| @@ -1,906 +1,902 | |||
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1 | 1 | import itertools |
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2 | 2 | |
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3 | 3 | import numpy |
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4 | 4 | |
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5 | 5 | from jroproc_base import ProcessingUnit, Operation |
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6 | 6 | from schainpy.model.data.jrodata import Spectra |
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7 | 7 | from schainpy.model.data.jrodata import hildebrand_sekhon |
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8 | 8 | |
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9 | 9 | class SpectraProc(ProcessingUnit): |
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10 | 10 | |
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11 | 11 | def __init__(self, **kwargs): |
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12 | 12 | |
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13 | 13 | ProcessingUnit.__init__(self, **kwargs) |
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14 | 14 | |
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15 | 15 | self.buffer = None |
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16 | 16 | self.firstdatatime = None |
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17 | 17 | self.profIndex = 0 |
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18 | 18 | self.dataOut = Spectra() |
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19 | 19 | self.id_min = None |
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20 | 20 | self.id_max = None |
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21 | 21 | |
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22 | 22 | def __updateSpecFromVoltage(self): |
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23 | 23 | |
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24 | 24 | self.dataOut.timeZone = self.dataIn.timeZone |
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25 | 25 | self.dataOut.dstFlag = self.dataIn.dstFlag |
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26 | 26 | self.dataOut.errorCount = self.dataIn.errorCount |
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27 | 27 | self.dataOut.useLocalTime = self.dataIn.useLocalTime |
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28 | 28 | |
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29 | 29 | self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() |
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30 | 30 | self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy() |
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31 | 31 | self.dataOut.channelList = self.dataIn.channelList |
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32 | 32 | self.dataOut.heightList = self.dataIn.heightList |
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33 | 33 | self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')]) |
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34 | 34 | |
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35 | 35 | self.dataOut.nBaud = self.dataIn.nBaud |
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36 | 36 | self.dataOut.nCode = self.dataIn.nCode |
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37 | 37 | self.dataOut.code = self.dataIn.code |
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38 | 38 | self.dataOut.nProfiles = self.dataOut.nFFTPoints |
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39 | 39 | |
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40 | 40 | self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock |
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41 | 41 | self.dataOut.utctime = self.firstdatatime |
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42 | 42 | self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada |
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43 | 43 | self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip |
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44 | 44 | self.dataOut.flagShiftFFT = False |
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45 | 45 | |
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46 | 46 | self.dataOut.nCohInt = self.dataIn.nCohInt |
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47 | 47 | self.dataOut.nIncohInt = 1 |
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48 | 48 | |
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49 | 49 | self.dataOut.windowOfFilter = self.dataIn.windowOfFilter |
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50 | 50 | |
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51 | 51 | self.dataOut.frequency = self.dataIn.frequency |
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52 | 52 | self.dataOut.realtime = self.dataIn.realtime |
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53 | 53 | |
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54 | 54 | self.dataOut.azimuth = self.dataIn.azimuth |
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55 | 55 | self.dataOut.zenith = self.dataIn.zenith |
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56 | 56 | |
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57 | 57 | self.dataOut.beam.codeList = self.dataIn.beam.codeList |
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58 | 58 | self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList |
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59 | 59 | self.dataOut.beam.zenithList = self.dataIn.beam.zenithList |
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60 | 60 | |
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61 | 61 | def __getFft(self): |
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62 | 62 | """ |
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63 | 63 | Convierte valores de Voltaje a Spectra |
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64 | 64 | |
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65 | 65 | Affected: |
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66 | 66 | self.dataOut.data_spc |
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67 | 67 | self.dataOut.data_cspc |
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68 | 68 | self.dataOut.data_dc |
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69 | 69 | self.dataOut.heightList |
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70 | 70 | self.profIndex |
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71 | 71 | self.buffer |
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72 | 72 | self.dataOut.flagNoData |
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73 | 73 | """ |
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74 | 74 | fft_volt = numpy.fft.fft(self.buffer,n=self.dataOut.nFFTPoints,axis=1) |
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75 | 75 | fft_volt = fft_volt.astype(numpy.dtype('complex')) |
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76 | 76 | dc = fft_volt[:,0,:] |
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77 | 77 | |
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78 | 78 | #calculo de self-spectra |
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79 | 79 | fft_volt = numpy.fft.fftshift(fft_volt,axes=(1,)) |
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80 | 80 | spc = fft_volt * numpy.conjugate(fft_volt) |
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81 | 81 | spc = spc.real |
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82 | 82 | |
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83 | 83 | blocksize = 0 |
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84 | 84 | blocksize += dc.size |
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85 | 85 | blocksize += spc.size |
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86 | 86 | |
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87 | 87 | cspc = None |
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88 | 88 | pairIndex = 0 |
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89 | 89 | if self.dataOut.pairsList != None: |
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90 | 90 | #calculo de cross-spectra |
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91 | 91 | cspc = numpy.zeros((self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex') |
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92 | 92 | for pair in self.dataOut.pairsList: |
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93 | 93 | if pair[0] not in self.dataOut.channelList: |
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94 | 94 | raise ValueError, "Error getting CrossSpectra: pair 0 of %s is not in channelList = %s" %(str(pair), str(self.dataOut.channelList)) |
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95 | 95 | if pair[1] not in self.dataOut.channelList: |
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96 | 96 | raise ValueError, "Error getting CrossSpectra: pair 1 of %s is not in channelList = %s" %(str(pair), str(self.dataOut.channelList)) |
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97 | 97 | |
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98 | 98 | cspc[pairIndex,:,:] = fft_volt[pair[0],:,:] * numpy.conjugate(fft_volt[pair[1],:,:]) |
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99 | 99 | pairIndex += 1 |
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100 | 100 | blocksize += cspc.size |
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101 | 101 | |
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102 | 102 | self.dataOut.data_spc = spc |
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103 | 103 | self.dataOut.data_cspc = cspc |
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104 | 104 | self.dataOut.data_dc = dc |
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105 | 105 | self.dataOut.blockSize = blocksize |
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106 | 106 | self.dataOut.flagShiftFFT = True |
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107 | 107 | |
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108 | 108 | def run(self, nProfiles=None, nFFTPoints=None, pairsList=[], ippFactor=None): |
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109 | 109 | |
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110 | 110 | self.dataOut.flagNoData = True |
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111 | 111 | |
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112 | 112 | if self.dataIn.type == "Spectra": |
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113 | 113 | self.dataOut.copy(self.dataIn) |
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114 | <<<<<<< HEAD | |
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115 | # self.__selectPairs(pairsList) | |
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116 | ======= | |
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117 | 114 | if not pairsList: |
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118 | 115 | pairsList = itertools.combinations(self.dataOut.channelList, 2) |
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119 | 116 | if self.dataOut.data_cspc is not None: |
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120 | 117 | self.__selectPairs(pairsList) |
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121 | >>>>>>> 8048843f4edfb980d0968f42f82054783b84e1cc | |
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122 | 118 | return True |
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123 | 119 | |
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124 | 120 | if self.dataIn.type == "Voltage": |
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125 | 121 | |
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126 | 122 | if nFFTPoints == None: |
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127 | 123 | raise ValueError, "This SpectraProc.run() need nFFTPoints input variable" |
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128 | 124 | |
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129 | 125 | if nProfiles == None: |
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130 | 126 | nProfiles = nFFTPoints |
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131 | 127 | |
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132 | 128 | if ippFactor == None: |
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133 | 129 | ippFactor = 1 |
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134 | 130 | |
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135 | 131 | self.dataOut.ippFactor = ippFactor |
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136 | 132 | |
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137 | 133 | self.dataOut.nFFTPoints = nFFTPoints |
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138 | 134 | self.dataOut.pairsList = pairsList |
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139 | 135 | |
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140 | 136 | if self.buffer is None: |
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141 | 137 | self.buffer = numpy.zeros( (self.dataIn.nChannels, |
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142 | 138 | nProfiles, |
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143 | 139 | self.dataIn.nHeights), |
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144 | 140 | dtype='complex') |
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145 | 141 | |
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146 | 142 | if self.dataIn.flagDataAsBlock: |
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147 | 143 | #data dimension: [nChannels, nProfiles, nSamples] |
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148 | 144 | nVoltProfiles = self.dataIn.data.shape[1] |
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149 | 145 | # nVoltProfiles = self.dataIn.nProfiles |
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150 | 146 | |
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151 | 147 | if nVoltProfiles == nProfiles: |
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152 | 148 | self.buffer = self.dataIn.data.copy() |
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153 | 149 | self.profIndex = nVoltProfiles |
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154 | 150 | |
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155 | 151 | elif nVoltProfiles < nProfiles: |
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156 | 152 | |
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157 | 153 | if self.profIndex == 0: |
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158 | 154 | self.id_min = 0 |
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159 | 155 | self.id_max = nVoltProfiles |
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160 | 156 | |
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161 | 157 | self.buffer[:,self.id_min:self.id_max,:] = self.dataIn.data |
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162 | 158 | self.profIndex += nVoltProfiles |
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163 | 159 | self.id_min += nVoltProfiles |
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164 | 160 | self.id_max += nVoltProfiles |
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165 | 161 | else: |
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166 | 162 | raise ValueError, "The type object %s has %d profiles, it should just has %d profiles"%(self.dataIn.type,self.dataIn.data.shape[1],nProfiles) |
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167 | 163 | self.dataOut.flagNoData = True |
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168 | 164 | return 0 |
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169 | 165 | else: |
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170 | 166 | self.buffer[:,self.profIndex,:] = self.dataIn.data.copy() |
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171 | 167 | self.profIndex += 1 |
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172 | 168 | |
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173 | 169 | if self.firstdatatime == None: |
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174 | 170 | self.firstdatatime = self.dataIn.utctime |
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175 | 171 | |
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176 | 172 | if self.profIndex == nProfiles: |
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177 | 173 | self.__updateSpecFromVoltage() |
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178 | 174 | self.__getFft() |
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179 | 175 | |
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180 | 176 | self.dataOut.flagNoData = False |
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181 | 177 | self.firstdatatime = None |
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182 | 178 | self.profIndex = 0 |
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183 | 179 | |
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184 | 180 | return True |
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185 | 181 | |
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186 | 182 | raise ValueError, "The type of input object '%s' is not valid"%(self.dataIn.type) |
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187 | 183 | |
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188 | 184 | def __selectPairs(self, pairsList): |
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189 | 185 | |
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190 | 186 | if not pairsList: |
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191 | 187 | return |
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192 | 188 | |
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193 | 189 | pairs = [] |
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194 | 190 | pairsIndex = [] |
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195 | 191 | |
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196 | 192 | for pair in pairsList: |
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197 | 193 | if pair[0] not in self.dataOut.channelList or pair[1] not in self.dataOut.channelList: |
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198 | 194 | continue |
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199 | 195 | pairs.append(pair) |
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200 | 196 | pairsIndex.append(pairs.index(pair)) |
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201 | 197 | |
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202 | 198 | self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndex] |
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203 | 199 | self.dataOut.pairsList = pairs |
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204 | 200 | self.dataOut.pairsIndexList = pairsIndex |
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205 | 201 | |
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206 | 202 | return |
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207 | 203 | |
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208 | 204 | def __selectPairsByChannel(self, channelList=None): |
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209 | 205 | |
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210 | 206 | if channelList == None: |
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211 | 207 | return |
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212 | 208 | |
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213 | 209 | pairsIndexListSelected = [] |
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214 | 210 | for pairIndex in self.dataOut.pairsIndexList: |
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215 | 211 | #First pair |
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216 | 212 | if self.dataOut.pairsList[pairIndex][0] not in channelList: |
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217 | 213 | continue |
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218 | 214 | #Second pair |
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219 | 215 | if self.dataOut.pairsList[pairIndex][1] not in channelList: |
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220 | 216 | continue |
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221 | 217 | |
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222 | 218 | pairsIndexListSelected.append(pairIndex) |
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223 | 219 | |
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224 | 220 | if not pairsIndexListSelected: |
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225 | 221 | self.dataOut.data_cspc = None |
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226 | 222 | self.dataOut.pairsList = [] |
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227 | 223 | return |
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228 | 224 | |
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229 | 225 | self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected] |
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230 | 226 | self.dataOut.pairsList = [self.dataOut.pairsList[i] for i in pairsIndexListSelected] |
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231 | 227 | |
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232 | 228 | return |
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233 | 229 | |
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234 | 230 | def selectChannels(self, channelList): |
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235 | 231 | |
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236 | 232 | channelIndexList = [] |
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237 | 233 | |
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238 | 234 | for channel in channelList: |
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239 | 235 | if channel not in self.dataOut.channelList: |
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240 | 236 | raise ValueError, "Error selecting channels, Channel %d is not valid.\nAvailable channels = %s" %(channel, str(self.dataOut.channelList)) |
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241 | 237 | |
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242 | 238 | index = self.dataOut.channelList.index(channel) |
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243 | 239 | channelIndexList.append(index) |
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244 | 240 | |
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245 | 241 | self.selectChannelsByIndex(channelIndexList) |
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246 | 242 | |
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247 | 243 | def selectChannelsByIndex(self, channelIndexList): |
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248 | 244 | """ |
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249 | 245 | Selecciona un bloque de datos en base a canales segun el channelIndexList |
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250 | 246 | |
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251 | 247 | Input: |
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252 | 248 | channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7] |
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253 | 249 | |
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254 | 250 | Affected: |
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255 | 251 | self.dataOut.data_spc |
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256 | 252 | self.dataOut.channelIndexList |
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257 | 253 | self.dataOut.nChannels |
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258 | 254 | |
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259 | 255 | Return: |
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260 | 256 | None |
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261 | 257 | """ |
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262 | 258 | |
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263 | 259 | for channelIndex in channelIndexList: |
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264 | 260 | if channelIndex not in self.dataOut.channelIndexList: |
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265 | 261 | raise ValueError, "Error selecting channels: The value %d in channelIndexList is not valid.\nAvailable channel indexes = " %(channelIndex, self.dataOut.channelIndexList) |
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266 | 262 | |
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267 | 263 | # nChannels = len(channelIndexList) |
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268 | 264 | |
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269 | 265 | data_spc = self.dataOut.data_spc[channelIndexList,:] |
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270 | 266 | data_dc = self.dataOut.data_dc[channelIndexList,:] |
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271 | 267 | |
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272 | 268 | self.dataOut.data_spc = data_spc |
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273 | 269 | self.dataOut.data_dc = data_dc |
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274 | 270 | |
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275 | 271 | self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList] |
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276 | 272 | # self.dataOut.nChannels = nChannels |
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277 | 273 | |
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278 | 274 | self.__selectPairsByChannel(self.dataOut.channelList) |
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279 | 275 | |
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280 | 276 | return 1 |
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281 | 277 | |
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282 | 278 | def selectHeights(self, minHei, maxHei): |
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283 | 279 | """ |
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284 | 280 | Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango |
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285 | 281 | minHei <= height <= maxHei |
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286 | 282 | |
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287 | 283 | Input: |
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288 | 284 | minHei : valor minimo de altura a considerar |
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289 | 285 | maxHei : valor maximo de altura a considerar |
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290 | 286 | |
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291 | 287 | Affected: |
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292 | 288 | Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex |
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293 | 289 | |
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294 | 290 | Return: |
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295 | 291 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 |
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296 | 292 | """ |
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297 | 293 | |
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298 | 294 | if (minHei > maxHei): |
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299 | 295 | raise ValueError, "Error selecting heights: Height range (%d,%d) is not valid" % (minHei, maxHei) |
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300 | 296 | |
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301 | 297 | if (minHei < self.dataOut.heightList[0]): |
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302 | 298 | minHei = self.dataOut.heightList[0] |
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303 | 299 | |
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304 | 300 | if (maxHei > self.dataOut.heightList[-1]): |
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305 | 301 | maxHei = self.dataOut.heightList[-1] |
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306 | 302 | |
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307 | 303 | minIndex = 0 |
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308 | 304 | maxIndex = 0 |
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309 | 305 | heights = self.dataOut.heightList |
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310 | 306 | |
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311 | 307 | inda = numpy.where(heights >= minHei) |
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312 | 308 | indb = numpy.where(heights <= maxHei) |
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313 | 309 | |
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314 | 310 | try: |
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315 | 311 | minIndex = inda[0][0] |
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316 | 312 | except: |
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317 | 313 | minIndex = 0 |
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318 | 314 | |
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319 | 315 | try: |
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320 | 316 | maxIndex = indb[0][-1] |
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321 | 317 | except: |
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322 | 318 | maxIndex = len(heights) |
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323 | 319 | |
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324 | 320 | self.selectHeightsByIndex(minIndex, maxIndex) |
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325 | 321 | |
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326 | 322 | return 1 |
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327 | 323 | |
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328 | 324 | def getBeaconSignal(self, tauindex = 0, channelindex = 0, hei_ref=None): |
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329 | 325 | newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex]) |
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330 | 326 | |
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331 | 327 | if hei_ref != None: |
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332 | 328 | newheis = numpy.where(self.dataOut.heightList>hei_ref) |
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333 | 329 | |
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334 | 330 | minIndex = min(newheis[0]) |
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335 | 331 | maxIndex = max(newheis[0]) |
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336 | 332 | data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1] |
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337 | 333 | heightList = self.dataOut.heightList[minIndex:maxIndex+1] |
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338 | 334 | |
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339 | 335 | # determina indices |
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340 | 336 | nheis = int(self.dataOut.radarControllerHeaderObj.txB/(self.dataOut.heightList[1]-self.dataOut.heightList[0])) |
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341 | 337 | avg_dB = 10*numpy.log10(numpy.sum(data_spc[channelindex,:,:],axis=0)) |
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342 | 338 | beacon_dB = numpy.sort(avg_dB)[-nheis:] |
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343 | 339 | beacon_heiIndexList = [] |
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344 | 340 | for val in avg_dB.tolist(): |
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345 | 341 | if val >= beacon_dB[0]: |
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346 | 342 | beacon_heiIndexList.append(avg_dB.tolist().index(val)) |
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347 | 343 | |
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348 | 344 | #data_spc = data_spc[:,:,beacon_heiIndexList] |
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349 | 345 | data_cspc = None |
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350 | 346 | if self.dataOut.data_cspc is not None: |
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351 | 347 | data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1] |
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352 | 348 | #data_cspc = data_cspc[:,:,beacon_heiIndexList] |
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353 | 349 | |
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354 | 350 | data_dc = None |
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355 | 351 | if self.dataOut.data_dc is not None: |
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356 | 352 | data_dc = self.dataOut.data_dc[:,minIndex:maxIndex+1] |
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357 | 353 | #data_dc = data_dc[:,beacon_heiIndexList] |
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358 | 354 | |
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359 | 355 | self.dataOut.data_spc = data_spc |
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360 | 356 | self.dataOut.data_cspc = data_cspc |
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361 | 357 | self.dataOut.data_dc = data_dc |
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362 | 358 | self.dataOut.heightList = heightList |
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363 | 359 | self.dataOut.beacon_heiIndexList = beacon_heiIndexList |
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364 | 360 | |
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365 | 361 | return 1 |
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366 | 362 | |
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367 | 363 | |
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368 | 364 | def selectHeightsByIndex(self, minIndex, maxIndex): |
|
|
369 | 365 | """ |
|
|
370 | 366 | Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango |
|
|
371 | 367 | minIndex <= index <= maxIndex |
|
|
372 | 368 | |
|
|
373 | 369 | Input: |
|
|
374 | 370 | minIndex : valor de indice minimo de altura a considerar |
|
|
375 | 371 | maxIndex : valor de indice maximo de altura a considerar |
|
|
376 | 372 | |
|
|
377 | 373 | Affected: |
|
|
378 | 374 | self.dataOut.data_spc |
|
|
379 | 375 | self.dataOut.data_cspc |
|
|
380 | 376 | self.dataOut.data_dc |
|
|
381 | 377 | self.dataOut.heightList |
|
|
382 | 378 | |
|
|
383 | 379 | Return: |
|
|
384 | 380 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 |
|
|
385 | 381 | """ |
|
|
386 | 382 | |
|
|
387 | 383 | if (minIndex < 0) or (minIndex > maxIndex): |
|
|
388 | 384 | raise ValueError, "Error selecting heights: Index range (%d,%d) is not valid" % (minIndex, maxIndex) |
|
|
389 | 385 | |
|
|
390 | 386 | if (maxIndex >= self.dataOut.nHeights): |
|
|
391 | 387 | maxIndex = self.dataOut.nHeights-1 |
|
|
392 | 388 | |
|
|
393 | 389 | #Spectra |
|
|
394 | 390 | data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1] |
|
|
395 | 391 | |
|
|
396 | 392 | data_cspc = None |
|
|
397 | 393 | if self.dataOut.data_cspc is not None: |
|
|
398 | 394 | data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1] |
|
|
399 | 395 | |
|
|
400 | 396 | data_dc = None |
|
|
401 | 397 | if self.dataOut.data_dc is not None: |
|
|
402 | 398 | data_dc = self.dataOut.data_dc[:,minIndex:maxIndex+1] |
|
|
403 | 399 | |
|
|
404 | 400 | self.dataOut.data_spc = data_spc |
|
|
405 | 401 | self.dataOut.data_cspc = data_cspc |
|
|
406 | 402 | self.dataOut.data_dc = data_dc |
|
|
407 | 403 | |
|
|
408 | 404 | self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1] |
|
|
409 | 405 | |
|
|
410 | 406 | return 1 |
|
|
411 | 407 | |
|
|
412 | 408 | def removeDC(self, mode = 2): |
|
|
413 | 409 | jspectra = self.dataOut.data_spc |
|
|
414 | 410 | jcspectra = self.dataOut.data_cspc |
|
|
415 | 411 | |
|
|
416 | 412 | |
|
|
417 | 413 | num_chan = jspectra.shape[0] |
|
|
418 | 414 | num_hei = jspectra.shape[2] |
|
|
419 | 415 | |
|
|
420 | 416 | if jcspectra is not None: |
|
|
421 | 417 | jcspectraExist = True |
|
|
422 | 418 | num_pairs = jcspectra.shape[0] |
|
|
423 | 419 | else: jcspectraExist = False |
|
|
424 | 420 | |
|
|
425 | 421 | freq_dc = jspectra.shape[1]/2 |
|
|
426 | 422 | ind_vel = numpy.array([-2,-1,1,2]) + freq_dc |
|
|
427 | 423 | |
|
|
428 | 424 | if ind_vel[0]<0: |
|
|
429 | 425 | ind_vel[range(0,1)] = ind_vel[range(0,1)] + self.num_prof |
|
|
430 | 426 | |
|
|
431 | 427 | if mode == 1: |
|
|
432 | 428 | jspectra[:,freq_dc,:] = (jspectra[:,ind_vel[1],:] + jspectra[:,ind_vel[2],:])/2 #CORRECCION |
|
|
433 | 429 | |
|
|
434 | 430 | if jcspectraExist: |
|
|
435 | 431 | jcspectra[:,freq_dc,:] = (jcspectra[:,ind_vel[1],:] + jcspectra[:,ind_vel[2],:])/2 |
|
|
436 | 432 | |
|
|
437 | 433 | if mode == 2: |
|
|
438 | 434 | |
|
|
439 | 435 | vel = numpy.array([-2,-1,1,2]) |
|
|
440 | 436 | xx = numpy.zeros([4,4]) |
|
|
441 | 437 | |
|
|
442 | 438 | for fil in range(4): |
|
|
443 | 439 | xx[fil,:] = vel[fil]**numpy.asarray(range(4)) |
|
|
444 | 440 | |
|
|
445 | 441 | xx_inv = numpy.linalg.inv(xx) |
|
|
446 | 442 | xx_aux = xx_inv[0,:] |
|
|
447 | 443 | |
|
|
448 | 444 | for ich in range(num_chan): |
|
|
449 | 445 | yy = jspectra[ich,ind_vel,:] |
|
|
450 | 446 | jspectra[ich,freq_dc,:] = numpy.dot(xx_aux,yy) |
|
|
451 | 447 | |
|
|
452 | 448 | junkid = jspectra[ich,freq_dc,:]<=0 |
|
|
453 | 449 | cjunkid = sum(junkid) |
|
|
454 | 450 | |
|
|
455 | 451 | if cjunkid.any(): |
|
|
456 | 452 | jspectra[ich,freq_dc,junkid.nonzero()] = (jspectra[ich,ind_vel[1],junkid] + jspectra[ich,ind_vel[2],junkid])/2 |
|
|
457 | 453 | |
|
|
458 | 454 | if jcspectraExist: |
|
|
459 | 455 | for ip in range(num_pairs): |
|
|
460 | 456 | yy = jcspectra[ip,ind_vel,:] |
|
|
461 | 457 | jcspectra[ip,freq_dc,:] = numpy.dot(xx_aux,yy) |
|
|
462 | 458 | |
|
|
463 | 459 | |
|
|
464 | 460 | self.dataOut.data_spc = jspectra |
|
|
465 | 461 | self.dataOut.data_cspc = jcspectra |
|
|
466 | 462 | |
|
|
467 | 463 | return 1 |
|
|
468 | 464 | |
|
|
469 | 465 | def removeInterference(self, interf = 2,hei_interf = None, nhei_interf = None, offhei_interf = None): |
|
|
470 | 466 | |
|
|
471 | 467 | jspectra = self.dataOut.data_spc |
|
|
472 | 468 | jcspectra = self.dataOut.data_cspc |
|
|
473 | 469 | jnoise = self.dataOut.getNoise() |
|
|
474 | 470 | num_incoh = self.dataOut.nIncohInt |
|
|
475 | 471 | |
|
|
476 | 472 | num_channel = jspectra.shape[0] |
|
|
477 | 473 | num_prof = jspectra.shape[1] |
|
|
478 | 474 | num_hei = jspectra.shape[2] |
|
|
479 | 475 | |
|
|
480 | 476 | #hei_interf |
|
|
481 | 477 | if hei_interf is None: |
|
|
482 | 478 | count_hei = num_hei/2 #Como es entero no importa |
|
|
483 | 479 | hei_interf = numpy.asmatrix(range(count_hei)) + num_hei - count_hei |
|
|
484 | 480 | hei_interf = numpy.asarray(hei_interf)[0] |
|
|
485 | 481 | #nhei_interf |
|
|
486 | 482 | if (nhei_interf == None): |
|
|
487 | 483 | nhei_interf = 5 |
|
|
488 | 484 | if (nhei_interf < 1): |
|
|
489 | 485 | nhei_interf = 1 |
|
|
490 | 486 | if (nhei_interf > count_hei): |
|
|
491 | 487 | nhei_interf = count_hei |
|
|
492 | 488 | if (offhei_interf == None): |
|
|
493 | 489 | offhei_interf = 0 |
|
|
494 | 490 | |
|
|
495 | 491 | ind_hei = range(num_hei) |
|
|
496 | 492 | # mask_prof = numpy.asarray(range(num_prof - 2)) + 1 |
|
|
497 | 493 | # mask_prof[range(num_prof/2 - 1,len(mask_prof))] += 1 |
|
|
498 | 494 | mask_prof = numpy.asarray(range(num_prof)) |
|
|
499 | 495 | num_mask_prof = mask_prof.size |
|
|
500 | 496 | comp_mask_prof = [0, num_prof/2] |
|
|
501 | 497 | |
|
|
502 | 498 | |
|
|
503 | 499 | #noise_exist: Determina si la variable jnoise ha sido definida y contiene la informacion del ruido de cada canal |
|
|
504 | 500 | if (jnoise.size < num_channel or numpy.isnan(jnoise).any()): |
|
|
505 | 501 | jnoise = numpy.nan |
|
|
506 | 502 | noise_exist = jnoise[0] < numpy.Inf |
|
|
507 | 503 | |
|
|
508 | 504 | #Subrutina de Remocion de la Interferencia |
|
|
509 | 505 | for ich in range(num_channel): |
|
|
510 | 506 | #Se ordena los espectros segun su potencia (menor a mayor) |
|
|
511 | 507 | power = jspectra[ich,mask_prof,:] |
|
|
512 | 508 | power = power[:,hei_interf] |
|
|
513 | 509 | power = power.sum(axis = 0) |
|
|
514 | 510 | psort = power.ravel().argsort() |
|
|
515 | 511 | |
|
|
516 | 512 | #Se estima la interferencia promedio en los Espectros de Potencia empleando |
|
|
517 | 513 | junkspc_interf = jspectra[ich,:,hei_interf[psort[range(offhei_interf, nhei_interf + offhei_interf)]]] |
|
|
518 | 514 | |
|
|
519 | 515 | if noise_exist: |
|
|
520 | 516 | # tmp_noise = jnoise[ich] / num_prof |
|
|
521 | 517 | tmp_noise = jnoise[ich] |
|
|
522 | 518 | junkspc_interf = junkspc_interf - tmp_noise |
|
|
523 | 519 | #junkspc_interf[:,comp_mask_prof] = 0 |
|
|
524 | 520 | |
|
|
525 | 521 | jspc_interf = junkspc_interf.sum(axis = 0) / nhei_interf |
|
|
526 | 522 | jspc_interf = jspc_interf.transpose() |
|
|
527 | 523 | #Calculando el espectro de interferencia promedio |
|
|
528 | 524 | noiseid = numpy.where(jspc_interf <= tmp_noise/ numpy.sqrt(num_incoh)) |
|
|
529 | 525 | noiseid = noiseid[0] |
|
|
530 | 526 | cnoiseid = noiseid.size |
|
|
531 | 527 | interfid = numpy.where(jspc_interf > tmp_noise/ numpy.sqrt(num_incoh)) |
|
|
532 | 528 | interfid = interfid[0] |
|
|
533 | 529 | cinterfid = interfid.size |
|
|
534 | 530 | |
|
|
535 | 531 | if (cnoiseid > 0): jspc_interf[noiseid] = 0 |
|
|
536 | 532 | |
|
|
537 | 533 | #Expandiendo los perfiles a limpiar |
|
|
538 | 534 | if (cinterfid > 0): |
|
|
539 | 535 | new_interfid = (numpy.r_[interfid - 1, interfid, interfid + 1] + num_prof)%num_prof |
|
|
540 | 536 | new_interfid = numpy.asarray(new_interfid) |
|
|
541 | 537 | new_interfid = {x for x in new_interfid} |
|
|
542 | 538 | new_interfid = numpy.array(list(new_interfid)) |
|
|
543 | 539 | new_cinterfid = new_interfid.size |
|
|
544 | 540 | else: new_cinterfid = 0 |
|
|
545 | 541 | |
|
|
546 | 542 | for ip in range(new_cinterfid): |
|
|
547 | 543 | ind = junkspc_interf[:,new_interfid[ip]].ravel().argsort() |
|
|
548 | 544 | jspc_interf[new_interfid[ip]] = junkspc_interf[ind[nhei_interf/2],new_interfid[ip]] |
|
|
549 | 545 | |
|
|
550 | 546 | |
|
|
551 | 547 | jspectra[ich,:,ind_hei] = jspectra[ich,:,ind_hei] - jspc_interf #Corregir indices |
|
|
552 | 548 | |
|
|
553 | 549 | #Removiendo la interferencia del punto de mayor interferencia |
|
|
554 | 550 | ListAux = jspc_interf[mask_prof].tolist() |
|
|
555 | 551 | maxid = ListAux.index(max(ListAux)) |
|
|
556 | 552 | |
|
|
557 | 553 | |
|
|
558 | 554 | if cinterfid > 0: |
|
|
559 | 555 | for ip in range(cinterfid*(interf == 2) - 1): |
|
|
560 | 556 | ind = (jspectra[ich,interfid[ip],:] < tmp_noise*(1 + 1/numpy.sqrt(num_incoh))).nonzero() |
|
|
561 | 557 | cind = len(ind) |
|
|
562 | 558 | |
|
|
563 | 559 | if (cind > 0): |
|
|
564 | 560 | jspectra[ich,interfid[ip],ind] = tmp_noise*(1 + (numpy.random.uniform(cind) - 0.5)/numpy.sqrt(num_incoh)) |
|
|
565 | 561 | |
|
|
566 | 562 | ind = numpy.array([-2,-1,1,2]) |
|
|
567 | 563 | xx = numpy.zeros([4,4]) |
|
|
568 | 564 | |
|
|
569 | 565 | for id1 in range(4): |
|
|
570 | 566 | xx[:,id1] = ind[id1]**numpy.asarray(range(4)) |
|
|
571 | 567 | |
|
|
572 | 568 | xx_inv = numpy.linalg.inv(xx) |
|
|
573 | 569 | xx = xx_inv[:,0] |
|
|
574 | 570 | ind = (ind + maxid + num_mask_prof)%num_mask_prof |
|
|
575 | 571 | yy = jspectra[ich,mask_prof[ind],:] |
|
|
576 | 572 | jspectra[ich,mask_prof[maxid],:] = numpy.dot(yy.transpose(),xx) |
|
|
577 | 573 | |
|
|
578 | 574 | |
|
|
579 | 575 | indAux = (jspectra[ich,:,:] < tmp_noise*(1-1/numpy.sqrt(num_incoh))).nonzero() |
|
|
580 | 576 | jspectra[ich,indAux[0],indAux[1]] = tmp_noise * (1 - 1/numpy.sqrt(num_incoh)) |
|
|
581 | 577 | |
|
|
582 | 578 | #Remocion de Interferencia en el Cross Spectra |
|
|
583 | 579 | if jcspectra is None: return jspectra, jcspectra |
|
|
584 | 580 | num_pairs = jcspectra.size/(num_prof*num_hei) |
|
|
585 | 581 | jcspectra = jcspectra.reshape(num_pairs, num_prof, num_hei) |
|
|
586 | 582 | |
|
|
587 | 583 | for ip in range(num_pairs): |
|
|
588 | 584 | |
|
|
589 | 585 | #------------------------------------------- |
|
|
590 | 586 | |
|
|
591 | 587 | cspower = numpy.abs(jcspectra[ip,mask_prof,:]) |
|
|
592 | 588 | cspower = cspower[:,hei_interf] |
|
|
593 | 589 | cspower = cspower.sum(axis = 0) |
|
|
594 | 590 | |
|
|
595 | 591 | cspsort = cspower.ravel().argsort() |
|
|
596 | 592 | junkcspc_interf = jcspectra[ip,:,hei_interf[cspsort[range(offhei_interf, nhei_interf + offhei_interf)]]] |
|
|
597 | 593 | junkcspc_interf = junkcspc_interf.transpose() |
|
|
598 | 594 | jcspc_interf = junkcspc_interf.sum(axis = 1)/nhei_interf |
|
|
599 | 595 | |
|
|
600 | 596 | ind = numpy.abs(jcspc_interf[mask_prof]).ravel().argsort() |
|
|
601 | 597 | |
|
|
602 | 598 | median_real = numpy.median(numpy.real(junkcspc_interf[mask_prof[ind[range(3*num_prof/4)]],:])) |
|
|
603 | 599 | median_imag = numpy.median(numpy.imag(junkcspc_interf[mask_prof[ind[range(3*num_prof/4)]],:])) |
|
|
604 | 600 | junkcspc_interf[comp_mask_prof,:] = numpy.complex(median_real, median_imag) |
|
|
605 | 601 | |
|
|
606 | 602 | for iprof in range(num_prof): |
|
|
607 | 603 | ind = numpy.abs(junkcspc_interf[iprof,:]).ravel().argsort() |
|
|
608 | 604 | jcspc_interf[iprof] = junkcspc_interf[iprof, ind[nhei_interf/2]] |
|
|
609 | 605 | |
|
|
610 | 606 | #Removiendo la Interferencia |
|
|
611 | 607 | jcspectra[ip,:,ind_hei] = jcspectra[ip,:,ind_hei] - jcspc_interf |
|
|
612 | 608 | |
|
|
613 | 609 | ListAux = numpy.abs(jcspc_interf[mask_prof]).tolist() |
|
|
614 | 610 | maxid = ListAux.index(max(ListAux)) |
|
|
615 | 611 | |
|
|
616 | 612 | ind = numpy.array([-2,-1,1,2]) |
|
|
617 | 613 | xx = numpy.zeros([4,4]) |
|
|
618 | 614 | |
|
|
619 | 615 | for id1 in range(4): |
|
|
620 | 616 | xx[:,id1] = ind[id1]**numpy.asarray(range(4)) |
|
|
621 | 617 | |
|
|
622 | 618 | xx_inv = numpy.linalg.inv(xx) |
|
|
623 | 619 | xx = xx_inv[:,0] |
|
|
624 | 620 | |
|
|
625 | 621 | ind = (ind + maxid + num_mask_prof)%num_mask_prof |
|
|
626 | 622 | yy = jcspectra[ip,mask_prof[ind],:] |
|
|
627 | 623 | jcspectra[ip,mask_prof[maxid],:] = numpy.dot(yy.transpose(),xx) |
|
|
628 | 624 | |
|
|
629 | 625 | #Guardar Resultados |
|
|
630 | 626 | self.dataOut.data_spc = jspectra |
|
|
631 | 627 | self.dataOut.data_cspc = jcspectra |
|
|
632 | 628 | |
|
|
633 | 629 | return 1 |
|
|
634 | 630 | |
|
|
635 | 631 | def setRadarFrequency(self, frequency=None): |
|
|
636 | 632 | |
|
|
637 | 633 | if frequency != None: |
|
|
638 | 634 | self.dataOut.frequency = frequency |
|
|
639 | 635 | |
|
|
640 | 636 | return 1 |
|
|
641 | 637 | |
|
|
642 | 638 | def getNoise(self, minHei=None, maxHei=None, minVel=None, maxVel=None): |
|
|
643 | 639 | #validacion de rango |
|
|
644 | 640 | if minHei == None: |
|
|
645 | 641 | minHei = self.dataOut.heightList[0] |
|
|
646 | 642 | |
|
|
647 | 643 | if maxHei == None: |
|
|
648 | 644 | maxHei = self.dataOut.heightList[-1] |
|
|
649 | 645 | |
|
|
650 | 646 | if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei): |
|
|
651 | 647 | print 'minHei: %.2f is out of the heights range'%(minHei) |
|
|
652 | 648 | print 'minHei is setting to %.2f'%(self.dataOut.heightList[0]) |
|
|
653 | 649 | minHei = self.dataOut.heightList[0] |
|
|
654 | 650 | |
|
|
655 | 651 | if (maxHei > self.dataOut.heightList[-1]) or (maxHei < minHei): |
|
|
656 | 652 | print 'maxHei: %.2f is out of the heights range'%(maxHei) |
|
|
657 | 653 | print 'maxHei is setting to %.2f'%(self.dataOut.heightList[-1]) |
|
|
658 | 654 | maxHei = self.dataOut.heightList[-1] |
|
|
659 | 655 | |
|
|
660 | 656 | # validacion de velocidades |
|
|
661 | 657 | velrange = self.dataOut.getVelRange(1) |
|
|
662 | 658 | |
|
|
663 | 659 | if minVel == None: |
|
|
664 | 660 | minVel = velrange[0] |
|
|
665 | 661 | |
|
|
666 | 662 | if maxVel == None: |
|
|
667 | 663 | maxVel = velrange[-1] |
|
|
668 | 664 | |
|
|
669 | 665 | if (minVel < velrange[0]) or (minVel > maxVel): |
|
|
670 | 666 | print 'minVel: %.2f is out of the velocity range'%(minVel) |
|
|
671 | 667 | print 'minVel is setting to %.2f'%(velrange[0]) |
|
|
672 | 668 | minVel = velrange[0] |
|
|
673 | 669 | |
|
|
674 | 670 | if (maxVel > velrange[-1]) or (maxVel < minVel): |
|
|
675 | 671 | print 'maxVel: %.2f is out of the velocity range'%(maxVel) |
|
|
676 | 672 | print 'maxVel is setting to %.2f'%(velrange[-1]) |
|
|
677 | 673 | maxVel = velrange[-1] |
|
|
678 | 674 | |
|
|
679 | 675 | # seleccion de indices para rango |
|
|
680 | 676 | minIndex = 0 |
|
|
681 | 677 | maxIndex = 0 |
|
|
682 | 678 | heights = self.dataOut.heightList |
|
|
683 | 679 | |
|
|
684 | 680 | inda = numpy.where(heights >= minHei) |
|
|
685 | 681 | indb = numpy.where(heights <= maxHei) |
|
|
686 | 682 | |
|
|
687 | 683 | try: |
|
|
688 | 684 | minIndex = inda[0][0] |
|
|
689 | 685 | except: |
|
|
690 | 686 | minIndex = 0 |
|
|
691 | 687 | |
|
|
692 | 688 | try: |
|
|
693 | 689 | maxIndex = indb[0][-1] |
|
|
694 | 690 | except: |
|
|
695 | 691 | maxIndex = len(heights) |
|
|
696 | 692 | |
|
|
697 | 693 | if (minIndex < 0) or (minIndex > maxIndex): |
|
|
698 | 694 | raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex) |
|
|
699 | 695 | |
|
|
700 | 696 | if (maxIndex >= self.dataOut.nHeights): |
|
|
701 | 697 | maxIndex = self.dataOut.nHeights-1 |
|
|
702 | 698 | |
|
|
703 | 699 | # seleccion de indices para velocidades |
|
|
704 | 700 | indminvel = numpy.where(velrange >= minVel) |
|
|
705 | 701 | indmaxvel = numpy.where(velrange <= maxVel) |
|
|
706 | 702 | try: |
|
|
707 | 703 | minIndexVel = indminvel[0][0] |
|
|
708 | 704 | except: |
|
|
709 | 705 | minIndexVel = 0 |
|
|
710 | 706 | |
|
|
711 | 707 | try: |
|
|
712 | 708 | maxIndexVel = indmaxvel[0][-1] |
|
|
713 | 709 | except: |
|
|
714 | 710 | maxIndexVel = len(velrange) |
|
|
715 | 711 | |
|
|
716 | 712 | #seleccion del espectro |
|
|
717 | 713 | data_spc = self.dataOut.data_spc[:,minIndexVel:maxIndexVel+1,minIndex:maxIndex+1] |
|
|
718 | 714 | #estimacion de ruido |
|
|
719 | 715 | noise = numpy.zeros(self.dataOut.nChannels) |
|
|
720 | 716 | |
|
|
721 | 717 | for channel in range(self.dataOut.nChannels): |
|
|
722 | 718 | daux = data_spc[channel,:,:] |
|
|
723 | 719 | noise[channel] = hildebrand_sekhon(daux, self.dataOut.nIncohInt) |
|
|
724 | 720 | |
|
|
725 | 721 | self.dataOut.noise_estimation = noise.copy() |
|
|
726 | 722 | |
|
|
727 | 723 | return 1 |
|
|
728 | 724 | |
|
|
729 | 725 | class IncohInt(Operation): |
|
|
730 | 726 | |
|
|
731 | 727 | |
|
|
732 | 728 | __profIndex = 0 |
|
|
733 | 729 | __withOverapping = False |
|
|
734 | 730 | |
|
|
735 | 731 | __byTime = False |
|
|
736 | 732 | __initime = None |
|
|
737 | 733 | __lastdatatime = None |
|
|
738 | 734 | __integrationtime = None |
|
|
739 | 735 | |
|
|
740 | 736 | __buffer_spc = None |
|
|
741 | 737 | __buffer_cspc = None |
|
|
742 | 738 | __buffer_dc = None |
|
|
743 | 739 | |
|
|
744 | 740 | __dataReady = False |
|
|
745 | 741 | |
|
|
746 | 742 | __timeInterval = None |
|
|
747 | 743 | |
|
|
748 | 744 | n = None |
|
|
749 | 745 | |
|
|
750 | 746 | |
|
|
751 | 747 | |
|
|
752 | 748 | def __init__(self, **kwargs): |
|
|
753 | 749 | |
|
|
754 | 750 | Operation.__init__(self, **kwargs) |
|
|
755 | 751 | # self.isConfig = False |
|
|
756 | 752 | |
|
|
757 | 753 | def setup(self, n=None, timeInterval=None, overlapping=False): |
|
|
758 | 754 | """ |
|
|
759 | 755 | Set the parameters of the integration class. |
|
|
760 | 756 | |
|
|
761 | 757 | Inputs: |
|
|
762 | 758 | |
|
|
763 | 759 | n : Number of coherent integrations |
|
|
764 | 760 | timeInterval : Time of integration. If the parameter "n" is selected this one does not work |
|
|
765 | 761 | overlapping : |
|
|
766 | 762 | |
|
|
767 | 763 | """ |
|
|
768 | 764 | |
|
|
769 | 765 | self.__initime = None |
|
|
770 | 766 | self.__lastdatatime = 0 |
|
|
771 | 767 | |
|
|
772 | 768 | self.__buffer_spc = 0 |
|
|
773 | 769 | self.__buffer_cspc = 0 |
|
|
774 | 770 | self.__buffer_dc = 0 |
|
|
775 | 771 | |
|
|
776 | 772 | self.__profIndex = 0 |
|
|
777 | 773 | self.__dataReady = False |
|
|
778 | 774 | self.__byTime = False |
|
|
779 | 775 | |
|
|
780 | 776 | if n is None and timeInterval is None: |
|
|
781 | 777 | raise ValueError, "n or timeInterval should be specified ..." |
|
|
782 | 778 | |
|
|
783 | 779 | if n is not None: |
|
|
784 | 780 | self.n = int(n) |
|
|
785 | 781 | else: |
|
|
786 | 782 | self.__integrationtime = int(timeInterval) #if (type(timeInterval)!=integer) -> change this line |
|
|
787 | 783 | self.n = None |
|
|
788 | 784 | self.__byTime = True |
|
|
789 | 785 | |
|
|
790 | 786 | def putData(self, data_spc, data_cspc, data_dc): |
|
|
791 | 787 | |
|
|
792 | 788 | """ |
|
|
793 | 789 | Add a profile to the __buffer_spc and increase in one the __profileIndex |
|
|
794 | 790 | |
|
|
795 | 791 | """ |
|
|
796 | 792 | |
|
|
797 | 793 | self.__buffer_spc += data_spc |
|
|
798 | 794 | |
|
|
799 | 795 | if data_cspc is None: |
|
|
800 | 796 | self.__buffer_cspc = None |
|
|
801 | 797 | else: |
|
|
802 | 798 | self.__buffer_cspc += data_cspc |
|
|
803 | 799 | |
|
|
804 | 800 | if data_dc is None: |
|
|
805 | 801 | self.__buffer_dc = None |
|
|
806 | 802 | else: |
|
|
807 | 803 | self.__buffer_dc += data_dc |
|
|
808 | 804 | |
|
|
809 | 805 | self.__profIndex += 1 |
|
|
810 | 806 | |
|
|
811 | 807 | return |
|
|
812 | 808 | |
|
|
813 | 809 | def pushData(self): |
|
|
814 | 810 | """ |
|
|
815 | 811 | Return the sum of the last profiles and the profiles used in the sum. |
|
|
816 | 812 | |
|
|
817 | 813 | Affected: |
|
|
818 | 814 | |
|
|
819 | 815 | self.__profileIndex |
|
|
820 | 816 | |
|
|
821 | 817 | """ |
|
|
822 | 818 | |
|
|
823 | 819 | data_spc = self.__buffer_spc |
|
|
824 | 820 | data_cspc = self.__buffer_cspc |
|
|
825 | 821 | data_dc = self.__buffer_dc |
|
|
826 | 822 | n = self.__profIndex |
|
|
827 | 823 | |
|
|
828 | 824 | self.__buffer_spc = 0 |
|
|
829 | 825 | self.__buffer_cspc = 0 |
|
|
830 | 826 | self.__buffer_dc = 0 |
|
|
831 | 827 | self.__profIndex = 0 |
|
|
832 | 828 | |
|
|
833 | 829 | return data_spc, data_cspc, data_dc, n |
|
|
834 | 830 | |
|
|
835 | 831 | def byProfiles(self, *args): |
|
|
836 | 832 | |
|
|
837 | 833 | self.__dataReady = False |
|
|
838 | 834 | avgdata_spc = None |
|
|
839 | 835 | avgdata_cspc = None |
|
|
840 | 836 | avgdata_dc = None |
|
|
841 | 837 | |
|
|
842 | 838 | self.putData(*args) |
|
|
843 | 839 | |
|
|
844 | 840 | if self.__profIndex == self.n: |
|
|
845 | 841 | |
|
|
846 | 842 | avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData() |
|
|
847 | 843 | self.n = n |
|
|
848 | 844 | self.__dataReady = True |
|
|
849 | 845 | |
|
|
850 | 846 | return avgdata_spc, avgdata_cspc, avgdata_dc |
|
|
851 | 847 | |
|
|
852 | 848 | def byTime(self, datatime, *args): |
|
|
853 | 849 | |
|
|
854 | 850 | self.__dataReady = False |
|
|
855 | 851 | avgdata_spc = None |
|
|
856 | 852 | avgdata_cspc = None |
|
|
857 | 853 | avgdata_dc = None |
|
|
858 | 854 | |
|
|
859 | 855 | self.putData(*args) |
|
|
860 | 856 | |
|
|
861 | 857 | if (datatime - self.__initime) >= self.__integrationtime: |
|
|
862 | 858 | avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData() |
|
|
863 | 859 | self.n = n |
|
|
864 | 860 | self.__dataReady = True |
|
|
865 | 861 | |
|
|
866 | 862 | return avgdata_spc, avgdata_cspc, avgdata_dc |
|
|
867 | 863 | |
|
|
868 | 864 | def integrate(self, datatime, *args): |
|
|
869 | 865 | |
|
|
870 | 866 | if self.__profIndex == 0: |
|
|
871 | 867 | self.__initime = datatime |
|
|
872 | 868 | |
|
|
873 | 869 | if self.__byTime: |
|
|
874 | 870 | avgdata_spc, avgdata_cspc, avgdata_dc = self.byTime(datatime, *args) |
|
|
875 | 871 | else: |
|
|
876 | 872 | avgdata_spc, avgdata_cspc, avgdata_dc = self.byProfiles(*args) |
|
|
877 | 873 | |
|
|
878 | 874 | if not self.__dataReady: |
|
|
879 | 875 | return None, None, None, None |
|
|
880 | 876 | |
|
|
881 | 877 | return self.__initime, avgdata_spc, avgdata_cspc, avgdata_dc |
|
|
882 | 878 | |
|
|
883 | 879 | def run(self, dataOut, n=None, timeInterval=None, overlapping=False): |
|
|
884 | 880 | if n==1: |
|
|
885 | 881 | return |
|
|
886 | 882 | |
|
|
887 | 883 | dataOut.flagNoData = True |
|
|
888 | 884 | |
|
|
889 | 885 | if not self.isConfig: |
|
|
890 | 886 | self.setup(n, timeInterval, overlapping) |
|
|
891 | 887 | self.isConfig = True |
|
|
892 | 888 | |
|
|
893 | 889 | avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(dataOut.utctime, |
|
|
894 | 890 | dataOut.data_spc, |
|
|
895 | 891 | dataOut.data_cspc, |
|
|
896 | 892 | dataOut.data_dc) |
|
|
897 | 893 | |
|
|
898 | 894 | if self.__dataReady: |
|
|
899 | 895 | |
|
|
900 | 896 | dataOut.data_spc = avgdata_spc |
|
|
901 | 897 | dataOut.data_cspc = avgdata_cspc |
|
|
902 | 898 | dataOut.data_dc = avgdata_dc |
|
|
903 | 899 | |
|
|
904 | 900 | dataOut.nIncohInt *= self.n |
|
|
905 | 901 | dataOut.utctime = avgdatatime |
|
|
906 | 902 | dataOut.flagNoData = False |
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