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1 | import numpy | |
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2 | ||
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3 | from jroproc_base import ProcessingUnit, Operation | |
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4 | from schainpy.model.data.jrodata import Spectra | |
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5 | from schainpy.model.data.jrodata import hildebrand_sekhon | |
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6 | ||
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7 | class SpectraAFCProc(ProcessingUnit): | |
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8 | ||
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9 | def __init__(self): | |
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10 | ||
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11 | ProcessingUnit.__init__(self) | |
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12 | ||
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13 | self.buffer = None | |
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14 | self.firstdatatime = None | |
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15 | self.profIndex = 0 | |
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16 | self.dataOut = Spectra() | |
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17 | self.id_min = None | |
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18 | self.id_max = None | |
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19 | ||
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20 | def __updateSpecFromVoltage(self): | |
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21 | ||
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22 | self.dataOut.plotting = "spectra_acf" | |
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23 | ||
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24 | self.dataOut.timeZone = self.dataIn.timeZone | |
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25 | self.dataOut.dstFlag = self.dataIn.dstFlag | |
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26 | self.dataOut.errorCount = self.dataIn.errorCount | |
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27 | self.dataOut.useLocalTime = self.dataIn.useLocalTime | |
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28 | ||
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29 | self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() | |
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30 | self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy() | |
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31 | self.dataOut.ippSeconds = self.dataIn.getDeltaH()*(10**-6)/0.15 | |
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32 | ||
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33 | self.dataOut.channelList = self.dataIn.channelList | |
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34 | self.dataOut.heightList = self.dataIn.heightList | |
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35 | self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')]) | |
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36 | ||
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37 | self.dataOut.nBaud = self.dataIn.nBaud | |
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38 | self.dataOut.nCode = self.dataIn.nCode | |
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39 | self.dataOut.code = self.dataIn.code | |
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40 | # self.dataOut.nProfiles = self.dataOut.nFFTPoints | |
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41 | ||
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42 | self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock | |
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43 | self.dataOut.utctime = self.firstdatatime | |
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44 | self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada | |
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45 | self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip | |
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46 | self.dataOut.flagShiftFFT = False | |
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47 | ||
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48 | self.dataOut.nCohInt = self.dataIn.nCohInt | |
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49 | self.dataOut.nIncohInt = 1 | |
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50 | ||
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51 | self.dataOut.windowOfFilter = self.dataIn.windowOfFilter | |
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52 | ||
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53 | self.dataOut.frequency = self.dataIn.frequency | |
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54 | self.dataOut.realtime = self.dataIn.realtime | |
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55 | ||
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56 | self.dataOut.azimuth = self.dataIn.azimuth | |
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57 | self.dataOut.zenith = self.dataIn.zenith | |
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58 | ||
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59 | self.dataOut.beam.codeList = self.dataIn.beam.codeList | |
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60 | self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList | |
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61 | self.dataOut.beam.zenithList = self.dataIn.beam.zenithList | |
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62 | ||
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63 | def __decodeData(self, nProfiles, code): | |
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64 | ||
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65 | if code is None: | |
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66 | return | |
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67 | ||
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68 | for i in range(nProfiles): | |
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69 | self.buffer[:,i,:] = self.buffer[:,i,:]*code[0][i] | |
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70 | ||
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71 | def __getFft(self): | |
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72 | """ | |
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73 | Convierte valores de Voltaje a Spectra | |
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74 | ||
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75 | Affected: | |
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76 | self.dataOut.data_spc | |
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77 | self.dataOut.data_cspc | |
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78 | self.dataOut.data_dc | |
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79 | self.dataOut.heightList | |
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80 | self.profIndex | |
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81 | self.buffer | |
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82 | self.dataOut.flagNoData | |
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83 | """ | |
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84 | nsegments = self.dataOut.nHeights | |
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85 | ||
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86 | _fft_buffer = numpy.zeros((self.dataOut.nChannels, self.dataOut.nProfiles, nsegments), dtype='complex') | |
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87 | ||
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88 | for i in range(nsegments): | |
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89 | try: | |
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90 | _fft_buffer[:,:,i] = self.buffer[:,i:i+self.dataOut.nProfiles] | |
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91 | ||
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92 | if self.code is not None: | |
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93 | _fft_buffer[:,:,i] = _fft_buffer[:,:,i]*self.code[0] | |
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94 | except: | |
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95 | pass | |
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96 | ||
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97 | fft_volt = numpy.fft.fft(_fft_buffer, n=self.dataOut.nFFTPoints, axis=1) | |
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98 | fft_volt = fft_volt.astype(numpy.dtype('complex')) | |
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99 | dc = fft_volt[:,0,:] | |
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100 | ||
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101 | #calculo de self-spectra | |
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102 | # fft_volt = numpy.fft.fftshift(fft_volt, axes=(1,)) | |
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103 | spc = fft_volt * numpy.conjugate(fft_volt) | |
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104 | ||
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105 | data = numpy.fft.ifft(spc, axis=1) | |
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106 | data = numpy.fft.fftshift(data, axes=(1,)) | |
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107 | ||
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108 | spc = data | |
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109 | ||
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110 | blocksize = 0 | |
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111 | blocksize += dc.size | |
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112 | blocksize += spc.size | |
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113 | ||
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114 | cspc = None | |
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115 | pairIndex = 0 | |
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116 | ||
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117 | if self.dataOut.pairsList != None: | |
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118 | #calculo de cross-spectra | |
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119 | cspc = numpy.zeros((self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex') | |
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120 | for pair in self.dataOut.pairsList: | |
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121 | if pair[0] not in self.dataOut.channelList: | |
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122 | raise ValueError, "Error getting CrossSpectra: pair 0 of %s is not in channelList = %s" %(str(pair), str(self.dataOut.channelList)) | |
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123 | if pair[1] not in self.dataOut.channelList: | |
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124 | raise ValueError, "Error getting CrossSpectra: pair 1 of %s is not in channelList = %s" %(str(pair), str(self.dataOut.channelList)) | |
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125 | ||
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126 | chan_index0 = self.dataOut.channelList.index(pair[0]) | |
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127 | chan_index1 = self.dataOut.channelList.index(pair[1]) | |
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128 | ||
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129 | cspc[pairIndex,:,:] = fft_volt[chan_index0,:,:] * numpy.conjugate(fft_volt[chan_index1,:,:]) | |
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130 | pairIndex += 1 | |
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131 | blocksize += cspc.size | |
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132 | ||
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133 | self.dataOut.data_spc = spc | |
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134 | self.dataOut.data_cspc = cspc | |
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135 | self.dataOut.data_dc = dc | |
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136 | self.dataOut.blockSize = blocksize | |
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137 | self.dataOut.flagShiftFFT = True | |
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138 | ||
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139 | def run(self, nProfiles=None, nFFTPoints=None, pairsList=[], code=None, nCode=1, nBaud=1): | |
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140 | ||
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141 | self.dataOut.flagNoData = True | |
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142 | ||
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143 | if code is not None: | |
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144 | self.code = numpy.array(code).reshape(nCode,nBaud) | |
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145 | else: | |
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146 | self.code = None | |
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147 | ||
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148 | if self.dataIn.type == "Voltage": | |
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149 | ||
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150 | if nFFTPoints == None: | |
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151 | raise ValueError, "This SpectraProc.run() need nFFTPoints input variable" | |
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152 | ||
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153 | if nProfiles == None: | |
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154 | nProfiles = nFFTPoints | |
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155 | ||
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156 | self.dataOut.ippFactor = 1 | |
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157 | ||
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158 | self.dataOut.nFFTPoints = nFFTPoints | |
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159 | self.dataOut.nProfiles = nProfiles | |
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160 | self.dataOut.pairsList = pairsList | |
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161 | ||
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162 | # if self.buffer is None: | |
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163 | # self.buffer = numpy.zeros( (self.dataIn.nChannels, nProfiles, self.dataIn.nHeights), | |
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164 | # dtype='complex') | |
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165 | ||
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166 | if not self.dataIn.flagDataAsBlock: | |
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167 | self.buffer = self.dataIn.data.copy() | |
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168 | ||
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169 | # for i in range(self.dataIn.nHeights): | |
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170 | # self.buffer[:, self.profIndex, self.profIndex:] = voltage_data[:,:self.dataIn.nHeights - self.profIndex] | |
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171 | # | |
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172 | # self.profIndex += 1 | |
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173 | ||
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174 | else: | |
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175 | raise ValueError, "" | |
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176 | ||
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177 | self.firstdatatime = self.dataIn.utctime | |
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178 | ||
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179 | self.profIndex == nProfiles | |
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180 | ||
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181 | self.__updateSpecFromVoltage() | |
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182 | ||
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183 | self.__getFft() | |
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184 | ||
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185 | self.dataOut.flagNoData = False | |
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186 | ||
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187 | return True | |
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188 | ||
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189 | raise ValueError, "The type of input object '%s' is not valid"%(self.dataIn.type) | |
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190 | ||
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191 | def __selectPairs(self, pairsList): | |
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192 | ||
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193 | if channelList == None: | |
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194 | return | |
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195 | ||
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196 | pairsIndexListSelected = [] | |
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197 | ||
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198 | for thisPair in pairsList: | |
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199 | ||
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200 | if thisPair not in self.dataOut.pairsList: | |
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201 | continue | |
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202 | ||
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203 | pairIndex = self.dataOut.pairsList.index(thisPair) | |
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204 | ||
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205 | pairsIndexListSelected.append(pairIndex) | |
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206 | ||
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207 | if not pairsIndexListSelected: | |
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208 | self.dataOut.data_cspc = None | |
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209 | self.dataOut.pairsList = [] | |
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210 | return | |
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211 | ||
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212 | self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected] | |
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213 | self.dataOut.pairsList = [self.dataOut.pairsList[i] for i in pairsIndexListSelected] | |
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214 | ||
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215 | return | |
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216 | ||
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217 | def __selectPairsByChannel(self, channelList=None): | |
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218 | ||
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219 | if channelList == None: | |
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220 | return | |
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221 | ||
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222 | pairsIndexListSelected = [] | |
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223 | for pairIndex in self.dataOut.pairsIndexList: | |
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224 | #First pair | |
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225 | if self.dataOut.pairsList[pairIndex][0] not in channelList: | |
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226 | continue | |
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227 | #Second pair | |
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228 | if self.dataOut.pairsList[pairIndex][1] not in channelList: | |
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229 | continue | |
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230 | ||
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231 | pairsIndexListSelected.append(pairIndex) | |
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232 | ||
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233 | if not pairsIndexListSelected: | |
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234 | self.dataOut.data_cspc = None | |
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235 | self.dataOut.pairsList = [] | |
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236 | return | |
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237 | ||
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238 | self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected] | |
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239 | self.dataOut.pairsList = [self.dataOut.pairsList[i] for i in pairsIndexListSelected] | |
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240 | ||
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241 | return | |
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242 | ||
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243 | def selectChannels(self, channelList): | |
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244 | ||
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245 | channelIndexList = [] | |
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246 | ||
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247 | for channel in channelList: | |
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248 | if channel not in self.dataOut.channelList: | |
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249 | raise ValueError, "Error selecting channels, Channel %d is not valid.\nAvailable channels = %s" %(channel, str(self.dataOut.channelList)) | |
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250 | ||
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251 | index = self.dataOut.channelList.index(channel) | |
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252 | channelIndexList.append(index) | |
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253 | ||
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254 | self.selectChannelsByIndex(channelIndexList) | |
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255 | ||
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256 | def selectChannelsByIndex(self, channelIndexList): | |
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257 | """ | |
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258 | Selecciona un bloque de datos en base a canales segun el channelIndexList | |
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259 | ||
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260 | Input: | |
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261 | channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7] | |
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262 | ||
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263 | Affected: | |
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264 | self.dataOut.data_spc | |
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265 | self.dataOut.channelIndexList | |
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266 | self.dataOut.nChannels | |
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267 | ||
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268 | Return: | |
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269 | None | |
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270 | """ | |
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271 | ||
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272 | for channelIndex in channelIndexList: | |
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273 | if channelIndex not in self.dataOut.channelIndexList: | |
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274 | raise ValueError, "Error selecting channels: The value %d in channelIndexList is not valid.\nAvailable channel indexes = " %(channelIndex, self.dataOut.channelIndexList) | |
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275 | ||
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276 | # nChannels = len(channelIndexList) | |
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277 | ||
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278 | data_spc = self.dataOut.data_spc[channelIndexList,:] | |
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279 | data_dc = self.dataOut.data_dc[channelIndexList,:] | |
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280 | ||
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281 | self.dataOut.data_spc = data_spc | |
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282 | self.dataOut.data_dc = data_dc | |
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283 | ||
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284 | self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList] | |
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285 | # self.dataOut.nChannels = nChannels | |
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286 | ||
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287 | self.__selectPairsByChannel(self.dataOut.channelList) | |
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288 | ||
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289 | return 1 | |
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290 | ||
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291 | def selectHeights(self, minHei, maxHei): | |
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292 | """ | |
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293 | Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango | |
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294 | minHei <= height <= maxHei | |
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295 | ||
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296 | Input: | |
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297 | minHei : valor minimo de altura a considerar | |
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298 | maxHei : valor maximo de altura a considerar | |
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299 | ||
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300 | Affected: | |
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301 | Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex | |
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302 | ||
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303 | Return: | |
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304 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 | |
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305 | """ | |
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306 | ||
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307 | if (minHei > maxHei): | |
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308 | raise ValueError, "Error selecting heights: Height range (%d,%d) is not valid" % (minHei, maxHei) | |
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309 | ||
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310 | if (minHei < self.dataOut.heightList[0]): | |
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311 | minHei = self.dataOut.heightList[0] | |
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312 | ||
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313 | if (maxHei > self.dataOut.heightList[-1]): | |
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314 | maxHei = self.dataOut.heightList[-1] | |
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315 | ||
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316 | minIndex = 0 | |
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317 | maxIndex = 0 | |
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318 | heights = self.dataOut.heightList | |
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319 | ||
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320 | inda = numpy.where(heights >= minHei) | |
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321 | indb = numpy.where(heights <= maxHei) | |
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322 | ||
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323 | try: | |
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324 | minIndex = inda[0][0] | |
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325 | except: | |
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326 | minIndex = 0 | |
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327 | ||
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328 | try: | |
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329 | maxIndex = indb[0][-1] | |
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330 | except: | |
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331 | maxIndex = len(heights) | |
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332 | ||
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333 | self.selectHeightsByIndex(minIndex, maxIndex) | |
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334 | ||
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335 | return 1 | |
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336 | ||
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337 | def getBeaconSignal(self, tauindex = 0, channelindex = 0, hei_ref=None): | |
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338 | newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex]) | |
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339 | ||
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340 | if hei_ref != None: | |
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341 | newheis = numpy.where(self.dataOut.heightList>hei_ref) | |
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342 | ||
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343 | minIndex = min(newheis[0]) | |
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344 | maxIndex = max(newheis[0]) | |
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345 | data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1] | |
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346 | heightList = self.dataOut.heightList[minIndex:maxIndex+1] | |
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347 | ||
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348 | # determina indices | |
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349 | nheis = int(self.dataOut.radarControllerHeaderObj.txB/(self.dataOut.heightList[1]-self.dataOut.heightList[0])) | |
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350 | avg_dB = 10*numpy.log10(numpy.sum(data_spc[channelindex,:,:],axis=0)) | |
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351 | beacon_dB = numpy.sort(avg_dB)[-nheis:] | |
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352 | beacon_heiIndexList = [] | |
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353 | for val in avg_dB.tolist(): | |
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354 | if val >= beacon_dB[0]: | |
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355 | beacon_heiIndexList.append(avg_dB.tolist().index(val)) | |
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356 | ||
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357 | #data_spc = data_spc[:,:,beacon_heiIndexList] | |
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358 | data_cspc = None | |
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359 | if self.dataOut.data_cspc is not None: | |
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360 | data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1] | |
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361 | #data_cspc = data_cspc[:,:,beacon_heiIndexList] | |
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362 | ||
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363 | data_dc = None | |
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364 | if self.dataOut.data_dc is not None: | |
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365 | data_dc = self.dataOut.data_dc[:,minIndex:maxIndex+1] | |
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366 | #data_dc = data_dc[:,beacon_heiIndexList] | |
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367 | ||
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368 | self.dataOut.data_spc = data_spc | |
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369 | self.dataOut.data_cspc = data_cspc | |
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370 | self.dataOut.data_dc = data_dc | |
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371 | self.dataOut.heightList = heightList | |
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372 | self.dataOut.beacon_heiIndexList = beacon_heiIndexList | |
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373 | ||
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374 | return 1 | |
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375 | ||
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376 | ||
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377 | def selectHeightsByIndex(self, minIndex, maxIndex): | |
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378 | """ | |
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379 | Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango | |
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380 | minIndex <= index <= maxIndex | |
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381 | ||
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382 | Input: | |
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383 | minIndex : valor de indice minimo de altura a considerar | |
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384 | maxIndex : valor de indice maximo de altura a considerar | |
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385 | ||
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386 | Affected: | |
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387 | self.dataOut.data_spc | |
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388 | self.dataOut.data_cspc | |
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389 | self.dataOut.data_dc | |
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390 | self.dataOut.heightList | |
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391 | ||
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392 | Return: | |
|
|
393 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 | |
|
|
394 | """ | |
|
|
395 | ||
|
|
396 | if (minIndex < 0) or (minIndex > maxIndex): | |
|
|
397 | raise ValueError, "Error selecting heights: Index range (%d,%d) is not valid" % (minIndex, maxIndex) | |
|
|
398 | ||
|
|
399 | if (maxIndex >= self.dataOut.nHeights): | |
|
|
400 | maxIndex = self.dataOut.nHeights-1 | |
|
|
401 | ||
|
|
402 | #Spectra | |
|
|
403 | data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1] | |
|
|
404 | ||
|
|
405 | data_cspc = None | |
|
|
406 | if self.dataOut.data_cspc is not None: | |
|
|
407 | data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1] | |
|
|
408 | ||
|
|
409 | data_dc = None | |
|
|
410 | if self.dataOut.data_dc is not None: | |
|
|
411 | data_dc = self.dataOut.data_dc[:,minIndex:maxIndex+1] | |
|
|
412 | ||
|
|
413 | self.dataOut.data_spc = data_spc | |
|
|
414 | self.dataOut.data_cspc = data_cspc | |
|
|
415 | self.dataOut.data_dc = data_dc | |
|
|
416 | ||
|
|
417 | self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1] | |
|
|
418 | ||
|
|
419 | return 1 | |
|
|
420 | ||
|
|
421 | def removeDC(self, mode = 2): | |
|
|
422 | jspectra = self.dataOut.data_spc | |
|
|
423 | jcspectra = self.dataOut.data_cspc | |
|
|
424 | ||
|
|
425 | ||
|
|
426 | num_chan = jspectra.shape[0] | |
|
|
427 | num_hei = jspectra.shape[2] | |
|
|
428 | ||
|
|
429 | if jcspectra is not None: | |
|
|
430 | jcspectraExist = True | |
|
|
431 | num_pairs = jcspectra.shape[0] | |
|
|
432 | else: jcspectraExist = False | |
|
|
433 | ||
|
|
434 | freq_dc = jspectra.shape[1]/2 | |
|
|
435 | ind_vel = numpy.array([-2,-1,1,2]) + freq_dc | |
|
|
436 | ||
|
|
437 | if ind_vel[0]<0: | |
|
|
438 | ind_vel[range(0,1)] = ind_vel[range(0,1)] + self.num_prof | |
|
|
439 | ||
|
|
440 | if mode == 1: | |
|
|
441 | jspectra[:,freq_dc,:] = (jspectra[:,ind_vel[1],:] + jspectra[:,ind_vel[2],:])/2 #CORRECCION | |
|
|
442 | ||
|
|
443 | if jcspectraExist: | |
|
|
444 | jcspectra[:,freq_dc,:] = (jcspectra[:,ind_vel[1],:] + jcspectra[:,ind_vel[2],:])/2 | |
|
|
445 | ||
|
|
446 | if mode == 2: | |
|
|
447 | ||
|
|
448 | vel = numpy.array([-2,-1,1,2]) | |
|
|
449 | xx = numpy.zeros([4,4]) | |
|
|
450 | ||
|
|
451 | for fil in range(4): | |
|
|
452 | xx[fil,:] = vel[fil]**numpy.asarray(range(4)) | |
|
|
453 | ||
|
|
454 | xx_inv = numpy.linalg.inv(xx) | |
|
|
455 | xx_aux = xx_inv[0,:] | |
|
|
456 | ||
|
|
457 | for ich in range(num_chan): | |
|
|
458 | yy = jspectra[ich,ind_vel,:] | |
|
|
459 | jspectra[ich,freq_dc,:] = numpy.dot(xx_aux,yy) | |
|
|
460 | ||
|
|
461 | junkid = jspectra[ich,freq_dc,:]<=0 | |
|
|
462 | cjunkid = sum(junkid) | |
|
|
463 | ||
|
|
464 | if cjunkid.any(): | |
|
|
465 | jspectra[ich,freq_dc,junkid.nonzero()] = (jspectra[ich,ind_vel[1],junkid] + jspectra[ich,ind_vel[2],junkid])/2 | |
|
|
466 | ||
|
|
467 | if jcspectraExist: | |
|
|
468 | for ip in range(num_pairs): | |
|
|
469 | yy = jcspectra[ip,ind_vel,:] | |
|
|
470 | jcspectra[ip,freq_dc,:] = numpy.dot(xx_aux,yy) | |
|
|
471 | ||
|
|
472 | ||
|
|
473 | self.dataOut.data_spc = jspectra | |
|
|
474 | self.dataOut.data_cspc = jcspectra | |
|
|
475 | ||
|
|
476 | return 1 | |
|
|
477 | ||
|
|
478 | def removeInterference(self, interf = 2,hei_interf = None, nhei_interf = None, offhei_interf = None): | |
|
|
479 | ||
|
|
480 | jspectra = self.dataOut.data_spc | |
|
|
481 | jcspectra = self.dataOut.data_cspc | |
|
|
482 | jnoise = self.dataOut.getNoise() | |
|
|
483 | num_incoh = self.dataOut.nIncohInt | |
|
|
484 | ||
|
|
485 | num_channel = jspectra.shape[0] | |
|
|
486 | num_prof = jspectra.shape[1] | |
|
|
487 | num_hei = jspectra.shape[2] | |
|
|
488 | ||
|
|
489 | #hei_interf | |
|
|
490 | if hei_interf is None: | |
|
|
491 | count_hei = num_hei/2 #Como es entero no importa | |
|
|
492 | hei_interf = numpy.asmatrix(range(count_hei)) + num_hei - count_hei | |
|
|
493 | hei_interf = numpy.asarray(hei_interf)[0] | |
|
|
494 | #nhei_interf | |
|
|
495 | if (nhei_interf == None): | |
|
|
496 | nhei_interf = 5 | |
|
|
497 | if (nhei_interf < 1): | |
|
|
498 | nhei_interf = 1 | |
|
|
499 | if (nhei_interf > count_hei): | |
|
|
500 | nhei_interf = count_hei | |
|
|
501 | if (offhei_interf == None): | |
|
|
502 | offhei_interf = 0 | |
|
|
503 | ||
|
|
504 | ind_hei = range(num_hei) | |
|
|
505 | # mask_prof = numpy.asarray(range(num_prof - 2)) + 1 | |
|
|
506 | # mask_prof[range(num_prof/2 - 1,len(mask_prof))] += 1 | |
|
|
507 | mask_prof = numpy.asarray(range(num_prof)) | |
|
|
508 | num_mask_prof = mask_prof.size | |
|
|
509 | comp_mask_prof = [0, num_prof/2] | |
|
|
510 | ||
|
|
511 | ||
|
|
512 | #noise_exist: Determina si la variable jnoise ha sido definida y contiene la informacion del ruido de cada canal | |
|
|
513 | if (jnoise.size < num_channel or numpy.isnan(jnoise).any()): | |
|
|
514 | jnoise = numpy.nan | |
|
|
515 | noise_exist = jnoise[0] < numpy.Inf | |
|
|
516 | ||
|
|
517 | #Subrutina de Remocion de la Interferencia | |
|
|
518 | for ich in range(num_channel): | |
|
|
519 | #Se ordena los espectros segun su potencia (menor a mayor) | |
|
|
520 | power = jspectra[ich,mask_prof,:] | |
|
|
521 | power = power[:,hei_interf] | |
|
|
522 | power = power.sum(axis = 0) | |
|
|
523 | psort = power.ravel().argsort() | |
|
|
524 | ||
|
|
525 | #Se estima la interferencia promedio en los Espectros de Potencia empleando | |
|
|
526 | junkspc_interf = jspectra[ich,:,hei_interf[psort[range(offhei_interf, nhei_interf + offhei_interf)]]] | |
|
|
527 | ||
|
|
528 | if noise_exist: | |
|
|
529 | # tmp_noise = jnoise[ich] / num_prof | |
|
|
530 | tmp_noise = jnoise[ich] | |
|
|
531 | junkspc_interf = junkspc_interf - tmp_noise | |
|
|
532 | #junkspc_interf[:,comp_mask_prof] = 0 | |
|
|
533 | ||
|
|
534 | jspc_interf = junkspc_interf.sum(axis = 0) / nhei_interf | |
|
|
535 | jspc_interf = jspc_interf.transpose() | |
|
|
536 | #Calculando el espectro de interferencia promedio | |
|
|
537 | noiseid = numpy.where(jspc_interf <= tmp_noise/ numpy.sqrt(num_incoh)) | |
|
|
538 | noiseid = noiseid[0] | |
|
|
539 | cnoiseid = noiseid.size | |
|
|
540 | interfid = numpy.where(jspc_interf > tmp_noise/ numpy.sqrt(num_incoh)) | |
|
|
541 | interfid = interfid[0] | |
|
|
542 | cinterfid = interfid.size | |
|
|
543 | ||
|
|
544 | if (cnoiseid > 0): jspc_interf[noiseid] = 0 | |
|
|
545 | ||
|
|
546 | #Expandiendo los perfiles a limpiar | |
|
|
547 | if (cinterfid > 0): | |
|
|
548 | new_interfid = (numpy.r_[interfid - 1, interfid, interfid + 1] + num_prof)%num_prof | |
|
|
549 | new_interfid = numpy.asarray(new_interfid) | |
|
|
550 | new_interfid = {x for x in new_interfid} | |
|
|
551 | new_interfid = numpy.array(list(new_interfid)) | |
|
|
552 | new_cinterfid = new_interfid.size | |
|
|
553 | else: new_cinterfid = 0 | |
|
|
554 | ||
|
|
555 | for ip in range(new_cinterfid): | |
|
|
556 | ind = junkspc_interf[:,new_interfid[ip]].ravel().argsort() | |
|
|
557 | jspc_interf[new_interfid[ip]] = junkspc_interf[ind[nhei_interf/2],new_interfid[ip]] | |
|
|
558 | ||
|
|
559 | ||
|
|
560 | jspectra[ich,:,ind_hei] = jspectra[ich,:,ind_hei] - jspc_interf #Corregir indices | |
|
|
561 | ||
|
|
562 | #Removiendo la interferencia del punto de mayor interferencia | |
|
|
563 | ListAux = jspc_interf[mask_prof].tolist() | |
|
|
564 | maxid = ListAux.index(max(ListAux)) | |
|
|
565 | ||
|
|
566 | ||
|
|
567 | if cinterfid > 0: | |
|
|
568 | for ip in range(cinterfid*(interf == 2) - 1): | |
|
|
569 | ind = (jspectra[ich,interfid[ip],:] < tmp_noise*(1 + 1/numpy.sqrt(num_incoh))).nonzero() | |
|
|
570 | cind = len(ind) | |
|
|
571 | ||
|
|
572 | if (cind > 0): | |
|
|
573 | jspectra[ich,interfid[ip],ind] = tmp_noise*(1 + (numpy.random.uniform(cind) - 0.5)/numpy.sqrt(num_incoh)) | |
|
|
574 | ||
|
|
575 | ind = numpy.array([-2,-1,1,2]) | |
|
|
576 | xx = numpy.zeros([4,4]) | |
|
|
577 | ||
|
|
578 | for id1 in range(4): | |
|
|
579 | xx[:,id1] = ind[id1]**numpy.asarray(range(4)) | |
|
|
580 | ||
|
|
581 | xx_inv = numpy.linalg.inv(xx) | |
|
|
582 | xx = xx_inv[:,0] | |
|
|
583 | ind = (ind + maxid + num_mask_prof)%num_mask_prof | |
|
|
584 | yy = jspectra[ich,mask_prof[ind],:] | |
|
|
585 | jspectra[ich,mask_prof[maxid],:] = numpy.dot(yy.transpose(),xx) | |
|
|
586 | ||
|
|
587 | ||
|
|
588 | indAux = (jspectra[ich,:,:] < tmp_noise*(1-1/numpy.sqrt(num_incoh))).nonzero() | |
|
|
589 | jspectra[ich,indAux[0],indAux[1]] = tmp_noise * (1 - 1/numpy.sqrt(num_incoh)) | |
|
|
590 | ||
|
|
591 | #Remocion de Interferencia en el Cross Spectra | |
|
|
592 | if jcspectra is None: return jspectra, jcspectra | |
|
|
593 | num_pairs = jcspectra.size/(num_prof*num_hei) | |
|
|
594 | jcspectra = jcspectra.reshape(num_pairs, num_prof, num_hei) | |
|
|
595 | ||
|
|
596 | for ip in range(num_pairs): | |
|
|
597 | ||
|
|
598 | #------------------------------------------- | |
|
|
599 | ||
|
|
600 | cspower = numpy.abs(jcspectra[ip,mask_prof,:]) | |
|
|
601 | cspower = cspower[:,hei_interf] | |
|
|
602 | cspower = cspower.sum(axis = 0) | |
|
|
603 | ||
|
|
604 | cspsort = cspower.ravel().argsort() | |
|
|
605 | junkcspc_interf = jcspectra[ip,:,hei_interf[cspsort[range(offhei_interf, nhei_interf + offhei_interf)]]] | |
|
|
606 | junkcspc_interf = junkcspc_interf.transpose() | |
|
|
607 | jcspc_interf = junkcspc_interf.sum(axis = 1)/nhei_interf | |
|
|
608 | ||
|
|
609 | ind = numpy.abs(jcspc_interf[mask_prof]).ravel().argsort() | |
|
|
610 | ||
|
|
611 | median_real = numpy.median(numpy.real(junkcspc_interf[mask_prof[ind[range(3*num_prof/4)]],:])) | |
|
|
612 | median_imag = numpy.median(numpy.imag(junkcspc_interf[mask_prof[ind[range(3*num_prof/4)]],:])) | |
|
|
613 | junkcspc_interf[comp_mask_prof,:] = numpy.complex(median_real, median_imag) | |
|
|
614 | ||
|
|
615 | for iprof in range(num_prof): | |
|
|
616 | ind = numpy.abs(junkcspc_interf[iprof,:]).ravel().argsort() | |
|
|
617 | jcspc_interf[iprof] = junkcspc_interf[iprof, ind[nhei_interf/2]] | |
|
|
618 | ||
|
|
619 | #Removiendo la Interferencia | |
|
|
620 | jcspectra[ip,:,ind_hei] = jcspectra[ip,:,ind_hei] - jcspc_interf | |
|
|
621 | ||
|
|
622 | ListAux = numpy.abs(jcspc_interf[mask_prof]).tolist() | |
|
|
623 | maxid = ListAux.index(max(ListAux)) | |
|
|
624 | ||
|
|
625 | ind = numpy.array([-2,-1,1,2]) | |
|
|
626 | xx = numpy.zeros([4,4]) | |
|
|
627 | ||
|
|
628 | for id1 in range(4): | |
|
|
629 | xx[:,id1] = ind[id1]**numpy.asarray(range(4)) | |
|
|
630 | ||
|
|
631 | xx_inv = numpy.linalg.inv(xx) | |
|
|
632 | xx = xx_inv[:,0] | |
|
|
633 | ||
|
|
634 | ind = (ind + maxid + num_mask_prof)%num_mask_prof | |
|
|
635 | yy = jcspectra[ip,mask_prof[ind],:] | |
|
|
636 | jcspectra[ip,mask_prof[maxid],:] = numpy.dot(yy.transpose(),xx) | |
|
|
637 | ||
|
|
638 | #Guardar Resultados | |
|
|
639 | self.dataOut.data_spc = jspectra | |
|
|
640 | self.dataOut.data_cspc = jcspectra | |
|
|
641 | ||
|
|
642 | return 1 | |
|
|
643 | ||
|
|
644 | def setRadarFrequency(self, frequency=None): | |
|
|
645 | ||
|
|
646 | if frequency != None: | |
|
|
647 | self.dataOut.frequency = frequency | |
|
|
648 | ||
|
|
649 | return 1 | |
|
|
650 | ||
|
|
651 | def getNoise(self, minHei=None, maxHei=None, minVel=None, maxVel=None): | |
|
|
652 | #validacion de rango | |
|
|
653 | if minHei == None: | |
|
|
654 | minHei = self.dataOut.heightList[0] | |
|
|
655 | ||
|
|
656 | if maxHei == None: | |
|
|
657 | maxHei = self.dataOut.heightList[-1] | |
|
|
658 | ||
|
|
659 | if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei): | |
|
|
660 | print 'minHei: %.2f is out of the heights range'%(minHei) | |
|
|
661 | print 'minHei is setting to %.2f'%(self.dataOut.heightList[0]) | |
|
|
662 | minHei = self.dataOut.heightList[0] | |
|
|
663 | ||
|
|
664 | if (maxHei > self.dataOut.heightList[-1]) or (maxHei < minHei): | |
|
|
665 | print 'maxHei: %.2f is out of the heights range'%(maxHei) | |
|
|
666 | print 'maxHei is setting to %.2f'%(self.dataOut.heightList[-1]) | |
|
|
667 | maxHei = self.dataOut.heightList[-1] | |
|
|
668 | ||
|
|
669 | # validacion de velocidades | |
|
|
670 | velrange = self.dataOut.getVelRange(1) | |
|
|
671 | ||
|
|
672 | if minVel == None: | |
|
|
673 | minVel = velrange[0] | |
|
|
674 | ||
|
|
675 | if maxVel == None: | |
|
|
676 | maxVel = velrange[-1] | |
|
|
677 | ||
|
|
678 | if (minVel < velrange[0]) or (minVel > maxVel): | |
|
|
679 | print 'minVel: %.2f is out of the velocity range'%(minVel) | |
|
|
680 | print 'minVel is setting to %.2f'%(velrange[0]) | |
|
|
681 | minVel = velrange[0] | |
|
|
682 | ||
|
|
683 | if (maxVel > velrange[-1]) or (maxVel < minVel): | |
|
|
684 | print 'maxVel: %.2f is out of the velocity range'%(maxVel) | |
|
|
685 | print 'maxVel is setting to %.2f'%(velrange[-1]) | |
|
|
686 | maxVel = velrange[-1] | |
|
|
687 | ||
|
|
688 | # seleccion de indices para rango | |
|
|
689 | minIndex = 0 | |
|
|
690 | maxIndex = 0 | |
|
|
691 | heights = self.dataOut.heightList | |
|
|
692 | ||
|
|
693 | inda = numpy.where(heights >= minHei) | |
|
|
694 | indb = numpy.where(heights <= maxHei) | |
|
|
695 | ||
|
|
696 | try: | |
|
|
697 | minIndex = inda[0][0] | |
|
|
698 | except: | |
|
|
699 | minIndex = 0 | |
|
|
700 | ||
|
|
701 | try: | |
|
|
702 | maxIndex = indb[0][-1] | |
|
|
703 | except: | |
|
|
704 | maxIndex = len(heights) | |
|
|
705 | ||
|
|
706 | if (minIndex < 0) or (minIndex > maxIndex): | |
|
|
707 | raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex) | |
|
|
708 | ||
|
|
709 | if (maxIndex >= self.dataOut.nHeights): | |
|
|
710 | maxIndex = self.dataOut.nHeights-1 | |
|
|
711 | ||
|
|
712 | # seleccion de indices para velocidades | |
|
|
713 | indminvel = numpy.where(velrange >= minVel) | |
|
|
714 | indmaxvel = numpy.where(velrange <= maxVel) | |
|
|
715 | try: | |
|
|
716 | minIndexVel = indminvel[0][0] | |
|
|
717 | except: | |
|
|
718 | minIndexVel = 0 | |
|
|
719 | ||
|
|
720 | try: | |
|
|
721 | maxIndexVel = indmaxvel[0][-1] | |
|
|
722 | except: | |
|
|
723 | maxIndexVel = len(velrange) | |
|
|
724 | ||
|
|
725 | #seleccion del espectro | |
|
|
726 | data_spc = self.dataOut.data_spc[:,minIndexVel:maxIndexVel+1,minIndex:maxIndex+1] | |
|
|
727 | #estimacion de ruido | |
|
|
728 | noise = numpy.zeros(self.dataOut.nChannels) | |
|
|
729 | ||
|
|
730 | for channel in range(self.dataOut.nChannels): | |
|
|
731 | daux = data_spc[channel,:,:] | |
|
|
732 | noise[channel] = hildebrand_sekhon(daux, self.dataOut.nIncohInt) | |
|
|
733 | ||
|
|
734 | self.dataOut.noise_estimation = noise.copy() | |
|
|
735 | ||
|
|
736 | return 1 No newline at end of file | |
| @@ -1,13 +1,14 | |||
|
|
1 | 1 | ''' |
|
|
2 | 2 | |
|
|
3 | 3 | $Author: murco $ |
|
|
4 | 4 | $Id: Processor.py 1 2012-11-12 18:56:07Z murco $ |
|
|
5 | 5 | ''' |
|
|
6 | 6 | |
|
|
7 | 7 | from jroproc_voltage import * |
|
|
8 | 8 | from jroproc_spectra import * |
|
|
9 | 9 | from jroproc_heispectra import * |
|
|
10 | 10 | from jroproc_amisr import * |
|
|
11 | 11 | from jroproc_correlation import * |
|
|
12 | 12 | from jroproc_parameters import * |
|
|
13 | from jroproc_spectra_lags import * No newline at end of file | |
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13 | from jroproc_spectra_lags import * | |
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14 | from jroproc_spectra_acf import * No newline at end of file | |
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1 | 1 | import numpy |
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2 | 2 | |
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3 | 3 | from jroproc_base import ProcessingUnit, Operation |
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4 | 4 | from schainpy.model.data.jrodata import Spectra |
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5 | 5 | from schainpy.model.data.jrodata import hildebrand_sekhon |
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6 | 6 | |
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7 | 7 | class SpectraLagsProc(ProcessingUnit): |
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8 | 8 | |
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9 | 9 | def __init__(self): |
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10 | 10 | |
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11 | 11 | ProcessingUnit.__init__(self) |
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12 | 12 | |
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13 | 13 | self.__input_buffer = None |
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14 | 14 | self.firstdatatime = None |
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15 | 15 | self.profIndex = 0 |
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16 | 16 | self.dataOut = Spectra() |
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17 | 17 | self.id_min = None |
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18 | 18 | self.id_max = None |
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19 | 19 | self.__codeIndex = 0 |
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20 | 20 | |
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21 | 21 | self.__lags_buffer = None |
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22 | 22 | |
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23 | 23 | def __updateSpecFromVoltage(self): |
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24 | 24 | |
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25 | 25 | self.dataOut.plotting = "spectra_lags" |
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26 | 26 | self.dataOut.timeZone = self.dataIn.timeZone |
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27 | 27 | self.dataOut.dstFlag = self.dataIn.dstFlag |
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28 | 28 | self.dataOut.errorCount = self.dataIn.errorCount |
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29 | 29 | self.dataOut.useLocalTime = self.dataIn.useLocalTime |
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30 | 30 | |
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31 | 31 | self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() |
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32 | 32 | self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy() |
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33 | 33 | self.dataOut.ippSeconds = self.dataIn.getDeltaH()*(10**-6)/0.15 |
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34 | 34 | |
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35 | 35 | self.dataOut.channelList = self.dataIn.channelList |
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36 | 36 | self.dataOut.heightList = self.dataIn.heightList |
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37 | 37 | self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')]) |
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38 | 38 | |
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39 | 39 | self.dataOut.nBaud = self.dataIn.nBaud |
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40 | 40 | self.dataOut.nCode = self.dataIn.nCode |
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41 | 41 | self.dataOut.code = self.dataIn.code |
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42 | 42 | # self.dataOut.nProfiles = self.dataOut.nFFTPoints |
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43 | 43 | |
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44 | 44 | self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock |
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45 | 45 | self.dataOut.utctime = self.firstdatatime |
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46 | 46 | self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada |
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47 | 47 | self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip |
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48 | 48 | self.dataOut.flagShiftFFT = False |
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49 | 49 | |
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50 | 50 | self.dataOut.nCohInt = self.dataIn.nCohInt |
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51 | 51 | self.dataOut.nIncohInt = 1 |
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52 | 52 | |
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53 | 53 | self.dataOut.windowOfFilter = self.dataIn.windowOfFilter |
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54 | 54 | |
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55 | 55 | self.dataOut.frequency = self.dataIn.frequency |
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56 | 56 | self.dataOut.realtime = self.dataIn.realtime |
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57 | 57 | |
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58 | 58 | self.dataOut.azimuth = self.dataIn.azimuth |
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59 | 59 | self.dataOut.zenith = self.dataIn.zenith |
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60 | 60 | |
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61 | 61 | self.dataOut.beam.codeList = self.dataIn.beam.codeList |
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62 | 62 | self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList |
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63 | 63 | self.dataOut.beam.zenithList = self.dataIn.beam.zenithList |
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64 | 64 | |
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65 | 65 | def __createLagsBlock(self, voltages): |
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66 | 66 | |
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67 | 67 | if self.__lags_buffer is None: |
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68 | 68 | self.__lags_buffer = numpy.zeros((self.dataOut.nChannels, self.dataOut.nProfiles, self.dataOut.nHeights), dtype='complex') |
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69 | 69 | |
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70 | 70 | nsegments = self.dataOut.nHeights - self.dataOut.nProfiles |
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71 | 71 | |
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72 | 72 | # codes = numpy.conjugate(self.__input_buffer[:,9:169])/10000 |
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73 | 73 | |
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74 | 74 | for i in range(nsegments): |
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75 | 75 | self.__lags_buffer[:,:,i] = voltages[:,i:i+self.dataOut.nProfiles]#*codes |
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76 | 76 | |
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77 | 77 | return self.__lags_buffer |
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78 | 78 | |
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79 | 79 | def __decodeData(self, volt_buffer, pulseIndex=None): |
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80 | 80 | |
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81 | 81 | if pulseIndex is None: |
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82 | 82 | return volt_buffer |
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83 | 83 | |
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84 | 84 | codes = numpy.conjugate(self.__input_buffer[:,pulseIndex[0]:pulseIndex[1]])/10000 |
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85 | 85 | |
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86 | 86 | nsegments = self.dataOut.nHeights - self.dataOut.nProfiles |
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87 | 87 | |
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88 | 88 | for i in range(nsegments): |
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89 | 89 | volt_buffer[:,:,i] = volt_buffer[:,:,i]*codes |
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90 | 90 | |
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91 | 91 | return volt_buffer |
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92 | 92 | |
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93 | 93 | def __getFft(self, datablock): |
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94 | 94 | """ |
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95 | 95 | Convierte valores de Voltaje a Spectra |
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96 | 96 | |
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97 | 97 | Affected: |
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98 | 98 | self.dataOut.data_spc |
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99 | 99 | self.dataOut.data_cspc |
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100 | 100 | self.dataOut.data_dc |
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101 | 101 | self.dataOut.heightList |
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102 | 102 | self.profIndex |
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103 | 103 | self.__input_buffer |
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104 | 104 | self.dataOut.flagNoData |
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105 | 105 | """ |
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106 | 106 | |
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107 | 107 | fft_volt = numpy.fft.fft(datablock, n=self.dataOut.nFFTPoints, axis=1) |
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108 | 108 | |
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109 |
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109 | dc = fft_volt[:,0,:] | |
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110 | 110 | |
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111 | 111 | #calculo de self-spectra |
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112 | 112 | fft_volt = numpy.fft.fftshift(fft_volt, axes=(1,)) |
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113 | 113 | spc = fft_volt * numpy.conjugate(fft_volt) |
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114 | 114 | spc = spc.real |
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115 | 115 | |
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116 | 116 | blocksize = 0 |
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117 |
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117 | blocksize += dc.size | |
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118 | 118 | blocksize += spc.size |
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119 | 119 | |
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120 | 120 | cspc = None |
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121 | 121 | pairIndex = 0 |
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122 | 122 | |
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123 |
if self.dataOut.pairsList != |
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123 | if self.dataOut.pairsList != []: | |
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124 | 124 | #calculo de cross-spectra |
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125 | 125 | cspc = numpy.zeros((self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex') |
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126 | 126 | for pair in self.dataOut.pairsList: |
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127 | 127 | if pair[0] not in self.dataOut.channelList: |
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128 | 128 | raise ValueError, "Error getting CrossSpectra: pair 0 of %s is not in channelList = %s" %(str(pair), str(self.dataOut.channelList)) |
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129 | 129 | if pair[1] not in self.dataOut.channelList: |
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130 | 130 | raise ValueError, "Error getting CrossSpectra: pair 1 of %s is not in channelList = %s" %(str(pair), str(self.dataOut.channelList)) |
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131 | 131 | |
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132 | 132 | chan_index0 = self.dataOut.channelList.index(pair[0]) |
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133 | 133 | chan_index1 = self.dataOut.channelList.index(pair[1]) |
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134 | 134 | |
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135 | 135 | cspc[pairIndex,:,:] = fft_volt[chan_index0,:,:] * numpy.conjugate(fft_volt[chan_index1,:,:]) |
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136 | 136 | pairIndex += 1 |
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137 | 137 | blocksize += cspc.size |
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138 | 138 | |
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139 | 139 | self.dataOut.data_spc = spc |
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140 | 140 | self.dataOut.data_cspc = cspc |
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141 |
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141 | self.dataOut.data_dc = dc | |
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142 | 142 | self.dataOut.blockSize = blocksize |
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143 | 143 | self.dataOut.flagShiftFFT = True |
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144 | 144 | |
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145 | 145 | def run(self, nProfiles=None, nFFTPoints=None, pairsList=[], code=None, nCode=None, nBaud=None, codeFromHeader=False, pulseIndex=None): |
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146 | 146 | |
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147 | 147 | self.dataOut.flagNoData = True |
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148 | 148 | |
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149 | 149 | self.code = None |
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150 | 150 | |
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151 | 151 | if codeFromHeader: |
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152 | 152 | if self.dataIn.code is not None: |
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153 | 153 | self.code = self.dataIn.code |
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154 | 154 | |
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155 | 155 | if code is not None: |
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156 | 156 | self.code = numpy.array(code).reshape(nCode,nBaud) |
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157 | 157 | |
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158 | 158 | if self.dataIn.type == "Voltage": |
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159 | 159 | |
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160 | 160 | if nFFTPoints == None: |
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161 | 161 | raise ValueError, "This SpectraProc.run() need nFFTPoints input variable" |
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162 | 162 | |
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163 | 163 | if nProfiles == None: |
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164 | 164 | nProfiles = nFFTPoints |
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165 | 165 | |
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166 | 166 | self.profIndex == nProfiles |
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167 | 167 | self.firstdatatime = self.dataIn.utctime |
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168 | 168 | |
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169 | 169 | self.dataOut.ippFactor = 1 |
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170 | 170 | self.dataOut.nFFTPoints = nFFTPoints |
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171 | 171 | self.dataOut.nProfiles = nProfiles |
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172 | 172 | self.dataOut.pairsList = pairsList |
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173 | 173 | |
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174 | 174 | self.__updateSpecFromVoltage() |
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175 | 175 | |
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176 | 176 | if not self.dataIn.flagDataAsBlock: |
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177 | 177 | self.__input_buffer = self.dataIn.data.copy() |
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178 | 178 | |
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179 | 179 | lags_block = self.__createLagsBlock(self.__input_buffer) |
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180 | 180 | |
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181 | 181 | lags_block = self.__decodeData(lags_block, pulseIndex) |
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182 | 182 | |
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183 | 183 | else: |
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184 | 184 | self.__input_buffer = self.dataIn.data.copy() |
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185 | 185 | |
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186 | 186 | self.__getFft(lags_block) |
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187 | 187 | |
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188 | 188 | self.dataOut.flagNoData = False |
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189 | 189 | |
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190 | 190 | return True |
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191 | 191 | |
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192 | 192 | raise ValueError, "The type of input object '%s' is not valid"%(self.dataIn.type) |
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193 | 193 | |
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194 | 194 | def __selectPairs(self, pairsList): |
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195 | 195 | |
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196 | 196 | if channelList == None: |
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197 | 197 | return |
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198 | 198 | |
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199 | 199 | pairsIndexListSelected = [] |
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200 | 200 | |
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201 | 201 | for thisPair in pairsList: |
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202 | 202 | |
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203 | 203 | if thisPair not in self.dataOut.pairsList: |
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204 | 204 | continue |
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205 | 205 | |
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206 | 206 | pairIndex = self.dataOut.pairsList.index(thisPair) |
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207 | 207 | |
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208 | 208 | pairsIndexListSelected.append(pairIndex) |
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209 | 209 | |
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210 | 210 | if not pairsIndexListSelected: |
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211 | 211 | self.dataOut.data_cspc = None |
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212 | 212 | self.dataOut.pairsList = [] |
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213 | 213 | return |
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214 | 214 | |
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215 | 215 | self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected] |
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216 | 216 | self.dataOut.pairsList = [self.dataOut.pairsList[i] for i in pairsIndexListSelected] |
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217 | 217 | |
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218 | 218 | return |
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219 | 219 | |
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220 | 220 | def __selectPairsByChannel(self, channelList=None): |
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221 | 221 | |
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222 | 222 | if channelList == None: |
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223 | 223 | return |
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224 | 224 | |
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225 | 225 | pairsIndexListSelected = [] |
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226 | 226 | for pairIndex in self.dataOut.pairsIndexList: |
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227 | 227 | #First pair |
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228 | 228 | if self.dataOut.pairsList[pairIndex][0] not in channelList: |
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229 | 229 | continue |
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230 | 230 | #Second pair |
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231 | 231 | if self.dataOut.pairsList[pairIndex][1] not in channelList: |
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232 | 232 | continue |
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233 | 233 | |
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234 | 234 | pairsIndexListSelected.append(pairIndex) |
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235 | 235 | |
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236 | 236 | if not pairsIndexListSelected: |
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237 | 237 | self.dataOut.data_cspc = None |
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238 | 238 | self.dataOut.pairsList = [] |
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239 | 239 | return |
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240 | 240 | |
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241 | 241 | self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected] |
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242 | 242 | self.dataOut.pairsList = [self.dataOut.pairsList[i] for i in pairsIndexListSelected] |
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243 | 243 | |
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244 | 244 | return |
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245 | 245 | |
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246 | 246 | def selectChannels(self, channelList): |
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247 | 247 | |
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248 | 248 | channelIndexList = [] |
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249 | 249 | |
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250 | 250 | for channel in channelList: |
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251 | 251 | if channel not in self.dataOut.channelList: |
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252 | 252 | raise ValueError, "Error selecting channels, Channel %d is not valid.\nAvailable channels = %s" %(channel, str(self.dataOut.channelList)) |
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253 | 253 | |
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254 | 254 | index = self.dataOut.channelList.index(channel) |
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255 | 255 | channelIndexList.append(index) |
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256 | 256 | |
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257 | 257 | self.selectChannelsByIndex(channelIndexList) |
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258 | 258 | |
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259 | 259 | def selectChannelsByIndex(self, channelIndexList): |
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260 | 260 | """ |
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261 | 261 | Selecciona un bloque de datos en base a canales segun el channelIndexList |
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262 | 262 | |
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263 | 263 | Input: |
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264 | 264 | channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7] |
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265 | 265 | |
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266 | 266 | Affected: |
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267 | 267 | self.dataOut.data_spc |
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268 | 268 | self.dataOut.channelIndexList |
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269 | 269 | self.dataOut.nChannels |
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270 | 270 | |
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271 | 271 | Return: |
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272 | 272 | None |
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273 | 273 | """ |
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274 | 274 | |
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275 | 275 | for channelIndex in channelIndexList: |
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276 | 276 | if channelIndex not in self.dataOut.channelIndexList: |
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277 | 277 | raise ValueError, "Error selecting channels: The value %d in channelIndexList is not valid.\nAvailable channel indexes = " %(channelIndex, self.dataOut.channelIndexList) |
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278 | 278 | |
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279 | 279 | # nChannels = len(channelIndexList) |
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280 | 280 | |
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281 | 281 | data_spc = self.dataOut.data_spc[channelIndexList,:] |
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282 | 282 | data_dc = self.dataOut.data_dc[channelIndexList,:] |
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283 | 283 | |
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284 | 284 | self.dataOut.data_spc = data_spc |
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285 | 285 | self.dataOut.data_dc = data_dc |
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286 | 286 | |
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287 | 287 | self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList] |
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288 | 288 | # self.dataOut.nChannels = nChannels |
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289 | 289 | |
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290 | 290 | self.__selectPairsByChannel(self.dataOut.channelList) |
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291 | 291 | |
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292 | 292 | return 1 |
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293 | 293 | |
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294 | 294 | def selectHeights(self, minHei, maxHei): |
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295 | 295 | """ |
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296 | 296 | Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango |
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297 | 297 | minHei <= height <= maxHei |
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298 | 298 | |
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299 | 299 | Input: |
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300 | 300 | minHei : valor minimo de altura a considerar |
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301 | 301 | maxHei : valor maximo de altura a considerar |
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302 | 302 | |
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303 | 303 | Affected: |
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304 | 304 | Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex |
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305 | 305 | |
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306 | 306 | Return: |
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307 | 307 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 |
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308 | 308 | """ |
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309 | 309 | |
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310 | 310 | if (minHei > maxHei): |
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311 | 311 | raise ValueError, "Error selecting heights: Height range (%d,%d) is not valid" % (minHei, maxHei) |
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312 | 312 | |
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313 | 313 | if (minHei < self.dataOut.heightList[0]): |
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314 | 314 | minHei = self.dataOut.heightList[0] |
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315 | 315 | |
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316 | 316 | if (maxHei > self.dataOut.heightList[-1]): |
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317 | 317 | maxHei = self.dataOut.heightList[-1] |
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318 | 318 | |
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319 | 319 | minIndex = 0 |
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320 | 320 | maxIndex = 0 |
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321 | 321 | heights = self.dataOut.heightList |
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322 | 322 | |
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323 | 323 | inda = numpy.where(heights >= minHei) |
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324 | 324 | indb = numpy.where(heights <= maxHei) |
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325 | 325 | |
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326 | 326 | try: |
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327 | 327 | minIndex = inda[0][0] |
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328 | 328 | except: |
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329 | 329 | minIndex = 0 |
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330 | 330 | |
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331 | 331 | try: |
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332 | 332 | maxIndex = indb[0][-1] |
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333 | 333 | except: |
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334 | 334 | maxIndex = len(heights) |
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335 | 335 | |
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336 | 336 | self.selectHeightsByIndex(minIndex, maxIndex) |
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337 | 337 | |
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338 | 338 | return 1 |
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339 | 339 | |
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340 | 340 | def getBeaconSignal(self, tauindex = 0, channelindex = 0, hei_ref=None): |
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341 | 341 | newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex]) |
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342 | 342 | |
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343 | 343 | if hei_ref != None: |
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344 | 344 | newheis = numpy.where(self.dataOut.heightList>hei_ref) |
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345 | 345 | |
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346 | 346 | minIndex = min(newheis[0]) |
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347 | 347 | maxIndex = max(newheis[0]) |
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348 | 348 | data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1] |
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349 | 349 | heightList = self.dataOut.heightList[minIndex:maxIndex+1] |
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350 | 350 | |
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351 | 351 | # determina indices |
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352 | 352 | nheis = int(self.dataOut.radarControllerHeaderObj.txB/(self.dataOut.heightList[1]-self.dataOut.heightList[0])) |
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353 | 353 | avg_dB = 10*numpy.log10(numpy.sum(data_spc[channelindex,:,:],axis=0)) |
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354 | 354 | beacon_dB = numpy.sort(avg_dB)[-nheis:] |
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355 | 355 | beacon_heiIndexList = [] |
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356 | 356 | for val in avg_dB.tolist(): |
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357 | 357 | if val >= beacon_dB[0]: |
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358 | 358 | beacon_heiIndexList.append(avg_dB.tolist().index(val)) |
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359 | 359 | |
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360 | 360 | #data_spc = data_spc[:,:,beacon_heiIndexList] |
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361 | 361 | data_cspc = None |
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362 | 362 | if self.dataOut.data_cspc is not None: |
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363 | 363 | data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1] |
|
|
364 | 364 | #data_cspc = data_cspc[:,:,beacon_heiIndexList] |
|
|
365 | 365 | |
|
|
366 | 366 | data_dc = None |
|
|
367 | 367 | if self.dataOut.data_dc is not None: |
|
|
368 | 368 | data_dc = self.dataOut.data_dc[:,minIndex:maxIndex+1] |
|
|
369 | 369 | #data_dc = data_dc[:,beacon_heiIndexList] |
|
|
370 | 370 | |
|
|
371 | 371 | self.dataOut.data_spc = data_spc |
|
|
372 | 372 | self.dataOut.data_cspc = data_cspc |
|
|
373 | 373 | self.dataOut.data_dc = data_dc |
|
|
374 | 374 | self.dataOut.heightList = heightList |
|
|
375 | 375 | self.dataOut.beacon_heiIndexList = beacon_heiIndexList |
|
|
376 | 376 | |
|
|
377 | 377 | return 1 |
|
|
378 | 378 | |
|
|
379 | 379 | |
|
|
380 | 380 | def selectHeightsByIndex(self, minIndex, maxIndex): |
|
|
381 | 381 | """ |
|
|
382 | 382 | Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango |
|
|
383 | 383 | minIndex <= index <= maxIndex |
|
|
384 | 384 | |
|
|
385 | 385 | Input: |
|
|
386 | 386 | minIndex : valor de indice minimo de altura a considerar |
|
|
387 | 387 | maxIndex : valor de indice maximo de altura a considerar |
|
|
388 | 388 | |
|
|
389 | 389 | Affected: |
|
|
390 | 390 | self.dataOut.data_spc |
|
|
391 | 391 | self.dataOut.data_cspc |
|
|
392 | 392 | self.dataOut.data_dc |
|
|
393 | 393 | self.dataOut.heightList |
|
|
394 | 394 | |
|
|
395 | 395 | Return: |
|
|
396 | 396 | 1 si el metodo se ejecuto con exito caso contrario devuelve 0 |
|
|
397 | 397 | """ |
|
|
398 | 398 | |
|
|
399 | 399 | if (minIndex < 0) or (minIndex > maxIndex): |
|
|
400 | 400 | raise ValueError, "Error selecting heights: Index range (%d,%d) is not valid" % (minIndex, maxIndex) |
|
|
401 | 401 | |
|
|
402 | 402 | if (maxIndex >= self.dataOut.nHeights): |
|
|
403 | 403 | maxIndex = self.dataOut.nHeights-1 |
|
|
404 | 404 | |
|
|
405 | 405 | #Spectra |
|
|
406 | 406 | data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1] |
|
|
407 | 407 | |
|
|
408 | 408 | data_cspc = None |
|
|
409 | 409 | if self.dataOut.data_cspc is not None: |
|
|
410 | 410 | data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1] |
|
|
411 | 411 | |
|
|
412 | 412 | data_dc = None |
|
|
413 | 413 | if self.dataOut.data_dc is not None: |
|
|
414 | 414 | data_dc = self.dataOut.data_dc[:,minIndex:maxIndex+1] |
|
|
415 | 415 | |
|
|
416 | 416 | self.dataOut.data_spc = data_spc |
|
|
417 | 417 | self.dataOut.data_cspc = data_cspc |
|
|
418 | 418 | self.dataOut.data_dc = data_dc |
|
|
419 | 419 | |
|
|
420 | 420 | self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1] |
|
|
421 | 421 | |
|
|
422 | 422 | return 1 |
|
|
423 | 423 | |
|
|
424 | 424 | def removeDC(self, mode = 2): |
|
|
425 | 425 | jspectra = self.dataOut.data_spc |
|
|
426 | 426 | jcspectra = self.dataOut.data_cspc |
|
|
427 | 427 | |
|
|
428 | 428 | |
|
|
429 | 429 | num_chan = jspectra.shape[0] |
|
|
430 | 430 | num_hei = jspectra.shape[2] |
|
|
431 | 431 | |
|
|
432 | 432 | if jcspectra is not None: |
|
|
433 | 433 | jcspectraExist = True |
|
|
434 | 434 | num_pairs = jcspectra.shape[0] |
|
|
435 | 435 | else: jcspectraExist = False |
|
|
436 | 436 | |
|
|
437 | 437 | freq_dc = jspectra.shape[1]/2 |
|
|
438 | 438 | ind_vel = numpy.array([-2,-1,1,2]) + freq_dc |
|
|
439 | 439 | |
|
|
440 | 440 | if ind_vel[0]<0: |
|
|
441 | 441 | ind_vel[range(0,1)] = ind_vel[range(0,1)] + self.num_prof |
|
|
442 | 442 | |
|
|
443 | 443 | if mode == 1: |
|
|
444 | 444 | jspectra[:,freq_dc,:] = (jspectra[:,ind_vel[1],:] + jspectra[:,ind_vel[2],:])/2 #CORRECCION |
|
|
445 | 445 | |
|
|
446 | 446 | if jcspectraExist: |
|
|
447 | 447 | jcspectra[:,freq_dc,:] = (jcspectra[:,ind_vel[1],:] + jcspectra[:,ind_vel[2],:])/2 |
|
|
448 | 448 | |
|
|
449 | 449 | if mode == 2: |
|
|
450 | 450 | |
|
|
451 | 451 | vel = numpy.array([-2,-1,1,2]) |
|
|
452 | 452 | xx = numpy.zeros([4,4]) |
|
|
453 | 453 | |
|
|
454 | 454 | for fil in range(4): |
|
|
455 | 455 | xx[fil,:] = vel[fil]**numpy.asarray(range(4)) |
|
|
456 | 456 | |
|
|
457 | 457 | xx_inv = numpy.linalg.inv(xx) |
|
|
458 | 458 | xx_aux = xx_inv[0,:] |
|
|
459 | 459 | |
|
|
460 | 460 | for ich in range(num_chan): |
|
|
461 | 461 | yy = jspectra[ich,ind_vel,:] |
|
|
462 | 462 | jspectra[ich,freq_dc,:] = numpy.dot(xx_aux,yy) |
|
|
463 | 463 | |
|
|
464 | 464 | junkid = jspectra[ich,freq_dc,:]<=0 |
|
|
465 | 465 | cjunkid = sum(junkid) |
|
|
466 | 466 | |
|
|
467 | 467 | if cjunkid.any(): |
|
|
468 | 468 | jspectra[ich,freq_dc,junkid.nonzero()] = (jspectra[ich,ind_vel[1],junkid] + jspectra[ich,ind_vel[2],junkid])/2 |
|
|
469 | 469 | |
|
|
470 | 470 | if jcspectraExist: |
|
|
471 | 471 | for ip in range(num_pairs): |
|
|
472 | 472 | yy = jcspectra[ip,ind_vel,:] |
|
|
473 | 473 | jcspectra[ip,freq_dc,:] = numpy.dot(xx_aux,yy) |
|
|
474 | 474 | |
|
|
475 | 475 | |
|
|
476 | 476 | self.dataOut.data_spc = jspectra |
|
|
477 | 477 | self.dataOut.data_cspc = jcspectra |
|
|
478 | 478 | |
|
|
479 | 479 | return 1 |
|
|
480 | 480 | |
|
|
481 | 481 | def removeInterference(self, interf = 2,hei_interf = None, nhei_interf = None, offhei_interf = None): |
|
|
482 | 482 | |
|
|
483 | 483 | jspectra = self.dataOut.data_spc |
|
|
484 | 484 | jcspectra = self.dataOut.data_cspc |
|
|
485 | 485 | jnoise = self.dataOut.getNoise() |
|
|
486 | 486 | num_incoh = self.dataOut.nIncohInt |
|
|
487 | 487 | |
|
|
488 | 488 | num_channel = jspectra.shape[0] |
|
|
489 | 489 | num_prof = jspectra.shape[1] |
|
|
490 | 490 | num_hei = jspectra.shape[2] |
|
|
491 | 491 | |
|
|
492 | 492 | #hei_interf |
|
|
493 | 493 | if hei_interf is None: |
|
|
494 | 494 | count_hei = num_hei/2 #Como es entero no importa |
|
|
495 | 495 | hei_interf = numpy.asmatrix(range(count_hei)) + num_hei - count_hei |
|
|
496 | 496 | hei_interf = numpy.asarray(hei_interf)[0] |
|
|
497 | 497 | #nhei_interf |
|
|
498 | 498 | if (nhei_interf == None): |
|
|
499 | 499 | nhei_interf = 5 |
|
|
500 | 500 | if (nhei_interf < 1): |
|
|
501 | 501 | nhei_interf = 1 |
|
|
502 | 502 | if (nhei_interf > count_hei): |
|
|
503 | 503 | nhei_interf = count_hei |
|
|
504 | 504 | if (offhei_interf == None): |
|
|
505 | 505 | offhei_interf = 0 |
|
|
506 | 506 | |
|
|
507 | 507 | ind_hei = range(num_hei) |
|
|
508 | 508 | # mask_prof = numpy.asarray(range(num_prof - 2)) + 1 |
|
|
509 | 509 | # mask_prof[range(num_prof/2 - 1,len(mask_prof))] += 1 |
|
|
510 | 510 | mask_prof = numpy.asarray(range(num_prof)) |
|
|
511 | 511 | num_mask_prof = mask_prof.size |
|
|
512 | 512 | comp_mask_prof = [0, num_prof/2] |
|
|
513 | 513 | |
|
|
514 | 514 | |
|
|
515 | 515 | #noise_exist: Determina si la variable jnoise ha sido definida y contiene la informacion del ruido de cada canal |
|
|
516 | 516 | if (jnoise.size < num_channel or numpy.isnan(jnoise).any()): |
|
|
517 | 517 | jnoise = numpy.nan |
|
|
518 | 518 | noise_exist = jnoise[0] < numpy.Inf |
|
|
519 | 519 | |
|
|
520 | 520 | #Subrutina de Remocion de la Interferencia |
|
|
521 | 521 | for ich in range(num_channel): |
|
|
522 | 522 | #Se ordena los espectros segun su potencia (menor a mayor) |
|
|
523 | 523 | power = jspectra[ich,mask_prof,:] |
|
|
524 | 524 | power = power[:,hei_interf] |
|
|
525 | 525 | power = power.sum(axis = 0) |
|
|
526 | 526 | psort = power.ravel().argsort() |
|
|
527 | 527 | |
|
|
528 | 528 | #Se estima la interferencia promedio en los Espectros de Potencia empleando |
|
|
529 | 529 | junkspc_interf = jspectra[ich,:,hei_interf[psort[range(offhei_interf, nhei_interf + offhei_interf)]]] |
|
|
530 | 530 | |
|
|
531 | 531 | if noise_exist: |
|
|
532 | 532 | # tmp_noise = jnoise[ich] / num_prof |
|
|
533 | 533 | tmp_noise = jnoise[ich] |
|
|
534 | 534 | junkspc_interf = junkspc_interf - tmp_noise |
|
|
535 | 535 | #junkspc_interf[:,comp_mask_prof] = 0 |
|
|
536 | 536 | |
|
|
537 | 537 | jspc_interf = junkspc_interf.sum(axis = 0) / nhei_interf |
|
|
538 | 538 | jspc_interf = jspc_interf.transpose() |
|
|
539 | 539 | #Calculando el espectro de interferencia promedio |
|
|
540 | 540 | noiseid = numpy.where(jspc_interf <= tmp_noise/ numpy.sqrt(num_incoh)) |
|
|
541 | 541 | noiseid = noiseid[0] |
|
|
542 | 542 | cnoiseid = noiseid.size |
|
|
543 | 543 | interfid = numpy.where(jspc_interf > tmp_noise/ numpy.sqrt(num_incoh)) |
|
|
544 | 544 | interfid = interfid[0] |
|
|
545 | 545 | cinterfid = interfid.size |
|
|
546 | 546 | |
|
|
547 | 547 | if (cnoiseid > 0): jspc_interf[noiseid] = 0 |
|
|
548 | 548 | |
|
|
549 | 549 | #Expandiendo los perfiles a limpiar |
|
|
550 | 550 | if (cinterfid > 0): |
|
|
551 | 551 | new_interfid = (numpy.r_[interfid - 1, interfid, interfid + 1] + num_prof)%num_prof |
|
|
552 | 552 | new_interfid = numpy.asarray(new_interfid) |
|
|
553 | 553 | new_interfid = {x for x in new_interfid} |
|
|
554 | 554 | new_interfid = numpy.array(list(new_interfid)) |
|
|
555 | 555 | new_cinterfid = new_interfid.size |
|
|
556 | 556 | else: new_cinterfid = 0 |
|
|
557 | 557 | |
|
|
558 | 558 | for ip in range(new_cinterfid): |
|
|
559 | 559 | ind = junkspc_interf[:,new_interfid[ip]].ravel().argsort() |
|
|
560 | 560 | jspc_interf[new_interfid[ip]] = junkspc_interf[ind[nhei_interf/2],new_interfid[ip]] |
|
|
561 | 561 | |
|
|
562 | 562 | |
|
|
563 | 563 | jspectra[ich,:,ind_hei] = jspectra[ich,:,ind_hei] - jspc_interf #Corregir indices |
|
|
564 | 564 | |
|
|
565 | 565 | #Removiendo la interferencia del punto de mayor interferencia |
|
|
566 | 566 | ListAux = jspc_interf[mask_prof].tolist() |
|
|
567 | 567 | maxid = ListAux.index(max(ListAux)) |
|
|
568 | 568 | |
|
|
569 | 569 | |
|
|
570 | 570 | if cinterfid > 0: |
|
|
571 | 571 | for ip in range(cinterfid*(interf == 2) - 1): |
|
|
572 | 572 | ind = (jspectra[ich,interfid[ip],:] < tmp_noise*(1 + 1/numpy.sqrt(num_incoh))).nonzero() |
|
|
573 | 573 | cind = len(ind) |
|
|
574 | 574 | |
|
|
575 | 575 | if (cind > 0): |
|
|
576 | 576 | jspectra[ich,interfid[ip],ind] = tmp_noise*(1 + (numpy.random.uniform(cind) - 0.5)/numpy.sqrt(num_incoh)) |
|
|
577 | 577 | |
|
|
578 | 578 | ind = numpy.array([-2,-1,1,2]) |
|
|
579 | 579 | xx = numpy.zeros([4,4]) |
|
|
580 | 580 | |
|
|
581 | 581 | for id1 in range(4): |
|
|
582 | 582 | xx[:,id1] = ind[id1]**numpy.asarray(range(4)) |
|
|
583 | 583 | |
|
|
584 | 584 | xx_inv = numpy.linalg.inv(xx) |
|
|
585 | 585 | xx = xx_inv[:,0] |
|
|
586 | 586 | ind = (ind + maxid + num_mask_prof)%num_mask_prof |
|
|
587 | 587 | yy = jspectra[ich,mask_prof[ind],:] |
|
|
588 | 588 | jspectra[ich,mask_prof[maxid],:] = numpy.dot(yy.transpose(),xx) |
|
|
589 | 589 | |
|
|
590 | 590 | |
|
|
591 | 591 | indAux = (jspectra[ich,:,:] < tmp_noise*(1-1/numpy.sqrt(num_incoh))).nonzero() |
|
|
592 | 592 | jspectra[ich,indAux[0],indAux[1]] = tmp_noise * (1 - 1/numpy.sqrt(num_incoh)) |
|
|
593 | 593 | |
|
|
594 | 594 | #Remocion de Interferencia en el Cross Spectra |
|
|
595 | 595 | if jcspectra is None: return jspectra, jcspectra |
|
|
596 | 596 | num_pairs = jcspectra.size/(num_prof*num_hei) |
|
|
597 | 597 | jcspectra = jcspectra.reshape(num_pairs, num_prof, num_hei) |
|
|
598 | 598 | |
|
|
599 | 599 | for ip in range(num_pairs): |
|
|
600 | 600 | |
|
|
601 | 601 | #------------------------------------------- |
|
|
602 | 602 | |
|
|
603 | 603 | cspower = numpy.abs(jcspectra[ip,mask_prof,:]) |
|
|
604 | 604 | cspower = cspower[:,hei_interf] |
|
|
605 | 605 | cspower = cspower.sum(axis = 0) |
|
|
606 | 606 | |
|
|
607 | 607 | cspsort = cspower.ravel().argsort() |
|
|
608 | 608 | junkcspc_interf = jcspectra[ip,:,hei_interf[cspsort[range(offhei_interf, nhei_interf + offhei_interf)]]] |
|
|
609 | 609 | junkcspc_interf = junkcspc_interf.transpose() |
|
|
610 | 610 | jcspc_interf = junkcspc_interf.sum(axis = 1)/nhei_interf |
|
|
611 | 611 | |
|
|
612 | 612 | ind = numpy.abs(jcspc_interf[mask_prof]).ravel().argsort() |
|
|
613 | 613 | |
|
|
614 | 614 | median_real = numpy.median(numpy.real(junkcspc_interf[mask_prof[ind[range(3*num_prof/4)]],:])) |
|
|
615 | 615 | median_imag = numpy.median(numpy.imag(junkcspc_interf[mask_prof[ind[range(3*num_prof/4)]],:])) |
|
|
616 | 616 | junkcspc_interf[comp_mask_prof,:] = numpy.complex(median_real, median_imag) |
|
|
617 | 617 | |
|
|
618 | 618 | for iprof in range(num_prof): |
|
|
619 | 619 | ind = numpy.abs(junkcspc_interf[iprof,:]).ravel().argsort() |
|
|
620 | 620 | jcspc_interf[iprof] = junkcspc_interf[iprof, ind[nhei_interf/2]] |
|
|
621 | 621 | |
|
|
622 | 622 | #Removiendo la Interferencia |
|
|
623 | 623 | jcspectra[ip,:,ind_hei] = jcspectra[ip,:,ind_hei] - jcspc_interf |
|
|
624 | 624 | |
|
|
625 | 625 | ListAux = numpy.abs(jcspc_interf[mask_prof]).tolist() |
|
|
626 | 626 | maxid = ListAux.index(max(ListAux)) |
|
|
627 | 627 | |
|
|
628 | 628 | ind = numpy.array([-2,-1,1,2]) |
|
|
629 | 629 | xx = numpy.zeros([4,4]) |
|
|
630 | 630 | |
|
|
631 | 631 | for id1 in range(4): |
|
|
632 | 632 | xx[:,id1] = ind[id1]**numpy.asarray(range(4)) |
|
|
633 | 633 | |
|
|
634 | 634 | xx_inv = numpy.linalg.inv(xx) |
|
|
635 | 635 | xx = xx_inv[:,0] |
|
|
636 | 636 | |
|
|
637 | 637 | ind = (ind + maxid + num_mask_prof)%num_mask_prof |
|
|
638 | 638 | yy = jcspectra[ip,mask_prof[ind],:] |
|
|
639 | 639 | jcspectra[ip,mask_prof[maxid],:] = numpy.dot(yy.transpose(),xx) |
|
|
640 | 640 | |
|
|
641 | 641 | #Guardar Resultados |
|
|
642 | 642 | self.dataOut.data_spc = jspectra |
|
|
643 | 643 | self.dataOut.data_cspc = jcspectra |
|
|
644 | 644 | |
|
|
645 | 645 | return 1 |
|
|
646 | 646 | |
|
|
647 | 647 | def setRadarFrequency(self, frequency=None): |
|
|
648 | 648 | |
|
|
649 | 649 | if frequency != None: |
|
|
650 | 650 | self.dataOut.frequency = frequency |
|
|
651 | 651 | |
|
|
652 | 652 | return 1 |
|
|
653 | 653 | |
|
|
654 | 654 | def getNoise(self, minHei=None, maxHei=None, minVel=None, maxVel=None): |
|
|
655 | 655 | #validacion de rango |
|
|
656 | 656 | if minHei == None: |
|
|
657 | 657 | minHei = self.dataOut.heightList[0] |
|
|
658 | 658 | |
|
|
659 | 659 | if maxHei == None: |
|
|
660 | 660 | maxHei = self.dataOut.heightList[-1] |
|
|
661 | 661 | |
|
|
662 | 662 | if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei): |
|
|
663 | 663 | print 'minHei: %.2f is out of the heights range'%(minHei) |
|
|
664 | 664 | print 'minHei is setting to %.2f'%(self.dataOut.heightList[0]) |
|
|
665 | 665 | minHei = self.dataOut.heightList[0] |
|
|
666 | 666 | |
|
|
667 | 667 | if (maxHei > self.dataOut.heightList[-1]) or (maxHei < minHei): |
|
|
668 | 668 | print 'maxHei: %.2f is out of the heights range'%(maxHei) |
|
|
669 | 669 | print 'maxHei is setting to %.2f'%(self.dataOut.heightList[-1]) |
|
|
670 | 670 | maxHei = self.dataOut.heightList[-1] |
|
|
671 | 671 | |
|
|
672 | 672 | # validacion de velocidades |
|
|
673 | 673 | velrange = self.dataOut.getVelRange(1) |
|
|
674 | 674 | |
|
|
675 | 675 | if minVel == None: |
|
|
676 | 676 | minVel = velrange[0] |
|
|
677 | 677 | |
|
|
678 | 678 | if maxVel == None: |
|
|
679 | 679 | maxVel = velrange[-1] |
|
|
680 | 680 | |
|
|
681 | 681 | if (minVel < velrange[0]) or (minVel > maxVel): |
|
|
682 | 682 | print 'minVel: %.2f is out of the velocity range'%(minVel) |
|
|
683 | 683 | print 'minVel is setting to %.2f'%(velrange[0]) |
|
|
684 | 684 | minVel = velrange[0] |
|
|
685 | 685 | |
|
|
686 | 686 | if (maxVel > velrange[-1]) or (maxVel < minVel): |
|
|
687 | 687 | print 'maxVel: %.2f is out of the velocity range'%(maxVel) |
|
|
688 | 688 | print 'maxVel is setting to %.2f'%(velrange[-1]) |
|
|
689 | 689 | maxVel = velrange[-1] |
|
|
690 | 690 | |
|
|
691 | 691 | # seleccion de indices para rango |
|
|
692 | 692 | minIndex = 0 |
|
|
693 | 693 | maxIndex = 0 |
|
|
694 | 694 | heights = self.dataOut.heightList |
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695 | 695 | |
|
|
696 | 696 | inda = numpy.where(heights >= minHei) |
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697 | 697 | indb = numpy.where(heights <= maxHei) |
|
|
698 | 698 | |
|
|
699 | 699 | try: |
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|
700 | 700 | minIndex = inda[0][0] |
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701 | 701 | except: |
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702 | 702 | minIndex = 0 |
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703 | 703 | |
|
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704 | 704 | try: |
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705 | 705 | maxIndex = indb[0][-1] |
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706 | 706 | except: |
|
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707 | 707 | maxIndex = len(heights) |
|
|
708 | 708 | |
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709 | 709 | if (minIndex < 0) or (minIndex > maxIndex): |
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710 | 710 | raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex) |
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|
711 | 711 | |
|
|
712 | 712 | if (maxIndex >= self.dataOut.nHeights): |
|
|
713 | 713 | maxIndex = self.dataOut.nHeights-1 |
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714 | 714 | |
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|
715 | 715 | # seleccion de indices para velocidades |
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|
716 | 716 | indminvel = numpy.where(velrange >= minVel) |
|
|
717 | 717 | indmaxvel = numpy.where(velrange <= maxVel) |
|
|
718 | 718 | try: |
|
|
719 | 719 | minIndexVel = indminvel[0][0] |
|
|
720 | 720 | except: |
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|
721 | 721 | minIndexVel = 0 |
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|
722 | 722 | |
|
|
723 | 723 | try: |
|
|
724 | 724 | maxIndexVel = indmaxvel[0][-1] |
|
|
725 | 725 | except: |
|
|
726 | 726 | maxIndexVel = len(velrange) |
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|
727 | 727 | |
|
|
728 | 728 | #seleccion del espectro |
|
|
729 | 729 | data_spc = self.dataOut.data_spc[:,minIndexVel:maxIndexVel+1,minIndex:maxIndex+1] |
|
|
730 | 730 | #estimacion de ruido |
|
|
731 | 731 | noise = numpy.zeros(self.dataOut.nChannels) |
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|
732 | 732 | |
|
|
733 | 733 | for channel in range(self.dataOut.nChannels): |
|
|
734 | 734 | daux = data_spc[channel,:,:] |
|
|
735 | 735 | noise[channel] = hildebrand_sekhon(daux, self.dataOut.nIncohInt) |
|
|
736 | 736 | |
|
|
737 | 737 | self.dataOut.noise_estimation = noise.copy() |
|
|
738 | 738 | |
|
|
739 | 739 | return 1 No newline at end of file |
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