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1 | # DIAS 19 Y 20 FEB 2014 | |
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2 | # Comprobacion de Resultados DBS con SA | |
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3 | ||
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4 | import os, sys | |
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5 | ||
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6 | path = os.path.split(os.getcwd())[0] | |
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7 | path = os.path.split(path)[0] | |
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8 | ||
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9 | sys.path.insert(0, path) | |
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10 | ||
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11 | from schainpy.controller import Project | |
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12 | ||
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13 | desc = "JASMET Experiment Test" | |
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14 | filename = "JASMETtest.xml" | |
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15 | ||
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16 | controllerObj = Project() | |
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17 | ||
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18 | controllerObj.setup(id = '191', name='test01', description=desc) | |
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19 | ||
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20 | #Experimentos | |
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21 | ||
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22 | #2014051 20 Feb 2014 | |
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23 | # path = '/home/soporte/Data/JASMET/JASMET_30/2014106' | |
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24 | # pathFigure = '/home/soporte/workspace/Graficos/JASMET/prueba1' | |
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25 | ||
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26 | remotefolder = "/home/wmaster/graficos" | |
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27 | path = '/media/joscanoa/84A65E64A65E5730/soporte/Data/JASMET/JASMET_30/' | |
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28 | ||
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29 | pathfile1 = os.path.join(os.environ['HOME'],'Pictures/JASMET30/meteor') | |
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30 | pathfile2 = os.path.join(os.environ['HOME'],'Pictures/JASMET30/wind') | |
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31 | pathfile3 = os.path.join(os.environ['HOME'],'Pictures/JASMET30/phase') | |
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32 | ||
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33 | pathfig = os.path.join(os.environ['HOME'],'Pictures/JASMET30/graph') | |
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34 | ||
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35 | startTime = '00:00:00' | |
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36 | endTime = '23:59:59' | |
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37 | xmin ='17.0' | |
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38 | xmax = '34.0' | |
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39 | ||
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40 | #------------------------------------------------------------------------------------------------ | |
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41 | readUnitConfObj = controllerObj.addReadUnit(datatype='VoltageReader', | |
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42 | path=path, | |
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43 | startDate='2014/04/15', | |
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44 | endDate='2014/04/16', | |
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45 | startTime=startTime, | |
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46 | endTime=endTime, | |
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47 | online=0, | |
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48 | delay=5, | |
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49 | walk=1) | |
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50 | ||
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51 | opObj11 = readUnitConfObj.addOperation(name='printNumberOfBlock') | |
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52 | ||
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53 | ||
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54 | #-------------------------------------------------------------------------------------------------- | |
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55 | ||
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56 | procUnitConfObj0 = controllerObj.addProcUnit(datatype='VoltageProc', inputId=readUnitConfObj.getId()) | |
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57 | ||
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58 | opObj00 = procUnitConfObj0.addOperation(name='selectChannels') | |
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59 | opObj00.addParameter(name='channelList', value='0, 1, 2, 3, 4', format='intlist') | |
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60 | ||
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61 | opObj01 = procUnitConfObj0.addOperation(name='setRadarFrequency') | |
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62 | # opObj01.addParameter(name='frequency', value='30.e6', format='float') | |
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63 | opObj01.addParameter(name='frequency', value='50.e6', format='float') | |
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64 | ||
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65 | #opObj11 = procUnitConfObj0.addOperation(name='Decoder', optype='other') | |
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66 | #-------------------------------------------------------------------------------------------------- | |
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67 | ||
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68 | procUnitConfObj1 = controllerObj.addProcUnit(datatype='ParametersProc', inputId=procUnitConfObj0.getId()) | |
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69 | procUnitConfObj1.addParameter(name='nSeconds', value='100', format='int') | |
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70 | ||
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71 | opObj10 = procUnitConfObj1.addOperation(name='MeteorDetection') | |
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72 | # opObj10.addParameter(name='predefinedPhaseShifts', value='-89.5, 41.5, 0.0, -138.0, -85.5', format='floatlist') | |
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73 | opObj10.addParameter(name='predefinedPhaseShifts', value='0, 0, 0, 0, 0', format='floatlist') | |
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74 | opObj10.addParameter(name='cohDetection', value='0', format='bool') | |
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75 | opObj10.addParameter(name='noise_multiple', value='4', format='int') | |
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76 | opObj10.addParameter(name='SNRThresh', value='5', format='float') | |
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77 | opObj10.addParameter(name='phaseThresh', value='20', format='float') | |
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78 | opObj10.addParameter(name='azimuth', value='45', format='float') | |
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79 | opObj10.addParameter(name='hmin', value='68', format='float') | |
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80 | opObj10.addParameter(name='hmax', value='112', format='float') | |
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81 | opObj10.addParameter(name='saveAll', value='1', format='bool') | |
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82 | ||
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83 | opObj12 = procUnitConfObj1.addOperation(name='HDF5Writer', optype='other') | |
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84 | opObj12.addParameter(name='path', value=pathfile1) | |
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85 | opObj12.addParameter(name='blocksPerFile', value='1000', format='int') | |
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86 | opObj12.addParameter(name='metadataList',value='type,inputUnit,heightList,paramInterval',format='list') | |
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87 | opObj12.addParameter(name='dataList',value='data_param',format='list') | |
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88 | opObj12.addParameter(name='mode',value='0',format='int') | |
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89 | #Tiene que ser de 3 dimensiones, append en lugar de aumentar una dimension | |
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90 | ||
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91 | opObj13 = procUnitConfObj1.addOperation(name='SkyMapPlot', optype='other') | |
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92 | opObj13.addParameter(name='id', value='1', format='int') | |
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93 | opObj13.addParameter(name='wintitle', value='Sky Map', format='str') | |
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94 | opObj13.addParameter(name='save', value='1', format='bool') | |
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95 | opObj13.addParameter(name='figpath', value=pathfig, format='str') | |
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96 | opObj13.addParameter(name='ftp', value='1', format='int') | |
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97 | opObj13.addParameter(name='exp_code', value='15', format='int') | |
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98 | opObj13.addParameter(name='sub_exp_code', value='1', format='int') | |
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99 | ||
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100 | ||
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101 | #-------------------------------------------------------------------------------------------------- | |
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102 | procUnitConfObj2 = controllerObj.addProcUnit(datatype='ParametersProc', inputId=procUnitConfObj1.getId()) | |
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103 | ||
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104 | opObj22 = procUnitConfObj2.addOperation(name='WindProfiler', optype='other') | |
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105 | opObj22.addParameter(name='technique', value='Meteors', format='str') | |
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106 | opObj22.addParameter(name='nHours', value='0.25', format='float') | |
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107 | opObj22.addParameter(name='SNRThresh', value='12.0', format='float') | |
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108 | ||
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109 | opObj23 = procUnitConfObj2.addOperation(name='WindProfilerPlot', optype='other') | |
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110 | opObj23.addParameter(name='id', value='2', format='int') | |
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111 | opObj23.addParameter(name='wintitle', value='Wind Profiler', format='str') | |
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112 | opObj23.addParameter(name='save', value='1', format='bool') | |
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113 | opObj23.addParameter(name='figpath', value = pathfig, format='str') | |
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114 | opObj23.addParameter(name='zmin', value='-120', format='int') | |
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115 | opObj23.addParameter(name='zmax', value='120', format='int') | |
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116 | # opObj12.addParameter(name='zmin_ver', value='-0.8', format='float') | |
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117 | # opObj12.addParameter(name='zmax_ver', value='0.8', format='float') | |
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118 | # opObj23.addParameter(name='SNRmin', value='-10', format='int') | |
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119 | # opObj23.addParameter(name='SNRmax', value='60', format='int') | |
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120 | # opObj23.addParameter(name='SNRthresh', value='0', format='float') | |
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121 | opObj23.addParameter(name='xmin', value=xmin, format='float') | |
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122 | opObj23.addParameter(name='xmax', value=xmax, format='float') | |
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123 | opObj23.addParameter(name='ftp', value='1', format='int') | |
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124 | opObj23.addParameter(name='exp_code', value='15', format='int') | |
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125 | opObj23.addParameter(name='sub_exp_code', value='1', format='int') | |
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126 | ||
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127 | opObj24 = procUnitConfObj2.addOperation(name='HDF5Writer', optype='other') | |
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128 | opObj24.addParameter(name='path', value=pathfile2) | |
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129 | opObj24.addParameter(name='blocksPerFile', value='1000', format='int') | |
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130 | opObj24.addParameter(name='metadataList',value='type,inputUnit,outputInterval',format='list') | |
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131 | opObj24.addParameter(name='dataList',value='data_output,utctime',format='list') | |
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132 | ||
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133 | #-------------------------------------------------------------------------------------------------- | |
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134 | procUnitConfObj3 = controllerObj.addProcUnit(datatype='ParametersProc', inputId=procUnitConfObj1.getId()) | |
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135 | ||
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136 | ||
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137 | opObj31 = procUnitConfObj3.addOperation(name='PhaseCalibration', optype='other') | |
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138 | opObj31.addParameter(name='nHours', value='0.25', format='float') | |
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139 | opObj31.addParameter(name='distances', value='-15, -15, 12, 12', format='intlist') | |
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140 | # opObj31.addParameter(name='distances', value='-25, -25, 20, 20', format='intlist') | |
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141 | opObj31.addParameter(name='pairs', value='(0,3),(1,2)', format='pairslist') | |
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142 | opObj31.addParameter(name='hmin', value='68', format='float') | |
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143 | opObj31.addParameter(name='hmax', value='112', format='float') | |
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144 | ||
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145 | opObj32 = procUnitConfObj3.addOperation(name='PhasePlot', optype='other') | |
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146 | opObj32.addParameter(name='id', value='201', format='int') | |
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147 | opObj32.addParameter(name='wintitle', value='PhaseCalibration', format='str') | |
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148 | # opObj32.addParameter(name='timerange', value='300', format='int') | |
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149 | opObj32.addParameter(name='xmin', value=xmin, format='float') | |
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150 | opObj32.addParameter(name='xmax', value=xmax, format='float') | |
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151 | # opObj32.addParameter(name='xmin', value='19', format='float') | |
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152 | # opObj32.addParameter(name='xmax', value='20', format='float') | |
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153 | opObj32.addParameter(name='ymin', value='-180', format='float') | |
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154 | opObj32.addParameter(name='ymax', value='180', format='float') | |
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155 | opObj32.addParameter(name='figpath', value=pathfig, format='str') | |
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156 | opObj32.addParameter(name='ftp', value='1', format='int') | |
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157 | opObj32.addParameter(name='exp_code', value='15', format='int') | |
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158 | opObj32.addParameter(name='sub_exp_code', value='1', format='int') | |
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159 | ||
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160 | opObj33 = procUnitConfObj3.addOperation(name='HDF5Writer', optype='other') | |
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161 | opObj33.addParameter(name='path', value=pathfile3) | |
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162 | opObj33.addParameter(name='blocksPerFile', value='1000', format='int') | |
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163 | opObj33.addParameter(name='metadataList',value='type,inputUnit,outputInterval',format='list') | |
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164 | opObj33.addParameter(name='dataList',value='data_output,utctime',format='list') | |
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165 | # opObj25.addParameter(name='mode',value='1,0,0',format='intlist') | |
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166 | ||
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167 | #-------------------------------------------------------------------------------------------------- | |
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168 | ||
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169 | procUnitConfObj4 = controllerObj.addProcUnit(name='SendToServer') | |
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170 | procUnitConfObj4.addParameter(name='server', value='jro-app.igp.gob.pe', format='str') | |
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171 | procUnitConfObj4.addParameter(name='username', value='wmaster', format='str') | |
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172 | procUnitConfObj4.addParameter(name='password', value='mst2010vhf', format='str') | |
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173 | procUnitConfObj4.addParameter(name='localfolder', value=pathfig, format='str') | |
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174 | procUnitConfObj4.addParameter(name='remotefolder', value=remotefolder, format='str') | |
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175 | procUnitConfObj4.addParameter(name='ext', value='.png', format='str') | |
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176 | procUnitConfObj4.addParameter(name='period', value=20, format='int') | |
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177 | procUnitConfObj4.addParameter(name='protocol', value='ftp', format='str') | |
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178 | ||
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179 | #-------------------------------------------------------------------------------------------------- | |
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180 | ||
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181 | print "Escribiendo el archivo XML" | |
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182 | controllerObj.writeXml(filename) | |
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183 | print "Leyendo el archivo XML" | |
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184 | controllerObj.readXml(filename) | |
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185 | ||
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186 | controllerObj.createObjects() | |
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187 | controllerObj.connectObjects() | |
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188 | controllerObj.run() No newline at end of file | |
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1 | """ | |
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2 | Se debe verficar que el disco de datos se encuentra montado en el sistema | |
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3 | """ | |
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4 | import os, sys | |
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5 | ||
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6 | path = os.path.split(os.getcwd())[0] | |
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7 | path = os.path.split(path)[0] | |
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8 | ||
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9 | sys.path.insert(0, path) | |
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10 | ||
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11 | from schainpy.controller import Project | |
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12 | ||
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13 | desc = "Meteor Experiment Test" | |
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14 | filename = "meteor20130812.xml" | |
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15 | ||
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16 | controllerObj = Project() | |
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17 | controllerObj.setup(id = '191', name='meteor_test01', description=desc) | |
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18 | ||
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19 | path = '/home/dsuarez/.gvfs/data on 10.10.20.13/Jasmet50' | |
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20 | ||
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21 | ||
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22 | readUnitConfObj = controllerObj.addReadUnit(datatype='Voltage', | |
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23 | path=path, | |
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24 | startDate='2014/04/16', | |
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25 | endDate='2014/04/16', | |
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26 | startTime='00:00:00', | |
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27 | endTime='23:59:59', | |
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28 | online=0, | |
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29 | walk=1) | |
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30 | ||
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31 | opObj11 = readUnitConfObj.addOperation(name='printNumberOfBlock') | |
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32 | ||
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33 | procUnitConfObj0 = controllerObj.addProcUnit(datatype='Voltage', inputId=readUnitConfObj.getId()) | |
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34 | ||
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35 | ||
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36 | opObj11 = procUnitConfObj0.addOperation(name='Decoder', optype='other') | |
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37 | ||
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38 | ||
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39 | opObj11 = procUnitConfObj0.addOperation(name='CohInt', optype='other') | |
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40 | opObj11.addParameter(name='n', value='2', format='int') | |
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41 | ||
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42 | opObj11 = procUnitConfObj0.addOperation(name='VoltageWriter', optype='other') | |
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43 | opObj11.addParameter(name='path', value='/home/jasmet/jasmet30_abril') | |
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44 | opObj11.addParameter(name='blocksPerFile', value='100', format='int') | |
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45 | opObj11.addParameter(name='profilesPerBlock', value='200', format='int') | |
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46 | ||
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47 | ||
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48 | ||
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49 | print "Escribiendo el archivo XML" | |
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50 | controllerObj.writeXml(filename) | |
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51 | print "Leyendo el archivo XML" | |
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52 | controllerObj.readXml(filename) | |
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53 | ||
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54 | controllerObj.createObjects() | |
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55 | controllerObj.connectObjects() | |
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56 | controllerObj.run() | |
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1 | 1 | ''' |
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2 | 2 | |
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3 | 3 | $Author: murco $ |
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4 | 4 | $Id: JROData.py 173 2012-11-20 15:06:21Z murco $ |
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5 | 5 | ''' |
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6 | 6 | |
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7 | 7 | import copy |
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8 | 8 | import numpy |
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9 | 9 | import datetime |
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10 | 10 | |
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11 | 11 | from jroheaderIO import SystemHeader, RadarControllerHeader |
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12 | 12 | |
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13 | 13 | def getNumpyDtype(dataTypeCode): |
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14 | 14 | |
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15 | 15 | if dataTypeCode == 0: |
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16 | 16 | numpyDtype = numpy.dtype([('real','<i1'),('imag','<i1')]) |
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17 | 17 | elif dataTypeCode == 1: |
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18 | 18 | numpyDtype = numpy.dtype([('real','<i2'),('imag','<i2')]) |
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19 | 19 | elif dataTypeCode == 2: |
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20 | 20 | numpyDtype = numpy.dtype([('real','<i4'),('imag','<i4')]) |
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21 | 21 | elif dataTypeCode == 3: |
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22 | 22 | numpyDtype = numpy.dtype([('real','<i8'),('imag','<i8')]) |
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23 | 23 | elif dataTypeCode == 4: |
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24 | 24 | numpyDtype = numpy.dtype([('real','<f4'),('imag','<f4')]) |
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25 | 25 | elif dataTypeCode == 5: |
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26 | 26 | numpyDtype = numpy.dtype([('real','<f8'),('imag','<f8')]) |
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27 | 27 | else: |
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28 | 28 | raise ValueError, 'dataTypeCode was not defined' |
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29 | 29 | |
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30 | 30 | return numpyDtype |
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31 | 31 | |
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32 | 32 | def getDataTypeCode(numpyDtype): |
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33 | 33 | |
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34 | 34 | if numpyDtype == numpy.dtype([('real','<i1'),('imag','<i1')]): |
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35 | 35 | datatype = 0 |
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36 | 36 | elif numpyDtype == numpy.dtype([('real','<i2'),('imag','<i2')]): |
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37 | 37 | datatype = 1 |
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38 | 38 | elif numpyDtype == numpy.dtype([('real','<i4'),('imag','<i4')]): |
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39 | 39 | datatype = 2 |
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40 | 40 | elif numpyDtype == numpy.dtype([('real','<i8'),('imag','<i8')]): |
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41 | 41 | datatype = 3 |
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42 | 42 | elif numpyDtype == numpy.dtype([('real','<f4'),('imag','<f4')]): |
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43 | 43 | datatype = 4 |
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44 | 44 | elif numpyDtype == numpy.dtype([('real','<f8'),('imag','<f8')]): |
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45 | 45 | datatype = 5 |
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46 | 46 | else: |
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47 | 47 | datatype = None |
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48 | 48 | |
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49 | 49 | return datatype |
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50 | 50 | |
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51 | 51 | def hildebrand_sekhon(data, navg): |
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52 | 52 | """ |
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53 | 53 | This method is for the objective determination of the noise level in Doppler spectra. This |
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54 | 54 | implementation technique is based on the fact that the standard deviation of the spectral |
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55 | 55 | densities is equal to the mean spectral density for white Gaussian noise |
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56 | 56 | |
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57 | 57 | Inputs: |
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58 | 58 | Data : heights |
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59 | 59 | navg : numbers of averages |
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60 | 60 | |
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61 | 61 | Return: |
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62 | 62 | -1 : any error |
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63 | 63 | anoise : noise's level |
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64 | 64 | """ |
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65 | 65 | |
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66 | 66 | sortdata = numpy.sort(data,axis=None) |
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67 | 67 | lenOfData = len(sortdata) |
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68 | 68 | nums_min = lenOfData/10 |
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69 | 69 | |
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70 | 70 | if (lenOfData/10) > 2: |
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71 | 71 | nums_min = lenOfData/10 |
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72 | 72 | else: |
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73 | 73 | nums_min = 2 |
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74 | 74 | |
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75 | 75 | sump = 0. |
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76 | 76 | |
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77 | 77 | sumq = 0. |
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78 | 78 | |
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79 | 79 | j = 0 |
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80 | 80 | |
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81 | 81 | cont = 1 |
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82 | 82 | |
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83 | 83 | while((cont==1)and(j<lenOfData)): |
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84 | 84 | |
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85 | 85 | sump += sortdata[j] |
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86 | 86 | |
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87 | 87 | sumq += sortdata[j]**2 |
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88 | 88 | |
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89 | 89 | if j > nums_min: |
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90 | 90 | rtest = float(j)/(j-1) + 1.0/navg |
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91 | 91 | if ((sumq*j) > (rtest*sump**2)): |
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92 | 92 | j = j - 1 |
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93 | 93 | sump = sump - sortdata[j] |
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94 | 94 | sumq = sumq - sortdata[j]**2 |
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95 | 95 | cont = 0 |
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96 | 96 | |
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97 | 97 | j += 1 |
|
|
98 | 98 | |
|
|
99 | 99 | lnoise = sump /j |
|
|
100 | 100 | stdv = numpy.sqrt((sumq - lnoise**2)/(j - 1)) |
|
|
101 | 101 | return lnoise |
|
|
102 | 102 | |
|
|
103 | 103 | class Beam: |
|
|
104 | 104 | def __init__(self): |
|
|
105 | 105 | self.codeList = [] |
|
|
106 | 106 | self.azimuthList = [] |
|
|
107 | 107 | self.zenithList = [] |
|
|
108 | 108 | |
|
|
109 | 109 | class GenericData(object): |
|
|
110 | 110 | |
|
|
111 | 111 | flagNoData = True |
|
|
112 | 112 | |
|
|
113 | 113 | def __init__(self): |
|
|
114 | 114 | |
|
|
115 | 115 | raise ValueError, "This class has not been implemented" |
|
|
116 | 116 | |
|
|
117 | 117 | def copy(self, inputObj=None): |
|
|
118 | 118 | |
|
|
119 | 119 | if inputObj == None: |
|
|
120 | 120 | return copy.deepcopy(self) |
|
|
121 | 121 | |
|
|
122 | 122 | for key in inputObj.__dict__.keys(): |
|
|
123 | 123 | self.__dict__[key] = inputObj.__dict__[key] |
|
|
124 | 124 | |
|
|
125 | 125 | def deepcopy(self): |
|
|
126 | 126 | |
|
|
127 | 127 | return copy.deepcopy(self) |
|
|
128 | 128 | |
|
|
129 | 129 | def isEmpty(self): |
|
|
130 | 130 | |
|
|
131 | 131 | return self.flagNoData |
|
|
132 | 132 | |
|
|
133 | 133 | class JROData(GenericData): |
|
|
134 | 134 | |
|
|
135 | 135 | # m_BasicHeader = BasicHeader() |
|
|
136 | 136 | # m_ProcessingHeader = ProcessingHeader() |
|
|
137 | 137 | |
|
|
138 | 138 | systemHeaderObj = SystemHeader() |
|
|
139 | 139 | |
|
|
140 | 140 | radarControllerHeaderObj = RadarControllerHeader() |
|
|
141 | 141 | |
|
|
142 | 142 | # data = None |
|
|
143 | 143 | |
|
|
144 | 144 | type = None |
|
|
145 | 145 | |
|
|
146 | 146 | datatype = None #dtype but in string |
|
|
147 | 147 | |
|
|
148 | 148 | # dtype = None |
|
|
149 | 149 | |
|
|
150 | 150 | # nChannels = None |
|
|
151 | 151 | |
|
|
152 | 152 | # nHeights = None |
|
|
153 | 153 | |
|
|
154 | 154 | nProfiles = None |
|
|
155 | 155 | |
|
|
156 | 156 | heightList = None |
|
|
157 | 157 | |
|
|
158 | 158 | channelList = None |
|
|
159 | 159 | |
|
|
160 | 160 | flagDiscontinuousBlock = False |
|
|
161 | 161 | |
|
|
162 | 162 | useLocalTime = False |
|
|
163 | 163 | |
|
|
164 | 164 | utctime = None |
|
|
165 | 165 | |
|
|
166 | 166 | timeZone = None |
|
|
167 | 167 | |
|
|
168 | 168 | dstFlag = None |
|
|
169 | 169 | |
|
|
170 | 170 | errorCount = None |
|
|
171 | 171 | |
|
|
172 | 172 | blocksize = None |
|
|
173 | 173 | |
|
|
174 | 174 | # nCode = None |
|
|
175 | 175 | # |
|
|
176 | 176 | # nBaud = None |
|
|
177 | 177 | # |
|
|
178 | 178 | # code = None |
|
|
179 | 179 | |
|
|
180 | 180 | flagDecodeData = False #asumo q la data no esta decodificada |
|
|
181 | 181 | |
|
|
182 | 182 | flagDeflipData = False #asumo q la data no esta sin flip |
|
|
183 | 183 | |
|
|
184 | 184 | flagShiftFFT = False |
|
|
185 | 185 | |
|
|
186 | 186 | # ippSeconds = None |
|
|
187 | 187 | |
|
|
188 | 188 | # timeInterval = None |
|
|
189 | 189 | |
|
|
190 | 190 | nCohInt = None |
|
|
191 | 191 | |
|
|
192 | 192 | # noise = None |
|
|
193 | 193 | |
|
|
194 | 194 | windowOfFilter = 1 |
|
|
195 | 195 | |
|
|
196 | 196 | #Speed of ligth |
|
|
197 | 197 | C = 3e8 |
|
|
198 | 198 | |
|
|
199 | 199 | frequency = 49.92e6 |
|
|
200 | 200 | |
|
|
201 | 201 | realtime = False |
|
|
202 | 202 | |
|
|
203 | 203 | beacon_heiIndexList = None |
|
|
204 | 204 | |
|
|
205 | 205 | last_block = None |
|
|
206 | 206 | |
|
|
207 | 207 | blocknow = None |
|
|
208 | 208 | |
|
|
209 | 209 | azimuth = None |
|
|
210 | 210 | |
|
|
211 | 211 | zenith = None |
|
|
212 | 212 | |
|
|
213 | 213 | beam = Beam() |
|
|
214 | 214 | |
|
|
215 | 215 | profileIndex = None |
|
|
216 | 216 | |
|
|
217 | 217 | def __init__(self): |
|
|
218 | 218 | |
|
|
219 | 219 | raise ValueError, "This class has not been implemented" |
|
|
220 | 220 | |
|
|
221 | 221 | def getNoise(self): |
|
|
222 | 222 | |
|
|
223 | 223 | raise ValueError, "Not implemented" |
|
|
224 | 224 | |
|
|
225 | 225 | def getNChannels(self): |
|
|
226 | 226 | |
|
|
227 | 227 | return len(self.channelList) |
|
|
228 | 228 | |
|
|
229 | 229 | def getChannelIndexList(self): |
|
|
230 | 230 | |
|
|
231 | 231 | return range(self.nChannels) |
|
|
232 | 232 | |
|
|
233 | 233 | def getNHeights(self): |
|
|
234 | 234 | |
|
|
235 | 235 | return len(self.heightList) |
|
|
236 | 236 | |
|
|
237 | 237 | def getHeiRange(self, extrapoints=0): |
|
|
238 | 238 | |
|
|
239 | 239 | heis = self.heightList |
|
|
240 | 240 | # deltah = self.heightList[1] - self.heightList[0] |
|
|
241 | 241 | # |
|
|
242 | 242 | # heis.append(self.heightList[-1]) |
|
|
243 | 243 | |
|
|
244 | 244 | return heis |
|
|
245 | 245 | |
|
|
246 | 246 | def getltctime(self): |
|
|
247 | 247 | |
|
|
248 | 248 | if self.useLocalTime: |
|
|
249 | 249 | return self.utctime - self.timeZone*60 |
|
|
250 | 250 | |
|
|
251 | 251 | return self.utctime |
|
|
252 | 252 | |
|
|
253 | 253 | def getDatatime(self): |
|
|
254 | 254 | |
|
|
255 | 255 | datatimeValue = datetime.datetime.utcfromtimestamp(self.ltctime) |
|
|
256 | 256 | return datatimeValue |
|
|
257 | 257 | |
|
|
258 | 258 | def getTimeRange(self): |
|
|
259 | 259 | |
|
|
260 | 260 | datatime = [] |
|
|
261 | 261 | |
|
|
262 | 262 | datatime.append(self.ltctime) |
|
|
263 | 263 | datatime.append(self.ltctime + self.timeInterval+60) |
|
|
264 | 264 | |
|
|
265 | 265 | datatime = numpy.array(datatime) |
|
|
266 | 266 | |
|
|
267 | 267 | return datatime |
|
|
268 | 268 | |
|
|
269 | 269 | def getFmax(self): |
|
|
270 | 270 | |
|
|
271 | 271 | PRF = 1./(self.ippSeconds * self.nCohInt) |
|
|
272 | 272 | |
|
|
273 | 273 | fmax = PRF/2. |
|
|
274 | 274 | |
|
|
275 | 275 | return fmax |
|
|
276 | 276 | |
|
|
277 | 277 | def getVmax(self): |
|
|
278 | 278 | |
|
|
279 | 279 | _lambda = self.C/self.frequency |
|
|
280 | 280 | |
|
|
281 | 281 | vmax = self.getFmax() * _lambda |
|
|
282 | 282 | |
|
|
283 | 283 | return vmax |
|
|
284 | 284 | |
|
|
285 | 285 | def get_ippSeconds(self): |
|
|
286 | 286 | ''' |
|
|
287 | 287 | ''' |
|
|
288 | 288 | return self.radarControllerHeaderObj.ippSeconds |
|
|
289 | 289 | |
|
|
290 | 290 | def set_ippSeconds(self, ippSeconds): |
|
|
291 | 291 | ''' |
|
|
292 | 292 | ''' |
|
|
293 | 293 | |
|
|
294 | 294 | self.radarControllerHeaderObj.ippSeconds = ippSeconds |
|
|
295 | 295 | |
|
|
296 | 296 | return |
|
|
297 | 297 | |
|
|
298 | 298 | def get_dtype(self): |
|
|
299 | 299 | ''' |
|
|
300 | 300 | ''' |
|
|
301 | 301 | return getNumpyDtype(self.datatype) |
|
|
302 | 302 | |
|
|
303 | 303 | def set_dtype(self, numpyDtype): |
|
|
304 | 304 | ''' |
|
|
305 | 305 | ''' |
|
|
306 | 306 | |
|
|
307 | 307 | self.datatype = getDataTypeCode(numpyDtype) |
|
|
308 | 308 | |
|
|
309 | 309 | def get_code(self): |
|
|
310 | 310 | ''' |
|
|
311 | 311 | ''' |
|
|
312 | 312 | return self.radarControllerHeaderObj.code |
|
|
313 | 313 | |
|
|
314 | 314 | def set_code(self, code): |
|
|
315 | 315 | ''' |
|
|
316 | 316 | ''' |
|
|
317 | 317 | self.radarControllerHeaderObj.code = code |
|
|
318 | 318 | |
|
|
319 | 319 | return |
|
|
320 | 320 | |
|
|
321 | 321 | def get_ncode(self): |
|
|
322 | 322 | ''' |
|
|
323 | 323 | ''' |
|
|
324 | 324 | return self.radarControllerHeaderObj.nCode |
|
|
325 | 325 | |
|
|
326 | 326 | def set_ncode(self, nCode): |
|
|
327 | 327 | ''' |
|
|
328 | 328 | ''' |
|
|
329 | 329 | self.radarControllerHeaderObj.nCode = nCode |
|
|
330 | 330 | |
|
|
331 | 331 | return |
|
|
332 | 332 | |
|
|
333 | 333 | def get_nbaud(self): |
|
|
334 | 334 | ''' |
|
|
335 | 335 | ''' |
|
|
336 | 336 | return self.radarControllerHeaderObj.nBaud |
|
|
337 | 337 | |
|
|
338 | 338 | def set_nbaud(self, nBaud): |
|
|
339 | 339 | ''' |
|
|
340 | 340 | ''' |
|
|
341 | 341 | self.radarControllerHeaderObj.nBaud = nBaud |
|
|
342 | 342 | |
|
|
343 | 343 | return |
|
|
344 | 344 | # def getTimeInterval(self): |
|
|
345 | 345 | # |
|
|
346 | 346 | # raise IOError, "This method should be implemented inside each Class" |
|
|
347 | 347 | |
|
|
348 | 348 | nChannels = property(getNChannels, "I'm the 'nChannel' property.") |
|
|
349 | 349 | channelIndexList = property(getChannelIndexList, "I'm the 'channelIndexList' property.") |
|
|
350 | 350 | nHeights = property(getNHeights, "I'm the 'nHeights' property.") |
|
|
351 | 351 | #noise = property(getNoise, "I'm the 'nHeights' property.") |
|
|
352 | 352 | datatime = property(getDatatime, "I'm the 'datatime' property") |
|
|
353 | 353 | ltctime = property(getltctime, "I'm the 'ltctime' property") |
|
|
354 | 354 | ippSeconds = property(get_ippSeconds, set_ippSeconds) |
|
|
355 | 355 | dtype = property(get_dtype, set_dtype) |
|
|
356 | 356 | # timeInterval = property(getTimeInterval, "I'm the 'timeInterval' property") |
|
|
357 | 357 | code = property(get_code, set_code) |
|
|
358 | 358 | nCode = property(get_ncode, set_ncode) |
|
|
359 | 359 | nBaud = property(get_nbaud, set_nbaud) |
|
|
360 | 360 | |
|
|
361 | 361 | class Voltage(JROData): |
|
|
362 | 362 | |
|
|
363 | 363 | #data es un numpy array de 2 dmensiones (canales, alturas) |
|
|
364 | 364 | data = None |
|
|
365 | 365 | |
|
|
366 | 366 | def __init__(self): |
|
|
367 | 367 | ''' |
|
|
368 | 368 | Constructor |
|
|
369 | 369 | ''' |
|
|
370 | 370 | |
|
|
371 | 371 | self.useLocalTime = True |
|
|
372 | 372 | |
|
|
373 | 373 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
|
374 | 374 | |
|
|
375 | 375 | self.systemHeaderObj = SystemHeader() |
|
|
376 | 376 | |
|
|
377 | 377 | self.type = "Voltage" |
|
|
378 | 378 | |
|
|
379 | 379 | self.data = None |
|
|
380 | 380 | |
|
|
381 | 381 | # self.dtype = None |
|
|
382 | 382 | |
|
|
383 | 383 | # self.nChannels = 0 |
|
|
384 | 384 | |
|
|
385 | 385 | # self.nHeights = 0 |
|
|
386 | 386 | |
|
|
387 | 387 | self.nProfiles = None |
|
|
388 | 388 | |
|
|
389 | 389 | self.heightList = None |
|
|
390 | 390 | |
|
|
391 | 391 | self.channelList = None |
|
|
392 | 392 | |
|
|
393 | 393 | # self.channelIndexList = None |
|
|
394 | 394 | |
|
|
395 | 395 | self.flagNoData = True |
|
|
396 | 396 | |
|
|
397 | 397 | self.flagDiscontinuousBlock = False |
|
|
398 | 398 | |
|
|
399 | 399 | self.utctime = None |
|
|
400 | 400 | |
|
|
401 | 401 | self.timeZone = None |
|
|
402 | 402 | |
|
|
403 | 403 | self.dstFlag = None |
|
|
404 | 404 | |
|
|
405 | 405 | self.errorCount = None |
|
|
406 | 406 | |
|
|
407 | 407 | self.nCohInt = None |
|
|
408 | 408 | |
|
|
409 | 409 | self.blocksize = None |
|
|
410 | 410 | |
|
|
411 | 411 | self.flagDecodeData = False #asumo q la data no esta decodificada |
|
|
412 | 412 | |
|
|
413 | 413 | self.flagDeflipData = False #asumo q la data no esta sin flip |
|
|
414 | 414 | |
|
|
415 | 415 | self.flagShiftFFT = False |
|
|
416 | 416 | |
|
|
417 | 417 | self.flagDataAsBlock = False #Asumo que la data es leida perfil a perfil |
|
|
418 | 418 | |
|
|
419 | 419 | self.profileIndex = 0 |
|
|
420 | 420 | |
|
|
421 | 421 | def getNoisebyHildebrand(self, channel = None): |
|
|
422 | 422 | """ |
|
|
423 | 423 | Determino el nivel de ruido usando el metodo Hildebrand-Sekhon |
|
|
424 | 424 | |
|
|
425 | 425 | Return: |
|
|
426 | 426 | noiselevel |
|
|
427 | 427 | """ |
|
|
428 | 428 | |
|
|
429 | 429 | if channel != None: |
|
|
430 | 430 | data = self.data[channel] |
|
|
431 | 431 | nChannels = 1 |
|
|
432 | 432 | else: |
|
|
433 | 433 | data = self.data |
|
|
434 | 434 | nChannels = self.nChannels |
|
|
435 | 435 | |
|
|
436 | 436 | noise = numpy.zeros(nChannels) |
|
|
437 | 437 | power = data * numpy.conjugate(data) |
|
|
438 | 438 | |
|
|
439 | 439 | for thisChannel in range(nChannels): |
|
|
440 | 440 | if nChannels == 1: |
|
|
441 | 441 | daux = power[:].real |
|
|
442 | 442 | else: |
|
|
443 | 443 | daux = power[thisChannel,:].real |
|
|
444 | 444 | noise[thisChannel] = hildebrand_sekhon(daux, self.nCohInt) |
|
|
445 | 445 | |
|
|
446 | 446 | return noise |
|
|
447 | 447 | |
|
|
448 | 448 | def getNoise(self, type = 1, channel = None): |
|
|
449 | 449 | |
|
|
450 | 450 | if type == 1: |
|
|
451 | 451 | noise = self.getNoisebyHildebrand(channel) |
|
|
452 | 452 | |
|
|
453 | 453 | return 10*numpy.log10(noise) |
|
|
454 | 454 | |
|
|
455 | 455 | def getPower(self, channel = None): |
|
|
456 | 456 | |
|
|
457 | 457 | if channel != None: |
|
|
458 | 458 | data = self.data[channel] |
|
|
459 | 459 | else: |
|
|
460 | 460 | data = self.data |
|
|
461 | 461 | |
|
|
462 | 462 | power = data * numpy.conjugate(data) |
|
|
463 | 463 | |
|
|
464 | 464 | return 10*numpy.log10(power.real) |
|
|
465 | 465 | |
|
|
466 | 466 | def getTimeInterval(self): |
|
|
467 | 467 | |
|
|
468 | 468 | timeInterval = self.ippSeconds * self.nCohInt |
|
|
469 | 469 | |
|
|
470 | 470 | return timeInterval |
|
|
471 | 471 | |
|
|
472 | 472 | noise = property(getNoise, "I'm the 'nHeights' property.") |
|
|
473 | 473 | timeInterval = property(getTimeInterval, "I'm the 'timeInterval' property") |
|
|
474 | 474 | |
|
|
475 | 475 | class Spectra(JROData): |
|
|
476 | 476 | |
|
|
477 | 477 | #data es un numpy array de 2 dmensiones (canales, perfiles, alturas) |
|
|
478 | 478 | data_spc = None |
|
|
479 | 479 | |
|
|
480 | 480 | #data es un numpy array de 2 dmensiones (canales, pares, alturas) |
|
|
481 | 481 | data_cspc = None |
|
|
482 | 482 | |
|
|
483 | 483 | #data es un numpy array de 2 dmensiones (canales, alturas) |
|
|
484 | 484 | data_dc = None |
|
|
485 | 485 | |
|
|
486 | 486 | nFFTPoints = None |
|
|
487 | 487 | |
|
|
488 | 488 | # nPairs = None |
|
|
489 | 489 | |
|
|
490 | 490 | pairsList = None |
|
|
491 | 491 | |
|
|
492 | 492 | nIncohInt = None |
|
|
493 | 493 | |
|
|
494 | 494 | wavelength = None #Necesario para cacular el rango de velocidad desde la frecuencia |
|
|
495 | 495 | |
|
|
496 | 496 | nCohInt = None #se requiere para determinar el valor de timeInterval |
|
|
497 | 497 | |
|
|
498 | 498 | ippFactor = None |
|
|
499 | 499 | |
|
|
500 | 500 | profileIndex = 0 |
|
|
501 | 501 | |
|
|
502 | 502 | def __init__(self): |
|
|
503 | 503 | ''' |
|
|
504 | 504 | Constructor |
|
|
505 | 505 | ''' |
|
|
506 | 506 | |
|
|
507 | 507 | self.useLocalTime = True |
|
|
508 | 508 | |
|
|
509 | 509 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
|
510 | 510 | |
|
|
511 | 511 | self.systemHeaderObj = SystemHeader() |
|
|
512 | 512 | |
|
|
513 | 513 | self.type = "Spectra" |
|
|
514 | 514 | |
|
|
515 | 515 | # self.data = None |
|
|
516 | 516 | |
|
|
517 | 517 | # self.dtype = None |
|
|
518 | 518 | |
|
|
519 | 519 | # self.nChannels = 0 |
|
|
520 | 520 | |
|
|
521 | 521 | # self.nHeights = 0 |
|
|
522 | 522 | |
|
|
523 | 523 | self.nProfiles = None |
|
|
524 | 524 | |
|
|
525 | 525 | self.heightList = None |
|
|
526 | 526 | |
|
|
527 | 527 | self.channelList = None |
|
|
528 | 528 | |
|
|
529 | 529 | # self.channelIndexList = None |
|
|
530 | 530 | |
|
|
531 | 531 | self.pairsList = None |
|
|
532 | 532 | |
|
|
533 | 533 | self.flagNoData = True |
|
|
534 | 534 | |
|
|
535 | 535 | self.flagDiscontinuousBlock = False |
|
|
536 | 536 | |
|
|
537 | 537 | self.utctime = None |
|
|
538 | 538 | |
|
|
539 | 539 | self.nCohInt = None |
|
|
540 | 540 | |
|
|
541 | 541 | self.nIncohInt = None |
|
|
542 | 542 | |
|
|
543 | 543 | self.blocksize = None |
|
|
544 | 544 | |
|
|
545 | 545 | self.nFFTPoints = None |
|
|
546 | 546 | |
|
|
547 | 547 | self.wavelength = None |
|
|
548 | 548 | |
|
|
549 | 549 | self.flagDecodeData = False #asumo q la data no esta decodificada |
|
|
550 | 550 | |
|
|
551 | 551 | self.flagDeflipData = False #asumo q la data no esta sin flip |
|
|
552 | 552 | |
|
|
553 | 553 | self.flagShiftFFT = False |
|
|
554 | 554 | |
|
|
555 | 555 | self.ippFactor = 1 |
|
|
556 | 556 | |
|
|
557 | 557 | #self.noise = None |
|
|
558 | 558 | |
|
|
559 | 559 | self.beacon_heiIndexList = [] |
|
|
560 | 560 | |
|
|
561 | 561 | self.noise_estimation = None |
|
|
562 | 562 | |
|
|
563 | 563 | |
|
|
564 | 564 | def getNoisebyHildebrand(self, xmin_index=None, xmax_index=None, ymin_index=None, ymax_index=None): |
|
|
565 | 565 | """ |
|
|
566 | 566 | Determino el nivel de ruido usando el metodo Hildebrand-Sekhon |
|
|
567 | 567 | |
|
|
568 | 568 | Return: |
|
|
569 | 569 | noiselevel |
|
|
570 | 570 | """ |
|
|
571 | 571 | |
|
|
572 | 572 | noise = numpy.zeros(self.nChannels) |
|
|
573 | 573 | |
|
|
574 | 574 | for channel in range(self.nChannels): |
|
|
575 | 575 | daux = self.data_spc[channel,xmin_index:xmax_index,ymin_index:ymax_index] |
|
|
576 | 576 | noise[channel] = hildebrand_sekhon(daux, self.nIncohInt) |
|
|
577 | 577 | |
|
|
578 | 578 | return noise |
|
|
579 | 579 | |
|
|
580 | 580 | def getNoise(self, xmin_index=None, xmax_index=None, ymin_index=None, ymax_index=None): |
|
|
581 | 581 | |
|
|
582 | 582 | if self.noise_estimation != None: |
|
|
583 | 583 | return self.noise_estimation #this was estimated by getNoise Operation defined in jroproc_spectra.py |
|
|
584 | 584 | else: |
|
|
585 | 585 | noise = self.getNoisebyHildebrand(xmin_index, xmax_index, ymin_index, ymax_index) |
|
|
586 | 586 | return noise |
|
|
587 | 587 | |
|
|
588 | 588 | |
|
|
589 | 589 | def getFreqRange(self, extrapoints=0): |
|
|
590 | 590 | |
|
|
591 | 591 | deltafreq = self.getFmax() / (self.nFFTPoints*self.ippFactor) |
|
|
592 | 592 | freqrange = deltafreq*(numpy.arange(self.nFFTPoints+extrapoints)-self.nFFTPoints/2.) - deltafreq/2 |
|
|
593 | 593 | |
|
|
594 | 594 | return freqrange |
|
|
595 | 595 | |
|
|
596 | 596 | def getVelRange(self, extrapoints=0): |
|
|
597 | 597 | |
|
|
598 | 598 | deltav = self.getVmax() / (self.nFFTPoints*self.ippFactor) |
|
|
599 | 599 | velrange = deltav*(numpy.arange(self.nFFTPoints+extrapoints)-self.nFFTPoints/2.) - deltav/2 |
|
|
600 | 600 | |
|
|
601 | 601 | return velrange |
|
|
602 | 602 | |
|
|
603 | 603 | def getNPairs(self): |
|
|
604 | 604 | |
|
|
605 | 605 | return len(self.pairsList) |
|
|
606 | 606 | |
|
|
607 | 607 | def getPairsIndexList(self): |
|
|
608 | 608 | |
|
|
609 | 609 | return range(self.nPairs) |
|
|
610 | 610 | |
|
|
611 | 611 | def getNormFactor(self): |
|
|
612 | 612 | pwcode = 1 |
|
|
613 | 613 | if self.flagDecodeData: |
|
|
614 | 614 | pwcode = numpy.sum(self.code[0]**2) |
|
|
615 | 615 | #normFactor = min(self.nFFTPoints,self.nProfiles)*self.nIncohInt*self.nCohInt*pwcode*self.windowOfFilter |
|
|
616 | 616 | normFactor = self.nProfiles*self.nIncohInt*self.nCohInt*pwcode*self.windowOfFilter |
|
|
617 | 617 | |
|
|
618 | 618 | return normFactor |
|
|
619 | 619 | |
|
|
620 | 620 | def getFlagCspc(self): |
|
|
621 | 621 | |
|
|
622 | 622 | if self.data_cspc == None: |
|
|
623 | 623 | return True |
|
|
624 | 624 | |
|
|
625 | 625 | return False |
|
|
626 | 626 | |
|
|
627 | 627 | def getFlagDc(self): |
|
|
628 | 628 | |
|
|
629 | 629 | if self.data_dc == None: |
|
|
630 | 630 | return True |
|
|
631 | 631 | |
|
|
632 | 632 | return False |
|
|
633 | 633 | |
|
|
634 | 634 | def getTimeInterval(self): |
|
|
635 | 635 | |
|
|
636 | 636 | timeInterval = self.ippSeconds * self.nCohInt * self.nIncohInt * self.nProfiles |
|
|
637 | 637 | |
|
|
638 | 638 | return timeInterval |
|
|
639 | 639 | |
|
|
640 | 640 | nPairs = property(getNPairs, "I'm the 'nPairs' property.") |
|
|
641 | 641 | pairsIndexList = property(getPairsIndexList, "I'm the 'pairsIndexList' property.") |
|
|
642 | 642 | normFactor = property(getNormFactor, "I'm the 'getNormFactor' property.") |
|
|
643 | 643 | flag_cspc = property(getFlagCspc) |
|
|
644 | 644 | flag_dc = property(getFlagDc) |
|
|
645 | 645 | noise = property(getNoise, "I'm the 'nHeights' property.") |
|
|
646 | 646 | timeInterval = property(getTimeInterval, "I'm the 'timeInterval' property") |
|
|
647 | 647 | |
|
|
648 | 648 | class SpectraHeis(Spectra): |
|
|
649 | 649 | |
|
|
650 | 650 | data_spc = None |
|
|
651 | 651 | |
|
|
652 | 652 | data_cspc = None |
|
|
653 | 653 | |
|
|
654 | 654 | data_dc = None |
|
|
655 | 655 | |
|
|
656 | 656 | nFFTPoints = None |
|
|
657 | 657 | |
|
|
658 | 658 | # nPairs = None |
|
|
659 | 659 | |
|
|
660 | 660 | pairsList = None |
|
|
661 | 661 | |
|
|
662 | 662 | nCohInt = None |
|
|
663 | 663 | |
|
|
664 | 664 | nIncohInt = None |
|
|
665 | 665 | |
|
|
666 | 666 | def __init__(self): |
|
|
667 | 667 | |
|
|
668 | 668 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
|
669 | 669 | |
|
|
670 | 670 | self.systemHeaderObj = SystemHeader() |
|
|
671 | 671 | |
|
|
672 | 672 | self.type = "SpectraHeis" |
|
|
673 | 673 | |
|
|
674 | 674 | # self.dtype = None |
|
|
675 | 675 | |
|
|
676 | 676 | # self.nChannels = 0 |
|
|
677 | 677 | |
|
|
678 | 678 | # self.nHeights = 0 |
|
|
679 | 679 | |
|
|
680 | 680 | self.nProfiles = None |
|
|
681 | 681 | |
|
|
682 | 682 | self.heightList = None |
|
|
683 | 683 | |
|
|
684 | 684 | self.channelList = None |
|
|
685 | 685 | |
|
|
686 | 686 | # self.channelIndexList = None |
|
|
687 | 687 | |
|
|
688 | 688 | self.flagNoData = True |
|
|
689 | 689 | |
|
|
690 | 690 | self.flagDiscontinuousBlock = False |
|
|
691 | 691 | |
|
|
692 | 692 | # self.nPairs = 0 |
|
|
693 | 693 | |
|
|
694 | 694 | self.utctime = None |
|
|
695 | 695 | |
|
|
696 | 696 | self.blocksize = None |
|
|
697 | 697 | |
|
|
698 | 698 | self.profileIndex = 0 |
|
|
699 | 699 | |
|
|
700 | 700 | self.nCohInt = 1 |
|
|
701 | 701 | |
|
|
702 | 702 | self.nIncohInt = 1 |
|
|
703 | 703 | |
|
|
704 | 704 | def getNormFactor(self): |
|
|
705 | 705 | pwcode = 1 |
|
|
706 | 706 | if self.flagDecodeData: |
|
|
707 | 707 | pwcode = numpy.sum(self.code[0]**2) |
|
|
708 | 708 | |
|
|
709 | 709 | normFactor = self.nIncohInt*self.nCohInt*pwcode |
|
|
710 | 710 | |
|
|
711 | 711 | return normFactor |
|
|
712 | 712 | |
|
|
713 | 713 | def getTimeInterval(self): |
|
|
714 | 714 | |
|
|
715 | 715 | timeInterval = self.ippSeconds * self.nCohInt * self.nIncohInt |
|
|
716 | 716 | |
|
|
717 | 717 | return timeInterval |
|
|
718 | 718 | |
|
|
719 | 719 | normFactor = property(getNormFactor, "I'm the 'getNormFactor' property.") |
|
|
720 | 720 | timeInterval = property(getTimeInterval, "I'm the 'timeInterval' property") |
|
|
721 | 721 | |
|
|
722 | 722 | class Fits(JROData): |
|
|
723 | 723 | |
|
|
724 | 724 | heightList = None |
|
|
725 | 725 | |
|
|
726 | 726 | channelList = None |
|
|
727 | 727 | |
|
|
728 | 728 | flagNoData = True |
|
|
729 | 729 | |
|
|
730 | 730 | flagDiscontinuousBlock = False |
|
|
731 | 731 | |
|
|
732 | 732 | useLocalTime = False |
|
|
733 | 733 | |
|
|
734 | 734 | utctime = None |
|
|
735 | 735 | |
|
|
736 | 736 | timeZone = None |
|
|
737 | 737 | |
|
|
738 | 738 | # ippSeconds = None |
|
|
739 | 739 | |
|
|
740 | 740 | # timeInterval = None |
|
|
741 | 741 | |
|
|
742 | 742 | nCohInt = None |
|
|
743 | 743 | |
|
|
744 | 744 | nIncohInt = None |
|
|
745 | 745 | |
|
|
746 | 746 | noise = None |
|
|
747 | 747 | |
|
|
748 | 748 | windowOfFilter = 1 |
|
|
749 | 749 | |
|
|
750 | 750 | #Speed of ligth |
|
|
751 | 751 | C = 3e8 |
|
|
752 | 752 | |
|
|
753 | 753 | frequency = 49.92e6 |
|
|
754 | 754 | |
|
|
755 | 755 | realtime = False |
|
|
756 | 756 | |
|
|
757 | 757 | |
|
|
758 | 758 | def __init__(self): |
|
|
759 | 759 | |
|
|
760 | 760 | self.type = "Fits" |
|
|
761 | 761 | |
|
|
762 | 762 | self.nProfiles = None |
|
|
763 | 763 | |
|
|
764 | 764 | self.heightList = None |
|
|
765 | 765 | |
|
|
766 | 766 | self.channelList = None |
|
|
767 | 767 | |
|
|
768 | 768 | # self.channelIndexList = None |
|
|
769 | 769 | |
|
|
770 | 770 | self.flagNoData = True |
|
|
771 | 771 | |
|
|
772 | 772 | self.utctime = None |
|
|
773 | 773 | |
|
|
774 | 774 | self.nCohInt = 1 |
|
|
775 | 775 | |
|
|
776 | 776 | self.nIncohInt = 1 |
|
|
777 | 777 | |
|
|
778 | 778 | self.useLocalTime = True |
|
|
779 | 779 | |
|
|
780 | 780 | self.profileIndex = 0 |
|
|
781 | 781 | |
|
|
782 | 782 | # self.utctime = None |
|
|
783 | 783 | # self.timeZone = None |
|
|
784 | 784 | # self.ltctime = None |
|
|
785 | 785 | # self.timeInterval = None |
|
|
786 | 786 | # self.header = None |
|
|
787 | 787 | # self.data_header = None |
|
|
788 | 788 | # self.data = None |
|
|
789 | 789 | # self.datatime = None |
|
|
790 | 790 | # self.flagNoData = False |
|
|
791 | 791 | # self.expName = '' |
|
|
792 | 792 | # self.nChannels = None |
|
|
793 | 793 | # self.nSamples = None |
|
|
794 | 794 | # self.dataBlocksPerFile = None |
|
|
795 | 795 | # self.comments = '' |
|
|
796 | 796 | # |
|
|
797 | 797 | |
|
|
798 | 798 | |
|
|
799 | 799 | def getltctime(self): |
|
|
800 | 800 | |
|
|
801 | 801 | if self.useLocalTime: |
|
|
802 | 802 | return self.utctime - self.timeZone*60 |
|
|
803 | 803 | |
|
|
804 | 804 | return self.utctime |
|
|
805 | 805 | |
|
|
806 | 806 | def getDatatime(self): |
|
|
807 | 807 | |
|
|
808 | 808 | datatime = datetime.datetime.utcfromtimestamp(self.ltctime) |
|
|
809 | 809 | return datatime |
|
|
810 | 810 | |
|
|
811 | 811 | def getTimeRange(self): |
|
|
812 | 812 | |
|
|
813 | 813 | datatime = [] |
|
|
814 | 814 | |
|
|
815 | 815 | datatime.append(self.ltctime) |
|
|
816 | 816 | datatime.append(self.ltctime + self.timeInterval) |
|
|
817 | 817 | |
|
|
818 | 818 | datatime = numpy.array(datatime) |
|
|
819 | 819 | |
|
|
820 | 820 | return datatime |
|
|
821 | 821 | |
|
|
822 | 822 | def getHeiRange(self): |
|
|
823 | 823 | |
|
|
824 | 824 | heis = self.heightList |
|
|
825 | 825 | |
|
|
826 | 826 | return heis |
|
|
827 | 827 | |
|
|
828 | 828 | def isEmpty(self): |
|
|
829 | 829 | |
|
|
830 | 830 | return self.flagNoData |
|
|
831 | 831 | |
|
|
832 | 832 | def getNHeights(self): |
|
|
833 | 833 | |
|
|
834 | 834 | return len(self.heightList) |
|
|
835 | 835 | |
|
|
836 | 836 | def getNChannels(self): |
|
|
837 | 837 | |
|
|
838 | 838 | return len(self.channelList) |
|
|
839 | 839 | |
|
|
840 | 840 | def getChannelIndexList(self): |
|
|
841 | 841 | |
|
|
842 | 842 | return range(self.nChannels) |
|
|
843 | 843 | |
|
|
844 | 844 | def getNoise(self, type = 1): |
|
|
845 | 845 | |
|
|
846 | 846 | #noise = numpy.zeros(self.nChannels) |
|
|
847 | 847 | |
|
|
848 | 848 | if type == 1: |
|
|
849 | 849 | noise = self.getNoisebyHildebrand() |
|
|
850 | 850 | |
|
|
851 | 851 | if type == 2: |
|
|
852 | 852 | noise = self.getNoisebySort() |
|
|
853 | 853 | |
|
|
854 | 854 | if type == 3: |
|
|
855 | 855 | noise = self.getNoisebyWindow() |
|
|
856 | 856 | |
|
|
857 | 857 | return noise |
|
|
858 | 858 | |
|
|
859 | 859 | def getTimeInterval(self): |
|
|
860 | 860 | |
|
|
861 | 861 | timeInterval = self.ippSeconds * self.nCohInt * self.nIncohInt |
|
|
862 | 862 | |
|
|
863 | 863 | return timeInterval |
|
|
864 | 864 | |
|
|
865 | 865 | datatime = property(getDatatime, "I'm the 'datatime' property") |
|
|
866 | 866 | nHeights = property(getNHeights, "I'm the 'nHeights' property.") |
|
|
867 | 867 | nChannels = property(getNChannels, "I'm the 'nChannel' property.") |
|
|
868 | 868 | channelIndexList = property(getChannelIndexList, "I'm the 'channelIndexList' property.") |
|
|
869 | 869 | noise = property(getNoise, "I'm the 'nHeights' property.") |
|
|
870 | 870 | datatime = property(getDatatime, "I'm the 'datatime' property") |
|
|
871 | 871 | ltctime = property(getltctime, "I'm the 'ltctime' property") |
|
|
872 | 872 | timeInterval = property(getTimeInterval, "I'm the 'timeInterval' property") |
|
|
873 | 873 | |
|
|
874 | 874 | class Correlation(JROData): |
|
|
875 | 875 | |
|
|
876 | 876 | noise = None |
|
|
877 | 877 | |
|
|
878 | 878 | SNR = None |
|
|
879 | 879 | |
|
|
880 | 880 | pairsAutoCorr = None #Pairs of Autocorrelation |
|
|
881 | 881 | |
|
|
882 | 882 | #-------------------------------------------------- |
|
|
883 | 883 | |
|
|
884 | 884 | data_corr = None |
|
|
885 | 885 | |
|
|
886 | 886 | data_volt = None |
|
|
887 | 887 | |
|
|
888 | 888 | lagT = None # each element value is a profileIndex |
|
|
889 | 889 | |
|
|
890 | 890 | lagR = None # each element value is in km |
|
|
891 | 891 | |
|
|
892 | 892 | pairsList = None |
|
|
893 | 893 | |
|
|
894 | 894 | calculateVelocity = None |
|
|
895 | 895 | |
|
|
896 | 896 | nPoints = None |
|
|
897 | 897 | |
|
|
898 | 898 | nAvg = None |
|
|
899 | 899 | |
|
|
900 | 900 | bufferSize = None |
|
|
901 | 901 | |
|
|
902 | 902 | def __init__(self): |
|
|
903 | 903 | ''' |
|
|
904 | 904 | Constructor |
|
|
905 | 905 | ''' |
|
|
906 | 906 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
|
907 | 907 | |
|
|
908 | 908 | self.systemHeaderObj = SystemHeader() |
|
|
909 | 909 | |
|
|
910 | 910 | self.type = "Correlation" |
|
|
911 | 911 | |
|
|
912 | 912 | self.data = None |
|
|
913 | 913 | |
|
|
914 | 914 | self.dtype = None |
|
|
915 | 915 | |
|
|
916 | 916 | self.nProfiles = None |
|
|
917 | 917 | |
|
|
918 | 918 | self.heightList = None |
|
|
919 | 919 | |
|
|
920 | 920 | self.channelList = None |
|
|
921 | 921 | |
|
|
922 | 922 | self.flagNoData = True |
|
|
923 | 923 | |
|
|
924 | 924 | self.flagDiscontinuousBlock = False |
|
|
925 | 925 | |
|
|
926 | 926 | self.utctime = None |
|
|
927 | 927 | |
|
|
928 | 928 | self.timeZone = None |
|
|
929 | 929 | |
|
|
930 | 930 | self.dstFlag = None |
|
|
931 | 931 | |
|
|
932 | 932 | self.errorCount = None |
|
|
933 | 933 | |
|
|
934 | 934 | self.blocksize = None |
|
|
935 | 935 | |
|
|
936 | 936 | self.flagDecodeData = False #asumo q la data no esta decodificada |
|
|
937 | 937 | |
|
|
938 | 938 | self.flagDeflipData = False #asumo q la data no esta sin flip |
|
|
939 | 939 | |
|
|
940 | 940 | self.pairsList = None |
|
|
941 | 941 | |
|
|
942 | 942 | self.nPoints = None |
|
|
943 | 943 | |
|
|
944 | 944 | def getLagTRange(self, extrapoints=0): |
|
|
945 | 945 | |
|
|
946 | 946 | lagTRange = self.lagT |
|
|
947 | 947 | diff = lagTRange[1] - lagTRange[0] |
|
|
948 | 948 | extra = numpy.arange(1,extrapoints + 1)*diff + lagTRange[-1] |
|
|
949 | 949 | lagTRange = numpy.hstack((lagTRange, extra)) |
|
|
950 | 950 | |
|
|
951 | 951 | return lagTRange |
|
|
952 | 952 | |
|
|
953 | 953 | def getLagRRange(self, extrapoints=0): |
|
|
954 | 954 | |
|
|
955 | 955 | return self.lagR |
|
|
956 | 956 | |
|
|
957 | 957 | def getPairsList(self): |
|
|
958 | 958 | |
|
|
959 | 959 | return self.pairsList |
|
|
960 | 960 | |
|
|
961 | 961 | def getCalculateVelocity(self): |
|
|
962 | 962 | |
|
|
963 | 963 | return self.calculateVelocity |
|
|
964 | 964 | |
|
|
965 | 965 | def getNPoints(self): |
|
|
966 | 966 | |
|
|
967 | 967 | return self.nPoints |
|
|
968 | 968 | |
|
|
969 | 969 | def getNAvg(self): |
|
|
970 | 970 | |
|
|
971 | 971 | return self.nAvg |
|
|
972 | 972 | |
|
|
973 | 973 | def getBufferSize(self): |
|
|
974 | 974 | |
|
|
975 | 975 | return self.bufferSize |
|
|
976 | 976 | |
|
|
977 | 977 | def getPairsAutoCorr(self): |
|
|
978 | 978 | pairsList = self.pairsList |
|
|
979 | 979 | pairsAutoCorr = numpy.zeros(self.nChannels, dtype = 'int')*numpy.nan |
|
|
980 | 980 | |
|
|
981 | 981 | for l in range(len(pairsList)): |
|
|
982 | 982 | firstChannel = pairsList[l][0] |
|
|
983 | 983 | secondChannel = pairsList[l][1] |
|
|
984 | 984 | |
|
|
985 | 985 | #Obteniendo pares de Autocorrelacion |
|
|
986 | 986 | if firstChannel == secondChannel: |
|
|
987 | 987 | pairsAutoCorr[firstChannel] = int(l) |
|
|
988 | 988 | |
|
|
989 | 989 | pairsAutoCorr = pairsAutoCorr.astype(int) |
|
|
990 | 990 | |
|
|
991 | 991 | return pairsAutoCorr |
|
|
992 | 992 | |
|
|
993 | 993 | def getNoise(self, mode = 2): |
|
|
994 | 994 | |
|
|
995 | 995 | indR = numpy.where(self.lagR == 0)[0][0] |
|
|
996 | 996 | indT = numpy.where(self.lagT == 0)[0][0] |
|
|
997 | 997 | |
|
|
998 | 998 | jspectra0 = self.data_corr[:,:,indR,:] |
|
|
999 | 999 | jspectra = copy.copy(jspectra0) |
|
|
1000 | 1000 | |
|
|
1001 | 1001 | num_chan = jspectra.shape[0] |
|
|
1002 | 1002 | num_hei = jspectra.shape[2] |
|
|
1003 | 1003 | |
|
|
1004 | 1004 | freq_dc = jspectra.shape[1]/2 |
|
|
1005 | 1005 | ind_vel = numpy.array([-2,-1,1,2]) + freq_dc |
|
|
1006 | 1006 | |
|
|
1007 | 1007 | if ind_vel[0]<0: |
|
|
1008 | 1008 | ind_vel[range(0,1)] = ind_vel[range(0,1)] + self.num_prof |
|
|
1009 | 1009 | |
|
|
1010 | 1010 | if mode == 1: |
|
|
1011 | 1011 | jspectra[:,freq_dc,:] = (jspectra[:,ind_vel[1],:] + jspectra[:,ind_vel[2],:])/2 #CORRECCION |
|
|
1012 | 1012 | |
|
|
1013 | 1013 | if mode == 2: |
|
|
1014 | 1014 | |
|
|
1015 | 1015 | vel = numpy.array([-2,-1,1,2]) |
|
|
1016 | 1016 | xx = numpy.zeros([4,4]) |
|
|
1017 | 1017 | |
|
|
1018 | 1018 | for fil in range(4): |
|
|
1019 | 1019 | xx[fil,:] = vel[fil]**numpy.asarray(range(4)) |
|
|
1020 | 1020 | |
|
|
1021 | 1021 | xx_inv = numpy.linalg.inv(xx) |
|
|
1022 | 1022 | xx_aux = xx_inv[0,:] |
|
|
1023 | 1023 | |
|
|
1024 | 1024 | for ich in range(num_chan): |
|
|
1025 | 1025 | yy = jspectra[ich,ind_vel,:] |
|
|
1026 | 1026 | jspectra[ich,freq_dc,:] = numpy.dot(xx_aux,yy) |
|
|
1027 | 1027 | |
|
|
1028 | 1028 | junkid = jspectra[ich,freq_dc,:]<=0 |
|
|
1029 | 1029 | cjunkid = sum(junkid) |
|
|
1030 | 1030 | |
|
|
1031 | 1031 | if cjunkid.any(): |
|
|
1032 | 1032 | jspectra[ich,freq_dc,junkid.nonzero()] = (jspectra[ich,ind_vel[1],junkid] + jspectra[ich,ind_vel[2],junkid])/2 |
|
|
1033 | 1033 | |
|
|
1034 | 1034 | noise = jspectra0[:,freq_dc,:] - jspectra[:,freq_dc,:] |
|
|
1035 | 1035 | |
|
|
1036 | 1036 | return noise |
|
|
1037 | 1037 | |
|
|
1038 | 1038 | def getTimeInterval(self): |
|
|
1039 | 1039 | |
|
|
1040 | 1040 | timeInterval = self.ippSeconds * self.nCohInt * self.nPoints |
|
|
1041 | 1041 | |
|
|
1042 | 1042 | return timeInterval |
|
|
1043 | 1043 | |
|
|
1044 | 1044 | timeInterval = property(getTimeInterval, "I'm the 'timeInterval' property") |
|
|
1045 | 1045 | # pairsList = property(getPairsList, "I'm the 'pairsList' property.") |
|
|
1046 | 1046 | # nPoints = property(getNPoints, "I'm the 'nPoints' property.") |
|
|
1047 | 1047 | calculateVelocity = property(getCalculateVelocity, "I'm the 'calculateVelocity' property.") |
|
|
1048 | 1048 | nAvg = property(getNAvg, "I'm the 'nAvg' property.") |
|
|
1049 | 1049 | bufferSize = property(getBufferSize, "I'm the 'bufferSize' property.") |
|
|
1050 | 1050 | |
|
|
1051 | 1051 | |
|
|
1052 | 1052 | class Parameters(JROData): |
|
|
1053 | 1053 | |
|
|
1054 | 1054 | #Information from previous data |
|
|
1055 | 1055 | |
|
|
1056 | 1056 | inputUnit = None #Type of data to be processed |
|
|
1057 | 1057 | |
|
|
1058 | 1058 | operation = None #Type of operation to parametrize |
|
|
1059 | 1059 | |
|
|
1060 | 1060 | normFactor = None #Normalization Factor |
|
|
1061 | 1061 | |
|
|
1062 | 1062 | groupList = None #List of Pairs, Groups, etc |
|
|
1063 | 1063 | |
|
|
1064 | 1064 | #Parameters |
|
|
1065 | 1065 | |
|
|
1066 | 1066 | data_param = None #Parameters obtained |
|
|
1067 | 1067 | |
|
|
1068 | 1068 | data_pre = None #Data Pre Parametrization |
|
|
1069 | 1069 | |
|
|
1070 | 1070 | data_SNR = None #Signal to Noise Ratio |
|
|
1071 | 1071 | |
|
|
1072 | heightRange = None #Heights | |
|
|
1072 | # heightRange = None #Heights | |
|
|
1073 | 1073 | |
|
|
1074 |
abscissa |
|
|
|
1074 | abscissaList = None #Abscissa, can be velocities, lags or time | |
|
|
1075 | 1075 | |
|
|
1076 | 1076 | noise = None #Noise Potency |
|
|
1077 | 1077 | |
|
|
1078 |
|
|
|
|
1078 | utctimeInit = None #Initial UTC time | |
|
|
1079 | 1079 | |
|
|
1080 | 1080 | paramInterval = None #Time interval to calculate Parameters in seconds |
|
|
1081 | 1081 | |
|
|
1082 | 1082 | #Fitting |
|
|
1083 | 1083 | |
|
|
1084 | 1084 | data_error = None #Error of the estimation |
|
|
1085 | 1085 | |
|
|
1086 | 1086 | constants = None |
|
|
1087 | 1087 | |
|
|
1088 | 1088 | library = None |
|
|
1089 | 1089 | |
|
|
1090 | 1090 | #Output signal |
|
|
1091 | 1091 | |
|
|
1092 | 1092 | outputInterval = None #Time interval to calculate output signal in seconds |
|
|
1093 | 1093 | |
|
|
1094 | 1094 | data_output = None #Out signal |
|
|
1095 | 1095 | |
|
|
1096 | ||
|
|
1097 | ||
|
|
1096 | 1098 | def __init__(self): |
|
|
1097 | 1099 | ''' |
|
|
1098 | 1100 | Constructor |
|
|
1099 | 1101 | ''' |
|
|
1100 | 1102 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
|
1101 | 1103 | |
|
|
1102 | 1104 | self.systemHeaderObj = SystemHeader() |
|
|
1103 | 1105 | |
|
|
1104 | 1106 | self.type = "Parameters" |
|
|
1105 | ||
|
|
1107 | ||
|
|
1106 | 1108 | def getTimeRange1(self): |
|
|
1107 | 1109 | |
|
|
1108 | 1110 | datatime = [] |
|
|
1109 | 1111 | |
|
|
1110 | datatime.append(self.ltctime) | |
|
|
1111 | datatime.append(self.ltctime + self.outputInterval - 1) | |
|
|
1112 | if self.useLocalTime: | |
|
|
1113 | time1 = self.utctimeInit - self.timeZone*60 | |
|
|
1114 | else: | |
|
|
1115 | time1 = utctimeInit | |
|
|
1116 | ||
|
|
1117 | # datatime.append(self.utctimeInit) | |
|
|
1118 | # datatime.append(self.utctimeInit + self.outputInterval) | |
|
|
1119 | datatime.append(time1) | |
|
|
1120 | datatime.append(time1 + self.outputInterval) | |
|
|
1112 | 1121 | |
|
|
1113 | 1122 | datatime = numpy.array(datatime) |
|
|
1114 | 1123 | |
|
|
1115 | return datatime | |
|
|
1124 | return datatime | |
| @@ -1,610 +1,610 | |||
|
|
1 | 1 | import os |
|
|
2 | 2 | import numpy |
|
|
3 | 3 | import time, datetime |
|
|
4 | 4 | import mpldriver |
|
|
5 | 5 | |
|
|
6 | 6 | from schainpy.model.proc.jroproc_base import Operation |
|
|
7 | 7 | |
|
|
8 | 8 | def isRealtime(utcdatatime): |
|
|
9 | 9 | utcnow = time.mktime(time.localtime()) |
|
|
10 | 10 | delta = abs(utcnow - utcdatatime) # abs |
|
|
11 | 11 | if delta >= 30.: |
|
|
12 | 12 | return False |
|
|
13 | 13 | return True |
|
|
14 | 14 | |
|
|
15 | 15 | class Figure(Operation): |
|
|
16 | 16 | |
|
|
17 | 17 | __driver = mpldriver |
|
|
18 | 18 | __isConfigThread = False |
|
|
19 | 19 | fig = None |
|
|
20 | 20 | |
|
|
21 | 21 | id = None |
|
|
22 | 22 | wintitle = None |
|
|
23 | 23 | width = None |
|
|
24 | 24 | height = None |
|
|
25 | 25 | nplots = None |
|
|
26 | 26 | timerange = None |
|
|
27 | 27 | |
|
|
28 | 28 | axesObjList = [] |
|
|
29 | 29 | |
|
|
30 | 30 | WIDTH = None |
|
|
31 | 31 | HEIGHT = None |
|
|
32 | 32 | PREFIX = 'fig' |
|
|
33 | 33 | |
|
|
34 | 34 | xmin = None |
|
|
35 | 35 | xmax = None |
|
|
36 | 36 | |
|
|
37 | 37 | counter_imagwr = 0 |
|
|
38 | 38 | |
|
|
39 | 39 | figfile = None |
|
|
40 | 40 | |
|
|
41 | 41 | def __init__(self): |
|
|
42 | 42 | |
|
|
43 | 43 | raise ValueError, "This method is not implemented" |
|
|
44 | 44 | |
|
|
45 | 45 | def __del__(self): |
|
|
46 | 46 | |
|
|
47 | 47 | self.__driver.closeFigure() |
|
|
48 | 48 | |
|
|
49 | 49 | def getFilename(self, name, ext='.png'): |
|
|
50 | 50 | |
|
|
51 | 51 | path = '%s%03d' %(self.PREFIX, self.id) |
|
|
52 | 52 | filename = '%s_%s%s' %(self.PREFIX, name, ext) |
|
|
53 | 53 | return os.path.join(path, filename) |
|
|
54 | 54 | |
|
|
55 | 55 | def getAxesObjList(self): |
|
|
56 | 56 | |
|
|
57 | 57 | return self.axesObjList |
|
|
58 | 58 | |
|
|
59 | 59 | def getSubplots(self): |
|
|
60 | 60 | |
|
|
61 | 61 | raise ValueError, "Abstract method: This method should be defined" |
|
|
62 | 62 | |
|
|
63 | 63 | def getScreenDim(self, widthplot, heightplot): |
|
|
64 | 64 | |
|
|
65 | 65 | nrow, ncol = self.getSubplots() |
|
|
66 | 66 | |
|
|
67 | 67 | widthscreen = widthplot*ncol |
|
|
68 | 68 | heightscreen = heightplot*nrow |
|
|
69 | 69 | |
|
|
70 | 70 | return widthscreen, heightscreen |
|
|
71 | 71 | |
|
|
72 | 72 | def getTimeLim(self, x, xmin=None, xmax=None, timerange=None): |
|
|
73 | 73 | |
|
|
74 | 74 | if self.xmin != None and self.xmax != None: |
|
|
75 | 75 | if timerange == None: |
|
|
76 | 76 | timerange = self.xmax - self.xmin |
|
|
77 | 77 | xmin = self.xmin + timerange |
|
|
78 | 78 | xmax = self.xmax + timerange |
|
|
79 | 79 | |
|
|
80 | 80 | return xmin, xmax |
|
|
81 | 81 | |
|
|
82 | 82 | if timerange == None and (xmin==None or xmax==None): |
|
|
83 | 83 | timerange = 14400 #seconds |
|
|
84 | 84 | #raise ValueError, "(timerange) or (xmin & xmax) should be defined" |
|
|
85 | 85 | |
|
|
86 | 86 | if timerange != None: |
|
|
87 | txmin = x[0] - x[0] % min(timerange/10, 10*60) | |
|
|
87 | txmin = x[0] #- x[0] % min(timerange/10, 10*60) | |
|
|
88 | 88 | else: |
|
|
89 | txmin = x[0] - x[0] % 10*60 | |
|
|
89 | txmin = x[0] #- x[0] % 10*60 | |
|
|
90 | 90 | |
|
|
91 | 91 | thisdatetime = datetime.datetime.utcfromtimestamp(txmin) |
|
|
92 | 92 | thisdate = datetime.datetime.combine(thisdatetime.date(), datetime.time(0,0,0)) |
|
|
93 | 93 | |
|
|
94 | 94 | if timerange != None: |
|
|
95 | 95 | xmin = (thisdatetime - thisdate).seconds/(60*60.) |
|
|
96 | 96 | xmax = xmin + timerange/(60*60.) |
|
|
97 | 97 | |
|
|
98 | 98 | mindt = thisdate + datetime.timedelta(hours=xmin) - datetime.timedelta(seconds=time.timezone) |
|
|
99 | 99 | xmin_sec = time.mktime(mindt.timetuple()) |
|
|
100 | 100 | |
|
|
101 | 101 | maxdt = thisdate + datetime.timedelta(hours=xmax) - datetime.timedelta(seconds=time.timezone) |
|
|
102 | 102 | xmax_sec = time.mktime(maxdt.timetuple()) |
|
|
103 | 103 | |
|
|
104 | 104 | return xmin_sec, xmax_sec |
|
|
105 | 105 | |
|
|
106 | 106 | def init(self, id, nplots, wintitle): |
|
|
107 | 107 | |
|
|
108 | 108 | raise ValueError, "This method has been replaced with createFigure" |
|
|
109 | 109 | |
|
|
110 | 110 | def createFigure(self, id, wintitle, widthplot=None, heightplot=None, show=True): |
|
|
111 | 111 | |
|
|
112 | 112 | """ |
|
|
113 | 113 | Crea la figura de acuerdo al driver y parametros seleccionados seleccionados. |
|
|
114 | 114 | Las dimensiones de la pantalla es calculada a partir de los atributos self.WIDTH |
|
|
115 | 115 | y self.HEIGHT y el numero de subplots (nrow, ncol) |
|
|
116 | 116 | |
|
|
117 | 117 | Input: |
|
|
118 | 118 | id : Los parametros necesarios son |
|
|
119 | 119 | wintitle : |
|
|
120 | 120 | |
|
|
121 | 121 | """ |
|
|
122 | 122 | |
|
|
123 | 123 | if widthplot == None: |
|
|
124 | 124 | widthplot = self.WIDTH |
|
|
125 | 125 | |
|
|
126 | 126 | if heightplot == None: |
|
|
127 | 127 | heightplot = self.HEIGHT |
|
|
128 | 128 | |
|
|
129 | 129 | self.id = id |
|
|
130 | 130 | |
|
|
131 | 131 | self.wintitle = wintitle |
|
|
132 | 132 | |
|
|
133 | 133 | self.widthscreen, self.heightscreen = self.getScreenDim(widthplot, heightplot) |
|
|
134 | 134 | |
|
|
135 | 135 | self.fig = self.__driver.createFigure(id=self.id, |
|
|
136 | 136 | wintitle=self.wintitle, |
|
|
137 | 137 | width=self.widthscreen, |
|
|
138 | 138 | height=self.heightscreen, |
|
|
139 | 139 | show=show) |
|
|
140 | 140 | |
|
|
141 | 141 | self.axesObjList = [] |
|
|
142 | 142 | self.counter_imagwr = 0 |
|
|
143 | 143 | |
|
|
144 | 144 | |
|
|
145 | 145 | def setDriver(self, driver=mpldriver): |
|
|
146 | 146 | |
|
|
147 | 147 | self.__driver = driver |
|
|
148 | 148 | |
|
|
149 | 149 | def setTitle(self, title): |
|
|
150 | 150 | |
|
|
151 | 151 | self.__driver.setTitle(self.fig, title) |
|
|
152 | 152 | |
|
|
153 | 153 | def setWinTitle(self, title): |
|
|
154 | 154 | |
|
|
155 | 155 | self.__driver.setWinTitle(self.fig, title=title) |
|
|
156 | 156 | |
|
|
157 | 157 | def setTextFromAxes(self, text): |
|
|
158 | 158 | |
|
|
159 | 159 | raise ValueError, "Este metodo ha sido reemplazaado con el metodo setText de la clase Axes" |
|
|
160 | 160 | |
|
|
161 | 161 | def makeAxes(self, nrow, ncol, xpos, ypos, colspan, rowspan): |
|
|
162 | 162 | |
|
|
163 | 163 | raise ValueError, "Este metodo ha sido reemplazaado con el metodo addAxes" |
|
|
164 | 164 | |
|
|
165 | 165 | def addAxes(self, *args): |
|
|
166 | 166 | """ |
|
|
167 | 167 | |
|
|
168 | 168 | Input: |
|
|
169 | 169 | *args : Los parametros necesarios son |
|
|
170 | 170 | nrow, ncol, xpos, ypos, colspan, rowspan |
|
|
171 | 171 | """ |
|
|
172 | 172 | |
|
|
173 | 173 | axesObj = Axes(self.fig, *args) |
|
|
174 | 174 | self.axesObjList.append(axesObj) |
|
|
175 | 175 | |
|
|
176 | 176 | def saveFigure(self, figpath, figfile, *args): |
|
|
177 | 177 | |
|
|
178 | 178 | filename = os.path.join(figpath, figfile) |
|
|
179 | 179 | |
|
|
180 | 180 | fullpath = os.path.split(filename)[0] |
|
|
181 | 181 | |
|
|
182 | 182 | if not os.path.exists(fullpath): |
|
|
183 | 183 | subpath = os.path.split(fullpath)[0] |
|
|
184 | 184 | |
|
|
185 | 185 | if not os.path.exists(subpath): |
|
|
186 | 186 | os.mkdir(subpath) |
|
|
187 | 187 | |
|
|
188 | 188 | os.mkdir(fullpath) |
|
|
189 | 189 | |
|
|
190 | 190 | self.__driver.saveFigure(self.fig, filename, *args) |
|
|
191 | 191 | |
|
|
192 | 192 | def save(self, figpath, figfile=None, save=True, ftp=False, wr_period=1, thisDatetime=None, update_figfile=True): |
|
|
193 | 193 | |
|
|
194 | 194 | self.counter_imagwr += 1 |
|
|
195 | 195 | if self.counter_imagwr < wr_period: |
|
|
196 | 196 | return |
|
|
197 | 197 | |
|
|
198 | 198 | self.counter_imagwr = 0 |
|
|
199 | 199 | |
|
|
200 | 200 | if save: |
|
|
201 | 201 | |
|
|
202 | 202 | if not figfile: |
|
|
203 | 203 | |
|
|
204 | 204 | if not thisDatetime: |
|
|
205 | 205 | raise ValueError, "Saving figure: figfile or thisDatetime should be defined" |
|
|
206 | 206 | return |
|
|
207 | 207 | |
|
|
208 | 208 | str_datetime = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
209 | 209 | figfile = self.getFilename(name = str_datetime) |
|
|
210 | 210 | |
|
|
211 | 211 | if self.figfile == None: |
|
|
212 | 212 | self.figfile = figfile |
|
|
213 | 213 | |
|
|
214 | 214 | if update_figfile: |
|
|
215 | 215 | self.figfile = figfile |
|
|
216 | 216 | |
|
|
217 | 217 | # store png plot to local folder |
|
|
218 | 218 | self.saveFigure(figpath, self.figfile) |
|
|
219 | 219 | |
|
|
220 | 220 | |
|
|
221 | 221 | if not ftp: |
|
|
222 | 222 | return |
|
|
223 | 223 | |
|
|
224 | 224 | if not thisDatetime: |
|
|
225 | 225 | return |
|
|
226 | 226 | |
|
|
227 | 227 | # store png plot to FTP server according to RT-Web format |
|
|
228 | 228 | ftp_filename = self.getNameToFtp(thisDatetime, self.FTP_WEI, self.EXP_CODE, self.SUB_EXP_CODE, self.PLOT_CODE, self.PLOT_POS) |
|
|
229 | 229 | # ftp_filename = os.path.join(figpath, name) |
|
|
230 | 230 | self.saveFigure(figpath, ftp_filename) |
|
|
231 | 231 | |
|
|
232 | 232 | def getNameToFtp(self, thisDatetime, FTP_WEI, EXP_CODE, SUB_EXP_CODE, PLOT_CODE, PLOT_POS): |
|
|
233 | 233 | YEAR_STR = '%4.4d'%thisDatetime.timetuple().tm_year |
|
|
234 | 234 | DOY_STR = '%3.3d'%thisDatetime.timetuple().tm_yday |
|
|
235 | 235 | FTP_WEI = '%2.2d'%FTP_WEI |
|
|
236 | 236 | EXP_CODE = '%3.3d'%EXP_CODE |
|
|
237 | 237 | SUB_EXP_CODE = '%2.2d'%SUB_EXP_CODE |
|
|
238 | 238 | PLOT_CODE = '%2.2d'%PLOT_CODE |
|
|
239 | 239 | PLOT_POS = '%2.2d'%PLOT_POS |
|
|
240 | 240 | name = YEAR_STR + DOY_STR + FTP_WEI + EXP_CODE + SUB_EXP_CODE + PLOT_CODE + PLOT_POS |
|
|
241 | 241 | return name |
|
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242 | 242 | |
|
|
243 | 243 | def draw(self): |
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244 | 244 | |
|
|
245 | 245 | self.__driver.draw(self.fig) |
|
|
246 | 246 | |
|
|
247 | 247 | def run(self): |
|
|
248 | 248 | |
|
|
249 | 249 | raise ValueError, "This method is not implemented" |
|
|
250 | 250 | |
|
|
251 | 251 | def close(self, show=False): |
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252 | 252 | |
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253 | 253 | self.__driver.closeFigure(show=show, fig=self.fig) |
|
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254 | 254 | |
|
|
255 | 255 | axesList = property(getAxesObjList) |
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256 | 256 | |
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257 | 257 | |
|
|
258 | 258 | class Axes: |
|
|
259 | 259 | |
|
|
260 | 260 | __driver = mpldriver |
|
|
261 | 261 | fig = None |
|
|
262 | 262 | ax = None |
|
|
263 | 263 | plot = None |
|
|
264 | 264 | __missing = 1E30 |
|
|
265 | 265 | __firsttime = None |
|
|
266 | 266 | |
|
|
267 | 267 | __showprofile = False |
|
|
268 | 268 | |
|
|
269 | 269 | xmin = None |
|
|
270 | 270 | xmax = None |
|
|
271 | 271 | ymin = None |
|
|
272 | 272 | ymax = None |
|
|
273 | 273 | zmin = None |
|
|
274 | 274 | zmax = None |
|
|
275 | 275 | |
|
|
276 | 276 | x_buffer = None |
|
|
277 | 277 | z_buffer = None |
|
|
278 | 278 | |
|
|
279 | 279 | decimationx = None |
|
|
280 | 280 | decimationy = None |
|
|
281 | 281 | |
|
|
282 | 282 | __MAXNUMX = 300 |
|
|
283 | 283 | __MAXNUMY = 150 |
|
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284 | 284 | |
|
|
285 | 285 | def __init__(self, *args): |
|
|
286 | 286 | |
|
|
287 | 287 | """ |
|
|
288 | 288 | |
|
|
289 | 289 | Input: |
|
|
290 | 290 | *args : Los parametros necesarios son |
|
|
291 | 291 | fig, nrow, ncol, xpos, ypos, colspan, rowspan |
|
|
292 | 292 | """ |
|
|
293 | 293 | |
|
|
294 | 294 | ax = self.__driver.createAxes(*args) |
|
|
295 | 295 | self.fig = args[0] |
|
|
296 | 296 | self.ax = ax |
|
|
297 | 297 | self.plot = None |
|
|
298 | 298 | |
|
|
299 | 299 | self.__firsttime = True |
|
|
300 | 300 | self.idlineList = [] |
|
|
301 | 301 | |
|
|
302 | 302 | self.x_buffer = numpy.array([]) |
|
|
303 | 303 | self.z_buffer = numpy.array([]) |
|
|
304 | 304 | |
|
|
305 | 305 | def setText(self, text): |
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|
306 | 306 | |
|
|
307 | 307 | self.__driver.setAxesText(self.ax, text) |
|
|
308 | 308 | |
|
|
309 | 309 | def setXAxisAsTime(self): |
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|
310 | 310 | pass |
|
|
311 | 311 | |
|
|
312 | 312 | def pline(self, x, y, |
|
|
313 | 313 | xmin=None, xmax=None, |
|
|
314 | 314 | ymin=None, ymax=None, |
|
|
315 | 315 | xlabel='', ylabel='', |
|
|
316 | 316 | title='', |
|
|
317 | 317 | **kwargs): |
|
|
318 | 318 | |
|
|
319 | 319 | """ |
|
|
320 | 320 | |
|
|
321 | 321 | Input: |
|
|
322 | 322 | x : |
|
|
323 | 323 | y : |
|
|
324 | 324 | xmin : |
|
|
325 | 325 | xmax : |
|
|
326 | 326 | ymin : |
|
|
327 | 327 | ymax : |
|
|
328 | 328 | xlabel : |
|
|
329 | 329 | ylabel : |
|
|
330 | 330 | title : |
|
|
331 | 331 | **kwargs : Los parametros aceptados son |
|
|
332 | 332 | |
|
|
333 | 333 | ticksize |
|
|
334 | 334 | ytick_visible |
|
|
335 | 335 | """ |
|
|
336 | 336 | |
|
|
337 | 337 | if self.__firsttime: |
|
|
338 | 338 | |
|
|
339 | 339 | if xmin == None: xmin = numpy.nanmin(x) |
|
|
340 | 340 | if xmax == None: xmax = numpy.nanmax(x) |
|
|
341 | 341 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
342 | 342 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
343 | 343 | |
|
|
344 | 344 | self.plot = self.__driver.createPline(self.ax, x, y, |
|
|
345 | 345 | xmin, xmax, |
|
|
346 | 346 | ymin, ymax, |
|
|
347 | 347 | xlabel=xlabel, |
|
|
348 | 348 | ylabel=ylabel, |
|
|
349 | 349 | title=title, |
|
|
350 | 350 | **kwargs) |
|
|
351 | 351 | |
|
|
352 | 352 | self.idlineList.append(0) |
|
|
353 | 353 | self.__firsttime = False |
|
|
354 | 354 | return |
|
|
355 | 355 | |
|
|
356 | 356 | self.__driver.pline(self.plot, x, y, xlabel=xlabel, |
|
|
357 | 357 | ylabel=ylabel, |
|
|
358 | 358 | title=title) |
|
|
359 | 359 | |
|
|
360 | 360 | def addpline(self, x, y, idline, **kwargs): |
|
|
361 | 361 | lines = self.ax.lines |
|
|
362 | 362 | |
|
|
363 | 363 | if idline in self.idlineList: |
|
|
364 | 364 | self.__driver.set_linedata(self.ax, x, y, idline) |
|
|
365 | 365 | |
|
|
366 | 366 | if idline not in(self.idlineList): |
|
|
367 | 367 | self.__driver.addpline(self.ax, x, y, **kwargs) |
|
|
368 | 368 | self.idlineList.append(idline) |
|
|
369 | 369 | |
|
|
370 | 370 | return |
|
|
371 | 371 | |
|
|
372 | 372 | def pmultiline(self, x, y, |
|
|
373 | 373 | xmin=None, xmax=None, |
|
|
374 | 374 | ymin=None, ymax=None, |
|
|
375 | 375 | xlabel='', ylabel='', |
|
|
376 | 376 | title='', |
|
|
377 | 377 | **kwargs): |
|
|
378 | 378 | |
|
|
379 | 379 | if self.__firsttime: |
|
|
380 | 380 | |
|
|
381 | 381 | if xmin == None: xmin = numpy.nanmin(x) |
|
|
382 | 382 | if xmax == None: xmax = numpy.nanmax(x) |
|
|
383 | 383 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
384 | 384 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
385 | 385 | |
|
|
386 | 386 | self.plot = self.__driver.createPmultiline(self.ax, x, y, |
|
|
387 | 387 | xmin, xmax, |
|
|
388 | 388 | ymin, ymax, |
|
|
389 | 389 | xlabel=xlabel, |
|
|
390 | 390 | ylabel=ylabel, |
|
|
391 | 391 | title=title, |
|
|
392 | 392 | **kwargs) |
|
|
393 | 393 | self.__firsttime = False |
|
|
394 | 394 | return |
|
|
395 | 395 | |
|
|
396 | 396 | self.__driver.pmultiline(self.plot, x, y, xlabel=xlabel, |
|
|
397 | 397 | ylabel=ylabel, |
|
|
398 | 398 | title=title) |
|
|
399 | 399 | |
|
|
400 | 400 | def pmultilineyaxis(self, x, y, |
|
|
401 | 401 | xmin=None, xmax=None, |
|
|
402 | 402 | ymin=None, ymax=None, |
|
|
403 | 403 | xlabel='', ylabel='', |
|
|
404 | 404 | title='', |
|
|
405 | 405 | **kwargs): |
|
|
406 | 406 | |
|
|
407 | 407 | if self.__firsttime: |
|
|
408 | 408 | |
|
|
409 | 409 | if xmin == None: xmin = numpy.nanmin(x) |
|
|
410 | 410 | if xmax == None: xmax = numpy.nanmax(x) |
|
|
411 | 411 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
412 | 412 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
413 | 413 | |
|
|
414 | 414 | self.plot = self.__driver.createPmultilineYAxis(self.ax, x, y, |
|
|
415 | 415 | xmin, xmax, |
|
|
416 | 416 | ymin, ymax, |
|
|
417 | 417 | xlabel=xlabel, |
|
|
418 | 418 | ylabel=ylabel, |
|
|
419 | 419 | title=title, |
|
|
420 | 420 | **kwargs) |
|
|
421 | 421 | if self.xmin == None: self.xmin = xmin |
|
|
422 | 422 | if self.xmax == None: self.xmax = xmax |
|
|
423 | 423 | if self.ymin == None: self.ymin = ymin |
|
|
424 | 424 | if self.ymax == None: self.ymax = ymax |
|
|
425 | 425 | |
|
|
426 | 426 | self.__firsttime = False |
|
|
427 | 427 | return |
|
|
428 | 428 | |
|
|
429 | 429 | self.__driver.pmultilineyaxis(self.plot, x, y, xlabel=xlabel, |
|
|
430 | 430 | ylabel=ylabel, |
|
|
431 | 431 | title=title) |
|
|
432 | 432 | |
|
|
433 | 433 | def pcolor(self, x, y, z, |
|
|
434 | 434 | xmin=None, xmax=None, |
|
|
435 | 435 | ymin=None, ymax=None, |
|
|
436 | 436 | zmin=None, zmax=None, |
|
|
437 | 437 | xlabel='', ylabel='', |
|
|
438 | 438 | title='', rti = False, colormap='jet', |
|
|
439 | 439 | **kwargs): |
|
|
440 | 440 | |
|
|
441 | 441 | """ |
|
|
442 | 442 | Input: |
|
|
443 | 443 | x : |
|
|
444 | 444 | y : |
|
|
445 | 445 | x : |
|
|
446 | 446 | xmin : |
|
|
447 | 447 | xmax : |
|
|
448 | 448 | ymin : |
|
|
449 | 449 | ymax : |
|
|
450 | 450 | zmin : |
|
|
451 | 451 | zmax : |
|
|
452 | 452 | xlabel : |
|
|
453 | 453 | ylabel : |
|
|
454 | 454 | title : |
|
|
455 | 455 | **kwargs : Los parametros aceptados son |
|
|
456 | 456 | ticksize=9, |
|
|
457 | 457 | cblabel='' |
|
|
458 | 458 | rti = True or False |
|
|
459 | 459 | """ |
|
|
460 | 460 | |
|
|
461 | 461 | if self.__firsttime: |
|
|
462 | 462 | |
|
|
463 | 463 | if xmin == None: xmin = numpy.nanmin(x) |
|
|
464 | 464 | if xmax == None: xmax = numpy.nanmax(x) |
|
|
465 | 465 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
466 | 466 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
467 | 467 | if zmin == None: zmin = numpy.nanmin(z) |
|
|
468 | 468 | if zmax == None: zmax = numpy.nanmax(z) |
|
|
469 | 469 | |
|
|
470 | 470 | |
|
|
471 | 471 | self.plot = self.__driver.createPcolor(self.ax, x, y, z, |
|
|
472 | 472 | xmin, xmax, |
|
|
473 | 473 | ymin, ymax, |
|
|
474 | 474 | zmin, zmax, |
|
|
475 | 475 | xlabel=xlabel, |
|
|
476 | 476 | ylabel=ylabel, |
|
|
477 | 477 | title=title, |
|
|
478 | 478 | colormap=colormap, |
|
|
479 | 479 | **kwargs) |
|
|
480 | 480 | |
|
|
481 | 481 | if self.xmin == None: self.xmin = xmin |
|
|
482 | 482 | if self.xmax == None: self.xmax = xmax |
|
|
483 | 483 | if self.ymin == None: self.ymin = ymin |
|
|
484 | 484 | if self.ymax == None: self.ymax = ymax |
|
|
485 | 485 | if self.zmin == None: self.zmin = zmin |
|
|
486 | 486 | if self.zmax == None: self.zmax = zmax |
|
|
487 | 487 | |
|
|
488 | 488 | self.__firsttime = False |
|
|
489 | 489 | return |
|
|
490 | 490 | |
|
|
491 | 491 | if rti: |
|
|
492 | 492 | self.__driver.addpcolor(self.ax, x, y, z, self.zmin, self.zmax, |
|
|
493 | 493 | xlabel=xlabel, |
|
|
494 | 494 | ylabel=ylabel, |
|
|
495 | 495 | title=title, |
|
|
496 | 496 | colormap=colormap) |
|
|
497 | 497 | return |
|
|
498 | 498 | |
|
|
499 | 499 | self.__driver.pcolor(self.plot, z, |
|
|
500 | 500 | xlabel=xlabel, |
|
|
501 | 501 | ylabel=ylabel, |
|
|
502 | 502 | title=title) |
|
|
503 | 503 | |
|
|
504 | 504 | def pcolorbuffer(self, x, y, z, |
|
|
505 | 505 | xmin=None, xmax=None, |
|
|
506 | 506 | ymin=None, ymax=None, |
|
|
507 | 507 | zmin=None, zmax=None, |
|
|
508 | 508 | xlabel='', ylabel='', |
|
|
509 | 509 | title='', rti = True, colormap='jet', |
|
|
510 | 510 | maxNumX = None, maxNumY = None, |
|
|
511 | 511 | **kwargs): |
|
|
512 | 512 | |
|
|
513 | 513 | if maxNumX == None: |
|
|
514 | 514 | maxNumX = self.__MAXNUMX |
|
|
515 | 515 | |
|
|
516 | 516 | if maxNumY == None: |
|
|
517 | 517 | maxNumY = self.__MAXNUMY |
|
|
518 | 518 | |
|
|
519 | 519 | if self.__firsttime: |
|
|
520 | 520 | self.z_buffer = z |
|
|
521 | 521 | self.x_buffer = numpy.hstack((self.x_buffer, x)) |
|
|
522 | 522 | |
|
|
523 | 523 | if xmin == None: xmin = numpy.nanmin(x) |
|
|
524 | 524 | if xmax == None: xmax = numpy.nanmax(x) |
|
|
525 | 525 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
526 | 526 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
527 | 527 | if zmin == None: zmin = numpy.nanmin(z) |
|
|
528 | 528 | if zmax == None: zmax = numpy.nanmax(z) |
|
|
529 | 529 | |
|
|
530 | 530 | |
|
|
531 | 531 | self.plot = self.__driver.createPcolor(self.ax, self.x_buffer, y, z, |
|
|
532 | 532 | xmin, xmax, |
|
|
533 | 533 | ymin, ymax, |
|
|
534 | 534 | zmin, zmax, |
|
|
535 | 535 | xlabel=xlabel, |
|
|
536 | 536 | ylabel=ylabel, |
|
|
537 | 537 | title=title, |
|
|
538 | 538 | colormap=colormap, |
|
|
539 | 539 | **kwargs) |
|
|
540 | 540 | |
|
|
541 | 541 | if self.xmin == None: self.xmin = xmin |
|
|
542 | 542 | if self.xmax == None: self.xmax = xmax |
|
|
543 | 543 | if self.ymin == None: self.ymin = ymin |
|
|
544 | 544 | if self.ymax == None: self.ymax = ymax |
|
|
545 | 545 | if self.zmin == None: self.zmin = zmin |
|
|
546 | 546 | if self.zmax == None: self.zmax = zmax |
|
|
547 | 547 | |
|
|
548 | 548 | self.__firsttime = False |
|
|
549 | 549 | return |
|
|
550 | 550 | |
|
|
551 | 551 | self.x_buffer = numpy.hstack((self.x_buffer, x[-1])) |
|
|
552 | 552 | self.z_buffer = numpy.hstack((self.z_buffer, z)) |
|
|
553 | 553 | |
|
|
554 | 554 | if self.decimationx == None: |
|
|
555 | 555 | deltax = float(self.xmax - self.xmin)/maxNumX |
|
|
556 | 556 | deltay = float(self.ymax - self.ymin)/maxNumY |
|
|
557 | 557 | |
|
|
558 | 558 | resolutionx = self.x_buffer[2]-self.x_buffer[0] |
|
|
559 | 559 | resolutiony = y[1]-y[0] |
|
|
560 | 560 | |
|
|
561 | 561 | self.decimationx = numpy.ceil(deltax / resolutionx) |
|
|
562 | 562 | self.decimationy = numpy.ceil(deltay / resolutiony) |
|
|
563 | 563 | |
|
|
564 | 564 | z_buffer = self.z_buffer.reshape(-1,len(y)) |
|
|
565 | 565 | |
|
|
566 | 566 | x_buffer = self.x_buffer[::self.decimationx] |
|
|
567 | 567 | y_buffer = y[::self.decimationy] |
|
|
568 | 568 | z_buffer = z_buffer[::self.decimationx, ::self.decimationy] |
|
|
569 | 569 | #=================================================== |
|
|
570 | 570 | |
|
|
571 | 571 | x_buffer, y_buffer, z_buffer = self.__fillGaps(x_buffer, y_buffer, z_buffer) |
|
|
572 | 572 | |
|
|
573 | 573 | self.__driver.addpcolorbuffer(self.ax, x_buffer, y_buffer, z_buffer, self.zmin, self.zmax, |
|
|
574 | 574 | xlabel=xlabel, |
|
|
575 | 575 | ylabel=ylabel, |
|
|
576 | 576 | title=title, |
|
|
577 | 577 | colormap=colormap) |
|
|
578 | 578 | |
|
|
579 | 579 | def polar(self, x, y, |
|
|
580 | 580 | title='', xlabel='',ylabel='',**kwargs): |
|
|
581 | 581 | |
|
|
582 | 582 | if self.__firsttime: |
|
|
583 | 583 | self.plot = self.__driver.createPolar(self.ax, x, y, title = title, xlabel = xlabel, ylabel = ylabel) |
|
|
584 | 584 | self.__firsttime = False |
|
|
585 | 585 | self.x_buffer = x |
|
|
586 | 586 | self.y_buffer = y |
|
|
587 | 587 | return |
|
|
588 | 588 | |
|
|
589 | 589 | self.x_buffer = numpy.hstack((self.x_buffer,x)) |
|
|
590 | 590 | self.y_buffer = numpy.hstack((self.y_buffer,y)) |
|
|
591 | 591 | self.__driver.polar(self.plot, self.x_buffer, self.y_buffer, xlabel=xlabel, |
|
|
592 | 592 | ylabel=ylabel, |
|
|
593 | 593 | title=title) |
|
|
594 | 594 | |
|
|
595 | 595 | def __fillGaps(self, x_buffer, y_buffer, z_buffer): |
|
|
596 | 596 | |
|
|
597 | 597 | deltas = x_buffer[1:] - x_buffer[0:-1] |
|
|
598 | 598 | x_median = numpy.median(deltas) |
|
|
599 | 599 | |
|
|
600 | 600 | index = numpy.where(deltas >= 2*x_median) |
|
|
601 | 601 | |
|
|
602 | 602 | if len(index[0]) != 0: |
|
|
603 | 603 | z_buffer[index[0],::] = self.__missing |
|
|
604 | 604 | z_buffer = numpy.ma.masked_inside(z_buffer,0.99*self.__missing,1.01*self.__missing) |
|
|
605 | 605 | |
|
|
606 | 606 | return x_buffer, y_buffer, z_buffer |
|
|
607 | 607 | |
|
|
608 | 608 | |
|
|
609 | 609 | |
|
|
610 | 610 | No newline at end of file |
| @@ -1,1164 +1,1362 | |||
|
|
1 | 1 | import os |
|
|
2 | 2 | import datetime |
|
|
3 | 3 | import numpy |
|
|
4 | 4 | |
|
|
5 | 5 | from figure import Figure, isRealtime |
|
|
6 | 6 | from plotting_codes import * |
|
|
7 | 7 | |
|
|
8 | 8 | class MomentsPlot(Figure): |
|
|
9 | 9 | |
|
|
10 | 10 | isConfig = None |
|
|
11 | 11 | __nsubplots = None |
|
|
12 | 12 | |
|
|
13 | 13 | WIDTHPROF = None |
|
|
14 | 14 | HEIGHTPROF = None |
|
|
15 | 15 | PREFIX = 'prm' |
|
|
16 | 16 | |
|
|
17 | 17 | def __init__(self): |
|
|
18 | 18 | |
|
|
19 | 19 | self.isConfig = False |
|
|
20 | 20 | self.__nsubplots = 1 |
|
|
21 | 21 | |
|
|
22 | 22 | self.WIDTH = 280 |
|
|
23 | 23 | self.HEIGHT = 250 |
|
|
24 | 24 | self.WIDTHPROF = 120 |
|
|
25 | 25 | self.HEIGHTPROF = 0 |
|
|
26 | 26 | self.counter_imagwr = 0 |
|
|
27 | 27 | |
|
|
28 | 28 | self.PLOT_CODE = MOMENTS_CODE |
|
|
29 | 29 | |
|
|
30 | 30 | self.FTP_WEI = None |
|
|
31 | 31 | self.EXP_CODE = None |
|
|
32 | 32 | self.SUB_EXP_CODE = None |
|
|
33 | 33 | self.PLOT_POS = None |
|
|
34 | 34 | |
|
|
35 | 35 | def getSubplots(self): |
|
|
36 | 36 | |
|
|
37 | 37 | ncol = int(numpy.sqrt(self.nplots)+0.9) |
|
|
38 | 38 | nrow = int(self.nplots*1./ncol + 0.9) |
|
|
39 | 39 | |
|
|
40 | 40 | return nrow, ncol |
|
|
41 | 41 | |
|
|
42 | 42 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
43 | 43 | |
|
|
44 | 44 | self.__showprofile = showprofile |
|
|
45 | 45 | self.nplots = nplots |
|
|
46 | 46 | |
|
|
47 | 47 | ncolspan = 1 |
|
|
48 | 48 | colspan = 1 |
|
|
49 | 49 | if showprofile: |
|
|
50 | 50 | ncolspan = 3 |
|
|
51 | 51 | colspan = 2 |
|
|
52 | 52 | self.__nsubplots = 2 |
|
|
53 | 53 | |
|
|
54 | 54 | self.createFigure(id = id, |
|
|
55 | 55 | wintitle = wintitle, |
|
|
56 | 56 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
57 | 57 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
58 | 58 | show=show) |
|
|
59 | 59 | |
|
|
60 | 60 | nrow, ncol = self.getSubplots() |
|
|
61 | 61 | |
|
|
62 | 62 | counter = 0 |
|
|
63 | 63 | for y in range(nrow): |
|
|
64 | 64 | for x in range(ncol): |
|
|
65 | 65 | |
|
|
66 | 66 | if counter >= self.nplots: |
|
|
67 | 67 | break |
|
|
68 | 68 | |
|
|
69 | 69 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
70 | 70 | |
|
|
71 | 71 | if showprofile: |
|
|
72 | 72 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan+colspan, 1, 1) |
|
|
73 | 73 | |
|
|
74 | 74 | counter += 1 |
|
|
75 | 75 | |
|
|
76 | 76 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile=True, |
|
|
77 | 77 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
|
|
78 | 78 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, |
|
|
79 | 79 | server=None, folder=None, username=None, password=None, |
|
|
80 | 80 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0, realtime=False): |
|
|
81 | 81 | |
|
|
82 | 82 | """ |
|
|
83 | 83 | |
|
|
84 | 84 | Input: |
|
|
85 | 85 | dataOut : |
|
|
86 | 86 | id : |
|
|
87 | 87 | wintitle : |
|
|
88 | 88 | channelList : |
|
|
89 | 89 | showProfile : |
|
|
90 | 90 | xmin : None, |
|
|
91 | 91 | xmax : None, |
|
|
92 | 92 | ymin : None, |
|
|
93 | 93 | ymax : None, |
|
|
94 | 94 | zmin : None, |
|
|
95 | 95 | zmax : None |
|
|
96 | 96 | """ |
|
|
97 | 97 | |
|
|
98 | 98 | if dataOut.flagNoData: |
|
|
99 | 99 | return None |
|
|
100 | 100 | |
|
|
101 | 101 | if realtime: |
|
|
102 | 102 | if not(isRealtime(utcdatatime = dataOut.utctime)): |
|
|
103 | 103 | print 'Skipping this plot function' |
|
|
104 | 104 | return |
|
|
105 | 105 | |
|
|
106 | 106 | if channelList == None: |
|
|
107 | 107 | channelIndexList = dataOut.channelIndexList |
|
|
108 | 108 | else: |
|
|
109 | 109 | channelIndexList = [] |
|
|
110 | 110 | for channel in channelList: |
|
|
111 | 111 | if channel not in dataOut.channelList: |
|
|
112 | 112 | raise ValueError, "Channel %d is not in dataOut.channelList" |
|
|
113 | 113 | channelIndexList.append(dataOut.channelList.index(channel)) |
|
|
114 | 114 | |
|
|
115 | 115 | factor = dataOut.normFactor |
|
|
116 | 116 | x = dataOut.abscissaList |
|
|
117 | 117 | y = dataOut.heightList |
|
|
118 | 118 | |
|
|
119 | 119 | z = dataOut.data_pre[channelIndexList,:,:]/factor |
|
|
120 | 120 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
|
121 | 121 | avg = numpy.average(z, axis=1) |
|
|
122 | 122 | noise = dataOut.noise/factor |
|
|
123 | 123 | |
|
|
124 | 124 | zdB = 10*numpy.log10(z) |
|
|
125 | 125 | avgdB = 10*numpy.log10(avg) |
|
|
126 | 126 | noisedB = 10*numpy.log10(noise) |
|
|
127 | 127 | |
|
|
128 | 128 | #thisDatetime = dataOut.datatime |
|
|
129 | 129 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
|
|
130 | 130 | title = wintitle + " Parameters" |
|
|
131 | 131 | xlabel = "Velocity (m/s)" |
|
|
132 | 132 | ylabel = "Range (Km)" |
|
|
133 | 133 | |
|
|
134 | 134 | if not self.isConfig: |
|
|
135 | 135 | |
|
|
136 | 136 | nplots = len(channelIndexList) |
|
|
137 | 137 | |
|
|
138 | 138 | self.setup(id=id, |
|
|
139 | 139 | nplots=nplots, |
|
|
140 | 140 | wintitle=wintitle, |
|
|
141 | 141 | showprofile=showprofile, |
|
|
142 | 142 | show=show) |
|
|
143 | 143 | |
|
|
144 | 144 | if xmin == None: xmin = numpy.nanmin(x) |
|
|
145 | 145 | if xmax == None: xmax = numpy.nanmax(x) |
|
|
146 | 146 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
147 | 147 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
148 | 148 | if zmin == None: zmin = numpy.nanmin(avgdB)*0.9 |
|
|
149 | 149 | if zmax == None: zmax = numpy.nanmax(avgdB)*0.9 |
|
|
150 | 150 | |
|
|
151 | 151 | self.FTP_WEI = ftp_wei |
|
|
152 | 152 | self.EXP_CODE = exp_code |
|
|
153 | 153 | self.SUB_EXP_CODE = sub_exp_code |
|
|
154 | 154 | self.PLOT_POS = plot_pos |
|
|
155 | 155 | |
|
|
156 | 156 | self.isConfig = True |
|
|
157 | 157 | |
|
|
158 | 158 | self.setWinTitle(title) |
|
|
159 | 159 | |
|
|
160 | 160 | for i in range(self.nplots): |
|
|
161 | 161 | str_datetime = '%s %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S")) |
|
|
162 | 162 | title = "Channel %d: %4.2fdB: %s" %(dataOut.channelList[i]+1, noisedB[i], str_datetime) |
|
|
163 | 163 | axes = self.axesList[i*self.__nsubplots] |
|
|
164 | 164 | axes.pcolor(x, y, zdB[i,:,:], |
|
|
165 | 165 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
|
|
166 | 166 | xlabel=xlabel, ylabel=ylabel, title=title, |
|
|
167 | 167 | ticksize=9, cblabel='') |
|
|
168 | 168 | #Mean Line |
|
|
169 | 169 | mean = dataOut.data_param[i, 1, :] |
|
|
170 | 170 | axes.addpline(mean, y, idline=0, color="black", linestyle="solid", lw=1) |
|
|
171 | 171 | |
|
|
172 | 172 | if self.__showprofile: |
|
|
173 | 173 | axes = self.axesList[i*self.__nsubplots +1] |
|
|
174 | 174 | axes.pline(avgdB[i], y, |
|
|
175 | 175 | xmin=zmin, xmax=zmax, ymin=ymin, ymax=ymax, |
|
|
176 | 176 | xlabel='dB', ylabel='', title='', |
|
|
177 | 177 | ytick_visible=False, |
|
|
178 | 178 | grid='x') |
|
|
179 | 179 | |
|
|
180 | 180 | noiseline = numpy.repeat(noisedB[i], len(y)) |
|
|
181 | 181 | axes.addpline(noiseline, y, idline=1, color="black", linestyle="dashed", lw=2) |
|
|
182 | 182 | |
|
|
183 | 183 | self.draw() |
|
|
184 | 184 | |
|
|
185 | 185 | self.save(figpath=figpath, |
|
|
186 | 186 | figfile=figfile, |
|
|
187 | 187 | save=save, |
|
|
188 | 188 | ftp=ftp, |
|
|
189 | 189 | wr_period=wr_period, |
|
|
190 | 190 | thisDatetime=thisDatetime) |
|
|
191 | 191 | |
|
|
192 | 192 | |
|
|
193 | 193 | |
|
|
194 | 194 | class SkyMapPlot(Figure): |
|
|
195 | 195 | |
|
|
196 | 196 | __isConfig = None |
|
|
197 | 197 | __nsubplots = None |
|
|
198 | 198 | |
|
|
199 | 199 | WIDTHPROF = None |
|
|
200 | 200 | HEIGHTPROF = None |
|
|
201 |
PREFIX = 'p |
|
|
|
201 | PREFIX = 'mmap' | |
|
|
202 | 202 | |
|
|
203 | 203 | def __init__(self): |
|
|
204 | 204 | |
|
|
205 | 205 | self.__isConfig = False |
|
|
206 | 206 | self.__nsubplots = 1 |
|
|
207 | 207 | |
|
|
208 | 208 | # self.WIDTH = 280 |
|
|
209 | 209 | # self.HEIGHT = 250 |
|
|
210 | 210 | self.WIDTH = 600 |
|
|
211 | 211 | self.HEIGHT = 600 |
|
|
212 | 212 | self.WIDTHPROF = 120 |
|
|
213 | 213 | self.HEIGHTPROF = 0 |
|
|
214 | 214 | self.counter_imagwr = 0 |
|
|
215 | 215 | |
|
|
216 | self.PLOT_CODE = SKYMAP_CODE | |
|
|
216 | self.PLOT_CODE = MSKYMAP_CODE | |
|
|
217 | 217 | |
|
|
218 | 218 | self.FTP_WEI = None |
|
|
219 | 219 | self.EXP_CODE = None |
|
|
220 | 220 | self.SUB_EXP_CODE = None |
|
|
221 | 221 | self.PLOT_POS = None |
|
|
222 | 222 | |
|
|
223 | 223 | def getSubplots(self): |
|
|
224 | 224 | |
|
|
225 | 225 | ncol = int(numpy.sqrt(self.nplots)+0.9) |
|
|
226 | 226 | nrow = int(self.nplots*1./ncol + 0.9) |
|
|
227 | 227 | |
|
|
228 | 228 | return nrow, ncol |
|
|
229 | 229 | |
|
|
230 | 230 | def setup(self, id, nplots, wintitle, showprofile=False, show=True): |
|
|
231 | 231 | |
|
|
232 | 232 | self.__showprofile = showprofile |
|
|
233 | 233 | self.nplots = nplots |
|
|
234 | 234 | |
|
|
235 | 235 | ncolspan = 1 |
|
|
236 | 236 | colspan = 1 |
|
|
237 | 237 | |
|
|
238 | 238 | self.createFigure(id = id, |
|
|
239 | 239 | wintitle = wintitle, |
|
|
240 | 240 | widthplot = self.WIDTH, #+ self.WIDTHPROF, |
|
|
241 | 241 | heightplot = self.HEIGHT,# + self.HEIGHTPROF, |
|
|
242 | 242 | show=show) |
|
|
243 | 243 | |
|
|
244 | 244 | nrow, ncol = 1,1 |
|
|
245 | 245 | counter = 0 |
|
|
246 | 246 | x = 0 |
|
|
247 | 247 | y = 0 |
|
|
248 | 248 | self.addAxes(1, 1, 0, 0, 1, 1, True) |
|
|
249 | 249 | |
|
|
250 | 250 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile=False, |
|
|
251 | 251 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
|
|
252 | 252 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, |
|
|
253 | 253 | server=None, folder=None, username=None, password=None, |
|
|
254 | 254 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0, realtime=False): |
|
|
255 | 255 | |
|
|
256 | 256 | """ |
|
|
257 | 257 | |
|
|
258 | 258 | Input: |
|
|
259 | 259 | dataOut : |
|
|
260 | 260 | id : |
|
|
261 | 261 | wintitle : |
|
|
262 | 262 | channelList : |
|
|
263 | 263 | showProfile : |
|
|
264 | 264 | xmin : None, |
|
|
265 | 265 | xmax : None, |
|
|
266 | 266 | ymin : None, |
|
|
267 | 267 | ymax : None, |
|
|
268 | 268 | zmin : None, |
|
|
269 | 269 | zmax : None |
|
|
270 | 270 | """ |
|
|
271 | 271 | |
|
|
272 | arrayParameters = dataOut.data_param | |
|
|
272 | arrayParameters = dataOut.data_param[0,:] | |
|
|
273 | 273 | error = arrayParameters[:,-1] |
|
|
274 | 274 | indValid = numpy.where(error == 0)[0] |
|
|
275 | 275 | finalMeteor = arrayParameters[indValid,:] |
|
|
276 | 276 | finalAzimuth = finalMeteor[:,4] |
|
|
277 | 277 | finalZenith = finalMeteor[:,5] |
|
|
278 | 278 | |
|
|
279 | 279 | x = finalAzimuth*numpy.pi/180 |
|
|
280 | 280 | y = finalZenith |
|
|
281 | 281 | |
|
|
282 | 282 | |
|
|
283 | 283 | #thisDatetime = dataOut.datatime |
|
|
284 | 284 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
|
|
285 | 285 | title = wintitle + " Parameters" |
|
|
286 | 286 | xlabel = "Zonal Zenith Angle (deg) " |
|
|
287 | 287 | ylabel = "Meridional Zenith Angle (deg)" |
|
|
288 | 288 | |
|
|
289 | 289 | if not self.__isConfig: |
|
|
290 | 290 | |
|
|
291 | 291 | nplots = 1 |
|
|
292 | 292 | |
|
|
293 | 293 | self.setup(id=id, |
|
|
294 | 294 | nplots=nplots, |
|
|
295 | 295 | wintitle=wintitle, |
|
|
296 | 296 | showprofile=showprofile, |
|
|
297 | 297 | show=show) |
|
|
298 | 298 | |
|
|
299 | 299 | self.FTP_WEI = ftp_wei |
|
|
300 | 300 | self.EXP_CODE = exp_code |
|
|
301 | 301 | self.SUB_EXP_CODE = sub_exp_code |
|
|
302 | 302 | self.PLOT_POS = plot_pos |
|
|
303 | 303 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
304 | 304 | self.firstdate = '%s %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S")) |
|
|
305 | 305 | self.__isConfig = True |
|
|
306 | 306 | |
|
|
307 | 307 | self.setWinTitle(title) |
|
|
308 | 308 | |
|
|
309 | 309 | i = 0 |
|
|
310 | 310 | str_datetime = '%s %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S")) |
|
|
311 | 311 | |
|
|
312 | 312 | axes = self.axesList[i*self.__nsubplots] |
|
|
313 | 313 | nevents = axes.x_buffer.shape[0] + x.shape[0] |
|
|
314 | 314 | title = "Meteor Detection Sky Map\n %s - %s \n Number of events: %5.0f\n" %(self.firstdate,str_datetime,nevents) |
|
|
315 | 315 | axes.polar(x, y, |
|
|
316 | 316 | title=title, xlabel=xlabel, ylabel=ylabel, |
|
|
317 | 317 | ticksize=9, cblabel='') |
|
|
318 | 318 | |
|
|
319 | 319 | self.draw() |
|
|
320 | 320 | |
|
|
321 | 321 | self.save(figpath=figpath, |
|
|
322 | 322 | figfile=figfile, |
|
|
323 | 323 | save=save, |
|
|
324 | 324 | ftp=ftp, |
|
|
325 | 325 | wr_period=wr_period, |
|
|
326 | 326 | thisDatetime=thisDatetime) |
|
|
327 | ||
|
|
327 | 328 | |
|
|
328 | 329 | class WindProfilerPlot(Figure): |
|
|
329 | 330 | |
|
|
330 | 331 | __isConfig = None |
|
|
331 | 332 | __nsubplots = None |
|
|
332 | 333 | |
|
|
333 | 334 | WIDTHPROF = None |
|
|
334 | 335 | HEIGHTPROF = None |
|
|
335 | 336 | PREFIX = 'wind' |
|
|
336 | 337 | |
|
|
337 | 338 | def __init__(self): |
|
|
338 | 339 | |
|
|
339 |
self.timerange = |
|
|
|
340 | self.timerange = None | |
|
|
340 | 341 | self.__isConfig = False |
|
|
341 | 342 | self.__nsubplots = 1 |
|
|
342 | 343 | |
|
|
343 | 344 | self.WIDTH = 800 |
|
|
344 | 345 | self.HEIGHT = 150 |
|
|
345 | 346 | self.WIDTHPROF = 120 |
|
|
346 | 347 | self.HEIGHTPROF = 0 |
|
|
347 | 348 | self.counter_imagwr = 0 |
|
|
348 | 349 | |
|
|
349 | 350 | self.PLOT_CODE = WIND_CODE |
|
|
350 | 351 | |
|
|
351 | 352 | self.FTP_WEI = None |
|
|
352 | 353 | self.EXP_CODE = None |
|
|
353 | 354 | self.SUB_EXP_CODE = None |
|
|
354 | 355 | self.PLOT_POS = None |
|
|
355 | 356 | self.tmin = None |
|
|
356 | 357 | self.tmax = None |
|
|
357 | 358 | |
|
|
358 | 359 | self.xmin = None |
|
|
359 | 360 | self.xmax = None |
|
|
360 | 361 | |
|
|
361 | 362 | self.figfile = None |
|
|
362 | 363 | |
|
|
363 | 364 | def getSubplots(self): |
|
|
364 | 365 | |
|
|
365 | 366 | ncol = 1 |
|
|
366 | 367 | nrow = self.nplots |
|
|
367 | 368 | |
|
|
368 | 369 | return nrow, ncol |
|
|
369 | 370 | |
|
|
370 | 371 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
371 | 372 | |
|
|
372 | 373 | self.__showprofile = showprofile |
|
|
373 | 374 | self.nplots = nplots |
|
|
374 | 375 | |
|
|
375 | 376 | ncolspan = 1 |
|
|
376 | 377 | colspan = 1 |
|
|
377 | 378 | |
|
|
378 | 379 | self.createFigure(id = id, |
|
|
379 | 380 | wintitle = wintitle, |
|
|
380 | 381 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
381 | 382 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
382 | 383 | show=show) |
|
|
383 | 384 | |
|
|
384 | 385 | nrow, ncol = self.getSubplots() |
|
|
385 | 386 | |
|
|
386 | 387 | counter = 0 |
|
|
387 | 388 | for y in range(nrow): |
|
|
388 | 389 | if counter >= self.nplots: |
|
|
389 | 390 | break |
|
|
390 | 391 | |
|
|
391 | 392 | self.addAxes(nrow, ncol*ncolspan, y, 0, colspan, 1) |
|
|
392 | 393 | counter += 1 |
|
|
393 | 394 | |
|
|
394 | def run(self, dataOut, id, wintitle="", channelList=None, | |
|
|
395 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile='False', | |
|
|
395 | 396 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
|
|
396 | 397 | zmax_ver = None, zmin_ver = None, SNRmin = None, SNRmax = None, |
|
|
397 | 398 | timerange=None, SNRthresh = None, |
|
|
398 | 399 | save=False, figpath='./', lastone=0,figfile=None, ftp=False, wr_period=1, show=True, |
|
|
399 | 400 | server=None, folder=None, username=None, password=None, |
|
|
400 | 401 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): |
|
|
401 | 402 | """ |
|
|
402 | 403 | |
|
|
403 | 404 | Input: |
|
|
404 | 405 | dataOut : |
|
|
405 | 406 | id : |
|
|
406 | 407 | wintitle : |
|
|
407 | 408 | channelList : |
|
|
408 | 409 | showProfile : |
|
|
409 | 410 | xmin : None, |
|
|
410 | 411 | xmax : None, |
|
|
411 | 412 | ymin : None, |
|
|
412 | 413 | ymax : None, |
|
|
413 | 414 | zmin : None, |
|
|
414 | 415 | zmax : None |
|
|
415 | 416 | """ |
|
|
416 | 417 | |
|
|
417 | 418 | if channelList == None: |
|
|
418 | 419 | channelIndexList = dataOut.channelIndexList |
|
|
419 | 420 | else: |
|
|
420 | 421 | channelIndexList = [] |
|
|
421 | 422 | for channel in channelList: |
|
|
422 | 423 | if channel not in dataOut.channelList: |
|
|
423 | 424 | raise ValueError, "Channel %d is not in dataOut.channelList" |
|
|
424 | 425 | channelIndexList.append(dataOut.channelList.index(channel)) |
|
|
425 | 426 | |
|
|
426 | if timerange != None: | |
|
|
427 | self.timerange = timerange | |
|
|
428 | ||
|
|
429 | tmin = None | |
|
|
430 | tmax = None | |
|
|
427 | # if timerange != None: | |
|
|
428 | # self.timerange = timerange | |
|
|
429 | # | |
|
|
430 | # tmin = None | |
|
|
431 | # tmax = None | |
|
|
431 | 432 | |
|
|
432 | 433 | x = dataOut.getTimeRange1() |
|
|
433 | 434 | # y = dataOut.heightList |
|
|
434 | 435 | y = dataOut.heightList |
|
|
435 | 436 | |
|
|
436 | 437 | z = dataOut.data_output.copy() |
|
|
437 | 438 | nplots = z.shape[0] #Number of wind dimensions estimated |
|
|
438 | 439 | nplotsw = nplots |
|
|
439 | 440 | |
|
|
440 | 441 | #If there is a SNR function defined |
|
|
441 | 442 | if dataOut.data_SNR != None: |
|
|
442 | 443 | nplots += 1 |
|
|
443 | 444 | SNR = dataOut.data_SNR |
|
|
444 | 445 | SNRavg = numpy.average(SNR, axis=0) |
|
|
445 | 446 | |
|
|
446 | 447 | SNRdB = 10*numpy.log10(SNR) |
|
|
447 | 448 | SNRavgdB = 10*numpy.log10(SNRavg) |
|
|
448 | 449 | |
|
|
449 | 450 | if SNRthresh == None: SNRthresh = -5.0 |
|
|
450 | 451 | ind = numpy.where(SNRavg < 10**(SNRthresh/10))[0] |
|
|
451 | 452 | |
|
|
452 | 453 | for i in range(nplotsw): |
|
|
453 | 454 | z[i,ind] = numpy.nan |
|
|
454 | 455 | |
|
|
455 | 456 | |
|
|
456 | showprofile = False | |
|
|
457 | # showprofile = False | |
|
|
457 | 458 | # thisDatetime = dataOut.datatime |
|
|
458 | 459 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
|
|
459 | 460 | title = wintitle + "Wind" |
|
|
460 | 461 | xlabel = "" |
|
|
461 | 462 | ylabel = "Range (Km)" |
|
|
462 | 463 | |
|
|
463 | 464 | if not self.__isConfig: |
|
|
464 | 465 | |
|
|
465 | 466 | self.setup(id=id, |
|
|
466 | 467 | nplots=nplots, |
|
|
467 | 468 | wintitle=wintitle, |
|
|
468 | 469 | showprofile=showprofile, |
|
|
469 | 470 | show=show) |
|
|
470 | 471 | |
|
|
472 | if timerange != None: | |
|
|
473 | self.timerange = timerange | |
|
|
474 | ||
|
|
471 | 475 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
472 | 476 | |
|
|
473 | 477 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
474 | 478 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
475 | 479 | |
|
|
476 | 480 | if zmax == None: zmax = numpy.nanmax(abs(z[range(2),:])) |
|
|
477 | 481 | #if numpy.isnan(zmax): zmax = 50 |
|
|
478 | 482 | if zmin == None: zmin = -zmax |
|
|
479 | 483 | |
|
|
480 | 484 | if nplotsw == 3: |
|
|
481 | 485 | if zmax_ver == None: zmax_ver = numpy.nanmax(abs(z[2,:])) |
|
|
482 | 486 | if zmin_ver == None: zmin_ver = -zmax_ver |
|
|
483 | 487 | |
|
|
484 | 488 | if dataOut.data_SNR != None: |
|
|
485 | 489 | if SNRmin == None: SNRmin = numpy.nanmin(SNRavgdB) |
|
|
486 | 490 | if SNRmax == None: SNRmax = numpy.nanmax(SNRavgdB) |
|
|
487 | 491 | |
|
|
492 | ||
|
|
488 | 493 | self.FTP_WEI = ftp_wei |
|
|
489 | 494 | self.EXP_CODE = exp_code |
|
|
490 | 495 | self.SUB_EXP_CODE = sub_exp_code |
|
|
491 | 496 | self.PLOT_POS = plot_pos |
|
|
492 |
|
|
|
|
497 | ||
|
|
493 | 498 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
494 | 499 | self.__isConfig = True |
|
|
495 | ||
|
|
496 |
|
|
|
|
500 | self.figfile = figfile | |
|
|
501 | ||
|
|
497 | 502 | self.setWinTitle(title) |
|
|
498 |
|
|
|
|
503 | ||
|
|
499 | 504 | if ((self.xmax - x[1]) < (x[1]-x[0])): |
|
|
500 | 505 | x[1] = self.xmax |
|
|
501 | 506 | |
|
|
502 | 507 | strWind = ['Zonal', 'Meridional', 'Vertical'] |
|
|
503 | 508 | strCb = ['Velocity (m/s)','Velocity (m/s)','Velocity (cm/s)'] |
|
|
504 | 509 | zmaxVector = [zmax, zmax, zmax_ver] |
|
|
505 | 510 | zminVector = [zmin, zmin, zmin_ver] |
|
|
506 | 511 | windFactor = [1,1,100] |
|
|
507 | 512 | |
|
|
508 | 513 | for i in range(nplotsw): |
|
|
509 | 514 | |
|
|
510 | 515 | title = "%s Wind: %s" %(strWind[i], thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
511 | 516 | axes = self.axesList[i*self.__nsubplots] |
|
|
512 | 517 | |
|
|
513 | 518 | z1 = z[i,:].reshape((1,-1))*windFactor[i] |
|
|
514 | 519 | |
|
|
515 | 520 | axes.pcolorbuffer(x, y, z1, |
|
|
516 | 521 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=zminVector[i], zmax=zmaxVector[i], |
|
|
517 | 522 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, |
|
|
518 | 523 | ticksize=9, cblabel=strCb[i], cbsize="1%", colormap="RdBu_r" ) |
|
|
519 | 524 | |
|
|
520 | 525 | if dataOut.data_SNR != None: |
|
|
521 | 526 | i += 1 |
|
|
522 | 527 | title = "Signal Noise Ratio (SNR): %s" %(thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
523 | 528 | axes = self.axesList[i*self.__nsubplots] |
|
|
524 | 529 | |
|
|
525 | 530 | SNRavgdB = SNRavgdB.reshape((1,-1)) |
|
|
526 | 531 | |
|
|
527 | 532 | axes.pcolorbuffer(x, y, SNRavgdB, |
|
|
528 | 533 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=SNRmin, zmax=SNRmax, |
|
|
529 | 534 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, |
|
|
530 | 535 | ticksize=9, cblabel='', cbsize="1%", colormap="jet") |
|
|
531 | 536 | |
|
|
532 | 537 | self.draw() |
|
|
533 | 538 | |
|
|
534 | 539 | if x[1] >= self.axesList[0].xmax: |
|
|
535 | 540 | self.counter_imagwr = wr_period |
|
|
536 | 541 | self.__isConfig = False |
|
|
537 | 542 | self.figfile = None |
|
|
538 | 543 | |
|
|
539 | 544 | self.save(figpath=figpath, |
|
|
540 | 545 | figfile=figfile, |
|
|
541 | 546 | save=save, |
|
|
542 | 547 | ftp=ftp, |
|
|
543 | 548 | wr_period=wr_period, |
|
|
544 | 549 | thisDatetime=thisDatetime, |
|
|
545 | 550 | update_figfile=False) |
|
|
546 | 551 | |
|
|
547 | 552 | |
|
|
548 | 553 | class ParametersPlot(Figure): |
|
|
549 | 554 | |
|
|
550 | 555 | __isConfig = None |
|
|
551 | 556 | __nsubplots = None |
|
|
552 | 557 | |
|
|
553 | 558 | WIDTHPROF = None |
|
|
554 | 559 | HEIGHTPROF = None |
|
|
555 | 560 | PREFIX = 'prm' |
|
|
556 | 561 | |
|
|
557 | 562 | def __init__(self): |
|
|
558 | 563 | |
|
|
559 | 564 | self.timerange = 2*60*60 |
|
|
560 | 565 | self.__isConfig = False |
|
|
561 | 566 | self.__nsubplots = 1 |
|
|
562 | 567 | |
|
|
563 | 568 | self.WIDTH = 800 |
|
|
564 | 569 | self.HEIGHT = 150 |
|
|
565 | 570 | self.WIDTHPROF = 120 |
|
|
566 | 571 | self.HEIGHTPROF = 0 |
|
|
567 | 572 | self.counter_imagwr = 0 |
|
|
568 | 573 | |
|
|
569 | 574 | self.PLOT_CODE = PARMS_CODE |
|
|
570 | 575 | |
|
|
571 | 576 | self.FTP_WEI = None |
|
|
572 | 577 | self.EXP_CODE = None |
|
|
573 | 578 | self.SUB_EXP_CODE = None |
|
|
574 | 579 | self.PLOT_POS = None |
|
|
575 | 580 | self.tmin = None |
|
|
576 | 581 | self.tmax = None |
|
|
577 | 582 | |
|
|
578 | 583 | self.xmin = None |
|
|
579 | 584 | self.xmax = None |
|
|
580 | 585 | |
|
|
581 | 586 | self.figfile = None |
|
|
582 | 587 | |
|
|
583 | 588 | def getSubplots(self): |
|
|
584 | 589 | |
|
|
585 | 590 | ncol = 1 |
|
|
586 | 591 | nrow = self.nplots |
|
|
587 | 592 | |
|
|
588 | 593 | return nrow, ncol |
|
|
589 | 594 | |
|
|
590 | 595 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
591 | 596 | |
|
|
592 | 597 | self.__showprofile = showprofile |
|
|
593 | 598 | self.nplots = nplots |
|
|
594 | 599 | |
|
|
595 | 600 | ncolspan = 1 |
|
|
596 | 601 | colspan = 1 |
|
|
597 | 602 | |
|
|
598 | 603 | self.createFigure(id = id, |
|
|
599 | 604 | wintitle = wintitle, |
|
|
600 | 605 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
601 | 606 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
602 | 607 | show=show) |
|
|
603 | 608 | |
|
|
604 | 609 | nrow, ncol = self.getSubplots() |
|
|
605 | 610 | |
|
|
606 | 611 | counter = 0 |
|
|
607 | 612 | for y in range(nrow): |
|
|
608 | 613 | for x in range(ncol): |
|
|
609 | 614 | |
|
|
610 | 615 | if counter >= self.nplots: |
|
|
611 | 616 | break |
|
|
612 | 617 | |
|
|
613 | 618 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
614 | 619 | |
|
|
615 | 620 | if showprofile: |
|
|
616 | 621 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan+colspan, 1, 1) |
|
|
617 | 622 | |
|
|
618 | 623 | counter += 1 |
|
|
619 | 624 | |
|
|
620 | 625 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile=False, |
|
|
621 | 626 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None,timerange=None, |
|
|
622 | 627 | parameterIndex = None, onlyPositive = False, |
|
|
623 | SNRthresh = -numpy.inf, SNR = True, SNRmin = None, SNRmax = None, | |
|
|
628 | SNRthresh = -numpy.inf, SNR = True, SNRmin = None, SNRmax = None, onlySNR = False, | |
|
|
624 | 629 | DOP = True, |
|
|
625 | 630 | zlabel = "", parameterName = "", parameterObject = "data_param", |
|
|
626 | 631 | save=False, figpath='./', lastone=0,figfile=None, ftp=False, wr_period=1, show=True, |
|
|
627 | 632 | server=None, folder=None, username=None, password=None, |
|
|
628 | 633 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): |
|
|
629 | 634 | |
|
|
630 | 635 | """ |
|
|
631 | 636 | |
|
|
632 | 637 | Input: |
|
|
633 | 638 | dataOut : |
|
|
634 | 639 | id : |
|
|
635 | 640 | wintitle : |
|
|
636 | 641 | channelList : |
|
|
637 | 642 | showProfile : |
|
|
638 | 643 | xmin : None, |
|
|
639 | 644 | xmax : None, |
|
|
640 | 645 | ymin : None, |
|
|
641 | 646 | ymax : None, |
|
|
642 | 647 | zmin : None, |
|
|
643 | 648 | zmax : None |
|
|
644 | 649 | """ |
|
|
645 | 650 | |
|
|
646 | 651 | data_param = getattr(dataOut, parameterObject) |
|
|
647 | 652 | |
|
|
648 | 653 | if channelList == None: |
|
|
649 | 654 | channelIndexList = numpy.arange(data_param.shape[0]) |
|
|
650 | 655 | else: |
|
|
651 | 656 | channelIndexList = numpy.array(channelList) |
|
|
652 | 657 | |
|
|
653 | 658 | nchan = len(channelIndexList) #Number of channels being plotted |
|
|
654 | 659 | |
|
|
655 | 660 | if nchan < 1: |
|
|
656 | 661 | return |
|
|
657 | 662 | |
|
|
658 | 663 | nGraphsByChannel = 0 |
|
|
659 | 664 | |
|
|
660 | 665 | if SNR: |
|
|
661 | 666 | nGraphsByChannel += 1 |
|
|
662 | 667 | if DOP: |
|
|
663 | 668 | nGraphsByChannel += 1 |
|
|
664 | 669 | |
|
|
665 | 670 | if nGraphsByChannel < 1: |
|
|
666 | 671 | return |
|
|
667 | 672 | |
|
|
668 | 673 | nplots = nGraphsByChannel*nchan |
|
|
669 | 674 | |
|
|
670 | 675 | if timerange != None: |
|
|
671 | 676 | self.timerange = timerange |
|
|
672 | 677 | |
|
|
673 | 678 | #tmin = None |
|
|
674 | 679 | #tmax = None |
|
|
675 | 680 | if parameterIndex == None: |
|
|
676 | 681 | parameterIndex = 1 |
|
|
677 | 682 | |
|
|
678 | 683 | x = dataOut.getTimeRange1() |
|
|
679 | 684 | y = dataOut.heightList |
|
|
680 | 685 | z = data_param[channelIndexList,parameterIndex,:].copy() |
|
|
681 | 686 | |
|
|
682 | 687 | zRange = dataOut.abscissaList |
|
|
683 | 688 | # nChannels = z.shape[0] #Number of wind dimensions estimated |
|
|
684 | 689 | # thisDatetime = dataOut.datatime |
|
|
685 | 690 | |
|
|
686 | 691 | if dataOut.data_SNR != None: |
|
|
687 | 692 | SNRarray = dataOut.data_SNR[channelIndexList,:] |
|
|
688 | 693 | SNRdB = 10*numpy.log10(SNRarray) |
|
|
689 | 694 | # SNRavgdB = 10*numpy.log10(SNRavg) |
|
|
690 | 695 | ind = numpy.where(SNRdB < 10**(SNRthresh/10)) |
|
|
691 | 696 | z[ind] = numpy.nan |
|
|
692 | ||
|
|
697 | ||
|
|
693 | 698 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
|
|
694 | 699 | title = wintitle + " Parameters Plot" #: %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
695 | 700 | xlabel = "" |
|
|
696 | 701 | ylabel = "Range (Km)" |
|
|
697 | 702 | |
|
|
703 | if (SNR and not onlySNR): nplots = 2*nplots | |
|
|
704 | ||
|
|
698 | 705 | if onlyPositive: |
|
|
699 | 706 | colormap = "jet" |
|
|
700 | 707 | zmin = 0 |
|
|
701 | 708 | else: colormap = "RdBu_r" |
|
|
702 | 709 | |
|
|
703 | 710 | if not self.__isConfig: |
|
|
704 | 711 | |
|
|
705 | 712 | self.setup(id=id, |
|
|
706 | 713 | nplots=nplots, |
|
|
707 | 714 | wintitle=wintitle, |
|
|
708 | 715 | showprofile=showprofile, |
|
|
709 | 716 | show=show) |
|
|
710 | 717 | |
|
|
711 | 718 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
712 | 719 | |
|
|
713 | 720 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
714 | 721 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
715 | 722 | if zmin == None: zmin = numpy.nanmin(zRange) |
|
|
716 | 723 | if zmax == None: zmax = numpy.nanmax(zRange) |
|
|
717 | 724 | |
|
|
718 | 725 | if SNR: |
|
|
719 | 726 | if SNRmin == None: SNRmin = numpy.nanmin(SNRdB) |
|
|
720 | 727 | if SNRmax == None: SNRmax = numpy.nanmax(SNRdB) |
|
|
721 | 728 | |
|
|
722 | 729 | self.FTP_WEI = ftp_wei |
|
|
723 | 730 | self.EXP_CODE = exp_code |
|
|
724 | 731 | self.SUB_EXP_CODE = sub_exp_code |
|
|
725 | 732 | self.PLOT_POS = plot_pos |
|
|
726 | 733 | |
|
|
727 | 734 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
728 | 735 | self.__isConfig = True |
|
|
729 | 736 | self.figfile = figfile |
|
|
730 | 737 | |
|
|
731 | 738 | self.setWinTitle(title) |
|
|
732 | 739 | |
|
|
733 | 740 | if ((self.xmax - x[1]) < (x[1]-x[0])): |
|
|
734 | 741 | x[1] = self.xmax |
|
|
735 | 742 | |
|
|
736 | 743 | for i in range(nchan): |
|
|
744 | ||
|
|
745 | if (SNR and not onlySNR): j = 2*i | |
|
|
746 | else: j = i | |
|
|
737 | 747 | |
|
|
738 | 748 | j = nGraphsByChannel*i |
|
|
749 | ||
|
|
750 | if ((dataOut.azimuth!=None) and (dataOut.zenith!=None)): | |
|
|
751 | title = title + '_' + 'azimuth,zenith=%2.2f,%2.2f'%(dataOut.azimuth, dataOut.zenith) | |
|
|
739 | 752 | |
|
|
753 | if not onlySNR: | |
|
|
754 | axes = self.axesList[j*self.__nsubplots] | |
|
|
755 | z1 = z[i,:].reshape((1,-1)) | |
|
|
756 | axes.pcolorbuffer(x, y, z1, | |
|
|
757 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, | |
|
|
758 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True,colormap=colormap, | |
|
|
759 | ticksize=9, cblabel=zlabel, cbsize="1%") | |
|
|
760 | ||
|
|
740 | 761 | if DOP: |
|
|
741 | 762 | title = "%s Channel %d: %s" %(parameterName, channelIndexList[i]+1, thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
742 | 763 | |
|
|
743 | 764 | if ((dataOut.azimuth!=None) and (dataOut.zenith!=None)): |
|
|
744 | 765 | title = title + '_' + 'azimuth,zenith=%2.2f,%2.2f'%(dataOut.azimuth, dataOut.zenith) |
|
|
745 | 766 | axes = self.axesList[j] |
|
|
746 | 767 | z1 = z[i,:].reshape((1,-1)) |
|
|
747 | 768 | axes.pcolorbuffer(x, y, z1, |
|
|
748 | 769 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
|
|
749 | 770 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True,colormap=colormap, |
|
|
750 | 771 | ticksize=9, cblabel=zlabel, cbsize="1%") |
|
|
751 | 772 | |
|
|
752 | 773 | if SNR: |
|
|
753 | 774 | title = "Channel %d Signal Noise Ratio (SNR): %s" %(channelIndexList[i]+1, thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
775 | axes = self.axesList[(j)*self.__nsubplots] | |
|
|
776 | if not onlySNR: | |
|
|
777 | axes = self.axesList[(j + 1)*self.__nsubplots] | |
|
|
778 | ||
|
|
754 | 779 | axes = self.axesList[(j + nGraphsByChannel-1)] |
|
|
780 | ||
|
|
755 | 781 | z1 = SNRdB[i,:].reshape((1,-1)) |
|
|
756 | 782 | axes.pcolorbuffer(x, y, z1, |
|
|
757 | 783 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=SNRmin, zmax=SNRmax, |
|
|
758 | 784 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True,colormap="jet", |
|
|
759 | 785 | ticksize=9, cblabel=zlabel, cbsize="1%") |
|
|
760 | 786 | |
|
|
761 | 787 | |
|
|
762 | 788 | |
|
|
763 | 789 | self.draw() |
|
|
764 | 790 | |
|
|
765 | 791 | if x[1] >= self.axesList[0].xmax: |
|
|
766 | 792 | self.counter_imagwr = wr_period |
|
|
767 | 793 | self.__isConfig = False |
|
|
768 | 794 | self.figfile = None |
|
|
769 | 795 | |
|
|
770 | 796 | self.save(figpath=figpath, |
|
|
771 | 797 | figfile=figfile, |
|
|
772 | 798 | save=save, |
|
|
773 | 799 | ftp=ftp, |
|
|
774 | 800 | wr_period=wr_period, |
|
|
775 | 801 | thisDatetime=thisDatetime, |
|
|
776 | 802 | update_figfile=False) |
|
|
777 | 803 | |
|
|
778 | 804 | class SpectralFittingPlot(Figure): |
|
|
779 | 805 | |
|
|
780 | 806 | __isConfig = None |
|
|
781 | 807 | __nsubplots = None |
|
|
782 | 808 | |
|
|
783 | 809 | WIDTHPROF = None |
|
|
784 | 810 | HEIGHTPROF = None |
|
|
785 | 811 | PREFIX = 'prm' |
|
|
786 | 812 | |
|
|
787 | 813 | |
|
|
788 | 814 | N = None |
|
|
789 | 815 | ippSeconds = None |
|
|
790 | 816 | |
|
|
791 | 817 | def __init__(self): |
|
|
792 | 818 | self.__isConfig = False |
|
|
793 | 819 | self.__nsubplots = 1 |
|
|
794 | 820 | |
|
|
795 | 821 | self.PLOT_CODE = SPECFIT_CODE |
|
|
796 | 822 | |
|
|
797 | 823 | self.WIDTH = 450 |
|
|
798 | 824 | self.HEIGHT = 250 |
|
|
799 | 825 | self.WIDTHPROF = 0 |
|
|
800 | 826 | self.HEIGHTPROF = 0 |
|
|
801 | 827 | |
|
|
802 | 828 | def getSubplots(self): |
|
|
803 | 829 | |
|
|
804 | 830 | ncol = int(numpy.sqrt(self.nplots)+0.9) |
|
|
805 | 831 | nrow = int(self.nplots*1./ncol + 0.9) |
|
|
806 | 832 | |
|
|
807 | 833 | return nrow, ncol |
|
|
808 | 834 | |
|
|
809 | 835 | def setup(self, id, nplots, wintitle, showprofile=False, show=True): |
|
|
810 | 836 | |
|
|
811 | 837 | showprofile = False |
|
|
812 | 838 | self.__showprofile = showprofile |
|
|
813 | 839 | self.nplots = nplots |
|
|
814 | 840 | |
|
|
815 | 841 | ncolspan = 5 |
|
|
816 | 842 | colspan = 4 |
|
|
817 | 843 | if showprofile: |
|
|
818 | 844 | ncolspan = 5 |
|
|
819 | 845 | colspan = 4 |
|
|
820 | 846 | self.__nsubplots = 2 |
|
|
821 | 847 | |
|
|
822 | 848 | self.createFigure(id = id, |
|
|
823 | 849 | wintitle = wintitle, |
|
|
824 | 850 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
825 | 851 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
826 | 852 | show=show) |
|
|
827 | 853 | |
|
|
828 | 854 | nrow, ncol = self.getSubplots() |
|
|
829 | 855 | |
|
|
830 | 856 | counter = 0 |
|
|
831 | 857 | for y in range(nrow): |
|
|
832 | 858 | for x in range(ncol): |
|
|
833 | 859 | |
|
|
834 | 860 | if counter >= self.nplots: |
|
|
835 | 861 | break |
|
|
836 | 862 | |
|
|
837 | 863 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
838 | 864 | |
|
|
839 | 865 | if showprofile: |
|
|
840 | 866 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan+colspan, 1, 1) |
|
|
841 | 867 | |
|
|
842 | 868 | counter += 1 |
|
|
843 | 869 | |
|
|
844 | 870 | def run(self, dataOut, id, cutHeight=None, fit=False, wintitle="", channelList=None, showprofile=True, |
|
|
845 | 871 | xmin=None, xmax=None, ymin=None, ymax=None, |
|
|
846 | 872 | save=False, figpath='./', figfile=None, show=True): |
|
|
847 | 873 | |
|
|
848 | 874 | """ |
|
|
849 | 875 | |
|
|
850 | 876 | Input: |
|
|
851 | 877 | dataOut : |
|
|
852 | 878 | id : |
|
|
853 | 879 | wintitle : |
|
|
854 | 880 | channelList : |
|
|
855 | 881 | showProfile : |
|
|
856 | 882 | xmin : None, |
|
|
857 | 883 | xmax : None, |
|
|
858 | 884 | zmin : None, |
|
|
859 | 885 | zmax : None |
|
|
860 | 886 | """ |
|
|
861 | 887 | |
|
|
862 | 888 | if cutHeight==None: |
|
|
863 | 889 | h=270 |
|
|
864 | 890 | heightindex = numpy.abs(cutHeight - dataOut.heightList).argmin() |
|
|
865 | 891 | cutHeight = dataOut.heightList[heightindex] |
|
|
866 | 892 | |
|
|
867 | 893 | factor = dataOut.normFactor |
|
|
868 | 894 | x = dataOut.abscissaList[:-1] |
|
|
869 | 895 | #y = dataOut.getHeiRange() |
|
|
870 | 896 | |
|
|
871 | 897 | z = dataOut.data_pre[:,:,heightindex]/factor |
|
|
872 | 898 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
|
873 | 899 | avg = numpy.average(z, axis=1) |
|
|
874 | 900 | listChannels = z.shape[0] |
|
|
875 | 901 | |
|
|
876 | 902 | #Reconstruct Function |
|
|
877 | 903 | if fit==True: |
|
|
878 | 904 | groupArray = dataOut.groupList |
|
|
879 | 905 | listChannels = groupArray.reshape((groupArray.size)) |
|
|
880 | 906 | listChannels.sort() |
|
|
881 | 907 | spcFitLine = numpy.zeros(z.shape) |
|
|
882 | 908 | constants = dataOut.constants |
|
|
883 | 909 | |
|
|
884 | 910 | nGroups = groupArray.shape[0] |
|
|
885 | 911 | nChannels = groupArray.shape[1] |
|
|
886 | 912 | nProfiles = z.shape[1] |
|
|
887 | 913 | |
|
|
888 | 914 | for f in range(nGroups): |
|
|
889 | 915 | groupChann = groupArray[f,:] |
|
|
890 | 916 | p = dataOut.data_param[f,:,heightindex] |
|
|
891 | 917 | # p = numpy.array([ 89.343967,0.14036615,0.17086219,18.89835291,1.58388365,1.55099167]) |
|
|
892 | 918 | fitLineAux = dataOut.library.modelFunction(p, constants)*nProfiles |
|
|
893 | 919 | fitLineAux = fitLineAux.reshape((nChannels,nProfiles)) |
|
|
894 | 920 | spcFitLine[groupChann,:] = fitLineAux |
|
|
895 | 921 | # spcFitLine = spcFitLine/factor |
|
|
896 | 922 | |
|
|
897 | 923 | z = z[listChannels,:] |
|
|
898 | 924 | spcFitLine = spcFitLine[listChannels,:] |
|
|
899 | 925 | spcFitLinedB = 10*numpy.log10(spcFitLine) |
|
|
900 | 926 | |
|
|
901 | 927 | zdB = 10*numpy.log10(z) |
|
|
902 | 928 | #thisDatetime = dataOut.datatime |
|
|
903 | 929 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[0]) |
|
|
904 | 930 | title = wintitle + " Doppler Spectra: %s" %(thisDatetime.strftime("%d-%b-%Y %H:%M:%S")) |
|
|
905 | 931 | xlabel = "Velocity (m/s)" |
|
|
906 | 932 | ylabel = "Spectrum" |
|
|
907 | 933 | |
|
|
908 | 934 | if not self.__isConfig: |
|
|
909 | 935 | |
|
|
910 | 936 | nplots = listChannels.size |
|
|
911 | 937 | |
|
|
912 | 938 | self.setup(id=id, |
|
|
913 | 939 | nplots=nplots, |
|
|
914 | 940 | wintitle=wintitle, |
|
|
915 | 941 | showprofile=showprofile, |
|
|
916 | 942 | show=show) |
|
|
917 | 943 | |
|
|
918 | 944 | if xmin == None: xmin = numpy.nanmin(x) |
|
|
919 | 945 | if xmax == None: xmax = numpy.nanmax(x) |
|
|
920 | 946 | if ymin == None: ymin = numpy.nanmin(zdB) |
|
|
921 | 947 | if ymax == None: ymax = numpy.nanmax(zdB)+2 |
|
|
922 | 948 | |
|
|
923 | 949 | self.__isConfig = True |
|
|
924 | 950 | |
|
|
925 | 951 | self.setWinTitle(title) |
|
|
926 | 952 | for i in range(self.nplots): |
|
|
927 | 953 | # title = "Channel %d: %4.2fdB" %(dataOut.channelList[i]+1, noisedB[i]) |
|
|
928 | 954 | title = "Height %4.1f km\nChannel %d:" %(cutHeight, listChannels[i]+1) |
|
|
929 | 955 | axes = self.axesList[i*self.__nsubplots] |
|
|
930 | 956 | if fit == False: |
|
|
931 | 957 | axes.pline(x, zdB[i,:], |
|
|
932 | 958 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, |
|
|
933 | 959 | xlabel=xlabel, ylabel=ylabel, title=title |
|
|
934 | 960 | ) |
|
|
935 | 961 | if fit == True: |
|
|
936 | 962 | fitline=spcFitLinedB[i,:] |
|
|
937 | 963 | y=numpy.vstack([zdB[i,:],fitline] ) |
|
|
938 | 964 | legendlabels=['Data','Fitting'] |
|
|
939 | 965 | axes.pmultilineyaxis(x, y, |
|
|
940 | 966 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, |
|
|
941 | 967 | xlabel=xlabel, ylabel=ylabel, title=title, |
|
|
942 | 968 | legendlabels=legendlabels, marker=None, |
|
|
943 | 969 | linestyle='solid', grid='both') |
|
|
944 | 970 | |
|
|
945 | 971 | self.draw() |
|
|
946 | 972 | |
|
|
947 | 973 | self.save(figpath=figpath, |
|
|
948 | 974 | figfile=figfile, |
|
|
949 | 975 | save=save, |
|
|
950 | 976 | ftp=ftp, |
|
|
951 | 977 | wr_period=wr_period, |
|
|
952 | 978 | thisDatetime=thisDatetime) |
|
|
953 | 979 | |
|
|
954 | 980 | |
|
|
955 | 981 | class EWDriftsPlot(Figure): |
|
|
956 | 982 | |
|
|
957 | 983 | __isConfig = None |
|
|
958 | 984 | __nsubplots = None |
|
|
959 | 985 | |
|
|
960 | 986 | WIDTHPROF = None |
|
|
961 | 987 | HEIGHTPROF = None |
|
|
962 | 988 | PREFIX = 'drift' |
|
|
963 | 989 | |
|
|
964 | 990 | def __init__(self): |
|
|
965 | 991 | |
|
|
966 | 992 | self.timerange = 2*60*60 |
|
|
967 | 993 | self.isConfig = False |
|
|
968 | 994 | self.__nsubplots = 1 |
|
|
969 | 995 | |
|
|
970 | 996 | self.WIDTH = 800 |
|
|
971 | 997 | self.HEIGHT = 150 |
|
|
972 | 998 | self.WIDTHPROF = 120 |
|
|
973 | 999 | self.HEIGHTPROF = 0 |
|
|
974 | 1000 | self.counter_imagwr = 0 |
|
|
975 | 1001 | |
|
|
976 | 1002 | self.PLOT_CODE = EWDRIFT_CODE |
|
|
977 | 1003 | |
|
|
978 | 1004 | self.FTP_WEI = None |
|
|
979 | 1005 | self.EXP_CODE = None |
|
|
980 | 1006 | self.SUB_EXP_CODE = None |
|
|
981 | 1007 | self.PLOT_POS = None |
|
|
982 | 1008 | self.tmin = None |
|
|
983 | 1009 | self.tmax = None |
|
|
984 | 1010 | |
|
|
985 | 1011 | self.xmin = None |
|
|
986 | 1012 | self.xmax = None |
|
|
987 | 1013 | |
|
|
988 | 1014 | self.figfile = None |
|
|
989 | 1015 | |
|
|
990 | 1016 | def getSubplots(self): |
|
|
991 | 1017 | |
|
|
992 | 1018 | ncol = 1 |
|
|
993 | 1019 | nrow = self.nplots |
|
|
994 | 1020 | |
|
|
995 | 1021 | return nrow, ncol |
|
|
996 | 1022 | |
|
|
997 | 1023 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
998 | 1024 | |
|
|
999 | 1025 | self.__showprofile = showprofile |
|
|
1000 | 1026 | self.nplots = nplots |
|
|
1001 | 1027 | |
|
|
1002 | 1028 | ncolspan = 1 |
|
|
1003 | 1029 | colspan = 1 |
|
|
1004 | 1030 | |
|
|
1005 | 1031 | self.createFigure(id = id, |
|
|
1006 | 1032 | wintitle = wintitle, |
|
|
1007 | 1033 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
1008 | 1034 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
1009 | 1035 | show=show) |
|
|
1010 | 1036 | |
|
|
1011 | 1037 | nrow, ncol = self.getSubplots() |
|
|
1012 | 1038 | |
|
|
1013 | 1039 | counter = 0 |
|
|
1014 | 1040 | for y in range(nrow): |
|
|
1015 | 1041 | if counter >= self.nplots: |
|
|
1016 | 1042 | break |
|
|
1017 | 1043 | |
|
|
1018 | 1044 | self.addAxes(nrow, ncol*ncolspan, y, 0, colspan, 1) |
|
|
1019 | 1045 | counter += 1 |
|
|
1020 | 1046 | |
|
|
1021 | 1047 | def run(self, dataOut, id, wintitle="", channelList=None, |
|
|
1022 | 1048 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
|
|
1023 | 1049 | zmaxVertical = None, zminVertical = None, zmaxZonal = None, zminZonal = None, |
|
|
1024 | 1050 | timerange=None, SNRthresh = -numpy.inf, SNRmin = None, SNRmax = None, SNR_1 = False, |
|
|
1025 | 1051 | save=False, figpath='./', lastone=0,figfile=None, ftp=False, wr_period=1, show=True, |
|
|
1026 | 1052 | server=None, folder=None, username=None, password=None, |
|
|
1027 | 1053 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): |
|
|
1028 | 1054 | """ |
|
|
1029 | 1055 | |
|
|
1030 | 1056 | Input: |
|
|
1031 | 1057 | dataOut : |
|
|
1032 | 1058 | id : |
|
|
1033 | 1059 | wintitle : |
|
|
1034 | 1060 | channelList : |
|
|
1035 | 1061 | showProfile : |
|
|
1036 | 1062 | xmin : None, |
|
|
1037 | 1063 | xmax : None, |
|
|
1038 | 1064 | ymin : None, |
|
|
1039 | 1065 | ymax : None, |
|
|
1040 | 1066 | zmin : None, |
|
|
1041 | 1067 | zmax : None |
|
|
1042 | 1068 | """ |
|
|
1043 | 1069 | |
|
|
1044 | 1070 | if timerange != None: |
|
|
1045 | 1071 | self.timerange = timerange |
|
|
1046 | 1072 | |
|
|
1047 | 1073 | tmin = None |
|
|
1048 | 1074 | tmax = None |
|
|
1049 | 1075 | |
|
|
1050 | 1076 | x = dataOut.getTimeRange1() |
|
|
1051 | 1077 | # y = dataOut.heightList |
|
|
1052 | 1078 | y = dataOut.heightList |
|
|
1053 | 1079 | |
|
|
1054 | 1080 | z = dataOut.data_output |
|
|
1055 | 1081 | nplots = z.shape[0] #Number of wind dimensions estimated |
|
|
1056 | 1082 | nplotsw = nplots |
|
|
1057 | 1083 | |
|
|
1058 | 1084 | #If there is a SNR function defined |
|
|
1059 | 1085 | if dataOut.data_SNR != None: |
|
|
1060 | 1086 | nplots += 1 |
|
|
1061 | 1087 | SNR = dataOut.data_SNR |
|
|
1062 | 1088 | |
|
|
1063 | 1089 | if SNR_1: |
|
|
1064 | 1090 | SNR += 1 |
|
|
1065 | 1091 | |
|
|
1066 | 1092 | SNRavg = numpy.average(SNR, axis=0) |
|
|
1067 | 1093 | |
|
|
1068 | 1094 | SNRdB = 10*numpy.log10(SNR) |
|
|
1069 | 1095 | SNRavgdB = 10*numpy.log10(SNRavg) |
|
|
1070 | 1096 | |
|
|
1071 | 1097 | ind = numpy.where(SNRavg < 10**(SNRthresh/10))[0] |
|
|
1072 | 1098 | |
|
|
1073 | 1099 | for i in range(nplotsw): |
|
|
1074 | 1100 | z[i,ind] = numpy.nan |
|
|
1075 | 1101 | |
|
|
1076 | 1102 | |
|
|
1077 | 1103 | showprofile = False |
|
|
1078 | 1104 | # thisDatetime = dataOut.datatime |
|
|
1079 | 1105 | thisDatetime = datetime.datetime.utcfromtimestamp(x[1]) |
|
|
1080 | 1106 | title = wintitle + " EW Drifts" |
|
|
1081 | 1107 | xlabel = "" |
|
|
1082 | 1108 | ylabel = "Height (Km)" |
|
|
1083 | 1109 | |
|
|
1084 | 1110 | if not self.__isConfig: |
|
|
1085 | 1111 | |
|
|
1086 | 1112 | self.setup(id=id, |
|
|
1087 | 1113 | nplots=nplots, |
|
|
1088 | 1114 | wintitle=wintitle, |
|
|
1089 | 1115 | showprofile=showprofile, |
|
|
1090 | 1116 | show=show) |
|
|
1091 | 1117 | |
|
|
1092 | 1118 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
1093 | 1119 | |
|
|
1094 | 1120 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
1095 | 1121 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
1096 | 1122 | |
|
|
1097 | 1123 | if zmaxZonal == None: zmaxZonal = numpy.nanmax(abs(z[0,:])) |
|
|
1098 | 1124 | if zminZonal == None: zminZonal = -zmaxZonal |
|
|
1099 | 1125 | if zmaxVertical == None: zmaxVertical = numpy.nanmax(abs(z[1,:])) |
|
|
1100 | 1126 | if zminVertical == None: zminVertical = -zmaxVertical |
|
|
1101 | 1127 | |
|
|
1102 | 1128 | if dataOut.data_SNR != None: |
|
|
1103 | 1129 | if SNRmin == None: SNRmin = numpy.nanmin(SNRavgdB) |
|
|
1104 | 1130 | if SNRmax == None: SNRmax = numpy.nanmax(SNRavgdB) |
|
|
1105 | 1131 | |
|
|
1106 | 1132 | self.FTP_WEI = ftp_wei |
|
|
1107 | 1133 | self.EXP_CODE = exp_code |
|
|
1108 | 1134 | self.SUB_EXP_CODE = sub_exp_code |
|
|
1109 | 1135 | self.PLOT_POS = plot_pos |
|
|
1110 | 1136 | |
|
|
1111 | 1137 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
1112 | 1138 | self.__isConfig = True |
|
|
1113 | 1139 | |
|
|
1114 | 1140 | |
|
|
1115 | 1141 | self.setWinTitle(title) |
|
|
1116 | 1142 | |
|
|
1117 | 1143 | if ((self.xmax - x[1]) < (x[1]-x[0])): |
|
|
1118 | 1144 | x[1] = self.xmax |
|
|
1119 | 1145 | |
|
|
1120 | 1146 | strWind = ['Zonal','Vertical'] |
|
|
1121 | 1147 | strCb = 'Velocity (m/s)' |
|
|
1122 | 1148 | zmaxVector = [zmaxZonal, zmaxVertical] |
|
|
1123 | 1149 | zminVector = [zminZonal, zminVertical] |
|
|
1124 | 1150 | |
|
|
1125 | 1151 | for i in range(nplotsw): |
|
|
1126 | 1152 | |
|
|
1127 | 1153 | title = "%s Drifts: %s" %(strWind[i], thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
1128 | 1154 | axes = self.axesList[i*self.__nsubplots] |
|
|
1129 | 1155 | |
|
|
1130 | 1156 | z1 = z[i,:].reshape((1,-1)) |
|
|
1131 | 1157 | |
|
|
1132 | 1158 | axes.pcolorbuffer(x, y, z1, |
|
|
1133 | 1159 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=zminVector[i], zmax=zmaxVector[i], |
|
|
1134 | 1160 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, |
|
|
1135 | 1161 | ticksize=9, cblabel=strCb, cbsize="1%", colormap="RdBu_r") |
|
|
1136 | 1162 | |
|
|
1137 | 1163 | if dataOut.data_SNR != None: |
|
|
1138 | 1164 | i += 1 |
|
|
1139 | 1165 | if SNR_1: |
|
|
1140 | 1166 | title = "Signal Noise Ratio + 1 (SNR+1): %s" %(thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
1141 | 1167 | else: |
|
|
1142 | 1168 | title = "Signal Noise Ratio (SNR): %s" %(thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
1143 | 1169 | axes = self.axesList[i*self.__nsubplots] |
|
|
1144 | 1170 | SNRavgdB = SNRavgdB.reshape((1,-1)) |
|
|
1145 | 1171 | |
|
|
1146 | 1172 | axes.pcolorbuffer(x, y, SNRavgdB, |
|
|
1147 | 1173 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=SNRmin, zmax=SNRmax, |
|
|
1148 | 1174 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, |
|
|
1149 | 1175 | ticksize=9, cblabel='', cbsize="1%", colormap="jet") |
|
|
1150 | 1176 | |
|
|
1151 | 1177 | self.draw() |
|
|
1152 | 1178 | |
|
|
1153 | 1179 | if x[1] >= self.axesList[0].xmax: |
|
|
1154 | 1180 | self.counter_imagwr = wr_period |
|
|
1155 | 1181 | self.__isConfig = False |
|
|
1156 | 1182 | self.figfile = None |
|
|
1157 | 1183 | |
|
|
1184 | ||
|
|
1185 | ||
|
|
1186 | ||
|
|
1187 | class PhasePlot(Figure): | |
|
|
1188 | ||
|
|
1189 | __isConfig = None | |
|
|
1190 | __nsubplots = None | |
|
|
1191 | ||
|
|
1192 | PREFIX = 'mphase' | |
|
|
1193 | ||
|
|
1194 | def __init__(self): | |
|
|
1195 | ||
|
|
1196 | self.timerange = 24*60*60 | |
|
|
1197 | self.__isConfig = False | |
|
|
1198 | self.__nsubplots = 1 | |
|
|
1199 | self.counter_imagwr = 0 | |
|
|
1200 | self.WIDTH = 600 | |
|
|
1201 | self.HEIGHT = 300 | |
|
|
1202 | self.WIDTHPROF = 120 | |
|
|
1203 | self.HEIGHTPROF = 0 | |
|
|
1204 | self.xdata = None | |
|
|
1205 | self.ydata = None | |
|
|
1206 | ||
|
|
1207 | self.PLOT_CODE = MPHASE_CODE | |
|
|
1208 | ||
|
|
1209 | self.FTP_WEI = None | |
|
|
1210 | self.EXP_CODE = None | |
|
|
1211 | self.SUB_EXP_CODE = None | |
|
|
1212 | self.PLOT_POS = None | |
|
|
1213 | ||
|
|
1214 | ||
|
|
1215 | self.filename_phase = None | |
|
|
1216 | ||
|
|
1217 | self.figfile = None | |
|
|
1218 | ||
|
|
1219 | def getSubplots(self): | |
|
|
1220 | ||
|
|
1221 | ncol = 1 | |
|
|
1222 | nrow = 1 | |
|
|
1223 | ||
|
|
1224 | return nrow, ncol | |
|
|
1225 | ||
|
|
1226 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): | |
|
|
1227 | ||
|
|
1228 | self.__showprofile = showprofile | |
|
|
1229 | self.nplots = nplots | |
|
|
1230 | ||
|
|
1231 | ncolspan = 7 | |
|
|
1232 | colspan = 6 | |
|
|
1233 | self.__nsubplots = 2 | |
|
|
1234 | ||
|
|
1235 | self.createFigure(id = id, | |
|
|
1236 | wintitle = wintitle, | |
|
|
1237 | widthplot = self.WIDTH+self.WIDTHPROF, | |
|
|
1238 | heightplot = self.HEIGHT+self.HEIGHTPROF, | |
|
|
1239 | show=show) | |
|
|
1240 | ||
|
|
1241 | nrow, ncol = self.getSubplots() | |
|
|
1242 | ||
|
|
1243 | self.addAxes(nrow, ncol*ncolspan, 0, 0, colspan, 1) | |
|
|
1244 | ||
|
|
1245 | ||
|
|
1246 | def run(self, dataOut, id, wintitle="", pairsList=None, showprofile='True', | |
|
|
1247 | xmin=None, xmax=None, ymin=None, ymax=None, | |
|
|
1248 | timerange=None, | |
|
|
1249 | save=False, figpath='', figfile=None, show=True, ftp=False, wr_period=1, | |
|
|
1250 | server=None, folder=None, username=None, password=None, | |
|
|
1251 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): | |
|
|
1252 | ||
|
|
1253 | ||
|
|
1254 | if timerange != None: | |
|
|
1255 | self.timerange = timerange | |
|
|
1256 | ||
|
|
1257 | tmin = None | |
|
|
1258 | tmax = None | |
|
|
1259 | x = dataOut.getTimeRange1() | |
|
|
1260 | y = dataOut.getHeiRange() | |
|
|
1261 | ||
|
|
1262 | ||
|
|
1263 | #thisDatetime = dataOut.datatime | |
|
|
1264 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[1]) | |
|
|
1265 | title = wintitle + " Phase of Beacon Signal" # : %s" %(thisDatetime.strftime("%d-%b-%Y")) | |
|
|
1266 | xlabel = "Local Time" | |
|
|
1267 | ylabel = "Phase" | |
|
|
1268 | ||
|
|
1269 | ||
|
|
1270 | #phase = numpy.zeros((len(pairsIndexList),len(dataOut.beacon_heiIndexList))) | |
|
|
1271 | phase_beacon = dataOut.data_output | |
|
|
1272 | ||
|
|
1273 | ||
|
|
1274 | if not self.__isConfig: | |
|
|
1275 | ||
|
|
1276 | self.nplots = phase_beacon.size | |
|
|
1277 | ||
|
|
1278 | self.setup(id=id, | |
|
|
1279 | nplots=self.nplots, | |
|
|
1280 | wintitle=wintitle, | |
|
|
1281 | showprofile=showprofile, | |
|
|
1282 | show=show) | |
|
|
1283 | ||
|
|
1284 | tmin, tmax = self.getTimeLim(x, xmin, xmax) | |
|
|
1285 | if ymin == None: ymin = numpy.nanmin(phase_beacon) - 10.0 | |
|
|
1286 | if ymax == None: ymax = numpy.nanmax(phase_beacon) + 10.0 | |
|
|
1287 | ||
|
|
1288 | self.FTP_WEI = ftp_wei | |
|
|
1289 | self.EXP_CODE = exp_code | |
|
|
1290 | self.SUB_EXP_CODE = sub_exp_code | |
|
|
1291 | self.PLOT_POS = plot_pos | |
|
|
1292 | ||
|
|
1293 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") | |
|
|
1294 | self.__isConfig = True | |
|
|
1295 | self.figfile = figfile | |
|
|
1296 | self.xdata = numpy.array([]) | |
|
|
1297 | self.ydata = numpy.array([]) | |
|
|
1298 | ||
|
|
1299 | #open file beacon phase | |
|
|
1300 | path = '%s%03d' %(self.PREFIX, self.id) | |
|
|
1301 | beacon_file = os.path.join(path,'%s.txt'%self.name) | |
|
|
1302 | self.filename_phase = os.path.join(figpath,beacon_file) | |
|
|
1303 | #self.save_phase(self.filename_phase) | |
|
|
1304 | ||
|
|
1305 | ||
|
|
1306 | #store data beacon phase | |
|
|
1307 | #self.save_data(self.filename_phase, phase_beacon, thisDatetime) | |
|
|
1308 | ||
|
|
1309 | self.setWinTitle(title) | |
|
|
1310 | ||
|
|
1311 | ||
|
|
1312 | title = "Phase Offset %s" %(thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) | |
|
|
1313 | ||
|
|
1314 | legendlabels = ["phase %d"%(chan) for chan in numpy.arange(self.nplots)] | |
|
|
1315 | ||
|
|
1316 | axes = self.axesList[0] | |
|
|
1317 | ||
|
|
1318 | self.xdata = numpy.hstack((self.xdata, x[0:1])) | |
|
|
1319 | ||
|
|
1320 | if len(self.ydata)==0: | |
|
|
1321 | self.ydata = phase_beacon.reshape(-1,1) | |
|
|
1322 | else: | |
|
|
1323 | self.ydata = numpy.hstack((self.ydata, phase_beacon.reshape(-1,1))) | |
|
|
1324 | ||
|
|
1325 | ||
|
|
1326 | axes.pmultilineyaxis(x=self.xdata, y=self.ydata, | |
|
|
1327 | xmin=tmin, xmax=tmax, ymin=ymin, ymax=ymax, | |
|
|
1328 | xlabel=xlabel, ylabel=ylabel, title=title, legendlabels=legendlabels, marker='x', markersize=8, linestyle="solid", | |
|
|
1329 | XAxisAsTime=True, grid='both' | |
|
|
1330 | ) | |
|
|
1331 | ||
|
|
1332 | self.draw() | |
|
|
1333 | ||
|
|
1334 | if x[1] >= self.axesList[0].xmax: | |
|
|
1335 | self.counter_imagwr = wr_period | |
|
|
1336 | del self.xdata | |
|
|
1337 | del self.ydata | |
|
|
1338 | self.__isConfig = False | |
|
|
1339 | ||
|
|
1340 | if self.figfile == None: | |
|
|
1341 | str_datetime = thisDatetime.strftime("%Y%m%d_%H%M%S") | |
|
|
1342 | self.figfile = self.getFilename(name = str_datetime) | |
|
|
1343 | ||
|
|
1344 | if figpath != '': | |
|
|
1345 | self.counter_imagwr += 1 | |
|
|
1346 | if (self.counter_imagwr>=wr_period): | |
|
|
1347 | # store png plot to local folder | |
|
|
1348 | self.saveFigure(figpath, self.figfile) | |
|
|
1349 | # store png plot to FTP server according to RT-Web format | |
|
|
1350 | name = self.getNameToFtp(thisDatetime, self.FTP_WEI, self.EXP_CODE, self.SUB_EXP_CODE, self.PLOT_CODE, self.PLOT_POS) | |
|
|
1351 | ftp_filename = os.path.join(figpath, name) | |
|
|
1352 | self.saveFigure(figpath, ftp_filename) | |
|
|
1353 | self.counter_imagwr = 0 | |
|
|
1354 | self.figfile = None | |
|
|
1355 | ||
|
|
1158 | 1356 | self.save(figpath=figpath, |
|
|
1159 | 1357 | figfile=figfile, |
|
|
1160 | 1358 | save=save, |
|
|
1161 | 1359 | ftp=ftp, |
|
|
1162 | 1360 | wr_period=wr_period, |
|
|
1163 | 1361 | thisDatetime=thisDatetime, |
|
|
1164 | update_figfile=False) No newline at end of file | |
|
|
1362 | update_figfile=False) | |
| @@ -1,435 +1,437 | |||
|
|
1 | 1 | import numpy |
|
|
2 | 2 | import datetime |
|
|
3 | 3 | import sys |
|
|
4 | 4 | import matplotlib |
|
|
5 | 5 | |
|
|
6 | 6 | if 'linux' in sys.platform: |
|
|
7 | 7 | matplotlib.use("TKAgg") |
|
|
8 | 8 | |
|
|
9 | 9 | if 'darwin' in sys.platform: |
|
|
10 | 10 | matplotlib.use("WXAgg") |
|
|
11 | 11 | #Qt4Agg', 'GTK', 'GTKAgg', 'ps', 'agg', 'cairo', 'MacOSX', 'GTKCairo', 'WXAgg', 'template', 'TkAgg', 'GTK3Cairo', 'GTK3Agg', 'svg', 'WebAgg', 'CocoaAgg', 'emf', 'gdk', 'WX' |
|
|
12 | 12 | import matplotlib.pyplot |
|
|
13 | 13 | |
|
|
14 | 14 | from mpl_toolkits.axes_grid1 import make_axes_locatable |
|
|
15 | 15 | from matplotlib.ticker import * |
|
|
16 | 16 | |
|
|
17 | 17 | ########################################### |
|
|
18 | 18 | #Actualizacion de las funciones del driver |
|
|
19 | 19 | ########################################### |
|
|
20 | 20 | |
|
|
21 | 21 | def createFigure(id, wintitle, width, height, facecolor="w", show=True): |
|
|
22 | 22 | |
|
|
23 | 23 | matplotlib.pyplot.ioff() |
|
|
24 | 24 | fig = matplotlib.pyplot.figure(num=id, facecolor=facecolor) |
|
|
25 | 25 | fig.canvas.manager.set_window_title(wintitle) |
|
|
26 | 26 | fig.canvas.manager.resize(width, height) |
|
|
27 | 27 | matplotlib.pyplot.ion() |
|
|
28 | 28 | if show: |
|
|
29 | 29 | matplotlib.pyplot.show() |
|
|
30 | 30 | |
|
|
31 | 31 | return fig |
|
|
32 | 32 | |
|
|
33 | 33 | def closeFigure(show=False, fig=None): |
|
|
34 | 34 | |
|
|
35 | 35 | matplotlib.pyplot.ioff() |
|
|
36 | 36 | matplotlib.pyplot.pause(0.1) |
|
|
37 | 37 | |
|
|
38 | 38 | if show: |
|
|
39 | 39 | matplotlib.pyplot.show() |
|
|
40 | 40 | |
|
|
41 | 41 | if fig != None: |
|
|
42 | 42 | matplotlib.pyplot.close(fig) |
|
|
43 | 43 | matplotlib.pyplot.pause(0.1) |
|
|
44 | 44 | matplotlib.pyplot.ion() |
|
|
45 | 45 | return |
|
|
46 | 46 | |
|
|
47 | 47 | matplotlib.pyplot.close("all") |
|
|
48 | 48 | matplotlib.pyplot.pause(0.1) |
|
|
49 | 49 | matplotlib.pyplot.ion() |
|
|
50 | 50 | return |
|
|
51 | 51 | |
|
|
52 | 52 | def saveFigure(fig, filename): |
|
|
53 | 53 | |
|
|
54 | 54 | matplotlib.pyplot.ioff() |
|
|
55 | 55 | fig.savefig(filename) |
|
|
56 | 56 | matplotlib.pyplot.ion() |
|
|
57 | 57 | |
|
|
58 | 58 | def setWinTitle(fig, title): |
|
|
59 | 59 | |
|
|
60 | 60 | fig.canvas.manager.set_window_title(title) |
|
|
61 | 61 | |
|
|
62 | 62 | def setTitle(fig, title): |
|
|
63 | 63 | |
|
|
64 | 64 | fig.suptitle(title) |
|
|
65 | 65 | |
|
|
66 | 66 | def createAxes(fig, nrow, ncol, xpos, ypos, colspan, rowspan, polar=False): |
|
|
67 | 67 | |
|
|
68 | 68 | matplotlib.pyplot.ioff() |
|
|
69 | 69 | matplotlib.pyplot.figure(fig.number) |
|
|
70 | 70 | axes = matplotlib.pyplot.subplot2grid((nrow, ncol), |
|
|
71 | 71 | (xpos, ypos), |
|
|
72 | 72 | colspan=colspan, |
|
|
73 | 73 | rowspan=rowspan, |
|
|
74 | 74 | polar=polar) |
|
|
75 | 75 | |
|
|
76 | 76 | matplotlib.pyplot.ion() |
|
|
77 | 77 | return axes |
|
|
78 | 78 | |
|
|
79 | 79 | def setAxesText(ax, text): |
|
|
80 | 80 | |
|
|
81 | 81 | ax.annotate(text, |
|
|
82 | 82 | xy = (.1, .99), |
|
|
83 | 83 | xycoords = 'figure fraction', |
|
|
84 | 84 | horizontalalignment = 'left', |
|
|
85 | 85 | verticalalignment = 'top', |
|
|
86 | 86 | fontsize = 10) |
|
|
87 | 87 | |
|
|
88 | 88 | def printLabels(ax, xlabel, ylabel, title): |
|
|
89 | 89 | |
|
|
90 | 90 | ax.set_xlabel(xlabel, size=11) |
|
|
91 | 91 | ax.set_ylabel(ylabel, size=11) |
|
|
92 | 92 | ax.set_title(title, size=8) |
|
|
93 | 93 | |
|
|
94 | 94 | def createPline(ax, x, y, xmin, xmax, ymin, ymax, xlabel='', ylabel='', title='', |
|
|
95 | 95 | ticksize=9, xtick_visible=True, ytick_visible=True, |
|
|
96 | 96 | nxticks=4, nyticks=10, |
|
|
97 | 97 | grid=None,color='blue'): |
|
|
98 | 98 | |
|
|
99 | 99 | """ |
|
|
100 | 100 | |
|
|
101 | 101 | Input: |
|
|
102 | 102 | grid : None, 'both', 'x', 'y' |
|
|
103 | 103 | """ |
|
|
104 | 104 | |
|
|
105 | 105 | matplotlib.pyplot.ioff() |
|
|
106 | 106 | |
|
|
107 | 107 | ax.set_xlim([xmin,xmax]) |
|
|
108 | 108 | ax.set_ylim([ymin,ymax]) |
|
|
109 | 109 | |
|
|
110 | 110 | printLabels(ax, xlabel, ylabel, title) |
|
|
111 | 111 | |
|
|
112 | 112 | ###################################################### |
|
|
113 | 113 | if (xmax-xmin)<=1: |
|
|
114 | 114 | xtickspos = numpy.linspace(xmin,xmax,nxticks) |
|
|
115 | 115 | xtickspos = numpy.array([float("%.1f"%i) for i in xtickspos]) |
|
|
116 | 116 | ax.set_xticks(xtickspos) |
|
|
117 | 117 | else: |
|
|
118 | 118 | xtickspos = numpy.arange(nxticks)*int((xmax-xmin)/(nxticks)) + int(xmin) |
|
|
119 | 119 | # xtickspos = numpy.arange(nxticks)*float(xmax-xmin)/float(nxticks) + int(xmin) |
|
|
120 | 120 | ax.set_xticks(xtickspos) |
|
|
121 | 121 | |
|
|
122 | 122 | for tick in ax.get_xticklabels(): |
|
|
123 | 123 | tick.set_visible(xtick_visible) |
|
|
124 | 124 | |
|
|
125 | 125 | for tick in ax.xaxis.get_major_ticks(): |
|
|
126 | 126 | tick.label.set_fontsize(ticksize) |
|
|
127 | 127 | |
|
|
128 | 128 | ###################################################### |
|
|
129 | 129 | for tick in ax.get_yticklabels(): |
|
|
130 | 130 | tick.set_visible(ytick_visible) |
|
|
131 | 131 | |
|
|
132 | 132 | for tick in ax.yaxis.get_major_ticks(): |
|
|
133 | 133 | tick.label.set_fontsize(ticksize) |
|
|
134 | 134 | |
|
|
135 | 135 | ax.plot(x, y, color=color) |
|
|
136 | 136 | iplot = ax.lines[-1] |
|
|
137 | 137 | |
|
|
138 | 138 | ###################################################### |
|
|
139 | 139 | if '0.' in matplotlib.__version__[0:2]: |
|
|
140 | 140 | print "The matplotlib version has to be updated to 1.1 or newer" |
|
|
141 | 141 | return iplot |
|
|
142 | 142 | |
|
|
143 | 143 | if '1.0.' in matplotlib.__version__[0:4]: |
|
|
144 | 144 | print "The matplotlib version has to be updated to 1.1 or newer" |
|
|
145 | 145 | return iplot |
|
|
146 | 146 | |
|
|
147 | 147 | if grid != None: |
|
|
148 | 148 | ax.grid(b=True, which='major', axis=grid) |
|
|
149 | 149 | |
|
|
150 | 150 | matplotlib.pyplot.tight_layout() |
|
|
151 | 151 | |
|
|
152 | 152 | matplotlib.pyplot.ion() |
|
|
153 | 153 | |
|
|
154 | 154 | return iplot |
|
|
155 | 155 | |
|
|
156 | 156 | def set_linedata(ax, x, y, idline): |
|
|
157 | 157 | |
|
|
158 | 158 | ax.lines[idline].set_data(x,y) |
|
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159 | 159 | |
|
|
160 | 160 | def pline(iplot, x, y, xlabel='', ylabel='', title=''): |
|
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161 | 161 | |
|
|
162 | 162 | ax = iplot.get_axes() |
|
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163 | 163 | |
|
|
164 | 164 | printLabels(ax, xlabel, ylabel, title) |
|
|
165 | 165 | |
|
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166 | 166 | set_linedata(ax, x, y, idline=0) |
|
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167 | 167 | |
|
|
168 | 168 | def addpline(ax, x, y, color, linestyle, lw): |
|
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169 | 169 | |
|
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170 | 170 | ax.plot(x,y,color=color,linestyle=linestyle,lw=lw) |
|
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171 | 171 | |
|
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172 | 172 | |
|
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173 | 173 | def createPcolor(ax, x, y, z, xmin, xmax, ymin, ymax, zmin, zmax, |
|
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174 | 174 | xlabel='', ylabel='', title='', ticksize = 9, |
|
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175 | 175 | colormap='jet',cblabel='', cbsize="5%", |
|
|
176 | 176 | XAxisAsTime=False): |
|
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177 | 177 | |
|
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178 | 178 | matplotlib.pyplot.ioff() |
|
|
179 | 179 | |
|
|
180 | 180 | divider = make_axes_locatable(ax) |
|
|
181 | 181 | ax_cb = divider.new_horizontal(size=cbsize, pad=0.05) |
|
|
182 | 182 | fig = ax.get_figure() |
|
|
183 | 183 | fig.add_axes(ax_cb) |
|
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184 | 184 | |
|
|
185 | 185 | ax.set_xlim([xmin,xmax]) |
|
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186 | 186 | ax.set_ylim([ymin,ymax]) |
|
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187 | 187 | |
|
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188 | 188 | printLabels(ax, xlabel, ylabel, title) |
|
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189 | 189 | |
|
|
190 | 190 | imesh = ax.pcolormesh(x,y,z.T, vmin=zmin, vmax=zmax, cmap=matplotlib.pyplot.get_cmap(colormap)) |
|
|
191 | 191 | cb = matplotlib.pyplot.colorbar(imesh, cax=ax_cb) |
|
|
192 | 192 | cb.set_label(cblabel) |
|
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193 | 193 | |
|
|
194 | 194 | # for tl in ax_cb.get_yticklabels(): |
|
|
195 | 195 | # tl.set_visible(True) |
|
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196 | 196 | |
|
|
197 | 197 | for tick in ax.yaxis.get_major_ticks(): |
|
|
198 | 198 | tick.label.set_fontsize(ticksize) |
|
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199 | 199 | |
|
|
200 | 200 | for tick in ax.xaxis.get_major_ticks(): |
|
|
201 | 201 | tick.label.set_fontsize(ticksize) |
|
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202 | 202 | |
|
|
203 | 203 | for tick in cb.ax.get_yticklabels(): |
|
|
204 | 204 | tick.set_fontsize(ticksize) |
|
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205 | 205 | |
|
|
206 | 206 | ax_cb.yaxis.tick_right() |
|
|
207 | 207 | |
|
|
208 | 208 | if '0.' in matplotlib.__version__[0:2]: |
|
|
209 | 209 | print "The matplotlib version has to be updated to 1.1 or newer" |
|
|
210 | 210 | return imesh |
|
|
211 | 211 | |
|
|
212 | 212 | if '1.0.' in matplotlib.__version__[0:4]: |
|
|
213 | 213 | print "The matplotlib version has to be updated to 1.1 or newer" |
|
|
214 | 214 | return imesh |
|
|
215 | 215 | |
|
|
216 | 216 | matplotlib.pyplot.tight_layout() |
|
|
217 | 217 | |
|
|
218 | 218 | if XAxisAsTime: |
|
|
219 | 219 | |
|
|
220 | 220 | func = lambda x, pos: ('%s') %(datetime.datetime.utcfromtimestamp(x).strftime("%H:%M:%S")) |
|
|
221 | 221 | ax.xaxis.set_major_formatter(FuncFormatter(func)) |
|
|
222 | 222 | ax.xaxis.set_major_locator(LinearLocator(7)) |
|
|
223 | 223 | |
|
|
224 | 224 | matplotlib.pyplot.ion() |
|
|
225 | 225 | return imesh |
|
|
226 | 226 | |
|
|
227 | 227 | def pcolor(imesh, z, xlabel='', ylabel='', title=''): |
|
|
228 | 228 | |
|
|
229 | 229 | z = z.T |
|
|
230 | 230 | ax = imesh.get_axes() |
|
|
231 | 231 | printLabels(ax, xlabel, ylabel, title) |
|
|
232 | 232 | imesh.set_array(z.ravel()) |
|
|
233 | 233 | |
|
|
234 | 234 | def addpcolor(ax, x, y, z, zmin, zmax, xlabel='', ylabel='', title='', colormap='jet'): |
|
|
235 | 235 | |
|
|
236 | 236 | printLabels(ax, xlabel, ylabel, title) |
|
|
237 | 237 | |
|
|
238 | 238 | ax.pcolormesh(x,y,z.T,vmin=zmin,vmax=zmax, cmap=matplotlib.pyplot.get_cmap(colormap)) |
|
|
239 | 239 | |
|
|
240 | 240 | def addpcolorbuffer(ax, x, y, z, zmin, zmax, xlabel='', ylabel='', title='', colormap='jet'): |
|
|
241 | 241 | |
|
|
242 | 242 | printLabels(ax, xlabel, ylabel, title) |
|
|
243 | 243 | |
|
|
244 | 244 | ax.collections.remove(ax.collections[0]) |
|
|
245 | 245 | |
|
|
246 | 246 | ax.pcolormesh(x,y,z.T,vmin=zmin,vmax=zmax, cmap=matplotlib.pyplot.get_cmap(colormap)) |
|
|
247 | 247 | |
|
|
248 | 248 | def createPmultiline(ax, x, y, xmin, xmax, ymin, ymax, xlabel='', ylabel='', title='', legendlabels=None, |
|
|
249 | 249 | ticksize=9, xtick_visible=True, ytick_visible=True, |
|
|
250 | 250 | nxticks=4, nyticks=10, |
|
|
251 | 251 | grid=None): |
|
|
252 | 252 | |
|
|
253 | 253 | """ |
|
|
254 | 254 | |
|
|
255 | 255 | Input: |
|
|
256 | 256 | grid : None, 'both', 'x', 'y' |
|
|
257 | 257 | """ |
|
|
258 | 258 | |
|
|
259 | 259 | matplotlib.pyplot.ioff() |
|
|
260 | 260 | |
|
|
261 | 261 | lines = ax.plot(x.T, y) |
|
|
262 | 262 | leg = ax.legend(lines, legendlabels, loc='upper right') |
|
|
263 | 263 | leg.get_frame().set_alpha(0.5) |
|
|
264 | 264 | ax.set_xlim([xmin,xmax]) |
|
|
265 | 265 | ax.set_ylim([ymin,ymax]) |
|
|
266 | 266 | printLabels(ax, xlabel, ylabel, title) |
|
|
267 | 267 | |
|
|
268 | 268 | xtickspos = numpy.arange(nxticks)*int((xmax-xmin)/(nxticks)) + int(xmin) |
|
|
269 | 269 | ax.set_xticks(xtickspos) |
|
|
270 | 270 | |
|
|
271 | 271 | for tick in ax.get_xticklabels(): |
|
|
272 | 272 | tick.set_visible(xtick_visible) |
|
|
273 | 273 | |
|
|
274 | 274 | for tick in ax.xaxis.get_major_ticks(): |
|
|
275 | 275 | tick.label.set_fontsize(ticksize) |
|
|
276 | 276 | |
|
|
277 | 277 | for tick in ax.get_yticklabels(): |
|
|
278 | 278 | tick.set_visible(ytick_visible) |
|
|
279 | 279 | |
|
|
280 | 280 | for tick in ax.yaxis.get_major_ticks(): |
|
|
281 | 281 | tick.label.set_fontsize(ticksize) |
|
|
282 | 282 | |
|
|
283 | 283 | iplot = ax.lines[-1] |
|
|
284 | 284 | |
|
|
285 | 285 | if '0.' in matplotlib.__version__[0:2]: |
|
|
286 | 286 | print "The matplotlib version has to be updated to 1.1 or newer" |
|
|
287 | 287 | return iplot |
|
|
288 | 288 | |
|
|
289 | 289 | if '1.0.' in matplotlib.__version__[0:4]: |
|
|
290 | 290 | print "The matplotlib version has to be updated to 1.1 or newer" |
|
|
291 | 291 | return iplot |
|
|
292 | 292 | |
|
|
293 | 293 | if grid != None: |
|
|
294 | 294 | ax.grid(b=True, which='major', axis=grid) |
|
|
295 | 295 | |
|
|
296 | 296 | matplotlib.pyplot.tight_layout() |
|
|
297 | 297 | |
|
|
298 | 298 | matplotlib.pyplot.ion() |
|
|
299 | 299 | |
|
|
300 | 300 | return iplot |
|
|
301 | 301 | |
|
|
302 | 302 | |
|
|
303 | 303 | def pmultiline(iplot, x, y, xlabel='', ylabel='', title=''): |
|
|
304 | 304 | |
|
|
305 | 305 | ax = iplot.get_axes() |
|
|
306 | 306 | |
|
|
307 | 307 | printLabels(ax, xlabel, ylabel, title) |
|
|
308 | 308 | |
|
|
309 | 309 | for i in range(len(ax.lines)): |
|
|
310 | 310 | line = ax.lines[i] |
|
|
311 | 311 | line.set_data(x[i,:],y) |
|
|
312 | 312 | |
|
|
313 | 313 | def createPmultilineYAxis(ax, x, y, xmin, xmax, ymin, ymax, xlabel='', ylabel='', title='', legendlabels=None, |
|
|
314 | 314 | ticksize=9, xtick_visible=True, ytick_visible=True, |
|
|
315 | 315 | nxticks=4, nyticks=10, marker='.', markersize=10, linestyle="None", |
|
|
316 | 316 | grid=None, XAxisAsTime=False): |
|
|
317 | 317 | |
|
|
318 | 318 | """ |
|
|
319 | 319 | |
|
|
320 | 320 | Input: |
|
|
321 | 321 | grid : None, 'both', 'x', 'y' |
|
|
322 | 322 | """ |
|
|
323 | 323 | |
|
|
324 | 324 | matplotlib.pyplot.ioff() |
|
|
325 | 325 | |
|
|
326 | 326 | # lines = ax.plot(x, y.T, marker=marker,markersize=markersize,linestyle=linestyle) |
|
|
327 | 327 | lines = ax.plot(x, y.T, linestyle=linestyle, marker=marker, markersize=markersize) |
|
|
328 | 328 | leg = ax.legend(lines, legendlabels, loc='upper left', bbox_to_anchor=(1.01, 1.00), numpoints=1, handlelength=1.5, \ |
|
|
329 | 329 | handletextpad=0.5, borderpad=0.5, labelspacing=0.5, borderaxespad=0.) |
|
|
330 | 330 | |
|
|
331 | 331 | for label in leg.get_texts(): label.set_fontsize(9) |
|
|
332 | 332 | |
|
|
333 | 333 | ax.set_xlim([xmin,xmax]) |
|
|
334 | 334 | ax.set_ylim([ymin,ymax]) |
|
|
335 | 335 | printLabels(ax, xlabel, ylabel, title) |
|
|
336 | 336 | |
|
|
337 | 337 | # xtickspos = numpy.arange(nxticks)*int((xmax-xmin)/(nxticks)) + int(xmin) |
|
|
338 | 338 | # ax.set_xticks(xtickspos) |
|
|
339 | 339 | |
|
|
340 | 340 | for tick in ax.get_xticklabels(): |
|
|
341 | 341 | tick.set_visible(xtick_visible) |
|
|
342 | 342 | |
|
|
343 | 343 | for tick in ax.xaxis.get_major_ticks(): |
|
|
344 | 344 | tick.label.set_fontsize(ticksize) |
|
|
345 | 345 | |
|
|
346 | 346 | for tick in ax.get_yticklabels(): |
|
|
347 | 347 | tick.set_visible(ytick_visible) |
|
|
348 | 348 | |
|
|
349 | 349 | for tick in ax.yaxis.get_major_ticks(): |
|
|
350 | 350 | tick.label.set_fontsize(ticksize) |
|
|
351 | 351 | |
|
|
352 | 352 | iplot = ax.lines[-1] |
|
|
353 | 353 | |
|
|
354 | 354 | if '0.' in matplotlib.__version__[0:2]: |
|
|
355 | 355 | print "The matplotlib version has to be updated to 1.1 or newer" |
|
|
356 | 356 | return iplot |
|
|
357 | 357 | |
|
|
358 | 358 | if '1.0.' in matplotlib.__version__[0:4]: |
|
|
359 | 359 | print "The matplotlib version has to be updated to 1.1 or newer" |
|
|
360 | 360 | return iplot |
|
|
361 | 361 | |
|
|
362 | 362 | if grid != None: |
|
|
363 | 363 | ax.grid(b=True, which='major', axis=grid) |
|
|
364 | 364 | |
|
|
365 | 365 | matplotlib.pyplot.tight_layout() |
|
|
366 | 366 | |
|
|
367 | 367 | if XAxisAsTime: |
|
|
368 | 368 | |
|
|
369 | 369 | func = lambda x, pos: ('%s') %(datetime.datetime.utcfromtimestamp(x).strftime("%H:%M:%S")) |
|
|
370 | 370 | ax.xaxis.set_major_formatter(FuncFormatter(func)) |
|
|
371 | 371 | ax.xaxis.set_major_locator(LinearLocator(7)) |
|
|
372 | 372 | |
|
|
373 | 373 | matplotlib.pyplot.ion() |
|
|
374 | 374 | |
|
|
375 | 375 | return iplot |
|
|
376 | 376 | |
|
|
377 | 377 | def pmultilineyaxis(iplot, x, y, xlabel='', ylabel='', title=''): |
|
|
378 | 378 | |
|
|
379 | 379 | ax = iplot.get_axes() |
|
|
380 | 380 | |
|
|
381 | 381 | printLabels(ax, xlabel, ylabel, title) |
|
|
382 | 382 | |
|
|
383 | 383 | for i in range(len(ax.lines)): |
|
|
384 | 384 | line = ax.lines[i] |
|
|
385 | 385 | line.set_data(x,y[i,:]) |
|
|
386 | 386 | |
|
|
387 | 387 | def createPolar(ax, x, y, |
|
|
388 | 388 | xlabel='', ylabel='', title='', ticksize = 9, |
|
|
389 | 389 | colormap='jet',cblabel='', cbsize="5%", |
|
|
390 | 390 | XAxisAsTime=False): |
|
|
391 | 391 | |
|
|
392 | 392 | matplotlib.pyplot.ioff() |
|
|
393 | 393 | |
|
|
394 | 394 | ax.plot(x,y,'bo', markersize=5) |
|
|
395 | 395 | # ax.set_rmax(90) |
|
|
396 | 396 | ax.set_ylim(0,90) |
|
|
397 | 397 | ax.set_yticks(numpy.arange(0,90,20)) |
|
|
398 | ax.text(0, -110, ylabel, rotation='vertical', va ='center', ha = 'center' ,size='11') | |
|
|
398 | # ax.text(0, -110, ylabel, rotation='vertical', va ='center', ha = 'center' ,size='11') | |
|
|
399 | # ax.text(0, 50, ylabel, rotation='vertical', va ='center', ha = 'left' ,size='11') | |
|
|
399 | 400 | # ax.text(100, 100, 'example', ha='left', va='center', rotation='vertical') |
|
|
400 | printLabels(ax, xlabel, '', title) | |
|
|
401 | ax.yaxis.labelpad = 230 | |
|
|
402 | printLabels(ax, xlabel, ylabel, title) | |
|
|
401 | 403 | iplot = ax.lines[-1] |
|
|
402 | 404 | |
|
|
403 | 405 | if '0.' in matplotlib.__version__[0:2]: |
|
|
404 | 406 | print "The matplotlib version has to be updated to 1.1 or newer" |
|
|
405 | 407 | return iplot |
|
|
406 | 408 | |
|
|
407 | 409 | if '1.0.' in matplotlib.__version__[0:4]: |
|
|
408 | 410 | print "The matplotlib version has to be updated to 1.1 or newer" |
|
|
409 | 411 | return iplot |
|
|
410 | 412 | |
|
|
411 | 413 | # if grid != None: |
|
|
412 | 414 | # ax.grid(b=True, which='major', axis=grid) |
|
|
413 | 415 | |
|
|
414 | 416 | matplotlib.pyplot.tight_layout() |
|
|
415 | 417 | |
|
|
416 | 418 | matplotlib.pyplot.ion() |
|
|
417 | 419 | |
|
|
418 | 420 | |
|
|
419 | 421 | return iplot |
|
|
420 | 422 | |
|
|
421 | 423 | def polar(iplot, x, y, xlabel='', ylabel='', title=''): |
|
|
422 | 424 | |
|
|
423 | 425 | ax = iplot.get_axes() |
|
|
424 | 426 | |
|
|
425 | 427 | # ax.text(0, -110, ylabel, rotation='vertical', va ='center', ha = 'center',size='11') |
|
|
426 |
printLabels(ax, xlabel, |
|
|
|
428 | printLabels(ax, xlabel, ylabel, title) | |
|
|
427 | 429 | |
|
|
428 | 430 | set_linedata(ax, x, y, idline=0) |
|
|
429 | 431 | |
|
|
430 | 432 | def draw(fig): |
|
|
431 | 433 | |
|
|
432 | 434 | if type(fig) == 'int': |
|
|
433 | 435 | raise ValueError, "Error drawing: Fig parameter should be a matplotlib figure object figure" |
|
|
434 | 436 | |
|
|
435 | 437 | fig.canvas.draw() |
| @@ -1,27 +1,28 | |||
|
|
1 | 1 | ''' |
|
|
2 | 2 | @author: roj-idl71 |
|
|
3 | 3 | ''' |
|
|
4 | 4 | #USED IN jroplot_spectra.py |
|
|
5 | 5 | RTI_CODE = 0 #Range time intensity (RTI). |
|
|
6 | 6 | SPEC_CODE = 1 #Spectra (and Cross-spectra) information. |
|
|
7 | 7 | CROSS_CODE = 2 #Cross-Correlation information. |
|
|
8 | 8 | COH_CODE = 3 #Coherence map. |
|
|
9 | 9 | BASE_CODE = 4 #Base lines graphic. |
|
|
10 | 10 | ROW_CODE = 5 #Row Spectra. |
|
|
11 | 11 | TOTAL_CODE = 6 #Total Power. |
|
|
12 | 12 | DRIFT_CODE = 7 #Drifts graphics. |
|
|
13 | 13 | HEIGHT_CODE = 8 #Height profile. |
|
|
14 | 14 | PHASE_CODE = 9 #Signal Phase. |
|
|
15 | 15 | |
|
|
16 | 16 | POWER_CODE = 16 |
|
|
17 | 17 | NOISE_CODE = 17 |
|
|
18 | 18 | BEACON_CODE = 18 |
|
|
19 | 19 | |
|
|
20 | 20 | #USED IN jroplot_parameters.py |
|
|
21 | ||
|
|
22 | MOMENTS_CODE = 20 | |
|
|
23 | SKYMAP_CODE = 21 | |
|
|
24 | 21 | WIND_CODE = 22 |
|
|
25 |
|
|
|
|
26 |
S |
|
|
|
27 | EWDRIFT_CODE = 25 | |
|
|
22 | MSKYMAP_CODE = 23 | |
|
|
23 | MPHASE_CODE = 24 | |
|
|
24 | ||
|
|
25 | MOMENTS_CODE = 25 | |
|
|
26 | PARMS_CODE = 26 | |
|
|
27 | SPECFIT_CODE = 27 | |
|
|
28 | EWDRIFT_CODE = 28 | |
| @@ -1,903 +1,1005 | |||
|
|
1 | 1 | import numpy |
|
|
2 | 2 | import time |
|
|
3 | 3 | import os |
|
|
4 | 4 | import h5py |
|
|
5 | 5 | import re |
|
|
6 | import tables | |
|
|
6 | 7 | |
|
|
7 | 8 | from schainpy.model.data.jrodata import * |
|
|
8 | 9 | from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation |
|
|
9 | 10 | from schainpy.model.io.jroIO_base import * |
|
|
10 | 11 | |
|
|
11 | 12 | |
|
|
12 | 13 | class HDF5Reader(ProcessingUnit): |
|
|
13 | 14 | |
|
|
14 | 15 | ext = ".hdf5" |
|
|
15 | 16 | |
|
|
16 | 17 | optchar = "D" |
|
|
17 | 18 | |
|
|
18 | 19 | timezone = None |
|
|
19 | 20 | |
|
|
20 | 21 | secStart = None |
|
|
21 | 22 | |
|
|
22 | 23 | secEnd = None |
|
|
23 | 24 | |
|
|
24 | 25 | fileIndex = None |
|
|
25 | 26 | |
|
|
26 | 27 | blockIndex = None |
|
|
27 | 28 | |
|
|
28 | 29 | blocksPerFile = None |
|
|
29 | 30 | |
|
|
30 | 31 | path = None |
|
|
31 | 32 | |
|
|
32 | 33 | #List of Files |
|
|
33 | 34 | |
|
|
34 | 35 | filenameList = None |
|
|
35 | 36 | |
|
|
36 | 37 | datetimeList = None |
|
|
37 | 38 | |
|
|
38 | 39 | #Hdf5 File |
|
|
39 | 40 | |
|
|
40 | 41 | fpMetadata = None |
|
|
41 | 42 | |
|
|
42 | 43 | pathMeta = None |
|
|
43 | 44 | |
|
|
44 | 45 | listMetaname = None |
|
|
45 | 46 | |
|
|
46 | 47 | listMeta = None |
|
|
47 | 48 | |
|
|
48 | 49 | listDataname = None |
|
|
49 | 50 | |
|
|
50 | 51 | listData = None |
|
|
51 | 52 | |
|
|
52 | 53 | listShapes = None |
|
|
53 | 54 | |
|
|
54 | 55 | fp = None |
|
|
55 | 56 | |
|
|
56 | 57 | #dataOut reconstruction |
|
|
57 | 58 | |
|
|
58 | 59 | dataOut = None |
|
|
59 | 60 | |
|
|
60 | 61 | nRecords = None |
|
|
61 | 62 | |
|
|
62 | 63 | |
|
|
63 | 64 | def __init__(self): |
|
|
64 | 65 | self.dataOut = self.__createObjByDefault() |
|
|
65 | 66 | return |
|
|
66 | 67 | |
|
|
67 | 68 | def __createObjByDefault(self): |
|
|
68 | 69 | |
|
|
69 | 70 | dataObj = Parameters() |
|
|
70 | 71 | |
|
|
71 | 72 | return dataObj |
|
|
72 | 73 | |
|
|
73 | 74 | def setup(self,path=None, |
|
|
74 | 75 | startDate=None, |
|
|
75 | 76 | endDate=None, |
|
|
76 | 77 | startTime=datetime.time(0,0,0), |
|
|
77 | 78 | endTime=datetime.time(23,59,59), |
|
|
78 | 79 | walk=True, |
|
|
79 | 80 | timezone='ut', |
|
|
80 | 81 | all=0, |
|
|
81 | 82 | online=False, |
|
|
82 | 83 | ext=None): |
|
|
83 | 84 | |
|
|
84 | 85 | if ext==None: |
|
|
85 | 86 | ext = self.ext |
|
|
86 | 87 | self.timezone = timezone |
|
|
87 | 88 | # self.all = all |
|
|
88 | 89 | # self.online = online |
|
|
89 | 90 | self.path = path |
|
|
90 | 91 | |
|
|
91 | 92 | startDateTime = datetime.datetime.combine(startDate,startTime) |
|
|
92 | 93 | endDateTime = datetime.datetime.combine(endDate,endTime) |
|
|
93 | 94 | secStart = (startDateTime-datetime.datetime(1970,1,1)).total_seconds() |
|
|
94 | 95 | secEnd = (endDateTime-datetime.datetime(1970,1,1)).total_seconds() |
|
|
95 | 96 | |
|
|
96 | 97 | self.secStart = secStart |
|
|
97 | 98 | self.secEnd = secEnd |
|
|
98 | 99 | |
|
|
99 | 100 | if not(online): |
|
|
100 | 101 | #Busqueda de archivos offline |
|
|
101 | 102 | self.__searchFilesOffline(path, startDate, endDate, ext, startTime, endTime, secStart, secEnd, walk) |
|
|
102 | 103 | else: |
|
|
103 | 104 | self.__searchFilesOnline(path, walk) |
|
|
104 | 105 | |
|
|
105 | 106 | if not(self.filenameList): |
|
|
106 | 107 | print "There is no files into the folder: %s"%(path) |
|
|
107 | 108 | sys.exit(-1) |
|
|
108 | 109 | |
|
|
109 | 110 | # self.__getExpParameters() |
|
|
110 | 111 | |
|
|
111 | 112 | self.fileIndex = -1 |
|
|
112 | 113 | |
|
|
113 | 114 | self.__setNextFileOffline() |
|
|
114 | 115 | |
|
|
115 | 116 | self.__readMetadata() |
|
|
116 | 117 | |
|
|
117 | 118 | self.blockIndex = 0 |
|
|
118 | 119 | |
|
|
119 | 120 | return |
|
|
120 | 121 | |
|
|
121 | 122 | def __searchFilesOffline(self, |
|
|
122 | 123 | path, |
|
|
123 | 124 | startDate, |
|
|
124 | 125 | endDate, |
|
|
125 | 126 | ext, |
|
|
126 | 127 | startTime=datetime.time(0,0,0), |
|
|
127 | 128 | endTime=datetime.time(23,59,59), |
|
|
128 | 129 | secStart = 0, |
|
|
129 | 130 | secEnd = numpy.inf, |
|
|
130 | 131 | walk=True): |
|
|
131 | 132 | |
|
|
132 | 133 | # self.__setParameters(path, startDate, endDate, startTime, endTime, walk) |
|
|
133 | 134 | # |
|
|
134 | 135 | # self.__checkPath() |
|
|
135 | 136 | # |
|
|
136 | 137 | # self.__findDataForDates() |
|
|
137 | 138 | # |
|
|
138 | 139 | # self.__selectDataForTimes() |
|
|
139 | 140 | # |
|
|
140 | 141 | # for i in range(len(self.filenameList)): |
|
|
141 | 142 | # print "%s" %(self.filenameList[i]) |
|
|
142 | 143 | |
|
|
143 | 144 | pathList = [] |
|
|
144 | 145 | |
|
|
145 | 146 | if not walk: |
|
|
146 | 147 | #pathList.append(path) |
|
|
147 | 148 | multi_path = path.split(',') |
|
|
148 | 149 | for single_path in multi_path: |
|
|
149 | 150 | pathList.append(single_path) |
|
|
150 | 151 | |
|
|
151 | 152 | else: |
|
|
152 | 153 | #dirList = [] |
|
|
153 | 154 | multi_path = path.split(',') |
|
|
154 | 155 | for single_path in multi_path: |
|
|
155 | 156 | dirList = [] |
|
|
156 | 157 | for thisPath in os.listdir(single_path): |
|
|
157 | 158 | if not os.path.isdir(os.path.join(single_path,thisPath)): |
|
|
158 | 159 | continue |
|
|
159 |
if not is |
|
|
|
160 | if not isDoyFolder(thisPath): | |
|
|
160 | 161 | continue |
|
|
161 | 162 | |
|
|
162 | 163 | dirList.append(thisPath) |
|
|
163 | 164 | |
|
|
164 | 165 | if not(dirList): |
|
|
165 | 166 | return None, None |
|
|
166 | 167 | |
|
|
167 | 168 | thisDate = startDate |
|
|
168 | 169 | |
|
|
169 | 170 | while(thisDate <= endDate): |
|
|
170 | 171 | year = thisDate.timetuple().tm_year |
|
|
171 | 172 | doy = thisDate.timetuple().tm_yday |
|
|
172 | 173 | |
|
|
173 | 174 | matchlist = fnmatch.filter(dirList, '?' + '%4.4d%3.3d' % (year,doy) + '*') |
|
|
174 | 175 | if len(matchlist) == 0: |
|
|
175 | 176 | thisDate += datetime.timedelta(1) |
|
|
176 | 177 | continue |
|
|
177 | 178 | for match in matchlist: |
|
|
178 | 179 | pathList.append(os.path.join(single_path,match)) |
|
|
179 | 180 | |
|
|
180 | 181 | thisDate += datetime.timedelta(1) |
|
|
181 | 182 | |
|
|
182 | 183 | if pathList == []: |
|
|
183 | 184 | print "Any folder was found for the date range: %s-%s" %(startDate, endDate) |
|
|
184 | 185 | return None, None |
|
|
185 | 186 | |
|
|
186 | 187 | print "%d folder(s) was(were) found for the date range: %s - %s" %(len(pathList), startDate, endDate) |
|
|
187 | 188 | |
|
|
188 | 189 | filenameList = [] |
|
|
189 | 190 | datetimeList = [] |
|
|
190 | 191 | pathDict = {} |
|
|
191 | 192 | filenameList_to_sort = [] |
|
|
192 | 193 | |
|
|
193 | 194 | for i in range(len(pathList)): |
|
|
194 | 195 | |
|
|
195 | 196 | thisPath = pathList[i] |
|
|
196 | 197 | |
|
|
197 | 198 | fileList = glob.glob1(thisPath, "*%s" %ext) |
|
|
198 | 199 | fileList.sort() |
|
|
199 | 200 | pathDict.setdefault(fileList[0]) |
|
|
200 | 201 | pathDict[fileList[0]] = i |
|
|
201 | 202 | filenameList_to_sort.append(fileList[0]) |
|
|
202 | 203 | |
|
|
203 | 204 | filenameList_to_sort.sort() |
|
|
204 | 205 | |
|
|
205 | 206 | for file in filenameList_to_sort: |
|
|
206 | 207 | thisPath = pathList[pathDict[file]] |
|
|
207 | 208 | |
|
|
208 | 209 | fileList = glob.glob1(thisPath, "*%s" %ext) |
|
|
209 | 210 | fileList.sort() |
|
|
210 | 211 | |
|
|
211 | 212 | for file in fileList: |
|
|
212 | 213 | |
|
|
213 | 214 | filename = os.path.join(thisPath,file) |
|
|
214 | 215 | thisDatetime = self.__isFileinThisTime(filename, secStart, secEnd) |
|
|
215 | 216 | |
|
|
216 | 217 | if not(thisDatetime): |
|
|
217 | 218 | continue |
|
|
218 | 219 | |
|
|
219 | 220 | filenameList.append(filename) |
|
|
220 | 221 | datetimeList.append(thisDatetime) |
|
|
221 | 222 | |
|
|
222 | 223 | if not(filenameList): |
|
|
223 | 224 | print "Any file was found for the time range %s - %s" %(startTime, endTime) |
|
|
224 | 225 | return None, None |
|
|
225 | 226 | |
|
|
226 | 227 | print "%d file(s) was(were) found for the time range: %s - %s" %(len(filenameList), startTime, endTime) |
|
|
227 | 228 | |
|
|
228 | 229 | |
|
|
229 | 230 | for i in range(len(filenameList)): |
|
|
230 | 231 | print "%s -> [%s]" %(filenameList[i], datetimeList[i].ctime()) |
|
|
231 | 232 | |
|
|
232 | 233 | self.filenameList = filenameList |
|
|
233 | 234 | self.datetimeList = datetimeList |
|
|
234 | 235 | |
|
|
235 | 236 | return pathList, filenameList |
|
|
236 | 237 | |
|
|
237 | 238 | def __isFileinThisTime(self, filename, startSeconds, endSeconds): |
|
|
238 | 239 | """ |
|
|
239 | 240 | Retorna 1 si el archivo de datos se encuentra dentro del rango de horas especificado. |
|
|
240 | 241 | |
|
|
241 | 242 | Inputs: |
|
|
242 | 243 | filename : nombre completo del archivo de datos en formato Jicamarca (.r) |
|
|
243 | 244 | |
|
|
244 | 245 | startTime : tiempo inicial del rango seleccionado en formato datetime.time |
|
|
245 | 246 | |
|
|
246 | 247 | endTime : tiempo final del rango seleccionado en formato datetime.time |
|
|
247 | 248 | |
|
|
248 | 249 | Return: |
|
|
249 | 250 | Boolean : Retorna True si el archivo de datos contiene datos en el rango de |
|
|
250 | 251 | fecha especificado, de lo contrario retorna False. |
|
|
251 | 252 | |
|
|
252 | 253 | Excepciones: |
|
|
253 | 254 | Si el archivo no existe o no puede ser abierto |
|
|
254 | 255 | Si la cabecera no puede ser leida. |
|
|
255 | 256 | |
|
|
256 | 257 | """ |
|
|
257 | 258 | |
|
|
258 | 259 | try: |
|
|
259 | 260 | fp = fp = h5py.File(filename,'r') |
|
|
260 | 261 | except IOError: |
|
|
261 | 262 | traceback.print_exc() |
|
|
262 | 263 | raise IOError, "The file %s can't be opened" %(filename) |
|
|
263 | 264 | |
|
|
264 | 265 | grp = fp['Data'] |
|
|
265 | 266 | timeAux = grp['time'] |
|
|
266 | 267 | time0 = timeAux[:][0].astype(numpy.float) #Time Vector |
|
|
267 | 268 | |
|
|
268 | 269 | fp.close() |
|
|
269 | 270 | |
|
|
270 | 271 | if self.timezone == 'lt': |
|
|
271 | 272 | time0 -= 5*3600 |
|
|
272 | 273 | |
|
|
273 | 274 | boolTimer = numpy.logical_and(time0 >= startSeconds,time0 < endSeconds) |
|
|
274 | 275 | |
|
|
275 | 276 | if not (numpy.any(boolTimer)): |
|
|
276 | 277 | return None |
|
|
277 | 278 | |
|
|
278 | 279 | thisDatetime = datetime.datetime.utcfromtimestamp(time0[0]) |
|
|
279 | 280 | return thisDatetime |
|
|
280 | 281 | |
|
|
281 | 282 | def __checkPath(self): |
|
|
282 | 283 | if os.path.exists(self.path): |
|
|
283 | 284 | self.status = 1 |
|
|
284 | 285 | else: |
|
|
285 | 286 | self.status = 0 |
|
|
286 | 287 | print 'Path:%s does not exists'%self.path |
|
|
287 | 288 | |
|
|
288 | 289 | return |
|
|
289 | 290 | |
|
|
290 | 291 | def __setNextFileOffline(self): |
|
|
291 | 292 | idFile = self.fileIndex |
|
|
292 | 293 | idFile += 1 |
|
|
293 | 294 | |
|
|
294 | 295 | if not(idFile < len(self.filenameList)): |
|
|
295 | 296 | print "No more Files" |
|
|
296 | 297 | return 0 |
|
|
297 | 298 | |
|
|
298 | 299 | filename = self.filenameList[idFile] |
|
|
299 | 300 | |
|
|
300 | 301 | filePointer = h5py.File(filename,'r') |
|
|
301 | 302 | |
|
|
302 | 303 | self.flagIsNewFile = 1 |
|
|
303 | 304 | self.fileIndex = idFile |
|
|
304 | 305 | self.filename = filename |
|
|
305 | 306 | |
|
|
306 | 307 | self.fp = filePointer |
|
|
307 | 308 | |
|
|
308 | 309 | print "Setting the file: %s"%self.filename |
|
|
309 | 310 | |
|
|
310 | 311 | self.__readMetadata() |
|
|
311 | 312 | self.__setBlockList() |
|
|
312 | 313 | # self.nRecords = self.fp['Data'].attrs['blocksPerFile'] |
|
|
313 | 314 | self.nRecords = self.fp['Data'].attrs['nRecords'] |
|
|
314 | 315 | self.blockIndex = 0 |
|
|
315 | 316 | return 1 |
|
|
316 | 317 | |
|
|
317 | 318 | def __setBlockList(self): |
|
|
318 | 319 | ''' |
|
|
319 | 320 | self.fp |
|
|
320 | 321 | self.startDateTime |
|
|
321 | 322 | self.endDateTime |
|
|
322 | 323 | |
|
|
323 | 324 | self.blockList |
|
|
324 | 325 | self.blocksPerFile |
|
|
325 | 326 | |
|
|
326 | 327 | ''' |
|
|
327 | 328 | filePointer = self.fp |
|
|
328 | 329 | secStart = self.secStart |
|
|
329 | 330 | secEnd = self.secEnd |
|
|
330 | 331 | |
|
|
331 | 332 | grp = filePointer['Data'] |
|
|
332 | 333 | timeVector = grp['time'].value.astype(numpy.float)[0] |
|
|
333 | 334 | |
|
|
334 | 335 | if self.timezone == 'lt': |
|
|
335 | 336 | timeVector -= 5*3600 |
|
|
336 | 337 | |
|
|
337 | 338 | ind = numpy.where(numpy.logical_and(timeVector >= secStart , timeVector < secEnd))[0] |
|
|
338 | 339 | |
|
|
339 | 340 | self.blockList = ind |
|
|
340 | 341 | self.blocksPerFile = len(ind) |
|
|
341 | 342 | |
|
|
342 | 343 | return |
|
|
343 | 344 | |
|
|
344 | 345 | def __readMetadata(self): |
|
|
345 | 346 | ''' |
|
|
346 | 347 | self.pathMeta |
|
|
347 | 348 | |
|
|
348 | 349 | self.listShapes |
|
|
349 | 350 | self.listMetaname |
|
|
350 | 351 | self.listMeta |
|
|
351 | 352 | |
|
|
352 | 353 | ''' |
|
|
353 | 354 | |
|
|
354 | 355 | grp = self.fp['Data'] |
|
|
355 | 356 | pathMeta = os.path.join(self.path, grp.attrs['metadata']) |
|
|
356 | 357 | |
|
|
357 | 358 | if pathMeta == self.pathMeta: |
|
|
358 | 359 | return |
|
|
359 | 360 | else: |
|
|
360 | 361 | self.pathMeta = pathMeta |
|
|
361 | 362 | |
|
|
362 | 363 | filePointer = h5py.File(self.pathMeta,'r') |
|
|
363 | 364 | groupPointer = filePointer['Metadata'] |
|
|
364 | 365 | |
|
|
365 | 366 | listMetaname = [] |
|
|
366 | 367 | listMetadata = [] |
|
|
367 | 368 | for item in groupPointer.items(): |
|
|
368 | 369 | name = item[0] |
|
|
369 | 370 | |
|
|
370 | 371 | if name=='array dimensions': |
|
|
371 | 372 | table = groupPointer[name][:] |
|
|
372 | 373 | listShapes = {} |
|
|
373 | 374 | for shapes in table: |
|
|
374 | 375 | listShapes[shapes[0]] = numpy.array([shapes[1],shapes[2],shapes[3],shapes[4]]) |
|
|
375 | 376 | else: |
|
|
376 | 377 | data = groupPointer[name].value |
|
|
377 | 378 | listMetaname.append(name) |
|
|
378 | 379 | listMetadata.append(data) |
|
|
379 | 380 | |
|
|
380 | 381 | if name=='type': |
|
|
381 | 382 | self.__initDataOut(data) |
|
|
382 | 383 | |
|
|
383 | 384 | filePointer.close() |
|
|
384 | 385 | |
|
|
385 | 386 | self.listShapes = listShapes |
|
|
386 | 387 | self.listMetaname = listMetaname |
|
|
387 | 388 | self.listMeta = listMetadata |
|
|
388 | 389 | |
|
|
389 | 390 | return |
|
|
390 | 391 | |
|
|
391 | 392 | def __readData(self): |
|
|
392 | 393 | grp = self.fp['Data'] |
|
|
393 | 394 | listdataname = [] |
|
|
394 | 395 | listdata = [] |
|
|
395 | 396 | |
|
|
396 | 397 | for item in grp.items(): |
|
|
397 | 398 | name = item[0] |
|
|
398 | 399 | |
|
|
399 | 400 | if name == 'time': |
|
|
400 | 401 | listdataname.append('utctime') |
|
|
401 | 402 | timeAux = grp[name].value.astype(numpy.float)[0] |
|
|
402 | 403 | listdata.append(timeAux) |
|
|
403 | 404 | continue |
|
|
404 | 405 | |
|
|
405 | 406 | listdataname.append(name) |
|
|
406 | 407 | array = self.__setDataArray(self.nRecords, grp[name],self.listShapes[name]) |
|
|
407 | 408 | listdata.append(array) |
|
|
408 | 409 | |
|
|
409 | 410 | self.listDataname = listdataname |
|
|
410 | 411 | self.listData = listdata |
|
|
411 | 412 | return |
|
|
412 | 413 | |
|
|
413 | 414 | def __setDataArray(self, nRecords, dataset, shapes): |
|
|
414 | 415 | |
|
|
415 | 416 | nChannels = shapes[0] #Dimension 0 |
|
|
416 | 417 | |
|
|
417 | 418 | nPoints = shapes[1] #Dimension 1, number of Points or Parameters |
|
|
418 | 419 | |
|
|
419 | 420 | nSamples = shapes[2] #Dimension 2, number of samples or ranges |
|
|
420 | 421 | |
|
|
421 | 422 | mode = shapes[3] |
|
|
422 | 423 | |
|
|
423 | 424 | # if nPoints>1: |
|
|
424 | 425 | # arrayData = numpy.zeros((nRecords,nChannels,nPoints,nSamples)) |
|
|
425 | 426 | # else: |
|
|
426 | 427 | # arrayData = numpy.zeros((nRecords,nChannels,nSamples)) |
|
|
427 | 428 | # |
|
|
428 | 429 | # chn = 'channel' |
|
|
429 | 430 | # |
|
|
430 | 431 | # for i in range(nChannels): |
|
|
431 | 432 | # |
|
|
432 | 433 | # data = dataset[chn + str(i)].value |
|
|
433 | 434 | # |
|
|
434 | 435 | # if nPoints>1: |
|
|
435 | 436 | # data = numpy.rollaxis(data,2) |
|
|
436 | 437 | # |
|
|
437 | 438 | # arrayData[:,i,:] = data |
|
|
438 | 439 | |
|
|
439 | 440 | arrayData = numpy.zeros((nRecords,nChannels,nPoints,nSamples)) |
|
|
440 | 441 | doSqueeze = False |
|
|
441 | 442 | if mode == 0: |
|
|
442 | 443 | strds = 'channel' |
|
|
443 | 444 | nDatas = nChannels |
|
|
444 | 445 | newShapes = (nRecords,nPoints,nSamples) |
|
|
445 | 446 | if nPoints == 1: |
|
|
446 | 447 | doSqueeze = True |
|
|
447 | 448 | axisSqueeze = 2 |
|
|
448 | 449 | else: |
|
|
449 | 450 | strds = 'param' |
|
|
450 | 451 | nDatas = nPoints |
|
|
451 | 452 | newShapes = (nRecords,nChannels,nSamples) |
|
|
452 | 453 | if nChannels == 1: |
|
|
453 | 454 | doSqueeze = True |
|
|
454 | 455 | axisSqueeze = 1 |
|
|
455 | 456 | |
|
|
456 | 457 | for i in range(nDatas): |
|
|
457 | 458 | |
|
|
458 | 459 | data = dataset[strds + str(i)].value |
|
|
459 | 460 | data = data.reshape(newShapes) |
|
|
460 | 461 | |
|
|
461 | 462 | if mode == 0: |
|
|
462 | 463 | arrayData[:,i,:,:] = data |
|
|
463 | 464 | else: |
|
|
464 | 465 | arrayData[:,:,i,:] = data |
|
|
465 | 466 | |
|
|
466 | 467 | if doSqueeze: |
|
|
467 | 468 | arrayData = numpy.squeeze(arrayData, axis=axisSqueeze) |
|
|
468 | 469 | |
|
|
469 | 470 | return arrayData |
|
|
470 | 471 | |
|
|
471 | 472 | def __initDataOut(self, type): |
|
|
472 | 473 | |
|
|
473 | 474 | # if type =='Parameters': |
|
|
474 | 475 | # self.dataOut = Parameters() |
|
|
475 | 476 | # elif type =='Spectra': |
|
|
476 | 477 | # self.dataOut = Spectra() |
|
|
477 | 478 | # elif type =='Voltage': |
|
|
478 | 479 | # self.dataOut = Voltage() |
|
|
479 | 480 | # elif type =='Correlation': |
|
|
480 | 481 | # self.dataOut = Correlation() |
|
|
481 | 482 | |
|
|
482 | 483 | return |
|
|
483 | 484 | |
|
|
484 | 485 | def __setDataOut(self): |
|
|
485 | 486 | listMeta = self.listMeta |
|
|
486 | 487 | listMetaname = self.listMetaname |
|
|
487 | 488 | listDataname = self.listDataname |
|
|
488 | 489 | listData = self.listData |
|
|
489 | 490 | |
|
|
490 | 491 | blockIndex = self.blockIndex |
|
|
491 | 492 | blockList = self.blockList |
|
|
492 | 493 | |
|
|
493 | 494 | for i in range(len(listMeta)): |
|
|
494 | 495 | setattr(self.dataOut,listMetaname[i],listMeta[i]) |
|
|
495 | 496 | |
|
|
496 | 497 | for j in range(len(listData)): |
|
|
497 | 498 | if listDataname[j]=='utctime': |
|
|
498 | 499 | # setattr(self.dataOut,listDataname[j],listData[j][blockList[blockIndex]]) |
|
|
499 | 500 | setattr(self.dataOut,'utctimeInit',listData[j][blockList[blockIndex]]) |
|
|
500 | 501 | continue |
|
|
501 | 502 | |
|
|
502 | 503 | setattr(self.dataOut,listDataname[j],listData[j][blockList[blockIndex],:]) |
|
|
503 | 504 | |
|
|
504 | 505 | return self.dataOut.data_param |
|
|
505 | 506 | |
|
|
506 | 507 | def getData(self): |
|
|
507 | 508 | |
|
|
508 | 509 | # if self.flagNoMoreFiles: |
|
|
509 | 510 | # self.dataOut.flagNoData = True |
|
|
510 | 511 | # print 'Process finished' |
|
|
511 | 512 | # return 0 |
|
|
512 | 513 | # |
|
|
513 | 514 | if self.blockIndex==self.blocksPerFile: |
|
|
514 | 515 | if not( self.__setNextFileOffline() ): |
|
|
515 | 516 | self.dataOut.flagNoData = True |
|
|
516 | 517 | return 0 |
|
|
517 | 518 | |
|
|
518 | 519 | # |
|
|
519 | 520 | # if self.datablock == None: # setear esta condicion cuando no hayan datos por leers |
|
|
520 | 521 | # self.dataOut.flagNoData = True |
|
|
521 | 522 | # return 0 |
|
|
522 | 523 | |
|
|
523 | 524 | self.__readData() |
|
|
524 | 525 | self.__setDataOut() |
|
|
525 | 526 | self.dataOut.flagNoData = False |
|
|
526 | 527 | |
|
|
527 | 528 | self.blockIndex += 1 |
|
|
528 | 529 | |
|
|
529 | 530 | return |
|
|
530 | 531 | |
|
|
531 | 532 | def run(self, **kwargs): |
|
|
532 | 533 | |
|
|
533 | 534 | if not(self.isConfig): |
|
|
534 | 535 | self.setup(**kwargs) |
|
|
535 | 536 | # self.setObjProperties() |
|
|
536 | 537 | self.isConfig = True |
|
|
537 | 538 | |
|
|
538 | 539 | self.getData() |
|
|
539 | 540 | |
|
|
540 | 541 | return |
|
|
541 | 542 | |
|
|
542 | 543 | class HDF5Writer(Operation): |
|
|
543 | 544 | |
|
|
544 | 545 | ext = ".hdf5" |
|
|
545 | 546 | |
|
|
546 | 547 | optchar = "D" |
|
|
547 | 548 | |
|
|
548 | 549 | metaoptchar = "M" |
|
|
549 | 550 | |
|
|
550 | 551 | metaFile = None |
|
|
551 | 552 | |
|
|
553 | filename = None | |
|
|
554 | ||
|
|
552 | 555 | path = None |
|
|
553 | 556 | |
|
|
554 | 557 | setFile = None |
|
|
555 | 558 | |
|
|
556 | 559 | fp = None |
|
|
557 | 560 | |
|
|
558 | 561 | grp = None |
|
|
559 | 562 | |
|
|
560 | 563 | ds = None |
|
|
561 | 564 | |
|
|
562 | 565 | firsttime = True |
|
|
563 | 566 | |
|
|
564 | 567 | #Configurations |
|
|
565 | 568 | |
|
|
566 | 569 | blocksPerFile = None |
|
|
567 | 570 | |
|
|
568 | 571 | blockIndex = None |
|
|
569 | 572 | |
|
|
570 | 573 | dataOut = None |
|
|
571 | 574 | |
|
|
572 | 575 | #Data Arrays |
|
|
573 | 576 | |
|
|
574 | 577 | dataList = None |
|
|
575 | 578 | |
|
|
576 | 579 | metadataList = None |
|
|
577 | 580 | |
|
|
578 | 581 | arrayDim = None |
|
|
579 | 582 | |
|
|
580 | 583 | tableDim = None |
|
|
581 | 584 | |
|
|
582 | 585 | # dtype = [('arrayName', 'S20'),('nChannels', 'i'), ('nPoints', 'i'), ('nSamples', 'i'),('mode', 'b')] |
|
|
583 | 586 | |
|
|
584 | 587 | dtype = [('arrayName', 'S20'),('nDimensions', 'i'), ('dim2', 'i'), ('dim1', 'i'),('dim0', 'i'),('mode', 'b')] |
|
|
585 | 588 | |
|
|
586 | 589 | mode = None |
|
|
587 | 590 | |
|
|
588 | 591 | nDatas = None #Number of datasets to be stored per array |
|
|
589 | ||
|
|
592 | ||
|
|
590 | 593 | nDims = None #Number Dimensions in each dataset |
|
|
591 | 594 | |
|
|
595 | nDimsForDs = None | |
|
|
596 | ||
|
|
592 | 597 | def __init__(self): |
|
|
593 | 598 | |
|
|
594 | 599 | Operation.__init__(self) |
|
|
595 | 600 | self.isConfig = False |
|
|
596 | 601 | return |
|
|
597 | 602 | |
|
|
598 | 603 | |
|
|
599 | 604 | def setup(self, dataOut, **kwargs): |
|
|
600 | 605 | |
|
|
601 | 606 | self.path = kwargs['path'] |
|
|
602 | 607 | |
|
|
603 | 608 | if kwargs.has_key('ext'): |
|
|
604 | 609 | self.ext = kwargs['ext'] |
|
|
605 | 610 | |
|
|
606 | 611 | if kwargs.has_key('blocksPerFile'): |
|
|
607 | 612 | self.blocksPerFile = kwargs['blocksPerFile'] |
|
|
608 | 613 | else: |
|
|
609 | 614 | self.blocksPerFile = 10 |
|
|
610 | 615 | |
|
|
611 | 616 | self.metadataList = kwargs['metadataList'] |
|
|
612 | 617 | |
|
|
613 | 618 | self.dataList = kwargs['dataList'] |
|
|
614 | 619 | |
|
|
615 | 620 | self.dataOut = dataOut |
|
|
616 | 621 | |
|
|
617 | 622 | if kwargs.has_key('mode'): |
|
|
618 | 623 | mode = kwargs['mode'] |
|
|
619 | 624 | |
|
|
620 | 625 | if type(mode) == int: |
|
|
621 | 626 | mode = numpy.zeros(len(self.dataList)) + mode |
|
|
622 | 627 | else: |
|
|
623 | 628 | mode = numpy.zeros(len(self.dataList)) |
|
|
624 | 629 | |
|
|
625 | 630 | self.mode = mode |
|
|
626 | 631 | |
|
|
627 | 632 | arrayDim = numpy.zeros((len(self.dataList),5)) |
|
|
628 | 633 | |
|
|
629 | 634 | #Table dimensions |
|
|
630 | 635 | |
|
|
631 | 636 | dtype0 = self.dtype |
|
|
632 | 637 | |
|
|
633 | 638 | tableList = [] |
|
|
634 | 639 | |
|
|
635 | 640 | for i in range(len(self.dataList)): |
|
|
636 | 641 | |
|
|
637 | 642 | dataAux = getattr(self.dataOut, self.dataList[i]) |
|
|
638 | 643 | |
|
|
639 | 644 | if type(dataAux)==float or type(dataAux)==int: |
|
|
640 | 645 | arrayDim[i,0] = 1 |
|
|
641 | 646 | else: |
|
|
642 | 647 | arrayDim0 = dataAux.shape |
|
|
643 | 648 | arrayDim[i,0] = len(arrayDim0) |
|
|
644 | 649 | arrayDim[i,4] = mode[i] |
|
|
645 | 650 | |
|
|
646 | 651 | if len(arrayDim0) == 3: |
|
|
647 | 652 | arrayDim[i,1:-1] = numpy.array(arrayDim0) |
|
|
648 | 653 | elif len(arrayDim0) == 2: |
|
|
649 | 654 | arrayDim[i,2:-1] = numpy.array(arrayDim0) #nHeights |
|
|
650 | 655 | elif len(arrayDim0) == 1: |
|
|
651 | 656 | arrayDim[i,3] = arrayDim0 |
|
|
652 | 657 | elif len(arrayDim0) == 0: |
|
|
653 | 658 | arrayDim[i,0] = 1 |
|
|
654 | 659 | arrayDim[i,3] = 1 |
|
|
655 | 660 | |
|
|
656 | 661 | table = numpy.array((self.dataList[i],) + tuple(arrayDim[i,:]),dtype = dtype0) |
|
|
657 | 662 | tableList.append(table) |
|
|
658 | 663 | |
|
|
659 | 664 | self.arrayDim = arrayDim |
|
|
660 | 665 | self.tableDim = numpy.array(tableList, dtype = dtype0) |
|
|
661 | 666 | self.blockIndex = 0 |
|
|
662 | 667 | |
|
|
663 | 668 | return |
|
|
664 | 669 | |
|
|
665 | 670 | def putMetadata(self): |
|
|
666 | 671 | |
|
|
667 | 672 | fp = self.createMetadataFile() |
|
|
668 | 673 | self.writeMetadata(fp) |
|
|
669 | 674 | fp.close() |
|
|
670 | 675 | return |
|
|
671 | 676 | |
|
|
672 | 677 | def createMetadataFile(self): |
|
|
673 | 678 | ext = self.ext |
|
|
674 | 679 | path = self.path |
|
|
675 | 680 | setFile = self.setFile |
|
|
676 | 681 | |
|
|
677 | 682 | timeTuple = time.localtime(self.dataOut.utctime) |
|
|
678 | subfolder = '' | |
|
|
679 | ||
|
|
683 | ||
|
|
684 | subfolder = '' | |
|
|
680 | 685 | fullpath = os.path.join( path, subfolder ) |
|
|
686 | ||
|
|
681 | 687 | if not( os.path.exists(fullpath) ): |
|
|
682 | 688 | os.mkdir(fullpath) |
|
|
683 | 689 | setFile = -1 #inicializo mi contador de seteo |
|
|
690 | ||
|
|
691 | subfolder = 'd%4.4d%3.3d' % (timeTuple.tm_year,timeTuple.tm_yday) | |
|
|
692 | fullpath = os.path.join( path, subfolder ) | |
|
|
693 | ||
|
|
694 | if not( os.path.exists(fullpath) ): | |
|
|
695 | os.mkdir(fullpath) | |
|
|
696 | setFile = -1 #inicializo mi contador de seteo | |
|
|
697 | ||
|
|
684 | 698 | else: |
|
|
685 | 699 | filesList = os.listdir( fullpath ) |
|
|
700 | filesList = sorted( filesList, key=str.lower ) | |
|
|
686 | 701 | if len( filesList ) > 0: |
|
|
687 |
filesList = |
|
|
|
702 | filesList = [k for k in filesList if 'M' in k] | |
|
|
688 | 703 | filen = filesList[-1] |
|
|
689 | 704 | # el filename debera tener el siguiente formato |
|
|
690 | 705 | # 0 1234 567 89A BCDE (hex) |
|
|
691 | 706 | # x YYYY DDD SSS .ext |
|
|
692 | 707 | if isNumber( filen[8:11] ): |
|
|
693 | 708 | setFile = int( filen[8:11] ) #inicializo mi contador de seteo al seteo del ultimo file |
|
|
694 | 709 | else: |
|
|
695 | 710 | setFile = -1 |
|
|
696 | 711 | else: |
|
|
697 | 712 | setFile = -1 #inicializo mi contador de seteo |
|
|
698 | 713 | |
|
|
699 | 714 | setFile += 1 |
|
|
700 | ||
|
|
715 | ||
|
|
701 | 716 | file = '%s%4.4d%3.3d%3.3d%s' % (self.metaoptchar, |
|
|
702 | 717 | timeTuple.tm_year, |
|
|
703 | 718 | timeTuple.tm_yday, |
|
|
704 | 719 | setFile, |
|
|
705 | 720 | ext ) |
|
|
706 | 721 | |
|
|
707 | 722 | filename = os.path.join( path, subfolder, file ) |
|
|
708 | 723 | self.metaFile = file |
|
|
709 | 724 | #Setting HDF5 File |
|
|
710 | 725 | fp = h5py.File(filename,'w') |
|
|
711 | 726 | |
|
|
712 | 727 | return fp |
|
|
713 | 728 | |
|
|
714 | 729 | def writeMetadata(self, fp): |
|
|
715 | 730 | |
|
|
716 | 731 | grp = fp.create_group("Metadata") |
|
|
717 | 732 | grp.create_dataset('array dimensions', data = self.tableDim, dtype = self.dtype) |
|
|
718 | 733 | |
|
|
719 | 734 | for i in range(len(self.metadataList)): |
|
|
720 | 735 | grp.create_dataset(self.metadataList[i], data=getattr(self.dataOut, self.metadataList[i])) |
|
|
721 | 736 | return |
|
|
722 | 737 | |
|
|
723 | 738 | def setNextFile(self): |
|
|
724 | 739 | |
|
|
725 | 740 | ext = self.ext |
|
|
726 | 741 | path = self.path |
|
|
727 | 742 | setFile = self.setFile |
|
|
728 | 743 | mode = self.mode |
|
|
729 | ||
|
|
730 | if self.fp != None: | |
|
|
731 | self.fp.close() | |
|
|
732 | ||
|
|
744 | ||
|
|
733 | 745 | timeTuple = time.localtime(self.dataOut.utctime) |
|
|
734 | 746 | subfolder = 'd%4.4d%3.3d' % (timeTuple.tm_year,timeTuple.tm_yday) |
|
|
735 | 747 | |
|
|
736 | 748 | fullpath = os.path.join( path, subfolder ) |
|
|
737 | if not( os.path.exists(fullpath) ): | |
|
|
738 |
|
|
|
|
739 | setFile = -1 #inicializo mi contador de seteo | |
|
|
740 | else: | |
|
|
749 | ||
|
|
750 | if os.path.exists(fullpath): | |
|
|
741 | 751 | filesList = os.listdir( fullpath ) |
|
|
752 | filesList = [k for k in filesList if 'D' in k] | |
|
|
742 | 753 | if len( filesList ) > 0: |
|
|
743 | 754 | filesList = sorted( filesList, key=str.lower ) |
|
|
744 | 755 | filen = filesList[-1] |
|
|
745 | 756 | # el filename debera tener el siguiente formato |
|
|
746 | 757 | # 0 1234 567 89A BCDE (hex) |
|
|
747 | 758 | # x YYYY DDD SSS .ext |
|
|
748 | 759 | if isNumber( filen[8:11] ): |
|
|
749 | 760 | setFile = int( filen[8:11] ) #inicializo mi contador de seteo al seteo del ultimo file |
|
|
750 | 761 | else: |
|
|
751 | 762 | setFile = -1 |
|
|
752 | 763 | else: |
|
|
753 | 764 | setFile = -1 #inicializo mi contador de seteo |
|
|
754 | 765 | |
|
|
755 | 766 | setFile += 1 |
|
|
756 | 767 | |
|
|
757 | 768 | file = '%s%4.4d%3.3d%3.3d%s' % (self.optchar, |
|
|
758 | 769 | timeTuple.tm_year, |
|
|
759 | 770 | timeTuple.tm_yday, |
|
|
760 | 771 | setFile, |
|
|
761 | 772 | ext ) |
|
|
762 | 773 | |
|
|
763 | 774 | filename = os.path.join( path, subfolder, file ) |
|
|
764 | 775 | |
|
|
765 | 776 | #Setting HDF5 File |
|
|
766 | 777 | fp = h5py.File(filename,'w') |
|
|
767 | 778 | grp = fp.create_group("Data") |
|
|
768 | 779 | grp.attrs['metadata'] = self.metaFile |
|
|
769 | 780 | |
|
|
770 | 781 | # grp.attrs['blocksPerFile'] = 0 |
|
|
771 | 782 | |
|
|
772 | 783 | ds = [] |
|
|
773 | 784 | data = [] |
|
|
785 | nDimsForDs = [] | |
|
|
774 | 786 | |
|
|
775 | 787 | nDatas = numpy.zeros(len(self.dataList)) |
|
|
776 | 788 | nDims = self.arrayDim[:,0] |
|
|
777 | 789 | |
|
|
790 | nDim1 = self.arrayDim[:,2] | |
|
|
791 | nDim0 = self.arrayDim[:,3] | |
|
|
792 | ||
|
|
778 | 793 | for i in range(len(self.dataList)): |
|
|
779 | 794 | |
|
|
780 | 795 | if nDims[i]==1: |
|
|
781 |
ds0 = grp.create_dataset(self.dataList[i], (1,1), maxshape=(1, |
|
|
|
796 | # ds0 = grp.create_dataset(self.dataList[i], (1,1), maxshape=(1,self.blocksPerFile) , chunks = True, dtype='S20') | |
|
|
797 | ds0 = grp.create_dataset(self.dataList[i], (1,1), maxshape=(1,self.blocksPerFile) , chunks = True, dtype=numpy.float64) | |
|
|
782 | 798 | ds.append(ds0) |
|
|
783 | 799 | data.append([]) |
|
|
784 | ||
|
|
800 | nDimsForDs.append(nDims[i]) | |
|
|
785 | 801 | else: |
|
|
786 | 802 | |
|
|
787 | 803 | if mode[i]==0: |
|
|
788 | 804 | strMode = "channel" |
|
|
789 | 805 | nDatas[i] = self.arrayDim[i,1] |
|
|
790 | 806 | else: |
|
|
791 | 807 | strMode = "param" |
|
|
792 | 808 | nDatas[i] = self.arrayDim[i,2] |
|
|
793 | 809 | |
|
|
794 | 810 | if nDims[i]==2: |
|
|
795 | 811 | nDatas[i] = self.arrayDim[i,2] |
|
|
796 | 812 | |
|
|
797 | 813 | grp0 = grp.create_group(self.dataList[i]) |
|
|
798 | 814 | |
|
|
799 | 815 | for j in range(int(nDatas[i])): |
|
|
800 | 816 | tableName = strMode + str(j) |
|
|
801 | 817 | |
|
|
802 | 818 | if nDims[i] == 3: |
|
|
803 |
ds0 = grp0.create_dataset(tableName, (1, |
|
|
|
819 | ds0 = grp0.create_dataset(tableName, (nDim1[i],nDim0[i],1) , data = numpy.zeros((nDim1[i],nDim0[i],1)) ,maxshape=(None,nDim0[i],None), chunks=True) | |
|
|
804 | 820 | else: |
|
|
805 |
ds0 = grp0.create_dataset(tableName, (1,1) , maxshape=(None, |
|
|
|
821 | ds0 = grp0.create_dataset(tableName, (1,nDim0[i]), data = numpy.zeros((1,nDim0[i])) , maxshape=(None,nDim0[i]), chunks=True) | |
|
|
806 | 822 | |
|
|
807 | 823 | ds.append(ds0) |
|
|
808 | 824 | data.append([]) |
|
|
809 | ||
|
|
825 | nDimsForDs.append(nDims[i]) | |
|
|
810 | 826 | self.nDatas = nDatas |
|
|
811 | 827 | self.nDims = nDims |
|
|
812 | ||
|
|
828 | self.nDimsForDs = nDimsForDs | |
|
|
813 | 829 | #Saving variables |
|
|
814 | 830 | print 'Writing the file: %s'%filename |
|
|
831 | self.filename = filename | |
|
|
815 | 832 | self.fp = fp |
|
|
816 | 833 | self.grp = grp |
|
|
834 | self.grp.attrs.modify('nRecords', 1) | |
|
|
817 | 835 | self.ds = ds |
|
|
818 | 836 | self.data = data |
|
|
819 | 837 | |
|
|
820 | 838 | self.setFile = setFile |
|
|
821 | 839 | self.firsttime = True |
|
|
822 | 840 | self.blockIndex = 0 |
|
|
823 | 841 | return |
|
|
824 | 842 | |
|
|
825 | 843 | def putData(self): |
|
|
844 | ||
|
|
845 | if not self.firsttime: | |
|
|
846 | self.fp.flush() | |
|
|
847 | self.fp.close() | |
|
|
848 | self.readBlock() | |
|
|
849 | ||
|
|
850 | if self.blockIndex == self.blocksPerFile: | |
|
|
851 | ||
|
|
852 | self.setNextFile() | |
|
|
853 | ||
|
|
826 | 854 | self.setBlock() |
|
|
827 | 855 | self.writeBlock() |
|
|
828 | 856 | |
|
|
829 | if self.blockIndex == self.blocksPerFile: | |
|
|
830 | self.setNextFile() | |
|
|
857 | ||
|
|
831 | 858 | return |
|
|
832 | 859 | |
|
|
860 | def readBlock(self): | |
|
|
861 | ||
|
|
862 | ''' | |
|
|
863 | data Array configured | |
|
|
864 | ||
|
|
865 | ||
|
|
866 | self.data | |
|
|
867 | ''' | |
|
|
868 | ds = self.ds | |
|
|
869 | #Setting HDF5 File | |
|
|
870 | fp = h5py.File(self.filename,'r+') | |
|
|
871 | grp = fp["Data"] | |
|
|
872 | ind = 0 | |
|
|
873 | ||
|
|
874 | # grp.attrs['blocksPerFile'] = 0 | |
|
|
875 | for i in range(len(self.dataList)): | |
|
|
876 | ||
|
|
877 | if self.nDims[i]==1: | |
|
|
878 | ds0 = grp[self.dataList[i]] | |
|
|
879 | ds[ind] = ds0 | |
|
|
880 | ind += 1 | |
|
|
881 | else: | |
|
|
882 | if self.mode[i]==0: | |
|
|
883 | strMode = "channel" | |
|
|
884 | else: | |
|
|
885 | strMode = "param" | |
|
|
886 | ||
|
|
887 | grp0 = grp[self.dataList[i]] | |
|
|
888 | ||
|
|
889 | for j in range(int(self.nDatas[i])): | |
|
|
890 | tableName = strMode + str(j) | |
|
|
891 | ds0 = grp0[tableName] | |
|
|
892 | ds[ind] = ds0 | |
|
|
893 | ind += 1 | |
|
|
894 | ||
|
|
895 | ||
|
|
896 | self.fp = fp | |
|
|
897 | self.grp = grp | |
|
|
898 | self.ds = ds | |
|
|
899 | ||
|
|
900 | return | |
|
|
901 | ||
|
|
902 | ||
|
|
833 | 903 | def setBlock(self): |
|
|
834 | 904 | ''' |
|
|
835 | 905 | data Array configured |
|
|
836 | 906 | |
|
|
837 | 907 | |
|
|
838 | 908 | self.data |
|
|
839 | 909 | ''' |
|
|
840 | 910 | #Creating Arrays |
|
|
841 | 911 | data = self.data |
|
|
842 | 912 | nDatas = self.nDatas |
|
|
843 | 913 | nDims = self.nDims |
|
|
844 | 914 | mode = self.mode |
|
|
845 | 915 | ind = 0 |
|
|
846 | 916 | |
|
|
847 | 917 | for i in range(len(self.dataList)): |
|
|
848 | 918 | dataAux = getattr(self.dataOut,self.dataList[i]) |
|
|
849 | 919 | |
|
|
850 | 920 | if nDims[i] == 1: |
|
|
851 | data[ind] = numpy.array([str(dataAux)]).reshape((1,1)) | |
|
|
852 | if not self.firsttime: | |
|
|
853 | data[ind] = numpy.hstack((self.ds[ind][:], self.data[ind])) | |
|
|
921 | # data[ind] = numpy.array([str(dataAux)]).reshape((1,1)) | |
|
|
922 | data[ind] = dataAux | |
|
|
923 | # if not self.firsttime: | |
|
|
924 | # data[ind] = numpy.hstack((self.ds[ind][:], self.data[ind])) | |
|
|
854 | 925 | ind += 1 |
|
|
855 | ||
|
|
856 | 926 | else: |
|
|
857 | 927 | for j in range(int(nDatas[i])): |
|
|
858 | 928 | if (mode[i] == 0) or (nDims[i] == 2): #In case division per channel or Dimensions is only 1 |
|
|
859 | 929 | data[ind] = dataAux[j,:] |
|
|
860 | 930 | else: |
|
|
861 | 931 | data[ind] = dataAux[:,j,:] |
|
|
862 | 932 | |
|
|
863 | if nDims[i] == 3: | |
|
|
864 | data[ind] = data[ind].reshape((data[ind].shape[0],data[ind].shape[1],1)) | |
|
|
933 | # if nDims[i] == 3: | |
|
|
934 | # data[ind] = data[ind].reshape((data[ind].shape[0],data[ind].shape[1],1)) | |
|
|
865 | 935 | |
|
|
866 | if not self.firsttime: | |
|
|
867 | data[ind] = numpy.dstack((self.ds[ind][:], data[ind])) | |
|
|
936 | # if not self.firsttime: | |
|
|
937 | # data[ind] = numpy.dstack((self.ds[ind][:], data[ind])) | |
|
|
868 | 938 | |
|
|
869 | else: | |
|
|
870 | data[ind] = data[ind].reshape((1,data[ind].shape[0])) | |
|
|
939 | # else: | |
|
|
940 | # data[ind] = data[ind].reshape((1,data[ind].shape[0])) | |
|
|
871 | 941 | |
|
|
872 | if not self.firsttime: | |
|
|
873 | data[ind] = numpy.vstack((self.ds[ind][:], data[ind])) | |
|
|
942 | # if not self.firsttime: | |
|
|
943 | # data[ind] = numpy.vstack((self.ds[ind][:], data[ind])) | |
|
|
874 | 944 | ind += 1 |
|
|
875 |
|
|
|
|
945 | ||
|
|
876 | 946 | self.data = data |
|
|
877 | 947 | return |
|
|
878 | 948 | |
|
|
879 | 949 | def writeBlock(self): |
|
|
880 | 950 | ''' |
|
|
881 | 951 | Saves the block in the HDF5 file |
|
|
882 | 952 | ''' |
|
|
883 | 953 | for i in range(len(self.ds)): |
|
|
884 | self.ds[i].resize(self.data[i].shape) | |
|
|
885 |
self.ds[i] |
|
|
|
954 | if self.firsttime: | |
|
|
955 | # self.ds[i].resize(self.data[i].shape) | |
|
|
956 | # self.ds[i][self.blockIndex,:] = self.data[i] | |
|
|
957 | if type(self.data[i]) == numpy.ndarray: | |
|
|
958 | nDims1 = len(self.ds[i].shape) | |
|
|
959 | ||
|
|
960 | if nDims1 == 3: | |
|
|
961 | self.data[i] = self.data[i].reshape((self.data[i].shape[0],self.data[i].shape[1],1)) | |
|
|
962 | ||
|
|
963 | self.ds[i].resize(self.data[i].shape) | |
|
|
964 | self.ds[i][:] = self.data[i] | |
|
|
965 | else: | |
|
|
966 | if self.nDimsForDs[i] == 1: | |
|
|
967 | self.ds[i].resize((self.ds[i].shape[0], self.ds[i].shape[1] + 1)) | |
|
|
968 | self.ds[i][0,-1] = self.data[i] | |
|
|
969 | elif self.nDimsForDs[i] == 2: | |
|
|
970 | self.ds[i].resize((self.ds[i].shape[0] + 1,self.ds[i].shape[1])) | |
|
|
971 | self.ds[i][self.blockIndex,:] = self.data[i] | |
|
|
972 | elif self.nDimsForDs[i] == 3: | |
|
|
973 | ||
|
|
974 | dataShape = self.data[i].shape | |
|
|
975 | dsShape = self.ds[i].shape | |
|
|
976 | ||
|
|
977 | if dataShape[0]==dsShape[0]: | |
|
|
978 | self.ds[i].resize((self.ds[i].shape[0],self.ds[i].shape[1],self.ds[i].shape[2]+1)) | |
|
|
979 | self.ds[i][:,:,-1] = self.data[i] | |
|
|
980 | else: | |
|
|
981 | self.ds[i].resize((self.ds[i].shape[0] + dataShape[0],self.ds[i].shape[1],self.ds[i].shape[2])) | |
|
|
982 | self.ds[i][dsShape[0]:,:,0] = self.data[i] | |
|
|
983 | # self.ds[i].append(self.data[i]) | |
|
|
984 | # self.fp.flush() | |
|
|
985 | # if not self.firsttime: | |
|
|
986 | # self.fp.root.Data._v_attrs.nRecords = self.blockIndex | |
|
|
987 | ||
|
|
988 | # if self.firsttime: | |
|
|
989 | # self.fp.close() | |
|
|
990 | # self.readBlock2() | |
|
|
886 | 991 | |
|
|
887 | 992 | self.blockIndex += 1 |
|
|
888 | ||
|
|
889 | self.grp.attrs.modify('nRecords', self.blockIndex) | |
|
|
890 | ||
|
|
891 | 993 | self.firsttime = False |
|
|
892 | 994 | return |
|
|
893 | 995 | |
|
|
894 | 996 | def run(self, dataOut, **kwargs): |
|
|
895 | 997 | if not(self.isConfig): |
|
|
896 | 998 | self.setup(dataOut, **kwargs) |
|
|
897 | 999 | self.isConfig = True |
|
|
898 | 1000 | self.putMetadata() |
|
|
899 | 1001 | self.setNextFile() |
|
|
900 | 1002 | |
|
|
901 | 1003 | self.putData() |
|
|
902 | 1004 | return |
|
|
903 | 1005 | |
| @@ -1,1786 +1,2144 | |||
|
|
1 | 1 | import numpy |
|
|
2 | 2 | import math |
|
|
3 | 3 | from scipy import optimize |
|
|
4 | 4 | from scipy import interpolate |
|
|
5 | 5 | from scipy import signal |
|
|
6 | 6 | from scipy import stats |
|
|
7 | 7 | import re |
|
|
8 | 8 | import datetime |
|
|
9 | 9 | import copy |
|
|
10 | 10 | import sys |
|
|
11 | 11 | import importlib |
|
|
12 | 12 | import itertools |
|
|
13 | 13 | |
|
|
14 | 14 | from jroproc_base import ProcessingUnit, Operation |
|
|
15 | 15 | from schainpy.model.data.jrodata import Parameters |
|
|
16 | 16 | |
|
|
17 | 17 | |
|
|
18 | 18 | class ParametersProc(ProcessingUnit): |
|
|
19 | 19 | |
|
|
20 | 20 | nSeconds = None |
|
|
21 | 21 | |
|
|
22 | 22 | def __init__(self): |
|
|
23 | 23 | ProcessingUnit.__init__(self) |
|
|
24 | 24 | |
|
|
25 | 25 | # self.objectDict = {} |
|
|
26 | 26 | self.buffer = None |
|
|
27 | 27 | self.firstdatatime = None |
|
|
28 | 28 | self.profIndex = 0 |
|
|
29 | 29 | self.dataOut = Parameters() |
|
|
30 | 30 | |
|
|
31 | 31 | def __updateObjFromInput(self): |
|
|
32 | 32 | |
|
|
33 | 33 | self.dataOut.inputUnit = self.dataIn.type |
|
|
34 | 34 | |
|
|
35 | 35 | self.dataOut.timeZone = self.dataIn.timeZone |
|
|
36 | 36 | self.dataOut.dstFlag = self.dataIn.dstFlag |
|
|
37 | 37 | self.dataOut.errorCount = self.dataIn.errorCount |
|
|
38 | 38 | self.dataOut.useLocalTime = self.dataIn.useLocalTime |
|
|
39 | 39 | |
|
|
40 | 40 | self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() |
|
|
41 | 41 | self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy() |
|
|
42 | 42 | self.dataOut.channelList = self.dataIn.channelList |
|
|
43 | 43 | self.dataOut.heightList = self.dataIn.heightList |
|
|
44 | 44 | self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')]) |
|
|
45 | 45 | # self.dataOut.nHeights = self.dataIn.nHeights |
|
|
46 | 46 | # self.dataOut.nChannels = self.dataIn.nChannels |
|
|
47 | 47 | self.dataOut.nBaud = self.dataIn.nBaud |
|
|
48 | 48 | self.dataOut.nCode = self.dataIn.nCode |
|
|
49 | 49 | self.dataOut.code = self.dataIn.code |
|
|
50 | 50 | # self.dataOut.nProfiles = self.dataOut.nFFTPoints |
|
|
51 | 51 | self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock |
|
|
52 | 52 | self.dataOut.utctime = self.firstdatatime |
|
|
53 | 53 | self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada |
|
|
54 | 54 | self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip |
|
|
55 | 55 | # self.dataOut.nCohInt = self.dataIn.nCohInt |
|
|
56 | 56 | # self.dataOut.nIncohInt = 1 |
|
|
57 | 57 | self.dataOut.ippSeconds = self.dataIn.ippSeconds |
|
|
58 | 58 | # self.dataOut.windowOfFilter = self.dataIn.windowOfFilter |
|
|
59 | 59 | self.dataOut.timeInterval = self.dataIn.timeInterval |
|
|
60 | 60 | self.dataOut.heightList = self.dataIn.getHeiRange() |
|
|
61 | 61 | self.dataOut.frequency = self.dataIn.frequency |
|
|
62 | 62 | |
|
|
63 | 63 | def run(self, nSeconds = None, nProfiles = None): |
|
|
64 | 64 | |
|
|
65 | 65 | |
|
|
66 | 66 | |
|
|
67 | 67 | if self.firstdatatime == None: |
|
|
68 | 68 | self.firstdatatime = self.dataIn.utctime |
|
|
69 | 69 | |
|
|
70 | 70 | #---------------------- Voltage Data --------------------------- |
|
|
71 | 71 | |
|
|
72 | 72 | if self.dataIn.type == "Voltage": |
|
|
73 | 73 | self.dataOut.flagNoData = True |
|
|
74 | 74 | if nSeconds != None: |
|
|
75 | 75 | self.nSeconds = nSeconds |
|
|
76 | 76 | self.nProfiles= int(numpy.floor(nSeconds/(self.dataIn.ippSeconds*self.dataIn.nCohInt))) |
|
|
77 | 77 | |
|
|
78 | 78 | if self.buffer == None: |
|
|
79 | 79 | self.buffer = numpy.zeros((self.dataIn.nChannels, |
|
|
80 | 80 | self.nProfiles, |
|
|
81 | 81 | self.dataIn.nHeights), |
|
|
82 | 82 | dtype='complex') |
|
|
83 | 83 | |
|
|
84 | 84 | self.buffer[:,self.profIndex,:] = self.dataIn.data.copy() |
|
|
85 | 85 | self.profIndex += 1 |
|
|
86 | 86 | |
|
|
87 | 87 | if self.profIndex == self.nProfiles: |
|
|
88 | 88 | |
|
|
89 | 89 | self.__updateObjFromInput() |
|
|
90 | 90 | self.dataOut.data_pre = self.buffer.copy() |
|
|
91 | 91 | self.dataOut.paramInterval = nSeconds |
|
|
92 | 92 | self.dataOut.flagNoData = False |
|
|
93 | 93 | |
|
|
94 | 94 | self.buffer = None |
|
|
95 | 95 | self.firstdatatime = None |
|
|
96 | 96 | self.profIndex = 0 |
|
|
97 | 97 | return |
|
|
98 | 98 | |
|
|
99 | 99 | #---------------------- Spectra Data --------------------------- |
|
|
100 | 100 | |
|
|
101 | 101 | if self.dataIn.type == "Spectra": |
|
|
102 | 102 | self.dataOut.data_pre = self.dataIn.data_spc.copy() |
|
|
103 | 103 | self.dataOut.abscissaList = self.dataIn.getVelRange(1) |
|
|
104 | 104 | self.dataOut.noise = self.dataIn.getNoise() |
|
|
105 | 105 | self.dataOut.normFactor = self.dataIn.normFactor |
|
|
106 | 106 | self.dataOut.groupList = self.dataIn.pairsList |
|
|
107 | 107 | self.dataOut.flagNoData = False |
|
|
108 | 108 | |
|
|
109 | 109 | #---------------------- Correlation Data --------------------------- |
|
|
110 | 110 | |
|
|
111 | 111 | if self.dataIn.type == "Correlation": |
|
|
112 | 112 | lagRRange = self.dataIn.lagR |
|
|
113 | 113 | indR = numpy.where(lagRRange == 0)[0][0] |
|
|
114 | 114 | |
|
|
115 | 115 | self.dataOut.data_pre = self.dataIn.data_corr.copy()[:,:,indR,:] |
|
|
116 | 116 | self.dataOut.abscissaList = self.dataIn.getLagTRange(1) |
|
|
117 | 117 | self.dataOut.noise = self.dataIn.noise |
|
|
118 | 118 | self.dataOut.normFactor = self.dataIn.normFactor |
|
|
119 | 119 | self.dataOut.data_SNR = self.dataIn.SNR |
|
|
120 | 120 | self.dataOut.groupList = self.dataIn.pairsList |
|
|
121 | 121 | self.dataOut.flagNoData = False |
|
|
122 | 122 | |
|
|
123 | 123 | #---------------------- Correlation Data --------------------------- |
|
|
124 | 124 | |
|
|
125 | 125 | if self.dataIn.type == "Parameters": |
|
|
126 | 126 | self.dataOut.copy(self.dataIn) |
|
|
127 | 127 | self.dataOut.flagNoData = False |
|
|
128 | 128 | |
|
|
129 | 129 | return True |
|
|
130 | 130 | |
|
|
131 | 131 | self.__updateObjFromInput() |
|
|
132 | 132 | self.firstdatatime = None |
|
|
133 | 133 | self.dataOut.utctimeInit = self.dataIn.utctime |
|
|
134 | 134 | self.dataOut.outputInterval = self.dataIn.timeInterval |
|
|
135 | 135 | |
|
|
136 | 136 | #------------------- Get Moments ---------------------------------- |
|
|
137 | 137 | def GetMoments(self, channelList = None): |
|
|
138 | 138 | ''' |
|
|
139 | 139 | Function GetMoments() |
|
|
140 | 140 | |
|
|
141 | 141 | Input: |
|
|
142 | 142 | channelList : simple channel list to select e.g. [2,3,7] |
|
|
143 | 143 | self.dataOut.data_pre |
|
|
144 | 144 | self.dataOut.abscissaList |
|
|
145 | 145 | self.dataOut.noise |
|
|
146 | 146 | |
|
|
147 | 147 | Affected: |
|
|
148 | 148 | self.dataOut.data_param |
|
|
149 | 149 | self.dataOut.data_SNR |
|
|
150 | 150 | |
|
|
151 | 151 | ''' |
|
|
152 | 152 | data = self.dataOut.data_pre |
|
|
153 | 153 | absc = self.dataOut.abscissaList[:-1] |
|
|
154 | 154 | noise = self.dataOut.noise |
|
|
155 | 155 | |
|
|
156 | 156 | data_param = numpy.zeros((data.shape[0], 4, data.shape[2])) |
|
|
157 | 157 | |
|
|
158 | 158 | if channelList== None: |
|
|
159 | 159 | channelList = self.dataIn.channelList |
|
|
160 | 160 | self.dataOut.channelList = channelList |
|
|
161 | 161 | |
|
|
162 | 162 | for ind in channelList: |
|
|
163 | 163 | data_param[ind,:,:] = self.__calculateMoments(data[ind,:,:], absc, noise[ind]) |
|
|
164 | 164 | |
|
|
165 | 165 | self.dataOut.data_param = data_param[:,1:,:] |
|
|
166 | 166 | self.dataOut.data_SNR = data_param[:,0] |
|
|
167 | 167 | return |
|
|
168 | 168 | |
|
|
169 | 169 | def __calculateMoments(self, oldspec, oldfreq, n0, nicoh = None, graph = None, smooth = None, type1 = None, fwindow = None, snrth = None, dc = None, aliasing = None, oldfd = None, wwauto = None): |
|
|
170 | 170 | |
|
|
171 | 171 | if (nicoh == None): nicoh = 1 |
|
|
172 | 172 | if (graph == None): graph = 0 |
|
|
173 | 173 | if (smooth == None): smooth = 0 |
|
|
174 | 174 | elif (self.smooth < 3): smooth = 0 |
|
|
175 | 175 | |
|
|
176 | 176 | if (type1 == None): type1 = 0 |
|
|
177 | 177 | if (fwindow == None): fwindow = numpy.zeros(oldfreq.size) + 1 |
|
|
178 | 178 | if (snrth == None): snrth = -3 |
|
|
179 | 179 | if (dc == None): dc = 0 |
|
|
180 | 180 | if (aliasing == None): aliasing = 0 |
|
|
181 | 181 | if (oldfd == None): oldfd = 0 |
|
|
182 | 182 | if (wwauto == None): wwauto = 0 |
|
|
183 | 183 | |
|
|
184 | 184 | if (n0 < 1.e-20): n0 = 1.e-20 |
|
|
185 | 185 | |
|
|
186 | 186 | freq = oldfreq |
|
|
187 | 187 | vec_power = numpy.zeros(oldspec.shape[1]) |
|
|
188 | 188 | vec_fd = numpy.zeros(oldspec.shape[1]) |
|
|
189 | 189 | vec_w = numpy.zeros(oldspec.shape[1]) |
|
|
190 | 190 | vec_snr = numpy.zeros(oldspec.shape[1]) |
|
|
191 | 191 | |
|
|
192 | 192 | for ind in range(oldspec.shape[1]): |
|
|
193 | 193 | |
|
|
194 | 194 | spec = oldspec[:,ind] |
|
|
195 | 195 | aux = spec*fwindow |
|
|
196 | 196 | max_spec = aux.max() |
|
|
197 | 197 | m = list(aux).index(max_spec) |
|
|
198 | 198 | |
|
|
199 | 199 | #Smooth |
|
|
200 | 200 | if (smooth == 0): spec2 = spec |
|
|
201 | 201 | else: spec2 = scipy.ndimage.filters.uniform_filter1d(spec,size=smooth) |
|
|
202 | 202 | |
|
|
203 | 203 | # Calculo de Momentos |
|
|
204 | 204 | bb = spec2[range(m,spec2.size)] |
|
|
205 | 205 | bb = (bb<n0).nonzero() |
|
|
206 | 206 | bb = bb[0] |
|
|
207 | 207 | |
|
|
208 | 208 | ss = spec2[range(0,m + 1)] |
|
|
209 | 209 | ss = (ss<n0).nonzero() |
|
|
210 | 210 | ss = ss[0] |
|
|
211 | 211 | |
|
|
212 | 212 | if (bb.size == 0): |
|
|
213 | 213 | bb0 = spec.size - 1 - m |
|
|
214 | 214 | else: |
|
|
215 | 215 | bb0 = bb[0] - 1 |
|
|
216 | 216 | if (bb0 < 0): |
|
|
217 | 217 | bb0 = 0 |
|
|
218 | 218 | |
|
|
219 | 219 | if (ss.size == 0): ss1 = 1 |
|
|
220 | 220 | else: ss1 = max(ss) + 1 |
|
|
221 | 221 | |
|
|
222 | 222 | if (ss1 > m): ss1 = m |
|
|
223 | 223 | |
|
|
224 | 224 | valid = numpy.asarray(range(int(m + bb0 - ss1 + 1))) + ss1 |
|
|
225 | 225 | power = ((spec2[valid] - n0)*fwindow[valid]).sum() |
|
|
226 | 226 | fd = ((spec2[valid]- n0)*freq[valid]*fwindow[valid]).sum()/power |
|
|
227 | 227 | w = math.sqrt(((spec2[valid] - n0)*fwindow[valid]*(freq[valid]- fd)**2).sum()/power) |
|
|
228 | 228 | snr = (spec2.mean()-n0)/n0 |
|
|
229 | 229 | |
|
|
230 | 230 | if (snr < 1.e-20) : |
|
|
231 | 231 | snr = 1.e-20 |
|
|
232 | 232 | |
|
|
233 | 233 | vec_power[ind] = power |
|
|
234 | 234 | vec_fd[ind] = fd |
|
|
235 | 235 | vec_w[ind] = w |
|
|
236 | 236 | vec_snr[ind] = snr |
|
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237 | 237 | |
|
|
238 | 238 | moments = numpy.vstack((vec_snr, vec_power, vec_fd, vec_w)) |
|
|
239 | 239 | return moments |
|
|
240 | 240 | |
|
|
241 | 241 | #------------------ Get SA Parameters -------------------------- |
|
|
242 | def GetSAParameters(self): | |
|
|
243 | data = self.dataOut.data_pre | |
|
|
244 | crossdata = self.dataIn.data_cspc | |
|
|
245 | a = 1 | |
|
|
246 | ||
|
|
247 | ||
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|
248 | 242 | |
|
|
243 | def GetSAParameters(self): | |
|
|
244 | pairslist = self.dataOut.groupList | |
|
|
245 | num_pairs = len(pairslist) | |
|
|
246 | ||
|
|
247 | vel = self.dataOut.abscissaList | |
|
|
248 | spectra = self.dataOut.data_pre | |
|
|
249 | cspectra = self.dataIn.data_cspc | |
|
|
250 | delta_v = vel[1] - vel[0] | |
|
|
251 | ||
|
|
252 | #Calculating the power spectrum | |
|
|
253 | spc_pow = numpy.sum(spectra, 3)*delta_v | |
|
|
254 | #Normalizing Spectra | |
|
|
255 | norm_spectra = spectra/spc_pow | |
|
|
256 | #Calculating the norm_spectra at peak | |
|
|
257 | max_spectra = numpy.max(norm_spectra, 3) | |
|
|
258 | ||
|
|
259 | #Normalizing Cross Spectra | |
|
|
260 | norm_cspectra = numpy.zeros(cspectra.shape) | |
|
|
261 | ||
|
|
262 | for i in range(num_chan): | |
|
|
263 | norm_cspectra[i,:,:] = cspectra[i,:,:]/numpy.sqrt(spc_pow[pairslist[i][0],:]*spc_pow[pairslist[i][1],:]) | |
|
|
264 | ||
|
|
265 | max_cspectra = numpy.max(norm_cspectra,2) | |
|
|
266 | max_cspectra_index = numpy.argmax(norm_cspectra, 2) | |
|
|
267 | ||
|
|
268 | for i in range(num_pairs): | |
|
|
269 | cspc_par[i,:,:] = __calculateMoments(norm_cspectra) | |
|
|
249 | 270 | #------------------- Get Lags ---------------------------------- |
|
|
250 | 271 | |
|
|
251 | 272 | def GetLags(self): |
|
|
252 | 273 | ''' |
|
|
253 | 274 | Function GetMoments() |
|
|
254 | 275 | |
|
|
255 | 276 | Input: |
|
|
256 | 277 | self.dataOut.data_pre |
|
|
257 | 278 | self.dataOut.abscissaList |
|
|
258 | 279 | self.dataOut.noise |
|
|
259 | 280 | self.dataOut.normFactor |
|
|
260 | 281 | self.dataOut.data_SNR |
|
|
261 | 282 | self.dataOut.groupList |
|
|
262 | 283 | self.dataOut.nChannels |
|
|
263 | 284 | |
|
|
264 | 285 | Affected: |
|
|
265 | 286 | self.dataOut.data_param |
|
|
266 | 287 | |
|
|
267 | 288 | ''' |
|
|
268 | 289 | |
|
|
269 | 290 | data = self.dataOut.data_pre |
|
|
270 | 291 | normFactor = self.dataOut.normFactor |
|
|
271 | 292 | nHeights = self.dataOut.nHeights |
|
|
272 | 293 | absc = self.dataOut.abscissaList[:-1] |
|
|
273 | 294 | noise = self.dataOut.noise |
|
|
274 | 295 | SNR = self.dataOut.data_SNR |
|
|
275 | 296 | pairsList = self.dataOut.groupList |
|
|
276 | 297 | nChannels = self.dataOut.nChannels |
|
|
277 | 298 | pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairsList, nChannels) |
|
|
278 | 299 | self.dataOut.data_param = numpy.zeros((len(pairsCrossCorr)*2 + 1, nHeights)) |
|
|
279 | 300 | |
|
|
280 | 301 | dataNorm = numpy.abs(data) |
|
|
281 | 302 | for l in range(len(pairsList)): |
|
|
282 | 303 | dataNorm[l,:,:] = dataNorm[l,:,:]/normFactor[l,:] |
|
|
283 | 304 | |
|
|
284 | 305 | self.dataOut.data_param[:-1,:] = self.__calculateTaus(dataNorm, pairsCrossCorr, pairsAutoCorr, absc) |
|
|
285 | 306 | self.dataOut.data_param[-1,:] = self.__calculateLag1Phase(data, pairsAutoCorr, absc) |
|
|
286 | 307 | return |
|
|
287 | 308 | |
|
|
288 | 309 | def __getPairsAutoCorr(self, pairsList, nChannels): |
|
|
289 | 310 | |
|
|
290 | 311 | pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan |
|
|
291 | 312 | |
|
|
292 | 313 | for l in range(len(pairsList)): |
|
|
293 | 314 | firstChannel = pairsList[l][0] |
|
|
294 | 315 | secondChannel = pairsList[l][1] |
|
|
295 | 316 | |
|
|
296 | 317 | #Obteniendo pares de Autocorrelacion |
|
|
297 | 318 | if firstChannel == secondChannel: |
|
|
298 | 319 | pairsAutoCorr[firstChannel] = int(l) |
|
|
299 | 320 | |
|
|
300 | 321 | pairsAutoCorr = pairsAutoCorr.astype(int) |
|
|
301 | 322 | |
|
|
302 | 323 | pairsCrossCorr = range(len(pairsList)) |
|
|
303 | 324 | pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr) |
|
|
304 | 325 | |
|
|
305 | 326 | return pairsAutoCorr, pairsCrossCorr |
|
|
306 | 327 | |
|
|
307 | 328 | def __calculateTaus(self, data, pairsCrossCorr, pairsAutoCorr, lagTRange): |
|
|
308 | 329 | |
|
|
309 | 330 | Pt0 = data.shape[1]/2 |
|
|
310 | 331 | #Funcion de Autocorrelacion |
|
|
311 | 332 | dataAutoCorr = stats.nanmean(data[pairsAutoCorr,:,:], axis = 0) |
|
|
312 | 333 | |
|
|
313 | 334 | #Obtencion Indice de TauCross |
|
|
314 | 335 | indCross = data[pairsCrossCorr,:,:].argmax(axis = 1) |
|
|
315 | 336 | #Obtencion Indice de TauAuto |
|
|
316 | 337 | indAuto = numpy.zeros(indCross.shape,dtype = 'int') |
|
|
317 | 338 | CCValue = data[pairsCrossCorr,Pt0,:] |
|
|
318 | 339 | for i in range(pairsCrossCorr.size): |
|
|
319 | 340 | indAuto[i,:] = numpy.abs(dataAutoCorr - CCValue[i,:]).argmin(axis = 0) |
|
|
320 | 341 | |
|
|
321 | 342 | #Obtencion de TauCross y TauAuto |
|
|
322 | 343 | tauCross = lagTRange[indCross] |
|
|
323 | 344 | tauAuto = lagTRange[indAuto] |
|
|
324 | 345 | |
|
|
325 | 346 | Nan1, Nan2 = numpy.where(tauCross == lagTRange[0]) |
|
|
326 | 347 | |
|
|
327 | 348 | tauCross[Nan1,Nan2] = numpy.nan |
|
|
328 | 349 | tauAuto[Nan1,Nan2] = numpy.nan |
|
|
329 | 350 | tau = numpy.vstack((tauCross,tauAuto)) |
|
|
330 | 351 | |
|
|
331 | 352 | return tau |
|
|
332 | 353 | |
|
|
333 | 354 | def __calculateLag1Phase(self, data, pairs, lagTRange): |
|
|
334 | 355 | data1 = stats.nanmean(data[pairs,:,:], axis = 0) |
|
|
335 | 356 | lag1 = numpy.where(lagTRange == 0)[0][0] + 1 |
|
|
336 | 357 | |
|
|
337 | 358 | phase = numpy.angle(data1[lag1,:]) |
|
|
338 | 359 | |
|
|
339 | 360 | return phase |
|
|
340 | 361 | #------------------- Detect Meteors ------------------------------ |
|
|
341 | 362 | |
|
|
342 |
def |
|
|
|
343 | predefinedPhaseShifts = None, centerReceiverIndex = 2, | |
|
|
363 | def MeteorDetection(self, hei_ref = None, tauindex = 0, | |
|
|
364 | predefinedPhaseShifts = None, centerReceiverIndex = 2, saveAll = False, | |
|
|
344 | 365 | cohDetection = False, cohDet_timeStep = 1, cohDet_thresh = 25, |
|
|
345 | 366 | noise_timeStep = 4, noise_multiple = 4, |
|
|
346 | 367 | multDet_timeLimit = 1, multDet_rangeLimit = 3, |
|
|
347 | 368 | phaseThresh = 20, SNRThresh = 8, |
|
|
348 | 369 | hmin = 70, hmax=110, azimuth = 0) : |
|
|
349 | 370 | |
|
|
350 | 371 | ''' |
|
|
351 | 372 | Function DetectMeteors() |
|
|
352 | 373 | Project developed with paper: |
|
|
353 | 374 | HOLDSWORTH ET AL. 2004 |
|
|
354 | 375 | |
|
|
355 | 376 | Input: |
|
|
356 | 377 | self.dataOut.data_pre |
|
|
357 | 378 | |
|
|
358 | 379 | centerReceiverIndex: From the channels, which is the center receiver |
|
|
359 | 380 | |
|
|
360 | 381 | hei_ref: Height reference for the Beacon signal extraction |
|
|
361 | 382 | tauindex: |
|
|
362 | 383 | predefinedPhaseShifts: Predefined phase offset for the voltge signals |
|
|
363 | 384 | |
|
|
364 | 385 | cohDetection: Whether to user Coherent detection or not |
|
|
365 | 386 | cohDet_timeStep: Coherent Detection calculation time step |
|
|
366 | 387 | cohDet_thresh: Coherent Detection phase threshold to correct phases |
|
|
367 | 388 | |
|
|
368 | 389 | noise_timeStep: Noise calculation time step |
|
|
369 | 390 | noise_multiple: Noise multiple to define signal threshold |
|
|
370 | 391 | |
|
|
371 | 392 | multDet_timeLimit: Multiple Detection Removal time limit in seconds |
|
|
372 | 393 | multDet_rangeLimit: Multiple Detection Removal range limit in km |
|
|
373 | 394 | |
|
|
374 | 395 | phaseThresh: Maximum phase difference between receiver to be consider a meteor |
|
|
375 | 396 | SNRThresh: Minimum SNR threshold of the meteor signal to be consider a meteor |
|
|
376 | 397 | |
|
|
377 | 398 | hmin: Minimum Height of the meteor to use it in the further wind estimations |
|
|
378 | 399 | hmax: Maximum Height of the meteor to use it in the further wind estimations |
|
|
379 | 400 | azimuth: Azimuth angle correction |
|
|
380 | 401 | |
|
|
381 | 402 | Affected: |
|
|
382 | 403 | self.dataOut.data_param |
|
|
383 | 404 | |
|
|
384 | 405 | Rejection Criteria (Errors): |
|
|
385 | 406 | 0: No error; analysis OK |
|
|
386 | 407 | 1: SNR < SNR threshold |
|
|
387 | 408 | 2: angle of arrival (AOA) ambiguously determined |
|
|
388 | 409 | 3: AOA estimate not feasible |
|
|
389 | 410 | 4: Large difference in AOAs obtained from different antenna baselines |
|
|
390 | 411 | 5: echo at start or end of time series |
|
|
391 | 412 | 6: echo less than 5 examples long; too short for analysis |
|
|
392 | 413 | 7: echo rise exceeds 0.3s |
|
|
393 | 414 | 8: echo decay time less than twice rise time |
|
|
394 | 415 | 9: large power level before echo |
|
|
395 | 416 | 10: large power level after echo |
|
|
396 | 417 | 11: poor fit to amplitude for estimation of decay time |
|
|
397 | 418 | 12: poor fit to CCF phase variation for estimation of radial drift velocity |
|
|
398 | 419 | 13: height unresolvable echo: not valid height within 70 to 110 km |
|
|
399 | 420 | 14: height ambiguous echo: more then one possible height within 70 to 110 km |
|
|
400 | 421 | 15: radial drift velocity or projected horizontal velocity exceeds 200 m/s |
|
|
401 | 422 | 16: oscilatory echo, indicating event most likely not an underdense echo |
|
|
402 | 423 | |
|
|
403 | 424 | 17: phase difference in meteor Reestimation |
|
|
404 | 425 | |
|
|
405 | 426 | Data Storage: |
|
|
406 | 427 | Meteors for Wind Estimation (8): |
|
|
407 | 428 | Day Hour | Range Height |
|
|
408 | 429 | Azimuth Zenith errorCosDir |
|
|
409 | 430 | VelRad errorVelRad |
|
|
410 | 431 | TypeError |
|
|
411 | 432 | |
|
|
412 | 433 | ''' |
|
|
413 | 434 | #Get Beacon signal |
|
|
414 | 435 | newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex]) |
|
|
415 | 436 | |
|
|
416 | 437 | if hei_ref != None: |
|
|
417 | 438 | newheis = numpy.where(self.dataOut.heightList>hei_ref) |
|
|
418 | 439 | |
|
|
419 | 440 | heiRang = self.dataOut.getHeiRange() |
|
|
420 | 441 | #Pairs List |
|
|
421 | 442 | pairslist = [] |
|
|
422 | 443 | nChannel = self.dataOut.nChannels |
|
|
423 | 444 | for i in range(nChannel): |
|
|
424 | 445 | if i != centerReceiverIndex: |
|
|
425 | 446 | pairslist.append((centerReceiverIndex,i)) |
|
|
426 | 447 | |
|
|
427 | 448 | #****************REMOVING HARDWARE PHASE DIFFERENCES*************** |
|
|
428 | 449 | # see if the user put in pre defined phase shifts |
|
|
429 | 450 | voltsPShift = self.dataOut.data_pre.copy() |
|
|
430 | 451 | |
|
|
431 | 452 | if predefinedPhaseShifts != None: |
|
|
432 | 453 | hardwarePhaseShifts = numpy.array(predefinedPhaseShifts)*numpy.pi/180 |
|
|
433 |
|
|
|
|
454 | ||
|
|
455 | elif beaconPhaseShifts: | |
|
|
434 | 456 | #get hardware phase shifts using beacon signal |
|
|
435 | 457 | hardwarePhaseShifts = self.__getHardwarePhaseDiff(self.dataOut.data_pre, pairslist, newheis, 10) |
|
|
436 | 458 | hardwarePhaseShifts = numpy.insert(hardwarePhaseShifts,centerReceiverIndex,0) |
|
|
437 | ||
|
|
459 | ||
|
|
460 | else: | |
|
|
461 | hardwarePhaseShifts = numpy.zeros(5) | |
|
|
462 | ||
|
|
463 | ||
|
|
438 | 464 | voltsPShift = numpy.zeros((self.dataOut.data_pre.shape[0],self.dataOut.data_pre.shape[1],self.dataOut.data_pre.shape[2]), dtype = 'complex') |
|
|
439 | 465 | for i in range(self.dataOut.data_pre.shape[0]): |
|
|
440 | 466 | voltsPShift[i,:,:] = self.__shiftPhase(self.dataOut.data_pre[i,:,:], hardwarePhaseShifts[i]) |
|
|
467 | ||
|
|
468 | ||
|
|
441 | 469 | #******************END OF REMOVING HARDWARE PHASE DIFFERENCES********* |
|
|
442 | 470 | |
|
|
443 | 471 | #Remove DC |
|
|
444 | 472 | voltsDC = numpy.mean(voltsPShift,1) |
|
|
445 | 473 | voltsDC = numpy.mean(voltsDC,1) |
|
|
446 | 474 | for i in range(voltsDC.shape[0]): |
|
|
447 | 475 | voltsPShift[i] = voltsPShift[i] - voltsDC[i] |
|
|
448 | 476 | |
|
|
449 | 477 | #Don't considerate last heights, theyre used to calculate Hardware Phase Shift |
|
|
450 | 478 | voltsPShift = voltsPShift[:,:,:newheis[0][0]] |
|
|
451 | 479 | |
|
|
452 | 480 | #************ FIND POWER OF DATA W/COH OR NON COH DETECTION (3.4) ********** |
|
|
453 | 481 | #Coherent Detection |
|
|
454 | 482 | if cohDetection: |
|
|
455 | 483 | #use coherent detection to get the net power |
|
|
456 | 484 | cohDet_thresh = cohDet_thresh*numpy.pi/180 |
|
|
457 | 485 | voltsPShift = self.__coherentDetection(voltsPShift, cohDet_timeStep, self.dataOut.timeInterval, pairslist, cohDet_thresh) |
|
|
458 | 486 | |
|
|
459 | 487 | #Non-coherent detection! |
|
|
460 | 488 | powerNet = numpy.nansum(numpy.abs(voltsPShift[:,:,:])**2,0) |
|
|
461 | 489 | #********** END OF COH/NON-COH POWER CALCULATION********************** |
|
|
462 | 490 | |
|
|
463 | 491 | #********** FIND THE NOISE LEVEL AND POSSIBLE METEORS **************** |
|
|
464 | 492 | #Get noise |
|
|
465 | 493 | noise, noise1 = self.__getNoise(powerNet, noise_timeStep, self.dataOut.timeInterval) |
|
|
466 | 494 | # noise = self.getNoise1(powerNet, noise_timeStep, self.dataOut.timeInterval) |
|
|
467 | 495 | #Get signal threshold |
|
|
468 | 496 | signalThresh = noise_multiple*noise |
|
|
469 | 497 | #Meteor echoes detection |
|
|
470 | 498 | listMeteors = self.__findMeteors(powerNet, signalThresh) |
|
|
471 | 499 | #******* END OF NOISE LEVEL AND POSSIBLE METEORS CACULATION ********** |
|
|
472 | 500 | |
|
|
473 | 501 | #************** REMOVE MULTIPLE DETECTIONS (3.5) *************************** |
|
|
474 | 502 | #Parameters |
|
|
475 | 503 | heiRange = self.dataOut.getHeiRange() |
|
|
476 | 504 | rangeInterval = heiRange[1] - heiRange[0] |
|
|
477 | 505 | rangeLimit = multDet_rangeLimit/rangeInterval |
|
|
478 | 506 | timeLimit = multDet_timeLimit/self.dataOut.timeInterval |
|
|
479 | 507 | #Multiple detection removals |
|
|
480 | 508 | listMeteors1 = self.__removeMultipleDetections(listMeteors, rangeLimit, timeLimit) |
|
|
481 | 509 | #************ END OF REMOVE MULTIPLE DETECTIONS ********************** |
|
|
482 | 510 | |
|
|
483 | 511 | #********************* METEOR REESTIMATION (3.7, 3.8, 3.9, 3.10) ******************** |
|
|
484 | 512 | #Parameters |
|
|
485 | 513 | phaseThresh = phaseThresh*numpy.pi/180 |
|
|
486 | 514 | thresh = [phaseThresh, noise_multiple, SNRThresh] |
|
|
487 | 515 | #Meteor reestimation (Errors N 1, 6, 12, 17) |
|
|
488 | 516 | listMeteors2, listMeteorsPower, listMeteorsVolts = self.__meteorReestimation(listMeteors1, voltsPShift, pairslist, thresh, noise, self.dataOut.timeInterval, self.dataOut.frequency) |
|
|
489 | 517 | # listMeteors2, listMeteorsPower, listMeteorsVolts = self.meteorReestimation3(listMeteors2, listMeteorsPower, listMeteorsVolts, voltsPShift, pairslist, thresh, noise) |
|
|
490 | 518 | #Estimation of decay times (Errors N 7, 8, 11) |
|
|
491 | 519 | listMeteors3 = self.__estimateDecayTime(listMeteors2, listMeteorsPower, self.dataOut.timeInterval, self.dataOut.frequency) |
|
|
492 | 520 | #******************* END OF METEOR REESTIMATION ******************* |
|
|
493 | 521 | |
|
|
494 | 522 | #********************* METEOR PARAMETERS CALCULATION (3.11, 3.12, 3.13) ************************** |
|
|
495 | 523 | #Calculating Radial Velocity (Error N 15) |
|
|
496 | 524 | radialStdThresh = 10 |
|
|
497 | 525 | listMeteors4 = self.__getRadialVelocity(listMeteors3, listMeteorsVolts, radialStdThresh, pairslist, self.dataOut.timeInterval) |
|
|
498 | 526 | |
|
|
499 | 527 | if len(listMeteors4) > 0: |
|
|
500 | #Setting New Array | |
|
|
501 | date = repr(self.dataOut.datatime) | |
|
|
502 | arrayMeteors4, arrayParameters = self.__setNewArrays(listMeteors4, date, heiRang) | |
|
|
503 | 528 | |
|
|
504 | #Calculate AOA (Error N 3, 4) | |
|
|
505 | #JONES ET AL. 1998 | |
|
|
506 | AOAthresh = numpy.pi/8 | |
|
|
507 | error = arrayParameters[:,-1] | |
|
|
508 | phases = -arrayMeteors4[:,9:13] | |
|
|
509 | 529 | pairsList = [] |
|
|
510 |
pair |
|
|
|
511 |
pair |
|
|
|
512 | arrayParameters[:,4:7], arrayParameters[:,-1] = self.__getAOA(phases, pairsList, error, AOAthresh, azimuth) | |
|
|
513 | ||
|
|
514 | #Calculate Heights (Error N 13 and 14) | |
|
|
515 | error = arrayParameters[:,-1] | |
|
|
516 | Ranges = arrayParameters[:,2] | |
|
|
517 | zenith = arrayParameters[:,5] | |
|
|
518 | arrayParameters[:,3], arrayParameters[:,-1] = self.__getHeights(Ranges, zenith, error, hmin, hmax) | |
|
|
530 | pairx = (0,3) | |
|
|
531 | pairy = (1,2) | |
|
|
532 | pairsList.append(pairx) | |
|
|
533 | pairsList.append(pairy) | |
|
|
534 | ||
|
|
535 | #Setting New Array | |
|
|
536 | date = repr(self.dataOut.datatime) | |
|
|
537 | arrayParameters = self.__setNewArrays(listMeteors4, date, heiRang) | |
|
|
538 | ||
|
|
539 | meteorOps = MeteorOperations() | |
|
|
540 | jph = numpy.array([0,0,0,0]) | |
|
|
541 | h = (hmin,hmax) | |
|
|
542 | arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, jph) | |
|
|
543 | ||
|
|
544 | # #Calculate AOA (Error N 3, 4) | |
|
|
545 | # #JONES ET AL. 1998 | |
|
|
546 | # error = arrayParameters[:,-1] | |
|
|
547 | # AOAthresh = numpy.pi/8 | |
|
|
548 | # phases = -arrayParameters[:,9:13] | |
|
|
549 | # arrayParameters[:,4:7], arrayParameters[:,-1] = meteorOps.getAOA(phases, pairsList, error, AOAthresh, azimuth) | |
|
|
550 | # | |
|
|
551 | # #Calculate Heights (Error N 13 and 14) | |
|
|
552 | # error = arrayParameters[:,-1] | |
|
|
553 | # Ranges = arrayParameters[:,2] | |
|
|
554 | # zenith = arrayParameters[:,5] | |
|
|
555 | # arrayParameters[:,3], arrayParameters[:,-1] = meteorOps.getHeights(Ranges, zenith, error, hmin, hmax) | |
|
|
556 | # error = arrayParameters[:,-1] | |
|
|
519 | 557 | #********************* END OF PARAMETERS CALCULATION ************************** |
|
|
520 | 558 | |
|
|
521 |
#***************************+ |
|
|
|
522 | self.dataOut.data_param = arrayParameters | |
|
|
559 | #***************************+ PASS DATA TO NEXT STEP ********************** | |
|
|
560 | arrayFinal = arrayParameters.reshape((1,arrayParameters.shape[0],arrayParameters.shape[1])) | |
|
|
561 | self.dataOut.data_param = arrayFinal | |
|
|
523 | 562 | |
|
|
524 | 563 | return |
|
|
525 | 564 | |
|
|
526 | 565 | def __getHardwarePhaseDiff(self, voltage0, pairslist, newheis, n): |
|
|
527 | 566 | |
|
|
528 | 567 | minIndex = min(newheis[0]) |
|
|
529 | 568 | maxIndex = max(newheis[0]) |
|
|
530 | 569 | |
|
|
531 | 570 | voltage = voltage0[:,:,minIndex:maxIndex+1] |
|
|
532 | 571 | nLength = voltage.shape[1]/n |
|
|
533 | 572 | nMin = 0 |
|
|
534 | 573 | nMax = 0 |
|
|
535 | 574 | phaseOffset = numpy.zeros((len(pairslist),n)) |
|
|
536 | 575 | |
|
|
537 | 576 | for i in range(n): |
|
|
538 | 577 | nMax += nLength |
|
|
539 | 578 | phaseCCF = -numpy.angle(self.__calculateCCF(voltage[:,nMin:nMax,:], pairslist, [0])) |
|
|
540 | 579 | phaseCCF = numpy.mean(phaseCCF, axis = 2) |
|
|
541 | 580 | phaseOffset[:,i] = phaseCCF.transpose() |
|
|
542 | 581 | nMin = nMax |
|
|
543 | 582 | # phaseDiff, phaseArrival = self.estimatePhaseDifference(voltage, pairslist) |
|
|
544 | 583 | |
|
|
545 | 584 | #Remove Outliers |
|
|
546 | 585 | factor = 2 |
|
|
547 | 586 | wt = phaseOffset - signal.medfilt(phaseOffset,(1,5)) |
|
|
548 | 587 | dw = numpy.std(wt,axis = 1) |
|
|
549 | 588 | dw = dw.reshape((dw.size,1)) |
|
|
550 | 589 | ind = numpy.where(numpy.logical_or(wt>dw*factor,wt<-dw*factor)) |
|
|
551 | 590 | phaseOffset[ind] = numpy.nan |
|
|
552 | 591 | phaseOffset = stats.nanmean(phaseOffset, axis=1) |
|
|
553 | 592 | |
|
|
554 | 593 | return phaseOffset |
|
|
555 | 594 | |
|
|
556 | 595 | def __shiftPhase(self, data, phaseShift): |
|
|
557 | 596 | #this will shift the phase of a complex number |
|
|
558 | 597 | dataShifted = numpy.abs(data) * numpy.exp((numpy.angle(data)+phaseShift)*1j) |
|
|
559 | 598 | return dataShifted |
|
|
560 | 599 | |
|
|
561 | 600 | def __estimatePhaseDifference(self, array, pairslist): |
|
|
562 | 601 | nChannel = array.shape[0] |
|
|
563 | 602 | nHeights = array.shape[2] |
|
|
564 | 603 | numPairs = len(pairslist) |
|
|
565 | 604 | # phaseCCF = numpy.zeros((nChannel, 5, nHeights)) |
|
|
566 | 605 | phaseCCF = numpy.angle(self.__calculateCCF(array, pairslist, [-2,-1,0,1,2])) |
|
|
567 | 606 | |
|
|
568 | 607 | #Correct phases |
|
|
569 | 608 | derPhaseCCF = phaseCCF[:,1:,:] - phaseCCF[:,0:-1,:] |
|
|
570 | 609 | indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) |
|
|
571 | 610 | |
|
|
572 | 611 | if indDer[0].shape[0] > 0: |
|
|
573 | 612 | for i in range(indDer[0].shape[0]): |
|
|
574 | 613 | signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i],indDer[2][i]]) |
|
|
575 | 614 | phaseCCF[indDer[0][i],indDer[1][i]+1:,:] += signo*2*numpy.pi |
|
|
576 | 615 | |
|
|
577 | 616 | # for j in range(numSides): |
|
|
578 | 617 | # phaseCCFAux = self.calculateCCF(arrayCenter, arraySides[j,:,:], [-2,1,0,1,2]) |
|
|
579 | 618 | # phaseCCF[j,:,:] = numpy.angle(phaseCCFAux) |
|
|
580 | 619 | # |
|
|
581 | 620 | #Linear |
|
|
582 | 621 | phaseInt = numpy.zeros((numPairs,1)) |
|
|
583 | 622 | angAllCCF = phaseCCF[:,[0,1,3,4],0] |
|
|
584 | 623 | for j in range(numPairs): |
|
|
585 | 624 | fit = stats.linregress([-2,-1,1,2],angAllCCF[j,:]) |
|
|
586 | 625 | phaseInt[j] = fit[1] |
|
|
587 | 626 | #Phase Differences |
|
|
588 | 627 | phaseDiff = phaseInt - phaseCCF[:,2,:] |
|
|
589 | 628 | phaseArrival = phaseInt.reshape(phaseInt.size) |
|
|
590 | 629 | |
|
|
591 | 630 | #Dealias |
|
|
592 | 631 | indAlias = numpy.where(phaseArrival > numpy.pi) |
|
|
593 | 632 | phaseArrival[indAlias] -= 2*numpy.pi |
|
|
594 | 633 | indAlias = numpy.where(phaseArrival < -numpy.pi) |
|
|
595 | 634 | phaseArrival[indAlias] += 2*numpy.pi |
|
|
596 | 635 | |
|
|
597 | 636 | return phaseDiff, phaseArrival |
|
|
598 | 637 | |
|
|
599 | 638 | def __coherentDetection(self, volts, timeSegment, timeInterval, pairslist, thresh): |
|
|
600 | 639 | #this function will run the coherent detection used in Holdworth et al. 2004 and return the net power |
|
|
601 | 640 | #find the phase shifts of each channel over 1 second intervals |
|
|
602 | 641 | #only look at ranges below the beacon signal |
|
|
603 | 642 | numProfPerBlock = numpy.ceil(timeSegment/timeInterval) |
|
|
604 | 643 | numBlocks = int(volts.shape[1]/numProfPerBlock) |
|
|
605 | 644 | numHeights = volts.shape[2] |
|
|
606 | 645 | nChannel = volts.shape[0] |
|
|
607 | 646 | voltsCohDet = volts.copy() |
|
|
608 | 647 | |
|
|
609 | 648 | pairsarray = numpy.array(pairslist) |
|
|
610 | 649 | indSides = pairsarray[:,1] |
|
|
611 | 650 | # indSides = numpy.array(range(nChannel)) |
|
|
612 | 651 | # indSides = numpy.delete(indSides, indCenter) |
|
|
613 | 652 | # |
|
|
614 | 653 | # listCenter = numpy.array_split(volts[indCenter,:,:], numBlocks, 0) |
|
|
615 | 654 | listBlocks = numpy.array_split(volts, numBlocks, 1) |
|
|
616 | 655 | |
|
|
617 | 656 | startInd = 0 |
|
|
618 | 657 | endInd = 0 |
|
|
619 | 658 | |
|
|
620 | 659 | for i in range(numBlocks): |
|
|
621 | 660 | startInd = endInd |
|
|
622 | 661 | endInd = endInd + listBlocks[i].shape[1] |
|
|
623 | 662 | |
|
|
624 | 663 | arrayBlock = listBlocks[i] |
|
|
625 | 664 | # arrayBlockCenter = listCenter[i] |
|
|
626 | 665 | |
|
|
627 | 666 | #Estimate the Phase Difference |
|
|
628 | 667 | phaseDiff, aux = self.__estimatePhaseDifference(arrayBlock, pairslist) |
|
|
629 | 668 | #Phase Difference RMS |
|
|
630 | 669 | arrayPhaseRMS = numpy.abs(phaseDiff) |
|
|
631 | 670 | phaseRMSaux = numpy.sum(arrayPhaseRMS < thresh,0) |
|
|
632 | 671 | indPhase = numpy.where(phaseRMSaux==4) |
|
|
633 | 672 | #Shifting |
|
|
634 | 673 | if indPhase[0].shape[0] > 0: |
|
|
635 | 674 | for j in range(indSides.size): |
|
|
636 | 675 | arrayBlock[indSides[j],:,indPhase] = self.__shiftPhase(arrayBlock[indSides[j],:,indPhase], phaseDiff[j,indPhase].transpose()) |
|
|
637 | 676 | voltsCohDet[:,startInd:endInd,:] = arrayBlock |
|
|
638 | 677 | |
|
|
639 | 678 | return voltsCohDet |
|
|
640 | 679 | |
|
|
641 | 680 | def __calculateCCF(self, volts, pairslist ,laglist): |
|
|
642 | 681 | |
|
|
643 | 682 | nHeights = volts.shape[2] |
|
|
644 | 683 | nPoints = volts.shape[1] |
|
|
645 | 684 | voltsCCF = numpy.zeros((len(pairslist), len(laglist), nHeights),dtype = 'complex') |
|
|
646 | 685 | |
|
|
647 | 686 | for i in range(len(pairslist)): |
|
|
648 | 687 | volts1 = volts[pairslist[i][0]] |
|
|
649 | 688 | volts2 = volts[pairslist[i][1]] |
|
|
650 | 689 | |
|
|
651 | 690 | for t in range(len(laglist)): |
|
|
652 | 691 | idxT = laglist[t] |
|
|
653 | 692 | if idxT >= 0: |
|
|
654 | 693 | vStacked = numpy.vstack((volts2[idxT:,:], |
|
|
655 | 694 | numpy.zeros((idxT, nHeights),dtype='complex'))) |
|
|
656 | 695 | else: |
|
|
657 | 696 | vStacked = numpy.vstack((numpy.zeros((-idxT, nHeights),dtype='complex'), |
|
|
658 | 697 | volts2[:(nPoints + idxT),:])) |
|
|
659 | 698 | voltsCCF[i,t,:] = numpy.sum((numpy.conjugate(volts1)*vStacked),axis=0) |
|
|
660 | 699 | |
|
|
661 | 700 | vStacked = None |
|
|
662 | 701 | return voltsCCF |
|
|
663 | 702 | |
|
|
664 | 703 | def __getNoise(self, power, timeSegment, timeInterval): |
|
|
665 | 704 | numProfPerBlock = numpy.ceil(timeSegment/timeInterval) |
|
|
666 | 705 | numBlocks = int(power.shape[0]/numProfPerBlock) |
|
|
667 | 706 | numHeights = power.shape[1] |
|
|
668 | 707 | |
|
|
669 | 708 | listPower = numpy.array_split(power, numBlocks, 0) |
|
|
670 | 709 | noise = numpy.zeros((power.shape[0], power.shape[1])) |
|
|
671 | 710 | noise1 = numpy.zeros((power.shape[0], power.shape[1])) |
|
|
672 | 711 | |
|
|
673 | 712 | startInd = 0 |
|
|
674 | 713 | endInd = 0 |
|
|
675 | 714 | |
|
|
676 | 715 | for i in range(numBlocks): #split por canal |
|
|
677 | 716 | startInd = endInd |
|
|
678 | 717 | endInd = endInd + listPower[i].shape[0] |
|
|
679 | 718 | |
|
|
680 | 719 | arrayBlock = listPower[i] |
|
|
681 | 720 | noiseAux = numpy.mean(arrayBlock, 0) |
|
|
682 | 721 | # noiseAux = numpy.median(noiseAux) |
|
|
683 | 722 | # noiseAux = numpy.mean(arrayBlock) |
|
|
684 | 723 | noise[startInd:endInd,:] = noise[startInd:endInd,:] + noiseAux |
|
|
685 | 724 | |
|
|
686 | 725 | noiseAux1 = numpy.mean(arrayBlock) |
|
|
687 | 726 | noise1[startInd:endInd,:] = noise1[startInd:endInd,:] + noiseAux1 |
|
|
688 | 727 | |
|
|
689 | 728 | return noise, noise1 |
|
|
690 | 729 | |
|
|
691 | 730 | def __findMeteors(self, power, thresh): |
|
|
692 | 731 | nProf = power.shape[0] |
|
|
693 | 732 | nHeights = power.shape[1] |
|
|
694 | 733 | listMeteors = [] |
|
|
695 | 734 | |
|
|
696 | 735 | for i in range(nHeights): |
|
|
697 | 736 | powerAux = power[:,i] |
|
|
698 | 737 | threshAux = thresh[:,i] |
|
|
699 | 738 | |
|
|
700 | 739 | indUPthresh = numpy.where(powerAux > threshAux)[0] |
|
|
701 | 740 | indDNthresh = numpy.where(powerAux <= threshAux)[0] |
|
|
702 | 741 | |
|
|
703 | 742 | j = 0 |
|
|
704 | 743 | |
|
|
705 | 744 | while (j < indUPthresh.size - 2): |
|
|
706 | 745 | if (indUPthresh[j + 2] == indUPthresh[j] + 2): |
|
|
707 | 746 | indDNAux = numpy.where(indDNthresh > indUPthresh[j]) |
|
|
708 | 747 | indDNthresh = indDNthresh[indDNAux] |
|
|
709 | 748 | |
|
|
710 | 749 | if (indDNthresh.size > 0): |
|
|
711 | 750 | indEnd = indDNthresh[0] - 1 |
|
|
712 | 751 | indInit = indUPthresh[j] |
|
|
713 | 752 | |
|
|
714 | 753 | meteor = powerAux[indInit:indEnd + 1] |
|
|
715 | 754 | indPeak = meteor.argmax() + indInit |
|
|
716 | 755 | FLA = sum(numpy.conj(meteor)*numpy.hstack((meteor[1:],0))) |
|
|
717 | 756 | |
|
|
718 | 757 | listMeteors.append(numpy.array([i,indInit,indPeak,indEnd,FLA])) #CHEQUEAR!!!!! |
|
|
719 | 758 | j = numpy.where(indUPthresh == indEnd)[0] + 1 |
|
|
720 | 759 | else: j+=1 |
|
|
721 | 760 | else: j+=1 |
|
|
722 | 761 | |
|
|
723 | 762 | return listMeteors |
|
|
724 | 763 | |
|
|
725 | 764 | def __removeMultipleDetections(self,listMeteors, rangeLimit, timeLimit): |
|
|
726 | 765 | |
|
|
727 | 766 | arrayMeteors = numpy.asarray(listMeteors) |
|
|
728 | 767 | listMeteors1 = [] |
|
|
729 | 768 | |
|
|
730 | 769 | while arrayMeteors.shape[0] > 0: |
|
|
731 | 770 | FLAs = arrayMeteors[:,4] |
|
|
732 | 771 | maxFLA = FLAs.argmax() |
|
|
733 | 772 | listMeteors1.append(arrayMeteors[maxFLA,:]) |
|
|
734 | 773 | |
|
|
735 | 774 | MeteorInitTime = arrayMeteors[maxFLA,1] |
|
|
736 | 775 | MeteorEndTime = arrayMeteors[maxFLA,3] |
|
|
737 | 776 | MeteorHeight = arrayMeteors[maxFLA,0] |
|
|
738 | 777 | |
|
|
739 | 778 | #Check neighborhood |
|
|
740 | 779 | maxHeightIndex = MeteorHeight + rangeLimit |
|
|
741 | 780 | minHeightIndex = MeteorHeight - rangeLimit |
|
|
742 | 781 | minTimeIndex = MeteorInitTime - timeLimit |
|
|
743 | 782 | maxTimeIndex = MeteorEndTime + timeLimit |
|
|
744 | 783 | |
|
|
745 | 784 | #Check Heights |
|
|
746 | 785 | indHeight = numpy.logical_and(arrayMeteors[:,0] >= minHeightIndex, arrayMeteors[:,0] <= maxHeightIndex) |
|
|
747 | 786 | indTime = numpy.logical_and(arrayMeteors[:,3] >= minTimeIndex, arrayMeteors[:,1] <= maxTimeIndex) |
|
|
748 | 787 | indBoth = numpy.where(numpy.logical_and(indTime,indHeight)) |
|
|
749 | 788 | |
|
|
750 | 789 | arrayMeteors = numpy.delete(arrayMeteors, indBoth, axis = 0) |
|
|
751 | 790 | |
|
|
752 | 791 | return listMeteors1 |
|
|
753 | 792 | |
|
|
754 | 793 | def __meteorReestimation(self, listMeteors, volts, pairslist, thresh, noise, timeInterval,frequency): |
|
|
755 | 794 | numHeights = volts.shape[2] |
|
|
756 | 795 | nChannel = volts.shape[0] |
|
|
757 | 796 | |
|
|
758 | 797 | thresholdPhase = thresh[0] |
|
|
759 | 798 | thresholdNoise = thresh[1] |
|
|
760 | 799 | thresholdDB = float(thresh[2]) |
|
|
761 | 800 | |
|
|
762 | 801 | thresholdDB1 = 10**(thresholdDB/10) |
|
|
763 | 802 | pairsarray = numpy.array(pairslist) |
|
|
764 | 803 | indSides = pairsarray[:,1] |
|
|
765 | 804 | |
|
|
766 | 805 | pairslist1 = list(pairslist) |
|
|
767 | 806 | pairslist1.append((0,1)) |
|
|
768 | 807 | pairslist1.append((3,4)) |
|
|
769 | 808 | |
|
|
770 | 809 | listMeteors1 = [] |
|
|
771 | 810 | listPowerSeries = [] |
|
|
772 | 811 | listVoltageSeries = [] |
|
|
773 | 812 | #volts has the war data |
|
|
774 | 813 | |
|
|
775 | 814 | if frequency == 30e6: |
|
|
776 | 815 | timeLag = 45*10**-3 |
|
|
777 | 816 | else: |
|
|
778 | 817 | timeLag = 15*10**-3 |
|
|
779 | 818 | lag = numpy.ceil(timeLag/timeInterval) |
|
|
780 | 819 | |
|
|
781 | 820 | for i in range(len(listMeteors)): |
|
|
782 | 821 | |
|
|
783 | 822 | ###################### 3.6 - 3.7 PARAMETERS REESTIMATION ######################### |
|
|
784 | 823 | meteorAux = numpy.zeros(16) |
|
|
785 | 824 | |
|
|
786 | 825 | #Loading meteor Data (mHeight, mStart, mPeak, mEnd) |
|
|
787 | 826 | mHeight = listMeteors[i][0] |
|
|
788 | 827 | mStart = listMeteors[i][1] |
|
|
789 | 828 | mPeak = listMeteors[i][2] |
|
|
790 | 829 | mEnd = listMeteors[i][3] |
|
|
791 | 830 | |
|
|
792 | 831 | #get the volt data between the start and end times of the meteor |
|
|
793 | 832 | meteorVolts = volts[:,mStart:mEnd+1,mHeight] |
|
|
794 | 833 | meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) |
|
|
795 | 834 | |
|
|
796 | 835 | #3.6. Phase Difference estimation |
|
|
797 | 836 | phaseDiff, aux = self.__estimatePhaseDifference(meteorVolts, pairslist) |
|
|
798 | 837 | |
|
|
799 | 838 | #3.7. Phase difference removal & meteor start, peak and end times reestimated |
|
|
800 | 839 | #meteorVolts0.- all Channels, all Profiles |
|
|
801 | 840 | meteorVolts0 = volts[:,:,mHeight] |
|
|
802 | 841 | meteorThresh = noise[:,mHeight]*thresholdNoise |
|
|
803 | 842 | meteorNoise = noise[:,mHeight] |
|
|
804 | 843 | meteorVolts0[indSides,:] = self.__shiftPhase(meteorVolts0[indSides,:], phaseDiff) #Phase Shifting |
|
|
805 | 844 | powerNet0 = numpy.nansum(numpy.abs(meteorVolts0)**2, axis = 0) #Power |
|
|
806 | 845 | |
|
|
807 | 846 | #Times reestimation |
|
|
808 | 847 | mStart1 = numpy.where(powerNet0[:mPeak] < meteorThresh[:mPeak])[0] |
|
|
809 | 848 | if mStart1.size > 0: |
|
|
810 | 849 | mStart1 = mStart1[-1] + 1 |
|
|
811 | 850 | |
|
|
812 | 851 | else: |
|
|
813 | 852 | mStart1 = mPeak |
|
|
814 | 853 | |
|
|
815 | 854 | mEnd1 = numpy.where(powerNet0[mPeak:] < meteorThresh[mPeak:])[0][0] + mPeak - 1 |
|
|
816 | 855 | mEndDecayTime1 = numpy.where(powerNet0[mPeak:] < meteorNoise[mPeak:])[0] |
|
|
817 | 856 | if mEndDecayTime1.size == 0: |
|
|
818 | 857 | mEndDecayTime1 = powerNet0.size |
|
|
819 | 858 | else: |
|
|
820 | 859 | mEndDecayTime1 = mEndDecayTime1[0] + mPeak - 1 |
|
|
821 | 860 | # mPeak1 = meteorVolts0[mStart1:mEnd1 + 1].argmax() |
|
|
822 | 861 | |
|
|
823 | 862 | #meteorVolts1.- all Channels, from start to end |
|
|
824 | 863 | meteorVolts1 = meteorVolts0[:,mStart1:mEnd1 + 1] |
|
|
825 | 864 | meteorVolts2 = meteorVolts0[:,mPeak + lag:mEnd1 + 1] |
|
|
826 | 865 | if meteorVolts2.shape[1] == 0: |
|
|
827 | 866 | meteorVolts2 = meteorVolts0[:,mPeak:mEnd1 + 1] |
|
|
828 | 867 | meteorVolts1 = meteorVolts1.reshape(meteorVolts1.shape[0], meteorVolts1.shape[1], 1) |
|
|
829 | 868 | meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1], 1) |
|
|
830 | 869 | ##################### END PARAMETERS REESTIMATION ######################### |
|
|
831 | 870 | |
|
|
832 | 871 | ##################### 3.8 PHASE DIFFERENCE REESTIMATION ######################## |
|
|
833 | 872 | # if mEnd1 - mStart1 > 4: #Error Number 6: echo less than 5 samples long; too short for analysis |
|
|
834 | 873 | if meteorVolts2.shape[1] > 0: |
|
|
835 | 874 | #Phase Difference re-estimation |
|
|
836 | 875 | phaseDiff1, phaseDiffint = self.__estimatePhaseDifference(meteorVolts2, pairslist1) #Phase Difference Estimation |
|
|
837 | 876 | # phaseDiff1, phaseDiffint = self.estimatePhaseDifference(meteorVolts2, pairslist) |
|
|
838 | 877 | meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1]) |
|
|
839 | 878 | phaseDiff11 = numpy.reshape(phaseDiff1, (phaseDiff1.shape[0],1)) |
|
|
840 | 879 | meteorVolts2[indSides,:] = self.__shiftPhase(meteorVolts2[indSides,:], phaseDiff11[0:4]) #Phase Shifting |
|
|
841 | 880 | |
|
|
842 | 881 | #Phase Difference RMS |
|
|
843 | 882 | phaseRMS1 = numpy.sqrt(numpy.mean(numpy.square(phaseDiff1))) |
|
|
844 | 883 | powerNet1 = numpy.nansum(numpy.abs(meteorVolts1[:,:])**2,0) |
|
|
845 | 884 | #Data from Meteor |
|
|
846 | 885 | mPeak1 = powerNet1.argmax() + mStart1 |
|
|
847 | 886 | mPeakPower1 = powerNet1.max() |
|
|
848 | 887 | noiseAux = sum(noise[mStart1:mEnd1 + 1,mHeight]) |
|
|
849 | 888 | mSNR1 = (sum(powerNet1)-noiseAux)/noiseAux |
|
|
850 | 889 | Meteor1 = numpy.array([mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1]) |
|
|
851 | 890 | Meteor1 = numpy.hstack((Meteor1,phaseDiffint)) |
|
|
852 | 891 | PowerSeries = powerNet0[mStart1:mEndDecayTime1 + 1] |
|
|
853 | 892 | #Vectorize |
|
|
854 | 893 | meteorAux[0:7] = [mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1] |
|
|
855 | 894 | meteorAux[7:11] = phaseDiffint[0:4] |
|
|
856 | 895 | |
|
|
857 | 896 | #Rejection Criterions |
|
|
858 | 897 | if phaseRMS1 > thresholdPhase: #Error Number 17: Phase variation |
|
|
859 | 898 | meteorAux[-1] = 17 |
|
|
860 | 899 | elif mSNR1 < thresholdDB1: #Error Number 1: SNR < threshold dB |
|
|
861 | 900 | meteorAux[-1] = 1 |
|
|
862 | 901 | |
|
|
863 | 902 | |
|
|
864 | 903 | else: |
|
|
865 | 904 | meteorAux[0:4] = [mHeight, mStart, mPeak, mEnd] |
|
|
866 | 905 | meteorAux[-1] = 6 #Error Number 6: echo less than 5 samples long; too short for analysis |
|
|
867 | 906 | PowerSeries = 0 |
|
|
868 | 907 | |
|
|
869 | 908 | listMeteors1.append(meteorAux) |
|
|
870 | 909 | listPowerSeries.append(PowerSeries) |
|
|
871 | 910 | listVoltageSeries.append(meteorVolts1) |
|
|
872 | 911 | |
|
|
873 | 912 | return listMeteors1, listPowerSeries, listVoltageSeries |
|
|
874 | 913 | |
|
|
875 | 914 | def __estimateDecayTime(self, listMeteors, listPower, timeInterval, frequency): |
|
|
876 | 915 | |
|
|
877 | 916 | threshError = 10 |
|
|
878 | 917 | #Depending if it is 30 or 50 MHz |
|
|
879 | 918 | if frequency == 30e6: |
|
|
880 | 919 | timeLag = 45*10**-3 |
|
|
881 | 920 | else: |
|
|
882 | 921 | timeLag = 15*10**-3 |
|
|
883 | 922 | lag = numpy.ceil(timeLag/timeInterval) |
|
|
884 | 923 | |
|
|
885 | 924 | listMeteors1 = [] |
|
|
886 | 925 | |
|
|
887 | 926 | for i in range(len(listMeteors)): |
|
|
888 | 927 | meteorPower = listPower[i] |
|
|
889 | 928 | meteorAux = listMeteors[i] |
|
|
890 | 929 | |
|
|
891 | 930 | if meteorAux[-1] == 0: |
|
|
892 | 931 | |
|
|
893 | 932 | try: |
|
|
894 | 933 | indmax = meteorPower.argmax() |
|
|
895 | 934 | indlag = indmax + lag |
|
|
896 | 935 | |
|
|
897 | 936 | y = meteorPower[indlag:] |
|
|
898 | 937 | x = numpy.arange(0, y.size)*timeLag |
|
|
899 | 938 | |
|
|
900 | 939 | #first guess |
|
|
901 | 940 | a = y[0] |
|
|
902 | 941 | tau = timeLag |
|
|
903 | 942 | #exponential fit |
|
|
904 | 943 | popt, pcov = optimize.curve_fit(self.__exponential_function, x, y, p0 = [a, tau]) |
|
|
905 | 944 | y1 = self.__exponential_function(x, *popt) |
|
|
906 | 945 | #error estimation |
|
|
907 | 946 | error = sum((y - y1)**2)/(numpy.var(y)*(y.size - popt.size)) |
|
|
908 | 947 | |
|
|
909 | 948 | decayTime = popt[1] |
|
|
910 | 949 | riseTime = indmax*timeInterval |
|
|
911 | 950 | meteorAux[11:13] = [decayTime, error] |
|
|
912 | 951 | |
|
|
913 | 952 | #Table items 7, 8 and 11 |
|
|
914 | 953 | if (riseTime > 0.3): #Number 7: Echo rise exceeds 0.3s |
|
|
915 | 954 | meteorAux[-1] = 7 |
|
|
916 | 955 | elif (decayTime < 2*riseTime) : #Number 8: Echo decay time less than than twice rise time |
|
|
917 | 956 | meteorAux[-1] = 8 |
|
|
918 | 957 | if (error > threshError): #Number 11: Poor fit to amplitude for estimation of decay time |
|
|
919 | 958 | meteorAux[-1] = 11 |
|
|
920 | 959 | |
|
|
921 | 960 | |
|
|
922 | 961 | except: |
|
|
923 | 962 | meteorAux[-1] = 11 |
|
|
924 | 963 | |
|
|
925 | 964 | |
|
|
926 | 965 | listMeteors1.append(meteorAux) |
|
|
927 | 966 | |
|
|
928 | 967 | return listMeteors1 |
|
|
929 | 968 | |
|
|
930 | 969 | #Exponential Function |
|
|
931 | 970 | |
|
|
932 | 971 | def __exponential_function(self, x, a, tau): |
|
|
933 | 972 | y = a*numpy.exp(-x/tau) |
|
|
934 | 973 | return y |
|
|
935 | 974 | |
|
|
936 | 975 | def __getRadialVelocity(self, listMeteors, listVolts, radialStdThresh, pairslist, timeInterval): |
|
|
937 | 976 | |
|
|
938 | 977 | pairslist1 = list(pairslist) |
|
|
939 | 978 | pairslist1.append((0,1)) |
|
|
940 | 979 | pairslist1.append((3,4)) |
|
|
941 | 980 | numPairs = len(pairslist1) |
|
|
942 | 981 | #Time Lag |
|
|
943 | 982 | timeLag = 45*10**-3 |
|
|
944 | 983 | c = 3e8 |
|
|
945 | 984 | lag = numpy.ceil(timeLag/timeInterval) |
|
|
946 | 985 | freq = 30e6 |
|
|
947 | 986 | |
|
|
948 | 987 | listMeteors1 = [] |
|
|
949 | 988 | |
|
|
950 | 989 | for i in range(len(listMeteors)): |
|
|
951 | meteor = listMeteors[i] | |
|
|
952 | meteorAux = numpy.hstack((meteor[:-1], 0, 0, meteor[-1])) | |
|
|
953 | if meteor[-1] == 0: | |
|
|
990 | meteorAux = listMeteors[i] | |
|
|
991 | if meteorAux[-1] == 0: | |
|
|
954 | 992 | mStart = listMeteors[i][1] |
|
|
955 | 993 | mPeak = listMeteors[i][2] |
|
|
956 | 994 | mLag = mPeak - mStart + lag |
|
|
957 | 995 | |
|
|
958 | 996 | #get the volt data between the start and end times of the meteor |
|
|
959 | 997 | meteorVolts = listVolts[i] |
|
|
960 | 998 | meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) |
|
|
961 | 999 | |
|
|
962 | 1000 | #Get CCF |
|
|
963 | 1001 | allCCFs = self.__calculateCCF(meteorVolts, pairslist1, [-2,-1,0,1,2]) |
|
|
964 | 1002 | |
|
|
965 | 1003 | #Method 2 |
|
|
966 | 1004 | slopes = numpy.zeros(numPairs) |
|
|
967 | 1005 | time = numpy.array([-2,-1,1,2])*timeInterval |
|
|
968 | 1006 | angAllCCF = numpy.angle(allCCFs[:,[0,1,3,4],0]) |
|
|
969 | 1007 | |
|
|
970 | 1008 | #Correct phases |
|
|
971 | 1009 | derPhaseCCF = angAllCCF[:,1:] - angAllCCF[:,0:-1] |
|
|
972 | 1010 | indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) |
|
|
973 | 1011 | |
|
|
974 | 1012 | if indDer[0].shape[0] > 0: |
|
|
975 | 1013 | for i in range(indDer[0].shape[0]): |
|
|
976 | 1014 | signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i]]) |
|
|
977 | 1015 | angAllCCF[indDer[0][i],indDer[1][i]+1:] += signo*2*numpy.pi |
|
|
978 | 1016 | |
|
|
979 | 1017 | # fit = scipy.stats.linregress(numpy.array([-2,-1,1,2])*timeInterval, numpy.array([phaseLagN2s[i],phaseLagN1s[i],phaseLag1s[i],phaseLag2s[i]])) |
|
|
980 | 1018 | for j in range(numPairs): |
|
|
981 | 1019 | fit = stats.linregress(time, angAllCCF[j,:]) |
|
|
982 | 1020 | slopes[j] = fit[0] |
|
|
983 | 1021 | |
|
|
984 | 1022 | #Remove Outlier |
|
|
985 | 1023 | # indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes))) |
|
|
986 | 1024 | # slopes = numpy.delete(slopes,indOut) |
|
|
987 | 1025 | # indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes))) |
|
|
988 | 1026 | # slopes = numpy.delete(slopes,indOut) |
|
|
989 | 1027 | |
|
|
990 | 1028 | radialVelocity = -numpy.mean(slopes)*(0.25/numpy.pi)*(c/freq) |
|
|
991 | 1029 | radialError = numpy.std(slopes)*(0.25/numpy.pi)*(c/freq) |
|
|
992 | 1030 | meteorAux[-2] = radialError |
|
|
993 | 1031 | meteorAux[-3] = radialVelocity |
|
|
994 | 1032 | |
|
|
995 | 1033 | #Setting Error |
|
|
996 | 1034 | #Number 15: Radial Drift velocity or projected horizontal velocity exceeds 200 m/s |
|
|
997 | 1035 | if numpy.abs(radialVelocity) > 200: |
|
|
998 | 1036 | meteorAux[-1] = 15 |
|
|
999 | 1037 | #Number 12: Poor fit to CCF variation for estimation of radial drift velocity |
|
|
1000 | 1038 | elif radialError > radialStdThresh: |
|
|
1001 | 1039 | meteorAux[-1] = 12 |
|
|
1002 | 1040 | |
|
|
1003 | 1041 | listMeteors1.append(meteorAux) |
|
|
1004 | 1042 | return listMeteors1 |
|
|
1005 | 1043 | |
|
|
1006 | 1044 | def __setNewArrays(self, listMeteors, date, heiRang): |
|
|
1007 | 1045 | |
|
|
1008 | 1046 | #New arrays |
|
|
1009 | 1047 | arrayMeteors = numpy.array(listMeteors) |
|
|
1010 |
arrayParameters = numpy.zeros((len(listMeteors), |
|
|
|
1048 | arrayParameters = numpy.zeros((len(listMeteors), 14)) | |
|
|
1011 | 1049 | |
|
|
1012 | 1050 | #Date inclusion |
|
|
1013 | 1051 | date = re.findall(r'\((.*?)\)', date) |
|
|
1014 | 1052 | date = date[0].split(',') |
|
|
1015 | 1053 | date = map(int, date) |
|
|
1016 | 1054 | date = [date[0]*10000 + date[1]*100 + date[2], date[3]*10000 + date[4]*100 + date[5]] |
|
|
1017 | 1055 | arrayDate = numpy.tile(date, (len(listMeteors), 1)) |
|
|
1018 | 1056 | |
|
|
1019 | 1057 | #Meteor array |
|
|
1020 | arrayMeteors[:,0] = heiRang[arrayMeteors[:,0].astype(int)] | |
|
|
1021 | arrayMeteors = numpy.hstack((arrayDate, arrayMeteors)) | |
|
|
1058 | # arrayMeteors[:,0] = heiRang[arrayMeteors[:,0].astype(int)] | |
|
|
1059 | # arrayMeteors = numpy.hstack((arrayDate, arrayMeteors)) | |
|
|
1022 | 1060 | |
|
|
1023 | 1061 | #Parameters Array |
|
|
1024 |
arrayParameters[:, |
|
|
|
1025 |
arrayParameters[:, |
|
|
|
1026 | ||
|
|
1027 | return arrayMeteors, arrayParameters | |
|
|
1062 | arrayParameters[:,:2] = arrayDate #Date | |
|
|
1063 | arrayParameters[:,2] = heiRang[arrayMeteors[:,0].astype(int)] #Range | |
|
|
1064 | arrayParameters[:,7:9] = arrayMeteors[:,-3:-1] #Radial velocity and its error | |
|
|
1065 | arrayParameters[:,9:13] = arrayMeteors[:,7:11] #Phases | |
|
|
1066 | arrayParameters[:,-1] = arrayMeteors[:,-1] #Error | |
|
|
1067 | ||
|
|
1068 | ||
|
|
1069 | return arrayParameters | |
|
|
1028 | 1070 | |
|
|
1029 | 1071 | def __getAOA(self, phases, pairsList, error, AOAthresh, azimuth): |
|
|
1030 | 1072 | |
|
|
1031 | 1073 | arrayAOA = numpy.zeros((phases.shape[0],3)) |
|
|
1032 | 1074 | cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList) |
|
|
1033 | 1075 | |
|
|
1034 | 1076 | arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth) |
|
|
1035 | 1077 | cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1) |
|
|
1036 | 1078 | arrayAOA[:,2] = cosDirError |
|
|
1037 | 1079 | |
|
|
1038 | 1080 | azimuthAngle = arrayAOA[:,0] |
|
|
1039 | 1081 | zenithAngle = arrayAOA[:,1] |
|
|
1040 | 1082 | |
|
|
1041 | 1083 | #Setting Error |
|
|
1042 | 1084 | #Number 3: AOA not fesible |
|
|
1043 | 1085 | indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0] |
|
|
1044 | 1086 | error[indInvalid] = 3 |
|
|
1045 | 1087 | #Number 4: Large difference in AOAs obtained from different antenna baselines |
|
|
1046 | 1088 | indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0] |
|
|
1047 | 1089 | error[indInvalid] = 4 |
|
|
1048 | 1090 | return arrayAOA, error |
|
|
1049 | 1091 | |
|
|
1050 | 1092 | def __getDirectionCosines(self, arrayPhase, pairsList): |
|
|
1051 | 1093 | |
|
|
1052 | 1094 | #Initializing some variables |
|
|
1053 | 1095 | ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi |
|
|
1054 | 1096 | ang_aux = ang_aux.reshape(1,ang_aux.size) |
|
|
1055 | 1097 | |
|
|
1056 | 1098 | cosdir = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
1057 | 1099 | cosdir0 = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
1058 | 1100 | |
|
|
1059 | 1101 | |
|
|
1060 | 1102 | for i in range(2): |
|
|
1061 | 1103 | #First Estimation |
|
|
1062 | 1104 | phi0_aux = arrayPhase[:,pairsList[i][0]] + arrayPhase[:,pairsList[i][1]] |
|
|
1063 | 1105 | #Dealias |
|
|
1064 | 1106 | indcsi = numpy.where(phi0_aux > numpy.pi) |
|
|
1065 | 1107 | phi0_aux[indcsi] -= 2*numpy.pi |
|
|
1066 | 1108 | indcsi = numpy.where(phi0_aux < -numpy.pi) |
|
|
1067 | 1109 | phi0_aux[indcsi] += 2*numpy.pi |
|
|
1068 | 1110 | #Direction Cosine 0 |
|
|
1069 | 1111 | cosdir0[:,i] = -(phi0_aux)/(2*numpy.pi*0.5) |
|
|
1070 | 1112 | |
|
|
1071 | 1113 | #Most-Accurate Second Estimation |
|
|
1072 | 1114 | phi1_aux = arrayPhase[:,pairsList[i][0]] - arrayPhase[:,pairsList[i][1]] |
|
|
1073 | 1115 | phi1_aux = phi1_aux.reshape(phi1_aux.size,1) |
|
|
1074 | 1116 | #Direction Cosine 1 |
|
|
1075 | 1117 | cosdir1 = -(phi1_aux + ang_aux)/(2*numpy.pi*4.5) |
|
|
1076 | 1118 | |
|
|
1077 | 1119 | #Searching the correct Direction Cosine |
|
|
1078 | 1120 | cosdir0_aux = cosdir0[:,i] |
|
|
1079 | 1121 | cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1) |
|
|
1080 | 1122 | #Minimum Distance |
|
|
1081 | 1123 | cosDiff = (cosdir1 - cosdir0_aux)**2 |
|
|
1082 | 1124 | indcos = cosDiff.argmin(axis = 1) |
|
|
1083 | 1125 | #Saving Value obtained |
|
|
1084 | 1126 | cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos] |
|
|
1085 | 1127 | |
|
|
1086 | 1128 | return cosdir0, cosdir |
|
|
1087 | 1129 | |
|
|
1088 | 1130 | def __calculateAOA(self, cosdir, azimuth): |
|
|
1089 | 1131 | cosdirX = cosdir[:,0] |
|
|
1090 | 1132 | cosdirY = cosdir[:,1] |
|
|
1091 | 1133 | |
|
|
1092 | 1134 | zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi |
|
|
1093 | 1135 | azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth #0 deg north, 90 deg east |
|
|
1094 | 1136 | angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose() |
|
|
1095 | 1137 | |
|
|
1096 | 1138 | return angles |
|
|
1097 | 1139 | |
|
|
1098 | 1140 | def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight): |
|
|
1099 | 1141 | |
|
|
1100 | 1142 | Ramb = 375 #Ramb = c/(2*PRF) |
|
|
1101 | 1143 | Re = 6371 #Earth Radius |
|
|
1102 | 1144 | heights = numpy.zeros(Ranges.shape) |
|
|
1103 | 1145 | |
|
|
1104 | 1146 | R_aux = numpy.array([0,1,2])*Ramb |
|
|
1105 | 1147 | R_aux = R_aux.reshape(1,R_aux.size) |
|
|
1106 | 1148 | |
|
|
1107 | 1149 | Ranges = Ranges.reshape(Ranges.size,1) |
|
|
1108 | 1150 | |
|
|
1109 | 1151 | Ri = Ranges + R_aux |
|
|
1110 | 1152 | hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re |
|
|
1111 | 1153 | |
|
|
1112 | 1154 | #Check if there is a height between 70 and 110 km |
|
|
1113 | 1155 | h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1) |
|
|
1114 | 1156 | ind_h = numpy.where(h_bool == 1)[0] |
|
|
1115 | 1157 | |
|
|
1116 | 1158 | hCorr = hi[ind_h, :] |
|
|
1117 | 1159 | ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight)) |
|
|
1118 | 1160 | |
|
|
1119 | 1161 | hCorr = hi[ind_hCorr] |
|
|
1120 | 1162 | heights[ind_h] = hCorr |
|
|
1121 | 1163 | |
|
|
1122 | 1164 | #Setting Error |
|
|
1123 | 1165 | #Number 13: Height unresolvable echo: not valid height within 70 to 110 km |
|
|
1124 | 1166 | #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km |
|
|
1125 | 1167 | |
|
|
1126 | 1168 | indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0] |
|
|
1127 | 1169 | error[indInvalid2] = 14 |
|
|
1128 | 1170 | indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0] |
|
|
1129 | 1171 | error[indInvalid1] = 13 |
|
|
1130 | 1172 | |
|
|
1131 | 1173 | return heights, error |
|
|
1132 | 1174 | |
|
|
1133 | 1175 | def SpectralFitting(self, getSNR = True, path=None, file=None, groupList=None): |
|
|
1134 | 1176 | |
|
|
1135 | 1177 | ''' |
|
|
1136 | 1178 | Function GetMoments() |
|
|
1137 | 1179 | |
|
|
1138 | 1180 | Input: |
|
|
1139 | 1181 | Output: |
|
|
1140 | 1182 | Variables modified: |
|
|
1141 | 1183 | ''' |
|
|
1142 | 1184 | if path != None: |
|
|
1143 | 1185 | sys.path.append(path) |
|
|
1144 | 1186 | self.dataOut.library = importlib.import_module(file) |
|
|
1145 | 1187 | |
|
|
1146 | 1188 | #To be inserted as a parameter |
|
|
1147 | 1189 | groupArray = numpy.array(groupList) |
|
|
1148 | 1190 | # groupArray = numpy.array([[0,1],[2,3]]) |
|
|
1149 | 1191 | self.dataOut.groupList = groupArray |
|
|
1150 | 1192 | |
|
|
1151 | 1193 | nGroups = groupArray.shape[0] |
|
|
1152 | 1194 | nChannels = self.dataIn.nChannels |
|
|
1153 | 1195 | nHeights=self.dataIn.heightList.size |
|
|
1154 | 1196 | |
|
|
1155 | 1197 | #Parameters Array |
|
|
1156 | 1198 | self.dataOut.data_param = None |
|
|
1157 | 1199 | |
|
|
1158 | 1200 | #Set constants |
|
|
1159 | 1201 | constants = self.dataOut.library.setConstants(self.dataIn) |
|
|
1160 | 1202 | self.dataOut.constants = constants |
|
|
1161 | 1203 | M = self.dataIn.normFactor |
|
|
1162 | 1204 | N = self.dataIn.nFFTPoints |
|
|
1163 | 1205 | ippSeconds = self.dataIn.ippSeconds |
|
|
1164 | 1206 | K = self.dataIn.nIncohInt |
|
|
1165 | 1207 | pairsArray = numpy.array(self.dataIn.pairsList) |
|
|
1166 | 1208 | |
|
|
1167 | 1209 | #List of possible combinations |
|
|
1168 | 1210 | listComb = itertools.combinations(numpy.arange(groupArray.shape[1]),2) |
|
|
1169 | 1211 | indCross = numpy.zeros(len(list(listComb)), dtype = 'int') |
|
|
1170 | 1212 | |
|
|
1171 | 1213 | if getSNR: |
|
|
1172 | 1214 | listChannels = groupArray.reshape((groupArray.size)) |
|
|
1173 | 1215 | listChannels.sort() |
|
|
1174 | 1216 | noise = self.dataIn.getNoise() |
|
|
1175 | 1217 | self.dataOut.data_SNR = self.__getSNR(self.dataIn.data_spc[listChannels,:,:], noise[listChannels]) |
|
|
1176 | 1218 | |
|
|
1177 | 1219 | for i in range(nGroups): |
|
|
1178 | 1220 | coord = groupArray[i,:] |
|
|
1179 | 1221 | |
|
|
1180 | 1222 | #Input data array |
|
|
1181 | 1223 | data = self.dataIn.data_spc[coord,:,:]/(M*N) |
|
|
1182 | 1224 | data = data.reshape((data.shape[0]*data.shape[1],data.shape[2])) |
|
|
1183 | 1225 | |
|
|
1184 | 1226 | #Cross Spectra data array for Covariance Matrixes |
|
|
1185 | 1227 | ind = 0 |
|
|
1186 | 1228 | for pairs in listComb: |
|
|
1187 | 1229 | pairsSel = numpy.array([coord[x],coord[y]]) |
|
|
1188 | 1230 | indCross[ind] = int(numpy.where(numpy.all(pairsArray == pairsSel, axis = 1))[0][0]) |
|
|
1189 | 1231 | ind += 1 |
|
|
1190 | 1232 | dataCross = self.dataIn.data_cspc[indCross,:,:]/(M*N) |
|
|
1191 | 1233 | dataCross = dataCross**2/K |
|
|
1192 | 1234 | |
|
|
1193 | 1235 | for h in range(nHeights): |
|
|
1194 | 1236 | # print self.dataOut.heightList[h] |
|
|
1195 | 1237 | |
|
|
1196 | 1238 | #Input |
|
|
1197 | 1239 | d = data[:,h] |
|
|
1198 | 1240 | |
|
|
1199 | 1241 | #Covariance Matrix |
|
|
1200 | 1242 | D = numpy.diag(d**2/K) |
|
|
1201 | 1243 | ind = 0 |
|
|
1202 | 1244 | for pairs in listComb: |
|
|
1203 | 1245 | #Coordinates in Covariance Matrix |
|
|
1204 | 1246 | x = pairs[0] |
|
|
1205 | 1247 | y = pairs[1] |
|
|
1206 | 1248 | #Channel Index |
|
|
1207 | 1249 | S12 = dataCross[ind,:,h] |
|
|
1208 | 1250 | D12 = numpy.diag(S12) |
|
|
1209 | 1251 | #Completing Covariance Matrix with Cross Spectras |
|
|
1210 | 1252 | D[x*N:(x+1)*N,y*N:(y+1)*N] = D12 |
|
|
1211 | 1253 | D[y*N:(y+1)*N,x*N:(x+1)*N] = D12 |
|
|
1212 | 1254 | ind += 1 |
|
|
1213 | 1255 | Dinv=numpy.linalg.inv(D) |
|
|
1214 | 1256 | L=numpy.linalg.cholesky(Dinv) |
|
|
1215 | 1257 | LT=L.T |
|
|
1216 | 1258 | |
|
|
1217 | 1259 | dp = numpy.dot(LT,d) |
|
|
1218 | 1260 | |
|
|
1219 | 1261 | #Initial values |
|
|
1220 | 1262 | data_spc = self.dataIn.data_spc[coord,:,h] |
|
|
1221 | 1263 | |
|
|
1222 | 1264 | if (h>0)and(error1[3]<5): |
|
|
1223 | 1265 | p0 = self.dataOut.data_param[i,:,h-1] |
|
|
1224 | 1266 | else: |
|
|
1225 | 1267 | p0 = numpy.array(self.dataOut.library.initialValuesFunction(data_spc, constants, i)) |
|
|
1226 | 1268 | |
|
|
1227 | 1269 | try: |
|
|
1228 | 1270 | #Least Squares |
|
|
1229 | 1271 | minp,covp,infodict,mesg,ier = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants),full_output=True) |
|
|
1230 | 1272 | # minp,covp = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants)) |
|
|
1231 | 1273 | #Chi square error |
|
|
1232 | 1274 | error0 = numpy.sum(infodict['fvec']**2)/(2*N) |
|
|
1233 | 1275 | #Error with Jacobian |
|
|
1234 | 1276 | error1 = self.dataOut.library.errorFunction(minp,constants,LT) |
|
|
1235 | 1277 | except: |
|
|
1236 | 1278 | minp = p0*numpy.nan |
|
|
1237 | 1279 | error0 = numpy.nan |
|
|
1238 | 1280 | error1 = p0*numpy.nan |
|
|
1239 | 1281 | |
|
|
1240 | 1282 | #Save |
|
|
1241 | 1283 | if self.dataOut.data_param == None: |
|
|
1242 | 1284 | self.dataOut.data_param = numpy.zeros((nGroups, p0.size, nHeights))*numpy.nan |
|
|
1243 | 1285 | self.dataOut.data_error = numpy.zeros((nGroups, p0.size + 1, nHeights))*numpy.nan |
|
|
1244 | 1286 | |
|
|
1245 | 1287 | self.dataOut.data_error[i,:,h] = numpy.hstack((error0,error1)) |
|
|
1246 | 1288 | self.dataOut.data_param[i,:,h] = minp |
|
|
1247 | 1289 | return |
|
|
1248 | 1290 | |
|
|
1249 | ||
|
|
1250 | 1291 | def __residFunction(self, p, dp, LT, constants): |
|
|
1251 | 1292 | |
|
|
1252 | 1293 | fm = self.dataOut.library.modelFunction(p, constants) |
|
|
1253 | 1294 | fmp=numpy.dot(LT,fm) |
|
|
1254 | 1295 | |
|
|
1255 | 1296 | return dp-fmp |
|
|
1256 | 1297 | |
|
|
1257 | 1298 | def __getSNR(self, z, noise): |
|
|
1258 | 1299 | |
|
|
1259 | 1300 | avg = numpy.average(z, axis=1) |
|
|
1260 | 1301 | SNR = (avg.T-noise)/noise |
|
|
1261 | 1302 | SNR = SNR.T |
|
|
1262 | 1303 | return SNR |
|
|
1263 | 1304 | |
|
|
1264 | 1305 | def __chisq(p,chindex,hindex): |
|
|
1265 | 1306 | #similar to Resid but calculates CHI**2 |
|
|
1266 | 1307 | [LT,d,fm]=setupLTdfm(p,chindex,hindex) |
|
|
1267 | 1308 | dp=numpy.dot(LT,d) |
|
|
1268 | 1309 | fmp=numpy.dot(LT,fm) |
|
|
1269 | 1310 | chisq=numpy.dot((dp-fmp).T,(dp-fmp)) |
|
|
1270 | 1311 | return chisq |
|
|
1271 | 1312 | |
|
|
1272 | 1313 | |
|
|
1273 | 1314 | |
|
|
1274 | 1315 | class WindProfiler(Operation): |
|
|
1275 | 1316 | |
|
|
1276 | 1317 | __isConfig = False |
|
|
1277 | 1318 | |
|
|
1278 | 1319 | __initime = None |
|
|
1279 | 1320 | __lastdatatime = None |
|
|
1280 | 1321 | __integrationtime = None |
|
|
1281 | 1322 | |
|
|
1282 | 1323 | __buffer = None |
|
|
1283 | 1324 | |
|
|
1284 | 1325 | __dataReady = False |
|
|
1285 | 1326 | |
|
|
1286 | 1327 | __firstdata = None |
|
|
1287 | 1328 | |
|
|
1288 | 1329 | n = None |
|
|
1289 | 1330 | |
|
|
1290 | 1331 | def __init__(self): |
|
|
1291 | 1332 | Operation.__init__(self) |
|
|
1292 | 1333 | |
|
|
1293 | 1334 | def __calculateCosDir(self, elev, azim): |
|
|
1294 | 1335 | zen = (90 - elev)*numpy.pi/180 |
|
|
1295 | 1336 | azim = azim*numpy.pi/180 |
|
|
1296 | 1337 | cosDirX = numpy.sqrt((1-numpy.cos(zen)**2)/((1+numpy.tan(azim)**2))) |
|
|
1297 | 1338 | cosDirY = numpy.sqrt(1-numpy.cos(zen)**2-cosDirX**2) |
|
|
1298 | 1339 | |
|
|
1299 | 1340 | signX = numpy.sign(numpy.cos(azim)) |
|
|
1300 | 1341 | signY = numpy.sign(numpy.sin(azim)) |
|
|
1301 | 1342 | |
|
|
1302 | 1343 | cosDirX = numpy.copysign(cosDirX, signX) |
|
|
1303 | 1344 | cosDirY = numpy.copysign(cosDirY, signY) |
|
|
1304 | 1345 | return cosDirX, cosDirY |
|
|
1305 | 1346 | |
|
|
1306 | 1347 | def __calculateAngles(self, theta_x, theta_y, azimuth): |
|
|
1307 | 1348 | |
|
|
1308 | 1349 | dir_cosw = numpy.sqrt(1-theta_x**2-theta_y**2) |
|
|
1309 | 1350 | zenith_arr = numpy.arccos(dir_cosw) |
|
|
1310 | 1351 | azimuth_arr = numpy.arctan2(theta_x,theta_y) + azimuth*math.pi/180 |
|
|
1311 | 1352 | |
|
|
1312 | 1353 | dir_cosu = numpy.sin(azimuth_arr)*numpy.sin(zenith_arr) |
|
|
1313 | 1354 | dir_cosv = numpy.cos(azimuth_arr)*numpy.sin(zenith_arr) |
|
|
1314 | 1355 | |
|
|
1315 | 1356 | return azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw |
|
|
1316 | 1357 | |
|
|
1317 | 1358 | def __calculateMatA(self, dir_cosu, dir_cosv, dir_cosw, horOnly): |
|
|
1318 | 1359 | |
|
|
1319 | 1360 | # |
|
|
1320 | 1361 | if horOnly: |
|
|
1321 | 1362 | A = numpy.c_[dir_cosu,dir_cosv] |
|
|
1322 | 1363 | else: |
|
|
1323 | 1364 | A = numpy.c_[dir_cosu,dir_cosv,dir_cosw] |
|
|
1324 | 1365 | A = numpy.asmatrix(A) |
|
|
1325 | 1366 | A1 = numpy.linalg.inv(A.transpose()*A)*A.transpose() |
|
|
1326 | 1367 | |
|
|
1327 | 1368 | return A1 |
|
|
1328 | 1369 | |
|
|
1329 | 1370 | def __correctValues(self, heiRang, phi, velRadial, SNR): |
|
|
1330 | ||
|
|
1331 | 1371 | listPhi = phi.tolist() |
|
|
1332 | 1372 | maxid = listPhi.index(max(listPhi)) |
|
|
1333 | 1373 | minid = listPhi.index(min(listPhi)) |
|
|
1334 | 1374 | |
|
|
1335 | 1375 | rango = range(len(phi)) |
|
|
1336 | 1376 | # rango = numpy.delete(rango,maxid) |
|
|
1337 | 1377 | |
|
|
1338 | 1378 | heiRang1 = heiRang*math.cos(phi[maxid]) |
|
|
1339 | 1379 | heiRangAux = heiRang*math.cos(phi[minid]) |
|
|
1340 | 1380 | indOut = (heiRang1 < heiRangAux[0]).nonzero() |
|
|
1341 | 1381 | heiRang1 = numpy.delete(heiRang1,indOut) |
|
|
1342 | 1382 | |
|
|
1343 | 1383 | velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
1344 | 1384 | SNR1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
1345 | 1385 | |
|
|
1346 | 1386 | for i in rango: |
|
|
1347 | 1387 | x = heiRang*math.cos(phi[i]) |
|
|
1348 | 1388 | y1 = velRadial[i,:] |
|
|
1349 | 1389 | f1 = interpolate.interp1d(x,y1,kind = 'cubic') |
|
|
1350 | 1390 | |
|
|
1351 | 1391 | x1 = heiRang1 |
|
|
1352 | 1392 | y11 = f1(x1) |
|
|
1353 | 1393 | |
|
|
1354 | 1394 | y2 = SNR[i,:] |
|
|
1355 | 1395 | f2 = interpolate.interp1d(x,y2,kind = 'cubic') |
|
|
1356 | 1396 | y21 = f2(x1) |
|
|
1357 | 1397 | |
|
|
1358 | 1398 | velRadial1[i,:] = y11 |
|
|
1359 | 1399 | SNR1[i,:] = y21 |
|
|
1360 | 1400 | |
|
|
1361 | 1401 | return heiRang1, velRadial1, SNR1 |
|
|
1362 | 1402 | |
|
|
1363 | 1403 | def __calculateVelUVW(self, A, velRadial): |
|
|
1364 | 1404 | |
|
|
1365 | 1405 | #Operacion Matricial |
|
|
1366 | 1406 | # velUVW = numpy.zeros((velRadial.shape[1],3)) |
|
|
1367 | 1407 | # for ind in range(velRadial.shape[1]): |
|
|
1368 | 1408 | # velUVW[ind,:] = numpy.dot(A,velRadial[:,ind]) |
|
|
1369 | 1409 | # velUVW = velUVW.transpose() |
|
|
1370 | 1410 | velUVW = numpy.zeros((A.shape[0],velRadial.shape[1])) |
|
|
1371 | 1411 | velUVW[:,:] = numpy.dot(A,velRadial) |
|
|
1372 | 1412 | |
|
|
1373 | 1413 | |
|
|
1374 | 1414 | return velUVW |
|
|
1375 | 1415 | |
|
|
1376 | 1416 | def techniqueDBS(self, velRadial0, dirCosx, disrCosy, azimuth, correct, horizontalOnly, heiRang, SNR0): |
|
|
1377 | 1417 | """ |
|
|
1378 | 1418 | Function that implements Doppler Beam Swinging (DBS) technique. |
|
|
1379 | 1419 | |
|
|
1380 | 1420 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, |
|
|
1381 | 1421 | Direction correction (if necessary), Ranges and SNR |
|
|
1382 | 1422 | |
|
|
1383 | 1423 | Output: Winds estimation (Zonal, Meridional and Vertical) |
|
|
1384 | 1424 | |
|
|
1385 | 1425 | Parameters affected: Winds, height range, SNR |
|
|
1386 | 1426 | """ |
|
|
1387 | 1427 | azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(dirCosx, disrCosy, azimuth) |
|
|
1388 | 1428 | heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, zenith_arr, correct*velRadial0, SNR0) |
|
|
1389 | 1429 | A = self.__calculateMatA(dir_cosu, dir_cosv, dir_cosw, horizontalOnly) |
|
|
1390 | 1430 | |
|
|
1391 | 1431 | #Calculo de Componentes de la velocidad con DBS |
|
|
1392 | 1432 | winds = self.__calculateVelUVW(A,velRadial1) |
|
|
1393 | 1433 | |
|
|
1394 | 1434 | return winds, heiRang1, SNR1 |
|
|
1395 | 1435 | |
|
|
1396 | 1436 | def __calculateDistance(self, posx, posy, pairsCrossCorr, pairsList, pairs, azimuth = None): |
|
|
1397 | 1437 | |
|
|
1398 | 1438 | posx = numpy.asarray(posx) |
|
|
1399 | 1439 | posy = numpy.asarray(posy) |
|
|
1400 | 1440 | |
|
|
1401 | 1441 | #Rotacion Inversa para alinear con el azimuth |
|
|
1402 | 1442 | if azimuth!= None: |
|
|
1403 | 1443 | azimuth = azimuth*math.pi/180 |
|
|
1404 | 1444 | posx1 = posx*math.cos(azimuth) + posy*math.sin(azimuth) |
|
|
1405 | 1445 | posy1 = -posx*math.sin(azimuth) + posy*math.cos(azimuth) |
|
|
1406 | 1446 | else: |
|
|
1407 | 1447 | posx1 = posx |
|
|
1408 | 1448 | posy1 = posy |
|
|
1409 | 1449 | |
|
|
1410 | 1450 | #Calculo de Distancias |
|
|
1411 | 1451 | distx = numpy.zeros(pairsCrossCorr.size) |
|
|
1412 | 1452 | disty = numpy.zeros(pairsCrossCorr.size) |
|
|
1413 | 1453 | dist = numpy.zeros(pairsCrossCorr.size) |
|
|
1414 | 1454 | ang = numpy.zeros(pairsCrossCorr.size) |
|
|
1415 | 1455 | |
|
|
1416 | 1456 | for i in range(pairsCrossCorr.size): |
|
|
1417 | 1457 | distx[i] = posx1[pairsList[pairsCrossCorr[i]][1]] - posx1[pairsList[pairsCrossCorr[i]][0]] |
|
|
1418 | 1458 | disty[i] = posy1[pairsList[pairsCrossCorr[i]][1]] - posy1[pairsList[pairsCrossCorr[i]][0]] |
|
|
1419 | 1459 | dist[i] = numpy.sqrt(distx[i]**2 + disty[i]**2) |
|
|
1420 | 1460 | ang[i] = numpy.arctan2(disty[i],distx[i]) |
|
|
1421 | 1461 | #Calculo de Matrices |
|
|
1422 | 1462 | nPairs = len(pairs) |
|
|
1423 | 1463 | ang1 = numpy.zeros((nPairs, 2, 1)) |
|
|
1424 | 1464 | dist1 = numpy.zeros((nPairs, 2, 1)) |
|
|
1425 | 1465 | |
|
|
1426 | 1466 | for j in range(nPairs): |
|
|
1427 | 1467 | dist1[j,0,0] = dist[pairs[j][0]] |
|
|
1428 | 1468 | dist1[j,1,0] = dist[pairs[j][1]] |
|
|
1429 | 1469 | ang1[j,0,0] = ang[pairs[j][0]] |
|
|
1430 | 1470 | ang1[j,1,0] = ang[pairs[j][1]] |
|
|
1431 | 1471 | |
|
|
1432 | 1472 | return distx,disty, dist1,ang1 |
|
|
1433 | 1473 | |
|
|
1434 | 1474 | def __calculateVelVer(self, phase, lagTRange, _lambda): |
|
|
1435 | 1475 | |
|
|
1436 | 1476 | Ts = lagTRange[1] - lagTRange[0] |
|
|
1437 | 1477 | velW = -_lambda*phase/(4*math.pi*Ts) |
|
|
1438 | 1478 | |
|
|
1439 | 1479 | return velW |
|
|
1440 | 1480 | |
|
|
1441 | 1481 | def __calculateVelHorDir(self, dist, tau1, tau2, ang): |
|
|
1442 | 1482 | nPairs = tau1.shape[0] |
|
|
1443 | 1483 | vel = numpy.zeros((nPairs,3,tau1.shape[2])) |
|
|
1444 | 1484 | |
|
|
1445 | 1485 | angCos = numpy.cos(ang) |
|
|
1446 | 1486 | angSin = numpy.sin(ang) |
|
|
1447 | 1487 | |
|
|
1448 | 1488 | vel0 = dist*tau1/(2*tau2**2) |
|
|
1449 | 1489 | vel[:,0,:] = (vel0*angCos).sum(axis = 1) |
|
|
1450 | 1490 | vel[:,1,:] = (vel0*angSin).sum(axis = 1) |
|
|
1451 | 1491 | |
|
|
1452 | 1492 | ind = numpy.where(numpy.isinf(vel)) |
|
|
1453 | 1493 | vel[ind] = numpy.nan |
|
|
1454 | 1494 | |
|
|
1455 | 1495 | return vel |
|
|
1456 | 1496 | |
|
|
1457 | 1497 | def __getPairsAutoCorr(self, pairsList, nChannels): |
|
|
1458 | 1498 | |
|
|
1459 | 1499 | pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan |
|
|
1460 | 1500 | |
|
|
1461 | 1501 | for l in range(len(pairsList)): |
|
|
1462 | 1502 | firstChannel = pairsList[l][0] |
|
|
1463 | 1503 | secondChannel = pairsList[l][1] |
|
|
1464 | 1504 | |
|
|
1465 | 1505 | #Obteniendo pares de Autocorrelacion |
|
|
1466 | 1506 | if firstChannel == secondChannel: |
|
|
1467 | 1507 | pairsAutoCorr[firstChannel] = int(l) |
|
|
1468 | 1508 | |
|
|
1469 | 1509 | pairsAutoCorr = pairsAutoCorr.astype(int) |
|
|
1470 | 1510 | |
|
|
1471 | 1511 | pairsCrossCorr = range(len(pairsList)) |
|
|
1472 | 1512 | pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr) |
|
|
1473 | 1513 | |
|
|
1474 | 1514 | return pairsAutoCorr, pairsCrossCorr |
|
|
1475 | 1515 | |
|
|
1476 | 1516 | def techniqueSA(self, pairsSelected, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, lagTRange, correctFactor): |
|
|
1477 | 1517 | """ |
|
|
1478 | 1518 | Function that implements Spaced Antenna (SA) technique. |
|
|
1479 | 1519 | |
|
|
1480 | 1520 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, |
|
|
1481 | 1521 | Direction correction (if necessary), Ranges and SNR |
|
|
1482 | 1522 | |
|
|
1483 | 1523 | Output: Winds estimation (Zonal, Meridional and Vertical) |
|
|
1484 | 1524 | |
|
|
1485 | 1525 | Parameters affected: Winds |
|
|
1486 | 1526 | """ |
|
|
1487 | 1527 | #Cross Correlation pairs obtained |
|
|
1488 | 1528 | pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairsList, nChannels) |
|
|
1489 | 1529 | pairsArray = numpy.array(pairsList)[pairsCrossCorr] |
|
|
1490 | 1530 | pairsSelArray = numpy.array(pairsSelected) |
|
|
1491 | 1531 | pairs = [] |
|
|
1492 | 1532 | |
|
|
1493 | 1533 | #Wind estimation pairs obtained |
|
|
1494 | 1534 | for i in range(pairsSelArray.shape[0]/2): |
|
|
1495 | 1535 | ind1 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i], axis = 1))[0][0] |
|
|
1496 | 1536 | ind2 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i + 1], axis = 1))[0][0] |
|
|
1497 | 1537 | pairs.append((ind1,ind2)) |
|
|
1498 | 1538 | |
|
|
1499 | 1539 | indtau = tau.shape[0]/2 |
|
|
1500 | 1540 | tau1 = tau[:indtau,:] |
|
|
1501 | 1541 | tau2 = tau[indtau:-1,:] |
|
|
1502 | 1542 | tau1 = tau1[pairs,:] |
|
|
1503 | 1543 | tau2 = tau2[pairs,:] |
|
|
1504 | 1544 | phase1 = tau[-1,:] |
|
|
1505 | 1545 | |
|
|
1506 | 1546 | #--------------------------------------------------------------------- |
|
|
1507 | 1547 | #Metodo Directo |
|
|
1508 | 1548 | distx, disty, dist, ang = self.__calculateDistance(position_x, position_y, pairsCrossCorr, pairsList, pairs,azimuth) |
|
|
1509 | 1549 | winds = self.__calculateVelHorDir(dist, tau1, tau2, ang) |
|
|
1510 | 1550 | winds = stats.nanmean(winds, axis=0) |
|
|
1511 | 1551 | #--------------------------------------------------------------------- |
|
|
1512 | 1552 | #Metodo General |
|
|
1513 | 1553 | # distx, disty, dist = self.calculateDistance(position_x,position_y,pairsCrossCorr, pairsList, azimuth) |
|
|
1514 | 1554 | # #Calculo Coeficientes de Funcion de Correlacion |
|
|
1515 | 1555 | # F,G,A,B,H = self.calculateCoef(tau1,tau2,distx,disty,n) |
|
|
1516 | 1556 | # #Calculo de Velocidades |
|
|
1517 | 1557 | # winds = self.calculateVelUV(F,G,A,B,H) |
|
|
1518 | 1558 | |
|
|
1519 | 1559 | #--------------------------------------------------------------------- |
|
|
1520 | 1560 | winds[2,:] = self.__calculateVelVer(phase1, lagTRange, _lambda) |
|
|
1521 | 1561 | winds = correctFactor*winds |
|
|
1522 | 1562 | return winds |
|
|
1523 | 1563 | |
|
|
1524 | 1564 | def __checkTime(self, currentTime, paramInterval, outputInterval): |
|
|
1525 | 1565 | |
|
|
1526 | 1566 | dataTime = currentTime + paramInterval |
|
|
1527 | 1567 | deltaTime = dataTime - self.__initime |
|
|
1528 | 1568 | |
|
|
1529 | 1569 | if deltaTime >= outputInterval or deltaTime < 0: |
|
|
1530 | 1570 | self.__dataReady = True |
|
|
1531 | 1571 | return |
|
|
1532 | 1572 | |
|
|
1533 | 1573 | def techniqueMeteors(self, arrayMeteor, meteorThresh, heightMin, heightMax): |
|
|
1534 | 1574 | ''' |
|
|
1535 | 1575 | Function that implements winds estimation technique with detected meteors. |
|
|
1536 | 1576 | |
|
|
1537 | 1577 | Input: Detected meteors, Minimum meteor quantity to wind estimation |
|
|
1538 | 1578 | |
|
|
1539 | 1579 | Output: Winds estimation (Zonal and Meridional) |
|
|
1540 | 1580 | |
|
|
1541 | 1581 | Parameters affected: Winds |
|
|
1542 |
''' |
|
|
|
1582 | ''' | |
|
|
1583 | print arrayMeteor.shape | |
|
|
1543 | 1584 | #Settings |
|
|
1544 | 1585 | nInt = (heightMax - heightMin)/2 |
|
|
1545 | 1586 | winds = numpy.zeros((2,nInt))*numpy.nan |
|
|
1546 | 1587 | |
|
|
1547 | 1588 | #Filter errors |
|
|
1548 | error = numpy.where(arrayMeteor[:,-1] == 0)[0] | |
|
|
1549 | finalMeteor = arrayMeteor[error,:] | |
|
|
1589 | error = numpy.where(arrayMeteor[0,:,-1] == 0)[0] | |
|
|
1590 | finalMeteor = arrayMeteor[0,error,:] | |
|
|
1550 | 1591 | |
|
|
1551 | 1592 | #Meteor Histogram |
|
|
1552 | 1593 | finalHeights = finalMeteor[:,3] |
|
|
1553 | 1594 | hist = numpy.histogram(finalHeights, bins = nInt, range = (heightMin,heightMax)) |
|
|
1554 | 1595 | nMeteorsPerI = hist[0] |
|
|
1555 | 1596 | heightPerI = hist[1] |
|
|
1556 | 1597 | |
|
|
1557 | 1598 | #Sort of meteors |
|
|
1558 | 1599 | indSort = finalHeights.argsort() |
|
|
1559 | 1600 | finalMeteor2 = finalMeteor[indSort,:] |
|
|
1560 | 1601 | |
|
|
1561 | 1602 | # Calculating winds |
|
|
1562 | 1603 | ind1 = 0 |
|
|
1563 | 1604 | ind2 = 0 |
|
|
1564 | 1605 | |
|
|
1565 | 1606 | for i in range(nInt): |
|
|
1566 | 1607 | nMet = nMeteorsPerI[i] |
|
|
1567 | 1608 | ind1 = ind2 |
|
|
1568 | 1609 | ind2 = ind1 + nMet |
|
|
1569 | 1610 | |
|
|
1570 | 1611 | meteorAux = finalMeteor2[ind1:ind2,:] |
|
|
1571 | 1612 | |
|
|
1572 | 1613 | if meteorAux.shape[0] >= meteorThresh: |
|
|
1573 | 1614 | vel = meteorAux[:, 7] |
|
|
1574 | 1615 | zen = meteorAux[:, 5]*numpy.pi/180 |
|
|
1575 | 1616 | azim = meteorAux[:, 4]*numpy.pi/180 |
|
|
1576 | 1617 | |
|
|
1577 | 1618 | n = numpy.cos(zen) |
|
|
1578 | 1619 | # m = (1 - n**2)/(1 - numpy.tan(azim)**2) |
|
|
1579 | 1620 | # l = m*numpy.tan(azim) |
|
|
1580 | 1621 | l = numpy.sin(zen)*numpy.sin(azim) |
|
|
1581 | 1622 | m = numpy.sin(zen)*numpy.cos(azim) |
|
|
1582 | 1623 | |
|
|
1583 | 1624 | A = numpy.vstack((l, m)).transpose() |
|
|
1584 | 1625 | A1 = numpy.dot(numpy.linalg.inv( numpy.dot(A.transpose(),A) ),A.transpose()) |
|
|
1585 | 1626 | windsAux = numpy.dot(A1, vel) |
|
|
1586 | 1627 | |
|
|
1587 | 1628 | winds[0,i] = windsAux[0] |
|
|
1588 | 1629 | winds[1,i] = windsAux[1] |
|
|
1589 | 1630 | |
|
|
1590 | 1631 | return winds, heightPerI[:-1] |
|
|
1591 | 1632 | |
|
|
1592 | 1633 | def run(self, dataOut, technique, **kwargs): |
|
|
1593 | 1634 | |
|
|
1594 | 1635 | param = dataOut.data_param |
|
|
1595 | if dataOut.abscissaList != None: | |
|
|
1596 | absc = dataOut.abscissaList[:-1] | |
|
|
1636 | # if dataOut.abscissaList != None: | |
|
|
1637 | # absc = dataOut.abscissaList[:-1] | |
|
|
1597 | 1638 | noise = dataOut.noise |
|
|
1598 |
heightList = dataOut. |
|
|
|
1639 | heightList = dataOut.heightList | |
|
|
1599 | 1640 | SNR = dataOut.data_SNR |
|
|
1600 | 1641 | |
|
|
1601 | 1642 | if technique == 'DBS': |
|
|
1602 | 1643 | |
|
|
1603 | 1644 | if kwargs.has_key('dirCosx') and kwargs.has_key('dirCosy'): |
|
|
1604 | 1645 | theta_x = numpy.array(kwargs['dirCosx']) |
|
|
1605 | 1646 | theta_y = numpy.array(kwargs['dirCosy']) |
|
|
1606 | 1647 | else: |
|
|
1607 | 1648 | elev = numpy.array(kwargs['elevation']) |
|
|
1608 | 1649 | azim = numpy.array(kwargs['azimuth']) |
|
|
1609 | 1650 | theta_x, theta_y = self.__calculateCosDir(elev, azim) |
|
|
1610 | 1651 | azimuth = kwargs['correctAzimuth'] |
|
|
1611 | 1652 | if kwargs.has_key('horizontalOnly'): |
|
|
1612 | 1653 | horizontalOnly = kwargs['horizontalOnly'] |
|
|
1613 | 1654 | else: horizontalOnly = False |
|
|
1614 | 1655 | if kwargs.has_key('correctFactor'): |
|
|
1615 | 1656 | correctFactor = kwargs['correctFactor'] |
|
|
1616 | 1657 | else: correctFactor = 1 |
|
|
1617 | 1658 | if kwargs.has_key('channelList'): |
|
|
1618 | 1659 | channelList = kwargs['channelList'] |
|
|
1660 | if len(channelList) == 2: | |
|
|
1661 | horizontalOnly = True | |
|
|
1619 | 1662 | arrayChannel = numpy.array(channelList) |
|
|
1620 | 1663 | param = param[arrayChannel,:,:] |
|
|
1621 | 1664 | theta_x = theta_x[arrayChannel] |
|
|
1622 | 1665 | theta_y = theta_y[arrayChannel] |
|
|
1623 | 1666 | |
|
|
1624 | 1667 | velRadial0 = param[:,1,:] #Radial velocity |
|
|
1625 | ||
|
|
1626 | if velRadial0.shape[0] != theta_x.shape[0] or velRadial0.shape[0] != theta_y.shape[0]: | |
|
|
1627 | raise ValueError, "The max number of channels is %d, and the length of cosine director is %d. Please check: dirCosX, dirCosY, elevation or azimuth arguments" %(velRadial0.shape[0], theta_x.shape[0]) | |
|
|
1628 | ||
|
|
1629 | if theta_x.shape[0] == 2: | |
|
|
1630 | horizontalOnly = True | |
|
|
1631 | ||
|
|
1632 | 1668 | dataOut.data_output, dataOut.heightList, dataOut.data_SNR = self.techniqueDBS(velRadial0, theta_x, theta_y, azimuth, correctFactor, horizontalOnly, heightList, SNR) #DBS Function |
|
|
1633 | 1669 | dataOut.utctimeInit = dataOut.utctime |
|
|
1634 | 1670 | dataOut.outputInterval = dataOut.timeInterval |
|
|
1635 | 1671 | |
|
|
1636 | 1672 | elif technique == 'SA': |
|
|
1637 | 1673 | |
|
|
1638 | 1674 | #Parameters |
|
|
1639 | 1675 | position_x = kwargs['positionX'] |
|
|
1640 | 1676 | position_y = kwargs['positionY'] |
|
|
1641 | 1677 | azimuth = kwargs['azimuth'] |
|
|
1642 | 1678 | |
|
|
1643 | 1679 | if kwargs.has_key('crosspairsList'): |
|
|
1644 | 1680 | pairs = kwargs['crosspairsList'] |
|
|
1645 | 1681 | else: |
|
|
1646 | 1682 | pairs = None |
|
|
1647 | 1683 | |
|
|
1648 | 1684 | if kwargs.has_key('correctFactor'): |
|
|
1649 | 1685 | correctFactor = kwargs['correctFactor'] |
|
|
1650 | 1686 | else: |
|
|
1651 | 1687 | correctFactor = 1 |
|
|
1652 | 1688 | |
|
|
1653 | 1689 | tau = dataOut.data_param |
|
|
1654 | 1690 | _lambda = dataOut.C/dataOut.frequency |
|
|
1655 | 1691 | pairsList = dataOut.groupList |
|
|
1656 | 1692 | nChannels = dataOut.nChannels |
|
|
1657 | 1693 | |
|
|
1658 | 1694 | dataOut.data_output = self.techniqueSA(pairs, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, absc, correctFactor) |
|
|
1659 | 1695 | dataOut.utctimeInit = dataOut.utctime |
|
|
1660 | 1696 | dataOut.outputInterval = dataOut.timeInterval |
|
|
1661 | 1697 | |
|
|
1662 | 1698 | elif technique == 'Meteors': |
|
|
1663 | 1699 | dataOut.flagNoData = True |
|
|
1664 | 1700 | self.__dataReady = False |
|
|
1665 | 1701 | |
|
|
1666 | 1702 | if kwargs.has_key('nHours'): |
|
|
1667 | 1703 | nHours = kwargs['nHours'] |
|
|
1668 | 1704 | else: |
|
|
1669 | 1705 | nHours = 1 |
|
|
1670 | 1706 | |
|
|
1671 | 1707 | if kwargs.has_key('meteorsPerBin'): |
|
|
1672 | 1708 | meteorThresh = kwargs['meteorsPerBin'] |
|
|
1673 | 1709 | else: |
|
|
1674 | 1710 | meteorThresh = 6 |
|
|
1675 | 1711 | |
|
|
1676 | 1712 | if kwargs.has_key('hmin'): |
|
|
1677 | 1713 | hmin = kwargs['hmin'] |
|
|
1678 | 1714 | else: hmin = 70 |
|
|
1679 | 1715 | if kwargs.has_key('hmax'): |
|
|
1680 | 1716 | hmax = kwargs['hmax'] |
|
|
1681 | 1717 | else: hmax = 110 |
|
|
1682 | 1718 | |
|
|
1683 | 1719 | dataOut.outputInterval = nHours*3600 |
|
|
1684 | 1720 | |
|
|
1685 | 1721 | if self.__isConfig == False: |
|
|
1686 | 1722 | # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) |
|
|
1687 | 1723 | #Get Initial LTC time |
|
|
1688 |
self.__initime = datetime.datetime.utcfromtimestamp( |
|
|
|
1724 | self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) | |
|
|
1689 | 1725 | self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
1690 | 1726 | |
|
|
1691 | 1727 | self.__isConfig = True |
|
|
1692 | 1728 | |
|
|
1693 | 1729 | if self.__buffer == None: |
|
|
1694 | 1730 | self.__buffer = dataOut.data_param |
|
|
1695 | 1731 | self.__firstdata = copy.copy(dataOut) |
|
|
1696 | 1732 | |
|
|
1697 | 1733 | else: |
|
|
1698 |
self.__buffer = numpy. |
|
|
|
1734 | self.__buffer = numpy.hstack((self.__buffer, dataOut.data_param)) | |
|
|
1699 | 1735 | |
|
|
1700 | 1736 | self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready |
|
|
1701 | 1737 | |
|
|
1702 | 1738 | if self.__dataReady: |
|
|
1703 | 1739 | dataOut.utctimeInit = self.__initime |
|
|
1704 | 1740 | |
|
|
1705 | 1741 | self.__initime += dataOut.outputInterval #to erase time offset |
|
|
1706 | 1742 | |
|
|
1707 | 1743 | dataOut.data_output, dataOut.heightList = self.techniqueMeteors(self.__buffer, meteorThresh, hmin, hmax) |
|
|
1708 | 1744 | dataOut.flagNoData = False |
|
|
1709 | 1745 | self.__buffer = None |
|
|
1710 | 1746 | |
|
|
1711 | 1747 | return |
|
|
1712 | 1748 | |
|
|
1713 | 1749 | class EWDriftsEstimation(Operation): |
|
|
1714 | 1750 | |
|
|
1715 | 1751 | |
|
|
1716 | 1752 | def __init__(self): |
|
|
1717 | 1753 | Operation.__init__(self) |
|
|
1718 | 1754 | |
|
|
1719 | 1755 | def __correctValues(self, heiRang, phi, velRadial, SNR): |
|
|
1720 | 1756 | listPhi = phi.tolist() |
|
|
1721 | 1757 | maxid = listPhi.index(max(listPhi)) |
|
|
1722 | 1758 | minid = listPhi.index(min(listPhi)) |
|
|
1723 | 1759 | |
|
|
1724 | 1760 | rango = range(len(phi)) |
|
|
1725 | 1761 | # rango = numpy.delete(rango,maxid) |
|
|
1726 | 1762 | |
|
|
1727 | 1763 | heiRang1 = heiRang*math.cos(phi[maxid]) |
|
|
1728 | 1764 | heiRangAux = heiRang*math.cos(phi[minid]) |
|
|
1729 | 1765 | indOut = (heiRang1 < heiRangAux[0]).nonzero() |
|
|
1730 | 1766 | heiRang1 = numpy.delete(heiRang1,indOut) |
|
|
1731 | 1767 | |
|
|
1732 | 1768 | velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
1733 | 1769 | SNR1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
1734 | 1770 | |
|
|
1735 | 1771 | for i in rango: |
|
|
1736 | 1772 | x = heiRang*math.cos(phi[i]) |
|
|
1737 | 1773 | y1 = velRadial[i,:] |
|
|
1738 | 1774 | f1 = interpolate.interp1d(x,y1,kind = 'cubic') |
|
|
1739 | 1775 | |
|
|
1740 | 1776 | x1 = heiRang1 |
|
|
1741 | 1777 | y11 = f1(x1) |
|
|
1742 | 1778 | |
|
|
1743 | 1779 | y2 = SNR[i,:] |
|
|
1744 | 1780 | f2 = interpolate.interp1d(x,y2,kind = 'cubic') |
|
|
1745 | 1781 | y21 = f2(x1) |
|
|
1746 | 1782 | |
|
|
1747 | 1783 | velRadial1[i,:] = y11 |
|
|
1748 | 1784 | SNR1[i,:] = y21 |
|
|
1749 | 1785 | |
|
|
1750 | 1786 | return heiRang1, velRadial1, SNR1 |
|
|
1751 | 1787 | |
|
|
1752 | 1788 | def run(self, dataOut, zenith, zenithCorrection): |
|
|
1753 | 1789 | heiRang = dataOut.heightList |
|
|
1754 | 1790 | velRadial = dataOut.data_param[:,3,:] |
|
|
1755 | 1791 | SNR = dataOut.data_SNR |
|
|
1756 | 1792 | |
|
|
1757 | 1793 | zenith = numpy.array(zenith) |
|
|
1758 | 1794 | zenith -= zenithCorrection |
|
|
1759 | 1795 | zenith *= numpy.pi/180 |
|
|
1760 | 1796 | |
|
|
1761 | 1797 | heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, numpy.abs(zenith), velRadial, SNR) |
|
|
1762 | 1798 | |
|
|
1763 | 1799 | alp = zenith[0] |
|
|
1764 | 1800 | bet = zenith[1] |
|
|
1765 | 1801 | |
|
|
1766 | 1802 | w_w = velRadial1[0,:] |
|
|
1767 | 1803 | w_e = velRadial1[1,:] |
|
|
1768 | 1804 | |
|
|
1769 | 1805 | w = (w_w*numpy.sin(bet) - w_e*numpy.sin(alp))/(numpy.cos(alp)*numpy.sin(bet) - numpy.cos(bet)*numpy.sin(alp)) |
|
|
1770 | 1806 | u = (w_w*numpy.cos(bet) - w_e*numpy.cos(alp))/(numpy.sin(alp)*numpy.cos(bet) - numpy.sin(bet)*numpy.cos(alp)) |
|
|
1771 | 1807 | |
|
|
1772 | 1808 | winds = numpy.vstack((u,w)) |
|
|
1773 | 1809 | |
|
|
1774 | 1810 | dataOut.heightList = heiRang1 |
|
|
1775 | 1811 | dataOut.data_output = winds |
|
|
1776 | 1812 | dataOut.data_SNR = SNR1 |
|
|
1777 | 1813 | |
|
|
1778 | 1814 | dataOut.utctimeInit = dataOut.utctime |
|
|
1779 | 1815 | dataOut.outputInterval = dataOut.timeInterval |
|
|
1780 | 1816 | return |
|
|
1781 | 1817 | |
|
|
1818 | class PhaseCalibration(Operation): | |
|
|
1819 | ||
|
|
1820 | __buffer = None | |
|
|
1821 | ||
|
|
1822 | __initime = None | |
|
|
1782 | 1823 | |
|
|
1824 | __dataReady = False | |
|
|
1825 | ||
|
|
1826 | __isConfig = False | |
|
|
1827 | ||
|
|
1828 | def __checkTime(self, currentTime, initTime, paramInterval, outputInterval): | |
|
|
1829 | ||
|
|
1830 | dataTime = currentTime + paramInterval | |
|
|
1831 | deltaTime = dataTime - initTime | |
|
|
1832 | ||
|
|
1833 | if deltaTime >= outputInterval or deltaTime < 0: | |
|
|
1834 | return True | |
|
|
1835 | ||
|
|
1836 | return False | |
|
|
1837 | ||
|
|
1838 | def __getGammas(self, pairs, k, d, phases): | |
|
|
1839 | gammas = numpy.zeros(2) | |
|
|
1840 | ||
|
|
1841 | for i in range(len(pairs)): | |
|
|
1842 | ||
|
|
1843 | pairi = pairs[i] | |
|
|
1844 | ||
|
|
1845 | #Calculating gamma | |
|
|
1846 | jdcos = phases[:,pairi[1]]/(k*d[pairi[1]]) | |
|
|
1847 | jgamma = numpy.angle(numpy.exp(1j*(k*d[pairi[0]]*jdcos - phases[:,pairi[0]]))) | |
|
|
1848 | ||
|
|
1849 | #Revised distribution | |
|
|
1850 | jgammaArray = numpy.hstack((jgamma,jgamma+0.5*numpy.pi,jgamma-0.5*numpy.pi)) | |
|
|
1851 | ||
|
|
1852 | #Histogram | |
|
|
1853 | nBins = 64.0 | |
|
|
1854 | rmin = -0.5*numpy.pi | |
|
|
1855 | rmax = 0.5*numpy.pi | |
|
|
1856 | phaseHisto = numpy.histogram(jgammaArray, bins=nBins, range=(rmin,rmax)) | |
|
|
1857 | ||
|
|
1858 | meteorsY = phaseHisto[0] | |
|
|
1859 | phasesX = phaseHisto[1][:-1] | |
|
|
1860 | width = phasesX[1] - phasesX[0] | |
|
|
1861 | phasesX += width/2 | |
|
|
1862 | ||
|
|
1863 | #Gaussian aproximation | |
|
|
1864 | bpeak = meteorsY.argmax() | |
|
|
1865 | peak = meteorsY.max() | |
|
|
1866 | jmin = bpeak - 5 | |
|
|
1867 | jmax = bpeak + 5 + 1 | |
|
|
1868 | ||
|
|
1869 | if jmin<0: | |
|
|
1870 | jmin = 0 | |
|
|
1871 | jmax = 6 | |
|
|
1872 | elif jmax > meteorsY.size: | |
|
|
1873 | jmin = meteorsY.size - 6 | |
|
|
1874 | jmax = meteorsY.size | |
|
|
1875 | ||
|
|
1876 | x0 = numpy.array([peak,bpeak,50]) | |
|
|
1877 | coeff = optimize.leastsq(self.__residualFunction, x0, args=(meteorsY[jmin:jmax], phasesX[jmin:jmax])) | |
|
|
1878 | ||
|
|
1879 | #Gammas | |
|
|
1880 | gammas[i] = coeff[0][1] | |
|
|
1881 | # gammas[i] = bpeak | |
|
|
1882 | ||
|
|
1883 | return gammas | |
|
|
1884 | ||
|
|
1885 | def __residualFunction(self, coeffs, y, t): | |
|
|
1886 | ||
|
|
1887 | return y - self.__gauss_function(t, coeffs) | |
|
|
1888 | ||
|
|
1889 | def __gauss_function(self, t, coeffs): | |
|
|
1890 | ||
|
|
1891 | return coeffs[0]*numpy.exp(-0.5*((t - coeffs[1]) / coeffs[2])**2) | |
|
|
1892 | ||
|
|
1893 | def __getPhases(self, azimuth, h, pairsList, d, gammas, meteorsArray): | |
|
|
1894 | meteorOps = MeteorOperations() | |
|
|
1895 | nchan = 4 | |
|
|
1896 | pairx = pairsList[0] | |
|
|
1897 | pairy = pairsList[1] | |
|
|
1898 | center_xangle = 0 | |
|
|
1899 | center_yangle = 0 | |
|
|
1900 | range_angle = numpy.array([8*numpy.pi,numpy.pi,numpy.pi/2,numpy.pi/4]) | |
|
|
1901 | ntimes = len(range_angle) | |
|
|
1902 | ||
|
|
1903 | nstepsx = 20.0 | |
|
|
1904 | nstepsy = 20.0 | |
|
|
1905 | ||
|
|
1906 | for iz in range(ntimes): | |
|
|
1907 | min_xangle = -range_angle[iz]/2 + center_xangle | |
|
|
1908 | max_xangle = range_angle[iz]/2 + center_xangle | |
|
|
1909 | min_yangle = -range_angle[iz]/2 + center_yangle | |
|
|
1910 | max_yangle = range_angle[iz]/2 + center_yangle | |
|
|
1911 | ||
|
|
1912 | inc_x = (max_xangle-min_xangle)/nstepsx | |
|
|
1913 | inc_y = (max_yangle-min_yangle)/nstepsy | |
|
|
1914 | ||
|
|
1915 | alpha_y = numpy.arange(nstepsy)*inc_y + min_yangle | |
|
|
1916 | alpha_x = numpy.arange(nstepsx)*inc_x + min_xangle | |
|
|
1917 | penalty = numpy.zeros((nstepsx,nstepsy)) | |
|
|
1918 | jph_array = numpy.zeros((nchan,nstepsx,nstepsy)) | |
|
|
1919 | jph = numpy.zeros(nchan) | |
|
|
1920 | ||
|
|
1921 | # Iterations looking for the offset | |
|
|
1922 | for iy in range(int(nstepsy)): | |
|
|
1923 | for ix in range(int(nstepsx)): | |
|
|
1924 | jph[pairy[1]] = alpha_y[iy] | |
|
|
1925 | jph[pairy[0]] = -gammas[1] + alpha_y[iy]*d[pairy[0]]/d[pairy[1]] | |
|
|
1926 | ||
|
|
1927 | jph[pairx[1]] = alpha_x[ix] | |
|
|
1928 | jph[pairx[0]] = -gammas[0] + alpha_x[ix]*d[pairx[0]]/d[pairx[1]] | |
|
|
1929 | ||
|
|
1930 | jph_array[:,ix,iy] = jph | |
|
|
1931 | ||
|
|
1932 | meteorsArray1 = meteorOps.getMeteorParams(meteorsArray, azimuth, h, pairsList, jph) | |
|
|
1933 | error = meteorsArray1[:,-1] | |
|
|
1934 | ind1 = numpy.where(error==0)[0] | |
|
|
1935 | penalty[ix,iy] = ind1.size | |
|
|
1936 | ||
|
|
1937 | i,j = numpy.unravel_index(penalty.argmax(), penalty.shape) | |
|
|
1938 | phOffset = jph_array[:,i,j] | |
|
|
1939 | ||
|
|
1940 | center_xangle = phOffset[pairx[1]] | |
|
|
1941 | center_yangle = phOffset[pairy[1]] | |
|
|
1942 | ||
|
|
1943 | phOffset = numpy.angle(numpy.exp(1j*jph_array[:,i,j])) | |
|
|
1944 | phOffset = phOffset*180/numpy.pi | |
|
|
1945 | return phOffset | |
|
|
1946 | ||
|
|
1947 | ||
|
|
1948 | def run(self, dataOut, pairs, distances, hmin, hmax, nHours = None): | |
|
|
1949 | ||
|
|
1950 | dataOut.flagNoData = True | |
|
|
1951 | self.__dataReady = False | |
|
|
1952 | ||
|
|
1953 | if nHours == None: | |
|
|
1954 | nHours = 1 | |
|
|
1955 | ||
|
|
1956 | dataOut.outputInterval = nHours*3600 | |
|
|
1957 | ||
|
|
1958 | if self.__isConfig == False: | |
|
|
1959 | # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) | |
|
|
1960 | #Get Initial LTC time | |
|
|
1961 | self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime) | |
|
|
1962 | self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() | |
|
|
1963 | ||
|
|
1964 | self.__isConfig = True | |
|
|
1965 | ||
|
|
1966 | if self.__buffer == None: | |
|
|
1967 | self.__buffer = dataOut.data_param.copy() | |
|
|
1968 | ||
|
|
1969 | else: | |
|
|
1970 | self.__buffer = numpy.hstack((self.__buffer, dataOut.data_param)) | |
|
|
1971 | ||
|
|
1972 | self.__dataReady = self.__checkTime(dataOut.utctime, self.__initime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready | |
|
|
1973 | ||
|
|
1974 | if self.__dataReady: | |
|
|
1975 | dataOut.utctimeInit = self.__initime | |
|
|
1976 | self.__initime += dataOut.outputInterval #to erase time offset | |
|
|
1977 | ||
|
|
1978 | freq = dataOut.frequency | |
|
|
1979 | c = dataOut.C #m/s | |
|
|
1980 | lamb = c/freq | |
|
|
1981 | k = 2*numpy.pi/lamb | |
|
|
1982 | azimuth = 0 | |
|
|
1983 | h = (hmin, hmax) | |
|
|
1984 | pairsList = ((0,3),(1,2)) | |
|
|
1985 | ||
|
|
1986 | meteorsArray = self.__buffer[0,:,:] | |
|
|
1987 | error = meteorsArray[:,-1] | |
|
|
1988 | boolError = (error==0)|(error==3)|(error==4)|(error==13)|(error==14) | |
|
|
1989 | ind1 = numpy.where(boolError)[0] | |
|
|
1990 | meteorsArray = meteorsArray[ind1,:] | |
|
|
1991 | meteorsArray[:,-1] = 0 | |
|
|
1992 | phases = meteorsArray[:,9:13] | |
|
|
1993 | ||
|
|
1994 | #Calculate Gammas | |
|
|
1995 | gammas = self.__getGammas(pairs, k, distances, phases) | |
|
|
1996 | # gammas = numpy.array([-21.70409463,45.76935864])*numpy.pi/180 | |
|
|
1997 | #Calculate Phases | |
|
|
1998 | phasesOff = self.__getPhases(azimuth, h, pairsList, distances, gammas, meteorsArray) | |
|
|
1999 | phasesOff = phasesOff.reshape((1,phasesOff.size)) | |
|
|
2000 | dataOut.data_output = -phasesOff | |
|
|
2001 | dataOut.flagNoData = False | |
|
|
2002 | self.__buffer = None | |
|
|
2003 | ||
|
|
2004 | ||
|
|
2005 | return | |
|
|
2006 | ||
|
|
2007 | class MeteorOperations(): | |
|
|
2008 | ||
|
|
2009 | def __init__(self): | |
|
|
2010 | ||
|
|
2011 | return | |
|
|
2012 | ||
|
|
2013 | def getMeteorParams(self, arrayParameters0, azimuth, h, pairsList, jph): | |
|
|
2014 | ||
|
|
2015 | arrayParameters = arrayParameters0.copy() | |
|
|
2016 | hmin = h[0] | |
|
|
2017 | hmax = h[1] | |
|
|
2018 | ||
|
|
2019 | #Calculate AOA (Error N 3, 4) | |
|
|
2020 | #JONES ET AL. 1998 | |
|
|
2021 | AOAthresh = numpy.pi/8 | |
|
|
2022 | error = arrayParameters[:,-1] | |
|
|
2023 | phases = -arrayParameters[:,9:13] + jph | |
|
|
2024 | arrayParameters[:,4:7], arrayParameters[:,-1] = self.__getAOA(phases, pairsList, error, AOAthresh, azimuth) | |
|
|
2025 | ||
|
|
2026 | #Calculate Heights (Error N 13 and 14) | |
|
|
2027 | error = arrayParameters[:,-1] | |
|
|
2028 | Ranges = arrayParameters[:,2] | |
|
|
2029 | zenith = arrayParameters[:,5] | |
|
|
2030 | arrayParameters[:,3], arrayParameters[:,-1] = self.__getHeights(Ranges, zenith, error, hmin, hmax) | |
|
|
2031 | ||
|
|
2032 | #----------------------- Get Final data ------------------------------------ | |
|
|
2033 | # error = arrayParameters[:,-1] | |
|
|
2034 | # ind1 = numpy.where(error==0)[0] | |
|
|
2035 | # arrayParameters = arrayParameters[ind1,:] | |
|
|
2036 | ||
|
|
2037 | return arrayParameters | |
|
|
2038 | ||
|
|
2039 | def __getAOA(self, phases, pairsList, error, AOAthresh, azimuth): | |
|
|
2040 | ||
|
|
2041 | arrayAOA = numpy.zeros((phases.shape[0],3)) | |
|
|
2042 | cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList) | |
|
|
2043 | ||
|
|
2044 | arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth) | |
|
|
2045 | cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1) | |
|
|
2046 | arrayAOA[:,2] = cosDirError | |
|
|
1783 | 2047 | |
|
|
2048 | azimuthAngle = arrayAOA[:,0] | |
|
|
2049 | zenithAngle = arrayAOA[:,1] | |
|
|
2050 | ||
|
|
2051 | #Setting Error | |
|
|
2052 | #Number 3: AOA not fesible | |
|
|
2053 | indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0] | |
|
|
2054 | error[indInvalid] = 3 | |
|
|
2055 | #Number 4: Large difference in AOAs obtained from different antenna baselines | |
|
|
2056 | indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0] | |
|
|
2057 | error[indInvalid] = 4 | |
|
|
2058 | return arrayAOA, error | |
|
|
2059 | ||
|
|
2060 | def __getDirectionCosines(self, arrayPhase, pairsList): | |
|
|
1784 | 2061 | |
|
|
2062 | #Initializing some variables | |
|
|
2063 | ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi | |
|
|
2064 | ang_aux = ang_aux.reshape(1,ang_aux.size) | |
|
|
2065 | ||
|
|
2066 | cosdir = numpy.zeros((arrayPhase.shape[0],2)) | |
|
|
2067 | cosdir0 = numpy.zeros((arrayPhase.shape[0],2)) | |
|
|
2068 | ||
|
|
2069 | ||
|
|
2070 | for i in range(2): | |
|
|
2071 | #First Estimation | |
|
|
2072 | phi0_aux = arrayPhase[:,pairsList[i][0]] + arrayPhase[:,pairsList[i][1]] | |
|
|
2073 | #Dealias | |
|
|
2074 | indcsi = numpy.where(phi0_aux > numpy.pi) | |
|
|
2075 | phi0_aux[indcsi] -= 2*numpy.pi | |
|
|
2076 | indcsi = numpy.where(phi0_aux < -numpy.pi) | |
|
|
2077 | phi0_aux[indcsi] += 2*numpy.pi | |
|
|
2078 | #Direction Cosine 0 | |
|
|
2079 | cosdir0[:,i] = -(phi0_aux)/(2*numpy.pi*0.5) | |
|
|
2080 | ||
|
|
2081 | #Most-Accurate Second Estimation | |
|
|
2082 | phi1_aux = arrayPhase[:,pairsList[i][0]] - arrayPhase[:,pairsList[i][1]] | |
|
|
2083 | phi1_aux = phi1_aux.reshape(phi1_aux.size,1) | |
|
|
2084 | #Direction Cosine 1 | |
|
|
2085 | cosdir1 = -(phi1_aux + ang_aux)/(2*numpy.pi*4.5) | |
|
|
2086 | ||
|
|
2087 | #Searching the correct Direction Cosine | |
|
|
2088 | cosdir0_aux = cosdir0[:,i] | |
|
|
2089 | cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1) | |
|
|
2090 | #Minimum Distance | |
|
|
2091 | cosDiff = (cosdir1 - cosdir0_aux)**2 | |
|
|
2092 | indcos = cosDiff.argmin(axis = 1) | |
|
|
2093 | #Saving Value obtained | |
|
|
2094 | cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos] | |
|
|
2095 | ||
|
|
2096 | return cosdir0, cosdir | |
|
|
2097 | ||
|
|
2098 | def __calculateAOA(self, cosdir, azimuth): | |
|
|
2099 | cosdirX = cosdir[:,0] | |
|
|
2100 | cosdirY = cosdir[:,1] | |
|
|
2101 | ||
|
|
2102 | zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi | |
|
|
2103 | azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth #0 deg north, 90 deg east | |
|
|
2104 | angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose() | |
|
|
2105 | ||
|
|
2106 | return angles | |
|
|
2107 | ||
|
|
2108 | def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight): | |
|
|
2109 | ||
|
|
2110 | Ramb = 375 #Ramb = c/(2*PRF) | |
|
|
2111 | Re = 6371 #Earth Radius | |
|
|
2112 | heights = numpy.zeros(Ranges.shape) | |
|
|
2113 | ||
|
|
2114 | R_aux = numpy.array([0,1,2])*Ramb | |
|
|
2115 | R_aux = R_aux.reshape(1,R_aux.size) | |
|
|
2116 | ||
|
|
2117 | Ranges = Ranges.reshape(Ranges.size,1) | |
|
|
2118 | ||
|
|
2119 | Ri = Ranges + R_aux | |
|
|
2120 | hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re | |
|
|
2121 | ||
|
|
2122 | #Check if there is a height between 70 and 110 km | |
|
|
2123 | h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1) | |
|
|
2124 | ind_h = numpy.where(h_bool == 1)[0] | |
|
|
2125 | ||
|
|
2126 | hCorr = hi[ind_h, :] | |
|
|
2127 | ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight)) | |
|
|
2128 | ||
|
|
2129 | hCorr = hi[ind_hCorr] | |
|
|
2130 | heights[ind_h] = hCorr | |
|
|
2131 | ||
|
|
2132 | #Setting Error | |
|
|
2133 | #Number 13: Height unresolvable echo: not valid height within 70 to 110 km | |
|
|
2134 | #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km | |
|
|
2135 | ||
|
|
2136 | indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0] | |
|
|
2137 | error[indInvalid2] = 14 | |
|
|
2138 | indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0] | |
|
|
2139 | error[indInvalid1] = 13 | |
|
|
2140 | ||
|
|
2141 | return heights, error | |
|
|
2142 | ||
|
|
1785 | 2143 | |
|
|
1786 | 2144 | No newline at end of file |
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