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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 | sys.path.append(path) | |
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8 | ||
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9 | from controller import * | |
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10 | ||
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11 | desc = "DBS Experiment Test" | |
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12 | filename = "DBStest.xml" | |
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13 | ||
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14 | controllerObj = Project() | |
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15 | ||
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16 | controllerObj.setup(id = '191', name='test01', description=desc) | |
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17 | ||
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18 | #Experimentos | |
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19 | ||
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20 | path = '/host/Jicamarca/EW_Drifts/d2012248' | |
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21 | pathFigure = '/home/propietario/workspace/Graficos/drifts' | |
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22 | ||
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23 | ||
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24 | path = "/home/soporte/Data/drifts" | |
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25 | pathFigure = '/home/soporte/workspace/Graficos/drifts/prueba' | |
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26 | ||
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27 | xmin = 11.75 | |
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28 | xmax = 14.75 | |
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29 | #------------------------------------------------------------------------------------------------ | |
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30 | readUnitConfObj = controllerObj.addReadUnit(datatype='VoltageReader', | |
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31 | path=path, | |
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32 | startDate='2012/01/01', | |
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33 | endDate='2012/12/31', | |
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34 | startTime='00:00:00', | |
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35 | endTime='23:59:59', | |
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36 | online=0, | |
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37 | walk=1) | |
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38 | ||
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39 | opObj11 = readUnitConfObj.addOperation(name='printNumberOfBlock') | |
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40 | ||
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41 | #-------------------------------------------------------------------------------------------------- | |
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42 | ||
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43 | procUnitConfObj0 = controllerObj.addProcUnit(datatype='VoltageProc', inputId=readUnitConfObj.getId()) | |
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44 | ||
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45 | opObj11 = procUnitConfObj0.addOperation(name='ProfileSelector', optype='other') | |
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46 | opObj11.addParameter(name='profileRangeList', value='0,127', format='intlist') | |
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47 | ||
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48 | opObj11 = procUnitConfObj0.addOperation(name='filterByHeights') | |
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49 | opObj11.addParameter(name='window', value='3', format='int') | |
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50 | ||
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51 | opObj11 = procUnitConfObj0.addOperation(name='Decoder', optype='other') | |
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52 | # opObj11.addParameter(name='code', value='1,-1', format='floatlist') | |
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53 | # opObj11.addParameter(name='nCode', value='2', format='int') | |
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54 | # opObj11.addParameter(name='nBaud', value='1', format='int') | |
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55 | ||
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56 | procUnitConfObj1 = controllerObj.addProcUnit(datatype='SpectraProc', inputId=procUnitConfObj0.getId()) | |
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57 | procUnitConfObj1.addParameter(name='nFFTPoints', value='128', format='int') | |
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58 | procUnitConfObj1.addParameter(name='nProfiles', value='128', format='int') | |
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59 | procUnitConfObj1.addParameter(name='pairsList', value='(0,1),(2,3)', format='pairsList')#,(2,3) | |
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60 | ||
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61 | opObj11 = procUnitConfObj1.addOperation(name='selectHeights') | |
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62 | # # opObj11.addParameter(name='minHei', value='320.0', format='float') | |
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63 | # # opObj11.addParameter(name='maxHei', value='350.0', format='float') | |
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64 | opObj11.addParameter(name='minHei', value='200.0', format='float') | |
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65 | opObj11.addParameter(name='maxHei', value='600.0', format='float') | |
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66 | ||
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67 | opObj11 = procUnitConfObj1.addOperation(name='selectChannels') | |
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68 | opObj11.addParameter(name='channelList', value='0,1,2,3', format='intlist') | |
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69 | ||
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70 | opObj11 = procUnitConfObj1.addOperation(name='IncohInt', optype='other') | |
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71 | opObj11.addParameter(name='timeInterval', value='300.0', format='float') | |
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72 | ||
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73 | opObj13 = procUnitConfObj1.addOperation(name='removeDC') | |
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74 | ||
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75 | # opObj14 = procUnitConfObj1.addOperation(name='SpectraPlot', optype='other') | |
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76 | # opObj14.addParameter(name='id', value='1', format='int') | |
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77 | # # opObj14.addParameter(name='wintitle', value='Con interf', format='str') | |
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78 | # opObj14.addParameter(name='save', value='1', format='bool') | |
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79 | # opObj14.addParameter(name='figpath', value=pathFigure, format='str') | |
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80 | # # opObj14.addParameter(name='zmin', value='5', format='int') | |
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81 | # opObj14.addParameter(name='zmax', value='30', format='int') | |
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82 | # | |
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83 | # opObj12 = procUnitConfObj1.addOperation(name='RTIPlot', optype='other') | |
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84 | # opObj12.addParameter(name='id', value='2', format='int') | |
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85 | # opObj12.addParameter(name='wintitle', value='RTI Plot', format='str') | |
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86 | # opObj12.addParameter(name='save', value='1', format='bool') | |
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87 | # opObj12.addParameter(name='figpath', value = pathFigure, format='str') | |
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88 | # opObj12.addParameter(name='xmin', value=xmin, format='float') | |
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89 | # opObj12.addParameter(name='xmax', value=xmax, format='float') | |
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90 | # # opObj12.addParameter(name='zmin', value='5', format='int') | |
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91 | # opObj12.addParameter(name='zmax', value='30', format='int') | |
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92 | ||
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93 | #-------------------------------------------------------------------------------------------------- | |
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94 | ||
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95 | procUnitConfObj2 = controllerObj.addProcUnit(datatype='ParametersProc', inputId=procUnitConfObj1.getId()) | |
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96 | opObj20 = procUnitConfObj2.addOperation(name='SpectralFitting') | |
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97 | opObj20.addParameter(name='path', value='/home/soporte/workspace/RemoteSystemsTempFiles', format='str') | |
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98 | opObj20.addParameter(name='file', value='modelSpectralFitting', format='str') | |
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99 | opObj20.addParameter(name='groupList', value='(0,1),(2,3)',format='multiList') | |
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100 | ||
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101 | opObj11 = procUnitConfObj2.addOperation(name='SpectralFittingPlot', optype='other') | |
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102 | opObj11.addParameter(name='id', value='3', format='int') | |
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103 | opObj11.addParameter(name='wintitle', value='DopplerPlot', format='str') | |
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104 | opObj11.addParameter(name='cutHeight', value='350', format='int') | |
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105 | opObj11.addParameter(name='fit', value='1', format='int')#1--True/include fit | |
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106 | opObj11.addParameter(name='save', value='1', format='bool') | |
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107 | opObj11.addParameter(name='figpath', value = pathFigure, format='str') | |
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108 | ||
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109 | opObj11 = procUnitConfObj2.addOperation(name='EWDriftsEstimation', optype='other') | |
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110 | opObj11.addParameter(name='zenith', value='-3.80208,3.10658', format='floatlist') | |
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111 | opObj11.addParameter(name='zenithCorrection', value='0.183201', format='float') | |
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112 | ||
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113 | opObj23 = procUnitConfObj2.addOperation(name='EWDriftsPlot', optype='other') | |
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114 | opObj23.addParameter(name='id', value='4', format='int') | |
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115 | opObj23.addParameter(name='wintitle', value='EW Drifts', format='str') | |
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116 | opObj23.addParameter(name='save', value='1', format='bool') | |
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117 | opObj23.addParameter(name='figpath', value = pathFigure, format='str') | |
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118 | opObj23.addParameter(name='zminZonal', value='-150', format='int') | |
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119 | opObj23.addParameter(name='zmaxZonal', value='150', format='int') | |
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120 | opObj23.addParameter(name='zminVertical', value='-30', format='float') | |
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121 | opObj23.addParameter(name='zmaxVertical', value='30', format='float') | |
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122 | opObj23.addParameter(name='SNR_1', value='1', format='bool') | |
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123 | opObj23.addParameter(name='SNRmax', value='5', format='int') | |
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124 | # opObj23.addParameter(name='SNRthresh', value='-50', format='float') | |
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125 | opObj23.addParameter(name='xmin', value=xmin, format='float') | |
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126 | opObj23.addParameter(name='xmax', value=xmax, format='float') | |
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127 | #-------------------------------------------------------------------------------------------------- | |
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128 | print "Escribiendo el archivo XML" | |
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129 | controllerObj.writeXml(filename) | |
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130 | print "Leyendo el archivo XML" | |
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131 | controllerObj.readXml(filename) | |
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132 | ||
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133 | controllerObj.createObjects() | |
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134 | controllerObj.connectObjects() | |
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135 | controllerObj.run() No newline at end of file | |
| @@ -1,967 +1,983 | |||
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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 | data = data.copy() |
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54 | 54 | |
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55 | 55 | sortdata = numpy.sort(data,axis=None) |
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56 | 56 | lenOfData = len(sortdata) |
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57 | 57 | nums_min = lenOfData/10 |
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58 | 58 | |
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59 | 59 | if (lenOfData/10) > 2: |
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60 | 60 | nums_min = lenOfData/10 |
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61 | 61 | else: |
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62 | 62 | nums_min = 2 |
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63 | 63 | |
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64 | 64 | sump = 0. |
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65 | 65 | |
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66 | 66 | sumq = 0. |
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67 | 67 | |
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68 | 68 | j = 0 |
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69 | 69 | |
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70 | 70 | cont = 1 |
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71 | 71 | |
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72 | 72 | while((cont==1)and(j<lenOfData)): |
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73 | 73 | |
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74 | 74 | sump += sortdata[j] |
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75 | 75 | |
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76 | 76 | sumq += sortdata[j]**2 |
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77 | 77 | |
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78 | 78 | j += 1 |
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79 | 79 | |
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80 | 80 | if j > nums_min: |
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81 | 81 | rtest = float(j)/(j-1) + 1.0/navg |
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82 | 82 | if ((sumq*j) > (rtest*sump**2)): |
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83 | 83 | j = j - 1 |
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84 | 84 | sump = sump - sortdata[j] |
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85 | 85 | sumq = sumq - sortdata[j]**2 |
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86 | 86 | cont = 0 |
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87 | 87 | |
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88 | 88 | lnoise = sump /j |
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89 | 89 | stdv = numpy.sqrt((sumq - lnoise**2)/(j - 1)) |
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90 | 90 | return lnoise |
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91 | 91 | |
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92 | 92 | class Beam: |
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93 | 93 | def __init__(self): |
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94 | 94 | self.codeList = [] |
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95 | 95 | self.azimuthList = [] |
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96 | 96 | self.zenithList = [] |
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97 | 97 | |
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98 | 98 | class GenericData(object): |
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99 | 99 | |
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100 | 100 | flagNoData = True |
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101 | 101 | |
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102 | 102 | def __init__(self): |
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103 | 103 | |
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104 | 104 | raise ValueError, "This class has not been implemented" |
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105 | 105 | |
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106 | 106 | def copy(self, inputObj=None): |
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107 | 107 | |
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108 | 108 | if inputObj == None: |
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109 | 109 | return copy.deepcopy(self) |
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110 | 110 | |
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111 | 111 | for key in inputObj.__dict__.keys(): |
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112 | 112 | self.__dict__[key] = inputObj.__dict__[key] |
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113 | 113 | |
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114 | 114 | def deepcopy(self): |
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115 | 115 | |
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116 | 116 | return copy.deepcopy(self) |
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117 | 117 | |
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118 | 118 | def isEmpty(self): |
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119 | 119 | |
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120 | 120 | return self.flagNoData |
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121 | 121 | |
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122 | 122 | class JROData(GenericData): |
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123 | 123 | |
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124 | 124 | # m_BasicHeader = BasicHeader() |
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125 | 125 | # m_ProcessingHeader = ProcessingHeader() |
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126 | 126 | |
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127 | 127 | systemHeaderObj = SystemHeader() |
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128 | 128 | |
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129 | 129 | radarControllerHeaderObj = RadarControllerHeader() |
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130 | 130 | |
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131 | 131 | # data = None |
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132 | 132 | |
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133 | 133 | type = None |
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134 | 134 | |
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135 | 135 | datatype = None #dtype but in string |
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136 | 136 | |
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137 | 137 | # dtype = None |
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138 | 138 | |
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139 | 139 | # nChannels = None |
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140 | 140 | |
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141 | 141 | # nHeights = None |
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142 | 142 | |
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143 | 143 | nProfiles = None |
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144 | 144 | |
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145 | 145 | heightList = None |
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146 | 146 | |
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147 | 147 | channelList = None |
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148 | 148 | |
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149 | 149 | flagTimeBlock = False |
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150 | 150 | |
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151 | 151 | useLocalTime = False |
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152 | 152 | |
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153 | 153 | utctime = None |
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154 | 154 | |
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155 | 155 | timeZone = None |
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156 | 156 | |
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157 | 157 | dstFlag = None |
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158 | 158 | |
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159 | 159 | errorCount = None |
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160 | 160 | |
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161 | 161 | blocksize = None |
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162 | 162 | |
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163 | 163 | nCode = None |
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164 | 164 | |
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165 | 165 | nBaud = None |
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166 | 166 | |
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167 | 167 | code = None |
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168 | 168 | |
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169 | 169 | flagDecodeData = False #asumo q la data no esta decodificada |
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170 | 170 | |
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171 | 171 | flagDeflipData = False #asumo q la data no esta sin flip |
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172 | 172 | |
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173 | 173 | flagShiftFFT = False |
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174 | 174 | |
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175 | 175 | # ippSeconds = None |
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176 | 176 | |
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177 | 177 | timeInterval = None |
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178 | 178 | |
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179 | 179 | nCohInt = None |
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180 | 180 | |
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181 | 181 | noise = None |
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182 | 182 | |
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183 | 183 | windowOfFilter = 1 |
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184 | 184 | |
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185 | 185 | #Speed of ligth |
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186 | 186 | C = 3e8 |
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187 | 187 | |
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188 | 188 | frequency = 49.92e6 |
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189 | 189 | |
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190 | 190 | realtime = False |
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191 | 191 | |
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192 | 192 | beacon_heiIndexList = None |
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193 | 193 | |
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194 | 194 | last_block = None |
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195 | 195 | |
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196 | 196 | blocknow = None |
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197 | 197 | |
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198 | 198 | azimuth = None |
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199 | 199 | |
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200 | 200 | zenith = None |
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201 | 201 | |
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202 | 202 | beam = Beam() |
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203 | 203 | |
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204 | 204 | def __init__(self): |
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205 | 205 | |
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206 | 206 | raise ValueError, "This class has not been implemented" |
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207 | 207 | |
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208 | 208 | def getNoise(self): |
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209 | 209 | |
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210 | 210 | raise ValueError, "Not implemented" |
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211 | 211 | |
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212 | 212 | def getNChannels(self): |
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213 | 213 | |
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214 | 214 | return len(self.channelList) |
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215 | 215 | |
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216 | 216 | def getChannelIndexList(self): |
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217 | 217 | |
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218 | 218 | return range(self.nChannels) |
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219 | 219 | |
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220 | 220 | def getNHeights(self): |
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221 | 221 | |
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222 | 222 | return len(self.heightList) |
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223 | 223 | |
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224 | 224 | def getHeiRange(self, extrapoints=0): |
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225 | 225 | |
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226 | 226 | heis = self.heightList |
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227 | 227 | # deltah = self.heightList[1] - self.heightList[0] |
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228 | 228 | # |
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229 | 229 | # heis.append(self.heightList[-1]) |
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230 | 230 | |
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231 | 231 | return heis |
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232 | 232 | |
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233 | 233 | def getltctime(self): |
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234 | 234 | |
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235 | 235 | if self.useLocalTime: |
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236 | 236 | return self.utctime - self.timeZone*60 |
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237 | 237 | |
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238 | 238 | return self.utctime |
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239 | 239 | |
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240 | 240 | def getDatatime(self): |
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241 | 241 | |
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242 | 242 | datatimeValue = datetime.datetime.utcfromtimestamp(self.ltctime) |
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243 | 243 | return datatimeValue |
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244 | 244 | |
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245 | 245 | def getTimeRange(self): |
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246 | 246 | |
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247 | 247 | datatime = [] |
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248 | 248 | |
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249 | 249 | datatime.append(self.ltctime) |
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250 | 250 | datatime.append(self.ltctime + self.timeInterval) |
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251 | 251 | |
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252 | 252 | datatime = numpy.array(datatime) |
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253 | 253 | |
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254 | 254 | return datatime |
|
|
255 | 255 | |
|
|
256 | 256 | def getFmax(self): |
|
|
257 | 257 | |
|
|
258 | 258 | PRF = 1./(self.ippSeconds * self.nCohInt) |
|
|
259 | 259 | |
|
|
260 | 260 | fmax = PRF/2. |
|
|
261 | 261 | |
|
|
262 | 262 | return fmax |
|
|
263 | 263 | |
|
|
264 | 264 | def getVmax(self): |
|
|
265 | 265 | |
|
|
266 | 266 | _lambda = self.C/self.frequency |
|
|
267 | 267 | |
|
|
268 | 268 | vmax = self.getFmax() * _lambda |
|
|
269 | 269 | |
|
|
270 | 270 | return vmax |
|
|
271 | 271 | |
|
|
272 | 272 | def get_ippSeconds(self): |
|
|
273 | 273 | ''' |
|
|
274 | 274 | ''' |
|
|
275 | 275 | return self.radarControllerHeaderObj.ippSeconds |
|
|
276 | 276 | |
|
|
277 | 277 | def set_ippSeconds(self, ippSeconds): |
|
|
278 | 278 | ''' |
|
|
279 | 279 | ''' |
|
|
280 | 280 | |
|
|
281 | 281 | self.radarControllerHeaderObj.ippSeconds = ippSeconds |
|
|
282 | 282 | |
|
|
283 | 283 | return |
|
|
284 | 284 | |
|
|
285 | 285 | def get_dtype(self): |
|
|
286 | 286 | ''' |
|
|
287 | 287 | ''' |
|
|
288 | 288 | return getNumpyDtype(self.datatype) |
|
|
289 | 289 | |
|
|
290 | 290 | def set_dtype(self, numpyDtype): |
|
|
291 | 291 | ''' |
|
|
292 | 292 | ''' |
|
|
293 | 293 | |
|
|
294 | 294 | self.datatype = getDataTypeCode(numpyDtype) |
|
|
295 | 295 | |
|
|
296 | 296 | nChannels = property(getNChannels, "I'm the 'nChannel' property.") |
|
|
297 | 297 | channelIndexList = property(getChannelIndexList, "I'm the 'channelIndexList' property.") |
|
|
298 | 298 | nHeights = property(getNHeights, "I'm the 'nHeights' property.") |
|
|
299 | 299 | #noise = property(getNoise, "I'm the 'nHeights' property.") |
|
|
300 | 300 | datatime = property(getDatatime, "I'm the 'datatime' property") |
|
|
301 | 301 | ltctime = property(getltctime, "I'm the 'ltctime' property") |
|
|
302 | 302 | ippSeconds = property(get_ippSeconds, set_ippSeconds) |
|
|
303 | 303 | dtype = property(get_dtype, set_dtype) |
|
|
304 | 304 | |
|
|
305 | 305 | class Voltage(JROData): |
|
|
306 | 306 | |
|
|
307 | 307 | #data es un numpy array de 2 dmensiones (canales, alturas) |
|
|
308 | 308 | data = None |
|
|
309 | 309 | |
|
|
310 | 310 | def __init__(self): |
|
|
311 | 311 | ''' |
|
|
312 | 312 | Constructor |
|
|
313 | 313 | ''' |
|
|
314 | 314 | |
|
|
315 | 315 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
|
316 | 316 | |
|
|
317 | 317 | self.systemHeaderObj = SystemHeader() |
|
|
318 | 318 | |
|
|
319 | 319 | self.type = "Voltage" |
|
|
320 | 320 | |
|
|
321 | 321 | self.data = None |
|
|
322 | 322 | |
|
|
323 | 323 | # self.dtype = None |
|
|
324 | 324 | |
|
|
325 | 325 | # self.nChannels = 0 |
|
|
326 | 326 | |
|
|
327 | 327 | # self.nHeights = 0 |
|
|
328 | 328 | |
|
|
329 | 329 | self.nProfiles = None |
|
|
330 | 330 | |
|
|
331 | 331 | self.heightList = None |
|
|
332 | 332 | |
|
|
333 | 333 | self.channelList = None |
|
|
334 | 334 | |
|
|
335 | 335 | # self.channelIndexList = None |
|
|
336 | 336 | |
|
|
337 | 337 | self.flagNoData = True |
|
|
338 | 338 | |
|
|
339 | 339 | self.flagTimeBlock = False |
|
|
340 | 340 | |
|
|
341 | 341 | self.utctime = None |
|
|
342 | 342 | |
|
|
343 | 343 | self.timeZone = None |
|
|
344 | 344 | |
|
|
345 | 345 | self.dstFlag = None |
|
|
346 | 346 | |
|
|
347 | 347 | self.errorCount = None |
|
|
348 | 348 | |
|
|
349 | 349 | self.nCohInt = None |
|
|
350 | 350 | |
|
|
351 | 351 | self.blocksize = None |
|
|
352 | 352 | |
|
|
353 | 353 | self.flagDecodeData = False #asumo q la data no esta decodificada |
|
|
354 | 354 | |
|
|
355 | 355 | self.flagDeflipData = False #asumo q la data no esta sin flip |
|
|
356 | 356 | |
|
|
357 | 357 | self.flagShiftFFT = False |
|
|
358 | 358 | |
|
|
359 | 359 | |
|
|
360 | 360 | def getNoisebyHildebrand(self): |
|
|
361 | 361 | """ |
|
|
362 | 362 | Determino el nivel de ruido usando el metodo Hildebrand-Sekhon |
|
|
363 | 363 | |
|
|
364 | 364 | Return: |
|
|
365 | 365 | noiselevel |
|
|
366 | 366 | """ |
|
|
367 | 367 | |
|
|
368 | 368 | for channel in range(self.nChannels): |
|
|
369 | 369 | daux = self.data_spc[channel,:,:] |
|
|
370 | 370 | self.noise[channel] = hildebrand_sekhon(daux, self.nCohInt) |
|
|
371 | 371 | |
|
|
372 | 372 | return self.noise |
|
|
373 | 373 | |
|
|
374 | 374 | def getNoise(self, type = 1): |
|
|
375 | 375 | |
|
|
376 | 376 | self.noise = numpy.zeros(self.nChannels) |
|
|
377 | 377 | |
|
|
378 | 378 | if type == 1: |
|
|
379 | 379 | noise = self.getNoisebyHildebrand() |
|
|
380 | 380 | |
|
|
381 | 381 | return 10*numpy.log10(noise) |
|
|
382 | 382 | |
|
|
383 | 383 | noise = property(getNoise, "I'm the 'nHeights' property.") |
|
|
384 | 384 | |
|
|
385 | 385 | class Spectra(JROData): |
|
|
386 | 386 | |
|
|
387 | 387 | #data es un numpy array de 2 dmensiones (canales, perfiles, alturas) |
|
|
388 | 388 | data_spc = None |
|
|
389 | 389 | |
|
|
390 | 390 | #data es un numpy array de 2 dmensiones (canales, pares, alturas) |
|
|
391 | 391 | data_cspc = None |
|
|
392 | 392 | |
|
|
393 | 393 | #data es un numpy array de 2 dmensiones (canales, alturas) |
|
|
394 | 394 | data_dc = None |
|
|
395 | 395 | |
|
|
396 | 396 | nFFTPoints = None |
|
|
397 | 397 | |
|
|
398 | 398 | # nPairs = None |
|
|
399 | 399 | |
|
|
400 | 400 | pairsList = None |
|
|
401 | 401 | |
|
|
402 | 402 | nIncohInt = None |
|
|
403 | 403 | |
|
|
404 | 404 | wavelength = None #Necesario para cacular el rango de velocidad desde la frecuencia |
|
|
405 | 405 | |
|
|
406 | 406 | nCohInt = None #se requiere para determinar el valor de timeInterval |
|
|
407 | 407 | |
|
|
408 | 408 | ippFactor = None |
|
|
409 | 409 | |
|
|
410 | 410 | def __init__(self): |
|
|
411 | 411 | ''' |
|
|
412 | 412 | Constructor |
|
|
413 | 413 | ''' |
|
|
414 | 414 | |
|
|
415 | 415 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
|
416 | 416 | |
|
|
417 | 417 | self.systemHeaderObj = SystemHeader() |
|
|
418 | 418 | |
|
|
419 | 419 | self.type = "Spectra" |
|
|
420 | 420 | |
|
|
421 | 421 | # self.data = None |
|
|
422 | 422 | |
|
|
423 | 423 | # self.dtype = None |
|
|
424 | 424 | |
|
|
425 | 425 | # self.nChannels = 0 |
|
|
426 | 426 | |
|
|
427 | 427 | # self.nHeights = 0 |
|
|
428 | 428 | |
|
|
429 | 429 | self.nProfiles = None |
|
|
430 | 430 | |
|
|
431 | 431 | self.heightList = None |
|
|
432 | 432 | |
|
|
433 | 433 | self.channelList = None |
|
|
434 | 434 | |
|
|
435 | 435 | # self.channelIndexList = None |
|
|
436 | 436 | |
|
|
437 | 437 | self.pairsList = None |
|
|
438 | 438 | |
|
|
439 | 439 | self.flagNoData = True |
|
|
440 | 440 | |
|
|
441 | 441 | self.flagTimeBlock = False |
|
|
442 | 442 | |
|
|
443 | 443 | self.utctime = None |
|
|
444 | 444 | |
|
|
445 | 445 | self.nCohInt = None |
|
|
446 | 446 | |
|
|
447 | 447 | self.nIncohInt = None |
|
|
448 | 448 | |
|
|
449 | 449 | self.blocksize = None |
|
|
450 | 450 | |
|
|
451 | 451 | self.nFFTPoints = None |
|
|
452 | 452 | |
|
|
453 | 453 | self.wavelength = None |
|
|
454 | 454 | |
|
|
455 | 455 | self.flagDecodeData = False #asumo q la data no esta decodificada |
|
|
456 | 456 | |
|
|
457 | 457 | self.flagDeflipData = False #asumo q la data no esta sin flip |
|
|
458 | 458 | |
|
|
459 | 459 | self.flagShiftFFT = False |
|
|
460 | 460 | |
|
|
461 | 461 | self.ippFactor = 1 |
|
|
462 | 462 | |
|
|
463 | 463 | #self.noise = None |
|
|
464 | 464 | |
|
|
465 | 465 | self.beacon_heiIndexList = [] |
|
|
466 | 466 | |
|
|
467 | 467 | self.noise_estimation = None |
|
|
468 | 468 | |
|
|
469 | 469 | |
|
|
470 | 470 | def getNoisebyHildebrand(self): |
|
|
471 | 471 | """ |
|
|
472 | 472 | Determino el nivel de ruido usando el metodo Hildebrand-Sekhon |
|
|
473 | 473 | |
|
|
474 | 474 | Return: |
|
|
475 | 475 | noiselevel |
|
|
476 | 476 | """ |
|
|
477 | 477 | |
|
|
478 | 478 | noise = numpy.zeros(self.nChannels) |
|
|
479 | 479 | for channel in range(self.nChannels): |
|
|
480 | 480 | daux = self.data_spc[channel,:,:] |
|
|
481 | 481 | noise[channel] = hildebrand_sekhon(daux, self.nIncohInt) |
|
|
482 | 482 | |
|
|
483 | 483 | return noise |
|
|
484 | 484 | |
|
|
485 | 485 | def getNoise(self): |
|
|
486 | 486 | if self.noise_estimation != None: |
|
|
487 | 487 | return self.noise_estimation #this was estimated by getNoise Operation defined in jroproc_spectra.py |
|
|
488 | 488 | else: |
|
|
489 | 489 | noise = self.getNoisebyHildebrand() |
|
|
490 | 490 | return noise |
|
|
491 | 491 | |
|
|
492 | 492 | |
|
|
493 | 493 | def getFreqRange(self, extrapoints=0): |
|
|
494 | 494 | |
|
|
495 | 495 | deltafreq = self.getFmax() / (self.nFFTPoints*self.ippFactor) |
|
|
496 | 496 | freqrange = deltafreq*(numpy.arange(self.nFFTPoints+extrapoints)-self.nFFTPoints/2.) - deltafreq/2 |
|
|
497 | 497 | |
|
|
498 | 498 | return freqrange |
|
|
499 | 499 | |
|
|
500 | 500 | def getVelRange(self, extrapoints=0): |
|
|
501 | 501 | |
|
|
502 | 502 | deltav = self.getVmax() / (self.nFFTPoints*self.ippFactor) |
|
|
503 | 503 | velrange = deltav*(numpy.arange(self.nFFTPoints+extrapoints)-self.nFFTPoints/2.) - deltav/2 |
|
|
504 | 504 | |
|
|
505 | 505 | return velrange |
|
|
506 | 506 | |
|
|
507 | 507 | def getNPairs(self): |
|
|
508 | 508 | |
|
|
509 | 509 | return len(self.pairsList) |
|
|
510 | 510 | |
|
|
511 | 511 | def getPairsIndexList(self): |
|
|
512 | 512 | |
|
|
513 | 513 | return range(self.nPairs) |
|
|
514 | 514 | |
|
|
515 | 515 | def getNormFactor(self): |
|
|
516 | 516 | pwcode = 1 |
|
|
517 | 517 | if self.flagDecodeData: |
|
|
518 | 518 | pwcode = numpy.sum(self.code[0]**2) |
|
|
519 | 519 | #normFactor = min(self.nFFTPoints,self.nProfiles)*self.nIncohInt*self.nCohInt*pwcode*self.windowOfFilter |
|
|
520 | 520 | normFactor = self.nProfiles*self.nIncohInt*self.nCohInt*pwcode*self.windowOfFilter |
|
|
521 | 521 | |
|
|
522 | 522 | return normFactor |
|
|
523 | 523 | |
|
|
524 | 524 | def getFlagCspc(self): |
|
|
525 | 525 | |
|
|
526 | 526 | if self.data_cspc == None: |
|
|
527 | 527 | return True |
|
|
528 | 528 | |
|
|
529 | 529 | return False |
|
|
530 | 530 | |
|
|
531 | 531 | def getFlagDc(self): |
|
|
532 | 532 | |
|
|
533 | 533 | if self.data_dc == None: |
|
|
534 | 534 | return True |
|
|
535 | 535 | |
|
|
536 | 536 | return False |
|
|
537 | 537 | |
|
|
538 | 538 | nPairs = property(getNPairs, "I'm the 'nPairs' property.") |
|
|
539 | 539 | pairsIndexList = property(getPairsIndexList, "I'm the 'pairsIndexList' property.") |
|
|
540 | 540 | normFactor = property(getNormFactor, "I'm the 'getNormFactor' property.") |
|
|
541 | 541 | flag_cspc = property(getFlagCspc) |
|
|
542 | 542 | flag_dc = property(getFlagDc) |
|
|
543 | 543 | noise = property(getNoise, "I'm the 'nHeights' property.") |
|
|
544 | 544 | |
|
|
545 | 545 | class SpectraHeis(Spectra): |
|
|
546 | 546 | |
|
|
547 | 547 | data_spc = None |
|
|
548 | 548 | |
|
|
549 | 549 | data_cspc = None |
|
|
550 | 550 | |
|
|
551 | 551 | data_dc = None |
|
|
552 | 552 | |
|
|
553 | 553 | nFFTPoints = None |
|
|
554 | 554 | |
|
|
555 | 555 | # nPairs = None |
|
|
556 | 556 | |
|
|
557 | 557 | pairsList = None |
|
|
558 | 558 | |
|
|
559 | 559 | nIncohInt = None |
|
|
560 | 560 | |
|
|
561 | 561 | def __init__(self): |
|
|
562 | 562 | |
|
|
563 | 563 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
|
564 | 564 | |
|
|
565 | 565 | self.systemHeaderObj = SystemHeader() |
|
|
566 | 566 | |
|
|
567 | 567 | self.type = "SpectraHeis" |
|
|
568 | 568 | |
|
|
569 | 569 | # self.dtype = None |
|
|
570 | 570 | |
|
|
571 | 571 | # self.nChannels = 0 |
|
|
572 | 572 | |
|
|
573 | 573 | # self.nHeights = 0 |
|
|
574 | 574 | |
|
|
575 | 575 | self.nProfiles = None |
|
|
576 | 576 | |
|
|
577 | 577 | self.heightList = None |
|
|
578 | 578 | |
|
|
579 | 579 | self.channelList = None |
|
|
580 | 580 | |
|
|
581 | 581 | # self.channelIndexList = None |
|
|
582 | 582 | |
|
|
583 | 583 | self.flagNoData = True |
|
|
584 | 584 | |
|
|
585 | 585 | self.flagTimeBlock = False |
|
|
586 | 586 | |
|
|
587 | 587 | # self.nPairs = 0 |
|
|
588 | 588 | |
|
|
589 | 589 | self.utctime = None |
|
|
590 | 590 | |
|
|
591 | 591 | self.blocksize = None |
|
|
592 | 592 | |
|
|
593 | 593 | def getNormFactor(self): |
|
|
594 | 594 | pwcode = 1 |
|
|
595 | 595 | if self.flagDecodeData: |
|
|
596 | 596 | pwcode = numpy.sum(self.code[0]**2) |
|
|
597 | 597 | |
|
|
598 | 598 | normFactor = self.nIncohInt*self.nCohInt*pwcode |
|
|
599 | 599 | |
|
|
600 | 600 | return normFactor |
|
|
601 | 601 | |
|
|
602 | 602 | normFactor = property(getNormFactor, "I'm the 'getNormFactor' property.") |
|
|
603 | 603 | |
|
|
604 | 604 | class Fits: |
|
|
605 | 605 | |
|
|
606 | 606 | heightList = None |
|
|
607 | 607 | |
|
|
608 | 608 | channelList = None |
|
|
609 | 609 | |
|
|
610 | 610 | flagNoData = True |
|
|
611 | 611 | |
|
|
612 | 612 | flagTimeBlock = False |
|
|
613 | 613 | |
|
|
614 | 614 | useLocalTime = False |
|
|
615 | 615 | |
|
|
616 | 616 | utctime = None |
|
|
617 | 617 | |
|
|
618 | 618 | timeZone = None |
|
|
619 | 619 | |
|
|
620 | 620 | # ippSeconds = None |
|
|
621 | 621 | |
|
|
622 | 622 | timeInterval = None |
|
|
623 | 623 | |
|
|
624 | 624 | nCohInt = None |
|
|
625 | 625 | |
|
|
626 | 626 | nIncohInt = None |
|
|
627 | 627 | |
|
|
628 | 628 | noise = None |
|
|
629 | 629 | |
|
|
630 | 630 | windowOfFilter = 1 |
|
|
631 | 631 | |
|
|
632 | 632 | #Speed of ligth |
|
|
633 | 633 | C = 3e8 |
|
|
634 | 634 | |
|
|
635 | 635 | frequency = 49.92e6 |
|
|
636 | 636 | |
|
|
637 | 637 | realtime = False |
|
|
638 | 638 | |
|
|
639 | 639 | |
|
|
640 | 640 | def __init__(self): |
|
|
641 | 641 | |
|
|
642 | 642 | self.type = "Fits" |
|
|
643 | 643 | |
|
|
644 | 644 | self.nProfiles = None |
|
|
645 | 645 | |
|
|
646 | 646 | self.heightList = None |
|
|
647 | 647 | |
|
|
648 | 648 | self.channelList = None |
|
|
649 | 649 | |
|
|
650 | 650 | # self.channelIndexList = None |
|
|
651 | 651 | |
|
|
652 | 652 | self.flagNoData = True |
|
|
653 | 653 | |
|
|
654 | 654 | self.utctime = None |
|
|
655 | 655 | |
|
|
656 | 656 | self.nCohInt = None |
|
|
657 | 657 | |
|
|
658 | 658 | self.nIncohInt = None |
|
|
659 | 659 | |
|
|
660 | 660 | self.useLocalTime = True |
|
|
661 | 661 | |
|
|
662 | 662 | # self.utctime = None |
|
|
663 | 663 | # self.timeZone = None |
|
|
664 | 664 | # self.ltctime = None |
|
|
665 | 665 | # self.timeInterval = None |
|
|
666 | 666 | # self.header = None |
|
|
667 | 667 | # self.data_header = None |
|
|
668 | 668 | # self.data = None |
|
|
669 | 669 | # self.datatime = None |
|
|
670 | 670 | # self.flagNoData = False |
|
|
671 | 671 | # self.expName = '' |
|
|
672 | 672 | # self.nChannels = None |
|
|
673 | 673 | # self.nSamples = None |
|
|
674 | 674 | # self.dataBlocksPerFile = None |
|
|
675 | 675 | # self.comments = '' |
|
|
676 | 676 | # |
|
|
677 | 677 | |
|
|
678 | 678 | |
|
|
679 | 679 | def getltctime(self): |
|
|
680 | 680 | |
|
|
681 | 681 | if self.useLocalTime: |
|
|
682 | 682 | return self.utctime - self.timeZone*60 |
|
|
683 | 683 | |
|
|
684 | 684 | return self.utctime |
|
|
685 | 685 | |
|
|
686 | 686 | def getDatatime(self): |
|
|
687 | 687 | |
|
|
688 | 688 | datatime = datetime.datetime.utcfromtimestamp(self.ltctime) |
|
|
689 | 689 | return datatime |
|
|
690 | 690 | |
|
|
691 | 691 | def getTimeRange(self): |
|
|
692 | 692 | |
|
|
693 | 693 | datatime = [] |
|
|
694 | 694 | |
|
|
695 | 695 | datatime.append(self.ltctime) |
|
|
696 | 696 | datatime.append(self.ltctime + self.timeInterval) |
|
|
697 | 697 | |
|
|
698 | 698 | datatime = numpy.array(datatime) |
|
|
699 | 699 | |
|
|
700 | 700 | return datatime |
|
|
701 | 701 | |
|
|
702 | 702 | def getHeiRange(self): |
|
|
703 | 703 | |
|
|
704 | 704 | heis = self.heightList |
|
|
705 | 705 | |
|
|
706 | 706 | return heis |
|
|
707 | 707 | |
|
|
708 | 708 | def isEmpty(self): |
|
|
709 | 709 | |
|
|
710 | 710 | return self.flagNoData |
|
|
711 | 711 | |
|
|
712 | 712 | def getNHeights(self): |
|
|
713 | 713 | |
|
|
714 | 714 | return len(self.heightList) |
|
|
715 | 715 | |
|
|
716 | 716 | def getNChannels(self): |
|
|
717 | 717 | |
|
|
718 | 718 | return len(self.channelList) |
|
|
719 | 719 | |
|
|
720 | 720 | def getChannelIndexList(self): |
|
|
721 | 721 | |
|
|
722 | 722 | return range(self.nChannels) |
|
|
723 | 723 | |
|
|
724 | 724 | def getNoise(self, type = 1): |
|
|
725 | 725 | |
|
|
726 | 726 | self.noise = numpy.zeros(self.nChannels) |
|
|
727 | 727 | |
|
|
728 | 728 | if type == 1: |
|
|
729 | 729 | noise = self.getNoisebyHildebrand() |
|
|
730 | 730 | |
|
|
731 | 731 | if type == 2: |
|
|
732 | 732 | noise = self.getNoisebySort() |
|
|
733 | 733 | |
|
|
734 | 734 | if type == 3: |
|
|
735 | 735 | noise = self.getNoisebyWindow() |
|
|
736 | 736 | |
|
|
737 | 737 | return noise |
|
|
738 | 738 | |
|
|
739 | 739 | datatime = property(getDatatime, "I'm the 'datatime' property") |
|
|
740 | 740 | nHeights = property(getNHeights, "I'm the 'nHeights' property.") |
|
|
741 | 741 | nChannels = property(getNChannels, "I'm the 'nChannel' property.") |
|
|
742 | 742 | channelIndexList = property(getChannelIndexList, "I'm the 'channelIndexList' property.") |
|
|
743 | 743 | noise = property(getNoise, "I'm the 'nHeights' property.") |
|
|
744 | 744 | datatime = property(getDatatime, "I'm the 'datatime' property") |
|
|
745 | 745 | ltctime = property(getltctime, "I'm the 'ltctime' property") |
|
|
746 | 746 | |
|
|
747 | 747 | class Correlation(JROData): |
|
|
748 | 748 | |
|
|
749 | 749 | noise = None |
|
|
750 | 750 | |
|
|
751 | 751 | SNR = None |
|
|
752 | 752 | |
|
|
753 | 753 | pairsAutoCorr = None #Pairs of Autocorrelation |
|
|
754 | 754 | |
|
|
755 | 755 | #-------------------------------------------------- |
|
|
756 | 756 | |
|
|
757 | 757 | data_corr = None |
|
|
758 | 758 | |
|
|
759 | 759 | data_volt = None |
|
|
760 | 760 | |
|
|
761 | 761 | lagT = None # each element value is a profileIndex |
|
|
762 | 762 | |
|
|
763 | 763 | lagR = None # each element value is in km |
|
|
764 | 764 | |
|
|
765 | 765 | pairsList = None |
|
|
766 | 766 | |
|
|
767 | 767 | calculateVelocity = None |
|
|
768 | 768 | |
|
|
769 | 769 | nPoints = None |
|
|
770 | 770 | |
|
|
771 | 771 | nAvg = None |
|
|
772 | 772 | |
|
|
773 | 773 | bufferSize = None |
|
|
774 | 774 | |
|
|
775 | 775 | def __init__(self): |
|
|
776 | 776 | ''' |
|
|
777 | 777 | Constructor |
|
|
778 | 778 | ''' |
|
|
779 | 779 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
|
780 | 780 | |
|
|
781 | 781 | self.systemHeaderObj = SystemHeader() |
|
|
782 | 782 | |
|
|
783 | 783 | self.type = "Correlation" |
|
|
784 | 784 | |
|
|
785 | 785 | self.data = None |
|
|
786 | 786 | |
|
|
787 | 787 | self.dtype = None |
|
|
788 | 788 | |
|
|
789 | 789 | self.nProfiles = None |
|
|
790 | 790 | |
|
|
791 | 791 | self.heightList = None |
|
|
792 | 792 | |
|
|
793 | 793 | self.channelList = None |
|
|
794 | 794 | |
|
|
795 | 795 | self.flagNoData = True |
|
|
796 | 796 | |
|
|
797 | 797 | self.flagTimeBlock = False |
|
|
798 | 798 | |
|
|
799 | 799 | self.utctime = None |
|
|
800 | 800 | |
|
|
801 | 801 | self.timeZone = None |
|
|
802 | 802 | |
|
|
803 | 803 | self.dstFlag = None |
|
|
804 | 804 | |
|
|
805 | 805 | self.errorCount = None |
|
|
806 | 806 | |
|
|
807 | 807 | self.blocksize = None |
|
|
808 | 808 | |
|
|
809 | 809 | self.flagDecodeData = False #asumo q la data no esta decodificada |
|
|
810 | 810 | |
|
|
811 | 811 | self.flagDeflipData = False #asumo q la data no esta sin flip |
|
|
812 | 812 | |
|
|
813 | 813 | self.pairsList = None |
|
|
814 | 814 | |
|
|
815 | 815 | self.nPoints = None |
|
|
816 | 816 | |
|
|
817 | 817 | def getLagTRange(self, extrapoints=0): |
|
|
818 | 818 | |
|
|
819 | 819 | lagTRange = self.lagT |
|
|
820 | 820 | diff = lagTRange[1] - lagTRange[0] |
|
|
821 | 821 | extra = numpy.arange(1,extrapoints + 1)*diff + lagTRange[-1] |
|
|
822 | 822 | lagTRange = numpy.hstack((lagTRange, extra)) |
|
|
823 | 823 | |
|
|
824 | 824 | return lagTRange |
|
|
825 | 825 | |
|
|
826 | 826 | def getLagRRange(self, extrapoints=0): |
|
|
827 | 827 | |
|
|
828 | 828 | return self.lagR |
|
|
829 | 829 | |
|
|
830 | 830 | def getPairsList(self): |
|
|
831 | 831 | |
|
|
832 | 832 | return self.pairsList |
|
|
833 | 833 | |
|
|
834 | 834 | def getCalculateVelocity(self): |
|
|
835 | 835 | |
|
|
836 | 836 | return self.calculateVelocity |
|
|
837 | 837 | |
|
|
838 | 838 | def getNPoints(self): |
|
|
839 | 839 | |
|
|
840 | 840 | return self.nPoints |
|
|
841 | 841 | |
|
|
842 | 842 | def getNAvg(self): |
|
|
843 | 843 | |
|
|
844 | 844 | return self.nAvg |
|
|
845 | 845 | |
|
|
846 | 846 | def getBufferSize(self): |
|
|
847 | 847 | |
|
|
848 | 848 | return self.bufferSize |
|
|
849 | 849 | |
|
|
850 | 850 | def getPairsAutoCorr(self): |
|
|
851 | 851 | pairsList = self.pairsList |
|
|
852 | 852 | pairsAutoCorr = numpy.zeros(self.nChannels, dtype = 'int')*numpy.nan |
|
|
853 | 853 | |
|
|
854 | 854 | for l in range(len(pairsList)): |
|
|
855 | 855 | firstChannel = pairsList[l][0] |
|
|
856 | 856 | secondChannel = pairsList[l][1] |
|
|
857 | 857 | |
|
|
858 | 858 | #Obteniendo pares de Autocorrelacion |
|
|
859 | 859 | if firstChannel == secondChannel: |
|
|
860 | 860 | pairsAutoCorr[firstChannel] = int(l) |
|
|
861 | 861 | |
|
|
862 | 862 | pairsAutoCorr = pairsAutoCorr.astype(int) |
|
|
863 | 863 | |
|
|
864 | 864 | return pairsAutoCorr |
|
|
865 | 865 | |
|
|
866 | 866 | def getNoise(self, mode = 2): |
|
|
867 | 867 | |
|
|
868 | 868 | indR = numpy.where(self.lagR == 0)[0][0] |
|
|
869 | 869 | indT = numpy.where(self.lagT == 0)[0][0] |
|
|
870 | 870 | |
|
|
871 | 871 | jspectra0 = self.data_corr[:,:,indR,:] |
|
|
872 | 872 | jspectra = copy.copy(jspectra0) |
|
|
873 | 873 | |
|
|
874 | 874 | num_chan = jspectra.shape[0] |
|
|
875 | 875 | num_hei = jspectra.shape[2] |
|
|
876 | 876 | |
|
|
877 | 877 | freq_dc = jspectra.shape[1]/2 |
|
|
878 | 878 | ind_vel = numpy.array([-2,-1,1,2]) + freq_dc |
|
|
879 | 879 | |
|
|
880 | 880 | if ind_vel[0]<0: |
|
|
881 | 881 | ind_vel[range(0,1)] = ind_vel[range(0,1)] + self.num_prof |
|
|
882 | 882 | |
|
|
883 | 883 | if mode == 1: |
|
|
884 | 884 | jspectra[:,freq_dc,:] = (jspectra[:,ind_vel[1],:] + jspectra[:,ind_vel[2],:])/2 #CORRECCION |
|
|
885 | 885 | |
|
|
886 | 886 | if mode == 2: |
|
|
887 | 887 | |
|
|
888 | 888 | vel = numpy.array([-2,-1,1,2]) |
|
|
889 | 889 | xx = numpy.zeros([4,4]) |
|
|
890 | 890 | |
|
|
891 | 891 | for fil in range(4): |
|
|
892 | 892 | xx[fil,:] = vel[fil]**numpy.asarray(range(4)) |
|
|
893 | 893 | |
|
|
894 | 894 | xx_inv = numpy.linalg.inv(xx) |
|
|
895 | 895 | xx_aux = xx_inv[0,:] |
|
|
896 | 896 | |
|
|
897 | 897 | for ich in range(num_chan): |
|
|
898 | 898 | yy = jspectra[ich,ind_vel,:] |
|
|
899 | 899 | jspectra[ich,freq_dc,:] = numpy.dot(xx_aux,yy) |
|
|
900 | 900 | |
|
|
901 | 901 | junkid = jspectra[ich,freq_dc,:]<=0 |
|
|
902 | 902 | cjunkid = sum(junkid) |
|
|
903 | 903 | |
|
|
904 | 904 | if cjunkid.any(): |
|
|
905 | 905 | jspectra[ich,freq_dc,junkid.nonzero()] = (jspectra[ich,ind_vel[1],junkid] + jspectra[ich,ind_vel[2],junkid])/2 |
|
|
906 | 906 | |
|
|
907 | 907 | noise = jspectra0[:,freq_dc,:] - jspectra[:,freq_dc,:] |
|
|
908 | 908 | |
|
|
909 | 909 | return noise |
|
|
910 | 910 | |
|
|
911 | 911 | # pairsList = property(getPairsList, "I'm the 'pairsList' property.") |
|
|
912 | 912 | # nPoints = property(getNPoints, "I'm the 'nPoints' property.") |
|
|
913 | 913 | calculateVelocity = property(getCalculateVelocity, "I'm the 'calculateVelocity' property.") |
|
|
914 | 914 | nAvg = property(getNAvg, "I'm the 'nAvg' property.") |
|
|
915 | 915 | bufferSize = property(getBufferSize, "I'm the 'bufferSize' property.") |
|
|
916 | 916 | |
|
|
917 | 917 | |
|
|
918 | 918 | class Parameters(JROData): |
|
|
919 | 919 | |
|
|
920 | #Information from previous data | |
|
|
921 | ||
|
|
920 | 922 | inputUnit = None #Type of data to be processed |
|
|
921 | 923 | |
|
|
922 | 924 | operation = None #Type of operation to parametrize |
|
|
923 | 925 | |
|
|
926 | normFactor = None #Normalization Factor | |
|
|
927 | ||
|
|
928 | groupList = None #List of Pairs, Groups, etc | |
|
|
929 | ||
|
|
930 | #Parameters | |
|
|
931 | ||
|
|
924 | 932 | data_param = None #Parameters obtained |
|
|
925 | 933 | |
|
|
926 | 934 | data_pre = None #Data Pre Parametrization |
|
|
927 | 935 | |
|
|
928 | 936 | heightRange = None #Heights |
|
|
929 | 937 | |
|
|
930 | 938 | abscissaRange = None #Abscissa, can be velocities, lags or time |
|
|
931 | 939 | |
|
|
932 | 940 | noise = None #Noise Potency |
|
|
933 | 941 | |
|
|
934 | 942 | SNR = None #Signal to Noise Ratio |
|
|
935 | 943 | |
|
|
936 | pairsList = None #List of Pairs for Cross correlations or Cross spectrum | |
|
|
937 | ||
|
|
938 | 944 | initUtcTime = None #Initial UTC time |
|
|
939 | 945 | |
|
|
940 | 946 | paramInterval = None #Time interval to calculate Parameters in seconds |
|
|
941 | 947 | |
|
|
942 | windsInterval = None #Time interval to calculate Winds in seconds | |
|
|
948 | #Fitting | |
|
|
949 | ||
|
|
950 | constants = None | |
|
|
951 | ||
|
|
952 | error = None | |
|
|
953 | ||
|
|
954 | library = None | |
|
|
955 | ||
|
|
956 | #Output signal | |
|
|
957 | ||
|
|
958 | outputInterval = None #Time interval to calculate output signal in seconds | |
|
|
959 | ||
|
|
960 | data_output = None #Out signal | |
|
|
943 | 961 | |
|
|
944 | normFactor = None #Normalization Factor | |
|
|
945 | 962 | |
|
|
946 | winds = None #Wind estimations | |
|
|
947 | 963 | |
|
|
948 | 964 | def __init__(self): |
|
|
949 | 965 | ''' |
|
|
950 | 966 | Constructor |
|
|
951 | 967 | ''' |
|
|
952 | 968 | self.radarControllerHeaderObj = RadarControllerHeader() |
|
|
953 | 969 | |
|
|
954 | 970 | self.systemHeaderObj = SystemHeader() |
|
|
955 | 971 | |
|
|
956 | 972 | self.type = "Parameters" |
|
|
957 | 973 | |
|
|
958 | 974 | def getTimeRange1(self): |
|
|
959 | 975 | |
|
|
960 | 976 | datatime = [] |
|
|
961 | 977 | |
|
|
962 | 978 | datatime.append(self.initUtcTime) |
|
|
963 |
datatime.append(self.initUtcTime + self. |
|
|
|
979 | datatime.append(self.initUtcTime + self.outputInterval - 1) | |
|
|
964 | 980 | |
|
|
965 | 981 | datatime = numpy.array(datatime) |
|
|
966 | 982 | |
|
|
967 | 983 | return datatime |
| @@ -1,774 +1,1178 | |||
|
|
1 | 1 | import os |
|
|
2 | 2 | import datetime |
|
|
3 | 3 | import numpy |
|
|
4 | 4 | |
|
|
5 | 5 | from figure import Figure, isRealtime |
|
|
6 | 6 | |
|
|
7 | 7 | class MomentsPlot(Figure): |
|
|
8 | 8 | |
|
|
9 | 9 | isConfig = None |
|
|
10 | 10 | __nsubplots = None |
|
|
11 | 11 | |
|
|
12 | 12 | WIDTHPROF = None |
|
|
13 | 13 | HEIGHTPROF = None |
|
|
14 | 14 | PREFIX = 'prm' |
|
|
15 | 15 | |
|
|
16 | 16 | def __init__(self): |
|
|
17 | 17 | |
|
|
18 | 18 | self.isConfig = False |
|
|
19 | 19 | self.__nsubplots = 1 |
|
|
20 | 20 | |
|
|
21 | 21 | self.WIDTH = 280 |
|
|
22 | 22 | self.HEIGHT = 250 |
|
|
23 | 23 | self.WIDTHPROF = 120 |
|
|
24 | 24 | self.HEIGHTPROF = 0 |
|
|
25 | 25 | self.counter_imagwr = 0 |
|
|
26 | 26 | |
|
|
27 | 27 | self.PLOT_CODE = 1 |
|
|
28 | 28 | self.FTP_WEI = None |
|
|
29 | 29 | self.EXP_CODE = None |
|
|
30 | 30 | self.SUB_EXP_CODE = None |
|
|
31 | 31 | self.PLOT_POS = None |
|
|
32 | 32 | |
|
|
33 | 33 | def getSubplots(self): |
|
|
34 | 34 | |
|
|
35 | 35 | ncol = int(numpy.sqrt(self.nplots)+0.9) |
|
|
36 | 36 | nrow = int(self.nplots*1./ncol + 0.9) |
|
|
37 | 37 | |
|
|
38 | 38 | return nrow, ncol |
|
|
39 | 39 | |
|
|
40 | 40 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
41 | 41 | |
|
|
42 | 42 | self.__showprofile = showprofile |
|
|
43 | 43 | self.nplots = nplots |
|
|
44 | 44 | |
|
|
45 | 45 | ncolspan = 1 |
|
|
46 | 46 | colspan = 1 |
|
|
47 | 47 | if showprofile: |
|
|
48 | 48 | ncolspan = 3 |
|
|
49 | 49 | colspan = 2 |
|
|
50 | 50 | self.__nsubplots = 2 |
|
|
51 | 51 | |
|
|
52 | 52 | self.createFigure(id = id, |
|
|
53 | 53 | wintitle = wintitle, |
|
|
54 | 54 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
55 | 55 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
56 | 56 | show=show) |
|
|
57 | 57 | |
|
|
58 | 58 | nrow, ncol = self.getSubplots() |
|
|
59 | 59 | |
|
|
60 | 60 | counter = 0 |
|
|
61 | 61 | for y in range(nrow): |
|
|
62 | 62 | for x in range(ncol): |
|
|
63 | 63 | |
|
|
64 | 64 | if counter >= self.nplots: |
|
|
65 | 65 | break |
|
|
66 | 66 | |
|
|
67 | 67 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
68 | 68 | |
|
|
69 | 69 | if showprofile: |
|
|
70 | 70 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan+colspan, 1, 1) |
|
|
71 | 71 | |
|
|
72 | 72 | counter += 1 |
|
|
73 | 73 | |
|
|
74 | 74 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile=True, |
|
|
75 | 75 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
|
|
76 | 76 | save=False, figpath='', figfile=None, show=True, ftp=False, wr_period=1, |
|
|
77 | 77 | server=None, folder=None, username=None, password=None, |
|
|
78 | 78 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0, realtime=False): |
|
|
79 | 79 | |
|
|
80 | 80 | """ |
|
|
81 | 81 | |
|
|
82 | 82 | Input: |
|
|
83 | 83 | dataOut : |
|
|
84 | 84 | id : |
|
|
85 | 85 | wintitle : |
|
|
86 | 86 | channelList : |
|
|
87 | 87 | showProfile : |
|
|
88 | 88 | xmin : None, |
|
|
89 | 89 | xmax : None, |
|
|
90 | 90 | ymin : None, |
|
|
91 | 91 | ymax : None, |
|
|
92 | 92 | zmin : None, |
|
|
93 | 93 | zmax : None |
|
|
94 | 94 | """ |
|
|
95 | 95 | |
|
|
96 | 96 | if dataOut.flagNoData: |
|
|
97 | 97 | return None |
|
|
98 | 98 | |
|
|
99 | 99 | if realtime: |
|
|
100 | 100 | if not(isRealtime(utcdatatime = dataOut.utctime)): |
|
|
101 | 101 | print 'Skipping this plot function' |
|
|
102 | 102 | return |
|
|
103 | 103 | |
|
|
104 | 104 | if channelList == None: |
|
|
105 | 105 | channelIndexList = dataOut.channelIndexList |
|
|
106 | 106 | else: |
|
|
107 | 107 | channelIndexList = [] |
|
|
108 | 108 | for channel in channelList: |
|
|
109 | 109 | if channel not in dataOut.channelList: |
|
|
110 | 110 | raise ValueError, "Channel %d is not in dataOut.channelList" |
|
|
111 | 111 | channelIndexList.append(dataOut.channelList.index(channel)) |
|
|
112 | 112 | |
|
|
113 | 113 | factor = dataOut.normFactor |
|
|
114 | 114 | x = dataOut.abscissaRange |
|
|
115 | 115 | y = dataOut.heightRange |
|
|
116 | 116 | |
|
|
117 | 117 | z = dataOut.data_pre[channelIndexList,:,:]/factor |
|
|
118 | 118 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
|
|
119 | 119 | avg = numpy.average(z, axis=1) |
|
|
120 | 120 | noise = dataOut.noise/factor |
|
|
121 | 121 | |
|
|
122 | 122 | zdB = 10*numpy.log10(z) |
|
|
123 | 123 | avgdB = 10*numpy.log10(avg) |
|
|
124 | 124 | noisedB = 10*numpy.log10(noise) |
|
|
125 | 125 | |
|
|
126 | 126 | #thisDatetime = dataOut.datatime |
|
|
127 | 127 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[1]) |
|
|
128 | 128 | title = wintitle + " Parameters" |
|
|
129 | 129 | xlabel = "Velocity (m/s)" |
|
|
130 | 130 | ylabel = "Range (Km)" |
|
|
131 | 131 | |
|
|
132 | 132 | if not self.isConfig: |
|
|
133 | 133 | |
|
|
134 | 134 | nplots = len(channelIndexList) |
|
|
135 | 135 | |
|
|
136 | 136 | self.setup(id=id, |
|
|
137 | 137 | nplots=nplots, |
|
|
138 | 138 | wintitle=wintitle, |
|
|
139 | 139 | showprofile=showprofile, |
|
|
140 | 140 | show=show) |
|
|
141 | 141 | |
|
|
142 | 142 | if xmin == None: xmin = numpy.nanmin(x) |
|
|
143 | 143 | if xmax == None: xmax = numpy.nanmax(x) |
|
|
144 | 144 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
145 | 145 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
146 | 146 | if zmin == None: zmin = numpy.nanmin(avgdB)*0.9 |
|
|
147 | 147 | if zmax == None: zmax = numpy.nanmax(avgdB)*0.9 |
|
|
148 | 148 | |
|
|
149 | 149 | self.FTP_WEI = ftp_wei |
|
|
150 | 150 | self.EXP_CODE = exp_code |
|
|
151 | 151 | self.SUB_EXP_CODE = sub_exp_code |
|
|
152 | 152 | self.PLOT_POS = plot_pos |
|
|
153 | 153 | |
|
|
154 | 154 | self.isConfig = True |
|
|
155 | 155 | |
|
|
156 | 156 | self.setWinTitle(title) |
|
|
157 | 157 | |
|
|
158 | 158 | for i in range(self.nplots): |
|
|
159 | 159 | str_datetime = '%s %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S")) |
|
|
160 | 160 | title = "Channel %d: %4.2fdB: %s" %(dataOut.channelList[i]+1, noisedB[i], str_datetime) |
|
|
161 | 161 | axes = self.axesList[i*self.__nsubplots] |
|
|
162 | 162 | axes.pcolor(x, y, zdB[i,:,:], |
|
|
163 | 163 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
|
|
164 | 164 | xlabel=xlabel, ylabel=ylabel, title=title, |
|
|
165 | 165 | ticksize=9, cblabel='') |
|
|
166 | 166 | #Mean Line |
|
|
167 | 167 | mean = dataOut.data_param[i, 1, :] |
|
|
168 | 168 | axes.addpline(mean, y, idline=0, color="black", linestyle="solid", lw=1) |
|
|
169 | 169 | |
|
|
170 | 170 | if self.__showprofile: |
|
|
171 | 171 | axes = self.axesList[i*self.__nsubplots +1] |
|
|
172 | 172 | axes.pline(avgdB[i], y, |
|
|
173 | 173 | xmin=zmin, xmax=zmax, ymin=ymin, ymax=ymax, |
|
|
174 | 174 | xlabel='dB', ylabel='', title='', |
|
|
175 | 175 | ytick_visible=False, |
|
|
176 | 176 | grid='x') |
|
|
177 | 177 | |
|
|
178 | 178 | noiseline = numpy.repeat(noisedB[i], len(y)) |
|
|
179 | 179 | axes.addpline(noiseline, y, idline=1, color="black", linestyle="dashed", lw=2) |
|
|
180 | 180 | |
|
|
181 | 181 | self.draw() |
|
|
182 | 182 | |
|
|
183 | 183 | if figfile == None: |
|
|
184 | 184 | str_datetime = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
185 | 185 | figfile = self.getFilename(name = str_datetime) |
|
|
186 | 186 | |
|
|
187 | 187 | if figpath != '': |
|
|
188 | 188 | self.counter_imagwr += 1 |
|
|
189 | 189 | if (self.counter_imagwr>=wr_period): |
|
|
190 | 190 | # store png plot to local folder |
|
|
191 | 191 | self.saveFigure(figpath, figfile) |
|
|
192 | 192 | # store png plot to FTP server according to RT-Web format |
|
|
193 | 193 | name = self.getNameToFtp(thisDatetime, self.FTP_WEI, self.EXP_CODE, self.SUB_EXP_CODE, self.PLOT_CODE, self.PLOT_POS) |
|
|
194 | 194 | ftp_filename = os.path.join(figpath, name) |
|
|
195 | 195 | self.saveFigure(figpath, ftp_filename) |
|
|
196 | 196 | self.counter_imagwr = 0 |
|
|
197 | 197 | |
|
|
198 | 198 | class SkyMapPlot(Figure): |
|
|
199 | 199 | |
|
|
200 | 200 | __isConfig = None |
|
|
201 | 201 | __nsubplots = None |
|
|
202 | 202 | |
|
|
203 | 203 | WIDTHPROF = None |
|
|
204 | 204 | HEIGHTPROF = None |
|
|
205 | 205 | PREFIX = 'prm' |
|
|
206 | 206 | |
|
|
207 | 207 | def __init__(self): |
|
|
208 | 208 | |
|
|
209 | 209 | self.__isConfig = False |
|
|
210 | 210 | self.__nsubplots = 1 |
|
|
211 | 211 | |
|
|
212 | 212 | # self.WIDTH = 280 |
|
|
213 | 213 | # self.HEIGHT = 250 |
|
|
214 | 214 | self.WIDTH = 600 |
|
|
215 | 215 | self.HEIGHT = 600 |
|
|
216 | 216 | self.WIDTHPROF = 120 |
|
|
217 | 217 | self.HEIGHTPROF = 0 |
|
|
218 | 218 | self.counter_imagwr = 0 |
|
|
219 | 219 | |
|
|
220 | 220 | self.PLOT_CODE = 1 |
|
|
221 | 221 | self.FTP_WEI = None |
|
|
222 | 222 | self.EXP_CODE = None |
|
|
223 | 223 | self.SUB_EXP_CODE = None |
|
|
224 | 224 | self.PLOT_POS = None |
|
|
225 | 225 | |
|
|
226 | 226 | def getSubplots(self): |
|
|
227 | 227 | |
|
|
228 | 228 | ncol = int(numpy.sqrt(self.nplots)+0.9) |
|
|
229 | 229 | nrow = int(self.nplots*1./ncol + 0.9) |
|
|
230 | 230 | |
|
|
231 | 231 | return nrow, ncol |
|
|
232 | 232 | |
|
|
233 | 233 | def setup(self, id, nplots, wintitle, showprofile=False, show=True): |
|
|
234 | 234 | |
|
|
235 | 235 | self.__showprofile = showprofile |
|
|
236 | 236 | self.nplots = nplots |
|
|
237 | 237 | |
|
|
238 | 238 | ncolspan = 1 |
|
|
239 | 239 | colspan = 1 |
|
|
240 | 240 | |
|
|
241 | 241 | self.createFigure(id = id, |
|
|
242 | 242 | wintitle = wintitle, |
|
|
243 | 243 | widthplot = self.WIDTH, #+ self.WIDTHPROF, |
|
|
244 | 244 | heightplot = self.HEIGHT,# + self.HEIGHTPROF, |
|
|
245 | 245 | show=show) |
|
|
246 | 246 | |
|
|
247 | 247 | nrow, ncol = 1,1 |
|
|
248 | 248 | counter = 0 |
|
|
249 | 249 | x = 0 |
|
|
250 | 250 | y = 0 |
|
|
251 | 251 | self.addAxes(1, 1, 0, 0, 1, 1, True) |
|
|
252 | 252 | |
|
|
253 | 253 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile=False, |
|
|
254 | 254 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
|
|
255 | 255 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, |
|
|
256 | 256 | server=None, folder=None, username=None, password=None, |
|
|
257 | 257 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0, realtime=False): |
|
|
258 | 258 | |
|
|
259 | 259 | """ |
|
|
260 | 260 | |
|
|
261 | 261 | Input: |
|
|
262 | 262 | dataOut : |
|
|
263 | 263 | id : |
|
|
264 | 264 | wintitle : |
|
|
265 | 265 | channelList : |
|
|
266 | 266 | showProfile : |
|
|
267 | 267 | xmin : None, |
|
|
268 | 268 | xmax : None, |
|
|
269 | 269 | ymin : None, |
|
|
270 | 270 | ymax : None, |
|
|
271 | 271 | zmin : None, |
|
|
272 | 272 | zmax : None |
|
|
273 | 273 | """ |
|
|
274 | 274 | |
|
|
275 | 275 | arrayParameters = dataOut.data_param |
|
|
276 | 276 | error = arrayParameters[:,-1] |
|
|
277 | 277 | indValid = numpy.where(error == 0)[0] |
|
|
278 | 278 | finalMeteor = arrayParameters[indValid,:] |
|
|
279 | 279 | finalAzimuth = finalMeteor[:,4] |
|
|
280 | 280 | finalZenith = finalMeteor[:,5] |
|
|
281 | 281 | |
|
|
282 | 282 | x = finalAzimuth*numpy.pi/180 |
|
|
283 | 283 | y = finalZenith |
|
|
284 | 284 | |
|
|
285 | 285 | |
|
|
286 | 286 | #thisDatetime = dataOut.datatime |
|
|
287 | 287 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[1]) |
|
|
288 | 288 | title = wintitle + " Parameters" |
|
|
289 | 289 | xlabel = "Zonal Zenith Angle (deg) " |
|
|
290 | 290 | ylabel = "Meridional Zenith Angle (deg)" |
|
|
291 | 291 | |
|
|
292 | 292 | if not self.__isConfig: |
|
|
293 | 293 | |
|
|
294 | 294 | nplots = 1 |
|
|
295 | 295 | |
|
|
296 | 296 | self.setup(id=id, |
|
|
297 | 297 | nplots=nplots, |
|
|
298 | 298 | wintitle=wintitle, |
|
|
299 | 299 | showprofile=showprofile, |
|
|
300 | 300 | show=show) |
|
|
301 | 301 | |
|
|
302 | 302 | self.FTP_WEI = ftp_wei |
|
|
303 | 303 | self.EXP_CODE = exp_code |
|
|
304 | 304 | self.SUB_EXP_CODE = sub_exp_code |
|
|
305 | 305 | self.PLOT_POS = plot_pos |
|
|
306 | 306 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
307 | 307 | self.firstdate = '%s %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S")) |
|
|
308 | 308 | self.__isConfig = True |
|
|
309 | 309 | |
|
|
310 | 310 | self.setWinTitle(title) |
|
|
311 | 311 | |
|
|
312 | 312 | i = 0 |
|
|
313 | 313 | str_datetime = '%s %s'%(thisDatetime.strftime("%Y/%m/%d"),thisDatetime.strftime("%H:%M:%S")) |
|
|
314 | 314 | |
|
|
315 | 315 | axes = self.axesList[i*self.__nsubplots] |
|
|
316 | 316 | nevents = axes.x_buffer.shape[0] + x.shape[0] |
|
|
317 | 317 | title = "Meteor Detection Sky Map\n %s - %s \n Number of events: %5.0f\n" %(self.firstdate,str_datetime,nevents) |
|
|
318 | 318 | axes.polar(x, y, |
|
|
319 | 319 | title=title, xlabel=xlabel, ylabel=ylabel, |
|
|
320 | 320 | ticksize=9, cblabel='') |
|
|
321 | 321 | |
|
|
322 | 322 | self.draw() |
|
|
323 | 323 | |
|
|
324 | 324 | if save: |
|
|
325 | 325 | |
|
|
326 | 326 | self.counter_imagwr += 1 |
|
|
327 | 327 | if (self.counter_imagwr==wr_period): |
|
|
328 | 328 | |
|
|
329 | 329 | if figfile == None: |
|
|
330 | 330 | figfile = self.getFilename(name = self.name) |
|
|
331 | 331 | self.saveFigure(figpath, figfile) |
|
|
332 | 332 | |
|
|
333 | 333 | if ftp: |
|
|
334 | 334 | #provisionalmente envia archivos en el formato de la web en tiempo real |
|
|
335 | 335 | name = self.getNameToFtp(thisDatetime, self.FTP_WEI, self.EXP_CODE, self.SUB_EXP_CODE, self.PLOT_CODE, self.PLOT_POS) |
|
|
336 | 336 | path = '%s%03d' %(self.PREFIX, self.id) |
|
|
337 | 337 | ftp_file = os.path.join(path,'ftp','%s.png'%name) |
|
|
338 | 338 | self.saveFigure(figpath, ftp_file) |
|
|
339 | 339 | ftp_filename = os.path.join(figpath,ftp_file) |
|
|
340 | 340 | |
|
|
341 | 341 | |
|
|
342 | 342 | try: |
|
|
343 | 343 | self.sendByFTP(ftp_filename, server, folder, username, password) |
|
|
344 | 344 | except: |
|
|
345 | 345 | self.counter_imagwr = 0 |
|
|
346 | 346 | raise ValueError, 'Error FTP' |
|
|
347 | 347 | |
|
|
348 | 348 | self.counter_imagwr = 0 |
|
|
349 | 349 | |
|
|
350 | 350 | |
|
|
351 | 351 | class WindProfilerPlot(Figure): |
|
|
352 | 352 | |
|
|
353 | 353 | __isConfig = None |
|
|
354 | 354 | __nsubplots = None |
|
|
355 | 355 | |
|
|
356 | 356 | WIDTHPROF = None |
|
|
357 | 357 | HEIGHTPROF = None |
|
|
358 | 358 | PREFIX = 'wind' |
|
|
359 | 359 | |
|
|
360 | 360 | def __init__(self): |
|
|
361 | 361 | |
|
|
362 | 362 | self.timerange = 2*60*60 |
|
|
363 | 363 | self.__isConfig = False |
|
|
364 | 364 | self.__nsubplots = 1 |
|
|
365 | 365 | |
|
|
366 | 366 | self.WIDTH = 800 |
|
|
367 | 367 | self.HEIGHT = 150 |
|
|
368 | 368 | self.WIDTHPROF = 120 |
|
|
369 | 369 | self.HEIGHTPROF = 0 |
|
|
370 | 370 | self.counter_imagwr = 0 |
|
|
371 | 371 | |
|
|
372 | 372 | self.PLOT_CODE = 0 |
|
|
373 | 373 | self.FTP_WEI = None |
|
|
374 | 374 | self.EXP_CODE = None |
|
|
375 | 375 | self.SUB_EXP_CODE = None |
|
|
376 | 376 | self.PLOT_POS = None |
|
|
377 | 377 | self.tmin = None |
|
|
378 | 378 | self.tmax = None |
|
|
379 | 379 | |
|
|
380 | 380 | self.xmin = None |
|
|
381 | 381 | self.xmax = None |
|
|
382 | 382 | |
|
|
383 | 383 | self.figfile = None |
|
|
384 | 384 | |
|
|
385 | 385 | def getSubplots(self): |
|
|
386 | 386 | |
|
|
387 | 387 | ncol = 1 |
|
|
388 | 388 | nrow = self.nplots |
|
|
389 | 389 | |
|
|
390 | 390 | return nrow, ncol |
|
|
391 | 391 | |
|
|
392 | 392 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
393 | 393 | |
|
|
394 | 394 | self.__showprofile = showprofile |
|
|
395 | 395 | self.nplots = nplots |
|
|
396 | 396 | |
|
|
397 | 397 | ncolspan = 1 |
|
|
398 | 398 | colspan = 1 |
|
|
399 | 399 | |
|
|
400 | 400 | self.createFigure(id = id, |
|
|
401 | 401 | wintitle = wintitle, |
|
|
402 | 402 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
403 | 403 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
404 | 404 | show=show) |
|
|
405 | 405 | |
|
|
406 | 406 | nrow, ncol = self.getSubplots() |
|
|
407 | 407 | |
|
|
408 | 408 | counter = 0 |
|
|
409 | 409 | for y in range(nrow): |
|
|
410 | 410 | if counter >= self.nplots: |
|
|
411 | 411 | break |
|
|
412 | 412 | |
|
|
413 | 413 | self.addAxes(nrow, ncol*ncolspan, y, 0, colspan, 1) |
|
|
414 | 414 | counter += 1 |
|
|
415 | 415 | |
|
|
416 | 416 | def run(self, dataOut, id, wintitle="", channelList=None, |
|
|
417 | 417 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
|
|
418 | 418 | zmax_ver = None, zmin_ver = None, SNRmin = None, SNRmax = None, |
|
|
419 | 419 | timerange=None, SNRthresh = None, |
|
|
420 | 420 | save=False, figpath='', lastone=0,figfile=None, ftp=False, wr_period=1, show=True, |
|
|
421 | 421 | server=None, folder=None, username=None, password=None, |
|
|
422 | 422 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): |
|
|
423 | 423 | """ |
|
|
424 | 424 | |
|
|
425 | 425 | Input: |
|
|
426 | 426 | dataOut : |
|
|
427 | 427 | id : |
|
|
428 | 428 | wintitle : |
|
|
429 | 429 | channelList : |
|
|
430 | 430 | showProfile : |
|
|
431 | 431 | xmin : None, |
|
|
432 | 432 | xmax : None, |
|
|
433 | 433 | ymin : None, |
|
|
434 | 434 | ymax : None, |
|
|
435 | 435 | zmin : None, |
|
|
436 | 436 | zmax : None |
|
|
437 | 437 | """ |
|
|
438 | 438 | |
|
|
439 | 439 | if channelList == None: |
|
|
440 | 440 | channelIndexList = dataOut.channelIndexList |
|
|
441 | 441 | else: |
|
|
442 | 442 | channelIndexList = [] |
|
|
443 | 443 | for channel in channelList: |
|
|
444 | 444 | if channel not in dataOut.channelList: |
|
|
445 | 445 | raise ValueError, "Channel %d is not in dataOut.channelList" |
|
|
446 | 446 | channelIndexList.append(dataOut.channelList.index(channel)) |
|
|
447 | 447 | |
|
|
448 | 448 | if timerange != None: |
|
|
449 | 449 | self.timerange = timerange |
|
|
450 | 450 | |
|
|
451 | 451 | tmin = None |
|
|
452 | 452 | tmax = None |
|
|
453 | 453 | |
|
|
454 | 454 | x = dataOut.getTimeRange1() |
|
|
455 | 455 | # y = dataOut.heightRange |
|
|
456 | 456 | y = dataOut.heightRange |
|
|
457 | 457 | |
|
|
458 |
z = dataOut. |
|
|
|
458 | z = dataOut.data_output.copy() | |
|
|
459 | 459 | nplots = z.shape[0] #Number of wind dimensions estimated |
|
|
460 | 460 | nplotsw = nplots |
|
|
461 | 461 | |
|
|
462 | 462 | #If there is a SNR function defined |
|
|
463 | 463 | if dataOut.SNR != None: |
|
|
464 | 464 | nplots += 1 |
|
|
465 | 465 | SNR = dataOut.SNR |
|
|
466 | 466 | SNRavg = numpy.average(SNR, axis=0) |
|
|
467 | 467 | |
|
|
468 | 468 | SNRdB = 10*numpy.log10(SNR) |
|
|
469 | 469 | SNRavgdB = 10*numpy.log10(SNRavg) |
|
|
470 | 470 | |
|
|
471 | 471 | if SNRthresh == None: SNRthresh = -5.0 |
|
|
472 | 472 | ind = numpy.where(SNRavg < 10**(SNRthresh/10))[0] |
|
|
473 | 473 | |
|
|
474 | 474 | for i in range(nplotsw): |
|
|
475 | 475 | z[i,ind] = numpy.nan |
|
|
476 | 476 | |
|
|
477 | 477 | |
|
|
478 | 478 | showprofile = False |
|
|
479 | 479 | # thisDatetime = dataOut.datatime |
|
|
480 | 480 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[1]) |
|
|
481 | 481 | title = wintitle + "Wind" |
|
|
482 | 482 | xlabel = "" |
|
|
483 | 483 | ylabel = "Range (Km)" |
|
|
484 | 484 | |
|
|
485 | 485 | if not self.__isConfig: |
|
|
486 | 486 | |
|
|
487 | 487 | self.setup(id=id, |
|
|
488 | 488 | nplots=nplots, |
|
|
489 | 489 | wintitle=wintitle, |
|
|
490 | 490 | showprofile=showprofile, |
|
|
491 | 491 | show=show) |
|
|
492 | 492 | |
|
|
493 | 493 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
494 | 494 | |
|
|
495 | 495 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
496 | 496 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
497 | 497 | |
|
|
498 | 498 | if zmax == None: zmax = numpy.nanmax(abs(z[range(2),:])) |
|
|
499 | 499 | #if numpy.isnan(zmax): zmax = 50 |
|
|
500 | 500 | if zmin == None: zmin = -zmax |
|
|
501 | 501 | |
|
|
502 | 502 | if nplotsw == 3: |
|
|
503 | 503 | if zmax_ver == None: zmax_ver = numpy.nanmax(abs(z[2,:])) |
|
|
504 | 504 | if zmin_ver == None: zmin_ver = -zmax_ver |
|
|
505 | 505 | |
|
|
506 | 506 | if dataOut.SNR != None: |
|
|
507 | 507 | if SNRmin == None: SNRmin = numpy.nanmin(SNRavgdB) |
|
|
508 | 508 | if SNRmax == None: SNRmax = numpy.nanmax(SNRavgdB) |
|
|
509 | 509 | |
|
|
510 | 510 | self.FTP_WEI = ftp_wei |
|
|
511 | 511 | self.EXP_CODE = exp_code |
|
|
512 | 512 | self.SUB_EXP_CODE = sub_exp_code |
|
|
513 | 513 | self.PLOT_POS = plot_pos |
|
|
514 | 514 | |
|
|
515 | 515 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
516 | 516 | self.__isConfig = True |
|
|
517 | 517 | |
|
|
518 | 518 | |
|
|
519 | 519 | self.setWinTitle(title) |
|
|
520 | 520 | |
|
|
521 | 521 | if ((self.xmax - x[1]) < (x[1]-x[0])): |
|
|
522 | 522 | x[1] = self.xmax |
|
|
523 | 523 | |
|
|
524 | 524 | strWind = ['Zonal', 'Meridional', 'Vertical'] |
|
|
525 | 525 | strCb = ['Velocity (m/s)','Velocity (m/s)','Velocity (cm/s)'] |
|
|
526 | 526 | zmaxVector = [zmax, zmax, zmax_ver] |
|
|
527 | 527 | zminVector = [zmin, zmin, zmin_ver] |
|
|
528 | 528 | windFactor = [1,1,100] |
|
|
529 | 529 | |
|
|
530 | 530 | for i in range(nplotsw): |
|
|
531 | 531 | |
|
|
532 | 532 | title = "%s Wind: %s" %(strWind[i], thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
533 | 533 | axes = self.axesList[i*self.__nsubplots] |
|
|
534 | 534 | |
|
|
535 | 535 | z1 = z[i,:].reshape((1,-1))*windFactor[i] |
|
|
536 | 536 | |
|
|
537 | 537 | axes.pcolorbuffer(x, y, z1, |
|
|
538 | 538 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=zminVector[i], zmax=zmaxVector[i], |
|
|
539 | 539 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, |
|
|
540 | 540 | ticksize=9, cblabel=strCb[i], cbsize="1%", colormap="RdBu_r" ) |
|
|
541 | 541 | |
|
|
542 | 542 | if dataOut.SNR != None: |
|
|
543 | 543 | i += 1 |
|
|
544 | 544 | title = "Signal Noise Ratio (SNR): %s" %(thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
545 | 545 | axes = self.axesList[i*self.__nsubplots] |
|
|
546 | 546 | |
|
|
547 | 547 | SNRavgdB = SNRavgdB.reshape((1,-1)) |
|
|
548 | 548 | |
|
|
549 | 549 | axes.pcolorbuffer(x, y, SNRavgdB, |
|
|
550 | 550 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=SNRmin, zmax=SNRmax, |
|
|
551 | 551 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, |
|
|
552 | 552 | ticksize=9, cblabel='', cbsize="1%", colormap="jet") |
|
|
553 | 553 | |
|
|
554 | 554 | self.draw() |
|
|
555 | 555 | |
|
|
556 | 556 | if self.figfile == None: |
|
|
557 | 557 | str_datetime = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
558 | 558 | self.figfile = self.getFilename(name = str_datetime) |
|
|
559 | 559 | |
|
|
560 | 560 | if figpath != '': |
|
|
561 | 561 | |
|
|
562 | 562 | self.counter_imagwr += 1 |
|
|
563 | 563 | if (self.counter_imagwr>=wr_period): |
|
|
564 | 564 | # store png plot to local folder |
|
|
565 | 565 | self.saveFigure(figpath, self.figfile) |
|
|
566 | 566 | # store png plot to FTP server according to RT-Web format |
|
|
567 | 567 | name = self.getNameToFtp(thisDatetime, self.FTP_WEI, self.EXP_CODE, self.SUB_EXP_CODE, self.PLOT_CODE, self.PLOT_POS) |
|
|
568 | 568 | ftp_filename = os.path.join(figpath, name) |
|
|
569 | 569 | self.saveFigure(figpath, ftp_filename) |
|
|
570 | 570 | |
|
|
571 | 571 | self.counter_imagwr = 0 |
|
|
572 | 572 | |
|
|
573 | 573 | if x[1] >= self.axesList[0].xmax: |
|
|
574 | 574 | self.counter_imagwr = wr_period |
|
|
575 | 575 | self.__isConfig = False |
|
|
576 | 576 | self.figfile = None |
|
|
577 | 577 | |
|
|
578 | 578 | |
|
|
579 | 579 | class ParametersPlot(Figure): |
|
|
580 | 580 | |
|
|
581 | 581 | __isConfig = None |
|
|
582 | 582 | __nsubplots = None |
|
|
583 | 583 | |
|
|
584 | 584 | WIDTHPROF = None |
|
|
585 | 585 | HEIGHTPROF = None |
|
|
586 | 586 | PREFIX = 'prm' |
|
|
587 | 587 | |
|
|
588 | 588 | def __init__(self): |
|
|
589 | 589 | |
|
|
590 | 590 | self.timerange = 2*60*60 |
|
|
591 | 591 | self.__isConfig = False |
|
|
592 | 592 | self.__nsubplots = 1 |
|
|
593 | 593 | |
|
|
594 | 594 | self.WIDTH = 800 |
|
|
595 | 595 | self.HEIGHT = 150 |
|
|
596 | 596 | self.WIDTHPROF = 120 |
|
|
597 | 597 | self.HEIGHTPROF = 0 |
|
|
598 | 598 | self.counter_imagwr = 0 |
|
|
599 | 599 | |
|
|
600 | 600 | self.PLOT_CODE = 0 |
|
|
601 | 601 | self.FTP_WEI = None |
|
|
602 | 602 | self.EXP_CODE = None |
|
|
603 | 603 | self.SUB_EXP_CODE = None |
|
|
604 | 604 | self.PLOT_POS = None |
|
|
605 | 605 | self.tmin = None |
|
|
606 | 606 | self.tmax = None |
|
|
607 | 607 | |
|
|
608 | 608 | self.xmin = None |
|
|
609 | 609 | self.xmax = None |
|
|
610 | 610 | |
|
|
611 | 611 | self.figfile = None |
|
|
612 | 612 | |
|
|
613 | 613 | def getSubplots(self): |
|
|
614 | 614 | |
|
|
615 | 615 | ncol = 1 |
|
|
616 | 616 | nrow = self.nplots |
|
|
617 | 617 | |
|
|
618 | 618 | return nrow, ncol |
|
|
619 | 619 | |
|
|
620 | 620 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): |
|
|
621 | 621 | |
|
|
622 | 622 | self.__showprofile = showprofile |
|
|
623 | 623 | self.nplots = nplots |
|
|
624 | 624 | |
|
|
625 | 625 | ncolspan = 1 |
|
|
626 | 626 | colspan = 1 |
|
|
627 | 627 | |
|
|
628 | 628 | self.createFigure(id = id, |
|
|
629 | 629 | wintitle = wintitle, |
|
|
630 | 630 | widthplot = self.WIDTH + self.WIDTHPROF, |
|
|
631 | 631 | heightplot = self.HEIGHT + self.HEIGHTPROF, |
|
|
632 | 632 | show=show) |
|
|
633 | 633 | |
|
|
634 | 634 | nrow, ncol = self.getSubplots() |
|
|
635 | 635 | |
|
|
636 | 636 | counter = 0 |
|
|
637 | 637 | for y in range(nrow): |
|
|
638 | 638 | for x in range(ncol): |
|
|
639 | 639 | |
|
|
640 | 640 | if counter >= self.nplots: |
|
|
641 | 641 | break |
|
|
642 | 642 | |
|
|
643 | 643 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
|
644 | 644 | |
|
|
645 | 645 | if showprofile: |
|
|
646 | 646 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan+colspan, 1, 1) |
|
|
647 | 647 | |
|
|
648 | 648 | counter += 1 |
|
|
649 | 649 | |
|
|
650 | 650 | def run(self, dataOut, id, wintitle="", channelList=None, showprofile=False, |
|
|
651 | 651 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None,timerange=None, |
|
|
652 | 652 | SNRmin = None, SNRmax = None, SNRthresh = None, paramIndex = None, onlyPositive = False, |
|
|
653 | 653 | zlabel = "", parameterName = "", |
|
|
654 | 654 | save=False, figpath='', lastone=0,figfile=None, ftp=False, wr_period=1, show=True, |
|
|
655 | 655 | server=None, folder=None, username=None, password=None, |
|
|
656 | 656 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): |
|
|
657 | 657 | |
|
|
658 | 658 | """ |
|
|
659 | 659 | |
|
|
660 | 660 | Input: |
|
|
661 | 661 | dataOut : |
|
|
662 | 662 | id : |
|
|
663 | 663 | wintitle : |
|
|
664 | 664 | channelList : |
|
|
665 | 665 | showProfile : |
|
|
666 | 666 | xmin : None, |
|
|
667 | 667 | xmax : None, |
|
|
668 | 668 | ymin : None, |
|
|
669 | 669 | ymax : None, |
|
|
670 | 670 | zmin : None, |
|
|
671 | 671 | zmax : None |
|
|
672 | 672 | """ |
|
|
673 | 673 | |
|
|
674 | 674 | if channelList == None: |
|
|
675 | 675 | channelIndexList = dataOut.channelIndexList |
|
|
676 | 676 | else: |
|
|
677 | 677 | channelIndexList = [] |
|
|
678 | 678 | for channel in channelList: |
|
|
679 | 679 | if channel not in dataOut.channelList: |
|
|
680 | 680 | raise ValueError, "Channel %d is not in dataOut.channelList" |
|
|
681 | 681 | channelIndexList.append(dataOut.channelList.index(channel)) |
|
|
682 | 682 | |
|
|
683 | 683 | if timerange != None: |
|
|
684 | 684 | self.timerange = timerange |
|
|
685 | 685 | |
|
|
686 | 686 | #tmin = None |
|
|
687 | 687 | #tmax = None |
|
|
688 | 688 | if paramIndex == None: |
|
|
689 | 689 | paramIndex = 1 |
|
|
690 | 690 | x = dataOut.getTimeRange1() |
|
|
691 | 691 | y = dataOut.heightRange |
|
|
692 | 692 | z = dataOut.data_param[channelIndexList,paramIndex,:].copy() |
|
|
693 | 693 | |
|
|
694 | 694 | zRange = dataOut.abscissaRange |
|
|
695 | 695 | nplots = z.shape[0] #Number of wind dimensions estimated |
|
|
696 | 696 | # thisDatetime = dataOut.datatime |
|
|
697 | 697 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[1]) |
|
|
698 | 698 | title = wintitle + " Parameters Plot" #: %s" %(thisDatetime.strftime("%d-%b-%Y")) |
|
|
699 | 699 | xlabel = "" |
|
|
700 | 700 | ylabel = "Range (Km)" |
|
|
701 | 701 | |
|
|
702 | 702 | if onlyPositive: |
|
|
703 | 703 | colormap = "jet" |
|
|
704 | 704 | zmin = 0 |
|
|
705 | 705 | else: colormap = "RdBu_r" |
|
|
706 | 706 | |
|
|
707 | 707 | if not self.__isConfig: |
|
|
708 | 708 | |
|
|
709 | 709 | self.setup(id=id, |
|
|
710 | 710 | nplots=nplots, |
|
|
711 | 711 | wintitle=wintitle, |
|
|
712 | 712 | showprofile=showprofile, |
|
|
713 | 713 | show=show) |
|
|
714 | 714 | |
|
|
715 | 715 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) |
|
|
716 | 716 | |
|
|
717 | 717 | if ymin == None: ymin = numpy.nanmin(y) |
|
|
718 | 718 | if ymax == None: ymax = numpy.nanmax(y) |
|
|
719 | 719 | if zmin == None: zmin = numpy.nanmin(zRange) |
|
|
720 | 720 | if zmax == None: zmax = numpy.nanmax(zRange) |
|
|
721 | 721 | |
|
|
722 | 722 | if dataOut.SNR != None: |
|
|
723 | 723 | if SNRmin == None: SNRmin = numpy.nanmin(SNRavgdB) |
|
|
724 | 724 | if SNRmax == None: SNRmax = numpy.nanmax(SNRavgdB) |
|
|
725 | 725 | |
|
|
726 | 726 | self.FTP_WEI = ftp_wei |
|
|
727 | 727 | self.EXP_CODE = exp_code |
|
|
728 | 728 | self.SUB_EXP_CODE = sub_exp_code |
|
|
729 | 729 | self.PLOT_POS = plot_pos |
|
|
730 | 730 | |
|
|
731 | 731 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
732 | 732 | self.__isConfig = True |
|
|
733 | 733 | self.figfile = figfile |
|
|
734 | 734 | |
|
|
735 | 735 | self.setWinTitle(title) |
|
|
736 | 736 | |
|
|
737 | 737 | if ((self.xmax - x[1]) < (x[1]-x[0])): |
|
|
738 | 738 | x[1] = self.xmax |
|
|
739 | 739 | |
|
|
740 | 740 | for i in range(nplots): |
|
|
741 | 741 | title = "%s Channel %d: %s" %(parameterName, dataOut.channelList[i]+1, thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) |
|
|
742 | 742 | |
|
|
743 | 743 | if ((dataOut.azimuth!=None) and (dataOut.zenith!=None)): |
|
|
744 | 744 | title = title + '_' + 'azimuth,zenith=%2.2f,%2.2f'%(dataOut.azimuth, dataOut.zenith) |
|
|
745 | 745 | axes = self.axesList[i*self.__nsubplots] |
|
|
746 | 746 | z1 = z[i,:].reshape((1,-1)) |
|
|
747 | 747 | axes.pcolorbuffer(x, y, z1, |
|
|
748 | 748 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
|
|
749 | 749 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True,colormap=colormap, |
|
|
750 | 750 | ticksize=9, cblabel=zlabel, cbsize="1%") |
|
|
751 | 751 | |
|
|
752 | 752 | self.draw() |
|
|
753 | 753 | |
|
|
754 | 754 | if self.figfile == None: |
|
|
755 | 755 | str_datetime = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
|
756 | 756 | self.figfile = self.getFilename(name = str_datetime) |
|
|
757 | 757 | |
|
|
758 | 758 | if figpath != '': |
|
|
759 | 759 | |
|
|
760 | 760 | self.counter_imagwr += 1 |
|
|
761 | 761 | if (self.counter_imagwr>=wr_period): |
|
|
762 | 762 | # store png plot to local folder |
|
|
763 | 763 | self.saveFigure(figpath, self.figfile) |
|
|
764 | 764 | # store png plot to FTP server according to RT-Web format |
|
|
765 | 765 | name = self.getNameToFtp(thisDatetime, self.FTP_WEI, self.EXP_CODE, self.SUB_EXP_CODE, self.PLOT_CODE, self.PLOT_POS) |
|
|
766 | 766 | ftp_filename = os.path.join(figpath, name) |
|
|
767 | 767 | self.saveFigure(figpath, ftp_filename) |
|
|
768 | 768 | |
|
|
769 | 769 | self.counter_imagwr = 0 |
|
|
770 | 770 | |
|
|
771 | 771 | if x[1] >= self.axesList[0].xmax: |
|
|
772 | 772 | self.counter_imagwr = wr_period |
|
|
773 | 773 | self.__isConfig = False |
|
|
774 | self.figfile = None | |
|
|
775 | ||
|
|
776 | ||
|
|
777 | class SpectralFittingPlot(Figure): | |
|
|
778 | ||
|
|
779 | __isConfig = None | |
|
|
780 | __nsubplots = None | |
|
|
781 | ||
|
|
782 | WIDTHPROF = None | |
|
|
783 | HEIGHTPROF = None | |
|
|
784 | PREFIX = 'prm' | |
|
|
785 | ||
|
|
786 | ||
|
|
787 | N = None | |
|
|
788 | ippSeconds = None | |
|
|
789 | ||
|
|
790 | def __init__(self): | |
|
|
791 | self.__isConfig = False | |
|
|
792 | self.__nsubplots = 1 | |
|
|
793 | ||
|
|
794 | self.WIDTH = 450 | |
|
|
795 | self.HEIGHT = 250 | |
|
|
796 | self.WIDTHPROF = 0 | |
|
|
797 | self.HEIGHTPROF = 0 | |
|
|
798 | ||
|
|
799 | def getSubplots(self): | |
|
|
800 | ||
|
|
801 | ncol = int(numpy.sqrt(self.nplots)+0.9) | |
|
|
802 | nrow = int(self.nplots*1./ncol + 0.9) | |
|
|
803 | ||
|
|
804 | return nrow, ncol | |
|
|
805 | ||
|
|
806 | def setup(self, id, nplots, wintitle, showprofile=False, show=True): | |
|
|
807 | ||
|
|
808 | showprofile = False | |
|
|
809 | self.__showprofile = showprofile | |
|
|
810 | self.nplots = nplots | |
|
|
811 | ||
|
|
812 | ncolspan = 5 | |
|
|
813 | colspan = 4 | |
|
|
814 | if showprofile: | |
|
|
815 | ncolspan = 5 | |
|
|
816 | colspan = 4 | |
|
|
817 | self.__nsubplots = 2 | |
|
|
818 | ||
|
|
819 | self.createFigure(id = id, | |
|
|
820 | wintitle = wintitle, | |
|
|
821 | widthplot = self.WIDTH + self.WIDTHPROF, | |
|
|
822 | heightplot = self.HEIGHT + self.HEIGHTPROF, | |
|
|
823 | show=show) | |
|
|
824 | ||
|
|
825 | nrow, ncol = self.getSubplots() | |
|
|
826 | ||
|
|
827 | counter = 0 | |
|
|
828 | for y in range(nrow): | |
|
|
829 | for x in range(ncol): | |
|
|
830 | ||
|
|
831 | if counter >= self.nplots: | |
|
|
832 | break | |
|
|
833 | ||
|
|
834 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) | |
|
|
835 | ||
|
|
836 | if showprofile: | |
|
|
837 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan+colspan, 1, 1) | |
|
|
838 | ||
|
|
839 | counter += 1 | |
|
|
840 | ||
|
|
841 | def run(self, dataOut, id, cutHeight=None, fit=False, wintitle="", channelList=None, showprofile=True, | |
|
|
842 | xmin=None, xmax=None, ymin=None, ymax=None, | |
|
|
843 | save=False, figpath='./', figfile=None, show=True): | |
|
|
844 | ||
|
|
845 | """ | |
|
|
846 | ||
|
|
847 | Input: | |
|
|
848 | dataOut : | |
|
|
849 | id : | |
|
|
850 | wintitle : | |
|
|
851 | channelList : | |
|
|
852 | showProfile : | |
|
|
853 | xmin : None, | |
|
|
854 | xmax : None, | |
|
|
855 | zmin : None, | |
|
|
856 | zmax : None | |
|
|
857 | """ | |
|
|
858 | ||
|
|
859 | if cutHeight==None: | |
|
|
860 | h=270 | |
|
|
861 | heightindex = numpy.abs(cutHeight - dataOut.heightList).argmin() | |
|
|
862 | cutHeight = dataOut.heightList[heightindex] | |
|
|
863 | ||
|
|
864 | factor = dataOut.normFactor | |
|
|
865 | x = dataOut.abscissaRange[:-1] | |
|
|
866 | #y = dataOut.getHeiRange() | |
|
|
867 | ||
|
|
868 | z = dataOut.data_pre[:,:,heightindex]/factor | |
|
|
869 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) | |
|
|
870 | avg = numpy.average(z, axis=1) | |
|
|
871 | listChannels = z.shape[0] | |
|
|
872 | ||
|
|
873 | #Reconstruct Function | |
|
|
874 | if fit==True: | |
|
|
875 | groupArray = dataOut.groupList | |
|
|
876 | listChannels = groupArray.reshape((groupArray.size)) | |
|
|
877 | listChannels.sort() | |
|
|
878 | spcFitLine = numpy.zeros(z.shape) | |
|
|
879 | constants = dataOut.constants | |
|
|
880 | ||
|
|
881 | nGroups = groupArray.shape[0] | |
|
|
882 | nChannels = groupArray.shape[1] | |
|
|
883 | nProfiles = z.shape[1] | |
|
|
884 | ||
|
|
885 | for f in range(nGroups): | |
|
|
886 | groupChann = groupArray[f,:] | |
|
|
887 | p = dataOut.data_param[f,:,heightindex] | |
|
|
888 | # p = numpy.array([ 89.343967,0.14036615,0.17086219,18.89835291,1.58388365,1.55099167]) | |
|
|
889 | fitLineAux = dataOut.library.modelFunction(p, constants)*nProfiles | |
|
|
890 | fitLineAux = fitLineAux.reshape((nChannels,nProfiles)) | |
|
|
891 | spcFitLine[groupChann,:] = fitLineAux | |
|
|
892 | # spcFitLine = spcFitLine/factor | |
|
|
893 | ||
|
|
894 | z = z[listChannels,:] | |
|
|
895 | spcFitLine = spcFitLine[listChannels,:] | |
|
|
896 | spcFitLinedB = 10*numpy.log10(spcFitLine) | |
|
|
897 | ||
|
|
898 | zdB = 10*numpy.log10(z) | |
|
|
899 | #thisDatetime = dataOut.datatime | |
|
|
900 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[1]) | |
|
|
901 | title = wintitle + " Doppler Spectra: %s" %(thisDatetime.strftime("%d-%b-%Y %H:%M:%S")) | |
|
|
902 | xlabel = "Velocity (m/s)" | |
|
|
903 | ylabel = "Spectrum" | |
|
|
904 | ||
|
|
905 | if not self.__isConfig: | |
|
|
906 | ||
|
|
907 | nplots = listChannels.size | |
|
|
908 | ||
|
|
909 | self.setup(id=id, | |
|
|
910 | nplots=nplots, | |
|
|
911 | wintitle=wintitle, | |
|
|
912 | showprofile=showprofile, | |
|
|
913 | show=show) | |
|
|
914 | ||
|
|
915 | if xmin == None: xmin = numpy.nanmin(x) | |
|
|
916 | if xmax == None: xmax = numpy.nanmax(x) | |
|
|
917 | if ymin == None: ymin = numpy.nanmin(zdB) | |
|
|
918 | if ymax == None: ymax = numpy.nanmax(zdB)+2 | |
|
|
919 | ||
|
|
920 | self.__isConfig = True | |
|
|
921 | ||
|
|
922 | self.setWinTitle(title) | |
|
|
923 | for i in range(self.nplots): | |
|
|
924 | # title = "Channel %d: %4.2fdB" %(dataOut.channelList[i]+1, noisedB[i]) | |
|
|
925 | title = "Height %4.1f km\nChannel %d:" %(cutHeight, listChannels[i]+1) | |
|
|
926 | axes = self.axesList[i*self.__nsubplots] | |
|
|
927 | if fit == False: | |
|
|
928 | axes.pline(x, zdB[i,:], | |
|
|
929 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, | |
|
|
930 | xlabel=xlabel, ylabel=ylabel, title=title | |
|
|
931 | ) | |
|
|
932 | if fit == True: | |
|
|
933 | fitline=spcFitLinedB[i,:] | |
|
|
934 | y=numpy.vstack([zdB[i,:],fitline] ) | |
|
|
935 | legendlabels=['Data','Fitting'] | |
|
|
936 | axes.pmultilineyaxis(x, y, | |
|
|
937 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, | |
|
|
938 | xlabel=xlabel, ylabel=ylabel, title=title, | |
|
|
939 | legendlabels=legendlabels, marker=None, | |
|
|
940 | linestyle='solid', grid='both') | |
|
|
941 | ||
|
|
942 | self.draw() | |
|
|
943 | ||
|
|
944 | if save: | |
|
|
945 | date = thisDatetime.strftime("%Y%m%d_%H%M%S") | |
|
|
946 | if figfile == None: | |
|
|
947 | figfile = self.getFilename(name = date) | |
|
|
948 | ||
|
|
949 | self.saveFigure(figpath, figfile) | |
|
|
950 | ||
|
|
951 | ||
|
|
952 | class EWDriftsPlot(Figure): | |
|
|
953 | ||
|
|
954 | __isConfig = None | |
|
|
955 | __nsubplots = None | |
|
|
956 | ||
|
|
957 | WIDTHPROF = None | |
|
|
958 | HEIGHTPROF = None | |
|
|
959 | PREFIX = 'drift' | |
|
|
960 | ||
|
|
961 | def __init__(self): | |
|
|
962 | ||
|
|
963 | self.timerange = 2*60*60 | |
|
|
964 | self.isConfig = False | |
|
|
965 | self.__nsubplots = 1 | |
|
|
966 | ||
|
|
967 | self.WIDTH = 800 | |
|
|
968 | self.HEIGHT = 150 | |
|
|
969 | self.WIDTHPROF = 120 | |
|
|
970 | self.HEIGHTPROF = 0 | |
|
|
971 | self.counter_imagwr = 0 | |
|
|
972 | ||
|
|
973 | self.PLOT_CODE = 0 | |
|
|
974 | self.FTP_WEI = None | |
|
|
975 | self.EXP_CODE = None | |
|
|
976 | self.SUB_EXP_CODE = None | |
|
|
977 | self.PLOT_POS = None | |
|
|
978 | self.tmin = None | |
|
|
979 | self.tmax = None | |
|
|
980 | ||
|
|
981 | self.xmin = None | |
|
|
982 | self.xmax = None | |
|
|
983 | ||
|
|
984 | self.figfile = None | |
|
|
985 | ||
|
|
986 | def getSubplots(self): | |
|
|
987 | ||
|
|
988 | ncol = 1 | |
|
|
989 | nrow = self.nplots | |
|
|
990 | ||
|
|
991 | return nrow, ncol | |
|
|
992 | ||
|
|
993 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): | |
|
|
994 | ||
|
|
995 | self.__showprofile = showprofile | |
|
|
996 | self.nplots = nplots | |
|
|
997 | ||
|
|
998 | ncolspan = 1 | |
|
|
999 | colspan = 1 | |
|
|
1000 | ||
|
|
1001 | self.createFigure(id = id, | |
|
|
1002 | wintitle = wintitle, | |
|
|
1003 | widthplot = self.WIDTH + self.WIDTHPROF, | |
|
|
1004 | heightplot = self.HEIGHT + self.HEIGHTPROF, | |
|
|
1005 | show=show) | |
|
|
1006 | ||
|
|
1007 | nrow, ncol = self.getSubplots() | |
|
|
1008 | ||
|
|
1009 | counter = 0 | |
|
|
1010 | for y in range(nrow): | |
|
|
1011 | if counter >= self.nplots: | |
|
|
1012 | break | |
|
|
1013 | ||
|
|
1014 | self.addAxes(nrow, ncol*ncolspan, y, 0, colspan, 1) | |
|
|
1015 | counter += 1 | |
|
|
1016 | ||
|
|
1017 | def run(self, dataOut, id, wintitle="", channelList=None, | |
|
|
1018 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, | |
|
|
1019 | zmaxVertical = None, zminVertical = None, zmaxZonal = None, zminZonal = None, | |
|
|
1020 | timerange=None, SNRthresh = -numpy.inf, SNRmin = None, SNRmax = None, SNR_1 = False, | |
|
|
1021 | save=False, figpath='', lastone=0,figfile=None, ftp=False, wr_period=1, show=True, | |
|
|
1022 | server=None, folder=None, username=None, password=None, | |
|
|
1023 | ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): | |
|
|
1024 | """ | |
|
|
1025 | ||
|
|
1026 | Input: | |
|
|
1027 | dataOut : | |
|
|
1028 | id : | |
|
|
1029 | wintitle : | |
|
|
1030 | channelList : | |
|
|
1031 | showProfile : | |
|
|
1032 | xmin : None, | |
|
|
1033 | xmax : None, | |
|
|
1034 | ymin : None, | |
|
|
1035 | ymax : None, | |
|
|
1036 | zmin : None, | |
|
|
1037 | zmax : None | |
|
|
1038 | """ | |
|
|
1039 | ||
|
|
1040 | if channelList == None: | |
|
|
1041 | channelIndexList = dataOut.channelIndexList | |
|
|
1042 | else: | |
|
|
1043 | channelIndexList = [] | |
|
|
1044 | for channel in channelList: | |
|
|
1045 | if channel not in dataOut.channelList: | |
|
|
1046 | raise ValueError, "Channel %d is not in dataOut.channelList" | |
|
|
1047 | channelIndexList.append(dataOut.channelList.index(channel)) | |
|
|
1048 | ||
|
|
1049 | if timerange != None: | |
|
|
1050 | self.timerange = timerange | |
|
|
1051 | ||
|
|
1052 | tmin = None | |
|
|
1053 | tmax = None | |
|
|
1054 | ||
|
|
1055 | x = dataOut.getTimeRange1() | |
|
|
1056 | # y = dataOut.heightRange | |
|
|
1057 | y = dataOut.heightList | |
|
|
1058 | ||
|
|
1059 | z = dataOut.data_output | |
|
|
1060 | nplots = z.shape[0] #Number of wind dimensions estimated | |
|
|
1061 | nplotsw = nplots | |
|
|
1062 | ||
|
|
1063 | #If there is a SNR function defined | |
|
|
1064 | if dataOut.SNR != None: | |
|
|
1065 | nplots += 1 | |
|
|
1066 | SNR = dataOut.SNR | |
|
|
1067 | ||
|
|
1068 | if SNR_1: | |
|
|
1069 | SNR += 1 | |
|
|
1070 | ||
|
|
1071 | SNRavg = numpy.average(SNR, axis=0) | |
|
|
1072 | ||
|
|
1073 | SNRdB = 10*numpy.log10(SNR) | |
|
|
1074 | SNRavgdB = 10*numpy.log10(SNRavg) | |
|
|
1075 | ||
|
|
1076 | ind = numpy.where(SNRavg < 10**(SNRthresh/10))[0] | |
|
|
1077 | ||
|
|
1078 | for i in range(nplotsw): | |
|
|
1079 | z[i,ind] = numpy.nan | |
|
|
1080 | ||
|
|
1081 | ||
|
|
1082 | showprofile = False | |
|
|
1083 | # thisDatetime = dataOut.datatime | |
|
|
1084 | thisDatetime = datetime.datetime.utcfromtimestamp(dataOut.getTimeRange()[1]) | |
|
|
1085 | title = wintitle + " EW Drifts" | |
|
|
1086 | xlabel = "" | |
|
|
1087 | ylabel = "Height (Km)" | |
|
|
1088 | ||
|
|
1089 | if not self.__isConfig: | |
|
|
1090 | ||
|
|
1091 | self.setup(id=id, | |
|
|
1092 | nplots=nplots, | |
|
|
1093 | wintitle=wintitle, | |
|
|
1094 | showprofile=showprofile, | |
|
|
1095 | show=show) | |
|
|
1096 | ||
|
|
1097 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) | |
|
|
1098 | ||
|
|
1099 | if ymin == None: ymin = numpy.nanmin(y) | |
|
|
1100 | if ymax == None: ymax = numpy.nanmax(y) | |
|
|
1101 | ||
|
|
1102 | if zmaxZonal == None: zmaxZonal = numpy.nanmax(abs(z[0,:])) | |
|
|
1103 | if zminZonal == None: zminZonal = -zmaxZonal | |
|
|
1104 | if zmaxVertical == None: zmaxVertical = numpy.nanmax(abs(z[1,:])) | |
|
|
1105 | if zminVertical == None: zminVertical = -zmaxVertical | |
|
|
1106 | ||
|
|
1107 | if dataOut.SNR != None: | |
|
|
1108 | if SNRmin == None: SNRmin = numpy.nanmin(SNRavgdB) | |
|
|
1109 | if SNRmax == None: SNRmax = numpy.nanmax(SNRavgdB) | |
|
|
1110 | ||
|
|
1111 | self.FTP_WEI = ftp_wei | |
|
|
1112 | self.EXP_CODE = exp_code | |
|
|
1113 | self.SUB_EXP_CODE = sub_exp_code | |
|
|
1114 | self.PLOT_POS = plot_pos | |
|
|
1115 | ||
|
|
1116 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") | |
|
|
1117 | self.__isConfig = True | |
|
|
1118 | ||
|
|
1119 | ||
|
|
1120 | self.setWinTitle(title) | |
|
|
1121 | ||
|
|
1122 | if ((self.xmax - x[1]) < (x[1]-x[0])): | |
|
|
1123 | x[1] = self.xmax | |
|
|
1124 | ||
|
|
1125 | strWind = ['Zonal','Vertical'] | |
|
|
1126 | strCb = 'Velocity (m/s)' | |
|
|
1127 | zmaxVector = [zmaxZonal, zmaxVertical] | |
|
|
1128 | zminVector = [zminZonal, zminVertical] | |
|
|
1129 | ||
|
|
1130 | for i in range(nplotsw): | |
|
|
1131 | ||
|
|
1132 | title = "%s Drifts: %s" %(strWind[i], thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) | |
|
|
1133 | axes = self.axesList[i*self.__nsubplots] | |
|
|
1134 | ||
|
|
1135 | z1 = z[i,:].reshape((1,-1)) | |
|
|
1136 | ||
|
|
1137 | axes.pcolorbuffer(x, y, z1, | |
|
|
1138 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=zminVector[i], zmax=zmaxVector[i], | |
|
|
1139 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, | |
|
|
1140 | ticksize=9, cblabel=strCb, cbsize="1%", colormap="RdBu_r") | |
|
|
1141 | ||
|
|
1142 | if dataOut.SNR != None: | |
|
|
1143 | i += 1 | |
|
|
1144 | if SNR_1: | |
|
|
1145 | title = "Signal Noise Ratio + 1 (SNR+1): %s" %(thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) | |
|
|
1146 | else: | |
|
|
1147 | title = "Signal Noise Ratio (SNR): %s" %(thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) | |
|
|
1148 | axes = self.axesList[i*self.__nsubplots] | |
|
|
1149 | SNRavgdB = SNRavgdB.reshape((1,-1)) | |
|
|
1150 | ||
|
|
1151 | axes.pcolorbuffer(x, y, SNRavgdB, | |
|
|
1152 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, zmin=SNRmin, zmax=SNRmax, | |
|
|
1153 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, | |
|
|
1154 | ticksize=9, cblabel='', cbsize="1%", colormap="jet") | |
|
|
1155 | ||
|
|
1156 | self.draw() | |
|
|
1157 | ||
|
|
1158 | if self.figfile == None: | |
|
|
1159 | str_datetime = thisDatetime.strftime("%Y%m%d_%H%M%S") | |
|
|
1160 | self.figfile = self.getFilename(name = str_datetime) | |
|
|
1161 | ||
|
|
1162 | if figpath != '': | |
|
|
1163 | ||
|
|
1164 | self.counter_imagwr += 1 | |
|
|
1165 | if (self.counter_imagwr>=wr_period): | |
|
|
1166 | # store png plot to local folder | |
|
|
1167 | self.saveFigure(figpath, self.figfile) | |
|
|
1168 | # store png plot to FTP server according to RT-Web format | |
|
|
1169 | name = self.getNameToFtp(thisDatetime, self.FTP_WEI, self.EXP_CODE, self.SUB_EXP_CODE, self.PLOT_CODE, self.PLOT_POS) | |
|
|
1170 | ftp_filename = os.path.join(figpath, name) | |
|
|
1171 | self.saveFigure(figpath, ftp_filename) | |
|
|
1172 | ||
|
|
1173 | self.counter_imagwr = 0 | |
|
|
1174 | ||
|
|
1175 | if x[1] >= self.axesList[0].xmax: | |
|
|
1176 | self.counter_imagwr = wr_period | |
|
|
1177 | self.__isConfig = False | |
|
|
774 | 1178 | self.figfile = None No newline at end of file |
| @@ -1,427 +1,427 | |||
|
|
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("TKAgg") |
|
|
11 | 11 | |
|
|
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=True): |
|
|
34 | 34 | |
|
|
35 | 35 | matplotlib.pyplot.ioff() |
|
|
36 | 36 | if show: |
|
|
37 | 37 | matplotlib.pyplot.show() |
|
|
38 | 38 | |
|
|
39 | 39 | return |
|
|
40 | 40 | |
|
|
41 | 41 | def saveFigure(fig, filename): |
|
|
42 | 42 | |
|
|
43 | 43 | matplotlib.pyplot.ioff() |
|
|
44 | 44 | fig.savefig(filename) |
|
|
45 | 45 | matplotlib.pyplot.ion() |
|
|
46 | 46 | |
|
|
47 | 47 | def setWinTitle(fig, title): |
|
|
48 | 48 | |
|
|
49 | 49 | fig.canvas.manager.set_window_title(title) |
|
|
50 | 50 | |
|
|
51 | 51 | def setTitle(fig, title): |
|
|
52 | 52 | |
|
|
53 | 53 | fig.suptitle(title) |
|
|
54 | 54 | |
|
|
55 | 55 | def createAxes(fig, nrow, ncol, xpos, ypos, colspan, rowspan, polar=False): |
|
|
56 | 56 | |
|
|
57 | 57 | matplotlib.pyplot.ioff() |
|
|
58 | 58 | matplotlib.pyplot.figure(fig.number) |
|
|
59 | 59 | axes = matplotlib.pyplot.subplot2grid((nrow, ncol), |
|
|
60 | 60 | (xpos, ypos), |
|
|
61 | 61 | colspan=colspan, |
|
|
62 | 62 | rowspan=rowspan, |
|
|
63 | 63 | polar=polar) |
|
|
64 | 64 | |
|
|
65 | 65 | matplotlib.pyplot.ion() |
|
|
66 | 66 | return axes |
|
|
67 | 67 | |
|
|
68 | 68 | def setAxesText(ax, text): |
|
|
69 | 69 | |
|
|
70 | 70 | ax.annotate(text, |
|
|
71 | 71 | xy = (.1, .99), |
|
|
72 | 72 | xycoords = 'figure fraction', |
|
|
73 | 73 | horizontalalignment = 'left', |
|
|
74 | 74 | verticalalignment = 'top', |
|
|
75 | 75 | fontsize = 10) |
|
|
76 | 76 | |
|
|
77 | 77 | def printLabels(ax, xlabel, ylabel, title): |
|
|
78 | 78 | |
|
|
79 | 79 | ax.set_xlabel(xlabel, size=11) |
|
|
80 | 80 | ax.set_ylabel(ylabel, size=11) |
|
|
81 | 81 | ax.set_title(title, size=8) |
|
|
82 | 82 | |
|
|
83 | 83 | def createPline(ax, x, y, xmin, xmax, ymin, ymax, xlabel='', ylabel='', title='', |
|
|
84 | 84 | ticksize=9, xtick_visible=True, ytick_visible=True, |
|
|
85 | 85 | nxticks=4, nyticks=10, |
|
|
86 | 86 | grid=None,color='blue'): |
|
|
87 | 87 | |
|
|
88 | 88 | """ |
|
|
89 | 89 | |
|
|
90 | 90 | Input: |
|
|
91 | 91 | grid : None, 'both', 'x', 'y' |
|
|
92 | 92 | """ |
|
|
93 | 93 | |
|
|
94 | 94 | matplotlib.pyplot.ioff() |
|
|
95 | 95 | |
|
|
96 | 96 | ax.set_xlim([xmin,xmax]) |
|
|
97 | 97 | ax.set_ylim([ymin,ymax]) |
|
|
98 | 98 | |
|
|
99 | 99 | printLabels(ax, xlabel, ylabel, title) |
|
|
100 | 100 | |
|
|
101 | 101 | ###################################################### |
|
|
102 | 102 | if (xmax-xmin)<=1: |
|
|
103 | 103 | xtickspos = numpy.linspace(xmin,xmax,nxticks) |
|
|
104 | 104 | xtickspos = numpy.array([float("%.1f"%i) for i in xtickspos]) |
|
|
105 | 105 | ax.set_xticks(xtickspos) |
|
|
106 | 106 | else: |
|
|
107 | 107 | xtickspos = numpy.arange(nxticks)*int((xmax-xmin)/(nxticks)) + int(xmin) |
|
|
108 | 108 | # xtickspos = numpy.arange(nxticks)*float(xmax-xmin)/float(nxticks) + int(xmin) |
|
|
109 | 109 | ax.set_xticks(xtickspos) |
|
|
110 | 110 | |
|
|
111 | 111 | for tick in ax.get_xticklabels(): |
|
|
112 | 112 | tick.set_visible(xtick_visible) |
|
|
113 | 113 | |
|
|
114 | 114 | for tick in ax.xaxis.get_major_ticks(): |
|
|
115 | 115 | tick.label.set_fontsize(ticksize) |
|
|
116 | 116 | |
|
|
117 | 117 | ###################################################### |
|
|
118 | 118 | for tick in ax.get_yticklabels(): |
|
|
119 | 119 | tick.set_visible(ytick_visible) |
|
|
120 | 120 | |
|
|
121 | 121 | for tick in ax.yaxis.get_major_ticks(): |
|
|
122 | 122 | tick.label.set_fontsize(ticksize) |
|
|
123 | 123 | |
|
|
124 | 124 | ax.plot(x, y, color=color) |
|
|
125 | 125 | iplot = ax.lines[-1] |
|
|
126 | 126 | |
|
|
127 | 127 | ###################################################### |
|
|
128 | 128 | if '0.' in matplotlib.__version__[0:2]: |
|
|
129 | 129 | print "The matplotlib version has to be updated to 1.1 or newer" |
|
|
130 | 130 | return iplot |
|
|
131 | 131 | |
|
|
132 | 132 | if '1.0.' in matplotlib.__version__[0:4]: |
|
|
133 | 133 | print "The matplotlib version has to be updated to 1.1 or newer" |
|
|
134 | 134 | return iplot |
|
|
135 | 135 | |
|
|
136 | 136 | if grid != None: |
|
|
137 | 137 | ax.grid(b=True, which='major', axis=grid) |
|
|
138 | 138 | |
|
|
139 | 139 | matplotlib.pyplot.tight_layout() |
|
|
140 | 140 | |
|
|
141 | 141 | matplotlib.pyplot.ion() |
|
|
142 | 142 | |
|
|
143 | 143 | return iplot |
|
|
144 | 144 | |
|
|
145 | 145 | def set_linedata(ax, x, y, idline): |
|
|
146 | 146 | |
|
|
147 | 147 | ax.lines[idline].set_data(x,y) |
|
|
148 | 148 | |
|
|
149 | 149 | def pline(iplot, x, y, xlabel='', ylabel='', title=''): |
|
|
150 | 150 | |
|
|
151 | 151 | ax = iplot.get_axes() |
|
|
152 | 152 | |
|
|
153 | 153 | printLabels(ax, xlabel, ylabel, title) |
|
|
154 | 154 | |
|
|
155 | 155 | set_linedata(ax, x, y, idline=0) |
|
|
156 | 156 | |
|
|
157 | 157 | def addpline(ax, x, y, color, linestyle, lw): |
|
|
158 | 158 | |
|
|
159 | 159 | ax.plot(x,y,color=color,linestyle=linestyle,lw=lw) |
|
|
160 | 160 | |
|
|
161 | 161 | |
|
|
162 | 162 | def createPcolor(ax, x, y, z, xmin, xmax, ymin, ymax, zmin, zmax, |
|
|
163 | 163 | xlabel='', ylabel='', title='', ticksize = 9, |
|
|
164 | 164 | colormap='jet',cblabel='', cbsize="5%", |
|
|
165 | 165 | XAxisAsTime=False): |
|
|
166 | 166 | |
|
|
167 | 167 | matplotlib.pyplot.ioff() |
|
|
168 | 168 | |
|
|
169 | 169 | divider = make_axes_locatable(ax) |
|
|
170 | 170 | ax_cb = divider.new_horizontal(size=cbsize, pad=0.05) |
|
|
171 | 171 | fig = ax.get_figure() |
|
|
172 | 172 | fig.add_axes(ax_cb) |
|
|
173 | 173 | |
|
|
174 | 174 | ax.set_xlim([xmin,xmax]) |
|
|
175 | 175 | ax.set_ylim([ymin,ymax]) |
|
|
176 | 176 | |
|
|
177 | 177 | printLabels(ax, xlabel, ylabel, title) |
|
|
178 | 178 | |
|
|
179 | 179 | imesh = ax.pcolormesh(x,y,z.T, vmin=zmin, vmax=zmax, cmap=matplotlib.pyplot.get_cmap(colormap)) |
|
|
180 | 180 | cb = matplotlib.pyplot.colorbar(imesh, cax=ax_cb) |
|
|
181 | 181 | cb.set_label(cblabel) |
|
|
182 | 182 | |
|
|
183 | 183 | # for tl in ax_cb.get_yticklabels(): |
|
|
184 | 184 | # tl.set_visible(True) |
|
|
185 | 185 | |
|
|
186 | 186 | for tick in ax.yaxis.get_major_ticks(): |
|
|
187 | 187 | tick.label.set_fontsize(ticksize) |
|
|
188 | 188 | |
|
|
189 | 189 | for tick in ax.xaxis.get_major_ticks(): |
|
|
190 | 190 | tick.label.set_fontsize(ticksize) |
|
|
191 | 191 | |
|
|
192 | 192 | for tick in cb.ax.get_yticklabels(): |
|
|
193 | 193 | tick.set_fontsize(ticksize) |
|
|
194 | 194 | |
|
|
195 | 195 | ax_cb.yaxis.tick_right() |
|
|
196 | 196 | |
|
|
197 | 197 | if '0.' in matplotlib.__version__[0:2]: |
|
|
198 | 198 | print "The matplotlib version has to be updated to 1.1 or newer" |
|
|
199 | 199 | return imesh |
|
|
200 | 200 | |
|
|
201 | 201 | if '1.0.' in matplotlib.__version__[0:4]: |
|
|
202 | 202 | print "The matplotlib version has to be updated to 1.1 or newer" |
|
|
203 | 203 | return imesh |
|
|
204 | 204 | |
|
|
205 | 205 | matplotlib.pyplot.tight_layout() |
|
|
206 | 206 | |
|
|
207 | 207 | if XAxisAsTime: |
|
|
208 | 208 | |
|
|
209 | 209 | func = lambda x, pos: ('%s') %(datetime.datetime.utcfromtimestamp(x).strftime("%H:%M:%S")) |
|
|
210 | 210 | ax.xaxis.set_major_formatter(FuncFormatter(func)) |
|
|
211 | 211 | ax.xaxis.set_major_locator(LinearLocator(7)) |
|
|
212 | 212 | |
|
|
213 | 213 | matplotlib.pyplot.ion() |
|
|
214 | 214 | return imesh |
|
|
215 | 215 | |
|
|
216 | 216 | def pcolor(imesh, z, xlabel='', ylabel='', title=''): |
|
|
217 | 217 | |
|
|
218 | 218 | z = z.T |
|
|
219 | 219 | |
|
|
220 | 220 | ax = imesh.get_axes() |
|
|
221 | 221 | |
|
|
222 | 222 | printLabels(ax, xlabel, ylabel, title) |
|
|
223 | 223 | |
|
|
224 | 224 | imesh.set_array(z.ravel()) |
|
|
225 | 225 | |
|
|
226 | 226 | def addpcolor(ax, x, y, z, zmin, zmax, xlabel='', ylabel='', title='', colormap='jet'): |
|
|
227 | 227 | |
|
|
228 | 228 | printLabels(ax, xlabel, ylabel, title) |
|
|
229 | 229 | |
|
|
230 | 230 | ax.pcolormesh(x,y,z.T,vmin=zmin,vmax=zmax, cmap=matplotlib.pyplot.get_cmap(colormap)) |
|
|
231 | 231 | |
|
|
232 | 232 | def addpcolorbuffer(ax, x, y, z, zmin, zmax, xlabel='', ylabel='', title='', colormap='jet'): |
|
|
233 | 233 | |
|
|
234 | 234 | printLabels(ax, xlabel, ylabel, title) |
|
|
235 | 235 | |
|
|
236 | 236 | ax.collections.remove(ax.collections[0]) |
|
|
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 createPmultiline(ax, x, y, xmin, xmax, ymin, ymax, xlabel='', ylabel='', title='', legendlabels=None, |
|
|
241 | 241 | ticksize=9, xtick_visible=True, ytick_visible=True, |
|
|
242 | 242 | nxticks=4, nyticks=10, |
|
|
243 | 243 | grid=None): |
|
|
244 | 244 | |
|
|
245 | 245 | """ |
|
|
246 | 246 | |
|
|
247 | 247 | Input: |
|
|
248 | 248 | grid : None, 'both', 'x', 'y' |
|
|
249 | 249 | """ |
|
|
250 | 250 | |
|
|
251 | 251 | matplotlib.pyplot.ioff() |
|
|
252 | 252 | |
|
|
253 | 253 | lines = ax.plot(x.T, y) |
|
|
254 | 254 | leg = ax.legend(lines, legendlabels, loc='upper right') |
|
|
255 | 255 | leg.get_frame().set_alpha(0.5) |
|
|
256 | 256 | ax.set_xlim([xmin,xmax]) |
|
|
257 | 257 | ax.set_ylim([ymin,ymax]) |
|
|
258 | 258 | printLabels(ax, xlabel, ylabel, title) |
|
|
259 | 259 | |
|
|
260 | 260 | xtickspos = numpy.arange(nxticks)*int((xmax-xmin)/(nxticks)) + int(xmin) |
|
|
261 | 261 | ax.set_xticks(xtickspos) |
|
|
262 | 262 | |
|
|
263 | 263 | for tick in ax.get_xticklabels(): |
|
|
264 | 264 | tick.set_visible(xtick_visible) |
|
|
265 | 265 | |
|
|
266 | 266 | for tick in ax.xaxis.get_major_ticks(): |
|
|
267 | 267 | tick.label.set_fontsize(ticksize) |
|
|
268 | 268 | |
|
|
269 | 269 | for tick in ax.get_yticklabels(): |
|
|
270 | 270 | tick.set_visible(ytick_visible) |
|
|
271 | 271 | |
|
|
272 | 272 | for tick in ax.yaxis.get_major_ticks(): |
|
|
273 | 273 | tick.label.set_fontsize(ticksize) |
|
|
274 | 274 | |
|
|
275 | 275 | iplot = ax.lines[-1] |
|
|
276 | 276 | |
|
|
277 | 277 | if '0.' in matplotlib.__version__[0:2]: |
|
|
278 | 278 | print "The matplotlib version has to be updated to 1.1 or newer" |
|
|
279 | 279 | return iplot |
|
|
280 | 280 | |
|
|
281 | 281 | if '1.0.' in matplotlib.__version__[0:4]: |
|
|
282 | 282 | print "The matplotlib version has to be updated to 1.1 or newer" |
|
|
283 | 283 | return iplot |
|
|
284 | 284 | |
|
|
285 | 285 | if grid != None: |
|
|
286 | 286 | ax.grid(b=True, which='major', axis=grid) |
|
|
287 | 287 | |
|
|
288 | 288 | matplotlib.pyplot.tight_layout() |
|
|
289 | 289 | |
|
|
290 | 290 | matplotlib.pyplot.ion() |
|
|
291 | 291 | |
|
|
292 | 292 | return iplot |
|
|
293 | 293 | |
|
|
294 | 294 | |
|
|
295 | 295 | def pmultiline(iplot, x, y, xlabel='', ylabel='', title=''): |
|
|
296 | 296 | |
|
|
297 | 297 | ax = iplot.get_axes() |
|
|
298 | 298 | |
|
|
299 | 299 | printLabels(ax, xlabel, ylabel, title) |
|
|
300 | 300 | |
|
|
301 | 301 | for i in range(len(ax.lines)): |
|
|
302 | 302 | line = ax.lines[i] |
|
|
303 | 303 | line.set_data(x[i,:],y) |
|
|
304 | 304 | |
|
|
305 | 305 | def createPmultilineYAxis(ax, x, y, xmin, xmax, ymin, ymax, xlabel='', ylabel='', title='', legendlabels=None, |
|
|
306 | 306 | ticksize=9, xtick_visible=True, ytick_visible=True, |
|
|
307 | 307 | nxticks=4, nyticks=10, marker='.', markersize=10, linestyle="None", |
|
|
308 | 308 | grid=None, XAxisAsTime=False): |
|
|
309 | 309 | |
|
|
310 | 310 | """ |
|
|
311 | 311 | |
|
|
312 | 312 | Input: |
|
|
313 | 313 | grid : None, 'both', 'x', 'y' |
|
|
314 | 314 | """ |
|
|
315 | 315 | |
|
|
316 | 316 | matplotlib.pyplot.ioff() |
|
|
317 | 317 | |
|
|
318 | 318 | # lines = ax.plot(x, y.T, marker=marker,markersize=markersize,linestyle=linestyle) |
|
|
319 |
lines = ax.plot(x, y.T, linestyle= |
|
|
|
319 | lines = ax.plot(x, y.T, linestyle=linestyle, marker=marker, markersize=markersize) | |
|
|
320 | 320 | leg = ax.legend(lines, legendlabels, loc='upper left', bbox_to_anchor=(1.01, 1.00), numpoints=1, handlelength=1.5, \ |
|
|
321 | 321 | handletextpad=0.5, borderpad=0.5, labelspacing=0.5, borderaxespad=0.) |
|
|
322 | 322 | |
|
|
323 | 323 | for label in leg.get_texts(): label.set_fontsize(9) |
|
|
324 | 324 | |
|
|
325 | 325 | ax.set_xlim([xmin,xmax]) |
|
|
326 | 326 | ax.set_ylim([ymin,ymax]) |
|
|
327 | 327 | printLabels(ax, xlabel, ylabel, title) |
|
|
328 | 328 | |
|
|
329 | 329 | # xtickspos = numpy.arange(nxticks)*int((xmax-xmin)/(nxticks)) + int(xmin) |
|
|
330 | 330 | # ax.set_xticks(xtickspos) |
|
|
331 | 331 | |
|
|
332 | 332 | for tick in ax.get_xticklabels(): |
|
|
333 | 333 | tick.set_visible(xtick_visible) |
|
|
334 | 334 | |
|
|
335 | 335 | for tick in ax.xaxis.get_major_ticks(): |
|
|
336 | 336 | tick.label.set_fontsize(ticksize) |
|
|
337 | 337 | |
|
|
338 | 338 | for tick in ax.get_yticklabels(): |
|
|
339 | 339 | tick.set_visible(ytick_visible) |
|
|
340 | 340 | |
|
|
341 | 341 | for tick in ax.yaxis.get_major_ticks(): |
|
|
342 | 342 | tick.label.set_fontsize(ticksize) |
|
|
343 | 343 | |
|
|
344 | 344 | iplot = ax.lines[-1] |
|
|
345 | 345 | |
|
|
346 | 346 | if '0.' in matplotlib.__version__[0:2]: |
|
|
347 | 347 | print "The matplotlib version has to be updated to 1.1 or newer" |
|
|
348 | 348 | return iplot |
|
|
349 | 349 | |
|
|
350 | 350 | if '1.0.' in matplotlib.__version__[0:4]: |
|
|
351 | 351 | print "The matplotlib version has to be updated to 1.1 or newer" |
|
|
352 | 352 | return iplot |
|
|
353 | 353 | |
|
|
354 | 354 | if grid != None: |
|
|
355 | 355 | ax.grid(b=True, which='major', axis=grid) |
|
|
356 | 356 | |
|
|
357 | 357 | matplotlib.pyplot.tight_layout() |
|
|
358 | 358 | |
|
|
359 | 359 | if XAxisAsTime: |
|
|
360 | 360 | |
|
|
361 | 361 | func = lambda x, pos: ('%s') %(datetime.datetime.utcfromtimestamp(x).strftime("%H:%M:%S")) |
|
|
362 | 362 | ax.xaxis.set_major_formatter(FuncFormatter(func)) |
|
|
363 | 363 | ax.xaxis.set_major_locator(LinearLocator(7)) |
|
|
364 | 364 | |
|
|
365 | 365 | matplotlib.pyplot.ion() |
|
|
366 | 366 | |
|
|
367 | 367 | return iplot |
|
|
368 | 368 | |
|
|
369 | 369 | def pmultilineyaxis(iplot, x, y, xlabel='', ylabel='', title=''): |
|
|
370 | 370 | |
|
|
371 | 371 | ax = iplot.get_axes() |
|
|
372 | 372 | |
|
|
373 | 373 | printLabels(ax, xlabel, ylabel, title) |
|
|
374 | 374 | |
|
|
375 | 375 | for i in range(len(ax.lines)): |
|
|
376 | 376 | line = ax.lines[i] |
|
|
377 | 377 | line.set_data(x,y[i,:]) |
|
|
378 | 378 | |
|
|
379 | 379 | def createPolar(ax, x, y, |
|
|
380 | 380 | xlabel='', ylabel='', title='', ticksize = 9, |
|
|
381 | 381 | colormap='jet',cblabel='', cbsize="5%", |
|
|
382 | 382 | XAxisAsTime=False): |
|
|
383 | 383 | |
|
|
384 | 384 | matplotlib.pyplot.ioff() |
|
|
385 | 385 | |
|
|
386 | 386 | ax.plot(x,y,'bo', markersize=5) |
|
|
387 | 387 | # ax.set_rmax(90) |
|
|
388 | 388 | ax.set_ylim(0,90) |
|
|
389 | 389 | ax.set_yticks(numpy.arange(0,90,20)) |
|
|
390 | 390 | ax.text(0, -110, ylabel, rotation='vertical', va ='center', ha = 'center' ,size='11') |
|
|
391 | 391 | # ax.text(100, 100, 'example', ha='left', va='center', rotation='vertical') |
|
|
392 | 392 | printLabels(ax, xlabel, '', title) |
|
|
393 | 393 | iplot = ax.lines[-1] |
|
|
394 | 394 | |
|
|
395 | 395 | if '0.' in matplotlib.__version__[0:2]: |
|
|
396 | 396 | print "The matplotlib version has to be updated to 1.1 or newer" |
|
|
397 | 397 | return iplot |
|
|
398 | 398 | |
|
|
399 | 399 | if '1.0.' in matplotlib.__version__[0:4]: |
|
|
400 | 400 | print "The matplotlib version has to be updated to 1.1 or newer" |
|
|
401 | 401 | return iplot |
|
|
402 | 402 | |
|
|
403 | 403 | # if grid != None: |
|
|
404 | 404 | # ax.grid(b=True, which='major', axis=grid) |
|
|
405 | 405 | |
|
|
406 | 406 | matplotlib.pyplot.tight_layout() |
|
|
407 | 407 | |
|
|
408 | 408 | matplotlib.pyplot.ion() |
|
|
409 | 409 | |
|
|
410 | 410 | |
|
|
411 | 411 | return iplot |
|
|
412 | 412 | |
|
|
413 | 413 | def polar(iplot, x, y, xlabel='', ylabel='', title=''): |
|
|
414 | 414 | |
|
|
415 | 415 | ax = iplot.get_axes() |
|
|
416 | 416 | |
|
|
417 | 417 | # ax.text(0, -110, ylabel, rotation='vertical', va ='center', ha = 'center',size='11') |
|
|
418 | 418 | printLabels(ax, xlabel, '', title) |
|
|
419 | 419 | |
|
|
420 | 420 | set_linedata(ax, x, y, idline=0) |
|
|
421 | 421 | |
|
|
422 | 422 | def draw(fig): |
|
|
423 | 423 | |
|
|
424 | 424 | if type(fig) == 'int': |
|
|
425 | 425 | raise ValueError, "This parameter should be of tpye matplotlib figure" |
|
|
426 | 426 | |
|
|
427 | 427 | fig.canvas.draw() |
| @@ -1,1539 +1,1749 | |||
|
|
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 | import importlib | |
|
|
12 | import itertools | |
|
|
11 | 13 | |
|
|
12 | 14 | from jroproc_base import ProcessingUnit, Operation |
|
|
13 | 15 | from model.data.jrodata import Parameters |
|
|
14 | 16 | |
|
|
15 | 17 | |
|
|
16 | 18 | class ParametersProc(ProcessingUnit): |
|
|
17 | 19 | |
|
|
18 | 20 | nSeconds = None |
|
|
19 | 21 | |
|
|
20 | 22 | def __init__(self): |
|
|
21 | 23 | ProcessingUnit.__init__(self) |
|
|
22 | 24 | |
|
|
23 | 25 | self.objectDict = {} |
|
|
24 | 26 | self.buffer = None |
|
|
25 | 27 | self.firstdatatime = None |
|
|
26 | 28 | self.profIndex = 0 |
|
|
27 | 29 | self.dataOut = Parameters() |
|
|
28 | 30 | |
|
|
29 | 31 | def __updateObjFromInput(self): |
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|
30 | 32 | |
|
|
31 | 33 | self.dataOut.inputUnit = self.dataIn.type |
|
|
32 | 34 | |
|
|
33 | 35 | self.dataOut.timeZone = self.dataIn.timeZone |
|
|
34 | 36 | self.dataOut.dstFlag = self.dataIn.dstFlag |
|
|
35 | 37 | self.dataOut.errorCount = self.dataIn.errorCount |
|
|
36 | 38 | self.dataOut.useLocalTime = self.dataIn.useLocalTime |
|
|
37 | 39 | |
|
|
38 | 40 | self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy() |
|
|
39 | 41 | self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy() |
|
|
40 | 42 | self.dataOut.channelList = self.dataIn.channelList |
|
|
41 | 43 | self.dataOut.heightList = self.dataIn.heightList |
|
|
42 | 44 | self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')]) |
|
|
43 | 45 | # self.dataOut.nHeights = self.dataIn.nHeights |
|
|
44 | 46 | # self.dataOut.nChannels = self.dataIn.nChannels |
|
|
45 | 47 | self.dataOut.nBaud = self.dataIn.nBaud |
|
|
46 | 48 | self.dataOut.nCode = self.dataIn.nCode |
|
|
47 | 49 | self.dataOut.code = self.dataIn.code |
|
|
48 | 50 | # self.dataOut.nProfiles = self.dataOut.nFFTPoints |
|
|
49 | 51 | self.dataOut.flagTimeBlock = self.dataIn.flagTimeBlock |
|
|
50 | 52 | self.dataOut.utctime = self.firstdatatime |
|
|
51 | 53 | self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada |
|
|
52 | 54 | self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip |
|
|
53 | 55 | # self.dataOut.nCohInt = self.dataIn.nCohInt |
|
|
54 | 56 | # self.dataOut.nIncohInt = 1 |
|
|
55 | 57 | self.dataOut.ippSeconds = self.dataIn.ippSeconds |
|
|
56 | 58 | # self.dataOut.windowOfFilter = self.dataIn.windowOfFilter |
|
|
57 | 59 | self.dataOut.timeInterval = self.dataIn.timeInterval |
|
|
58 | 60 | self.dataOut.heightRange = self.dataIn.getHeiRange() |
|
|
59 | 61 | self.dataOut.frequency = self.dataIn.frequency |
|
|
60 | 62 | |
|
|
61 | 63 | def run(self, nSeconds = None, nProfiles = None): |
|
|
62 | 64 | |
|
|
63 | self.dataOut.flagNoData = True | |
|
|
65 | ||
|
|
64 | 66 | |
|
|
65 | 67 | if self.firstdatatime == None: |
|
|
66 | 68 | self.firstdatatime = self.dataIn.utctime |
|
|
67 | 69 | |
|
|
68 | 70 | #---------------------- Voltage Data --------------------------- |
|
|
69 | 71 | |
|
|
70 | 72 | if self.dataIn.type == "Voltage": |
|
|
73 | self.dataOut.flagNoData = True | |
|
|
71 | 74 | if nSeconds != None: |
|
|
72 | 75 | self.nSeconds = nSeconds |
|
|
73 | 76 | self.nProfiles= int(numpy.floor(nSeconds/(self.dataIn.ippSeconds*self.dataIn.nCohInt))) |
|
|
74 | 77 | |
|
|
75 | 78 | if self.buffer == None: |
|
|
76 | 79 | self.buffer = numpy.zeros((self.dataIn.nChannels, |
|
|
77 | 80 | self.nProfiles, |
|
|
78 | 81 | self.dataIn.nHeights), |
|
|
79 | 82 | dtype='complex') |
|
|
80 | 83 | |
|
|
81 | 84 | self.buffer[:,self.profIndex,:] = self.dataIn.data.copy() |
|
|
82 | 85 | self.profIndex += 1 |
|
|
83 | 86 | |
|
|
84 | 87 | if self.profIndex == self.nProfiles: |
|
|
85 | 88 | |
|
|
86 | 89 | self.__updateObjFromInput() |
|
|
87 | 90 | self.dataOut.data_pre = self.buffer.copy() |
|
|
88 | 91 | self.dataOut.paramInterval = nSeconds |
|
|
89 | 92 | self.dataOut.flagNoData = False |
|
|
90 | 93 | |
|
|
91 | 94 | self.buffer = None |
|
|
92 | 95 | self.firstdatatime = None |
|
|
93 | 96 | self.profIndex = 0 |
|
|
94 | 97 | return |
|
|
95 | 98 | |
|
|
96 | 99 | #---------------------- Spectra Data --------------------------- |
|
|
97 | 100 | |
|
|
98 | 101 | if self.dataIn.type == "Spectra": |
|
|
99 | 102 | self.dataOut.data_pre = self.dataIn.data_spc.copy() |
|
|
100 | 103 | self.dataOut.abscissaRange = self.dataIn.getVelRange(1) |
|
|
101 | 104 | self.dataOut.noise = self.dataIn.getNoise() |
|
|
102 | 105 | self.dataOut.normFactor = self.dataIn.normFactor |
|
|
106 | self.dataOut.flagNoData = False | |
|
|
103 | 107 | |
|
|
104 | 108 | #---------------------- Correlation Data --------------------------- |
|
|
105 | 109 | |
|
|
106 | 110 | if self.dataIn.type == "Correlation": |
|
|
107 | 111 | lagRRange = self.dataIn.lagR |
|
|
108 | 112 | indR = numpy.where(lagRRange == 0)[0][0] |
|
|
109 | 113 | |
|
|
110 | 114 | self.dataOut.data_pre = self.dataIn.data_corr.copy()[:,:,indR,:] |
|
|
111 | 115 | self.dataOut.abscissaRange = self.dataIn.getLagTRange(1) |
|
|
112 | 116 | self.dataOut.noise = self.dataIn.noise |
|
|
113 | 117 | self.dataOut.normFactor = self.dataIn.normFactor |
|
|
114 | 118 | self.dataOut.SNR = self.dataIn.SNR |
|
|
115 |
self.dataOut. |
|
|
|
119 | self.dataOut.groupList = self.dataIn.pairsList | |
|
|
120 | self.dataOut.flagNoData = False | |
|
|
116 | 121 | |
|
|
117 | 122 | |
|
|
118 | 123 | self.__updateObjFromInput() |
|
|
119 | self.dataOut.flagNoData = False | |
|
|
120 | 124 | self.firstdatatime = None |
|
|
121 | 125 | self.dataOut.initUtcTime = self.dataIn.ltctime |
|
|
122 |
self.dataOut. |
|
|
|
126 | self.dataOut.outputInterval = self.dataIn.timeInterval | |
|
|
123 | 127 | |
|
|
124 | 128 | #------------------- Get Moments ---------------------------------- |
|
|
125 | 129 | def GetMoments(self, channelList = None): |
|
|
126 | 130 | ''' |
|
|
127 | 131 | Function GetMoments() |
|
|
128 | 132 | |
|
|
129 | 133 | Input: |
|
|
130 | 134 | channelList : simple channel list to select e.g. [2,3,7] |
|
|
131 | 135 | self.dataOut.data_pre |
|
|
132 | 136 | self.dataOut.abscissaRange |
|
|
133 | 137 | self.dataOut.noise |
|
|
134 | 138 | |
|
|
135 | 139 | Affected: |
|
|
136 | 140 | self.dataOut.data_param |
|
|
137 | 141 | self.dataOut.SNR |
|
|
138 | 142 | |
|
|
139 | 143 | ''' |
|
|
140 | 144 | data = self.dataOut.data_pre |
|
|
141 | 145 | absc = self.dataOut.abscissaRange[:-1] |
|
|
142 | 146 | noise = self.dataOut.noise |
|
|
143 | 147 | |
|
|
144 | 148 | data_param = numpy.zeros((data.shape[0], 4, data.shape[2])) |
|
|
145 | 149 | |
|
|
146 | 150 | if channelList== None: |
|
|
147 | 151 | channelList = self.dataIn.channelList |
|
|
148 | 152 | self.dataOut.channelList = channelList |
|
|
149 | 153 | |
|
|
150 | 154 | for ind in channelList: |
|
|
151 | 155 | data_param[ind,:,:] = self.__calculateMoments(data[ind,:,:], absc, noise[ind]) |
|
|
152 | 156 | |
|
|
153 | 157 | self.dataOut.data_param = data_param[:,1:,:] |
|
|
154 | 158 | self.dataOut.SNR = data_param[:,0] |
|
|
155 | 159 | return |
|
|
156 | 160 | |
|
|
157 | 161 | 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): |
|
|
158 | 162 | |
|
|
159 | 163 | if (nicoh == None): nicoh = 1 |
|
|
160 | 164 | if (graph == None): graph = 0 |
|
|
161 | 165 | if (smooth == None): smooth = 0 |
|
|
162 | 166 | elif (self.smooth < 3): smooth = 0 |
|
|
163 | 167 | |
|
|
164 | 168 | if (type1 == None): type1 = 0 |
|
|
165 | 169 | if (fwindow == None): fwindow = numpy.zeros(oldfreq.size) + 1 |
|
|
166 | 170 | if (snrth == None): snrth = -3 |
|
|
167 | 171 | if (dc == None): dc = 0 |
|
|
168 | 172 | if (aliasing == None): aliasing = 0 |
|
|
169 | 173 | if (oldfd == None): oldfd = 0 |
|
|
170 | 174 | if (wwauto == None): wwauto = 0 |
|
|
171 | 175 | |
|
|
172 | 176 | if (n0 < 1.e-20): n0 = 1.e-20 |
|
|
173 | 177 | |
|
|
174 | 178 | freq = oldfreq |
|
|
175 | 179 | vec_power = numpy.zeros(oldspec.shape[1]) |
|
|
176 | 180 | vec_fd = numpy.zeros(oldspec.shape[1]) |
|
|
177 | 181 | vec_w = numpy.zeros(oldspec.shape[1]) |
|
|
178 | 182 | vec_snr = numpy.zeros(oldspec.shape[1]) |
|
|
179 | 183 | |
|
|
180 | 184 | for ind in range(oldspec.shape[1]): |
|
|
181 | 185 | |
|
|
182 | 186 | spec = oldspec[:,ind] |
|
|
183 | 187 | aux = spec*fwindow |
|
|
184 | 188 | max_spec = aux.max() |
|
|
185 | 189 | m = list(aux).index(max_spec) |
|
|
186 | 190 | |
|
|
187 | 191 | #Smooth |
|
|
188 | 192 | if (smooth == 0): spec2 = spec |
|
|
189 | 193 | else: spec2 = scipy.ndimage.filters.uniform_filter1d(spec,size=smooth) |
|
|
190 | 194 | |
|
|
191 | 195 | # Calculo de Momentos |
|
|
192 | 196 | bb = spec2[range(m,spec2.size)] |
|
|
193 | 197 | bb = (bb<n0).nonzero() |
|
|
194 | 198 | bb = bb[0] |
|
|
195 | 199 | |
|
|
196 | 200 | ss = spec2[range(0,m + 1)] |
|
|
197 | 201 | ss = (ss<n0).nonzero() |
|
|
198 | 202 | ss = ss[0] |
|
|
199 | 203 | |
|
|
200 | 204 | if (bb.size == 0): |
|
|
201 | 205 | bb0 = spec.size - 1 - m |
|
|
202 | 206 | else: |
|
|
203 | 207 | bb0 = bb[0] - 1 |
|
|
204 | 208 | if (bb0 < 0): |
|
|
205 | 209 | bb0 = 0 |
|
|
206 | 210 | |
|
|
207 | 211 | if (ss.size == 0): ss1 = 1 |
|
|
208 | 212 | else: ss1 = max(ss) + 1 |
|
|
209 | 213 | |
|
|
210 | 214 | if (ss1 > m): ss1 = m |
|
|
211 | 215 | |
|
|
212 | 216 | valid = numpy.asarray(range(int(m + bb0 - ss1 + 1))) + ss1 |
|
|
213 | 217 | power = ((spec2[valid] - n0)*fwindow[valid]).sum() |
|
|
214 | 218 | fd = ((spec2[valid]- n0)*freq[valid]*fwindow[valid]).sum()/power |
|
|
215 | 219 | w = math.sqrt(((spec2[valid] - n0)*fwindow[valid]*(freq[valid]- fd)**2).sum()/power) |
|
|
216 | 220 | snr = (spec2.mean()-n0)/n0 |
|
|
217 | 221 | |
|
|
218 | 222 | if (snr < 1.e-20) : |
|
|
219 | 223 | snr = 1.e-20 |
|
|
220 | 224 | |
|
|
221 | 225 | vec_power[ind] = power |
|
|
222 | 226 | vec_fd[ind] = fd |
|
|
223 | 227 | vec_w[ind] = w |
|
|
224 | 228 | vec_snr[ind] = snr |
|
|
225 | 229 | |
|
|
226 | 230 | moments = numpy.vstack((vec_snr, vec_power, vec_fd, vec_w)) |
|
|
227 | 231 | return moments |
|
|
228 | 232 | |
|
|
229 | 233 | #------------------- Get Lags ---------------------------------- |
|
|
230 | 234 | |
|
|
231 | 235 | def GetLags(self): |
|
|
232 | 236 | ''' |
|
|
233 | 237 | Function GetMoments() |
|
|
234 | 238 | |
|
|
235 | 239 | Input: |
|
|
236 | 240 | self.dataOut.data_pre |
|
|
237 | 241 | self.dataOut.abscissaRange |
|
|
238 | 242 | self.dataOut.noise |
|
|
239 | 243 | self.dataOut.normFactor |
|
|
240 | 244 | self.dataOut.SNR |
|
|
241 |
self.dataOut. |
|
|
|
245 | self.dataOut.groupList | |
|
|
242 | 246 | self.dataOut.nChannels |
|
|
243 | 247 | |
|
|
244 | 248 | Affected: |
|
|
245 | 249 | self.dataOut.data_param |
|
|
246 | 250 | |
|
|
247 | 251 | ''' |
|
|
248 | 252 | data = self.dataOut.data_pre |
|
|
249 | 253 | normFactor = self.dataOut.normFactor |
|
|
250 | 254 | nHeights = self.dataOut.nHeights |
|
|
251 | 255 | absc = self.dataOut.abscissaRange[:-1] |
|
|
252 | 256 | noise = self.dataOut.noise |
|
|
253 | 257 | SNR = self.dataOut.SNR |
|
|
254 |
pairsList = self.dataOut. |
|
|
|
258 | pairsList = self.dataOut.groupList | |
|
|
255 | 259 | nChannels = self.dataOut.nChannels |
|
|
256 | 260 | pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairsList, nChannels) |
|
|
257 | 261 | self.dataOut.data_param = numpy.zeros((len(pairsCrossCorr)*2 + 1, nHeights)) |
|
|
258 | 262 | |
|
|
259 | 263 | dataNorm = numpy.abs(data) |
|
|
260 | 264 | for l in range(len(pairsList)): |
|
|
261 | 265 | dataNorm[l,:,:] = dataNorm[l,:,:]/normFactor[l,:] |
|
|
262 | 266 | |
|
|
263 | 267 | self.dataOut.data_param[:-1,:] = self.__calculateTaus(dataNorm, pairsCrossCorr, pairsAutoCorr, absc) |
|
|
264 | 268 | self.dataOut.data_param[-1,:] = self.__calculateLag1Phase(data, pairsAutoCorr, absc) |
|
|
265 | 269 | return |
|
|
266 | 270 | |
|
|
267 | 271 | def __getPairsAutoCorr(self, pairsList, nChannels): |
|
|
268 | 272 | |
|
|
269 | 273 | pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan |
|
|
270 | 274 | |
|
|
271 | 275 | for l in range(len(pairsList)): |
|
|
272 | 276 | firstChannel = pairsList[l][0] |
|
|
273 | 277 | secondChannel = pairsList[l][1] |
|
|
274 | 278 | |
|
|
275 | 279 | #Obteniendo pares de Autocorrelacion |
|
|
276 | 280 | if firstChannel == secondChannel: |
|
|
277 | 281 | pairsAutoCorr[firstChannel] = int(l) |
|
|
278 | 282 | |
|
|
279 | 283 | pairsAutoCorr = pairsAutoCorr.astype(int) |
|
|
280 | 284 | |
|
|
281 | 285 | pairsCrossCorr = range(len(pairsList)) |
|
|
282 | 286 | pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr) |
|
|
283 | 287 | |
|
|
284 | 288 | return pairsAutoCorr, pairsCrossCorr |
|
|
285 | 289 | |
|
|
286 | 290 | def __calculateTaus(self, data, pairsCrossCorr, pairsAutoCorr, lagTRange): |
|
|
287 | 291 | |
|
|
288 | 292 | Pt0 = data.shape[1]/2 |
|
|
289 | 293 | #Funcion de Autocorrelacion |
|
|
290 | 294 | dataAutoCorr = stats.nanmean(data[pairsAutoCorr,:,:], axis = 0) |
|
|
291 | 295 | |
|
|
292 | 296 | #Obtencion Indice de TauCross |
|
|
293 | 297 | indCross = data[pairsCrossCorr,:,:].argmax(axis = 1) |
|
|
294 | 298 | #Obtencion Indice de TauAuto |
|
|
295 | 299 | indAuto = numpy.zeros(indCross.shape,dtype = 'int') |
|
|
296 | 300 | CCValue = data[pairsCrossCorr,Pt0,:] |
|
|
297 | 301 | for i in range(pairsCrossCorr.size): |
|
|
298 | 302 | indAuto[i,:] = numpy.abs(dataAutoCorr - CCValue[i,:]).argmin(axis = 0) |
|
|
299 | 303 | |
|
|
300 | 304 | #Obtencion de TauCross y TauAuto |
|
|
301 | 305 | tauCross = lagTRange[indCross] |
|
|
302 | 306 | tauAuto = lagTRange[indAuto] |
|
|
303 | 307 | |
|
|
304 | 308 | Nan1, Nan2 = numpy.where(tauCross == lagTRange[0]) |
|
|
305 | 309 | |
|
|
306 | 310 | tauCross[Nan1,Nan2] = numpy.nan |
|
|
307 | 311 | tauAuto[Nan1,Nan2] = numpy.nan |
|
|
308 | 312 | tau = numpy.vstack((tauCross,tauAuto)) |
|
|
309 | 313 | |
|
|
310 | 314 | return tau |
|
|
311 | 315 | |
|
|
312 | 316 | def __calculateLag1Phase(self, data, pairs, lagTRange): |
|
|
313 | 317 | data1 = stats.nanmean(data[pairs,:,:], axis = 0) |
|
|
314 | 318 | lag1 = numpy.where(lagTRange == 0)[0][0] + 1 |
|
|
315 | 319 | |
|
|
316 | 320 | phase = numpy.angle(data1[lag1,:]) |
|
|
317 | 321 | |
|
|
318 | 322 | return phase |
|
|
319 | 323 | #------------------- Detect Meteors ------------------------------ |
|
|
320 | 324 | |
|
|
321 | 325 | def DetectMeteors(self, hei_ref = None, tauindex = 0, |
|
|
322 | 326 | predefinedPhaseShifts = None, centerReceiverIndex = 2, |
|
|
323 | 327 | cohDetection = False, cohDet_timeStep = 1, cohDet_thresh = 25, |
|
|
324 | 328 | noise_timeStep = 4, noise_multiple = 4, |
|
|
325 | 329 | multDet_timeLimit = 1, multDet_rangeLimit = 3, |
|
|
326 | 330 | phaseThresh = 20, SNRThresh = 8, |
|
|
327 | 331 | hmin = 70, hmax=110, azimuth = 0) : |
|
|
328 | 332 | |
|
|
329 | 333 | ''' |
|
|
330 | 334 | Function DetectMeteors() |
|
|
331 | 335 | Project developed with paper: |
|
|
332 | 336 | HOLDSWORTH ET AL. 2004 |
|
|
333 | 337 | |
|
|
334 | 338 | Input: |
|
|
335 | 339 | self.dataOut.data_pre |
|
|
336 | 340 | |
|
|
337 | 341 | centerReceiverIndex: From the channels, which is the center receiver |
|
|
338 | 342 | |
|
|
339 | 343 | hei_ref: Height reference for the Beacon signal extraction |
|
|
340 | 344 | tauindex: |
|
|
341 | 345 | predefinedPhaseShifts: Predefined phase offset for the voltge signals |
|
|
342 | 346 | |
|
|
343 | 347 | cohDetection: Whether to user Coherent detection or not |
|
|
344 | 348 | cohDet_timeStep: Coherent Detection calculation time step |
|
|
345 | 349 | cohDet_thresh: Coherent Detection phase threshold to correct phases |
|
|
346 | 350 | |
|
|
347 | 351 | noise_timeStep: Noise calculation time step |
|
|
348 | 352 | noise_multiple: Noise multiple to define signal threshold |
|
|
349 | 353 | |
|
|
350 | 354 | multDet_timeLimit: Multiple Detection Removal time limit in seconds |
|
|
351 | 355 | multDet_rangeLimit: Multiple Detection Removal range limit in km |
|
|
352 | 356 | |
|
|
353 | 357 | phaseThresh: Maximum phase difference between receiver to be consider a meteor |
|
|
354 | 358 | SNRThresh: Minimum SNR threshold of the meteor signal to be consider a meteor |
|
|
355 | 359 | |
|
|
356 | 360 | hmin: Minimum Height of the meteor to use it in the further wind estimations |
|
|
357 | 361 | hmax: Maximum Height of the meteor to use it in the further wind estimations |
|
|
358 | 362 | azimuth: Azimuth angle correction |
|
|
359 | 363 | |
|
|
360 | 364 | Affected: |
|
|
361 | 365 | self.dataOut.data_param |
|
|
362 | 366 | |
|
|
363 | 367 | Rejection Criteria (Errors): |
|
|
364 | 368 | 0: No error; analysis OK |
|
|
365 | 369 | 1: SNR < SNR threshold |
|
|
366 | 370 | 2: angle of arrival (AOA) ambiguously determined |
|
|
367 | 371 | 3: AOA estimate not feasible |
|
|
368 | 372 | 4: Large difference in AOAs obtained from different antenna baselines |
|
|
369 | 373 | 5: echo at start or end of time series |
|
|
370 | 374 | 6: echo less than 5 examples long; too short for analysis |
|
|
371 | 375 | 7: echo rise exceeds 0.3s |
|
|
372 | 376 | 8: echo decay time less than twice rise time |
|
|
373 | 377 | 9: large power level before echo |
|
|
374 | 378 | 10: large power level after echo |
|
|
375 | 379 | 11: poor fit to amplitude for estimation of decay time |
|
|
376 | 380 | 12: poor fit to CCF phase variation for estimation of radial drift velocity |
|
|
377 | 381 | 13: height unresolvable echo: not valid height within 70 to 110 km |
|
|
378 | 382 | 14: height ambiguous echo: more then one possible height within 70 to 110 km |
|
|
379 | 383 | 15: radial drift velocity or projected horizontal velocity exceeds 200 m/s |
|
|
380 | 384 | 16: oscilatory echo, indicating event most likely not an underdense echo |
|
|
381 | 385 | |
|
|
382 | 386 | 17: phase difference in meteor Reestimation |
|
|
383 | 387 | |
|
|
384 | 388 | Data Storage: |
|
|
385 | 389 | Meteors for Wind Estimation (8): |
|
|
386 | 390 | Day Hour | Range Height |
|
|
387 | 391 | Azimuth Zenith errorCosDir |
|
|
388 | 392 | VelRad errorVelRad |
|
|
389 | 393 | TypeError |
|
|
390 | 394 | |
|
|
391 | 395 | ''' |
|
|
392 | 396 | #Get Beacon signal |
|
|
393 | 397 | newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex]) |
|
|
394 | 398 | |
|
|
395 | 399 | if hei_ref != None: |
|
|
396 | 400 | newheis = numpy.where(self.dataOut.heightList>hei_ref) |
|
|
397 | 401 | |
|
|
398 | 402 | heiRang = self.dataOut.getHeiRange() |
|
|
399 | 403 | #Pairs List |
|
|
400 | 404 | pairslist = [] |
|
|
401 | 405 | nChannel = self.dataOut.nChannels |
|
|
402 | 406 | for i in range(nChannel): |
|
|
403 | 407 | if i != centerReceiverIndex: |
|
|
404 | 408 | pairslist.append((centerReceiverIndex,i)) |
|
|
405 | 409 | |
|
|
406 | 410 | #****************REMOVING HARDWARE PHASE DIFFERENCES*************** |
|
|
407 | 411 | # see if the user put in pre defined phase shifts |
|
|
408 | 412 | voltsPShift = self.dataOut.data_pre.copy() |
|
|
409 | 413 | |
|
|
410 | 414 | if predefinedPhaseShifts != None: |
|
|
411 | 415 | hardwarePhaseShifts = numpy.array(predefinedPhaseShifts)*numpy.pi/180 |
|
|
412 | 416 | else: |
|
|
413 | 417 | #get hardware phase shifts using beacon signal |
|
|
414 | 418 | hardwarePhaseShifts = self.__getHardwarePhaseDiff(self.dataOut.data_pre, pairslist, newheis, 10) |
|
|
415 | 419 | hardwarePhaseShifts = numpy.insert(hardwarePhaseShifts,centerReceiverIndex,0) |
|
|
416 | 420 | |
|
|
417 | 421 | voltsPShift = numpy.zeros((self.dataOut.data_pre.shape[0],self.dataOut.data_pre.shape[1],self.dataOut.data_pre.shape[2]), dtype = 'complex') |
|
|
418 | 422 | for i in range(self.dataOut.data_pre.shape[0]): |
|
|
419 | 423 | voltsPShift[i,:,:] = self.__shiftPhase(self.dataOut.data_pre[i,:,:], hardwarePhaseShifts[i]) |
|
|
420 | 424 | #******************END OF REMOVING HARDWARE PHASE DIFFERENCES********* |
|
|
421 | 425 | |
|
|
422 | 426 | #Remove DC |
|
|
423 | 427 | voltsDC = numpy.mean(voltsPShift,1) |
|
|
424 | 428 | voltsDC = numpy.mean(voltsDC,1) |
|
|
425 | 429 | for i in range(voltsDC.shape[0]): |
|
|
426 | 430 | voltsPShift[i] = voltsPShift[i] - voltsDC[i] |
|
|
427 | 431 | |
|
|
428 | 432 | #Don't considerate last heights, theyre used to calculate Hardware Phase Shift |
|
|
429 | 433 | voltsPShift = voltsPShift[:,:,:newheis[0][0]] |
|
|
430 | 434 | |
|
|
431 | 435 | #************ FIND POWER OF DATA W/COH OR NON COH DETECTION (3.4) ********** |
|
|
432 | 436 | #Coherent Detection |
|
|
433 | 437 | if cohDetection: |
|
|
434 | 438 | #use coherent detection to get the net power |
|
|
435 | 439 | cohDet_thresh = cohDet_thresh*numpy.pi/180 |
|
|
436 | 440 | voltsPShift = self.__coherentDetection(voltsPShift, cohDet_timeStep, self.dataOut.timeInterval, pairslist, cohDet_thresh) |
|
|
437 | 441 | |
|
|
438 | 442 | #Non-coherent detection! |
|
|
439 | 443 | powerNet = numpy.nansum(numpy.abs(voltsPShift[:,:,:])**2,0) |
|
|
440 | 444 | #********** END OF COH/NON-COH POWER CALCULATION********************** |
|
|
441 | 445 | |
|
|
442 | 446 | #********** FIND THE NOISE LEVEL AND POSSIBLE METEORS **************** |
|
|
443 | 447 | #Get noise |
|
|
444 | 448 | noise, noise1 = self.__getNoise(powerNet, noise_timeStep, self.dataOut.timeInterval) |
|
|
445 | 449 | # noise = self.getNoise1(powerNet, noise_timeStep, self.dataOut.timeInterval) |
|
|
446 | 450 | #Get signal threshold |
|
|
447 | 451 | signalThresh = noise_multiple*noise |
|
|
448 | 452 | #Meteor echoes detection |
|
|
449 | 453 | listMeteors = self.__findMeteors(powerNet, signalThresh) |
|
|
450 | 454 | #******* END OF NOISE LEVEL AND POSSIBLE METEORS CACULATION ********** |
|
|
451 | 455 | |
|
|
452 | 456 | #************** REMOVE MULTIPLE DETECTIONS (3.5) *************************** |
|
|
453 | 457 | #Parameters |
|
|
454 | 458 | heiRange = self.dataOut.getHeiRange() |
|
|
455 | 459 | rangeInterval = heiRange[1] - heiRange[0] |
|
|
456 | 460 | rangeLimit = multDet_rangeLimit/rangeInterval |
|
|
457 | 461 | timeLimit = multDet_timeLimit/self.dataOut.timeInterval |
|
|
458 | 462 | #Multiple detection removals |
|
|
459 | 463 | listMeteors1 = self.__removeMultipleDetections(listMeteors, rangeLimit, timeLimit) |
|
|
460 | 464 | #************ END OF REMOVE MULTIPLE DETECTIONS ********************** |
|
|
461 | 465 | |
|
|
462 | 466 | #********************* METEOR REESTIMATION (3.7, 3.8, 3.9, 3.10) ******************** |
|
|
463 | 467 | #Parameters |
|
|
464 | 468 | phaseThresh = phaseThresh*numpy.pi/180 |
|
|
465 | 469 | thresh = [phaseThresh, noise_multiple, SNRThresh] |
|
|
466 | 470 | #Meteor reestimation (Errors N 1, 6, 12, 17) |
|
|
467 | 471 | listMeteors2, listMeteorsPower, listMeteorsVolts = self.__meteorReestimation(listMeteors1, voltsPShift, pairslist, thresh, noise, self.dataOut.timeInterval, self.dataOut.frequency) |
|
|
468 | 472 | # listMeteors2, listMeteorsPower, listMeteorsVolts = self.meteorReestimation3(listMeteors2, listMeteorsPower, listMeteorsVolts, voltsPShift, pairslist, thresh, noise) |
|
|
469 | 473 | #Estimation of decay times (Errors N 7, 8, 11) |
|
|
470 | 474 | listMeteors3 = self.__estimateDecayTime(listMeteors2, listMeteorsPower, self.dataOut.timeInterval, self.dataOut.frequency) |
|
|
471 | 475 | #******************* END OF METEOR REESTIMATION ******************* |
|
|
472 | 476 | |
|
|
473 | 477 | #********************* METEOR PARAMETERS CALCULATION (3.11, 3.12, 3.13) ************************** |
|
|
474 | 478 | #Calculating Radial Velocity (Error N 15) |
|
|
475 | 479 | radialStdThresh = 10 |
|
|
476 | 480 | listMeteors4 = self.__getRadialVelocity(listMeteors3, listMeteorsVolts, radialStdThresh, pairslist, self.dataOut.timeInterval) |
|
|
477 | 481 | |
|
|
478 | 482 | if len(listMeteors4) > 0: |
|
|
479 | 483 | #Setting New Array |
|
|
480 | 484 | date = repr(self.dataOut.datatime) |
|
|
481 | 485 | arrayMeteors4, arrayParameters = self.__setNewArrays(listMeteors4, date, heiRang) |
|
|
482 | 486 | |
|
|
483 | 487 | #Calculate AOA (Error N 3, 4) |
|
|
484 | 488 | #JONES ET AL. 1998 |
|
|
485 | 489 | AOAthresh = numpy.pi/8 |
|
|
486 | 490 | error = arrayParameters[:,-1] |
|
|
487 | 491 | phases = -arrayMeteors4[:,9:13] |
|
|
488 | 492 | pairsList = [] |
|
|
489 | 493 | pairsList.append((0,3)) |
|
|
490 | 494 | pairsList.append((1,2)) |
|
|
491 | 495 | arrayParameters[:,4:7], arrayParameters[:,-1] = self.__getAOA(phases, pairsList, error, AOAthresh, azimuth) |
|
|
492 | 496 | |
|
|
493 | 497 | #Calculate Heights (Error N 13 and 14) |
|
|
494 | 498 | error = arrayParameters[:,-1] |
|
|
495 | 499 | Ranges = arrayParameters[:,2] |
|
|
496 | 500 | zenith = arrayParameters[:,5] |
|
|
497 | 501 | arrayParameters[:,3], arrayParameters[:,-1] = self.__getHeights(Ranges, zenith, error, hmin, hmax) |
|
|
498 | 502 | #********************* END OF PARAMETERS CALCULATION ************************** |
|
|
499 | 503 | |
|
|
500 | 504 | #***************************+ SAVE DATA IN HDF5 FORMAT ********************** |
|
|
501 | 505 | self.dataOut.data_param = arrayParameters |
|
|
502 | 506 | |
|
|
503 | 507 | return |
|
|
504 | 508 | |
|
|
505 | 509 | def __getHardwarePhaseDiff(self, voltage0, pairslist, newheis, n): |
|
|
506 | 510 | |
|
|
507 | 511 | minIndex = min(newheis[0]) |
|
|
508 | 512 | maxIndex = max(newheis[0]) |
|
|
509 | 513 | |
|
|
510 | 514 | voltage = voltage0[:,:,minIndex:maxIndex+1] |
|
|
511 | 515 | nLength = voltage.shape[1]/n |
|
|
512 | 516 | nMin = 0 |
|
|
513 | 517 | nMax = 0 |
|
|
514 | 518 | phaseOffset = numpy.zeros((len(pairslist),n)) |
|
|
515 | 519 | |
|
|
516 | 520 | for i in range(n): |
|
|
517 | 521 | nMax += nLength |
|
|
518 | 522 | phaseCCF = -numpy.angle(self.__calculateCCF(voltage[:,nMin:nMax,:], pairslist, [0])) |
|
|
519 | 523 | phaseCCF = numpy.mean(phaseCCF, axis = 2) |
|
|
520 | 524 | phaseOffset[:,i] = phaseCCF.transpose() |
|
|
521 | 525 | nMin = nMax |
|
|
522 | 526 | # phaseDiff, phaseArrival = self.estimatePhaseDifference(voltage, pairslist) |
|
|
523 | 527 | |
|
|
524 | 528 | #Remove Outliers |
|
|
525 | 529 | factor = 2 |
|
|
526 | 530 | wt = phaseOffset - signal.medfilt(phaseOffset,(1,5)) |
|
|
527 | 531 | dw = numpy.std(wt,axis = 1) |
|
|
528 | 532 | dw = dw.reshape((dw.size,1)) |
|
|
529 | 533 | ind = numpy.where(numpy.logical_or(wt>dw*factor,wt<-dw*factor)) |
|
|
530 | 534 | phaseOffset[ind] = numpy.nan |
|
|
531 | 535 | phaseOffset = stats.nanmean(phaseOffset, axis=1) |
|
|
532 | 536 | |
|
|
533 | 537 | return phaseOffset |
|
|
534 | 538 | |
|
|
535 | 539 | def __shiftPhase(self, data, phaseShift): |
|
|
536 | 540 | #this will shift the phase of a complex number |
|
|
537 | 541 | dataShifted = numpy.abs(data) * numpy.exp((numpy.angle(data)+phaseShift)*1j) |
|
|
538 | 542 | return dataShifted |
|
|
539 | 543 | |
|
|
540 | 544 | def __estimatePhaseDifference(self, array, pairslist): |
|
|
541 | 545 | nChannel = array.shape[0] |
|
|
542 | 546 | nHeights = array.shape[2] |
|
|
543 | 547 | numPairs = len(pairslist) |
|
|
544 | 548 | # phaseCCF = numpy.zeros((nChannel, 5, nHeights)) |
|
|
545 | 549 | phaseCCF = numpy.angle(self.__calculateCCF(array, pairslist, [-2,-1,0,1,2])) |
|
|
546 | 550 | |
|
|
547 | 551 | #Correct phases |
|
|
548 | 552 | derPhaseCCF = phaseCCF[:,1:,:] - phaseCCF[:,0:-1,:] |
|
|
549 | 553 | indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) |
|
|
550 | 554 | |
|
|
551 | 555 | if indDer[0].shape[0] > 0: |
|
|
552 | 556 | for i in range(indDer[0].shape[0]): |
|
|
553 | 557 | signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i],indDer[2][i]]) |
|
|
554 | 558 | phaseCCF[indDer[0][i],indDer[1][i]+1:,:] += signo*2*numpy.pi |
|
|
555 | 559 | |
|
|
556 | 560 | # for j in range(numSides): |
|
|
557 | 561 | # phaseCCFAux = self.calculateCCF(arrayCenter, arraySides[j,:,:], [-2,1,0,1,2]) |
|
|
558 | 562 | # phaseCCF[j,:,:] = numpy.angle(phaseCCFAux) |
|
|
559 | 563 | # |
|
|
560 | 564 | #Linear |
|
|
561 | 565 | phaseInt = numpy.zeros((numPairs,1)) |
|
|
562 | 566 | angAllCCF = phaseCCF[:,[0,1,3,4],0] |
|
|
563 | 567 | for j in range(numPairs): |
|
|
564 | 568 | fit = stats.linregress([-2,-1,1,2],angAllCCF[j,:]) |
|
|
565 | 569 | phaseInt[j] = fit[1] |
|
|
566 | 570 | #Phase Differences |
|
|
567 | 571 | phaseDiff = phaseInt - phaseCCF[:,2,:] |
|
|
568 | 572 | phaseArrival = phaseInt.reshape(phaseInt.size) |
|
|
569 | 573 | |
|
|
570 | 574 | #Dealias |
|
|
571 | 575 | indAlias = numpy.where(phaseArrival > numpy.pi) |
|
|
572 | 576 | phaseArrival[indAlias] -= 2*numpy.pi |
|
|
573 | 577 | indAlias = numpy.where(phaseArrival < -numpy.pi) |
|
|
574 | 578 | phaseArrival[indAlias] += 2*numpy.pi |
|
|
575 | 579 | |
|
|
576 | 580 | return phaseDiff, phaseArrival |
|
|
577 | 581 | |
|
|
578 | 582 | def __coherentDetection(self, volts, timeSegment, timeInterval, pairslist, thresh): |
|
|
579 | 583 | #this function will run the coherent detection used in Holdworth et al. 2004 and return the net power |
|
|
580 | 584 | #find the phase shifts of each channel over 1 second intervals |
|
|
581 | 585 | #only look at ranges below the beacon signal |
|
|
582 | 586 | numProfPerBlock = numpy.ceil(timeSegment/timeInterval) |
|
|
583 | 587 | numBlocks = int(volts.shape[1]/numProfPerBlock) |
|
|
584 | 588 | numHeights = volts.shape[2] |
|
|
585 | 589 | nChannel = volts.shape[0] |
|
|
586 | 590 | voltsCohDet = volts.copy() |
|
|
587 | 591 | |
|
|
588 | 592 | pairsarray = numpy.array(pairslist) |
|
|
589 | 593 | indSides = pairsarray[:,1] |
|
|
590 | 594 | # indSides = numpy.array(range(nChannel)) |
|
|
591 | 595 | # indSides = numpy.delete(indSides, indCenter) |
|
|
592 | 596 | # |
|
|
593 | 597 | # listCenter = numpy.array_split(volts[indCenter,:,:], numBlocks, 0) |
|
|
594 | 598 | listBlocks = numpy.array_split(volts, numBlocks, 1) |
|
|
595 | 599 | |
|
|
596 | 600 | startInd = 0 |
|
|
597 | 601 | endInd = 0 |
|
|
598 | 602 | |
|
|
599 | 603 | for i in range(numBlocks): |
|
|
600 | 604 | startInd = endInd |
|
|
601 | 605 | endInd = endInd + listBlocks[i].shape[1] |
|
|
602 | 606 | |
|
|
603 | 607 | arrayBlock = listBlocks[i] |
|
|
604 | 608 | # arrayBlockCenter = listCenter[i] |
|
|
605 | 609 | |
|
|
606 | 610 | #Estimate the Phase Difference |
|
|
607 | 611 | phaseDiff, aux = self.__estimatePhaseDifference(arrayBlock, pairslist) |
|
|
608 | 612 | #Phase Difference RMS |
|
|
609 | 613 | arrayPhaseRMS = numpy.abs(phaseDiff) |
|
|
610 | 614 | phaseRMSaux = numpy.sum(arrayPhaseRMS < thresh,0) |
|
|
611 | 615 | indPhase = numpy.where(phaseRMSaux==4) |
|
|
612 | 616 | #Shifting |
|
|
613 | 617 | if indPhase[0].shape[0] > 0: |
|
|
614 | 618 | for j in range(indSides.size): |
|
|
615 | 619 | arrayBlock[indSides[j],:,indPhase] = self.__shiftPhase(arrayBlock[indSides[j],:,indPhase], phaseDiff[j,indPhase].transpose()) |
|
|
616 | 620 | voltsCohDet[:,startInd:endInd,:] = arrayBlock |
|
|
617 | 621 | |
|
|
618 | 622 | return voltsCohDet |
|
|
619 | 623 | |
|
|
620 | 624 | def __calculateCCF(self, volts, pairslist ,laglist): |
|
|
621 | 625 | |
|
|
622 | 626 | nHeights = volts.shape[2] |
|
|
623 | 627 | nPoints = volts.shape[1] |
|
|
624 | 628 | voltsCCF = numpy.zeros((len(pairslist), len(laglist), nHeights),dtype = 'complex') |
|
|
625 | 629 | |
|
|
626 | 630 | for i in range(len(pairslist)): |
|
|
627 | 631 | volts1 = volts[pairslist[i][0]] |
|
|
628 | 632 | volts2 = volts[pairslist[i][1]] |
|
|
629 | 633 | |
|
|
630 | 634 | for t in range(len(laglist)): |
|
|
631 | 635 | idxT = laglist[t] |
|
|
632 | 636 | if idxT >= 0: |
|
|
633 | 637 | vStacked = numpy.vstack((volts2[idxT:,:], |
|
|
634 | 638 | numpy.zeros((idxT, nHeights),dtype='complex'))) |
|
|
635 | 639 | else: |
|
|
636 | 640 | vStacked = numpy.vstack((numpy.zeros((-idxT, nHeights),dtype='complex'), |
|
|
637 | 641 | volts2[:(nPoints + idxT),:])) |
|
|
638 | 642 | voltsCCF[i,t,:] = numpy.sum((numpy.conjugate(volts1)*vStacked),axis=0) |
|
|
639 | 643 | |
|
|
640 | 644 | vStacked = None |
|
|
641 | 645 | return voltsCCF |
|
|
642 | 646 | |
|
|
643 | 647 | def __getNoise(self, power, timeSegment, timeInterval): |
|
|
644 | 648 | numProfPerBlock = numpy.ceil(timeSegment/timeInterval) |
|
|
645 | 649 | numBlocks = int(power.shape[0]/numProfPerBlock) |
|
|
646 | 650 | numHeights = power.shape[1] |
|
|
647 | 651 | |
|
|
648 | 652 | listPower = numpy.array_split(power, numBlocks, 0) |
|
|
649 | 653 | noise = numpy.zeros((power.shape[0], power.shape[1])) |
|
|
650 | 654 | noise1 = numpy.zeros((power.shape[0], power.shape[1])) |
|
|
651 | 655 | |
|
|
652 | 656 | startInd = 0 |
|
|
653 | 657 | endInd = 0 |
|
|
654 | 658 | |
|
|
655 | 659 | for i in range(numBlocks): #split por canal |
|
|
656 | 660 | startInd = endInd |
|
|
657 | 661 | endInd = endInd + listPower[i].shape[0] |
|
|
658 | 662 | |
|
|
659 | 663 | arrayBlock = listPower[i] |
|
|
660 | 664 | noiseAux = numpy.mean(arrayBlock, 0) |
|
|
661 | 665 | # noiseAux = numpy.median(noiseAux) |
|
|
662 | 666 | # noiseAux = numpy.mean(arrayBlock) |
|
|
663 | 667 | noise[startInd:endInd,:] = noise[startInd:endInd,:] + noiseAux |
|
|
664 | 668 | |
|
|
665 | 669 | noiseAux1 = numpy.mean(arrayBlock) |
|
|
666 | 670 | noise1[startInd:endInd,:] = noise1[startInd:endInd,:] + noiseAux1 |
|
|
667 | 671 | |
|
|
668 | 672 | return noise, noise1 |
|
|
669 | 673 | |
|
|
670 | 674 | def __findMeteors(self, power, thresh): |
|
|
671 | 675 | nProf = power.shape[0] |
|
|
672 | 676 | nHeights = power.shape[1] |
|
|
673 | 677 | listMeteors = [] |
|
|
674 | 678 | |
|
|
675 | 679 | for i in range(nHeights): |
|
|
676 | 680 | powerAux = power[:,i] |
|
|
677 | 681 | threshAux = thresh[:,i] |
|
|
678 | 682 | |
|
|
679 | 683 | indUPthresh = numpy.where(powerAux > threshAux)[0] |
|
|
680 | 684 | indDNthresh = numpy.where(powerAux <= threshAux)[0] |
|
|
681 | 685 | |
|
|
682 | 686 | j = 0 |
|
|
683 | 687 | |
|
|
684 | 688 | while (j < indUPthresh.size - 2): |
|
|
685 | 689 | if (indUPthresh[j + 2] == indUPthresh[j] + 2): |
|
|
686 | 690 | indDNAux = numpy.where(indDNthresh > indUPthresh[j]) |
|
|
687 | 691 | indDNthresh = indDNthresh[indDNAux] |
|
|
688 | 692 | |
|
|
689 | 693 | if (indDNthresh.size > 0): |
|
|
690 | 694 | indEnd = indDNthresh[0] - 1 |
|
|
691 | 695 | indInit = indUPthresh[j] |
|
|
692 | 696 | |
|
|
693 | 697 | meteor = powerAux[indInit:indEnd + 1] |
|
|
694 | 698 | indPeak = meteor.argmax() + indInit |
|
|
695 | 699 | FLA = sum(numpy.conj(meteor)*numpy.hstack((meteor[1:],0))) |
|
|
696 | 700 | |
|
|
697 | 701 | listMeteors.append(numpy.array([i,indInit,indPeak,indEnd,FLA])) #CHEQUEAR!!!!! |
|
|
698 | 702 | j = numpy.where(indUPthresh == indEnd)[0] + 1 |
|
|
699 | 703 | else: j+=1 |
|
|
700 | 704 | else: j+=1 |
|
|
701 | 705 | |
|
|
702 | 706 | return listMeteors |
|
|
703 | 707 | |
|
|
704 | 708 | def __removeMultipleDetections(self,listMeteors, rangeLimit, timeLimit): |
|
|
705 | 709 | |
|
|
706 | 710 | arrayMeteors = numpy.asarray(listMeteors) |
|
|
707 | 711 | listMeteors1 = [] |
|
|
708 | 712 | |
|
|
709 | 713 | while arrayMeteors.shape[0] > 0: |
|
|
710 | 714 | FLAs = arrayMeteors[:,4] |
|
|
711 | 715 | maxFLA = FLAs.argmax() |
|
|
712 | 716 | listMeteors1.append(arrayMeteors[maxFLA,:]) |
|
|
713 | 717 | |
|
|
714 | 718 | MeteorInitTime = arrayMeteors[maxFLA,1] |
|
|
715 | 719 | MeteorEndTime = arrayMeteors[maxFLA,3] |
|
|
716 | 720 | MeteorHeight = arrayMeteors[maxFLA,0] |
|
|
717 | 721 | |
|
|
718 | 722 | #Check neighborhood |
|
|
719 | 723 | maxHeightIndex = MeteorHeight + rangeLimit |
|
|
720 | 724 | minHeightIndex = MeteorHeight - rangeLimit |
|
|
721 | 725 | minTimeIndex = MeteorInitTime - timeLimit |
|
|
722 | 726 | maxTimeIndex = MeteorEndTime + timeLimit |
|
|
723 | 727 | |
|
|
724 | 728 | #Check Heights |
|
|
725 | 729 | indHeight = numpy.logical_and(arrayMeteors[:,0] >= minHeightIndex, arrayMeteors[:,0] <= maxHeightIndex) |
|
|
726 | 730 | indTime = numpy.logical_and(arrayMeteors[:,3] >= minTimeIndex, arrayMeteors[:,1] <= maxTimeIndex) |
|
|
727 | 731 | indBoth = numpy.where(numpy.logical_and(indTime,indHeight)) |
|
|
728 | 732 | |
|
|
729 | 733 | arrayMeteors = numpy.delete(arrayMeteors, indBoth, axis = 0) |
|
|
730 | 734 | |
|
|
731 | 735 | return listMeteors1 |
|
|
732 | 736 | |
|
|
733 | 737 | def __meteorReestimation(self, listMeteors, volts, pairslist, thresh, noise, timeInterval,frequency): |
|
|
734 | 738 | numHeights = volts.shape[2] |
|
|
735 | 739 | nChannel = volts.shape[0] |
|
|
736 | 740 | |
|
|
737 | 741 | thresholdPhase = thresh[0] |
|
|
738 | 742 | thresholdNoise = thresh[1] |
|
|
739 | 743 | thresholdDB = float(thresh[2]) |
|
|
740 | 744 | |
|
|
741 | 745 | thresholdDB1 = 10**(thresholdDB/10) |
|
|
742 | 746 | pairsarray = numpy.array(pairslist) |
|
|
743 | 747 | indSides = pairsarray[:,1] |
|
|
744 | 748 | |
|
|
745 | 749 | pairslist1 = list(pairslist) |
|
|
746 | 750 | pairslist1.append((0,1)) |
|
|
747 | 751 | pairslist1.append((3,4)) |
|
|
748 | 752 | |
|
|
749 | 753 | listMeteors1 = [] |
|
|
750 | 754 | listPowerSeries = [] |
|
|
751 | 755 | listVoltageSeries = [] |
|
|
752 | 756 | #volts has the war data |
|
|
753 | 757 | |
|
|
754 | 758 | if frequency == 30e6: |
|
|
755 | 759 | timeLag = 45*10**-3 |
|
|
756 | 760 | else: |
|
|
757 | 761 | timeLag = 15*10**-3 |
|
|
758 | 762 | lag = numpy.ceil(timeLag/timeInterval) |
|
|
759 | 763 | |
|
|
760 | 764 | for i in range(len(listMeteors)): |
|
|
761 | 765 | |
|
|
762 | 766 | ###################### 3.6 - 3.7 PARAMETERS REESTIMATION ######################### |
|
|
763 | 767 | meteorAux = numpy.zeros(16) |
|
|
764 | 768 | |
|
|
765 | 769 | #Loading meteor Data (mHeight, mStart, mPeak, mEnd) |
|
|
766 | 770 | mHeight = listMeteors[i][0] |
|
|
767 | 771 | mStart = listMeteors[i][1] |
|
|
768 | 772 | mPeak = listMeteors[i][2] |
|
|
769 | 773 | mEnd = listMeteors[i][3] |
|
|
770 | 774 | |
|
|
771 | 775 | #get the volt data between the start and end times of the meteor |
|
|
772 | 776 | meteorVolts = volts[:,mStart:mEnd+1,mHeight] |
|
|
773 | 777 | meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) |
|
|
774 | 778 | |
|
|
775 | 779 | #3.6. Phase Difference estimation |
|
|
776 | 780 | phaseDiff, aux = self.__estimatePhaseDifference(meteorVolts, pairslist) |
|
|
777 | 781 | |
|
|
778 | 782 | #3.7. Phase difference removal & meteor start, peak and end times reestimated |
|
|
779 | 783 | #meteorVolts0.- all Channels, all Profiles |
|
|
780 | 784 | meteorVolts0 = volts[:,:,mHeight] |
|
|
781 | 785 | meteorThresh = noise[:,mHeight]*thresholdNoise |
|
|
782 | 786 | meteorNoise = noise[:,mHeight] |
|
|
783 | 787 | meteorVolts0[indSides,:] = self.__shiftPhase(meteorVolts0[indSides,:], phaseDiff) #Phase Shifting |
|
|
784 | 788 | powerNet0 = numpy.nansum(numpy.abs(meteorVolts0)**2, axis = 0) #Power |
|
|
785 | 789 | |
|
|
786 | 790 | #Times reestimation |
|
|
787 | 791 | mStart1 = numpy.where(powerNet0[:mPeak] < meteorThresh[:mPeak])[0] |
|
|
788 | 792 | if mStart1.size > 0: |
|
|
789 | 793 | mStart1 = mStart1[-1] + 1 |
|
|
790 | 794 | |
|
|
791 | 795 | else: |
|
|
792 | 796 | mStart1 = mPeak |
|
|
793 | 797 | |
|
|
794 | 798 | mEnd1 = numpy.where(powerNet0[mPeak:] < meteorThresh[mPeak:])[0][0] + mPeak - 1 |
|
|
795 | 799 | mEndDecayTime1 = numpy.where(powerNet0[mPeak:] < meteorNoise[mPeak:])[0] |
|
|
796 | 800 | if mEndDecayTime1.size == 0: |
|
|
797 | 801 | mEndDecayTime1 = powerNet0.size |
|
|
798 | 802 | else: |
|
|
799 | 803 | mEndDecayTime1 = mEndDecayTime1[0] + mPeak - 1 |
|
|
800 | 804 | # mPeak1 = meteorVolts0[mStart1:mEnd1 + 1].argmax() |
|
|
801 | 805 | |
|
|
802 | 806 | #meteorVolts1.- all Channels, from start to end |
|
|
803 | 807 | meteorVolts1 = meteorVolts0[:,mStart1:mEnd1 + 1] |
|
|
804 | 808 | meteorVolts2 = meteorVolts0[:,mPeak + lag:mEnd1 + 1] |
|
|
805 | 809 | if meteorVolts2.shape[1] == 0: |
|
|
806 | 810 | meteorVolts2 = meteorVolts0[:,mPeak:mEnd1 + 1] |
|
|
807 | 811 | meteorVolts1 = meteorVolts1.reshape(meteorVolts1.shape[0], meteorVolts1.shape[1], 1) |
|
|
808 | 812 | meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1], 1) |
|
|
809 | 813 | ##################### END PARAMETERS REESTIMATION ######################### |
|
|
810 | 814 | |
|
|
811 | 815 | ##################### 3.8 PHASE DIFFERENCE REESTIMATION ######################## |
|
|
812 | 816 | # if mEnd1 - mStart1 > 4: #Error Number 6: echo less than 5 samples long; too short for analysis |
|
|
813 | 817 | if meteorVolts2.shape[1] > 0: |
|
|
814 | 818 | #Phase Difference re-estimation |
|
|
815 | 819 | phaseDiff1, phaseDiffint = self.__estimatePhaseDifference(meteorVolts2, pairslist1) #Phase Difference Estimation |
|
|
816 | 820 | # phaseDiff1, phaseDiffint = self.estimatePhaseDifference(meteorVolts2, pairslist) |
|
|
817 | 821 | meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1]) |
|
|
818 | 822 | phaseDiff11 = numpy.reshape(phaseDiff1, (phaseDiff1.shape[0],1)) |
|
|
819 | 823 | meteorVolts2[indSides,:] = self.__shiftPhase(meteorVolts2[indSides,:], phaseDiff11[0:4]) #Phase Shifting |
|
|
820 | 824 | |
|
|
821 | 825 | #Phase Difference RMS |
|
|
822 | 826 | phaseRMS1 = numpy.sqrt(numpy.mean(numpy.square(phaseDiff1))) |
|
|
823 | 827 | powerNet1 = numpy.nansum(numpy.abs(meteorVolts1[:,:])**2,0) |
|
|
824 | 828 | #Data from Meteor |
|
|
825 | 829 | mPeak1 = powerNet1.argmax() + mStart1 |
|
|
826 | 830 | mPeakPower1 = powerNet1.max() |
|
|
827 | 831 | noiseAux = sum(noise[mStart1:mEnd1 + 1,mHeight]) |
|
|
828 | 832 | mSNR1 = (sum(powerNet1)-noiseAux)/noiseAux |
|
|
829 | 833 | Meteor1 = numpy.array([mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1]) |
|
|
830 | 834 | Meteor1 = numpy.hstack((Meteor1,phaseDiffint)) |
|
|
831 | 835 | PowerSeries = powerNet0[mStart1:mEndDecayTime1 + 1] |
|
|
832 | 836 | #Vectorize |
|
|
833 | 837 | meteorAux[0:7] = [mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1] |
|
|
834 | 838 | meteorAux[7:11] = phaseDiffint[0:4] |
|
|
835 | 839 | |
|
|
836 | 840 | #Rejection Criterions |
|
|
837 | 841 | if phaseRMS1 > thresholdPhase: #Error Number 17: Phase variation |
|
|
838 | 842 | meteorAux[-1] = 17 |
|
|
839 | 843 | elif mSNR1 < thresholdDB1: #Error Number 1: SNR < threshold dB |
|
|
840 | 844 | meteorAux[-1] = 1 |
|
|
841 | 845 | |
|
|
842 | 846 | |
|
|
843 | 847 | else: |
|
|
844 | 848 | meteorAux[0:4] = [mHeight, mStart, mPeak, mEnd] |
|
|
845 | 849 | meteorAux[-1] = 6 #Error Number 6: echo less than 5 samples long; too short for analysis |
|
|
846 | 850 | PowerSeries = 0 |
|
|
847 | 851 | |
|
|
848 | 852 | listMeteors1.append(meteorAux) |
|
|
849 | 853 | listPowerSeries.append(PowerSeries) |
|
|
850 | 854 | listVoltageSeries.append(meteorVolts1) |
|
|
851 | 855 | |
|
|
852 | 856 | return listMeteors1, listPowerSeries, listVoltageSeries |
|
|
853 | 857 | |
|
|
854 | 858 | def __estimateDecayTime(self, listMeteors, listPower, timeInterval, frequency): |
|
|
855 | 859 | |
|
|
856 | 860 | threshError = 10 |
|
|
857 | 861 | #Depending if it is 30 or 50 MHz |
|
|
858 | 862 | if frequency == 30e6: |
|
|
859 | 863 | timeLag = 45*10**-3 |
|
|
860 | 864 | else: |
|
|
861 | 865 | timeLag = 15*10**-3 |
|
|
862 | 866 | lag = numpy.ceil(timeLag/timeInterval) |
|
|
863 | 867 | |
|
|
864 | 868 | listMeteors1 = [] |
|
|
865 | 869 | |
|
|
866 | 870 | for i in range(len(listMeteors)): |
|
|
867 | 871 | meteorPower = listPower[i] |
|
|
868 | 872 | meteorAux = listMeteors[i] |
|
|
869 | 873 | |
|
|
870 | 874 | if meteorAux[-1] == 0: |
|
|
871 | 875 | |
|
|
872 | 876 | try: |
|
|
873 | 877 | indmax = meteorPower.argmax() |
|
|
874 | 878 | indlag = indmax + lag |
|
|
875 | 879 | |
|
|
876 | 880 | y = meteorPower[indlag:] |
|
|
877 | 881 | x = numpy.arange(0, y.size)*timeLag |
|
|
878 | 882 | |
|
|
879 | 883 | #first guess |
|
|
880 | 884 | a = y[0] |
|
|
881 | 885 | tau = timeLag |
|
|
882 | 886 | #exponential fit |
|
|
883 | 887 | popt, pcov = optimize.curve_fit(self.__exponential_function, x, y, p0 = [a, tau]) |
|
|
884 | 888 | y1 = self.__exponential_function(x, *popt) |
|
|
885 | 889 | #error estimation |
|
|
886 | 890 | error = sum((y - y1)**2)/(numpy.var(y)*(y.size - popt.size)) |
|
|
887 | 891 | |
|
|
888 | 892 | decayTime = popt[1] |
|
|
889 | 893 | riseTime = indmax*timeInterval |
|
|
890 | 894 | meteorAux[11:13] = [decayTime, error] |
|
|
891 | 895 | |
|
|
892 | 896 | #Table items 7, 8 and 11 |
|
|
893 | 897 | if (riseTime > 0.3): #Number 7: Echo rise exceeds 0.3s |
|
|
894 | 898 | meteorAux[-1] = 7 |
|
|
895 | 899 | elif (decayTime < 2*riseTime) : #Number 8: Echo decay time less than than twice rise time |
|
|
896 | 900 | meteorAux[-1] = 8 |
|
|
897 | 901 | if (error > threshError): #Number 11: Poor fit to amplitude for estimation of decay time |
|
|
898 | 902 | meteorAux[-1] = 11 |
|
|
899 | 903 | |
|
|
900 | 904 | |
|
|
901 | 905 | except: |
|
|
902 | 906 | meteorAux[-1] = 11 |
|
|
903 | 907 | |
|
|
904 | 908 | |
|
|
905 | 909 | listMeteors1.append(meteorAux) |
|
|
906 | 910 | |
|
|
907 | 911 | return listMeteors1 |
|
|
908 | 912 | |
|
|
909 | 913 | #Exponential Function |
|
|
910 | 914 | |
|
|
911 | 915 | def __exponential_function(self, x, a, tau): |
|
|
912 | 916 | y = a*numpy.exp(-x/tau) |
|
|
913 | 917 | return y |
|
|
914 | 918 | |
|
|
915 | 919 | def __getRadialVelocity(self, listMeteors, listVolts, radialStdThresh, pairslist, timeInterval): |
|
|
916 | 920 | |
|
|
917 | 921 | pairslist1 = list(pairslist) |
|
|
918 | 922 | pairslist1.append((0,1)) |
|
|
919 | 923 | pairslist1.append((3,4)) |
|
|
920 | 924 | numPairs = len(pairslist1) |
|
|
921 | 925 | #Time Lag |
|
|
922 | 926 | timeLag = 45*10**-3 |
|
|
923 | 927 | c = 3e8 |
|
|
924 | 928 | lag = numpy.ceil(timeLag/timeInterval) |
|
|
925 | 929 | freq = 30e6 |
|
|
926 | 930 | |
|
|
927 | 931 | listMeteors1 = [] |
|
|
928 | 932 | |
|
|
929 | 933 | for i in range(len(listMeteors)): |
|
|
930 | 934 | meteor = listMeteors[i] |
|
|
931 | 935 | meteorAux = numpy.hstack((meteor[:-1], 0, 0, meteor[-1])) |
|
|
932 | 936 | if meteor[-1] == 0: |
|
|
933 | 937 | mStart = listMeteors[i][1] |
|
|
934 | 938 | mPeak = listMeteors[i][2] |
|
|
935 | 939 | mLag = mPeak - mStart + lag |
|
|
936 | 940 | |
|
|
937 | 941 | #get the volt data between the start and end times of the meteor |
|
|
938 | 942 | meteorVolts = listVolts[i] |
|
|
939 | 943 | meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1) |
|
|
940 | 944 | |
|
|
941 | 945 | #Get CCF |
|
|
942 | 946 | allCCFs = self.__calculateCCF(meteorVolts, pairslist1, [-2,-1,0,1,2]) |
|
|
943 | 947 | |
|
|
944 | 948 | #Method 2 |
|
|
945 | 949 | slopes = numpy.zeros(numPairs) |
|
|
946 | 950 | time = numpy.array([-2,-1,1,2])*timeInterval |
|
|
947 | 951 | angAllCCF = numpy.angle(allCCFs[:,[0,1,3,4],0]) |
|
|
948 | 952 | |
|
|
949 | 953 | #Correct phases |
|
|
950 | 954 | derPhaseCCF = angAllCCF[:,1:] - angAllCCF[:,0:-1] |
|
|
951 | 955 | indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi) |
|
|
952 | 956 | |
|
|
953 | 957 | if indDer[0].shape[0] > 0: |
|
|
954 | 958 | for i in range(indDer[0].shape[0]): |
|
|
955 | 959 | signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i]]) |
|
|
956 | 960 | angAllCCF[indDer[0][i],indDer[1][i]+1:] += signo*2*numpy.pi |
|
|
957 | 961 | |
|
|
958 | 962 | # fit = scipy.stats.linregress(numpy.array([-2,-1,1,2])*timeInterval, numpy.array([phaseLagN2s[i],phaseLagN1s[i],phaseLag1s[i],phaseLag2s[i]])) |
|
|
959 | 963 | for j in range(numPairs): |
|
|
960 | 964 | fit = stats.linregress(time, angAllCCF[j,:]) |
|
|
961 | 965 | slopes[j] = fit[0] |
|
|
962 | 966 | |
|
|
963 | 967 | #Remove Outlier |
|
|
964 | 968 | # indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes))) |
|
|
965 | 969 | # slopes = numpy.delete(slopes,indOut) |
|
|
966 | 970 | # indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes))) |
|
|
967 | 971 | # slopes = numpy.delete(slopes,indOut) |
|
|
968 | 972 | |
|
|
969 | 973 | radialVelocity = -numpy.mean(slopes)*(0.25/numpy.pi)*(c/freq) |
|
|
970 | 974 | radialError = numpy.std(slopes)*(0.25/numpy.pi)*(c/freq) |
|
|
971 | 975 | meteorAux[-2] = radialError |
|
|
972 | 976 | meteorAux[-3] = radialVelocity |
|
|
973 | 977 | |
|
|
974 | 978 | #Setting Error |
|
|
975 | 979 | #Number 15: Radial Drift velocity or projected horizontal velocity exceeds 200 m/s |
|
|
976 | 980 | if numpy.abs(radialVelocity) > 200: |
|
|
977 | 981 | meteorAux[-1] = 15 |
|
|
978 | 982 | #Number 12: Poor fit to CCF variation for estimation of radial drift velocity |
|
|
979 | 983 | elif radialError > radialStdThresh: |
|
|
980 | 984 | meteorAux[-1] = 12 |
|
|
981 | 985 | |
|
|
982 | 986 | listMeteors1.append(meteorAux) |
|
|
983 | 987 | return listMeteors1 |
|
|
984 | 988 | |
|
|
985 | 989 | def __setNewArrays(self, listMeteors, date, heiRang): |
|
|
986 | 990 | |
|
|
987 | 991 | #New arrays |
|
|
988 | 992 | arrayMeteors = numpy.array(listMeteors) |
|
|
989 | 993 | arrayParameters = numpy.zeros((len(listMeteors),10)) |
|
|
990 | 994 | |
|
|
991 | 995 | #Date inclusion |
|
|
992 | 996 | date = re.findall(r'\((.*?)\)', date) |
|
|
993 | 997 | date = date[0].split(',') |
|
|
994 | 998 | date = map(int, date) |
|
|
995 | 999 | date = [date[0]*10000 + date[1]*100 + date[2], date[3]*10000 + date[4]*100 + date[5]] |
|
|
996 | 1000 | arrayDate = numpy.tile(date, (len(listMeteors), 1)) |
|
|
997 | 1001 | |
|
|
998 | 1002 | #Meteor array |
|
|
999 | 1003 | arrayMeteors[:,0] = heiRang[arrayMeteors[:,0].astype(int)] |
|
|
1000 | 1004 | arrayMeteors = numpy.hstack((arrayDate, arrayMeteors)) |
|
|
1001 | 1005 | |
|
|
1002 | 1006 | #Parameters Array |
|
|
1003 | 1007 | arrayParameters[:,0:3] = arrayMeteors[:,0:3] |
|
|
1004 | 1008 | arrayParameters[:,-3:] = arrayMeteors[:,-3:] |
|
|
1005 | 1009 | |
|
|
1006 | 1010 | return arrayMeteors, arrayParameters |
|
|
1007 | 1011 | |
|
|
1008 | 1012 | def __getAOA(self, phases, pairsList, error, AOAthresh, azimuth): |
|
|
1009 | 1013 | |
|
|
1010 | 1014 | arrayAOA = numpy.zeros((phases.shape[0],3)) |
|
|
1011 | 1015 | cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList) |
|
|
1012 | 1016 | |
|
|
1013 | 1017 | arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth) |
|
|
1014 | 1018 | cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1) |
|
|
1015 | 1019 | arrayAOA[:,2] = cosDirError |
|
|
1016 | 1020 | |
|
|
1017 | 1021 | azimuthAngle = arrayAOA[:,0] |
|
|
1018 | 1022 | zenithAngle = arrayAOA[:,1] |
|
|
1019 | 1023 | |
|
|
1020 | 1024 | #Setting Error |
|
|
1021 | 1025 | #Number 3: AOA not fesible |
|
|
1022 | 1026 | indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0] |
|
|
1023 | 1027 | error[indInvalid] = 3 |
|
|
1024 | 1028 | #Number 4: Large difference in AOAs obtained from different antenna baselines |
|
|
1025 | 1029 | indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0] |
|
|
1026 | 1030 | error[indInvalid] = 4 |
|
|
1027 | 1031 | return arrayAOA, error |
|
|
1028 | 1032 | |
|
|
1029 | 1033 | def __getDirectionCosines(self, arrayPhase, pairsList): |
|
|
1030 | 1034 | |
|
|
1031 | 1035 | #Initializing some variables |
|
|
1032 | 1036 | ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi |
|
|
1033 | 1037 | ang_aux = ang_aux.reshape(1,ang_aux.size) |
|
|
1034 | 1038 | |
|
|
1035 | 1039 | cosdir = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
1036 | 1040 | cosdir0 = numpy.zeros((arrayPhase.shape[0],2)) |
|
|
1037 | 1041 | |
|
|
1038 | 1042 | |
|
|
1039 | 1043 | for i in range(2): |
|
|
1040 | 1044 | #First Estimation |
|
|
1041 | 1045 | phi0_aux = arrayPhase[:,pairsList[i][0]] + arrayPhase[:,pairsList[i][1]] |
|
|
1042 | 1046 | #Dealias |
|
|
1043 | 1047 | indcsi = numpy.where(phi0_aux > numpy.pi) |
|
|
1044 | 1048 | phi0_aux[indcsi] -= 2*numpy.pi |
|
|
1045 | 1049 | indcsi = numpy.where(phi0_aux < -numpy.pi) |
|
|
1046 | 1050 | phi0_aux[indcsi] += 2*numpy.pi |
|
|
1047 | 1051 | #Direction Cosine 0 |
|
|
1048 | 1052 | cosdir0[:,i] = -(phi0_aux)/(2*numpy.pi*0.5) |
|
|
1049 | 1053 | |
|
|
1050 | 1054 | #Most-Accurate Second Estimation |
|
|
1051 | 1055 | phi1_aux = arrayPhase[:,pairsList[i][0]] - arrayPhase[:,pairsList[i][1]] |
|
|
1052 | 1056 | phi1_aux = phi1_aux.reshape(phi1_aux.size,1) |
|
|
1053 | 1057 | #Direction Cosine 1 |
|
|
1054 | 1058 | cosdir1 = -(phi1_aux + ang_aux)/(2*numpy.pi*4.5) |
|
|
1055 | 1059 | |
|
|
1056 | 1060 | #Searching the correct Direction Cosine |
|
|
1057 | 1061 | cosdir0_aux = cosdir0[:,i] |
|
|
1058 | 1062 | cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1) |
|
|
1059 | 1063 | #Minimum Distance |
|
|
1060 | 1064 | cosDiff = (cosdir1 - cosdir0_aux)**2 |
|
|
1061 | 1065 | indcos = cosDiff.argmin(axis = 1) |
|
|
1062 | 1066 | #Saving Value obtained |
|
|
1063 | 1067 | cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos] |
|
|
1064 | 1068 | |
|
|
1065 | 1069 | return cosdir0, cosdir |
|
|
1066 | 1070 | |
|
|
1067 | 1071 | def __calculateAOA(self, cosdir, azimuth): |
|
|
1068 | 1072 | cosdirX = cosdir[:,0] |
|
|
1069 | 1073 | cosdirY = cosdir[:,1] |
|
|
1070 | 1074 | |
|
|
1071 | 1075 | zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi |
|
|
1072 | 1076 | azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth #0 deg north, 90 deg east |
|
|
1073 | 1077 | angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose() |
|
|
1074 | 1078 | |
|
|
1075 | 1079 | return angles |
|
|
1076 | 1080 | |
|
|
1077 | 1081 | def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight): |
|
|
1078 | 1082 | |
|
|
1079 | 1083 | Ramb = 375 #Ramb = c/(2*PRF) |
|
|
1080 | 1084 | Re = 6371 #Earth Radius |
|
|
1081 | 1085 | heights = numpy.zeros(Ranges.shape) |
|
|
1082 | 1086 | |
|
|
1083 | 1087 | R_aux = numpy.array([0,1,2])*Ramb |
|
|
1084 | 1088 | R_aux = R_aux.reshape(1,R_aux.size) |
|
|
1085 | 1089 | |
|
|
1086 | 1090 | Ranges = Ranges.reshape(Ranges.size,1) |
|
|
1087 | 1091 | |
|
|
1088 | 1092 | Ri = Ranges + R_aux |
|
|
1089 | 1093 | hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re |
|
|
1090 | 1094 | |
|
|
1091 | 1095 | #Check if there is a height between 70 and 110 km |
|
|
1092 | 1096 | h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1) |
|
|
1093 | 1097 | ind_h = numpy.where(h_bool == 1)[0] |
|
|
1094 | 1098 | |
|
|
1095 | 1099 | hCorr = hi[ind_h, :] |
|
|
1096 | 1100 | ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight)) |
|
|
1097 | 1101 | |
|
|
1098 | 1102 | hCorr = hi[ind_hCorr] |
|
|
1099 | 1103 | heights[ind_h] = hCorr |
|
|
1100 | 1104 | |
|
|
1101 | 1105 | #Setting Error |
|
|
1102 | 1106 | #Number 13: Height unresolvable echo: not valid height within 70 to 110 km |
|
|
1103 | 1107 | #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km |
|
|
1104 | 1108 | |
|
|
1105 | 1109 | indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0] |
|
|
1106 | 1110 | error[indInvalid2] = 14 |
|
|
1107 | 1111 | indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0] |
|
|
1108 | 1112 | error[indInvalid1] = 13 |
|
|
1109 | 1113 | |
|
|
1110 | 1114 | return heights, error |
|
|
1111 | 1115 | |
|
|
1116 | def SpectralFitting(self, getSNR = True, path=None, file=None, groupList=None): | |
|
|
1117 | ||
|
|
1118 | ''' | |
|
|
1119 | Function GetMoments() | |
|
|
1120 | ||
|
|
1121 | Input: | |
|
|
1122 | Output: | |
|
|
1123 | Variables modified: | |
|
|
1124 | ''' | |
|
|
1125 | if path != None: | |
|
|
1126 | sys.path.append(path) | |
|
|
1127 | self.dataOut.library = importlib.import_module(file) | |
|
|
1128 | ||
|
|
1129 | #To be inserted as a parameter | |
|
|
1130 | groupArray = numpy.array(groupList) | |
|
|
1131 | # groupArray = numpy.array([[0,1],[2,3]]) | |
|
|
1132 | self.dataOut.groupList = groupArray | |
|
|
1133 | ||
|
|
1134 | nGroups = groupArray.shape[0] | |
|
|
1135 | nChannels = self.dataIn.nChannels | |
|
|
1136 | nHeights=self.dataIn.heightList.size | |
|
|
1137 | ||
|
|
1138 | #Parameters Array | |
|
|
1139 | self.dataOut.data_param = None | |
|
|
1140 | ||
|
|
1141 | #Set constants | |
|
|
1142 | constants = self.dataOut.library.setConstants(self.dataIn) | |
|
|
1143 | self.dataOut.constants = constants | |
|
|
1144 | M = self.dataIn.normFactor | |
|
|
1145 | N = self.dataIn.nFFTPoints | |
|
|
1146 | ippSeconds = self.dataIn.ippSeconds | |
|
|
1147 | K = self.dataIn.nIncohInt | |
|
|
1148 | pairsArray = numpy.array(self.dataIn.pairsList) | |
|
|
1149 | ||
|
|
1150 | #List of possible combinations | |
|
|
1151 | listComb = itertools.combinations(numpy.arange(groupArray.shape[1]),2) | |
|
|
1152 | indCross = numpy.zeros(len(list(listComb)), dtype = 'int') | |
|
|
1153 | ||
|
|
1154 | if getSNR: | |
|
|
1155 | listChannels = groupArray.reshape((groupArray.size)) | |
|
|
1156 | listChannels.sort() | |
|
|
1157 | noise = self.dataIn.getNoise() | |
|
|
1158 | self.dataOut.SNR = self.__getSNR(self.dataIn.data_spc[listChannels,:,:], noise[listChannels]) | |
|
|
1159 | ||
|
|
1160 | for i in range(nGroups): | |
|
|
1161 | coord = groupArray[i,:] | |
|
|
1162 | ||
|
|
1163 | #Input data array | |
|
|
1164 | data = self.dataIn.data_spc[coord,:,:]/(M*N) | |
|
|
1165 | data = data.reshape((data.shape[0]*data.shape[1],data.shape[2])) | |
|
|
1166 | ||
|
|
1167 | #Cross Spectra data array for Covariance Matrixes | |
|
|
1168 | ind = 0 | |
|
|
1169 | for pairs in listComb: | |
|
|
1170 | pairsSel = numpy.array([coord[x],coord[y]]) | |
|
|
1171 | indCross[ind] = int(numpy.where(numpy.all(pairsArray == pairsSel, axis = 1))[0][0]) | |
|
|
1172 | ind += 1 | |
|
|
1173 | dataCross = self.dataIn.data_cspc[indCross,:,:]/(M*N) | |
|
|
1174 | dataCross = dataCross**2/K | |
|
|
1175 | ||
|
|
1176 | for h in range(nHeights): | |
|
|
1177 | # print self.dataOut.heightList[h] | |
|
|
1178 | ||
|
|
1179 | #Input | |
|
|
1180 | d = data[:,h] | |
|
|
1181 | ||
|
|
1182 | #Covariance Matrix | |
|
|
1183 | D = numpy.diag(d**2/K) | |
|
|
1184 | ind = 0 | |
|
|
1185 | for pairs in listComb: | |
|
|
1186 | #Coordinates in Covariance Matrix | |
|
|
1187 | x = pairs[0] | |
|
|
1188 | y = pairs[1] | |
|
|
1189 | #Channel Index | |
|
|
1190 | S12 = dataCross[ind,:,h] | |
|
|
1191 | D12 = numpy.diag(S12) | |
|
|
1192 | #Completing Covariance Matrix with Cross Spectras | |
|
|
1193 | D[x*N:(x+1)*N,y*N:(y+1)*N] = D12 | |
|
|
1194 | D[y*N:(y+1)*N,x*N:(x+1)*N] = D12 | |
|
|
1195 | ind += 1 | |
|
|
1196 | Dinv=numpy.linalg.inv(D) | |
|
|
1197 | L=numpy.linalg.cholesky(Dinv) | |
|
|
1198 | LT=L.T | |
|
|
1199 | ||
|
|
1200 | dp = numpy.dot(LT,d) | |
|
|
1201 | ||
|
|
1202 | #Initial values | |
|
|
1203 | data_spc = self.dataIn.data_spc[coord,:,h] | |
|
|
1204 | p0 = self.dataOut.library.initialValuesFunction(data_spc, constants) | |
|
|
1205 | ||
|
|
1206 | #Least Squares | |
|
|
1207 | minp,covp,infodict,mesg,ier = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants),full_output=True) | |
|
|
1208 | # minp,covp = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants)) | |
|
|
1209 | #Chi square error | |
|
|
1210 | error0 = numpy.sum(infodict['fvec']**2)/(2*N) | |
|
|
1211 | # error0 = 0 | |
|
|
1212 | #Error with Jacobian | |
|
|
1213 | error1 = self.dataOut.library.errorFunction(minp,constants,LT) | |
|
|
1214 | #Save | |
|
|
1215 | if self.dataOut.data_param == None: | |
|
|
1216 | self.dataOut.data_param = numpy.zeros((nGroups, minp.size, nHeights))*numpy.nan | |
|
|
1217 | self.dataOut.error = numpy.zeros((nGroups, error1.size + 1, nHeights))*numpy.nan | |
|
|
1218 | ||
|
|
1219 | self.dataOut.error[i,:,h] = numpy.hstack((error0,error1)) | |
|
|
1220 | self.dataOut.data_param[i,:,h] = minp | |
|
|
1221 | return | |
|
|
1222 | ||
|
|
1223 | ||
|
|
1224 | def __residFunction(self, p, dp, LT, constants): | |
|
|
1225 | ||
|
|
1226 | fm = self.dataOut.library.modelFunction(p, constants) | |
|
|
1227 | fmp=numpy.dot(LT,fm) | |
|
|
1228 | ||
|
|
1229 | return dp-fmp | |
|
|
1230 | ||
|
|
1231 | def __getSNR(self, z, noise): | |
|
|
1232 | ||
|
|
1233 | avg = numpy.average(z, axis=1) | |
|
|
1234 | SNR = (avg.T-noise)/noise | |
|
|
1235 | SNR = SNR.T | |
|
|
1236 | return SNR | |
|
|
1237 | ||
|
|
1238 | def __chisq(p,chindex,hindex): | |
|
|
1239 | #similar to Resid but calculates CHI**2 | |
|
|
1240 | [LT,d,fm]=setupLTdfm(p,chindex,hindex) | |
|
|
1241 | dp=numpy.dot(LT,d) | |
|
|
1242 | fmp=numpy.dot(LT,fm) | |
|
|
1243 | chisq=numpy.dot((dp-fmp).T,(dp-fmp)) | |
|
|
1244 | return chisq | |
|
|
1245 | ||
|
|
1246 | ||
|
|
1112 | 1247 | |
|
|
1113 | 1248 | class WindProfiler(Operation): |
|
|
1114 | 1249 | |
|
|
1115 | 1250 | __isConfig = False |
|
|
1116 | 1251 | |
|
|
1117 | 1252 | __initime = None |
|
|
1118 | 1253 | __lastdatatime = None |
|
|
1119 | 1254 | __integrationtime = None |
|
|
1120 | 1255 | |
|
|
1121 | 1256 | __buffer = None |
|
|
1122 | 1257 | |
|
|
1123 | 1258 | __dataReady = False |
|
|
1124 | 1259 | |
|
|
1125 | 1260 | __firstdata = None |
|
|
1126 | 1261 | |
|
|
1127 | 1262 | n = None |
|
|
1128 | 1263 | |
|
|
1129 | 1264 | def __init__(self): |
|
|
1130 | 1265 | Operation.__init__(self) |
|
|
1131 | 1266 | |
|
|
1132 | 1267 | def __calculateCosDir(self, elev, azim): |
|
|
1133 | 1268 | zen = (90 - elev)*numpy.pi/180 |
|
|
1134 | 1269 | azim = azim*numpy.pi/180 |
|
|
1135 | 1270 | cosDirX = numpy.sqrt((1-numpy.cos(zen)**2)/((1+numpy.tan(azim)**2))) |
|
|
1136 | 1271 | cosDirY = numpy.sqrt(1-numpy.cos(zen)**2-cosDirX**2) |
|
|
1137 | 1272 | |
|
|
1138 | 1273 | signX = numpy.sign(numpy.cos(azim)) |
|
|
1139 | 1274 | signY = numpy.sign(numpy.sin(azim)) |
|
|
1140 | 1275 | |
|
|
1141 | 1276 | cosDirX = numpy.copysign(cosDirX, signX) |
|
|
1142 | 1277 | cosDirY = numpy.copysign(cosDirY, signY) |
|
|
1143 | 1278 | return cosDirX, cosDirY |
|
|
1144 | 1279 | |
|
|
1145 | 1280 | def __calculateAngles(self, theta_x, theta_y, azimuth): |
|
|
1146 | 1281 | |
|
|
1147 | 1282 | dir_cosw = numpy.sqrt(1-theta_x**2-theta_y**2) |
|
|
1148 | 1283 | zenith_arr = numpy.arccos(dir_cosw) |
|
|
1149 | 1284 | azimuth_arr = numpy.arctan2(theta_x,theta_y) + azimuth*math.pi/180 |
|
|
1150 | 1285 | |
|
|
1151 | 1286 | dir_cosu = numpy.sin(azimuth_arr)*numpy.sin(zenith_arr) |
|
|
1152 | 1287 | dir_cosv = numpy.cos(azimuth_arr)*numpy.sin(zenith_arr) |
|
|
1153 | 1288 | |
|
|
1154 | 1289 | return azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw |
|
|
1155 | 1290 | |
|
|
1156 | 1291 | def __calculateMatA(self, dir_cosu, dir_cosv, dir_cosw, horOnly): |
|
|
1157 | 1292 | |
|
|
1158 | 1293 | # |
|
|
1159 | 1294 | if horOnly: |
|
|
1160 | 1295 | A = numpy.c_[dir_cosu,dir_cosv] |
|
|
1161 | 1296 | else: |
|
|
1162 | 1297 | A = numpy.c_[dir_cosu,dir_cosv,dir_cosw] |
|
|
1163 | 1298 | A = numpy.asmatrix(A) |
|
|
1164 | 1299 | A1 = numpy.linalg.inv(A.transpose()*A)*A.transpose() |
|
|
1165 | 1300 | |
|
|
1166 | 1301 | return A1 |
|
|
1167 | 1302 | |
|
|
1168 | 1303 | def __correctValues(self, heiRang, phi, velRadial, SNR): |
|
|
1169 | 1304 | listPhi = phi.tolist() |
|
|
1170 | 1305 | maxid = listPhi.index(max(listPhi)) |
|
|
1171 | 1306 | minid = listPhi.index(min(listPhi)) |
|
|
1172 | 1307 | |
|
|
1173 | 1308 | rango = range(len(phi)) |
|
|
1174 | 1309 | # rango = numpy.delete(rango,maxid) |
|
|
1175 | 1310 | |
|
|
1176 | 1311 | heiRang1 = heiRang*math.cos(phi[maxid]) |
|
|
1177 | 1312 | heiRangAux = heiRang*math.cos(phi[minid]) |
|
|
1178 | 1313 | indOut = (heiRang1 < heiRangAux[0]).nonzero() |
|
|
1179 | 1314 | heiRang1 = numpy.delete(heiRang1,indOut) |
|
|
1180 | 1315 | |
|
|
1181 | 1316 | velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
1182 | 1317 | SNR1 = numpy.zeros([len(phi),len(heiRang1)]) |
|
|
1183 | 1318 | |
|
|
1184 | 1319 | for i in rango: |
|
|
1185 | 1320 | x = heiRang*math.cos(phi[i]) |
|
|
1186 | 1321 | y1 = velRadial[i,:] |
|
|
1187 | 1322 | f1 = interpolate.interp1d(x,y1,kind = 'cubic') |
|
|
1188 | 1323 | |
|
|
1189 | 1324 | x1 = heiRang1 |
|
|
1190 | 1325 | y11 = f1(x1) |
|
|
1191 | 1326 | |
|
|
1192 | 1327 | y2 = SNR[i,:] |
|
|
1193 | 1328 | f2 = interpolate.interp1d(x,y2,kind = 'cubic') |
|
|
1194 | 1329 | y21 = f2(x1) |
|
|
1195 | 1330 | |
|
|
1196 | 1331 | velRadial1[i,:] = y11 |
|
|
1197 | 1332 | SNR1[i,:] = y21 |
|
|
1198 | 1333 | |
|
|
1199 | 1334 | return heiRang1, velRadial1, SNR1 |
|
|
1200 | 1335 | |
|
|
1201 | 1336 | def __calculateVelUVW(self, A, velRadial): |
|
|
1202 | 1337 | |
|
|
1203 | 1338 | #Operacion Matricial |
|
|
1204 | 1339 | # velUVW = numpy.zeros((velRadial.shape[1],3)) |
|
|
1205 | 1340 | # for ind in range(velRadial.shape[1]): |
|
|
1206 | 1341 | # velUVW[ind,:] = numpy.dot(A,velRadial[:,ind]) |
|
|
1207 | 1342 | # velUVW = velUVW.transpose() |
|
|
1208 | 1343 | velUVW = numpy.zeros((A.shape[0],velRadial.shape[1])) |
|
|
1209 | 1344 | velUVW[:,:] = numpy.dot(A,velRadial) |
|
|
1210 | 1345 | |
|
|
1211 | 1346 | |
|
|
1212 | 1347 | return velUVW |
|
|
1213 | 1348 | |
|
|
1214 | 1349 | def techniqueDBS(self, velRadial0, dirCosx, disrCosy, azimuth, correct, horizontalOnly, heiRang, SNR0): |
|
|
1215 | 1350 | """ |
|
|
1216 | 1351 | Function that implements Doppler Beam Swinging (DBS) technique. |
|
|
1217 | 1352 | |
|
|
1218 | 1353 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, |
|
|
1219 | 1354 | Direction correction (if necessary), Ranges and SNR |
|
|
1220 | 1355 | |
|
|
1221 | 1356 | Output: Winds estimation (Zonal, Meridional and Vertical) |
|
|
1222 | 1357 | |
|
|
1223 | 1358 | Parameters affected: Winds, height range, SNR |
|
|
1224 | 1359 | """ |
|
|
1225 | 1360 | azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(dirCosx, disrCosy, azimuth) |
|
|
1226 | 1361 | heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, zenith_arr, correct*velRadial0, SNR0) |
|
|
1227 | 1362 | A = self.__calculateMatA(dir_cosu, dir_cosv, dir_cosw, horizontalOnly) |
|
|
1228 | 1363 | |
|
|
1229 | 1364 | #Calculo de Componentes de la velocidad con DBS |
|
|
1230 | 1365 | winds = self.__calculateVelUVW(A,velRadial1) |
|
|
1231 | 1366 | |
|
|
1232 | 1367 | return winds, heiRang1, SNR1 |
|
|
1233 | 1368 | |
|
|
1234 | 1369 | def __calculateDistance(self, posx, posy, pairsCrossCorr, pairsList, pairs, azimuth = None): |
|
|
1235 | 1370 | |
|
|
1236 | 1371 | posx = numpy.asarray(posx) |
|
|
1237 | 1372 | posy = numpy.asarray(posy) |
|
|
1238 | 1373 | |
|
|
1239 | 1374 | #Rotacion Inversa para alinear con el azimuth |
|
|
1240 | 1375 | if azimuth!= None: |
|
|
1241 | 1376 | azimuth = azimuth*math.pi/180 |
|
|
1242 | 1377 | posx1 = posx*math.cos(azimuth) + posy*math.sin(azimuth) |
|
|
1243 | 1378 | posy1 = -posx*math.sin(azimuth) + posy*math.cos(azimuth) |
|
|
1244 | 1379 | else: |
|
|
1245 | 1380 | posx1 = posx |
|
|
1246 | 1381 | posy1 = posy |
|
|
1247 | 1382 | |
|
|
1248 | 1383 | #Calculo de Distancias |
|
|
1249 | 1384 | distx = numpy.zeros(pairsCrossCorr.size) |
|
|
1250 | 1385 | disty = numpy.zeros(pairsCrossCorr.size) |
|
|
1251 | 1386 | dist = numpy.zeros(pairsCrossCorr.size) |
|
|
1252 | 1387 | ang = numpy.zeros(pairsCrossCorr.size) |
|
|
1253 | 1388 | |
|
|
1254 | 1389 | for i in range(pairsCrossCorr.size): |
|
|
1255 | 1390 | distx[i] = posx1[pairsList[pairsCrossCorr[i]][1]] - posx1[pairsList[pairsCrossCorr[i]][0]] |
|
|
1256 | 1391 | disty[i] = posy1[pairsList[pairsCrossCorr[i]][1]] - posy1[pairsList[pairsCrossCorr[i]][0]] |
|
|
1257 | 1392 | dist[i] = numpy.sqrt(distx[i]**2 + disty[i]**2) |
|
|
1258 | 1393 | ang[i] = numpy.arctan2(disty[i],distx[i]) |
|
|
1259 | 1394 | #Calculo de Matrices |
|
|
1260 | 1395 | nPairs = len(pairs) |
|
|
1261 | 1396 | ang1 = numpy.zeros((nPairs, 2, 1)) |
|
|
1262 | 1397 | dist1 = numpy.zeros((nPairs, 2, 1)) |
|
|
1263 | 1398 | |
|
|
1264 | 1399 | for j in range(nPairs): |
|
|
1265 | 1400 | dist1[j,0,0] = dist[pairs[j][0]] |
|
|
1266 | 1401 | dist1[j,1,0] = dist[pairs[j][1]] |
|
|
1267 | 1402 | ang1[j,0,0] = ang[pairs[j][0]] |
|
|
1268 | 1403 | ang1[j,1,0] = ang[pairs[j][1]] |
|
|
1269 | 1404 | |
|
|
1270 | 1405 | return distx,disty, dist1,ang1 |
|
|
1271 | 1406 | |
|
|
1272 | 1407 | def __calculateVelVer(self, phase, lagTRange, _lambda): |
|
|
1273 | 1408 | |
|
|
1274 | 1409 | Ts = lagTRange[1] - lagTRange[0] |
|
|
1275 | 1410 | velW = -_lambda*phase/(4*math.pi*Ts) |
|
|
1276 | 1411 | |
|
|
1277 | 1412 | return velW |
|
|
1278 | 1413 | |
|
|
1279 | 1414 | def __calculateVelHorDir(self, dist, tau1, tau2, ang): |
|
|
1280 | 1415 | nPairs = tau1.shape[0] |
|
|
1281 | 1416 | vel = numpy.zeros((nPairs,3,tau1.shape[2])) |
|
|
1282 | 1417 | |
|
|
1283 | 1418 | angCos = numpy.cos(ang) |
|
|
1284 | 1419 | angSin = numpy.sin(ang) |
|
|
1285 | 1420 | |
|
|
1286 | 1421 | vel0 = dist*tau1/(2*tau2**2) |
|
|
1287 | 1422 | vel[:,0,:] = (vel0*angCos).sum(axis = 1) |
|
|
1288 | 1423 | vel[:,1,:] = (vel0*angSin).sum(axis = 1) |
|
|
1289 | 1424 | |
|
|
1290 | 1425 | ind = numpy.where(numpy.isinf(vel)) |
|
|
1291 | 1426 | vel[ind] = numpy.nan |
|
|
1292 | 1427 | |
|
|
1293 | 1428 | return vel |
|
|
1294 | 1429 | |
|
|
1295 | 1430 | def __getPairsAutoCorr(self, pairsList, nChannels): |
|
|
1296 | 1431 | |
|
|
1297 | 1432 | pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan |
|
|
1298 | 1433 | |
|
|
1299 | 1434 | for l in range(len(pairsList)): |
|
|
1300 | 1435 | firstChannel = pairsList[l][0] |
|
|
1301 | 1436 | secondChannel = pairsList[l][1] |
|
|
1302 | 1437 | |
|
|
1303 | 1438 | #Obteniendo pares de Autocorrelacion |
|
|
1304 | 1439 | if firstChannel == secondChannel: |
|
|
1305 | 1440 | pairsAutoCorr[firstChannel] = int(l) |
|
|
1306 | 1441 | |
|
|
1307 | 1442 | pairsAutoCorr = pairsAutoCorr.astype(int) |
|
|
1308 | 1443 | |
|
|
1309 | 1444 | pairsCrossCorr = range(len(pairsList)) |
|
|
1310 | 1445 | pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr) |
|
|
1311 | 1446 | |
|
|
1312 | 1447 | return pairsAutoCorr, pairsCrossCorr |
|
|
1313 | 1448 | |
|
|
1314 | 1449 | def techniqueSA(self, pairsSelected, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, lagTRange, correctFactor): |
|
|
1315 | 1450 | """ |
|
|
1316 | 1451 | Function that implements Spaced Antenna (SA) technique. |
|
|
1317 | 1452 | |
|
|
1318 | 1453 | Input: Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth, |
|
|
1319 | 1454 | Direction correction (if necessary), Ranges and SNR |
|
|
1320 | 1455 | |
|
|
1321 | 1456 | Output: Winds estimation (Zonal, Meridional and Vertical) |
|
|
1322 | 1457 | |
|
|
1323 | 1458 | Parameters affected: Winds |
|
|
1324 | 1459 | """ |
|
|
1325 | 1460 | #Cross Correlation pairs obtained |
|
|
1326 | 1461 | pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairsList, nChannels) |
|
|
1327 | 1462 | pairsArray = numpy.array(pairsList)[pairsCrossCorr] |
|
|
1328 | 1463 | pairsSelArray = numpy.array(pairsSelected) |
|
|
1329 | 1464 | pairs = [] |
|
|
1330 | 1465 | |
|
|
1331 | 1466 | #Wind estimation pairs obtained |
|
|
1332 | 1467 | for i in range(pairsSelArray.shape[0]/2): |
|
|
1333 | 1468 | ind1 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i], axis = 1))[0][0] |
|
|
1334 | 1469 | ind2 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i + 1], axis = 1))[0][0] |
|
|
1335 | 1470 | pairs.append((ind1,ind2)) |
|
|
1336 | 1471 | |
|
|
1337 | 1472 | indtau = tau.shape[0]/2 |
|
|
1338 | 1473 | tau1 = tau[:indtau,:] |
|
|
1339 | 1474 | tau2 = tau[indtau:-1,:] |
|
|
1340 | 1475 | tau1 = tau1[pairs,:] |
|
|
1341 | 1476 | tau2 = tau2[pairs,:] |
|
|
1342 | 1477 | phase1 = tau[-1,:] |
|
|
1343 | 1478 | |
|
|
1344 | 1479 | #--------------------------------------------------------------------- |
|
|
1345 | 1480 | #Metodo Directo |
|
|
1346 | 1481 | distx, disty, dist, ang = self.__calculateDistance(position_x, position_y, pairsCrossCorr, pairsList, pairs,azimuth) |
|
|
1347 | 1482 | winds = self.__calculateVelHorDir(dist, tau1, tau2, ang) |
|
|
1348 | 1483 | winds = stats.nanmean(winds, axis=0) |
|
|
1349 | 1484 | #--------------------------------------------------------------------- |
|
|
1350 | 1485 | #Metodo General |
|
|
1351 | 1486 | # distx, disty, dist = self.calculateDistance(position_x,position_y,pairsCrossCorr, pairsList, azimuth) |
|
|
1352 | 1487 | # #Calculo Coeficientes de Funcion de Correlacion |
|
|
1353 | 1488 | # F,G,A,B,H = self.calculateCoef(tau1,tau2,distx,disty,n) |
|
|
1354 | 1489 | # #Calculo de Velocidades |
|
|
1355 | 1490 | # winds = self.calculateVelUV(F,G,A,B,H) |
|
|
1356 | 1491 | |
|
|
1357 | 1492 | #--------------------------------------------------------------------- |
|
|
1358 | 1493 | winds[2,:] = self.__calculateVelVer(phase1, lagTRange, _lambda) |
|
|
1359 | 1494 | winds = correctFactor*winds |
|
|
1360 | 1495 | return winds |
|
|
1361 | 1496 | |
|
|
1362 |
def __checkTime(self, currentTime, paramInterval, |
|
|
|
1497 | def __checkTime(self, currentTime, paramInterval, outputInterval): | |
|
|
1363 | 1498 | |
|
|
1364 | 1499 | dataTime = currentTime + paramInterval |
|
|
1365 | 1500 | deltaTime = dataTime - self.__initime |
|
|
1366 | 1501 | |
|
|
1367 |
if deltaTime >= |
|
|
|
1502 | if deltaTime >= outputInterval or deltaTime < 0: | |
|
|
1368 | 1503 | self.__dataReady = True |
|
|
1369 | 1504 | return |
|
|
1370 | 1505 | |
|
|
1371 | 1506 | def techniqueMeteors(self, arrayMeteor, meteorThresh, heightMin, heightMax): |
|
|
1372 | 1507 | ''' |
|
|
1373 | 1508 | Function that implements winds estimation technique with detected meteors. |
|
|
1374 | 1509 | |
|
|
1375 | 1510 | Input: Detected meteors, Minimum meteor quantity to wind estimation |
|
|
1376 | 1511 | |
|
|
1377 | 1512 | Output: Winds estimation (Zonal and Meridional) |
|
|
1378 | 1513 | |
|
|
1379 | 1514 | Parameters affected: Winds |
|
|
1380 | 1515 | ''' |
|
|
1381 | 1516 | #Settings |
|
|
1382 | 1517 | nInt = (heightMax - heightMin)/2 |
|
|
1383 | 1518 | winds = numpy.zeros((2,nInt))*numpy.nan |
|
|
1384 | 1519 | |
|
|
1385 | 1520 | #Filter errors |
|
|
1386 | 1521 | error = numpy.where(arrayMeteor[:,-1] == 0)[0] |
|
|
1387 | 1522 | finalMeteor = arrayMeteor[error,:] |
|
|
1388 | 1523 | |
|
|
1389 | 1524 | #Meteor Histogram |
|
|
1390 | 1525 | finalHeights = finalMeteor[:,3] |
|
|
1391 | 1526 | hist = numpy.histogram(finalHeights, bins = nInt, range = (heightMin,heightMax)) |
|
|
1392 | 1527 | nMeteorsPerI = hist[0] |
|
|
1393 | 1528 | heightPerI = hist[1] |
|
|
1394 | 1529 | |
|
|
1395 | 1530 | #Sort of meteors |
|
|
1396 | 1531 | indSort = finalHeights.argsort() |
|
|
1397 | 1532 | finalMeteor2 = finalMeteor[indSort,:] |
|
|
1398 | 1533 | |
|
|
1399 | 1534 | # Calculating winds |
|
|
1400 | 1535 | ind1 = 0 |
|
|
1401 | 1536 | ind2 = 0 |
|
|
1402 | 1537 | |
|
|
1403 | 1538 | for i in range(nInt): |
|
|
1404 | 1539 | nMet = nMeteorsPerI[i] |
|
|
1405 | 1540 | ind1 = ind2 |
|
|
1406 | 1541 | ind2 = ind1 + nMet |
|
|
1407 | 1542 | |
|
|
1408 | 1543 | meteorAux = finalMeteor2[ind1:ind2,:] |
|
|
1409 | 1544 | |
|
|
1410 | 1545 | if meteorAux.shape[0] >= meteorThresh: |
|
|
1411 | 1546 | vel = meteorAux[:, 7] |
|
|
1412 | 1547 | zen = meteorAux[:, 5]*numpy.pi/180 |
|
|
1413 | 1548 | azim = meteorAux[:, 4]*numpy.pi/180 |
|
|
1414 | 1549 | |
|
|
1415 | 1550 | n = numpy.cos(zen) |
|
|
1416 | 1551 | # m = (1 - n**2)/(1 - numpy.tan(azim)**2) |
|
|
1417 | 1552 | # l = m*numpy.tan(azim) |
|
|
1418 | 1553 | l = numpy.sin(zen)*numpy.sin(azim) |
|
|
1419 | 1554 | m = numpy.sin(zen)*numpy.cos(azim) |
|
|
1420 | 1555 | |
|
|
1421 | 1556 | A = numpy.vstack((l, m)).transpose() |
|
|
1422 | 1557 | A1 = numpy.dot(numpy.linalg.inv( numpy.dot(A.transpose(),A) ),A.transpose()) |
|
|
1423 | 1558 | windsAux = numpy.dot(A1, vel) |
|
|
1424 | 1559 | |
|
|
1425 | 1560 | winds[0,i] = windsAux[0] |
|
|
1426 | 1561 | winds[1,i] = windsAux[1] |
|
|
1427 | 1562 | |
|
|
1428 | 1563 | return winds, heightPerI[:-1] |
|
|
1429 | 1564 | |
|
|
1430 | 1565 | def run(self, dataOut, technique, **kwargs): |
|
|
1431 | 1566 | |
|
|
1432 | 1567 | param = dataOut.data_param |
|
|
1433 | 1568 | if dataOut.abscissaRange != None: |
|
|
1434 | 1569 | absc = dataOut.abscissaRange[:-1] |
|
|
1435 | 1570 | noise = dataOut.noise |
|
|
1436 | 1571 | heightRange = dataOut.getHeiRange() |
|
|
1437 | 1572 | SNR = dataOut.SNR |
|
|
1438 | 1573 | |
|
|
1439 | 1574 | if technique == 'DBS': |
|
|
1440 | 1575 | |
|
|
1441 | 1576 | if kwargs.has_key('dirCosx') and kwargs.has_key('dirCosy'): |
|
|
1442 | 1577 | theta_x = numpy.array(kwargs['dirCosx']) |
|
|
1443 | 1578 | theta_y = numpy.array(kwargs['dirCosy']) |
|
|
1444 | 1579 | else: |
|
|
1445 | 1580 | elev = numpy.array(kwargs['elevation']) |
|
|
1446 | 1581 | azim = numpy.array(kwargs['azimuth']) |
|
|
1447 | 1582 | theta_x, theta_y = self.__calculateCosDir(elev, azim) |
|
|
1448 | 1583 | azimuth = kwargs['correctAzimuth'] |
|
|
1449 | 1584 | if kwargs.has_key('horizontalOnly'): |
|
|
1450 | 1585 | horizontalOnly = kwargs['horizontalOnly'] |
|
|
1451 | 1586 | else: horizontalOnly = False |
|
|
1452 | 1587 | if kwargs.has_key('correctFactor'): |
|
|
1453 | 1588 | correctFactor = kwargs['correctFactor'] |
|
|
1454 | 1589 | else: correctFactor = 1 |
|
|
1455 | 1590 | if kwargs.has_key('channelList'): |
|
|
1456 | 1591 | channelList = kwargs['channelList'] |
|
|
1457 | 1592 | if len(channelList) == 2: |
|
|
1458 | 1593 | horizontalOnly = True |
|
|
1459 | 1594 | arrayChannel = numpy.array(channelList) |
|
|
1460 | 1595 | param = param[arrayChannel,:,:] |
|
|
1461 | 1596 | theta_x = theta_x[arrayChannel] |
|
|
1462 | 1597 | theta_y = theta_y[arrayChannel] |
|
|
1463 | 1598 | |
|
|
1464 | 1599 | velRadial0 = param[:,1,:] #Radial velocity |
|
|
1465 |
dataOut. |
|
|
|
1600 | dataOut.data_output, dataOut.heightRange, dataOut.SNR = self.techniqueDBS(velRadial0, theta_x, theta_y, azimuth, correctFactor, horizontalOnly, heightRange, SNR) #DBS Function | |
|
|
1466 | 1601 | |
|
|
1467 | 1602 | elif technique == 'SA': |
|
|
1468 | 1603 | |
|
|
1469 | 1604 | #Parameters |
|
|
1470 | 1605 | position_x = kwargs['positionX'] |
|
|
1471 | 1606 | position_y = kwargs['positionY'] |
|
|
1472 | 1607 | azimuth = kwargs['azimuth'] |
|
|
1473 | 1608 | |
|
|
1474 | 1609 | if kwargs.has_key('crosspairsList'): |
|
|
1475 | 1610 | pairs = kwargs['crosspairsList'] |
|
|
1476 | 1611 | else: |
|
|
1477 | 1612 | pairs = None |
|
|
1478 | 1613 | |
|
|
1479 | 1614 | if kwargs.has_key('correctFactor'): |
|
|
1480 | 1615 | correctFactor = kwargs['correctFactor'] |
|
|
1481 | 1616 | else: |
|
|
1482 | 1617 | correctFactor = 1 |
|
|
1483 | 1618 | |
|
|
1484 | 1619 | tau = dataOut.data_param |
|
|
1485 | 1620 | _lambda = dataOut.C/dataOut.frequency |
|
|
1486 |
pairsList = dataOut. |
|
|
|
1621 | pairsList = dataOut.groupList | |
|
|
1487 | 1622 | nChannels = dataOut.nChannels |
|
|
1488 | 1623 | |
|
|
1489 |
dataOut. |
|
|
|
1624 | dataOut.data_output = self.techniqueSA(pairs, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, absc, correctFactor) | |
|
|
1490 | 1625 | dataOut.initUtcTime = dataOut.ltctime |
|
|
1491 |
dataOut. |
|
|
|
1626 | dataOut.outputInterval = dataOut.timeInterval | |
|
|
1492 | 1627 | |
|
|
1493 | 1628 | elif technique == 'Meteors': |
|
|
1494 | 1629 | dataOut.flagNoData = True |
|
|
1495 | 1630 | self.__dataReady = False |
|
|
1496 | 1631 | |
|
|
1497 | 1632 | if kwargs.has_key('nHours'): |
|
|
1498 | 1633 | nHours = kwargs['nHours'] |
|
|
1499 | 1634 | else: |
|
|
1500 | 1635 | nHours = 1 |
|
|
1501 | 1636 | |
|
|
1502 | 1637 | if kwargs.has_key('meteorsPerBin'): |
|
|
1503 | 1638 | meteorThresh = kwargs['meteorsPerBin'] |
|
|
1504 | 1639 | else: |
|
|
1505 | 1640 | meteorThresh = 6 |
|
|
1506 | 1641 | |
|
|
1507 | 1642 | if kwargs.has_key('hmin'): |
|
|
1508 | 1643 | hmin = kwargs['hmin'] |
|
|
1509 | 1644 | else: hmin = 70 |
|
|
1510 | 1645 | if kwargs.has_key('hmax'): |
|
|
1511 | 1646 | hmax = kwargs['hmax'] |
|
|
1512 | 1647 | else: hmax = 110 |
|
|
1513 | 1648 | |
|
|
1514 |
dataOut. |
|
|
|
1649 | dataOut.outputInterval = nHours*3600 | |
|
|
1515 | 1650 | |
|
|
1516 | 1651 | if self.__isConfig == False: |
|
|
1517 | 1652 | # self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03) |
|
|
1518 | 1653 | #Get Initial LTC time |
|
|
1519 | 1654 | self.__initime = (dataOut.datatime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds() |
|
|
1520 | 1655 | self.__isConfig = True |
|
|
1521 | 1656 | |
|
|
1522 | 1657 | if self.__buffer == None: |
|
|
1523 | 1658 | self.__buffer = dataOut.data_param |
|
|
1524 | 1659 | self.__firstdata = copy.copy(dataOut) |
|
|
1525 | 1660 | |
|
|
1526 | 1661 | else: |
|
|
1527 | 1662 | self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param)) |
|
|
1528 | 1663 | |
|
|
1529 |
self.__checkTime(dataOut.ltctime, dataOut.paramInterval, dataOut. |
|
|
|
1664 | self.__checkTime(dataOut.ltctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready | |
|
|
1530 | 1665 | |
|
|
1531 | 1666 | if self.__dataReady: |
|
|
1532 | 1667 | dataOut.initUtcTime = self.__initime |
|
|
1533 |
self.__initime = self.__initime + dataOut. |
|
|
|
1668 | self.__initime = self.__initime + dataOut.outputInterval #to erase time offset | |
|
|
1534 | 1669 | |
|
|
1535 |
dataOut. |
|
|
|
1670 | dataOut.data_output, dataOut.heightRange = self.techniqueMeteors(self.__buffer, meteorThresh, hmin, hmax) | |
|
|
1536 | 1671 | dataOut.flagNoData = False |
|
|
1537 | 1672 | self.__buffer = None |
|
|
1538 | 1673 | |
|
|
1539 | return No newline at end of file | |
|
|
1674 | return | |
|
|
1675 | ||
|
|
1676 | class EWDriftsEstimation(Operation): | |
|
|
1677 | ||
|
|
1678 | ||
|
|
1679 | def __init__(self): | |
|
|
1680 | Operation.__init__(self) | |
|
|
1681 | ||
|
|
1682 | def __correctValues(self, heiRang, phi, velRadial, SNR): | |
|
|
1683 | listPhi = phi.tolist() | |
|
|
1684 | maxid = listPhi.index(max(listPhi)) | |
|
|
1685 | minid = listPhi.index(min(listPhi)) | |
|
|
1686 | ||
|
|
1687 | rango = range(len(phi)) | |
|
|
1688 | # rango = numpy.delete(rango,maxid) | |
|
|
1689 | ||
|
|
1690 | heiRang1 = heiRang*math.cos(phi[maxid]) | |
|
|
1691 | heiRangAux = heiRang*math.cos(phi[minid]) | |
|
|
1692 | indOut = (heiRang1 < heiRangAux[0]).nonzero() | |
|
|
1693 | heiRang1 = numpy.delete(heiRang1,indOut) | |
|
|
1694 | ||
|
|
1695 | velRadial1 = numpy.zeros([len(phi),len(heiRang1)]) | |
|
|
1696 | SNR1 = numpy.zeros([len(phi),len(heiRang1)]) | |
|
|
1697 | ||
|
|
1698 | for i in rango: | |
|
|
1699 | x = heiRang*math.cos(phi[i]) | |
|
|
1700 | y1 = velRadial[i,:] | |
|
|
1701 | f1 = interpolate.interp1d(x,y1,kind = 'cubic') | |
|
|
1702 | ||
|
|
1703 | x1 = heiRang1 | |
|
|
1704 | y11 = f1(x1) | |
|
|
1705 | ||
|
|
1706 | y2 = SNR[i,:] | |
|
|
1707 | f2 = interpolate.interp1d(x,y2,kind = 'cubic') | |
|
|
1708 | y21 = f2(x1) | |
|
|
1709 | ||
|
|
1710 | velRadial1[i,:] = y11 | |
|
|
1711 | SNR1[i,:] = y21 | |
|
|
1712 | ||
|
|
1713 | return heiRang1, velRadial1, SNR1 | |
|
|
1714 | ||
|
|
1715 | def run(self, dataOut, zenith, zenithCorrection): | |
|
|
1716 | heiRang = dataOut.heightList | |
|
|
1717 | velRadial = dataOut.data_param[:,3,:] | |
|
|
1718 | SNR = dataOut.SNR | |
|
|
1719 | ||
|
|
1720 | zenith = numpy.array(zenith) | |
|
|
1721 | zenith -= zenithCorrection | |
|
|
1722 | zenith *= numpy.pi/180 | |
|
|
1723 | ||
|
|
1724 | heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, numpy.abs(zenith), velRadial, SNR) | |
|
|
1725 | ||
|
|
1726 | alp = zenith[0] | |
|
|
1727 | bet = zenith[1] | |
|
|
1728 | ||
|
|
1729 | w_w = velRadial1[0,:] | |
|
|
1730 | w_e = velRadial1[1,:] | |
|
|
1731 | ||
|
|
1732 | w = (w_w*numpy.sin(bet) - w_e*numpy.sin(alp))/(numpy.cos(alp)*numpy.sin(bet) - numpy.cos(bet)*numpy.sin(alp)) | |
|
|
1733 | u = (w_w*numpy.cos(bet) - w_e*numpy.cos(alp))/(numpy.sin(alp)*numpy.cos(bet) - numpy.sin(bet)*numpy.cos(alp)) | |
|
|
1734 | ||
|
|
1735 | winds = numpy.vstack((u,w)) | |
|
|
1736 | ||
|
|
1737 | dataOut.heightList = heiRang1 | |
|
|
1738 | dataOut.data_output = winds | |
|
|
1739 | dataOut.SNR = SNR1 | |
|
|
1740 | ||
|
|
1741 | dataOut.initUtcTime = dataOut.ltctime | |
|
|
1742 | dataOut.outputInterval = dataOut.timeInterval | |
|
|
1743 | return | |
|
|
1744 | ||
|
|
1745 | ||
|
|
1746 | ||
|
|
1747 | ||
|
|
1748 | ||
|
|
1749 | No newline at end of file | |
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