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1 | #!/usr/bin/env python | |
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2 | ''' | |
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3 | Created on Jul 7, 2014 | |
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4 | ||
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5 | @author: roj-idl71 | |
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6 | ''' | |
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7 | import os, sys, json | |
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8 | ||
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9 | #path = os.path.dirname(os.getcwd()) | |
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10 | #path = os.path.join(path, 'source') | |
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11 | #sys.path.insert(0, path) | |
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12 | ||
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13 | from schainpy.controller import Project | |
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14 | ||
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15 | if __name__ == '__main__': | |
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16 | ||
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17 | desc = "JULIA raw experiment " | |
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18 | filename = "schain.xml" | |
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19 | ||
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20 | dpath = '/home/roberto/puma/JULIA_NEW/JULIA_EW/D2022/' | |
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21 | dpath = '/home/roberto/puma/JULIA_NEW/JULIA_EW/D2021/' | |
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22 | dpath = '/home/roberto/puma/JULIA_NEW/JULIA_EW/D2017/' | |
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23 | dpath = '/home/roberto/puma/JULIA_NEW/JULIA_EW/D2021/' | |
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24 | dpath = '/home/roberto/puma/JULIA_NEW/JULIA_EW/D2018/' | |
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25 | #dpath = '/home/roberto/puma/JULIA_NEW/JULIA_EW/D2017/' | |
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26 | #dpath = '/home/roberto/Folder_aux/D2019/' | |
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27 | ||
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28 | ##dpath = '/home/roberto/puma/JULIA_EW_IMAGING/JULIA_EW/D2017/' | |
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29 | dpath = '/home/soporte/PUMA/JULIA_EW_IMAGING/JULIA_EW/D2017/' | |
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30 | ||
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31 | figpath = '/home/soporte/DATA/Pictures/JULIA/EEJ/Skew_but_dop_is_shift'+'/'+dpath[-5:-1] | |
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32 | ppath = "/home/soporte/DATA/MLT/Oblique/2022_03/data_reshape" | |
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33 | spcdata = '/home/soporte/DATA/JULIA/EEJ/SPC' | |
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34 | path_parameters = '/home/soporte/DATA/JULIA/EEJ/Params' | |
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35 | db_range=['25','35'] | |
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36 | db_range=['10','20'] | |
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37 | #db_range=['14','20'] | |
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38 | db_range=['10','23'] | |
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39 | db_range=['13','20'] | |
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40 | db_range=['21','30'] | |
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41 | db_range=['15','30'] | |
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42 | db_range=['13','28'] | |
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43 | tiempo=['7','18'] | |
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44 | altura1=[2,20] | |
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45 | altura1=[90,220] | |
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46 | velocity=['-80','80'] | |
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47 | period=60 | |
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48 | # PROJECT 1 | |
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49 | ||
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50 | show_spc = 0 | |
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51 | save_spc = 0 | |
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52 | fitting = 1 | |
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53 | save_params = 1 | |
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54 | plot_params = 0 | |
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55 | ||
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56 | controllerObj = Project() | |
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57 | controllerObj.setup(id = '191', name='altura1', description=desc) | |
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58 | ||
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59 | readUnitConfObj1 = controllerObj.addReadUnit(datatype='SpectraReader', | |
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60 | path=dpath, | |
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61 | startDate='2017/09/01', #Check 21-29 Jun 2021, 18 May 2022 | |
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62 | endDate='2017/09/30', #05,06,07-01-18 | |
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63 | #startTime='06:00:00', | |
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64 | #endTime='18:00:00', | |
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65 | startTime='07:00:00', | |
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66 | #startTime='10:00:00', | |
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67 | #startTime='08:38:01', | |
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68 | #startTime='11:16:00', | |
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69 | #startTime='08:13:00', | |
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70 | endTime='17:59:59', | |
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71 | #startTime='16:30:00', | |
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72 | #endTime='17:30:59', | |
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73 | online=0, | |
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74 | walk=1, | |
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75 | expLabel='150EEJ', | |
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76 | getByBlock=1, | |
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77 | delay=20) | |
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78 | ||
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79 | # opObj00 = readUnitConfObj.addOperation(name='printInfo') | |
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80 | # opObj00 = readUnitConfObj.addOperation(name='printNumberOfBlock') | |
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81 | ||
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82 | procUnitConfObj1 = controllerObj.addProcUnit(datatype='SpectraProc', inputId=readUnitConfObj1.getId()) | |
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83 | ||
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84 | opObj11 = procUnitConfObj1.addOperation(name='selectChannels') | |
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85 | opObj11.addParameter(name='channelList', value='2,', format='intlist') | |
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86 | ||
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87 | ''' | |
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88 | opObj11 = procUnitConfObj1SPC.addOperation(name='removeDC') | |
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89 | opObj11.addParameter(name='mode', value='2', format='int') | |
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90 | ''' | |
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91 | ||
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92 | #opObj11 = procUnitConfObj1.addOperation(name='dopplerFlip') #It fixes the Doppler | |
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93 | #opObj11.addParameter(name='chann', value='0', format='int') | |
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94 | ||
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95 | opObj11 = procUnitConfObj1.addOperation(name='removeInterference') | |
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96 | ||
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97 | opObj11 = procUnitConfObj1.addOperation(name='IncohInt', optype='other') | |
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98 | #opObj11.addParameter(name='n', value='20', format='int') | |
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99 | opObj11.addParameter(name='n', value='1', format='int') | |
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100 | ||
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101 | opObj11 = procUnitConfObj1.addOperation(name='GetSNR', optype='other') | |
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102 | ||
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103 | if save_spc: | |
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104 | dataList=['data_spc', | |
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105 | 'utctime'] | |
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106 | metadataList=['nFFTPoints','VelRange','normFactor', | |
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107 | 'heightList','timeZone'] | |
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108 | ||
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109 | op221 = procUnitConfObj1.addOperation(name='HDFWriter', optype='external') | |
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110 | op221.addParameter(name='path', value=spcdata) | |
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111 | #op221.addParameter(name='mode', value=1, format='int') | |
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112 | op221.addParameter(name='dataList', value=dataList) | |
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113 | op221.addParameter(name='metadataList', value=metadataList) | |
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114 | #op221.addParameter(name='blocksPerFile', value=500) | |
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115 | op221.addParameter(name='blocksPerFile', value=2000) | |
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116 | ||
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117 | if show_spc: | |
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118 | #''' | |
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119 | # opObj11 = procUnitConfObj1SPC.addOperation(name='removeInterference') | |
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120 | ||
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121 | opObj11 = procUnitConfObj1.addOperation(name='SpectraPlot', optype='other') | |
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122 | opObj11.addParameter(name='id', value='1', format='int') | |
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123 | opObj11.addParameter(name='wintitle', value='Oblique', format='str') | |
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124 | opObj11.addParameter(name='zmin', value=db_range[0], format='int') | |
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125 | opObj11.addParameter(name='zmax', value=db_range[1], format='int') | |
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126 | opObj11.addParameter(name='xaxis', value='velocity', format='str') | |
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127 | opObj11.addParameter(name='ymin', value=altura1[0], format='int') | |
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128 | opObj11.addParameter(name='ymax', value=altura1[1], format='int') | |
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129 | # opObj11.addParameter(name='xmin', value=velocity[0], format='int') | |
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130 | # opObj11.addParameter(name='xmax', value=velocity[1], format='int') | |
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131 | opObj11.addParameter(name='showprofile', value='1', format='int') | |
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132 | opObj11.addParameter(name='save', value=figpath, format='str') | |
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133 | #''' | |
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134 | #''' | |
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135 | opObj11 = procUnitConfObj1.addOperation(name='RTIPlot', optype='other') | |
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136 | opObj11.addParameter(name='id', value='10', format='int') | |
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137 | opObj11.addParameter(name='wintitle', value='JULIA EEJ RTI', format='str') | |
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138 | opObj11.addParameter(name='xmin', value=tiempo[0], format='float') | |
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139 | opObj11.addParameter(name='xmax', value=tiempo[1], format='float') | |
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140 | opObj11.addParameter(name='ymin', value=altura1[0], format='int') | |
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141 | opObj11.addParameter(name='ymax', value=altura1[1], format='int') | |
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142 | opObj11.addParameter(name='zmin', value=db_range[0], format='int') | |
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143 | opObj11.addParameter(name='zmax', value=db_range[1], format='int') | |
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144 | opObj11.addParameter(name='showprofile', value='1', format='int') | |
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145 | #opObj11.addParameter(name='save_period', value=40, format='str') | |
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146 | opObj11.addParameter(name='save', value=figpath, format='str') | |
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147 | #opObj11.addParameter(name='throttle', value=1, format='str') | |
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148 | #''' | |
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149 | ''' | |
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150 | opObj11 = procUnitConfObj1.addOperation(name='SnrPlot', optype='other') | |
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151 | opObj11.addParameter(name='id', value='10', format='int') | |
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152 | opObj11.addParameter(name='wintitle', value='JULIA EEJ SNR', format='str') | |
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153 | opObj11.addParameter(name='xmin', value=tiempo[0], format='float') | |
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154 | opObj11.addParameter(name='xmax', value=tiempo[1], format='float') | |
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155 | opObj11.addParameter(name='ymin', value=altura1[0], format='int') | |
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156 | opObj11.addParameter(name='ymax', value=altura1[1], format='int') | |
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157 | opObj11.addParameter(name='zmin', value=0.1, format='int') | |
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158 | opObj11.addParameter(name='zmax', value=50, format='int') | |
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159 | #opObj11.addParameter(name='showprofile', value='1', format='int') | |
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160 | opObj11.addParameter(name='save', value=figpath, format='str') | |
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161 | ''' | |
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162 | if fitting: | |
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163 | Dop = 'Max' #Plot and Save the Pos[Max_val] as the Doppler Shift | |
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164 | Dop = 'Shift' #Plot and Save the Skew Gaussian Shift as the Doppler Shift | |
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165 | opObj11 = procUnitConfObj1.addOperation(name='Oblique_Gauss_Fit', optype='other') | |
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166 | opObj11.addParameter(name='mode', value=9, format='int') #Skew | |
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167 | opObj11.addParameter(name='Dop', value=Dop) | |
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168 | #opObj11.addParameter(name='mode', value=11, format='int') | |
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169 | ||
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170 | if save_params: | |
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171 | ''' | |
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172 | dataList=['powerdB', 'Oblique_params', 'Oblique_param_errors', 'dplr_2_u', 'data_snr', | |
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173 | 'utctime'] | |
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174 | metadataList=['VelRange', | |
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175 | 'heightList','timeZone'] | |
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176 | ||
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177 | op221 = procUnitConfObj1.addOperation(name='HDFWriter', optype='external') | |
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178 | op221.addParameter(name='path', value=path_parameters) | |
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179 | #op221.addParameter(name='mode', value=1, format='int') | |
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180 | op221.addParameter(name='dataList', value=dataList) | |
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181 | op221.addParameter(name='metadataList', value=metadataList) | |
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182 | #op221.addParameter(name='blocksPerFile', value=500) | |
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183 | op221.addParameter(name='blocksPerFile', value=2000) | |
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184 | ''' | |
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185 | ||
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186 | one = {'gdlatr': 'lat', 'gdlonr': 'lon', 'inttms': 'paramInterval'} #reader gdlatr-->lat only 1D | |
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187 | ||
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188 | two = { | |
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189 | 'snl': 'snl', #DeberΓa salir como el original pero mΓ‘s limpio | |
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190 | 'RANGE': 'heightList', #<----- nmonics | |
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191 | 'DOPP_T1_EEJ': ('Dop_EEJ_T1', (0)), | |
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192 | 'DDOPP_T1_EEJ': ('Err_Dop_EEJ_T1', (0)), | |
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193 | 'SPEC_W_T1_EEJ': ('Spec_W_T1', (0)), | |
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194 | 'DSPEC_W_T1_EEJ': ('Err_Spec_W_T1', (0)), | |
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195 | 'DOPP_T2_EEJ': ('Dop_EEJ_T2', (0)), | |
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196 | 'DDOPP_T2_EEJ': ('Err_Dop_EEJ_T2', (0)), | |
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197 | 'SPEC_W_T2_EEJ': ('Spec_W_T2', (0)), | |
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198 | 'DSPEC_W_T2_EEJ': ('Err_Spec_W_T2', (0)), | |
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199 | } #writer | |
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200 | ||
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201 | ||
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202 | ind = ['range'] | |
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203 | ||
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204 | meta = { | |
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205 | 'kinst': 840, #instrumnet code, 840 for JULIA, 14 for JULIA MP CSR | |
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206 | 'kindat': 1962, #type of data #Este es el nuevo e igual para JULIA y JULIA CSR | |
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207 | 'catalog': { | |
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208 | 'principleInvestigator': 'Danny ScipiΓ³n', | |
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209 | 'expPurpose': 'Equatorial Electrojet Parameters', | |
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210 | }, | |
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211 | 'header': { | |
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212 | 'analyst': 'D. Hysell' | |
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213 | } | |
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214 | } | |
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215 | ||
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216 | ||
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217 | op_writer = procUnitConfObj1.addOperation(name='MADWriter', optype='external') | |
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218 | #op_writer.addParameter(name='path', value='/home/roberto/DATA/hdf5_outputs/Madrigal/EEJ') | |
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219 | #op_writer.addParameter(name='path', value='/home/roberto/DATA/hdf5_outputs/Madrigal/EEJ/Dop_Max_Val') | |
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220 | #op_writer.addParameter(name='path', value='/home/roberto/DATA/hdf5_outputs/Madrigal/EEJ/'+Dop+'/'+dpath[-5:-1]) | |
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221 | #op_writer.addParameter(name='path', value='/home/roberto/DATA/hdf5_outputs/Madrigal/EEJ/CorrectFiles/no_snl_Test/01/'+Dop+'/'+dpath[-5:-1]) | |
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222 | op_writer.addParameter(name='path', value='/home/soporte/DATA/hdf5_outputs/Madrigal/EEJ/FinalFiles/'+Dop+'/'+dpath[-5:-1]) | |
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223 | op_writer.addParameter(name='format', value='hdf5', format='str') | |
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224 | op_writer.addParameter(name='oneDDict', value=json.dumps(one), format='str') | |
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225 | op_writer.addParameter(name='twoDDict', value=json.dumps(two), format='str') | |
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226 | op_writer.addParameter(name='ind2DList', value=json.dumps(ind), format='str') | |
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227 | op_writer.addParameter(name='metadata', value=json.dumps(meta), format='str') | |
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228 | ||
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229 | ||
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230 | if plot_params: | |
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231 | ''' | |
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232 | opObj11 = procUnitConfObj1.addOperation(name='DopplerEEJPlot', optype='other') | |
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233 | opObj11.addParameter(name='id', value='10', format='int') | |
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234 | opObj11.addParameter(name='wintitle', value='Doppler EEJ', format='str') | |
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235 | opObj11.addParameter(name='xmin', value=tiempo[0], format='float') | |
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236 | opObj11.addParameter(name='xmax', value=tiempo[1], format='float') | |
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237 | opObj11.addParameter(name='ymin', value=altura1[0], format='int') | |
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238 | opObj11.addParameter(name='ymax', value=altura1[1], format='int') | |
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239 | #opObj11.addParameter(name='zmin', value=-250, format='int') | |
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240 | #opObj11.addParameter(name='zmax', value=250, format='int') | |
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241 | opObj11.addParameter(name='zlimits', value='(-400,400),(-250,250)') | |
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242 | #opObj11.addParameter(name='showprofile', value='1', format='int') | |
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243 | opObj11.addParameter(name='save', value=figpath, format='str') | |
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244 | #opObj11.addParameter(name='EEJtype', value=1, format='int') | |
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245 | ||
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246 | opObj11 = procUnitConfObj1.addOperation(name='SpcWidthEEJPlot', optype='other') | |
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247 | opObj11.addParameter(name='id', value='10', format='int') | |
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248 | opObj11.addParameter(name='wintitle', value='Spectral Width EEJ', format='str') | |
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249 | opObj11.addParameter(name='xmin', value=tiempo[0], format='float') | |
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250 | opObj11.addParameter(name='xmax', value=tiempo[1], format='float') | |
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251 | opObj11.addParameter(name='ymin', value=altura1[0], format='int') | |
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252 | opObj11.addParameter(name='ymax', value=altura1[1], format='int') | |
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253 | #opObj11.addParameter(name='zmin', value=0., format='int') | |
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254 | #opObj11.addParameter(name='zmax', value=250, format='int') | |
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255 | opObj11.addParameter(name='zlimits', value='(0.1,100),(0.1,250)') | |
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256 | #opObj11.addParameter(name='showprofile', value='1', format='int') | |
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257 | opObj11.addParameter(name='save', value=figpath, format='str') | |
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258 | #opObj11.addParameter(name='EEJtype', value=1, format='int') | |
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259 | ''' | |
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260 | opObj11 = procUnitConfObj1.addOperation(name='SpectraObliquePlot', optype='other') | |
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261 | opObj11.addParameter(name='id', value='1', format='int') | |
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262 | opObj11.addParameter(name='wintitle', value='Oblique', format='str') | |
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263 | opObj11.addParameter(name='zmin', value=db_range[0], format='int') | |
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264 | opObj11.addParameter(name='zmax', value=db_range[1], format='int') | |
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265 | opObj11.addParameter(name='xaxis', value='velocity', format='str') | |
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266 | opObj11.addParameter(name='ymin', value=altura1[0], format='int') | |
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267 | opObj11.addParameter(name='ymax', value=altura1[1], format='int') | |
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268 | # opObj11.addParameter(name='xmin', value=velocity[0], format='int') | |
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269 | # opObj11.addParameter(name='xmax', value=velocity[1], format='int') | |
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270 | opObj11.addParameter(name='showprofile', value='1', format='int') | |
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271 | opObj11.addParameter(name='save', value=figpath+'/400', format='str') | |
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272 | ||
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273 | ''' | |
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274 | opObj11 = procUnitConfObj1.addOperation(name='DopplerEEJPlot', optype='other') | |
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275 | opObj11.addParameter(name='id', value='10', format='int') | |
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276 | opObj11.addParameter(name='wintitle', value='Doppler EEJ Type II', format='str') | |
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277 | opObj11.addParameter(name='xmin', value=tiempo[0], format='float') | |
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278 | opObj11.addParameter(name='xmax', value=tiempo[1], format='float') | |
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279 | # opObj11.addParameter(name='ymin', value=altura1[0], format='int') | |
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280 | # opObj11.addParameter(name='ymax', value=altura1[1], format='int') | |
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281 | opObj11.addParameter(name='zmin', value=-250, format='int') | |
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282 | opObj11.addParameter(name='zmax', value=250, format='int') | |
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283 | #opObj11.addParameter(name='showprofile', value='1', format='int') | |
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284 | opObj11.addParameter(name='save', value=figpath, format='str') | |
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285 | opObj11.addParameter(name='EEJtype', value=2, format='int') | |
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286 | ''' | |
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287 | ||
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288 | ||
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289 | ||
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290 | ||
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291 | controllerObj.start() No newline at end of file | |
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1 | 1 | # Copyright (c) 2012-2020 Jicamarca Radio Observatory |
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2 | 2 | # All rights reserved. |
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3 | 3 | # |
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4 | 4 | # Distributed under the terms of the BSD 3-clause license. |
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5 | 5 | """Spectra processing Unit and operations |
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6 | 6 | |
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7 | 7 | Here you will find the processing unit `SpectraProc` and several operations |
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8 | 8 | to work with Spectra data type |
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9 | 9 | """ |
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10 | 10 | |
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11 | 11 | import time |
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12 | 12 | import itertools |
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13 | 13 | import numpy |
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14 | 14 | # repositorio |
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15 | 15 | from schainpy.model.proc.jroproc_base import ProcessingUnit, MPDecorator, Operation |
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16 | 16 | from schainpy.model.data.jrodata import Spectra |
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17 | 17 | from schainpy.model.data.jrodata import hildebrand_sekhon |
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18 | 18 | from schainpy.utils import log |
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19 | 19 | |
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20 | 20 | |
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21 | 21 | class SpectraProc(ProcessingUnit): |
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22 | 22 | |
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23 | 23 | def __init__(self): |
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24 | 24 | |
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25 | 25 | ProcessingUnit.__init__(self) |
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26 | 26 | |
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27 | 27 | self.buffer = None |
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28 | 28 | self.firstdatatime = None |
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29 | 29 | self.profIndex = 0 |
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30 | 30 | self.dataOut = Spectra() |
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31 | 31 | self.id_min = None |
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32 | 32 | self.id_max = None |
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33 | 33 | self.setupReq = False #Agregar a todas las unidades de proc |
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34 | 34 | |
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35 | 35 | def __updateSpecFromVoltage(self): |
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36 | 36 | |
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37 | 37 | self.dataOut.timeZone = self.dataIn.timeZone |
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38 | 38 | self.dataOut.dstFlag = self.dataIn.dstFlag |
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39 | 39 | self.dataOut.errorCount = self.dataIn.errorCount |
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40 | 40 | self.dataOut.useLocalTime = self.dataIn.useLocalTime |
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41 | 41 | try: |
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42 | 42 | self.dataOut.processingHeaderObj = self.dataIn.processingHeaderObj.copy() |
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43 | 43 | except: |
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44 | 44 | pass |
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45 | 45 | self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() |
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46 | 46 | self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy() |
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47 | 47 | self.dataOut.channelList = self.dataIn.channelList |
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48 | 48 | self.dataOut.heightList = self.dataIn.heightList |
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49 | 49 | self.dataOut.dtype = numpy.dtype([('real', '<f4'), ('imag', '<f4')]) |
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50 | 50 | self.dataOut.nProfiles = self.dataOut.nFFTPoints |
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51 | 51 | self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock |
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52 | 52 | self.dataOut.utctime = self.firstdatatime |
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53 | 53 | self.dataOut.flagDecodeData = self.dataIn.flagDecodeData |
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54 | 54 | self.dataOut.flagDeflipData = self.dataIn.flagDeflipData |
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55 | 55 | self.dataOut.flagShiftFFT = False |
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56 | 56 | self.dataOut.nCohInt = self.dataIn.nCohInt |
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57 | 57 | self.dataOut.nIncohInt = 1 |
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58 | 58 | self.dataOut.windowOfFilter = self.dataIn.windowOfFilter |
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59 | 59 | self.dataOut.frequency = self.dataIn.frequency |
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60 | 60 | self.dataOut.realtime = self.dataIn.realtime |
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61 | 61 | self.dataOut.azimuth = self.dataIn.azimuth |
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62 | 62 | self.dataOut.zenith = self.dataIn.zenith |
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63 | 63 | self.dataOut.beam.codeList = self.dataIn.beam.codeList |
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64 | 64 | self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList |
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65 | 65 | self.dataOut.beam.zenithList = self.dataIn.beam.zenithList |
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66 | 66 | self.dataOut.runNextUnit = self.dataIn.runNextUnit |
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67 | 67 | try: |
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68 | 68 | self.dataOut.step = self.dataIn.step |
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69 | 69 | except: |
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70 | 70 | pass |
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71 | 71 | |
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72 | 72 | def __getFft(self): |
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73 | 73 | """ |
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74 | 74 | Convierte valores de Voltaje a Spectra |
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75 | 75 | |
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76 | 76 | Affected: |
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77 | 77 | self.dataOut.data_spc |
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78 | 78 | self.dataOut.data_cspc |
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79 | 79 | self.dataOut.data_dc |
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80 | 80 | self.dataOut.heightList |
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81 | 81 | self.profIndex |
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82 | 82 | self.buffer |
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83 | 83 | self.dataOut.flagNoData |
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84 | 84 | """ |
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85 | 85 | fft_volt = numpy.fft.fft( |
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86 | 86 | self.buffer, n=self.dataOut.nFFTPoints, axis=1) |
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87 | 87 | fft_volt = fft_volt.astype(numpy.dtype('complex')) |
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88 | 88 | dc = fft_volt[:, 0, :] |
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89 | 89 | |
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90 | 90 | # calculo de self-spectra |
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91 | 91 | fft_volt = numpy.fft.fftshift(fft_volt, axes=(1,)) |
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92 | 92 | spc = fft_volt * numpy.conjugate(fft_volt) |
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93 | 93 | spc = spc.real |
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94 | 94 | |
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95 | 95 | blocksize = 0 |
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96 | 96 | blocksize += dc.size |
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97 | 97 | blocksize += spc.size |
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98 | 98 | |
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99 | 99 | cspc = None |
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100 | 100 | pairIndex = 0 |
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101 | 101 | if self.dataOut.pairsList != None: |
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102 | 102 | # calculo de cross-spectra |
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103 | 103 | cspc = numpy.zeros( |
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104 | 104 | (self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex') |
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105 | 105 | for pair in self.dataOut.pairsList: |
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106 | 106 | if pair[0] not in self.dataOut.channelList: |
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107 | 107 | raise ValueError("Error getting CrossSpectra: pair 0 of %s is not in channelList = %s" % ( |
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108 | 108 | str(pair), str(self.dataOut.channelList))) |
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109 | 109 | if pair[1] not in self.dataOut.channelList: |
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110 | 110 | raise ValueError("Error getting CrossSpectra: pair 1 of %s is not in channelList = %s" % ( |
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111 | 111 | str(pair), str(self.dataOut.channelList))) |
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112 | 112 | |
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113 | 113 | cspc[pairIndex, :, :] = fft_volt[pair[0], :, :] * \ |
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114 | 114 | numpy.conjugate(fft_volt[pair[1], :, :]) |
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115 | 115 | pairIndex += 1 |
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116 | 116 | blocksize += cspc.size |
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117 | 117 | |
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118 | 118 | self.dataOut.data_spc = spc |
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119 | 119 | self.dataOut.data_cspc = cspc |
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120 | 120 | self.dataOut.data_dc = dc |
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121 | 121 | self.dataOut.blockSize = blocksize |
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122 | 122 | self.dataOut.flagShiftFFT = False |
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123 | 123 | |
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124 | 124 | def run(self, nProfiles=None, nFFTPoints=None, pairsList=None, ippFactor=None, shift_fft=False, runNextUnit = 0): |
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125 | 125 | |
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126 | 126 | self.dataIn.runNextUnit = runNextUnit |
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127 | 127 | if self.dataIn.type == "Spectra": |
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128 | 128 | self.dataOut.copy(self.dataIn) |
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129 | 129 | if shift_fft: |
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130 | 130 | #desplaza a la derecha en el eje 2 determinadas posiciones |
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131 | 131 | shift = int(self.dataOut.nFFTPoints/2) |
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132 | 132 | self.dataOut.data_spc = numpy.roll(self.dataOut.data_spc, shift , axis=1) |
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133 | 133 | |
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134 | 134 | if self.dataOut.data_cspc is not None: |
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135 | 135 | #desplaza a la derecha en el eje 2 determinadas posiciones |
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136 | 136 | self.dataOut.data_cspc = numpy.roll(self.dataOut.data_cspc, shift, axis=1) |
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137 | 137 | if pairsList: |
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138 | 138 | self.__selectPairs(pairsList) |
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139 | 139 | |
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140 | 140 | elif self.dataIn.type == "Voltage": |
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141 | 141 | |
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142 | 142 | self.dataOut.flagNoData = True |
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143 | 143 | |
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144 | 144 | if nFFTPoints == None: |
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145 | 145 | raise ValueError("This SpectraProc.run() need nFFTPoints input variable") |
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146 | 146 | |
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147 | 147 | if nProfiles == None: |
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148 | 148 | nProfiles = nFFTPoints |
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149 | 149 | |
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150 | 150 | if ippFactor == None: |
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151 | 151 | self.dataOut.ippFactor = 1 |
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152 | 152 | |
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153 | 153 | self.dataOut.nFFTPoints = nFFTPoints |
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154 | 154 | |
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155 | 155 | if self.buffer is None: |
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156 | 156 | self.buffer = numpy.zeros((self.dataIn.nChannels, |
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157 | 157 | nProfiles, |
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158 | 158 | self.dataIn.nHeights), |
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159 | 159 | dtype='complex') |
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160 | 160 | |
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161 | 161 | if self.dataIn.flagDataAsBlock: |
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162 | 162 | nVoltProfiles = self.dataIn.data.shape[1] |
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163 | 163 | if nVoltProfiles == nProfiles: |
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164 | 164 | self.buffer = self.dataIn.data.copy() |
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165 | 165 | self.profIndex = nVoltProfiles |
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166 | 166 | |
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167 | 167 | elif nVoltProfiles < nProfiles: |
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168 | 168 | |
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169 | 169 | if self.profIndex == 0: |
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170 | 170 | self.id_min = 0 |
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171 | 171 | self.id_max = nVoltProfiles |
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172 | 172 | |
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173 | 173 | self.buffer[:, self.id_min:self.id_max, |
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174 | 174 | :] = self.dataIn.data |
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175 | 175 | self.profIndex += nVoltProfiles |
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176 | 176 | self.id_min += nVoltProfiles |
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177 | 177 | self.id_max += nVoltProfiles |
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178 | 178 | elif nVoltProfiles > nProfiles: |
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179 | 179 | self.reader.bypass = True |
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180 | 180 | if self.profIndex == 0: |
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181 | 181 | self.id_min = 0 |
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182 | 182 | self.id_max = nProfiles |
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183 | 183 | |
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184 | 184 | self.buffer = self.dataIn.data[:, self.id_min:self.id_max,:] |
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185 | 185 | self.profIndex += nProfiles |
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186 | 186 | self.id_min += nProfiles |
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187 | 187 | self.id_max += nProfiles |
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188 | 188 | if self.id_max == nVoltProfiles: |
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189 | 189 | self.reader.bypass = False |
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190 | 190 | |
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191 | 191 | else: |
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192 | 192 | raise ValueError("The type object %s has %d profiles, it should just has %d profiles" % ( |
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193 | 193 | self.dataIn.type, self.dataIn.data.shape[1], nProfiles)) |
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194 | 194 | self.dataOut.flagNoData = True |
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195 | 195 | else: |
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196 | 196 | self.buffer[:, self.profIndex, :] = self.dataIn.data.copy() |
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197 | 197 | self.profIndex += 1 |
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198 | 198 | |
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199 | 199 | if self.firstdatatime == None: |
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200 | 200 | self.firstdatatime = self.dataIn.utctime |
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201 | 201 | |
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202 | 202 | if self.profIndex == nProfiles: |
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203 | 203 | self.__updateSpecFromVoltage() |
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204 | 204 | if pairsList == None: |
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205 | 205 | self.dataOut.pairsList = [pair for pair in itertools.combinations(self.dataOut.channelList, 2)] |
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206 | 206 | else: |
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207 | 207 | self.dataOut.pairsList = pairsList |
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208 | 208 | self.__getFft() |
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209 | 209 | self.dataOut.flagNoData = False |
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210 | 210 | self.firstdatatime = None |
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211 | 211 | #if not self.reader.bypass: |
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212 | 212 | self.profIndex = 0 |
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213 | 213 | else: |
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214 | 214 | raise ValueError("The type of input object '%s' is not valid".format( |
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215 | 215 | self.dataIn.type)) |
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216 | 216 | |
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217 | 217 | def __selectPairs(self, pairsList): |
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218 | 218 | |
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219 | 219 | if not pairsList: |
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220 | 220 | return |
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221 | 221 | |
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222 | 222 | pairs = [] |
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223 | 223 | pairsIndex = [] |
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224 | 224 | |
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225 | 225 | for pair in pairsList: |
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226 | 226 | if pair[0] not in self.dataOut.channelList or pair[1] not in self.dataOut.channelList: |
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227 | 227 | continue |
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228 | 228 | pairs.append(pair) |
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229 | 229 | pairsIndex.append(pairs.index(pair)) |
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230 | 230 | |
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231 | 231 | self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndex] |
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232 | 232 | self.dataOut.pairsList = pairs |
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233 | 233 | |
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234 | 234 | return |
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235 | 235 | |
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236 | 236 | def selectFFTs(self, minFFT, maxFFT ): |
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237 | 237 | """ |
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238 | 238 | Selecciona un bloque de datos en base a un grupo de valores de puntos FFTs segun el rango |
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239 | 239 | minFFT<= FFT <= maxFFT |
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240 | 240 | """ |
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241 | 241 | |
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242 | 242 | if (minFFT > maxFFT): |
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243 | 243 | raise ValueError("Error selecting heights: Height range (%d,%d) is not valid" % (minFFT, maxFFT)) |
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244 | 244 | |
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245 | 245 | if (minFFT < self.dataOut.getFreqRange()[0]): |
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246 | 246 | minFFT = self.dataOut.getFreqRange()[0] |
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247 | 247 | |
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248 | 248 | if (maxFFT > self.dataOut.getFreqRange()[-1]): |
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249 | 249 | maxFFT = self.dataOut.getFreqRange()[-1] |
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250 | 250 | |
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251 | 251 | minIndex = 0 |
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252 | 252 | maxIndex = 0 |
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253 | 253 | FFTs = self.dataOut.getFreqRange() |
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254 | 254 | |
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255 | 255 | inda = numpy.where(FFTs >= minFFT) |
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256 | 256 | indb = numpy.where(FFTs <= maxFFT) |
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257 | 257 | |
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258 | 258 | try: |
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259 | 259 | minIndex = inda[0][0] |
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260 | 260 | except: |
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261 | 261 | minIndex = 0 |
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262 | 262 | |
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263 | 263 | try: |
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264 | 264 | maxIndex = indb[0][-1] |
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265 | 265 | except: |
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266 | 266 | maxIndex = len(FFTs) |
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267 | 267 | |
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268 | 268 | self.selectFFTsByIndex(minIndex, maxIndex) |
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269 | 269 | |
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270 | 270 | return 1 |
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271 | 271 | |
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272 | 272 | def getBeaconSignal(self, tauindex=0, channelindex=0, hei_ref=None): |
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273 | 273 | newheis = numpy.where( |
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274 | 274 | self.dataOut.heightList > self.dataOut.radarControllerHeaderObj.Taus[tauindex]) |
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275 | 275 | |
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276 | 276 | if hei_ref != None: |
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277 | 277 | newheis = numpy.where(self.dataOut.heightList > hei_ref) |
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278 | 278 | |
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279 | 279 | minIndex = min(newheis[0]) |
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280 | 280 | maxIndex = max(newheis[0]) |
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281 | 281 | data_spc = self.dataOut.data_spc[:, :, minIndex:maxIndex + 1] |
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282 | 282 | heightList = self.dataOut.heightList[minIndex:maxIndex + 1] |
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283 | 283 | |
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284 | 284 | # determina indices |
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285 | 285 | nheis = int(self.dataOut.radarControllerHeaderObj.txB / |
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286 | 286 | (self.dataOut.heightList[1] - self.dataOut.heightList[0])) |
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287 | 287 | avg_dB = 10 * \ |
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288 | 288 | numpy.log10(numpy.sum(data_spc[channelindex, :, :], axis=0)) |
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289 | 289 | beacon_dB = numpy.sort(avg_dB)[-nheis:] |
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290 | 290 | beacon_heiIndexList = [] |
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291 | 291 | for val in avg_dB.tolist(): |
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292 | 292 | if val >= beacon_dB[0]: |
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293 | 293 | beacon_heiIndexList.append(avg_dB.tolist().index(val)) |
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294 | 294 | |
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295 | 295 | data_cspc = None |
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296 | 296 | if self.dataOut.data_cspc is not None: |
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297 | 297 | data_cspc = self.dataOut.data_cspc[:, :, minIndex:maxIndex + 1] |
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298 | 298 | |
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299 | 299 | data_dc = None |
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300 | 300 | if self.dataOut.data_dc is not None: |
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301 | 301 | data_dc = self.dataOut.data_dc[:, minIndex:maxIndex + 1] |
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302 | 302 | |
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303 | 303 | self.dataOut.data_spc = data_spc |
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304 | 304 | self.dataOut.data_cspc = data_cspc |
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305 | 305 | self.dataOut.data_dc = data_dc |
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306 | 306 | self.dataOut.heightList = heightList |
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307 | 307 | self.dataOut.beacon_heiIndexList = beacon_heiIndexList |
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308 | 308 | |
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309 | 309 | return 1 |
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310 | 310 | |
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311 | 311 | def selectFFTsByIndex(self, minIndex, maxIndex): |
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312 | 312 | """ |
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313 | 313 | |
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314 | 314 | """ |
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315 | 315 | |
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316 | 316 | if (minIndex < 0) or (minIndex > maxIndex): |
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317 | 317 | raise ValueError("Error selecting heights: Index range (%d,%d) is not valid" % (minIndex, maxIndex)) |
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318 | 318 | |
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319 | 319 | if (maxIndex >= self.dataOut.nProfiles): |
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320 | 320 | maxIndex = self.dataOut.nProfiles-1 |
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321 | 321 | |
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322 | 322 | #Spectra |
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323 | 323 | data_spc = self.dataOut.data_spc[:,minIndex:maxIndex+1,:] |
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324 | 324 | |
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325 | 325 | data_cspc = None |
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326 | 326 | if self.dataOut.data_cspc is not None: |
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327 | 327 | data_cspc = self.dataOut.data_cspc[:,minIndex:maxIndex+1,:] |
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328 | 328 | |
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329 | 329 | data_dc = None |
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330 | 330 | if self.dataOut.data_dc is not None: |
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331 | 331 | data_dc = self.dataOut.data_dc[minIndex:maxIndex+1,:] |
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332 | 332 | |
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333 | 333 | self.dataOut.data_spc = data_spc |
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334 | 334 | self.dataOut.data_cspc = data_cspc |
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335 | 335 | self.dataOut.data_dc = data_dc |
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336 | 336 | |
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337 | 337 | self.dataOut.ippSeconds = self.dataOut.ippSeconds*(self.dataOut.nFFTPoints / numpy.shape(data_cspc)[1]) |
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338 | 338 | self.dataOut.nFFTPoints = numpy.shape(data_cspc)[1] |
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339 | 339 | self.dataOut.profilesPerBlock = numpy.shape(data_cspc)[1] |
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340 | 340 | |
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341 | 341 | return 1 |
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342 | 342 | |
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343 | 343 | def getNoise(self, minHei=None, maxHei=None, minVel=None, maxVel=None): |
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344 | 344 | # validacion de rango |
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345 | 345 | if minHei == None: |
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346 | 346 | minHei = self.dataOut.heightList[0] |
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347 | 347 | |
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348 | 348 | if maxHei == None: |
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349 | 349 | maxHei = self.dataOut.heightList[-1] |
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350 | 350 | |
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351 | 351 | if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei): |
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352 | 352 | print('minHei: %.2f is out of the heights range' % (minHei)) |
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353 | 353 | print('minHei is setting to %.2f' % (self.dataOut.heightList[0])) |
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354 | 354 | minHei = self.dataOut.heightList[0] |
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355 | 355 | |
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356 | 356 | if (maxHei > self.dataOut.heightList[-1]) or (maxHei < minHei): |
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357 | 357 | print('maxHei: %.2f is out of the heights range' % (maxHei)) |
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358 | 358 | print('maxHei is setting to %.2f' % (self.dataOut.heightList[-1])) |
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359 | 359 | maxHei = self.dataOut.heightList[-1] |
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360 | 360 | |
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361 | 361 | # validacion de velocidades |
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362 | 362 | velrange = self.dataOut.getVelRange(1) |
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363 | 363 | |
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364 | 364 | if minVel == None: |
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365 | 365 | minVel = velrange[0] |
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366 | 366 | |
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367 | 367 | if maxVel == None: |
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368 | 368 | maxVel = velrange[-1] |
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369 | 369 | |
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370 | 370 | if (minVel < velrange[0]) or (minVel > maxVel): |
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371 | 371 | print('minVel: %.2f is out of the velocity range' % (minVel)) |
|
|
372 | 372 | print('minVel is setting to %.2f' % (velrange[0])) |
|
|
373 | 373 | minVel = velrange[0] |
|
|
374 | 374 | |
|
|
375 | 375 | if (maxVel > velrange[-1]) or (maxVel < minVel): |
|
|
376 | 376 | print('maxVel: %.2f is out of the velocity range' % (maxVel)) |
|
|
377 | 377 | print('maxVel is setting to %.2f' % (velrange[-1])) |
|
|
378 | 378 | maxVel = velrange[-1] |
|
|
379 | 379 | |
|
|
380 | 380 | # seleccion de indices para rango |
|
|
381 | 381 | minIndex = 0 |
|
|
382 | 382 | maxIndex = 0 |
|
|
383 | 383 | heights = self.dataOut.heightList |
|
|
384 | 384 | |
|
|
385 | 385 | inda = numpy.where(heights >= minHei) |
|
|
386 | 386 | indb = numpy.where(heights <= maxHei) |
|
|
387 | 387 | |
|
|
388 | 388 | try: |
|
|
389 | 389 | minIndex = inda[0][0] |
|
|
390 | 390 | except: |
|
|
391 | 391 | minIndex = 0 |
|
|
392 | 392 | |
|
|
393 | 393 | try: |
|
|
394 | 394 | maxIndex = indb[0][-1] |
|
|
395 | 395 | except: |
|
|
396 | 396 | maxIndex = len(heights) |
|
|
397 | 397 | |
|
|
398 | 398 | if (minIndex < 0) or (minIndex > maxIndex): |
|
|
399 | 399 | raise ValueError("some value in (%d,%d) is not valid" % ( |
|
|
400 | 400 | minIndex, maxIndex)) |
|
|
401 | 401 | |
|
|
402 | 402 | if (maxIndex >= self.dataOut.nHeights): |
|
|
403 | 403 | maxIndex = self.dataOut.nHeights - 1 |
|
|
404 | 404 | |
|
|
405 | 405 | # seleccion de indices para velocidades |
|
|
406 | 406 | indminvel = numpy.where(velrange >= minVel) |
|
|
407 | 407 | indmaxvel = numpy.where(velrange <= maxVel) |
|
|
408 | 408 | try: |
|
|
409 | 409 | minIndexVel = indminvel[0][0] |
|
|
410 | 410 | except: |
|
|
411 | 411 | minIndexVel = 0 |
|
|
412 | 412 | |
|
|
413 | 413 | try: |
|
|
414 | 414 | maxIndexVel = indmaxvel[0][-1] |
|
|
415 | 415 | except: |
|
|
416 | 416 | maxIndexVel = len(velrange) |
|
|
417 | 417 | |
|
|
418 | 418 | # seleccion del espectro |
|
|
419 | 419 | data_spc = self.dataOut.data_spc[:, |
|
|
420 | 420 | minIndexVel:maxIndexVel + 1, minIndex:maxIndex + 1] |
|
|
421 | 421 | # estimacion de ruido |
|
|
422 | 422 | noise = numpy.zeros(self.dataOut.nChannels) |
|
|
423 | 423 | |
|
|
424 | 424 | for channel in range(self.dataOut.nChannels): |
|
|
425 | 425 | daux = data_spc[channel, :, :] |
|
|
426 | 426 | sortdata = numpy.sort(daux, axis=None) |
|
|
427 | 427 | noise[channel] = hildebrand_sekhon(sortdata, self.dataOut.nIncohInt) |
|
|
428 | 428 | |
|
|
429 | 429 | self.dataOut.noise_estimation = noise.copy() |
|
|
430 | 430 | |
|
|
431 | 431 | return 1 |
|
|
432 | 432 | |
|
|
433 | ||
|
|
434 | class GetSNR(Operation): | |
|
|
435 | ''' | |
|
|
436 | Written by R. Flores | |
|
|
437 | ''' | |
|
|
438 | """Operation to get SNR. | |
|
|
439 | ||
|
|
440 | Parameters: | |
|
|
441 | ----------- | |
|
|
442 | ||
|
|
443 | Example | |
|
|
444 | -------- | |
|
|
445 | ||
|
|
446 | op = proc_unit.addOperation(name='GetSNR', optype='other') | |
|
|
447 | ||
|
|
448 | """ | |
|
|
449 | ||
|
|
450 | def __init__(self, **kwargs): | |
|
|
451 | ||
|
|
452 | Operation.__init__(self, **kwargs) | |
|
|
453 | ||
|
|
454 | def run(self,dataOut): | |
|
|
455 | ||
|
|
456 | noise = dataOut.getNoise(ymin_index=-10) #RegiΓ³n superior donde solo deberΓa de haber ruido | |
|
|
457 | dataOut.data_snr = (dataOut.data_spc.sum(axis=1)-noise[:,None]*dataOut.nFFTPoints)/(noise[:,None]*dataOut.nFFTPoints) #It works apparently | |
|
|
458 | dataOut.snl = numpy.log10(dataOut.data_snr) | |
|
|
459 | dataOut.snl = numpy.where(dataOut.data_snr<.01, numpy.nan, dataOut.snl) | |
|
|
460 | ||
|
|
461 | return dataOut | |
|
|
462 | ||
|
|
433 | 463 | class removeDC(Operation): |
|
|
434 | 464 | |
|
|
435 | 465 | def run(self, dataOut, mode=2): |
|
|
436 | 466 | self.dataOut = dataOut |
|
|
437 | 467 | jspectra = self.dataOut.data_spc |
|
|
438 | 468 | jcspectra = self.dataOut.data_cspc |
|
|
439 | 469 | |
|
|
440 | 470 | num_chan = jspectra.shape[0] |
|
|
441 | 471 | num_hei = jspectra.shape[2] |
|
|
442 | 472 | |
|
|
443 | 473 | if jcspectra is not None: |
|
|
444 | 474 | jcspectraExist = True |
|
|
445 | 475 | num_pairs = jcspectra.shape[0] |
|
|
446 | 476 | else: |
|
|
447 | 477 | jcspectraExist = False |
|
|
448 | 478 | |
|
|
449 | 479 | freq_dc = int(jspectra.shape[1] / 2) |
|
|
450 | 480 | ind_vel = numpy.array([-2, -1, 1, 2]) + freq_dc |
|
|
451 | 481 | ind_vel = ind_vel.astype(int) |
|
|
452 | 482 | |
|
|
453 | 483 | if ind_vel[0] < 0: |
|
|
454 | 484 | ind_vel[list(range(0, 1))] = ind_vel[list(range(0, 1))] + self.num_prof |
|
|
455 | 485 | |
|
|
456 | 486 | if mode == 1: |
|
|
457 | 487 | jspectra[:, freq_dc, :] = ( |
|
|
458 | 488 | jspectra[:, ind_vel[1], :] + jspectra[:, ind_vel[2], :]) / 2 # CORRECCION |
|
|
459 | 489 | |
|
|
460 | 490 | if jcspectraExist: |
|
|
461 | 491 | jcspectra[:, freq_dc, :] = ( |
|
|
462 | 492 | jcspectra[:, ind_vel[1], :] + jcspectra[:, ind_vel[2], :]) / 2 |
|
|
463 | 493 | |
|
|
464 | 494 | if mode == 2: |
|
|
465 | 495 | |
|
|
466 | 496 | vel = numpy.array([-2, -1, 1, 2]) |
|
|
467 | 497 | xx = numpy.zeros([4, 4]) |
|
|
468 | 498 | |
|
|
469 | 499 | for fil in range(4): |
|
|
470 | 500 | xx[fil, :] = vel[fil]**numpy.asarray(list(range(4))) |
|
|
471 | 501 | |
|
|
472 | 502 | xx_inv = numpy.linalg.inv(xx) |
|
|
473 | 503 | xx_aux = xx_inv[0, :] |
|
|
474 | 504 | |
|
|
475 | 505 | for ich in range(num_chan): |
|
|
476 | 506 | yy = jspectra[ich, ind_vel, :] |
|
|
477 | 507 | jspectra[ich, freq_dc, :] = numpy.dot(xx_aux, yy) |
|
|
478 | 508 | |
|
|
479 | 509 | junkid = jspectra[ich, freq_dc, :] <= 0 |
|
|
480 | 510 | cjunkid = sum(junkid) |
|
|
481 | 511 | |
|
|
482 | 512 | if cjunkid.any(): |
|
|
483 | 513 | jspectra[ich, freq_dc, junkid.nonzero()] = ( |
|
|
484 | 514 | jspectra[ich, ind_vel[1], junkid] + jspectra[ich, ind_vel[2], junkid]) / 2 |
|
|
485 | 515 | |
|
|
486 | 516 | if jcspectraExist: |
|
|
487 | 517 | for ip in range(num_pairs): |
|
|
488 | 518 | yy = jcspectra[ip, ind_vel, :] |
|
|
489 | 519 | jcspectra[ip, freq_dc, :] = numpy.dot(xx_aux, yy) |
|
|
490 | 520 | |
|
|
491 | 521 | self.dataOut.data_spc = jspectra |
|
|
492 | 522 | self.dataOut.data_cspc = jcspectra |
|
|
493 | 523 | |
|
|
494 | 524 | return self.dataOut |
|
|
495 | 525 | |
|
|
496 | 526 | class removeInterference(Operation): |
|
|
497 | 527 | |
|
|
498 | 528 | def removeInterference2(self): |
|
|
499 | 529 | |
|
|
500 | 530 | cspc = self.dataOut.data_cspc |
|
|
501 | 531 | spc = self.dataOut.data_spc |
|
|
502 | 532 | Heights = numpy.arange(cspc.shape[2]) |
|
|
503 | 533 | realCspc = numpy.abs(cspc) |
|
|
504 | 534 | |
|
|
505 | 535 | for i in range(cspc.shape[0]): |
|
|
506 | 536 | LinePower= numpy.sum(realCspc[i], axis=0) |
|
|
507 | 537 | Threshold = numpy.amax(LinePower)-numpy.sort(LinePower)[len(Heights)-int(len(Heights)*0.1)] |
|
|
508 | 538 | SelectedHeights = Heights[ numpy.where(LinePower < Threshold) ] |
|
|
509 | 539 | InterferenceSum = numpy.sum(realCspc[i,:,SelectedHeights],axis=0) |
|
|
510 | 540 | InterferenceThresholdMin = numpy.sort(InterferenceSum)[int(len(InterferenceSum)*0.98)] |
|
|
511 | 541 | InterferenceThresholdMax = numpy.sort(InterferenceSum)[int(len(InterferenceSum)*0.99)] |
|
|
512 | 542 | |
|
|
513 | 543 | |
|
|
514 | 544 | InterferenceRange = numpy.where(([InterferenceSum > InterferenceThresholdMin]))# , InterferenceSum < InterferenceThresholdMax]) ) |
|
|
515 | 545 | #InterferenceRange = numpy.where( ([InterferenceRange < InterferenceThresholdMax])) |
|
|
516 | 546 | if len(InterferenceRange)<int(cspc.shape[1]*0.3): |
|
|
517 | 547 | cspc[i,InterferenceRange,:] = numpy.NaN |
|
|
518 | 548 | |
|
|
519 | 549 | self.dataOut.data_cspc = cspc |
|
|
520 | 550 | |
|
|
521 | 551 | def removeInterference(self, interf=2, hei_interf=None, nhei_interf=None, offhei_interf=None): |
|
|
522 | 552 | |
|
|
523 | 553 | jspectra = self.dataOut.data_spc |
|
|
524 | 554 | jcspectra = self.dataOut.data_cspc |
|
|
525 | 555 | jnoise = self.dataOut.getNoise() |
|
|
526 | 556 | num_incoh = self.dataOut.nIncohInt |
|
|
527 | 557 | |
|
|
528 | 558 | num_channel = jspectra.shape[0] |
|
|
529 | 559 | num_prof = jspectra.shape[1] |
|
|
530 | 560 | num_hei = jspectra.shape[2] |
|
|
531 | 561 | |
|
|
532 | 562 | # hei_interf |
|
|
533 | 563 | if hei_interf is None: |
|
|
534 | 564 | count_hei = int(num_hei / 2) |
|
|
535 | 565 | hei_interf = numpy.asmatrix(list(range(count_hei))) + num_hei - count_hei |
|
|
536 | 566 | hei_interf = numpy.asarray(hei_interf)[0] |
|
|
537 | 567 | # nhei_interf |
|
|
538 | 568 | if (nhei_interf == None): |
|
|
539 | 569 | nhei_interf = 5 |
|
|
540 | 570 | if (nhei_interf < 1): |
|
|
541 | 571 | nhei_interf = 1 |
|
|
542 | 572 | if (nhei_interf > count_hei): |
|
|
543 | 573 | nhei_interf = count_hei |
|
|
544 | 574 | if (offhei_interf == None): |
|
|
545 | 575 | offhei_interf = 0 |
|
|
546 | 576 | |
|
|
547 | 577 | ind_hei = list(range(num_hei)) |
|
|
548 | 578 | # mask_prof = numpy.asarray(range(num_prof - 2)) + 1 |
|
|
549 | 579 | # mask_prof[range(num_prof/2 - 1,len(mask_prof))] += 1 |
|
|
550 | 580 | mask_prof = numpy.asarray(list(range(num_prof))) |
|
|
551 | 581 | num_mask_prof = mask_prof.size |
|
|
552 | 582 | comp_mask_prof = [0, num_prof / 2] |
|
|
553 | 583 | |
|
|
554 | 584 | # noise_exist: Determina si la variable jnoise ha sido definida y contiene la informacion del ruido de cada canal |
|
|
555 | 585 | if (jnoise.size < num_channel or numpy.isnan(jnoise).any()): |
|
|
556 | 586 | jnoise = numpy.nan |
|
|
557 | 587 | noise_exist = jnoise[0] < numpy.Inf |
|
|
558 | 588 | |
|
|
559 | 589 | # Subrutina de Remocion de la Interferencia |
|
|
560 | 590 | for ich in range(num_channel): |
|
|
561 | 591 | # Se ordena los espectros segun su potencia (menor a mayor) |
|
|
562 | 592 | power = jspectra[ich, mask_prof, :] |
|
|
563 | 593 | power = power[:, hei_interf] |
|
|
564 | 594 | power = power.sum(axis=0) |
|
|
565 | 595 | psort = power.ravel().argsort() |
|
|
566 | 596 | |
|
|
567 | 597 | # Se estima la interferencia promedio en los Espectros de Potencia empleando |
|
|
568 | 598 | junkspc_interf = jspectra[ich, :, hei_interf[psort[list(range( |
|
|
569 | 599 | offhei_interf, nhei_interf + offhei_interf))]]] |
|
|
570 | 600 | |
|
|
571 | 601 | if noise_exist: |
|
|
572 | 602 | # tmp_noise = jnoise[ich] / num_prof |
|
|
573 | 603 | tmp_noise = jnoise[ich] |
|
|
574 | 604 | junkspc_interf = junkspc_interf - tmp_noise |
|
|
575 | 605 | #junkspc_interf[:,comp_mask_prof] = 0 |
|
|
576 | 606 | |
|
|
577 | 607 | jspc_interf = junkspc_interf.sum(axis=0) / nhei_interf |
|
|
578 | 608 | jspc_interf = jspc_interf.transpose() |
|
|
579 | 609 | # Calculando el espectro de interferencia promedio |
|
|
580 | 610 | noiseid = numpy.where( |
|
|
581 | 611 | jspc_interf <= tmp_noise / numpy.sqrt(num_incoh)) |
|
|
582 | 612 | noiseid = noiseid[0] |
|
|
583 | 613 | cnoiseid = noiseid.size |
|
|
584 | 614 | interfid = numpy.where( |
|
|
585 | 615 | jspc_interf > tmp_noise / numpy.sqrt(num_incoh)) |
|
|
586 | 616 | interfid = interfid[0] |
|
|
587 | 617 | cinterfid = interfid.size |
|
|
588 | 618 | |
|
|
589 | 619 | if (cnoiseid > 0): |
|
|
590 | 620 | jspc_interf[noiseid] = 0 |
|
|
591 | 621 | |
|
|
592 | 622 | # Expandiendo los perfiles a limpiar |
|
|
593 | 623 | if (cinterfid > 0): |
|
|
594 | 624 | new_interfid = ( |
|
|
595 | 625 | numpy.r_[interfid - 1, interfid, interfid + 1] + num_prof) % num_prof |
|
|
596 | 626 | new_interfid = numpy.asarray(new_interfid) |
|
|
597 | 627 | new_interfid = {x for x in new_interfid} |
|
|
598 | 628 | new_interfid = numpy.array(list(new_interfid)) |
|
|
599 | 629 | new_cinterfid = new_interfid.size |
|
|
600 | 630 | else: |
|
|
601 | 631 | new_cinterfid = 0 |
|
|
602 | 632 | |
|
|
603 | 633 | for ip in range(new_cinterfid): |
|
|
604 | 634 | ind = junkspc_interf[:, new_interfid[ip]].ravel().argsort() |
|
|
605 | 635 | jspc_interf[new_interfid[ip] |
|
|
606 | 636 | ] = junkspc_interf[ind[nhei_interf // 2], new_interfid[ip]] |
|
|
607 | 637 | |
|
|
608 | 638 | jspectra[ich, :, ind_hei] = jspectra[ich, :, |
|
|
609 | 639 | ind_hei] - jspc_interf # Corregir indices |
|
|
610 | 640 | |
|
|
611 | 641 | # Removiendo la interferencia del punto de mayor interferencia |
|
|
612 | 642 | ListAux = jspc_interf[mask_prof].tolist() |
|
|
613 | 643 | maxid = ListAux.index(max(ListAux)) |
|
|
614 | 644 | |
|
|
615 | 645 | if cinterfid > 0: |
|
|
616 | 646 | for ip in range(cinterfid * (interf == 2) - 1): |
|
|
617 | 647 | ind = (jspectra[ich, interfid[ip], :] < tmp_noise * |
|
|
618 | 648 | (1 + 1 / numpy.sqrt(num_incoh))).nonzero() |
|
|
619 | 649 | cind = len(ind) |
|
|
620 | 650 | |
|
|
621 | 651 | if (cind > 0): |
|
|
622 | 652 | jspectra[ich, interfid[ip], ind] = tmp_noise * \ |
|
|
623 | 653 | (1 + (numpy.random.uniform(cind) - 0.5) / |
|
|
624 | 654 | numpy.sqrt(num_incoh)) |
|
|
625 | 655 | |
|
|
626 | 656 | ind = numpy.array([-2, -1, 1, 2]) |
|
|
627 | 657 | xx = numpy.zeros([4, 4]) |
|
|
628 | 658 | |
|
|
629 | 659 | for id1 in range(4): |
|
|
630 | 660 | xx[:, id1] = ind[id1]**numpy.asarray(list(range(4))) |
|
|
631 | 661 | |
|
|
632 | 662 | xx_inv = numpy.linalg.inv(xx) |
|
|
633 | 663 | xx = xx_inv[:, 0] |
|
|
634 | 664 | ind = (ind + maxid + num_mask_prof) % num_mask_prof |
|
|
635 | 665 | yy = jspectra[ich, mask_prof[ind], :] |
|
|
636 | 666 | jspectra[ich, mask_prof[maxid], :] = numpy.dot( |
|
|
637 | 667 | yy.transpose(), xx) |
|
|
638 | 668 | |
|
|
639 | 669 | indAux = (jspectra[ich, :, :] < tmp_noise * |
|
|
640 | 670 | (1 - 1 / numpy.sqrt(num_incoh))).nonzero() |
|
|
641 | 671 | jspectra[ich, indAux[0], indAux[1]] = tmp_noise * \ |
|
|
642 | 672 | (1 - 1 / numpy.sqrt(num_incoh)) |
|
|
643 | 673 | |
|
|
644 | 674 | # Remocion de Interferencia en el Cross Spectra |
|
|
645 | 675 | if jcspectra is None: |
|
|
646 | 676 | return jspectra, jcspectra |
|
|
647 | 677 | num_pairs = int(jcspectra.size / (num_prof * num_hei)) |
|
|
648 | 678 | jcspectra = jcspectra.reshape(num_pairs, num_prof, num_hei) |
|
|
649 | 679 | |
|
|
650 | 680 | for ip in range(num_pairs): |
|
|
651 | 681 | |
|
|
652 | 682 | #------------------------------------------- |
|
|
653 | 683 | |
|
|
654 | 684 | cspower = numpy.abs(jcspectra[ip, mask_prof, :]) |
|
|
655 | 685 | cspower = cspower[:, hei_interf] |
|
|
656 | 686 | cspower = cspower.sum(axis=0) |
|
|
657 | 687 | |
|
|
658 | 688 | cspsort = cspower.ravel().argsort() |
|
|
659 | 689 | junkcspc_interf = jcspectra[ip, :, hei_interf[cspsort[list(range( |
|
|
660 | 690 | offhei_interf, nhei_interf + offhei_interf))]]] |
|
|
661 | 691 | junkcspc_interf = junkcspc_interf.transpose() |
|
|
662 | 692 | jcspc_interf = junkcspc_interf.sum(axis=1) / nhei_interf |
|
|
663 | 693 | |
|
|
664 | 694 | ind = numpy.abs(jcspc_interf[mask_prof]).ravel().argsort() |
|
|
665 | 695 | |
|
|
666 | 696 | median_real = int(numpy.median(numpy.real( |
|
|
667 | 697 | junkcspc_interf[mask_prof[ind[list(range(3 * num_prof // 4))]], :]))) |
|
|
668 | 698 | median_imag = int(numpy.median(numpy.imag( |
|
|
669 | 699 | junkcspc_interf[mask_prof[ind[list(range(3 * num_prof // 4))]], :]))) |
|
|
670 | 700 | comp_mask_prof = [int(e) for e in comp_mask_prof] |
|
|
671 | 701 | junkcspc_interf[comp_mask_prof, :] = numpy.complex( |
|
|
672 | 702 | median_real, median_imag) |
|
|
673 | 703 | |
|
|
674 | 704 | for iprof in range(num_prof): |
|
|
675 | 705 | ind = numpy.abs(junkcspc_interf[iprof, :]).ravel().argsort() |
|
|
676 | 706 | jcspc_interf[iprof] = junkcspc_interf[iprof, ind[nhei_interf // 2]] |
|
|
677 | 707 | |
|
|
678 | 708 | # Removiendo la Interferencia |
|
|
679 | 709 | jcspectra[ip, :, ind_hei] = jcspectra[ip, |
|
|
680 | 710 | :, ind_hei] - jcspc_interf |
|
|
681 | 711 | |
|
|
682 | 712 | ListAux = numpy.abs(jcspc_interf[mask_prof]).tolist() |
|
|
683 | 713 | maxid = ListAux.index(max(ListAux)) |
|
|
684 | 714 | |
|
|
685 | 715 | ind = numpy.array([-2, -1, 1, 2]) |
|
|
686 | 716 | xx = numpy.zeros([4, 4]) |
|
|
687 | 717 | |
|
|
688 | 718 | for id1 in range(4): |
|
|
689 | 719 | xx[:, id1] = ind[id1]**numpy.asarray(list(range(4))) |
|
|
690 | 720 | |
|
|
691 | 721 | xx_inv = numpy.linalg.inv(xx) |
|
|
692 | 722 | xx = xx_inv[:, 0] |
|
|
693 | 723 | |
|
|
694 | 724 | ind = (ind + maxid + num_mask_prof) % num_mask_prof |
|
|
695 | 725 | yy = jcspectra[ip, mask_prof[ind], :] |
|
|
696 | 726 | jcspectra[ip, mask_prof[maxid], :] = numpy.dot(yy.transpose(), xx) |
|
|
697 | 727 | |
|
|
698 | 728 | # Guardar Resultados |
|
|
699 | 729 | self.dataOut.data_spc = jspectra |
|
|
700 | 730 | self.dataOut.data_cspc = jcspectra |
|
|
701 | 731 | |
|
|
702 | 732 | return 1 |
|
|
703 | 733 | |
|
|
704 | 734 | def run(self, dataOut, interf=2,hei_interf=None, nhei_interf=None, offhei_interf=None, mode=1): |
|
|
705 | 735 | |
|
|
706 | 736 | self.dataOut = dataOut |
|
|
707 | 737 | |
|
|
708 | 738 | if mode == 1: |
|
|
709 | 739 | self.removeInterference(interf=2,hei_interf=None, nhei_interf=None, offhei_interf=None) |
|
|
710 | 740 | elif mode == 2: |
|
|
711 | 741 | self.removeInterference2() |
|
|
712 | 742 | |
|
|
713 | 743 | return self.dataOut |
|
|
714 | 744 | |
|
|
715 | 745 | |
|
|
716 | 746 | class deflip(Operation): |
|
|
717 | 747 | |
|
|
718 | 748 | def run(self, dataOut): |
|
|
719 | 749 | # arreglo 1: (num_chan, num_profiles, num_heights) |
|
|
720 | 750 | self.dataOut = dataOut |
|
|
721 | 751 | |
|
|
722 | 752 | # JULIA-oblicua, indice 2 |
|
|
723 | 753 | # arreglo 2: (num_profiles, num_heights) |
|
|
724 | 754 | jspectra = self.dataOut.data_spc[2] |
|
|
725 | 755 | jspectra_tmp=numpy.zeros(jspectra.shape) |
|
|
726 | 756 | num_profiles=jspectra.shape[0] |
|
|
727 | 757 | freq_dc = int(num_profiles / 2) |
|
|
728 | 758 | # Flip con for |
|
|
729 | 759 | for j in range(num_profiles): |
|
|
730 | 760 | jspectra_tmp[num_profiles-j-1]= jspectra[j] |
|
|
731 | 761 | # Intercambio perfil de DC con perfil inmediato anterior |
|
|
732 | 762 | jspectra_tmp[freq_dc-1]= jspectra[freq_dc-1] |
|
|
733 | 763 | jspectra_tmp[freq_dc]= jspectra[freq_dc] |
|
|
734 | 764 | # canal modificado es re-escrito en el arreglo de canales |
|
|
735 | 765 | self.dataOut.data_spc[2] = jspectra_tmp |
|
|
736 | 766 | |
|
|
737 | 767 | return self.dataOut |
|
|
738 | 768 | |
|
|
739 | 769 | |
|
|
740 | 770 | class IncohInt(Operation): |
|
|
741 | 771 | |
|
|
742 | 772 | __profIndex = 0 |
|
|
743 | 773 | __withOverapping = False |
|
|
744 | 774 | |
|
|
745 | 775 | __byTime = False |
|
|
746 | 776 | __initime = None |
|
|
747 | 777 | __lastdatatime = None |
|
|
748 | 778 | __integrationtime = None |
|
|
749 | 779 | |
|
|
750 | 780 | __buffer_spc = None |
|
|
751 | 781 | __buffer_cspc = None |
|
|
752 | 782 | __buffer_dc = None |
|
|
753 | 783 | |
|
|
754 | 784 | __dataReady = False |
|
|
755 | 785 | |
|
|
756 | 786 | __timeInterval = None |
|
|
757 | 787 | |
|
|
758 | 788 | n = None |
|
|
759 | 789 | |
|
|
760 | 790 | def __init__(self): |
|
|
761 | 791 | |
|
|
762 | 792 | Operation.__init__(self) |
|
|
763 | 793 | |
|
|
764 | 794 | def setup(self, n=None, timeInterval=None, overlapping=False): |
|
|
765 | 795 | """ |
|
|
766 | 796 | Set the parameters of the integration class. |
|
|
767 | 797 | |
|
|
768 | 798 | Inputs: |
|
|
769 | 799 | |
|
|
770 | 800 | n : Number of coherent integrations |
|
|
771 | 801 | timeInterval : Time of integration. If the parameter "n" is selected this one does not work |
|
|
772 | 802 | overlapping : |
|
|
773 | 803 | |
|
|
774 | 804 | """ |
|
|
775 | 805 | |
|
|
776 | 806 | self.__initime = None |
|
|
777 | 807 | self.__lastdatatime = 0 |
|
|
778 | 808 | |
|
|
779 | 809 | self.__buffer_spc = 0 |
|
|
780 | 810 | self.__buffer_cspc = 0 |
|
|
781 | 811 | self.__buffer_dc = 0 |
|
|
782 | 812 | |
|
|
783 | 813 | self.__profIndex = 0 |
|
|
784 | 814 | self.__dataReady = False |
|
|
785 | 815 | self.__byTime = False |
|
|
786 | 816 | |
|
|
787 | 817 | if n is None and timeInterval is None: |
|
|
788 | 818 | raise ValueError("n or timeInterval should be specified ...") |
|
|
789 | 819 | |
|
|
790 | 820 | if n is not None: |
|
|
791 | 821 | self.n = int(n) |
|
|
792 | 822 | else: |
|
|
793 | 823 | |
|
|
794 | 824 | self.__integrationtime = int(timeInterval) |
|
|
795 | 825 | self.n = None |
|
|
796 | 826 | self.__byTime = True |
|
|
797 | 827 | |
|
|
798 | 828 | def putData(self, data_spc, data_cspc, data_dc): |
|
|
799 | 829 | """ |
|
|
800 | 830 | Add a profile to the __buffer_spc and increase in one the __profileIndex |
|
|
801 | 831 | |
|
|
802 | 832 | """ |
|
|
803 | 833 | |
|
|
804 | 834 | self.__buffer_spc += data_spc |
|
|
805 | 835 | |
|
|
806 | 836 | if data_cspc is None: |
|
|
807 | 837 | self.__buffer_cspc = None |
|
|
808 | 838 | else: |
|
|
809 | 839 | self.__buffer_cspc += data_cspc |
|
|
810 | 840 | |
|
|
811 | 841 | if data_dc is None: |
|
|
812 | 842 | self.__buffer_dc = None |
|
|
813 | 843 | else: |
|
|
814 | 844 | self.__buffer_dc += data_dc |
|
|
815 | 845 | |
|
|
816 | 846 | self.__profIndex += 1 |
|
|
817 | 847 | |
|
|
818 | 848 | return |
|
|
819 | 849 | |
|
|
820 | 850 | def pushData(self): |
|
|
821 | 851 | """ |
|
|
822 | 852 | Return the sum of the last profiles and the profiles used in the sum. |
|
|
823 | 853 | |
|
|
824 | 854 | Affected: |
|
|
825 | 855 | |
|
|
826 | 856 | self.__profileIndex |
|
|
827 | 857 | |
|
|
828 | 858 | """ |
|
|
829 | 859 | |
|
|
830 | 860 | data_spc = self.__buffer_spc |
|
|
831 | 861 | data_cspc = self.__buffer_cspc |
|
|
832 | 862 | data_dc = self.__buffer_dc |
|
|
833 | 863 | n = self.__profIndex |
|
|
834 | 864 | |
|
|
835 | 865 | self.__buffer_spc = 0 |
|
|
836 | 866 | self.__buffer_cspc = 0 |
|
|
837 | 867 | self.__buffer_dc = 0 |
|
|
838 | 868 | self.__profIndex = 0 |
|
|
839 | 869 | |
|
|
840 | 870 | return data_spc, data_cspc, data_dc, n |
|
|
841 | 871 | |
|
|
842 | 872 | def byProfiles(self, *args): |
|
|
843 | 873 | |
|
|
844 | 874 | self.__dataReady = False |
|
|
845 | 875 | avgdata_spc = None |
|
|
846 | 876 | avgdata_cspc = None |
|
|
847 | 877 | avgdata_dc = None |
|
|
848 | 878 | |
|
|
849 | 879 | self.putData(*args) |
|
|
850 | 880 | |
|
|
851 | 881 | if self.__profIndex == self.n: |
|
|
852 | 882 | |
|
|
853 | 883 | avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData() |
|
|
854 | 884 | self.n = n |
|
|
855 | 885 | self.__dataReady = True |
|
|
856 | 886 | |
|
|
857 | 887 | return avgdata_spc, avgdata_cspc, avgdata_dc |
|
|
858 | 888 | |
|
|
859 | 889 | def byTime(self, datatime, *args): |
|
|
860 | 890 | |
|
|
861 | 891 | self.__dataReady = False |
|
|
862 | 892 | avgdata_spc = None |
|
|
863 | 893 | avgdata_cspc = None |
|
|
864 | 894 | avgdata_dc = None |
|
|
865 | 895 | |
|
|
866 | 896 | self.putData(*args) |
|
|
867 | 897 | |
|
|
868 | 898 | if (datatime - self.__initime) >= self.__integrationtime: |
|
|
869 | 899 | avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData() |
|
|
870 | 900 | self.n = n |
|
|
871 | 901 | self.__dataReady = True |
|
|
872 | 902 | |
|
|
873 | 903 | return avgdata_spc, avgdata_cspc, avgdata_dc |
|
|
874 | 904 | |
|
|
875 | 905 | def integrate(self, datatime, *args): |
|
|
876 | 906 | |
|
|
877 | 907 | if self.__profIndex == 0: |
|
|
878 | 908 | self.__initime = datatime |
|
|
879 | 909 | |
|
|
880 | 910 | if self.__byTime: |
|
|
881 | 911 | avgdata_spc, avgdata_cspc, avgdata_dc = self.byTime( |
|
|
882 | 912 | datatime, *args) |
|
|
883 | 913 | else: |
|
|
884 | 914 | avgdata_spc, avgdata_cspc, avgdata_dc = self.byProfiles(*args) |
|
|
885 | 915 | |
|
|
886 | 916 | if not self.__dataReady: |
|
|
887 | 917 | return None, None, None, None |
|
|
888 | 918 | |
|
|
889 | 919 | return self.__initime, avgdata_spc, avgdata_cspc, avgdata_dc |
|
|
890 | 920 | |
|
|
891 | 921 | def run(self, dataOut, n=None, timeInterval=None, overlapping=False): |
|
|
892 | 922 | if n == 1: |
|
|
893 | 923 | return dataOut |
|
|
894 | 924 | |
|
|
895 | 925 | dataOut.flagNoData = True |
|
|
896 | 926 | |
|
|
897 | 927 | if not self.isConfig: |
|
|
898 | 928 | self.setup(n, timeInterval, overlapping) |
|
|
899 | 929 | self.isConfig = True |
|
|
900 | 930 | |
|
|
901 | 931 | avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(dataOut.utctime, |
|
|
902 | 932 | dataOut.data_spc, |
|
|
903 | 933 | dataOut.data_cspc, |
|
|
904 | 934 | dataOut.data_dc) |
|
|
905 | 935 | |
|
|
906 | 936 | if self.__dataReady: |
|
|
907 | 937 | |
|
|
908 | 938 | dataOut.data_spc = avgdata_spc |
|
|
909 | 939 | dataOut.data_cspc = avgdata_cspc |
|
|
910 | 940 | dataOut.data_dc = avgdata_dc |
|
|
911 | 941 | dataOut.nIncohInt *= self.n |
|
|
912 | 942 | dataOut.utctime = avgdatatime |
|
|
913 | 943 | dataOut.flagNoData = False |
|
|
914 | 944 | |
|
|
915 | 945 | return dataOut |
|
|
916 | 946 | |
|
|
917 | 947 | class dopplerFlip(Operation): |
|
|
918 | 948 | |
|
|
919 | 949 | def run(self, dataOut): |
|
|
920 | 950 | # arreglo 1: (num_chan, num_profiles, num_heights) |
|
|
921 | 951 | self.dataOut = dataOut |
|
|
922 | 952 | # JULIA-oblicua, indice 2 |
|
|
923 | 953 | # arreglo 2: (num_profiles, num_heights) |
|
|
924 | 954 | jspectra = self.dataOut.data_spc[2] |
|
|
925 | 955 | jspectra_tmp = numpy.zeros(jspectra.shape) |
|
|
926 | 956 | num_profiles = jspectra.shape[0] |
|
|
927 | 957 | freq_dc = int(num_profiles / 2) |
|
|
928 | 958 | # Flip con for |
|
|
929 | 959 | for j in range(num_profiles): |
|
|
930 | 960 | jspectra_tmp[num_profiles-j-1]= jspectra[j] |
|
|
931 | 961 | # Intercambio perfil de DC con perfil inmediato anterior |
|
|
932 | 962 | jspectra_tmp[freq_dc-1]= jspectra[freq_dc-1] |
|
|
933 | 963 | jspectra_tmp[freq_dc]= jspectra[freq_dc] |
|
|
934 | 964 | # canal modificado es re-escrito en el arreglo de canales |
|
|
935 | 965 | self.dataOut.data_spc[2] = jspectra_tmp |
|
|
936 | 966 | |
|
|
937 | 967 | return self.dataOut No newline at end of file |
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