |
|
1253 | |||
| 1301 |
|
1254 | |||
| 1302 |
|
|
1255 | ''' using frequency as abscissa. Due to three channels, the offzenith angle is zero | |
| 1303 | and Vrad equal to Vver. formula taken from Briggs 92, figure 4. |
|
1256 | and Vrad equal to Vver. formula taken from Briggs 92, figure 4. | |
| 1304 | ''' |
|
1257 | ''' | |
| 1305 |
|
|
1258 | if numpy.abs( popt[1] ) < 3.5 and len(FrecRange) > 4: | |
| 1306 |
|
|
1259 | Vver = 0.5 * radarWavelength * popt[1] * 100 # *100 to get cm (/s) | |
| 1307 |
|
|
1260 | else: | |
| 1308 |
|
|
1261 | Vver = numpy.NaN | |
| 1309 |
|
1262 | |||
| 1310 |
|
|
1263 | error_code = 0 | |
| 1311 |
|
1264 | |||
| 1312 |
|
|
1265 | return Vzon, Vmer, Vver, error_code | |
| 1313 |
|
1266 | |||
| 1314 |
|
1267 | |||
| 1315 |
|
|
1268 | class SpectralMoments(Operation): | |
| 1316 |
|
1269 | |||
| 1317 |
|
|
1270 | ''' | |
| 1318 | Function SpectralMoments() |
|
1271 | Function SpectralMoments() | |
| 1319 |
|
1272 | |||
| 1320 | Calculates moments (power, mean, standard deviation) and SNR of the signal |
|
1273 | Calculates moments (power, mean, standard deviation) and SNR of the signal | |
| 1321 |
|
1274 | |||
| 1322 | Type of dataIn: Spectra |
|
1275 | Type of dataIn: Spectra | |
| 1323 |
|
1276 | |||
| 1324 | Configuration Parameters: |
|
1277 | Configuration Parameters: | |
| 1325 |
|
1278 | |||
| 1326 | dirCosx : Cosine director in X axis |
|
1279 | dirCosx : Cosine director in X axis | |
| 1327 | dirCosy : Cosine director in Y axis |
|
1280 | dirCosy : Cosine director in Y axis | |
| 1328 |
|
1281 | |||
| 1329 | elevation : |
|
1282 | elevation : | |
| 1330 | azimuth : |
|
1283 | azimuth : | |
| 1331 |
|
1284 | |||
| 1332 | Input: |
|
1285 | Input: | |
| 1333 | channelList : simple channel list to select e.g. [2,3,7] |
|
1286 | channelList : simple channel list to select e.g. [2,3,7] | |
| 1334 | self.dataOut.data_pre : Spectral data |
|
1287 | self.dataOut.data_pre : Spectral data | |
| 1335 | self.dataOut.abscissaList : List of frequencies |
|
1288 | self.dataOut.abscissaList : List of frequencies | |
| 1336 | self.dataOut.noise : Noise level per channel |
|
1289 | self.dataOut.noise : Noise level per channel | |
| 1337 |
|
1290 | |||
| 1338 | Affected: |
|
1291 | Affected: | |
| 1339 | self.dataOut.moments : Parameters per channel |
|
1292 | self.dataOut.moments : Parameters per channel | |
| 1340 | self.dataOut.data_SNR : SNR per channel |
|
1293 | self.dataOut.data_SNR : SNR per channel | |
| 1341 |
|
1294 | |||
| 1342 | ''' |
|
1295 | ''' | |
| 1343 |
|
1296 | |||
| 1344 |
|
|
1297 | def run(self, dataOut): | |
| 1345 |
|
1298 | |||
| 1346 |
|
|
1299 | data = dataOut.data_pre[0] | |
| 1347 |
|
|
1300 | absc = dataOut.abscissaList[:-1] | |
| 1348 |
|
|
1301 | noise = dataOut.noise | |
| 1349 |
|
|
1302 | nChannel = data.shape[0] | |
| 1350 |
|
|
1303 | data_param = numpy.zeros((nChannel, 4, data.shape[2])) | |
| 1351 |
|
1304 | |||
| 1352 |
|
|
1305 | for ind in range(nChannel): | |
| 1353 |
|
|
1306 | data_param[ind,:,:] = self.__calculateMoments( data[ind,:,:] , absc , noise[ind] ) | |
| 1354 |
|
1307 | |||
| 1355 |
|
|
1308 | dataOut.moments = data_param[:,1:,:] | |
| 1356 |
|
|
1309 | dataOut.data_SNR = data_param[:,0] | |
| 1357 |
|
|
1310 | dataOut.data_POW = data_param[:,1] | |
| 1358 |
|
|
1311 | dataOut.data_DOP = data_param[:,2] | |
| 1359 |
|
|
1312 | dataOut.data_WIDTH = data_param[:,3] | |
| 1360 |
|
1313 | |||
| 1361 |
|
|
1314 | return dataOut | |
| 1362 |
|
1315 | |||
| 1363 |
|
|
1316 | def __calculateMoments(self, oldspec, oldfreq, n0, | |
| 1364 |
|
|
1317 | nicoh = None, graph = None, smooth = None, type1 = None, fwindow = None, snrth = None, dc = None, aliasing = None, oldfd = None, wwauto = None): | |
| 1365 |
|
1318 | |||
| 1366 |
|
|
1319 | if (nicoh is None): nicoh = 1 | |
| 1367 |
|
|
1320 | if (graph is None): graph = 0 | |
| 1368 |
|
|
1321 | if (smooth is None): smooth = 0 | |
| 1369 |
|
|
1322 | elif (self.smooth < 3): smooth = 0 | |
| 1370 |
|
1323 | |||
| 1371 |
|
|
1324 | if (type1 is None): type1 = 0 | |
| 1372 |
|
|
1325 | if (fwindow is None): fwindow = numpy.zeros(oldfreq.size) + 1 | |
| 1373 |
|
|
1326 | if (snrth is None): snrth = -3 | |
| 1374 |
|
|
1327 | if (dc is None): dc = 0 | |
| 1375 |
|
|
1328 | if (aliasing is None): aliasing = 0 | |
| 1376 |
|
|
1329 | if (oldfd is None): oldfd = 0 | |
| 1377 |
|
|
1330 | if (wwauto is None): wwauto = 0 | |
| 1378 |
|
1331 | |||
| 1379 |
|
|
1332 | if (n0 < 1.e-20): n0 = 1.e-20 | |
| 1380 |
|
1333 | |||
| 1381 |
|
|
1334 | freq = oldfreq | |
| 1382 |
|
|
1335 | vec_power = numpy.zeros(oldspec.shape[1]) | |
| 1383 |
|
|
1336 | vec_fd = numpy.zeros(oldspec.shape[1]) | |
| 1384 |
|
|
1337 | vec_w = numpy.zeros(oldspec.shape[1]) | |
| 1385 |
|
|
1338 | vec_snr = numpy.zeros(oldspec.shape[1]) | |
| 1386 |
|
1339 | |||
| 1387 |
|
|
1340 | # oldspec = numpy.ma.masked_invalid(oldspec) | |
| 1388 |
|
1341 | |||
| 1389 |
|
|
1342 | for ind in range(oldspec.shape[1]): | |
| 1390 |
|
1343 | |||
| 1391 |
|
|
1344 | spec = oldspec[:,ind] | |
| 1392 |
|
|
1345 | aux = spec*fwindow | |
| 1393 |
|
|
1346 | max_spec = aux.max() | |
| 1394 |
|
|
1347 | m = aux.tolist().index(max_spec) | |
| 1395 |
|
1348 | |||
| 1396 |
|
|
1349 | # Smooth | |
| 1397 |
|
|
1350 | if (smooth == 0): | |
| 1398 |
|
|
1351 | spec2 = spec | |
| 1399 |
|
|
1352 | else: | |
| 1400 |
|
|
1353 | spec2 = scipy.ndimage.filters.uniform_filter1d(spec,size=smooth) | |
| 1401 |
|
1354 | |||
| 1402 |
|
|
1355 | # Moments Estimation | |
| 1403 |
|
|
1356 | bb = spec2[numpy.arange(m,spec2.size)] | |
| 1404 |
|
|
1357 | bb = (bb<n0).nonzero() | |
| 1405 |
|
|
1358 | bb = bb[0] | |
| 1406 |
|
1359 | |||
| 1407 |
|
|
1360 | ss = spec2[numpy.arange(0,m + 1)] | |
| 1408 |
|
|
1361 | ss = (ss<n0).nonzero() | |
| 1409 |
|
|
1362 | ss = ss[0] | |
| 1410 |
|
1363 | |||
| 1411 |
|
|
1364 | if (bb.size == 0): | |
| 1412 |
|
|
1365 | bb0 = spec.size - 1 - m | |
| 1413 |
|
|
1366 | else: | |
| 1414 |
|
|
1367 | bb0 = bb[0] - 1 | |
| 1415 |
|
|
1368 | if (bb0 < 0): | |
| 1416 |
|
|
1369 | bb0 = 0 | |
| 1417 |
|
1370 | |||
| 1418 |
|
|
1371 | if (ss.size == 0): | |
| 1419 |
|
|
1372 | ss1 = 1 | |
| 1420 |
|
|
1373 | else: | |
| 1421 |
|
|
1374 | ss1 = max(ss) + 1 | |
| 1422 |
|
1375 | |||
| 1423 |
|
|
1376 | if (ss1 > m): | |
| 1424 |
|
|
1377 | ss1 = m | |
| 1425 |
|
1378 | |||
| 1426 |
|
|
1379 | valid = numpy.arange(int(m + bb0 - ss1 + 1)) + ss1 | |
| 1427 |
|
1380 | |||
| 1428 |
|
|
1381 | signal_power = ((spec2[valid] - n0) * fwindow[valid]).mean() # D. Scipión added with correct definition | |
|
|
1382 | total_power = (spec2[valid] * fwindow[valid]).mean() # D. Scipión added with correct definition | |||
|
|
1383 | power = ((spec2[valid] - n0) * fwindow[valid]).sum() | |||
| 1429 |
|
|
1384 | fd = ((spec2[valid]- n0)*freq[valid] * fwindow[valid]).sum() / power | |
| 1430 |
|
|
1385 | w = numpy.sqrt(((spec2[valid] - n0)*fwindow[valid]*(freq[valid]- fd)**2).sum() / power) | |
| 1431 |
|
|
1386 | snr = (spec2.mean()-n0)/n0 | |
| 1432 |
|
|
1387 | if (snr < 1.e-20) : | |
| 1433 |
|
|
1388 | snr = 1.e-20 | |
| 1434 |
|
1389 | |||
| 1435 | vec_power[ind] = power |
|
1390 | # vec_power[ind] = power #D. Scipión replaced with the line below | |
|
|
1391 | vec_power[ind] = total_power | |||
| 1436 |
|
|
1392 | vec_fd[ind] = fd | |
| 1437 |
|
|
1393 | vec_w[ind] = w | |
| 1438 |
|
|
1394 | vec_snr[ind] = snr | |
| 1439 |
|
1395 | |||
| 1440 |
|
|
1396 | return numpy.vstack((vec_snr, vec_power, vec_fd, vec_w)) | |
| 1441 |
|
1397 | |||
| 1442 |
|
|
1398 | #------------------ Get SA Parameters -------------------------- | |
| 1443 |
|
1399 | |||
| 1444 |
|
|
1400 | def GetSAParameters(self): | |
| 1445 |
|
|
1401 | #SA en frecuencia | |
| 1446 |
|
|
1402 | pairslist = self.dataOut.groupList | |
| 1447 |
|
|
1403 | num_pairs = len(pairslist) | |
| 1448 |
|
1404 | |||
| 1449 |
|
|
1405 | vel = self.dataOut.abscissaList | |
| 1450 |
|
|
1406 | spectra = self.dataOut.data_pre | |
| 1451 |
|
|
1407 | cspectra = self.dataIn.data_cspc | |
| 1452 |
|
|
1408 | delta_v = vel[1] - vel[0] | |
| 1453 |
|
1409 | |||
| 1454 |
|
|
1410 | #Calculating the power spectrum | |
| 1455 |
|
|
1411 | spc_pow = numpy.sum(spectra, 3)*delta_v | |
| 1456 |
|
|
1412 | #Normalizing Spectra | |
| 1457 |
|
|
1413 | norm_spectra = spectra/spc_pow | |
| 1458 |
|
|
1414 | #Calculating the norm_spectra at peak | |
| 1459 |
|
|
1415 | max_spectra = numpy.max(norm_spectra, 3) | |
| 1460 |
|
1416 | |||
| 1461 |
|
|
1417 | #Normalizing Cross Spectra | |
| 1462 |
|
|
1418 | norm_cspectra = numpy.zeros(cspectra.shape) | |
| 1463 |
|
1419 | |||
| 1464 |
|
|
1420 | for i in range(num_chan): | |
| 1465 |
|
|
1421 | norm_cspectra[i,:,:] = cspectra[i,:,:]/numpy.sqrt(spc_pow[pairslist[i][0],:]*spc_pow[pairslist[i][1],:]) | |
| 1466 |
|
1422 | |||
| 1467 |
|
|
1423 | max_cspectra = numpy.max(norm_cspectra,2) | |
| 1468 |
|
|
1424 | max_cspectra_index = numpy.argmax(norm_cspectra, 2) | |
| 1469 |
|
1425 | |||
| 1470 |
|
|
1426 | for i in range(num_pairs): | |
| 1471 |
|
|
1427 | cspc_par[i,:,:] = __calculateMoments(norm_cspectra) | |
| 1472 |
|
|
1428 | #------------------- Get Lags ---------------------------------- | |
| 1473 |
|
1429 | |||
| 1474 |
|
|
1430 | class SALags(Operation): | |
| 1475 |
|
|
1431 | ''' | |
| 1476 | Function GetMoments() |
|
1432 | Function GetMoments() | |
| 1477 |
|
1433 | |||
| 1478 | Input: |
|
1434 | Input: | |
| 1479 | self.dataOut.data_pre |
|
1435 | self.dataOut.data_pre | |
| 1480 | self.dataOut.abscissaList |
|
1436 | self.dataOut.abscissaList | |
| 1481 | self.dataOut.noise |
|
1437 | self.dataOut.noise | |
| 1482 | self.dataOut.normFactor |
|
1438 | self.dataOut.normFactor | |
| 1483 | self.dataOut.data_SNR |
|
1439 | self.dataOut.data_SNR | |
| 1484 | self.dataOut.groupList |
|
1440 | self.dataOut.groupList | |
| 1485 | self.dataOut.nChannels |
|
1441 | self.dataOut.nChannels | |
| 1486 |
|
1442 | |||
| 1487 | Affected: |
|
1443 | Affected: | |
| 1488 | self.dataOut.data_param |
|
1444 | self.dataOut.data_param | |
| 1489 |
|
1445 | |||
| 1490 | ''' |
|
1446 | ''' | |
| 1491 |
|
|
1447 | def run(self, dataOut): | |
| 1492 |
|
|
1448 | data_acf = dataOut.data_pre[0] | |
| 1493 |
|
|
1449 | data_ccf = dataOut.data_pre[1] | |
| 1494 |
|
|
1450 | normFactor_acf = dataOut.normFactor[0] | |
| 1495 |
|
|
1451 | normFactor_ccf = dataOut.normFactor[1] | |
| 1496 |
|
|
1452 | pairs_acf = dataOut.groupList[0] | |
| 1497 |
|
|
1453 | pairs_ccf = dataOut.groupList[1] | |
| 1498 |
|
1454 | |||
| 1499 |
|
|
1455 | nHeights = dataOut.nHeights | |
| 1500 |
|
|
1456 | absc = dataOut.abscissaList | |
| 1501 |
|
|
1457 | noise = dataOut.noise | |
| 1502 |
|
|
1458 | SNR = dataOut.data_SNR | |
| 1503 |
|
|
1459 | nChannels = dataOut.nChannels | |
| 1504 |
|
|
1460 | # pairsList = dataOut.groupList | |
| 1505 |
|
|
1461 | # pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairsList, nChannels) | |
| 1506 |
|
1462 | |||
| 1507 |
|
|
1463 | for l in range(len(pairs_acf)): | |
| 1508 |
|
|
1464 | data_acf[l,:,:] = data_acf[l,:,:]/normFactor_acf[l,:] | |
| 1509 |
|
1465 | |||
| 1510 |
|
|
1466 | for l in range(len(pairs_ccf)): | |
| 1511 |
|
|
1467 | data_ccf[l,:,:] = data_ccf[l,:,:]/normFactor_ccf[l,:] | |
| 1512 |
|
1468 | |||
| 1513 |
|
|
1469 | dataOut.data_param = numpy.zeros((len(pairs_ccf)*2 + 1, nHeights)) | |
| 1514 |
|
|
1470 | dataOut.data_param[:-1,:] = self.__calculateTaus(data_acf, data_ccf, absc) | |
| 1515 |
|
|
1471 | dataOut.data_param[-1,:] = self.__calculateLag1Phase(data_acf, absc) | |
| 1516 |
|
|
1472 | return | |
| 1517 |
|
1473 | |||
| 1518 |
|
|
1474 | # def __getPairsAutoCorr(self, pairsList, nChannels): | |
| 1519 |
|
|
1475 | # | |
| 1520 |
|
|
1476 | # pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan | |
| 1521 |
|
|
1477 | # | |
| 1522 |
|
|
1478 | # for l in range(len(pairsList)): | |
| 1523 |
|
|
1479 | # firstChannel = pairsList[l][0] | |
| 1524 |
|
|
1480 | # secondChannel = pairsList[l][1] | |
| 1525 |
|
|
1481 | # | |
| 1526 |
|
|
1482 | # #Obteniendo pares de Autocorrelacion | |
| 1527 |
|
|
1483 | # if firstChannel == secondChannel: | |
| 1528 |
|
|
1484 | # pairsAutoCorr[firstChannel] = int(l) | |
| 1529 |
|
|
1485 | # | |
| 1530 |
|
|
1486 | # pairsAutoCorr = pairsAutoCorr.astype(int) | |
| 1531 |
|
|
1487 | # | |
| 1532 |
|
|
1488 | # pairsCrossCorr = range(len(pairsList)) | |
| 1533 |
|
|
1489 | # pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr) | |
| 1534 |
|
|
1490 | # | |
| 1535 |
|
|
1491 | # return pairsAutoCorr, pairsCrossCorr | |
| 1536 |
|
1492 | |||
| 1537 |
|
|
1493 | def __calculateTaus(self, data_acf, data_ccf, lagRange): | |
| 1538 |
|
1494 | |||
| 1539 |
|
|
1495 | lag0 = data_acf.shape[1]/2 | |
| 1540 |
|
|
1496 | #Funcion de Autocorrelacion | |
| 1541 |
|
|
1497 | mean_acf = stats.nanmean(data_acf, axis = 0) | |
| 1542 |
|
1498 | |||
| 1543 |
|
|
1499 | #Obtencion Indice de TauCross | |
| 1544 |
|
|
1500 | ind_ccf = data_ccf.argmax(axis = 1) | |
| 1545 |
|
|
1501 | #Obtencion Indice de TauAuto | |
| 1546 |
|
|
1502 | ind_acf = numpy.zeros(ind_ccf.shape,dtype = 'int') | |
| 1547 |
|
|
1503 | ccf_lag0 = data_ccf[:,lag0,:] | |
| 1548 |
|
1504 | |||
| 1549 |
|
|
1505 | for i in range(ccf_lag0.shape[0]): | |
| 1550 |
|
|
1506 | ind_acf[i,:] = numpy.abs(mean_acf - ccf_lag0[i,:]).argmin(axis = 0) | |
| 1551 |
|
1507 | |||
| 1552 |
|
|
1508 | #Obtencion de TauCross y TauAuto | |
| 1553 |
|
|
1509 | tau_ccf = lagRange[ind_ccf] | |
| 1554 |
|
|
1510 | tau_acf = lagRange[ind_acf] | |
| 1555 |
|
1511 | |||
| 1556 |
|
|
1512 | Nan1, Nan2 = numpy.where(tau_ccf == lagRange[0]) | |
| 1557 |
|
1513 | |||
| 1558 |
|
|
1514 | tau_ccf[Nan1,Nan2] = numpy.nan | |
| 1559 |
|
|
1515 | tau_acf[Nan1,Nan2] = numpy.nan | |
| 1560 |
|
|
1516 | tau = numpy.vstack((tau_ccf,tau_acf)) | |
| 1561 |
|
1517 | |||
| 1562 |
|
|
1518 | return tau | |
| 1563 |
|
1519 | |||
| 1564 |
|
|
1520 | def __calculateLag1Phase(self, data, lagTRange): | |
| 1565 |
|
|
1521 | data1 = stats.nanmean(data, axis = 0) | |
| 1566 |
|
|
1522 | lag1 = numpy.where(lagTRange == 0)[0][0] + 1 | |
| 1567 |
|
1523 | |||
| 1568 |
|
|
1524 | phase = numpy.angle(data1[lag1,:]) | |
| 1569 |
|
1525 | |||
| 1570 |
|
|
1526 | return phase | |
| 1571 |
|
1527 | |||
| 1572 |
|
|
1528 | class SpectralFitting(Operation): | |
| 1573 |
|
|
1529 | ''' | |
| 1574 | Function GetMoments() |
|
1530 | Function GetMoments() | |
| 1575 |
|
1531 | |||
| 1576 | Input: |
|
1532 | Input: | |
| 1577 | Output: |
|
1533 | Output: | |
| 1578 | Variables modified: |
|
1534 | Variables modified: | |
| 1579 | ''' |
|
1535 | ''' | |
| 1580 |
|
1536 | |||
| 1581 |
|
|
1537 | def run(self, dataOut, getSNR = True, path=None, file=None, groupList=None): | |
| 1582 |
|
1538 | |||
| 1583 |
|
1539 | |||
| 1584 |
|
|
1540 | if path != None: | |
| 1585 |
|
|
1541 | sys.path.append(path) | |
| 1586 |
|
|
1542 | self.dataOut.library = importlib.import_module(file) | |
| 1587 |
|
1543 | |||
| 1588 |
|
|
1544 | #To be inserted as a parameter | |
| 1589 |
|
|
1545 | groupArray = numpy.array(groupList) | |
| 1590 |
|
|
1546 | # groupArray = numpy.array([[0,1],[2,3]]) | |
| 1591 |
|
|
1547 | self.dataOut.groupList = groupArray | |
| 1592 |
|
1548 | |||
| 1593 |
|
|
1549 | nGroups = groupArray.shape[0] | |
| 1594 |
|
|
1550 | nChannels = self.dataIn.nChannels | |
| 1595 |
|
|
1551 | nHeights=self.dataIn.heightList.size | |
| 1596 |
|
1552 | |||
| 1597 |
|
|
1553 | #Parameters Array | |
| 1598 |
|
|
1554 | self.dataOut.data_param = None | |
| 1599 |
|
1555 | |||
| 1600 |
|
|
1556 | #Set constants | |
| 1601 |
|
|
1557 | constants = self.dataOut.library.setConstants(self.dataIn) | |
| 1602 |
|
|
1558 | self.dataOut.constants = constants | |
| 1603 |
|
|
1559 | M = self.dataIn.normFactor | |
| 1604 |
|
|
1560 | N = self.dataIn.nFFTPoints | |
| 1605 |
|
|
1561 | ippSeconds = self.dataIn.ippSeconds | |
| 1606 |
|
|
1562 | K = self.dataIn.nIncohInt | |
| 1607 |
|
|
1563 | pairsArray = numpy.array(self.dataIn.pairsList) | |
| 1608 |
|
1564 | |||
| 1609 |
|
|
1565 | #List of possible combinations | |
| 1610 |
|
|
1566 | listComb = itertools.combinations(numpy.arange(groupArray.shape[1]),2) | |
| 1611 |
|
|
1567 | indCross = numpy.zeros(len(list(listComb)), dtype = 'int') | |
| 1612 |
|
1568 | |||
| 1613 |
|
|
1569 | if getSNR: | |
| 1614 |
|
|
1570 | listChannels = groupArray.reshape((groupArray.size)) | |
| 1615 |
|
|
1571 | listChannels.sort() | |
| 1616 |
|
|
1572 | noise = self.dataIn.getNoise() | |
| 1617 |
|
|
1573 | self.dataOut.data_SNR = self.__getSNR(self.dataIn.data_spc[listChannels,:,:], noise[listChannels]) | |
| 1618 |
|
1574 | |||
| 1619 |
|
|
1575 | for i in range(nGroups): | |
| 1620 |
|
|
1576 | coord = groupArray[i,:] | |
| 1621 |
|
1577 | |||
| 1622 |
|
|
1578 | #Input data array | |
| 1623 |
|
|
1579 | data = self.dataIn.data_spc[coord,:,:]/(M*N) | |
| 1624 |
|
|
1580 | data = data.reshape((data.shape[0]*data.shape[1],data.shape[2])) | |
| 1625 |
|
1581 | |||
| 1626 |
|
|
1582 | #Cross Spectra data array for Covariance Matrixes | |
| 1627 |
|
|
1583 | ind = 0 | |
| 1628 |
|
|
1584 | for pairs in listComb: | |
| 1629 |
|
|
1585 | pairsSel = numpy.array([coord[x],coord[y]]) | |
| 1630 |
|
|
1586 | indCross[ind] = int(numpy.where(numpy.all(pairsArray == pairsSel, axis = 1))[0][0]) | |
| 1631 |
|
|
1587 | ind += 1 | |
| 1632 |
|
|
1588 | dataCross = self.dataIn.data_cspc[indCross,:,:]/(M*N) | |
| 1633 |
|
|
1589 | dataCross = dataCross**2/K | |
| 1634 |
|
1590 | |||
| 1635 |
|
|
1591 | for h in range(nHeights): | |
| 1636 |
|
1592 | |||
| 1637 |
|
|
1593 | #Input | |
| 1638 |
|
|
1594 | d = data[:,h] | |
| 1639 |
|
1595 | |||
| 1640 |
|
|
1596 | #Covariance Matrix | |
| 1641 |
|
|
1597 | D = numpy.diag(d**2/K) | |
| 1642 |
|
|
1598 | ind = 0 | |
| 1643 |
|
|
1599 | for pairs in listComb: | |
| 1644 |
|
|
1600 | #Coordinates in Covariance Matrix | |
| 1645 |
|
|
1601 | x = pairs[0] | |
| 1646 |
|
|
1602 | y = pairs[1] | |
| 1647 |
|
|
1603 | #Channel Index | |
| 1648 |
|
|
1604 | S12 = dataCross[ind,:,h] | |
| 1649 |
|
|
1605 | D12 = numpy.diag(S12) | |
| 1650 |
|
|
1606 | #Completing Covariance Matrix with Cross Spectras | |
| 1651 |
|
|
1607 | D[x*N:(x+1)*N,y*N:(y+1)*N] = D12 | |
| 1652 |
|
|
1608 | D[y*N:(y+1)*N,x*N:(x+1)*N] = D12 | |
| 1653 |
|
|
1609 | ind += 1 | |
| 1654 |
|
|
1610 | Dinv=numpy.linalg.inv(D) | |
| 1655 |
|
|
1611 | L=numpy.linalg.cholesky(Dinv) | |
| 1656 |
|
|
1612 | LT=L.T | |
| 1657 |
|
1613 | |||
| 1658 |
|
|
1614 | dp = numpy.dot(LT,d) | |
| 1659 |
|
1615 | |||
| 1660 |
|
|
1616 | #Initial values | |
| 1661 |
|
|
1617 | data_spc = self.dataIn.data_spc[coord,:,h] | |
| 1662 |
|
1618 | |||
| 1663 |
|
|
1619 | if (h>0)and(error1[3]<5): | |
| 1664 |
|
|
1620 | p0 = self.dataOut.data_param[i,:,h-1] | |
| 1665 |
|
|
1621 | else: | |
| 1666 |
|
|
1622 | p0 = numpy.array(self.dataOut.library.initialValuesFunction(data_spc, constants, i)) | |
| 1667 |
|
1623 | |||
| 1668 |
|
|
1624 | try: | |
| 1669 |
|
|
1625 | #Least Squares | |
| 1670 |
|
|
1626 | minp,covp,infodict,mesg,ier = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants),full_output=True) | |
| 1671 |
|
|
1627 | # minp,covp = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants)) | |
| 1672 |
|
|
1628 | #Chi square error | |
| 1673 |
|
|
1629 | error0 = numpy.sum(infodict['fvec']**2)/(2*N) | |
| 1674 |
|
|
1630 | #Error with Jacobian | |
| 1675 |
|
|
1631 | error1 = self.dataOut.library.errorFunction(minp,constants,LT) | |
| 1676 |
|
|
1632 | except: | |
| 1677 |
|
|
1633 | minp = p0*numpy.nan | |
| 1678 |
|
|
1634 | error0 = numpy.nan | |
| 1679 |
|
|
1635 | error1 = p0*numpy.nan | |
| 1680 |
|
1636 | |||
| 1681 |
|
|
1637 | #Save | |
| 1682 |
|
|
1638 | if self.dataOut.data_param is None: | |
| 1683 |
|
|
1639 | self.dataOut.data_param = numpy.zeros((nGroups, p0.size, nHeights))*numpy.nan | |
| 1684 |
|
|
1640 | self.dataOut.data_error = numpy.zeros((nGroups, p0.size + 1, nHeights))*numpy.nan | |
| 1685 |
|
1641 | |||
| 1686 |
|
|
1642 | self.dataOut.data_error[i,:,h] = numpy.hstack((error0,error1)) | |
| 1687 |
|
|
1643 | self.dataOut.data_param[i,:,h] = minp | |
| 1688 |
|
|
1644 | return | |
| 1689 |
|
1645 | |||
| 1690 |
|
|
1646 | def __residFunction(self, p, dp, LT, constants): | |
| 1691 |
|
1647 | |||
| 1692 |
|
|
1648 | fm = self.dataOut.library.modelFunction(p, constants) | |
| 1693 |
|
|
1649 | fmp=numpy.dot(LT,fm) | |
| 1694 |
|
1650 | |||
| 1695 |
|
|
1651 | return dp-fmp | |
| 1696 |
|
1652 | |||
| 1697 |
|
|
1653 | def __getSNR(self, z, noise): | |
| 1698 |
|
1654 | |||
| 1699 |
|
|
1655 | avg = numpy.average(z, axis=1) | |
| 1700 |
|
|
1656 | SNR = (avg.T-noise)/noise | |
| 1701 |
|
|
1657 | SNR = SNR.T | |
| 1702 |
|
|
1658 | return SNR | |
| 1703 |
|
1659 | |||
| 1704 |
|
|
1660 | def __chisq(p,chindex,hindex): | |
| 1705 |
|
|
1661 | #similar to Resid but calculates CHI**2 | |
| 1706 |
|
|
1662 | [LT,d,fm]=setupLTdfm(p,chindex,hindex) | |
| 1707 |
|
|
1663 | dp=numpy.dot(LT,d) | |
| 1708 |
|
|
1664 | fmp=numpy.dot(LT,fm) | |
| 1709 |
|
|
1665 | chisq=numpy.dot((dp-fmp).T,(dp-fmp)) | |
| 1710 |
|
|
1666 | return chisq | |
| 1711 |
|
1667 | |||
| 1712 |
|
|
1668 | class WindProfiler(Operation): | |
| 1713 |
|
1669 | |||
| 1714 |
|
|
1670 | __isConfig = False | |
| 1715 |
|
1671 | |||
| 1716 |
|
|
1672 | __initime = None | |
| 1717 |
|
|
1673 | __lastdatatime = None | |
| 1718 |
|
|
1674 | __integrationtime = None | |
| 1719 |
|
1675 | |||
| 1720 |
|
|
1676 | __buffer = None | |
| 1721 |
|
1677 | |||
| 1722 |
|
|
1678 | __dataReady = False | |
| 1723 |
|
1679 | |||
| 1724 |
|
|
1680 | __firstdata = None | |
| 1725 |
|
1681 | |||
| 1726 |
|
|
1682 | n = None | |
| 1727 |
|
1683 | |||
| 1728 |
|
|
1684 | def __init__(self): | |
| 1729 |
|
|
1685 | Operation.__init__(self) | |
| 1730 |
|
1686 | |||
| 1731 |
|
|
1687 | def __calculateCosDir(self, elev, azim): | |
| 1732 |
|
|
1688 | zen = (90 - elev)*numpy.pi/180 | |
| 1733 |
|
|
1689 | azim = azim*numpy.pi/180 | |
| 1734 |
|
|
1690 | cosDirX = numpy.sqrt((1-numpy.cos(zen)**2)/((1+numpy.tan(azim)**2))) | |
| 1735 |
|
|
1691 | cosDirY = numpy.sqrt(1-numpy.cos(zen)**2-cosDirX**2) | |
| 1736 |
|
1692 | |||
| 1737 |
|
|
1693 | signX = numpy.sign(numpy.cos(azim)) | |
| 1738 |
|
|
1694 | signY = numpy.sign(numpy.sin(azim)) | |
| 1739 |
|
1695 | |||
| 1740 |
|
|
1696 | cosDirX = numpy.copysign(cosDirX, signX) | |
| 1741 |
|
|
1697 | cosDirY = numpy.copysign(cosDirY, signY) | |
| 1742 |
|
|
1698 | return cosDirX, cosDirY | |
| 1743 |
|
1699 | |||
| 1744 |
|
|
1700 | def __calculateAngles(self, theta_x, theta_y, azimuth): | |
| 1745 |
|
1701 | |||
| 1746 |
|
|
1702 | dir_cosw = numpy.sqrt(1-theta_x**2-theta_y**2) | |
| 1747 |
|
|
1703 | zenith_arr = numpy.arccos(dir_cosw) | |
| 1748 |
|
|
1704 | azimuth_arr = numpy.arctan2(theta_x,theta_y) + azimuth*math.pi/180 | |
| 1749 |
|
1705 | |||
| 1750 |
|
|
1706 | dir_cosu = numpy.sin(azimuth_arr)*numpy.sin(zenith_arr) | |
| 1751 |
|
|
1707 | dir_cosv = numpy.cos(azimuth_arr)*numpy.sin(zenith_arr) | |
| 1752 |
|
1708 | |||
| 1753 |
|
|
1709 | return azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw | |
| 1754 |
|
1710 | |||
| 1755 |
|
|
1711 | def __calculateMatA(self, dir_cosu, dir_cosv, dir_cosw, horOnly): | |
| 1756 |
|
1712 | |||
| 1757 |
|
|
1713 | # | |
| 1758 |
|
|
1714 | if horOnly: | |
| 1759 |
|
|
1715 | A = numpy.c_[dir_cosu,dir_cosv] | |
| 1760 |
|
|
1716 | else: | |
| 1761 |
|
|
1717 | A = numpy.c_[dir_cosu,dir_cosv,dir_cosw] | |
| 1762 |
|
|
1718 | A = numpy.asmatrix(A) | |
| 1763 |
|
|
1719 | A1 = numpy.linalg.inv(A.transpose()*A)*A.transpose() | |
| 1764 |
|
1720 | |||
| 1765 |
|
|
1721 | return A1 | |
| 1766 |
|
1722 | |||
| 1767 |
|
|
1723 | def __correctValues(self, heiRang, phi, velRadial, SNR): | |
| 1768 |
|
|
1724 | listPhi = phi.tolist() | |
| 1769 |
|
|
1725 | maxid = listPhi.index(max(listPhi)) | |
| 1770 |
|
|
1726 | minid = listPhi.index(min(listPhi)) | |
| 1771 |
|
1727 | |||
| 1772 |
|
|
1728 | rango = list(range(len(phi))) | |
| 1773 |
|
|
1729 | # rango = numpy.delete(rango,maxid) | |
| 1774 |
|
1730 | |||
| 1775 |
|
|
1731 | heiRang1 = heiRang*math.cos(phi[maxid]) | |
| 1776 |
|
|
1732 | heiRangAux = heiRang*math.cos(phi[minid]) | |
| 1777 |
|
|
1733 | indOut = (heiRang1 < heiRangAux[0]).nonzero() | |
| 1778 |
|
|
1734 | heiRang1 = numpy.delete(heiRang1,indOut) | |
| 1779 |
|
1735 | |||
| 1780 |
|
|
1736 | velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) | |
| 1781 |
|
|
1737 | SNR1 = numpy.zeros([len(phi),len(heiRang1)]) | |
| 1782 |
|
1738 | |||
| 1783 |
|
|
1739 | for i in rango: | |
| 1784 |
|
|
1740 | x = heiRang*math.cos(phi[i]) | |
| 1785 |
|
|
1741 | y1 = velRadial[i,:] | |
| 1786 |
|
|
1742 | f1 = interpolate.interp1d(x,y1,kind = 'cubic') | |
| 1787 |
|
1743 | |||
| 1788 |
|
|
1744 | x1 = heiRang1 | |
| 1789 |
|
|
1745 | y11 = f1(x1) | |
| 1790 |
|
1746 | |||
| 1791 |
|
|
1747 | y2 = SNR[i,:] | |
| 1792 |
|
|
1748 | f2 = interpolate.interp1d(x,y2,kind = 'cubic') | |
| 1793 |
|
|
1749 | y21 = f2(x1) | |
| 1794 |
|
1750 | |||
| 1795 |
|
|
1751 | velRadial1[i,:] = y11 | |
| 1796 |
|
|
1752 | SNR1[i,:] = y21 | |
| 1797 |
|
1753 | |||
| 1798 |
|
|
1754 | return heiRang1, velRadial1, SNR1 | |
| 1799 |
|
1755 | |||
| 1800 |
|
|
1756 | def __calculateVelUVW(self, A, velRadial): | |
| 1801 |
|
1757 | |||
| 1802 |
|
|
1758 | #Operacion Matricial | |
| 1803 |
|
|
1759 | # velUVW = numpy.zeros((velRadial.shape[1],3)) | |
| 1804 |
|
|
1760 | # for ind in range(velRadial.shape[1]): | |
| 1805 |
|
|
1761 | # velUVW[ind,:] = numpy.dot(A,velRadial[:,ind]) | |
| 1806 |
|
|
1762 | # velUVW = velUVW.transpose() | |
| 1807 |
|
|
1763 | velUVW = numpy.zeros((A.shape[0],velRadial.shape[1])) | |
| 1808 |
|
|
1764 | velUVW[:,:] = numpy.dot(A,velRadial) | |
| 1809 |
|
1765 | |||
| 1810 |
|
1766 | |||
| 1811 |
|
|
1767 | return velUVW | |
| 1812 |
|
1768 | |||
| 1813 |
|
|
1769 | # def techniqueDBS(self, velRadial0, dirCosx, disrCosy, azimuth, correct, horizontalOnly, heiRang, SNR0): | |
| 1814 |
|
1770 | |||
| 1815 |
|
|
1771 | def techniqueDBS(self, kwargs): | |
| 1816 |
|
|
1772 | """ | |
| 1817 | Function that implements Doppler Beam Swinging (DBS) technique. |
|
1773 | Function that implements Doppler Beam Swinging (DBS) technique. | |
| 1818 |
|
1774 | |||
| 1819 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, |
|
1775 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, | |
| 1820 | Direction correction (if necessary), Ranges and SNR |
|
1776 | Direction correction (if necessary), Ranges and SNR | |
| 1821 |
|
1777 | |||
| 1822 | Output: Winds estimation (Zonal, Meridional and Vertical) |
|
1778 | Output: Winds estimation (Zonal, Meridional and Vertical) | |
| 1823 |
|
1779 | |||
| 1824 | Parameters affected: Winds, height range, SNR |
|
1780 | Parameters affected: Winds, height range, SNR | |
| 1825 | """ |
|
1781 | """ | |
| 1826 |
|
|
1782 | velRadial0 = kwargs['velRadial'] | |
| 1827 |
|
|
1783 | heiRang = kwargs['heightList'] | |
| 1828 |
|
|
1784 | SNR0 = kwargs['SNR'] | |
| 1829 |
|
1785 | |||
| 1830 |
|
|
1786 | if 'dirCosx' in kwargs and 'dirCosy' in kwargs: | |
| 1831 |
|
|
1787 | theta_x = numpy.array(kwargs['dirCosx']) | |
| 1832 |
|
|
1788 | theta_y = numpy.array(kwargs['dirCosy']) | |
| 1833 |
|
|
1789 | else: | |
| 1834 |
|
|
1790 | elev = numpy.array(kwargs['elevation']) | |
| 1835 |
|
|
1791 | azim = numpy.array(kwargs['azimuth']) | |
| 1836 |
|
|
1792 | theta_x, theta_y = self.__calculateCosDir(elev, azim) | |
| 1837 |
|
|
1793 | azimuth = kwargs['correctAzimuth'] | |
| 1838 |
|
|
1794 | if 'horizontalOnly' in kwargs: | |
| 1839 |
|
|
1795 | horizontalOnly = kwargs['horizontalOnly'] | |
| 1840 |
|
|
1796 | else: horizontalOnly = False | |
| 1841 |
|
|
1797 | if 'correctFactor' in kwargs: | |
| 1842 |
|
|
1798 | correctFactor = kwargs['correctFactor'] | |
| 1843 |
|
|
1799 | else: correctFactor = 1 | |
| 1844 |
|
|
1800 | if 'channelList' in kwargs: | |
| 1845 |
|
|
1801 | channelList = kwargs['channelList'] | |
| 1846 |
|
|
1802 | if len(channelList) == 2: | |
| 1847 |
|
|
1803 | horizontalOnly = True | |
| 1848 |
|
|
1804 | arrayChannel = numpy.array(channelList) | |
| 1849 |
|
|
1805 | param = param[arrayChannel,:,:] | |
| 1850 |
|
|
1806 | theta_x = theta_x[arrayChannel] | |
| 1851 |
|
|
1807 | theta_y = theta_y[arrayChannel] | |
| 1852 |
|
1808 | |||
| 1853 |
|
|
1809 | azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth) | |
| 1854 |
|
|
1810 | heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, zenith_arr, correctFactor*velRadial0, SNR0) | |
| 1855 |
|
|
1811 | A = self.__calculateMatA(dir_cosu, dir_cosv, dir_cosw, horizontalOnly) | |
| 1856 |
|
1812 | |||
| 1857 |
|
|
1813 | #Calculo de Componentes de la velocidad con DBS | |
| 1858 |
|
|
1814 | winds = self.__calculateVelUVW(A,velRadial1) | |
| 1859 |
|
1815 | |||
| 1860 |
|
|
1816 | return winds, heiRang1, SNR1 | |
| 1861 |
|
1817 | |||
| 1862 |
|
|
1818 | def __calculateDistance(self, posx, posy, pairs_ccf, azimuth = None): | |
| 1863 |
|
1819 | |||
| 1864 |
|
|
1820 | nPairs = len(pairs_ccf) | |
| 1865 |
|
|
1821 | posx = numpy.asarray(posx) | |
| 1866 |
|
|
1822 | posy = numpy.asarray(posy) | |
| 1867 |
|
1823 | |||
| 1868 |
|
|
1824 | #Rotacion Inversa para alinear con el azimuth | |
| 1869 |
|
|
1825 | if azimuth!= None: | |
| 1870 |
|
|
1826 | azimuth = azimuth*math.pi/180 | |
| 1871 |
|
|
1827 | posx1 = posx*math.cos(azimuth) + posy*math.sin(azimuth) | |
| 1872 |
|
|
1828 | posy1 = -posx*math.sin(azimuth) + posy*math.cos(azimuth) | |
| 1873 |
|
|
1829 | else: | |
| 1874 |
|
|
1830 | posx1 = posx | |
| 1875 |
|
|
1831 | posy1 = posy | |
| 1876 |
|
1832 | |||
| 1877 |
|
|
1833 | #Calculo de Distancias | |
| 1878 |
|
|
1834 | distx = numpy.zeros(nPairs) | |
| 1879 |
|
|
1835 | disty = numpy.zeros(nPairs) | |
| 1880 |
|
|
1836 | dist = numpy.zeros(nPairs) | |
| 1881 |
|
|
1837 | ang = numpy.zeros(nPairs) | |
| 1882 |
|
1838 | |||
| 1883 |
|
|
1839 | for i in range(nPairs): | |
| 1884 |
|
|
1840 | distx[i] = posx1[pairs_ccf[i][1]] - posx1[pairs_ccf[i][0]] | |
| 1885 |
|
|
1841 | disty[i] = posy1[pairs_ccf[i][1]] - posy1[pairs_ccf[i][0]] | |
| 1886 |
|
|
1842 | dist[i] = numpy.sqrt(distx[i]**2 + disty[i]**2) | |
| 1887 |
|
|
1843 | ang[i] = numpy.arctan2(disty[i],distx[i]) | |
| 1888 |
|
1844 | |||
| 1889 |
|
|
1845 | return distx, disty, dist, ang | |
| 1890 |
|
|
1846 | #Calculo de Matrices | |
| 1891 |
|
|
1847 | # nPairs = len(pairs) | |
| 1892 |
|
|
1848 | # ang1 = numpy.zeros((nPairs, 2, 1)) | |
| 1893 |
|
|
1849 | # dist1 = numpy.zeros((nPairs, 2, 1)) | |
| 1894 |
|
|
1850 | # | |
| 1895 |
|
|
1851 | # for j in range(nPairs): | |
| 1896 |
|
|
1852 | # dist1[j,0,0] = dist[pairs[j][0]] | |
| 1897 |
|
|
1853 | # dist1[j,1,0] = dist[pairs[j][1]] | |
| 1898 |
|
|
1854 | # ang1[j,0,0] = ang[pairs[j][0]] | |
| 1899 |
|
|
1855 | # ang1[j,1,0] = ang[pairs[j][1]] | |
| 1900 |
|
|
1856 | # | |
| 1901 |
|
|
1857 | # return distx,disty, dist1,ang1 | |
| 1902 |
|
1858 | |||
| 1903 |
|
1859 | |||
| 1904 |
|
|
1860 | def __calculateVelVer(self, phase, lagTRange, _lambda): | |
| 1905 |
|
1861 | |||
| 1906 |
|
|
1862 | Ts = lagTRange[1] - lagTRange[0] | |
| 1907 |
|
|
1863 | velW = -_lambda*phase/(4*math.pi*Ts) | |
| 1908 |
|
1864 | |||
| 1909 |
|
|
1865 | return velW | |
| 1910 |
|
1866 | |||
| 1911 |
|
|
1867 | def __calculateVelHorDir(self, dist, tau1, tau2, ang): | |
| 1912 |
|
|
1868 | nPairs = tau1.shape[0] | |
| 1913 |
|
|
1869 | nHeights = tau1.shape[1] | |
| 1914 |
|
|
1870 | vel = numpy.zeros((nPairs,3,nHeights)) | |
| 1915 |
|
|
1871 | dist1 = numpy.reshape(dist, (dist.size,1)) | |
| 1916 |
|
1872 | |||
| 1917 |
|
|
1873 | angCos = numpy.cos(ang) | |
| 1918 |
|
|
1874 | angSin = numpy.sin(ang) | |
| 1919 |
|
1875 | |||
| 1920 |
|
|
1876 | vel0 = dist1*tau1/(2*tau2**2) | |
| 1921 |
|
|
1877 | vel[:,0,:] = (vel0*angCos).sum(axis = 1) | |
| 1922 |
|
|
1878 | vel[:,1,:] = (vel0*angSin).sum(axis = 1) | |
| 1923 |
|
1879 | |||
| 1924 |
|
|
1880 | ind = numpy.where(numpy.isinf(vel)) | |
| 1925 |
|
|
1881 | vel[ind] = numpy.nan | |
| 1926 |
|
1882 | |||
| 1927 |
|
|
1883 | return vel | |
| 1928 |
|
1884 | |||
| 1929 |
|
|
1885 | # def __getPairsAutoCorr(self, pairsList, nChannels): | |
| 1930 |
|
|
1886 | # | |
| 1931 |
|
|
1887 | # pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan | |
| 1932 |
|
|
1888 | # | |
| 1933 |
|
|
1889 | # for l in range(len(pairsList)): | |
| 1934 |
|
|
1890 | # firstChannel = pairsList[l][0] | |
| 1935 |
|
|
1891 | # secondChannel = pairsList[l][1] | |
| 1936 |
|
|
1892 | # | |
| 1937 |
|
|
1893 | # #Obteniendo pares de Autocorrelacion | |
| 1938 |
|
|
1894 | # if firstChannel == secondChannel: | |
| 1939 |
|
|
1895 | # pairsAutoCorr[firstChannel] = int(l) | |
| 1940 |
|
|
1896 | # | |
| 1941 |
|
|
1897 | # pairsAutoCorr = pairsAutoCorr.astype(int) | |
| 1942 |
|
|
1898 | # | |
| 1943 |
|
|
1899 | # pairsCrossCorr = range(len(pairsList)) | |
| 1944 |
|
|
1900 | # pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr) | |
| 1945 |
|
|
1901 | # | |
| 1946 |
|
|
1902 | # return pairsAutoCorr, pairsCrossCorr | |
| 1947 |
|
1903 | |||
| 1948 |
|
|
1904 | # def techniqueSA(self, pairsSelected, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, lagTRange, correctFactor): | |
| 1949 |
|
|
1905 | def techniqueSA(self, kwargs): | |
| 1950 |
|
1906 | |||
| 1951 |
|
|
1907 | """ | |
| 1952 | Function that implements Spaced Antenna (SA) technique. |
|
1908 | Function that implements Spaced Antenna (SA) technique. | |
| 1953 |
|
1909 | |||
| 1954 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, |
|
1910 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, | |
| 1955 | Direction correction (if necessary), Ranges and SNR |
|
1911 | Direction correction (if necessary), Ranges and SNR | |
| 1956 |
|
1912 | |||
| 1957 | Output: Winds estimation (Zonal, Meridional and Vertical) |
|
1913 | Output: Winds estimation (Zonal, Meridional and Vertical) | |
| 1958 |
|
1914 | |||
| 1959 | Parameters affected: Winds |
|
1915 | Parameters affected: Winds | |
| 1960 | """ |
|
1916 | """ | |
| 1961 |
|
|
1917 | position_x = kwargs['positionX'] | |
| 1962 |
|
|
1918 | position_y = kwargs['positionY'] | |
| 1963 |
|
|
1919 | azimuth = kwargs['azimuth'] | |
| 1964 |
|
1920 | |||
| 1965 |
|
|
1921 | if 'correctFactor' in kwargs: | |
| 1966 |
|
|
1922 | correctFactor = kwargs['correctFactor'] | |
| 1967 |
|
|
1923 | else: | |
| 1968 |
|
|
1924 | correctFactor = 1 | |
| 1969 |
|
1925 | |||
| 1970 |
|
|
1926 | groupList = kwargs['groupList'] | |
| 1971 |
|
|
1927 | pairs_ccf = groupList[1] | |
| 1972 |
|
|
1928 | tau = kwargs['tau'] | |
| 1973 |
|
|
1929 | _lambda = kwargs['_lambda'] | |
| 1974 |
|
1930 | |||
| 1975 |
|
|
1931 | #Cross Correlation pairs obtained | |
| 1976 |
|
|
1932 | # pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairssList, nChannels) | |
| 1977 |
|
|
1933 | # pairsArray = numpy.array(pairsList)[pairsCrossCorr] | |
| 1978 |
|
|
1934 | # pairsSelArray = numpy.array(pairsSelected) | |
| 1979 |
|
|
1935 | # pairs = [] | |
| 1980 |
|
|
1936 | # | |
| 1981 |
|
|
1937 | # #Wind estimation pairs obtained | |
| 1982 |
|
|
1938 | # for i in range(pairsSelArray.shape[0]/2): | |
| 1983 |
|
|
1939 | # ind1 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i], axis = 1))[0][0] | |
| 1984 |
|
|
1940 | # ind2 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i + 1], axis = 1))[0][0] | |
| 1985 |
|
|
1941 | # pairs.append((ind1,ind2)) | |
| 1986 |
|
1942 | |||
| 1987 |
|
|
1943 | indtau = tau.shape[0]/2 | |
| 1988 |
|
|
1944 | tau1 = tau[:indtau,:] | |
| 1989 |
|
|
1945 | tau2 = tau[indtau:-1,:] | |
| 1990 |
|
|
1946 | # tau1 = tau1[pairs,:] | |
| 1991 |
|
|
1947 | # tau2 = tau2[pairs,:] | |
| 1992 |
|
|
1948 | phase1 = tau[-1,:] | |
| 1993 |
|
1949 | |||
| 1994 |
|
|
1950 | #--------------------------------------------------------------------- | |
| 1995 |
|
|
1951 | #Metodo Directo | |
| 1996 |
|
|
1952 | distx, disty, dist, ang = self.__calculateDistance(position_x, position_y, pairs_ccf,azimuth) | |
| 1997 |
|
|
1953 | winds = self.__calculateVelHorDir(dist, tau1, tau2, ang) | |
| 1998 |
|
|
1954 | winds = stats.nanmean(winds, axis=0) | |
| 1999 |
|
|
1955 | #--------------------------------------------------------------------- | |
| 2000 |
|
|
1956 | #Metodo General | |
| 2001 |
|
|
1957 | # distx, disty, dist = self.calculateDistance(position_x,position_y,pairsCrossCorr, pairsList, azimuth) | |
| 2002 |
|
|
1958 | # #Calculo Coeficientes de Funcion de Correlacion | |
| 2003 |
|
|
1959 | # F,G,A,B,H = self.calculateCoef(tau1,tau2,distx,disty,n) | |
| 2004 |
|
|
1960 | # #Calculo de Velocidades | |
| 2005 |
|
|
1961 | # winds = self.calculateVelUV(F,G,A,B,H) | |
| 2006 |
|
1962 | |||
| 2007 |
|
|
1963 | #--------------------------------------------------------------------- | |
| 2008 |
|
|
1964 | winds[2,:] = self.__calculateVelVer(phase1, lagTRange, _lambda) | |
| 2009 |
|
|
1965 | winds = correctFactor*winds | |
| 2010 |
|
|
1966 | return winds | |
| 2011 |
|
1967 | |||
| 2012 |
|
|
1968 | def __checkTime(self, currentTime, paramInterval, outputInterval): | |
| 2013 |
|
1969 | |||
| 2014 |
|
|
1970 | dataTime = currentTime + paramInterval | |
| 2015 |
|
|
1971 | deltaTime = dataTime - self.__initime | |
| 2016 |
|
1972 | |||
| 2017 |
|
|
1973 | if deltaTime >= outputInterval or deltaTime < 0: | |
| 2018 |
|
|
1974 | self.__dataReady = True | |
| 2019 |
|
|
1975 | return | |
| 2020 |
|
1976 | |||
| 2021 |
|
|
1977 | def techniqueMeteors(self, arrayMeteor, meteorThresh, heightMin, heightMax): | |
| 2022 |
|
|
1978 | ''' | |
| 2023 | Function that implements winds estimation technique with detected meteors. |
|
1979 | Function that implements winds estimation technique with detected meteors. | |
| 2024 |
|
1980 | |||
| 2025 | Input: Detected meteors, Minimum meteor quantity to wind estimation |
|
1981 | Input: Detected meteors, Minimum meteor quantity to wind estimation | |
| 2026 |
|
1982 | |||
| 2027 | Output: Winds estimation (Zonal and Meridional) |
|
1983 | Output: Winds estimation (Zonal and Meridional) | |
| 2028 |
|
1984 | |||
| 2029 | Parameters affected: Winds |
|
1985 | Parameters affected: Winds | |
| 2030 | ''' |
|
1986 | ''' | |
| 2031 |
|
|
1987 | #Settings | |
| 2032 |
|
|
1988 | nInt = (heightMax - heightMin)/2 | |
| 2033 |
|
|
1989 | nInt = int(nInt) | |
| 2034 |
|
|
1990 | winds = numpy.zeros((2,nInt))*numpy.nan | |
| 2035 |
|
1991 | |||
| 2036 |
|
|
1992 | #Filter errors | |
| 2037 |
|
|
1993 | error = numpy.where(arrayMeteor[:,-1] == 0)[0] | |
| 2038 |
|
|
1994 | finalMeteor = arrayMeteor[error,:] | |
| 2039 |
|
1995 | |||
| 2040 |
|
|
1996 | #Meteor Histogram | |
| 2041 |
|
|
1997 | finalHeights = finalMeteor[:,2] | |
| 2042 |
|
|
1998 | hist = numpy.histogram(finalHeights, bins = nInt, range = (heightMin,heightMax)) | |
| 2043 |
|
|
1999 | nMeteorsPerI = hist[0] | |
| 2044 |
|
|
2000 | heightPerI = hist[1] | |
| 2045 |
|
2001 | |||
| 2046 |
|
|
2002 | #Sort of meteors | |
| 2047 |
|
|
2003 | indSort = finalHeights.argsort() | |
| 2048 |
|
|
2004 | finalMeteor2 = finalMeteor[indSort,:] | |
| 2049 |
|
2005 | |||
| 2050 |
|
|
2006 | # Calculating winds | |
| 2051 |
|
|
2007 | ind1 = 0 | |
| 2052 |
|
|
2008 | ind2 = 0 | |
| 2053 |
|
2009 | |||
| 2054 |
|
|
2010 | for i in range(nInt): | |
| 2055 |
|
|
2011 | nMet = nMeteorsPerI[i] | |
| 2056 |
|
|
2012 | ind1 = ind2 | |
| 2057 |
|
|
2013 | ind2 = ind1 + nMet | |
| 2058 |
|
2014 | |||
| 2059 |
|
|
2015 | meteorAux = finalMeteor2[ind1:ind2,:] | |
| 2060 |
|
2016 | |||
| 2061 |
|
|
2017 | if meteorAux.shape[0] >= meteorThresh: | |
| 2062 |
|
|
2018 | vel = meteorAux[:, 6] | |
| 2063 |
|
|
2019 | zen = meteorAux[:, 4]*numpy.pi/180 | |
| 2064 |
|
|
2020 | azim = meteorAux[:, 3]*numpy.pi/180 | |
| 2065 |
|
2021 | |||
| 2066 |
|
|
2022 | n = numpy.cos(zen) | |
| 2067 |
|
|
2023 | # m = (1 - n**2)/(1 - numpy.tan(azim)**2) | |
| 2068 |
|
|
2024 | # l = m*numpy.tan(azim) | |
| 2069 |
|
|
2025 | l = numpy.sin(zen)*numpy.sin(azim) | |
| 2070 |
|
|
2026 | m = numpy.sin(zen)*numpy.cos(azim) | |
| 2071 |
|
2027 | |||
| 2072 |
|
|
2028 | A = numpy.vstack((l, m)).transpose() | |
| 2073 |
|
|
2029 | A1 = numpy.dot(numpy.linalg.inv( numpy.dot(A.transpose(),A) ),A.transpose()) | |
| 2074 |
|
|
2030 | windsAux = numpy.dot(A1, vel) | |
| 2075 |
|
2031 | |||
| 2076 |
|
|
2032 | winds[0,i] = windsAux[0] | |
| 2077 |
|
|
2033 | winds[1,i] = windsAux[1] | |
| 2078 |
|
2034 | |||
| 2079 |
|
|
2035 | return winds, heightPerI[:-1] | |
| 2080 |
|
2036 | |||
| 2081 |
|
|
2037 | def techniqueNSM_SA(self, **kwargs): | |
| 2082 |
|
|
2038 | metArray = kwargs['metArray'] | |
| 2083 |
|
|
2039 | heightList = kwargs['heightList'] | |
| 2084 |
|
|
2040 | timeList = kwargs['timeList'] | |
| 2085 |
|
2041 | |||
| 2086 |
|
|
2042 | rx_location = kwargs['rx_location'] | |
| 2087 |
|
|
2043 | groupList = kwargs['groupList'] | |
| 2088 |
|
|
2044 | azimuth = kwargs['azimuth'] | |
| 2089 |
|
|
2045 | dfactor = kwargs['dfactor'] | |
| 2090 |
|
|
2046 | k = kwargs['k'] | |
| 2091 |
|
2047 | |||
| 2092 |
|
|
2048 | azimuth1, dist = self.__calculateAzimuth1(rx_location, groupList, azimuth) | |
| 2093 |
|
|
2049 | d = dist*dfactor | |
| 2094 |
|
|
2050 | #Phase calculation | |
| 2095 |
|
|
2051 | metArray1 = self.__getPhaseSlope(metArray, heightList, timeList) | |
| 2096 |
|
2052 | |||
| 2097 |
|
|
2053 | metArray1[:,-2] = metArray1[:,-2]*metArray1[:,2]*1000/(k*d[metArray1[:,1].astype(int)]) #angles into velocities | |
| 2098 |
|
2054 | |||
| 2099 |
|
|
2055 | velEst = numpy.zeros((heightList.size,2))*numpy.nan | |
| 2100 |
|
|
2056 | azimuth1 = azimuth1*numpy.pi/180 | |
| 2101 |
|
2057 | |||
| 2102 |
|
|
2058 | for i in range(heightList.size): | |
| 2103 |
|
|
2059 | h = heightList[i] | |
| 2104 |
|
|
2060 | indH = numpy.where((metArray1[:,2] == h)&(numpy.abs(metArray1[:,-2]) < 100))[0] | |
| 2105 |
|
|
2061 | metHeight = metArray1[indH,:] | |
| 2106 |
|
|
2062 | if metHeight.shape[0] >= 2: | |
| 2107 |
|
|
2063 | velAux = numpy.asmatrix(metHeight[:,-2]).T #Radial Velocities | |
| 2108 |
|
|
2064 | iazim = metHeight[:,1].astype(int) | |
| 2109 |
|
|
2065 | azimAux = numpy.asmatrix(azimuth1[iazim]).T #Azimuths | |
| 2110 |
|
|
2066 | A = numpy.hstack((numpy.cos(azimAux),numpy.sin(azimAux))) | |
| 2111 |
|
|
2067 | A = numpy.asmatrix(A) | |
| 2112 |
|
|
2068 | A1 = numpy.linalg.pinv(A.transpose()*A)*A.transpose() | |
| 2113 |
|
|
2069 | velHor = numpy.dot(A1,velAux) | |
| 2114 |
|
2070 | |||
| 2115 |
|
|
2071 | velEst[i,:] = numpy.squeeze(velHor) | |
| 2116 |
|
|
2072 | return velEst | |
| 2117 |
|
2073 | |||
| 2118 |
|
|
2074 | def __getPhaseSlope(self, metArray, heightList, timeList): | |
| 2119 |
|
|
2075 | meteorList = [] | |
| 2120 |
|
|
2076 | #utctime sec1 height SNR velRad ph0 ph1 ph2 coh0 coh1 coh2 | |
| 2121 |
|
|
2077 | #Putting back together the meteor matrix | |
| 2122 |
|
|
2078 | utctime = metArray[:,0] | |
| 2123 |
|
|
2079 | uniqueTime = numpy.unique(utctime) | |
| 2124 |
|
2080 | |||
| 2125 |
|
|
2081 | phaseDerThresh = 0.5 | |
| 2126 |
|
|
2082 | ippSeconds = timeList[1] - timeList[0] | |
| 2127 |
|
|
2083 | sec = numpy.where(timeList>1)[0][0] | |
| 2128 |
|
|
2084 | nPairs = metArray.shape[1] - 6 | |
| 2129 |
|
|
2085 | nHeights = len(heightList) | |
| 2130 |
|
2086 | |||
| 2131 |
|
|
2087 | for t in uniqueTime: | |
| 2132 |
|
|
2088 | metArray1 = metArray[utctime==t,:] | |
| 2133 |
|
|
2089 | # phaseDerThresh = numpy.pi/4 #reducir Phase thresh | |
| 2134 |
|
|
2090 | tmet = metArray1[:,1].astype(int) | |
| 2135 |
|
|
2091 | hmet = metArray1[:,2].astype(int) | |
| 2136 |
|
2092 | |||
| 2137 |
|
|
2093 | metPhase = numpy.zeros((nPairs, heightList.size, timeList.size - 1)) | |
| 2138 |
|
|
2094 | metPhase[:,:] = numpy.nan | |
| 2139 |
|
|
2095 | metPhase[:,hmet,tmet] = metArray1[:,6:].T | |
| 2140 |
|
2096 | |||
| 2141 |
|
|
2097 | #Delete short trails | |
| 2142 |
|
|
2098 | metBool = ~numpy.isnan(metPhase[0,:,:]) | |
| 2143 |
|
|
2099 | heightVect = numpy.sum(metBool, axis = 1) | |
| 2144 |
|
|
2100 | metBool[heightVect<sec,:] = False | |
| 2145 |
|
|
2101 | metPhase[:,heightVect<sec,:] = numpy.nan | |
| 2146 |
|
2102 | |||
| 2147 |
|
|
2103 | #Derivative | |
| 2148 |
|
|
2104 | metDer = numpy.abs(metPhase[:,:,1:] - metPhase[:,:,:-1]) | |
| 2149 |
|
|
2105 | phDerAux = numpy.dstack((numpy.full((nPairs,nHeights,1), False, dtype=bool),metDer > phaseDerThresh)) | |
| 2150 |
|
|
2106 | metPhase[phDerAux] = numpy.nan | |
| 2151 |
|
2107 | |||
| 2152 |
|
|
2108 | #--------------------------METEOR DETECTION ----------------------------------------- | |
| 2153 |
|
|
2109 | indMet = numpy.where(numpy.any(metBool,axis=1))[0] | |
| 2154 |
|
2110 | |||
| 2155 |
|
|
2111 | for p in numpy.arange(nPairs): | |
| 2156 |
|
|
2112 | phase = metPhase[p,:,:] | |
| 2157 |
|
|
2113 | phDer = metDer[p,:,:] | |
| 2158 |
|
2114 | |||
| 2159 |
|
|
2115 | for h in indMet: | |
| 2160 |
|
|
2116 | height = heightList[h] | |
| 2161 |
|
|
2117 | phase1 = phase[h,:] #82 | |
| 2162 |
|
|
2118 | phDer1 = phDer[h,:] | |
| 2163 |
|
2119 | |||
| 2164 |
|
|
2120 | phase1[~numpy.isnan(phase1)] = numpy.unwrap(phase1[~numpy.isnan(phase1)]) #Unwrap | |
| 2165 |
|
2121 | |||
| 2166 |
|
|
2122 | indValid = numpy.where(~numpy.isnan(phase1))[0] | |
| 2167 |
|
|
2123 | initMet = indValid[0] | |
| 2168 |
|
|
2124 | endMet = 0 | |
| 2169 |
|
2125 | |||
| 2170 |
|
|
2126 | for i in range(len(indValid)-1): | |
| 2171 |
|
2127 | |||
| 2172 |
|
|
2128 | #Time difference | |
| 2173 |
|
|
2129 | inow = indValid[i] | |
| 2174 |
|
|
2130 | inext = indValid[i+1] | |
| 2175 |
|
|
2131 | idiff = inext - inow | |
| 2176 |
|
|
2132 | #Phase difference | |
| 2177 |
|
|
2133 | phDiff = numpy.abs(phase1[inext] - phase1[inow]) | |
| 2178 |
|
2134 | |||
| 2179 |
|
|
2135 | if idiff>sec or phDiff>numpy.pi/4 or inext==indValid[-1]: #End of Meteor | |
| 2180 |
|
|
2136 | sizeTrail = inow - initMet + 1 | |
| 2181 |
|
|
2137 | if sizeTrail>3*sec: #Too short meteors | |
| 2182 |
|
|
2138 | x = numpy.arange(initMet,inow+1)*ippSeconds | |
| 2183 |
|
|
2139 | y = phase1[initMet:inow+1] | |
| 2184 |
|
|
2140 | ynnan = ~numpy.isnan(y) | |
| 2185 |
|
|
2141 | x = x[ynnan] | |
| 2186 |
|
|
2142 | y = y[ynnan] | |
| 2187 |
|
|
2143 | slope, intercept, r_value, p_value, std_err = stats.linregress(x,y) | |
| 2188 |
|
|
2144 | ylin = x*slope + intercept | |
| 2189 |
|
|
2145 | rsq = r_value**2 | |
| 2190 |
|
|
2146 | if rsq > 0.5: | |
| 2191 |
|
|
2147 | vel = slope#*height*1000/(k*d) | |
| 2192 |
|
|
2148 | estAux = numpy.array([utctime,p,height, vel, rsq]) | |
| 2193 |
|
|
2149 | meteorList.append(estAux) | |
| 2194 |
|
|
2150 | initMet = inext | |
| 2195 |
|
|
2151 | metArray2 = numpy.array(meteorList) | |
| 2196 |
|
2152 | |||
| 2197 |
|
|
2153 | return metArray2 | |
| 2198 |
|
2154 | |||
| 2199 |
|
|
2155 | def __calculateAzimuth1(self, rx_location, pairslist, azimuth0): | |
| 2200 |
|
2156 | |||
| 2201 |
|
|
2157 | azimuth1 = numpy.zeros(len(pairslist)) | |
| 2202 |
|
|
2158 | dist = numpy.zeros(len(pairslist)) | |
| 2203 |
|
2159 | |||
| 2204 |
|
|
2160 | for i in range(len(rx_location)): | |
| 2205 |
|
|
2161 | ch0 = pairslist[i][0] | |
| 2206 |
|
|
2162 | ch1 = pairslist[i][1] | |
| 2207 |
|
2163 | |||
| 2208 |
|
|
2164 | diffX = rx_location[ch0][0] - rx_location[ch1][0] | |
| 2209 |
|
|
2165 | diffY = rx_location[ch0][1] - rx_location[ch1][1] | |
| 2210 |
|
|
2166 | azimuth1[i] = numpy.arctan2(diffY,diffX)*180/numpy.pi | |
| 2211 |
|
|
2167 | dist[i] = numpy.sqrt(diffX**2 + diffY**2) | |
| 2212 |
|
2168 | |||
| 2213 |
|
|
2169 | azimuth1 -= azimuth0 | |
| 2214 |
|
|
2170 | return azimuth1, dist | |
| 2215 |
|
2171 | |||
| 2216 |
|
|
2172 | def techniqueNSM_DBS(self, **kwargs): | |
| 2217 |
|
|
2173 | metArray = kwargs['metArray'] | |
| 2218 |
|
|
2174 | heightList = kwargs['heightList'] | |
| 2219 |
|
|
2175 | timeList = kwargs['timeList'] | |
| 2220 |
|
|
2176 | azimuth = kwargs['azimuth'] | |
| 2221 |
|
|
2177 | theta_x = numpy.array(kwargs['theta_x']) | |
| 2222 |
|
|
2178 | theta_y = numpy.array(kwargs['theta_y']) | |
| 2223 |
|
2179 | |||
| 2224 |
|
|
2180 | utctime = metArray[:,0] | |
| 2225 |
|
|
2181 | cmet = metArray[:,1].astype(int) | |
| 2226 |
|
|
2182 | hmet = metArray[:,3].astype(int) | |
| 2227 |
|
|
2183 | SNRmet = metArray[:,4] | |
| 2228 |
|
|
2184 | vmet = metArray[:,5] | |
| 2229 |
|
|
2185 | spcmet = metArray[:,6] | |
| 2230 |
|
2186 | |||
| 2231 |
|
|
2187 | nChan = numpy.max(cmet) + 1 | |
| 2232 |
|
|
2188 | nHeights = len(heightList) | |
| 2233 |
|
2189 | |||
| 2234 |
|
|
2190 | azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth) | |
| 2235 |
|
|
2191 | hmet = heightList[hmet] | |
| 2236 |
|
|
2192 | h1met = hmet*numpy.cos(zenith_arr[cmet]) #Corrected heights | |
| 2237 |
|
2193 | |||
| 2238 |
|
|
2194 | velEst = numpy.zeros((heightList.size,2))*numpy.nan | |
| 2239 |
|
2195 | |||
| 2240 |
|
|
2196 | for i in range(nHeights - 1): | |
| 2241 |
|
|
2197 | hmin = heightList[i] | |
| 2242 |
|
|
2198 | hmax = heightList[i + 1] | |
| 2243 |
|
2199 | |||
| 2244 |
|
|
2200 | thisH = (h1met>=hmin) & (h1met<hmax) & (cmet!=2) & (SNRmet>8) & (vmet<50) & (spcmet<10) | |
| 2245 |
|
|
2201 | indthisH = numpy.where(thisH) | |
| 2246 |
|
2202 | |||
| 2247 |
|
|
2203 | if numpy.size(indthisH) > 3: | |
| 2248 |
|
2204 | |||
| 2249 |
|
|
2205 | vel_aux = vmet[thisH] | |
| 2250 |
|
|
2206 | chan_aux = cmet[thisH] | |
| 2251 |
|
|
2207 | cosu_aux = dir_cosu[chan_aux] | |
| 2252 |
|
|
2208 | cosv_aux = dir_cosv[chan_aux] | |
| 2253 |
|
|
2209 | cosw_aux = dir_cosw[chan_aux] | |
| 2254 |
|
2210 | |||
| 2255 |
|
|
2211 | nch = numpy.size(numpy.unique(chan_aux)) | |
| 2256 |
|
|
2212 | if nch > 1: | |
| 2257 |
|
|
2213 | A = self.__calculateMatA(cosu_aux, cosv_aux, cosw_aux, True) | |
| 2258 |
|
|
2214 | velEst[i,:] = numpy.dot(A,vel_aux) | |
| 2259 |
|
2215 | |||
| 2260 |
|
|
2216 | return velEst | |
| 2261 |
|
2217 | |||
| 2262 |
|
|
2218 | def run(self, dataOut, technique, nHours=1, hmin=70, hmax=110, **kwargs): | |
| 2263 |
|
2219 | |||
| 2264 |
|
|
2220 | param = dataOut.data_param | |
| 2265 |
|
|
2221 | if dataOut.abscissaList != None: | |
| 2266 |
|
|
2222 | absc = dataOut.abscissaList[:-1] | |
| 2267 |
|
|
2223 | # noise = dataOut.noise | |
| 2268 |
|
|
2224 | heightList = dataOut.heightList | |
| 2269 |
|
|
2225 | SNR = dataOut.data_SNR | |
| 2270 |
|
2226 | |||
| 2271 |
|
|
2227 | if technique == 'DBS': | |
| 2272 |
|
2228 | |||
| 2273 |
|
|
2229 | kwargs['velRadial'] = param[:,1,:] #Radial velocity | |
| 2274 |
|
|
2230 | kwargs['heightList'] = heightList | |
| 2275 |
|
|
2231 | kwargs['SNR'] = SNR | |
| 2276 |
|
2232 | |||
| 2277 |
|
|
2233 | dataOut.data_output, dataOut.heightList, dataOut.data_SNR = self.techniqueDBS(kwargs) #DBS Function | |
| 2278 |
|
|
2234 | dataOut.utctimeInit = dataOut.utctime | |
| 2279 |
|
|
2235 | dataOut.outputInterval = dataOut.paramInterval | |
| 2280 |
|
2236 | |||
| 2281 |
|
|
2237 | elif technique == 'SA': | |
| 2282 |
|
2238 | |||
| 2283 |
|
|
2239 | #Parameters | |
| 2284 |
|
|
2240 | # position_x = kwargs['positionX'] | |
| 2285 |
|
|
2241 | # position_y = kwargs['positionY'] | |
| 2286 |
|
|
2242 | # azimuth = kwargs['azimuth'] | |
| 2287 |
|
|
2243 | # | |
| 2288 |
|
|
2244 | # if kwargs.has_key('crosspairsList'): | |
| 2289 |
|
|
2245 | # pairs = kwargs['crosspairsList'] | |
| 2290 |
|
|
2246 | # else: | |
| 2291 |
|
|
2247 | # pairs = None | |
| 2292 |
|
|
2248 | # | |
| 2293 |
|
|
2249 | # if kwargs.has_key('correctFactor'): | |
| 2294 |
|
|
2250 | # correctFactor = kwargs['correctFactor'] | |
| 2295 |
|
|
2251 | # else: | |
| 2296 |
|
|
2252 | # correctFactor = 1 | |
| 2297 |
|
2253 | |||
| 2298 |
|
|
2254 | # tau = dataOut.data_param | |
| 2299 |
|
|
2255 | # _lambda = dataOut.C/dataOut.frequency | |
| 2300 |
|
|
2256 | # pairsList = dataOut.groupList | |
| 2301 |
|
|
2257 | # nChannels = dataOut.nChannels | |
| 2302 |
|
2258 | |||
| 2303 |
|
|
2259 | kwargs['groupList'] = dataOut.groupList | |
| 2304 |
|
|
2260 | kwargs['tau'] = dataOut.data_param | |
| 2305 |
|
|
2261 | kwargs['_lambda'] = dataOut.C/dataOut.frequency | |
| 2306 |
|
|
2262 | # dataOut.data_output = self.techniqueSA(pairs, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, absc, correctFactor) | |
| 2307 |
|
|
2263 | dataOut.data_output = self.techniqueSA(kwargs) | |
| 2308 |
|
|
2264 | dataOut.utctimeInit = dataOut.utctime | |
| 2309 |
|
|
2265 | dataOut.outputInterval = dataOut.timeInterval | |
| 2310 |
|
2266 | |||
| 2311 |
|
|
2267 | elif technique == 'Meteors': | |
| 2312 |
|
|
2268 | dataOut.flagNoData = True | |
| 2313 |
|
|
2269 | self.__dataReady = False | |
| 2314 |
|
2270 | |||
| 2315 |
|
|
2271 | if 'nHours' in kwargs: | |
| 2316 |
|
|
2272 | nHours = kwargs['nHours'] | |
| 2317 |
|
|
2273 | else: | |
| 2318 |
|
|
2274 | nHours = 1 | |
| 2319 |
|
2275 | |||
| 2320 |
|
|
2276 | if 'meteorsPerBin' in kwargs: | |
| 2321 |
|
|
2277 | meteorThresh = kwargs['meteorsPerBin'] | |
| 2322 |
|
|
2278 | else: | |
| 2323 |
|
|
2279 | meteorThresh = 6 | |
| 2324 |
|
2280 | |||
| 2325 |
|
|
2281 | if 'hmin' in kwargs: | |
| 2326 |
|
|
2282 | hmin = kwargs['hmin'] | |
| 2327 |
|
|
2283 | else: hmin = 70 | |
| 2328 |
|
|
2284 | if 'hmax' in kwargs: | |
| 2329 |
|
|
2285 | hmax = kwargs['hmax'] | |
| 2330 |
|
|
2286 | else: hmax = 110 | |
| 2331 |
|
2287 | |||
| 2332 |
|
|
2288 | dataOut.outputInterval = nHours*3600 | |
| 2333 |
|
2289 | |||
| 2334 |
|
|
2290 | if self.__isConfig == False: | |
| 2335 |
|
|
2291 | # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) | |
| 2336 |
|
|
2292 | #Get Initial LTC time | |
| 2337 |
|
|
2293 | self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) | |
| 2338 |
|
|
2294 | self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() | |
| 2339 |
|
2295 | |||
| 2340 |
|
|
2296 | self.__isConfig = True | |
| 2341 |
|
2297 | |||
| 2342 |
|
|
2298 | if self.__buffer is None: | |
| 2343 |
|
|
2299 | self.__buffer = dataOut.data_param | |
| 2344 |
|
|
2300 | self.__firstdata = copy.copy(dataOut) | |
| 2345 |
|
2301 | |||
| 2346 |
|
|
2302 | else: | |
| 2347 |
|
|
2303 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) | |
| 2348 |
|
2304 | |||
| 2349 |
|
|
2305 | self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready | |
| 2350 |
|
2306 | |||
| 2351 |
|
|
2307 | if self.__dataReady: | |
| 2352 |
|
|
2308 | dataOut.utctimeInit = self.__initime | |
| 2353 |
|
2309 | |||
| 2354 |
|
|
2310 | self.__initime += dataOut.outputInterval #to erase time offset | |
| 2355 |
|
2311 | |||
| 2356 |
|
|
2312 | dataOut.data_output, dataOut.heightList = self.techniqueMeteors(self.__buffer, meteorThresh, hmin, hmax) | |
| 2357 |
|
|
2313 | dataOut.flagNoData = False | |
| 2358 |
|
|
2314 | self.__buffer = None | |
| 2359 |
|
2315 | |||
| 2360 |
|
|
2316 | elif technique == 'Meteors1': | |
| 2361 |
|
|
2317 | dataOut.flagNoData = True | |
| 2362 |
|
|
2318 | self.__dataReady = False | |
| 2363 |
|
2319 | |||
| 2364 |
|
|
2320 | if 'nMins' in kwargs: | |
| 2365 |
|
|
2321 | nMins = kwargs['nMins'] | |
| 2366 |
|
|
2322 | else: nMins = 20 | |
| 2367 |
|
|
2323 | if 'rx_location' in kwargs: | |
| 2368 |
|
|
2324 | rx_location = kwargs['rx_location'] | |
| 2369 |
|
|
2325 | else: rx_location = [(0,1),(1,1),(1,0)] | |
| 2370 |
|
|
2326 | if 'azimuth' in kwargs: | |
| 2371 |
|
|
2327 | azimuth = kwargs['azimuth'] | |
| 2372 |
|
|
2328 | else: azimuth = 51.06 | |
| 2373 |
|
|
2329 | if 'dfactor' in kwargs: | |
| 2374 |
|
|
2330 | dfactor = kwargs['dfactor'] | |
| 2375 |
|
|
2331 | if 'mode' in kwargs: | |
| 2376 |
|
|
2332 | mode = kwargs['mode'] | |
| 2377 |
|
|
2333 | if 'theta_x' in kwargs: | |
| 2378 |
|
|
2334 | theta_x = kwargs['theta_x'] | |
| 2379 |
|
|
2335 | if 'theta_y' in kwargs: | |
| 2380 |
|
|
2336 | theta_y = kwargs['theta_y'] | |
| 2381 |
|
|
2337 | else: mode = 'SA' | |
| 2382 |
|
2338 | |||
| 2383 |
|
|
2339 | #Borrar luego esto | |
| 2384 |
|
|
2340 | if dataOut.groupList is None: | |
| 2385 |
|
|
2341 | dataOut.groupList = [(0,1),(0,2),(1,2)] | |
| 2386 |
|
|
2342 | groupList = dataOut.groupList | |
| 2387 |
|
|
2343 | C = 3e8 | |
| 2388 |
|
|
2344 | freq = 50e6 | |
| 2389 |
|
|
2345 | lamb = C/freq | |
| 2390 |
|
|
2346 | k = 2*numpy.pi/lamb | |
| 2391 |
|
2347 | |||
| 2392 |
|
|
2348 | timeList = dataOut.abscissaList | |
| 2393 |
|
|
2349 | heightList = dataOut.heightList | |
| 2394 |
|
2350 | |||
| 2395 |
|
|
2351 | if self.__isConfig == False: | |
| 2396 |
|
|
2352 | dataOut.outputInterval = nMins*60 | |
| 2397 |
|
|
2353 | # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) | |
| 2398 |
|
|
2354 | #Get Initial LTC time | |
| 2399 |
|
|
2355 | initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) | |
| 2400 |
|
|
2356 | minuteAux = initime.minute | |
| 2401 |
|
|
2357 | minuteNew = int(numpy.floor(minuteAux/nMins)*nMins) | |
| 2402 |
|
|
2358 | self.__initime = (initime.replace(minute = minuteNew, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() | |
| 2403 |
|
2359 | |||
| 2404 |
|
|
2360 | self.__isConfig = True | |
| 2405 |
|
2361 | |||
| 2406 |
|
|
2362 | if self.__buffer is None: | |
| 2407 |
|
|
2363 | self.__buffer = dataOut.data_param | |
| 2408 |
|
|
2364 | self.__firstdata = copy.copy(dataOut) | |
| 2409 |
|
2365 | |||
| 2410 |
|
|
2366 | else: | |
| 2411 |
|
|
2367 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) | |
| 2412 |
|
2368 | |||
| 2413 |
|
|
2369 | self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready | |
| 2414 |
|
2370 | |||
| 2415 |
|
|
2371 | if self.__dataReady: | |
| 2416 |
|
|
2372 | dataOut.utctimeInit = self.__initime | |
| 2417 |
|
|
2373 | self.__initime += dataOut.outputInterval #to erase time offset | |
| 2418 |
|
2374 | |||
| 2419 |
|
|
2375 | metArray = self.__buffer | |
| 2420 |
|
|
2376 | if mode == 'SA': | |
| 2421 |
|
|
2377 | dataOut.data_output = self.techniqueNSM_SA(rx_location=rx_location, groupList=groupList, azimuth=azimuth, dfactor=dfactor, k=k,metArray=metArray, heightList=heightList,timeList=timeList) | |
| 2422 |
|
|
2378 | elif mode == 'DBS': | |
| 2423 |
|
|
2379 | dataOut.data_output = self.techniqueNSM_DBS(metArray=metArray,heightList=heightList,timeList=timeList, azimuth=azimuth, theta_x=theta_x, theta_y=theta_y) | |
| 2424 |
|
|
2380 | dataOut.data_output = dataOut.data_output.T | |
| 2425 |
|
|
2381 | dataOut.flagNoData = False | |
| 2426 |
|
|
2382 | self.__buffer = None | |
| 2427 |
|
2383 | |||
| 2428 |
|
|
2384 | return | |
| 2429 |
|
2385 | |||
| 2430 |
|
|
2386 | class EWDriftsEstimation(Operation): | |
| 2431 |
|
2387 | |||
| 2432 |
|
|
2388 | def __init__(self): | |
| 2433 |
|
|
2389 | Operation.__init__(self) | |
| 2434 |
|
2390 | |||
| 2435 |
|
|
2391 | def __correctValues(self, heiRang, phi, velRadial, SNR): | |
| 2436 |
|
|
2392 | listPhi = phi.tolist() | |
| 2437 |
|
|
2393 | maxid = listPhi.index(max(listPhi)) | |
| 2438 |
|
|
2394 | minid = listPhi.index(min(listPhi)) | |
| 2439 |
|
2395 | |||
| 2440 |
|
|
2396 | rango = list(range(len(phi))) | |
| 2441 |
|
|
2397 | # rango = numpy.delete(rango,maxid) | |
| 2442 |
|
2398 | |||
| 2443 |
|
|
2399 | heiRang1 = heiRang*math.cos(phi[maxid]) | |
| 2444 |
|
|
2400 | heiRangAux = heiRang*math.cos(phi[minid]) | |
| 2445 |
|
|
2401 | indOut = (heiRang1 < heiRangAux[0]).nonzero() | |
| 2446 |
|
|
2402 | heiRang1 = numpy.delete(heiRang1,indOut) | |
| 2447 |
|
2403 | |||
| 2448 |
|
|
2404 | velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) | |
| 2449 |
|
|
2405 | SNR1 = numpy.zeros([len(phi),len(heiRang1)]) | |
| 2450 |
|
2406 | |||
| 2451 |
|
|
2407 | for i in rango: | |
| 2452 |
|
|
2408 | x = heiRang*math.cos(phi[i]) | |
| 2453 |
|
|
2409 | y1 = velRadial[i,:] | |
| 2454 |
|
|
2410 | f1 = interpolate.interp1d(x,y1,kind = 'cubic') | |
| 2455 |
|
2411 | |||
| 2456 |
|
|
2412 | x1 = heiRang1 | |
| 2457 |
|
|
2413 | y11 = f1(x1) | |
| 2458 |
|
2414 | |||
| 2459 |
|
|
2415 | y2 = SNR[i,:] | |
| 2460 |
|
|
2416 | f2 = interpolate.interp1d(x,y2,kind = 'cubic') | |
| 2461 |
|
|
2417 | y21 = f2(x1) | |
| 2462 |
|
2418 | |||
| 2463 |
|
|
2419 | velRadial1[i,:] = y11 | |
| 2464 |
|
|
2420 | SNR1[i,:] = y21 | |
| 2465 |
|
2421 | |||
| 2466 |
|
|
2422 | return heiRang1, velRadial1, SNR1 | |
| 2467 |
|
2423 | |||
| 2468 |
|
|
2424 | def run(self, dataOut, zenith, zenithCorrection): | |
| 2469 |
|
|
2425 | heiRang = dataOut.heightList | |
| 2470 |
|
|
2426 | velRadial = dataOut.data_param[:,3,:] | |
| 2471 |
|
|
2427 | SNR = dataOut.data_SNR | |
| 2472 |
|
2428 | |||
| 2473 |
|
|
2429 | zenith = numpy.array(zenith) | |
| 2474 |
|
|
2430 | zenith -= zenithCorrection | |
| 2475 |
|
|
2431 | zenith *= numpy.pi/180 | |
| 2476 |
|
2432 | |||
| 2477 |
|
|
2433 | heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, numpy.abs(zenith), velRadial, SNR) | |
| 2478 |
|
2434 | |||
| 2479 |
|
|
2435 | alp = zenith[0] | |
| 2480 |
|
|
2436 | bet = zenith[1] | |
| 2481 |
|
2437 | |||
| 2482 |
|
|
2438 | w_w = velRadial1[0,:] | |
| 2483 |
|
|
2439 | w_e = velRadial1[1,:] | |
| 2484 |
|
2440 | |||
| 2485 |
|
|
2441 | w = (w_w*numpy.sin(bet) - w_e*numpy.sin(alp))/(numpy.cos(alp)*numpy.sin(bet) - numpy.cos(bet)*numpy.sin(alp)) | |
| 2486 |
|
|
2442 | u = (w_w*numpy.cos(bet) - w_e*numpy.cos(alp))/(numpy.sin(alp)*numpy.cos(bet) - numpy.sin(bet)*numpy.cos(alp)) | |
| 2487 |
|
2443 | |||
| 2488 |
|
|
2444 | winds = numpy.vstack((u,w)) | |
| 2489 |
|
2445 | |||
| 2490 |
|
|
2446 | dataOut.heightList = heiRang1 | |
| 2491 |
|
|
2447 | dataOut.data_output = winds | |
| 2492 |
|
|
2448 | dataOut.data_SNR = SNR1 | |
| 2493 |
|
2449 | |||
| 2494 |
|
|
2450 | dataOut.utctimeInit = dataOut.utctime | |
| 2495 |
|
|
2451 | dataOut.outputInterval = dataOut.timeInterval | |
| 2496 |
|
|
2452 | return | |
| 2497 |
|
2453 | |||
| 2498 |
|
|
2454 | #--------------- Non Specular Meteor ---------------- | |
| 2499 |
|
2455 | |||
| 2500 |
|
|
2456 | class NonSpecularMeteorDetection(Operation): | |
| 2501 |
|
2457 | |||
| 2502 |
|
|
2458 | def run(self, dataOut, mode, SNRthresh=8, phaseDerThresh=0.5, cohThresh=0.8, allData = False): | |
| 2503 |
|
|
2459 | data_acf = dataOut.data_pre[0] | |
| 2504 |
|
|
2460 | data_ccf = dataOut.data_pre[1] | |
| 2505 |
|
|
2461 | pairsList = dataOut.groupList[1] | |
| 2506 |
|
2462 | |||
| 2507 |
|
|
2463 | lamb = dataOut.C/dataOut.frequency | |
| 2508 |
|
|
2464 | tSamp = dataOut.ippSeconds*dataOut.nCohInt | |
| 2509 |
|
|
2465 | paramInterval = dataOut.paramInterval | |
| 2510 |
|
2466 | |||
| 2511 |
|
|
2467 | nChannels = data_acf.shape[0] | |
| 2512 |
|
|
2468 | nLags = data_acf.shape[1] | |
| 2513 |
|
|
2469 | nProfiles = data_acf.shape[2] | |
| 2514 |
|
|
2470 | nHeights = dataOut.nHeights | |
| 2515 |
|
|
2471 | nCohInt = dataOut.nCohInt | |
| 2516 |
|
|
2472 | sec = numpy.round(nProfiles/dataOut.paramInterval) | |
| 2517 |
|
|
2473 | heightList = dataOut.heightList | |
| 2518 |
|
|
2474 | ippSeconds = dataOut.ippSeconds*dataOut.nCohInt*dataOut.nAvg | |
| 2519 |
|
|
2475 | utctime = dataOut.utctime | |
| 2520 |
|
2476 | |||
| 2521 |
|
|
2477 | dataOut.abscissaList = numpy.arange(0,paramInterval+ippSeconds,ippSeconds) | |
| 2522 |
|
2478 | |||
| 2523 |
|
|
2479 | #------------------------ SNR -------------------------------------- | |
| 2524 |
|
|
2480 | power = data_acf[:,0,:,:].real | |
| 2525 |
|
|
2481 | noise = numpy.zeros(nChannels) | |
| 2526 |
|
|
2482 | SNR = numpy.zeros(power.shape) | |
| 2527 |
|
|
2483 | for i in range(nChannels): | |
| 2528 |
|
|
2484 | noise[i] = hildebrand_sekhon(power[i,:], nCohInt) | |
| 2529 |
|
|
2485 | SNR[i] = (power[i]-noise[i])/noise[i] | |
| 2530 |
|
|
2486 | SNRm = numpy.nanmean(SNR, axis = 0) | |
| 2531 |
|
|
2487 | SNRdB = 10*numpy.log10(SNR) | |
| 2532 |
|
2488 | |||
| 2533 |
|
|
2489 | if mode == 'SA': | |
| 2534 |
|
|
2490 | dataOut.groupList = dataOut.groupList[1] | |
| 2535 |
|
|
2491 | nPairs = data_ccf.shape[0] | |
| 2536 |
|
|
2492 | #---------------------- Coherence and Phase -------------------------- | |
| 2537 |
|
|
2493 | phase = numpy.zeros(data_ccf[:,0,:,:].shape) | |
| 2538 |
|
|
2494 | # phase1 = numpy.copy(phase) | |
| 2539 |
|
|
2495 | coh1 = numpy.zeros(data_ccf[:,0,:,:].shape) | |
| 2540 |
|
2496 | |||
| 2541 |
|
|
2497 | for p in range(nPairs): | |
| 2542 |
|
|
2498 | ch0 = pairsList[p][0] | |
| 2543 |
|
|
2499 | ch1 = pairsList[p][1] | |
| 2544 |
|
|
2500 | ccf = data_ccf[p,0,:,:]/numpy.sqrt(data_acf[ch0,0,:,:]*data_acf[ch1,0,:,:]) | |
| 2545 |
|
|
2501 | phase[p,:,:] = ndimage.median_filter(numpy.angle(ccf), size = (5,1)) #median filter | |
| 2546 |
|
|
2502 | # phase1[p,:,:] = numpy.angle(ccf) #median filter | |
| 2547 |
|
|
2503 | coh1[p,:,:] = ndimage.median_filter(numpy.abs(ccf), 5) #median filter | |
| 2548 |
|
|
2504 | # coh1[p,:,:] = numpy.abs(ccf) #median filter | |
| 2549 |
|
|
2505 | coh = numpy.nanmax(coh1, axis = 0) | |
| 2550 |
|
|
2506 | # struc = numpy.ones((5,1)) | |
| 2551 |
|
|
2507 | # coh = ndimage.morphology.grey_dilation(coh, size=(10,1)) | |
| 2552 |
|
|
2508 | #---------------------- Radial Velocity ---------------------------- | |
| 2553 |
|
|
2509 | phaseAux = numpy.mean(numpy.angle(data_acf[:,1,:,:]), axis = 0) | |
| 2554 |
|
|
2510 | velRad = phaseAux*lamb/(4*numpy.pi*tSamp) | |
| 2555 |
|
2511 | |||
| 2556 |
|
|
2512 | if allData: | |
| 2557 |
|
|
2513 | boolMetFin = ~numpy.isnan(SNRm) | |
| 2558 |
|
|
2514 | # coh[:-1,:] = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0) | |
| 2559 |
|
|
2515 | else: | |
| 2560 |
|
|
2516 | #------------------------ Meteor mask --------------------------------- | |
| 2561 |
|
|
2517 | # #SNR mask | |
| 2562 |
|
|
2518 | # boolMet = (SNRdB>SNRthresh)#|(~numpy.isnan(SNRdB)) | |
| 2563 |
|
|
2519 | # | |
| 2564 |
|
|
2520 | # #Erase small objects | |
| 2565 |
|
|
2521 | # boolMet1 = self.__erase_small(boolMet, 2*sec, 5) | |
| 2566 |
|
|
2522 | # | |
| 2567 |
|
|
2523 | # auxEEJ = numpy.sum(boolMet1,axis=0) | |
| 2568 |
|
|
2524 | # indOver = auxEEJ>nProfiles*0.8 #Use this later | |
| 2569 |
|
|
2525 | # indEEJ = numpy.where(indOver)[0] | |
| 2570 |
|
|
2526 | # indNEEJ = numpy.where(~indOver)[0] | |
| 2571 |
|
|
2527 | # | |
| 2572 |
|
|
2528 | # boolMetFin = boolMet1 | |
| 2573 |
|
|
2529 | # | |
| 2574 |
|
|
2530 | # if indEEJ.size > 0: | |
| 2575 |
|
|
2531 | # boolMet1[:,indEEJ] = False #Erase heights with EEJ | |
| 2576 |
|
|
2532 | # | |
| 2577 |
|
|
2533 | # boolMet2 = coh > cohThresh | |
| 2578 |
|
|
2534 | # boolMet2 = self.__erase_small(boolMet2, 2*sec,5) | |
| 2579 |
|
|
2535 | # | |
| 2580 |
|
|
2536 | # #Final Meteor mask | |
| 2581 |
|
|
2537 | # boolMetFin = boolMet1|boolMet2 | |
| 2582 |
|
2538 | |||
| 2583 |
|
|
2539 | #Coherence mask | |
| 2584 |
|
|
2540 | boolMet1 = coh > 0.75 | |
| 2585 |
|
|
2541 | struc = numpy.ones((30,1)) | |
| 2586 |
|
|
2542 | boolMet1 = ndimage.morphology.binary_dilation(boolMet1, structure=struc) | |
| 2587 |
|
2543 | |||
| 2588 |
|
|
2544 | #Derivative mask | |
| 2589 |
|
|
2545 | derPhase = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0) | |
| 2590 |
|
|
2546 | boolMet2 = derPhase < 0.2 | |
| 2591 |
|
|
2547 | # boolMet2 = ndimage.morphology.binary_opening(boolMet2) | |
| 2592 |
|
|
2548 | # boolMet2 = ndimage.morphology.binary_closing(boolMet2, structure = numpy.ones((10,1))) | |
| 2593 |
|
|
2549 | boolMet2 = ndimage.median_filter(boolMet2,size=5) | |
| 2594 |
|
|
2550 | boolMet2 = numpy.vstack((boolMet2,numpy.full((1,nHeights), True, dtype=bool))) | |
| 2595 |
|
|
2551 | # #Final mask | |
| 2596 |
|
|
2552 | # boolMetFin = boolMet2 | |
| 2597 |
|
|
2553 | boolMetFin = boolMet1&boolMet2 | |
| 2598 |
|
|
2554 | # boolMetFin = ndimage.morphology.binary_dilation(boolMetFin) | |
| 2599 |
|
|
2555 | #Creating data_param | |
| 2600 |
|
|
2556 | coordMet = numpy.where(boolMetFin) | |
| 2601 |
|
2557 | |||
| 2602 |
|
|
2558 | tmet = coordMet[0] | |
| 2603 |
|
|
2559 | hmet = coordMet[1] | |
| 2604 |
|
2560 | |||
| 2605 |
|
|
2561 | data_param = numpy.zeros((tmet.size, 6 + nPairs)) | |
| 2606 |
|
|
2562 | data_param[:,0] = utctime | |
| 2607 |
|
|
2563 | data_param[:,1] = tmet | |
| 2608 |
|
|
2564 | data_param[:,2] = hmet | |
| 2609 |
|
|
2565 | data_param[:,3] = SNRm[tmet,hmet] | |
| 2610 |
|
|
2566 | data_param[:,4] = velRad[tmet,hmet] | |
| 2611 |
|
|
2567 | data_param[:,5] = coh[tmet,hmet] | |
| 2612 |
|
|
2568 | data_param[:,6:] = phase[:,tmet,hmet].T | |
| 2613 |
|
2569 | |||
| 2614 |
|
|
2570 | elif mode == 'DBS': | |
| 2615 |
|
|
2571 | dataOut.groupList = numpy.arange(nChannels) | |
| 2616 |
|
2572 | |||
| 2617 |
|
|
2573 | #Radial Velocities | |
| 2618 |
|
|
2574 | phase = numpy.angle(data_acf[:,1,:,:]) | |
| 2619 |
|
|
2575 | # phase = ndimage.median_filter(numpy.angle(data_acf[:,1,:,:]), size = (1,5,1)) | |
| 2620 |
|
|
2576 | velRad = phase*lamb/(4*numpy.pi*tSamp) | |
| 2621 |
|
2577 | |||
| 2622 |
|
|
2578 | #Spectral width | |
| 2623 |
|
|
2579 | # acf1 = ndimage.median_filter(numpy.abs(data_acf[:,1,:,:]), size = (1,5,1)) | |
| 2624 |
|
|
2580 | # acf2 = ndimage.median_filter(numpy.abs(data_acf[:,2,:,:]), size = (1,5,1)) | |
| 2625 |
|
|
2581 | acf1 = data_acf[:,1,:,:] | |
| 2626 |
|
|
2582 | acf2 = data_acf[:,2,:,:] | |
| 2627 |
|
2583 | |||
| 2628 |
|
|
2584 | spcWidth = (lamb/(2*numpy.sqrt(6)*numpy.pi*tSamp))*numpy.sqrt(numpy.log(acf1/acf2)) | |
| 2629 |
|
|
2585 | # velRad = ndimage.median_filter(velRad, size = (1,5,1)) | |
| 2630 |
|
|
2586 | if allData: | |
| 2631 |
|
|
2587 | boolMetFin = ~numpy.isnan(SNRdB) | |
| 2632 |
|
|
2588 | else: | |
| 2633 |
|
|
2589 | #SNR | |
| 2634 |
|
|
2590 | boolMet1 = (SNRdB>SNRthresh) #SNR mask | |
| 2635 |
|
|
2591 | boolMet1 = ndimage.median_filter(boolMet1, size=(1,5,5)) | |
| 2636 |
|
2592 | |||
| 2637 |
|
|
2593 | #Radial velocity | |
| 2638 |
|
|
2594 | boolMet2 = numpy.abs(velRad) < 20 | |
| 2639 |
|
|
2595 | boolMet2 = ndimage.median_filter(boolMet2, (1,5,5)) | |
| 2640 |
|
2596 | |||
| 2641 |
|
|
2597 | #Spectral Width | |
| 2642 |
|
|
2598 | boolMet3 = spcWidth < 30 | |
| 2643 |
|
|
2599 | boolMet3 = ndimage.median_filter(boolMet3, (1,5,5)) | |
| 2644 |
|
|
2600 | # boolMetFin = self.__erase_small(boolMet1, 10,5) | |
| 2645 |
|
|
2601 | boolMetFin = boolMet1&boolMet2&boolMet3 | |
| 2646 |
|
2602 | |||
| 2647 |
|
|
2603 | #Creating data_param | |
| 2648 |
|
|
2604 | coordMet = numpy.where(boolMetFin) | |
| 2649 |
|
2605 | |||
| 2650 |
|
|
2606 | cmet = coordMet[0] | |
| 2651 |
|
|
2607 | tmet = coordMet[1] | |
| 2652 |
|
|
2608 | hmet = coordMet[2] | |
| 2653 |
|
2609 | |||
| 2654 |
|
|
2610 | data_param = numpy.zeros((tmet.size, 7)) | |
| 2655 |
|
|
2611 | data_param[:,0] = utctime | |
| 2656 |
|
|
2612 | data_param[:,1] = cmet | |
| 2657 |
|
|
2613 | data_param[:,2] = tmet | |
| 2658 |
|
|
2614 | data_param[:,3] = hmet | |
| 2659 |
|
|
2615 | data_param[:,4] = SNR[cmet,tmet,hmet].T | |
| 2660 |
|
|
2616 | data_param[:,5] = velRad[cmet,tmet,hmet].T | |
| 2661 |
|
|
2617 | data_param[:,6] = spcWidth[cmet,tmet,hmet].T | |
| 2662 |
|
2618 | |||
| 2663 |
|
|
2619 | # self.dataOut.data_param = data_int | |
| 2664 |
|
|
2620 | if len(data_param) == 0: | |
| 2665 |
|
|
2621 | dataOut.flagNoData = True | |
| 2666 |
|
|
2622 | else: | |
| 2667 |
|
|
2623 | dataOut.data_param = data_param | |
| 2668 |
|
2624 | |||
| 2669 |
|
|
2625 | def __erase_small(self, binArray, threshX, threshY): | |
| 2670 |
|
|
2626 | labarray, numfeat = ndimage.measurements.label(binArray) | |
| 2671 |
|
|
2627 | binArray1 = numpy.copy(binArray) | |
| 2672 |
|
2628 | |||
| 2673 |
|
|
2629 | for i in range(1,numfeat + 1): | |
| 2674 |
|
|
2630 | auxBin = (labarray==i) | |
| 2675 |
|
|
2631 | auxSize = auxBin.sum() | |
| 2676 |
|
2632 | |||
| 2677 |
|
|
2633 | x,y = numpy.where(auxBin) | |
| 2678 |
|
|
2634 | widthX = x.max() - x.min() | |
| 2679 |
|
|
2635 | widthY = y.max() - y.min() | |
| 2680 |
|
2636 | |||
| 2681 |
|
|
2637 | #width X: 3 seg -> 12.5*3 | |
| 2682 |
|
|
2638 | #width Y: | |
| 2683 |
|
2639 | |||
| 2684 |
|
|
2640 | if (auxSize < 50) or (widthX < threshX) or (widthY < threshY): | |
| 2685 |
|
|
2641 | binArray1[auxBin] = False | |
| 2686 |
|
2642 | |||
| 2687 |
|
|
2643 | return binArray1 | |
| 2688 |
|
2644 | |||
| 2689 |
|
|
2645 | #--------------- Specular Meteor ---------------- | |
| 2690 |
|
2646 | |||
| 2691 |
|
|
2647 | class SMDetection(Operation): | |
| 2692 |
|
|
2648 | ''' | |
| 2693 | Function DetectMeteors() |
|
2649 | Function DetectMeteors() | |
| 2694 | Project developed with paper: |
|
2650 | Project developed with paper: | |
| 2695 | HOLDSWORTH ET AL. 2004 |
|
2651 | HOLDSWORTH ET AL. 2004 | |
| 2696 |
|
2652 | |||
| 2697 | Input: |
|
2653 | Input: | |
| 2698 | self.dataOut.data_pre |
|
2654 | self.dataOut.data_pre | |
| 2699 |
|
2655 | |||
| 2700 | centerReceiverIndex: From the channels, which is the center receiver |
|
2656 | centerReceiverIndex: From the channels, which is the center receiver | |
| 2701 |
|
2657 | |||
| 2702 | hei_ref: Height reference for the Beacon signal extraction |
|
2658 | hei_ref: Height reference for the Beacon signal extraction | |
| 2703 | tauindex: |
|
2659 | tauindex: | |
| 2704 | predefinedPhaseShifts: Predefined phase offset for the voltge signals |
|
2660 | predefinedPhaseShifts: Predefined phase offset for the voltge signals | |
| 2705 |
|
2661 | |||
| 2706 | cohDetection: Whether to user Coherent detection or not |
|
2662 | cohDetection: Whether to user Coherent detection or not | |
| 2707 | cohDet_timeStep: Coherent Detection calculation time step |
|
2663 | cohDet_timeStep: Coherent Detection calculation time step | |
| 2708 | cohDet_thresh: Coherent Detection phase threshold to correct phases |
|
2664 | cohDet_thresh: Coherent Detection phase threshold to correct phases | |
| 2709 |
|
2665 | |||
| 2710 | noise_timeStep: Noise calculation time step |
|
2666 | noise_timeStep: Noise calculation time step | |
| 2711 | noise_multiple: Noise multiple to define signal threshold |
|
2667 | noise_multiple: Noise multiple to define signal threshold | |
| 2712 |
|
2668 | |||
| 2713 | multDet_timeLimit: Multiple Detection Removal time limit in seconds |
|
2669 | multDet_timeLimit: Multiple Detection Removal time limit in seconds | |
| 2714 | multDet_rangeLimit: Multiple Detection Removal range limit in km |
|
2670 | multDet_rangeLimit: Multiple Detection Removal range limit in km | |
| 2715 |
|
2671 | |||
| 2716 | phaseThresh: Maximum phase difference between receiver to be consider a meteor |
|
2672 | phaseThresh: Maximum phase difference between receiver to be consider a meteor | |
| 2717 | SNRThresh: Minimum SNR threshold of the meteor signal to be consider a meteor |
|
2673 | SNRThresh: Minimum SNR threshold of the meteor signal to be consider a meteor | |
| 2718 |
|
2674 | |||
| 2719 | hmin: Minimum Height of the meteor to use it in the further wind estimations |
|
2675 | hmin: Minimum Height of the meteor to use it in the further wind estimations | |
| 2720 | hmax: Maximum Height of the meteor to use it in the further wind estimations |
|
2676 | hmax: Maximum Height of the meteor to use it in the further wind estimations | |
| 2721 | azimuth: Azimuth angle correction |
|
2677 | azimuth: Azimuth angle correction | |
| 2722 |
|
2678 | |||
| 2723 | Affected: |
|
2679 | Affected: | |
| 2724 | self.dataOut.data_param |
|
2680 | self.dataOut.data_param | |
| 2725 |
|
2681 | |||
| 2726 | Rejection Criteria (Errors): |
|
2682 | Rejection Criteria (Errors): | |
| 2727 | 0: No error; analysis OK |
|
2683 | 0: No error; analysis OK | |
| 2728 | 1: SNR < SNR threshold |
|
2684 | 1: SNR < SNR threshold | |
| 2729 | 2: angle of arrival (AOA) ambiguously determined |
|
2685 | 2: angle of arrival (AOA) ambiguously determined | |
| 2730 | 3: AOA estimate not feasible |
|
2686 | 3: AOA estimate not feasible | |
| 2731 | 4: Large difference in AOAs obtained from different antenna baselines |
|
2687 | 4: Large difference in AOAs obtained from different antenna baselines | |
| 2732 | 5: echo at start or end of time series |
|
2688 | 5: echo at start or end of time series | |
| 2733 | 6: echo less than 5 examples long; too short for analysis |
|
2689 | 6: echo less than 5 examples long; too short for analysis | |
| 2734 | 7: echo rise exceeds 0.3s |
|
2690 | 7: echo rise exceeds 0.3s | |
| 2735 | 8: echo decay time less than twice rise time |
|
2691 | 8: echo decay time less than twice rise time | |
| 2736 | 9: large power level before echo |
|
2692 | 9: large power level before echo | |
| 2737 | 10: large power level after echo |
|
2693 | 10: large power level after echo | |
| 2738 | 11: poor fit to amplitude for estimation of decay time |
|
2694 | 11: poor fit to amplitude for estimation of decay time | |
| 2739 | 12: poor fit to CCF phase variation for estimation of radial drift velocity |
|
2695 | 12: poor fit to CCF phase variation for estimation of radial drift velocity | |
| 2740 | 13: height unresolvable echo: not valid height within 70 to 110 km |
|
2696 | 13: height unresolvable echo: not valid height within 70 to 110 km | |
| 2741 | 14: height ambiguous echo: more then one possible height within 70 to 110 km |
|
2697 | 14: height ambiguous echo: more then one possible height within 70 to 110 km | |
| 2742 | 15: radial drift velocity or projected horizontal velocity exceeds 200 m/s |
|
2698 | 15: radial drift velocity or projected horizontal velocity exceeds 200 m/s | |
| 2743 | 16: oscilatory echo, indicating event most likely not an underdense echo |
|
2699 | 16: oscilatory echo, indicating event most likely not an underdense echo | |
| 2744 |
|
2700 | |||
| 2745 | 17: phase difference in meteor Reestimation |
|
2701 | 17: phase difference in meteor Reestimation | |
| 2746 |
|
2702 | |||
| 2747 | Data Storage: |
|
2703 | Data Storage: | |
| 2748 | Meteors for Wind Estimation (8): |
|
2704 | Meteors for Wind Estimation (8): | |
| 2749 | Utc Time | Range Height |
|
2705 | Utc Time | Range Height | |
| 2750 | Azimuth Zenith errorCosDir |
|
2706 | Azimuth Zenith errorCosDir | |
| 2751 | VelRad errorVelRad |
|
2707 | VelRad errorVelRad | |
| 2752 | Phase0 Phase1 Phase2 Phase3 |
|
2708 | Phase0 Phase1 Phase2 Phase3 | |
| 2753 | TypeError |
|
2709 | TypeError | |
| 2754 |
|
2710 | |||
| 2755 | ''' |
|
2711 | ''' | |
| 2756 |
|
2712 | |||
| 2757 |
|
|
2713 | def run(self, dataOut, hei_ref = None, tauindex = 0, | |
| 2758 |
|
|
2714 | phaseOffsets = None, | |
| 2759 |
|
|
2715 | cohDetection = False, cohDet_timeStep = 1, cohDet_thresh = 25, | |
| 2760 |
|
|
2716 | noise_timeStep = 4, noise_multiple = 4, | |
| 2761 |
|
|
2717 | multDet_timeLimit = 1, multDet_rangeLimit = 3, | |
| 2762 |
|
|
2718 | phaseThresh = 20, SNRThresh = 5, | |
| 2763 |
|
|
2719 | hmin = 50, hmax=150, azimuth = 0, | |
| 2764 |
|
|
2720 | channelPositions = None) : | |
| 2765 |
|
2721 | |||
| 2766 |
|
2722 | |||
| 2767 |
|
|
2723 | #Getting Pairslist | |
| 2768 |
|
|
2724 | if channelPositions is None: | |
| 2769 |
|
|
2725 | # channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T | |
| 2770 |
|
|
2726 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella | |
| 2771 |
|
|
2727 | meteorOps = SMOperations() | |
| 2772 |
|
|
2728 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) | |
| 2773 |
|
|
2729 | heiRang = dataOut.heightList | |
| 2774 |
|
|
2730 | #Get Beacon signal - No Beacon signal anymore | |
| 2775 |
|
|
2731 | # newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex]) | |
| 2776 |
|
|
2732 | # | |
| 2777 |
|
|
2733 | # if hei_ref != None: | |
| 2778 |
|
|
2734 | # newheis = numpy.where(self.dataOut.heightList>hei_ref) | |
| 2779 |
|
|
2735 | # | |
| 2780 |
|
2736 | |||
| 2781 |
|
2737 | |||
| 2782 |
|
|
2738 | #****************REMOVING HARDWARE PHASE DIFFERENCES*************** | |
| 2783 |
|
|
2739 | # see if the user put in pre defined phase shifts | |
| 2784 |
|
|
2740 | voltsPShift = dataOut.data_pre.copy() | |
| 2785 |
|
2741 | |||
| 2786 |
|
|
2742 | # if predefinedPhaseShifts != None: | |
| 2787 |
|
|
2743 | # hardwarePhaseShifts = numpy.array(predefinedPhaseShifts)*numpy.pi/180 | |
| 2788 |
|
|
2744 | # | |
| 2789 |
|
|
2745 | # # elif beaconPhaseShifts: | |
| 2790 |
|
|
2746 | # # #get hardware phase shifts using beacon signal | |
| 2791 |
|
|
2747 | # # hardwarePhaseShifts = self.__getHardwarePhaseDiff(self.dataOut.data_pre, pairslist, newheis, 10) | |
| 2792 |
|
|
2748 | # # hardwarePhaseShifts = numpy.insert(hardwarePhaseShifts,centerReceiverIndex,0) | |
| 2793 |
|
|
2749 | # | |
| 2794 |
|
|
2750 | # else: | |
| 2795 |
|
|
2751 | # hardwarePhaseShifts = numpy.zeros(5) | |
| 2796 |
|
|
2752 | # | |
| 2797 |
|
|
2753 | # voltsPShift = numpy.zeros((self.dataOut.data_pre.shape[0],self.dataOut.data_pre.shape[1],self.dataOut.data_pre.shape[2]), dtype = 'complex') | |
| 2798 |
|
|
2754 | # for i in range(self.dataOut.data_pre.shape[0]): | |
| 2799 |
|
|
2755 | # voltsPShift[i,:,:] = self.__shiftPhase(self.dataOut.data_pre[i,:,:], hardwarePhaseShifts[i]) | |
| 2800 |
|
2756 | |||
| 2801 |
|
|
2757 | #******************END OF REMOVING HARDWARE PHASE DIFFERENCES********* | |
| 2802 |
|
2758 | |||
| 2803 |
|
|
2759 | #Remove DC | |
| 2804 |
|
|
2760 | voltsDC = numpy.mean(voltsPShift,1) | |
| 2805 |
|
|
2761 | voltsDC = numpy.mean(voltsDC,1) | |
| 2806 |
|
|
2762 | for i in range(voltsDC.shape[0]): | |
| 2807 |
|
|
2763 | voltsPShift[i] = voltsPShift[i] - voltsDC[i] | |
| 2808 |
|
2764 | |||
| 2809 |
|
|
2765 | #Don't considerate last heights, theyre used to calculate Hardware Phase Shift | |
| 2810 |
|
|
2766 | # voltsPShift = voltsPShift[:,:,:newheis[0][0]] | |
| 2811 |
|
2767 | |||
| 2812 |
|
|
2768 | #************ FIND POWER OF DATA W/COH OR NON COH DETECTION (3.4) ********** | |
| 2813 |
|
|
2769 | #Coherent Detection | |
| 2814 |
|
|
2770 | if cohDetection: | |
| 2815 |
|
|
2771 | #use coherent detection to get the net power | |
| 2816 |
|
|
2772 | cohDet_thresh = cohDet_thresh*numpy.pi/180 | |
| 2817 |
|
|
2773 | voltsPShift = self.__coherentDetection(voltsPShift, cohDet_timeStep, dataOut.timeInterval, pairslist0, cohDet_thresh) | |
| 2818 |
|
2774 | |||
| 2819 |
|
|
2775 | #Non-coherent detection! | |
| 2820 |
|
|
2776 | powerNet = numpy.nansum(numpy.abs(voltsPShift[:,:,:])**2,0) | |
| 2821 |
|
|
2777 | #********** END OF COH/NON-COH POWER CALCULATION********************** | |
| 2822 |
|
2778 | |||
| 2823 |
|
|
2779 | #********** FIND THE NOISE LEVEL AND POSSIBLE METEORS **************** | |
| 2824 |
|
|
2780 | #Get noise | |
| 2825 |
|
|
2781 | noise, noise1 = self.__getNoise(powerNet, noise_timeStep, dataOut.timeInterval) | |
| 2826 |
|
|
2782 | # noise = self.getNoise1(powerNet, noise_timeStep, self.dataOut.timeInterval) | |
| 2827 |
|
|
2783 | #Get signal threshold | |
| 2828 |
|
|
2784 | signalThresh = noise_multiple*noise | |
| 2829 |
|
|
2785 | #Meteor echoes detection | |
| 2830 |
|
|
2786 | listMeteors = self.__findMeteors(powerNet, signalThresh) | |
| 2831 |
|
|
2787 | #******* END OF NOISE LEVEL AND POSSIBLE METEORS CACULATION ********** | |
| 2832 |
|
2788 | |||
| 2833 |
|
|
2789 | #************** REMOVE MULTIPLE DETECTIONS (3.5) *************************** | |
| 2834 |
|
|
2790 | #Parameters | |
| 2835 |
|
|
2791 | heiRange = dataOut.heightList | |
| 2836 |
|
|
2792 | rangeInterval = heiRange[1] - heiRange[0] | |
| 2837 |
|
|
2793 | rangeLimit = multDet_rangeLimit/rangeInterval | |
| 2838 |
|
|
2794 | timeLimit = multDet_timeLimit/dataOut.timeInterval | |
| 2839 |
|
|
2795 | #Multiple detection removals | |
| 2840 |
|
|
2796 | listMeteors1 = self.__removeMultipleDetections(listMeteors, rangeLimit, timeLimit) | |
| 2841 |
|
|
2797 | #************ END OF REMOVE MULTIPLE DETECTIONS ********************** | |
| 2842 |
|
2798 | |||
| 2843 |
|
|
2799 | #********************* METEOR REESTIMATION (3.7, 3.8, 3.9, 3.10) ******************** | |
| 2844 |
|
|
2800 | #Parameters | |
| 2845 |
|
|
2801 | phaseThresh = phaseThresh*numpy.pi/180 | |
| 2846 |
|
|
2802 | thresh = [phaseThresh, noise_multiple, SNRThresh] | |
| 2847 |
|
|
2803 | #Meteor reestimation (Errors N 1, 6, 12, 17) | |
| 2848 |
|
|
2804 | listMeteors2, listMeteorsPower, listMeteorsVolts = self.__meteorReestimation(listMeteors1, voltsPShift, pairslist0, thresh, noise, dataOut.timeInterval, dataOut.frequency) | |
| 2849 |
|
|
2805 | # listMeteors2, listMeteorsPower, listMeteorsVolts = self.meteorReestimation3(listMeteors2, listMeteorsPower, listMeteorsVolts, voltsPShift, pairslist, thresh, noise) | |
| 2850 |
|
|
2806 | #Estimation of decay times (Errors N 7, 8, 11) | |
| 2851 |
|
|
2807 | listMeteors3 = self.__estimateDecayTime(listMeteors2, listMeteorsPower, dataOut.timeInterval, dataOut.frequency) | |
| 2852 |
|
|
2808 | #******************* END OF METEOR REESTIMATION ******************* | |
| 2853 |
|
2809 | |||
| 2854 |
|
|
2810 | #********************* METEOR PARAMETERS CALCULATION (3.11, 3.12, 3.13) ************************** | |
| 2855 |
|
|
2811 | #Calculating Radial Velocity (Error N 15) | |
| 2856 |
|
|
2812 | radialStdThresh = 10 | |
| 2857 |
|
|
2813 | listMeteors4 = self.__getRadialVelocity(listMeteors3, listMeteorsVolts, radialStdThresh, pairslist0, dataOut.timeInterval) | |
| 2858 |
|
2814 | |||
| 2859 |
|
|
2815 | if len(listMeteors4) > 0: | |
| 2860 |
|
|
2816 | #Setting New Array | |
| 2861 |
|
|
2817 | date = dataOut.utctime | |
| 2862 |
|
|
2818 | arrayParameters = self.__setNewArrays(listMeteors4, date, heiRang) | |
| 2863 |
|
2819 | |||
| 2864 |
|
|
2820 | #Correcting phase offset | |
| 2865 |
|
|
2821 | if phaseOffsets != None: | |
| 2866 |
|
|
2822 | phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180 | |
| 2867 |
|
|
2823 | arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets) | |
| 2868 |
|
2824 | |||
| 2869 |
|
|
2825 | #Second Pairslist | |
| 2870 |
|
|
2826 | pairsList = [] | |
| 2871 |
|
|
2827 | pairx = (0,1) | |
| 2872 |
|
|
2828 | pairy = (2,3) | |
| 2873 |
|
|
2829 | pairsList.append(pairx) | |
| 2874 |
|
|
2830 | pairsList.append(pairy) | |
| 2875 |
|
2831 | |||
| 2876 |
|
|
2832 | jph = numpy.array([0,0,0,0]) | |
| 2877 |
|
|
2833 | h = (hmin,hmax) | |
| 2878 |
|
|
2834 | arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph) | |
| 2879 |
|
2835 | |||
| 2880 |
|
|
2836 | # #Calculate AOA (Error N 3, 4) | |
| 2881 |
|
|
2837 | # #JONES ET AL. 1998 | |
| 2882 |
|
|
2838 | # error = arrayParameters[:,-1] | |
| 2883 |
|
|
2839 | # AOAthresh = numpy.pi/8 | |
| 2884 |
|
|
2840 | # phases = -arrayParameters[:,9:13] | |
| 2885 |
|
|
2841 | # arrayParameters[:,4:7], arrayParameters[:,-1] = meteorOps.getAOA(phases, pairsList, error, AOAthresh, azimuth) | |
| 2886 |
|
|
2842 | # | |
| 2887 |
|
|
2843 | # #Calculate Heights (Error N 13 and 14) | |
| 2888 |
|
|
2844 | # error = arrayParameters[:,-1] | |
| 2889 |
|
|
2845 | # Ranges = arrayParameters[:,2] | |
| 2890 |
|
|
2846 | # zenith = arrayParameters[:,5] | |
| 2891 |
|
|
2847 | # arrayParameters[:,3], arrayParameters[:,-1] = meteorOps.getHeights(Ranges, zenith, error, hmin, hmax) | |
| 2892 |
|
|
2848 | # error = arrayParameters[:,-1] | |
| 2893 |
|
|
2849 | #********************* END OF PARAMETERS CALCULATION ************************** | |
| 2894 |
|
2850 | |||
| 2895 |
|
|
2851 | #***************************+ PASS DATA TO NEXT STEP ********************** | |
| 2896 |
|
|
2852 | # arrayFinal = arrayParameters.reshape((1,arrayParameters.shape[0],arrayParameters.shape[1])) | |
| 2897 |
|
|
2853 | dataOut.data_param = arrayParameters | |
| 2898 |
|
2854 | |||
| 2899 |
|
|
2855 | if arrayParameters is None: | |
| 2900 |
|
|
2856 | dataOut.flagNoData = True | |
| 2901 |
|
|
2857 | else: | |
| 2902 |
|
|
2858 | dataOut.flagNoData = True | |
| 2903 |
|
2859 | |||
| 2904 |
|
|
2860 | return | |
| 2905 |
|
2861 | |||
| 2906 |
|
|
2862 | def __getHardwarePhaseDiff(self, voltage0, pairslist, newheis, n): | |
| 2907 |
|
2863 | |||
| 2908 |
|
|
2864 | minIndex = min(newheis[0]) | |
| 2909 |
|
|
2865 | maxIndex = max(newheis[0]) | |
| 2910 |
|
2866 | |||
| 2911 |
|
|
2867 | voltage = voltage0[:,:,minIndex:maxIndex+1] | |
| 2912 |
|
|
2868 | nLength = voltage.shape[1]/n | |
| 2913 |
|
|
2869 | nMin = 0 | |
| 2914 |
|
|
2870 | nMax = 0 | |
| 2915 |
|
|
2871 | phaseOffset = numpy.zeros((len(pairslist),n)) | |
| 2916 |
|
2872 | |||
| 2917 |
|
|
2873 | for i in range(n): | |
| 2918 |
|
|
2874 | nMax += nLength | |
| 2919 |
|
|
2875 | phaseCCF = -numpy.angle(self.__calculateCCF(voltage[:,nMin:nMax,:], pairslist, [0])) | |
| 2920 |
|
|
2876 | phaseCCF = numpy.mean(phaseCCF, axis = 2) | |
| 2921 |
|
|
2877 | phaseOffset[:,i] = phaseCCF.transpose() | |
| 2922 |
|
|
2878 | nMin = nMax | |
| 2923 |
|
|
2879 | # phaseDiff, phaseArrival = self.estimatePhaseDifference(voltage, pairslist) | |
| 2924 |
|
2880 | |||
| 2925 |
|
|
2881 | #Remove Outliers | |
| 2926 |
|
|
2882 | factor = 2 | |
| 2927 |
|
|
2883 | wt = phaseOffset - signal.medfilt(phaseOffset,(1,5)) | |
| 2928 |
|
|
2884 | dw = numpy.std(wt,axis = 1) | |
| 2929 |
|
|
2885 | dw = dw.reshape((dw.size,1)) | |
| 2930 |
|
|
2886 | ind = numpy.where(numpy.logical_or(wt>dw*factor,wt<-dw*factor)) | |
| 2931 |
|
|
2887 | phaseOffset[ind] = numpy.nan | |
| 2932 |
|
|
2888 | phaseOffset = stats.nanmean(phaseOffset, axis=1) | |
| 2933 |
|
2889 | |||
| 2934 |
|
|
2890 | return phaseOffset | |
| 2935 |
|
2891 | |||
| 2936 |
|
|
2892 | def __shiftPhase(self, data, phaseShift): | |
| 2937 |
|
|
2893 | #this will shift the phase of a complex number | |
| 2938 |
|
|
2894 | dataShifted = numpy.abs(data) * numpy.exp((numpy.angle(data)+phaseShift)*1j) | |
| 2939 |
|
|
2895 | return dataShifted | |
| 2940 |
|
2896 | |||
| 2941 |
|
|
2897 | def __estimatePhaseDifference(self, array, pairslist): | |
| 2942 |
|
|
2898 | nChannel = array.shape[0] | |
| 2943 |
|
|
2899 | nHeights = array.shape[2] | |
| 2944 |
|
|
2900 | numPairs = len(pairslist) | |
| 2945 |
|
|
2901 | # phaseCCF = numpy.zeros((nChannel, 5, nHeights)) | |
| 2946 |
|
|
2902 | phaseCCF = numpy.angle(self.__calculateCCF(array, pairslist, [-2,-1,0,1,2])) | |
| 2947 |
|
2903 | |||
| 2948 |
|
|
2904 | #Correct phases | |
| 2949 |
|
|
2905 | derPhaseCCF = phaseCCF[:,1:,:] - phaseCCF[:,0:-1,:] | |
| 2950 |
|
|
2906 | indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) | |
| 2951 |
|
2907 | |||
| 2952 |
|
|
2908 | if indDer[0].shape[0] > 0: | |
| 2953 |
|
|
2909 | for i in range(indDer[0].shape[0]): | |
| 2954 |
|
|
2910 | signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i],indDer[2][i]]) | |
| 2955 |
|
|
2911 | phaseCCF[indDer[0][i],indDer[1][i]+1:,:] += signo*2*numpy.pi | |
| 2956 |
|
2912 | |||
| 2957 |
|
|
2913 | # for j in range(numSides): | |
| 2958 |
|
|
2914 | # phaseCCFAux = self.calculateCCF(arrayCenter, arraySides[j,:,:], [-2,1,0,1,2]) | |
| 2959 |
|
|
2915 | # phaseCCF[j,:,:] = numpy.angle(phaseCCFAux) | |
| 2960 |
|
|
2916 | # | |
| 2961 |
|
|
2917 | #Linear | |
| 2962 |
|
|
2918 | phaseInt = numpy.zeros((numPairs,1)) | |
| 2963 |
|
|
2919 | angAllCCF = phaseCCF[:,[0,1,3,4],0] | |
| 2964 |
|
|
2920 | for j in range(numPairs): | |
| 2965 |
|
|
2921 | fit = stats.linregress([-2,-1,1,2],angAllCCF[j,:]) | |
| 2966 |
|
|
2922 | phaseInt[j] = fit[1] | |
| 2967 |
|
|
2923 | #Phase Differences | |
| 2968 |
|
|
2924 | phaseDiff = phaseInt - phaseCCF[:,2,:] | |
| 2969 |
|
|
2925 | phaseArrival = phaseInt.reshape(phaseInt.size) | |
| 2970 |
|
2926 | |||
| 2971 |
|
|
2927 | #Dealias | |
| 2972 |
|
|
2928 | phaseArrival = numpy.angle(numpy.exp(1j*phaseArrival)) | |
| 2973 |
|
|
2929 | # indAlias = numpy.where(phaseArrival > numpy.pi) | |
| 2974 |
|
|
2930 | # phaseArrival[indAlias] -= 2*numpy.pi | |
| 2975 |
|
|
2931 | # indAlias = numpy.where(phaseArrival < -numpy.pi) | |
| 2976 |
|
|
2932 | # phaseArrival[indAlias] += 2*numpy.pi | |
| 2977 |
|
2933 | |||
| 2978 |
|
|
2934 | return phaseDiff, phaseArrival | |
| 2979 |
|
2935 | |||
| 2980 |
|
|
2936 | def __coherentDetection(self, volts, timeSegment, timeInterval, pairslist, thresh): | |
| 2981 |
|
|
2937 | #this function will run the coherent detection used in Holdworth et al. 2004 and return the net power | |
| 2982 |
|
|
2938 | #find the phase shifts of each channel over 1 second intervals | |
| 2983 |
|
|
2939 | #only look at ranges below the beacon signal | |
| 2984 |
|
|
2940 | numProfPerBlock = numpy.ceil(timeSegment/timeInterval) | |
| 2985 |
|
|
2941 | numBlocks = int(volts.shape[1]/numProfPerBlock) | |
| 2986 |
|
|
2942 | numHeights = volts.shape[2] | |
| 2987 |
|
|
2943 | nChannel = volts.shape[0] | |
| 2988 |
|
|
2944 | voltsCohDet = volts.copy() | |
| 2989 |
|
2945 | |||
| 2990 |
|
|
2946 | pairsarray = numpy.array(pairslist) | |
| 2991 |
|
|
2947 | indSides = pairsarray[:,1] | |
| 2992 |
|
|
2948 | # indSides = numpy.array(range(nChannel)) | |
| 2993 |
|
|
2949 | # indSides = numpy.delete(indSides, indCenter) | |
| 2994 |
|
|
2950 | # | |
| 2995 |
|
|
2951 | # listCenter = numpy.array_split(volts[indCenter,:,:], numBlocks, 0) | |
| 2996 |
|
|
2952 | listBlocks = numpy.array_split(volts, numBlocks, 1) | |
| 2997 |
|
2953 | |||
| 2998 |
|
|
2954 | startInd = 0 | |
| 2999 |
|
|
2955 | endInd = 0 | |
| 3000 |
|
2956 | |||
| 3001 |
|
|
2957 | for i in range(numBlocks): | |
| 3002 |
|
|
2958 | startInd = endInd | |
| 3003 |
|
|
2959 | endInd = endInd + listBlocks[i].shape[1] | |
| 3004 |
|
2960 | |||
| 3005 |
|
|
2961 | arrayBlock = listBlocks[i] | |
| 3006 |
|
|
2962 | # arrayBlockCenter = listCenter[i] | |
| 3007 |
|
2963 | |||
| 3008 |
|
|
2964 | #Estimate the Phase Difference | |
| 3009 |
|
|
2965 | phaseDiff, aux = self.__estimatePhaseDifference(arrayBlock, pairslist) | |
| 3010 |
|
|
2966 | #Phase Difference RMS | |
| 3011 |
|
|
2967 | arrayPhaseRMS = numpy.abs(phaseDiff) | |
| 3012 |
|
|
2968 | phaseRMSaux = numpy.sum(arrayPhaseRMS < thresh,0) | |
| 3013 |
|
|
2969 | indPhase = numpy.where(phaseRMSaux==4) | |
| 3014 |
|
|
2970 | #Shifting | |
| 3015 |
|
|
2971 | if indPhase[0].shape[0] > 0: | |
| 3016 |
|
|
2972 | for j in range(indSides.size): | |
| 3017 |
|
|
2973 | arrayBlock[indSides[j],:,indPhase] = self.__shiftPhase(arrayBlock[indSides[j],:,indPhase], phaseDiff[j,indPhase].transpose()) | |
| 3018 |
|
|
2974 | voltsCohDet[:,startInd:endInd,:] = arrayBlock | |
| 3019 |
|
2975 | |||
| 3020 |
|
|
2976 | return voltsCohDet | |
| 3021 |
|
2977 | |||
| 3022 |
|
|
2978 | def __calculateCCF(self, volts, pairslist ,laglist): | |
| 3023 |
|
2979 | |||
| 3024 |
|
|
2980 | nHeights = volts.shape[2] | |
| 3025 |
|
|
2981 | nPoints = volts.shape[1] | |
| 3026 |
|
|
2982 | voltsCCF = numpy.zeros((len(pairslist), len(laglist), nHeights),dtype = 'complex') | |
| 3027 |
|
2983 | |||
| 3028 |
|
|
2984 | for i in range(len(pairslist)): | |
| 3029 |
|
|
2985 | volts1 = volts[pairslist[i][0]] | |
| 3030 |
|
|
2986 | volts2 = volts[pairslist[i][1]] | |
| 3031 |
|
2987 | |||
| 3032 |
|
|
2988 | for t in range(len(laglist)): | |
| 3033 |
|
|
2989 | idxT = laglist[t] | |
| 3034 |
|
|
2990 | if idxT >= 0: | |
| 3035 |
|
|
2991 | vStacked = numpy.vstack((volts2[idxT:,:], | |
| 3036 |
|
|
2992 | numpy.zeros((idxT, nHeights),dtype='complex'))) | |
| 3037 |
|
|
2993 | else: | |
| 3038 |
|
|
2994 | vStacked = numpy.vstack((numpy.zeros((-idxT, nHeights),dtype='complex'), | |
| 3039 |
|
|
2995 | volts2[:(nPoints + idxT),:])) | |
| 3040 |
|
|
2996 | voltsCCF[i,t,:] = numpy.sum((numpy.conjugate(volts1)*vStacked),axis=0) | |
| 3041 |
|
2997 | |||
| 3042 |
|
|
2998 | vStacked = None | |
| 3043 |
|
|
2999 | return voltsCCF | |
| 3044 |
|
3000 | |||
| 3045 |
|
|
3001 | def __getNoise(self, power, timeSegment, timeInterval): | |
| 3046 |
|
|
3002 | numProfPerBlock = numpy.ceil(timeSegment/timeInterval) | |
| 3047 |
|
|
3003 | numBlocks = int(power.shape[0]/numProfPerBlock) | |
| 3048 |
|
|
3004 | numHeights = power.shape[1] | |
| 3049 |
|
3005 | |||
| 3050 |
|
|
3006 | listPower = numpy.array_split(power, numBlocks, 0) | |
| 3051 |
|
|
3007 | noise = numpy.zeros((power.shape[0], power.shape[1])) | |
| 3052 |
|
|
3008 | noise1 = numpy.zeros((power.shape[0], power.shape[1])) | |
| 3053 |
|
3009 | |||
| 3054 |
|
|
3010 | startInd = 0 | |
| 3055 |
|
|
3011 | endInd = 0 | |
| 3056 |
|
3012 | |||
| 3057 |
|
|
3013 | for i in range(numBlocks): #split por canal | |
| 3058 |
|
|
3014 | startInd = endInd | |
| 3059 |
|
|
3015 | endInd = endInd + listPower[i].shape[0] | |
| 3060 |
|
3016 | |||
| 3061 |
|
|
3017 | arrayBlock = listPower[i] | |
| 3062 |
|
|
3018 | noiseAux = numpy.mean(arrayBlock, 0) | |
| 3063 |
|
|
3019 | # noiseAux = numpy.median(noiseAux) | |
| 3064 |
|
|
3020 | # noiseAux = numpy.mean(arrayBlock) | |
| 3065 |
|
|
3021 | noise[startInd:endInd,:] = noise[startInd:endInd,:] + noiseAux | |
| 3066 |
|
3022 | |||
| 3067 |
|
|
3023 | noiseAux1 = numpy.mean(arrayBlock) | |
| 3068 |
|
|
3024 | noise1[startInd:endInd,:] = noise1[startInd:endInd,:] + noiseAux1 | |
| 3069 |
|
3025 | |||
| 3070 |
|
|
3026 | return noise, noise1 | |
| 3071 |
|
3027 | |||
| 3072 |
|
|
3028 | def __findMeteors(self, power, thresh): | |
| 3073 |
|
|
3029 | nProf = power.shape[0] | |
| 3074 |
|
|
3030 | nHeights = power.shape[1] | |
| 3075 |
|
|
3031 | listMeteors = [] | |
| 3076 |
|
3032 | |||
| 3077 |
|
|
3033 | for i in range(nHeights): | |
| 3078 |
|
|
3034 | powerAux = power[:,i] | |
| 3079 |
|
|
3035 | threshAux = thresh[:,i] | |
| 3080 |
|
3036 | |||
| 3081 |
|
|
3037 | indUPthresh = numpy.where(powerAux > threshAux)[0] | |
| 3082 |
|
|
3038 | indDNthresh = numpy.where(powerAux <= threshAux)[0] | |
| 3083 |
|
3039 | |||
| 3084 |
|
|
3040 | j = 0 | |
| 3085 |
|
3041 | |||
| 3086 |
|
|
3042 | while (j < indUPthresh.size - 2): | |
| 3087 |
|
|
3043 | if (indUPthresh[j + 2] == indUPthresh[j] + 2): | |
| 3088 |
|
|
3044 | indDNAux = numpy.where(indDNthresh > indUPthresh[j]) | |
| 3089 |
|
|
3045 | indDNthresh = indDNthresh[indDNAux] | |
| 3090 |
|
3046 | |||
| 3091 |
|
|
3047 | if (indDNthresh.size > 0): | |
| 3092 |
|
|
3048 | indEnd = indDNthresh[0] - 1 | |
| 3093 |
|
|
3049 | indInit = indUPthresh[j] | |
| 3094 |
|
3050 | |||
| 3095 |
|
|
3051 | meteor = powerAux[indInit:indEnd + 1] | |
| 3096 |
|
|
3052 | indPeak = meteor.argmax() + indInit | |
| 3097 |
|
|
3053 | FLA = sum(numpy.conj(meteor)*numpy.hstack((meteor[1:],0))) | |
| 3098 |
|
3054 | |||
| 3099 |
|
|
3055 | listMeteors.append(numpy.array([i,indInit,indPeak,indEnd,FLA])) #CHEQUEAR!!!!! | |
| 3100 |
|
|
3056 | j = numpy.where(indUPthresh == indEnd)[0] + 1 | |
| 3101 |
|
|
3057 | else: j+=1 | |
| 3102 |
|
|
3058 | else: j+=1 | |
| 3103 |
|
3059 | |||
| 3104 |
|
|
3060 | return listMeteors | |
| 3105 |
|
3061 | |||
| 3106 |
|
|
3062 | def __removeMultipleDetections(self,listMeteors, rangeLimit, timeLimit): | |
| 3107 |
|
3063 | |||
| 3108 |
|
|
3064 | arrayMeteors = numpy.asarray(listMeteors) | |
| 3109 |
|
|
3065 | listMeteors1 = [] | |
| 3110 |
|
3066 | |||
| 3111 |
|
|
3067 | while arrayMeteors.shape[0] > 0: | |
| 3112 |
|
|
3068 | FLAs = arrayMeteors[:,4] | |
| 3113 |
|
|
3069 | maxFLA = FLAs.argmax() | |
| 3114 |
|
|
3070 | listMeteors1.append(arrayMeteors[maxFLA,:]) | |
| 3115 |
|
3071 | |||
| 3116 |
|
|
3072 | MeteorInitTime = arrayMeteors[maxFLA,1] | |
| 3117 |
|
|
3073 | MeteorEndTime = arrayMeteors[maxFLA,3] | |
| 3118 |
|
|
3074 | MeteorHeight = arrayMeteors[maxFLA,0] | |
| 3119 |
|
3075 | |||
| 3120 |
|
|
3076 | #Check neighborhood | |
| 3121 |
|
|
3077 | maxHeightIndex = MeteorHeight + rangeLimit | |
| 3122 |
|
|
3078 | minHeightIndex = MeteorHeight - rangeLimit | |
| 3123 |
|
|
3079 | minTimeIndex = MeteorInitTime - timeLimit | |
| 3124 |
|
|
3080 | maxTimeIndex = MeteorEndTime + timeLimit | |
| 3125 |
|
3081 | |||
| 3126 |
|
|
3082 | #Check Heights | |
| 3127 |
|
|
3083 | indHeight = numpy.logical_and(arrayMeteors[:,0] >= minHeightIndex, arrayMeteors[:,0] <= maxHeightIndex) | |
| 3128 |
|
|
3084 | indTime = numpy.logical_and(arrayMeteors[:,3] >= minTimeIndex, arrayMeteors[:,1] <= maxTimeIndex) | |
| 3129 |
|
|
3085 | indBoth = numpy.where(numpy.logical_and(indTime,indHeight)) | |
| 3130 |
|
3086 | |||
| 3131 |
|
|
3087 | arrayMeteors = numpy.delete(arrayMeteors, indBoth, axis = 0) | |
| 3132 |
|
3088 | |||
| 3133 |
|
|
3089 | return listMeteors1 | |
| 3134 |
|
3090 | |||
| 3135 |
|
|
3091 | def __meteorReestimation(self, listMeteors, volts, pairslist, thresh, noise, timeInterval,frequency): | |
| 3136 |
|
|
3092 | numHeights = volts.shape[2] | |
| 3137 |
|
|
3093 | nChannel = volts.shape[0] | |
| 3138 |
|
3094 | |||
| 3139 |
|
|
3095 | thresholdPhase = thresh[0] | |
| 3140 |
|
|
3096 | thresholdNoise = thresh[1] | |
| 3141 |
|
|
3097 | thresholdDB = float(thresh[2]) | |
| 3142 |
|
3098 | |||
| 3143 |
|
|
3099 | thresholdDB1 = 10**(thresholdDB/10) | |
| 3144 |
|
|
3100 | pairsarray = numpy.array(pairslist) | |
| 3145 |
|
|
3101 | indSides = pairsarray[:,1] | |
| 3146 |
|
3102 | |||
| 3147 |
|
|
3103 | pairslist1 = list(pairslist) | |
| 3148 |
|
|
3104 | pairslist1.append((0,1)) | |
| 3149 |
|
|
3105 | pairslist1.append((3,4)) | |
| 3150 |
|
3106 | |||
| 3151 |
|
|
3107 | listMeteors1 = [] | |
| 3152 |
|
|
3108 | listPowerSeries = [] | |
| 3153 |
|
|
3109 | listVoltageSeries = [] | |
| 3154 |
|
|
3110 | #volts has the war data | |
| 3155 |
|
3111 | |||
| 3156 |
|
|
3112 | if frequency == 30e6: | |
| 3157 |
|
|
3113 | timeLag = 45*10**-3 | |
| 3158 |
|
|
3114 | else: | |
| 3159 |
|
|
3115 | timeLag = 15*10**-3 | |
| 3160 |
|
|
3116 | lag = numpy.ceil(timeLag/timeInterval) | |
| 3161 |
|
3117 | |||
| 3162 |
|
|
3118 | for i in range(len(listMeteors)): | |
| 3163 |
|
3119 | |||
| 3164 |
|
|
3120 | ###################### 3.6 - 3.7 PARAMETERS REESTIMATION ######################### | |
| 3165 |
|
|
3121 | meteorAux = numpy.zeros(16) | |
| 3166 |
|
3122 | |||
| 3167 |
|
|
3123 | #Loading meteor Data (mHeight, mStart, mPeak, mEnd) | |
| 3168 |
|
|
3124 | mHeight = listMeteors[i][0] | |
| 3169 |
|
|
3125 | mStart = listMeteors[i][1] | |
| 3170 |
|
|
3126 | mPeak = listMeteors[i][2] | |
| 3171 |
|
|
3127 | mEnd = listMeteors[i][3] | |
| 3172 |
|
3128 | |||
| 3173 |
|
|
3129 | #get the volt data between the start and end times of the meteor | |
| 3174 |
|
|
3130 | meteorVolts = volts[:,mStart:mEnd+1,mHeight] | |
| 3175 |
|
|
3131 | meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) | |
| 3176 |
|
3132 | |||
| 3177 |
|
|
3133 | #3.6. Phase Difference estimation | |
| 3178 |
|
|
3134 | phaseDiff, aux = self.__estimatePhaseDifference(meteorVolts, pairslist) | |
| 3179 |
|
3135 | |||
| 3180 |
|
|
3136 | #3.7. Phase difference removal & meteor start, peak and end times reestimated | |
| 3181 |
|
|
3137 | #meteorVolts0.- all Channels, all Profiles | |
| 3182 |
|
|
3138 | meteorVolts0 = volts[:,:,mHeight] | |
| 3183 |
|
|
3139 | meteorThresh = noise[:,mHeight]*thresholdNoise | |
| 3184 |
|
|
3140 | meteorNoise = noise[:,mHeight] | |
| 3185 |
|
|
3141 | meteorVolts0[indSides,:] = self.__shiftPhase(meteorVolts0[indSides,:], phaseDiff) #Phase Shifting | |
| 3186 |
|
|
3142 | powerNet0 = numpy.nansum(numpy.abs(meteorVolts0)**2, axis = 0) #Power | |
| 3187 |
|
3143 | |||
| 3188 |
|
|
3144 | #Times reestimation | |
| 3189 |
|
|
3145 | mStart1 = numpy.where(powerNet0[:mPeak] < meteorThresh[:mPeak])[0] | |
| 3190 |
|
|
3146 | if mStart1.size > 0: | |
| 3191 |
|
|
3147 | mStart1 = mStart1[-1] + 1 | |
| 3192 |
|
3148 | |||
| 3193 |
|
|
3149 | else: | |
| 3194 |
|
|
3150 | mStart1 = mPeak | |
| 3195 |
|
3151 | |||
| 3196 |
|
|
3152 | mEnd1 = numpy.where(powerNet0[mPeak:] < meteorThresh[mPeak:])[0][0] + mPeak - 1 | |
| 3197 |
|
|
3153 | mEndDecayTime1 = numpy.where(powerNet0[mPeak:] < meteorNoise[mPeak:])[0] | |
| 3198 |
|
|
3154 | if mEndDecayTime1.size == 0: | |
| 3199 |
|
|
3155 | mEndDecayTime1 = powerNet0.size | |
| 3200 |
|
|
3156 | else: | |
| 3201 |
|
|
3157 | mEndDecayTime1 = mEndDecayTime1[0] + mPeak - 1 | |
| 3202 |
|
|
3158 | # mPeak1 = meteorVolts0[mStart1:mEnd1 + 1].argmax() | |
| 3203 |
|
3159 | |||
| 3204 |
|
|
3160 | #meteorVolts1.- all Channels, from start to end | |
| 3205 |
|
|
3161 | meteorVolts1 = meteorVolts0[:,mStart1:mEnd1 + 1] | |
| 3206 |
|
|
3162 | meteorVolts2 = meteorVolts0[:,mPeak + lag:mEnd1 + 1] | |
| 3207 |
|
|
3163 | if meteorVolts2.shape[1] == 0: | |
| 3208 |
|
|
3164 | meteorVolts2 = meteorVolts0[:,mPeak:mEnd1 + 1] | |
| 3209 |
|
|
3165 | meteorVolts1 = meteorVolts1.reshape(meteorVolts1.shape[0], meteorVolts1.shape[1], 1) | |
| 3210 |
|
|
3166 | meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1], 1) | |
| 3211 |
|
|
3167 | ##################### END PARAMETERS REESTIMATION ######################### | |
| 3212 |
|
3168 | |||
| 3213 |
|
|
3169 | ##################### 3.8 PHASE DIFFERENCE REESTIMATION ######################## | |
| 3214 |
|
|
3170 | # if mEnd1 - mStart1 > 4: #Error Number 6: echo less than 5 samples long; too short for analysis | |
| 3215 |
|
|
3171 | if meteorVolts2.shape[1] > 0: | |
| 3216 |
|
|
3172 | #Phase Difference re-estimation | |
| 3217 |
|
|
3173 | phaseDiff1, phaseDiffint = self.__estimatePhaseDifference(meteorVolts2, pairslist1) #Phase Difference Estimation | |
| 3218 |
|
|
3174 | # phaseDiff1, phaseDiffint = self.estimatePhaseDifference(meteorVolts2, pairslist) | |
| 3219 |
|
|
3175 | meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1]) | |
| 3220 |
|
|
3176 | phaseDiff11 = numpy.reshape(phaseDiff1, (phaseDiff1.shape[0],1)) | |
| 3221 |
|
|
3177 | meteorVolts2[indSides,:] = self.__shiftPhase(meteorVolts2[indSides,:], phaseDiff11[0:4]) #Phase Shifting | |
| 3222 |
|
3178 | |||
| 3223 |
|
|
3179 | #Phase Difference RMS | |
| 3224 |
|
|
3180 | phaseRMS1 = numpy.sqrt(numpy.mean(numpy.square(phaseDiff1))) | |
| 3225 |
|
|
3181 | powerNet1 = numpy.nansum(numpy.abs(meteorVolts1[:,:])**2,0) | |
| 3226 |
|
|
3182 | #Data from Meteor | |
| 3227 |
|
|
3183 | mPeak1 = powerNet1.argmax() + mStart1 | |
| 3228 |
|
|
3184 | mPeakPower1 = powerNet1.max() | |
| 3229 |
|
|
3185 | noiseAux = sum(noise[mStart1:mEnd1 + 1,mHeight]) | |
| 3230 |
|
|
3186 | mSNR1 = (sum(powerNet1)-noiseAux)/noiseAux | |
| 3231 |
|
|
3187 | Meteor1 = numpy.array([mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1]) | |
| 3232 |
|
|
3188 | Meteor1 = numpy.hstack((Meteor1,phaseDiffint)) | |
| 3233 |
|
|
3189 | PowerSeries = powerNet0[mStart1:mEndDecayTime1 + 1] | |
| 3234 |
|
|
3190 | #Vectorize | |
| 3235 |
|
|
3191 | meteorAux[0:7] = [mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1] | |
| 3236 |
|
|
3192 | meteorAux[7:11] = phaseDiffint[0:4] | |
| 3237 |
|
3193 | |||
| 3238 |
|
|
3194 | #Rejection Criterions | |
| 3239 |
|
|
3195 | if phaseRMS1 > thresholdPhase: #Error Number 17: Phase variation | |
| 3240 |
|
|
3196 | meteorAux[-1] = 17 | |
| 3241 |
|
|
3197 | elif mSNR1 < thresholdDB1: #Error Number 1: SNR < threshold dB | |
| 3242 |
|
|
3198 | meteorAux[-1] = 1 | |
| 3243 |
|
3199 | |||
| 3244 |
|
3200 | |||
| 3245 |
|
|
3201 | else: | |
| 3246 |
|
|
3202 | meteorAux[0:4] = [mHeight, mStart, mPeak, mEnd] | |
| 3247 |
|
|
3203 | meteorAux[-1] = 6 #Error Number 6: echo less than 5 samples long; too short for analysis | |
| 3248 |
|
|
3204 | PowerSeries = 0 | |
| 3249 |
|
3205 | |||
| 3250 |
|
|
3206 | listMeteors1.append(meteorAux) | |
| 3251 |
|
|
3207 | listPowerSeries.append(PowerSeries) | |
| 3252 |
|
|
3208 | listVoltageSeries.append(meteorVolts1) | |
| 3253 |
|
3209 | |||
| 3254 |
|
|
3210 | return listMeteors1, listPowerSeries, listVoltageSeries | |
| 3255 |
|
3211 | |||
| 3256 |
|
|
3212 | def __estimateDecayTime(self, listMeteors, listPower, timeInterval, frequency): | |
| 3257 |
|
3213 | |||
| 3258 |
|
|
3214 | threshError = 10 | |
| 3259 |
|
|
3215 | #Depending if it is 30 or 50 MHz | |
| 3260 |
|
|
3216 | if frequency == 30e6: | |
| 3261 |
|
|
3217 | timeLag = 45*10**-3 | |
| 3262 |
|
|
3218 | else: | |
| 3263 |
|
|
3219 | timeLag = 15*10**-3 | |
| 3264 |
|
|
3220 | lag = numpy.ceil(timeLag/timeInterval) | |
| 3265 |
|
3221 | |||
| 3266 |
|
|
3222 | listMeteors1 = [] | |
| 3267 |
|
3223 | |||
| 3268 |
|
|
3224 | for i in range(len(listMeteors)): | |
| 3269 |
|
|
3225 | meteorPower = listPower[i] | |
| 3270 |
|
|
3226 | meteorAux = listMeteors[i] | |
| 3271 |
|
3227 | |||
| 3272 |
|
|
3228 | if meteorAux[-1] == 0: | |
| 3273 |
|
3229 | |||
| 3274 |
|
|
3230 | try: | |
| 3275 |
|
|
3231 | indmax = meteorPower.argmax() | |
| 3276 |
|
|
3232 | indlag = indmax + lag | |
| 3277 |
|
3233 | |||
| 3278 |
|
|
3234 | y = meteorPower[indlag:] | |
| 3279 |
|
|
3235 | x = numpy.arange(0, y.size)*timeLag | |
| 3280 |
|
3236 | |||
| 3281 |
|
|
3237 | #first guess | |
| 3282 |
|
|
3238 | a = y[0] | |
| 3283 |
|
|
3239 | tau = timeLag | |
| 3284 |
|
|
3240 | #exponential fit | |
| 3285 |
|
|
3241 | popt, pcov = optimize.curve_fit(self.__exponential_function, x, y, p0 = [a, tau]) | |
| 3286 |
|
|
3242 | y1 = self.__exponential_function(x, *popt) | |
| 3287 |
|
|
3243 | #error estimation | |
| 3288 |
|
|
3244 | error = sum((y - y1)**2)/(numpy.var(y)*(y.size - popt.size)) | |
| 3289 |
|
3245 | |||
| 3290 |
|
|
3246 | decayTime = popt[1] | |
| 3291 |
|
|
3247 | riseTime = indmax*timeInterval | |
| 3292 |
|
|
3248 | meteorAux[11:13] = [decayTime, error] | |
| 3293 |
|
3249 | |||
| 3294 |
|
|
3250 | #Table items 7, 8 and 11 | |
| 3295 |
|
|
3251 | if (riseTime > 0.3): #Number 7: Echo rise exceeds 0.3s | |
| 3296 |
|
|
3252 | meteorAux[-1] = 7 | |
| 3297 |
|
|
3253 | elif (decayTime < 2*riseTime) : #Number 8: Echo decay time less than than twice rise time | |
| 3298 |
|
|
3254 | meteorAux[-1] = 8 | |
| 3299 |
|
|
3255 | if (error > threshError): #Number 11: Poor fit to amplitude for estimation of decay time | |
| 3300 |
|
|
3256 | meteorAux[-1] = 11 | |
| 3301 |
|
3257 | |||
| 3302 |
|
3258 | |||
| 3303 |
|
|
3259 | except: | |
| 3304 |
|
|
3260 | meteorAux[-1] = 11 | |
| 3305 |
|
3261 | |||
| 3306 |
|
3262 | |||
| 3307 |
|
|
3263 | listMeteors1.append(meteorAux) | |
| 3308 |
|
3264 | |||
| 3309 |
|
|
3265 | return listMeteors1 | |
| 3310 |
|
3266 | |||
| 3311 |
|
|
3267 | #Exponential Function | |
| 3312 |
|
3268 | |||
| 3313 |
|
|
3269 | def __exponential_function(self, x, a, tau): | |
| 3314 |
|
|
3270 | y = a*numpy.exp(-x/tau) | |
| 3315 |
|
|
3271 | return y | |
| 3316 |
|
3272 | |||
| 3317 |
|
|
3273 | def __getRadialVelocity(self, listMeteors, listVolts, radialStdThresh, pairslist, timeInterval): | |
| 3318 |
|
3274 | |||
| 3319 |
|
|
3275 | pairslist1 = list(pairslist) | |
| 3320 |
|
|
3276 | pairslist1.append((0,1)) | |
| 3321 |
|
|
3277 | pairslist1.append((3,4)) | |
| 3322 |
|
|
3278 | numPairs = len(pairslist1) | |
| 3323 |
|
|
3279 | #Time Lag | |
| 3324 |
|
|
3280 | timeLag = 45*10**-3 | |
| 3325 |
|
|
3281 | c = 3e8 | |
| 3326 |
|
|
3282 | lag = numpy.ceil(timeLag/timeInterval) | |
| 3327 |
|
|
3283 | freq = 30e6 | |
| 3328 |
|
3284 | |||
| 3329 |
|
|
3285 | listMeteors1 = [] | |
| 3330 |
|
3286 | |||
| 3331 |
|
|
3287 | for i in range(len(listMeteors)): | |
| 3332 |
|
|
3288 | meteorAux = listMeteors[i] | |
| 3333 |
|
|
3289 | if meteorAux[-1] == 0: | |
| 3334 |
|
|
3290 | mStart = listMeteors[i][1] | |
| 3335 |
|
|
3291 | mPeak = listMeteors[i][2] | |
| 3336 |
|
|
3292 | mLag = mPeak - mStart + lag | |
| 3337 |
|
3293 | |||
| 3338 |
|
|
3294 | #get the volt data between the start and end times of the meteor | |
| 3339 |
|
|
3295 | meteorVolts = listVolts[i] | |
| 3340 |
|
|
3296 | meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) | |
| 3341 |
|
3297 | |||
| 3342 |
|
|
3298 | #Get CCF | |
| 3343 |
|
|
3299 | allCCFs = self.__calculateCCF(meteorVolts, pairslist1, [-2,-1,0,1,2]) | |
| 3344 |
|
3300 | |||
| 3345 |
|
|
3301 | #Method 2 | |
| 3346 |
|
|
3302 | slopes = numpy.zeros(numPairs) | |
| 3347 |
|
|
3303 | time = numpy.array([-2,-1,1,2])*timeInterval | |
| 3348 |
|
|
3304 | angAllCCF = numpy.angle(allCCFs[:,[0,1,3,4],0]) | |
| 3349 |
|
3305 | |||
| 3350 |
|
|
3306 | #Correct phases | |
| 3351 |
|
|
3307 | derPhaseCCF = angAllCCF[:,1:] - angAllCCF[:,0:-1] | |
| 3352 |
|
|
3308 | indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) | |
| 3353 |
|
3309 | |||
| 3354 |
|
|
3310 | if indDer[0].shape[0] > 0: | |
| 3355 |
|
|
3311 | for i in range(indDer[0].shape[0]): | |
| 3356 |
|
|
3312 | signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i]]) | |
| 3357 |
|
|
3313 | angAllCCF[indDer[0][i],indDer[1][i]+1:] += signo*2*numpy.pi | |
| 3358 |
|
3314 | |||
| 3359 |
|
|
3315 | # fit = scipy.stats.linregress(numpy.array([-2,-1,1,2])*timeInterval, numpy.array([phaseLagN2s[i],phaseLagN1s[i],phaseLag1s[i],phaseLag2s[i]])) | |
| 3360 |
|
|
3316 | for j in range(numPairs): | |
| 3361 |
|
|
3317 | fit = stats.linregress(time, angAllCCF[j,:]) | |
| 3362 |
|
|
3318 | slopes[j] = fit[0] | |
| 3363 |
|
3319 | |||
| 3364 |
|
|
3320 | #Remove Outlier | |
| 3365 |
|
|
3321 | # indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes))) | |
| 3366 |
|
|
3322 | # slopes = numpy.delete(slopes,indOut) | |
| 3367 |
|
|
3323 | # indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes))) | |
| 3368 |
|
|
3324 | # slopes = numpy.delete(slopes,indOut) | |
| 3369 |
|
3325 | |||
| 3370 |
|
|
3326 | radialVelocity = -numpy.mean(slopes)*(0.25/numpy.pi)*(c/freq) | |
| 3371 |
|
|
3327 | radialError = numpy.std(slopes)*(0.25/numpy.pi)*(c/freq) | |
| 3372 |
|
|
3328 | meteorAux[-2] = radialError | |
| 3373 |
|
|
3329 | meteorAux[-3] = radialVelocity | |
| 3374 |
|
3330 | |||
| 3375 |
|
|
3331 | #Setting Error | |
| 3376 |
|
|
3332 | #Number 15: Radial Drift velocity or projected horizontal velocity exceeds 200 m/s | |
| 3377 |
|
|
3333 | if numpy.abs(radialVelocity) > 200: | |
| 3378 |
|
|
3334 | meteorAux[-1] = 15 | |
| 3379 |
|
|
3335 | #Number 12: Poor fit to CCF variation for estimation of radial drift velocity | |
| 3380 |
|
|
3336 | elif radialError > radialStdThresh: | |
| 3381 |
|
|
3337 | meteorAux[-1] = 12 | |
| 3382 |
|
3338 | |||
| 3383 |
|
|
3339 | listMeteors1.append(meteorAux) | |
| 3384 |
|
|
3340 | return listMeteors1 | |
| 3385 |
|
3341 | |||
| 3386 |
|
|
3342 | def __setNewArrays(self, listMeteors, date, heiRang): | |
| 3387 |
|
3343 | |||
| 3388 |
|
|
3344 | #New arrays | |
| 3389 |
|
|
3345 | arrayMeteors = numpy.array(listMeteors) | |
| 3390 |
|
|
3346 | arrayParameters = numpy.zeros((len(listMeteors), 13)) | |
| 3391 |
|
3347 | |||
| 3392 |
|
|
3348 | #Date inclusion | |
| 3393 |
|
|
3349 | # date = re.findall(r'\((.*?)\)', date) | |
| 3394 |
|
|
3350 | # date = date[0].split(',') | |
| 3395 |
|
|
3351 | # date = map(int, date) | |
| 3396 |
|
|
3352 | # | |
| 3397 |
|
|
3353 | # if len(date)<6: | |
| 3398 |
|
|
3354 | # date.append(0) | |
| 3399 |
|
|
3355 | # | |
| 3400 |
|
|
3356 | # date = [date[0]*10000 + date[1]*100 + date[2], date[3]*10000 + date[4]*100 + date[5]] | |
| 3401 |
|
|
3357 | # arrayDate = numpy.tile(date, (len(listMeteors), 1)) | |
| 3402 |
|
|
3358 | arrayDate = numpy.tile(date, (len(listMeteors))) | |
| 3403 |
|
3359 | |||
| 3404 |
|
|
3360 | #Meteor array | |
| 3405 |
|
|
3361 | # arrayMeteors[:,0] = heiRang[arrayMeteors[:,0].astype(int)] | |
| 3406 |
|
|
3362 | # arrayMeteors = numpy.hstack((arrayDate, arrayMeteors)) | |
| 3407 |
|
3363 | |||
| 3408 |
|
|
3364 | #Parameters Array | |
| 3409 |
|
|
3365 | arrayParameters[:,0] = arrayDate #Date | |
| 3410 |
|
|
3366 | arrayParameters[:,1] = heiRang[arrayMeteors[:,0].astype(int)] #Range | |
| 3411 |
|
|
3367 | arrayParameters[:,6:8] = arrayMeteors[:,-3:-1] #Radial velocity and its error | |
| 3412 |
|
|
3368 | arrayParameters[:,8:12] = arrayMeteors[:,7:11] #Phases | |
| 3413 |
|
|
3369 | arrayParameters[:,-1] = arrayMeteors[:,-1] #Error | |
| 3414 |
|
3370 | |||
| 3415 |
|
3371 | |||
| 3416 |
|
|
3372 | return arrayParameters | |
| 3417 |
|
3373 | |||
| 3418 |
|
|
3374 | class CorrectSMPhases(Operation): | |
| 3419 |
|
3375 | |||
| 3420 |
|
|
3376 | def run(self, dataOut, phaseOffsets, hmin = 50, hmax = 150, azimuth = 45, channelPositions = None): | |
| 3421 |
|
3377 | |||
| 3422 |
|
|
3378 | arrayParameters = dataOut.data_param | |
| 3423 |
|
|
3379 | pairsList = [] | |
| 3424 |
|
|
3380 | pairx = (0,1) | |
| 3425 |
|
|
3381 | pairy = (2,3) | |
| 3426 |
|
|
3382 | pairsList.append(pairx) | |
| 3427 |
|
|
3383 | pairsList.append(pairy) | |
| 3428 |
|
|
3384 | jph = numpy.zeros(4) | |
| 3429 |
|
3385 | |||
| 3430 |
|
|
3386 | phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180 | |
| 3431 |
|
|
3387 | # arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets) | |
| 3432 |
|
|
3388 | arrayParameters[:,8:12] = numpy.angle(numpy.exp(1j*(arrayParameters[:,8:12] + phaseOffsets))) | |
| 3433 |
|
3389 | |||
| 3434 |
|
|
3390 | meteorOps = SMOperations() | |
| 3435 |
|
|
3391 | if channelPositions is None: | |
| 3436 |
|
|
3392 | # channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T | |
| 3437 |
|
|
3393 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella | |
| 3438 |
|
3394 | |||
| 3439 |
|
|
3395 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) | |
| 3440 |
|
|
3396 | h = (hmin,hmax) | |
| 3441 |
|
3397 | |||
| 3442 |
|
|
3398 | arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph) | |
| 3443 |
|
3399 | |||
| 3444 |
|
|
3400 | dataOut.data_param = arrayParameters | |
| 3445 |
|
|
3401 | return | |
| 3446 |
|
3402 | |||
| 3447 |
|
|
3403 | class SMPhaseCalibration(Operation): | |
| 3448 |
|
3404 | |||
| 3449 |
|
|
3405 | __buffer = None | |
| 3450 |
|
3406 | |||
| 3451 |
|
|
3407 | __initime = None | |
| 3452 |
|
3408 | |||
| 3453 |
|
|
3409 | __dataReady = False | |
| 3454 |
|
3410 | |||
| 3455 |
|
|
3411 | __isConfig = False | |
| 3456 |
|
3412 | |||
| 3457 |
|
|
3413 | def __checkTime(self, currentTime, initTime, paramInterval, outputInterval): | |
| 3458 |
|
3414 | |||
| 3459 |
|
|
3415 | dataTime = currentTime + paramInterval | |
| 3460 |
|
|
3416 | deltaTime = dataTime - initTime | |
| 3461 |
|
3417 | |||
| 3462 |
|
|
3418 | if deltaTime >= outputInterval or deltaTime < 0: | |
| 3463 |
|
|
3419 | return True | |
| 3464 |
|
3420 | |||
| 3465 |
|
|
3421 | return False | |
| 3466 |
|
3422 | |||
| 3467 |
|
|
3423 | def __getGammas(self, pairs, d, phases): | |
| 3468 |
|
|
3424 | gammas = numpy.zeros(2) | |
| 3469 |
|
3425 | |||
| 3470 |
|
|
3426 | for i in range(len(pairs)): | |
| 3471 |
|
3427 | |||
| 3472 |
|
|
3428 | pairi = pairs[i] | |
| 3473 |
|
3429 | |||
| 3474 |
|
|
3430 | phip3 = phases[:,pairi[0]] | |
| 3475 |
|
|
3431 | d3 = d[pairi[0]] | |
| 3476 |
|
|
3432 | phip2 = phases[:,pairi[1]] | |
| 3477 |
|
|
3433 | d2 = d[pairi[1]] | |
| 3478 |
|
|
3434 | #Calculating gamma | |
| 3479 |
|
|
3435 | # jdcos = alp1/(k*d1) | |
| 3480 |
|
|
3436 | # jgamma = numpy.angle(numpy.exp(1j*(d0*alp1/d1 - alp0))) | |
| 3481 |
|
|
3437 | jgamma = -phip2*d3/d2 - phip3 | |
| 3482 |
|
|
3438 | jgamma = numpy.angle(numpy.exp(1j*jgamma)) | |
| 3483 |
|
|
3439 | # jgamma[jgamma>numpy.pi] -= 2*numpy.pi | |
| 3484 |
|
|
3440 | # jgamma[jgamma<-numpy.pi] += 2*numpy.pi | |
| 3485 |
|
3441 | |||
| 3486 |
|
|
3442 | #Revised distribution | |
| 3487 |
|
|
3443 | jgammaArray = numpy.hstack((jgamma,jgamma+0.5*numpy.pi,jgamma-0.5*numpy.pi)) | |
| 3488 |
|
3444 | |||
| 3489 |
|
|
3445 | #Histogram | |
| 3490 |
|
|
3446 | nBins = 64 | |
| 3491 |
|
|
3447 | rmin = -0.5*numpy.pi | |
| 3492 |
|
|
3448 | rmax = 0.5*numpy.pi | |
| 3493 |
|
|
3449 | phaseHisto = numpy.histogram(jgammaArray, bins=nBins, range=(rmin,rmax)) | |
| 3494 |
|
3450 | |||
| 3495 |
|
|
3451 | meteorsY = phaseHisto[0] | |
| 3496 |
|
|
3452 | phasesX = phaseHisto[1][:-1] | |
| 3497 |
|
|
3453 | width = phasesX[1] - phasesX[0] | |
| 3498 |
|
|
3454 | phasesX += width/2 | |
| 3499 |
|
3455 | |||
| 3500 |
|
|
3456 | #Gaussian aproximation | |
| 3501 |
|
|
3457 | bpeak = meteorsY.argmax() | |
| 3502 |
|
|
3458 | peak = meteorsY.max() | |
| 3503 |
|
|
3459 | jmin = bpeak - 5 | |
| 3504 |
|
|
3460 | jmax = bpeak + 5 + 1 | |
| 3505 |
|
3461 | |||
| 3506 |
|
|
3462 | if jmin<0: | |
| 3507 |
|
|
3463 | jmin = 0 | |
| 3508 |
|
|
3464 | jmax = 6 | |
| 3509 |
|
|
3465 | elif jmax > meteorsY.size: | |
| 3510 |
|
|
3466 | jmin = meteorsY.size - 6 | |
| 3511 |
|
|
3467 | jmax = meteorsY.size | |
| 3512 |
|
3468 | |||
| 3513 |
|
|
3469 | x0 = numpy.array([peak,bpeak,50]) | |
| 3514 |
|
|
3470 | coeff = optimize.leastsq(self.__residualFunction, x0, args=(meteorsY[jmin:jmax], phasesX[jmin:jmax])) | |
| 3515 |
|
3471 | |||
| 3516 |
|
|
3472 | #Gammas | |
| 3517 |
|
|
3473 | gammas[i] = coeff[0][1] | |
| 3518 |
|
3474 | |||
| 3519 |
|
|
3475 | return gammas | |
| 3520 |
|
3476 | |||
| 3521 |
|
|
3477 | def __residualFunction(self, coeffs, y, t): | |
| 3522 |
|
3478 | |||
| 3523 |
|
|
3479 | return y - self.__gauss_function(t, coeffs) | |
| 3524 |
|
3480 | |||
| 3525 |
|
|
3481 | def __gauss_function(self, t, coeffs): | |
| 3526 |
|
3482 | |||
| 3527 |
|
|
3483 | return coeffs[0]*numpy.exp(-0.5*((t - coeffs[1]) / coeffs[2])**2) | |
| 3528 |
|
3484 | |||
| 3529 |
|
|
3485 | def __getPhases(self, azimuth, h, pairsList, d, gammas, meteorsArray): | |
| 3530 |
|
|
3486 | meteorOps = SMOperations() | |
| 3531 |
|
|
3487 | nchan = 4 | |
| 3532 |
|
|
3488 | pairx = pairsList[0] #x es 0 | |
| 3533 |
|
|
3489 | pairy = pairsList[1] #y es 1 | |
| 3534 |
|
|
3490 | center_xangle = 0 | |
| 3535 |
|
|
3491 | center_yangle = 0 | |
| 3536 |
|
|
3492 | range_angle = numpy.array([10*numpy.pi,numpy.pi,numpy.pi/2,numpy.pi/4]) | |
| 3537 |
|
|
3493 | ntimes = len(range_angle) | |
| 3538 |
|
3494 | |||
| 3539 |
|
|
3495 | nstepsx = 20 | |
| 3540 |
|
|
3496 | nstepsy = 20 | |
| 3541 |
|
3497 | |||
| 3542 |
|
|
3498 | for iz in range(ntimes): | |
| 3543 |
|
|
3499 | min_xangle = -range_angle[iz]/2 + center_xangle | |
| 3544 |
|
|
3500 | max_xangle = range_angle[iz]/2 + center_xangle | |
| 3545 |
|
|
3501 | min_yangle = -range_angle[iz]/2 + center_yangle | |
| 3546 |
|
|
3502 | max_yangle = range_angle[iz]/2 + center_yangle | |
| 3547 |
|
3503 | |||
| 3548 |
|
|
3504 | inc_x = (max_xangle-min_xangle)/nstepsx | |
| 3549 |
|
|
3505 | inc_y = (max_yangle-min_yangle)/nstepsy | |
| 3550 |
|
3506 | |||
| 3551 |
|
|
3507 | alpha_y = numpy.arange(nstepsy)*inc_y + min_yangle | |
| 3552 |
|
|
3508 | alpha_x = numpy.arange(nstepsx)*inc_x + min_xangle | |
| 3553 |
|
|
3509 | penalty = numpy.zeros((nstepsx,nstepsy)) | |
| 3554 |
|
|
3510 | jph_array = numpy.zeros((nchan,nstepsx,nstepsy)) | |
| 3555 |
|
|
3511 | jph = numpy.zeros(nchan) | |
| 3556 |
|
3512 | |||
| 3557 |
|
|
3513 | # Iterations looking for the offset | |
| 3558 |
|
|
3514 | for iy in range(int(nstepsy)): | |
| 3559 |
|
|
3515 | for ix in range(int(nstepsx)): | |
| 3560 |
|
|
3516 | d3 = d[pairsList[1][0]] | |
| 3561 |
|
|
3517 | d2 = d[pairsList[1][1]] | |
| 3562 |
|
|
3518 | d5 = d[pairsList[0][0]] | |
| 3563 |
|
|
3519 | d4 = d[pairsList[0][1]] | |
| 3564 |
|
3520 | |||
| 3565 |
|
|
3521 | alp2 = alpha_y[iy] #gamma 1 | |
| 3566 |
|
|
3522 | alp4 = alpha_x[ix] #gamma 0 | |
| 3567 |
|
3523 | |||
| 3568 |
|
|
3524 | alp3 = -alp2*d3/d2 - gammas[1] | |
| 3569 |
|
|
3525 | alp5 = -alp4*d5/d4 - gammas[0] | |
| 3570 |
|
|
3526 | # jph[pairy[1]] = alpha_y[iy] | |
| 3571 |
|
|
3527 | # jph[pairy[0]] = -gammas[1] - alpha_y[iy]*d[pairy[1]]/d[pairy[0]] | |
| 3572 |
|
3528 | |||
| 3573 |
|
|
3529 | # jph[pairx[1]] = alpha_x[ix] | |
| 3574 |
|
|
3530 | # jph[pairx[0]] = -gammas[0] - alpha_x[ix]*d[pairx[1]]/d[pairx[0]] | |
| 3575 |
|
|
3531 | jph[pairsList[0][1]] = alp4 | |
| 3576 |
|
|
3532 | jph[pairsList[0][0]] = alp5 | |
| 3577 |
|
|
3533 | jph[pairsList[1][0]] = alp3 | |
| 3578 |
|
|
3534 | jph[pairsList[1][1]] = alp2 | |
| 3579 |
|
|
3535 | jph_array[:,ix,iy] = jph | |
| 3580 |
|
|
3536 | # d = [2.0,2.5,2.5,2.0] | |
| 3581 |
|
|
3537 | #falta chequear si va a leer bien los meteoros | |
| 3582 |
|
|
3538 | meteorsArray1 = meteorOps.getMeteorParams(meteorsArray, azimuth, h, pairsList, d, jph) | |
| 3583 |
|
|
3539 | error = meteorsArray1[:,-1] | |
| 3584 |
|
|
3540 | ind1 = numpy.where(error==0)[0] | |
| 3585 |
|
|
3541 | penalty[ix,iy] = ind1.size | |
| 3586 |
|
3542 | |||
| 3587 |
|
|
3543 | i,j = numpy.unravel_index(penalty.argmax(), penalty.shape) | |
| 3588 |
|
|
3544 | phOffset = jph_array[:,i,j] | |
| 3589 |
|
3545 | |||
| 3590 |
|
|
3546 | center_xangle = phOffset[pairx[1]] | |
| 3591 |
|
|
3547 | center_yangle = phOffset[pairy[1]] | |
| 3592 |
|
3548 | |||
| 3593 |
|
|
3549 | phOffset = numpy.angle(numpy.exp(1j*jph_array[:,i,j])) | |
| 3594 |
|
|
3550 | phOffset = phOffset*180/numpy.pi | |
| 3595 |
|
|
3551 | return phOffset | |
| 3596 |
|
3552 | |||
| 3597 |
|
3553 | |||
| 3598 |
|
|
3554 | def run(self, dataOut, hmin, hmax, channelPositions=None, nHours = 1): | |
| 3599 |
|
3555 | |||
| 3600 |
|
|
3556 | dataOut.flagNoData = True | |
| 3601 |
|
|
3557 | self.__dataReady = False | |
| 3602 |
|
|
3558 | dataOut.outputInterval = nHours*3600 | |
| 3603 |
|
3559 | |||
| 3604 |
|
|
3560 | if self.__isConfig == False: | |
| 3605 |
|
|
3561 | # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) | |
| 3606 |
|
|
3562 | #Get Initial LTC time | |
| 3607 |
|
|
3563 | self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) | |
| 3608 |
|
|
3564 | self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() | |
| 3609 |
|
3565 | |||
| 3610 |
|
|
3566 | self.__isConfig = True | |
| 3611 |
|
3567 | |||
| 3612 |
|
|
3568 | if self.__buffer is None: | |
| 3613 |
|
|
3569 | self.__buffer = dataOut.data_param.copy() | |
| 3614 |
|
3570 | |||
| 3615 |
|
|
3571 | else: | |
| 3616 |
|
|
3572 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) | |
| 3617 |
|
3573 | |||
| 3618 |
|
|
3574 | self.__dataReady = self.__checkTime(dataOut.utctime, self.__initime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready | |
| 3619 |
|
3575 | |||
| 3620 |
|
|
3576 | if self.__dataReady: | |
| 3621 |
|
|
3577 | dataOut.utctimeInit = self.__initime | |
| 3622 |
|
|
3578 | self.__initime += dataOut.outputInterval #to erase time offset | |
| 3623 |
|
3579 | |||
| 3624 |
|
|
3580 | freq = dataOut.frequency | |
| 3625 |
|
|
3581 | c = dataOut.C #m/s | |
| 3626 |
|
|
3582 | lamb = c/freq | |
| 3627 |
|
|
3583 | k = 2*numpy.pi/lamb | |
| 3628 |
|
|
3584 | azimuth = 0 | |
| 3629 |
|
|
3585 | h = (hmin, hmax) | |
| 3630 |
|
|
3586 | # pairs = ((0,1),(2,3)) #Estrella | |
| 3631 |
|
|
3587 | # pairs = ((1,0),(2,3)) #T | |
| 3632 |
|
3588 | |||
| 3633 |
|
|
3589 | if channelPositions is None: | |
| 3634 |
|
|
3590 | # channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)] #T | |
| 3635 |
|
|
3591 | channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)] #Estrella | |
| 3636 |
|
|
3592 | meteorOps = SMOperations() | |
| 3637 |
|
|
3593 | pairslist0, distances = meteorOps.getPhasePairs(channelPositions) | |
| 3638 |
|
3594 | |||
| 3639 |
|
|
3595 | #Checking correct order of pairs | |
| 3640 |
|
|
3596 | pairs = [] | |
| 3641 |
|
|
3597 | if distances[1] > distances[0]: | |
| 3642 |
|
|
3598 | pairs.append((1,0)) | |
| 3643 |
|
|
3599 | else: | |
| 3644 |
|
|
3600 | pairs.append((0,1)) | |
| 3645 |
|
3601 | |||
| 3646 |
|
|
3602 | if distances[3] > distances[2]: | |
| 3647 |
|
|
3603 | pairs.append((3,2)) | |
| 3648 |
|
|
3604 | else: | |
| 3649 |
|
|
3605 | pairs.append((2,3)) | |
| 3650 |
|
|
3606 | # distances1 = [-distances[0]*lamb, distances[1]*lamb, -distances[2]*lamb, distances[3]*lamb] | |
| 3651 |
|
3607 | |||
| 3652 |
|
|
3608 | meteorsArray = self.__buffer | |
| 3653 |
|
|
3609 | error = meteorsArray[:,-1] | |
| 3654 |
|
|
3610 | boolError = (error==0)|(error==3)|(error==4)|(error==13)|(error==14) | |
| 3655 |
|
|
3611 | ind1 = numpy.where(boolError)[0] | |
| 3656 |
|
|
3612 | meteorsArray = meteorsArray[ind1,:] | |
| 3657 |
|
|
3613 | meteorsArray[:,-1] = 0 | |
| 3658 |
|
|
3614 | phases = meteorsArray[:,8:12] | |
| 3659 |
|
3615 | |||
| 3660 |
|
|
3616 | #Calculate Gammas | |
| 3661 |
|
|
3617 | gammas = self.__getGammas(pairs, distances, phases) | |
| 3662 |
|
|
3618 | # gammas = numpy.array([-21.70409463,45.76935864])*numpy.pi/180 | |
| 3663 |
|
|
3619 | #Calculate Phases | |
| 3664 |
|
|
3620 | phasesOff = self.__getPhases(azimuth, h, pairs, distances, gammas, meteorsArray) | |
| 3665 |
|
|
3621 | phasesOff = phasesOff.reshape((1,phasesOff.size)) | |
| 3666 |
|
|
3622 | dataOut.data_output = -phasesOff | |
| 3667 |
|
|
3623 | dataOut.flagNoData = False | |
| 3668 |
|
|
3624 | self.__buffer = None | |
| 3669 |
|
3625 | |||
| 3670 |
|
3626 | |||
| 3671 |
|
|
3627 | return | |
| 3672 |
|
3628 | |||
| 3673 |
|
|
3629 | class SMOperations(): | |
| 3674 |
|
3630 | |||
| 3675 |
|
|
3631 | def __init__(self): | |
| 3676 |
|
3632 | |||
| 3677 |
|
|
3633 | return | |
| 3678 |
|
3634 | |||
| 3679 |
|
|
3635 | def getMeteorParams(self, arrayParameters0, azimuth, h, pairsList, distances, jph): | |
| 3680 |
|
3636 | |||
| 3681 |
|
|
3637 | arrayParameters = arrayParameters0.copy() | |
| 3682 |
|
|
3638 | hmin = h[0] | |
| 3683 |
|
|
3639 | hmax = h[1] | |
| 3684 |
|
3640 | |||
| 3685 |
|
|
3641 | #Calculate AOA (Error N 3, 4) | |
| 3686 |
|
|
3642 | #JONES ET AL. 1998 | |
| 3687 |
|
|
3643 | AOAthresh = numpy.pi/8 | |
| 3688 |
|
|
3644 | error = arrayParameters[:,-1] | |
| 3689 |
|
|
3645 | phases = -arrayParameters[:,8:12] + jph | |
| 3690 |
|
|
3646 | # phases = numpy.unwrap(phases) | |
| 3691 |
|
|
3647 | arrayParameters[:,3:6], arrayParameters[:,-1] = self.__getAOA(phases, pairsList, distances, error, AOAthresh, azimuth) | |
| 3692 |
|
3648 | |||
| 3693 |
|
|
3649 | #Calculate Heights (Error N 13 and 14) | |
| 3694 |
|
|
3650 | error = arrayParameters[:,-1] | |
| 3695 |
|
|
3651 | Ranges = arrayParameters[:,1] | |
| 3696 |
|
|
3652 | zenith = arrayParameters[:,4] | |
| 3697 |
|
|
3653 | arrayParameters[:,2], arrayParameters[:,-1] = self.__getHeights(Ranges, zenith, error, hmin, hmax) | |
| 3698 |
|
3654 | |||
| 3699 |
|
|
3655 | #----------------------- Get Final data ------------------------------------ | |
| 3700 |
|
|
3656 | # error = arrayParameters[:,-1] | |
| 3701 |
|
|
3657 | # ind1 = numpy.where(error==0)[0] | |
| 3702 |
|
|
3658 | # arrayParameters = arrayParameters[ind1,:] | |
| 3703 |
|
3659 | |||
| 3704 |
|
|
3660 | return arrayParameters | |
| 3705 |
|
3661 | |||
| 3706 |
|
|
3662 | def __getAOA(self, phases, pairsList, directions, error, AOAthresh, azimuth): | |
| 3707 |
|
3663 | |||
| 3708 |
|
|
3664 | arrayAOA = numpy.zeros((phases.shape[0],3)) | |
| 3709 |
|
|
3665 | cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList,directions) | |
| 3710 |
|
3666 | |||
| 3711 |
|
|
3667 | arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth) | |
| 3712 |
|
|
3668 | cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1) | |
| 3713 |
|
|
3669 | arrayAOA[:,2] = cosDirError | |
| 3714 |
|
3670 | |||
| 3715 |
|
|
3671 | azimuthAngle = arrayAOA[:,0] | |
| 3716 |
|
|
3672 | zenithAngle = arrayAOA[:,1] | |
| 3717 |
|
3673 | |||
| 3718 |
|
|
3674 | #Setting Error | |
| 3719 |
|
|
3675 | indError = numpy.where(numpy.logical_or(error == 3, error == 4))[0] | |
| 3720 |
|
|
3676 | error[indError] = 0 | |
| 3721 |
|
|
3677 | #Number 3: AOA not fesible | |
| 3722 |
|
|
3678 | indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0] | |
| 3723 |
|
|
3679 | error[indInvalid] = 3 | |
| 3724 |
|
|
3680 | #Number 4: Large difference in AOAs obtained from different antenna baselines | |
| 3725 |
|
|
3681 | indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0] | |
| 3726 |
|
|
3682 | error[indInvalid] = 4 | |
| 3727 |
|
|
3683 | return arrayAOA, error | |
| 3728 |
|
3684 | |||
| 3729 |
|
|
3685 | def __getDirectionCosines(self, arrayPhase, pairsList, distances): | |
| 3730 |
|
3686 | |||
| 3731 |
|
|
3687 | #Initializing some variables | |
| 3732 |
|
|
3688 | ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi | |
| 3733 |
|
|
3689 | ang_aux = ang_aux.reshape(1,ang_aux.size) | |
| 3734 |
|
3690 | |||
| 3735 |
|
|
3691 | cosdir = numpy.zeros((arrayPhase.shape[0],2)) | |
| 3736 |
|
|
3692 | cosdir0 = numpy.zeros((arrayPhase.shape[0],2)) | |
| 3737 |
|
3693 | |||
| 3738 |
|
3694 | |||
| 3739 |
|
|
3695 | for i in range(2): | |
| 3740 |
|
|
3696 | ph0 = arrayPhase[:,pairsList[i][0]] | |
| 3741 |
|
|
3697 | ph1 = arrayPhase[:,pairsList[i][1]] | |
| 3742 |
|
|
3698 | d0 = distances[pairsList[i][0]] | |
| 3743 |
|
|
3699 | d1 = distances[pairsList[i][1]] | |
| 3744 |
|
3700 | |||
| 3745 |
|
|
3701 | ph0_aux = ph0 + ph1 | |
| 3746 |
|
|
3702 | ph0_aux = numpy.angle(numpy.exp(1j*ph0_aux)) | |
| 3747 |
|
|
3703 | # ph0_aux[ph0_aux > numpy.pi] -= 2*numpy.pi | |
| 3748 |
|
|
3704 | # ph0_aux[ph0_aux < -numpy.pi] += 2*numpy.pi | |
| 3749 |
|
|
3705 | #First Estimation | |
| 3750 |
|
|
3706 | cosdir0[:,i] = (ph0_aux)/(2*numpy.pi*(d0 - d1)) | |
| 3751 |
|
3707 | |||
| 3752 |
|
|
3708 | #Most-Accurate Second Estimation | |
| 3753 |
|
|
3709 | phi1_aux = ph0 - ph1 | |
| 3754 |
|
|
3710 | phi1_aux = phi1_aux.reshape(phi1_aux.size,1) | |
| 3755 |
|
|
3711 | #Direction Cosine 1 | |
| 3756 |
|
|
3712 | cosdir1 = (phi1_aux + ang_aux)/(2*numpy.pi*(d0 + d1)) | |
| 3757 |
|
3713 | |||
| 3758 |
|
|
3714 | #Searching the correct Direction Cosine | |
| 3759 |
|
|
3715 | cosdir0_aux = cosdir0[:,i] | |
| 3760 |
|
|
3716 | cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1) | |
| 3761 |
|
|
3717 | #Minimum Distance | |
| 3762 |
|
|
3718 | cosDiff = (cosdir1 - cosdir0_aux)**2 | |
| 3763 |
|
|
3719 | indcos = cosDiff.argmin(axis = 1) | |
| 3764 |
|
|
3720 | #Saving Value obtained | |
| 3765 |
|
|
3721 | cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos] | |
| 3766 |
|
3722 | |||
| 3767 |
|
|
3723 | return cosdir0, cosdir | |
| 3768 |
|
3724 | |||
| 3769 |
|
|
3725 | def __calculateAOA(self, cosdir, azimuth): | |
| 3770 |
|
|
3726 | cosdirX = cosdir[:,0] | |
| 3771 |
|
|
3727 | cosdirY = cosdir[:,1] | |
| 3772 |
|
3728 | |||
| 3773 |
|
|
3729 | zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi | |
| 3774 |
|
|
3730 | azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth#0 deg north, 90 deg east | |
| 3775 |
|
|
3731 | angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose() | |
| 3776 |
|
3732 | |||
| 3777 |
|
|
3733 | return angles | |
| 3778 |
|
3734 | |||
| 3779 |
|
|
3735 | def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight): | |
| 3780 |
|
3736 | |||
| 3781 |
|
|
3737 | Ramb = 375 #Ramb = c/(2*PRF) | |
| 3782 |
|
|
3738 | Re = 6371 #Earth Radius | |
| 3783 |
|
|
3739 | heights = numpy.zeros(Ranges.shape) | |
| 3784 |
|
3740 | |||
| 3785 |
|
|
3741 | R_aux = numpy.array([0,1,2])*Ramb | |
| 3786 |
|
|
3742 | R_aux = R_aux.reshape(1,R_aux.size) | |
| 3787 |
|
3743 | |||
| 3788 |
|
|
3744 | Ranges = Ranges.reshape(Ranges.size,1) | |
| 3789 |
|
3745 | |||
| 3790 |
|
|
3746 | Ri = Ranges + R_aux | |
| 3791 |
|
|
3747 | hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re | |
| 3792 |
|
3748 | |||
| 3793 |
|
|
3749 | #Check if there is a height between 70 and 110 km | |
| 3794 |
|
|
3750 | h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1) | |
| 3795 |
|
|
3751 | ind_h = numpy.where(h_bool == 1)[0] | |
| 3796 |
|
3752 | |||
| 3797 |
|
|
3753 | hCorr = hi[ind_h, :] | |
| 3798 |
|
|
3754 | ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight)) | |
| 3799 |
|
3755 | |||
| 3800 |
|
|
3756 | hCorr = hi[ind_hCorr][:len(ind_h)] | |
| 3801 |
|
|
3757 | heights[ind_h] = hCorr | |
| 3802 |
|
3758 | |||
| 3803 |
|
|
3759 | #Setting Error | |
| 3804 |
|
|
3760 | #Number 13: Height unresolvable echo: not valid height within 70 to 110 km | |
| 3805 |
|
|
3761 | #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km | |
| 3806 |
|
|
3762 | indError = numpy.where(numpy.logical_or(error == 13, error == 14))[0] | |
| 3807 |
|
|
3763 | error[indError] = 0 | |
| 3808 |
|
|
3764 | indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0] | |
| 3809 |
|
|
3765 | error[indInvalid2] = 14 | |
| 3810 |
|
|
3766 | indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0] | |
| 3811 |
|
|
3767 | error[indInvalid1] = 13 | |
| 3812 |
|
3768 | |||
| 3813 |
|
|
3769 | return heights, error | |
| 3814 |
|
3770 | |||
| 3815 |
|
|
3771 | def getPhasePairs(self, channelPositions): | |
| 3816 |
|
|
3772 | chanPos = numpy.array(channelPositions) | |
| 3817 |
|
|
3773 | listOper = list(itertools.combinations(list(range(5)),2)) | |
| 3818 |
|
3774 | |||
| 3819 |
|
|
3775 | distances = numpy.zeros(4) | |
| 3820 |
|
|
3776 | axisX = [] | |
| 3821 |
|
|
3777 | axisY = [] | |
| 3822 |
|
|
3778 | distX = numpy.zeros(3) | |
| 3823 |
|
|
3779 | distY = numpy.zeros(3) | |
| 3824 |
|
|
3780 | ix = 0 | |
| 3825 |
|
|
3781 | iy = 0 | |
| 3826 |
|
3782 | |||
| 3827 |
|
|
3783 | pairX = numpy.zeros((2,2)) | |
| 3828 |
|
|
3784 | pairY = numpy.zeros((2,2)) | |
| 3829 |
|
3785 | |||
| 3830 |
|
|
3786 | for i in range(len(listOper)): | |
| 3831 |
|
|
3787 | pairi = listOper[i] | |
| 3832 |
|
3788 | |||
| 3833 |
|
|
3789 | posDif = numpy.abs(chanPos[pairi[0],:] - chanPos[pairi[1],:]) | |
| 3834 |
|
3790 | |||
| 3835 |
|
|
3791 | if posDif[0] == 0: | |
| 3836 |
|
|
3792 | axisY.append(pairi) | |
| 3837 |
|
|
3793 | distY[iy] = posDif[1] | |
| 3838 |
|
|
3794 | iy += 1 | |
| 3839 |
|
|
3795 | elif posDif[1] == 0: | |
| 3840 |
|
|
3796 | axisX.append(pairi) | |
| 3841 |
|
|
3797 | distX[ix] = posDif[0] | |
| 3842 |
|
|
3798 | ix += 1 | |
| 3843 |
|
3799 | |||
| 3844 |
|
|
3800 | for i in range(2): | |
| 3845 |
|
|
3801 | if i==0: | |
| 3846 |
|
|
3802 | dist0 = distX | |
| 3847 |
|
|
3803 | axis0 = axisX | |
| 3848 |
|
|
3804 | else: | |
| 3849 |
|
|
3805 | dist0 = distY | |
| 3850 |
|
|
3806 | axis0 = axisY | |
| 3851 |
|
3807 | |||
| 3852 |
|
|
3808 | side = numpy.argsort(dist0)[:-1] | |
| 3853 |
|
|
3809 | axis0 = numpy.array(axis0)[side,:] | |
| 3854 |
|
|
3810 | chanC = int(numpy.intersect1d(axis0[0,:], axis0[1,:])[0]) | |
| 3855 |
|
|
3811 | axis1 = numpy.unique(numpy.reshape(axis0,4)) | |
| 3856 |
|
|
3812 | side = axis1[axis1 != chanC] | |
| 3857 |
|
|
3813 | diff1 = chanPos[chanC,i] - chanPos[side[0],i] | |
| 3858 |
|
|
3814 | diff2 = chanPos[chanC,i] - chanPos[side[1],i] | |
| 3859 |
|
|
3815 | if diff1<0: | |
| 3860 |
|
|
3816 | chan2 = side[0] | |
| 3861 |
|
|
3817 | d2 = numpy.abs(diff1) | |
| 3862 |
|
|
3818 | chan1 = side[1] | |
| 3863 |
|
|
3819 | d1 = numpy.abs(diff2) | |
| 3864 |
|
|
3820 | else: | |
| 3865 |
|
|
3821 | chan2 = side[1] | |
| 3866 |
|
|
3822 | d2 = numpy.abs(diff2) | |
| 3867 |
|
|
3823 | chan1 = side[0] | |
| 3868 |
|
|
3824 | d1 = numpy.abs(diff1) | |
| 3869 |
|
3825 | |||
| 3870 |
|
|
3826 | if i==0: | |
| 3871 |
|
|
3827 | chanCX = chanC | |
| 3872 |
|
|
3828 | chan1X = chan1 | |
| 3873 |
|
|
3829 | chan2X = chan2 | |
| 3874 |
|
|
3830 | distances[0:2] = numpy.array([d1,d2]) | |
| 3875 |
|
|
3831 | else: | |
| 3876 |
|
|
3832 | chanCY = chanC | |
| 3877 |
|
|
3833 | chan1Y = chan1 | |
| 3878 |
|
|
3834 | chan2Y = chan2 | |
| 3879 |
|
|
3835 | distances[2:4] = numpy.array([d1,d2]) | |
| 3880 |
|
|
3836 | # axisXsides = numpy.reshape(axisX[ix,:],4) | |
| 3881 |
|
|
3837 | # | |
| 3882 |
|
|
3838 | # channelCentX = int(numpy.intersect1d(pairX[0,:], pairX[1,:])[0]) | |
| 3883 |
|
|
3839 | # channelCentY = int(numpy.intersect1d(pairY[0,:], pairY[1,:])[0]) | |
| 3884 |
|
|
3840 | # | |
| 3885 |
|
|
3841 | # ind25X = numpy.where(pairX[0,:] != channelCentX)[0][0] | |
| 3886 |
|
|
3842 | # ind20X = numpy.where(pairX[1,:] != channelCentX)[0][0] | |
| 3887 |
|
|
3843 | # channel25X = int(pairX[0,ind25X]) | |
| 3888 |
|
|
3844 | # channel20X = int(pairX[1,ind20X]) | |
| 3889 |
|
|
3845 | # ind25Y = numpy.where(pairY[0,:] != channelCentY)[0][0] | |
| 3890 |
|
|
3846 | # ind20Y = numpy.where(pairY[1,:] != channelCentY)[0][0] | |
| 3891 |
|
|
3847 | # channel25Y = int(pairY[0,ind25Y]) | |
| 3892 |
|
|
3848 | # channel20Y = int(pairY[1,ind20Y]) | |
| 3893 |
|
3849 | |||
| 3894 |
|
|
3850 | # pairslist = [(channelCentX, channel25X),(channelCentX, channel20X),(channelCentY,channel25Y),(channelCentY, channel20Y)] | |
| 3895 |
|
|
3851 | pairslist = [(chanCX, chan1X),(chanCX, chan2X),(chanCY,chan1Y),(chanCY, chan2Y)] | |
| 3896 |
|
3852 | |||
| 3897 |
|
|
3853 | return pairslist, distances | |
| 3898 |
|
|
3854 | # def __getAOA(self, phases, pairsList, error, AOAthresh, azimuth): | |
| 3899 |
|
|
3855 | # | |
| 3900 |
|
|
3856 | # arrayAOA = numpy.zeros((phases.shape[0],3)) | |
| 3901 |
|
|
3857 | # cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList) | |
| 3902 |
|
|
3858 | # | |
| 3903 |
|
|
3859 | # arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth) | |
| 3904 |
|
|
3860 | # cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1) | |
| 3905 |
|
|
3861 | # arrayAOA[:,2] = cosDirError | |
| 3906 |
|
|
3862 | # | |
| 3907 |
|
|
3863 | # azimuthAngle = arrayAOA[:,0] | |
| 3908 |
|
|
3864 | # zenithAngle = arrayAOA[:,1] | |
| 3909 |
|
|
3865 | # | |
| 3910 |
|
|
3866 | # #Setting Error | |
| 3911 |
|
|
3867 | # #Number 3: AOA not fesible | |
| 3912 |
|
|
3868 | # indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0] | |
| 3913 |
|
|
3869 | # error[indInvalid] = 3 | |
| 3914 |
|
|
3870 | # #Number 4: Large difference in AOAs obtained from different antenna baselines | |
| 3915 |
|
|
3871 | # indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0] | |
| 3916 |
|
|
3872 | # error[indInvalid] = 4 | |
| 3917 |
|
|
3873 | # return arrayAOA, error | |
| 3918 |
|
|
3874 | # | |
| 3919 |
|
|
3875 | # def __getDirectionCosines(self, arrayPhase, pairsList): | |
| 3920 |
|
|
3876 | # | |
| 3921 |
|
|
3877 | # #Initializing some variables | |
| 3922 |
|
|
3878 | # ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi | |
| 3923 |
|
|
3879 | # ang_aux = ang_aux.reshape(1,ang_aux.size) | |
| 3924 |
|
|
3880 | # | |
| 3925 |
|
|
3881 | # cosdir = numpy.zeros((arrayPhase.shape[0],2)) | |
| 3926 |
|
|
3882 | # cosdir0 = numpy.zeros((arrayPhase.shape[0],2)) | |
| 3927 |
|
|
3883 | # | |
| 3928 |
|
|
3884 | # | |
| 3929 |
|
|
3885 | # for i in range(2): | |
| 3930 |
|
|
3886 | # #First Estimation | |
| 3931 |
|
|
3887 | # phi0_aux = arrayPhase[:,pairsList[i][0]] + arrayPhase[:,pairsList[i][1]] | |
| 3932 |
|
|
3888 | # #Dealias | |
| 3933 |
|
|
3889 | # indcsi = numpy.where(phi0_aux > numpy.pi) | |
| 3934 |
|
|
3890 | # phi0_aux[indcsi] -= 2*numpy.pi | |
| 3935 |
|
|
3891 | # indcsi = numpy.where(phi0_aux < -numpy.pi) | |
| 3936 |
|
|
3892 | # phi0_aux[indcsi] += 2*numpy.pi | |
| 3937 |
|
|
3893 | # #Direction Cosine 0 | |
| 3938 |
|
|
3894 | # cosdir0[:,i] = -(phi0_aux)/(2*numpy.pi*0.5) | |
| 3939 |
|
|
3895 | # | |
| 3940 |
|
|
3896 | # #Most-Accurate Second Estimation | |
| 3941 |
|
|
3897 | # phi1_aux = arrayPhase[:,pairsList[i][0]] - arrayPhase[:,pairsList[i][1]] | |
| 3942 |
|
|
3898 | # phi1_aux = phi1_aux.reshape(phi1_aux.size,1) | |
| 3943 |
|
|
3899 | # #Direction Cosine 1 | |
| 3944 |
|
|
3900 | # cosdir1 = -(phi1_aux + ang_aux)/(2*numpy.pi*4.5) | |
| 3945 |
|
|
3901 | # | |
| 3946 |
|
|
3902 | # #Searching the correct Direction Cosine | |
| 3947 |
|
|
3903 | # cosdir0_aux = cosdir0[:,i] | |
| 3948 |
|
|
3904 | # cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1) | |
| 3949 |
|
|
3905 | # #Minimum Distance | |
| 3950 |
|
|
3906 | # cosDiff = (cosdir1 - cosdir0_aux)**2 | |
| 3951 |
|
|
3907 | # indcos = cosDiff.argmin(axis = 1) | |
| 3952 |
|
|
3908 | # #Saving Value obtained | |
| 3953 |
|
|
3909 | # cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos] | |
| 3954 |
|
|
3910 | # | |
| 3955 |
|
|
3911 | # return cosdir0, cosdir | |
| 3956 |
|
|
3912 | # | |
| 3957 |
|
|
3913 | # def __calculateAOA(self, cosdir, azimuth): | |
| 3958 |
|
|
3914 | # cosdirX = cosdir[:,0] | |
| 3959 |
|
|
3915 | # cosdirY = cosdir[:,1] | |
| 3960 |
|
|
3916 | # | |
| 3961 |
|
|
3917 | # zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi | |
| 3962 |
|
|
3918 | # azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth #0 deg north, 90 deg east | |
| 3963 |
|
|
3919 | # angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose() | |
| 3964 |
|
|
3920 | # | |
| 3965 |
|
|
3921 | # return angles | |
| 3966 |
|
|
3922 | # | |
| 3967 |
|
|
3923 | # def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight): | |
| 3968 |
|
|
3924 | # | |
| 3969 |
|
|
3925 | # Ramb = 375 #Ramb = c/(2*PRF) | |
| 3970 |
|
|
3926 | # Re = 6371 #Earth Radius | |
| 3971 |
|
|
3927 | # heights = numpy.zeros(Ranges.shape) | |
| 3972 |
|
|
3928 | # | |
| 3973 |
|
|
3929 | # R_aux = numpy.array([0,1,2])*Ramb | |
| 3974 |
|
|
3930 | # R_aux = R_aux.reshape(1,R_aux.size) | |
| 3975 |
|
|
3931 | # | |
| 3976 |
|
|
3932 | # Ranges = Ranges.reshape(Ranges.size,1) | |
| 3977 |
|
|
3933 | # | |
| 3978 |
|
|
3934 | # Ri = Ranges + R_aux | |
| 3979 |
|
|
3935 | # hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re | |
| 3980 |
|
|
3936 | # | |
| 3981 |
|
|
3937 | # #Check if there is a height between 70 and 110 km | |
| 3982 |
|
|
3938 | # h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1) | |
| 3983 |
|
|
3939 | # ind_h = numpy.where(h_bool == 1)[0] | |
| 3984 |
|
|
3940 | # | |
| 3985 |
|
|
3941 | # hCorr = hi[ind_h, :] | |
| 3986 |
|
|
3942 | # ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight)) | |
| 3987 |
|
|
3943 | # | |
| 3988 |
|
|
3944 | # hCorr = hi[ind_hCorr] | |
| 3989 |
|
|
3945 | # heights[ind_h] = hCorr | |
| 3990 |
|
|
3946 | # | |
| 3991 |
|
|
3947 | # #Setting Error | |
| 3992 |
|
|
3948 | # #Number 13: Height unresolvable echo: not valid height within 70 to 110 km | |
| 3993 |
|
|
3949 | # #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km | |
| 3994 |
|
|
3950 | # | |
| 3995 |
|
|
3951 | # indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0] | |
| 3996 |
|
|
3952 | # error[indInvalid2] = 14 | |
| 3997 |
|
|
3953 | # indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0] | |
| 3998 |
|
|
3954 | # error[indInvalid1] = 13 | |
| 3999 |
|
|
3955 | # | |
| 4000 |
|
|
3956 | # return heights, error | |
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